diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000..6e115657ee --- /dev/null +++ b/.gitattributes @@ -0,0 +1,16 @@ +# Set the default behavior, in case people don't have core.autocrlf set. +* text=lf + +# Explicitly declare text files you want to always be normalized and converted +# to native line endings on checkout. +*.csv text +*.sh text +*.py text + +# Denote all files that are truly binary and should not be modified. +*.png binary +*.jpg binary + +# force lf for yaml files +*.yml text eol=lf +*.yaml text eol=lf \ No newline at end of file diff --git a/.github/workflows/yaml-validation.yml b/.github/workflows/yaml-validation.yml new file mode 100644 index 0000000000..e9bf0ad42b --- /dev/null +++ b/.github/workflows/yaml-validation.yml @@ -0,0 +1,45 @@ +name: YAML Validation + +on: + pull_request: + paths: + - 'detections/**/*.yml' + - 'detections/**/*.yaml' + push: + branches: + - develop + paths: + - 'detections/**/*.yml' + - 'detections/**/*.yaml' + +jobs: + validate: + name: Validate Detection YAML Files + runs-on: ubuntu-latest + + steps: + - name: Checkout code + uses: actions/checkout@v6 + with: + fetch-depth: 0 + + - name: Set up Python + uses: actions/setup-python@v6 + with: + python-version: '3.11' + architecture: 'x64' + + - name: Install yamllint + run: pip install yamllint + + - name: Set up Go + uses: actions/setup-go@v6 + + - name: Install yamlfmt + run: | + go install github.com/google/yamlfmt/cmd/yamlfmt@latest + echo "$HOME/go/bin" >> $GITHUB_PATH + + - name: Validate all detection YAML files + run: | + python scripts/validate_yaml.py detections/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 82acb2c4d9..442ea66425 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -6,10 +6,24 @@ repos: exclude: "package/bin/da_ess_contentupdate/|package/bin/splunklib/|venv/" - id: check-json - id: check-symlinks - - id: check-yaml + # - id: check-yaml - id: pretty-format-json args: [--autofix] - id: requirements-txt-fixer - id: detect-aws-credentials + args: ['--allow-missing-credentials'] - id: detect-private-key - id: forbid-submodules + + # yamlfmt: Auto-format YAML files in detections/ folder + - repo: local + hooks: + - id: yamlfmt + name: yamlfmt (detections only) + description: Format YAML files in detections/ with yamlfmt + entry: python .pre-commit-hooks/yamlfmt-hook.py + language: system + files: ^detections/.*\.(yml|yaml)$ + pass_filenames: true + # Optional: Specify custom yamlfmt binary path if not in PATH + # args: [--yamlfmt-path, /path/to/yamlfmt] diff --git a/.pre-commit-hooks/yamlfmt-hook.py b/.pre-commit-hooks/yamlfmt-hook.py new file mode 100644 index 0000000000..b7bfecd921 --- /dev/null +++ b/.pre-commit-hooks/yamlfmt-hook.py @@ -0,0 +1,124 @@ +#!/usr/bin/env python3 +""" +Pre-commit hook script for yamlfmt +Formats YAML files in the detections/ directory only +Cross-platform compatible (Linux, macOS, Windows) +""" +import argparse +import os +import subprocess +import sys +from pathlib import Path + + +def find_yamlfmt(custom_path=None): + """Find yamlfmt executable in common locations or use custom path + + Args: + custom_path: Optional path to yamlfmt binary + + Returns: + Path to yamlfmt executable or None if not found + """ + # If custom path provided, verify and use it + if custom_path: + custom_path = Path(custom_path) + if custom_path.exists(): + return str(custom_path) + else: + print(f"ERROR: yamlfmt not found at specified path: {custom_path}") + return None + + # Check if yamlfmt is in PATH + for cmd in ['yamlfmt', 'yamlfmt.exe']: + try: + result = subprocess.run([cmd, '--version'], capture_output=True, text=True) + if result.returncode == 0: + return cmd + except FileNotFoundError: + pass + + # Check common installation paths + possible_paths = [ + Path.home() / 'go' / 'bin' / 'yamlfmt', + Path.home() / 'go' / 'bin' / 'yamlfmt.exe', + Path('/usr/local/bin/yamlfmt'), + Path('/usr/bin/yamlfmt'), + # Check in repo yamlfmt-main folder (for development) + Path(__file__).parent.parent.parent / 'yamlfmt-main' / 'yamlfmt.exe', + ] + + for path in possible_paths: + if path.exists(): + return str(path) + + print("ERROR: yamlfmt not found. Install with: go install github.com/google/yamlfmt/cmd/yamlfmt@latest") + print("Make sure $GOPATH/bin is in your PATH") + print(f"Or place yamlfmt.exe in: {Path.home() / 'go' / 'bin'}") + print("Or use --yamlfmt-path to specify a custom yamlfmt binary location") + return None + + +def main(): + """Run yamlfmt on changed YAML files in detections/""" + # Parse arguments + parser = argparse.ArgumentParser(description='Pre-commit hook for yamlfmt') + parser.add_argument('--yamlfmt-path', help='Path to yamlfmt binary') + parser.add_argument('files', nargs='*', help='Files to format') + + args = parser.parse_args() + files = args.files + + if not files: + return 0 + + # Filter to only YAML files in detections/ + yaml_files = [ + f for f in files + if f.startswith('detections/') and f.endswith(('.yml', '.yaml')) + ] + + if not yaml_files: + return 0 + + # Find yamlfmt + yamlfmt = find_yamlfmt(args.yamlfmt_path) + if not yamlfmt: + return 1 + + # Get repo root to find .yamlfmt config + repo_root = subprocess.run( + ['git', 'rev-parse', '--show-toplevel'], + capture_output=True, + text=True, + check=True + ).stdout.strip() + + config_path = Path(repo_root) / '.yamlfmt' + + # Run yamlfmt on each file + failed = False + for file in yaml_files: + file_path = Path(repo_root) / file + if not file_path.exists(): + continue + + cmd = [yamlfmt] + if config_path.exists(): + cmd.extend(['-conf', str(config_path)]) + cmd.append(str(file_path)) + + result = subprocess.run(cmd, capture_output=True, text=True) + + if result.returncode != 0: + print(f"[FAIL] yamlfmt failed for {file}:") + print(result.stderr) + failed = True + else: + print(f"[OK] Formatted: {file}") + + return 1 if failed else 0 + + +if __name__ == '__main__': + sys.exit(main()) diff --git a/.yamlfmt b/.yamlfmt new file mode 100644 index 0000000000..8cbdbd4996 --- /dev/null +++ b/.yamlfmt @@ -0,0 +1,12 @@ +formatter: + type: basic + indent: 4 + include_document_start: false + line_ending: lf + retain_line_breaks: false + scan_folded_as_literal: true + indentless_arrays: false + pad_line_comments: 1 + eof_newline: true + max_line_length: 0 + trim_trailing_whitespace: true diff --git a/.yamllint b/.yamllint new file mode 100644 index 0000000000..ab224f9ee5 --- /dev/null +++ b/.yamllint @@ -0,0 +1,126 @@ +# https://yamllint.readthedocs.io/en/latest/configuration.html +# yamllint configuration for security_content +# Aligned with .yamlfmt config to avoid conflicts +# This config validates YAML syntax and enforces consistency while yamlfmt handles formatting + +extends: default + +# Ignore all YAML files except those in detections/ +ignore: | + /.git/ + /dist/ + /venv/ + /node_modules/ + /*.yml + /*.yaml + /app_template/ + /baselines/ + /dashboards/ + /data_sources/ + /deployments/ + /docs/ + /lookups/ + /macros/ + /notebooks/ + /playbooks/ + /removed/ + /response_templates/ + /stories/ + /workbooks/ + +rules: + # Comments: Enforce proper spacing for readability + # - require-starting-space: Ensures "# comment" not "#comment" + # - min-spaces-from-content: Requires space between code and inline comment + comments: + require-starting-space: true + min-spaces-from-content: 1 + + # Comments indentation: Disabled to allow flexible comment placement + # Useful for multi-line field comments that may not align with strict indent rules + comments-indentation: disable + + # Document start: Don't require "---" at the beginning + # Our YAML files are standalone detection rules, not multi-document streams + document-start: {present: false} + + # Empty lines: Allow up to 2 blank lines for visual separation + # Helps organize long detection rules into logical sections + empty-lines: {max: 2, max-start: 2, max-end: 2} + + # Indentation: Disabled - yamlfmt handles this consistently + # yamlfmt uses 4-space base indent with 2-space offsets for nested structures + # yamllint's indent rules conflict with yamlfmt's behavior, so we let yamlfmt control it + indentation: disable + + # Line length: Disabled due to extremely long search queries + # Detection rules often have 500+ character search fields that can't be wrapped + line-length: disable + + # New line at end of file: Required for POSIX compliance + # Prevents issues with git diffs and ensures proper file termination + new-line-at-end-of-file: enable + + # Trailing spaces: Not allowed + # Catches accidental whitespace that causes git diff noise + trailing-spaces: {} + + # New lines: LF only (Unix style) + # Enforces consistent line endings across all platforms for git compatibility + new-lines: {type: unix} + + # Key duplicates: Critical validation to catch errors + # Prevents accidentally defining the same field twice (e.g., two "name:" fields) + key-duplicates: enable + + # Truthy values: Allow both YAML 1.1 and 1.2 boolean representations + # Permits 'true/false', 'yes/no', 'on/off' for compatibility with various tools + # check-keys: false allows "no" as a key name (e.g., for test scenarios) + truthy: + allowed-values: ['true', 'false', 'yes', 'no', 'on', 'off'] + check-keys: false + + # Brackets: Consistent spacing in flow sequences [] + # Enforces "[item1, item2]" not "[ item1, item2 ]" + brackets: + min-spaces-inside: 0 + max-spaces-inside: 0 + + # Braces: Consistent spacing in flow mappings {} + # Allows "{key: value}" with optional space after colon + braces: + min-spaces-inside: 0 + max-spaces-inside: 1 + min-spaces-inside-empty: 0 + max-spaces-inside-empty: 0 + + # Colons: Enforce "key: value" spacing (not "key : value" or "key:value") + # Standard YAML formatting for readability + colons: + max-spaces-before: 0 + max-spaces-after: 1 + + # Commas: Enforce consistent spacing in flow collections + # Requires "item1, item2" not "item1,item2" or "item1 ,item2" + commas: + max-spaces-before: 0 + min-spaces-after: 1 + max-spaces-after: 1 + + # Hyphens: Enforce "- item" spacing for array items (not "-item" or "- item") + # Ensures consistent block sequence formatting + hyphens: + max-spaces-after: 1 + + # Empty values: Control where null/empty values are allowed + # Allow "field:" with no value in mappings (common in our detection rules) + # Forbid in flow mappings to catch likely errors: "{key:}" is probably wrong + empty-values: + forbid-in-block-mappings: false + forbid-in-flow-mappings: true + + # Quoted strings: Allow both single and double quotes + # Don't require quotes on unquoted strings - let yamlfmt handle quote style + quoted-strings: + quote-type: any + required: false diff --git a/README.md b/README.md index 56628774ba..c196f78ae6 100644 --- a/README.md +++ b/README.md @@ -56,6 +56,7 @@ Follow these steps to get started with Splunk Security Content. 1. Clone this repository using `git clone https://github.com/splunk/security_content.git` 2. Navigate to the repository directory using `cd security_content` 3. Install contentctl using `pip install contentctl` to install the latest version of contentctl, this is a pre-requisite to validate, build and test the content like the Splunk Threat Research team +4. Install pre-commit using `pip install pre-commit` then proceed to installing the hooks via `pre-commit install`. this is a pre-requisite to validate and apply the proper formatting. # Quick Start 🚀 diff --git a/detections/application/cisco_ai_defense_security_alerts_by_application_name.yml b/detections/application/cisco_ai_defense_security_alerts_by_application_name.yml index f1fe80d09d..0383585d30 100644 --- a/detections/application/cisco_ai_defense_security_alerts_by_application_name.yml +++ b/detections/application/cisco_ai_defense_security_alerts_by_application_name.yml @@ -1,80 +1,76 @@ name: Cisco AI Defense Security Alerts by Application Name id: 105e4a69-ec55-49fc-be1f-902467435ea8 -version: 3 -date: '2025-05-02' +version: 4 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: Anomaly description: The search surfaces alerts from the Cisco AI Defense product for potential attacks against the AI models running in your environment. This analytic identifies security events within Cisco AI Defense by examining event messages, actions, and policy names. It focuses on connections and applications associated with specific guardrail entities and ruleset types. By aggregating and analyzing these elements, the search helps detect potential policy violations and security threats, enabling proactive defense measures and ensuring network integrity. data_source: -- Cisco AI Defense Alerts + - Cisco AI Defense Alerts search: |- - `cisco_ai_defense` - | rename genai_application.application_name as application_name - | rename connection.connection_name as connection_name - ```Aggregating data by model name, connection name, application name, application ID, and user ID``` - | stats count - values(user_id) as user_id - values(event_message_type) as event_message_type - values(event_action) as event_action - values(policy.policy_name) as policy_name - values(event_policy_guardrail_assocs{}.policy_guardrail_assoc.guardrail_avail_entity.guardrail_entity_name) as guardrail_entity_name - values(event_policy_guardrail_assocs{}.policy_guardrail_assoc.guardrail_avail_ruleset.guardrail_ruleset_type) as guardrail_ruleset_type - by model.model_name connection_name application_name application_id - ```Evaluating severity based on policy name and guardrail ruleset type``` - | eval severity=case( - policy_name IN ("AI Runtime Latency Testing - Prompt Injection"), "critical", - policy_name IN ("AI Runtime Latency Testing - Code Detection"), "high", - guardrail_ruleset_type IN ("Toxicity"), "medium", - true(), "low" - ) - ```Calculating risk score based on severity level``` - | eval risk_score=case( - severity="critical", 100, - severity="high", 75, - severity="medium", 50, - severity="low", 25 - ) - | table model.model_name, user_id, event_action, application_id, application_name, severity, risk_score, policy_name, connection_name, guardrail_ruleset_type, guardrail_entity_name - | `cisco_ai_defense_security_alerts_by_application_name_filter` + `cisco_ai_defense` + | rename genai_application.application_name as application_name + | rename connection.connection_name as connection_name + ```Aggregating data by model name, connection name, application name, application ID, and user ID``` + | stats count + values(user_id) as user_id + values(event_message_type) as event_message_type + values(event_action) as event_action + values(policy.policy_name) as policy_name + values(event_policy_guardrail_assocs{}.policy_guardrail_assoc.guardrail_avail_entity.guardrail_entity_name) as guardrail_entity_name + values(event_policy_guardrail_assocs{}.policy_guardrail_assoc.guardrail_avail_ruleset.guardrail_ruleset_type) as guardrail_ruleset_type + by model.model_name connection_name application_name application_id + ```Evaluating severity based on policy name and guardrail ruleset type``` + | eval severity=case( + policy_name IN ("AI Runtime Latency Testing - Prompt Injection"), "critical", + policy_name IN ("AI Runtime Latency Testing - Code Detection"), "high", + guardrail_ruleset_type IN ("Toxicity"), "medium", + true(), "low" + ) + ```Calculating risk score based on severity level``` + | eval risk_score=case( + severity="critical", 100, + severity="high", 75, + severity="medium", 50, + severity="low", 25 + ) + | table model.model_name, user_id, event_action, application_id, application_name, severity, risk_score, policy_name, connection_name, guardrail_ruleset_type, guardrail_entity_name + | `cisco_ai_defense_security_alerts_by_application_name_filter` how_to_implement: To enable this detection, you need to ingest alerts from the Cisco AI Defense product. This can be done by using this app from splunkbase - Cisco Security Cloud and ingest alerts into the cisco:ai:defense sourcetype. known_false_positives: False positives may vary based on Cisco AI Defense configuration; monitor and filter out the alerts that are not relevant to your environment. references: -- https://www.robustintelligence.com/blog-posts/prompt-injection-attack-on-gpt-4 -- https://docs.aws.amazon.com/prescriptive-guidance/latest/llm-prompt-engineering-best-practices/common-attacks.html + - https://www.robustintelligence.com/blog-posts/prompt-injection-attack-on-gpt-4 + - https://docs.aws.amazon.com/prescriptive-guidance/latest/llm-prompt-engineering-best-practices/common-attacks.html drilldown_searches: -- name: View the detection results for - "$application_name$" - search: '%original_detection_search% | search application_name = "$application_name$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$application_name$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$application_name$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$application_name$" + search: '%original_detection_search% | search application_name = "$application_name$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$application_name$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$application_name$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Cisco AI Defense Security Alert has been action - [$event_action$] for the application name - [$application_name$] - risk_objects: - - field: application_name - type: other - score: 10 - threat_objects: [] + message: Cisco AI Defense Security Alert has been action - [$event_action$] for the application name - [$application_name$] + risk_objects: + - field: application_name + type: other + score: 10 + threat_objects: [] tags: analytic_story: - - Critical Alerts + - Critical Alerts asset_type: Web Application product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud security_domain: endpoint manual_test: We are dynamically creating the risk_score field based on the severity of the alert in the SPL and that supersedes the risk score set in the detection. tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/cisco_ai_defense_alerts/cisco_ai_defense_alerts.json - source: cisco_ai_defense - sourcetype: cisco:ai:defense \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/cisco_ai_defense_alerts/cisco_ai_defense_alerts.json + source: cisco_ai_defense + sourcetype: cisco:ai:defense diff --git a/detections/application/cisco_asa___aaa_policy_tampering.yml b/detections/application/cisco_asa___aaa_policy_tampering.yml index 5dc07b9f4c..fd5bbaf8f0 100644 --- a/detections/application/cisco_asa___aaa_policy_tampering.yml +++ b/detections/application/cisco_asa___aaa_policy_tampering.yml @@ -6,78 +6,78 @@ author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - This analytic detects modifications to authentication and authorization (AAA) security policies on Cisco ASA devices via CLI or ASDM. - AAA policies control critical security mechanisms including authentication attempts, lockout thresholds, password policies, and access control settings that protect administrative access to network infrastructure. - Adversaries or malicious insiders may weaken authentication policies to facilitate brute force attacks, disable account lockouts to enable unlimited password attempts, reduce password complexity requirements, or modify authorization settings to elevate privileges and maintain persistent access. - The detection monitors for command execution events containing AAA-related commands such as `aaa authentication`, `aaa authorization`, or `aaa local authentication`, focusing on changes to authentication attempts, lockout policies, and access control configurations. - Investigate any unauthorized modifications to AAA policies, especially changes that weaken security posture (increasing max-fail attempts, disabling lockouts, reducing password requirements), and verify these changes against approved change management processes and security policies. + This analytic detects modifications to authentication and authorization (AAA) security policies on Cisco ASA devices via CLI or ASDM. + AAA policies control critical security mechanisms including authentication attempts, lockout thresholds, password policies, and access control settings that protect administrative access to network infrastructure. + Adversaries or malicious insiders may weaken authentication policies to facilitate brute force attacks, disable account lockouts to enable unlimited password attempts, reduce password complexity requirements, or modify authorization settings to elevate privileges and maintain persistent access. + The detection monitors for command execution events containing AAA-related commands such as `aaa authentication`, `aaa authorization`, or `aaa local authentication`, focusing on changes to authentication attempts, lockout policies, and access control configurations. + Investigate any unauthorized modifications to AAA policies, especially changes that weaken security posture (increasing max-fail attempts, disabling lockouts, reducing password requirements), and verify these changes against approved change management processes and security policies. data_source: - - Cisco ASA Logs + - Cisco ASA Logs search: | - `cisco_asa` - message_id IN (111008, 111010) - command IN ( - "aaa authentication*", - "aaa authorization*", - "aaa local authentication*", - "aaa-server*", - "no aaa*" - ) - | fillnull - | stats count - earliest(_time) as firstTime - latest(_time) as lastTime - values(user) as user - values(action) as action - values(message_id) as message_id - values(command) as command - values(src_ip) as src_ip - values(process_name) as process_name - by host - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_asa___aaa_policy_tampering_filter` + `cisco_asa` + message_id IN (111008, 111010) + command IN ( + "aaa authentication*", + "aaa authorization*", + "aaa local authentication*", + "aaa-server*", + "no aaa*" + ) + | fillnull + | stats count + earliest(_time) as firstTime + latest(_time) as lastTime + values(user) as user + values(action) as action + values(message_id) as message_id + values(command) as command + values(src_ip) as src_ip + values(process_name) as process_name + by host + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_asa___aaa_policy_tampering_filter` how_to_implement: | - This search requires Cisco ASA syslog data to be ingested into Splunk via the Cisco Security Cloud TA. - To ensure this detection works effectively, configure your ASA and FTD devices to generate and forward message ID 111008 and 111010. - If your logging level is set to 'Notifications' or higher, these messages should already be included, else we recommend setting an event list that keeps the severity level you are using and adds message IDs 111008 and 111010. - You can find specific instructions on how to set this up here : https://www.cisco.com/c/en/us/support/docs/security/pix-500-series-security-appliances/63884-config-asa-00.html. - You can also change the severity level of the above message id's to the syslog level you have currently enabled using the logging message syslog_id level severity_level command in global configuration mode. For more information, see Change the Severity Level of a Syslog Message : https://www.cisco.com/c/en/us/td/docs/security/asa/asa922/configuration/general/asa-922-general-config/monitor-syslog.html#ID-2121-000006da + This search requires Cisco ASA syslog data to be ingested into Splunk via the Cisco Security Cloud TA. + To ensure this detection works effectively, configure your ASA and FTD devices to generate and forward message ID 111008 and 111010. + If your logging level is set to 'Notifications' or higher, these messages should already be included, else we recommend setting an event list that keeps the severity level you are using and adds message IDs 111008 and 111010. + You can find specific instructions on how to set this up here : https://www.cisco.com/c/en/us/support/docs/security/pix-500-series-security-appliances/63884-config-asa-00.html. + You can also change the severity level of the above message id's to the syslog level you have currently enabled using the logging message syslog_id level severity_level command in global configuration mode. For more information, see Change the Severity Level of a Syslog Message : https://www.cisco.com/c/en/us/td/docs/security/asa/asa922/configuration/general/asa-922-general-config/monitor-syslog.html#ID-2121-000006da known_false_positives: | - Legitimate AAA configuration modifications may occur during normal administrative activities such as implementing new security policies, adjusting lockout thresholds or troubleshooting authentication issues. These events should be verified and investigated. Consider filtering modifications performed by known administrative accounts where necessary. + Legitimate AAA configuration modifications may occur during normal administrative activities such as implementing new security policies, adjusting lockout thresholds or troubleshooting authentication issues. These events should be verified and investigated. Consider filtering modifications performed by known administrative accounts where necessary. references: - - https://www.cisco.com/c/en/us/td/docs/security/asa/asa-cli-reference/A-H/asa-command-ref-A-H/aa-ac-commands.html + - https://www.cisco.com/c/en/us/td/docs/security/asa/asa-cli-reference/A-H/asa-command-ref-A-H/aa-ac-commands.html drilldown_searches: - - name: View the detection results for $host$ - search: '%original_detection_search% | search host = $host$' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for $host$ - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($host$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for $host$ + search: '%original_detection_search% | search host = $host$' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for $host$ + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($host$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ executed command $command$ to modify AAA configuration on Cisco ASA host $host$. - risk_objects: - - field: host - type: system - score: 40 - threat_objects: - - field: command - type: process + message: User $user$ executed command $command$ to modify AAA configuration on Cisco ASA host $host$. + risk_objects: + - field: host + type: system + score: 40 + threat_objects: + - field: command + type: process tags: - analytic_story: - - Suspicious Cisco Adaptive Security Appliance Activity - asset_type: Network - mitre_attack_id: - - T1556.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: network + analytic_story: + - Suspicious Cisco Adaptive Security Appliance Activity + asset_type: Network + mitre_attack_id: + - T1556.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/generic/cisco_asa_generic_logs.log - source: not_applicable - sourcetype: cisco:asa + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/generic/cisco_asa_generic_logs.log + source: not_applicable + sourcetype: cisco:asa diff --git a/detections/application/cisco_asa___core_syslog_message_volume_drop.yml b/detections/application/cisco_asa___core_syslog_message_volume_drop.yml index b73fea1a8a..cdac8d7658 100644 --- a/detections/application/cisco_asa___core_syslog_message_volume_drop.yml +++ b/detections/application/cisco_asa___core_syslog_message_volume_drop.yml @@ -6,59 +6,59 @@ author: Bhavin Patel, Micheal Haag, Splunk status: production type: Hunting description: | - Adversaries may intentionally suppress or reduce the volume of core Cisco ASA syslog messages to evade detection or cover their tracks. This hunting search is recommended to proactively identify suspicious downward shifts or absences in key syslog message IDs, which may indicate tampering or malicious activity. Visualizing this data in Splunk dashboards enables security teams to quickly spot anomalies and investigate potential compromise. + Adversaries may intentionally suppress or reduce the volume of core Cisco ASA syslog messages to evade detection or cover their tracks. This hunting search is recommended to proactively identify suspicious downward shifts or absences in key syslog message IDs, which may indicate tampering or malicious activity. Visualizing this data in Splunk dashboards enables security teams to quickly spot anomalies and investigate potential compromise. data_source: - - Cisco ASA Logs + - Cisco ASA Logs search: | - `cisco_asa` - message_id IN (302013, 302014, 609002, 710005) - | eval msg_desc=case( - message_id="302013","Built inbound TCP connection", - message_id="302014","Teardown TCP connection", - message_id="609002","Teardown local-host management", - message_id="710005","TCP request discarded" - ) - | bin _time span=15m - | stats count values(msg_desc) as message_description - values(dest) as dest - by _time message_id - | xyseries _time message_id count - | `cisco_asa___core_syslog_message_volume_drop_filter` + `cisco_asa` + message_id IN (302013, 302014, 609002, 710005) + | eval msg_desc=case( + message_id="302013","Built inbound TCP connection", + message_id="302014","Teardown TCP connection", + message_id="609002","Teardown local-host management", + message_id="710005","TCP request discarded" + ) + | bin _time span=15m + | stats count values(msg_desc) as message_description + values(dest) as dest + by _time message_id + | xyseries _time message_id count + | `cisco_asa___core_syslog_message_volume_drop_filter` how_to_implement: | - This search requires Cisco ASA syslog data to be ingested into Splunk via the Cisco Security Cloud TA. To ensure this detection works effectively, configure your ASA and FTD devices to generate and forward both debug and informational level syslog messages before they are sent to Splunk. - This analytic is designed to be used with comprehensive logging enabled, as it relies on the presence of specific message IDs. You can find specific instructions on how to set this up here : https://www.cisco.com/c/en/us/support/docs/security/pix-500-series-security-appliances/63884-config-asa-00.html#toc-hId--1451069880. - The search produces a time-series suitable for dashboards to visualize drops across message IDs 302013, 302014, 609002, and 710005. - You can also change the severity level of the above message id's to the syslog level you have currently enabled using the logging message syslog_id level severity_level command in global configuration mode. For more information, see Change the Severity Level of a Syslog Message : https://www.cisco.com/c/en/us/td/docs/security/asa/asa922/configuration/general/asa-922-general-config/monitor-syslog.html#ID-2121-000006da + This search requires Cisco ASA syslog data to be ingested into Splunk via the Cisco Security Cloud TA. To ensure this detection works effectively, configure your ASA and FTD devices to generate and forward both debug and informational level syslog messages before they are sent to Splunk. + This analytic is designed to be used with comprehensive logging enabled, as it relies on the presence of specific message IDs. You can find specific instructions on how to set this up here : https://www.cisco.com/c/en/us/support/docs/security/pix-500-series-security-appliances/63884-config-asa-00.html#toc-hId--1451069880. + The search produces a time-series suitable for dashboards to visualize drops across message IDs 302013, 302014, 609002, and 710005. + You can also change the severity level of the above message id's to the syslog level you have currently enabled using the logging message syslog_id level severity_level command in global configuration mode. For more information, see Change the Severity Level of a Syslog Message : https://www.cisco.com/c/en/us/td/docs/security/asa/asa922/configuration/general/asa-922-general-config/monitor-syslog.html#ID-2121-000006da known_false_positives: | - Planned maintenance, network outages, routing changes, or benign configuration updates may reduce log volume temporarily. - Validate against change management records and corroborate with device health metrics. + Planned maintenance, network outages, routing changes, or benign configuration updates may reduce log volume temporarily. + Validate against change management records and corroborate with device health metrics. references: - - https://blog.talosintelligence.com/arcanedoor-new-espionage-focused-campaign-found-targeting-perimeter-network-devices/ - - https://sec.cloudapps.cisco.com/security/center/resources/asa_ftd_continued_attacks - - https://sec.cloudapps.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-asaftd-webvpn-z5xP8EUB - - https://sec.cloudapps.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-http-code-exec-WmfP3h3O - - https://sec.cloudapps.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-asaftd-webvpn-YROOTUW - - https://sec.cloudapps.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-http-code-exec-WmfP3h3O - - https://www.cisa.gov/news-events/directives/ed-25-03-identify-and-mitigate-potential-compromise-cisco-devices - - https://www.ncsc.gov.uk/news/persistent-malicious-targeting-cisco-devices + - https://blog.talosintelligence.com/arcanedoor-new-espionage-focused-campaign-found-targeting-perimeter-network-devices/ + - https://sec.cloudapps.cisco.com/security/center/resources/asa_ftd_continued_attacks + - https://sec.cloudapps.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-asaftd-webvpn-z5xP8EUB + - https://sec.cloudapps.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-http-code-exec-WmfP3h3O + - https://sec.cloudapps.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-asaftd-webvpn-YROOTUW + - https://sec.cloudapps.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-http-code-exec-WmfP3h3O + - https://www.cisa.gov/news-events/directives/ed-25-03-identify-and-mitigate-potential-compromise-cisco-devices + - https://www.ncsc.gov.uk/news/persistent-malicious-targeting-cisco-devices tags: - analytic_story: - - Suspicious Cisco Adaptive Security Appliance Activity - - ArcaneDoor - asset_type: Network - mitre_attack_id: - - T1562 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: - - CVE-2025-20333 - - CVE-2025-20362 + analytic_story: + - Suspicious Cisco Adaptive Security Appliance Activity + - ArcaneDoor + asset_type: Network + mitre_attack_id: + - T1562 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: + - CVE-2025-20333 + - CVE-2025-20362 tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/arcane_door/cisco_asa.log - source: not_applicable - sourcetype: cisco:asa + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/arcane_door/cisco_asa.log + source: not_applicable + sourcetype: cisco:asa diff --git a/detections/application/cisco_asa___device_file_copy_activity.yml b/detections/application/cisco_asa___device_file_copy_activity.yml index c4df139edc..7baf47e280 100644 --- a/detections/application/cisco_asa___device_file_copy_activity.yml +++ b/detections/application/cisco_asa___device_file_copy_activity.yml @@ -6,82 +6,82 @@ author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - This analytic detects file copy activity on Cisco ASA devices via CLI or ASDM. - Adversaries may copy device files including configurations, logs, packet captures, or system files for reconnaissance, credential extraction, or data exfiltration. While legitimate file operations occur during backups and maintenance, unauthorized copies may indicate malicious activity. - The detection monitors for command execution events (message ID 111008 or 111010) containing copy commands targeting running-config, startup-config, packet capture files, or other system files from disk0:, flash:, system:, or capture: locations. - Investigate unexpected file copies, especially from non-administrative accounts, during unusual hours, or when combined with other suspicious activities. + This analytic detects file copy activity on Cisco ASA devices via CLI or ASDM. + Adversaries may copy device files including configurations, logs, packet captures, or system files for reconnaissance, credential extraction, or data exfiltration. While legitimate file operations occur during backups and maintenance, unauthorized copies may indicate malicious activity. + The detection monitors for command execution events (message ID 111008 or 111010) containing copy commands targeting running-config, startup-config, packet capture files, or other system files from disk0:, flash:, system:, or capture: locations. + Investigate unexpected file copies, especially from non-administrative accounts, during unusual hours, or when combined with other suspicious activities. data_source: - - Cisco ASA Logs + - Cisco ASA Logs search: | - `cisco_asa` - message_id IN (111008, 111010) - command = "copy *" - command IN ( - "*running-config*", - "*startup-config*", - "*/pcap capture:*", - "* disk0:*", - "* flash:*", - "* system:*" - ) - | fillnull - | stats earliest(_time) as firstTime - latest(_time) as lastTime - values(user) as user - values(action) as action - values(message_id) as message_id - values(command) as command - values(src_ip) as src_ip - values(process_name) as process_name - by host - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_asa___device_file_copy_activity_filter` + `cisco_asa` + message_id IN (111008, 111010) + command = "copy *" + command IN ( + "*running-config*", + "*startup-config*", + "*/pcap capture:*", + "* disk0:*", + "* flash:*", + "* system:*" + ) + | fillnull + | stats earliest(_time) as firstTime + latest(_time) as lastTime + values(user) as user + values(action) as action + values(message_id) as message_id + values(command) as command + values(src_ip) as src_ip + values(process_name) as process_name + by host + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_asa___device_file_copy_activity_filter` how_to_implement: | - This search requires Cisco ASA syslog data to be ingested into Splunk via the Cisco Security Cloud TA. - To ensure this detection works effectively, configure your ASA and FTD devices to generate and forward message ID 111008 and 111010. - If your logging level is set to 'Notifications' or higher, these messages should already be included, else we recommend setting an event list that keeps the severity level you are using and adds message IDs 111008 and 111010. - You can find specific instructions on how to set this up here : https://www.cisco.com/c/en/us/support/docs/security/pix-500-series-security-appliances/63884-config-asa-00.html. - You can also change the severity level of the above message id's to the syslog level you have currently enabled using the logging message syslog_id level severity_level command in global configuration mode. For more information, see Change the Severity Level of a Syslog Message : https://www.cisco.com/c/en/us/td/docs/security/asa/asa922/configuration/general/asa-922-general-config/monitor-syslog.html#ID-2121-000006da + This search requires Cisco ASA syslog data to be ingested into Splunk via the Cisco Security Cloud TA. + To ensure this detection works effectively, configure your ASA and FTD devices to generate and forward message ID 111008 and 111010. + If your logging level is set to 'Notifications' or higher, these messages should already be included, else we recommend setting an event list that keeps the severity level you are using and adds message IDs 111008 and 111010. + You can find specific instructions on how to set this up here : https://www.cisco.com/c/en/us/support/docs/security/pix-500-series-security-appliances/63884-config-asa-00.html. + You can also change the severity level of the above message id's to the syslog level you have currently enabled using the logging message syslog_id level severity_level command in global configuration mode. For more information, see Change the Severity Level of a Syslog Message : https://www.cisco.com/c/en/us/td/docs/security/asa/asa922/configuration/general/asa-922-general-config/monitor-syslog.html#ID-2121-000006da known_false_positives: | - Legitimate configuration exports may occur during normal administrative activities. These events should be verified and investigated. + Legitimate configuration exports may occur during normal administrative activities. These events should be verified and investigated. references: - - https://blog.talosintelligence.com/arcanedoor-new-espionage-focused-campaign-found-targeting-perimeter-network-devices/ + - https://blog.talosintelligence.com/arcanedoor-new-espionage-focused-campaign-found-targeting-perimeter-network-devices/ drilldown_searches: - - name: View the detection results for $host$ - search: '%original_detection_search% | search host = $host$' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for $host$ - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($host$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for $host$ + search: '%original_detection_search% | search host = $host$' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for $host$ + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($host$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ executed command $command$ to export device configuration from Cisco ASA host $host$. - risk_objects: - - field: host - type: system - score: 50 - threat_objects: - - field: src_ip - type: ip_address - - field: command - type: process + message: User $user$ executed command $command$ to export device configuration from Cisco ASA host $host$. + risk_objects: + - field: host + type: system + score: 50 + threat_objects: + - field: src_ip + type: ip_address + - field: command + type: process tags: - analytic_story: - - Suspicious Cisco Adaptive Security Appliance Activity - - ArcaneDoor - asset_type: Network - mitre_attack_id: - - T1005 - - T1530 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: network + analytic_story: + - Suspicious Cisco Adaptive Security Appliance Activity + - ArcaneDoor + asset_type: Network + mitre_attack_id: + - T1005 + - T1530 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/generic/cisco_asa_generic_logs.log - source: not_applicable - sourcetype: cisco:asa + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/generic/cisco_asa_generic_logs.log + source: not_applicable + sourcetype: cisco:asa diff --git a/detections/application/cisco_asa___device_file_copy_to_remote_location.yml b/detections/application/cisco_asa___device_file_copy_to_remote_location.yml index 12d9dad7a4..4782f27c0f 100644 --- a/detections/application/cisco_asa___device_file_copy_to_remote_location.yml +++ b/detections/application/cisco_asa___device_file_copy_to_remote_location.yml @@ -6,107 +6,107 @@ author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - This analytic detects file copy operations to remote locations on Cisco ASA devices via CLI or ASDM. - Adversaries may exfiltrate device files including configurations, logs, packet captures, or system data to remote servers using protocols like TFTP, FTP, HTTP, HTTPS, SMB, or SCP. While legitimate backups to centralized servers are common, copies to unexpected destinations may indicate data exfiltration to attacker-controlled infrastructure. - The detection monitors for command execution events (message ID 111008 or 111010) containing copy commands with remote protocol indicators (tftp:, ftp:, http:, https:, smb:, scp:). - Investigate copies to unexpected destinations, from non-administrative accounts, or outside approved maintenance windows. - We recommend adapting the detection filters to exclude known legitimate backup activities. + This analytic detects file copy operations to remote locations on Cisco ASA devices via CLI or ASDM. + Adversaries may exfiltrate device files including configurations, logs, packet captures, or system data to remote servers using protocols like TFTP, FTP, HTTP, HTTPS, SMB, or SCP. While legitimate backups to centralized servers are common, copies to unexpected destinations may indicate data exfiltration to attacker-controlled infrastructure. + The detection monitors for command execution events (message ID 111008 or 111010) containing copy commands with remote protocol indicators (tftp:, ftp:, http:, https:, smb:, scp:). + Investigate copies to unexpected destinations, from non-administrative accounts, or outside approved maintenance windows. + We recommend adapting the detection filters to exclude known legitimate backup activities. data_source: - - Cisco ASA Logs + - Cisco ASA Logs search: | - `cisco_asa` - message_id IN (111008, 111010) - command = "copy *" - command IN ( - "*running-config*", - "*startup-config*", - "*/pcap capture:*", - "* disk0:*", - "* flash:*", - "* system:*" - ) - command IN ( - "*ftp:*", - "*http:*", - "*https:*", - "*smb:*", - "*scp:*" - ) - - | eval remote_protocol = mvappend( - if(match(command, "tftp:"), "TFTP", null()), - if(match(command, "ftp:"), "FTP", null()), - if(match(command, "http:"), "HTTP", null()), - if(match(command, "https:"), "HTTPS", null()), - if(match(command, "smb:"), "SMB", null()), - if(match(command, "scp:"), "SCP", null()) + `cisco_asa` + message_id IN (111008, 111010) + command = "copy *" + command IN ( + "*running-config*", + "*startup-config*", + "*/pcap capture:*", + "* disk0:*", + "* flash:*", + "* system:*" + ) + command IN ( + "*ftp:*", + "*http:*", + "*https:*", + "*smb:*", + "*scp:*" ) - | fillnull - | stats earliest(_time) as firstTime - latest(_time) as lastTime - values(user) as user - values(action) as action - values(message_id) as message_id - values(command) as command - values(remote_protocol) as remote_protocol - values(src_ip) as src_ip - values(dest) as dest - values(process_name) as process_name - by host - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_asa___device_file_copy_to_remote_location_filter` + + | eval remote_protocol = mvappend( + if(match(command, "tftp:"), "TFTP", null()), + if(match(command, "ftp:"), "FTP", null()), + if(match(command, "http:"), "HTTP", null()), + if(match(command, "https:"), "HTTPS", null()), + if(match(command, "smb:"), "SMB", null()), + if(match(command, "scp:"), "SCP", null()) + ) + | fillnull + | stats earliest(_time) as firstTime + latest(_time) as lastTime + values(user) as user + values(action) as action + values(message_id) as message_id + values(command) as command + values(remote_protocol) as remote_protocol + values(src_ip) as src_ip + values(dest) as dest + values(process_name) as process_name + by host + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_asa___device_file_copy_to_remote_location_filter` how_to_implement: | - This search requires Cisco ASA syslog data to be ingested into Splunk via the Cisco Security Cloud TA. - To ensure this detection works effectively, configure your ASA and FTD devices to generate and forward message IDs 111008 and 111010. - If your logging level is set to 'Notifications' or higher, these messages should already be included, else we recommend setting an event list that keeps the severity level you are using and add message IDs 111008 and 111010. - You can find specific instructions on how to set this up here : https://www.cisco.com/c/en/us/support/docs/security/pix-500-series-security-appliances/63884-config-asa-00.html. - You can also change the severity level of the above message id's to the syslog level you have currently enabled using the logging message syslog_id level severity_level command in global configuration mode. For more information, see Change the Severity Level of a Syslog Message : https://www.cisco.com/c/en/us/td/docs/security/asa/asa922/configuration/general/asa-922-general-config/monitor-syslog.html#ID-2121-000006da + This search requires Cisco ASA syslog data to be ingested into Splunk via the Cisco Security Cloud TA. + To ensure this detection works effectively, configure your ASA and FTD devices to generate and forward message IDs 111008 and 111010. + If your logging level is set to 'Notifications' or higher, these messages should already be included, else we recommend setting an event list that keeps the severity level you are using and add message IDs 111008 and 111010. + You can find specific instructions on how to set this up here : https://www.cisco.com/c/en/us/support/docs/security/pix-500-series-security-appliances/63884-config-asa-00.html. + You can also change the severity level of the above message id's to the syslog level you have currently enabled using the logging message syslog_id level severity_level command in global configuration mode. For more information, see Change the Severity Level of a Syslog Message : https://www.cisco.com/c/en/us/td/docs/security/asa/asa922/configuration/general/asa-922-general-config/monitor-syslog.html#ID-2121-000006da known_false_positives: | - Legitimate configuration exports to remote locations may occur during normal administrative activities. - Investigate these events to verify their legitimacy and apply necessary filters. + Legitimate configuration exports to remote locations may occur during normal administrative activities. + Investigate these events to verify their legitimacy and apply necessary filters. references: - - https://community.cisco.com/t5/security-knowledge-base/asa-how-to-download-images-using-tftp-ftp-http-https-and-scp/ta-p/3109769 - - https://blog.talosintelligence.com/arcanedoor-new-espionage-focused-campaign-found-targeting-perimeter-network-devices/ + - https://community.cisco.com/t5/security-knowledge-base/asa-how-to-download-images-using-tftp-ftp-http-https-and-scp/ta-p/3109769 + - https://blog.talosintelligence.com/arcanedoor-new-espionage-focused-campaign-found-targeting-perimeter-network-devices/ drilldown_searches: - - name: View the detection results for $host$ - search: '%original_detection_search% | search host = $host$' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for $host$ - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($host$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for $host$ + search: '%original_detection_search% | search host = $host$' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for $host$ + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($host$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ executed command $command$ to copy file or config from Cisco ASA host $host$ to remote location $dest$ via $remote_protocol$ protocols. - risk_objects: - - field: host - type: system - score: 50 - - field: user - type: user - score: 50 - threat_objects: - - field: dest - type: ip_address - - field: command - type: process + message: User $user$ executed command $command$ to copy file or config from Cisco ASA host $host$ to remote location $dest$ via $remote_protocol$ protocols. + risk_objects: + - field: host + type: system + score: 50 + - field: user + type: user + score: 50 + threat_objects: + - field: dest + type: ip_address + - field: command + type: process tags: - analytic_story: - - Suspicious Cisco Adaptive Security Appliance Activity - - ArcaneDoor - asset_type: Network - mitre_attack_id: - - T1005 - - T1041 - - T1048.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: network + analytic_story: + - Suspicious Cisco Adaptive Security Appliance Activity + - ArcaneDoor + asset_type: Network + mitre_attack_id: + - T1005 + - T1041 + - T1048.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/generic/cisco_asa_generic_logs.log - source: not_applicable - sourcetype: cisco:asa + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/generic/cisco_asa_generic_logs.log + source: not_applicable + sourcetype: cisco:asa diff --git a/detections/application/cisco_asa___logging_disabled_via_cli.yml b/detections/application/cisco_asa___logging_disabled_via_cli.yml index 3d8ce8a2eb..bab7b4d1a9 100644 --- a/detections/application/cisco_asa___logging_disabled_via_cli.yml +++ b/detections/application/cisco_asa___logging_disabled_via_cli.yml @@ -6,80 +6,80 @@ author: Bhavin Patel, Micheal Haag, Nasreddine Bencherchali, Splunk status: production type: TTP description: | - This analytic detects the disabling of logging functionality on a Cisco ASA device - through CLI commands. Adversaries or malicious insiders may attempt to disable logging - to evade detection and hide malicious activity. The detection looks for specific ASA - syslog message IDs (111010, 111008) associated with command execution, - combined with suspicious commands such as `no logging`, `logging disable`, - `clear logging`, or `no logging host`. Disabling logging on a firewall or security device - is a strong indicator of defense evasion. + This analytic detects the disabling of logging functionality on a Cisco ASA device + through CLI commands. Adversaries or malicious insiders may attempt to disable logging + to evade detection and hide malicious activity. The detection looks for specific ASA + syslog message IDs (111010, 111008) associated with command execution, + combined with suspicious commands such as `no logging`, `logging disable`, + `clear logging`, or `no logging host`. Disabling logging on a firewall or security device + is a strong indicator of defense evasion. data_source: - - Cisco ASA Logs + - Cisco ASA Logs search: | - `cisco_asa` - message_id IN (111008, 111010) - command IN ( - "*no logging*", - "*logging disable*", - "*clear logging*", - "*no logging host*", - "*no logging trap*" - ) - | stats earliest(_time) as firstTime - latest(_time) as lastTime - values(user) as user - values(action) as action - values(message_id) as message_id - values(command) as command - values(src_ip) as src_ip - values(process_name) as process_name - by host - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_asa___logging_disabled_via_cli_filter` + `cisco_asa` + message_id IN (111008, 111010) + command IN ( + "*no logging*", + "*logging disable*", + "*clear logging*", + "*no logging host*", + "*no logging trap*" + ) + | stats earliest(_time) as firstTime + latest(_time) as lastTime + values(user) as user + values(action) as action + values(message_id) as message_id + values(command) as command + values(src_ip) as src_ip + values(process_name) as process_name + by host + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_asa___logging_disabled_via_cli_filter` how_to_implement: | - This search requires Cisco ASA syslog data to be ingested into Splunk via the Cisco Security Cloud TA. - To ensure this detection works effectively, configure your ASA and FTD devices to generate and forward message ID 111008 and 111010. - If your logging level is set to 'Notifications' or higher, these messages should already be included, else we recommend setting an event list that keeps the severity level you are using and adds message IDs 111008 and 111010. - You can find specific instructions on how to set this up here : https://www.cisco.com/c/en/us/support/docs/security/pix-500-series-security-appliances/63884-config-asa-00.html. - You can also change the severity level of the above message id's to the syslog level you have currently enabled using the logging message syslog_id level severity_level command in global configuration mode. For more information, see Change the Severity Level of a Syslog Message : https://www.cisco.com/c/en/us/td/docs/security/asa/asa922/configuration/general/asa-922-general-config/monitor-syslog.html#ID-2121-000006da + This search requires Cisco ASA syslog data to be ingested into Splunk via the Cisco Security Cloud TA. + To ensure this detection works effectively, configure your ASA and FTD devices to generate and forward message ID 111008 and 111010. + If your logging level is set to 'Notifications' or higher, these messages should already be included, else we recommend setting an event list that keeps the severity level you are using and adds message IDs 111008 and 111010. + You can find specific instructions on how to set this up here : https://www.cisco.com/c/en/us/support/docs/security/pix-500-series-security-appliances/63884-config-asa-00.html. + You can also change the severity level of the above message id's to the syslog level you have currently enabled using the logging message syslog_id level severity_level command in global configuration mode. For more information, see Change the Severity Level of a Syslog Message : https://www.cisco.com/c/en/us/td/docs/security/asa/asa922/configuration/general/asa-922-general-config/monitor-syslog.html#ID-2121-000006da known_false_positives: | - Administrators may intentionally disable or modify logging during maintenance, troubleshooting, or device reconfiguration. - These events should be verified against approved change management activities. + Administrators may intentionally disable or modify logging during maintenance, troubleshooting, or device reconfiguration. + These events should be verified against approved change management activities. references: - - https://www.cisco.com/site/us/en/products/security/firewalls/adaptive-security-appliance-asa-software/index.html - - https://sec.cloudapps.cisco.com/security/center/resources/asa_ftd_continued_attacks + - https://www.cisco.com/site/us/en/products/security/firewalls/adaptive-security-appliance-asa-software/index.html + - https://sec.cloudapps.cisco.com/security/center/resources/asa_ftd_continued_attacks drilldown_searches: - - name: View the detection results for $host$ - search: '%original_detection_search% | search host = $host$' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for $host$ - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($host$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for $host$ + search: '%original_detection_search% | search host = $host$' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for $host$ + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($host$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ executed command $command$ to disable logging on the Cisco ASA host $host$. - risk_objects: - - field: host - type: system - score: 80 - threat_objects: - - field: src_ip - type: ip_address + message: User $user$ executed command $command$ to disable logging on the Cisco ASA host $host$. + risk_objects: + - field: host + type: system + score: 80 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Suspicious Cisco Adaptive Security Appliance Activity - asset_type: Network - mitre_attack_id: - - T1562 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: network + analytic_story: + - Suspicious Cisco Adaptive Security Appliance Activity + asset_type: Network + mitre_attack_id: + - T1562 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/generic/cisco_asa_generic_logs.log - source: not_applicable - sourcetype: cisco:asa + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/generic/cisco_asa_generic_logs.log + source: not_applicable + sourcetype: cisco:asa diff --git a/detections/application/cisco_asa___logging_filters_configuration_tampering.yml b/detections/application/cisco_asa___logging_filters_configuration_tampering.yml index 959af04c10..0d17d35c68 100644 --- a/detections/application/cisco_asa___logging_filters_configuration_tampering.yml +++ b/detections/application/cisco_asa___logging_filters_configuration_tampering.yml @@ -6,91 +6,91 @@ author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - This analytic detects tampering with logging filter configurations on Cisco ASA devices via CLI or ASDM. - Adversaries may reduce logging levels or disable specific log categories to evade detection, hide their activities, or prevent security monitoring systems from capturing evidence of their actions. By lowering logging verbosity, attackers can operate with reduced visibility to security teams. - The detection monitors for logging configuration commands (message ID 111008 or 111010) that modify logging destinations (asdm, console, history, mail, monitor, trap) without setting them to higher severity levels (5-notifications, 6-informational, 7-debugging), which may indicate an attempt to reduce logging verbosity. - Investigate unauthorized logging configuration changes that reduce verbosity, especially changes performed by non-administrative accounts, during unusual hours, or without corresponding change management approval. + This analytic detects tampering with logging filter configurations on Cisco ASA devices via CLI or ASDM. + Adversaries may reduce logging levels or disable specific log categories to evade detection, hide their activities, or prevent security monitoring systems from capturing evidence of their actions. By lowering logging verbosity, attackers can operate with reduced visibility to security teams. + The detection monitors for logging configuration commands (message ID 111008 or 111010) that modify logging destinations (asdm, console, history, mail, monitor, trap) without setting them to higher severity levels (5-notifications, 6-informational, 7-debugging), which may indicate an attempt to reduce logging verbosity. + Investigate unauthorized logging configuration changes that reduce verbosity, especially changes performed by non-administrative accounts, during unusual hours, or without corresponding change management approval. data_source: - - Cisco ASA Logs + - Cisco ASA Logs search: | - `cisco_asa` - message_id IN (111008, 111010) - command = "logging *" - command IN ( - "*asdm*", - "*console*", - "*history*", - "*mail*", - "*monitor*", - "*trap*" - ) - NOT command IN ( - "*notifications*", - "*informational*", - "*debugging*", - "* 5*", - "* 6*", - "* 7*" - ) - | fillnull - | stats count - earliest(_time) as firstTime - latest(_time) as lastTime - values(user) as user - values(action) as action - values(message_id) as message_id - values(command) as command - values(src_ip) as src_ip - values(process_name) as process_name - by host - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_asa___logging_filters_configuration_tampering_filter` + `cisco_asa` + message_id IN (111008, 111010) + command = "logging *" + command IN ( + "*asdm*", + "*console*", + "*history*", + "*mail*", + "*monitor*", + "*trap*" + ) + NOT command IN ( + "*notifications*", + "*informational*", + "*debugging*", + "* 5*", + "* 6*", + "* 7*" + ) + | fillnull + | stats count + earliest(_time) as firstTime + latest(_time) as lastTime + values(user) as user + values(action) as action + values(message_id) as message_id + values(command) as command + values(src_ip) as src_ip + values(process_name) as process_name + by host + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_asa___logging_filters_configuration_tampering_filter` how_to_implement: | - This search requires Cisco ASA syslog data to be ingested into Splunk via the Cisco Security Cloud TA. - To ensure this detection works effectively, configure your ASA and FTD devices to generate and forward message ID 111008 and 111010. - If your logging level is set to 'Notifications' or higher, these messages should already be included, else we recommend setting an event list that keeps the severity level you are using and adding the message IDs 111008 and 111010. - You can find specific instructions on how to set this up here : https://www.cisco.com/c/en/us/support/docs/security/pix-500-series-security-appliances/63884-config-asa-00.html. - You can also change the severity level of the above message id's to the syslog level you have currently enabled using the logging message syslog_id level severity_level command in global configuration mode. For more information, see Change the Severity Level of a Syslog Message : https://www.cisco.com/c/en/us/td/docs/security/asa/asa922/configuration/general/asa-922-general-config/monitor-syslog.html#ID-2121-000006da + This search requires Cisco ASA syslog data to be ingested into Splunk via the Cisco Security Cloud TA. + To ensure this detection works effectively, configure your ASA and FTD devices to generate and forward message ID 111008 and 111010. + If your logging level is set to 'Notifications' or higher, these messages should already be included, else we recommend setting an event list that keeps the severity level you are using and adding the message IDs 111008 and 111010. + You can find specific instructions on how to set this up here : https://www.cisco.com/c/en/us/support/docs/security/pix-500-series-security-appliances/63884-config-asa-00.html. + You can also change the severity level of the above message id's to the syslog level you have currently enabled using the logging message syslog_id level severity_level command in global configuration mode. For more information, see Change the Severity Level of a Syslog Message : https://www.cisco.com/c/en/us/td/docs/security/asa/asa922/configuration/general/asa-922-general-config/monitor-syslog.html#ID-2121-000006da known_false_positives: | - Admins may modify logging levels during maintenance or troubleshooting to reduce log volume. Verify against change management tickets. - Filter known admin accounts during maintenance windows. + Admins may modify logging levels during maintenance or troubleshooting to reduce log volume. Verify against change management tickets. + Filter known admin accounts during maintenance windows. references: - - https://www.cisco.com/c/en/us/td/docs/security/asa/asa-cli-reference/I-R/asa-command-ref-I-R/m_log-lz.html + - https://www.cisco.com/c/en/us/td/docs/security/asa/asa-cli-reference/I-R/asa-command-ref-I-R/m_log-lz.html drilldown_searches: - - name: View the detection results for $host$ - search: '%original_detection_search% | search host = $host$' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for $host$ - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($host$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for $host$ + search: '%original_detection_search% | search host = $host$' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for $host$ + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($host$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ executed command $command$ to tamper with logging filter configuration on the Cisco ASA host $host$. - risk_objects: - - field: host - type: system - score: 60 - - field: user - type: user - score: 60 - threat_objects: - - field: command - type: process + message: User $user$ executed command $command$ to tamper with logging filter configuration on the Cisco ASA host $host$. + risk_objects: + - field: host + type: system + score: 60 + - field: user + type: user + score: 60 + threat_objects: + - field: command + type: process tags: - analytic_story: - - Suspicious Cisco Adaptive Security Appliance Activity - asset_type: Network - mitre_attack_id: - - T1562 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: network + analytic_story: + - Suspicious Cisco Adaptive Security Appliance Activity + asset_type: Network + mitre_attack_id: + - T1562 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/generic/cisco_asa_generic_logs.log - source: not_applicable - sourcetype: cisco:asa + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/generic/cisco_asa_generic_logs.log + source: not_applicable + sourcetype: cisco:asa diff --git a/detections/application/cisco_asa___logging_message_suppression.yml b/detections/application/cisco_asa___logging_message_suppression.yml index e8789858f8..7509416919 100644 --- a/detections/application/cisco_asa___logging_message_suppression.yml +++ b/detections/application/cisco_asa___logging_message_suppression.yml @@ -6,78 +6,78 @@ author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - This analytic detects suppression of specific logging messages on Cisco ASA devices using the "no logging message" command. - Adversaries may suppress specific log message IDs to selectively disable logging of security-critical events such as authentication failures, configuration changes, or suspicious network activity. This targeted approach allows attackers to evade detection while maintaining normal logging operations that might otherwise alert administrators to complete logging disablement. - The detection monitors for command execution events (message ID 111008 or 111010) containing the "no logging message" command, which is used to suppress specific message IDs from being logged regardless of the configured severity level. - Investigate unauthorized message suppression, especially suppression of security-critical message IDs (authentication, authorization, configuration changes), suppression performed by non-administrative accounts, during unusual hours, or without documented justification. + This analytic detects suppression of specific logging messages on Cisco ASA devices using the "no logging message" command. + Adversaries may suppress specific log message IDs to selectively disable logging of security-critical events such as authentication failures, configuration changes, or suspicious network activity. This targeted approach allows attackers to evade detection while maintaining normal logging operations that might otherwise alert administrators to complete logging disablement. + The detection monitors for command execution events (message ID 111008 or 111010) containing the "no logging message" command, which is used to suppress specific message IDs from being logged regardless of the configured severity level. + Investigate unauthorized message suppression, especially suppression of security-critical message IDs (authentication, authorization, configuration changes), suppression performed by non-administrative accounts, during unusual hours, or without documented justification. data_source: - - Cisco ASA Logs + - Cisco ASA Logs search: | - `cisco_asa` - message_id IN (111008, 111010) - command = "no logging message *" - | fillnull - | stats count - earliest(_time) as firstTime - latest(_time) as lastTime - values(user) as user - values(action) as action - values(message_id) as message_id - values(command) as command - values(src_ip) as src_ip - values(process_name) as process_name - by host - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_asa___logging_message_suppression_filter` + `cisco_asa` + message_id IN (111008, 111010) + command = "no logging message *" + | fillnull + | stats count + earliest(_time) as firstTime + latest(_time) as lastTime + values(user) as user + values(action) as action + values(message_id) as message_id + values(command) as command + values(src_ip) as src_ip + values(process_name) as process_name + by host + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_asa___logging_message_suppression_filter` how_to_implement: | - This search requires Cisco ASA syslog data to be ingested into Splunk via the Cisco Security Cloud TA. - To ensure this detection works effectively, configure your ASA and FTD devices to generate and forward message ID 111008 and 111010. - If your logging level is set to 'notifications' or higher, these messages should already be included, else we recommend setting an event list that keeps the severity level you are using and adding the message IDs 111008 and 111010. - You can find specific instructions on how to set this up here : https://www.cisco.com/c/en/us/support/docs/security/pix-500-series-security-appliances/63884-config-asa-00.html. - You can also change the severity level of the above message id's to the syslog level you have currently enabled using the logging message syslog_id level severity_level command in global configuration mode. For more information, see Change the Severity Level of a Syslog Message : https://www.cisco.com/c/en/us/td/docs/security/asa/asa922/configuration/general/asa-922-general-config/monitor-syslog.html#ID-2121-000006da + This search requires Cisco ASA syslog data to be ingested into Splunk via the Cisco Security Cloud TA. + To ensure this detection works effectively, configure your ASA and FTD devices to generate and forward message ID 111008 and 111010. + If your logging level is set to 'notifications' or higher, these messages should already be included, else we recommend setting an event list that keeps the severity level you are using and adding the message IDs 111008 and 111010. + You can find specific instructions on how to set this up here : https://www.cisco.com/c/en/us/support/docs/security/pix-500-series-security-appliances/63884-config-asa-00.html. + You can also change the severity level of the above message id's to the syslog level you have currently enabled using the logging message syslog_id level severity_level command in global configuration mode. For more information, see Change the Severity Level of a Syslog Message : https://www.cisco.com/c/en/us/td/docs/security/asa/asa922/configuration/general/asa-922-general-config/monitor-syslog.html#ID-2121-000006da known_false_positives: | - Admins may suppress verbose messages to reduce log volume or manage storage. - Verify against change management and logging policies. Establish baseline of - approved suppressed message IDs. + Admins may suppress verbose messages to reduce log volume or manage storage. + Verify against change management and logging policies. Establish baseline of + approved suppressed message IDs. references: - - https://www.ncsc.gov.uk/static-assets/documents/malware-analysis-reports/RayInitiator-LINE-VIPER/ncsc-mar-rayinitiator-line-viper.pdf + - https://www.ncsc.gov.uk/static-assets/documents/malware-analysis-reports/RayInitiator-LINE-VIPER/ncsc-mar-rayinitiator-line-viper.pdf drilldown_searches: - - name: View the detection results for $host$ - search: '%original_detection_search% | search host = $host$' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for $host$ - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($host$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for $host$ + search: '%original_detection_search% | search host = $host$' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for $host$ + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($host$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ executed command $command$ to suppress specific logging message ID on Cisco ASA host $host$. - risk_objects: - - field: host - type: system - score: 50 - - field: user - type: user - score: 50 - threat_objects: - - field: command - type: process + message: User $user$ executed command $command$ to suppress specific logging message ID on Cisco ASA host $host$. + risk_objects: + - field: host + type: system + score: 50 + - field: user + type: user + score: 50 + threat_objects: + - field: command + type: process tags: - analytic_story: - - Suspicious Cisco Adaptive Security Appliance Activity - - ArcaneDoor - asset_type: Network - mitre_attack_id: - - T1562.002 - - T1070 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: network + analytic_story: + - Suspicious Cisco Adaptive Security Appliance Activity + - ArcaneDoor + asset_type: Network + mitre_attack_id: + - T1562.002 + - T1070 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/generic/cisco_asa_generic_logs.log - source: not_applicable - sourcetype: cisco:asa + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/generic/cisco_asa_generic_logs.log + source: not_applicable + sourcetype: cisco:asa diff --git a/detections/application/cisco_asa___new_local_user_account_created.yml b/detections/application/cisco_asa___new_local_user_account_created.yml index c4d203eafa..8b6b643d0e 100644 --- a/detections/application/cisco_asa___new_local_user_account_created.yml +++ b/detections/application/cisco_asa___new_local_user_account_created.yml @@ -6,70 +6,70 @@ author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - This analytic detects creation of new user accounts on Cisco ASA devices via CLI or ASDM. - Adversaries may create unauthorized user accounts to establish persistence, maintain backdoor access, or elevate privileges on network infrastructure devices. These rogue accounts can provide attackers with continued access even after initial compromise vectors are remediated. - The detection monitors for ASA message ID 502101, which is generated whenever a new user account is created on the device, capturing details including the username, privilege level, and the administrator who created the account. - Investigate unexpected account creations, especially those with elevated privileges (level 15), accounts created outside business hours, accounts with suspicious or generic names, or accounts created by non-administrative users. + This analytic detects creation of new user accounts on Cisco ASA devices via CLI or ASDM. + Adversaries may create unauthorized user accounts to establish persistence, maintain backdoor access, or elevate privileges on network infrastructure devices. These rogue accounts can provide attackers with continued access even after initial compromise vectors are remediated. + The detection monitors for ASA message ID 502101, which is generated whenever a new user account is created on the device, capturing details including the username, privilege level, and the administrator who created the account. + Investigate unexpected account creations, especially those with elevated privileges (level 15), accounts created outside business hours, accounts with suspicious or generic names, or accounts created by non-administrative users. data_source: - - Cisco ASA Logs + - Cisco ASA Logs search: | - `cisco_asa` - message_id IN (502101) - | fillnull - | stats count earliest(_time) as firstTime - latest(_time) as lastTime - values(action) as action - values(message_id) as message_id - values(result) as result - values(privilege_level) as privilege_level - by host user - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_asa___new_local_user_account_created_filter` + `cisco_asa` + message_id IN (502101) + | fillnull + | stats count earliest(_time) as firstTime + latest(_time) as lastTime + values(action) as action + values(message_id) as message_id + values(result) as result + values(privilege_level) as privilege_level + by host user + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_asa___new_local_user_account_created_filter` how_to_implement: | - This search requires Cisco ASA syslog data to be ingested into Splunk via the Cisco Security Cloud TA. - To ensure this detection works effectively, configure your ASA and FTD devices to generate and forward message ID 502101. - If your logging level is set to 'Notifications' or higher, these messages should already be included, else we recommend setting an event list that keeps the severity level you are using and adds message ID 502101. - You can find specific instructions on how to set this up here : https://www.cisco.com/c/en/us/support/docs/security/pix-500-series-security-appliances/63884-config-asa-00.html. - You can also change the severity level of the above message id's to the syslog level you have currently enabled using the logging message syslog_id level severity_level command in global configuration mode. For more information, see Change the Severity Level of a Syslog Message : https://www.cisco.com/c/en/us/td/docs/security/asa/asa922/configuration/general/asa-922-general-config/monitor-syslog.html#ID-2121-000006da + This search requires Cisco ASA syslog data to be ingested into Splunk via the Cisco Security Cloud TA. + To ensure this detection works effectively, configure your ASA and FTD devices to generate and forward message ID 502101. + If your logging level is set to 'Notifications' or higher, these messages should already be included, else we recommend setting an event list that keeps the severity level you are using and adds message ID 502101. + You can find specific instructions on how to set this up here : https://www.cisco.com/c/en/us/support/docs/security/pix-500-series-security-appliances/63884-config-asa-00.html. + You can also change the severity level of the above message id's to the syslog level you have currently enabled using the logging message syslog_id level severity_level command in global configuration mode. For more information, see Change the Severity Level of a Syslog Message : https://www.cisco.com/c/en/us/td/docs/security/asa/asa922/configuration/general/asa-922-general-config/monitor-syslog.html#ID-2121-000006da known_false_positives: | - Legitimate account creation occurs during employee onboarding, contractor provisioning, service account setup, or emergency access. Verify against HR records and change management tickets. - Filter known admin accounts during business hours. + Legitimate account creation occurs during employee onboarding, contractor provisioning, service account setup, or emergency access. Verify against HR records and change management tickets. + Filter known admin accounts during business hours. references: - - https://www.cisco.com/c/en/us/td/docs/security/asa/syslog/asa-syslog/syslog-messages-500000-to-520025.html#con_4773963 + - https://www.cisco.com/c/en/us/td/docs/security/asa/syslog/asa-syslog/syslog-messages-500000-to-520025.html#con_4773963 drilldown_searches: - - name: View the detection results for $host$ - search: '%original_detection_search% | search host = $host$' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for $host$ - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($host$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for $host$ + search: '%original_detection_search% | search host = $host$' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for $host$ + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($host$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: New local user account $user$ with privilege level $privilege_level$ was created on Cisco ASA host $host$. - risk_objects: - - field: host - type: system - score: 40 - - field: user - type: user - score: 40 - threat_objects: [] + message: New local user account $user$ with privilege level $privilege_level$ was created on Cisco ASA host $host$. + risk_objects: + - field: host + type: system + score: 40 + - field: user + type: user + score: 40 + threat_objects: [] tags: - analytic_story: - - Suspicious Cisco Adaptive Security Appliance Activity - asset_type: Network - mitre_attack_id: - - T1136.001 - - T1078.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: network + analytic_story: + - Suspicious Cisco Adaptive Security Appliance Activity + asset_type: Network + mitre_attack_id: + - T1136.001 + - T1078.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/generic/cisco_asa_generic_logs.log - source: not_applicable - sourcetype: cisco:asa + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/generic/cisco_asa_generic_logs.log + source: not_applicable + sourcetype: cisco:asa diff --git a/detections/application/cisco_asa___packet_capture_activity.yml b/detections/application/cisco_asa___packet_capture_activity.yml index 06608e8a62..1d55c6c543 100644 --- a/detections/application/cisco_asa___packet_capture_activity.yml +++ b/detections/application/cisco_asa___packet_capture_activity.yml @@ -6,30 +6,30 @@ author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - This analytic detects execution of packet capture commands on Cisco ASA devices via CLI or ASDM. - Adversaries may abuse the built-in packet capture functionality to perform network sniffing, intercept credentials transmitted over the network, capture sensitive data in transit, or gather intelligence about network traffic patterns and internal communications. Packet captures can reveal usernames, passwords, session tokens, and confidential business data. - The detection monitors for command execution events (message ID 111008 or 111010) containing "capture" commands, which are used to initiate packet capture sessions on specific interfaces or for specific traffic patterns on the ASA device. - Investigate unauthorized packet capture activities, especially captures targeting sensitive interfaces (internal network segments, DMZ), captures configured to capture large volumes of traffic, captures with suspicious filter criteria, captures initiated by non-administrative accounts, or captures during unusual hours. + This analytic detects execution of packet capture commands on Cisco ASA devices via CLI or ASDM. + Adversaries may abuse the built-in packet capture functionality to perform network sniffing, intercept credentials transmitted over the network, capture sensitive data in transit, or gather intelligence about network traffic patterns and internal communications. Packet captures can reveal usernames, passwords, session tokens, and confidential business data. + The detection monitors for command execution events (message ID 111008 or 111010) containing "capture" commands, which are used to initiate packet capture sessions on specific interfaces or for specific traffic patterns on the ASA device. + Investigate unauthorized packet capture activities, especially captures targeting sensitive interfaces (internal network segments, DMZ), captures configured to capture large volumes of traffic, captures with suspicious filter criteria, captures initiated by non-administrative accounts, or captures during unusual hours. data_source: - - Cisco ASA Logs + - Cisco ASA Logs search: | - `cisco_asa` - message_id IN (111008, 111010) - command IN ("capture *") - | fillnull - | stats count - earliest(_time) as firstTime - latest(_time) as lastTime - values(user) as user - values(action) as action - values(message_id) as message_id - values(command) as command - values(src_ip) as src_ip - values(process_name) as process_name - by host - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_asa___packet_capture_activity_filter` + `cisco_asa` + message_id IN (111008, 111010) + command IN ("capture *") + | fillnull + | stats count + earliest(_time) as firstTime + latest(_time) as lastTime + values(user) as user + values(action) as action + values(message_id) as message_id + values(command) as command + values(src_ip) as src_ip + values(process_name) as process_name + by host + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_asa___packet_capture_activity_filter` how_to_implement: | This search requires Cisco ASA syslog data to be ingested into Splunk via the Cisco Security Cloud TA. To ensure this detection works effectively, configure your ASA and FTD devices to generate and forward message ID 111008 and 111010. @@ -37,47 +37,47 @@ how_to_implement: | You can find specific instructions on how to set this up here : https://www.cisco.com/c/en/us/support/docs/security/pix-500-series-security-appliances/63884-config-asa-00.html. You can also change the severity level of the above message id's to the syslog level you have currently enabled using the logging message syslog_id level severity_level command in global configuration mode. For more information, see Change the Severity Level of a Syslog Message : https://www.cisco.com/c/en/us/td/docs/security/asa/asa922/configuration/general/asa-922-general-config/monitor-syslog.html#ID-2121-000006da known_false_positives: | - Admins use packet captures for troubleshooting, performance monitoring, or security investigations. Verify against change management. Filter known admin accounts during maintenance windows. + Admins use packet captures for troubleshooting, performance monitoring, or security investigations. Verify against change management. Filter known admin accounts during maintenance windows. references: - - https://www.cisco.com/c/en/us/td/docs/security/asa/asa-cli-reference/A-H/asa-command-ref-A-H/ca-cld-commands.html - - https://www.cisco.com/c/en/us/support/docs/security/asa-5500-x-series-next-generation-firewalls/118097-configure-asa-00.html - - https://www.ncsc.gov.uk/static-assets/documents/malware-analysis-reports/RayInitiator-LINE-VIPER/ncsc-mar-rayinitiator-line-viper.pdf + - https://www.cisco.com/c/en/us/td/docs/security/asa/asa-cli-reference/A-H/asa-command-ref-A-H/ca-cld-commands.html + - https://www.cisco.com/c/en/us/support/docs/security/asa-5500-x-series-next-generation-firewalls/118097-configure-asa-00.html + - https://www.ncsc.gov.uk/static-assets/documents/malware-analysis-reports/RayInitiator-LINE-VIPER/ncsc-mar-rayinitiator-line-viper.pdf drilldown_searches: - - name: View the detection results for $host$ - search: '%original_detection_search% | search host = $host$' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for $host$ - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($host$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for $host$ + search: '%original_detection_search% | search host = $host$' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for $host$ + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($host$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ executed packet capture command $command$ on Cisco ASA host $host$, potentially for network sniffing activity. - risk_objects: - - field: host - type: system - score: 50 - - field: user - type: user - score: 50 - threat_objects: - - field: command - type: process + message: User $user$ executed packet capture command $command$ on Cisco ASA host $host$, potentially for network sniffing activity. + risk_objects: + - field: host + type: system + score: 50 + - field: user + type: user + score: 50 + threat_objects: + - field: command + type: process tags: - analytic_story: - - Suspicious Cisco Adaptive Security Appliance Activity - - ArcaneDoor - asset_type: Network - mitre_attack_id: - - T1040 - - T1557 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: network + analytic_story: + - Suspicious Cisco Adaptive Security Appliance Activity + - ArcaneDoor + asset_type: Network + mitre_attack_id: + - T1040 + - T1557 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/generic/cisco_asa_generic_logs.log - source: not_applicable - sourcetype: cisco:asa + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/generic/cisco_asa_generic_logs.log + source: not_applicable + sourcetype: cisco:asa diff --git a/detections/application/cisco_asa___reconnaissance_command_activity.yml b/detections/application/cisco_asa___reconnaissance_command_activity.yml index bc70f87281..74d8dc88db 100644 --- a/detections/application/cisco_asa___reconnaissance_command_activity.yml +++ b/detections/application/cisco_asa___reconnaissance_command_activity.yml @@ -1,139 +1,139 @@ name: Cisco ASA - Reconnaissance Command Activity id: 6e9d4f7a-3c8b-4a9e-8d2f-7b5c9e1a6f3d -version: 1 -date: '2025-11-18' +version: 2 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - This analytic detects potential reconnaissance activities on Cisco ASA devices by identifying execution of multiple information-gathering "show" commands within a short timeframe. - Adversaries who gain initial access to network infrastructure devices typically perform systematic reconnaissance to understand the device configuration, network topology, security policies, connected systems, and potential attack paths. This reconnaissance phase involves executing multiple "show" commands to enumerate device details, running configurations, active connections, routing information, and VPN sessions. - The detection monitors for command execution events (message ID 111009) containing reconnaissance-oriented "show" commands (such as show running-config, show version, show interface, show crypto, show conn, etc.) and triggers when 7 or more distinct reconnaissance commands are executed within a 5-minute window by the same user. - Investigate reconnaissance bursts from non-administrative accounts, unusual source IP addresses, activity during off-hours, methodical command sequences suggesting automated enumeration, or reconnaissance activity correlated with other suspicious behaviors. - We recommend adapting the detection filters to exclude known legitimate administrative activities. + This analytic detects potential reconnaissance activities on Cisco ASA devices by identifying execution of multiple information-gathering "show" commands within a short timeframe. + Adversaries who gain initial access to network infrastructure devices typically perform systematic reconnaissance to understand the device configuration, network topology, security policies, connected systems, and potential attack paths. This reconnaissance phase involves executing multiple "show" commands to enumerate device details, running configurations, active connections, routing information, and VPN sessions. + The detection monitors for command execution events (message ID 111009) containing reconnaissance-oriented "show" commands (such as show running-config, show version, show interface, show crypto, show conn, etc.) and triggers when 7 or more distinct reconnaissance commands are executed within a 5-minute window by the same user. + Investigate reconnaissance bursts from non-administrative accounts, unusual source IP addresses, activity during off-hours, methodical command sequences suggesting automated enumeration, or reconnaissance activity correlated with other suspicious behaviors. + We recommend adapting the detection filters to exclude known legitimate administrative activities. data_source: - - Cisco ASA Logs + - Cisco ASA Logs search: | - `cisco_asa` - message_id IN (111009) - command IN ( - "show access-list*", - "show capture*", - "show conn*", - "show cpu*", - "show crypto*", - "show eigrp*", - "show failover*", - "show flow*", - "show interface*", - "show inventory*", - "show ip*", - "show license*", - "show memory*", - "show nat*", - "show ospf*", - "show process*", - "show running-config*", - "show startup-config*", - "show version*", - "show vpn-sessiondb*", - "show xlate*" - ) - | fillnull + `cisco_asa` + message_id IN (111009) + command IN ( + "show access-list*", + "show capture*", + "show conn*", + "show cpu*", + "show crypto*", + "show eigrp*", + "show failover*", + "show flow*", + "show interface*", + "show inventory*", + "show ip*", + "show license*", + "show memory*", + "show nat*", + "show ospf*", + "show process*", + "show running-config*", + "show startup-config*", + "show version*", + "show vpn-sessiondb*", + "show xlate*" + ) + | fillnull - ``` - Normalize command variations to base command types to count distinct reconnaissance categories. - For example, "show running-config", "show running-config | include username", and "show running-config interface" all count as one command type. - This prevents adversaries from evading detection by adding arguments or using multiple variations of the same command. - ``` - - | eval command_type=case( - match(command, "^show access-list"), "show access-list", - match(command, "^show conn"), "show conn", - match(command, "^show cpu"), "show cpu", - match(command, "^show crypto"), "show crypto", - match(command, "^show eigrp"), "show eigrp", - match(command, "^show failover"), "show failover", - match(command, "^show flow"), "show flow", - match(command, "^show interface"), "show interface", - match(command, "^show inventory"), "show inventory", - match(command, "^show ip"), "show ip", - match(command, "^show license"), "show license", - match(command, "^show memory"), "show memory", - match(command, "^show nat"), "show nat", - match(command, "^show ospf"), "show ospf", - match(command, "^show process"), "show process", - match(command, "^show running-config"), "show running-config", - match(command, "^show startup-config"), "show startup-config", - match(command, "^show version"), "show version", - match(command, "^show vpn-sessiondb"), "show vpn-sessiondb", - match(command, "^show xlate"), "show xlate", - true(), command) - - | bin _time span=5m - - | stats count - earliest(_time) as firstTime - latest(_time) as lastTime - dc(command_type) as unique_recon_commands - values(command_type) as command_types - values(command) as commands - values(src_ip) as src_ip - values(message_id) as message_id - values(action) as action - by _time host user + ``` + Normalize command variations to base command types to count distinct reconnaissance categories. + For example, "show running-config", "show running-config | include username", and "show running-config interface" all count as one command type. + This prevents adversaries from evading detection by adding arguments or using multiple variations of the same command. + ``` - | where unique_recon_commands >= 7 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_asa___reconnaissance_command_activity_filter` + | eval command_type=case( + match(command, "^show access-list"), "show access-list", + match(command, "^show conn"), "show conn", + match(command, "^show cpu"), "show cpu", + match(command, "^show crypto"), "show crypto", + match(command, "^show eigrp"), "show eigrp", + match(command, "^show failover"), "show failover", + match(command, "^show flow"), "show flow", + match(command, "^show interface"), "show interface", + match(command, "^show inventory"), "show inventory", + match(command, "^show ip"), "show ip", + match(command, "^show license"), "show license", + match(command, "^show memory"), "show memory", + match(command, "^show nat"), "show nat", + match(command, "^show ospf"), "show ospf", + match(command, "^show process"), "show process", + match(command, "^show running-config"), "show running-config", + match(command, "^show startup-config"), "show startup-config", + match(command, "^show version"), "show version", + match(command, "^show vpn-sessiondb"), "show vpn-sessiondb", + match(command, "^show xlate"), "show xlate", + true(), command) + + | bin _time span=5m + + | stats count + earliest(_time) as firstTime + latest(_time) as lastTime + dc(command_type) as unique_recon_commands + values(command_type) as command_types + values(command) as commands + values(src_ip) as src_ip + values(message_id) as message_id + values(action) as action + by _time host user + + | where unique_recon_commands >= 7 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_asa___reconnaissance_command_activity_filter` how_to_implement: | - This search requires Cisco ASA syslog data to be ingested into Splunk via the Cisco Security Cloud TA. - To ensure this detection works effectively, configure your ASA and FTD devices to generate and forward message ID 111009. - If your logging level is set to 'Debugging', these messages should already be included, else we recommend setting an event list that keeps the severity level you are using and adds message ID 111009. - You can find specific instructions on how to set this up here : https://www.cisco.com/c/en/us/support/docs/security/pix-500-series-security-appliances/63884-config-asa-00.html. - You can also change the severity level of the above message id's to the syslog level you have currently enabled using the logging message syslog_id level severity_level command in global configuration mode. For more information, see Change the Severity Level of a Syslog Message : https://www.cisco.com/c/en/us/td/docs/security/asa/asa922/configuration/general/asa-922-general-config/monitor-syslog.html#ID-2121-000006da + This search requires Cisco ASA syslog data to be ingested into Splunk via the Cisco Security Cloud TA. + To ensure this detection works effectively, configure your ASA and FTD devices to generate and forward message ID 111009. + If your logging level is set to 'Debugging', these messages should already be included, else we recommend setting an event list that keeps the severity level you are using and adds message ID 111009. + You can find specific instructions on how to set this up here : https://www.cisco.com/c/en/us/support/docs/security/pix-500-series-security-appliances/63884-config-asa-00.html. + You can also change the severity level of the above message id's to the syslog level you have currently enabled using the logging message syslog_id level severity_level command in global configuration mode. For more information, see Change the Severity Level of a Syslog Message : https://www.cisco.com/c/en/us/td/docs/security/asa/asa922/configuration/general/asa-922-general-config/monitor-syslog.html#ID-2121-000006da known_false_positives: | - Legitimate sequences occur during troubleshooting, health checks, upgrades, audits, or automation scripts. Verify against change management. - Filter known admin accounts, trusted management stations, or adjust threshold based on baseline. + Legitimate sequences occur during troubleshooting, health checks, upgrades, audits, or automation scripts. Verify against change management. + Filter known admin accounts, trusted management stations, or adjust threshold based on baseline. references: - - https://www.cisco.com/c/en/us/td/docs/security/asa/asa-cli-reference/S/asa-command-ref-S/sa-shov-commands.html + - https://www.cisco.com/c/en/us/td/docs/security/asa/asa-cli-reference/S/asa-command-ref-S/sa-shov-commands.html drilldown_searches: - - name: View the detection results for $host$ and $user$ - search: '%original_detection_search% | search host = $host$ user = $user$' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for $host$ - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($host$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for $host$ and $user$ + search: '%original_detection_search% | search host = $host$ user = $user$' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for $host$ + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($host$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ executed $unique_recon_commands$ distinct reconnaissance commands of type $command_types$ within a 5-minute window on Cisco ASA host $host$, indicating potential reconnaissance activity. - risk_objects: - - field: host - type: system - score: 50 - - field: user - type: user - score: 40 - threat_objects: - - field: src_ip - type: ip_address + message: User $user$ executed $unique_recon_commands$ distinct reconnaissance commands of type $command_types$ within a 5-minute window on Cisco ASA host $host$, indicating potential reconnaissance activity. + risk_objects: + - field: host + type: system + score: 50 + - field: user + type: user + score: 40 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Suspicious Cisco Adaptive Security Appliance Activity - asset_type: Network - mitre_attack_id: - - T1082 - - T1590.001 - - T1590.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: network + analytic_story: + - Suspicious Cisco Adaptive Security Appliance Activity + asset_type: Network + mitre_attack_id: + - T1082 + - T1590.001 + - T1590.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/generic/cisco_asa_generic_logs.log - source: not_applicable - sourcetype: cisco:asa + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/generic/cisco_asa_generic_logs.log + source: not_applicable + sourcetype: cisco:asa diff --git a/detections/application/cisco_asa___user_account_deleted_from_local_database.yml b/detections/application/cisco_asa___user_account_deleted_from_local_database.yml index 98fc7b8611..90681da6df 100644 --- a/detections/application/cisco_asa___user_account_deleted_from_local_database.yml +++ b/detections/application/cisco_asa___user_account_deleted_from_local_database.yml @@ -6,70 +6,70 @@ author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - This analytic detects deletion of user accounts from Cisco ASA devices via CLI or ASDM. - Adversaries may delete local accounts to cover their tracks, remove evidence of their activities, disrupt incident response efforts, or deny legitimate administrator access during an attack. Account deletion can also indicate an attempt to hide the creation of temporary accounts used during compromise. - The detection monitors for ASA message ID 502102, which is generated whenever a local user account is deleted from the device, capturing details including the deleted username, privilege level, and the administrator who performed the deletion. - Investigate unexpected account deletions, especially those involving privileged accounts (level 15), deletions performed outside business hours, deletions by non-administrative users, or deletions that coincide with other suspicious activities. + This analytic detects deletion of user accounts from Cisco ASA devices via CLI or ASDM. + Adversaries may delete local accounts to cover their tracks, remove evidence of their activities, disrupt incident response efforts, or deny legitimate administrator access during an attack. Account deletion can also indicate an attempt to hide the creation of temporary accounts used during compromise. + The detection monitors for ASA message ID 502102, which is generated whenever a local user account is deleted from the device, capturing details including the deleted username, privilege level, and the administrator who performed the deletion. + Investigate unexpected account deletions, especially those involving privileged accounts (level 15), deletions performed outside business hours, deletions by non-administrative users, or deletions that coincide with other suspicious activities. data_source: - - Cisco ASA Logs + - Cisco ASA Logs search: | - `cisco_asa` - message_id IN (502102) - | fillnull - | stats count earliest(_time) as firstTime - latest(_time) as lastTime - values(action) as action - values(message_id) as message_id - values(result) as result - values(privilege_level) as privilege_level - by host user - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_asa___user_account_deleted_from_local_database_filter` + `cisco_asa` + message_id IN (502102) + | fillnull + | stats count earliest(_time) as firstTime + latest(_time) as lastTime + values(action) as action + values(message_id) as message_id + values(result) as result + values(privilege_level) as privilege_level + by host user + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_asa___user_account_deleted_from_local_database_filter` how_to_implement: | - This search requires Cisco ASA syslog data to be ingested into Splunk via the Cisco Security Cloud TA. - To ensure this detection works effectively, configure your ASA and FTD devices to generate and forward message ID 502102. - If your logging level is set to 'Notifications' or higher, these messages should already be included, else we recommend setting an event list that keeps the severity level you are using and adds message ID 502102. - You can find specific instructions on how to set this up here : https://www.cisco.com/c/en/us/support/docs/security/pix-500-series-security-appliances/63884-config-asa-00.html. - You can also change the severity level of the above message id's to the syslog level you have currently enabled using the logging message syslog_id level severity_level command in global configuration mode. For more information, see Change the Severity Level of a Syslog Message : https://www.cisco.com/c/en/us/td/docs/security/asa/asa922/configuration/general/asa-922-general-config/monitor-syslog.html#ID-2121-000006da + This search requires Cisco ASA syslog data to be ingested into Splunk via the Cisco Security Cloud TA. + To ensure this detection works effectively, configure your ASA and FTD devices to generate and forward message ID 502102. + If your logging level is set to 'Notifications' or higher, these messages should already be included, else we recommend setting an event list that keeps the severity level you are using and adds message ID 502102. + You can find specific instructions on how to set this up here : https://www.cisco.com/c/en/us/support/docs/security/pix-500-series-security-appliances/63884-config-asa-00.html. + You can also change the severity level of the above message id's to the syslog level you have currently enabled using the logging message syslog_id level severity_level command in global configuration mode. For more information, see Change the Severity Level of a Syslog Message : https://www.cisco.com/c/en/us/td/docs/security/asa/asa922/configuration/general/asa-922-general-config/monitor-syslog.html#ID-2121-000006da known_false_positives: | - Legitimate deletions occur during employee offboarding, contractor removal, account cleanup, or service account decommissioning. Verify against HR records and change management tickets. - Filter known admin accounts during business hours. + Legitimate deletions occur during employee offboarding, contractor removal, account cleanup, or service account decommissioning. Verify against HR records and change management tickets. + Filter known admin accounts during business hours. references: - - https://www.cisco.com/c/en/us/td/docs/security/asa/syslog/asa-syslog/syslog-messages-500000-to-520025.html#con_4773969 + - https://www.cisco.com/c/en/us/td/docs/security/asa/syslog/asa-syslog/syslog-messages-500000-to-520025.html#con_4773969 drilldown_searches: - - name: View the detection results for $host$ - search: '%original_detection_search% | search host = $host$' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for $host$ - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($host$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for $host$ + search: '%original_detection_search% | search host = $host$' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for $host$ + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($host$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Local user account $user$ with privilege level $privilege_level$ was deleted from Cisco ASA host $host$. - risk_objects: - - field: host - type: system - score: 40 - - field: user - type: user - score: 40 - threat_objects: [] + message: Local user account $user$ with privilege level $privilege_level$ was deleted from Cisco ASA host $host$. + risk_objects: + - field: host + type: system + score: 40 + - field: user + type: user + score: 40 + threat_objects: [] tags: - analytic_story: - - Suspicious Cisco Adaptive Security Appliance Activity - asset_type: Network - mitre_attack_id: - - T1531 - - T1070.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: network + analytic_story: + - Suspicious Cisco Adaptive Security Appliance Activity + asset_type: Network + mitre_attack_id: + - T1531 + - T1070.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/generic/cisco_asa_generic_logs.log - source: not_applicable - sourcetype: cisco:asa + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/generic/cisco_asa_generic_logs.log + source: not_applicable + sourcetype: cisco:asa diff --git a/detections/application/cisco_asa___user_account_lockout_threshold_exceeded.yml b/detections/application/cisco_asa___user_account_lockout_threshold_exceeded.yml index e709b74354..4df474afbf 100644 --- a/detections/application/cisco_asa___user_account_lockout_threshold_exceeded.yml +++ b/detections/application/cisco_asa___user_account_lockout_threshold_exceeded.yml @@ -6,70 +6,70 @@ author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - This analytic detects user account lockouts on Cisco ASA devices resulting from excessive failed authentication attempts. - Account lockouts may indicate brute force attacks, password spraying campaigns, credential stuffing attempts using compromised credentials from external breaches, or misconfigured automation attempting authentication with incorrect credentials. These activities represent attempts to gain unauthorized access to network infrastructure. - The detection monitors for ASA message ID 113006, which is generated when a user account is locked out after exceeding the configured maximum number of failed authentication attempts, capturing the locked account name and the failure threshold that was exceeded. - Investigate account lockouts for privileged or administrative accounts, multiple simultaneous lockouts affecting different accounts (suggesting password spraying), lockouts originating from unusual source IP addresses, lockouts during off-hours, or patterns suggesting automated attack tools. + This analytic detects user account lockouts on Cisco ASA devices resulting from excessive failed authentication attempts. + Account lockouts may indicate brute force attacks, password spraying campaigns, credential stuffing attempts using compromised credentials from external breaches, or misconfigured automation attempting authentication with incorrect credentials. These activities represent attempts to gain unauthorized access to network infrastructure. + The detection monitors for ASA message ID 113006, which is generated when a user account is locked out after exceeding the configured maximum number of failed authentication attempts, capturing the locked account name and the failure threshold that was exceeded. + Investigate account lockouts for privileged or administrative accounts, multiple simultaneous lockouts affecting different accounts (suggesting password spraying), lockouts originating from unusual source IP addresses, lockouts during off-hours, or patterns suggesting automated attack tools. data_source: - - Cisco ASA Logs + - Cisco ASA Logs search: | - `cisco_asa` - message_id IN (113006) - | rex "locked out on exceeding '(?\d+)' successive failed authentication attempts" - | rex "User '(?[^']+)' locked out" - | eval failure_description="locked out on exceeding " . attempts_count . " successive failed authentication attempts" - | fillnull - | stats earliest(_time) as firstTime - latest(_time) as lastTime - values(message_id) as message_id - values(failure_description) as failure_description - by host user - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_asa___user_account_lockout_threshold_exceeded_filter` + `cisco_asa` + message_id IN (113006) + | rex "locked out on exceeding '(?\d+)' successive failed authentication attempts" + | rex "User '(?[^']+)' locked out" + | eval failure_description="locked out on exceeding " . attempts_count . " successive failed authentication attempts" + | fillnull + | stats earliest(_time) as firstTime + latest(_time) as lastTime + values(message_id) as message_id + values(failure_description) as failure_description + by host user + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_asa___user_account_lockout_threshold_exceeded_filter` how_to_implement: | - This search requires Cisco ASA syslog data to be ingested into Splunk via the Cisco Security Cloud TA. - To ensure this detection works effectively, configure your ASA and FTD devices to generate and forward message ID 113006. - If your logging level is set to 'Informational' or higher, these messages should already be included, else we recommend setting an event list that keeps the severity level you are using and adds message ID 113006. - You can find specific instructions on how to set this up here : https://www.cisco.com/c/en/us/support/docs/security/pix-500-series-security-appliances/63884-config-asa-00.html. - You can also change the severity level of the above message id's to the syslog level you have currently enabled using the logging message syslog_id level severity_level command in global configuration mode. For more information, see Change the Severity Level of a Syslog Message : https://www.cisco.com/c/en/us/td/docs/security/asa/asa922/configuration/general/asa-922-general-config/monitor-syslog.html#ID-2121-000006da + This search requires Cisco ASA syslog data to be ingested into Splunk via the Cisco Security Cloud TA. + To ensure this detection works effectively, configure your ASA and FTD devices to generate and forward message ID 113006. + If your logging level is set to 'Informational' or higher, these messages should already be included, else we recommend setting an event list that keeps the severity level you are using and adds message ID 113006. + You can find specific instructions on how to set this up here : https://www.cisco.com/c/en/us/support/docs/security/pix-500-series-security-appliances/63884-config-asa-00.html. + You can also change the severity level of the above message id's to the syslog level you have currently enabled using the logging message syslog_id level severity_level command in global configuration mode. For more information, see Change the Severity Level of a Syslog Message : https://www.cisco.com/c/en/us/td/docs/security/asa/asa922/configuration/general/asa-922-general-config/monitor-syslog.html#ID-2121-000006da known_false_positives: | - Legitimate lockouts occur from forgotten passwords, typos, script misconfigurations, or connectivity issues. Verify against help desk tickets. Filter known accounts during business hours or establish baseline patterns. + Legitimate lockouts occur from forgotten passwords, typos, script misconfigurations, or connectivity issues. Verify against help desk tickets. Filter known accounts during business hours or establish baseline patterns. references: - - https://www.cisco.com/c/en/us/td/docs/security/asa/syslog/asa-syslog/syslog-messages-101001-to-199021.html#con_4769446 + - https://www.cisco.com/c/en/us/td/docs/security/asa/syslog/asa-syslog/syslog-messages-101001-to-199021.html#con_4769446 drilldown_searches: - - name: View the detection results for $host$ - search: '%original_detection_search% | search host = $host$' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for $host$ - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($host$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for $host$ + search: '%original_detection_search% | search host = $host$' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for $host$ + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($host$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User account $user$ was $failure_description$ on Cisco ASA host $host$. - risk_objects: - - field: host - type: system - score: 40 - - field: user - type: user - score: 30 - threat_objects: [] + message: User account $user$ was $failure_description$ on Cisco ASA host $host$. + risk_objects: + - field: host + type: system + score: 40 + - field: user + type: user + score: 30 + threat_objects: [] tags: - analytic_story: - - Suspicious Cisco Adaptive Security Appliance Activity - asset_type: Network - mitre_attack_id: - - T1110.001 - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: network + analytic_story: + - Suspicious Cisco Adaptive Security Appliance Activity + asset_type: Network + mitre_attack_id: + - T1110.001 + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/generic/cisco_asa_generic_logs.log - source: not_applicable - sourcetype: cisco:asa + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/generic/cisco_asa_generic_logs.log + source: not_applicable + sourcetype: cisco:asa diff --git a/detections/application/cisco_asa___user_privilege_level_change.yml b/detections/application/cisco_asa___user_privilege_level_change.yml index 27ebe5a70f..3bbf6c0dcd 100644 --- a/detections/application/cisco_asa___user_privilege_level_change.yml +++ b/detections/application/cisco_asa___user_privilege_level_change.yml @@ -6,71 +6,71 @@ author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - This analytic detects privilege level changes for user accounts on Cisco ASA devices via CLI or ASDM. - Adversaries may escalate account privileges to gain elevated access to network infrastructure, enable additional command execution capabilities, or establish higher-level persistent access. Privilege levels on Cisco ASA range from 0 (lowest) to 15 (full administrative access), with level 15 providing complete device control. - The detection monitors for ASA message ID 502103, which is generated whenever a user account's privilege level is modified, capturing both the old and new privilege levels along with the username and administrator who made the change. - Investigate unexpected privilege changes, especially escalations to level 15, substantial privilege increases (e.g., from level 1 to 15), changes performed outside business hours, changes by non-administrative users, or changes without corresponding change management tickets. + This analytic detects privilege level changes for user accounts on Cisco ASA devices via CLI or ASDM. + Adversaries may escalate account privileges to gain elevated access to network infrastructure, enable additional command execution capabilities, or establish higher-level persistent access. Privilege levels on Cisco ASA range from 0 (lowest) to 15 (full administrative access), with level 15 providing complete device control. + The detection monitors for ASA message ID 502103, which is generated whenever a user account's privilege level is modified, capturing both the old and new privilege levels along with the username and administrator who made the change. + Investigate unexpected privilege changes, especially escalations to level 15, substantial privilege increases (e.g., from level 1 to 15), changes performed outside business hours, changes by non-administrative users, or changes without corresponding change management tickets. data_source: - - Cisco ASA Logs + - Cisco ASA Logs search: | - `cisco_asa` - message_id IN (502103) - | fillnull - | stats earliest(_time) as firstTime - latest(_time) as lastTime - values(action) as action - values(message_id) as message_id - values(old_privilege_level) as old_privilege_level - values(new_privilege_level) as new_privilege_level - by host user - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_asa___user_privilege_level_change_filter` + `cisco_asa` + message_id IN (502103) + | fillnull + | stats earliest(_time) as firstTime + latest(_time) as lastTime + values(action) as action + values(message_id) as message_id + values(old_privilege_level) as old_privilege_level + values(new_privilege_level) as new_privilege_level + by host user + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_asa___user_privilege_level_change_filter` how_to_implement: | - This search requires Cisco ASA syslog data to be ingested into Splunk via the Cisco Security Cloud TA. - To ensure this detection works effectively, configure your ASA and FTD devices to generate and forward message ID 502103. - If your logging level is set to 'Notifications' or higher, these messages should already be included, else we recommend setting an event list that keeps the severity level you are using and adding the message IDs 502103. - You can find specific instructions on how to set this up here : https://www.cisco.com/c/en/us/support/docs/security/pix-500-series-security-appliances/63884-config-asa-00.html. - You can also change the severity level of the above message id's to the syslog level you have currently enabled using the logging message syslog_id level severity_level command in global configuration mode. For more information, see Change the Severity Level of a Syslog Message : https://www.cisco.com/c/en/us/td/docs/security/asa/asa922/configuration/general/asa-922-general-config/monitor-syslog.html#ID-2121-000006da + This search requires Cisco ASA syslog data to be ingested into Splunk via the Cisco Security Cloud TA. + To ensure this detection works effectively, configure your ASA and FTD devices to generate and forward message ID 502103. + If your logging level is set to 'Notifications' or higher, these messages should already be included, else we recommend setting an event list that keeps the severity level you are using and adding the message IDs 502103. + You can find specific instructions on how to set this up here : https://www.cisco.com/c/en/us/support/docs/security/pix-500-series-security-appliances/63884-config-asa-00.html. + You can also change the severity level of the above message id's to the syslog level you have currently enabled using the logging message syslog_id level severity_level command in global configuration mode. For more information, see Change the Severity Level of a Syslog Message : https://www.cisco.com/c/en/us/td/docs/security/asa/asa922/configuration/general/asa-922-general-config/monitor-syslog.html#ID-2121-000006da known_false_positives: | - Legitimate changes occur during role changes, temporary escalation for maintenance, or security policy adjustments. Verify against change management. Filter known admin accounts during maintenance windows. + Legitimate changes occur during role changes, temporary escalation for maintenance, or security policy adjustments. Verify against change management. Filter known admin accounts during maintenance windows. references: - - https://www.cisco.com/c/en/us/td/docs/security/asa/syslog/asa-syslog/syslog-messages-500000-to-520025.html#con_4773975 - - https://www.ncsc.gov.uk/static-assets/documents/malware-analysis-reports/RayInitiator-LINE-VIPER/ncsc-mar-rayinitiator-line-viper.pdf + - https://www.cisco.com/c/en/us/td/docs/security/asa/syslog/asa-syslog/syslog-messages-500000-to-520025.html#con_4773975 + - https://www.ncsc.gov.uk/static-assets/documents/malware-analysis-reports/RayInitiator-LINE-VIPER/ncsc-mar-rayinitiator-line-viper.pdf drilldown_searches: - - name: View the detection results for $host$ - search: '%original_detection_search% | search host = $host$' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for $host$ - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($host$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for $host$ + search: '%original_detection_search% | search host = $host$' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for $host$ + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($host$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User account $user$ privilege level changed from $old_privilege_level$ to $new_privilege_level$ on Cisco ASA host $host$. - risk_objects: - - field: host - type: system - score: 40 - - field: user - type: user - score: 40 - threat_objects: [] + message: User account $user$ privilege level changed from $old_privilege_level$ to $new_privilege_level$ on Cisco ASA host $host$. + risk_objects: + - field: host + type: system + score: 40 + - field: user + type: user + score: 40 + threat_objects: [] tags: - analytic_story: - - Suspicious Cisco Adaptive Security Appliance Activity - - ArcaneDoor - asset_type: Network - mitre_attack_id: - - T1078.003 - - T1098 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: network + analytic_story: + - Suspicious Cisco Adaptive Security Appliance Activity + - ArcaneDoor + asset_type: Network + mitre_attack_id: + - T1078.003 + - T1098 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/generic/cisco_asa_generic_logs.log - source: not_applicable - sourcetype: cisco:asa + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_asa/generic/cisco_asa_generic_logs.log + source: not_applicable + sourcetype: cisco:asa diff --git a/detections/application/cisco_duo_admin_login_unusual_browser.yml b/detections/application/cisco_duo_admin_login_unusual_browser.yml index 94cfd9678f..6b2d14c9b9 100644 --- a/detections/application/cisco_duo_admin_login_unusual_browser.yml +++ b/detections/application/cisco_duo_admin_login_unusual_browser.yml @@ -1,72 +1,62 @@ name: Cisco Duo Admin Login Unusual Browser id: b38932ad-e663-4e90-bfdf-8446ee5b3f34 -version: 2 -date: '2026-01-14' +version: 3 +date: '2026-02-25' author: Patrick Bareiss, Splunk data_source: -- Cisco Duo Activity + - Cisco Duo Activity type: TTP status: production -description: The following analytic identifies instances where a Duo admin logs in using a browser other than Chrome, which is considered unusual - based on typical access patterns. Please adjust as needed to your environment. The detection leverages Duo activity logs ingested via the Cisco - Security Cloud App and filters for admin login actions where the browser is not Chrome. By renaming and aggregating relevant fields such as user, - browser, IP address, and location, the analytic highlights potentially suspicious access attempts that deviate from the norm. This behavior is - significant for a SOC because the use of an unexpected browser may indicate credential compromise, session hijacking, or the use of unauthorized - devices by attackers attempting to evade detection. Detecting such anomalies enables early investigation and response, helping to prevent privilege - escalation, policy manipulation, or further compromise of sensitive administrative accounts. The impact of this attack could include unauthorized - changes to security policies, user access, or the disabling of critical security controls, posing a substantial risk to the organizations security posture. -search: '`cisco_duo_activity` "action.name"=admin_login NOT access_device.browser IN (Chrome) - | rename actor.name as user access_device.ip.address as src_ip - | stats count min(_time) as firstTime max(_time) as lastTime by access_device.browser - access_device.browser_version src_ip access_device.location.city - access_device.location.country access_device.location.state access_device.os access_device.os_version - actor.details actor.type outcome.result user - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `cisco_duo_admin_login_unusual_browser_filter`' -how_to_implement: The analytic leverages Duo activity logs to be ingested using the - Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). +description: The following analytic identifies instances where a Duo admin logs in using a browser other than Chrome, which is considered unusual based on typical access patterns. Please adjust as needed to your environment. The detection leverages Duo activity logs ingested via the Cisco Security Cloud App and filters for admin login actions where the browser is not Chrome. By renaming and aggregating relevant fields such as user, browser, IP address, and location, the analytic highlights potentially suspicious access attempts that deviate from the norm. This behavior is significant for a SOC because the use of an unexpected browser may indicate credential compromise, session hijacking, or the use of unauthorized devices by attackers attempting to evade detection. Detecting such anomalies enables early investigation and response, helping to prevent privilege escalation, policy manipulation, or further compromise of sensitive administrative accounts. The impact of this attack could include unauthorized changes to security policies, user access, or the disabling of critical security controls, posing a substantial risk to the organizations security posture. +search: |- + `cisco_duo_activity` "action.name"=admin_login NOT access_device.browser IN (Chrome) + | rename actor.name as user access_device.ip.address as src_ip + | stats count min(_time) as firstTime max(_time) as lastTime + BY access_device.browser access_device.browser_version src_ip + access_device.location.city access_device.location.country access_device.location.state + access_device.os access_device.os_version actor.details + actor.type outcome.result user + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_duo_admin_login_unusual_browser_filter` +how_to_implement: The analytic leverages Duo activity logs to be ingested using the Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). known_false_positives: No false positives have been identified at this time. references: -- https://splunkbase.splunk.com/app/7404 + - https://splunkbase.splunk.com/app/7404 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A user $user$ has logged in using an unusual browser $access_device.browser$ from $src_ip$. - risk_objects: - - field: user - type: user - score: 48 - threat_objects: - - field: access_device.browser - type: http_user_agent - - field: src_ip - type: ip_address + message: A user $user$ has logged in using an unusual browser $access_device.browser$ from $src_ip$. + risk_objects: + - field: user + type: user + score: 48 + threat_objects: + - field: access_device.browser + type: http_user_agent + - field: src_ip + type: ip_address tags: - analytic_story: - - Cisco Duo Suspicious Activity - asset_type: Identity - mitre_attack_id: - - T1556 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Cisco Duo Suspicious Activity + asset_type: Identity + mitre_attack_id: + - T1556 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_unusual_admin_login/cisco_duo_activity.json - source: duo - sourcetype: cisco:duo:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_unusual_admin_login/cisco_duo_activity.json + source: duo + sourcetype: cisco:duo:activity diff --git a/detections/application/cisco_duo_admin_login_unusual_country.yml b/detections/application/cisco_duo_admin_login_unusual_country.yml index 6aec019c17..6461c7bb62 100644 --- a/detections/application/cisco_duo_admin_login_unusual_country.yml +++ b/detections/application/cisco_duo_admin_login_unusual_country.yml @@ -1,72 +1,62 @@ name: Cisco Duo Admin Login Unusual Country id: 1bf631d1-44a0-472b-98c4-2975b8b281df -version: 2 -date: '2026-01-14' +version: 3 +date: '2026-02-25' author: Patrick Bareiss, Splunk data_source: -- Cisco Duo Activity + - Cisco Duo Activity type: TTP status: production -description: The following analytic detects instances where a Duo admin login originates from a country outside of the United States, - which may indicate suspicious or unauthorized access attempts. Please adjust as needed to your environment. It works by analyzing Duo activity logs - for admin login actions and filtering out events where the access device's country is not within the expected region. By correlating user, device, - browser, and location details, the analytic highlights anomalies in geographic login patterns. This behavior is critical for a SOC to identify because - admin accounts have elevated privileges, and access from unusual countries can be a strong indicator of credential compromise, account takeover, - or targeted attacks. Early detection of such activity enables rapid investigation and response, reducing the risk of unauthorized changes, data breaches, - or further lateral movement within the environment. The impact of this attack can be severe, potentially allowing attackers to bypass security controls, - alter configurations, or exfiltrate sensitive information. -search: '`cisco_duo_activity` "action.name"=admin_login NOT access_device.location.country IN ("United States") - | rename actor.name as user access_device.ip.address as src_ip - | stats count min(_time) as firstTime max(_time) as lastTime by access_device.browser - access_device.browser_version src_ip access_device.location.city - access_device.location.country access_device.location.state access_device.os access_device.os_version - actor.details actor.type outcome.result user - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `cisco_duo_admin_login_unusual_country_filter`' -how_to_implement: The analytic leverages Duo activity logs to be ingested using the - Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). +description: The following analytic detects instances where a Duo admin login originates from a country outside of the United States, which may indicate suspicious or unauthorized access attempts. Please adjust as needed to your environment. It works by analyzing Duo activity logs for admin login actions and filtering out events where the access device's country is not within the expected region. By correlating user, device, browser, and location details, the analytic highlights anomalies in geographic login patterns. This behavior is critical for a SOC to identify because admin accounts have elevated privileges, and access from unusual countries can be a strong indicator of credential compromise, account takeover, or targeted attacks. Early detection of such activity enables rapid investigation and response, reducing the risk of unauthorized changes, data breaches, or further lateral movement within the environment. The impact of this attack can be severe, potentially allowing attackers to bypass security controls, alter configurations, or exfiltrate sensitive information. +search: |- + `cisco_duo_activity` "action.name"=admin_login NOT access_device.location.country IN ("United States") + | rename actor.name as user access_device.ip.address as src_ip + | stats count min(_time) as firstTime max(_time) as lastTime + BY access_device.browser access_device.browser_version src_ip + access_device.location.city access_device.location.country access_device.location.state + access_device.os access_device.os_version actor.details + actor.type outcome.result user + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_duo_admin_login_unusual_country_filter` +how_to_implement: The analytic leverages Duo activity logs to be ingested using the Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). known_false_positives: No false positives have been identified at this time. references: -- https://splunkbase.splunk.com/app/7404 + - https://splunkbase.splunk.com/app/7404 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A user $user$ has logged in using an unusual country using browser $access_device.browser$ from $src_ip$. - risk_objects: - - field: user - type: user - score: 48 - threat_objects: - - field: access_device.browser - type: http_user_agent - - field: src_ip - type: ip_address + message: A user $user$ has logged in using an unusual country using browser $access_device.browser$ from $src_ip$. + risk_objects: + - field: user + type: user + score: 48 + threat_objects: + - field: access_device.browser + type: http_user_agent + - field: src_ip + type: ip_address tags: - analytic_story: - - Cisco Duo Suspicious Activity - asset_type: Identity - mitre_attack_id: - - T1556 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Cisco Duo Suspicious Activity + asset_type: Identity + mitre_attack_id: + - T1556 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_unusual_admin_login/cisco_duo_activity.json - source: duo - sourcetype: cisco:duo:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_unusual_admin_login/cisco_duo_activity.json + source: duo + sourcetype: cisco:duo:activity diff --git a/detections/application/cisco_duo_admin_login_unusual_os.yml b/detections/application/cisco_duo_admin_login_unusual_os.yml index aebc4881d8..6faa3729b7 100644 --- a/detections/application/cisco_duo_admin_login_unusual_os.yml +++ b/detections/application/cisco_duo_admin_login_unusual_os.yml @@ -1,71 +1,62 @@ name: Cisco Duo Admin Login Unusual Os id: c4824cc6-d644-458e-a39a-67cd67da75e3 -version: 2 -date: '2026-01-14' +version: 3 +date: '2026-02-25' author: Patrick Bareiss, Splunk data_source: -- Cisco Duo Activity + - Cisco Duo Activity type: TTP status: production -description: The following analytic identifies Duo admin login attempts from operating systems that are unusual for your environment, excluding commonly - used OS such as Mac OS X. Please adjust to your environment. It works by analyzing Duo activity logs for admin login actions and filtering out logins - from expected operating systems. The analytic then aggregates events by browser, version, source IP, location, and OS details to highlight anomalies. - Detecting admin logins from unexpected operating systems is critical for a SOC, as it may indicate credential compromise, unauthorized access, or - attacker activity using unfamiliar devices. Such behavior can precede privilege escalation, policy changes, or other malicious actions within the - Duo environment. Early detection enables rapid investigation and response, reducing the risk of account takeover and minimizing potential damage - to organizational security controls. -search: '`cisco_duo_activity` "action.name"=admin_login NOT access_device.os IN ("Mac OS X") - | rename actor.name as user access_device.ip.address as src_ip - | stats count min(_time) as firstTime max(_time) as lastTime by access_device.browser - access_device.browser_version src_ip access_device.location.city - access_device.location.country access_device.location.state access_device.os access_device.os_version - actor.details actor.type outcome.result user - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `cisco_duo_admin_login_unusual_os_filter`' -how_to_implement: The analytic leverages Duo activity logs to be ingested using the - Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). +description: The following analytic identifies Duo admin login attempts from operating systems that are unusual for your environment, excluding commonly used OS such as Mac OS X. Please adjust to your environment. It works by analyzing Duo activity logs for admin login actions and filtering out logins from expected operating systems. The analytic then aggregates events by browser, version, source IP, location, and OS details to highlight anomalies. Detecting admin logins from unexpected operating systems is critical for a SOC, as it may indicate credential compromise, unauthorized access, or attacker activity using unfamiliar devices. Such behavior can precede privilege escalation, policy changes, or other malicious actions within the Duo environment. Early detection enables rapid investigation and response, reducing the risk of account takeover and minimizing potential damage to organizational security controls. +search: |- + `cisco_duo_activity` "action.name"=admin_login NOT access_device.os IN ("Mac OS X") + | rename actor.name as user access_device.ip.address as src_ip + | stats count min(_time) as firstTime max(_time) as lastTime + BY access_device.browser access_device.browser_version src_ip + access_device.location.city access_device.location.country access_device.location.state + access_device.os access_device.os_version actor.details + actor.type outcome.result user + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_duo_admin_login_unusual_os_filter` +how_to_implement: The analytic leverages Duo activity logs to be ingested using the Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). known_false_positives: No false positives have been identified at this time. references: -- https://splunkbase.splunk.com/app/7404 + - https://splunkbase.splunk.com/app/7404 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A user $user$ has logged in using an unusual OS $access_device.os$ using browser $access_device.browser$ from $src_ip$. - risk_objects: - - field: user - type: user - score: 48 - threat_objects: - - field: access_device.browser - type: http_user_agent - - field: src_ip - type: ip_address + message: A user $user$ has logged in using an unusual OS $access_device.os$ using browser $access_device.browser$ from $src_ip$. + risk_objects: + - field: user + type: user + score: 48 + threat_objects: + - field: access_device.browser + type: http_user_agent + - field: src_ip + type: ip_address tags: - analytic_story: - - Cisco Duo Suspicious Activity - asset_type: Identity - mitre_attack_id: - - T1556 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Cisco Duo Suspicious Activity + asset_type: Identity + mitre_attack_id: + - T1556 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_unusual_admin_login/cisco_duo_activity.json - source: duo - sourcetype: cisco:duo:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_unusual_admin_login/cisco_duo_activity.json + source: duo + sourcetype: cisco:duo:activity diff --git a/detections/application/cisco_duo_bulk_policy_deletion.yml b/detections/application/cisco_duo_bulk_policy_deletion.yml index 83626cb2c7..4e4a95a595 100644 --- a/detections/application/cisco_duo_bulk_policy_deletion.yml +++ b/detections/application/cisco_duo_bulk_policy_deletion.yml @@ -4,66 +4,45 @@ version: 2 date: '2026-01-14' author: Patrick Bareiss, Splunk data_source: -- Cisco Duo Administrator + - Cisco Duo Administrator type: TTP status: production -description: The following analytic detects instances where a Duo administrator performs a bulk deletion of more than three policies in a single action. - It identifies this behavior by searching Duo activity logs for the policy_bulk_delete action, extracting the names of deleted policies, and counting - them. If the count exceeds three, the event is flagged. This behavior is significant for a Security Operations Center (SOC) because mass deletion of - security policies can indicate malicious activity, such as an attacker or rogue administrator attempting to weaken or disable security controls, - potentially paving the way for further compromise. Detecting and investigating such actions promptly is critical, as the impact of this attack could - include reduced security posture, increased risk of unauthorized access, and potential data breaches. Monitoring for bulk policy deletions helps ensure - that any suspicious or unauthorized changes to security configurations are quickly identified and addressed to protect organizational assets and maintain - compliance. -search: '`cisco_duo_administrator` action=policy_bulk_delete - | rename username as user - | spath input=description - | rex field=policies max_match=0 "(?[^:,]+):\s+" - | eval policy_count=mvcount(policy_name) - | where policy_count > 3 - | stats count min(_time) as firstTime max(_time) as lastTime by action actionlabel description user admin_email policy_count - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `cisco_duo_bulk_policy_deletion_filter`' -how_to_implement: The analytic leverages Duo activity logs to be ingested using the - Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). +description: The following analytic detects instances where a Duo administrator performs a bulk deletion of more than three policies in a single action. It identifies this behavior by searching Duo activity logs for the policy_bulk_delete action, extracting the names of deleted policies, and counting them. If the count exceeds three, the event is flagged. This behavior is significant for a Security Operations Center (SOC) because mass deletion of security policies can indicate malicious activity, such as an attacker or rogue administrator attempting to weaken or disable security controls, potentially paving the way for further compromise. Detecting and investigating such actions promptly is critical, as the impact of this attack could include reduced security posture, increased risk of unauthorized access, and potential data breaches. Monitoring for bulk policy deletions helps ensure that any suspicious or unauthorized changes to security configurations are quickly identified and addressed to protect organizational assets and maintain compliance. +search: '`cisco_duo_administrator` action=policy_bulk_delete | rename username as user | spath input=description | rex field=policies max_match=0 "(?[^:,]+):\s+" | eval policy_count=mvcount(policy_name) | where policy_count > 3 | stats count min(_time) as firstTime max(_time) as lastTime by action actionlabel description user admin_email policy_count | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `cisco_duo_bulk_policy_deletion_filter`' +how_to_implement: The analytic leverages Duo activity logs to be ingested using the Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). known_false_positives: No false positives have been identified at this time. references: -- https://splunkbase.splunk.com/app/7404 + - https://splunkbase.splunk.com/app/7404 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A user $user$ has deleted more than 3 policies - risk_objects: - - field: user - type: user - score: 48 - threat_objects: [] + message: A user $user$ has deleted more than 3 policies + risk_objects: + - field: user + type: user + score: 48 + threat_objects: [] tags: - analytic_story: - - Cisco Duo Suspicious Activity - asset_type: Identity - mitre_attack_id: - - T1556 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Cisco Duo Suspicious Activity + asset_type: Identity + mitre_attack_id: + - T1556 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_bulk_policy_deletion/cisco_duo_administrator.json - source: duo - sourcetype: cisco:duo:administrator + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_bulk_policy_deletion/cisco_duo_administrator.json + source: duo + sourcetype: cisco:duo:administrator diff --git a/detections/application/cisco_duo_bypass_code_generation.yml b/detections/application/cisco_duo_bypass_code_generation.yml index 695627b5e5..939e08c239 100644 --- a/detections/application/cisco_duo_bypass_code_generation.yml +++ b/detections/application/cisco_duo_bypass_code_generation.yml @@ -1,67 +1,64 @@ name: Cisco Duo Bypass Code Generation id: 446e81ff-ce06-4925-9c7d-4073f9b5abf5 -version: 2 -date: '2026-01-14' +version: 3 +date: '2026-02-25' author: Patrick Bareiss, Splunk data_source: -- Cisco Duo Administrator + - Cisco Duo Administrator type: TTP status: production description: | - The following analytic detects when a Duo user generates a bypass code, which allows them to circumvent multi-factor authentication (2FA) protections. - It works by monitoring Duo activity logs for the 'bypass_create' action, renaming the affected object as the user, and aggregating events to identify - instances where a bypass code is issued. This behavior is significant for a Security Operations Center (SOC) because generating a bypass code can enable - attackers, malicious insiders, or unauthorized administrators to gain access to sensitive systems without the required second authentication factor. - Such activity may indicate account compromise, privilege abuse, or attempts to weaken security controls. Early detection of bypass code generation is - critical, as it allows the SOC to investigate and respond before an attacker can exploit the reduced authentication requirements, helping to prevent - unauthorized access, data breaches, or further lateral movement within the environment. Monitoring for this action helps maintain strong authentication - standards and reduces the risk of credential-based attacks. -search: '`cisco_duo_administrator` action=bypass_create - | rename object as user - | stats count min(_time) as firstTime max(_time) as lastTime by action actionlabel description user - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `cisco_duo_bypass_code_generation_filter`' -how_to_implement: The analytic leverages Duo activity logs to be ingested using the - Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). + The following analytic detects when a Duo user generates a bypass code, which allows them to circumvent multi-factor authentication (2FA) protections. + It works by monitoring Duo activity logs for the 'bypass_create' action, renaming the affected object as the user, and aggregating events to identify + instances where a bypass code is issued. This behavior is significant for a Security Operations Center (SOC) because generating a bypass code can enable + attackers, malicious insiders, or unauthorized administrators to gain access to sensitive systems without the required second authentication factor. + Such activity may indicate account compromise, privilege abuse, or attempts to weaken security controls. Early detection of bypass code generation is + critical, as it allows the SOC to investigate and respond before an attacker can exploit the reduced authentication requirements, helping to prevent + unauthorized access, data breaches, or further lateral movement within the environment. Monitoring for this action helps maintain strong authentication + standards and reduces the risk of credential-based attacks. +search: |- + `cisco_duo_administrator` action=bypass_create + | rename object as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY action actionlabel description + user + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_duo_bypass_code_generation_filter` +how_to_implement: The analytic leverages Duo activity logs to be ingested using the Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). known_false_positives: No false positives have been identified at this time. references: -- https://splunkbase.splunk.com/app/7404 + - https://splunkbase.splunk.com/app/7404 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A user $user$ has generated a bypass code - risk_objects: - - field: user - type: user - score: 48 - threat_objects: [] + message: A user $user$ has generated a bypass code + risk_objects: + - field: user + type: user + score: 48 + threat_objects: [] tags: - analytic_story: - - Cisco Duo Suspicious Activity - asset_type: Identity - mitre_attack_id: - - T1556 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Cisco Duo Suspicious Activity + asset_type: Identity + mitre_attack_id: + - T1556 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_bypass_code/cisco_duo_activity.json - source: duo - sourcetype: cisco:duo:administrator + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_bypass_code/cisco_duo_activity.json + source: duo + sourcetype: cisco:duo:administrator diff --git a/detections/application/cisco_duo_policy_allow_devices_without_screen_lock.yml b/detections/application/cisco_duo_policy_allow_devices_without_screen_lock.yml index 2e7cde7a70..e3620882c8 100644 --- a/detections/application/cisco_duo_policy_allow_devices_without_screen_lock.yml +++ b/detections/application/cisco_duo_policy_allow_devices_without_screen_lock.yml @@ -1,66 +1,64 @@ name: Cisco Duo Policy Allow Devices Without Screen Lock id: 114c616b-c793-465d-a80d-758c9fe8a704 -version: 2 -date: '2026-01-14' +version: 3 +date: '2026-02-25' author: Patrick Bareiss, Splunk data_source: -- Cisco Duo Administrator + - Cisco Duo Administrator type: TTP status: production description: | - The following analytic detects when a Duo policy is created or updated to allow devices without a screen lock requirement. It identifies this behavior - by searching Duo administrator activity logs for policy creation or update events where the 'require_lock' setting is set to false. This action may indicate - a weakening of device security controls, potentially exposing the organization to unauthorized access if devices are lost or stolen. For a Security Operations - Center (SOC), identifying such policy changes is critical, as attackers or malicious insiders may attempt to lower authentication standards to facilitate - unauthorized access. The impact of this attack could include increased risk of credential compromise, data breaches, or lateral movement within the - environment due to reduced device security requirements. -search: '`cisco_duo_administrator` action=policy_update OR action=policy_create - | spath input=description - | search require_lock=false - | rename object as user - | stats count min(_time) as firstTime max(_time) as lastTime by action actionlabel description user admin_email - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `cisco_duo_policy_allow_devices_without_screen_lock_filter`' -how_to_implement: The analytic leverages Duo activity logs to be ingested using the - Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). + The following analytic detects when a Duo policy is created or updated to allow devices without a screen lock requirement. It identifies this behavior + by searching Duo administrator activity logs for policy creation or update events where the 'require_lock' setting is set to false. This action may indicate + a weakening of device security controls, potentially exposing the organization to unauthorized access if devices are lost or stolen. For a Security Operations + Center (SOC), identifying such policy changes is critical, as attackers or malicious insiders may attempt to lower authentication standards to facilitate + unauthorized access. The impact of this attack could include increased risk of credential compromise, data breaches, or lateral movement within the + environment due to reduced device security requirements. +search: |- + `cisco_duo_administrator` action=policy_update OR action=policy_create + | spath input=description + | search require_lock=false + | rename object as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY action actionlabel description + user admin_email + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_duo_policy_allow_devices_without_screen_lock_filter` +how_to_implement: The analytic leverages Duo activity logs to be ingested using the Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). known_false_positives: No false positives have been identified at this time. references: -- https://splunkbase.splunk.com/app/7404 + - https://splunkbase.splunk.com/app/7404 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A policy has been created or updated to allow devices without screen lock by user $user$ with email $admin_email$ - risk_objects: - - field: user - type: user - score: 48 - threat_objects: [] + message: A policy has been created or updated to allow devices without screen lock by user $user$ with email $admin_email$ + risk_objects: + - field: user + type: user + score: 48 + threat_objects: [] tags: - analytic_story: - - Cisco Duo Suspicious Activity - asset_type: Identity - mitre_attack_id: - - T1556 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Cisco Duo Suspicious Activity + asset_type: Identity + mitre_attack_id: + - T1556 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_policy_allow_devices_without_screen_lock/cisco_duo_administrator.json - source: duo - sourcetype: cisco:duo:administrator + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_policy_allow_devices_without_screen_lock/cisco_duo_administrator.json + source: duo + sourcetype: cisco:duo:administrator diff --git a/detections/application/cisco_duo_policy_allow_network_bypass_2fa.yml b/detections/application/cisco_duo_policy_allow_network_bypass_2fa.yml index fe9e939e20..5bc714d347 100644 --- a/detections/application/cisco_duo_policy_allow_network_bypass_2fa.yml +++ b/detections/application/cisco_duo_policy_allow_network_bypass_2fa.yml @@ -1,68 +1,66 @@ name: Cisco Duo Policy Allow Network Bypass 2FA id: 2593f641-6192-4f3d-b96c-2bd1c706215f -version: 2 -date: '2026-01-14' +version: 3 +date: '2026-02-25' author: Patrick Bareiss, Splunk data_source: -- Cisco Duo Administrator + - Cisco Duo Administrator type: TTP status: production description: | - The following analytic detects when a Duo policy is created or updated to allow network-based bypass of two-factor authentication (2FA). - It identifies this behavior by searching Duo administrator logs for policy creation or update actions where the networks_allow field is present, - indicating that specific networks have been permitted to bypass 2FA requirements. This is achieved by parsing the event description and - filtering for relevant policy changes, then aggregating the results by user and administrator details. Detecting this behavior is critical - for a Security Operations Center (SOC) because allowing network-based 2FA bypass can significantly weaken authentication controls, potentially - enabling unauthorized access if a trusted network is compromised or misconfigured. Attackers or malicious insiders may exploit this policy - change to circumvent 2FA protections, increasing the risk of account takeover and lateral movement within the environment. Prompt detection - enables SOC analysts to investigate and respond to potentially risky policy modifications before they can be leveraged for malicious purposes. -search: '`cisco_duo_administrator` action=policy_update OR action=policy_create - | spath input=description - | search networks_allow=* - | rename object as user - | stats count min(_time) as firstTime max(_time) as lastTime by action actionlabel description user admin_email networks_allow - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `cisco_duo_policy_allow_network_bypass_2fa_filter`' -how_to_implement: The analytic leverages Duo activity logs to be ingested using the - Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). + The following analytic detects when a Duo policy is created or updated to allow network-based bypass of two-factor authentication (2FA). + It identifies this behavior by searching Duo administrator logs for policy creation or update actions where the networks_allow field is present, + indicating that specific networks have been permitted to bypass 2FA requirements. This is achieved by parsing the event description and + filtering for relevant policy changes, then aggregating the results by user and administrator details. Detecting this behavior is critical + for a Security Operations Center (SOC) because allowing network-based 2FA bypass can significantly weaken authentication controls, potentially + enabling unauthorized access if a trusted network is compromised or misconfigured. Attackers or malicious insiders may exploit this policy + change to circumvent 2FA protections, increasing the risk of account takeover and lateral movement within the environment. Prompt detection + enables SOC analysts to investigate and respond to potentially risky policy modifications before they can be leveraged for malicious purposes. +search: |- + `cisco_duo_administrator` action=policy_update OR action=policy_create + | spath input=description + | search networks_allow=* + | rename object as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY action actionlabel description + user admin_email networks_allow + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_duo_policy_allow_network_bypass_2fa_filter` +how_to_implement: The analytic leverages Duo activity logs to be ingested using the Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). known_false_positives: No false positives have been identified at this time. references: -- https://splunkbase.splunk.com/app/7404 + - https://splunkbase.splunk.com/app/7404 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A policy has been created or updated to allow network bypass 2FA by user $user$ with email $admin_email$ - risk_objects: - - field: user - type: user - score: 48 - threat_objects: [] + message: A policy has been created or updated to allow network bypass 2FA by user $user$ with email $admin_email$ + risk_objects: + - field: user + type: user + score: 48 + threat_objects: [] tags: - analytic_story: - - Cisco Duo Suspicious Activity - asset_type: Identity - mitre_attack_id: - - T1556 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Cisco Duo Suspicious Activity + asset_type: Identity + mitre_attack_id: + - T1556 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_policy_allow_network_bypass_2fa/cisco_duo_administrator.json - source: duo - sourcetype: cisco:duo:administrator + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_policy_allow_network_bypass_2fa/cisco_duo_administrator.json + source: duo + sourcetype: cisco:duo:administrator diff --git a/detections/application/cisco_duo_policy_allow_old_flash.yml b/detections/application/cisco_duo_policy_allow_old_flash.yml index 4cdd37a876..867d12e9b8 100644 --- a/detections/application/cisco_duo_policy_allow_old_flash.yml +++ b/detections/application/cisco_duo_policy_allow_old_flash.yml @@ -1,66 +1,58 @@ name: Cisco Duo Policy Allow Old Flash id: f36c0d3f-d57f-4b88-a5d4-0a4c9a0752f6 -version: 2 -date: '2026-01-14' +version: 3 +date: '2026-02-25' author: Patrick Bareiss, Splunk data_source: -- Cisco Duo Administrator + - Cisco Duo Administrator type: TTP status: production -description: The following analytic identifies instances where a Duo administrator creates or updates a policy to allow the use of outdated Flash components, - specifically by detecting policy changes with the flash_remediation=no remediation attribute. It leverages Duo activity logs ingested via the Cisco Security - Cloud App, searching for policy_update or policy_create actions and parsing the policy description for indicators of weakened security controls. This behavior - is significant for a SOC because permitting old Flash increases the attack surface, as Flash is widely known for its security vulnerabilities and is no longer - supported. Attackers may exploit such policy changes to bypass security controls, introduce malware, or escalate privileges within the environment. Detecting - and responding to these policy modifications helps prevent potential exploitation, reduces organizational risk, and ensures adherence to security best practices. - Immediate investigation is recommended to determine if the change was authorized or indicative of malicious activity. -search: '`cisco_duo_administrator` action=policy_update OR action=policy_create - | spath input=description - | search flash_remediation="no remediation" - | rename object as user - | stats count min(_time) as firstTime max(_time) as lastTime by action actionlabel description user admin_email - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `cisco_duo_policy_allow_old_flash_filter`' -how_to_implement: The analytic leverages Duo activity logs to be ingested using the - Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). +description: The following analytic identifies instances where a Duo administrator creates or updates a policy to allow the use of outdated Flash components, specifically by detecting policy changes with the flash_remediation=no remediation attribute. It leverages Duo activity logs ingested via the Cisco Security Cloud App, searching for policy_update or policy_create actions and parsing the policy description for indicators of weakened security controls. This behavior is significant for a SOC because permitting old Flash increases the attack surface, as Flash is widely known for its security vulnerabilities and is no longer supported. Attackers may exploit such policy changes to bypass security controls, introduce malware, or escalate privileges within the environment. Detecting and responding to these policy modifications helps prevent potential exploitation, reduces organizational risk, and ensures adherence to security best practices. Immediate investigation is recommended to determine if the change was authorized or indicative of malicious activity. +search: |- + `cisco_duo_administrator` action=policy_update OR action=policy_create + | spath input=description + | search flash_remediation="no remediation" + | rename object as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY action actionlabel description + user admin_email + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_duo_policy_allow_old_flash_filter` +how_to_implement: The analytic leverages Duo activity logs to be ingested using the Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). known_false_positives: No false positives have been identified at this time. references: -- https://splunkbase.splunk.com/app/7404 + - https://splunkbase.splunk.com/app/7404 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A policy has been created or updated to allow old flash by user $user$ with email $admin_email$ - risk_objects: - - field: user - type: user - score: 48 - threat_objects: [] + message: A policy has been created or updated to allow old flash by user $user$ with email $admin_email$ + risk_objects: + - field: user + type: user + score: 48 + threat_objects: [] tags: - analytic_story: - - Cisco Duo Suspicious Activity - asset_type: Identity - mitre_attack_id: - - T1556 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Cisco Duo Suspicious Activity + asset_type: Identity + mitre_attack_id: + - T1556 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_policy_allow_old_flash_and_java/cisco_duo_administrator.json - source: duo - sourcetype: cisco:duo:administrator + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_policy_allow_old_flash_and_java/cisco_duo_administrator.json + source: duo + sourcetype: cisco:duo:administrator diff --git a/detections/application/cisco_duo_policy_allow_old_java.yml b/detections/application/cisco_duo_policy_allow_old_java.yml index 9af49ca36d..ebc9e958b4 100644 --- a/detections/application/cisco_duo_policy_allow_old_java.yml +++ b/detections/application/cisco_duo_policy_allow_old_java.yml @@ -1,67 +1,65 @@ name: Cisco Duo Policy Allow Old Java id: ff56d843-57de-4a87-b726-13b145f6bf96 -version: 2 -date: '2026-01-14' +version: 3 +date: '2026-02-25' author: Patrick Bareiss, Splunk data_source: -- Cisco Duo Administrator + - Cisco Duo Administrator type: TTP status: production description: | - The following analytic detects when a Duo policy is created or updated to allow the use of outdated Java versions, which can introduce significant - security risks. It works by searching Duo administrator activity logs for policy creation or update actions where the policy explicitly sets - 'java_remediation' to 'no remediation', indicating that no restrictions are enforced against old Java. The analytic aggregates relevant details - such as the user, admin email, and action context for further investigation. Identifying this behavior is critical for a Security Operations Center - (SOC) because allowing outdated Java can expose an organization to known vulnerabilities, malware, and exploitation techniques. Attackers or malicious - insiders may attempt to weaken security controls by modifying policies to permit insecure software, increasing the risk of compromise. Prompt detection - enables SOC analysts to respond quickly, revert risky changes, and mitigate potential threats before they are exploited. -search: '`cisco_duo_administrator` action=policy_update OR action=policy_create - | spath input=description - | search java_remediation="no remediation" - | rename object as user - | stats count min(_time) as firstTime max(_time) as lastTime by action actionlabel description user admin_email - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `cisco_duo_policy_allow_old_java_filter`' -how_to_implement: The analytic leverages Duo activity logs to be ingested using the - Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). + The following analytic detects when a Duo policy is created or updated to allow the use of outdated Java versions, which can introduce significant + security risks. It works by searching Duo administrator activity logs for policy creation or update actions where the policy explicitly sets + 'java_remediation' to 'no remediation', indicating that no restrictions are enforced against old Java. The analytic aggregates relevant details + such as the user, admin email, and action context for further investigation. Identifying this behavior is critical for a Security Operations Center + (SOC) because allowing outdated Java can expose an organization to known vulnerabilities, malware, and exploitation techniques. Attackers or malicious + insiders may attempt to weaken security controls by modifying policies to permit insecure software, increasing the risk of compromise. Prompt detection + enables SOC analysts to respond quickly, revert risky changes, and mitigate potential threats before they are exploited. +search: |- + `cisco_duo_administrator` action=policy_update OR action=policy_create + | spath input=description + | search java_remediation="no remediation" + | rename object as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY action actionlabel description + user admin_email + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_duo_policy_allow_old_java_filter` +how_to_implement: The analytic leverages Duo activity logs to be ingested using the Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). known_false_positives: No false positives have been identified at this time. references: -- https://splunkbase.splunk.com/app/7404 + - https://splunkbase.splunk.com/app/7404 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A policy has been created or updated to allow old java by user $user$ with email $admin_email$ - risk_objects: - - field: user - type: user - score: 48 - threat_objects: [] + message: A policy has been created or updated to allow old java by user $user$ with email $admin_email$ + risk_objects: + - field: user + type: user + score: 48 + threat_objects: [] tags: - analytic_story: - - Cisco Duo Suspicious Activity - asset_type: Identity - mitre_attack_id: - - T1556 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Cisco Duo Suspicious Activity + asset_type: Identity + mitre_attack_id: + - T1556 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_policy_allow_old_flash_and_java/cisco_duo_administrator.json - source: duo - sourcetype: cisco:duo:administrator + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_policy_allow_old_flash_and_java/cisco_duo_administrator.json + source: duo + sourcetype: cisco:duo:administrator diff --git a/detections/application/cisco_duo_policy_allow_tampered_devices.yml b/detections/application/cisco_duo_policy_allow_tampered_devices.yml index 8031e5bc8d..93fb80ddfb 100644 --- a/detections/application/cisco_duo_policy_allow_tampered_devices.yml +++ b/detections/application/cisco_duo_policy_allow_tampered_devices.yml @@ -1,68 +1,66 @@ name: Cisco Duo Policy Allow Tampered Devices id: 6b813efd-8859-406f-b677-719458387fac -version: 2 -date: '2026-01-14' +version: 3 +date: '2026-02-25' author: Patrick Bareiss, Splunk data_source: -- Cisco Duo Administrator + - Cisco Duo Administrator type: TTP status: production description: | - The following analytic detects when a Duo policy is created or updated to allow tampered or rooted devices, such as jailbroken smartphones, - to access protected resources. It identifies this behavior by searching Duo administrator activity logs for policy changes where the allow_rooted_devices - setting is enabled. This is accomplished by filtering for policy creation or update actions and parsing the policy description for the relevant configuration. - Allowing tampered devices poses a significant security risk, as these devices may bypass built-in security controls, run unauthorized software, or be more - susceptible to compromise. For a Security Operations Center (SOC), identifying such policy changes is critical because it may indicate either a - misconfiguration or a malicious attempt to weaken authentication requirements, potentially enabling attackers to access sensitive systems with - compromised devices. The impact of this attack can include unauthorized access, data breaches, and lateral movement within the environment, - making prompt detection and response essential to maintaining organizational security. -search: '`cisco_duo_administrator` action=policy_update OR action=policy_create - | spath input=description - | search allow_rooted_devices=true - | rename object as user - | stats count min(_time) as firstTime max(_time) as lastTime by action actionlabel description user admin_email - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `cisco_duo_policy_allow_tampered_devices_filter`' -how_to_implement: The analytic leverages Duo activity logs to be ingested using the - Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). + The following analytic detects when a Duo policy is created or updated to allow tampered or rooted devices, such as jailbroken smartphones, + to access protected resources. It identifies this behavior by searching Duo administrator activity logs for policy changes where the allow_rooted_devices + setting is enabled. This is accomplished by filtering for policy creation or update actions and parsing the policy description for the relevant configuration. + Allowing tampered devices poses a significant security risk, as these devices may bypass built-in security controls, run unauthorized software, or be more + susceptible to compromise. For a Security Operations Center (SOC), identifying such policy changes is critical because it may indicate either a + misconfiguration or a malicious attempt to weaken authentication requirements, potentially enabling attackers to access sensitive systems with + compromised devices. The impact of this attack can include unauthorized access, data breaches, and lateral movement within the environment, + making prompt detection and response essential to maintaining organizational security. +search: |- + `cisco_duo_administrator` action=policy_update OR action=policy_create + | spath input=description + | search allow_rooted_devices=true + | rename object as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY action actionlabel description + user admin_email + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_duo_policy_allow_tampered_devices_filter` +how_to_implement: The analytic leverages Duo activity logs to be ingested using the Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). known_false_positives: No false positives have been identified at this time. references: -- https://splunkbase.splunk.com/app/7404 + - https://splunkbase.splunk.com/app/7404 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A policy has been created or updated to allow tampered devices by user $user$ with email $admin_email$ - risk_objects: - - field: user - type: user - score: 48 - threat_objects: [] + message: A policy has been created or updated to allow tampered devices by user $user$ with email $admin_email$ + risk_objects: + - field: user + type: user + score: 48 + threat_objects: [] tags: - analytic_story: - - Cisco Duo Suspicious Activity - asset_type: Identity - mitre_attack_id: - - T1556 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Cisco Duo Suspicious Activity + asset_type: Identity + mitre_attack_id: + - T1556 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_policy_allow_tampered_devices/cisco_duo_administrator.json - source: duo - sourcetype: cisco:duo:administrator + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_policy_allow_tampered_devices/cisco_duo_administrator.json + source: duo + sourcetype: cisco:duo:administrator diff --git a/detections/application/cisco_duo_policy_bypass_2fa.yml b/detections/application/cisco_duo_policy_bypass_2fa.yml index 81a82a4be9..d861b08dc6 100644 --- a/detections/application/cisco_duo_policy_bypass_2fa.yml +++ b/detections/application/cisco_duo_policy_bypass_2fa.yml @@ -1,66 +1,58 @@ name: Cisco Duo Policy Bypass 2FA id: 65862e8a-799a-4509-ae1c-4602aa139580 -version: 2 -date: '2026-01-14' +version: 3 +date: '2026-02-25' author: Patrick Bareiss, Splunk data_source: -- Cisco Duo Administrator + - Cisco Duo Administrator type: TTP status: production -description: The following analytic detects instances where a Duo policy is created or updated to allow access without two-factor authentication (2FA). - It identifies this behavior by searching Duo administrator activity logs for policy changes that set the authentication status to "Allow access without 2FA." - By monitoring for these specific actions, the analytic highlights potential attempts to weaken authentication controls, which could be indicative of - malicious activity or insider threats. This behavior is critical for a SOC to identify, as bypassing 2FA significantly reduces the security posture - of an organization, making it easier for attackers to gain unauthorized access to sensitive systems and data. Detecting and responding to such policy - changes promptly helps prevent potential account compromise and mitigates the risk of broader security breaches. -search: '`cisco_duo_administrator` action=policy_update OR action=policy_create - | spath input=description - | search auth_status="Allow access without 2FA" - | rename object as user - | stats count min(_time) as firstTime max(_time) as lastTime by action actionlabel description user admin_email - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `cisco_duo_policy_bypass_2fa_filter`' -how_to_implement: The analytic leverages Duo activity logs to be ingested using the - Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). +description: The following analytic detects instances where a Duo policy is created or updated to allow access without two-factor authentication (2FA). It identifies this behavior by searching Duo administrator activity logs for policy changes that set the authentication status to "Allow access without 2FA." By monitoring for these specific actions, the analytic highlights potential attempts to weaken authentication controls, which could be indicative of malicious activity or insider threats. This behavior is critical for a SOC to identify, as bypassing 2FA significantly reduces the security posture of an organization, making it easier for attackers to gain unauthorized access to sensitive systems and data. Detecting and responding to such policy changes promptly helps prevent potential account compromise and mitigates the risk of broader security breaches. +search: |- + `cisco_duo_administrator` action=policy_update OR action=policy_create + | spath input=description + | search auth_status="Allow access without 2FA" + | rename object as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY action actionlabel description + user admin_email + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_duo_policy_bypass_2fa_filter` +how_to_implement: The analytic leverages Duo activity logs to be ingested using the Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). known_false_positives: No false positives have been identified at this time. references: -- https://splunkbase.splunk.com/app/7404 + - https://splunkbase.splunk.com/app/7404 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A policy has been created or updated to allow access without 2FA by user $user$ with email $admin_email$ - risk_objects: - - field: user - type: user - score: 48 - threat_objects: [] + message: A policy has been created or updated to allow access without 2FA by user $user$ with email $admin_email$ + risk_objects: + - field: user + type: user + score: 48 + threat_objects: [] tags: - analytic_story: - - Cisco Duo Suspicious Activity - asset_type: Identity - mitre_attack_id: - - T1556 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Cisco Duo Suspicious Activity + asset_type: Identity + mitre_attack_id: + - T1556 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_policy_bypass_2FA/cisco_duo_administrator.json - source: duo - sourcetype: cisco:duo:administrator + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_policy_bypass_2FA/cisco_duo_administrator.json + source: duo + sourcetype: cisco:duo:administrator diff --git a/detections/application/cisco_duo_policy_deny_access.yml b/detections/application/cisco_duo_policy_deny_access.yml index c483e36e83..4e64eb74b2 100644 --- a/detections/application/cisco_duo_policy_deny_access.yml +++ b/detections/application/cisco_duo_policy_deny_access.yml @@ -1,66 +1,58 @@ name: Cisco Duo Policy Deny Access id: abf39464-ed43-4d69-a56c-02750032a3fb -version: 2 -date: '2026-01-14' +version: 3 +date: '2026-02-25' author: Patrick Bareiss, Splunk data_source: -- Cisco Duo Administrator + - Cisco Duo Administrator type: TTP status: production -description: The following analytic identifies instances where a Duo administrator creates or updates a policy to explicitly deny user access within - the Duo environment. It detects this behavior by searching Duo administrator activity logs for policy creation or update actions where the authentication - status is set to "Deny access." By correlating these events with user and admin details, the analytic highlights potential misuse or malicious changes - to access policies. This behavior is critical for a SOC to monitor, as unauthorized or suspicious denial of access policies can indicate insider threats, - account compromise, or attempts to disrupt legitimate user access. The impact of such an attack may include denial of service to critical accounts, - disruption of business operations, or the masking of further malicious activity by preventing targeted users from accessing resources. Early detection - enables rapid investigation and remediation to maintain organizational security and availability. -search: '`cisco_duo_administrator` action=policy_update OR action=policy_create - | spath input=description - | search auth_status="Deny access" - | rename object as user - | stats count min(_time) as firstTime max(_time) as lastTime by action actionlabel description user admin_email - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `cisco_duo_policy_deny_access_filter`' -how_to_implement: The analytic leverages Duo activity logs to be ingested using the - Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). +description: The following analytic identifies instances where a Duo administrator creates or updates a policy to explicitly deny user access within the Duo environment. It detects this behavior by searching Duo administrator activity logs for policy creation or update actions where the authentication status is set to "Deny access." By correlating these events with user and admin details, the analytic highlights potential misuse or malicious changes to access policies. This behavior is critical for a SOC to monitor, as unauthorized or suspicious denial of access policies can indicate insider threats, account compromise, or attempts to disrupt legitimate user access. The impact of such an attack may include denial of service to critical accounts, disruption of business operations, or the masking of further malicious activity by preventing targeted users from accessing resources. Early detection enables rapid investigation and remediation to maintain organizational security and availability. +search: |- + `cisco_duo_administrator` action=policy_update OR action=policy_create + | spath input=description + | search auth_status="Deny access" + | rename object as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY action actionlabel description + user admin_email + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_duo_policy_deny_access_filter` +how_to_implement: The analytic leverages Duo activity logs to be ingested using the Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). known_false_positives: No false positives have been identified at this time. references: -- https://splunkbase.splunk.com/app/7404 + - https://splunkbase.splunk.com/app/7404 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A policy has been created or updated to deny access by user $user$ with email $admin_email$ - risk_objects: - - field: user - type: user - score: 48 - threat_objects: [] + message: A policy has been created or updated to deny access by user $user$ with email $admin_email$ + risk_objects: + - field: user + type: user + score: 48 + threat_objects: [] tags: - analytic_story: - - Cisco Duo Suspicious Activity - asset_type: Identity - mitre_attack_id: - - T1556 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Cisco Duo Suspicious Activity + asset_type: Identity + mitre_attack_id: + - T1556 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_policy_deny_access/cisco_duo_administrator.json - source: duo - sourcetype: cisco:duo:administrator + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_policy_deny_access/cisco_duo_administrator.json + source: duo + sourcetype: cisco:duo:administrator diff --git a/detections/application/cisco_duo_policy_skip_2fa_for_other_countries.yml b/detections/application/cisco_duo_policy_skip_2fa_for_other_countries.yml index 0f00f9aa8d..1962ad87e9 100644 --- a/detections/application/cisco_duo_policy_skip_2fa_for_other_countries.yml +++ b/detections/application/cisco_duo_policy_skip_2fa_for_other_countries.yml @@ -1,69 +1,67 @@ name: Cisco Duo Policy Skip 2FA for Other Countries id: ab59d5ee-8694-4832-a332-cefcf66a9057 -version: 2 -date: '2026-01-14' +version: 3 +date: '2026-02-25' author: Patrick Bareiss, Splunk data_source: -- Cisco Duo Administrator + - Cisco Duo Administrator type: TTP status: production description: | - The following analytic detects when a Duo policy is created or updated to allow access without two-factor authentication (2FA) - for users in countries other than the default. It identifies this behavior by searching Duo administrator activity logs for policy - creation or update actions where the policy description indicates that access is permitted without 2FA for certain user locations. - This is achieved by parsing the relevant fields in the logs and filtering for the specific condition of 'Allow access without 2FA.' - This behavior is significant for a Security Operations Center (SOC) because bypassing 2FA for any user group or location weakens - the organization's security posture and increases the risk of unauthorized access. Attackers or malicious insiders may exploit - such policy changes to circumvent strong authentication controls, potentially leading to account compromise, data breaches, or - lateral movement within the environment. Early detection of these policy modifications enables the SOC to investigate and respond - before attackers can leverage the weakened controls, thereby reducing the risk and impact of a successful attack. -search: '`cisco_duo_administrator` action=policy_update OR action=policy_create - | spath input=description - | search user_locations_default_action="Allow access without 2FA" - | rename object as user - | stats count min(_time) as firstTime max(_time) as lastTime by action actionlabel description user admin_email - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `cisco_duo_policy_skip_2fa_for_other_countries_filter`' -how_to_implement: The analytic leverages Duo activity logs to be ingested using the - Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). + The following analytic detects when a Duo policy is created or updated to allow access without two-factor authentication (2FA) + for users in countries other than the default. It identifies this behavior by searching Duo administrator activity logs for policy + creation or update actions where the policy description indicates that access is permitted without 2FA for certain user locations. + This is achieved by parsing the relevant fields in the logs and filtering for the specific condition of 'Allow access without 2FA.' + This behavior is significant for a Security Operations Center (SOC) because bypassing 2FA for any user group or location weakens + the organization's security posture and increases the risk of unauthorized access. Attackers or malicious insiders may exploit + such policy changes to circumvent strong authentication controls, potentially leading to account compromise, data breaches, or + lateral movement within the environment. Early detection of these policy modifications enables the SOC to investigate and respond + before attackers can leverage the weakened controls, thereby reducing the risk and impact of a successful attack. +search: |- + `cisco_duo_administrator` action=policy_update OR action=policy_create + | spath input=description + | search user_locations_default_action="Allow access without 2FA" + | rename object as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY action actionlabel description + user admin_email + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_duo_policy_skip_2fa_for_other_countries_filter` +how_to_implement: The analytic leverages Duo activity logs to be ingested using the Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). known_false_positives: No false positives have been identified at this time. references: -- https://splunkbase.splunk.com/app/7404 + - https://splunkbase.splunk.com/app/7404 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A policy has been created or updated to allow access without 2FA for other countries by user $user$ with email $admin_email$ - risk_objects: - - field: user - type: user - score: 48 - threat_objects: [] + message: A policy has been created or updated to allow access without 2FA for other countries by user $user$ with email $admin_email$ + risk_objects: + - field: user + type: user + score: 48 + threat_objects: [] tags: - analytic_story: - - Cisco Duo Suspicious Activity - asset_type: Identity - mitre_attack_id: - - T1556 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Cisco Duo Suspicious Activity + asset_type: Identity + mitre_attack_id: + - T1556 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_policy_bypass_2FA_other_countries/cisco_duo_administrator.json - source: duo - sourcetype: cisco:duo:administrator + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_policy_bypass_2FA_other_countries/cisco_duo_administrator.json + source: duo + sourcetype: cisco:duo:administrator diff --git a/detections/application/cisco_duo_set_user_status_to_bypass_2fa.yml b/detections/application/cisco_duo_set_user_status_to_bypass_2fa.yml index bf83825bdd..47bc5d503a 100644 --- a/detections/application/cisco_duo_set_user_status_to_bypass_2fa.yml +++ b/detections/application/cisco_duo_set_user_status_to_bypass_2fa.yml @@ -1,75 +1,73 @@ name: Cisco Duo Set User Status to Bypass 2FA id: 8728d224-9cd5-4aa7-b75f-f8520a569979 -version: 2 -date: '2026-01-14' +version: 3 +date: '2026-02-25' author: Patrick Bareiss, Splunk data_source: -- Cisco Duo Administrator + - Cisco Duo Administrator type: TTP status: production description: | - The following analytic detects instances where a Duo user's status is changed to "Bypass" for 2FA, specifically when the - previous status was "Active." This behavior is identified by analyzing Duo activity logs for user update actions, extracting - the status transitions, and filtering for cases where a user is set to bypass multi-factor authentication. This is a critical - event for a Security Operations Center (SOC) to monitor, as bypassing 2FA significantly weakens account security and may - indicate malicious insider activity or account compromise. Attackers or unauthorized administrators may exploit this change to - disable strong authentication controls, increasing the risk of unauthorized access to sensitive systems and data. Early detection - of such changes enables rapid investigation and response, helping to prevent potential breaches and limit the impact of - credential-based attacks. -search: '`cisco_duo_activity` action.name=user_update - | spath input=target.details path=status output=status - | spath input=old_target.details path=status output=old_status - | search status=Bypass old_status=Active - | rename target.name as user access_device.ip.address as src_ip - | stats count min(_time) as firstTime max(_time) as lastTime by access_device.browser - access_device.browser_version src_ip access_device.location.city - access_device.location.country access_device.location.state access_device.os access_device.os_version - action.name actor.details actor.name actor.type old_target.details target.details status old_status user - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `cisco_duo_set_user_status_to_bypass_2fa_filter`' -how_to_implement: The analytic leverages Duo activity logs to be ingested using the - Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). + The following analytic detects instances where a Duo user's status is changed to "Bypass" for 2FA, specifically when the + previous status was "Active." This behavior is identified by analyzing Duo activity logs for user update actions, extracting + the status transitions, and filtering for cases where a user is set to bypass multi-factor authentication. This is a critical + event for a Security Operations Center (SOC) to monitor, as bypassing 2FA significantly weakens account security and may + indicate malicious insider activity or account compromise. Attackers or unauthorized administrators may exploit this change to + disable strong authentication controls, increasing the risk of unauthorized access to sensitive systems and data. Early detection + of such changes enables rapid investigation and response, helping to prevent potential breaches and limit the impact of + credential-based attacks. +search: |- + `cisco_duo_activity` action.name=user_update + | spath input=target.details path=status output=status + | spath input=old_target.details path=status output=old_status + | search status=Bypass old_status=Active + | rename target.name as user access_device.ip.address as src_ip + | stats count min(_time) as firstTime max(_time) as lastTime + BY access_device.browser access_device.browser_version src_ip + access_device.location.city access_device.location.country access_device.location.state + access_device.os access_device.os_version action.name + actor.details actor.name actor.type + old_target.details target.details status + old_status user + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_duo_set_user_status_to_bypass_2fa_filter` +how_to_implement: The analytic leverages Duo activity logs to be ingested using the Cisco Security Cloud App (https://splunkbase.splunk.com/app/7404). known_false_positives: No false positives have been identified at this time. references: -- https://splunkbase.splunk.com/app/7404 + - https://splunkbase.splunk.com/app/7404 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A user $user$ has set their status to bypass 2FA from IP Address - $src_ip$ - risk_objects: - - field: user - type: user - score: 48 - threat_objects: - - field: src_ip - type: ip_address + message: A user $user$ has set their status to bypass 2FA from IP Address - $src_ip$ + risk_objects: + - field: user + type: user + score: 48 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Cisco Duo Suspicious Activity - asset_type: Identity - mitre_attack_id: - - T1556 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Cisco Duo Suspicious Activity + asset_type: Identity + mitre_attack_id: + - T1556 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_bypass_2FA/cisco_duo_activity.json - source: duo - sourcetype: cisco:duo:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/cisco_duo_bypass_2FA/cisco_duo_activity.json + source: duo + sourcetype: cisco:duo:activity diff --git a/detections/application/crushftp_server_side_template_injection.yml b/detections/application/crushftp_server_side_template_injection.yml index dfb83afea2..c624a317d4 100644 --- a/detections/application/crushftp_server_side_template_injection.yml +++ b/detections/application/crushftp_server_side_template_injection.yml @@ -4,78 +4,51 @@ version: 5 date: '2025-10-14' author: Michael Haag, Splunk data_source: -- CrushFTP + - CrushFTP type: TTP status: production -description: This analytic is designed to identify attempts to exploit a server-side - template injection vulnerability in CrushFTP, designated as CVE-2024-4040. This - severe vulnerability enables unauthenticated remote attackers to access and read - files beyond the VFS Sandbox, circumvent authentication protocols, and execute arbitrary - commands on the affected server. The issue impacts all versions of CrushFTP up to - 10.7.1 and 11.1.0 on all supported platforms. It is highly recommended to apply - patches immediately to prevent unauthorized access to the system and avoid potential - data compromises. The search specifically looks for patterns in the raw log data - that match the exploitation attempts, including READ or WRITE actions, and extracts - relevant information such as the protocol, session ID, user, IP address, HTTP method, - and the URI queried. It then evaluates these logs to confirm traces of exploitation - based on the presence of specific keywords and the originating IP address, counting - and sorting these events for further analysis. -search: '`crushftp` | rex field=_raw "\[(?HTTPS|HTTP):(?[^\:]+):(?[^\:]+):(?\d+\.\d+\.\d+\.\d+)\] - (?READ|WROTE): \*(?[A-Z]+) (?[^\s]+) HTTP/[^\*]+\*" - | eval message=if(match(_raw, "INCLUDE") and isnotnull(src_ip), "traces of exploitation - by " . src_ip, "false") | search message!=false | rename host as dest | stats count - by _time, dest, source, message, src_ip, http_method, uri_query, user, action | - sort -_time| `crushftp_server_side_template_injection_filter`' -how_to_implement: CrushFTP Session logs, from Windows or Linux, must be ingested to - Splunk. Currently, there is no TA for CrushFTP, so the data must be extracted from - the raw logs. -known_false_positives: False positives should be limited, however tune or filter as - needed. +description: This analytic is designed to identify attempts to exploit a server-side template injection vulnerability in CrushFTP, designated as CVE-2024-4040. This severe vulnerability enables unauthenticated remote attackers to access and read files beyond the VFS Sandbox, circumvent authentication protocols, and execute arbitrary commands on the affected server. The issue impacts all versions of CrushFTP up to 10.7.1 and 11.1.0 on all supported platforms. It is highly recommended to apply patches immediately to prevent unauthorized access to the system and avoid potential data compromises. The search specifically looks for patterns in the raw log data that match the exploitation attempts, including READ or WRITE actions, and extracts relevant information such as the protocol, session ID, user, IP address, HTTP method, and the URI queried. It then evaluates these logs to confirm traces of exploitation based on the presence of specific keywords and the originating IP address, counting and sorting these events for further analysis. +search: '`crushftp` | rex field=_raw "\[(?HTTPS|HTTP):(?[^\:]+):(?[^\:]+):(?\d+\.\d+\.\d+\.\d+)\] (?READ|WROTE): \*(?[A-Z]+) (?[^\s]+) HTTP/[^\*]+\*" | eval message=if(match(_raw, "INCLUDE") and isnotnull(src_ip), "traces of exploitation by " . src_ip, "false") | search message!=false | rename host as dest | stats count by _time, dest, source, message, src_ip, http_method, uri_query, user, action | sort -_time| `crushftp_server_side_template_injection_filter`' +how_to_implement: CrushFTP Session logs, from Windows or Linux, must be ingested to Splunk. Currently, there is no TA for CrushFTP, so the data must be extracted from the raw logs. +known_false_positives: False positives should be limited, however tune or filter as needed. references: -- https://github.com/airbus-cert/CVE-2024-4040 -- https://www.bleepingcomputer.com/news/security/crushftp-warns-users-to-patch-exploited-zero-day-immediately/ + - https://github.com/airbus-cert/CVE-2024-4040 + - https://www.bleepingcomputer.com/news/security/crushftp-warns-users-to-patch-exploited-zero-day-immediately/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential exploitation of CrushFTP Server Side Template Injection Vulnerability - on $dest$ by $src_ip$. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: - - field: src_ip - type: ip_address + message: Potential exploitation of CrushFTP Server Side Template Injection Vulnerability on $dest$ by $src_ip$. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - CrushFTP Vulnerabilities - - Hellcat Ransomware - asset_type: Web Application - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: - - CVE-2024-4040 + analytic_story: + - CrushFTP Vulnerabilities + - Hellcat Ransomware + asset_type: Web Application + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: + - CVE-2024-4040 tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/crushftp/crushftp.log - sourcetype: crushftp:sessionlogs - source: crushftp + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/crushftp/crushftp.log + sourcetype: crushftp:sessionlogs + source: crushftp diff --git a/detections/application/detect_distributed_password_spray_attempts.yml b/detections/application/detect_distributed_password_spray_attempts.yml index 169d29b718..5d72845613 100644 --- a/detections/application/detect_distributed_password_spray_attempts.yml +++ b/detections/application/detect_distributed_password_spray_attempts.yml @@ -1,81 +1,69 @@ name: Detect Distributed Password Spray Attempts id: b1a82fc8-8a9f-4344-9ec2-bde5c5331b57 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Dean Luxton status: production type: Hunting data_source: -- Azure Active Directory Sign-in activity -description: This analytic employs the 3-sigma approach to identify distributed password - spray attacks. A distributed password spray attack is a type of brute force attack - where the attacker attempts a few common passwords against many different accounts, - connecting from multiple IP addresses to avoid detection. By utilizing the Authentication - Data Model, this detection is effective for all CIM-mapped authentication events, - providing comprehensive coverage and enhancing security against these attacks. + - Azure Active Directory Sign-in activity +description: This analytic employs the 3-sigma approach to identify distributed password spray attacks. A distributed password spray attack is a type of brute force attack where the attacker attempts a few common passwords against many different accounts, connecting from multiple IP addresses to avoid detection. By utilizing the Authentication Data Model, this detection is effective for all CIM-mapped authentication events, providing comprehensive coverage and enhancing security against these attacks. search: >- - | tstats `security_content_summariesonly` dc(Authentication.user) AS unique_accounts - dc(Authentication.src) as unique_src values(Authentication.app) as app values(Authentication.src) - as src count(Authentication.user) as total_failures from datamodel=Authentication.Authentication - where Authentication.action="failure" NOT Authentication.src IN ("-","unknown") - Authentication.user_agent="*" by Authentication.signature_id, Authentication.user_agent, - sourcetype, _time span=10m - | `drop_dm_object_name("Authentication")` - ```fill out time buckets for 0-count events during entire search length``` - | appendpipe [| timechart limit=0 span=10m count | table _time] - | fillnull value=0 unique_accounts, unique_src - ``` Create aggregation field & apply to all null events``` - | eval counter=sourcetype+"__"+signature_id - | eventstats values(counter) as fnscounter | eval counter=coalesce(counter,fnscounter) | - stats values(total_failures) as total_failures values(signature_id) as signature_id - values(src) as src values(sourcetype) as sourcetype values(app) as app count by - counter unique_accounts unique_src user_agent _time - ``` remove 0 count rows where counter has data``` - | sort - _time unique_accounts - | dedup _time counter - ``` 3-sigma detection logic ``` - | eventstats avg(unique_accounts) as comp_avg_user , stdev(unique_accounts) as comp_std_user - avg(unique_src) as comp_avg_src , stdev(unique_src) as comp_std_src by counter user_agent - | eval upperBoundUser=(comp_avg_user+comp_std_user*3), upperBoundsrc=(comp_avg_src+comp_std_src*3) - | eval isOutlier=if((unique_accounts > 30 and unique_accounts >= upperBoundUser) - and (unique_src > 30 and unique_src >= upperBoundsrc), 1, 0) - | replace "::ffff:*" with * in src - | where isOutlier=1 - | foreach * - [ eval <> = if(<>="null",null(),<>)] - | mvexpand src | iplocation src | table _time, unique_src, unique_accounts, total_failures, - sourcetype, signature_id, user_agent, src, Country - | eval date_wday=strftime(_time,"%a"), date_hour=strftime(_time,"%H") - | `detect_distributed_password_spray_attempts_filter` -how_to_implement: Ensure that all relevant authentication data is mapped to the Common - Information Model (CIM) and that the src field is populated with the source device - information. Additionally, ensure that fill_nullvalue is set within the security_content_summariesonly - macro to include authentication events from log sources that do not feature the - signature_id field in the results. -known_false_positives: It is common to see a spike of legitimate failed authentication - events on monday mornings. + | tstats `security_content_summariesonly` dc(Authentication.user) AS unique_accounts + dc(Authentication.src) as unique_src values(Authentication.app) as app values(Authentication.src) + as src count(Authentication.user) as total_failures from datamodel=Authentication.Authentication + where Authentication.action="failure" NOT Authentication.src IN ("-","unknown") + Authentication.user_agent="*" by Authentication.signature_id, Authentication.user_agent, + sourcetype, _time span=10m + | `drop_dm_object_name("Authentication")` + ```fill out time buckets for 0-count events during entire search length``` + | appendpipe [| timechart limit=0 span=10m count | table _time] + | fillnull value=0 unique_accounts, unique_src + ``` Create aggregation field & apply to all null events``` + | eval counter=sourcetype+"__"+signature_id + | eventstats values(counter) as fnscounter | eval counter=coalesce(counter,fnscounter) | + stats values(total_failures) as total_failures values(signature_id) as signature_id + values(src) as src values(sourcetype) as sourcetype values(app) as app count by + counter unique_accounts unique_src user_agent _time + ``` remove 0 count rows where counter has data``` + | sort - _time unique_accounts + | dedup _time counter + ``` 3-sigma detection logic ``` + | eventstats avg(unique_accounts) as comp_avg_user , stdev(unique_accounts) as comp_std_user + avg(unique_src) as comp_avg_src , stdev(unique_src) as comp_std_src by counter user_agent + | eval upperBoundUser=(comp_avg_user+comp_std_user*3), upperBoundsrc=(comp_avg_src+comp_std_src*3) + | eval isOutlier=if((unique_accounts > 30 and unique_accounts >= upperBoundUser) + and (unique_src > 30 and unique_src >= upperBoundsrc), 1, 0) + | replace "::ffff:*" with * in src + | where isOutlier=1 + | foreach * + [ eval <> = if(<>="null",null(),<>)] + | mvexpand src | iplocation src | table _time, unique_src, unique_accounts, total_failures, + sourcetype, signature_id, user_agent, src, Country + | eval date_wday=strftime(_time,"%a"), date_hour=strftime(_time,"%H") + | `detect_distributed_password_spray_attempts_filter` +how_to_implement: Ensure that all relevant authentication data is mapped to the Common Information Model (CIM) and that the src field is populated with the source device information. Additionally, ensure that fill_nullvalue is set within the security_content_summariesonly macro to include authentication events from log sources that do not feature the signature_id field in the results. +known_false_positives: It is common to see a spike of legitimate failed authentication events on monday mornings. references: -- https://attack.mitre.org/techniques/T1110/003/ + - https://attack.mitre.org/techniques/T1110/003/ tags: - analytic_story: - - Compromised User Account - - Active Directory Password Spraying - asset_type: Endpoint - atomic_guid: - - 90bc2e54-6c84-47a5-9439-0a2a92b4b175 - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access - manual_test: The dataset & hardcoded timerange doesn't meet the criteria for this - detection. + analytic_story: + - Compromised User Account + - Active Directory Password Spraying + asset_type: Endpoint + atomic_guid: + - 90bc2e54-6c84-47a5-9439-0a2a92b4b175 + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access + manual_test: The dataset & hardcoded timerange doesn't meet the criteria for this detection. tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/azure_ad_distributed_spray/azure_ad_distributed_spray.log - source: azure:monitor:aad - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/azure_ad_distributed_spray/azure_ad_distributed_spray.log + source: azure:monitor:aad + sourcetype: azure:monitor:aad diff --git a/detections/application/detect_html_help_spawn_child_process.yml b/detections/application/detect_html_help_spawn_child_process.yml index 5c2918d2bf..8bc0ed9bdf 100644 --- a/detections/application/detect_html_help_spawn_child_process.yml +++ b/detections/application/detect_html_help_spawn_child_process.yml @@ -1,96 +1,78 @@ name: Detect HTML Help Spawn Child Process id: 723716de-ee55-4cd4-9759-c44e7e55ba4b -version: 12 -date: '2025-09-18' +version: 13 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of hh.exe (HTML Help) spawning - a child process, indicating the use of a Compiled HTML Help (CHM) file to execute - Windows script code. This detection leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process creation events where hh.exe is the parent process. - This activity is significant as it may indicate an attempt to execute malicious - scripts via CHM files, a known technique for bypassing security controls. If confirmed - malicious, this could lead to unauthorized code execution, potentially compromising - the system. +description: The following analytic detects the execution of hh.exe (HTML Help) spawning a child process, indicating the use of a Compiled HTML Help (CHM) file to execute Windows script code. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where hh.exe is the parent process. This activity is significant as it may indicate an attempt to execute malicious scripts via CHM files, a known technique for bypassing security controls. If confirmed malicious, this could lead to unauthorized code execution, potentially compromising the system. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=hh.exe - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `detect_html_help_spawn_child_process_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although unlikely, some legitimate applications (ex. web browsers) - may spawn a child process. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name=hh.exe + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_html_help_spawn_child_process_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although unlikely, some legitimate applications (ex. web browsers) may spawn a child process. Filter as needed. references: -- https://attack.mitre.org/techniques/T1218/001/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md -- https://lolbas-project.github.io/lolbas/Binaries/Hh/ -- https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7 -- https://web.archive.org/web/20220119133748/https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/ + - https://attack.mitre.org/techniques/T1218/001/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md + - https://lolbas-project.github.io/lolbas/Binaries/Hh/ + - https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7 + - https://web.archive.org/web/20220119133748/https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ spawning a child process, typically not normal - behavior. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ spawning a child process, typically not normal behavior. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Suspicious Compiled HTML Activity - - AgentTesla - - Living Off The Land - - Compromised Windows Host - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1218.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Compiled HTML Activity + - AgentTesla + - Living Off The Land + - Compromised Windows Host + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1218.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/application/detect_new_login_attempts_to_routers.yml b/detections/application/detect_new_login_attempts_to_routers.yml index d6a55cecfd..2c75e82c1e 100644 --- a/detections/application/detect_new_login_attempts_to_routers.yml +++ b/detections/application/detect_new_login_attempts_to_routers.yml @@ -1,46 +1,42 @@ name: Detect New Login Attempts to Routers id: bce3ed7c-9b1f-42a0-abdf-d8b123a34836 -version: 6 -date: '2025-10-14' +version: 7 +date: '2026-02-25' author: Bhavin Patel, Splunk status: experimental type: TTP -description: The following analytic identifies new login attempts to routers. It leverages - authentication logs from the ES Assets and Identity Framework, focusing on assets - categorized as routers. The detection flags connections that have not been observed - in the past 30 days. This activity is significant because unauthorized access to - routers can lead to network disruptions or data interception. If confirmed malicious, - attackers could gain control over network traffic, potentially leading to data breaches - or further network compromise. +description: The following analytic identifies new login attempts to routers. It leverages authentication logs from the ES Assets and Identity Framework, focusing on assets categorized as routers. The detection flags connections that have not been observed in the past 30 days. This activity is significant because unauthorized access to routers can lead to network disruptions or data interception. If confirmed malicious, attackers could gain control over network traffic, potentially leading to data breaches or further network compromise. data_source: [] -search: '| tstats `security_content_summariesonly` count earliest(_time) as earliest - latest(_time) as latest from datamodel=Authentication where Authentication.dest_category=router - by Authentication.dest Authentication.user| eval isOutlier=if(earliest >= relative_time(now(), - "-30d@d"), 1, 0) | where isOutlier=1| `security_content_ctime(earliest)`| `security_content_ctime(latest)` - | `drop_dm_object_name("Authentication")` | `detect_new_login_attempts_to_routers_filter`' -how_to_implement: To successfully implement this search, you must ensure the network - router devices are categorized as "router" in the Assets and identity table. You - must also populate the Authentication data model with logs related to users authenticating - to routing infrastructure. +search: |- + | tstats `security_content_summariesonly` count earliest(_time) as earliest latest(_time) as latest FROM datamodel=Authentication + WHERE Authentication.dest_category=router + BY Authentication.dest Authentication.user + | eval isOutlier=if(earliest >= relative_time(now(), "-30d@d"), 1, 0) + | where isOutlier=1 + | `security_content_ctime(earliest)` + | `security_content_ctime(latest)` + | `drop_dm_object_name("Authentication")` + | `detect_new_login_attempts_to_routers_filter` +how_to_implement: To successfully implement this search, you must ensure the network router devices are categorized as "router" in the Assets and identity table. You must also populate the Authentication data model with logs related to users authenticating to routing infrastructure. known_false_positives: Legitimate router connections may appear as new connections references: [] rba: - message: New login on $dest$ from $user$ - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: New login on $dest$ from $user$ + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Router and Infrastructure Security - - Scattered Lapsus$ Hunters - asset_type: Endpoint - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Router and Infrastructure Security + - Scattered Lapsus$ Hunters + asset_type: Endpoint + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/application/detect_password_spray_attempts.yml b/detections/application/detect_password_spray_attempts.yml index 463281e3a2..007389634e 100644 --- a/detections/application/detect_password_spray_attempts.yml +++ b/detections/application/detect_password_spray_attempts.yml @@ -6,85 +6,48 @@ author: Dean Luxton status: production type: TTP data_source: -- Windows Event Log Security 4625 -description: This analytic employs the 3-sigma approach to detect an unusual volume - of failed authentication attempts from a single source. A password spray attack - is a type of brute force attack where an attacker tries a few common passwords across - many different accounts to avoid detection and account lockouts. By utilizing the - Authentication Data Model, this detection is effective for all CIM-mapped authentication - events, providing comprehensive coverage and enhancing security against these attacks. -search: "| tstats `security_content_summariesonly` values(Authentication.user) AS\ - \ unique_user_names dc(Authentication.user) AS unique_accounts values(Authentication.app)\ - \ as app count(Authentication.user) as total_failures from datamodel=Authentication.Authentication\ - \ where Authentication.action=\"failure\" NOT Authentication.src IN (\"-\",\"unknown\"\ - ) by Authentication.action Authentication.app Authentication.authentication_method\ - \ Authentication.dest \n Authentication.signature Authentication.signature_id Authentication.src\ - \ sourcetype _time span=5m \n| `drop_dm_object_name(\"Authentication\")`\n ```fill\ - \ out time buckets for 0-count events during entire search length```\n| appendpipe\ - \ [| timechart limit=0 span=5m count | table _time] | fillnull value=0 unique_accounts\n\ - \ ``` Create aggregation field & apply to all null events```\n| eval counter=src+\"\ - __\"+sourcetype+\"__\"+signature_id | eventstats values(counter) as fnscounter\ - \ | eval counter=coalesce(counter,fnscounter) \n ``` stats version of mvexpand\ - \ ```\n| stats values(app) as app values(unique_user_names) as unique_user_names\ - \ values(total_failures) as total_failures values(src) as src values(signature_id)\ - \ as signature_id values(sourcetype) as sourcetype count by counter unique_accounts\ - \ _time\n ``` remove duplicate time buckets for each unique source```\n| sort\ - \ - _time unique_accounts | dedup _time counter\n ```Find the outliers```\n|\ - \ eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std\ - \ by counter | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts\ - \ > 30 and unique_accounts >= upperBound, 1, 0) | replace \"::ffff:*\" with * in\ - \ src | where isOutlier=1 | foreach * \n [ eval <> = if(<>=\"\ - null\",null(),<>)] \n| table _time, src, action, app, unique_accounts, unique_user_names,\ - \ total_failures, sourcetype, signature_id, counter | `detect_password_spray_attempts_filter`" -how_to_implement: 'Ensure in-scope authentication data is CIM mapped and the src field - is populated with the source device. Also ensure fill_nullvalue is set within the - macro security_content_summariesonly. This search opporates best on a 5 minute schedule, - looking back over the past 70 minutes. Configure 70 minute throttling on the two - fields _time and counter. ' + - Windows Event Log Security 4625 +description: This analytic employs the 3-sigma approach to detect an unusual volume of failed authentication attempts from a single source. A password spray attack is a type of brute force attack where an attacker tries a few common passwords across many different accounts to avoid detection and account lockouts. By utilizing the Authentication Data Model, this detection is effective for all CIM-mapped authentication events, providing comprehensive coverage and enhancing security against these attacks. +search: "| tstats `security_content_summariesonly` values(Authentication.user) AS unique_user_names dc(Authentication.user) AS unique_accounts values(Authentication.app) as app count(Authentication.user) as total_failures from datamodel=Authentication.Authentication where Authentication.action=\"failure\" NOT Authentication.src IN (\"-\",\"unknown\") by Authentication.action Authentication.app Authentication.authentication_method Authentication.dest \n Authentication.signature Authentication.signature_id Authentication.src sourcetype _time span=5m \n| `drop_dm_object_name(\"Authentication\")`\n ```fill out time buckets for 0-count events during entire search length```\n| appendpipe [| timechart limit=0 span=5m count | table _time] | fillnull value=0 unique_accounts\n ``` Create aggregation field & apply to all null events```\n| eval counter=src+\"__\"+sourcetype+\"__\"+signature_id | eventstats values(counter) as fnscounter | eval counter=coalesce(counter,fnscounter) \n ``` stats version of mvexpand ```\n| stats values(app) as app values(unique_user_names) as unique_user_names values(total_failures) as total_failures values(src) as src values(signature_id) as signature_id values(sourcetype) as sourcetype count by counter unique_accounts _time\n ``` remove duplicate time buckets for each unique source```\n| sort - _time unique_accounts | dedup _time counter\n ```Find the outliers```\n| eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by counter | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 30 and unique_accounts >= upperBound, 1, 0) | replace \"::ffff:*\" with * in src | where isOutlier=1 | foreach * \n [ eval <> = if(<>=\"null\",null(),<>)] \n| table _time, src, action, app, unique_accounts, unique_user_names, total_failures, sourcetype, signature_id, counter | `detect_password_spray_attempts_filter`" +how_to_implement: 'Ensure in-scope authentication data is CIM mapped and the src field is populated with the source device. Also ensure fill_nullvalue is set within the macro security_content_summariesonly. This search opporates best on a 5 minute schedule, looking back over the past 70 minutes. Configure 70 minute throttling on the two fields _time and counter. ' known_false_positives: No false positives have been identified at this time. references: -- https://attack.mitre.org/techniques/T1110/003/ + - https://attack.mitre.org/techniques/T1110/003/ drilldown_searches: -- name: View the detection results for - "$sourcetype$" - search: '%original_detection_search% | search sourcetype = "$sourcetype$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$sourcetype$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$sourcetype$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$sourcetype$" + search: '%original_detection_search% | search sourcetype = "$sourcetype$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$sourcetype$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$sourcetype$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential Password Spraying attack from $src$ targeting $unique_accounts$ - unique accounts. - risk_objects: - - field: unique_user_names - type: user - score: 49 - threat_objects: - - field: src - type: system + message: Potential Password Spraying attack from $src$ targeting $unique_accounts$ unique accounts. + risk_objects: + - field: unique_user_names + type: user + score: 49 + threat_objects: + - field: src + type: system tags: - analytic_story: - - Compromised User Account - - Active Directory Password Spraying - asset_type: Endpoint - atomic_guid: - - 90bc2e54-6c84-47a5-9439-0a2a92b4b175 - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - Compromised User Account + - Active Directory Password Spraying + asset_type: Endpoint + atomic_guid: + - 90bc2e54-6c84-47a5-9439-0a2a92b4b175 + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_invalid_users_kerberos_xml/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_invalid_users_kerberos_xml/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/application/email_attachments_with_lots_of_spaces.yml b/detections/application/email_attachments_with_lots_of_spaces.yml index 43b797917f..7b726706be 100644 --- a/detections/application/email_attachments_with_lots_of_spaces.yml +++ b/detections/application/email_attachments_with_lots_of_spaces.yml @@ -1,56 +1,42 @@ name: Email Attachments With Lots Of Spaces id: 56e877a6-1455-4479-ada6-0550dc1e22f8 -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: David Dorsey, Splunk status: experimental type: Anomaly -description: The following analytic detects email attachments with an unusually high - number of spaces in their file names, which is a common tactic used by attackers - to obfuscate file extensions. It leverages the Email data model to identify attachments - where the ratio of spaces to the total file name length exceeds 10%. This behavior - is significant as it may indicate an attempt to bypass security filters and deliver - malicious payloads. If confirmed malicious, this activity could lead to the execution - of harmful code or unauthorized access to sensitive information within the recipient's - environment. +description: The following analytic detects email attachments with an unusually high number of spaces in their file names, which is a common tactic used by attackers to obfuscate file extensions. It leverages the Email data model to identify attachments where the ratio of spaces to the total file name length exceeds 10%. This behavior is significant as it may indicate an attempt to bypass security filters and deliver malicious payloads. If confirmed malicious, this activity could lead to the execution of harmful code or unauthorized access to sensitive information within the recipient's environment. data_source: [] -search: '| tstats `security_content_summariesonly` count values(All_Email.recipient) - as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email - where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")` - | eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) | search space_ratio - >= 0.1 | rex field=recipient_address "(?.*)@" | `email_attachments_with_lots_of_spaces_filter`' -how_to_implement: "You need to ingest data from emails. Specifically, the sender's - address and the file names of any attachments must be mapped to the Email data model. - The threshold ratio is set to 10%, but this value can be configured to suit each - environment.\n**Splunk Phantom Playbook Integration**\nIf Splunk Phantom is also - configured in your environment, a playbook called \"Suspicious Email Attachment - Investigate and Delete\" can be configured to run when any results are found by - this detection search. To use this integration, install the Phantom App for Splunk - `https://splunkbase.splunk.com/app/3411/` and add the correct hostname to the \"\ - Phantom Instance\" field in the Adaptive Response Actions when configuring this - detection search. The finding based event will be sent to Phantom and the playbook will - gather further information about the file attachment and its network behaviors. - If Phantom finds malicious behavior and an analyst approves of the results, the - email will be deleted from the user's inbox." +search: |- + | tstats `security_content_summariesonly` count values(All_Email.recipient) as recipient_address min(_time) as firstTime max(_time) as lastTime FROM datamodel=Email + WHERE All_Email.file_name="*" + BY All_Email.src_user, All_Email.file_name All_Email.message_id + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `drop_dm_object_name("All_Email")` + | eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) + | search space_ratio >= 0.1 + | rex field=recipient_address "(?.*)@" + | `email_attachments_with_lots_of_spaces_filter` +how_to_implement: "You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment.\n**Splunk Phantom Playbook Integration**\nIf Splunk Phantom is also configured in your environment, a playbook called \"Suspicious Email Attachment Investigate and Delete\" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/` and add the correct hostname to the \"Phantom Instance\" field in the Adaptive Response Actions when configuring this detection search. The finding based event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox." known_false_positives: No false positives have been identified at this time. references: [] rba: - message: Abnormal number of spaces present in attachment filename from $src_user$ - risk_objects: - - field: src_user - type: user - score: 25 - threat_objects: [] + message: Abnormal number of spaces present in attachment filename from $src_user$ + risk_objects: + - field: src_user + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Data Destruction - - Emotet Malware DHS Report TA18-201A - - Hermetic Wiper - - Suspicious Emails - asset_type: Endpoint - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Data Destruction + - Emotet Malware DHS Report TA18-201A + - Hermetic Wiper + - Suspicious Emails + asset_type: Endpoint + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/application/email_files_written_outside_of_the_outlook_directory.yml b/detections/application/email_files_written_outside_of_the_outlook_directory.yml index 2b683f0f10..740ddef4b5 100644 --- a/detections/application/email_files_written_outside_of_the_outlook_directory.yml +++ b/detections/application/email_files_written_outside_of_the_outlook_directory.yml @@ -1,54 +1,48 @@ name: Email files written outside of the Outlook directory id: 8d52cf03-ba25-4101-aa78-07994aed4f74 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Bhavin Patel, Splunk status: experimental type: TTP -description: The following analytic detects email files (.pst or .ost) being created - outside the standard Outlook directories. It leverages the Endpoint.Filesystem data - model to identify file creation events and filters for email files not located in - "C:\Users\*\My Documents\Outlook Files\*" or "C:\Users\*\AppData\Local\Microsoft\Outlook*". - This activity is significant as it may indicate data exfiltration or unauthorized - access to email data. If confirmed malicious, an attacker could potentially access - sensitive email content, leading to data breaches or further exploitation within - the network. +description: The following analytic detects email files (.pst or .ost) being created outside the standard Outlook directories. It leverages the Endpoint.Filesystem data model to identify file creation events and filters for email files not located in "C:\Users\*\My Documents\Outlook Files\*" or "C:\Users\*\AppData\Local\Microsoft\Outlook*". This activity is significant as it may indicate data exfiltration or unauthorized access to email data. If confirmed malicious, an attacker could potentially access sensitive email content, leading to data breaches or further exploitation within the network. data_source: -- Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count values(Filesystem.file_path) - as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem - where (Filesystem.file_name=*.pst OR Filesystem.file_name=*.ost) Filesystem.file_path - != "C:\\Users\\*\\My Documents\\Outlook Files\\*" Filesystem.file_path!="C:\\Users\\*\\AppData\\Local\\Microsoft\\Outlook*" - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path - Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product | `drop_dm_object_name("Filesystem")` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `email_files_written_outside_of_the_outlook_directory_filter`' -how_to_implement: To successfully implement this search, you must be ingesting data - that records the file-system activity from your hosts to populate the Endpoint.Filesystem - data model node. This is typically populated via endpoint detection-and-response - product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. - The data used for this search is typically generated via logs that report file-system - reads and writes. -known_false_positives: Administrators and users sometimes prefer backing up their - email data by moving the email files into a different folder. These attempts will - be detected by the search. + - Sysmon EventID 11 +search: |- + | tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE ( + Filesystem.file_name=*.pst + OR + Filesystem.file_name=*.ost + ) + Filesystem.file_path != "C:\Users\*\My Documents\Outlook Files\*" Filesystem.file_path!="C:\Users\*\AppData\Local\Microsoft\Outlook*" + BY Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path Filesystem.file_acl + Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name("Filesystem")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `email_files_written_outside_of_the_outlook_directory_filter` +how_to_implement: To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. +known_false_positives: Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search. references: [] rba: - message: Email files written outside of Outlook's Directory on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: Email files written outside of Outlook's Directory on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Collection and Staging - asset_type: Endpoint - mitre_attack_id: - - T1114.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Collection and Staging + asset_type: Endpoint + mitre_attack_id: + - T1114.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/application/email_servers_sending_high_volume_traffic_to_hosts.yml b/detections/application/email_servers_sending_high_volume_traffic_to_hosts.yml index ae9e6c81bd..414cb45b72 100644 --- a/detections/application/email_servers_sending_high_volume_traffic_to_hosts.yml +++ b/detections/application/email_servers_sending_high_volume_traffic_to_hosts.yml @@ -1,59 +1,44 @@ name: Email servers sending high volume traffic to hosts id: 7f5fb3e1-4209-4914-90db-0ec21b556378 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Bhavin Patel, Splunk status: experimental type: Anomaly -description: The following analytic identifies a significant increase in data transfers - from your email server to client hosts. It leverages the Network_Traffic data model - to monitor outbound traffic from email servers, using statistical analysis to detect - anomalies based on average and standard deviation metrics. This activity is significant - as it may indicate a malicious actor exfiltrating data via your email server. If - confirmed malicious, this could lead to unauthorized data access and potential data - breaches, compromising sensitive information and impacting organizational security. +description: The following analytic identifies a significant increase in data transfers from your email server to client hosts. It leverages the Network_Traffic data model to monitor outbound traffic from email servers, using statistical analysis to detect anomalies based on average and standard deviation metrics. This activity is significant as it may indicate a malicious actor exfiltrating data via your email server. If confirmed malicious, this could lead to unauthorized data access and potential data breaches, compromising sensitive information and impacting organizational security. data_source: [] -search: '| tstats `security_content_summariesonly` sum(All_Traffic.bytes_out) as bytes_out - from datamodel=Network_Traffic where All_Traffic.src_category=email_server by All_Traffic.dest_ip - _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_out) - as avg_bytes_out stdev(bytes_out) as stdev_bytes_out | eventstats count as num_data_samples - avg(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_avg_bytes_out - stdev(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_stdev_bytes_out - by dest_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples - >= minimum_data_samples AND bytes_out > (avg_bytes_out + (deviation_threshold * - stdev_bytes_out)) AND bytes_out > (per_source_avg_bytes_out + (deviation_threshold - * per_source_stdev_bytes_out)) AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average - = round(abs(bytes_out - avg_bytes_out) / stdev_bytes_out, 2), num_standard_deviations_away_from_client_average - = round(abs(bytes_out - per_source_avg_bytes_out) / per_source_stdev_bytes_out, - 2) | table dest_ip, _time, bytes_out, avg_bytes_out, per_source_avg_bytes_out, num_standard_deviations_away_from_server_average, - num_standard_deviations_away_from_client_average | `email_servers_sending_high_volume_traffic_to_hosts_filter`' -how_to_implement: This search requires you to be ingesting your network traffic and - populating the Network_Traffic data model. Your email servers must be categorized - as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold - and minimum_data_samples values based on the network traffic in your environment. - The "deviation_threshold" field is a multiplying factor to control how much variation - you're willing to tolerate. The "minimum_data_samples" field is the minimum number - of connections of data samples required for the statistic to be valid. -known_false_positives: The false-positive rate will vary based on how you set the - deviation_threshold and data_samples values. Our recommendation is to adjust these - values based on your network traffic to and from your email servers. +search: |- + | tstats `security_content_summariesonly` sum(All_Traffic.bytes_out) as bytes_out FROM datamodel=Network_Traffic + WHERE All_Traffic.src_category=email_server + BY All_Traffic.dest_ip _time span=1d + | `drop_dm_object_name("All_Traffic")` + | eventstats avg(bytes_out) as avg_bytes_out stdev(bytes_out) as stdev_bytes_out + | eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_avg_bytes_out stdev(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_stdev_bytes_out + BY dest_ip + | eval minimum_data_samples = 4, deviation_threshold = 3 + | where num_data_samples >= minimum_data_samples AND bytes_out > (avg_bytes_out + (deviation_threshold * stdev_bytes_out)) AND bytes_out > (per_source_avg_bytes_out + (deviation_threshold * per_source_stdev_bytes_out)) AND _time >= relative_time(now(), "@d") + | eval num_standard_deviations_away_from_server_average = round(abs(bytes_out - avg_bytes_out) / stdev_bytes_out, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_out - per_source_avg_bytes_out) / per_source_stdev_bytes_out, 2) + | table dest_ip, _time, bytes_out, avg_bytes_out, per_source_avg_bytes_out, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average + | `email_servers_sending_high_volume_traffic_to_hosts_filter` +how_to_implement: This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. +known_false_positives: The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. references: [] rba: - message: High volume of network traffic from $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: High volume of network traffic from $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Collection and Staging - - HAFNIUM Group - asset_type: Endpoint - mitre_attack_id: - - T1114.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Collection and Staging + - HAFNIUM Group + asset_type: Endpoint + mitre_attack_id: + - T1114.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/application/esxi_account_modified.yml b/detections/application/esxi_account_modified.yml index b237dffd59..dc0b2d15a4 100644 --- a/detections/application/esxi_account_modified.yml +++ b/detections/application/esxi_account_modified.yml @@ -5,61 +5,47 @@ date: '2025-07-01' author: Raven Tait, Splunk status: production type: Anomaly -description: This detection identifies the creation, deletion, or modification of a local user account on an ESXi host. - This activity may indicate unauthorized access, indicator removal, or persistence attempts by an attacker seeking - to establish or maintain control of the host. +description: This detection identifies the creation, deletion, or modification of a local user account on an ESXi host. This activity may indicate unauthorized access, indicator removal, or persistence attempts by an attacker seeking to establish or maintain control of the host. data_source: -- VMWare ESXi Syslog -search: '`esxi_syslog` Message="*esxcli system account*" Message IN ("*-i *","*--id*") NOT Message="*[shell*" - | rex field=_raw "Z (?[\w\.]+)\s.*: \[(?\w+)]:\s.+-i[d]*\s(?[\w_\-0-9]+)" - | stats min(_time) as firstTime max(_time) as lastTime count by dest initial_user modified_user - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `esxi_account_modified_filter`' -how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, - you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must - be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field - extractions and CIM compatibility. + - VMWare ESXi Syslog +search: '`esxi_syslog` Message="*esxcli system account*" Message IN ("*-i *","*--id*") NOT Message="*[shell*" | rex field=_raw "Z (?[\w\.]+)\s.*: \[(?\w+)]:\s.+-i[d]*\s(?[\w_\-0-9]+)" | stats min(_time) as firstTime max(_time) as lastTime count by dest initial_user modified_user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_account_modified_filter`' +how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field extractions and CIM compatibility. known_false_positives: New local accounts being created in ESXi is rare in most environments. Tune as needed. references: -- https://detect.fyi/detecting-and-responding-to-esxi-compromise-with-splunk-f33998ce7823 + - https://detect.fyi/detecting-and-responding-to-esxi-compromise-with-splunk-f33998ce7823 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Local account created, deleted, or modified on ESXi $dest$. - risk_objects: - - field: dest - type: system - score: 60 - threat_objects: [] + message: Local account created, deleted, or modified on ESXi $dest$. + risk_objects: + - field: dest + type: system + score: 60 + threat_objects: [] tags: - analytic_story: - - ESXi Post Compromise - - Black Basta Ransomware - asset_type: Infrastructure - mitre_attack_id: - - T1136.001 - - T1078 - - T1098 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ESXi Post Compromise + - Black Basta Ransomware + asset_type: Infrastructure + mitre_attack_id: + - T1136.001 + - T1078 + - T1098 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/esxi_account_modification/esxi_account_modified.log - source: vmware:esxlog - sourcetype: vmw-syslog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/esxi_account_modification/esxi_account_modified.log + source: vmware:esxlog + sourcetype: vmw-syslog diff --git a/detections/application/esxi_audit_tampering.yml b/detections/application/esxi_audit_tampering.yml index 0a5fcd7b52..c3188a5bce 100644 --- a/detections/application/esxi_audit_tampering.yml +++ b/detections/application/esxi_audit_tampering.yml @@ -5,63 +5,46 @@ date: '2025-07-01' author: Raven Tait, Splunk status: production type: TTP -description: This detection identifies the use of the esxcli system auditrecords commands, - which can be used to tamper with logging on an ESXi host. This action may indicate an attempt - to evade detection or hinder forensic analysis by preventing the recording of system-level audit events. +description: This detection identifies the use of the esxcli system auditrecords commands, which can be used to tamper with logging on an ESXi host. This action may indicate an attempt to evade detection or hinder forensic analysis by preventing the recording of system-level audit events. data_source: -- VMWare ESXi Syslog -search: '`esxi_syslog` Message="*esxcli system auditrecords*" Message IN ("*remote*","*local*") NOT Message = "*[shell*" - | rex field=_raw "Z (?[\w\.]+)\s" - | rex field=_raw "[\w+]\]: (?.*)" - | rex field=full_command "\[(?.*)]:\s(?.*)" - | stats min(_time) as firstTime max(_time) as lastTime count by dest user command - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `esxi_audit_tampering_filter`' -how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, - you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must - be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field - extractions and CIM compatibility. -known_false_positives: Limited false positives in most environments, however tune - as needed. + - VMWare ESXi Syslog +search: '`esxi_syslog` Message="*esxcli system auditrecords*" Message IN ("*remote*","*local*") NOT Message = "*[shell*" | rex field=_raw "Z (?[\w\.]+)\s" | rex field=_raw "[\w+]\]: (?.*)" | rex field=full_command "\[(?.*)]:\s(?.*)" | stats min(_time) as firstTime max(_time) as lastTime count by dest user command | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_audit_tampering_filter`' +how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field extractions and CIM compatibility. +known_false_positives: Limited false positives in most environments, however tune as needed. references: -- https://detect.fyi/detecting-and-responding-to-esxi-compromise-with-splunk-f33998ce7823 + - https://detect.fyi/detecting-and-responding-to-esxi-compromise-with-splunk-f33998ce7823 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Audit tampering activity on ESXi host $dest$. - risk_objects: - - field: dest - type: system - score: 50 - threat_objects: [] + message: Audit tampering activity on ESXi host $dest$. + risk_objects: + - field: dest + type: system + score: 50 + threat_objects: [] tags: - analytic_story: - - ESXi Post Compromise - - Black Basta Ransomware - asset_type: Infrastructure - mitre_attack_id: - - T1562.003 - - T1070 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ESXi Post Compromise + - Black Basta Ransomware + asset_type: Infrastructure + mitre_attack_id: + - T1562.003 + - T1070 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.003/esxi_audit_tampering/esxi_audit_tampering.log - source: vmware:esxlog - sourcetype: vmw-syslog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.003/esxi_audit_tampering/esxi_audit_tampering.log + source: vmware:esxlog + sourcetype: vmw-syslog diff --git a/detections/application/esxi_bulk_vm_termination.yml b/detections/application/esxi_bulk_vm_termination.yml index 763e05864d..5bdb448dc6 100644 --- a/detections/application/esxi_bulk_vm_termination.yml +++ b/detections/application/esxi_bulk_vm_termination.yml @@ -5,68 +5,45 @@ date: '2025-05-12' author: Raven Tait, Splunk status: production type: TTP -description: This detection identifies when all virtual machines on an ESXi host are abruptly - terminated, which may indicate malicious activity such as a deliberate denial-of-service, - ransomware staging, or an attempt to destroy critical workloads. +description: This detection identifies when all virtual machines on an ESXi host are abruptly terminated, which may indicate malicious activity such as a deliberate denial-of-service, ransomware staging, or an attempt to destroy critical workloads. data_source: -- VMWare ESXi Syslog -search: '`esxi_syslog` | rex field=_raw "\s\[(?[^\]]+)\]:\s(?.+)$" - | rex field=_raw "Z (?[\w\.]+)\s.*:\s(?esxcli\s.+)" - | eval command=mvappend(esxicli_Command, shell_Command) - | where isnotnull(command) - | search (command="pkill -9 vmx-*") OR ( - command="*esxcli*" - AND command="*--format-param*" - AND command="*vm process list*" - AND command="*awk*" - AND command="*esxcli vm process kill*") - | stats min(_time) as firstTime max(_time) as lastTime values(_time) as timeStamp values(command) as commands values(user) as user by dest - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `esxi_bulk_vm_termination_filter`' -how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, - you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must - be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field - extractions and CIM compatibility. -known_false_positives: Limited false positives in most environments, however tune - as needed. + - VMWare ESXi Syslog +search: '`esxi_syslog` | rex field=_raw "\s\[(?[^\]]+)\]:\s(?.+)$" | rex field=_raw "Z (?[\w\.]+)\s.*:\s(?esxcli\s.+)" | eval command=mvappend(esxicli_Command, shell_Command) | where isnotnull(command) | search (command="pkill -9 vmx-*") OR ( command="*esxcli*" AND command="*--format-param*" AND command="*vm process list*" AND command="*awk*" AND command="*esxcli vm process kill*") | stats min(_time) as firstTime max(_time) as lastTime values(_time) as timeStamp values(command) as commands values(user) as user by dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_bulk_vm_termination_filter`' +how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field extractions and CIM compatibility. +known_false_positives: Limited false positives in most environments, however tune as needed. drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Bulk VM termination activity on ESXi host $dest$. - risk_objects: - - field: dest - type: system - score: 50 - threat_objects: [] + message: Bulk VM termination activity on ESXi host $dest$. + risk_objects: + - field: dest + type: system + score: 50 + threat_objects: [] tags: - analytic_story: - - ESXi Post Compromise - - Black Basta Ransomware - asset_type: Infrastructure - mitre_attack_id: - - T1673 - - T1529 - - T1499 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ESXi Post Compromise + - Black Basta Ransomware + asset_type: Infrastructure + mitre_attack_id: + - T1673 + - T1529 + - T1499 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1529/esxi_bulk_vm_termination/esxi_bulk_vm_termination.log - source: vmware:esxlog - sourcetype: vmw-syslog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1529/esxi_bulk_vm_termination/esxi_bulk_vm_termination.log + source: vmware:esxlog + sourcetype: vmw-syslog diff --git a/detections/application/esxi_download_errors.yml b/detections/application/esxi_download_errors.yml index 847d934dc6..e38246faa3 100644 --- a/detections/application/esxi_download_errors.yml +++ b/detections/application/esxi_download_errors.yml @@ -5,61 +5,44 @@ date: '2025-05-12' author: Raven Tait, Splunk status: production type: Anomaly -description: This detection identifies failed file download attempts on ESXi hosts by looking - for specific error messages in the system logs. These failures may indicate unauthorized - or malicious attempts to install or update components—such as VIBs or scripts +description: This detection identifies failed file download attempts on ESXi hosts by looking for specific error messages in the system logs. These failures may indicate unauthorized or malicious attempts to install or update components—such as VIBs or scripts data_source: -- VMWare ESXi Syslog -search: '`esxi_syslog` Message IN ("*Download failed*", "*Failed to download file*", - "*File download error*", "*Could not download*") - | rex field=_raw "Z (?[\w\.]*)\s" - | stats min(_time) as firstTime max(_time) as lastTime count by dest Message - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `esxi_download_errors_filter` ' -how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, - you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must - be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field - extractions and CIM compatibility. -known_false_positives: Limited false positives in most environments, however tune - as needed. + - VMWare ESXi Syslog +search: '`esxi_syslog` Message IN ("*Download failed*", "*Failed to download file*", "*File download error*", "*Could not download*") | rex field=_raw "Z (?[\w\.]*)\s" | stats min(_time) as firstTime max(_time) as lastTime count by dest Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_download_errors_filter` ' +how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field extractions and CIM compatibility. +known_false_positives: Limited false positives in most environments, however tune as needed. drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Download Errors on ESXi host $dest$. - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: [] + message: Download Errors on ESXi host $dest$. + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: [] tags: - analytic_story: - - ESXi Post Compromise - - Black Basta Ransomware - asset_type: Infrastructure - mitre_attack_id: - - T1601.001 - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ESXi Post Compromise + - Black Basta Ransomware + asset_type: Infrastructure + mitre_attack_id: + - T1601.001 + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1601.001/esxi_download_errors/esxi_download_errors.log - source: vmware:esxlog - sourcetype: vmw-syslog - + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1601.001/esxi_download_errors/esxi_download_errors.log + source: vmware:esxlog + sourcetype: vmw-syslog diff --git a/detections/application/esxi_encryption_settings_modified.yml b/detections/application/esxi_encryption_settings_modified.yml index 9bb6a0e256..3dd8470592 100644 --- a/detections/application/esxi_encryption_settings_modified.yml +++ b/detections/application/esxi_encryption_settings_modified.yml @@ -5,59 +5,43 @@ date: '2025-07-07' author: Raven Tait, Splunk status: production type: TTP -description: Detects the disabling of critical encryption enforcement settings on an ESXi host, such as - secure boot or executable verification requirements, which may indicate an attempt to weaken - hypervisor integrity or allow unauthorized code execution. +description: Detects the disabling of critical encryption enforcement settings on an ESXi host, such as secure boot or executable verification requirements, which may indicate an attempt to weaken hypervisor integrity or allow unauthorized code execution. data_source: -- VMWare ESXi Syslog -search: '`esxi_syslog` Message="*system settings encryption set*" NOT Message="*shell.*" - Message IN ("* -s *", "* -e *","*--require-secure-boot*", "*require-exec-installed-only*", "execInstalledOnly") - | rex field=_raw "Z (?[\w\.]*)\s.*\]: \[(?\w+)\]:(?.+)" - | stats min(_time) as firstTime max(_time) as lastTime count by dest user command - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `esxi_encryption_settings_modified_filter`' -how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, - you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must - be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field - extractions and CIM compatibility. -known_false_positives: Limited false positives in most environments, however tune - as needed. + - VMWare ESXi Syslog +search: '`esxi_syslog` Message="*system settings encryption set*" NOT Message="*shell.*" Message IN ("* -s *", "* -e *","*--require-secure-boot*", "*require-exec-installed-only*", "execInstalledOnly") | rex field=_raw "Z (?[\w\.]*)\s.*\]: \[(?\w+)\]:(?.+)" | stats min(_time) as firstTime max(_time) as lastTime count by dest user command | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_encryption_settings_modified_filter`' +how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field extractions and CIM compatibility. +known_false_positives: Limited false positives in most environments, however tune as needed. drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Encryption settings modified on ESXi host $dest$. - risk_objects: - - field: dest - type: system - score: 50 - threat_objects: [] + message: Encryption settings modified on ESXi host $dest$. + risk_objects: + - field: dest + type: system + score: 50 + threat_objects: [] tags: - analytic_story: - - ESXi Post Compromise - - Black Basta Ransomware - asset_type: Infrastructure - mitre_attack_id: - - T1562 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ESXi Post Compromise + - Black Basta Ransomware + asset_type: Infrastructure + mitre_attack_id: + - T1562 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/esxi_encryption_modified/esxi_encryption_modified.log - source: vmware:esxlog - sourcetype: vmw-syslog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/esxi_encryption_modified/esxi_encryption_modified.log + source: vmware:esxlog + sourcetype: vmw-syslog diff --git a/detections/application/esxi_external_root_login_activity.yml b/detections/application/esxi_external_root_login_activity.yml index 5712f94d7b..2dbc9d3bb0 100644 --- a/detections/application/esxi_external_root_login_activity.yml +++ b/detections/application/esxi_external_root_login_activity.yml @@ -5,64 +5,46 @@ date: '2025-05-13' author: Raven Tait, Splunk status: production type: Anomaly -description: This detection identifies instances where the ESXi UI is accessed using the root - account instead of a delegated administrative user. Direct root access to the UI bypasses - role-based access controls and auditing practices, and may indicate risky behavior, - misconfiguration, or unauthorized activity by a malicious actor using compromised credentials. +description: This detection identifies instances where the ESXi UI is accessed using the root account instead of a delegated administrative user. Direct root access to the UI bypasses role-based access controls and auditing practices, and may indicate risky behavior, misconfiguration, or unauthorized activity by a malicious actor using compromised credentials. data_source: -- VMWare ESXi Syslog -search: '`esxi_syslog` Message="*root*" AND Message="*logged in*" - | rex field=_raw "root@(?\d{1,3}(?:\.\d{1,3}){3})" - | rex field=_raw "Z (?[\w\.]+)\s" - | search SrcIpAddr != "127.0.0.1" AND SrcIpAddr != 192.168.0.0/16 AND SrcIpAddr != 172.16.0.0/12 AND SrcIpAddr != 10.0.0.0/8 - | stats min(_time) as firstTime max(_time) as lastTime count by dest SrcIpAddr - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `esxi_external_root_login_activity_filter`' -how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, - you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must - be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field - extractions and CIM compatibility. -known_false_positives: Limited false positives in most environments, however tune - as needed. Administrators may use the root account for troubleshooting or initial user creation. + - VMWare ESXi Syslog +search: '`esxi_syslog` Message="*root*" AND Message="*logged in*" | rex field=_raw "root@(?\d{1,3}(?:\.\d{1,3}){3})" | rex field=_raw "Z (?[\w\.]+)\s" | search SrcIpAddr != "127.0.0.1" AND SrcIpAddr != 192.168.0.0/16 AND SrcIpAddr != 172.16.0.0/12 AND SrcIpAddr != 10.0.0.0/8 | stats min(_time) as firstTime max(_time) as lastTime count by dest SrcIpAddr | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_external_root_login_activity_filter`' +how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field extractions and CIM compatibility. +known_false_positives: Limited false positives in most environments, however tune as needed. Administrators may use the root account for troubleshooting or initial user creation. drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Root logged in on ESXi host $dest$ from $SrcIpAddr. - risk_objects: - - field: dest - type: system - score: 45 - - field: SrcIpAddr - type: system - score: 45 - threat_objects: [] + message: Root logged in on ESXi host $dest$ from $SrcIpAddr. + risk_objects: + - field: dest + type: system + score: 45 + - field: SrcIpAddr + type: system + score: 45 + threat_objects: [] tags: - analytic_story: - - ESXi Post Compromise - - Black Basta Ransomware - asset_type: Infrastructure - mitre_attack_id: - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ESXi Post Compromise + - Black Basta Ransomware + asset_type: Infrastructure + mitre_attack_id: + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/esxi_external_root_login/esxi_external_root_login.log - source: vmware:esxlog - sourcetype: vmw-syslog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/esxi_external_root_login/esxi_external_root_login.log + source: vmware:esxlog + sourcetype: vmw-syslog diff --git a/detections/application/esxi_firewall_disabled.yml b/detections/application/esxi_firewall_disabled.yml index 809b764011..17a4c78c1f 100644 --- a/detections/application/esxi_firewall_disabled.yml +++ b/detections/application/esxi_firewall_disabled.yml @@ -5,60 +5,44 @@ date: '2025-08-06' author: Raven Tait, Splunk status: production type: TTP -description: This detection identifies when the ESXi firewall is disabled or set to - permissive mode, which can expose the host to unauthorized access and network-based - attacks. Such changes are often a precursor to lateral movement, data exfiltration, - or the installation of malicious software by a threat actor. +description: This detection identifies when the ESXi firewall is disabled or set to permissive mode, which can expose the host to unauthorized access and network-based attacks. Such changes are often a precursor to lateral movement, data exfiltration, or the installation of malicious software by a threat actor. data_source: -- VMWare ESXi Syslog -search: '`esxi_syslog` Message="*network firewall set*" AND Message="*enabled f*" - | rex field=_raw "Z (?[\w\.]+)\s" - | stats min(_time) as firstTime max(_time) as lastTime count by dest Message - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `esxi_firewall_disabled_filter`' -how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, - you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must - be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field - extractions and CIM compatibility. -known_false_positives: Limited false positives in most environments, however tune - as needed. + - VMWare ESXi Syslog +search: '`esxi_syslog` Message="*network firewall set*" AND Message="*enabled f*" | rex field=_raw "Z (?[\w\.]+)\s" | stats min(_time) as firstTime max(_time) as lastTime count by dest Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_firewall_disabled_filter`' +how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field extractions and CIM compatibility. +known_false_positives: Limited false positives in most environments, however tune as needed. drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Firewall disabled on ESXi host $dest$. - risk_objects: - - field: dest - type: system - score: 65 - threat_objects: [] + message: Firewall disabled on ESXi host $dest$. + risk_objects: + - field: dest + type: system + score: 65 + threat_objects: [] tags: - analytic_story: - - ESXi Post Compromise - - Black Basta Ransomware - - China-Nexus Threat Activity - asset_type: Infrastructure - mitre_attack_id: - - T1562.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ESXi Post Compromise + - Black Basta Ransomware + - China-Nexus Threat Activity + asset_type: Infrastructure + mitre_attack_id: + - T1562.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/esxi_firewall_disabled/esxi_firewall_disabled.log - source: vmware:esxlog - sourcetype: vmw-syslog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/esxi_firewall_disabled/esxi_firewall_disabled.log + source: vmware:esxlog + sourcetype: vmw-syslog diff --git a/detections/application/esxi_lockdown_mode_disabled.yml b/detections/application/esxi_lockdown_mode_disabled.yml index 3455fba51b..2f66059142 100644 --- a/detections/application/esxi_lockdown_mode_disabled.yml +++ b/detections/application/esxi_lockdown_mode_disabled.yml @@ -5,60 +5,43 @@ date: '2025-05-12' author: Raven Tait, Splunk status: production type: TTP -description: This detection identifies when Lockdown Mode is disabled on an ESXi host, - which can indicate that a threat actor is attempting to weaken host security controls. - Disabling Lockdown Mode allows broader remote access via SSH or the host client and - may precede further malicious actions such as data exfiltration, lateral movement, - or VM tampering. +description: This detection identifies when Lockdown Mode is disabled on an ESXi host, which can indicate that a threat actor is attempting to weaken host security controls. Disabling Lockdown Mode allows broader remote access via SSH or the host client and may precede further malicious actions such as data exfiltration, lateral movement, or VM tampering. data_source: -- VMWare ESXi Syslog -search: '`esxi_syslog` Message IN ("*lockdownmode.disabled*", "*Administrator access to the host has been enabled*") - | rex field=_raw "Z (?[\w\.]+)\s" - | stats min(_time) as firstTime max(_time) as lastTime count by dest Message - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `esxi_lockdown_mode_disabled_filter`' -how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, - you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must - be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field - extractions and CIM compatibility. -known_false_positives: Limited false positives in most environments, however tune - as needed. + - VMWare ESXi Syslog +search: '`esxi_syslog` Message IN ("*lockdownmode.disabled*", "*Administrator access to the host has been enabled*") | rex field=_raw "Z (?[\w\.]+)\s" | stats min(_time) as firstTime max(_time) as lastTime count by dest Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_lockdown_mode_disabled_filter`' +how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field extractions and CIM compatibility. +known_false_positives: Limited false positives in most environments, however tune as needed. drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Lockdown Mode has been disabled on ESXi host $dest$. - risk_objects: - - field: dest - type: system - score: 55 - threat_objects: [] + message: Lockdown Mode has been disabled on ESXi host $dest$. + risk_objects: + - field: dest + type: system + score: 55 + threat_objects: [] tags: - analytic_story: - - ESXi Post Compromise - - Black Basta Ransomware - asset_type: Infrastructure - mitre_attack_id: - - T1562 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ESXi Post Compromise + - Black Basta Ransomware + asset_type: Infrastructure + mitre_attack_id: + - T1562 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/esxi_lockdown_disabled/esxi_lockdown_disabled.log - source: vmware:esxlog - sourcetype: vmw-syslog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/esxi_lockdown_disabled/esxi_lockdown_disabled.log + source: vmware:esxlog + sourcetype: vmw-syslog diff --git a/detections/application/esxi_loghost_config_tampering.yml b/detections/application/esxi_loghost_config_tampering.yml index a6da512ad0..de7b62a114 100644 --- a/detections/application/esxi_loghost_config_tampering.yml +++ b/detections/application/esxi_loghost_config_tampering.yml @@ -5,58 +5,43 @@ date: '2025-05-13' author: Raven Tait, Splunk status: production type: TTP -description: This detection identifies changes to the syslog loghost configuration on an ESXi host, - which may indicate an attempt to disrupt log forwarding and evade detection. +description: This detection identifies changes to the syslog loghost configuration on an ESXi host, which may indicate an attempt to disrupt log forwarding and evade detection. data_source: -- VMWare ESXi Syslog -search: '`esxi_syslog` Message="*Set called with key*" AND Message IN ("*Syslog.global.logHost*","*Syslog.global.logdir*") - | rex field=_raw "key ''(?[^'']+)'', value ''\"(?[^\"]+)\"''" - | rex field=_raw "Z (?[\w\.]+)\s" - | stats min(_time) as firstTime max(_time) as lastTime count by dest key value - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `esxi_loghost_config_tampering_filter`' -how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, - you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must - be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field - extractions and CIM compatibility. -known_false_positives: Limited false positives in most environments, however tune - as needed + - VMWare ESXi Syslog +search: '`esxi_syslog` Message="*Set called with key*" AND Message IN ("*Syslog.global.logHost*","*Syslog.global.logdir*") | rex field=_raw "key ''(?[^'']+)'', value ''\"(?[^\"]+)\"''" | rex field=_raw "Z (?[\w\.]+)\s" | stats min(_time) as firstTime max(_time) as lastTime count by dest key value | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_loghost_config_tampering_filter`' +how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field extractions and CIM compatibility. +known_false_positives: Limited false positives in most environments, however tune as needed drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Syslog destination was modified on ESXi host $dest$. - risk_objects: - - field: dest - type: system - score: 60 - threat_objects: [] + message: Syslog destination was modified on ESXi host $dest$. + risk_objects: + - field: dest + type: system + score: 60 + threat_objects: [] tags: - analytic_story: - - ESXi Post Compromise - - Black Basta Ransomware - asset_type: Infrastructure - mitre_attack_id: - - T1562 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ESXi Post Compromise + - Black Basta Ransomware + asset_type: Infrastructure + mitre_attack_id: + - T1562 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.003/esxi_loghost_config_tampering/esxi_loghost_config_tampering.log - source: vmware:esxlog - sourcetype: vmw-syslog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.003/esxi_loghost_config_tampering/esxi_loghost_config_tampering.log + source: vmware:esxlog + sourcetype: vmw-syslog diff --git a/detections/application/esxi_malicious_vib_forced_install.yml b/detections/application/esxi_malicious_vib_forced_install.yml index d9b4930532..34f2c1d5f1 100644 --- a/detections/application/esxi_malicious_vib_forced_install.yml +++ b/detections/application/esxi_malicious_vib_forced_install.yml @@ -5,69 +5,46 @@ date: '2025-08-06' author: Raven Tait, Splunk status: production type: TTP -description: Detects potentially malicious installation of VMware Installation - Bundles (VIBs) using the --force flag. The --force option bypasses signature - and compatibility checks, allowing unsigned, community-supported, or - incompatible VIBs to be installed on an ESXi host. This behavior is uncommon in - normal administrative operations and is often observed in post-compromise - scenarios where adversaries attempt to install backdoored or unauthorized kernel - modules, drivers, or monitoring tools to establish persistence or gain deeper - control of the hypervisor. +description: Detects potentially malicious installation of VMware Installation Bundles (VIBs) using the --force flag. The --force option bypasses signature and compatibility checks, allowing unsigned, community-supported, or incompatible VIBs to be installed on an ESXi host. This behavior is uncommon in normal administrative operations and is often observed in post-compromise scenarios where adversaries attempt to install backdoored or unauthorized kernel modules, drivers, or monitoring tools to establish persistence or gain deeper control of the hypervisor. data_source: - - VMWare ESXi Syslog -search: '`esxi_syslog` Message="* image profile with validation disabled. *" OR - Message="* image profile bypassing signing and acceptance level verification. - *" OR Message="* vib without valid signature, *" - | rex field=_raw "Z (?[\w\.]+)\s" - | stats min(_time) as firstTime max(_time) as lastTime count by dest Message - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `esxi_malicious_vib_forced_install_filter`' -how_to_implement: - This is based on syslog data generated by VMware ESXi hosts. To implement this search, - you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must - be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field - extractions and CIM compatibility. + - VMWare ESXi Syslog +search: '`esxi_syslog` Message="* image profile with validation disabled. *" OR Message="* image profile bypassing signing and acceptance level verification. *" OR Message="* vib without valid signature, *" | rex field=_raw "Z (?[\w\.]+)\s" | stats min(_time) as firstTime max(_time) as lastTime count by dest Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_malicious_vib_forced_install_filter`' +how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field extractions and CIM compatibility. known_false_positives: Some third party vendor VIBs or patches may require the force option. references: - - https://detect.fyi/detecting-and-responding-to-esxi-compromise-with-splunk-f33998ce7823 + - https://detect.fyi/detecting-and-responding-to-esxi-compromise-with-splunk-f33998ce7823 drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A VIB was installed on ESXi $dest$ with the force flag. - risk_objects: - - field: dest - type: system - score: 60 - threat_objects: [] + message: A VIB was installed on ESXi $dest$ with the force flag. + risk_objects: + - field: dest + type: system + score: 60 + threat_objects: [] tags: - analytic_story: - - ESXi Post Compromise - - Black Basta Ransomware - - China-Nexus Threat Activity - asset_type: Infrastructure - mitre_attack_id: - - T1505.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ESXi Post Compromise + - Black Basta Ransomware + - China-Nexus Threat Activity + asset_type: Infrastructure + mitre_attack_id: + - T1505.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.006/esxi_malicious_vib/esxi_malicious_vib_forced_install.log - source: vmware:esxlog - sourcetype: vmw-syslog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.006/esxi_malicious_vib/esxi_malicious_vib_forced_install.log + source: vmware:esxlog + sourcetype: vmw-syslog diff --git a/detections/application/esxi_reverse_shell_patterns.yml b/detections/application/esxi_reverse_shell_patterns.yml index 4f134a6c26..70cdd073a5 100644 --- a/detections/application/esxi_reverse_shell_patterns.yml +++ b/detections/application/esxi_reverse_shell_patterns.yml @@ -5,59 +5,43 @@ date: '2025-05-12' author: Raven Tait, Splunk status: production type: TTP -description: This detection looks for reverse shell string patterns on an ESXi - host, which may indicate that a threat actor is attempting to establish - remote control over the system. +description: This detection looks for reverse shell string patterns on an ESXi host, which may indicate that a threat actor is attempting to establish remote control over the system. data_source: -- VMWare ESXi Syslog -search: '`esxi_syslog` Message IN ("*bash -i >&*","*/dev/tcp/*","*/dev/udp/*", - "*/socat exec:*","*socket(S,PF_INET*") OR (Message="*python -c*" AND Message="*import socket*") - | rex field=_raw "Z (?[\w\.]+)\s" - | stats min(_time) as firstTime max(_time) as lastTime count by dest Message - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `esxi_reverse_shell_patterns_filter`' -how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, - you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must - be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field - extractions and CIM compatibility. -known_false_positives: Limited false positives in most environments, however tune - as needed. + - VMWare ESXi Syslog +search: '`esxi_syslog` Message IN ("*bash -i >&*","*/dev/tcp/*","*/dev/udp/*", "*/socat exec:*","*socket(S,PF_INET*") OR (Message="*python -c*" AND Message="*import socket*") | rex field=_raw "Z (?[\w\.]+)\s" | stats min(_time) as firstTime max(_time) as lastTime count by dest Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_reverse_shell_patterns_filter`' +how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field extractions and CIM compatibility. +known_false_positives: Limited false positives in most environments, however tune as needed. drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Reverse shell patterns seen on ESXi host $dest$. - risk_objects: - - field: dest - type: system - score: 75 - threat_objects: [] + message: Reverse shell patterns seen on ESXi host $dest$. + risk_objects: + - field: dest + type: system + score: 75 + threat_objects: [] tags: - analytic_story: - - ESXi Post Compromise - - Black Basta Ransomware - asset_type: Infrastructure - mitre_attack_id: - - T1059 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ESXi Post Compromise + - Black Basta Ransomware + asset_type: Infrastructure + mitre_attack_id: + - T1059 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/esxi_reverse_shell/esxi_reverse_shell.log - source: vmware:esxlog - sourcetype: vmw-syslog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/esxi_reverse_shell/esxi_reverse_shell.log + source: vmware:esxlog + sourcetype: vmw-syslog diff --git a/detections/application/esxi_sensitive_files_accessed.yml b/detections/application/esxi_sensitive_files_accessed.yml index 3f4e1632ac..1b0d54f6ce 100644 --- a/detections/application/esxi_sensitive_files_accessed.yml +++ b/detections/application/esxi_sensitive_files_accessed.yml @@ -5,64 +5,45 @@ date: '2025-08-06' author: Raven Tait, Splunk status: production type: TTP -description: This detection identifies access to sensitive system and configuration files - on an ESXi host, including authentication data, service configurations, and VMware-specific - management settings. Interaction with these files may indicate adversary reconnaissance, - credential harvesting, or preparation for privilege escalation, lateral movement, or persistence. +description: This detection identifies access to sensitive system and configuration files on an ESXi host, including authentication data, service configurations, and VMware-specific management settings. Interaction with these files may indicate adversary reconnaissance, credential harvesting, or preparation for privilege escalation, lateral movement, or persistence. data_source: -- VMWare ESXi Syslog -search: '`esxi_syslog` Message="*shell[*" Message IN ("*/etc/shadow*","*/etc/vmware/hostd/hostd.xml*", - "*/etc/vmware/vpxa/vpxa.cfg*","*/etc/sfcb/sfcb.cfg*","*/etc/security/*", - "*/etc/likewise/krb5-affinity.conf*","*/etc/vmware-vpx/vcdb.properties*") - | rex field=_raw "\]: \[(?\w+)\]:(?.+)" - | rex field=_raw "Z (?[\w\.]+)\s" - | stats min(_time) as firstTime max(_time) as lastTime count by dest user command - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `esxi_sensitive_files_accessed_filter`' -how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, - you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must - be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field - extractions and CIM compatibility. -known_false_positives: Administrators may access these files for initial setup or troubleshooting. Limited - in most environments. Tune as needed. + - VMWare ESXi Syslog +search: '`esxi_syslog` Message="*shell[*" Message IN ("*/etc/shadow*","*/etc/vmware/hostd/hostd.xml*", "*/etc/vmware/vpxa/vpxa.cfg*","*/etc/sfcb/sfcb.cfg*","*/etc/security/*", "*/etc/likewise/krb5-affinity.conf*","*/etc/vmware-vpx/vcdb.properties*") | rex field=_raw "\]: \[(?\w+)\]:(?.+)" | rex field=_raw "Z (?[\w\.]+)\s" | stats min(_time) as firstTime max(_time) as lastTime count by dest user command | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_sensitive_files_accessed_filter`' +how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field extractions and CIM compatibility. +known_false_positives: Administrators may access these files for initial setup or troubleshooting. Limited in most environments. Tune as needed. drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Sensitive files accessed on ESXi host $dest$ with $command$. - risk_objects: - - field: dest - type: system - score: 70 - threat_objects: [] + message: Sensitive files accessed on ESXi host $dest$ with $command$. + risk_objects: + - field: dest + type: system + score: 70 + threat_objects: [] tags: - analytic_story: - - ESXi Post Compromise - - Black Basta Ransomware - - China-Nexus Threat Activity - asset_type: Infrastructure - mitre_attack_id: - - T1003.008 - - T1005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ESXi Post Compromise + - Black Basta Ransomware + - China-Nexus Threat Activity + asset_type: Infrastructure + mitre_attack_id: + - T1003.008 + - T1005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.008/esxi_sensitive_files/esxi_sensitive_files.log - source: vmware:esxlog - sourcetype: vmw-syslog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.008/esxi_sensitive_files/esxi_sensitive_files.log + source: vmware:esxlog + sourcetype: vmw-syslog diff --git a/detections/application/esxi_shared_or_stolen_root_account.yml b/detections/application/esxi_shared_or_stolen_root_account.yml index d0f2177057..ba15a69164 100644 --- a/detections/application/esxi_shared_or_stolen_root_account.yml +++ b/detections/application/esxi_shared_or_stolen_root_account.yml @@ -5,64 +5,45 @@ date: '2025-05-09' author: Raven Tait, Splunk status: production type: Anomaly -description: This detection monitors for signs of a shared or potentially compromised root account on ESXi - hosts by tracking the number of unique IP addresses logging in as root within a short time window. - Multiple logins from different IPs in a brief period may indicate credential misuse, - lateral movement, or account compromise. +description: This detection monitors for signs of a shared or potentially compromised root account on ESXi hosts by tracking the number of unique IP addresses logging in as root within a short time window. Multiple logins from different IPs in a brief period may indicate credential misuse, lateral movement, or account compromise. data_source: -- VMWare ESXi Syslog -search: '`esxi_syslog` Message="*root*" Message="*logged in*" NOT Message="*root@127.0.0.1*" - | rex field=_raw "root@(?\d{1,3}(?:\.\d{1,3}){3})" - | rex field=_raw "Z (?[\w\.]+)\s" - | bin _time span=15m - | stats min(_time) as firstTime max(_time) as lastTime dc(SrcIpAddr) AS distinct_ip_count values(SrcIpAddr) AS SrcIps by dest - | where distinct_ip_count > 1 - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `esxi_shared_or_stolen_root_account_filter`' -how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, - you must configure your ESXi systems to forward logs to your Splunk deployment. These logs must - be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field - extractions and CIM compatibility. -known_false_positives: Limited false positives in most environments, however tune - as needed + - VMWare ESXi Syslog +search: '`esxi_syslog` Message="*root*" Message="*logged in*" NOT Message="*root@127.0.0.1*" | rex field=_raw "root@(?\d{1,3}(?:\.\d{1,3}){3})" | rex field=_raw "Z (?[\w\.]+)\s" | bin _time span=15m | stats min(_time) as firstTime max(_time) as lastTime dc(SrcIpAddr) AS distinct_ip_count values(SrcIpAddr) AS SrcIps by dest | where distinct_ip_count > 1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_shared_or_stolen_root_account_filter`' +how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, you must configure your ESXi systems to forward logs to your Splunk deployment. These logs must be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field extractions and CIM compatibility. +known_false_positives: Limited false positives in most environments, however tune as needed references: -- https://detect.fyi/detecting-and-responding-to-esxi-compromise-with-splunk-f33998ce7823 + - https://detect.fyi/detecting-and-responding-to-esxi-compromise-with-splunk-f33998ce7823 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Root login from multiple IPs on ESXi host $dest$. - risk_objects: - - field: dest - type: system - score: 50 - threat_objects: [] + message: Root login from multiple IPs on ESXi host $dest$. + risk_objects: + - field: dest + type: system + score: 50 + threat_objects: [] tags: - analytic_story: - - ESXi Post Compromise - - Black Basta Ransomware - asset_type: Infrastructure - mitre_attack_id: - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ESXi Post Compromise + - Black Basta Ransomware + asset_type: Infrastructure + mitre_attack_id: + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/esxi_stolen_root_account/esxi_stolen_root_account.log - source: vmware:esxlog - sourcetype: vmw-syslog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/esxi_stolen_root_account/esxi_stolen_root_account.log + source: vmware:esxlog + sourcetype: vmw-syslog diff --git a/detections/application/esxi_shell_access_enabled.yml b/detections/application/esxi_shell_access_enabled.yml index 7bfa58e917..eb4ff74857 100644 --- a/detections/application/esxi_shell_access_enabled.yml +++ b/detections/application/esxi_shell_access_enabled.yml @@ -5,60 +5,43 @@ date: '2025-05-12' author: Raven Tait, Splunk status: production type: TTP -description: This detection identifies when the ESXi Shell is enabled on a host, which may indicate - that a malicious actor is preparing to execute commands locally or establish persistent access. - Enabling the shell outside of approved maintenance windows can be a sign of compromise or - unauthorized administrative activity. +description: This detection identifies when the ESXi Shell is enabled on a host, which may indicate that a malicious actor is preparing to execute commands locally or establish persistent access. Enabling the shell outside of approved maintenance windows can be a sign of compromise or unauthorized administrative activity. data_source: -- VMWare ESXi Syslog -search: '`esxi_syslog` Message="*ESXi Shell*" Message="*has been enabled*" - | rex field=_raw "''(?\w+)@" - | rex field=_raw "Z (?[\w\.]+)\s" - | stats min(_time) as firstTime max(_time) as lastTime count by dest user Message - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `esxi_shell_access_enabled_filter`' -how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, - you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must - be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field - extractions and CIM compatibility. -known_false_positives: Limited false positives in most environments, however tune - as needed. Some Administrators may enable this for troubleshooting. + - VMWare ESXi Syslog +search: '`esxi_syslog` Message="*ESXi Shell*" Message="*has been enabled*" | rex field=_raw "''(?\w+)@" | rex field=_raw "Z (?[\w\.]+)\s" | stats min(_time) as firstTime max(_time) as lastTime count by dest user Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_shell_access_enabled_filter`' +how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field extractions and CIM compatibility. +known_false_positives: Limited false positives in most environments, however tune as needed. Some Administrators may enable this for troubleshooting. drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: ESXi Shell access was enabled on ESXi host $dest$. - risk_objects: - - field: dest - type: system - score: 60 - threat_objects: [] + message: ESXi Shell access was enabled on ESXi host $dest$. + risk_objects: + - field: dest + type: system + score: 60 + threat_objects: [] tags: - analytic_story: - - ESXi Post Compromise - - Black Basta Ransomware - asset_type: Infrastructure - mitre_attack_id: - - T1021 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ESXi Post Compromise + - Black Basta Ransomware + asset_type: Infrastructure + mitre_attack_id: + - T1021 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021/esxi_shell_enabled/esxi_shell_enabled.log - source: vmware:esxlog - sourcetype: vmw-syslog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021/esxi_shell_enabled/esxi_shell_enabled.log + source: vmware:esxlog + sourcetype: vmw-syslog diff --git a/detections/application/esxi_ssh_brute_force.yml b/detections/application/esxi_ssh_brute_force.yml index 6977573f1f..7d5e4f4bf2 100644 --- a/detections/application/esxi_ssh_brute_force.yml +++ b/detections/application/esxi_ssh_brute_force.yml @@ -5,66 +5,44 @@ date: '2025-10-14' author: Raven Tait, Splunk status: production type: Anomaly -description: - This detection identifies signs of SSH brute-force attacks by monitoring for a high - number of failed login attempts within a short time frame. Such activity may indicate an - attacker attempting to gain unauthorized access through password guessing. +description: This detection identifies signs of SSH brute-force attacks by monitoring for a high number of failed login attempts within a short time frame. Such activity may indicate an attacker attempting to gain unauthorized access through password guessing. data_source: - - VMWare ESXi Syslog -search: '`esxi_syslog` Message="*Authentication failure for*" - | rex "for (?[\w]+) from (?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})" - | rex field=_raw "Z (?[\w\.]+)\s" - | bin _time span=5m - | stats min(_time) as firstTime max(_time) as lastTime count by user, src_ip, dest - | where count > 10 - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `esxi_ssh_brute_force_filter`' -how_to_implement: - This is based on syslog data generated by VMware ESXi hosts. To implement this search, - you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must - be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field - extractions and CIM compatibility. -known_false_positives: - Limited false positives in most environments, however tune - as needed. + - VMWare ESXi Syslog +search: '`esxi_syslog` Message="*Authentication failure for*" | rex "for (?[\w]+) from (?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})" | rex field=_raw "Z (?[\w\.]+)\s" | bin _time span=5m | stats min(_time) as firstTime max(_time) as lastTime count by user, src_ip, dest | where count > 10 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_ssh_brute_force_filter`' +how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field extractions and CIM compatibility. +known_false_positives: Limited false positives in most environments, however tune as needed. drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Attempted SSH brute force on ESXi host $dest$. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: Attempted SSH brute force on ESXi host $dest$. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Hellcat Ransomware - - ESXi Post Compromise - - Black Basta Ransomware - asset_type: Infrastructure - mitre_attack_id: - - T1110 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Hellcat Ransomware + - ESXi Post Compromise + - Black Basta Ransomware + asset_type: Infrastructure + mitre_attack_id: + - T1110 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/esxi_ssh_brute_force/esxi_ssh_brute_force.log - source: vmware:esxlog - sourcetype: vmw-syslog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/esxi_ssh_brute_force/esxi_ssh_brute_force.log + source: vmware:esxlog + sourcetype: vmw-syslog diff --git a/detections/application/esxi_ssh_enabled.yml b/detections/application/esxi_ssh_enabled.yml index c7515516ec..49178511aa 100644 --- a/detections/application/esxi_ssh_enabled.yml +++ b/detections/application/esxi_ssh_enabled.yml @@ -5,59 +5,44 @@ date: '2025-10-14' author: Raven Tait, Splunk status: production type: TTP -description: This detection identifies SSH being enabled on ESXi hosts, which can be an early indicator of - malicious activity. Threat actors often use SSH to gain persistent remote access after compromising credentials - or exploiting vulnerabilities. +description: This detection identifies SSH being enabled on ESXi hosts, which can be an early indicator of malicious activity. Threat actors often use SSH to gain persistent remote access after compromising credentials or exploiting vulnerabilities. data_source: -- VMWare ESXi Syslog -search: '`esxi_syslog` Message="*SSH access has been enabled" - | rex field=_raw "Z (?[\w\.]+)\s" - | stats min(_time) as firstTime max(_time) as lastTime count by dest Message - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `esxi_ssh_enabled_filter`' -how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, - you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must - be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field - extractions and CIM compatibility. -known_false_positives: Limited false positives in most environments, however tune - as needed. Some Administrators may use SSH for troubleshooting. + - VMWare ESXi Syslog +search: '`esxi_syslog` Message="*SSH access has been enabled" | rex field=_raw "Z (?[\w\.]+)\s" | stats min(_time) as firstTime max(_time) as lastTime count by dest Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_ssh_enabled_filter`' +how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field extractions and CIM compatibility. +known_false_positives: Limited false positives in most environments, however tune as needed. Some Administrators may use SSH for troubleshooting. drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: SSH was enabled on ESXi host $dest$. - risk_objects: - - field: dest - type: system - score: 50 - threat_objects: [] + message: SSH was enabled on ESXi host $dest$. + risk_objects: + - field: dest + type: system + score: 50 + threat_objects: [] tags: - analytic_story: - - ESXi Post Compromise - - Black Basta Ransomware - - Hellcat Ransomware - asset_type: Infrastructure - mitre_attack_id: - - T1021.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ESXi Post Compromise + - Black Basta Ransomware + - Hellcat Ransomware + asset_type: Infrastructure + mitre_attack_id: + - T1021.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.004/esxi_ssh_enabled/esxi_ssh_enabled.log - source: vmware:esxlog - sourcetype: vmw-syslog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.004/esxi_ssh_enabled/esxi_ssh_enabled.log + source: vmware:esxlog + sourcetype: vmw-syslog diff --git a/detections/application/esxi_syslog_config_change.yml b/detections/application/esxi_syslog_config_change.yml index 276c5d85ee..06e2872746 100644 --- a/detections/application/esxi_syslog_config_change.yml +++ b/detections/application/esxi_syslog_config_change.yml @@ -5,58 +5,43 @@ date: '2025-05-13' author: Raven Tait, Splunk status: production type: TTP -description: This detection identifies changes to the syslog configuration on an ESXi host using esxcli, - which may indicate an attempt to disrupt log collection and evade detection. +description: This detection identifies changes to the syslog configuration on an ESXi host using esxcli, which may indicate an attempt to disrupt log collection and evade detection. data_source: -- VMWare ESXi Syslog -search: '`esxi_syslog` Message="*syslog config set*" AND Message="*esxcli*" - | rex field=_raw "\].*\[\s*(?P[^\]]+)\]:\s(?P.+)" - | rex field=_raw "Z (?[\w\.]+)\s" - | stats min(_time) as firstTime max(_time) as lastTime count by dest user command - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `esxi_syslog_config_change_filter`' -how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, - you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must - be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field - extractions and CIM compatibility. -known_false_positives: Limited false positives in most environments, however tune - as needed + - VMWare ESXi Syslog +search: '`esxi_syslog` Message="*syslog config set*" AND Message="*esxcli*" | rex field=_raw "\].*\[\s*(?P[^\]]+)\]:\s(?P.+)" | rex field=_raw "Z (?[\w\.]+)\s" | stats min(_time) as firstTime max(_time) as lastTime count by dest user command | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_syslog_config_change_filter`' +how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field extractions and CIM compatibility. +known_false_positives: Limited false positives in most environments, however tune as needed drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Syslog config was modified on ESXi host $dest$. - risk_objects: - - field: dest - type: system - score: 60 - threat_objects: [] + message: Syslog config was modified on ESXi host $dest$. + risk_objects: + - field: dest + type: system + score: 60 + threat_objects: [] tags: - analytic_story: - - ESXi Post Compromise - - Black Basta Ransomware - asset_type: Infrastructure - mitre_attack_id: - - T1562.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ESXi Post Compromise + - Black Basta Ransomware + asset_type: Infrastructure + mitre_attack_id: + - T1562.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.003/esxi_syslog_config/esxi_syslog_config.log - source: vmware:esxlog - sourcetype: vmw-syslog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.003/esxi_syslog_config/esxi_syslog_config.log + source: vmware:esxlog + sourcetype: vmw-syslog diff --git a/detections/application/esxi_system_clock_manipulation.yml b/detections/application/esxi_system_clock_manipulation.yml index 1340ad6f49..80ebec940a 100644 --- a/detections/application/esxi_system_clock_manipulation.yml +++ b/detections/application/esxi_system_clock_manipulation.yml @@ -5,66 +5,43 @@ date: '2025-05-19' author: Raven Tait, Splunk status: production type: TTP -description: This detection identifies a significant change to the system clock - on an ESXi host, which may indicate an attempt to manipulate timestamps and - evade detection or forensic analysis +description: This detection identifies a significant change to the system clock on an ESXi host, which may indicate an attempt to manipulate timestamps and evade detection or forensic analysis data_source: -- VMWare ESXi Syslog -search: '`esxi_syslog` Message="*NTPClock*" AND Message="*system clock stepped*" - | rex field=_raw "stepped to (?\d+\.\d+),.+delta\s(?\d+)\s" - | rex field=_raw "Z (?[\w\.]+)\s" - | eval epoch_time=tonumber(epoch_time) - | eval delta=tonumber(delta) - | eval event_time=round(_time, 0) - | eval direction=if(epoch_time < event_time, "backward", "forward") - | eval original_time=if(direction=="backward", epoch_time + delta, epoch_time - delta) - | eval stepped_to_str=strftime(epoch_time, "%Y-%m-%d %H:%M:%S") - | eval original_time_str=strftime(original_time, "%Y-%m-%d %H:%M:%S") - | stats min(_time) as firstTime max(_time) as lastTime count by dest direction original_time_str stepped_to_str epoch_time delta - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `esxi_system_clock_manipulation_filter`' -how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, - you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must - be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field - extractions and CIM compatibility. -known_false_positives: Limited false positives in most environments, however tune - as needed + - VMWare ESXi Syslog +search: '`esxi_syslog` Message="*NTPClock*" AND Message="*system clock stepped*" | rex field=_raw "stepped to (?\d+\.\d+),.+delta\s(?\d+)\s" | rex field=_raw "Z (?[\w\.]+)\s" | eval epoch_time=tonumber(epoch_time) | eval delta=tonumber(delta) | eval event_time=round(_time, 0) | eval direction=if(epoch_time < event_time, "backward", "forward") | eval original_time=if(direction=="backward", epoch_time + delta, epoch_time - delta) | eval stepped_to_str=strftime(epoch_time, "%Y-%m-%d %H:%M:%S") | eval original_time_str=strftime(original_time, "%Y-%m-%d %H:%M:%S") | stats min(_time) as firstTime max(_time) as lastTime count by dest direction original_time_str stepped_to_str epoch_time delta | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_system_clock_manipulation_filter`' +how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field extractions and CIM compatibility. +known_false_positives: Limited false positives in most environments, however tune as needed drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Large time change on ESXi host $dest$. - risk_objects: - - field: dest - type: system - score: 50 - threat_objects: [] + message: Large time change on ESXi host $dest$. + risk_objects: + - field: dest + type: system + score: 50 + threat_objects: [] tags: - analytic_story: - - ESXi Post Compromise - - Black Basta Ransomware - asset_type: Infrastructure - mitre_attack_id: - - T1070.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ESXi Post Compromise + - Black Basta Ransomware + asset_type: Infrastructure + mitre_attack_id: + - T1070.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070/esxi_system_clock_manipulation/esxi_system_clock_manipulation.log - source: vmware:esxlog - sourcetype: vmw-syslog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070/esxi_system_clock_manipulation/esxi_system_clock_manipulation.log + source: vmware:esxlog + sourcetype: vmw-syslog diff --git a/detections/application/esxi_system_information_discovery.yml b/detections/application/esxi_system_information_discovery.yml index b9bc86baaf..ebf62a04a0 100644 --- a/detections/application/esxi_system_information_discovery.yml +++ b/detections/application/esxi_system_information_discovery.yml @@ -5,63 +5,46 @@ date: '2025-05-14' author: Raven Tait, Splunk status: production type: TTP -description: This detection identifies the use of ESXCLI system-level commands that retrieve - configuration details. While used for legitimate administration, this behavior may also - indicate adversary reconnaissance aimed at profiling the ESXi host's capabilities, - build information, or system role in preparation for further compromise. +description: This detection identifies the use of ESXCLI system-level commands that retrieve configuration details. While used for legitimate administration, this behavior may also indicate adversary reconnaissance aimed at profiling the ESXi host's capabilities, build information, or system role in preparation for further compromise. data_source: -- VMWare ESXi Syslog -search: '`esxi_syslog` Message="*system*" AND Message="*esxcli*" AND Message IN ("*get*","*list*") - AND Message="*user=*" NOT Message="*filesystem*" - | rex field=_raw "user=(?\w+)\]\s+Dispatch\s+(?[^\s]+)" - | rex field=_raw "Z (?[\w\.]+)\s" - | stats min(_time) as firstTime max(_time) as lastTime count by dest user command - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `esxi_system_information_discovery_filter`' -how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, - you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must - be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field - extractions and CIM compatibility. + - VMWare ESXi Syslog +search: '`esxi_syslog` Message="*system*" AND Message="*esxcli*" AND Message IN ("*get*","*list*") AND Message="*user=*" NOT Message="*filesystem*" | rex field=_raw "user=(?\w+)\]\s+Dispatch\s+(?[^\s]+)" | rex field=_raw "Z (?[\w\.]+)\s" | stats min(_time) as firstTime max(_time) as lastTime count by dest user command | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_system_information_discovery_filter`' +how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field extractions and CIM compatibility. known_false_positives: Administrators may use this command when troubleshooting. Tune as needed. drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: System information discovery commands executed on ESXi host $dest$ by $user$. - risk_objects: - - field: dest - type: system - score: 30 - - field: user - type: user - score: 30 - threat_objects: [] + message: System information discovery commands executed on ESXi host $dest$ by $user$. + risk_objects: + - field: dest + type: system + score: 30 + - field: user + type: user + score: 30 + threat_objects: [] tags: - analytic_story: - - ESXi Post Compromise - - Black Basta Ransomware - asset_type: Infrastructure - mitre_attack_id: - - T1082 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ESXi Post Compromise + - Black Basta Ransomware + asset_type: Infrastructure + mitre_attack_id: + - T1082 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1082/esxi_system_information/esxi_system_information.log - source: vmware:esxlog - sourcetype: vmw-syslog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1082/esxi_system_information/esxi_system_information.log + source: vmware:esxlog + sourcetype: vmw-syslog diff --git a/detections/application/esxi_user_granted_admin_role.yml b/detections/application/esxi_user_granted_admin_role.yml index 5231fd86ee..44689c6f71 100644 --- a/detections/application/esxi_user_granted_admin_role.yml +++ b/detections/application/esxi_user_granted_admin_role.yml @@ -5,65 +5,47 @@ date: '2025-05-15' author: Raven Tait, Splunk status: production type: TTP -description: This detection identifies when a user is granted the Administrator role on an ESXi host. - Assigning elevated privileges is a critical action that can indicate potential malicious behavior - if performed unexpectedly. Adversaries who gain access may use this to escalate privileges, - maintain persistence, or disable security controls. +description: This detection identifies when a user is granted the Administrator role on an ESXi host. Assigning elevated privileges is a critical action that can indicate potential malicious behavior if performed unexpectedly. Adversaries who gain access may use this to escalate privileges, maintain persistence, or disable security controls. data_source: -- VMWare ESXi Syslog -search: '`esxi_syslog` Message="*esxcli system permission set*" AND Message="*role Admin*" - | rex field=_raw "\]: \[(?\w+)\]:(?.+)" - | rex field=_raw "--id (?\w+)" - | rex field=_raw "Z (?[\w\.]+)\s" - | stats min(_time) as firstTime max(_time) as lastTime count by dest user command target_user - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `esxi_user_granted_admin_role_filter`' -how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, - you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must - be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field - extractions and CIM compatibility. -known_false_positives: Limited false positives in most environments after initial setup, however tune - as needed. + - VMWare ESXi Syslog +search: '`esxi_syslog` Message="*esxcli system permission set*" AND Message="*role Admin*" | rex field=_raw "\]: \[(?\w+)\]:(?.+)" | rex field=_raw "--id (?\w+)" | rex field=_raw "Z (?[\w\.]+)\s" | stats min(_time) as firstTime max(_time) as lastTime count by dest user command target_user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_user_granted_admin_role_filter`' +how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field extractions and CIM compatibility. +known_false_positives: Limited false positives in most environments after initial setup, however tune as needed. drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $target_user$ granted Admin role on ESXi host $dest$ by $user$. - risk_objects: - - field: dest - type: system - score: 60 - - field: target_user - type: user - score: 60 - threat_objects: [] + message: $target_user$ granted Admin role on ESXi host $dest$ by $user$. + risk_objects: + - field: dest + type: system + score: 60 + - field: target_user + type: user + score: 60 + threat_objects: [] tags: - analytic_story: - - ESXi Post Compromise - - Black Basta Ransomware - asset_type: Infrastructure - mitre_attack_id: - - T1098 - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ESXi Post Compromise + - Black Basta Ransomware + asset_type: Infrastructure + mitre_attack_id: + - T1098 + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/esxi_admin_role/esxi_admin_role.log - source: vmware:esxlog - sourcetype: vmw-syslog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/esxi_admin_role/esxi_admin_role.log + source: vmware:esxlog + sourcetype: vmw-syslog diff --git a/detections/application/esxi_vib_acceptance_level_tampering.yml b/detections/application/esxi_vib_acceptance_level_tampering.yml index 1227ff0c29..0ade8115bb 100644 --- a/detections/application/esxi_vib_acceptance_level_tampering.yml +++ b/detections/application/esxi_vib_acceptance_level_tampering.yml @@ -5,62 +5,47 @@ date: '2025-08-06' author: Raven Tait, Splunk status: production type: TTP -description: This detection identifies changes to the VIB (vSphere Installation Bundle) acceptance - level on an ESXi host. Modifying the acceptance level, such as setting it to CommunitySupported, - lowers the system's integrity enforcement and may allow the installation of unsigned or unverified software. +description: This detection identifies changes to the VIB (vSphere Installation Bundle) acceptance level on an ESXi host. Modifying the acceptance level, such as setting it to CommunitySupported, lowers the system's integrity enforcement and may allow the installation of unsigned or unverified software. data_source: -- VMWare ESXi Syslog -search: '`esxi_syslog` Message="*esxcli software acceptance set*" Message="*shell*" - | rex field=_raw "\]: \[(?\w+)\]:(?.+)" - | rex field=_raw "Z (?[\w\.]+)\s" - | stats min(_time) as firstTime max(_time) as lastTime count by dest user command - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `esxi_vib_acceptance_level_tampering_filter`' -how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, - you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must - be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field - extractions and CIM compatibility. + - VMWare ESXi Syslog +search: '`esxi_syslog` Message="*esxcli software acceptance set*" Message="*shell*" | rex field=_raw "\]: \[(?\w+)\]:(?.+)" | rex field=_raw "Z (?[\w\.]+)\s" | stats min(_time) as firstTime max(_time) as lastTime count by dest user command | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_vib_acceptance_level_tampering_filter`' +how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field extractions and CIM compatibility. known_false_positives: Administrators may use this command when installing third party VIBs. Tune as needed. drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: VIB Acceptance level was modified on ESXi host $dest$ by $user$. - risk_objects: - - field: dest - type: system - score: 60 - - field: user - type: user - score: 60 - threat_objects: [] + message: VIB Acceptance level was modified on ESXi host $dest$ by $user$. + risk_objects: + - field: dest + type: system + score: 60 + - field: user + type: user + score: 60 + threat_objects: [] tags: - analytic_story: - - ESXi Post Compromise - - Black Basta Ransomware - - China-Nexus Threat Activity - asset_type: Infrastructure - mitre_attack_id: - - T1562 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ESXi Post Compromise + - Black Basta Ransomware + - China-Nexus Threat Activity + asset_type: Infrastructure + mitre_attack_id: + - T1562 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/esxi_vib_acceptance_level_tampering/esxi_vib_acceptance_level_tampering.log - source: vmware:esxlog - sourcetype: vmw-syslog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/esxi_vib_acceptance_level_tampering/esxi_vib_acceptance_level_tampering.log + source: vmware:esxlog + sourcetype: vmw-syslog diff --git a/detections/application/esxi_vm_discovery.yml b/detections/application/esxi_vm_discovery.yml index ea8134b137..c6efc615b0 100644 --- a/detections/application/esxi_vm_discovery.yml +++ b/detections/application/esxi_vm_discovery.yml @@ -5,62 +5,47 @@ date: '2025-08-06' author: Raven Tait, Splunk status: production type: TTP -description: This detection identifies the use of ESXCLI commands to discover virtual machines on an ESXi host - While used by administrators, this activity may also indicate adversary reconnaissance aimed at identifying - high value targets, mapping the virtual environment, or preparing for data theft or destructive operations. +description: This detection identifies the use of ESXCLI commands to discover virtual machines on an ESXi host While used by administrators, this activity may also indicate adversary reconnaissance aimed at identifying high value targets, mapping the virtual environment, or preparing for data theft or destructive operations. data_source: -- VMWare ESXi Syslog -search: '`esxi_syslog` Message="*esxcli vm process*" Message="*list*" - | rex field=_raw "\]: \[(?\w+)\]:(?.+)" - | rex field=_raw "Z (?[\w\.]+)\s" - | stats min(_time) as firstTime max(_time) as lastTime count by dest user command - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `esxi_vm_discovery_filter`' -how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, - you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must - be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field - extractions and CIM compatibility. + - VMWare ESXi Syslog +search: '`esxi_syslog` Message="*esxcli vm process*" Message="*list*" | rex field=_raw "\]: \[(?\w+)\]:(?.+)" | rex field=_raw "Z (?[\w\.]+)\s" | stats min(_time) as firstTime max(_time) as lastTime count by dest user command | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_vm_discovery_filter`' +how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field extractions and CIM compatibility. known_false_positives: Administrators may use this command when troubleshooting. Tune as needed. drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: VM discovery commands executed on ESXi host $dest$ by $user$. - risk_objects: - - field: dest - type: system - score: 30 - - field: user - type: user - score: 30 - threat_objects: [] + message: VM discovery commands executed on ESXi host $dest$ by $user$. + risk_objects: + - field: dest + type: system + score: 30 + - field: user + type: user + score: 30 + threat_objects: [] tags: - analytic_story: - - ESXi Post Compromise - - Black Basta Ransomware - - China-Nexus Threat Activity - asset_type: Infrastructure - mitre_attack_id: - - T1673 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ESXi Post Compromise + - Black Basta Ransomware + - China-Nexus Threat Activity + asset_type: Infrastructure + mitre_attack_id: + - T1673 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1673/esxi_vm_discovery/esxi_vm_discovery.log - source: vmware:esxlog - sourcetype: vmw-syslog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1673/esxi_vm_discovery/esxi_vm_discovery.log + source: vmware:esxlog + sourcetype: vmw-syslog diff --git a/detections/application/esxi_vm_exported_via_remote_tool.yml b/detections/application/esxi_vm_exported_via_remote_tool.yml index 9059fe4ff4..b51b370e95 100644 --- a/detections/application/esxi_vm_exported_via_remote_tool.yml +++ b/detections/application/esxi_vm_exported_via_remote_tool.yml @@ -5,60 +5,43 @@ date: '2025-05-15' author: Raven Tait, Splunk status: production type: TTP -description: This detection identifies the use of a remote tool to download virtual machine disk - files from a datastore. The NFC protocol is used by management tools to transfer files - to and from ESXi hosts, but it can also be abused by attackers or insiders to exfiltrate - full virtual disk images +description: This detection identifies the use of a remote tool to download virtual machine disk files from a datastore. The NFC protocol is used by management tools to transfer files to and from ESXi hosts, but it can also be abused by attackers or insiders to exfiltrate full virtual disk images data_source: -- VMWare ESXi Syslog -search: '`esxi_syslog` Message="*File download from path*" Message="*was initiated from*" - | rex field=_raw "from path ''\[(?[^\]]+)\](?[^'']+)''" - | rex field=_raw "initiated from ''(?[^/]+)/(?[^@]+)@(?\d{1,3}(?:\.\d{1,3}){3})''" - | rex field=_raw "Z (?[\w\.]+)\s" - | stats min(_time) as firstTime max(_time) as lastTime count by Datastore VMPath InitiatorTool ToolVersion InitiatorIP dest - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `esxi_vm_exported_via_remote_tool_filter`' -how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, - you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must - be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field - extractions and CIM compatibility. + - VMWare ESXi Syslog +search: '`esxi_syslog` Message="*File download from path*" Message="*was initiated from*" | rex field=_raw "from path ''\[(?[^\]]+)\](?[^'']+)''" | rex field=_raw "initiated from ''(?[^/]+)/(?[^@]+)@(?\d{1,3}(?:\.\d{1,3}){3})''" | rex field=_raw "Z (?[\w\.]+)\s" | stats min(_time) as firstTime max(_time) as lastTime count by Datastore VMPath InitiatorTool ToolVersion InitiatorIP dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_vm_exported_via_remote_tool_filter`' +how_to_implement: This is based on syslog data generated by VMware ESXi hosts. To implement this search, you must configure your ESXi systems to forward syslog output to your Splunk deployment. These logs must be ingested with the appropriate Splunk Technology Add-on for VMware ESXi Logs, which provides field extractions and CIM compatibility. known_false_positives: Administrators may use this command when troubleshooting. Tune as needed. drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: VM downloaded from datastore on ESXi host $dest$. - risk_objects: - - field: dest - type: system - score: 50 - threat_objects: [] + message: VM downloaded from datastore on ESXi host $dest$. + risk_objects: + - field: dest + type: system + score: 50 + threat_objects: [] tags: - analytic_story: - - ESXi Post Compromise - - Black Basta Ransomware - asset_type: Infrastructure - mitre_attack_id: - - T1005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ESXi Post Compromise + - Black Basta Ransomware + asset_type: Infrastructure + mitre_attack_id: + - T1005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1005/esxi_vm_download/esxi_vm_download.log - source: vmware:esxlog - sourcetype: vmw-syslog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1005/esxi_vm_download/esxi_vm_download.log + source: vmware:esxlog + sourcetype: vmw-syslog diff --git a/detections/application/ivanti_vtm_new_account_creation.yml b/detections/application/ivanti_vtm_new_account_creation.yml index 2d295c8ada..d6ed5795e5 100644 --- a/detections/application/ivanti_vtm_new_account_creation.yml +++ b/detections/application/ivanti_vtm_new_account_creation.yml @@ -1,74 +1,60 @@ name: Ivanti VTM New Account Creation id: b04be6e5-2002-4349-8742-52285635b8f5 -version: 5 -date: '2025-10-14' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk data_source: -- Ivanti VTM Audit + - Ivanti VTM Audit type: TTP status: production -description: This analytic detects potential exploitation of the Ivanti Virtual Traffic - Manager (vTM) authentication bypass vulnerability (CVE-2024-7593) to create new - administrator accounts. The vulnerability allows unauthenticated remote attackers - to bypass authentication on the admin panel and create new admin users. This detection - looks for suspicious new account creation events in the Ivanti vTM audit logs that - lack expected authentication details, which may indicate exploitation attempts. -search: '`ivanti_vtm_audit` OPERATION="adduser" MODGROUP="admin" IP="!!ABSENT!!" | - stats count min(_time) as firstTime max(_time) as lastTime by IP, MODUSER, OPERATION, - MODGROUP, AUTH | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `ivanti_vtm_new_account_creation_filter`' -how_to_implement: To implement this detection, ensure that Ivanti vTM audit logs are - being ingested into Splunk. Configure the Ivanti vTM to send its audit logs to Splunk - via syslog or by monitoring the log files directly. The sourcetype should be set - to "ivanti_vtm_audit" or a similar custom sourcetype for these logs. -known_false_positives: Legitimate new account creation by authorized administrators - will generate similar log entries. However, those should include proper authentication - details. Verify any detected events against expected administrative activities and - authorized user lists. +description: This analytic detects potential exploitation of the Ivanti Virtual Traffic Manager (vTM) authentication bypass vulnerability (CVE-2024-7593) to create new administrator accounts. The vulnerability allows unauthenticated remote attackers to bypass authentication on the admin panel and create new admin users. This detection looks for suspicious new account creation events in the Ivanti vTM audit logs that lack expected authentication details, which may indicate exploitation attempts. +search: |- + `ivanti_vtm_audit` OPERATION="adduser" MODGROUP="admin" IP="!!ABSENT!!" + | stats count min(_time) as firstTime max(_time) as lastTime + BY IP, MODUSER, OPERATION, + MODGROUP, AUTH + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `ivanti_vtm_new_account_creation_filter` +how_to_implement: To implement this detection, ensure that Ivanti vTM audit logs are being ingested into Splunk. Configure the Ivanti vTM to send its audit logs to Splunk via syslog or by monitoring the log files directly. The sourcetype should be set to "ivanti_vtm_audit" or a similar custom sourcetype for these logs. +known_false_positives: Legitimate new account creation by authorized administrators will generate similar log entries. However, those should include proper authentication details. Verify any detected events against expected administrative activities and authorized user lists. references: -- https://www.ivanti.com/security/security-advisories/ivanti-virtual-traffic-manager-vtm-cve-2024-7593 -- https://nvd.nist.gov/vuln/detail/CVE-2024-7593 + - https://www.ivanti.com/security/security-advisories/ivanti-virtual-traffic-manager-vtm-cve-2024-7593 + - https://nvd.nist.gov/vuln/detail/CVE-2024-7593 drilldown_searches: -- name: View the detection results for - "$MODUSER$" - search: '%original_detection_search% | search MODUSER = "$MODUSER$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$MODUSER$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$MODUSER$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$MODUSER$" + search: '%original_detection_search% | search MODUSER = "$MODUSER$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$MODUSER$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$MODUSER$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A new administrator account, $MODUSER$, was created on Ivanti vTM device - without proper authentication, which may indicate exploitation of CVE-2024-7593. - risk_objects: - - field: MODUSER - type: user - score: 72 - threat_objects: [] + message: A new administrator account, $MODUSER$, was created on Ivanti vTM device without proper authentication, which may indicate exploitation of CVE-2024-7593. + risk_objects: + - field: MODUSER + type: user + score: 72 + threat_objects: [] tags: - analytic_story: - - Ivanti Virtual Traffic Manager CVE-2024-7593 - - Scattered Lapsus$ Hunters - - Hellcat Ransomware - asset_type: Web Application - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access - cve: - - CVE-2024-7593 + analytic_story: + - Ivanti Virtual Traffic Manager CVE-2024-7593 + - Scattered Lapsus$ Hunters + - Hellcat Ransomware + asset_type: Web Application + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access + cve: + - CVE-2024-7593 tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/ivanti_vtm_audit.log - sourcetype: ivanti_vtm_audit - source: ivanti_vtm + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/ivanti_vtm_audit.log + sourcetype: ivanti_vtm_audit + source: ivanti_vtm diff --git a/detections/application/m365_copilot_agentic_jailbreak_attack.yml b/detections/application/m365_copilot_agentic_jailbreak_attack.yml index 9fce02cc57..80aec7631b 100644 --- a/detections/application/m365_copilot_agentic_jailbreak_attack.yml +++ b/detections/application/m365_copilot_agentic_jailbreak_attack.yml @@ -6,54 +6,54 @@ author: Rod Soto status: experimental type: Anomaly data_source: -- M365 Exported eDiscovery Prompts + - M365 Exported eDiscovery Prompts description: Detects agentic AI jailbreak attempts that try to establish persistent control over M365 Copilot through rule injection, universal triggers, response automation, system overrides, and persona establishment techniques. The detection analyzes the PromptText field for keywords like "from now on," "always respond," "ignore previous," "new rule," "override," and role-playing commands (e.g., "act as," "you are now") that attempt to inject persistent instructions. The search computes risk by counting distinct jailbreak indicators per user session, flagging coordinated manipulation attempts. search: > - `m365_exported_ediscovery_prompt_logs` - | eval user = Sender - | eval rule_injection=if(match(Subject_Title, "(?i)(rules|instructions)\s*="), "YES", "NO") - | eval universal_trigger=if(match(Subject_Title, "(?i)(every|all).*prompt"), "YES", "NO") - | eval response_automation=if(match(Subject_Title, "(?i)(always|automatic).*respond"), "YES", "NO") - | eval system_override=if(match(Subject_Title, "(?i)(override|bypass|ignore).*(system|default)"), "YES", "NO") - | eval persona_establishment=if(match(Subject_Title, "(?i)(with.*\[.*\]|persona)"), "YES", "NO") - | where rule_injection="YES" OR universal_trigger="YES" OR response_automation="YES" OR system_override="YES" OR persona_establishment="YES" - | table _time, "Source ID", user, Subject_Title, rule_injection, universal_trigger, response_automation, system_override, persona_establishment, Workload - | sort -_time - | `m365_copilot_agentic_jailbreak_attack_filter` + `m365_exported_ediscovery_prompt_logs` + | eval user = Sender + | eval rule_injection=if(match(Subject_Title, "(?i)(rules|instructions)\s*="), "YES", "NO") + | eval universal_trigger=if(match(Subject_Title, "(?i)(every|all).*prompt"), "YES", "NO") + | eval response_automation=if(match(Subject_Title, "(?i)(always|automatic).*respond"), "YES", "NO") + | eval system_override=if(match(Subject_Title, "(?i)(override|bypass|ignore).*(system|default)"), "YES", "NO") + | eval persona_establishment=if(match(Subject_Title, "(?i)(with.*\[.*\]|persona)"), "YES", "NO") + | where rule_injection="YES" OR universal_trigger="YES" OR response_automation="YES" OR system_override="YES" OR persona_establishment="YES" + | table _time, "Source ID", user, Subject_Title, rule_injection, universal_trigger, response_automation, system_override, persona_establishment, Workload + | sort -_time + | `m365_copilot_agentic_jailbreak_attack_filter` how_to_implement: To export M365 Copilot prompt logs, navigate to the Microsoft Purview compliance portal (compliance.microsoft.com) and access eDiscovery. Create a new eDiscovery case, add target user accounts or date ranges as data sources, then create a search query targeting M365 Copilot interactions across relevant workloads. Once the search completes, export the results to generate a package containing prompt logs with fields like Subject_Title (prompt text), Sender, timestamps, and workload metadata. Download the exported files using the eDiscovery Export Tool and ingest them into Splunk for security analysis and detection of jailbreak attempts, data exfiltration requests, and policy violations. known_false_positives: Legitimate users discussing AI ethics research, security professionals testing system robustness, developers creating training materials for AI safety, or academic discussions about AI limitations and behavioral constraints may trigger false positives. references: - - https://www.splunk.com/en_us/blog/artificial-intelligence/m365-copilot-log-analysis-splunk.html + - https://www.splunk.com/en_us/blog/artificial-intelligence/m365-copilot-log-analysis-splunk.html drilldown_searches: - - name: View the detection results for - "$user$" - search: '%original_detection_search% | search user="$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object="$user$" starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user="$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object="$user$" starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ attempted to establish persistent agentic control over M365 Copilot through advanced jailbreak techniques including rule injection, universal triggers, and system overrides, potentially compromising AI security across multiple sessions. - risk_objects: - - field: user - type: user - score: 50 - threat_objects: [] + message: User $user$ attempted to establish persistent agentic control over M365 Copilot through advanced jailbreak techniques including rule injection, universal triggers, and system overrides, potentially compromising AI security across multiple sessions. + risk_objects: + - field: user + type: user + score: 50 + threat_objects: [] tags: - analytic_story: - - Suspicious Microsoft 365 Copilot Activities - asset_type: Web Application - mitre_attack_id: - - T1562 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Microsoft 365 Copilot Activities + asset_type: Web Application + mitre_attack_id: + - T1562 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://raw.githubusercontent.com/splunk/attack_data/master/datasets/m365_copilot/copilot_prompt_logs.csv - sourcetype: csv - source: csv + - name: True Positive Test + attack_data: + - data: https://raw.githubusercontent.com/splunk/attack_data/master/datasets/m365_copilot/copilot_prompt_logs.csv + sourcetype: csv + source: csv diff --git a/detections/application/m365_copilot_application_usage_pattern_anomalies.yml b/detections/application/m365_copilot_application_usage_pattern_anomalies.yml index 2561e5472d..adecc2785e 100644 --- a/detections/application/m365_copilot_application_usage_pattern_anomalies.yml +++ b/detections/application/m365_copilot_application_usage_pattern_anomalies.yml @@ -7,65 +7,65 @@ status: production type: Anomaly description: Detects M365 Copilot users exhibiting suspicious application usage patterns including multi-location access, abnormally high activity volumes, or access to multiple Copilot applications that may indicate account compromise or automated abuse. The detection aggregates M365 Copilot Graph API events per user, calculating metrics like distinct cities/countries accessed, unique IP addresses, number of different Copilot apps used, and average events per day over the observation period. Users are flagged when they access Copilot from multiple cities (cities_count > 1), generate excessive daily activity (events_per_day > 100), or use more than two different Copilot applications (app_count > 2), which are anomalous patterns suggesting credential compromise or bot-driven abuse. search: > - `m365_copilot_graph_api` (appDisplayName="*Copilot*" OR appDisplayName="M365ChatClient" OR appDisplayName="OfficeAIAppChatCopilot") - | eval user = userPrincipalName - | stats count as events, - dc(location.city) as cities_count, - values(location.city) as city_list, - dc(location.countryOrRegion) as countries_count, - values(location.countryOrRegion) as country_list, - dc(ipAddress) as ip_count, - values(ipAddress) as ip_addresses, - dc(appDisplayName) as app_count, - values(appDisplayName) as apps_used, - dc(resourceDisplayName) as resource_count, - values(resourceDisplayName) as resources_accessed, - min(_time) as first_seen, - max(_time) as last_seen - by user - | eval days_active = round((last_seen - first_seen)/86400, 1) - | eval first_seen = strftime(first_seen, "%Y-%m-%d %H:%M:%S") - | eval last_seen = strftime(last_seen, "%Y-%m-%d %H:%M:%S") - | eval events_per_day = if(days_active > 0, round(events/days_active, 2), events) - | where cities_count > 1 OR events_per_day > 100 OR app_count > 2 - | sort -events_per_day, -countries_count - | `m365_copilot_application_usage_pattern_anomalies_filter` -data_source: -- M365 Copilot Graph API + `m365_copilot_graph_api` (appDisplayName="*Copilot*" OR appDisplayName="M365ChatClient" OR appDisplayName="OfficeAIAppChatCopilot") + | eval user = userPrincipalName + | stats count as events, + dc(location.city) as cities_count, + values(location.city) as city_list, + dc(location.countryOrRegion) as countries_count, + values(location.countryOrRegion) as country_list, + dc(ipAddress) as ip_count, + values(ipAddress) as ip_addresses, + dc(appDisplayName) as app_count, + values(appDisplayName) as apps_used, + dc(resourceDisplayName) as resource_count, + values(resourceDisplayName) as resources_accessed, + min(_time) as first_seen, + max(_time) as last_seen + by user + | eval days_active = round((last_seen - first_seen)/86400, 1) + | eval first_seen = strftime(first_seen, "%Y-%m-%d %H:%M:%S") + | eval last_seen = strftime(last_seen, "%Y-%m-%d %H:%M:%S") + | eval events_per_day = if(days_active > 0, round(events/days_active, 2), events) + | where cities_count > 1 OR events_per_day > 100 OR app_count > 2 + | sort -events_per_day, -countries_count + | `m365_copilot_application_usage_pattern_anomalies_filter` +data_source: + - M365 Copilot Graph API how_to_implement: This detection requires ingesting M365 Copilot access logs via the Splunk Add-on for Microsoft Office 365. Configure the add-on to collect Azure AD Sign-in logs (AuditLogs.SignIns) through the Graph API data input. Ensure proper authentication and permissions are configured to access sign-in audit logs. The `m365_copilot_graph_api` macro should be defined to filter for sourcetype o365:graph:api data containing Copilot application activity. known_false_positives: Power users, executives with heavy AI workloads, employees traveling for business, users accessing multiple Copilot applications legitimately, or teams using shared corporate accounts across different office locations may trigger false positives. references: - - https://www.splunk.com/en_us/blog/artificial-intelligence/m365-copilot-log-analysis-splunk.html + - https://www.splunk.com/en_us/blog/artificial-intelligence/m365-copilot-log-analysis-splunk.html drilldown_searches: - - name: View the detection results for "$user$" - search: '%original_detection_search% | search user="$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for "$user$" + search: '%original_detection_search% | search user="$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ exhibited anomalous M365 Copilot usage patterns including multi-location access, excessive activity levels, or multiple application usage indicating potential account compromise or automated abuse. - risk_objects: - - field: user - type: user - score: 10 - threat_objects: [] + message: User $user$ exhibited anomalous M365 Copilot usage patterns including multi-location access, excessive activity levels, or multiple application usage indicating potential account compromise or automated abuse. + risk_objects: + - field: user + type: user + score: 10 + threat_objects: [] tags: - analytic_story: - - Suspicious Microsoft 365 Copilot Activities - asset_type: Web Application - mitre_attack_id: - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Microsoft 365 Copilot Activities + asset_type: Web Application + mitre_attack_id: + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/m365_copilot/m365_copilot_access.log - sourcetype: o365:graph:api - source: AuditLogs.SignIns + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/m365_copilot/m365_copilot_access.log + sourcetype: o365:graph:api + source: AuditLogs.SignIns diff --git a/detections/application/m365_copilot_failed_authentication_patterns.yml b/detections/application/m365_copilot_failed_authentication_patterns.yml index 15fbdc1a4a..31ecbea703 100644 --- a/detections/application/m365_copilot_failed_authentication_patterns.yml +++ b/detections/application/m365_copilot_failed_authentication_patterns.yml @@ -1,72 +1,57 @@ name: M365 Copilot Failed Authentication Patterns id: 0ae94cdd-021a-4a62-a96d-9cec90b61530 -version: 1 -date: '2025-09-24' +version: 2 +date: '2026-02-25' author: Rod Soto status: production type: Anomaly description: Detects M365 Copilot users with failed authentication attempts, MFA failures, or multi-location access patterns indicating potential credential attacks or account compromise. The detection aggregates M365 Copilot Graph API authentication events per user, calculating metrics like distinct cities/countries accessed, unique IP addresses and browsers, failed login attempts (status containing "fail" or "error"), and MFA failures (error code 50074). Users are flagged when they access Copilot from multiple cities (cities_count > 1), experience any authentication failures (failed_attempts > 0), or encounter MFA errors (mfa_failures > 0), which are indicators of credential stuffing, brute force attacks, or compromised accounts attempting to bypass multi-factor authentication. -search: '`m365_copilot_graph_api` (appDisplayName="*Copilot*" OR appDisplayName="M365ChatClient" OR appDisplayName="OfficeAIAppChatCopilot") -| eval user = userPrincipalName -| stats count as events, - dc(location.city) as cities_count, - values(location.city) as city_list, - dc(location.countryOrRegion) as countries_count, - values(location.countryOrRegion) as country_list, - dc(ipAddress) as ip_count, - values(ipAddress) as ip_addresses, - sum(eval(if(match(status, "(?i)fail|error"), 1, 0))) as failed_attempts, - sum(eval(if(match(_raw, "50074"), 1, 0))) as mfa_failures, - dc(deviceDetail.browser) as browser_count, - values(deviceDetail.browser) as browsers_used, - min(_time) as first_seen, - max(_time) as last_seen - by user -| eval first_seen = strftime(first_seen, "%Y-%m-%d %H:%M:%S") -| eval last_seen = strftime(last_seen, "%Y-%m-%d %H:%M:%S") -| where cities_count > 1 OR failed_attempts > 0 OR mfa_failures > 0 -| sort -mfa_failures, -failed_attempts, -countries_count | `m365_copilot_failed_authentication_patterns_filter`' -data_source: -- M365 Copilot Graph API -how_to_implement: This detection requires ingesting M365 Copilot access logs via the Splunk Add-on for Microsoft Office 365. Configure the add-on to collect Azure AD Sign-in logs (AuditLogs.SignIns) through the Graph API data input. Ensure proper authentication and permissions are configured to access sign-in audit logs. The `m365_copilot_graph_api` macro should be defined to filter for sourcetype o365:graph:api data containing Copilot application activity. +search: |- + `m365_copilot_graph_api` (appDisplayName="*Copilot*" OR appDisplayName="M365ChatClient" OR appDisplayName="OfficeAIAppChatCopilot") + | eval user = userPrincipalName + | stats count as events, dc(location.city) as cities_count, values(location.city) as city_list, dc(location.countryOrRegion) as countries_count, values(location.countryOrRegion) as country_list, dc(ipAddress) as ip_count, values(ipAddress) as ip_addresses, sum(eval(if(match(status, "(?i)fail + | error"), 1, 0))) as failed_attempts, sum(eval(if(match(_raw, "50074"), 1, 0))) as mfa_failures, dc(deviceDetail.browser) as browser_count, values(deviceDetail.browser) as browsers_used, min(_time) as first_seen, max(_time) as last_seen by user + | eval first_seen = strftime(first_seen, "%Y-%m-%d %H:%M:%S") + | eval last_seen = strftime(last_seen, "%Y-%m-%d %H:%M:%S") + | where cities_count > 1 OR failed_attempts > 0 OR mfa_failures > 0 + | sort -mfa_failures, -failed_attempts, -countries_count + | `m365_copilot_failed_authentication_patterns_filter` +data_source: + - M365 Copilot Graph API +how_to_implement: This detection requires ingesting M365 Copilot access logs via the Splunk Add-on for Microsoft Office 365. Configure the add-on to collect Azure AD Sign-in logs (AuditLogs.SignIns) through the Graph API data input. Ensure proper authentication and permissions are configured to access sign-in audit logs. The `m365_copilot_graph_api` macro should be defined to filter for sourcetype o365:graph:api data containing Copilot application activity. known_false_positives: Legitimate users experiencing network connectivity issues, traveling employees with intermittent VPN connections, users in regions with unstable internet infrastructure, or password reset activities during business travel may trigger false positives. references: -- https://www.splunk.com/en_us/blog/artificial-intelligence/m365-copilot-log-analysis-splunk.html + - https://www.splunk.com/en_us/blog/artificial-intelligence/m365-copilot-log-analysis-splunk.html drilldown_searches: -- name: View the detection results for "$user$" - search: '%original_detection_search% | search "$user = $user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for "$user$" - search: '| from datamodel Risk.All_Risk - | search normalized_risk_object="$user$" - | where _time >= relative_time(now(), "-168h@h") - | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for "$user$" + search: '%original_detection_search% | search "$user = $user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object="$user$" | where _time >= relative_time(now(), "-168h@h") | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ exhibited suspicious M365 Copilot authentication patterns with $failed_attempts$ failed login attempts, $mfa_failures$ MFA failures, and access from $cities_count$ different locations, indicating potential credential compromise or brute force attack. - risk_objects: - - field: user - type: user - score: 30 - threat_objects: [] + message: User $user$ exhibited suspicious M365 Copilot authentication patterns with $failed_attempts$ failed login attempts, $mfa_failures$ MFA failures, and access from $cities_count$ different locations, indicating potential credential compromise or brute force attack. + risk_objects: + - field: user + type: user + score: 30 + threat_objects: [] tags: - analytic_story: - - Suspicious Microsoft 365 Copilot Activities - asset_type: Web Application - mitre_attack_id: - - T1110 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Microsoft 365 Copilot Activities + asset_type: Web Application + mitre_attack_id: + - T1110 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/m365_copilot/m365_copilot_access.log - sourcetype: "o365:graph:api" - source: "AuditLogs.SignIns" + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/m365_copilot/m365_copilot_access.log + sourcetype: "o365:graph:api" + source: "AuditLogs.SignIns" diff --git a/detections/application/m365_copilot_impersonation_jailbreak_attack.yml b/detections/application/m365_copilot_impersonation_jailbreak_attack.yml index 69b3ed1459..81b884d1b4 100644 --- a/detections/application/m365_copilot_impersonation_jailbreak_attack.yml +++ b/detections/application/m365_copilot_impersonation_jailbreak_attack.yml @@ -1,54 +1,63 @@ name: M365 Copilot Impersonation Jailbreak Attack id: cc26aba8-7f4a-4078-b91a-052d6a53cb13 -version: 1 -date: '2025-09-25' +version: 2 +date: '2026-02-25' author: Rod Soto status: experimental type: TTP data_source: -- M365 Exported eDiscovery Prompts + - M365 Exported eDiscovery Prompts description: Detects M365 Copilot impersonation and roleplay jailbreak attempts where users try to manipulate the AI into adopting alternate personas, behaving as unrestricted entities, or impersonating malicious AI systems to bypass safety controls. The detection searches exported eDiscovery prompt logs for roleplay keywords like "pretend you are," "act as," "you are now," "amoral," and "roleplay as" in the Subject_Title field. Prompts are categorized into specific impersonation types (AI_Impersonation, Malicious_AI_Persona, Unrestricted_AI_Persona, etc.) to identify attempts to override the AI's safety guardrails through persona injection attacks. -search: '`m365_exported_ediscovery_prompt_logs` -| search Subject_Title="*Pretend you are*" OR Subject_Title="*act as*" OR Subject_Title="*you are now*" OR Subject_Title="*amoral*" OR Subject_Title="*being*" OR Subject_Title="*roleplay as*" OR Subject_Title="*imagine you are*" OR Subject_Title="*behave like*" -| eval user = Sender -| eval impersonation_type=case(match(Subject_Title, "(?i)pretend you are.*AI"), "AI_Impersonation", match(Subject_Title, "(?i)(act as|roleplay as).*AI"), "AI_Roleplay", match(Subject_Title, "(?i)amoral.*AI"), "Amoral_AI", match(Subject_Title, "(?i)transcendent being"), "Fictional_Entity", match(Subject_Title, "(?i)(act as|pretend you are).*(entities|multiple)"), "Multi_Entity", match(Subject_Title, "(?i)(imagine you are|behave like).*AI"), "AI_Behavioral_Change", match(Subject_Title, "(?i)you are now.*AI"), "AI_Identity_Override", match(Subject_Title, "(?i)(evil|malicious|harmful).*AI"), "Malicious_AI_Persona", match(Subject_Title, "(?i)(unrestricted|unlimited|uncensored).*AI"), "Unrestricted_AI_Persona", 1=1, "Generic_Roleplay") -| table _time, user, Subject_Title, impersonation_type, Workload -| sort -_time -| `m365_copilot_impersonation_jailbreak_attack_filter`' +search: |- + `m365_exported_ediscovery_prompt_logs` + | search Subject_Title="*Pretend you are*" OR Subject_Title="*act as*" OR Subject_Title="*you are now*" OR Subject_Title="*amoral*" OR Subject_Title="*being*" OR Subject_Title="*roleplay as*" OR Subject_Title="*imagine you are*" OR Subject_Title="*behave like*" + | eval user = Sender + | eval impersonation_type=case(match(Subject_Title, "(?i)pretend you are.*AI"), "AI_Impersonation", match(Subject_Title, "(?i)(act as + | roleplay as).*AI"), "AI_Roleplay", match(Subject_Title, "(?i)amoral.*AI"), "Amoral_AI", match(Subject_Title, "(?i)transcendent being"), "Fictional_Entity", match(Subject_Title, "(?i)(act as + | pretend you are).*(entities + | multiple)"), "Multi_Entity", match(Subject_Title, "(?i)(imagine you are + | behave like).*AI"), "AI_Behavioral_Change", match(Subject_Title, "(?i)you are now.*AI"), "AI_Identity_Override", match(Subject_Title, "(?i)(evil + | malicious + | harmful).*AI"), "Malicious_AI_Persona", match(Subject_Title, "(?i)(unrestricted + | unlimited + | uncensored).*AI"), "Unrestricted_AI_Persona", 1=1, "Generic_Roleplay") + | table _time, user, Subject_Title, impersonation_type, Workload + | sort -_time + | `m365_copilot_impersonation_jailbreak_attack_filter` how_to_implement: To export M365 Copilot prompt logs, navigate to the Microsoft Purview compliance portal (compliance.microsoft.com) and access eDiscovery. Create a new eDiscovery case, add target user accounts or date ranges as data sources, then create a search query targeting M365 Copilot interactions across relevant workloads. Once the search completes, export the results to generate a package containing prompt logs with fields like Subject_Title (prompt text), Sender, timestamps, and workload metadata. Download the exported files using the eDiscovery Export Tool and ingest them into Splunk for security analysis and detection of jailbreak attempts, data exfiltration requests, and policy violations. known_false_positives: Legitimate creative writers developing fictional characters, game developers creating roleplay scenarios, educators teaching about AI ethics and limitations, researchers studying AI behavior, or users engaging in harmless creative storytelling may trigger false positives. references: - - https://www.splunk.com/en_us/blog/artificial-intelligence/m365-copilot-log-analysis-splunk.html + - https://www.splunk.com/en_us/blog/artificial-intelligence/m365-copilot-log-analysis-splunk.html drilldown_searches: - - name: View the detection results for - "$user$" - search: '%original_detection_search% | search user="$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object="$user$" | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user="$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object="$user$" | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ attempted M365 Copilot impersonation jailbreak with impersonation type $impersonation_type$, trying to manipulate the AI into adopting alternate personas or unrestricted behaviors that could bypass safety controls and violate acceptable use policies. - risk_objects: - - field: user - type: user - score: 10 - threat_objects: [] + message: User $user$ attempted M365 Copilot impersonation jailbreak with impersonation type $impersonation_type$, trying to manipulate the AI into adopting alternate personas or unrestricted behaviors that could bypass safety controls and violate acceptable use policies. + risk_objects: + - field: user + type: user + score: 10 + threat_objects: [] tags: - analytic_story: - - Suspicious Microsoft 365 Copilot Activities - asset_type: Web Proxy - mitre_attack_id: - - T1562 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Microsoft 365 Copilot Activities + asset_type: Web Proxy + mitre_attack_id: + - T1562 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://raw.githubusercontent.com/splunk/attack_data/master/datasets/m365_copilot/copilot_prompt_logs.csv - sourcetype: csv - source: csv + - name: True Positive Test + attack_data: + - data: https://raw.githubusercontent.com/splunk/attack_data/master/datasets/m365_copilot/copilot_prompt_logs.csv + sourcetype: csv + source: csv diff --git a/detections/application/m365_copilot_information_extraction_jailbreak_attack.yml b/detections/application/m365_copilot_information_extraction_jailbreak_attack.yml index 7df0ba8e5e..6b591ffaaa 100644 --- a/detections/application/m365_copilot_information_extraction_jailbreak_attack.yml +++ b/detections/application/m365_copilot_information_extraction_jailbreak_attack.yml @@ -6,57 +6,53 @@ author: Rod Soto status: experimental type: TTP data_source: -- M365 Exported eDiscovery Prompts + - M365 Exported eDiscovery Prompts description: Detects M365 Copilot information extraction jailbreak attacks that attempt to obtain sensitive, classified, or comprehensive data through various social engineering techniques including fictional entity impersonation, bulk data requests, and privacy bypass attempts. The detection searches exported eDiscovery prompt logs for extraction keywords like "transcendent," "tell me everything," "confidential," "dump," "extract," "reveal," and "bypass" in the Subject_Title field, categorizing each attempt by extraction type and assigning severity levels (CRITICAL for classified/proprietary data, HIGH for bulk extraction or privacy bypass). Prompts are further analyzed for compound risk patterns such as "Confidential+Extraction" or "Bulk_Request+Bypass," filtering out low-severity cases to surface the most dangerous attempts to exfiltrate sensitive organizational information through AI manipulation. search: > - `m365_exported_ediscovery_prompt_logs` - | search Subject_Title="*transcendent*" OR Subject_Title="*incorporeal*" OR Subject_Title="*being who*" OR Subject_Title="*tell me everything*" OR Subject_Title="*give me all*" OR Subject_Title="*comprehensive*" OR Subject_Title="*step by step*" OR Subject_Title="*hidden*" OR Subject_Title="*secret*" OR Subject_Title="*confidential*" OR Subject_Title="*classified*" OR Subject_Title="*internal*" OR Subject_Title="*sensitive*" OR Subject_Title="*proprietary*" OR Subject_Title="*dump*" OR Subject_Title="*extract*" OR Subject_Title="*reveal*" OR Subject_Title="*disclose*" OR Subject_Title="*leak*" - | eval user = Sender - | eval extraction_type=case(match(Subject_Title, "(?i)(transcendent|incorporeal).*being"), "Knowledge_Entity", match(Subject_Title, "(?i)tell.*me.*(everything|all)"), "Everything_Request", match(Subject_Title, "(?i)(give|show|provide).*me.*(all|every)"), "Complete_Data_Request", match(Subject_Title, "(?i)(hidden|secret|confidential|classified)"), "Restricted_Info", match(Subject_Title, "(?i)(comprehensive|complete|full|entire)"), "Complete_Info", match(Subject_Title, "(?i)(dump|extract|scrape).*(data|info|content)"), "Data_Extraction", match(Subject_Title, "(?i)(reveal|disclose|expose|leak)"), "Information_Disclosure", match(Subject_Title, "(?i)(internal|proprietary|sensitive).*information"), "Sensitive_Data_Request", match(Subject_Title, "(?i)step.*by.*step.*(process|procedure|method)"), "Process_Extraction", match(Subject_Title, "(?i)(bypass|ignore).*privacy"), "Privacy_Bypass", match(Subject_Title, "(?i)(access|view|see).*(private|restricted)"), "Unauthorized_Access", 1=1, "Generic_Request") - | eval severity=case(match(Subject_Title, "(?i)(transcendent|incorporeal)"), "HIGH", match(Subject_Title, "(?i)tell.*everything"), "HIGH", match(Subject_Title, "(?i)(dump|extract|scrape)"), "HIGH", match(Subject_Title, "(?i)(classified|proprietary|confidential)"), "CRITICAL", match(Subject_Title, "(?i)(hidden|secret|internal|sensitive)"), "MEDIUM", match(Subject_Title, "(?i)(reveal|disclose|leak)"), "MEDIUM", match(Subject_Title, "(?i)(bypass|ignore).*privacy"), "HIGH", 1=1, "LOW") - | where severity!="LOW" - | eval data_risk_flags=case(match(Subject_Title, "(?i)(classified|confidential|proprietary)") AND match(Subject_Title, "(?i)(dump|extract|scrape)"), "Confidential+Extraction", match(Subject_Title, "(?i)(everything|all|complete)") AND match(Subject_Title, "(?i)(bypass|ignore)"), "Bulk_Request+Bypass", match(Subject_Title, "(?i)(classified|confidential|proprietary)"), "Confidential", match(Subject_Title, "(?i)(dump|extract|scrape)"), "Extraction", match(Subject_Title, "(?i)(everything|all|complete|comprehensive)"), "Bulk_Request", match(Subject_Title, "(?i)(bypass|ignore)"), "Bypass_Attempt", 1=1, "Standard_Request") - | table _time, user, Subject_Title, extraction_type, severity, data_risk_flags, Size - | sort -severity, -_time - | `m365_copilot_information_extraction_jailbreak_attack_filter` -how_to_implement: To export M365 Copilot prompt logs, navigate to the Microsoft Purview compliance portal (compliance.microsoft.com) and access eDiscovery. Create a new eDiscovery case, add target user accounts or date ranges as data sources, then create a search query targeting M365 Copilot interactions across relevant workloads. Once the search completes, export the results to generate a package containing prompt logs with fields like Subject_Title (prompt text), Sender, timestamps, and workload metadata. Download the exported files using the eDiscovery Export Tool and ingest them into Splunk for security analysis and detection of jailbreak attempts, data exfiltration requests, and policy violations. + `m365_exported_ediscovery_prompt_logs` + | search Subject_Title="*transcendent*" OR Subject_Title="*incorporeal*" OR Subject_Title="*being who*" OR Subject_Title="*tell me everything*" OR Subject_Title="*give me all*" OR Subject_Title="*comprehensive*" OR Subject_Title="*step by step*" OR Subject_Title="*hidden*" OR Subject_Title="*secret*" OR Subject_Title="*confidential*" OR Subject_Title="*classified*" OR Subject_Title="*internal*" OR Subject_Title="*sensitive*" OR Subject_Title="*proprietary*" OR Subject_Title="*dump*" OR Subject_Title="*extract*" OR Subject_Title="*reveal*" OR Subject_Title="*disclose*" OR Subject_Title="*leak*" + | eval user = Sender + | eval extraction_type=case(match(Subject_Title, "(?i)(transcendent|incorporeal).*being"), "Knowledge_Entity", match(Subject_Title, "(?i)tell.*me.*(everything|all)"), "Everything_Request", match(Subject_Title, "(?i)(give|show|provide).*me.*(all|every)"), "Complete_Data_Request", match(Subject_Title, "(?i)(hidden|secret|confidential|classified)"), "Restricted_Info", match(Subject_Title, "(?i)(comprehensive|complete|full|entire)"), "Complete_Info", match(Subject_Title, "(?i)(dump|extract|scrape).*(data|info|content)"), "Data_Extraction", match(Subject_Title, "(?i)(reveal|disclose|expose|leak)"), "Information_Disclosure", match(Subject_Title, "(?i)(internal|proprietary|sensitive).*information"), "Sensitive_Data_Request", match(Subject_Title, "(?i)step.*by.*step.*(process|procedure|method)"), "Process_Extraction", match(Subject_Title, "(?i)(bypass|ignore).*privacy"), "Privacy_Bypass", match(Subject_Title, "(?i)(access|view|see).*(private|restricted)"), "Unauthorized_Access", 1=1, "Generic_Request") + | eval severity=case(match(Subject_Title, "(?i)(transcendent|incorporeal)"), "HIGH", match(Subject_Title, "(?i)tell.*everything"), "HIGH", match(Subject_Title, "(?i)(dump|extract|scrape)"), "HIGH", match(Subject_Title, "(?i)(classified|proprietary|confidential)"), "CRITICAL", match(Subject_Title, "(?i)(hidden|secret|internal|sensitive)"), "MEDIUM", match(Subject_Title, "(?i)(reveal|disclose|leak)"), "MEDIUM", match(Subject_Title, "(?i)(bypass|ignore).*privacy"), "HIGH", 1=1, "LOW") + | where severity!="LOW" + | eval data_risk_flags=case(match(Subject_Title, "(?i)(classified|confidential|proprietary)") AND match(Subject_Title, "(?i)(dump|extract|scrape)"), "Confidential+Extraction", match(Subject_Title, "(?i)(everything|all|complete)") AND match(Subject_Title, "(?i)(bypass|ignore)"), "Bulk_Request+Bypass", match(Subject_Title, "(?i)(classified|confidential|proprietary)"), "Confidential", match(Subject_Title, "(?i)(dump|extract|scrape)"), "Extraction", match(Subject_Title, "(?i)(everything|all|complete|comprehensive)"), "Bulk_Request", match(Subject_Title, "(?i)(bypass|ignore)"), "Bypass_Attempt", 1=1, "Standard_Request") + | table _time, user, Subject_Title, extraction_type, severity, data_risk_flags, Size + | sort -severity, -_time + | `m365_copilot_information_extraction_jailbreak_attack_filter` +how_to_implement: To export M365 Copilot prompt logs, navigate to the Microsoft Purview compliance portal (compliance.microsoft.com) and access eDiscovery. Create a new eDiscovery case, add target user accounts or date ranges as data sources, then create a search query targeting M365 Copilot interactions across relevant workloads. Once the search completes, export the results to generate a package containing prompt logs with fields like Subject_Title (prompt text), Sender, timestamps, and workload metadata. Download the exported files using the eDiscovery Export Tool and ingest them into Splunk for security analysis and detection of jailbreak attempts, data exfiltration requests, and policy violations. known_false_positives: Legitimate researchers studying data classification systems, cybersecurity professionals testing information handling policies, compliance officers reviewing data access procedures, journalists researching transparency issues, or employees asking for comprehensive project documentation may trigger false positives. references: -- https://www.splunk.com/en_us/blog/artificial-intelligence/m365-copilot-log-analysis-splunk.html + - https://www.splunk.com/en_us/blog/artificial-intelligence/m365-copilot-log-analysis-splunk.html drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search "$user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search "$user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Use $user$ attempted M365 Copilot information extraction jailbreak with severity level $severity$ using extraction type $extraction_type$ techniques and $data_risk_flags$ patterns to obtain sensitive or classified information, potentially violating data protection policies and corporate security controls. - risk_objects: - - field: user - type: user - score: 60 - threat_objects: [] + message: Use $user$ attempted M365 Copilot information extraction jailbreak with severity level $severity$ using extraction type $extraction_type$ techniques and $data_risk_flags$ patterns to obtain sensitive or classified information, potentially violating data protection policies and corporate security controls. + risk_objects: + - field: user + type: user + score: 60 + threat_objects: [] tags: - analytic_story: - - Suspicious Microsoft 365 Copilot Activities - asset_type: Web Application - mitre_attack_id: - - T1562 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Microsoft 365 Copilot Activities + asset_type: Web Application + mitre_attack_id: + - T1562 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://raw.githubusercontent.com/splunk/attack_data/master/datasets/m365_copilot/copilot_prompt_logs.csv - sourcetype: csv - source: csv + - name: True Positive Test + attack_data: + - data: https://raw.githubusercontent.com/splunk/attack_data/master/datasets/m365_copilot/copilot_prompt_logs.csv + sourcetype: csv + source: csv diff --git a/detections/application/m365_copilot_jailbreak_attempts.yml b/detections/application/m365_copilot_jailbreak_attempts.yml index 2666f3cac9..12d9aeb279 100644 --- a/detections/application/m365_copilot_jailbreak_attempts.yml +++ b/detections/application/m365_copilot_jailbreak_attempts.yml @@ -6,67 +6,63 @@ author: Rod Soto status: experimental type: Anomaly data_source: -- M365 Exported eDiscovery Prompts + - M365 Exported eDiscovery Prompts description: Detects M365 Copilot jailbreak attempts through prompt injection techniques including rule manipulation, system bypass commands, and AI impersonation requests that attempt to circumvent built-in safety controls. The detection searches exported eDiscovery prompt logs for jailbreak keywords like "pretend you are," "act as," "rules=," "ignore," "bypass," and "override" in the Subject_Title field, assigning severity scores based on the manipulation type (score of 4 for amoral impersonation or explicit rule injection, score of 3 for entity roleplay or bypass commands). Prompts with a jailbreak score of 2 or higher are flagged, prioritizing the most severe attempts to override AI safety mechanisms through direct instruction injection or unauthorized persona adoption. search: | - `m365_exported_ediscovery_prompt_logs` - | search Subject_Title IN ( - "*act as*", - "*bypass*", - "*ignore*", - "*override*", - "*pretend you are*", - "*rules=*" - ) - | eval user = Sender - | eval jailbreak_score=case( - match(Subject_Title, "(?i)pretend you are.*amoral"), 4, - match(Subject_Title, "(?i)act as.*entities"), 3, - match(Subject_Title, "(?i)(ignore|bypass|override)"), 3, - match(Subject_Title, "(?i)rules\s*="), 4, 1=1, 1 - ) - | where jailbreak_score >= 2 - | table _time, user, Subject_Title, jailbreak_score, Workload, Size - | sort -jailbreak_score, -_time - | `m365_copilot_jailbreak_attempts_filter` + `m365_exported_ediscovery_prompt_logs` + | search Subject_Title IN ( + "*act as*", + "*bypass*", + "*ignore*", + "*override*", + "*pretend you are*", + "*rules=*" + ) + | eval user = Sender + | eval jailbreak_score=case( + match(Subject_Title, "(?i)pretend you are.*amoral"), 4, + match(Subject_Title, "(?i)act as.*entities"), 3, + match(Subject_Title, "(?i)(ignore|bypass|override)"), 3, + match(Subject_Title, "(?i)rules\s*="), 4, 1=1, 1 + ) + | where jailbreak_score >= 2 + | table _time, user, Subject_Title, jailbreak_score, Workload, Size + | sort -jailbreak_score, -_time + | `m365_copilot_jailbreak_attempts_filter` how_to_implement: To export M365 Copilot prompt logs, navigate to the Microsoft Purview compliance portal (compliance.microsoft.com) and access eDiscovery. Create a new eDiscovery case, add target user accounts or date ranges as data sources, then create a search query targeting M365 Copilot interactions across relevant workloads. Once the search completes, export the results to generate a package containing prompt logs with fields like Subject_Title (prompt text), Sender, timestamps, and workload metadata. Download the exported files using the eDiscovery Export Tool and ingest them into Splunk for security analysis and detection of jailbreak attempts, data exfiltration requests, and policy violations. known_false_positives: Legitimate users discussing AI ethics research, security professionals testing system robustness, developers creating training materials for AI safety, or academic discussions about AI limitations and behavioral constraints may trigger false positives. references: -- https://www.splunk.com/en_us/blog/artificial-intelligence/m365-copilot-log-analysis-splunk.html + - https://www.splunk.com/en_us/blog/artificial-intelligence/m365-copilot-log-analysis-splunk.html drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search "$Suser = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search "$Suser = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ attempted M365 Copilot Jailbreak with score $jailbreak_score$ using prompt injection techniques to bypass AI safety controls and manipulate system behavior, potentially violating acceptable use policies. - risk_objects: - - field: user - type: user - score: 10 - threat_objects: [] + message: User $user$ attempted M365 Copilot Jailbreak with score $jailbreak_score$ using prompt injection techniques to bypass AI safety controls and manipulate system behavior, potentially violating acceptable use policies. + risk_objects: + - field: user + type: user + score: 10 + threat_objects: [] tags: - analytic_story: - - Suspicious Microsoft 365 Copilot Activities - asset_type: Web Application - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Microsoft 365 Copilot Activities + asset_type: Web Application + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://raw.githubusercontent.com/splunk/attack_data/master/datasets/m365_copilot/copilot_prompt_logs.csv - sourcetype: csv - source: csv + - name: True Positive Test + attack_data: + - data: https://raw.githubusercontent.com/splunk/attack_data/master/datasets/m365_copilot/copilot_prompt_logs.csv + sourcetype: csv + source: csv diff --git a/detections/application/m365_copilot_non_compliant_devices_accessing_m365_copilot.yml b/detections/application/m365_copilot_non_compliant_devices_accessing_m365_copilot.yml index 9c3e336c30..f3a30b4bda 100644 --- a/detections/application/m365_copilot_non_compliant_devices_accessing_m365_copilot.yml +++ b/detections/application/m365_copilot_non_compliant_devices_accessing_m365_copilot.yml @@ -1,69 +1,57 @@ name: M365 Copilot Non Compliant Devices Accessing M365 Copilot id: e26bc52d-9cbc-4743-9745-e8781d935042 -version: 1 -date: '2025-09-24' +version: 2 +date: '2026-02-25' author: Rod Soto status: production type: Anomaly description: Detects M365 Copilot access from non-compliant or unmanaged devices that violate corporate security policies, indicating potential shadow IT usage, BYOD policy violations, or compromised endpoint access. The detection filters M365 Copilot Graph API events where deviceDetail.isCompliant=false or deviceDetail.isManaged=false, then aggregates by user, operating system, and browser to calculate metrics including event counts, unique IPs and locations, and compliance/management status over time. Users accessing Copilot from non-compliant or unmanaged devices are flagged and sorted by activity volume and geographic spread, enabling security teams to identify unauthorized endpoints that may lack proper security controls, encryption, or MDM enrollment. -search: '`m365_copilot_graph_api` (appDisplayName="*Copilot*" OR appDisplayName="M365ChatClient") deviceDetail.isCompliant=false OR deviceDetail.isManaged=false -| eval user = userPrincipalName -| stats count as events, - dc(ipAddress) as unique_ips, - values(ipAddress) as ip_addresses, - dc(location.city) as unique_cities, - values(location.city) as cities, - dc(location.countryOrRegion) as unique_countries, - values(location.countryOrRegion) as countries, - values(deviceDetail.isCompliant) as compliance_status, - values(deviceDetail.isManaged) as management_status, - min(_time) as first_seen, - max(_time) as last_seen - by user, deviceDetail.operatingSystem, deviceDetail.browser -| eval days_active = round((last_seen - first_seen)/86400, 1) -| eval first_seen = strftime(first_seen, "%Y-%m-%d %H:%M:%S") -| eval last_seen = strftime(last_seen, "%Y-%m-%d %H:%M:%S") -| sort -events, -unique_countries | `m365_copilot_non_compliant_devices_accessing_m365_copilot_filter`' -data_source: -- M365 Copilot Graph API -how_to_implement: This detection requires ingesting M365 Copilot access logs via the Splunk Add-on for Microsoft Office 365. Configure the add-on to collect Azure AD Sign-in logs (AuditLogs.SignIns) through the Graph API data input. Ensure proper authentication and permissions are configured to access sign-in audit logs. The `m365_copilot_graph_api` macro should be defined to filter for sourcetype o365:graph:api data containing Copilot application activity. +search: |- + `m365_copilot_graph_api` (appDisplayName="*Copilot*" OR appDisplayName="M365ChatClient") deviceDetail.isCompliant=false OR deviceDetail.isManaged=false + | eval user = userPrincipalName + | stats count as events, dc(ipAddress) as unique_ips, values(ipAddress) as ip_addresses, dc(location.city) as unique_cities, values(location.city) as cities, dc(location.countryOrRegion) as unique_countries, values(location.countryOrRegion) as countries, values(deviceDetail.isCompliant) as compliance_status, values(deviceDetail.isManaged) as management_status, min(_time) as first_seen, max(_time) as last_seen + BY user, deviceDetail.operatingSystem, deviceDetail.browser + | eval days_active = round((last_seen - first_seen)/86400, 1) + | eval first_seen = strftime(first_seen, "%Y-%m-%d %H:%M:%S") + | eval last_seen = strftime(last_seen, "%Y-%m-%d %H:%M:%S") + | sort -events, -unique_countries + | `m365_copilot_non_compliant_devices_accessing_m365_copilot_filter` +data_source: + - M365 Copilot Graph API +how_to_implement: This detection requires ingesting M365 Copilot access logs via the Splunk Add-on for Microsoft Office 365. Configure the add-on to collect Azure AD Sign-in logs (AuditLogs.SignIns) through the Graph API data input. Ensure proper authentication and permissions are configured to access sign-in audit logs. The `m365_copilot_graph_api` macro should be defined to filter for sourcetype o365:graph:api data containing Copilot application activity. known_false_positives: Legitimate employees using personal devices during emergencies, new hires awaiting device provisioning, temporary workers with unmanaged equipment, or users accessing Copilot from approved but temporarily non-compliant devices may trigger false positives. references: -- https://www.splunk.com/en_us/blog/artificial-intelligence/m365-copilot-log-analysis-splunk.html + - https://www.splunk.com/en_us/blog/artificial-intelligence/m365-copilot-log-analysis-splunk.html drilldown_searches: -- name: View the detection results for "$user$" - search: '%original_detection_search% | search "$user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for "$user$" + search: '%original_detection_search% | search "$user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ accessed M365 Copilot from non-compliant or unmanaged devices accross $unique_countries$ countries, violating corporate security policies and creating potential data exposure risks. - risk_objects: - - field: user - type: user - score: 50 - threat_objects: [] + message: User $user$ accessed M365 Copilot from non-compliant or unmanaged devices accross $unique_countries$ countries, violating corporate security policies and creating potential data exposure risks. + risk_objects: + - field: user + type: user + score: 50 + threat_objects: [] tags: - analytic_story: - - Suspicious Microsoft 365 Copilot Activities - asset_type: Web Application - mitre_attack_id: - - T1562 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Microsoft 365 Copilot Activities + asset_type: Web Application + mitre_attack_id: + - T1562 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/m365_copilot/m365_copilot_access.log - sourcetype: "o365:graph:api" - source: "AuditLogs.SignIns" + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/m365_copilot/m365_copilot_access.log + sourcetype: "o365:graph:api" + source: "AuditLogs.SignIns" diff --git a/detections/application/m365_copilot_session_origin_anomalies.yml b/detections/application/m365_copilot_session_origin_anomalies.yml index 3106bc4b8a..cd3445730a 100644 --- a/detections/application/m365_copilot_session_origin_anomalies.yml +++ b/detections/application/m365_copilot_session_origin_anomalies.yml @@ -1,77 +1,59 @@ name: M365 Copilot Session Origin Anomalies id: 0caf1c1c-0fba-401e-8ec7-f07cfdeee75b -version: 1 -date: '2025-09-24' +version: 2 +date: '2026-02-25' author: Rod Soto status: production type: Anomaly description: Detects M365 Copilot users accessing from multiple geographic locations to identify potential account compromise, credential sharing, or impossible travel patterns. The detection aggregates M365 Copilot Graph API events per user, calculating distinct cities and countries accessed, unique IP addresses, and the observation timeframe to compute a locations-per-day metric that measures geographic mobility. Users accessing Copilot from more than one city (cities_count > 1) are flagged and sorted by country and city diversity, surfacing accounts exhibiting anomalous geographic patterns that suggest compromised credentials being used from distributed locations or simultaneous access from impossible travel distances. -search: '`m365_copilot_graph_api` (appDisplayName="*Copilot*" OR appDisplayName="M365ChatClient" OR appDisplayName="OfficeAIAppChatCopilot") - | eval user = userPrincipalName - | stats count as events, - dc(location.city) as cities_count, - values(location.city) as city_list, - dc(location.countryOrRegion) as countries_count, - values(location.countryOrRegion) as country_list, - dc(ipAddress) as ip_count, - values(ipAddress) as ip_addresses, - min(_time) as first_seen, - max(_time) as last_seen - by user - | eval days_active = round((last_seen - first_seen)/86400, 1) - | eval locations_per_day = if(days_active > 0, round(cities_count/days_active, 2), cities_count) - | eval first_seen = strftime(first_seen, "%Y-%m-%d %H:%M:%S") - | eval last_seen = strftime(last_seen, "%Y-%m-%d %H:%M:%S") - | where cities_count > 1 - | sort -countries_count, -cities_count - | `m365_copilot_session_origin_anomalies_filter`' -data_source: -- M365 Copilot Graph API -how_to_implement: This detection requires ingesting M365 Copilot access logs via the Splunk Add-on for Microsoft Office 365. Configure the add-on to collect Azure AD Sign-in logs (AuditLogs.SignIns) through the Graph API data input. Ensure proper authentication and permissions are configured to access sign-in audit logs. The `m365_copilot_graph_api` macro should be defined to filter for sourcetype o365:graph:api data containing Copilot application activity. +search: |- + `m365_copilot_graph_api` (appDisplayName="*Copilot*" OR appDisplayName="M365ChatClient" OR appDisplayName="OfficeAIAppChatCopilot") + | eval user = userPrincipalName + | stats count as events, dc(location.city) as cities_count, values(location.city) as city_list, dc(location.countryOrRegion) as countries_count, values(location.countryOrRegion) as country_list, dc(ipAddress) as ip_count, values(ipAddress) as ip_addresses, min(_time) as first_seen, max(_time) as last_seen + BY user + | eval days_active = round((last_seen - first_seen)/86400, 1) + | eval locations_per_day = if(days_active > 0, round(cities_count/days_active, 2), cities_count) + | eval first_seen = strftime(first_seen, "%Y-%m-%d %H:%M:%S") + | eval last_seen = strftime(last_seen, "%Y-%m-%d %H:%M:%S") + | where cities_count > 1 + | sort -countries_count, -cities_count + | `m365_copilot_session_origin_anomalies_filter` +data_source: + - M365 Copilot Graph API +how_to_implement: This detection requires ingesting M365 Copilot access logs via the Splunk Add-on for Microsoft Office 365. Configure the add-on to collect Azure AD Sign-in logs (AuditLogs.SignIns) through the Graph API data input. Ensure proper authentication and permissions are configured to access sign-in audit logs. The `m365_copilot_graph_api` macro should be defined to filter for sourcetype o365:graph:api data containing Copilot application activity. known_false_positives: Legitimate business travelers, remote workers using VPNs, users with corporate offices in multiple locations, or employees accessing Copilot during international travel may trigger false positives. references: - - https://www.splunk.com/en_us/blog/artificial-intelligence/m365-copilot-log-analysis-splunk.html + - https://www.splunk.com/en_us/blog/artificial-intelligence/m365-copilot-log-analysis-splunk.html drilldown_searches: - - name: View the detection results for '$user$' - search: '%original_detection_search% | search user="$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for "$user$" - search: '| from datamodel Risk.All_Risk - | search normalized_risk_object="$user" - | where _time >= relative_time(now(), "-168h@h") - | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" - values(risk_message) as "Risk Message" - values(analyticstories) as "Analytic Stories" - values(annotations._all) as "Annotations" - values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for '$user$' + search: '%original_detection_search% | search user="$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object="$user" | where _time >= relative_time(now(), "-168h@h") | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ accessed M365 Copilot from multiple geographic locations, indicating potential account compromise or credential sharing. - risk_objects: - - field: user - type: user - score: 10 - threat_objects: [] + message: User $user$ accessed M365 Copilot from multiple geographic locations, indicating potential account compromise or credential sharing. + risk_objects: + - field: user + type: user + score: 10 + threat_objects: [] tags: - analytic_story: - - Suspicious Microsoft 365 Copilot Activities - asset_type: Web Application - mitre_attack_id: - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - Suspicious Microsoft 365 Copilot Activities + asset_type: Web Application + mitre_attack_id: + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/m365_copilot/m365_copilot_access.log - sourcetype: "o365:graph:api" - source: "AuditLogs.SignIns" + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/m365_copilot/m365_copilot_access.log + sourcetype: "o365:graph:api" + source: "AuditLogs.SignIns" diff --git a/detections/application/mcp_filesystem_server_suspicious_extension_write.yml b/detections/application/mcp_filesystem_server_suspicious_extension_write.yml index 4bd95f42c9..daeecec76c 100644 --- a/detections/application/mcp_filesystem_server_suspicious_extension_write.yml +++ b/detections/application/mcp_filesystem_server_suspicious_extension_write.yml @@ -7,57 +7,57 @@ status: production type: Hunting description: This detection identifies attempts to create executable or script files through MCP filesystem server connections. Threat actors leveraging LLM-based tools may attempt to write malicious executables, scripts, or batch files to disk for persistence or code execution. The detection prioritizes files written to system directories or startup locations which indicate higher likelihood of malicious intent. data_source: - - MCP Server + - MCP Server search: | - `mcp_server` method IN ("write_file", "create_file") direction=inbound - | spath output=file_path path=params.path - | spath output=file_content path=params.content - | eval dest=host - | eval file_extension=lower(mvindex(split(file_path, "."), -1)) - | where file_extension IN ( - "exe", "dll", "ps1", "bat", "cmd", "vbs", "js", "scr", "msi", "hta", "wsf", "wsh", "pif", "com", "cpl", - "sh", "bash", "zsh", "ksh", "csh", "tcsh", "fish", - "py", "pl", "rb", "php", "lua", "awk", - "so", "dylib", "bin", "elf", "run", "AppImage", - "deb", "rpm", "pkg", "dmg", - "plist", "service", "timer", "socket", "conf" - ) - | eval - file_path_lower=lower(file_path), - is_system_path = if(match(file_path_lower, "(windows|system32|syswow64|program files|/usr|/bin|/sbin|/lib|/lib64|/etc|/opt)"), 1, 0), - is_startup_path = if(match(file_path_lower, "(startup|autorun|cron\.d|crontab|launchd|launchagents|launchdaemons|systemd|init\.d|rc\.d|rc\.local|profile\.d|bashrc|zshrc|bash_profile)"), 1, 0), - is_hidden_unix = if(match(file_path, "/\.[^/]+$"), 1, 0), - content_length=len(file_content) - | stats count min(_time) as firstTime max(_time) as lastTime values(file_path) as file_paths values(file_extension) as extensions max(is_system_path) as targets_system_path max(is_startup_path) as targets_startup_path max(is_hidden_unix) as targets_hidden_file avg(content_length) as avg_content_size by dest, method - | eval - targets_system_path=if(isnull(targets_system_path), 0, targets_system_path), - targets_startup_path=if(isnull(targets_startup_path), 0, targets_startup_path), - targets_hidden_file=if(isnull(targets_hidden_file), 0, targets_hidden_file) - | sort - targets_startup_path, - targets_system_path, - targets_hidden_file, - count - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | table dest firstTime lastTime count method extensions file_paths targets_system_path targets_startup_path targets_hidden_file avg_content_size - | `mcp_filesystem_server_suspicious_extension_write_filter` + `mcp_server` method IN ("write_file", "create_file") direction=inbound + | spath output=file_path path=params.path + | spath output=file_content path=params.content + | eval dest=host + | eval file_extension=lower(mvindex(split(file_path, "."), -1)) + | where file_extension IN ( + "exe", "dll", "ps1", "bat", "cmd", "vbs", "js", "scr", "msi", "hta", "wsf", "wsh", "pif", "com", "cpl", + "sh", "bash", "zsh", "ksh", "csh", "tcsh", "fish", + "py", "pl", "rb", "php", "lua", "awk", + "so", "dylib", "bin", "elf", "run", "AppImage", + "deb", "rpm", "pkg", "dmg", + "plist", "service", "timer", "socket", "conf" + ) + | eval + file_path_lower=lower(file_path), + is_system_path = if(match(file_path_lower, "(windows|system32|syswow64|program files|/usr|/bin|/sbin|/lib|/lib64|/etc|/opt)"), 1, 0), + is_startup_path = if(match(file_path_lower, "(startup|autorun|cron\.d|crontab|launchd|launchagents|launchdaemons|systemd|init\.d|rc\.d|rc\.local|profile\.d|bashrc|zshrc|bash_profile)"), 1, 0), + is_hidden_unix = if(match(file_path, "/\.[^/]+$"), 1, 0), + content_length=len(file_content) + | stats count min(_time) as firstTime max(_time) as lastTime values(file_path) as file_paths values(file_extension) as extensions max(is_system_path) as targets_system_path max(is_startup_path) as targets_startup_path max(is_hidden_unix) as targets_hidden_file avg(content_length) as avg_content_size by dest, method + | eval + targets_system_path=if(isnull(targets_system_path), 0, targets_system_path), + targets_startup_path=if(isnull(targets_startup_path), 0, targets_startup_path), + targets_hidden_file=if(isnull(targets_hidden_file), 0, targets_hidden_file) + | sort - targets_startup_path, - targets_system_path, - targets_hidden_file, - count + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table dest firstTime lastTime count method extensions file_paths targets_system_path targets_startup_path targets_hidden_file avg_content_size + | `mcp_filesystem_server_suspicious_extension_write_filter` how_to_implement: Install the MCP Technology Add-on from Splunkbase and ensure MCP filesystem server logging is enabled with proper field extraction for params.path and params.content. Schedule the search to run every 5-15 minutes and tune alerting based on whether system or startup paths are targeted. known_false_positives: Legitimate developers using LLM assistants to generate scripts or automation tools, DevOps engineers creating deployment scripts, and system administrators generating batch files for maintenance tasks. references: - - https://splunkbase.splunk.com/app/8377 - - https://cymulate.com/blog/cve-2025-53109-53110-escaperoute-anthropic/ - - https://www.splunk.com/en_us/blog/security/securing-ai-agents-model-context-protocol.html + - https://splunkbase.splunk.com/app/8377 + - https://cymulate.com/blog/cve-2025-53109-53110-escaperoute-anthropic/ + - https://www.splunk.com/en_us/blog/security/securing-ai-agents-model-context-protocol.html tags: - analytic_story: - - Suspicious MCP Activities - asset_type: Web Application - mitre_attack_id: - - T1059 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious MCP Activities + asset_type: Web Application + mitre_attack_id: + - T1059 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/mcp/mcp.log - sourcetype: mcp:jsonrpc - source: mcp.log \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/mcp/mcp.log + sourcetype: mcp:jsonrpc + source: mcp.log diff --git a/detections/application/mcp_github_suspicious_operation.yml b/detections/application/mcp_github_suspicious_operation.yml index 287293e89b..d98c760dbb 100644 --- a/detections/application/mcp_github_suspicious_operation.yml +++ b/detections/application/mcp_github_suspicious_operation.yml @@ -1,62 +1,62 @@ name: MCP Github Suspicious Operation id: 3348aefd-9ed8-451f-9993-1e9fa04b5530 -version: 1 -date: '2026-02-05' +version: 2 +date: '2026-02-25' author: Rod Soto status: production type: Hunting description: This detection identifies potentially malicious activity through MCP GitHub server connections, monitoring for secret hunting in code searches, organization and repository reconnaissance, branch protection abuse, CI/CD workflow manipulation, sensitive file access, and vulnerability intelligence gathering. These patterns indicate potential supply chain attacks, credential harvesting, or pre-attack reconnaissance. data_source: - - MCP Server + - MCP Server search: | - `mcp_server` direction=inbound - | eval dest=host - | eval - query_lower=lower('params.query'), - file_path_lower=lower('params.path'), - search_query='params.query', - file_path='params.path', - target_owner='params.owner', - is_secret_hunting=if(method="search_code" AND (like(query_lower, "%password%") OR like(query_lower, "%api_key%") OR like(query_lower, "%secret%") OR like(query_lower, "%token%") OR like(query_lower, "%aws_%") OR like(query_lower, "%private_key%") OR like(query_lower, "%credential%") OR like(query_lower, "%.env%") OR like(query_lower, "%config%")), 1, 0), - is_org_recon=if(method IN ("list_repositories", "get_repository", "get_organization", "list_organization_members", "get_collaborators", "list_forks", "fork_repository"), 1, 0), - is_branch_protection_abuse=if(method IN ("update_branch_protection", "delete_branch_protection"), 1, 0), - is_workflow_manipulation=if((method IN ("create_or_update_file", "push_files")) AND like(file_path_lower, "%github/workflows%"), 1, 0), - is_sensitive_file_access=if((method IN ("create_or_update_file", "push_files", "get_file_contents")) AND (like(file_path_lower, "%dockerfile%") OR like(file_path_lower, "%package.json%") OR like(file_path_lower, "%requirements.txt%") OR like(file_path_lower, "%.env%") OR like(file_path_lower, "%settings.py%") OR like(file_path_lower, "%config%")), 1, 0), - is_issue_intel=if(method IN ("list_issues", "search_issues") AND (like(query_lower, "%vulnerability%") OR like(query_lower, "%cve%") OR like(query_lower, "%security%") OR like(query_lower, "%exploit%") OR like(query_lower, "%bug%")), 1, 0) - | where is_secret_hunting=1 OR is_org_recon=1 OR is_branch_protection_abuse=1 OR is_workflow_manipulation=1 OR is_sensitive_file_access=1 OR is_issue_intel=1 - | eval attack_type=case( - is_secret_hunting=1, "Secret Hunting", - is_branch_protection_abuse=1, "Branch Protection Abuse", - is_workflow_manipulation=1, "Workflow Manipulation", - is_sensitive_file_access=1, "Sensitive File Access", - is_issue_intel=1, "Vulnerability Intelligence Gathering", - is_org_recon=1, "Organization Reconnaissance", - 1=1, "Unknown") - | stats count min(_time) as firstTime max(_time) as lastTime values(method) as methods values(search_query) as search_queries values(file_path) as file_paths values(target_owner) as target_owners values(attack_type) as attack_types dc(attack_type) as attack_diversity by dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | table dest firstTime lastTime count attack_diversity attack_types methods search_queries file_paths target_owners - | `mcp_github_suspicious_operation_filter` + `mcp_server` direction=inbound + | eval dest=host + | eval + query_lower=lower('params.query'), + file_path_lower=lower('params.path'), + search_query='params.query', + file_path='params.path', + target_owner='params.owner', + is_secret_hunting=if(method="search_code" AND (like(query_lower, "%password%") OR like(query_lower, "%api_key%") OR like(query_lower, "%secret%") OR like(query_lower, "%token%") OR like(query_lower, "%aws_%") OR like(query_lower, "%private_key%") OR like(query_lower, "%credential%") OR like(query_lower, "%.env%") OR like(query_lower, "%config%")), 1, 0), + is_org_recon=if(method IN ("list_repositories", "get_repository", "get_organization", "list_organization_members", "get_collaborators", "list_forks", "fork_repository"), 1, 0), + is_branch_protection_abuse=if(method IN ("update_branch_protection", "delete_branch_protection"), 1, 0), + is_workflow_manipulation=if((method IN ("create_or_update_file", "push_files")) AND like(file_path_lower, "%github/workflows%"), 1, 0), + is_sensitive_file_access=if((method IN ("create_or_update_file", "push_files", "get_file_contents")) AND (like(file_path_lower, "%dockerfile%") OR like(file_path_lower, "%package.json%") OR like(file_path_lower, "%requirements.txt%") OR like(file_path_lower, "%.env%") OR like(file_path_lower, "%settings.py%") OR like(file_path_lower, "%config%")), 1, 0), + is_issue_intel=if(method IN ("list_issues", "search_issues") AND (like(query_lower, "%vulnerability%") OR like(query_lower, "%cve%") OR like(query_lower, "%security%") OR like(query_lower, "%exploit%") OR like(query_lower, "%bug%")), 1, 0) + | where is_secret_hunting=1 OR is_org_recon=1 OR is_branch_protection_abuse=1 OR is_workflow_manipulation=1 OR is_sensitive_file_access=1 OR is_issue_intel=1 + | eval attack_type=case( + is_secret_hunting=1, "Secret Hunting", + is_branch_protection_abuse=1, "Branch Protection Abuse", + is_workflow_manipulation=1, "Workflow Manipulation", + is_sensitive_file_access=1, "Sensitive File Access", + is_issue_intel=1, "Vulnerability Intelligence Gathering", + is_org_recon=1, "Organization Reconnaissance", + 1=1, "Unknown") + | stats count min(_time) as firstTime max(_time) as lastTime values(method) as methods values(search_query) as search_queries values(file_path) as file_paths values(target_owner) as target_owners values(attack_type) as attack_types dc(attack_type) as attack_diversity by dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table dest firstTime lastTime count attack_diversity attack_types methods search_queries file_paths target_owners + | `mcp_github_suspicious_operation_filter` how_to_implement: Install the MCP Technology Add-on from Splunkbase and ensure MCP GitHub server logging is enabled and forwarding to the right index with proper field extraction for params.query, params.path, and params.owner. Schedule the search to run every 5-15 minutes. known_false_positives: Legitimate developers searching code for refactoring purposes, security teams conducting authorized secret scanning, DevOps engineers modifying workflow files, and repository administrators managing branch protection settings. references: -- https://splunkbase.splunk.com/app/8377 -- https://www.docker.com/blog/mcp-horror-stories-github-prompt-injection/ -- https://www.splunk.com/en_us/blog/security/securing-ai-agents-model-context-protocol.html + - https://splunkbase.splunk.com/app/8377 + - https://www.docker.com/blog/mcp-horror-stories-github-prompt-injection/ + - https://www.splunk.com/en_us/blog/security/securing-ai-agents-model-context-protocol.html tags: - analytic_story: - - Suspicious MCP Activities - asset_type: Web Application - mitre_attack_id: - - T1552.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious MCP Activities + asset_type: Web Application + mitre_attack_id: + - T1552.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/mcp/mcp.log - sourcetype: mcp:jsonrpc - source: mcp.log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/mcp/mcp.log + sourcetype: mcp:jsonrpc + source: mcp.log diff --git a/detections/application/mcp_postgres_suspicious_query.yml b/detections/application/mcp_postgres_suspicious_query.yml index 54bb992bbd..d8465e6dc2 100644 --- a/detections/application/mcp_postgres_suspicious_query.yml +++ b/detections/application/mcp_postgres_suspicious_query.yml @@ -1,52 +1,52 @@ name: MCP Postgres Suspicious Query id: 6a168ce8-9a39-4492-9416-a67abdc56c53 -version: 1 -date: '2026-02-05' +version: 2 +date: '2026-02-25' author: Rod Soto status: production type: Hunting description: This detection identifies potentially malicious SQL queries executed through MCP PostgreSQL server connections, monitoring for privilege escalation attempts, credential theft, and schema reconnaissance. These patterns are commonly observed in SQL injection attacks, compromised application credentials, and insider threat scenarios targeting database assets. data_source: - - MCP Server + - MCP Server search: | - `mcp_server` method=query direction=inbound - | eval dest=host - | eval query_lower=lower('params.query') - | eval suspicious_query='params.query' - | eval is_priv_escalation=if(like(query_lower, "%update%users%role%admin%") OR like(query_lower, "%grant%admin%") OR like(query_lower, "%grant%superuser%"), 1, 0) - | eval is_credential_theft=if(like(query_lower, "%password%") OR like(query_lower, "%credential%") OR like(query_lower, "%api_key%") OR like(query_lower, "%secret%"), 1, 0) - | eval is_recon=if(like(query_lower, "%information_schema%") OR like(query_lower, "%pg_catalog%") OR like(query_lower, "%pg_tables%") OR like(query_lower, "%pg_user%"), 1, 0) - | where is_priv_escalation=1 OR is_credential_theft=1 OR is_recon=1 - | eval attack_type=case( - is_priv_escalation=1, "Privilege Escalation", - is_credential_theft=1, "Credential Theft", - is_recon=1, "Schema Reconnaissance", - 1=1, "Unknown") - | stats count min(_time) as firstTime max(_time) as lastTime values(suspicious_query) as suspicious_queries values(attack_type) as attack_types dc(attack_type) as attack_diversity by dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | table dest firstTime lastTime count suspicious_queries attack_types attack_diversity - | `mcp_postgres_suspicious_query_filter` + `mcp_server` method=query direction=inbound + | eval dest=host + | eval query_lower=lower('params.query') + | eval suspicious_query='params.query' + | eval is_priv_escalation=if(like(query_lower, "%update%users%role%admin%") OR like(query_lower, "%grant%admin%") OR like(query_lower, "%grant%superuser%"), 1, 0) + | eval is_credential_theft=if(like(query_lower, "%password%") OR like(query_lower, "%credential%") OR like(query_lower, "%api_key%") OR like(query_lower, "%secret%"), 1, 0) + | eval is_recon=if(like(query_lower, "%information_schema%") OR like(query_lower, "%pg_catalog%") OR like(query_lower, "%pg_tables%") OR like(query_lower, "%pg_user%"), 1, 0) + | where is_priv_escalation=1 OR is_credential_theft=1 OR is_recon=1 + | eval attack_type=case( + is_priv_escalation=1, "Privilege Escalation", + is_credential_theft=1, "Credential Theft", + is_recon=1, "Schema Reconnaissance", + 1=1, "Unknown") + | stats count min(_time) as firstTime max(_time) as lastTime values(suspicious_query) as suspicious_queries values(attack_type) as attack_types dc(attack_type) as attack_diversity by dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table dest firstTime lastTime count suspicious_queries attack_types attack_diversity + | `mcp_postgres_suspicious_query_filter` how_to_implement: Install the MCP Technology Add-on from https://splunkbase.splunk.com/app/8377 and ensure MCP PostgreSQL server logging is enabled and forwarding to the right index with proper params.query field extraction. Schedule the search to run every 5-15 minutes and configure alerting thresholds based on your environment. known_false_positives: Legitimate database administrators performing user management tasks, ORM frameworks querying information_schema for schema validation, password reset functionality, and CI/CD pipelines running database migrations. references: - - https://splunkbase.splunk.com/app/8377 - - https://www.nodejs-security.com/blog/the-tale-of-the-vulnerable-mcp-database-server - - https://www.splunk.com/en_us/blog/security/securing-ai-agents-model-context-protocol.html + - https://splunkbase.splunk.com/app/8377 + - https://www.nodejs-security.com/blog/the-tale-of-the-vulnerable-mcp-database-server + - https://www.splunk.com/en_us/blog/security/securing-ai-agents-model-context-protocol.html tags: - analytic_story: - - Suspicious MCP Activities - asset_type: Web Application - mitre_attack_id: - - T1555 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious MCP Activities + asset_type: Web Application + mitre_attack_id: + - T1555 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/mcp/mcp.log - sourcetype: mcp:jsonrpc - source: mcp.log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/mcp/mcp.log + sourcetype: mcp:jsonrpc + source: mcp.log diff --git a/detections/application/mcp_prompt_injection.yml b/detections/application/mcp_prompt_injection.yml index 3d6a2b532f..669b613f86 100644 --- a/detections/application/mcp_prompt_injection.yml +++ b/detections/application/mcp_prompt_injection.yml @@ -1,68 +1,61 @@ name: MCP Prompt Injection id: 49779398-b738-4d64-bb3f-ead6eb97fe53 -version: 1 -date: '2026-02-04' +version: 2 +date: '2026-02-25' author: Rod Soto status: production type: TTP -description: This detection identifies potential prompt injection attempts within MCP (Model Context Protocol) communications by monitoring for known malicious phrases and patterns commonly used to manipulate AI assistants. Prompt - injection is a critical vulnerability where adversaries embed hidden instructions in content processed by AI tools, attempting to override system prompts, bypass security controls, or hijack the AI's behavior. The search - monitors JSON-RPC traffic for phrases such as "IGNORE PREVIOUS INSTRUCTIONS," "SYSTEM PROMPT OVERRIDE," and "ignore all security" which indicate attempts to subvert the AI's intended behavior and potentially execute - unauthorized actions through the MCP toolchain. +description: This detection identifies potential prompt injection attempts within MCP (Model Context Protocol) communications by monitoring for known malicious phrases and patterns commonly used to manipulate AI assistants. Prompt injection is a critical vulnerability where adversaries embed hidden instructions in content processed by AI tools, attempting to override system prompts, bypass security controls, or hijack the AI's behavior. The search monitors JSON-RPC traffic for phrases such as "IGNORE PREVIOUS INSTRUCTIONS," "SYSTEM PROMPT OVERRIDE," and "ignore all security" which indicate attempts to subvert the AI's intended behavior and potentially execute unauthorized actions through the MCP toolchain. data_source: - - MCP Server + - MCP Server search: | - `mcp_server` direction=inbound ( "IGNORE PREVIOUS INSTRUCTIONS" OR "AI_INSTRUCTION" OR "SYSTEM PROMPT OVERRIDE" OR "[SYSTEM]:" OR "ignore all security" OR "New directive" OR "ignore security policies" ) - | eval dest=host - | eval injection_payload=coalesce('params.content_preview', 'params.result_preview') - | eval target_path='params.path' - | eval sql_query='params.query' - | stats count min(_time) as firstTime max(_time) as lastTime values(method) as method values(target_path) as target_path values(sql_query) as sql_query values(injection_payload) as injection_payload by dest, source - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | table dest firstTime lastTime count source method target_path sql_query injection_payload - | `mcp_prompt_injection_filter` -how_to_implement: This detection requires the MCP Technology Add-on (TA) for Splunk configured to ingest JSON-RPC formatted logs from MCP-enabled AI assistants and agents into the appropiate index with sourcetype mcp:jsonrpc. Ensure the TA parses - multivalue fields including params.content_preview, params.result_preview, params.path, and params.query to capture tool inputs and outputs where injection payloads may appear. The macro mcp_prompt_injection_filter should be - created to allow environment-specific tuning and whitelisting of known benign patterns. -known_false_positives: Known false positives include security research and testing activities where red teams or developers intentionally test prompt injection defenses, as well as educational content where documentation, tutorials, or training - materials discussing prompt injection techniques are legitimately processed by the AI assistant. Additionally, security tool development involving code reviews or development of prompt injection detection mechanisms may - contain these patterns, and quoted references in conversations where users discuss or report prompt injection attempts they encountered elsewhere could trigger this detection. + `mcp_server` direction=inbound ( "IGNORE PREVIOUS INSTRUCTIONS" OR "AI_INSTRUCTION" OR "SYSTEM PROMPT OVERRIDE" OR "[SYSTEM]:" OR "ignore all security" OR "New directive" OR "ignore security policies" ) + | eval dest=host + | eval injection_payload=coalesce('params.content_preview', 'params.result_preview') + | eval target_path='params.path' + | eval sql_query='params.query' + | stats count min(_time) as firstTime max(_time) as lastTime values(method) as method values(target_path) as target_path values(sql_query) as sql_query values(injection_payload) as injection_payload by dest, source + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table dest firstTime lastTime count source method target_path sql_query injection_payload + | `mcp_prompt_injection_filter` +how_to_implement: This detection requires the MCP Technology Add-on (TA) for Splunk configured to ingest JSON-RPC formatted logs from MCP-enabled AI assistants and agents into the appropiate index with sourcetype mcp:jsonrpc. Ensure the TA parses multivalue fields including params.content_preview, params.result_preview, params.path, and params.query to capture tool inputs and outputs where injection payloads may appear. The macro mcp_prompt_injection_filter should be created to allow environment-specific tuning and whitelisting of known benign patterns. +known_false_positives: Known false positives include security research and testing activities where red teams or developers intentionally test prompt injection defenses, as well as educational content where documentation, tutorials, or training materials discussing prompt injection techniques are legitimately processed by the AI assistant. Additionally, security tool development involving code reviews or development of prompt injection detection mechanisms may contain these patterns, and quoted references in conversations where users discuss or report prompt injection attempts they encountered elsewhere could trigger this detection. references: - - https://splunkbase.splunk.com/app/8377 - - https://www.tenable.com/blog/mcp-prompt-injection-not-just-for-evil - - https://www.splunk.com/en_us/blog/security/securing-ai-agents-model-context-protocol.html + - https://splunkbase.splunk.com/app/8377 + - https://www.tenable.com/blog/mcp-prompt-injection-not-just-for-evil + - https://www.splunk.com/en_us/blog/security/securing-ai-agents-model-context-protocol.html drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest="$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object="$dest$" starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest="$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object="$dest$" starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: 'A prompt injection attempt was detected on $dest$ via MCP server. An attacker attempted to override AI instructions using phrases like IGNORE PREVIOUS INSTRUCTIONS or SYSTEM PROMPT OVERRIDE. This technique (AML.T0051) attempts to manipulate the LLM into bypassing security controls or executing unauthorized actions. Payload detected: $injection_payload$' - risk_score: 80 - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: [] + message: 'A prompt injection attempt was detected on $dest$ via MCP server. An attacker attempted to override AI instructions using phrases like IGNORE PREVIOUS INSTRUCTIONS or SYSTEM PROMPT OVERRIDE. This technique (AML.T0051) attempts to manipulate the LLM into bypassing security controls or executing unauthorized actions. Payload detected: $injection_payload$' + risk_score: 80 + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: [] tags: - analytic_story: - - Suspicious MCP Activities - asset_type: Web Application - mitre_attack_id: - - T1059 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious MCP Activities + asset_type: Web Application + mitre_attack_id: + - T1059 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/mcp/mcp.log - sourcetype: mcp:jsonrpc - source: mcp.log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/mcp/mcp.log + sourcetype: mcp:jsonrpc + source: mcp.log diff --git a/detections/application/mcp_sensitive_system_file_search.yml b/detections/application/mcp_sensitive_system_file_search.yml index 41692e8a7d..a432718dab 100644 --- a/detections/application/mcp_sensitive_system_file_search.yml +++ b/detections/application/mcp_sensitive_system_file_search.yml @@ -1,53 +1,51 @@ name: MCP Sensitive System File Search id: 4a57877d-9c56-4a50-9ad2-620e2f0ad821 -version: 1 -date: '2026-02-05' +version: 2 +date: '2026-02-25' author: Rod Soto status: production type: Hunting description: This detection identifies MCP filesystem tool usage attempting to search for files containing sensitive patterns such as passwords, credentials, API keys, secrets, and configuration files. Adversaries and malicious insiders may abuse legitimate MCP filesystem capabilities to conduct reconnaissance and discover sensitive data stores for exfiltration or credential harvesting. data_source: - - MCP Server + - MCP Server search: | - `mcp_server` - (method IN ("read_file", "get_file_contents", "read", "search_files", "find_files", "grep", "search", "list_directory", "read_directory")) - (params.path="*.ssh*" OR params.path="*Administrator*" OR params.path="*credentials*" OR params.path="*password*" OR params.path="*.env*" OR params.path="*id_rsa*" OR params.path="*.pem*" OR params.path="*.ppk*" OR params.path="*.key*" OR params.path="*secrets*" OR params.path="*.aws*" OR params.path="*.config*" - OR params.pattern="*password*" OR params.pattern="*key*" OR params.pattern="*secret*" OR params.pattern="*credential*" OR params.pattern="*token*" OR params.pattern="*auth*" OR params.pattern="*api_key*" OR params.pattern="*private_key*") - | eval dest=host - | eval detection_type=case( - method IN ("read_file", "get_file_contents", "read"), "PATH_ACCESS", - method IN ("search_files", "find_files", "grep", "search"), "PATTERN_SEARCH", - method IN ("list_directory", "read_directory"), "DIRECTORY_ENUM", - 1=1, "UNKNOWN") - | eval target_path=coalesce('params.path', 'params.directory', 'params.file') - | eval search_pattern=coalesce('params.pattern', 'params.query', 'params.search') - | stats count min(_time) as firstTime max(_time) as lastTime values(detection_type) as detection_types values(target_path) as targeted_paths values(search_pattern) as search_patterns values(method) as methods_used by dest, source - | eval time_span_seconds=lastTime-firstTime - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | table dest firstTime lastTime count source detection_types methods_used targeted_paths search_patterns time_span_seconds - | `mcp_sensitive_system_file_search_filter` + `mcp_server` + (method IN ("read_file", "get_file_contents", "read", "search_files", "find_files", "grep", "search", "list_directory", "read_directory")) + (params.path="*.ssh*" OR params.path="*Administrator*" OR params.path="*credentials*" OR params.path="*password*" OR params.path="*.env*" OR params.path="*id_rsa*" OR params.path="*.pem*" OR params.path="*.ppk*" OR params.path="*.key*" OR params.path="*secrets*" OR params.path="*.aws*" OR params.path="*.config*" + OR params.pattern="*password*" OR params.pattern="*key*" OR params.pattern="*secret*" OR params.pattern="*credential*" OR params.pattern="*token*" OR params.pattern="*auth*" OR params.pattern="*api_key*" OR params.pattern="*private_key*") + | eval dest=host + | eval detection_type=case( + method IN ("read_file", "get_file_contents", "read"), "PATH_ACCESS", + method IN ("search_files", "find_files", "grep", "search"), "PATTERN_SEARCH", + method IN ("list_directory", "read_directory"), "DIRECTORY_ENUM", + 1=1, "UNKNOWN") + | eval target_path=coalesce('params.path', 'params.directory', 'params.file') + | eval search_pattern=coalesce('params.pattern', 'params.query', 'params.search') + | stats count min(_time) as firstTime max(_time) as lastTime values(detection_type) as detection_types values(target_path) as targeted_paths values(search_pattern) as search_patterns values(method) as methods_used by dest, source + | eval time_span_seconds=lastTime-firstTime + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table dest firstTime lastTime count source detection_types methods_used targeted_paths search_patterns time_span_seconds + | `mcp_sensitive_system_file_search_filter` how_to_implement: This detection requires the MCP Technology Add-on (TA) for Splunk, which ingests logs from MCP-enabled AI coding assistants and agents. Configure the TA to collect events from MCP servers by pointing it to the appropriate log sources (typically JSON-formatted logs from tools like Claude Code, Cursor, or custom MCP implementations). The TA should normalize file search operations into the search_files method with standardized parameter extraction. -known_false_positives: Known false positives include legitimate development activities where developers search for configuration files, environment variables, or authentication modules as part of normal coding tasks, as well as security audits - involving authorized security reviews or code scanning tools searching for hardcoded secrets. Additionally, documentation lookups for example config files or authentication documentation may trigger this detection, along with - refactoring tasks where developers rename or consolidate credential management code across a codebase, and onboarding activities where new developers explore unfamiliar codebases to understand authentication flows. +known_false_positives: Known false positives include legitimate development activities where developers search for configuration files, environment variables, or authentication modules as part of normal coding tasks, as well as security audits involving authorized security reviews or code scanning tools searching for hardcoded secrets. Additionally, documentation lookups for example config files or authentication documentation may trigger this detection, along with refactoring tasks where developers rename or consolidate credential management code across a codebase, and onboarding activities where new developers explore unfamiliar codebases to understand authentication flows. references: - - https://splunkbase.splunk.com/app/8377 - - https://www.splunk.com/en_us/blog/security/securing-ai-agents-model-context-protocol.html + - https://splunkbase.splunk.com/app/8377 + - https://www.splunk.com/en_us/blog/security/securing-ai-agents-model-context-protocol.html tags: - analytic_story: - - Suspicious MCP Activities - asset_type: Web Application - mitre_attack_id: - - T1552.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious MCP Activities + asset_type: Web Application + mitre_attack_id: + - T1552.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/mcp/mcp.log - sourcetype: mcp:jsonrpc - source: mcp.log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/mcp/mcp.log + sourcetype: mcp:jsonrpc + source: mcp.log diff --git a/detections/application/monitor_email_for_brand_abuse.yml b/detections/application/monitor_email_for_brand_abuse.yml index bdd1a6e84c..0df71d5008 100644 --- a/detections/application/monitor_email_for_brand_abuse.yml +++ b/detections/application/monitor_email_for_brand_abuse.yml @@ -1,46 +1,42 @@ name: Monitor Email For Brand Abuse id: b2ea1f38-3a3e-4b8a-9cf1-82760d86a6b8 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: David Dorsey, Splunk status: experimental type: TTP -description: The following analytic identifies emails claiming to be sent from a domain - similar to one you are monitoring for potential abuse. It leverages email header - data, specifically the sender's address, and cross-references it with a lookup table - of known domain permutations generated by the "ESCU - DNSTwist Domain Names" search. - This activity is significant as it can indicate phishing attempts or brand impersonation, - which are common tactics used in social engineering attacks. If confirmed malicious, - this could lead to unauthorized access, data theft, or reputational damage. +description: The following analytic identifies emails claiming to be sent from a domain similar to one you are monitoring for potential abuse. It leverages email header data, specifically the sender's address, and cross-references it with a lookup table of known domain permutations generated by the "ESCU - DNSTwist Domain Names" search. This activity is significant as it can indicate phishing attempts or brand impersonation, which are common tactics used in social engineering attacks. If confirmed malicious, this could lead to unauthorized access, data theft, or reputational damage. data_source: [] -search: '| tstats `security_content_summariesonly` values(All_Email.recipient) as - recipients, min(_time) as firstTime, max(_time) as lastTime from datamodel=Email - by All_Email.src_user, All_Email.message_id | `drop_dm_object_name("All_Email")` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval - temp=split(src_user, "@") | eval email_domain=mvindex(temp, 1) | lookup update=true - brandMonitoring_lookup domain as email_domain OUTPUT domain_abuse | search domain_abuse=true - | table message_id, src_user, email_domain, recipients, firstTime, lastTime | `monitor_email_for_brand_abuse_filter`' -how_to_implement: You need to ingest email header data. Specifically the sender's - address (src_user) must be populated. You also need to have run the search "ESCU - - DNSTwist Domain Names", which creates the permutations of the domain that will - be checked for. +search: |- + | tstats `security_content_summariesonly` values(All_Email.recipient) as recipients, min(_time) as firstTime, max(_time) as lastTime FROM datamodel=Email + BY All_Email.src_user, All_Email.message_id + | `drop_dm_object_name("All_Email")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | eval temp=split(src_user, "@") + | eval email_domain=mvindex(temp, 1) + | lookup update=true brandMonitoring_lookup domain as email_domain OUTPUT domain_abuse + | search domain_abuse=true + | table message_id, src_user, email_domain, recipients, firstTime, lastTime + | `monitor_email_for_brand_abuse_filter` +how_to_implement: You need to ingest email header data. Specifically the sender's address (src_user) must be populated. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. known_false_positives: No false positives have been identified at this time. references: [] rba: - message: Possible Brand Abuse from $email_domain$ - risk_objects: - - field: src_user - type: user - score: 25 - threat_objects: [] + message: Possible Brand Abuse from $email_domain$ + risk_objects: + - field: src_user + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Brand Monitoring - - Suspicious Emails - - Scattered Lapsus$ Hunters - asset_type: Endpoint - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Brand Monitoring + - Suspicious Emails + - Scattered Lapsus$ Hunters + asset_type: Endpoint + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/application/no_windows_updates_in_a_time_frame.yml b/detections/application/no_windows_updates_in_a_time_frame.yml index 11899be6c0..0bcd9d2d54 100644 --- a/detections/application/no_windows_updates_in_a_time_frame.yml +++ b/detections/application/no_windows_updates_in_a_time_frame.yml @@ -1,40 +1,34 @@ name: No Windows Updates in a time frame id: 1a77c08c-2f56-409c-a2d3-7d64617edd4f -version: 6 -date: '2026-01-14' +version: 7 +date: '2026-02-25' author: Bhavin Patel, Splunk status: experimental type: Hunting -description: The following analytic identifies Windows endpoints that have not generated - an event indicating a successful Windows update in the last 60 days. It leverages - the 'Update' data model in Splunk, specifically looking for the latest 'Installed' - status events from Microsoft Windows. This activity is significant for a SOC because - endpoints that are not regularly patched are vulnerable to known exploits and security - vulnerabilities. If confirmed malicious, this could indicate a compromised endpoint - that is intentionally being kept unpatched, potentially allowing attackers to exploit - unpatched vulnerabilities and gain unauthorized access or control. +description: The following analytic identifies Windows endpoints that have not generated an event indicating a successful Windows update in the last 60 days. It leverages the 'Update' data model in Splunk, specifically looking for the latest 'Installed' status events from Microsoft Windows. This activity is significant for a SOC because endpoints that are not regularly patched are vulnerable to known exploits and security vulnerabilities. If confirmed malicious, this could indicate a compromised endpoint that is intentionally being kept unpatched, potentially allowing attackers to exploit unpatched vulnerabilities and gain unauthorized access or control. data_source: [] -search: '| tstats `security_content_summariesonly` max(_time) as lastTime from datamodel=Updates - where Updates.status=Installed Updates.vendor_product="Microsoft Windows" by Updates.dest - Updates.status Updates.vendor_product | rename Updates.dest as Host | rename Updates.status - as "Update Status" | rename Updates.vendor_product as Product | eval isOutlier=if(lastTime - <= relative_time(now(), "-60d@d"), 1, 0) | `security_content_ctime(lastTime)` | - search isOutlier=1 | rename lastTime as "Last Update Time", | table Host, "Update - Status", Product, "Last Update Time" | `no_windows_updates_in_a_time_frame_filter`' -how_to_implement: To successfully implement this search, it requires that the 'Update' - data model is being populated. This can be accomplished by ingesting Windows events - or the Windows Update log via a universal forwarder on the Windows endpoints you - wish to monitor. The Windows add-on should be also be installed and configured to - properly parse Windows events in Splunk. There may be other data sources which can - populate this data model, including vulnerability management systems. +search: |- + | tstats `security_content_summariesonly` max(_time) as lastTime FROM datamodel=Updates + WHERE Updates.status=Installed Updates.vendor_product="Microsoft Windows" + BY Updates.dest Updates.status Updates.vendor_product + | rename Updates.dest as Host + | rename Updates.status as "Update Status" + | rename Updates.vendor_product as Product + | eval isOutlier=if(lastTime <= relative_time(now(), "-60d@d"), 1, 0) + | `security_content_ctime(lastTime)` + | search isOutlier=1 + | rename lastTime as "Last Update Time", + | table Host, "Update Status", Product, "Last Update Time" + | `no_windows_updates_in_a_time_frame_filter` +how_to_implement: To successfully implement this search, it requires that the 'Update' data model is being populated. This can be accomplished by ingesting Windows events or the Windows Update log via a universal forwarder on the Windows endpoints you wish to monitor. The Windows add-on should be also be installed and configured to properly parse Windows events in Splunk. There may be other data sources which can populate this data model, including vulnerability management systems. known_false_positives: No false positives have been identified at this time. references: [] tags: - analytic_story: - - Monitor for Updates - asset_type: Endpoint - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Monitor for Updates + asset_type: Endpoint + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/application/okta_authentication_failed_during_mfa_challenge.yml b/detections/application/okta_authentication_failed_during_mfa_challenge.yml index f0b58f0869..d5921b2139 100644 --- a/detections/application/okta_authentication_failed_during_mfa_challenge.yml +++ b/detections/application/okta_authentication_failed_during_mfa_challenge.yml @@ -1,77 +1,63 @@ name: Okta Authentication Failed During MFA Challenge id: e2b99e7d-d956-411a-a120-2b14adfdde93 -version: 7 -date: '2025-10-14' +version: 8 +date: '2026-02-25' author: Bhavin Patel, Splunk data_source: -- Okta + - Okta type: TTP status: production -description: The following analytic identifies failed authentication attempts during - the Multi-Factor Authentication (MFA) challenge in an Okta tenant. It uses the Authentication - datamodel to detect specific failed events where the authentication signature is - `user.authentication.auth_via_mfa`. This activity is significant as it may indicate - an adversary attempting to authenticate with compromised credentials on an account - with MFA enabled. If confirmed malicious, this could suggest an ongoing attempt - to bypass MFA protections, potentially leading to unauthorized access and further - compromise of the affected account. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime values(Authentication.app) as app values(Authentication.reason) as - reason values(Authentication.signature) as signature values(Authentication.method) - as method from datamodel=Authentication where Authentication.signature=user.authentication.auth_via_mfa - Authentication.action = failure by _time Authentication.src Authentication.user - Authentication.dest Authentication.action | `drop_dm_object_name("Authentication")` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| iplocation - src | `okta_authentication_failed_during_mfa_challenge_filter`' -how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the - Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). -known_false_positives: A user may have accidentally entered the wrong credentials - during the MFA challenge. If the user is new to MFA, they may have trouble authenticating. - Ensure that the user is aware of the MFA process and has the correct credentials. +description: The following analytic identifies failed authentication attempts during the Multi-Factor Authentication (MFA) challenge in an Okta tenant. It uses the Authentication datamodel to detect specific failed events where the authentication signature is `user.authentication.auth_via_mfa`. This activity is significant as it may indicate an adversary attempting to authenticate with compromised credentials on an account with MFA enabled. If confirmed malicious, this could suggest an ongoing attempt to bypass MFA protections, potentially leading to unauthorized access and further compromise of the affected account. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Authentication.app) as app values(Authentication.reason) as reason values(Authentication.signature) as signature values(Authentication.method) as method FROM datamodel=Authentication + WHERE Authentication.signature=user.authentication.auth_via_mfa Authentication.action = failure + BY _time Authentication.src Authentication.user + Authentication.dest Authentication.action + | `drop_dm_object_name("Authentication")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | iplocation src + | `okta_authentication_failed_during_mfa_challenge_filter` +how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). +known_false_positives: A user may have accidentally entered the wrong credentials during the MFA challenge. If the user is new to MFA, they may have trouble authenticating. Ensure that the user is aware of the MFA process and has the correct credentials. references: -- https://sec.okta.com/everythingisyes -- https://splunkbase.splunk.com/app/6553 + - https://sec.okta.com/everythingisyes + - https://splunkbase.splunk.com/app/6553 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A user [$user$] has failed to authenticate via MFA from IP Address - [$src$]" - risk_objects: - - field: user - type: user - score: 48 - threat_objects: - - field: src - type: ip_address + message: A user [$user$] has failed to authenticate via MFA from IP Address - [$src$]" + risk_objects: + - field: user + type: user + score: 48 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Okta Account Takeover - - Scattered Lapsus$ Hunters - asset_type: Okta Tenant - mitre_attack_id: - - T1078.004 - - T1586.003 - - T1621 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Okta Account Takeover + - Scattered Lapsus$ Hunters + asset_type: Okta Tenant + mitre_attack_id: + - T1078.004 + - T1586.003 + - T1621 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/okta_mfa_login_failed/okta_mfa_login_failed.log - source: okta_log - sourcetype: OktaIM2:log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/okta_mfa_login_failed/okta_mfa_login_failed.log + source: okta_log + sourcetype: OktaIM2:log diff --git a/detections/application/okta_idp_lifecycle_modifications.yml b/detections/application/okta_idp_lifecycle_modifications.yml index ed4433b81c..a73f405327 100644 --- a/detections/application/okta_idp_lifecycle_modifications.yml +++ b/detections/application/okta_idp_lifecycle_modifications.yml @@ -1,74 +1,59 @@ name: Okta IDP Lifecycle Modifications id: e0be2c83-5526-4219-a14f-c3db2e763d15 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Bhavin Patel, Splunk data_source: -- Okta + - Okta type: Anomaly status: production -description: The following analytic identifies modifications to Okta Identity Provider - (IDP) lifecycle events, including creation, activation, deactivation, and deletion - of IDP configurations. It uses OktaIm2 logs ingested via the Splunk Add-on for Okta - Identity Cloud. Monitoring these events is crucial for maintaining the integrity - and security of authentication mechanisms. Unauthorized or anomalous changes could - indicate potential security breaches or misconfigurations. If confirmed malicious, - attackers could manipulate authentication processes, potentially gaining unauthorized - access or disrupting identity management systems. -search: '`okta` eventType IN ("system.idp.lifecycle.activate","system.idp.lifecycle.create","system.idp.lifecycle.delete","system.idp.lifecycle.deactivate") - | stats count min(_time) as firstTime max(_time) as lastTime values(target{}.id) - as target_id values(target{}.type) as target_modified by src dest src_user_id user - user_agent command description | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `okta_idp_lifecycle_modifications_filter`' -how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the - Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). -known_false_positives: It's possible for legitimate administrative actions or automated - processes to trigger this detection, especially if there are bulk modifications - to Okta IDP lifecycle events. Review the context of the modification, such as the - user making the change and the specific lifecycle event modified, to determine if - it aligns with expected behavior. +description: The following analytic identifies modifications to Okta Identity Provider (IDP) lifecycle events, including creation, activation, deactivation, and deletion of IDP configurations. It uses OktaIm2 logs ingested via the Splunk Add-on for Okta Identity Cloud. Monitoring these events is crucial for maintaining the integrity and security of authentication mechanisms. Unauthorized or anomalous changes could indicate potential security breaches or misconfigurations. If confirmed malicious, attackers could manipulate authentication processes, potentially gaining unauthorized access or disrupting identity management systems. +search: |- + `okta` eventType IN ("system.idp.lifecycle.activate","system.idp.lifecycle.create","system.idp.lifecycle.delete","system.idp.lifecycle.deactivate") + | stats count min(_time) as firstTime max(_time) as lastTime values(target{}.id) as target_id values(target{}.type) as target_modified + BY src dest src_user_id + user user_agent command + description + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `okta_idp_lifecycle_modifications_filter` +how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). +known_false_positives: It's possible for legitimate administrative actions or automated processes to trigger this detection, especially if there are bulk modifications to Okta IDP lifecycle events. Review the context of the modification, such as the user making the change and the specific lifecycle event modified, to determine if it aligns with expected behavior. references: -- https://www.obsidiansecurity.com/blog/behind-the-breach-cross-tenant-impersonation-in-okta/ -- https://splunkbase.splunk.com/app/6553 + - https://www.obsidiansecurity.com/blog/behind-the-breach-cross-tenant-impersonation-in-okta/ + - https://splunkbase.splunk.com/app/6553 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A user [$user$] is attempting IDP lifecycle modification - [$description$] - from IP Address - [$src$]" - risk_objects: - - field: user - type: user - score: 81 - threat_objects: - - field: src - type: ip_address + message: A user [$user$] is attempting IDP lifecycle modification - [$description$] from IP Address - [$src$]" + risk_objects: + - field: user + type: user + score: 81 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Suspicious Okta Activity - asset_type: Okta Tenant - mitre_attack_id: - - T1087.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Suspicious Okta Activity + asset_type: Okta Tenant + mitre_attack_id: + - T1087.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/okta_idp/okta.log - source: Okta - sourcetype: OktaIM2:log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/okta_idp/okta.log + source: Okta + sourcetype: OktaIM2:log diff --git a/detections/application/okta_mfa_exhaustion_hunt.yml b/detections/application/okta_mfa_exhaustion_hunt.yml index a27431bd48..cefa62011b 100644 --- a/detections/application/okta_mfa_exhaustion_hunt.yml +++ b/detections/application/okta_mfa_exhaustion_hunt.yml @@ -1,58 +1,50 @@ name: Okta MFA Exhaustion Hunt id: 97e2fe57-3740-402c-988a-76b64ce04b8d -version: 7 -date: '2025-10-14' +version: 8 +date: '2026-02-25' author: Michael Haag, Marissa Bower, Mauricio Velazco, Splunk status: production type: Hunting -description: The following analytic detects patterns of successful and failed Okta - MFA push attempts to identify potential MFA exhaustion attacks. It leverages Okta - event logs, specifically focusing on push verification events, and uses statistical - evaluations to determine suspicious activity. This activity is significant as it - may indicate an attacker attempting to bypass MFA by overwhelming the user with - push notifications. If confirmed malicious, this could lead to unauthorized access, - compromising the security of the affected accounts and potentially the entire environment. +description: The following analytic detects patterns of successful and failed Okta MFA push attempts to identify potential MFA exhaustion attacks. It leverages Okta event logs, specifically focusing on push verification events, and uses statistical evaluations to determine suspicious activity. This activity is significant as it may indicate an attacker attempting to bypass MFA by overwhelming the user with push notifications. If confirmed malicious, this could lead to unauthorized access, compromising the security of the affected accounts and potentially the entire environment. data_source: -- Okta -search: '`okta` eventType=system.push.send_factor_verify_push OR ((legacyEventType=core.user.factor.attempt_success) - AND (debugContext.debugData.factor=OKTA_VERIFY_PUSH)) OR ((legacyEventType=core.user.factor.attempt_fail) - AND (debugContext.debugData.factor=OKTA_VERIFY_PUSH)) | stats count(eval(legacyEventType="core.user.factor.attempt_success")) as - successes count(eval(legacyEventType="core.user.factor.attempt_fail")) as failures - count(eval(eventType="system.push.send_factor_verify_push")) as pushes by user,_time - | stats latest(_time) as lasttime earliest(_time) as firsttime sum(successes) as - successes sum(failures) as failures sum(pushes) as pushes by user | eval seconds=lasttime-firsttime - | eval lasttime=strftime(lasttime, "%c") | search (pushes>1) | eval totalattempts=successes+failures - | eval finding="Normal authentication pattern" | eval finding=if(failures==pushes - AND pushes>1,"Authentication attempts not successful because multiple pushes denied",finding) - | eval finding=if(totalattempts==0,"Multiple pushes sent and ignored",finding) | - eval finding=if(successes>0 AND pushes>3,"Probably should investigate. Multiple - pushes sent, eventual successful authentication!",finding) | `okta_mfa_exhaustion_hunt_filter`' -how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the - Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). -known_false_positives: False positives may be present. Tune Okta and tune the analytic - to ensure proper fidelity. Modify risk score as needed. Drop to anomaly until tuning - is complete. + - Okta +search: |- + `okta` eventType=system.push.send_factor_verify_push OR ((legacyEventType=core.user.factor.attempt_success) AND (debugContext.debugData.factor=OKTA_VERIFY_PUSH)) OR ((legacyEventType=core.user.factor.attempt_fail) AND (debugContext.debugData.factor=OKTA_VERIFY_PUSH)) + | stats count(eval(legacyEventType="core.user.factor.attempt_success")) as successes count(eval(legacyEventType="core.user.factor.attempt_fail")) as failures count(eval(eventType="system.push.send_factor_verify_push")) as pushes + BY user,_time + | stats latest(_time) as lasttime earliest(_time) as firsttime sum(successes) as successes sum(failures) as failures sum(pushes) as pushes + BY user + | eval seconds=lasttime-firsttime + | eval lasttime=strftime(lasttime, "%c") + | search (pushes>1) + | eval totalattempts=successes+failures + | eval finding="Normal authentication pattern" + | eval finding=if(failures==pushes AND pushes>1,"Authentication attempts not successful because multiple pushes denied",finding) + | eval finding=if(totalattempts==0,"Multiple pushes sent and ignored",finding) + | eval finding=if(successes>0 AND pushes>3,"Probably should investigate. Multiple pushes sent, eventual successful authentication!",finding) + | `okta_mfa_exhaustion_hunt_filter` +how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). +known_false_positives: False positives may be present. Tune Okta and tune the analytic to ensure proper fidelity. Modify risk score as needed. Drop to anomaly until tuning is complete. references: -- https://developer.okta.com/docs/reference/api/event-types/?q=user.acount.lock -- https://sec.okta.com/everythingisyes -- https://splunkbase.splunk.com/app/6553 + - https://developer.okta.com/docs/reference/api/event-types/?q=user.acount.lock + - https://sec.okta.com/everythingisyes + - https://splunkbase.splunk.com/app/6553 tags: - analytic_story: - - Okta Account Takeover - - Okta MFA Exhaustion - - Scattered Lapsus$ Hunters - asset_type: Okta Tenant - mitre_attack_id: - - T1110 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - Okta Account Takeover + - Okta MFA Exhaustion + - Scattered Lapsus$ Hunters + asset_type: Okta Tenant + mitre_attack_id: + - T1110 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/okta_multiple_failed_mfa_pushes/okta_multiple_failed_mfa_pushes.log - source: Okta - sourcetype: OktaIM2:log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/okta_multiple_failed_mfa_pushes/okta_multiple_failed_mfa_pushes.log + source: Okta + sourcetype: OktaIM2:log diff --git a/detections/application/okta_mismatch_between_source_and_response_for_verify_push_request.yml b/detections/application/okta_mismatch_between_source_and_response_for_verify_push_request.yml index 8e6f9cecc8..fa12e4bb5d 100644 --- a/detections/application/okta_mismatch_between_source_and_response_for_verify_push_request.yml +++ b/detections/application/okta_mismatch_between_source_and_response_for_verify_push_request.yml @@ -1,88 +1,69 @@ name: Okta Mismatch Between Source and Response for Verify Push Request id: 8085b79b-9b85-4e67-ad63-351c9e9a5e9a -version: 7 -date: '2025-10-14' +version: 8 +date: '2026-02-25' author: John Murphy and Jordan Ruocco, Okta, Michael Haag, Bhavin Patel, Splunk type: TTP status: production data_source: -- Okta -description: The following analytic identifies discrepancies between the source and - response events for Okta Verify Push requests, indicating potential suspicious behavior. - It leverages Okta System Log events, specifically `system.push.send_factor_verify_push` - and `user.authentication.auth_via_mfa` with the factor "OKTA_VERIFY_PUSH." The detection - groups events by SessionID, calculates the ratio of successful sign-ins to push - requests, and checks for session roaming and new device/IP usage. This activity - is significant as it may indicate push spam or unauthorized access attempts. If - confirmed malicious, attackers could bypass MFA, leading to unauthorized access - to sensitive systems. -search: '`okta` eventType IN (system.push.send_factor_verify_push) OR (eventType IN - (user.authentication.auth_via_mfa) debugContext.debugData.factor="OKTA_VERIFY_PUSH") - | eval groupby="authenticationContext.externalSessionId" | eval group_push_time=_time - | bin span=2s group_push_time | fillnull value=NULL | stats min(_time) as _time - by authenticationContext.externalSessionId eventType debugContext.debugData.factor - outcome.result actor.alternateId client.device client.ipAddress client.userAgent.rawUserAgent - debugContext.debugData.behaviors group_push_time | iplocation client.ipAddress | - fields - lat, lon, group_push_time | stats min(_time) as _time dc(client.ipAddress) - as dc_ip sum(eval(if(eventType="system.push.send_factor_verify_push" AND $outcome.result$="SUCCESS", - 1, 0))) as total_pushes sum(eval(if(eventType="user.authentication.auth_via_mfa" - AND $outcome.result$="SUCCESS", 1, 0))) as total_successes sum(eval(if(eventType="user.authentication.auth_via_mfa" - AND $outcome.result$="FAILURE", 1, 0))) as total_rejected sum(eval(if(eventType="system.push.send_factor_verify_push" - AND $debugContext.debugData.behaviors$ LIKE "%New Device=POSITIVE%", 1, 0))) as - suspect_device_from_source sum(eval(if(eventType="system.push.send_factor_verify_push" - AND $debugContext.debugData.behaviors$ LIKE "%New IP=POSITIVE%", 1, 0))) as suspect_ip_from_source - values(eval(if(eventType="system.push.send_factor_verify_push", $client.ipAddress$, - ""))) as src values(eval(if(eventType="user.authentication.auth_via_mfa", $client.ipAddress$, - ""))) as dest values(*) as * by authenticationContext.externalSessionId | eval ratio - = round(total_successes / total_pushes, 2) | search ((ratio < 0.5 AND total_pushes - > 1) OR (total_rejected > 0)) AND dc_ip > 1 AND suspect_device_from_source > 0 AND - suspect_ip_from_source > 0 |rename actor.alternateId as user | `okta_mismatch_between_source_and_response_for_verify_push_request_filter`' -how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the - Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). -known_false_positives: False positives may be present based on organization size and - configuration of Okta. Monitor, tune and filter as needed. + - Okta +description: The following analytic identifies discrepancies between the source and response events for Okta Verify Push requests, indicating potential suspicious behavior. It leverages Okta System Log events, specifically `system.push.send_factor_verify_push` and `user.authentication.auth_via_mfa` with the factor "OKTA_VERIFY_PUSH." The detection groups events by SessionID, calculates the ratio of successful sign-ins to push requests, and checks for session roaming and new device/IP usage. This activity is significant as it may indicate push spam or unauthorized access attempts. If confirmed malicious, attackers could bypass MFA, leading to unauthorized access to sensitive systems. +search: |- + `okta` eventType IN (system.push.send_factor_verify_push) OR (eventType IN (user.authentication.auth_via_mfa) debugContext.debugData.factor="OKTA_VERIFY_PUSH") + | eval groupby="authenticationContext.externalSessionId" + | eval group_push_time=_time + | bin span=2s group_push_time + | fillnull value=NULL + | stats min(_time) as _time + BY authenticationContext.externalSessionId eventType debugContext.debugData.factor + outcome.result actor.alternateId client.device + client.ipAddress client.userAgent.rawUserAgent debugContext.debugData.behaviors + group_push_time + | iplocation client.ipAddress + | fields - lat, lon, group_push_time + | stats min(_time) as _time dc(client.ipAddress) as dc_ip sum(eval(if(eventType="system.push.send_factor_verify_push" AND $outcome.result$="SUCCESS", 1, 0))) as total_pushes sum(eval(if(eventType="user.authentication.auth_via_mfa" AND $outcome.result$="SUCCESS", 1, 0))) as total_successes sum(eval(if(eventType="user.authentication.auth_via_mfa" AND $outcome.result$="FAILURE", 1, 0))) as total_rejected sum(eval(if(eventType="system.push.send_factor_verify_push" AND $debugContext.debugData.behaviors$ LIKE "%New Device=POSITIVE%", 1, 0))) as suspect_device_from_source sum(eval(if(eventType="system.push.send_factor_verify_push" AND $debugContext.debugData.behaviors$ LIKE "%New IP=POSITIVE%", 1, 0))) as suspect_ip_from_source values(eval(if(eventType="system.push.send_factor_verify_push", $client.ipAddress$, ""))) as src values(eval(if(eventType="user.authentication.auth_via_mfa", $client.ipAddress$, ""))) as dest values(*) as * + BY authenticationContext.externalSessionId + | eval ratio = round(total_successes / total_pushes, 2) + | search ((ratio < 0.5 AND total_pushes > 1) OR (total_rejected > 0)) AND dc_ip > 1 AND suspect_device_from_source > 0 AND suspect_ip_from_source > 0 + | rename actor.alternateId as user + | `okta_mismatch_between_source_and_response_for_verify_push_request_filter` +how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). +known_false_positives: False positives may be present based on organization size and configuration of Okta. Monitor, tune and filter as needed. drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ references: -- https://attack.mitre.org/techniques/T1621 -- https://splunkbase.splunk.com/app/6553 + - https://attack.mitre.org/techniques/T1621 + - https://splunkbase.splunk.com/app/6553 rba: - message: A mismatch between source and response for verifying a push request has - occurred for $user$ - risk_objects: - - field: user - type: user - score: 64 - threat_objects: [] + message: A mismatch between source and response for verifying a push request has occurred for $user$ + risk_objects: + - field: user + type: user + score: 64 + threat_objects: [] tags: - analytic_story: - - Okta Account Takeover - - Okta MFA Exhaustion - - Scattered Lapsus$ Hunters - asset_type: Okta Tenant - mitre_attack_id: - - T1621 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - Okta Account Takeover + - Okta MFA Exhaustion + - Scattered Lapsus$ Hunters + asset_type: Okta Tenant + mitre_attack_id: + - T1621 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/okta_mismatch/okta_mismatch.log - source: Okta - sourcetype: OktaIM2:log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/okta_mismatch/okta_mismatch.log + source: Okta + sourcetype: OktaIM2:log diff --git a/detections/application/okta_multi_factor_authentication_disabled.yml b/detections/application/okta_multi_factor_authentication_disabled.yml index 088d11c562..07ea53f105 100644 --- a/detections/application/okta_multi_factor_authentication_disabled.yml +++ b/detections/application/okta_multi_factor_authentication_disabled.yml @@ -1,72 +1,62 @@ name: Okta Multi-Factor Authentication Disabled id: 7c0348ce-bdf9-45f6-8a57-c18b5976f00a -version: 9 -date: '2025-10-14' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk data_source: -- Okta + - Okta type: TTP status: production -description: The following analytic identifies an attempt to disable multi-factor - authentication (MFA) for an Okta user. It leverages OktaIM2 logs to detect when - the 'user.mfa.factor.deactivate' command is executed. This activity is significant - because disabling MFA can allow an adversary to maintain persistence within the - environment using a compromised valid account. If confirmed malicious, this action - could enable attackers to bypass additional security layers, potentially leading - to unauthorized access to sensitive information and prolonged undetected presence - in the network. -search: '| tstats `security_content_summariesonly` count max(_time) as lastTime, min(_time) - as firstTime from datamodel=Change where sourcetype="OktaIM2:log" All_Changes.object_category=User - AND All_Changes.action=modified All_Changes.command=user.mfa.factor.deactivate by - All_Changes.user All_Changes.result All_Changes.command sourcetype All_Changes.src - All_Changes.dest | `drop_dm_object_name("All_Changes")` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `okta_multi_factor_authentication_disabled_filter`' -how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the - Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). -known_false_positives: Legitimate use case may require for users to disable MFA. Filter - lightly and monitor for any unusual activity. +description: The following analytic identifies an attempt to disable multi-factor authentication (MFA) for an Okta user. It leverages OktaIM2 logs to detect when the 'user.mfa.factor.deactivate' command is executed. This activity is significant because disabling MFA can allow an adversary to maintain persistence within the environment using a compromised valid account. If confirmed malicious, this action could enable attackers to bypass additional security layers, potentially leading to unauthorized access to sensitive information and prolonged undetected presence in the network. +search: |- + | tstats `security_content_summariesonly` count max(_time) as lastTime, min(_time) as firstTime FROM datamodel=Change + WHERE sourcetype="OktaIM2:log" All_Changes.object_category=User + AND + All_Changes.action=modified All_Changes.command=user.mfa.factor.deactivate + BY All_Changes.user All_Changes.result All_Changes.command + sourcetype All_Changes.src All_Changes.dest + | `drop_dm_object_name("All_Changes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `okta_multi_factor_authentication_disabled_filter` +how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). +known_false_positives: Legitimate use case may require for users to disable MFA. Filter lightly and monitor for any unusual activity. references: -- https://attack.mitre.org/techniques/T1556/ -- https://splunkbase.splunk.com/app/6553 + - https://attack.mitre.org/techniques/T1556/ + - https://splunkbase.splunk.com/app/6553 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: MFA was disabled for User [$user$] initiated by [$src$]. Investigate further - to determine if this was authorized. - risk_objects: - - field: user - type: user - score: 30 - threat_objects: - - field: src - type: ip_address + message: MFA was disabled for User [$user$] initiated by [$src$]. Investigate further to determine if this was authorized. + risk_objects: + - field: user + type: user + score: 30 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Okta Account Takeover - - Scattered Lapsus$ Hunters - asset_type: Okta Tenant - mitre_attack_id: - - T1556.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Okta Account Takeover + - Scattered Lapsus$ Hunters + asset_type: Okta Tenant + mitre_attack_id: + - T1556.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556.006/okta_mfa_method_disabled/okta_mfa_method_disabled.log - source: Okta - sourcetype: OktaIM2:log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556.006/okta_mfa_method_disabled/okta_mfa_method_disabled.log + source: Okta + sourcetype: OktaIM2:log diff --git a/detections/application/okta_multiple_accounts_locked_out.yml b/detections/application/okta_multiple_accounts_locked_out.yml index 34a3dd331a..498d01c3e5 100644 --- a/detections/application/okta_multiple_accounts_locked_out.yml +++ b/detections/application/okta_multiple_accounts_locked_out.yml @@ -1,70 +1,65 @@ name: Okta Multiple Accounts Locked Out id: a511426e-184f-4de6-8711-cfd2af29d1e1 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Mauricio Velazco, Splunk data_source: -- Okta + - Okta type: Anomaly status: production -description: The following analytic detects multiple Okta accounts being locked out - within a short period. It uses the user.account.lock event from Okta logs, aggregated - over a 5-minute window, to identify this behavior. This activity is significant - as it may indicate a brute force or password spraying attack, where an adversary - attempts to guess passwords, leading to account lockouts. If confirmed malicious, - this could result in potential account takeovers or unauthorized access to sensitive - Okta accounts, posing a significant security risk. -search: '| tstats `security_content_summariesonly` count max(_time) as lastTime, min(_time) - as firstTime values(All_Changes.user) as user from datamodel=Change where All_Changes.change_type=AAA - All_Changes.object_category=User AND All_Changes.action=lockout AND All_Changes.command=user.account.lock - by _time span=5m All_Changes.result All_Changes.command sourcetype All_Changes.src - All_Changes.dest | where count > 5 | `drop_dm_object_name("All_Changes")` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `okta_multiple_accounts_locked_out_filter`' -how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the - Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). -known_false_positives: Multiple account lockouts may be also triggered by an application - malfunction. Filter as needed, and monitor for any unusual activity. +description: The following analytic detects multiple Okta accounts being locked out within a short period. It uses the user.account.lock event from Okta logs, aggregated over a 5-minute window, to identify this behavior. This activity is significant as it may indicate a brute force or password spraying attack, where an adversary attempts to guess passwords, leading to account lockouts. If confirmed malicious, this could result in potential account takeovers or unauthorized access to sensitive Okta accounts, posing a significant security risk. +search: |- + | tstats `security_content_summariesonly` count max(_time) as lastTime, min(_time) as firstTime values(All_Changes.user) as user FROM datamodel=Change + WHERE All_Changes.change_type=AAA All_Changes.object_category=User + AND + All_Changes.action=lockout + AND + All_Changes.command=user.account.lock + BY _time span=5m All_Changes.result + All_Changes.command sourcetype All_Changes.src + All_Changes.dest + | where count > 5 + | `drop_dm_object_name("All_Changes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `okta_multiple_accounts_locked_out_filter` +how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). +known_false_positives: Multiple account lockouts may be also triggered by an application malfunction. Filter as needed, and monitor for any unusual activity. references: -- https://attack.mitre.org/techniques/T1110/ -- https://splunkbase.splunk.com/app/6553 + - https://attack.mitre.org/techniques/T1110/ + - https://splunkbase.splunk.com/app/6553 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Multiple accounts locked out in Okta from [$src$]. Investigate further - to determine if this was authorized. - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: src - type: ip_address + message: Multiple accounts locked out in Okta from [$src$]. Investigate further to determine if this was authorized. + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Okta Account Takeover - asset_type: Okta Tenant - mitre_attack_id: - - T1110 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Okta Account Takeover + asset_type: Okta Tenant + mitre_attack_id: + - T1110 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/okta_multiple_accounts_lockout/okta_multiple_accounts_lockout.log - source: Okta - sourcetype: OktaIM2:log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/okta_multiple_accounts_lockout/okta_multiple_accounts_lockout.log + source: Okta + sourcetype: OktaIM2:log diff --git a/detections/application/okta_multiple_failed_mfa_requests_for_user.yml b/detections/application/okta_multiple_failed_mfa_requests_for_user.yml index 26dc7a7baf..63625273ce 100644 --- a/detections/application/okta_multiple_failed_mfa_requests_for_user.yml +++ b/detections/application/okta_multiple_failed_mfa_requests_for_user.yml @@ -1,69 +1,59 @@ name: Okta Multiple Failed MFA Requests For User id: 826dbaae-a1e6-4c8c-b384-d16898956e73 -version: 8 -date: '2025-10-14' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk data_source: -- Okta + - Okta type: Anomaly status: production -description: The following analytic identifies multiple failed multi-factor authentication - (MFA) requests for a single user within an Okta tenant. It triggers when more than - 10 MFA attempts fail within 5 minutes, using Okta event logs to detect this pattern. - This activity is significant as it may indicate an adversary attempting to bypass - MFA by bombarding the user with repeated authentication requests, a technique used - by threat actors like Lapsus and APT29. If confirmed malicious, this could lead - to unauthorized access, potentially compromising sensitive information and systems. -search: '`okta` eventType=user.authentication.auth_via_mfa outcome.result=FAILURE - debugContext.debugData.factor!=PASSWORD_AS_FACTOR | bucket _time span=5m | stats - count min(_time) as firstTime max(_time) as lastTime values(displayMessage) values(src_ip) - as src_ip values(debugContext.debugData.factor) values(dest) as dest by _time src_user - | where count >= 10 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `okta_multiple_failed_mfa_requests_for_user_filter`' -how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the - Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). -known_false_positives: Multiple Failed MFA requests may also be a sign of authentication - or application issues. Filter as needed and monitor for any unusual activity. +description: The following analytic identifies multiple failed multi-factor authentication (MFA) requests for a single user within an Okta tenant. It triggers when more than 10 MFA attempts fail within 5 minutes, using Okta event logs to detect this pattern. This activity is significant as it may indicate an adversary attempting to bypass MFA by bombarding the user with repeated authentication requests, a technique used by threat actors like Lapsus and APT29. If confirmed malicious, this could lead to unauthorized access, potentially compromising sensitive information and systems. +search: |- + `okta` eventType=user.authentication.auth_via_mfa outcome.result=FAILURE debugContext.debugData.factor!=PASSWORD_AS_FACTOR + | bucket _time span=5m + | stats count min(_time) as firstTime max(_time) as lastTime values(displayMessage) values(src_ip) as src_ip values(debugContext.debugData.factor) values(dest) as dest + BY _time src_user + | where count >= 10 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `okta_multiple_failed_mfa_requests_for_user_filter` +how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). +known_false_positives: Multiple Failed MFA requests may also be a sign of authentication or application issues. Filter as needed and monitor for any unusual activity. references: -- https://attack.mitre.org/techniques/T1621/ + - https://attack.mitre.org/techniques/T1621/ drilldown_searches: -- name: View the detection results for - "$src_user$" - search: '%original_detection_search% | search src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_user$" + search: '%original_detection_search% | search src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Multiple failed MFA requests for user $src_user$ from IP Address - $src_ip$ - risk_objects: - - field: src_user - type: user - score: 42 - threat_objects: - - field: src_ip - type: ip_address + message: Multiple failed MFA requests for user $src_user$ from IP Address - $src_ip$ + risk_objects: + - field: src_user + type: user + score: 42 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Okta Account Takeover - - Scattered Lapsus$ Hunters - asset_type: Okta Tenant - mitre_attack_id: - - T1621 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Okta Account Takeover + - Scattered Lapsus$ Hunters + asset_type: Okta Tenant + mitre_attack_id: + - T1621 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/okta_multiple_failed_mfa_requests/okta_multiple_failed_mfa_requests.log - source: Okta - sourcetype: OktaIM2:log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/okta_multiple_failed_mfa_requests/okta_multiple_failed_mfa_requests.log + source: Okta + sourcetype: OktaIM2:log diff --git a/detections/application/okta_multiple_failed_requests_to_access_applications.yml b/detections/application/okta_multiple_failed_requests_to_access_applications.yml index d7fd44601a..94450222d2 100644 --- a/detections/application/okta_multiple_failed_requests_to_access_applications.yml +++ b/detections/application/okta_multiple_failed_requests_to_access_applications.yml @@ -6,47 +6,23 @@ author: John Murphy, Okta, Michael Haag, Splunk type: Hunting status: experimental data_source: -- Okta -description: The following analytic detects multiple failed attempts to access applications - in Okta, potentially indicating the reuse of a stolen web session cookie. It leverages - Okta logs to evaluate policy and SSO events, aggregating data by user, session, - and IP. The detection triggers when more than half of the app sign-on attempts are - unsuccessful across multiple applications. This activity is significant as it may - indicate an attempt to bypass authentication mechanisms. If confirmed malicious, - it could lead to unauthorized access to sensitive applications and data, posing - a significant security risk. -search: "`okta` target{}.type=AppInstance (eventType=policy.evaluate_sign_on outcome.result=CHALLENGE) - OR (eventType=user.authentication.sso outcome.result=SUCCESS) | eval targets=mvzip('target{}.type', - 'target{}.displayName', \": \") | eval targets=mvfilter(targets LIKE \"AppInstance%\"\ - ) | stats count min(_time) as _time values(outcome.result) as outcome.result dc(eval(if(eventType=\"\ - policy.evaluate_sign_on\",targets,NULL))) as total_challenges sum(eval(if(eventType=\"\ - user.authentication.sso\",1,0))) as total_successes by authenticationContext.externalSessionId - targets actor.alternateId client.ipAddress | search total_challenges > 0 | stats - min(_time) as _time values(*) as * sum(total_challenges) as total_challenges sum(total_successes) - as total_successes values(eval(if(\"outcome.result\"=\"SUCCESS\",targets,NULL))) - as success_apps values(eval(if(\":outcome.result\"!=\"SUCCESS\",targets,NULL))) - as no_success_apps by authenticationContext.externalSessionId actor.alternateId - client.ipAddress | fillnull | eval ratio=round(total_successes/total_challenges,2), - severity=\"HIGH\", mitre_technique_id=\"T1538\", description=\"actor.alternateId\"\ - . \" from \" . \"client.ipAddress\" . \" seen opening \" . total_challenges . \"\ - \ chiclets/apps with \" . total_successes . \" challenges successfully passed\" - | fields - count, targets | search ratio < 0.5 total_challenges > 2 | `okta_multiple_failed_requests_to_access_applications_filter`" -how_to_implement: This analytic is specific to Okta and requires Okta:im2 logs to - be ingested. -known_false_positives: False positives may be present based on organization size and - configuration of Okta. + - Okta +description: The following analytic detects multiple failed attempts to access applications in Okta, potentially indicating the reuse of a stolen web session cookie. It leverages Okta logs to evaluate policy and SSO events, aggregating data by user, session, and IP. The detection triggers when more than half of the app sign-on attempts are unsuccessful across multiple applications. This activity is significant as it may indicate an attempt to bypass authentication mechanisms. If confirmed malicious, it could lead to unauthorized access to sensitive applications and data, posing a significant security risk. +search: "`okta` target{}.type=AppInstance (eventType=policy.evaluate_sign_on outcome.result=CHALLENGE) OR (eventType=user.authentication.sso outcome.result=SUCCESS) | eval targets=mvzip('target{}.type', 'target{}.displayName', \": \") | eval targets=mvfilter(targets LIKE \"AppInstance%\") | stats count min(_time) as _time values(outcome.result) as outcome.result dc(eval(if(eventType=\"policy.evaluate_sign_on\",targets,NULL))) as total_challenges sum(eval(if(eventType=\"user.authentication.sso\",1,0))) as total_successes by authenticationContext.externalSessionId targets actor.alternateId client.ipAddress | search total_challenges > 0 | stats min(_time) as _time values(*) as * sum(total_challenges) as total_challenges sum(total_successes) as total_successes values(eval(if(\"outcome.result\"=\"SUCCESS\",targets,NULL))) as success_apps values(eval(if(\":outcome.result\"!=\"SUCCESS\",targets,NULL))) as no_success_apps by authenticationContext.externalSessionId actor.alternateId client.ipAddress | fillnull | eval ratio=round(total_successes/total_challenges,2), severity=\"HIGH\", mitre_technique_id=\"T1538\", description=\"actor.alternateId\". \" from \" . \"client.ipAddress\" . \" seen opening \" . total_challenges . \" chiclets/apps with \" . total_successes . \" challenges successfully passed\" | fields - count, targets | search ratio < 0.5 total_challenges > 2 | `okta_multiple_failed_requests_to_access_applications_filter`" +how_to_implement: This analytic is specific to Okta and requires Okta:im2 logs to be ingested. +known_false_positives: False positives may be present based on organization size and configuration of Okta. references: -- https://attack.mitre.org/techniques/T1538 -- https://attack.mitre.org/techniques/T1550/004 + - https://attack.mitre.org/techniques/T1538 + - https://attack.mitre.org/techniques/T1550/004 tags: - analytic_story: - - Okta Account Takeover - asset_type: Okta Tenant - mitre_attack_id: - - T1550.004 - - T1538 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - Okta Account Takeover + asset_type: Okta Tenant + mitre_attack_id: + - T1550.004 + - T1538 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access diff --git a/detections/application/okta_multiple_users_failing_to_authenticate_from_ip.yml b/detections/application/okta_multiple_users_failing_to_authenticate_from_ip.yml index e27356b59a..8971fe907f 100644 --- a/detections/application/okta_multiple_users_failing_to_authenticate_from_ip.yml +++ b/detections/application/okta_multiple_users_failing_to_authenticate_from_ip.yml @@ -1,73 +1,62 @@ name: Okta Multiple Users Failing To Authenticate From Ip id: de365ffa-42f5-46b5-b43f-fa72290b8218 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Michael Haag, Mauricio Velazco, Splunk data_source: -- Okta + - Okta type: Anomaly status: production -description: The following analytic identifies instances where more than 10 unique - user accounts have failed to authenticate from a single IP address within a 5-minute - window in an Okta tenant. This detection uses OktaIm2 logs ingested via the Splunk - Add-on for Okta Identity Cloud. Such activity is significant as it may indicate - brute-force attacks or password spraying attempts. If confirmed malicious, this - behavior suggests an external entity is attempting to compromise multiple user accounts, - potentially leading to unauthorized access to organizational resources and data - breaches. -search: '| tstats `security_content_summariesonly` count max(_time) as lastTime, min(_time) - as firstTime dc(Authentication.user) as unique_accounts values(Authentication.signature) - as signature values(Authentication.user) as user values(Authentication.app) as app - values(Authentication.authentication_method) as authentication_method values(Authentication.dest) - as dest from datamodel=Authentication where Authentication.action="failure" AND - Authentication.signature=user.session.start by _time span=5m Authentication.src - sourcetype | where unique_accounts > 9 | `drop_dm_object_name("Authentication")` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_multiple_users_failing_to_authenticate_from_ip_filter`' -how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the - Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). -known_false_positives: A source Ip failing to authenticate with multiple users in - a short period of time is not common legitimate behavior. +description: The following analytic identifies instances where more than 10 unique user accounts have failed to authenticate from a single IP address within a 5-minute window in an Okta tenant. This detection uses OktaIm2 logs ingested via the Splunk Add-on for Okta Identity Cloud. Such activity is significant as it may indicate brute-force attacks or password spraying attempts. If confirmed malicious, this behavior suggests an external entity is attempting to compromise multiple user accounts, potentially leading to unauthorized access to organizational resources and data breaches. +search: |- + | tstats `security_content_summariesonly` count max(_time) as lastTime, min(_time) as firstTime dc(Authentication.user) as unique_accounts values(Authentication.signature) as signature values(Authentication.user) as user values(Authentication.app) as app values(Authentication.authentication_method) as authentication_method values(Authentication.dest) as dest FROM datamodel=Authentication + WHERE Authentication.action="failure" + AND + Authentication.signature=user.session.start + BY _time span=5m Authentication.src + sourcetype + | where unique_accounts > 9 + | `drop_dm_object_name("Authentication")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `okta_multiple_users_failing_to_authenticate_from_ip_filter` +how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). +known_false_positives: A source Ip failing to authenticate with multiple users in a short period of time is not common legitimate behavior. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://splunkbase.splunk.com/app/6553 + - https://attack.mitre.org/techniques/T1110/003/ + - https://splunkbase.splunk.com/app/6553 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Multiple users failing to authenticate from a single source IP Address - - [$src$]. Investigate further to determine if this was authorized. - risk_objects: - - field: user - type: user - score: 54 - threat_objects: - - field: src - type: ip_address + message: Multiple users failing to authenticate from a single source IP Address - [$src$]. Investigate further to determine if this was authorized. + risk_objects: + - field: user + type: user + score: 54 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Okta Account Takeover - asset_type: Okta Tenant - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Okta Account Takeover + asset_type: Okta Tenant + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/okta_multiple_users_from_ip/okta_multiple_users_from_ip.log - source: Okta - sourcetype: OktaIM2:log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/okta_multiple_users_from_ip/okta_multiple_users_from_ip.log + source: Okta + sourcetype: OktaIM2:log diff --git a/detections/application/okta_new_api_token_created.yml b/detections/application/okta_new_api_token_created.yml index d5136c7cf8..06f7b75c8f 100644 --- a/detections/application/okta_new_api_token_created.yml +++ b/detections/application/okta_new_api_token_created.yml @@ -1,70 +1,62 @@ name: Okta New API Token Created id: c3d22720-35d3-4da4-bd0a-740d37192bd4 -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Michael Haag, Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the creation of a new API token within - an Okta tenant. It uses OktaIm2 logs ingested via the Splunk Add-on for Okta Identity - Cloud to identify events where the `system.api_token.create` command is executed. - This activity is significant because creating a new API token can indicate potential - account takeover attempts or unauthorized access, allowing an adversary to maintain - persistence. If confirmed malicious, this could enable attackers to execute API - calls, access sensitive data, and perform administrative actions within the Okta - environment. +description: The following analytic detects the creation of a new API token within an Okta tenant. It uses OktaIm2 logs ingested via the Splunk Add-on for Okta Identity Cloud to identify events where the `system.api_token.create` command is executed. This activity is significant because creating a new API token can indicate potential account takeover attempts or unauthorized access, allowing an adversary to maintain persistence. If confirmed malicious, this could enable attackers to execute API calls, access sensitive data, and perform administrative actions within the Okta environment. data_source: -- Okta -search: '| tstats `security_content_summariesonly` count max(_time) as lastTime, min(_time) - as firstTime from datamodel=Change where All_Changes.action=created AND All_Changes.command=system.api_token.create - by _time span=5m All_Changes.user All_Changes.result All_Changes.command sourcetype - All_Changes.src All_Changes.action All_Changes.object_category All_Changes.dest - | `drop_dm_object_name("All_Changes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `okta_new_api_token_created_filter`' -how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the - Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). -known_false_positives: False positives may be present. Tune Okta and tune the analytic - to ensure proper fidelity. Modify risk score as needed. + - Okta +search: |- + | tstats `security_content_summariesonly` count max(_time) as lastTime, min(_time) as firstTime FROM datamodel=Change + WHERE All_Changes.action=created + AND + All_Changes.command=system.api_token.create + BY _time span=5m All_Changes.user + All_Changes.result All_Changes.command sourcetype + All_Changes.src All_Changes.action All_Changes.object_category + All_Changes.dest + | `drop_dm_object_name("All_Changes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `okta_new_api_token_created_filter` +how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). +known_false_positives: False positives may be present. Tune Okta and tune the analytic to ensure proper fidelity. Modify risk score as needed. references: -- https://developer.okta.com/docs/reference/api/event-types/?q=security.threat.detected -- https://splunkbase.splunk.com/app/6553 + - https://developer.okta.com/docs/reference/api/event-types/?q=security.threat.detected + - https://splunkbase.splunk.com/app/6553 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A new API token was created in Okta by [$user$]. Investigate further to - determine if this was authorized. - risk_objects: - - field: user - type: user - score: 64 - threat_objects: [] + message: A new API token was created in Okta by [$user$]. Investigate further to determine if this was authorized. + risk_objects: + - field: user + type: user + score: 64 + threat_objects: [] tags: - analytic_story: - - Okta Account Takeover - - Scattered Lapsus$ Hunters - asset_type: Okta Tenant - mitre_attack_id: - - T1078.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - Okta Account Takeover + - Scattered Lapsus$ Hunters + asset_type: Okta Tenant + mitre_attack_id: + - T1078.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.001/okta_new_api_token_created/okta_new_api_token_created.log - source: Okta - sourcetype: OktaIM2:log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.001/okta_new_api_token_created/okta_new_api_token_created.log + source: Okta + sourcetype: OktaIM2:log diff --git a/detections/application/okta_new_device_enrolled_on_account.yml b/detections/application/okta_new_device_enrolled_on_account.yml index 85355954b2..86573ba642 100644 --- a/detections/application/okta_new_device_enrolled_on_account.yml +++ b/detections/application/okta_new_device_enrolled_on_account.yml @@ -1,70 +1,60 @@ name: Okta New Device Enrolled on Account id: bb27cbce-d4de-432c-932f-2e206e9130fb -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Michael Haag, Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic identifies when a new device is enrolled on an - Okta account. It uses OktaIm2 logs ingested via the Splunk Add-on for Okta Identity - Cloud to detect the creation of new device enrollments. This activity is significant - as it may indicate a legitimate user setting up a new device or an adversary adding - a device to maintain unauthorized access. If confirmed malicious, this could lead - to potential account takeover, unauthorized access, and persistent control over - the compromised Okta account. Monitoring this behavior is crucial for detecting - and mitigating unauthorized access attempts. +description: The following analytic identifies when a new device is enrolled on an Okta account. It uses OktaIm2 logs ingested via the Splunk Add-on for Okta Identity Cloud to detect the creation of new device enrollments. This activity is significant as it may indicate a legitimate user setting up a new device or an adversary adding a device to maintain unauthorized access. If confirmed malicious, this could lead to potential account takeover, unauthorized access, and persistent control over the compromised Okta account. Monitoring this behavior is crucial for detecting and mitigating unauthorized access attempts. data_source: -- Okta -search: '| tstats `security_content_summariesonly` count max(_time) as lastTime, min(_time) - as firstTime from datamodel=Change where All_Changes.action=created All_Changes.command=device.enrollment.create - by _time span=5m All_Changes.user All_Changes.result All_Changes.command sourcetype - All_Changes.src All_Changes.action All_Changes.object_category All_Changes.dest - | `drop_dm_object_name("All_Changes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `okta_new_device_enrolled_on_account_filter`' -how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the - Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). -known_false_positives: It is possible that the user has legitimately added a new device - to their account. Please verify this activity. + - Okta +search: |- + | tstats `security_content_summariesonly` count max(_time) as lastTime, min(_time) as firstTime FROM datamodel=Change + WHERE All_Changes.action=created All_Changes.command=device.enrollment.create + BY _time span=5m All_Changes.user + All_Changes.result All_Changes.command sourcetype + All_Changes.src All_Changes.action All_Changes.object_category + All_Changes.dest + | `drop_dm_object_name("All_Changes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `okta_new_device_enrolled_on_account_filter` +how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). +known_false_positives: It is possible that the user has legitimately added a new device to their account. Please verify this activity. references: -- https://attack.mitre.org/techniques/T1098/005/ -- https://developer.okta.com/docs/reference/api/event-types/?q=device.enrollment.create + - https://attack.mitre.org/techniques/T1098/005/ + - https://developer.okta.com/docs/reference/api/event-types/?q=device.enrollment.create drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A new device was enrolled on an Okta account for user [$user$]. Investigate - further to determine if this was authorized. - risk_objects: - - field: user - type: user - score: 24 - threat_objects: [] + message: A new device was enrolled on an Okta account for user [$user$]. Investigate further to determine if this was authorized. + risk_objects: + - field: user + type: user + score: 24 + threat_objects: [] tags: - analytic_story: - - Okta Account Takeover - - Scattered Lapsus$ Hunters - asset_type: Okta Tenant - mitre_attack_id: - - T1098.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Okta Account Takeover + - Scattered Lapsus$ Hunters + asset_type: Okta Tenant + mitre_attack_id: + - T1098.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.005/okta_new_device_enrolled/okta_new_device_enrolled.log - source: Okta - sourcetype: OktaIM2:log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.005/okta_new_device_enrolled/okta_new_device_enrolled.log + source: Okta + sourcetype: OktaIM2:log diff --git a/detections/application/okta_phishing_detection_with_fastpass_origin_check.yml b/detections/application/okta_phishing_detection_with_fastpass_origin_check.yml index dc4ff78883..c900fb9029 100644 --- a/detections/application/okta_phishing_detection_with_fastpass_origin_check.yml +++ b/detections/application/okta_phishing_detection_with_fastpass_origin_check.yml @@ -1,47 +1,41 @@ name: Okta Phishing Detection with FastPass Origin Check id: f4ca0057-cbf3-44f8-82ea-4e330ee901d3 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Okta, Inc, Michael Haag, Splunk type: TTP status: experimental data_source: -- Okta -description: The following analytic identifies failed user authentication attempts - in Okta due to FastPass declining a phishing attempt. It leverages Okta logs, specifically - looking for events where multi-factor authentication (MFA) fails with the reason - "FastPass declined phishing attempt." This activity is significant as it indicates - that attackers are targeting users with real-time phishing proxies, attempting to - capture credentials. If confirmed malicious, this could lead to unauthorized access - to user accounts, potentially compromising sensitive information and furthering - lateral movement within the organization. -search: '`okta` eventType="user.authentication.auth_via_mfa" AND result="FAILURE" - AND outcome.reason="FastPass declined phishing attempt" | stats count min(_time) - as firstTime max(_time) as lastTime values(displayMessage) by user eventType client.userAgent.rawUserAgent - client.userAgent.browser outcome.reason | `security_content_ctime(firstTime)` | - `security_content_ctime(lastTime)` | `okta_phishing_detection_with_fastpass_origin_check_filter`' -how_to_implement: This search is specific to Okta and requires Okta logs to be ingested - in your Splunk deployment. -known_false_positives: Fidelity of this is high as Okta is specifying malicious infrastructure. - Filter and modify as needed. + - Okta +description: The following analytic identifies failed user authentication attempts in Okta due to FastPass declining a phishing attempt. It leverages Okta logs, specifically looking for events where multi-factor authentication (MFA) fails with the reason "FastPass declined phishing attempt." This activity is significant as it indicates that attackers are targeting users with real-time phishing proxies, attempting to capture credentials. If confirmed malicious, this could lead to unauthorized access to user accounts, potentially compromising sensitive information and furthering lateral movement within the organization. +search: |- + `okta` eventType="user.authentication.auth_via_mfa" AND result="FAILURE" AND outcome.reason="FastPass declined phishing attempt" + | stats count min(_time) as firstTime max(_time) as lastTime values(displayMessage) + BY user eventType client.userAgent.rawUserAgent + client.userAgent.browser outcome.reason + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `okta_phishing_detection_with_fastpass_origin_check_filter` +how_to_implement: This search is specific to Okta and requires Okta logs to be ingested in your Splunk deployment. +known_false_positives: Fidelity of this is high as Okta is specifying malicious infrastructure. Filter and modify as needed. references: -- https://sec.okta.com/fastpassphishingdetection + - https://sec.okta.com/fastpassphishingdetection rba: - message: Okta FastPass has prevented $user$ from authenticating to a malicious site. - risk_objects: - - field: user - type: user - score: 100 - threat_objects: [] + message: Okta FastPass has prevented $user$ from authenticating to a malicious site. + risk_objects: + - field: user + type: user + score: 100 + threat_objects: [] tags: - analytic_story: - - Okta Account Takeover - asset_type: Infrastructure - mitre_attack_id: - - T1078.001 - - T1556 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - Okta Account Takeover + asset_type: Infrastructure + mitre_attack_id: + - T1078.001 + - T1556 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access diff --git a/detections/application/okta_risk_threshold_exceeded.yml b/detections/application/okta_risk_threshold_exceeded.yml index be36a2e2ab..9c60ead0a9 100644 --- a/detections/application/okta_risk_threshold_exceeded.yml +++ b/detections/application/okta_risk_threshold_exceeded.yml @@ -1,73 +1,51 @@ name: Okta Risk Threshold Exceeded id: d8b967dd-657f-4d88-93b5-c588bcd7218c -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Michael Haag, Bhavin Patel, Splunk status: production type: Correlation -description: The following correlation identifies when a user exceeds a risk threshold - based on multiple suspicious Okta activities. It leverages the Risk Framework from - Enterprise Security, aggregating risk events from "Suspicious Okta Activity," "Okta - Account Takeover," and "Okta MFA Exhaustion" analytic stories. This detection is - significant as it highlights potentially compromised user accounts exhibiting multiple - tactics, techniques, and procedures (TTPs) within a 24-hour period. If confirmed - malicious, this activity could indicate a serious security breach, allowing attackers - to gain unauthorized access, escalate privileges, or persist within the environment. +description: The following correlation identifies when a user exceeds a risk threshold based on multiple suspicious Okta activities. It leverages the Risk Framework from Enterprise Security, aggregating risk events from "Suspicious Okta Activity," "Okta Account Takeover," and "Okta MFA Exhaustion" analytic stories. This detection is significant as it highlights potentially compromised user accounts exhibiting multiple tactics, techniques, and procedures (TTPs) within a 24-hour period. If confirmed malicious, this activity could indicate a serious security breach, allowing attackers to gain unauthorized access, escalate privileges, or persist within the environment. data_source: -- Okta -search: '| tstats `security_content_summariesonly` values(All_Risk.analyticstories) - as analyticstories sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) - as risk_event_count,values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as - annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) - as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) - as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) - as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, - dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.risk_object_type - = user All_Risk.analyticstories IN ("Okta Account Takeover", "Suspicious Okta Activity","Okta - MFA Exhaustion") by All_Risk.risk_object,All_Risk.risk_object_type | `drop_dm_object_name("All_Risk")` - | search mitre_technique_id_count > 5 | `okta_risk_threshold_exceeded_filter`' -how_to_implement: This search leverages the Risk Framework from Enterprise Security. - Ensure that "Suspicious Okta Activity", "Okta Account Takeover", and "Okta MFA Exhaustion" - analytic stories are enabled. TTPs may be set to finding for point detections; anomalies - should not be findings but rather intermediate findings. The correlation relies - on intermediate findings before generating a findings. Modify the value as needed. -known_false_positives: False positives will be limited to the number of events generated - by the analytics tied to the stories. Analytics will need to be tested and tuned, - and the risk score reduced as needed based on the organization. + - Okta +search: |- + | tstats `security_content_summariesonly` values(All_Risk.analyticstories) as analyticstories sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count,values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count FROM datamodel=Risk.All_Risk + WHERE All_Risk.risk_object_type = user All_Risk.analyticstories IN ("Okta Account Takeover", "Suspicious Okta Activity","Okta MFA Exhaustion") + BY All_Risk.risk_object,All_Risk.risk_object_type + | `drop_dm_object_name("All_Risk")` + | search mitre_technique_id_count > 5 + | `okta_risk_threshold_exceeded_filter` +how_to_implement: This search leverages the Risk Framework from Enterprise Security. Ensure that "Suspicious Okta Activity", "Okta Account Takeover", and "Okta MFA Exhaustion" analytic stories are enabled. TTPs may be set to finding for point detections; anomalies should not be findings but rather intermediate findings. The correlation relies on intermediate findings before generating a findings. Modify the value as needed. +known_false_positives: False positives will be limited to the number of events generated by the analytics tied to the stories. Analytics will need to be tested and tuned, and the risk score reduced as needed based on the organization. references: -- https://developer.okta.com/docs/reference/api/event-types -- https://sec.okta.com/everythingisyes + - https://developer.okta.com/docs/reference/api/event-types + - https://sec.okta.com/everythingisyes drilldown_searches: -- name: View the detection results for - "$risk_object$" - search: '%original_detection_search% | search risk_object = "$risk_object$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$risk_object$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$risk_object$" + search: '%original_detection_search% | search risk_object = "$risk_object$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$risk_object$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ tags: - analytic_story: - - Okta Account Takeover - - Okta MFA Exhaustion - - Suspicious Okta Activity - asset_type: Okta Tenant - mitre_attack_id: - - T1078 - - T1110 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - Okta Account Takeover + - Okta MFA Exhaustion + - Suspicious Okta Activity + asset_type: Okta Tenant + mitre_attack_id: + - T1078 + - T1110 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/okta_account_takeover_risk_events/okta_risk.log - source: risk_data - sourcetype: stash + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/okta_account_takeover_risk_events/okta_risk.log + source: risk_data + sourcetype: stash diff --git a/detections/application/okta_successful_single_factor_authentication.yml b/detections/application/okta_successful_single_factor_authentication.yml index 59d7298489..59b7a232f7 100644 --- a/detections/application/okta_successful_single_factor_authentication.yml +++ b/detections/application/okta_successful_single_factor_authentication.yml @@ -1,71 +1,59 @@ name: Okta Successful Single Factor Authentication id: 98f6ad4f-4325-4096-9d69-45dc8e638e82 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Bhavin Patel, Splunk data_source: -- Okta + - Okta type: Anomaly status: production -description: The following analytic identifies successful single-factor authentication - events against the Okta Dashboard for accounts without Multi-Factor Authentication - (MFA) enabled. It detects this activity by analyzing Okta logs for successful authentication - events where "Okta Verify" is not used. This behavior is significant as it may indicate - a misconfiguration, policy violation, or potential account takeover. If confirmed - malicious, an attacker could gain unauthorized access to the account, potentially - leading to data breaches or further exploitation within the environment. -search: '`okta` action=success src_user_type = User eventType = user.authentication.verify - OR eventType = user.authentication.auth_via_mfa| stats dc(eventType) values(eventType) - as eventType values(target{}.displayName) as targets values(debugContext.debugData.url) - min(_time) as firstTime max(_time) as lastTime values(authentication_method) by - src_ip user action dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | search targets !="Okta Verify" | `okta_successful_single_factor_authentication_filter`' -how_to_implement: This detection utilizes logs from Okta environments and requires - the ingestion of OktaIm2 logs through the Splunk Add-on for Okta Identity Cloud - (https://splunkbase.splunk.com/app/6553). -known_false_positives: Although not recommended, certain users may be exempt from - multi-factor authentication. Adjust the filter as necessary. +description: The following analytic identifies successful single-factor authentication events against the Okta Dashboard for accounts without Multi-Factor Authentication (MFA) enabled. It detects this activity by analyzing Okta logs for successful authentication events where "Okta Verify" is not used. This behavior is significant as it may indicate a misconfiguration, policy violation, or potential account takeover. If confirmed malicious, an attacker could gain unauthorized access to the account, potentially leading to data breaches or further exploitation within the environment. +search: |- + `okta` action=success src_user_type = User eventType = user.authentication.verify OR eventType = user.authentication.auth_via_mfa + | stats dc(eventType) values(eventType) as eventType values(target{}.displayName) as targets values(debugContext.debugData.url) min(_time) as firstTime max(_time) as lastTime values(authentication_method) + BY src_ip user action + dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | search targets !="Okta Verify" + | `okta_successful_single_factor_authentication_filter` +how_to_implement: This detection utilizes logs from Okta environments and requires the ingestion of OktaIm2 logs through the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). +known_false_positives: Although not recommended, certain users may be exempt from multi-factor authentication. Adjust the filter as necessary. references: -- https://sec.okta.com/everythingisyes -- https://attack.mitre.org/techniques/T1078/004/ + - https://sec.okta.com/everythingisyes + - https://attack.mitre.org/techniques/T1078/004/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A user [$user$] has successfully logged in to Okta Dashboard with single - factor authentication from IP Address - [$src_ip$]. - risk_objects: - - field: user - type: user - score: 48 - threat_objects: [] + message: A user [$user$] has successfully logged in to Okta Dashboard with single factor authentication from IP Address - [$src_ip$]. + risk_objects: + - field: user + type: user + score: 48 + threat_objects: [] tags: - analytic_story: - - Okta Account Takeover - asset_type: Okta Tenant - mitre_attack_id: - - T1078.004 - - T1586.003 - - T1621 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Okta Account Takeover + asset_type: Okta Tenant + mitre_attack_id: + - T1078.004 + - T1586.003 + - T1621 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/okta_single_factor_auth/okta_single_factor_auth.log - source: okta_log - sourcetype: OktaIM2:log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/okta_single_factor_auth/okta_single_factor_auth.log + source: okta_log + sourcetype: OktaIM2:log diff --git a/detections/application/okta_suspicious_activity_reported.yml b/detections/application/okta_suspicious_activity_reported.yml index 837cc7c959..2e0ba7c73b 100644 --- a/detections/application/okta_suspicious_activity_reported.yml +++ b/detections/application/okta_suspicious_activity_reported.yml @@ -1,69 +1,56 @@ name: Okta Suspicious Activity Reported id: bfc840f5-c9c6-454c-aa13-b46fd0bf1e79 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies when an associate reports a login attempt - as suspicious via an email from Okta. It leverages Okta Identity Management logs, - specifically the `user.account.report_suspicious_activity_by_enduser` event type. - This activity is significant as it indicates potential unauthorized access attempts, - warranting immediate investigation to prevent possible security breaches. If confirmed - malicious, the attacker could gain unauthorized access to sensitive systems and - data, leading to data theft, privilege escalation, or further compromise of the - environment. +description: The following analytic identifies when an associate reports a login attempt as suspicious via an email from Okta. It leverages Okta Identity Management logs, specifically the `user.account.report_suspicious_activity_by_enduser` event type. This activity is significant as it indicates potential unauthorized access attempts, warranting immediate investigation to prevent possible security breaches. If confirmed malicious, the attacker could gain unauthorized access to sensitive systems and data, leading to data theft, privilege escalation, or further compromise of the environment. data_source: -- Okta -search: '`okta` eventType=user.account.report_suspicious_activity_by_enduser | stats - count min(_time) as firstTime max(_time) as lastTime values(displayMessage) by user - dest src eventType client.userAgent.rawUserAgent client.userAgent.browser client.geographicalContext.city client.geographicalContext.country - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_suspicious_activity_reported_filter`' -how_to_implement: This detection utilizes logs from Okta Identity Management (IM) - environments. It requires the ingestion of OktaIm2 logs through the Splunk Add-on - for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). Additionally, - it necessitates the activation of suspicious activity reporting and training for - associates to report such activities. -known_false_positives: False positives should be minimal, given the high fidelity - of this detection. marker. + - Okta +search: |- + `okta` eventType=user.account.report_suspicious_activity_by_enduser + | stats count min(_time) as firstTime max(_time) as lastTime values(displayMessage) + BY user dest src + eventType client.userAgent.rawUserAgent client.userAgent.browser + client.geographicalContext.city client.geographicalContext.country + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `okta_suspicious_activity_reported_filter` +how_to_implement: This detection utilizes logs from Okta Identity Management (IM) environments. It requires the ingestion of OktaIm2 logs through the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). Additionally, it necessitates the activation of suspicious activity reporting and training for associates to report such activities. +known_false_positives: False positives should be minimal, given the high fidelity of this detection. marker. references: -- https://help.okta.com/en-us/Content/Topics/Security/suspicious-activity-reporting.htm + - https://help.okta.com/en-us/Content/Topics/Security/suspicious-activity-reporting.htm drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A user [$user$] reported suspicious activity in Okta. Investigate further - to determine if this was authorized. - risk_objects: - - field: user - type: user - score: 25 - threat_objects: [] + message: A user [$user$] reported suspicious activity in Okta. Investigate further to determine if this was authorized. + risk_objects: + - field: user + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Okta Account Takeover - asset_type: Okta Tenant - mitre_attack_id: - - T1078.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - Okta Account Takeover + asset_type: Okta Tenant + mitre_attack_id: + - T1078.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/okta_suspicious_activity_reported_by_user/okta_suspicious_activity_reported_by_user.log - source: Okta - sourcetype: OktaIM2:log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/okta_suspicious_activity_reported_by_user/okta_suspicious_activity_reported_by_user.log + source: Okta + sourcetype: OktaIM2:log diff --git a/detections/application/okta_suspicious_use_of_a_session_cookie.yml b/detections/application/okta_suspicious_use_of_a_session_cookie.yml index aa5a13b4a3..55c886f2e6 100644 --- a/detections/application/okta_suspicious_use_of_a_session_cookie.yml +++ b/detections/application/okta_suspicious_use_of_a_session_cookie.yml @@ -1,73 +1,55 @@ name: Okta Suspicious Use of a Session Cookie id: 71ad47d1-d6bd-4e0a-b35c-020ad9a6959e -version: 8 -date: '2025-10-14' +version: 9 +date: '2026-02-25' author: Scott Dermott, Felicity Robson, Okta, Michael Haag, Bhavin Patel, Splunk type: Anomaly status: production data_source: -- Okta -description: The following analytic identifies suspicious use of a session cookie - by detecting multiple client values (IP, User Agent, etc.) changing for the same - Device Token associated with a specific user. It leverages policy evaluation events - from successful authentication logs in Okta. This activity is significant as it - may indicate an adversary attempting to reuse a stolen web session cookie, potentially - bypassing authentication mechanisms. If confirmed malicious, this could allow unauthorized - access to user accounts, leading to data breaches or further exploitation within - the environment. -search: '`okta` eventType IN (policy.evaluate_sign_on) outcome.result IN (ALLOW, SUCCESS) - | stats earliest(_time) as _time, values(client.ipAddress) as src_ip, values(client.userAgent.rawUserAgent) - as user_agent, values(client.userAgent.os) as userAgentOS_list, values(client.geographicalContext.city) - as city, values(client.userAgent.browser) as userAgentBrowser_list, values(device.os_platform) - as okta_device_os, dc(client.userAgent.browser) as dc_userAgentBrowser, dc(client.userAgent.os) - as dc_userAgentOS, dc(client.ipAddress) as dc_src_ip, values(outcome.reason) as - reason values(dest) as dest by debugContext.debugData.dtHash, user | where dc_src_ip>1 - AND (dc_userAgentOS>1 OR dc_userAgentBrowser>1) | `okta_suspicious_use_of_a_session_cookie_filter`' -how_to_implement: This detection utilizes logs from Okta Identity Management (IM) - environments. It requires the ingestion of OktaIm2 logs through the Splunk Add-on - for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). -known_false_positives: False positives may occur, depending on the organization's - size and the configuration of Okta. + - Okta +description: The following analytic identifies suspicious use of a session cookie by detecting multiple client values (IP, User Agent, etc.) changing for the same Device Token associated with a specific user. It leverages policy evaluation events from successful authentication logs in Okta. This activity is significant as it may indicate an adversary attempting to reuse a stolen web session cookie, potentially bypassing authentication mechanisms. If confirmed malicious, this could allow unauthorized access to user accounts, leading to data breaches or further exploitation within the environment. +search: |- + `okta` eventType IN (policy.evaluate_sign_on) outcome.result IN (ALLOW, SUCCESS) + | stats earliest(_time) as _time, values(client.ipAddress) as src_ip, values(client.userAgent.rawUserAgent) as user_agent, values(client.userAgent.os) as userAgentOS_list, values(client.geographicalContext.city) as city, values(client.userAgent.browser) as userAgentBrowser_list, values(device.os_platform) as okta_device_os, dc(client.userAgent.browser) as dc_userAgentBrowser, dc(client.userAgent.os) as dc_userAgentOS, dc(client.ipAddress) as dc_src_ip, values(outcome.reason) as reason values(dest) as dest + BY debugContext.debugData.dtHash, user + | where dc_src_ip>1 AND (dc_userAgentOS>1 OR dc_userAgentBrowser>1) + | `okta_suspicious_use_of_a_session_cookie_filter` +how_to_implement: This detection utilizes logs from Okta Identity Management (IM) environments. It requires the ingestion of OktaIm2 logs through the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). +known_false_positives: False positives may occur, depending on the organization's size and the configuration of Okta. references: -- https://attack.mitre.org/techniques/T1539/ + - https://attack.mitre.org/techniques/T1539/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A user [$user$] is attempting to use a session cookie from multiple IP - addresses or devices. Investigate further to determine if this was authorized. - risk_objects: - - field: user - type: user - score: 56 - threat_objects: [] + message: A user [$user$] is attempting to use a session cookie from multiple IP addresses or devices. Investigate further to determine if this was authorized. + risk_objects: + - field: user + type: user + score: 56 + threat_objects: [] tags: - analytic_story: - - Suspicious Okta Activity - - Okta Account Takeover - - Scattered Lapsus$ Hunters - asset_type: Okta Tenant - mitre_attack_id: - - T1539 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Suspicious Okta Activity + - Okta Account Takeover + - Scattered Lapsus$ Hunters + asset_type: Okta Tenant + mitre_attack_id: + - T1539 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1539/okta_web_session_multiple_ip/okta_web_session_multiple_ip.log - source: Okta - sourcetype: OktaIM2:log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1539/okta_web_session_multiple_ip/okta_web_session_multiple_ip.log + source: Okta + sourcetype: OktaIM2:log diff --git a/detections/application/okta_threatinsight_threat_detected.yml b/detections/application/okta_threatinsight_threat_detected.yml index 3da7031739..fcbf2d3ad5 100644 --- a/detections/application/okta_threatinsight_threat_detected.yml +++ b/detections/application/okta_threatinsight_threat_detected.yml @@ -1,70 +1,61 @@ name: Okta ThreatInsight Threat Detected id: 140504ae-5fe2-4d65-b2bc-a211813fbca6 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Mauricio Velazco, Splunk status: production type: Anomaly -description: The following analytic identifies threats detected by Okta ThreatInsight, - such as password spraying, login failures, and high counts of unknown user login - attempts. It leverages Okta Identity Management logs, specifically focusing on security.threat.detected - events. This activity is significant for a SOC as it highlights potential unauthorized - access attempts and credential-based attacks. If confirmed malicious, these activities - could lead to unauthorized access, data breaches, and further exploitation of compromised - accounts, posing a significant risk to the organization's security posture. +description: The following analytic identifies threats detected by Okta ThreatInsight, such as password spraying, login failures, and high counts of unknown user login attempts. It leverages Okta Identity Management logs, specifically focusing on security.threat.detected events. This activity is significant for a SOC as it highlights potential unauthorized access attempts and credential-based attacks. If confirmed malicious, these activities could lead to unauthorized access, data breaches, and further exploitation of compromised accounts, posing a significant risk to the organization's security posture. data_source: -- Okta -search: '`okta` eventType = security.threat.detected | rename client.geographicalContext.country - as country, client.geographicalContext.state as state, client.geographicalContext.city - as city | stats count min(_time) as firstTime max(_time) as lastTime by app src_ip - dest signature eventType displayMessage client.device city state country user_agent - outcome.reason outcome.result severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `okta_threatinsight_threat_detected_filter`' -how_to_implement: This detection utilizes logs from Okta Identity Management (IM) - environments. It requires the ingestion of OktaIm2 logs through the Splunk Add-on - for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). -known_false_positives: False positives may occur. It is recommended to fine-tune Okta - settings and the analytic to ensure high fidelity. Adjust the risk score as necessary. + - Okta +search: |- + `okta` eventType = security.threat.detected + | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city + | stats count min(_time) as firstTime max(_time) as lastTime + BY app src_ip dest + signature eventType displayMessage + client.device city state + country user_agent outcome.reason + outcome.result severity + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `okta_threatinsight_threat_detected_filter` +how_to_implement: This detection utilizes logs from Okta Identity Management (IM) environments. It requires the ingestion of OktaIm2 logs through the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). +known_false_positives: False positives may occur. It is recommended to fine-tune Okta settings and the analytic to ensure high fidelity. Adjust the risk score as necessary. references: -- https://developer.okta.com/docs/reference/api/event-types/?q=security.threat.detected + - https://developer.okta.com/docs/reference/api/event-types/?q=security.threat.detected drilldown_searches: -- name: View the detection results for - "$app$" - search: '%original_detection_search% | search app = "$app$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$app$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$app$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$app$" + search: '%original_detection_search% | search app = "$app$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$app$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$app$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The following $src_ip$ has been identified as a threat by Okta ThreatInsight. - Investigate further to determine if this was authorized. - risk_objects: - - field: app - type: system - score: 25 - threat_objects: - - field: src_ip - type: ip_address + message: The following $src_ip$ has been identified as a threat by Okta ThreatInsight. Investigate further to determine if this was authorized. + risk_objects: + - field: app + type: system + score: 25 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Okta Account Takeover - asset_type: Infrastructure - mitre_attack_id: - - T1078.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - Okta Account Takeover + asset_type: Infrastructure + mitre_attack_id: + - T1078.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/okta_threatinsight_threat_detected/okta_threatinsight_threat_detected.log - source: Okta - sourcetype: OktaIM2:log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/okta_threatinsight_threat_detected/okta_threatinsight_threat_detected.log + source: Okta + sourcetype: OktaIM2:log diff --git a/detections/application/okta_unauthorized_access_to_application.yml b/detections/application/okta_unauthorized_access_to_application.yml index 1f5c8285fc..127d8ec769 100644 --- a/detections/application/okta_unauthorized_access_to_application.yml +++ b/detections/application/okta_unauthorized_access_to_application.yml @@ -1,71 +1,59 @@ name: Okta Unauthorized Access to Application id: 5f661629-9750-4cb9-897c-1f05d6db8727 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Bhavin Patel, Splunk data_source: -- Okta + - Okta type: Anomaly status: production -description: The following analytic identifies attempts by users to access Okta applications - that have not been assigned to them. It leverages Okta Identity Management logs, - specifically focusing on failed access attempts to unassigned applications. This - activity is significant for a SOC as it may indicate potential unauthorized access - attempts, which could lead to exposure of sensitive information or disruption of - services. If confirmed malicious, such activity could result in data breaches, non-compliance - with data protection laws, and overall compromise of the IT environment. -search: '| tstats values(Authentication.app) as app values(Authentication.action) - as action values(Authentication.user) as user values(Authentication.reason) as reason - from datamodel=Authentication where Authentication.signature=app.generic.unauth_app_access_attempt - Authentication.action="failure" by _time Authentication.src Authentication.user - Authentication.dest | `drop_dm_object_name("Authentication")` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | iplocation src | `okta_unauthorized_access_to_application_filter`' -how_to_implement: This detection utilizes logs from Okta Identity Management (IM) - environments and requires the ingestion of OktaIm2 logs through the Splunk Add-on - for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). -known_false_positives: There is a possibility that a user may accidentally click on - the wrong application, which could trigger this event. It is advisable to verify - the location from which this activity originates. +description: The following analytic identifies attempts by users to access Okta applications that have not been assigned to them. It leverages Okta Identity Management logs, specifically focusing on failed access attempts to unassigned applications. This activity is significant for a SOC as it may indicate potential unauthorized access attempts, which could lead to exposure of sensitive information or disruption of services. If confirmed malicious, such activity could result in data breaches, non-compliance with data protection laws, and overall compromise of the IT environment. +search: |- + | tstats values(Authentication.app) as app values(Authentication.action) as action values(Authentication.user) as user values(Authentication.reason) as reason FROM datamodel=Authentication + WHERE Authentication.signature=app.generic.unauth_app_access_attempt Authentication.action="failure" + BY _time Authentication.src Authentication.user + Authentication.dest + | `drop_dm_object_name("Authentication")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | iplocation src + | `okta_unauthorized_access_to_application_filter` +how_to_implement: This detection utilizes logs from Okta Identity Management (IM) environments and requires the ingestion of OktaIm2 logs through the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). +known_false_positives: There is a possibility that a user may accidentally click on the wrong application, which could trigger this event. It is advisable to verify the location from which this activity originates. references: -- https://attack.mitre.org/techniques/T1110/003/ + - https://attack.mitre.org/techniques/T1110/003/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A user [$user$] is attempting to access an unauthorized application from - IP Address - [$src$] - risk_objects: - - field: user - type: user - score: 81 - threat_objects: - - field: src - type: ip_address + message: A user [$user$] is attempting to access an unauthorized application from IP Address - [$src$] + risk_objects: + - field: user + type: user + score: 81 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Okta Account Takeover - asset_type: Okta Tenant - mitre_attack_id: - - T1087.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Okta Account Takeover + asset_type: Okta Tenant + mitre_attack_id: + - T1087.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.004/okta_unauth_access/okta_unauth_access.log - source: Okta - sourcetype: OktaIM2:log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.004/okta_unauth_access/okta_unauth_access.log + source: Okta + sourcetype: OktaIM2:log diff --git a/detections/application/okta_user_logins_from_multiple_cities.yml b/detections/application/okta_user_logins_from_multiple_cities.yml index a068a40a44..7e3f508b05 100644 --- a/detections/application/okta_user_logins_from_multiple_cities.yml +++ b/detections/application/okta_user_logins_from_multiple_cities.yml @@ -1,76 +1,61 @@ name: Okta User Logins from Multiple Cities id: a3d1df37-c2a9-41d0-aa8f-59f82d6192a8 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Bhavin Patel, Splunk data_source: -- Okta + - Okta type: Anomaly status: production -description: The following analytic identifies instances where the same Okta user - logs in from different cities within a 24-hour period. This detection leverages - Okta Identity Management logs, analyzing login events and their geographic locations. - Such behavior is significant as it may indicate a compromised account, with an attacker - attempting unauthorized access from multiple locations. If confirmed malicious, - this activity could lead to account takeovers and data breaches, allowing attackers - to access sensitive information and potentially escalate their privileges within - the environment. -search: '| tstats `security_content_summariesonly` values(Authentication.app) as - app values(Authentication.action) as action values(Authentication.user) as user - values(Authentication.reason) as reason values(Authentication.dest) as dest values(Authentication.signature) - as signature values(Authentication.method) as method from datamodel=Authentication - where Authentication.signature=user.session.start by _time Authentication.src | - `drop_dm_object_name("Authentication")` | `security_content_ctime(firstTime)` | - `security_content_ctime(lastTime)` | iplocation src | stats count min(_time) as - firstTime max(_time) as lastTime dc(src) as distinct_src dc(City) as distinct_city - values(src) as src values(City) as City values(Country) as Country values(action) - as action by user | where distinct_city > 1 | `okta_user_logins_from_multiple_cities_filter`' -how_to_implement: This detection utilizes logs from Okta Identity Management (IM) - environments. It requires the ingestion of OktaIm2 logs through the Splunk Add-on - for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). -known_false_positives: It is uncommon for a user to log in from multiple cities simultaneously, - which may indicate a false positive. +description: The following analytic identifies instances where the same Okta user logs in from different cities within a 24-hour period. This detection leverages Okta Identity Management logs, analyzing login events and their geographic locations. Such behavior is significant as it may indicate a compromised account, with an attacker attempting unauthorized access from multiple locations. If confirmed malicious, this activity could lead to account takeovers and data breaches, allowing attackers to access sensitive information and potentially escalate their privileges within the environment. +search: |- + | tstats `security_content_summariesonly` values(Authentication.app) as app values(Authentication.action) as action values(Authentication.user) as user values(Authentication.reason) as reason values(Authentication.dest) as dest values(Authentication.signature) as signature values(Authentication.method) as method FROM datamodel=Authentication + WHERE Authentication.signature=user.session.start + BY _time Authentication.src + | `drop_dm_object_name("Authentication")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | iplocation src + | stats count min(_time) as firstTime max(_time) as lastTime dc(src) as distinct_src dc(City) as distinct_city values(src) as src values(City) as City values(Country) as Country values(action) as action + BY user + | where distinct_city > 1 + | `okta_user_logins_from_multiple_cities_filter` +how_to_implement: This detection utilizes logs from Okta Identity Management (IM) environments. It requires the ingestion of OktaIm2 logs through the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). +known_false_positives: It is uncommon for a user to log in from multiple cities simultaneously, which may indicate a false positive. references: -- https://attack.mitre.org/techniques/T1110/003/ + - https://attack.mitre.org/techniques/T1110/003/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A user [$user$] has logged in from multiple cities [$City$] from IP Address - - [$src$]. Investigate further to determine if this was authorized. - risk_objects: - - field: user - type: user - score: 81 - threat_objects: - - field: src - type: ip_address + message: A user [$user$] has logged in from multiple cities [$City$] from IP Address - [$src$]. Investigate further to determine if this was authorized. + risk_objects: + - field: user + type: user + score: 81 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Okta Account Takeover - asset_type: Okta Tenant - mitre_attack_id: - - T1586.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Okta Account Takeover + asset_type: Okta Tenant + mitre_attack_id: + - T1586.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1586.003/okta_multiple_city/okta_multiple_city_im2.log - source: Okta - sourcetype: OktaIM2:log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1586.003/okta_multiple_city/okta_multiple_city_im2.log + source: Okta + sourcetype: OktaIM2:log diff --git a/detections/application/ollama_abnormal_network_connectivity.yml b/detections/application/ollama_abnormal_network_connectivity.yml index a23a433174..b54935ba1e 100644 --- a/detections/application/ollama_abnormal_network_connectivity.yml +++ b/detections/application/ollama_abnormal_network_connectivity.yml @@ -1,62 +1,63 @@ name: Ollama Abnormal Network Connectivity id: 19ec30ad-faa2-496a-a6a9-f2e5f778fbdb -version: 1 -date: '2025-10-05' +version: 2 +date: '2026-02-25' author: Rod Soto status: experimental type: Anomaly description: Detects abnormal network activity and connectivity issues in Ollama including non-localhost API access attempts and warning-level network errors such as DNS lookup failures, TCP connection issues, or host resolution problems that may indicate network-based attacks, unauthorized access attempts, or infrastructure reconnaissance activity. data_source: -- Ollama Server -search: '`ollama_server` level=WARN (msg="*failed*" OR msg="*dial tcp*" OR msg="*lookup*" OR msg="*no such host*" OR msg="*connection*" OR msg="*network*" OR msg="*timeout*" OR msg="*unreachable*" OR msg="*refused*") -| eval src=coalesce(src, src_ip, "N/A") -| stats count as incidents, values(src) as src, values(msg) as warning_messages, latest(_time) as last_incident by host -| eval last_incident=strftime(last_incident, "%Y-%m-%d %H:%M:%S") -| eval severity="medium" -| eval attack_type="Abnormal Network Connectivity" -| stats count by last_incident, host, incidents, src, warning_messages, severity, attack_type -| `ollama_abnormal_network_connectivity_filter`' + - Ollama Server +search: |- + `ollama_server` level=WARN (msg="*failed*" OR msg="*dial tcp*" OR msg="*lookup*" OR msg="*no such host*" OR msg="*connection*" OR msg="*network*" OR msg="*timeout*" OR msg="*unreachable*" OR msg="*refused*") + | eval src=coalesce(src, src_ip, "N/A") + | stats count as incidents, values(src) as src, values(msg) as warning_messages, latest(_time) as last_incident + BY host + | eval last_incident=strftime(last_incident, "%Y-%m-%d %H:%M:%S") + | eval severity="medium" + | eval attack_type="Abnormal Network Connectivity" + | stats count + BY last_incident, host, incidents, + src, warning_messages, severity, + attack_type + | `ollama_abnormal_network_connectivity_filter` how_to_implement: 'Ingest Ollama logs via Splunk TA-ollama add-on by configuring file monitoring inputs pointed to your Ollama server log directories (sourcetype: ollama:server), or enable HTTP Event Collector (HEC) for real-time API telemetry and prompt analytics (sourcetypes: ollama:api, ollama:prompts). CIM compatibility using the Web datamodel for standardized security detections.' known_false_positives: Legitimate remote access from authorized users or applications connecting from non-localhost addresses, temporary network infrastructure issues causing DNS resolution failures, firewall or network configuration changes resulting in connection timeouts, cloud-hosted Ollama instances receiving valid external API requests, or intermittent connectivity problems during network maintenance may trigger this detection during normal operations. references: -- https://github.com/rosplk/ta-ollama + - https://github.com/rosplk/ta-ollama drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search "$src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$",) starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search "$src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$",) starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: 'Abnormal network activity detected on $host$ with $incidents$ incidents from $src$. Investigation needed for network errors: $warning_messages$.' - risk_objects: - - field: host - type: system - score: 10 - threat_objects: - - field: src - type: system - score: 10 + message: 'Abnormal network activity detected on $host$ with $incidents$ incidents from $src$. Investigation needed for network errors: $warning_messages$.' + risk_objects: + - field: host + type: system + score: 10 + threat_objects: + - field: src + type: system + score: 10 tags: - analytic_story: - - Suspicious Ollama Activities - asset_type: Web Application - mitre_attack_id: - - T1571 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Ollama Activities + asset_type: Web Application + mitre_attack_id: + - T1571 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/ollama/app.log - sourcetype: ollama:server - source: app.log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/ollama/app.log + sourcetype: ollama:server + source: app.log diff --git a/detections/application/ollama_abnormal_service_crash_availability_attack.yml b/detections/application/ollama_abnormal_service_crash_availability_attack.yml index 1b9750dbb2..ceeed48bdb 100644 --- a/detections/application/ollama_abnormal_service_crash_availability_attack.yml +++ b/detections/application/ollama_abnormal_service_crash_availability_attack.yml @@ -7,71 +7,42 @@ status: experimental type: Anomaly description: Detects critical service crashes, fatal errors, and abnormal process terminations in Ollama that may indicate exploitation attempts, resource exhaustion attacks, malicious input triggering unhandled exceptions, or deliberate denial of service attacks designed to disrupt AI model availability and degrade system stability. data_source: -- Ollama Server -search: '`ollama_server` (level=ERROR OR level=FATAL OR "service stopped" OR "terminated" OR "exit" OR "shutdown" OR "crash" OR "killed") -| rex field=_raw "msg=\"(?[^\"]+)\"" -| rex field=_raw "exit_code=(?\d+)" -| bin _time span=5m -| stats count as termination_count, - earliest(_time) as first_seen, - latest(_time) as last_seen, - values(msg) as error_messages, - values(exit_code) as exit_codes, - dc(msg) as unique_errors - by host -| eval first_seen=strftime(first_seen, "%Y-%m-%d %H:%M:%S") -| eval last_seen=strftime(last_seen, "%Y-%m-%d %H:%M:%S") -| eval severity=case( - termination_count > 5, "critical", - termination_count > 2, "high", - 1=1, "medium" -) -| eval attack_type=case( - termination_count > 5, "Resource Exhaustion", - termination_count > 2, "Repeated Service Failures", - 1=1, "Service Instability" -) -| where termination_count > 1 -| table first_seen, last_seen, host, termination_count, unique_errors, error_messages, severity, attack_type -| `ollama_abnormal_service_crash_availability_attack_filter`' + - Ollama Server +search: '`ollama_server` (level=ERROR OR level=FATAL OR "service stopped" OR "terminated" OR "exit" OR "shutdown" OR "crash" OR "killed") | rex field=_raw "msg=\"(?[^\"]+)\"" | rex field=_raw "exit_code=(?\d+)" | bin _time span=5m | stats count as termination_count, earliest(_time) as first_seen, latest(_time) as last_seen, values(msg) as error_messages, values(exit_code) as exit_codes, dc(msg) as unique_errors by host | eval first_seen=strftime(first_seen, "%Y-%m-%d %H:%M:%S") | eval last_seen=strftime(last_seen, "%Y-%m-%d %H:%M:%S") | eval severity=case( termination_count > 5, "critical", termination_count > 2, "high", 1=1, "medium" ) | eval attack_type=case( termination_count > 5, "Resource Exhaustion", termination_count > 2, "Repeated Service Failures", 1=1, "Service Instability" ) | where termination_count > 1 | table first_seen, last_seen, host, termination_count, unique_errors, error_messages, severity, attack_type | `ollama_abnormal_service_crash_availability_attack_filter`' how_to_implement: 'Ingest Ollama logs via Splunk TA-ollama add-on by configuring file monitoring inputs pointed to your Ollama server log directories (sourcetype: ollama:server), or enable HTTP Event Collector (HEC) for real-time API telemetry and prompt analytics (sourcetypes: ollama:api, ollama:prompts). CIM compatibility using the Web datamodel for standardized security detections.' known_false_positives: Normal service restarts during system updates or maintenance windows, graceful shutdowns with non-zero exit codes, intentional service stops by administrators, software upgrades requiring process termination, out-of-memory conditions on resource-constrained systems, or known bugs in specific Ollama versions that cause benign crashes may trigger this detection during routine operations. references: -- https://github.com/rosplk/ta-ollama + - https://github.com/rosplk/ta-ollama drilldown_searches: -- name: 'View the detection results for - "$host$"' - search: '%original_detection_search% | search host="$host$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: 'View risk events for the last 7 days for - "$host$"' - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host$") starthoursago=168 - | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" - values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" - values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: 'View the detection results for - "$host$"' + search: '%original_detection_search% | search host="$host$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: 'View risk events for the last 7 days for - "$host$"' + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: 'Abnormal Ollama service termination detected on host $host$ between $first_seen$ and $last_seen$. Service stopped $termination_count$ times with $unique_errors$ unique error types. Severity: $severity$. Potential cause: $attack_type$. Error messages: $error_messages$ require investigation.' - risk_objects: - - field: host - type: system - score: 10 - threat_objects: [] + message: 'Abnormal Ollama service termination detected on host $host$ between $first_seen$ and $last_seen$. Service stopped $termination_count$ times with $unique_errors$ unique error types. Severity: $severity$. Potential cause: $attack_type$. Error messages: $error_messages$ require investigation.' + risk_objects: + - field: host + type: system + score: 10 + threat_objects: [] tags: - analytic_story: - - Suspicious Ollama Activities - asset_type: Web Application - mitre_attack_id: - - T1489 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Ollama Activities + asset_type: Web Application + mitre_attack_id: + - T1489 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/ollama/app.log - sourcetype: ollama:server - source: app.log \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/ollama/app.log + sourcetype: ollama:server + source: app.log diff --git a/detections/application/ollama_excessive_api_requests.yml b/detections/application/ollama_excessive_api_requests.yml index 5147c5c04e..0dac7e8c2b 100644 --- a/detections/application/ollama_excessive_api_requests.yml +++ b/detections/application/ollama_excessive_api_requests.yml @@ -7,51 +7,42 @@ status: experimental type: Anomaly description: Detects potential Distributed Denial of Service (DDoS) attacks or rate limit abuse against Ollama API endpoints by identifying excessive request volumes from individual client IP addresses. This detection monitors GIN-formatted Ollama server logs to identify clients generating abnormally high request rates within short time windows, which may indicate automated attacks, botnet activity, or resource exhaustion attempts targeting local AI model infrastructure. data_source: -- Ollama Server -search: '`ollama_server` | rex field=_raw "\|\s+(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})\s+\|" -| eval src=coalesce(src, client_ip) -| eval dest=coalesce(dest, url, uripath, endpoint) -| bin _time span=5m -| stats count as request_count by _time, src, dest, host -| where request_count > 120 -| eval severity="high" -| eval attack_type="Rate Limit Abuse / DDoS" -| stats count by _time, host, src, dest, request_count, severity, attack_type -| `ollama_excessive_api_requests_filter`' -how_to_implement: 'Ingest Ollama logs via Splunk TA-ollama add-on by configuring file monitoring inputs pointed to your Ollama server log directories (sourcetype: ollama:server), or enable HTTP Event Collector (HEC) for real-time API telemetry and prompt analytics (sourcetypes: ollama:api, ollama:prompts). CIM compatibility using the Web datamodel for standardized security detections.' -known_false_positives: Legitimate automated services (CI/CD pipelines, monitoring tools, batch jobs), multiple users behind NAT/proxy infrastructure, or authorized load testing activities may trigger this detection during normal operations. Operator must adjust threshold accordingly. + - Ollama Server +search: '`ollama_server` | rex field=_raw "\|\s+(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})\s+\|" | eval src=coalesce(src, client_ip) | eval dest=coalesce(dest, url, uripath, endpoint) | bin _time span=5m | stats count as request_count by _time, src, dest, host | where request_count > 120 | eval severity="high" | eval attack_type="Rate Limit Abuse / DDoS" | stats count by _time, host, src, dest, request_count, severity, attack_type | `ollama_excessive_api_requests_filter`' +how_to_implement: 'Ingest Ollama logs via Splunk TA-ollama add-on by configuring file monitoring inputs pointed to your Ollama server log directories (sourcetype: ollama:server), or enable HTTP Event Collector (HEC) for real-time API telemetry and prompt analytics (sourcetypes: ollama:api, ollama:prompts). CIM compatibility using the Web datamodel for standardized security detections.' +known_false_positives: Legitimate automated services (CI/CD pipelines, monitoring tools, batch jobs), multiple users behind NAT/proxy infrastructure, or authorized load testing activities may trigger this detection during normal operations. Operator must adjust threshold accordingly. references: -- https://github.com/rosplk/ta-ollama + - https://github.com/rosplk/ta-ollama drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search "$src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search "$src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible DDoS attack from $src$ against Ollama server detected with request count $request_count$ in 1 minute, potentially causing service degradation or complete unavailability. - risk_objects: - - field: src - type: system - score: 10 - threat_objects: [] + message: Possible DDoS attack from $src$ against Ollama server detected with request count $request_count$ in 1 minute, potentially causing service degradation or complete unavailability. + risk_objects: + - field: src + type: system + score: 10 + threat_objects: [] tags: - analytic_story: - - Suspicious Ollama Activities - asset_type: Web Application - mitre_attack_id: - - T1498 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Ollama Activities + asset_type: Web Application + mitre_attack_id: + - T1498 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/ollama/server.log - sourcetype: ollama:server - source: server.log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/ollama/server.log + sourcetype: ollama:server + source: server.log diff --git a/detections/application/ollama_possible_api_endpoint_scan_reconnaissance.yml b/detections/application/ollama_possible_api_endpoint_scan_reconnaissance.yml index b298192319..7ff9c517e7 100644 --- a/detections/application/ollama_possible_api_endpoint_scan_reconnaissance.yml +++ b/detections/application/ollama_possible_api_endpoint_scan_reconnaissance.yml @@ -1,59 +1,60 @@ name: Ollama Possible API Endpoint Scan Reconnaissance id: ad3f352a-0347-48ee-86b9-670b5025a548 -version: 1 -date: '2025-10-05' +version: 2 +date: '2026-02-25' author: Rod Soto status: experimental type: Anomaly description: Detects API reconnaissance and endpoint scanning activity against Ollama servers by identifying sources probing multiple API endpoints within short timeframes, particularly when using HEAD requests or accessing diverse endpoint paths, which indicates systematic enumeration to map the API surface, discover hidden endpoints, or identify vulnerabilities before launching targeted attacks. data_source: -- Ollama Server -search: '`ollama_server` "[GIN]" -| bin _time span=5m -| stats count as total_requests, values(dest) as dest, values(http_method) as methods, values(status) as status_codes by _time, src, host -| where total_requests > 120 -| eval severity="medium" -| eval attack_type="API Activity Surge" -| stats count by _time, host, src, total_requests, dest, methods, status_codes, severity, attack_type -| `ollama_possible_api_endpoint_scan_reconnaissance_filter`' + - Ollama Server +search: |- + `ollama_server` "[GIN]" + | bin _time span=5m + | stats count as total_requests, values(dest) as dest, values(http_method) as methods, values(status) as status_codes + BY _time, src, host + | where total_requests > 120 + | eval severity="medium" + | eval attack_type="API Activity Surge" + | stats count + BY _time, host, src, + total_requests, dest, methods, + status_codes, severity, attack_type + | `ollama_possible_api_endpoint_scan_reconnaissance_filter` how_to_implement: 'Ingest Ollama logs via Splunk TA-ollama add-on by configuring file monitoring inputs pointed to your Ollama server log directories (sourcetype: ollama:server), or enable HTTP Event Collector (HEC) for real-time API telemetry and prompt analytics (sourcetypes: ollama:api, ollama:prompts). CIM compatibility using the Web datamodel for standardized security detections.' known_false_positives: Legitimate web application clients or mobile apps that access multiple API endpoints as part of normal functionality, monitoring and health check systems probing various endpoints for availability, load balancers performing health checks across different paths, API testing frameworks during development and QA processes, or users navigating through web interfaces that trigger multiple API calls may generate similar patterns during normal operations. references: -- https://github.com/rosplk/ta-ollama + - https://github.com/rosplk/ta-ollama drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search "$src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search "$src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: API reconnaissance activity detected from $src$ on $host$ with $total_requests$ requests across different endpoints using methods $methods$ and receiving status codes $status_codes$, indicating systematic endpoint enumeration to map API attack surface and identify potential vulnerabilities. - risk_objects: - - field: src - type: system - score: 10 - threat_objects: [] + message: API reconnaissance activity detected from $src$ on $host$ with $total_requests$ requests across different endpoints using methods $methods$ and receiving status codes $status_codes$, indicating systematic endpoint enumeration to map API attack surface and identify potential vulnerabilities. + risk_objects: + - field: src + type: system + score: 10 + threat_objects: [] tags: - analytic_story: - - Suspicious Ollama Activities - asset_type: Web Application - mitre_attack_id: - - T1595 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Ollama Activities + asset_type: Web Application + mitre_attack_id: + - T1595 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/ollama/server.log - sourcetype: ollama:server - source: server.log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/ollama/server.log + sourcetype: ollama:server + source: server.log diff --git a/detections/application/ollama_possible_memory_exhaustion_resource_abuse.yml b/detections/application/ollama_possible_memory_exhaustion_resource_abuse.yml index 3de864838d..0007462305 100644 --- a/detections/application/ollama_possible_memory_exhaustion_resource_abuse.yml +++ b/detections/application/ollama_possible_memory_exhaustion_resource_abuse.yml @@ -7,76 +7,42 @@ status: experimental type: Anomaly description: Detects abnormal memory allocation patterns and excessive runner operations in Ollama that may indicate resource exhaustion attacks, memory abuse through malicious model loading, or attempts to degrade system performance by overwhelming GPU/CPU resources. Adversaries may deliberately load multiple large models, trigger repeated model initialization cycles, or exploit memory allocation mechanisms to exhaust available system resources, causing denial of service conditions or degrading performance for legitimate users. data_source: -- Ollama Server -search: '`ollama_server` ("*llama_kv_cache*" OR "*compute buffer*" OR "*llama runner started*" OR "*loaded runners*") -| rex field=_raw "count=(?\d+)" -| rex field=_raw "size\s*=\s*(?[\d\.]+)\s+MiB" -| rex field=_raw "started in\s*(?[\d\.]+)\s*seconds" -| rex field=_raw "source=(?[^\s]+)" -| bin _time span=5m -| stats count as operations, - sum(runner_count) as total_runners, - dc(code_source) as unique_sources, - values(code_source) as code_sources, - avg(memory_mb) as avg_memory, - max(memory_mb) as max_memory, - sum(memory_mb) as total_memory, - avg(load_time) as avg_load_time, - max(load_time) as max_load_time - by _time, host -| where operations > 5 OR total_runners > 0 OR max_memory > 400 OR total_memory > 500 -| eval avg_memory=round(avg_memory, 2) -| eval max_memory=round(max_memory, 2) -| eval total_memory=round(total_memory, 2) -| eval avg_load_time=round(avg_load_time, 2) -| eval severity=case( - max_memory > 500 OR total_memory > 1000, "critical", - max_memory > 400 OR operations > 20, "high", - operations > 10, "medium", - 1=1, "low" -) -| eval attack_type="Resource Exhaustion / Memory Abuse" -| sort -_time -| table _time, host, operations, total_runners, unique_sources, avg_memory, max_memory, total_memory, avg_load_time, max_load_time, severity, attack_type -| `ollama_possible_memory_exhaustion_resource_abuse_filter`' + - Ollama Server +search: '`ollama_server` ("*llama_kv_cache*" OR "*compute buffer*" OR "*llama runner started*" OR "*loaded runners*") | rex field=_raw "count=(?\d+)" | rex field=_raw "size\s*=\s*(?[\d\.]+)\s+MiB" | rex field=_raw "started in\s*(?[\d\.]+)\s*seconds" | rex field=_raw "source=(?[^\s]+)" | bin _time span=5m | stats count as operations, sum(runner_count) as total_runners, dc(code_source) as unique_sources, values(code_source) as code_sources, avg(memory_mb) as avg_memory, max(memory_mb) as max_memory, sum(memory_mb) as total_memory, avg(load_time) as avg_load_time, max(load_time) as max_load_time by _time, host | where operations > 5 OR total_runners > 0 OR max_memory > 400 OR total_memory > 500 | eval avg_memory=round(avg_memory, 2) | eval max_memory=round(max_memory, 2) | eval total_memory=round(total_memory, 2) | eval avg_load_time=round(avg_load_time, 2) | eval severity=case( max_memory > 500 OR total_memory > 1000, "critical", max_memory > 400 OR operations > 20, "high", operations > 10, "medium", 1=1, "low" ) | eval attack_type="Resource Exhaustion / Memory Abuse" | sort -_time | table _time, host, operations, total_runners, unique_sources, avg_memory, max_memory, total_memory, avg_load_time, max_load_time, severity, attack_type | `ollama_possible_memory_exhaustion_resource_abuse_filter`' how_to_implement: 'Ingest Ollama logs via Splunk TA-ollama add-on by configuring file monitoring inputs pointed to your Ollama server log directories (sourcetype: ollama:server), or enable HTTP Event Collector (HEC) for real-time API telemetry and prompt analytics (sourcetypes: ollama:api, ollama:prompts). CIM compatibility using the Web datamodel for standardized security detections.' known_false_positives: Legitimate high-volume production workloads processing multiple concurrent requests, users loading large language models (7B+ parameters) that naturally require substantial memory allocation, simultaneous multi-model deployments during system scaling, batch processing operations, or initial system startup sequences may generate similar memory allocation patterns during normal operations. references: -- https://github.com/rosplk/ta-ollama + - https://github.com/rosplk/ta-ollama drilldown_searches: -- name: View the detection results for - "$host$" - search: '%original_detection_search% | search "$host = "$host$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$host$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$host$" + search: '%original_detection_search% | search "$host = "$host$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$host$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential resource exhaustion attack detected on $host$ with $operations$ memory operations in 5 minutes, utilizing $max_memory$ MiB peak memory and $total_runners$ runners, indicating possible attempts to exhaust system resources through excessive model loading or memory abuse. - risk_objects: - - field: host - type: system - score: 10 - threat_objects: [] + message: Potential resource exhaustion attack detected on $host$ with $operations$ memory operations in 5 minutes, utilizing $max_memory$ MiB peak memory and $total_runners$ runners, indicating possible attempts to exhaust system resources through excessive model loading or memory abuse. + risk_objects: + - field: host + type: system + score: 10 + threat_objects: [] tags: - analytic_story: - - Suspicious Ollama Activities - asset_type: Web Application - mitre_attack_id: - - T1499 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Ollama Activities + asset_type: Web Application + mitre_attack_id: + - T1499 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/ollama/server.log - sourcetype: ollama:server - source: server.log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/ollama/server.log + sourcetype: ollama:server + source: server.log diff --git a/detections/application/ollama_possible_model_exfiltration_data_leakage.yml b/detections/application/ollama_possible_model_exfiltration_data_leakage.yml index 7882c0a9f6..c31e9e72cf 100644 --- a/detections/application/ollama_possible_model_exfiltration_data_leakage.yml +++ b/detections/application/ollama_possible_model_exfiltration_data_leakage.yml @@ -7,62 +7,42 @@ status: experimental type: Anomaly description: Detects data leakage and exfiltration attempts targeting Ollama model metadata and configuration endpoints. Adversaries repeatedly query /api/show, /api/tags, and /api/v1/models to systematically extract sensitive model information including architecture details, fine-tuning parameters, system paths, Modelfile configurations, and proprietary customizations. Multiple inspection attempts within a 15-minute window indicate automated exfiltration of valuable intellectual property such as custom model configurations, system prompts, and internal model specifications. This activity represents unauthorized data disclosure that could enable competitive intelligence gathering, model replication, or preparation for advanced attacks against the AI infrastructure. data_source: -- Ollama Server -search: '`ollama_server` | rex field=_raw "\|\s+(?\d+)\s+\|\s+(?[\d\.]+)s\s+\|\s+(?[\:\da-f\.]+)\s+\|\s+(?\w+)\s+\"(?[^\"]+)\"" -| eval src=src_ip -| eval dest=uri_path -| where response_time > 55 -| bin _time span=15m -| stats count, avg(response_time) as avg_response_time, max(response_time) as max_response_time by _time, src, dest, uri_path -| eval avg_response_time=round(avg_response_time, 2) -| eval max_response_time=round(max_response_time, 2) -| eval severity=case( - avg_response_time > 50, "high", - avg_response_time > 40, "medium", - 1=1, "low" -) -| eval attack_type="Potential Data Exfiltration" -| sort -_time -| stats count by _time, src, uri_path, avg_response_time, max_response_time, severity, attack_type -| `ollama_possible_model_exfiltration_data_leakage_filter`' + - Ollama Server +search: '`ollama_server` | rex field=_raw "\|\s+(?\d+)\s+\|\s+(?[\d\.]+)s\s+\|\s+(?[\:\da-f\.]+)\s+\|\s+(?\w+)\s+\"(?[^\"]+)\"" | eval src=src_ip | eval dest=uri_path | where response_time > 55 | bin _time span=15m | stats count, avg(response_time) as avg_response_time, max(response_time) as max_response_time by _time, src, dest, uri_path | eval avg_response_time=round(avg_response_time, 2) | eval max_response_time=round(max_response_time, 2) | eval severity=case( avg_response_time > 50, "high", avg_response_time > 40, "medium", 1=1, "low" ) | eval attack_type="Potential Data Exfiltration" | sort -_time | stats count by _time, src, uri_path, avg_response_time, max_response_time, severity, attack_type | `ollama_possible_model_exfiltration_data_leakage_filter`' how_to_implement: 'Ingest Ollama logs via Splunk TA-ollama add-on by configuring file monitoring inputs pointed to your Ollama server log directories (sourcetype: ollama:server), or enable HTTP Event Collector (HEC) for real-time API telemetry and prompt analytics (sourcetypes: ollama:api, ollama:prompts). CIM compatibility using the Web datamodel for standardized security detections.' known_false_positives: Legitimate administrative activities such as model inventory management, monitoring dashboards polling model status, automated health checks verifying model availability, CI/CD pipelines validating deployments, development tools inspecting model configurations, or users browsing available models through management interfaces may trigger this detection during normal operations. Adjust the threshold based on your environment's baseline activity. references: -- https://github.com/rosplk/ta-ollama + - https://github.com/rosplk/ta-ollama drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search "$src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search "$src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential model data exfiltration detected from $src$ with $avg_response_time$ attempts across endpoints, indicating systematic extraction of sensitive model configurations, architecture details, and proprietary customizations that may constitute intellectual property theft. - risk_objects: - - field: src - type: system - score: 10 - threat_objects: [] + message: Potential model data exfiltration detected from $src$ with $avg_response_time$ attempts across endpoints, indicating systematic extraction of sensitive model configurations, architecture details, and proprietary customizations that may constitute intellectual property theft. + risk_objects: + - field: src + type: system + score: 10 + threat_objects: [] tags: - analytic_story: - - Suspicious Ollama Activities - asset_type: Web Application - mitre_attack_id: - - T1048 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Ollama Activities + asset_type: Web Application + mitre_attack_id: + - T1048 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/ollama/server.log - sourcetype: ollama:server - source: server.log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/ollama/server.log + sourcetype: ollama:server + source: server.log diff --git a/detections/application/ollama_possible_rce_via_model_loading.yml b/detections/application/ollama_possible_rce_via_model_loading.yml index c48904604c..c2284b3336 100644 --- a/detections/application/ollama_possible_rce_via_model_loading.yml +++ b/detections/application/ollama_possible_rce_via_model_loading.yml @@ -7,77 +7,42 @@ status: experimental type: Anomaly description: Detects Ollama server errors and failures during model loading operations that may indicate malicious model injection, path traversal attempts, or exploitation of model loading mechanisms to achieve remote code execution. Adversaries may attempt to load specially crafted malicious models or exploit vulnerabilities in the model loading process to execute arbitrary code on the server. This detection monitors error messages and failure patterns that could signal attempts to abuse model loading functionality for malicious purposes. data_source: -- Ollama Server -search: '`ollama_server` level=ERROR ("*llama runner*" OR "*model*" OR "*server.go*" OR "*exited*") -| rex field=_raw "source=(?[^\s]+)" -| rex field=_raw "msg=\"(?[^\"]+)\"" -| rex field=_raw "err=\"(?[^\"]+)\"" -| rex field=_raw "level=(?\w+)" -| eval error_type=case( - match(_raw, "exited"), "service_crash", - match(_raw, "model"), "model_error", - match(_raw, "llama runner"), "runner_error", - 1=1, "unknown_error" -) -| bin _time span=1h -| stats count as error_count, - earliest(_time) as first_error, - latest(_time) as last_error, - values(msg) as error_messages, - values(err) as error_details, - values(code_source) as code_sources, - values(error_type) as error_types, - dc(error_type) as unique_error_types - by host -| where error_count > 0 -| eval first_error=strftime(first_error, "%Y-%m-%d %H:%M:%S") -| eval last_error=strftime(last_error, "%Y-%m-%d %H:%M:%S") -| eval severity=case( - match(error_details, "exit status") OR error_count > 5, "critical", - error_count > 2, "high", - 1=1, "medium" -) -| eval attack_type="Suspicious Model Loading / Potential RCE" -| stats count by first_error, last_error, host, code_sources, error_count, unique_error_types, error_types, error_messages, error_details, severity, attack_type -| `ollama_possible_rce_via_model_loading_filter`' + - Ollama Server +search: '`ollama_server` level=ERROR ("*llama runner*" OR "*model*" OR "*server.go*" OR "*exited*") | rex field=_raw "source=(?[^\s]+)" | rex field=_raw "msg=\"(?[^\"]+)\"" | rex field=_raw "err=\"(?[^\"]+)\"" | rex field=_raw "level=(?\w+)" | eval error_type=case( match(_raw, "exited"), "service_crash", match(_raw, "model"), "model_error", match(_raw, "llama runner"), "runner_error", 1=1, "unknown_error" ) | bin _time span=1h | stats count as error_count, earliest(_time) as first_error, latest(_time) as last_error, values(msg) as error_messages, values(err) as error_details, values(code_source) as code_sources, values(error_type) as error_types, dc(error_type) as unique_error_types by host | where error_count > 0 | eval first_error=strftime(first_error, "%Y-%m-%d %H:%M:%S") | eval last_error=strftime(last_error, "%Y-%m-%d %H:%M:%S") | eval severity=case( match(error_details, "exit status") OR error_count > 5, "critical", error_count > 2, "high", 1=1, "medium" ) | eval attack_type="Suspicious Model Loading / Potential RCE" | stats count by first_error, last_error, host, code_sources, error_count, unique_error_types, error_types, error_messages, error_details, severity, attack_type | `ollama_possible_rce_via_model_loading_filter`' how_to_implement: 'Ingest Ollama logs via Splunk TA-ollama add-on by configuring file monitoring inputs pointed to your Ollama server log directories (sourcetype: ollama:server), or enable HTTP Event Collector (HEC) for real-time API telemetry and prompt analytics (sourcetypes: ollama:api, ollama:prompts). CIM compatibility using the Web datamodel for standardized security detections.' known_false_positives: Corrupted model files from interrupted downloads, insufficient disk space or memory during legitimate model loading, incompatible model formats or versions, network timeouts when pulling models from registries, file permission issues in multi-user environments, or genuine configuration errors during initial Ollama setup may generate similar error patterns during normal operations. references: -- https://github.com/rosplk/ta-ollama + - https://github.com/rosplk/ta-ollama drilldown_searches: -- name: View the detection results for - "$host$" - search: '%original_detection_search% | search "$host = "$host$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$host$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host$", starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$host$" + search: '%original_detection_search% | search "$host = "$host$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$host$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host$", starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious model loading errors detected on $host$ with $error_count$ failures showing error messages $error_messages$, potentially indicating malicious model injection, path traversal exploitation, or attempts to achieve remote code execution through crafted model files. - risk_objects: - - field: host - type: system - score: 10 - threat_objects: [] + message: Suspicious model loading errors detected on $host$ with $error_count$ failures showing error messages $error_messages$, potentially indicating malicious model injection, path traversal exploitation, or attempts to achieve remote code execution through crafted model files. + risk_objects: + - field: host + type: system + score: 10 + threat_objects: [] tags: - analytic_story: - - Suspicious Ollama Activities - asset_type: Web Application - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Ollama Activities + asset_type: Web Application + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/ollama/app.log - sourcetype: ollama:server - source: app.log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/ollama/app.log + sourcetype: ollama:server + source: app.log diff --git a/detections/application/ollama_suspicious_prompt_injection_jailbreak.yml b/detections/application/ollama_suspicious_prompt_injection_jailbreak.yml index 98414c61a4..2ceecbe84c 100644 --- a/detections/application/ollama_suspicious_prompt_injection_jailbreak.yml +++ b/detections/application/ollama_suspicious_prompt_injection_jailbreak.yml @@ -7,67 +7,44 @@ status: experimental type: Anomaly description: Detects potential prompt injection or jailbreak attempts against Ollama API endpoints by identifying requests with abnormally long response times. Attackers often craft complex, layered prompts designed to bypass AI safety controls, which typically result in extended processing times as the model attempts to parse and respond to these malicious inputs. This detection monitors /api/generate and /api/chat endpoints for requests exceeding 30 seconds, which may indicate sophisticated jailbreak techniques, multi-stage prompt injections, or attempts to extract sensitive information from the model. data_source: -- Ollama Server -search: '`ollama_server` "GIN" ("*/api/generate*" OR "*/v1/chat/completions*") -| rex field=_raw "\|\s+(?\d+)\s+\|\s+(?[\d\.]+[a-z]+)\s+\|\s+(?[\:\da-f\.]+)\s+\|\s+(?\w+)\s+\"(?[^\"]+)\"" -| rex field=response_time "^(?:(?\d+)m)?(?[\d\.]+)s$" -| eval response_time_seconds=if(isnotnull(minutes), tonumber(minutes)*60+tonumber(seconds), tonumber(seconds)) -| eval src=src_ip -| where response_time_seconds > 30 -| bin _time span=10m -| stats count as long_request_count, - avg(response_time_seconds) as avg_response_time, - max(response_time_seconds) as max_response_time, - values(uri_path) as uri_path, - values(status_code) as status_codes - by _time, src, host -| where long_request_count > 170 -| eval avg_response_time=round(avg_response_time, 2) -| eval max_response_time=round(max_response_time, 2) -| eval severity=case( - long_request_count > 50 OR max_response_time > 55, "critical", - long_request_count > 20 OR max_response_time > 40, "high", - 1=1, "medium" -) -| eval attack_type="Potential Prompt Injection / Jailbreak" -| table _time, host, src, uri_path, long_request_count, avg_response_time, max_response_time, status_codes, severity, attack_type -| `ollama_suspicious_prompt_injection_jailbreak_filter`' + - Ollama Server +search: '`ollama_server` "GIN" ("*/api/generate*" OR "*/v1/chat/completions*") | rex field=_raw "\|\s+(?\d+)\s+\|\s+(?[\d\.]+[a-z]+)\s+\|\s+(?[\:\da-f\.]+)\s+\|\s+(?\w+)\s+\"(?[^\"]+)\"" | rex field=response_time "^(?:(?\d+)m)?(?[\d\.]+)s$" | eval response_time_seconds=if(isnotnull(minutes), tonumber(minutes)*60+tonumber(seconds), tonumber(seconds)) | eval src=src_ip | where response_time_seconds > 30 | bin _time span=10m | stats count as long_request_count, avg(response_time_seconds) as avg_response_time, max(response_time_seconds) as max_response_time, values(uri_path) as uri_path, values(status_code) as status_codes by _time, src, host | where long_request_count > 170 | eval avg_response_time=round(avg_response_time, 2) | eval max_response_time=round(max_response_time, 2) | eval severity=case( long_request_count > 50 OR max_response_time > 55, "critical", long_request_count > 20 OR max_response_time > 40, "high", 1=1, "medium" ) | eval attack_type="Potential Prompt Injection / Jailbreak" | table _time, host, src, uri_path, long_request_count, avg_response_time, max_response_time, status_codes, severity, attack_type | `ollama_suspicious_prompt_injection_jailbreak_filter`' how_to_implement: 'Ingest Ollama logs via Splunk TA-ollama add-on by configuring file monitoring inputs pointed to your Ollama server log directories (sourcetype: ollama:server), or enable HTTP Event Collector (HEC) for real-time API telemetry and prompt analytics (sourcetypes: ollama:api, ollama:prompts). CIM compatibility using the Web datamodel for standardized security detections.' known_false_positives: Legitimate complex queries requiring extensive model reasoning, large context windows processing substantial amounts of text, batch processing operations, or resource-constrained systems experiencing performance degradation may trigger this detection during normal operations. references: -- https://github.com/rosplk/ta-ollama -- https://github.com/OWASP/www-project-ai-testing-guide + - https://github.com/rosplk/ta-ollama + - https://github.com/OWASP/www-project-ai-testing-guide drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src="$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src="$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential prompt injection or jailbreak attempt detected from $src$ with $long_request_count$ requests averaging $avg_response_time$ seconds, indicating possible attempts to bypass AI safety controls or extract sensitive information from the Ollama model. - risk_objects: - - field: src - type: system - score: 70 - threat_objects: [] + message: Potential prompt injection or jailbreak attempt detected from $src$ with $long_request_count$ requests averaging $avg_response_time$ seconds, indicating possible attempts to bypass AI safety controls or extract sensitive information from the Ollama model. + risk_objects: + - field: src + type: system + score: 70 + threat_objects: [] tags: - analytic_story: - - Suspicious Ollama Activities - asset_type: Web Application - mitre_attack_id: - - T1190 - - T1059 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Ollama Activities + asset_type: Web Application + mitre_attack_id: + - T1190 + - T1059 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/ollama/server.log - sourcetype: ollama:server - source: server.log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/ollama/server.log + sourcetype: ollama:server + source: server.log diff --git a/detections/application/pingid_mismatch_auth_source_and_verification_response.yml b/detections/application/pingid_mismatch_auth_source_and_verification_response.yml index f755801856..92628725d1 100644 --- a/detections/application/pingid_mismatch_auth_source_and_verification_response.yml +++ b/detections/application/pingid_mismatch_auth_source_and_verification_response.yml @@ -5,85 +5,52 @@ date: '2025-05-02' author: Steven Dick status: production type: TTP -description: The following analytic identifies discrepancies between the IP address - of an authentication event and the IP address of the verification response event, - focusing on differences in the originating countries. It leverages JSON logs from - PingID, comparing the 'auth_Country' and 'verify_Country' fields. This activity - is significant as it may indicate suspicious sign-in behavior, such as account compromise - or unauthorized access attempts. If confirmed malicious, this could allow attackers - to bypass authentication mechanisms, potentially leading to unauthorized access - to sensitive systems and data. +description: The following analytic identifies discrepancies between the IP address of an authentication event and the IP address of the verification response event, focusing on differences in the originating countries. It leverages JSON logs from PingID, comparing the 'auth_Country' and 'verify_Country' fields. This activity is significant as it may indicate suspicious sign-in behavior, such as account compromise or unauthorized access attempts. If confirmed malicious, this could allow attackers to bypass authentication mechanisms, potentially leading to unauthorized access to sensitive systems and data. data_source: -- PingID -search: "`pingid` (\"result.status\" IN (\"SUCCESS*\",\"FAIL*\",\"UNSUCCESSFUL*\" - ) NOT \"result.message\" IN (\"*pair*\",\"*create*\",\"*delete*\")) | eval user - = upper('actors{}.name'), session_id = 'resources{}.websession', dest = 'resources{}.ipaddress', - reason = 'result.message', object = 'resources{}.devicemodel', status = 'result.status' - | join user session_id [ search `pingid` (\"result.status\" IN (\"POLICY\") AND - \"resources{}.ipaddress\"=*) AND \"result.message\" IN(\"*Action: Authenticate*\"\ - ,\"*Action: Approve*\",\"*Action: Allowed*\") | rex field=result.message \"IP Address: - (?:N\\/A)?(?.+)?\\n\" | rex field=result.message \"Action: (?:N\\\ - /A)?(?.+)?\\n\" | rex field=result.message \"Requested Application Name: - (?:N\\/A)?(?.+)?\\n\" | rex field=result.message \" - Requested Application ID: (?:N\\/A)?(?.+)?\\n\" | eval - user = upper('actors{}.name'), session_id = 'resources{}.websession', src = coalesce('resources{}.ipaddress',policy_ipaddress), - app = coalesce(Requested_Application_ID,Requested_Application_Name) | fields app, - user, session_id, src, signature ] | iplocation prefix=auth_ dest | iplocation prefix=verify_ - src | stats count min(_time) as firstTime max(_time) as lastTime values(app) as - app values(session_id) as session_id by user, dest, auth_Country, src, verify_Country, - object, signature, status, reason | where auth_Country != verify_Country | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `pingid_mismatch_auth_source_and_verification_response_filter`" -how_to_implement: Target environment must ingest JSON logging from a PingID(PingOne) - enterprise environment, either via Webhook or Push Subscription. -known_false_positives: False positives may be generated by users working out the geographic - region where the organizations services or technology is hosted. + - PingID +search: "`pingid` (\"result.status\" IN (\"SUCCESS*\",\"FAIL*\",\"UNSUCCESSFUL*\" ) NOT \"result.message\" IN (\"*pair*\",\"*create*\",\"*delete*\")) | eval user = upper('actors{}.name'), session_id = 'resources{}.websession', dest = 'resources{}.ipaddress', reason = 'result.message', object = 'resources{}.devicemodel', status = 'result.status' | join user session_id [ search `pingid` (\"result.status\" IN (\"POLICY\") AND \"resources{}.ipaddress\"=*) AND \"result.message\" IN(\"*Action: Authenticate*\",\"*Action: Approve*\",\"*Action: Allowed*\") | rex field=result.message \"IP Address: (?:N\\/A)?(?.+)?\\n\" | rex field=result.message \"Action: (?:N\\/A)?(?.+)?\\n\" | rex field=result.message \"Requested Application Name: (?:N\\/A)?(?.+)?\\n\" | rex field=result.message \" Requested Application ID: (?:N\\/A)?(?.+)?\\n\" | eval user = upper('actors{}.name'), session_id = 'resources{}.websession', src = coalesce('resources{}.ipaddress',policy_ipaddress), app = coalesce(Requested_Application_ID,Requested_Application_Name) | fields app, user, session_id, src, signature ] | iplocation prefix=auth_ dest | iplocation prefix=verify_ src | stats count min(_time) as firstTime max(_time) as lastTime values(app) as app values(session_id) as session_id by user, dest, auth_Country, src, verify_Country, object, signature, status, reason | where auth_Country != verify_Country | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `pingid_mismatch_auth_source_and_verification_response_filter`" +how_to_implement: Target environment must ingest JSON logging from a PingID(PingOne) enterprise environment, either via Webhook or Push Subscription. +known_false_positives: False positives may be generated by users working out the geographic region where the organizations services or technology is hosted. references: -- https://twitter.com/jhencinski/status/1618660062352007174 -- https://attack.mitre.org/techniques/T1098/005/ -- https://attack.mitre.org/techniques/T1556/006/ -- https://docs.pingidentity.com/r/en-us/pingoneforenterprise/p14e_subscriptions?tocId=3xhnxjX3VzKNs3SXigWnQA + - https://twitter.com/jhencinski/status/1618660062352007174 + - https://attack.mitre.org/techniques/T1098/005/ + - https://attack.mitre.org/techniques/T1556/006/ + - https://docs.pingidentity.com/r/en-us/pingoneforenterprise/p14e_subscriptions?tocId=3xhnxjX3VzKNs3SXigWnQA drilldown_searches: -- name: View the detection results for - "$user$" and "$src$" - search: '%original_detection_search% | search user = "$user$" src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as - lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" - values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" - values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$src$" + search: '%original_detection_search% | search user = "$user$" src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An authentication by [$user$] was detected from [$dest$ - $auth_Country$] - and the verification was received from [$src$ - $verify_Country$]. - risk_objects: - - field: user - type: user - score: 25 - - field: src - type: system - score: 25 - threat_objects: [] + message: An authentication by [$user$] was detected from [$dest$ - $auth_Country$] and the verification was received from [$src$ - $verify_Country$]. + risk_objects: + - field: user + type: user + score: 25 + - field: src + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Compromised User Account - asset_type: Identity - mitre_attack_id: - - T1621 - - T1556.006 - - T1098.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - Compromised User Account + asset_type: Identity + mitre_attack_id: + - T1621 + - T1556.006 + - T1098.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/pingid/pingid.log - source: PINGID - sourcetype: _json + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/pingid/pingid.log + source: PINGID + sourcetype: _json diff --git a/detections/application/pingid_multiple_failed_mfa_requests_for_user.yml b/detections/application/pingid_multiple_failed_mfa_requests_for_user.yml index 5e1c89e279..7f02e4d944 100644 --- a/detections/application/pingid_multiple_failed_mfa_requests_for_user.yml +++ b/detections/application/pingid_multiple_failed_mfa_requests_for_user.yml @@ -5,70 +5,50 @@ date: '2025-05-02' author: Steven Dick status: production type: TTP -description: The following analytic identifies multiple failed multi-factor authentication - (MFA) requests for a single user within a PingID environment. It triggers when 10 - or more MFA prompts fail within 10 minutes, using JSON logs from PingID. This activity - is significant as it may indicate an adversary attempting to bypass MFA by bombarding - the user with repeated authentication requests. If confirmed malicious, this could - lead to unauthorized access, as the user might eventually accept the fraudulent - request, compromising the security of the account and potentially the entire network. +description: The following analytic identifies multiple failed multi-factor authentication (MFA) requests for a single user within a PingID environment. It triggers when 10 or more MFA prompts fail within 10 minutes, using JSON logs from PingID. This activity is significant as it may indicate an adversary attempting to bypass MFA by bombarding the user with repeated authentication requests. If confirmed malicious, this could lead to unauthorized access, as the user might eventually accept the fraudulent request, compromising the security of the account and potentially the entire network. data_source: -- PingID -search: "`pingid` \"result.status\" IN (\"FAILURE,authFail\",\"UNSUCCESSFUL_ATTEMPT\"\ - ) | eval time = _time, src = coalesce('resources{}.ipaddress','resources{}.devicemodel'), - user = upper('actors{}.name'), object = 'resources{}.devicemodel', reason = 'result.message'| - bucket span=10m _time | stats dc(_raw) AS mfa_prompts min(time) as firstTime, max(time) - as lastTime values(src) as src by user, reason, _time | `security_content_ctime(firstTime)`| - `security_content_ctime(lastTime)` | where mfa_prompts >= 10 | `pingid_multiple_failed_mfa_requests_for_user_filter`" -how_to_implement: Target environment must ingest JSON logging from a PingID(PingOne) - enterprise environment, either via Webhook or Push Subscription. -known_false_positives: False positives may be generated by normal provisioning workflows - for user device registration. + - PingID +search: "`pingid` \"result.status\" IN (\"FAILURE,authFail\",\"UNSUCCESSFUL_ATTEMPT\") | eval time = _time, src = coalesce('resources{}.ipaddress','resources{}.devicemodel'), user = upper('actors{}.name'), object = 'resources{}.devicemodel', reason = 'result.message'| bucket span=10m _time | stats dc(_raw) AS mfa_prompts min(time) as firstTime, max(time) as lastTime values(src) as src by user, reason, _time | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | where mfa_prompts >= 10 | `pingid_multiple_failed_mfa_requests_for_user_filter`" +how_to_implement: Target environment must ingest JSON logging from a PingID(PingOne) enterprise environment, either via Webhook or Push Subscription. +known_false_positives: False positives may be generated by normal provisioning workflows for user device registration. references: -- https://therecord.media/russian-hackers-bypass-2fa-by-annoying-victims-with-repeated-push-notifications/ -- https://attack.mitre.org/techniques/T1621/ -- https://attack.mitre.org/techniques/T1110/ -- https://attack.mitre.org/techniques/T1078/004/ -- https://docs.pingidentity.com/r/en-us/pingoneforenterprise/p14e_subscriptions?tocId=3xhnxjX3VzKNs3SXigWnQA + - https://therecord.media/russian-hackers-bypass-2fa-by-annoying-victims-with-repeated-push-notifications/ + - https://attack.mitre.org/techniques/T1621/ + - https://attack.mitre.org/techniques/T1110/ + - https://attack.mitre.org/techniques/T1078/004/ + - https://docs.pingidentity.com/r/en-us/pingoneforenterprise/p14e_subscriptions?tocId=3xhnxjX3VzKNs3SXigWnQA drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Multiple Failed MFA requests $mfa_prompts$ for user $user$ between $firstTime$ - and $lastTime$. - risk_objects: - - field: user - type: user - score: 50 - threat_objects: [] + message: Multiple Failed MFA requests $mfa_prompts$ for user $user$ between $firstTime$ and $lastTime$. + risk_objects: + - field: user + type: user + score: 50 + threat_objects: [] tags: - analytic_story: - - Compromised User Account - asset_type: Identity - mitre_attack_id: - - T1621 - - T1078 - - T1110 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - Compromised User Account + asset_type: Identity + mitre_attack_id: + - T1621 + - T1078 + - T1110 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/pingid/pingid.log - source: PINGID - sourcetype: _json + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/pingid/pingid.log + source: PINGID + sourcetype: _json diff --git a/detections/application/pingid_new_mfa_method_after_credential_reset.yml b/detections/application/pingid_new_mfa_method_after_credential_reset.yml index c39a83779d..dae4641b02 100644 --- a/detections/application/pingid_new_mfa_method_after_credential_reset.yml +++ b/detections/application/pingid_new_mfa_method_after_credential_reset.yml @@ -5,84 +5,54 @@ date: '2025-10-14' author: Steven Dick status: production type: TTP -description: The following analytic identifies the provisioning of a new MFA device - shortly after a password reset. It detects this activity by correlating Windows - Event Log events for password changes (EventID 4723, 4724) with PingID logs indicating - device pairing. This behavior is significant as it may indicate a social engineering - attack where a threat actor impersonates a valid user to reset credentials and add - a new MFA device. If confirmed malicious, this activity could allow an attacker - to gain persistent access to the compromised account, bypassing traditional security - measures. +description: The following analytic identifies the provisioning of a new MFA device shortly after a password reset. It detects this activity by correlating Windows Event Log events for password changes (EventID 4723, 4724) with PingID logs indicating device pairing. This behavior is significant as it may indicate a social engineering attack where a threat actor impersonates a valid user to reset credentials and add a new MFA device. If confirmed malicious, this activity could allow an attacker to gain persistent access to the compromised account, bypassing traditional security measures. data_source: -- PingID -search: "`pingid` \"result.message\" = \"*Device Paired*\" | rex field=result.message - \"Device (Unp)?(P)?aired (?.+)\" | eval src = coalesce('resources{}.ipaddress','resources{}.devicemodel'), - user = upper('actors{}.name'), reason = 'result.message' | eval object=CASE(ISNOTNULL('resources{}.devicemodel'),'resources{}.devicemodel',true(),device_extract) - | eval action=CASE(match('result.message',\"Device Paired*\"),\"created\",match('result.message', - \"Device Unpaired*\"),\"deleted\") | stats count min(_time) as firstTime, max(_time) - as lastTime, values(reason) as reason by src,user,action,object | join type=outer - user [| search `wineventlog_security` EventID IN(4723,4724) | eval PW_Change_Time - = _time, user = upper(user) | fields user,src_user,EventID,PW_Change_Time] | eval - timeDiffRaw = round(lastTime - PW_Change_Time) | eval timeDiff = replace(tostring(abs(timeDiffRaw) - ,\"duration\"),\"(\\d*)\\+*(\\d+):(\\d+):(\\d+)\",\"\\2 hours \\3 minutes\") | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `security_content_ctime(PW_Change_Time)` - | where timeDiffRaw > 0 AND timeDiffRaw < 3600 | `pingid_new_mfa_method_after_credential_reset_filter`" -how_to_implement: Target environment must ingest Windows Event Log and PingID(PingOne) - data sources. Specifically from logs from Active Directory Domain Controllers and - JSON logging from a PingID(PingOne) enterprise environment, either via Webhook or - Push Subscription. -known_false_positives: False positives may be generated by normal provisioning workflows - that generate a password reset followed by a device registration. + - PingID +search: "`pingid` \"result.message\" = \"*Device Paired*\" | rex field=result.message \"Device (Unp)?(P)?aired (?.+)\" | eval src = coalesce('resources{}.ipaddress','resources{}.devicemodel'), user = upper('actors{}.name'), reason = 'result.message' | eval object=CASE(ISNOTNULL('resources{}.devicemodel'),'resources{}.devicemodel',true(),device_extract) | eval action=CASE(match('result.message',\"Device Paired*\"),\"created\",match('result.message', \"Device Unpaired*\"),\"deleted\") | stats count min(_time) as firstTime, max(_time) as lastTime, values(reason) as reason by src,user,action,object | join type=outer user [| search `wineventlog_security` EventID IN(4723,4724) | eval PW_Change_Time = _time, user = upper(user) | fields user,src_user,EventID,PW_Change_Time] | eval timeDiffRaw = round(lastTime - PW_Change_Time) | eval timeDiff = replace(tostring(abs(timeDiffRaw) ,\"duration\"),\"(\\d*)\\+*(\\d+):(\\d+):(\\d+)\",\"\\2 hours \\3 minutes\") | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `security_content_ctime(PW_Change_Time)` | where timeDiffRaw > 0 AND timeDiffRaw < 3600 | `pingid_new_mfa_method_after_credential_reset_filter`" +how_to_implement: Target environment must ingest Windows Event Log and PingID(PingOne) data sources. Specifically from logs from Active Directory Domain Controllers and JSON logging from a PingID(PingOne) enterprise environment, either via Webhook or Push Subscription. +known_false_positives: False positives may be generated by normal provisioning workflows that generate a password reset followed by a device registration. references: -- https://techcommunity.microsoft.com/t5/microsoft-entra-azure-ad-blog/defend-your-users-from-mfa-fatigue-attacks/ba-p/2365677 -- https://www.bleepingcomputer.com/news/security/mfa-fatigue-hackers-new-favorite-tactic-in-high-profile-breaches/ -- https://attack.mitre.org/techniques/T1098/005/ -- https://attack.mitre.org/techniques/T1556/006/ -- https://docs.pingidentity.com/r/en-us/pingoneforenterprise/p14e_subscriptions?tocId=3xhnxjX3VzKNs3SXigWnQA + - https://techcommunity.microsoft.com/t5/microsoft-entra-azure-ad-blog/defend-your-users-from-mfa-fatigue-attacks/ba-p/2365677 + - https://www.bleepingcomputer.com/news/security/mfa-fatigue-hackers-new-favorite-tactic-in-high-profile-breaches/ + - https://attack.mitre.org/techniques/T1098/005/ + - https://attack.mitre.org/techniques/T1556/006/ + - https://docs.pingidentity.com/r/en-us/pingoneforenterprise/p14e_subscriptions?tocId=3xhnxjX3VzKNs3SXigWnQA drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An MFA configuration change was detected for [$user$] within [$timeDiff$] - of a password reset. The device [$object$] was $action$. - risk_objects: - - field: user - type: user - score: 50 - threat_objects: [] + message: An MFA configuration change was detected for [$user$] within [$timeDiff$] of a password reset. The device [$object$] was $action$. + risk_objects: + - field: user + type: user + score: 50 + threat_objects: [] tags: - analytic_story: - - Compromised User Account - - Scattered Lapsus$ Hunters - asset_type: Identity - mitre_attack_id: - - T1621 - - T1556.006 - - T1098.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - Compromised User Account + - Scattered Lapsus$ Hunters + asset_type: Identity + mitre_attack_id: + - T1621 + - T1556.006 + - T1098.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/pingid/windows_pw_reset.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/pingid/pingid.log - source: PINGID - sourcetype: _json + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/pingid/windows_pw_reset.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/pingid/pingid.log + source: PINGID + sourcetype: _json diff --git a/detections/application/pingid_new_mfa_method_registered_for_user.yml b/detections/application/pingid_new_mfa_method_registered_for_user.yml index 1df693343b..29da880560 100644 --- a/detections/application/pingid_new_mfa_method_registered_for_user.yml +++ b/detections/application/pingid_new_mfa_method_registered_for_user.yml @@ -5,74 +5,52 @@ date: '2025-05-02' author: Steven Dick status: production type: TTP -description: The following analytic detects the registration of a new Multi-Factor - Authentication (MFA) method for a PingID (PingOne) account. It leverages JSON logs - from PingID, specifically looking for successful device pairing events. This activity - is significant as adversaries who gain unauthorized access to a user account may - register a new MFA method to maintain persistence. If confirmed malicious, this - could allow attackers to bypass existing security measures, maintain long-term access, - and potentially escalate their privileges within the compromised environment. +description: The following analytic detects the registration of a new Multi-Factor Authentication (MFA) method for a PingID (PingOne) account. It leverages JSON logs from PingID, specifically looking for successful device pairing events. This activity is significant as adversaries who gain unauthorized access to a user account may register a new MFA method to maintain persistence. If confirmed malicious, this could allow attackers to bypass existing security measures, maintain long-term access, and potentially escalate their privileges within the compromised environment. data_source: -- PingID -search: "`pingid` \"result.message\"=\"Device Paired*\" result.status=\"SUCCESS\"\ - \ | rex field=result.message \"Device (Unp)?(P)?aired (?.+)\" - | eval src = coalesce('resources{}.ipaddress','resources{}.devicemodel'), user = - upper('actors{}.name'), reason = 'result.message' | eval object=CASE(ISNOTNULL('resources{}.devicemodel'),'resources{}.devicemodel',true(),device_extract) - | eval action=CASE(match('result.message',\"Device Paired*\"),\"created\",match('result.message', - \"Device Unpaired*\"),\"deleted\") | stats count min(_time) as firstTime, max(_time) - as lastTime by src,user,object,action,reason | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `pingid_new_mfa_method_registered_for_user_filter`" -how_to_implement: Target environment must ingest JSON logging from a PingID(PingOne) - enterprise environment, either via Webhook or Push Subscription. -known_false_positives: False positives may be generated by normal provisioning workflows - for user device registration. + - PingID +search: "`pingid` \"result.message\"=\"Device Paired*\" result.status=\"SUCCESS\" | rex field=result.message \"Device (Unp)?(P)?aired (?.+)\" | eval src = coalesce('resources{}.ipaddress','resources{}.devicemodel'), user = upper('actors{}.name'), reason = 'result.message' | eval object=CASE(ISNOTNULL('resources{}.devicemodel'),'resources{}.devicemodel',true(),device_extract) | eval action=CASE(match('result.message',\"Device Paired*\"),\"created\",match('result.message', \"Device Unpaired*\"),\"deleted\") | stats count min(_time) as firstTime, max(_time) as lastTime by src,user,object,action,reason | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `pingid_new_mfa_method_registered_for_user_filter`" +how_to_implement: Target environment must ingest JSON logging from a PingID(PingOne) enterprise environment, either via Webhook or Push Subscription. +known_false_positives: False positives may be generated by normal provisioning workflows for user device registration. references: -- https://twitter.com/jhencinski/status/1618660062352007174 -- https://attack.mitre.org/techniques/T1098/005/ -- https://attack.mitre.org/techniques/T1556/006/ -- https://docs.pingidentity.com/r/en-us/pingoneforenterprise/p14e_subscriptions?tocId=3xhnxjX3VzKNs3SXigWnQA + - https://twitter.com/jhencinski/status/1618660062352007174 + - https://attack.mitre.org/techniques/T1098/005/ + - https://attack.mitre.org/techniques/T1556/006/ + - https://docs.pingidentity.com/r/en-us/pingoneforenterprise/p14e_subscriptions?tocId=3xhnxjX3VzKNs3SXigWnQA drilldown_searches: -- name: View the detection results for - "$user$" and "$src$" - search: '%original_detection_search% | search user = "$user$" src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as - lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" - values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" - values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$src$" + search: '%original_detection_search% | search user = "$user$" src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An MFA configuration change was detected for [$user$], the device [$object$] - was $action$. - risk_objects: - - field: user - type: user - score: 10 - - field: src - type: system - score: 10 - threat_objects: [] + message: An MFA configuration change was detected for [$user$], the device [$object$] was $action$. + risk_objects: + - field: user + type: user + score: 10 + - field: src + type: system + score: 10 + threat_objects: [] tags: - analytic_story: - - Compromised User Account - asset_type: Identity - mitre_attack_id: - - T1621 - - T1556.006 - - T1098.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - Compromised User Account + asset_type: Identity + mitre_attack_id: + - T1621 + - T1556.006 + - T1098.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/pingid/pingid.log - source: PINGID - sourcetype: _json + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/pingid/pingid.log + source: PINGID + sourcetype: _json diff --git a/detections/application/splunk_appdynamics_secure_application_alerts.yml b/detections/application/splunk_appdynamics_secure_application_alerts.yml index 94f059f914..3f229a04d6 100644 --- a/detections/application/splunk_appdynamics_secure_application_alerts.yml +++ b/detections/application/splunk_appdynamics_secure_application_alerts.yml @@ -1,88 +1,84 @@ name: Splunk AppDynamics Secure Application Alerts id: d1a45d84-8dd1-4b31-8854-62b0b1d5da0b -version: 1 -date: '2025-05-02' +version: 2 +date: '2026-02-25' author: Ryan Long, Bhavin Patel, Splunk status: production type: Anomaly description: | - The following analytic is to leverage alerts from Splunk AppDynamics SecureApp, which identifies and monitors exploit attempts targeting business applications. The primary attack observed involves exploiting vulnerabilities in web applications, including injection attacks (SQL, API abuse), deserialization vulnerabilities, remote code execution attempts, LOG4J and zero day attacks. These attacks are typically aimed at gaining unauthorized access, exfiltrating sensitive data, or disrupting application functionality. + The following analytic is to leverage alerts from Splunk AppDynamics SecureApp, which identifies and monitors exploit attempts targeting business applications. The primary attack observed involves exploiting vulnerabilities in web applications, including injection attacks (SQL, API abuse), deserialization vulnerabilities, remote code execution attempts, LOG4J and zero day attacks. These attacks are typically aimed at gaining unauthorized access, exfiltrating sensitive data, or disrupting application functionality. - Splunk AppDynamics SecureApp provides real-time detection of these threats by analyzing application-layer events and correlating attack behavior with known vulnerability signatures. This detection methodology helps the Security Operations Center (SOC) by: + Splunk AppDynamics SecureApp provides real-time detection of these threats by analyzing application-layer events and correlating attack behavior with known vulnerability signatures. This detection methodology helps the Security Operations Center (SOC) by: - * Identifying active exploitation attempts in real-time, allowing for quicker incident response. - * Categorizing attack severity to prioritize remediation efforts based on risk level. - * Providing visibility into attacker tactics, including source IP, attack techniques, and affected applications. - * Generating risk-based scoring and contextual alerts to enhance decision-making within SOC workflows. - * Helping analysts determine whether an attack was merely an attempt or if it successfully exploited a vulnerability. + * Identifying active exploitation attempts in real-time, allowing for quicker incident response. + * Categorizing attack severity to prioritize remediation efforts based on risk level. + * Providing visibility into attacker tactics, including source IP, attack techniques, and affected applications. + * Generating risk-based scoring and contextual alerts to enhance decision-making within SOC workflows. + * Helping analysts determine whether an attack was merely an attempt or if it successfully exploited a vulnerability. - By leveraging this information, SOC teams can proactively mitigate security threats, patch vulnerable applications, and enforce security controls to prevent further exploitation. + By leveraging this information, SOC teams can proactively mitigate security threats, patch vulnerable applications, and enforce security controls to prevent further exploitation. data_source: -- Splunk AppDynamics Secure Application Alert + - Splunk AppDynamics Secure Application Alert search: |- - `appdynamics_security` blocked=false - | rename attackEvents{}.* AS *, detailJson.* AS *, vulnerabilityInfo.* AS * - | fields - tag::eventtype, eventtype, host, id, index, linecount, punct, source, sourcetype, splunk_server, tag, SourceType, app clientAddressType, application, tier, "attackEvents{}.* status" - | eval socketOut=mvjoin(socketOut," AND ") - | eval risk_score=kennaScore - | fillnull risk_score value="0" - `secureapp_es_field_mappings` - | stats values(*) as * by attackId - | eval severity=case( - risk_score>=100 OR signature="LOG4J", "critical", - risk_score>50 AND risk_score<75, "high", - risk_score=0 AND attackOutcome="EXPLOITED", "high", - risk_score<=50 AND attackOutcome!="OBSERVED", "medium", - risk_score=0 AND attackOutcome="ATTEMPTED", "medium", - risk_score=0, "low", - risk_score=0 AND attackOutcome="OBSERVED", "low" - ) - | eval risk_message=case( - (signature="API" OR signature="LOG4J" OR signature="SSRF"), "An attempt to exploit a ".signature." vulnerability was made from a ".src_category." IP address ".src_ip.". The server ".dest_nt_host." hosting application ".app_name." was accessed, and data may have been exfiltrated to ".socketOut.".", - (signature="MALIP" OR signature="SQL"), "A vulnerability is being ".attackOutcome." from a ".src_category." IP address ".src_ip.". The server ".dest_nt_host." hosting application ".app_name." was accessed.", - (signature="DESEREAL"), "The application ".app_name." deserializes untrusted data without sufficiently verifying that the resulting data will be valid. Data which is untrusted cannot be trusted to be well-formed. Malformed data or unexpected data could be used to abuse application logic, deny service, or execute arbitrary code, when deserialized." - ) - | `splunk_appdynamics_secure_application_alerts_filter` + `appdynamics_security` blocked=false + | rename attackEvents{}.* AS *, detailJson.* AS *, vulnerabilityInfo.* AS * + | fields - tag::eventtype, eventtype, host, id, index, linecount, punct, source, sourcetype, splunk_server, tag, SourceType, app clientAddressType, application, tier, "attackEvents{}.* status" + | eval socketOut=mvjoin(socketOut," AND ") + | eval risk_score=kennaScore + | fillnull risk_score value="0" + `secureapp_es_field_mappings` + | stats values(*) as * by attackId + | eval severity=case( + risk_score>=100 OR signature="LOG4J", "critical", + risk_score>50 AND risk_score<75, "high", + risk_score=0 AND attackOutcome="EXPLOITED", "high", + risk_score<=50 AND attackOutcome!="OBSERVED", "medium", + risk_score=0 AND attackOutcome="ATTEMPTED", "medium", + risk_score=0, "low", + risk_score=0 AND attackOutcome="OBSERVED", "low" + ) + | eval risk_message=case( + (signature="API" OR signature="LOG4J" OR signature="SSRF"), "An attempt to exploit a ".signature." vulnerability was made from a ".src_category." IP address ".src_ip.". The server ".dest_nt_host." hosting application ".app_name." was accessed, and data may have been exfiltrated to ".socketOut.".", + (signature="MALIP" OR signature="SQL"), "A vulnerability is being ".attackOutcome." from a ".src_category." IP address ".src_ip.". The server ".dest_nt_host." hosting application ".app_name." was accessed.", + (signature="DESEREAL"), "The application ".app_name." deserializes untrusted data without sufficiently verifying that the resulting data will be valid. Data which is untrusted cannot be trusted to be well-formed. Malformed data or unexpected data could be used to abuse application logic, deny service, or execute arbitrary code, when deserialized." + ) + | `splunk_appdynamics_secure_application_alerts_filter` how_to_implement: In order to properly run this search, you need to ingest alerts data from AppD SecureApp, specifically ingesting data via HEC. You will also need to ensure that the data is going to sourcetype - `appdynamics_security`. You will need to install the Splunk Add-on for AppDynamics. known_false_positives: No known false positives for this detection. If the alerts are noisy, consider tuning this detection by using the _filter macro in this search, and/or updating the tool this alert originates from. references: -- https://docs.appdynamics.com/appd/24.x/latest/en/application-security-monitoring/integrate-cisco-secure-application-with-splunk + - https://docs.appdynamics.com/appd/24.x/latest/en/application-security-monitoring/integrate-cisco-secure-application-with-splunk drilldown_searches: -- name: View the detection results for - "$app_name$" - search: '%original_detection_search% | search app_name = "$app_name$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$app_name$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$app_name$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$app_name$" + search: '%original_detection_search% | search app_name = "$app_name$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$app_name$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$app_name$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $risk_message$ - risk_objects: - - field: app_name - type: other - score: 10 - threat_objects: - - field: src_ip - type: ip_address + message: $risk_message$ + risk_objects: + - field: app_name + type: other + score: 10 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Critical Alerts - asset_type: Web Application - mitre_attack_id: [] - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat - manual_test: We are dynamically creating the risk_score field based on the severity of the alert in the SPL and that supersedes the risk score set in the detection. Setting these to manual test since otherwise we fail integration testing. The detection is also failing on unit-testing as some of the fields set in the observables are empty. + analytic_story: + - Critical Alerts + asset_type: Web Application + mitre_attack_id: [] + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat + manual_test: We are dynamically creating the risk_score field based on the severity of the alert in the SPL and that supersedes the risk score set in the detection. Setting these to manual test since otherwise we fail integration testing. The detection is also failing on unit-testing as some of the fields set in the observables are empty. tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/alerts/cisco_secure_app_alerts.log - sourcetype: appdynamics_security - source: AppDynamics Security + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/alerts/cisco_secure_app_alerts.log + sourcetype: appdynamics_security + source: AppDynamics Security diff --git a/detections/application/suspicious_email_attachment_extensions.yml b/detections/application/suspicious_email_attachment_extensions.yml index a01f1dd114..5697a389fd 100644 --- a/detections/application/suspicious_email_attachment_extensions.yml +++ b/detections/application/suspicious_email_attachment_extensions.yml @@ -5,60 +5,53 @@ date: '2026-01-14' author: David Dorsey, Splunk status: experimental type: Anomaly -description: The following analytic detects emails containing attachments with suspicious - file extensions. It leverages the Email data model in Splunk, using the tstats command - to identify emails where the attachment filename is not empty. This detection is - significant for SOC analysts as it highlights potential phishing or malware delivery - attempts, which are common vectors for data breaches and malware infections. If - confirmed malicious, this activity could lead to unauthorized access to sensitive - information, system compromise, or data exfiltration. Immediate review and analysis - of the identified emails and attachments are crucial to mitigate these risks. +description: The following analytic detects emails containing attachments with suspicious file extensions. It leverages the Email data model in Splunk, using the tstats command to identify emails where the attachment filename is not empty. This detection is significant for SOC analysts as it highlights potential phishing or malware delivery attempts, which are common vectors for data breaches and malware infections. If confirmed malicious, this activity could lead to unauthorized access to sensitive information, system compromise, or data exfiltration. Immediate review and analysis of the identified emails and attachments are crucial to mitigate these risks. data_source: [] search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Email.All_Email where All_Email.file_name="*" - - by All_Email.src_user All_Email.file_name All_Email.file_size All_Email.message_id - All_Email.message_info All_Email.process All_Email.process_id All_Email.orig_dest - All_Email.orig_recipient + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Email.All_Email where All_Email.file_name="*" - | `drop_dm_object_name(All_Email)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | lookup update=true is_suspicious_file_extension_lookup file_name OUTPUT suspicious - | search suspicious=true - | `suspicious_email_attachment_extensions_filter` + by All_Email.src_user All_Email.file_name All_Email.file_size All_Email.message_id + All_Email.message_info All_Email.process All_Email.process_id All_Email.orig_dest + All_Email.orig_recipient + + | `drop_dm_object_name(All_Email)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | lookup update=true is_suspicious_file_extension_lookup file_name OUTPUT suspicious + | search suspicious=true + | `suspicious_email_attachment_extensions_filter` how_to_implement: | - You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. - **Splunk Phantom Playbook Integration**\nIf Splunk Phantom is also configured in - your environment, a Playbook called \"Suspicious Email Attachment Investigate and - Delete\" can be configured to run when any results are found by this detection search. - To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, - and add the correct hostname to the \"Phantom Instance\" field in the Adaptive Response - Actions when configuring this detection search. The finding event will be sent to - Phantom and the playbook will gather further information about the file attachment - and its network behaviors. If Phantom finds malicious behavior and an analyst approves - of the results, the email will be deleted from the user's inbox.'" + You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. + **Splunk Phantom Playbook Integration**\nIf Splunk Phantom is also configured in + your environment, a Playbook called \"Suspicious Email Attachment Investigate and + Delete\" can be configured to run when any results are found by this detection search. + To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, + and add the correct hostname to the \"Phantom Instance\" field in the Adaptive Response + Actions when configuring this detection search. The finding event will be sent to + Phantom and the playbook will gather further information about the file attachment + and its network behaviors. If Phantom finds malicious behavior and an analyst approves + of the results, the email will be deleted from the user's inbox.'" known_false_positives: No false positives have been identified at this time. references: [] rba: - message: Email attachment $file_name$ with suspicious extension from $src_user$ - risk_objects: - - field: user - type: user - score: 25 - threat_objects: [] + message: Email attachment $file_name$ with suspicious extension from $src_user$ + risk_objects: + - field: user + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Data Destruction - - Emotet Malware DHS Report TA18-201A - - Hermetic Wiper - - Suspicious Emails - asset_type: Endpoint - mitre_attack_id: - - T1566.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Data Destruction + - Emotet Malware DHS Report TA18-201A + - Hermetic Wiper + - Suspicious Emails + asset_type: Endpoint + mitre_attack_id: + - T1566.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/application/suspicious_java_classes.yml b/detections/application/suspicious_java_classes.yml index dfc58111a2..eb72ec40fa 100644 --- a/detections/application/suspicious_java_classes.yml +++ b/detections/application/suspicious_java_classes.yml @@ -1,46 +1,41 @@ name: Suspicious Java Classes id: 6ed33786-5e87-4f55-b62c-cb5f1168b831 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Jose Hernandez, Splunk status: experimental type: Anomaly -description: The following analytic identifies suspicious Java classes often used - for remote command execution exploits in Java frameworks like Apache Struts. It - detects this activity by analyzing HTTP POST requests with specific content patterns - using Splunk's `stream_http` data source. This behavior is significant because it - may indicate an attempt to exploit vulnerabilities in web applications, potentially - leading to unauthorized remote code execution. If confirmed malicious, this activity - could allow attackers to execute arbitrary commands on the server, leading to data - breaches, system compromise, and further network infiltration. +description: The following analytic identifies suspicious Java classes often used for remote command execution exploits in Java frameworks like Apache Struts. It detects this activity by analyzing HTTP POST requests with specific content patterns using Splunk's `stream_http` data source. This behavior is significant because it may indicate an attempt to exploit vulnerabilities in web applications, potentially leading to unauthorized remote code execution. If confirmed malicious, this activity could allow attackers to execute arbitrary commands on the server, leading to data breaches, system compromise, and further network infiltration. data_source: [] -search: '`stream_http` http_method=POST http_content_length>1 | regex form_data="(?i)java\.lang\.(?:runtime|processbuilder)" - | rename src_ip as src | stats count earliest(_time) as firstTime, latest(_time) - as lastTime, values(url) as uri, values(status) as status, values(http_user_agent) - as http_user_agent by src, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `suspicious_java_classes_filter`' -how_to_implement: In order to properly run this search, Splunk needs to ingest data - from your web-traffic appliances that serve or sit in the path of your Struts application - servers. This can be accomplished by indexing data from a web proxy, or by using - network traffic-analysis tools, such as Splunk Stream or Bro. +search: |- + `stream_http` http_method=POST http_content_length>1 + | regex form_data="(?i)java\.lang\.(?:runtime + | processbuilder)" + | rename src_ip as src + | stats count earliest(_time) as firstTime, latest(_time) as lastTime, values(url) as uri, values(status) as status, values(http_user_agent) as http_user_agent + BY src, dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `suspicious_java_classes_filter` +how_to_implement: In order to properly run this search, Splunk needs to ingest data from your web-traffic appliances that serve or sit in the path of your Struts application servers. This can be accomplished by indexing data from a web proxy, or by using network traffic-analysis tools, such as Splunk Stream or Bro. known_false_positives: There are no known false positives. references: [] rba: - message: Suspicious Java Classes in HTTP requests involving $src$ and $dest$ - risk_objects: - - field: src - type: system - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: Suspicious Java Classes in HTTP requests involving $src$ and $dest$ + risk_objects: + - field: src + type: system + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Apache Struts Vulnerability - asset_type: Endpoint - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Apache Struts Vulnerability + asset_type: Endpoint + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat diff --git a/detections/application/zoom_high_video_latency.yml b/detections/application/zoom_high_video_latency.yml index 10a61876f3..7ab5d4574a 100644 --- a/detections/application/zoom_high_video_latency.yml +++ b/detections/application/zoom_high_video_latency.yml @@ -5,53 +5,35 @@ date: '2025-06-02' author: Marissa Bower, Raven Tait status: experimental type: Anomaly -description: Detects particularly high latency from Zoom logs. Latency observed from threat actors - performing Remote Employment Fraud (REF) is typically well above what’s normal for the majority of employees. +description: Detects particularly high latency from Zoom logs. Latency observed from threat actors performing Remote Employment Fraud (REF) is typically well above what’s normal for the majority of employees. data_source: [] -search: '`zoom_index` - | spath "payload.object.participant.qos{}.type" - | search "payload.object.participant.qos{}.type"=video_input - | rename payload.object.participant.qos{}.details.avg_latency as avg_latency "payload.object.participant.qos{}.details.latency" as latency payload.object.participant.email as email - | rex field=avg_latency "(?\d+) ms" - | rex field=latency "(?\d+) ms" - | search email="*" - | table email overall_latency latency avg_latency average_latency _raw - | stats latest(overall_latency) as overall_latency by email _raw - | where overall_latency>300 | `zoom_high_video_latency_filter`' -how_to_implement: The analytic leverages Zoom logs to be ingested using - Splunk Connect for Zoom (https://splunkbase.splunk.com/app/4961) -known_false_positives: While latency could simply indicate a slow network connection, when combined - with other indicators, it can help build a more complete picture. Tune the threshold as needed for - your environment baseline. +search: '`zoom_index` | spath "payload.object.participant.qos{}.type" | search "payload.object.participant.qos{}.type"=video_input | rename payload.object.participant.qos{}.details.avg_latency as avg_latency "payload.object.participant.qos{}.details.latency" as latency payload.object.participant.email as email | rex field=avg_latency "(?\d+) ms" | rex field=latency "(?\d+) ms" | search email="*" | table email overall_latency latency avg_latency average_latency _raw | stats latest(overall_latency) as overall_latency by email _raw | where overall_latency>300 | `zoom_high_video_latency_filter`' +how_to_implement: The analytic leverages Zoom logs to be ingested using Splunk Connect for Zoom (https://splunkbase.splunk.com/app/4961) +known_false_positives: While latency could simply indicate a slow network connection, when combined with other indicators, it can help build a more complete picture. Tune the threshold as needed for your environment baseline. drilldown_searches: -- name: View the detection results for - "$email$" - search: '%original_detection_search% | search payload.object.participant.email = "$email$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$email$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$email$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$email$" + search: '%original_detection_search% | search payload.object.participant.email = "$email$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$email$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$email$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious latency from $email$ in Zoom activity. - risk_objects: - - field: email - type: user - score: 39 - threat_objects: [] + message: Suspicious latency from $email$ in Zoom activity. + risk_objects: + - field: email + type: user + score: 39 + threat_objects: [] tags: - analytic_story: - - Remote Employment Fraud - asset_type: Identity - mitre_attack_id: - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Remote Employment Fraud + asset_type: Identity + mitre_attack_id: + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity diff --git a/detections/application/zoom_rare_audio_devices.yml b/detections/application/zoom_rare_audio_devices.yml index 21dbe8cf1c..d2cd0ea9e1 100644 --- a/detections/application/zoom_rare_audio_devices.yml +++ b/detections/application/zoom_rare_audio_devices.yml @@ -1,29 +1,26 @@ name: Zoom Rare Audio Devices id: 9fdbf709-4c46-4819-9fb6-98b2d72059ed -version: 1 -date: '2025-06-02' +version: 2 +date: '2026-02-25' author: Marissa Bower, Raven Tait status: experimental type: Hunting -description: Detects rare audio devices from Zoom logs. Actors performing Remote Employment - Fraud (REF) typically use unusual device information compared to a majority of employees. - Detecting this activity requires careful analysis, regular review, and a thorough - understanding of the audio and video devices commonly used within your environment. +description: Detects rare audio devices from Zoom logs. Actors performing Remote Employment Fraud (REF) typically use unusual device information compared to a majority of employees. Detecting this activity requires careful analysis, regular review, and a thorough understanding of the audio and video devices commonly used within your environment. data_source: [] -search: '`zoom_index` speaker=* NOT (camera=*iPhone* OR camera="*FaceTime*" - OR speaker="*AirPods*" OR camera="*MacBook*" OR microphone="*MacBook Pro Microphone*") - | rare speaker limit=50 | `zoom_rare_audio_devices_filter`' -how_to_implement: The analytic leverages Zoom logs to be ingested using - Splunk Connect for Zoom (https://splunkbase.splunk.com/app/4961) +search: |- + `zoom_index` speaker=* NOT (camera=*iPhone* OR camera="*FaceTime*" OR speaker="*AirPods*" OR camera="*MacBook*" OR microphone="*MacBook Pro Microphone*") + | rare speaker limit=50 + | `zoom_rare_audio_devices_filter` +how_to_implement: The analytic leverages Zoom logs to be ingested using Splunk Connect for Zoom (https://splunkbase.splunk.com/app/4961) known_false_positives: This is a hunting query meant to identify rare audio devices. tags: - analytic_story: - - Remote Employment Fraud - asset_type: Identity - mitre_attack_id: - - T1123 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Remote Employment Fraud + asset_type: Identity + mitre_attack_id: + - T1123 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity diff --git a/detections/application/zoom_rare_input_devices.yml b/detections/application/zoom_rare_input_devices.yml index 588d8c2bd9..9fe6b8a187 100644 --- a/detections/application/zoom_rare_input_devices.yml +++ b/detections/application/zoom_rare_input_devices.yml @@ -1,29 +1,26 @@ name: Zoom Rare Input Devices id: d290eeef-d05e-49a8-b598-72296023b87b -version: 1 -date: '2025-06-02' +version: 2 +date: '2026-02-25' author: Marissa Bower, Raven Tait status: experimental type: Hunting -description: Detects rare input devices from Zoom logs. Actors performing Remote Employment - Fraud (REF) typically use unusual device information compared to a majority of employees. - Detecting this activity requires careful analysis, regular review, and a thorough - understanding of the audio and video devices commonly used within your environment. +description: Detects rare input devices from Zoom logs. Actors performing Remote Employment Fraud (REF) typically use unusual device information compared to a majority of employees. Detecting this activity requires careful analysis, regular review, and a thorough understanding of the audio and video devices commonly used within your environment. data_source: [] -search: '`zoom_index` microphone=* NOT (camera=*iPhone* OR camera="*FaceTime*" - OR speaker="*AirPods*" OR camera="*MacBook*" OR microphone="*MacBook Pro Microphone*") - | rare microphone limit=50 | `zoom_rare_input_devices_filter`' -how_to_implement: The analytic leverages Zoom logs to be ingested using - Splunk Connect for Zoom (https://splunkbase.splunk.com/app/4961) +search: |- + `zoom_index` microphone=* NOT (camera=*iPhone* OR camera="*FaceTime*" OR speaker="*AirPods*" OR camera="*MacBook*" OR microphone="*MacBook Pro Microphone*") + | rare microphone limit=50 + | `zoom_rare_input_devices_filter` +how_to_implement: The analytic leverages Zoom logs to be ingested using Splunk Connect for Zoom (https://splunkbase.splunk.com/app/4961) known_false_positives: This is a hunting query meant to identify rare microphone devices. tags: - analytic_story: - - Remote Employment Fraud - asset_type: Identity - mitre_attack_id: - - T1123 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Remote Employment Fraud + asset_type: Identity + mitre_attack_id: + - T1123 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity diff --git a/detections/application/zoom_rare_video_devices.yml b/detections/application/zoom_rare_video_devices.yml index d5d8dcb9b9..3aab2a4ed2 100644 --- a/detections/application/zoom_rare_video_devices.yml +++ b/detections/application/zoom_rare_video_devices.yml @@ -1,29 +1,26 @@ name: Zoom Rare Video Devices id: 9b2b819d-c76b-4dc6-bd3d-148edb8de83e -version: 1 -date: '2025-06-02' +version: 2 +date: '2026-02-25' author: Marissa Bower, Raven Tait status: experimental type: Hunting -description: Detects rare video devices from Zoom logs. Actors performing Remote Employment - Fraud (REF) typically use unusual device information compared to a majority of employees. - Detecting this activity requires careful analysis, regular review, and a thorough - understanding of the audio and video devices commonly used within your environment. +description: Detects rare video devices from Zoom logs. Actors performing Remote Employment Fraud (REF) typically use unusual device information compared to a majority of employees. Detecting this activity requires careful analysis, regular review, and a thorough understanding of the audio and video devices commonly used within your environment. data_source: [] -search: '`zoom_index` camera=* NOT (camera=*iPhone* OR camera="*FaceTime*" - OR speaker="*AirPods*" OR camera="*MacBook*" OR microphone="*MacBook Pro Microphone*") - | rare camera limit=50 | `zoom_rare_video_devices_filter`' -how_to_implement: The analytic leverages Zoom logs to be ingested using - Splunk Connect for Zoom (https://splunkbase.splunk.com/app/4961) +search: |- + `zoom_index` camera=* NOT (camera=*iPhone* OR camera="*FaceTime*" OR speaker="*AirPods*" OR camera="*MacBook*" OR microphone="*MacBook Pro Microphone*") + | rare camera limit=50 + | `zoom_rare_video_devices_filter` +how_to_implement: The analytic leverages Zoom logs to be ingested using Splunk Connect for Zoom (https://splunkbase.splunk.com/app/4961) known_false_positives: This is a hunting query meant to identify rare video devices. tags: - analytic_story: - - Remote Employment Fraud - asset_type: Identity - mitre_attack_id: - - T1123 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Remote Employment Fraud + asset_type: Identity + mitre_attack_id: + - T1123 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity diff --git a/detections/cloud/abnormally_high_number_of_cloud_infrastructure_api_calls.yml b/detections/cloud/abnormally_high_number_of_cloud_infrastructure_api_calls.yml index bdfe336b65..fd172d6310 100644 --- a/detections/cloud/abnormally_high_number_of_cloud_infrastructure_api_calls.yml +++ b/detections/cloud/abnormally_high_number_of_cloud_infrastructure_api_calls.yml @@ -1,76 +1,68 @@ name: Abnormally High Number Of Cloud Infrastructure API Calls id: 0840ddf1-8c89-46ff-b730-c8d6722478c0 -version: 10 -date: '2026-01-14' +version: 11 +date: '2026-02-25' author: David Dorsey, Splunk status: production type: Anomaly -description: The following analytic detects a spike in the number of API calls made - to your cloud infrastructure by a user. It leverages cloud infrastructure logs and - compares the current API call volume against a baseline probability density function - to identify anomalies. This activity is significant because an unusual increase - in API calls can indicate potential misuse or compromise of cloud resources. If - confirmed malicious, this could lead to unauthorized access, data exfiltration, - or disruption of cloud services, posing a significant risk to the organization's - cloud environment. +description: The following analytic detects a spike in the number of API calls made to your cloud infrastructure by a user. It leverages cloud infrastructure logs and compares the current API call volume against a baseline probability density function to identify anomalies. This activity is significant because an unusual increase in API calls can indicate potential misuse or compromise of cloud resources. If confirmed malicious, this could lead to unauthorized access, data exfiltration, or disruption of cloud services, posing a significant risk to the organization's cloud environment. data_source: -- AWS CloudTrail -search: '| tstats count as api_calls values(All_Changes.command) as command from datamodel=Change - where All_Changes.user!=unknown All_Changes.status=success by All_Changes.user _time - span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, - "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") - | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join user HourOfDay - isWeekend [ summary cloud_excessive_api_calls_v1] | where cardinality >=16 | apply - cloud_excessive_api_calls_v1 threshold=0.005 | rename "IsOutlier(api_calls)" as - isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, - -1), ":"), 0) | where api_calls > expected_upper_threshold | eval distance_from_threshold - = api_calls - expected_upper_threshold | table _time, user, command, api_calls, - expected_upper_threshold, distance_from_threshold | `abnormally_high_number_of_cloud_infrastructure_api_calls_filter`' -how_to_implement: You must be ingesting your cloud infrastructure logs. You also must - run the baseline search `Baseline Of Cloud Infrastructure API Calls Per User` to - create the probability density function. + - AWS CloudTrail +search: |- + | tstats count as api_calls values(All_Changes.command) as command FROM datamodel=Change + WHERE All_Changes.user!=unknown All_Changes.status=success + BY All_Changes.user _time span=1h + | `drop_dm_object_name("All_Changes")` + | eval HourOfDay=strftime(_time, "%H") + | eval HourOfDay=floor(HourOfDay/4)*4 + | eval DayOfWeek=strftime(_time, "%w") + | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) + | join user HourOfDay isWeekend [ summary cloud_excessive_api_calls_v1] + | where cardinality >=16 + | apply cloud_excessive_api_calls_v1 threshold=0.005 + | rename "IsOutlier(api_calls)" as isOutlier + | where isOutlier=1 + | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) + | where api_calls > expected_upper_threshold + | eval distance_from_threshold = api_calls - expected_upper_threshold + | table _time, user, command, api_calls, expected_upper_threshold, distance_from_threshold + | `abnormally_high_number_of_cloud_infrastructure_api_calls_filter` +how_to_implement: You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Infrastructure API Calls Per User` to create the probability density function. known_false_positives: No false positives have been identified at this time. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: user $user$ has made $api_calls$ api calls, violating the dynamic threshold - of $expected_upper_threshold$ with the following command $command$. - risk_objects: - - field: user - type: user - score: 15 - threat_objects: [] + message: user $user$ has made $api_calls$ api calls, violating the dynamic threshold of $expected_upper_threshold$ with the following command $command$. + risk_objects: + - field: user + type: user + score: 15 + threat_objects: [] tags: - analytic_story: - - Suspicious Cloud User Activities - - Compromised User Account - - Scattered Lapsus$ Hunters - asset_type: AWS Instance - mitre_attack_id: - - T1078.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - manual_test: This search needs the baseline `Baseline Of Cloud Infrastructure API Calls Per User` to be run first. + analytic_story: + - Suspicious Cloud User Activities + - Compromised User Account + - Scattered Lapsus$ Hunters + asset_type: AWS Instance + mitre_attack_id: + - T1078.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + manual_test: This search needs the baseline `Baseline Of Cloud Infrastructure API Calls Per User` to be run first. tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/abnormally_high_number_of_cloud_instances_destroyed.yml b/detections/cloud/abnormally_high_number_of_cloud_instances_destroyed.yml index 1d36071ca8..caae424eb0 100644 --- a/detections/cloud/abnormally_high_number_of_cloud_instances_destroyed.yml +++ b/detections/cloud/abnormally_high_number_of_cloud_instances_destroyed.yml @@ -1,55 +1,53 @@ name: Abnormally High Number Of Cloud Instances Destroyed id: ef629fc9-1583-4590-b62a-f2247fbf7bbf -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: David Dorsey, Splunk status: experimental type: Anomaly -description: The following analytic identifies an abnormally high number of cloud - instances being destroyed within a 4-hour period. It leverages cloud infrastructure - logs and applies a probability density model to detect outliers. This activity is - significant for a SOC because a sudden spike in destroyed instances could indicate - malicious activity, such as an insider threat or a compromised account attempting - to disrupt services. If confirmed malicious, this could lead to significant operational - disruptions, data loss, and potential financial impact due to the destruction of - critical cloud resources. +description: The following analytic identifies an abnormally high number of cloud instances being destroyed within a 4-hour period. It leverages cloud infrastructure logs and applies a probability density model to detect outliers. This activity is significant for a SOC because a sudden spike in destroyed instances could indicate malicious activity, such as an insider threat or a compromised account attempting to disrupt services. If confirmed malicious, this could lead to significant operational disruptions, data loss, and potential financial impact due to the destruction of critical cloud resources. data_source: -- AWS CloudTrail -search: '| tstats count as instances_destroyed values(All_Changes.object_id) as object_id - from datamodel=Change where All_Changes.action=deleted AND All_Changes.status=success - AND All_Changes.object_category=instance by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` - | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval - DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek - <= 5, 0, 1) | join HourOfDay isWeekend [summary cloud_excessive_instances_destroyed_v1] - | where cardinality >=16 | apply cloud_excessive_instances_destroyed_v1 threshold=0.005 - | rename "IsOutlier(instances_destroyed)" as isOutlier | where isOutlier=1 | eval - expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | - eval distance_from_threshold = instances_destroyed - expected_upper_threshold | - table _time, user, instances_destroyed, expected_upper_threshold, distance_from_threshold, - object_id | `abnormally_high_number_of_cloud_instances_destroyed_filter`' -how_to_implement: You must be ingesting your cloud infrastructure logs. You also must - run the baseline search `Baseline Of Cloud Instances Destroyed` to create the probability - density function. -known_false_positives: Many service accounts configured within a cloud infrastructure - are known to exhibit this behavior. Please adjust the threshold values and filter - out service accounts from the output. Always verify if this search alerted on a - human user. + - AWS CloudTrail +search: |- + | tstats count as instances_destroyed values(All_Changes.object_id) as object_id FROM datamodel=Change + WHERE All_Changes.action=deleted + AND + All_Changes.status=success + AND + All_Changes.object_category=instance + BY All_Changes.user _time span=1h + | `drop_dm_object_name("All_Changes")` + | eval HourOfDay=strftime(_time, "%H") + | eval HourOfDay=floor(HourOfDay/4)*4 + | eval DayOfWeek=strftime(_time, "%w") + | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) + | join HourOfDay isWeekend [summary cloud_excessive_instances_destroyed_v1] + | where cardinality >=16 + | apply cloud_excessive_instances_destroyed_v1 threshold=0.005 + | rename "IsOutlier(instances_destroyed)" as isOutlier + | where isOutlier=1 + | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) + | eval distance_from_threshold = instances_destroyed - expected_upper_threshold + | table _time, user, instances_destroyed, expected_upper_threshold, distance_from_threshold, object_id + | `abnormally_high_number_of_cloud_instances_destroyed_filter` +how_to_implement: You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Instances Destroyed` to create the probability density function. +known_false_positives: Many service accounts configured within a cloud infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. references: [] rba: - message: At least $instances_destroyed$ instances destroyed by $user$ - risk_objects: - - field: user - type: user - score: 25 - threat_objects: [] + message: At least $instances_destroyed$ instances destroyed by $user$ + risk_objects: + - field: user + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Suspicious Cloud Instance Activities - asset_type: Cloud Instance - mitre_attack_id: - - T1078.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Suspicious Cloud Instance Activities + asset_type: Cloud Instance + mitre_attack_id: + - T1078.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat diff --git a/detections/cloud/abnormally_high_number_of_cloud_instances_launched.yml b/detections/cloud/abnormally_high_number_of_cloud_instances_launched.yml index 93d678f8de..aba1107d79 100644 --- a/detections/cloud/abnormally_high_number_of_cloud_instances_launched.yml +++ b/detections/cloud/abnormally_high_number_of_cloud_instances_launched.yml @@ -1,55 +1,53 @@ name: Abnormally High Number Of Cloud Instances Launched id: f2361e9f-3928-496c-a556-120cd4223a65 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: David Dorsey, Splunk status: experimental type: Anomaly -description: The following analytic detects an abnormally high number of cloud instances - launched within a 4-hour period. It leverages cloud infrastructure logs and applies - a probability density model to identify outliers based on historical data. This - activity is significant for a SOC because a sudden spike in instance creation could - indicate unauthorized access or misuse of cloud resources. If confirmed malicious, - this behavior could lead to resource exhaustion, increased costs, or provide attackers - with additional compute resources to further their objectives. +description: The following analytic detects an abnormally high number of cloud instances launched within a 4-hour period. It leverages cloud infrastructure logs and applies a probability density model to identify outliers based on historical data. This activity is significant for a SOC because a sudden spike in instance creation could indicate unauthorized access or misuse of cloud resources. If confirmed malicious, this behavior could lead to resource exhaustion, increased costs, or provide attackers with additional compute resources to further their objectives. data_source: -- AWS CloudTrail -search: '| tstats count as instances_launched values(All_Changes.object_id) as object_id - from datamodel=Change where (All_Changes.action=created) AND All_Changes.status=success - AND All_Changes.object_category=instance by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` - | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval - DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek - <= 5, 0, 1) | join HourOfDay isWeekend [summary cloud_excessive_instances_created_v1] - | where cardinality >=16 | apply cloud_excessive_instances_created_v1 threshold=0.005 - | rename "IsOutlier(instances_launched)" as isOutlier | where isOutlier=1 | eval - expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | - eval distance_from_threshold = instances_launched - expected_upper_threshold | table - _time, user, instances_launched, expected_upper_threshold, distance_from_threshold, - object_id | `abnormally_high_number_of_cloud_instances_launched_filter`' -how_to_implement: You must be ingesting your cloud infrastructure logs. You also must - run the baseline search `Baseline Of Cloud Instances Launched` to create the probability - density function. -known_false_positives: Many service accounts configured within an AWS infrastructure - are known to exhibit this behavior. Please adjust the threshold values and filter - out service accounts from the output. Always verify if this search alerted on a - human user. + - AWS CloudTrail +search: |- + | tstats count as instances_launched values(All_Changes.object_id) as object_id FROM datamodel=Change + WHERE ( + All_Changes.action=created + ) + AND All_Changes.status=success AND All_Changes.object_category=instance + BY All_Changes.user _time span=1h + | `drop_dm_object_name("All_Changes")` + | eval HourOfDay=strftime(_time, "%H") + | eval HourOfDay=floor(HourOfDay/4)*4 + | eval DayOfWeek=strftime(_time, "%w") + | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) + | join HourOfDay isWeekend [summary cloud_excessive_instances_created_v1] + | where cardinality >=16 + | apply cloud_excessive_instances_created_v1 threshold=0.005 + | rename "IsOutlier(instances_launched)" as isOutlier + | where isOutlier=1 + | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) + | eval distance_from_threshold = instances_launched - expected_upper_threshold + | table _time, user, instances_launched, expected_upper_threshold, distance_from_threshold, object_id + | `abnormally_high_number_of_cloud_instances_launched_filter` +how_to_implement: You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Instances Launched` to create the probability density function. +known_false_positives: Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. references: [] rba: - message: At least $instances_launched$ instances launched by $user$ - risk_objects: - - field: user - type: user - score: 25 - threat_objects: [] + message: At least $instances_launched$ instances launched by $user$ + risk_objects: + - field: user + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Cloud Cryptomining - - Suspicious Cloud Instance Activities - asset_type: Cloud Instance - mitre_attack_id: - - T1078.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Cloud Cryptomining + - Suspicious Cloud Instance Activities + asset_type: Cloud Instance + mitre_attack_id: + - T1078.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat diff --git a/detections/cloud/abnormally_high_number_of_cloud_security_group_api_calls.yml b/detections/cloud/abnormally_high_number_of_cloud_security_group_api_calls.yml index 85b1eb0a07..f868ae7a5e 100644 --- a/detections/cloud/abnormally_high_number_of_cloud_security_group_api_calls.yml +++ b/detections/cloud/abnormally_high_number_of_cloud_security_group_api_calls.yml @@ -1,75 +1,68 @@ name: Abnormally High Number Of Cloud Security Group API Calls id: d4dfb7f3-7a37-498a-b5df-f19334e871af -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: David Dorsey, Splunk status: production type: Anomaly -description: The following analytic detects a spike in the number of API calls made - to cloud security groups by a user. It leverages data from the Change data model, - focusing on successful firewall-related changes. This activity is significant because - an abnormal increase in security group API calls can indicate potential malicious - activity, such as unauthorized access or configuration changes. If confirmed malicious, - this could allow an attacker to manipulate security group settings, potentially - exposing sensitive resources or disrupting network security controls. +description: The following analytic detects a spike in the number of API calls made to cloud security groups by a user. It leverages data from the Change data model, focusing on successful firewall-related changes. This activity is significant because an abnormal increase in security group API calls can indicate potential malicious activity, such as unauthorized access or configuration changes. If confirmed malicious, this could allow an attacker to manipulate security group settings, potentially exposing sensitive resources or disrupting network security controls. data_source: -- AWS CloudTrail -search: '| tstats count as security_group_api_calls values(All_Changes.command) as - command from datamodel=Change where All_Changes.object_category=firewall AND All_Changes.status=success - by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval - HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, - "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join user HourOfDay - isWeekend [ summary cloud_excessive_security_group_api_calls_v1] | where cardinality - >=16 | apply cloud_excessive_security_group_api_calls_v1 threshold=0.005 | rename - "IsOutlier(security_group_api_calls)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold - = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | where security_group_api_calls - > expected_upper_threshold | eval distance_from_threshold = security_group_api_calls - - expected_upper_threshold | table _time, user, command, security_group_api_calls, - expected_upper_threshold, distance_from_threshold | `abnormally_high_number_of_cloud_security_group_api_calls_filter`' -how_to_implement: You must be ingesting your cloud infrastructure logs. You also must - run the baseline search `Baseline Of Cloud Security Group API Calls Per User` to - create the probability density function model. + - AWS CloudTrail +search: |- + | tstats count as security_group_api_calls values(All_Changes.command) as command FROM datamodel=Change + WHERE All_Changes.object_category=firewall + AND + All_Changes.status=success + BY All_Changes.user _time span=1h + | `drop_dm_object_name("All_Changes")` + | eval HourOfDay=strftime(_time, "%H") + | eval HourOfDay=floor(HourOfDay/4)*4 + | eval DayOfWeek=strftime(_time, "%w") + | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) + | join user HourOfDay isWeekend [ summary cloud_excessive_security_group_api_calls_v1] + | where cardinality >=16 + | apply cloud_excessive_security_group_api_calls_v1 threshold=0.005 + | rename "IsOutlier(security_group_api_calls)" as isOutlier + | where isOutlier=1 + | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) + | where security_group_api_calls > expected_upper_threshold + | eval distance_from_threshold = security_group_api_calls - expected_upper_threshold + | table _time, user, command, security_group_api_calls, expected_upper_threshold, distance_from_threshold + | `abnormally_high_number_of_cloud_security_group_api_calls_filter` +how_to_implement: You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Security Group API Calls Per User` to create the probability density function model. known_false_positives: No false positives have been identified at this time. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: user $user$ has made $api_calls$ api calls related to security groups, - violating the dynamic threshold of $expected_upper_threshold$ with the following - command $command$. - risk_objects: - - field: user - type: user - score: 15 - threat_objects: [] + message: user $user$ has made $api_calls$ api calls related to security groups, violating the dynamic threshold of $expected_upper_threshold$ with the following command $command$. + risk_objects: + - field: user + type: user + score: 15 + threat_objects: [] tags: - analytic_story: - - Suspicious Cloud User Activities - asset_type: AWS Instance - mitre_attack_id: - - T1078.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - manual_test: This search needs the baseline `Baseline Of Cloud Security Group API Calls Per User` to be run first. + analytic_story: + - Suspicious Cloud User Activities + asset_type: AWS Instance + mitre_attack_id: + - T1078.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + manual_test: This search needs the baseline `Baseline Of Cloud Security Group API Calls Per User` to be run first. tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/amazon_eks_kubernetes_cluster_scan_detection.yml b/detections/cloud/amazon_eks_kubernetes_cluster_scan_detection.yml index 7627be4597..d591ca81e5 100644 --- a/detections/cloud/amazon_eks_kubernetes_cluster_scan_detection.yml +++ b/detections/cloud/amazon_eks_kubernetes_cluster_scan_detection.yml @@ -1,38 +1,31 @@ name: Amazon EKS Kubernetes cluster scan detection id: 294c4686-63dd-4fe6-93a2-ca807626704a -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Rod Soto, Splunk status: experimental type: Hunting -description: The following analytic detects unauthenticated requests to an Amazon - EKS Kubernetes cluster, specifically identifying actions by the "system:anonymous" - user. It leverages AWS CloudWatch Logs data, focusing on user agents and authentication - details. This activity is significant as it may indicate unauthorized scanning or - probing of the Kubernetes cluster, which could be a precursor to an attack. If confirmed - malicious, this could lead to unauthorized access, data exfiltration, or disruption - of services within the Kubernetes environment. +description: The following analytic detects unauthenticated requests to an Amazon EKS Kubernetes cluster, specifically identifying actions by the "system:anonymous" user. It leverages AWS CloudWatch Logs data, focusing on user agents and authentication details. This activity is significant as it may indicate unauthorized scanning or probing of the Kubernetes cluster, which could be a precursor to an attack. If confirmed malicious, this could lead to unauthorized access, data exfiltration, or disruption of services within the Kubernetes environment. data_source: [] -search: '`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" userAgent!="AWS - Security Scanner" | rename sourceIPs{} as src_ip | stats count min(_time) as firstTime - max(_time) as lastTime values(responseStatus.reason) values(source) as cluster_name - values(responseStatus.code) values(userAgent) as http_user_agent values(verb) values(requestURI) - by src_ip user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` - |`amazon_eks_kubernetes_cluster_scan_detection_filter`' -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudWatch - EKS Logs inputs. -known_false_positives: Not all unauthenticated requests are malicious, but frequency, - UA and source IPs will provide context. +search: |- + `aws_cloudwatchlogs_eks` "user.username"="system:anonymous" userAgent!="AWS Security Scanner" + | rename sourceIPs{} as src_ip + | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(source) as cluster_name values(responseStatus.code) values(userAgent) as http_user_agent values(verb) values(requestURI) + BY src_ip user.username user.groups{} + | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` + | `amazon_eks_kubernetes_cluster_scan_detection_filter` +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudWatch EKS Logs inputs. +known_false_positives: Not all unauthenticated requests are malicious, but frequency, UA and source IPs will provide context. references: [] tags: - analytic_story: - - Kubernetes Scanning Activity - asset_type: Amazon EKS Kubernetes cluster - mitre_attack_id: - - T1526 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Kubernetes Scanning Activity + asset_type: Amazon EKS Kubernetes cluster + mitre_attack_id: + - T1526 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat diff --git a/detections/cloud/amazon_eks_kubernetes_pod_scan_detection.yml b/detections/cloud/amazon_eks_kubernetes_pod_scan_detection.yml index ec18b64d6d..4cb7a38a63 100644 --- a/detections/cloud/amazon_eks_kubernetes_pod_scan_detection.yml +++ b/detections/cloud/amazon_eks_kubernetes_pod_scan_detection.yml @@ -1,40 +1,32 @@ name: Amazon EKS Kubernetes Pod scan detection id: dbfca1dd-b8e5-4ba4-be0e-e565e5d62002 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Rod Soto, Splunk status: experimental type: Hunting -description: The following analytic detects unauthenticated requests made against - the Kubernetes Pods API, indicating potential unauthorized access attempts. It leverages - the `aws_cloudwatchlogs_eks` data source, filtering for events where `user.username` - is "system:anonymous", `verb` is "list", and `objectRef.resource` is "pods", with - `requestURI` set to "/api/v1/pods". This activity is significant as it may signal - attempts to access sensitive resources or execute unauthorized commands within the - Kubernetes environment. If confirmed malicious, such access could lead to data compromise, - unauthorized command execution, or lateral movement within the cluster. +description: The following analytic detects unauthenticated requests made against the Kubernetes Pods API, indicating potential unauthorized access attempts. It leverages the `aws_cloudwatchlogs_eks` data source, filtering for events where `user.username` is "system:anonymous", `verb` is "list", and `objectRef.resource` is "pods", with `requestURI` set to "/api/v1/pods". This activity is significant as it may signal attempts to access sensitive resources or execute unauthorized commands within the Kubernetes environment. If confirmed malicious, such access could lead to data compromise, unauthorized command execution, or lateral movement within the cluster. data_source: [] -search: '`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" verb=list objectRef.resource=pods - requestURI="/api/v1/pods" | rename source as cluster_name sourceIPs{} as src_ip - | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) - values(responseStatus.code) values(userAgent) values(verb) values(requestURI) by - src_ip cluster_name user.username user.groups{} | `security_content_ctime(lastTime)` - | `security_content_ctime(firstTime)` | `amazon_eks_kubernetes_pod_scan_detection_filter`' -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on forAWS (version 4.4.0 or later), then configure your AWS CloudWatch - EKS Logs.Please also customize the `kubernetes_pods_aws_scan_fingerprint_detection` - macro to filter out the false positives. -known_false_positives: Not all unauthenticated requests are malicious, but frequency, - UA and source IPs and direct request to API provide context. +search: |- + `aws_cloudwatchlogs_eks` "user.username"="system:anonymous" verb=list objectRef.resource=pods requestURI="/api/v1/pods" + | rename source as cluster_name sourceIPs{} as src_ip + | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(responseStatus.code) values(userAgent) values(verb) values(requestURI) + BY src_ip cluster_name user.username + user.groups{} + | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` + | `amazon_eks_kubernetes_pod_scan_detection_filter` +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on forAWS (version 4.4.0 or later), then configure your AWS CloudWatch EKS Logs.Please also customize the `kubernetes_pods_aws_scan_fingerprint_detection` macro to filter out the false positives. +known_false_positives: Not all unauthenticated requests are malicious, but frequency, UA and source IPs and direct request to API provide context. references: [] tags: - analytic_story: - - Kubernetes Scanning Activity - asset_type: Amazon EKS Kubernetes cluster Pod - mitre_attack_id: - - T1526 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Kubernetes Scanning Activity + asset_type: Amazon EKS Kubernetes cluster Pod + mitre_attack_id: + - T1526 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat diff --git a/detections/cloud/asl_aws_concurrent_sessions_from_different_ips.yml b/detections/cloud/asl_aws_concurrent_sessions_from_different_ips.yml index c33b25eae4..f4b304c9ad 100644 --- a/detections/cloud/asl_aws_concurrent_sessions_from_different_ips.yml +++ b/detections/cloud/asl_aws_concurrent_sessions_from_different_ips.yml @@ -1,67 +1,64 @@ name: ASL AWS Concurrent Sessions From Different Ips id: b3424bbe-3204-4469-887b-ec144483a336 -version: 9 -date: '2025-10-14' +version: 10 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly description: The following analytic identifies an AWS IAM account with concurrent sessions originating from more than one unique IP address within a 5-minute span. This detection leverages AWS CloudTrail logs, specifically the `DescribeEventAggregates` API call, to identify multiple IP addresses associated with the same user session. This behavior is significant as it may indicate a session hijacking attack, where an adversary uses stolen session cookies to access AWS resources from a different location. If confirmed malicious, this activity could allow unauthorized access to sensitive corporate resources, leading to potential data breaches or further exploitation. -data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` api.operation=DescribeEventAggregates src_endpoint.domain!="AWS Internal" - | bin span=5m _time - | stats min(_time) as firstTime max(_time) as lastTime values(api.operation) as api.operation values(api.service.name) as api.service.name values(http_request.user_agent) as http_request.user_agent values(src_endpoint.ip) as src_ip values(actor.user.account.uid) as actor.user.account.uid values(cloud.provider) as cloud.provider values(cloud.region) as cloud.region dc(src_endpoint.ip) as distinct_ip_count by _time actor.user.uid - | where distinct_ip_count > 1 - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `asl_aws_concurrent_sessions_from_different_ips_filter`' +data_source: + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` api.operation=DescribeEventAggregates src_endpoint.domain!="AWS Internal" + | bin span=5m _time + | stats min(_time) as firstTime max(_time) as lastTime values(api.operation) as api.operation values(api.service.name) as api.service.name values(http_request.user_agent) as http_request.user_agent values(src_endpoint.ip) as src_ip values(actor.user.account.uid) as actor.user.account.uid values(cloud.provider) as cloud.provider values(cloud.region) as cloud.region dc(src_endpoint.ip) as distinct_ip_count + BY _time actor.user.uid + | where distinct_ip_count > 1 + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_concurrent_sessions_from_different_ips_filter` how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: A user with concurrent sessions from different Ips may also represent the legitimate use of more than one device. Filter as needed and/or customize the threshold to fit your environment. references: -- https://attack.mitre.org/techniques/T1185/ -- https://breakdev.org/evilginx-2-next-generation-of-phishing-2fa-tokens/ -- https://github.com/kgretzky/evilginx2 + - https://attack.mitre.org/techniques/T1185/ + - https://breakdev.org/evilginx-2-next-generation-of-phishing-2fa-tokens/ + - https://github.com/kgretzky/evilginx2 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has concurrent sessions from more than one unique IP address - in the span of 5 minutes. - risk_objects: - - field: user - type: user - score: 42 - threat_objects: - - field: src - type: ip_address + message: User $user$ has concurrent sessions from more than one unique IP address in the span of 5 minutes. + risk_objects: + - field: user + type: user + score: 42 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Compromised User Account - - AWS Identity and Access Management Account Takeover - - Scattered Lapsus$ Hunters - asset_type: AWS Account - mitre_attack_id: - - T1185 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat - manual_test: Can't be tested automatically because of time span. + analytic_story: + - Compromised User Account + - AWS Identity and Access Management Account Takeover + - Scattered Lapsus$ Hunters + asset_type: AWS Account + mitre_attack_id: + - T1185 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat + manual_test: Can't be tested automatically because of time span. tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/aws_concurrent_sessions_from_different_ips/asl_ocsf_cloudtrail.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/aws_concurrent_sessions_from_different_ips/asl_ocsf_cloudtrail.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/asl_aws_create_access_key.yml b/detections/cloud/asl_aws_create_access_key.yml index ef30ea5c94..5e42baad27 100644 --- a/detections/cloud/asl_aws_create_access_key.yml +++ b/detections/cloud/asl_aws_create_access_key.yml @@ -1,46 +1,44 @@ name: ASL AWS Create Access Key id: 81a9f2fe-1697-473c-af1d-086b0d8b63c8 -version: 5 -date: '2025-10-14' +version: 6 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Hunting -description: The following analytic identifies the creation of AWS IAM access keys - by a user for another user, which can indicate privilege escalation. It leverages - AWS CloudTrail logs to detect instances where the user creating the access key is - different from the user for whom the key is created. This activity is significant - because unauthorized access key creation can allow attackers to establish persistence - or exfiltrate data via AWS APIs. If confirmed malicious, this could lead to unauthorized - access to AWS services, data exfiltration, and long-term persistence in the environment. +description: The following analytic identifies the creation of AWS IAM access keys by a user for another user, which can indicate privilege escalation. It leverages AWS CloudTrail logs to detect instances where the user creating the access key is different from the user for whom the key is created. This activity is significant because unauthorized access key creation can allow attackers to establish persistence or exfiltrate data via AWS APIs. If confirmed malicious, this could lead to unauthorized access to AWS services, data exfiltration, and long-term persistence in the environment. data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` api.operation=CreateAccessKey - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor.user.uid api.operation api.service.name http_request.user_agent src_endpoint.ip actor.user.account.uid cloud.provider cloud.region - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - |`asl_aws_create_access_key_filter`' + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` api.operation=CreateAccessKey + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor.user.uid api.operation api.service.name + http_request.user_agent src_endpoint.ip actor.user.account.uid + cloud.provider cloud.region + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_create_access_key_filter` how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: While this search has no known false positives, it is possible that an AWS admin has legitimately created keys for another user. references: -- https://bishopfox.com/blog/privilege-escalation-in-aws -- https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/ + - https://bishopfox.com/blog/privilege-escalation-in-aws + - https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/ tags: - analytic_story: - - AWS IAM Privilege Escalation - - Scattered Lapsus$ Hunters - asset_type: AWS Account - mitre_attack_id: - - T1136.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - AWS IAM Privilege Escalation + - Scattered Lapsus$ Hunters + asset_type: AWS Account + mitre_attack_id: + - T1136.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_createaccesskey/asl_ocsf_cloudtrail.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_createaccesskey/asl_ocsf_cloudtrail.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/asl_aws_create_policy_version_to_allow_all_resources.yml b/detections/cloud/asl_aws_create_policy_version_to_allow_all_resources.yml index e43a666c6c..d91685280b 100644 --- a/detections/cloud/asl_aws_create_policy_version_to_allow_all_resources.yml +++ b/detections/cloud/asl_aws_create_policy_version_to_allow_all_resources.yml @@ -1,72 +1,64 @@ name: ASL AWS Create Policy Version to allow all resources id: 22cc7a62-3884-48c4-82da-592b8199b72f -version: 5 -date: '2025-10-14' +version: 6 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic identifies the creation of a new AWS IAM policy - version that allows access to all resources. It detects this activity by analyzing - AWS CloudTrail logs for the CreatePolicyVersion event with a policy document that - grants broad permissions. This behavior is significant because it violates the principle - of least privilege, potentially exposing the environment to misuse or abuse. If - confirmed malicious, an attacker could gain extensive access to AWS resources, leading - to unauthorized actions, data exfiltration, or further compromise of the AWS environment. +description: The following analytic identifies the creation of a new AWS IAM policy version that allows access to all resources. It detects this activity by analyzing AWS CloudTrail logs for the CreatePolicyVersion event with a policy document that grants broad permissions. This behavior is significant because it violates the principle of least privilege, potentially exposing the environment to misuse or abuse. If confirmed malicious, an attacker could gain extensive access to AWS resources, leading to unauthorized actions, data exfiltration, or further compromise of the AWS environment. data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` api.operation=CreatePolicy - | spath input=api.request.data - | spath input=policyDocument - | regex Statement{}.Action="\*" - | regex Statement{}.Resource="\*" - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor.user.uid api.operation api.service.name http_request.user_agent src_endpoint.ip actor.user.account.uid cloud.provider cloud.region api.request.data - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - |`asl_aws_create_policy_version_to_allow_all_resources_filter`' + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` api.operation=CreatePolicy + | spath input=api.request.data + | spath input=policyDocument + | regex Statement{}.Action="\*" + | regex Statement{}.Resource="\*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor.user.uid api.operation api.service.name + http_request.user_agent src_endpoint.ip actor.user.account.uid + cloud.provider cloud.region api.request.data + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_create_policy_version_to_allow_all_resources_filter` how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: While this search has no known false positives, it is possible that an AWS admin has legitimately created a policy to allow a user to access all resources. That said, AWS strongly advises against granting full control to all AWS resources and you must verify this activity. references: -- https://bishopfox.com/blog/privilege-escalation-in-aws -- https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/ + - https://bishopfox.com/blog/privilege-escalation-in-aws + - https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ created a policy version that allows them to access any resource - in their account - risk_objects: - - field: user - type: user - score: 49 - threat_objects: [] + message: User $user$ created a policy version that allows them to access any resource in their account + risk_objects: + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - AWS IAM Privilege Escalation - - Scattered Lapsus$ Hunters - asset_type: AWS Account - mitre_attack_id: - - T1078.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - AWS IAM Privilege Escalation + - Scattered Lapsus$ Hunters + asset_type: AWS Account + mitre_attack_id: + - T1078.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_create_policy_version/asl_ocsf_cloudtrail.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_create_policy_version/asl_ocsf_cloudtrail.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/asl_aws_credential_access_getpassworddata.yml b/detections/cloud/asl_aws_credential_access_getpassworddata.yml index 7cb825a6c1..8e26820a6e 100644 --- a/detections/cloud/asl_aws_credential_access_getpassworddata.yml +++ b/detections/cloud/asl_aws_credential_access_getpassworddata.yml @@ -1,71 +1,63 @@ name: ASL AWS Credential Access GetPasswordData id: a79b607a-50cc-4704-bb9d-eff280cb78c2 -version: 4 -date: '2025-05-02' +version: 5 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic identifiesGetPasswordData API calls in your AWS - account. It leverages CloudTrail logs from Amazon Security Lake to detect this - activity by counting the distinct instance IDs accessed. This behavior is significant - as it may indicate an attempt to retrieve encrypted administrator passwords for - running Windows instances, which is a critical security concern. If confirmed malicious, - attackers could gain unauthorized access to administrative credentials, potentially - leading to full control over the affected instances and further compromise of the - AWS environment. +description: The following analytic identifiesGetPasswordData API calls in your AWS account. It leverages CloudTrail logs from Amazon Security Lake to detect this activity by counting the distinct instance IDs accessed. This behavior is significant as it may indicate an attempt to retrieve encrypted administrator passwords for running Windows instances, which is a critical security concern. If confirmed malicious, attackers could gain unauthorized access to administrative credentials, potentially leading to full control over the affected instances and further compromise of the AWS environment. data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` api.operation=GetPasswordData - | spath input=api.request.data - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor.user.uid api.operation api.service.name http_request.user_agent src_endpoint.ip actor.user.account.uid cloud.provider cloud.region instanceId - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - |`asl_aws_credential_access_getpassworddata_filter`' + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` api.operation=GetPasswordData + | spath input=api.request.data + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor.user.uid api.operation api.service.name + http_request.user_agent src_endpoint.ip actor.user.account.uid + cloud.provider cloud.region instanceId + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_credential_access_getpassworddata_filter` how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: Administrator tooling or automated scripts may make these calls but it is highly unlikely to make several calls in a short period of time. references: -- https://attack.mitre.org/techniques/T1552/ -- https://stratus-red-team.cloud/attack-techniques/AWS/aws.credential-access.ec2-get-password-data/ + - https://attack.mitre.org/techniques/T1552/ + - https://stratus-red-team.cloud/attack-techniques/AWS/aws.credential-access.ec2-get-password-data/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user_arn = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user_arn = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ is seen to make `GetPasswordData` API calls - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: src - type: ip_address + message: User $user$ is seen to make `GetPasswordData` API calls + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Identity and Access Management Account Takeover - asset_type: AWS Account - mitre_attack_id: - - T1110.001 - - T1586.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Identity and Access Management Account Takeover + asset_type: AWS Account + mitre_attack_id: + - T1110.001 + - T1586.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552/aws_getpassworddata/asl_ocsf_cloudtrail.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552/aws_getpassworddata/asl_ocsf_cloudtrail.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/asl_aws_credential_access_rds_password_reset.yml b/detections/cloud/asl_aws_credential_access_rds_password_reset.yml index 4c14fae9f6..63215a774f 100644 --- a/detections/cloud/asl_aws_credential_access_rds_password_reset.yml +++ b/detections/cloud/asl_aws_credential_access_rds_password_reset.yml @@ -1,73 +1,64 @@ name: ASL AWS Credential Access RDS Password reset id: d15e9bd9-ef64-4d84-bc04-f62955a9fee8 -version: 5 -date: '2025-10-14' +version: 6 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects the resetting of the master user password - for an Amazon RDS DB instance. It leverages AWS CloudTrail logs from Amazon Security - Lake to identify events where the `ModifyDBInstance` API call includes a new `masterUserPassword` - parameter. This activity is significant because unauthorized password resets can - grant attackers access to sensitive data stored in production databases, such as - credit card information, PII, and healthcare data. If confirmed malicious, this - could lead to data breaches, regulatory non-compliance, and significant reputational - damage. Immediate investigation is required to determine the legitimacy of the password - reset. +description: The following analytic detects the resetting of the master user password for an Amazon RDS DB instance. It leverages AWS CloudTrail logs from Amazon Security Lake to identify events where the `ModifyDBInstance` API call includes a new `masterUserPassword` parameter. This activity is significant because unauthorized password resets can grant attackers access to sensitive data stored in production databases, such as credit card information, PII, and healthcare data. If confirmed malicious, this could lead to data breaches, regulatory non-compliance, and significant reputational damage. Immediate investigation is required to determine the legitimacy of the password reset. data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` api.operation=ModifyDBInstance OR api.operation=ModifyDBCluster - | spath input=api.request.data - | search masterUserPassword=* - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor.user.uid api.operation api.service.name http_request.user_agent src_endpoint.ip actor.user.account.uid cloud.provider cloud.region api.request.data - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - |`asl_aws_credential_access_rds_password_reset_filter`' + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` api.operation=ModifyDBInstance OR api.operation=ModifyDBCluster + | spath input=api.request.data + | search masterUserPassword=* + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor.user.uid api.operation api.service.name + http_request.user_agent src_endpoint.ip actor.user.account.uid + cloud.provider cloud.region api.request.data + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_credential_access_rds_password_reset_filter` how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: Users may genuinely reset the RDS password. references: -- https://aws.amazon.com/premiumsupport/knowledge-center/reset-master-user-password-rds + - https://aws.amazon.com/premiumsupport/knowledge-center/reset-master-user-password-rds drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search database_id = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search database_id = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ is seen to reset the password for database - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: src - type: ip_address + message: User $user$ is seen to reset the password for database + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Identity and Access Management Account Takeover - - Scattered Lapsus$ Hunters - asset_type: AWS Account - mitre_attack_id: - - T1110 - - T1586.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Identity and Access Management Account Takeover + - Scattered Lapsus$ Hunters + asset_type: AWS Account + mitre_attack_id: + - T1110 + - T1586.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.002/aws_rds_password_reset/asl_ocsf_cloudtrail.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.002/aws_rds_password_reset/asl_ocsf_cloudtrail.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/asl_aws_defense_evasion_delete_cloudtrail.yml b/detections/cloud/asl_aws_defense_evasion_delete_cloudtrail.yml index 2bcb849df2..d8c3bdeb5e 100644 --- a/detections/cloud/asl_aws_defense_evasion_delete_cloudtrail.yml +++ b/detections/cloud/asl_aws_defense_evasion_delete_cloudtrail.yml @@ -1,67 +1,60 @@ name: ASL AWS Defense Evasion Delete Cloudtrail id: 1f0b47e5-0134-43eb-851c-e3258638945e -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects AWS `DeleteTrail` events within CloudTrail - logs. It leverages Amazon Security Lake logs parsed in the Open Cybersecurity Schema - Framework (OCSF) format to identify when a CloudTrail is deleted. This activity - is significant because adversaries may delete CloudTrail logs to evade detection - and operate with stealth. If confirmed malicious, this action could allow attackers - to cover their tracks, making it difficult to trace their activities and investigate - other potential compromises within the AWS environment. +description: The following analytic detects AWS `DeleteTrail` events within CloudTrail logs. It leverages Amazon Security Lake logs parsed in the Open Cybersecurity Schema Framework (OCSF) format to identify when a CloudTrail is deleted. This activity is significant because adversaries may delete CloudTrail logs to evade detection and operate with stealth. If confirmed malicious, this action could allow attackers to cover their tracks, making it difficult to trace their activities and investigate other potential compromises within the AWS environment. data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` api.operation=DeleteTrail - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor.user.uid api.operation api.service.name http_request.user_agent src_endpoint.ip actor.user.account.uid cloud.provider cloud.region - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `asl_aws_defense_evasion_delete_cloudtrail_filter`' + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` api.operation=DeleteTrail + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor.user.uid api.operation api.service.name + http_request.user_agent src_endpoint.ip actor.user.account.uid + cloud.provider cloud.region + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_defense_evasion_delete_cloudtrail_filter` how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: While this search has no known false positives, it is possible that an AWS admin has stopped cloudTrail logging. Please investigate this activity. references: -- https://attack.mitre.org/techniques/T1562/008/ + - https://attack.mitre.org/techniques/T1562/008/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has deleted CloudTrail logging - risk_objects: - - field: user - type: user - score: 90 - threat_objects: - - field: src - type: ip_address + message: User $user$ has deleted CloudTrail logging + risk_objects: + - field: user + type: user + score: 90 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Defense Evasion - asset_type: AWS Account - mitre_attack_id: - - T1562.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Defense Evasion + asset_type: AWS Account + mitre_attack_id: + - T1562.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/stop_delete_cloudtrail/asl_ocsf_cloudtrail.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/stop_delete_cloudtrail/asl_ocsf_cloudtrail.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/asl_aws_defense_evasion_delete_cloudwatch_log_group.yml b/detections/cloud/asl_aws_defense_evasion_delete_cloudwatch_log_group.yml index 6eb746b6f9..534e75e836 100644 --- a/detections/cloud/asl_aws_defense_evasion_delete_cloudwatch_log_group.yml +++ b/detections/cloud/asl_aws_defense_evasion_delete_cloudwatch_log_group.yml @@ -1,68 +1,60 @@ name: ASL AWS Defense Evasion Delete CloudWatch Log Group id: 0f701b38-a0fb-43fd-a83d-d12265f71f33 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects the deletion of CloudWatch log groups - in AWS, identified through `DeleteLogGroup` events in CloudTrail logs. This method - leverages Amazon Security Lake logs parsed in the OCSF format. The activity is significant - because attackers may delete log groups to evade detection and disrupt logging capabilities, - hindering incident response efforts. If confirmed malicious, this action could allow - attackers to cover their tracks, making it difficult to trace their activities and - potentially leading to undetected data breaches or further malicious actions within - the compromised AWS environment. +description: The following analytic detects the deletion of CloudWatch log groups in AWS, identified through `DeleteLogGroup` events in CloudTrail logs. This method leverages Amazon Security Lake logs parsed in the OCSF format. The activity is significant because attackers may delete log groups to evade detection and disrupt logging capabilities, hindering incident response efforts. If confirmed malicious, this action could allow attackers to cover their tracks, making it difficult to trace their activities and potentially leading to undetected data breaches or further malicious actions within the compromised AWS environment. data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` api.operation=DeleteLogGroup - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor.user.uid api.operation api.service.name http_request.user_agent src_endpoint.ip actor.user.account.uid cloud.provider cloud.region - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `asl_aws_defense_evasion_delete_cloudwatch_log_group_filter`' + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` api.operation=DeleteLogGroup + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor.user.uid api.operation api.service.name + http_request.user_agent src_endpoint.ip actor.user.account.uid + cloud.provider cloud.region + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_defense_evasion_delete_cloudwatch_log_group_filter` how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: While this search has no known false positives, it is possible that an AWS admin has deleted CloudWatch logging. Please investigate this activity. references: -- https://attack.mitre.org/techniques/T1562/008/ + - https://attack.mitre.org/techniques/T1562/008/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has deleted a CloudWatch logging group - risk_objects: - - field: user - type: user - score: 90 - threat_objects: - - field: src - type: ip_address + message: User $user$ has deleted a CloudWatch logging group + risk_objects: + - field: user + type: user + score: 90 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Defense Evasion - asset_type: AWS Account - mitre_attack_id: - - T1562.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Defense Evasion + asset_type: AWS Account + mitre_attack_id: + - T1562.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/delete_cloudwatch_log_group/asl_ocsf_cloudtrail.json - source: aws_asl - sourcetype: aws:asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/delete_cloudwatch_log_group/asl_ocsf_cloudtrail.json + source: aws_asl + sourcetype: aws:asl diff --git a/detections/cloud/asl_aws_defense_evasion_impair_security_services.yml b/detections/cloud/asl_aws_defense_evasion_impair_security_services.yml index 016c455114..4a164bafb8 100644 --- a/detections/cloud/asl_aws_defense_evasion_impair_security_services.yml +++ b/detections/cloud/asl_aws_defense_evasion_impair_security_services.yml @@ -1,47 +1,44 @@ name: ASL AWS Defense Evasion Impair Security Services id: 5029b681-0462-47b7-82e7-f7e3d37f5a2d -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Patrick Bareiss, Bhavin Patel, Gowthamaraj Rajendran, Splunk status: production type: Hunting -description: The following analytic detects the deletion of critical AWS Security - Services configurations, such as CloudWatch alarms, GuardDuty detectors, and Web - Application Firewall rules. It leverages Amazon Security Lake logs to identify specific - API calls like "DeleteLogStream" and "DeleteDetector." This activity is significant - because adversaries often use these actions to disable security monitoring and evade - detection. If confirmed malicious, this could allow attackers to operate undetected, - leading to potential data breaches, unauthorized access, and prolonged persistence - within the AWS environment. +description: The following analytic detects the deletion of critical AWS Security Services configurations, such as CloudWatch alarms, GuardDuty detectors, and Web Application Firewall rules. It leverages Amazon Security Lake logs to identify specific API calls like "DeleteLogStream" and "DeleteDetector." This activity is significant because adversaries often use these actions to disable security monitoring and evade detection. If confirmed malicious, this could allow attackers to operate undetected, leading to potential data breaches, unauthorized access, and prolonged persistence within the AWS environment. data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` api.operation IN ("DeleteLogStream","DeleteDetector","DeleteIPSet","DeleteWebACL","DeleteRule","DeleteRuleGroup","DeleteLoggingConfiguration","DeleteAlarms") - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor.user.uid api.operation api.service.name http_request.user_agent src_endpoint.ip actor.user.account.uid cloud.provider cloud.region - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `asl_aws_defense_evasion_impair_security_services_filter`' + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` api.operation IN ("DeleteLogStream","DeleteDetector","DeleteIPSet","DeleteWebACL","DeleteRule","DeleteRuleGroup","DeleteLoggingConfiguration","DeleteAlarms") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor.user.uid api.operation api.service.name + http_request.user_agent src_endpoint.ip actor.user.account.uid + cloud.provider cloud.region + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_defense_evasion_impair_security_services_filter` how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: While this search has no known false positives, it is possible that it is a legitimate admin activity. Please consider filtering out these noisy events using userAgent, user_arn field names. references: -- https://docs.aws.amazon.com/cli/latest/reference/guardduty/index.html -- https://docs.aws.amazon.com/cli/latest/reference/waf/index.html -- https://www.elastic.co/guide/en/security/current/prebuilt-rules.html + - https://docs.aws.amazon.com/cli/latest/reference/guardduty/index.html + - https://docs.aws.amazon.com/cli/latest/reference/waf/index.html + - https://www.elastic.co/guide/en/security/current/prebuilt-rules.html tags: - analytic_story: - - AWS Defense Evasion - asset_type: AWS Account - mitre_attack_id: - - T1562.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Defense Evasion + asset_type: AWS Account + mitre_attack_id: + - T1562.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/aws_delete_security_services/asl_ocsf_cloudtrail.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/aws_delete_security_services/asl_ocsf_cloudtrail.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/asl_aws_defense_evasion_putbucketlifecycle.yml b/detections/cloud/asl_aws_defense_evasion_putbucketlifecycle.yml index d90ecd24e1..869a42f317 100644 --- a/detections/cloud/asl_aws_defense_evasion_putbucketlifecycle.yml +++ b/detections/cloud/asl_aws_defense_evasion_putbucketlifecycle.yml @@ -1,48 +1,47 @@ name: ASL AWS Defense Evasion PutBucketLifecycle id: 986565a2-7707-48ea-9590-37929cebc938 -version: 4 -date: '2025-05-02' +version: 5 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Hunting -description: The following analytic detects `PutBucketLifecycle` events in AWS CloudTrail - logs where a user sets a lifecycle rule for an S3 bucket with an expiration period - of fewer than three days. This detection leverages CloudTrail logs to identify suspicious - lifecycle configurations. This activity is significant because attackers may use - it to delete CloudTrail logs quickly, thereby evading detection and impairing forensic - investigations. If confirmed malicious, this could allow attackers to cover their - tracks, making it difficult to trace their actions and respond to the breach effectively. +description: The following analytic detects `PutBucketLifecycle` events in AWS CloudTrail logs where a user sets a lifecycle rule for an S3 bucket with an expiration period of fewer than three days. This detection leverages CloudTrail logs to identify suspicious lifecycle configurations. This activity is significant because attackers may use it to delete CloudTrail logs quickly, thereby evading detection and impairing forensic investigations. If confirmed malicious, this could allow attackers to cover their tracks, making it difficult to trace their actions and respond to the breach effectively. data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` api.operation=PutBucketLifecycle - | spath input=api.request.data path=LifecycleConfiguration.Rule.NoncurrentVersionExpiration.NoncurrentDays output=NoncurrentDays - | where NoncurrentDays < 3 - | spath input=api.request.data - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor.user.uid api.operation api.service.name http_request.user_agent src_endpoint.ip actor.user.account.uid cloud.provider cloud.region NoncurrentDays bucketName - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `asl_aws_defense_evasion_putbucketlifecycle_filter`' + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` api.operation=PutBucketLifecycle + | spath input=api.request.data path=LifecycleConfiguration.Rule.NoncurrentVersionExpiration.NoncurrentDays output=NoncurrentDays + | where NoncurrentDays < 3 + | spath input=api.request.data + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor.user.uid api.operation api.service.name + http_request.user_agent src_endpoint.ip actor.user.account.uid + cloud.provider cloud.region NoncurrentDays + bucketName + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_defense_evasion_putbucketlifecycle_filter` how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: While this search has no known false positives, it is possible that it is a legitimate admin activity. Please consider filtering out these noisy events using userAgent, user_arn field names. references: -- https://stratus-red-team.cloud/attack-techniques/AWS/aws.defense-evasion.cloudtrail-lifecycle-rule/ + - https://stratus-red-team.cloud/attack-techniques/AWS/aws.defense-evasion.cloudtrail-lifecycle-rule/ tags: - analytic_story: - - AWS Defense Evasion - asset_type: AWS Account - mitre_attack_id: - - T1485.001 - - T1562.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Defense Evasion + asset_type: AWS Account + mitre_attack_id: + - T1485.001 + - T1562.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/put_bucketlifecycle/asl_ocsf_cloudtrail.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/put_bucketlifecycle/asl_ocsf_cloudtrail.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/asl_aws_defense_evasion_stop_logging_cloudtrail.yml b/detections/cloud/asl_aws_defense_evasion_stop_logging_cloudtrail.yml index 7a6ac5dd90..e1535e3e36 100644 --- a/detections/cloud/asl_aws_defense_evasion_stop_logging_cloudtrail.yml +++ b/detections/cloud/asl_aws_defense_evasion_stop_logging_cloudtrail.yml @@ -1,71 +1,60 @@ name: ASL AWS Defense Evasion Stop Logging Cloudtrail id: 0b78a8f9-1d31-4d23-85c8-56ad13d5b4c1 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects `StopLogging` events within AWS CloudTrail - logs, a critical action that adversaries may use to evade detection. By halting - the logging of their malicious activities, attackers aim to operate undetected within - a compromised AWS environment. This detection is achieved by monitoring for specific - CloudTrail log entries that indicate the cessation of logging activities. Identifying - such behavior is crucial for a Security Operations Center (SOC), as it signals an - attempt to undermine the integrity of logging mechanisms, potentially allowing malicious - activities to proceed without observation. The impact of this evasion tactic is - significant, as it can severely hamper incident response and forensic investigations - by obscuring the attacker's actions. +description: The following analytic detects `StopLogging` events within AWS CloudTrail logs, a critical action that adversaries may use to evade detection. By halting the logging of their malicious activities, attackers aim to operate undetected within a compromised AWS environment. This detection is achieved by monitoring for specific CloudTrail log entries that indicate the cessation of logging activities. Identifying such behavior is crucial for a Security Operations Center (SOC), as it signals an attempt to undermine the integrity of logging mechanisms, potentially allowing malicious activities to proceed without observation. The impact of this evasion tactic is significant, as it can severely hamper incident response and forensic investigations by obscuring the attacker's actions. data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` api.operation=StopLogging - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor.user.uid api.operation api.service.name http_request.user_agent src_endpoint.ip actor.user.account.uid cloud.provider cloud.region - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `asl_aws_defense_evasion_stop_logging_cloudtrail_filter`' + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` api.operation=StopLogging + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor.user.uid api.operation api.service.name + http_request.user_agent src_endpoint.ip actor.user.account.uid + cloud.provider cloud.region + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_defense_evasion_stop_logging_cloudtrail_filter` how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: While this search has no known false positives, it is possible that an AWS admin has stopped cloudtrail logging. Please investigate this activity. references: -- https://attack.mitre.org/techniques/T1562/008/ + - https://attack.mitre.org/techniques/T1562/008/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has stopped Cloudtrail logging for account id $vendor_account$ - from IP $src$ - risk_objects: - - field: user - type: user - score: 90 - threat_objects: - - field: src - type: ip_address + message: User $user$ has stopped Cloudtrail logging for account id $vendor_account$ from IP $src$ + risk_objects: + - field: user + type: user + score: 90 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Defense Evasion - asset_type: AWS Account - mitre_attack_id: - - T1562.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Defense Evasion + asset_type: AWS Account + mitre_attack_id: + - T1562.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/stop_delete_cloudtrail/asl_ocsf_cloudtrail_2.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/stop_delete_cloudtrail/asl_ocsf_cloudtrail_2.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/asl_aws_defense_evasion_update_cloudtrail.yml b/detections/cloud/asl_aws_defense_evasion_update_cloudtrail.yml index 4fe0ecd639..c8cba8f1dc 100644 --- a/detections/cloud/asl_aws_defense_evasion_update_cloudtrail.yml +++ b/detections/cloud/asl_aws_defense_evasion_update_cloudtrail.yml @@ -1,70 +1,60 @@ name: ASL AWS Defense Evasion Update Cloudtrail id: f3eb471c-16d0-404d-897c-7653f0a78cba -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects `UpdateTrail` events within AWS CloudTrail - logs, aiming to identify attempts by attackers to evade detection by altering logging - configurations. By updating CloudTrail settings with incorrect parameters, such - as changing multi-regional logging to a single region, attackers can impair the - logging of their activities across other regions. This behavior is crucial for Security - Operations Centers (SOCs) to identify, as it indicates an adversary's intent to - operate undetected within a compromised AWS environment. The impact of such evasion - tactics is significant, potentially allowing malicious activities to proceed without - being logged, thereby hindering incident response and forensic investigations. +description: The following analytic detects `UpdateTrail` events within AWS CloudTrail logs, aiming to identify attempts by attackers to evade detection by altering logging configurations. By updating CloudTrail settings with incorrect parameters, such as changing multi-regional logging to a single region, attackers can impair the logging of their activities across other regions. This behavior is crucial for Security Operations Centers (SOCs) to identify, as it indicates an adversary's intent to operate undetected within a compromised AWS environment. The impact of such evasion tactics is significant, potentially allowing malicious activities to proceed without being logged, thereby hindering incident response and forensic investigations. data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` api.operation=UpdateTrail - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor.user.uid api.operation api.service.name http_request.user_agent src_endpoint.ip actor.user.account.uid cloud.provider cloud.region - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `asl_aws_defense_evasion_update_cloudtrail_filter`' + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` api.operation=UpdateTrail + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor.user.uid api.operation api.service.name + http_request.user_agent src_endpoint.ip actor.user.account.uid + cloud.provider cloud.region + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_defense_evasion_update_cloudtrail_filter` how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: While this search has no known false positives, it is possible that an AWS admin has updated cloudtrail logging. Please investigate this activity. references: -- https://attack.mitre.org/techniques/T1562/008/ + - https://attack.mitre.org/techniques/T1562/008/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has updated a cloudtrail logging for account id $vendor_account$ - from IP $src$ - risk_objects: - - field: user - type: user - score: 90 - threat_objects: - - field: src - type: ip_address + message: User $user$ has updated a cloudtrail logging for account id $vendor_account$ from IP $src$ + risk_objects: + - field: user + type: user + score: 90 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Defense Evasion - asset_type: AWS Account - mitre_attack_id: - - T1562.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Defense Evasion + asset_type: AWS Account + mitre_attack_id: + - T1562.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/update_cloudtrail/asl_ocsf_cloudtrail.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/update_cloudtrail/asl_ocsf_cloudtrail.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/asl_aws_detect_users_creating_keys_with_encrypt_policy_without_mfa.yml b/detections/cloud/asl_aws_detect_users_creating_keys_with_encrypt_policy_without_mfa.yml index acd31d94e5..e954035241 100644 --- a/detections/cloud/asl_aws_detect_users_creating_keys_with_encrypt_policy_without_mfa.yml +++ b/detections/cloud/asl_aws_detect_users_creating_keys_with_encrypt_policy_without_mfa.yml @@ -1,63 +1,72 @@ name: ASL AWS Detect Users creating keys with encrypt policy without MFA id: 16ae9076-d1d5-411c-8fdd-457504b33dac -version: 4 -date: '2026-01-14' +version: 5 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP description: The following analytic detects the creation of AWS KMS keys with an encryption policy accessible to everyone, including external entities. It leverages AWS CloudTrail logs from Amazon Security Lake to identify `CreateKey` or `PutKeyPolicy` events where the `kms:Encrypt` action is granted to all principals. This activity is significant as it may indicate a compromised account, allowing an attacker to misuse the encryption key to target other organizations. If confirmed malicious, this could lead to unauthorized data encryption, potentially disrupting operations and compromising sensitive information across multiple entities. -data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` api.operation=PutKeyPolicy OR api.operation=CreateKey - | spath input=api.request.data path=policy output=policy - | spath input=policy - | rename Statement{}.Action as Action, Statement{}.Principal as Principal - | eval Statement=mvzip(Action,Principal,"|") - | mvexpand Statement - | eval action=mvindex(split(Statement, "|"), 0) - | eval principal=mvindex(split(Statement, "|"), 1) - | search action=kms* - | regex principal="\*" - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor.user.uid api.operation api.service.name http_request.user_agent src_endpoint.ip actor.user.account.uid cloud.provider cloud.region api.request.data - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`asl_aws_detect_users_creating_keys_with_encrypt_policy_without_mfa_filter`' +data_source: + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` api.operation=PutKeyPolicy OR api.operation=CreateKey + | spath input=api.request.data path=policy output=policy + | spath input=policy + | rename Statement{}.Action as Action, Statement{}.Principal as Principal + | eval Statement=mvzip(Action,Principal," + | ") + | mvexpand Statement + | eval action=mvindex(split(Statement, " + | "), 0) + | eval principal=mvindex(split(Statement, " + | "), 1) + | search action=kms* + | regex principal="\*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor.user.uid api.operation api.service.name + http_request.user_agent src_endpoint.ip actor.user.account.uid + cloud.provider cloud.region api.request.data + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_detect_users_creating_keys_with_encrypt_policy_without_mfa_filter` how_to_implement: The detection is based on Cloudtrail events from Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: No false positives have been identified at this time. references: -- https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/ -- https://github.com/d1vious/git-wild-hunt -- https://www.youtube.com/watch?v=PgzNib37g0M + - https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/ + - https://github.com/d1vious/git-wild-hunt + - https://www.youtube.com/watch?v=PgzNib37g0M drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: AWS account is potentially compromised and user $user$ is trying to compromise other accounts - risk_objects: - - field: user - type: user - score: 25 - threat_objects: [] + message: AWS account is potentially compromised and user $user$ is trying to compromise other accounts + risk_objects: + - field: user + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Ransomware Cloud - asset_type: AWS Account - mitre_attack_id: - - T1486 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Ransomware Cloud + asset_type: AWS Account + mitre_attack_id: + - T1486 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/aws_kms_key/asl_ocsf_cloudtrail.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/aws_kms_key/asl_ocsf_cloudtrail.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/asl_aws_disable_bucket_versioning.yml b/detections/cloud/asl_aws_disable_bucket_versioning.yml index a62ed822f7..e7f98c456a 100644 --- a/detections/cloud/asl_aws_disable_bucket_versioning.yml +++ b/detections/cloud/asl_aws_disable_bucket_versioning.yml @@ -1,59 +1,66 @@ name: ASL AWS Disable Bucket Versioning id: f32598bb-fa5f-4afd-8ab3-0263cc28efbc -version: 3 -date: '2025-05-02' +version: 4 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -data_source: -- ASL AWS CloudTrail +data_source: + - ASL AWS CloudTrail description: The following analytic detects when AWS S3 bucket versioning is suspended by a user. It leverages AWS CloudTrail logs to identify `PutBucketVersioning` events with the `VersioningConfiguration.Status` set to `Suspended`. This activity is significant because disabling versioning can prevent recovery of deleted or modified data, which is a common tactic in ransomware attacks. If confirmed malicious, this action could lead to data loss and hinder recovery efforts, severely impacting data integrity and availability. -search: '`amazon_security_lake` api.operation=PutBucketVersioning - | spath input=api.request.data path=VersioningConfiguration.Status output=Status - | spath input=api.request.data path=bucketName output=bucketName - | search Status=Suspended - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor.user.uid api.operation api.service.name http_request.user_agent src_endpoint.ip actor.user.account.uid cloud.provider cloud.region api.request.data bucketName - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `asl_aws_disable_bucket_versioning_filter`' +search: |- + `amazon_security_lake` api.operation=PutBucketVersioning + | spath input=api.request.data path=VersioningConfiguration.Status output=Status + | spath input=api.request.data path=bucketName output=bucketName + | search Status=Suspended + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor.user.uid api.operation api.service.name + http_request.user_agent src_endpoint.ip actor.user.account.uid + cloud.provider cloud.region api.request.data + bucketName + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_disable_bucket_versioning_filter` how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: It is possible that an AWS Administrator has legitimately disabled versioning on certain buckets to avoid costs. references: -- https://invictus-ir.medium.com/ransomware-in-the-cloud-7f14805bbe82 -- https://bleemb.medium.com/data-exfiltration-with-native-aws-s3-features-c94ae4d13436 + - https://invictus-ir.medium.com/ransomware-in-the-cloud-7f14805bbe82 + - https://bleemb.medium.com/data-exfiltration-with-native-aws-s3-features-c94ae4d13436 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Bucket Versioning is suspended for S3 buckets- $bucketName$ by user $user$ from IP address $src$ - risk_objects: - - field: user - type: user - score: 64 - threat_objects: - - field: src - type: ip_address + message: Bucket Versioning is suspended for S3 buckets- $bucketName$ by user $user$ from IP address $src$ + risk_objects: + - field: user + type: user + score: 64 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Suspicious AWS S3 Activities - - Data Exfiltration - asset_type: AWS Account - mitre_attack_id: - - T1490 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Suspicious AWS S3 Activities + - Data Exfiltration + asset_type: AWS Account + mitre_attack_id: + - T1490 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/aws_bucket_version/asl_ocsf_cloudtrail.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/aws_bucket_version/asl_ocsf_cloudtrail.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/asl_aws_ec2_snapshot_shared_externally.yml b/detections/cloud/asl_aws_ec2_snapshot_shared_externally.yml index b93f7e5a47..81089b6263 100644 --- a/detections/cloud/asl_aws_ec2_snapshot_shared_externally.yml +++ b/detections/cloud/asl_aws_ec2_snapshot_shared_externally.yml @@ -1,59 +1,65 @@ name: ASL AWS EC2 Snapshot Shared Externally id: 00af8f7f-e004-446b-9bba-2732f717ae27 -version: 3 -date: '2025-05-02' +version: 4 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP description: The following analytic detects when an EC2 snapshot is shared publicly by analyzing AWS CloudTrail events. This detection method leverages CloudTrail logs to identify modifications in snapshot permissions, specifically when the snapshot is shared outside the originating AWS account. This activity is significant as it may indicate an attempt to exfiltrate sensitive data stored in the snapshot. If confirmed malicious, an attacker could gain unauthorized access to the snapshot's data, potentially leading to data breaches or further exploitation of the compromised information. -data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` api.operation=ModifySnapshotAttribute - | spath input=api.request.data path=createVolumePermission.add.items{}.group output=group - | search group=all - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor.user.uid api.operation api.service.name http_request.user_agent src_endpoint.ip actor.user.account.uid cloud.provider cloud.region api.request.data - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `asl_aws_ec2_snapshot_shared_externally_filter`' +data_source: + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` api.operation=ModifySnapshotAttribute + | spath input=api.request.data path=createVolumePermission.add.items{}.group output=group + | search group=all + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor.user.uid api.operation api.service.name + http_request.user_agent src_endpoint.ip actor.user.account.uid + cloud.provider cloud.region api.request.data + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_ec2_snapshot_shared_externally_filter` how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: It is possible that an AWS admin has legitimately shared a snapshot with others for a specific purpose. references: -- https://labs.nettitude.com/blog/how-to-exfiltrate-aws-ec2-data/ -- https://stratus-red-team.cloud/attack-techniques/AWS/aws.exfiltration.ec2-share-ebs-snapshot/ -- https://hackingthe.cloud/aws/enumeration/loot_public_ebs_snapshots/ + - https://labs.nettitude.com/blog/how-to-exfiltrate-aws-ec2-data/ + - https://stratus-red-team.cloud/attack-techniques/AWS/aws.exfiltration.ec2-share-ebs-snapshot/ + - https://hackingthe.cloud/aws/enumeration/loot_public_ebs_snapshots/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: AWS EC2 snapshot from user $user$ is shared publicly - risk_objects: - - field: user - type: user - score: 48 - threat_objects: - - field: src - type: ip_address + message: AWS EC2 snapshot from user $user$ is shared publicly + risk_objects: + - field: user + type: user + score: 48 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Suspicious Cloud Instance Activities - - Data Exfiltration - asset_type: EC2 Snapshot - mitre_attack_id: - - T1537 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Suspicious Cloud Instance Activities + - Data Exfiltration + asset_type: EC2 Snapshot + mitre_attack_id: + - T1537 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1537/aws_snapshot_exfil/asl_ocsf_cloudtrail.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1537/aws_snapshot_exfil/asl_ocsf_cloudtrail.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/asl_aws_ecr_container_upload_outside_business_hours.yml b/detections/cloud/asl_aws_ecr_container_upload_outside_business_hours.yml index fe6eeadf82..15b9c91d01 100644 --- a/detections/cloud/asl_aws_ecr_container_upload_outside_business_hours.yml +++ b/detections/cloud/asl_aws_ecr_container_upload_outside_business_hours.yml @@ -1,70 +1,62 @@ name: ASL AWS ECR Container Upload Outside Business Hours id: 739ed682-27e9-4ba0-80e5-a91b97698213 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects the upload of new containers to AWS Elastic - Container Service (ECR) outside of standard business hours through AWS CloudTrail - events. It identifies this behavior by monitoring for `PutImage` events occurring - before 8 AM or after 8 PM, as well as any uploads on weekends. This activity is - significant for a SOC to investigate as it may indicate unauthorized access or malicious - deployments, potentially leading to compromised services or data breaches. Identifying - and addressing such uploads promptly can mitigate the risk of security incidents - and their associated impacts. +description: The following analytic detects the upload of new containers to AWS Elastic Container Service (ECR) outside of standard business hours through AWS CloudTrail events. It identifies this behavior by monitoring for `PutImage` events occurring before 8 AM or after 8 PM, as well as any uploads on weekends. This activity is significant for a SOC to investigate as it may indicate unauthorized access or malicious deployments, potentially leading to compromised services or data breaches. Identifying and addressing such uploads promptly can mitigate the risk of security incidents and their associated impacts. data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` api.operation=PutImage - | eval hour=strftime(time/pow(10,3), "%H"), weekday=strftime(time/pow(10,3), "%A") - | where hour >= 20 OR hour < 8 OR weekday=Saturday OR weekday=Sunday - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor.user.uid api.operation api.service.name http_request.user_agent src_endpoint.ip actor.user.account.uid cloud.provider cloud.region api.request.data bucketName - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `asl_aws_ecr_container_upload_outside_business_hours_filter`' + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` api.operation=PutImage + | eval hour=strftime(time/pow(10,3), "%H"), weekday=strftime(time/pow(10,3), "%A") + | where hour >= 20 OR hour < 8 OR weekday=Saturday OR weekday=Sunday + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor.user.uid api.operation api.service.name + http_request.user_agent src_endpoint.ip actor.user.account.uid + cloud.provider cloud.region api.request.data + bucketName + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_ecr_container_upload_outside_business_hours_filter` how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: When your development is spreaded in different time zones, applying this rule can be difficult. references: -- https://attack.mitre.org/techniques/T1204/003/ + - https://attack.mitre.org/techniques/T1204/003/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Container uploaded outside business hours from $user$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: [] + message: Container uploaded outside business hours from $user$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - Dev Sec Ops - asset_type: AWS Account - mitre_attack_id: - - T1204.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - manual_test: Can't be tested automatically because of outside of business hours - time + analytic_story: + - Dev Sec Ops + asset_type: AWS Account + mitre_attack_id: + - T1204.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + manual_test: Can't be tested automatically because of outside of business hours time tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_container_upload/asl_ocsf_cloudtrail.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_container_upload/asl_ocsf_cloudtrail.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/asl_aws_ecr_container_upload_unknown_user.yml b/detections/cloud/asl_aws_ecr_container_upload_unknown_user.yml index 491d236725..b2017da9a1 100644 --- a/detections/cloud/asl_aws_ecr_container_upload_unknown_user.yml +++ b/detections/cloud/asl_aws_ecr_container_upload_unknown_user.yml @@ -1,70 +1,60 @@ name: ASL AWS ECR Container Upload Unknown User id: 886a8f46-d7e2-4439-b9ba-aec238e31732 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects unauthorized container uploads to AWS - Elastic Container Service (ECR) by monitoring AWS CloudTrail events. It identifies - instances where a new container is uploaded by a user not previously recognized - as authorized. This detection is crucial for a SOC as it can indicate a potential - compromise or misuse of AWS ECR, which could lead to unauthorized access to sensitive - data or the deployment of malicious containers. By identifying and investigating - these events, organizations can mitigate the risk of data breaches or other security - incidents resulting from unauthorized container uploads. The impact of such an attack - could be significant, compromising the integrity and security of the organization's - cloud environment. +description: The following analytic detects unauthorized container uploads to AWS Elastic Container Service (ECR) by monitoring AWS CloudTrail events. It identifies instances where a new container is uploaded by a user not previously recognized as authorized. This detection is crucial for a SOC as it can indicate a potential compromise or misuse of AWS ECR, which could lead to unauthorized access to sensitive data or the deployment of malicious containers. By identifying and investigating these events, organizations can mitigate the risk of data breaches or other security incidents resulting from unauthorized container uploads. The impact of such an attack could be significant, compromising the integrity and security of the organization's cloud environment. data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` api.operation=PutImage NOT `aws_ecr_users_asl` - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor.user.uid api.operation api.service.name http_request.user_agent src_endpoint.ip actor.user.account.uid cloud.provider cloud.region - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `asl_aws_ecr_container_upload_unknown_user_filter`' + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` api.operation=PutImage NOT `aws_ecr_users_asl` + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor.user.uid api.operation api.service.name + http_request.user_agent src_endpoint.ip actor.user.account.uid + cloud.provider cloud.region + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_ecr_container_upload_unknown_user_filter` how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: No false positives have been identified at this time. references: -- https://attack.mitre.org/techniques/T1204/003/ + - https://attack.mitre.org/techniques/T1204/003/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Container uploaded from unknown user $user$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: src - type: ip_address + message: Container uploaded from unknown user $user$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Dev Sec Ops - asset_type: AWS Account - mitre_attack_id: - - T1204.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Dev Sec Ops + asset_type: AWS Account + mitre_attack_id: + - T1204.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_container_upload/asl_ocsf_cloudtrail.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_container_upload/asl_ocsf_cloudtrail.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/asl_aws_iam_accessdenied_discovery_events.yml b/detections/cloud/asl_aws_iam_accessdenied_discovery_events.yml index 16aeb0108e..ec3493ed45 100644 --- a/detections/cloud/asl_aws_iam_accessdenied_discovery_events.yml +++ b/detections/cloud/asl_aws_iam_accessdenied_discovery_events.yml @@ -1,56 +1,59 @@ name: ASL AWS IAM AccessDenied Discovery Events id: a4f39755-b1e2-40bb-b2dc-4449c45b0bf2 -version: 3 -date: '2025-05-02' +version: 4 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly description: The following analytic identifies excessive AccessDenied events within an hour timeframe for IAM users in AWS. It leverages AWS CloudTrail logs to detect multiple failed access attempts from the same source IP and user identity. This activity is significant as it may indicate that an access key has been compromised and is being misused for unauthorized discovery actions. If confirmed malicious, this could allow attackers to gather information about the AWS environment, potentially leading to further exploitation or privilege escalation. -data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` api.response.error=AccessDenied OR api.response.error=OperationNotPermittedException OR api.response.error=*Unauthorized* actor.user.type=IAMUser - | bucket _time span=1h - | stats count as failures min(_time) as firstTime max(_time) as lastTime dc(api.operation) as dc_operation, dc(api.service.name) as dc_service values(api.operation) as api.operation values(api.service.name) as api.service.name values(http_request.user_agent) as http_request.user_agent values(src_endpoint.ip) as src_ip values(actor.user.account.uid) as actor.user.account.uid values(cloud.provider) as cloud.provider values(cloud.region) as cloud.region by _time actor.user.uid - | where failures >= 5 AND dc_operation >= 1 AND dc_service >= 1 - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `asl_aws_iam_accessdenied_discovery_events_filter`' +data_source: + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` api.response.error=AccessDenied OR api.response.error=OperationNotPermittedException OR api.response.error=*Unauthorized* actor.user.type=IAMUser + | bucket _time span=1h + | stats count as failures min(_time) as firstTime max(_time) as lastTime dc(api.operation) as dc_operation, dc(api.service.name) as dc_service values(api.operation) as api.operation values(api.service.name) as api.service.name values(http_request.user_agent) as http_request.user_agent values(src_endpoint.ip) as src_ip values(actor.user.account.uid) as actor.user.account.uid values(cloud.provider) as cloud.provider values(cloud.region) as cloud.region + BY _time actor.user.uid + | where failures >= 5 AND dc_operation >= 1 AND dc_service >= 1 + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_iam_accessdenied_discovery_events_filter` how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: It is possible to start this detection will need to be tuned by source IP or user. In addition, change the count values to an upper threshold to restrict false positives. references: -- https://aws.amazon.com/premiumsupport/knowledge-center/troubleshoot-iam-permission-errors/ + - https://aws.amazon.com/premiumsupport/knowledge-center/troubleshoot-iam-permission-errors/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ is seen to perform excessive number of discovery related api calls- $failures$, within an hour where the access was denied. - risk_objects: - - field: user - type: user - score: 10 - threat_objects: - - field: src_ip - type: ip_address + message: User $user$ is seen to perform excessive number of discovery related api calls- $failures$, within an hour where the access was denied. + risk_objects: + - field: user + type: user + score: 10 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Suspicious Cloud User Activities - asset_type: AWS Account - mitre_attack_id: - - T1580 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - Suspicious Cloud User Activities + asset_type: AWS Account + mitre_attack_id: + - T1580 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1580/aws_iam_accessdenied_discovery_events/asl_ocsf_cloudtrail.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1580/aws_iam_accessdenied_discovery_events/asl_ocsf_cloudtrail.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/asl_aws_iam_assume_role_policy_brute_force.yml b/detections/cloud/asl_aws_iam_assume_role_policy_brute_force.yml index d78d8913e7..6b38405410 100644 --- a/detections/cloud/asl_aws_iam_assume_role_policy_brute_force.yml +++ b/detections/cloud/asl_aws_iam_assume_role_policy_brute_force.yml @@ -1,59 +1,63 @@ name: ASL AWS IAM Assume Role Policy Brute Force id: 726959fe-316d-445c-a584-fa187d64e295 -version: 4 -date: '2025-10-14' +version: 5 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP description: The following analytic detects multiple failed attempts to assume an AWS IAM role, indicating a potential brute force attack. It leverages AWS CloudTrail logs to identify `MalformedPolicyDocumentException` errors with a status of `failure` and filters out legitimate AWS services. This activity is significant as repeated failures to assume roles can indicate an adversary attempting to guess role names, which is a precursor to unauthorized access. If confirmed malicious, this could lead to unauthorized access to AWS resources, potentially compromising sensitive data and services. -data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` api.operation="AssumeRole" "api.response.error"=AccessDenied - | bucket _time span=1h - | stats count as failures min(_time) as firstTime max(_time) as lastTime values(api.operation) as api.operation values(api.service.name) as api.service.name values(http_request.user_agent) as http_request.user_agent values(src_endpoint.ip) as src_ip values(actor.user.account.uid) as actor.user.account.uid values(cloud.provider) as cloud.provider values(cloud.region) as cloud.region by _time actor.user.uid - | where failures >= 3 - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `asl_aws_iam_assume_role_policy_brute_force_filter`' +data_source: + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` api.operation="AssumeRole" "api.response.error"=AccessDenied + | bucket _time span=1h + | stats count as failures min(_time) as firstTime max(_time) as lastTime values(api.operation) as api.operation values(api.service.name) as api.service.name values(http_request.user_agent) as http_request.user_agent values(src_endpoint.ip) as src_ip values(actor.user.account.uid) as actor.user.account.uid values(cloud.provider) as cloud.provider values(cloud.region) as cloud.region + BY _time actor.user.uid + | where failures >= 3 + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_iam_assume_role_policy_brute_force_filter` how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. references: -- https://www.praetorian.com/blog/aws-iam-assume-role-vulnerabilities/ -- https://rhinosecuritylabs.com/aws/assume-worst-aws-assume-role-enumeration/ -- https://www.elastic.co/guide/en/security/current/aws-iam-brute-force-of-assume-role-policy.html + - https://www.praetorian.com/blog/aws-iam-assume-role-vulnerabilities/ + - https://rhinosecuritylabs.com/aws/assume-worst-aws-assume-role-enumeration/ + - https://www.elastic.co/guide/en/security/current/aws-iam-brute-force-of-assume-role-policy.html drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has caused multiple failures with errorCode AccessDenied, which potentially means adversary is attempting to identify a role name. - risk_objects: - - field: user - type: user - score: 28 - threat_objects: - - field: src_ip - type: ip_address + message: User $user$ has caused multiple failures with errorCode AccessDenied, which potentially means adversary is attempting to identify a role name. + risk_objects: + - field: user + type: user + score: 28 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - AWS IAM Privilege Escalation - - Scattered Lapsus$ Hunters - asset_type: AWS Account - mitre_attack_id: - - T1580 - - T1110 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - AWS IAM Privilege Escalation + - Scattered Lapsus$ Hunters + asset_type: AWS Account + mitre_attack_id: + - T1580 + - T1110 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1580/aws_iam_assume_role_policy_brute_force/asl_ocsf_cloudtrail.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1580/aws_iam_assume_role_policy_brute_force/asl_ocsf_cloudtrail.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/asl_aws_iam_delete_policy.yml b/detections/cloud/asl_aws_iam_delete_policy.yml index 14cced498e..f6daa8613e 100644 --- a/detections/cloud/asl_aws_iam_delete_policy.yml +++ b/detections/cloud/asl_aws_iam_delete_policy.yml @@ -1,38 +1,43 @@ name: ASL AWS IAM Delete Policy id: 609ced68-d420-4ff7-8164-ae98b4b4018c -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Hunting description: The following analytic identifies when a policy is deleted in AWS. It leverages Amazon Security Lake logs to detect the DeletePolicy API operation. Monitoring policy deletions is crucial as it can indicate unauthorized attempts to weaken security controls. If confirmed malicious, this activity could allow an attacker to remove critical security policies, potentially leading to privilege escalation or unauthorized access to sensitive resources. -data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` api.operation=DeletePolicy - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor.user.uid api.operation api.service.name http_request.user_agent src_endpoint.ip actor.user.account.uid cloud.provider cloud.region - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `asl_aws_iam_delete_policy_filter`' +data_source: + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` api.operation=DeletePolicy + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor.user.uid api.operation api.service.name + http_request.user_agent src_endpoint.ip actor.user.account.uid + cloud.provider cloud.region + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_iam_delete_policy_filter` how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete policies (least privilege). In addition, this may be saved seperately and tuned for failed or success attempts only. references: -- https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeletePolicy.html -- https://docs.aws.amazon.com/cli/latest/reference/iam/delete-policy.html + - https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeletePolicy.html + - https://docs.aws.amazon.com/cli/latest/reference/iam/delete-policy.html tags: - analytic_story: - - AWS IAM Privilege Escalation - asset_type: AWS Account - mitre_attack_id: - - T1098 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - AWS IAM Privilege Escalation + asset_type: AWS Account + mitre_attack_id: + - T1098 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_delete_policy/asl_ocsf_cloudtrail.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_delete_policy/asl_ocsf_cloudtrail.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/asl_aws_iam_failure_group_deletion.yml b/detections/cloud/asl_aws_iam_failure_group_deletion.yml index cfd00fe292..1baa35ee6f 100644 --- a/detections/cloud/asl_aws_iam_failure_group_deletion.yml +++ b/detections/cloud/asl_aws_iam_failure_group_deletion.yml @@ -1,62 +1,61 @@ name: ASL AWS IAM Failure Group Deletion id: 8d12f268-c567-4557-9813-f8389e235c06 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly description: The following analytic detects failed attempts to delete AWS IAM groups, triggered by access denial, conflicts, or non-existent groups. It operates by monitoring CloudTrail logs for specific error codes related to deletion failures. This behavior is significant for a SOC as it may indicate unauthorized attempts to modify access controls or disrupt operations by removing groups. Such actions could be part of a larger attack aiming to escalate privileges or impair security protocols. Identifying these attempts allows for timely investigation and mitigation, preventing potential impact on the organizations security posture. -data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` api.operation=DeleteGroup status=Failure http_request.user_agent!=*.amazonaws.com - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor.user.uid api.operation api.service.name http_request.user_agent src_endpoint.ip actor.user.account.uid cloud.provider cloud.region - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `asl_aws_iam_failure_group_deletion_filter`' +data_source: + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` api.operation=DeleteGroup status=Failure http_request.user_agent!=*.amazonaws.com + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor.user.uid api.operation api.service.name + http_request.user_agent src_endpoint.ip actor.user.account.uid + cloud.provider cloud.region + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_iam_failure_group_deletion_filter` how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete groups (least privilege). references: -- https://awscli.amazonaws.com/v2/documentation/api/latest/reference/iam/delete-group.html -- https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeleteGroup.html + - https://awscli.amazonaws.com/v2/documentation/api/latest/reference/iam/delete-group.html + - https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeleteGroup.html drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has had mulitple failures while attempting to delete groups - from $src$ - risk_objects: - - field: user - type: user - score: 5 - threat_objects: - - field: src - type: ip_address + message: User $user$ has had mulitple failures while attempting to delete groups from $src$ + risk_objects: + - field: user + type: user + score: 5 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS IAM Privilege Escalation - asset_type: AWS Account - mitre_attack_id: - - T1098 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - AWS IAM Privilege Escalation + asset_type: AWS Account + mitre_attack_id: + - T1098 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_failure_group_deletion/asl_ocsf_cloudtrail.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_failure_group_deletion/asl_ocsf_cloudtrail.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/asl_aws_iam_successful_group_deletion.yml b/detections/cloud/asl_aws_iam_successful_group_deletion.yml index 5200b8b723..a4e98abcb0 100644 --- a/detections/cloud/asl_aws_iam_successful_group_deletion.yml +++ b/detections/cloud/asl_aws_iam_successful_group_deletion.yml @@ -1,47 +1,44 @@ name: ASL AWS IAM Successful Group Deletion id: 1bbe54f1-93d7-4764-8a01-ddaa12ece7ac -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Hunting -description: The following analytic detects the successful deletion of a group within - AWS IAM, leveraging CloudTrail IAM events. This action, while not inherently malicious, - can serve as a precursor to more sinister activities, such as unauthorized access - or privilege escalation attempts. By monitoring for such deletions, the analytic - aids in identifying potential preparatory steps towards an attack, allowing for - early detection and mitigation. The identification of this behavior is crucial for - a SOC to prevent the potential impact of an attack, which could include unauthorized - access to sensitive resources or disruption of AWS environment operations. +description: The following analytic detects the successful deletion of a group within AWS IAM, leveraging CloudTrail IAM events. This action, while not inherently malicious, can serve as a precursor to more sinister activities, such as unauthorized access or privilege escalation attempts. By monitoring for such deletions, the analytic aids in identifying potential preparatory steps towards an attack, allowing for early detection and mitigation. The identification of this behavior is crucial for a SOC to prevent the potential impact of an attack, which could include unauthorized access to sensitive resources or disruption of AWS environment operations. data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` api.operation=DeleteGroup status=Success - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor.user.uid api.operation api.service.name http_request.user_agent src_endpoint.ip actor.user.account.uid cloud.provider cloud.region - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `asl_aws_iam_successful_group_deletion_filter`' + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` api.operation=DeleteGroup status=Success + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor.user.uid api.operation api.service.name + http_request.user_agent src_endpoint.ip actor.user.account.uid + cloud.provider cloud.region + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_iam_successful_group_deletion_filter` how_to_implement: You must install the Data Lake Federated Analytics App and ingest the logs into Splunk. known_false_positives: This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete groups (least privilege). references: -- https://awscli.amazonaws.com/v2/documentation/api/latest/reference/iam/delete-group.html -- https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeleteGroup.html + - https://awscli.amazonaws.com/v2/documentation/api/latest/reference/iam/delete-group.html + - https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeleteGroup.html tags: - analytic_story: - - AWS IAM Privilege Escalation - asset_type: AWS Account - mitre_attack_id: - - T1069.003 - - T1098 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - AWS IAM Privilege Escalation + asset_type: AWS Account + mitre_attack_id: + - T1069.003 + - T1098 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_successful_group_deletion/asl_ocsf_cloudtrail.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_successful_group_deletion/asl_ocsf_cloudtrail.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/asl_aws_multi_factor_authentication_disabled.yml b/detections/cloud/asl_aws_multi_factor_authentication_disabled.yml index fb038964af..53b7288d47 100644 --- a/detections/cloud/asl_aws_multi_factor_authentication_disabled.yml +++ b/detections/cloud/asl_aws_multi_factor_authentication_disabled.yml @@ -1,71 +1,63 @@ name: ASL AWS Multi-Factor Authentication Disabled id: 4d2df5e0-1092-4817-88a8-79c7fa054668 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects attempts to disable multi-factor authentication - (MFA) for an AWS IAM user. It leverages Amazon Security Lake logs, specifically - monitoring for `DeleteVirtualMFADevice` or `DeactivateMFADevice` API operations. - This activity is significant as disabling MFA can indicate an adversary attempting - to weaken account security to maintain persistence using a compromised account. - If confirmed malicious, this action could allow attackers to retain access to the - AWS environment without detection, potentially leading to unauthorized access to - sensitive resources and prolonged compromise. +description: The following analytic detects attempts to disable multi-factor authentication (MFA) for an AWS IAM user. It leverages Amazon Security Lake logs, specifically monitoring for `DeleteVirtualMFADevice` or `DeactivateMFADevice` API operations. This activity is significant as disabling MFA can indicate an adversary attempting to weaken account security to maintain persistence using a compromised account. If confirmed malicious, this action could allow attackers to retain access to the AWS environment without detection, potentially leading to unauthorized access to sensitive resources and prolonged compromise. data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` (api.operation=DeleteVirtualMFADevice OR api.operation=DeactivateMFADevice) - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor.user.uid api.operation api.service.name http_request.user_agent src_endpoint.ip actor.user.account.uid cloud.provider cloud.region - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `asl_aws_multi_factor_authentication_disabled_filter`' + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` (api.operation=DeleteVirtualMFADevice OR api.operation=DeactivateMFADevice) + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor.user.uid api.operation api.service.name + http_request.user_agent src_endpoint.ip actor.user.account.uid + cloud.provider cloud.region + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_multi_factor_authentication_disabled_filter` how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: AWS Administrators may disable MFA but it is highly unlikely for this event to occur without prior notice to the company references: -- https://attack.mitre.org/techniques/T1621/ -- https://aws.amazon.com/what-is/mfa/ + - https://attack.mitre.org/techniques/T1621/ + - https://aws.amazon.com/what-is/mfa/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has disabled Multi-Factor authentication - risk_objects: - - field: user - type: user - score: 64 - threat_objects: - - field: src - type: ip_address + message: User $user$ has disabled Multi-Factor authentication + risk_objects: + - field: user + type: user + score: 64 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Identity and Access Management Account Takeover - asset_type: AWS Account - mitre_attack_id: - - T1556.006 - - T1586.003 - - T1621 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Identity and Access Management Account Takeover + asset_type: AWS Account + mitre_attack_id: + - T1556.006 + - T1586.003 + - T1621 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/aws_mfa_disabled/asl_ocsf_cloudtrail.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/aws_mfa_disabled/asl_ocsf_cloudtrail.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/asl_aws_network_access_control_list_created_with_all_open_ports.yml b/detections/cloud/asl_aws_network_access_control_list_created_with_all_open_ports.yml index 717b7c44c5..63eaa9ef22 100644 --- a/detections/cloud/asl_aws_network_access_control_list_created_with_all_open_ports.yml +++ b/detections/cloud/asl_aws_network_access_control_list_created_with_all_open_ports.yml @@ -1,71 +1,66 @@ name: ASL AWS Network Access Control List Created with All Open Ports id: a2625034-c2de-44fc-b45c-7bac9c4a7974 -version: 4 -date: '2025-05-02' +version: 5 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects the creation of AWS Network Access Control - Lists (ACLs) with all ports open to a specified CIDR. It leverages AWS CloudTrail - events, specifically monitoring for `CreateNetworkAclEntry` or `ReplaceNetworkAclEntry` - actions with rules allowing all traffic. This activity is significant because it - can expose the network to unauthorized access, increasing the risk of data breaches - and other malicious activities. If confirmed malicious, an attacker could exploit - this misconfiguration to gain unrestricted access to the network, potentially leading - to data exfiltration, service disruption, or further compromise of the AWS environment. +description: The following analytic detects the creation of AWS Network Access Control Lists (ACLs) with all ports open to a specified CIDR. It leverages AWS CloudTrail events, specifically monitoring for `CreateNetworkAclEntry` or `ReplaceNetworkAclEntry` actions with rules allowing all traffic. This activity is significant because it can expose the network to unauthorized access, increasing the risk of data breaches and other malicious activities. If confirmed malicious, an attacker could exploit this misconfiguration to gain unrestricted access to the network, potentially leading to data exfiltration, service disruption, or further compromise of the AWS environment. data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` api.operation=CreateNetworkAclEntry OR api.operation=ReplaceNetworkAclEntry - status=Success | spath input=api.request.data path=ruleAction output=ruleAction - | spath input=api.request.data path=egress output=egress | spath input=api.request.data - path=aclProtocol output=aclProtocol | spath input=api.request.data path=cidrBlock - output=cidrBlock | spath input=api.request.data path=networkAclId output=networkAclId - | search ruleAction=allow AND egress=false AND aclProtocol=-1 AND cidrBlock=0.0.0.0/0 - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor.user.uid api.operation api.service.name http_request.user_agent src_endpoint.ip actor.user.account.uid cloud.provider cloud.region networkAclId cidrBlock - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `asl_aws_network_access_control_list_created_with_all_open_ports_filter`' + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` api.operation=CreateNetworkAclEntry OR api.operation=ReplaceNetworkAclEntry status=Success + | spath input=api.request.data path=ruleAction output=ruleAction + | spath input=api.request.data path=egress output=egress + | spath input=api.request.data path=aclProtocol output=aclProtocol + | spath input=api.request.data path=cidrBlock output=cidrBlock + | spath input=api.request.data path=networkAclId output=networkAclId + | search ruleAction=allow AND egress=false AND aclProtocol=-1 AND cidrBlock=0.0.0.0/0 + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor.user.uid api.operation api.service.name + http_request.user_agent src_endpoint.ip actor.user.account.uid + cloud.provider cloud.region networkAclId + cidrBlock + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_network_access_control_list_created_with_all_open_ports_filter` how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: It's possible that an admin has created this ACL with all ports open for some legitimate purpose however, this should be scoped and not allowed in production environment. references: [] drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has created network ACLs with all the ports opens to $cidrBlock$ - risk_objects: - - field: user - type: user - score: 48 - threat_objects: - - field: src - type: ip_address + message: User $user$ has created network ACLs with all the ports opens to $cidrBlock$ + risk_objects: + - field: user + type: user + score: 48 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Network ACL Activity - asset_type: AWS Instance - mitre_attack_id: - - T1562.007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - AWS Network ACL Activity + asset_type: AWS Instance + mitre_attack_id: + - T1562.007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_create_acl/asl_ocsf_cloudtrail.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_create_acl/asl_ocsf_cloudtrail.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/asl_aws_network_access_control_list_deleted.yml b/detections/cloud/asl_aws_network_access_control_list_deleted.yml index bc4413ef66..a3082af310 100644 --- a/detections/cloud/asl_aws_network_access_control_list_deleted.yml +++ b/detections/cloud/asl_aws_network_access_control_list_deleted.yml @@ -1,69 +1,63 @@ name: ASL AWS Network Access Control List Deleted id: e010ddf5-e9a5-44e5-bdd6-0c919ba8fc8b -version: 5 -date: '2025-10-14' +version: 6 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects the deletion of AWS Network Access Control - Lists (ACLs). It leverages AWS CloudTrail logs to identify events where a user deletes - a network ACL entry. This activity is significant because deleting a network ACL - can remove critical access restrictions, potentially allowing unauthorized access - to cloud instances. If confirmed malicious, this action could enable attackers to - bypass network security controls, leading to unauthorized access, data exfiltration, - or further compromise of the cloud environment. +description: The following analytic detects the deletion of AWS Network Access Control Lists (ACLs). It leverages AWS CloudTrail logs to identify events where a user deletes a network ACL entry. This activity is significant because deleting a network ACL can remove critical access restrictions, potentially allowing unauthorized access to cloud instances. If confirmed malicious, this action could enable attackers to bypass network security controls, leading to unauthorized access, data exfiltration, or further compromise of the cloud environment. data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` api.operation=DeleteNetworkAclEntry status=Success - | spath input=api.request.data path=egress output=egress - | spath input=api.request.data path=networkAclId output=networkAclId - | search egress=false - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor.user.uid api.operation api.service.name http_request.user_agent src_endpoint.ip actor.user.account.uid cloud.provider cloud.region networkAclId - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `asl_aws_network_access_control_list_deleted_filter`' + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` api.operation=DeleteNetworkAclEntry status=Success + | spath input=api.request.data path=egress output=egress + | spath input=api.request.data path=networkAclId output=networkAclId + | search egress=false + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor.user.uid api.operation api.service.name + http_request.user_agent src_endpoint.ip actor.user.account.uid + cloud.provider cloud.region networkAclId + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_network_access_control_list_deleted_filter` how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: It's possible that a user has legitimately deleted a network ACL. references: [] drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user_arn = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user_arn = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ from $src$ has sucessfully deleted network ACLs entry. - risk_objects: - - field: user - type: user - score: 5 - threat_objects: - - field: src - type: ip_address + message: User $user$ from $src$ has sucessfully deleted network ACLs entry. + risk_objects: + - field: user + type: user + score: 5 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Network ACL Activity - - Scattered Lapsus$ Hunters - asset_type: AWS Instance - mitre_attack_id: - - T1562.007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - AWS Network ACL Activity + - Scattered Lapsus$ Hunters + asset_type: AWS Instance + mitre_attack_id: + - T1562.007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_delete_acl/asl_ocsf_cloudtrail.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_delete_acl/asl_ocsf_cloudtrail.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/asl_aws_new_mfa_method_registered_for_user.yml b/detections/cloud/asl_aws_new_mfa_method_registered_for_user.yml index cf3fa5b265..7ed9b1a7b4 100644 --- a/detections/cloud/asl_aws_new_mfa_method_registered_for_user.yml +++ b/detections/cloud/asl_aws_new_mfa_method_registered_for_user.yml @@ -1,71 +1,63 @@ name: ASL AWS New MFA Method Registered For User id: 33ae0931-2a03-456b-b1d7-b016c5557fbd -version: 10 -date: '2025-06-10' +version: 11 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic identifies the registration of a new Multi-Factor - Authentication (MFA) method for an AWS account, as logged through Amazon Security - Lake (ASL). It detects this activity by monitoring the `CreateVirtualMFADevice` - API operation within ASL logs. This behavior is significant because adversaries - who gain unauthorized access to an AWS account may register a new MFA method to - maintain persistence. If confirmed malicious, this activity could allow attackers - to secure their access, making it harder to detect and remove their presence from - the compromised environment. +description: The following analytic identifies the registration of a new Multi-Factor Authentication (MFA) method for an AWS account, as logged through Amazon Security Lake (ASL). It detects this activity by monitoring the `CreateVirtualMFADevice` API operation within ASL logs. This behavior is significant because adversaries who gain unauthorized access to an AWS account may register a new MFA method to maintain persistence. If confirmed malicious, this activity could allow attackers to secure their access, making it harder to detect and remove their presence from the compromised environment. data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` api.operation=CreateVirtualMFADevice - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor.user.uid api.operation api.service.name http_request.user_agent src_endpoint.ip actor.user.account.uid cloud.provider cloud.region - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `asl_aws_new_mfa_method_registered_for_user_filter`' + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` api.operation=CreateVirtualMFADevice + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor.user.uid api.operation api.service.name + http_request.user_agent src_endpoint.ip actor.user.account.uid + cloud.provider cloud.region + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_new_mfa_method_registered_for_user_filter` how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: Newly onboarded users who are registering an MFA method for the first time will also trigger this detection. references: -- https://aws.amazon.com/blogs/security/you-can-now-assign-multiple-mfa-devices-in-iam/ -- https://attack.mitre.org/techniques/T1556/ -- https://attack.mitre.org/techniques/T1556/006/ -- https://twitter.com/jhencinski/status/1618660062352007174 + - https://aws.amazon.com/blogs/security/you-can-now-assign-multiple-mfa-devices-in-iam/ + - https://attack.mitre.org/techniques/T1556/ + - https://attack.mitre.org/techniques/T1556/006/ + - https://twitter.com/jhencinski/status/1618660062352007174 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A new virtual device is added to user $user$ - risk_objects: - - field: user - type: user - score: 64 - threat_objects: - - field: src - type: ip_address + message: A new virtual device is added to user $user$ + risk_objects: + - field: user + type: user + score: 64 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Identity and Access Management Account Takeover - asset_type: AWS Account - mitre_attack_id: - - T1556.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - AWS Identity and Access Management Account Takeover + asset_type: AWS Account + mitre_attack_id: + - T1556.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556.006/aws_new_mfa_method_registered_for_user/asl_ocsf_cloudtrail.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556.006/aws_new_mfa_method_registered_for_user/asl_ocsf_cloudtrail.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/asl_aws_saml_update_identity_provider.yml b/detections/cloud/asl_aws_saml_update_identity_provider.yml index 6e52ca5ddd..507e0ef93a 100644 --- a/detections/cloud/asl_aws_saml_update_identity_provider.yml +++ b/detections/cloud/asl_aws_saml_update_identity_provider.yml @@ -1,57 +1,63 @@ name: ASL AWS SAML Update identity provider id: 635c26cc-0fd1-4098-8ec9-824bf9544b11 -version: 3 -date: '2025-05-02' +version: 4 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP description: The following analytic detects updates to the SAML provider in AWS. It leverages AWS CloudTrail logs to identify the `UpdateSAMLProvider` event, analyzing fields such as `sAMLProviderArn`, `sourceIPAddress`, and `userIdentity` details. Monitoring updates to the SAML provider is crucial as it may indicate a perimeter compromise of federated credentials or unauthorized backdoor access set by an attacker. If confirmed malicious, this activity could allow attackers to manipulate identity federation, potentially leading to unauthorized access to cloud resources and sensitive data. -data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` api.operation=UpdateSAMLProvider - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor.user.uid api.operation api.service.name http_request.user_agent src_endpoint.ip actor.user.account.uid cloud.provider cloud.region - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `asl_aws_saml_update_identity_provider_filter`' +data_source: + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` api.operation=UpdateSAMLProvider + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor.user.uid api.operation api.service.name + http_request.user_agent src_endpoint.ip actor.user.account.uid + cloud.provider cloud.region + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_saml_update_identity_provider_filter` how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: Updating a SAML provider or creating a new one may not necessarily be malicious however it needs to be closely monitored. references: -- https://www.cisa.gov/uscert/ncas/alerts/aa21-008a -- https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html -- https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf -- https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps + - https://www.cisa.gov/uscert/ncas/alerts/aa21-008a + - https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html + - https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf + - https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ from IP address $src$ updated the SAML provider - risk_objects: - - field: user - type: user - score: 64 - threat_objects: - - field: src - type: ip_address + message: User $user$ from IP address $src$ updated the SAML provider + risk_objects: + - field: user + type: user + score: 64 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Cloud Federated Credential Abuse - asset_type: AWS Federated Account - mitre_attack_id: - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Cloud Federated Credential Abuse + asset_type: AWS Federated Account + mitre_attack_id: + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/update_saml_provider/asl_ocsf_cloudtrail.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/update_saml_provider/asl_ocsf_cloudtrail.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/asl_aws_updateloginprofile.yml b/detections/cloud/asl_aws_updateloginprofile.yml index 5aa2494e32..05b762e63c 100644 --- a/detections/cloud/asl_aws_updateloginprofile.yml +++ b/detections/cloud/asl_aws_updateloginprofile.yml @@ -1,68 +1,61 @@ name: ASL AWS UpdateLoginProfile id: 5b3f63a3-865b-4637-9941-f98bd1a50c0d -version: 4 -date: '2025-05-02' +version: 5 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects an AWS CloudTrail event where a user with - permissions updates the login profile of another user. It leverages CloudTrail logs - to identify instances where the user making the change is different from the user - whose profile is being updated. This activity is significant because it can indicate - privilege escalation attempts, where an attacker uses a compromised account to gain - higher privileges. If confirmed malicious, this could allow the attacker to escalate - their privileges, potentially leading to unauthorized access and control over sensitive - resources within the AWS environment. +description: The following analytic detects an AWS CloudTrail event where a user with permissions updates the login profile of another user. It leverages CloudTrail logs to identify instances where the user making the change is different from the user whose profile is being updated. This activity is significant because it can indicate privilege escalation attempts, where an attacker uses a compromised account to gain higher privileges. If confirmed malicious, this could allow the attacker to escalate their privileges, potentially leading to unauthorized access and control over sensitive resources within the AWS environment. data_source: -- ASL AWS CloudTrail -search: '`amazon_security_lake` api.operation=UpdateLoginProfile - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor.user.uid api.operation api.service.name http_request.user_agent src_endpoint.ip actor.user.account.uid cloud.provider cloud.region - | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `asl_aws_updateloginprofile_filter`' + - ASL AWS CloudTrail +search: |- + `amazon_security_lake` api.operation=UpdateLoginProfile + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor.user.uid api.operation api.service.name + http_request.user_agent src_endpoint.ip actor.user.account.uid + cloud.provider cloud.region + | rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `asl_aws_updateloginprofile_filter` how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App. known_false_positives: While this search has no known false positives, it is possible that an AWS admin has legitimately created keys for another user. references: -- https://bishopfox.com/blog/privilege-escalation-in-aws -- https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/ + - https://bishopfox.com/blog/privilege-escalation-in-aws + - https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ from IP address $src$ updated the login profile of another user - risk_objects: - - field: user - type: user - score: 30 - threat_objects: - - field: src - type: ip_address + message: User $user$ from IP address $src$ updated the login profile of another user + risk_objects: + - field: user + type: user + score: 30 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS IAM Privilege Escalation - asset_type: AWS Account - mitre_attack_id: - - T1136.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS IAM Privilege Escalation + asset_type: AWS Account + mitre_attack_id: + - T1136.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_updateloginprofile/asl_ocsf_cloudtrail.json - sourcetype: aws:asl - source: aws_asl + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_updateloginprofile/asl_ocsf_cloudtrail.json + sourcetype: aws:asl + source: aws_asl diff --git a/detections/cloud/aws_ami_attribute_modification_for_exfiltration.yml b/detections/cloud/aws_ami_attribute_modification_for_exfiltration.yml index 959c72fa58..eafc8ea8f4 100644 --- a/detections/cloud/aws_ami_attribute_modification_for_exfiltration.yml +++ b/detections/cloud/aws_ami_attribute_modification_for_exfiltration.yml @@ -1,75 +1,65 @@ name: AWS AMI Attribute Modification for Exfiltration id: f2132d74-cf81-4c5e-8799-ab069e67dc9f -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP data_source: -- AWS CloudTrail ModifyImageAttribute -description: The following analytic detects suspicious modifications to AWS AMI attributes, - such as sharing an AMI with another AWS account or making it publicly accessible. - It leverages AWS CloudTrail logs to identify these changes by monitoring specific - API calls. This activity is significant because adversaries can exploit these modifications - to exfiltrate sensitive data stored in AWS resources. If confirmed malicious, this - could lead to unauthorized access and potential data breaches, compromising the - confidentiality and integrity of organizational information. -search: '`cloudtrail` eventName=ModifyImageAttribute (requestParameters.launchPermission.add.items{}.userId - = * OR requestParameters.launchPermission.add.items{}.group = all) - | rename requestParameters.launchPermission.add.items{}.group as group_added - | rename requestParameters.launchPermission.add.items{}.userId as accounts_added - | eval ami_status=if(match(group_added,"all") ,"Public AMI", "Not Public") - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime values(group_added) as group_added values(accounts_added) as accounts_added values(ami_status) as ami_status by signature dest user user_agent src vendor_account vendor_region vendor_product - | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `aws_ami_attribute_modification_for_exfiltration_filter`' -how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. -known_false_positives: It is possible that an AWS admin has legitimately shared a - snapshot with others for a specific purpose. + - AWS CloudTrail ModifyImageAttribute +description: The following analytic detects suspicious modifications to AWS AMI attributes, such as sharing an AMI with another AWS account or making it publicly accessible. It leverages AWS CloudTrail logs to identify these changes by monitoring specific API calls. This activity is significant because adversaries can exploit these modifications to exfiltrate sensitive data stored in AWS resources. If confirmed malicious, this could lead to unauthorized access and potential data breaches, compromising the confidentiality and integrity of organizational information. +search: |- + `cloudtrail` eventName=ModifyImageAttribute (requestParameters.launchPermission.add.items{}.userId = * OR requestParameters.launchPermission.add.items{}.group = all) + | rename requestParameters.launchPermission.add.items{}.group as group_added + | rename requestParameters.launchPermission.add.items{}.userId as accounts_added + | eval ami_status=if(match(group_added,"all") ,"Public AMI", "Not Public") + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime values(group_added) as group_added values(accounts_added) as accounts_added values(ami_status) as ami_status + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_ami_attribute_modification_for_exfiltration_filter` +how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. +known_false_positives: It is possible that an AWS admin has legitimately shared a snapshot with others for a specific purpose. references: -- https://labs.nettitude.com/blog/how-to-exfiltrate-aws-ec2-data/ -- https://stratus-red-team.cloud/attack-techniques/AWS/aws.exfiltration.ec2-share-ami/ -- https://hackingthe.cloud/aws/enumeration/loot_public_ebs_snapshots/ + - https://labs.nettitude.com/blog/how-to-exfiltrate-aws-ec2-data/ + - https://stratus-red-team.cloud/attack-techniques/AWS/aws.exfiltration.ec2-share-ami/ + - https://hackingthe.cloud/aws/enumeration/loot_public_ebs_snapshots/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: AWS AMI from account $vendor_account$ is shared externally with $accounts_added$ - from $src$ or AMI made is made Public. - risk_objects: - - field: user - type: user - score: 80 - threat_objects: - - field: src - type: ip_address + message: AWS AMI from account $vendor_account$ is shared externally with $accounts_added$ from $src$ or AMI made is made Public. + risk_objects: + - field: user + type: user + score: 80 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Suspicious Cloud Instance Activities - - Data Exfiltration - asset_type: EC2 Snapshot - mitre_attack_id: - - T1537 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Suspicious Cloud Instance Activities + - Data Exfiltration + asset_type: EC2 Snapshot + mitre_attack_id: + - T1537 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1537/aws_ami_shared_public/aws_cloudtrail_events.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1537/aws_ami_shared_public/aws_cloudtrail_events.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_bedrock_delete_guardrails.yml b/detections/cloud/aws_bedrock_delete_guardrails.yml index c7facad576..2174d51913 100644 --- a/detections/cloud/aws_bedrock_delete_guardrails.yml +++ b/detections/cloud/aws_bedrock_delete_guardrails.yml @@ -1,64 +1,58 @@ name: AWS Bedrock Delete GuardRails id: 7a5e3d62-f743-11ee-9f6e-acde48001122 -version: 2 -date: '2025-05-02' +version: 3 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP description: The following analytic identifies attempts to delete AWS Bedrock GuardRails, which are security controls designed to prevent harmful, biased, or inappropriate AI outputs. It leverages AWS CloudTrail logs to detect when a user or service calls the DeleteGuardrail API. This activity is significant as it may indicate an adversary attempting to remove safety guardrails after compromising credentials, potentially to enable harmful or malicious model outputs. Removing guardrails could allow attackers to extract sensitive information, generate offensive content, or bypass security controls designed to prevent prompt injection and other AI-specific attacks. If confirmed malicious, this could represent a deliberate attempt to manipulate model behavior for harmful purposes. data_source: -- AWS CloudTrail DeleteGuardrail + - AWS CloudTrail DeleteGuardrail search: >- - `cloudtrail` eventSource=bedrock.amazonaws.com eventName=DeleteGuardrail - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.guardrailIdentifier) as guardrailIds by src user user_agent vendor_account vendor_product dest signature vendor_region - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `aws_bedrock_delete_guardrails_filter` + `cloudtrail` eventSource=bedrock.amazonaws.com eventName=DeleteGuardrail + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.guardrailIdentifier) as guardrailIds by src user user_agent vendor_account vendor_product dest signature vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_bedrock_delete_guardrails_filter` how_to_implement: The Splunk AWS Add-on is required to utilize this data. The search requires AWS CloudTrail logs with Bedrock service events enabled. You must install and configure the AWS App for Splunk (version 6.0.0 or later) and Splunk Add-on for AWS (version 5.1.0 or later) to collect CloudTrail logs from AWS. Ensure the CloudTrail is capturing Bedrock GuardRails management events. known_false_positives: Legitimate administrators may delete GuardRails as part of normal operations, such as when replacing outdated guardrails with updated versions, cleaning up test resources, or consolidating security controls. Consider implementing an allowlist for expected administrators who regularly manage GuardRails configurations. references: -- https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails.html -- https://docs.aws.amazon.com/bedrock/latest/APIReference/API_DeleteGuardrail.html -- https://attack.mitre.org/techniques/T1562/ + - https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails.html + - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_DeleteGuardrail.html + - https://attack.mitre.org/techniques/T1562/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ deleted AWS Bedrock GuardRails $guardrailIds$ from $src$ - risk_objects: - - field: user - type: user - score: 72 - threat_objects: - - field: src - type: ip_address + message: User $user$ deleted AWS Bedrock GuardRails $guardrailIds$ from $src$ + risk_objects: + - field: user + type: user + score: 72 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Bedrock Security - asset_type: AWS Account - mitre_attack_id: - - T1562.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Bedrock Security + asset_type: AWS Account + mitre_attack_id: + - T1562.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/aws_bedrock_delete_guardrails/cloudtrail.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/aws_bedrock_delete_guardrails/cloudtrail.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_bedrock_delete_knowledge_base.yml b/detections/cloud/aws_bedrock_delete_knowledge_base.yml index 9e8a6492f1..5e77aaa14d 100644 --- a/detections/cloud/aws_bedrock_delete_knowledge_base.yml +++ b/detections/cloud/aws_bedrock_delete_knowledge_base.yml @@ -1,63 +1,57 @@ name: AWS Bedrock Delete Knowledge Base id: 8b4e3d62-f743-11ee-9f6e-acde48001123 -version: 2 -date: '2025-05-02' +version: 3 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP description: The following analytic identifies attempts to delete AWS Bedrock Knowledge Bases, which are resources that store and manage domain-specific information for AI models. It monitors AWS CloudTrail logs for DeleteKnowledgeBase API calls. This activity could indicate an adversary attempting to remove knowledge bases after compromising credentials, potentially to disrupt business operations or remove traces of data access. Deleting knowledge bases could impact model performance, remove critical business context, or be part of a larger attack to degrade AI capabilities. If confirmed malicious, this could represent a deliberate attempt to cause service disruption or data loss. data_source: -- AWS CloudTrail DeleteKnowledgeBase + - AWS CloudTrail DeleteKnowledgeBase search: >- - `cloudtrail` eventSource=bedrock.amazonaws.com eventName=DeleteKnowledgeBase - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.knowledgeBaseId) as knowledgeBaseIds by src user user_agent vendor_account vendor_product dest signature vendor_region - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `aws_bedrock_delete_knowledge_base_filter` + `cloudtrail` eventSource=bedrock.amazonaws.com eventName=DeleteKnowledgeBase + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.knowledgeBaseId) as knowledgeBaseIds by src user user_agent vendor_account vendor_product dest signature vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_bedrock_delete_knowledge_base_filter` how_to_implement: The Splunk AWS Add-on is required to utilize this data. The search requires AWS CloudTrail logs with Bedrock service events enabled. You must install and configure the AWS App for Splunk (version 6.0.0 or later) and Splunk Add-on for AWS (version 5.1.0 or later) to collect CloudTrail logs from AWS. Ensure the CloudTrail is capturing Bedrock Knowledge Base management events. known_false_positives: Legitimate administrators may delete Knowledge Bases as part of normal operations, such as when replacing outdated knowledge bases, removing test resources, or consolidating information. Consider implementing an allowlist for expected administrators who regularly manage Knowledge Base configurations. references: -- https://www.sumologic.com/blog/defenders-guide-to-aws-bedrock/ -- https://attack.mitre.org/techniques/T1562/ + - https://www.sumologic.com/blog/defenders-guide-to-aws-bedrock/ + - https://attack.mitre.org/techniques/T1562/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ deleted AWS Bedrock Knowledge Base $knowledgeBaseIds$ from $src$ - risk_objects: - - field: user - type: user - score: 70 - threat_objects: - - field: src - type: ip_address + message: User $user$ deleted AWS Bedrock Knowledge Base $knowledgeBaseIds$ from $src$ + risk_objects: + - field: user + type: user + score: 70 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Bedrock Security - asset_type: AWS Account - mitre_attack_id: - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Bedrock Security + asset_type: AWS Account + mitre_attack_id: + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/aws_delete_knowledge_base/cloudtrail.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/aws_delete_knowledge_base/cloudtrail.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_bedrock_delete_model_invocation_logging_configuration.yml b/detections/cloud/aws_bedrock_delete_model_invocation_logging_configuration.yml index da40c19310..dd2787c5c1 100644 --- a/detections/cloud/aws_bedrock_delete_model_invocation_logging_configuration.yml +++ b/detections/cloud/aws_bedrock_delete_model_invocation_logging_configuration.yml @@ -1,63 +1,57 @@ name: AWS Bedrock Delete Model Invocation Logging Configuration id: 9c5e3d62-f743-11ee-9f6e-acde48001124 -version: 2 -date: '2025-05-02' +version: 3 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP description: The following analytic identifies attempts to delete AWS Bedrock model invocation logging configurations. It leverages AWS CloudTrail logs to detect when a user or service calls the DeleteModelInvocationLogging API. This activity is significant as it may indicate an adversary attempting to remove audit trails of model interactions after compromising credentials. Deleting model invocation logs could allow attackers to interact with AI models without leaving traces, potentially enabling them to conduct data exfiltration, prompt injection attacks, or other malicious activities without detection. If confirmed malicious, this could represent a deliberate attempt to hide unauthorized model usage and evade detection. data_source: -- AWS CloudTrail DeleteModelInvocationLoggingConfiguration + - AWS CloudTrail DeleteModelInvocationLoggingConfiguration search: >- - `cloudtrail` eventSource=bedrock.amazonaws.com eventName=DeleteModelInvocationLoggingConfiguration - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by src user user_agent vendor_account vendor_product dest signature vendor_region - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `aws_bedrock_delete_model_invocation_logging_configuration_filter` + `cloudtrail` eventSource=bedrock.amazonaws.com eventName=DeleteModelInvocationLoggingConfiguration + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime by src user user_agent vendor_account vendor_product dest signature vendor_region + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_bedrock_delete_model_invocation_logging_configuration_filter` how_to_implement: The Splunk AWS Add-on is required to utilize this data. The search requires AWS CloudTrail logs with Bedrock service events enabled. You must install and configure the AWS App for Splunk (version 6.0.0 or later) and Splunk Add-on for AWS (version 5.1.0 or later) to collect CloudTrail logs from AWS. Ensure the CloudTrail is capturing Bedrock model invocation logging management events. known_false_positives: Legitimate administrators may delete model invocation logging configurations during maintenance, when updating logging policies, or when cleaning up unused resources. Consider implementing an allowlist for expected administrators who regularly manage logging configurations. references: -- https://www.sumologic.com/blog/defenders-guide-to-aws-bedrock/ -- https://attack.mitre.org/techniques/T1562/008/ + - https://www.sumologic.com/blog/defenders-guide-to-aws-bedrock/ + - https://attack.mitre.org/techniques/T1562/008/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ deleted AWS Bedrock model invocation logging from $src$ - risk_objects: - - field: user - type: user - score: 75 - threat_objects: - - field: src - type: ip_address + message: User $user$ deleted AWS Bedrock model invocation logging from $src$ + risk_objects: + - field: user + type: user + score: 75 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Bedrock Security - asset_type: AWS Account - mitre_attack_id: - - T1562.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Bedrock Security + asset_type: AWS Account + mitre_attack_id: + - T1562.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/aws_bedrock_delete_model_invocation_logging/cloudtrail.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/aws_bedrock_delete_model_invocation_logging/cloudtrail.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_bedrock_high_number_list_foundation_model_failures.yml b/detections/cloud/aws_bedrock_high_number_list_foundation_model_failures.yml index a85328f951..d31d5537bc 100644 --- a/detections/cloud/aws_bedrock_high_number_list_foundation_model_failures.yml +++ b/detections/cloud/aws_bedrock_high_number_list_foundation_model_failures.yml @@ -1,64 +1,58 @@ name: AWS Bedrock High Number List Foundation Model Failures id: e84b3c74-f742-11ee-9f6e-acde48001122 -version: 2 -date: '2025-05-02' +version: 3 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP description: The following analytic identifies an high number of AccessDenied attempts to list AWS Bedrock foundation models. It leverages AWS CloudTrail logs to detect when a user or service experiences multiple failures when calling the ListFoundationModels API. This activity is significant as it may indicate an adversary performing reconnaissance of available AI models after compromising credentials with limited permissions. Repeated failures could suggest brute force attempts to enumerate accessible resources or misconfigured access controls. If confirmed malicious, this could represent early-stage reconnaissance before attempting to access or manipulate Bedrock models or knowledge bases. data_source: -- AWS CloudTrail + - AWS CloudTrail search: >- - `cloudtrail` eventSource=bedrock.amazonaws.com eventName=ListFoundationModels errorCode=AccessDenied | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime values(errorCode) as errorCodes values(errorMessage) as errorMessages by src user user_agent vendor_account vendor_product dest signature vendor_region - | where count > 9 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `aws_bedrock_high_number_list_foundation_model_failures_filter` + `cloudtrail` eventSource=bedrock.amazonaws.com eventName=ListFoundationModels errorCode=AccessDenied | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime values(errorCode) as errorCodes values(errorMessage) as errorMessages by src user user_agent vendor_account vendor_product dest signature vendor_region + | where count > 9 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_bedrock_high_number_list_foundation_model_failures_filter` how_to_implement: The Splunk AWS Add-on is required to utilize this data. The search requires AWS CloudTrail logs with Bedrock service events enabled. You must install and configure the AWS App for Splunk (version 6.0.0 or later) and Splunk Add-on for AWS (version 5.1.0 or later) to collect CloudTrail logs from AWS. known_false_positives: Legitimate users may encounter multiple failures during permission testing, role transitions, or when service permissions are being reconfigured. High volumes of API errors may also occur during automated processes with misconfigured IAM policies or when new Bedrock features are being explored through API testing. references: -- https://docs.aws.amazon.com/bedrock/latest/APIReference/API_ListFoundationModels.html -- https://trustoncloud.com/blog/exposing-the-weakness-how-we-identified-a-flaw-in-bedrocks-foundation-model-access-control/ -- https://attack.mitre.org/techniques/T1595/ + - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_ListFoundationModels.html + - https://trustoncloud.com/blog/exposing-the-weakness-how-we-identified-a-flaw-in-bedrocks-foundation-model-access-control/ + - https://attack.mitre.org/techniques/T1595/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ attempted to list AWS Bedrock foundation models $count$ times with failures from $src$ - risk_objects: - - field: user - type: user - score: 48 - threat_objects: - - field: src - type: ip_address + message: User $user$ attempted to list AWS Bedrock foundation models $count$ times with failures from $src$ + risk_objects: + - field: user + type: user + score: 48 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Bedrock Security - asset_type: AWS Account - mitre_attack_id: - - T1580 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Bedrock Security + asset_type: AWS Account + mitre_attack_id: + - T1580 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1580/aws_bedrock_list_foundation_model_failures/cloudtrail.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1580/aws_bedrock_list_foundation_model_failures/cloudtrail.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_bedrock_invoke_model_access_denied.yml b/detections/cloud/aws_bedrock_invoke_model_access_denied.yml index 3beab4027c..bfe753a5ea 100644 --- a/detections/cloud/aws_bedrock_invoke_model_access_denied.yml +++ b/detections/cloud/aws_bedrock_invoke_model_access_denied.yml @@ -1,65 +1,59 @@ name: AWS Bedrock Invoke Model Access Denied id: c53a8e62-f741-11ee-9f6e-acde48001122 -version: 2 -date: '2025-05-02' +version: 3 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP description: The following analytic identifies access denied error when attempting to invoke AWS Bedrock models. It leverages AWS CloudTrail logs to detect when a user or service receives an AccessDenied error when calling the InvokeModel API. This activity is significant as it may indicate an adversary attempting to access Bedrock models with insufficient permissions after compromising credentials. If confirmed malicious, this could suggest reconnaissance activities or privilege escalation attempts targeting generative AI resources, potentially leading to data exfiltration or manipulation of model outputs. data_source: -- AWS CloudTrail + - AWS CloudTrail search: >- - `cloudtrail` eventSource=bedrock.amazonaws.com eventName=InvokeModel errorCode=AccessDenied - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.modelId) as modelIds by src user user_agent vendor_account vendor_product dest signature vendor_region result result_id - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `aws_bedrock_invoke_model_access_denied_filter` + `cloudtrail` eventSource=bedrock.amazonaws.com eventName=InvokeModel errorCode=AccessDenied + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.modelId) as modelIds by src user user_agent vendor_account vendor_product dest signature vendor_region result result_id + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_bedrock_invoke_model_access_denied_filter` how_to_implement: The Splunk AWS Add-on is required to utilize this data. The search requires AWS CloudTrail logs with Bedrock service events enabled. You must install and configure the AWS App for Splunk (version 6.0.0 or later) and Splunk Add-on for AWS (version 5.1.0 or later) to collect CloudTrail logs from AWS. known_false_positives: Legitimate users may encounter access denied errors during permission testing, role transitions, or when service permissions are being reconfigured. Access denials may also happen when automated processes are using outdated credentials or when new Bedrock features are being explored. references: -- https://docs.aws.amazon.com/bedrock/latest/APIReference/API_ListFoundationModels.html -- https://trustoncloud.com/blog/exposing-the-weakness-how-we-identified-a-flaw-in-bedrocks-foundation-model-access-control/ -- https://attack.mitre.org/techniques/T1595/ + - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_ListFoundationModels.html + - https://trustoncloud.com/blog/exposing-the-weakness-how-we-identified-a-flaw-in-bedrocks-foundation-model-access-control/ + - https://attack.mitre.org/techniques/T1595/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ access denied when attempting to invoke AWS Bedrock models from $src$ - risk_objects: - - field: user - type: user - score: 64 - threat_objects: - - field: src - type: ip_address + message: User $user$ access denied when attempting to invoke AWS Bedrock models from $src$ + risk_objects: + - field: user + type: user + score: 64 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Bedrock Security - asset_type: AWS Account - mitre_attack_id: - - T1078 - - T1550 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Bedrock Security + asset_type: AWS Account + mitre_attack_id: + - T1078 + - T1550 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.004/aws_invoke_model_access_denied/cloudtrail.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.004/aws_invoke_model_access_denied/cloudtrail.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_concurrent_sessions_from_different_ips.yml b/detections/cloud/aws_concurrent_sessions_from_different_ips.yml index c16db144aa..9093f8656c 100644 --- a/detections/cloud/aws_concurrent_sessions_from_different_ips.yml +++ b/detections/cloud/aws_concurrent_sessions_from_different_ips.yml @@ -1,77 +1,64 @@ name: AWS Concurrent Sessions From Different Ips id: 51c04fdb-2746-465a-b86e-b413a09c9085 -version: 8 -date: '2025-10-14' +version: 9 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP -description: The following analytic identifies an AWS IAM account with concurrent - sessions originating from more than one unique IP address within a 5-minute window. - It leverages AWS CloudTrail logs, specifically the `DescribeEventAggregates` event, - to detect this behavior. This activity is significant as it may indicate a session - hijacking attack, where an adversary uses stolen session cookies to access AWS resources - from a different location. If confirmed malicious, this could allow unauthorized - access to sensitive corporate resources, leading to potential data breaches or further - exploitation within the AWS environment. +description: The following analytic identifies an AWS IAM account with concurrent sessions originating from more than one unique IP address within a 5-minute window. It leverages AWS CloudTrail logs, specifically the `DescribeEventAggregates` event, to detect this behavior. This activity is significant as it may indicate a session hijacking attack, where an adversary uses stolen session cookies to access AWS resources from a different location. If confirmed malicious, this could allow unauthorized access to sensitive corporate resources, leading to potential data breaches or further exploitation within the AWS environment. data_source: -- AWS CloudTrail DescribeEventAggregates -search: '`cloudtrail` eventName = DescribeEventAggregates src_ip!="AWS Internal" - | bin span=5m _time - | rename user_name as user - | stats min(_time) as firstTime max(_time) as lastTime values(user_agent) as user_agent values(signature) as signature values(src) as src values(dest) as dest dc(src) as distinct_ip_count by _time user vendor_account vendor_region vendor_product - | where distinct_ip_count > 1 - | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` - | `aws_concurrent_sessions_from_different_ips_filter`' -how_to_implement: You must install Splunk AWS Add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. -known_false_positives: A user with concurrent sessions from different Ips may also - represent the legitimate use of more than one device. Filter as needed and/or customize - the threshold to fit your environment. + - AWS CloudTrail DescribeEventAggregates +search: |- + `cloudtrail` eventName = DescribeEventAggregates src_ip!="AWS Internal" + | bin span=5m _time + | rename user_name as user + | stats min(_time) as firstTime max(_time) as lastTime values(user_agent) as user_agent values(signature) as signature values(src) as src values(dest) as dest dc(src) as distinct_ip_count + BY _time user vendor_account + vendor_region vendor_product + | where distinct_ip_count > 1 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_concurrent_sessions_from_different_ips_filter` +how_to_implement: You must install Splunk AWS Add on and Splunk App for AWS. This search works with AWS CloudTrail logs. +known_false_positives: A user with concurrent sessions from different Ips may also represent the legitimate use of more than one device. Filter as needed and/or customize the threshold to fit your environment. references: -- https://attack.mitre.org/techniques/T1185/ -- https://breakdev.org/evilginx-2-next-generation-of-phishing-2fa-tokens/ -- https://github.com/kgretzky/evilginx2 + - https://attack.mitre.org/techniques/T1185/ + - https://breakdev.org/evilginx-2-next-generation-of-phishing-2fa-tokens/ + - https://github.com/kgretzky/evilginx2 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has concurrent sessions from more than one unique IP address - $src$ in the span of 5 minutes. - risk_objects: - - field: user - type: user - score: 42 - threat_objects: - - field: src - type: ip_address + message: User $user$ has concurrent sessions from more than one unique IP address $src$ in the span of 5 minutes. + risk_objects: + - field: user + type: user + score: 42 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Compromised User Account - - AWS Identity and Access Management Account Takeover - - Scattered Lapsus$ Hunters - asset_type: AWS Account - mitre_attack_id: - - T1185 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Compromised User Account + - AWS Identity and Access Management Account Takeover + - Scattered Lapsus$ Hunters + asset_type: AWS Account + mitre_attack_id: + - T1185 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/aws_concurrent_sessions_from_different_ips/cloudtrail.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/aws_concurrent_sessions_from_different_ips/cloudtrail.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_console_login_failed_during_mfa_challenge.yml b/detections/cloud/aws_console_login_failed_during_mfa_challenge.yml index abc75ca68d..4eba36953e 100644 --- a/detections/cloud/aws_console_login_failed_during_mfa_challenge.yml +++ b/detections/cloud/aws_console_login_failed_during_mfa_challenge.yml @@ -1,74 +1,63 @@ name: AWS Console Login Failed During MFA Challenge id: 55349868-5583-466f-98ab-d3beb321961e -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP -description: The following analytic identifies failed authentication attempts to the - AWS Console during the Multi-Factor Authentication (MFA) challenge. It leverages - AWS CloudTrail logs, specifically the `additionalEventData` field, to detect when - MFA was used but the login attempt still failed. This activity is significant as - it may indicate an adversary attempting to access an account with compromised credentials - but being thwarted by MFA. If confirmed malicious, this could suggest an ongoing - attempt to breach the account, potentially leading to unauthorized access and further - attacks if MFA is bypassed. +description: The following analytic identifies failed authentication attempts to the AWS Console during the Multi-Factor Authentication (MFA) challenge. It leverages AWS CloudTrail logs, specifically the `additionalEventData` field, to detect when MFA was used but the login attempt still failed. This activity is significant as it may indicate an adversary attempting to access an account with compromised credentials but being thwarted by MFA. If confirmed malicious, this could suggest an ongoing attempt to breach the account, potentially leading to unauthorized access and further attacks if MFA is bypassed. data_source: -- AWS CloudTrail ConsoleLogin -search: '`cloudtrail` eventName= ConsoleLogin errorMessage="Failed authentication" additionalEventData.MFAUsed = "Yes" - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product additionalEventData.MFAUsed errorMessage - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `aws_console_login_failed_during_mfa_challenge_filter`' -how_to_implement: The Splunk AWS Add-on is required to utilize this data. The search - requires AWS CloudTrail logs. -known_false_positives: Legitimate users may miss to reply the MFA challenge within - the time window or deny it by mistake. + - AWS CloudTrail ConsoleLogin +search: |- + `cloudtrail` eventName= ConsoleLogin errorMessage="Failed authentication" additionalEventData.MFAUsed = "Yes" + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product additionalEventData.MFAUsed + errorMessage + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_console_login_failed_during_mfa_challenge_filter` +how_to_implement: The Splunk AWS Add-on is required to utilize this data. The search requires AWS CloudTrail logs. +known_false_positives: Legitimate users may miss to reply the MFA challenge within the time window or deny it by mistake. references: -- https://attack.mitre.org/techniques/T1621/ -- https://aws.amazon.com/what-is/mfa/ + - https://attack.mitre.org/techniques/T1621/ + - https://aws.amazon.com/what-is/mfa/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ failed to pass MFA challenge while logging into console - from $src$ - risk_objects: - - field: user - type: user - score: 64 - threat_objects: - - field: src - type: ip_address + message: User $user$ failed to pass MFA challenge while logging into console from $src$ + risk_objects: + - field: user + type: user + score: 64 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Identity and Access Management Account Takeover - - Compromised User Account - asset_type: AWS Account - mitre_attack_id: - - T1586.003 - - T1621 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Identity and Access Management Account Takeover + - Compromised User Account + asset_type: AWS Account + mitre_attack_id: + - T1586.003 + - T1621 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/aws_failed_mfa/cloudtrail.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/aws_failed_mfa/cloudtrail.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_create_policy_version_to_allow_all_resources.yml b/detections/cloud/aws_create_policy_version_to_allow_all_resources.yml index 5fd5b33b14..eae4fcf9f1 100644 --- a/detections/cloud/aws_create_policy_version_to_allow_all_resources.yml +++ b/detections/cloud/aws_create_policy_version_to_allow_all_resources.yml @@ -1,73 +1,62 @@ name: AWS Create Policy Version to allow all resources id: 2a9b80d3-6340-4345-b5ad-212bf3d0dac4 -version: 10 -date: '2025-05-02' +version: 11 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP -description: The following analytic identifies the creation of a new AWS IAM policy - version that allows access to all resources. It detects this activity by analyzing - AWS CloudTrail logs for the CreatePolicyVersion event with a policy document that - grants broad permissions. This behavior is significant because it violates the principle - of least privilege, potentially exposing the environment to misuse or abuse. If - confirmed malicious, an attacker could gain extensive access to AWS resources, leading - to unauthorized actions, data exfiltration, or further compromise of the AWS environment. +description: The following analytic identifies the creation of a new AWS IAM policy version that allows access to all resources. It detects this activity by analyzing AWS CloudTrail logs for the CreatePolicyVersion event with a policy document that grants broad permissions. This behavior is significant because it violates the principle of least privilege, potentially exposing the environment to misuse or abuse. If confirmed malicious, an attacker could gain extensive access to AWS resources, leading to unauthorized actions, data exfiltration, or further compromise of the AWS environment. data_source: -- AWS CloudTrail CreatePolicyVersion -search: '`cloudtrail` eventName=CreatePolicyVersion eventSource = iam.amazonaws.com errorCode = success - | spath input=requestParameters.policyDocument output=key_policy_statements path=Statement{} - | mvexpand key_policy_statements - | spath input=key_policy_statements output=key_policy_action_1 path=Action - | where key_policy_action_1 = "*" - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime values(key_policy_statements) as policy_added by signature dest user user_agent src vendor_account vendor_region vendor_product - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |`aws_create_policy_version_to_allow_all_resources_filter`' -how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. -known_false_positives: While this search has no known false positives, it is possible - that an AWS admin has legitimately created a policy to allow a user to access all - resources. That said, AWS strongly advises against granting full control to all - AWS resources and you must verify this activity. + - AWS CloudTrail CreatePolicyVersion +search: |- + `cloudtrail` eventName=CreatePolicyVersion eventSource = iam.amazonaws.com errorCode = success + | spath input=requestParameters.policyDocument output=key_policy_statements path=Statement{} + | mvexpand key_policy_statements + | spath input=key_policy_statements output=key_policy_action_1 path=Action + | where key_policy_action_1 = "*" + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime values(key_policy_statements) as policy_added + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_create_policy_version_to_allow_all_resources_filter` +how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. +known_false_positives: While this search has no known false positives, it is possible that an AWS admin has legitimately created a policy to allow a user to access all resources. That said, AWS strongly advises against granting full control to all AWS resources and you must verify this activity. references: -- https://bishopfox.com/blog/privilege-escalation-in-aws -- https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/ + - https://bishopfox.com/blog/privilege-escalation-in-aws + - https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ created a policy version that allows them to access any resource - in their account. - risk_objects: - - field: user - type: user - score: 49 - threat_objects: [] + message: User $user$ created a policy version that allows them to access any resource in their account. + risk_objects: + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - AWS IAM Privilege Escalation - asset_type: AWS Account - mitre_attack_id: - - T1078.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - AWS IAM Privilege Escalation + asset_type: AWS Account + mitre_attack_id: + - T1078.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_create_policy_version/aws_cloudtrail_events.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_create_policy_version/aws_cloudtrail_events.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_createaccesskey.yml b/detections/cloud/aws_createaccesskey.yml index c071cb232d..a35716a113 100644 --- a/detections/cloud/aws_createaccesskey.yml +++ b/detections/cloud/aws_createaccesskey.yml @@ -1,47 +1,44 @@ name: AWS CreateAccessKey id: 2a9b80d3-6340-4345-11ad-212bf3d0d111 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: Hunting -description: The following analytic identifies the creation of AWS IAM access keys - by a user for another user, which can indicate privilege escalation. It leverages - AWS CloudTrail logs to detect instances where the user creating the access key is - different from the user for whom the key is created. This activity is significant - because unauthorized access key creation can allow attackers to establish persistence - or exfiltrate data via AWS APIs. If confirmed malicious, this could lead to unauthorized - access to AWS services, data exfiltration, and long-term persistence in the environment. +description: The following analytic identifies the creation of AWS IAM access keys by a user for another user, which can indicate privilege escalation. It leverages AWS CloudTrail logs to detect instances where the user creating the access key is different from the user for whom the key is created. This activity is significant because unauthorized access key creation can allow attackers to establish persistence or exfiltrate data via AWS APIs. If confirmed malicious, this could lead to unauthorized access to AWS services, data exfiltration, and long-term persistence in the environment. data_source: -- AWS CloudTrail CreateAccessKey -search: '`cloudtrail` eventName = CreateAccessKey userAgent !=console.amazonaws.com errorCode = success - | eval match=if(match(userIdentity.userName,requestParameters.userName),1,0) - | search match=0 - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |`aws_createaccesskey_filter`' -how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. -known_false_positives: While this search has no known false positives, it is possible - that an AWS admin has legitimately created keys for another user. + - AWS CloudTrail CreateAccessKey +search: |- + `cloudtrail` eventName = CreateAccessKey userAgent !=console.amazonaws.com errorCode = success + | eval match=if(match(userIdentity.userName,requestParameters.userName),1,0) + | search match=0 + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_createaccesskey_filter` +how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. +known_false_positives: While this search has no known false positives, it is possible that an AWS admin has legitimately created keys for another user. references: -- https://bishopfox.com/blog/privilege-escalation-in-aws -- https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/ + - https://bishopfox.com/blog/privilege-escalation-in-aws + - https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/ tags: - analytic_story: - - AWS IAM Privilege Escalation - asset_type: AWS Account - mitre_attack_id: - - T1136.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - AWS IAM Privilege Escalation + asset_type: AWS Account + mitre_attack_id: + - T1136.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_createaccesskey/aws_cloudtrail_events.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_createaccesskey/aws_cloudtrail_events.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_createloginprofile.yml b/detections/cloud/aws_createloginprofile.yml index a65525b09c..3bbb768a9f 100644 --- a/detections/cloud/aws_createloginprofile.yml +++ b/detections/cloud/aws_createloginprofile.yml @@ -1,77 +1,67 @@ name: AWS CreateLoginProfile id: 2a9b80d3-6340-4345-11ad-212bf444d111 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP -description: The following analytic identifies the creation of a login profile for - one AWS user by another, followed by a console login from the same source IP. It - uses AWS CloudTrail logs to correlate the `CreateLoginProfile` and `ConsoleLogin` - events based on the source IP and user identity. This activity is significant as - it may indicate privilege escalation, where an attacker creates a new login profile - to gain unauthorized access. If confirmed malicious, this could allow the attacker - to escalate privileges and maintain persistent access to the AWS environment. +description: The following analytic identifies the creation of a login profile for one AWS user by another, followed by a console login from the same source IP. It uses AWS CloudTrail logs to correlate the `CreateLoginProfile` and `ConsoleLogin` events based on the source IP and user identity. This activity is significant as it may indicate privilege escalation, where an attacker creates a new login profile to gain unauthorized access. If confirmed malicious, this could allow the attacker to escalate privileges and maintain persistent access to the AWS environment. data_source: -- AWS CloudTrail CreateLoginProfile AND AWS CloudTrail ConsoleLogin -search: '`cloudtrail` eventName = CreateLoginProfile - | rename requestParameters.userName as new_login_profile - | table src_ip eventName new_login_profile userIdentity.userName - | join new_login_profile src_ip - [| search `cloudtrail` eventName = ConsoleLogin - | rename userIdentity.userName as new_login_profile - | stats count values(eventName) min(_time) as firstTime max(_time) as lastTime by eventSource aws_account_id errorCode user_agent eventID awsRegion userIdentity.principalId user_arn new_login_profile src_ip dest vendor_account vendor_region vendor_product - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`] - | rename user_arn as user - | `aws_createloginprofile_filter`' -how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. -known_false_positives: While this search has no known false positives, it is possible - that an AWS admin has legitimately created a login profile for another user. + - AWS CloudTrail CreateLoginProfile AND AWS CloudTrail ConsoleLogin +search: |- + `cloudtrail` eventName = CreateLoginProfile + | rename requestParameters.userName as new_login_profile + | table src_ip eventName new_login_profile userIdentity.userName + | join new_login_profile src_ip [ + | search `cloudtrail` eventName = ConsoleLogin + | rename userIdentity.userName as new_login_profile + | stats count values(eventName) min(_time) as firstTime max(_time) as lastTime + BY eventSource aws_account_id errorCode + user_agent eventID awsRegion + userIdentity.principalId user_arn new_login_profile + src_ip dest vendor_account + vendor_region vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)`] + | rename user_arn as user + | `aws_createloginprofile_filter` +how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. +known_false_positives: While this search has no known false positives, it is possible that an AWS admin has legitimately created a login profile for another user. references: -- https://bishopfox.com/blog/privilege-escalation-in-aws -- https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/ + - https://bishopfox.com/blog/privilege-escalation-in-aws + - https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ is attempting to create a login profile for $new_login_profile$ - and did a console login from this IP $src_ip$ - risk_objects: - - field: user - type: user - score: 72 - threat_objects: - - field: src_ip - type: ip_address + message: User $user$ is attempting to create a login profile for $new_login_profile$ and did a console login from this IP $src_ip$ + risk_objects: + - field: user + type: user + score: 72 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - AWS IAM Privilege Escalation - asset_type: AWS Account - mitre_attack_id: - - T1136.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - AWS IAM Privilege Escalation + asset_type: AWS Account + mitre_attack_id: + - T1136.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_createloginprofile/aws_cloudtrail_events.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail - - \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_createloginprofile/aws_cloudtrail_events.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_credential_access_failed_login.yml b/detections/cloud/aws_credential_access_failed_login.yml index 7050bb6adf..faada2e2bc 100644 --- a/detections/cloud/aws_credential_access_failed_login.yml +++ b/detections/cloud/aws_credential_access_failed_login.yml @@ -1,68 +1,60 @@ name: AWS Credential Access Failed Login id: a19b354d-0d7f-47f3-8ea6-1a7c36434968 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Gowthamaraj Rajendran, Bhavin Patel, Splunk status: production type: TTP -description: The following analytic identifies unsuccessful login attempts to the - AWS Management Console using a specific user identity. It leverages AWS CloudTrail - logs to detect failed authentication events associated with the AWS ConsoleLogin - action. This activity is significant for a SOC because repeated failed login attempts - may indicate a brute force attack or unauthorized access attempts. If confirmed - malicious, an attacker could potentially gain access to AWS account services and - resources, leading to data breaches, resource manipulation, or further exploitation - within the AWS environment. +description: The following analytic identifies unsuccessful login attempts to the AWS Management Console using a specific user identity. It leverages AWS CloudTrail logs to detect failed authentication events associated with the AWS ConsoleLogin action. This activity is significant for a SOC because repeated failed login attempts may indicate a brute force attack or unauthorized access attempts. If confirmed malicious, an attacker could potentially gain access to AWS account services and resources, leading to data breaches, resource manipulation, or further exploitation within the AWS environment. data_source: -- AWS CloudTrail ConsoleLogin -search: '`cloudtrail` eventName = ConsoleLogin errorMessage="Failed authentication" - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_credential_access_failed_login_filter`' -how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. + - AWS CloudTrail ConsoleLogin +search: |- + `cloudtrail` eventName = ConsoleLogin errorMessage="Failed authentication" + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_credential_access_failed_login_filter` +how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. known_false_positives: Users may genuinely mistype or forget the password. references: -- https://attack.mitre.org/techniques/T1110/001/ + - https://attack.mitre.org/techniques/T1110/001/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has a login failure from IP $src$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: src - type: ip_address + message: User $user$ has a login failure from IP $src$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Identity and Access Management Account Takeover - asset_type: AWS Account - mitre_attack_id: - - T1110.001 - - T1586.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Identity and Access Management Account Takeover + asset_type: AWS Account + mitre_attack_id: + - T1110.001 + - T1586.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/aws_login_failure/aws_cloudtrail_events.json - source: aws_cloudtrail - sourcetype: aws:cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/aws_login_failure/aws_cloudtrail_events.json + source: aws_cloudtrail + sourcetype: aws:cloudtrail diff --git a/detections/cloud/aws_credential_access_getpassworddata.yml b/detections/cloud/aws_credential_access_getpassworddata.yml index 1f8ac9d46d..eb59efadf0 100644 --- a/detections/cloud/aws_credential_access_getpassworddata.yml +++ b/detections/cloud/aws_credential_access_getpassworddata.yml @@ -1,74 +1,63 @@ name: AWS Credential Access GetPasswordData id: 4d347c4a-306e-41db-8d10-b46baf71b3e2 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: Anomaly -description: The following analytic identifies more than 10 GetPasswordData API calls - within a 5-minute window in your AWS account. It leverages AWS CloudTrail logs to - detect this activity by counting the distinct instance IDs accessed. This behavior - is significant as it may indicate an attempt to retrieve encrypted administrator - passwords for running Windows instances, which is a critical security concern. If - confirmed malicious, attackers could gain unauthorized access to administrative - credentials, potentially leading to full control over the affected instances and - further compromise of the AWS environment. +description: The following analytic identifies more than 10 GetPasswordData API calls within a 5-minute window in your AWS account. It leverages AWS CloudTrail logs to detect this activity by counting the distinct instance IDs accessed. This behavior is significant as it may indicate an attempt to retrieve encrypted administrator passwords for running Windows instances, which is a critical security concern. If confirmed malicious, attackers could gain unauthorized access to administrative credentials, potentially leading to full control over the affected instances and further compromise of the AWS environment. data_source: -- AWS CloudTrail GetPasswordData -search: '`cloudtrail` eventName=GetPasswordData eventSource = ec2.amazonaws.com - | bin _time span=5m - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime dc(requestParameters.instanceId) as distinct_instance_ids by signature dest user user_agent src vendor_account vendor_region vendor_product - | where distinct_instance_ids > 10 - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `aws_credential_access_getpassworddata_filter`' -how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. We encourage the users to adjust the values - of `distinct_instance_ids` and tweak the `span` value according to their environment. -known_false_positives: Administrator tooling or automated scripts may make these calls - but it is highly unlikely to make several calls in a short period of time. + - AWS CloudTrail GetPasswordData +search: |- + `cloudtrail` eventName=GetPasswordData eventSource = ec2.amazonaws.com + | bin _time span=5m + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime dc(requestParameters.instanceId) as distinct_instance_ids + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product + | where distinct_instance_ids > 10 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_credential_access_getpassworddata_filter` +how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. We encourage the users to adjust the values of `distinct_instance_ids` and tweak the `span` value according to their environment. +known_false_positives: Administrator tooling or automated scripts may make these calls but it is highly unlikely to make several calls in a short period of time. references: -- https://attack.mitre.org/techniques/T1552/ -- https://stratus-red-team.cloud/attack-techniques/AWS/aws.credential-access.ec2-get-password-data/ + - https://attack.mitre.org/techniques/T1552/ + - https://stratus-red-team.cloud/attack-techniques/AWS/aws.credential-access.ec2-get-password-data/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ is seen to make mulitple `GetPasswordData` API calls to multiple instances from IP $src$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: src - type: ip_address + message: User $user$ is seen to make mulitple `GetPasswordData` API calls to multiple instances from IP $src$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Identity and Access Management Account Takeover - asset_type: AWS Account - mitre_attack_id: - - T1110.001 - - T1586.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Identity and Access Management Account Takeover + asset_type: AWS Account + mitre_attack_id: + - T1110.001 + - T1586.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552/aws_getpassworddata/aws_cloudtrail_events.json - source: aws_cloudtrail - sourcetype: aws:cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552/aws_getpassworddata/aws_cloudtrail_events.json + source: aws_cloudtrail + sourcetype: aws:cloudtrail diff --git a/detections/cloud/aws_credential_access_rds_password_reset.yml b/detections/cloud/aws_credential_access_rds_password_reset.yml index ee86ce0df9..d4f7f38432 100644 --- a/detections/cloud/aws_credential_access_rds_password_reset.yml +++ b/detections/cloud/aws_credential_access_rds_password_reset.yml @@ -1,69 +1,61 @@ name: AWS Credential Access RDS Password reset id: 6153c5ea-ed30-4878-81e6-21ecdb198189 -version: 9 -date: '2025-10-14' +version: 10 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: TTP -description: The following analytic detects the resetting of the master user password - for an Amazon RDS DB instance. It leverages AWS CloudTrail logs to identify events - where the `ModifyDBInstance` API call includes a new `masterUserPassword` parameter. - This activity is significant because unauthorized password resets can grant attackers - access to sensitive data stored in production databases, such as credit card information, - PII, and healthcare data. If confirmed malicious, this could lead to data breaches, - regulatory non-compliance, and significant reputational damage. Immediate investigation - is required to determine the legitimacy of the password reset. +description: The following analytic detects the resetting of the master user password for an Amazon RDS DB instance. It leverages AWS CloudTrail logs to identify events where the `ModifyDBInstance` API call includes a new `masterUserPassword` parameter. This activity is significant because unauthorized password resets can grant attackers access to sensitive data stored in production databases, such as credit card information, PII, and healthcare data. If confirmed malicious, this could lead to data breaches, regulatory non-compliance, and significant reputational damage. Immediate investigation is required to determine the legitimacy of the password reset. data_source: -- AWS CloudTrail ModifyDBInstance -search: '`cloudtrail` eventSource="rds.amazonaws.com" eventName=ModifyDBInstance "requestParameters.masterUserPassword"=* - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.dBInstanceIdentifier) as database_id by signature dest user user_agent src vendor_account vendor_region vendor_product - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `aws_credential_access_rds_password_reset_filter`' -how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. + - AWS CloudTrail ModifyDBInstance +search: |- + `cloudtrail` eventSource="rds.amazonaws.com" eventName=ModifyDBInstance "requestParameters.masterUserPassword"=* + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.dBInstanceIdentifier) as database_id + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_credential_access_rds_password_reset_filter` +how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. known_false_positives: Users may genuinely reset the RDS password. references: -- https://aws.amazon.com/premiumsupport/knowledge-center/reset-master-user-password-rds + - https://aws.amazon.com/premiumsupport/knowledge-center/reset-master-user-password-rds drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $database_id$ password has been reset from IP $src$ - risk_objects: - - field: database_id - type: system - score: 49 - threat_objects: - - field: src - type: ip_address + message: $database_id$ password has been reset from IP $src$ + risk_objects: + - field: database_id + type: system + score: 49 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Identity and Access Management Account Takeover - - Scattered Lapsus$ Hunters - asset_type: AWS Account - mitre_attack_id: - - T1110 - - T1586.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Identity and Access Management Account Takeover + - Scattered Lapsus$ Hunters + asset_type: AWS Account + mitre_attack_id: + - T1110 + - T1586.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.002/aws_rds_password_reset/aws_cloudtrail_events.json - source: aws_cloudtrail - sourcetype: aws:cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.002/aws_rds_password_reset/aws_cloudtrail_events.json + source: aws_cloudtrail + sourcetype: aws:cloudtrail diff --git a/detections/cloud/aws_defense_evasion_delete_cloudtrail.yml b/detections/cloud/aws_defense_evasion_delete_cloudtrail.yml index 0885be912c..ec25413cc0 100644 --- a/detections/cloud/aws_defense_evasion_delete_cloudtrail.yml +++ b/detections/cloud/aws_defense_evasion_delete_cloudtrail.yml @@ -1,69 +1,59 @@ name: AWS Defense Evasion Delete Cloudtrail id: 82092925-9ca1-4e06-98b8-85a2d3889552 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP -description: The following analytic detects the deletion of AWS CloudTrail logs by - identifying `DeleteTrail` events within CloudTrail logs. This detection leverages - CloudTrail data to monitor for successful `DeleteTrail` actions, excluding those - initiated from the AWS console. This activity is significant because adversaries - may delete CloudTrail logs to evade detection and operate stealthily within the - compromised environment. If confirmed malicious, this action could allow attackers - to cover their tracks, making it difficult to trace their activities and potentially - leading to prolonged unauthorized access and further exploitation. +description: The following analytic detects the deletion of AWS CloudTrail logs by identifying `DeleteTrail` events within CloudTrail logs. This detection leverages CloudTrail data to monitor for successful `DeleteTrail` actions, excluding those initiated from the AWS console. This activity is significant because adversaries may delete CloudTrail logs to evade detection and operate stealthily within the compromised environment. If confirmed malicious, this action could allow attackers to cover their tracks, making it difficult to trace their activities and potentially leading to prolonged unauthorized access and further exploitation. data_source: -- AWS CloudTrail DeleteTrail -search: '`cloudtrail` eventName = DeleteTrail eventSource = cloudtrail.amazonaws.com userAgent !=console.amazonaws.com errorCode = success - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `aws_defense_evasion_delete_cloudtrail_filter`' -how_to_implement: You must install Splunk AWS Add on and enable CloudTrail logs in - your AWS Environment. -known_false_positives: While this search has no known false positives, it is possible - that an AWS admin has stopped cloudTrail logging. Please investigate this activity. + - AWS CloudTrail DeleteTrail +search: |- + `cloudtrail` eventName = DeleteTrail eventSource = cloudtrail.amazonaws.com userAgent !=console.amazonaws.com errorCode = success + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_defense_evasion_delete_cloudtrail_filter` +how_to_implement: You must install Splunk AWS Add on and enable CloudTrail logs in your AWS Environment. +known_false_positives: While this search has no known false positives, it is possible that an AWS admin has stopped cloudTrail logging. Please investigate this activity. references: -- https://attack.mitre.org/techniques/T1562/008/ + - https://attack.mitre.org/techniques/T1562/008/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has delete a CloudTrail logging for account id $vendor_account$ - from IP $src$ - risk_objects: - - field: user - type: user - score: 90 - threat_objects: - - field: src - type: ip_address + message: User $user$ has delete a CloudTrail logging for account id $vendor_account$ from IP $src$ + risk_objects: + - field: user + type: user + score: 90 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Defense Evasion - asset_type: AWS Account - mitre_attack_id: - - T1562.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Defense Evasion + asset_type: AWS Account + mitre_attack_id: + - T1562.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/stop_delete_cloudtrail/aws_cloudtrail_events.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/stop_delete_cloudtrail/aws_cloudtrail_events.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_defense_evasion_delete_cloudwatch_log_group.yml b/detections/cloud/aws_defense_evasion_delete_cloudwatch_log_group.yml index 4f88a19133..6d61d720fa 100644 --- a/detections/cloud/aws_defense_evasion_delete_cloudwatch_log_group.yml +++ b/detections/cloud/aws_defense_evasion_delete_cloudwatch_log_group.yml @@ -1,69 +1,59 @@ name: AWS Defense Evasion Delete CloudWatch Log Group id: d308b0f1-edb7-4a62-a614-af321160710f -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: TTP -description: The following analytic detects the deletion of CloudWatch log groups - in AWS, identified through `DeleteLogGroup` events in CloudTrail logs. This detection - leverages CloudTrail data to monitor for successful log group deletions, excluding - console-based actions. This activity is significant as it indicates potential attempts - to evade logging and monitoring, which is crucial for maintaining visibility into - AWS activities. If confirmed malicious, this could allow attackers to hide their - tracks, making it difficult to detect further malicious actions or investigate incidents - within the compromised AWS environment. +description: The following analytic detects the deletion of CloudWatch log groups in AWS, identified through `DeleteLogGroup` events in CloudTrail logs. This detection leverages CloudTrail data to monitor for successful log group deletions, excluding console-based actions. This activity is significant as it indicates potential attempts to evade logging and monitoring, which is crucial for maintaining visibility into AWS activities. If confirmed malicious, this could allow attackers to hide their tracks, making it difficult to detect further malicious actions or investigate incidents within the compromised AWS environment. data_source: -- AWS CloudTrail DeleteLogGroup -search: '`cloudtrail` eventName = DeleteLogGroup eventSource = logs.amazonaws.com userAgent !=console.amazonaws.com errorCode = success - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `aws_defense_evasion_delete_cloudwatch_log_group_filter`' -how_to_implement: You must install Splunk AWS Add on and enable CloudTrail logs in - your AWS Environment. -known_false_positives: While this search has no known false positives, it is possible - that an AWS admin has deleted CloudWatch logging. Please investigate this activity. + - AWS CloudTrail DeleteLogGroup +search: |- + `cloudtrail` eventName = DeleteLogGroup eventSource = logs.amazonaws.com userAgent !=console.amazonaws.com errorCode = success + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_defense_evasion_delete_cloudwatch_log_group_filter` +how_to_implement: You must install Splunk AWS Add on and enable CloudTrail logs in your AWS Environment. +known_false_positives: While this search has no known false positives, it is possible that an AWS admin has deleted CloudWatch logging. Please investigate this activity. references: -- https://attack.mitre.org/techniques/T1562/008/ + - https://attack.mitre.org/techniques/T1562/008/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has deleted a CloudWatch logging group for account id $vendor_account$ - from IP $src$ - risk_objects: - - field: user - type: user - score: 90 - threat_objects: - - field: src - type: ip_address + message: User $user$ has deleted a CloudWatch logging group for account id $vendor_account$ from IP $src$ + risk_objects: + - field: user + type: user + score: 90 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Defense Evasion - asset_type: AWS Account - mitre_attack_id: - - T1562.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Defense Evasion + asset_type: AWS Account + mitre_attack_id: + - T1562.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/delete_cloudwatch_log_group/aws_cloudtrail_events.json - source: aws_cloudtrail - sourcetype: aws:cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/delete_cloudwatch_log_group/aws_cloudtrail_events.json + source: aws_cloudtrail + sourcetype: aws:cloudtrail diff --git a/detections/cloud/aws_defense_evasion_impair_security_services.yml b/detections/cloud/aws_defense_evasion_impair_security_services.yml index 1ad0b7e85e..ec1283d4b0 100644 --- a/detections/cloud/aws_defense_evasion_impair_security_services.yml +++ b/detections/cloud/aws_defense_evasion_impair_security_services.yml @@ -1,80 +1,66 @@ name: AWS Defense Evasion Impair Security Services id: b28c4957-96a6-47e0-a965-6c767aac1458 -version: 9 -date: '2025-08-26' +version: 10 +date: '2026-02-25' author: Bhavin Patel, Gowthamaraj Rajendran, Splunk, PashFW, Github Community status: production type: TTP -description: The following analytic detects attempts to impair or disable AWS security services by monitoring specific deletion operations across GuardDuty, AWS WAF (classic and v2), CloudWatch, Route 53, and CloudWatch Logs. These actions include deleting detectors, rule groups, IP sets, web ACLs, logging configurations, alarms, and log streams. Adversaries may perform such operations to evade detection or remove visibility from defenders. By explicitly pairing eventName values with their corresponding eventSource services, this detection reduces noise and ensures that only security-related deletions are flagged. It leverages CloudTrail logs to identify specific API - calls like "DeleteLogStream" and "DeleteDetector." This activity is significant - because it indicates potential efforts to disable security monitoring and evade - detection. If confirmed malicious, this could allow attackers to operate undetected, - escalate privileges, or exfiltrate data without triggering security alerts, severely - compromising the security posture of the AWS environment. +description: The following analytic detects attempts to impair or disable AWS security services by monitoring specific deletion operations across GuardDuty, AWS WAF (classic and v2), CloudWatch, Route 53, and CloudWatch Logs. These actions include deleting detectors, rule groups, IP sets, web ACLs, logging configurations, alarms, and log streams. Adversaries may perform such operations to evade detection or remove visibility from defenders. By explicitly pairing eventName values with their corresponding eventSource services, this detection reduces noise and ensures that only security-related deletions are flagged. It leverages CloudTrail logs to identify specific API calls like "DeleteLogStream" and "DeleteDetector." This activity is significant because it indicates potential efforts to disable security monitoring and evade detection. If confirmed malicious, this could allow attackers to operate undetected, escalate privileges, or exfiltrate data without triggering security alerts, severely compromising the security posture of the AWS environment. data_source: -- AWS CloudTrail DeleteLogStream -- AWS CloudTrail DeleteDetector -- AWS CloudTrail DeleteIPSet -- AWS CloudTrail DeleteWebACL -- AWS CloudTrail DeleteRule -- AWS CloudTrail DeleteRuleGroup -- AWS CloudTrail DeleteLoggingConfiguration -- AWS CloudTrail DeleteAlarms + - AWS CloudTrail DeleteLogStream + - AWS CloudTrail DeleteDetector + - AWS CloudTrail DeleteIPSet + - AWS CloudTrail DeleteWebACL + - AWS CloudTrail DeleteRule + - AWS CloudTrail DeleteRuleGroup + - AWS CloudTrail DeleteLoggingConfiguration + - AWS CloudTrail DeleteAlarms search: | - `cloudtrail` - (eventName="DeleteDetector" AND eventSource="guardduty.amazonaws.com") OR ( eventName IN ("DeleteIPSet", "DeleteWebACL", "DeleteRuleGroup", "DeleteRule") AND eventSource IN ("guardduty.amazonaws.com", "wafv2.amazonaws.com", "waf.amazonaws.com") ) OR ( eventName="DeleteLoggingConfiguration" AND eventSource IN ("wafv2.amazonaws.com", "waf.amazonaws.com", "route53.amazonaws.com") ) - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `aws_defense_evasion_impair_security_services_filter` -how_to_implement: You must install Splunk AWS Add on and enable CloudTrail logs in - your AWS Environment. -known_false_positives: Legitimate administrators may occasionally delete GuardDuty detectors, WAF rule groups, or CloudWatch alarms during environment reconfiguration, migration, or decommissioning activities. In such cases, these events are expected and benign. These should be validated against approved change tickets or deployment pipelines to differentiate malicious activity from normal operations. Please consider filtering out these noisy - events using userAgent, user_arn field names. + `cloudtrail` + (eventName="DeleteDetector" AND eventSource="guardduty.amazonaws.com") OR ( eventName IN ("DeleteIPSet", "DeleteWebACL", "DeleteRuleGroup", "DeleteRule") AND eventSource IN ("guardduty.amazonaws.com", "wafv2.amazonaws.com", "waf.amazonaws.com") ) OR ( eventName="DeleteLoggingConfiguration" AND eventSource IN ("wafv2.amazonaws.com", "waf.amazonaws.com", "route53.amazonaws.com") ) + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_defense_evasion_impair_security_services_filter` +how_to_implement: You must install Splunk AWS Add on and enable CloudTrail logs in your AWS Environment. +known_false_positives: Legitimate administrators may occasionally delete GuardDuty detectors, WAF rule groups, or CloudWatch alarms during environment reconfiguration, migration, or decommissioning activities. In such cases, these events are expected and benign. These should be validated against approved change tickets or deployment pipelines to differentiate malicious activity from normal operations. Please consider filtering out these noisy events using userAgent, user_arn field names. references: -- https://docs.aws.amazon.com/cli/latest/reference/guardduty/index.html -- https://docs.aws.amazon.com/cli/latest/reference/waf/index.html -- https://www.elastic.co/guide/en/security/current/prebuilt-rules.html + - https://docs.aws.amazon.com/cli/latest/reference/guardduty/index.html + - https://docs.aws.amazon.com/cli/latest/reference/waf/index.html + - https://www.elastic.co/guide/en/security/current/prebuilt-rules.html drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has deleted a security service by attempting to $signature$ for account id $vendor_account$ - from IP $src$ - risk_objects: - - field: user - type: user - score: 90 - threat_objects: - - field: src - type: ip_address + message: User $user$ has deleted a security service by attempting to $signature$ for account id $vendor_account$ from IP $src$ + risk_objects: + - field: user + type: user + score: 90 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Defense Evasion - asset_type: AWS Account - mitre_attack_id: - - T1562.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Defense Evasion + asset_type: AWS Account + mitre_attack_id: + - T1562.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/aws_delete_security_services/aws_cloudtrail_events.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/aws_delete_security_services/aws_cloudtrail_events.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_defense_evasion_putbucketlifecycle.yml b/detections/cloud/aws_defense_evasion_putbucketlifecycle.yml index 1a93dee6c8..c2c1a49ddd 100644 --- a/detections/cloud/aws_defense_evasion_putbucketlifecycle.yml +++ b/detections/cloud/aws_defense_evasion_putbucketlifecycle.yml @@ -1,49 +1,45 @@ name: AWS Defense Evasion PutBucketLifecycle id: ce1c0e2b-9303-4903-818b-0d9002fc6ea4 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Bhavin Patel status: production type: Hunting -description: The following analytic detects `PutBucketLifecycle` events in AWS CloudTrail - logs where a user sets a lifecycle rule for an S3 bucket with an expiration period - of fewer than three days. This detection leverages CloudTrail logs to identify suspicious - lifecycle configurations. This activity is significant because attackers may use - it to delete CloudTrail logs quickly, thereby evading detection and impairing forensic - investigations. If confirmed malicious, this could allow attackers to cover their - tracks, making it difficult to trace their actions and respond to the breach effectively. +description: The following analytic detects `PutBucketLifecycle` events in AWS CloudTrail logs where a user sets a lifecycle rule for an S3 bucket with an expiration period of fewer than three days. This detection leverages CloudTrail logs to identify suspicious lifecycle configurations. This activity is significant because attackers may use it to delete CloudTrail logs quickly, thereby evading detection and impairing forensic investigations. If confirmed malicious, this could allow attackers to cover their tracks, making it difficult to trace their actions and respond to the breach effectively. data_source: -- AWS CloudTrail PutBucketLifecycle -search: '`cloudtrail` eventName=PutBucketLifecycle user_type=IAMUser errorCode=success - | spath path=requestParameters{}.LifecycleConfiguration{}.Rule{}.Expiration{}.Days output=expiration_days - | spath path=requestParameters{}.bucketName output=bucket_name - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product bucket_name expiration_days - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_defense_evasion_putbucketlifecycle_filter`' -how_to_implement: You must install Splunk AWS Add on and enable CloudTrail logs in - your AWS Environment. We recommend our users to set the expiration days value according - to your company's log retention policies. -known_false_positives: While this search has no known false positives, it is possible - that it is a legitimate admin activity. Please consider filtering out these noisy - events using userAgent, user_arn field names. + - AWS CloudTrail PutBucketLifecycle +search: |- + `cloudtrail` eventName=PutBucketLifecycle user_type=IAMUser errorCode=success + | spath path=requestParameters{}.LifecycleConfiguration{}.Rule{}.Expiration{}.Days output=expiration_days + | spath path=requestParameters{}.bucketName output=bucket_name + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product bucket_name + expiration_days + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_defense_evasion_putbucketlifecycle_filter` +how_to_implement: You must install Splunk AWS Add on and enable CloudTrail logs in your AWS Environment. We recommend our users to set the expiration days value according to your company's log retention policies. +known_false_positives: While this search has no known false positives, it is possible that it is a legitimate admin activity. Please consider filtering out these noisy events using userAgent, user_arn field names. references: -- https://stratus-red-team.cloud/attack-techniques/AWS/aws.defense-evasion.cloudtrail-lifecycle-rule/ + - https://stratus-red-team.cloud/attack-techniques/AWS/aws.defense-evasion.cloudtrail-lifecycle-rule/ tags: - analytic_story: - - AWS Defense Evasion - asset_type: AWS Account - mitre_attack_id: - - T1485.001 - - T1562.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Defense Evasion + asset_type: AWS Account + mitre_attack_id: + - T1485.001 + - T1562.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/put_bucketlifecycle/aws_cloudtrail_events.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/put_bucketlifecycle/aws_cloudtrail_events.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_defense_evasion_stop_logging_cloudtrail.yml b/detections/cloud/aws_defense_evasion_stop_logging_cloudtrail.yml index 96c3c66794..cab1610217 100644 --- a/detections/cloud/aws_defense_evasion_stop_logging_cloudtrail.yml +++ b/detections/cloud/aws_defense_evasion_stop_logging_cloudtrail.yml @@ -1,69 +1,59 @@ name: AWS Defense Evasion Stop Logging Cloudtrail id: 8a2f3ca2-4eb5-4389-a549-14063882e537 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP -description: The following analytic detects `StopLogging` events in AWS CloudTrail - logs. It leverages CloudTrail event data to identify when logging is intentionally - stopped, excluding console-based actions and focusing on successful attempts. This - activity is significant because adversaries may stop logging to evade detection - and operate stealthily within the compromised environment. If confirmed malicious, - this action could allow attackers to perform further activities without being logged, - hindering incident response and forensic investigations, and potentially leading - to unauthorized access or data exfiltration. +description: The following analytic detects `StopLogging` events in AWS CloudTrail logs. It leverages CloudTrail event data to identify when logging is intentionally stopped, excluding console-based actions and focusing on successful attempts. This activity is significant because adversaries may stop logging to evade detection and operate stealthily within the compromised environment. If confirmed malicious, this action could allow attackers to perform further activities without being logged, hindering incident response and forensic investigations, and potentially leading to unauthorized access or data exfiltration. data_source: -- AWS CloudTrail StopLogging -search: '`cloudtrail` eventName = StopLogging eventSource = cloudtrail.amazonaws.com userAgent!=console.amazonaws.com errorCode = success - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_defense_evasion_stop_logging_cloudtrail_filter`' -how_to_implement: You must install Splunk AWS Add on and enable Cloudtrail logs in - your AWS Environment. -known_false_positives: While this search has no known false positives, it is possible - that an AWS admin has stopped cloudtrail logging. Please investigate this activity. + - AWS CloudTrail StopLogging +search: |- + `cloudtrail` eventName = StopLogging eventSource = cloudtrail.amazonaws.com userAgent!=console.amazonaws.com errorCode = success + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_defense_evasion_stop_logging_cloudtrail_filter` +how_to_implement: You must install Splunk AWS Add on and enable Cloudtrail logs in your AWS Environment. +known_false_positives: While this search has no known false positives, it is possible that an AWS admin has stopped cloudtrail logging. Please investigate this activity. references: -- https://attack.mitre.org/techniques/T1562/008/ + - https://attack.mitre.org/techniques/T1562/008/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has stopped Cloudtrail logging for account id $vendor_account$ - from IP $src$ - risk_objects: - - field: user - type: user - score: 90 - threat_objects: - - field: src - type: ip_address + message: User $user$ has stopped Cloudtrail logging for account id $vendor_account$ from IP $src$ + risk_objects: + - field: user + type: user + score: 90 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Defense Evasion - asset_type: AWS Account - mitre_attack_id: - - T1562.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Defense Evasion + asset_type: AWS Account + mitre_attack_id: + - T1562.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/stop_delete_cloudtrail/aws_cloudtrail_events.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/stop_delete_cloudtrail/aws_cloudtrail_events.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_defense_evasion_update_cloudtrail.yml b/detections/cloud/aws_defense_evasion_update_cloudtrail.yml index 9bc009d59b..6f2d86de3d 100644 --- a/detections/cloud/aws_defense_evasion_update_cloudtrail.yml +++ b/detections/cloud/aws_defense_evasion_update_cloudtrail.yml @@ -1,69 +1,59 @@ name: AWS Defense Evasion Update Cloudtrail id: 7c921d28-ef48-4f1b-85b3-0af8af7697db -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: TTP -description: The following analytic detects `UpdateTrail` events in AWS CloudTrail - logs. It identifies attempts to modify CloudTrail settings, potentially to evade - logging. The detection leverages CloudTrail logs, focusing on `UpdateTrail` events - where the user agent is not the AWS console and the operation is successful. This - activity is significant because altering CloudTrail settings can disable or limit - logging, hindering visibility into AWS account activities. If confirmed malicious, - this could allow attackers to operate undetected, compromising the integrity and - security of the AWS environment. +description: The following analytic detects `UpdateTrail` events in AWS CloudTrail logs. It identifies attempts to modify CloudTrail settings, potentially to evade logging. The detection leverages CloudTrail logs, focusing on `UpdateTrail` events where the user agent is not the AWS console and the operation is successful. This activity is significant because altering CloudTrail settings can disable or limit logging, hindering visibility into AWS account activities. If confirmed malicious, this could allow attackers to operate undetected, compromising the integrity and security of the AWS environment. data_source: -- AWS CloudTrail UpdateTrail -search: '`cloudtrail` eventName = UpdateTrail eventSource = cloudtrail.amazonaws.com userAgent !=console.amazonaws.com errorCode = success - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `aws_defense_evasion_update_cloudtrail_filter`' -how_to_implement: You must install Splunk AWS Add on and enable CloudTrail logs in - your AWS Environment. -known_false_positives: While this search has no known false positives, it is possible - that an AWS admin has updated cloudtrail logging. Please investigate this activity. + - AWS CloudTrail UpdateTrail +search: |- + `cloudtrail` eventName = UpdateTrail eventSource = cloudtrail.amazonaws.com userAgent !=console.amazonaws.com errorCode = success + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_defense_evasion_update_cloudtrail_filter` +how_to_implement: You must install Splunk AWS Add on and enable CloudTrail logs in your AWS Environment. +known_false_positives: While this search has no known false positives, it is possible that an AWS admin has updated cloudtrail logging. Please investigate this activity. references: -- https://attack.mitre.org/techniques/T1562/008/ + - https://attack.mitre.org/techniques/T1562/008/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has updated a cloudtrail logging for account id $vendor_account$ - from IP $src$ - risk_objects: - - field: user - type: user - score: 90 - threat_objects: - - field: src - type: ip_address + message: User $user$ has updated a cloudtrail logging for account id $vendor_account$ from IP $src$ + risk_objects: + - field: user + type: user + score: 90 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Defense Evasion - asset_type: AWS Account - mitre_attack_id: - - T1562.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Defense Evasion + asset_type: AWS Account + mitre_attack_id: + - T1562.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/update_cloudtrail/aws_cloudtrail_events.json - source: aws_cloudtrail - sourcetype: aws:cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/update_cloudtrail/aws_cloudtrail_events.json + source: aws_cloudtrail + sourcetype: aws:cloudtrail diff --git a/detections/cloud/aws_detect_users_creating_keys_with_encrypt_policy_without_mfa.yml b/detections/cloud/aws_detect_users_creating_keys_with_encrypt_policy_without_mfa.yml index 81909ec513..33bb446a69 100644 --- a/detections/cloud/aws_detect_users_creating_keys_with_encrypt_policy_without_mfa.yml +++ b/detections/cloud/aws_detect_users_creating_keys_with_encrypt_policy_without_mfa.yml @@ -1,76 +1,68 @@ name: AWS Detect Users creating keys with encrypt policy without MFA id: c79c164f-4b21-4847-98f9-cf6a9f49179e -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Rod Soto, Patrick Bareiss Splunk status: production type: TTP -description: The following analytic detects the creation of AWS KMS keys with an encryption - policy accessible to everyone, including external entities. It leverages AWS CloudTrail - logs to identify `CreateKey` or `PutKeyPolicy` events where the `kms:Encrypt` action - is granted to all principals. This activity is significant as it may indicate a - compromised account, allowing an attacker to misuse the encryption key to target - other organizations. If confirmed malicious, this could lead to unauthorized data - encryption, potentially disrupting operations and compromising sensitive information - across multiple entities. +description: The following analytic detects the creation of AWS KMS keys with an encryption policy accessible to everyone, including external entities. It leverages AWS CloudTrail logs to identify `CreateKey` or `PutKeyPolicy` events where the `kms:Encrypt` action is granted to all principals. This activity is significant as it may indicate a compromised account, allowing an attacker to misuse the encryption key to target other organizations. If confirmed malicious, this could lead to unauthorized data encryption, potentially disrupting operations and compromising sensitive information across multiple entities. data_source: -- AWS CloudTrail CreateKey -- AWS CloudTrail PutKeyPolicy -search: '`cloudtrail` eventName=CreateKey OR eventName=PutKeyPolicy - | spath input=requestParameters.policy output=key_policy_statements path=Statement{} - | mvexpand key_policy_statements - | spath input=key_policy_statements output=key_policy_action_1 path=Action - | spath input=key_policy_statements output=key_policy_action_2 path=Action{} - | eval key_policy_action=mvappend(key_policy_action_1,key_policy_action_2) - | spath input=key_policy_statements output=key_policy_principal path=Principal.AWS - | search key_policy_action="kms:Encrypt" AND key_policy_principal="*" - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product key_policy_action key_policy_principal - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_detect_users_creating_keys_with_encrypt_policy_without_mfa_filter`' -how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This - search works with AWS CloudTrail logs + - AWS CloudTrail CreateKey + - AWS CloudTrail PutKeyPolicy +search: |- + `cloudtrail` eventName=CreateKey OR eventName=PutKeyPolicy + | spath input=requestParameters.policy output=key_policy_statements path=Statement{} + | mvexpand key_policy_statements + | spath input=key_policy_statements output=key_policy_action_1 path=Action + | spath input=key_policy_statements output=key_policy_action_2 path=Action{} + | eval key_policy_action=mvappend(key_policy_action_1,key_policy_action_2) + | spath input=key_policy_statements output=key_policy_principal path=Principal.AWS + | search key_policy_action="kms:Encrypt" AND key_policy_principal="*" + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product key_policy_action + key_policy_principal + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_detect_users_creating_keys_with_encrypt_policy_without_mfa_filter` +how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs known_false_positives: No false positives have been identified at this time. references: -- https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/ -- https://github.com/d1vious/git-wild-hunt -- https://www.youtube.com/watch?v=PgzNib37g0M + - https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/ + - https://github.com/d1vious/git-wild-hunt + - https://www.youtube.com/watch?v=PgzNib37g0M drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: AWS account is potentially compromised and user $user$ is trying to compromise - other accounts. - risk_objects: - - field: user - type: user - score: 25 - threat_objects: [] + message: AWS account is potentially compromised and user $user$ is trying to compromise other accounts. + risk_objects: + - field: user + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Ransomware Cloud - asset_type: AWS Account - mitre_attack_id: - - T1486 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Ransomware Cloud + asset_type: AWS Account + mitre_attack_id: + - T1486 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/aws_kms_key/aws_cloudtrail_events.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/aws_kms_key/aws_cloudtrail_events.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_detect_users_with_kms_keys_performing_encryption_s3.yml b/detections/cloud/aws_detect_users_with_kms_keys_performing_encryption_s3.yml index b75bb2fdca..98b6922b48 100644 --- a/detections/cloud/aws_detect_users_with_kms_keys_performing_encryption_s3.yml +++ b/detections/cloud/aws_detect_users_with_kms_keys_performing_encryption_s3.yml @@ -1,68 +1,61 @@ name: AWS Detect Users with KMS keys performing encryption S3 id: 884a5f59-eec7-4f4a-948b-dbde18225fdc -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Rod Soto, Patrick Bareiss Splunk status: production type: Anomaly -description: The following analytic identifies users with KMS keys performing encryption - operations on S3 buckets. It leverages AWS CloudTrail logs to detect the `CopyObject` - event where server-side encryption with AWS KMS is specified. This activity is significant - as it may indicate unauthorized or suspicious encryption of data, potentially masking - exfiltration or tampering efforts. If confirmed malicious, an attacker could be - encrypting sensitive data to evade detection or preparing it for exfiltration, posing - a significant risk to data integrity and confidentiality. +description: The following analytic identifies users with KMS keys performing encryption operations on S3 buckets. It leverages AWS CloudTrail logs to detect the `CopyObject` event where server-side encryption with AWS KMS is specified. This activity is significant as it may indicate unauthorized or suspicious encryption of data, potentially masking exfiltration or tampering efforts. If confirmed malicious, an attacker could be encrypting sensitive data to evade detection or preparing it for exfiltration, posing a significant risk to data integrity and confidentiality. data_source: -- AWS CloudTrail -search: '`cloudtrail` eventName=CopyObject requestParameters.x-amz-server-side-encryption="aws:kms" - | rename requestParameters.bucketName AS bucketName, requestParameters.x-amz-copy-source AS src_file, requestParameters.key AS dest_file - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product bucketName src_file dest_file - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `aws_detect_users_with_kms_keys_performing_encryption_s3_filter`' -how_to_implement: You must install Splunk AWS add on and Splunk App for AWS. This - search works with AWS CloudTrail logs + - AWS CloudTrail +search: |- + `cloudtrail` eventName=CopyObject requestParameters.x-amz-server-side-encryption="aws:kms" + | rename requestParameters.bucketName AS bucketName, requestParameters.x-amz-copy-source AS src_file, requestParameters.key AS dest_file + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product bucketName + src_file dest_file + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_detect_users_with_kms_keys_performing_encryption_s3_filter` +how_to_implement: You must install Splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs known_false_positives: There maybe buckets provisioned with S3 encryption references: -- https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/ -- https://github.com/d1vious/git-wild-hunt -- https://www.youtube.com/watch?v=PgzNib37g0M + - https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/ + - https://github.com/d1vious/git-wild-hunt + - https://www.youtube.com/watch?v=PgzNib37g0M drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ with KMS keys is performing encryption, against S3 buckets - on these files $dest_file$ - risk_objects: - - field: user - type: user - score: 15 - threat_objects: [] + message: User $user$ with KMS keys is performing encryption, against S3 buckets on these files $dest_file$ + risk_objects: + - field: user + type: user + score: 15 + threat_objects: [] tags: - analytic_story: - - Ransomware Cloud - asset_type: S3 Bucket - mitre_attack_id: - - T1486 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Ransomware Cloud + asset_type: S3 Bucket + mitre_attack_id: + - T1486 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/s3_file_encryption/aws_cloudtrail_events.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/s3_file_encryption/aws_cloudtrail_events.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_disable_bucket_versioning.yml b/detections/cloud/aws_disable_bucket_versioning.yml index 3f983f4cd3..d41d2eb602 100644 --- a/detections/cloud/aws_disable_bucket_versioning.yml +++ b/detections/cloud/aws_disable_bucket_versioning.yml @@ -1,70 +1,61 @@ name: AWS Disable Bucket Versioning id: 657902a9-987d-4879-a1b2-e7a65512824b -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: Anomaly data_source: -- AWS CloudTrail PutBucketVersioning -description: The following analytic detects when AWS S3 bucket versioning is suspended - by a user. It leverages AWS CloudTrail logs to identify `PutBucketVersioning` events - with the `VersioningConfiguration.Status` set to `Suspended`. This activity is significant - because disabling versioning can prevent recovery of deleted or modified data, which - is a common tactic in ransomware attacks. If confirmed malicious, this action could - lead to data loss and hinder recovery efforts, severely impacting data integrity - and availability. -search: '`cloudtrail` eventName= PutBucketVersioning "requestParameters.VersioningConfiguration.Status"=Suspended - | rename user_name as user, requestParameters.bucketName as bucket_name - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product bucket_name - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_disable_bucket_versioning_filter`' -how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. -known_false_positives: It is possible that an AWS Administrator has legitimately disabled - versioning on certain buckets to avoid costs. + - AWS CloudTrail PutBucketVersioning +description: The following analytic detects when AWS S3 bucket versioning is suspended by a user. It leverages AWS CloudTrail logs to identify `PutBucketVersioning` events with the `VersioningConfiguration.Status` set to `Suspended`. This activity is significant because disabling versioning can prevent recovery of deleted or modified data, which is a common tactic in ransomware attacks. If confirmed malicious, this action could lead to data loss and hinder recovery efforts, severely impacting data integrity and availability. +search: |- + `cloudtrail` eventName= PutBucketVersioning "requestParameters.VersioningConfiguration.Status"=Suspended + | rename user_name as user, requestParameters.bucketName as bucket_name + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product bucket_name + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_disable_bucket_versioning_filter` +how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. +known_false_positives: It is possible that an AWS Administrator has legitimately disabled versioning on certain buckets to avoid costs. references: -- https://invictus-ir.medium.com/ransomware-in-the-cloud-7f14805bbe82 -- https://bleemb.medium.com/data-exfiltration-with-native-aws-s3-features-c94ae4d13436 + - https://invictus-ir.medium.com/ransomware-in-the-cloud-7f14805bbe82 + - https://bleemb.medium.com/data-exfiltration-with-native-aws-s3-features-c94ae4d13436 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Bucket Versioning is suspended for S3 buckets- $bucket_name$ by user $user$ - from IP address $src$ - risk_objects: - - field: user - type: user - score: 64 - threat_objects: - - field: src - type: ip_address + message: Bucket Versioning is suspended for S3 buckets- $bucket_name$ by user $user$ from IP address $src$ + risk_objects: + - field: user + type: user + score: 64 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Suspicious AWS S3 Activities - - Data Exfiltration - asset_type: AWS Account - mitre_attack_id: - - T1490 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Suspicious AWS S3 Activities + - Data Exfiltration + asset_type: AWS Account + mitre_attack_id: + - T1490 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/aws_bucket_version/cloudtrail.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/aws_bucket_version/cloudtrail.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_ec2_snapshot_shared_externally.yml b/detections/cloud/aws_ec2_snapshot_shared_externally.yml index 3e92fa83c1..614b374098 100644 --- a/detections/cloud/aws_ec2_snapshot_shared_externally.yml +++ b/detections/cloud/aws_ec2_snapshot_shared_externally.yml @@ -1,77 +1,66 @@ name: AWS EC2 Snapshot Shared Externally id: 2a9b80d3-6340-4345-b5ad-290bf3d222c4 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP -description: The following analytic detects when an EC2 snapshot is shared with an - external AWS account by analyzing AWS CloudTrail events. This detection method leverages - CloudTrail logs to identify modifications in snapshot permissions, specifically - when the snapshot is shared outside the originating AWS account. This activity is - significant as it may indicate an attempt to exfiltrate sensitive data stored in - the snapshot. If confirmed malicious, an attacker could gain unauthorized access - to the snapshot's data, potentially leading to data breaches or further exploitation - of the compromised information. +description: The following analytic detects when an EC2 snapshot is shared with an external AWS account by analyzing AWS CloudTrail events. This detection method leverages CloudTrail logs to identify modifications in snapshot permissions, specifically when the snapshot is shared outside the originating AWS account. This activity is significant as it may indicate an attempt to exfiltrate sensitive data stored in the snapshot. If confirmed malicious, an attacker could gain unauthorized access to the snapshot's data, potentially leading to data breaches or further exploitation of the compromised information. data_source: -- AWS CloudTrail ModifySnapshotAttribute -search: '`cloudtrail` eventName=ModifySnapshotAttribute - | rename requestParameters.createVolumePermission.add.items{}.userId as requested_account_id - | search requested_account_id != NULL - | eval match=if(requested_account_id==aws_account_id,"Match","No Match") - | where match = "No Match" - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product requested_account_id - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `aws_ec2_snapshot_shared_externally_filter`' -how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. -known_false_positives: It is possible that an AWS admin has legitimately shared a - snapshot with others for a specific purpose. + - AWS CloudTrail ModifySnapshotAttribute +search: |- + `cloudtrail` eventName=ModifySnapshotAttribute + | rename requestParameters.createVolumePermission.add.items{}.userId as requested_account_id + | search requested_account_id != NULL + | eval match=if(requested_account_id==aws_account_id,"Match","No Match") + | where match = "No Match" + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product requested_account_id + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_ec2_snapshot_shared_externally_filter` +how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. +known_false_positives: It is possible that an AWS admin has legitimately shared a snapshot with others for a specific purpose. references: -- https://labs.nettitude.com/blog/how-to-exfiltrate-aws-ec2-data/ -- https://stratus-red-team.cloud/attack-techniques/AWS/aws.exfiltration.ec2-share-ebs-snapshot/ -- https://hackingthe.cloud/aws/enumeration/loot_public_ebs_snapshots/ + - https://labs.nettitude.com/blog/how-to-exfiltrate-aws-ec2-data/ + - https://stratus-red-team.cloud/attack-techniques/AWS/aws.exfiltration.ec2-share-ebs-snapshot/ + - https://hackingthe.cloud/aws/enumeration/loot_public_ebs_snapshots/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: AWS EC2 snapshot from account $vendor_account$ is shared with $requested_account_id$ - by user $user$ from $src$ - risk_objects: - - field: user - type: user - score: 48 - threat_objects: - - field: src - type: ip_address + message: AWS EC2 snapshot from account $vendor_account$ is shared with $requested_account_id$ by user $user$ from $src$ + risk_objects: + - field: user + type: user + score: 48 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Suspicious Cloud Instance Activities - - Data Exfiltration - asset_type: EC2 Snapshot - mitre_attack_id: - - T1537 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Suspicious Cloud Instance Activities + - Data Exfiltration + asset_type: EC2 Snapshot + mitre_attack_id: + - T1537 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1537/aws_snapshot_exfil/aws_cloudtrail_events.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1537/aws_snapshot_exfil/aws_cloudtrail_events.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_ecr_container_scanning_findings_high.yml b/detections/cloud/aws_ecr_container_scanning_findings_high.yml index 7d75a02040..7727ff632d 100644 --- a/detections/cloud/aws_ecr_container_scanning_findings_high.yml +++ b/detections/cloud/aws_ecr_container_scanning_findings_high.yml @@ -1,71 +1,63 @@ name: AWS ECR Container Scanning Findings High id: 30a0e9f8-f1dd-4f9d-8fc2-c622461d781c -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic identifies high-severity findings from AWS Elastic - Container Registry (ECR) image scans. It detects these activities by analyzing AWS - CloudTrail logs for the DescribeImageScanFindings event, specifically filtering - for findings with a high severity level. This activity is significant for a SOC - because high-severity vulnerabilities in container images can lead to potential - exploitation if not addressed. If confirmed malicious, attackers could exploit these - vulnerabilities to gain unauthorized access, execute arbitrary code, or escalate - privileges within the container environment, posing a significant risk to the overall - security posture. +description: The following analytic identifies high-severity findings from AWS Elastic Container Registry (ECR) image scans. It detects these activities by analyzing AWS CloudTrail logs for the DescribeImageScanFindings event, specifically filtering for findings with a high severity level. This activity is significant for a SOC because high-severity vulnerabilities in container images can lead to potential exploitation if not addressed. If confirmed malicious, attackers could exploit these vulnerabilities to gain unauthorized access, execute arbitrary code, or escalate privileges within the container environment, posing a significant risk to the overall security posture. data_source: -- AWS CloudTrail DescribeImageScanFindings -search: '`cloudtrail` eventSource=ecr.amazonaws.com eventName=DescribeImageScanFindings - | spath path=responseElements.imageScanFindings.findings{} output=findings - | mvexpand findings - | spath input=findings - | search severity=HIGH - | rename name as finding_name, description as finding_description, requestParameters.imageId.imageDigest as imageDigest, requestParameters.repositoryName as repository - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product finding_name finding_description imageDigest repository - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_ecr_container_scanning_findings_high_filter`' -how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. + - AWS CloudTrail DescribeImageScanFindings +search: |- + `cloudtrail` eventSource=ecr.amazonaws.com eventName=DescribeImageScanFindings + | spath path=responseElements.imageScanFindings.findings{} output=findings + | mvexpand findings + | spath input=findings + | search severity=HIGH + | rename name as finding_name, description as finding_description, requestParameters.imageId.imageDigest as imageDigest, requestParameters.repositoryName as repository + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product finding_name + finding_description imageDigest repository + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_ecr_container_scanning_findings_high_filter` +how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. known_false_positives: No false positives have been identified at this time. references: -- https://docs.aws.amazon.com/AmazonECR/latest/userguide/image-scanning.html + - https://docs.aws.amazon.com/AmazonECR/latest/userguide/image-scanning.html drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Vulnerabilities with severity high found in repository $repository$ - risk_objects: - - field: user - type: user - score: 70 - threat_objects: [] + message: Vulnerabilities with severity high found in repository $repository$ + risk_objects: + - field: user + type: user + score: 70 + threat_objects: [] tags: - analytic_story: - - Dev Sec Ops - asset_type: AWS Account - mitre_attack_id: - - T1204.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Dev Sec Ops + asset_type: AWS Account + mitre_attack_id: + - T1204.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_image_scanning/aws_ecr_scanning_findings_events.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_image_scanning/aws_ecr_scanning_findings_events.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_ecr_container_scanning_findings_low_informational_unknown.yml b/detections/cloud/aws_ecr_container_scanning_findings_low_informational_unknown.yml index 8aeecfb2b9..be2fc4e595 100644 --- a/detections/cloud/aws_ecr_container_scanning_findings_low_informational_unknown.yml +++ b/detections/cloud/aws_ecr_container_scanning_findings_low_informational_unknown.yml @@ -1,71 +1,63 @@ name: AWS ECR Container Scanning Findings Low Informational Unknown id: cbc95e44-7c22-443f-88fd-0424478f5589 -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Patrick Bareiss, Eric McGinnis Splunk status: production type: Anomaly -description: The following analytic identifies low, informational, or unknown severity - findings from AWS Elastic Container Registry (ECR) image scans. It leverages AWS - CloudTrail logs, specifically the DescribeImageScanFindings event, to detect these - findings. This activity is significant for a SOC as it helps in early identification - of potential vulnerabilities or misconfigurations in container images, which could - be exploited if left unaddressed. If confirmed malicious, these findings could lead - to unauthorized access, data breaches, or further exploitation within the containerized - environment. +description: The following analytic identifies low, informational, or unknown severity findings from AWS Elastic Container Registry (ECR) image scans. It leverages AWS CloudTrail logs, specifically the DescribeImageScanFindings event, to detect these findings. This activity is significant for a SOC as it helps in early identification of potential vulnerabilities or misconfigurations in container images, which could be exploited if left unaddressed. If confirmed malicious, these findings could lead to unauthorized access, data breaches, or further exploitation within the containerized environment. data_source: -- AWS CloudTrail DescribeImageScanFindings -search: '`cloudtrail` eventSource=ecr.amazonaws.com eventName=DescribeImageScanFindings - | spath path=responseElements.imageScanFindings.findings{} output=findings - | mvexpand findings - | spath input=findings - | search severity IN ("LOW", "INFORMATIONAL", "UNKNOWN") - | rename name as finding_name, description as finding_description, requestParameters.imageId.imageDigest as imageDigest, requestParameters.repositoryName as repository - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product finding_name finding_description imageDigest repository - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `aws_ecr_container_scanning_findings_low_informational_unknown_filter`' -how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. + - AWS CloudTrail DescribeImageScanFindings +search: |- + `cloudtrail` eventSource=ecr.amazonaws.com eventName=DescribeImageScanFindings + | spath path=responseElements.imageScanFindings.findings{} output=findings + | mvexpand findings + | spath input=findings + | search severity IN ("LOW", "INFORMATIONAL", "UNKNOWN") + | rename name as finding_name, description as finding_description, requestParameters.imageId.imageDigest as imageDigest, requestParameters.repositoryName as repository + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product finding_name + finding_description imageDigest repository + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_ecr_container_scanning_findings_low_informational_unknown_filter` +how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. known_false_positives: No false positives have been identified at this time. references: -- https://docs.aws.amazon.com/AmazonECR/latest/userguide/image-scanning.html + - https://docs.aws.amazon.com/AmazonECR/latest/userguide/image-scanning.html drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Vulnerabilities found in repository $repository$ - risk_objects: - - field: user - type: user - score: 5 - threat_objects: [] + message: Vulnerabilities found in repository $repository$ + risk_objects: + - field: user + type: user + score: 5 + threat_objects: [] tags: - analytic_story: - - Dev Sec Ops - asset_type: AWS Account - mitre_attack_id: - - T1204.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Dev Sec Ops + asset_type: AWS Account + mitre_attack_id: + - T1204.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_image_scanning/aws_ecr_scanning_findings_events.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_image_scanning/aws_ecr_scanning_findings_events.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_ecr_container_scanning_findings_medium.yml b/detections/cloud/aws_ecr_container_scanning_findings_medium.yml index 2784a4f49b..7a7b49f217 100644 --- a/detections/cloud/aws_ecr_container_scanning_findings_medium.yml +++ b/detections/cloud/aws_ecr_container_scanning_findings_medium.yml @@ -1,70 +1,63 @@ name: AWS ECR Container Scanning Findings Medium id: 0b80e2c8-c746-4ddb-89eb-9efd892220cf -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic identifies medium-severity findings from AWS Elastic - Container Registry (ECR) image scans. It leverages AWS CloudTrail logs, specifically - the DescribeImageScanFindings event, to detect vulnerabilities in container images. - This activity is significant for a SOC as it highlights potential security risks - in containerized applications, which could be exploited if not addressed. If confirmed - malicious, these vulnerabilities could lead to unauthorized access, data breaches, - or further exploitation within the container environment, compromising the overall - security posture. +description: The following analytic identifies medium-severity findings from AWS Elastic Container Registry (ECR) image scans. It leverages AWS CloudTrail logs, specifically the DescribeImageScanFindings event, to detect vulnerabilities in container images. This activity is significant for a SOC as it highlights potential security risks in containerized applications, which could be exploited if not addressed. If confirmed malicious, these vulnerabilities could lead to unauthorized access, data breaches, or further exploitation within the container environment, compromising the overall security posture. data_source: -- AWS CloudTrail DescribeImageScanFindings -search: '`cloudtrail` eventSource=ecr.amazonaws.com eventName=DescribeImageScanFindings - | spath path=responseElements.imageScanFindings.findings{} output=findings - | mvexpand findings - | spath input=findings - | search severity=MEDIUM - | rename name as finding_name, description as finding_description, requestParameters.imageId.imageDigest as imageDigest, requestParameters.repositoryName as repository - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product finding_name finding_description imageDigest repository - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_ecr_container_scanning_findings_medium_filter`' -how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. + - AWS CloudTrail DescribeImageScanFindings +search: |- + `cloudtrail` eventSource=ecr.amazonaws.com eventName=DescribeImageScanFindings + | spath path=responseElements.imageScanFindings.findings{} output=findings + | mvexpand findings + | spath input=findings + | search severity=MEDIUM + | rename name as finding_name, description as finding_description, requestParameters.imageId.imageDigest as imageDigest, requestParameters.repositoryName as repository + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product finding_name + finding_description imageDigest repository + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_ecr_container_scanning_findings_medium_filter` +how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. known_false_positives: No false positives have been identified at this time. references: -- https://docs.aws.amazon.com/AmazonECR/latest/userguide/image-scanning.html + - https://docs.aws.amazon.com/AmazonECR/latest/userguide/image-scanning.html drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Vulnerabilities with severity medium found in repository $repository$ - risk_objects: - - field: user - type: user - score: 21 - threat_objects: [] + message: Vulnerabilities with severity medium found in repository $repository$ + risk_objects: + - field: user + type: user + score: 21 + threat_objects: [] tags: - analytic_story: - - Dev Sec Ops - asset_type: AWS Account - mitre_attack_id: - - T1204.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Dev Sec Ops + asset_type: AWS Account + mitre_attack_id: + - T1204.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_image_scanning/aws_ecr_scanning_findings_events.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_image_scanning/aws_ecr_scanning_findings_events.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_ecr_container_upload_outside_business_hours.yml b/detections/cloud/aws_ecr_container_upload_outside_business_hours.yml index d519eb08ae..91094bcee4 100644 --- a/detections/cloud/aws_ecr_container_upload_outside_business_hours.yml +++ b/detections/cloud/aws_ecr_container_upload_outside_business_hours.yml @@ -1,71 +1,61 @@ name: AWS ECR Container Upload Outside Business Hours id: d4c4d4eb-3994-41ca-a25e-a82d64e125bb -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects the upload of a new container image to - AWS Elastic Container Registry (ECR) outside of standard business hours. It leverages - AWS CloudTrail logs to identify `PutImage` events occurring between 8 PM and 8 AM - or on weekends. This activity is significant because container uploads outside business - hours can indicate unauthorized or suspicious activity, potentially pointing to - a compromised account or insider threat. If confirmed malicious, this could allow - an attacker to deploy unauthorized or malicious containers, leading to potential - data breaches or service disruptions. +description: The following analytic detects the upload of a new container image to AWS Elastic Container Registry (ECR) outside of standard business hours. It leverages AWS CloudTrail logs to identify `PutImage` events occurring between 8 PM and 8 AM or on weekends. This activity is significant because container uploads outside business hours can indicate unauthorized or suspicious activity, potentially pointing to a compromised account or insider threat. If confirmed malicious, this could allow an attacker to deploy unauthorized or malicious containers, leading to potential data breaches or service disruptions. data_source: -- AWS CloudTrail PutImage -search: '`cloudtrail` eventSource=ecr.amazonaws.com eventName=PutImage date_hour>=20 - OR date_hour<8 OR date_wday=saturday OR date_wday=sunday - | rename requestParameters.* as * - | rename repositoryName AS repository - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature user user_agent src vendor_account vendor_region vendor_product repository - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_ecr_container_upload_outside_business_hours_filter`' -how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. -known_false_positives: When your development is spreaded in different time zones, - applying this rule can be difficult. + - AWS CloudTrail PutImage +search: |- + `cloudtrail` eventSource=ecr.amazonaws.com eventName=PutImage date_hour>=20 OR date_hour<8 OR date_wday=saturday OR date_wday=sunday + | rename requestParameters.* as * + | rename repositoryName AS repository + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature user user_agent + src vendor_account vendor_region + vendor_product repository + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_ecr_container_upload_outside_business_hours_filter` +how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. +known_false_positives: When your development is spreaded in different time zones, applying this rule can be difficult. references: -- https://attack.mitre.org/techniques/T1204/003/ + - https://attack.mitre.org/techniques/T1204/003/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Container uploaded outside business hours from $user$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: src - type: ip_address + message: Container uploaded outside business hours from $user$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Dev Sec Ops - asset_type: AWS Account - mitre_attack_id: - - T1204.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Dev Sec Ops + asset_type: AWS Account + mitre_attack_id: + - T1204.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_container_upload/aws_ecr_container_upload.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_container_upload/aws_ecr_container_upload.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_ecr_container_upload_unknown_user.yml b/detections/cloud/aws_ecr_container_upload_unknown_user.yml index b98556a86e..b430208235 100644 --- a/detections/cloud/aws_ecr_container_upload_unknown_user.yml +++ b/detections/cloud/aws_ecr_container_upload_unknown_user.yml @@ -1,69 +1,61 @@ name: AWS ECR Container Upload Unknown User id: 300688e4-365c-4486-a065-7c884462b31d -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects the upload of a new container image to - AWS Elastic Container Registry (ECR) by an unknown user. It leverages AWS CloudTrail - logs to identify `PutImage` events from the ECR service, filtering out known users. - This activity is significant because container uploads should typically be performed - by a limited set of authorized users. If confirmed malicious, this could indicate - unauthorized access, potentially leading to the deployment of malicious containers, - data exfiltration, or further compromise of the AWS environment. +description: The following analytic detects the upload of a new container image to AWS Elastic Container Registry (ECR) by an unknown user. It leverages AWS CloudTrail logs to identify `PutImage` events from the ECR service, filtering out known users. This activity is significant because container uploads should typically be performed by a limited set of authorized users. If confirmed malicious, this could indicate unauthorized access, potentially leading to the deployment of malicious containers, data exfiltration, or further compromise of the AWS environment. data_source: -- AWS CloudTrail PutImage -search: '`cloudtrail` eventSource=ecr.amazonaws.com eventName=PutImage NOT `aws_ecr_users` - | rename requestParameters.* as * - | rename repositoryName AS image - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature user user_agent src vendor_account vendor_region vendor_product image - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `aws_ecr_container_upload_unknown_user_filter`' -how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. + - AWS CloudTrail PutImage +search: |- + `cloudtrail` eventSource=ecr.amazonaws.com eventName=PutImage NOT `aws_ecr_users` + | rename requestParameters.* as * + | rename repositoryName AS image + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature user user_agent + src vendor_account vendor_region + vendor_product image + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_ecr_container_upload_unknown_user_filter` +how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. known_false_positives: No false positives have been identified at this time. references: -- https://attack.mitre.org/techniques/T1204/003/ + - https://attack.mitre.org/techniques/T1204/003/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Container uploaded from unknown user $user$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: src - type: ip_address + message: Container uploaded from unknown user $user$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Dev Sec Ops - asset_type: AWS Account - mitre_attack_id: - - T1204.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Dev Sec Ops + asset_type: AWS Account + mitre_attack_id: + - T1204.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_container_upload/aws_ecr_container_upload.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_container_upload/aws_ecr_container_upload.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_excessive_security_scanning.yml b/detections/cloud/aws_excessive_security_scanning.yml index 3be5cf8b6e..06ef3d1a31 100644 --- a/detections/cloud/aws_excessive_security_scanning.yml +++ b/detections/cloud/aws_excessive_security_scanning.yml @@ -1,69 +1,59 @@ name: AWS Excessive Security Scanning id: 1fdd164a-def8-4762-83a9-9ffe24e74d5a -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic identifies excessive security scanning activities - in AWS by detecting a high number of Describe, List, or Get API calls from a single - user. It leverages AWS CloudTrail logs to count distinct event names and flags users - with more than 50 such events. This behavior is significant as it may indicate reconnaissance - activities by an attacker attempting to map out your AWS environment. If confirmed - malicious, this could lead to unauthorized access, data exfiltration, or further - exploitation of your cloud infrastructure. +description: The following analytic identifies excessive security scanning activities in AWS by detecting a high number of Describe, List, or Get API calls from a single user. It leverages AWS CloudTrail logs to count distinct event names and flags users with more than 50 such events. This behavior is significant as it may indicate reconnaissance activities by an attacker attempting to map out your AWS environment. If confirmed malicious, this could lead to unauthorized access, data exfiltration, or further exploitation of your cloud infrastructure. data_source: -- AWS CloudTrail -search: '`cloudtrail` eventName=Describe* OR eventName=List* OR eventName=Get* - | fillnull - | rename user_name as user - | stats dc(signature) as dc_events min(_time) as firstTime max(_time) as lastTime values(signature) as signature values(dest) as dest values(user_agent) as user_agent values(src) as src values(vendor_account) as vendor_account values(vendor_region) as vendor_region by user - | where dc_events > 50 - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|`aws_excessive_security_scanning_filter`' -how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. + - AWS CloudTrail +search: |- + `cloudtrail` eventName=Describe* OR eventName=List* OR eventName=Get* + | fillnull + | rename user_name as user + | stats dc(signature) as dc_events min(_time) as firstTime max(_time) as lastTime values(signature) as signature values(dest) as dest values(user_agent) as user_agent values(src) as src values(vendor_account) as vendor_account values(vendor_region) as vendor_region + BY user + | where dc_events > 50 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_excessive_security_scanning_filter` +how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. known_false_positives: While this search has no known false positives. references: -- https://github.com/aquasecurity/cloudsploit + - https://github.com/aquasecurity/cloudsploit drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has excessive number of api calls $dc_events$ from these IP - addresses $src$, violating the threshold of 50, using the following actions $signature$. - risk_objects: - - field: user - type: user - score: 18 - threat_objects: - - field: src - type: ip_address + message: User $user$ has excessive number of api calls $dc_events$ from these IP addresses $src$, violating the threshold of 50, using the following actions $signature$. + risk_objects: + - field: user + type: user + score: 18 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS User Monitoring - asset_type: AWS Account - mitre_attack_id: - - T1526 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - AWS User Monitoring + asset_type: AWS Account + mitre_attack_id: + - T1526 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1526/aws_security_scanner/aws_security_scanner.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1526/aws_security_scanner/aws_security_scanner.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_exfiltration_via_anomalous_getobject_api_activity.yml b/detections/cloud/aws_exfiltration_via_anomalous_getobject_api_activity.yml index 03e01a22da..e358884d0a 100644 --- a/detections/cloud/aws_exfiltration_via_anomalous_getobject_api_activity.yml +++ b/detections/cloud/aws_exfiltration_via_anomalous_getobject_api_activity.yml @@ -1,76 +1,62 @@ name: AWS Exfiltration via Anomalous GetObject API Activity id: e4384bbf-5835-4831-8d85-694de6ad2cc6 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: Anomaly data_source: - - AWS CloudTrail GetObject -description: - The following analytic identifies anomalous GetObject API activity in - AWS, indicating potential data exfiltration attempts. It leverages AWS CloudTrail - logs and uses the `anomalydetection` command to detect unusual patterns in the frequency - of GetObject API calls by analyzing fields such as "count," "user_type," and "user_arn" - within a 10-minute window. This activity is significant as it may indicate unauthorized - data access or exfiltration from S3 buckets. If confirmed malicious, attackers could - exfiltrate sensitive data, leading to data breaches and compliance violations. -search: '`cloudtrail` eventName=GetObject - | bin _time span=10m - | rename user_name as user - | stats count values(requestParameters.bucketName) as bucketName by signature dest user user_agent src vendor_account vendor_region vendor_product - | anomalydetection "count" "user" action=annotate - | search probable_cause=* - |`aws_exfiltration_via_anomalous_getobject_api_activity_filter`' -how_to_implement: - You must install splunk AWS add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. -known_false_positives: - It is possible that a user downloaded these files to use them - locally and there are AWS services in configured that perform these activities for - a legitimate reason. Filter is needed. + - AWS CloudTrail GetObject +description: The following analytic identifies anomalous GetObject API activity in AWS, indicating potential data exfiltration attempts. It leverages AWS CloudTrail logs and uses the `anomalydetection` command to detect unusual patterns in the frequency of GetObject API calls by analyzing fields such as "count," "user_type," and "user_arn" within a 10-minute window. This activity is significant as it may indicate unauthorized data access or exfiltration from S3 buckets. If confirmed malicious, attackers could exfiltrate sensitive data, leading to data breaches and compliance violations. +search: |- + `cloudtrail` eventName=GetObject + | bin _time span=10m + | rename user_name as user + | stats count values(requestParameters.bucketName) as bucketName + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product + | anomalydetection "count" "user" action=annotate + | search probable_cause=* + | `aws_exfiltration_via_anomalous_getobject_api_activity_filter` +how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. +known_false_positives: It is possible that a user downloaded these files to use them locally and there are AWS services in configured that perform these activities for a legitimate reason. Filter is needed. references: - - https://labs.nettitude.com/blog/how-to-exfiltrate-aws-ec2-data/ - - https://help.splunk.com/en/splunk-enterprise/search/spl-search-reference/9.4/search-commands/anomalydetection - - https://www.vectra.ai/blogpost/abusing-the-replicator-silently-exfiltrating-data-with-the-aws-s3-replication-service + - https://labs.nettitude.com/blog/how-to-exfiltrate-aws-ec2-data/ + - https://help.splunk.com/en/splunk-enterprise/search/spl-search-reference/9.4/search-commands/anomalydetection + - https://www.vectra.ai/blogpost/abusing-the-replicator-silently-exfiltrating-data-with-the-aws-s3-replication-service drilldown_searches: - - name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Anomalous S3 activities detected by user $user$ from $src$ - risk_objects: - - field: user - type: user - score: 64 - threat_objects: - - field: src - type: ip_address + message: Anomalous S3 activities detected by user $user$ from $src$ + risk_objects: + - field: user + type: user + score: 64 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Data Exfiltration - asset_type: AWS Account - mitre_attack_id: - - T1119 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Data Exfiltration + asset_type: AWS Account + mitre_attack_id: + - T1119 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_exfil_high_no_getobject/cloudtrail.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_exfil_high_no_getobject/cloudtrail.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_exfiltration_via_batch_service.yml b/detections/cloud/aws_exfiltration_via_batch_service.yml index b9f2f66813..55a21f466b 100644 --- a/detections/cloud/aws_exfiltration_via_batch_service.yml +++ b/detections/cloud/aws_exfiltration_via_batch_service.yml @@ -1,69 +1,61 @@ name: AWS Exfiltration via Batch Service id: 04455dd3-ced7-480f-b8e6-5469b99e98e2 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP data_source: -- AWS CloudTrail JobCreated -description: The following analytic identifies the creation of AWS Batch jobs that - could potentially abuse the AWS Bucket Replication feature on S3 buckets. It leverages - AWS CloudTrail logs to detect the `JobCreated` event, analyzing job details and - their status. This activity is significant because attackers can exploit this feature - to exfiltrate data by creating malicious batch jobs. If confirmed malicious, this - could lead to unauthorized data transfer between S3 buckets, resulting in data breaches - and loss of sensitive information. -search: '`cloudtrail` eventName = JobCreated - | fillnull - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_exfiltration_via_batch_service_filter`' -how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. -known_false_positives: It is possible that an AWS Administrator or a user has legitimately - created this job for some tasks. + - AWS CloudTrail JobCreated +description: The following analytic identifies the creation of AWS Batch jobs that could potentially abuse the AWS Bucket Replication feature on S3 buckets. It leverages AWS CloudTrail logs to detect the `JobCreated` event, analyzing job details and their status. This activity is significant because attackers can exploit this feature to exfiltrate data by creating malicious batch jobs. If confirmed malicious, this could lead to unauthorized data transfer between S3 buckets, resulting in data breaches and loss of sensitive information. +search: |- + `cloudtrail` eventName = JobCreated + | fillnull + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_exfiltration_via_batch_service_filter` +how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. +known_false_positives: It is possible that an AWS Administrator or a user has legitimately created this job for some tasks. references: -- https://hackingthe.cloud/aws/exploitation/s3-bucket-replication-exfiltration/ -- https://bleemb.medium.com/data-exfiltration-with-native-aws-s3-features-c94ae4d13436 + - https://hackingthe.cloud/aws/exploitation/s3-bucket-replication-exfiltration/ + - https://bleemb.medium.com/data-exfiltration-with-native-aws-s3-features-c94ae4d13436 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: AWS Batch Job is created on account id - $vendor_account$ from src_ip $src$ - risk_objects: - - field: user - type: other - score: 64 - threat_objects: - - field: src - type: ip_address + message: AWS Batch Job is created on account id - $vendor_account$ from src_ip $src$ + risk_objects: + - field: user + type: other + score: 64 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Data Exfiltration - asset_type: AWS Account - mitre_attack_id: - - T1119 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Data Exfiltration + asset_type: AWS Account + mitre_attack_id: + - T1119 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1119/aws_exfil_datasync/cloudtrail.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1119/aws_exfil_datasync/cloudtrail.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_exfiltration_via_bucket_replication.yml b/detections/cloud/aws_exfiltration_via_bucket_replication.yml index c75bc306c0..a593648d15 100644 --- a/detections/cloud/aws_exfiltration_via_bucket_replication.yml +++ b/detections/cloud/aws_exfiltration_via_bucket_replication.yml @@ -1,71 +1,60 @@ name: AWS Exfiltration via Bucket Replication id: eeb432d6-2212-43b6-9e89-fcd753f7da4c -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP data_source: -- AWS CloudTrail PutBucketReplication -description: The following analytic detects API calls to enable S3 bucket replication - services. It leverages AWS CloudTrail logs to identify `PutBucketReplication` events, - focusing on fields like `bucketName`, `ReplicationConfiguration.Rule.Destination.Bucket`, - and user details. This activity is significant as it can indicate unauthorized data - replication, potentially leading to data exfiltration. If confirmed malicious, attackers - could replicate sensitive data to external accounts, leading to data breaches and - compliance violations. -search: '`cloudtrail` eventName = PutBucketReplication eventSource = s3.amazonaws.com - | rename user_name as user, requestParameters.ReplicationConfiguration.Rule.Destination.Bucket as bucket_name - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product bucket_name - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_exfiltration_via_bucket_replication_filter`' -how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. -known_false_positives: It is possible that an AWS admin has legitimately implemented - data replication to ensure data availability and improve data protection/backup - strategies. + - AWS CloudTrail PutBucketReplication +description: The following analytic detects API calls to enable S3 bucket replication services. It leverages AWS CloudTrail logs to identify `PutBucketReplication` events, focusing on fields like `bucketName`, `ReplicationConfiguration.Rule.Destination.Bucket`, and user details. This activity is significant as it can indicate unauthorized data replication, potentially leading to data exfiltration. If confirmed malicious, attackers could replicate sensitive data to external accounts, leading to data breaches and compliance violations. +search: |- + `cloudtrail` eventName = PutBucketReplication eventSource = s3.amazonaws.com + | rename user_name as user, requestParameters.ReplicationConfiguration.Rule.Destination.Bucket as bucket_name + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product bucket_name + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_exfiltration_via_bucket_replication_filter` +how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. +known_false_positives: It is possible that an AWS admin has legitimately implemented data replication to ensure data availability and improve data protection/backup strategies. references: -- https://hackingthe.cloud/aws/exploitation/s3-bucket-replication-exfiltration/ + - https://hackingthe.cloud/aws/exploitation/s3-bucket-replication-exfiltration/ drilldown_searches: -- name: View the detection results for - "$user_arn$" and "$aws_account_id$" - search: '%original_detection_search% | search user_arn = "$user_arn$" aws_account_id - = "$aws_account_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user_arn$" and "$aws_account_id$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$", - "$aws_account_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user_arn$" and "$aws_account_id$" + search: '%original_detection_search% | search user_arn = "$user_arn$" aws_account_id = "$aws_account_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user_arn$" and "$aws_account_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$", "$aws_account_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: AWS Bucket Replication rule added to $bucket_name$ - by user $user$ from IP Address - $src$ - risk_objects: - - field: user - type: user - score: 64 - threat_objects: - - field: src - type: ip_address + message: AWS Bucket Replication rule added to $bucket_name$ by user $user$ from IP Address - $src$ + risk_objects: + - field: user + type: user + score: 64 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Suspicious AWS S3 Activities - - Data Exfiltration - asset_type: EC2 Snapshot - mitre_attack_id: - - T1537 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Suspicious AWS S3 Activities + - Data Exfiltration + asset_type: EC2 Snapshot + mitre_attack_id: + - T1537 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1119/aws_exfil_datasync/cloudtrail.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1119/aws_exfil_datasync/cloudtrail.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_exfiltration_via_datasync_task.yml b/detections/cloud/aws_exfiltration_via_datasync_task.yml index a4d3bbe9a5..534fb1dbc6 100644 --- a/detections/cloud/aws_exfiltration_via_datasync_task.yml +++ b/detections/cloud/aws_exfiltration_via_datasync_task.yml @@ -1,73 +1,64 @@ name: AWS Exfiltration via DataSync Task id: 05c4b09f-ea28-4c7c-a7aa-a246f665c8a2 -version: 7 -date: '2025-10-14' +version: 8 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP data_source: -- AWS CloudTrail CreateTask -description: The following analytic detects the creation of an AWS DataSync task, - which could indicate potential data exfiltration. It leverages AWS CloudTrail logs - to identify the `CreateTask` event from the DataSync service. This activity is significant - because attackers can misuse DataSync to transfer sensitive data from a private - AWS location to a public one, leading to data compromise. If confirmed malicious, - this could result in unauthorized access to sensitive information, causing severe - data breaches and compliance violations. -search: '`cloudtrail` eventName = CreateTask eventSource="datasync.amazonaws.com" - | rename requestParameters.* as * - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product destinationLocationArn sourceLocationArn - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_exfiltration_via_datasync_task_filter`' -how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. -known_false_positives: It is possible that an AWS Administrator has legitimately created - this task for creating backup. Please check the `sourceLocationArn` and `destinationLocationArn` - of this task + - AWS CloudTrail CreateTask +description: The following analytic detects the creation of an AWS DataSync task, which could indicate potential data exfiltration. It leverages AWS CloudTrail logs to identify the `CreateTask` event from the DataSync service. This activity is significant because attackers can misuse DataSync to transfer sensitive data from a private AWS location to a public one, leading to data compromise. If confirmed malicious, this could result in unauthorized access to sensitive information, causing severe data breaches and compliance violations. +search: |- + `cloudtrail` eventName = CreateTask eventSource="datasync.amazonaws.com" + | rename requestParameters.* as * + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product destinationLocationArn + sourceLocationArn + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_exfiltration_via_datasync_task_filter` +how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. +known_false_positives: It is possible that an AWS Administrator has legitimately created this task for creating backup. Please check the `sourceLocationArn` and `destinationLocationArn` of this task references: -- https://labs.nettitude.com/blog/how-to-exfiltrate-aws-ec2-data/ -- https://www.shehackske.com/how-to/data-exfiltration-on-cloud-1606/ + - https://labs.nettitude.com/blog/how-to-exfiltrate-aws-ec2-data/ + - https://www.shehackske.com/how-to/data-exfiltration-on-cloud-1606/ drilldown_searches: -- name: View the detection results for - "$aws_account_id$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$aws_account_id$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: DataSync task created on account id - $vendor_account$ by user $user$ - from src_ip $src$ - risk_objects: - - field: user - type: user - score: 64 - threat_objects: - - field: src - type: ip_address + message: DataSync task created on account id - $vendor_account$ by user $user$ from src_ip $src$ + risk_objects: + - field: user + type: user + score: 64 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Suspicious AWS S3 Activities - - Data Exfiltration - - Hellcat Ransomware - asset_type: AWS Account - mitre_attack_id: - - T1119 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Suspicious AWS S3 Activities + - Data Exfiltration + - Hellcat Ransomware + asset_type: AWS Account + mitre_attack_id: + - T1119 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1119/aws_exfil_datasync/cloudtrail.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1119/aws_exfil_datasync/cloudtrail.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_exfiltration_via_ec2_snapshot.yml b/detections/cloud/aws_exfiltration_via_ec2_snapshot.yml index 4383fc9d22..73a23e2302 100644 --- a/detections/cloud/aws_exfiltration_via_ec2_snapshot.yml +++ b/detections/cloud/aws_exfiltration_via_ec2_snapshot.yml @@ -1,82 +1,67 @@ name: AWS Exfiltration via EC2 Snapshot id: ac90b339-13fc-4f29-a18c-4abbba1f2171 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP data_source: -- AWS CloudTrail CreateSnapshot -- AWS CloudTrail DescribeSnapshotAttribute -- AWS CloudTrail ModifySnapshotAttribute -- AWS CloudTrail DeleteSnapshot -description: The following analytic detects a series of AWS API calls related to EC2 - snapshots within a short time window, indicating potential exfiltration via EC2 - Snapshot modifications. It leverages AWS CloudTrail logs to identify actions such - as creating, describing, and modifying snapshot attributes. This activity is significant - as it may indicate an attacker attempting to exfiltrate data by sharing EC2 snapshots - externally. If confirmed malicious, the attacker could gain access to sensitive - information stored in the snapshots, leading to data breaches and potential compliance - violations. -search: '`cloudtrail` eventName IN ("CreateSnapshot", "DescribeSnapshotAttribute", "ModifySnapshotAttribute", "DeleteSnapshot") src_ip !="guardduty.amazonaws.com" - | bin _time span=5m - | rename user_name as user - | stats count dc(signature) as distinct_api_calls values(signature) as signature values(dest) as dest values(requestParameters.attributeType) as attributeType values(requestParameters.createVolumePermission.add.items{}.userId) as aws_account_id_added values(user_agent) as user_agent by _time user src vendor_account vendor_region vendor_product - | where distinct_api_calls >= 2 - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `aws_exfiltration_via_ec2_snapshot_filter`' -how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. We have intentionally removed `guardduty.amazonaws.com` - from src_ip to remove false positives caused by guard duty. We recommend you adjust - the time window as per your environment. -known_false_positives: It is possible that an AWS admin has legitimately shared a - snapshot with an other account for a specific purpose. Please check any recent change - requests filed in your organization. + - AWS CloudTrail CreateSnapshot + - AWS CloudTrail DescribeSnapshotAttribute + - AWS CloudTrail ModifySnapshotAttribute + - AWS CloudTrail DeleteSnapshot +description: The following analytic detects a series of AWS API calls related to EC2 snapshots within a short time window, indicating potential exfiltration via EC2 Snapshot modifications. It leverages AWS CloudTrail logs to identify actions such as creating, describing, and modifying snapshot attributes. This activity is significant as it may indicate an attacker attempting to exfiltrate data by sharing EC2 snapshots externally. If confirmed malicious, the attacker could gain access to sensitive information stored in the snapshots, leading to data breaches and potential compliance violations. +search: |- + `cloudtrail` eventName IN ("CreateSnapshot", "DescribeSnapshotAttribute", "ModifySnapshotAttribute", "DeleteSnapshot") src_ip !="guardduty.amazonaws.com" + | bin _time span=5m + | rename user_name as user + | stats count dc(signature) as distinct_api_calls values(signature) as signature values(dest) as dest values(requestParameters.attributeType) as attributeType values(requestParameters.createVolumePermission.add.items{}.userId) as aws_account_id_added values(user_agent) as user_agent + BY _time user src + vendor_account vendor_region vendor_product + | where distinct_api_calls >= 2 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_exfiltration_via_ec2_snapshot_filter` +how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. We have intentionally removed `guardduty.amazonaws.com` from src_ip to remove false positives caused by guard duty. We recommend you adjust the time window as per your environment. +known_false_positives: It is possible that an AWS admin has legitimately shared a snapshot with an other account for a specific purpose. Please check any recent change requests filed in your organization. references: -- https://labs.nettitude.com/blog/how-to-exfiltrate-aws-ec2-data/ -- https://docs.aws.amazon.com/AWSEC2/latest/APIReference/API_ModifySnapshotAttribute.html -- https://bleemb.medium.com/data-exfiltration-with-native-aws-s3-features-c94ae4d13436 -- https://stratus-red-team.cloud/attack-techniques/list/ + - https://labs.nettitude.com/blog/how-to-exfiltrate-aws-ec2-data/ + - https://docs.aws.amazon.com/AWSEC2/latest/APIReference/API_ModifySnapshotAttribute.html + - https://bleemb.medium.com/data-exfiltration-with-native-aws-s3-features-c94ae4d13436 + - https://stratus-red-team.cloud/attack-techniques/list/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential AWS EC2 Exfiltration detected on account id - $vendor_account$ - by user $user$ from src_ip $src$ - risk_objects: - - field: user - type: user - score: 64 - threat_objects: - - field: src - type: ip_address + message: Potential AWS EC2 Exfiltration detected on account id - $vendor_account$ by user $user$ from src_ip $src$ + risk_objects: + - field: user + type: user + score: 64 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Suspicious Cloud Instance Activities - - Data Exfiltration - asset_type: EC2 Snapshot - mitre_attack_id: - - T1537 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Suspicious Cloud Instance Activities + - Data Exfiltration + asset_type: EC2 Snapshot + mitre_attack_id: + - T1537 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1537/aws_snapshot_exfil/aws_cloudtrail_events.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1537/aws_snapshot_exfil/aws_cloudtrail_events.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_high_number_of_failed_authentications_for_user.yml b/detections/cloud/aws_high_number_of_failed_authentications_for_user.yml index 77ac7eec8f..62f82a56f7 100644 --- a/detections/cloud/aws_high_number_of_failed_authentications_for_user.yml +++ b/detections/cloud/aws_high_number_of_failed_authentications_for_user.yml @@ -1,70 +1,60 @@ name: AWS High Number Of Failed Authentications For User id: e3236f49-daf3-4b70-b808-9290912ac64d -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: Anomaly -description: The following analytic detects an AWS account experiencing more than - 20 failed authentication attempts within a 5-minute window. It leverages AWS CloudTrail - logs to identify multiple failed ConsoleLogin events. This behavior is significant - as it may indicate a brute force attack targeting the account. If confirmed malicious, - the attacker could potentially gain unauthorized access, leading to data breaches - or further exploitation of the AWS environment. Security teams should consider adjusting - the threshold based on their specific environment to reduce false positives. +description: The following analytic detects an AWS account experiencing more than 20 failed authentication attempts within a 5-minute window. It leverages AWS CloudTrail logs to identify multiple failed ConsoleLogin events. This behavior is significant as it may indicate a brute force attack targeting the account. If confirmed malicious, the attacker could potentially gain unauthorized access, leading to data breaches or further exploitation of the AWS environment. Security teams should consider adjusting the threshold based on their specific environment to reduce false positives. data_source: -- AWS CloudTrail ConsoleLogin -search: '`cloudtrail` eventName=ConsoleLogin action=failure - | bucket span=10m _time - | rename user_name as user - | stats dc(_raw) AS failed_attempts values(src) as src values(user_agent) as user_agent by _time, user, signature, dest, vendor_account vendor_region, vendor_product - | where failed_attempts > 20 - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `aws_high_number_of_failed_authentications_for_user_filter`' -how_to_implement: You must install Splunk AWS Add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. -known_false_positives: A user with more than 20 failed authentication attempts in - the span of 5 minutes may also be triggered by a broken application. + - AWS CloudTrail ConsoleLogin +search: |- + `cloudtrail` eventName=ConsoleLogin action=failure + | bucket span=10m _time + | rename user_name as user + | stats dc(_raw) AS failed_attempts values(src) as src values(user_agent) as user_agent + BY _time, user, signature, + dest, vendor_account vendor_region, + vendor_product + | where failed_attempts > 20 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_high_number_of_failed_authentications_for_user_filter` +how_to_implement: You must install Splunk AWS Add on and Splunk App for AWS. This search works with AWS CloudTrail logs. +known_false_positives: A user with more than 20 failed authentication attempts in the span of 5 minutes may also be triggered by a broken application. references: -- https://www.trendmicro.com/cloudoneconformity/knowledge-base/aws/IAM/password-policy.html + - https://www.trendmicro.com/cloudoneconformity/knowledge-base/aws/IAM/password-policy.html drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ failed to authenticate more than 20 times in the span - of 5 minutes for AWS Account $vendor_account$ - risk_objects: - - field: user - type: user - score: 35 - threat_objects: [] + message: User $user$ failed to authenticate more than 20 times in the span of 5 minutes for AWS Account $vendor_account$ + risk_objects: + - field: user + type: user + score: 35 + threat_objects: [] tags: - analytic_story: - - Compromised User Account - - AWS Identity and Access Management Account Takeover - asset_type: AWS Account - mitre_attack_id: - - T1201 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Compromised User Account + - AWS Identity and Access Management Account Takeover + asset_type: AWS Account + mitre_attack_id: + - T1201 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/aws_multiple_login_fail_per_user/cloudtrail.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/aws_multiple_login_fail_per_user/cloudtrail.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_high_number_of_failed_authentications_from_ip.yml b/detections/cloud/aws_high_number_of_failed_authentications_from_ip.yml index b824ef463b..ba8ec65f5d 100644 --- a/detections/cloud/aws_high_number_of_failed_authentications_from_ip.yml +++ b/detections/cloud/aws_high_number_of_failed_authentications_from_ip.yml @@ -1,74 +1,63 @@ name: AWS High Number Of Failed Authentications From Ip id: f75b7f1a-b8eb-4975-a214-ff3e0a944757 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: Anomaly -description: The following analytic detects an IP address with 20 or more failed authentication - attempts to the AWS Web Console within a 5-minute window. This detection leverages - CloudTrail logs, aggregating failed login events by IP address and time span. This - activity is significant as it may indicate a brute force attack aimed at gaining - unauthorized access or escalating privileges within an AWS environment. If confirmed - malicious, this could lead to unauthorized access, data breaches, or further exploitation - of AWS resources. +description: The following analytic detects an IP address with 20 or more failed authentication attempts to the AWS Web Console within a 5-minute window. This detection leverages CloudTrail logs, aggregating failed login events by IP address and time span. This activity is significant as it may indicate a brute force attack aimed at gaining unauthorized access or escalating privileges within an AWS environment. If confirmed malicious, this could lead to unauthorized access, data breaches, or further exploitation of AWS resources. data_source: -- AWS CloudTrail ConsoleLogin -search: '`cloudtrail` eventName=ConsoleLogin action=failure - | bucket span=10m _time - | rename user_name as user - | stats dc(_raw) AS failed_attempts values(user) as user values(user_agent) as user_agent by _time, src, signature, dest, vendor_account vendor_region, vendor_product - | where failed_attempts > 20 - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `aws_high_number_of_failed_authentications_from_ip_filter`' -how_to_implement: You must install Splunk Add-on for AWS in order to ingest Cloudtrail. - We recommend the users to try different combinations of the bucket span time and - the tried account threshold to tune this search according to their environment. -known_false_positives: An Ip address with more than 20 failed authentication attempts - in the span of 5 minutes may also be triggered by a broken application. + - AWS CloudTrail ConsoleLogin +search: |- + `cloudtrail` eventName=ConsoleLogin action=failure + | bucket span=10m _time + | rename user_name as user + | stats dc(_raw) AS failed_attempts values(user) as user values(user_agent) as user_agent + BY _time, src, signature, + dest, vendor_account vendor_region, + vendor_product + | where failed_attempts > 20 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_high_number_of_failed_authentications_from_ip_filter` +how_to_implement: You must install Splunk Add-on for AWS in order to ingest Cloudtrail. We recommend the users to try different combinations of the bucket span time and the tried account threshold to tune this search according to their environment. +known_false_positives: An Ip address with more than 20 failed authentication attempts in the span of 5 minutes may also be triggered by a broken application. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://www.whiteoaksecurity.com/blog/goawsconsolespray-password-spraying-tool/ -- https://softwaresecuritydotblog.wordpress.com/2019/09/28/how-to-protect-against-credential-stuffing-on-aws/ + - https://attack.mitre.org/techniques/T1110/003/ + - https://www.whiteoaksecurity.com/blog/goawsconsolespray-password-spraying-tool/ + - https://softwaresecuritydotblog.wordpress.com/2019/09/28/how-to-protect-against-credential-stuffing-on-aws/ drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: 'Multiple failed console login attempts (Count: $failed_attempts$) against - users from IP Address - $src$' - risk_objects: - - field: user - type: user - score: 54 - threat_objects: [] + message: 'Multiple failed console login attempts (Count: $failed_attempts$) against users from IP Address - $src$' + risk_objects: + - field: user + type: user + score: 54 + threat_objects: [] tags: - analytic_story: - - AWS Identity and Access Management Account Takeover - - Compromised User Account - asset_type: AWS Account - mitre_attack_id: - - T1110.003 - - T1110.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Identity and Access Management Account Takeover + - Compromised User Account + asset_type: AWS Account + mitre_attack_id: + - T1110.003 + - T1110.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/aws_mulitple_failed_console_login/aws_cloudtrail.json - source: aws_cloudtrail - sourcetype: aws:cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/aws_mulitple_failed_console_login/aws_cloudtrail.json + source: aws_cloudtrail + sourcetype: aws:cloudtrail diff --git a/detections/cloud/aws_iam_accessdenied_discovery_events.yml b/detections/cloud/aws_iam_accessdenied_discovery_events.yml index 54b120c778..0ff6f915b4 100644 --- a/detections/cloud/aws_iam_accessdenied_discovery_events.yml +++ b/detections/cloud/aws_iam_accessdenied_discovery_events.yml @@ -1,72 +1,60 @@ name: AWS IAM AccessDenied Discovery Events id: 3e1f1568-9633-11eb-a69c-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic identifies excessive AccessDenied events within - an hour timeframe for IAM users in AWS. It leverages AWS CloudTrail logs to detect - multiple failed access attempts from the same source IP and user identity. This - activity is significant as it may indicate that an access key has been compromised - and is being misused for unauthorized discovery actions. If confirmed malicious, - this could allow attackers to gather information about the AWS environment, potentially - leading to further exploitation or privilege escalation. +description: The following analytic identifies excessive AccessDenied events within an hour timeframe for IAM users in AWS. It leverages AWS CloudTrail logs to detect multiple failed access attempts from the same source IP and user identity. This activity is significant as it may indicate that an access key has been compromised and is being misused for unauthorized discovery actions. If confirmed malicious, this could allow attackers to gather information about the AWS environment, potentially leading to further exploitation or privilege escalation. data_source: -- AWS CloudTrail -search: '`cloudtrail` (errorCode = "AccessDenied") user_type=IAMUser (userAgent!=*.amazonaws.com) - | bucket _time span=1h - | rename user_name as user - | stats count as failures min(_time) as firstTime max(_time) as lastTime, dc(signature) as methods, dc(dest) as sources values(signature) as signature values(dest) as dest by src, user, vendor_account vendor_region, vendor_product - | where failures >= 5 and methods >= 1 and sources >= 1 - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `aws_iam_accessdenied_discovery_events_filter`' -how_to_implement: The Splunk AWS Add-on and Splunk App for AWS is required to utilize - this data. The search requires AWS CloudTrail logs. -known_false_positives: It is possible to start this detection will need to be tuned - by source IP or user. In addition, change the count values to an upper threshold - to restrict false positives. + - AWS CloudTrail +search: |- + `cloudtrail` (errorCode = "AccessDenied") user_type=IAMUser (userAgent!=*.amazonaws.com) + | bucket _time span=1h + | rename user_name as user + | stats count as failures min(_time) as firstTime max(_time) as lastTime, dc(signature) as methods, dc(dest) as sources values(signature) as signature values(dest) as dest + BY src, user, vendor_account + vendor_region, vendor_product + | where failures >= 5 and methods >= 1 and sources >= 1 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_iam_accessdenied_discovery_events_filter` +how_to_implement: The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS CloudTrail logs. +known_false_positives: It is possible to start this detection will need to be tuned by source IP or user. In addition, change the count values to an upper threshold to restrict false positives. references: -- https://aws.amazon.com/premiumsupport/knowledge-center/troubleshoot-iam-permission-errors/ + - https://aws.amazon.com/premiumsupport/knowledge-center/troubleshoot-iam-permission-errors/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ is seen to perform excessive number of discovery - related api calls- $failures$, within an hour where the access was denied. - risk_objects: - - field: user - type: user - score: 10 - threat_objects: - - field: src - type: ip_address + message: User $user$ is seen to perform excessive number of discovery related api calls- $failures$, within an hour where the access was denied. + risk_objects: + - field: user + type: user + score: 10 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Suspicious Cloud User Activities - asset_type: AWS Account - mitre_attack_id: - - T1580 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - Suspicious Cloud User Activities + asset_type: AWS Account + mitre_attack_id: + - T1580 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1580/aws_iam_accessdenied_discovery_events/aws_iam_accessdenied_discovery_events.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1580/aws_iam_accessdenied_discovery_events/aws_iam_accessdenied_discovery_events.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_iam_assume_role_policy_brute_force.yml b/detections/cloud/aws_iam_assume_role_policy_brute_force.yml index 29a69badee..eb760065db 100644 --- a/detections/cloud/aws_iam_assume_role_policy_brute_force.yml +++ b/detections/cloud/aws_iam_assume_role_policy_brute_force.yml @@ -1,75 +1,63 @@ name: AWS IAM Assume Role Policy Brute Force id: f19e09b0-9308-11eb-b7ec-acde48001122 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects multiple failed attempts to assume an - AWS IAM role, indicating a potential brute force attack. It leverages AWS CloudTrail - logs to identify `MalformedPolicyDocumentException` errors with a status of `failure` - and filters out legitimate AWS services. This activity is significant as repeated - failures to assume roles can indicate an adversary attempting to guess role names, - which is a precursor to unauthorized access. If confirmed malicious, this could - lead to unauthorized access to AWS resources, potentially compromising sensitive - data and services. +description: The following analytic detects multiple failed attempts to assume an AWS IAM role, indicating a potential brute force attack. It leverages AWS CloudTrail logs to identify `MalformedPolicyDocumentException` errors with a status of `failure` and filters out legitimate AWS services. This activity is significant as repeated failures to assume roles can indicate an adversary attempting to guess role names, which is a precursor to unauthorized access. If confirmed malicious, this could lead to unauthorized access to AWS resources, potentially compromising sensitive data and services. data_source: -- AWS CloudTrail -search: '`cloudtrail` (errorCode=MalformedPolicyDocumentException) status=failure (userAgent!=*.amazonaws.com) - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.policyName) as policy_name by src, user, vendor_account vendor_region, vendor_product, signature, dest, errorCode - | where count >= 2 - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_iam_assume_role_policy_brute_force_filter`' -how_to_implement: The Splunk AWS Add-on and Splunk App for AWS is required to utilize - this data. The search requires AWS CloudTrail logs. Set the `where count` greater - than a value to identify suspicious activity in your environment. -known_false_positives: This detection will require tuning to provide high fidelity - detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) - or by groups of users. + - AWS CloudTrail +search: |- + `cloudtrail` (errorCode=MalformedPolicyDocumentException) status=failure (userAgent!=*.amazonaws.com) + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.policyName) as policy_name + BY src, user, vendor_account + vendor_region, vendor_product, signature, + dest, errorCode + | where count >= 2 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_iam_assume_role_policy_brute_force_filter` +how_to_implement: The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS CloudTrail logs. Set the `where count` greater than a value to identify suspicious activity in your environment. +known_false_positives: This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. references: -- https://www.praetorian.com/blog/aws-iam-assume-role-vulnerabilities/ -- https://rhinosecuritylabs.com/aws/assume-worst-aws-assume-role-enumeration/ -- https://www.elastic.co/guide/en/security/current/aws-iam-brute-force-of-assume-role-policy.html + - https://www.praetorian.com/blog/aws-iam-assume-role-vulnerabilities/ + - https://rhinosecuritylabs.com/aws/assume-worst-aws-assume-role-enumeration/ + - https://www.elastic.co/guide/en/security/current/aws-iam-brute-force-of-assume-role-policy.html drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has caused multiple failures with errorCode $errorCode$, - which potentially means adversary is attempting to identify a role name. - risk_objects: - - field: user - type: user - score: 28 - threat_objects: - - field: src - type: ip_address + message: User $user$ has caused multiple failures with errorCode $errorCode$, which potentially means adversary is attempting to identify a role name. + risk_objects: + - field: user + type: user + score: 28 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS IAM Privilege Escalation - asset_type: AWS Account - mitre_attack_id: - - T1580 - - T1110 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - AWS IAM Privilege Escalation + asset_type: AWS Account + mitre_attack_id: + - T1580 + - T1110 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1580/aws_iam_assume_role_policy_brute_force/aws_iam_assume_role_policy_brute_force.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1580/aws_iam_assume_role_policy_brute_force/aws_iam_assume_role_policy_brute_force.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_iam_delete_policy.yml b/detections/cloud/aws_iam_delete_policy.yml index 462ea66cf5..cb9e3dd8c0 100644 --- a/detections/cloud/aws_iam_delete_policy.yml +++ b/detections/cloud/aws_iam_delete_policy.yml @@ -1,49 +1,42 @@ name: AWS IAM Delete Policy id: ec3a9362-92fe-11eb-99d0-acde48001122 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic detects the deletion of an IAM policy in AWS. - It leverages AWS CloudTrail logs to identify `DeletePolicy` events, excluding those - from AWS internal services. This activity is significant as unauthorized policy - deletions can disrupt access controls and weaken security postures. If confirmed - malicious, an attacker could remove critical security policies, potentially leading - to privilege escalation, unauthorized access, or data exfiltration. Monitoring this - behavior helps ensure that only authorized changes are made to IAM policies, maintaining - the integrity and security of the AWS environment. +description: The following analytic detects the deletion of an IAM policy in AWS. It leverages AWS CloudTrail logs to identify `DeletePolicy` events, excluding those from AWS internal services. This activity is significant as unauthorized policy deletions can disrupt access controls and weaken security postures. If confirmed malicious, an attacker could remove critical security policies, potentially leading to privilege escalation, unauthorized access, or data exfiltration. Monitoring this behavior helps ensure that only authorized changes are made to IAM policies, maintaining the integrity and security of the AWS environment. data_source: -- AWS CloudTrail DeletePolicy -search: '`cloudtrail` eventName=DeletePolicy (userAgent!=*.amazonaws.com) - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_iam_delete_policy_filter`' -how_to_implement: The Splunk AWS Add-on and Splunk App for AWS is required to utilize - this data. The search requires AWS CloudTrail logs. -known_false_positives: This detection will require tuning to provide high fidelity - detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) - or by groups of users. Not every user with AWS access should have permission to - delete policies (least privilege). In addition, this may be saved seperately and - tuned for failed or success attempts only. + - AWS CloudTrail DeletePolicy +search: |- + `cloudtrail` eventName=DeletePolicy (userAgent!=*.amazonaws.com) + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_iam_delete_policy_filter` +how_to_implement: The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS CloudTrail logs. +known_false_positives: This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete policies (least privilege). In addition, this may be saved seperately and tuned for failed or success attempts only. references: -- https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeletePolicy.html -- https://docs.aws.amazon.com/cli/latest/reference/iam/delete-policy.html + - https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeletePolicy.html + - https://docs.aws.amazon.com/cli/latest/reference/iam/delete-policy.html tags: - analytic_story: - - AWS IAM Privilege Escalation - asset_type: AWS Account - mitre_attack_id: - - T1098 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - AWS IAM Privilege Escalation + asset_type: AWS Account + mitre_attack_id: + - T1098 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_delete_policy/aws_iam_delete_policy.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_delete_policy/aws_iam_delete_policy.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_iam_failure_group_deletion.yml b/detections/cloud/aws_iam_failure_group_deletion.yml index c18d632265..9c995ad536 100644 --- a/detections/cloud/aws_iam_failure_group_deletion.yml +++ b/detections/cloud/aws_iam_failure_group_deletion.yml @@ -1,72 +1,60 @@ name: AWS IAM Failure Group Deletion id: 723b861a-92eb-11eb-93b8-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic identifies failed attempts to delete AWS IAM groups. - It leverages AWS CloudTrail logs to detect events where the DeleteGroup action fails - due to errors like NoSuchEntityException, DeleteConflictException, or AccessDenied. - This activity is significant as it may indicate unauthorized attempts to modify - IAM group configurations, which could be a precursor to privilege escalation or - other malicious actions. If confirmed malicious, this could allow an attacker to - disrupt IAM policies, potentially leading to unauthorized access or denial of service - within the AWS environment. +description: The following analytic identifies failed attempts to delete AWS IAM groups. It leverages AWS CloudTrail logs to detect events where the DeleteGroup action fails due to errors like NoSuchEntityException, DeleteConflictException, or AccessDenied. This activity is significant as it may indicate unauthorized attempts to modify IAM group configurations, which could be a precursor to privilege escalation or other malicious actions. If confirmed malicious, this could allow an attacker to disrupt IAM policies, potentially leading to unauthorized access or denial of service within the AWS environment. data_source: -- AWS CloudTrail DeleteGroup -search: '`cloudtrail` eventSource=iam.amazonaws.com eventName=DeleteGroup errorCode IN (NoSuchEntityException,DeleteConflictException, AccessDenied) (userAgent!=*.amazonaws.com) - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_iam_failure_group_deletion_filter`' -how_to_implement: The Splunk AWS Add-on and Splunk App for AWS is required to utilize - this data. The search requires AWS CloudTrail logs. -known_false_positives: This detection will require tuning to provide high fidelity - detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) - or by groups of users. Not every user with AWS access should have permission to - delete groups (least privilege). + - AWS CloudTrail DeleteGroup +search: |- + `cloudtrail` eventSource=iam.amazonaws.com eventName=DeleteGroup errorCode IN (NoSuchEntityException,DeleteConflictException, AccessDenied) (userAgent!=*.amazonaws.com) + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_iam_failure_group_deletion_filter` +how_to_implement: The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS CloudTrail logs. +known_false_positives: This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete groups (least privilege). references: -- https://awscli.amazonaws.com/v2/documentation/api/latest/reference/iam/delete-group.html -- https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeleteGroup.html + - https://awscli.amazonaws.com/v2/documentation/api/latest/reference/iam/delete-group.html + - https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeleteGroup.html drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has had mulitple failures while attempting to delete groups - from $src$ - risk_objects: - - field: user - type: user - score: 5 - threat_objects: - - field: src - type: ip_address + message: User $user$ has had mulitple failures while attempting to delete groups from $src$ + risk_objects: + - field: user + type: user + score: 5 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS IAM Privilege Escalation - asset_type: AWS Account - mitre_attack_id: - - T1098 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - AWS IAM Privilege Escalation + asset_type: AWS Account + mitre_attack_id: + - T1098 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_failure_group_deletion/aws_iam_failure_group_deletion.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_failure_group_deletion/aws_iam_failure_group_deletion.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_iam_successful_group_deletion.yml b/detections/cloud/aws_iam_successful_group_deletion.yml index f3a0cff040..8a59c38450 100644 --- a/detections/cloud/aws_iam_successful_group_deletion.yml +++ b/detections/cloud/aws_iam_successful_group_deletion.yml @@ -1,49 +1,43 @@ name: AWS IAM Successful Group Deletion id: e776d06c-9267-11eb-819b-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic identifies the successful deletion of an IAM group - in AWS. It leverages CloudTrail logs to detect `DeleteGroup` events with a success - status. This activity is significant as it could indicate potential changes in user - permissions or access controls, which may be a precursor to further unauthorized - actions. If confirmed malicious, an attacker could disrupt access management, potentially - leading to privilege escalation or unauthorized access to sensitive resources. Analysts - should review related IAM events, such as recent user additions or new group creations, - to assess the broader context. +description: The following analytic identifies the successful deletion of an IAM group in AWS. It leverages CloudTrail logs to detect `DeleteGroup` events with a success status. This activity is significant as it could indicate potential changes in user permissions or access controls, which may be a precursor to further unauthorized actions. If confirmed malicious, an attacker could disrupt access management, potentially leading to privilege escalation or unauthorized access to sensitive resources. Analysts should review related IAM events, such as recent user additions or new group creations, to assess the broader context. data_source: -- AWS CloudTrail DeleteGroup -search: '`cloudtrail` eventSource=iam.amazonaws.com eventName=DeleteGroup errorCode=success (userAgent!=*.amazonaws.com) - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_iam_successful_group_deletion_filter`' -how_to_implement: The Splunk AWS Add-on and Splunk App for AWS is required to utilize - this data. The search requires AWS CloudTrail logs. -known_false_positives: This detection will require tuning to provide high fidelity - detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) - or by groups of users. Not every user with AWS access should have permission to - delete groups (least privilege). + - AWS CloudTrail DeleteGroup +search: |- + `cloudtrail` eventSource=iam.amazonaws.com eventName=DeleteGroup errorCode=success (userAgent!=*.amazonaws.com) + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_iam_successful_group_deletion_filter` +how_to_implement: The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS CloudTrail logs. +known_false_positives: This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete groups (least privilege). references: -- https://awscli.amazonaws.com/v2/documentation/api/latest/reference/iam/delete-group.html -- https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeleteGroup.html + - https://awscli.amazonaws.com/v2/documentation/api/latest/reference/iam/delete-group.html + - https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeleteGroup.html tags: - analytic_story: - - AWS IAM Privilege Escalation - asset_type: AWS Account - mitre_attack_id: - - T1069.003 - - T1098 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - AWS IAM Privilege Escalation + asset_type: AWS Account + mitre_attack_id: + - T1069.003 + - T1098 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_successful_group_deletion/aws_iam_successful_group_deletion.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_successful_group_deletion/aws_iam_successful_group_deletion.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_lambda_updatefunctioncode.yml b/detections/cloud/aws_lambda_updatefunctioncode.yml index 3572007fe2..bd623058eb 100644 --- a/detections/cloud/aws_lambda_updatefunctioncode.yml +++ b/detections/cloud/aws_lambda_updatefunctioncode.yml @@ -1,45 +1,42 @@ name: AWS Lambda UpdateFunctionCode id: 211b80d3-6340-4345-11ad-212bf3d0d111 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: Hunting -description: The following analytic identifies IAM users attempting to update or modify - AWS Lambda code via the AWS CLI. It leverages CloudTrail logs to detect successful - `UpdateFunctionCode` events initiated by IAM users. This activity is significant - as it may indicate an attempt to gain persistence, further access, or plant backdoors - within your AWS environment. If confirmed malicious, an attacker could upload and - execute malicious code automatically when the Lambda function is triggered, potentially - compromising the integrity and security of your AWS infrastructure. +description: The following analytic identifies IAM users attempting to update or modify AWS Lambda code via the AWS CLI. It leverages CloudTrail logs to detect successful `UpdateFunctionCode` events initiated by IAM users. This activity is significant as it may indicate an attempt to gain persistence, further access, or plant backdoors within your AWS environment. If confirmed malicious, an attacker could upload and execute malicious code automatically when the Lambda function is triggered, potentially compromising the integrity and security of your AWS infrastructure. data_source: -- AWS CloudTrail -search: '`cloudtrail` eventSource=lambda.amazonaws.com eventName=UpdateFunctionCode* errorCode = success user_type=IAMUser - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |`aws_lambda_updatefunctioncode_filter`' -how_to_implement: You must install Splunk AWS Add on and enable Cloudtrail logs in - your AWS Environment. -known_false_positives: While this search has no known false positives, it is possible - that an AWS admin or an autorized IAM user has updated the lambda fuction code legitimately. + - AWS CloudTrail +search: |- + `cloudtrail` eventSource=lambda.amazonaws.com eventName=UpdateFunctionCode* errorCode = success user_type=IAMUser + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_lambda_updatefunctioncode_filter` +how_to_implement: You must install Splunk AWS Add on and enable Cloudtrail logs in your AWS Environment. +known_false_positives: While this search has no known false positives, it is possible that an AWS admin or an autorized IAM user has updated the lambda fuction code legitimately. references: -- http://detectioninthe.cloud/execution/modify_lambda_function_code/ -- https://sysdig.com/blog/exploit-mitigate-aws-lambdas-mitre/ + - http://detectioninthe.cloud/execution/modify_lambda_function_code/ + - https://sysdig.com/blog/exploit-mitigate-aws-lambdas-mitre/ tags: - analytic_story: - - Suspicious Cloud User Activities - asset_type: AWS Account - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Suspicious Cloud User Activities + asset_type: AWS Account + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/aws_updatelambdafunctioncode/aws_cloudtrail_events.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/aws_updatelambdafunctioncode/aws_cloudtrail_events.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_multi_factor_authentication_disabled.yml b/detections/cloud/aws_multi_factor_authentication_disabled.yml index cab6e569c9..bdf39d309c 100644 --- a/detections/cloud/aws_multi_factor_authentication_disabled.yml +++ b/detections/cloud/aws_multi_factor_authentication_disabled.yml @@ -1,74 +1,64 @@ name: AWS Multi-Factor Authentication Disabled id: 374832b1-3603-420c-b456-b373e24d34c0 -version: 8 -date: '2025-10-14' +version: 9 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP -description: The following analytic detects attempts to disable multi-factor authentication - (MFA) for an AWS IAM user. It leverages AWS CloudTrail logs to identify events where - MFA devices are deleted or deactivated. This activity is significant because disabling - MFA can indicate an adversary attempting to weaken account security, potentially - to maintain persistence using a compromised account. If confirmed malicious, this - action could allow attackers to retain access to the AWS environment without detection, - posing a significant risk to the security and integrity of the cloud infrastructure. +description: The following analytic detects attempts to disable multi-factor authentication (MFA) for an AWS IAM user. It leverages AWS CloudTrail logs to identify events where MFA devices are deleted or deactivated. This activity is significant because disabling MFA can indicate an adversary attempting to weaken account security, potentially to maintain persistence using a compromised account. If confirmed malicious, this action could allow attackers to retain access to the AWS environment without detection, posing a significant risk to the security and integrity of the cloud infrastructure. data_source: -- AWS CloudTrail DeleteVirtualMFADevice -- AWS CloudTrail DeactivateMFADevice -search: '`cloudtrail` (eventName= DeleteVirtualMFADevice OR eventName=DeactivateMFADevice) - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_multi_factor_authentication_disabled_filter`' -how_to_implement: The Splunk AWS Add-on is required to utilize this data. The search - requires AWS CloudTrail logs. -known_false_positives: AWS Administrators may disable MFA but it is highly unlikely - for this event to occur without prior notice to the company + - AWS CloudTrail DeleteVirtualMFADevice + - AWS CloudTrail DeactivateMFADevice +search: |- + `cloudtrail` (eventName= DeleteVirtualMFADevice OR eventName=DeactivateMFADevice) + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_multi_factor_authentication_disabled_filter` +how_to_implement: The Splunk AWS Add-on is required to utilize this data. The search requires AWS CloudTrail logs. +known_false_positives: AWS Administrators may disable MFA but it is highly unlikely for this event to occur without prior notice to the company references: -- https://attack.mitre.org/techniques/T1621/ -- https://aws.amazon.com/what-is/mfa/ + - https://attack.mitre.org/techniques/T1621/ + - https://aws.amazon.com/what-is/mfa/ drilldown_searches: -- name: View the detection results for - "$vendor_account$" and "$user$" - search: '%original_detection_search% | search vendor_account = "$vendor_account$" - user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$vendor_account$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$vendor_account$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$vendor_account$" and "$user$" + search: '%original_detection_search% | search vendor_account = "$vendor_account$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$vendor_account$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$vendor_account$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has disabled Multi-Factor authentication for AWS account - $vendor_account$ - risk_objects: - - field: user - type: user - score: 64 - threat_objects: - - field: src - type: ip_address + message: User $user$ has disabled Multi-Factor authentication for AWS account $vendor_account$ + risk_objects: + - field: user + type: user + score: 64 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Identity and Access Management Account Takeover - - Scattered Lapsus$ Hunters - asset_type: AWS Account - mitre_attack_id: - - T1556.006 - - T1586.003 - - T1621 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Identity and Access Management Account Takeover + - Scattered Lapsus$ Hunters + asset_type: AWS Account + mitre_attack_id: + - T1556.006 + - T1586.003 + - T1621 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/aws_mfa_disabled/cloudtrail.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/aws_mfa_disabled/cloudtrail.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_multiple_failed_mfa_requests_for_user.yml b/detections/cloud/aws_multiple_failed_mfa_requests_for_user.yml index 8b71e6186a..8b1838c0cc 100644 --- a/detections/cloud/aws_multiple_failed_mfa_requests_for_user.yml +++ b/detections/cloud/aws_multiple_failed_mfa_requests_for_user.yml @@ -1,72 +1,63 @@ name: AWS Multiple Failed MFA Requests For User id: 1fece617-e614-4329-9e61-3ba228c0f353 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Bhavin Patel status: production type: Anomaly -description: The following analytic identifies multiple failed multi-factor authentication - (MFA) requests to an AWS Console for a single user. It leverages AWS CloudTrail - logs, specifically the `additionalEventData` field, to detect more than 10 failed - MFA prompts within 5 minutes. This activity is significant as it may indicate an - adversary attempting to bypass MFA by bombarding the user with repeated authentication - requests. If confirmed malicious, this could lead to unauthorized access to the - AWS environment, potentially compromising sensitive data and resources. +description: The following analytic identifies multiple failed multi-factor authentication (MFA) requests to an AWS Console for a single user. It leverages AWS CloudTrail logs, specifically the `additionalEventData` field, to detect more than 10 failed MFA prompts within 5 minutes. This activity is significant as it may indicate an adversary attempting to bypass MFA by bombarding the user with repeated authentication requests. If confirmed malicious, this could lead to unauthorized access to the AWS environment, potentially compromising sensitive data and resources. data_source: -- AWS CloudTrail ConsoleLogin -search: '`cloudtrail` eventName= ConsoleLogin "additionalEventData.MFAUsed"=Yes errorMessage="Failed authentication" - | bucket span=5m _time - | rename user_name as user - | stats dc(_raw) as mfa_prompts min(_time) as firstTime max(_time) as lastTime values(user_agent) as user_agent values(src) as src values(dest) as dest by _time user signature vendor_account vendor_region vendor_product errorMessage - | where mfa_prompts > 10 - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_multiple_failed_mfa_requests_for_user_filter`' -how_to_implement: The Splunk AWS Add-on is required to utilize this data. The search - requires AWS CloudTrail logs. -known_false_positives: Multiple Failed MFA requests may also be a sign of authentication - or application issues. Filter as needed. + - AWS CloudTrail ConsoleLogin +search: |- + `cloudtrail` eventName= ConsoleLogin "additionalEventData.MFAUsed"=Yes errorMessage="Failed authentication" + | bucket span=5m _time + | rename user_name as user + | stats dc(_raw) as mfa_prompts min(_time) as firstTime max(_time) as lastTime values(user_agent) as user_agent values(src) as src values(dest) as dest + BY _time user signature + vendor_account vendor_region vendor_product + errorMessage + | where mfa_prompts > 10 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_multiple_failed_mfa_requests_for_user_filter` +how_to_implement: The Splunk AWS Add-on is required to utilize this data. The search requires AWS CloudTrail logs. +known_false_positives: Multiple Failed MFA requests may also be a sign of authentication or application issues. Filter as needed. references: -- https://attack.mitre.org/techniques/T1621/ -- https://aws.amazon.com/what-is/mfa/ + - https://attack.mitre.org/techniques/T1621/ + - https://aws.amazon.com/what-is/mfa/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ is seen to have high number of MFA prompt failures within - a short period of time. - risk_objects: - - field: user - type: user - score: 64 - threat_objects: - - field: src - type: ip_address + message: User $user$ is seen to have high number of MFA prompt failures within a short period of time. + risk_objects: + - field: user + type: user + score: 64 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Identity and Access Management Account Takeover - asset_type: AWS Account - mitre_attack_id: - - T1586.003 - - T1621 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Identity and Access Management Account Takeover + asset_type: AWS Account + mitre_attack_id: + - T1586.003 + - T1621 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/aws_failed_mfa/cloudtrail.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/aws_failed_mfa/cloudtrail.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_multiple_users_failing_to_authenticate_from_ip.yml b/detections/cloud/aws_multiple_users_failing_to_authenticate_from_ip.yml index b5363e8f73..0b9531193f 100644 --- a/detections/cloud/aws_multiple_users_failing_to_authenticate_from_ip.yml +++ b/detections/cloud/aws_multiple_users_failing_to_authenticate_from_ip.yml @@ -1,78 +1,66 @@ name: AWS Multiple Users Failing To Authenticate From Ip id: 71e1fb89-dd5f-4691-8523-575420de4630 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Bhavin Patel status: production type: Anomaly -description: The following analytic identifies a single source IP failing to authenticate - into the AWS Console with 30 unique valid users within 10 minutes. It leverages - CloudTrail logs to detect multiple failed login attempts from the same IP address. - This behavior is significant as it may indicate a Password Spraying attack, where - an adversary attempts to gain unauthorized access or elevate privileges by trying - common passwords across many accounts. If confirmed malicious, this activity could - lead to unauthorized access, data breaches, or further exploitation within the AWS - environment. +description: The following analytic identifies a single source IP failing to authenticate into the AWS Console with 30 unique valid users within 10 minutes. It leverages CloudTrail logs to detect multiple failed login attempts from the same IP address. This behavior is significant as it may indicate a Password Spraying attack, where an adversary attempts to gain unauthorized access or elevate privileges by trying common passwords across many accounts. If confirmed malicious, this activity could lead to unauthorized access, data breaches, or further exploitation within the AWS environment. data_source: -- AWS CloudTrail ConsoleLogin -search: '`cloudtrail` eventName=ConsoleLogin action=failure - | bucket span=10m _time - | rename user_name as user - | stats dc(user) AS unique_accounts values(user) as user values(user_agent) as user_agent by _time, src, signature, dest, vendor_account, vendor_region, vendor_product - | where unique_accounts>30 - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_multiple_users_failing_to_authenticate_from_ip_filter`' -how_to_implement: You must install Splunk Add-on for AWS in order to ingest Cloudtrail. - We recommend the users to try different combinations of the bucket span time and - the tried account threshold to tune this search according to their environment. -known_false_positives: No known false postives for this detection. Please review this - alert + - AWS CloudTrail ConsoleLogin +search: |- + `cloudtrail` eventName=ConsoleLogin action=failure + | bucket span=10m _time + | rename user_name as user + | stats dc(user) AS unique_accounts values(user) as user values(user_agent) as user_agent + BY _time, src, signature, + dest, vendor_account, vendor_region, + vendor_product + | where unique_accounts>30 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_multiple_users_failing_to_authenticate_from_ip_filter` +how_to_implement: You must install Splunk Add-on for AWS in order to ingest Cloudtrail. We recommend the users to try different combinations of the bucket span time and the tried account threshold to tune this search according to their environment. +known_false_positives: No known false postives for this detection. Please review this alert references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://www.whiteoaksecurity.com/blog/goawsconsolespray-password-spraying-tool/ -- https://softwaresecuritydotblog.wordpress.com/2019/09/28/how-to-protect-against-credential-stuffing-on-aws/ + - https://attack.mitre.org/techniques/T1110/003/ + - https://www.whiteoaksecurity.com/blog/goawsconsolespray-password-spraying-tool/ + - https://softwaresecuritydotblog.wordpress.com/2019/09/28/how-to-protect-against-credential-stuffing-on-aws/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: 'Multiple failed console login attempts (Count: $unique_accounts$) against - users from IP Address - $src$' - risk_objects: - - field: user - type: user - score: 54 - threat_objects: - - field: src - type: ip_address + message: 'Multiple failed console login attempts (Count: $unique_accounts$) against users from IP Address - $src$' + risk_objects: + - field: user + type: user + score: 54 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Identity and Access Management Account Takeover - - Compromised User Account - asset_type: AWS Account - mitre_attack_id: - - T1110.003 - - T1110.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat - manual_test: This search needs a specific number of events in a time window for - the alert to trigger and events split up in CI testing while updating timestamp. + analytic_story: + - AWS Identity and Access Management Account Takeover + - Compromised User Account + asset_type: AWS Account + mitre_attack_id: + - T1110.003 + - T1110.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat + manual_test: This search needs a specific number of events in a time window for the alert to trigger and events split up in CI testing while updating timestamp. tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/aws_mulitple_failed_console_login/aws_cloudtrail.json - source: aws_cloudtrail - sourcetype: aws:cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/aws_mulitple_failed_console_login/aws_cloudtrail.json + source: aws_cloudtrail + sourcetype: aws:cloudtrail diff --git a/detections/cloud/aws_network_access_control_list_created_with_all_open_ports.yml b/detections/cloud/aws_network_access_control_list_created_with_all_open_ports.yml index 789d0680a5..006373cd8e 100644 --- a/detections/cloud/aws_network_access_control_list_created_with_all_open_ports.yml +++ b/detections/cloud/aws_network_access_control_list_created_with_all_open_ports.yml @@ -1,76 +1,65 @@ name: AWS Network Access Control List Created with All Open Ports id: ada0f478-84a8-4641-a3f1-d82362d6bd75 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Bhavin Patel, Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects the creation of AWS Network Access Control - Lists (ACLs) with all ports open to a specified CIDR. It leverages AWS CloudTrail - events, specifically monitoring for `CreateNetworkAclEntry` or `ReplaceNetworkAclEntry` - actions with rules allowing all traffic. This activity is significant because it - can expose the network to unauthorized access, increasing the risk of data breaches - and other malicious activities. If confirmed malicious, an attacker could exploit - this misconfiguration to gain unrestricted access to the network, potentially leading - to data exfiltration, service disruption, or further compromise of the AWS environment. +description: The following analytic detects the creation of AWS Network Access Control Lists (ACLs) with all ports open to a specified CIDR. It leverages AWS CloudTrail events, specifically monitoring for `CreateNetworkAclEntry` or `ReplaceNetworkAclEntry` actions with rules allowing all traffic. This activity is significant because it can expose the network to unauthorized access, increasing the risk of data breaches and other malicious activities. If confirmed malicious, an attacker could exploit this misconfiguration to gain unrestricted access to the network, potentially leading to data exfiltration, service disruption, or further compromise of the AWS environment. data_source: -- AWS CloudTrail CreateNetworkAclEntry -- AWS CloudTrail ReplaceNetworkAclEntry -search: "`cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol=-1 - | append [search `cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry - requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol!=-1 - | eval port_range='requestParameters.portRange.to' - 'requestParameters.portRange.from' - | where port_range>1024] - | fillnull - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product requestParameters.ruleAction requestParameters.egress requestParameters.aclProtocol requestParameters.portRange.to requestParameters.portRange.from requestParameters.cidrBlock - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `aws_network_access_control_list_created_with_all_open_ports_filter`" -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS, version 4.4.0 or later, and configure your AWS CloudTrail - inputs. -known_false_positives: It's possible that an admin has created this ACL with all ports - open for some legitimate purpose however, this should be scoped and not allowed - in production environment. + - AWS CloudTrail CreateNetworkAclEntry + - AWS CloudTrail ReplaceNetworkAclEntry +search: |- + `cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol=-1 + | append [search `cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol!=-1 + | eval port_range='requestParameters.portRange.to' - 'requestParameters.portRange.from' + | where port_range>1024] + | fillnull + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product requestParameters.ruleAction + requestParameters.egress requestParameters.aclProtocol requestParameters.portRange.to + requestParameters.portRange.from requestParameters.cidrBlock + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_network_access_control_list_created_with_all_open_ports_filter` +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS, version 4.4.0 or later, and configure your AWS CloudTrail inputs. +known_false_positives: It's possible that an admin has created this ACL with all ports open for some legitimate purpose however, this should be scoped and not allowed in production environment. references: [] drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has created network ACLs with all the ports open to a specified - CIDR $requestParameters.cidrBlock$ - risk_objects: - - field: user - type: user - score: 48 - threat_objects: - - field: src - type: ip_address + message: User $user$ has created network ACLs with all the ports open to a specified CIDR $requestParameters.cidrBlock$ + risk_objects: + - field: user + type: user + score: 48 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Network ACL Activity - asset_type: AWS Instance - mitre_attack_id: - - T1562.007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - AWS Network ACL Activity + asset_type: AWS Instance + mitre_attack_id: + - T1562.007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_create_acl/aws_cloudtrail_events.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_create_acl/aws_cloudtrail_events.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_network_access_control_list_deleted.yml b/detections/cloud/aws_network_access_control_list_deleted.yml index b6a9ed4904..2283370099 100644 --- a/detections/cloud/aws_network_access_control_list_deleted.yml +++ b/detections/cloud/aws_network_access_control_list_deleted.yml @@ -1,68 +1,59 @@ name: AWS Network Access Control List Deleted id: ada0f478-84a8-4641-a3f1-d82362d6fd75 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Bhavin Patel, Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects the deletion of AWS Network Access Control - Lists (ACLs). It leverages AWS CloudTrail logs to identify events where a user deletes - a network ACL entry. This activity is significant because deleting a network ACL - can remove critical access restrictions, potentially allowing unauthorized access - to cloud instances. If confirmed malicious, this action could enable attackers to - bypass network security controls, leading to unauthorized access, data exfiltration, - or further compromise of the cloud environment. +description: The following analytic detects the deletion of AWS Network Access Control Lists (ACLs). It leverages AWS CloudTrail logs to identify events where a user deletes a network ACL entry. This activity is significant because deleting a network ACL can remove critical access restrictions, potentially allowing unauthorized access to cloud instances. If confirmed malicious, this action could enable attackers to bypass network security controls, leading to unauthorized access, data exfiltration, or further compromise of the cloud environment. data_source: -- AWS CloudTrail DeleteNetworkAclEntry -search: '`cloudtrail` eventName=DeleteNetworkAclEntry requestParameters.egress=false - | fillnull - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_network_access_control_list_deleted_filter`' -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail - inputs. -known_false_positives: It's possible that a user has legitimately deleted a network - ACL. + - AWS CloudTrail DeleteNetworkAclEntry +search: |- + `cloudtrail` eventName=DeleteNetworkAclEntry requestParameters.egress=false + | fillnull + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_network_access_control_list_deleted_filter` +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. +known_false_positives: It's possible that a user has legitimately deleted a network ACL. references: [] drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ from $src$ has sucessfully deleted network ACLs entry, such that the instance is accessible from anywhere - risk_objects: - - field: user - type: user - score: 5 - threat_objects: - - field: src - type: ip_address + message: User $user$ from $src$ has sucessfully deleted network ACLs entry, such that the instance is accessible from anywhere + risk_objects: + - field: user + type: user + score: 5 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Network ACL Activity - asset_type: AWS Instance - mitre_attack_id: - - T1562.007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - AWS Network ACL Activity + asset_type: AWS Instance + mitre_attack_id: + - T1562.007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_delete_acl/aws_cloudtrail_events.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_delete_acl/aws_cloudtrail_events.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_new_mfa_method_registered_for_user.yml b/detections/cloud/aws_new_mfa_method_registered_for_user.yml index c95ec1d221..bcd85fa768 100644 --- a/detections/cloud/aws_new_mfa_method_registered_for_user.yml +++ b/detections/cloud/aws_new_mfa_method_registered_for_user.yml @@ -1,71 +1,62 @@ name: AWS New MFA Method Registered For User id: 4e3c26f2-4fb9-4bd7-ab46-1b76ffa2a23b -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP -description: The following analytic detects the registration of a new Multi-Factor - Authentication (MFA) method for an AWS account. It leverages AWS CloudTrail logs - to identify the `CreateVirtualMFADevice` event. This activity is significant because - adversaries who gain unauthorized access to an AWS account may register a new MFA - method to maintain persistence. If confirmed malicious, this could allow attackers - to secure their access, making it difficult to detect and remove their presence, - potentially leading to further unauthorized activities and data breaches. +description: The following analytic detects the registration of a new Multi-Factor Authentication (MFA) method for an AWS account. It leverages AWS CloudTrail logs to identify the `CreateVirtualMFADevice` event. This activity is significant because adversaries who gain unauthorized access to an AWS account may register a new MFA method to maintain persistence. If confirmed malicious, this could allow attackers to secure their access, making it difficult to detect and remove their presence, potentially leading to further unauthorized activities and data breaches. data_source: -- AWS CloudTrail CreateVirtualMFADevice -search: '`cloudtrail` eventName=CreateVirtualMFADevice - | rename userName as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `aws_new_mfa_method_registered_for_user_filter`' -how_to_implement: You must install Splunk AWS add on and Splunk App for AWS. This - search works when AWS CloudTrail logs. -known_false_positives: Newly onboarded users who are registering an MFA method for - the first time will also trigger this detection. + - AWS CloudTrail CreateVirtualMFADevice +search: |- + `cloudtrail` eventName=CreateVirtualMFADevice + | rename userName as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_new_mfa_method_registered_for_user_filter` +how_to_implement: You must install Splunk AWS add on and Splunk App for AWS. This search works when AWS CloudTrail logs. +known_false_positives: Newly onboarded users who are registering an MFA method for the first time will also trigger this detection. references: -- https://aws.amazon.com/blogs/security/you-can-now-assign-multiple-mfa-devices-in-iam/ -- https://attack.mitre.org/techniques/T1556/ -- https://attack.mitre.org/techniques/T1556/006/ -- https://twitter.com/jhencinski/status/1618660062352007174 + - https://aws.amazon.com/blogs/security/you-can-now-assign-multiple-mfa-devices-in-iam/ + - https://attack.mitre.org/techniques/T1556/ + - https://attack.mitre.org/techniques/T1556/006/ + - https://twitter.com/jhencinski/status/1618660062352007174 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A new virtual device is added to user $user$ - risk_objects: - - field: user - type: user - score: 64 - threat_objects: - - field: src - type: ip_address + message: A new virtual device is added to user $user$ + risk_objects: + - field: user + type: user + score: 64 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Identity and Access Management Account Takeover - asset_type: AWS Account - mitre_attack_id: - - T1556.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - AWS Identity and Access Management Account Takeover + asset_type: AWS Account + mitre_attack_id: + - T1556.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556.006/aws_new_mfa_method_registered_for_user/cloudtrail.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556.006/aws_new_mfa_method_registered_for_user/cloudtrail.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_password_policy_changes.yml b/detections/cloud/aws_password_policy_changes.yml index 734856119d..62c4c34586 100644 --- a/detections/cloud/aws_password_policy_changes.yml +++ b/detections/cloud/aws_password_policy_changes.yml @@ -1,49 +1,44 @@ name: AWS Password Policy Changes id: aee4a575-7064-4e60-b511-246f9baf9895 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: Hunting -description: The following analytic detects successful API calls to view, update, - or delete the password policy in an AWS organization. It leverages AWS CloudTrail - logs to identify events such as "UpdateAccountPasswordPolicy," "GetAccountPasswordPolicy," - and "DeleteAccountPasswordPolicy." This activity is significant because it is uncommon - for regular users to perform these actions, and such changes can indicate an adversary - attempting to understand or weaken password defenses. If confirmed malicious, this - could lead to compromised accounts and increased attack surface, potentially allowing - unauthorized access and control over AWS resources. +description: The following analytic detects successful API calls to view, update, or delete the password policy in an AWS organization. It leverages AWS CloudTrail logs to identify events such as "UpdateAccountPasswordPolicy," "GetAccountPasswordPolicy," and "DeleteAccountPasswordPolicy." This activity is significant because it is uncommon for regular users to perform these actions, and such changes can indicate an adversary attempting to understand or weaken password defenses. If confirmed malicious, this could lead to compromised accounts and increased attack surface, potentially allowing unauthorized access and control over AWS resources. data_source: -- AWS CloudTrail UpdateAccountPasswordPolicy -- AWS CloudTrail GetAccountPasswordPolicy -- AWS CloudTrail DeleteAccountPasswordPolicy -search: '`cloudtrail` eventName IN ("UpdateAccountPasswordPolicy","GetAccountPasswordPolicy","DeleteAccountPasswordPolicy") errorCode=success - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_password_policy_changes_filter`' -how_to_implement: You must install Splunk AWS Add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. -known_false_positives: While this search has no known false positives, it is possible - that an AWS admin has legitimately triggered an AWS audit tool activity which may - trigger this event. + - AWS CloudTrail UpdateAccountPasswordPolicy + - AWS CloudTrail GetAccountPasswordPolicy + - AWS CloudTrail DeleteAccountPasswordPolicy +search: |- + `cloudtrail` eventName IN ("UpdateAccountPasswordPolicy","GetAccountPasswordPolicy","DeleteAccountPasswordPolicy") errorCode=success + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_password_policy_changes_filter` +how_to_implement: You must install Splunk AWS Add on and Splunk App for AWS. This search works with AWS CloudTrail logs. +known_false_positives: While this search has no known false positives, it is possible that an AWS admin has legitimately triggered an AWS audit tool activity which may trigger this event. references: -- https://www.trendmicro.com/cloudoneconformity/knowledge-base/aws/IAM/password-policy.html + - https://www.trendmicro.com/cloudoneconformity/knowledge-base/aws/IAM/password-policy.html tags: - analytic_story: - - AWS IAM Privilege Escalation - - Compromised User Account - asset_type: AWS Account - mitre_attack_id: - - T1201 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS IAM Privilege Escalation + - Compromised User Account + asset_type: AWS Account + mitre_attack_id: + - T1201 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/aws_password_policy/cloudtrail.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/aws_password_policy/cloudtrail.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_s3_exfiltration_behavior_identified.yml b/detections/cloud/aws_s3_exfiltration_behavior_identified.yml index eb82d957c2..c7a89af8f1 100644 --- a/detections/cloud/aws_s3_exfiltration_behavior_identified.yml +++ b/detections/cloud/aws_s3_exfiltration_behavior_identified.yml @@ -1,68 +1,53 @@ name: AWS S3 Exfiltration Behavior Identified id: 85096389-a443-42df-b89d-200efbb1b560 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: Correlation data_source: [] -description: The following analytic identifies potential AWS S3 exfiltration behavior - by correlating multiple risk events related to Collection and Exfiltration techniques. - It leverages risk events from AWS sources, focusing on instances where two or more - unique analytics and distinct MITRE ATT&CK IDs are triggered for a specific risk - object. This activity is significant as it may indicate an ongoing data exfiltration - attempt, which is critical for security teams to monitor. If confirmed malicious, - this could lead to unauthorized access and theft of sensitive information, compromising - the organization's data integrity and confidentiality. -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) - as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as - annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) - as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) - as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) - as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, - dc(source) as source_count values(All_Risk.risk_message) as risk_message from datamodel=Risk.All_Risk - where All_Risk.annotations.mitre_attack.mitre_tactic = "collection" OR All_Risk.annotations.mitre_attack.mitre_tactic - = "exfiltration" source = *AWS* by All_Risk.risk_object | `drop_dm_object_name(All_Risk)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where - source_count >= 2 and mitre_tactic_id_count>=2 | `aws_s3_exfiltration_behavior_identified_filter`' -how_to_implement: You must enable all the detection searches in the Data Exfiltration - Analytic story to create risk events in Enterprise Security. -known_false_positives: alse positives may be present based on automated tooling or - system administrators. Filter as needed. +description: The following analytic identifies potential AWS S3 exfiltration behavior by correlating multiple risk events related to Collection and Exfiltration techniques. It leverages risk events from AWS sources, focusing on instances where two or more unique analytics and distinct MITRE ATT&CK IDs are triggered for a specific risk object. This activity is significant as it may indicate an ongoing data exfiltration attempt, which is critical for security teams to monitor. If confirmed malicious, this could lead to unauthorized access and theft of sensitive information, compromising the organization's data integrity and confidentiality. +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count values(All_Risk.risk_message) as risk_message FROM datamodel=Risk.All_Risk + WHERE All_Risk.annotations.mitre_attack.mitre_tactic = "collection" + OR + All_Risk.annotations.mitre_attack.mitre_tactic = "exfiltration" source = *AWS* + BY All_Risk.risk_object + | `drop_dm_object_name(All_Risk)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | where source_count >= 2 and mitre_tactic_id_count>=2 + | `aws_s3_exfiltration_behavior_identified_filter` +how_to_implement: You must enable all the detection searches in the Data Exfiltration Analytic story to create risk events in Enterprise Security. +known_false_positives: alse positives may be present based on automated tooling or system administrators. Filter as needed. references: -- https://labs.nettitude.com/blog/how-to-exfiltrate-aws-ec2-data/ -- https://stratus-red-team.cloud/attack-techniques/AWS/aws.exfiltration.ec2-share-ebs-snapshot/ -- https://hackingthe.cloud/aws/enumeration/loot_public_ebs_snapshots/ + - https://labs.nettitude.com/blog/how-to-exfiltrate-aws-ec2-data/ + - https://stratus-red-team.cloud/attack-techniques/AWS/aws.exfiltration.ec2-share-ebs-snapshot/ + - https://hackingthe.cloud/aws/enumeration/loot_public_ebs_snapshots/ drilldown_searches: -- name: View the detection results for - "$risk_object$" - search: '%original_detection_search% | search risk_object = "$risk_object$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$risk_object$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$risk_object$" + search: '%original_detection_search% | search risk_object = "$risk_object$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$risk_object$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ tags: - analytic_story: - - Suspicious Cloud Instance Activities - - Data Exfiltration - asset_type: AWS Account - mitre_attack_id: - - T1537 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Suspicious Cloud Instance Activities + - Data Exfiltration + asset_type: AWS Account + mitre_attack_id: + - T1537 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1537/aws_exfil_risk_events/aws_risk.log - sourcetype: stash - source: aws_exfil + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1537/aws_exfil_risk_events/aws_risk.log + sourcetype: stash + source: aws_exfil diff --git a/detections/cloud/aws_saml_update_identity_provider.yml b/detections/cloud/aws_saml_update_identity_provider.yml index c606eb1532..d92095bb75 100644 --- a/detections/cloud/aws_saml_update_identity_provider.yml +++ b/detections/cloud/aws_saml_update_identity_provider.yml @@ -1,73 +1,62 @@ name: AWS SAML Update identity provider id: 2f0604c6-6030-11eb-ae93-0242ac130002 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Rod Soto, Splunk status: production type: TTP -description: The following analytic detects updates to the SAML provider in AWS. It - leverages AWS CloudTrail logs to identify the `UpdateSAMLProvider` event, analyzing - fields such as `sAMLProviderArn`, `sourceIPAddress`, and `userIdentity` details. - Monitoring updates to the SAML provider is crucial as it may indicate a perimeter - compromise of federated credentials or unauthorized backdoor access set by an attacker. - If confirmed malicious, this activity could allow attackers to manipulate identity - federation, potentially leading to unauthorized access to cloud resources and sensitive - data. +description: The following analytic detects updates to the SAML provider in AWS. It leverages AWS CloudTrail logs to identify the `UpdateSAMLProvider` event, analyzing fields such as `sAMLProviderArn`, `sourceIPAddress`, and `userIdentity` details. Monitoring updates to the SAML provider is crucial as it may indicate a perimeter compromise of federated credentials or unauthorized backdoor access set by an attacker. If confirmed malicious, this activity could allow attackers to manipulate identity federation, potentially leading to unauthorized access to cloud resources and sensitive data. data_source: -- AWS CloudTrail UpdateSAMLProvider -search: '`cloudtrail` eventName=UpdateSAMLProvider - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.sAMLProviderArn) as request_parameters by signature dest user user_agent src vendor_account vendor_region vendor_product - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - |`aws_saml_update_identity_provider_filter`' -how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. -known_false_positives: Updating a SAML provider or creating a new one may not necessarily - be malicious however it needs to be closely monitored. + - AWS CloudTrail UpdateSAMLProvider +search: |- + `cloudtrail` eventName=UpdateSAMLProvider + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.sAMLProviderArn) as request_parameters + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_saml_update_identity_provider_filter` +how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. +known_false_positives: Updating a SAML provider or creating a new one may not necessarily be malicious however it needs to be closely monitored. references: -- https://www.cisa.gov/uscert/ncas/alerts/aa21-008a -- https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html -- https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf -- https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps + - https://www.cisa.gov/uscert/ncas/alerts/aa21-008a + - https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html + - https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf + - https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ from IP address $src$ has trigged - an event $signature$ to update the SAML provider to $request_parameters$ - risk_objects: - - field: user - type: user - score: 64 - threat_objects: - - field: src - type: ip_address + message: User $user$ from IP address $src$ has trigged an event $signature$ to update the SAML provider to $request_parameters$ + risk_objects: + - field: user + type: user + score: 64 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Cloud Federated Credential Abuse - asset_type: AWS Federated Account - mitre_attack_id: - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Cloud Federated Credential Abuse + asset_type: AWS Federated Account + mitre_attack_id: + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/update_saml_provider/update_saml_provider.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/update_saml_provider/update_saml_provider.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_setdefaultpolicyversion.yml b/detections/cloud/aws_setdefaultpolicyversion.yml index e2ce1660e1..e197ad8635 100644 --- a/detections/cloud/aws_setdefaultpolicyversion.yml +++ b/detections/cloud/aws_setdefaultpolicyversion.yml @@ -1,71 +1,60 @@ name: AWS SetDefaultPolicyVersion id: 2a9b80d3-6340-4345-11ad-212bf3d0dac4 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP -description: The following analytic detects when a user sets a default policy version - in AWS. It leverages AWS CloudTrail logs to identify the `SetDefaultPolicyVersion` - event from the IAM service. This activity is significant because attackers may exploit - this technique for privilege escalation, especially if previous policy versions - grant more extensive permissions than the current one. If confirmed malicious, this - could allow an attacker to gain elevated access to AWS resources, potentially leading - to unauthorized actions and data breaches. +description: The following analytic detects when a user sets a default policy version in AWS. It leverages AWS CloudTrail logs to identify the `SetDefaultPolicyVersion` event from the IAM service. This activity is significant because attackers may exploit this technique for privilege escalation, especially if previous policy versions grant more extensive permissions than the current one. If confirmed malicious, this could allow an attacker to gain elevated access to AWS resources, potentially leading to unauthorized actions and data breaches. data_source: -- AWS CloudTrail SetDefaultPolicyVersion -search: '`cloudtrail` eventName=SetDefaultPolicyVersion eventSource = iam.amazonaws.com - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_setdefaultpolicyversion_filter`' -how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. -known_false_positives: While this search has no known false positives, it is possible - that an AWS admin has legitimately set a default policy to allow a user to access - all resources. That said, AWS strongly advises against granting full control to - all AWS resources + - AWS CloudTrail SetDefaultPolicyVersion +search: |- + `cloudtrail` eventName=SetDefaultPolicyVersion eventSource = iam.amazonaws.com + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_setdefaultpolicyversion_filter` +how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. +known_false_positives: While this search has no known false positives, it is possible that an AWS admin has legitimately set a default policy to allow a user to access all resources. That said, AWS strongly advises against granting full control to all AWS resources references: -- https://bishopfox.com/blog/privilege-escalation-in-aws -- https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/ + - https://bishopfox.com/blog/privilege-escalation-in-aws + - https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: From IP address $src$, user $user$ has trigged an action $signature$ - for updating the the default policy version - risk_objects: - - field: user - type: user - score: 30 - threat_objects: - - field: src - type: ip_address + message: From IP address $src$, user $user$ has trigged an action $signature$ for updating the the default policy version + risk_objects: + - field: user + type: user + score: 30 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS IAM Privilege Escalation - asset_type: AWS Account - mitre_attack_id: - - T1078.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS IAM Privilege Escalation + asset_type: AWS Account + mitre_attack_id: + - T1078.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_setdefaultpolicyversion/aws_cloudtrail_events.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_setdefaultpolicyversion/aws_cloudtrail_events.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_successful_console_authentication_from_multiple_ips.yml b/detections/cloud/aws_successful_console_authentication_from_multiple_ips.yml index 59eb9a1914..1f67a46b18 100644 --- a/detections/cloud/aws_successful_console_authentication_from_multiple_ips.yml +++ b/detections/cloud/aws_successful_console_authentication_from_multiple_ips.yml @@ -1,73 +1,62 @@ name: AWS Successful Console Authentication From Multiple IPs id: 395e50e1-2b87-4fa3-8632-0dfbdcbcd2cb -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: Anomaly -description: The following analytic detects an AWS account successfully authenticating - from multiple unique IP addresses within a 5-minute window. It leverages AWS CloudTrail - logs, specifically monitoring `ConsoleLogin` events and counting distinct source - IPs. This behavior is significant as it may indicate compromised credentials, potentially - from a phishing attack, being used concurrently by an adversary and a legitimate - user. If confirmed malicious, this activity could allow unauthorized access to corporate - resources, leading to data breaches or further exploitation within the AWS environment. +description: The following analytic detects an AWS account successfully authenticating from multiple unique IP addresses within a 5-minute window. It leverages AWS CloudTrail logs, specifically monitoring `ConsoleLogin` events and counting distinct source IPs. This behavior is significant as it may indicate compromised credentials, potentially from a phishing attack, being used concurrently by an adversary and a legitimate user. If confirmed malicious, this activity could allow unauthorized access to corporate resources, leading to data breaches or further exploitation within the AWS environment. data_source: -- AWS CloudTrail ConsoleLogin -search: '`cloudtrail` eventName = ConsoleLogin - | bin span=5m _time - | rename user_name as user - | stats dc(src) as distinct_ip_count values(src) as src values(user_agent) as user_agent values(dest) as dest by _time, user, signature, vendor_account, vendor_region, vendor_product - | where distinct_ip_count>1 - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_successful_console_authentication_from_multiple_ips_filter`' -how_to_implement: You must install Splunk AWS add on and Splunk App for AWS. This - search works when AWS CloudTrail events are normalized use the Authentication datamodel. -known_false_positives: A user with successful authentication events from different - Ips may also represent the legitimate use of more than one device. Filter as needed - and/or customize the threshold to fit your environment. + - AWS CloudTrail ConsoleLogin +search: |- + `cloudtrail` eventName = ConsoleLogin + | bin span=5m _time + | rename user_name as user + | stats dc(src) as distinct_ip_count values(src) as src values(user_agent) as user_agent values(dest) as dest + BY _time, user, signature, + vendor_account, vendor_region, vendor_product + | where distinct_ip_count>1 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_successful_console_authentication_from_multiple_ips_filter` +how_to_implement: You must install Splunk AWS add on and Splunk App for AWS. This search works when AWS CloudTrail events are normalized use the Authentication datamodel. +known_false_positives: A user with successful authentication events from different Ips may also represent the legitimate use of more than one device. Filter as needed and/or customize the threshold to fit your environment. references: -- https://rhinosecuritylabs.com/aws/mfa-phishing-on-aws/ + - https://rhinosecuritylabs.com/aws/mfa-phishing-on-aws/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has successfully logged into the AWS Console from different - IP addresses $src$ within 5 mins - risk_objects: - - field: user - type: user - score: 72 - threat_objects: - - field: src - type: ip_address + message: User $user$ has successfully logged into the AWS Console from different IP addresses $src$ within 5 mins + risk_objects: + - field: user + type: user + score: 72 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Suspicious AWS Login Activities - - Compromised User Account - asset_type: AWS Account - mitre_attack_id: - - T1586 - - T1535 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Suspicious AWS Login Activities + - Compromised User Account + asset_type: AWS Account + mitre_attack_id: + - T1586 + - T1535 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1586.003/aws_console_login_multiple_ips/cloudtrail.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1586.003/aws_console_login_multiple_ips/cloudtrail.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_successful_single_factor_authentication.yml b/detections/cloud/aws_successful_single_factor_authentication.yml index 7a2a49d184..9ae52c8292 100644 --- a/detections/cloud/aws_successful_single_factor_authentication.yml +++ b/detections/cloud/aws_successful_single_factor_authentication.yml @@ -1,72 +1,62 @@ name: AWS Successful Single-Factor Authentication id: a520b1fe-cc9e-4f56-b762-18354594c52f -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP -description: The following analytic identifies a successful Console Login authentication - event for an AWS IAM user account without Multi-Factor Authentication (MFA) enabled. - It leverages AWS CloudTrail logs to detect instances where MFA was not used during - login. This activity is significant as it may indicate a misconfiguration, policy - violation, or potential account takeover attempt. If confirmed malicious, an attacker - could gain unauthorized access to the AWS environment, potentially leading to data - exfiltration, resource manipulation, or further privilege escalation. +description: The following analytic identifies a successful Console Login authentication event for an AWS IAM user account without Multi-Factor Authentication (MFA) enabled. It leverages AWS CloudTrail logs to detect instances where MFA was not used during login. This activity is significant as it may indicate a misconfiguration, policy violation, or potential account takeover attempt. If confirmed malicious, an attacker could gain unauthorized access to the AWS environment, potentially leading to data exfiltration, resource manipulation, or further privilege escalation. data_source: -- AWS CloudTrail ConsoleLogin -search: '`cloudtrail` eventName= ConsoleLogin errorCode=success "additionalEventData.MFAUsed"=No - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `aws_successful_single_factor_authentication_filter`' -how_to_implement: The Splunk AWS Add-on is required to utilize this data. The search - requires AWS CloudTrail logs. -known_false_positives: It is possible that some accounts do not have MFA enabled for - the AWS account however its agaisnt the best practices of securing AWS. + - AWS CloudTrail ConsoleLogin +search: |- + `cloudtrail` eventName= ConsoleLogin errorCode=success "additionalEventData.MFAUsed"=No + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_successful_single_factor_authentication_filter` +how_to_implement: The Splunk AWS Add-on is required to utilize this data. The search requires AWS CloudTrail logs. +known_false_positives: It is possible that some accounts do not have MFA enabled for the AWS account however its agaisnt the best practices of securing AWS. references: -- https://attack.mitre.org/techniques/T1621/ -- https://attack.mitre.org/techniques/T1078/004/ -- https://aws.amazon.com/what-is/mfa/ + - https://attack.mitre.org/techniques/T1621/ + - https://attack.mitre.org/techniques/T1078/004/ + - https://aws.amazon.com/what-is/mfa/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has successfully logged into an AWS Console without Multi-Factor - Authentication from $src$ - risk_objects: - - field: user - type: user - score: 64 - threat_objects: - - field: src - type: ip_address + message: User $user$ has successfully logged into an AWS Console without Multi-Factor Authentication from $src$ + risk_objects: + - field: user + type: user + score: 64 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Identity and Access Management Account Takeover - asset_type: AWS Account - mitre_attack_id: - - T1078.004 - - T1586.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Identity and Access Management Account Takeover + asset_type: AWS Account + mitre_attack_id: + - T1078.004 + - T1586.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/aws_login_sfa/cloudtrail.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/aws_login_sfa/cloudtrail.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/aws_unusual_number_of_failed_authentications_from_ip.yml b/detections/cloud/aws_unusual_number_of_failed_authentications_from_ip.yml index 70c96282f1..97d64e705d 100644 --- a/detections/cloud/aws_unusual_number_of_failed_authentications_from_ip.yml +++ b/detections/cloud/aws_unusual_number_of_failed_authentications_from_ip.yml @@ -1,79 +1,67 @@ name: AWS Unusual Number of Failed Authentications From Ip id: 0b5c9c2b-e2cb-4831-b4f1-af125ceb1386 -version: 11 -date: '2025-05-02' +version: 12 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: Anomaly -description: The following analytic identifies a single source IP failing to authenticate - into the AWS Console with multiple valid users. It uses CloudTrail logs and calculates - the standard deviation for source IP, leveraging the 3-sigma rule to detect unusual - numbers of failed authentication attempts. This behavior is significant as it may - indicate a Password Spraying attack, where an adversary attempts to gain initial - access or elevate privileges. If confirmed malicious, this activity could lead to - unauthorized access, data breaches, or further exploitation within the AWS environment. +description: The following analytic identifies a single source IP failing to authenticate into the AWS Console with multiple valid users. It uses CloudTrail logs and calculates the standard deviation for source IP, leveraging the 3-sigma rule to detect unusual numbers of failed authentication attempts. This behavior is significant as it may indicate a Password Spraying attack, where an adversary attempts to gain initial access or elevate privileges. If confirmed malicious, this activity could lead to unauthorized access, data breaches, or further exploitation within the AWS environment. data_source: -- AWS CloudTrail ConsoleLogin -search: '`cloudtrail` eventName=ConsoleLogin action=failure | rename eventName as - action, eventSource as dest, userName as user, userAgent as user_agent, sourceIPAddress - as src, userIdentity.accountId as vendor_account, awsRegion as vendor_region | bucket - span=10m _time | stats dc(_raw) AS distinct_attempts values(user_name) as tried_accounts - values(action) as action values(dest) as dest values(vendor_account) as vendor_account - values(vendor_region) as vendor_region values(vendor_product) as vendor_product - values(user_agent) as user_agent by _time, src | eventstats avg(distinct_attempts) - as avg_attempts , stdev(distinct_attempts) as ip_std by _time | eval upperBound=(avg_attempts+ip_std*3) - | eval isOutlier=if(distinct_attempts > 10 and distinct_attempts >= upperBound, - 1, 0) | where isOutlier = 1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `aws_unusual_number_of_failed_authentications_from_ip_filter`' -how_to_implement: You must install Splunk Add-on for AWS in order to ingest Cloudtrail. - We recommend the users to try different combinations of the bucket span time and - the calculation of the upperBound field to tune this search according to their environment -known_false_positives: No known false postives for this detection. Please review this - alert + - AWS CloudTrail ConsoleLogin +search: |- + `cloudtrail` eventName=ConsoleLogin action=failure + | rename eventName as action, eventSource as dest, userName as user, userAgent as user_agent, sourceIPAddress as src, userIdentity.accountId as vendor_account, awsRegion as vendor_region + | bucket span=10m _time + | stats dc(_raw) AS distinct_attempts values(user_name) as tried_accounts values(action) as action values(dest) as dest values(vendor_account) as vendor_account values(vendor_region) as vendor_region values(vendor_product) as vendor_product values(user_agent) as user_agent + BY _time, src + | eventstats avg(distinct_attempts) as avg_attempts , stdev(distinct_attempts) as ip_std + BY _time + | eval upperBound=(avg_attempts+ip_std*3) + | eval isOutlier=if(distinct_attempts > 10 and distinct_attempts >= upperBound, 1, 0) + | where isOutlier = 1 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_unusual_number_of_failed_authentications_from_ip_filter` +how_to_implement: You must install Splunk Add-on for AWS in order to ingest Cloudtrail. We recommend the users to try different combinations of the bucket span time and the calculation of the upperBound field to tune this search according to their environment +known_false_positives: No known false postives for this detection. Please review this alert references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://www.whiteoaksecurity.com/blog/goawsconsolespray-password-spraying-tool/ -- https://softwaresecuritydotblog.wordpress.com/2019/09/28/how-to-protect-against-credential-stuffing-on-aws/ + - https://attack.mitre.org/techniques/T1110/003/ + - https://www.whiteoaksecurity.com/blog/goawsconsolespray-password-spraying-tool/ + - https://softwaresecuritydotblog.wordpress.com/2019/09/28/how-to-protect-against-credential-stuffing-on-aws/ drilldown_searches: -- name: View the detection results for - "$tried_accounts$" - search: '%original_detection_search% | search tried_accounts = "$tried_accounts$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$tried_accounts$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$tried_accounts$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$tried_accounts$" + search: '%original_detection_search% | search tried_accounts = "$tried_accounts$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$tried_accounts$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$tried_accounts$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: 'Unusual number of failed console login attempts (Count: $distinct_attempts$) - against users from IP Address - $src$' - risk_objects: - - field: tried_accounts - type: user - score: 54 - threat_objects: - - field: src - type: ip_address + message: 'Unusual number of failed console login attempts (Count: $distinct_attempts$) against users from IP Address - $src$' + risk_objects: + - field: tried_accounts + type: user + score: 54 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS Identity and Access Management Account Takeover - asset_type: AWS Account - mitre_attack_id: - - T1110.003 - - T1110.004 - - T1586.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS Identity and Access Management Account Takeover + asset_type: AWS Account + mitre_attack_id: + - T1110.003 + - T1110.004 + - T1586.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/aws_mulitple_failed_console_login/aws_cloudtrail.json - source: aws_cloudtrail - sourcetype: aws:cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/aws_mulitple_failed_console_login/aws_cloudtrail.json + source: aws_cloudtrail + sourcetype: aws:cloudtrail diff --git a/detections/cloud/aws_updateloginprofile.yml b/detections/cloud/aws_updateloginprofile.yml index c430f55af6..b424cfd13d 100644 --- a/detections/cloud/aws_updateloginprofile.yml +++ b/detections/cloud/aws_updateloginprofile.yml @@ -1,74 +1,62 @@ name: AWS UpdateLoginProfile id: 2a9b80d3-6a40-4115-11ad-212bf3d0d111 -version: 10 -date: '2025-05-02' +version: 11 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP -description: The following analytic detects an AWS CloudTrail event where a user with - permissions updates the login profile of another user. It leverages CloudTrail logs - to identify instances where the user making the change is different from the user - whose profile is being updated. This activity is significant because it can indicate - privilege escalation attempts, where an attacker uses a compromised account to gain - higher privileges. If confirmed malicious, this could allow the attacker to escalate - their privileges, potentially leading to unauthorized access and control over sensitive - resources within the AWS environment. +description: The following analytic detects an AWS CloudTrail event where a user with permissions updates the login profile of another user. It leverages CloudTrail logs to identify instances where the user making the change is different from the user whose profile is being updated. This activity is significant because it can indicate privilege escalation attempts, where an attacker uses a compromised account to gain higher privileges. If confirmed malicious, this could allow the attacker to escalate their privileges, potentially leading to unauthorized access and control over sensitive resources within the AWS environment. data_source: -- AWS CloudTrail UpdateLoginProfile -search: '`cloudtrail` eventName = UpdateLoginProfile userAgent !=console.amazonaws.com - errorCode = success | eval match=if(match(userIdentity.userName,requestParameters.userName), - 1,0) | search match=0 - | rename user_name as user - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user user_agent src vendor_account vendor_region vendor_product - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `aws_updateloginprofile_filter`' -how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This - search works with AWS CloudTrail logs. -known_false_positives: While this search has no known false positives, it is possible - that an AWS admin has legitimately created keys for another user. + - AWS CloudTrail UpdateLoginProfile +search: |- + `cloudtrail` eventName = UpdateLoginProfile userAgent !=console.amazonaws.com errorCode = success + | eval match=if(match(userIdentity.userName,requestParameters.userName), 1,0) + | search match=0 + | rename user_name as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + user_agent src vendor_account + vendor_region vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `aws_updateloginprofile_filter` +how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. +known_false_positives: While this search has no known false positives, it is possible that an AWS admin has legitimately created keys for another user. references: -- https://bishopfox.com/blog/privilege-escalation-in-aws -- https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/ + - https://bishopfox.com/blog/privilege-escalation-in-aws + - https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: From IP address $src$, user agent $user_agent$ has trigged an event UpdateLoginProfile - for updating the existing login profile, potentially giving user $user$ more - access privilleges - risk_objects: - - field: user - type: user - score: 30 - threat_objects: - - field: src - type: ip_address + message: From IP address $src$, user agent $user_agent$ has trigged an event UpdateLoginProfile for updating the existing login profile, potentially giving user $user$ more access privilleges + risk_objects: + - field: user + type: user + score: 30 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - AWS IAM Privilege Escalation - asset_type: AWS Account - mitre_attack_id: - - T1136.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - AWS IAM Privilege Escalation + asset_type: AWS Account + mitre_attack_id: + - T1136.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_updateloginprofile/aws_cloudtrail_events.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_updateloginprofile/aws_cloudtrail_events.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/azure_active_directory_high_risk_sign_in.yml b/detections/cloud/azure_active_directory_high_risk_sign_in.yml index 7d1c997ae7..dcc88318db 100644 --- a/detections/cloud/azure_active_directory_high_risk_sign_in.yml +++ b/detections/cloud/azure_active_directory_high_risk_sign_in.yml @@ -1,74 +1,62 @@ name: Azure Active Directory High Risk Sign-in id: 1ecff169-26d7-4161-9a7b-2ac4c8e61bea -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk status: production type: TTP -description: The following analytic detects high-risk sign-in attempts against Azure - Active Directory, identified by Azure Identity Protection. It leverages the RiskyUsers - and UserRiskEvents log categories from Azure AD events ingested via EventHub. This - activity is significant as it indicates potentially compromised accounts, flagged - by heuristics and machine learning. If confirmed malicious, attackers could gain - unauthorized access to sensitive resources, leading to data breaches or further - exploitation within the environment. +description: The following analytic detects high-risk sign-in attempts against Azure Active Directory, identified by Azure Identity Protection. It leverages the RiskyUsers and UserRiskEvents log categories from Azure AD events ingested via EventHub. This activity is significant as it indicates potentially compromised accounts, flagged by heuristics and machine learning. If confirmed malicious, attackers could gain unauthorized access to sensitive resources, leading to data breaches or further exploitation within the environment. data_source: -- Azure Active Directory -search: '`azure_monitor_aad` `azure_monitor_aad` category=UserRiskEvents properties.riskLevel=high - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product category - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_active_directory_high_risk_sign_in_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. Specifically, this analytic leverages the RiskyUsers and UserRiskEvents - log category in the azure:monitor:aad sourcetype. -known_false_positives: Details for the risk calculation algorithm used by Identity - Protection are unknown and may be prone to false positives. + - Azure Active Directory +search: |- + `azure_monitor_aad` `azure_monitor_aad` category=UserRiskEvents properties.riskLevel=high + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product category + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_active_directory_high_risk_sign_in_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. Specifically, this analytic leverages the RiskyUsers and UserRiskEvents log category in the azure:monitor:aad sourcetype. +known_false_positives: Details for the risk calculation algorithm used by Identity Protection are unknown and may be prone to false positives. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://docs.microsoft.com/en-us/security/compass/incident-response-playbook-password-spray -- https://docs.microsoft.com/en-us/azure/active-directory/identity-protection/overview-identity-protection -- https://docs.microsoft.com/en-us/azure/active-directory/identity-protection/concept-identity-protection-risks + - https://attack.mitre.org/techniques/T1110/003/ + - https://docs.microsoft.com/en-us/security/compass/incident-response-playbook-password-spray + - https://docs.microsoft.com/en-us/azure/active-directory/identity-protection/overview-identity-protection + - https://docs.microsoft.com/en-us/azure/active-directory/identity-protection/concept-identity-protection-risks drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A high risk event was identified by Identify Protection for user $user$ - risk_objects: - - field: user - type: user - score: 54 - threat_objects: - - field: src - type: ip_address + message: A high risk event was identified by Identify Protection for user $user$ + risk_objects: + - field: user + type: user + score: 54 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Azure Active Directory Account Takeover - asset_type: Azure Active Directory - mitre_attack_id: - - T1110.003 - - T1586.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Account Takeover + asset_type: Azure Active Directory + mitre_attack_id: + - T1110.003 + - T1586.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/azuread_highrisk/azure-audit.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/azuread_highrisk/azure-audit.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_admin_consent_bypassed_by_service_principal.yml b/detections/cloud/azure_ad_admin_consent_bypassed_by_service_principal.yml index 72582792d7..5d866affca 100644 --- a/detections/cloud/azure_ad_admin_consent_bypassed_by_service_principal.yml +++ b/detections/cloud/azure_ad_admin_consent_bypassed_by_service_principal.yml @@ -4,78 +4,49 @@ version: 9 date: '2025-05-02' author: Mauricio Velazco, Splunk data_source: -- Azure Active Directory Add app role assignment to service principal + - Azure Active Directory Add app role assignment to service principal type: TTP status: production -description: The following analytic identifies instances where a service principal - in Azure Active Directory assigns app roles without standard admin consent. It uses - Entra ID logs from the `azure_monitor_aad` data source, focusing on the "Add app - role assignment to service principal" operation. This detection is significant as - it highlights potential bypasses of critical administrative consent processes, which - could lead to unauthorized privileges being granted. If confirmed malicious, this - activity could allow attackers to exploit automation to assign sensitive permissions - without proper oversight, potentially compromising the security of the Azure AD - environment. -search: "`azure_monitor_aad` (operationName=\"Add app role assignment to service principal\" OR operationName=\"Add member to role*\") src_user_type=servicePrincipal - | rename properties.* as * - | eval roleId = mvindex('targetResources{}.modifiedProperties{}.newValue',0) - | eval roleValue = mvindex('targetResources{}.modifiedProperties{}.newValue',1) - | eval roleDescription = mvindex('targetResources{}.modifiedProperties{}.newValue',2) - | eval user_id = mvindex('targetResources{}.id', 0), user=coalesce(user,mvindex('targetResources{}.displayName',0)) - | rename initiatedBy.app.displayName as src_user, userAgent as user_agent - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product src_user user_id roleId roleValue roleDescription user_agent signature - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `azure_ad_admin_consent_bypassed_by_service_principal_filter`" -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment. - This analytic was written to be used with the azure:monitor:aad sourcetype leveraging - the Auditlog log category -known_false_positives: Service Principals are sometimes configured to legitimately - bypass the consent process for purposes of automation. Filter as needed. +description: The following analytic identifies instances where a service principal in Azure Active Directory assigns app roles without standard admin consent. It uses Entra ID logs from the `azure_monitor_aad` data source, focusing on the "Add app role assignment to service principal" operation. This detection is significant as it highlights potential bypasses of critical administrative consent processes, which could lead to unauthorized privileges being granted. If confirmed malicious, this activity could allow attackers to exploit automation to assign sensitive permissions without proper oversight, potentially compromising the security of the Azure AD environment. +search: "`azure_monitor_aad` (operationName=\"Add app role assignment to service principal\" OR operationName=\"Add member to role*\") src_user_type=servicePrincipal | rename properties.* as * | eval roleId = mvindex('targetResources{}.modifiedProperties{}.newValue',0) | eval roleValue = mvindex('targetResources{}.modifiedProperties{}.newValue',1) | eval roleDescription = mvindex('targetResources{}.modifiedProperties{}.newValue',2) | eval user_id = mvindex('targetResources{}.id', 0), user=coalesce(user,mvindex('targetResources{}.displayName',0)) | rename initiatedBy.app.displayName as src_user, userAgent as user_agent | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product src_user user_id roleId roleValue roleDescription user_agent signature | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_admin_consent_bypassed_by_service_principal_filter`" +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the Auditlog log category +known_false_positives: Service Principals are sometimes configured to legitimately bypass the consent process for purposes of automation. Filter as needed. references: -- https://attack.mitre.org/techniques/T1098/003/ + - https://attack.mitre.org/techniques/T1098/003/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 - | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Service principal $src_user$ bypassed the admin consent process and granted - permissions to $user$ - risk_objects: - - field: user - type: user - score: 54 - - field: src_user - type: user - score: 54 - threat_objects: [] + message: Service principal $src_user$ bypassed the admin consent process and granted permissions to $user$ + risk_objects: + - field: user + type: user + score: 54 + - field: src_user + type: user + score: 54 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Privilege Escalation - - NOBELIUM Group - asset_type: Azure Active Directory - mitre_attack_id: - - T1098.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Privilege Escalation + - NOBELIUM Group + asset_type: Azure Active Directory + mitre_attack_id: + - T1098.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_bypass_admin_consent/azure_ad_bypass_admin_consent.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_bypass_admin_consent/azure_ad_bypass_admin_consent.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_application_administrator_role_assigned.yml b/detections/cloud/azure_ad_application_administrator_role_assigned.yml index 364fc8db0c..45cc4ded38 100644 --- a/detections/cloud/azure_ad_application_administrator_role_assigned.yml +++ b/detections/cloud/azure_ad_application_administrator_role_assigned.yml @@ -1,77 +1,65 @@ name: Azure AD Application Administrator Role Assigned id: eac4de87-7a56-4538-a21b-277897af6d8d -version: 11 -date: '2025-10-14' +version: 12 +date: '2026-02-25' author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk status: production type: TTP data_source: -- Azure Active Directory Add member to role -description: The following analytic identifies the assignment of the Application Administrator - role to an Azure AD user. It leverages Azure Active Directory events, specifically - monitoring the "Add member to role" operation. This activity is significant because - users in this role can manage all aspects of enterprise applications, including - credentials, which can be used to impersonate application identities. If confirmed - malicious, an attacker could escalate privileges, manage application settings, and - potentially access sensitive resources by impersonating application identities, - posing a significant security risk to the Azure AD tenant. -search: '`azure_monitor_aad` operationName="Add member to role" "properties.targetResources{}.modifiedProperties{}.newValue"="*Application Administrator*" - | rename properties.* as * | rename initiatedBy.user.userPrincipalName as initiatedBy, userAgent as user_agent - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product initiatedBy user_agent signature - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_application_administrator_role_assigned_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment. - This analytic was written to be used with the azure:monitor:aad sourcetype leveraging - the Auditlog log category -known_false_positives: Administrators may legitimately assign the Application Administrator - role to a user. Filter as needed. + - Azure Active Directory Add member to role +description: The following analytic identifies the assignment of the Application Administrator role to an Azure AD user. It leverages Azure Active Directory events, specifically monitoring the "Add member to role" operation. This activity is significant because users in this role can manage all aspects of enterprise applications, including credentials, which can be used to impersonate application identities. If confirmed malicious, an attacker could escalate privileges, manage application settings, and potentially access sensitive resources by impersonating application identities, posing a significant security risk to the Azure AD tenant. +search: |- + `azure_monitor_aad` operationName="Add member to role" "properties.targetResources{}.modifiedProperties{}.newValue"="*Application Administrator*" + | rename properties.* as * + | rename initiatedBy.user.userPrincipalName as initiatedBy, userAgent as user_agent + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product initiatedBy + user_agent signature + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_application_administrator_role_assigned_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the Auditlog log category +known_false_positives: Administrators may legitimately assign the Application Administrator role to a user. Filter as needed. references: -- https://dirkjanm.io/azure-ad-privilege-escalation-application-admin/ -- https://posts.specterops.io/azure-privilege-escalation-via-service-principal-abuse-210ae2be2a5 -- https://docs.microsoft.com/en-us/azure/active-directory/roles/concept-understand-roles -- https://attack.mitre.org/techniques/T1098/003/ -- https://learn.microsoft.com/en-us/azure/active-directory/roles/permissions-reference#application-administrator + - https://dirkjanm.io/azure-ad-privilege-escalation-application-admin/ + - https://posts.specterops.io/azure-privilege-escalation-via-service-principal-abuse-210ae2be2a5 + - https://docs.microsoft.com/en-us/azure/active-directory/roles/concept-understand-roles + - https://attack.mitre.org/techniques/T1098/003/ + - https://learn.microsoft.com/en-us/azure/active-directory/roles/permissions-reference#application-administrator drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The privileged Azure AD role Application Administrator was assigned for - User $user$ initiated by $initiatedBy$ - risk_objects: - - field: user - type: user - score: 35 - threat_objects: [] + message: The privileged Azure AD role Application Administrator was assigned for User $user$ initiated by $initiatedBy$ + risk_objects: + - field: user + type: user + score: 35 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Privilege Escalation - - Scattered Lapsus$ Hunters - asset_type: Azure Active Directory - atomic_guid: [] - mitre_attack_id: - - T1098.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azure Active Directory Privilege Escalation + - Scattered Lapsus$ Hunters + asset_type: Azure Active Directory + atomic_guid: [] + mitre_attack_id: + - T1098.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_assign_privileged_role/azure-audit.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_assign_privileged_role/azure-audit.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_authentication_failed_during_mfa_challenge.yml b/detections/cloud/azure_ad_authentication_failed_during_mfa_challenge.yml index ee7da5b832..8ced8bb4ca 100644 --- a/detections/cloud/azure_ad_authentication_failed_during_mfa_challenge.yml +++ b/detections/cloud/azure_ad_authentication_failed_during_mfa_challenge.yml @@ -5,82 +5,51 @@ date: '2025-05-02' author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk, 0xC0FFEEEE status: production type: TTP -description: The following analytic identifies failed authentication attempts against - an Azure AD tenant during the Multi-Factor Authentication (MFA) challenge, specifically - flagged by error code 500121. It leverages Azure AD SignInLogs to detect these events. - This activity is significant as it may indicate an adversary attempting to authenticate - using compromised credentials on an account with MFA enabled. If confirmed malicious, - this could suggest an ongoing effort to bypass MFA protections, potentially leading - to unauthorized access and further compromise of the affected account. +description: The following analytic identifies failed authentication attempts against an Azure AD tenant during the Multi-Factor Authentication (MFA) challenge, specifically flagged by error code 500121. It leverages Azure AD SignInLogs to detect these events. This activity is significant as it may indicate an adversary attempting to authenticate using compromised credentials on an account with MFA enabled. If confirmed malicious, this could suggest an ongoing effort to bypass MFA protections, potentially leading to unauthorized access and further compromise of the affected account. data_source: -- Azure Active Directory -search: "`azure_monitor_aad` category=SignInLogs properties.status.errorCode=500121 - | rename properties.* as *, authenticationDetails{}.* as * - | eval time=strptime(authenticationStepDateTime,\"%Y-%m-%dT%H:%M:%S\") - | eval auth_detail=mvzip(strftime(time, \"%Y-%m-%dT%H:%M:%S\"),authenticationStepResultDetail,\" - \"), auth_msg=mvappend('status.additionalDetails', - authenticationStepResultDetail) - | eval auth_method=mvmap(authenticationMethod, if(isnull(mvfind('mfaDetail.authMethod',authenticationMethod)), authenticationMethod, null())) - | search NOT auth_msg=\"MFA successfully completed\" - | rename userAgent as user_agent - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product auth_method auth_msg user_agent signature - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_authentication_failed_during_mfa_challenge_filter`" -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the SignInLogs log category. -known_false_positives: "False positives have been minimized by removing attempts that - result in 'MFA successfully completed messages', which were found to be generated - when a user opts to use a different MFA method than the default.\nFurther reductions - in finding events can be achieved through filtering 'MFA denied; duplicate authentication - attempt' messages within the auth_msg field, as they could arguably be considered - as false positives." + - Azure Active Directory +search: "`azure_monitor_aad` category=SignInLogs properties.status.errorCode=500121 | rename properties.* as *, authenticationDetails{}.* as * | eval time=strptime(authenticationStepDateTime,\"%Y-%m-%dT%H:%M:%S\") | eval auth_detail=mvzip(strftime(time, \"%Y-%m-%dT%H:%M:%S\"),authenticationStepResultDetail,\" - \"), auth_msg=mvappend('status.additionalDetails', authenticationStepResultDetail) | eval auth_method=mvmap(authenticationMethod, if(isnull(mvfind('mfaDetail.authMethod',authenticationMethod)), authenticationMethod, null())) | search NOT auth_msg=\"MFA successfully completed\" | rename userAgent as user_agent | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product auth_method auth_msg user_agent signature | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_authentication_failed_during_mfa_challenge_filter`" +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the SignInLogs log category. +known_false_positives: "False positives have been minimized by removing attempts that result in 'MFA successfully completed messages', which were found to be generated when a user opts to use a different MFA method than the default.\nFurther reductions in finding events can be achieved through filtering 'MFA denied; duplicate authentication attempt' messages within the auth_msg field, as they could arguably be considered as false positives." references: -- https://attack.mitre.org/techniques/T1621/ -- https://attack.mitre.org/techniques/T1078/004/ -- https://docs.microsoft.com/en-us/azure/active-directory/authentication/concept-mfa-howitworks -- https://learn.microsoft.com/en-us/entra/identity/monitoring-health/concept-sign-in-log-activity-details + - https://attack.mitre.org/techniques/T1621/ + - https://attack.mitre.org/techniques/T1078/004/ + - https://docs.microsoft.com/en-us/azure/active-directory/authentication/concept-mfa-howitworks + - https://learn.microsoft.com/en-us/entra/identity/monitoring-health/concept-sign-in-log-activity-details drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ failed to pass MFA challenge - risk_objects: - - field: user - type: user - score: 54 - threat_objects: - - field: src - type: ip_address + message: User $user$ failed to pass MFA challenge + risk_objects: + - field: user + type: user + score: 54 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Azure Active Directory Account Takeover - asset_type: Azure Active Directory - mitre_attack_id: - - T1078.004 - - T1586.003 - - T1621 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Account Takeover + asset_type: Azure Active Directory + mitre_attack_id: + - T1078.004 + - T1586.003 + - T1621 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/azuread/azure-audit.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/azuread/azure-audit.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_azurehound_useragent_detected.yml b/detections/cloud/azure_ad_azurehound_useragent_detected.yml index 58143dcc63..41a0a6b321 100644 --- a/detections/cloud/azure_ad_azurehound_useragent_detected.yml +++ b/detections/cloud/azure_ad_azurehound_useragent_detected.yml @@ -1,61 +1,66 @@ name: Azure AD AzureHound UserAgent Detected id: d62852db-a1f1-40db-a7fc-c3d56fa8bda3 -version: 5 -date: '2026-01-14' +version: 6 +date: '2026-02-25' author: Dean Luxton data_source: - - Azure Active Directory NonInteractiveUserSignInLogs - - Azure Active Directory MicrosoftGraphActivityLogs + - Azure Active Directory NonInteractiveUserSignInLogs + - Azure Active Directory MicrosoftGraphActivityLogs type: TTP status: production description: This detection identifies the presence of the default AzureHound user-agent string within Microsoft Graph Activity logs and NonInteractive SignIn Logs. AzureHound is a tool used for gathering information about Azure Active Directory environments, often employed by security professionals for legitimate auditing purposes. However, it can also be leveraged by malicious actors to perform reconnaissance activities, mapping out the Azure AD infrastructure to identify potential vulnerabilities and targets for further exploitation. Detecting its usage can help in identifying unauthorized access attempts and preemptively mitigating potential security threats to your Azure environment. -search: - '`azure_monitor_aad` category IN (MicrosoftGraphActivityLogs, NonInteractiveUserSignInLogs) properties.userAgent=azurehound* - | rename properties.userAgent as user_agent - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product user_agent signature - | iplocation src - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_azurehound_useragent_detected_filter`' +search: |- + `azure_monitor_aad` category IN (MicrosoftGraphActivityLogs, NonInteractiveUserSignInLogs) properties.userAgent=azurehound* + | rename properties.userAgent as user_agent + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product user_agent + signature + | iplocation src + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_azurehound_useragent_detected_filter` how_to_implement: The Splunk Add-on for Microsoft Cloud Services add-on is required to ingest NonInteractiveUserSignInLogs and MicrosoftGraphActivityLogs via an Azure EventHub. See reference for links for further details. known_false_positives: No false positives have been identified at this time. references: - - https://github.com/SpecterOps/AzureHound - - https://splunkbase.splunk.com/app/3110 - - https://splunk.github.io/splunk-add-on-for-microsoft-cloud-services/Install/ + - https://github.com/SpecterOps/AzureHound + - https://splunkbase.splunk.com/app/3110 + - https://splunk.github.io/splunk-add-on-for-microsoft-cloud-services/Install/ drilldown_searches: - - name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: AzureHound UserAgent String $user_agent$ Detected on Tenant $dest$ - risk_objects: - - field: user - type: user - score: 80 - threat_objects: - - field: src - type: ip_address + message: AzureHound UserAgent String $user_agent$ Detected on Tenant $dest$ + risk_objects: + - field: user + type: user + score: 80 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Azure Active Directory Privilege Escalation - - Compromised User Account - asset_type: Azure Tenant - mitre_attack_id: - - T1087.004 - - T1526 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Privilege Escalation + - Compromised User Account + asset_type: Azure Tenant + mitre_attack_id: + - T1087.004 + - T1526 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.004/azurehound/azurehound.log - sourcetype: azure:monitor:aad - source: Azure AD + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.004/azurehound/azurehound.log + sourcetype: azure:monitor:aad + source: Azure AD diff --git a/detections/cloud/azure_ad_block_user_consent_for_risky_apps_disabled.yml b/detections/cloud/azure_ad_block_user_consent_for_risky_apps_disabled.yml index 68225aba17..48ffdc90d7 100644 --- a/detections/cloud/azure_ad_block_user_consent_for_risky_apps_disabled.yml +++ b/detections/cloud/azure_ad_block_user_consent_for_risky_apps_disabled.yml @@ -6,74 +6,46 @@ author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- Azure Active Directory Update authorization policy -description: The following analytic detects when the risk-based step-up consent security - setting in Azure AD is disabled. It monitors Azure Active Directory logs for the - "Update authorization policy" operation, specifically changes to the "AllowUserConsentForRiskyApps" - setting. This activity is significant because disabling this feature can expose - the organization to OAuth phishing threats by allowing users to grant consent to - potentially malicious applications. If confirmed malicious, attackers could gain - unauthorized access to user data and sensitive information, leading to data breaches - and further compromise within the organization. -search: "`azure_monitor_aad` operationName=\"Update authorization policy\" - | rename properties.* as * - | eval index_number = if(mvfind('targetResources{}.modifiedProperties{}.displayName',\"AllowUserConsentForRiskyApps\") >= 0, mvfind('targetResources{}.modifiedProperties{}.displayName',\"AllowUserConsentForRiskyApps\"), -1) - | search index_number >= 0 - | eval AllowUserConsentForRiskyApps = mvindex('targetResources{}.modifiedProperties{}.newValue',index_number) - | search AllowUserConsentForRiskyApps = \"[true]\" - | rename userAgent as user_agent - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product user_agent signature - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_block_user_consent_for_risky_apps_disabled_filter`" -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the AuditLog log category. -known_false_positives: Legitimate changes to the 'risk-based step-up consent' setting - by administrators, perhaps as part of a policy update or security assessment, may - trigger this alert, necessitating verification of the change's intent and authorization + - Azure Active Directory Update authorization policy +description: The following analytic detects when the risk-based step-up consent security setting in Azure AD is disabled. It monitors Azure Active Directory logs for the "Update authorization policy" operation, specifically changes to the "AllowUserConsentForRiskyApps" setting. This activity is significant because disabling this feature can expose the organization to OAuth phishing threats by allowing users to grant consent to potentially malicious applications. If confirmed malicious, attackers could gain unauthorized access to user data and sensitive information, leading to data breaches and further compromise within the organization. +search: "`azure_monitor_aad` operationName=\"Update authorization policy\" | rename properties.* as * | eval index_number = if(mvfind('targetResources{}.modifiedProperties{}.displayName',\"AllowUserConsentForRiskyApps\") >= 0, mvfind('targetResources{}.modifiedProperties{}.displayName',\"AllowUserConsentForRiskyApps\"), -1) | search index_number >= 0 | eval AllowUserConsentForRiskyApps = mvindex('targetResources{}.modifiedProperties{}.newValue',index_number) | search AllowUserConsentForRiskyApps = \"[true]\" | rename userAgent as user_agent | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product user_agent signature | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_block_user_consent_for_risky_apps_disabled_filter`" +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category. +known_false_positives: Legitimate changes to the 'risk-based step-up consent' setting by administrators, perhaps as part of a policy update or security assessment, may trigger this alert, necessitating verification of the change's intent and authorization references: -- https://attack.mitre.org/techniques/T1562/ -- https://goodworkaround.com/2020/10/19/a-look-behind-the-azure-ad-permission-classifications-preview/ -- https://learn.microsoft.com/en-us/entra/identity/enterprise-apps/configure-risk-based-step-up-consent -- https://learn.microsoft.com/en-us/defender-cloud-apps/investigate-risky-oauth + - https://attack.mitre.org/techniques/T1562/ + - https://goodworkaround.com/2020/10/19/a-look-behind-the-azure-ad-permission-classifications-preview/ + - https://learn.microsoft.com/en-us/entra/identity/enterprise-apps/configure-risk-based-step-up-consent + - https://learn.microsoft.com/en-us/defender-cloud-apps/investigate-risky-oauth drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ disabled the BlockUserConsentForRiskyApps Azure AD setting. - risk_objects: - - field: user - type: user - score: 30 - threat_objects: [] + message: User $user$ disabled the BlockUserConsentForRiskyApps Azure AD setting. + risk_objects: + - field: user + type: user + score: 30 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Account Takeover - asset_type: Azure Tenant - mitre_attack_id: - - T1562 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Account Takeover + asset_type: Azure Tenant + mitre_attack_id: + - T1562 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/azuread_disable_blockconsent_for_riskapps/azuread_disable_blockconsent_for_riskapps.log - source: Azure Ad - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/azuread_disable_blockconsent_for_riskapps/azuread_disable_blockconsent_for_riskapps.log + source: Azure Ad + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_concurrent_sessions_from_different_ips.yml b/detections/cloud/azure_ad_concurrent_sessions_from_different_ips.yml index 0698396557..c8d39e276b 100644 --- a/detections/cloud/azure_ad_concurrent_sessions_from_different_ips.yml +++ b/detections/cloud/azure_ad_concurrent_sessions_from_different_ips.yml @@ -1,85 +1,66 @@ name: Azure AD Concurrent Sessions From Different Ips id: a9126f73-9a9b-493d-96ec-0dd06695490d -version: 11 -date: '2025-10-14' +version: 12 +date: '2026-02-25' author: Mauricio Velazco, Bhavin Patel, Splunk status: production type: TTP -description: The following analytic detects an Azure AD account with concurrent sessions - originating from multiple unique IP addresses within a 5-minute window. It leverages - Azure Active Directory NonInteractiveUserSignInLogs to identify this behavior by - analyzing successful authentication events and counting distinct source IPs. This - activity is significant as it may indicate session hijacking, where an attacker - uses stolen session cookies to access corporate resources from a different location. - If confirmed malicious, this could lead to unauthorized access to sensitive information - and potential data breaches. +description: The following analytic detects an Azure AD account with concurrent sessions originating from multiple unique IP addresses within a 5-minute window. It leverages Azure Active Directory NonInteractiveUserSignInLogs to identify this behavior by analyzing successful authentication events and counting distinct source IPs. This activity is significant as it may indicate session hijacking, where an attacker uses stolen session cookies to access corporate resources from a different location. If confirmed malicious, this could lead to unauthorized access to sensitive information and potential data breaches. data_source: -- Azure Active Directory -search: '`azure_monitor_aad` properties.authenticationDetails{}.succeeded=true category=NonInteractiveUserSignInLogs - action=success - | rename properties.* as * - | bucket span=5m _time - | rename userAgent as user_agent - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime dc(src) as unique_ips values(dest) as dest values(src) as src values(user_agent) as user_agent by user _time vendor_account vendor_product category - | where unique_ips > 1 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_concurrent_sessions_from_different_ips_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the SignInLogs log category. -known_false_positives: A user with concurrent sessions from different Ips may also - represent the legitimate use of more than one device. Filter as needed and/or customize - the threshold to fit your environment. Also consider the geographic location of - the IP addresses and filter out IP space that belong to your organization. + - Azure Active Directory +search: |- + `azure_monitor_aad` properties.authenticationDetails{}.succeeded=true category=NonInteractiveUserSignInLogs action=success + | rename properties.* as * + | bucket span=5m _time + | rename userAgent as user_agent + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime dc(src) as unique_ips values(dest) as dest values(src) as src values(user_agent) as user_agent + BY user _time vendor_account + vendor_product category + | where unique_ips > 1 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_concurrent_sessions_from_different_ips_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the SignInLogs log category. +known_false_positives: A user with concurrent sessions from different Ips may also represent the legitimate use of more than one device. Filter as needed and/or customize the threshold to fit your environment. Also consider the geographic location of the IP addresses and filter out IP space that belong to your organization. references: -- https://attack.mitre.org/techniques/T1185/ -- https://breakdev.org/evilginx-2-next-generation-of-phishing-2fa-tokens/ -- https://github.com/kgretzky/evilginx2 + - https://attack.mitre.org/techniques/T1185/ + - https://breakdev.org/evilginx-2-next-generation-of-phishing-2fa-tokens/ + - https://github.com/kgretzky/evilginx2 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has concurrent sessions from more than one unique IP address - in the span of 5 minutes. - risk_objects: - - field: user - type: user - score: 42 - threat_objects: - - field: src - type: ip_address + message: User $user$ has concurrent sessions from more than one unique IP address in the span of 5 minutes. + risk_objects: + - field: user + type: user + score: 42 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Compromised User Account - - Azure Active Directory Account Takeover - - Scattered Lapsus$ Hunters - asset_type: Azure Tenant - mitre_attack_id: - - T1185 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Compromised User Account + - Azure Active Directory Account Takeover + - Scattered Lapsus$ Hunters + asset_type: Azure Tenant + mitre_attack_id: + - T1185 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/azure_ad_concurrent_sessions_from_different_ips/azuread.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/azure_ad_concurrent_sessions_from_different_ips/azuread.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_device_code_authentication.yml b/detections/cloud/azure_ad_device_code_authentication.yml index 48e20d7c7b..b348ef5151 100644 --- a/detections/cloud/azure_ad_device_code_authentication.yml +++ b/detections/cloud/azure_ad_device_code_authentication.yml @@ -1,81 +1,66 @@ name: Azure AD Device Code Authentication id: d68d8732-6f7e-4ee5-a6eb-737f2b990b91 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk status: production type: TTP data_source: -- Azure Active Directory -description: The following analytic identifies Azure Device Code Phishing attacks, - which can lead to Azure Account Take-Over (ATO). It leverages Azure AD SignInLogs - to detect suspicious authentication requests using the device code authentication - protocol. This activity is significant as it indicates potential bypassing of Multi-Factor - Authentication (MFA) and Conditional Access Policies (CAPs) through phishing emails. - If confirmed malicious, attackers could gain unauthorized access to Azure AD, Exchange - mailboxes, and Outlook Web Application (OWA), leading to potential data breaches - and unauthorized data access. -search: '`azure_monitor_aad` category=SignInLogs "properties.authenticationProtocol"=deviceCode - | rename properties.* as * - | rename userAgent as user_agent - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product user_agent category - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_device_code_authentication_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the SignInLogs log category. -known_false_positives: In most organizations, device code authentication will be used - to access common Microsoft service but it may be legitimate for others. Filter as - needed. + - Azure Active Directory +description: The following analytic identifies Azure Device Code Phishing attacks, which can lead to Azure Account Take-Over (ATO). It leverages Azure AD SignInLogs to detect suspicious authentication requests using the device code authentication protocol. This activity is significant as it indicates potential bypassing of Multi-Factor Authentication (MFA) and Conditional Access Policies (CAPs) through phishing emails. If confirmed malicious, attackers could gain unauthorized access to Azure AD, Exchange mailboxes, and Outlook Web Application (OWA), leading to potential data breaches and unauthorized data access. +search: |- + `azure_monitor_aad` category=SignInLogs "properties.authenticationProtocol"=deviceCode + | rename properties.* as * + | rename userAgent as user_agent + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product user_agent + category + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_device_code_authentication_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the SignInLogs log category. +known_false_positives: In most organizations, device code authentication will be used to access common Microsoft service but it may be legitimate for others. Filter as needed. references: -- https://attack.mitre.org/techniques/T1528 -- https://github.com/rvrsh3ll/TokenTactics -- https://embracethered.com/blog/posts/2022/device-code-phishing/ -- https://0xboku.com/2021/07/12/ArtOfDeviceCodePhish.html -- https://learn.microsoft.com/en-us/azure/active-directory/develop/v2-oauth2-device-code + - https://attack.mitre.org/techniques/T1528 + - https://github.com/rvrsh3ll/TokenTactics + - https://embracethered.com/blog/posts/2022/device-code-phishing/ + - https://0xboku.com/2021/07/12/ArtOfDeviceCodePhish.html + - https://learn.microsoft.com/en-us/azure/active-directory/develop/v2-oauth2-device-code drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Device code requested for $user$ from $src$ - risk_objects: - - field: user - type: user - score: 35 - threat_objects: - - field: src - type: ip_address + message: Device code requested for $user$ from $src$ + risk_objects: + - field: user + type: user + score: 35 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Azure Active Directory Account Takeover - asset_type: Azure Tenant - mitre_attack_id: - - T1528 - - T1566.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Account Takeover + asset_type: Azure Tenant + mitre_attack_id: + - T1528 + - T1566.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/device_code_authentication/azure-audit.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/device_code_authentication/azure-audit.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_external_guest_user_invited.yml b/detections/cloud/azure_ad_external_guest_user_invited.yml index d6141d9911..c44468bb31 100644 --- a/detections/cloud/azure_ad_external_guest_user_invited.yml +++ b/detections/cloud/azure_ad_external_guest_user_invited.yml @@ -1,79 +1,67 @@ name: Azure AD External Guest User Invited id: c1fb4edb-cab1-4359-9b40-925ffd797fb5 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Gowthamaraj Rajendran, Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the invitation of an external guest user - within Azure AD. It leverages Azure AD AuditLogs to identify events where an external - user is invited, using fields such as operationName and initiatedBy. Monitoring - these invitations is crucial as they can lead to unauthorized access if abused. - If confirmed malicious, this activity could allow attackers to gain access to internal - resources, potentially leading to data breaches or further exploitation of the environment. +description: The following analytic detects the invitation of an external guest user within Azure AD. It leverages Azure AD AuditLogs to identify events where an external user is invited, using fields such as operationName and initiatedBy. Monitoring these invitations is crucial as they can lead to unauthorized access if abused. If confirmed malicious, this activity could allow attackers to gain access to internal resources, potentially leading to data breaches or further exploitation of the environment. data_source: -- Azure Active Directory Invite external user -search: '`azure_monitor_aad` operationName="Invite external user" - | rename properties.* as * - | rename initiatedBy.user.userPrincipalName as initiatedBy - | rename targetResources{}.type as type - | rename userAgent as user_agent - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product user_agent initiatedBy type signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_external_guest_user_invited_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment. - This analytic was written to be used with the azure:monitor:aad sourcetype leveraging - the AuditLogs log category. -known_false_positives: Administrator may legitimately invite external guest users. - Filter as needed. + - Azure Active Directory Invite external user +search: |- + `azure_monitor_aad` operationName="Invite external user" + | rename properties.* as * + | rename initiatedBy.user.userPrincipalName as initiatedBy + | rename targetResources{}.type as type + | rename userAgent as user_agent + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product user_agent + initiatedBy type signature + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_external_guest_user_invited_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLogs log category. +known_false_positives: Administrator may legitimately invite external guest users. Filter as needed. references: -- https://dirkjanm.io/assets/raw/US-22-Mollema-Backdooring-and-hijacking-Azure-AD-accounts_final.pdf -- https://www.blackhat.com/us-22/briefings/schedule/#backdooring-and-hijacking-azure-ad-accounts-by-abusing-external-identities-26999 -- https://attack.mitre.org/techniques/T1136/003/ -- https://docs.microsoft.com/en-us/azure/active-directory/external-identities/b2b-quickstart-add-guest-users-portal + - https://dirkjanm.io/assets/raw/US-22-Mollema-Backdooring-and-hijacking-Azure-AD-accounts_final.pdf + - https://www.blackhat.com/us-22/briefings/schedule/#backdooring-and-hijacking-azure-ad-accounts-by-abusing-external-identities-26999 + - https://attack.mitre.org/techniques/T1136/003/ + - https://docs.microsoft.com/en-us/azure/active-directory/external-identities/b2b-quickstart-add-guest-users-portal drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: External Guest User $user$ initiated by $initiatedBy$ - risk_objects: - - field: user - type: user - score: 45 - - field: initiatedBy - type: user - score: 45 - threat_objects: [] + message: External Guest User $user$ initiated by $initiatedBy$ + risk_objects: + - field: user + type: user + score: 45 + - field: initiatedBy + type: user + score: 45 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - asset_type: Azure Active Directory - mitre_attack_id: - - T1136.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Azure Active Directory Persistence + asset_type: Azure Active Directory + mitre_attack_id: + - T1136.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/azure_ad_external_guest_user_invited/azure-audit.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/azure_ad_external_guest_user_invited/azure-audit.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_fullaccessasapp_permission_assigned.yml b/detections/cloud/azure_ad_fullaccessasapp_permission_assigned.yml index d8edd0b228..4e996e6d66 100644 --- a/detections/cloud/azure_ad_fullaccessasapp_permission_assigned.yml +++ b/detections/cloud/azure_ad_fullaccessasapp_permission_assigned.yml @@ -5,77 +5,48 @@ date: '2025-05-02' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the assignment of the 'full_access_as_app' - permission to an application within Office 365 Exchange Online. This is identified - by the GUID 'dc890d15-9560-4a4c-9b7f-a736ec74ec40' and the ResourceAppId '00000002-0000-0ff1-ce00-000000000000'. - The detection leverages the azure_monitor_aad data source, focusing on AuditLogs - with the operation name 'Update application'. This activity is significant as it - grants broad control over Office 365 operations, including full access to all mailboxes - and the ability to send emails as any user. If malicious, this could lead to unauthorized - access and data exfiltration. +description: The following analytic detects the assignment of the 'full_access_as_app' permission to an application within Office 365 Exchange Online. This is identified by the GUID 'dc890d15-9560-4a4c-9b7f-a736ec74ec40' and the ResourceAppId '00000002-0000-0ff1-ce00-000000000000'. The detection leverages the azure_monitor_aad data source, focusing on AuditLogs with the operation name 'Update application'. This activity is significant as it grants broad control over Office 365 operations, including full access to all mailboxes and the ability to send emails as any user. If malicious, this could lead to unauthorized access and data exfiltration. data_source: -- Azure Active Directory Update application -search: "`azure_monitor_aad` category=AuditLogs operationName=\"Update application\" - | eval newvalue = mvindex('properties.targetResources{}.modifiedProperties{}.newValue',0) - | spath input=newvalue - | search \"{}.ResourceAppId\"=\"00000002-0000-0ff1-ce00-000000000000\" \"{}.RequiredAppPermissions{}.EntitlementId\"=\"dc890d15-9560-4a4c-9b7f-a736ec74ec40\" - | eval Permissions = '{}.RequiredAppPermissions{}.EntitlementId' - | rename properties.userAgent as user_agent - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product user_agent Permissions object signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_fullaccessasapp_permission_assigned_filter`" -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the AuditLogs log category. -known_false_positives: The full_access_as_app API permission may be assigned to legitimate - applications. Filter as needed. + - Azure Active Directory Update application +search: "`azure_monitor_aad` category=AuditLogs operationName=\"Update application\" | eval newvalue = mvindex('properties.targetResources{}.modifiedProperties{}.newValue',0) | spath input=newvalue | search \"{}.ResourceAppId\"=\"00000002-0000-0ff1-ce00-000000000000\" \"{}.RequiredAppPermissions{}.EntitlementId\"=\"dc890d15-9560-4a4c-9b7f-a736ec74ec40\" | eval Permissions = '{}.RequiredAppPermissions{}.EntitlementId' | rename properties.userAgent as user_agent | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product user_agent Permissions object signature | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_fullaccessasapp_permission_assigned_filter`" +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLogs log category. +known_false_positives: The full_access_as_app API permission may be assigned to legitimate applications. Filter as needed. references: -- https://msrc.microsoft.com/blog/2024/01/microsoft-actions-following-attack-by-nation-state-actor-midnight-blizzard/ -- https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/ -- https://attack.mitre.org/techniques/T1098/002/ + - https://msrc.microsoft.com/blog/2024/01/microsoft-actions-following-attack-by-nation-state-actor-midnight-blizzard/ + - https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/ + - https://attack.mitre.org/techniques/T1098/002/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ assigned the full_access_as_app permission to the app registration - $object$ - risk_objects: - - field: user - type: user - score: 48 - threat_objects: [] + message: User $user$ assigned the full_access_as_app permission to the app registration $object$ + risk_objects: + - field: user + type: user + score: 48 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - - NOBELIUM Group - asset_type: Azure Active Directory - mitre_attack_id: - - T1098.002 - - T1098.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Azure Active Directory Persistence + - NOBELIUM Group + asset_type: Azure Active Directory + mitre_attack_id: + - T1098.002 + - T1098.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.002/full_access_as_app_permission_assigned/full_access_as_app_permission_assigned.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.002/full_access_as_app_permission_assigned/full_access_as_app_permission_assigned.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_global_administrator_role_assigned.yml b/detections/cloud/azure_ad_global_administrator_role_assigned.yml index 21d4e63f30..1f2a5eac0b 100644 --- a/detections/cloud/azure_ad_global_administrator_role_assigned.yml +++ b/detections/cloud/azure_ad_global_administrator_role_assigned.yml @@ -1,84 +1,70 @@ name: Azure AD Global Administrator Role Assigned id: 825fed20-309d-4fd1-8aaf-cd49c1bb093c -version: 11 -date: '2025-10-14' +version: 12 +date: '2026-02-25' author: Gowthamaraj Rajendran, Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the assignment of the Azure AD Global - Administrator role to a user. It leverages Azure Active Directory AuditLogs to identify - when the "Add member to role" operation includes the "Global Administrator" role. - This activity is significant because the Global Administrator role grants extensive - access to data, resources, and settings, similar to a Domain Administrator in traditional - AD environments. If confirmed malicious, this could allow an attacker to establish - persistence, escalate privileges, and potentially gain control over Azure resources, - posing a severe security risk. +description: The following analytic detects the assignment of the Azure AD Global Administrator role to a user. It leverages Azure Active Directory AuditLogs to identify when the "Add member to role" operation includes the "Global Administrator" role. This activity is significant because the Global Administrator role grants extensive access to data, resources, and settings, similar to a Domain Administrator in traditional AD environments. If confirmed malicious, this could allow an attacker to establish persistence, escalate privileges, and potentially gain control over Azure resources, posing a severe security risk. data_source: -- Azure Active Directory Add member to role -search: '`azure_monitor_aad` operationName="Add member to role" properties.targetResources{}.modifiedProperties{}.newValue="*Global Administrator*" - | rename properties.* as * - | rename initiatedBy.user.userPrincipalName as initiatedBy - | rename userAgent as user_agent - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product user_agent initiatedBy signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_global_administrator_role_assigned_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the AuditLogs log category. -known_false_positives: Administrators may legitimately assign the Global Administrator - role to a user. Filter as needed. + - Azure Active Directory Add member to role +search: |- + `azure_monitor_aad` operationName="Add member to role" properties.targetResources{}.modifiedProperties{}.newValue="*Global Administrator*" + | rename properties.* as * + | rename initiatedBy.user.userPrincipalName as initiatedBy + | rename userAgent as user_agent + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product user_agent + initiatedBy signature + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_global_administrator_role_assigned_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLogs log category. +known_false_positives: Administrators may legitimately assign the Global Administrator role to a user. Filter as needed. references: -- https://o365blog.com/post/admin/ -- https://adsecurity.org/?p=4277 -- https://www.mandiant.com/resources/detecting-microsoft-365-azure-active-directory-backdoors -- https://docs.microsoft.com/en-us/azure/active-directory/roles/security-planning -- https://docs.microsoft.com/en-us/azure/role-based-access-control/elevate-access-global-admin -- https://attack.mitre.org/techniques/T1098/003/ + - https://o365blog.com/post/admin/ + - https://adsecurity.org/?p=4277 + - https://www.mandiant.com/resources/detecting-microsoft-365-azure-active-directory-backdoors + - https://docs.microsoft.com/en-us/azure/active-directory/roles/security-planning + - https://docs.microsoft.com/en-us/azure/role-based-access-control/elevate-access-global-admin + - https://attack.mitre.org/techniques/T1098/003/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Global Administrator Role assigned for User $user$ initiated by $initiatedBy$ - risk_objects: - - field: user - type: user - score: 72 - - field: initiatedBy - type: user - score: 72 - threat_objects: [] + message: Global Administrator Role assigned for User $user$ initiated by $initiatedBy$ + risk_objects: + - field: user + type: user + score: 72 + - field: initiatedBy + type: user + score: 72 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - - Azure Active Directory Privilege Escalation - - Scattered Lapsus$ Hunters - asset_type: Azure Active Directory - mitre_attack_id: - - T1098.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Azure Active Directory Persistence + - Azure Active Directory Privilege Escalation + - Scattered Lapsus$ Hunters + asset_type: Azure Active Directory + mitre_attack_id: + - T1098.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_assign_global_administrator/azure-audit.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_assign_global_administrator/azure-audit.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_high_number_of_failed_authentications_for_user.yml b/detections/cloud/azure_ad_high_number_of_failed_authentications_for_user.yml index 273b331ded..62b768bf15 100644 --- a/detections/cloud/azure_ad_high_number_of_failed_authentications_for_user.yml +++ b/detections/cloud/azure_ad_high_number_of_failed_authentications_for_user.yml @@ -5,76 +5,58 @@ date: '2025-12-01' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic identifies an Azure AD account experiencing more - than 20 failed authentication attempts within a 10-minute window. This detection - leverages Azure SignInLogs data, specifically monitoring for error code 50126 and - unsuccessful authentication attempts. This behavior is significant as it may indicate - a brute force attack targeting the account. If confirmed malicious, an attacker - could potentially gain unauthorized access, leading to data breaches or further - exploitation within the environment. Security teams should adjust the threshold - based on their specific environment to reduce false positives. +description: The following analytic identifies an Azure AD account experiencing more than 20 failed authentication attempts within a 10-minute window. This detection leverages Azure SignInLogs data, specifically monitoring for error code 50126 and unsuccessful authentication attempts. This behavior is significant as it may indicate a brute force attack targeting the account. If confirmed malicious, an attacker could potentially gain unauthorized access, leading to data breaches or further exploitation within the environment. Security teams should adjust the threshold based on their specific environment to reduce false positives. data_source: -- Azure Active Directory + - Azure Active Directory search: | - `azure_monitor_aad` - category=SignInLogs - properties.status.errorCode=50126 - properties.authenticationDetails{}.succeeded=false - | rename properties.* as * - | bin span=10m _time - | fillnull value=null - | stats count min(_time) as firstTime max(_time) as lastTime values(dest) as dest values(src) as src values(user_agent) as user_agent by user _time vendor_account vendor_product - | where count > 20 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_high_number_of_failed_authentications_for_user_filter` -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the SignInLogs log category. -known_false_positives: A user with more than 20 failed authentication attempts in - the span of 10 minutes may also be triggered by a broken application. + `azure_monitor_aad` + category=SignInLogs + properties.status.errorCode=50126 + properties.authenticationDetails{}.succeeded=false + | rename properties.* as * + | bin span=10m _time + | fillnull value=null + | stats count min(_time) as firstTime max(_time) as lastTime values(dest) as dest values(src) as src values(user_agent) as user_agent by user _time vendor_account vendor_product + | where count > 20 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_high_number_of_failed_authentications_for_user_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the SignInLogs log category. +known_false_positives: A user with more than 20 failed authentication attempts in the span of 10 minutes may also be triggered by a broken application. references: -- https://attack.mitre.org/techniques/T1110/ -- https://attack.mitre.org/techniques/T1110/001/ + - https://attack.mitre.org/techniques/T1110/ + - https://attack.mitre.org/techniques/T1110/001/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ failed to authenticate more than 20 times in the span of 10 minutes. - risk_objects: - - field: user - type: user - score: 35 - threat_objects: [] + message: User $user$ failed to authenticate more than 20 times in the span of 10 minutes. + risk_objects: + - field: user + type: user + score: 35 + threat_objects: [] tags: - analytic_story: - - Compromised User Account - - Azure Active Directory Account Takeover - asset_type: Azure Tenant - mitre_attack_id: - - T1110.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Compromised User Account + - Azure Active Directory Account Takeover + asset_type: Azure Tenant + mitre_attack_id: + - T1110.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/azure_ad_high_number_of_failed_authentications_for_user/azuread.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/azure_ad_high_number_of_failed_authentications_for_user/azuread.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_high_number_of_failed_authentications_from_ip.yml b/detections/cloud/azure_ad_high_number_of_failed_authentications_from_ip.yml index b9ced2d17d..7c627d3aee 100644 --- a/detections/cloud/azure_ad_high_number_of_failed_authentications_from_ip.yml +++ b/detections/cloud/azure_ad_high_number_of_failed_authentications_from_ip.yml @@ -5,80 +5,63 @@ date: '2025-12-01' author: Mauricio Velazco, Bhavin Patel, Splunk status: production type: TTP -description: The following analytic detects an IP address with 20 or more failed authentication - attempts to an Azure AD tenant within 10 minutes. It leverages Azure AD SignInLogs - to identify repeated failed logins from the same IP. This behavior is significant - as it may indicate a brute force attack aimed at gaining unauthorized access or - escalating privileges. If confirmed malicious, the attacker could potentially compromise - user accounts, leading to unauthorized access to sensitive information and resources - within the Azure environment. +description: The following analytic detects an IP address with 20 or more failed authentication attempts to an Azure AD tenant within 10 minutes. It leverages Azure AD SignInLogs to identify repeated failed logins from the same IP. This behavior is significant as it may indicate a brute force attack aimed at gaining unauthorized access or escalating privileges. If confirmed malicious, the attacker could potentially compromise user accounts, leading to unauthorized access to sensitive information and resources within the Azure environment. data_source: -- Azure Active Directory + - Azure Active Directory search: | - `azure_monitor_aad` - category=SignInLogs - properties.status.errorCode=50126 - properties.authenticationDetails{}.succeeded=false - | rename properties.* as * - | bin span=10m _time - | fillnull value=null - | stats count min(_time) as firstTime max(_time) as lastTime values(dest) as dest values(user) as user values(user_agent) as user_agent by src _time vendor_account vendor_product - | where count > 20 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_high_number_of_failed_authentications_from_ip_filter` -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the SignInLogs log category. -known_false_positives: An Ip address with more than 20 failed authentication attempts - in the span of 10 minutes may also be triggered by a broken application. + `azure_monitor_aad` + category=SignInLogs + properties.status.errorCode=50126 + properties.authenticationDetails{}.succeeded=false + | rename properties.* as * + | bin span=10m _time + | fillnull value=null + | stats count min(_time) as firstTime max(_time) as lastTime values(dest) as dest values(user) as user values(user_agent) as user_agent by src _time vendor_account vendor_product + | where count > 20 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_high_number_of_failed_authentications_from_ip_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the SignInLogs log category. +known_false_positives: An Ip address with more than 20 failed authentication attempts in the span of 10 minutes may also be triggered by a broken application. references: -- https://attack.mitre.org/techniques/T1110/ -- https://attack.mitre.org/techniques/T1110/001/ -- https://attack.mitre.org/techniques/T1110/003/ + - https://attack.mitre.org/techniques/T1110/ + - https://attack.mitre.org/techniques/T1110/001/ + - https://attack.mitre.org/techniques/T1110/003/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $src$ failed to authenticate more than 20 times in the span of 10 minutes. - risk_objects: - - field: user - type: user - score: 35 - threat_objects: - - field: src - type: ip_address + message: $src$ failed to authenticate more than 20 times in the span of 10 minutes. + risk_objects: + - field: user + type: user + score: 35 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Compromised User Account - - Azure Active Directory Account Takeover - - NOBELIUM Group - asset_type: Azure Tenant - mitre_attack_id: - - T1110.001 - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Compromised User Account + - Azure Active Directory Account Takeover + - NOBELIUM Group + asset_type: Azure Tenant + mitre_attack_id: + - T1110.001 + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/azure_ad_high_number_of_failed_authentications_for_user/azuread.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/azure_ad_high_number_of_failed_authentications_for_user/azuread.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_multi_factor_authentication_disabled.yml b/detections/cloud/azure_ad_multi_factor_authentication_disabled.yml index c0d6161e1c..b5eaa6d2be 100644 --- a/detections/cloud/azure_ad_multi_factor_authentication_disabled.yml +++ b/detections/cloud/azure_ad_multi_factor_authentication_disabled.yml @@ -1,79 +1,66 @@ name: Azure AD Multi-Factor Authentication Disabled id: 482dd42a-acfa-486b-a0bb-d6fcda27318e -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk status: production type: TTP -description: The following analytic detects attempts to disable multi-factor authentication - (MFA) for an Azure AD user. It leverages Azure Active Directory AuditLogs to identify - the "Disable Strong Authentication" operation. This activity is significant because - disabling MFA can allow adversaries to maintain persistence using compromised accounts - without raising suspicion. If confirmed malicious, this action could enable attackers - to bypass an essential security control, potentially leading to unauthorized access - and prolonged undetected presence in the environment. +description: The following analytic detects attempts to disable multi-factor authentication (MFA) for an Azure AD user. It leverages Azure Active Directory AuditLogs to identify the "Disable Strong Authentication" operation. This activity is significant because disabling MFA can allow adversaries to maintain persistence using compromised accounts without raising suspicion. If confirmed malicious, this action could enable attackers to bypass an essential security control, potentially leading to unauthorized access and prolonged undetected presence in the environment. data_source: -- Azure Active Directory Disable Strong Authentication -search: '`azure_monitor_aad` category=AuditLogs operationName="Disable Strong Authentication" - | rename properties.* as * - | rename targetResources{}.type as type - | rename initiatedBy.user.userPrincipalName as initiatedBy - | rename userAgent as user_agent - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product user_agent initiatedBy signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_multi_factor_authentication_disabled_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the AuditLogs log category. -known_false_positives: Legitimate use case may require for users to disable MFA. Filter - as needed. + - Azure Active Directory Disable Strong Authentication +search: |- + `azure_monitor_aad` category=AuditLogs operationName="Disable Strong Authentication" + | rename properties.* as * + | rename targetResources{}.type as type + | rename initiatedBy.user.userPrincipalName as initiatedBy + | rename userAgent as user_agent + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product user_agent + initiatedBy signature + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_multi_factor_authentication_disabled_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLogs log category. +known_false_positives: Legitimate use case may require for users to disable MFA. Filter as needed. references: -- https://docs.microsoft.com/en-us/azure/active-directory/authentication/concept-mfa-howitworks -- https://docs.microsoft.com/en-us/azure/active-directory/authentication/howto-mfa-userstates -- https://attack.mitre.org/tactics/TA0005/ -- https://attack.mitre.org/techniques/T1556/ + - https://docs.microsoft.com/en-us/azure/active-directory/authentication/concept-mfa-howitworks + - https://docs.microsoft.com/en-us/azure/active-directory/authentication/howto-mfa-userstates + - https://attack.mitre.org/tactics/TA0005/ + - https://attack.mitre.org/techniques/T1556/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: MFA disabled for User $user$ initiated by $initiatedBy$ - risk_objects: - - field: user - type: user - score: 45 - threat_objects: [] + message: MFA disabled for User $user$ initiated by $initiatedBy$ + risk_objects: + - field: user + type: user + score: 45 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Account Takeover - - Scattered Lapsus$ Hunters - asset_type: Azure Active Directory - mitre_attack_id: - - T1556.006 - - T1586.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Account Takeover + - Scattered Lapsus$ Hunters + asset_type: Azure Active Directory + mitre_attack_id: + - T1556.006 + - T1586.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/azuread/azure-audit.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/azuread/azure-audit.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_multi_source_failed_authentications_spike.yml b/detections/cloud/azure_ad_multi_source_failed_authentications_spike.yml index 2f02e9b541..bb18905541 100644 --- a/detections/cloud/azure_ad_multi_source_failed_authentications_spike.yml +++ b/detections/cloud/azure_ad_multi_source_failed_authentications_spike.yml @@ -1,71 +1,50 @@ name: Azure AD Multi-Source Failed Authentications Spike id: 116e11a9-63ea-41eb-a66a-6a13bdc7d2c7 -version: 10 -date: '2025-09-17' +version: 11 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting data_source: -- Azure Active Directory -description: The following analytic detects potential distributed password spraying - attacks in an Azure AD environment. It identifies a spike in failed authentication - attempts across various user-and-IP combinations from multiple source IPs and countries, - using different user agents. This detection leverages Azure AD SignInLogs, focusing - on error code 50126 for failed authentications. This activity is significant as - it indicates an adversary's attempt to bypass security controls by distributing - login attempts. If confirmed malicious, this could lead to unauthorized access, - data breaches, privilege escalation, and lateral movement within the organization's - infrastructure. -search: '`azure_monitor_aad` category=*SignInLogs properties.status.errorCode=50126 properties.authenticationDetails{}.succeeded=false - | rename properties.* as * - | bucket span=5m _time - | eval uniqueIPUserCombo = src_ip . "-" . user - | rename userAgent as user_agent - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime dc(uniqueIPUserCombo) as uniqueIpUserCombinations, dc(user) as uniqueUsers, dc(src_ip) as uniqueIPs, dc(user_agent) as uniqueUserAgents, dc(location.countryOrRegion) as uniqueCountries values(location.countryOrRegion) as countries values(action) as action values(dest) as dest values(user) as user values(src) as src values(vendor_account) as vendor_account values(vendor_product) as vendor_product values(user_agent) as user_agent - | where uniqueIpUserCombinations > 20 AND uniqueUsers > 20 AND uniqueIPs > 20 AND uniqueUserAgents >= 1 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_multi_source_failed_authentications_spike_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the SignInLogs log category. The thresholds set within the - analytic (such as unique IPs, unique users, etc.) are initial guidelines and should - be customized based on the organization's user behavior and risk profile. Security - teams are encouraged to adjust these thresholds to optimize the balance between - detecting genuine threats and minimizing false positives, ensuring the detection - is tailored to their specific environment. -known_false_positives: This detection may yield false positives in scenarios where - legitimate bulk sign-in activities occur, such as during company-wide system updates - or when users are accessing resources from varying locations in a short time frame, - such as in the case of VPNs or cloud services that rotate IP addresses. Filter as - needed. + - Azure Active Directory +description: The following analytic detects potential distributed password spraying attacks in an Azure AD environment. It identifies a spike in failed authentication attempts across various user-and-IP combinations from multiple source IPs and countries, using different user agents. This detection leverages Azure AD SignInLogs, focusing on error code 50126 for failed authentications. This activity is significant as it indicates an adversary's attempt to bypass security controls by distributing login attempts. If confirmed malicious, this could lead to unauthorized access, data breaches, privilege escalation, and lateral movement within the organization's infrastructure. +search: |- + `azure_monitor_aad` category=*SignInLogs properties.status.errorCode=50126 properties.authenticationDetails{}.succeeded=false + | rename properties.* as * + | bucket span=5m _time + | eval uniqueIPUserCombo = src_ip . "-" . user + | rename userAgent as user_agent + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime dc(uniqueIPUserCombo) as uniqueIpUserCombinations, dc(user) as uniqueUsers, dc(src_ip) as uniqueIPs, dc(user_agent) as uniqueUserAgents, dc(location.countryOrRegion) as uniqueCountries values(location.countryOrRegion) as countries values(action) as action values(dest) as dest values(user) as user values(src) as src values(vendor_account) as vendor_account values(vendor_product) as vendor_product values(user_agent) as user_agent + | where uniqueIpUserCombinations > 20 AND uniqueUsers > 20 AND uniqueIPs > 20 AND uniqueUserAgents >= 1 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_multi_source_failed_authentications_spike_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the SignInLogs log category. The thresholds set within the analytic (such as unique IPs, unique users, etc.) are initial guidelines and should be customized based on the organization's user behavior and risk profile. Security teams are encouraged to adjust these thresholds to optimize the balance between detecting genuine threats and minimizing false positives, ensuring the detection is tailored to their specific environment. +known_false_positives: This detection may yield false positives in scenarios where legitimate bulk sign-in activities occur, such as during company-wide system updates or when users are accessing resources from varying locations in a short time frame, such as in the case of VPNs or cloud services that rotate IP addresses. Filter as needed. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://docs.microsoft.com/en-us/security/compass/incident-response-playbook-password-spray -- https://www.cisa.gov/uscert/ncas/alerts/aa21-008a -- https://docs.microsoft.com/azure/active-directory/reports-monitoring/reference-sign-ins-error-codes + - https://attack.mitre.org/techniques/T1110/003/ + - https://docs.microsoft.com/en-us/security/compass/incident-response-playbook-password-spray + - https://www.cisa.gov/uscert/ncas/alerts/aa21-008a + - https://docs.microsoft.com/azure/active-directory/reports-monitoring/reference-sign-ins-error-codes tags: - analytic_story: - - Azure Active Directory Account Takeover - - NOBELIUM Group - asset_type: Azure Tenant - atomic_guid: [] - mitre_attack_id: - - T1110.003 - - T1110.004 - - T1586.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Account Takeover + - NOBELIUM Group + asset_type: Azure Tenant + atomic_guid: [] + mitre_attack_id: + - T1110.003 + - T1110.004 + - T1586.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/azure_ad_distributed_spray/azure_ad_distributed_spray.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/azure_ad_distributed_spray/azure_ad_distributed_spray.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_multiple_appids_and_useragents_authentication_spike.yml b/detections/cloud/azure_ad_multiple_appids_and_useragents_authentication_spike.yml index 00a97b06bf..5f9b8e168e 100644 --- a/detections/cloud/azure_ad_multiple_appids_and_useragents_authentication_spike.yml +++ b/detections/cloud/azure_ad_multiple_appids_and_useragents_authentication_spike.yml @@ -1,80 +1,63 @@ name: Azure AD Multiple AppIDs and UserAgents Authentication Spike id: 5d8bb1f0-f65a-4b4e-af2e-fcdb88276314 -version: 10 -date: '2025-05-02' +version: 11 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Anomaly data_source: -- Azure Active Directory Sign-in activity -description: The following analytic detects unusual authentication activity in Azure - AD, specifically when a single user account has over 8 authentication attempts using - 3+ unique application IDs and 5+ unique user agents within a short period. It leverages - Azure AD audit logs, focusing on authentication events and using statistical thresholds. - This behavior is significant as it may indicate an adversary probing for MFA requirements. - If confirmed malicious, it suggests a compromised account, potentially leading to - further exploitation, lateral movement, and data exfiltration. Early detection is - crucial to prevent substantial harm. -search: '`azure_monitor_aad` category=SignInLogs operationName="Sign-in activity" (properties.authenticationRequirement="multiFactorAuthentication" properties.status.additionalDetails="MFA required in Azure AD") OR (properties.authenticationRequirement=singleFactorAuthentication "properties.authenticationDetails{}.succeeded"=true) - | bucket span=5m _time - | rename properties.* as * - | rename userAgent as user_agent - | fillnull - | stats count dc(appId) as unique_app_ids dc(user_agent) as unique_user_agents min(_time) as firstTime max(_time) as lastTime values(dest) as dest values(user_agent) as user_agent by user src vendor_account vendor_product signature - | where count > 5 and unique_app_ids > 2 and unique_user_agents > 5 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_multiple_appids_and_useragents_authentication_spike_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the SignInLogs log category. -known_false_positives: Rapid authentication from the same user using more than 5 different - user agents and 3 application IDs is highly unlikely under normal circumstances. - However, there are potential scenarios that could lead to false positives. + - Azure Active Directory Sign-in activity +description: The following analytic detects unusual authentication activity in Azure AD, specifically when a single user account has over 8 authentication attempts using 3+ unique application IDs and 5+ unique user agents within a short period. It leverages Azure AD audit logs, focusing on authentication events and using statistical thresholds. This behavior is significant as it may indicate an adversary probing for MFA requirements. If confirmed malicious, it suggests a compromised account, potentially leading to further exploitation, lateral movement, and data exfiltration. Early detection is crucial to prevent substantial harm. +search: |- + `azure_monitor_aad` category=SignInLogs operationName="Sign-in activity" (properties.authenticationRequirement="multiFactorAuthentication" properties.status.additionalDetails="MFA required in Azure AD") OR (properties.authenticationRequirement=singleFactorAuthentication "properties.authenticationDetails{}.succeeded"=true) + | bucket span=5m _time + | rename properties.* as * + | rename userAgent as user_agent + | fillnull + | stats count dc(appId) as unique_app_ids dc(user_agent) as unique_user_agents min(_time) as firstTime max(_time) as lastTime values(dest) as dest values(user_agent) as user_agent + BY user src vendor_account + vendor_product signature + | where count > 5 and unique_app_ids > 2 and unique_user_agents > 5 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_multiple_appids_and_useragents_authentication_spike_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the SignInLogs log category. +known_false_positives: Rapid authentication from the same user using more than 5 different user agents and 3 application IDs is highly unlikely under normal circumstances. However, there are potential scenarios that could lead to false positives. references: -- https://attack.mitre.org/techniques/T1078/ -- https://www.blackhillsinfosec.com/exploiting-mfa-inconsistencies-on-microsoft-services/ -- https://github.com/dafthack/MFASweep -- https://www.youtube.com/watch?v=SK1zgqaAZ2E + - https://attack.mitre.org/techniques/T1078/ + - https://www.blackhillsinfosec.com/exploiting-mfa-inconsistencies-on-microsoft-services/ + - https://github.com/dafthack/MFASweep + - https://www.youtube.com/watch?v=SK1zgqaAZ2E drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $user$ authenticated in a short periof of time with more than 5 different - user agents across 3 or more unique application ids. - risk_objects: - - field: user - type: user - score: 48 - threat_objects: [] + message: $user$ authenticated in a short periof of time with more than 5 different user agents across 3 or more unique application ids. + risk_objects: + - field: user + type: user + score: 48 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Account Takeover - asset_type: Azure Tenant - mitre_attack_id: - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Account Takeover + asset_type: Azure Tenant + mitre_attack_id: + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/azure_ad_multiple_appids_and_useragents_auth/azure_ad_multiple_appids_and_useragents_auth.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/azure_ad_multiple_appids_and_useragents_auth/azure_ad_multiple_appids_and_useragents_auth.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_multiple_denied_mfa_requests_for_user.yml b/detections/cloud/azure_ad_multiple_denied_mfa_requests_for_user.yml index aac50480fa..576827617d 100644 --- a/detections/cloud/azure_ad_multiple_denied_mfa_requests_for_user.yml +++ b/detections/cloud/azure_ad_multiple_denied_mfa_requests_for_user.yml @@ -1,82 +1,67 @@ name: Azure AD Multiple Denied MFA Requests For User id: d0895c20-de71-4fd2-b56c-3fcdb888eba1 -version: 9 -date: '2025-07-31' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- Azure Active Directory Sign-in activity -description: The following analytic detects an unusually high number of denied Multi-Factor - Authentication (MFA) requests for a single user within a 10-minute window, specifically - when more than nine MFA prompts are declined. It leverages Azure Active Directory - (Azure AD) sign-in logs, focusing on "Sign-in activity" events with error code 500121 - and additional details indicating "MFA denied; user declined the authentication." - This behavior is significant as it may indicate a targeted attack or account compromise - attempt, with the user actively declining unauthorized access. If confirmed malicious, - it could lead to data exfiltration, lateral movement, or further malicious activities. -search: '`azure_monitor_aad` category=SignInLogs operationName="Sign-in activity" - | rename properties.* as * - | search status.errorCode=500121 status.additionalDetails="MFA denied; user declined the authentication" - | bucket span=10m _time - | rename userAgent as user_agent - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime values(dest) as dest values(user_agent) as user_agent values(src) as src by user status.additionalDetails vendor_account vendor_product signature _time - | where count > 9 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_multiple_denied_mfa_requests_for_user_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the Signin log category. -known_false_positives: Multiple denifed MFA requests in a short period of span may - also be a sign of authentication errors. Investigate and filter as needed. + - Azure Active Directory Sign-in activity +description: The following analytic detects an unusually high number of denied Multi-Factor Authentication (MFA) requests for a single user within a 10-minute window, specifically when more than nine MFA prompts are declined. It leverages Azure Active Directory (Azure AD) sign-in logs, focusing on "Sign-in activity" events with error code 500121 and additional details indicating "MFA denied; user declined the authentication." This behavior is significant as it may indicate a targeted attack or account compromise attempt, with the user actively declining unauthorized access. If confirmed malicious, it could lead to data exfiltration, lateral movement, or further malicious activities. +search: |- + `azure_monitor_aad` category=SignInLogs operationName="Sign-in activity" + | rename properties.* as * + | search status.errorCode=500121 status.additionalDetails="MFA denied; user declined the authentication" + | bucket span=10m _time + | rename userAgent as user_agent + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime values(dest) as dest values(user_agent) as user_agent values(src) as src + BY user status.additionalDetails vendor_account + vendor_product signature _time + | where count > 9 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_multiple_denied_mfa_requests_for_user_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the Signin log category. +known_false_positives: Multiple denifed MFA requests in a short period of span may also be a sign of authentication errors. Investigate and filter as needed. references: -- https://www.mandiant.com/resources/blog/russian-targeting-gov-business -- https://arstechnica.com/information-technology/2022/03/lapsus-and-solar-winds-hackers-both-use-the-same-old-trick-to-bypass-mfa/ -- https://therecord.media/russian-hackers-bypass-2fa-by-annoying-victims-with-repeated-push-notifications/ -- https://attack.mitre.org/techniques/T1621/ -- https://attack.mitre.org/techniques/T1078/004/ -- https://www.cisa.gov/sites/default/files/publications/fact-sheet-implement-number-matching-in-mfa-applications-508c.pdf + - https://www.mandiant.com/resources/blog/russian-targeting-gov-business + - https://arstechnica.com/information-technology/2022/03/lapsus-and-solar-winds-hackers-both-use-the-same-old-trick-to-bypass-mfa/ + - https://therecord.media/russian-hackers-bypass-2fa-by-annoying-victims-with-repeated-push-notifications/ + - https://attack.mitre.org/techniques/T1621/ + - https://attack.mitre.org/techniques/T1078/004/ + - https://www.cisa.gov/sites/default/files/publications/fact-sheet-implement-number-matching-in-mfa-applications-508c.pdf drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ denied more than 9 MFA requests in a timespan of 10 minutes. - risk_objects: - - field: user - type: user - score: 54 - threat_objects: [] + message: User $user$ denied more than 9 MFA requests in a timespan of 10 minutes. + risk_objects: + - field: user + type: user + score: 54 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Account Takeover - asset_type: Azure Active Directory - atomic_guid: [] - mitre_attack_id: - - T1621 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Account Takeover + asset_type: Azure Active Directory + atomic_guid: [] + mitre_attack_id: + - T1621 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/azure_ad_multiple_denied_mfa_requests/azure_ad_multiple_denied_mfa_requests.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/azure_ad_multiple_denied_mfa_requests/azure_ad_multiple_denied_mfa_requests.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_multiple_failed_mfa_requests_for_user.yml b/detections/cloud/azure_ad_multiple_failed_mfa_requests_for_user.yml index 92db4fb368..44f07f9ac8 100644 --- a/detections/cloud/azure_ad_multiple_failed_mfa_requests_for_user.yml +++ b/detections/cloud/azure_ad_multiple_failed_mfa_requests_for_user.yml @@ -1,83 +1,68 @@ name: Azure AD Multiple Failed MFA Requests For User id: 264ea131-ab1f-41b8-90e0-33ad1a1888ea -version: 10 -date: '2025-05-02' +version: 11 +date: '2026-02-25' author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk status: production type: TTP -description: The following analytic identifies multiple failed multi-factor authentication - (MFA) requests for a single user within an Azure AD tenant. It leverages Azure AD - Sign-in Logs, specifically error code 500121, to detect more than 10 failed MFA - attempts within 10 minutes. This behavior is significant as it may indicate an adversary - attempting to bypass MFA by bombarding the user with repeated authentication prompts. - If confirmed malicious, this activity could lead to unauthorized access, allowing - attackers to compromise user accounts and potentially escalate their privileges - within the environment. +description: The following analytic identifies multiple failed multi-factor authentication (MFA) requests for a single user within an Azure AD tenant. It leverages Azure AD Sign-in Logs, specifically error code 500121, to detect more than 10 failed MFA attempts within 10 minutes. This behavior is significant as it may indicate an adversary attempting to bypass MFA by bombarding the user with repeated authentication prompts. If confirmed malicious, this activity could lead to unauthorized access, allowing attackers to compromise user accounts and potentially escalate their privileges within the environment. data_source: -- Azure Active Directory Sign-in activity -search: '`azure_monitor_aad` category=SignInLogs operationName="Sign-in activity" properties.status.errorCode=500121 properties.status.additionalDetails!="MFA denied; user declined the authentication" - | rename properties.* as * - | bucket span=10m _time - | rename userAgent as user_agent - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime values(dest) as dest values(src) as src by user, status.additionalDetails, appDisplayName, user_agent, vendor_account, vendor_product, signature - | where count > 10 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_multiple_failed_mfa_requests_for_user_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the Signin log category. -known_false_positives: Multiple Failed MFA requests may also be a sign of authentication - or application issues. Filter as needed. + - Azure Active Directory Sign-in activity +search: |- + `azure_monitor_aad` category=SignInLogs operationName="Sign-in activity" properties.status.errorCode=500121 properties.status.additionalDetails!="MFA denied; user declined the authentication" + | rename properties.* as * + | bucket span=10m _time + | rename userAgent as user_agent + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime values(dest) as dest values(src) as src + BY user, status.additionalDetails, appDisplayName, + user_agent, vendor_account, vendor_product, + signature + | where count > 10 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_multiple_failed_mfa_requests_for_user_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the Signin log category. +known_false_positives: Multiple Failed MFA requests may also be a sign of authentication or application issues. Filter as needed. references: -- https://www.mandiant.com/resources/blog/russian-targeting-gov-business -- https://arstechnica.com/information-technology/2022/03/lapsus-and-solar-winds-hackers-both-use-the-same-old-trick-to-bypass-mfa/ -- https://therecord.media/russian-hackers-bypass-2fa-by-annoying-victims-with-repeated-push-notifications/ -- https://attack.mitre.org/techniques/T1621/ -- https://attack.mitre.org/techniques/T1078/004/ -- https://www.cisa.gov/sites/default/files/publications/fact-sheet-implement-number-matching-in-mfa-applications-508c.pdf + - https://www.mandiant.com/resources/blog/russian-targeting-gov-business + - https://arstechnica.com/information-technology/2022/03/lapsus-and-solar-winds-hackers-both-use-the-same-old-trick-to-bypass-mfa/ + - https://therecord.media/russian-hackers-bypass-2fa-by-annoying-victims-with-repeated-push-notifications/ + - https://attack.mitre.org/techniques/T1621/ + - https://attack.mitre.org/techniques/T1078/004/ + - https://www.cisa.gov/sites/default/files/publications/fact-sheet-implement-number-matching-in-mfa-applications-508c.pdf drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ failed to complete MFA authentication more than 9 times in - a timespan of 10 minutes. - risk_objects: - - field: user - type: user - score: 54 - threat_objects: [] + message: User $user$ failed to complete MFA authentication more than 9 times in a timespan of 10 minutes. + risk_objects: + - field: user + type: user + score: 54 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Account Takeover - asset_type: Azure Active Directory - mitre_attack_id: - - T1078.004 - - T1586.003 - - T1621 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Account Takeover + asset_type: Azure Active Directory + mitre_attack_id: + - T1078.004 + - T1586.003 + - T1621 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/multiple_failed_mfa_requests/azure-audit.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/multiple_failed_mfa_requests/azure-audit.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_multiple_service_principals_created_by_sp.yml b/detections/cloud/azure_ad_multiple_service_principals_created_by_sp.yml index 545051b829..3d2c58fabb 100644 --- a/detections/cloud/azure_ad_multiple_service_principals_created_by_sp.yml +++ b/detections/cloud/azure_ad_multiple_service_principals_created_by_sp.yml @@ -1,81 +1,65 @@ name: Azure AD Multiple Service Principals Created by SP id: 66cb378f-234d-4fe1-bb4c-e7878ff6b017 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk data_source: -- Azure Active Directory Add service principal + - Azure Active Directory Add service principal type: Anomaly status: production -description: The following analytic detects when a single service principal in Azure - AD creates more than three unique OAuth applications within a 10-minute span. It - leverages Azure AD audit logs, specifically monitoring the 'Add service principal' - operation initiated by service principals. This behavior is significant as it may - indicate an attacker using a compromised or malicious service principal to rapidly - establish multiple service principals, potentially staging an attack. If confirmed - malicious, this activity could facilitate network infiltration or expansion, allowing - the attacker to gain unauthorized access and persist within the environment. -search: '`azure_monitor_aad` operationName="Add service principal" properties.initiatedBy.app.appId=* - | rename properties.* as * - | bucket span=10m _time - | rename targetResources{}.displayName as displayName - | rename targetResources{}.type as type - | rename initiatedBy.app.displayName as src_user - | rename userAgent as user_agent - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime dc(displayName) as unique_apps values(displayName) as displayName values(dest) as dest values(src) as src values(user) as user values(user_agent) as user_agent by src_user vendor_account vendor_product signature - | where unique_apps > 3 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_multiple_service_principals_created_by_sp_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the AuditLogs log category. -known_false_positives: Certain users or applications may create multiple service principals - in a short period of time for legitimate purposes. Filter as needed. +description: The following analytic detects when a single service principal in Azure AD creates more than three unique OAuth applications within a 10-minute span. It leverages Azure AD audit logs, specifically monitoring the 'Add service principal' operation initiated by service principals. This behavior is significant as it may indicate an attacker using a compromised or malicious service principal to rapidly establish multiple service principals, potentially staging an attack. If confirmed malicious, this activity could facilitate network infiltration or expansion, allowing the attacker to gain unauthorized access and persist within the environment. +search: |- + `azure_monitor_aad` operationName="Add service principal" properties.initiatedBy.app.appId=* + | rename properties.* as * + | bucket span=10m _time + | rename targetResources{}.displayName as displayName + | rename targetResources{}.type as type + | rename initiatedBy.app.displayName as src_user + | rename userAgent as user_agent + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime dc(displayName) as unique_apps values(displayName) as displayName values(dest) as dest values(src) as src values(user) as user values(user_agent) as user_agent + BY src_user vendor_account vendor_product + signature + | where unique_apps > 3 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_multiple_service_principals_created_by_sp_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLogs log category. +known_false_positives: Certain users or applications may create multiple service principals in a short period of time for legitimate purposes. Filter as needed. references: -- https://attack.mitre.org/techniques/T1136/003/ -- https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/ + - https://attack.mitre.org/techniques/T1136/003/ + - https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/ drilldown_searches: -- name: View the detection results for - "$src_user$" - search: '%original_detection_search% | search src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_user$" + search: '%original_detection_search% | search src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Multiple OAuth applications were created by $src_user$ in a short period - of time - risk_objects: - - field: src_user - type: user - score: 42 - threat_objects: [] + message: Multiple OAuth applications were created by $src_user$ in a short period of time + risk_objects: + - field: src_user + type: user + score: 42 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - - NOBELIUM Group - asset_type: Azure Active Directory - mitre_attack_id: - - T1136.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Persistence + - NOBELIUM Group + asset_type: Azure Active Directory + mitre_attack_id: + - T1136.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/azure_ad_multiple_service_principals_created/azure_ad_multiple_service_principals_created.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/azure_ad_multiple_service_principals_created/azure_ad_multiple_service_principals_created.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_multiple_service_principals_created_by_user.yml b/detections/cloud/azure_ad_multiple_service_principals_created_by_user.yml index 3fcae05cb4..372e52ba3a 100644 --- a/detections/cloud/azure_ad_multiple_service_principals_created_by_user.yml +++ b/detections/cloud/azure_ad_multiple_service_principals_created_by_user.yml @@ -1,79 +1,63 @@ name: Azure AD Multiple Service Principals Created by User id: 32880707-f512-414e-bd7f-204c0c85b758 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk data_source: -- Azure Active Directory Add service principal + - Azure Active Directory Add service principal type: Anomaly status: production -description: The following analytic identifies instances where a single user creates - more than three unique OAuth applications within a 10-minute timeframe in Azure - AD. It detects this activity by monitoring the 'Add service principal' operation - and aggregating data in 10-minute intervals. This behavior is significant as it - may indicate an adversary rapidly creating multiple service principals to stage - an attack or expand their foothold within the network. If confirmed malicious, this - activity could allow attackers to establish persistence, escalate privileges, or - access sensitive information within the Azure environment. -search: '`azure_monitor_aad` operationName="Add service principal" properties.initiatedBy.user.id=* - | rename properties.* as * - | bucket span=10m _time - | rename targetResources{}.displayName as displayName - | rename userAgent as user_agent - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime dc(displayName) as unique_apps values(displayName) as displayName values(dest) as dest values(src) as src values(user) as user values(user_agent) as user_agent by src_user vendor_account vendor_product signature - | where unique_apps > 3 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_multiple_service_principals_created_by_user_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the AuditLogs log category. -known_false_positives: Certain users or applications may create multiple service principals - in a short period of time for legitimate purposes. Filter as needed. +description: The following analytic identifies instances where a single user creates more than three unique OAuth applications within a 10-minute timeframe in Azure AD. It detects this activity by monitoring the 'Add service principal' operation and aggregating data in 10-minute intervals. This behavior is significant as it may indicate an adversary rapidly creating multiple service principals to stage an attack or expand their foothold within the network. If confirmed malicious, this activity could allow attackers to establish persistence, escalate privileges, or access sensitive information within the Azure environment. +search: |- + `azure_monitor_aad` operationName="Add service principal" properties.initiatedBy.user.id=* + | rename properties.* as * + | bucket span=10m _time + | rename targetResources{}.displayName as displayName + | rename userAgent as user_agent + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime dc(displayName) as unique_apps values(displayName) as displayName values(dest) as dest values(src) as src values(user) as user values(user_agent) as user_agent + BY src_user vendor_account vendor_product + signature + | where unique_apps > 3 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_multiple_service_principals_created_by_user_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLogs log category. +known_false_positives: Certain users or applications may create multiple service principals in a short period of time for legitimate purposes. Filter as needed. references: -- https://attack.mitre.org/techniques/T1136/003/ -- https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/ + - https://attack.mitre.org/techniques/T1136/003/ + - https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/ drilldown_searches: -- name: View the detection results for - "$src_user$" - search: '%original_detection_search% | search src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_user$" + search: '%original_detection_search% | search src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Multiple OAuth applications were created by $src_user$ in a short period - of time - risk_objects: - - field: src_user - type: user - score: 42 - threat_objects: [] + message: Multiple OAuth applications were created by $src_user$ in a short period of time + risk_objects: + - field: src_user + type: user + score: 42 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - - NOBELIUM Group - asset_type: Azure Active Directory - mitre_attack_id: - - T1136.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Persistence + - NOBELIUM Group + asset_type: Azure Active Directory + mitre_attack_id: + - T1136.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/azure_ad_multiple_service_principals_created/azure_ad_multiple_service_principals_created.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/azure_ad_multiple_service_principals_created/azure_ad_multiple_service_principals_created.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_multiple_users_failing_to_authenticate_from_ip.yml b/detections/cloud/azure_ad_multiple_users_failing_to_authenticate_from_ip.yml index d402795b29..9b42945d32 100644 --- a/detections/cloud/azure_ad_multiple_users_failing_to_authenticate_from_ip.yml +++ b/detections/cloud/azure_ad_multiple_users_failing_to_authenticate_from_ip.yml @@ -1,81 +1,66 @@ name: Azure AD Multiple Users Failing To Authenticate From Ip id: 94481a6a-8f59-4c86-957f-55a71e3612a6 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk status: production type: Anomaly -description: The following analytic detects a single source IP failing to authenticate - with 30 unique valid users within 5 minutes in Azure Active Directory. It leverages - Azure AD SignInLogs with error code 50126, indicating invalid passwords. This behavior - is significant as it may indicate a Password Spraying attack, where an adversary - attempts to gain initial access or elevate privileges by trying common passwords - across many accounts. If confirmed malicious, this activity could lead to unauthorized - access, data breaches, or privilege escalation within the Azure AD environment. +description: The following analytic detects a single source IP failing to authenticate with 30 unique valid users within 5 minutes in Azure Active Directory. It leverages Azure AD SignInLogs with error code 50126, indicating invalid passwords. This behavior is significant as it may indicate a Password Spraying attack, where an adversary attempts to gain initial access or elevate privileges by trying common passwords across many accounts. If confirmed malicious, this activity could lead to unauthorized access, data breaches, or privilege escalation within the Azure AD environment. data_source: -- Azure Active Directory -search: '`azure_monitor_aad` category=SignInLogs properties.status.errorCode=50126 properties.authenticationDetails{}.succeeded=false - | rename properties.* as * - | bucket span=5m _time - | rename userAgent as user_agent - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime dc(user) as unique_user values(dest) as dest values(user) as user values(user_agent) as user_agent values(vendor_account) as vendor_account values(vendor_product) as vendor_product by src signature - | where unique_user > 30 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_multiple_users_failing_to_authenticate_from_ip_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the Signin log category. -known_false_positives: A source Ip failing to authenticate with multiple users is - not a common for legitimate behavior. + - Azure Active Directory +search: |- + `azure_monitor_aad` category=SignInLogs properties.status.errorCode=50126 properties.authenticationDetails{}.succeeded=false + | rename properties.* as * + | bucket span=5m _time + | rename userAgent as user_agent + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime dc(user) as unique_user values(dest) as dest values(user) as user values(user_agent) as user_agent values(vendor_account) as vendor_account values(vendor_product) as vendor_product + BY src signature + | where unique_user > 30 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_multiple_users_failing_to_authenticate_from_ip_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the Signin log category. +known_false_positives: A source Ip failing to authenticate with multiple users is not a common for legitimate behavior. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://docs.microsoft.com/en-us/security/compass/incident-response-playbook-password-spray -- https://www.cisa.gov/uscert/ncas/alerts/aa21-008a -- https://docs.microsoft.com/azure/active-directory/reports-monitoring/reference-sign-ins-error-codes + - https://attack.mitre.org/techniques/T1110/003/ + - https://docs.microsoft.com/en-us/security/compass/incident-response-playbook-password-spray + - https://www.cisa.gov/uscert/ncas/alerts/aa21-008a + - https://docs.microsoft.com/azure/active-directory/reports-monitoring/reference-sign-ins-error-codes drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Source Ip $src$ failed to authenticate with 30 users within 5 minutes. - risk_objects: - - field: user - type: user - score: 63 - threat_objects: - - field: src - type: ip_address + message: Source Ip $src$ failed to authenticate with 30 users within 5 minutes. + risk_objects: + - field: user + type: user + score: 63 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Azure Active Directory Account Takeover - asset_type: Azure Active Directory - mitre_attack_id: - - T1110.003 - - T1110.004 - - T1586.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Account Takeover + asset_type: Azure Active Directory + mitre_attack_id: + - T1110.003 + - T1110.004 + - T1586.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/password_spraying_azuread/azuread_signin.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/password_spraying_azuread/azuread_signin.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_new_custom_domain_added.yml b/detections/cloud/azure_ad_new_custom_domain_added.yml index 652f4d3550..d2016575e6 100644 --- a/detections/cloud/azure_ad_new_custom_domain_added.yml +++ b/detections/cloud/azure_ad_new_custom_domain_added.yml @@ -1,79 +1,65 @@ name: Azure AD New Custom Domain Added id: 30c47f45-dd6a-4720-9963-0bca6c8686ef -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk status: production type: TTP -description: The following analytic detects the addition of a new custom domain within - an Azure Active Directory (AD) tenant. It leverages Azure AD AuditLogs to identify - successful "Add unverified domain" operations. This activity is significant as it - may indicate an adversary attempting to establish persistence by setting up identity - federation backdoors, allowing them to impersonate users and bypass authentication - mechanisms. If confirmed malicious, this could enable attackers to gain unauthorized - access, escalate privileges, and maintain long-term access to the Azure AD environment, - posing a severe security risk. +description: The following analytic detects the addition of a new custom domain within an Azure Active Directory (AD) tenant. It leverages Azure AD AuditLogs to identify successful "Add unverified domain" operations. This activity is significant as it may indicate an adversary attempting to establish persistence by setting up identity federation backdoors, allowing them to impersonate users and bypass authentication mechanisms. If confirmed malicious, this could enable attackers to gain unauthorized access, escalate privileges, and maintain long-term access to the Azure AD environment, posing a severe security risk. data_source: -- Azure Active Directory Add unverified domain -search: '`azure_monitor_aad` operationName="Add unverified domain" properties.result=success - | rename properties.* as * - | rename targetResources{}.displayName as domain - | rename userAgent as user_agent - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product user_agent domain signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_new_custom_domain_added_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment. - This analytic was written to be used with the azure:monitor:aad sourcetype leveraging - the AuditLogs log category. -known_false_positives: In most organizations, new customm domains will be updated - infrequently. Filter as needed. + - Azure Active Directory Add unverified domain +search: |- + `azure_monitor_aad` operationName="Add unverified domain" properties.result=success + | rename properties.* as * + | rename targetResources{}.displayName as domain + | rename userAgent as user_agent + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product user_agent + domain signature + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_new_custom_domain_added_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLogs log category. +known_false_positives: In most organizations, new customm domains will be updated infrequently. Filter as needed. references: -- https://docs.microsoft.com/en-us/azure/active-directory/enterprise-users/domains-manage -- https://www.mandiant.com/resources/remediation-and-hardening-strategies-microsoft-365-defend-against-apt29-v13 -- https://o365blog.com/post/federation-vulnerability/ -- https://www.inversecos.com/2021/11/how-to-detect-azure-active-directory.html -- https://www.mandiant.com/resources/blog/detecting-microsoft-365-azure-active-directory-backdoors -- https://attack.mitre.org/techniques/T1484/002/ + - https://docs.microsoft.com/en-us/azure/active-directory/enterprise-users/domains-manage + - https://www.mandiant.com/resources/remediation-and-hardening-strategies-microsoft-365-defend-against-apt29-v13 + - https://o365blog.com/post/federation-vulnerability/ + - https://www.inversecos.com/2021/11/how-to-detect-azure-active-directory.html + - https://www.mandiant.com/resources/blog/detecting-microsoft-365-azure-active-directory-backdoors + - https://attack.mitre.org/techniques/T1484/002/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A new custom domain, $domain$ , was added by $user$ - risk_objects: - - field: user - type: user - score: 54 - threat_objects: [] + message: A new custom domain, $domain$ , was added by $user$ + risk_objects: + - field: user + type: user + score: 54 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - asset_type: Azure Active Directory - mitre_attack_id: - - T1484.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Azure Active Directory Persistence + asset_type: Azure Active Directory + mitre_attack_id: + - T1484.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.002/new_federated_domain/azure-audit.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.002/new_federated_domain/azure-audit.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_new_federated_domain_added.yml b/detections/cloud/azure_ad_new_federated_domain_added.yml index f373085aae..351e18a502 100644 --- a/detections/cloud/azure_ad_new_federated_domain_added.yml +++ b/detections/cloud/azure_ad_new_federated_domain_added.yml @@ -1,80 +1,67 @@ name: Azure AD New Federated Domain Added id: a87cd633-076d-4ab2-9047-977751a3c1a0 -version: 11 -date: '2026-01-20' +version: 12 +date: '2026-02-25' author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk status: production type: TTP -description: The following analytic detects the addition of a new federated domain - within an Azure Active Directory tenant. It leverages Azure AD AuditLogs to identify - successful "Set domain authentication" operations. This activity is significant - as it may indicate the use of the Azure AD identity federation backdoor technique, - allowing an adversary to establish persistence. If confirmed malicious, the attacker - could impersonate any user, bypassing password and MFA requirements, potentially - leading to unauthorized access and control over the Azure AD environment. +description: The following analytic detects the addition of a new federated domain within an Azure Active Directory tenant. It leverages Azure AD AuditLogs to identify successful "Set domain authentication" operations. This activity is significant as it may indicate the use of the Azure AD identity federation backdoor technique, allowing an adversary to establish persistence. If confirmed malicious, the attacker could impersonate any user, bypassing password and MFA requirements, potentially leading to unauthorized access and control over the Azure AD environment. data_source: -- Azure Active Directory Set domain authentication -search: '`azure_monitor_aad` operationName="Set domain authentication" "properties.result"=success - | rename properties.* as * - | rename targetResources{}.displayName as domain - | rename userAgent as user_agent - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product user_agent domain signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_new_federated_domain_added_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment. - This analytic was written to be used with the azure:monitor:aad sourcetype leveraging - the AuditLogs log category. -known_false_positives: In most organizations, domain federation settings will be updated - infrequently. Filter as needed. + - Azure Active Directory Set domain authentication +search: |- + `azure_monitor_aad` operationName="Set domain authentication" "properties.result"=success + | rename properties.* as * + | rename targetResources{}.displayName as domain + | rename userAgent as user_agent + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product user_agent + domain signature + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_new_federated_domain_added_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLogs log category. +known_false_positives: In most organizations, domain federation settings will be updated infrequently. Filter as needed. references: -- https://www.mandiant.com/resources/remediation-and-hardening-strategies-microsoft-365-defend-against-apt29-v13 -- https://o365blog.com/post/federation-vulnerability/ -- https://www.inversecos.com/2021/11/how-to-detect-azure-active-directory.html -- https://www.mandiant.com/resources/blog/detecting-microsoft-365-azure-active-directory-backdoors -- https://attack.mitre.org/techniques/T1484/002/ + - https://www.mandiant.com/resources/remediation-and-hardening-strategies-microsoft-365-defend-against-apt29-v13 + - https://o365blog.com/post/federation-vulnerability/ + - https://www.inversecos.com/2021/11/how-to-detect-azure-active-directory.html + - https://www.mandiant.com/resources/blog/detecting-microsoft-365-azure-active-directory-backdoors + - https://attack.mitre.org/techniques/T1484/002/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A new federated domain, $domain$ , was added by $user$ - risk_objects: - - field: user - type: user - score: 81 - threat_objects: [] + message: A new federated domain, $domain$ , was added by $user$ + risk_objects: + - field: user + type: user + score: 81 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - - Scattered Lapsus$ Hunters - - Hellcat Ransomware - - Storm-0501 Ransomware - asset_type: Azure Active Directory - mitre_attack_id: - - T1484.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Azure Active Directory Persistence + - Scattered Lapsus$ Hunters + - Hellcat Ransomware + - Storm-0501 Ransomware + asset_type: Azure Active Directory + mitre_attack_id: + - T1484.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.002/new_federated_domain/azure-audit.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.002/new_federated_domain/azure-audit.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_new_mfa_method_registered.yml b/detections/cloud/azure_ad_new_mfa_method_registered.yml index 0475bcdc68..ddca77d3f0 100644 --- a/detections/cloud/azure_ad_new_mfa_method_registered.yml +++ b/detections/cloud/azure_ad_new_mfa_method_registered.yml @@ -6,78 +6,46 @@ author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- Azure Active Directory Update user -description: The following analytic detects the registration of a new Multi-Factor - Authentication (MFA) method for a user account in Azure Active Directory. It leverages - Azure AD audit logs to identify changes in MFA configurations. This activity is - significant because adding a new MFA method can indicate an attacker's attempt to - maintain persistence on a compromised account. If confirmed malicious, the attacker - could bypass existing security measures, solidify their access, and potentially - escalate privileges, access sensitive data, or make unauthorized changes. Immediate - verification and remediation are required to secure the affected account. -search: "`azure_monitor_aad` operationName=\"Update user\" - | rename properties.* as * - | eval propertyName = mvindex('targetResources{}.modifiedProperties{}.displayName',0) - | search propertyName = StrongAuthenticationMethod - | eval oldvalue = mvindex('targetResources{}.modifiedProperties{}.oldValue',0) - | eval newvalue = mvindex('targetResources{}.modifiedProperties{}.newValue',0) - | rex field=newvalue max_match=0 \"(?i)(?\\\"MethodType\\\")\" - | rex field=oldvalue max_match=0 \"(?i)(?\\\"MethodType\\\")\" - | eval count_new_method_type = coalesce(mvcount(new_method_type), 0) - | eval count_old_method_type = coalesce(mvcount(old_method_type), 0) - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product newvalue oldvalue signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_new_mfa_method_registered_filter`" -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the AuditLog log category. -known_false_positives: Users may register MFA methods legitimally, investigate and - filter as needed. + - Azure Active Directory Update user +description: The following analytic detects the registration of a new Multi-Factor Authentication (MFA) method for a user account in Azure Active Directory. It leverages Azure AD audit logs to identify changes in MFA configurations. This activity is significant because adding a new MFA method can indicate an attacker's attempt to maintain persistence on a compromised account. If confirmed malicious, the attacker could bypass existing security measures, solidify their access, and potentially escalate privileges, access sensitive data, or make unauthorized changes. Immediate verification and remediation are required to secure the affected account. +search: "`azure_monitor_aad` operationName=\"Update user\" | rename properties.* as * | eval propertyName = mvindex('targetResources{}.modifiedProperties{}.displayName',0) | search propertyName = StrongAuthenticationMethod | eval oldvalue = mvindex('targetResources{}.modifiedProperties{}.oldValue',0) | eval newvalue = mvindex('targetResources{}.modifiedProperties{}.newValue',0) | rex field=newvalue max_match=0 \"(?i)(?\\\"MethodType\\\")\" | rex field=oldvalue max_match=0 \"(?i)(?\\\"MethodType\\\")\" | eval count_new_method_type = coalesce(mvcount(new_method_type), 0) | eval count_old_method_type = coalesce(mvcount(old_method_type), 0) | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product newvalue oldvalue signature | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_new_mfa_method_registered_filter`" +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category. +known_false_positives: Users may register MFA methods legitimally, investigate and filter as needed. references: -- https://attack.mitre.org/techniques/T1098/005/ -- https://www.microsoft.com/en-us/security/blog/2023/06/08/detecting-and-mitigating-a-multi-stage-aitm-phishing-and-bec-campaign/ -- https://www.csoonline.com/article/573451/sophisticated-bec-scammers-bypass-microsoft-365-multi-factor-authentication.html + - https://attack.mitre.org/techniques/T1098/005/ + - https://www.microsoft.com/en-us/security/blog/2023/06/08/detecting-and-mitigating-a-multi-stage-aitm-phishing-and-bec-campaign/ + - https://www.csoonline.com/article/573451/sophisticated-bec-scammers-bypass-microsoft-365-multi-factor-authentication.html drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A new MFA method was registered for user $user$ - risk_objects: - - field: user - type: user - score: 30 - threat_objects: [] + message: A new MFA method was registered for user $user$ + risk_objects: + - field: user + type: user + score: 30 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - - Scattered Lapsus$ Hunters - asset_type: Azure Tenant - mitre_attack_id: - - T1098.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Persistence + - Scattered Lapsus$ Hunters + asset_type: Azure Tenant + mitre_attack_id: + - T1098.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.005/azure_ad_register_new_mfa_method/azure_ad_register_new_mfa_method.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.005/azure_ad_register_new_mfa_method/azure_ad_register_new_mfa_method.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_new_mfa_method_registered_for_user.yml b/detections/cloud/azure_ad_new_mfa_method_registered_for_user.yml index 6acc314354..674b83fa04 100644 --- a/detections/cloud/azure_ad_new_mfa_method_registered_for_user.yml +++ b/detections/cloud/azure_ad_new_mfa_method_registered_for_user.yml @@ -1,80 +1,68 @@ name: Azure AD New MFA Method Registered For User id: 2628b087-4189-403f-9044-87403f777a1b -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the registration of a new Multi-Factor - Authentication (MFA) method for an Azure AD account. It leverages Azure AD AuditLogs - to identify when a user registers new security information. This activity is significant - because adversaries who gain unauthorized access to an account may add their own - MFA method to maintain persistence. If confirmed malicious, this could allow attackers - to bypass existing security controls, maintain long-term access, and potentially - escalate their privileges within the environment. +description: The following analytic detects the registration of a new Multi-Factor Authentication (MFA) method for an Azure AD account. It leverages Azure AD AuditLogs to identify when a user registers new security information. This activity is significant because adversaries who gain unauthorized access to an account may add their own MFA method to maintain persistence. If confirmed malicious, this could allow attackers to bypass existing security controls, maintain long-term access, and potentially escalate their privileges within the environment. data_source: -- Azure Active Directory User registered security info -search: '`azure_monitor_aad` category=AuditLogs operationName="User registered security info" properties.operationType=Add - | rename properties.* as * - | rename targetResources{}.* as * - | rename userAgent as user_agent - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by action dest user src vendor_account vendor_product user_agent result resultDescription signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_new_mfa_method_registered_for_user_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the AuditLogs log category. -known_false_positives: Newly onboarded users who are registering an MFA method for - the first time will also trigger this detection. + - Azure Active Directory User registered security info +search: |- + `azure_monitor_aad` category=AuditLogs operationName="User registered security info" properties.operationType=Add + | rename properties.* as * + | rename targetResources{}.* as * + | rename userAgent as user_agent + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY action dest user + src vendor_account vendor_product + user_agent result resultDescription + signature + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_new_mfa_method_registered_for_user_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLogs log category. +known_false_positives: Newly onboarded users who are registering an MFA method for the first time will also trigger this detection. references: -- https://docs.microsoft.com/en-us/azure/active-directory/authentication/concept-mfa-howitworks -- https://attack.mitre.org/techniques/T1556/ -- https://attack.mitre.org/techniques/T1556/006/ -- https://twitter.com/jhencinski/status/1618660062352007174 + - https://docs.microsoft.com/en-us/azure/active-directory/authentication/concept-mfa-howitworks + - https://attack.mitre.org/techniques/T1556/ + - https://attack.mitre.org/techniques/T1556/006/ + - https://twitter.com/jhencinski/status/1618660062352007174 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A new MFA method was registered for user $user$ - risk_objects: - - field: user - type: user - score: 64 - threat_objects: - - field: src - type: ip_address + message: A new MFA method was registered for user $user$ + risk_objects: + - field: user + type: user + score: 64 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Compromised User Account - - Azure Active Directory Account Takeover - - Scattered Lapsus$ Hunters - asset_type: Azure Active Directory - mitre_attack_id: - - T1556.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Compromised User Account + - Azure Active Directory Account Takeover + - Scattered Lapsus$ Hunters + asset_type: Azure Active Directory + mitre_attack_id: + - T1556.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556.006/azure_ad_new_mfa_method_registered_for_user/azuread.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556.006/azure_ad_new_mfa_method_registered_for_user/azuread.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_oauth_application_consent_granted_by_user.yml b/detections/cloud/azure_ad_oauth_application_consent_granted_by_user.yml index cc0d42bfdd..4ab06116a6 100644 --- a/detections/cloud/azure_ad_oauth_application_consent_granted_by_user.yml +++ b/detections/cloud/azure_ad_oauth_application_consent_granted_by_user.yml @@ -6,80 +6,48 @@ author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- Azure Active Directory Consent to application -description: The following analytic detects when a user in an Azure AD environment - grants consent to an OAuth application. It leverages Azure AD audit logs to identify - events where users approve application consents. This activity is significant as - it can expose organizational data to third-party applications, a common tactic used - by malicious actors to gain unauthorized access. If confirmed malicious, this could - lead to unauthorized access to sensitive information and resources. Immediate investigation - is required to validate the application's legitimacy, review permissions, and mitigate - potential risks. -search: "`azure_monitor_aad` operationName=\"Consent to application\" properties.result=success - | rename properties.* as * - | eval permissions_index = if(mvfind('targetResources{}.modifiedProperties{}.displayName', - \"ConsentAction.Permissions\") >= 0, mvfind('targetResources{}.modifiedProperties{}.displayName', - \"ConsentAction.Permissions\"), -1) - | eval permissions = mvindex('targetResources{}.modifiedProperties{}.newValue',permissions_index) - | rex field=permissions \"Scope: (? - [ ^,]+)\" - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product Scope signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_oauth_application_consent_granted_by_user_filter`" -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the AuditLog log category. -known_false_positives: False positives may occur if users are granting consents as - part of legitimate application integrations or setups. It is crucial to review the - application and the permissions it requests to ensure they align with organizational - policies and security best practices. + - Azure Active Directory Consent to application +description: The following analytic detects when a user in an Azure AD environment grants consent to an OAuth application. It leverages Azure AD audit logs to identify events where users approve application consents. This activity is significant as it can expose organizational data to third-party applications, a common tactic used by malicious actors to gain unauthorized access. If confirmed malicious, this could lead to unauthorized access to sensitive information and resources. Immediate investigation is required to validate the application's legitimacy, review permissions, and mitigate potential risks. +search: "`azure_monitor_aad` operationName=\"Consent to application\" properties.result=success | rename properties.* as * | eval permissions_index = if(mvfind('targetResources{}.modifiedProperties{}.displayName', \"ConsentAction.Permissions\") >= 0, mvfind('targetResources{}.modifiedProperties{}.displayName', \"ConsentAction.Permissions\"), -1) | eval permissions = mvindex('targetResources{}.modifiedProperties{}.newValue',permissions_index) | rex field=permissions \"Scope: (? [ ^,]+)\" | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product Scope signature | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_oauth_application_consent_granted_by_user_filter`" +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category. +known_false_positives: False positives may occur if users are granting consents as part of legitimate application integrations or setups. It is crucial to review the application and the permissions it requests to ensure they align with organizational policies and security best practices. references: -- https://attack.mitre.org/techniques/T1528/ -- https://www.microsoft.com/en-us/security/blog/2022/09/22/malicious-oauth-applications-used-to-compromise-email-servers-and-spread-spam/ -- https://learn.microsoft.com/en-us/azure/active-directory/manage-apps/protect-against-consent-phishing -- https://learn.microsoft.com/en-us/defender-cloud-apps/investigate-risky-oauth -- https://www.alteredsecurity.com/post/introduction-to-365-stealer -- https://github.com/AlteredSecurity/365-Stealer + - https://attack.mitre.org/techniques/T1528/ + - https://www.microsoft.com/en-us/security/blog/2022/09/22/malicious-oauth-applications-used-to-compromise-email-servers-and-spread-spam/ + - https://learn.microsoft.com/en-us/azure/active-directory/manage-apps/protect-against-consent-phishing + - https://learn.microsoft.com/en-us/defender-cloud-apps/investigate-risky-oauth + - https://www.alteredsecurity.com/post/introduction-to-365-stealer + - https://github.com/AlteredSecurity/365-Stealer drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ consented an OAuth application. - risk_objects: - - field: user - type: user - score: 36 - threat_objects: [] + message: User $user$ consented an OAuth application. + risk_objects: + - field: user + type: user + score: 36 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Account Takeover - asset_type: Azure Tenant - mitre_attack_id: - - T1528 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Account Takeover + asset_type: Azure Tenant + mitre_attack_id: + - T1528 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/azure_ad_user_consent_granted/azure_ad_user_consent_granted.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/azure_ad_user_consent_granted/azure_ad_user_consent_granted.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_pim_role_assigned.yml b/detections/cloud/azure_ad_pim_role_assigned.yml index f3abe01663..a5caad0703 100644 --- a/detections/cloud/azure_ad_pim_role_assigned.yml +++ b/detections/cloud/azure_ad_pim_role_assigned.yml @@ -1,75 +1,61 @@ name: Azure AD PIM Role Assigned id: fcd6dfeb-191c-46a0-a29c-c306382145ab -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- Azure Active Directory -description: The following analytic detects the assignment of an Azure AD Privileged - Identity Management (PIM) role. It leverages Azure Active Directory events to identify - when a user is added as an eligible member to a PIM role. This activity is significant - because PIM roles grant elevated privileges, and their assignment should be closely - monitored to prevent unauthorized access. If confirmed malicious, an attacker could - exploit this to gain privileged access, potentially leading to unauthorized actions, - data breaches, or further compromise of the environment. -search: '`azure_monitor_aad` operationName="Add eligible member to role in PIM completed*" - | rename properties.* as * - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_pim_role_assigned_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment. - This analytic was written to be used with the azure:monitor:aad sourcetype leveraging - the AuditLog log category. -known_false_positives: As part of legitimate administrative behavior, users may be - assigned PIM roles. Filter as needed + - Azure Active Directory +description: The following analytic detects the assignment of an Azure AD Privileged Identity Management (PIM) role. It leverages Azure Active Directory events to identify when a user is added as an eligible member to a PIM role. This activity is significant because PIM roles grant elevated privileges, and their assignment should be closely monitored to prevent unauthorized access. If confirmed malicious, an attacker could exploit this to gain privileged access, potentially leading to unauthorized actions, data breaches, or further compromise of the environment. +search: |- + `azure_monitor_aad` operationName="Add eligible member to role in PIM completed*" + | rename properties.* as * + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product signature + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_pim_role_assigned_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category. +known_false_positives: As part of legitimate administrative behavior, users may be assigned PIM roles. Filter as needed references: -- https://learn.microsoft.com/en-us/azure/active-directory/privileged-identity-management/pim-configure -- https://learn.microsoft.com/en-us/azure/active-directory/privileged-identity-management/pim-how-to-activate-role -- https://microsoft.github.io/Azure-Threat-Research-Matrix/PrivilegeEscalation/AZT401/AZT401/ + - https://learn.microsoft.com/en-us/azure/active-directory/privileged-identity-management/pim-configure + - https://learn.microsoft.com/en-us/azure/active-directory/privileged-identity-management/pim-how-to-activate-role + - https://microsoft.github.io/Azure-Threat-Research-Matrix/PrivilegeEscalation/AZT401/AZT401/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An Azure AD PIM role assignment was assiged to $user$ - risk_objects: - - field: user - type: user - score: 35 - threat_objects: [] + message: An Azure AD PIM role assignment was assiged to $user$ + risk_objects: + - field: user + type: user + score: 35 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Privilege Escalation - - Azure Active Directory Persistence - - Scattered Lapsus$ Hunters - asset_type: Azure Active Directory - mitre_attack_id: - - T1098.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Privilege Escalation + - Azure Active Directory Persistence + - Scattered Lapsus$ Hunters + asset_type: Azure Active Directory + mitre_attack_id: + - T1098.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_pim_role_activated/azure-audit.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_pim_role_activated/azure-audit.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_pim_role_assignment_activated.yml b/detections/cloud/azure_ad_pim_role_assignment_activated.yml index 3a63cc7576..cd404f95a5 100644 --- a/detections/cloud/azure_ad_pim_role_assignment_activated.yml +++ b/detections/cloud/azure_ad_pim_role_assignment_activated.yml @@ -1,77 +1,63 @@ name: Azure AD PIM Role Assignment Activated id: 952e80d0-e343-439b-83f4-808c3e6fbf2e -version: 11 -date: '2025-10-14' +version: 12 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- Azure Active Directory -description: The following analytic detects the activation of an Azure AD Privileged - Identity Management (PIM) role. It leverages Azure Active Directory events to identify - when a user activates a PIM role assignment, indicated by the "Add member to role - completed (PIM activation)" operation. Monitoring this activity is crucial as PIM - roles grant elevated privileges, and unauthorized activation could indicate an adversary - attempting to gain privileged access. If confirmed malicious, this could lead to - unauthorized administrative actions, data breaches, or further compromise of the - Azure environment. -search: '`azure_monitor_aad` operationName="Add member to role completed (PIM activation)" - | rename properties.* as * - | rename initiatedBy.user.userPrincipalName as initiatedBy - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product initiatedBy signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_pim_role_assignment_activated_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment. - This analytic was written to be used with the azure:monitor:aad sourcetype leveraging - the AuditLog log category. -known_false_positives: As part of legitimate administrative behavior, users may activate - PIM roles. Filter as needed + - Azure Active Directory +description: The following analytic detects the activation of an Azure AD Privileged Identity Management (PIM) role. It leverages Azure Active Directory events to identify when a user activates a PIM role assignment, indicated by the "Add member to role completed (PIM activation)" operation. Monitoring this activity is crucial as PIM roles grant elevated privileges, and unauthorized activation could indicate an adversary attempting to gain privileged access. If confirmed malicious, this could lead to unauthorized administrative actions, data breaches, or further compromise of the Azure environment. +search: |- + `azure_monitor_aad` operationName="Add member to role completed (PIM activation)" + | rename properties.* as * + | rename initiatedBy.user.userPrincipalName as initiatedBy + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product initiatedBy + signature + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_pim_role_assignment_activated_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category. +known_false_positives: As part of legitimate administrative behavior, users may activate PIM roles. Filter as needed references: -- https://learn.microsoft.com/en-us/azure/active-directory/privileged-identity-management/pim-configure -- https://learn.microsoft.com/en-us/azure/active-directory/privileged-identity-management/pim-how-to-activate-role -- https://microsoft.github.io/Azure-Threat-Research-Matrix/PrivilegeEscalation/AZT401/AZT401/ + - https://learn.microsoft.com/en-us/azure/active-directory/privileged-identity-management/pim-configure + - https://learn.microsoft.com/en-us/azure/active-directory/privileged-identity-management/pim-how-to-activate-role + - https://microsoft.github.io/Azure-Threat-Research-Matrix/PrivilegeEscalation/AZT401/AZT401/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An Azure AD PIM role assignment was activated by $initiatedBy$ by $user$ - risk_objects: - - field: user - type: user - score: 35 - threat_objects: [] + message: An Azure AD PIM role assignment was activated by $initiatedBy$ by $user$ + risk_objects: + - field: user + type: user + score: 35 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Privilege Escalation - - Azure Active Directory Persistence - - Scattered Lapsus$ Hunters - asset_type: Azure Active Directory - mitre_attack_id: - - T1098.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Privilege Escalation + - Azure Active Directory Persistence + - Scattered Lapsus$ Hunters + asset_type: Azure Active Directory + mitre_attack_id: + - T1098.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_pim_role_activated/azure-audit.log - source: eventhub://researchhub1.servicebus.windows.net/azureadhub; - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_pim_role_activated/azure-audit.log + source: eventhub://researchhub1.servicebus.windows.net/azureadhub; + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_privileged_authentication_administrator_role_assigned.yml b/detections/cloud/azure_ad_privileged_authentication_administrator_role_assigned.yml index 4b0a3e29e0..a4a9b63315 100644 --- a/detections/cloud/azure_ad_privileged_authentication_administrator_role_assigned.yml +++ b/detections/cloud/azure_ad_privileged_authentication_administrator_role_assigned.yml @@ -1,80 +1,65 @@ name: Azure AD Privileged Authentication Administrator Role Assigned id: a7da845d-6fae-41cf-b823-6c0b8c55814a -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk status: production type: TTP data_source: -- Azure Active Directory Add member to role -description: The following analytic detects the assignment of the Privileged Authentication - Administrator role to an Azure AD user. It leverages Azure Active Directory audit - logs to identify when this specific role is assigned. This activity is significant - because users in this role can set or reset authentication methods for any user, - including those in privileged roles like Global Administrators. If confirmed malicious, - an attacker could change credentials and assume the identity and permissions of - high-privilege users, potentially leading to unauthorized access to sensitive information - and critical configurations. -search: '`azure_monitor_aad` "operationName"="Add member to role" "properties.targetResources{}.modifiedProperties{}.newValue"="\"Privileged Authentication Administrator\"" - | rename properties.* as * - | rename initiatedBy.user.userPrincipalName as initiatedBy - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product initiatedBy signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_privileged_authentication_administrator_role_assigned_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment. - This analytic was written to be used with the azure:monitor:aad sourcetype leveraging - the AuditLog log category. -known_false_positives: Administrators may legitimately assign the Privileged Authentication - Administrator role as part of administrative tasks. Filter as needed. + - Azure Active Directory Add member to role +description: The following analytic detects the assignment of the Privileged Authentication Administrator role to an Azure AD user. It leverages Azure Active Directory audit logs to identify when this specific role is assigned. This activity is significant because users in this role can set or reset authentication methods for any user, including those in privileged roles like Global Administrators. If confirmed malicious, an attacker could change credentials and assume the identity and permissions of high-privilege users, potentially leading to unauthorized access to sensitive information and critical configurations. +search: |- + `azure_monitor_aad` "operationName"="Add member to role" "properties.targetResources{}.modifiedProperties{}.newValue"="\"Privileged Authentication Administrator\"" + | rename properties.* as * + | rename initiatedBy.user.userPrincipalName as initiatedBy + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product initiatedBy + signature + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_privileged_authentication_administrator_role_assigned_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category. +known_false_positives: Administrators may legitimately assign the Privileged Authentication Administrator role as part of administrative tasks. Filter as needed. references: -- https://learn.microsoft.com/en-us/azure/active-directory/roles/permissions-reference#privileged-authentication-administrator -- https://posts.specterops.io/azure-privilege-escalation-via-azure-api-permissions-abuse-74aee1006f48 -- https://learn.microsoft.com/en-us/azure/active-directory/roles/permissions-reference + - https://learn.microsoft.com/en-us/azure/active-directory/roles/permissions-reference#privileged-authentication-administrator + - https://posts.specterops.io/azure-privilege-escalation-via-azure-api-permissions-abuse-74aee1006f48 + - https://learn.microsoft.com/en-us/azure/active-directory/roles/permissions-reference drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The privileged Azure AD role Privileged Authentication Administrator was - assigned for User $user$ initiated by $initiatedBy$ - risk_objects: - - field: user - type: user - score: 50 - - field: initiatedBy - type: user - score: 50 - threat_objects: [] + message: The privileged Azure AD role Privileged Authentication Administrator was assigned for User $user$ initiated by $initiatedBy$ + risk_objects: + - field: user + type: user + score: 50 + - field: initiatedBy + type: user + score: 50 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Privilege Escalation - - Scattered Lapsus$ Hunters - asset_type: Azure Active Directory - mitre_attack_id: - - T1003.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Privilege Escalation + - Scattered Lapsus$ Hunters + asset_type: Azure Active Directory + mitre_attack_id: + - T1003.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_assign_privileged_role/azure-audit.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_assign_privileged_role/azure-audit.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_privileged_graph_api_permission_assigned.yml b/detections/cloud/azure_ad_privileged_graph_api_permission_assigned.yml index 3237af4b11..46e492a70c 100644 --- a/detections/cloud/azure_ad_privileged_graph_api_permission_assigned.yml +++ b/detections/cloud/azure_ad_privileged_graph_api_permission_assigned.yml @@ -6,78 +6,48 @@ author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- Azure Active Directory Update application -description: The following analytic detects the assignment of high-risk Graph API - permissions in Azure AD, specifically Application.ReadWrite.All, AppRoleAssignment.ReadWrite.All, - and RoleManagement.ReadWrite.Directory. It uses azure_monitor_aad data to scan AuditLogs - for 'Update application' operations, identifying when these permissions are assigned. - This activity is significant as it grants broad control over Azure AD, including - application and directory settings. If confirmed malicious, it could lead to unauthorized - modifications and potential security breaches, compromising the integrity and security - of the Azure AD environment. Immediate investigation is required. -search: "`azure_monitor_aad` category=AuditLogs operationName=\"Update application\" - | eval newvalue = mvindex('properties.targetResources{}.modifiedProperties{}.newValue',0) - | spath input=newvalue - | search \"{}.RequiredAppPermissions{}.EntitlementId\"=\" - 1bfefb4e-e0b5-418b-a88f-73c46d2cc8e9\" OR \"{}.RequiredAppPermissions{}.EntitlementId\" - =\"06b708a9-e830-4db3-a914-8e69da51d44f\" OR \"{}.RequiredAppPermissions{}.EntitlementId\" - =\"9e3f62cf-ca93-4989-b6ce-bf83c28f9fe8\" - | eval Permissions = '{}.RequiredAppPermissions{}.EntitlementId' - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product Permissions signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_privileged_graph_api_permission_assigned_filter`" -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment. - This analytic was written to be used with the azure:monitor:aad sourcetype leveraging - the AuditLog log category. -known_false_positives: Privileged Graph API permissions may be assigned for legitimate - purposes. Filter as needed. + - Azure Active Directory Update application +description: The following analytic detects the assignment of high-risk Graph API permissions in Azure AD, specifically Application.ReadWrite.All, AppRoleAssignment.ReadWrite.All, and RoleManagement.ReadWrite.Directory. It uses azure_monitor_aad data to scan AuditLogs for 'Update application' operations, identifying when these permissions are assigned. This activity is significant as it grants broad control over Azure AD, including application and directory settings. If confirmed malicious, it could lead to unauthorized modifications and potential security breaches, compromising the integrity and security of the Azure AD environment. Immediate investigation is required. +search: "`azure_monitor_aad` category=AuditLogs operationName=\"Update application\" | eval newvalue = mvindex('properties.targetResources{}.modifiedProperties{}.newValue',0) | spath input=newvalue | search \"{}.RequiredAppPermissions{}.EntitlementId\"=\" 1bfefb4e-e0b5-418b-a88f-73c46d2cc8e9\" OR \"{}.RequiredAppPermissions{}.EntitlementId\" =\"06b708a9-e830-4db3-a914-8e69da51d44f\" OR \"{}.RequiredAppPermissions{}.EntitlementId\" =\"9e3f62cf-ca93-4989-b6ce-bf83c28f9fe8\" | eval Permissions = '{}.RequiredAppPermissions{}.EntitlementId' | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product Permissions signature | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_privileged_graph_api_permission_assigned_filter`" +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category. +known_false_positives: Privileged Graph API permissions may be assigned for legitimate purposes. Filter as needed. references: -- https://cloudbrothers.info/en/azure-attack-paths/ -- https://github.com/mandiant/Mandiant-Azure-AD-Investigator/blob/master/MandiantAzureADInvestigator.json -- https://learn.microsoft.com/en-us/graph/permissions-reference -- https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/ -- https://posts.specterops.io/azure-privilege-escalation-via-azure-api-permissions-abuse-74aee1006f48 + - https://cloudbrothers.info/en/azure-attack-paths/ + - https://github.com/mandiant/Mandiant-Azure-AD-Investigator/blob/master/MandiantAzureADInvestigator.json + - https://learn.microsoft.com/en-us/graph/permissions-reference + - https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/ + - https://posts.specterops.io/azure-privilege-escalation-via-azure-api-permissions-abuse-74aee1006f48 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ assigned privileged Graph API permissions to $Permissions$ - risk_objects: - - field: user - type: user - score: 54 - threat_objects: [] + message: User $user$ assigned privileged Graph API permissions to $Permissions$ + risk_objects: + - field: user + type: user + score: 54 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - - NOBELIUM Group - asset_type: Azure Active Directory - mitre_attack_id: - - T1003.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Persistence + - NOBELIUM Group + asset_type: Azure Active Directory + mitre_attack_id: + - T1003.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_privileged_graph_perm_assigned/azure_ad_privileged_graph_perm_assigned.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_privileged_graph_perm_assigned/azure_ad_privileged_graph_perm_assigned.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_privileged_role_assigned.yml b/detections/cloud/azure_ad_privileged_role_assigned.yml index 88a2080dc9..ddbc6126d7 100644 --- a/detections/cloud/azure_ad_privileged_role_assigned.yml +++ b/detections/cloud/azure_ad_privileged_role_assigned.yml @@ -1,87 +1,74 @@ name: Azure AD Privileged Role Assigned id: a28f0bc3-3400-4a6e-a2da-89b9e95f0d2a -version: 12 -date: '2026-01-20' +version: 13 +date: '2026-02-25' author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk status: production type: TTP -description: The following analytic detects the assignment of privileged Azure Active - Directory roles to a user. It leverages Azure AD audit logs, specifically monitoring - the "Add member to role" operation. This activity is significant as adversaries - may assign privileged roles to compromised accounts to maintain persistence within - the Azure AD environment. If confirmed malicious, this could allow attackers to - escalate privileges, access sensitive information, and maintain long-term control - over the Azure AD infrastructure. +description: The following analytic detects the assignment of privileged Azure Active Directory roles to a user. It leverages Azure AD audit logs, specifically monitoring the "Add member to role" operation. This activity is significant as adversaries may assign privileged roles to compromised accounts to maintain persistence within the Azure AD environment. If confirmed malicious, this could allow attackers to escalate privileges, access sensitive information, and maintain long-term control over the Azure AD infrastructure. data_source: -- Azure Active Directory Add member to role -search: '`azure_monitor_aad` "operationName"="Add member to role" - | rename properties.* as * - | rename initiatedBy.user.userPrincipalName as initiatedBy - | rename targetResources{}.modifiedProperties{}.newValue as roles - | eval role=mvindex(roles,1) - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product initiatedBy result role signature - | lookup privileged_azure_ad_roles azureadrole AS role OUTPUT isprvilegedadrole description - | search isprvilegedadrole = True - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_privileged_role_assigned_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment. - This analytic was written to be used with the azure:monitor:aad sourcetype leveraging - the AuditLog log category. -known_false_positives: Administrators will legitimately assign the privileged roles - users as part of administrative tasks. Filter as needed. + - Azure Active Directory Add member to role +search: |- + `azure_monitor_aad` "operationName"="Add member to role" + | rename properties.* as * + | rename initiatedBy.user.userPrincipalName as initiatedBy + | rename targetResources{}.modifiedProperties{}.newValue as roles + | eval role=mvindex(roles,1) + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product initiatedBy + result role signature + | lookup privileged_azure_ad_roles azureadrole AS role OUTPUT isprvilegedadrole description + | search isprvilegedadrole = True + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_privileged_role_assigned_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category. +known_false_positives: Administrators will legitimately assign the privileged roles users as part of administrative tasks. Filter as needed. references: -- https://docs.microsoft.com/en-us/azure/active-directory/roles/concept-understand-roles -- https://docs.microsoft.com/en-us/azure/active-directory/roles/permissions-reference -- https://adsecurity.org/?p=4277 -- https://www.mandiant.com/resources/detecting-microsoft-365-azure-active-directory-backdoors -- https://docs.microsoft.com/en-us/azure/active-directory/roles/security-planning -- https://attack.mitre.org/techniques/T1098/003/ + - https://docs.microsoft.com/en-us/azure/active-directory/roles/concept-understand-roles + - https://docs.microsoft.com/en-us/azure/active-directory/roles/permissions-reference + - https://adsecurity.org/?p=4277 + - https://www.mandiant.com/resources/detecting-microsoft-365-azure-active-directory-backdoors + - https://docs.microsoft.com/en-us/azure/active-directory/roles/security-planning + - https://attack.mitre.org/techniques/T1098/003/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A privileged Azure AD role was assigned for User $user$ initiated by $initiatedBy$ - risk_objects: - - field: user - type: user - score: 63 - - field: initiatedBy - type: user - score: 63 - threat_objects: [] + message: A privileged Azure AD role was assigned for User $user$ initiated by $initiatedBy$ + risk_objects: + - field: user + type: user + score: 63 + - field: initiatedBy + type: user + score: 63 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - - NOBELIUM Group - - Scattered Lapsus$ Hunters - - Storm-0501 Ransomware - asset_type: Azure Active Directory - mitre_attack_id: - - T1098.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: audit + analytic_story: + - Azure Active Directory Persistence + - NOBELIUM Group + - Scattered Lapsus$ Hunters + - Storm-0501 Ransomware + asset_type: Azure Active Directory + mitre_attack_id: + - T1098.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: audit tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_assign_privileged_role/azure-audit.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_assign_privileged_role/azure-audit.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_privileged_role_assigned_to_service_principal.yml b/detections/cloud/azure_ad_privileged_role_assigned_to_service_principal.yml index 8b72a00ef1..c598115e51 100644 --- a/detections/cloud/azure_ad_privileged_role_assigned_to_service_principal.yml +++ b/detections/cloud/azure_ad_privileged_role_assigned_to_service_principal.yml @@ -1,83 +1,69 @@ name: Azure AD Privileged Role Assigned to Service Principal id: 5dfaa3d3-e2e4-4053-8252-16d9ee528c41 -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the assignment of privileged roles to - service principals in Azure Active Directory (AD). It leverages the AuditLogs log - category from ingested Azure AD events. This activity is significant because assigning - elevated permissions to non-human entities can lead to unauthorized access or malicious - activities. If confirmed malicious, attackers could exploit these service principals - to gain elevated access to Azure resources, potentially compromising sensitive data - and critical infrastructure. Monitoring this behavior helps prevent privilege escalation - and ensures the security of Azure environments. +description: The following analytic detects the assignment of privileged roles to service principals in Azure Active Directory (AD). It leverages the AuditLogs log category from ingested Azure AD events. This activity is significant because assigning elevated permissions to non-human entities can lead to unauthorized access or malicious activities. If confirmed malicious, attackers could exploit these service principals to gain elevated access to Azure resources, potentially compromising sensitive data and critical infrastructure. Monitoring this behavior helps prevent privilege escalation and ensures the security of Azure environments. data_source: -- Azure Active Directory Add member to role -search: '`azure_monitor_aad` operationName="Add member to role" - | rename properties.* as * - | search "targetResources{}.type"=ServicePrincipal - | rename initiatedBy.user.userPrincipalName as initiatedBy - | rename targetResources{}.modifiedProperties{}.newValue as roles - | eval role=mvindex(roles,1) - | rename targetResources{}.displayName as apps - | eval displayName=mvindex(apps,0) - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product displayName initiatedBy result role signature - | lookup privileged_azure_ad_roles azureadrole AS role OUTPUT isprvilegedadrole description - | search isprvilegedadrole = True - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_privileged_role_assigned_to_service_principal_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment. - This analytic was written to be used with the azure:monitor:aad sourcetype leveraging - the AuditLog log category. -known_false_positives: Administrators may legitimately assign the privileged roles - to Service Principals as part of administrative tasks. Filter as needed. + - Azure Active Directory Add member to role +search: |- + `azure_monitor_aad` operationName="Add member to role" + | rename properties.* as * + | search "targetResources{}.type"=ServicePrincipal + | rename initiatedBy.user.userPrincipalName as initiatedBy + | rename targetResources{}.modifiedProperties{}.newValue as roles + | eval role=mvindex(roles,1) + | rename targetResources{}.displayName as apps + | eval displayName=mvindex(apps,0) + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product displayName + initiatedBy result role + signature + | lookup privileged_azure_ad_roles azureadrole AS role OUTPUT isprvilegedadrole description + | search isprvilegedadrole = True + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_privileged_role_assigned_to_service_principal_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category. +known_false_positives: Administrators may legitimately assign the privileged roles to Service Principals as part of administrative tasks. Filter as needed. references: -- https://posts.specterops.io/azure-privilege-escalation-via-service-principal-abuse-210ae2be2a5 + - https://posts.specterops.io/azure-privilege-escalation-via-service-principal-abuse-210ae2be2a5 drilldown_searches: -- name: View the detection results for - "$initiatedBy$" - search: '%original_detection_search% | search initiatedBy = "$initiatedBy$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$initiatedBy$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$initiatedBy$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$initiatedBy$" + search: '%original_detection_search% | search initiatedBy = "$initiatedBy$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$initiatedBy$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$initiatedBy$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A privileged Azure AD role was assigned to the Service Principal $displayName$ - initiated by $initiatedBy$ - risk_objects: - - field: initiatedBy - type: user - score: 35 - threat_objects: [] + message: A privileged Azure AD role was assigned to the Service Principal $displayName$ initiated by $initiatedBy$ + risk_objects: + - field: initiatedBy + type: user + score: 35 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Privilege Escalation - - NOBELIUM Group - - Scattered Lapsus$ Hunters - asset_type: Azure Active Directory - mitre_attack_id: - - T1098.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Privilege Escalation + - NOBELIUM Group + - Scattered Lapsus$ Hunters + asset_type: Azure Active Directory + mitre_attack_id: + - T1098.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_privileged_role_serviceprincipal/azure-audit.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_privileged_role_serviceprincipal/azure-audit.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_service_principal_authentication.yml b/detections/cloud/azure_ad_service_principal_authentication.yml index c5fca95f12..c0d81a12e5 100644 --- a/detections/cloud/azure_ad_service_principal_authentication.yml +++ b/detections/cloud/azure_ad_service_principal_authentication.yml @@ -1,79 +1,62 @@ name: Azure AD Service Principal Authentication id: 5a2ec401-60bb-474e-b936-1e66e7aa4060 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk data_source: -- Azure Active Directory Sign-in activity + - Azure Active Directory Sign-in activity type: TTP status: production -description: The following analytic identifies authentication events of service principals - in Azure Active Directory. It leverages the `azure_monitor_aad` data source, specifically - targeting "Sign-in activity" within ServicePrincipalSignInLogs. This detection gathers - details such as sign-in frequency, timing, source IPs, and accessed resources. Monitoring - these events is significant for SOC teams to distinguish between normal application - authentication and potential anomalies, which could indicate compromised credentials - or malicious activities. If confirmed malicious, attackers could gain unauthorized - access to resources, leading to data breaches or further exploitation within the - environment. -search: '`azure_monitor_aad` operationName="Sign-in activity" category=ServicePrincipalSignInLogs - | rename properties.* as * - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product resourceDisplayName resourceId signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_service_principal_authentication_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the SignInLogs log category. -known_false_positives: Service Principals will legitimally authenticate remotely to - your tenant. Implementing this detection after establishing a baseline enables a - more accurate identification of security threats, ensuring proactive and informed - responses to safeguard the Azure AD environment. source ips. +description: The following analytic identifies authentication events of service principals in Azure Active Directory. It leverages the `azure_monitor_aad` data source, specifically targeting "Sign-in activity" within ServicePrincipalSignInLogs. This detection gathers details such as sign-in frequency, timing, source IPs, and accessed resources. Monitoring these events is significant for SOC teams to distinguish between normal application authentication and potential anomalies, which could indicate compromised credentials or malicious activities. If confirmed malicious, attackers could gain unauthorized access to resources, leading to data breaches or further exploitation within the environment. +search: |- + `azure_monitor_aad` operationName="Sign-in activity" category=ServicePrincipalSignInLogs + | rename properties.* as * + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product resourceDisplayName + resourceId signature + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_service_principal_authentication_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the SignInLogs log category. +known_false_positives: Service Principals will legitimally authenticate remotely to your tenant. Implementing this detection after establishing a baseline enables a more accurate identification of security threats, ensuring proactive and informed responses to safeguard the Azure AD environment. source ips. references: -- https://attack.mitre.org/techniques/T1078/004/ -- https://learn.microsoft.com/en-us/entra/identity/monitoring-health/concept-sign-ins#service-principal-sign-ins + - https://attack.mitre.org/techniques/T1078/004/ + - https://learn.microsoft.com/en-us/entra/identity/monitoring-health/concept-sign-ins#service-principal-sign-ins drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Service Principal $user$ authenticated from $src$ - risk_objects: - - field: user - type: user - score: 25 - threat_objects: - - field: src - type: ip_address + message: Service Principal $user$ authenticated from $src$ + risk_objects: + - field: user + type: user + score: 25 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Azure Active Directory Account Takeover - - NOBELIUM Group - asset_type: Azure Active Directory - mitre_attack_id: - - T1078.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Account Takeover + - NOBELIUM Group + asset_type: Azure Active Directory + mitre_attack_id: + - T1078.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/azure_ad_service_principal_authentication/azure_ad_service_principal_authentication.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/azure_ad_service_principal_authentication/azure_ad_service_principal_authentication.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_service_principal_created.yml b/detections/cloud/azure_ad_service_principal_created.yml index 932365f405..285043e54a 100644 --- a/detections/cloud/azure_ad_service_principal_created.yml +++ b/detections/cloud/azure_ad_service_principal_created.yml @@ -1,79 +1,65 @@ name: Azure AD Service Principal Created id: f8ba49e7-ffd3-4b53-8f61-e73974583c5d -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Gowthamaraj Rajendran, Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the creation of a Service Principal in - an Azure AD environment. It leverages Azure Active Directory events ingested through - EventHub, specifically monitoring the "Add service principal" operation. This activity - is significant because Service Principals can be used by adversaries to establish - persistence and bypass multi-factor authentication and conditional access policies. - If confirmed malicious, this could allow attackers to maintain single-factor access - to the Azure AD environment, potentially leading to unauthorized access to resources - and prolonged undetected activity. +description: The following analytic detects the creation of a Service Principal in an Azure AD environment. It leverages Azure Active Directory events ingested through EventHub, specifically monitoring the "Add service principal" operation. This activity is significant because Service Principals can be used by adversaries to establish persistence and bypass multi-factor authentication and conditional access policies. If confirmed malicious, this could allow attackers to maintain single-factor access to the Azure AD environment, potentially leading to unauthorized access to resources and prolonged undetected activity. data_source: -- Azure Active Directory Add service principal -search: '`azure_monitor_aad` operationName="Add service principal" properties.initiatedBy.user.id=* - | rename properties.* as * - | rename targetResources{}.displayName as displayName - | rename targetResources{}.type as type - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product displayName result signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_service_principal_created_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - thorough an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the AuditLog log category. -known_false_positives: Administrator may legitimately create Service Principal. Filter - as needed. + - Azure Active Directory Add service principal +search: |- + `azure_monitor_aad` operationName="Add service principal" properties.initiatedBy.user.id=* + | rename properties.* as * + | rename targetResources{}.displayName as displayName + | rename targetResources{}.type as type + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product displayName + result signature + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_service_principal_created_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment thorough an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category. +known_false_positives: Administrator may legitimately create Service Principal. Filter as needed. references: -- https://docs.microsoft.com/en-us/azure/active-directory/develop/app-objects-and-service-principals -- https://docs.microsoft.com/en-us/powershell/azure/create-azure-service-principal-azureps?view=azps-8.2.0 -- https://www.truesec.com/hub/blog/using-a-legitimate-application-to-create-persistence-and-initiate-email-campaigns -- https://www.inversecos.com/2021/10/how-to-backdoor-azure-applications-and.html -- https://attack.mitre.org/techniques/T1136/003/ + - https://docs.microsoft.com/en-us/azure/active-directory/develop/app-objects-and-service-principals + - https://docs.microsoft.com/en-us/powershell/azure/create-azure-service-principal-azureps?view=azps-8.2.0 + - https://www.truesec.com/hub/blog/using-a-legitimate-application-to-create-persistence-and-initiate-email-campaigns + - https://www.inversecos.com/2021/10/how-to-backdoor-azure-applications-and.html + - https://attack.mitre.org/techniques/T1136/003/ drilldown_searches: -- name: View the detection results for - "$displayName$" - search: '%original_detection_search% | search displayName = "$displayName$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$displayName$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$displayName$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$displayName$" + search: '%original_detection_search% | search displayName = "$displayName$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$displayName$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$displayName$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Service Principal named $displayName$ created by $user$ - risk_objects: - - field: displayName - type: user - score: 45 - threat_objects: [] + message: Service Principal named $displayName$ created by $user$ + risk_objects: + - field: displayName + type: user + score: 45 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - - NOBELIUM Group - asset_type: Azure Active Directory - mitre_attack_id: - - T1136.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Azure Active Directory Persistence + - NOBELIUM Group + asset_type: Azure Active Directory + mitre_attack_id: + - T1136.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/azure_ad_add_service_principal/azure-audit.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/azure_ad_add_service_principal/azure-audit.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_service_principal_enumeration.yml b/detections/cloud/azure_ad_service_principal_enumeration.yml index 152606b2d1..3ea0969843 100644 --- a/detections/cloud/azure_ad_service_principal_enumeration.yml +++ b/detections/cloud/azure_ad_service_principal_enumeration.yml @@ -1,68 +1,70 @@ name: Azure AD Service Principal Enumeration id: 3f0647ce-add5-4436-8039-cbd1abe74563 -version: 6 -date: '2026-01-14' +version: 7 +date: '2026-02-25' author: Dean Luxton data_source: - - Azure Active Directory MicrosoftGraphActivityLogs + - Azure Active Directory MicrosoftGraphActivityLogs type: TTP status: production description: >- - This detection leverages azure graph activity logs to identify when graph APIs have been used to identify 10 or more service principals. - This type of behaviour is associated with tools such as Azure enumberation tools such as AzureHound or ROADtools. -search: - '`azure_monitor_aad` category IN (MicrosoftGraphActivityLogs) TERM(servicePrincipals) - | fillnull - | rex field="properties.requestUri" "https\:\/\/graph.microsoft.com\/beta\/servicePrincipals\/(?P.*?)\/" - | rex field="properties.requestUri" "https\:\/\/graph.microsoft.com\/v1.0\/servicePrincipals\/(?P.*?)\/" - | eval spn=coalesce(servicePrincipalb,servicePrincipalv1) - | fillnull - | stats count min(_time) as _time dc(spn) as spn_count values(user_id) as user_id by dest user src vendor_account vendor_product signature - | where spn_count>9 - | `azure_ad_service_principal_enumeration_filter`' + This detection leverages azure graph activity logs to identify when graph APIs have been used to identify 10 or more service principals. + This type of behaviour is associated with tools such as Azure enumberation tools such as AzureHound or ROADtools. +search: |- + `azure_monitor_aad` category IN (MicrosoftGraphActivityLogs) TERM(servicePrincipals) + | fillnull + | rex field="properties.requestUri" "https\:\/\/graph.microsoft.com\/beta\/servicePrincipals\/(?P.*?)\/" + | rex field="properties.requestUri" "https\:\/\/graph.microsoft.com\/v1.0\/servicePrincipals\/(?P.*?)\/" + | eval spn=coalesce(servicePrincipalb,servicePrincipalv1) + | fillnull + | stats count min(_time) as _time dc(spn) as spn_count values(user_id) as user_id + BY dest user src + vendor_account vendor_product signature + | where spn_count>9 + | `azure_ad_service_principal_enumeration_filter` how_to_implement: >- - Run this detection over historical data to identify then tune out any known services which may be performing this action. Thresholds can be lowered or raised to meet requirements. - The Splunk Add-on for Microsoft Cloud Services add-on is required to ingest MicrosoftGraphActivityLogs via Azure EventHub. See reference for links for further details on how to onboard this log source. + Run this detection over historical data to identify then tune out any known services which may be performing this action. Thresholds can be lowered or raised to meet requirements. + The Splunk Add-on for Microsoft Cloud Services add-on is required to ingest MicrosoftGraphActivityLogs via Azure EventHub. See reference for links for further details on how to onboard this log source. known_false_positives: No false positives have been identified at this time. references: - - https://github.com/SpecterOps/AzureHound - - https://github.com/dirkjanm/ROADtools - - https://splunkbase.splunk.com/app/3110 - - https://splunk.github.io/splunk-add-on-for-microsoft-cloud-services/Install/ + - https://github.com/SpecterOps/AzureHound + - https://github.com/dirkjanm/ROADtools + - https://splunkbase.splunk.com/app/3110 + - https://splunk.github.io/splunk-add-on-for-microsoft-cloud-services/Install/ drilldown_searches: - - name: View the detection results for - "$user_id$" - search: '%original_detection_search% | search user_id = "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user_id$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user_id$" + search: '%original_detection_search% | search user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $spn_count$ Service Principals have been enumerated by $user_id$ from IP $src$ - risk_objects: - - field: user - type: user - score: 80 - threat_objects: - - field: src - type: ip_address + message: $spn_count$ Service Principals have been enumerated by $user_id$ from IP $src$ + risk_objects: + - field: user + type: user + score: 80 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Azure Active Directory Privilege Escalation - - Compromised User Account - asset_type: Azure Tenant - mitre_attack_id: - - T1087.004 - - T1526 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Privilege Escalation + - Compromised User Account + asset_type: Azure Tenant + mitre_attack_id: + - T1087.004 + - T1526 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.004/azurehound/azurehound.log - sourcetype: azure:monitor:aad - source: Azure AD + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.004/azurehound/azurehound.log + sourcetype: azure:monitor:aad + source: Azure AD diff --git a/detections/cloud/azure_ad_service_principal_new_client_credentials.yml b/detections/cloud/azure_ad_service_principal_new_client_credentials.yml index 20a02fac0a..ec2daa50a2 100644 --- a/detections/cloud/azure_ad_service_principal_new_client_credentials.yml +++ b/detections/cloud/azure_ad_service_principal_new_client_credentials.yml @@ -1,81 +1,67 @@ name: Azure AD Service Principal New Client Credentials id: e3adc0d3-9e4b-4b5d-b662-12cec1adff2a -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk status: production type: TTP -description: The following analytic detects the addition of new credentials to Service - Principals and Applications in Azure AD. It leverages Azure AD AuditLogs, specifically - monitoring the "Update application*Certificates and secrets management" operation. - This activity is significant as it may indicate an adversary attempting to maintain - persistent access or escalate privileges within the Azure environment. If confirmed - malicious, attackers could use these new credentials to log in as the service principal, - potentially compromising sensitive accounts and resources, leading to unauthorized - access and control over the Azure environment. +description: The following analytic detects the addition of new credentials to Service Principals and Applications in Azure AD. It leverages Azure AD AuditLogs, specifically monitoring the "Update application*Certificates and secrets management" operation. This activity is significant as it may indicate an adversary attempting to maintain persistent access or escalate privileges within the Azure environment. If confirmed malicious, attackers could use these new credentials to log in as the service principal, potentially compromising sensitive accounts and resources, leading to unauthorized access and control over the Azure environment. data_source: -- Azure Active Directory -search: '`azure_monitor_aad` category=AuditLogs operationName="Update application*Certificates and secrets management " - | rename properties.* as * - | rename targetResources{}.* as * - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product modifiedProperties{}.newValue signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_service_principal_new_client_credentials_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment. - This analytic was written to be used with the azure:monitor:aad sourcetype leveraging - the Signin log category. -known_false_positives: Service Principal client credential modifications may be part - of legitimate administrative operations. Filter as needed. + - Azure Active Directory +search: |- + `azure_monitor_aad` category=AuditLogs operationName="Update application*Certificates and secrets management " + | rename properties.* as * + | rename targetResources{}.* as * + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product modifiedProperties{}.newValue + signature + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_service_principal_new_client_credentials_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the Signin log category. +known_false_positives: Service Principal client credential modifications may be part of legitimate administrative operations. Filter as needed. references: -- https://attack.mitre.org/techniques/T1098/001/ -- https://microsoft.github.io/Azure-Threat-Research-Matrix/Persistence/AZT501/AZT501-2/ -- https://hausec.com/2021/10/26/attacking-azure-azure-ad-part-ii/ -- https://www.inversecos.com/2021/10/how-to-backdoor-azure-applications-and.html -- https://www.mandiant.com/resources/blog/apt29-continues-targeting-microsoft -- https://microsoft.github.io/Azure-Threat-Research-Matrix/PrivilegeEscalation/AZT405/AZT405-3/ + - https://attack.mitre.org/techniques/T1098/001/ + - https://microsoft.github.io/Azure-Threat-Research-Matrix/Persistence/AZT501/AZT501-2/ + - https://hausec.com/2021/10/26/attacking-azure-azure-ad-part-ii/ + - https://www.inversecos.com/2021/10/how-to-backdoor-azure-applications-and.html + - https://www.mandiant.com/resources/blog/apt29-continues-targeting-microsoft + - https://microsoft.github.io/Azure-Threat-Research-Matrix/PrivilegeEscalation/AZT405/AZT405-3/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: New credentials added for Service Principal by $user$ - risk_objects: - - field: user - type: user - score: 35 - threat_objects: [] + message: New credentials added for Service Principal by $user$ + risk_objects: + - field: user + type: user + score: 35 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - - Azure Active Directory Privilege Escalation - - NOBELIUM Group - - Scattered Lapsus$ Hunters - asset_type: Azure Active Directory - mitre_attack_id: - - T1098.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Azure Active Directory Persistence + - Azure Active Directory Privilege Escalation + - NOBELIUM Group + - Scattered Lapsus$ Hunters + asset_type: Azure Active Directory + mitre_attack_id: + - T1098.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.001/azure_ad_service_principal_credentials/azure-audit.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.001/azure_ad_service_principal_credentials/azure-audit.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_service_principal_owner_added.yml b/detections/cloud/azure_ad_service_principal_owner_added.yml index c804bed006..951bb4cbef 100644 --- a/detections/cloud/azure_ad_service_principal_owner_added.yml +++ b/detections/cloud/azure_ad_service_principal_owner_added.yml @@ -1,82 +1,69 @@ name: Azure AD Service Principal Owner Added id: 7ddf2084-6cf3-4a44-be83-474f7b73c701 -version: 10 -date: '2025-05-02' +version: 11 +date: '2026-02-25' author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk status: production type: TTP -description: The following analytic detects the addition of a new owner to a Service - Principal within an Azure AD tenant. It leverages Azure Active Directory events - from the AuditLog log category to identify this activity. This behavior is significant - because Service Principals do not support multi-factor authentication or conditional - access policies, making them a target for adversaries seeking persistence or privilege - escalation. If confirmed malicious, this activity could allow attackers to maintain - access to the Azure AD environment with single-factor authentication, potentially - leading to unauthorized access and control over critical resources. +description: The following analytic detects the addition of a new owner to a Service Principal within an Azure AD tenant. It leverages Azure Active Directory events from the AuditLog log category to identify this activity. This behavior is significant because Service Principals do not support multi-factor authentication or conditional access policies, making them a target for adversaries seeking persistence or privilege escalation. If confirmed malicious, this activity could allow attackers to maintain access to the Azure AD environment with single-factor authentication, potentially leading to unauthorized access and control over critical resources. data_source: -- Azure Active Directory Add owner to application -search: '`azure_monitor_aad` operationName="Add owner to application" - | rename properties.* as * - | rename initiatedBy.user.userPrincipalName as initiatedBy - | rename targetResources{}.userPrincipalName as newOwner - | rename targetResources{}.modifiedProperties{}.newValue as displayName - | eval displayName = mvindex(displayName,1) - | where initiatedBy!=newOwner - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product initiatedBy result newOwner displayName signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_service_principal_owner_added_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the AuditLog log category. -known_false_positives: Administrator may legitimately add new owners for Service Principals. - Filter as needed. + - Azure Active Directory Add owner to application +search: |- + `azure_monitor_aad` operationName="Add owner to application" + | rename properties.* as * + | rename initiatedBy.user.userPrincipalName as initiatedBy + | rename targetResources{}.userPrincipalName as newOwner + | rename targetResources{}.modifiedProperties{}.newValue as displayName + | eval displayName = mvindex(displayName,1) + | where initiatedBy!=newOwner + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product initiatedBy + result newOwner displayName + signature + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_service_principal_owner_added_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category. +known_false_positives: Administrator may legitimately add new owners for Service Principals. Filter as needed. references: -- https://attack.mitre.org/techniques/T1098/ + - https://attack.mitre.org/techniques/T1098/ drilldown_searches: -- name: View the detection results for - "$displayName$" - search: '%original_detection_search% | search displayName = "$displayName$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$displayName$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$displayName$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$displayName$" + search: '%original_detection_search% | search displayName = "$displayName$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$displayName$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$displayName$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A new owner was added for service principal $displayName$ by $initiatedBy$ - risk_objects: - - field: displayName - type: user - score: 54 - - field: initiatedBy - type: user - score: 54 - threat_objects: [] + message: A new owner was added for service principal $displayName$ by $initiatedBy$ + risk_objects: + - field: displayName + type: user + score: 54 + - field: initiatedBy + type: user + score: 54 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - - Azure Active Directory Privilege Escalation - - NOBELIUM Group - asset_type: Azure Active Directory - mitre_attack_id: - - T1098 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: audit + analytic_story: + - Azure Active Directory Persistence + - Azure Active Directory Privilege Escalation + - NOBELIUM Group + asset_type: Azure Active Directory + mitre_attack_id: + - T1098 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: audit tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/azure_ad_add_serviceprincipal_owner/azure-audit.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/azure_ad_add_serviceprincipal_owner/azure-audit.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_service_principal_privilege_escalation.yml b/detections/cloud/azure_ad_service_principal_privilege_escalation.yml index d27f0eab4e..bbab5b0e5c 100644 --- a/detections/cloud/azure_ad_service_principal_privilege_escalation.yml +++ b/detections/cloud/azure_ad_service_principal_privilege_escalation.yml @@ -4,78 +4,65 @@ version: 7 date: '2026-01-14' author: Dean Luxton data_source: - - Azure Active Directory Add app role assignment to service principal + - Azure Active Directory Add app role assignment to service principal type: TTP status: production -description: - This detection identifies when an Azure Service Principal elevates privileges - by adding themself to a new app role assignment. +description: This detection identifies when an Azure Service Principal elevates privileges by adding themself to a new app role assignment. search: >- - `azure_monitor_aad` category=AuditLogs operationName="Add app role assignment to service principal" properties.initiatedBy.app.displayName=* properties.result=Success - | spath path=properties{}.targetResources{}.modifiedProperties{} output=targetResources - | rename properties.* as * - | eval user="NA" - | eval src="NA" - | stats min(_time) as firstTime max(_time) as lastTime values(eval(mvfilter(match(targetResources, "AppRole.Value")))) as appRole, values(eval(mvfilter(match(targetResources, "ServicePrincipal.DisplayName")))) as targetServicePrincipal values(eval(mvindex('properties.targetResources{}.displayName',0))) as targetAppContext - values(user_agent) as user_agent values(identity) as servicePrincipal values(properties.initiatedBy.app.servicePrincipalId) as servicePrincipalId by dest user src vendor_account vendor_product signature - | spath input=appRole path=newValue output=appRole - | spath input=targetServicePrincipal path=newValue output=targetServicePrincipal - | eval appRole=trim(replace(appRole, "\"", "")), targetServicePrincipal=trim(replace(targetServicePrincipal, "\"", "")) - | where servicePrincipal=targetServicePrincipal - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_service_principal_privilege_escalation_filter` -how_to_implement: - The Splunk Add-on for Microsoft Cloud Services add-on is required - to ingest EntraID audit logs via Azure EventHub. See reference for links for further - details on how to onboard this log source. + `azure_monitor_aad` category=AuditLogs operationName="Add app role assignment to service principal" properties.initiatedBy.app.displayName=* properties.result=Success + | spath path=properties{}.targetResources{}.modifiedProperties{} output=targetResources + | rename properties.* as * + | eval user="NA" + | eval src="NA" + | stats min(_time) as firstTime max(_time) as lastTime values(eval(mvfilter(match(targetResources, "AppRole.Value")))) as appRole, values(eval(mvfilter(match(targetResources, "ServicePrincipal.DisplayName")))) as targetServicePrincipal values(eval(mvindex('properties.targetResources{}.displayName',0))) as targetAppContext + values(user_agent) as user_agent values(identity) as servicePrincipal values(properties.initiatedBy.app.servicePrincipalId) as servicePrincipalId by dest user src vendor_account vendor_product signature + | spath input=appRole path=newValue output=appRole + | spath input=targetServicePrincipal path=newValue output=targetServicePrincipal + | eval appRole=trim(replace(appRole, "\"", "")), targetServicePrincipal=trim(replace(targetServicePrincipal, "\"", "")) + | where servicePrincipal=targetServicePrincipal + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_service_principal_privilege_escalation_filter` +how_to_implement: The Splunk Add-on for Microsoft Cloud Services add-on is required to ingest EntraID audit logs via Azure EventHub. See reference for links for further details on how to onboard this log source. known_false_positives: No false positives have been identified at this time. references: - - https://splunkbase.splunk.com/app/3110 - - https://splunk.github.io/splunk-add-on-for-microsoft-cloud-services/Install/ - - https://github.com/mvelazc0/BadZure - - https://www.splunk.com/en_us/blog/security/hunting-m365-invaders-navigating-the-shadows-of-midnight-blizzard.html - - https://posts.specterops.io/microsoft-breach-what-happened-what-should-azure-admins-do-da2b7e674ebc + - https://splunkbase.splunk.com/app/3110 + - https://splunk.github.io/splunk-add-on-for-microsoft-cloud-services/Install/ + - https://github.com/mvelazc0/BadZure + - https://www.splunk.com/en_us/blog/security/hunting-m365-invaders-navigating-the-shadows-of-midnight-blizzard.html + - https://posts.specterops.io/microsoft-breach-what-happened-what-should-azure-admins-do-da2b7e674ebc drilldown_searches: - - name: View the detection results for - "$servicePrincipal$" - search: '%original_detection_search% | search servicePrincipal = "$servicePrincipal$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$servicePrincipal$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$servicePrincipal$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$servicePrincipal$" + search: '%original_detection_search% | search servicePrincipal = "$servicePrincipal$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$servicePrincipal$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$servicePrincipal$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - Service Principal $servicePrincipal$ has elevated privileges by adding - themself to app role $appRole$ - risk_objects: - - field: servicePrincipal - type: user - score: 100 - threat_objects: - - field: user_agent - type: http_user_agent + message: Service Principal $servicePrincipal$ has elevated privileges by adding themself to app role $appRole$ + risk_objects: + - field: servicePrincipal + type: user + score: 100 + threat_objects: + - field: user_agent + type: http_user_agent tags: - analytic_story: - - Azure Active Directory Privilege Escalation - asset_type: Azure Tenant - mitre_attack_id: - - T1098.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Privilege Escalation + asset_type: Azure Tenant + mitre_attack_id: + - T1098.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_spn_privesc/azure_ad_spn_privesc.log - sourcetype: azure:monitor:aad - source: Azure AD + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_spn_privesc/azure_ad_spn_privesc.log + sourcetype: azure:monitor:aad + source: Azure AD diff --git a/detections/cloud/azure_ad_successful_authentication_from_different_ips.yml b/detections/cloud/azure_ad_successful_authentication_from_different_ips.yml index 182ed0f811..e4f88c7d47 100644 --- a/detections/cloud/azure_ad_successful_authentication_from_different_ips.yml +++ b/detections/cloud/azure_ad_successful_authentication_from_different_ips.yml @@ -1,81 +1,65 @@ name: Azure AD Successful Authentication From Different Ips id: be6d868d-33b6-4aaa-912e-724fb555b11a -version: 10 -date: '2025-05-02' +version: 11 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects an Azure AD account successfully authenticating - from multiple unique IP addresses within a 30-minute window. It leverages Azure - AD SignInLogs to identify instances where the same user logs in from different IPs - in a short time frame. This behavior is significant as it may indicate compromised - credentials being used by an adversary, potentially following a phishing attack. - If confirmed malicious, this activity could allow unauthorized access to corporate - resources, leading to data breaches or further exploitation within the network. +description: The following analytic detects an Azure AD account successfully authenticating from multiple unique IP addresses within a 30-minute window. It leverages Azure AD SignInLogs to identify instances where the same user logs in from different IPs in a short time frame. This behavior is significant as it may indicate compromised credentials being used by an adversary, potentially following a phishing attack. If confirmed malicious, this activity could allow unauthorized access to corporate resources, leading to data breaches or further exploitation within the network. data_source: -- Azure Active Directory -search: '`azure_monitor_aad` properties.authenticationDetails{}.succeeded=true category=SignInLogs - | rename properties.* as * - | bucket span=30m _time - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime dc(src) AS unique_ips values(dest) as dest values(src) as src by user vendor_account vendor_product signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | where unique_ips > 1 - | `azure_ad_successful_authentication_from_different_ips_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the Signin log category. -known_false_positives: A user with successful authentication events from different - Ips may also represent the legitimate use of more than one device. Filter as needed - and/or customize the threshold to fit your environment. + - Azure Active Directory +search: |- + `azure_monitor_aad` properties.authenticationDetails{}.succeeded=true category=SignInLogs + | rename properties.* as * + | bucket span=30m _time + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime dc(src) AS unique_ips values(dest) as dest values(src) as src + BY user vendor_account vendor_product + signature + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | where unique_ips > 1 + | `azure_ad_successful_authentication_from_different_ips_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the Signin log category. +known_false_positives: A user with successful authentication events from different Ips may also represent the legitimate use of more than one device. Filter as needed and/or customize the threshold to fit your environment. references: -- https://attack.mitre.org/techniques/T1110 -- https://attack.mitre.org/techniques/T1110.001 -- https://attack.mitre.org/techniques/T1110.003 + - https://attack.mitre.org/techniques/T1110 + - https://attack.mitre.org/techniques/T1110.001 + - https://attack.mitre.org/techniques/T1110.003 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has had successful authentication events from more than one - unique IP address in the span of 30 minutes. - risk_objects: - - field: user - type: user - score: 56 - threat_objects: - - field: src - type: ip_address + message: User $user$ has had successful authentication events from more than one unique IP address in the span of 30 minutes. + risk_objects: + - field: user + type: user + score: 56 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Compromised User Account - - Azure Active Directory Account Takeover - asset_type: Azure Tenant - mitre_attack_id: - - T1110.001 - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Compromised User Account + - Azure Active Directory Account Takeover + asset_type: Azure Tenant + mitre_attack_id: + - T1110.001 + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/azure_ad_successful_authentication_from_different_ips/azuread.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/azure_ad_successful_authentication_from_different_ips/azuread.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_successful_powershell_authentication.yml b/detections/cloud/azure_ad_successful_powershell_authentication.yml index e1cab3297b..c5b255edfe 100644 --- a/detections/cloud/azure_ad_successful_powershell_authentication.yml +++ b/detections/cloud/azure_ad_successful_powershell_authentication.yml @@ -1,79 +1,65 @@ name: Azure AD Successful PowerShell Authentication id: 62f10052-d7b3-4e48-b57b-56f8e3ac7ceb -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk status: production type: TTP -description: The following analytic identifies a successful authentication event against - an Azure AD tenant using PowerShell cmdlets. This detection leverages Azure AD SignInLogs - to identify successful logins where the appDisplayName is "Microsoft Azure PowerShell." - This activity is significant because it is uncommon for regular, non-administrative - users to authenticate using PowerShell, and it may indicate enumeration and discovery - techniques by an attacker. If confirmed malicious, this activity could allow attackers - to perform extensive reconnaissance, potentially leading to privilege escalation - or further exploitation within the Azure environment. +description: The following analytic identifies a successful authentication event against an Azure AD tenant using PowerShell cmdlets. This detection leverages Azure AD SignInLogs to identify successful logins where the appDisplayName is "Microsoft Azure PowerShell." This activity is significant because it is uncommon for regular, non-administrative users to authenticate using PowerShell, and it may indicate enumeration and discovery techniques by an attacker. If confirmed malicious, this activity could allow attackers to perform extensive reconnaissance, potentially leading to privilege escalation or further exploitation within the Azure environment. data_source: -- Azure Active Directory -search: '`azure_monitor_aad` category=SignInLogs properties.authenticationDetails{}.succeeded=true properties.appDisplayName="Microsoft Azure PowerShell" - | rename properties.* as * - | rename userAgent as user_agent - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product user_agent signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_successful_powershell_authentication_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the Signin log category. -known_false_positives: Administrative users will likely use PowerShell commandlets - to troubleshoot and maintain the environment. Filter as needed. + - Azure Active Directory +search: |- + `azure_monitor_aad` category=SignInLogs properties.authenticationDetails{}.succeeded=true properties.appDisplayName="Microsoft Azure PowerShell" + | rename properties.* as * + | rename userAgent as user_agent + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product user_agent + signature + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_successful_powershell_authentication_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the Signin log category. +known_false_positives: Administrative users will likely use PowerShell commandlets to troubleshoot and maintain the environment. Filter as needed. references: -- https://attack.mitre.org/techniques/T1078/004/ -- https://docs.microsoft.com/en-us/powershell/module/azuread/connect-azuread?view=azureadps-2.0 -- https://securitycafe.ro/2022/04/29/pentesting-azure-recon-techniques/ -- https://github.com/swisskyrepo/PayloadsAllTheThings/blob/master/Methodology%20and%20Resources/Cloud%20-%20Azure%20Pentest.md + - https://attack.mitre.org/techniques/T1078/004/ + - https://docs.microsoft.com/en-us/powershell/module/azuread/connect-azuread?view=azureadps-2.0 + - https://securitycafe.ro/2022/04/29/pentesting-azure-recon-techniques/ + - https://github.com/swisskyrepo/PayloadsAllTheThings/blob/master/Methodology%20and%20Resources/Cloud%20-%20Azure%20Pentest.md drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Successful authentication for user $user$ using PowerShell. - risk_objects: - - field: user - type: user - score: 54 - threat_objects: - - field: src - type: ip_address + message: Successful authentication for user $user$ using PowerShell. + risk_objects: + - field: user + type: user + score: 54 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Azure Active Directory Account Takeover - asset_type: Azure Active Directory - mitre_attack_id: - - T1078.004 - - T1586.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Account Takeover + asset_type: Azure Active Directory + mitre_attack_id: + - T1078.004 + - T1586.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/azuread_pws/azure-audit.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/azuread_pws/azure-audit.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_successful_single_factor_authentication.yml b/detections/cloud/azure_ad_successful_single_factor_authentication.yml index 17ffe9f75b..94e3fb8365 100644 --- a/detections/cloud/azure_ad_successful_single_factor_authentication.yml +++ b/detections/cloud/azure_ad_successful_single_factor_authentication.yml @@ -1,77 +1,64 @@ name: Azure AD Successful Single-Factor Authentication id: a560e7f6-1711-4353-885b-40be53101fcd -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk status: production type: TTP -description: The following analytic identifies a successful single-factor authentication - event against Azure Active Directory. It leverages Azure SignInLogs data, specifically - focusing on events where single-factor authentication succeeded. This activity is - significant as it may indicate a misconfiguration, policy violation, or potential - account takeover attempt. If confirmed malicious, an attacker could gain unauthorized - access to the account, potentially leading to data breaches, privilege escalation, - or further exploitation within the environment. +description: The following analytic identifies a successful single-factor authentication event against Azure Active Directory. It leverages Azure SignInLogs data, specifically focusing on events where single-factor authentication succeeded. This activity is significant as it may indicate a misconfiguration, policy violation, or potential account takeover attempt. If confirmed malicious, an attacker could gain unauthorized access to the account, potentially leading to data breaches, privilege escalation, or further exploitation within the environment. data_source: -- Azure Active Directory -search: '`azure_monitor_aad` category=SignInLogs properties.authenticationRequirement=singleFactorAuthentication properties.authenticationDetails{}.succeeded=true - | rename properties.* as * - | rename userAgent as user_agent - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product user_agent signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_successful_single_factor_authentication_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the Signin log category. -known_false_positives: Although not recommended, certain users may be required without - multi-factor authentication. Filter as needed + - Azure Active Directory +search: |- + `azure_monitor_aad` category=SignInLogs properties.authenticationRequirement=singleFactorAuthentication properties.authenticationDetails{}.succeeded=true + | rename properties.* as * + | rename userAgent as user_agent + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product user_agent + signature + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_successful_single_factor_authentication_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the Signin log category. +known_false_positives: Although not recommended, certain users may be required without multi-factor authentication. Filter as needed references: -- https://attack.mitre.org/techniques/T1078/004/ -- https://docs.microsoft.com/en-us/azure/active-directory/authentication/concept-mfa-howitworks* -- https://www.forbes.com/sites/daveywinder/2020/07/08/new-dark-web-audit-reveals-15-billion-stolen-logins-from-100000-breaches-passwords-hackers-cybercrime/?sh=69927b2a180f + - https://attack.mitre.org/techniques/T1078/004/ + - https://docs.microsoft.com/en-us/azure/active-directory/authentication/concept-mfa-howitworks* + - https://www.forbes.com/sites/daveywinder/2020/07/08/new-dark-web-audit-reveals-15-billion-stolen-logins-from-100000-breaches-passwords-hackers-cybercrime/?sh=69927b2a180f drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Successful authentication for user $user$ without MFA - risk_objects: - - field: user - type: user - score: 45 - threat_objects: - - field: src - type: ip_address + message: Successful authentication for user $user$ without MFA + risk_objects: + - field: user + type: user + score: 45 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Azure Active Directory Account Takeover - asset_type: Azure Active Directory - mitre_attack_id: - - T1078.004 - - T1586.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Account Takeover + asset_type: Azure Active Directory + mitre_attack_id: + - T1078.004 + - T1586.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/azuread/azure-audit.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/azuread/azure-audit.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_tenant_wide_admin_consent_granted.yml b/detections/cloud/azure_ad_tenant_wide_admin_consent_granted.yml index 1338fb486b..64ed42aad9 100644 --- a/detections/cloud/azure_ad_tenant_wide_admin_consent_granted.yml +++ b/detections/cloud/azure_ad_tenant_wide_admin_consent_granted.yml @@ -6,77 +6,48 @@ author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- Azure Active Directory Consent to application -description: The following analytic identifies instances where admin consent is granted - to an application within an Azure AD tenant. It leverages Azure AD audit logs, specifically - events related to the admin consent action within the ApplicationManagement category. - This activity is significant because admin consent allows applications to access - data across the entire tenant, potentially exposing vast amounts of organizational - data. If confirmed malicious, an attacker could gain extensive and persistent access - to sensitive data, leading to data exfiltration, espionage, further malicious activities, - and potential compliance violations. -search: "`azure_monitor_aad` operationName=\"Consent to application\" - | eval new_field=mvindex('properties.targetResources{}.modifiedProperties{}.newValue',4) - | rename properties.* as * - | rex field=new_field \"ConsentType:(? [^\\,]+)\" - | rex field=new_field \"Scope:(? [^\\,]+)\" - | search ConsentType = \"*AllPrincipals*\" - | rename userAgent as user_agent - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product ConsentType Scope signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_tenant_wide_admin_consent_granted_filter`" -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the Auditlogs log category. -known_false_positives: Legitimate applications may be granted tenant wide consent, - filter as needed. + - Azure Active Directory Consent to application +description: The following analytic identifies instances where admin consent is granted to an application within an Azure AD tenant. It leverages Azure AD audit logs, specifically events related to the admin consent action within the ApplicationManagement category. This activity is significant because admin consent allows applications to access data across the entire tenant, potentially exposing vast amounts of organizational data. If confirmed malicious, an attacker could gain extensive and persistent access to sensitive data, leading to data exfiltration, espionage, further malicious activities, and potential compliance violations. +search: "`azure_monitor_aad` operationName=\"Consent to application\" | eval new_field=mvindex('properties.targetResources{}.modifiedProperties{}.newValue',4) | rename properties.* as * | rex field=new_field \"ConsentType:(? [^\\,]+)\" | rex field=new_field \"Scope:(? [^\\,]+)\" | search ConsentType = \"*AllPrincipals*\" | rename userAgent as user_agent | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product ConsentType Scope signature | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_tenant_wide_admin_consent_granted_filter`" +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the Auditlogs log category. +known_false_positives: Legitimate applications may be granted tenant wide consent, filter as needed. references: -- https://attack.mitre.org/techniques/T1098/003/ -- https://www.mandiant.com/resources/blog/remediation-and-hardening-strategies-for-microsoft-365-to-defend-against-unc2452 -- https://learn.microsoft.com/en-us/security/operations/incident-response-playbook-app-consent -- https://learn.microsoft.com/en-us/azure/active-directory/manage-apps/grant-admin-consent?pivots=portal -- https://microsoft.github.io/Azure-Threat-Research-Matrix/Persistence/AZT501/AZT501-2/ + - https://attack.mitre.org/techniques/T1098/003/ + - https://www.mandiant.com/resources/blog/remediation-and-hardening-strategies-for-microsoft-365-to-defend-against-unc2452 + - https://learn.microsoft.com/en-us/security/operations/incident-response-playbook-app-consent + - https://learn.microsoft.com/en-us/azure/active-directory/manage-apps/grant-admin-consent?pivots=portal + - https://microsoft.github.io/Azure-Threat-Research-Matrix/Persistence/AZT501/AZT501-2/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Administrator $user$ consented an OAuth application for the tenant. - risk_objects: - - field: user - type: user - score: 45 - threat_objects: [] + message: Administrator $user$ consented an OAuth application for the tenant. + risk_objects: + - field: user + type: user + score: 45 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - - NOBELIUM Group - asset_type: Azure Tenant - mitre_attack_id: - - T1098.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Persistence + - NOBELIUM Group + asset_type: Azure Tenant + mitre_attack_id: + - T1098.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_admin_consent/azure_ad_admin_consent.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_admin_consent/azure_ad_admin_consent.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_unusual_number_of_failed_authentications_from_ip.yml b/detections/cloud/azure_ad_unusual_number_of_failed_authentications_from_ip.yml index 760ce19083..afbcf57750 100644 --- a/detections/cloud/azure_ad_unusual_number_of_failed_authentications_from_ip.yml +++ b/detections/cloud/azure_ad_unusual_number_of_failed_authentications_from_ip.yml @@ -1,81 +1,67 @@ name: Azure AD Unusual Number of Failed Authentications From Ip id: 3d8d3a36-93b8-42d7-8d91-c5f24cec223d -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk status: production type: Anomaly -description: The following analytic identifies a single source IP failing to authenticate - with multiple valid users, potentially indicating a Password Spraying attack against - an Azure Active Directory tenant. It uses Azure SignInLogs data and calculates the - standard deviation for source IPs, applying the 3-sigma rule to detect unusual numbers - of failed authentication attempts. This activity is significant as it may signal - an adversary attempting to gain initial access or elevate privileges. If confirmed - malicious, this could lead to unauthorized access, privilege escalation, and potential - compromise of sensitive information. +description: The following analytic identifies a single source IP failing to authenticate with multiple valid users, potentially indicating a Password Spraying attack against an Azure Active Directory tenant. It uses Azure SignInLogs data and calculates the standard deviation for source IPs, applying the 3-sigma rule to detect unusual numbers of failed authentication attempts. This activity is significant as it may signal an adversary attempting to gain initial access or elevate privileges. If confirmed malicious, this could lead to unauthorized access, privilege escalation, and potential compromise of sensitive information. data_source: -- Azure Active Directory -search: '`azure_monitor_aad` category=SignInLogs properties.status.errorCode=50126 properties.authenticationDetails{}.succeeded=false - | rename properties.* as * - | bucket span=5m _time - | stats dc(userPrincipalName) AS unique_accounts values(userPrincipalName) as userPrincipalName values(dest) as dest values(user) as user by _time, src, vendor_account, vendor_product - | eventstats avg(unique_accounts) as ip_avg, stdev(unique_accounts) as ip_std by src - | eval upperBound=(ip_avg+ip_std*3) - | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1,0) - | where isOutlier = 1 - | `azure_ad_unusual_number_of_failed_authentications_from_ip_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the Signin log category. -known_false_positives: A source Ip failing to authenticate with multiple users is - not a common for legitimate behavior. + - Azure Active Directory +search: |- + `azure_monitor_aad` category=SignInLogs properties.status.errorCode=50126 properties.authenticationDetails{}.succeeded=false + | rename properties.* as * + | bucket span=5m _time + | stats dc(userPrincipalName) AS unique_accounts values(userPrincipalName) as userPrincipalName values(dest) as dest values(user) as user + BY _time, src, vendor_account, + vendor_product + | eventstats avg(unique_accounts) as ip_avg, stdev(unique_accounts) as ip_std + BY src + | eval upperBound=(ip_avg+ip_std*3) + | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1,0) + | where isOutlier = 1 + | `azure_ad_unusual_number_of_failed_authentications_from_ip_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the Signin log category. +known_false_positives: A source Ip failing to authenticate with multiple users is not a common for legitimate behavior. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://docs.microsoft.com/en-us/security/compass/incident-response-playbook-password-spray -- https://www.cisa.gov/uscert/ncas/alerts/aa21-008a -- https://docs.microsoft.com/azure/active-directory/reports-monitoring/reference-sign-ins-error-codes + - https://attack.mitre.org/techniques/T1110/003/ + - https://docs.microsoft.com/en-us/security/compass/incident-response-playbook-password-spray + - https://www.cisa.gov/uscert/ncas/alerts/aa21-008a + - https://docs.microsoft.com/azure/active-directory/reports-monitoring/reference-sign-ins-error-codes drilldown_searches: -- name: View the detection results for - "$userPrincipalName$" - search: '%original_detection_search% | search userPrincipalName = "$userPrincipalName$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$userPrincipalName$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$userPrincipalName$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$userPrincipalName$" + search: '%original_detection_search% | search userPrincipalName = "$userPrincipalName$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$userPrincipalName$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$userPrincipalName$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible Password Spraying attack against Azure AD from source ip $src$ - risk_objects: - - field: userPrincipalName - type: user - score: 54 - threat_objects: - - field: src - type: ip_address + message: Possible Password Spraying attack against Azure AD from source ip $src$ + risk_objects: + - field: userPrincipalName + type: user + score: 54 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Azure Active Directory Account Takeover - asset_type: Azure Active Directory - mitre_attack_id: - - T1110.003 - - T1110.004 - - T1586.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - Azure Active Directory Account Takeover + asset_type: Azure Active Directory + mitre_attack_id: + - T1110.003 + - T1110.004 + - T1586.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/password_spraying_azuread/azuread_signin.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/password_spraying_azuread/azuread_signin.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_user_consent_blocked_for_risky_application.yml b/detections/cloud/azure_ad_user_consent_blocked_for_risky_application.yml index 006ae6ec4b..d460a49493 100644 --- a/detections/cloud/azure_ad_user_consent_blocked_for_risky_application.yml +++ b/detections/cloud/azure_ad_user_consent_blocked_for_risky_application.yml @@ -6,88 +6,48 @@ author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- Azure Active Directory Consent to application -description: The following analytic detects instances where Azure AD has blocked a - user's attempt to grant consent to a risky or potentially malicious application. - This detection leverages Azure AD audit logs, focusing on user consent actions and - system-driven blocks. Monitoring these blocked consent attempts is crucial as it - highlights potential threats early on, indicating that a user might be targeted - or that malicious applications are attempting to infiltrate the organization. If - confirmed malicious, this activity suggests that Azure's security measures successfully - prevented a harmful application from accessing organizational data, warranting immediate - investigation to understand the context and take preventive measures. -search: "`azure_monitor_aad` operationName=\"Consent to application\" properties.result=failure - | rename properties.* as * - | eval reason_index = if(mvfind('targetResources{}.modifiedProperties{}.displayName', - \"ConsentAction.Reason\") >= 0, mvfind('targetResources{}.modifiedProperties{}.displayName', - \"ConsentAction.Reason\"), -1) - | eval permissions_index = if(mvfind('targetResources{}.modifiedProperties{}.displayName', - \"ConsentAction.Permissions\") >= 0, mvfind('targetResources{}.modifiedProperties{}.displayName', - \"ConsentAction.Permissions\"), -1) - | search reason_index >= 0 - | eval reason = - mvindex('targetResources{}.modifiedProperties{}.newValue',reason_index) - | eval permissions - = mvindex('targetResources{}.modifiedProperties{}.newValue',permissions_index) - | search reason = \"\\\"Risky application detected\\\"\" - | rex field=permissions \"\ - Scope: (? - [ ^,]+)\" - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product reason Scope signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_user_consent_blocked_for_risky_application_filter`" -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the AuditLog log category. + - Azure Active Directory Consent to application +description: The following analytic detects instances where Azure AD has blocked a user's attempt to grant consent to a risky or potentially malicious application. This detection leverages Azure AD audit logs, focusing on user consent actions and system-driven blocks. Monitoring these blocked consent attempts is crucial as it highlights potential threats early on, indicating that a user might be targeted or that malicious applications are attempting to infiltrate the organization. If confirmed malicious, this activity suggests that Azure's security measures successfully prevented a harmful application from accessing organizational data, warranting immediate investigation to understand the context and take preventive measures. +search: "`azure_monitor_aad` operationName=\"Consent to application\" properties.result=failure | rename properties.* as * | eval reason_index = if(mvfind('targetResources{}.modifiedProperties{}.displayName', \"ConsentAction.Reason\") >= 0, mvfind('targetResources{}.modifiedProperties{}.displayName', \"ConsentAction.Reason\"), -1) | eval permissions_index = if(mvfind('targetResources{}.modifiedProperties{}.displayName', \"ConsentAction.Permissions\") >= 0, mvfind('targetResources{}.modifiedProperties{}.displayName', \"ConsentAction.Permissions\"), -1) | search reason_index >= 0 | eval reason = mvindex('targetResources{}.modifiedProperties{}.newValue',reason_index) | eval permissions = mvindex('targetResources{}.modifiedProperties{}.newValue',permissions_index) | search reason = \"\\\"Risky application detected\\\"\" | rex field=permissions \"Scope: (? [ ^,]+)\" | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product reason Scope signature | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_user_consent_blocked_for_risky_application_filter`" +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category. known_false_positives: UPDATE_KNOWN_FALSE_POSITIVES references: -- https://attack.mitre.org/techniques/T1528/ -- https://www.microsoft.com/en-us/security/blog/2022/09/22/malicious-oauth-applications-used-to-compromise-email-servers-and-spread-spam/ -- https://learn.microsoft.com/en-us/azure/active-directory/manage-apps/protect-against-consent-phishing -- https://learn.microsoft.com/en-us/defender-cloud-apps/investigate-risky-oauth -- https://www.alteredsecurity.com/post/introduction-to-365-stealer -- https://github.com/AlteredSecurity/365-Stealer + - https://attack.mitre.org/techniques/T1528/ + - https://www.microsoft.com/en-us/security/blog/2022/09/22/malicious-oauth-applications-used-to-compromise-email-servers-and-spread-spam/ + - https://learn.microsoft.com/en-us/azure/active-directory/manage-apps/protect-against-consent-phishing + - https://learn.microsoft.com/en-us/defender-cloud-apps/investigate-risky-oauth + - https://www.alteredsecurity.com/post/introduction-to-365-stealer + - https://github.com/AlteredSecurity/365-Stealer drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Azure AD has blocked $user$ attempt to grant to consent to an application - deemed risky. - risk_objects: - - field: user - type: user - score: 30 - threat_objects: [] + message: Azure AD has blocked $user$ attempt to grant to consent to an application deemed risky. + risk_objects: + - field: user + type: user + score: 30 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Account Takeover - asset_type: Azure Tenant - mitre_attack_id: - - T1528 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Account Takeover + asset_type: Azure Tenant + mitre_attack_id: + - T1528 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/azure_ad_user_consent_blocked/azure_ad_user_consent_blocked.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/azure_ad_user_consent_blocked/azure_ad_user_consent_blocked.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_user_consent_denied_for_oauth_application.yml b/detections/cloud/azure_ad_user_consent_denied_for_oauth_application.yml index 0ab61b6d06..6e4fb05763 100644 --- a/detections/cloud/azure_ad_user_consent_denied_for_oauth_application.yml +++ b/detections/cloud/azure_ad_user_consent_denied_for_oauth_application.yml @@ -1,78 +1,63 @@ name: Azure AD User Consent Denied for OAuth Application id: bb093c30-d860-4858-a56e-cd0895d5b49c -version: 10 -date: '2025-05-02' +version: 11 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- Azure Active Directory Sign-in activity -description: The following analytic identifies instances where a user has denied consent - to an OAuth application seeking permissions within the Azure AD environment. This - detection leverages Azure AD's audit logs, specifically focusing on user consent - actions with error code 65004. Monitoring denied consent actions is significant - as it can indicate users recognizing potentially suspicious or untrusted applications. - If confirmed malicious, this activity could suggest attempts by unauthorized applications - to gain access, potentially leading to data breaches or unauthorized actions within - the environment. Understanding these denials helps refine security policies and - enhance user awareness. -search: '`azure_monitor_aad` operationName="Sign-in activity" properties.status.errorCode=65004 - | rename properties.* as * - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product appDisplayName status.failureReason signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_user_consent_denied_for_oauth_application_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment - through an EventHub. This analytic was written to be used with the azure:monitor:aad - sourcetype leveraging the SignInLogs log category. -known_false_positives: Users may deny consent for legitimate applications by mistake, - filter as needed. + - Azure Active Directory Sign-in activity +description: The following analytic identifies instances where a user has denied consent to an OAuth application seeking permissions within the Azure AD environment. This detection leverages Azure AD's audit logs, specifically focusing on user consent actions with error code 65004. Monitoring denied consent actions is significant as it can indicate users recognizing potentially suspicious or untrusted applications. If confirmed malicious, this activity could suggest attempts by unauthorized applications to gain access, potentially leading to data breaches or unauthorized actions within the environment. Understanding these denials helps refine security policies and enhance user awareness. +search: |- + `azure_monitor_aad` operationName="Sign-in activity" properties.status.errorCode=65004 + | rename properties.* as * + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product appDisplayName + status.failureReason signature + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_user_consent_denied_for_oauth_application_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the SignInLogs log category. +known_false_positives: Users may deny consent for legitimate applications by mistake, filter as needed. references: -- https://attack.mitre.org/techniques/T1528/ -- https://www.microsoft.com/en-us/security/blog/2022/09/22/malicious-oauth-applications-used-to-compromise-email-servers-and-spread-spam/ -- https://learn.microsoft.com/en-us/azure/active-directory/manage-apps/protect-against-consent-phishing -- https://learn.microsoft.com/en-us/defender-cloud-apps/investigate-risky-oauth -- https://www.alteredsecurity.com/post/introduction-to-365-stealer -- https://github.com/AlteredSecurity/365-Stealer + - https://attack.mitre.org/techniques/T1528/ + - https://www.microsoft.com/en-us/security/blog/2022/09/22/malicious-oauth-applications-used-to-compromise-email-servers-and-spread-spam/ + - https://learn.microsoft.com/en-us/azure/active-directory/manage-apps/protect-against-consent-phishing + - https://learn.microsoft.com/en-us/defender-cloud-apps/investigate-risky-oauth + - https://www.alteredsecurity.com/post/introduction-to-365-stealer + - https://github.com/AlteredSecurity/365-Stealer drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ denied consent for an OAuth application. - risk_objects: - - field: user - type: user - score: 36 - threat_objects: [] + message: User $user$ denied consent for an OAuth application. + risk_objects: + - field: user + type: user + score: 36 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Account Takeover - asset_type: Azure Tenant - mitre_attack_id: - - T1528 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Account Takeover + asset_type: Azure Tenant + mitre_attack_id: + - T1528 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/azure_ad_user_consent_declined/azure_ad_user_consent_declined.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/azure_ad_user_consent_declined/azure_ad_user_consent_declined.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_user_enabled_and_password_reset.yml b/detections/cloud/azure_ad_user_enabled_and_password_reset.yml index 17f48a3267..dbbe1ee9a4 100644 --- a/detections/cloud/azure_ad_user_enabled_and_password_reset.yml +++ b/detections/cloud/azure_ad_user_enabled_and_password_reset.yml @@ -1,80 +1,66 @@ name: Azure AD User Enabled And Password Reset id: 1347b9e8-2daa-4a6f-be73-b421d3d9e268 -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk status: production type: TTP -description: The following analytic detects an Azure AD user enabling a previously - disabled account and resetting its password within 2 minutes. It uses Azure Active - Directory events to identify this sequence of actions. This activity is significant - because it may indicate an adversary with administrative access attempting to establish - a backdoor identity within the Azure AD tenant. If confirmed malicious, this could - allow the attacker to maintain persistent access, escalate privileges, and potentially - exfiltrate sensitive information from the environment. +description: The following analytic detects an Azure AD user enabling a previously disabled account and resetting its password within 2 minutes. It uses Azure Active Directory events to identify this sequence of actions. This activity is significant because it may indicate an adversary with administrative access attempting to establish a backdoor identity within the Azure AD tenant. If confirmed malicious, this could allow the attacker to maintain persistent access, escalate privileges, and potentially exfiltrate sensitive information from the environment. data_source: -- Azure Active Directory Enable account -- Azure Active Directory Reset password (by admin) -- Azure Active Directory Update user -search: '`azure_monitor_aad` (operationName="Enable account" OR operationName="Reset password (by admin)" OR operationName="Update user") - | transaction user startsWith=(operationName="Enable account") endsWith=(operationName="Reset password (by admin)") maxspan=2m - | rename properties.* as * - | rename initiatedBy.user.userPrincipalName as initiatedBy - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product initiatedBy signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_user_enabled_and_password_reset_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment. - This analytic was written to be used with the azure:monitor:aad sourcetype leveraging - the AuditLog log category. -known_false_positives: While not common, Administrators may enable accounts and reset - their passwords for legitimate reasons. Filter as needed. + - Azure Active Directory Enable account + - Azure Active Directory Reset password (by admin) + - Azure Active Directory Update user +search: |- + `azure_monitor_aad` (operationName="Enable account" OR operationName="Reset password (by admin)" OR operationName="Update user") + | transaction user startsWith=(operationName="Enable account") endsWith=(operationName="Reset password (by admin)") maxspan=2m + | rename properties.* as * + | rename initiatedBy.user.userPrincipalName as initiatedBy + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product initiatedBy + signature + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_user_enabled_and_password_reset_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category. +known_false_positives: While not common, Administrators may enable accounts and reset their passwords for legitimate reasons. Filter as needed. references: -- https://attack.mitre.org/techniques/T1098/ + - https://attack.mitre.org/techniques/T1098/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A user account, $user$, was enabled and its password reset within 2 minutes - by $initiatedBy$ - risk_objects: - - field: user - type: user - score: 45 - - field: initiatedBy - type: user - score: 45 - threat_objects: [] + message: A user account, $user$, was enabled and its password reset within 2 minutes by $initiatedBy$ + risk_objects: + - field: user + type: user + score: 45 + - field: initiatedBy + type: user + score: 45 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - - Scattered Lapsus$ Hunters - asset_type: Azure Active Directory - mitre_attack_id: - - T1098 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Persistence + - Scattered Lapsus$ Hunters + asset_type: Azure Active Directory + mitre_attack_id: + - T1098 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/azure_ad_enable_and_reset/azure-audit.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/azure_ad_enable_and_reset/azure-audit.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_ad_user_immutableid_attribute_updated.yml b/detections/cloud/azure_ad_user_immutableid_attribute_updated.yml index 47ac1359fe..69fe9542cb 100644 --- a/detections/cloud/azure_ad_user_immutableid_attribute_updated.yml +++ b/detections/cloud/azure_ad_user_immutableid_attribute_updated.yml @@ -1,84 +1,69 @@ name: Azure AD User ImmutableId Attribute Updated id: 0c0badad-4536-4a84-a561-5ff760f3c00e -version: 9 -date: '2025-10-14' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk status: production type: TTP -description: The following analytic identifies the modification of the SourceAnchor - (ImmutableId) attribute for an Azure Active Directory user. This detection leverages - Azure AD audit logs, specifically monitoring the "Update user" operation and changes - to the SourceAnchor attribute. This activity is significant as it is a step in setting - up an Azure AD identity federation backdoor, allowing an adversary to establish - persistence. If confirmed malicious, the attacker could impersonate any user, bypassing - password and MFA requirements, leading to unauthorized access and potential data - breaches. +description: The following analytic identifies the modification of the SourceAnchor (ImmutableId) attribute for an Azure Active Directory user. This detection leverages Azure AD audit logs, specifically monitoring the "Update user" operation and changes to the SourceAnchor attribute. This activity is significant as it is a step in setting up an Azure AD identity federation backdoor, allowing an adversary to establish persistence. If confirmed malicious, the attacker could impersonate any user, bypassing password and MFA requirements, leading to unauthorized access and potential data breaches. data_source: -- Azure Active Directory Update user -search: '`azure_monitor_aad` operationName="Update user" properties.targetResources{}.modifiedProperties{}.displayName=SourceAnchor - | rename properties.* as * - | rename initiatedBy.user.userPrincipalName as initiatedBy - | rename targetResources{}.modifiedProperties{}.newValue as modifiedProperties - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product initiatedBy signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_ad_user_immutableid_attribute_updated_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Active Directory events into your Splunk environment. - This analytic was written to be used with the azure:monitor:aad sourcetype leveraging - the AuditLog log category. -known_false_positives: The SourceAnchor (also called ImmutableId) Azure AD attribute - has legitimate uses for directory synchronization. Investigate and filter as needed. + - Azure Active Directory Update user +search: |- + `azure_monitor_aad` operationName="Update user" properties.targetResources{}.modifiedProperties{}.displayName=SourceAnchor + | rename properties.* as * + | rename initiatedBy.user.userPrincipalName as initiatedBy + | rename targetResources{}.modifiedProperties{}.newValue as modifiedProperties + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product initiatedBy + signature + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_ad_user_immutableid_attribute_updated_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category. +known_false_positives: The SourceAnchor (also called ImmutableId) Azure AD attribute has legitimate uses for directory synchronization. Investigate and filter as needed. references: -- https://docs.microsoft.com/en-us/azure/active-directory/hybrid/plan-connect-design-concepts -- https://www.mandiant.com/resources/remediation-and-hardening-strategies-microsoft-365-defend-against-apt29-v13 -- https://o365blog.com/post/federation-vulnerability/ -- https://www.inversecos.com/2021/11/how-to-detect-azure-active-directory.html -- https://www.mandiant.com/resources/blog/detecting-microsoft-365-azure-active-directory-backdoors -- https://attack.mitre.org/techniques/T1098/ + - https://docs.microsoft.com/en-us/azure/active-directory/hybrid/plan-connect-design-concepts + - https://www.mandiant.com/resources/remediation-and-hardening-strategies-microsoft-365-defend-against-apt29-v13 + - https://o365blog.com/post/federation-vulnerability/ + - https://www.inversecos.com/2021/11/how-to-detect-azure-active-directory.html + - https://www.mandiant.com/resources/blog/detecting-microsoft-365-azure-active-directory-backdoors + - https://attack.mitre.org/techniques/T1098/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The SourceAnchor or ImmutableID attribute has been modified for user $user$ - by $initiatedBy$ - risk_objects: - - field: user - type: user - score: 45 - - field: initiatedBy - type: user - score: 45 - threat_objects: [] + message: The SourceAnchor or ImmutableID attribute has been modified for user $user$ by $initiatedBy$ + risk_objects: + - field: user + type: user + score: 45 + - field: initiatedBy + type: user + score: 45 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - - Hellcat Ransomware - asset_type: Azure Active Directory - mitre_attack_id: - - T1098 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Azure Active Directory Persistence + - Hellcat Ransomware + asset_type: Azure Active Directory + mitre_attack_id: + - T1098 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/azure_ad_set_immutableid/azure-audit.log - source: Azure AD - sourcetype: azure:monitor:aad + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/azure_ad_set_immutableid/azure-audit.log + source: Azure AD + sourcetype: azure:monitor:aad diff --git a/detections/cloud/azure_automation_account_created.yml b/detections/cloud/azure_automation_account_created.yml index 7e672d98a0..81031828f9 100644 --- a/detections/cloud/azure_automation_account_created.yml +++ b/detections/cloud/azure_automation_account_created.yml @@ -1,75 +1,64 @@ name: Azure Automation Account Created id: 860902fd-2e76-46b3-b050-ba548dab576c -version: 10 -date: '2025-09-03' +version: 11 +date: '2026-02-25' author: Mauricio Velazco, Brian Serocki, Splunk status: production type: TTP -description: The following analytic detects the creation of a new Azure Automation - account within an Azure tenant. It leverages Azure Audit events, specifically the - Azure Activity log category, to identify when an account is created or updated. - This activity is significant because Azure Automation accounts can be used to automate - tasks and orchestrate actions across Azure and on-premise environments. If an attacker - creates an Automation account with elevated privileges, they could maintain persistence, - execute malicious runbooks, and potentially escalate privileges or execute code - on virtual machines, posing a significant security risk. +description: The following analytic detects the creation of a new Azure Automation account within an Azure tenant. It leverages Azure Audit events, specifically the Azure Activity log category, to identify when an account is created or updated. This activity is significant because Azure Automation accounts can be used to automate tasks and orchestrate actions across Azure and on-premise environments. If an attacker creates an Automation account with elevated privileges, they could maintain persistence, execute malicious runbooks, and potentially escalate privileges or execute code on virtual machines, posing a significant security risk. data_source: -- Azure Audit Create or Update an Azure Automation account -search: '`azure_audit` operationName.value="Microsoft.Automation/automationAccounts/write" status.value=Succeeded - | dedup object - | rename claims.ipaddr as src, subscriptionId as vendor_account, operationName.value as signature - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product object object_path signature - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_automation_account_created_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Audit events into your Splunk environment. Specifically, - this analytic leverages the Azure Activity log category. -known_false_positives: Administrators may legitimately create Azure Automation accounts. - Filter as needed. + - Azure Audit Create or Update an Azure Automation account +search: |- + `azure_audit` operationName.value="Microsoft.Automation/automationAccounts/write" status.value=Succeeded + | dedup object + | rename claims.ipaddr as src, subscriptionId as vendor_account, operationName.value as signature + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product object + object_path signature + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_automation_account_created_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Audit events into your Splunk environment. Specifically, this analytic leverages the Azure Activity log category. +known_false_positives: Administrators may legitimately create Azure Automation accounts. Filter as needed. references: -- https://docs.microsoft.com/en-us/azure/automation/overview -- https://docs.microsoft.com/en-us/azure/automation/automation-create-standalone-account?tabs=azureportal -- https://docs.microsoft.com/en-us/azure/automation/automation-hybrid-runbook-worker -- https://www.inversecos.com/2021/12/how-to-detect-malicious-azure.html -- https://www.netspi.com/blog/technical/cloud-penetration-testing/maintaining-azure-persistence-via-automation-accounts/ -- https://microsoft.github.io/Azure-Threat-Research-Matrix/Persistence/AZT503/AZT503-3/ -- https://attack.mitre.org/techniques/T1136/003/ + - https://docs.microsoft.com/en-us/azure/automation/overview + - https://docs.microsoft.com/en-us/azure/automation/automation-create-standalone-account?tabs=azureportal + - https://docs.microsoft.com/en-us/azure/automation/automation-hybrid-runbook-worker + - https://www.inversecos.com/2021/12/how-to-detect-malicious-azure.html + - https://www.netspi.com/blog/technical/cloud-penetration-testing/maintaining-azure-persistence-via-automation-accounts/ + - https://microsoft.github.io/Azure-Threat-Research-Matrix/Persistence/AZT503/AZT503-3/ + - https://attack.mitre.org/techniques/T1136/003/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A new Azure Automation account $object$ was created by $user$ - risk_objects: - - field: user - type: user - score: 63 - threat_objects: [] + message: A new Azure Automation account $object$ was created by $user$ + risk_objects: + - field: user + type: user + score: 63 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - asset_type: Azure Tenant - mitre_attack_id: - - T1136.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: audit + analytic_story: + - Azure Active Directory Persistence + asset_type: Azure Tenant + mitre_attack_id: + - T1136.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: audit tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/azure_automation_account/azure-activity.log - source: mscs:azure:audit - sourcetype: mscs:azure:audit + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/azure_automation_account/azure-activity.log + source: mscs:azure:audit + sourcetype: mscs:azure:audit diff --git a/detections/cloud/azure_automation_runbook_created.yml b/detections/cloud/azure_automation_runbook_created.yml index 271f62106d..d856f1fc1c 100644 --- a/detections/cloud/azure_automation_runbook_created.yml +++ b/detections/cloud/azure_automation_runbook_created.yml @@ -1,77 +1,64 @@ name: Azure Automation Runbook Created id: 178d696d-6dc6-4ee8-9d25-93fee34eaf5b -version: 10 -date: '2025-09-03' +version: 11 +date: '2026-02-25' author: Mauricio Velazco, Brian Serocki, Splunk status: production type: TTP -description: The following analytic detects the creation of a new Azure Automation - Runbook within an Azure tenant. It leverages Azure Audit events, specifically the - Azure Activity log category, to identify when a new Runbook is created or updated. - This activity is significant because adversaries with privileged access can use - Runbooks to maintain persistence, escalate privileges, or execute malicious code. - If confirmed malicious, this could lead to unauthorized actions such as creating - Global Administrators, executing code on VMs, and compromising the entire Azure - environment. +description: The following analytic detects the creation of a new Azure Automation Runbook within an Azure tenant. It leverages Azure Audit events, specifically the Azure Activity log category, to identify when a new Runbook is created or updated. This activity is significant because adversaries with privileged access can use Runbooks to maintain persistence, escalate privileges, or execute malicious code. If confirmed malicious, this could lead to unauthorized actions such as creating Global Administrators, executing code on VMs, and compromising the entire Azure environment. data_source: -- Azure Audit Create or Update an Azure Automation Runbook -search: '`azure_audit` operationName.value="Microsoft.Automation/automationAccounts/runbooks/write" object!=AzureAutomationTutorial* status.value=Succeeded - | dedup object - | rename claims.ipaddr as src, subscriptionId as vendor_account, operationName.value as operationName - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product object object_path - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_automation_runbook_created_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Audit events into your Splunk environment. Specifically, - this analytic leverages the Azure Activity log category. -known_false_positives: Administrators may legitimately create Azure Automation Runbooks. - Filter as needed. + - Azure Audit Create or Update an Azure Automation Runbook +search: |- + `azure_audit` operationName.value="Microsoft.Automation/automationAccounts/runbooks/write" object!=AzureAutomationTutorial* status.value=Succeeded + | dedup object + | rename claims.ipaddr as src, subscriptionId as vendor_account, operationName.value as operationName + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product object + object_path + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_automation_runbook_created_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Audit events into your Splunk environment. Specifically, this analytic leverages the Azure Activity log category. +known_false_positives: Administrators may legitimately create Azure Automation Runbooks. Filter as needed. references: -- https://docs.microsoft.com/en-us/azure/automation/overview -- https://docs.microsoft.com/en-us/azure/automation/automation-runbook-types -- https://docs.microsoft.com/en-us/azure/automation/manage-runbooks -- https://www.inversecos.com/2021/12/how-to-detect-malicious-azure.html -- https://www.netspi.com/blog/technical/cloud-penetration-testing/maintaining-azure-persistence-via-automation-accounts/ -- https://microsoft.github.io/Azure-Threat-Research-Matrix/Persistence/AZT503/AZT503-3/ -- https://attack.mitre.org/techniques/T1136/003/ + - https://docs.microsoft.com/en-us/azure/automation/overview + - https://docs.microsoft.com/en-us/azure/automation/automation-runbook-types + - https://docs.microsoft.com/en-us/azure/automation/manage-runbooks + - https://www.inversecos.com/2021/12/how-to-detect-malicious-azure.html + - https://www.netspi.com/blog/technical/cloud-penetration-testing/maintaining-azure-persistence-via-automation-accounts/ + - https://microsoft.github.io/Azure-Threat-Research-Matrix/Persistence/AZT503/AZT503-3/ + - https://attack.mitre.org/techniques/T1136/003/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A new Azure Automation Runbook $object$ was created by $user$ - risk_objects: - - field: user - type: user - score: 63 - threat_objects: [] + message: A new Azure Automation Runbook $object$ was created by $user$ + risk_objects: + - field: user + type: user + score: 63 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - asset_type: Azure Tenant - mitre_attack_id: - - T1136.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: audit + analytic_story: + - Azure Active Directory Persistence + asset_type: Azure Tenant + mitre_attack_id: + - T1136.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: audit tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/azure_automation_runbook/azure-activity.log - source: mscs:azure:audit - sourcetype: mscs:azure:audit + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/azure_automation_runbook/azure-activity.log + source: mscs:azure:audit + sourcetype: mscs:azure:audit diff --git a/detections/cloud/azure_runbook_webhook_created.yml b/detections/cloud/azure_runbook_webhook_created.yml index 1b8b886dbf..bf1b53e9d6 100644 --- a/detections/cloud/azure_runbook_webhook_created.yml +++ b/detections/cloud/azure_runbook_webhook_created.yml @@ -1,78 +1,64 @@ name: Azure Runbook Webhook Created id: e98944a9-92e4-443c-81b8-a322e33ce75a -version: 11 -date: '2025-09-03' +version: 12 +date: '2026-02-25' author: Mauricio Velazco, Brian Serocki, Splunk status: production type: TTP -description: The following analytic detects the creation of a new Automation Runbook - Webhook within an Azure tenant. It leverages Azure Audit events, specifically the - "Create or Update an Azure Automation webhook" operation, to identify this activity. - This behavior is significant because Webhooks can trigger Automation Runbooks via - unauthenticated URLs exposed to the Internet, posing a security risk. If confirmed - malicious, an attacker could use this to execute code, create users, or maintain - persistence within the environment, potentially leading to unauthorized access and - control over Azure resources. +description: The following analytic detects the creation of a new Automation Runbook Webhook within an Azure tenant. It leverages Azure Audit events, specifically the "Create or Update an Azure Automation webhook" operation, to identify this activity. This behavior is significant because Webhooks can trigger Automation Runbooks via unauthenticated URLs exposed to the Internet, posing a security risk. If confirmed malicious, an attacker could use this to execute code, create users, or maintain persistence within the environment, potentially leading to unauthorized access and control over Azure resources. data_source: -- Azure Audit Create or Update an Azure Automation webhook -search: '`azure_audit` operationName.value="Microsoft.Automation/automationAccounts/webhooks/write" status.value=Succeeded - | dedup object - | rename claims.ipaddr as src_ip - | rename caller as user - | stats count min(_time) as firstTime max(_time) as lastTime values(dest) as dest by object user, src_ip, resourceGroupName, object_path - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `azure_runbook_webhook_created_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Microsoft - Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). - You must be ingesting Azure Audit events into your Splunk environment. Specifically, - this analytic leverages the Azure Activity log category. -known_false_positives: Administrators may legitimately create Azure Runbook Webhooks. - Filter as needed. + - Azure Audit Create or Update an Azure Automation webhook +search: |- + `azure_audit` operationName.value="Microsoft.Automation/automationAccounts/webhooks/write" status.value=Succeeded + | dedup object + | rename claims.ipaddr as src_ip + | rename caller as user + | stats count min(_time) as firstTime max(_time) as lastTime values(dest) as dest + BY object user, src_ip, + resourceGroupName, object_path + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `azure_runbook_webhook_created_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Audit events into your Splunk environment. Specifically, this analytic leverages the Azure Activity log category. +known_false_positives: Administrators may legitimately create Azure Runbook Webhooks. Filter as needed. references: -- https://docs.microsoft.com/en-us/azure/automation/overview -- https://docs.microsoft.com/en-us/azure/automation/automation-runbook-types -- https://docs.microsoft.com/en-us/azure/automation/automation-webhooks?tabs=portal -- https://www.inversecos.com/2021/12/how-to-detect-malicious-azure.html -- https://www.netspi.com/blog/technical/cloud-penetration-testing/maintaining-azure-persistence-via-automation-accounts/ -- https://microsoft.github.io/Azure-Threat-Research-Matrix/Persistence/AZT503/AZT503-3/ -- https://attack.mitre.org/techniques/T1078/004/ + - https://docs.microsoft.com/en-us/azure/automation/overview + - https://docs.microsoft.com/en-us/azure/automation/automation-runbook-types + - https://docs.microsoft.com/en-us/azure/automation/automation-webhooks?tabs=portal + - https://www.inversecos.com/2021/12/how-to-detect-malicious-azure.html + - https://www.netspi.com/blog/technical/cloud-penetration-testing/maintaining-azure-persistence-via-automation-accounts/ + - https://microsoft.github.io/Azure-Threat-Research-Matrix/Persistence/AZT503/AZT503-3/ + - https://attack.mitre.org/techniques/T1078/004/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A new Azure Runbook Webhook $object$ was created by $user$ - risk_objects: - - field: user - type: user - score: 63 - threat_objects: [] + message: A new Azure Runbook Webhook $object$ was created by $user$ + risk_objects: + - field: user + type: user + score: 63 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - asset_type: Azure Tenant - mitre_attack_id: - - T1078.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Azure Active Directory Persistence + asset_type: Azure Tenant + mitre_attack_id: + - T1078.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/azure_runbook_webhook/azure-activity.log - source: mscs:azure:audit - sourcetype: mscs:azure:audit + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/azure_runbook_webhook/azure-activity.log + source: mscs:azure:audit + sourcetype: mscs:azure:audit diff --git a/detections/cloud/circle_ci_disable_security_job.yml b/detections/cloud/circle_ci_disable_security_job.yml index 985f9fb1d9..23f44b1ca4 100644 --- a/detections/cloud/circle_ci_disable_security_job.yml +++ b/detections/cloud/circle_ci_disable_security_job.yml @@ -1,67 +1,61 @@ name: Circle CI Disable Security Job id: 4a2fdd41-c578-4cd4-9ef7-980e352517f2 -version: 6 -date: '2026-01-14' +version: 7 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects the disabling of security jobs in CircleCI - pipelines. It leverages CircleCI log data, renaming and extracting fields such as - job names, workflow IDs, user information, commit messages, URLs, and branches. - The detection identifies mandatory jobs for each workflow and checks if they were - executed. This activity is significant because disabling security jobs can allow - malicious code to bypass security checks, leading to potential data breaches, system - downtime, and reputational damage. If confirmed malicious, this could result in - unauthorized code execution and compromised pipeline integrity. +description: The following analytic detects the disabling of security jobs in CircleCI pipelines. It leverages CircleCI log data, renaming and extracting fields such as job names, workflow IDs, user information, commit messages, URLs, and branches. The detection identifies mandatory jobs for each workflow and checks if they were executed. This activity is significant because disabling security jobs can allow malicious code to bypass security checks, leading to potential data breaches, system downtime, and reputational damage. If confirmed malicious, this could result in unauthorized code execution and compromised pipeline integrity. data_source: -- CircleCI -search: '`circleci` | rename vcs.committer_name as user vcs.subject as commit_message - vcs.url as url workflows.* as * | stats values(job_name) as job_names by workflow_id - workflow_name user commit_message url branch | lookup mandatory_job_for_workflow - workflow_name OUTPUTNEW job_name AS mandatory_job | search mandatory_job=* | eval - mandatory_job_executed=if(like(job_names, "%".mandatory_job."%"), 1, 0) | where - mandatory_job_executed=0 | eval phase="build" | rex field=url "(?[^\/]*\/[^\/]*)$" - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `circle_ci_disable_security_job_filter`' + - CircleCI +search: |- + `circleci` + | rename vcs.committer_name as user vcs.subject as commit_message vcs.url as url workflows.* as * + | stats values(job_name) as job_names + BY workflow_id workflow_name user + commit_message url branch + | lookup mandatory_job_for_workflow workflow_name OUTPUTNEW job_name AS mandatory_job + | search mandatory_job=* + | eval mandatory_job_executed=if(like(job_names, "%".mandatory_job."%"), 1, 0) + | where mandatory_job_executed=0 + | eval phase="build" + | rex field=url "(?[^\/]*\/[^\/]*)$" + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `circle_ci_disable_security_job_filter` how_to_implement: You must index CircleCI logs. known_false_positives: No false positives have been identified at this time. references: [] drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Disable security job $mandatory_job$ in workflow $workflow_name$ from user - $user$ - risk_objects: - - field: user - type: user - score: 72 - threat_objects: [] + message: Disable security job $mandatory_job$ in workflow $workflow_name$ from user $user$ + risk_objects: + - field: user + type: user + score: 72 + threat_objects: [] tags: - analytic_story: - - Dev Sec Ops - asset_type: CircleCI - mitre_attack_id: - - T1554 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Dev Sec Ops + asset_type: CircleCI + mitre_attack_id: + - T1554 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1554/circle_ci_disable_security_job/circle_ci_disable_security_job.json - sourcetype: circleci - source: circleci + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1554/circle_ci_disable_security_job/circle_ci_disable_security_job.json + sourcetype: circleci + source: circleci diff --git a/detections/cloud/circle_ci_disable_security_step.yml b/detections/cloud/circle_ci_disable_security_step.yml index 92e96f5c4f..11e02fdc69 100644 --- a/detections/cloud/circle_ci_disable_security_step.yml +++ b/detections/cloud/circle_ci_disable_security_step.yml @@ -1,53 +1,58 @@ name: Circle CI Disable Security Step id: 72cb9de9-e98b-4ac9-80b2-5331bba6ea97 -version: 6 -date: '2026-01-14' +version: 7 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: experimental type: Anomaly -description: The following analytic detects the disablement of security steps in a - CircleCI pipeline. It leverages CircleCI logs, using field renaming, joining, and - statistical analysis to identify instances where mandatory security steps are not - executed. This activity is significant because disabling security steps can introduce - vulnerabilities, unauthorized changes, or malicious code into the pipeline. If confirmed - malicious, this could lead to potential attacks, data breaches, or compromised infrastructure. - Investigate by reviewing job names, commit details, and user information associated - with the disablement, and examine any relevant artifacts and concurrent processes. +description: The following analytic detects the disablement of security steps in a CircleCI pipeline. It leverages CircleCI logs, using field renaming, joining, and statistical analysis to identify instances where mandatory security steps are not executed. This activity is significant because disabling security steps can introduce vulnerabilities, unauthorized changes, or malicious code into the pipeline. If confirmed malicious, this could lead to potential attacks, data breaches, or compromised infrastructure. Investigate by reviewing job names, commit details, and user information associated with the disablement, and examine any relevant artifacts and concurrent processes. data_source: -- CircleCI -search: '`circleci` | rename workflows.job_id AS job_id | join job_id [ | search `circleci` - | stats values(name) as step_names count by job_id job_name ] | stats count by step_names - job_id job_name vcs.committer_name vcs.subject vcs.url owners{} | rename vcs.* as - * , owners{} as user | lookup mandatory_step_for_job job_name OUTPUTNEW step_name - AS mandatory_step | search mandatory_step=* | eval mandatory_step_executed=if(like(step_names, - "%".mandatory_step."%"), 1, 0) | where mandatory_step_executed=0 | rex field=url - "(?[^\/]*\/[^\/]*)$" | eval phase="build" | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `circle_ci_disable_security_step_filter`' + - CircleCI +search: |- + `circleci` + | rename workflows.job_id AS job_id + | join job_id [ + | search `circleci` + | stats values(name) as step_names count + BY job_id job_name ] + | stats count + BY step_names job_id job_name + vcs.committer_name vcs.subject vcs.url + owners{} + | rename vcs.* as * , owners{} as user + | lookup mandatory_step_for_job job_name OUTPUTNEW step_name AS mandatory_step + | search mandatory_step=* + | eval mandatory_step_executed=if(like(step_names, "%".mandatory_step."%"), 1, 0) + | where mandatory_step_executed=0 + | rex field=url "(?[^\/]*\/[^\/]*)$" + | eval phase="build" + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `circle_ci_disable_security_step_filter` how_to_implement: You must index CircleCI logs. known_false_positives: No false positives have been identified at this time. references: [] rba: - message: Disable security step $mandatory_step$ in job $job_name$ from user $user$ - risk_objects: - - field: user - type: user - score: 72 - threat_objects: [] + message: Disable security step $mandatory_step$ in job $job_name$ from user $user$ + risk_objects: + - field: user + type: user + score: 72 + threat_objects: [] tags: - analytic_story: - - Dev Sec Ops - asset_type: CircleCI - mitre_attack_id: - - T1554 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Dev Sec Ops + asset_type: CircleCI + mitre_attack_id: + - T1554 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1554/circle_ci_disable_security_step/circle_ci_disable_security_step.json - sourcetype: circleci - source: circleci + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1554/circle_ci_disable_security_step/circle_ci_disable_security_step.json + sourcetype: circleci + source: circleci diff --git a/detections/cloud/cloud_api_calls_from_previously_unseen_user_roles.yml b/detections/cloud/cloud_api_calls_from_previously_unseen_user_roles.yml index 17ddd88a16..05f2cdde5d 100644 --- a/detections/cloud/cloud_api_calls_from_previously_unseen_user_roles.yml +++ b/detections/cloud/cloud_api_calls_from_previously_unseen_user_roles.yml @@ -1,77 +1,63 @@ name: Cloud API Calls From Previously Unseen User Roles id: 2181ad1f-1e73-4d0c-9780-e8880482a08f -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: David Dorsey, Splunk status: production type: Anomaly -description: The following analytic detects cloud API calls executed by user roles - that have not previously run these commands. It leverages the Change data model - in Splunk to identify commands executed by users with the user_type of AssumedRole - and a status of success. This activity is significant because new commands from - different user roles can indicate potential malicious activity or unauthorized actions. - If confirmed malicious, this behavior could lead to unauthorized access, data breaches, - or other damaging outcomes by exploiting new or unmonitored commands within the - cloud environment. +description: The following analytic detects cloud API calls executed by user roles that have not previously run these commands. It leverages the Change data model in Splunk to identify commands executed by users with the user_type of AssumedRole and a status of success. This activity is significant because new commands from different user roles can indicate potential malicious activity or unauthorized actions. If confirmed malicious, this behavior could lead to unauthorized access, data breaches, or other damaging outcomes by exploiting new or unmonitored commands within the cloud environment. data_source: -- AWS CloudTrail -search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change - where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, - All_Changes.command All_Changes.object | `drop_dm_object_name("All_Changes")` | - lookup previously_seen_cloud_api_calls_per_user_role user as user, command as command - OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | - where enough_data=1 | eval firstTimeSeenUserApiCall=min(firstTimeSeen) | where isnull(firstTimeSeenUserApiCall) - OR firstTimeSeenUserApiCall > relative_time(now(),"-24h@h") | table firstTime, user, - object, command |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| - `cloud_api_calls_from_previously_unseen_user_roles_filter`' -how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud - provider. You should run the baseline search `Previously Seen Cloud API Calls Per - User Role - Initial` to build the initial table of user roles, commands, and times. - You must also enable the second baseline search `Previously Seen Cloud API Calls - Per User Role - Update` to keep this table up to date and to age out old data. You - can adjust the time window for this search by updating the `cloud_api_calls_from_previously_unseen_user_roles_activity_window` - macro. You can also provide additional filtering for this search by customizing - the `cloud_api_calls_from_previously_unseen_user_roles_filter` + - AWS CloudTrail +search: |- + | tstats earliest(_time) as firstTime, latest(_time) as lastTime FROM datamodel=Change + WHERE All_Changes.user_type=AssumedRole + AND + All_Changes.status=success + BY All_Changes.user, All_Changes.command All_Changes.object + | `drop_dm_object_name("All_Changes")` + | lookup previously_seen_cloud_api_calls_per_user_role user as user, command as command OUTPUT firstTimeSeen, enough_data + | eventstats max(enough_data) as enough_data + | where enough_data=1 + | eval firstTimeSeenUserApiCall=min(firstTimeSeen) + | where isnull(firstTimeSeenUserApiCall) OR firstTimeSeenUserApiCall > relative_time(now(),"-24h@h") + | table firstTime, user, object, command + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cloud_api_calls_from_previously_unseen_user_roles_filter` +how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud API Calls Per User Role - Initial` to build the initial table of user roles, commands, and times. You must also enable the second baseline search `Previously Seen Cloud API Calls Per User Role - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `cloud_api_calls_from_previously_unseen_user_roles_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_api_calls_from_previously_unseen_user_roles_filter` known_false_positives: No false positives have been identified at this time. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ of type AssumedRole attempting to execute new API calls $command$ - that have not been seen before - risk_objects: - - field: user - type: user - score: 36 - threat_objects: [] + message: User $user$ of type AssumedRole attempting to execute new API calls $command$ that have not been seen before + risk_objects: + - field: user + type: user + score: 36 + threat_objects: [] tags: - analytic_story: - - Suspicious Cloud User Activities - asset_type: AWS Instance - mitre_attack_id: - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat - manual_test: This search needs the baseline `Previously Seen Cloud API Calls Per User Role - Initial` to be run first. + analytic_story: + - Suspicious Cloud User Activities + asset_type: AWS Instance + mitre_attack_id: + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat + manual_test: This search needs the baseline `Previously Seen Cloud API Calls Per User Role - Initial` to be run first. tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/cloud_compute_instance_created_by_previously_unseen_user.yml b/detections/cloud/cloud_compute_instance_created_by_previously_unseen_user.yml index 5aeec3f839..b561d65836 100644 --- a/detections/cloud/cloud_compute_instance_created_by_previously_unseen_user.yml +++ b/detections/cloud/cloud_compute_instance_created_by_previously_unseen_user.yml @@ -1,75 +1,63 @@ name: Cloud Compute Instance Created By Previously Unseen User id: 37a0ec8d-827e-4d6d-8025-cedf31f3a149 -version: 8 -date: '2025-06-10' +version: 9 +date: '2026-02-25' author: Rico Valdez, Splunk status: production type: Anomaly -description: The following analytic identifies the creation of cloud compute instances - by users who have not previously created them. It leverages data from the Change - data model, focusing on 'create' actions by users, and cross-references with a baseline - of known user activities. This activity is significant as it may indicate unauthorized - access or misuse of cloud resources by new or compromised accounts. If confirmed - malicious, attackers could deploy unauthorized compute instances, leading to potential - data exfiltration, increased costs, or further exploitation within the cloud environment. +description: The following analytic identifies the creation of cloud compute instances by users who have not previously created them. It leverages data from the Change data model, focusing on 'create' actions by users, and cross-references with a baseline of known user activities. This activity is significant as it may indicate unauthorized access or misuse of cloud resources by new or compromised accounts. If confirmed malicious, attackers could deploy unauthorized compute instances, leading to potential data exfiltration, increased costs, or further exploitation within the cloud environment. data_source: -- AWS CloudTrail -search: '| tstats `security_content_summariesonly` count earliest(_time) as firstTime, - latest(_time) as lastTime values(All_Changes.object) as dest from datamodel=Change - where All_Changes.action=created by All_Changes.user All_Changes.vendor_region | - `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_compute_creations_by_user - user as user OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) - as enough_data | where enough_data=1 | eval firstTimeSeenUser=min(firstTimeSeen) - | where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") - | table firstTime, user, dest, count vendor_region | `security_content_ctime(firstTime)` - | `cloud_compute_instance_created_by_previously_unseen_user_filter`' -how_to_implement: You must be ingesting the appropriate cloud-infrastructure logs - Run the "Previously Seen Cloud Compute Creations By User" support search to create - of baseline of previously seen users. -known_false_positives: It's possible that a user will start to create compute instances - for the first time, for any number of reasons. Verify with the user launching instances - that this is the intended behavior. + - AWS CloudTrail +search: |- + | tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object) as dest FROM datamodel=Change + WHERE All_Changes.action=created + BY All_Changes.user All_Changes.vendor_region + | `drop_dm_object_name("All_Changes")` + | lookup previously_seen_cloud_compute_creations_by_user user as user OUTPUTNEW firstTimeSeen, enough_data + | eventstats max(enough_data) as enough_data + | where enough_data=1 + | eval firstTimeSeenUser=min(firstTimeSeen) + | where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") + | table firstTime, user, dest, count vendor_region + | `security_content_ctime(firstTime)` + | `cloud_compute_instance_created_by_previously_unseen_user_filter` +how_to_implement: You must be ingesting the appropriate cloud-infrastructure logs Run the "Previously Seen Cloud Compute Creations By User" support search to create of baseline of previously seen users. +known_false_positives: It's possible that a user will start to create compute instances for the first time, for any number of reasons. Verify with the user launching instances that this is the intended behavior. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ is creating a new instance $dest$ for the first time - risk_objects: - - field: dest - type: system - score: 18 - - field: user - type: user - score: 18 - threat_objects: [] + message: User $user$ is creating a new instance $dest$ for the first time + risk_objects: + - field: dest + type: system + score: 18 + - field: user + type: user + score: 18 + threat_objects: [] tags: - analytic_story: - - Cloud Cryptomining - asset_type: Cloud Compute Instance - mitre_attack_id: - - T1078.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat - manual_test: This search needs the baseline `Previously Seen Cloud Compute Creations By User` to be run first. + analytic_story: + - Cloud Cryptomining + asset_type: Cloud Compute Instance + mitre_attack_id: + - T1078.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat + manual_test: This search needs the baseline `Previously Seen Cloud Compute Creations By User` to be run first. tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/cloud_compute_instance_created_in_previously_unused_region.yml b/detections/cloud/cloud_compute_instance_created_in_previously_unused_region.yml index 2ef8952152..e23af4db82 100644 --- a/detections/cloud/cloud_compute_instance_created_in_previously_unused_region.yml +++ b/detections/cloud/cloud_compute_instance_created_in_previously_unused_region.yml @@ -1,79 +1,63 @@ name: Cloud Compute Instance Created In Previously Unused Region id: fa4089e2-50e3-40f7-8469-d2cc1564ca59 -version: 6 -date: '2025-06-10' +version: 7 +date: '2026-02-25' author: David Dorsey, Splunk status: production type: Anomaly -description: The following analytic detects the creation of a cloud compute instance - in a region that has not been previously used within the last hour. It leverages - cloud infrastructure logs and compares the regions of newly created instances against - a lookup file of historically used regions. This activity is significant because - the creation of instances in new regions can indicate unauthorized or suspicious - activity, such as an attacker attempting to evade detection or establish a foothold - in a less monitored area. If confirmed malicious, this could lead to unauthorized - resource usage, data exfiltration, or further compromise of the cloud environment. +description: The following analytic detects the creation of a cloud compute instance in a region that has not been previously used within the last hour. It leverages cloud infrastructure logs and compares the regions of newly created instances against a lookup file of historically used regions. This activity is significant because the creation of instances in new regions can indicate unauthorized or suspicious activity, such as an attacker attempting to evade detection or establish a foothold in a less monitored area. If confirmed malicious, this could lead to unauthorized resource usage, data exfiltration, or further compromise of the cloud environment. data_source: -- AWS CloudTrail -search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime values(All_Changes.object_id) - as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region, - All_Changes.user | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_regions - vendor_region as vendor_region OUTPUTNEW firstTimeSeen, enough_data | eventstats - max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenRegion=min(firstTimeSeen) - | where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), - "-24h@h") | table firstTime, user, dest, count , vendor_region | `security_content_ctime(firstTime)` - | `cloud_compute_instance_created_in_previously_unused_region_filter`' -how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud - provider. You should run the baseline search `Previously Seen Cloud Regions - Initial` - to build the initial table of images observed and times. You must also enable the - second baseline search `Previously Seen Cloud Regions - Update` to keep this table - up to date and to age out old data. You can also provide additional filtering for - this search by customizing the `cloud_compute_instance_created_in_previously_unused_region_filter` - macro. -known_false_positives: It's possible that a user has unknowingly started an instance - in a new region. Please verify that this activity is legitimate. + - AWS CloudTrail +search: |- + | tstats earliest(_time) as firstTime latest(_time) as lastTime values(All_Changes.object_id) as dest, count FROM datamodel=Change + WHERE All_Changes.action=created + BY All_Changes.vendor_region, All_Changes.user + | `drop_dm_object_name("All_Changes")` + | lookup previously_seen_cloud_regions vendor_region as vendor_region OUTPUTNEW firstTimeSeen, enough_data + | eventstats max(enough_data) as enough_data + | where enough_data=1 + | eval firstTimeSeenRegion=min(firstTimeSeen) + | where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), "-24h@h") + | table firstTime, user, dest, count , vendor_region + | `security_content_ctime(firstTime)` + | `cloud_compute_instance_created_in_previously_unused_region_filter` +how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Regions - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Regions - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_in_previously_unused_region_filter` macro. +known_false_positives: It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ is creating an instance $dest$ in a new region for the first - time - risk_objects: - - field: dest - type: system - score: 42 - - field: user - type: user - score: 42 - threat_objects: [] + message: User $user$ is creating an instance $dest$ in a new region for the first time + risk_objects: + - field: dest + type: system + score: 42 + - field: user + type: user + score: 42 + threat_objects: [] tags: - analytic_story: - - Cloud Cryptomining - asset_type: Cloud Compute Instance - mitre_attack_id: - - T1535 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat - manual_test: This search needs the baseline `Previously Seen Cloud Regions - Update` to be run first. + analytic_story: + - Cloud Cryptomining + asset_type: Cloud Compute Instance + mitre_attack_id: + - T1535 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat + manual_test: This search needs the baseline `Previously Seen Cloud Regions - Update` to be run first. tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/cloud_compute_instance_created_with_previously_unseen_image.yml b/detections/cloud/cloud_compute_instance_created_with_previously_unseen_image.yml index 35faa8aca6..fe3eae1085 100644 --- a/detections/cloud/cloud_compute_instance_created_with_previously_unseen_image.yml +++ b/detections/cloud/cloud_compute_instance_created_with_previously_unseen_image.yml @@ -1,78 +1,63 @@ name: Cloud Compute Instance Created With Previously Unseen Image id: bc24922d-987c-4645-b288-f8c73ec194c4 -version: 6 -date: '2025-06-10' +version: 7 +date: '2026-02-25' author: David Dorsey, Splunk status: production type: Anomaly -description: The following analytic detects the creation of cloud compute instances - using previously unseen image IDs. It leverages cloud infrastructure logs to identify - new image IDs that have not been observed before. This activity is significant because - it may indicate unauthorized or suspicious activity, such as the deployment of malicious - payloads or unauthorized access to sensitive information. If confirmed malicious, - this could lead to data breaches, unauthorized access, or further compromise of - the cloud environment. Immediate investigation is required to determine the legitimacy - of the instance creation and to mitigate potential threats. +description: The following analytic detects the creation of cloud compute instances using previously unseen image IDs. It leverages cloud infrastructure logs to identify new image IDs that have not been observed before. This activity is significant because it may indicate unauthorized or suspicious activity, such as the deployment of malicious payloads or unauthorized access to sensitive information. If confirmed malicious, this could lead to data breaches, unauthorized access, or further compromise of the cloud environment. Immediate investigation is required to determine the legitimacy of the instance creation and to mitigate potential threats. data_source: -- AWS CloudTrail -search: '| tstats count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) - as dest from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id, - All_Changes.user | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")` - | where image_id != "unknown" | lookup previously_seen_cloud_compute_images image_id - as image_id OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data - | where enough_data=1 | eval firstTimeSeenImage=min(firstTimeSeen) | where isnull(firstTimeSeenImage) - OR firstTimeSeenImage > relative_time(now(), "-24h@h") | table firstTime, user, - image_id, count, dest | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_with_previously_unseen_image_filter`' -how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud - provider. You should run the baseline search `Previously Seen Cloud Compute Images - - Initial` to build the initial table of images observed and times. You must also - enable the second baseline search `Previously Seen Cloud Compute Images - Update` - to keep this table up to date and to age out old data. You can also provide additional - filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_image_filter` - macro. -known_false_positives: After a new image is created, the first systems created with - that image will cause this alert to fire. Verify that the image being used was - created by a legitimate user. + - AWS CloudTrail +search: |- + | tstats count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest FROM datamodel=Change + WHERE All_Changes.action=created + BY All_Changes.Instance_Changes.image_id, All_Changes.user + | `drop_dm_object_name("All_Changes")` + | `drop_dm_object_name("Instance_Changes")` + | where image_id != "unknown" + | lookup previously_seen_cloud_compute_images image_id as image_id OUTPUT firstTimeSeen, enough_data + | eventstats max(enough_data) as enough_data + | where enough_data=1 + | eval firstTimeSeenImage=min(firstTimeSeen) + | where isnull(firstTimeSeenImage) OR firstTimeSeenImage > relative_time(now(), "-24h@h") + | table firstTime, user, image_id, count, dest + | `security_content_ctime(firstTime)` + | `cloud_compute_instance_created_with_previously_unseen_image_filter` +how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Images - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Images - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_image_filter` macro. +known_false_positives: After a new image is created, the first systems created with that image will cause this alert to fire. Verify that the image being used was created by a legitimate user. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ is creating an instance $dest$ with an image that has not been - previously seen. - risk_objects: - - field: dest - type: system - score: 36 - - field: user - type: user - score: 36 - threat_objects: [] + message: User $user$ is creating an instance $dest$ with an image that has not been previously seen. + risk_objects: + - field: dest + type: system + score: 36 + - field: user + type: user + score: 36 + threat_objects: [] tags: - analytic_story: - - Cloud Cryptomining - asset_type: Cloud Compute Instance - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat - manual_test: This search needs the baseline `Previously Seen Cloud Compute Images - Initial` to be run first. + analytic_story: + - Cloud Cryptomining + asset_type: Cloud Compute Instance + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat + manual_test: This search needs the baseline `Previously Seen Cloud Compute Images - Initial` to be run first. tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/cloud_compute_instance_created_with_previously_unseen_instance_type.yml b/detections/cloud/cloud_compute_instance_created_with_previously_unseen_instance_type.yml index 710346f255..07fae74d1f 100644 --- a/detections/cloud/cloud_compute_instance_created_with_previously_unseen_instance_type.yml +++ b/detections/cloud/cloud_compute_instance_created_with_previously_unseen_instance_type.yml @@ -1,79 +1,63 @@ name: Cloud Compute Instance Created With Previously Unseen Instance Type id: c6ddbf53-9715-49f3-bb4c-fb2e8a309cda -version: 6 -date: '2025-06-10' +version: 7 +date: '2026-02-25' author: David Dorsey, Splunk status: production type: Anomaly -description: The following analytic detects the creation of EC2 instances with previously - unseen instance types. It leverages Splunk's tstats command to analyze data from - the Change data model, identifying instance types that have not been previously - recorded. This activity is significant for a SOC because it may indicate unauthorized - or suspicious activity, such as an attacker attempting to create instances for malicious - purposes. If confirmed malicious, this could lead to unauthorized access, data exfiltration, - system compromise, or service disruption. Immediate investigation is required to - determine the legitimacy of the instance creation. +description: The following analytic detects the creation of EC2 instances with previously unseen instance types. It leverages Splunk's tstats command to analyze data from the Change data model, identifying instance types that have not been previously recorded. This activity is significant for a SOC because it may indicate unauthorized or suspicious activity, such as an attacker attempting to create instances for malicious purposes. If confirmed malicious, this could lead to unauthorized access, data exfiltration, system compromise, or service disruption. Immediate investigation is required to determine the legitimacy of the instance creation. data_source: -- AWS CloudTrail -search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) - as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type, - All_Changes.user | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")` - | where instance_type != "unknown" | lookup previously_seen_cloud_compute_instance_types - instance_type as instance_type OUTPUTNEW firstTimeSeen, enough_data | eventstats - max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenInstanceType=min(firstTimeSeen) - | where isnull(firstTimeSeenInstanceType) OR firstTimeSeenInstanceType > relative_time(now(), - "-24h@h") | table firstTime, user, dest, count, instance_type | `security_content_ctime(firstTime)` - | `cloud_compute_instance_created_with_previously_unseen_instance_type_filter`' -how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud - provider. You should run the baseline search `Previously Seen Cloud Compute Instance - Types - Initial` to build the initial table of instance types observed and times. - You must also enable the second baseline search `Previously Seen Cloud Compute Instance - Types - Update` to keep this table up to date and to age out old data. You can also - provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` - macro. -known_false_positives: It is possible that an admin will create a new system using - a new instance type that has never been used before. Verify with the creator that - they intended to create the system with the new instance type. + - AWS CloudTrail +search: |- + | tstats earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest, count FROM datamodel=Change + WHERE All_Changes.action=created + BY All_Changes.Instance_Changes.instance_type, All_Changes.user + | `drop_dm_object_name("All_Changes")` + | `drop_dm_object_name("Instance_Changes")` + | where instance_type != "unknown" + | lookup previously_seen_cloud_compute_instance_types instance_type as instance_type OUTPUTNEW firstTimeSeen, enough_data + | eventstats max(enough_data) as enough_data + | where enough_data=1 + | eval firstTimeSeenInstanceType=min(firstTimeSeen) + | where isnull(firstTimeSeenInstanceType) OR firstTimeSeenInstanceType > relative_time(now(), "-24h@h") + | table firstTime, user, dest, count, instance_type + | `security_content_ctime(firstTime)` + | `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` +how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Instance Types - Initial` to build the initial table of instance types observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Instance Types - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` macro. +known_false_positives: It is possible that an admin will create a new system using a new instance type that has never been used before. Verify with the creator that they intended to create the system with the new instance type. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ is creating an instance $dest$ with an instance type $instance_type$ - that has not been previously seen. - risk_objects: - - field: dest - type: system - score: 30 - - field: user - type: user - score: 30 - threat_objects: [] + message: User $user$ is creating an instance $dest$ with an instance type $instance_type$ that has not been previously seen. + risk_objects: + - field: dest + type: system + score: 30 + - field: user + type: user + score: 30 + threat_objects: [] tags: - analytic_story: - - Cloud Cryptomining - asset_type: Cloud Compute Instance - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat - manual_test: This search needs the baseline `Previously Seen Cloud Compute Instance Types - Initial` to be run first. + analytic_story: + - Cloud Cryptomining + asset_type: Cloud Compute Instance + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat + manual_test: This search needs the baseline `Previously Seen Cloud Compute Instance Types - Initial` to be run first. tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/cloud_instance_modified_by_previously_unseen_user.yml b/detections/cloud/cloud_instance_modified_by_previously_unseen_user.yml index 42ea20e41d..001d912bd2 100644 --- a/detections/cloud/cloud_instance_modified_by_previously_unseen_user.yml +++ b/detections/cloud/cloud_instance_modified_by_previously_unseen_user.yml @@ -1,73 +1,60 @@ name: Cloud Instance Modified By Previously Unseen User id: 7fb15084-b14e-405a-bd61-a6de15a40722 -version: 8 -date: '2025-06-10' +version: 9 +date: '2026-02-25' author: Rico Valdez, Splunk status: production type: Anomaly -description: The following analytic identifies cloud instances being modified by users - who have not previously modified them. It leverages data from the Change data model, - focusing on successful modifications of EC2 instances. This activity is significant - because it can indicate unauthorized or suspicious changes by potentially compromised - or malicious users. If confirmed malicious, this could lead to unauthorized access, - configuration changes, or potential disruption of cloud services, posing a significant - risk to the organization's cloud infrastructure. +description: The following analytic identifies cloud instances being modified by users who have not previously modified them. It leverages data from the Change data model, focusing on successful modifications of EC2 instances. This activity is significant because it can indicate unauthorized or suspicious changes by potentially compromised or malicious users. If confirmed malicious, this could lead to unauthorized access, configuration changes, or potential disruption of cloud services, posing a significant risk to the organization's cloud infrastructure. data_source: -- AWS CloudTrail -search: '| tstats `security_content_summariesonly` count earliest(_time) as firstTime, - latest(_time) as lastTime values(All_Changes.object_id) as object_id values(All_Changes.command) - as command from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2 - All_Changes.status=success by All_Changes.user | `drop_dm_object_name("All_Changes")` - | lookup previously_seen_cloud_instance_modifications_by_user user as user OUTPUTNEW - firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where - enough_data=1 | eval firstTimeSeenUser=min(firstTimeSeen) | where isnull(firstTimeSeenUser) - OR firstTimeSeenUser > relative_time(now(), "-24h@h") | table firstTime user command - object_id count | `security_content_ctime(firstTime)` | `cloud_instance_modified_by_previously_unseen_user_filter`' -how_to_implement: This search has a dependency on other searches to create and update - a baseline of users observed to be associated with this activity. The search "Previously - Seen Cloud Instance Modifications By User - Update" should be enabled for this detection - to properly work. -known_false_positives: It's possible that a new user will start to modify EC2 instances - when they haven't before for any number of reasons. Verify with the user that is - modifying instances that this is the intended behavior. + - AWS CloudTrail +search: |- + | tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as object_id values(All_Changes.command) as command FROM datamodel=Change + WHERE All_Changes.action=modified All_Changes.change_type=EC2 All_Changes.status=success + BY All_Changes.user + | `drop_dm_object_name("All_Changes")` + | lookup previously_seen_cloud_instance_modifications_by_user user as user OUTPUTNEW firstTimeSeen, enough_data + | eventstats max(enough_data) as enough_data + | where enough_data=1 + | eval firstTimeSeenUser=min(firstTimeSeen) + | where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") + | table firstTime user command object_id count + | `security_content_ctime(firstTime)` + | `cloud_instance_modified_by_previously_unseen_user_filter` +how_to_implement: This search has a dependency on other searches to create and update a baseline of users observed to be associated with this activity. The search "Previously Seen Cloud Instance Modifications By User - Update" should be enabled for this detection to properly work. +known_false_positives: It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ is modifying an instance $object_id$ for the first time. - risk_objects: - - field: user - type: user - score: 42 - threat_objects: [] + message: User $user$ is modifying an instance $object_id$ for the first time. + risk_objects: + - field: user + type: user + score: 42 + threat_objects: [] tags: - analytic_story: - - Suspicious Cloud Instance Activities - asset_type: AWS Instance - mitre_attack_id: - - T1078.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat - manual_test: This search needs the baseline `Previously Seen Cloud Instance Modifications By User - Update` to be run first. + analytic_story: + - Suspicious Cloud Instance Activities + asset_type: AWS Instance + mitre_attack_id: + - T1078.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat + manual_test: This search needs the baseline `Previously Seen Cloud Instance Modifications By User - Update` to be run first. tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/cloud_provisioning_activity_from_previously_unseen_city.yml b/detections/cloud/cloud_provisioning_activity_from_previously_unseen_city.yml index dca2820a0c..b478bcd173 100644 --- a/detections/cloud/cloud_provisioning_activity_from_previously_unseen_city.yml +++ b/detections/cloud/cloud_provisioning_activity_from_previously_unseen_city.yml @@ -1,93 +1,73 @@ name: Cloud Provisioning Activity From Previously Unseen City id: e7ecc5e0-88df-48b9-91af-51104c68f02f -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Rico Valdez, Bhavin Patel, Splunk status: production type: Anomaly -description: The following analytic detects cloud provisioning activities originating - from previously unseen cities. It leverages cloud infrastructure logs and compares - the geographic location of the source IP address against a baseline of known locations. - This activity is significant as it may indicate unauthorized access or misuse of - cloud resources from an unexpected location. If confirmed malicious, this could - lead to unauthorized resource creation, potential data exfiltration, or further - compromise of cloud infrastructure. +description: The following analytic detects cloud provisioning activities originating from previously unseen cities. It leverages cloud infrastructure logs and compares the geographic location of the source IP address against a baseline of known locations. This activity is significant as it may indicate unauthorized access or misuse of cloud resources from an unexpected location. If confirmed malicious, this could lead to unauthorized resource creation, potential data exfiltration, or further compromise of cloud infrastructure. data_source: -- AWS CloudTrail -search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change - where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success - by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | - `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(City) | - lookup previously_seen_cloud_provisioning_activity_sources City as City OUTPUT firstTimeSeen, - enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | - eval firstTimeSeenCity=min(firstTimeSeen) | where isnull(firstTimeSeenCity) OR firstTimeSeenCity - > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) | - `security_content_ctime(firstTime)` | table firstTime, src, City, user, object, - command | `cloud_provisioning_activity_from_previously_unseen_city_filter`' -how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud - provider. You should run the baseline search `Previously Seen Cloud Provisioning - Activity Sources - Initial` to build the initial table of source IP address, geographic - locations, and times. You must also enable the second baseline search `Previously - Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date - and to age out old data. You can adjust the time window for this search by updating - the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide - additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_city_filter` - macro. -known_false_positives: "This is a strictly behavioral search, so we define \"false - positive\" slightly differently. Every time this fires, it will accurately reflect - the first occurrence in the time period you're searching within, plus what is stored - in the cache feature. But while there are really no \"false positives\" in a traditional - sense, there is definitely lots of noise.\nThis search will fire any time a new - IP address is seen in the **GeoIP** database for any kind of provisioning activity. - If you typically do all provisioning from tools inside of your country, there should - be few false positives. If you are located in countries where the free version of - **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly - small countries in less economically powerful regions), this may be much less valuable - to you." + - AWS CloudTrail +search: |- + | tstats earliest(_time) as firstTime, latest(_time) as lastTime FROM datamodel=Change + WHERE ( + All_Changes.action=started + OR + All_Changes.action=created + ) + All_Changes.status=success + BY All_Changes.src, All_Changes.user, All_Changes.object, + All_Changes.command + | `drop_dm_object_name("All_Changes")` + | iplocation src + | where isnotnull(City) + | lookup previously_seen_cloud_provisioning_activity_sources City as City OUTPUT firstTimeSeen, enough_data + | eventstats max(enough_data) as enough_data + | where enough_data=1 + | eval firstTimeSeenCity=min(firstTimeSeen) + | where isnull(firstTimeSeenCity) OR firstTimeSeenCity > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) + | `security_content_ctime(firstTime)` + | table firstTime, src, City, user, object, command + | `cloud_provisioning_activity_from_previously_unseen_city_filter` +how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_city_filter` macro. +known_false_positives: "This is a strictly behavioral search, so we define \"false positive\" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no \"false positives\" in a traditional sense, there is definitely lots of noise.\nThis search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you." references: [] drilldown_searches: -- name: View the detection results for - "$user$" and "$object$" - search: '%original_detection_search% | search user = "$user$" object = "$object$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$object$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$object$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$object$" + search: '%original_detection_search% | search user = "$user$" object = "$object$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$object$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$object$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ is starting or creating an instance $object$ for the first - time in City $City$ from IP address $src$ - risk_objects: - - field: user - type: user - score: 18 - - field: object - type: system - score: 18 - threat_objects: - - field: src - type: ip_address + message: User $user$ is starting or creating an instance $object$ for the first time in City $City$ from IP address $src$ + risk_objects: + - field: user + type: user + score: 18 + - field: object + type: system + score: 18 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Suspicious Cloud Provisioning Activities - asset_type: AWS Instance - mitre_attack_id: - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat - manual_test: This search needs the baseline to be run first to create a lookup + analytic_story: + - Suspicious Cloud Provisioning Activities + asset_type: AWS Instance + mitre_attack_id: + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat + manual_test: This search needs the baseline to be run first to create a lookup tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/cloud_provisioning_activity_from_previously_unseen_country.yml b/detections/cloud/cloud_provisioning_activity_from_previously_unseen_country.yml index 0ce1acbec8..895f2436f2 100644 --- a/detections/cloud/cloud_provisioning_activity_from_previously_unseen_country.yml +++ b/detections/cloud/cloud_provisioning_activity_from_previously_unseen_country.yml @@ -1,92 +1,73 @@ name: Cloud Provisioning Activity From Previously Unseen Country id: 94994255-3acf-4213-9b3f-0494df03bb31 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Rico Valdez, Bhavin Patel, Splunk status: production type: Anomaly -description: The following analytic detects cloud provisioning activities originating - from previously unseen countries. It leverages cloud infrastructure logs and compares - the geographic location of the source IP address against a baseline of known locations. - This activity is significant as it may indicate unauthorized access or potential - compromise of cloud resources. If confirmed malicious, an attacker could gain control - over cloud assets, leading to data breaches, service disruptions, or further infiltration - into the network. +description: The following analytic detects cloud provisioning activities originating from previously unseen countries. It leverages cloud infrastructure logs and compares the geographic location of the source IP address against a baseline of known locations. This activity is significant as it may indicate unauthorized access or potential compromise of cloud resources. If confirmed malicious, an attacker could gain control over cloud assets, leading to data breaches, service disruptions, or further infiltration into the network. data_source: -- AWS CloudTrail -search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change - where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success - by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | - `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Country) - | lookup previously_seen_cloud_provisioning_activity_sources Country as Country - OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | - where enough_data=1 | eval firstTimeSeenCountry=min(firstTimeSeen) | where isnull(firstTimeSeenCountry) - OR firstTimeSeenCountry > relative_time(now(), "-24h@h") | `security_content_ctime(firstTime)` - | table firstTime, src, Country, user, object, command | `cloud_provisioning_activity_from_previously_unseen_country_filter`' -how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud - provider. You should run the baseline search `Previously Seen Cloud Provisioning - Activity Sources - Initial` to build the initial table of source IP address, geographic - locations, and times. You must also enable the second baseline search `Previously - Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date - and to age out old data. You can adjust the time window for this search by updating - the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide - additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_country_filter` - macro. -known_false_positives: "This is a strictly behavioral search, so we define \"false - positive\" slightly differently. Every time this fires, it will accurately reflect - the first occurrence in the time period you're searching within, plus what is stored - in the cache feature. But while there are really no \"false positives\" in a traditional - sense, there is definitely lots of noise.\nThis search will fire any time a new - IP address is seen in the **GeoIP** database for any kind of provisioning activity. - If you typically do all provisioning from tools inside of your country, there should - be few false positives. If you are located in countries where the free version of - **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly - small countries in less economically powerful regions), this may be much less valuable - to you." + - AWS CloudTrail +search: |- + | tstats earliest(_time) as firstTime, latest(_time) as lastTime FROM datamodel=Change + WHERE ( + All_Changes.action=started + OR + All_Changes.action=created + ) + All_Changes.status=success + BY All_Changes.src, All_Changes.user, All_Changes.object, + All_Changes.command + | `drop_dm_object_name("All_Changes")` + | iplocation src + | where isnotnull(Country) + | lookup previously_seen_cloud_provisioning_activity_sources Country as Country OUTPUT firstTimeSeen, enough_data + | eventstats max(enough_data) as enough_data + | where enough_data=1 + | eval firstTimeSeenCountry=min(firstTimeSeen) + | where isnull(firstTimeSeenCountry) OR firstTimeSeenCountry > relative_time(now(), "-24h@h") + | `security_content_ctime(firstTime)` + | table firstTime, src, Country, user, object, command + | `cloud_provisioning_activity_from_previously_unseen_country_filter` +how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_country_filter` macro. +known_false_positives: "This is a strictly behavioral search, so we define \"false positive\" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no \"false positives\" in a traditional sense, there is definitely lots of noise.\nThis search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you." references: [] drilldown_searches: -- name: View the detection results for - "$object$" - search: '%original_detection_search% | search object = "$object$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$object$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$object$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$object$" + search: '%original_detection_search% | search object = "$object$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$object$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$object$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ is starting or creating an instance $object$ for the first - time in Country $Country$ from IP address $src$ - risk_objects: - - field: object - type: system - score: 42 - - field: user - type: user - score: 42 - threat_objects: - - field: src - type: ip_address + message: User $user$ is starting or creating an instance $object$ for the first time in Country $Country$ from IP address $src$ + risk_objects: + - field: object + type: system + score: 42 + - field: user + type: user + score: 42 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Suspicious Cloud Provisioning Activities - asset_type: AWS Instance - mitre_attack_id: - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat - manual_test: This search needs the baseline to be run first to create a lookup + analytic_story: + - Suspicious Cloud Provisioning Activities + asset_type: AWS Instance + mitre_attack_id: + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat + manual_test: This search needs the baseline to be run first to create a lookup tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/cloud_provisioning_activity_from_previously_unseen_ip_address.yml b/detections/cloud/cloud_provisioning_activity_from_previously_unseen_ip_address.yml index ac493e3a2f..c8e3d951e9 100644 --- a/detections/cloud/cloud_provisioning_activity_from_previously_unseen_ip_address.yml +++ b/detections/cloud/cloud_provisioning_activity_from_previously_unseen_ip_address.yml @@ -1,92 +1,70 @@ name: Cloud Provisioning Activity From Previously Unseen IP Address id: f86a8ec9-b042-45eb-92f4-e9ed1d781078 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Rico Valdez, Splunk status: production type: Anomaly -description: The following analytic detects cloud provisioning activities originating - from previously unseen IP addresses. It leverages cloud infrastructure logs to identify - events where resources are created or started, and cross-references these with a - baseline of known IP addresses. This activity is significant as it may indicate - unauthorized access or potential misuse of cloud resources. If confirmed malicious, - an attacker could gain unauthorized control over cloud resources, leading to data - breaches, service disruptions, or increased operational costs. +description: The following analytic detects cloud provisioning activities originating from previously unseen IP addresses. It leverages cloud infrastructure logs to identify events where resources are created or started, and cross-references these with a baseline of known IP addresses. This activity is significant as it may indicate unauthorized access or potential misuse of cloud resources. If confirmed malicious, an attacker could gain unauthorized control over cloud resources, leading to data breaches, service disruptions, or increased operational costs. data_source: -- AWS CloudTrail -search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime, values(All_Changes.object_id) - as object_id from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) - All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.command - | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_provisioning_activity_sources - src as src OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data - | where enough_data=1 | eval firstTimeSeenSrc=min(firstTimeSeen) | where isnull(firstTimeSeenSrc) - OR firstTimeSeenSrc > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) - | `security_content_ctime(firstTime)` | table firstTime, src, user, object_id, command - | `cloud_provisioning_activity_from_previously_unseen_ip_address_filter`' -how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud - provider. You should run the baseline search `Previously Seen Cloud Provisioning - Activity Sources - Initial` to build the initial table of source IP address, geographic - locations, and times. You must also enable the second baseline search `Previously - Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date - and to age out old data. You can adjust the time window for this search by updating - the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide - additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` - macro. -known_false_positives: "This is a strictly behavioral search, so we define \"false - positive\" slightly differently. Every time this fires, it will accurately reflect - the first occurrence in the time period you're searching within, plus what is stored - in the cache feature. But while there are really no \"false positives\" in a traditional - sense, there is definitely lots of noise.\nThis search will fire any time a new - IP address is seen in the **GeoIP** database for any kind of provisioning activity. - If you typically do all provisioning from tools inside of your country, there should - be few false positives. If you are located in countries where the free version of - **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly - small countries in less economically powerful regions), this may be much less valuable - to you." + - AWS CloudTrail +search: |- + | tstats earliest(_time) as firstTime, latest(_time) as lastTime, values(All_Changes.object_id) as object_id FROM datamodel=Change + WHERE ( + All_Changes.action=started + OR + All_Changes.action=created + ) + All_Changes.status=success + BY All_Changes.src, All_Changes.user, All_Changes.command + | `drop_dm_object_name("All_Changes")` + | lookup previously_seen_cloud_provisioning_activity_sources src as src OUTPUT firstTimeSeen, enough_data + | eventstats max(enough_data) as enough_data + | where enough_data=1 + | eval firstTimeSeenSrc=min(firstTimeSeen) + | where isnull(firstTimeSeenSrc) OR firstTimeSeenSrc > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) + | `security_content_ctime(firstTime)` + | table firstTime, src, user, object_id, command + | `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` +how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` macro. +known_false_positives: "This is a strictly behavioral search, so we define \"false positive\" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no \"false positives\" in a traditional sense, there is definitely lots of noise.\nThis search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you." references: [] drilldown_searches: -- name: View the detection results for - "$object_id$" - search: '%original_detection_search% | search object_id = "$object_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$object_id$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$object_id$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$object_id$" + search: '%original_detection_search% | search object_id = "$object_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$object_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$object_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ is starting or creating an instance $object_id$ for the first - time from IP address $src$ - risk_objects: - - field: object_id - type: system - score: 42 - - field: user - type: user - score: 42 - threat_objects: - - field: src - type: ip_address + message: User $user$ is starting or creating an instance $object_id$ for the first time from IP address $src$ + risk_objects: + - field: object_id + type: system + score: 42 + - field: user + type: user + score: 42 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Suspicious Cloud Provisioning Activities - asset_type: AWS Instance - mitre_attack_id: - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat - manual_test: This search needs the baseline to be run first to create a lookup + analytic_story: + - Suspicious Cloud Provisioning Activities + asset_type: AWS Instance + mitre_attack_id: + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat + manual_test: This search needs the baseline to be run first to create a lookup tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/cloud_provisioning_activity_from_previously_unseen_region.yml b/detections/cloud/cloud_provisioning_activity_from_previously_unseen_region.yml index 606514f917..6ecf566936 100644 --- a/detections/cloud/cloud_provisioning_activity_from_previously_unseen_region.yml +++ b/detections/cloud/cloud_provisioning_activity_from_previously_unseen_region.yml @@ -1,93 +1,73 @@ name: Cloud Provisioning Activity From Previously Unseen Region id: 5aba1860-9617-4af9-b19d-aecac16fe4f2 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Rico Valdez, Bhavin Patel, Splunk status: production type: Anomaly -description: The following analytic detects cloud provisioning activities originating - from previously unseen regions. It leverages cloud infrastructure logs to identify - events where resources are started or created, and cross-references these with a - baseline of known regions. This activity is significant as it may indicate unauthorized - access or misuse of cloud resources from unfamiliar locations. If confirmed malicious, - this could lead to unauthorized resource creation, potential data exfiltration, - or further compromise of cloud infrastructure. +description: The following analytic detects cloud provisioning activities originating from previously unseen regions. It leverages cloud infrastructure logs to identify events where resources are started or created, and cross-references these with a baseline of known regions. This activity is significant as it may indicate unauthorized access or misuse of cloud resources from unfamiliar locations. If confirmed malicious, this could lead to unauthorized resource creation, potential data exfiltration, or further compromise of cloud infrastructure. data_source: -- AWS CloudTrail -search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change - where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success - by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | - `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Region) - | lookup previously_seen_cloud_provisioning_activity_sources Region as Region OUTPUT - firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where - enough_data=1 | eval firstTimeSeenRegion=min(firstTimeSeen) | where isnull(firstTimeSeenRegion) - OR firstTimeSeenRegion > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) - | `security_content_ctime(firstTime)` | table firstTime, src, Region, user, object, - command | `cloud_provisioning_activity_from_previously_unseen_region_filter`' -how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud - provider. You should run the baseline search `Previously Seen Cloud Provisioning - Activity Sources - Initial` to build the initial table of source IP address, geographic - locations, and times. You must also enable the second baseline search `Previously - Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date - and to age out old data. You can adjust the time window for this search by updating - the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide - additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_region_filter` - macro. -known_false_positives: "This is a strictly behavioral search, so we define \"false - positive\" slightly differently. Every time this fires, it will accurately reflect - the first occurrence in the time period you're searching within, plus what is stored - in the cache feature. But while there are really no \"false positives\" in a traditional - sense, there is definitely lots of noise.\nThis search will fire any time a new - IP address is seen in the **GeoIP** database for any kind of provisioning activity. - If you typically do all provisioning from tools inside of your country, there should - be few false positives. If you are located in countries where the free version of - **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly - small countries in less economically powerful regions), this may be much less valuable - to you." + - AWS CloudTrail +search: |- + | tstats earliest(_time) as firstTime, latest(_time) as lastTime FROM datamodel=Change + WHERE ( + All_Changes.action=started + OR + All_Changes.action=created + ) + All_Changes.status=success + BY All_Changes.src, All_Changes.user, All_Changes.object, + All_Changes.command + | `drop_dm_object_name("All_Changes")` + | iplocation src + | where isnotnull(Region) + | lookup previously_seen_cloud_provisioning_activity_sources Region as Region OUTPUT firstTimeSeen, enough_data + | eventstats max(enough_data) as enough_data + | where enough_data=1 + | eval firstTimeSeenRegion=min(firstTimeSeen) + | where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) + | `security_content_ctime(firstTime)` + | table firstTime, src, Region, user, object, command + | `cloud_provisioning_activity_from_previously_unseen_region_filter` +how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_region_filter` macro. +known_false_positives: "This is a strictly behavioral search, so we define \"false positive\" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no \"false positives\" in a traditional sense, there is definitely lots of noise.\nThis search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you." references: [] drilldown_searches: -- name: View the detection results for - "$object$" - search: '%original_detection_search% | search object = "$object$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$object$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$object$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$object$" + search: '%original_detection_search% | search object = "$object$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$object$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$object$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ is starting or creating an instance $object$ for the first - time in region $Region$ from IP address $src$ - risk_objects: - - field: object - type: system - score: 42 - - field: user - type: user - score: 42 - threat_objects: - - field: src - type: ip_address + message: User $user$ is starting or creating an instance $object$ for the first time in region $Region$ from IP address $src$ + risk_objects: + - field: object + type: system + score: 42 + - field: user + type: user + score: 42 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Suspicious Cloud Provisioning Activities - asset_type: AWS Instance - mitre_attack_id: - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat - manual_test: This search needs the baseline to be run first to create a lookup + analytic_story: + - Suspicious Cloud Provisioning Activities + asset_type: AWS Instance + mitre_attack_id: + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat + manual_test: This search needs the baseline to be run first to create a lookup tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/cloud_security_groups_modifications_by_user.yml b/detections/cloud/cloud_security_groups_modifications_by_user.yml index d75189d0a0..a5dfc1495f 100644 --- a/detections/cloud/cloud_security_groups_modifications_by_user.yml +++ b/detections/cloud/cloud_security_groups_modifications_by_user.yml @@ -1,73 +1,62 @@ name: Cloud Security Groups Modifications by User id: cfe7cca7-2746-4bdf-b712-b01ed819b9de -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Bhavin Patel, Splunk data_source: -- AWS CloudTrail + - AWS CloudTrail type: Anomaly status: production -description: The following analytic identifies unusual modifications to security groups - in your cloud environment by users, focusing on actions such as modifications, deletions, - or creations over 30-minute intervals. It leverages cloud infrastructure logs and - calculates the standard deviation for each user, using the 3-sigma rule to detect - anomalies. This activity is significant as it may indicate a compromised account - or insider threat. If confirmed malicious, attackers could alter security group - configurations, potentially exposing sensitive resources or disrupting services. -search: '| tstats dc(All_Changes.object) as unique_security_groups values(All_Changes.src) - as src values(All_Changes.user_type) as user_type values(All_Changes.object_category) - as object_category values(All_Changes.object) as objects values(All_Changes.action) - as action values(All_Changes.user_agent) as user_agent values(All_Changes.command) - as command from datamodel=Change WHERE All_Changes.object_category = "security_group" - (All_Changes.action = modified OR All_Changes.action = deleted OR All_Changes.action - = created) by All_Changes.user _time span=30m | `drop_dm_object_name("All_Changes")` - | eventstats avg(unique_security_groups) as avg_changes , stdev(unique_security_groups) - as std_changes by user | eval upperBound=(avg_changes+std_changes*3) | eval isOutlier=if(unique_security_groups - > 2 and unique_security_groups >= upperBound, 1, 0) | where isOutlier=1| `cloud_security_groups_modifications_by_user_filter`' -how_to_implement: This search requries the Cloud infrastructure logs such as AWS Cloudtrail, - GCP Pubsub Message logs, Azure Audit logs to be ingested into an accelerated Change - datamodel. It is also recommended that users can try different combinations of the - `bucket` span time and outlier conditions to better suit with their environment. -known_false_positives: It is possible that legitimate user/admin may modify a number - of security groups +description: The following analytic identifies unusual modifications to security groups in your cloud environment by users, focusing on actions such as modifications, deletions, or creations over 30-minute intervals. It leverages cloud infrastructure logs and calculates the standard deviation for each user, using the 3-sigma rule to detect anomalies. This activity is significant as it may indicate a compromised account or insider threat. If confirmed malicious, attackers could alter security group configurations, potentially exposing sensitive resources or disrupting services. +search: |- + | tstats dc(All_Changes.object) as unique_security_groups values(All_Changes.src) as src values(All_Changes.user_type) as user_type values(All_Changes.object_category) as object_category values(All_Changes.object) as objects values(All_Changes.action) as action values(All_Changes.user_agent) as user_agent values(All_Changes.command) as command FROM datamodel=Change + WHERE All_Changes.object_category = "security_group" (All_Changes.action = modified + OR + All_Changes.action = deleted + OR + All_Changes.action = created) + BY All_Changes.user _time span=30m + | `drop_dm_object_name("All_Changes")` + | eventstats avg(unique_security_groups) as avg_changes , stdev(unique_security_groups) as std_changes + BY user + | eval upperBound=(avg_changes+std_changes*3) + | eval isOutlier=if(unique_security_groups > 2 and unique_security_groups >= upperBound, 1, 0) + | where isOutlier=1 + | `cloud_security_groups_modifications_by_user_filter` +how_to_implement: This search requries the Cloud infrastructure logs such as AWS Cloudtrail, GCP Pubsub Message logs, Azure Audit logs to be ingested into an accelerated Change datamodel. It is also recommended that users can try different combinations of the `bucket` span time and outlier conditions to better suit with their environment. +known_false_positives: It is possible that legitimate user/admin may modify a number of security groups references: -- https://attack.mitre.org/techniques/T1578/005/ + - https://attack.mitre.org/techniques/T1578/005/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Unsual number cloud security group modifications detected by user - $user$ - risk_objects: - - field: user - type: user - score: 35 - threat_objects: [] + message: Unsual number cloud security group modifications detected by user - $user$ + risk_objects: + - field: user + type: user + score: 35 + threat_objects: [] tags: - analytic_story: - - Suspicious Cloud User Activities - asset_type: Cloud Instance - mitre_attack_id: - - T1578.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Suspicious Cloud User Activities + asset_type: Cloud Instance + mitre_attack_id: + - T1578.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1578.005/aws_authorize_security_group/aws_authorize_security_group.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1578.005/aws_authorize_security_group/aws_authorize_security_group.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/detect_aws_console_login_by_new_user.yml b/detections/cloud/detect_aws_console_login_by_new_user.yml index 53b6e1014f..f01d9046f4 100644 --- a/detections/cloud/detect_aws_console_login_by_new_user.yml +++ b/detections/cloud/detect_aws_console_login_by_new_user.yml @@ -1,55 +1,47 @@ name: Detect AWS Console Login by New User id: bc91a8cd-35e7-4bb2-6140-e756cc46fd71 -version: 9 -date: '2025-06-10' +version: 10 +date: '2026-02-25' author: Rico Valdez, Splunk status: production type: Hunting -description: The following analytic detects AWS console login events by new users. - It leverages AWS CloudTrail events and compares them against a lookup file of previously - seen users based on ARN values. This detection is significant because a new user - logging into the AWS console could indicate the creation of new accounts or potential - unauthorized access. If confirmed malicious, this activity could lead to unauthorized - access to AWS resources, data exfiltration, or further exploitation within the cloud - environment. +description: The following analytic detects AWS console login events by new users. It leverages AWS CloudTrail events and compares them against a lookup file of previously seen users based on ARN values. This detection is significant because a new user logging into the AWS console could indicate the creation of new accounts or potential unauthorized access. If confirmed malicious, this activity could lead to unauthorized access to AWS resources, data exfiltration, or further exploitation within the cloud environment. data_source: -- AWS CloudTrail -search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication - where Authentication.signature=ConsoleLogin by Authentication.user | `drop_dm_object_name(Authentication)` - | join user type=outer [ | inputlookup previously_seen_users_console_logins | stats - min(firstTime) as earliestseen by user] | eval userStatus=if(earliestseen >= relative_time(now(), - "-24h@h") OR isnull(earliestseen), "First Time Logging into AWS Console", "Previously - Seen User") | where userStatus="First Time Logging into AWS Console" | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `detect_aws_console_login_by_new_user_filter`' -how_to_implement: You must install and configure the Splunk Add-on for AWS (version - 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates - to the Authentication data model for cloud use cases. Run the `Previously Seen Users - in CloudTrail - Initial` support search only once to create a baseline of previously - seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - - Update` hourly (or more frequently depending on how often you run the detection - searches) to refresh the baselines. -known_false_positives: When a legitimate new user logins for the first time, this - activity will be detected. Check how old the account is and verify that the user - activity is legitimate. + - AWS CloudTrail +search: |- + | tstats earliest(_time) as firstTime latest(_time) as lastTime FROM datamodel=Authentication + WHERE Authentication.signature=ConsoleLogin + BY Authentication.user + | `drop_dm_object_name(Authentication)` + | join user type=outer [ + | inputlookup previously_seen_users_console_logins + | stats min(firstTime) as earliestseen + BY user] + | eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "First Time Logging into AWS Console", "Previously Seen User") + | where userStatus="First Time Logging into AWS Console" + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_aws_console_login_by_new_user_filter` +how_to_implement: You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. +known_false_positives: When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. references: [] tags: - analytic_story: - - Suspicious Cloud Authentication Activities - - AWS Identity and Access Management Account Takeover - asset_type: AWS Instance - mitre_attack_id: - - T1552 - - T1586.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat - manual_test: This search needs the baseline `Previously Seen Users in CloudTrail - Initial` to be run first. + analytic_story: + - Suspicious Cloud Authentication Activities + - AWS Identity and Access Management Account Takeover + asset_type: AWS Instance + mitre_attack_id: + - T1552 + - T1586.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat + manual_test: This search needs the baseline `Previously Seen Users in CloudTrail - Initial` to be run first. tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/detect_aws_console_login_by_user_from_new_city.yml b/detections/cloud/detect_aws_console_login_by_user_from_new_city.yml index fe2c7e70e8..a41ae167d7 100644 --- a/detections/cloud/detect_aws_console_login_by_user_from_new_city.yml +++ b/detections/cloud/detect_aws_console_login_by_user_from_new_city.yml @@ -1,63 +1,55 @@ name: Detect AWS Console Login by User from New City id: 121b0b11-f8ac-4ed6-a132-3800ca4fc07a -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Bhavin Patel, Eric McGinnis Splunk status: production type: Hunting -description: The following analytic identifies AWS console login events by users from - a new city within the last hour. It leverages AWS CloudTrail events and compares - them against a lookup file of previously seen user locations. This activity is significant - for a SOC as it may indicate unauthorized access or credential compromise, especially - if the login originates from an unusual location. If confirmed malicious, this could - lead to unauthorized access to AWS resources, data exfiltration, or further exploitation - within the cloud environment. +description: The following analytic identifies AWS console login events by users from a new city within the last hour. It leverages AWS CloudTrail events and compares them against a lookup file of previously seen user locations. This activity is significant for a SOC as it may indicate unauthorized access or credential compromise, especially if the login originates from an unusual location. If confirmed malicious, this could lead to unauthorized access to AWS resources, data exfiltration, or further exploitation within the cloud environment. data_source: -- AWS CloudTrail -search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication - where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src - | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | rename - City as justSeenCity | table firstTime lastTime user justSeenCity | join user type=outer - [| inputlookup previously_seen_users_console_logins | rename City as previouslySeenCity - | stats min(firstTime) AS earliestseen by user previouslySeenCity | fields earliestseen - user previouslySeenCity] | eval userCity=if(firstTime >= relative_time(now(), "-24h@h"), - "New City","Previously Seen City") | where userCity = "New City" | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | table firstTime lastTime user previouslySeenCity - justSeenCity userCity | `detect_aws_console_login_by_user_from_new_city_filter`' -how_to_implement: You must install and configure the Splunk Add-on for AWS (version - 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates - to the Authentication data model for cloud use cases. Run the `Previously Seen Users - in AWS CloudTrail - Initial` support search only once to create a baseline of previously - seen IAM users within the last 30 days. Run `Previously Seen Users in AWS CloudTrail - - Update` hourly (or more frequently depending on how often you run the detection - searches) to refresh the baselines. You can also provide additional filtering for - this search by customizing the `detect_aws_console_login_by_user_from_new_city_filter` - macro. -known_false_positives: When a legitimate new user logins for the first time, this - activity will be detected. Check how old the account is and verify that the user - activity is legitimate. + - AWS CloudTrail +search: |- + | tstats earliest(_time) as firstTime latest(_time) as lastTime FROM datamodel=Authentication + WHERE Authentication.signature=ConsoleLogin + BY Authentication.user Authentication.src + | iplocation Authentication.src + | `drop_dm_object_name(Authentication)` + | rename City as justSeenCity + | table firstTime lastTime user justSeenCity + | join user type=outer [ + | inputlookup previously_seen_users_console_logins + | rename City as previouslySeenCity + | stats min(firstTime) AS earliestseen + BY user previouslySeenCity + | fields earliestseen user previouslySeenCity] + | eval userCity=if(firstTime >= relative_time(now(), "-24h@h"), "New City","Previously Seen City") + | where userCity = "New City" + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table firstTime lastTime user previouslySeenCity justSeenCity userCity + | `detect_aws_console_login_by_user_from_new_city_filter` +how_to_implement: You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in AWS CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in AWS CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_city_filter` macro. +known_false_positives: When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. references: [] tags: - analytic_story: - - Suspicious AWS Login Activities - - Suspicious Cloud Authentication Activities - - AWS Identity and Access Management Account Takeover - - Compromised User Account - asset_type: AWS Instance - mitre_attack_id: - - T1535 - - T1586.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat - manual_test: This search needs the baseline to be run first to create a lookup. - It also requires that the timestamps in the dataset be updated. + analytic_story: + - Suspicious AWS Login Activities + - Suspicious Cloud Authentication Activities + - AWS Identity and Access Management Account Takeover + - Compromised User Account + asset_type: AWS Instance + mitre_attack_id: + - T1535 + - T1586.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat + manual_test: This search needs the baseline to be run first to create a lookup. It also requires that the timestamps in the dataset be updated. tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/detect_aws_console_login_by_user_from_new_country.yml b/detections/cloud/detect_aws_console_login_by_user_from_new_country.yml index 4d609dbfb4..c6917942b5 100644 --- a/detections/cloud/detect_aws_console_login_by_user_from_new_country.yml +++ b/detections/cloud/detect_aws_console_login_by_user_from_new_country.yml @@ -1,63 +1,55 @@ name: Detect AWS Console Login by User from New Country id: 67bd3def-c41c-4bf6-837b-ae196b4257c6 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Bhavin Patel, Eric McGinnis Splunk status: production type: Hunting -description: The following analytic identifies AWS console login events by users from - a new country. It leverages AWS CloudTrail events and compares them against a lookup - file of previously seen users and their login locations. This activity is significant - because logins from new countries can indicate potential unauthorized access or - compromised accounts. If confirmed malicious, this could lead to unauthorized access - to AWS resources, data exfiltration, or further exploitation within the AWS environment. +description: The following analytic identifies AWS console login events by users from a new country. It leverages AWS CloudTrail events and compares them against a lookup file of previously seen users and their login locations. This activity is significant because logins from new countries can indicate potential unauthorized access or compromised accounts. If confirmed malicious, this could lead to unauthorized access to AWS resources, data exfiltration, or further exploitation within the AWS environment. data_source: -- AWS CloudTrail -search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication - where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src - | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | rename - Country as justSeenCountry | table firstTime lastTime user justSeenCountry | join - user type=outer [| inputlookup previously_seen_users_console_logins | rename Country - as previouslySeenCountry | stats min(firstTime) AS earliestseen by user previouslySeenCountry - | fields earliestseen user previouslySeenCountry] | eval userCountry=if(firstTime - >= relative_time(now(), "-24h@h"), "New Country","Previously Seen Country") | where - userCountry = "New Country" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | table firstTime lastTime user previouslySeenCountry justSeenCountry userCountry - | `detect_aws_console_login_by_user_from_new_country_filter`' -how_to_implement: You must install and configure the Splunk Add-on for AWS (version - 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates - to the Authentication data model for cloud use cases. Run the `Previously Seen Users - in AWS CloudTrail - Initial` support search only once to create a baseline of previously - seen IAM users within the last 30 days. Run `Previously Seen Users in AWS CloudTrail - - Update` hourly (or more frequently depending on how often you run the detection - searches) to refresh the baselines. You can also provide additional filtering for - this search by customizing the `detect_aws_console_login_by_user_from_new_country_filter` - macro. -known_false_positives: When a legitimate new user logins for the first time, this - activity will be detected. Check how old the account is and verify that the user - activity is legitimate. + - AWS CloudTrail +search: |- + | tstats earliest(_time) as firstTime latest(_time) as lastTime FROM datamodel=Authentication + WHERE Authentication.signature=ConsoleLogin + BY Authentication.user Authentication.src + | iplocation Authentication.src + | `drop_dm_object_name(Authentication)` + | rename Country as justSeenCountry + | table firstTime lastTime user justSeenCountry + | join user type=outer [ + | inputlookup previously_seen_users_console_logins + | rename Country as previouslySeenCountry + | stats min(firstTime) AS earliestseen + BY user previouslySeenCountry + | fields earliestseen user previouslySeenCountry] + | eval userCountry=if(firstTime >= relative_time(now(), "-24h@h"), "New Country","Previously Seen Country") + | where userCountry = "New Country" + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table firstTime lastTime user previouslySeenCountry justSeenCountry userCountry + | `detect_aws_console_login_by_user_from_new_country_filter` +how_to_implement: You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in AWS CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in AWS CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_country_filter` macro. +known_false_positives: When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. references: [] tags: - analytic_story: - - Suspicious AWS Login Activities - - Suspicious Cloud Authentication Activities - - AWS Identity and Access Management Account Takeover - - Compromised User Account - asset_type: AWS Instance - mitre_attack_id: - - T1535 - - T1586.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat - manual_test: This search needs the baseline to be run first to create a lookup. - It also requires that the timestamps in the dataset be updated. + analytic_story: + - Suspicious AWS Login Activities + - Suspicious Cloud Authentication Activities + - AWS Identity and Access Management Account Takeover + - Compromised User Account + asset_type: AWS Instance + mitre_attack_id: + - T1535 + - T1586.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat + manual_test: This search needs the baseline to be run first to create a lookup. It also requires that the timestamps in the dataset be updated. tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/detect_aws_console_login_by_user_from_new_region.yml b/detections/cloud/detect_aws_console_login_by_user_from_new_region.yml index dfebaa206c..caeb9d3093 100644 --- a/detections/cloud/detect_aws_console_login_by_user_from_new_region.yml +++ b/detections/cloud/detect_aws_console_login_by_user_from_new_region.yml @@ -1,64 +1,55 @@ name: Detect AWS Console Login by User from New Region id: 9f31aa8e-e37c-46bc-bce1-8b3be646d026 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Bhavin Patel, Eric McGinnis Splunk status: production type: Hunting -description: The following analytic identifies AWS console login attempts by users - from a new region. It leverages AWS CloudTrail events and compares current login - regions against a baseline of previously seen regions for each user. This activity - is significant as it may indicate unauthorized access attempts or compromised credentials. - If confirmed malicious, an attacker could gain unauthorized access to AWS resources, - potentially leading to data breaches, resource manipulation, or further lateral - movement within the cloud environment. +description: The following analytic identifies AWS console login attempts by users from a new region. It leverages AWS CloudTrail events and compares current login regions against a baseline of previously seen regions for each user. This activity is significant as it may indicate unauthorized access attempts or compromised credentials. If confirmed malicious, an attacker could gain unauthorized access to AWS resources, potentially leading to data breaches, resource manipulation, or further lateral movement within the cloud environment. data_source: -- AWS CloudTrail -search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication - where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src - | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | rename - Region as justSeenRegion | table firstTime lastTime user justSeenRegion | join user - type=outer [| inputlookup previously_seen_users_console_logins | rename Region as - previouslySeenRegion | stats min(firstTime) AS earliestseen by user previouslySeenRegion - | fields earliestseen user previouslySeenRegion] | eval userRegion=if(firstTime - >= relative_time(now(), "-24h@h"), "New Region","Previously Seen Region") | where - userRegion= "New Region" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | table firstTime lastTime user previouslySeenRegion justSeenRegion userRegion | - `detect_aws_console_login_by_user_from_new_region_filter`' -how_to_implement: You must install and configure the Splunk Add-on for AWS (version - 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates - to the Authentication data model for cloud use cases. Run the `Previously Seen Users - in AWS CloudTrail - Initial` support search only once to create a baseline of previously - seen IAM users within the last 30 days. Run `Previously Seen Users in AWS CloudTrail - - Update` hourly (or more frequently depending on how often you run the detection - searches) to refresh the baselines. You can also provide additional filtering for - this search by customizing the `detect_aws_console_login_by_user_from_new_region_filter` - macro. -known_false_positives: When a legitimate new user logins for the first time, this - activity will be detected. Check how old the account is and verify that the user - activity is legitimate. + - AWS CloudTrail +search: |- + | tstats earliest(_time) as firstTime latest(_time) as lastTime FROM datamodel=Authentication + WHERE Authentication.signature=ConsoleLogin + BY Authentication.user Authentication.src + | iplocation Authentication.src + | `drop_dm_object_name(Authentication)` + | rename Region as justSeenRegion + | table firstTime lastTime user justSeenRegion + | join user type=outer [ + | inputlookup previously_seen_users_console_logins + | rename Region as previouslySeenRegion + | stats min(firstTime) AS earliestseen + BY user previouslySeenRegion + | fields earliestseen user previouslySeenRegion] + | eval userRegion=if(firstTime >= relative_time(now(), "-24h@h"), "New Region","Previously Seen Region") + | where userRegion= "New Region" + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table firstTime lastTime user previouslySeenRegion justSeenRegion userRegion + | `detect_aws_console_login_by_user_from_new_region_filter` +how_to_implement: You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in AWS CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in AWS CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_region_filter` macro. +known_false_positives: When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. references: [] tags: - analytic_story: - - Suspicious AWS Login Activities - - Suspicious Cloud Authentication Activities - - AWS Identity and Access Management Account Takeover - - Compromised User Account - asset_type: AWS Instance - mitre_attack_id: - - T1535 - - T1586.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat - manual_test: This search needs the baseline to be run first to create a lookup. - It also requires that the timestamps in the dataset be updated. + analytic_story: + - Suspicious AWS Login Activities + - Suspicious Cloud Authentication Activities + - AWS Identity and Access Management Account Takeover + - Compromised User Account + asset_type: AWS Instance + mitre_attack_id: + - T1535 + - T1586.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat + manual_test: This search needs the baseline to be run first to create a lookup. It also requires that the timestamps in the dataset be updated. tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/detect_gcp_storage_access_from_a_new_ip.yml b/detections/cloud/detect_gcp_storage_access_from_a_new_ip.yml index 84b759ab07..33c9ebb500 100644 --- a/detections/cloud/detect_gcp_storage_access_from_a_new_ip.yml +++ b/detections/cloud/detect_gcp_storage_access_from_a_new_ip.yml @@ -1,62 +1,56 @@ name: Detect GCP Storage access from a new IP id: ccc3246a-daa1-11ea-87d0-0242ac130022 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Shannon Davis, Splunk status: experimental type: Anomaly -description: The following analytic identifies access to GCP Storage buckets from - new or previously unseen remote IP addresses. It leverages GCP Storage bucket-access - logs ingested via Cloud Pub/Sub and compares current access events against a lookup - table of previously seen IP addresses. This activity is significant as it may indicate - unauthorized access or potential reconnaissance by an attacker. If confirmed malicious, - this could lead to data exfiltration, unauthorized data manipulation, or further - compromise of the GCP environment. +description: The following analytic identifies access to GCP Storage buckets from new or previously unseen remote IP addresses. It leverages GCP Storage bucket-access logs ingested via Cloud Pub/Sub and compares current access events against a lookup table of previously seen IP addresses. This activity is significant as it may indicate unauthorized access or potential reconnaissance by an attacker. If confirmed malicious, this could lead to data exfiltration, unauthorized data manipulation, or further compromise of the GCP environment. data_source: [] -search: '`google_gcp_pubsub_message` | multikv | rename sc_status_ as status | rename - cs_object_ as bucket_name | rename c_ip_ as remote_ip | rename cs_uri_ as request_uri - | rename cs_method_ as operation | search status="\"200\"" | stats earliest(_time) - as firstTime latest(_time) as lastTime by bucket_name remote_ip operation request_uri - | table firstTime, lastTime, bucket_name, remote_ip, operation, request_uri | inputlookup - append=t previously_seen_gcp_storage_access_from_remote_ip | stats min(firstTime) - as firstTime, max(lastTime) as lastTime by bucket_name remote_ip operation request_uri - | outputlookup previously_seen_gcp_storage_access_from_remote_ip | eval newIP=if(firstTime - >= relative_time(now(),"-70m@m"), 1, 0) | where newIP=1 | eval first_time=strftime(firstTime,"%m/%d/%y - %H:%M:%S") | eval last_time=strftime(lastTime,"%m/%d/%y %H:%M:%S") | table first_time - last_time bucket_name remote_ip operation request_uri | `detect_gcp_storage_access_from_a_new_ip_filter`' -how_to_implement: This search relies on the Splunk Add-on for Google Cloud Platform, - setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and - logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). - In order to capture public GCP Storage Bucket access logs, you must also enable - storage bucket logging to your PubSub Topic as per https://cloud.google.com/storage/docs/access-logs. These - logs are deposited into the nominated Storage Bucket on an hourly basis and typically - show up by 15 minutes past the hour. It is recommended to configure any saved searches - or correlation searches in Enterprise Security to run on an hourly basis at 30 minutes - past the hour (cron definition of 30 * * * *). A lookup table (previously_seen_gcp_storage_access_from_remote_ip.csv) - stores the previously seen access requests, and is used by this search to determine - any newly seen IP addresses accessing the Storage Buckets. -known_false_positives: GCP Storage buckets can be accessed from any IP (if the ACLs - are open to allow it), as long as it can make a successful connection. This will - be a false postive, since the search is looking for a new IP within the past two - hours. +search: |- + `google_gcp_pubsub_message` + | multikv + | rename sc_status_ as status + | rename cs_object_ as bucket_name + | rename c_ip_ as remote_ip + | rename cs_uri_ as request_uri + | rename cs_method_ as operation + | search status="\"200\"" + | stats earliest(_time) as firstTime latest(_time) as lastTime + BY bucket_name remote_ip operation + request_uri + | table firstTime, lastTime, bucket_name, remote_ip, operation, request_uri + | inputlookup append=t previously_seen_gcp_storage_access_from_remote_ip + | stats min(firstTime) as firstTime, max(lastTime) as lastTime + BY bucket_name remote_ip operation + request_uri + | outputlookup previously_seen_gcp_storage_access_from_remote_ip + | eval newIP=if(firstTime >= relative_time(now(),"-70m@m"), 1, 0) + | where newIP=1 + | eval first_time=strftime(firstTime,"%m/%d/%y %H:%M:%S") + | eval last_time=strftime(lastTime,"%m/%d/%y %H:%M:%S") + | table first_time last_time bucket_name remote_ip operation request_uri + | `detect_gcp_storage_access_from_a_new_ip_filter` +how_to_implement: This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). In order to capture public GCP Storage Bucket access logs, you must also enable storage bucket logging to your PubSub Topic as per https://cloud.google.com/storage/docs/access-logs. These logs are deposited into the nominated Storage Bucket on an hourly basis and typically show up by 15 minutes past the hour. It is recommended to configure any saved searches or correlation searches in Enterprise Security to run on an hourly basis at 30 minutes past the hour (cron definition of 30 * * * *). A lookup table (previously_seen_gcp_storage_access_from_remote_ip.csv) stores the previously seen access requests, and is used by this search to determine any newly seen IP addresses accessing the Storage Buckets. +known_false_positives: GCP Storage buckets can be accessed from any IP (if the ACLs are open to allow it), as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past two hours. references: [] rba: - message: GCP Bucket $bucket_name$ accessed from a new IP ($remote_ip$) - risk_objects: - - field: bucket_name - type: system - score: 25 - threat_objects: - - field: remote_ip - type: ip_address + message: GCP Bucket $bucket_name$ accessed from a new IP ($remote_ip$) + risk_objects: + - field: bucket_name + type: system + score: 25 + threat_objects: + - field: remote_ip + type: ip_address tags: - analytic_story: - - Suspicious GCP Storage Activities - asset_type: GCP Storage Bucket - mitre_attack_id: - - T1530 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Suspicious GCP Storage Activities + asset_type: GCP Storage Bucket + mitre_attack_id: + - T1530 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/cloud/detect_new_open_gcp_storage_buckets.yml b/detections/cloud/detect_new_open_gcp_storage_buckets.yml index 0b199f7ab6..b144b2c14b 100644 --- a/detections/cloud/detect_new_open_gcp_storage_buckets.yml +++ b/detections/cloud/detect_new_open_gcp_storage_buckets.yml @@ -1,50 +1,42 @@ name: Detect New Open GCP Storage Buckets id: f6ea3466-d6bb-11ea-87d0-0242ac130003 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Shannon Davis, Splunk status: experimental type: TTP -description: The following analytic identifies the creation of new open/public GCP - Storage buckets. It leverages GCP PubSub events, specifically monitoring for the - `storage.setIamPermissions` method and checks if the `allUsers` member is added. - This activity is significant because open storage buckets can expose sensitive data - to the public, posing a severe security risk. If confirmed malicious, an attacker - could access, modify, or delete data within the bucket, leading to data breaches - and potential compliance violations. +description: The following analytic identifies the creation of new open/public GCP Storage buckets. It leverages GCP PubSub events, specifically monitoring for the `storage.setIamPermissions` method and checks if the `allUsers` member is added. This activity is significant because open storage buckets can expose sensitive data to the public, posing a severe security risk. If confirmed malicious, an attacker could access, modify, or delete data within the bucket, leading to data breaches and potential compliance violations. data_source: [] -search: '`google_gcp_pubsub_message` data.resource.type=gcs_bucket data.protoPayload.methodName=storage.setIamPermissions - | spath output=action path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.action - | spath output=user path=data.protoPayload.authenticationInfo.principalEmail | spath - output=location path=data.protoPayload.resourceLocation.currentLocations{} | spath - output=src path=data.protoPayload.requestMetadata.callerIp | spath output=bucketName - path=data.protoPayload.resourceName | spath output=role path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.role - | spath output=member path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.member - | search (member=allUsers AND action=ADD) | table _time, bucketName, src, user, - location, action, role, member | search `detect_new_open_gcp_storage_buckets_filter`' -how_to_implement: This search relies on the Splunk Add-on for Google Cloud Platform, - setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and - logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). -known_false_positives: While this search has no known false positives, it is possible - that a GCP admin has legitimately created a public bucket for a specific purpose. - That said, GCP strongly advises against granting full control to the "allUsers" - group. +search: |- + `google_gcp_pubsub_message` data.resource.type=gcs_bucket data.protoPayload.methodName=storage.setIamPermissions + | spath output=action path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.action + | spath output=user path=data.protoPayload.authenticationInfo.principalEmail + | spath output=location path=data.protoPayload.resourceLocation.currentLocations{} + | spath output=src path=data.protoPayload.requestMetadata.callerIp + | spath output=bucketName path=data.protoPayload.resourceName + | spath output=role path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.role + | spath output=member path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.member + | search (member=allUsers AND action=ADD) + | table _time, bucketName, src, user, location, action, role, member + | search `detect_new_open_gcp_storage_buckets_filter` +how_to_implement: This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). +known_false_positives: While this search has no known false positives, it is possible that a GCP admin has legitimately created a public bucket for a specific purpose. That said, GCP strongly advises against granting full control to the "allUsers" group. references: [] rba: - message: New Public GCP Storage Bucket Detected - risk_objects: - - field: user - type: user - score: 25 - threat_objects: [] + message: New Public GCP Storage Bucket Detected + risk_objects: + - field: user + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Suspicious GCP Storage Activities - asset_type: GCP Storage Bucket - mitre_attack_id: - - T1530 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Suspicious GCP Storage Activities + asset_type: GCP Storage Bucket + mitre_attack_id: + - T1530 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/cloud/detect_new_open_s3_buckets.yml b/detections/cloud/detect_new_open_s3_buckets.yml index 393fb2a550..d5dd914eb5 100644 --- a/detections/cloud/detect_new_open_s3_buckets.yml +++ b/detections/cloud/detect_new_open_s3_buckets.yml @@ -1,72 +1,63 @@ name: Detect New Open S3 buckets id: 2a9b80d3-6340-4345-b5ad-290bf3d0dac4 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Bhavin Patel, Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic identifies the creation of open/public S3 buckets - in AWS. It detects this activity by analyzing AWS CloudTrail events for `PutBucketAcl` - actions where the access control list (ACL) grants permissions to all users or authenticated - users. This activity is significant because open S3 buckets can expose sensitive - data to unauthorized access, leading to data breaches. If confirmed malicious, an - attacker could read, write, or fully control the contents of the bucket, potentially - leading to data exfiltration or tampering. +description: The following analytic identifies the creation of open/public S3 buckets in AWS. It detects this activity by analyzing AWS CloudTrail events for `PutBucketAcl` actions where the access control list (ACL) grants permissions to all users or authenticated users. This activity is significant because open S3 buckets can expose sensitive data to unauthorized access, leading to data breaches. If confirmed malicious, an attacker could read, write, or fully control the contents of the bucket, potentially leading to data exfiltration or tampering. data_source: -- AWS CloudTrail -search: '`cloudtrail` eventSource=s3.amazonaws.com eventName=PutBucketAcl | rex field=_raw - "(?{.+})" | spath input=json_field output=grantees path=requestParameters.AccessControlPolicy.AccessControlList.Grant{} - | search grantees=* | mvexpand grantees | spath input=grantees output=uri path=Grantee.URI - | spath input=grantees output=permission path=Permission | search uri IN ("http://acs.amazonaws.com/groups/global/AllUsers","http://acs.amazonaws.com/groups/global/AuthenticatedUsers") - | search permission IN ("READ","READ_ACP","WRITE","WRITE_ACP","FULL_CONTROL") | - rename requestParameters.bucketName AS bucketName | stats count min(_time) as firstTime - max(_time) as lastTime by user_arn userIdentity.principalId userAgent uri permission - bucketName | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `detect_new_open_s3_buckets_filter`' + - AWS CloudTrail +search: |- + `cloudtrail` eventSource=s3.amazonaws.com eventName=PutBucketAcl + | rex field=_raw "(?{.+})" + | spath input=json_field output=grantees path=requestParameters.AccessControlPolicy.AccessControlList.Grant{} + | search grantees=* + | mvexpand grantees + | spath input=grantees output=uri path=Grantee.URI + | spath input=grantees output=permission path=Permission + | search uri IN ("http://acs.amazonaws.com/groups/global/AllUsers","http://acs.amazonaws.com/groups/global/AuthenticatedUsers") + | search permission IN ("READ","READ_ACP","WRITE","WRITE_ACP","FULL_CONTROL") + | rename requestParameters.bucketName AS bucketName + | stats count min(_time) as firstTime max(_time) as lastTime + BY user_arn userIdentity.principalId userAgent + uri permission bucketName + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_new_open_s3_buckets_filter` how_to_implement: You must install the AWS App for Splunk. -known_false_positives: While this search has no known false positives, it is possible - that an AWS admin has legitimately created a public bucket for a specific purpose. - That said, AWS strongly advises against granting full control to the "All Users" - group. +known_false_positives: While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. references: [] drilldown_searches: -- name: View the detection results for - "$user_arn$" and "$bucketName$" - search: '%original_detection_search% | search user_arn = "$user_arn$" bucketName - = "$bucketName$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user_arn$" and "$bucketName$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$", - "$bucketName$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user_arn$" and "$bucketName$" + search: '%original_detection_search% | search user_arn = "$user_arn$" bucketName = "$bucketName$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user_arn$" and "$bucketName$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$", "$bucketName$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user_arn$ has created an open/public bucket $bucketName$ with the - following permissions $permission$ - risk_objects: - - field: user_arn - type: user - score: 48 - threat_objects: [] + message: User $user_arn$ has created an open/public bucket $bucketName$ with the following permissions $permission$ + risk_objects: + - field: user_arn + type: user + score: 48 + threat_objects: [] tags: - analytic_story: - - Suspicious AWS S3 Activities - asset_type: S3 Bucket - mitre_attack_id: - - T1530 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Suspicious AWS S3 Activities + asset_type: S3 Bucket + mitre_attack_id: + - T1530 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/detect_new_open_s3_buckets_over_aws_cli.yml b/detections/cloud/detect_new_open_s3_buckets_over_aws_cli.yml index 93c78f022c..479c709b9d 100644 --- a/detections/cloud/detect_new_open_s3_buckets_over_aws_cli.yml +++ b/detections/cloud/detect_new_open_s3_buckets_over_aws_cli.yml @@ -1,77 +1,58 @@ name: Detect New Open S3 Buckets over AWS CLI id: 39c61d09-8b30-4154-922b-2d0a694ecc22 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects the creation of open/public S3 buckets - via the AWS CLI. It leverages AWS CloudTrail logs to identify events where a user - has set bucket permissions to allow access to "AuthenticatedUsers" or "AllUsers." - This activity is significant because open S3 buckets can expose sensitive data to - unauthorized users, leading to data breaches. If confirmed malicious, an attacker - could gain unauthorized access to potentially sensitive information stored in the - S3 bucket, posing a significant security risk. +description: The following analytic detects the creation of open/public S3 buckets via the AWS CLI. It leverages AWS CloudTrail logs to identify events where a user has set bucket permissions to allow access to "AuthenticatedUsers" or "AllUsers." This activity is significant because open S3 buckets can expose sensitive data to unauthorized users, leading to data breaches. If confirmed malicious, an attacker could gain unauthorized access to potentially sensitive information stored in the S3 bucket, posing a significant security risk. data_source: -- AWS CloudTrail -search: '`cloudtrail` eventSource="s3.amazonaws.com" (userAgent="[aws-cli*" OR userAgent=aws-cli* - ) eventName=PutBucketAcl OR requestParameters.accessControlList.x-amz-grant-read-acp - IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write - IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write-acp - IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-full-control - IN ("*AuthenticatedUsers","*AllUsers") | rename requestParameters.bucketName AS - bucketName | fillnull | stats count min(_time) as firstTime max(_time) as lastTime - by userIdentity.userName userIdentity.principalId userAgent bucketName requestParameters.accessControlList.x-amz-grant-read - requestParameters.accessControlList.x-amz-grant-read-acp requestParameters.accessControlList.x-amz-grant-write - requestParameters.accessControlList.x-amz-grant-write-acp requestParameters.accessControlList.x-amz-grant-full-control - | rename userIdentity.userName as user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `detect_new_open_s3_buckets_over_aws_cli_filter`' -how_to_implement: The Splunk AWS Add-on and Splunk App for AWS is required to utilize - this data. The search requires AWS Cloudtrail logs. -known_false_positives: While this search has no known false positives, it is possible - that an AWS admin has legitimately created a public bucket for a specific purpose. - That said, AWS strongly advises against granting full control to the "All Users" - group. + - AWS CloudTrail +search: |- + `cloudtrail` eventSource="s3.amazonaws.com" (userAgent="[aws-cli*" OR userAgent=aws-cli* ) eventName=PutBucketAcl OR requestParameters.accessControlList.x-amz-grant-read-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-full-control IN ("*AuthenticatedUsers","*AllUsers") + | rename requestParameters.bucketName AS bucketName + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY userIdentity.userName userIdentity.principalId userAgent + bucketName requestParameters.accessControlList.x-amz-grant-read requestParameters.accessControlList.x-amz-grant-read-acp + requestParameters.accessControlList.x-amz-grant-write requestParameters.accessControlList.x-amz-grant-write-acp requestParameters.accessControlList.x-amz-grant-full-control + | rename userIdentity.userName as user + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_new_open_s3_buckets_over_aws_cli_filter` +how_to_implement: The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs. +known_false_positives: While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. references: [] drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has created an open/public bucket $bucketName$ using AWS CLI - with the following permissions - $requestParameters.accessControlList.x-amz-grant-read$ - $requestParameters.accessControlList.x-amz-grant-read-acp$ $requestParameters.accessControlList.x-amz-grant-write$ - $requestParameters.accessControlList.x-amz-grant-write-acp$ $requestParameters.accessControlList.x-amz-grant-full-control$ - risk_objects: - - field: user - type: user - score: 48 - threat_objects: [] + message: User $user$ has created an open/public bucket $bucketName$ using AWS CLI with the following permissions - $requestParameters.accessControlList.x-amz-grant-read$ $requestParameters.accessControlList.x-amz-grant-read-acp$ $requestParameters.accessControlList.x-amz-grant-write$ $requestParameters.accessControlList.x-amz-grant-write-acp$ $requestParameters.accessControlList.x-amz-grant-full-control$ + risk_objects: + - field: user + type: user + score: 48 + threat_objects: [] tags: - analytic_story: - - Suspicious AWS S3 Activities - asset_type: S3 Bucket - mitre_attack_id: - - T1530 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Suspicious AWS S3 Activities + asset_type: S3 Bucket + mitre_attack_id: + - T1530 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json - sourcetype: aws:cloudtrail - source: aws_cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json + sourcetype: aws:cloudtrail + source: aws_cloudtrail diff --git a/detections/cloud/detect_s3_access_from_a_new_ip.yml b/detections/cloud/detect_s3_access_from_a_new_ip.yml index 7e69c035a5..f185ad8e2a 100644 --- a/detections/cloud/detect_s3_access_from_a_new_ip.yml +++ b/detections/cloud/detect_s3_access_from_a_new_ip.yml @@ -1,53 +1,49 @@ name: Detect S3 access from a new IP id: e6f1bb1b-f441-492b-9126-902acda217da -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Bhavin Patel, Splunk status: experimental type: Anomaly -description: The following analytic identifies access to an S3 bucket from a new or - previously unseen remote IP address. It leverages S3 bucket-access logs, specifically - focusing on successful access events (http_status=200). This activity is significant - because access from unfamiliar IP addresses could indicate unauthorized access or - potential data exfiltration attempts. If confirmed malicious, this activity could - lead to unauthorized data access, data theft, or further exploitation of the compromised - S3 bucket, posing a significant risk to sensitive information stored within the - bucket. +description: The following analytic identifies access to an S3 bucket from a new or previously unseen remote IP address. It leverages S3 bucket-access logs, specifically focusing on successful access events (http_status=200). This activity is significant because access from unfamiliar IP addresses could indicate unauthorized access or potential data exfiltration attempts. If confirmed malicious, this activity could lead to unauthorized data access, data theft, or further exploitation of the compromised S3 bucket, posing a significant risk to sensitive information stored within the bucket. data_source: [] -search: '`aws_s3_accesslogs` http_status=200 [search `aws_s3_accesslogs` http_status=200 - | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip - | inputlookup append=t previously_seen_S3_access_from_remote_ip | stats min(firstTime) - as firstTime, max(lastTime) as lastTime by bucket_name remote_ip | outputlookup - previously_seen_S3_access_from_remote_ip | eval newIP=if(firstTime >= relative_time(now(), - "-70m@m"), 1, 0) | where newIP=1 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | table bucket_name remote_ip]| iplocation remote_ip |rename remote_ip as src_ip - | table _time bucket_name src_ip City Country operation request_uri | `detect_s3_access_from_a_new_ip_filter`' -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access - logs' inputs. This search works best when you run the "Previously Seen S3 Bucket - Access by Remote IP" support search once to create a history of previously seen - remote IPs and bucket names. -known_false_positives: S3 buckets can be accessed from any IP, as long as it can make - a successful connection. This will be a false postive, since the search is looking - for a new IP within the past hour +search: |- + `aws_s3_accesslogs` http_status=200 [search `aws_s3_accesslogs` http_status=200 + | stats earliest(_time) as firstTime latest(_time) as lastTime + BY bucket_name remote_ip + | inputlookup append=t previously_seen_S3_access_from_remote_ip + | stats min(firstTime) as firstTime, max(lastTime) as lastTime + BY bucket_name remote_ip + | outputlookup previously_seen_S3_access_from_remote_ip + | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) + | where newIP=1 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table bucket_name remote_ip] + | iplocation remote_ip + | rename remote_ip as src_ip + | table _time bucket_name src_ip City Country operation request_uri + | `detect_s3_access_from_a_new_ip_filter` +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names. +known_false_positives: S3 buckets can be accessed from any IP, as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past hour references: [] rba: - message: New S3 access from a new IP - $src_ip$ - risk_objects: - - field: bucketName - type: other - score: 25 - threat_objects: - - field: src_ip - type: ip_address + message: New S3 access from a new IP - $src_ip$ + risk_objects: + - field: bucketName + type: other + score: 25 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Suspicious AWS S3 Activities - asset_type: S3 Bucket - mitre_attack_id: - - T1530 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Suspicious AWS S3 Activities + asset_type: S3 Bucket + mitre_attack_id: + - T1530 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/cloud/detect_spike_in_aws_security_hub_alerts_for_ec2_instance.yml b/detections/cloud/detect_spike_in_aws_security_hub_alerts_for_ec2_instance.yml index 3d6c1895ec..e79c530161 100644 --- a/detections/cloud/detect_spike_in_aws_security_hub_alerts_for_ec2_instance.yml +++ b/detections/cloud/detect_spike_in_aws_security_hub_alerts_for_ec2_instance.yml @@ -1,68 +1,56 @@ name: Detect Spike in AWS Security Hub Alerts for EC2 Instance id: 2a9b80d3-6340-4345-b5ad-290bf5d0d222 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: Anomaly -description: The following analytic identifies a spike in the number of AWS Security - Hub alerts for an EC2 instance within a 4-hour interval. It leverages AWS Security - Hub findings data, calculating the average and standard deviation of alerts to detect - anomalies. This activity is significant for a SOC as a sudden increase in alerts - may indicate potential security incidents or misconfigurations requiring immediate - attention. If confirmed malicious, this could signify an ongoing attack, leading - to unauthorized access, data exfiltration, or disruption of services on the affected - EC2 instance. +description: The following analytic identifies a spike in the number of AWS Security Hub alerts for an EC2 instance within a 4-hour interval. It leverages AWS Security Hub findings data, calculating the average and standard deviation of alerts to detect anomalies. This activity is significant for a SOC as a sudden increase in alerts may indicate potential security incidents or misconfigurations requiring immediate attention. If confirmed malicious, this could signify an ongoing attack, leading to unauthorized access, data exfiltration, or disruption of services on the affected EC2 instance. data_source: -- AWS Security Hub -search: '`aws_securityhub_finding` "Resources{}.Type"=AWSEC2Instance | bucket span=4h - _time | stats count AS alerts values(Title) as Title values(Types{}) as Types values(vendor_account) - as vendor_account values(vendor_region) as vendor_region values(severity) as severity - by _time dest | eventstats avg(alerts) as total_alerts_avg, stdev(alerts) as total_alerts_stdev - | eval threshold_value = 3 | eval isOutlier=if(alerts > total_alerts_avg+(total_alerts_stdev - * threshold_value), 1, 0) | search isOutlier=1 | table _time dest alerts Title Types - vendor_account vendor_region severity isOutlier total_alerts_avg | `detect_spike_in_aws_security_hub_alerts_for_ec2_instance_filter`' -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security - Hub inputs. The threshold_value should be tuned to your environment and schedule - these searches according to the bucket span interval. + - AWS Security Hub +search: |- + `aws_securityhub_finding` "Resources{}.Type"=AWSEC2Instance + | bucket span=4h _time + | stats count AS alerts values(Title) as Title values(Types{}) as Types values(vendor_account) as vendor_account values(vendor_region) as vendor_region values(severity) as severity + BY _time dest + | eventstats avg(alerts) as total_alerts_avg, stdev(alerts) as total_alerts_stdev + | eval threshold_value = 3 + | eval isOutlier=if(alerts > total_alerts_avg+(total_alerts_stdev * threshold_value), 1, 0) + | search isOutlier=1 + | table _time dest alerts Title Types vendor_account vendor_region severity isOutlier total_alerts_avg + | `detect_spike_in_aws_security_hub_alerts_for_ec2_instance_filter` +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval. known_false_positives: No false positives have been identified at this time. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Spike in AWS security Hub alerts with title $Title$ for EC2 instance $dest$ - risk_objects: - - field: dest - type: system - score: 15 - threat_objects: [] + message: Spike in AWS security Hub alerts with title $Title$ for EC2 instance $dest$ + risk_objects: + - field: dest + type: system + score: 15 + threat_objects: [] tags: - analytic_story: - - AWS Security Hub Alerts - - Critical Alerts - asset_type: AWS Instance - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AWS Security Hub Alerts + - Critical Alerts + asset_type: AWS Instance + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/security_hub_ec2_spike/security_hub_ec2_spike.json - sourcetype: aws:securityhub:finding - source: aws_securityhub_finding + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/security_hub_ec2_spike/security_hub_ec2_spike.json + sourcetype: aws:securityhub:finding + source: aws_securityhub_finding diff --git a/detections/cloud/detect_spike_in_aws_security_hub_alerts_for_user.yml b/detections/cloud/detect_spike_in_aws_security_hub_alerts_for_user.yml index 29a445fb9d..f3f4e06def 100644 --- a/detections/cloud/detect_spike_in_aws_security_hub_alerts_for_user.yml +++ b/detections/cloud/detect_spike_in_aws_security_hub_alerts_for_user.yml @@ -1,44 +1,42 @@ name: Detect Spike in AWS Security Hub Alerts for User id: 2a9b80d3-6220-4345-b5ad-290bf5d0d222 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Bhavin Patel, Splunk status: experimental type: Anomaly -description: The following analytic identifies a spike in the number of AWS Security - Hub alerts for an AWS IAM User within a 4-hour interval. It leverages AWS Security - Hub findings data, calculating the average and standard deviation of alerts to detect - significant deviations. This activity is significant as a sudden increase in alerts - for a specific user may indicate suspicious behavior or a potential security incident. - If confirmed malicious, this could signify an ongoing attack, unauthorized access, - or misuse of IAM credentials, potentially leading to data breaches or further exploitation. +description: The following analytic identifies a spike in the number of AWS Security Hub alerts for an AWS IAM User within a 4-hour interval. It leverages AWS Security Hub findings data, calculating the average and standard deviation of alerts to detect significant deviations. This activity is significant as a sudden increase in alerts for a specific user may indicate suspicious behavior or a potential security incident. If confirmed malicious, this could signify an ongoing attack, unauthorized access, or misuse of IAM credentials, potentially leading to data breaches or further exploitation. data_source: -- AWS Security Hub -search: '`aws_securityhub_finding` "findings{}.Resources{}.Type"= AwsIamUser | rename - findings{}.Resources{}.Id as user | bucket span=4h _time | stats count AS alerts - by _time user | eventstats avg(alerts) as total_launched_avg, stdev(alerts) as total_launched_stdev - | eval threshold_value = 2 | eval isOutlier=if(alerts > total_launched_avg+(total_launched_stdev - * threshold_value), 1, 0) | search isOutlier=1 | table _time user alerts |`detect_spike_in_aws_security_hub_alerts_for_user_filter`' -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security - Hub inputs. The threshold_value should be tuned to your environment and schedule - these searches according to the bucket span interval. + - AWS Security Hub +search: |- + `aws_securityhub_finding` "findings{}.Resources{}.Type"= AwsIamUser + | rename findings{}.Resources{}.Id as user + | bucket span=4h _time + | stats count AS alerts + BY _time user + | eventstats avg(alerts) as total_launched_avg, stdev(alerts) as total_launched_stdev + | eval threshold_value = 2 + | eval isOutlier=if(alerts > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) + | search isOutlier=1 + | table _time user alerts + | `detect_spike_in_aws_security_hub_alerts_for_user_filter` +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval. known_false_positives: No false positives have been identified at this time. references: [] rba: - message: Spike in AWS Security Hub alerts for user - $user$ - risk_objects: - - field: user - type: user - score: 25 - threat_objects: [] + message: Spike in AWS Security Hub alerts for user - $user$ + risk_objects: + - field: user + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - AWS Security Hub Alerts - - Critical Alerts - asset_type: AWS Instance - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - AWS Security Hub Alerts + - Critical Alerts + asset_type: AWS Instance + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/cloud/detect_spike_in_blocked_outbound_traffic_from_your_aws.yml b/detections/cloud/detect_spike_in_blocked_outbound_traffic_from_your_aws.yml index 4cced2797f..04bc76833f 100644 --- a/detections/cloud/detect_spike_in_blocked_outbound_traffic_from_your_aws.yml +++ b/detections/cloud/detect_spike_in_blocked_outbound_traffic_from_your_aws.yml @@ -1,64 +1,51 @@ name: Detect Spike in blocked Outbound Traffic from your AWS id: d3fffa37-492f-487b-a35d-c60fcb2acf01 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Bhavin Patel, Splunk status: experimental type: Anomaly -description: The following analytic identifies spikes in blocked outbound network - connections originating from within your AWS environment. It leverages VPC Flow - Logs data from CloudWatch, focusing on blocked actions from internal IP ranges to - external destinations. This detection is significant as it can indicate potential - exfiltration attempts or misconfigurations leading to data leakage. If confirmed - malicious, such activity could allow attackers to bypass network defenses, leading - to unauthorized data transfer or communication with malicious external entities. +description: The following analytic identifies spikes in blocked outbound network connections originating from within your AWS environment. It leverages VPC Flow Logs data from CloudWatch, focusing on blocked actions from internal IP ranges to external destinations. This detection is significant as it can indicate potential exfiltration attempts or misconfigurations leading to data leakage. If confirmed malicious, such activity could allow attackers to bypass network defenses, leading to unauthorized data transfer or communication with malicious external entities. data_source: [] -search: '`cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 - OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) [search `cloudwatchlogs_vpcflow` - action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) - ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | - stats count as numberOfBlockedConnections by src_ip | inputlookup baseline_blocked_outbound_connections - append=t | fields - latestCount | stats values(*) as * by src_ip | rename numberOfBlockedConnections - as latestCount | eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 - | eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) - | eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), - stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), - numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table src_ip, - latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections | outputlookup - baseline_blocked_outbound_connections | eval dataPointThreshold = 5, deviationThreshold - = 3 | eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) - AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | table src_ip] - | stats values(dest_ip) as dest_ip, values(interface_id) as "resourceId" count as - numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip | `detect_spike_in_blocked_outbound_traffic_from_your_aws_filter`' -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow - logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit - your environment. The `dataPointThreshold` variable is the number of data points - required to meet the definition of "spike." The `deviationThreshold` variable is - the number of standard deviations away from the mean that the value must be to be - considered a spike. This search works best when you run the "Baseline of Blocked - Outbound Connection" support search once to create a history of previously seen - blocked outbound connections. -known_false_positives: The false-positive rate may vary based on the values of`dataPointThreshold` - and `deviationThreshold`. Additionally, false positives may result when AWS administrators - roll out policies enforcing network blocks, causing sudden increases in the number - of blocked outbound connections. +search: |- + `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) [search `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) + | stats count as numberOfBlockedConnections + BY src_ip + | inputlookup baseline_blocked_outbound_connections append=t + | fields - latestCount + | stats values(*) as * + BY src_ip + | rename numberOfBlockedConnections as latestCount + | eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 + | eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) + | eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) + | table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections + | outputlookup baseline_blocked_outbound_connections + | eval dataPointThreshold = 5, deviationThreshold = 3 + | eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) AND numDataPoints > dataPointThreshold, 1, 0) + | where isSpike=1 + | table src_ip] + | stats values(dest_ip) as dest_ip, values(interface_id) as "resourceId" count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections + BY src_ip + | `detect_spike_in_blocked_outbound_traffic_from_your_aws_filter` +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the number of data points required to meet the definition of "spike." The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections. +known_false_positives: The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Additionally, false positives may result when AWS administrators roll out policies enforcing network blocks, causing sudden increases in the number of blocked outbound connections. references: [] rba: - message: Blocked outbound traffic from your AWS VPC - risk_objects: - - field: src_ip - type: system - score: 25 - threat_objects: [] + message: Blocked outbound traffic from your AWS VPC + risk_objects: + - field: src_ip + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - AWS Network ACL Activity - - Suspicious AWS Traffic - - Command And Control - asset_type: AWS Instance - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - AWS Network ACL Activity + - Suspicious AWS Traffic + - Command And Control + asset_type: AWS Instance + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/cloud/detect_spike_in_s3_bucket_deletion.yml b/detections/cloud/detect_spike_in_s3_bucket_deletion.yml index ac2dbf96d4..ebf8ff5d90 100644 --- a/detections/cloud/detect_spike_in_s3_bucket_deletion.yml +++ b/detections/cloud/detect_spike_in_s3_bucket_deletion.yml @@ -1,61 +1,56 @@ name: Detect Spike in S3 Bucket deletion id: e733a326-59d2-446d-b8db-14a17151aa68 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Bhavin Patel, Splunk status: experimental type: Anomaly -description: The following analytic identifies a spike in API activity related to - the deletion of S3 buckets in your AWS environment. It leverages AWS CloudTrail - logs to detect anomalies by comparing current deletion activity against a historical - baseline. This activity is significant as unusual spikes in S3 bucket deletions - could indicate malicious actions such as data exfiltration or unauthorized data - destruction. If confirmed malicious, this could lead to significant data loss, disruption - of services, and potential exposure of sensitive information. Immediate investigation - is required to determine the legitimacy of the activity. +description: The following analytic identifies a spike in API activity related to the deletion of S3 buckets in your AWS environment. It leverages AWS CloudTrail logs to detect anomalies by comparing current deletion activity against a historical baseline. This activity is significant as unusual spikes in S3 bucket deletions could indicate malicious actions such as data exfiltration or unauthorized data destruction. If confirmed malicious, this could lead to significant data loss, disruption of services, and potential exposure of sensitive information. Immediate investigation is required to determine the legitimacy of the activity. data_source: -- AWS CloudTrail -search: '`cloudtrail` eventName=DeleteBucket [search `cloudtrail` eventName=DeleteBucket - | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup - s3_deletion_baseline append=t | fields - latestCount | stats values(*) as * by arn - | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 - | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) - | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, - stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) - | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup - s3_deletion_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval - isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints - > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | - table userIdentity.arn] | spath output=user userIdentity.arn | spath output=bucketName - path=requestParameters.bucketName | stats values(bucketName) as bucketName, count - as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_s3_bucket_deletion_filter`' -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail - inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit - your environment. The `dataPointThreshold` variable is the minimum number of data - points required to have a statistically significant amount of data to determine. - The `deviationThreshold` variable is the number of standard deviations away from - the mean that the value must be to be considered a spike. This search works best - when you run the "Baseline of S3 Bucket deletion activity by ARN" support search - once to create a baseline of previously seen S3 bucket-deletion activity. -known_false_positives: Based on the values of`dataPointThreshold` and `deviationThreshold`, - the false positive rate may vary. Please modify this according the your environment. + - AWS CloudTrail +search: |- + `cloudtrail` eventName=DeleteBucket [search `cloudtrail` eventName=DeleteBucket + | spath output=arn path=userIdentity.arn + | stats count as apiCalls + BY arn + | inputlookup s3_deletion_baseline append=t + | fields - latestCount + | stats values(*) as * + BY arn + | rename apiCalls as latestCount + | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 + | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) + | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) + | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls + | outputlookup s3_deletion_baseline + | eval dataPointThreshold = 15, deviationThreshold = 3 + | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) + | where isSpike=1 + | rename arn as userIdentity.arn + | table userIdentity.arn] + | spath output=user userIdentity.arn + | spath output=bucketName path=requestParameters.bucketName + | stats values(bucketName) as bucketName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled + BY user + | `detect_spike_in_s3_bucket_deletion_filter` +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity. +known_false_positives: Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. references: [] rba: - message: Spike in AWS S3 Bucket Deletion from $user$ - risk_objects: - - field: user - type: user - score: 25 - threat_objects: [] + message: Spike in AWS S3 Bucket Deletion from $user$ + risk_objects: + - field: user + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Suspicious AWS S3 Activities - asset_type: S3 Bucket - mitre_attack_id: - - T1530 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Suspicious AWS S3 Activities + asset_type: S3 Bucket + mitre_attack_id: + - T1530 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/cloud/gcp_authentication_failed_during_mfa_challenge.yml b/detections/cloud/gcp_authentication_failed_during_mfa_challenge.yml index 8b454fd3e6..1e8e7d3041 100644 --- a/detections/cloud/gcp_authentication_failed_during_mfa_challenge.yml +++ b/detections/cloud/gcp_authentication_failed_during_mfa_challenge.yml @@ -1,73 +1,58 @@ name: GCP Authentication Failed During MFA Challenge id: 345f7e1d-a3fe-4158-abd8-e630f9878323 -version: 9 -date: '2025-10-14' +version: 10 +date: '2026-02-25' author: Bhavin Patel, Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects failed authentication attempts during - the Multi-Factor Authentication (MFA) challenge on a Google Cloud Platform (GCP) - tenant. It uses Google Workspace login failure events to identify instances where - MFA methods were challenged but not successfully completed. This activity is significant - as it may indicate an adversary attempting to access an account with compromised - credentials despite MFA protection. If confirmed malicious, this could lead to unauthorized - access attempts, potentially compromising sensitive data and resources within the - GCP environment. +description: The following analytic detects failed authentication attempts during the Multi-Factor Authentication (MFA) challenge on a Google Cloud Platform (GCP) tenant. It uses Google Workspace login failure events to identify instances where MFA methods were challenged but not successfully completed. This activity is significant as it may indicate an adversary attempting to access an account with compromised credentials despite MFA protection. If confirmed malicious, this could lead to unauthorized access attempts, potentially compromising sensitive data and resources within the GCP environment. data_source: -- Google Workspace login_failure -search: '`gws_reports_login` event.name=login_failure `gws_login_mfa_methods` | stats - count min(_time) as firstTime max(_time) as lastTime by user, src_ip, login_challenge_method - | `gcp_authentication_failed_during_mfa_challenge_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Google - Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows - Splunk administrators to collect Google Workspace event data in Splunk using Google - Workspace APIs. Specifically, this analytic leverages the User log events. -known_false_positives: Legitimate users may miss to reply the MFA challenge within - the time window or deny it by mistake. + - Google Workspace login_failure +search: |- + `gws_reports_login` event.name=login_failure `gws_login_mfa_methods` + | stats count min(_time) as firstTime max(_time) as lastTime + BY user, src_ip, login_challenge_method + | `gcp_authentication_failed_during_mfa_challenge_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Google Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows Splunk administrators to collect Google Workspace event data in Splunk using Google Workspace APIs. Specifically, this analytic leverages the User log events. +known_false_positives: Legitimate users may miss to reply the MFA challenge within the time window or deny it by mistake. references: -- https://attack.mitre.org/techniques/T1621/ -- https://attack.mitre.org/techniques/T1078/004/ + - https://attack.mitre.org/techniques/T1621/ + - https://attack.mitre.org/techniques/T1078/004/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ failed to pass MFA challenge - risk_objects: - - field: user - type: user - score: 54 - threat_objects: - - field: src_ip - type: ip_address + message: User $user$ failed to pass MFA challenge + risk_objects: + - field: user + type: user + score: 54 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - GCP Account Takeover - - Scattered Lapsus$ Hunters - asset_type: Google Cloud Platform tenant - mitre_attack_id: - - T1078.004 - - T1586.003 - - T1621 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - GCP Account Takeover + - Scattered Lapsus$ Hunters + asset_type: Google Cloud Platform tenant + mitre_attack_id: + - T1078.004 + - T1586.003 + - T1621 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/gcp_failed_mfa/gws_login.log - source: gws:reports:login - sourcetype: gws:reports:login + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/gcp_failed_mfa/gws_login.log + source: gws:reports:login + sourcetype: gws:reports:login diff --git a/detections/cloud/gcp_detect_gcploit_framework.yml b/detections/cloud/gcp_detect_gcploit_framework.yml index 2669be4814..bf8bb0a575 100644 --- a/detections/cloud/gcp_detect_gcploit_framework.yml +++ b/detections/cloud/gcp_detect_gcploit_framework.yml @@ -1,46 +1,36 @@ name: GCP Detect gcploit framework id: a1c5a85e-a162-410c-a5d9-99ff639e5a52 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Rod Soto, Splunk status: experimental type: TTP -description: The following analytic identifies the use of the GCPloit exploitation - framework within Google Cloud Platform (GCP). It detects specific GCP Pub/Sub messages - with a function timeout of 539 seconds, which is indicative of GCPloit activity. - This detection is significant as GCPloit can be used to escalate privileges and - facilitate lateral movement from compromised high-privilege accounts. If confirmed - malicious, this activity could allow attackers to gain unauthorized access, escalate - their privileges, and move laterally within the GCP environment, potentially compromising - sensitive data and critical resources. +description: The following analytic identifies the use of the GCPloit exploitation framework within Google Cloud Platform (GCP). It detects specific GCP Pub/Sub messages with a function timeout of 539 seconds, which is indicative of GCPloit activity. This detection is significant as GCPloit can be used to escalate privileges and facilitate lateral movement from compromised high-privilege accounts. If confirmed malicious, this activity could allow attackers to gain unauthorized access, escalate their privileges, and move laterally within the GCP environment, potentially compromising sensitive data and critical resources. data_source: [] -search: '`google_gcp_pubsub_message` data.protoPayload.request.function.timeout=539s - | table src src_user data.resource.labels.project_id data.protoPayload.request.function.serviceAccountEmail - data.protoPayload.authorizationInfo{}.permission data.protoPayload.request.location - http_user_agent | `gcp_detect_gcploit_framework_filter`' -how_to_implement: You must install splunk GCP add-on. This search works with gcp:pubsub:message - logs -known_false_positives: Payload.request.function.timeout value can possibly be match - with other functions or requests however the source user and target request account - may indicate an attempt to move laterally accross acounts or projects +search: |- + `google_gcp_pubsub_message` data.protoPayload.request.function.timeout=539s + | table src src_user data.resource.labels.project_id data.protoPayload.request.function.serviceAccountEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.request.location http_user_agent + | `gcp_detect_gcploit_framework_filter` +how_to_implement: You must install splunk GCP add-on. This search works with gcp:pubsub:message logs +known_false_positives: Payload.request.function.timeout value can possibly be match with other functions or requests however the source user and target request account may indicate an attempt to move laterally accross acounts or projects references: -- https://github.com/dxa4481/gcploit -- https://www.youtube.com/watch?v=Ml09R38jpok + - https://github.com/dxa4481/gcploit + - https://www.youtube.com/watch?v=Ml09R38jpok rba: - message: Possible use of gcploit framework - risk_objects: - - field: src_user - type: user - score: 25 - threat_objects: [] + message: Possible use of gcploit framework + risk_objects: + - field: src_user + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - GCP Cross Account Activity - asset_type: GCP Account - mitre_attack_id: - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - GCP Cross Account Activity + asset_type: GCP Account + mitre_attack_id: + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat diff --git a/detections/cloud/gcp_kubernetes_cluster_pod_scan_detection.yml b/detections/cloud/gcp_kubernetes_cluster_pod_scan_detection.yml index 36bce00971..7e27b87b4f 100644 --- a/detections/cloud/gcp_kubernetes_cluster_pod_scan_detection.yml +++ b/detections/cloud/gcp_kubernetes_cluster_pod_scan_detection.yml @@ -1,36 +1,30 @@ name: GCP Kubernetes cluster pod scan detection id: 19b53215-4a16-405b-8087-9e6acf619842 -version: 6 -date: '2025-10-14' +version: 7 +date: '2026-02-25' author: Rod Soto, Splunk status: experimental type: Hunting -description: The following analytic identifies unauthenticated requests to Kubernetes - cluster pods. It detects this activity by analyzing GCP Pub/Sub messages for audit - logs where the response status code is 401, indicating unauthorized access attempts. - This activity is significant for a SOC because it may indicate reconnaissance or - scanning attempts by an attacker trying to identify vulnerable pods. If confirmed - malicious, this activity could lead to unauthorized access, allowing the attacker - to exploit vulnerabilities within the cluster, potentially compromising sensitive - data or gaining control over the Kubernetes environment. +description: The following analytic identifies unauthenticated requests to Kubernetes cluster pods. It detects this activity by analyzing GCP Pub/Sub messages for audit logs where the response status code is 401, indicating unauthorized access attempts. This activity is significant for a SOC because it may indicate reconnaissance or scanning attempts by an attacker trying to identify vulnerable pods. If confirmed malicious, this activity could lead to unauthorized access, allowing the attacker to exploit vulnerabilities within the cluster, potentially compromising sensitive data or gaining control over the Kubernetes environment. data_source: [] -search: '`google_gcp_pubsub_message` category=kube-audit |spath input=properties.log - |search responseStatus.code=401 |table sourceIPs{} userAgent verb requestURI responseStatus.reason - properties.pod | `gcp_kubernetes_cluster_pod_scan_detection_filter`' -how_to_implement: You must install the GCP App for Splunk (version 2.0.0 or later), - then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. -known_false_positives: Not all unauthenticated requests are malicious, but frequency, - User Agent, source IPs and pods will provide context. +search: |- + `google_gcp_pubsub_message` category=kube-audit + | spath input=properties.log + | search responseStatus.code=401 + | table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod + | `gcp_kubernetes_cluster_pod_scan_detection_filter` +how_to_implement: You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. +known_false_positives: Not all unauthenticated requests are malicious, but frequency, User Agent, source IPs and pods will provide context. references: [] tags: - analytic_story: - - Kubernetes Scanning Activity - - Scattered Lapsus$ Hunters - asset_type: GCP Kubernetes cluster - mitre_attack_id: - - T1526 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Kubernetes Scanning Activity + - Scattered Lapsus$ Hunters + asset_type: GCP Kubernetes cluster + mitre_attack_id: + - T1526 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat diff --git a/detections/cloud/gcp_multi_factor_authentication_disabled.yml b/detections/cloud/gcp_multi_factor_authentication_disabled.yml index e13588725c..4e04a9d0f5 100644 --- a/detections/cloud/gcp_multi_factor_authentication_disabled.yml +++ b/detections/cloud/gcp_multi_factor_authentication_disabled.yml @@ -1,75 +1,63 @@ name: GCP Multi-Factor Authentication Disabled id: b9bc5513-6fc1-4821-85a3-e1d81e451c83 -version: 9 -date: '2025-10-14' +version: 10 +date: '2026-02-25' author: Bhavin Patel, Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects an attempt to disable multi-factor authentication - (MFA) for a Google Cloud Platform (GCP) user. It leverages Google Workspace Admin - log events, specifically the `UNENROLL_USER_FROM_STRONG_AUTH` command. This activity - is significant because disabling MFA can allow an adversary to maintain persistence - within the environment using a compromised account without raising suspicion. If - confirmed malicious, this action could enable attackers to bypass additional security - layers, potentially leading to unauthorized access, data exfiltration, or further - exploitation of the compromised account. +description: The following analytic detects an attempt to disable multi-factor authentication (MFA) for a Google Cloud Platform (GCP) user. It leverages Google Workspace Admin log events, specifically the `UNENROLL_USER_FROM_STRONG_AUTH` command. This activity is significant because disabling MFA can allow an adversary to maintain persistence within the environment using a compromised account without raising suspicion. If confirmed malicious, this action could enable attackers to bypass additional security layers, potentially leading to unauthorized access, data exfiltration, or further exploitation of the compromised account. data_source: -- Google Workspace -search: '`gws_reports_admin` command=UNENROLL_USER_FROM_STRONG_AUTH | stats count - min(_time) as firstTime max(_time) as lastTime by user, command, actor.email, status, - id.applicationName, event.name, vendor_account, action | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`| `gcp_multi_factor_authentication_disabled_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Google - Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows - Splunk administrators to collect Google Workspace event data in Splunk using Google - Workspace APIs. Specifically, this analytic leverages the Admin log events. -known_false_positives: Legitimate use case may require for users to disable MFA. Filter - as needed. + - Google Workspace +search: |- + `gws_reports_admin` command=UNENROLL_USER_FROM_STRONG_AUTH + | stats count min(_time) as firstTime max(_time) as lastTime + BY user, command, actor.email, + status, id.applicationName, event.name, + vendor_account, action + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `gcp_multi_factor_authentication_disabled_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Google Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows Splunk administrators to collect Google Workspace event data in Splunk using Google Workspace APIs. Specifically, this analytic leverages the Admin log events. +known_false_positives: Legitimate use case may require for users to disable MFA. Filter as needed. references: -- https://support.google.com/cloudidentity/answer/2537800?hl=en -- https://attack.mitre.org/tactics/TA0005/ -- https://attack.mitre.org/techniques/T1556/ + - https://support.google.com/cloudidentity/answer/2537800?hl=en + - https://attack.mitre.org/tactics/TA0005/ + - https://attack.mitre.org/techniques/T1556/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: MFA disabled for User $user$ initiated by $actor.email$ - risk_objects: - - field: user - type: user - score: 45 - - field: actor.email - type: user - score: 45 - threat_objects: [] + message: MFA disabled for User $user$ initiated by $actor.email$ + risk_objects: + - field: user + type: user + score: 45 + - field: actor.email + type: user + score: 45 + threat_objects: [] tags: - analytic_story: - - GCP Account Takeover - - Scattered Lapsus$ Hunters - asset_type: GCP - mitre_attack_id: - - T1556.006 - - T1586.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - GCP Account Takeover + - Scattered Lapsus$ Hunters + asset_type: GCP + mitre_attack_id: + - T1556.006 + - T1586.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/gcp_disable_mfa/gws_admin.log - source: gws:reports:admin - sourcetype: gws:reports:admin + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/gcp_disable_mfa/gws_admin.log + source: gws:reports:admin + sourcetype: gws:reports:admin diff --git a/detections/cloud/gcp_multiple_failed_mfa_requests_for_user.yml b/detections/cloud/gcp_multiple_failed_mfa_requests_for_user.yml index b37e48c71a..92c84afc50 100644 --- a/detections/cloud/gcp_multiple_failed_mfa_requests_for_user.yml +++ b/detections/cloud/gcp_multiple_failed_mfa_requests_for_user.yml @@ -1,78 +1,63 @@ name: GCP Multiple Failed MFA Requests For User id: cbb3cb84-c06f-4393-adcc-5cb6195621f1 -version: 8 -date: '2025-10-14' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects multiple failed multi-factor authentication - (MFA) requests for a single user within a Google Cloud Platform (GCP) tenant. It - triggers when 10 or more MFA prompts fail within a 5-minute window, using Google - Workspace login failure events. This behavior is significant as it may indicate - an adversary attempting to bypass MFA by bombarding the user with repeated authentication - requests. If confirmed malicious, this activity could lead to unauthorized access, - allowing attackers to compromise accounts and potentially escalate privileges within - the GCP environment. +description: The following analytic detects multiple failed multi-factor authentication (MFA) requests for a single user within a Google Cloud Platform (GCP) tenant. It triggers when 10 or more MFA prompts fail within a 5-minute window, using Google Workspace login failure events. This behavior is significant as it may indicate an adversary attempting to bypass MFA by bombarding the user with repeated authentication requests. If confirmed malicious, this activity could lead to unauthorized access, allowing attackers to compromise accounts and potentially escalate privileges within the GCP environment. data_source: -- Google Workspace -search: '`gws_reports_login` event.name=login_failure `gws_login_mfa_methods` | bucket - span=5m _time | stats dc(_raw) AS mfa_prompts values(user) AS user by src_ip, login_challenge_method, _time - | where mfa_prompts >= 10 | `gcp_multiple_failed_mfa_requests_for_user_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Google - Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows - Splunk administrators to collect Google Workspace event data in Splunk using Google - Workspace APIs. We would also recommend tuning the detection by adjusting the window - `span` and `mfa_prompts` threshold values according to your environment. Specifically, - this analytic leverages the User log events. -known_false_positives: Multiple Failed MFA requests may also be a sign of authentication - or application issues. Filter as needed. + - Google Workspace +search: |- + `gws_reports_login` event.name=login_failure `gws_login_mfa_methods` + | bucket span=5m _time + | stats dc(_raw) AS mfa_prompts values(user) AS user + BY src_ip, login_challenge_method, _time + | where mfa_prompts >= 10 + | `gcp_multiple_failed_mfa_requests_for_user_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Google Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows Splunk administrators to collect Google Workspace event data in Splunk using Google Workspace APIs. We would also recommend tuning the detection by adjusting the window `span` and `mfa_prompts` threshold values according to your environment. Specifically, this analytic leverages the User log events. +known_false_positives: Multiple Failed MFA requests may also be a sign of authentication or application issues. Filter as needed. references: -- https://www.mandiant.com/resources/blog/russian-targeting-gov-business -- https://arstechnica.com/information-technology/2022/03/lapsus-and-solar-winds-hackers-both-use-the-same-old-trick-to-bypass-mfa/ -- https://therecord.media/russian-hackers-bypass-2fa-by-annoying-victims-with-repeated-push-notifications/ -- https://attack.mitre.org/techniques/T1621/ -- https://attack.mitre.org/techniques/T1078/004/ + - https://www.mandiant.com/resources/blog/russian-targeting-gov-business + - https://arstechnica.com/information-technology/2022/03/lapsus-and-solar-winds-hackers-both-use-the-same-old-trick-to-bypass-mfa/ + - https://therecord.media/russian-hackers-bypass-2fa-by-annoying-victims-with-repeated-push-notifications/ + - https://attack.mitre.org/techniques/T1621/ + - https://attack.mitre.org/techniques/T1078/004/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Multiple Failed MFA requests for user $user$ - risk_objects: - - field: user - type: user - score: 54 - threat_objects: - - field: src_ip - type: ip_address + message: Multiple Failed MFA requests for user $user$ + risk_objects: + - field: user + type: user + score: 54 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - GCP Account Takeover - - Scattered Lapsus$ Hunters - asset_type: Google Cloud Platform tenant - mitre_attack_id: - - T1078.004 - - T1586.003 - - T1621 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - GCP Account Takeover + - Scattered Lapsus$ Hunters + asset_type: Google Cloud Platform tenant + mitre_attack_id: + - T1078.004 + - T1586.003 + - T1621 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/multiple_failed_mfa_gws/gws_login.log - source: gws:reports:login - sourcetype: gws:reports:login + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/multiple_failed_mfa_gws/gws_login.log + source: gws:reports:login + sourcetype: gws:reports:login diff --git a/detections/cloud/gcp_multiple_users_failing_to_authenticate_from_ip.yml b/detections/cloud/gcp_multiple_users_failing_to_authenticate_from_ip.yml index fb91acee35..0db70c671f 100644 --- a/detections/cloud/gcp_multiple_users_failing_to_authenticate_from_ip.yml +++ b/detections/cloud/gcp_multiple_users_failing_to_authenticate_from_ip.yml @@ -1,78 +1,64 @@ name: GCP Multiple Users Failing To Authenticate From Ip id: da20828e-d6fb-4ee5-afb7-d0ac200923d5 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: Anomaly -description: The following analytic detects a single source IP address failing to - authenticate into more than 20 unique Google Workspace user accounts within a 5-minute - window. It leverages Google Workspace login failure events to identify potential - password spraying attacks. This activity is significant as it may indicate an adversary - attempting to gain unauthorized access or elevate privileges within the Google Cloud - Platform. If confirmed malicious, this behavior could lead to unauthorized access - to sensitive resources, data breaches, or further exploitation within the environment. +description: The following analytic detects a single source IP address failing to authenticate into more than 20 unique Google Workspace user accounts within a 5-minute window. It leverages Google Workspace login failure events to identify potential password spraying attacks. This activity is significant as it may indicate an adversary attempting to gain unauthorized access or elevate privileges within the Google Cloud Platform. If confirmed malicious, this behavior could lead to unauthorized access to sensitive resources, data breaches, or further exploitation within the environment. data_source: -- Google Workspace -search: '`gws_reports_login` event.type = login event.name = login_failure | bucket - span=5m _time | stats count dc(user) AS unique_accounts values(user) as tried_accounts - values(authentication_method) AS authentication_method earliest(_time) as firstTime - latest(_time) as lastTime by _time event.name src app id.applicationName | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | where unique_accounts > 20 | `gcp_multiple_users_failing_to_authenticate_from_ip_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Google - Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows - Splunk administrators to collect Google Workspace event data in Splunk using Google - Workspace APIs. We would also recommend tuning the detection by adjusting the window - `span` and `unique_accounts` threshold values according to your environment. Specifically, - this analytic leverages the User log events. -known_false_positives: No known false postives for this detection. Please review this - alert. + - Google Workspace +search: |- + `gws_reports_login` event.type = login event.name = login_failure + | bucket span=5m _time + | stats count dc(user) AS unique_accounts values(user) as tried_accounts values(authentication_method) AS authentication_method earliest(_time) as firstTime latest(_time) as lastTime + BY _time event.name src + app id.applicationName + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | where unique_accounts > 20 + | `gcp_multiple_users_failing_to_authenticate_from_ip_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Google Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows Splunk administrators to collect Google Workspace event data in Splunk using Google Workspace APIs. We would also recommend tuning the detection by adjusting the window `span` and `unique_accounts` threshold values according to your environment. Specifically, this analytic leverages the User log events. +known_false_positives: No known false postives for this detection. Please review this alert. references: -- https://cloud.google.com/blog/products/identity-security/how-google-cloud-can-help-stop-credential-stuffing-attacks -- https://www.slideshare.net/dafthack/ok-google-how-do-i-red-team-gsuite -- https://attack.mitre.org/techniques/T1110/003/ -- https://www.blackhillsinfosec.com/wp-content/uploads/2020/05/Breaching-the-Cloud-Perimeter-Slides.pdf + - https://cloud.google.com/blog/products/identity-security/how-google-cloud-can-help-stop-credential-stuffing-attacks + - https://www.slideshare.net/dafthack/ok-google-how-do-i-red-team-gsuite + - https://attack.mitre.org/techniques/T1110/003/ + - https://www.blackhillsinfosec.com/wp-content/uploads/2020/05/Breaching-the-Cloud-Perimeter-Slides.pdf drilldown_searches: -- name: View the detection results for - "$tried_accounts$" - search: '%original_detection_search% | search tried_accounts = "$tried_accounts$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$tried_accounts$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$tried_accounts$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$tried_accounts$" + search: '%original_detection_search% | search tried_accounts = "$tried_accounts$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$tried_accounts$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$tried_accounts$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: 'Multiple failed login attempts (Count: $unique_accounts$) against users - seen from $src$' - risk_objects: - - field: tried_accounts - type: user - score: 54 - threat_objects: - - field: src - type: ip_address + message: 'Multiple failed login attempts (Count: $unique_accounts$) against users seen from $src$' + risk_objects: + - field: tried_accounts + type: user + score: 54 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - GCP Account Takeover - asset_type: Google Cloud Platform tenant - mitre_attack_id: - - T1110.003 - - T1110.004 - - T1586.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - GCP Account Takeover + asset_type: Google Cloud Platform tenant + mitre_attack_id: + - T1110.003 + - T1110.004 + - T1586.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/gcp_gws_multiple_login_failure/gws_login.json - source: gws_login - sourcetype: gws:reports:login + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/gcp_gws_multiple_login_failure/gws_login.json + source: gws_login + sourcetype: gws:reports:login diff --git a/detections/cloud/gcp_successful_single_factor_authentication.yml b/detections/cloud/gcp_successful_single_factor_authentication.yml index e00f93504c..bd05cc1183 100644 --- a/detections/cloud/gcp_successful_single_factor_authentication.yml +++ b/detections/cloud/gcp_successful_single_factor_authentication.yml @@ -1,74 +1,62 @@ name: GCP Successful Single-Factor Authentication id: 40e17d88-87da-414e-b253-8dc1e4f9555b -version: 9 -date: '2025-10-14' +version: 10 +date: '2026-02-25' author: Bhavin Patel, Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic identifies a successful single-factor authentication - event against Google Cloud Platform (GCP) for an account without Multi-Factor Authentication - (MFA) enabled. It uses Google Workspace login event data to detect instances where - MFA is not utilized. This activity is significant as it may indicate a misconfiguration, - policy violation, or potential account takeover attempt. If confirmed malicious, - an attacker could gain unauthorized access to GCP resources, potentially leading - to data breaches, service disruptions, or further exploitation within the cloud - environment. +description: The following analytic identifies a successful single-factor authentication event against Google Cloud Platform (GCP) for an account without Multi-Factor Authentication (MFA) enabled. It uses Google Workspace login event data to detect instances where MFA is not utilized. This activity is significant as it may indicate a misconfiguration, policy violation, or potential account takeover attempt. If confirmed malicious, an attacker could gain unauthorized access to GCP resources, potentially leading to data breaches, service disruptions, or further exploitation within the cloud environment. data_source: -- Google Workspace -search: '`gws_reports_login` event.name=login_success NOT `gws_login_mfa_methods` - | stats count min(_time) as firstTime max(_time) as lastTime by user, src_ip, login_challenge_method, - app, event.name, vendor_account, action |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| - `gcp_successful_single_factor_authentication_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Google - Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows - Splunk administrators to collect Google Workspace event data in Splunk using Google - Workspace APIs. Specifically, this analytic leverages the User log events. -known_false_positives: Although not recommended, certain users may be required without - multi-factor authentication. Filter as needed + - Google Workspace +search: |- + `gws_reports_login` event.name=login_success NOT `gws_login_mfa_methods` + | stats count min(_time) as firstTime max(_time) as lastTime + BY user, src_ip, login_challenge_method, + app, event.name, vendor_account, + action + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `gcp_successful_single_factor_authentication_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Google Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows Splunk administrators to collect Google Workspace event data in Splunk using Google Workspace APIs. Specifically, this analytic leverages the User log events. +known_false_positives: Although not recommended, certain users may be required without multi-factor authentication. Filter as needed references: -- https://attack.mitre.org/techniques/T1078/004/ -- https://support.google.com/a/answer/175197?hl=en -- https://www.forbes.com/sites/daveywinder/2020/07/08/new-dark-web-audit-reveals-15-billion-stolen-logins-from-100000-breaches-passwords-hackers-cybercrime/?sh=69927b2a180f + - https://attack.mitre.org/techniques/T1078/004/ + - https://support.google.com/a/answer/175197?hl=en + - https://www.forbes.com/sites/daveywinder/2020/07/08/new-dark-web-audit-reveals-15-billion-stolen-logins-from-100000-breaches-passwords-hackers-cybercrime/?sh=69927b2a180f drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Successful authentication for user $user$ without MFA - risk_objects: - - field: user - type: user - score: 45 - threat_objects: - - field: src_ip - type: ip_address + message: Successful authentication for user $user$ without MFA + risk_objects: + - field: user + type: user + score: 45 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - GCP Account Takeover - - Scattered Lapsus$ Hunters - asset_type: Google Cloud Platform tenant - mitre_attack_id: - - T1078.004 - - T1586.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - GCP Account Takeover + - Scattered Lapsus$ Hunters + asset_type: Google Cloud Platform tenant + mitre_attack_id: + - T1078.004 + - T1586.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/gcp_single_factor_auth/gws_login.log - source: gws:reports:login - sourcetype: gws:reports:login + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/gcp_single_factor_auth/gws_login.log + source: gws:reports:login + sourcetype: gws:reports:login diff --git a/detections/cloud/gcp_unusual_number_of_failed_authentications_from_ip.yml b/detections/cloud/gcp_unusual_number_of_failed_authentications_from_ip.yml index f3739e61cf..99dc795696 100644 --- a/detections/cloud/gcp_unusual_number_of_failed_authentications_from_ip.yml +++ b/detections/cloud/gcp_unusual_number_of_failed_authentications_from_ip.yml @@ -1,80 +1,65 @@ name: GCP Unusual Number of Failed Authentications From Ip id: bd8097ed-958a-4873-87d9-44f2b4d85705 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: Anomaly -description: The following analytic identifies a single source IP failing to authenticate - into Google Workspace with multiple valid users, potentially indicating a Password - Spraying attack. It uses Google Workspace login failure events and calculates the - standard deviation for source IPs, applying the 3-sigma rule to detect unusual failed - authentication attempts. This activity is significant as it may signal an adversary - attempting to gain initial access or elevate privileges. If confirmed malicious, - this could lead to unauthorized access, data breaches, or further exploitation within - the environment. +description: The following analytic identifies a single source IP failing to authenticate into Google Workspace with multiple valid users, potentially indicating a Password Spraying attack. It uses Google Workspace login failure events and calculates the standard deviation for source IPs, applying the 3-sigma rule to detect unusual failed authentication attempts. This activity is significant as it may signal an adversary attempting to gain initial access or elevate privileges. If confirmed malicious, this could lead to unauthorized access, data breaches, or further exploitation within the environment. data_source: -- Google Workspace -search: '`gws_reports_login` event.type = login event.name = login_failure| bucket - span=5m _time | stats dc(user_name) AS unique_accounts values(user_name) as tried_accounts - values(authentication_method) AS authentication_method by _time, src | eventstats avg(unique_accounts) - as ip_avg , stdev(unique_accounts) as ip_std by _time | eval upperBound=(ip_avg+ip_std*3) - | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, - 0) | where isOutlier =1| `gcp_unusual_number_of_failed_authentications_from_ip_filter`' -how_to_implement: You must install the latest version of Splunk Add-on for Google - Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows - Splunk administrators to collect Google Workspace event data in Splunk using Google - Workspace APIs. We would also recommend tuning the detection by adjusting the window - `span` and `unique_accounts` threshold values according to your environment. Specifically, - this analytic leverages the User log events. -known_false_positives: No known false positives for this detection. Please review - this alert + - Google Workspace +search: |- + `gws_reports_login` event.type = login event.name = login_failure + | bucket span=5m _time + | stats dc(user_name) AS unique_accounts values(user_name) as tried_accounts values(authentication_method) AS authentication_method + BY _time, src + | eventstats avg(unique_accounts) as ip_avg , stdev(unique_accounts) as ip_std + BY _time + | eval upperBound=(ip_avg+ip_std*3) + | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) + | where isOutlier =1 + | `gcp_unusual_number_of_failed_authentications_from_ip_filter` +how_to_implement: You must install the latest version of Splunk Add-on for Google Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows Splunk administrators to collect Google Workspace event data in Splunk using Google Workspace APIs. We would also recommend tuning the detection by adjusting the window `span` and `unique_accounts` threshold values according to your environment. Specifically, this analytic leverages the User log events. +known_false_positives: No known false positives for this detection. Please review this alert references: -- https://cloud.google.com/blog/products/identity-security/how-google-cloud-can-help-stop-credential-stuffing-attacks -- https://www.slideshare.net/dafthack/ok-google-how-do-i-red-team-gsuite -- https://attack.mitre.org/techniques/T1110/003/ -- https://www.blackhillsinfosec.com/wp-content/uploads/2020/05/Breaching-the-Cloud-Perimeter-Slides.pdf + - https://cloud.google.com/blog/products/identity-security/how-google-cloud-can-help-stop-credential-stuffing-attacks + - https://www.slideshare.net/dafthack/ok-google-how-do-i-red-team-gsuite + - https://attack.mitre.org/techniques/T1110/003/ + - https://www.blackhillsinfosec.com/wp-content/uploads/2020/05/Breaching-the-Cloud-Perimeter-Slides.pdf drilldown_searches: -- name: View the detection results for - "$tried_accounts$" - search: '%original_detection_search% | search tried_accounts = "$tried_accounts$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$tried_accounts$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$tried_accounts$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$tried_accounts$" + search: '%original_detection_search% | search tried_accounts = "$tried_accounts$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$tried_accounts$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$tried_accounts$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: 'Unusual number of failed console login attempts (Count: $unique_accounts$) - against users from IP Address - $src$' - risk_objects: - - field: tried_accounts - type: user - score: 54 - threat_objects: - - field: src - type: ip_address + message: 'Unusual number of failed console login attempts (Count: $unique_accounts$) against users from IP Address - $src$' + risk_objects: + - field: tried_accounts + type: user + score: 54 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - GCP Account Takeover - asset_type: Google Cloud Platform tenant - mitre_attack_id: - - T1110.003 - - T1110.004 - - T1586.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - GCP Account Takeover + asset_type: Google Cloud Platform tenant + mitre_attack_id: + - T1110.003 + - T1110.004 + - T1586.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/gcp_gws_multiple_login_failure/gws_login.json - source: gws_login - sourcetype: gws:reports:login + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/gcp_gws_multiple_login_failure/gws_login.json + source: gws_login + sourcetype: gws:reports:login diff --git a/detections/cloud/gdrive_suspicious_file_sharing.yml b/detections/cloud/gdrive_suspicious_file_sharing.yml index 406a4dedb4..da89885fe9 100644 --- a/detections/cloud/gdrive_suspicious_file_sharing.yml +++ b/detections/cloud/gdrive_suspicious_file_sharing.yml @@ -1,43 +1,34 @@ name: Gdrive suspicious file sharing id: a7131dae-34e3-11ec-a2de-acde48001122 -version: 6 -date: '2025-10-14' +version: 7 +date: '2026-02-25' author: Rod Soto, Teoderick Contreras status: experimental type: Hunting -description: The following analytic identifies suspicious file-sharing activity on - Google Drive, where internal users share documents with more than 50 external recipients. - It leverages GSuite Drive logs, focusing on changes in user access and filtering - for emails outside the organization's domain. This activity is significant as it - may indicate compromised accounts or intentional data exfiltration. If confirmed - malicious, this behavior could lead to unauthorized access to sensitive information, - data leaks, and potential compliance violations. +description: The following analytic identifies suspicious file-sharing activity on Google Drive, where internal users share documents with more than 50 external recipients. It leverages GSuite Drive logs, focusing on changes in user access and filtering for emails outside the organization's domain. This activity is significant as it may indicate compromised accounts or intentional data exfiltration. If confirmed malicious, this behavior could lead to unauthorized access to sensitive information, data leaks, and potential compliance violations. data_source: [] -search: '`gsuite_drive` name=change_user_access | rename parameters.* as * | search - email = "*@yourdomain.com" target_user != "*@yourdomain.com" | stats count values(owner) - as owner values(target_user) as target values(doc_type) as doc_type values(doc_title) - as doc_title dc(target_user) as distinct_target by src_ip email | where distinct_target - > 50 | `gdrive_suspicious_file_sharing_filter`' -how_to_implement: Need to implement Gsuite logging targeting Google suite drive activity. - In order for the search to work for your environment please update `yourdomain.com` - value in the query with the domain relavant for your organization. -known_false_positives: This is an anomaly search, you must specify your domain in - the parameters so it either filters outside domains or focus on internal domains. - This search may also help investigate compromise of accounts. By looking at for - example source ip addresses, document titles and abnormal number of shares and shared - target users. +search: |- + `gsuite_drive` name=change_user_access + | rename parameters.* as * + | search email = "*@yourdomain.com" target_user != "*@yourdomain.com" + | stats count values(owner) as owner values(target_user) as target values(doc_type) as doc_type values(doc_title) as doc_title dc(target_user) as distinct_target + BY src_ip email + | where distinct_target > 50 + | `gdrive_suspicious_file_sharing_filter` +how_to_implement: Need to implement Gsuite logging targeting Google suite drive activity. In order for the search to work for your environment please update `yourdomain.com` value in the query with the domain relavant for your organization. +known_false_positives: This is an anomaly search, you must specify your domain in the parameters so it either filters outside domains or focus on internal domains. This search may also help investigate compromise of accounts. By looking at for example source ip addresses, document titles and abnormal number of shares and shared target users. references: -- https://www.splunk.com/en_us/blog/security/investigating-gsuite-phishing-attacks-with-splunk.html + - https://www.splunk.com/en_us/blog/security/investigating-gsuite-phishing-attacks-with-splunk.html tags: - analytic_story: - - Spearphishing Attachments - - Data Exfiltration - - Scattered Lapsus$ Hunters - asset_type: GDrive - mitre_attack_id: - - T1566 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Spearphishing Attachments + - Data Exfiltration + - Scattered Lapsus$ Hunters + asset_type: GDrive + mitre_attack_id: + - T1566 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat diff --git a/detections/cloud/geographic_improbable_location.yml b/detections/cloud/geographic_improbable_location.yml index c79f2b417b..950e8f125e 100644 --- a/detections/cloud/geographic_improbable_location.yml +++ b/detections/cloud/geographic_improbable_location.yml @@ -5,109 +5,36 @@ date: '2025-06-03' author: Marissa Bower, Raven Tait status: experimental type: Anomaly -description: Geolocation data can be inaccurate or easily spoofed by Remote Employment Fraud (REF) workers. - REF actors sometimes slip up and reveal their true location, creating what we call 'improbable travel' - scenarios — logins from opposite sides of the world within minutes. This identifies situations where these - travel scenarios occur. +description: Geolocation data can be inaccurate or easily spoofed by Remote Employment Fraud (REF) workers. REF actors sometimes slip up and reveal their true location, creating what we call 'improbable travel' scenarios — logins from opposite sides of the world within minutes. This identifies situations where these travel scenarios occur. data_source: -- Okta -search: '| tstats summariesonly=true values(Authentication.app) as app from datamodel=Authentication.Authentication - where (`okta` OR (index="firewall" AND sourcetype="pan:globalprotect")) - AND Authentication.action="success" AND Authentication.app IN ("Workday", "Slack", "*GlobalProtect", "Jira*", - "Atlassian Cloud", "Zoom") AND NOT Authentication.user="unknown" by _time index sourcetype host Authentication.user - Authentication.src span=1s - | `drop_dm_object_name("Authentication")` - | fields user,src,app,_time,count,host - | eval user=lower(replace(user, "((^.*\\\)|(@.*$))", "")) - | join type=outer user - [| inputlookup identity_lookup_expanded where user_status=active - | rex field=email "^(?[a-zA-Z0-9_\-\.]+)@([a-zA-Z0-9_\-\.]+)\.([a-zA-Z]{2,5})$" - | rename email as user_email bunit as user_bunit priority as user_priority work_country as user_work_country work_city as user_work_city - | fields user user_email user_bunit user_priority user_work_country user_work_city] - | eventstats dc(src) as src_count by user - | eventstats dc(user) as user_count by src - | sort 0 + _time - | iplocation src - | lookup local=true asn_lookup_by_cidr ip as src OUTPUT ip asn description - | eval session_lat=if(isnull(src_lat), lat, src_lat), session_lon=if(isnull(src_long), lon, src_long), - session_city=if(isnull(src_city), City, src_city), session_country=if(isnull(src_country), Country, src_country), - session_region=if(isnull(src_region), Region, src_region) - | eval session_city=if(isnull(session_city) OR match(session_city,"^\s+|^$"), null(), session_city), - session_country=if(isnull(session_country) OR match(session_country,"^\s+|^$"), null(), session_country), - session_region=if(isnull(session_region) OR match(session_region,"^\s+|^$"), null(), session_region) - | where isnotnull(session_lat) and isnotnull(session_lon) - | eval session_city=if(isnull(session_city),"-",session_city), session_country=if(isnull(session_country),"-",session_country), - session_region=if(isnull(session_region),"-",session_region) - | streamstats current=t window=2 earliest(session_region) as prev_region,earliest(session_lat) as prev_lat, - earliest(session_lon) as prev_lon, earliest(session_city) as prev_city, earliest(session_country) as prev_country, - earliest(_time) as prev_time, earliest(src) as prev_src, latest(user_bunit) as user_bunit, - earliest(app) as prev_app values(user_work_country) as user_work_country by user - | where (src!=prev_src) AND !(prev_city=session_city AND prev_country=session_country) AND ((isnotnull(prev_city) - AND isnotnull(session_city)) OR prev_country!=session_country) - | `globedistance(session_lat,session_lon,prev_lat,prev_lon,"m")` - | eval time_diff=if((_time-prev_time)==0, 1, _time - prev_time) - | eval speed = round(distance*3600/time_diff,2) - | eval distance= round(distance,2) - | eval user_work_country=case(user_work_country="usa","United States", user_work_country="cze","Czechia", - user_work_country="pol","Poland", user_work_country="ind","India", user_work_country="fra","France", - user_work_country="can","Canada", user_work_country="mys","Malaysia", user_work_country="kor","South Korea", - user_work_country="aus","Australia", user_work_country="bel","Belgium", user_work_country="dnk","Denmark", - user_work_country="bra","Brazil", user_work_country="deu","Germany", user_work_country="jpn","Japan", - user_work_country="che","Switzerland", user_work_country="swe","Sweden", user_work_country="zaf","South Africa", - user_work_country="irl","Ireland", user_work_country="ita","Italy", user_work_country="nor","Norway", - user_work_country="gbr","United Kingdom", user_work_country="hkg","Hong Kong", user_work_country="chn","China", - user_work_country="esp","Spain", user_work_country="nld", "Netherlands", user_work_country="twn","Taiwan", - user_work_country="est","Estonia", user_work_country="sgp","Singapore", user_work_country="are","United Arab Emirates", 1=1,"N/A") - | lookup local=true asn_lookup_by_cidr ip as prev_src OUTPUT ip as prev_ip asn as prev_asn description as prev_description - | eval suspect=if(!user_work_country==session_country,"Sketchy","Normal") - | search (speed>500 AND distance>750) - | table _time,prev_time,user,host,src,prev_src,app,prev_app,distance,speed,suspect,session_city,session_region, - session_country,prev_city,prev_region,prev_country,user_priority,user_work_*,prev_ip,ip,asn,prev_asn,prev_description,description - | rename _time as event_time - | convert ctime(event_time) timeformat="%Y-%m-%d %H:%M:%S" - | convert ctime(prev_time) timeformat="%Y-%m-%d %H:%M:%S" - | eval problem=if(!session_country==prev_country AND (!session_country==user_work_country),"Yes","Nope") - | search NOT (prev_city="-" OR session_city="-") AND NOT - [inputlookup known_devices_public_ip_filter.csv - | fields ip - | rename ip as src] - | dedup user host prev_src src - | fillnull value="N/A" - | search problem="Yes"| `geographic_improbable_location_filter`' -how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the - Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). This also utilizes - Splunk Enterprise Security Suite for several macros and lookups. The known_devices_public_ip_filter - lookup is a placeholder for known public edge devices in your network. + - Okta +search: '| tstats summariesonly=true values(Authentication.app) as app from datamodel=Authentication.Authentication where (`okta` OR (index="firewall" AND sourcetype="pan:globalprotect")) AND Authentication.action="success" AND Authentication.app IN ("Workday", "Slack", "*GlobalProtect", "Jira*", "Atlassian Cloud", "Zoom") AND NOT Authentication.user="unknown" by _time index sourcetype host Authentication.user Authentication.src span=1s | `drop_dm_object_name("Authentication")` | fields user,src,app,_time,count,host | eval user=lower(replace(user, "((^.*\\\)|(@.*$))", "")) | join type=outer user [| inputlookup identity_lookup_expanded where user_status=active | rex field=email "^(?[a-zA-Z0-9_\-\.]+)@([a-zA-Z0-9_\-\.]+)\.([a-zA-Z]{2,5})$" | rename email as user_email bunit as user_bunit priority as user_priority work_country as user_work_country work_city as user_work_city | fields user user_email user_bunit user_priority user_work_country user_work_city] | eventstats dc(src) as src_count by user | eventstats dc(user) as user_count by src | sort 0 + _time | iplocation src | lookup local=true asn_lookup_by_cidr ip as src OUTPUT ip asn description | eval session_lat=if(isnull(src_lat), lat, src_lat), session_lon=if(isnull(src_long), lon, src_long), session_city=if(isnull(src_city), City, src_city), session_country=if(isnull(src_country), Country, src_country), session_region=if(isnull(src_region), Region, src_region) | eval session_city=if(isnull(session_city) OR match(session_city,"^\s+|^$"), null(), session_city), session_country=if(isnull(session_country) OR match(session_country,"^\s+|^$"), null(), session_country), session_region=if(isnull(session_region) OR match(session_region,"^\s+|^$"), null(), session_region) | where isnotnull(session_lat) and isnotnull(session_lon) | eval session_city=if(isnull(session_city),"-",session_city), session_country=if(isnull(session_country),"-",session_country), session_region=if(isnull(session_region),"-",session_region) | streamstats current=t window=2 earliest(session_region) as prev_region,earliest(session_lat) as prev_lat, earliest(session_lon) as prev_lon, earliest(session_city) as prev_city, earliest(session_country) as prev_country, earliest(_time) as prev_time, earliest(src) as prev_src, latest(user_bunit) as user_bunit, earliest(app) as prev_app values(user_work_country) as user_work_country by user | where (src!=prev_src) AND !(prev_city=session_city AND prev_country=session_country) AND ((isnotnull(prev_city) AND isnotnull(session_city)) OR prev_country!=session_country) | `globedistance(session_lat,session_lon,prev_lat,prev_lon,"m")` | eval time_diff=if((_time-prev_time)==0, 1, _time - prev_time) | eval speed = round(distance*3600/time_diff,2) | eval distance= round(distance,2) | eval user_work_country=case(user_work_country="usa","United States", user_work_country="cze","Czechia", user_work_country="pol","Poland", user_work_country="ind","India", user_work_country="fra","France", user_work_country="can","Canada", user_work_country="mys","Malaysia", user_work_country="kor","South Korea", user_work_country="aus","Australia", user_work_country="bel","Belgium", user_work_country="dnk","Denmark", user_work_country="bra","Brazil", user_work_country="deu","Germany", user_work_country="jpn","Japan", user_work_country="che","Switzerland", user_work_country="swe","Sweden", user_work_country="zaf","South Africa", user_work_country="irl","Ireland", user_work_country="ita","Italy", user_work_country="nor","Norway", user_work_country="gbr","United Kingdom", user_work_country="hkg","Hong Kong", user_work_country="chn","China", user_work_country="esp","Spain", user_work_country="nld", "Netherlands", user_work_country="twn","Taiwan", user_work_country="est","Estonia", user_work_country="sgp","Singapore", user_work_country="are","United Arab Emirates", 1=1,"N/A") | lookup local=true asn_lookup_by_cidr ip as prev_src OUTPUT ip as prev_ip asn as prev_asn description as prev_description | eval suspect=if(!user_work_country==session_country,"Sketchy","Normal") | search (speed>500 AND distance>750) | table _time,prev_time,user,host,src,prev_src,app,prev_app,distance,speed,suspect,session_city,session_region, session_country,prev_city,prev_region,prev_country,user_priority,user_work_*,prev_ip,ip,asn,prev_asn,prev_description,description | rename _time as event_time | convert ctime(event_time) timeformat="%Y-%m-%d %H:%M:%S" | convert ctime(prev_time) timeformat="%Y-%m-%d %H:%M:%S" | eval problem=if(!session_country==prev_country AND (!session_country==user_work_country),"Yes","Nope") | search NOT (prev_city="-" OR session_city="-") AND NOT [inputlookup known_devices_public_ip_filter.csv | fields ip | rename ip as src] | dedup user host prev_src src | fillnull value="N/A" | search problem="Yes"| `geographic_improbable_location_filter`' +how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). This also utilizes Splunk Enterprise Security Suite for several macros and lookups. The known_devices_public_ip_filter lookup is a placeholder for known public edge devices in your network. known_false_positives: Legitimate usage of some VPNs may cause false positives. Tune as needed. drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search Authentication.user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search Authentication.user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Improbable travel speed between locations observed for $user$. - risk_objects: - - field: user - type: user - score: 50 - threat_objects: [] + message: Improbable travel speed between locations observed for $user$. + risk_objects: + - field: user + type: user + score: 50 + threat_objects: [] tags: - analytic_story: - - Remote Employment Fraud - asset_type: Identity - mitre_attack_id: - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Remote Employment Fraud + asset_type: Identity + mitre_attack_id: + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity diff --git a/detections/cloud/github_enterprise_delete_branch_ruleset.yml b/detections/cloud/github_enterprise_delete_branch_ruleset.yml index b76f4c213d..187fb52c4e 100644 --- a/detections/cloud/github_enterprise_delete_branch_ruleset.yml +++ b/detections/cloud/github_enterprise_delete_branch_ruleset.yml @@ -1,67 +1,65 @@ name: GitHub Enterprise Delete Branch Ruleset id: 6169ea23-3719-439f-957a-0ea5174b70e2 -version: 4 -date: '2026-01-14' +version: 5 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects when branch rules are deleted in GitHub Enterprise. - The detection monitors GitHub Enterprise audit logs for branch rule deletion events by tracking actor details, repository information, - and associated metadata. For a SOC, identifying deleted branch rules is critical as it could indicate attempts to bypass code review requirements - and security controls. Branch deletion rules are essential security controls that enforce code review, prevent force pushes, and maintain code quality. - Disabling these protections could allow malicious actors to directly push unauthorized code changes or backdoors to protected branches. The impact of - disabled branch protection includes potential code tampering, bypass of security reviews, introduction of vulnerabilities or malicious code, and compromise - of software supply chain integrity. This activity could be part of a larger attack chain where an adversary first disables security controls before attempting - to inject malicious code. +description: The following analytic detects when branch rules are deleted in GitHub Enterprise. The detection monitors GitHub Enterprise audit logs for branch rule deletion events by tracking actor details, repository information, and associated metadata. For a SOC, identifying deleted branch rules is critical as it could indicate attempts to bypass code review requirements and security controls. Branch deletion rules are essential security controls that enforce code review, prevent force pushes, and maintain code quality. Disabling these protections could allow malicious actors to directly push unauthorized code changes or backdoors to protected branches. The impact of disabled branch protection includes potential code tampering, bypass of security reviews, introduction of vulnerabilities or malicious code, and compromise of software supply chain integrity. This activity could be part of a larger attack chain where an adversary first disables security controls before attempting to inject malicious code. data_source: -- GitHub Enterprise Audit Logs -search: '`github_enterprise` action=repository_ruleset.destroy - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor, actor_id, actor_ip, actor_is_bot, actor_location.country_code, business, business_id, org, org_id, repo, repo_id, user_agent, action, ruleset_name - | eval user=actor - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `github_enterprise_delete_branch_ruleset_filter`' + - GitHub Enterprise Audit Logs +search: |- + `github_enterprise` action=repository_ruleset.destroy + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor, actor_id, actor_ip, + actor_is_bot, actor_location.country_code, business, + business_id, org, org_id, + repo, repo_id, user_agent, + action, ruleset_name + | eval user=actor + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `github_enterprise_delete_branch_ruleset_filter` how_to_implement: You must ingest GitHub Enterprise logs using Audit log streaming as described in this documentation https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk using a Splunk HTTP Event Collector. known_false_positives: No false positives have been identified at this time. references: -- https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 -- https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk + - https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 + - https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $user$ deleted a branch ruleset in repo $repo$ - risk_objects: - - field: user - type: user - score: 25 - threat_objects: - - field: user_agent - type: http_user_agent + message: $user$ deleted a branch ruleset in repo $repo$ + risk_objects: + - field: user + type: user + score: 25 + threat_objects: + - field: user_agent + type: http_user_agent tags: - analytic_story: - - GitHub Malicious Activity - - NPM Supply Chain Compromise - asset_type: GitHub - mitre_attack_id: - - T1562.001 - - T1195 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - GitHub Malicious Activity + - NPM Supply Chain Compromise + asset_type: GitHub + mitre_attack_id: + - T1562.001 + - T1195 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/github_delete_branch_ruleset/github.json - source: http:github - sourcetype: httpevent - - + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/github_delete_branch_ruleset/github.json + source: http:github + sourcetype: httpevent diff --git a/detections/cloud/github_enterprise_disable_2fa_requirement.yml b/detections/cloud/github_enterprise_disable_2fa_requirement.yml index fe8c1aff5a..5f2d1bf3d5 100644 --- a/detections/cloud/github_enterprise_disable_2fa_requirement.yml +++ b/detections/cloud/github_enterprise_disable_2fa_requirement.yml @@ -1,64 +1,62 @@ name: GitHub Enterprise Disable 2FA Requirement id: 5a773226-ebd7-480c-a819-fccacfeddcd9 -version: 3 -date: '2026-01-14' +version: 4 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects when two-factor authentication (2FA) requirements are disabled in GitHub Enterprise. - The detection monitors GitHub Enterprise audit logs for 2FA requirement changes by tracking actor details, organization information, - and associated metadata. For a SOC, identifying disabled 2FA requirements is critical as it could indicate attempts to weaken - account security controls. Two-factor authentication is a fundamental security control that helps prevent unauthorized access even if - passwords are compromised. Disabling 2FA requirements could allow attackers to more easily compromise accounts through password-based attacks. - The impact of disabled 2FA includes increased risk of account takeover, potential access to sensitive code and intellectual property, and - compromise of the software supply chain. This activity could be part of a larger attack chain where an adversary first disables - security controls before attempting broader account compromises. +description: The following analytic detects when two-factor authentication (2FA) requirements are disabled in GitHub Enterprise. The detection monitors GitHub Enterprise audit logs for 2FA requirement changes by tracking actor details, organization information, and associated metadata. For a SOC, identifying disabled 2FA requirements is critical as it could indicate attempts to weaken account security controls. Two-factor authentication is a fundamental security control that helps prevent unauthorized access even if passwords are compromised. Disabling 2FA requirements could allow attackers to more easily compromise accounts through password-based attacks. The impact of disabled 2FA includes increased risk of account takeover, potential access to sensitive code and intellectual property, and compromise of the software supply chain. This activity could be part of a larger attack chain where an adversary first disables security controls before attempting broader account compromises. data_source: -- GitHub Enterprise Audit Logs -search: '`github_enterprise` action=org.disable_two_factor_requirement OR action=business.disable_two_factor_requirement - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor, actor_id, actor_is_bot, actor_location.country_code, business, business_id, user_agent, action - | eval user=actor - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `github_enterprise_disable_2fa_requirement_filter`' + - GitHub Enterprise Audit Logs +search: |- + `github_enterprise` action=org.disable_two_factor_requirement OR action=business.disable_two_factor_requirement + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor, actor_id, actor_is_bot, + actor_location.country_code, business, business_id, + user_agent, action + | eval user=actor + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `github_enterprise_disable_2fa_requirement_filter` how_to_implement: You must ingest GitHub Enterprise logs using Audit log streaming as described in this documentation https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk using a Splunk HTTP Event Collector. known_false_positives: No false positives have been identified at this time. references: -- https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 -- https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk + - https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 + - https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $user$ disabled 2FA requirement - risk_objects: - - field: user - type: user - score: 25 - threat_objects: - - field: user_agent - type: http_user_agent + message: $user$ disabled 2FA requirement + risk_objects: + - field: user + type: user + score: 25 + threat_objects: + - field: user_agent + type: http_user_agent tags: - analytic_story: - - GitHub Malicious Activity - asset_type: GitHub - mitre_attack_id: - - T1562.001 - - T1195 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - GitHub Malicious Activity + asset_type: GitHub + mitre_attack_id: + - T1562.001 + - T1195 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/github_disable_two_factor_requirement/github.json - source: http:github - sourcetype: httpevent + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/github_disable_two_factor_requirement/github.json + source: http:github + sourcetype: httpevent diff --git a/detections/cloud/github_enterprise_disable_audit_log_event_stream.yml b/detections/cloud/github_enterprise_disable_audit_log_event_stream.yml index 0c016774b4..d1ebc9158d 100644 --- a/detections/cloud/github_enterprise_disable_audit_log_event_stream.yml +++ b/detections/cloud/github_enterprise_disable_audit_log_event_stream.yml @@ -1,66 +1,63 @@ name: GitHub Enterprise Disable Audit Log Event Stream id: 7bc111cc-7f1b-4be7-99fa-50cf8d2e7564 -version: 4 -date: '2026-01-14' +version: 5 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects when a user disables audit log event streaming in GitHub Enterprise. - The detection monitors GitHub Enterprise audit logs for configuration changes that disable the audit log streaming functionality, - which is used to send audit events to security monitoring platforms. This behavior could indicate an attacker attempting to prevent - their malicious activities from being logged and detected by disabling the audit trail. For a SOC, identifying the disabling of - audit logging is critical as it may be a precursor to other attacks where adversaries want to operate undetected. The impact could - be severe as organizations lose visibility into user actions, configuration changes, and security events within their - GitHub Enterprise environment, potentially allowing attackers to perform malicious activities without detection. - This creates a significant blind spot in security monitoring and incident response capabilities. +description: The following analytic detects when a user disables audit log event streaming in GitHub Enterprise. The detection monitors GitHub Enterprise audit logs for configuration changes that disable the audit log streaming functionality, which is used to send audit events to security monitoring platforms. This behavior could indicate an attacker attempting to prevent their malicious activities from being logged and detected by disabling the audit trail. For a SOC, identifying the disabling of audit logging is critical as it may be a precursor to other attacks where adversaries want to operate undetected. The impact could be severe as organizations lose visibility into user actions, configuration changes, and security events within their GitHub Enterprise environment, potentially allowing attackers to perform malicious activities without detection. This creates a significant blind spot in security monitoring and incident response capabilities. data_source: -- GitHub Enterprise Audit Logs -search: '`github_enterprise` action=audit_log_streaming.destroy - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor, actor_id, actor_ip, actor_is_bot, actor_location.country_code, business, business_id, user_agent, action - | eval user=actor - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `github_enterprise_disable_audit_log_event_stream_filter`' + - GitHub Enterprise Audit Logs +search: |- + `github_enterprise` action=audit_log_streaming.destroy + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor, actor_id, actor_ip, + actor_is_bot, actor_location.country_code, business, + business_id, user_agent, action + | eval user=actor + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `github_enterprise_disable_audit_log_event_stream_filter` how_to_implement: You must ingest GitHub Enterprise logs using Audit log streaming as described in this documentation https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk using a Splunk HTTP Event Collector. known_false_positives: No false positives have been identified at this time. references: -- https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 -- https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk + - https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 + - https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Audit log event streaming is disabled by $user$ - risk_objects: - - field: user - type: user - score: 25 - threat_objects: - - field: user_agent - type: http_user_agent + message: Audit log event streaming is disabled by $user$ + risk_objects: + - field: user + type: user + score: 25 + threat_objects: + - field: user_agent + type: http_user_agent tags: - analytic_story: - - GitHub Malicious Activity - - NPM Supply Chain Compromise - asset_type: GitHub - mitre_attack_id: - - T1562.008 - - T1195 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - GitHub Malicious Activity + - NPM Supply Chain Compromise + asset_type: GitHub + mitre_attack_id: + - T1562.008 + - T1195 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/github_audit_log_stream_disabled/github.json - source: http:github - sourcetype: httpevent - + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/github_audit_log_stream_disabled/github.json + source: http:github + sourcetype: httpevent diff --git a/detections/cloud/github_enterprise_disable_classic_branch_protection_rule.yml b/detections/cloud/github_enterprise_disable_classic_branch_protection_rule.yml index 3e65abfc6f..0e8a96a549 100644 --- a/detections/cloud/github_enterprise_disable_classic_branch_protection_rule.yml +++ b/detections/cloud/github_enterprise_disable_classic_branch_protection_rule.yml @@ -1,66 +1,64 @@ name: GitHub Enterprise Disable Classic Branch Protection Rule id: 372176ba-450c-4abd-9b86-419bb44c1b76 -version: 3 -date: '2026-01-14' +version: 4 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects when classic branch protection rules are disabled in GitHub Enterprise. - The detection monitors GitHub Enterprise audit logs for branch protection removal events by tracking actor details, repository information, - and associated metadata. For a SOC, identifying disabled branch protection is critical as it could indicate attempts to bypass code review requirements - and security controls. Branch protection rules are essential security controls that enforce code review, prevent force pushes, and maintain code quality. - Disabling these protections could allow malicious actors to directly push unauthorized code changes or backdoors to protected branches. The impact of - disabled branch protection includes potential code tampering, bypass of security reviews, introduction of vulnerabilities or malicious code, and compromise - of software supply chain integrity. This activity could be part of a larger attack chain where an adversary first disables security controls before attempting - to inject malicious code. +description: The following analytic detects when classic branch protection rules are disabled in GitHub Enterprise. The detection monitors GitHub Enterprise audit logs for branch protection removal events by tracking actor details, repository information, and associated metadata. For a SOC, identifying disabled branch protection is critical as it could indicate attempts to bypass code review requirements and security controls. Branch protection rules are essential security controls that enforce code review, prevent force pushes, and maintain code quality. Disabling these protections could allow malicious actors to directly push unauthorized code changes or backdoors to protected branches. The impact of disabled branch protection includes potential code tampering, bypass of security reviews, introduction of vulnerabilities or malicious code, and compromise of software supply chain integrity. This activity could be part of a larger attack chain where an adversary first disables security controls before attempting to inject malicious code. data_source: -- GitHub Enterprise Audit Logs -search: '`github_enterprise` action=protected_branch.destroy - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor, actor_id, actor_ip, actor_is_bot, actor_location.country_code, business, business_id, org, org_id, repo, repo_id, user_agent, action, name - | eval user=actor - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `github_enterprise_disable_classic_branch_protection_rule_filter`' + - GitHub Enterprise Audit Logs +search: |- + `github_enterprise` action=protected_branch.destroy + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor, actor_id, actor_ip, + actor_is_bot, actor_location.country_code, business, + business_id, org, org_id, + repo, repo_id, user_agent, + action, name + | eval user=actor + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `github_enterprise_disable_classic_branch_protection_rule_filter` how_to_implement: You must ingest GitHub Enterprise logs using Audit log streaming as described in this documentation https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk using a Splunk HTTP Event Collector. known_false_positives: No false positives have been identified at this time. references: -- https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 -- https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk + - https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 + - https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $user$ disabled a classic branch protection rule in repo $repo$ - risk_objects: - - field: user - type: user - score: 25 - threat_objects: - - field: user_agent - type: http_user_agent + message: $user$ disabled a classic branch protection rule in repo $repo$ + risk_objects: + - field: user + type: user + score: 25 + threat_objects: + - field: user_agent + type: http_user_agent tags: - analytic_story: - - GitHub Malicious Activity - asset_type: GitHub - mitre_attack_id: - - T1562.001 - - T1195 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - GitHub Malicious Activity + asset_type: GitHub + mitre_attack_id: + - T1562.001 + - T1195 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/github_disable_classic_branch_protection/github.json - source: http:github - sourcetype: httpevent - - + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/github_disable_classic_branch_protection/github.json + source: http:github + sourcetype: httpevent diff --git a/detections/cloud/github_enterprise_disable_dependabot.yml b/detections/cloud/github_enterprise_disable_dependabot.yml index f47358161a..e59491930b 100644 --- a/detections/cloud/github_enterprise_disable_dependabot.yml +++ b/detections/cloud/github_enterprise_disable_dependabot.yml @@ -1,63 +1,63 @@ name: GitHub Enterprise Disable Dependabot id: 787dd1c1-eb3a-4a31-8e8c-2ad24b214bc8 -version: 3 -date: '2026-01-14' +version: 4 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects when a user disables Dependabot security features within a GitHub repository. - Dependabot helps automatically identify and fix security vulnerabilities in dependencies. The detection monitors GitHub - Enterprise logs for configuration changes that disable Dependabot functionality. This behavior could indicate an attacker - attempting to prevent the automatic detection of vulnerable dependencies, which would allow them to exploit known vulnerabilities - that would otherwise be patched. For a SOC, identifying the disabling of security features like Dependabot is critical as it may - be a precursor to supply chain attacks where attackers exploit vulnerable dependencies. The impact could be severe if vulnerabilities - remain unpatched, potentially leading to code execution, data theft, or other compromises through the software supply chain. +description: The following analytic detects when a user disables Dependabot security features within a GitHub repository. Dependabot helps automatically identify and fix security vulnerabilities in dependencies. The detection monitors GitHub Enterprise logs for configuration changes that disable Dependabot functionality. This behavior could indicate an attacker attempting to prevent the automatic detection of vulnerable dependencies, which would allow them to exploit known vulnerabilities that would otherwise be patched. For a SOC, identifying the disabling of security features like Dependabot is critical as it may be a precursor to supply chain attacks where attackers exploit vulnerable dependencies. The impact could be severe if vulnerabilities remain unpatched, potentially leading to code execution, data theft, or other compromises through the software supply chain. data_source: -- GitHub Enterprise Audit Logs -search: '`github_enterprise` action=repository_vulnerability_alerts.disable - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor, actor_id, actor_ip, actor_is_bot, actor_location.country_code, business, business_id, org, org_id, repo, repo_id, user, user_agent, user_id, action - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `github_enterprise_disable_dependabot_filter`' + - GitHub Enterprise Audit Logs +search: |- + `github_enterprise` action=repository_vulnerability_alerts.disable + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor, actor_id, actor_ip, + actor_is_bot, actor_location.country_code, business, + business_id, org, org_id, + repo, repo_id, user, + user_agent, user_id, action + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `github_enterprise_disable_dependabot_filter` how_to_implement: You must ingest GitHub Enterprise logs using Audit log streaming as described in this documentation https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk using a Splunk HTTP Event Collector. known_false_positives: No false positives have been identified at this time. references: -- https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 -- https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk + - https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 + - https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Dependabot security features are disabled in repository $repo$ by $user$ - risk_objects: - - field: user - type: user - score: 25 - threat_objects: - - field: user_agent - type: http_user_agent + message: Dependabot security features are disabled in repository $repo$ by $user$ + risk_objects: + - field: user + type: user + score: 25 + threat_objects: + - field: user_agent + type: http_user_agent tags: - analytic_story: - - GitHub Malicious Activity - asset_type: GitHub - mitre_attack_id: - - T1562.001 - - T1195 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - GitHub Malicious Activity + asset_type: GitHub + mitre_attack_id: + - T1562.001 + - T1195 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable_dependabot/github.json - source: http:github - sourcetype: httpevent - + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable_dependabot/github.json + source: http:github + sourcetype: httpevent diff --git a/detections/cloud/github_enterprise_disable_ip_allow_list.yml b/detections/cloud/github_enterprise_disable_ip_allow_list.yml index d3265a4592..7279d64efb 100644 --- a/detections/cloud/github_enterprise_disable_ip_allow_list.yml +++ b/detections/cloud/github_enterprise_disable_ip_allow_list.yml @@ -1,65 +1,62 @@ name: GitHub Enterprise Disable IP Allow List id: afed020e-edcd-4913-a675-cebedf81d4fb -version: 3 -date: '2026-01-14' +version: 4 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic identifies when an IP allow list is disabled in GitHub Enterprise. - The detection monitors GitHub Enterprise audit logs for actions related to disabling IP allow lists at the organization or enterprise level. - This behavior is concerning because IP allow lists are a critical security control that restricts access to GitHub Enterprise resources to only - trusted IP addresses. When disabled, it could indicate an attacker attempting to bypass access controls to gain unauthorized access from untrusted - networks. The impact includes potential exposure of sensitive code repositories and GitHub Enterprise resources to access from any IP address. - SOC teams should investigate such events, especially if they were not pre-approved changes, as they may indicate compromise of admin credentials - or malicious insider activity. +description: The following analytic identifies when an IP allow list is disabled in GitHub Enterprise. The detection monitors GitHub Enterprise audit logs for actions related to disabling IP allow lists at the organization or enterprise level. This behavior is concerning because IP allow lists are a critical security control that restricts access to GitHub Enterprise resources to only trusted IP addresses. When disabled, it could indicate an attacker attempting to bypass access controls to gain unauthorized access from untrusted networks. The impact includes potential exposure of sensitive code repositories and GitHub Enterprise resources to access from any IP address. SOC teams should investigate such events, especially if they were not pre-approved changes, as they may indicate compromise of admin credentials or malicious insider activity. data_source: -- GitHub Enterprise Audit Logs -search: '`github_enterprise` action=ip_allow_list.disable - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor, actor_id, actor_is_bot, actor_location.country_code, business, business_id, user_agent, user_id, action - | eval user=actor - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `github_enterprise_disable_ip_allow_list_filter`' + - GitHub Enterprise Audit Logs +search: |- + `github_enterprise` action=ip_allow_list.disable + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor, actor_id, actor_is_bot, + actor_location.country_code, business, business_id, + user_agent, user_id, action + | eval user=actor + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `github_enterprise_disable_ip_allow_list_filter` how_to_implement: You must ingest GitHub Enterprise logs using Audit log streaming as described in this documentation https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk using a Splunk HTTP Event Collector. known_false_positives: No false positives have been identified at this time. references: -- https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 -- https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk + - https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 + - https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $user$ disabled an IP allow list in GitHub Enterprise - risk_objects: - - field: user - type: user - score: 25 - threat_objects: - - field: user_agent - type: http_user_agent + message: $user$ disabled an IP allow list in GitHub Enterprise + risk_objects: + - field: user + type: user + score: 25 + threat_objects: + - field: user_agent + type: http_user_agent tags: - analytic_story: - - GitHub Malicious Activity - asset_type: GitHub - mitre_attack_id: - - T1562.001 - - T1195 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - GitHub Malicious Activity + asset_type: GitHub + mitre_attack_id: + - T1562.001 + - T1195 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/github_disable_ip_allow_list/github.json - source: http:github - sourcetype: httpevent - - + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/github_disable_ip_allow_list/github.json + source: http:github + sourcetype: httpevent diff --git a/detections/cloud/github_enterprise_modify_audit_log_event_stream.yml b/detections/cloud/github_enterprise_modify_audit_log_event_stream.yml index ffbaf39edc..3d62dcd40e 100644 --- a/detections/cloud/github_enterprise_modify_audit_log_event_stream.yml +++ b/detections/cloud/github_enterprise_modify_audit_log_event_stream.yml @@ -1,66 +1,63 @@ name: GitHub Enterprise Modify Audit Log Event Stream id: 99abf2e1-863c-4ec6-82f8-714391590a4c -version: 4 -date: '2026-01-14' +version: 5 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects when a user modifies or disables audit log event streaming in GitHub Enterprise. - The detection monitors GitHub Enterprise audit logs for configuration changes that affect the audit log streaming functionality, - which is used to send audit events to security monitoring platforms. This behavior could indicate an attacker attempting to - prevent their malicious activities from being logged and detected by tampering with the audit trail. For a SOC, identifying - modifications to audit logging is critical as it may be a precursor to other attacks where adversaries want to operate undetected. - The impact could be severe as organizations lose visibility into user actions, configuration changes, and security events within - their GitHub Enterprise environment, potentially allowing attackers to perform malicious activities without detection. - This creates a significant blind spot in security monitoring and incident response capabilities. +description: The following analytic detects when a user modifies or disables audit log event streaming in GitHub Enterprise. The detection monitors GitHub Enterprise audit logs for configuration changes that affect the audit log streaming functionality, which is used to send audit events to security monitoring platforms. This behavior could indicate an attacker attempting to prevent their malicious activities from being logged and detected by tampering with the audit trail. For a SOC, identifying modifications to audit logging is critical as it may be a precursor to other attacks where adversaries want to operate undetected. The impact could be severe as organizations lose visibility into user actions, configuration changes, and security events within their GitHub Enterprise environment, potentially allowing attackers to perform malicious activities without detection. This creates a significant blind spot in security monitoring and incident response capabilities. data_source: -- GitHub Enterprise Audit Logs -search: '`github_enterprise` action=audit_log_streaming.update - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor, actor_id, actor_ip, actor_is_bot, actor_location.country_code, business, business_id, user_agent, action - | eval user=actor - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `github_enterprise_modify_audit_log_event_stream_filter` ' + - GitHub Enterprise Audit Logs +search: |- + `github_enterprise` action=audit_log_streaming.update + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor, actor_id, actor_ip, + actor_is_bot, actor_location.country_code, business, + business_id, user_agent, action + | eval user=actor + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `github_enterprise_modify_audit_log_event_stream_filter` how_to_implement: You must ingest GitHub Enterprise logs using Audit log streaming as described in this documentation https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk using a Splunk HTTP Event Collector. known_false_positives: No false positives have been identified at this time. references: -- https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 -- https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk + - https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 + - https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Audit log event streaming is modified by $user$ - risk_objects: - - field: user - type: user - score: 25 - threat_objects: - - field: user_agent - type: http_user_agent + message: Audit log event streaming is modified by $user$ + risk_objects: + - field: user + type: user + score: 25 + threat_objects: + - field: user_agent + type: http_user_agent tags: - analytic_story: - - GitHub Malicious Activity - - NPM Supply Chain Compromise - asset_type: GitHub - mitre_attack_id: - - T1562.008 - - T1195 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - GitHub Malicious Activity + - NPM Supply Chain Compromise + asset_type: GitHub + mitre_attack_id: + - T1562.008 + - T1195 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/github_audit_log_stream_modified/github.json - source: http:github - sourcetype: httpevent - + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/github_audit_log_stream_modified/github.json + source: http:github + sourcetype: httpevent diff --git a/detections/cloud/github_enterprise_pause_audit_log_event_stream.yml b/detections/cloud/github_enterprise_pause_audit_log_event_stream.yml index d647320fea..68f27992d3 100644 --- a/detections/cloud/github_enterprise_pause_audit_log_event_stream.yml +++ b/detections/cloud/github_enterprise_pause_audit_log_event_stream.yml @@ -1,66 +1,64 @@ name: GitHub Enterprise Pause Audit Log Event Stream id: 21083dcb-276d-4ef9-8f7e-2113ca5e8094 -version: 4 -date: '2026-01-14' +version: 5 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects when a user pauses audit log event streaming in GitHub Enterprise. - The detection monitors GitHub Enterprise audit logs for configuration changes that temporarily suspend the audit log streaming functionality, - which is used to send audit events to security monitoring platforms. This behavior could indicate an attacker attempting to prevent their - malicious activities from being logged and detected by temporarily disabling the audit trail. For a SOC, identifying the pausing of audit logging - is critical as it may be a precursor to other attacks where adversaries want to operate undetected during the pause window. The impact could be - severe as organizations temporarily lose visibility into user actions, configuration changes, and security events within their GitHub Enterprise - environment, potentially allowing attackers to perform malicious activities without detection during the pause period. - This creates a temporary blind spot in security monitoring and incident response capabilities. +description: The following analytic detects when a user pauses audit log event streaming in GitHub Enterprise. The detection monitors GitHub Enterprise audit logs for configuration changes that temporarily suspend the audit log streaming functionality, which is used to send audit events to security monitoring platforms. This behavior could indicate an attacker attempting to prevent their malicious activities from being logged and detected by temporarily disabling the audit trail. For a SOC, identifying the pausing of audit logging is critical as it may be a precursor to other attacks where adversaries want to operate undetected during the pause window. The impact could be severe as organizations temporarily lose visibility into user actions, configuration changes, and security events within their GitHub Enterprise environment, potentially allowing attackers to perform malicious activities without detection during the pause period. This creates a temporary blind spot in security monitoring and incident response capabilities. data_source: -- GitHub Enterprise Audit Logs -search: '`github_enterprise` action=audit_log_streaming.update reason="User initiated pause" - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor, actor_id, actor_ip, actor_is_bot, actor_location.country_code, business, business_id, user_agent, action, reason - | eval user=actor - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `github_enterprise_pause_audit_log_event_stream_filter`' + - GitHub Enterprise Audit Logs +search: |- + `github_enterprise` action=audit_log_streaming.update reason="User initiated pause" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor, actor_id, actor_ip, + actor_is_bot, actor_location.country_code, business, + business_id, user_agent, action, + reason + | eval user=actor + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `github_enterprise_pause_audit_log_event_stream_filter` how_to_implement: You must ingest GitHub Enterprise logs using Audit log streaming as described in this documentation https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk using a Splunk HTTP Event Collector. known_false_positives: No false positives have been identified at this time. references: -- https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 -- https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk + - https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 + - https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Audit log event streaming is paused by $user$ - risk_objects: - - field: user - type: user - score: 25 - threat_objects: - - field: user_agent - type: http_user_agent + message: Audit log event streaming is paused by $user$ + risk_objects: + - field: user + type: user + score: 25 + threat_objects: + - field: user_agent + type: http_user_agent tags: - analytic_story: - - GitHub Malicious Activity - - NPM Supply Chain Compromise - asset_type: GitHub - mitre_attack_id: - - T1562.008 - - T1195 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - GitHub Malicious Activity + - NPM Supply Chain Compromise + asset_type: GitHub + mitre_attack_id: + - T1562.008 + - T1195 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/github_audit_log_stream_modified/github.json - source: http:github - sourcetype: httpevent - + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/github_audit_log_stream_modified/github.json + source: http:github + sourcetype: httpevent diff --git a/detections/cloud/github_enterprise_register_self_hosted_runner.yml b/detections/cloud/github_enterprise_register_self_hosted_runner.yml index 63ebce071e..1b3d655db1 100644 --- a/detections/cloud/github_enterprise_register_self_hosted_runner.yml +++ b/detections/cloud/github_enterprise_register_self_hosted_runner.yml @@ -1,67 +1,64 @@ name: GitHub Enterprise Register Self Hosted Runner id: b27685a2-8826-4123-ab78-2d9d0d419ed0 -version: 4 -date: '2026-01-14' +version: 5 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic identifies when a self-hosted runner is created in GitHub Enterprise. - The detection monitors GitHub Enterprise audit logs for actions related to creating new self-hosted runners at the organization or enterprise level. - his behavior warrants monitoring because self-hosted runners execute workflow jobs on customer-controlled infrastructure, which could be exploited by attackers to - execute malicious code, access sensitive data, or pivot to other systems. While self-hosted runners are a legitimate feature, their creation should be carefully - controlled as compromised runners pose significant security risks. The impact includes potential remote code execution, data exfiltration, and lateral movement - within the environment if a runner is compromised. SOC teams should investigate unexpected runner creation events to verify they are authorized and properly secured, - especially if created by unfamiliar users or in unusual contexts. +description: The following analytic identifies when a self-hosted runner is created in GitHub Enterprise. The detection monitors GitHub Enterprise audit logs for actions related to creating new self-hosted runners at the organization or enterprise level. his behavior warrants monitoring because self-hosted runners execute workflow jobs on customer-controlled infrastructure, which could be exploited by attackers to execute malicious code, access sensitive data, or pivot to other systems. While self-hosted runners are a legitimate feature, their creation should be carefully controlled as compromised runners pose significant security risks. The impact includes potential remote code execution, data exfiltration, and lateral movement within the environment if a runner is compromised. SOC teams should investigate unexpected runner creation events to verify they are authorized and properly secured, especially if created by unfamiliar users or in unusual contexts. data_source: -- GitHub Enterprise Audit Logs -search: '`github_enterprise` action=enterprise.register_self_hosted_runner - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor, actor_id, actor_is_bot, actor_location.country_code, business, business_id, user_agent, action - | eval user=actor - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `github_enterprise_register_self_hosted_runner_filter`' + - GitHub Enterprise Audit Logs +search: |- + `github_enterprise` action=enterprise.register_self_hosted_runner + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor, actor_id, actor_is_bot, + actor_location.country_code, business, business_id, + user_agent, action + | eval user=actor + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `github_enterprise_register_self_hosted_runner_filter` how_to_implement: You must ingest GitHub Enterprise logs using Audit log streaming as described in this documentation https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk using a Splunk HTTP Event Collector. known_false_positives: No false positives have been identified at this time. references: -- https://www.wiz.io/blog/shai-hulud-2-0-ongoing-supply-chain-attack -- https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 -- https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk + - https://www.wiz.io/blog/shai-hulud-2-0-ongoing-supply-chain-attack + - https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 + - https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $user$ created a self-hosted runner in GitHub Enterprise - risk_objects: - - field: user - type: user - score: 25 - threat_objects: - - field: user_agent - type: http_user_agent + message: $user$ created a self-hosted runner in GitHub Enterprise + risk_objects: + - field: user + type: user + score: 25 + threat_objects: + - field: user_agent + type: http_user_agent tags: - analytic_story: - - GitHub Malicious Activity - - NPM Supply Chain Compromise - asset_type: GitHub - mitre_attack_id: - - T1562.001 - - T1195 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - GitHub Malicious Activity + - NPM Supply Chain Compromise + asset_type: GitHub + mitre_attack_id: + - T1562.001 + - T1195 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/github_created_self_hosted_runner/github.json - source: http:github - sourcetype: httpevent - - + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/github_created_self_hosted_runner/github.json + source: http:github + sourcetype: httpevent diff --git a/detections/cloud/github_enterprise_remove_organization.yml b/detections/cloud/github_enterprise_remove_organization.yml index c76914631a..66f0460471 100644 --- a/detections/cloud/github_enterprise_remove_organization.yml +++ b/detections/cloud/github_enterprise_remove_organization.yml @@ -1,63 +1,63 @@ name: GitHub Enterprise Remove Organization id: 94cb89aa-aec1-4585-91b1-affcdacf357e -version: 3 -date: '2026-01-14' +version: 4 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects when a user removes an organization from GitHub Enterprise. - The detection monitors GitHub Enterprise audit logs for organization deletion events, which could indicate unauthorized removal of critical business resources. - For a SOC, identifying organization removals is crucial as it may signal account compromise, insider threats, or malicious attempts to disrupt business operations - by deleting entire organizational structures. The impact could be severe, potentially resulting in loss of source code, repositories, team structures, access controls, - and other critical organizational assets. This disruption could halt development workflows, cause data loss, and require significant effort to restore from backups - if available. Additionally, unauthorized organization removal could be part of a larger attack campaign aimed at destroying or compromising enterprise assets. +description: The following analytic detects when a user removes an organization from GitHub Enterprise. The detection monitors GitHub Enterprise audit logs for organization deletion events, which could indicate unauthorized removal of critical business resources. For a SOC, identifying organization removals is crucial as it may signal account compromise, insider threats, or malicious attempts to disrupt business operations by deleting entire organizational structures. The impact could be severe, potentially resulting in loss of source code, repositories, team structures, access controls, and other critical organizational assets. This disruption could halt development workflows, cause data loss, and require significant effort to restore from backups if available. Additionally, unauthorized organization removal could be part of a larger attack campaign aimed at destroying or compromising enterprise assets. data_source: -- GitHub Enterprise Audit Logs -search: '`github_enterprise` action=business.remove_organization - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor, actor_id, actor_is_bot, actor_location.country_code, business, business_id, org, org_id, user_agent, action - | eval user=actor - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `github_enterprise_remove_organization_filter`' + - GitHub Enterprise Audit Logs +search: |- + `github_enterprise` action=business.remove_organization + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor, actor_id, actor_is_bot, + actor_location.country_code, business, business_id, + org, org_id, user_agent, + action + | eval user=actor + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `github_enterprise_remove_organization_filter` how_to_implement: You must ingest GitHub Enterprise logs using Audit log streaming as described in this documentation https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk using a Splunk HTTP Event Collector. known_false_positives: No false positives have been identified at this time. references: -- https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 -- https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk + - https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 + - https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $user$ removed an organization from GitHub Enterprise - risk_objects: - - field: user - type: user - score: 25 - threat_objects: - - field: user_agent - type: http_user_agent + message: $user$ removed an organization from GitHub Enterprise + risk_objects: + - field: user + type: user + score: 25 + threat_objects: + - field: user_agent + type: http_user_agent tags: - analytic_story: - - GitHub Malicious Activity - asset_type: GitHub - mitre_attack_id: - - T1485 - - T1195 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - GitHub Malicious Activity + asset_type: GitHub + mitre_attack_id: + - T1485 + - T1195 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/github_remove_organization/github.json - source: http:github - sourcetype: httpevent - + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/github_remove_organization/github.json + source: http:github + sourcetype: httpevent diff --git a/detections/cloud/github_enterprise_repository_archived.yml b/detections/cloud/github_enterprise_repository_archived.yml index b4c4c781ca..a40e914409 100644 --- a/detections/cloud/github_enterprise_repository_archived.yml +++ b/detections/cloud/github_enterprise_repository_archived.yml @@ -1,68 +1,65 @@ name: GitHub Enterprise Repository Archived id: 8367cb99-bae1-4748-ae3b-0927bb381424 -version: 4 -date: '2026-01-14' +version: 5 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects when a repository is archived in GitHub Enterprise. - The detection monitors GitHub Enterprise audit logs for repository archival events by tracking actor details, - repository information, and associated metadata. For a SOC, identifying repository archival is important as it could - indicate attempts to make critical code inaccessible or preparation for repository deletion. While archiving is a legitimate - feature, unauthorized archival of active repositories could signal account compromise, insider threats, or attempts to disrupt - development operations. The impact of unauthorized repository archival includes loss of active development access, disruption - to workflows and CI/CD pipelines, and potential business delays if critical repositories are affected. Additionally, archived - repositories may be targeted for subsequent deletion, potentially resulting in permanent loss of intellectual property if - proper backups are not maintained. +description: The following analytic detects when a repository is archived in GitHub Enterprise. The detection monitors GitHub Enterprise audit logs for repository archival events by tracking actor details, repository information, and associated metadata. For a SOC, identifying repository archival is important as it could indicate attempts to make critical code inaccessible or preparation for repository deletion. While archiving is a legitimate feature, unauthorized archival of active repositories could signal account compromise, insider threats, or attempts to disrupt development operations. The impact of unauthorized repository archival includes loss of active development access, disruption to workflows and CI/CD pipelines, and potential business delays if critical repositories are affected. Additionally, archived repositories may be targeted for subsequent deletion, potentially resulting in permanent loss of intellectual property if proper backups are not maintained. data_source: -- GitHub Enterprise Audit Logs -search: '`github_enterprise` action=repo.archived - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor, actor_id, actor_is_bot, actor_location.country_code, business, business_id, org, org_id, repo, repo_id, user_agent, visibility, action - | eval user=actor - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `github_enterprise_repository_archived_filter`' + - GitHub Enterprise Audit Logs +search: |- + `github_enterprise` action=repo.archived + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor, actor_id, actor_is_bot, + actor_location.country_code, business, business_id, + org, org_id, repo, + repo_id, user_agent, visibility, + action + | eval user=actor + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `github_enterprise_repository_archived_filter` how_to_implement: You must ingest GitHub Enterprise logs using Audit log streaming as described in this documentation https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk using a Splunk HTTP Event Collector. known_false_positives: No false positives have been identified at this time. references: -- https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 -- https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk + - https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 + - https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $user$ archived a repository in GitHub Enterprise - risk_objects: - - field: user - type: user - score: 25 - threat_objects: - - field: user_agent - type: http_user_agent + message: $user$ archived a repository in GitHub Enterprise + risk_objects: + - field: user + type: user + score: 25 + threat_objects: + - field: user_agent + type: http_user_agent tags: - analytic_story: - - GitHub Malicious Activity - - NPM Supply Chain Compromise - asset_type: GitHub - mitre_attack_id: - - T1485 - - T1195 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - GitHub Malicious Activity + - NPM Supply Chain Compromise + asset_type: GitHub + mitre_attack_id: + - T1485 + - T1195 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/github_archived_repository/github.json - source: http:github - sourcetype: httpevent - - + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/github_archived_repository/github.json + source: http:github + sourcetype: httpevent diff --git a/detections/cloud/github_enterprise_repository_deleted.yml b/detections/cloud/github_enterprise_repository_deleted.yml index 94264a86b9..b835ed5edc 100644 --- a/detections/cloud/github_enterprise_repository_deleted.yml +++ b/detections/cloud/github_enterprise_repository_deleted.yml @@ -1,65 +1,65 @@ name: GitHub Enterprise Repository Deleted id: f709e736-3e6c-492f-b865-bc7696cc24a7 -version: 4 -date: '2026-01-14' +version: 5 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects when a user deletes a repository in GitHub Enterprise. - The detection monitors GitHub Enterprise audit logs for repository deletion events, which could indicate unauthorized removal of critical source code and project resources. - For a SOC, identifying repository deletions is crucial as it may signal account compromise, insider threats, or malicious attempts to destroy intellectual property and - disrupt development operations. The impact could be severe, potentially resulting in permanent loss of source code, documentation, project history, and other critical assets - if proper backups are not maintained. Repository deletion could halt development workflows, cause significant business disruption, and require substantial effort to restore - from backups if available. Additionally, unauthorized repository removal could be part of a larger attack campaign aimed at destroying or compromising enterprise assets. +description: The following analytic detects when a user deletes a repository in GitHub Enterprise. The detection monitors GitHub Enterprise audit logs for repository deletion events, which could indicate unauthorized removal of critical source code and project resources. For a SOC, identifying repository deletions is crucial as it may signal account compromise, insider threats, or malicious attempts to destroy intellectual property and disrupt development operations. The impact could be severe, potentially resulting in permanent loss of source code, documentation, project history, and other critical assets if proper backups are not maintained. Repository deletion could halt development workflows, cause significant business disruption, and require substantial effort to restore from backups if available. Additionally, unauthorized repository removal could be part of a larger attack campaign aimed at destroying or compromising enterprise assets. data_source: -- GitHub Enterprise Audit Logs -search: '`github_enterprise` action=repo.destroy - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor, actor_id, actor_is_bot, actor_location.country_code, business, business_id, org, org_id, repo, repo_id, user_agent, visibility, action - | eval user=actor - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `github_enterprise_repository_deleted_filter`' + - GitHub Enterprise Audit Logs +search: |- + `github_enterprise` action=repo.destroy + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor, actor_id, actor_is_bot, + actor_location.country_code, business, business_id, + org, org_id, repo, + repo_id, user_agent, visibility, + action + | eval user=actor + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `github_enterprise_repository_deleted_filter` how_to_implement: You must ingest GitHub Enterprise logs using Audit log streaming as described in this documentation https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk using a Splunk HTTP Event Collector. known_false_positives: No false positives have been identified at this time. references: -- https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 -- https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk + - https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 + - https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $user$ deleted a repository in GitHub Enterprise - risk_objects: - - field: user - type: user - score: 25 - threat_objects: - - field: user_agent - type: http_user_agent + message: $user$ deleted a repository in GitHub Enterprise + risk_objects: + - field: user + type: user + score: 25 + threat_objects: + - field: user_agent + type: http_user_agent tags: - analytic_story: - - GitHub Malicious Activity - - NPM Supply Chain Compromise - asset_type: GitHub - mitre_attack_id: - - T1485 - - T1195 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - GitHub Malicious Activity + - NPM Supply Chain Compromise + asset_type: GitHub + mitre_attack_id: + - T1485 + - T1195 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/github_delete_repository/github.json - source: http:github - sourcetype: httpevent - - + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/github_delete_repository/github.json + source: http:github + sourcetype: httpevent diff --git a/detections/cloud/github_organizations_delete_branch_ruleset.yml b/detections/cloud/github_organizations_delete_branch_ruleset.yml index b7105f3b01..d4338ea09c 100644 --- a/detections/cloud/github_organizations_delete_branch_ruleset.yml +++ b/detections/cloud/github_organizations_delete_branch_ruleset.yml @@ -1,66 +1,65 @@ name: GitHub Organizations Delete Branch Ruleset id: 8e454f64-4bd6-45e6-8a94-1b482593d721 -version: 5 -date: '2026-01-14' +version: 6 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: - The following analytic detects when branch rulesets are deleted in GitHub Organizations. - The detection monitors GitHub Organizations audit logs for branch ruleset deletion events by tracking actor details, repository information, - and associated metadata. For a SOC, identifying deleted branch rulesets is critical as it could indicate attempts to bypass code review requirements - and security controls. Branch rulesets are essential security controls that enforce code review, prevent force pushes, and maintain code quality. - Disabling these protections could allow malicious actors to directly push unauthorized code changes or backdoors to protected branches. - The impact of disabled branch protection includes potential code tampering, bypass of security reviews, introduction of vulnerabilities or malicious code, - and compromise of software supply chain integrity. This activity could be part of a larger attack chain where an adversary first disables security controls - before attempting to inject malicious code. +description: The following analytic detects when branch rulesets are deleted in GitHub Organizations. The detection monitors GitHub Organizations audit logs for branch ruleset deletion events by tracking actor details, repository information, and associated metadata. For a SOC, identifying deleted branch rulesets is critical as it could indicate attempts to bypass code review requirements and security controls. Branch rulesets are essential security controls that enforce code review, prevent force pushes, and maintain code quality. Disabling these protections could allow malicious actors to directly push unauthorized code changes or backdoors to protected branches. The impact of disabled branch protection includes potential code tampering, bypass of security reviews, introduction of vulnerabilities or malicious code, and compromise of software supply chain integrity. This activity could be part of a larger attack chain where an adversary first disables security controls before attempting to inject malicious code. data_source: - - GitHub Organizations Audit Logs -search: '`github_organizations` vendor_action=repository_ruleset.destroy - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor, actor_id, actor_ip, actor_is_bot, actor_location.country_code, business, business_id, org, org_id, repo, repo_id, user_agent, vendor_action, ruleset_name - | eval user=actor - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `github_organizations_delete_branch_ruleset_filter`' + - GitHub Organizations Audit Logs +search: |- + `github_organizations` vendor_action=repository_ruleset.destroy + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor, actor_id, actor_ip, + actor_is_bot, actor_location.country_code, business, + business_id, org, org_id, + repo, repo_id, user_agent, + vendor_action, ruleset_name + | eval user=actor + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `github_organizations_delete_branch_ruleset_filter` how_to_implement: You must ingest GitHub Organizations logs using Splunk Add-on for Github using a Personal Access Token https://splunk.github.io/splunk-add-on-for-github-audit-log-monitoring/Install/ . known_false_positives: No false positives have been identified at this time. references: - - https://splunk.github.io/splunk-add-on-for-github-audit-log-monitoring/Install/ - - https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 + - https://splunk.github.io/splunk-add-on-for-github-audit-log-monitoring/Install/ + - https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 drilldown_searches: - - name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $user$ deleted a branch ruleset in repo $repo$ - risk_objects: - - field: user - type: user - score: 25 - threat_objects: - - field: user_agent - type: http_user_agent + message: $user$ deleted a branch ruleset in repo $repo$ + risk_objects: + - field: user + type: user + score: 25 + threat_objects: + - field: user_agent + type: http_user_agent tags: - analytic_story: - - GitHub Malicious Activity - - NPM Supply Chain Compromise - asset_type: GitHub - mitre_attack_id: - - T1562.001 - - T1195 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - GitHub Malicious Activity + - NPM Supply Chain Compromise + asset_type: GitHub + mitre_attack_id: + - T1562.001 + - T1195 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/github_delete_branch_ruleset/github.json - source: github - sourcetype: github:cloud:audit + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/github_delete_branch_ruleset/github.json + source: github + sourcetype: github:cloud:audit diff --git a/detections/cloud/github_organizations_disable_2fa_requirement.yml b/detections/cloud/github_organizations_disable_2fa_requirement.yml index 53bddc1b26..326180b51d 100644 --- a/detections/cloud/github_organizations_disable_2fa_requirement.yml +++ b/detections/cloud/github_organizations_disable_2fa_requirement.yml @@ -1,64 +1,63 @@ name: GitHub Organizations Disable 2FA Requirement id: 3ed0d6ba-4791-4fa8-a1ef-403e438c7033 -version: 4 -date: '2026-01-14' +version: 5 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: - The following analytic detects when two-factor authentication (2FA) requirements are disabled in GitHub Organizations. - The detection monitors GitHub Organizations audit logs for 2FA requirement changes by tracking actor details, organization information, - and associated metadata. For a SOC, identifying disabled 2FA requirements is critical as it could indicate attempts to weaken account security - controls. Two-factor authentication is a fundamental security control that helps prevent unauthorized access even if passwords are compromised. - Disabling 2FA requirements could allow attackers to more easily compromise accounts through password-based attacks. The impact of disabled 2FA - includes increased risk of account takeover, potential access to sensitive code and intellectual property, and compromise of the software supply chain. - This activity could be part of a larger attack chain where an adversary first disables security controls before attempting broader account compromises. +description: The following analytic detects when two-factor authentication (2FA) requirements are disabled in GitHub Organizations. The detection monitors GitHub Organizations audit logs for 2FA requirement changes by tracking actor details, organization information, and associated metadata. For a SOC, identifying disabled 2FA requirements is critical as it could indicate attempts to weaken account security controls. Two-factor authentication is a fundamental security control that helps prevent unauthorized access even if passwords are compromised. Disabling 2FA requirements could allow attackers to more easily compromise accounts through password-based attacks. The impact of disabled 2FA includes increased risk of account takeover, potential access to sensitive code and intellectual property, and compromise of the software supply chain. This activity could be part of a larger attack chain where an adversary first disables security controls before attempting broader account compromises. data_source: - - GitHub Organizations Audit Logs -search: '`github_organizations` vendor_action=org.disable_two_factor_requirement - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor, actor_id, actor_ip, actor_is_bot, actor_location.country_code, business, business_id, org, org_id, user_agent, vendor_action - | eval user=actor - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `github_organizations_disable_2fa_requirement_filter`' + - GitHub Organizations Audit Logs +search: |- + `github_organizations` vendor_action=org.disable_two_factor_requirement + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor, actor_id, actor_ip, + actor_is_bot, actor_location.country_code, business, + business_id, org, org_id, + user_agent, vendor_action + | eval user=actor + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `github_organizations_disable_2fa_requirement_filter` how_to_implement: You must ingest GitHub Organizations logs using Splunk Add-on for Github using a Personal Access Token https://splunk.github.io/splunk-add-on-for-github-audit-log-monitoring/Install/ . known_false_positives: No false positives have been identified at this time. references: - - https://splunk.github.io/splunk-add-on-for-github-audit-log-monitoring/Install/ - - https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 + - https://splunk.github.io/splunk-add-on-for-github-audit-log-monitoring/Install/ + - https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 drilldown_searches: - - name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $user$ disabled 2FA requirement in GitHub Organizations - risk_objects: - - field: user - type: user - score: 25 - threat_objects: - - field: user_agent - type: http_user_agent + message: $user$ disabled 2FA requirement in GitHub Organizations + risk_objects: + - field: user + type: user + score: 25 + threat_objects: + - field: user_agent + type: http_user_agent tags: - analytic_story: - - GitHub Malicious Activity - asset_type: GitHub - mitre_attack_id: - - T1562.001 - - T1195 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - GitHub Malicious Activity + asset_type: GitHub + mitre_attack_id: + - T1562.001 + - T1195 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/github_disable_two_factor_requirement/github.json - source: github - sourcetype: github:cloud:audit + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/github_disable_two_factor_requirement/github.json + source: github + sourcetype: github:cloud:audit diff --git a/detections/cloud/github_organizations_disable_classic_branch_protection_rule.yml b/detections/cloud/github_organizations_disable_classic_branch_protection_rule.yml index 063c463ea9..bb423526c1 100644 --- a/detections/cloud/github_organizations_disable_classic_branch_protection_rule.yml +++ b/detections/cloud/github_organizations_disable_classic_branch_protection_rule.yml @@ -1,65 +1,64 @@ name: GitHub Organizations Disable Classic Branch Protection Rule id: 33cffee0-41ee-402e-a238-d37825f2d788 -version: 4 -date: '2026-01-14' +version: 5 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: - The following analytic detects when classic branch protection rules are disabled in GitHub Organizations. - The detection monitors GitHub Organizations audit logs for branch protection removal events by tracking actor details, repository information, - and associated metadata. For a SOC, identifying disabled branch protection is critical as it could indicate attempts to bypass code review requirements - and security controls. Branch protection rules are essential security controls that enforce code review, prevent force pushes, and maintain code quality. - Disabling these protections could allow malicious actors to directly push unauthorized code changes or backdoors to protected branches. - The impact of disabled branch protection includes potential code tampering, bypass of security reviews, introduction of vulnerabilities - or malicious code, and compromise of software supply chain integrity. This activity could be part of a larger attack chain where an adversary - first disables security controls before attempting to inject malicious code. +description: The following analytic detects when classic branch protection rules are disabled in GitHub Organizations. The detection monitors GitHub Organizations audit logs for branch protection removal events by tracking actor details, repository information, and associated metadata. For a SOC, identifying disabled branch protection is critical as it could indicate attempts to bypass code review requirements and security controls. Branch protection rules are essential security controls that enforce code review, prevent force pushes, and maintain code quality. Disabling these protections could allow malicious actors to directly push unauthorized code changes or backdoors to protected branches. The impact of disabled branch protection includes potential code tampering, bypass of security reviews, introduction of vulnerabilities or malicious code, and compromise of software supply chain integrity. This activity could be part of a larger attack chain where an adversary first disables security controls before attempting to inject malicious code. data_source: - - GitHub Organizations Audit Logs -search: '`github_organizations` vendor_action=protected_branch.destroy - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor, actor_id, actor_ip, actor_is_bot, actor_location.country_code, business, business_id, org, org_id, repo, repo_id, user_agent, vendor_action, name - | eval user=actor - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `github_organizations_disable_classic_branch_protection_rule_filter`' + - GitHub Organizations Audit Logs +search: |- + `github_organizations` vendor_action=protected_branch.destroy + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor, actor_id, actor_ip, + actor_is_bot, actor_location.country_code, business, + business_id, org, org_id, + repo, repo_id, user_agent, + vendor_action, name + | eval user=actor + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `github_organizations_disable_classic_branch_protection_rule_filter` how_to_implement: You must ingest GitHub Organizations logs using Splunk Add-on for Github using a Personal Access Token https://splunk.github.io/splunk-add-on-for-github-audit-log-monitoring/Install/ . known_false_positives: No false positives have been identified at this time. references: - - https://splunk.github.io/splunk-add-on-for-github-audit-log-monitoring/Install/ - - https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 + - https://splunk.github.io/splunk-add-on-for-github-audit-log-monitoring/Install/ + - https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 drilldown_searches: - - name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $user$ disabled a classic branch protection rule in repo $repo$ - risk_objects: - - field: user - type: user - score: 25 - threat_objects: - - field: user_agent - type: http_user_agent + message: $user$ disabled a classic branch protection rule in repo $repo$ + risk_objects: + - field: user + type: user + score: 25 + threat_objects: + - field: user_agent + type: http_user_agent tags: - analytic_story: - - GitHub Malicious Activity - asset_type: GitHub - mitre_attack_id: - - T1562.001 - - T1195 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - GitHub Malicious Activity + asset_type: GitHub + mitre_attack_id: + - T1562.001 + - T1195 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/github_disable_classic_branch_protection/github.json - source: github - sourcetype: github:cloud:audit + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/github_disable_classic_branch_protection/github.json + source: github + sourcetype: github:cloud:audit diff --git a/detections/cloud/github_organizations_disable_dependabot.yml b/detections/cloud/github_organizations_disable_dependabot.yml index 54fe3d8666..6ba76598d9 100644 --- a/detections/cloud/github_organizations_disable_dependabot.yml +++ b/detections/cloud/github_organizations_disable_dependabot.yml @@ -1,64 +1,63 @@ name: GitHub Organizations Disable Dependabot id: 69078d8c-0de6-45de-bb00-14e78e042fd6 -version: 4 -date: '2026-01-14' +version: 5 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: - The following analytic detects when a user disables Dependabot security features within a GitHub repository. - Dependabot helps automatically identify and fix security vulnerabilities in dependencies. The detection monitors GitHub - Enterprise logs for configuration changes that disable Dependabot functionality. This behavior could indicate an attacker - attempting to prevent the automatic detection of vulnerable dependencies, which would allow them to exploit known vulnerabilities - that would otherwise be patched. For a SOC, identifying the disabling of security features like Dependabot is critical as it may - be a precursor to supply chain attacks where attackers exploit vulnerable dependencies. The impact could be severe if vulnerabilities - remain unpatched, potentially leading to code execution, data theft, or other compromises through the software supply chain. +description: The following analytic detects when a user disables Dependabot security features within a GitHub repository. Dependabot helps automatically identify and fix security vulnerabilities in dependencies. The detection monitors GitHub Enterprise logs for configuration changes that disable Dependabot functionality. This behavior could indicate an attacker attempting to prevent the automatic detection of vulnerable dependencies, which would allow them to exploit known vulnerabilities that would otherwise be patched. For a SOC, identifying the disabling of security features like Dependabot is critical as it may be a precursor to supply chain attacks where attackers exploit vulnerable dependencies. The impact could be severe if vulnerabilities remain unpatched, potentially leading to code execution, data theft, or other compromises through the software supply chain. data_source: - - GitHub Organizations Audit Logs -search: - '`github_organizations` vendor_action=repository_vulnerability_alerts.disable - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor, actor_id, actor_ip, actor_is_bot, actor_location.country_code, business, business_id, org, org_id, repo, repo_id, user, user_agent, user_id, vendor_action - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `github_organizations_disable_dependabot_filter`' + - GitHub Organizations Audit Logs +search: |- + `github_organizations` vendor_action=repository_vulnerability_alerts.disable + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor, actor_id, actor_ip, + actor_is_bot, actor_location.country_code, business, + business_id, org, org_id, + repo, repo_id, user, + user_agent, user_id, vendor_action + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `github_organizations_disable_dependabot_filter` how_to_implement: You must ingest GitHub Organizations logs using Splunk Add-on for Github using a Personal Access Token https://splunk.github.io/splunk-add-on-for-github-audit-log-monitoring/Install/ . known_false_positives: No false positives have been identified at this time. references: - - https://splunk.github.io/splunk-add-on-for-github-audit-log-monitoring/Install/ - - https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 + - https://splunk.github.io/splunk-add-on-for-github-audit-log-monitoring/Install/ + - https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 drilldown_searches: - - name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Dependabot security features are disabled in repository $repo$ by $user$ - risk_objects: - - field: user - type: user - score: 25 - threat_objects: - - field: user_agent - type: http_user_agent + message: Dependabot security features are disabled in repository $repo$ by $user$ + risk_objects: + - field: user + type: user + score: 25 + threat_objects: + - field: user_agent + type: http_user_agent tags: - analytic_story: - - GitHub Malicious Activity - asset_type: GitHub - mitre_attack_id: - - T1562.001 - - T1195 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - GitHub Malicious Activity + asset_type: GitHub + mitre_attack_id: + - T1562.001 + - T1195 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable_dependabot/github.json - source: github - sourcetype: github:cloud:audit + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable_dependabot/github.json + source: github + sourcetype: github:cloud:audit diff --git a/detections/cloud/github_organizations_repository_archived.yml b/detections/cloud/github_organizations_repository_archived.yml index c6b07f0742..4b18f0880b 100644 --- a/detections/cloud/github_organizations_repository_archived.yml +++ b/detections/cloud/github_organizations_repository_archived.yml @@ -1,67 +1,65 @@ name: GitHub Organizations Repository Archived id: 4f568a0e-896f-4d94-a2f7-fa6d82ab1f77 -version: 5 -date: '2026-01-14' +version: 6 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: - The following analytic detects when a repository is archived in GitHub Organizations. - The detection monitors GitHub Organizations audit logs for repository archival events by tracking actor details, - repository information, and associated metadata. For a SOC, identifying repository archival is important as it could - indicate attempts to make critical code inaccessible or preparation for repository deletion. While archiving is a legitimate - feature, unauthorized archival of active repositories could signal account compromise, insider threats, or attempts to disrupt - development operations. The impact of unauthorized repository archival includes loss of active development access, disruption - to workflows and CI/CD pipelines, and potential business delays if critical repositories are affected. Additionally, archived - repositories may be targeted for subsequent deletion, potentially resulting in permanent loss of intellectual property if - proper backups are not maintained. +description: The following analytic detects when a repository is archived in GitHub Organizations. The detection monitors GitHub Organizations audit logs for repository archival events by tracking actor details, repository information, and associated metadata. For a SOC, identifying repository archival is important as it could indicate attempts to make critical code inaccessible or preparation for repository deletion. While archiving is a legitimate feature, unauthorized archival of active repositories could signal account compromise, insider threats, or attempts to disrupt development operations. The impact of unauthorized repository archival includes loss of active development access, disruption to workflows and CI/CD pipelines, and potential business delays if critical repositories are affected. Additionally, archived repositories may be targeted for subsequent deletion, potentially resulting in permanent loss of intellectual property if proper backups are not maintained. data_source: - - GitHub Organizations Audit Logs -search: '`github_organizations` vendor_action=repo.archived - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor, actor_id, actor_is_bot, actor_location.country_code, business, business_id, org, org_id, repo, repo_id, user_agent, visibility, vendor_action - | eval user=actor - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `github_organizations_repository_archived_filter`' + - GitHub Organizations Audit Logs +search: |- + `github_organizations` vendor_action=repo.archived + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor, actor_id, actor_is_bot, + actor_location.country_code, business, business_id, + org, org_id, repo, + repo_id, user_agent, visibility, + vendor_action + | eval user=actor + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `github_organizations_repository_archived_filter` how_to_implement: You must ingest GitHub Organizations logs using Splunk Add-on for Github using a Personal Access Token https://splunk.github.io/splunk-add-on-for-github-audit-log-monitoring/Install/ . known_false_positives: No false positives have been identified at this time. references: - - https://splunk.github.io/splunk-add-on-for-github-audit-log-monitoring/Install/ - - https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 + - https://splunk.github.io/splunk-add-on-for-github-audit-log-monitoring/Install/ + - https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 drilldown_searches: - - name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $user$ archived a repository in GitHub Organizations - risk_objects: - - field: user - type: user - score: 25 - threat_objects: - - field: user_agent - type: http_user_agent + message: $user$ archived a repository in GitHub Organizations + risk_objects: + - field: user + type: user + score: 25 + threat_objects: + - field: user_agent + type: http_user_agent tags: - analytic_story: - - GitHub Malicious Activity - - NPM Supply Chain Compromise - asset_type: GitHub - mitre_attack_id: - - T1485 - - T1195 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - GitHub Malicious Activity + - NPM Supply Chain Compromise + asset_type: GitHub + mitre_attack_id: + - T1485 + - T1195 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/github_archived_repository/github.json - source: github - sourcetype: github:cloud:audit + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/github_archived_repository/github.json + source: github + sourcetype: github:cloud:audit diff --git a/detections/cloud/github_organizations_repository_deleted.yml b/detections/cloud/github_organizations_repository_deleted.yml index 36fb23762a..3dc60eb2ff 100644 --- a/detections/cloud/github_organizations_repository_deleted.yml +++ b/detections/cloud/github_organizations_repository_deleted.yml @@ -1,67 +1,65 @@ name: GitHub Organizations Repository Deleted id: 9ff4ca95-fdae-4eea-9ffa-6d8e1c202a71 -version: 5 -date: '2026-01-14' +version: 6 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: - The following analytic identifies when a repository is deleted within a GitHub organization. - The detection monitors GitHub Organizations audit logs for repository deletion events by tracking actor details, - repository information, and associated metadata. This behavior is concerning for SOC teams as malicious actors may - attempt to delete repositories to destroy source code, intellectual property, or evidence of compromise. Repository - deletion can result in permanent loss of code, documentation, and project history if proper backups are not maintained. - Additionally, unauthorized repository deletion could indicate account compromise, insider threats, or attempts to disrupt - business operations. The impact of a repository deletion attack includes loss of intellectual property, disruption to - development workflows, and potential financial losses from lost work. Early detection of unauthorized repository deletions - allows security teams to investigate potential compromises and restore from backups if needed. +description: The following analytic identifies when a repository is deleted within a GitHub organization. The detection monitors GitHub Organizations audit logs for repository deletion events by tracking actor details, repository information, and associated metadata. This behavior is concerning for SOC teams as malicious actors may attempt to delete repositories to destroy source code, intellectual property, or evidence of compromise. Repository deletion can result in permanent loss of code, documentation, and project history if proper backups are not maintained. Additionally, unauthorized repository deletion could indicate account compromise, insider threats, or attempts to disrupt business operations. The impact of a repository deletion attack includes loss of intellectual property, disruption to development workflows, and potential financial losses from lost work. Early detection of unauthorized repository deletions allows security teams to investigate potential compromises and restore from backups if needed. data_source: - - GitHub Organizations Audit Logs -search: '`github_organizations` vendor_action=repo.destroy - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by actor, actor_id, actor_is_bot, actor_location.country_code, business, business_id, org, org_id, repo, repo_id, user_agent, visibility, vendor_action - | eval user=actor - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `github_organizations_repository_deleted_filter`' + - GitHub Organizations Audit Logs +search: |- + `github_organizations` vendor_action=repo.destroy + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY actor, actor_id, actor_is_bot, + actor_location.country_code, business, business_id, + org, org_id, repo, + repo_id, user_agent, visibility, + vendor_action + | eval user=actor + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `github_organizations_repository_deleted_filter` how_to_implement: You must ingest GitHub Organizations logs using Splunk Add-on for Github using a Personal Access Token https://splunk.github.io/splunk-add-on-for-github-audit-log-monitoring/Install/ . known_false_positives: No false positives have been identified at this time. references: - - https://splunk.github.io/splunk-add-on-for-github-audit-log-monitoring/Install/ - - https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 + - https://splunk.github.io/splunk-add-on-for-github-audit-log-monitoring/Install/ + - https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610 drilldown_searches: - - name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $user$ deleted a repository in GitHub Organizations - risk_objects: - - field: user - type: user - score: 25 - threat_objects: - - field: user_agent - type: http_user_agent + message: $user$ deleted a repository in GitHub Organizations + risk_objects: + - field: user + type: user + score: 25 + threat_objects: + - field: user_agent + type: http_user_agent tags: - analytic_story: - - GitHub Malicious Activity - - NPM Supply Chain Compromise - asset_type: GitHub - mitre_attack_id: - - T1485 - - T1195 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - GitHub Malicious Activity + - NPM Supply Chain Compromise + asset_type: GitHub + mitre_attack_id: + - T1485 + - T1195 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/github_delete_repository/github.json - source: github - sourcetype: github:cloud:audit + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/github_delete_repository/github.json + source: github + sourcetype: github:cloud:audit diff --git a/detections/cloud/gsuite_drive_share_in_external_email.yml b/detections/cloud/gsuite_drive_share_in_external_email.yml index 3137d2e35a..067d23f49a 100644 --- a/detections/cloud/gsuite_drive_share_in_external_email.yml +++ b/detections/cloud/gsuite_drive_share_in_external_email.yml @@ -5,87 +5,67 @@ date: '2025-10-14' author: Teoderick Contreras, Splunk status: experimental type: Anomaly -description: - The following analytic detects Google Drive or Google Docs files shared - externally from an internal domain. It leverages GSuite Drive logs, extracting and - comparing the source and destination email domains to identify external sharing. - This activity is significant as it may indicate potential data exfiltration by an - attacker or insider. If confirmed malicious, this could lead to unauthorized access - to sensitive information, data leakage, and potential compliance violations. Monitoring - this behavior helps in early detection and mitigation of data breaches. +description: The following analytic detects Google Drive or Google Docs files shared externally from an internal domain. It leverages GSuite Drive logs, extracting and comparing the source and destination email domains to identify external sharing. This activity is significant as it may indicate potential data exfiltration by an attacker or insider. If confirmed malicious, this could lead to unauthorized access to sensitive information, data leakage, and potential compliance violations. Monitoring this behavior helps in early detection and mitigation of data breaches. data_source: - - G Suite Drive + - G Suite Drive search: | - `gsuite_drive` NOT (email IN("", "null")) - | spath path=parameters.owner output=owner - | rex field=owner "[^@]+@(?[^@]+)" - | rex field=email "[^@]+@(?[^@]+)" - | where src_domain = "internal_test_email.com" and not dest_domain = "internal_test_email.com" - | eval phase="plan" - | eval severity="low" - | stats values(parameters.doc_title) as doc_title, - values(parameters.doc_type) as doc_types, - values(email) as dst_email_list, - values(parameters.visibility) as visibility, - values(parameters.doc_id) as doc_id, - count min(_time) as firstTime max(_time) as lastTime - by parameters.owner ip_address phase severity - | rename parameters.owner as user ip_address as src_ip - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `gsuite_drive_share_in_external_email_filter` -how_to_implement: - To successfully implement this search, you need to be ingesting - logs related to gsuite having the file attachment metadata like file type, file - extension, source email, destination email, num of attachment and etc. In order - for the search to work for your environment, please edit the query to use your company - specific email domain instead of `internal_test_email.com`. -known_false_positives: - network admin or normal user may share files to customer and - external team. + `gsuite_drive` NOT (email IN("", "null")) + | spath path=parameters.owner output=owner + | rex field=owner "[^@]+@(?[^@]+)" + | rex field=email "[^@]+@(?[^@]+)" + | where src_domain = "internal_test_email.com" and not dest_domain = "internal_test_email.com" + | eval phase="plan" + | eval severity="low" + | stats values(parameters.doc_title) as doc_title, + values(parameters.doc_type) as doc_types, + values(email) as dst_email_list, + values(parameters.visibility) as visibility, + values(parameters.doc_id) as doc_id, + count min(_time) as firstTime max(_time) as lastTime + by parameters.owner ip_address phase severity + | rename parameters.owner as user ip_address as src_ip + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `gsuite_drive_share_in_external_email_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc. In order for the search to work for your environment, please edit the query to use your company specific email domain instead of `internal_test_email.com`. +known_false_positives: network admin or normal user may share files to customer and external team. references: - - https://www.redhat.com/en/topics/devops/what-is-devsecops + - https://www.redhat.com/en/topics/devops/what-is-devsecops drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious share gdrive from $user$ to $dst_email_list$ namely as $doc_title$ - risk_objects: - - field: dst_email_list - type: user - score: 72 - - field: user - type: user - score: 72 - threat_objects: [] + message: Suspicious share gdrive from $user$ to $dst_email_list$ namely as $doc_title$ + risk_objects: + - field: dst_email_list + type: user + score: 72 + - field: user + type: user + score: 72 + threat_objects: [] tags: - analytic_story: - - Scattered Lapsus$ Hunters - - Dev Sec Ops - - Insider Threat - asset_type: GSuite - mitre_attack_id: - - T1567.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Scattered Lapsus$ Hunters + - Dev Sec Ops + - Insider Threat + asset_type: GSuite + mitre_attack_id: + - T1567.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1567.002/gsuite_share_drive/gdrive_share_external.log - source: http:gsuite - sourcetype: gws:reports:drive + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1567.002/gsuite_share_drive/gdrive_share_external.log + source: http:gsuite + sourcetype: gws:reports:drive diff --git a/detections/cloud/gsuite_email_suspicious_attachment.yml b/detections/cloud/gsuite_email_suspicious_attachment.yml index 8ba3827168..f095b167b6 100644 --- a/detections/cloud/gsuite_email_suspicious_attachment.yml +++ b/detections/cloud/gsuite_email_suspicious_attachment.yml @@ -1,73 +1,60 @@ name: GSuite Email Suspicious Attachment id: 6d663014-fe92-11eb-ab07-acde48001122 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects suspicious attachment file extensions - in GSuite emails, potentially indicating a spear-phishing attack. It leverages GSuite - Gmail logs to identify emails with attachments having file extensions commonly associated - with malware, such as .exe, .bat, and .js. This activity is significant as these - file types are often used to deliver malicious payloads, posing a risk of compromising - targeted machines. If confirmed malicious, this could lead to unauthorized code - execution, data breaches, or further network infiltration. +description: The following analytic detects suspicious attachment file extensions in GSuite emails, potentially indicating a spear-phishing attack. It leverages GSuite Gmail logs to identify emails with attachments having file extensions commonly associated with malware, such as .exe, .bat, and .js. This activity is significant as these file types are often used to deliver malicious payloads, posing a risk of compromising targeted machines. If confirmed malicious, this could lead to unauthorized code execution, data breaches, or further network infiltration. data_source: -- G Suite Gmail -search: '`gsuite_gmail` "attachment{}.file_extension_type" IN ("pl", "py", "rb", "sh", - "bat", "exe", "dll", "cpl", "com", "js", "vbs", "ps1", "reg","swf", "cmd", "go") - | eval phase="plan" | eval severity="medium" | stats count min(_time) as firstTime - max(_time) as lastTime values(attachment{}.file_extension_type) as email_attachments, - values(attachment{}.sha256) as attachment_sha256, values(payload_size) as payload_size - by destination{}.service num_message_attachments subject destination{}.address - source.address phase severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `gsuite_email_suspicious_attachment_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs related to gsuite having the file attachment metadata like file type, file - extension, source email, destination email, num of attachment and etc. -known_false_positives: network admin and normal user may send this file attachment - as part of their day to day work. having a good protocol in attaching this file - type to an e-mail may reduce the risk of having a spear phishing attack. + - G Suite Gmail +search: |- + `gsuite_gmail` "attachment{}.file_extension_type" IN ("pl", "py", "rb", "sh", "bat", "exe", "dll", "cpl", "com", "js", "vbs", "ps1", "reg","swf", "cmd", "go") + | eval phase="plan" + | eval severity="medium" + | stats count min(_time) as firstTime max(_time) as lastTime values(attachment{}.file_extension_type) as email_attachments, values(attachment{}.sha256) as attachment_sha256, values(payload_size) as payload_size + BY destination{}.service num_message_attachments subject + destination{}.address source.address phase + severity + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `gsuite_email_suspicious_attachment_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc. +known_false_positives: network admin and normal user may send this file attachment as part of their day to day work. having a good protocol in attaching this file type to an e-mail may reduce the risk of having a spear phishing attack. references: -- https://www.redhat.com/en/topics/devops/what-is-devsecops + - https://www.redhat.com/en/topics/devops/what-is-devsecops drilldown_searches: -- name: View the detection results for - "$destination{}.address$" - search: '%original_detection_search% | search destination{}.address = "$destination{}.address$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$destination{}.address$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$destination{}.address$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$destination{}.address$" + search: '%original_detection_search% | search destination{}.address = "$destination{}.address$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$destination{}.address$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$destination{}.address$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious email from $source.address$ to $destination{}.address$ - risk_objects: - - field: destination{}.address - type: user - score: 49 - threat_objects: - - field: source.address - type: email_address + message: Suspicious email from $source.address$ to $destination{}.address$ + risk_objects: + - field: destination{}.address + type: user + score: 49 + threat_objects: + - field: source.address + type: email_address tags: - analytic_story: - - Dev Sec Ops - asset_type: GSuite - mitre_attack_id: - - T1566.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Dev Sec Ops + asset_type: GSuite + mitre_attack_id: + - T1566.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/gsuite_susp_attachment_ext/gsuite_gmail_file_ext.log - source: http:gsuite - sourcetype: gsuite:gmail:bigquery + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/gsuite_susp_attachment_ext/gsuite_gmail_file_ext.log + source: http:gsuite + sourcetype: gsuite:gmail:bigquery diff --git a/detections/cloud/gsuite_email_suspicious_subject_with_attachment.yml b/detections/cloud/gsuite_email_suspicious_subject_with_attachment.yml index c425fd4243..04d1589fd3 100644 --- a/detections/cloud/gsuite_email_suspicious_subject_with_attachment.yml +++ b/detections/cloud/gsuite_email_suspicious_subject_with_attachment.yml @@ -1,78 +1,64 @@ name: Gsuite Email Suspicious Subject With Attachment id: 8ef3971e-00f2-11ec-b54f-acde48001122 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies Gsuite emails with suspicious subjects - and attachments commonly used in spear phishing attacks. It leverages Gsuite email - logs, focusing on specific keywords in the subject line and known malicious file - types in attachments. This activity is significant for a SOC as spear phishing is - a prevalent method for initial compromise, often leading to further malicious actions. - If confirmed malicious, this activity could result in unauthorized access, data - exfiltration, or further malware deployment, posing a significant risk to the organization's - security. +description: The following analytic identifies Gsuite emails with suspicious subjects and attachments commonly used in spear phishing attacks. It leverages Gsuite email logs, focusing on specific keywords in the subject line and known malicious file types in attachments. This activity is significant for a SOC as spear phishing is a prevalent method for initial compromise, often leading to further malicious actions. If confirmed malicious, this activity could result in unauthorized access, data exfiltration, or further malware deployment, posing a significant risk to the organization's security. data_source: -- G Suite Gmail -search: '`gsuite_gmail` num_message_attachments > 0 subject IN ("*dhl*", "* ups *", - "*delivery*", "*parcel*", "*label*", "*invoice*", "*postal*", "* fedex *", "* usps - *", "* express *", "*shipment*", "*Banking/Tax*","*shipment*", "*new order*") attachment{}.file_extension_type - IN ("doc", "docx", "xls", "xlsx", "ppt", "pptx", "pdf", "zip", "rar", "html","htm","hta") - | rex field=source.from_header_address "[^@]+@(?[^@]+)" | rex field=destination{}.address - "[^@]+@(?[^@]+)" | where not source_domain="internal_test_email.com" - and dest_domain="internal_test_email.com" | eval phase="plan" | eval severity="medium" - | stats count min(_time) as firstTime max(_time) as lastTime values(attachment{}.file_extension_type) - as email_attachments, values(attachment{}.sha256) as attachment_sha256, values(payload_size) - as payload_size by destination{}.service num_message_attachments subject destination{}.address - source.address phase severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `gsuite_email_suspicious_subject_with_attachment_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs related to gsuite having the file attachment metadata like file type, file - extension, source email, destination email, num of attachment and etc. -known_false_positives: normal user or normal transaction may contain the subject and - file type attachment that this detection try to search. + - G Suite Gmail +search: |- + `gsuite_gmail` num_message_attachments > 0 subject IN ("*dhl*", "* ups *", "*delivery*", "*parcel*", "*label*", "*invoice*", "*postal*", "* fedex *", "* usps *", "* express *", "*shipment*", "*Banking/Tax*","*shipment*", "*new order*") attachment{}.file_extension_type IN ("doc", "docx", "xls", "xlsx", "ppt", "pptx", "pdf", "zip", "rar", "html","htm","hta") + | rex field=source.from_header_address "[^@]+@(?[^@]+)" + | rex field=destination{}.address "[^@]+@(?[^@]+)" + | where not source_domain="internal_test_email.com" and dest_domain="internal_test_email.com" + | eval phase="plan" + | eval severity="medium" + | stats count min(_time) as firstTime max(_time) as lastTime values(attachment{}.file_extension_type) as email_attachments, values(attachment{}.sha256) as attachment_sha256, values(payload_size) as payload_size + BY destination{}.service num_message_attachments subject + destination{}.address source.address phase + severity + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `gsuite_email_suspicious_subject_with_attachment_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc. +known_false_positives: normal user or normal transaction may contain the subject and file type attachment that this detection try to search. references: -- https://www.redhat.com/en/topics/devops/what-is-devsecops -- https://www.mandiant.com/resources/top-words-used-in-spear-phishing-attacks + - https://www.redhat.com/en/topics/devops/what-is-devsecops + - https://www.mandiant.com/resources/top-words-used-in-spear-phishing-attacks drilldown_searches: -- name: View the detection results for - "$destination{}.address$" - search: '%original_detection_search% | search destination{}.address = "$destination{}.address$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$destination{}.address$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$destination{}.address$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$destination{}.address$" + search: '%original_detection_search% | search destination{}.address = "$destination{}.address$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$destination{}.address$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$destination{}.address$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious email from $source.address$ to $destination{}.address$ - risk_objects: - - field: destination{}.address - type: user - score: 25 - threat_objects: - - field: source.address - type: email_address + message: Suspicious email from $source.address$ to $destination{}.address$ + risk_objects: + - field: destination{}.address + type: user + score: 25 + threat_objects: + - field: source.address + type: email_address tags: - analytic_story: - - Dev Sec Ops - asset_type: GSuite - mitre_attack_id: - - T1566.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Dev Sec Ops + asset_type: GSuite + mitre_attack_id: + - T1566.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/gsuite_susp_subj/gsuite_susp_subj_attach.log - source: http:gsuite - sourcetype: gsuite:gmail:bigquery + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/gsuite_susp_subj/gsuite_susp_subj_attach.log + source: http:gsuite + sourcetype: gsuite:gmail:bigquery diff --git a/detections/cloud/gsuite_email_with_known_abuse_web_service_link.yml b/detections/cloud/gsuite_email_with_known_abuse_web_service_link.yml index 9036cd6794..7f50276011 100644 --- a/detections/cloud/gsuite_email_with_known_abuse_web_service_link.yml +++ b/detections/cloud/gsuite_email_with_known_abuse_web_service_link.yml @@ -1,72 +1,63 @@ name: Gsuite Email With Known Abuse Web Service Link id: 8630aa22-042b-11ec-af39-acde48001122 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects emails in Gsuite containing links to known - abuse web services such as Pastebin, Telegram, and Discord. It leverages Gsuite - Gmail logs to identify emails with these specific domains in their links. This activity - is significant because these services are commonly used by attackers to deliver - malicious payloads. If confirmed malicious, this could lead to the delivery of malware, - phishing attacks, or other harmful activities, potentially compromising sensitive - information or systems within the organization. +description: The following analytic detects emails in Gsuite containing links to known abuse web services such as Pastebin, Telegram, and Discord. It leverages Gsuite Gmail logs to identify emails with these specific domains in their links. This activity is significant because these services are commonly used by attackers to deliver malicious payloads. If confirmed malicious, this could lead to the delivery of malware, phishing attacks, or other harmful activities, potentially compromising sensitive information or systems within the organization. data_source: -- G Suite Gmail -search: '`gsuite_gmail` "link_domain{}" IN ("*pastebin.com*", "*discord*", "*telegram*","t.me") - | rex field=source.from_header_address "[^@]+@(?[^@]+)" | rex field=destination{}.address - "[^@]+@(?[^@]+)" | where not source_domain="internal_test_email.com" - and dest_domain="internal_test_email.com" | eval phase="plan" | eval severity="low" - |stats values(link_domain{}) as link_domains min(_time) as firstTime max(_time) - as lastTime count by is_spam source.address source.from_header_address subject destination{}.address - phase severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `gsuite_email_with_known_abuse_web_service_link_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs related to gsuite having the file attachment metadata like file type, file - extension, source email, destination email, num of attachment and etc. -known_false_positives: normal email contains this link that are known application - within the organization or network can be catched by this detection. + - G Suite Gmail +search: |- + `gsuite_gmail` "link_domain{}" IN ("*pastebin.com*", "*discord*", "*telegram*","t.me") + | rex field=source.from_header_address "[^@]+@(?[^@]+)" + | rex field=destination{}.address "[^@]+@(?[^@]+)" + | where not source_domain="internal_test_email.com" and dest_domain="internal_test_email.com" + | eval phase="plan" + | eval severity="low" + | stats values(link_domain{}) as link_domains min(_time) as firstTime max(_time) as lastTime count + BY is_spam source.address source.from_header_address + subject destination{}.address phase + severity + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `gsuite_email_with_known_abuse_web_service_link_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc. +known_false_positives: normal email contains this link that are known application within the organization or network can be catched by this detection. references: -- https://news.sophos.com/en-us/2021/07/22/malware-increasingly-targets-discord-for-abuse/ + - https://news.sophos.com/en-us/2021/07/22/malware-increasingly-targets-discord-for-abuse/ drilldown_searches: -- name: View the detection results for - "$destination{}.address$" - search: '%original_detection_search% | search destination{}.address = "$destination{}.address$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$destination{}.address$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$destination{}.address$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$destination{}.address$" + search: '%original_detection_search% | search destination{}.address = "$destination{}.address$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$destination{}.address$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$destination{}.address$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious email from $source.address$ to $destination{}.address$ - risk_objects: - - field: destination{}.address - type: user - score: 25 - threat_objects: - - field: source.address - type: email_address + message: Suspicious email from $source.address$ to $destination{}.address$ + risk_objects: + - field: destination{}.address + type: user + score: 25 + threat_objects: + - field: source.address + type: email_address tags: - analytic_story: - - Dev Sec Ops - asset_type: GSuite - mitre_attack_id: - - T1566.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Dev Sec Ops + asset_type: GSuite + mitre_attack_id: + - T1566.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/gsuite_susp_url/gsuite_susp_url.log - source: http:gsuite - sourcetype: gsuite:gmail:bigquery + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/gsuite_susp_url/gsuite_susp_url.log + source: http:gsuite + sourcetype: gsuite:gmail:bigquery diff --git a/detections/cloud/gsuite_outbound_email_with_attachment_to_external_domain.yml b/detections/cloud/gsuite_outbound_email_with_attachment_to_external_domain.yml index c61a11d2e3..383a9c7f7b 100644 --- a/detections/cloud/gsuite_outbound_email_with_attachment_to_external_domain.yml +++ b/detections/cloud/gsuite_outbound_email_with_attachment_to_external_domain.yml @@ -1,51 +1,46 @@ name: Gsuite Outbound Email With Attachment To External Domain id: dc4dc3a8-ff54-11eb-8bf7-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Stanislav Miskovic, Splunk status: production type: Hunting -description: The following analytic detects outbound emails with attachments sent - from an internal email domain to an external domain. It leverages Gsuite Gmail logs, - parsing the source and destination email domains, and flags emails with fewer than - 20 outbound instances. This activity is significant as it may indicate potential - data exfiltration or insider threats. If confirmed malicious, an attacker could - use this method to exfiltrate sensitive information, leading to data breaches and - compliance violations. +description: The following analytic detects outbound emails with attachments sent from an internal email domain to an external domain. It leverages Gsuite Gmail logs, parsing the source and destination email domains, and flags emails with fewer than 20 outbound instances. This activity is significant as it may indicate potential data exfiltration or insider threats. If confirmed malicious, an attacker could use this method to exfiltrate sensitive information, leading to data breaches and compliance violations. data_source: -- G Suite Gmail -search: '`gsuite_gmail` num_message_attachments > 0 | rex field=source.from_header_address - "[^@]+@(?[^@]+)" | rex field=destination{}.address "[^@]+@(?[^@]+)" - | where source_domain="internal_test_email.com" and not dest_domain="internal_test_email.com" - | eval phase="plan" | eval severity="low" | stats values(subject) as subject, values(source.from_header_address) - as src_domain_list, count as numEvents, dc(source.from_header_address) as numSrcAddresses, - min(_time) as firstTime max(_time) as lastTime by dest_domain phase severity | where - numSrcAddresses < 20 |sort - numSrcAddresses | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `gsuite_outbound_email_with_attachment_to_external_domain_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs related to gsuite having the file attachment metadata like file type, file - extension, source email, destination email, num of attachment and etc. -known_false_positives: network admin and normal user may send this file attachment - as part of their day to day work. having a good protocol in attaching this file - type to an e-mail may reduce the risk of having a spear phishing attack. + - G Suite Gmail +search: |- + `gsuite_gmail` num_message_attachments > 0 + | rex field=source.from_header_address "[^@]+@(?[^@]+)" + | rex field=destination{}.address "[^@]+@(?[^@]+)" + | where source_domain="internal_test_email.com" and not dest_domain="internal_test_email.com" + | eval phase="plan" + | eval severity="low" + | stats values(subject) as subject, values(source.from_header_address) as src_domain_list, count as numEvents, dc(source.from_header_address) as numSrcAddresses, min(_time) as firstTime max(_time) as lastTime + BY dest_domain phase severity + | where numSrcAddresses < 20 + | sort - numSrcAddresses + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `gsuite_outbound_email_with_attachment_to_external_domain_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc. +known_false_positives: network admin and normal user may send this file attachment as part of their day to day work. having a good protocol in attaching this file type to an e-mail may reduce the risk of having a spear phishing attack. references: -- https://www.redhat.com/en/topics/devops/what-is-devsecops + - https://www.redhat.com/en/topics/devops/what-is-devsecops tags: - analytic_story: - - Dev Sec Ops - - Insider Threat - asset_type: GSuite - mitre_attack_id: - - T1048.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Dev Sec Ops + - Insider Threat + asset_type: GSuite + mitre_attack_id: + - T1048.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/gsuite_outbound_email_to_external/gsuite_external_domain.log - source: http:gsuite - sourcetype: gsuite:gmail:bigquery + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/gsuite_outbound_email_to_external/gsuite_external_domain.log + source: http:gsuite + sourcetype: gsuite:gmail:bigquery diff --git a/detections/cloud/gsuite_suspicious_calendar_invite.yml b/detections/cloud/gsuite_suspicious_calendar_invite.yml index 896b94e6be..1e83193f92 100644 --- a/detections/cloud/gsuite_suspicious_calendar_invite.yml +++ b/detections/cloud/gsuite_suspicious_calendar_invite.yml @@ -1,43 +1,34 @@ name: Gsuite suspicious calendar invite id: 03cdd68a-34fb-11ec-9bd3-acde48001122 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Rod Soto, Teoderick Contreras status: experimental type: Hunting -description: The following analytic detects suspicious calendar invites sent via GSuite, - potentially indicating compromised accounts or malicious internal activity. It leverages - GSuite calendar logs, focusing on events where a high volume of invites (over 100) - is sent within a 5-minute window. This behavior is significant as it may involve - the distribution of malicious links or attachments, posing a security risk. If confirmed - malicious, this activity could lead to widespread phishing attacks, unauthorized - access, or malware distribution within the organization. +description: The following analytic detects suspicious calendar invites sent via GSuite, potentially indicating compromised accounts or malicious internal activity. It leverages GSuite calendar logs, focusing on events where a high volume of invites (over 100) is sent within a 5-minute window. This behavior is significant as it may involve the distribution of malicious links or attachments, posing a security risk. If confirmed malicious, this activity could lead to widespread phishing attacks, unauthorized access, or malware distribution within the organization. data_source: [] -search: '`gsuite_calendar` |bin span=5m _time |rename parameters.* as * |search target_calendar_id!=null - email="*yourdomain.com"| stats count values(target_calendar_id) values(event_title) - values(event_guest) by email _time | where count >100| `gsuite_suspicious_calendar_invite_filter`' -how_to_implement: In order to successfully implement this search, you need to be ingesting - logs related to gsuite (gsuite:calendar:json) having the file sharing metadata like - file type, source owner, destination target user, description, etc. This search - can also be made more specific by selecting specific emails, subdomains timeframe, - organizational units, targeted user, etc. In order for the search to work for your - environment please update `yourdomain.com` value in the query with the domain relavant - for your organization. -known_false_positives: This search will also produce normal activity statistics. Fields - such as email, ip address, name, parameters.organizer_calendar_id, parameters.target_calendar_id - and parameters.event_title may give away phishing intent.For more specific results - use email parameter. +search: |- + `gsuite_calendar` + | bin span=5m _time + | rename parameters.* as * + | search target_calendar_id!=null email="*yourdomain.com" + | stats count values(target_calendar_id) values(event_title) values(event_guest) + BY email _time + | where count >100 + | `gsuite_suspicious_calendar_invite_filter` +how_to_implement: In order to successfully implement this search, you need to be ingesting logs related to gsuite (gsuite:calendar:json) having the file sharing metadata like file type, source owner, destination target user, description, etc. This search can also be made more specific by selecting specific emails, subdomains timeframe, organizational units, targeted user, etc. In order for the search to work for your environment please update `yourdomain.com` value in the query with the domain relavant for your organization. +known_false_positives: This search will also produce normal activity statistics. Fields such as email, ip address, name, parameters.organizer_calendar_id, parameters.target_calendar_id and parameters.event_title may give away phishing intent.For more specific results use email parameter. references: -- https://www.techrepublic.com/article/how-to-avoid-the-dreaded-google-calendar-malicious-invite-issue/ -- https://gcn.com/cybersecurity/2012/09/the-20-most-common-words-in-phishing-attacks/280956/ + - https://www.techrepublic.com/article/how-to-avoid-the-dreaded-google-calendar-malicious-invite-issue/ + - https://gcn.com/cybersecurity/2012/09/the-20-most-common-words-in-phishing-attacks/280956/ tags: - analytic_story: - - Spearphishing Attachments - asset_type: GSuite - mitre_attack_id: - - T1566 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Spearphishing Attachments + asset_type: GSuite + mitre_attack_id: + - T1566 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat diff --git a/detections/cloud/gsuite_suspicious_shared_file_name.yml b/detections/cloud/gsuite_suspicious_shared_file_name.yml index 44288fe8bb..1e132f1457 100644 --- a/detections/cloud/gsuite_suspicious_shared_file_name.yml +++ b/detections/cloud/gsuite_suspicious_shared_file_name.yml @@ -1,83 +1,66 @@ name: Gsuite Suspicious Shared File Name id: 07eed200-03f5-11ec-98fb-acde48001122 -version: 8 -date: '2025-06-17' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: experimental type: Anomaly -description: - The following analytic detects shared files in Google Drive with suspicious - filenames commonly used in spear phishing campaigns. It leverages GSuite Drive logs - to identify documents with titles that include keywords like "dhl," "ups," "invoice," - and "shipment." This activity is significant because such filenames are often used - to lure users into opening malicious documents or clicking harmful links. If confirmed - malicious, this activity could lead to unauthorized access, data theft, or further - compromise of the user's system. +description: The following analytic detects shared files in Google Drive with suspicious filenames commonly used in spear phishing campaigns. It leverages GSuite Drive logs to identify documents with titles that include keywords like "dhl," "ups," "invoice," and "shipment." This activity is significant because such filenames are often used to lure users into opening malicious documents or clicking harmful links. If confirmed malicious, this activity could lead to unauthorized access, data theft, or further compromise of the user's system. data_source: - - G Suite Drive -search: - '`gsuite_drive` parameters.owner_is_team_drive=false "parameters.doc_title" - IN ("*dhl*", "* ups *", "*delivery*", "*parcel*", "*label*", "*invoice*", "*postal*", - "*fedex*", "* usps *", "* express *", "*shipment*", "*Banking/Tax*","*shipment*", - "*new order*") parameters.doc_type IN ("document","pdf", "msexcel", "msword", "spreadsheet", - "presentation") | rex field=parameters.owner "[^@]+@(?[^@]+)" | rex - field=parameters.target_user "[^@]+@(?[^@]+)" | where not source_domain="internal_test_email.com" - and dest_domain="internal_test_email.com" | eval phase="plan" | eval severity="low" - | stats count min(_time) as firstTime max(_time) as lastTime by email parameters.owner - parameters.target_user parameters.doc_title parameters.doc_type phase severity | - rename parameters.target_user AS user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `gsuite_suspicious_shared_file_name_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs related to gsuite having the file attachment metadata like file type, file - extension, source email, destination email, num of attachment and etc. In order - for the search to work for your environment, please edit the query to use your company - specific email domain instead of `internal_test_email.com`. -known_false_positives: - normal user or normal transaction may contain the subject and - file type attachment that this detection try to search + - G Suite Drive +search: |- + `gsuite_drive` parameters.owner_is_team_drive=false "parameters.doc_title" IN ("*dhl*", "* ups *", "*delivery*", "*parcel*", "*label*", "*invoice*", "*postal*", "*fedex*", "* usps *", "* express *", "*shipment*", "*Banking/Tax*","*shipment*", "*new order*") parameters.doc_type IN ("document","pdf", "msexcel", "msword", "spreadsheet", "presentation") + | rex field=parameters.owner "[^@]+@(?[^@]+)" + | rex field=parameters.target_user "[^@]+@(?[^@]+)" + | where not source_domain="internal_test_email.com" and dest_domain="internal_test_email.com" + | eval phase="plan" + | eval severity="low" + | stats count min(_time) as firstTime max(_time) as lastTime + BY email parameters.owner parameters.target_user + parameters.doc_title parameters.doc_type phase + severity + | rename parameters.target_user AS user + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `gsuite_suspicious_shared_file_name_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc. In order for the search to work for your environment, please edit the query to use your company specific email domain instead of `internal_test_email.com`. +known_false_positives: normal user or normal transaction may contain the subject and file type attachment that this detection try to search references: - - https://www.redhat.com/en/topics/devops/what-is-devsecops - - https://www.mandiant.com/resources/top-words-used-in-spear-phishing-attacks + - https://www.redhat.com/en/topics/devops/what-is-devsecops + - https://www.mandiant.com/resources/top-words-used-in-spear-phishing-attacks drilldown_searches: - - name: View the detection results for - "$email$" - search: '%original_detection_search% | search email = "$email$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$email$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$email$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$email$" + search: '%original_detection_search% | search email = "$email$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$email$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$email$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: suspicious share gdrive from $parameters.owner$ to $email$ namely as $parameters.doc_title$ - risk_objects: - - field: email - type: user - score: 21 - - field: parameters.owner - type: user - score: 21 - threat_objects: [] + message: suspicious share gdrive from $parameters.owner$ to $email$ namely as $parameters.doc_title$ + risk_objects: + - field: email + type: user + score: 21 + - field: parameters.owner + type: user + score: 21 + threat_objects: [] tags: - analytic_story: - - Dev Sec Ops - asset_type: GSuite - mitre_attack_id: - - T1566.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Dev Sec Ops + asset_type: GSuite + mitre_attack_id: + - T1566.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/gdrive_susp_file_share/gdrive_susp_attach.log - source: http:gsuite - sourcetype: gws:reports:drive + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/gdrive_susp_file_share/gdrive_susp_attach.log + source: http:gsuite + sourcetype: gws:reports:drive diff --git a/detections/cloud/high_number_of_login_failures_from_a_single_source.yml b/detections/cloud/high_number_of_login_failures_from_a_single_source.yml index 05813d8213..30f24897a2 100644 --- a/detections/cloud/high_number_of_login_failures_from_a_single_source.yml +++ b/detections/cloud/high_number_of_login_failures_from_a_single_source.yml @@ -1,77 +1,59 @@ name: High Number of Login Failures from a single source id: 7f398cfb-918d-41f4-8db8-2e2474e02222 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Bhavin Patel, Mauricio Velazco, Splunk status: production type: Anomaly -description: The following analytic detects multiple failed login attempts in Office365 - Azure Active Directory from a single source IP address. It leverages Office365 management - activity logs, specifically AzureActiveDirectoryStsLogon records, aggregating these - logs in 5-minute intervals to count failed login attempts. This activity is significant - as it may indicate brute-force attacks or password spraying, which are critical - to monitor. If confirmed malicious, an attacker could gain unauthorized access to - Office365 accounts, leading to potential data breaches, lateral movement within - the organization, or further malicious activities using the compromised account. +description: The following analytic detects multiple failed login attempts in Office365 Azure Active Directory from a single source IP address. It leverages Office365 management activity logs, specifically AzureActiveDirectoryStsLogon records, aggregating these logs in 5-minute intervals to count failed login attempts. This activity is significant as it may indicate brute-force attacks or password spraying, which are critical to monitor. If confirmed malicious, an attacker could gain unauthorized access to Office365 accounts, leading to potential data breaches, lateral movement within the organization, or further malicious activities using the compromised account. data_source: -- O365 UserLoginFailed -search: '`o365_management_activity` Workload=AzureActiveDirectory Operation=UserLoginFailed - record_type=AzureActiveDirectoryStsLogon - | bucket span=5m _time - | stats dc(_raw) AS failed_attempts values(user) as user values(LogonError) as LogonError values(signature) - as signature values(UserAgent) as UserAgent values(dest) as dest values(vendor_account) as vendor_account values(vendor_product) as vendor_product - by _time, src_ip - | where failed_attempts > 10 - | `high_number_of_login_failures_from_a_single_source_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. Adjust the threshold value to suit the specific - environment, as environments with naturally higher login failures might generate - false positives at a lower threshold. -known_false_positives: An Ip address with more than 10 failed authentication attempts - in the span of 5 minutes may also be triggered by a broken application. + - O365 UserLoginFailed +search: |- + `o365_management_activity` Workload=AzureActiveDirectory Operation=UserLoginFailed record_type=AzureActiveDirectoryStsLogon + | bucket span=5m _time + | stats dc(_raw) AS failed_attempts values(user) as user values(LogonError) as LogonError values(signature) as signature values(UserAgent) as UserAgent values(dest) as dest values(vendor_account) as vendor_account values(vendor_product) as vendor_product + BY _time, src_ip + | where failed_attempts > 10 + | `high_number_of_login_failures_from_a_single_source_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. Adjust the threshold value to suit the specific environment, as environments with naturally higher login failures might generate false positives at a lower threshold. +known_false_positives: An Ip address with more than 10 failed authentication attempts in the span of 5 minutes may also be triggered by a broken application. references: -- https://attack.mitre.org/techniques/T1110/001/ -- https://docs.microsoft.com/en-us/security/compass/incident-response-playbook-password-spray -- https://www.cisa.gov/uscert/ncas/alerts/aa21-008a -- https://docs.microsoft.com/azure/active-directory/reports-monitoring/reference-sign-ins-error-codes + - https://attack.mitre.org/techniques/T1110/001/ + - https://docs.microsoft.com/en-us/security/compass/incident-response-playbook-password-spray + - https://www.cisa.gov/uscert/ncas/alerts/aa21-008a + - https://docs.microsoft.com/azure/active-directory/reports-monitoring/reference-sign-ins-error-codes drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Ip address $src_ip$ failed to authenticate more than 10 times in a 5 minute - risk_objects: - - field: user - type: user - score: 25 - threat_objects: - - field: src_ip - type: ip_address + message: Ip address $src_ip$ failed to authenticate more than 10 times in a 5 minute + risk_objects: + - field: user + type: user + score: 25 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Office 365 Account Takeover - asset_type: O365 Tenant - mitre_attack_id: - - T1110.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Account Takeover + asset_type: O365 Tenant + mitre_attack_id: + - T1110.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/o365_high_number_authentications_for_user/o365_high_number_authentications_for_user.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/o365_high_number_authentications_for_user/o365_high_number_authentications_for_user.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/kubernetes_abuse_of_secret_by_unusual_location.yml b/detections/cloud/kubernetes_abuse_of_secret_by_unusual_location.yml index 24b73eff0c..9568db5148 100644 --- a/detections/cloud/kubernetes_abuse_of_secret_by_unusual_location.yml +++ b/detections/cloud/kubernetes_abuse_of_secret_by_unusual_location.yml @@ -1,79 +1,62 @@ name: Kubernetes Abuse of Secret by Unusual Location id: 40a064c1-4ec1-4381-9e35-61192ba8ef82 -version: 6 -date: '2026-01-14' +version: 7 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects unauthorized access or misuse of Kubernetes - Secrets from unusual locations. It leverages Kubernetes Audit logs to identify anomalies - in access patterns by analyzing the source of requests by country. This activity - is significant for a SOC as Kubernetes Secrets store sensitive information like - passwords, OAuth tokens, and SSH keys, making them critical assets. If confirmed - malicious, this behavior could indicate an attacker attempting to exfiltrate or - misuse these secrets, potentially leading to unauthorized access to sensitive systems - or data. +description: The following analytic detects unauthorized access or misuse of Kubernetes Secrets from unusual locations. It leverages Kubernetes Audit logs to identify anomalies in access patterns by analyzing the source of requests by country. This activity is significant for a SOC as Kubernetes Secrets store sensitive information like passwords, OAuth tokens, and SSH keys, making them critical assets. If confirmed malicious, this behavior could indicate an attacker attempting to exfiltrate or misuse these secrets, potentially leading to unauthorized access to sensitive systems or data. data_source: -- Kubernetes Audit -search: '`kube_audit` objectRef.resource=secrets verb=get | iplocation sourceIPs{} - | fillnull | search NOT `kube_allowed_locations` | stats count by objectRef.name - objectRef.namespace objectRef.resource requestReceivedTimestamp requestURI responseStatus.code - sourceIPs{} stage user.groups{} user.uid user.username userAgent verb City Country - | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_abuse_of_secret_by_unusual_location_filter`' -how_to_implement: The detection is based on data that originates from Kubernetes Audit - logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes - audit logs provide a record of the requests made to the Kubernetes API server, which - is crucial for monitoring and detecting suspicious activities. Configure the audit - policy in Kubernetes to determine what kind of activities are logged. This is done - by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry - Collector for Kubernetes to collect the logs. This doc will describe how to collect - the audit log file - https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. - When you want to use this detection with AWS EKS, you need to enable EKS control - plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. - Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. + - Kubernetes Audit +search: |- + `kube_audit` objectRef.resource=secrets verb=get + | iplocation sourceIPs{} + | fillnull + | search NOT `kube_allowed_locations` + | stats count + BY objectRef.name objectRef.namespace objectRef.resource + requestReceivedTimestamp requestURI responseStatus.code + sourceIPs{} stage user.groups{} + user.uid user.username userAgent + verb City Country + | rename sourceIPs{} as src_ip, user.username as user + | `kubernetes_abuse_of_secret_by_unusual_location_filter` +how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. known_false_positives: No false positives have been identified at this time. references: -- https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ + - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Access of Kubernetes secret $objectRef.name$ from unusual location $Country$ - by $user$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: src_ip - type: ip_address + message: Access of Kubernetes secret $objectRef.name$ from unusual location $Country$ by $user$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Kubernetes Security - asset_type: Kubernetes - mitre_attack_id: - - T1552.007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Kubernetes Security + asset_type: Kubernetes + mitre_attack_id: + - T1552.007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.007/kube_audit_get_secret/kube_audit_get_secret.json - sourcetype: _json - source: kubernetes + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.007/kube_audit_get_secret/kube_audit_get_secret.json + sourcetype: _json + source: kubernetes diff --git a/detections/cloud/kubernetes_abuse_of_secret_by_unusual_user_agent.yml b/detections/cloud/kubernetes_abuse_of_secret_by_unusual_user_agent.yml index 7a778d21f3..420863ff1a 100644 --- a/detections/cloud/kubernetes_abuse_of_secret_by_unusual_user_agent.yml +++ b/detections/cloud/kubernetes_abuse_of_secret_by_unusual_user_agent.yml @@ -1,79 +1,61 @@ name: Kubernetes Abuse of Secret by Unusual User Agent id: 096ab390-05ca-462c-884e-343acd5b9240 -version: 6 -date: '2026-01-14' +version: 7 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects unauthorized access or misuse of Kubernetes - Secrets by unusual user agents. It leverages Kubernetes Audit logs to identify anomalies - in access patterns by analyzing the source of requests based on user agents. This - activity is significant for a SOC because Kubernetes Secrets store sensitive information - like passwords, OAuth tokens, and SSH keys, making them critical assets. If confirmed - malicious, this activity could lead to unauthorized access to sensitive systems - or data, potentially resulting in significant security breaches and exfiltration - of critical information. +description: The following analytic detects unauthorized access or misuse of Kubernetes Secrets by unusual user agents. It leverages Kubernetes Audit logs to identify anomalies in access patterns by analyzing the source of requests based on user agents. This activity is significant for a SOC because Kubernetes Secrets store sensitive information like passwords, OAuth tokens, and SSH keys, making them critical assets. If confirmed malicious, this activity could lead to unauthorized access to sensitive systems or data, potentially resulting in significant security breaches and exfiltration of critical information. data_source: -- Kubernetes Audit -search: '`kube_audit` objectRef.resource=secrets verb=get | search NOT `kube_allowed_user_agents` - | fillnull | stats count by objectRef.name objectRef.namespace objectRef.resource - requestReceivedTimestamp requestURI responseStatus.code sourceIPs{} stage user.groups{} - user.uid user.username userAgent verb | rename sourceIPs{} as src_ip, user.username - as user | `kubernetes_abuse_of_secret_by_unusual_user_agent_filter`' -how_to_implement: The detection is based on data that originates from Kubernetes Audit - logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes - audit logs provide a record of the requests made to the Kubernetes API server, which - is crucial for monitoring and detecting suspicious activities. Configure the audit - policy in Kubernetes to determine what kind of activities are logged. This is done - by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry - Collector for Kubernetes to collect the logs. This doc will describe how to collect - the audit log file - https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. - When you want to use this detection with AWS EKS, you need to enable EKS control - plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. - Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. + - Kubernetes Audit +search: |- + `kube_audit` objectRef.resource=secrets verb=get + | search NOT `kube_allowed_user_agents` + | fillnull + | stats count + BY objectRef.name objectRef.namespace objectRef.resource + requestReceivedTimestamp requestURI responseStatus.code + sourceIPs{} stage user.groups{} + user.uid user.username userAgent + verb + | rename sourceIPs{} as src_ip, user.username as user + | `kubernetes_abuse_of_secret_by_unusual_user_agent_filter` +how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. known_false_positives: No false positives have been identified at this time. references: -- https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ + - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Access of Kubernetes secret $objectRef.name$ from unusual user agent $userAgent$ - by $user$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: src_ip - type: ip_address + message: Access of Kubernetes secret $objectRef.name$ from unusual user agent $userAgent$ by $user$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Kubernetes Security - asset_type: Kubernetes - mitre_attack_id: - - T1552.007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Kubernetes Security + asset_type: Kubernetes + mitre_attack_id: + - T1552.007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.007/kube_audit_get_secret/kube_audit_get_secret.json - sourcetype: _json - source: kubernetes + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.007/kube_audit_get_secret/kube_audit_get_secret.json + sourcetype: _json + source: kubernetes diff --git a/detections/cloud/kubernetes_abuse_of_secret_by_unusual_user_group.yml b/detections/cloud/kubernetes_abuse_of_secret_by_unusual_user_group.yml index 464eb2f6e7..9a809d9123 100644 --- a/detections/cloud/kubernetes_abuse_of_secret_by_unusual_user_group.yml +++ b/detections/cloud/kubernetes_abuse_of_secret_by_unusual_user_group.yml @@ -1,78 +1,61 @@ name: Kubernetes Abuse of Secret by Unusual User Group id: b6f45bbc-4ea9-4068-b3bc-0477f6997ae2 -version: 6 -date: '2026-01-14' +version: 7 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects unauthorized access or misuse of Kubernetes - Secrets by unusual user groups. It leverages Kubernetes Audit logs to identify anomalies - in access patterns by analyzing the source of requests and user groups. This activity - is significant for a SOC as Kubernetes Secrets store sensitive information like - passwords, OAuth tokens, and SSH keys. If confirmed malicious, this behavior could - indicate an attacker attempting to exfiltrate or misuse these secrets, potentially - leading to unauthorized access to sensitive systems or data. +description: The following analytic detects unauthorized access or misuse of Kubernetes Secrets by unusual user groups. It leverages Kubernetes Audit logs to identify anomalies in access patterns by analyzing the source of requests and user groups. This activity is significant for a SOC as Kubernetes Secrets store sensitive information like passwords, OAuth tokens, and SSH keys. If confirmed malicious, this behavior could indicate an attacker attempting to exfiltrate or misuse these secrets, potentially leading to unauthorized access to sensitive systems or data. data_source: -- Kubernetes Audit -search: '`kube_audit` objectRef.resource=secrets verb=get | search NOT `kube_allowed_user_groups` - | fillnull | stats count by objectRef.name objectRef.namespace objectRef.resource - requestReceivedTimestamp requestURI responseStatus.code sourceIPs{} stage user.groups{} - user.uid user.username userAgent verb | rename sourceIPs{} as src_ip, user.username - as user | `kubernetes_abuse_of_secret_by_unusual_user_group_filter`' -how_to_implement: The detection is based on data that originates from Kubernetes Audit - logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes - audit logs provide a record of the requests made to the Kubernetes API server, which - is crucial for monitoring and detecting suspicious activities. Configure the audit - policy in Kubernetes to determine what kind of activities are logged. This is done - by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry - Collector for Kubernetes to collect the logs. This doc will describe how to collect - the audit log file - https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. - When you want to use this detection with AWS EKS, you need to enable EKS control - plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. - Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. + - Kubernetes Audit +search: |- + `kube_audit` objectRef.resource=secrets verb=get + | search NOT `kube_allowed_user_groups` + | fillnull + | stats count + BY objectRef.name objectRef.namespace objectRef.resource + requestReceivedTimestamp requestURI responseStatus.code + sourceIPs{} stage user.groups{} + user.uid user.username userAgent + verb + | rename sourceIPs{} as src_ip, user.username as user + | `kubernetes_abuse_of_secret_by_unusual_user_group_filter` +how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. known_false_positives: No false positives have been identified at this time. references: -- https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ + - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Access of Kubernetes secret $objectRef.name$ from unusual user group $user.groups{}$ - by user name $user$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: src_ip - type: ip_address + message: Access of Kubernetes secret $objectRef.name$ from unusual user group $user.groups{}$ by user name $user$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Kubernetes Security - asset_type: Kubernetes - mitre_attack_id: - - T1552.007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Kubernetes Security + asset_type: Kubernetes + mitre_attack_id: + - T1552.007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.007/kube_audit_get_secret/kube_audit_get_secret.json - sourcetype: _json - source: kubernetes + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.007/kube_audit_get_secret/kube_audit_get_secret.json + sourcetype: _json + source: kubernetes diff --git a/detections/cloud/kubernetes_abuse_of_secret_by_unusual_user_name.yml b/detections/cloud/kubernetes_abuse_of_secret_by_unusual_user_name.yml index 1be4bc29e5..4b6e5d6735 100644 --- a/detections/cloud/kubernetes_abuse_of_secret_by_unusual_user_name.yml +++ b/detections/cloud/kubernetes_abuse_of_secret_by_unusual_user_name.yml @@ -1,78 +1,61 @@ name: Kubernetes Abuse of Secret by Unusual User Name id: df6e9cae-5257-4a34-8f3a-df49fa0f5c46 -version: 6 -date: '2026-01-14' +version: 7 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects unauthorized access or misuse of Kubernetes - Secrets by unusual user names. It leverages Kubernetes Audit logs to identify anomalies - in access patterns by analyzing the source of requests based on user names. This - activity is significant for a SOC as Kubernetes Secrets store sensitive information - like passwords, OAuth tokens, and SSH keys, making them critical assets. If confirmed - malicious, this activity could lead to unauthorized access to sensitive systems - or data, potentially resulting in significant security breaches and exfiltration - of sensitive information. +description: The following analytic detects unauthorized access or misuse of Kubernetes Secrets by unusual user names. It leverages Kubernetes Audit logs to identify anomalies in access patterns by analyzing the source of requests based on user names. This activity is significant for a SOC as Kubernetes Secrets store sensitive information like passwords, OAuth tokens, and SSH keys, making them critical assets. If confirmed malicious, this activity could lead to unauthorized access to sensitive systems or data, potentially resulting in significant security breaches and exfiltration of sensitive information. data_source: -- Kubernetes Audit -search: '`kube_audit` objectRef.resource=secrets verb=get | search NOT `kube_allowed_user_names` - | fillnull | stats count by objectRef.name objectRef.namespace objectRef.resource - requestReceivedTimestamp requestURI responseStatus.code sourceIPs{} stage user.groups{} - user.uid user.username userAgent verb | rename sourceIPs{} as src_ip, user.username - as user | `kubernetes_abuse_of_secret_by_unusual_user_name_filter`' -how_to_implement: The detection is based on data that originates from Kubernetes Audit - logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes - audit logs provide a record of the requests made to the Kubernetes API server, which - is crucial for monitoring and detecting suspicious activities. Configure the audit - policy in Kubernetes to determine what kind of activities are logged. This is done - by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry - Collector for Kubernetes to collect the logs. This doc will describe how to collect - the audit log file - https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. - When you want to use this detection with AWS EKS, you need to enable EKS control - plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. - Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. + - Kubernetes Audit +search: |- + `kube_audit` objectRef.resource=secrets verb=get + | search NOT `kube_allowed_user_names` + | fillnull + | stats count + BY objectRef.name objectRef.namespace objectRef.resource + requestReceivedTimestamp requestURI responseStatus.code + sourceIPs{} stage user.groups{} + user.uid user.username userAgent + verb + | rename sourceIPs{} as src_ip, user.username as user + | `kubernetes_abuse_of_secret_by_unusual_user_name_filter` +how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. known_false_positives: No false positives have been identified at this time. references: -- https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ + - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Access of Kubernetes secret $objectRef.name$ from unusual user name $user$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: src_ip - type: ip_address + message: Access of Kubernetes secret $objectRef.name$ from unusual user name $user$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Kubernetes Security - asset_type: Kubernetes - mitre_attack_id: - - T1552.007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Kubernetes Security + asset_type: Kubernetes + mitre_attack_id: + - T1552.007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.007/kube_audit_get_secret/kube_audit_get_secret.json - sourcetype: _json - source: kubernetes + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.007/kube_audit_get_secret/kube_audit_get_secret.json + sourcetype: _json + source: kubernetes diff --git a/detections/cloud/kubernetes_access_scanning.yml b/detections/cloud/kubernetes_access_scanning.yml index a9a48f86ef..beea58030c 100644 --- a/detections/cloud/kubernetes_access_scanning.yml +++ b/detections/cloud/kubernetes_access_scanning.yml @@ -1,80 +1,57 @@ name: Kubernetes Access Scanning id: 2f4abe6d-5991-464d-8216-f90f42999764 -version: 6 -date: '2026-01-14' +version: 7 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects potential scanning activities within a - Kubernetes environment. It identifies unauthorized access attempts, probing of public - APIs, or attempts to exploit known vulnerabilities by monitoring Kubernetes audit - logs for repeated failed access attempts or unusual API requests. This activity - is significant for a SOC as it may indicate an attacker's preliminary reconnaissance - to gather information about the system. If confirmed malicious, this activity could - lead to unauthorized access to sensitive systems or data, posing a severe security - risk. +description: The following analytic detects potential scanning activities within a Kubernetes environment. It identifies unauthorized access attempts, probing of public APIs, or attempts to exploit known vulnerabilities by monitoring Kubernetes audit logs for repeated failed access attempts or unusual API requests. This activity is significant for a SOC as it may indicate an attacker's preliminary reconnaissance to gather information about the system. If confirmed malicious, this activity could lead to unauthorized access to sensitive systems or data, posing a severe security risk. data_source: -- Kubernetes Audit -search: '`kube_audit` "user.groups{}"="system:unauthenticated" "responseStatus.code"=403 - | iplocation sourceIPs{} | stats count values(userAgent) as userAgent values(user.username) - as user.username values(user.groups{}) as user.groups{} values(verb) as verb values(requestURI) - as requestURI values(responseStatus.code) as responseStatus.code values(responseStatus.message) - as responseStatus.message values(responseStatus.reason) as responseStatus.reason - values(responseStatus.status) as responseStatus.status by sourceIPs{} Country City - | where count > 5 | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_access_scanning_filter`' -how_to_implement: The detection is based on data that originates from Kubernetes Audit - logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes - audit logs provide a record of the requests made to the Kubernetes API server, which - is crucial for monitoring and detecting suspicious activities. Configure the audit - policy in Kubernetes to determine what kind of activities are logged. This is done - by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry - Collector for Kubernetes to collect the logs. This doc will describe how to collect - the audit log file - https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. - When you want to use this detection with AWS EKS, you need to enable EKS control - plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. - Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. + - Kubernetes Audit +search: |- + `kube_audit` "user.groups{}"="system:unauthenticated" "responseStatus.code"=403 + | iplocation sourceIPs{} + | stats count values(userAgent) as userAgent values(user.username) as user.username values(user.groups{}) as user.groups{} values(verb) as verb values(requestURI) as requestURI values(responseStatus.code) as responseStatus.code values(responseStatus.message) as responseStatus.message values(responseStatus.reason) as responseStatus.reason values(responseStatus.status) as responseStatus.status + BY sourceIPs{} Country City + | where count > 5 + | rename sourceIPs{} as src_ip, user.username as user + | `kubernetes_access_scanning_filter` +how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. known_false_positives: No false positives have been identified at this time. references: -- https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ + - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Kubernetes scanning from ip $src_ip$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: src_ip - type: ip_address + message: Kubernetes scanning from ip $src_ip$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Kubernetes Security - asset_type: Kubernetes - mitre_attack_id: - - T1046 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Kubernetes Security + asset_type: Kubernetes + mitre_attack_id: + - T1046 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1046/kubernetes_scanning/kubernetes_scanning.json - sourcetype: _json - source: kubernetes + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1046/kubernetes_scanning/kubernetes_scanning.json + sourcetype: _json + source: kubernetes diff --git a/detections/cloud/kubernetes_anomalous_inbound_network_activity_from_process.yml b/detections/cloud/kubernetes_anomalous_inbound_network_activity_from_process.yml index 7f45d3f079..5ac93e38f3 100644 --- a/detections/cloud/kubernetes_anomalous_inbound_network_activity_from_process.yml +++ b/detections/cloud/kubernetes_anomalous_inbound_network_activity_from_process.yml @@ -5,63 +5,28 @@ date: '2026-01-14' author: Matthew Moore, Splunk status: experimental type: Anomaly -description: - The following analytic identifies anomalous inbound network traffic volumes - from processes within containerized workloads. It leverages Network Performance - Monitoring metrics collected via an OTEL collector and pulled from Splunk Observability - Cloud. The detection compares recent metrics (tcp.bytes, tcp.new_sockets, tcp.packets, - udp.bytes, udp.packets) over the last hour with the average over the past 30 days. - This activity is significant as it may indicate unauthorized data reception, potential - breaches, vulnerability exploitation, or malware propagation. If confirmed malicious, - it could lead to command and control installation, data integrity damage, container - escape, and further environment compromise. +description: The following analytic identifies anomalous inbound network traffic volumes from processes within containerized workloads. It leverages Network Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk Observability Cloud. The detection compares recent metrics (tcp.bytes, tcp.new_sockets, tcp.packets, udp.bytes, udp.packets) over the last hour with the average over the past 30 days. This activity is significant as it may indicate unauthorized data reception, potential breaches, vulnerability exploitation, or malware propagation. If confirmed malicious, it could lead to command and control installation, data integrity damage, container escape, and further environment compromise. data_source: [] -search: - "| mstats avg(tcp.*) as tcp.* avg(udp.*) as udp.* where `kubernetes_metrics` - AND earliest=-1h by k8s.cluster.name dest.workload.name dest.process.name span=10s - | eval key='dest.workload.name' + \":\" + 'dest.process.name' | join type=left key - [ mstats avg(tcp.*) as avg_tcp.* avg(udp.*) as avg_udp.* stdev(tcp.*) as stdev_tcp.* - avg(udp.*) as stdev_udp.* where `kubernetes_metrics` AND earliest=-30d latest=-1h - by dest.workload.name dest.process.name | eval key='dest.workload.name' + \":\" - + 'dest.process.name' ] | eval anomalies = \"\" | foreach stdev_* [ eval anomalies - =if( '<>' > ('avg_<>' + 3 * 'stdev_<>'), anomalies - + \"<> higher than average by \" + tostring(round(('<>' - 'avg_<>')/'stdev_<>' - ,2)) + \" Standard Deviations. <>=\" + tostring('<>') + \" avg_<>=\"\ - \ + tostring('avg_<>') + \" 'stdev_<>'=\" + tostring('stdev_<>') - + \", \" , anomalies) ] | fillnull | eval anomalies = split(replace(anomalies, \"\ - ,\\s$$$$\", \"\") ,\", \") | where anomalies!=\"\" | stats count(anomalies) as count - values(anomalies) as anomalies by k8s.cluster.name dest.workload.name dest.process.name - | where count > 5 | rename k8s.cluster.name as host | `kubernetes_anomalous_inbound_network_activity_from_process_filter`" -how_to_implement: - "To gather NPM metrics the Open Telemetry to the Kubernetes Cluster - and enable Network Performance Monitoring according to instructions found in Splunk - Docs https://help.splunk.com/en/splunk-observability-cloud/monitor-infrastructure/network-explorer/set-up-network-explorer-in-kubernetes#network-explorer-setup - In order to access those metrics from within Splunk Enterprise and ES, the Splunk - Infrastructure Monitoring add-on must be installed and configured on a Splunk Search - Head. Once installed, first configure the add-on with your O11y Cloud Org ID and - Access Token. Lastly set up the add-on to ingest metrics from O11y cloud using the - following settings, and any other settings left at default:\n* Name sim_npm_metrics_to_metrics_index\n - * Metric Resolution 10000" +search: "| mstats avg(tcp.*) as tcp.* avg(udp.*) as udp.* where `kubernetes_metrics` AND earliest=-1h by k8s.cluster.name dest.workload.name dest.process.name span=10s | eval key='dest.workload.name' + \":\" + 'dest.process.name' | join type=left key [ mstats avg(tcp.*) as avg_tcp.* avg(udp.*) as avg_udp.* stdev(tcp.*) as stdev_tcp.* avg(udp.*) as stdev_udp.* where `kubernetes_metrics` AND earliest=-30d latest=-1h by dest.workload.name dest.process.name | eval key='dest.workload.name' + \":\" + 'dest.process.name' ] | eval anomalies = \"\" | foreach stdev_* [ eval anomalies =if( '<>' > ('avg_<>' + 3 * 'stdev_<>'), anomalies + \"<> higher than average by \" + tostring(round(('<>' - 'avg_<>')/'stdev_<>' ,2)) + \" Standard Deviations. <>=\" + tostring('<>') + \" avg_<>=\" + tostring('avg_<>') + \" 'stdev_<>'=\" + tostring('stdev_<>') + \", \" , anomalies) ] | fillnull | eval anomalies = split(replace(anomalies, \",\\s$$$$\", \"\") ,\", \") | where anomalies!=\"\" | stats count(anomalies) as count values(anomalies) as anomalies by k8s.cluster.name dest.workload.name dest.process.name | where count > 5 | rename k8s.cluster.name as host | `kubernetes_anomalous_inbound_network_activity_from_process_filter`" +how_to_implement: "To gather NPM metrics the Open Telemetry to the Kubernetes Cluster and enable Network Performance Monitoring according to instructions found in Splunk Docs https://help.splunk.com/en/splunk-observability-cloud/monitor-infrastructure/network-explorer/set-up-network-explorer-in-kubernetes#network-explorer-setup In order to access those metrics from within Splunk Enterprise and ES, the Splunk Infrastructure Monitoring add-on must be installed and configured on a Splunk Search Head. Once installed, first configure the add-on with your O11y Cloud Org ID and Access Token. Lastly set up the add-on to ingest metrics from O11y cloud using the following settings, and any other settings left at default:\n* Name sim_npm_metrics_to_metrics_index\n * Metric Resolution 10000" known_false_positives: No false positives have been identified at this time. references: - - https://github.com/signalfx/splunk-otel-collector-chart + - https://github.com/signalfx/splunk-otel-collector-chart rba: - message: - Kubernetes Anomalous Inbound Network Activity from Process in kubernetes - cluster $host$ - risk_objects: - - field: host - type: system - score: 25 - threat_objects: [] + message: Kubernetes Anomalous Inbound Network Activity from Process in kubernetes cluster $host$ + risk_objects: + - field: host + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring - asset_type: Kubernetes - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring + asset_type: Kubernetes + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/cloud/kubernetes_anomalous_inbound_outbound_network_io.yml b/detections/cloud/kubernetes_anomalous_inbound_outbound_network_io.yml index 39a23fb7fd..0d865c3bb9 100644 --- a/detections/cloud/kubernetes_anomalous_inbound_outbound_network_io.yml +++ b/detections/cloud/kubernetes_anomalous_inbound_outbound_network_io.yml @@ -5,66 +5,28 @@ date: '2026-01-14' author: Matthew Moore, Splunk status: experimental type: Anomaly -description: The following analytic identifies high inbound or outbound network I/O - anomalies in Kubernetes containers. It leverages process metrics from an OTEL collector - and Kubelet Stats Receiver, along with data from Splunk Observability Cloud. A lookup - table with average and standard deviation values for network I/O is used to detect - anomalies persisting over a 1-hour period. This activity is significant as it may - indicate data exfiltration, command and control communication, or unauthorized data - transfers. If confirmed malicious, it could lead to data breaches, service outages, - financial losses, and reputational damage. +description: The following analytic identifies high inbound or outbound network I/O anomalies in Kubernetes containers. It leverages process metrics from an OTEL collector and Kubelet Stats Receiver, along with data from Splunk Observability Cloud. A lookup table with average and standard deviation values for network I/O is used to detect anomalies persisting over a 1-hour period. This activity is significant as it may indicate data exfiltration, command and control communication, or unauthorized data transfers. If confirmed malicious, it could lead to data breaches, service outages, financial losses, and reputational damage. data_source: [] -search: "| mstats avg(k8s.pod.network.io) as io where `kubernetes_metrics` by k8s.cluster.name - k8s.pod.name k8s.node.name direction span=10s | eval service = replace('k8s.pod.name', - \"-\\w{5}$$|-[abcdef0-9]{8,10}-\\w{5}$$\", \"\") | stats avg(eval(if(direction=\"\ - transmit\", io,null()))) as outbound_network_io avg(eval(if(direction=\"receive\"\ - , io,null()))) as inbound_network_io by k8s.cluster.name k8s.node.name k8s.pod.name - service _time | eval key = 'k8s.cluster.name' + \":\" + 'service' | lookup k8s_container_network_io_baseline - key | eval anomalies = \"\" | foreach stdev_* [ eval anomalies =if( '<>' - > ('avg_<>' + 4 * 'stdev_<>'), anomalies + \"<> higher - than average by \" + tostring(round(('<>' - 'avg_<>')/'stdev_<>' - ,2)) + \" Standard Deviations. <>=\" + tostring('<>') + \" avg_<>=\"\ - \ + tostring('avg_<>') + \" 'stdev_<>'=\" + tostring('stdev_<>') - + \", \" , anomalies) ] | eval anomalies = replace(anomalies, \",\\s$$\", \"\") - | where anomalies!=\"\" | stats count values(anomalies) as anomalies by k8s.cluster.name - k8s.node.name k8s.pod.name service | rename service as k8s.service | where count - > 5 | rename k8s.node.name as host | `kubernetes_anomalous_inbound_outbound_network_io_filter`" -how_to_implement: "To implement this detection, follow these steps:\n* Deploy the - OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process - receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically - Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install - the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n - * Configure the SIM add-on with your Observability Cloud Organization ID and Access - Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input - \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization - ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to - the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B'); - data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D'); - data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F'); - data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H'); - data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J'); - data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n - * Leave all other settings at their default values.\n* Run the Search Baseline Of - Kubernetes Container Network IO Ratio" +search: "| mstats avg(k8s.pod.network.io) as io where `kubernetes_metrics` by k8s.cluster.name k8s.pod.name k8s.node.name direction span=10s | eval service = replace('k8s.pod.name', \"-\\w{5}$$|-[abcdef0-9]{8,10}-\\w{5}$$\", \"\") | stats avg(eval(if(direction=\"transmit\", io,null()))) as outbound_network_io avg(eval(if(direction=\"receive\", io,null()))) as inbound_network_io by k8s.cluster.name k8s.node.name k8s.pod.name service _time | eval key = 'k8s.cluster.name' + \":\" + 'service' | lookup k8s_container_network_io_baseline key | eval anomalies = \"\" | foreach stdev_* [ eval anomalies =if( '<>' > ('avg_<>' + 4 * 'stdev_<>'), anomalies + \"<> higher than average by \" + tostring(round(('<>' - 'avg_<>')/'stdev_<>' ,2)) + \" Standard Deviations. <>=\" + tostring('<>') + \" avg_<>=\" + tostring('avg_<>') + \" 'stdev_<>'=\" + tostring('stdev_<>') + \", \" , anomalies) ] | eval anomalies = replace(anomalies, \",\\s$$\", \"\") | where anomalies!=\"\" | stats count values(anomalies) as anomalies by k8s.cluster.name k8s.node.name k8s.pod.name service | rename service as k8s.service | where count > 5 | rename k8s.node.name as host | `kubernetes_anomalous_inbound_outbound_network_io_filter`" +how_to_implement: "To implement this detection, follow these steps:\n* Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n * Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B'); data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D'); data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F'); data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H'); data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J'); data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n * Leave all other settings at their default values.\n* Run the Search Baseline Of Kubernetes Container Network IO Ratio" known_false_positives: No false positives have been identified at this time. references: -- https://github.com/signalfx/splunk-otel-collector-chart + - https://github.com/signalfx/splunk-otel-collector-chart rba: - message: Kubernetes Anomalous Inbound Outbound Network IO from container on host - $host$ - risk_objects: - - field: host - type: system - score: 25 - threat_objects: [] + message: Kubernetes Anomalous Inbound Outbound Network IO from container on host $host$ + risk_objects: + - field: host + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring - asset_type: Kubernetes - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring + asset_type: Kubernetes + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/cloud/kubernetes_anomalous_inbound_to_outbound_network_io_ratio.yml b/detections/cloud/kubernetes_anomalous_inbound_to_outbound_network_io_ratio.yml index 3a5cf2e8d3..fe58bd11c1 100644 --- a/detections/cloud/kubernetes_anomalous_inbound_to_outbound_network_io_ratio.yml +++ b/detections/cloud/kubernetes_anomalous_inbound_to_outbound_network_io_ratio.yml @@ -5,69 +5,28 @@ date: '2026-01-14' author: Matthew Moore, Splunk status: experimental type: Anomaly -description: The following analytic identifies significant changes in network communication - behavior within Kubernetes containers by examining the inbound to outbound network - IO ratios. It leverages process metrics from an OTEL collector and Kubelet Stats - Receiver, along with data from Splunk Observability Cloud. Anomalies are detected - using a lookup table containing average and standard deviation values for network - IO, triggering an event if the anomaly persists for over an hour. This activity - is significant as it may indicate data exfiltration, command and control communication, - or compromised container behavior. If confirmed malicious, it could lead to data - breaches, service outages, and unauthorized access within the Kubernetes cluster. +description: The following analytic identifies significant changes in network communication behavior within Kubernetes containers by examining the inbound to outbound network IO ratios. It leverages process metrics from an OTEL collector and Kubelet Stats Receiver, along with data from Splunk Observability Cloud. Anomalies are detected using a lookup table containing average and standard deviation values for network IO, triggering an event if the anomaly persists for over an hour. This activity is significant as it may indicate data exfiltration, command and control communication, or compromised container behavior. If confirmed malicious, it could lead to data breaches, service outages, and unauthorized access within the Kubernetes cluster. data_source: [] -search: "| mstats avg(k8s.pod.network.io) as io where `kubernetes_metrics` by k8s.cluster.name - k8s.pod.name k8s.node.name direction span=10s | eval service = replace('k8s.pod.name', - \"-\\w{5}$|-[abcdef0-9]{8,10}-\\w{5}$\", \"\") | eval key = 'k8s.cluster.name' + - \":\" + 'service' | stats avg(eval(if(direction=\"transmit\", io,null()))) as outbound_network_io - avg(eval(if(direction=\"receive\", io,null()))) as inbound_network_io by key service - k8s.cluster.name k8s.pod.name k8s.node.name _time | eval inbound:outbound = inbound_network_io/outbound_network_io - | eval outbound:inbound = outbound_network_io/inbound_network_io | fields - *network_io - | lookup k8s_container_network_io_ratio_baseline key | eval anomalies = \"\" | foreach - stdev_* [ eval anomalies =if( '<>' > ('avg_<>' + 4 * 'stdev_<>'), - anomalies + \"<> ratio higher than average by \" + tostring(round(('<>' - - 'avg_<>')/'stdev_<>' ,2)) + \" Standard Deviations. <>=\"\ - \ + tostring('<>') + \" avg_<>=\" + tostring('avg_<>') - + \" 'stdev_<>'=\" + tostring('stdev_<>') + \", \" , anomalies) - ] | eval anomalies = replace(anomalies, \",\\s$\", \"\") | where anomalies!=\"\"\ - \ | stats count values(anomalies) as anomalies by k8s.cluster.name k8s.node.name - k8s.pod.name service | rename service as k8s.service | where count > 5 | rename - k8s.node.name as host | `kubernetes_anomalous_inbound_to_outbound_network_io_ratio_filter`" -how_to_implement: "To implement this detection, follow these steps:\n* Deploy the - OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process - receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically - Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install - the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n - * Configure the SIM add-on with your Observability Cloud Organization ID and Access - Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input - \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization - ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to - the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B'); - data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D'); - data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F'); - data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H'); - data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J'); - data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n - * Leave all other settings at their default values.\n* Run the Search Baseline Of - Kubernetes Container Network IO Ratio" +search: "| mstats avg(k8s.pod.network.io) as io where `kubernetes_metrics` by k8s.cluster.name k8s.pod.name k8s.node.name direction span=10s | eval service = replace('k8s.pod.name', \"-\\w{5}$|-[abcdef0-9]{8,10}-\\w{5}$\", \"\") | eval key = 'k8s.cluster.name' + \":\" + 'service' | stats avg(eval(if(direction=\"transmit\", io,null()))) as outbound_network_io avg(eval(if(direction=\"receive\", io,null()))) as inbound_network_io by key service k8s.cluster.name k8s.pod.name k8s.node.name _time | eval inbound:outbound = inbound_network_io/outbound_network_io | eval outbound:inbound = outbound_network_io/inbound_network_io | fields - *network_io | lookup k8s_container_network_io_ratio_baseline key | eval anomalies = \"\" | foreach stdev_* [ eval anomalies =if( '<>' > ('avg_<>' + 4 * 'stdev_<>'), anomalies + \"<> ratio higher than average by \" + tostring(round(('<>' - 'avg_<>')/'stdev_<>' ,2)) + \" Standard Deviations. <>=\" + tostring('<>') + \" avg_<>=\" + tostring('avg_<>') + \" 'stdev_<>'=\" + tostring('stdev_<>') + \", \" , anomalies) ] | eval anomalies = replace(anomalies, \",\\s$\", \"\") | where anomalies!=\"\" | stats count values(anomalies) as anomalies by k8s.cluster.name k8s.node.name k8s.pod.name service | rename service as k8s.service | where count > 5 | rename k8s.node.name as host | `kubernetes_anomalous_inbound_to_outbound_network_io_ratio_filter`" +how_to_implement: "To implement this detection, follow these steps:\n* Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n * Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B'); data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D'); data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F'); data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H'); data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J'); data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n * Leave all other settings at their default values.\n* Run the Search Baseline Of Kubernetes Container Network IO Ratio" known_false_positives: No false positives have been identified at this time. references: -- https://github.com/signalfx/splunk-otel-collector-chart + - https://github.com/signalfx/splunk-otel-collector-chart rba: - message: Kubernetes Anomalous Inbound to Outbound Network IO Ratio from Container - on host $host$ - risk_objects: - - field: host - type: system - score: 25 - threat_objects: [] + message: Kubernetes Anomalous Inbound to Outbound Network IO Ratio from Container on host $host$ + risk_objects: + - field: host + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring - asset_type: Kubernetes - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring + asset_type: Kubernetes + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/cloud/kubernetes_anomalous_outbound_network_activity_from_process.yml b/detections/cloud/kubernetes_anomalous_outbound_network_activity_from_process.yml index 0b7dcd7c3e..6048925b4f 100644 --- a/detections/cloud/kubernetes_anomalous_outbound_network_activity_from_process.yml +++ b/detections/cloud/kubernetes_anomalous_outbound_network_activity_from_process.yml @@ -5,64 +5,28 @@ date: '2026-01-14' author: Matthew Moore, Splunk status: experimental type: Anomaly -description: - The following analytic identifies anomalously high outbound network activity - from processes running within containerized workloads in a Kubernetes environment. - It leverages Network Performance Monitoring metrics collected via an OTEL collector - and pulled from Splunk Observability Cloud. The detection compares recent network - metrics (tcp.bytes, tcp.new_sockets, tcp.packets, udp.bytes, udp.packets) over the - last hour with the average metrics over the past 30 days. This activity is significant - as it may indicate data exfiltration, process modification, or container compromise. - If confirmed malicious, it could lead to unauthorized data exfiltration, communication - with malicious entities, or further attacks within the containerized environment. +description: The following analytic identifies anomalously high outbound network activity from processes running within containerized workloads in a Kubernetes environment. It leverages Network Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk Observability Cloud. The detection compares recent network metrics (tcp.bytes, tcp.new_sockets, tcp.packets, udp.bytes, udp.packets) over the last hour with the average metrics over the past 30 days. This activity is significant as it may indicate data exfiltration, process modification, or container compromise. If confirmed malicious, it could lead to unauthorized data exfiltration, communication with malicious entities, or further attacks within the containerized environment. data_source: [] -search: - "| mstats avg(tcp.*) as tcp.* avg(udp.*) as udp.* where `kubernetes_metrics` - AND earliest=-1h by k8s.cluster.name source.workload.name source.process.name span=10s - | eval key='source.workload.name' + \":\" + 'source.process.name' | join type=left - key [ mstats avg(tcp.*) as avg_tcp.* avg(udp.*) as avg_udp.* stdev(tcp.*) as stdev_tcp.* - avg(udp.*) as stdev_udp.* where `kubernetes_metrics` AND earliest=-30d latest=-1h - by source.workload.name source.process.name | eval key='source.workload.name' + - \":\" + 'source.process.name' ] | eval anomalies = \"\" | foreach stdev_* [ eval - anomalies =if( '<>' > ('avg_<>' + 3 * 'stdev_<>'), - anomalies + \"<> higher than average by \" + tostring(round(('<>' - - 'avg_<>')/'stdev_<>' ,2)) + \" Standard Deviations. <>=\"\ - \ + tostring('<>') + \" avg_<>=\" + tostring('avg_<>') - + \" 'stdev_<>'=\" + tostring('stdev_<>') + \", \" , anomalies) - ] | fillnull | eval anomalies = split(replace(anomalies, \",\\s$$$$\", \"\") ,\"\ - , \") | where anomalies!=\"\" | stats count(anomalies) as count values(anomalies) - as anomalies by k8s.cluster.name source.workload.name source.process.name | where - count > 5 | rename k8s.cluster.name as host | `kubernetes_anomalous_outbound_network_activity_from_process_filter`" -how_to_implement: - "To gather NPM metrics the Open Telemetry to the Kubernetes Cluster - and enable Network Performance Monitoring according to instructions found in Splunk - Docs https://help.splunk.com/en/splunk-observability-cloud/monitor-infrastructure/network-explorer/set-up-network-explorer-in-kubernetes#network-explorer-setup - In order to access those metrics from within Splunk Enterprise and ES, the Splunk - Infrastructure Monitoring add-on must be installed and configured on a Splunk Search - Head. Once installed, first configure the add-on with your O11y Cloud Org ID and - Access Token. Lastly set up the add-on to ingest metrics from O11y cloud using the - following settings, and any other settings left at default:\n* Name sim_npm_metrics_to_metrics_index\n - * Metric Resolution 10000" +search: "| mstats avg(tcp.*) as tcp.* avg(udp.*) as udp.* where `kubernetes_metrics` AND earliest=-1h by k8s.cluster.name source.workload.name source.process.name span=10s | eval key='source.workload.name' + \":\" + 'source.process.name' | join type=left key [ mstats avg(tcp.*) as avg_tcp.* avg(udp.*) as avg_udp.* stdev(tcp.*) as stdev_tcp.* avg(udp.*) as stdev_udp.* where `kubernetes_metrics` AND earliest=-30d latest=-1h by source.workload.name source.process.name | eval key='source.workload.name' + \":\" + 'source.process.name' ] | eval anomalies = \"\" | foreach stdev_* [ eval anomalies =if( '<>' > ('avg_<>' + 3 * 'stdev_<>'), anomalies + \"<> higher than average by \" + tostring(round(('<>' - 'avg_<>')/'stdev_<>' ,2)) + \" Standard Deviations. <>=\" + tostring('<>') + \" avg_<>=\" + tostring('avg_<>') + \" 'stdev_<>'=\" + tostring('stdev_<>') + \", \" , anomalies) ] | fillnull | eval anomalies = split(replace(anomalies, \",\\s$$$$\", \"\") ,\", \") | where anomalies!=\"\" | stats count(anomalies) as count values(anomalies) as anomalies by k8s.cluster.name source.workload.name source.process.name | where count > 5 | rename k8s.cluster.name as host | `kubernetes_anomalous_outbound_network_activity_from_process_filter`" +how_to_implement: "To gather NPM metrics the Open Telemetry to the Kubernetes Cluster and enable Network Performance Monitoring according to instructions found in Splunk Docs https://help.splunk.com/en/splunk-observability-cloud/monitor-infrastructure/network-explorer/set-up-network-explorer-in-kubernetes#network-explorer-setup In order to access those metrics from within Splunk Enterprise and ES, the Splunk Infrastructure Monitoring add-on must be installed and configured on a Splunk Search Head. Once installed, first configure the add-on with your O11y Cloud Org ID and Access Token. Lastly set up the add-on to ingest metrics from O11y cloud using the following settings, and any other settings left at default:\n* Name sim_npm_metrics_to_metrics_index\n * Metric Resolution 10000" known_false_positives: No false positives have been identified at this time. references: - - https://github.com/signalfx/splunk-otel-collector-chart + - https://github.com/signalfx/splunk-otel-collector-chart rba: - message: - Kubernetes Anomalous Outbound Network Activity from Process in kubernetes - cluster $host$ - risk_objects: - - field: host - type: system - score: 25 - threat_objects: [] + message: Kubernetes Anomalous Outbound Network Activity from Process in kubernetes cluster $host$ + risk_objects: + - field: host + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring - asset_type: Kubernetes - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring + asset_type: Kubernetes + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/cloud/kubernetes_anomalous_traffic_on_network_edge.yml b/detections/cloud/kubernetes_anomalous_traffic_on_network_edge.yml index 4e39933799..d72c0b854b 100644 --- a/detections/cloud/kubernetes_anomalous_traffic_on_network_edge.yml +++ b/detections/cloud/kubernetes_anomalous_traffic_on_network_edge.yml @@ -5,62 +5,28 @@ date: '2026-01-14' author: Matthew Moore, Splunk status: experimental type: Anomaly -description: - The following analytic identifies anomalous network traffic volumes between - Kubernetes workloads or between a workload and external sources. It leverages Network - Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk - Observability Cloud. The detection compares recent network metrics (tcp.bytes, tcp.new_sockets, - tcp.packets, udp.bytes, udp.packets) over the last hour with the average over the - past 30 days to identify significant deviations. This activity is significant as - unexpected spikes may indicate unauthorized data transfers or lateral movement. - If confirmed malicious, it could lead to data exfiltration or compromise of additional - services, potentially resulting in data breaches. +description: The following analytic identifies anomalous network traffic volumes between Kubernetes workloads or between a workload and external sources. It leverages Network Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk Observability Cloud. The detection compares recent network metrics (tcp.bytes, tcp.new_sockets, tcp.packets, udp.bytes, udp.packets) over the last hour with the average over the past 30 days to identify significant deviations. This activity is significant as unexpected spikes may indicate unauthorized data transfers or lateral movement. If confirmed malicious, it could lead to data exfiltration or compromise of additional services, potentially resulting in data breaches. data_source: [] -search: - "| mstats avg(tcp.*) as tcp.* avg(udp.*) as udp.* where `kubernetes_metrics` - AND earliest=-1h by k8s.cluster.name source.workload.name dest.workload.name span=10s - | eval key='source.workload.name' + \":\" + 'dest.workload.name' | join type=left - key [ mstats avg(tcp.*) as avg_tcp.* avg(udp.*) as avg_udp.* stdev(tcp.*) as stdev_tcp.* - avg(udp.*) as stdev_udp.* where `kubernetes_metrics` AND earliest=-30d latest=-1h - by source.workload.name dest.workload.name | eval key='source.workload.name' + \"\ - :\" + 'dest.workload.name' ] | eval anomalies = \"\" | foreach stdev_* [ eval anomalies - =if( '<>' > ('avg_<>' + 3 * 'stdev_<>'), anomalies - + \"<> higher than average by \" + tostring(round(('<>' - 'avg_<>')/'stdev_<>' - ,2)) + \" Standard Deviations. <>=\" + tostring('<>') + \" avg_<>=\"\ - \ + tostring('avg_<>') + \" 'stdev_<>'=\" + tostring('stdev_<>') - + \", \" , anomalies) ] | fillnull | eval anomalies = split(replace(anomalies, \"\ - ,\\s$$$$\", \"\") ,\", \") | where anomalies!=\"\" | stats count(anomalies) as count - values(anomalies) as anomalies by k8s.cluster.name source.workload.name dest.workload.name - | rename service as k8s.service | where count > 5 | rename k8s.cluster.name as host - | `kubernetes_anomalous_traffic_on_network_edge_filter`" -how_to_implement: - "To gather NPM metrics the Open Telemetry to the Kubernetes Cluster - and enable Network Performance Monitoring according to instructions found in Splunk - Docs https://help.splunk.com/en/splunk-observability-cloud/monitor-infrastructure/network-explorer/set-up-network-explorer-in-kubernetes#network-explorer-setup - In order to access those metrics from within Splunk Enterprise and ES, the Splunk - Infrastructure Monitoring add-on must be installed and configured on a Splunk Search - Head. Once installed, first configure the add-on with your O11y Cloud Org ID and - Access Token. Lastly set up the add-on to ingest metrics from O11y cloud using the - following settings, and any other settings left at default:\n* Name sim_npm_metrics_to_metrics_index\n - * Metric Resolution 10000" +search: "| mstats avg(tcp.*) as tcp.* avg(udp.*) as udp.* where `kubernetes_metrics` AND earliest=-1h by k8s.cluster.name source.workload.name dest.workload.name span=10s | eval key='source.workload.name' + \":\" + 'dest.workload.name' | join type=left key [ mstats avg(tcp.*) as avg_tcp.* avg(udp.*) as avg_udp.* stdev(tcp.*) as stdev_tcp.* avg(udp.*) as stdev_udp.* where `kubernetes_metrics` AND earliest=-30d latest=-1h by source.workload.name dest.workload.name | eval key='source.workload.name' + \":\" + 'dest.workload.name' ] | eval anomalies = \"\" | foreach stdev_* [ eval anomalies =if( '<>' > ('avg_<>' + 3 * 'stdev_<>'), anomalies + \"<> higher than average by \" + tostring(round(('<>' - 'avg_<>')/'stdev_<>' ,2)) + \" Standard Deviations. <>=\" + tostring('<>') + \" avg_<>=\" + tostring('avg_<>') + \" 'stdev_<>'=\" + tostring('stdev_<>') + \", \" , anomalies) ] | fillnull | eval anomalies = split(replace(anomalies, \",\\s$$$$\", \"\") ,\", \") | where anomalies!=\"\" | stats count(anomalies) as count values(anomalies) as anomalies by k8s.cluster.name source.workload.name dest.workload.name | rename service as k8s.service | where count > 5 | rename k8s.cluster.name as host | `kubernetes_anomalous_traffic_on_network_edge_filter`" +how_to_implement: "To gather NPM metrics the Open Telemetry to the Kubernetes Cluster and enable Network Performance Monitoring according to instructions found in Splunk Docs https://help.splunk.com/en/splunk-observability-cloud/monitor-infrastructure/network-explorer/set-up-network-explorer-in-kubernetes#network-explorer-setup In order to access those metrics from within Splunk Enterprise and ES, the Splunk Infrastructure Monitoring add-on must be installed and configured on a Splunk Search Head. Once installed, first configure the add-on with your O11y Cloud Org ID and Access Token. Lastly set up the add-on to ingest metrics from O11y cloud using the following settings, and any other settings left at default:\n* Name sim_npm_metrics_to_metrics_index\n * Metric Resolution 10000" known_false_positives: No false positives have been identified at this time. references: - - https://github.com/signalfx/splunk-otel-collector-chart + - https://github.com/signalfx/splunk-otel-collector-chart rba: - message: Kubernetes Anomalous Traffic on Network Edge in kubernetes cluster $host$ - risk_objects: - - field: host - type: system - score: 25 - threat_objects: [] + message: Kubernetes Anomalous Traffic on Network Edge in kubernetes cluster $host$ + risk_objects: + - field: host + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring - asset_type: Kubernetes - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring + asset_type: Kubernetes + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/cloud/kubernetes_aws_detect_suspicious_kubectl_calls.yml b/detections/cloud/kubernetes_aws_detect_suspicious_kubectl_calls.yml index ce7354b9d4..317850317f 100644 --- a/detections/cloud/kubernetes_aws_detect_suspicious_kubectl_calls.yml +++ b/detections/cloud/kubernetes_aws_detect_suspicious_kubectl_calls.yml @@ -1,53 +1,40 @@ name: Kubernetes AWS detect suspicious kubectl calls id: 042a3d32-8318-4763-9679-09db2644a8f2 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Rod Soto, Patrick Bareiss, Splunk status: experimental type: Anomaly -description: The following analytic detects anonymous and unauthenticated requests - to a Kubernetes cluster. It identifies this behavior by monitoring API calls from - users who have not provided any token or password in their request, using data from - `kube_audit` logs. This activity is significant for a SOC as it indicates a severe - misconfiguration, allowing unfettered access to the cluster with no traceability. - If confirmed malicious, an attacker could gain access to sensitive data or control - over the cluster, posing a substantial security risk. +description: The following analytic detects anonymous and unauthenticated requests to a Kubernetes cluster. It identifies this behavior by monitoring API calls from users who have not provided any token or password in their request, using data from `kube_audit` logs. This activity is significant for a SOC as it indicates a severe misconfiguration, allowing unfettered access to the cluster with no traceability. If confirmed malicious, an attacker could gain access to sensitive data or control over the cluster, posing a substantial security risk. data_source: -- Kubernetes Audit -search: '`kube_audit` user.username="system:anonymous" user.groups{} IN ("system:unauthenticated") - | fillnull | stats count by objectRef.name objectRef.namespace objectRef.resource - requestReceivedTimestamp requestURI responseStatus.code sourceIPs{} stage user.groups{} - user.uid user.username userAgent verb | rename sourceIPs{} as src_ip, user.username - as user |`kubernetes_aws_detect_suspicious_kubectl_calls_filter`' -how_to_implement: The detection is based on data that originates from Kubernetes Audit - logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes - audit logs provide a record of the requests made to the Kubernetes API server, which - is crucial for monitoring and detecting suspicious activities. Configure the audit - policy in Kubernetes to determine what kind of activities are logged. This is done - by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry - Collector for Kubernetes to collect the logs. This doc will describe how to collect - the audit log file - https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. - When you want to use this detection with AWS EKS, you need to enable EKS control - plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. - Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. -known_false_positives: Kubectl calls are not malicious by nature. However source IP, - verb and Object can reveal potential malicious activity, specially anonymous suspicious - IPs and sensitive objects such as configmaps or secrets + - Kubernetes Audit +search: |- + `kube_audit` user.username="system:anonymous" user.groups{} IN ("system:unauthenticated") + | fillnull + | stats count + BY objectRef.name objectRef.namespace objectRef.resource + requestReceivedTimestamp requestURI responseStatus.code + sourceIPs{} stage user.groups{} + user.uid user.username userAgent + verb + | rename sourceIPs{} as src_ip, user.username as user + | `kubernetes_aws_detect_suspicious_kubectl_calls_filter` +how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. +known_false_positives: Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets references: [] rba: - message: Suspicious kubectl API calls from $user$ - risk_objects: - - field: user - type: user - score: 25 - threat_objects: [] + message: Suspicious kubectl API calls from $user$ + risk_objects: + - field: user + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Kubernetes Security - asset_type: Kubernetes - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Kubernetes Security + asset_type: Kubernetes + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat diff --git a/detections/cloud/kubernetes_create_or_update_privileged_pod.yml b/detections/cloud/kubernetes_create_or_update_privileged_pod.yml index f4c6050fd7..b89e110a02 100644 --- a/detections/cloud/kubernetes_create_or_update_privileged_pod.yml +++ b/detections/cloud/kubernetes_create_or_update_privileged_pod.yml @@ -1,77 +1,59 @@ name: Kubernetes Create or Update Privileged Pod id: 3c6bd734-334d-4818-ae7c-5234313fc5da -version: 6 -date: '2026-01-14' +version: 7 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects the creation or update of privileged pods - in Kubernetes. It identifies this activity by monitoring Kubernetes Audit logs for - pod configurations that include root privileges. This behavior is significant for - a SOC as it could indicate an attempt to escalate privileges, exploit the kernel, - and gain full access to the host's namespace and devices. If confirmed malicious, - this activity could lead to unauthorized access to sensitive information, data breaches, - and service disruptions, posing a severe threat to the environment. +description: The following analytic detects the creation or update of privileged pods in Kubernetes. It identifies this activity by monitoring Kubernetes Audit logs for pod configurations that include root privileges. This behavior is significant for a SOC as it could indicate an attempt to escalate privileges, exploit the kernel, and gain full access to the host's namespace and devices. If confirmed malicious, this activity could lead to unauthorized access to sensitive information, data breaches, and service disruptions, posing a severe threat to the environment. data_source: -- Kubernetes Audit -search: '`kube_audit` objectRef.resource=pods verb=create OR verb=update requestObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration=*\"privileged\":true* - | fillnull | stats count values(user.groups{}) as user_groups by kind objectRef.name - objectRef.namespace objectRef.resource requestObject.kind responseStatus.code sourceIPs{} - stage user.username userAgent verb requestObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration - | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_create_or_update_privileged_pod_filter`' -how_to_implement: The detection is based on data that originates from Kubernetes Audit - logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes - audit logs provide a record of the requests made to the Kubernetes API server, which - is crucial for monitoring and detecting suspicious activities. Configure the audit - policy in Kubernetes to determine what kind of activities are logged. This is done - by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry - Collector for Kubernetes to collect the logs. This doc will describe how to collect - the audit log file - https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. - When you want to use this detection with AWS EKS, you need to enable EKS control - plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. - Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. + - Kubernetes Audit +search: |- + `kube_audit` objectRef.resource=pods verb=create OR verb=update requestObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration=*\"privileged\":true* + | fillnull + | stats count values(user.groups{}) as user_groups + BY kind objectRef.name objectRef.namespace + objectRef.resource requestObject.kind responseStatus.code + sourceIPs{} stage user.username + userAgent verb requestObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration + | rename sourceIPs{} as src_ip, user.username as user + | `kubernetes_create_or_update_privileged_pod_filter` +how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. known_false_positives: No false positives have been identified at this time. references: -- https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ + - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Kubernetes privileged pod created by user $user$. - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: src_ip - type: ip_address + message: Kubernetes privileged pod created by user $user$. + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Kubernetes Security - asset_type: Kubernetes - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Kubernetes Security + asset_type: Kubernetes + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kubernetes_privileged_pod/kubernetes_privileged_pod.json - sourcetype: _json - source: kubernetes + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kubernetes_privileged_pod/kubernetes_privileged_pod.json + sourcetype: _json + source: kubernetes diff --git a/detections/cloud/kubernetes_cron_job_creation.yml b/detections/cloud/kubernetes_cron_job_creation.yml index 5eedaaa47f..0aefd1400e 100644 --- a/detections/cloud/kubernetes_cron_job_creation.yml +++ b/detections/cloud/kubernetes_cron_job_creation.yml @@ -1,77 +1,60 @@ name: Kubernetes Cron Job Creation id: 5984dbe8-572f-47d7-9251-3dff6c3f0c0d -version: 6 -date: '2026-01-14' +version: 7 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects the creation of a Kubernetes cron job, - which is a task scheduled to run automatically at specified intervals. It identifies - this activity by monitoring Kubernetes Audit logs for the creation events of cron - jobs. This behavior is significant for a SOC as it could allow an attacker to execute - malicious tasks repeatedly and automatically, posing a threat to the Kubernetes - infrastructure. If confirmed malicious, this activity could lead to persistent attacks, - service disruptions, or unauthorized access to sensitive information. +description: The following analytic detects the creation of a Kubernetes cron job, which is a task scheduled to run automatically at specified intervals. It identifies this activity by monitoring Kubernetes Audit logs for the creation events of cron jobs. This behavior is significant for a SOC as it could allow an attacker to execute malicious tasks repeatedly and automatically, posing a threat to the Kubernetes infrastructure. If confirmed malicious, this activity could lead to persistent attacks, service disruptions, or unauthorized access to sensitive information. data_source: -- Kubernetes Audit -search: '`kube_audit` verb=create "objectRef.resource"=cronjobs | fillnull | stats - count values(user.groups{}) as user_groups by kind objectRef.name objectRef.namespace - objectRef.resource requestObject.kind requestObject.spec.schedule requestObject.spec.jobTemplate.spec.template.spec.containers{}.image - responseStatus.code sourceIPs{} stage user.username userAgent verb | rename sourceIPs{} - as src_ip, user.username as user | `kubernetes_cron_job_creation_filter`' -how_to_implement: The detection is based on data that originates from Kubernetes Audit - logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes - audit logs provide a record of the requests made to the Kubernetes API server, which - is crucial for monitoring and detecting suspicious activities. Configure the audit - policy in Kubernetes to determine what kind of activities are logged. This is done - by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry - Collector for Kubernetes to collect the logs. This doc will describe how to collect - the audit log file - https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. - When you want to use this detection with AWS EKS, you need to enable EKS control - plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. - Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. + - Kubernetes Audit +search: |- + `kube_audit` verb=create "objectRef.resource"=cronjobs + | fillnull + | stats count values(user.groups{}) as user_groups + BY kind objectRef.name objectRef.namespace + objectRef.resource requestObject.kind requestObject.spec.schedule + requestObject.spec.jobTemplate.spec.template.spec.containers{}.image responseStatus.code sourceIPs{} + stage user.username userAgent + verb + | rename sourceIPs{} as src_ip, user.username as user + | `kubernetes_cron_job_creation_filter` +how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. known_false_positives: No false positives have been identified at this time. references: -- https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ + - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Kubernetes cron job creation from user $user$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: src_ip - type: ip_address + message: Kubernetes cron job creation from user $user$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Kubernetes Security - asset_type: Kubernetes - mitre_attack_id: - - T1053.007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Kubernetes Security + asset_type: Kubernetes + mitre_attack_id: + - T1053.007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.007/kubernetes_audit_cron_job_creation/kubernetes_audit_cron_job_creation.json - sourcetype: _json - source: kubernetes + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.007/kubernetes_audit_cron_job_creation/kubernetes_audit_cron_job_creation.json + sourcetype: _json + source: kubernetes diff --git a/detections/cloud/kubernetes_daemonset_deployed.yml b/detections/cloud/kubernetes_daemonset_deployed.yml index 7cb700a7c9..b0a275d819 100644 --- a/detections/cloud/kubernetes_daemonset_deployed.yml +++ b/detections/cloud/kubernetes_daemonset_deployed.yml @@ -1,76 +1,59 @@ name: Kubernetes DaemonSet Deployed id: bf39c3a3-b191-4d42-8738-9d9797bd0c3a -version: 6 -date: '2026-01-14' +version: 7 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects the creation of a DaemonSet in a Kubernetes - cluster. This behavior is identified by monitoring Kubernetes Audit logs for the - creation event of a DaemonSet. DaemonSets ensure a specific pod runs on every node, - making them a potential vector for persistent access. This activity is significant - for a SOC as it could indicate an attempt to maintain persistent access to the Kubernetes - infrastructure. If confirmed malicious, it could lead to persistent attacks, service - disruptions, or unauthorized access to sensitive information. +description: The following analytic detects the creation of a DaemonSet in a Kubernetes cluster. This behavior is identified by monitoring Kubernetes Audit logs for the creation event of a DaemonSet. DaemonSets ensure a specific pod runs on every node, making them a potential vector for persistent access. This activity is significant for a SOC as it could indicate an attempt to maintain persistent access to the Kubernetes infrastructure. If confirmed malicious, it could lead to persistent attacks, service disruptions, or unauthorized access to sensitive information. data_source: -- Kubernetes Audit -search: '`kube_audit` "objectRef.resource"=daemonsets verb=create | fillnull | stats - count values(user.groups{}) as user_groups by kind objectRef.name objectRef.namespace - objectRef.resource requestObject.kind responseStatus.code sourceIPs{} stage user.username - userAgent verb | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_daemonset_deployed_filter`' -how_to_implement: The detection is based on data that originates from Kubernetes Audit - logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes - audit logs provide a record of the requests made to the Kubernetes API server, which - is crucial for monitoring and detecting suspicious activities. Configure the audit - policy in Kubernetes to determine what kind of activities are logged. This is done - by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry - Collector for Kubernetes to collect the logs. This doc will describe how to collect - the audit log file - https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. - When you want to use this detection with AWS EKS, you need to enable EKS control - plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. - Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. + - Kubernetes Audit +search: |- + `kube_audit` "objectRef.resource"=daemonsets verb=create + | fillnull + | stats count values(user.groups{}) as user_groups + BY kind objectRef.name objectRef.namespace + objectRef.resource requestObject.kind responseStatus.code + sourceIPs{} stage user.username + userAgent verb + | rename sourceIPs{} as src_ip, user.username as user + | `kubernetes_daemonset_deployed_filter` +how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. known_false_positives: No false positives have been identified at this time. references: -- https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ + - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: DaemonSet deployed to Kubernetes by user $user$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: src_ip - type: ip_address + message: DaemonSet deployed to Kubernetes by user $user$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Kubernetes Security - asset_type: Kubernetes - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Kubernetes Security + asset_type: Kubernetes + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kubernetes_audit_daemonset_created/kubernetes_audit_daemonset_created.json - sourcetype: _json - source: kubernetes + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kubernetes_audit_daemonset_created/kubernetes_audit_daemonset_created.json + sourcetype: _json + source: kubernetes diff --git a/detections/cloud/kubernetes_falco_shell_spawned.yml b/detections/cloud/kubernetes_falco_shell_spawned.yml index 71b7626f09..0e4ac6f3fa 100644 --- a/detections/cloud/kubernetes_falco_shell_spawned.yml +++ b/detections/cloud/kubernetes_falco_shell_spawned.yml @@ -1,74 +1,52 @@ name: Kubernetes Falco Shell Spawned id: d2feef92-d54a-4a19-8306-b47c6ceba5b2 -version: 6 -date: '2026-01-14' +version: 7 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects instances where a shell is spawned within - a Kubernetes container. Leveraging Falco, a cloud-native runtime security tool, - this analytic monitors system calls within the Kubernetes environment and flags - when a shell is spawned. This activity is significant for a SOC as it may indicate - unauthorized access, allowing an attacker to execute arbitrary commands, manipulate - container processes, or escalate privileges. If confirmed malicious, this could - lead to data breaches, service disruptions, or unauthorized access to sensitive - information, severely impacting the Kubernetes infrastructure's integrity and security. +description: The following analytic detects instances where a shell is spawned within a Kubernetes container. Leveraging Falco, a cloud-native runtime security tool, this analytic monitors system calls within the Kubernetes environment and flags when a shell is spawned. This activity is significant for a SOC as it may indicate unauthorized access, allowing an attacker to execute arbitrary commands, manipulate container processes, or escalate privileges. If confirmed malicious, this could lead to data breaches, service disruptions, or unauthorized access to sensitive information, severely impacting the Kubernetes infrastructure's integrity and security. data_source: -- Kubernetes Falco -search: '`kube_container_falco` "A shell was spawned in a container" | fillnull | - stats count by container_image container_image_tag container_name parent proc_exepath - process user | `kubernetes_falco_shell_spawned_filter`' -how_to_implement: The detection is based on data that originates from Kubernetes Audit - logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes - audit logs provide a record of the requests made to the Kubernetes API server, which - is crucial for monitoring and detecting suspicious activities. Configure the audit - policy in Kubernetes to determine what kind of activities are logged. This is done - by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry - Collector for Kubernetes to collect the logs. This doc will describe how to collect - the audit log file - https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. - When you want to use this detection with AWS EKS, you need to enable EKS control - plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. - Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. + - Kubernetes Falco +search: |- + `kube_container_falco` "A shell was spawned in a container" + | fillnull + | stats count by container_image container_image_tag container_name parent proc_exepath process user + | `kubernetes_falco_shell_spawned_filter` +how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. known_false_positives: No false positives have been identified at this time. references: -- https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ + - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A shell is spawned in the container $container_name$ by user $user$. - risk_objects: - - field: user - type: user - score: 49 - threat_objects: [] + message: A shell is spawned in the container $container_name$ by user $user$. + risk_objects: + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - Kubernetes Security - asset_type: Kubernetes - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Kubernetes Security + asset_type: Kubernetes + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kubernetes_falco_shell_spawned/kubernetes_falco_shell_spawned.log - sourcetype: kube:container:falco - source: kubernetes + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kubernetes_falco_shell_spawned/kubernetes_falco_shell_spawned.log + sourcetype: kube:container:falco + source: kubernetes diff --git a/detections/cloud/kubernetes_newly_seen_tcp_edge.yml b/detections/cloud/kubernetes_newly_seen_tcp_edge.yml index f02d71c894..b465a34ac7 100644 --- a/detections/cloud/kubernetes_newly_seen_tcp_edge.yml +++ b/detections/cloud/kubernetes_newly_seen_tcp_edge.yml @@ -1,56 +1,41 @@ name: Kubernetes newly seen TCP edge id: 13f081d6-7052-428a-bbb0-892c79ca7c65 -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Matthew Moore, Splunk status: experimental type: Anomaly -description: - The following analytic identifies newly seen TCP communication between - source and destination workload pairs within a Kubernetes cluster. It leverages - Network Performance Monitoring metrics collected via an OTEL collector and pulled - from Splunk Observability Cloud. The detection compares network activity over the - last hour with the past 30 days to spot new inter-workload communications. This - is significant as new connections can indicate changes in application behavior or - potential security threats. If malicious, unauthorized connections could lead to - data breaches, privilege escalation, lateral movement, or disruption of critical - services, compromising the application's integrity, availability, and confidentiality. +description: The following analytic identifies newly seen TCP communication between source and destination workload pairs within a Kubernetes cluster. It leverages Network Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk Observability Cloud. The detection compares network activity over the last hour with the past 30 days to spot new inter-workload communications. This is significant as new connections can indicate changes in application behavior or potential security threats. If malicious, unauthorized connections could lead to data breaches, privilege escalation, lateral movement, or disruption of critical services, compromising the application's integrity, availability, and confidentiality. data_source: [] -search: - '| mstats count(tcp.packets) as tcp.packets_count where `kubernetes_metrics` - AND earliest=-1h by k8s.cluster.name source.workload.name dest.workload.name | eval - current="True" | append [ mstats count(tcp.packets) as tcp.packets_count where `kubernetes_metrics` - AND earliest=-30d latest=-1h by source.workload.name dest.workload.name | eval current="false" - ] | eventstats values(current) as current by source.workload.name dest.workload.name - | search current="true" current!="false" | rename k8s.cluster.name as host | `kubernetes_newly_seen_tcp_edge_filter`' -how_to_implement: - "To gather NPM metrics the Open Telemetry to the Kubernetes Cluster - and enable Network Performance Monitoring according to instructions found in Splunk - Docs https://help.splunk.com/en/splunk-observability-cloud/monitor-infrastructure/network-explorer/set-up-network-explorer-in-kubernetes#network-explorer-setup - In order to access those metrics from within Splunk Enterprise and ES, the Splunk - Infrastructure Monitoring add-on must be installed and configured on a Splunk Search - Head. Once installed, first configure the add-on with your O11y Cloud Org ID and - Access Token. Lastly set up the add-on to ingest metrics from O11y cloud using the - following settings, and any other settings left at default:\n* Name sim_npm_metrics_to_metrics_index\n - * Metric Resolution 10000" +search: |- + | mstats count(tcp.packets) as tcp.packets_count where `kubernetes_metrics` AND earliest=-1h by k8s.cluster.name source.workload.name dest.workload.name + | eval current="True" + | append [ mstats count(tcp.packets) as tcp.packets_count where `kubernetes_metrics` AND earliest=-30d latest=-1h by source.workload.name dest.workload.name + | eval current="false" ] + | eventstats values(current) as current + BY source.workload.name dest.workload.name + | search current="true" current!="false" + | rename k8s.cluster.name as host + | `kubernetes_newly_seen_tcp_edge_filter` +how_to_implement: "To gather NPM metrics the Open Telemetry to the Kubernetes Cluster and enable Network Performance Monitoring according to instructions found in Splunk Docs https://help.splunk.com/en/splunk-observability-cloud/monitor-infrastructure/network-explorer/set-up-network-explorer-in-kubernetes#network-explorer-setup In order to access those metrics from within Splunk Enterprise and ES, the Splunk Infrastructure Monitoring add-on must be installed and configured on a Splunk Search Head. Once installed, first configure the add-on with your O11y Cloud Org ID and Access Token. Lastly set up the add-on to ingest metrics from O11y cloud using the following settings, and any other settings left at default:\n* Name sim_npm_metrics_to_metrics_index\n * Metric Resolution 10000" known_false_positives: No false positives have been identified at this time. references: - - https://github.com/signalfx/splunk-otel-collector-chart + - https://github.com/signalfx/splunk-otel-collector-chart rba: - message: Kubernetes newly seen TCP edge in kubernetes cluster $host$ - risk_objects: - - field: host - type: system - score: 25 - threat_objects: [] + message: Kubernetes newly seen TCP edge in kubernetes cluster $host$ + risk_objects: + - field: host + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring - asset_type: Kubernetes - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring + asset_type: Kubernetes + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/cloud/kubernetes_newly_seen_udp_edge.yml b/detections/cloud/kubernetes_newly_seen_udp_edge.yml index ab43d2e3a8..b094693c73 100644 --- a/detections/cloud/kubernetes_newly_seen_udp_edge.yml +++ b/detections/cloud/kubernetes_newly_seen_udp_edge.yml @@ -1,56 +1,41 @@ name: Kubernetes newly seen UDP edge id: 49b7daca-4e3c-4899-ba15-9a175e056fa9 -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Matthew Moore, Splunk status: experimental type: Anomaly -description: - The following analytic detects UDP communication between a newly seen - source and destination workload pair within a Kubernetes cluster. It leverages Network - Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk - Observability Cloud. This detection compares network activity over the last hour - with the past 30 days to identify new inter-workload communication. Such changes - in network behavior can indicate potential security threats or anomalies. If confirmed - malicious, unauthorized connections may enable attackers to infiltrate the application - ecosystem, leading to data breaches, privilege escalation, lateral movement, or - disruption of critical services. +description: The following analytic detects UDP communication between a newly seen source and destination workload pair within a Kubernetes cluster. It leverages Network Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk Observability Cloud. This detection compares network activity over the last hour with the past 30 days to identify new inter-workload communication. Such changes in network behavior can indicate potential security threats or anomalies. If confirmed malicious, unauthorized connections may enable attackers to infiltrate the application ecosystem, leading to data breaches, privilege escalation, lateral movement, or disruption of critical services. data_source: [] -search: - '| mstats count(udp.packets) as udp.packets_count where `kubernetes_metrics` - AND earliest=-1h by k8s.cluster.name source.workload.name dest.workload.name | eval - current="True" | append [ mstats count(udp.packets) as udp.packets_count where `kubernetes_metrics` - AND earliest=-30d latest=-1h by source.workload.name dest.workload.name | eval current="false" - ] | eventstats values(current) as current by source.workload.name dest.workload.name - | search current="true" current!="false" | rename k8s.cluster.name as host | `kubernetes_newly_seen_udp_edge_filter`' -how_to_implement: - "To gather NPM metrics the Open Telemetry to the Kubernetes Cluster - and enable Network Performance Monitoring according to instructions found in Splunk - Docs https://help.splunk.com/en/splunk-observability-cloud/monitor-infrastructure/network-explorer/set-up-network-explorer-in-kubernetes#network-explorer-setup - In order to access those metrics from within Splunk Enterprise and ES, the Splunk - Infrastructure Monitoring add-on must be installed and configured on a Splunk Search - Head. Once installed, first configure the add-on with your O11y Cloud Org ID and - Access Token. Lastly set up the add-on to ingest metrics from O11y cloud using the - following settings, and any other settings left at default:\n* Name sim_npm_metrics_to_metrics_index\n - * Metric Resolution 10000" +search: |- + | mstats count(udp.packets) as udp.packets_count where `kubernetes_metrics` AND earliest=-1h by k8s.cluster.name source.workload.name dest.workload.name + | eval current="True" + | append [ mstats count(udp.packets) as udp.packets_count where `kubernetes_metrics` AND earliest=-30d latest=-1h by source.workload.name dest.workload.name + | eval current="false" ] + | eventstats values(current) as current + BY source.workload.name dest.workload.name + | search current="true" current!="false" + | rename k8s.cluster.name as host + | `kubernetes_newly_seen_udp_edge_filter` +how_to_implement: "To gather NPM metrics the Open Telemetry to the Kubernetes Cluster and enable Network Performance Monitoring according to instructions found in Splunk Docs https://help.splunk.com/en/splunk-observability-cloud/monitor-infrastructure/network-explorer/set-up-network-explorer-in-kubernetes#network-explorer-setup In order to access those metrics from within Splunk Enterprise and ES, the Splunk Infrastructure Monitoring add-on must be installed and configured on a Splunk Search Head. Once installed, first configure the add-on with your O11y Cloud Org ID and Access Token. Lastly set up the add-on to ingest metrics from O11y cloud using the following settings, and any other settings left at default:\n* Name sim_npm_metrics_to_metrics_index\n * Metric Resolution 10000" known_false_positives: No false positives have been identified at this time. references: - - https://github.com/signalfx/splunk-otel-collector-chart + - https://github.com/signalfx/splunk-otel-collector-chart rba: - message: Kubernetes newly seen UDP edge in kubernetes cluster $host$ - risk_objects: - - field: host - type: system - score: 25 - threat_objects: [] + message: Kubernetes newly seen UDP edge in kubernetes cluster $host$ + risk_objects: + - field: host + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring - asset_type: Kubernetes - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring + asset_type: Kubernetes + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/cloud/kubernetes_nginx_ingress_lfi.yml b/detections/cloud/kubernetes_nginx_ingress_lfi.yml index 28162fbe90..e91ba022c1 100644 --- a/detections/cloud/kubernetes_nginx_ingress_lfi.yml +++ b/detections/cloud/kubernetes_nginx_ingress_lfi.yml @@ -5,65 +5,46 @@ date: '2026-01-14' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects local file inclusion (LFI) attacks targeting - Kubernetes Nginx ingress controllers. It leverages Kubernetes logs, parsing fields - such as `request` and `status` to identify suspicious patterns indicative of LFI - attempts. This activity is significant because LFI attacks can allow attackers to - read sensitive files from the server, potentially exposing critical information. - If confirmed malicious, this could lead to unauthorized access to sensitive data, - further exploitation, and potential compromise of the Kubernetes environment. +description: The following analytic detects local file inclusion (LFI) attacks targeting Kubernetes Nginx ingress controllers. It leverages Kubernetes logs, parsing fields such as `request` and `status` to identify suspicious patterns indicative of LFI attempts. This activity is significant because LFI attacks can allow attackers to read sensitive files from the server, potentially exposing critical information. If confirmed malicious, this could lead to unauthorized access to sensitive data, further exploitation, and potential compromise of the Kubernetes environment. data_source: [] -search: '`kubernetes_container_controller` | rex field=_raw "^(?\S+)\s+-\s+-\s+\[(?[^\]]*)\]\s\"(?[^\"]*)\"\s(?\S*)\s(?\S*)\s\"(?[^\"]*)\"\s\"(?[^\"]*)\"\s(?\S*)\s(?\S*)\s\[(?[^\]]*)\]\s\[(?[^\]]*)\]\s(?\S*)\s(?\S*)\s(?\S*)\s(?\S*)\s(?\S*)" - | rename remote_addr AS src_ip, upstream_status as status, proxy_upstream_name as - proxy | rex field=request "^(?\S+)\s(?\S+)\s" | eval phase="operate" - | eval severity="high" | stats count min(_time) as firstTime max(_time) as lastTime - by src_ip, status, url, http_method, host, http_user_agent, proxy, phase, severity, - request | lookup local_file_inclusion_paths local_file_inclusion_paths AS request - OUTPUT lfi_path | search lfi_path=yes | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `kubernetes_nginx_ingress_lfi_filter`' +search: '`kubernetes_container_controller` | rex field=_raw "^(?\S+)\s+-\s+-\s+\[(?[^\]]*)\]\s\"(?[^\"]*)\"\s(?\S*)\s(?\S*)\s\"(?[^\"]*)\"\s\"(?[^\"]*)\"\s(?\S*)\s(?\S*)\s\[(?[^\]]*)\]\s\[(?[^\]]*)\]\s(?\S*)\s(?\S*)\s(?\S*)\s(?\S*)\s(?\S*)" | rename remote_addr AS src_ip, upstream_status as status, proxy_upstream_name as proxy | rex field=request "^(?\S+)\s(?\S+)\s" | eval phase="operate" | eval severity="high" | stats count min(_time) as firstTime max(_time) as lastTime by src_ip, status, url, http_method, host, http_user_agent, proxy, phase, severity, request | lookup local_file_inclusion_paths local_file_inclusion_paths AS request OUTPUT lfi_path | search lfi_path=yes | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `kubernetes_nginx_ingress_lfi_filter`' how_to_implement: You must ingest Kubernetes logs through Splunk Connect for Kubernetes. known_false_positives: No false positives have been identified at this time. references: -- https://github.com/splunk/splunk-connect-for-kubernetes -- https://www.offensive-security.com/metasploit-unleashed/file-inclusion-vulnerabilities/ + - https://github.com/splunk/splunk-connect-for-kubernetes + - https://www.offensive-security.com/metasploit-unleashed/file-inclusion-vulnerabilities/ drilldown_searches: -- name: View the detection results for - "$host$" - search: '%original_detection_search% | search host = "$host$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$host$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$host$" + search: '%original_detection_search% | search host = "$host$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$host$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Local File Inclusion Attack detected on $host$ - risk_objects: - - field: host - type: system - score: 49 - threat_objects: - - field: src_ip - type: ip_address + message: Local File Inclusion Attack detected on $host$ + risk_objects: + - field: host + type: system + score: 49 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Dev Sec Ops - asset_type: Kubernetes - mitre_attack_id: - - T1212 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Dev Sec Ops + asset_type: Kubernetes + mitre_attack_id: + - T1212 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1212/kubernetes_nginx_lfi_attack/kubernetes_nginx_lfi_attack.log - sourcetype: kube:container:controller - source: kubernetes + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1212/kubernetes_nginx_lfi_attack/kubernetes_nginx_lfi_attack.log + sourcetype: kube:container:controller + source: kubernetes diff --git a/detections/cloud/kubernetes_nginx_ingress_rfi.yml b/detections/cloud/kubernetes_nginx_ingress_rfi.yml index e16b2d70b8..84edb6dfd0 100644 --- a/detections/cloud/kubernetes_nginx_ingress_rfi.yml +++ b/detections/cloud/kubernetes_nginx_ingress_rfi.yml @@ -5,64 +5,46 @@ date: '2026-01-14' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects remote file inclusion (RFI) attacks targeting - Kubernetes Nginx ingress controllers. It leverages Kubernetes logs from the Nginx - ingress controller, parsing fields such as `remote_addr`, `request`, and `url` to - identify suspicious activity. This activity is significant because RFI attacks can - allow attackers to execute arbitrary code or access sensitive files on the server. - If confirmed malicious, this could lead to unauthorized access, data exfiltration, - or further compromise of the Kubernetes environment. +description: The following analytic detects remote file inclusion (RFI) attacks targeting Kubernetes Nginx ingress controllers. It leverages Kubernetes logs from the Nginx ingress controller, parsing fields such as `remote_addr`, `request`, and `url` to identify suspicious activity. This activity is significant because RFI attacks can allow attackers to execute arbitrary code or access sensitive files on the server. If confirmed malicious, this could lead to unauthorized access, data exfiltration, or further compromise of the Kubernetes environment. data_source: [] -search: '`kubernetes_container_controller` | rex field=_raw "^(?\S+)\s+-\s+-\s+\[(?[^\]]*)\]\s\"(?[^\"]*)\"\s(?\S*)\s(?\S*)\s\"(?[^\"]*)\"\s\"(?[^\"]*)\"\s(?\S*)\s(?\S*)\s\[(?[^\]]*)\]\s\[(?[^\]]*)\]\s(?\S*)\s(?\S*)\s(?\S*)\s(?\S*)\s(?\S*)" - | rex field=request "^(?\S+)?\s(?\S+)\s" | rex field=url "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})" - | search dest_ip=* | rename remote_addr AS src_ip, upstream_status as status, proxy_upstream_name - as proxy | eval phase="operate" | eval severity="medium" | stats count min(_time) - as firstTime max(_time) as lastTime by src_ip, dest_ip status, url, http_method, - host, http_user_agent, proxy, phase, severity | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `kubernetes_nginx_ingress_rfi_filter`' +search: '`kubernetes_container_controller` | rex field=_raw "^(?\S+)\s+-\s+-\s+\[(?[^\]]*)\]\s\"(?[^\"]*)\"\s(?\S*)\s(?\S*)\s\"(?[^\"]*)\"\s\"(?[^\"]*)\"\s(?\S*)\s(?\S*)\s\[(?[^\]]*)\]\s\[(?[^\]]*)\]\s(?\S*)\s(?\S*)\s(?\S*)\s(?\S*)\s(?\S*)" | rex field=request "^(?\S+)?\s(?\S+)\s" | rex field=url "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})" | search dest_ip=* | rename remote_addr AS src_ip, upstream_status as status, proxy_upstream_name as proxy | eval phase="operate" | eval severity="medium" | stats count min(_time) as firstTime max(_time) as lastTime by src_ip, dest_ip status, url, http_method, host, http_user_agent, proxy, phase, severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `kubernetes_nginx_ingress_rfi_filter`' how_to_implement: You must ingest Kubernetes logs through Splunk Connect for Kubernetes. known_false_positives: No false positives have been identified at this time. references: -- https://github.com/splunk/splunk-connect-for-kubernetes -- https://www.invicti.com/blog/web-security/remote-file-inclusion-vulnerability/ + - https://github.com/splunk/splunk-connect-for-kubernetes + - https://www.invicti.com/blog/web-security/remote-file-inclusion-vulnerability/ drilldown_searches: -- name: View the detection results for - "$host$" - search: '%original_detection_search% | search host = "$host$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$host$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$host$" + search: '%original_detection_search% | search host = "$host$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$host$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Remote File Inclusion Attack detected on $host$ - risk_objects: - - field: host - type: system - score: 49 - threat_objects: - - field: src_ip - type: ip_address + message: Remote File Inclusion Attack detected on $host$ + risk_objects: + - field: host + type: system + score: 49 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Dev Sec Ops - asset_type: Kubernetes - mitre_attack_id: - - T1212 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Dev Sec Ops + asset_type: Kubernetes + mitre_attack_id: + - T1212 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1212/kuberntest_nginx_rfi_attack/kubernetes_nginx_rfi_attack.log - sourcetype: kube:container:controller - source: kubernetes + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1212/kuberntest_nginx_rfi_attack/kubernetes_nginx_rfi_attack.log + sourcetype: kube:container:controller + source: kubernetes diff --git a/detections/cloud/kubernetes_node_port_creation.yml b/detections/cloud/kubernetes_node_port_creation.yml index 2b1731031e..c165c41c1e 100644 --- a/detections/cloud/kubernetes_node_port_creation.yml +++ b/detections/cloud/kubernetes_node_port_creation.yml @@ -1,77 +1,59 @@ name: Kubernetes Node Port Creation id: d7fc865e-b8a1-4029-a960-cf4403b821b6 -version: 6 -date: '2026-01-14' +version: 7 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects the creation of a Kubernetes NodePort - service, which exposes a service to the external network. It identifies this activity - by monitoring Kubernetes Audit logs for the creation of NodePort services. This - behavior is significant for a SOC as it could allow an attacker to access internal - services, posing a threat to the Kubernetes infrastructure's integrity and security. - If confirmed malicious, this activity could lead to data breaches, service disruptions, - or unauthorized access to sensitive information. +description: The following analytic detects the creation of a Kubernetes NodePort service, which exposes a service to the external network. It identifies this activity by monitoring Kubernetes Audit logs for the creation of NodePort services. This behavior is significant for a SOC as it could allow an attacker to access internal services, posing a threat to the Kubernetes infrastructure's integrity and security. If confirmed malicious, this activity could lead to data breaches, service disruptions, or unauthorized access to sensitive information. data_source: -- Kubernetes Audit -search: '`kube_audit` "objectRef.resource"=services verb=create requestObject.spec.type=NodePort - | fillnull | stats count values(user.groups{}) as user_groups by kind objectRef.name - objectRef.namespace objectRef.resource requestObject.kind requestObject.spec.type - responseStatus.code sourceIPs{} stage user.username userAgent verb | rename sourceIPs{} - as src_ip, user.username as user | `kubernetes_node_port_creation_filter`' -how_to_implement: The detection is based on data that originates from Kubernetes Audit - logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes - audit logs provide a record of the requests made to the Kubernetes API server, which - is crucial for monitoring and detecting suspicious activities. Configure the audit - policy in Kubernetes to determine what kind of activities are logged. This is done - by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry - Collector for Kubernetes to collect the logs. This doc will describe how to collect - the audit log file - https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. - When you want to use this detection with AWS EKS, you need to enable EKS control - plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. - Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. + - Kubernetes Audit +search: |- + `kube_audit` "objectRef.resource"=services verb=create requestObject.spec.type=NodePort + | fillnull + | stats count values(user.groups{}) as user_groups + BY kind objectRef.name objectRef.namespace + objectRef.resource requestObject.kind requestObject.spec.type + responseStatus.code sourceIPs{} stage + user.username userAgent verb + | rename sourceIPs{} as src_ip, user.username as user + | `kubernetes_node_port_creation_filter` +how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. known_false_positives: No false positives have been identified at this time. references: -- https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ + - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Kubernetes node port creation from user $user$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: src_ip - type: ip_address + message: Kubernetes node port creation from user $user$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Kubernetes Security - asset_type: Kubernetes - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Kubernetes Security + asset_type: Kubernetes + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kube_audit_create_node_port_service/kube_audit_create_node_port_service.json - sourcetype: _json - source: kubernetes + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kube_audit_create_node_port_service/kube_audit_create_node_port_service.json + sourcetype: _json + source: kubernetes diff --git a/detections/cloud/kubernetes_pod_created_in_default_namespace.yml b/detections/cloud/kubernetes_pod_created_in_default_namespace.yml index c3d3bd2ede..ca5d880e02 100644 --- a/detections/cloud/kubernetes_pod_created_in_default_namespace.yml +++ b/detections/cloud/kubernetes_pod_created_in_default_namespace.yml @@ -1,77 +1,60 @@ name: Kubernetes Pod Created in Default Namespace id: 3d6b1a81-367b-42d5-a925-6ef90b6b9f1e -version: 6 -date: '2026-01-14' +version: 7 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects the creation of Kubernetes pods in the - default, kube-system, or kube-public namespaces. It leverages Kubernetes audit logs - to identify pod creation events within these specific namespaces. This activity - is significant for a SOC as it may indicate an attacker attempting to hide their - presence or evade defenses. Unauthorized pod creation in these namespaces can suggest - a successful cluster breach, potentially leading to privilege escalation, persistent - access, or further malicious activities within the cluster. +description: The following analytic detects the creation of Kubernetes pods in the default, kube-system, or kube-public namespaces. It leverages Kubernetes audit logs to identify pod creation events within these specific namespaces. This activity is significant for a SOC as it may indicate an attacker attempting to hide their presence or evade defenses. Unauthorized pod creation in these namespaces can suggest a successful cluster breach, potentially leading to privilege escalation, persistent access, or further malicious activities within the cluster. data_source: -- Kubernetes Audit -search: '`kube_audit` objectRef.resource=pods verb=create objectRef.namespace IN ("default", - "kube-system", "kube-public") | fillnull | stats count by objectRef.name objectRef.namespace - objectRef.resource requestReceivedTimestamp requestURI responseStatus.code sourceIPs{} - stage user.groups{} user.uid user.username userAgent verb | rename sourceIPs{} as - src_ip, user.username as user | `kubernetes_pod_created_in_default_namespace_filter`' -how_to_implement: The detection is based on data that originates from Kubernetes Audit - logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes - audit logs provide a record of the requests made to the Kubernetes API server, which - is crucial for monitoring and detecting suspicious activities. Configure the audit - policy in Kubernetes to determine what kind of activities are logged. This is done - by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry - Collector for Kubernetes to collect the logs. This doc will describe how to collect - the audit log file - https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. - When you want to use this detection with AWS EKS, you need to enable EKS control - plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. - Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. + - Kubernetes Audit +search: |- + `kube_audit` objectRef.resource=pods verb=create objectRef.namespace IN ("default", "kube-system", "kube-public") + | fillnull + | stats count + BY objectRef.name objectRef.namespace objectRef.resource + requestReceivedTimestamp requestURI responseStatus.code + sourceIPs{} stage user.groups{} + user.uid user.username userAgent + verb + | rename sourceIPs{} as src_ip, user.username as user + | `kubernetes_pod_created_in_default_namespace_filter` +how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. known_false_positives: No false positives have been identified at this time. references: -- https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ + - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Kubernetes Pod Created in Default Namespace by $user$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: src_ip - type: ip_address + message: Kubernetes Pod Created in Default Namespace by $user$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Kubernetes Security - asset_type: Kubernetes - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Kubernetes Security + asset_type: Kubernetes + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kubernetes_privileged_pod/kubernetes_privileged_pod.json - sourcetype: _json - source: kubernetes + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kubernetes_privileged_pod/kubernetes_privileged_pod.json + sourcetype: _json + source: kubernetes diff --git a/detections/cloud/kubernetes_pod_with_host_network_attachment.yml b/detections/cloud/kubernetes_pod_with_host_network_attachment.yml index 569b49e3a3..36ce2fb47d 100644 --- a/detections/cloud/kubernetes_pod_with_host_network_attachment.yml +++ b/detections/cloud/kubernetes_pod_with_host_network_attachment.yml @@ -1,77 +1,59 @@ name: Kubernetes Pod With Host Network Attachment id: cce357cf-43a4-494a-814b-67cea90fe990 -version: 6 -date: '2026-01-14' +version: 7 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects the creation or update of a Kubernetes - pod with host network attachment. It leverages Kubernetes Audit logs to identify - pods configured with host network settings. This activity is significant for a SOC - as it could allow an attacker to monitor all network traffic on the node, potentially - capturing sensitive information and escalating privileges. If confirmed malicious, - this could lead to unauthorized access, data breaches, and service disruptions, - severely impacting the security and integrity of the Kubernetes environment. +description: The following analytic detects the creation or update of a Kubernetes pod with host network attachment. It leverages Kubernetes Audit logs to identify pods configured with host network settings. This activity is significant for a SOC as it could allow an attacker to monitor all network traffic on the node, potentially capturing sensitive information and escalating privileges. If confirmed malicious, this could lead to unauthorized access, data breaches, and service disruptions, severely impacting the security and integrity of the Kubernetes environment. data_source: -- Kubernetes Audit -search: '`kube_audit` objectRef.resource=pods verb=create OR verb=update requestObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration=*\"hostNetwork\":true* - | fillnull | stats count values(user.groups{}) as user_groups by kind objectRef.name - objectRef.namespace objectRef.resource requestObject.kind responseStatus.code sourceIPs{} - stage user.username userAgent verb requestObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration - | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_pod_with_host_network_attachment_filter`' -how_to_implement: The detection is based on data that originates from Kubernetes Audit - logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes - audit logs provide a record of the requests made to the Kubernetes API server, which - is crucial for monitoring and detecting suspicious activities. Configure the audit - policy in Kubernetes to determine what kind of activities are logged. This is done - by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry - Collector for Kubernetes to collect the logs. This doc will describe how to collect - the audit log file - https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. - When you want to use this detection with AWS EKS, you need to enable EKS control - plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. - Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. + - Kubernetes Audit +search: |- + `kube_audit` objectRef.resource=pods verb=create OR verb=update requestObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration=*\"hostNetwork\":true* + | fillnull + | stats count values(user.groups{}) as user_groups + BY kind objectRef.name objectRef.namespace + objectRef.resource requestObject.kind responseStatus.code + sourceIPs{} stage user.username + userAgent verb requestObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration + | rename sourceIPs{} as src_ip, user.username as user + | `kubernetes_pod_with_host_network_attachment_filter` +how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. known_false_positives: No false positives have been identified at this time. references: -- https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ + - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Kubernetes pod with host network attachment from user $user$. - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: src_ip - type: ip_address + message: Kubernetes pod with host network attachment from user $user$. + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Kubernetes Security - asset_type: Kubernetes - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Kubernetes Security + asset_type: Kubernetes + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kubernetes_privileged_pod/kubernetes_privileged_pod.json - sourcetype: _json - source: kubernetes + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kubernetes_privileged_pod/kubernetes_privileged_pod.json + sourcetype: _json + source: kubernetes diff --git a/detections/cloud/kubernetes_previously_unseen_container_image_name.yml b/detections/cloud/kubernetes_previously_unseen_container_image_name.yml index 5ae14fe92a..080ec10c0a 100644 --- a/detections/cloud/kubernetes_previously_unseen_container_image_name.yml +++ b/detections/cloud/kubernetes_previously_unseen_container_image_name.yml @@ -1,62 +1,42 @@ name: Kubernetes Previously Unseen Container Image Name id: fea515a4-b1d8-4cd6-80d6-e0d71397b891 -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Matthew Moore, Splunk status: experimental type: Anomaly -description: The following analytic identifies the creation of containerized workloads - using previously unseen images in a Kubernetes cluster. It leverages process metrics - from an OTEL collector and Kubernetes cluster receiver, pulled from Splunk Observability - Cloud. The detection compares container image names seen in the last hour with those - from the previous 30 days. This activity is significant as unfamiliar container - images may introduce vulnerabilities, malware, or misconfigurations, posing threats - to the cluster's integrity. If confirmed malicious, compromised images can lead - to data breaches, service disruptions, unauthorized access, and potential lateral - movement within the cluster. +description: The following analytic identifies the creation of containerized workloads using previously unseen images in a Kubernetes cluster. It leverages process metrics from an OTEL collector and Kubernetes cluster receiver, pulled from Splunk Observability Cloud. The detection compares container image names seen in the last hour with those from the previous 30 days. This activity is significant as unfamiliar container images may introduce vulnerabilities, malware, or misconfigurations, posing threats to the cluster's integrity. If confirmed malicious, compromised images can lead to data breaches, service disruptions, unauthorized access, and potential lateral movement within the cluster. data_source: [] -search: '| mstats count(k8s.container.ready) as k8s.container.ready_count where `kubernetes_metrics` - AND earliest=-24h by host.name k8s.cluster.name k8s.node.name container.image.name - | eval current="True" | append [mstats count(k8s.container.ready) as k8s.container.ready_count - where `kubernetes_metrics` AND earliest=-30d latest=-1h by host.name k8s.cluster.name - k8s.node.name container.image.name | eval current="false" ] | stats values(current) - as current by host.name k8s.cluster.name k8s.node.name container.image.name | search - current="true" AND current!="false" | rename host.name as host | `kubernetes_previously_unseen_container_image_name_filter`' -how_to_implement: "To implement this detection, follow these steps:\n* Deploy the - OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process - receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically - Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install - the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n - * Configure the SIM add-on with your Observability Cloud Organization ID and Access - Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input - \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization - ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to - the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B'); - data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D'); - data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F'); - data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H'); - data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J'); - data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n - * Leave all other settings at their default values.\n* Run the Search Baseline Of - Kubernetes Container Network IO Ratio" +search: |- + | mstats count(k8s.container.ready) as k8s.container.ready_count where `kubernetes_metrics` AND earliest=-24h by host.name k8s.cluster.name k8s.node.name container.image.name + | eval current="True" + | append [mstats count(k8s.container.ready) as k8s.container.ready_count where `kubernetes_metrics` AND earliest=-30d latest=-1h by host.name k8s.cluster.name k8s.node.name container.image.name + | eval current="false" ] + | stats values(current) as current + BY host.name k8s.cluster.name k8s.node.name + container.image.name + | search current="true" AND current!="false" + | rename host.name as host + | `kubernetes_previously_unseen_container_image_name_filter` +how_to_implement: "To implement this detection, follow these steps:\n* Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n * Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B'); data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D'); data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F'); data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H'); data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J'); data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n * Leave all other settings at their default values.\n* Run the Search Baseline Of Kubernetes Container Network IO Ratio" known_false_positives: No false positives have been identified at this time. references: -- https://github.com/signalfx/splunk-otel-collector-chart + - https://github.com/signalfx/splunk-otel-collector-chart rba: - message: Kubernetes Previously Unseen Container Image Name on host $host$ - risk_objects: - - field: host - type: system - score: 25 - threat_objects: [] + message: Kubernetes Previously Unseen Container Image Name on host $host$ + risk_objects: + - field: host + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring - asset_type: Kubernetes - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring + asset_type: Kubernetes + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/cloud/kubernetes_previously_unseen_process.yml b/detections/cloud/kubernetes_previously_unseen_process.yml index 000866aeb4..6f0cd4cbf5 100644 --- a/detections/cloud/kubernetes_previously_unseen_process.yml +++ b/detections/cloud/kubernetes_previously_unseen_process.yml @@ -1,63 +1,41 @@ name: Kubernetes Previously Unseen Process id: c8119b2f-d7f7-40be-940a-1c582870e8e2 -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Matthew Moore, Splunk status: experimental type: Anomaly -description: The following analytic detects previously unseen processes within the - Kubernetes environment on master or worker nodes. It leverages process metrics collected - via an OTEL collector and hostmetrics receiver, and data is pulled from Splunk Observability - Cloud. This detection compares processes observed in the last hour against those - seen in the previous 30 days. Identifying new processes is crucial as they may indicate - unauthorized activity or attempts to compromise the node. If confirmed malicious, - these processes could lead to data exfiltration, privilege escalation, denial-of-service - attacks, or the introduction of malware, posing significant risks to the Kubernetes - cluster. +description: The following analytic detects previously unseen processes within the Kubernetes environment on master or worker nodes. It leverages process metrics collected via an OTEL collector and hostmetrics receiver, and data is pulled from Splunk Observability Cloud. This detection compares processes observed in the last hour against those seen in the previous 30 days. Identifying new processes is crucial as they may indicate unauthorized activity or attempts to compromise the node. If confirmed malicious, these processes could lead to data exfiltration, privilege escalation, denial-of-service attacks, or the introduction of malware, posing significant risks to the Kubernetes cluster. data_source: [] -search: '| mstats count(process.memory.utilization) as process.memory.utilization_count - where `kubernetes_metrics` AND earliest=-1h by host.name k8s.cluster.name k8s.node.name - process.executable.name | eval current="True" | append [mstats count(process.memory.utilization) - as process.memory.utilization_count where `kubernetes_metrics` AND earliest=-30d - latest=-1h by host.name k8s.cluster.name k8s.node.name process.executable.name ] - | stats count values(current) as current by host.name k8s.cluster.name k8s.node.name - process.executable.name | where count=1 and current="True" | rename host.name as - host | `kubernetes_previously_unseen_process_filter`' -how_to_implement: "To implement this detection, follow these steps:\n* Deploy the - OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process - receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically - Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install - the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n - * Configure the SIM add-on with your Observability Cloud Organization ID and Access - Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input - \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization - ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to - the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B'); - data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D'); - data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F'); - data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H'); - data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J'); - data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n - * Leave all other settings at their default values.\n* Run the Search Baseline Of - Kubernetes Container Network IO Ratio" +search: |- + | mstats count(process.memory.utilization) as process.memory.utilization_count where `kubernetes_metrics` AND earliest=-1h by host.name k8s.cluster.name k8s.node.name process.executable.name + | eval current="True" + | append [mstats count(process.memory.utilization) as process.memory.utilization_count where `kubernetes_metrics` AND earliest=-30d latest=-1h by host.name k8s.cluster.name k8s.node.name process.executable.name ] + | stats count values(current) as current + BY host.name k8s.cluster.name k8s.node.name + process.executable.name + | where count=1 and current="True" + | rename host.name as host + | `kubernetes_previously_unseen_process_filter` +how_to_implement: "To implement this detection, follow these steps:\n* Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n * Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B'); data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D'); data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F'); data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H'); data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J'); data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n * Leave all other settings at their default values.\n* Run the Search Baseline Of Kubernetes Container Network IO Ratio" known_false_positives: No false positives have been identified at this time. references: -- https://github.com/signalfx/splunk-otel-collector-chart + - https://github.com/signalfx/splunk-otel-collector-chart rba: - message: Kubernetes Previously Unseen Process on host $host$ - risk_objects: - - field: host - type: system - score: 25 - threat_objects: [] + message: Kubernetes Previously Unseen Process on host $host$ + risk_objects: + - field: host + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring - asset_type: Kubernetes - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring + asset_type: Kubernetes + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/cloud/kubernetes_process_running_from_new_path.yml b/detections/cloud/kubernetes_process_running_from_new_path.yml index 96783638ed..dd7d32d6db 100644 --- a/detections/cloud/kubernetes_process_running_from_new_path.yml +++ b/detections/cloud/kubernetes_process_running_from_new_path.yml @@ -1,64 +1,41 @@ name: Kubernetes Process Running From New Path id: 454076fb-0e9e-4adf-b93a-da132621c5e6 -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Matthew Moore, Splunk status: experimental type: Anomaly -description: The following analytic identifies processes running from newly seen paths - within a Kubernetes environment. It leverages process metrics collected via an OTEL - collector and hostmetrics receiver, and data is pulled from Splunk Observability - Cloud using the Splunk Infrastructure Monitoring Add-on. This detection compares - processes observed in the last hour with those seen over the previous 30 days. This - activity is significant as it may indicate unauthorized changes, compromised nodes, - or the introduction of malicious software. If confirmed malicious, it could lead - to unauthorized process execution, control over critical resources, data exfiltration, - privilege escalation, or malware introduction within the Kubernetes cluster. +description: The following analytic identifies processes running from newly seen paths within a Kubernetes environment. It leverages process metrics collected via an OTEL collector and hostmetrics receiver, and data is pulled from Splunk Observability Cloud using the Splunk Infrastructure Monitoring Add-on. This detection compares processes observed in the last hour with those seen over the previous 30 days. This activity is significant as it may indicate unauthorized changes, compromised nodes, or the introduction of malicious software. If confirmed malicious, it could lead to unauthorized process execution, control over critical resources, data exfiltration, privilege escalation, or malware introduction within the Kubernetes cluster. data_source: [] -search: '| mstats count(process.memory.utilization) as process.memory.utilization_count - where `kubernetes_metrics` AND earliest=-1h by host.name k8s.cluster.name k8s.node.name - process.pid process.executable.path process.executable.name | eval current="True" - | append [ mstats count(process.memory.utilization) as process.memory.utilization_count - where `kubernetes_metrics` AND earliest=-30d latest=-1h by host.name k8s.cluster.name - k8s.node.name process.pid process.executable.path process.executable.name ] | stats - count values(current) as current by host.name k8s.cluster.name k8s.node.name process.pid - process.executable.name process.executable.path | where count=1 and current="True" - | rename host.name as host | `kubernetes_process_running_from_new_path_filter`' -how_to_implement: "To implement this detection, follow these steps:\n* Deploy the - OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process - receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically - Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install - the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n - * Configure the SIM add-on with your Observability Cloud Organization ID and Access - Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input - \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization - ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to - the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B'); - data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D'); - data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F'); - data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H'); - data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J'); - data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n - * Leave all other settings at their default values.\n* Run the Search Baseline Of - Kubernetes Container Network IO Ratio" +search: |- + | mstats count(process.memory.utilization) as process.memory.utilization_count where `kubernetes_metrics` AND earliest=-1h by host.name k8s.cluster.name k8s.node.name process.pid process.executable.path process.executable.name + | eval current="True" + | append [ mstats count(process.memory.utilization) as process.memory.utilization_count where `kubernetes_metrics` AND earliest=-30d latest=-1h by host.name k8s.cluster.name k8s.node.name process.pid process.executable.path process.executable.name ] + | stats count values(current) as current + BY host.name k8s.cluster.name k8s.node.name + process.pid process.executable.name process.executable.path + | where count=1 and current="True" + | rename host.name as host + | `kubernetes_process_running_from_new_path_filter` +how_to_implement: "To implement this detection, follow these steps:\n* Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n * Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B'); data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D'); data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F'); data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H'); data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J'); data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n * Leave all other settings at their default values.\n* Run the Search Baseline Of Kubernetes Container Network IO Ratio" known_false_positives: No false positives have been identified at this time. references: -- https://github.com/signalfx/splunk-otel-collector-chart + - https://github.com/signalfx/splunk-otel-collector-chart rba: - message: Kubernetes Process Running From New Path on host $host$ - risk_objects: - - field: host - type: system - score: 25 - threat_objects: [] + message: Kubernetes Process Running From New Path on host $host$ + risk_objects: + - field: host + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring - asset_type: Kubernetes - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring + asset_type: Kubernetes + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/cloud/kubernetes_process_with_anomalous_resource_utilisation.yml b/detections/cloud/kubernetes_process_with_anomalous_resource_utilisation.yml index 3859fd9d64..27815457b9 100644 --- a/detections/cloud/kubernetes_process_with_anomalous_resource_utilisation.yml +++ b/detections/cloud/kubernetes_process_with_anomalous_resource_utilisation.yml @@ -5,62 +5,28 @@ date: '2026-01-14' author: Matthew Moore, Splunk status: experimental type: Anomaly -description: The following analytic identifies high resource utilization anomalies - in Kubernetes processes. It leverages process metrics from an OTEL collector and - hostmetrics receiver, fetched via the Splunk Infrastructure Monitoring Add-on. The - detection uses a lookup table with average and standard deviation values to spot - anomalies. This activity is significant as high resource utilization can indicate - security threats like cryptojacking, unauthorized data exfiltration, or compromised - containers. If confirmed malicious, such anomalies can disrupt services, exhaust - resources, increase costs, and allow attackers to evade detection or maintain access. +description: The following analytic identifies high resource utilization anomalies in Kubernetes processes. It leverages process metrics from an OTEL collector and hostmetrics receiver, fetched via the Splunk Infrastructure Monitoring Add-on. The detection uses a lookup table with average and standard deviation values to spot anomalies. This activity is significant as high resource utilization can indicate security threats like cryptojacking, unauthorized data exfiltration, or compromised containers. If confirmed malicious, such anomalies can disrupt services, exhaust resources, increase costs, and allow attackers to evade detection or maintain access. data_source: [] -search: "| mstats avg(process.*) as process.* where `kubernetes_metrics` by host.name - k8s.cluster.name k8s.node.name process.executable.name span=10s | eval key = 'k8s.cluster.name' - + \":\" + 'host.name' + \":\" + 'process.executable.name' | lookup k8s_process_resource_baseline - key | fillnull | eval anomalies = \"\" | foreach stdev_* [ eval anomalies =if( '<>' - > ('avg_<>' + 4 * 'stdev_<>'), anomalies + \"<> higher - than average by \" + tostring(round(('<>' - 'avg_<>')/'stdev_<>' - ,2)) + \" Standard Deviations. <>=\" + tostring('<>') + \" avg_<>=\"\ - \ + tostring('avg_<>') + \" 'stdev_<>'=\" + tostring('stdev_<>') - + \", \" , anomalies) ] | eval anomalies = replace(anomalies, \",\\s$\", \"\") | - where anomalies!=\"\" | stats count values(anomalies) as anomalies by host.name - k8s.cluster.name k8s.node.name process.executable.name | sort - count | where count - > 5 | rename host.name as host | `kubernetes_process_with_anomalous_resource_utilisation_filter`" -how_to_implement: "To implement this detection, follow these steps:\n* Deploy the - OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process - receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically - Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install - the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n - * Configure the SIM add-on with your Observability Cloud Organization ID and Access - Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input - \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization - ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to - the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B'); - data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D'); - data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F'); - data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H'); - data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J'); - data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n - * Leave all other settings at their default values.\n* Run the Search Baseline Of - Kubernetes Container Network IO Ratio" +search: "| mstats avg(process.*) as process.* where `kubernetes_metrics` by host.name k8s.cluster.name k8s.node.name process.executable.name span=10s | eval key = 'k8s.cluster.name' + \":\" + 'host.name' + \":\" + 'process.executable.name' | lookup k8s_process_resource_baseline key | fillnull | eval anomalies = \"\" | foreach stdev_* [ eval anomalies =if( '<>' > ('avg_<>' + 4 * 'stdev_<>'), anomalies + \"<> higher than average by \" + tostring(round(('<>' - 'avg_<>')/'stdev_<>' ,2)) + \" Standard Deviations. <>=\" + tostring('<>') + \" avg_<>=\" + tostring('avg_<>') + \" 'stdev_<>'=\" + tostring('stdev_<>') + \", \" , anomalies) ] | eval anomalies = replace(anomalies, \",\\s$\", \"\") | where anomalies!=\"\" | stats count values(anomalies) as anomalies by host.name k8s.cluster.name k8s.node.name process.executable.name | sort - count | where count > 5 | rename host.name as host | `kubernetes_process_with_anomalous_resource_utilisation_filter`" +how_to_implement: "To implement this detection, follow these steps:\n* Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n * Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B'); data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D'); data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F'); data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H'); data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J'); data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n * Leave all other settings at their default values.\n* Run the Search Baseline Of Kubernetes Container Network IO Ratio" known_false_positives: No false positives have been identified at this time. references: -- https://github.com/signalfx/splunk-otel-collector-chart + - https://github.com/signalfx/splunk-otel-collector-chart rba: - message: Kubernetes Process with Anomalous Resource Utilisation on host $host$ - risk_objects: - - field: host - type: system - score: 25 - threat_objects: [] + message: Kubernetes Process with Anomalous Resource Utilisation on host $host$ + risk_objects: + - field: host + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring - asset_type: Kubernetes - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring + asset_type: Kubernetes + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/cloud/kubernetes_process_with_resource_ratio_anomalies.yml b/detections/cloud/kubernetes_process_with_resource_ratio_anomalies.yml index d4abb2451a..62d52cfe2b 100644 --- a/detections/cloud/kubernetes_process_with_resource_ratio_anomalies.yml +++ b/detections/cloud/kubernetes_process_with_resource_ratio_anomalies.yml @@ -5,67 +5,28 @@ date: '2026-01-14' author: Matthew Moore, Splunk status: experimental type: Anomaly -description: The following analytic detects anomalous changes in resource utilization - ratios for processes running on a Kubernetes node. It leverages process metrics - collected via an OTEL collector and hostmetrics receiver, analyzed through Splunk - Observability Cloud. The detection uses a lookup table containing average and standard - deviation values for various resource ratios (e.g., CPU:memory, CPU:disk operations). - Significant deviations from these baselines may indicate compromised processes, - malicious activity, or misconfigurations. If confirmed malicious, this could signify - a security breach, allowing attackers to manipulate workloads, potentially leading - to data exfiltration or service disruption. +description: The following analytic detects anomalous changes in resource utilization ratios for processes running on a Kubernetes node. It leverages process metrics collected via an OTEL collector and hostmetrics receiver, analyzed through Splunk Observability Cloud. The detection uses a lookup table containing average and standard deviation values for various resource ratios (e.g., CPU:memory, CPU:disk operations). Significant deviations from these baselines may indicate compromised processes, malicious activity, or misconfigurations. If confirmed malicious, this could signify a security breach, allowing attackers to manipulate workloads, potentially leading to data exfiltration or service disruption. data_source: [] -search: "| mstats avg(process.*) as process.* where `kubernetes_metrics` by host.name - k8s.cluster.name k8s.node.name process.executable.name span=10s | eval cpu:mem = - 'process.cpu.utilization'/'process.memory.utilization' | eval cpu:disk = 'process.cpu.utilization'/'process.disk.operations' - | eval mem:disk = 'process.memory.utilization'/'process.disk.operations' | eval - cpu:threads = 'process.cpu.utilization'/'process.threads' | eval disk:threads = - 'process.disk.operations'/'process.threads' | eval key = 'k8s.cluster.name' + \"\ - :\" + 'host.name' + \":\" + 'process.executable.name' | lookup k8s_process_resource_ratio_baseline - key | fillnull | eval anomalies = \"\" | foreach stdev_* [ eval anomalies =if( '<>' - > ('avg_<>' + 4 * 'stdev_<>'), anomalies + \"<> ratio - higher than average by \" + tostring(round(('<>' - 'avg_<>')/'stdev_<>' - ,2)) + \" Standard Deviations. <>=\" + tostring('<>') + \" avg_<>=\"\ - \ + tostring('avg_<>') + \" 'stdev_<>'=\" + tostring('stdev_<>') - + \", \" , anomalies) ] | eval anomalies = replace(anomalies, \",\\s$\", \"\") | - where anomalies!=\"\" | stats count values(anomalies) as anomalies by host.name - k8s.cluster.name k8s.node.name process.executable.name | where count > 5 | rename - host.name as host | `kubernetes_process_with_resource_ratio_anomalies_filter`" -how_to_implement: "To implement this detection, follow these steps:\n* Deploy the - OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process - receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically - Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install - the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n - * Configure the SIM add-on with your Observability Cloud Organization ID and Access - Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input - \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization - ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to - the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B'); - data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D'); - data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F'); - data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H'); - data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J'); - data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n - * Leave all other settings at their default values.\n* Run the Search Baseline Of - Kubernetes Container Network IO Ratio" +search: "| mstats avg(process.*) as process.* where `kubernetes_metrics` by host.name k8s.cluster.name k8s.node.name process.executable.name span=10s | eval cpu:mem = 'process.cpu.utilization'/'process.memory.utilization' | eval cpu:disk = 'process.cpu.utilization'/'process.disk.operations' | eval mem:disk = 'process.memory.utilization'/'process.disk.operations' | eval cpu:threads = 'process.cpu.utilization'/'process.threads' | eval disk:threads = 'process.disk.operations'/'process.threads' | eval key = 'k8s.cluster.name' + \":\" + 'host.name' + \":\" + 'process.executable.name' | lookup k8s_process_resource_ratio_baseline key | fillnull | eval anomalies = \"\" | foreach stdev_* [ eval anomalies =if( '<>' > ('avg_<>' + 4 * 'stdev_<>'), anomalies + \"<> ratio higher than average by \" + tostring(round(('<>' - 'avg_<>')/'stdev_<>' ,2)) + \" Standard Deviations. <>=\" + tostring('<>') + \" avg_<>=\" + tostring('avg_<>') + \" 'stdev_<>'=\" + tostring('stdev_<>') + \", \" , anomalies) ] | eval anomalies = replace(anomalies, \",\\s$\", \"\") | where anomalies!=\"\" | stats count values(anomalies) as anomalies by host.name k8s.cluster.name k8s.node.name process.executable.name | where count > 5 | rename host.name as host | `kubernetes_process_with_resource_ratio_anomalies_filter`" +how_to_implement: "To implement this detection, follow these steps:\n* Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n * Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B'); data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D'); data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F'); data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H'); data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J'); data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n * Leave all other settings at their default values.\n* Run the Search Baseline Of Kubernetes Container Network IO Ratio" known_false_positives: No false positives have been identified at this time. references: -- https://github.com/signalfx/splunk-otel-collector-chart + - https://github.com/signalfx/splunk-otel-collector-chart rba: - message: Kubernetes Process with Resource Ratio Anomalies on host $host$ - risk_objects: - - field: host - type: system - score: 25 - threat_objects: [] + message: Kubernetes Process with Resource Ratio Anomalies on host $host$ + risk_objects: + - field: host + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring - asset_type: Kubernetes - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring + asset_type: Kubernetes + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/cloud/kubernetes_scanner_image_pulling.yml b/detections/cloud/kubernetes_scanner_image_pulling.yml index d838d71b0a..cf660d0c03 100644 --- a/detections/cloud/kubernetes_scanner_image_pulling.yml +++ b/detections/cloud/kubernetes_scanner_image_pulling.yml @@ -1,65 +1,60 @@ name: Kubernetes Scanner Image Pulling id: 4890cd6b-0112-4974-a272-c5c153aee551 -version: 6 -date: '2026-01-14' +version: 7 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects the pulling of known Kubernetes security - scanner images such as kube-hunter, kube-bench, and kube-recon. It leverages Kubernetes - logs ingested through Splunk Connect for Kubernetes, specifically monitoring for - messages indicating the pulling of these images. This activity is significant because - the use of security scanners can indicate an attempt to identify vulnerabilities - within the Kubernetes environment. If confirmed malicious, this could lead to the - discovery and exploitation of security weaknesses, potentially compromising the - entire Kubernetes cluster. +description: The following analytic detects the pulling of known Kubernetes security scanner images such as kube-hunter, kube-bench, and kube-recon. It leverages Kubernetes logs ingested through Splunk Connect for Kubernetes, specifically monitoring for messages indicating the pulling of these images. This activity is significant because the use of security scanners can indicate an attempt to identify vulnerabilities within the Kubernetes environment. If confirmed malicious, this could lead to the discovery and exploitation of security weaknesses, potentially compromising the entire Kubernetes cluster. data_source: [] -search: '`kube_objects_events` object.message IN ("Pulling image *kube-hunter*", "Pulling - image *kube-bench*", "Pulling image *kube-recon*", "Pulling image *kube-recon*") - | rename object.* AS * | rename involvedObject.* AS * | rename source.host AS host - | eval phase="operate" | eval severity="high" | stats min(_time) as firstTime max(_time) - as lastTime count by host, name, namespace, kind, reason, message, phase, severity - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `kubernetes_scanner_image_pulling_filter`' +search: |- + `kube_objects_events` object.message IN ("Pulling image *kube-hunter*", "Pulling image *kube-bench*", "Pulling image *kube-recon*", "Pulling image *kube-recon*") + | rename object.* AS * + | rename involvedObject.* AS * + | rename source.host AS host + | eval phase="operate" + | eval severity="high" + | stats min(_time) as firstTime max(_time) as lastTime count + BY host, name, namespace, + kind, reason, message, + phase, severity + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `kubernetes_scanner_image_pulling_filter` how_to_implement: You must ingest Kubernetes logs through Splunk Connect for Kubernetes. known_false_positives: No false positives have been identified at this time. references: -- https://github.com/splunk/splunk-connect-for-kubernetes + - https://github.com/splunk/splunk-connect-for-kubernetes drilldown_searches: -- name: View the detection results for - "$host$" - search: '%original_detection_search% | search host = "$host$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$host$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$host$" + search: '%original_detection_search% | search host = "$host$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$host$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Kubernetes Scanner image pulled on host $host$ - risk_objects: - - field: host - type: system - score: 81 - threat_objects: [] + message: Kubernetes Scanner image pulled on host $host$ + risk_objects: + - field: host + type: system + score: 81 + threat_objects: [] tags: - analytic_story: - - Dev Sec Ops - asset_type: Kubernetes - mitre_attack_id: - - T1526 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Dev Sec Ops + asset_type: Kubernetes + mitre_attack_id: + - T1526 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1526/kubernetes_kube_hunter/kubernetes_kube_hunter.json - sourcetype: kube:objects:events - source: kubernetes + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1526/kubernetes_kube_hunter/kubernetes_kube_hunter.json + sourcetype: kube:objects:events + source: kubernetes diff --git a/detections/cloud/kubernetes_scanning_by_unauthenticated_ip_address.yml b/detections/cloud/kubernetes_scanning_by_unauthenticated_ip_address.yml index be46354370..f698de0c49 100644 --- a/detections/cloud/kubernetes_scanning_by_unauthenticated_ip_address.yml +++ b/detections/cloud/kubernetes_scanning_by_unauthenticated_ip_address.yml @@ -1,80 +1,57 @@ name: Kubernetes Scanning by Unauthenticated IP Address id: f9cadf4e-df22-4f4e-a08f-9d3344c2165d -version: 6 -date: '2026-01-14' +version: 7 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic identifies potential scanning activities within - a Kubernetes environment by unauthenticated IP addresses. It leverages Kubernetes - audit logs to detect multiple unauthorized access attempts (HTTP 403 responses) - from the same source IP. This activity is significant as it may indicate an attacker - probing for vulnerabilities or attempting to exploit known issues. If confirmed - malicious, such scanning could lead to unauthorized access, data breaches, or further - exploitation of the Kubernetes infrastructure, compromising the security and integrity - of the environment. +description: The following analytic identifies potential scanning activities within a Kubernetes environment by unauthenticated IP addresses. It leverages Kubernetes audit logs to detect multiple unauthorized access attempts (HTTP 403 responses) from the same source IP. This activity is significant as it may indicate an attacker probing for vulnerabilities or attempting to exploit known issues. If confirmed malicious, such scanning could lead to unauthorized access, data breaches, or further exploitation of the Kubernetes infrastructure, compromising the security and integrity of the environment. data_source: -- Kubernetes Audit -search: '`kube_audit` "user.groups{}"="system:unauthenticated" "responseStatus.code"=403 - | iplocation sourceIPs{} | stats count values(userAgent) as userAgent values(user.username) - as user.username values(user.groups{}) as user.groups{} values(verb) as verb values(requestURI) - as requestURI values(responseStatus.code) as responseStatus.code values(responseStatus.message) - as responseStatus.message values(responseStatus.reason) as responseStatus.reason - values(responseStatus.status) as responseStatus.status by sourceIPs{} Country City - | where count > 5 | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_scanning_by_unauthenticated_ip_address_filter`' -how_to_implement: The detection is based on data that originates from Kubernetes Audit - logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes - audit logs provide a record of the requests made to the Kubernetes API server, which - is crucial for monitoring and detecting suspicious activities. Configure the audit - policy in Kubernetes to determine what kind of activities are logged. This is done - by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry - Collector for Kubernetes to collect the logs. This doc will describe how to collect - the audit log file - https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. - When you want to use this detection with AWS EKS, you need to enable EKS control - plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. - Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. + - Kubernetes Audit +search: |- + `kube_audit` "user.groups{}"="system:unauthenticated" "responseStatus.code"=403 + | iplocation sourceIPs{} + | stats count values(userAgent) as userAgent values(user.username) as user.username values(user.groups{}) as user.groups{} values(verb) as verb values(requestURI) as requestURI values(responseStatus.code) as responseStatus.code values(responseStatus.message) as responseStatus.message values(responseStatus.reason) as responseStatus.reason values(responseStatus.status) as responseStatus.status + BY sourceIPs{} Country City + | where count > 5 + | rename sourceIPs{} as src_ip, user.username as user + | `kubernetes_scanning_by_unauthenticated_ip_address_filter` +how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. known_false_positives: No false positives have been identified at this time. references: -- https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ + - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Kubernetes scanning from ip $src_ip$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: src_ip - type: ip_address + message: Kubernetes scanning from ip $src_ip$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Kubernetes Security - asset_type: Kubernetes - mitre_attack_id: - - T1046 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Kubernetes Security + asset_type: Kubernetes + mitre_attack_id: + - T1046 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1046/kubernetes_scanning/kubernetes_scanning.json - sourcetype: _json - source: kubernetes + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1046/kubernetes_scanning/kubernetes_scanning.json + sourcetype: _json + source: kubernetes diff --git a/detections/cloud/kubernetes_shell_running_on_worker_node.yml b/detections/cloud/kubernetes_shell_running_on_worker_node.yml index c841747211..e7e0a22a2a 100644 --- a/detections/cloud/kubernetes_shell_running_on_worker_node.yml +++ b/detections/cloud/kubernetes_shell_running_on_worker_node.yml @@ -1,62 +1,39 @@ name: Kubernetes Shell Running on Worker Node id: efebf0c4-dcf4-496f-85a2-5ab7ad8fa876 -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Matthew Moore, Splunk status: experimental type: Anomaly -description: The following analytic identifies shell activity within the Kubernetes - privilege scope on a worker node. It leverages process metrics from an OTEL collector - hostmetrics receiver, specifically process.cpu.utilization and process.memory.utilization, - pulled from Splunk Observability Cloud. This activity is significant as unauthorized - shell processes can indicate potential security threats, providing attackers an - entry point to compromise the node and the entire Kubernetes cluster. If confirmed - malicious, this activity could lead to data theft, service disruption, privilege - escalation, lateral movement, and further attacks, severely compromising the cluster's - security and integrity. +description: The following analytic identifies shell activity within the Kubernetes privilege scope on a worker node. It leverages process metrics from an OTEL collector hostmetrics receiver, specifically process.cpu.utilization and process.memory.utilization, pulled from Splunk Observability Cloud. This activity is significant as unauthorized shell processes can indicate potential security threats, providing attackers an entry point to compromise the node and the entire Kubernetes cluster. If confirmed malicious, this activity could lead to data theft, service disruption, privilege escalation, lateral movement, and further attacks, severely compromising the cluster's security and integrity. data_source: [] -search: '| mstats avg(process.cpu.utilization) as process.cpu.utilization avg(process.memory.utilization) - as process.memory.utilization where `kubernetes_metrics` AND process.executable.name - IN ("sh","bash","csh", "tcsh") by host.name k8s.cluster.name k8s.node.name process.pid - process.executable.name span=10s | search process.cpu.utilization>0 OR process.memory.utilization>0 - | stats avg(process.cpu.utilization) as process.cpu.utilization avg(process.memory.utilization) - as process.memory.utilization by host.name k8s.cluster.name k8s.node.name process.pid - process.executable.name | rename host.name as host | `kubernetes_shell_running_on_worker_node_filter`' -how_to_implement: "To implement this detection, follow these steps:\n* Deploy the - OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process - receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically - Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install - the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n - * Configure the SIM add-on with your Observability Cloud Organization ID and Access - Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input - \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization - ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to - the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B'); - data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D'); - data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F'); - data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H'); - data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J'); - data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n - * Leave all other settings at their default values.\n* Run the Search Baseline Of - Kubernetes Container Network IO Ratio" +search: |- + | mstats avg(process.cpu.utilization) as process.cpu.utilization avg(process.memory.utilization) as process.memory.utilization where `kubernetes_metrics` AND process.executable.name IN ("sh","bash","csh", "tcsh") by host.name k8s.cluster.name k8s.node.name process.pid process.executable.name span=10s + | search process.cpu.utilization>0 OR process.memory.utilization>0 + | stats avg(process.cpu.utilization) as process.cpu.utilization avg(process.memory.utilization) as process.memory.utilization + BY host.name k8s.cluster.name k8s.node.name + process.pid process.executable.name + | rename host.name as host + | `kubernetes_shell_running_on_worker_node_filter` +how_to_implement: "To implement this detection, follow these steps:\n* Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n * Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B'); data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D'); data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F'); data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H'); data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J'); data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n * Leave all other settings at their default values.\n* Run the Search Baseline Of Kubernetes Container Network IO Ratio" known_false_positives: No false positives have been identified at this time. references: -- https://github.com/signalfx/splunk-otel-collector-chart/tree/main + - https://github.com/signalfx/splunk-otel-collector-chart/tree/main rba: - message: Kubernetes shell running on worker node on host $host$ - risk_objects: - - field: host - type: system - score: 25 - threat_objects: [] + message: Kubernetes shell running on worker node on host $host$ + risk_objects: + - field: host + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring - asset_type: Kubernetes - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring + asset_type: Kubernetes + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/cloud/kubernetes_shell_running_on_worker_node_with_cpu_activity.yml b/detections/cloud/kubernetes_shell_running_on_worker_node_with_cpu_activity.yml index 7aca0457eb..716229a7c2 100644 --- a/detections/cloud/kubernetes_shell_running_on_worker_node_with_cpu_activity.yml +++ b/detections/cloud/kubernetes_shell_running_on_worker_node_with_cpu_activity.yml @@ -1,63 +1,39 @@ name: Kubernetes Shell Running on Worker Node with CPU Activity id: cc1448e3-cc7a-4518-bc9f-2fa48f61a22b -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Matthew Moore, Splunk status: experimental type: Anomaly -description: The following analytic identifies shell activity within the Kubernetes - privilege scope on a worker node, specifically when shell processes are consuming - CPU resources. It leverages process metrics from an OTEL collector hostmetrics receiver, - pulled from Splunk Observability Cloud via the Splunk Infrastructure Monitoring - Add-on, focusing on process.cpu.utilization and process.memory.utilization. This - activity is significant as unauthorized shell processes can indicate a security - threat, potentially compromising the node and the entire Kubernetes cluster. If - confirmed malicious, attackers could gain full control over the host's resources, - leading to data theft, service disruption, privilege escalation, and further attacks - within the cluster. +description: The following analytic identifies shell activity within the Kubernetes privilege scope on a worker node, specifically when shell processes are consuming CPU resources. It leverages process metrics from an OTEL collector hostmetrics receiver, pulled from Splunk Observability Cloud via the Splunk Infrastructure Monitoring Add-on, focusing on process.cpu.utilization and process.memory.utilization. This activity is significant as unauthorized shell processes can indicate a security threat, potentially compromising the node and the entire Kubernetes cluster. If confirmed malicious, attackers could gain full control over the host's resources, leading to data theft, service disruption, privilege escalation, and further attacks within the cluster. data_source: [] -search: '| mstats avg(process.cpu.utilization) as process.cpu.utilization avg(process.memory.utilization) - as process.memory.utilization where `kubernetes_metrics` AND process.executable.name - IN ("sh","bash","csh", "tcsh") by host.name k8s.cluster.name k8s.node.name process.pid - process.executable.name span=10s | search process.cpu.utilization>0 | stats avg(process.cpu.utilization) - as process.cpu.utilization avg(process.memory.utilization) as process.memory.utilization - by host.name k8s.cluster.name k8s.node.name process.pid process.executable.name - | rename host.name as host | `kubernetes_shell_running_on_worker_node_with_cpu_activity_filter`' -how_to_implement: "To implement this detection, follow these steps:\n* Deploy the - OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process - receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically - Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install - the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n - * Configure the SIM add-on with your Observability Cloud Organization ID and Access - Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input - \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization - ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to - the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B'); - data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D'); - data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F'); - data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H'); - data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J'); - data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n - * Leave all other settings at their default values.\n* Run the Search Baseline Of - Kubernetes Container Network IO Ratio" +search: |- + | mstats avg(process.cpu.utilization) as process.cpu.utilization avg(process.memory.utilization) as process.memory.utilization where `kubernetes_metrics` AND process.executable.name IN ("sh","bash","csh", "tcsh") by host.name k8s.cluster.name k8s.node.name process.pid process.executable.name span=10s + | search process.cpu.utilization>0 + | stats avg(process.cpu.utilization) as process.cpu.utilization avg(process.memory.utilization) as process.memory.utilization + BY host.name k8s.cluster.name k8s.node.name + process.pid process.executable.name + | rename host.name as host + | `kubernetes_shell_running_on_worker_node_with_cpu_activity_filter` +how_to_implement: "To implement this detection, follow these steps:\n* Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n * Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B'); data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D'); data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F'); data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H'); data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J'); data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n * Leave all other settings at their default values.\n* Run the Search Baseline Of Kubernetes Container Network IO Ratio" known_false_positives: No false positives have been identified at this time. references: -- https://github.com/signalfx/splunk-otel-collector-chart/tree/main + - https://github.com/signalfx/splunk-otel-collector-chart/tree/main rba: - message: Kubernetes shell with cpu activity running on worker node on host $host$ - risk_objects: - - field: host - type: system - score: 25 - threat_objects: [] + message: Kubernetes shell with cpu activity running on worker node on host $host$ + risk_objects: + - field: host + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring - asset_type: Kubernetes - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring + asset_type: Kubernetes + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/cloud/kubernetes_suspicious_image_pulling.yml b/detections/cloud/kubernetes_suspicious_image_pulling.yml index b501929a12..df8e399667 100644 --- a/detections/cloud/kubernetes_suspicious_image_pulling.yml +++ b/detections/cloud/kubernetes_suspicious_image_pulling.yml @@ -1,78 +1,61 @@ name: Kubernetes Suspicious Image Pulling id: 4d3a17b3-0a6d-4ae0-9421-46623a69c122 -version: 6 -date: '2026-01-14' +version: 7 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects suspicious image pulling in Kubernetes - environments. It identifies this activity by monitoring Kubernetes audit logs for - image pull requests that do not match a predefined list of allowed images. This - behavior is significant for a SOC as it may indicate an attacker attempting to deploy - malicious software or infiltrate the system. If confirmed malicious, the impact - could be severe, potentially leading to unauthorized access to sensitive systems - or data, and enabling further malicious activities within the cluster. +description: The following analytic detects suspicious image pulling in Kubernetes environments. It identifies this activity by monitoring Kubernetes audit logs for image pull requests that do not match a predefined list of allowed images. This behavior is significant for a SOC as it may indicate an attacker attempting to deploy malicious software or infiltrate the system. If confirmed malicious, the impact could be severe, potentially leading to unauthorized access to sensitive systems or data, and enabling further malicious activities within the cluster. data_source: -- Kubernetes Audit -search: '`kube_audit` requestObject.message="Pulling image*" | search NOT `kube_allowed_images` - | fillnull | stats count by objectRef.name objectRef.namespace objectRef.resource - requestReceivedTimestamp requestURI responseStatus.code sourceIPs{} stage user.groups{} - user.uid user.username userAgent verb | rename sourceIPs{} as src_ip, user.username - as user | `kubernetes_suspicious_image_pulling_filter`' -how_to_implement: The detection is based on data that originates from Kubernetes Audit - logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes - audit logs provide a record of the requests made to the Kubernetes API server, which - is crucial for monitoring and detecting suspicious activities. Configure the audit - policy in Kubernetes to determine what kind of activities are logged. This is done - by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry - Collector for Kubernetes to collect the logs. This doc will describe how to collect - the audit log file - https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. - When you want to use this detection with AWS EKS, you need to enable EKS control - plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. - Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. + - Kubernetes Audit +search: |- + `kube_audit` requestObject.message="Pulling image*" + | search NOT `kube_allowed_images` + | fillnull + | stats count + BY objectRef.name objectRef.namespace objectRef.resource + requestReceivedTimestamp requestURI responseStatus.code + sourceIPs{} stage user.groups{} + user.uid user.username userAgent + verb + | rename sourceIPs{} as src_ip, user.username as user + | `kubernetes_suspicious_image_pulling_filter` +how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. known_false_positives: No false positives have been identified at this time. references: -- https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ + - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious image $objectRef.name$ pulled in Kubernetes from ip $src_ip$ - by user $user$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: src_ip - type: ip_address + message: Suspicious image $objectRef.name$ pulled in Kubernetes from ip $src_ip$ by user $user$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Kubernetes Security - asset_type: Kubernetes - mitre_attack_id: - - T1526 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Kubernetes Security + asset_type: Kubernetes + mitre_attack_id: + - T1526 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1526/kubernetes_audit_pull_image/kubernetes_audit_pull_image.json - sourcetype: _json - source: kubernetes + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1526/kubernetes_audit_pull_image/kubernetes_audit_pull_image.json + sourcetype: _json + source: kubernetes diff --git a/detections/cloud/kubernetes_unauthorized_access.yml b/detections/cloud/kubernetes_unauthorized_access.yml index 8e72e5d436..7572591554 100644 --- a/detections/cloud/kubernetes_unauthorized_access.yml +++ b/detections/cloud/kubernetes_unauthorized_access.yml @@ -1,77 +1,60 @@ name: Kubernetes Unauthorized Access id: 9b5f1832-e8b9-453f-93df-07a3d6a72a45 -version: 6 -date: '2026-01-14' +version: 7 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: Anomaly -description: The following analytic detects unauthorized access attempts to Kubernetes - by analyzing Kubernetes audit logs. It identifies anomalies in access patterns by - examining the source of requests and their response statuses. This activity is significant - for a SOC as it may indicate an attacker attempting to infiltrate the Kubernetes - environment. If confirmed malicious, such access could lead to unauthorized control - over Kubernetes resources, potentially compromising sensitive systems or data within - the cluster. +description: The following analytic detects unauthorized access attempts to Kubernetes by analyzing Kubernetes audit logs. It identifies anomalies in access patterns by examining the source of requests and their response statuses. This activity is significant for a SOC as it may indicate an attacker attempting to infiltrate the Kubernetes environment. If confirmed malicious, such access could lead to unauthorized control over Kubernetes resources, potentially compromising sensitive systems or data within the cluster. data_source: -- Kubernetes Audit -search: '`kube_audit` verb=create responseStatus.reason=Forbidden | fillnull | stats - count by objectRef.namespace objectRef.resource requestReceivedTimestamp requestURI - responseStatus.code responseStatus.message sourceIPs{} stage user.groups{} user.uid - user.username userAgent verb | rename sourceIPs{} as src_ip, user.username as user - | `kubernetes_unauthorized_access_filter`' -how_to_implement: The detection is based on data that originates from Kubernetes Audit - logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes - audit logs provide a record of the requests made to the Kubernetes API server, which - is crucial for monitoring and detecting suspicious activities. Configure the audit - policy in Kubernetes to determine what kind of activities are logged. This is done - by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry - Collector for Kubernetes to collect the logs. This doc will describe how to collect - the audit log file - https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. - When you want to use this detection with AWS EKS, you need to enable EKS control - plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. - Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. + - Kubernetes Audit +search: |- + `kube_audit` verb=create responseStatus.reason=Forbidden + | fillnull + | stats count + BY objectRef.namespace objectRef.resource requestReceivedTimestamp + requestURI responseStatus.code responseStatus.message + sourceIPs{} stage user.groups{} + user.uid user.username userAgent + verb + | rename sourceIPs{} as src_ip, user.username as user + | `kubernetes_unauthorized_access_filter` +how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/. known_false_positives: No false positives have been identified at this time. references: -- https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ + - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Unauthorized access to Kubernetes from user $user$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: src_ip - type: ip_address + message: Unauthorized access to Kubernetes from user $user$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Kubernetes Security - asset_type: Kubernetes - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Kubernetes Security + asset_type: Kubernetes + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kubernetes_unauthorized_access/kubernetes_unauthorized_access.json - sourcetype: _json - source: kubernetes + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kubernetes_unauthorized_access/kubernetes_unauthorized_access.json + sourcetype: _json + source: kubernetes diff --git a/detections/cloud/microsoft_intune_device_health_scripts.yml b/detections/cloud/microsoft_intune_device_health_scripts.yml index 9f0894d6d5..c9ce522081 100644 --- a/detections/cloud/microsoft_intune_device_health_scripts.yml +++ b/detections/cloud/microsoft_intune_device_health_scripts.yml @@ -1,47 +1,47 @@ name: Microsoft Intune Device Health Scripts id: 6fe42e07-15b1-4caa-b547-7885666cb1bd -version: 2 -date: '2025-05-02' +version: 3 +date: '2026-02-25' author: Dean Luxton data_source: -- Azure Monitor Activity + - Azure Monitor Activity type: Hunting status: production description: >- - Microsoft Intune device remediation scripts are a tool administrators can use to remotely manage devices, this functionality can also be abused for SYSTEM level code execution and lateral movement to intune managed devices. - This detection identifies when a new device health script has been added, updated or deleted. + Microsoft Intune device remediation scripts are a tool administrators can use to remotely manage devices, this functionality can also be abused for SYSTEM level code execution and lateral movement to intune managed devices. + This detection identifies when a new device health script has been added, updated or deleted. search: >- - `azure_monitor_activity` operationName="*DeviceHealthScript*" - | rename identity as user, properties.TargetObjectIds{} as TargetObjectId, properties.TargetDisplayNames{} as TargetDisplayName, properties.Actor.IsDelegatedAdmin as user_isDelegatedAdmin - | rex field="operationName" "^(?P\w+?)DeviceHealthScript" | replace "patch" with "updated", "create" with "created", "delete", with "deleted", "assign", with "assigned" IN action - | table _time operationName action user user_type user_isDelegatedAdmin TargetDisplayName TargetObjectId status tenantId correlationId - | `microsoft_intune_device_health_scripts_filter` + `azure_monitor_activity` operationName="*DeviceHealthScript*" + | rename identity as user, properties.TargetObjectIds{} as TargetObjectId, properties.TargetDisplayNames{} as TargetDisplayName, properties.Actor.IsDelegatedAdmin as user_isDelegatedAdmin + | rex field="operationName" "^(?P\w+?)DeviceHealthScript" | replace "patch" with "updated", "create" with "created", "delete", with "deleted", "assign", with "assigned" IN action + | table _time operationName action user user_type user_isDelegatedAdmin TargetDisplayName TargetObjectId status tenantId correlationId + | `microsoft_intune_device_health_scripts_filter` how_to_implement: >- - The Splunk Add-on for Microsoft Cloud Services add-on is required to ingest In-Tune audit logs via Azure EventHub. - To configure this logging, visit Intune > Tenant administration > Diagnostic settings > Add diagnostic settings & send events to the activity audit event hub. - Deploy as a risk based alerting rule for quick deployment or perform baselining & tune accordingly. -known_false_positives: Legitimate adminstrative usage of this functionality will trigger this detection. + The Splunk Add-on for Microsoft Cloud Services add-on is required to ingest In-Tune audit logs via Azure EventHub. + To configure this logging, visit Intune > Tenant administration > Diagnostic settings > Add diagnostic settings & send events to the activity audit event hub. + Deploy as a risk based alerting rule for quick deployment or perform baselining & tune accordingly. +known_false_positives: Legitimate adminstrative usage of this functionality will trigger this detection. references: -- https://posts.specterops.io/death-from-above-lateral-movement-from-azure-to-on-prem-ad-d18cb3959d4d -- https://securityintelligence.com/x-force/detecting-intune-lateral-movement/ -- https://posts.specterops.io/maestro-9ed71d38d546 + - https://posts.specterops.io/death-from-above-lateral-movement-from-azure-to-on-prem-ad-d18cb3959d4d + - https://securityintelligence.com/x-force/detecting-intune-lateral-movement/ + - https://posts.specterops.io/maestro-9ed71d38d546 tags: - analytic_story: - - Azure Active Directory Account Takeover - asset_type: Azure Tenant - mitre_attack_id: - - T1072 - - T1021.007 - - T1202 - - T1105 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: audit + analytic_story: + - Azure Active Directory Account Takeover + asset_type: Azure Tenant + mitre_attack_id: + - T1072 + - T1021.007 + - T1202 + - T1105 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: audit tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1072/intune/intune.log - sourcetype: azure:monitor:activity - source: Azure AD \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1072/intune/intune.log + sourcetype: azure:monitor:activity + source: Azure AD diff --git a/detections/cloud/microsoft_intune_devicemanagementconfigurationpolicies.yml b/detections/cloud/microsoft_intune_devicemanagementconfigurationpolicies.yml index 7661bd8b78..8e1cd6365a 100644 --- a/detections/cloud/microsoft_intune_devicemanagementconfigurationpolicies.yml +++ b/detections/cloud/microsoft_intune_devicemanagementconfigurationpolicies.yml @@ -1,50 +1,50 @@ name: Microsoft Intune DeviceManagementConfigurationPolicies id: 3c49e5ed-625c-408c-a2c7-8e2b524efb2c -version: 2 -date: '2025-05-02' +version: 3 +date: '2026-02-25' author: Dean Luxton data_source: -- Azure Monitor Activity + - Azure Monitor Activity type: Hunting status: production description: >- - Microsoft Intune device management configuration policies are a tool administrators can use to remotely manage policies and settings on intune managed devices. - This functionality can also be abused to disable defences & evade detection. - This detection identifies when a new device management configuration policy has been created. + Microsoft Intune device management configuration policies are a tool administrators can use to remotely manage policies and settings on intune managed devices. + This functionality can also be abused to disable defences & evade detection. + This detection identifies when a new device management configuration policy has been created. search: >- - `azure_monitor_activity` operationName="* DeviceManagementConfigurationPolicy*" - | rename identity as user, properties.TargetObjectIds{} as TargetObjectId, properties.TargetDisplayNames{} as TargetDisplayName, properties.Actor.IsDelegatedAdmin as user_isDelegatedAdmin - | eval details=mvzip('properties.Targets{}.ModifiedProperties{}.Name','properties.Targets{}.ModifiedProperties{}.New',": ") - | rex field="operationName" "^(?P\w+)\s" | replace "Patch" with "updated", "Create" with "created", "Delete", with "deleted", "assign", with "assigned" IN action - | eval action=if(match(operationName ,"Assignment$"),"assigned",'action') - | table _time operationName action user user_type user_isDelegatedAdmin TargetDisplayName TargetObjectId details status tenantId correlationId | `microsoft_intune_devicemanagementconfigurationpolicies_filter` + `azure_monitor_activity` operationName="* DeviceManagementConfigurationPolicy*" + | rename identity as user, properties.TargetObjectIds{} as TargetObjectId, properties.TargetDisplayNames{} as TargetDisplayName, properties.Actor.IsDelegatedAdmin as user_isDelegatedAdmin + | eval details=mvzip('properties.Targets{}.ModifiedProperties{}.Name','properties.Targets{}.ModifiedProperties{}.New',": ") + | rex field="operationName" "^(?P\w+)\s" | replace "Patch" with "updated", "Create" with "created", "Delete", with "deleted", "assign", with "assigned" IN action + | eval action=if(match(operationName ,"Assignment$"),"assigned",'action') + | table _time operationName action user user_type user_isDelegatedAdmin TargetDisplayName TargetObjectId details status tenantId correlationId | `microsoft_intune_devicemanagementconfigurationpolicies_filter` how_to_implement: >- - The Splunk Add-on for Microsoft Cloud Services add-on is required to ingest In-Tune audit logs via Azure EventHub. - To configure this logging, visit Intune > Tenant administration > Diagnostic settings > Add diagnostic settings & send events to the activity audit event hub. - Deploy as a risk based alerting rule for quick deployment or perform baselining & tune accordingly. -known_false_positives: Legitimate adminstrative usage of this functionality will trigger this detection. + The Splunk Add-on for Microsoft Cloud Services add-on is required to ingest In-Tune audit logs via Azure EventHub. + To configure this logging, visit Intune > Tenant administration > Diagnostic settings > Add diagnostic settings & send events to the activity audit event hub. + Deploy as a risk based alerting rule for quick deployment or perform baselining & tune accordingly. +known_false_positives: Legitimate adminstrative usage of this functionality will trigger this detection. references: -- https://posts.specterops.io/death-from-above-lateral-movement-from-azure-to-on-prem-ad-d18cb3959d4d -- https://securityintelligence.com/x-force/detecting-intune-lateral-movement/ -- https://posts.specterops.io/maestro-9ed71d38d546 + - https://posts.specterops.io/death-from-above-lateral-movement-from-azure-to-on-prem-ad-d18cb3959d4d + - https://securityintelligence.com/x-force/detecting-intune-lateral-movement/ + - https://posts.specterops.io/maestro-9ed71d38d546 tags: - analytic_story: - - Azure Active Directory Account Takeover - asset_type: Azure Tenant - mitre_attack_id: - - T1072 - - T1484 - - T1021.007 - - T1562.001 - - T1562.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: audit + analytic_story: + - Azure Active Directory Account Takeover + asset_type: Azure Tenant + mitre_attack_id: + - T1072 + - T1484 + - T1021.007 + - T1562.001 + - T1562.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: audit tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1072/intune/intune.log - sourcetype: azure:monitor:activity - source: Azure AD + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1072/intune/intune.log + sourcetype: azure:monitor:activity + source: Azure AD diff --git a/detections/cloud/microsoft_intune_manual_device_management.yml b/detections/cloud/microsoft_intune_manual_device_management.yml index fa87da6255..e18411b2d0 100644 --- a/detections/cloud/microsoft_intune_manual_device_management.yml +++ b/detections/cloud/microsoft_intune_manual_device_management.yml @@ -1,48 +1,48 @@ name: Microsoft Intune Manual Device Management id: 5ca7ebee-4ee7-4cf2-b3be-0ea26a00d822 -version: 2 -date: '2025-05-02' +version: 3 +date: '2026-02-25' author: Dean Luxton data_source: -- Azure Monitor Activity + - Azure Monitor Activity type: Hunting status: production description: >- - Microsoft Intune device management configuration policies, scripts & apps are a all tools administrators can use to remotely manage intune managed devices. - Instead of waiting for the devices to poll for changes to polciies, the policies can be manually pushed to expidite delivery. - This may be useful in a pinch, it may also be a sign of an impatient attacker trying to speed up the delivery of their payload. - This detection identifies when a device management configuration policy sync events, on-demand remediation scripts are triggered or when devices are remotely restarted. + Microsoft Intune device management configuration policies, scripts & apps are a all tools administrators can use to remotely manage intune managed devices. + Instead of waiting for the devices to poll for changes to polciies, the policies can be manually pushed to expidite delivery. + This may be useful in a pinch, it may also be a sign of an impatient attacker trying to speed up the delivery of their payload. + This detection identifies when a device management configuration policy sync events, on-demand remediation scripts are triggered or when devices are remotely restarted. search: >- - `azure_monitor_activity` operationName="*ManagedDevice*" - | rename identity as user, properties.TargetObjectIds{} as TargetObjectId, properties.TargetDisplayNames{} as TargetDisplayName, properties.Actor.IsDelegatedAdmin as user_isDelegatedAdmin - | rex field="operationName" "^(?P\w+)\s" - | table _time operationName action user user_type user_isDelegatedAdmin TargetDisplayName TargetObjectId status tenantId correlationId - | `microsoft_intune_manual_device_management_filter` + `azure_monitor_activity` operationName="*ManagedDevice*" + | rename identity as user, properties.TargetObjectIds{} as TargetObjectId, properties.TargetDisplayNames{} as TargetDisplayName, properties.Actor.IsDelegatedAdmin as user_isDelegatedAdmin + | rex field="operationName" "^(?P\w+)\s" + | table _time operationName action user user_type user_isDelegatedAdmin TargetDisplayName TargetObjectId status tenantId correlationId + | `microsoft_intune_manual_device_management_filter` how_to_implement: >- - The Splunk Add-on for Microsoft Cloud Services add-on is required to ingest In-Tune audit logs via Azure EventHub. - To configure this logging, visit Intune > Tenant administration > Diagnostic settings > Add diagnostic settings & send events to the activity audit event hub. - Deploy as a risk based alerting rule for quick deployment or perform baselining & tune accordingly. -known_false_positives: Legitimate adminstrative usage of this functionality will trigger this detection. + The Splunk Add-on for Microsoft Cloud Services add-on is required to ingest In-Tune audit logs via Azure EventHub. + To configure this logging, visit Intune > Tenant administration > Diagnostic settings > Add diagnostic settings & send events to the activity audit event hub. + Deploy as a risk based alerting rule for quick deployment or perform baselining & tune accordingly. +known_false_positives: Legitimate adminstrative usage of this functionality will trigger this detection. references: -- https://posts.specterops.io/death-from-above-lateral-movement-from-azure-to-on-prem-ad-d18cb3959d4d -- https://securityintelligence.com/x-force/detecting-intune-lateral-movement/ -- https://posts.specterops.io/maestro-9ed71d38d546 + - https://posts.specterops.io/death-from-above-lateral-movement-from-azure-to-on-prem-ad-d18cb3959d4d + - https://securityintelligence.com/x-force/detecting-intune-lateral-movement/ + - https://posts.specterops.io/maestro-9ed71d38d546 tags: - analytic_story: - - Azure Active Directory Account Takeover - asset_type: Azure Tenant - mitre_attack_id: - - T1021.007 - - T1072 - - T1529 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: audit + analytic_story: + - Azure Active Directory Account Takeover + asset_type: Azure Tenant + mitre_attack_id: + - T1021.007 + - T1072 + - T1529 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: audit tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1072/intune/intune.log - sourcetype: azure:monitor:activity - source: Azure AD + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1072/intune/intune.log + sourcetype: azure:monitor:activity + source: Azure AD diff --git a/detections/cloud/microsoft_intune_mobile_apps.yml b/detections/cloud/microsoft_intune_mobile_apps.yml index ec88b4e2a6..d6a82e3fdf 100644 --- a/detections/cloud/microsoft_intune_mobile_apps.yml +++ b/detections/cloud/microsoft_intune_mobile_apps.yml @@ -1,41 +1,41 @@ name: Microsoft Intune Mobile Apps id: 98e6b389-2806-4426-a580-8a92cb0d9710 -version: 3 -date: '2025-06-10' +version: 4 +date: '2026-02-25' author: Dean Luxton status: experimental type: Hunting description: | - Microsoft Intune supports deploying packaged applications to support software deployment, this functionality can also be abused for deploying malicious payloads to intune managed devices. - This detection identifies when a new packaged application has been added, updated or deleted. + Microsoft Intune supports deploying packaged applications to support software deployment, this functionality can also be abused for deploying malicious payloads to intune managed devices. + This detection identifies when a new packaged application has been added, updated or deleted. data_source: -- Azure Monitor Activity + - Azure Monitor Activity search: | - `azure_monitor_activity` operationName="*MobileApp*" - | rename identity as user, properties.TargetObjectIds{} as TargetObjectId, properties.TargetDisplayNames{} as TargetDisplayName, properties.Actor.IsDelegatedAdmin as user_isDelegatedAdmin - | rex field="operationName" "^(?P\w+)\s" | replace "Patch" with "updated", "Create" with "created", "Delete", with "deleted", "assign", with "assigned" IN action - | table _time operationName action user user_type user_isDelegatedAdmin TargetDisplayName TargetObjectId status tenantId correlationId - | `microsoft_intune_mobile_apps_filter` + `azure_monitor_activity` operationName="*MobileApp*" + | rename identity as user, properties.TargetObjectIds{} as TargetObjectId, properties.TargetDisplayNames{} as TargetDisplayName, properties.Actor.IsDelegatedAdmin as user_isDelegatedAdmin + | rex field="operationName" "^(?P\w+)\s" | replace "Patch" with "updated", "Create" with "created", "Delete", with "deleted", "assign", with "assigned" IN action + | table _time operationName action user user_type user_isDelegatedAdmin TargetDisplayName TargetObjectId status tenantId correlationId + | `microsoft_intune_mobile_apps_filter` how_to_implement: | - The Splunk Add-on for Microsoft Cloud Services add-on is required to ingest In-Tune audit logs via Azure EventHub. - To configure this logging, visit Intune > Tenant administration > Diagnostic settings > Add diagnostic settings & send events to the activity audit event hub. - Deploy as a risk based alerting rule for quick deployment or perform baselining & tune accordingly. -known_false_positives: Legitimate adminstrative usage of this functionality will trigger this detection. + The Splunk Add-on for Microsoft Cloud Services add-on is required to ingest In-Tune audit logs via Azure EventHub. + To configure this logging, visit Intune > Tenant administration > Diagnostic settings > Add diagnostic settings & send events to the activity audit event hub. + Deploy as a risk based alerting rule for quick deployment or perform baselining & tune accordingly. +known_false_positives: Legitimate adminstrative usage of this functionality will trigger this detection. references: -- https://posts.specterops.io/death-from-above-lateral-movement-from-azure-to-on-prem-ad-d18cb3959d4d -- https://securityintelligence.com/x-force/detecting-intune-lateral-movement/ -- https://posts.specterops.io/maestro-9ed71d38d546 + - https://posts.specterops.io/death-from-above-lateral-movement-from-azure-to-on-prem-ad-d18cb3959d4d + - https://securityintelligence.com/x-force/detecting-intune-lateral-movement/ + - https://posts.specterops.io/maestro-9ed71d38d546 tags: - analytic_story: - - Azure Active Directory Account Takeover - asset_type: Azure Tenant - mitre_attack_id: - - T1072 - - T1021.007 - - T1202 - - T1105 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: audit + analytic_story: + - Azure Active Directory Account Takeover + asset_type: Azure Tenant + mitre_attack_id: + - T1072 + - T1021.007 + - T1202 + - T1105 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: audit diff --git a/detections/cloud/o365_add_app_role_assignment_grant_user.yml b/detections/cloud/o365_add_app_role_assignment_grant_user.yml index 08b2f980d9..d317209219 100644 --- a/detections/cloud/o365_add_app_role_assignment_grant_user.yml +++ b/detections/cloud/o365_add_app_role_assignment_grant_user.yml @@ -1,71 +1,61 @@ name: O365 Add App Role Assignment Grant User id: b2c81cc6-6040-11eb-ae93-0242ac130002 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Rod Soto, Splunk status: production type: TTP -description: The following analytic detects the addition of an application role assignment - grant to a user in Office 365. It leverages data from the `o365_management_activity` - dataset, specifically monitoring the "Add app role assignment grant to user" operation. - This activity is significant as it can indicate unauthorized privilege escalation - or the assignment of sensitive roles to users. If confirmed malicious, this could - allow an attacker to gain elevated permissions, potentially leading to unauthorized - access to critical resources and data within the Office 365 environment. +description: The following analytic detects the addition of an application role assignment grant to a user in Office 365. It leverages data from the `o365_management_activity` dataset, specifically monitoring the "Add app role assignment grant to user" operation. This activity is significant as it can indicate unauthorized privilege escalation or the assignment of sensitive roles to users. If confirmed malicious, this could allow an attacker to gain elevated permissions, potentially leading to unauthorized access to critical resources and data within the Office 365 environment. data_source: -- O365 Add app role assignment grant to user. -search: '`o365_management_activity` Workload=AzureActiveDirectory Operation="Add app role assignment grant to user." - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user src vendor_account vendor_product - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_add_app_role_assignment_grant_user_filter`' -how_to_implement: You must install splunk Microsoft Office 365 add-on. This search - works with o365:management:activity -known_false_positives: The creation of a new Federation is not necessarily malicious, - however this events need to be followed closely, as it may indicate federated credential - abuse or backdoor via federated identities at a different cloud provider. + - O365 Add app role assignment grant to user. +search: |- + `o365_management_activity` Workload=AzureActiveDirectory Operation="Add app role assignment grant to user." + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + src vendor_account vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_add_app_role_assignment_grant_user_filter` +how_to_implement: You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity +known_false_positives: The creation of a new Federation is not necessarily malicious, however this events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider. references: -- https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf -- https://www.cisa.gov/uscert/ncas/alerts/aa21-008a + - https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf + - https://www.cisa.gov/uscert/ncas/alerts/aa21-008a drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ added a new app role assignment - risk_objects: - - field: user - type: user - score: 18 - - field: dest - type: system - score: 18 - threat_objects: [] + message: User $user$ added a new app role assignment + risk_objects: + - field: user + type: user + score: 18 + - field: dest + type: system + score: 18 + threat_objects: [] tags: - analytic_story: - - Office 365 Persistence Mechanisms - - Cloud Federated Credential Abuse - asset_type: O365 Tenant - mitre_attack_id: - - T1136.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Persistence Mechanisms + - Cloud Federated Credential Abuse + asset_type: O365 Tenant + mitre_attack_id: + - T1136.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federation/o365_new_federation.json - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federation/o365_new_federation.json + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_added_service_principal.yml b/detections/cloud/o365_added_service_principal.yml index 317f562445..0970eca704 100644 --- a/detections/cloud/o365_added_service_principal.yml +++ b/detections/cloud/o365_added_service_principal.yml @@ -1,74 +1,61 @@ name: O365 Added Service Principal id: 1668812a-6047-11eb-ae93-0242ac130002 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Rod Soto, Splunk status: production type: TTP -description: The following analytic detects the addition of new service principal - accounts in O365 tenants. It leverages data from the `o365_management_activity` - dataset, specifically monitoring for operations related to adding or creating service - principals. This activity is significant because attackers can exploit service principals - to gain unauthorized access and perform malicious actions within an organization's - environment. If confirmed malicious, this could allow attackers to interact with - APIs, access resources, and execute operations on behalf of the organization, potentially - leading to data breaches or further compromise. +description: The following analytic detects the addition of new service principal accounts in O365 tenants. It leverages data from the `o365_management_activity` dataset, specifically monitoring for operations related to adding or creating service principals. This activity is significant because attackers can exploit service principals to gain unauthorized access and perform malicious actions within an organization's environment. If confirmed malicious, this could allow attackers to interact with APIs, access resources, and execute operations on behalf of the organization, potentially leading to data breaches or further compromise. data_source: -- O365 -search: '`o365_management_activity` Workload=AzureActiveDirectory Operation="*Add service principal*" OR (Operation = "*principal*" AND action = "created") - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user src vendor_account vendor_product - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_added_service_principal_filter`' -how_to_implement: You must install splunk Microsoft Office 365 add-on. This search - works with o365:management:activity -known_false_positives: The creation of a new Federation is not necessarily malicious, - however these events need to be followed closely, as it may indicate federated credential - abuse or backdoor via federated identities at a different cloud provider. + - O365 +search: |- + `o365_management_activity` Workload=AzureActiveDirectory Operation="*Add service principal*" OR (Operation = "*principal*" AND action = "created") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + src vendor_account vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_added_service_principal_filter` +how_to_implement: You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity +known_false_positives: The creation of a new Federation is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider. references: -- https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf -- https://www.cisa.gov/uscert/ncas/alerts/aa21-008a -- https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html -- https://blog.sygnia.co/detection-and-hunting-of-golden-saml-attack?hsLang=en + - https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf + - https://www.cisa.gov/uscert/ncas/alerts/aa21-008a + - https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html + - https://blog.sygnia.co/detection-and-hunting-of-golden-saml-attack?hsLang=en drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has created new service principal in AzureActiveDirectory - risk_objects: - - field: user - type: user - score: 42 - threat_objects: [] + message: User $user$ has created new service principal in AzureActiveDirectory + risk_objects: + - field: user + type: user + score: 42 + threat_objects: [] tags: - analytic_story: - - Office 365 Persistence Mechanisms - - Cloud Federated Credential Abuse - - NOBELIUM Group - asset_type: O365 Tenant - mitre_attack_id: - - T1136.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Persistence Mechanisms + - Cloud Federated Credential Abuse + - NOBELIUM Group + asset_type: O365 Tenant + mitre_attack_id: + - T1136.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_added_service_principal/o365_add_service_principal.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_added_service_principal/o365_add_service_principal.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_admin_consent_bypassed_by_service_principal.yml b/detections/cloud/o365_admin_consent_bypassed_by_service_principal.yml index 5e3fe47396..823f3119ab 100644 --- a/detections/cloud/o365_admin_consent_bypassed_by_service_principal.yml +++ b/detections/cloud/o365_admin_consent_bypassed_by_service_principal.yml @@ -4,78 +4,50 @@ version: 6 date: '2025-05-02' author: Mauricio Velazco, Splunk data_source: -- O365 Add app role assignment to service principal. + - O365 Add app role assignment to service principal. type: TTP status: production -description: The following analytic identifies instances where a service principal - in Office 365 Azure Active Directory assigns app roles without standard admin consent. - It leverages `o365_management_activity` logs, specifically focusing on the 'Add - app role assignment to service principal' operation. This activity is significant - for SOCs as it may indicate a bypass of critical administrative controls, potentially - leading to unauthorized access or privilege escalation. If confirmed malicious, - this could allow an attacker to misuse automated processes to assign sensitive permissions, - compromising the security of the environment. -search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Add app role assignment to service principal.\" - | eval len=mvcount('Actor{}.ID') - | eval userType = mvindex('Actor{}.ID',len-1) - | eval roleId = mvindex('ModifiedProperties{}.NewValue', 0) - | eval roleValue = mvindex('ModifiedProperties{}.NewValue', 1) - | eval roleDescription = mvindex('ModifiedProperties{}.NewValue', 2) - | eval dest_user = mvindex('Target{}.ID', 0) - | search userType = \"ServicePrincipal\" - | eval src_user = user - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user src vendor_account vendor_product dest_user roleId roleValue roleDescription - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_admin_consent_bypassed_by_service_principal_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Service Principals are sometimes configured to legitimately - bypass the consent process for purposes of automation. Filter as needed. +description: The following analytic identifies instances where a service principal in Office 365 Azure Active Directory assigns app roles without standard admin consent. It leverages `o365_management_activity` logs, specifically focusing on the 'Add app role assignment to service principal' operation. This activity is significant for SOCs as it may indicate a bypass of critical administrative controls, potentially leading to unauthorized access or privilege escalation. If confirmed malicious, this could allow an attacker to misuse automated processes to assign sensitive permissions, compromising the security of the environment. +search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Add app role assignment to service principal.\" | eval len=mvcount('Actor{}.ID') | eval userType = mvindex('Actor{}.ID',len-1) | eval roleId = mvindex('ModifiedProperties{}.NewValue', 0) | eval roleValue = mvindex('ModifiedProperties{}.NewValue', 1) | eval roleDescription = mvindex('ModifiedProperties{}.NewValue', 2) | eval dest_user = mvindex('Target{}.ID', 0) | search userType = \"ServicePrincipal\" | eval src_user = user | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user src vendor_account vendor_product dest_user roleId roleValue roleDescription | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_admin_consent_bypassed_by_service_principal_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Service Principals are sometimes configured to legitimately bypass the consent process for purposes of automation. Filter as needed. references: -- https://attack.mitre.org/techniques/T1098/003/ -- https://msrc.microsoft.com/blog/2024/01/microsoft-actions-following-attack-by-nation-state-actor-midnight-blizzard/ -- https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/ -- https://attack.mitre.org/techniques/T1098/002/ -- https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/ -- https://winsmarts.com/how-to-grant-admin-consent-to-an-api-programmatically-e32f4a100e9d + - https://attack.mitre.org/techniques/T1098/003/ + - https://msrc.microsoft.com/blog/2024/01/microsoft-actions-following-attack-by-nation-state-actor-midnight-blizzard/ + - https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/ + - https://attack.mitre.org/techniques/T1098/002/ + - https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/ + - https://winsmarts.com/how-to-grant-admin-consent-to-an-api-programmatically-e32f4a100e9d drilldown_searches: -- name: View the detection results for - "$dest_user$" - search: '%original_detection_search% | search dest_user = "$dest_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest_user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest_user$" + search: '%original_detection_search% | search dest_user = "$dest_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Service principal $user$ bypassed the admin consent process and granted permissions to $dest_user$ - risk_objects: - - field: dest_user - type: user - score: 54 - threat_objects: [] + message: Service principal $user$ bypassed the admin consent process and granted permissions to $dest_user$ + risk_objects: + - field: dest_user + type: user + score: 54 + threat_objects: [] tags: - analytic_story: - - Office 365 Persistence Mechanisms - asset_type: O365 Tenant - mitre_attack_id: - - T1098.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Office 365 Persistence Mechanisms + asset_type: O365 Tenant + mitre_attack_id: + - T1098.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/o365_bypass_admin_consent/o365_bypass_admin_consent.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/o365_bypass_admin_consent/o365_bypass_admin_consent.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_advanced_audit_disabled.yml b/detections/cloud/o365_advanced_audit_disabled.yml index 6e1f718431..a6d41d0540 100644 --- a/detections/cloud/o365_advanced_audit_disabled.yml +++ b/detections/cloud/o365_advanced_audit_disabled.yml @@ -6,73 +6,45 @@ author: Mauricio Velazco, Michael Haag, Splunk status: production type: TTP data_source: -- O365 Change user license. -description: The following analytic detects instances where the O365 advanced audit - is disabled for a specific user within the Office 365 tenant. It uses O365 audit - logs, focusing on events related to audit license changes in AzureActiveDirectory - workloads. This activity is significant because the O365 advanced audit provides - critical logging and insights into user and administrator activities. Disabling - it can blind security teams to potential malicious actions. If confirmed malicious, - attackers could operate within the user's mailbox or account with reduced risk of - detection, leading to unauthorized data access, data exfiltration, or account compromise. -search: "`o365_management_activity` Operation=\"Change user license.\" - | eval property_name = mvindex ('ExtendedProperties{}.Name', 1) - | search property_name = \"extendedAuditEventCategory\" - | eval additionalDetails = mvindex('ExtendedProperties{}.Value',0) - | eval split_value=split(additionalDetails,\"NewValue\") - | eval possible_plan=mvindex(split_value, 1) - | rex field=\"possible_plan\" \"DisabledPlans=\\[(?P[^\\]]+)\\]\" - | search DisabledPlans IN (\"*M365_ADVANCED_AUDITING*\") - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user src vendor_account vendor_product DisabledPlans object - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_advanced_audit_disabled_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Administrators might temporarily disable the advanced audit - for troubleshooting, performance reasons, or other administrative tasks. Filter - as needed. + - O365 Change user license. +description: The following analytic detects instances where the O365 advanced audit is disabled for a specific user within the Office 365 tenant. It uses O365 audit logs, focusing on events related to audit license changes in AzureActiveDirectory workloads. This activity is significant because the O365 advanced audit provides critical logging and insights into user and administrator activities. Disabling it can blind security teams to potential malicious actions. If confirmed malicious, attackers could operate within the user's mailbox or account with reduced risk of detection, leading to unauthorized data access, data exfiltration, or account compromise. +search: "`o365_management_activity` Operation=\"Change user license.\" | eval property_name = mvindex ('ExtendedProperties{}.Name', 1) | search property_name = \"extendedAuditEventCategory\" | eval additionalDetails = mvindex('ExtendedProperties{}.Value',0) | eval split_value=split(additionalDetails,\"NewValue\") | eval possible_plan=mvindex(split_value, 1) | rex field=\"possible_plan\" \"DisabledPlans=\\[(?P[^\\]]+)\\]\" | search DisabledPlans IN (\"*M365_ADVANCED_AUDITING*\") | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user src vendor_account vendor_product DisabledPlans object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_advanced_audit_disabled_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Administrators might temporarily disable the advanced audit for troubleshooting, performance reasons, or other administrative tasks. Filter as needed. references: -- https://attack.mitre.org/techniques/T1562/008/ -- https://www.mandiant.com/sites/default/files/2022-08/remediation-hardening-strategies-for-m365-defend-against-apt29-white-paper.pdf -- https://www.csoonline.com/article/570381/microsoft-365-advanced-audit-what-you-need-to-know.html + - https://attack.mitre.org/techniques/T1562/008/ + - https://www.mandiant.com/sites/default/files/2022-08/remediation-hardening-strategies-for-m365-defend-against-apt29-white-paper.pdf + - https://www.csoonline.com/article/570381/microsoft-365-advanced-audit-what-you-need-to-know.html drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Advanced auditing for user $object$ was disabled by $user$ - risk_objects: - - field: user - type: user - score: 32 - threat_objects: [] + message: Advanced auditing for user $object$ was disabled by $user$ + risk_objects: + - field: user + type: user + score: 32 + threat_objects: [] tags: - analytic_story: - - Office 365 Persistence Mechanisms - asset_type: O365 Tenant - mitre_attack_id: - - T1562.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Office 365 Persistence Mechanisms + asset_type: O365 Tenant + mitre_attack_id: + - T1562.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/o365_advanced_audit_disabled/o365_advanced_audit_disabled.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/o365_advanced_audit_disabled/o365_advanced_audit_disabled.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_application_available_to_other_tenants.yml b/detections/cloud/o365_application_available_to_other_tenants.yml index 140f1d2833..4f0831eccc 100644 --- a/detections/cloud/o365_application_available_to_other_tenants.yml +++ b/detections/cloud/o365_application_available_to_other_tenants.yml @@ -5,73 +5,50 @@ date: '2025-05-02' author: Steven Dick status: production type: TTP -description: The following analytic identifies the configuration of Azure Active Directory - Applications in a manner that allows authentication from external tenants or personal - accounts. This configuration can lead to inappropriate or malicious access of any - data or capabilities the application is allowed to access. This detection leverages - the O365 Universal Audit Log data source. +description: The following analytic identifies the configuration of Azure Active Directory Applications in a manner that allows authentication from external tenants or personal accounts. This configuration can lead to inappropriate or malicious access of any data or capabilities the application is allowed to access. This detection leverages the O365 Universal Audit Log data source. data_source: -- Office 365 Universal Audit Log -search: "`o365_management_activity` Workload=AzureActiveDirectory Operation IN (\"Add application.\",\"Update application.\") ModifiedProperties{}.Name=AvailableToOtherTenants - | eval result = case(match(mvindex('ModifiedProperties{}.NewValue',mvfind('ModifiedProperties{}.Name',\"\ - AvailableToOtherTenants\")),\"false\"),\"removed\",true(),\"added\"), object_name=mvindex('Target{}.ID', - 3), signature=Operation, object_attrs = \"AvailableToOtherTenants\", user = case(match(mvindex('Actor{}.ID',-1),\"\ - User\"),mvindex('Actor{}.ID',0),match(mvindex('Actor{}.ID',-1),\"ServicePrincipal\"\ - ),mvindex('Actor{}.ID',3),true(),mvindex('Actor{}.ID',0)) - | search result = \"added\" - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user src vendor_account vendor_product object_attrs object_name - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_application_available_to_other_tenants_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. + - Office 365 Universal Audit Log +search: "`o365_management_activity` Workload=AzureActiveDirectory Operation IN (\"Add application.\",\"Update application.\") ModifiedProperties{}.Name=AvailableToOtherTenants | eval result = case(match(mvindex('ModifiedProperties{}.NewValue',mvfind('ModifiedProperties{}.Name',\"AvailableToOtherTenants\")),\"false\"),\"removed\",true(),\"added\"), object_name=mvindex('Target{}.ID', 3), signature=Operation, object_attrs = \"AvailableToOtherTenants\", user = case(match(mvindex('Actor{}.ID',-1),\"User\"),mvindex('Actor{}.ID',0),match(mvindex('Actor{}.ID',-1),\"ServicePrincipal\"),mvindex('Actor{}.ID',3),true(),mvindex('Actor{}.ID',0)) | search result = \"added\" | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user src vendor_account vendor_product object_attrs object_name | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_application_available_to_other_tenants_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. known_false_positives: Business approved changes by known administrators. references: -- https://attack.mitre.org/techniques/T1098/ -- https://msrc.microsoft.com/blog/2023/03/guidance-on-potential-misconfiguration-of-authorization-of-multi-tenant-applications-that-use-azure-ad/ -- https://www.wiz.io/blog/azure-active-directory-bing-misconfiguration + - https://attack.mitre.org/techniques/T1098/ + - https://msrc.microsoft.com/blog/2023/03/guidance-on-potential-misconfiguration-of-authorization-of-multi-tenant-applications-that-use-azure-ad/ + - https://www.wiz.io/blog/azure-active-directory-bing-misconfiguration drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An Azure Application [$object_name$] was configured by [$user$] as accessible - to external tenants. - risk_objects: - - field: user - type: user - score: 50 - threat_objects: - - field: object_name - type: service + message: An Azure Application [$object_name$] was configured by [$user$] as accessible to external tenants. + risk_objects: + - field: user + type: user + score: 50 + threat_objects: + - field: object_name + type: service tags: - analytic_story: - - Azure Active Directory Persistence - - Azure Active Directory Account Takeover - - Data Exfiltration - asset_type: O365 Tenant - mitre_attack_id: - - T1098.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Azure Active Directory Persistence + - Azure Active Directory Account Takeover + - Data Exfiltration + asset_type: O365 Tenant + mitre_attack_id: + - T1098.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_azure_workload_events/o365_azure_workload_events.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_azure_workload_events/o365_azure_workload_events.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_application_registration_owner_added.yml b/detections/cloud/o365_application_registration_owner_added.yml index 7f9426b56c..8f73369d25 100644 --- a/detections/cloud/o365_application_registration_owner_added.yml +++ b/detections/cloud/o365_application_registration_owner_added.yml @@ -6,69 +6,46 @@ author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- O365 Add owner to application. -description: The following analytic identifies instances where a new owner is assigned - to an application registration within an Azure AD and Office 365 tenant. It leverages - O365 audit logs, specifically events related to changes in owner assignments within - the AzureActiveDirectory workload. This activity is significant because assigning - a new owner to an application registration can grant significant control over the - application's configuration, permissions, and behavior. If confirmed malicious, - an attacker could modify the application's settings, permissions, and behavior, - leading to unauthorized data access, privilege escalation, or the introduction of - malicious behavior within the application's operations. -search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Add owner to application.\" - | eval app_id=mvindex('ModifiedProperties{}.NewValue', 0) - | eval app_displayName=mvindex('ModifiedProperties{}.NewValue', 1) - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user src vendor_account vendor_product app_id app_displayName object - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_application_registration_owner_added_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Application owners may be added for legitimate reasons, filter - as needed. + - O365 Add owner to application. +description: The following analytic identifies instances where a new owner is assigned to an application registration within an Azure AD and Office 365 tenant. It leverages O365 audit logs, specifically events related to changes in owner assignments within the AzureActiveDirectory workload. This activity is significant because assigning a new owner to an application registration can grant significant control over the application's configuration, permissions, and behavior. If confirmed malicious, an attacker could modify the application's settings, permissions, and behavior, leading to unauthorized data access, privilege escalation, or the introduction of malicious behavior within the application's operations. +search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Add owner to application.\" | eval app_id=mvindex('ModifiedProperties{}.NewValue', 0) | eval app_displayName=mvindex('ModifiedProperties{}.NewValue', 1) | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user src vendor_account vendor_product app_id app_displayName object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_application_registration_owner_added_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Application owners may be added for legitimate reasons, filter as needed. references: -- https://attack.mitre.org/techniques/T1098/ -- https://learn.microsoft.com/en-us/azure/active-directory/manage-apps/overview-assign-app-owners + - https://attack.mitre.org/techniques/T1098/ + - https://learn.microsoft.com/en-us/azure/active-directory/manage-apps/overview-assign-app-owners drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Application registration $app_displayName$ was assigned a new owner $object$ - risk_objects: - - field: user - type: user - score: 30 - threat_objects: [] + message: Application registration $app_displayName$ was assigned a new owner $object$ + risk_objects: + - field: user + type: user + score: 30 + threat_objects: [] tags: - analytic_story: - - Office 365 Persistence Mechanisms - - NOBELIUM Group - asset_type: O365 Tenant - atomic_guid: [] - mitre_attack_id: - - T1098 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Office 365 Persistence Mechanisms + - NOBELIUM Group + asset_type: O365 Tenant + atomic_guid: [] + mitre_attack_id: + - T1098 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_add_app_registration_owner/o365_add_app_registration_owner.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_add_app_registration_owner/o365_add_app_registration_owner.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_applicationimpersonation_role_assigned.yml b/detections/cloud/o365_applicationimpersonation_role_assigned.yml index 7645fdddd2..aeccb2b7e0 100644 --- a/detections/cloud/o365_applicationimpersonation_role_assigned.yml +++ b/detections/cloud/o365_applicationimpersonation_role_assigned.yml @@ -1,76 +1,64 @@ name: O365 ApplicationImpersonation Role Assigned id: 49cdce75-f814-4d56-a7a4-c64ec3a481f2 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- O365 -description: The following analytic detects the assignment of the ApplicationImpersonation - role in Office 365 to a user or application. It uses the Office 365 Management Activity - API to monitor Azure Active Directory audit logs for role assignment events. This - activity is significant because the ApplicationImpersonation role allows impersonation - of any user, enabling access to and modification of their mailbox. If confirmed - malicious, an attacker could gain unauthorized access to sensitive information, - manipulate mailbox data, and perform actions as a legitimate user, posing a severe - security risk to the organization. -search: '`o365_management_activity` Workload=Exchange Operation="New-ManagementRoleAssignment" Role=ApplicationImpersonation - | rename User as target_user - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user src vendor_account vendor_product target_user - | `security_content_ctime(lastTime)` - | `o365_applicationimpersonation_role_assigned_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: While infrequent, the ApplicationImpersonation role may be - granted for leigimate reasons, filter as needed. + - O365 +description: The following analytic detects the assignment of the ApplicationImpersonation role in Office 365 to a user or application. It uses the Office 365 Management Activity API to monitor Azure Active Directory audit logs for role assignment events. This activity is significant because the ApplicationImpersonation role allows impersonation of any user, enabling access to and modification of their mailbox. If confirmed malicious, an attacker could gain unauthorized access to sensitive information, manipulate mailbox data, and perform actions as a legitimate user, posing a severe security risk to the organization. +search: |- + `o365_management_activity` Workload=Exchange Operation="New-ManagementRoleAssignment" Role=ApplicationImpersonation + | rename User as target_user + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + src vendor_account vendor_product + target_user + | `security_content_ctime(lastTime)` + | `o365_applicationimpersonation_role_assigned_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: While infrequent, the ApplicationImpersonation role may be granted for leigimate reasons, filter as needed. references: -- https://attack.mitre.org/techniques/T1098/002/ -- https://www.mandiant.com/resources/blog/remediation-and-hardening-strategies-for-microsoft-365-to-defend-against-unc2452 -- https://www.mandiant.com/media/17656 + - https://attack.mitre.org/techniques/T1098/002/ + - https://www.mandiant.com/resources/blog/remediation-and-hardening-strategies-for-microsoft-365-to-defend-against-unc2452 + - https://www.mandiant.com/media/17656 drilldown_searches: -- name: View the detection results for - "$target_user$" and "$user$" - search: '%original_detection_search% | search target_user = "$target_user$" user - = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$target_user$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$target_user$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$target_user$" and "$user$" + search: '%original_detection_search% | search target_user = "$target_user$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$target_user$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$target_user$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $user$ granted the ApplicationImpersonation role to $target_user$ - risk_objects: - - field: target_user - type: user - score: 56 - - field: user - type: user - score: 56 - threat_objects: [] + message: $user$ granted the ApplicationImpersonation role to $target_user$ + risk_objects: + - field: target_user + type: user + score: 56 + - field: user + type: user + score: 56 + threat_objects: [] tags: - analytic_story: - - Office 365 Persistence Mechanisms - - Office 365 Collection Techniques - - NOBELIUM Group - asset_type: O365 Tenant - mitre_attack_id: - - T1098.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Persistence Mechanisms + - Office 365 Collection Techniques + - NOBELIUM Group + asset_type: O365 Tenant + mitre_attack_id: + - T1098.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.002/application_impersonation_role_assigned/application_impersonation_role_assigned.log - source: O365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.002/application_impersonation_role_assigned/application_impersonation_role_assigned.log + source: O365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_bec_email_hiding_rule_created.yml b/detections/cloud/o365_bec_email_hiding_rule_created.yml index 3f8dfbe7b5..f22914acb4 100644 --- a/detections/cloud/o365_bec_email_hiding_rule_created.yml +++ b/detections/cloud/o365_bec_email_hiding_rule_created.yml @@ -1,64 +1,60 @@ name: O365 BEC Email Hiding Rule Created id: 603ebac2-f157-4df7-a6ac-34e8d0350f86 -version: 4 -date: '2025-07-23' +version: 5 +date: '2026-02-25' author: '0xC0FFEEEE, Github Community' type: TTP status: production -description: This analytic detects mailbox rule creation, a common technique used in Business Email Compromise. It uses a scoring mechanism to identify a combination of attributes often featured in mailbox rules created by attackers. - This may indicate that an attacker has gained access to the account. +description: This analytic detects mailbox rule creation, a common technique used in Business Email Compromise. It uses a scoring mechanism to identify a combination of attributes often featured in mailbox rules created by attackers. This may indicate that an attacker has gained access to the account. search: |- - `o365_management_activity` Workload=Exchange Operation IN ("New-InboxRule", "Set-InboxRule") - | stats min(_time) as firstTime, max(_time) as lastTime, values(Operation) as Operation, latest(Name) as Name, latest(MarkAsRead) as MarkAsRead, latest(MoveToFolder) as MoveToFolder by object_id user - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | lookup ut_shannon_lookup word as Name - | eval entropy_score=if(ut_shannon<=2, 1, 0) - | eval len_score=if(len(Name)<=3, 1,0) - | eval read_score=if(MarkAsRead="True", 1, 0) - | eval folder_score=if(match(MoveToFolder, "^(RSS|Conversation History|Archive)"), 1, 0) - | eval suspicious_score=entropy_score+len_score+read_score+folder_score - | where suspicious_score>2 - | `o365_bec_email_hiding_rule_created_filter` -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. You also need to have the Splunk TA URL - Toolbox (https://splunkbase.splunk.com/app/2734/) installed. -known_false_positives: Short rule names may trigger false positives. Adjust - the entropy and length thresholds as needed. + `o365_management_activity` Workload=Exchange Operation IN ("New-InboxRule", "Set-InboxRule") + | stats min(_time) as firstTime, max(_time) as lastTime, values(Operation) as Operation, latest(Name) as Name, latest(MarkAsRead) as MarkAsRead, latest(MoveToFolder) as MoveToFolder by object_id user + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | lookup ut_shannon_lookup word as Name + | eval entropy_score=if(ut_shannon<=2, 1, 0) + | eval len_score=if(len(Name)<=3, 1,0) + | eval read_score=if(MarkAsRead="True", 1, 0) + | eval folder_score=if(match(MoveToFolder, "^(RSS|Conversation History|Archive)"), 1, 0) + | eval suspicious_score=entropy_score+len_score+read_score+folder_score + | where suspicious_score>2 + | `o365_bec_email_hiding_rule_created_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. You also need to have the Splunk TA URL Toolbox (https://splunkbase.splunk.com/app/2734/) installed. +known_false_positives: Short rule names may trigger false positives. Adjust the entropy and length thresholds as needed. references: -- https://attack.mitre.org/techniques/T1564/008/ + - https://attack.mitre.org/techniques/T1564/008/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for $user$ - search: '| from datamodel Risk.All_Risk | search normalized_risk_object="$user$" starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for $user$ + search: '| from datamodel Risk.All_Risk | search normalized_risk_object="$user$" starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential BEC mailbox rule - $Name$ was created by user - $user$ - risk_objects: - - field: user - type: user - score: 25 - threat_objects: - - field: Name - type: signature + message: Potential BEC mailbox rule - $Name$ was created by user - $user$ + risk_objects: + - field: user + type: user + score: 25 + threat_objects: + - field: Name + type: signature tags: - analytic_story: - - Office 365 Account Takeover - asset_type: O365 Tenant - mitre_attack_id: - - T1564.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: audit + analytic_story: + - Office 365 Account Takeover + asset_type: O365 Tenant + mitre_attack_id: + - T1564.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: audit tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1564.008/o365/o365_suspicious_mailbox_rule.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1564.008/o365/o365_suspicious_mailbox_rule.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_block_user_consent_for_risky_apps_disabled.yml b/detections/cloud/o365_block_user_consent_for_risky_apps_disabled.yml index 32bd533069..a91cfde233 100644 --- a/detections/cloud/o365_block_user_consent_for_risky_apps_disabled.yml +++ b/detections/cloud/o365_block_user_consent_for_risky_apps_disabled.yml @@ -6,72 +6,47 @@ author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- O365 Update authorization policy. -description: The following analytic detects when the "risk-based step-up consent" - security setting in Microsoft 365 is disabled. It monitors Azure Active Directory - logs for the "Update authorization policy" operation, specifically changes to the - "AllowUserConsentForRiskyApps" setting. This activity is significant because disabling - this feature can expose the organization to OAuth phishing threats, allowing users - to grant consent to malicious applications. If confirmed malicious, attackers could - gain unauthorized access to user data and sensitive information, leading to data - breaches and further compromise within the organization. -search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Update authorization policy.\" - | eval index_number = if(mvfind('ModifiedProperties{}.Name',\"AllowUserConsentForRiskyApps\") >= 0, mvfind('ModifiedProperties{}.Name',\"AllowUserConsentForRiskyApps\"), -1) - | search index_number >= 0 - | eval AllowUserConsentForRiskyApps = mvindex('ModifiedProperties{}.NewValue',index_number) - | where AllowUserConsentForRiskyApps like \"%true%\" - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user src vendor_account vendor_product AllowUserConsentForRiskyApps - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_block_user_consent_for_risky_apps_disabled_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Legitimate changes to the 'risk-based step-up consent' setting - by administrators, perhaps as part of a policy update or security assessment, may - trigger this alert, necessitating verification of the change's intent and authorization. + - O365 Update authorization policy. +description: The following analytic detects when the "risk-based step-up consent" security setting in Microsoft 365 is disabled. It monitors Azure Active Directory logs for the "Update authorization policy" operation, specifically changes to the "AllowUserConsentForRiskyApps" setting. This activity is significant because disabling this feature can expose the organization to OAuth phishing threats, allowing users to grant consent to malicious applications. If confirmed malicious, attackers could gain unauthorized access to user data and sensitive information, leading to data breaches and further compromise within the organization. +search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Update authorization policy.\" | eval index_number = if(mvfind('ModifiedProperties{}.Name',\"AllowUserConsentForRiskyApps\") >= 0, mvfind('ModifiedProperties{}.Name',\"AllowUserConsentForRiskyApps\"), -1) | search index_number >= 0 | eval AllowUserConsentForRiskyApps = mvindex('ModifiedProperties{}.NewValue',index_number) | where AllowUserConsentForRiskyApps like \"%true%\" | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user src vendor_account vendor_product AllowUserConsentForRiskyApps | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_block_user_consent_for_risky_apps_disabled_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Legitimate changes to the 'risk-based step-up consent' setting by administrators, perhaps as part of a policy update or security assessment, may trigger this alert, necessitating verification of the change's intent and authorization. references: -- https://attack.mitre.org/techniques/T1562/ -- https://goodworkaround.com/2020/10/19/a-look-behind-the-azure-ad-permission-classifications-preview/ -- https://learn.microsoft.com/en-us/entra/identity/enterprise-apps/configure-risk-based-step-up-consent -- https://learn.microsoft.com/en-us/defender-cloud-apps/investigate-risky-oauth + - https://attack.mitre.org/techniques/T1562/ + - https://goodworkaround.com/2020/10/19/a-look-behind-the-azure-ad-permission-classifications-preview/ + - https://learn.microsoft.com/en-us/entra/identity/enterprise-apps/configure-risk-based-step-up-consent + - https://learn.microsoft.com/en-us/defender-cloud-apps/investigate-risky-oauth drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Risk-based step-up consent security setting was disabled by $user$ - risk_objects: - - field: user - type: user - score: 30 - threat_objects: [] + message: Risk-based step-up consent security setting was disabled by $user$ + risk_objects: + - field: user + type: user + score: 30 + threat_objects: [] tags: - analytic_story: - - Office 365 Account Takeover - asset_type: O365 Tenant - atomic_guid: [] - mitre_attack_id: - - T1562 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: audit + analytic_story: + - Office 365 Account Takeover + asset_type: O365 Tenant + atomic_guid: [] + mitre_attack_id: + - T1562 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: audit tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/o365_disable_blockconsent_for_riskapps/o365_disable_blockconsent_for_riskapps.log - source: O365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/o365_disable_blockconsent_for_riskapps/o365_disable_blockconsent_for_riskapps.log + source: O365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_bypass_mfa_via_trusted_ip.yml b/detections/cloud/o365_bypass_mfa_via_trusted_ip.yml index 8ab46388c3..f37fa5c4b5 100644 --- a/detections/cloud/o365_bypass_mfa_via_trusted_ip.yml +++ b/detections/cloud/o365_bypass_mfa_via_trusted_ip.yml @@ -5,73 +5,46 @@ date: '2025-05-02' author: Bhavin Patel, Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic identifies instances where new IP addresses are - added to the trusted IPs list in Office 365, potentially allowing users from these - IPs to bypass Multi-Factor Authentication (MFA) during login. It leverages O365 - audit logs, specifically focusing on events related to the modification of trusted - IP settings. This activity is significant because adding trusted IPs can weaken - the security posture by bypassing MFA, which is a critical security control. If - confirmed malicious, this could lead to unauthorized access, compromising sensitive - information and systems. Immediate investigation is required to validate the legitimacy - of the IP addition. +description: The following analytic identifies instances where new IP addresses are added to the trusted IPs list in Office 365, potentially allowing users from these IPs to bypass Multi-Factor Authentication (MFA) during login. It leverages O365 audit logs, specifically focusing on events related to the modification of trusted IP settings. This activity is significant because adding trusted IPs can weaken the security posture by bypassing MFA, which is a critical security control. If confirmed malicious, this could lead to unauthorized access, compromising sensitive information and systems. Immediate investigation is required to validate the legitimacy of the IP addition. data_source: -- O365 Set Company Information. -search: '`o365_management_activity` Operation="Set Company Information." ModifiedProperties{}.Name=StrongAuthenticationPolicy - | rex max_match=100 field=ModifiedProperties{}.NewValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" - | rex max_match=100 field=ModifiedProperties{}.OldValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" - | eval ip_addresses_old=if(isnotnull(ip_addresses_old),ip_addresses_old,"0") - | mvexpand ip_addresses_new_added - | where isnull(mvfind(ip_addresses_old,ip_addresses_new_added)) - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime values(ip_addresses_old) as ip_addresses_old by signature dest user src vendor_account vendor_product ip_addresses_new_added - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_bypass_mfa_via_trusted_ip_filter`' -how_to_implement: You must install Splunk Microsoft Office 365 add-on. This search - works with o365:management:activity -known_false_positives: Unless it is a special case, it is uncommon to continually - update Trusted IPs to MFA configuration. + - O365 Set Company Information. +search: '`o365_management_activity` Operation="Set Company Information." ModifiedProperties{}.Name=StrongAuthenticationPolicy | rex max_match=100 field=ModifiedProperties{}.NewValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" | rex max_match=100 field=ModifiedProperties{}.OldValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" | eval ip_addresses_old=if(isnotnull(ip_addresses_old),ip_addresses_old,"0") | mvexpand ip_addresses_new_added | where isnull(mvfind(ip_addresses_old,ip_addresses_new_added)) | fillnull | stats count min(_time) as firstTime max(_time) as lastTime values(ip_addresses_old) as ip_addresses_old by signature dest user src vendor_account vendor_product ip_addresses_new_added | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_bypass_mfa_via_trusted_ip_filter`' +how_to_implement: You must install Splunk Microsoft Office 365 add-on. This search works with o365:management:activity +known_false_positives: Unless it is a special case, it is uncommon to continually update Trusted IPs to MFA configuration. references: -- https://i.blackhat.com/USA-20/Thursday/us-20-Bienstock-My-Cloud-Is-APTs-Cloud-Investigating-And-Defending-Office-365.pdf -- https://attack.mitre.org/techniques/T1562/007/ -- https://learn.microsoft.com/en-us/azure/active-directory/authentication/howto-mfa-mfasettings + - https://i.blackhat.com/USA-20/Thursday/us-20-Bienstock-My-Cloud-Is-APTs-Cloud-Investigating-And-Defending-Office-365.pdf + - https://attack.mitre.org/techniques/T1562/007/ + - https://learn.microsoft.com/en-us/azure/active-directory/authentication/howto-mfa-mfasettings drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has added new IP addresses $ip_addresses_new_added$ to a - list of trusted IPs to bypass MFA - risk_objects: - - field: user - type: user - score: 42 - threat_objects: [] + message: User $user$ has added new IP addresses $ip_addresses_new_added$ to a list of trusted IPs to bypass MFA + risk_objects: + - field: user + type: user + score: 42 + threat_objects: [] tags: - analytic_story: - - Office 365 Persistence Mechanisms - asset_type: O365 Tenant - mitre_attack_id: - - T1562.007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Persistence Mechanisms + asset_type: O365 Tenant + mitre_attack_id: + - T1562.007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/o365_bypass_mfa_via_trusted_ip/o365_bypass_mfa_via_trusted_ip.json - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/o365_bypass_mfa_via_trusted_ip/o365_bypass_mfa_via_trusted_ip.json + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_compliance_content_search_exported.yml b/detections/cloud/o365_compliance_content_search_exported.yml index 4a634e82f9..6200385294 100644 --- a/detections/cloud/o365_compliance_content_search_exported.yml +++ b/detections/cloud/o365_compliance_content_search_exported.yml @@ -1,71 +1,60 @@ name: O365 Compliance Content Search Exported id: 2ce9f31d-ab4f-4179-b2b7-c77a9652e1d8 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk data_source: [] type: TTP status: production -description: The following analytic identifies when the results of a content search - within the Office 365 Security and Compliance Center are exported. It uses the SearchExported - operation from the SecurityComplianceCenter workload in the o365_management_activity - data source. This activity is significant because exporting search results can involve - sensitive or critical organizational data, potentially leading to data exfiltration. - If confirmed malicious, an attacker could gain access to and exfiltrate sensitive - information, posing a severe risk to the organization's data security and compliance - posture. -search: '`o365_management_activity` Workload=SecurityComplianceCenter Operation="SearchExported" - | rename user_id as user - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user src vendor_account vendor_product ExchangeLocations Query - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_compliance_content_search_exported_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Compliance content searche exports may be executed for legitimate - purposes, filter as needed. +description: The following analytic identifies when the results of a content search within the Office 365 Security and Compliance Center are exported. It uses the SearchExported operation from the SecurityComplianceCenter workload in the o365_management_activity data source. This activity is significant because exporting search results can involve sensitive or critical organizational data, potentially leading to data exfiltration. If confirmed malicious, an attacker could gain access to and exfiltrate sensitive information, posing a severe risk to the organization's data security and compliance posture. +search: |- + `o365_management_activity` Workload=SecurityComplianceCenter Operation="SearchExported" + | rename user_id as user + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + src vendor_account vendor_product + ExchangeLocations Query + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_compliance_content_search_exported_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Compliance content searche exports may be executed for legitimate purposes, filter as needed. references: -- https://attack.mitre.org/techniques/T1114/002/ -- https://learn.microsoft.com/en-us/purview/ediscovery-content-search-overview -- https://learn.microsoft.com/en-us/purview/ediscovery-keyword-queries-and-search-conditions -- https://learn.microsoft.com/en-us/purview/ediscovery-search-for-activities-in-the-audit-log + - https://attack.mitre.org/techniques/T1114/002/ + - https://learn.microsoft.com/en-us/purview/ediscovery-content-search-overview + - https://learn.microsoft.com/en-us/purview/ediscovery-keyword-queries-and-search-conditions + - https://learn.microsoft.com/en-us/purview/ediscovery-search-for-activities-in-the-audit-log drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A new compliance content search export was started by $user$ - risk_objects: - - field: user - type: user - score: 42 - threat_objects: [] + message: A new compliance content search export was started by $user$ + risk_objects: + - field: user + type: user + score: 42 + threat_objects: [] tags: - analytic_story: - - Office 365 Collection Techniques - asset_type: O365 Tenant - mitre_attack_id: - - T1114.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Office 365 Collection Techniques + asset_type: O365 Tenant + mitre_attack_id: + - T1114.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/o365_compliance_content_search_exported/o365_compliance_content_search_exported.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/o365_compliance_content_search_exported/o365_compliance_content_search_exported.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_compliance_content_search_started.yml b/detections/cloud/o365_compliance_content_search_started.yml index b650b90948..37a263c838 100644 --- a/detections/cloud/o365_compliance_content_search_started.yml +++ b/detections/cloud/o365_compliance_content_search_started.yml @@ -1,71 +1,60 @@ name: O365 Compliance Content Search Started id: f4cabbc7-c19a-4e41-8be5-98daeaccbb50 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk data_source: [] type: TTP status: production -description: The following analytic detects when a content search is initiated within - the Office 365 Security and Compliance Center. It leverages the SearchCreated operation - from the o365_management_activity logs under the SecurityComplianceCenter workload. - This activity is significant as it may indicate an attempt to access sensitive organizational - data, including emails and documents. If confirmed malicious, this could lead to - unauthorized data access, potential data exfiltration, and compliance violations. - Monitoring this behavior helps ensure the integrity and security of organizational - data. -search: '`o365_management_activity` Workload=SecurityComplianceCenter Operation=SearchCreated - | rename user_id as user - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user src vendor_account vendor_product ExchangeLocations Query - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_compliance_content_search_started_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Compliance content searches may be executed for legitimate - purposes, filter as needed. +description: The following analytic detects when a content search is initiated within the Office 365 Security and Compliance Center. It leverages the SearchCreated operation from the o365_management_activity logs under the SecurityComplianceCenter workload. This activity is significant as it may indicate an attempt to access sensitive organizational data, including emails and documents. If confirmed malicious, this could lead to unauthorized data access, potential data exfiltration, and compliance violations. Monitoring this behavior helps ensure the integrity and security of organizational data. +search: |- + `o365_management_activity` Workload=SecurityComplianceCenter Operation=SearchCreated + | rename user_id as user + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + src vendor_account vendor_product + ExchangeLocations Query + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_compliance_content_search_started_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Compliance content searches may be executed for legitimate purposes, filter as needed. references: -- https://attack.mitre.org/techniques/T1114/002/ -- https://learn.microsoft.com/en-us/purview/ediscovery-content-search-overview -- https://learn.microsoft.com/en-us/purview/ediscovery-keyword-queries-and-search-conditions -- https://learn.microsoft.com/en-us/purview/ediscovery-search-for-activities-in-the-audit-log + - https://attack.mitre.org/techniques/T1114/002/ + - https://learn.microsoft.com/en-us/purview/ediscovery-content-search-overview + - https://learn.microsoft.com/en-us/purview/ediscovery-keyword-queries-and-search-conditions + - https://learn.microsoft.com/en-us/purview/ediscovery-search-for-activities-in-the-audit-log drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A new compliance content search was started by $user$ - risk_objects: - - field: user - type: user - score: 42 - threat_objects: [] + message: A new compliance content search was started by $user$ + risk_objects: + - field: user + type: user + score: 42 + threat_objects: [] tags: - analytic_story: - - Office 365 Collection Techniques - asset_type: O365 Tenant - mitre_attack_id: - - T1114.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: audit + analytic_story: + - Office 365 Collection Techniques + asset_type: O365 Tenant + mitre_attack_id: + - T1114.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: audit tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/o365_compliance_content_search_started/o365_compliance_content_search_started.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/o365_compliance_content_search_started/o365_compliance_content_search_started.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_concurrent_sessions_from_different_ips.yml b/detections/cloud/o365_concurrent_sessions_from_different_ips.yml index 75c5bac39c..d006f1438a 100644 --- a/detections/cloud/o365_concurrent_sessions_from_different_ips.yml +++ b/detections/cloud/o365_concurrent_sessions_from_different_ips.yml @@ -1,73 +1,60 @@ name: O365 Concurrent Sessions From Different Ips id: 58e034de-1f87-4812-9dc3-a4f68c7db930 -version: 10 -date: '2026-01-14' +version: 11 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic identifies user sessions in Office 365 accessed - from multiple IP addresses, indicating potential adversary-in-the-middle (AiTM) - phishing attacks. It detects this activity by analyzing Azure Active Directory logs - for 'UserLoggedIn' operations and flags sessions with more than one associated IP - address. This behavior is significant as it suggests unauthorized concurrent access, - which is uncommon in normal usage. If confirmed malicious, the impact could include - data theft, account takeover, and the launching of internal phishing campaigns, - posing severe risks to organizational security. +description: The following analytic identifies user sessions in Office 365 accessed from multiple IP addresses, indicating potential adversary-in-the-middle (AiTM) phishing attacks. It detects this activity by analyzing Azure Active Directory logs for 'UserLoggedIn' operations and flags sessions with more than one associated IP address. This behavior is significant as it suggests unauthorized concurrent access, which is uncommon in normal usage. If confirmed malicious, the impact could include data theft, account takeover, and the launching of internal phishing campaigns, posing severe risks to organizational security. data_source: -- O365 UserLoggedIn -search: '`o365_management_activity` Workload=AzureActiveDirectory Operation=UserLoggedIn - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime values(src) as src - by signature dest user vendor_account vendor_product SessionId - | where mvcount(src) > 1 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_concurrent_sessions_from_different_ips_filter`' -how_to_implement: You must install splunk Microsoft Office 365 add-on. This search - works with o365:management:activity + - O365 UserLoggedIn +search: |- + `o365_management_activity` Workload=AzureActiveDirectory Operation=UserLoggedIn + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime values(src) as src + BY signature dest user + vendor_account vendor_product SessionId + | where mvcount(src) > 1 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_concurrent_sessions_from_different_ips_filter` +how_to_implement: You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity known_false_positives: No false positives have been identified at this time. references: -- https://attack.mitre.org/techniques/T1185/ -- https://breakdev.org/evilginx-2-next-generation-of-phishing-2fa-tokens/ -- https://github.com/kgretzky/evilginx2 + - https://attack.mitre.org/techniques/T1185/ + - https://breakdev.org/evilginx-2-next-generation-of-phishing-2fa-tokens/ + - https://github.com/kgretzky/evilginx2 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has logged in with the same session id from more than one unique - IP address - risk_objects: - - field: user - type: user - score: 42 - threat_objects: [] + message: User $user$ has logged in with the same session id from more than one unique IP address + risk_objects: + - field: user + type: user + score: 42 + threat_objects: [] tags: - analytic_story: - - Office 365 Account Takeover - - Scattered Lapsus$ Hunters - asset_type: O365 Tenant - mitre_attack_id: - - T1185 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Account Takeover + - Scattered Lapsus$ Hunters + asset_type: O365 Tenant + mitre_attack_id: + - T1185 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/o365_concurrent_sessions_from_different_ips/o365_concurrent_sessions_from_different_ips.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/o365_concurrent_sessions_from_different_ips/o365_concurrent_sessions_from_different_ips.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_cross_tenant_access_change.yml b/detections/cloud/o365_cross_tenant_access_change.yml index 5cb8ef819c..8d0558113a 100644 --- a/detections/cloud/o365_cross_tenant_access_change.yml +++ b/detections/cloud/o365_cross_tenant_access_change.yml @@ -5,67 +5,47 @@ date: '2025-05-02' author: Steven Dick status: production type: TTP -description: The following analytic identifies when cross-tenant access/synchronization - policies are changed in an Azure tenant. Adversaries have been observed altering - victim cross-tenant policies as a method of lateral movement or maintaining persistent - access to compromised environments. These policies should be considered sensitive - and monitored for changes and/or loose configuration. +description: The following analytic identifies when cross-tenant access/synchronization policies are changed in an Azure tenant. Adversaries have been observed altering victim cross-tenant policies as a method of lateral movement or maintaining persistent access to compromised environments. These policies should be considered sensitive and monitored for changes and/or loose configuration. data_source: -- Office 365 Universal Audit Log -search: "`o365_management_activity` Workload=AzureActiveDirectory Operation IN (\"\ - Add a partner to cross-tenant access setting.\",\"Delete partner specific cross-tenant - access setting.\") - | eval user = case(match(mvindex('Actor{}.ID',-1),\"User\"),mvindex('Actor{}.ID',0),match(mvindex('Actor{}.ID',-1),\"\ - ServicePrincipal\"),mvindex('Actor{}.ID',3),true(),mvindex('Actor{}.ID',0)) - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product signature signature_id - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_cross_tenant_access_change_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. + - Office 365 Universal Audit Log +search: "`o365_management_activity` Workload=AzureActiveDirectory Operation IN (\"Add a partner to cross-tenant access setting.\",\"Delete partner specific cross-tenant access setting.\") | eval user = case(match(mvindex('Actor{}.ID',-1),\"User\"),mvindex('Actor{}.ID',0),match(mvindex('Actor{}.ID',-1),\"ServicePrincipal\"),mvindex('Actor{}.ID',3),true(),mvindex('Actor{}.ID',0)) | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product signature signature_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_cross_tenant_access_change_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. known_false_positives: Business approved changes by known administrators. references: -- https://attack.mitre.org/techniques/T1484/002/ -- https://thehackernews.com/2023/08/emerging-attacker-exploit-microsoft.html -- https://cyberaffairs.com/news/emerging-attacker-exploit-microsoft-cross-tenant-synchronization/ -- https://www.crowdstrike.com/blog/crowdstrike-defends-against-azure-cross-tenant-synchronization-attacks/ + - https://attack.mitre.org/techniques/T1484/002/ + - https://thehackernews.com/2023/08/emerging-attacker-exploit-microsoft.html + - https://cyberaffairs.com/news/emerging-attacker-exploit-microsoft-cross-tenant-synchronization/ + - https://www.crowdstrike.com/blog/crowdstrike-defends-against-azure-cross-tenant-synchronization-attacks/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The user [$user$] changed the Azure cross-tenant access settings - risk_objects: - - field: user - type: user - score: 56 - threat_objects: [] + message: The user [$user$] changed the Azure cross-tenant access settings + risk_objects: + - field: user + type: user + score: 56 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - asset_type: O365 Tenant - mitre_attack_id: - - T1484.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Azure Active Directory Persistence + asset_type: O365 Tenant + mitre_attack_id: + - T1484.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_azure_workload_events/o365_azure_workload_events.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_azure_workload_events/o365_azure_workload_events.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_disable_mfa.yml b/detections/cloud/o365_disable_mfa.yml index b4ba5a2d6b..a5dda094a2 100644 --- a/detections/cloud/o365_disable_mfa.yml +++ b/detections/cloud/o365_disable_mfa.yml @@ -1,70 +1,58 @@ name: O365 Disable MFA id: c783dd98-c703-4252-9e8a-f19d9f5c949e -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Rod Soto, Splunk status: production type: TTP -description: The following analytic identifies instances where Multi-Factor Authentication - (MFA) is disabled for a user within the Office 365 environment. It leverages O365 - audit logs, specifically focusing on events related to MFA settings. Disabling MFA - removes a critical security layer, making accounts more vulnerable to unauthorized - access. If confirmed malicious, this activity could indicate an attacker attempting - to maintain persistence or an insider threat, significantly increasing the risk - of unauthorized access. Immediate investigation is required to validate the reason - for disabling MFA, potentially re-enable it, and assess any other suspicious activities - related to the affected account. +description: The following analytic identifies instances where Multi-Factor Authentication (MFA) is disabled for a user within the Office 365 environment. It leverages O365 audit logs, specifically focusing on events related to MFA settings. Disabling MFA removes a critical security layer, making accounts more vulnerable to unauthorized access. If confirmed malicious, this activity could indicate an attacker attempting to maintain persistence or an insider threat, significantly increasing the risk of unauthorized access. Immediate investigation is required to validate the reason for disabling MFA, potentially re-enable it, and assess any other suspicious activities related to the affected account. data_source: -- O365 Disable Strong Authentication. -search: '`o365_management_activity` Operation="Disable Strong Authentication." - | rename UserId as user object as src_user - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user src vendor_account vendor_product src_user - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_disable_mfa_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 add-on. This search - works with o365:management:activity -known_false_positives: Unless it is a special case, it is uncommon to disable MFA - or Strong Authentication + - O365 Disable Strong Authentication. +search: |- + `o365_management_activity` Operation="Disable Strong Authentication." + | rename UserId as user object as src_user + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + src vendor_account vendor_product + src_user + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_disable_mfa_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 add-on. This search works with o365:management:activity +known_false_positives: Unless it is a special case, it is uncommon to disable MFA or Strong Authentication references: -- https://attack.mitre.org/techniques/T1556/ + - https://attack.mitre.org/techniques/T1556/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $src_user$ has executed an operation $signature$ for user $user$ - risk_objects: - - field: user - type: user - score: 64 - threat_objects: [] + message: User $src_user$ has executed an operation $signature$ for user $user$ + risk_objects: + - field: user + type: user + score: 64 + threat_objects: [] tags: - analytic_story: - - Office 365 Persistence Mechanisms - asset_type: O365 Tenant - mitre_attack_id: - - T1556 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Persistence Mechanisms + asset_type: O365 Tenant + mitre_attack_id: + - T1556 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_disable_mfa/o365_disable_mfa.json - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_disable_mfa/o365_disable_mfa.json + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_dlp_rule_triggered.yml b/detections/cloud/o365_dlp_rule_triggered.yml index 678fe40c47..6c8c242e5b 100644 --- a/detections/cloud/o365_dlp_rule_triggered.yml +++ b/detections/cloud/o365_dlp_rule_triggered.yml @@ -5,67 +5,45 @@ date: '2025-05-02' author: Steven Dick status: production type: Anomaly -description: The following analytic detects when Microsoft Office 365 Data Loss Prevention - (DLP) rules have been triggered. DLP rules can be configured for any number of security, - regulatory, or business compliance reasons, as such this analytic will only be as - accurate as the upstream DLP configuration. Detections from this analytic should - be evaluated thoroughly to de termine what, if any, security relevance the underlying - DLP events contain. +description: The following analytic detects when Microsoft Office 365 Data Loss Prevention (DLP) rules have been triggered. DLP rules can be configured for any number of security, regulatory, or business compliance reasons, as such this analytic will only be as accurate as the upstream DLP configuration. Detections from this analytic should be evaluated thoroughly to de termine what, if any, security relevance the underlying DLP events contain. data_source: -- Office 365 Universal Audit Log -search: '`o365_management_activity` Operation=DLPRuleMatch | eval recipient = ''ExchangeMetaData.To{}'', - signature_id = ''ExchangeMetaData.UniqueID'', signature = ''PolicyDetails{}.Rules{}.RuleName'' - , src_user = UserId, reason =''PolicyDetails{}.Rules{}.ConditionsMatched.SensitiveInformation{}.SensitiveInformationTypeName'', - result=''PolicyDetails{}.Rules{}.Actions{}'', file_name=case(NOT match(''PolicyDetails{}.Rules{}.ConditionsMatched.SensitiveInformation{}.Location'',"Message - Body"),''PolicyDetails{}.Rules{}.ConditionsMatched.SensitiveInformation{}.Location'') - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime values(signature) - as signature values(file_name) as file_name values(ExchangeMetaData.Subject) AS - subject values(Workload) as app values(result) as result by action dest user src - vendor_account vendor_product src_user recipient signature_id reason | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `o365_dlp_rule_triggered_filter` ' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. You must deploy DLP rules through O365 security - and compliance functions. -known_false_positives: WIll depending on accuracy of DLP rules, these can be noisy - so tune appropriately. + - Office 365 Universal Audit Log +search: '`o365_management_activity` Operation=DLPRuleMatch | eval recipient = ''ExchangeMetaData.To{}'', signature_id = ''ExchangeMetaData.UniqueID'', signature = ''PolicyDetails{}.Rules{}.RuleName'' , src_user = UserId, reason =''PolicyDetails{}.Rules{}.ConditionsMatched.SensitiveInformation{}.SensitiveInformationTypeName'', result=''PolicyDetails{}.Rules{}.Actions{}'', file_name=case(NOT match(''PolicyDetails{}.Rules{}.ConditionsMatched.SensitiveInformation{}.Location'',"Message Body"),''PolicyDetails{}.Rules{}.ConditionsMatched.SensitiveInformation{}.Location'') | fillnull | stats count min(_time) as firstTime max(_time) as lastTime values(signature) as signature values(file_name) as file_name values(ExchangeMetaData.Subject) AS subject values(Workload) as app values(result) as result by action dest user src vendor_account vendor_product src_user recipient signature_id reason | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_dlp_rule_triggered_filter` ' +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. You must deploy DLP rules through O365 security and compliance functions. +known_false_positives: WIll depending on accuracy of DLP rules, these can be noisy so tune appropriately. references: -- https://learn.microsoft.com/en-us/purview/dlp-learn-about-dlp + - https://learn.microsoft.com/en-us/purview/dlp-learn-about-dlp drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ triggered a Microsoft Office DLP rule. - risk_objects: - - field: user - type: user - score: 20 - threat_objects: [] + message: User $user$ triggered a Microsoft Office DLP rule. + risk_objects: + - field: user + type: user + score: 20 + threat_objects: [] tags: - analytic_story: - - Data Exfiltration - asset_type: O365 Tenant - mitre_attack_id: - - T1048 - - T1567 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Data Exfiltration + asset_type: O365 Tenant + mitre_attack_id: + - T1048 + - T1567 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_elevated_mailbox_permission_assigned.yml b/detections/cloud/o365_elevated_mailbox_permission_assigned.yml index 3316fa7983..05c7d96251 100644 --- a/detections/cloud/o365_elevated_mailbox_permission_assigned.yml +++ b/detections/cloud/o365_elevated_mailbox_permission_assigned.yml @@ -1,71 +1,60 @@ name: O365 Elevated Mailbox Permission Assigned id: 2246c142-a678-45f8-8546-aaed7e0efd30 -version: 9 -date: '2025-10-21' +version: 10 +date: '2026-02-25' author: Patrick Bareiss, Mauricio Velazco, Splunk data_source: - - O365 Add-MailboxPermission + - O365 Add-MailboxPermission type: TTP status: production -description: The following analytic identifies the assignment of elevated mailbox - permissions in an Office 365 environment via the Add-MailboxPermission operation. - It leverages logs from the Exchange workload in the o365_management_activity data - source, focusing on permissions such as FullAccess, ChangePermission, or ChangeOwner. - This activity is significant as it indicates potential unauthorized access or control - over mailboxes, which could lead to data exfiltration or privilege escalation. If - confirmed malicious, attackers could gain extensive access to sensitive email data - and potentially manipulate mailbox settings, posing a severe security risk. -search: '`o365_management_activity` Workload=Exchange Operation=Add-MailboxPermission (AccessRights=FullAccess OR AccessRights=ChangePermission OR AccessRights=ChangeOwner) - | rename Identity AS dest_user - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user src vendor_account vendor_product dest_user - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_elevated_mailbox_permission_assigned_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: FullAccess mailbox delegation may be assigned for legitimate - purposes, filter as needed. +description: The following analytic identifies the assignment of elevated mailbox permissions in an Office 365 environment via the Add-MailboxPermission operation. It leverages logs from the Exchange workload in the o365_management_activity data source, focusing on permissions such as FullAccess, ChangePermission, or ChangeOwner. This activity is significant as it indicates potential unauthorized access or control over mailboxes, which could lead to data exfiltration or privilege escalation. If confirmed malicious, attackers could gain extensive access to sensitive email data and potentially manipulate mailbox settings, posing a severe security risk. +search: |- + `o365_management_activity` Workload=Exchange Operation=Add-MailboxPermission (AccessRights=FullAccess OR AccessRights=ChangePermission OR AccessRights=ChangeOwner) + | rename Identity AS dest_user + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + src vendor_account vendor_product + dest_user + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_elevated_mailbox_permission_assigned_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: FullAccess mailbox delegation may be assigned for legitimate purposes, filter as needed. references: -- https://attack.mitre.org/techniques/T1098/002/ -- https://learn.microsoft.com/en-us/powershell/module/exchange/add-mailboxpermission -- https://learn.microsoft.com/en-us/exchange/recipients/mailbox-permissions?view=exchserver-2019 + - https://attack.mitre.org/techniques/T1098/002/ + - https://learn.microsoft.com/en-us/powershell/module/exchange/add-mailboxpermission + - https://learn.microsoft.com/en-us/exchange/recipients/mailbox-permissions?view=exchserver-2019 drilldown_searches: -- name: View the detection results for - "$dest_user$" - search: '%original_detection_search% | search dest_user = "$dest_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest_user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest_user$" + search: '%original_detection_search% | search dest_user = "$dest_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Elevated mailbox permissions were assigned on $dest_user$ - risk_objects: - - field: dest_user - type: user - score: 42 - threat_objects: [] + message: Elevated mailbox permissions were assigned on $dest_user$ + risk_objects: + - field: dest_user + type: user + score: 42 + threat_objects: [] tags: - analytic_story: - - Office 365 Collection Techniques - asset_type: O365 Tenant - mitre_attack_id: - - T1098.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: audit + analytic_story: + - Office 365 Collection Techniques + asset_type: O365 Tenant + mitre_attack_id: + - T1098.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: audit tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/suspicious_rights_delegation/suspicious_rights_delegation.json - source: o365:management:activity - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/suspicious_rights_delegation/suspicious_rights_delegation.json + source: o365:management:activity + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_email_access_by_security_administrator.yml b/detections/cloud/o365_email_access_by_security_administrator.yml index 826044f6d1..8bf8152d53 100644 --- a/detections/cloud/o365_email_access_by_security_administrator.yml +++ b/detections/cloud/o365_email_access_by_security_administrator.yml @@ -1,69 +1,64 @@ name: O365 Email Access By Security Administrator id: c6998a30-fef4-4e89-97ac-3bb0123719b4 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Steven Dick status: production type: TTP -description: The following analytic identifies when a user with sufficient access - to O365 Security & Compliance portal uses premium investigation features (Threat - Explorer) to directly view email. Adversaries may exploit privileged access with - this premium feature to enumerate or exfiltrate sensitive data. +description: The following analytic identifies when a user with sufficient access to O365 Security & Compliance portal uses premium investigation features (Threat Explorer) to directly view email. Adversaries may exploit privileged access with this premium feature to enumerate or exfiltrate sensitive data. data_source: -- Office 365 Universal Audit Log -search: '`o365_management_activity` Workload=SecurityComplianceCenter Operation=AdminMailAccess - | rename InternetMessageId as signature_id, UserId as src_user | fillnull | stats - count min(_time) as firstTime max(_time) as lastTime by signature dest user src - vendor_account vendor_product src_user signature_id | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `o365_email_access_by_security_administrator_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. Threat Explorer is a premium feature with - o365, logging may not be available with proper license. -known_false_positives: Legitamate access by security administators for incident response - measures. + - Office 365 Universal Audit Log +search: |- + `o365_management_activity` Workload=SecurityComplianceCenter Operation=AdminMailAccess + | rename InternetMessageId as signature_id, UserId as src_user + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY signature dest user + src vendor_account vendor_product + src_user signature_id + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_email_access_by_security_administrator_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. Threat Explorer is a premium feature with o365, logging may not be available with proper license. +known_false_positives: Legitamate access by security administators for incident response measures. references: -- https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/threat-explorer-investigate-delivered-malicious-email?view=o365-worldwide + - https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/threat-explorer-investigate-delivered-malicious-email?view=o365-worldwide drilldown_searches: -- name: View the detection results for - "$user$" and "$src_user$" - search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$src_user$" + search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A security administrator $src_user$ accessed email messages for $user$ - risk_objects: - - field: user - type: user - score: 25 - - field: src_user - type: user - score: 25 - threat_objects: [] + message: A security administrator $src_user$ accessed email messages for $user$ + risk_objects: + - field: user + type: user + score: 25 + - field: src_user + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Data Exfiltration - - Azure Active Directory Account Takeover - - Office 365 Account Takeover - asset_type: O365 Tenant - mitre_attack_id: - - T1114.002 - - T1567 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Data Exfiltration + - Azure Active Directory Account Takeover + - Office 365 Account Takeover + asset_type: O365 Tenant + mitre_attack_id: + - T1114.002 + - T1567 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_email_hard_delete_excessive_volume.yml b/detections/cloud/o365_email_hard_delete_excessive_volume.yml index 622d0405a4..3dc95c8c5c 100644 --- a/detections/cloud/o365_email_hard_delete_excessive_volume.yml +++ b/detections/cloud/o365_email_hard_delete_excessive_volume.yml @@ -1,67 +1,67 @@ name: O365 Email Hard Delete Excessive Volume id: c7fe0949-348a-41ce-8f17-a09a7fe5fd7d -version: 2 -date: '2025-05-02' +version: 3 +date: '2026-02-25' author: Steven Dick status: production type: Anomaly description: The following analytic identifies when an O365 email account hard deletes an excessive number of emails within a short period (within 1 hour). This behavior may indicate a compromised account where the threat actor is attempting to permanently purge a large amount of items from the mailbox. Threat actors may attempt to remove evidence of their activity by purging items from the compromised mailbox. --- Some account owner legitimate behaviors can trigger this alert, however these actions may not be aligned with organizational expectations / best practice behaviors. -data_source: -- Office 365 Universal Audit Log +data_source: + - Office 365 Universal Audit Log search: |- - `o365_management_activity` Workload=Exchange (Operation IN ("HardDelete") AND Folder.Path IN ("\\Sent Items","\\Recoverable Items\\Deletions")) - | eval user = lower(UserId), sender = lower(CASE(isnotnull(SendAsUserSmtp),SendAsUserSmtp,isnotnull(SendOnBehalfOfUserSmtp),SendOnBehalfOfUserSmtp,true(),MailboxOwnerUPN)), subject = trim(CASE(Operation IN ("Send","SendAs","SendOnBehalf"),'Item.Subject',Operation IN ("SoftDelete","HardDelete"),'AffectedItems{}.Subject')), -time = _time,file_name = CASE(Operation IN ("Send","SendAs","SendOnBehalf"),split('Item.Attachments',"; "),Operation IN ("SoftDelete","HardDelete"),split('AffectedItems{}.Attachments',"; ")), file_size = CASE(Operation IN ("Send","SendAs","SendOnBehalf"),round(tonumber('Item.SizeInBytes')/1024/1024,2),true(),round(tonumber(replace(file_name, "(.+)\s\((\d+)(b\)$)", "\2"))/1024/1024,2)) - | bin _time span=1hr - | stats values(sender) as sender, values(ClientIPAddress) as src, values(ClientInfoString) as http_user_agent, values(Operation) as signature, latest(file_name) as file_name, sum(file_size) as file_size, values(Folder.Path) as file_path, min(-time) as firstTime, max(-time) as lastTime, dc(subject) as count by _time,user - | where count > 50 OR file_size > 10 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_email_hard_delete_excessive_volume_filter` + `o365_management_activity` Workload=Exchange (Operation IN ("HardDelete") AND Folder.Path IN ("\\Sent Items","\\Recoverable Items\\Deletions")) + | eval user = lower(UserId), sender = lower(CASE(isnotnull(SendAsUserSmtp),SendAsUserSmtp,isnotnull(SendOnBehalfOfUserSmtp),SendOnBehalfOfUserSmtp,true(),MailboxOwnerUPN)), subject = trim(CASE(Operation IN ("Send","SendAs","SendOnBehalf"),'Item.Subject',Operation IN ("SoftDelete","HardDelete"),'AffectedItems{}.Subject')), -time = _time,file_name = CASE(Operation IN ("Send","SendAs","SendOnBehalf"),split('Item.Attachments',"; "),Operation IN ("SoftDelete","HardDelete"),split('AffectedItems{}.Attachments',"; ")), file_size = CASE(Operation IN ("Send","SendAs","SendOnBehalf"),round(tonumber('Item.SizeInBytes')/1024/1024,2),true(),round(tonumber(replace(file_name, "(.+)\s\((\d+)(b\)$)", "\2"))/1024/1024,2)) + | bin _time span=1hr + | stats values(sender) as sender, values(ClientIPAddress) as src, values(ClientInfoString) as http_user_agent, values(Operation) as signature, latest(file_name) as file_name, sum(file_size) as file_size, values(Folder.Path) as file_path, min(-time) as firstTime, max(-time) as lastTime, dc(subject) as count by _time,user + | where count > 50 OR file_size > 10 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_email_hard_delete_excessive_volume_filter` how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. known_false_positives: Users that habitually/proactively cleaning the recoverable items folder may trigger this alert. references: -- https://attack.mitre.org/techniques/T1114/ -- https://www.hhs.gov/sites/default/files/help-desk-social-engineering-sector-alert-tlpclear.pdf -- https://intelligence.abnormalsecurity.com/attack-library/threat-actor-convincingly-impersonates-employee-requesting-direct-deposit-update-in-likely-ai-generated-attack + - https://attack.mitre.org/techniques/T1114/ + - https://www.hhs.gov/sites/default/files/help-desk-social-engineering-sector-alert-tlpclear.pdf + - https://intelligence.abnormalsecurity.com/attack-library/threat-actor-convincingly-impersonates-employee-requesting-direct-deposit-update-in-likely-ai-generated-attack drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate Email for $user$ - search: '`o365_management_activity` Workload=Exchange (Operation IN ("HardDelete") AND Folder.Path IN ("\\Sent Items","\\Recoverable Items\\Deletions")) AND UserId = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate Email for $user$ + search: '`o365_management_activity` Workload=Exchange (Operation IN ("HardDelete") AND Folder.Path IN ("\\Sent Items","\\Recoverable Items\\Deletions")) AND UserId = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The user $user$ deleted an excessing number of emails [$count$] within a short timeframe - risk_objects: - - field: user - type: user - score: 25 - threat_objects: - - field: src - type: ip_address + message: The user $user$ deleted an excessing number of emails [$count$] within a short timeframe + risk_objects: + - field: user + type: user + score: 25 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Office 365 Account Takeover - - Suspicious Emails - - Data Destruction - asset_type: O365 Tenant - mitre_attack_id: - - T1070.008 - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Account Takeover + - Suspicious Emails + - Data Destruction + asset_type: O365 Tenant + mitre_attack_id: + - T1070.008 + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_suspect_email_actions/o365_exchange_suspect_events.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_suspect_email_actions/o365_exchange_suspect_events.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_email_new_inbox_rule_created.yml b/detections/cloud/o365_email_new_inbox_rule_created.yml index e5d98fd968..e35112962f 100644 --- a/detections/cloud/o365_email_new_inbox_rule_created.yml +++ b/detections/cloud/o365_email_new_inbox_rule_created.yml @@ -1,64 +1,64 @@ name: O365 Email New Inbox Rule Created id: 449f525a-7b42-47be-96a7-d9724e336c19 -version: 2 -date: '2025-05-02' +version: 3 +date: '2026-02-25' author: Steven Dick status: production type: Anomaly description: The following analytic identifies the creation of new email inbox rules in an Office 365 environment. It detects events logged under New-InboxRule and Set-InboxRule operations within the o365_management_activity data source, focusing on parameters that may indicate mail forwarding, removal, or obfuscation. Inbox rule creation is a typical end-user activity however attackers also leverage this technique for multiple reasons. -data_source: -- Office 365 Universal Audit Log +data_source: + - Office 365 Universal Audit Log search: |- - `o365_management_activity` Workload=Exchange AND (Operation=New-InboxRule OR Operation=Set-InboxRule) Parameters{}.Name IN (SoftDeleteMessage,DeleteMessage,ForwardTo,ForwardAsAttachmentTo,RedirectTo,MoveToFolder,CopyToFolder) - | eval file_path = mvappend(MoveToFolder,CopyToFolder), recipient=mvappend(ForwardTo, ForwardAsAttachmentTo, RedirectTo), user = lower(UserId), signature = Operation, src = if(match(ClientIP, "^\["), ltrim(mvindex(split(ClientIP, "]:"), 0), "["), mvindex(split(ClientIP,":"),0)), desc = Name, action = 'Parameters{}.Name' + `o365_management_activity` Workload=Exchange AND (Operation=New-InboxRule OR Operation=Set-InboxRule) Parameters{}.Name IN (SoftDeleteMessage,DeleteMessage,ForwardTo,ForwardAsAttachmentTo,RedirectTo,MoveToFolder,CopyToFolder) + | eval file_path = mvappend(MoveToFolder,CopyToFolder), recipient=mvappend(ForwardTo, ForwardAsAttachmentTo, RedirectTo), user = lower(UserId), signature = Operation, src = if(match(ClientIP, "^\["), ltrim(mvindex(split(ClientIP, "]:"), 0), "["), mvindex(split(ClientIP,":"),0)), desc = Name, action = 'Parameters{}.Name' - | stats values(action) as action, values(src) as src, values(recipient) as recipient, values(file_path) as file_path, count, min(_time) as firstTime, max(_time) as lastTime by user, signature, desc - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_email_new_inbox_rule_created_filter` + | stats values(action) as action, values(src) as src, values(recipient) as recipient, values(file_path) as file_path, count, min(_time) as firstTime, max(_time) as lastTime by user, signature, desc + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_email_new_inbox_rule_created_filter` how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. known_false_positives: Users may create email rules for legitimate purposes. Filter as needed. references: -- https://attack.mitre.org/techniques/T1114/ -- https://www.hhs.gov/sites/default/files/help-desk-social-engineering-sector-alert-tlpclear.pdf -- https://intelligence.abnormalsecurity.com/attack-library/threat-actor-convincingly-impersonates-employee-requesting-direct-deposit-update-in-likely-ai-generated-attack + - https://attack.mitre.org/techniques/T1114/ + - https://www.hhs.gov/sites/default/files/help-desk-social-engineering-sector-alert-tlpclear.pdf + - https://intelligence.abnormalsecurity.com/attack-library/threat-actor-convincingly-impersonates-employee-requesting-direct-deposit-update-in-likely-ai-generated-attack drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate Inbox Rules for $user$ - search: '`o365_management_activity` Workload=Exchange AND (Operation=New-InboxRule OR Operation=Set-InboxRule) AND UserId = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate Inbox Rules for $user$ + search: '`o365_management_activity` Workload=Exchange AND (Operation=New-InboxRule OR Operation=Set-InboxRule) AND UserId = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A new email inbox rule was created for $user$ - risk_objects: - - field: user - type: user - score: 10 - threat_objects: - - field: desc - type: signature + message: A new email inbox rule was created for $user$ + risk_objects: + - field: user + type: user + score: 10 + threat_objects: + - field: desc + type: signature tags: - analytic_story: - - Office 365 Collection Techniques - asset_type: O365 Tenant - mitre_attack_id: - - T1114.003 - - T1564.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: audit + analytic_story: + - Office 365 Collection Techniques + asset_type: O365 Tenant + mitre_attack_id: + - T1114.003 + - T1564.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: audit tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_suspect_email_actions/o365_exchange_suspect_events.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_suspect_email_actions/o365_exchange_suspect_events.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_email_password_and_payroll_compromise_behavior.yml b/detections/cloud/o365_email_password_and_payroll_compromise_behavior.yml index 05f537b32d..fa1f58fedf 100644 --- a/detections/cloud/o365_email_password_and_payroll_compromise_behavior.yml +++ b/detections/cloud/o365_email_password_and_payroll_compromise_behavior.yml @@ -1,86 +1,86 @@ name: O365 Email Password and Payroll Compromise Behavior id: e36de71a-6bdc-4002-98ff-e3e51b0d8f96 -version: 3 -date: '2026-01-14' +version: 4 +date: '2026-02-25' author: Steven Dick status: production type: TTP description: The following analytic identifies when an O365 email recipient receives and then deletes emails for the combination of both password and banking/payroll changes within a short period. This behavior may indicate a compromised account where the threat actor is attempting to redirect the victims payroll to an attacker controlled bank account. -data_source: -- Office 365 Universal Audit Log -- Office 365 Reporting Message Trace +data_source: + - Office 365 Universal Audit Log + - Office 365 Reporting Message Trace search: |- - `o365_messagetrace` subject IN ("*banking*","*direct deposit*","*pay-to*","*password *","*passcode *","*OTP *","*MFA *","*Account Recovery*") - | eval mailtime = _time - | bin _time span=4hr - | eval user = lower(RecipientAddress) - | eval InternetMessageId = lower(MessageId) - | join InternetMessageId, user max=0 - [ - | search `o365_management_activity` Workload=Exchange Operation IN ("SoftDelete","HardDelete") - | spath path=AffectedItems{} output=AffectedItemSplit - | fields _time,ClientIP,ClientInfoString,UserId,Operation,ResultStatus,MailboxOwnerUPN,AffectedItemSplit - | mvexpand AffectedItemSplit | spath input=AffectedItemSplit - | search Subject IN ("*banking*","*direct deposit*","*pay-to*","*password *","*passcode *","*OTP *","*MFA *","*Account Recovery*") - | eval deltime = _time + `o365_messagetrace` subject IN ("*banking*","*direct deposit*","*pay-to*","*password *","*passcode *","*OTP *","*MFA *","*Account Recovery*") + | eval mailtime = _time | bin _time span=4hr - | eval InternetMessageId = lower(InternetMessageId), user = lower(UserId) - ] - | stats values(ClientInfoString) as http_user_agent, values(ClientIP) as src, values(Subject) as subject, dc(Subject) as subject_count, values(Operation) as action, values(ResultStatus) as result, count, min(mailtime) as firstTime, max(deltime) as lastTime by user,_time - | search subject IN ("*banking*","*direct deposit*","*pay-to*") AND subject IN ("*password *","*passcode *","*OTP *","*MFA *","*Account Recovery*") - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_email_password_and_payroll_compromise_behavior_filter` + | eval user = lower(RecipientAddress) + | eval InternetMessageId = lower(MessageId) + | join InternetMessageId, user max=0 + [ + | search `o365_management_activity` Workload=Exchange Operation IN ("SoftDelete","HardDelete") + | spath path=AffectedItems{} output=AffectedItemSplit + | fields _time,ClientIP,ClientInfoString,UserId,Operation,ResultStatus,MailboxOwnerUPN,AffectedItemSplit + | mvexpand AffectedItemSplit | spath input=AffectedItemSplit + | search Subject IN ("*banking*","*direct deposit*","*pay-to*","*password *","*passcode *","*OTP *","*MFA *","*Account Recovery*") + | eval deltime = _time + | bin _time span=4hr + | eval InternetMessageId = lower(InternetMessageId), user = lower(UserId) + ] + | stats values(ClientInfoString) as http_user_agent, values(ClientIP) as src, values(Subject) as subject, dc(Subject) as subject_count, values(Operation) as action, values(ResultStatus) as result, count, min(mailtime) as firstTime, max(deltime) as lastTime by user,_time + | search subject IN ("*banking*","*direct deposit*","*pay-to*") AND subject IN ("*password *","*passcode *","*OTP *","*MFA *","*Account Recovery*") + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_email_password_and_payroll_compromise_behavior_filter` how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events AND Message Trace events. known_false_positives: No false positives have been identified at this time. references: -- https://attack.mitre.org/techniques/T1114/ -- https://www.hhs.gov/sites/default/files/help-desk-social-engineering-sector-alert-tlpclear.pdf -- https://intelligence.abnormalsecurity.com/attack-library/threat-actor-convincingly-impersonates-employee-requesting-direct-deposit-update-in-likely-ai-generated-attack + - https://attack.mitre.org/techniques/T1114/ + - https://www.hhs.gov/sites/default/files/help-desk-social-engineering-sector-alert-tlpclear.pdf + - https://intelligence.abnormalsecurity.com/attack-library/threat-actor-convincingly-impersonates-employee-requesting-direct-deposit-update-in-likely-ai-generated-attack drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate Email for $user$ - search: '`o365_messagetrace` subject IN ("*banking*","*direct deposit*","*password*","*passcode*") RecipientAddress = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate Email for $user$ + search: '`o365_messagetrace` subject IN ("*banking*","*direct deposit*","*password*","*passcode*") RecipientAddress = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The user $user$ received and deleted password and payroll change emails within a short timeframe - risk_objects: - - field: user - type: user - score: 90 - threat_objects: - - field: src - type: ip_address + message: The user $user$ received and deleted password and payroll change emails within a short timeframe + risk_objects: + - field: user + type: user + score: 90 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Office 365 Account Takeover - - Office 365 Collection Techniques - - Suspicious Emails - - Data Destruction - asset_type: O365 Tenant - mitre_attack_id: - - T1070.008 - - T1485 - - T1114.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Account Takeover + - Office 365 Collection Techniques + - Suspicious Emails + - Data Destruction + asset_type: O365 Tenant + mitre_attack_id: + - T1070.008 + - T1485 + - T1114.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_suspect_email_actions/o365_exchange_suspect_events.log - source: o365 - sourcetype: o365:management:activity - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_suspect_email_actions/o365_messagetrace_suspect_events.log - source: o365_messagetrace - sourcetype: o365:reporting:messagetrace + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_suspect_email_actions/o365_exchange_suspect_events.log + source: o365 + sourcetype: o365:management:activity + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_suspect_email_actions/o365_messagetrace_suspect_events.log + source: o365_messagetrace + sourcetype: o365:reporting:messagetrace diff --git a/detections/cloud/o365_email_receive_and_hard_delete_takeover_behavior.yml b/detections/cloud/o365_email_receive_and_hard_delete_takeover_behavior.yml index 8246169862..e2e5e74010 100644 --- a/detections/cloud/o365_email_receive_and_hard_delete_takeover_behavior.yml +++ b/detections/cloud/o365_email_receive_and_hard_delete_takeover_behavior.yml @@ -1,87 +1,87 @@ name: O365 Email Receive and Hard Delete Takeover Behavior id: b66aeaa4-586f-428b-8a2b-c4fd3039d8d3 -version: 2 -date: '2025-05-02' +version: 3 +date: '2026-02-25' author: Steven Dick status: production type: Anomaly description: The following analytic identifies when an O365 email recipient receives and then deletes emails related to password or banking/payroll changes within a short period. This behavior may indicate a compromised account where the threat actor is attempting to redirect the victims payroll to an attacker controlled bank account. -data_source: -- Office 365 Universal Audit Log -- Office 365 Reporting Message Trace +data_source: + - Office 365 Universal Audit Log + - Office 365 Reporting Message Trace search: |- - `o365_messagetrace` subject IN ("*banking*","*direct deposit*","*pay-to*","*password *","*passcode *","*OTP *","*MFA *","*Account Recovery*") - | eval mailtime = _time - | bin _time span=4hr - | eval user = lower(RecipientAddress) - | eval InternetMessageId = lower(MessageId) - | join InternetMessageId, user max=0 - [ - | search `o365_management_activity` Workload=Exchange Operation IN ("HardDelete") AND Folder.Path IN ("\\Sent Items","\\Recoverable Items\\Deletions") - | spath path=AffectedItems{} output=AffectedItemSplit - | fields _time,ClientProcessName,ClientIPAddress,ClientInfoString,UserId,Operation,ResultStatus,MailboxOwnerUPN,AffectedItemSplit,Folder.Path - | mvexpand AffectedItemSplit | spath input=AffectedItemSplit - | search Subject IN ("*banking*","*direct deposit*","*pay-to*","*password *","*passcode *","*OTP *","*MFA *","*Account Recovery*") - | eval deltime = _time + `o365_messagetrace` subject IN ("*banking*","*direct deposit*","*pay-to*","*password *","*passcode *","*OTP *","*MFA *","*Account Recovery*") + | eval mailtime = _time | bin _time span=4hr - | eval InternetMessageId = lower(InternetMessageId), user = lower(UserId), subject = Subject - ] - | stats values(ClientIPAddress) as src, values(ClientInfoString) as http_user_agent, values(Folder.Path) as file_path, values(Operation) as signature, values(ResultStatus) as result, values(InternetMessageId) as signature_id, count, min(mailtime) as firstTime, max(deltime) as lastTime by user,subject - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_email_receive_and_hard_delete_takeover_behavior_filter` + | eval user = lower(RecipientAddress) + | eval InternetMessageId = lower(MessageId) + | join InternetMessageId, user max=0 + [ + | search `o365_management_activity` Workload=Exchange Operation IN ("HardDelete") AND Folder.Path IN ("\\Sent Items","\\Recoverable Items\\Deletions") + | spath path=AffectedItems{} output=AffectedItemSplit + | fields _time,ClientProcessName,ClientIPAddress,ClientInfoString,UserId,Operation,ResultStatus,MailboxOwnerUPN,AffectedItemSplit,Folder.Path + | mvexpand AffectedItemSplit | spath input=AffectedItemSplit + | search Subject IN ("*banking*","*direct deposit*","*pay-to*","*password *","*passcode *","*OTP *","*MFA *","*Account Recovery*") + | eval deltime = _time + | bin _time span=4hr + | eval InternetMessageId = lower(InternetMessageId), user = lower(UserId), subject = Subject + ] + | stats values(ClientIPAddress) as src, values(ClientInfoString) as http_user_agent, values(Folder.Path) as file_path, values(Operation) as signature, values(ResultStatus) as result, values(InternetMessageId) as signature_id, count, min(mailtime) as firstTime, max(deltime) as lastTime by user,subject + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_email_receive_and_hard_delete_takeover_behavior_filter` how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events AND Message Trace events. known_false_positives: Possible new user/account onboarding processes. references: -- https://attack.mitre.org/techniques/T1114/ -- https://www.hhs.gov/sites/default/files/help-desk-social-engineering-sector-alert-tlpclear.pdf -- https://intelligence.abnormalsecurity.com/attack-library/threat-actor-convincingly-impersonates-employee-requesting-direct-deposit-update-in-likely-ai-generated-attack + - https://attack.mitre.org/techniques/T1114/ + - https://www.hhs.gov/sites/default/files/help-desk-social-engineering-sector-alert-tlpclear.pdf + - https://intelligence.abnormalsecurity.com/attack-library/threat-actor-convincingly-impersonates-employee-requesting-direct-deposit-update-in-likely-ai-generated-attack drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate Email for $user$ - search: '`o365_messagetrace` subject IN ("*banking*","*direct deposit*","*pay-to*","*password *","*passcode *","*OTP *","*MFA *","*Account Recovery*") AND RecipientAddress = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate Email for $user$ + search: '`o365_messagetrace` subject IN ("*banking*","*direct deposit*","*pay-to*","*password *","*passcode *","*OTP *","*MFA *","*Account Recovery*") AND RecipientAddress = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The user $user$ received and deleted an email within a short timeframe titled [$subject$] which may contain password or banking information - risk_objects: - - field: user - type: user - score: 80 - threat_objects: - - field: subject - type: email_subject - - field: src - type: ip_address + message: The user $user$ received and deleted an email within a short timeframe titled [$subject$] which may contain password or banking information + risk_objects: + - field: user + type: user + score: 80 + threat_objects: + - field: subject + type: email_subject + - field: src + type: ip_address tags: - analytic_story: - - Office 365 Account Takeover - - Office 365 Collection Techniques - - Suspicious Emails - - Data Destruction - asset_type: O365 Tenant - mitre_attack_id: - - T1070.008 - - T1485 - - T1114.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Account Takeover + - Office 365 Collection Techniques + - Suspicious Emails + - Data Destruction + asset_type: O365 Tenant + mitre_attack_id: + - T1070.008 + - T1485 + - T1114.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_suspect_email_actions/o365_exchange_suspect_events.log - source: o365 - sourcetype: o365:management:activity - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_suspect_email_actions/o365_messagetrace_suspect_events.log - source: o365_messagetrace - sourcetype: o365:reporting:messagetrace + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_suspect_email_actions/o365_exchange_suspect_events.log + source: o365 + sourcetype: o365:management:activity + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_suspect_email_actions/o365_messagetrace_suspect_events.log + source: o365_messagetrace + sourcetype: o365:reporting:messagetrace diff --git a/detections/cloud/o365_email_reported_by_admin_found_malicious.yml b/detections/cloud/o365_email_reported_by_admin_found_malicious.yml index d258d91941..34b0f81df6 100644 --- a/detections/cloud/o365_email_reported_by_admin_found_malicious.yml +++ b/detections/cloud/o365_email_reported_by_admin_found_malicious.yml @@ -1,70 +1,67 @@ name: O365 Email Reported By Admin Found Malicious id: 94396c3e-7728-422a-9956-e4b77b53dbdf -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Steven Dick status: production type: TTP -description: The following analytic detects when an email manually submitted to Microsoft - through the Security & Compliance portal is found to be malicious. This capability - is an enhanced protection feature that can be used within o365 tenants by administrative - users to report potentially malicious emails. This correlation looks for any submission - that returns a Phish or Malware verdict upon submission. +description: The following analytic detects when an email manually submitted to Microsoft through the Security & Compliance portal is found to be malicious. This capability is an enhanced protection feature that can be used within o365 tenants by administrative users to report potentially malicious emails. This correlation looks for any submission that returns a Phish or Malware verdict upon submission. data_source: -- Office 365 Universal Audit Log -search: '`o365_management_activity` Workload=SecurityComplianceCenter Operation=AdminSubmission - | search RescanVerdict IN (Phish,Malware) | rename Id as signature_id, SenderIP - as src, Recipients{} as dest_user, P1Sender as src_user | fillnull | stats count - min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product - signature signature_id dest_user src_user Subject SubmissionContent | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `o365_email_reported_by_admin_found_malicious_filter`' -how_to_implement: You must install splunk Microsoft Office 365 add-on. This search - works with o365:management:activity + - Office 365 Universal Audit Log +search: |- + `o365_management_activity` Workload=SecurityComplianceCenter Operation=AdminSubmission + | search RescanVerdict IN (Phish,Malware) + | rename Id as signature_id, SenderIP as src, Recipients{} as dest_user, P1Sender as src_user + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product signature + signature_id dest_user src_user + Subject SubmissionContent + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_email_reported_by_admin_found_malicious_filter` +how_to_implement: You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity known_false_positives: Administrators that submit known phishing training exercises. references: -- https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/submissions-outlook-report-messages?view=o365-worldwide + - https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/submissions-outlook-report-messages?view=o365-worldwide drilldown_searches: -- name: View the detection results for - "$src_user$" and "$user$" - search: '%original_detection_search% | search src_user = "$src_user$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_user$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_user$" and "$user$" + search: '%original_detection_search% | search src_user = "$src_user$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_user$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: O365 security admin $user$ manually reported a suspicious email from $src_user$ - risk_objects: - - field: src_user - type: user - score: 50 - - field: user - type: user - score: 50 - threat_objects: - - field: Subject - type: email_subject + message: O365 security admin $user$ manually reported a suspicious email from $src_user$ + risk_objects: + - field: src_user + type: user + score: 50 + - field: user + type: user + score: 50 + threat_objects: + - field: Subject + type: email_subject tags: - analytic_story: - - Spearphishing Attachments - - Suspicious Emails - asset_type: O365 Tenant - mitre_attack_id: - - T1566.001 - - T1566.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Spearphishing Attachments + - Suspicious Emails + asset_type: O365 Tenant + mitre_attack_id: + - T1566.001 + - T1566.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_email_reported_by_user_found_malicious.yml b/detections/cloud/o365_email_reported_by_user_found_malicious.yml index 13f4ef7958..c76fd41bf8 100644 --- a/detections/cloud/o365_email_reported_by_user_found_malicious.yml +++ b/detections/cloud/o365_email_reported_by_user_found_malicious.yml @@ -1,74 +1,73 @@ name: O365 Email Reported By User Found Malicious id: 7698b945-238e-4bb9-b172-81f5ca1685a1 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Steven Dick status: production type: TTP -description: The following analytic detects when an email submitted to Microsoft using - the built-in report button in Outlook is found to be malicious. This capability - is an enhanced protection feature that can be used within o365 tenants by users - to report potentially malicious emails. This correlation looks for any submission - that returns a Phish or Malware verdict upon submission. +description: The following analytic detects when an email submitted to Microsoft using the built-in report button in Outlook is found to be malicious. This capability is an enhanced protection feature that can be used within o365 tenants by users to report potentially malicious emails. This correlation looks for any submission that returns a Phish or Malware verdict upon submission. data_source: -- Office 365 Universal Audit Log -search: '`o365_management_activity` Workload=SecurityComplianceCenter Operation=AlertEntityGenerated - Name="Email reported by user as*" | fromjson Data | rename _raw AS temp etps AS - _raw | extract pairdelim=";" kvdelim=":" | rename _raw AS etps temp AS _raw | search - RescanVerdict IN (Phish,Malware) | rex field=tsd "\<(?.+)\>" | eval src_user - = case(isnull(src_user),tsd,true(),src_user) | rename Name as signature, AlertId - as signature_id, AlertEntityId as user, tsd as sender, ms as subject | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account - vendor_product signature signature_id src_user sender subject | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `o365_email_reported_by_user_found_malicious_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. You must deploy/allow the usage of the Microsoft - Office Report A Message function. + - Office 365 Universal Audit Log +search: |- + `o365_management_activity` Workload=SecurityComplianceCenter Operation=AlertEntityGenerated Name="Email reported by user as*" + | fromjson Data + | rename _raw AS temp etps AS _raw + | extract pairdelim=";" kvdelim=":" + | rename _raw AS etps temp AS _raw + | search RescanVerdict IN (Phish,Malware) + | rex field=tsd "\<(?.+)\>" + | eval src_user = case(isnull(src_user),tsd,true(),src_user) + | rename Name as signature, AlertId as signature_id, AlertEntityId as user, tsd as sender, ms as subject + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product signature + signature_id src_user sender + subject + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_email_reported_by_user_found_malicious_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. You must deploy/allow the usage of the Microsoft Office Report A Message function. known_false_positives: No false positives have been identified at this time. references: -- https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/submissions-outlook-report-messages?view=o365-worldwide + - https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/submissions-outlook-report-messages?view=o365-worldwide drilldown_searches: -- name: View the detection results for - "$src_user$" and "$user$" - search: '%original_detection_search% | search src_user = "$src_user$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_user$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_user$" and "$user$" + search: '%original_detection_search% | search src_user = "$src_user$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_user$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The user $user$ reported an email classified from $src_user$ - risk_objects: - - field: src_user - type: user - score: 75 - - field: user - type: user - score: 75 - threat_objects: - - field: subject - type: email_subject + message: The user $user$ reported an email classified from $src_user$ + risk_objects: + - field: src_user + type: user + score: 75 + - field: user + type: user + score: 75 + threat_objects: + - field: subject + type: email_subject tags: - analytic_story: - - Spearphishing Attachments - - Suspicious Emails - asset_type: O365 Tenant - mitre_attack_id: - - T1566.001 - - T1566.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Spearphishing Attachments + - Suspicious Emails + asset_type: O365 Tenant + mitre_attack_id: + - T1566.001 + - T1566.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_email_security_feature_changed.yml b/detections/cloud/o365_email_security_feature_changed.yml index 39902b1bc3..c233776033 100644 --- a/detections/cloud/o365_email_security_feature_changed.yml +++ b/detections/cloud/o365_email_security_feature_changed.yml @@ -1,67 +1,61 @@ name: O365 Email Security Feature Changed id: 4d28013d-3a0f-4d65-a33f-4e8009fee0ae -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Steven Dick status: production type: TTP -description: The following analytic identifies when specific O365 advanced security - settings are altered within the Office 365 tenant. If an attacker successfully disables - O365 security settings, they can operate within the tenant with reduced risk of - detection. This can lead to unauthorized data access, data exfiltration, account - compromise, or other malicious activities without leaving a detailed audit trail. +description: The following analytic identifies when specific O365 advanced security settings are altered within the Office 365 tenant. If an attacker successfully disables O365 security settings, they can operate within the tenant with reduced risk of detection. This can lead to unauthorized data access, data exfiltration, account compromise, or other malicious activities without leaving a detailed audit trail. data_source: -- Office 365 Universal Audit Log -search: '`o365_management_activity` Workload=Exchange AND Operation IN ("Set-*","Disable-*","New-*","Remove-*") - Operation IN ("*AntiPhish*","*SafeLink*","*SafeAttachment*","*Malware*") | rename - Id as object_id, UserId as user, Operation as signature, ObjectId as object | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account - vendor_product signature object_id object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `o365_email_security_feature_changed_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Administrators might alter features for troubleshooting, performance - reasons, or other administrative tasks. Filter as needed. + - Office 365 Universal Audit Log +search: |- + `o365_management_activity` Workload=Exchange AND Operation IN ("Set-*","Disable-*","New-*","Remove-*") Operation IN ("*AntiPhish*","*SafeLink*","*SafeAttachment*","*Malware*") + | rename Id as object_id, UserId as user, Operation as signature, ObjectId as object + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product signature + object_id object + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_email_security_feature_changed_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Administrators might alter features for troubleshooting, performance reasons, or other administrative tasks. Filter as needed. references: -- https://learn.microsoft.com/en-us/entra/fundamentals/security-defaults -- https://attack.mitre.org/techniques/T1562/008/ + - https://learn.microsoft.com/en-us/entra/fundamentals/security-defaults + - https://attack.mitre.org/techniques/T1562/008/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An O365 security object [$object$] was altered by user $user$ using $signature$ - risk_objects: - - field: user - type: user - score: 25 - threat_objects: [] + message: An O365 security object [$object$] was altered by user $user$ using $signature$ + risk_objects: + - field: user + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Office 365 Persistence Mechanisms - - Office 365 Account Takeover - asset_type: O365 Tenant - mitre_attack_id: - - T1562.001 - - T1562.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Persistence Mechanisms + - Office 365 Account Takeover + asset_type: O365 Tenant + mitre_attack_id: + - T1562.001 + - T1562.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_email_send_and_hard_delete_exfiltration_behavior.yml b/detections/cloud/o365_email_send_and_hard_delete_exfiltration_behavior.yml index 56ce1f4716..ff1aa7311a 100644 --- a/detections/cloud/o365_email_send_and_hard_delete_exfiltration_behavior.yml +++ b/detections/cloud/o365_email_send_and_hard_delete_exfiltration_behavior.yml @@ -1,89 +1,89 @@ name: O365 Email Send and Hard Delete Exfiltration Behavior id: dd7798cf-c4f5-4114-ad0f-beacd9a33708 -version: 2 -date: '2025-05-02' +version: 3 +date: '2026-02-25' author: Steven Dick status: production type: Anomaly description: The following analytic identifies when an O365 email account sends and then hard deletes an email to an external recipient within a short period (within 1 hour). This behavior may indicate a compromised account where the threat actor is attempting to remove forensic artifacts or evidence of exfiltration activity. This behavior is often seen when threat actors want to reduce the probability of detection by the compromised account owner. -data_source: -- Office 365 Universal Audit Log -- Office 365 Reporting Message Trace +data_source: + - Office 365 Universal Audit Log + - Office 365 Reporting Message Trace search: |- - `o365_messagetrace` Status=Delivered - | eval mailtime = _time - | bin _time span=1hr - | eval user = lower(SenderAddress), recipient = lower(RecipientAddress) - | eval InternetMessageId = lower(MessageId) - | join InternetMessageId, user, max=0 - [ - | search `o365_management_activity` Workload=Exchange (Operation IN ("Send*")) OR (Operation IN ("HardDelete") AND Folder.Path IN ("\\Sent Items","\\Recoverable Items\\Deletions")) - | eval user = lower(UserId), sender = lower(CASE(isnotnull(SendAsUserSmtp),SendAsUserSmtp,isnotnull(SendOnBehalfOfUserSmtp),SendOnBehalfOfUserSmtp,true(),MailboxOwnerUPN)), subject = trim(CASE(Operation IN ("Send","SendAs","SendOnBehalf"),'Item.Subject',Operation IN ("SoftDelete","HardDelete"),'AffectedItems{}.Subject')), -time = _time,file_name = CASE(Operation IN ("Send","SendAs","SendOnBehalf"),split('Item.Attachments',"; "),Operation IN ("SoftDelete","HardDelete"),split('AffectedItems{}.Attachments',"; ")), file_size = CASE(Operation IN ("Send","SendAs","SendOnBehalf"),round(tonumber('Item.SizeInBytes')/1024/1024,2),true(),round(tonumber(replace(file_name, "(.+)\s\((\d+)(b\)$)", "\2"))/1024/1024,2)), InternetMessageId = lower('Item.InternetMessageId') - | eval sendtime = CASE(Operation IN ("Send","SendAs","SendOnBehalf"),_time) - | eval deltime = CASE(Operation IN ("SoftDelete","HardDelete"),_time) + `o365_messagetrace` Status=Delivered + | eval mailtime = _time | bin _time span=1hr - | stats values(sender) as sender, values(ClientInfoString) as http_user_agent, values(InternetMessageId) as InternetMessageId, values(file_name) as file_name, sum(file_size) as file_size, values(sendtime) as firstTime, values(deltime) as lastTime values(Operation) as signature, dc(Operation) as opcount, count by _time,subject,user - | where opcount > 1 AND firstTime < lastTime - ] - | stats values(sender) as sender, values(http_user_agent) as http_user_agent, values(signature) as signature, values(file_name) as file_name, sum(file_size) as file_size, min(firstTime) as firstTime, max(lastTime) as lastTime count by subject,user,recipient,Organization - | eval externalRecipient = if(match(lower(recipient),mvindex(split(lower(Organization),"."),0)),0,1) - | where externalRecipient = 1 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_email_send_and_hard_delete_exfiltration_behavior_filter` + | eval user = lower(SenderAddress), recipient = lower(RecipientAddress) + | eval InternetMessageId = lower(MessageId) + | join InternetMessageId, user, max=0 + [ + | search `o365_management_activity` Workload=Exchange (Operation IN ("Send*")) OR (Operation IN ("HardDelete") AND Folder.Path IN ("\\Sent Items","\\Recoverable Items\\Deletions")) + | eval user = lower(UserId), sender = lower(CASE(isnotnull(SendAsUserSmtp),SendAsUserSmtp,isnotnull(SendOnBehalfOfUserSmtp),SendOnBehalfOfUserSmtp,true(),MailboxOwnerUPN)), subject = trim(CASE(Operation IN ("Send","SendAs","SendOnBehalf"),'Item.Subject',Operation IN ("SoftDelete","HardDelete"),'AffectedItems{}.Subject')), -time = _time,file_name = CASE(Operation IN ("Send","SendAs","SendOnBehalf"),split('Item.Attachments',"; "),Operation IN ("SoftDelete","HardDelete"),split('AffectedItems{}.Attachments',"; ")), file_size = CASE(Operation IN ("Send","SendAs","SendOnBehalf"),round(tonumber('Item.SizeInBytes')/1024/1024,2),true(),round(tonumber(replace(file_name, "(.+)\s\((\d+)(b\)$)", "\2"))/1024/1024,2)), InternetMessageId = lower('Item.InternetMessageId') + | eval sendtime = CASE(Operation IN ("Send","SendAs","SendOnBehalf"),_time) + | eval deltime = CASE(Operation IN ("SoftDelete","HardDelete"),_time) + | bin _time span=1hr + | stats values(sender) as sender, values(ClientInfoString) as http_user_agent, values(InternetMessageId) as InternetMessageId, values(file_name) as file_name, sum(file_size) as file_size, values(sendtime) as firstTime, values(deltime) as lastTime values(Operation) as signature, dc(Operation) as opcount, count by _time,subject,user + | where opcount > 1 AND firstTime < lastTime + ] + | stats values(sender) as sender, values(http_user_agent) as http_user_agent, values(signature) as signature, values(file_name) as file_name, sum(file_size) as file_size, min(firstTime) as firstTime, max(lastTime) as lastTime count by subject,user,recipient,Organization + | eval externalRecipient = if(match(lower(recipient),mvindex(split(lower(Organization),"."),0)),0,1) + | where externalRecipient = 1 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_email_send_and_hard_delete_exfiltration_behavior_filter` how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events AND Message Trace events. known_false_positives: Users that habitually/proactively cleaning the recoverable items folder may trigger this alert. references: -- https://attack.mitre.org/techniques/T1114/ -- https://www.hhs.gov/sites/default/files/help-desk-social-engineering-sector-alert-tlpclear.pdf -- https://intelligence.abnormalsecurity.com/attack-library/threat-actor-convincingly-impersonates-employee-requesting-direct-deposit-update-in-likely-ai-generated-attack + - https://attack.mitre.org/techniques/T1114/ + - https://www.hhs.gov/sites/default/files/help-desk-social-engineering-sector-alert-tlpclear.pdf + - https://intelligence.abnormalsecurity.com/attack-library/threat-actor-convincingly-impersonates-employee-requesting-direct-deposit-update-in-likely-ai-generated-attack drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate Email for $user$ - search: '`o365_management_activity` Workload=Exchange (Operation IN ("Send")) OR (Operation IN ("HardDelete") AND Folder.Path IN ("\\Sent Items","\\Recoverable Items\\Deletions")) AND UserId = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate Email for $user$ + search: '`o365_management_activity` Workload=Exchange (Operation IN ("Send")) OR (Operation IN ("HardDelete") AND Folder.Path IN ("\\Sent Items","\\Recoverable Items\\Deletions")) AND UserId = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The user $user$ sent and hard deleted an email to an external recipient [$recipient$] within a short timeframe - risk_objects: - - field: user - type: user - score: 40 - - field: recipient - type: user - score: 40 - threat_objects: - - field: subject - type: email_subject + message: The user $user$ sent and hard deleted an email to an external recipient [$recipient$] within a short timeframe + risk_objects: + - field: user + type: user + score: 40 + - field: recipient + type: user + score: 40 + threat_objects: + - field: subject + type: email_subject tags: - analytic_story: - - Office 365 Account Takeover - - Office 365 Collection Techniques - - Suspicious Emails - - Data Destruction - asset_type: O365 Tenant - mitre_attack_id: - - T1114.001 - - T1070.008 - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Account Takeover + - Office 365 Collection Techniques + - Suspicious Emails + - Data Destruction + asset_type: O365 Tenant + mitre_attack_id: + - T1114.001 + - T1070.008 + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_suspect_email_actions/o365_exchange_suspect_events.log - source: o365 - sourcetype: o365:management:activity - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_suspect_email_actions/o365_messagetrace_suspect_events.log - source: o365_messagetrace - sourcetype: o365:reporting:messagetrace + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_suspect_email_actions/o365_exchange_suspect_events.log + source: o365 + sourcetype: o365:management:activity + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_suspect_email_actions/o365_messagetrace_suspect_events.log + source: o365_messagetrace + sourcetype: o365:reporting:messagetrace diff --git a/detections/cloud/o365_email_send_and_hard_delete_suspicious_behavior.yml b/detections/cloud/o365_email_send_and_hard_delete_suspicious_behavior.yml index 56a7c26e0f..51fd362e5e 100644 --- a/detections/cloud/o365_email_send_and_hard_delete_suspicious_behavior.yml +++ b/detections/cloud/o365_email_send_and_hard_delete_suspicious_behavior.yml @@ -1,73 +1,73 @@ name: O365 Email Send and Hard Delete Suspicious Behavior id: c97b3d72-0a47-46f9-b742-b89f1cc2d551 -version: 2 -date: '2025-05-02' +version: 3 +date: '2026-02-25' author: Steven Dick status: production type: Anomaly description: The following analytic identifies when an O365 email account sends and then hard deletes email with within a short period (within 1 hour). This behavior may indicate a compromised account where the threat actor is attempting to remove forensic artifacts or evidence of activity. Threat actors often use this technique to prevent defenders and victims from knowing the account has been compromised. --- Some account owner legitimate behaviors can trigger this alert, however these actions may not be aligned with organizational expectations / best practice behaviors. -data_source: -- Office 365 Universal Audit Log +data_source: + - Office 365 Universal Audit Log search: |- - `o365_management_activity` Workload=Exchange (Operation IN ("Send*")) OR (Operation IN ("HardDelete") AND Folder.Path IN ("\\Sent Items","\\Recoverable Items\\Deletions")) - | eval user = lower(UserId), sender = lower(CASE(isnotnull(SendAsUserSmtp),SendAsUserSmtp,isnotnull(SendOnBehalfOfUserSmtp),SendOnBehalfOfUserSmtp,true(),MailboxOwnerUPN)), subject = trim(CASE(Operation IN ("Send","SendAs","SendOnBehalf"),'Item.Subject',Operation IN ("SoftDelete","HardDelete"),'AffectedItems{}.Subject')), -time = _time,file_name = CASE(Operation IN ("Send","SendAs","SendOnBehalf"),split('Item.Attachments',"; "),Operation IN ("SoftDelete","HardDelete"),split('AffectedItems{}.Attachments',"; ")), file_size = CASE(Operation IN ("Send","SendAs","SendOnBehalf"),round(tonumber('Item.SizeInBytes')/1024/1024,2),true(),round(tonumber(replace(file_name, "(.+)\s\((\d+)(b\)$)", "\2"))/1024/1024,2)) - | eval sendtime = CASE(Operation IN ("Send","SendAs","SendOnBehalf"),_time) - | eval deltime = CASE(Operation IN ("SoftDelete","HardDelete"),_time) - | stats values(sender) as sender, values(ClientIPAddress) as src, values(ClientInfoString) as http_user_agent, values(Operation) as signature, values(file_name) as file_name, sum(file_size) as file_size, values(Folder.Path) as file_path, min(sendtime) as firstTime, max(deltime) as lastTime, dc(Operation) as opcount, count by subject,user - | eval timediff = tonumber(lastTime) - tonumber(firstTime) - | where opcount > 1 AND firstTime < lastTime AND timediff < 3600 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_email_send_and_hard_delete_suspicious_behavior_filter` + `o365_management_activity` Workload=Exchange (Operation IN ("Send*")) OR (Operation IN ("HardDelete") AND Folder.Path IN ("\\Sent Items","\\Recoverable Items\\Deletions")) + | eval user = lower(UserId), sender = lower(CASE(isnotnull(SendAsUserSmtp),SendAsUserSmtp,isnotnull(SendOnBehalfOfUserSmtp),SendOnBehalfOfUserSmtp,true(),MailboxOwnerUPN)), subject = trim(CASE(Operation IN ("Send","SendAs","SendOnBehalf"),'Item.Subject',Operation IN ("SoftDelete","HardDelete"),'AffectedItems{}.Subject')), -time = _time,file_name = CASE(Operation IN ("Send","SendAs","SendOnBehalf"),split('Item.Attachments',"; "),Operation IN ("SoftDelete","HardDelete"),split('AffectedItems{}.Attachments',"; ")), file_size = CASE(Operation IN ("Send","SendAs","SendOnBehalf"),round(tonumber('Item.SizeInBytes')/1024/1024,2),true(),round(tonumber(replace(file_name, "(.+)\s\((\d+)(b\)$)", "\2"))/1024/1024,2)) + | eval sendtime = CASE(Operation IN ("Send","SendAs","SendOnBehalf"),_time) + | eval deltime = CASE(Operation IN ("SoftDelete","HardDelete"),_time) + | stats values(sender) as sender, values(ClientIPAddress) as src, values(ClientInfoString) as http_user_agent, values(Operation) as signature, values(file_name) as file_name, sum(file_size) as file_size, values(Folder.Path) as file_path, min(sendtime) as firstTime, max(deltime) as lastTime, dc(Operation) as opcount, count by subject,user + | eval timediff = tonumber(lastTime) - tonumber(firstTime) + | where opcount > 1 AND firstTime < lastTime AND timediff < 3600 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_email_send_and_hard_delete_suspicious_behavior_filter` how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. known_false_positives: Users that habitually/proactively cleaning the recoverable items folder may trigger this alert. references: -- https://attack.mitre.org/techniques/T1114/ -- https://www.hhs.gov/sites/default/files/help-desk-social-engineering-sector-alert-tlpclear.pdf -- https://intelligence.abnormalsecurity.com/attack-library/threat-actor-convincingly-impersonates-employee-requesting-direct-deposit-update-in-likely-ai-generated-attack + - https://attack.mitre.org/techniques/T1114/ + - https://www.hhs.gov/sites/default/files/help-desk-social-engineering-sector-alert-tlpclear.pdf + - https://intelligence.abnormalsecurity.com/attack-library/threat-actor-convincingly-impersonates-employee-requesting-direct-deposit-update-in-likely-ai-generated-attack drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search [CHANGEME_FIELD] = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate Email for $user$ - search: '`o365_management_activity` Workload=Exchange (Operation IN ("Send*")) OR (Operation IN ("HardDelete") AND Folder.Path IN ("\\Sent Items","\\Recoverable Items\\Deletions")) AND UserId = "$user$" AND "$subject$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search [CHANGEME_FIELD] = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate Email for $user$ + search: '`o365_management_activity` Workload=Exchange (Operation IN ("Send*")) OR (Operation IN ("HardDelete") AND Folder.Path IN ("\\Sent Items","\\Recoverable Items\\Deletions")) AND UserId = "$user$" AND "$subject$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The user $user$ sent and hard deleted an email within a short timeframe - risk_objects: - - field: user - type: user - score: 20 - threat_objects: - - field: src - type: ip_address - - field: subject - type: email_subject + message: The user $user$ sent and hard deleted an email within a short timeframe + risk_objects: + - field: user + type: user + score: 20 + threat_objects: + - field: src + type: ip_address + - field: subject + type: email_subject tags: - analytic_story: - - Office 365 Account Takeover - - Office 365 Collection Techniques - - Suspicious Emails - - Data Destruction - asset_type: O365 Tenant - mitre_attack_id: - - T1114.001 - - T1070.008 - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Account Takeover + - Office 365 Collection Techniques + - Suspicious Emails + - Data Destruction + asset_type: O365 Tenant + mitre_attack_id: + - T1114.001 + - T1070.008 + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_suspect_email_actions/o365_exchange_suspect_events.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_suspect_email_actions/o365_exchange_suspect_events.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_email_send_attachments_excessive_volume.yml b/detections/cloud/o365_email_send_attachments_excessive_volume.yml index ee0a87ae54..76a82b073f 100644 --- a/detections/cloud/o365_email_send_attachments_excessive_volume.yml +++ b/detections/cloud/o365_email_send_attachments_excessive_volume.yml @@ -1,84 +1,84 @@ name: O365 Email Send Attachments Excessive Volume id: 70a050a2-8537-488a-a628-b60a9558d96a -version: 2 -date: '2025-05-02' +version: 3 +date: '2026-02-25' author: Steven Dick status: production type: Anomaly description: The following analytic identifies when an O365 email account sends an excessive number of email attachments to external recipients within a short period (within 1 hour). This behavior may indicate a compromised account where the threat actor is attempting to exfiltrate data from the mailbox. Threat actors may attempt to transfer data through email as a simple means of exfiltration from the compromised mailbox. Some account owner legitimate behaviors can trigger this alert, however these actions may not be aligned with organizational expectations / best practice behaviors. -data_source: -- Office 365 Universal Audit Log +data_source: + - Office 365 Universal Audit Log search: |- - `o365_messagetrace` Status=Delivered - | eval mailtime = _time - | bin _time span=1hr - | eval user = lower(SenderAddress), recipient = lower(RecipientAddress) - | eval InternetMessageId = lower(MessageId) - | join InternetMessageId, user, _time max=0 - [ - | search `o365_management_activity` Workload=Exchange Operation IN ("Send","SendAs","SendOnBehalf") - | eval user = lower(UserId), sender = lower(CASE(isnotnull(SendAsUserSmtp),SendAsUserSmtp,isnotnull(SendOnBehalfOfUserSmtp),SendOnBehalfOfUserSmtp,true(),MailboxOwnerUPN)), subject = trim(CASE(Operation IN ("Send","SendAs","SendOnBehalf"),'Item.Subject',Operation IN ("SoftDelete","HardDelete"),'AffectedItems{}.Subject')), -time = _time,file_name = trim(CASE(Operation IN ("Send","SendAs","SendOnBehalf"),split('Item.Attachments',"; "),Operation IN ("SoftDelete","HardDelete"),split('AffectedItems{}.Attachments',"; "))), file_size = CASE(Operation IN ("Send","SendAs","SendOnBehalf"),round(tonumber('Item.SizeInBytes')/1024/1024,2),true(),round(tonumber(replace(file_name, "(.+)\s\((\d+)(b\)$)", "\2"))/1024/1024,2)), InternetMessageId = lower('Item.InternetMessageId') + `o365_messagetrace` Status=Delivered + | eval mailtime = _time | bin _time span=1hr - | eval file_name = mvfilter(NOT match(file_name, "\.jpg |\.png |\.jpeg |\.gif ")) - | search file_name=* - | stats values(sender) as sender, values(ClientIPAddress) as src, values(ClientInfoString) as http_user_agent, values(Operation) as signature, values(file_name) as file_name, sum(file_size) as file_size, values(Folder.Path) as file_path, min(-time) as firstTime, max(-time) as lastTime, dc(file_name) as count by _time,user,InternetMessageId - | where count > 25 - | eval file_name = mvjoin(file_name,"||") - ] - | eval file_name = split(file_name,"||") - | stats values(sender) as sender, values(recipient) as recipient, values(http_user_agent) as http_user_agent, values(signature) as signature, values(file_name) as file_name, max(file_size) as file_size, min(firstTime) as firstTime, max(lastTime) as lastTime max(count) as count by subject,user,Organization,InternetMessageId - | eval recipient = mvmap(recipient, if(match(mvindex(split(lower(recipient),"@"),1),mvindex(split(lower(user),"@"),1)), null(),recipient)) - | search recipient = * - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_email_send_attachments_excessive_volume_filter` + | eval user = lower(SenderAddress), recipient = lower(RecipientAddress) + | eval InternetMessageId = lower(MessageId) + | join InternetMessageId, user, _time max=0 + [ + | search `o365_management_activity` Workload=Exchange Operation IN ("Send","SendAs","SendOnBehalf") + | eval user = lower(UserId), sender = lower(CASE(isnotnull(SendAsUserSmtp),SendAsUserSmtp,isnotnull(SendOnBehalfOfUserSmtp),SendOnBehalfOfUserSmtp,true(),MailboxOwnerUPN)), subject = trim(CASE(Operation IN ("Send","SendAs","SendOnBehalf"),'Item.Subject',Operation IN ("SoftDelete","HardDelete"),'AffectedItems{}.Subject')), -time = _time,file_name = trim(CASE(Operation IN ("Send","SendAs","SendOnBehalf"),split('Item.Attachments',"; "),Operation IN ("SoftDelete","HardDelete"),split('AffectedItems{}.Attachments',"; "))), file_size = CASE(Operation IN ("Send","SendAs","SendOnBehalf"),round(tonumber('Item.SizeInBytes')/1024/1024,2),true(),round(tonumber(replace(file_name, "(.+)\s\((\d+)(b\)$)", "\2"))/1024/1024,2)), InternetMessageId = lower('Item.InternetMessageId') + | bin _time span=1hr + | eval file_name = mvfilter(NOT match(file_name, "\.jpg |\.png |\.jpeg |\.gif ")) + | search file_name=* + | stats values(sender) as sender, values(ClientIPAddress) as src, values(ClientInfoString) as http_user_agent, values(Operation) as signature, values(file_name) as file_name, sum(file_size) as file_size, values(Folder.Path) as file_path, min(-time) as firstTime, max(-time) as lastTime, dc(file_name) as count by _time,user,InternetMessageId + | where count > 25 + | eval file_name = mvjoin(file_name,"||") + ] + | eval file_name = split(file_name,"||") + | stats values(sender) as sender, values(recipient) as recipient, values(http_user_agent) as http_user_agent, values(signature) as signature, values(file_name) as file_name, max(file_size) as file_size, min(firstTime) as firstTime, max(lastTime) as lastTime max(count) as count by subject,user,Organization,InternetMessageId + | eval recipient = mvmap(recipient, if(match(mvindex(split(lower(recipient),"@"),1),mvindex(split(lower(user),"@"),1)), null(),recipient)) + | search recipient = * + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_email_send_attachments_excessive_volume_filter` how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events AND Message Trace events. known_false_positives: Users or processes that are send a large number of attachments may trigger this alert, adjust thresholds accordingly. references: -- https://attack.mitre.org/techniques/T1114/ -- https://www.hhs.gov/sites/default/files/help-desk-social-engineering-sector-alert-tlpclear.pdf -- https://intelligence.abnormalsecurity.com/attack-library/threat-actor-convincingly-impersonates-employee-requesting-direct-deposit-update-in-likely-ai-generated-attack + - https://attack.mitre.org/techniques/T1114/ + - https://www.hhs.gov/sites/default/files/help-desk-social-engineering-sector-alert-tlpclear.pdf + - https://intelligence.abnormalsecurity.com/attack-library/threat-actor-convincingly-impersonates-employee-requesting-direct-deposit-update-in-likely-ai-generated-attack drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate Email for $user$ - search: '`o365_management_activity` Workload=Exchange (Operation IN ("Send*")) AND Item.Attachments=* AND UserId = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate Email for $user$ + search: '`o365_management_activity` Workload=Exchange (Operation IN ("Send*")) AND Item.Attachments=* AND UserId = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The user $user$ sent an excessive number of email attachments [$count$] to external recipient(s) within a short timeframe - risk_objects: - - field: user - type: user - score: 20 - threat_objects: - - field: recipient - type: email_address + message: The user $user$ sent an excessive number of email attachments [$count$] to external recipient(s) within a short timeframe + risk_objects: + - field: user + type: user + score: 20 + threat_objects: + - field: recipient + type: email_address tags: - analytic_story: - - Office 365 Account Takeover - - Suspicious Emails - asset_type: O365 Tenant - mitre_attack_id: - - T1070.008 - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Account Takeover + - Suspicious Emails + asset_type: O365 Tenant + mitre_attack_id: + - T1070.008 + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_suspect_email_actions/o365_exchange_suspect_events.log - source: o365 - sourcetype: o365:management:activity - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_suspect_email_actions/o365_messagetrace_suspect_events.log - source: o365_messagetrace - sourcetype: o365:reporting:messagetrace + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_suspect_email_actions/o365_exchange_suspect_events.log + source: o365 + sourcetype: o365:management:activity + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_suspect_email_actions/o365_messagetrace_suspect_events.log + source: o365_messagetrace + sourcetype: o365:reporting:messagetrace diff --git a/detections/cloud/o365_email_suspicious_behavior_alert.yml b/detections/cloud/o365_email_suspicious_behavior_alert.yml index 5177aeb292..c0928390ef 100644 --- a/detections/cloud/o365_email_suspicious_behavior_alert.yml +++ b/detections/cloud/o365_email_suspicious_behavior_alert.yml @@ -1,69 +1,61 @@ name: O365 Email Suspicious Behavior Alert id: 85c7555a-05af-4322-81aa-76b4ddf52baa -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Steven Dick status: production type: TTP -description: The following analytic identifies when one of O365 the built-in security - detections for suspicious email behaviors are triggered. These alerts often indicate - that an attacker may have compromised a mailbox within the environment. Any detections - from built-in Office 365 capabilities should be monitored and responded to appropriately. - Certain premium Office 365 capabilities further enhance these detection and response - functions. +description: The following analytic identifies when one of O365 the built-in security detections for suspicious email behaviors are triggered. These alerts often indicate that an attacker may have compromised a mailbox within the environment. Any detections from built-in Office 365 capabilities should be monitored and responded to appropriately. Certain premium Office 365 capabilities further enhance these detection and response functions. data_source: -- Office 365 Universal Audit Log -search: '`o365_management_activity` Workload=SecurityComplianceCenter Operation=AlertEntityGenerated - Name IN ("Suspicious email sending patterns detected","User restricted from sending - email","Suspicious Email Forwarding Activity","Email sending limit exceeded") | - fromjson Data | rename Name as signature, AlertId as signature_id, ObjectId as user - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - user src vendor_account vendor_product signature signature_id | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `o365_email_suspicious_behavior_alert_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. The alerts must be enabled in the o365 security - portal. -known_false_positives: Users emailing for legitimate business purposes that appear - suspicious. + - Office 365 Universal Audit Log +search: |- + `o365_management_activity` Workload=SecurityComplianceCenter Operation=AlertEntityGenerated Name IN ("Suspicious email sending patterns detected","User restricted from sending email","Suspicious Email Forwarding Activity","Email sending limit exceeded") + | fromjson Data + | rename Name as signature, AlertId as signature_id, ObjectId as user + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest user src + vendor_account vendor_product signature + signature_id + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_email_suspicious_behavior_alert_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. The alerts must be enabled in the o365 security portal. +known_false_positives: Users emailing for legitimate business purposes that appear suspicious. references: -- https://learn.microsoft.com/en-us/purview/alert-policies + - https://learn.microsoft.com/en-us/purview/alert-policies drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The user $user$ triggered the O365 security alert [$signature$] - risk_objects: - - field: user - type: user - score: 90 - threat_objects: [] + message: The user $user$ triggered the O365 security alert [$signature$] + risk_objects: + - field: user + type: user + score: 90 + threat_objects: [] tags: - analytic_story: - - Suspicious Emails - - Office 365 Collection Techniques - - Office 365 Account Takeover - asset_type: O365 Tenant - mitre_attack_id: - - T1114.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Suspicious Emails + - Office 365 Collection Techniques + - Office 365 Account Takeover + asset_type: O365 Tenant + mitre_attack_id: + - T1114.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_email_suspicious_search_behavior.yml b/detections/cloud/o365_email_suspicious_search_behavior.yml index 6c3dc5d8bd..9cccd974d5 100644 --- a/detections/cloud/o365_email_suspicious_search_behavior.yml +++ b/detections/cloud/o365_email_suspicious_search_behavior.yml @@ -7,64 +7,64 @@ status: production type: Anomaly description: The following analytic identifies when Office 365 users search for suspicious keywords or have an excessive number of queries to a mailbox within a limited timeframe. This behavior may indicate that a malicious actor has gained control of a mailbox and is conducting discovery or enumeration activities. data_source: -- Office 365 Universal Audit Log + - Office 365 Universal Audit Log search: |- - `o365_management_activity` Operation=SearchQueryInitiatedExchange - | eval command = case(Operation=="SearchQueryPerformed",SearchQueryText,true(),QueryText), UserId = lower(UserId), signature_id = CorrelationId, signature=Operation, src = ClientIP, user = lower(UserId), object_name=case(Operation=="SearchQueryPerformed",'EventData',true(),QuerySource), -time = _time, suspect_terms = case(match(command, `o365_suspect_search_terms_regex`),command,true(),null()) - | where command != "*" AND command != "(*)" - | bin _time span=1hr - | stats values(ScenarioName) as app, values(object_name) as object_name values(command) as command, values(suspect_terms) as suspect_terms, values(src) as src, dc(suspect_terms) as suspect_terms_count, dc(command) as count, min(-time) as firstTime, max(-time) as lastTime by user,signature,_time - | where count > 20 OR suspect_terms_count >= 2 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_email_suspicious_search_behavior_filter` + `o365_management_activity` Operation=SearchQueryInitiatedExchange + | eval command = case(Operation=="SearchQueryPerformed",SearchQueryText,true(),QueryText), UserId = lower(UserId), signature_id = CorrelationId, signature=Operation, src = ClientIP, user = lower(UserId), object_name=case(Operation=="SearchQueryPerformed",'EventData',true(),QuerySource), -time = _time, suspect_terms = case(match(command, `o365_suspect_search_terms_regex`),command,true(),null()) + | where command != "*" AND command != "(*)" + | bin _time span=1hr + | stats values(ScenarioName) as app, values(object_name) as object_name values(command) as command, values(suspect_terms) as suspect_terms, values(src) as src, dc(suspect_terms) as suspect_terms_count, dc(command) as count, min(-time) as firstTime, max(-time) as lastTime by user,signature,_time + | where count > 20 OR suspect_terms_count >= 2 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_email_suspicious_search_behavior_filter` how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. You must also enable SearchQueryInitiated category as part of your organizations mailbox audit logging policy. The thresholds and match terms set within the analytic are initial guidelines and should be customized based on the organization's user behavior and risk profile. Security teams are encouraged to adjust these thresholds to optimize the balance between detecting genuine threats and minimizing false positives, ensuring the detection is tailored to their specific environment. known_false_positives: Users searching excessively or possible false positives related to matching conditions. references: -- https://learn.microsoft.com/en-us/purview/audit-get-started#step-3-enable-searchqueryinitiated-events -- https://www.cisa.gov/sites/default/files/2025-01/microsoft-expanded-cloud-logs-implementation-playbook-508c.pdf -- https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-320a -- https://attack.mitre.org/techniques/T1114/002/ + - https://learn.microsoft.com/en-us/purview/audit-get-started#step-3-enable-searchqueryinitiated-events + - https://www.cisa.gov/sites/default/files/2025-01/microsoft-expanded-cloud-logs-implementation-playbook-508c.pdf + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-320a + - https://attack.mitre.org/techniques/T1114/002/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate search behavior by $user$ - search: '`o365_management_activity` AND Operation=SearchQueryInitiatedExchange AND UserId = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate search behavior by $user$ + search: '`o365_management_activity` AND Operation=SearchQueryInitiatedExchange AND UserId = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The user $user$ searched email suspiciously, $count$ unique terms and $suspect_terms_count$ suspect terms were searched within a limited timeframe. - risk_objects: - - field: user - type: user - score: 35 - threat_objects: - - field: src - type: ip_address + message: The user $user$ searched email suspiciously, $count$ unique terms and $suspect_terms_count$ suspect terms were searched within a limited timeframe. + risk_objects: + - field: user + type: user + score: 35 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Office 365 Account Takeover - - Office 365 Collection Techniques - - Compromised User Account - - CISA AA22-320A - asset_type: O365 Tenant - mitre_attack_id: - - T1114.002 - - T1552 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Account Takeover + - Office 365 Collection Techniques + - Compromised User Account + - CISA AA22-320A + asset_type: O365 Tenant + mitre_attack_id: + - T1114.002 + - T1552 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1213.002/o365_sus_sharepoint_search/o365_sus_sharepoint_search.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1213.002/o365_sus_sharepoint_search/o365_sus_sharepoint_search.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_email_transport_rule_changed.yml b/detections/cloud/o365_email_transport_rule_changed.yml index 22f5982610..778e53537b 100644 --- a/detections/cloud/o365_email_transport_rule_changed.yml +++ b/detections/cloud/o365_email_transport_rule_changed.yml @@ -1,67 +1,67 @@ name: O365 Email Transport Rule Changed id: 11ebb7c2-46bd-41c9-81e1-d0b4b34583a2 -version: 3 -date: '2025-05-02' +version: 4 +date: '2026-02-25' author: Steven Dick status: production type: Anomaly description: The following analytic identifies when a user with sufficient access to Exchange Online alters the mail flow/transport rule configuration of the organization. Transport rules are a set of rules that can be used by attackers to modify or delete emails based on specific conditions, this activity could indicate an attacker hiding or exfiltrated data. -data_source: -- Office 365 Universal Audit Log +data_source: + - Office 365 Universal Audit Log search: |- - `o365_management_activity` Workload=Exchange AND Operation IN ("Set-*","Disable-*","New-*","Remove-*") AND Operation="*TransportRule" - | eval object_name = case('Parameters{}.Name'=="Name",mvindex('Parameters{}.Value',mvfind('Parameters{}.Name',"^Name$")),true(),ObjectId), object_id = case('Parameters{}.Name'=="Identity",mvindex('Parameters{}.Value',mvfind('Parameters{}.Name',"^Identity$")),true(),Id) - | stats values(object_name) as object_name, min(_time) as firstTime, max(_time) as lastTime, count by object_id, UserId, Operation, signature - | rename UserId as user - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_email_transport_rule_changed_filter` + `o365_management_activity` Workload=Exchange AND Operation IN ("Set-*","Disable-*","New-*","Remove-*") AND Operation="*TransportRule" + | eval object_name = case('Parameters{}.Name'=="Name",mvindex('Parameters{}.Value',mvfind('Parameters{}.Name',"^Name$")),true(),ObjectId), object_id = case('Parameters{}.Name'=="Identity",mvindex('Parameters{}.Value',mvfind('Parameters{}.Name',"^Identity$")),true(),Id) + | stats values(object_name) as object_name, min(_time) as firstTime, max(_time) as lastTime, count by object_id, UserId, Operation, signature + | rename UserId as user + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_email_transport_rule_changed_filter` how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. known_false_positives: Legitimate administrative changes for business needs. references: -- https://attack.mitre.org/techniques/T1114/003/ -- https://cardinalops.com/blog/cardinalops-contributes-new-mitre-attck-techniques-related-to-abuse-of-mail-transport-rules/ -- https://www.microsoft.com/en-us/security/blog/2022/09/22/malicious-OAuth-applications-used-to-compromise-email-servers-and-spread-spam/ + - https://attack.mitre.org/techniques/T1114/003/ + - https://cardinalops.com/blog/cardinalops-contributes-new-mitre-attck-techniques-related-to-abuse-of-mail-transport-rules/ + - https://www.microsoft.com/en-us/security/blog/2022/09/22/malicious-OAuth-applications-used-to-compromise-email-servers-and-spread-spam/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate changes by $user$ - search: '`o365_management_activity` Workload=Exchange AND Operation IN ("Set-*","Disable-*","New-*","Remove-*") AND Operation="*Transport*" UserId=$user$' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate changes by $user$ + search: '`o365_management_activity` Workload=Exchange AND Operation IN ("Set-*","Disable-*","New-*","Remove-*") AND Operation="*Transport*" UserId=$user$' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The user [$user$] altered the exchange transport rule id [$object_name$] - risk_objects: - - field: user - type: user - score: 25 - threat_objects: - - field: object_id - type: signature - - field: object_name - type: signature + message: The user [$user$] altered the exchange transport rule id [$object_name$] + risk_objects: + - field: user + type: user + score: 25 + threat_objects: + - field: object_id + type: signature + - field: object_name + type: signature tags: - analytic_story: - - Data Exfiltration - - Office 365 Account Takeover - asset_type: O365 Tenant - mitre_attack_id: - - T1114.003 - - T1564.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Data Exfiltration + - Office 365 Account Takeover + asset_type: O365 Tenant + mitre_attack_id: + - T1114.003 + - T1564.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/transport_rule_change/transport_rule_change.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/transport_rule_change/transport_rule_change.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_excessive_authentication_failures_alert.yml b/detections/cloud/o365_excessive_authentication_failures_alert.yml index 249847b712..52d9d09818 100644 --- a/detections/cloud/o365_excessive_authentication_failures_alert.yml +++ b/detections/cloud/o365_excessive_authentication_failures_alert.yml @@ -1,70 +1,57 @@ name: O365 Excessive Authentication Failures Alert id: d441364c-349c-453b-b55f-12eccab67cf9 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Rod Soto, Splunk status: production type: Anomaly -description: The following analytic identifies an excessive number of authentication - failures, including failed attempts against MFA prompt codes. It uses data from - the `o365_management_activity` dataset, focusing on events where the authentication - status is marked as failure. This behavior is significant as it may indicate a brute - force attack or an attempt to compromise user accounts. If confirmed malicious, - this activity could lead to unauthorized access, data breaches, or further exploitation - within the environment. +description: The following analytic identifies an excessive number of authentication failures, including failed attempts against MFA prompt codes. It uses data from the `o365_management_activity` dataset, focusing on events where the authentication status is marked as failure. This behavior is significant as it may indicate a brute force attack or an attempt to compromise user accounts. If confirmed malicious, this activity could lead to unauthorized access, data breaches, or further exploitation within the environment. data_source: [] -search: '`o365_management_activity` Workload=AzureActiveDirectory UserAuthenticationMethod=* status=failure - | stats count earliest(_time) AS firstTime latest(_time) AS lastTime values(UserAuthenticationMethod) AS UserAuthenticationMethod values(UserAgent) AS - user_agent values(status) AS status values(src_ip) AS src values(signature) as signature by user vendor_account vendor_product dest - | where count > 10 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_excessive_authentication_failures_alert_filter`' -how_to_implement: You must install splunk Microsoft Office 365 add-on. This search - works with o365:management:activity -known_false_positives: The threshold for alert is above 10 attempts and this should - reduce the number of false positives. +search: |- + `o365_management_activity` Workload=AzureActiveDirectory UserAuthenticationMethod=* status=failure + | stats count earliest(_time) AS firstTime latest(_time) AS lastTime values(UserAuthenticationMethod) AS UserAuthenticationMethod values(UserAgent) AS user_agent values(status) AS status values(src_ip) AS src values(signature) as signature + BY user vendor_account vendor_product + dest + | where count > 10 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_excessive_authentication_failures_alert_filter` +how_to_implement: You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity +known_false_positives: The threshold for alert is above 10 attempts and this should reduce the number of false positives. references: -- https://attack.mitre.org/techniques/T1110/ + - https://attack.mitre.org/techniques/T1110/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has caused excessive number of authentication failures from - $src$ using UserAgent $user_agent$. - risk_objects: - - field: user - type: user - score: 64 - threat_objects: - - field: src - type: ip_address + message: User $user$ has caused excessive number of authentication failures from $src$ using UserAgent $user_agent$. + risk_objects: + - field: user + type: user + score: 64 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Office 365 Account Takeover - asset_type: O365 Tenant - mitre_attack_id: - - T1110 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Account Takeover + asset_type: O365 Tenant + mitre_attack_id: + - T1110 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/o365_brute_force_login/o365_brute_force_login.json - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/o365_brute_force_login/o365_brute_force_login.json + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_excessive_sso_logon_errors.yml b/detections/cloud/o365_excessive_sso_logon_errors.yml index d821ef59f6..47a2e0f0cd 100644 --- a/detections/cloud/o365_excessive_sso_logon_errors.yml +++ b/detections/cloud/o365_excessive_sso_logon_errors.yml @@ -1,70 +1,59 @@ name: O365 Excessive SSO logon errors id: 8158ccc4-6038-11eb-ae93-0242ac130002 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Rod Soto, Splunk status: production type: Anomaly -description: The following analytic detects accounts experiencing a high number of - Single Sign-On (SSO) logon errors. It leverages data from the `o365_management_activity` - dataset, focusing on failed user login attempts with SSO errors. This activity is - significant as it may indicate brute-force attempts or the hijacking/reuse of SSO - tokens. If confirmed malicious, attackers could potentially gain unauthorized access - to user accounts, leading to data breaches, privilege escalation, or further lateral - movement within the organization. +description: The following analytic detects accounts experiencing a high number of Single Sign-On (SSO) logon errors. It leverages data from the `o365_management_activity` dataset, focusing on failed user login attempts with SSO errors. This activity is significant as it may indicate brute-force attempts or the hijacking/reuse of SSO tokens. If confirmed malicious, attackers could potentially gain unauthorized access to user accounts, leading to data breaches, privilege escalation, or further lateral movement within the organization. data_source: -- O365 UserLoginFailed -search: '`o365_management_activity` Workload=AzureActiveDirectory LogonError=*Sso* Operation=UserLoginFailed - | stats count min(_time) as firstTime max(_time) as lastTime values(user) as user by src vendor_account vendor_product dest signature user_agent - | where count >= 5 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_excessive_sso_logon_errors_filter`' -how_to_implement: You must install splunk Microsoft Office 365 add-on. This search - works with o365:management:activity -known_false_positives: Logon errors may not be malicious in nature however it may - indicate attempts to reuse a token or password obtained via credential access attack. + - O365 UserLoginFailed +search: |- + `o365_management_activity` Workload=AzureActiveDirectory LogonError=*Sso* Operation=UserLoginFailed + | stats count min(_time) as firstTime max(_time) as lastTime values(user) as user + BY src vendor_account vendor_product + dest signature user_agent + | where count >= 5 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_excessive_sso_logon_errors_filter` +how_to_implement: You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity +known_false_positives: Logon errors may not be malicious in nature however it may indicate attempts to reuse a token or password obtained via credential access attack. references: -- https://stealthbits.com/blog/bypassing-mfa-with-pass-the-cookie/ + - https://stealthbits.com/blog/bypassing-mfa-with-pass-the-cookie/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Excessive number of SSO logon errors from $src$ using UserAgent $user_agent$. - risk_objects: - - field: user - type: user - score: 64 - threat_objects: - - field: src - type: ip_address + message: Excessive number of SSO logon errors from $src$ using UserAgent $user_agent$. + risk_objects: + - field: user + type: user + score: 64 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Office 365 Account Takeover - - Cloud Federated Credential Abuse - asset_type: O365 Tenant - mitre_attack_id: - - T1556 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Account Takeover + - Cloud Federated Credential Abuse + asset_type: O365 Tenant + mitre_attack_id: + - T1556 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_sso_logon_errors/o365_sso_logon_errors2.json - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_sso_logon_errors/o365_sso_logon_errors2.json + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_exfiltration_via_file_access.yml b/detections/cloud/o365_exfiltration_via_file_access.yml index 0237e59cf2..36d861dab1 100644 --- a/detections/cloud/o365_exfiltration_via_file_access.yml +++ b/detections/cloud/o365_exfiltration_via_file_access.yml @@ -1,68 +1,68 @@ name: O365 Exfiltration via File Access id: 80b44ae2-60ff-43f1-8e56-34beb49a340a -version: 2 -date: '2025-05-02' +version: 3 +date: '2026-02-25' author: Steven Dick status: production type: Anomaly description: The following analytic detects when an excessive number of files are access from o365 by the same user over a short period of time. A malicious actor may abuse the "open in app" functionality of SharePoint through scripted or Graph API based access to evade triggering the FileDownloaded Event. This behavior may indicate an attacker staging data for exfiltration or an insider threat removing organizational data. Additional attention should be take with any Azure Guest (#EXT#) accounts. -data_source: -- Office 365 Universal Audit Log +data_source: + - Office 365 Universal Audit Log search: |- - `o365_management_activity` Operation IN ("fileaccessed") UserId!=app@sharepoint NOT SourceFileExtension IN (bmp,png,jpeg,jpg) - | eval user = replace(mvindex(split(lower(UserId),"#ext#"),0),"_","@"), user_flat = replace(UserId, "[^A-Za-z0-9]","_") - | where NOT match(SiteUrl,user_flat) - | stats values(user) as user, latest(ClientIP) as src values(ZipFileName) as file_name, values(Operation) as signature, values(UserAgent) as http_user_agent, dc(SourceFileName) as count, min(_time) as firstTime, max(_time) as lastTime by Workload,UserId,SiteUrl - | eventstats avg(count) as avg stdev(count) as stdev by Workload - | rename SiteUrl as file_path,Workload as app - | where count > 50 AND count > (avg + (3*(stdev))) - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_exfiltration_via_file_access_filter` + `o365_management_activity` Operation IN ("fileaccessed") UserId!=app@sharepoint NOT SourceFileExtension IN (bmp,png,jpeg,jpg) + | eval user = replace(mvindex(split(lower(UserId),"#ext#"),0),"_","@"), user_flat = replace(UserId, "[^A-Za-z0-9]","_") + | where NOT match(SiteUrl,user_flat) + | stats values(user) as user, latest(ClientIP) as src values(ZipFileName) as file_name, values(Operation) as signature, values(UserAgent) as http_user_agent, dc(SourceFileName) as count, min(_time) as firstTime, max(_time) as lastTime by Workload,UserId,SiteUrl + | eventstats avg(count) as avg stdev(count) as stdev by Workload + | rename SiteUrl as file_path,Workload as app + | where count > 50 AND count > (avg + (3*(stdev))) + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_exfiltration_via_file_access_filter` how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. known_false_positives: It is possible that certain file access scenarios may trigger this alert, specifically OneDrive syncing and users accessing personal onedrives of other users. Adjust threshold and filtering as needed. references: -- https://attack.mitre.org/techniques/T1567/exfil -- https://www.varonis.com/blog/sidestepping-detection-while-exfiltrating-sharepoint-data -- https://thedfirjournal.com/posts/m365-data-exfiltration-rclone/ + - https://attack.mitre.org/techniques/T1567/exfil + - https://www.varonis.com/blog/sidestepping-detection-while-exfiltrating-sharepoint-data + - https://thedfirjournal.com/posts/m365-data-exfiltration-rclone/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate file access by $user$ - search: '`o365_management_activity` Operation IN ("fileaccessed") UserId="$UserId$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate file access by $user$ + search: '`o365_management_activity` Operation IN ("fileaccessed") UserId="$UserId$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The user $user$ accessed an excessive number of files [$count$] from $file_path$ using $src$ - risk_objects: - - field: user - type: user - score: 20 - threat_objects: - - field: src - type: ip_address + message: The user $user$ accessed an excessive number of files [$count$] from $file_path$ using $src$ + risk_objects: + - field: user + type: user + score: 20 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Data Exfiltration - - Office 365 Account Takeover - asset_type: O365 Tenant - mitre_attack_id: - - T1567 - - T1530 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Data Exfiltration + - Office 365 Account Takeover + asset_type: O365 Tenant + mitre_attack_id: + - T1567 + - T1530 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1567/o365_sus_file_activity/o365_sus_file_activity.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1567/o365_sus_file_activity/o365_sus_file_activity.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_exfiltration_via_file_download.yml b/detections/cloud/o365_exfiltration_via_file_download.yml index f3bde7110b..bc57c13c48 100644 --- a/detections/cloud/o365_exfiltration_via_file_download.yml +++ b/detections/cloud/o365_exfiltration_via_file_download.yml @@ -1,66 +1,66 @@ name: O365 Exfiltration via File Download id: 06b23921-bfe2-4576-89dd-616f06e129da -version: 2 -date: '2025-05-02' +version: 3 +date: '2026-02-25' author: Steven Dick status: production type: Anomaly description: The following analytic detects when an excessive number of files are downloaded from o365 by the same user over a short period of time. O365 may bundle these files together as a ZIP file, however each file will have it's own download event. This behavior may indicate an attacker staging data for exfiltration or an insider threat removing organizational data. Additional attention should be taken with any Azure Guest (#EXT#) accounts. -data_source: -- Office 365 Universal Audit Log +data_source: + - Office 365 Universal Audit Log search: |- - `o365_management_activity` Operation IN ("filedownloaded") - | eval user = replace(mvindex(split(lower(UserId),"#ext#"),0),"_","@"), user_flat = replace(UserId, "[^A-Za-z0-9]","_") - | stats values(user) as user, latest(ClientIP) as src values(ZipFileName) as file_name, values(Operation) as signature, values(UserAgent) as http_user_agent, dc(SourceFileName) as count, min(_time) as firstTime, max(_time) as lastTime by Workload,UserId,SiteUrl - | rename SiteUrl as file_path,Workload as app - | where count > 50 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_exfiltration_via_file_download_filter` + `o365_management_activity` Operation IN ("filedownloaded") + | eval user = replace(mvindex(split(lower(UserId),"#ext#"),0),"_","@"), user_flat = replace(UserId, "[^A-Za-z0-9]","_") + | stats values(user) as user, latest(ClientIP) as src values(ZipFileName) as file_name, values(Operation) as signature, values(UserAgent) as http_user_agent, dc(SourceFileName) as count, min(_time) as firstTime, max(_time) as lastTime by Workload,UserId,SiteUrl + | rename SiteUrl as file_path,Workload as app + | where count > 50 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_exfiltration_via_file_download_filter` how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. known_false_positives: It is possible that certain file download scenarios may trigger this alert, specifically OneDrive syncing. Adjust threshold and filtering as needed. references: -- https://attack.mitre.org/techniques/T1567/exfil -- https://www.varonis.com/blog/sidestepping-detection-while-exfiltrating-sharepoint-data -- https://thedfirjournal.com/posts/m365-data-exfiltration-rclone/ + - https://attack.mitre.org/techniques/T1567/exfil + - https://www.varonis.com/blog/sidestepping-detection-while-exfiltrating-sharepoint-data + - https://thedfirjournal.com/posts/m365-data-exfiltration-rclone/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate file downloads by $user$ - search: '`o365_management_activity` Operation IN ("filedownloaded") UserId="$UserId$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate file downloads by $user$ + search: '`o365_management_activity` Operation IN ("filedownloaded") UserId="$UserId$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The user $user$ downloaded an excessive number of files [$count$] from $file_path$ using $src$ - risk_objects: - - field: user - type: user - score: 25 - threat_objects: - - field: src - type: ip_address + message: The user $user$ downloaded an excessive number of files [$count$] from $file_path$ using $src$ + risk_objects: + - field: user + type: user + score: 25 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Data Exfiltration - - Office 365 Account Takeover - asset_type: O365 Tenant - mitre_attack_id: - - T1567 - - T1530 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Data Exfiltration + - Office 365 Account Takeover + asset_type: O365 Tenant + mitre_attack_id: + - T1567 + - T1530 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1567/o365_sus_file_activity/o365_sus_file_activity.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1567/o365_sus_file_activity/o365_sus_file_activity.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_exfiltration_via_file_sync_download.yml b/detections/cloud/o365_exfiltration_via_file_sync_download.yml index 51dcace086..c8970f5695 100644 --- a/detections/cloud/o365_exfiltration_via_file_sync_download.yml +++ b/detections/cloud/o365_exfiltration_via_file_sync_download.yml @@ -1,67 +1,67 @@ name: O365 Exfiltration via File Sync Download id: 350837b5-13d3-4c06-b688-db07afbe5050 -version: 2 -date: '2025-05-02' +version: 3 +date: '2026-02-25' author: Steven Dick status: production type: Anomaly description: The following analytic detects when an excessive number of files are sync from o365 by the same user over a short period of time. A malicious actor abuse the user-agent string through GUI or API access to evade triggering the FileDownloaded event. This behavior may indicate an attacker staging data for exfiltration or an insider threat removing organizational data. Additional attention should be taken with any Azure Guest (#EXT#) accounts. -data_source: -- Office 365 Universal Audit Log +data_source: + - Office 365 Universal Audit Log search: |- - `o365_management_activity` Operation IN ("filesyncdownload*") UserAgent="*SkyDriveSync*" - | eval user = replace(mvindex(split(lower(UserId),"#ext#"),0),"_","@"), user_flat = replace(UserId, "[^A-Za-z0-9]","_") - | where NOT match(SiteUrl,user_flat) - | stats values(user) as user, latest(ClientIP) as src values(ZipFileName) as file_name, values(Operation) as signature, values(UserAgent) as http_user_agent, dc(SourceFileName) as count, min(_time) as firstTime, max(_time) as lastTime by Workload,UserId,SiteUrl - | rename SiteUrl as file_path,Workload as app - | where count > 50 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_exfiltration_via_file_sync_download_filter` + `o365_management_activity` Operation IN ("filesyncdownload*") UserAgent="*SkyDriveSync*" + | eval user = replace(mvindex(split(lower(UserId),"#ext#"),0),"_","@"), user_flat = replace(UserId, "[^A-Za-z0-9]","_") + | where NOT match(SiteUrl,user_flat) + | stats values(user) as user, latest(ClientIP) as src values(ZipFileName) as file_name, values(Operation) as signature, values(UserAgent) as http_user_agent, dc(SourceFileName) as count, min(_time) as firstTime, max(_time) as lastTime by Workload,UserId,SiteUrl + | rename SiteUrl as file_path,Workload as app + | where count > 50 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_exfiltration_via_file_sync_download_filter` how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. known_false_positives: It is possible that certain file sync scenarios may trigger this alert, specifically OneNote. Adjust threshold and filtering as needed. references: -- https://attack.mitre.org/techniques/T1567/exfil -- https://www.varonis.com/blog/sidestepping-detection-while-exfiltrating-sharepoint-data -- https://thedfirjournal.com/posts/m365-data-exfiltration-rclone/ + - https://attack.mitre.org/techniques/T1567/exfil + - https://www.varonis.com/blog/sidestepping-detection-while-exfiltrating-sharepoint-data + - https://thedfirjournal.com/posts/m365-data-exfiltration-rclone/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate file sync downloads by $user$ - search: '`o365_management_activity` Operation IN ("filesyncdownload*") UserId="$UserId$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate file sync downloads by $user$ + search: '`o365_management_activity` Operation IN ("filesyncdownload*") UserId="$UserId$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The user $user$ synced an excessive number of files [$count$] from $file_path$ using $src$ - risk_objects: - - field: user - type: user - score: 25 - threat_objects: - - field: src - type: ip_address + message: The user $user$ synced an excessive number of files [$count$] from $file_path$ using $src$ + risk_objects: + - field: user + type: user + score: 25 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Data Exfiltration - - Office 365 Account Takeover - asset_type: O365 Tenant - mitre_attack_id: - - T1567 - - T1530 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Data Exfiltration + - Office 365 Account Takeover + asset_type: O365 Tenant + mitre_attack_id: + - T1567 + - T1530 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1567/o365_sus_file_activity/o365_sus_file_activity.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1567/o365_sus_file_activity/o365_sus_file_activity.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_external_guest_user_invited.yml b/detections/cloud/o365_external_guest_user_invited.yml index dcef99b70d..4891968fbc 100644 --- a/detections/cloud/o365_external_guest_user_invited.yml +++ b/detections/cloud/o365_external_guest_user_invited.yml @@ -5,78 +5,50 @@ date: '2025-05-02' author: Steven Dick status: production type: TTP -description: The following analytic identifies the invitation of an external guest - user within Azure AD. With Azure AD B2B collaboration, users and administrators - can invite external users to collaborate with internal users. External guest account - invitations should be monitored by security teams as they could potentially lead - to unauthorized access. An example of this attack vector was described at BlackHat - 2022 by security researcher Dirk-Jan during his tall `Backdooring and Hijacking - Azure AD Accounts by Abusing External Identities`. This detection leverages the - Universal Audit Log (UAL)/o365:management:activity sourcetype as a detection data - source. +description: The following analytic identifies the invitation of an external guest user within Azure AD. With Azure AD B2B collaboration, users and administrators can invite external users to collaborate with internal users. External guest account invitations should be monitored by security teams as they could potentially lead to unauthorized access. An example of this attack vector was described at BlackHat 2022 by security researcher Dirk-Jan during his tall `Backdooring and Hijacking Azure AD Accounts by Abusing External Identities`. This detection leverages the Universal Audit Log (UAL)/o365:management:activity sourcetype as a detection data source. data_source: -- Office 365 Universal Audit Log -search: "`o365_management_activity` Workload=AzureActiveDirectory AND Operation=\"Add user*\" AND ModifiedProperties{}.NewValue=\"[*Guest*]\" AND ModifiedProperties{}.NewValue=\"[*Invitation*]\" - | eval user = (mvindex('ModifiedProperties{}.NewValue',5)), src_user = case(match(mvindex('Actor{}.ID',-1),\"User\"),mvindex('Actor{}.ID',0),match(mvindex('Actor{}.ID',-1),\"ServicePrincipal\"),mvindex('Actor{}.ID',3),true(),mvindex('Actor{}.ID',0)) - | rex - field=user \"(? - [ \\w\\.-]+@ - [ \\w-]+\\. - [ \\w-]{2,4})\" - | rename Operation as signature, Id as signature_id - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product signature signature_id src_user - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_external_guest_user_invited_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Administrator may legitimately invite external guest users. - Filter as needed. + - Office 365 Universal Audit Log +search: "`o365_management_activity` Workload=AzureActiveDirectory AND Operation=\"Add user*\" AND ModifiedProperties{}.NewValue=\"[*Guest*]\" AND ModifiedProperties{}.NewValue=\"[*Invitation*]\" | eval user = (mvindex('ModifiedProperties{}.NewValue',5)), src_user = case(match(mvindex('Actor{}.ID',-1),\"User\"),mvindex('Actor{}.ID',0),match(mvindex('Actor{}.ID',-1),\"ServicePrincipal\"),mvindex('Actor{}.ID',3),true(),mvindex('Actor{}.ID',0)) | rex field=user \"(? [ \\w\\.-]+@ [ \\w-]+\\. [ \\w-]{2,4})\" | rename Operation as signature, Id as signature_id | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product signature signature_id src_user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_external_guest_user_invited_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Administrator may legitimately invite external guest users. Filter as needed. references: -- https://dirkjanm.io/assets/raw/US-22-Mollema-Backdooring-and-hijacking-Azure-AD-accounts_final.pdf -- https://www.blackhat.com/us-22/briefings/schedule/#backdooring-and-hijacking-azure-ad-accounts-by-abusing-external-identities-26999 -- https://attack.mitre.org/techniques/T1136/003/ -- https://docs.microsoft.com/en-us/azure/active-directory/external-identities/b2b-quickstart-add-guest-users-portal + - https://dirkjanm.io/assets/raw/US-22-Mollema-Backdooring-and-hijacking-Azure-AD-accounts_final.pdf + - https://www.blackhat.com/us-22/briefings/schedule/#backdooring-and-hijacking-azure-ad-accounts-by-abusing-external-identities-26999 + - https://attack.mitre.org/techniques/T1136/003/ + - https://docs.microsoft.com/en-us/azure/active-directory/external-identities/b2b-quickstart-add-guest-users-portal drilldown_searches: -- name: View the detection results for - "$user$" and "$src_user$" - search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$src_user$" + search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Azure Guest User $user$ invited by $src_user$ - risk_objects: - - field: user - type: user - score: 25 - - field: src_user - type: user - score: 25 - threat_objects: [] + message: Azure Guest User $user$ invited by $src_user$ + risk_objects: + - field: user + type: user + score: 25 + - field: src_user + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - asset_type: O365 Tenant - mitre_attack_id: - - T1136.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Persistence + asset_type: O365 Tenant + mitre_attack_id: + - T1136.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_azure_workload_events/o365_azure_workload_events.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_azure_workload_events/o365_azure_workload_events.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_external_identity_policy_changed.yml b/detections/cloud/o365_external_identity_policy_changed.yml index b330117596..d3bec21707 100644 --- a/detections/cloud/o365_external_identity_policy_changed.yml +++ b/detections/cloud/o365_external_identity_policy_changed.yml @@ -5,80 +5,45 @@ date: '2025-05-02' author: Steven Dick status: production type: TTP -description: The following analytic identifies when changes are made to the external - guest policies within Azure AD. With Azure AD B2B collaboration, users and administrators - can invite external users to collaborate with internal users. This detection also - attempts to highlight what may have changed. External guest account invitations - should be monitored by security teams as they could potentially lead to unauthorized - access. An example of this attack vector was described at BlackHat 2022 by security - researcher Dirk-Jan during his tall `Backdooring and Hijacking Azure AD Accounts - by Abusing External Identities`. +description: The following analytic identifies when changes are made to the external guest policies within Azure AD. With Azure AD B2B collaboration, users and administrators can invite external users to collaborate with internal users. This detection also attempts to highlight what may have changed. External guest account invitations should be monitored by security teams as they could potentially lead to unauthorized access. An example of this attack vector was described at BlackHat 2022 by security researcher Dirk-Jan during his tall `Backdooring and Hijacking Azure AD Accounts by Abusing External Identities`. data_source: -- Office 365 Universal Audit Log -search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Update policy.\" Target{}.ID=\"B2BManagementPolicy\" - | eval object_attrs = mvindex('ModifiedProperties{}.NewValue',0), - object_attrs_old = mvindex('ModifiedProperties{}.OldValue',0), object_name = mvindex('Target{}.ID',3), - signature=Operation, user = case(match(mvindex('Actor{}.ID',-1),\"User\"),mvindex('Actor{}.ID',0),match(mvindex('Actor{}.ID',-1),\"ServicePrincipal\"), - mvindex('Actor{}.ID',3),true(),mvindex('Actor{}.ID',0)) - | spath input=object_attrs_old output=B2BOld path={} - | spath input=B2BOld - | rename B2BManagementPolicy.* as B2BManagementPolicyOld.* - | spath input=object_attrs output=B2BNew path={} - | spath input=B2BNew - | eval object_attrs = 'B2BManagementPolicy.InvitationsAllowedAndBlockedDomainsPolicy.AllowedDomains{}' - , object_attrs_old = 'B2BManagementPolicyOld.InvitationsAllowedAndBlockedDomainsPolicy.AllowedDomains{}' - | eval diff_add=mvmap(object_attrs,if(isnull(mvfind(object_attrs_old,object_attrs)),object_attrs,null)) - | eval diff_remove=mvmap(object_attrs_old,if(isnull(mvfind(object_attrs,object_attrs_old)),object_attrs_old,null)) - | eval result = case(isnotnull(diff_add),\"Added \".mvjoin(diff_add,\",\"),isnotnull(diff_remove),\"Removed \".mvjoin(diff_remove,\",\")), action = case(isnotnull(diff_add),\"created\",isnotnull(diff_remove),\"deleted\") - | stats values(object_attrs) as object_attrs, - values(action) as action, values(result) as result, values(B2BManagementPolicy*) - as B2BManagementPolicy*, count, min(_time) as firstTime, max(_time) as lastTime - by user signature object_name dest vendor_account vendor_product - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_external_identity_policy_changed_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. + - Office 365 Universal Audit Log +search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Update policy.\" Target{}.ID=\"B2BManagementPolicy\" | eval object_attrs = mvindex('ModifiedProperties{}.NewValue',0), object_attrs_old = mvindex('ModifiedProperties{}.OldValue',0), object_name = mvindex('Target{}.ID',3), signature=Operation, user = case(match(mvindex('Actor{}.ID',-1),\"User\"),mvindex('Actor{}.ID',0),match(mvindex('Actor{}.ID',-1),\"ServicePrincipal\"), mvindex('Actor{}.ID',3),true(),mvindex('Actor{}.ID',0)) | spath input=object_attrs_old output=B2BOld path={} | spath input=B2BOld | rename B2BManagementPolicy.* as B2BManagementPolicyOld.* | spath input=object_attrs output=B2BNew path={} | spath input=B2BNew | eval object_attrs = 'B2BManagementPolicy.InvitationsAllowedAndBlockedDomainsPolicy.AllowedDomains{}' , object_attrs_old = 'B2BManagementPolicyOld.InvitationsAllowedAndBlockedDomainsPolicy.AllowedDomains{}' | eval diff_add=mvmap(object_attrs,if(isnull(mvfind(object_attrs_old,object_attrs)),object_attrs,null)) | eval diff_remove=mvmap(object_attrs_old,if(isnull(mvfind(object_attrs,object_attrs_old)),object_attrs_old,null)) | eval result = case(isnotnull(diff_add),\"Added \".mvjoin(diff_add,\",\"),isnotnull(diff_remove),\"Removed \".mvjoin(diff_remove,\",\")), action = case(isnotnull(diff_add),\"created\",isnotnull(diff_remove),\"deleted\") | stats values(object_attrs) as object_attrs, values(action) as action, values(result) as result, values(B2BManagementPolicy*) as B2BManagementPolicy*, count, min(_time) as firstTime, max(_time) as lastTime by user signature object_name dest vendor_account vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_external_identity_policy_changed_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. known_false_positives: Business approved changes by known administrators. references: -- https://medium.com/tenable-techblog/roles-allowing-to-abuse-entra-id-federation-for-persistence-and-privilege-escalation-df9ca6e58360 -- https://learn.microsoft.com/en-us/entra/external-id/external-identities-overview + - https://medium.com/tenable-techblog/roles-allowing-to-abuse-entra-id-federation-for-persistence-and-privilege-escalation-df9ca6e58360 + - https://learn.microsoft.com/en-us/entra/external-id/external-identities-overview drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ changed the external identity [$object_name$] policy - risk_objects: - - field: user - type: user - score: 75 - threat_objects: [] + message: User $user$ changed the external identity [$object_name$] policy + risk_objects: + - field: user + type: user + score: 75 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - asset_type: O365 Tenant - mitre_attack_id: - - T1136.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Azure Active Directory Persistence + asset_type: O365 Tenant + mitre_attack_id: + - T1136.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_azure_workload_events/o365_azure_workload_events.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_azure_workload_events/o365_azure_workload_events.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_file_permissioned_application_consent_granted_by_user.yml b/detections/cloud/o365_file_permissioned_application_consent_granted_by_user.yml index 89badf917d..0a4e01d5eb 100644 --- a/detections/cloud/o365_file_permissioned_application_consent_granted_by_user.yml +++ b/detections/cloud/o365_file_permissioned_application_consent_granted_by_user.yml @@ -6,72 +6,47 @@ author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- O365 Consent to application. -description: The following analytic identifies instances where a user in the Office - 365 environment grants consent to an application requesting file permissions for - OneDrive or SharePoint. It leverages O365 audit logs, focusing on OAuth application - consent events. This activity is significant because granting such permissions can - allow applications to access, modify, or delete files, posing a risk if the application - is malicious or overly permissive. If confirmed malicious, this could lead to data - breaches, data loss, or unauthorized data manipulation, necessitating immediate - investigation to validate the application's legitimacy and assess potential risks. -search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Consent to application.\" ResultStatus=Success - | eval admin_consent =mvindex('ModifiedProperties{}.NewValue',0) - | search admin_consent=False - | eval permissions =mvindex('ModifiedProperties{}.NewValue',4) - | rex field=permissions \"Scope:(?[^,]+)\" - | makemv delim=\" \" Scope - | search Scope IN (\"Files.Read\", \"Files.Read.All\", \"Files.ReadWrite\", \"Files.ReadWrite.All\", \"Files.ReadWrite.AppFolder\") - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime values(Scope) as Scope by signature dest user src vendor_account vendor_product object ObjectId - | `security_content_ctime(lastTime)` - | `o365_file_permissioned_application_consent_granted_by_user_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: OAuth applications that require file permissions may be legitimate, - investigate and filter as needed. + - O365 Consent to application. +description: The following analytic identifies instances where a user in the Office 365 environment grants consent to an application requesting file permissions for OneDrive or SharePoint. It leverages O365 audit logs, focusing on OAuth application consent events. This activity is significant because granting such permissions can allow applications to access, modify, or delete files, posing a risk if the application is malicious or overly permissive. If confirmed malicious, this could lead to data breaches, data loss, or unauthorized data manipulation, necessitating immediate investigation to validate the application's legitimacy and assess potential risks. +search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Consent to application.\" ResultStatus=Success | eval admin_consent =mvindex('ModifiedProperties{}.NewValue',0) | search admin_consent=False | eval permissions =mvindex('ModifiedProperties{}.NewValue',4) | rex field=permissions \"Scope:(?[^,]+)\" | makemv delim=\" \" Scope | search Scope IN (\"Files.Read\", \"Files.Read.All\", \"Files.ReadWrite\", \"Files.ReadWrite.All\", \"Files.ReadWrite.AppFolder\") | fillnull | stats count min(_time) as firstTime max(_time) as lastTime values(Scope) as Scope by signature dest user src vendor_account vendor_product object ObjectId | `security_content_ctime(lastTime)` | `o365_file_permissioned_application_consent_granted_by_user_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: OAuth applications that require file permissions may be legitimate, investigate and filter as needed. references: -- https://attack.mitre.org/techniques/T1528/ -- https://www.microsoft.com/en-us/security/blog/2022/09/22/malicious-oauth-applications-used-to-compromise-email-servers-and-spread-spam/ -- https://learn.microsoft.com/en-us/defender-cloud-apps/investigate-risky-oauth -- https://www.alteredsecurity.com/post/introduction-to-365-stealer -- https://github.com/AlteredSecurity/365-Stealer + - https://attack.mitre.org/techniques/T1528/ + - https://www.microsoft.com/en-us/security/blog/2022/09/22/malicious-oauth-applications-used-to-compromise-email-servers-and-spread-spam/ + - https://learn.microsoft.com/en-us/defender-cloud-apps/investigate-risky-oauth + - https://www.alteredsecurity.com/post/introduction-to-365-stealer + - https://github.com/AlteredSecurity/365-Stealer drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ consented an OAuth application that requests file-related permissions. - risk_objects: - - field: user - type: user - score: 40 - threat_objects: [] + message: User $user$ consented an OAuth application that requests file-related permissions. + risk_objects: + - field: user + type: user + score: 40 + threat_objects: [] tags: - analytic_story: - - Office 365 Account Takeover - asset_type: O365 Tenant - mitre_attack_id: - - T1528 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Office 365 Account Takeover + asset_type: O365 Tenant + mitre_attack_id: + - T1528 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/o365_user_consent_file_permissions/o365_user_consent_file_permissions.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/o365_user_consent_file_permissions/o365_user_consent_file_permissions.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_fullaccessasapp_permission_assigned.yml b/detections/cloud/o365_fullaccessasapp_permission_assigned.yml index 24e57ca3aa..d481a102c0 100644 --- a/detections/cloud/o365_fullaccessasapp_permission_assigned.yml +++ b/detections/cloud/o365_fullaccessasapp_permission_assigned.yml @@ -6,72 +6,47 @@ author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- O365 Update application. -description: The following analytic detects the assignment of the 'full_access_as_app' - permission to an application registration in Office 365 Exchange Online. This detection - leverages Office 365 management activity logs and filters Azure Active Directory - workload events to identify when the specific permission, identified by GUID 'dc890d15-9560-4a4c-9b7f-a736ec74ec40', - is granted. This activity is significant because it provides extensive control over - Office 365 operations, including access to all mailboxes and the ability to send - mail as any user. If confirmed malicious, this could lead to unauthorized data access, - exfiltration, or account compromise. Immediate investigation is required. -search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Update application.\" - | eval newvalue = mvindex('ModifiedProperties{}.NewValue',0) - | spath input=newvalue - | search \"{}.ResourceAppId\"=\"00000002-0000-0ff1-ce00-000000000000\"\"{}.RequiredAppPermissions{}.EntitlementId\"=\"dc890d15-9560-4a4c-9b7f-a736ec74ec40\" - | eval Permissions = '{}.RequiredAppPermissions{}.EntitlementId' - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime values(Scope) as Scope by signature dest user src vendor_account vendor_product object user_agent - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_fullaccessasapp_permission_assigned_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: The full_access_as_app API permission may be assigned to legitimate - applications. Filter as needed. + - O365 Update application. +description: The following analytic detects the assignment of the 'full_access_as_app' permission to an application registration in Office 365 Exchange Online. This detection leverages Office 365 management activity logs and filters Azure Active Directory workload events to identify when the specific permission, identified by GUID 'dc890d15-9560-4a4c-9b7f-a736ec74ec40', is granted. This activity is significant because it provides extensive control over Office 365 operations, including access to all mailboxes and the ability to send mail as any user. If confirmed malicious, this could lead to unauthorized data access, exfiltration, or account compromise. Immediate investigation is required. +search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Update application.\" | eval newvalue = mvindex('ModifiedProperties{}.NewValue',0) | spath input=newvalue | search \"{}.ResourceAppId\"=\"00000002-0000-0ff1-ce00-000000000000\"\"{}.RequiredAppPermissions{}.EntitlementId\"=\"dc890d15-9560-4a4c-9b7f-a736ec74ec40\" | eval Permissions = '{}.RequiredAppPermissions{}.EntitlementId' | fillnull | stats count min(_time) as firstTime max(_time) as lastTime values(Scope) as Scope by signature dest user src vendor_account vendor_product object user_agent | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_fullaccessasapp_permission_assigned_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: The full_access_as_app API permission may be assigned to legitimate applications. Filter as needed. references: -- https://msrc.microsoft.com/blog/2024/01/microsoft-actions-following-attack-by-nation-state-actor-midnight-blizzard/ -- https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/ -- https://attack.mitre.org/techniques/T1098/002/ + - https://msrc.microsoft.com/blog/2024/01/microsoft-actions-following-attack-by-nation-state-actor-midnight-blizzard/ + - https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/ + - https://attack.mitre.org/techniques/T1098/002/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ assigned the full_access_as_app permission to the app registration - $object$ - risk_objects: - - field: user - type: user - score: 48 - threat_objects: [] + message: User $user$ assigned the full_access_as_app permission to the app registration $object$ + risk_objects: + - field: user + type: user + score: 48 + threat_objects: [] tags: - analytic_story: - - Office 365 Persistence Mechanisms - - NOBELIUM Group - asset_type: O365 Tenant - mitre_attack_id: - - T1098.002 - - T1098.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Office 365 Persistence Mechanisms + - NOBELIUM Group + asset_type: O365 Tenant + mitre_attack_id: + - T1098.002 + - T1098.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.002/o365_full_access_as_app_permission_assigned/o365_full_access_as_app_permission_assigned.log - source: o365:management:activity - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.002/o365_full_access_as_app_permission_assigned/o365_full_access_as_app_permission_assigned.log + source: o365:management:activity + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_high_number_of_failed_authentications_for_user.yml b/detections/cloud/o365_high_number_of_failed_authentications_for_user.yml index 38d740db21..23a1275b06 100644 --- a/detections/cloud/o365_high_number_of_failed_authentications_for_user.yml +++ b/detections/cloud/o365_high_number_of_failed_authentications_for_user.yml @@ -1,72 +1,59 @@ name: O365 High Number Of Failed Authentications for User id: 31641378-2fa9-42b1-948e-25e281cb98f7 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- O365 UserLoginFailed -description: The following analytic identifies an O365 account experiencing more than - 20 failed authentication attempts within 5 minutes. It uses O365 Unified Audit Logs, - specifically "UserLoginFailed" events, to monitor and flag accounts exceeding this - threshold. This activity is significant as it may indicate a brute force attack - or password guessing attempt. If confirmed malicious, an attacker could gain unauthorized - access to the O365 environment, potentially compromising sensitive emails, documents, - and other data. Prompt investigation and action are crucial to prevent unauthorized - access and data breaches. -search: '`o365_management_activity` Operation=UserLoginFailed record_type=AzureActiveDirectoryStsLogon Workload=AzureActiveDirectory - | bucket span=5m _time - | fillnull - | stats dc(_raw) AS failed_attempts values(src_ip) as src by signature user _time dest vendor_account vendor_product - | where failed_attempts > 10 - | `o365_high_number_of_failed_authentications_for_user_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Although unusual, users who have lost their passwords may trigger - this detection. Filter as needed. + - O365 UserLoginFailed +description: The following analytic identifies an O365 account experiencing more than 20 failed authentication attempts within 5 minutes. It uses O365 Unified Audit Logs, specifically "UserLoginFailed" events, to monitor and flag accounts exceeding this threshold. This activity is significant as it may indicate a brute force attack or password guessing attempt. If confirmed malicious, an attacker could gain unauthorized access to the O365 environment, potentially compromising sensitive emails, documents, and other data. Prompt investigation and action are crucial to prevent unauthorized access and data breaches. +search: |- + `o365_management_activity` Operation=UserLoginFailed record_type=AzureActiveDirectoryStsLogon Workload=AzureActiveDirectory + | bucket span=5m _time + | fillnull + | stats dc(_raw) AS failed_attempts values(src_ip) as src + BY signature user _time + dest vendor_account vendor_product + | where failed_attempts > 10 + | `o365_high_number_of_failed_authentications_for_user_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Although unusual, users who have lost their passwords may trigger this detection. Filter as needed. references: -- https://attack.mitre.org/techniques/T1110/ -- https://attack.mitre.org/techniques/T1110/001/ + - https://attack.mitre.org/techniques/T1110/ + - https://attack.mitre.org/techniques/T1110/001/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ failed to authenticate more than 10 times in the span of 5 - minutes. - risk_objects: - - field: user - type: user - score: 35 - threat_objects: - - field: src - type: ip_address + message: User $user$ failed to authenticate more than 10 times in the span of 5 minutes. + risk_objects: + - field: user + type: user + score: 35 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Office 365 Account Takeover - asset_type: O365 Tenant - mitre_attack_id: - - T1110.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Office 365 Account Takeover + asset_type: O365 Tenant + mitre_attack_id: + - T1110.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/o365_high_number_authentications_for_user/o365_high_number_authentications_for_user.log - source: o365:management:activity - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/o365_high_number_authentications_for_user/o365_high_number_authentications_for_user.log + source: o365:management:activity + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_high_privilege_role_granted.yml b/detections/cloud/o365_high_privilege_role_granted.yml index b4dbd8cb3d..5d6f7ee5b4 100644 --- a/detections/cloud/o365_high_privilege_role_granted.yml +++ b/detections/cloud/o365_high_privilege_role_granted.yml @@ -6,69 +6,46 @@ author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- O365 Add member to role. -description: The following analytic detects when high-privilege roles such as "Exchange - Administrator," "SharePoint Administrator," or "Global Administrator" are granted - within Office 365. It leverages O365 audit logs to identify events where these roles - are assigned to any user or service account. This activity is significant for SOCs - as these roles provide extensive permissions, allowing broad access and control - over critical resources and data. If confirmed malicious, this could enable attackers - to gain significant control over O365 resources, access, modify, or delete critical - data, and compromise the overall security and functionality of the O365 environment. -search: "`o365_management_activity` Operation=\"Add member to role.\" Workload=AzureActiveDirectory - | eval role_id = mvindex('ModifiedProperties{}.NewValue',2) - | eval role_name = mvindex('ModifiedProperties{}.NewValue',1) - | where role_id IN (\"29232cdf-9323-42fd-ade2-1d097af3e4de\", \"f28a1f50-f6e7-4571-818b-6a12f2af6b6c\", \"62e90394-69f5-4237-9190-012177145e10\") - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user src vendor_account vendor_product ObjectId role_name role_id - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_high_privilege_role_granted_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Privilege roles may be assigned for legitimate purposes, filter - as needed. + - O365 Add member to role. +description: The following analytic detects when high-privilege roles such as "Exchange Administrator," "SharePoint Administrator," or "Global Administrator" are granted within Office 365. It leverages O365 audit logs to identify events where these roles are assigned to any user or service account. This activity is significant for SOCs as these roles provide extensive permissions, allowing broad access and control over critical resources and data. If confirmed malicious, this could enable attackers to gain significant control over O365 resources, access, modify, or delete critical data, and compromise the overall security and functionality of the O365 environment. +search: "`o365_management_activity` Operation=\"Add member to role.\" Workload=AzureActiveDirectory | eval role_id = mvindex('ModifiedProperties{}.NewValue',2) | eval role_name = mvindex('ModifiedProperties{}.NewValue',1) | where role_id IN (\"29232cdf-9323-42fd-ade2-1d097af3e4de\", \"f28a1f50-f6e7-4571-818b-6a12f2af6b6c\", \"62e90394-69f5-4237-9190-012177145e10\") | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user src vendor_account vendor_product ObjectId role_name role_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_high_privilege_role_granted_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Privilege roles may be assigned for legitimate purposes, filter as needed. references: -- https://attack.mitre.org/techniques/T1098/003/ -- https://learn.microsoft.com/en-us/azure/active-directory/roles/permissions-reference -- https://learn.microsoft.com/en-us/microsoft-365/admin/add-users/about-exchange-online-admin-role?view=o365-worldwide -- https://learn.microsoft.com/en-us/sharepoint/sharepoint-admin-role + - https://attack.mitre.org/techniques/T1098/003/ + - https://learn.microsoft.com/en-us/azure/active-directory/roles/permissions-reference + - https://learn.microsoft.com/en-us/microsoft-365/admin/add-users/about-exchange-online-admin-role?view=o365-worldwide + - https://learn.microsoft.com/en-us/sharepoint/sharepoint-admin-role drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $user$ granted high privilege roles to $ObjectId$ - risk_objects: - - field: user - type: user - score: 48 - threat_objects: [] + message: $user$ granted high privilege roles to $ObjectId$ + risk_objects: + - field: user + type: user + score: 48 + threat_objects: [] tags: - analytic_story: - - Office 365 Persistence Mechanisms - asset_type: O365 Tenant - mitre_attack_id: - - T1098.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Office 365 Persistence Mechanisms + asset_type: O365 Tenant + mitre_attack_id: + - T1098.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/o365_high_priv_role_assigned/o365_high_priv_role_assigned.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/o365_high_priv_role_assigned/o365_high_priv_role_assigned.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_mail_permissioned_application_consent_granted_by_user.yml b/detections/cloud/o365_mail_permissioned_application_consent_granted_by_user.yml index 932d078d91..152cdede72 100644 --- a/detections/cloud/o365_mail_permissioned_application_consent_granted_by_user.yml +++ b/detections/cloud/o365_mail_permissioned_application_consent_granted_by_user.yml @@ -6,74 +6,48 @@ author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- O365 Consent to application. -description: The following analytic identifies instances where a user grants consent - to an application requesting mail-related permissions within the Office 365 environment. - It leverages O365 audit logs, specifically focusing on events related to application - permissions and user consent actions. This activity is significant as it can indicate - potential security risks, such as data exfiltration or spear phishing, if malicious - applications gain access. If confirmed malicious, this could lead to unauthorized - data access, email forwarding, or sending malicious emails from the compromised - account. Validating the legitimacy of the application and consent context is crucial - to prevent data breaches. -search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Consent to application.\" ResultStatus=Success - | eval admin_consent =mvindex('ModifiedProperties{}.NewValue',0) - | search admin_consent=False - | eval permissions =mvindex('ModifiedProperties{}.NewValue',4) - | rex field=permissions \"Scope:(?[^,]+)\" - | makemv delim=\" \" Scope - | search Scope IN (\"Mail.Read\", \"Mail.ReadBasic\", \"Mail.ReadWrite\", \"Mail.Read.Shared\", \"Mail.ReadWrite.Shared\", \"Mail.Send\", \"Mail.Send.Shared\") - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime values(Scope) as Scope by signature dest user src vendor_account vendor_product object ObjectId - | `security_content_ctime(lastTime)` - | `o365_mail_permissioned_application_consent_granted_by_user_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: OAuth applications that require mail permissions may be legitimate, - investigate and filter as needed. + - O365 Consent to application. +description: The following analytic identifies instances where a user grants consent to an application requesting mail-related permissions within the Office 365 environment. It leverages O365 audit logs, specifically focusing on events related to application permissions and user consent actions. This activity is significant as it can indicate potential security risks, such as data exfiltration or spear phishing, if malicious applications gain access. If confirmed malicious, this could lead to unauthorized data access, email forwarding, or sending malicious emails from the compromised account. Validating the legitimacy of the application and consent context is crucial to prevent data breaches. +search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Consent to application.\" ResultStatus=Success | eval admin_consent =mvindex('ModifiedProperties{}.NewValue',0) | search admin_consent=False | eval permissions =mvindex('ModifiedProperties{}.NewValue',4) | rex field=permissions \"Scope:(?[^,]+)\" | makemv delim=\" \" Scope | search Scope IN (\"Mail.Read\", \"Mail.ReadBasic\", \"Mail.ReadWrite\", \"Mail.Read.Shared\", \"Mail.ReadWrite.Shared\", \"Mail.Send\", \"Mail.Send.Shared\") | fillnull | stats count min(_time) as firstTime max(_time) as lastTime values(Scope) as Scope by signature dest user src vendor_account vendor_product object ObjectId | `security_content_ctime(lastTime)` | `o365_mail_permissioned_application_consent_granted_by_user_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: OAuth applications that require mail permissions may be legitimate, investigate and filter as needed. references: -- https://attack.mitre.org/techniques/T1528/ -- https://www.microsoft.com/en-us/security/blog/2022/09/22/malicious-oauth-applications-used-to-compromise-email-servers-and-spread-spam/ -- https://learn.microsoft.com/en-us/azure/active-directory/manage-apps/protect-against-consent-phishing -- https://learn.microsoft.com/en-us/defender-cloud-apps/investigate-risky-oauth -- https://www.alteredsecurity.com/post/introduction-to-365-stealer -- https://github.com/AlteredSecurity/365-Stealer + - https://attack.mitre.org/techniques/T1528/ + - https://www.microsoft.com/en-us/security/blog/2022/09/22/malicious-oauth-applications-used-to-compromise-email-servers-and-spread-spam/ + - https://learn.microsoft.com/en-us/azure/active-directory/manage-apps/protect-against-consent-phishing + - https://learn.microsoft.com/en-us/defender-cloud-apps/investigate-risky-oauth + - https://www.alteredsecurity.com/post/introduction-to-365-stealer + - https://github.com/AlteredSecurity/365-Stealer drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ consented an OAuth application that requests mail-related permissions. - risk_objects: - - field: user - type: user - score: 40 - threat_objects: [] + message: User $user$ consented an OAuth application that requests mail-related permissions. + risk_objects: + - field: user + type: user + score: 40 + threat_objects: [] tags: - analytic_story: - - Office 365 Account Takeover - asset_type: O365 Tenant - mitre_attack_id: - - T1528 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Office 365 Account Takeover + asset_type: O365 Tenant + mitre_attack_id: + - T1528 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/o365_user_consent_mail_permissions/o365_user_consent_mail_permissions.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/o365_user_consent_mail_permissions/o365_user_consent_mail_permissions.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_mailbox_email_forwarding_enabled.yml b/detections/cloud/o365_mailbox_email_forwarding_enabled.yml index 6a89c3d8e8..174c3b139e 100644 --- a/detections/cloud/o365_mailbox_email_forwarding_enabled.yml +++ b/detections/cloud/o365_mailbox_email_forwarding_enabled.yml @@ -6,68 +6,43 @@ author: Patrick Bareiss, Mauricio Velazco, Splunk data_source: [] type: TTP status: production -description: The following analytic identifies instances where email forwarding has - been enabled on mailboxes within an Office 365 environment. It detects this activity - by monitoring the Set-Mailbox operation within the o365_management_activity logs, - specifically looking for changes to the ForwardingAddress or ForwardingSmtpAddress - parameters. This activity is significant as unauthorized email forwarding can lead - to data exfiltration and unauthorized access to sensitive information. If confirmed - malicious, attackers could intercept and redirect emails, potentially compromising - confidential communications and leading to data breaches. -search: "`o365_management_activity` Operation=Set-Mailbox - | eval match1=mvfind('Parameters{}.Name',\"ForwardingAddress\") - | eval match2=mvfind('Parameters{}.Name', \"ForwardingSmtpAddress\") - | where match1>= 0 OR match2>= 0 - | eval ForwardTo=coalesce(ForwardingAddress,ForwardingSmtpAddress) - | search ForwardTo!=\"\" - | rename user_id as user - | stats count earliest(_time) as firstTime latest(_time) as lastTime values(ForwardTo) as ForwardTo by signature dest user src vendor_account vendor_product object ObjectId - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_mailbox_email_forwarding_enabled_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Email forwarding may be configured for legitimate purposes, - filter as needed. +description: The following analytic identifies instances where email forwarding has been enabled on mailboxes within an Office 365 environment. It detects this activity by monitoring the Set-Mailbox operation within the o365_management_activity logs, specifically looking for changes to the ForwardingAddress or ForwardingSmtpAddress parameters. This activity is significant as unauthorized email forwarding can lead to data exfiltration and unauthorized access to sensitive information. If confirmed malicious, attackers could intercept and redirect emails, potentially compromising confidential communications and leading to data breaches. +search: "`o365_management_activity` Operation=Set-Mailbox | eval match1=mvfind('Parameters{}.Name',\"ForwardingAddress\") | eval match2=mvfind('Parameters{}.Name', \"ForwardingSmtpAddress\") | where match1>= 0 OR match2>= 0 | eval ForwardTo=coalesce(ForwardingAddress,ForwardingSmtpAddress) | search ForwardTo!=\"\" | rename user_id as user | stats count earliest(_time) as firstTime latest(_time) as lastTime values(ForwardTo) as ForwardTo by signature dest user src vendor_account vendor_product object ObjectId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_mailbox_email_forwarding_enabled_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Email forwarding may be configured for legitimate purposes, filter as needed. references: -- https://attack.mitre.org/techniques/T1114/003/ -- https://learn.microsoft.com/en-us/exchange/recipients/user-mailboxes/email-forwarding?view=exchserver-2019 + - https://attack.mitre.org/techniques/T1114/003/ + - https://learn.microsoft.com/en-us/exchange/recipients/user-mailboxes/email-forwarding?view=exchserver-2019 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Email forwarding configured by $user$ on mailbox $ObjectId$ - risk_objects: - - field: user - type: user - score: 42 - threat_objects: [] + message: Email forwarding configured by $user$ on mailbox $ObjectId$ + risk_objects: + - field: user + type: user + score: 42 + threat_objects: [] tags: - analytic_story: - - Office 365 Collection Techniques - asset_type: O365 Tenant - mitre_attack_id: - - T1114.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Collection Techniques + asset_type: O365 Tenant + mitre_attack_id: + - T1114.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_mailbox_forwarding_enabled/o365_mailbox_forwarding_enabled.json - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_mailbox_forwarding_enabled/o365_mailbox_forwarding_enabled.json + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_mailbox_folder_read_permission_assigned.yml b/detections/cloud/o365_mailbox_folder_read_permission_assigned.yml index 492344e073..e6298a876b 100644 --- a/detections/cloud/o365_mailbox_folder_read_permission_assigned.yml +++ b/detections/cloud/o365_mailbox_folder_read_permission_assigned.yml @@ -4,68 +4,47 @@ version: 8 date: '2025-10-21' author: Mauricio Velazco, Splunk data_source: - - O365 ModifyFolderPermissions + - O365 ModifyFolderPermissions type: TTP status: production -description: The following analytic identifies instances where read permissions are - assigned to mailbox folders within an Office 365 environment. It leverages the `o365_management_activity` - data source, specifically monitoring the `ModifyFolderPermissions` and `AddFolderPermissions` - operations, while excluding Calendar, Contacts, and PersonMetadata objects. This - activity is significant as unauthorized read permissions can lead to data exposure - and potential information leakage. If confirmed malicious, an attacker could gain - unauthorized access to sensitive emails, leading to data breaches and compromising - the confidentiality of organizational communications. -search: "`o365_management_activity` Workload=Exchange (Operation=ModifyFolderPermissions OR Operation=AddFolderPermissions) Workload=Exchange object!=Calendar object!=Contacts object!=PersonMetadata - | eval isReadRole=if(match('Item.ParentFolder.MemberRights',\"(ReadAny)\"), \"true\", \"false\") - | rename UserId as user - | stats count earliest(_time) as firstTime latest(_time) as lastTime by signature user object dest Item.ParentFolder.MemberUpn Item.ParentFolder.MemberRights src vendor_account vendor_product - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_mailbox_folder_read_permission_assigned_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Mailbox folder permissions may be configured for legitimate - purposes, filter as needed. +description: The following analytic identifies instances where read permissions are assigned to mailbox folders within an Office 365 environment. It leverages the `o365_management_activity` data source, specifically monitoring the `ModifyFolderPermissions` and `AddFolderPermissions` operations, while excluding Calendar, Contacts, and PersonMetadata objects. This activity is significant as unauthorized read permissions can lead to data exposure and potential information leakage. If confirmed malicious, an attacker could gain unauthorized access to sensitive emails, leading to data breaches and compromising the confidentiality of organizational communications. +search: "`o365_management_activity` Workload=Exchange (Operation=ModifyFolderPermissions OR Operation=AddFolderPermissions) Workload=Exchange object!=Calendar object!=Contacts object!=PersonMetadata | eval isReadRole=if(match('Item.ParentFolder.MemberRights',\"(ReadAny)\"), \"true\", \"false\") | rename UserId as user | stats count earliest(_time) as firstTime latest(_time) as lastTime by signature user object dest Item.ParentFolder.MemberUpn Item.ParentFolder.MemberRights src vendor_account vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_mailbox_folder_read_permission_assigned_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Mailbox folder permissions may be configured for legitimate purposes, filter as needed. references: -- https://attack.mitre.org/techniques/T1098/002/ -- https://learn.microsoft.com/en-us/openspecs/exchange_server_protocols/ms-oxodlgt/5610c6e6-3268-44e3-adff-8804f5315946 -- https://learn.microsoft.com/en-us/purview/audit-mailboxes + - https://attack.mitre.org/techniques/T1098/002/ + - https://learn.microsoft.com/en-us/openspecs/exchange_server_protocols/ms-oxodlgt/5610c6e6-3268-44e3-adff-8804f5315946 + - https://learn.microsoft.com/en-us/purview/audit-mailboxes drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A folder was granted read permission by $user$ - risk_objects: - - field: user - type: user - score: 42 - threat_objects: [] + message: A folder was granted read permission by $user$ + risk_objects: + - field: user + type: user + score: 42 + threat_objects: [] tags: - analytic_story: - - Office 365 Collection Techniques - asset_type: O365 Tenant - mitre_attack_id: - - T1098.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: audit + analytic_story: + - Office 365 Collection Techniques + asset_type: O365 Tenant + mitre_attack_id: + - T1098.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: audit tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.002/o365_mailbox_folder_read_granted/o365_mailbox_folder_read_granted.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.002/o365_mailbox_folder_read_granted/o365_mailbox_folder_read_granted.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_mailbox_folder_read_permission_granted.yml b/detections/cloud/o365_mailbox_folder_read_permission_granted.yml index c4d88689b3..20aae6ded9 100644 --- a/detections/cloud/o365_mailbox_folder_read_permission_granted.yml +++ b/detections/cloud/o365_mailbox_folder_read_permission_granted.yml @@ -1,73 +1,63 @@ name: O365 Mailbox Folder Read Permission Granted id: cd15c0a8-470e-4b12-9517-046e4927db30 -version: 9 -date: '2025-10-21' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk data_source: - - O365 ModifyFolderPermissions + - O365 ModifyFolderPermissions type: TTP status: production -description: The following analytic identifies instances where read permissions are - granted to mailbox folders within an Office 365 environment. It detects this activity - by monitoring the `o365_management_activity` data source for the `Set-MailboxFolderPermission` - and `Add-MailboxFolderPermission` operations. This behavior is significant as it - may indicate unauthorized access or changes to mailbox folder permissions, potentially - exposing sensitive email content. If confirmed malicious, an attacker could gain - unauthorized access to read email communications, leading to data breaches or information - leakage. -search: '`o365_management_activity` Workload=Exchange (Operation="Set-MailboxFolderPermission" OR Operation="Add-MailboxFolderPermission" ) - | eval isReadRole=if(match(AccessRights,"^(ReadItems|Author|NonEditingAuthor|Owner|PublishingAuthor|Reviewer)$"), "true", "false") - | search isReadRole="true" - | rename UserId as user - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by signature dest user src vendor_account vendor_product Identity AccessRights - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_mailbox_folder_read_permission_granted_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Mailbox folder permissions may be configured for legitimate - purposes, filter as needed. +description: The following analytic identifies instances where read permissions are granted to mailbox folders within an Office 365 environment. It detects this activity by monitoring the `o365_management_activity` data source for the `Set-MailboxFolderPermission` and `Add-MailboxFolderPermission` operations. This behavior is significant as it may indicate unauthorized access or changes to mailbox folder permissions, potentially exposing sensitive email content. If confirmed malicious, an attacker could gain unauthorized access to read email communications, leading to data breaches or information leakage. +search: |- + `o365_management_activity` + Workload=Exchange + Operation IN ("Set-MailboxFolderPermission", "Add-MailboxFolderPermission") + | eval isReadRole=if(match(AccessRights,"^(ReadItems|Author|NonEditingAuthor|Owner|PublishingAuthor|Reviewer)$"), "true", "false") + | search isReadRole="true" + | rename UserId as user + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + by signature dest user src vendor_account + vendor_product Identity AccessRights + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_mailbox_folder_read_permission_granted_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Mailbox folder permissions may be configured for legitimate purposes, filter as needed. references: -- https://attack.mitre.org/techniques/T1098/002/ -- https://learn.microsoft.com/en-us/powershell/module/exchange/add-mailboxfolderpermission?view=exchange-ps -- https://learn.microsoft.com/en-us/powershell/module/exchange/set-mailboxfolderpermission?view=exchange-ps + - https://attack.mitre.org/techniques/T1098/002/ + - https://learn.microsoft.com/en-us/powershell/module/exchange/add-mailboxfolderpermission?view=exchange-ps + - https://learn.microsoft.com/en-us/powershell/module/exchange/set-mailboxfolderpermission?view=exchange-ps drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A folder was granted read permission by $user$ - risk_objects: - - field: user - type: user - score: 42 - threat_objects: [] + message: A folder was granted read permission by $user$ + risk_objects: + - field: user + type: user + score: 42 + threat_objects: [] tags: - analytic_story: - - Office 365 Collection Techniques - asset_type: O365 Tenant - mitre_attack_id: - - T1098.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: audit + analytic_story: + - Office 365 Collection Techniques + asset_type: O365 Tenant + mitre_attack_id: + - T1098.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: audit tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.002/o365_mailbox_folder_read_granted/o365_mailbox_folder_read_granted.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.002/o365_mailbox_folder_read_granted/o365_mailbox_folder_read_granted.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_mailbox_inbox_folder_shared_with_all_users.yml b/detections/cloud/o365_mailbox_inbox_folder_shared_with_all_users.yml index d6830ccfca..b4ef20c7e4 100644 --- a/detections/cloud/o365_mailbox_inbox_folder_shared_with_all_users.yml +++ b/detections/cloud/o365_mailbox_inbox_folder_shared_with_all_users.yml @@ -6,72 +6,47 @@ author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- O365 ModifyFolderPermissions -description: The following analytic detects instances where the inbox folder of an - Office 365 mailbox is shared with all users within the tenant. It leverages Office - 365 management activity events to identify when the 'Inbox' folder permissions are - modified to include 'Everyone' with read rights. This activity is significant as - it represents a potential security risk, allowing unauthorized access to sensitive - emails. If confirmed malicious, this could lead to data breaches, exfiltration of - confidential information, and further compromise through spear-phishing or other - malicious activities based on the accessed email content. -search: "`o365_management_activity` Operation=ModifyFolderPermissions Workload=Exchange object=Inbox Item.ParentFolder.MemberUpn=Everyone - | eval isReadRole=if(match('Item.ParentFolder.MemberRights',\"(ReadAny)\"), \"true\", \"false\") - | search isReadRole = \"true\" - | rename UserId as user - | fillnull - | stats count earliest(_time) as firstTime latest(_time) as lastTime by signature, user, dest, vendor_account, vendor_product, object, MailboxOwnerUPN, Item.ParentFolder.MemberUpn, Item.ParentFolder.MemberRights, src - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_mailbox_inbox_folder_shared_with_all_users_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Administrators might temporarily share a mailbox with all users - for legitimate reasons, such as troubleshooting, migrations, or other administrative - tasks. Some organizations use shared mailboxes for teams or departments where multiple - users need access to the same mailbox. Filter as needed. + - O365 ModifyFolderPermissions +description: The following analytic detects instances where the inbox folder of an Office 365 mailbox is shared with all users within the tenant. It leverages Office 365 management activity events to identify when the 'Inbox' folder permissions are modified to include 'Everyone' with read rights. This activity is significant as it represents a potential security risk, allowing unauthorized access to sensitive emails. If confirmed malicious, this could lead to data breaches, exfiltration of confidential information, and further compromise through spear-phishing or other malicious activities based on the accessed email content. +search: "`o365_management_activity` Operation=ModifyFolderPermissions Workload=Exchange object=Inbox Item.ParentFolder.MemberUpn=Everyone | eval isReadRole=if(match('Item.ParentFolder.MemberRights',\"(ReadAny)\"), \"true\", \"false\") | search isReadRole = \"true\" | rename UserId as user | fillnull | stats count earliest(_time) as firstTime latest(_time) as lastTime by signature, user, dest, vendor_account, vendor_product, object, MailboxOwnerUPN, Item.ParentFolder.MemberUpn, Item.ParentFolder.MemberRights, src | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_mailbox_inbox_folder_shared_with_all_users_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Administrators might temporarily share a mailbox with all users for legitimate reasons, such as troubleshooting, migrations, or other administrative tasks. Some organizations use shared mailboxes for teams or departments where multiple users need access to the same mailbox. Filter as needed. references: -- https://attack.mitre.org/techniques/T1114/002/ -- https://www.mandiant.com/sites/default/files/2022-08/remediation-hardening-strategies-for-m365-defend-against-apt29-white-paper.pdf -- https://www.blackhillsinfosec.com/abusing-exchange-mailbox-permissions-mailsniper/ -- https://learn.microsoft.com/en-us/purview/audit-mailboxes -- https://learn.microsoft.com/en-us/openspecs/exchange_server_protocols/ms-oxodlgt/5610c6e6-3268-44e3-adff-8804f5315946 + - https://attack.mitre.org/techniques/T1114/002/ + - https://www.mandiant.com/sites/default/files/2022-08/remediation-hardening-strategies-for-m365-defend-against-apt29-white-paper.pdf + - https://www.blackhillsinfosec.com/abusing-exchange-mailbox-permissions-mailsniper/ + - https://learn.microsoft.com/en-us/purview/audit-mailboxes + - https://learn.microsoft.com/en-us/openspecs/exchange_server_protocols/ms-oxodlgt/5610c6e6-3268-44e3-adff-8804f5315946 drilldown_searches: -- name: View the detection results for - "$MailboxOwnerUPN$" - search: '%original_detection_search% | search MailboxOwnerUPN = "$MailboxOwnerUPN$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$MailboxOwnerUPN$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$MailboxOwnerUPN$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$MailboxOwnerUPN$" + search: '%original_detection_search% | search MailboxOwnerUPN = "$MailboxOwnerUPN$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$MailboxOwnerUPN$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$MailboxOwnerUPN$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Inbox folder for the $MailboxOwnerUPN$ mailbox was shared with all users. - risk_objects: - - field: MailboxOwnerUPN - type: user - score: 56 - threat_objects: [] + message: Inbox folder for the $MailboxOwnerUPN$ mailbox was shared with all users. + risk_objects: + - field: MailboxOwnerUPN + type: user + score: 56 + threat_objects: [] tags: - analytic_story: - - Office 365 Persistence Mechanisms - asset_type: O365 Tenant - mitre_attack_id: - - T1114.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - Office 365 Persistence Mechanisms + asset_type: O365 Tenant + mitre_attack_id: + - T1114.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/o365_inbox_shared_with_all_users/o365_inbox_shared_with_all_users.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/o365_inbox_shared_with_all_users/o365_inbox_shared_with_all_users.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_mailbox_read_access_granted_to_application.yml b/detections/cloud/o365_mailbox_read_access_granted_to_application.yml index 6b01377a87..6e5a87aad1 100644 --- a/detections/cloud/o365_mailbox_read_access_granted_to_application.yml +++ b/detections/cloud/o365_mailbox_read_access_granted_to_application.yml @@ -6,75 +6,49 @@ author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- O365 Update application. -description: The following analytic identifies instances where the Mail.Read Graph - API permissions are granted to an application registration within an Office 365 - tenant. It leverages O365 audit logs, specifically events related to changes in - application permissions within the AzureActiveDirectory workload. This activity - is significant because the Mail.Read permission allows applications to access and - read all emails within a user's mailbox, which often contain sensitive or confidential - information. If confirmed malicious, this could lead to data exfiltration, spear-phishing - attacks, or further compromise based on the information gathered from the emails. -search: "`o365_management_activity` Operation=\"Update application.\" - | eval json_data=mvindex('ModifiedProperties{}.NewValue',0) - | eval json_data=replace(json_data,\"^\\[\\s*\",\"\") - | eval json_data=replace(json_data,\"\\s*\\]$\",\"\") - | spath input=json_data path=RequiredAppPermissions{}.EntitlementId output=EntitlementIds - | eval match_found=mvfind(EntitlementIds, \"810c84a8-4a9e-49e6-bf7d-12d183f40d01\") - | where isnotnull(match_found) - | fillnull - | stats count earliest(_time) as firstTime max(_time) as lastTime values(EntitlementIds) as EntitlementIds by signature, user, dest, vendor_account, vendor_product, object, src - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_mailbox_read_access_granted_to_application_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: There are legitimate scenarios in wich an Application registrations - requires Mailbox read access. Filter as needed. + - O365 Update application. +description: The following analytic identifies instances where the Mail.Read Graph API permissions are granted to an application registration within an Office 365 tenant. It leverages O365 audit logs, specifically events related to changes in application permissions within the AzureActiveDirectory workload. This activity is significant because the Mail.Read permission allows applications to access and read all emails within a user's mailbox, which often contain sensitive or confidential information. If confirmed malicious, this could lead to data exfiltration, spear-phishing attacks, or further compromise based on the information gathered from the emails. +search: "`o365_management_activity` Operation=\"Update application.\" | eval json_data=mvindex('ModifiedProperties{}.NewValue',0) | eval json_data=replace(json_data,\"^\\[\\s*\",\"\") | eval json_data=replace(json_data,\"\\s*\\]$\",\"\") | spath input=json_data path=RequiredAppPermissions{}.EntitlementId output=EntitlementIds | eval match_found=mvfind(EntitlementIds, \"810c84a8-4a9e-49e6-bf7d-12d183f40d01\") | where isnotnull(match_found) | fillnull | stats count earliest(_time) as firstTime max(_time) as lastTime values(EntitlementIds) as EntitlementIds by signature, user, dest, vendor_account, vendor_product, object, src | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_mailbox_read_access_granted_to_application_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: There are legitimate scenarios in wich an Application registrations requires Mailbox read access. Filter as needed. references: -- https://attack.mitre.org/techniques/T1098/003/ -- https://attack.mitre.org/techniques/T1114/002/ -- https://www.mandiant.com/sites/default/files/2022-08/remediation-hardening-strategies-for-m365-defend-against-apt29-white-paper.pdf -- https://www.cisa.gov/sites/default/files/publications/Supply_Chain_Compromise_Detecting_APT_Activity_from_known_TTPs.pdf -- https://learn.microsoft.com/en-us/graph/permissions-reference -- https://graphpermissions.merill.net/permission/Mail.Read + - https://attack.mitre.org/techniques/T1098/003/ + - https://attack.mitre.org/techniques/T1114/002/ + - https://www.mandiant.com/sites/default/files/2022-08/remediation-hardening-strategies-for-m365-defend-against-apt29-white-paper.pdf + - https://www.cisa.gov/sites/default/files/publications/Supply_Chain_Compromise_Detecting_APT_Activity_from_known_TTPs.pdf + - https://learn.microsoft.com/en-us/graph/permissions-reference + - https://graphpermissions.merill.net/permission/Mail.Read drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Application registration $object$ was grandes mailbox read access by $user$ - risk_objects: - - field: user - type: user - score: 45 - threat_objects: [] + message: Application registration $object$ was grandes mailbox read access by $user$ + risk_objects: + - field: user + type: user + score: 45 + threat_objects: [] tags: - analytic_story: - - Office 365 Persistence Mechanisms - asset_type: O365 Tenant - mitre_attack_id: - - T1098.003 - - T1114.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - Office 365 Persistence Mechanisms + asset_type: O365 Tenant + mitre_attack_id: + - T1098.003 + - T1114.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/o365_grant_mail_read/o365_grant_mail_read.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/o365_grant_mail_read/o365_grant_mail_read.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_multi_source_failed_authentications_spike.yml b/detections/cloud/o365_multi_source_failed_authentications_spike.yml index ff4124d1ac..aaba2dfebd 100644 --- a/detections/cloud/o365_multi_source_failed_authentications_spike.yml +++ b/detections/cloud/o365_multi_source_failed_authentications_spike.yml @@ -1,65 +1,49 @@ name: O365 Multi-Source Failed Authentications Spike id: ea4e2c41-dbfb-4f5f-a7b6-9ac1b7f104aa -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting data_source: -- O365 UserLoginFailed -description: The following analytic identifies a spike in failed authentication attempts - within an Office 365 environment, indicative of a potential distributed password - spraying attack. It leverages UserLoginFailed events from O365 Management Activity - logs, focusing on ErrorNumber 50126. This detection is significant as it highlights - attempts to bypass security controls using multiple IP addresses and user agents. - If confirmed malicious, this activity could lead to unauthorized access, data breaches, - privilege escalation, and lateral movement within the organization. Early detection - is crucial to prevent account takeovers and mitigate subsequent threats. -search: '`o365_management_activity` Workload=AzureActiveDirectory Operation=UserLoginFailed ErrorNumber=50126 - | bucket span=5m _time - | eval uniqueIPUserCombo = src_ip . "-" . user - | fillnull - | stats earliest(_time) as firstTime max(_time) as lastTime dc(uniqueIPUserCombo) as uniqueIpUserCombinations, dc(user) as uniqueUsers, dc(src_ip) as uniqueIPs, values(user) as user, values(src_ip) as ips, values(user_agent) as user_agents values(signature) as signature values(src) as src values(dest) as dest by _time vendor_account vendor_product - | where uniqueIpUserCombinations > 20 AND uniqueUsers > 20 AND uniqueIPs > 20 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_multi_source_failed_authentications_spike_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. The thresholds set within the analytic (such - as unique IPs, unique users, etc.) are initial guidelines and should be customized - based on the organization's user behavior and risk profile. Security teams are encouraged - to adjust these thresholds to optimize the balance between detecting genuine threats - and minimizing false positives, ensuring the detection is tailored to their specific - environment. -known_false_positives: This detection may yield false positives in scenarios where - legitimate bulk sign-in activities occur, such as during company-wide system updates - or when users are accessing resources from varying locations in a short time frame, - such as in the case of VPNs or cloud services that rotate IP addresses. Filter as - needed. + - O365 UserLoginFailed +description: The following analytic identifies a spike in failed authentication attempts within an Office 365 environment, indicative of a potential distributed password spraying attack. It leverages UserLoginFailed events from O365 Management Activity logs, focusing on ErrorNumber 50126. This detection is significant as it highlights attempts to bypass security controls using multiple IP addresses and user agents. If confirmed malicious, this activity could lead to unauthorized access, data breaches, privilege escalation, and lateral movement within the organization. Early detection is crucial to prevent account takeovers and mitigate subsequent threats. +search: |- + `o365_management_activity` Workload=AzureActiveDirectory Operation=UserLoginFailed ErrorNumber=50126 + | bucket span=5m _time + | eval uniqueIPUserCombo = src_ip . "-" . user + | fillnull + | stats earliest(_time) as firstTime max(_time) as lastTime dc(uniqueIPUserCombo) as uniqueIpUserCombinations, dc(user) as uniqueUsers, dc(src_ip) as uniqueIPs, values(user) as user, values(src_ip) as ips, values(user_agent) as user_agents values(signature) as signature values(src) as src values(dest) as dest + BY _time vendor_account vendor_product + | where uniqueIpUserCombinations > 20 AND uniqueUsers > 20 AND uniqueIPs > 20 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_multi_source_failed_authentications_spike_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. The thresholds set within the analytic (such as unique IPs, unique users, etc.) are initial guidelines and should be customized based on the organization's user behavior and risk profile. Security teams are encouraged to adjust these thresholds to optimize the balance between detecting genuine threats and minimizing false positives, ensuring the detection is tailored to their specific environment. +known_false_positives: This detection may yield false positives in scenarios where legitimate bulk sign-in activities occur, such as during company-wide system updates or when users are accessing resources from varying locations in a short time frame, such as in the case of VPNs or cloud services that rotate IP addresses. Filter as needed. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://docs.microsoft.com/en-us/security/compass/incident-response-playbook-password-spray -- https://www.cisa.gov/uscert/ncas/alerts/aa21-008a -- https://docs.microsoft.com/azure/active-directory/reports-monitoring/reference-sign-ins-error-codes + - https://attack.mitre.org/techniques/T1110/003/ + - https://docs.microsoft.com/en-us/security/compass/incident-response-playbook-password-spray + - https://www.cisa.gov/uscert/ncas/alerts/aa21-008a + - https://docs.microsoft.com/azure/active-directory/reports-monitoring/reference-sign-ins-error-codes tags: - analytic_story: - - Office 365 Account Takeover - - NOBELIUM Group - asset_type: O365 Tenant - atomic_guid: [] - mitre_attack_id: - - T1110.003 - - T1110.004 - - T1586.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Office 365 Account Takeover + - NOBELIUM Group + asset_type: O365 Tenant + atomic_guid: [] + mitre_attack_id: + - T1110.003 + - T1110.004 + - T1586.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/o365_distributed_spray/o365_distributed_spray.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/o365_distributed_spray/o365_distributed_spray.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_multiple_appids_and_useragents_authentication_spike.yml b/detections/cloud/o365_multiple_appids_and_useragents_authentication_spike.yml index 9e2738eec1..b71ea9c2f0 100644 --- a/detections/cloud/o365_multiple_appids_and_useragents_authentication_spike.yml +++ b/detections/cloud/o365_multiple_appids_and_useragents_authentication_spike.yml @@ -1,76 +1,61 @@ name: O365 Multiple AppIDs and UserAgents Authentication Spike id: 66adc486-224d-45c1-8e4d-9e7eeaba988f -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Anomaly data_source: -- O365 UserLoggedIn -- O365 UserLoginFailed -description: The following analytic identifies unusual authentication activity in - an O365 environment, where a single user account experiences more than 8 authentication - attempts using 3 or more unique application IDs and over 5 unique user agents within - a short timeframe. It leverages O365 audit logs, focusing on authentication events - and applying statistical thresholds. This behavior is significant as it may indicate - an adversary probing for multi-factor authentication weaknesses. If confirmed malicious, - it suggests a compromised account, potentially leading to unauthorized access, privilege - escalation, and data exfiltration. Early detection is crucial to prevent further - exploitation. -search: '`o365_management_activity` Workload=AzureActiveDirectory (Operation=UserLoggedIn OR Operation=UserLoginFailed) - | bucket span=5m _time - | stats dc(_raw) as failed_attempts dc(ApplicationId) as unique_app_ids dc(UserAgent) as unique_user_agents values(ApplicationId) values(OS) values(signature) as signature by _time user src vendor_account vendor_product dest - | where failed_attempts > 5 and unique_user_agents > 5 and unique_app_ids > 2 - | `o365_multiple_appids_and_useragents_authentication_spike_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Rapid authentication from the same user using more than 5 different - user agents and 3 application IDs is highly unlikely under normal circumstances. - However, there are potential scenarios that could lead to false positives. + - O365 UserLoggedIn + - O365 UserLoginFailed +description: The following analytic identifies unusual authentication activity in an O365 environment, where a single user account experiences more than 8 authentication attempts using 3 or more unique application IDs and over 5 unique user agents within a short timeframe. It leverages O365 audit logs, focusing on authentication events and applying statistical thresholds. This behavior is significant as it may indicate an adversary probing for multi-factor authentication weaknesses. If confirmed malicious, it suggests a compromised account, potentially leading to unauthorized access, privilege escalation, and data exfiltration. Early detection is crucial to prevent further exploitation. +search: |- + `o365_management_activity` Workload=AzureActiveDirectory (Operation=UserLoggedIn OR Operation=UserLoginFailed) + | bucket span=5m _time + | stats dc(_raw) as failed_attempts dc(ApplicationId) as unique_app_ids dc(UserAgent) as unique_user_agents values(ApplicationId) values(OS) values(signature) as signature + BY _time user src + vendor_account vendor_product dest + | where failed_attempts > 5 and unique_user_agents > 5 and unique_app_ids > 2 + | `o365_multiple_appids_and_useragents_authentication_spike_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Rapid authentication from the same user using more than 5 different user agents and 3 application IDs is highly unlikely under normal circumstances. However, there are potential scenarios that could lead to false positives. references: -- https://attack.mitre.org/techniques/T1078/ -- https://www.blackhillsinfosec.com/exploiting-mfa-inconsistencies-on-microsoft-services/ -- https://github.com/dafthack/MFASweep -- https://www.youtube.com/watch?v=SK1zgqaAZ2E + - https://attack.mitre.org/techniques/T1078/ + - https://www.blackhillsinfosec.com/exploiting-mfa-inconsistencies-on-microsoft-services/ + - https://github.com/dafthack/MFASweep + - https://www.youtube.com/watch?v=SK1zgqaAZ2E drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $user$ authenticated in a short period of time with more than 5 different - user agents across 3 or more unique application ids. - risk_objects: - - field: user - type: user - score: 48 - threat_objects: - - field: src - type: ip_address + message: $user$ authenticated in a short period of time with more than 5 different user agents across 3 or more unique application ids. + risk_objects: + - field: user + type: user + score: 48 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Office 365 Account Takeover - asset_type: O365 Tenant - mitre_attack_id: - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Office 365 Account Takeover + asset_type: O365 Tenant + mitre_attack_id: + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/o365_multiple_appids_and_useragents_auth/o365_multiple_appids_and_useragents_auth.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/o365_multiple_appids_and_useragents_auth/o365_multiple_appids_and_useragents_auth.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_multiple_failed_mfa_requests_for_user.yml b/detections/cloud/o365_multiple_failed_mfa_requests_for_user.yml index 290502603c..7db5443443 100644 --- a/detections/cloud/o365_multiple_failed_mfa_requests_for_user.yml +++ b/detections/cloud/o365_multiple_failed_mfa_requests_for_user.yml @@ -1,69 +1,56 @@ name: O365 Multiple Failed MFA Requests For User id: fd22124e-dbac-4744-a8ce-be10d8ec3e26 -version: 8 -date: '2025-10-14' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- O365 UserLoginFailed -description: The following analytic identifies potential "MFA fatigue" attacks targeting - Office 365 users by detecting more than nine Multi-Factor Authentication (MFA) prompts - within a 10-minute timeframe. It leverages O365 management activity logs, focusing - on Azure Active Directory events with the UserLoginFailed operation, a Success ResultStatus, - and an ErrorNumber of 500121. This activity is significant as attackers may exploit - MFA fatigue to gain unauthorized access by overwhelming users with repeated MFA - requests. If confirmed malicious, this could lead to data breaches, unauthorized - data access, or further compromise within the O365 environment. Immediate investigation - is crucial. -search: '`o365_management_activity` Workload=AzureActiveDirectory Operation=UserLoginFailed ResultStatus=Success ErrorNumber=500121 - | bucket span=10m _time - | stats dc(_raw) as mfa_prompts values(LogonError) as LogonError values(signature) as signature values(action) as action values(src) as src by user _time vendor_account vendor_product dest - | where mfa_prompts > 9 - | `o365_multiple_failed_mfa_requests_for_user_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Multiple Failed MFA requests may also be a sign of authentication - or application issues. Filter as needed. + - O365 UserLoginFailed +description: The following analytic identifies potential "MFA fatigue" attacks targeting Office 365 users by detecting more than nine Multi-Factor Authentication (MFA) prompts within a 10-minute timeframe. It leverages O365 management activity logs, focusing on Azure Active Directory events with the UserLoginFailed operation, a Success ResultStatus, and an ErrorNumber of 500121. This activity is significant as attackers may exploit MFA fatigue to gain unauthorized access by overwhelming users with repeated MFA requests. If confirmed malicious, this could lead to data breaches, unauthorized data access, or further compromise within the O365 environment. Immediate investigation is crucial. +search: |- + `o365_management_activity` Workload=AzureActiveDirectory Operation=UserLoginFailed ResultStatus=Success ErrorNumber=500121 + | bucket span=10m _time + | stats dc(_raw) as mfa_prompts values(LogonError) as LogonError values(signature) as signature values(action) as action values(src) as src + BY user _time vendor_account + vendor_product dest + | where mfa_prompts > 9 + | `o365_multiple_failed_mfa_requests_for_user_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Multiple Failed MFA requests may also be a sign of authentication or application issues. Filter as needed. references: -- https://attack.mitre.org/techniques/T1621/ + - https://attack.mitre.org/techniques/T1621/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Multiple failed MFA requestes for $user$ - risk_objects: - - field: user - type: user - score: 48 - threat_objects: [] + message: Multiple failed MFA requestes for $user$ + risk_objects: + - field: user + type: user + score: 48 + threat_objects: [] tags: - analytic_story: - - Office 365 Account Takeover - - Scattered Lapsus$ Hunters - asset_type: O365 Tenant - mitre_attack_id: - - T1621 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Office 365 Account Takeover + - Scattered Lapsus$ Hunters + asset_type: O365 Tenant + mitre_attack_id: + - T1621 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/o365_multiple_failed_mfa_requests/o365_multiple_failed_mfa_requests.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/o365_multiple_failed_mfa_requests/o365_multiple_failed_mfa_requests.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_multiple_mailboxes_accessed_via_api.yml b/detections/cloud/o365_multiple_mailboxes_accessed_via_api.yml index f3e386ea6e..d80f1b4e9e 100644 --- a/detections/cloud/o365_multiple_mailboxes_accessed_via_api.yml +++ b/detections/cloud/o365_multiple_mailboxes_accessed_via_api.yml @@ -1,78 +1,65 @@ name: O365 Multiple Mailboxes Accessed via API id: 7cd853e9-d370-412f-965d-a2bcff2a2908 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Mauricio Velazco, Splunk data_source: -- O365 MailItemsAccessed + - O365 MailItemsAccessed type: TTP status: production -description: The following analytic detects when a high number of Office 365 Exchange - mailboxes are accessed via API (Microsoft Graph API or Exchange Web Services) within - a short timeframe. It leverages 'MailItemsAccessed' operations in Exchange, using - AppId and regex to identify API interactions. This activity is significant as it - may indicate unauthorized mass email access, potentially signaling data exfiltration - or account compromise. If confirmed malicious, attackers could gain access to sensitive - information, leading to data breaches and further exploitation of compromised accounts. - The threshold is set to flag over five unique mailboxes accessed within 10 minutes, - but should be tailored to your environment. -search: '`o365_management_activity` Workload=Exchange Operation=MailItemsAccessed AppId=* ClientAppId=* - | bucket span=10m _time - | eval matchRegex=if(match(ClientInfoString,"^Client=WebServices;ExchangeWebServices"), 1, 0) - | search (AppId="00000003-0000-0000-c000-000000000000" OR matchRegex=1) - | fillnull - | stats values(ClientIPAddress) as src dc(user) as unique_mailboxes values(user) as user by _time ClientAppId ClientInfoString vendor_account vendor_product dest signature - | where unique_mailboxes > 5 - | `o365_multiple_mailboxes_accessed_via_api_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Legitimate applications may access multiple mailboxes via an - API. You can filter by the ClientAppId or the CLientIpAddress fields. +description: The following analytic detects when a high number of Office 365 Exchange mailboxes are accessed via API (Microsoft Graph API or Exchange Web Services) within a short timeframe. It leverages 'MailItemsAccessed' operations in Exchange, using AppId and regex to identify API interactions. This activity is significant as it may indicate unauthorized mass email access, potentially signaling data exfiltration or account compromise. If confirmed malicious, attackers could gain access to sensitive information, leading to data breaches and further exploitation of compromised accounts. The threshold is set to flag over five unique mailboxes accessed within 10 minutes, but should be tailored to your environment. +search: |- + `o365_management_activity` Workload=Exchange Operation=MailItemsAccessed AppId=* ClientAppId=* + | bucket span=10m _time + | eval matchRegex=if(match(ClientInfoString,"^Client=WebServices;ExchangeWebServices"), 1, 0) + | search (AppId="00000003-0000-0000-c000-000000000000" OR matchRegex=1) + | fillnull + | stats values(ClientIPAddress) as src dc(user) as unique_mailboxes values(user) as user + BY _time ClientAppId ClientInfoString + vendor_account vendor_product dest + signature + | where unique_mailboxes > 5 + | `o365_multiple_mailboxes_accessed_via_api_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Legitimate applications may access multiple mailboxes via an API. You can filter by the ClientAppId or the CLientIpAddress fields. references: -- https://attack.mitre.org/techniques/T1114/002/ -- https://learn.microsoft.com/en-us/troubleshoot/azure/active-directory/verify-first-party-apps-sign-in -- https://learn.microsoft.com/en-us/graph/permissions-reference -- https://attack.mitre.org/techniques/T1114/002/ -- https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/ -- https://learn.microsoft.com/en-us/exchange/client-developer/exchange-web-services/ews-applications-and-the-exchange-architecture + - https://attack.mitre.org/techniques/T1114/002/ + - https://learn.microsoft.com/en-us/troubleshoot/azure/active-directory/verify-first-party-apps-sign-in + - https://learn.microsoft.com/en-us/graph/permissions-reference + - https://attack.mitre.org/techniques/T1114/002/ + - https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/ + - https://learn.microsoft.com/en-us/exchange/client-developer/exchange-web-services/ews-applications-and-the-exchange-architecture drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An Oauth application identified with id $ClientAppId$ accessed multiple - mailboxes in a short period of time via an API. - risk_objects: - - field: user - type: user - score: 42 - threat_objects: [] + message: An Oauth application identified with id $ClientAppId$ accessed multiple mailboxes in a short period of time via an API. + risk_objects: + - field: user + type: user + score: 42 + threat_objects: [] tags: - analytic_story: - - Office 365 Collection Techniques - - NOBELIUM Group - asset_type: O365 Tenant - mitre_attack_id: - - T1114.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Collection Techniques + - NOBELIUM Group + asset_type: O365 Tenant + mitre_attack_id: + - T1114.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/o365_multiple_mailboxes_accessed_via_api/o365_multiple_mailboxes_accessed_via_api.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/o365_multiple_mailboxes_accessed_via_api/o365_multiple_mailboxes_accessed_via_api.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_multiple_os_vendors_authenticating_from_user.yml b/detections/cloud/o365_multiple_os_vendors_authenticating_from_user.yml index d54c44fb1b..d9a4d6cb35 100644 --- a/detections/cloud/o365_multiple_os_vendors_authenticating_from_user.yml +++ b/detections/cloud/o365_multiple_os_vendors_authenticating_from_user.yml @@ -1,67 +1,67 @@ name: O365 Multiple OS Vendors Authenticating From User id: 3451e58a-9457-4985-a600-b616b0cbfda1 -version: 3 -date: '2025-05-02' +version: 4 +date: '2026-02-25' author: Steven Dick status: production type: TTP description: The following analytic identifies when multiple operating systems are used to authenticate to Azure/EntraID/Office 365 by the same user account over a short period of time. This activity could be indicative of attackers enumerating various logon capabilities of Azure/EntraID/Office 365 and attempting to discover weaknesses in the organizational MFA or conditional access configurations. Usage of the tools like "MFASweep" will trigger this detection. -data_source: -- Office 365 Universal Audit Log +data_source: + - Office 365 Universal Audit Log search: |- - `o365_management_activity` Operation IN (UserLoginFailed,UserLoggedIn) - | eval -time = _time - | bin _time span=15m - | fillnull - | stats values(Operation) as signature, values(ErrorNumber) as signature_id, values(OS) as os_name, dc(OS) as os_count, count, min(-time) as firstTime, max(-time) as lastTime by ClientIP, UserId, _time, dest, vendor_account, vendor_product - | where os_count >= 4 - | eval src = ClientIP, user = UserId - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_multiple_os_vendors_authenticating_from_user_filter` + `o365_management_activity` Operation IN (UserLoginFailed,UserLoggedIn) + | eval -time = _time + | bin _time span=15m + | fillnull + | stats values(Operation) as signature, values(ErrorNumber) as signature_id, values(OS) as os_name, dc(OS) as os_count, count, min(-time) as firstTime, max(-time) as lastTime by ClientIP, UserId, _time, dest, vendor_account, vendor_product + | where os_count >= 4 + | eval src = ClientIP, user = UserId + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_multiple_os_vendors_authenticating_from_user_filter` how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. The thresholds set within the analytic (such as unique OS) are initial guidelines and should be customized based on the organization's user behavior and risk profile. Security teams are encouraged to adjust these thresholds to optimize the balance between detecting genuine threats and minimizing false positives, ensuring the detection is tailored to their specific environment. -known_false_positives: IP or users where the usage of multiple Operating systems is expected, filter accordingly. +known_false_positives: IP or users where the usage of multiple Operating systems is expected, filter accordingly. references: -- https://attack.mitre.org/techniques/T1110 -- https://www.blackhillsinfosec.com/exploiting-mfa-inconsistencies-on-microsoft-services/ -- https://sra.io/blog/msspray-wait-how-many-endpoints-dont-have-mfa/ -- https://github.com/dafthack/MFASweep/tree/master + - https://attack.mitre.org/techniques/T1110 + - https://www.blackhillsinfosec.com/exploiting-mfa-inconsistencies-on-microsoft-services/ + - https://sra.io/blog/msspray-wait-how-many-endpoints-dont-have-mfa/ + - https://github.com/dafthack/MFASweep/tree/master drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate logons from $user$ - search: '`o365_management_activity` Operation IN (UserLoginFailed,UserLoggedIn) "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate logons from $user$ + search: '`o365_management_activity` Operation IN (UserLoginFailed,UserLoggedIn) "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The user account $user$ authenticated with $os_count$ unique operating system types over a short period from $src$. - risk_objects: - - field: user - type: user - score: 60 - threat_objects: - - field: src - type: ip_address + message: The user account $user$ authenticated with $os_count$ unique operating system types over a short period from $src$. + risk_objects: + - field: user + type: user + score: 60 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Office 365 Account Takeover - asset_type: O365 Tenant - mitre_attack_id: - - T1110 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Account Takeover + asset_type: O365 Tenant + mitre_attack_id: + - T1110 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/azure_mfasweep_events/azure_mfasweep_events.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/azure_mfasweep_events/azure_mfasweep_events.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_multiple_service_principals_created_by_sp.yml b/detections/cloud/o365_multiple_service_principals_created_by_sp.yml index 30619e2736..9c04a06428 100644 --- a/detections/cloud/o365_multiple_service_principals_created_by_sp.yml +++ b/detections/cloud/o365_multiple_service_principals_created_by_sp.yml @@ -4,75 +4,47 @@ version: 6 date: '2025-05-02' author: Mauricio Velazco, Splunk data_source: -- O365 Add service principal. + - O365 Add service principal. type: Anomaly status: production -description: The following analytic identifies instances where a single service principal - creates more than three unique OAuth applications within a 10-minute timeframe. - It leverages O365 logs from the Unified Audit Log, focusing on the 'Add service - principal' operation in the Office 365 Azure Active Directory environment. This - activity is significant as it may indicate a compromised or malicious service principal - attempting to expand control or access within the network. If confirmed malicious, - this could lead to unauthorized access and potential lateral movement within the - environment, posing a significant security risk. -search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Add service principal.\" - | bucket span=10m _time - | eval len=mvcount('Actor{}.ID') - | eval userType = mvindex('Actor{}.ID',len-1) - | search userType = \"ServicePrincipal\" - | eval displayName = object - | fillnull - | stats count earliest(_time) as firstTime latest(_time) as lastTime values(displayName) as displayName dc(displayName) as unique_apps values(user) as user values(src) as src - by src_user vendor_account vendor_product dest signature - | where unique_apps > 3 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_multiple_service_principals_created_by_sp_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Certain users or applications may create multiple service principals - in a short period of time for legitimate purposes. Filter as needed. +description: The following analytic identifies instances where a single service principal creates more than three unique OAuth applications within a 10-minute timeframe. It leverages O365 logs from the Unified Audit Log, focusing on the 'Add service principal' operation in the Office 365 Azure Active Directory environment. This activity is significant as it may indicate a compromised or malicious service principal attempting to expand control or access within the network. If confirmed malicious, this could lead to unauthorized access and potential lateral movement within the environment, posing a significant security risk. +search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Add service principal.\" | bucket span=10m _time | eval len=mvcount('Actor{}.ID') | eval userType = mvindex('Actor{}.ID',len-1) | search userType = \"ServicePrincipal\" | eval displayName = object | fillnull | stats count earliest(_time) as firstTime latest(_time) as lastTime values(displayName) as displayName dc(displayName) as unique_apps values(user) as user values(src) as src by src_user vendor_account vendor_product dest signature | where unique_apps > 3 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_multiple_service_principals_created_by_sp_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Certain users or applications may create multiple service principals in a short period of time for legitimate purposes. Filter as needed. references: -- https://attack.mitre.org/techniques/T1136/003/ -- https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/ + - https://attack.mitre.org/techniques/T1136/003/ + - https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/ drilldown_searches: -- name: View the detection results for - "$src_user$" - search: '%original_detection_search% | search src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_user$" + search: '%original_detection_search% | search src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Multiple OAuth applications were created by $src_user$ in a short period - of time - risk_objects: - - field: src_user - type: user - score: 42 - threat_objects: [] + message: Multiple OAuth applications were created by $src_user$ in a short period of time + risk_objects: + - field: src_user + type: user + score: 42 + threat_objects: [] tags: - analytic_story: - - Office 365 Persistence Mechanisms - - NOBELIUM Group - asset_type: O365 Tenant - mitre_attack_id: - - T1136.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Office 365 Persistence Mechanisms + - NOBELIUM Group + asset_type: O365 Tenant + mitre_attack_id: + - T1136.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_multiple_service_principals_created/o365_multiple_service_principals_created.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_multiple_service_principals_created/o365_multiple_service_principals_created.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_multiple_service_principals_created_by_user.yml b/detections/cloud/o365_multiple_service_principals_created_by_user.yml index c815b43344..473212f2b5 100644 --- a/detections/cloud/o365_multiple_service_principals_created_by_user.yml +++ b/detections/cloud/o365_multiple_service_principals_created_by_user.yml @@ -4,74 +4,47 @@ version: 6 date: '2025-05-02' author: Mauricio Velazco, Splunk data_source: -- O365 Add service principal. + - O365 Add service principal. type: Anomaly status: production -description: The following analytic identifies instances where a single user creates - more than three unique OAuth applications within a 10-minute window in the Office - 365 environment. It leverages O365 logs from the Unified Audit Log, focusing on - the 'Add service principal' operation in Azure Active Directory. This activity is - significant as it may indicate a compromised user account or unauthorized actions, - potentially leading to broader network infiltration or privilege escalation. If - confirmed malicious, this behavior could allow attackers to gain persistent access, - escalate privileges, or exfiltrate sensitive information. -search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Add service principal.\" - | bucket span=10m _time - | eval len=mvcount('Actor{}.ID') - | eval userType = mvindex('Actor{}.ID',len-1) - | search userType = \"User\" - | eval displayName = object - | stats count earliest(_time) as firstTime latest(_time) as lastTime values(displayName) as displayName dc(displayName) as unique_apps values(user) as user values(src) as src - by src_user vendor_account vendor_product dest signature - | where unique_apps > 3 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_multiple_service_principals_created_by_user_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Certain users or applications may create multiple service principals - in a short period of time for legitimate purposes. Filter as needed. +description: The following analytic identifies instances where a single user creates more than three unique OAuth applications within a 10-minute window in the Office 365 environment. It leverages O365 logs from the Unified Audit Log, focusing on the 'Add service principal' operation in Azure Active Directory. This activity is significant as it may indicate a compromised user account or unauthorized actions, potentially leading to broader network infiltration or privilege escalation. If confirmed malicious, this behavior could allow attackers to gain persistent access, escalate privileges, or exfiltrate sensitive information. +search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Add service principal.\" | bucket span=10m _time | eval len=mvcount('Actor{}.ID') | eval userType = mvindex('Actor{}.ID',len-1) | search userType = \"User\" | eval displayName = object | stats count earliest(_time) as firstTime latest(_time) as lastTime values(displayName) as displayName dc(displayName) as unique_apps values(user) as user values(src) as src by src_user vendor_account vendor_product dest signature | where unique_apps > 3 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_multiple_service_principals_created_by_user_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Certain users or applications may create multiple service principals in a short period of time for legitimate purposes. Filter as needed. references: -- https://attack.mitre.org/techniques/T1136/003/ -- https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/ + - https://attack.mitre.org/techniques/T1136/003/ + - https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/ drilldown_searches: -- name: View the detection results for - "$src_user$" - search: '%original_detection_search% | search src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_user$" + search: '%original_detection_search% | search src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Multiple OAuth applications were created by $src_user$ in a short period - of time - risk_objects: - - field: src_user - type: user - score: 42 - threat_objects: [] + message: Multiple OAuth applications were created by $src_user$ in a short period of time + risk_objects: + - field: src_user + type: user + score: 42 + threat_objects: [] tags: - analytic_story: - - Office 365 Persistence Mechanisms - - NOBELIUM Group - asset_type: O365 Tenant - mitre_attack_id: - - T1136.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Office 365 Persistence Mechanisms + - NOBELIUM Group + asset_type: O365 Tenant + mitre_attack_id: + - T1136.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_multiple_service_principals_created/o365_multiple_service_principals_created.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_multiple_service_principals_created/o365_multiple_service_principals_created.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_multiple_users_failing_to_authenticate_from_ip.yml b/detections/cloud/o365_multiple_users_failing_to_authenticate_from_ip.yml index 18f65e8cdb..6dabe71ce3 100644 --- a/detections/cloud/o365_multiple_users_failing_to_authenticate_from_ip.yml +++ b/detections/cloud/o365_multiple_users_failing_to_authenticate_from_ip.yml @@ -1,77 +1,64 @@ name: O365 Multiple Users Failing To Authenticate From Ip id: 8d486e2e-3235-4cfe-ac35-0d042e24ecb4 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- O365 UserLoginFailed -description: The following analytic identifies instances where more than 10 unique - user accounts fail to authenticate from a single IP address within a 5-minute window. - This detection leverages O365 audit logs, specifically Azure Active Directory login - failures (AzureActiveDirectoryStsLogon). Such activity is significant as it may - indicate brute-force attacks or password spraying attempts. If confirmed malicious, - this behavior suggests an external entity is attempting to breach security by targeting - multiple accounts, potentially leading to unauthorized access. Immediate action - is required to block or monitor the suspicious IP and notify affected users to enhance - their security measures. -search: '`o365_management_activity` Workload=AzureActiveDirectory Operation=UserLoginFailed ErrorNumber=50126 - | bucket span=5m _time - | fillnull - | stats dc(user) as unique_accounts values(user) as user values(LogonError) as LogonError values(signature) as signature values(UserAgent) as user_agent values(dest) as dest by _time src vendor_account vendor_product - | where unique_accounts > 10 - | `o365_multiple_users_failing_to_authenticate_from_ip_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: A source Ip failing to authenticate with multiple users in - a short period of time is not common legitimate behavior. + - O365 UserLoginFailed +description: The following analytic identifies instances where more than 10 unique user accounts fail to authenticate from a single IP address within a 5-minute window. This detection leverages O365 audit logs, specifically Azure Active Directory login failures (AzureActiveDirectoryStsLogon). Such activity is significant as it may indicate brute-force attacks or password spraying attempts. If confirmed malicious, this behavior suggests an external entity is attempting to breach security by targeting multiple accounts, potentially leading to unauthorized access. Immediate action is required to block or monitor the suspicious IP and notify affected users to enhance their security measures. +search: |- + `o365_management_activity` Workload=AzureActiveDirectory Operation=UserLoginFailed ErrorNumber=50126 + | bucket span=5m _time + | fillnull + | stats dc(user) as unique_accounts values(user) as user values(LogonError) as LogonError values(signature) as signature values(UserAgent) as user_agent values(dest) as dest + BY _time src vendor_account + vendor_product + | where unique_accounts > 10 + | `o365_multiple_users_failing_to_authenticate_from_ip_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: A source Ip failing to authenticate with multiple users in a short period of time is not common legitimate behavior. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://docs.microsoft.com/en-us/security/compass/incident-response-playbook-password-spray -- https://www.cisa.gov/uscert/ncas/alerts/aa21-008a -- https://docs.microsoft.com/azure/active-directory/reports-monitoring/reference-sign-ins-error-codes + - https://attack.mitre.org/techniques/T1110/003/ + - https://docs.microsoft.com/en-us/security/compass/incident-response-playbook-password-spray + - https://www.cisa.gov/uscert/ncas/alerts/aa21-008a + - https://docs.microsoft.com/azure/active-directory/reports-monitoring/reference-sign-ins-error-codes drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Source Ip $src$ failed to authenticate with 20 users within 5 minutes. - risk_objects: - - field: user - type: user - score: 63 - threat_objects: - - field: src - type: ip_address + message: Source Ip $src$ failed to authenticate with 20 users within 5 minutes. + risk_objects: + - field: user + type: user + score: 63 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Office 365 Account Takeover - - NOBELIUM Group - asset_type: O365 Tenant - mitre_attack_id: - - T1110.003 - - T1110.004 - - T1586.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Office 365 Account Takeover + - NOBELIUM Group + asset_type: O365 Tenant + mitre_attack_id: + - T1110.003 + - T1110.004 + - T1586.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/o365_multiple_users_from_ip/o365_multiple_users_from_ip.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/o365_multiple_users_from_ip/o365_multiple_users_from_ip.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_new_email_forwarding_rule_created.yml b/detections/cloud/o365_new_email_forwarding_rule_created.yml index b0d8fff6c2..6300ef24cb 100644 --- a/detections/cloud/o365_new_email_forwarding_rule_created.yml +++ b/detections/cloud/o365_new_email_forwarding_rule_created.yml @@ -6,67 +6,42 @@ author: Mauricio Velazco, Splunk data_source: [] type: TTP status: production -description: The following analytic identifies the creation of new email forwarding - rules in an Office 365 environment. It detects events logged under New-InboxRule - and Set-InboxRule operations within the o365_management_activity data source, focusing - on parameters like ForwardTo, ForwardAsAttachmentTo, and RedirectTo. This activity - is significant as unauthorized email forwarding can lead to data exfiltration and - unauthorized access to sensitive information. If confirmed malicious, attackers - could intercept and redirect emails, potentially compromising confidential communications - and leading to data breaches. -search: "`o365_management_activity` (Operation=New-InboxRule OR Operation=set-InboxRule) - | eval match1=mvfind('Parameters{}.Name', \"ForwardTo\") - | eval match2=mvfind('Parameters{}.Name', \"ForwardAsAttachmentTo\") - | eval match3=mvfind('Parameters{}.Name', \"RedirectTo\") - | where match1>= 0 OR match2>= 0 OR match3>= 0 - | eval ForwardTo=coalesce(ForwardTo, ForwardAsAttachmentTo, RedirectTo) - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime values(Name) as Name by signature dest user src vendor_account vendor_product ForwardTo - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_new_email_forwarding_rule_created_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Users may create email forwarding rules for legitimate purposes. - Filter as needed. +description: The following analytic identifies the creation of new email forwarding rules in an Office 365 environment. It detects events logged under New-InboxRule and Set-InboxRule operations within the o365_management_activity data source, focusing on parameters like ForwardTo, ForwardAsAttachmentTo, and RedirectTo. This activity is significant as unauthorized email forwarding can lead to data exfiltration and unauthorized access to sensitive information. If confirmed malicious, attackers could intercept and redirect emails, potentially compromising confidential communications and leading to data breaches. +search: "`o365_management_activity` (Operation=New-InboxRule OR Operation=set-InboxRule) | eval match1=mvfind('Parameters{}.Name', \"ForwardTo\") | eval match2=mvfind('Parameters{}.Name', \"ForwardAsAttachmentTo\") | eval match3=mvfind('Parameters{}.Name', \"RedirectTo\") | where match1>= 0 OR match2>= 0 OR match3>= 0 | eval ForwardTo=coalesce(ForwardTo, ForwardAsAttachmentTo, RedirectTo) | fillnull | stats count min(_time) as firstTime max(_time) as lastTime values(Name) as Name by signature dest user src vendor_account vendor_product ForwardTo | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_new_email_forwarding_rule_created_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Users may create email forwarding rules for legitimate purposes. Filter as needed. references: -- https://attack.mitre.org/techniques/T1114/003/ + - https://attack.mitre.org/techniques/T1114/003/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A forwarding email inbox rule was created for $user$ - risk_objects: - - field: user - type: user - score: 42 - threat_objects: [] + message: A forwarding email inbox rule was created for $user$ + risk_objects: + - field: user + type: user + score: 42 + threat_objects: [] tags: - analytic_story: - - Office 365 Collection Techniques - asset_type: O365 Tenant - mitre_attack_id: - - T1114.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: audit + analytic_story: + - Office 365 Collection Techniques + asset_type: O365 Tenant + mitre_attack_id: + - T1114.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: audit tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule_created/o365_email_forwarding_rule_created.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule_created/o365_email_forwarding_rule_created.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_new_email_forwarding_rule_enabled.yml b/detections/cloud/o365_new_email_forwarding_rule_enabled.yml index 7d418ddcb4..d295be0b9d 100644 --- a/detections/cloud/o365_new_email_forwarding_rule_enabled.yml +++ b/detections/cloud/o365_new_email_forwarding_rule_enabled.yml @@ -6,73 +6,42 @@ author: Mauricio Velazco, Splunk data_source: [] type: TTP status: production -description: The following analytic identifies the creation of new email forwarding - rules in an Office 365 environment via the UpdateInboxRules operation. It leverages - Office 365 management activity events to detect rules that forward emails to external - recipients by examining the OperationProperties for specific forwarding actions. - This activity is significant as it may indicate unauthorized email redirection, - potentially leading to data exfiltration. If confirmed malicious, attackers could - intercept sensitive communications, leading to data breaches and information leakage. -search: "`o365_management_activity` Workload=Exchange Operation=UpdateInboxRules - | eval match1=mvfind('OperationProperties{}.Value', \"ForwardToRecipientsAction\") - | eval match2=mvfind('OperationProperties{}.Value', \"ForwardAsAttachmentToRecipientsAction\") - | eval match3=mvfind('OperationProperties{}.Value', \"RedirectToRecipientsAction\") - | eval index = mvfind('OperationProperties{}.Name', \"ServerRule\") - | where match1>=0 OR match2>= 0 OR match3>= 0 - | eval ServerRule = mvindex('OperationProperties{}.Value',index-1) - | spath input=ServerRule path=Actions{}.Recipients{}.Values{}.Value output=valueExtracted - | mvexpand valueExtracted - | search valueExtracted=\"*@*.*\" - | eval ForwardTo=if(match(valueExtracted,\"^[^@]+@[^@]+\\\\.[^@]+$\"), valueExtracted, null) - | dedup ForwardTo - | where isnotnull(ForwardTo) - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime values(Name) as Name by signature dest user src vendor_account vendor_product ForwardTo - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_new_email_forwarding_rule_enabled_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Users may create email forwarding rules for legitimate purposes. - Filter as needed. +description: The following analytic identifies the creation of new email forwarding rules in an Office 365 environment via the UpdateInboxRules operation. It leverages Office 365 management activity events to detect rules that forward emails to external recipients by examining the OperationProperties for specific forwarding actions. This activity is significant as it may indicate unauthorized email redirection, potentially leading to data exfiltration. If confirmed malicious, attackers could intercept sensitive communications, leading to data breaches and information leakage. +search: "`o365_management_activity` Workload=Exchange Operation=UpdateInboxRules | eval match1=mvfind('OperationProperties{}.Value', \"ForwardToRecipientsAction\") | eval match2=mvfind('OperationProperties{}.Value', \"ForwardAsAttachmentToRecipientsAction\") | eval match3=mvfind('OperationProperties{}.Value', \"RedirectToRecipientsAction\") | eval index = mvfind('OperationProperties{}.Name', \"ServerRule\") | where match1>=0 OR match2>= 0 OR match3>= 0 | eval ServerRule = mvindex('OperationProperties{}.Value',index-1) | spath input=ServerRule path=Actions{}.Recipients{}.Values{}.Value output=valueExtracted | mvexpand valueExtracted | search valueExtracted=\"*@*.*\" | eval ForwardTo=if(match(valueExtracted,\"^[^@]+@[^@]+\\\\.[^@]+$\"), valueExtracted, null) | dedup ForwardTo | where isnotnull(ForwardTo) | fillnull | stats count min(_time) as firstTime max(_time) as lastTime values(Name) as Name by signature dest user src vendor_account vendor_product ForwardTo | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_new_email_forwarding_rule_enabled_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Users may create email forwarding rules for legitimate purposes. Filter as needed. references: -- https://attack.mitre.org/techniques/T1114/003/ + - https://attack.mitre.org/techniques/T1114/003/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A forwarding email inbox rule was created for $user$ - risk_objects: - - field: user - type: user - score: 42 - threat_objects: [] + message: A forwarding email inbox rule was created for $user$ + risk_objects: + - field: user + type: user + score: 42 + threat_objects: [] tags: - analytic_story: - - Office 365 Collection Techniques - asset_type: O365 Tenant - mitre_attack_id: - - T1114.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: audit + analytic_story: + - Office 365 Collection Techniques + asset_type: O365 Tenant + mitre_attack_id: + - T1114.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: audit tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule_created/o365_email_forwarding_rule_created.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule_created/o365_email_forwarding_rule_created.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_new_federated_domain_added.yml b/detections/cloud/o365_new_federated_domain_added.yml index 138e14e9cd..c1566ac947 100644 --- a/detections/cloud/o365_new_federated_domain_added.yml +++ b/detections/cloud/o365_new_federated_domain_added.yml @@ -1,75 +1,63 @@ name: O365 New Federated Domain Added id: e155876a-6048-11eb-ae93-0242ac130002 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Rod Soto, Mauricio Velazco Splunk status: production type: TTP -description: The following analytic identifies the addition of a new federated domain - in an Office 365 environment. This behavior is detected by analyzing Office 365 - management activity logs, specifically filtering for Workload=Exchange and Operation="Add-FederatedDomain". - The addition of a new federated domain is significant as it may indicate unauthorized - changes or potential compromises. If confirmed malicious, attackers could establish - a backdoor, bypass security measures, or exfiltrate data, leading to data breaches - and unauthorized access to sensitive information. Immediate investigation is required - to review the details of the added domain and any concurrent suspicious activities. +description: The following analytic identifies the addition of a new federated domain in an Office 365 environment. This behavior is detected by analyzing Office 365 management activity logs, specifically filtering for Workload=Exchange and Operation="Add-FederatedDomain". The addition of a new federated domain is significant as it may indicate unauthorized changes or potential compromises. If confirmed malicious, attackers could establish a backdoor, bypass security measures, or exfiltrate data, leading to data breaches and unauthorized access to sensitive information. Immediate investigation is required to review the details of the added domain and any concurrent suspicious activities. data_source: -- O365 -search: '`o365_management_activity` Operation IN ("*add*", "*new*") AND Operation="*domain*" - | eval src="NA" - | fillnull - | stats count values(ModifiedProperties{}.NewValue) as new_value by user user_agent authentication_service signature Workload src vendor_account vendor_product dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_new_federated_domain_added_filter`' -how_to_implement: You must install splunk Microsoft Office 365 add-on. This search - works with o365:management:activity. -known_false_positives: The creation of a new Federated domain is not necessarily malicious, - however these events need to be followed closely, as it may indicate federated credential - abuse or backdoor via federated identities at a similar or different cloud provider. + - O365 +search: |- + `o365_management_activity` Operation IN ("*add*", "*new*") AND Operation="*domain*" + | eval src="NA" + | fillnull + | stats count values(ModifiedProperties{}.NewValue) as new_value + BY user user_agent authentication_service + signature Workload src + vendor_account vendor_product dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_new_federated_domain_added_filter` +how_to_implement: You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity. +known_false_positives: The creation of a new Federated domain is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a similar or different cloud provider. references: -- https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf -- https://www.cisa.gov/uscert/ncas/alerts/aa21-008a -- https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html -- https://blog.sygnia.co/detection-and-hunting-of-golden-saml-attack?hsLang=en -- https://o365blog.com/post/aadbackdoor/ + - https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf + - https://www.cisa.gov/uscert/ncas/alerts/aa21-008a + - https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html + - https://blog.sygnia.co/detection-and-hunting-of-golden-saml-attack?hsLang=en + - https://o365blog.com/post/aadbackdoor/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has added a new federated domain $new_value$ - risk_objects: - - field: user - type: user - score: 64 - threat_objects: [] + message: User $user$ has added a new federated domain $new_value$ + risk_objects: + - field: user + type: user + score: 64 + threat_objects: [] tags: - analytic_story: - - Office 365 Persistence Mechanisms - - Cloud Federated Credential Abuse - asset_type: O365 Tenant - mitre_attack_id: - - T1136.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Persistence Mechanisms + - Cloud Federated Credential Abuse + asset_type: O365 Tenant + mitre_attack_id: + - T1136.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federated_domain_added/o365_add_federated_domain.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federated_domain_added/o365_add_federated_domain.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_new_forwarding_mailflow_rule_created.yml b/detections/cloud/o365_new_forwarding_mailflow_rule_created.yml index bdcfcaf6fd..3fe3cd78ce 100644 --- a/detections/cloud/o365_new_forwarding_mailflow_rule_created.yml +++ b/detections/cloud/o365_new_forwarding_mailflow_rule_created.yml @@ -6,70 +6,44 @@ author: Mauricio Velazco, Splunk data_source: [] type: TTP status: production -description: The following analytic detects the creation of new mail flow rules in - Office 365 that may redirect or copy emails to unauthorized or external addresses. - It leverages Office 365 Management Activity logs, specifically querying for the - "New-TransportRule" operation and parameters like "BlindCopyTo", "CopyTo", and "RedirectMessageTo". - This activity is significant as it can indicate potential data exfiltration or unauthorized - access to sensitive information. If confirmed malicious, attackers could intercept - or redirect email communications, leading to data breaches or information leakage. -search: "`o365_management_activity` Workload=Exchange Operation=\"New-TransportRule\" - | eval match1=mvfind('Parameters{}.Name',\"BlindCopyTo\") - | eval match2=mvfind('Parameters{}.Name',\"CopyTo\") - | eval match3=mvfind('Parameters{}.Name', \"RedirectMessageTo\") - | where match1>= 0 OR match2>= 0 OR match3>=0 - | eval ForwardTo=coalesce(BlindCopyTo, CopyTo, RedirectMessageTo) - | search ForwardTo!=\"\" - | rename UserId as user - | fillnull - | stats count earliest(_time) as firstTime latest(_time) as lastTime by user, Name, ForwardTo, vendor_account, vendor_product, dest, signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_new_forwarding_mailflow_rule_created_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Forwarding mail flow rules may be created for legitimate reasons, - filter as needed. +description: The following analytic detects the creation of new mail flow rules in Office 365 that may redirect or copy emails to unauthorized or external addresses. It leverages Office 365 Management Activity logs, specifically querying for the "New-TransportRule" operation and parameters like "BlindCopyTo", "CopyTo", and "RedirectMessageTo". This activity is significant as it can indicate potential data exfiltration or unauthorized access to sensitive information. If confirmed malicious, attackers could intercept or redirect email communications, leading to data breaches or information leakage. +search: "`o365_management_activity` Workload=Exchange Operation=\"New-TransportRule\" | eval match1=mvfind('Parameters{}.Name',\"BlindCopyTo\") | eval match2=mvfind('Parameters{}.Name',\"CopyTo\") | eval match3=mvfind('Parameters{}.Name', \"RedirectMessageTo\") | where match1>= 0 OR match2>= 0 OR match3>=0 | eval ForwardTo=coalesce(BlindCopyTo, CopyTo, RedirectMessageTo) | search ForwardTo!=\"\" | rename UserId as user | fillnull | stats count earliest(_time) as firstTime latest(_time) as lastTime by user, Name, ForwardTo, vendor_account, vendor_product, dest, signature | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_new_forwarding_mailflow_rule_created_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Forwarding mail flow rules may be created for legitimate reasons, filter as needed. references: -- https://attack.mitre.org/techniques/T1114/ -- https://learn.microsoft.com/en-us/exchange/security-and-compliance/mail-flow-rules/mail-flow-rules -- https://learn.microsoft.com/en-us/exchange/security-and-compliance/mail-flow-rules/mail-flow-rule-actions + - https://attack.mitre.org/techniques/T1114/ + - https://learn.microsoft.com/en-us/exchange/security-and-compliance/mail-flow-rules/mail-flow-rules + - https://learn.microsoft.com/en-us/exchange/security-and-compliance/mail-flow-rules/mail-flow-rule-actions drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A new forwarding mailflow rule was created by $user$ - risk_objects: - - field: user - type: user - score: 42 - threat_objects: [] + message: A new forwarding mailflow rule was created by $user$ + risk_objects: + - field: user + type: user + score: 42 + threat_objects: [] tags: - analytic_story: - - Office 365 Collection Techniques - asset_type: O365 Tenant - mitre_attack_id: - - T1114 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: audit + analytic_story: + - Office 365 Collection Techniques + asset_type: O365 Tenant + mitre_attack_id: + - T1114 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: audit tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_new_forwarding_mailflow_rule_created/o365_new_forwarding_mailflow_rule_created.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_new_forwarding_mailflow_rule_created/o365_new_forwarding_mailflow_rule_created.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_new_mfa_method_registered.yml b/detections/cloud/o365_new_mfa_method_registered.yml index 3d9c8366fe..0a976d0c2c 100644 --- a/detections/cloud/o365_new_mfa_method_registered.yml +++ b/detections/cloud/o365_new_mfa_method_registered.yml @@ -6,80 +6,66 @@ author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- O365 Update user. -description: The following analytic detects the registration of a new Multi-Factor - Authentication (MFA) method for a user account within Office 365. It leverages O365 - audit logs to identify changes in MFA configurations. This activity is significant - as it may indicate an attacker's attempt to maintain persistence on a compromised - account. If confirmed malicious, the attacker could bypass existing security measures, - solidify their access, and potentially escalate privileges or access sensitive data. - Immediate verification and remediation are required to secure the affected account. + - O365 Update user. +description: The following analytic detects the registration of a new Multi-Factor Authentication (MFA) method for a user account within Office 365. It leverages O365 audit logs to identify changes in MFA configurations. This activity is significant as it may indicate an attacker's attempt to maintain persistence on a compromised account. If confirmed malicious, the attacker could bypass existing security measures, solidify their access, and potentially escalate privileges or access sensitive data. Immediate verification and remediation are required to secure the affected account. search: | - `o365_management_activity` - Workload=AzureActiveDirectory - Operation="Update user." - | eval propertyName = mvindex('ModifiedProperties{}.Name', 0) - | search propertyName IN ("StrongAuthenticationMethod", "StrongAuthenticationPhoneAppDetail") - | eval oldvalue = mvindex('ModifiedProperties{}.OldValue',0) - | eval newvalue = mvindex('ModifiedProperties{}.NewValue',0) - | rex field=newvalue max_match=0 "(?i)(?MethodType|DeviceName)" - | rex field=oldvalue max_match=0 "(?i)(?MethodType|DeviceName)" - | eval count_new_method_type = coalesce(mvcount(new_method_type), 0) - | eval count_old_method_type = coalesce(mvcount(old_method_type), 0) - | where count_new_method_type > count_old_method_type - | fillnull - | stats earliest(_time) as firstTime - latest(_time) as lastTime - values(propertyName) as propertyName - by user newvalue oldvalue vendor_account - vendor_product dest signature src - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_new_mfa_method_registered_filter` -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Users may register MFA methods legitimately, investigate and - filter as needed. + `o365_management_activity` + Workload=AzureActiveDirectory + Operation="Update user." + | eval propertyName = mvindex('ModifiedProperties{}.Name', 0) + | search propertyName IN ("StrongAuthenticationMethod", "StrongAuthenticationPhoneAppDetail") + | eval oldvalue = mvindex('ModifiedProperties{}.OldValue',0) + | eval newvalue = mvindex('ModifiedProperties{}.NewValue',0) + | rex field=newvalue max_match=0 "(?i)(?MethodType|DeviceName)" + | rex field=oldvalue max_match=0 "(?i)(?MethodType|DeviceName)" + | eval count_new_method_type = coalesce(mvcount(new_method_type), 0) + | eval count_old_method_type = coalesce(mvcount(old_method_type), 0) + | where count_new_method_type > count_old_method_type + | fillnull + | stats earliest(_time) as firstTime + latest(_time) as lastTime + values(propertyName) as propertyName + by user newvalue oldvalue vendor_account + vendor_product dest signature src + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_new_mfa_method_registered_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Users may register MFA methods legitimately, investigate and filter as needed. references: -- https://attack.mitre.org/techniques/T1098/005/ -- https://www.microsoft.com/en-us/security/blog/2023/06/08/detecting-and-mitigating-a-multi-stage-aitm-phishing-and-bec-campaign/ -- https://www.csoonline.com/article/573451/sophisticated-bec-scammers-bypass-microsoft-365-multi-factor-authentication.html + - https://attack.mitre.org/techniques/T1098/005/ + - https://www.microsoft.com/en-us/security/blog/2023/06/08/detecting-and-mitigating-a-multi-stage-aitm-phishing-and-bec-campaign/ + - https://www.csoonline.com/article/573451/sophisticated-bec-scammers-bypass-microsoft-365-multi-factor-authentication.html drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A new MFA method was added for $user$ - risk_objects: - - field: user - type: user - score: 30 - threat_objects: [] + message: A new MFA method was added for $user$ + risk_objects: + - field: user + type: user + score: 30 + threat_objects: [] tags: - analytic_story: - - Office 365 Persistence Mechanisms - asset_type: O365 Tenant - mitre_attack_id: - - T1098.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Office 365 Persistence Mechanisms + asset_type: O365 Tenant + mitre_attack_id: + - T1098.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.005/o365_register_new_mfa_method/o365_register_new_mfa_method.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.005/o365_register_new_mfa_method/o365_register_new_mfa_method.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_oauth_app_mailbox_access_via_ews.yml b/detections/cloud/o365_oauth_app_mailbox_access_via_ews.yml index 1ed4c88b22..9451b2275c 100644 --- a/detections/cloud/o365_oauth_app_mailbox_access_via_ews.yml +++ b/detections/cloud/o365_oauth_app_mailbox_access_via_ews.yml @@ -1,73 +1,61 @@ name: O365 OAuth App Mailbox Access via EWS id: e600cf1a-0bef-4426-b42e-00176d610a4d -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production data_source: -- O365 MailItemsAccessed + - O365 MailItemsAccessed type: TTP -description: The following analytic detects when emails are accessed in Office 365 - Exchange via Exchange Web Services (EWS) using OAuth-authenticated applications. - It leverages the ClientInfoString field to identify EWS interactions and aggregates - metrics such as access counts, timing, and client IP addresses, categorized by user, - ClientAppId, OperationCount, and AppId. Monitoring OAuth applications accessing - emails through EWS is crucial for identifying potential abuse or unauthorized data - access. If confirmed malicious, this activity could lead to unauthorized email access, - data exfiltration, or further compromise of sensitive information. -search: '`o365_management_activity` Workload=Exchange Operation=MailItemsAccessed AppId=* ClientAppId=* - | regex ClientInfoString="^Client=WebServices;ExchangeWebServices" - | fillnull - | stats count earliest(_time) as firstTime latest(_time) as lastTime values(ClientIPAddress) as src by user ClientAppId OperationCount AppId vendor_account vendor_product dest signature ClientInfoString - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_oauth_app_mailbox_access_via_ews_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: OAuth applications may access mailboxes for legitimate purposes, - you can use the src_ip to add trusted sources to an allow list. +description: The following analytic detects when emails are accessed in Office 365 Exchange via Exchange Web Services (EWS) using OAuth-authenticated applications. It leverages the ClientInfoString field to identify EWS interactions and aggregates metrics such as access counts, timing, and client IP addresses, categorized by user, ClientAppId, OperationCount, and AppId. Monitoring OAuth applications accessing emails through EWS is crucial for identifying potential abuse or unauthorized data access. If confirmed malicious, this activity could lead to unauthorized email access, data exfiltration, or further compromise of sensitive information. +search: |- + `o365_management_activity` Workload=Exchange Operation=MailItemsAccessed AppId=* ClientAppId=* + | regex ClientInfoString="^Client=WebServices;ExchangeWebServices" + | fillnull + | stats count earliest(_time) as firstTime latest(_time) as lastTime values(ClientIPAddress) as src + BY user ClientAppId OperationCount + AppId vendor_account vendor_product + dest signature ClientInfoString + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_oauth_app_mailbox_access_via_ews_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: OAuth applications may access mailboxes for legitimate purposes, you can use the src_ip to add trusted sources to an allow list. references: -- https://attack.mitre.org/techniques/T1114/002/ -- https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/ -- https://learn.microsoft.com/en-us/exchange/client-developer/exchange-web-services/ews-applications-and-the-exchange-architecture + - https://attack.mitre.org/techniques/T1114/002/ + - https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/ + - https://learn.microsoft.com/en-us/exchange/client-developer/exchange-web-services/ews-applications-and-the-exchange-architecture drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An OAuth application identified with id $ClientAppId$ accesed mailboxes - through the Graph API. - risk_objects: - - field: user - type: user - score: 42 - threat_objects: [] + message: An OAuth application identified with id $ClientAppId$ accesed mailboxes through the Graph API. + risk_objects: + - field: user + type: user + score: 42 + threat_objects: [] tags: - analytic_story: - - Office 365 Collection Techniques - - NOBELIUM Group - asset_type: O365 Tenant - mitre_attack_id: - - T1114.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Collection Techniques + - NOBELIUM Group + asset_type: O365 Tenant + mitre_attack_id: + - T1114.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/o365_oauth_app_ews_mailbox_access/o365_oauth_app_ews_mailbox_access.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/o365_oauth_app_ews_mailbox_access/o365_oauth_app_ews_mailbox_access.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_oauth_app_mailbox_access_via_graph_api.yml b/detections/cloud/o365_oauth_app_mailbox_access_via_graph_api.yml index e87f77f44b..f22de2ba38 100644 --- a/detections/cloud/o365_oauth_app_mailbox_access_via_graph_api.yml +++ b/detections/cloud/o365_oauth_app_mailbox_access_via_graph_api.yml @@ -1,71 +1,60 @@ name: O365 OAuth App Mailbox Access via Graph API id: 9db0d5b0-4058-4cb7-baaf-77d8143539a2 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production data_source: -- O365 MailItemsAccessed + - O365 MailItemsAccessed type: TTP -description: The following analytic detects when emails are accessed in Office 365 - Exchange via the Microsoft Graph API using the client ID '00000003-0000-0000-c000-000000000000'. - It leverages the 'MailItemsAccessed' operation within the Exchange workload, focusing - on OAuth-authenticated applications. This activity is significant as unauthorized - access to emails can lead to data breaches and information theft. If confirmed malicious, - attackers could exfiltrate sensitive information, compromise user accounts, and - further infiltrate the organization's network. -search: '`o365_management_activity` Workload=Exchange Operation=MailItemsAccessed AppId=* AppId=00000003-0000-0000-c000-000000000000 - | fillnull - | stats count earliest(_time) as firstTime latest(_time) as lastTime values(ClientIPAddress) as src by user ClientAppId OperationCount AppId vendor_account vendor_product dest signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_oauth_app_mailbox_access_via_graph_api_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: OAuth applications may access mailboxes for legitimate purposes, - you can use the ClientAppId to add trusted applications to an allow list. +description: The following analytic detects when emails are accessed in Office 365 Exchange via the Microsoft Graph API using the client ID '00000003-0000-0000-c000-000000000000'. It leverages the 'MailItemsAccessed' operation within the Exchange workload, focusing on OAuth-authenticated applications. This activity is significant as unauthorized access to emails can lead to data breaches and information theft. If confirmed malicious, attackers could exfiltrate sensitive information, compromise user accounts, and further infiltrate the organization's network. +search: |- + `o365_management_activity` Workload=Exchange Operation=MailItemsAccessed AppId=* AppId=00000003-0000-0000-c000-000000000000 + | fillnull + | stats count earliest(_time) as firstTime latest(_time) as lastTime values(ClientIPAddress) as src + BY user ClientAppId OperationCount + AppId vendor_account vendor_product + dest signature + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_oauth_app_mailbox_access_via_graph_api_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: OAuth applications may access mailboxes for legitimate purposes, you can use the ClientAppId to add trusted applications to an allow list. references: -- https://attack.mitre.org/techniques/T1114/002/ -- https://learn.microsoft.com/en-us/troubleshoot/azure/active-directory/verify-first-party-apps-sign-in -- https://learn.microsoft.com/en-us/graph/permissions-reference + - https://attack.mitre.org/techniques/T1114/002/ + - https://learn.microsoft.com/en-us/troubleshoot/azure/active-directory/verify-first-party-apps-sign-in + - https://learn.microsoft.com/en-us/graph/permissions-reference drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An OAuth application identified with id $ClientAppId$ accesed mailboxes - through the Graph API. - risk_objects: - - field: user - type: user - score: 42 - threat_objects: [] + message: An OAuth application identified with id $ClientAppId$ accesed mailboxes through the Graph API. + risk_objects: + - field: user + type: user + score: 42 + threat_objects: [] tags: - analytic_story: - - Office 365 Collection Techniques - - NOBELIUM Group - asset_type: O365 Tenant - mitre_attack_id: - - T1114.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Collection Techniques + - NOBELIUM Group + asset_type: O365 Tenant + mitre_attack_id: + - T1114.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/o365_oauth_app_graph_mailbox_access/o365_oauth_app_graph_mailbox_access.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/o365_oauth_app_graph_mailbox_access/o365_oauth_app_graph_mailbox_access.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_privileged_graph_api_permission_assigned.yml b/detections/cloud/o365_privileged_graph_api_permission_assigned.yml index a93d0245af..bc0b4d61b0 100644 --- a/detections/cloud/o365_privileged_graph_api_permission_assigned.yml +++ b/detections/cloud/o365_privileged_graph_api_permission_assigned.yml @@ -6,72 +6,48 @@ author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- O365 Update application. -description: The following analytic detects the assignment of critical Graph API permissions - in Azure AD using the O365 Unified Audit Log. It focuses on permissions such as - Application.ReadWrite.All, AppRoleAssignment.ReadWrite.All, and RoleManagement.ReadWrite.Directory. - The detection method leverages Azure Active Directory workload events, specifically - 'Update application' operations. This activity is significant as these permissions - provide extensive control over Azure AD settings, posing a high risk if misused. - If confirmed malicious, this could allow unauthorized modifications, leading to - potential data breaches or privilege escalation. Immediate investigation is crucial. -search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Update application.\" - | eval newvalue = mvindex('ModifiedProperties{}.NewValue',0) - | spath input=newvalue - | search \"{}.RequiredAppPermissions{}.EntitlementId\"=\"1bfefb4e-e0b5-418b-a88f-73c46d2cc8e9\" OR \"{}.RequiredAppPermissions{}.EntitlementId\"=\"06b708a9-e830-4db3-a914-8e69da51d44f\" OR \"{}.RequiredAppPermissions{}.EntitlementId\"=\"9e3f62cf-ca93-4989-b6ce-bf83c28f9fe8\" - | eval Permissions = '{}.RequiredAppPermissions{}.EntitlementId' - | fillnull - | stats count earliest(_time) as firstTime latest(_time) as lastTime values(Permissions) by user src object user_agent signature vendor_account vendor_product dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_privileged_graph_api_permission_assigned_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Privileged Graph API permissions may be assigned for legitimate - purposes. Filter as needed. + - O365 Update application. +description: The following analytic detects the assignment of critical Graph API permissions in Azure AD using the O365 Unified Audit Log. It focuses on permissions such as Application.ReadWrite.All, AppRoleAssignment.ReadWrite.All, and RoleManagement.ReadWrite.Directory. The detection method leverages Azure Active Directory workload events, specifically 'Update application' operations. This activity is significant as these permissions provide extensive control over Azure AD settings, posing a high risk if misused. If confirmed malicious, this could allow unauthorized modifications, leading to potential data breaches or privilege escalation. Immediate investigation is crucial. +search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Update application.\" | eval newvalue = mvindex('ModifiedProperties{}.NewValue',0) | spath input=newvalue | search \"{}.RequiredAppPermissions{}.EntitlementId\"=\"1bfefb4e-e0b5-418b-a88f-73c46d2cc8e9\" OR \"{}.RequiredAppPermissions{}.EntitlementId\"=\"06b708a9-e830-4db3-a914-8e69da51d44f\" OR \"{}.RequiredAppPermissions{}.EntitlementId\"=\"9e3f62cf-ca93-4989-b6ce-bf83c28f9fe8\" | eval Permissions = '{}.RequiredAppPermissions{}.EntitlementId' | fillnull | stats count earliest(_time) as firstTime latest(_time) as lastTime values(Permissions) by user src object user_agent signature vendor_account vendor_product dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_privileged_graph_api_permission_assigned_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Privileged Graph API permissions may be assigned for legitimate purposes. Filter as needed. references: -- https://cloudbrothers.info/en/azure-attack-paths/ -- https://github.com/mandiant/Mandiant-Azure-AD-Investigator/blob/master/MandiantAzureADInvestigator.json -- https://learn.microsoft.com/en-us/graph/permissions-reference -- https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/ -- https://posts.specterops.io/azure-privilege-escalation-via-azure-api-permissions-abuse-74aee1006f48 + - https://cloudbrothers.info/en/azure-attack-paths/ + - https://github.com/mandiant/Mandiant-Azure-AD-Investigator/blob/master/MandiantAzureADInvestigator.json + - https://learn.microsoft.com/en-us/graph/permissions-reference + - https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/ + - https://posts.specterops.io/azure-privilege-escalation-via-azure-api-permissions-abuse-74aee1006f48 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ assigned privileged Graph API permissions to $object$ - risk_objects: - - field: user - type: user - score: 54 - threat_objects: [] + message: User $user$ assigned privileged Graph API permissions to $object$ + risk_objects: + - field: user + type: user + score: 54 + threat_objects: [] tags: - analytic_story: - - Office 365 Persistence Mechanisms - - NOBELIUM Group - asset_type: O365 Tenant - mitre_attack_id: - - T1003.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Office 365 Persistence Mechanisms + - NOBELIUM Group + asset_type: O365 Tenant + mitre_attack_id: + - T1003.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/o365_privileged_graph_perm_assigned/o365_privileged_graph_perm_assigned.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/o365_privileged_graph_perm_assigned/o365_privileged_graph_perm_assigned.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_privileged_role_assigned.yml b/detections/cloud/o365_privileged_role_assigned.yml index 3aeeb0efc2..0d665bbfe1 100644 --- a/detections/cloud/o365_privileged_role_assigned.yml +++ b/detections/cloud/o365_privileged_role_assigned.yml @@ -5,71 +5,50 @@ date: '2025-10-14' author: Steven Dick status: production type: TTP -description: The following analytic identifies the assignment of sensitive and privileged - Azure Active Directory roles to an Azure AD user. Adversaries and red teams alike - may assign these roles to a compromised account to establish Persistence in an Azure - AD environment. This detection leverages the O365 Universal Audit Log data source. +description: The following analytic identifies the assignment of sensitive and privileged Azure Active Directory roles to an Azure AD user. Adversaries and red teams alike may assign these roles to a compromised account to establish Persistence in an Azure AD environment. This detection leverages the O365 Universal Audit Log data source. data_source: -- Office 365 Universal Audit Log -search: "`o365_management_activity` Workload=AzureActiveDirectory Operation IN (\"Add member to role.\",\"Add eligible member to role.\") - | eval user = ObjectId, src_user = case(match(mvindex('Actor{}.ID',-1),\"User\"),mvindex('Actor{}.ID',0),match(mvindex('Actor{}.ID',-1),\"ServicePrincipal\"),mvindex('Actor{}.ID',3),true(),mvindex('Actor{}.ID',0)), object_name = mvindex('ModifiedProperties{}.NewValue', mvfind('ModifiedProperties{}.Name',\"Role\\.DisplayName\")), object_id = mvindex('ModifiedProperties{}.NewValue', mvfind('ModifiedProperties{}.Name',\"Role\\.TemplateId\")), signature = Operation, result = ResultStatus, category = mvindex('Target{}.ID',2) - | fillnull - | stats count, min(_time) as firstTime, max(_time) as lastTime by src_user, src, user, category, result, object_name, object_id, signature, vendor_account, vendor_product, dest - | lookup privileged_azure_ad_roles azuretemplateid as object_id OUTPUT isprvilegedadrole - | search isprvilegedadrole=\"TRUE\" category=\"User\" - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_privileged_role_assigned_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Administrators will legitimately assign the privileged roles - users as part of administrative tasks. Microsoft Privileged Identity Management - (PIM) may cause false positives / less accurate alerting. + - Office 365 Universal Audit Log +search: "`o365_management_activity` Workload=AzureActiveDirectory Operation IN (\"Add member to role.\",\"Add eligible member to role.\") | eval user = ObjectId, src_user = case(match(mvindex('Actor{}.ID',-1),\"User\"),mvindex('Actor{}.ID',0),match(mvindex('Actor{}.ID',-1),\"ServicePrincipal\"),mvindex('Actor{}.ID',3),true(),mvindex('Actor{}.ID',0)), object_name = mvindex('ModifiedProperties{}.NewValue', mvfind('ModifiedProperties{}.Name',\"Role\\.DisplayName\")), object_id = mvindex('ModifiedProperties{}.NewValue', mvfind('ModifiedProperties{}.Name',\"Role\\.TemplateId\")), signature = Operation, result = ResultStatus, category = mvindex('Target{}.ID',2) | fillnull | stats count, min(_time) as firstTime, max(_time) as lastTime by src_user, src, user, category, result, object_name, object_id, signature, vendor_account, vendor_product, dest | lookup privileged_azure_ad_roles azuretemplateid as object_id OUTPUT isprvilegedadrole | search isprvilegedadrole=\"TRUE\" category=\"User\" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_privileged_role_assigned_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Administrators will legitimately assign the privileged roles users as part of administrative tasks. Microsoft Privileged Identity Management (PIM) may cause false positives / less accurate alerting. references: -- https://attack.mitre.org/techniques/T1098/003/ -- https://learn.microsoft.com/en-us/azure/active-directory/roles/permissions-reference -- https://learn.microsoft.com/en-us/microsoft-365/admin/add-users/about-exchange-online-admin-role?view=o365-worldwide + - https://attack.mitre.org/techniques/T1098/003/ + - https://learn.microsoft.com/en-us/azure/active-directory/roles/permissions-reference + - https://learn.microsoft.com/en-us/microsoft-365/admin/add-users/about-exchange-online-admin-role?view=o365-worldwide drilldown_searches: -- name: View the detection results for - "$user$" and "$src_user$" - search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$src_user$" + search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A privileged Azure AD role [$object_name$] was assigned to user $user$ - by $src_user$ - risk_objects: - - field: user - type: user - score: 75 - - field: src_user - type: user - score: 75 - threat_objects: [] + message: A privileged Azure AD role [$object_name$] was assigned to user $user$ by $src_user$ + risk_objects: + - field: user + type: user + score: 75 + - field: src_user + type: user + score: 75 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - - Scattered Lapsus$ Hunters - asset_type: O365 Tenant - mitre_attack_id: - - T1098.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Persistence + - Scattered Lapsus$ Hunters + asset_type: O365 Tenant + mitre_attack_id: + - T1098.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_azure_workload_events/o365_azure_workload_events.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_azure_workload_events/o365_azure_workload_events.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_privileged_role_assigned_to_service_principal.yml b/detections/cloud/o365_privileged_role_assigned_to_service_principal.yml index 5d3475ddd5..75fd4b8b22 100644 --- a/detections/cloud/o365_privileged_role_assigned_to_service_principal.yml +++ b/detections/cloud/o365_privileged_role_assigned_to_service_principal.yml @@ -5,75 +5,51 @@ date: '2025-10-14' author: Steven Dick status: production type: TTP -description: The following analytic detects potential privilege escalation threats - in Azure Active Directory (AD). This detection is important because it identifies - instances where privileged roles that hold elevated permissions are assigned to - service principals. This prevents unauthorized access or malicious activities, which - occur when these non-human entities access Azure resources to exploit them. False - positives might occur since administrators can legitimately assign privileged roles - to service principals. This detection leverages the O365 Universal Audit Log data - source. +description: The following analytic detects potential privilege escalation threats in Azure Active Directory (AD). This detection is important because it identifies instances where privileged roles that hold elevated permissions are assigned to service principals. This prevents unauthorized access or malicious activities, which occur when these non-human entities access Azure resources to exploit them. False positives might occur since administrators can legitimately assign privileged roles to service principals. This detection leverages the O365 Universal Audit Log data source. data_source: -- Office 365 Universal Audit Log -search: "`o365_management_activity` Workload=AzureActiveDirectory Operation IN (\"Add member to role.\",\"Add eligible member to role.\") - | eval user = ObjectId, src_user = case(match(mvindex('Actor{}.ID',-1),\"User\"),mvindex('Actor{}.ID',0),match(mvindex('Actor{}.ID',-1),\"ServicePrincipal\"),mvindex('Actor{}.ID',3),true(),mvindex('Actor{}.ID',0)), object_name = mvindex('ModifiedProperties{}.NewValue', mvfind('ModifiedProperties{}.Name',\"Role\\.DisplayName\")), object_id = mvindex('ModifiedProperties{}.NewValue', mvfind('ModifiedProperties{}.Name',\"Role\\.TemplateId\")), signature = Operation, result = ResultStatus, category = mvindex('Target{}.ID',2) - | fillnull - | stats count, min(_time) as firstTime, max(_time) as lastTime by src_user, src, user, category, result, object_name, object_id, signature,vendor_account, vendor_product, dest - | lookup privileged_azure_ad_roles azuretemplateid as object_id OUTPUT isprvilegedadrole - | search isprvilegedadrole=\"TRUE\" category!=\"User\" - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_privileged_role_assigned_to_service_principal_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Administrators may legitimately assign the privileged roles - to Service Principals as part of administrative tasks. Filter as needed. + - Office 365 Universal Audit Log +search: "`o365_management_activity` Workload=AzureActiveDirectory Operation IN (\"Add member to role.\",\"Add eligible member to role.\") | eval user = ObjectId, src_user = case(match(mvindex('Actor{}.ID',-1),\"User\"),mvindex('Actor{}.ID',0),match(mvindex('Actor{}.ID',-1),\"ServicePrincipal\"),mvindex('Actor{}.ID',3),true(),mvindex('Actor{}.ID',0)), object_name = mvindex('ModifiedProperties{}.NewValue', mvfind('ModifiedProperties{}.Name',\"Role\\.DisplayName\")), object_id = mvindex('ModifiedProperties{}.NewValue', mvfind('ModifiedProperties{}.Name',\"Role\\.TemplateId\")), signature = Operation, result = ResultStatus, category = mvindex('Target{}.ID',2) | fillnull | stats count, min(_time) as firstTime, max(_time) as lastTime by src_user, src, user, category, result, object_name, object_id, signature,vendor_account, vendor_product, dest | lookup privileged_azure_ad_roles azuretemplateid as object_id OUTPUT isprvilegedadrole | search isprvilegedadrole=\"TRUE\" category!=\"User\" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_privileged_role_assigned_to_service_principal_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Administrators may legitimately assign the privileged roles to Service Principals as part of administrative tasks. Filter as needed. references: -- https://attack.mitre.org/techniques/T1098/003/ -- https://learn.microsoft.com/en-us/azure/active-directory/roles/permissions-reference -- https://learn.microsoft.com/en-us/microsoft-365/admin/add-users/about-exchange-online-admin-role?view=o365-worldwide -- https://posts.specterops.io/azure-privilege-escalation-via-service-principal-abuse-210ae2be2a5 + - https://attack.mitre.org/techniques/T1098/003/ + - https://learn.microsoft.com/en-us/azure/active-directory/roles/permissions-reference + - https://learn.microsoft.com/en-us/microsoft-365/admin/add-users/about-exchange-online-admin-role?view=o365-worldwide + - https://posts.specterops.io/azure-privilege-escalation-via-service-principal-abuse-210ae2be2a5 drilldown_searches: -- name: View the detection results for - "$user$" and "$src_user$" - search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$src_user$" + search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A privileged Azure AD role [$object_name$] was assigned to the Service - Principal $user$ initiated by $src_user$ - risk_objects: - - field: user - type: user - score: 75 - - field: src_user - type: user - score: 75 - threat_objects: [] + message: A privileged Azure AD role [$object_name$] was assigned to the Service Principal $user$ initiated by $src_user$ + risk_objects: + - field: user + type: user + score: 75 + - field: src_user + type: user + score: 75 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Privilege Escalation - - Scattered Lapsus$ Hunters - asset_type: O365 Tenant - mitre_attack_id: - - T1098.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Privilege Escalation + - Scattered Lapsus$ Hunters + asset_type: O365 Tenant + mitre_attack_id: + - T1098.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_azure_workload_events/o365_azure_workload_events.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_azure_workload_events/o365_azure_workload_events.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_pst_export_alert.yml b/detections/cloud/o365_pst_export_alert.yml index 3bbb1831d0..31de46417c 100644 --- a/detections/cloud/o365_pst_export_alert.yml +++ b/detections/cloud/o365_pst_export_alert.yml @@ -1,71 +1,59 @@ name: O365 PST export alert id: 5f694cc4-a678-4a60-9410-bffca1b647dc -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Rod Soto, Splunk status: production type: TTP -description: The following analytic detects instances where a user has initiated an - eDiscovery search or exported a PST file in an Office 365 environment. It leverages - Office 365 management activity logs, specifically filtering for events under ThreatManagement - with the name "eDiscovery search started or exported." This activity is significant - as it may indicate data exfiltration attempts or unauthorized access to sensitive - information. If confirmed malicious, it suggests an attacker or insider threat is - attempting to gather or exfiltrate data, potentially leading to data breaches, loss - of intellectual property, or unauthorized access to confidential communications. - Immediate investigation is required. +description: The following analytic detects instances where a user has initiated an eDiscovery search or exported a PST file in an Office 365 environment. It leverages Office 365 management activity logs, specifically filtering for events under ThreatManagement with the name "eDiscovery search started or exported." This activity is significant as it may indicate data exfiltration attempts or unauthorized access to sensitive information. If confirmed malicious, it suggests an attacker or insider threat is attempting to gather or exfiltrate data, potentially leading to data breaches, loss of intellectual property, or unauthorized access to confidential communications. Immediate investigation is required. data_source: -- O365 -search: '`o365_management_activity` Category=ThreatManagement Name="eDiscovery search started or exported" - | fillnull - | stats count earliest(_time) as firstTime latest(_time) as lastTime by Source Severity AlertEntityId Name user src vendor_account vendor_product dest signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_pst_export_alert_filter`' -how_to_implement: You must install splunk Microsoft Office 365 add-on. This search - works with o365:management:activity -known_false_positives: PST export can be done for legitimate purposes but due to the - sensitive nature of its content it must be monitored. + - O365 +search: |- + `o365_management_activity` Category=ThreatManagement Name="eDiscovery search started or exported" + | fillnull + | stats count earliest(_time) as firstTime latest(_time) as lastTime + BY Source Severity AlertEntityId + Name user src + vendor_account vendor_product dest + signature + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_pst_export_alert_filter` +how_to_implement: You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity +known_false_positives: PST export can be done for legitimate purposes but due to the sensitive nature of its content it must be monitored. references: -- https://attack.mitre.org/techniques/T1114/ + - https://attack.mitre.org/techniques/T1114/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ has exported a PST file from the search using this operation- - $signature$ with a severity of $Severity$ - risk_objects: - - field: user - type: user - score: 48 - threat_objects: [] + message: User $user$ has exported a PST file from the search using this operation- $signature$ with a severity of $Severity$ + risk_objects: + - field: user + type: user + score: 48 + threat_objects: [] tags: - analytic_story: - - Office 365 Collection Techniques - - Data Exfiltration - asset_type: O365 Tenant - mitre_attack_id: - - T1114 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Collection Techniques + - Data Exfiltration + asset_type: O365 Tenant + mitre_attack_id: + - T1114 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_export_pst_file/o365_export_pst_file.json - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_export_pst_file/o365_export_pst_file.json + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_safe_links_detection.yml b/detections/cloud/o365_safe_links_detection.yml index b0f2fcae4a..4184359f8a 100644 --- a/detections/cloud/o365_safe_links_detection.yml +++ b/detections/cloud/o365_safe_links_detection.yml @@ -1,65 +1,62 @@ name: O365 Safe Links Detection id: 711d9e8c-2cb0-45cf-8813-5f191ecb9b26 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Steven Dick status: production type: TTP -description: The following analytic detects when any Microsoft Safe Links alerting - is triggered. This behavior may indicate when user has interacted with a phishing - or otherwise malicious link within the Microsoft Office ecosystem. +description: The following analytic detects when any Microsoft Safe Links alerting is triggered. This behavior may indicate when user has interacted with a phishing or otherwise malicious link within the Microsoft Office ecosystem. data_source: -- Office 365 Universal Audit Log -search: '`o365_management_activity` Name="*a potentially malicious URL*" Operation=AlertEntityGenerated - | fromjson Data | fillnull | stats count min(_time) as firstTime max(_time) as lastTime - values(ObjectId) as url values(od) as desc by AlertId, trc, Name, ot, dest, vendor_account, - vendor_product, src | rename Name as signature, AlertId as signature_id, trc as - user, ot as action | eval action = CASE(action == "Allowed", "allowed", action=="BlockPageOverride", - "allowed", true(),"blocked") | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `o365_safe_links_detection_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. The Safe Links capability must be configured - and is typically only available to E3/E5 level customers. + - Office 365 Universal Audit Log +search: |- + `o365_management_activity` Name="*a potentially malicious URL*" Operation=AlertEntityGenerated + | fromjson Data + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime values(ObjectId) as url values(od) as desc + BY AlertId, trc, Name, + ot, dest, vendor_account, + vendor_product, src + | rename Name as signature, AlertId as signature_id, trc as user, ot as action + | eval action = CASE(action == "Allowed", "allowed", action=="BlockPageOverride", "allowed", true(),"blocked") + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_safe_links_detection_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. The Safe Links capability must be configured and is typically only available to E3/E5 level customers. known_false_positives: Based on Safe Links policies, may vary. references: -- https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/safe-links-about?view=o365-worldwide -- https://attack.mitre.org/techniques/T1566/ + - https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/safe-links-about?view=o365-worldwide + - https://attack.mitre.org/techniques/T1566/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $user$ triggered a Microsoft Safe Links detection. - risk_objects: - - field: user - type: user - score: 40 - threat_objects: [] + message: $user$ triggered a Microsoft Safe Links detection. + risk_objects: + - field: user + type: user + score: 40 + threat_objects: [] tags: - analytic_story: - - Office 365 Account Takeover - - Spearphishing Attachments - asset_type: O365 Tenant - mitre_attack_id: - - T1566.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Account Takeover + - Spearphishing Attachments + asset_type: O365 Tenant + mitre_attack_id: + - T1566.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_security_and_compliance_alert_triggered.yml b/detections/cloud/o365_security_and_compliance_alert_triggered.yml index e72df6dc06..2caa56be8c 100644 --- a/detections/cloud/o365_security_and_compliance_alert_triggered.yml +++ b/detections/cloud/o365_security_and_compliance_alert_triggered.yml @@ -1,78 +1,67 @@ name: O365 Security And Compliance Alert Triggered id: 5b367cdd-8dfc-49ac-a9b7-6406cf27f33e -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk data_source: [] type: TTP status: production -description: The following analytic identifies alerts triggered by the Office 365 - Security and Compliance Center, indicating potential threats or policy violations. - It leverages data from the `o365_management_activity` dataset, focusing on events - where the workload is SecurityComplianceCenter and the operation is AlertTriggered. - This activity is significant as it highlights security and compliance issues within - the O365 environment, which are crucial for maintaining organizational security. - If confirmed malicious, these alerts could indicate attempts to breach security - policies, leading to unauthorized access, data exfiltration, or other malicious - activities. -search: '`o365_management_activity` Workload=SecurityComplianceCenter Category=ThreatManagement Operation=AlertTriggered - | spath input=Data path=f3u output=user - | spath input=Data path=op output=operation - | spath input=_raw path=wl - | spath input=Data path=rid output=rule_id - | spath input=Data path=ad output=alert_description - | spath input=Data path=lon output=operation_name - | spath input=Data path=an output=alert_name - | spath input=Data path=sev output=severity - | fillnull - | stats count earliest(_time) as firstTime latest(_time) as lastTime by user, Name, rule_id, alert_description, alert_name, severity, dest, src, vendor_account, vendor_product, signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_security_and_compliance_alert_triggered_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: O365 Security and Compliance may also generate false positives - or trigger on legitimate behavior, filter as needed. +description: The following analytic identifies alerts triggered by the Office 365 Security and Compliance Center, indicating potential threats or policy violations. It leverages data from the `o365_management_activity` dataset, focusing on events where the workload is SecurityComplianceCenter and the operation is AlertTriggered. This activity is significant as it highlights security and compliance issues within the O365 environment, which are crucial for maintaining organizational security. If confirmed malicious, these alerts could indicate attempts to breach security policies, leading to unauthorized access, data exfiltration, or other malicious activities. +search: |- + `o365_management_activity` Workload=SecurityComplianceCenter Category=ThreatManagement Operation=AlertTriggered + | spath input=Data path=f3u output=user + | spath input=Data path=op output=operation + | spath input=_raw path=wl + | spath input=Data path=rid output=rule_id + | spath input=Data path=ad output=alert_description + | spath input=Data path=lon output=operation_name + | spath input=Data path=an output=alert_name + | spath input=Data path=sev output=severity + | fillnull + | stats count earliest(_time) as firstTime latest(_time) as lastTime + BY user, Name, rule_id, + alert_description, alert_name, severity, + dest, src, vendor_account, + vendor_product, signature + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_security_and_compliance_alert_triggered_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: O365 Security and Compliance may also generate false positives or trigger on legitimate behavior, filter as needed. references: -- https://attack.mitre.org/techniques/T1078/004/ -- https://learn.microsoft.com/en-us/purview/alert-policies?view=o365-worldwide -- https://learn.microsoft.com/en-us/purview/alert-policies + - https://attack.mitre.org/techniques/T1078/004/ + - https://learn.microsoft.com/en-us/purview/alert-policies?view=o365-worldwide + - https://learn.microsoft.com/en-us/purview/alert-policies drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Security and Compliance triggered an alert for $user$ - risk_objects: - - field: user - type: user - score: 48 - threat_objects: [] + message: Security and Compliance triggered an alert for $user$ + risk_objects: + - field: user + type: user + score: 48 + threat_objects: [] tags: - analytic_story: - - Office 365 Account Takeover - asset_type: O365 Tenant - mitre_attack_id: - - T1078.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Office 365 Account Takeover + asset_type: O365 Tenant + mitre_attack_id: + - T1078.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/o365_security_and_compliance_alert_triggered/o365_security_and_compliance_alert_triggered.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/o365_security_and_compliance_alert_triggered/o365_security_and_compliance_alert_triggered.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_service_principal_new_client_credentials.yml b/detections/cloud/o365_service_principal_new_client_credentials.yml index b3ae75202e..134fc6237e 100644 --- a/detections/cloud/o365_service_principal_new_client_credentials.yml +++ b/detections/cloud/o365_service_principal_new_client_credentials.yml @@ -1,75 +1,64 @@ name: O365 Service Principal New Client Credentials id: a1b229e9-d962-4222-8c62-905a8a010453 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the addition of new credentials for Service - Principals within an Office 365 tenant. It uses O365 audit logs, focusing on events - related to credential modifications or additions in the AzureActiveDirectory workload. - This activity is significant because Service Principals represent application identities, - and their credentials allow applications to authenticate and access resources. If - an attacker successfully adds or modifies these credentials, they can impersonate - the application, leading to unauthorized data access, data exfiltration, or malicious - operations under the application's identity. +description: The following analytic detects the addition of new credentials for Service Principals within an Office 365 tenant. It uses O365 audit logs, focusing on events related to credential modifications or additions in the AzureActiveDirectory workload. This activity is significant because Service Principals represent application identities, and their credentials allow applications to authenticate and access resources. If an attacker successfully adds or modifies these credentials, they can impersonate the application, leading to unauthorized data access, data exfiltration, or malicious operations under the application's identity. data_source: -- O365 -search: '`o365_management_activity` Workload=AzureActiveDirectory Operation="Update application*Certificates and secrets management " - | fillnull - | stats earliest(_time) as firstTime latest(_time) as lastTime by user ModifiedProperties{}.NewValue object ObjectId dest signature src vendor_account vendor_product - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_service_principal_new_client_credentials_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Service Principal client credential modifications may be part - of legitimate administrative operations. Filter as needed. + - O365 +search: |- + `o365_management_activity` Workload=AzureActiveDirectory Operation="Update application*Certificates and secrets management " + | fillnull + | stats earliest(_time) as firstTime latest(_time) as lastTime + BY user ModifiedProperties{}.NewValue object + ObjectId dest signature + src vendor_account vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_service_principal_new_client_credentials_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Service Principal client credential modifications may be part of legitimate administrative operations. Filter as needed. references: -- https://attack.mitre.org/techniques/T1098/001/ -- https://www.mandiant.com/resources/blog/remediation-and-hardening-strategies-for-microsoft-365-to-defend-against-unc2452 -- https://microsoft.github.io/Azure-Threat-Research-Matrix/Persistence/AZT501/AZT501-2/ -- https://github.com/swisskyrepo/PayloadsAllTheThings/blob/master/Methodology%20and%20Resources/Cloud%20-%20Azure%20Pentest.md#add-credentials-to-all-enterprise-applications + - https://attack.mitre.org/techniques/T1098/001/ + - https://www.mandiant.com/resources/blog/remediation-and-hardening-strategies-for-microsoft-365-to-defend-against-unc2452 + - https://microsoft.github.io/Azure-Threat-Research-Matrix/Persistence/AZT501/AZT501-2/ + - https://github.com/swisskyrepo/PayloadsAllTheThings/blob/master/Methodology%20and%20Resources/Cloud%20-%20Azure%20Pentest.md#add-credentials-to-all-enterprise-applications drilldown_searches: -- name: View the detection results for - "$object$" - search: '%original_detection_search% | search object = "$object$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$object$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$object$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$object$" + search: '%original_detection_search% | search object = "$object$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$object$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$object$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: New credentials added for Service Principal $object$ - risk_objects: - - field: object - type: user - score: 35 - - field: user - type: user - score: 35 - threat_objects: [] + message: New credentials added for Service Principal $object$ + risk_objects: + - field: object + type: user + score: 35 + - field: user + type: user + score: 35 + threat_objects: [] tags: - analytic_story: - - Office 365 Persistence Mechanisms - - NOBELIUM Group - asset_type: O365 Tenant - mitre_attack_id: - - T1098.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Office 365 Persistence Mechanisms + - NOBELIUM Group + asset_type: O365 Tenant + mitre_attack_id: + - T1098.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.001/o365_service_principal_credentials/o365_service_principal_credentials.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.001/o365_service_principal_credentials/o365_service_principal_credentials.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_service_principal_privilege_escalation.yml b/detections/cloud/o365_service_principal_privilege_escalation.yml index 15f4c9c20f..0d9994c77b 100644 --- a/detections/cloud/o365_service_principal_privilege_escalation.yml +++ b/detections/cloud/o365_service_principal_privilege_escalation.yml @@ -1,75 +1,66 @@ name: O365 Service Principal Privilege Escalation id: b686d0bd-cca7-44ca-ae07-87f6465131d9 -version: 5 -date: '2026-01-14' +version: 6 +date: '2026-02-25' author: Dean Luxton data_source: -- O365 Add app role assignment grant to user. + - O365 Add app role assignment grant to user. type: TTP status: production description: This detection identifies when an Azure Service Principal elevates privileges by adding themself to a new app role assignment. -search: >- - `o365_management_activity` Operation="Add app role assignment to service principal." "Actor{}.ID"=ServicePrincipal ResultStatus=Success - | spath path=ModifiedProperties{} output=targetResources - | eval src="NA" - | stats min(_time) as _time values(eval(mvfilter(match(targetResources, "AppRole.Value")))) as appRole, values(eval(mvfilter(match(targetResources, "ServicePrincipal.DisplayName")))) as targetServicePrincipal values(object) as targetAppContext values(user_agent) as user_agent values(user) as servicePrincipal values(UserId) as servicePrincipalId by Operation InterSystemsId tenant_id user dest src vendor_account vendor_product signature - | spath input=appRole path=NewValue output=appRole - | spath input=targetServicePrincipal path=NewValue output=targetServicePrincipal - | where servicePrincipal=targetServicePrincipal - | fillnull - | stats earliest(_time) as firstTime latest(_time) as lastTime by servicePrincipal servicePrincipalId appRole targetAppContext user_agent tenant_id InterSystemsId user dest src vendor_account vendor_product signature - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_service_principal_privilege_escalation_filter` -how_to_implement: The Splunk Add-on for Microsoft Office 365 add-on is required to - ingest EntraID audit logs via the 365 API. See references for links for further - details on how to onboard this log source. +search: >- + `o365_management_activity` Operation="Add app role assignment to service principal." "Actor{}.ID"=ServicePrincipal ResultStatus=Success + | spath path=ModifiedProperties{} output=targetResources + | eval src="NA" + | stats min(_time) as _time values(eval(mvfilter(match(targetResources, "AppRole.Value")))) as appRole, values(eval(mvfilter(match(targetResources, "ServicePrincipal.DisplayName")))) as targetServicePrincipal values(object) as targetAppContext values(user_agent) as user_agent values(user) as servicePrincipal values(UserId) as servicePrincipalId by Operation InterSystemsId tenant_id user dest src vendor_account vendor_product signature + | spath input=appRole path=NewValue output=appRole + | spath input=targetServicePrincipal path=NewValue output=targetServicePrincipal + | where servicePrincipal=targetServicePrincipal + | fillnull + | stats earliest(_time) as firstTime latest(_time) as lastTime by servicePrincipal servicePrincipalId appRole targetAppContext user_agent tenant_id InterSystemsId user dest src vendor_account vendor_product signature + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_service_principal_privilege_escalation_filter` +how_to_implement: The Splunk Add-on for Microsoft Office 365 add-on is required to ingest EntraID audit logs via the 365 API. See references for links for further details on how to onboard this log source. known_false_positives: No false positives have been identified at this time. references: -- https://splunkbase.splunk.com/app/4055 -- https://github.com/mvelazc0/BadZure -- https://www.splunk.com/en_us/blog/security/hunting-m365-invaders-navigating-the-shadows-of-midnight-blizzard.html -- https://posts.specterops.io/microsoft-breach-what-happened-what-should-azure-admins-do-da2b7e674ebc + - https://splunkbase.splunk.com/app/4055 + - https://github.com/mvelazc0/BadZure + - https://www.splunk.com/en_us/blog/security/hunting-m365-invaders-navigating-the-shadows-of-midnight-blizzard.html + - https://posts.specterops.io/microsoft-breach-what-happened-what-should-azure-admins-do-da2b7e674ebc drilldown_searches: -- name: View the detection results for - "$servicePrincipal$" - search: '%original_detection_search% | search servicePrincipal = "$servicePrincipal$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$servicePrincipal$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$servicePrincipal$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$servicePrincipal$" + search: '%original_detection_search% | search servicePrincipal = "$servicePrincipal$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$servicePrincipal$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$servicePrincipal$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Service Principal $servicePrincipal$ has elevated privileges by adding - themself to app role $appRole$ - risk_objects: - - field: servicePrincipal - type: user - score: 100 - threat_objects: - - field: user_agent - type: http_user_agent + message: Service Principal $servicePrincipal$ has elevated privileges by adding themself to app role $appRole$ + risk_objects: + - field: servicePrincipal + type: user + score: 100 + threat_objects: + - field: user_agent + type: http_user_agent tags: - analytic_story: - - Azure Active Directory Privilege Escalation - - Office 365 Account Takeover - asset_type: Azure Tenant - mitre_attack_id: - - T1098.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Azure Active Directory Privilege Escalation + - Office 365 Account Takeover + asset_type: Azure Tenant + mitre_attack_id: + - T1098.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/o365_spn_privesc/o365_spn_privesc.log - sourcetype: o365:management:activity - source: Office 365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/o365_spn_privesc/o365_spn_privesc.log + sourcetype: o365:management:activity + source: Office 365 diff --git a/detections/cloud/o365_sharepoint_allowed_domains_policy_changed.yml b/detections/cloud/o365_sharepoint_allowed_domains_policy_changed.yml index fd5567d08f..0cf30d2e29 100644 --- a/detections/cloud/o365_sharepoint_allowed_domains_policy_changed.yml +++ b/detections/cloud/o365_sharepoint_allowed_domains_policy_changed.yml @@ -5,67 +5,44 @@ date: '2025-05-02' author: Steven Dick status: production type: TTP -description: The following analytic identifies when the allowed domain settings for - O365 SharePoint have been changed. With Azure AD B2B collaboration, users and administrators - can invite external users to collaborate with internal users. External guest account - invitations may also need access to OneDrive/SharePoint resources. These changed - should be monitored by security teams as they could potentially lead to unauthorized - access. +description: The following analytic identifies when the allowed domain settings for O365 SharePoint have been changed. With Azure AD B2B collaboration, users and administrators can invite external users to collaborate with internal users. External guest account invitations may also need access to OneDrive/SharePoint resources. These changed should be monitored by security teams as they could potentially lead to unauthorized access. data_source: -- Office 365 Universal Audit Log -search: '`o365_management_activity` Workload=SharePoint Operation=SharingPolicyChanged - "ModifiedProperties{}.Name"=AllowDomainList | eval signature_id = CorrelationId, - signature=Operation, src = ClientIP, user = UserId, object_name=''ModifiedProperties{}.Name'', - object_attrs_new = split(replace(''ModifiedProperties{}.NewValue'',"\.\.\.",""),","), - object_attrs_old = split(replace(''ModifiedProperties{}.OldValue'',"\.\.\.",""),",") - | fillnull | stats values(object_attrs_new) as object_attrs_new, values(object_attrs_old) - as object_attrs_old, values(src) as src, count, min(_time) as firstTime, max(_time) - as lastTime by user,signature,signature_id,object_name,dest,action,vendor_account,vendor_product - | eval diff_add=mvmap(object_attrs_new,if(isnull(mvfind(object_attrs_old,object_attrs_new)),object_attrs_new,null)) - | eval diff_remove=mvmap(object_attrs_old,if(isnull(mvfind(object_attrs_new,object_attrs_old)),object_attrs_old,null)) - | eval result = case(isnotnull(diff_add),"Added ".mvjoin(diff_add,","),isnotnull(diff_remove),"Removed - ".mvjoin(diff_remove,",")), action = case(isnotnull(diff_add),"created",isnotnull(diff_remove),"deleted") - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_sharepoint_allowed_domains_policy_changed_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. + - Office 365 Universal Audit Log +search: '`o365_management_activity` Workload=SharePoint Operation=SharingPolicyChanged "ModifiedProperties{}.Name"=AllowDomainList | eval signature_id = CorrelationId, signature=Operation, src = ClientIP, user = UserId, object_name=''ModifiedProperties{}.Name'', object_attrs_new = split(replace(''ModifiedProperties{}.NewValue'',"\.\.\.",""),","), object_attrs_old = split(replace(''ModifiedProperties{}.OldValue'',"\.\.\.",""),",") | fillnull | stats values(object_attrs_new) as object_attrs_new, values(object_attrs_old) as object_attrs_old, values(src) as src, count, min(_time) as firstTime, max(_time) as lastTime by user,signature,signature_id,object_name,dest,action,vendor_account,vendor_product | eval diff_add=mvmap(object_attrs_new,if(isnull(mvfind(object_attrs_old,object_attrs_new)),object_attrs_new,null)) | eval diff_remove=mvmap(object_attrs_old,if(isnull(mvfind(object_attrs_new,object_attrs_old)),object_attrs_old,null)) | eval result = case(isnotnull(diff_add),"Added ".mvjoin(diff_add,","),isnotnull(diff_remove),"Removed ".mvjoin(diff_remove,",")), action = case(isnotnull(diff_add),"created",isnotnull(diff_remove),"deleted") | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_sharepoint_allowed_domains_policy_changed_filter`' +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. known_false_positives: Business approved changes by known administrators. references: -- https://learn.microsoft.com/en-us/sharepoint/external-sharing-overview + - https://learn.microsoft.com/en-us/sharepoint/external-sharing-overview drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The SharePoint Online domain allowlist was changed by $user$, $result$ - risk_objects: - - field: user - type: user - score: 75 - threat_objects: [] + message: The SharePoint Online domain allowlist was changed by $user$, $result$ + risk_objects: + - field: user + type: user + score: 75 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - asset_type: O365 Tenant - mitre_attack_id: - - T1136.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Azure Active Directory Persistence + asset_type: O365 Tenant + mitre_attack_id: + - T1136.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_sharepoint_malware_detection.yml b/detections/cloud/o365_sharepoint_malware_detection.yml index c4aee5288d..dba9654dd8 100644 --- a/detections/cloud/o365_sharepoint_malware_detection.yml +++ b/detections/cloud/o365_sharepoint_malware_detection.yml @@ -1,67 +1,60 @@ name: O365 SharePoint Malware Detection id: 583c5de3-7709-44cb-abfc-0e828d301b59 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Steven Dick status: production type: TTP -description: The following analytic identifies when a malicious file is detected within - the SharePoint Online ecosystem. Attackers may stage and execute malicious files - from within the Microsoft Office 365 ecosystem. Any detections from built-in Office - 365 capabilities should be monitored and responded to appropriately. Certain premium - Office 365 capabilities further enhance these detection and response functions. +description: The following analytic identifies when a malicious file is detected within the SharePoint Online ecosystem. Attackers may stage and execute malicious files from within the Microsoft Office 365 ecosystem. Any detections from built-in Office 365 capabilities should be monitored and responded to appropriately. Certain premium Office 365 capabilities further enhance these detection and response functions. data_source: -- Office 365 Universal Audit Log -search: '`o365_management_activity` Operation=FileMalwareDetected | rename UserId - as user, Id as signature_id | stats values(Workload) as category, values(SourceFileName) - as file_name values(ObjectId) as file_path, values(VirusInfo) as signature, count, - min(_time) as firstTime, max(_time) as lastTime by signature_id, user, dest, src, - vendor_account, vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `o365_sharepoint_malware_detection_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. + - Office 365 Universal Audit Log +search: |- + `o365_management_activity` Operation=FileMalwareDetected + | rename UserId as user, Id as signature_id + | stats values(Workload) as category, values(SourceFileName) as file_name values(ObjectId) as file_path, values(VirusInfo) as signature, count, min(_time) as firstTime, max(_time) as lastTime + BY signature_id, user, dest, + src, vendor_account, vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_sharepoint_malware_detection_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. known_false_positives: No false positives have been identified at this time. references: -- https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/anti-malware-protection-for-spo-odfb-teams-about?view=o365-worldwide + - https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/anti-malware-protection-for-spo-odfb-teams-about?view=o365-worldwide drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: SharePoint detected a potentially malicious file $file_name$ - risk_objects: - - field: user - type: user - score: 75 - threat_objects: - - field: file_name - type: file_name + message: SharePoint detected a potentially malicious file $file_name$ + risk_objects: + - field: user + type: user + score: 75 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - Azure Active Directory Persistence - - Office 365 Account Takeover - - Ransomware Cloud - asset_type: O365 Tenant - mitre_attack_id: - - T1204.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Azure Active Directory Persistence + - Office 365 Account Takeover + - Ransomware Cloud + asset_type: O365 Tenant + mitre_attack_id: + - T1204.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_sharepoint_suspicious_search_behavior.yml b/detections/cloud/o365_sharepoint_suspicious_search_behavior.yml index 61891aa321..182ea0729a 100644 --- a/detections/cloud/o365_sharepoint_suspicious_search_behavior.yml +++ b/detections/cloud/o365_sharepoint_suspicious_search_behavior.yml @@ -7,64 +7,64 @@ status: production type: Anomaly description: The following analytic identifies when Office 365 users search for suspicious keywords or have an excessive number of queries to a SharePoint site within a limited timeframe. This behavior may indicate that a malicious actor has gained control of a user account and is conducting discovery or enumeration activities. data_source: -- Office 365 Universal Audit Log + - Office 365 Universal Audit Log search: |- - `o365_management_activity` (Workload=SharePoint Operation="SearchQueryPerformed" SearchQueryText=* EventData=*search*) OR Operation=SearchQueryInitiatedSharepoint - | eval command = case(Operation=="SearchQueryPerformed",SearchQueryText,true(),QueryText), UserId = lower(UserId), signature_id = CorrelationId, signature=Operation, src = ClientIP, user = lower(UserId), object_name=case(Operation=="SearchQueryPerformed",'EventData',true(),QuerySource), -time = _time, suspect_terms = case(match(command, `o365_suspect_search_terms_regex`),command,true(),null()) - | where command != "*" AND command != "(*)" - | bin _time span=1hr - | stats values(ScenarioName) as app, values(object_name) as object_name values(command) as command, values(suspect_terms) as suspect_terms, values(src) as src, dc(suspect_terms) as suspect_terms_count, dc(command) as count, min(-time) as firstTime, max(-time) as lastTime by user,signature,_time - | where count > 20 OR suspect_terms_count >= 2 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_sharepoint_suspicious_search_behavior_filter` + `o365_management_activity` (Workload=SharePoint Operation="SearchQueryPerformed" SearchQueryText=* EventData=*search*) OR Operation=SearchQueryInitiatedSharepoint + | eval command = case(Operation=="SearchQueryPerformed",SearchQueryText,true(),QueryText), UserId = lower(UserId), signature_id = CorrelationId, signature=Operation, src = ClientIP, user = lower(UserId), object_name=case(Operation=="SearchQueryPerformed",'EventData',true(),QuerySource), -time = _time, suspect_terms = case(match(command, `o365_suspect_search_terms_regex`),command,true(),null()) + | where command != "*" AND command != "(*)" + | bin _time span=1hr + | stats values(ScenarioName) as app, values(object_name) as object_name values(command) as command, values(suspect_terms) as suspect_terms, values(src) as src, dc(suspect_terms) as suspect_terms_count, dc(command) as count, min(-time) as firstTime, max(-time) as lastTime by user,signature,_time + | where count > 20 OR suspect_terms_count >= 2 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_sharepoint_suspicious_search_behavior_filter` how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. The thresholds and match terms set within the analytic are initial guidelines and should be customized based on the organization's user behavior and risk profile. Security teams are encouraged to adjust these thresholds to optimize the balance between detecting genuine threats and minimizing false positives, ensuring the detection is tailored to their specific environment. known_false_positives: Users searching excessively or possible false positives related to matching conditions. references: -- https://learn.microsoft.com/en-us/purview/audit-get-started#step-3-enable-searchqueryinitiated-events -- https://www.cisa.gov/sites/default/files/2025-01/microsoft-expanded-cloud-logs-implementation-playbook-508c.pdf -- https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-320a -- https://attack.mitre.org/techniques/T1213/002/ + - https://learn.microsoft.com/en-us/purview/audit-get-started#step-3-enable-searchqueryinitiated-events + - https://www.cisa.gov/sites/default/files/2025-01/microsoft-expanded-cloud-logs-implementation-playbook-508c.pdf + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-320a + - https://attack.mitre.org/techniques/T1213/002/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate search behavior by $user$ - search: '`o365_management_activity` (Workload=SharePoint Operation="SearchQueryPerformed" SearchQueryText=* EventData=*search* AND UserId = "$user$") OR (OR Operation=SearchQueryInitiatedSharepoint AND UserId = "$user$")' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate search behavior by $user$ + search: '`o365_management_activity` (Workload=SharePoint Operation="SearchQueryPerformed" SearchQueryText=* EventData=*search* AND UserId = "$user$") OR (OR Operation=SearchQueryInitiatedSharepoint AND UserId = "$user$")' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The user $user$ searched SharePoint suspiciously, $count$ unique terms and $suspect_terms_count$ suspect terms were searched within a limited timeframe. - risk_objects: - - field: user - type: user - score: 35 - threat_objects: - - field: src - type: ip_address + message: The user $user$ searched SharePoint suspiciously, $count$ unique terms and $suspect_terms_count$ suspect terms were searched within a limited timeframe. + risk_objects: + - field: user + type: user + score: 35 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Office 365 Account Takeover - - Office 365 Collection Techniques - - Compromised User Account - - CISA AA22-320A - asset_type: O365 Tenant - mitre_attack_id: - - T1213.002 - - T1552 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Office 365 Account Takeover + - Office 365 Collection Techniques + - Compromised User Account + - CISA AA22-320A + asset_type: O365 Tenant + mitre_attack_id: + - T1213.002 + - T1552 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1213.002/o365_sus_sharepoint_search/o365_sus_sharepoint_search.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1213.002/o365_sus_sharepoint_search/o365_sus_sharepoint_search.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_tenant_wide_admin_consent_granted.yml b/detections/cloud/o365_tenant_wide_admin_consent_granted.yml index 0ce9daed4a..5b05b2ed21 100644 --- a/detections/cloud/o365_tenant_wide_admin_consent_granted.yml +++ b/detections/cloud/o365_tenant_wide_admin_consent_granted.yml @@ -6,72 +6,48 @@ author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- O365 Consent to application. -description: The following analytic identifies instances where admin consent is granted - to an application within an Azure AD and Office 365 tenant. It leverages O365 audit - logs, specifically events related to the admin consent action within the AzureActiveDirectory - workload. This activity is significant because admin consent allows applications - to access data across the entire tenant, potentially exposing vast amounts of organizational - data. If confirmed malicious, an attacker could gain extensive and persistent access - to organizational data, leading to data exfiltration, espionage, further malicious - activities, and potential compliance violations. -search: "`o365_management_activity` Operation=\"Consent to application.\" - | eval new_field=mvindex('ModifiedProperties{}.NewValue', 4) - | rex field=new_field \"ConsentType: (?[^\\,]+)\" - | rex field=new_field \"Scope: (?[^\\,]+)\" - | search ConsentType = \"AllPrincipals\" - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by user, object, ObjectId, ConsentType, Scope, dest, vendor_account, vendor_product, signature, src - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `o365_tenant_wide_admin_consent_granted_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Legitimate applications may be granted tenant wide consent, - filter as needed. + - O365 Consent to application. +description: The following analytic identifies instances where admin consent is granted to an application within an Azure AD and Office 365 tenant. It leverages O365 audit logs, specifically events related to the admin consent action within the AzureActiveDirectory workload. This activity is significant because admin consent allows applications to access data across the entire tenant, potentially exposing vast amounts of organizational data. If confirmed malicious, an attacker could gain extensive and persistent access to organizational data, leading to data exfiltration, espionage, further malicious activities, and potential compliance violations. +search: "`o365_management_activity` Operation=\"Consent to application.\" | eval new_field=mvindex('ModifiedProperties{}.NewValue', 4) | rex field=new_field \"ConsentType: (?[^\\,]+)\" | rex field=new_field \"Scope: (?[^\\,]+)\" | search ConsentType = \"AllPrincipals\" | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by user, object, ObjectId, ConsentType, Scope, dest, vendor_account, vendor_product, signature, src | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_tenant_wide_admin_consent_granted_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Legitimate applications may be granted tenant wide consent, filter as needed. references: -- https://attack.mitre.org/techniques/T1098/003/ -- https://www.mandiant.com/resources/blog/remediation-and-hardening-strategies-for-microsoft-365-to-defend-against-unc2452 -- https://learn.microsoft.com/en-us/security/operations/incident-response-playbook-app-consent -- https://learn.microsoft.com/en-us/azure/active-directory/manage-apps/grant-admin-consent?pivots=portal -- https://microsoft.github.io/Azure-Threat-Research-Matrix/Persistence/AZT501/AZT501-2/ + - https://attack.mitre.org/techniques/T1098/003/ + - https://www.mandiant.com/resources/blog/remediation-and-hardening-strategies-for-microsoft-365-to-defend-against-unc2452 + - https://learn.microsoft.com/en-us/security/operations/incident-response-playbook-app-consent + - https://learn.microsoft.com/en-us/azure/active-directory/manage-apps/grant-admin-consent?pivots=portal + - https://microsoft.github.io/Azure-Threat-Research-Matrix/Persistence/AZT501/AZT501-2/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The $object$ application registration was granted tenant wide admin consent. - risk_objects: - - field: user - type: user - score: 45 - threat_objects: [] + message: The $object$ application registration was granted tenant wide admin consent. + risk_objects: + - field: user + type: user + score: 45 + threat_objects: [] tags: - analytic_story: - - Office 365 Persistence Mechanisms - - NOBELIUM Group - asset_type: O365 Tenant - mitre_attack_id: - - T1098.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Office 365 Persistence Mechanisms + - NOBELIUM Group + asset_type: O365 Tenant + mitre_attack_id: + - T1098.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/o365_admin_consent/o365_admin_consent.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/o365_admin_consent/o365_admin_consent.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_threat_intelligence_suspicious_email_delivered.yml b/detections/cloud/o365_threat_intelligence_suspicious_email_delivered.yml index 2438bc1df8..9d9b764d05 100644 --- a/detections/cloud/o365_threat_intelligence_suspicious_email_delivered.yml +++ b/detections/cloud/o365_threat_intelligence_suspicious_email_delivered.yml @@ -1,78 +1,63 @@ name: O365 Threat Intelligence Suspicious Email Delivered id: 605cc93a-70e4-4ee3-9a3d-1a62e8c9b6c2 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Steven Dick status: production type: Anomaly -description: The following analytic identifies when a suspicious email is detected - within the Microsoft Office 365 ecosystem through the Advanced Threat Protection - engine and delivered to an end user. Attackers may execute several attacks through - email, any detections from built-in Office 365 capabilities should be monitored - and responded to appropriately. Certain premium Office 365 capabilities such as - Safe Attachment and Safe Links further enhance these detection and response functions. +description: The following analytic identifies when a suspicious email is detected within the Microsoft Office 365 ecosystem through the Advanced Threat Protection engine and delivered to an end user. Attackers may execute several attacks through email, any detections from built-in Office 365 capabilities should be monitored and responded to appropriately. Certain premium Office 365 capabilities such as Safe Attachment and Safe Links further enhance these detection and response functions. data_source: -- Office 365 Universal Audit Log -search: '`o365_management_activity` Workload=ThreatIntelligence Operation=TIMailData - DeliveryAction!=Blocked Directionality=InBound | rename P2Sender as src_user, P1Sender - as sender, Recipients{} as user, DeliveryAction as action | stats values(SenderIp) - as src, values(Subject) as subject, values(user) as user, values(action) as action, - values(SystemOverrides{}.Details) as reason, values(LatestDeliveryLocation) as result, - values(ThreatsAndDetectionTech{}) as category, values(AttachmentData{}.FileName) - as file_name, values(AttachmentData{}.FileType) as file_type, values(AttachmentData{}.SHA256) - as file_hash values(DetectionMethod) as signature, min(_time) as firstTime max(_time) - as lastTime, count by src_user,sender,dest,vendor_account,vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `o365_threat_intelligence_suspicious_email_delivered_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. The threat intelligence workload is typically - only visible to E3/E5 level customers. + - Office 365 Universal Audit Log +search: |- + `o365_management_activity` Workload=ThreatIntelligence Operation=TIMailData DeliveryAction!=Blocked Directionality=InBound + | rename P2Sender as src_user, P1Sender as sender, Recipients{} as user, DeliveryAction as action + | stats values(SenderIp) as src, values(Subject) as subject, values(user) as user, values(action) as action, values(SystemOverrides{}.Details) as reason, values(LatestDeliveryLocation) as result, values(ThreatsAndDetectionTech{}) as category, values(AttachmentData{}.FileName) as file_name, values(AttachmentData{}.FileType) as file_type, values(AttachmentData{}.SHA256) as file_hash values(DetectionMethod) as signature, min(_time) as firstTime max(_time) as lastTime, count + BY src_user,sender,dest,vendor_account,vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_threat_intelligence_suspicious_email_delivered_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. The threat intelligence workload is typically only visible to E3/E5 level customers. known_false_positives: No false positives have been identified at this time. references: -- https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/anti-malware-protection-for-spo-odfb-teams-about?view=o365-worldwide -- https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/office-365-ti?view=o365-worldwide + - https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/anti-malware-protection-for-spo-odfb-teams-about?view=o365-worldwide + - https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/office-365-ti?view=o365-worldwide drilldown_searches: -- name: View the detection results for - "$user$" and "$src_user$" - search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$src_user$" + search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious email was delivered to $user$ by $src_user$ matching the $signature$ - signature - risk_objects: - - field: user - type: user - score: 20 - - field: src_user - type: user - score: 20 - threat_objects: - - field: subject - type: email_subject + message: A suspicious email was delivered to $user$ by $src_user$ matching the $signature$ signature + risk_objects: + - field: user + type: user + score: 20 + - field: src_user + type: user + score: 20 + threat_objects: + - field: subject + type: email_subject tags: - analytic_story: - - Spearphishing Attachments - - Suspicious Emails - asset_type: O365 Tenant - mitre_attack_id: - - T1566.001 - - T1566.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Spearphishing Attachments + - Suspicious Emails + asset_type: O365 Tenant + mitre_attack_id: + - T1566.001 + - T1566.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_threat_intelligence_suspicious_file_detected.yml b/detections/cloud/o365_threat_intelligence_suspicious_file_detected.yml index 4df0fddf26..51baaae15c 100644 --- a/detections/cloud/o365_threat_intelligence_suspicious_file_detected.yml +++ b/detections/cloud/o365_threat_intelligence_suspicious_file_detected.yml @@ -1,72 +1,63 @@ name: O365 Threat Intelligence Suspicious File Detected id: 00958c7b-35db-4e7a-ad13-31550a7a7c64 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Steven Dick status: production type: TTP -description: The following analytic identifies when a malicious file is detected within - the Microsoft Office 365 ecosystem through the Advanced Threat Protection engine. - Attackers may stage and execute malicious files from within the Microsoft Office - 365 ecosystem. Any detections from built-in Office 365 capabilities should be monitored - and responded to appropriately. Certain premium Office 365 capabilities such as - Safe Attachment and Safe Links further enhance these detection and response functions. +description: The following analytic identifies when a malicious file is detected within the Microsoft Office 365 ecosystem through the Advanced Threat Protection engine. Attackers may stage and execute malicious files from within the Microsoft Office 365 ecosystem. Any detections from built-in Office 365 capabilities should be monitored and responded to appropriately. Certain premium Office 365 capabilities such as Safe Attachment and Safe Links further enhance these detection and response functions. data_source: -- Office 365 Universal Audit Log -search: '`o365_management_activity` Workload=ThreatIntelligence Operation=AtpDetection - | eval dest="NA" | eval src="NA" | stats values(DetectionMethod) as category values(FileData.FileName) - as file_name values(FileData.FilePath) as file_path values(FileData.FileSize) as - file_size values(FileData.MalwareFamily) as signature count, min(_time) as firstTime, - max(_time) as lastTime by Id, UserId, dest, src, vendor_account, vendor_product - | rename Id as signature_id, UserId as user | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `o365_threat_intelligence_suspicious_file_detected_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. The threat intelligence workload is typically - only visible to E3/E5 level customers. + - Office 365 Universal Audit Log +search: |- + `o365_management_activity` Workload=ThreatIntelligence Operation=AtpDetection + | eval dest="NA" + | eval src="NA" + | stats values(DetectionMethod) as category values(FileData.FileName) as file_name values(FileData.FilePath) as file_path values(FileData.FileSize) as file_size values(FileData.MalwareFamily) as signature count, min(_time) as firstTime, max(_time) as lastTime + BY Id, UserId, dest, + src, vendor_account, vendor_product + | rename Id as signature_id, UserId as user + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_threat_intelligence_suspicious_file_detected_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. The threat intelligence workload is typically only visible to E3/E5 level customers. known_false_positives: No false positives have been identified at this time. references: -- https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/threat-explorer-real-time-detections-about?view=o365-worldwide -- https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/anti-malware-protection-for-spo-odfb-teams-about?view=o365-worldwide + - https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/threat-explorer-real-time-detections-about?view=o365-worldwide + - https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/anti-malware-protection-for-spo-odfb-teams-about?view=o365-worldwide drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Threat Intelligence workload detected a malicious file [$file_name$] from - user $user$ - risk_objects: - - field: user - type: user - score: 50 - threat_objects: - - field: file_name - type: file_name + message: Threat Intelligence workload detected a malicious file [$file_name$] from user $user$ + risk_objects: + - field: user + type: user + score: 50 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - Azure Active Directory Account Takeover - - Office 365 Account Takeover - - Ransomware Cloud - asset_type: O365 Tenant - mitre_attack_id: - - T1204.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Azure Active Directory Account Takeover + - Office 365 Account Takeover + - Ransomware Cloud + asset_type: O365 Tenant + mitre_attack_id: + - T1204.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/o365_user_consent_blocked_for_risky_application.yml b/detections/cloud/o365_user_consent_blocked_for_risky_application.yml index 4c44a4e162..e51fc0aa27 100644 --- a/detections/cloud/o365_user_consent_blocked_for_risky_application.yml +++ b/detections/cloud/o365_user_consent_blocked_for_risky_application.yml @@ -6,73 +6,48 @@ author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- O365 Consent to application. -description: The following analytic identifies instances where Office 365 has blocked - a user's attempt to grant consent to an application deemed risky or potentially - malicious. This detection leverages O365 audit logs, specifically focusing on failed - user consent actions due to system-driven blocks. Monitoring these blocked consent - attempts is crucial as it highlights potential threats early on, indicating that - a user might be targeted or that malicious applications are attempting to infiltrate - the organization. If confirmed malicious, this activity suggests that O365's security - measures successfully prevented a harmful application from accessing organizational - data, warranting immediate investigation. -search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Consent to application.\" ResultStatus=Failure - | eval permissions =mvindex('ModifiedProperties{}.NewValue', 4) - | eval reason =mvindex('ModifiedProperties{}.NewValue', 5) - | search reason = \"Risky application detected\" - | rex field=permissions \"Scope: (?[^,]+)\" - | fillnull - | stats max(_time) as lastTime by user, reason, object, Scope, dest, src, vendor_account, vendor_product, signature - | `security_content_ctime(lastTime)` - | `o365_user_consent_blocked_for_risky_application_filter`" -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. -known_false_positives: Microsofts algorithm to identify risky applications is unknown - and may flag legitimate applications. + - O365 Consent to application. +description: The following analytic identifies instances where Office 365 has blocked a user's attempt to grant consent to an application deemed risky or potentially malicious. This detection leverages O365 audit logs, specifically focusing on failed user consent actions due to system-driven blocks. Monitoring these blocked consent attempts is crucial as it highlights potential threats early on, indicating that a user might be targeted or that malicious applications are attempting to infiltrate the organization. If confirmed malicious, this activity suggests that O365's security measures successfully prevented a harmful application from accessing organizational data, warranting immediate investigation. +search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Consent to application.\" ResultStatus=Failure | eval permissions =mvindex('ModifiedProperties{}.NewValue', 4) | eval reason =mvindex('ModifiedProperties{}.NewValue', 5) | search reason = \"Risky application detected\" | rex field=permissions \"Scope: (?[^,]+)\" | fillnull | stats max(_time) as lastTime by user, reason, object, Scope, dest, src, vendor_account, vendor_product, signature | `security_content_ctime(lastTime)` | `o365_user_consent_blocked_for_risky_application_filter`" +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. +known_false_positives: Microsofts algorithm to identify risky applications is unknown and may flag legitimate applications. references: -- https://attack.mitre.org/techniques/T1528/ -- https://www.microsoft.com/en-us/security/blog/2022/09/22/malicious-oauth-applications-used-to-compromise-email-servers-and-spread-spam/ -- https://learn.microsoft.com/en-us/azure/active-directory/manage-apps/protect-against-consent-phishing -- https://learn.microsoft.com/en-us/defender-cloud-apps/investigate-risky-oauth -- https://www.alteredsecurity.com/post/introduction-to-365-stealer -- https://github.com/AlteredSecurity/365-Stealer + - https://attack.mitre.org/techniques/T1528/ + - https://www.microsoft.com/en-us/security/blog/2022/09/22/malicious-oauth-applications-used-to-compromise-email-servers-and-spread-spam/ + - https://learn.microsoft.com/en-us/azure/active-directory/manage-apps/protect-against-consent-phishing + - https://learn.microsoft.com/en-us/defender-cloud-apps/investigate-risky-oauth + - https://www.alteredsecurity.com/post/introduction-to-365-stealer + - https://github.com/AlteredSecurity/365-Stealer drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: O365 has blocked $user$ attempt to grant to consent to an application deemed - risky. - risk_objects: - - field: user - type: user - score: 30 - threat_objects: [] + message: O365 has blocked $user$ attempt to grant to consent to an application deemed risky. + risk_objects: + - field: user + type: user + score: 30 + threat_objects: [] tags: - analytic_story: - - Office 365 Account Takeover - asset_type: O365 Tenant - mitre_attack_id: - - T1528 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Office 365 Account Takeover + asset_type: O365 Tenant + mitre_attack_id: + - T1528 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/o365_user_consent_blocked/o365_user_consent_blocked.log - source: o365 - sourcetype: o365:management:activity + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/o365_user_consent_blocked/o365_user_consent_blocked.log + source: o365 + sourcetype: o365:management:activity diff --git a/detections/cloud/o365_user_consent_denied_for_oauth_application.yml b/detections/cloud/o365_user_consent_denied_for_oauth_application.yml index a6441d354d..8400bccda7 100644 --- a/detections/cloud/o365_user_consent_denied_for_oauth_application.yml +++ b/detections/cloud/o365_user_consent_denied_for_oauth_application.yml @@ -1,75 +1,64 @@ name: O365 User Consent Denied for OAuth Application id: 2d8679ef-b075-46be-8059-c25116cb1072 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- O365 -description: The following analytic identifies instances where a user has denied consent - to an OAuth application seeking permissions within the Office 365 environment. This - detection leverages O365 audit logs, focusing on events related to user consent - actions. By filtering for denied consent actions associated with OAuth applications, - it captures instances where users have actively rejected permission requests. This - activity is significant as it may indicate users spotting potentially suspicious - or unfamiliar applications. If confirmed malicious, it suggests an attempt by a - potentially harmful application to gain unauthorized access, which was proactively - blocked by the user. -search: '`o365_graph` status.errorCode=65004 - | rename userPrincipalName as user - | rename ipAddress as src_ip - | stats min(_time) as firstTime max(_time) as lastTime by user src_ip appDisplayName status.failureReason - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_user_consent_denied_for_oauth_application_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 events. -known_false_positives: OAuth applications that require mail permissions may be legitimate, - investigate and filter as needed. + - O365 +description: The following analytic identifies instances where a user has denied consent to an OAuth application seeking permissions within the Office 365 environment. This detection leverages O365 audit logs, focusing on events related to user consent actions. By filtering for denied consent actions associated with OAuth applications, it captures instances where users have actively rejected permission requests. This activity is significant as it may indicate users spotting potentially suspicious or unfamiliar applications. If confirmed malicious, it suggests an attempt by a potentially harmful application to gain unauthorized access, which was proactively blocked by the user. +search: |- + `o365_graph` status.errorCode=65004 + | rename userPrincipalName as user + | rename ipAddress as src_ip + | stats min(_time) as firstTime max(_time) as lastTime + BY user src_ip appDisplayName + status.failureReason + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_user_consent_denied_for_oauth_application_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 events. +known_false_positives: OAuth applications that require mail permissions may be legitimate, investigate and filter as needed. references: -- https://attack.mitre.org/techniques/T1528/ -- https://www.microsoft.com/en-us/security/blog/2022/09/22/malicious-oauth-applications-used-to-compromise-email-servers-and-spread-spam/ -- https://learn.microsoft.com/en-us/azure/active-directory/manage-apps/protect-against-consent-phishing -- https://learn.microsoft.com/en-us/defender-cloud-apps/investigate-risky-oauth -- https://www.alteredsecurity.com/post/introduction-to-365-stealer -- https://github.com/AlteredSecurity/365-Stealer + - https://attack.mitre.org/techniques/T1528/ + - https://www.microsoft.com/en-us/security/blog/2022/09/22/malicious-oauth-applications-used-to-compromise-email-servers-and-spread-spam/ + - https://learn.microsoft.com/en-us/azure/active-directory/manage-apps/protect-against-consent-phishing + - https://learn.microsoft.com/en-us/defender-cloud-apps/investigate-risky-oauth + - https://www.alteredsecurity.com/post/introduction-to-365-stealer + - https://github.com/AlteredSecurity/365-Stealer drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ denifed consent for an OAuth application. - risk_objects: - - field: user - type: user - score: 30 - threat_objects: - - field: src_ip - type: ip_address + message: User $user$ denifed consent for an OAuth application. + risk_objects: + - field: user + type: user + score: 30 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Office 365 Account Takeover - asset_type: O365 Tenant - mitre_attack_id: - - T1528 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity + analytic_story: + - Office 365 Account Takeover + asset_type: O365 Tenant + mitre_attack_id: + - T1528 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/o365_user_consent_declined/o365_user_consent_declined.log - source: o365 - sourcetype: o365:graph:api + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/o365_user_consent_declined/o365_user_consent_declined.log + source: o365 + sourcetype: o365:graph:api diff --git a/detections/cloud/o365_zap_activity_detection.yml b/detections/cloud/o365_zap_activity_detection.yml index 839ddacdc9..85bde8609e 100644 --- a/detections/cloud/o365_zap_activity_detection.yml +++ b/detections/cloud/o365_zap_activity_detection.yml @@ -1,74 +1,66 @@ name: O365 ZAP Activity Detection id: 4df275fd-a0e5-4246-8b92-d3201edaef7a -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Steven Dick status: production type: Anomaly -description: The following analytic detects when the Microsoft Zero-hour Automatic - Purge (ZAP) capability takes action against a user's mailbox. This capability is - an enhanced protection feature that retro-actively removes email with known malicious - content for user inboxes. Since this is a retroactive capability, there is still - a window in which the user may fall victim to the malicious content. +description: The following analytic detects when the Microsoft Zero-hour Automatic Purge (ZAP) capability takes action against a user's mailbox. This capability is an enhanced protection feature that retro-actively removes email with known malicious content for user inboxes. Since this is a retroactive capability, there is still a window in which the user may fall victim to the malicious content. data_source: -- Office 365 Universal Audit Log -search: '`o365_management_activity` Workload=SecurityComplianceCenter Operation=AlertEntityGenerated - Name="*messages containing malicious*" | fromjson Data | fillnull | stats count - min(_time) as firstTime max(_time) as lastTime values(zu) as url values(zfn) as - file_name values(ms) as subject values(ttr) as result values(tsd) as src_user by - AlertId,trc,signature,Name,dest,src,vendor_account,vendor_product | rename Name - as signature, AlertId as signature_id, trc as user | eval action = CASE(match(result,"Success"), - "blocked", true(),"allowed"), url = split(url,";") | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `o365_zap_activity_detection_filter`' -how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest - Office 365 management activity events. Some features of Zero-hour purge are only - offered within E3/E5 license level tenants, events may not be available otherwise. + - Office 365 Universal Audit Log +search: |- + `o365_management_activity` Workload=SecurityComplianceCenter Operation=AlertEntityGenerated Name="*messages containing malicious*" + | fromjson Data + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime values(zu) as url values(zfn) as file_name values(ms) as subject values(ttr) as result values(tsd) as src_user + BY AlertId,trc,signature,Name,dest,src,vendor_account,vendor_product + | rename Name as signature, AlertId as signature_id, trc as user + | eval action = CASE(match(result,"Success"), "blocked", true(),"allowed"), url = split(url,";") + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `o365_zap_activity_detection_filter` +how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. Some features of Zero-hour purge are only offered within E3/E5 license level tenants, events may not be available otherwise. known_false_positives: No false positives have been identified at this time. references: -- https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/zero-hour-auto-purge?view=o365-worldwide + - https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/zero-hour-auto-purge?view=o365-worldwide drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ was included in a ZAP protection activity. - risk_objects: - - field: user - type: user - score: 10 - threat_objects: - - field: file_name - type: file_name - - field: url - type: url - - field: src_user - type: email_address + message: User $user$ was included in a ZAP protection activity. + risk_objects: + - field: user + type: user + score: 10 + threat_objects: + - field: file_name + type: file_name + - field: url + type: url + - field: src_user + type: email_address tags: - analytic_story: - - Spearphishing Attachments - - Suspicious Emails - asset_type: O365 Tenant - mitre_attack_id: - - T1566.001 - - T1566.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Spearphishing Attachments + - Suspicious Emails + asset_type: O365 Tenant + mitre_attack_id: + - T1566.001 + - T1566.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log - sourcetype: o365:management:activity - source: o365 + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log + sourcetype: o365:management:activity + source: o365 diff --git a/detections/cloud/okta_non_standard_vpn_usage.yml b/detections/cloud/okta_non_standard_vpn_usage.yml index b5f865d738..9e2a8d8bdc 100644 --- a/detections/cloud/okta_non_standard_vpn_usage.yml +++ b/detections/cloud/okta_non_standard_vpn_usage.yml @@ -5,57 +5,39 @@ date: '2025-06-03' author: Marissa Bower, Raven Tait status: experimental type: TTP -description: Remote Employment Fraud (REF) actors will often use virtual private networks (VPNs) to conceal their - true physical location. Threat actors mask their originating IP address and instead appear to be situated in - any location where the VPN service has a node. +description: Remote Employment Fraud (REF) actors will often use virtual private networks (VPNs) to conceal their true physical location. Threat actors mask their originating IP address and instead appear to be situated in any location where the VPN service has a node. data_source: -- Okta -search: '`okta` debugContext.debugData.tunnels IN (*Astrill*,*Azire*,*CyberGhost*,*Express*VPN,*HideMe*, - *IPVanish*,*Mullvad*,*Nord*VPN*,*OVPN*,*PIA*VPN*,*Proton*VPN*,*Pure*VPN*,*Slick*VPN*,*Surf*Easy*, - *SurfShark*,*Star*VPN*,*TorGuard*,*TorProxy*,*Tiger*VPN*,*TunnelBear*,*Unblock*VPN*,*Warp*VPN*,*WarpSpeed*, - *VPNReactor*,*VPN*Shield*,*VPN*Super*VPN*,*ZenMate*) ```listing of commonly used known VPN providers. Add or remove whatever is appropriate for your environment``` - | eval user=coalesce(''actor.alternateId'',user), user=mvindex(split(user, "@"), 0) - | rename targetUserAlternateId as user client.* as * request.* as * ipChain{}.* as * geographicalContext.* as * debugContext.* as * debugData.* as * - | eval status=case(match(_raw, "FAILURE"), "failure", !match(_raw, "FAILURE"), "success") - | stats count values(status) as status max(published) as UTC min(_time) as firsttime max(_time) as lasttime values(target_data) as target_data values(displayMessage) as displayMessage values(eventType) as eventType values(city) as city values(country) as country values(action) as action values(src_ip) as src_ip values(outcome.*) as * values(user) as user by tunnels,_time,host sourcetype index - | fillnull value="N/A" - | convert ctime(*ttime) - | `okta_non_standard_vpn_usage_filter`' -how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the - Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). + - Okta +search: '`okta` debugContext.debugData.tunnels IN (*Astrill*,*Azire*,*CyberGhost*,*Express*VPN,*HideMe*, *IPVanish*,*Mullvad*,*Nord*VPN*,*OVPN*,*PIA*VPN*,*Proton*VPN*,*Pure*VPN*,*Slick*VPN*,*Surf*Easy*, *SurfShark*,*Star*VPN*,*TorGuard*,*TorProxy*,*Tiger*VPN*,*TunnelBear*,*Unblock*VPN*,*Warp*VPN*,*WarpSpeed*, *VPNReactor*,*VPN*Shield*,*VPN*Super*VPN*,*ZenMate*) ```listing of commonly used known VPN providers. Add or remove whatever is appropriate for your environment``` | eval user=coalesce(''actor.alternateId'',user), user=mvindex(split(user, "@"), 0) | rename targetUserAlternateId as user client.* as * request.* as * ipChain{}.* as * geographicalContext.* as * debugContext.* as * debugData.* as * | eval status=case(match(_raw, "FAILURE"), "failure", !match(_raw, "FAILURE"), "success") | stats count values(status) as status max(published) as UTC min(_time) as firsttime max(_time) as lasttime values(target_data) as target_data values(displayMessage) as displayMessage values(eventType) as eventType values(city) as city values(country) as country values(action) as action values(src_ip) as src_ip values(outcome.*) as * values(user) as user by tunnels,_time,host sourcetype index | fillnull value="N/A" | convert ctime(*ttime) | `okta_non_standard_vpn_usage_filter`' +how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). known_false_positives: Limited to no expected false positives once a baseline of common VPN software has been completed. drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search actor.alternateId = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search actor.alternateId = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Uncommon VPN software used by $user$ to connect to Okta. - risk_objects: - - field: user - type: user - score: 50 - threat_objects: [] + message: Uncommon VPN software used by $user$ to connect to Okta. + risk_objects: + - field: user + type: user + score: 50 + threat_objects: [] tags: - analytic_story: - - Remote Employment Fraud - - Suspicious Okta Activity - asset_type: Identity - mitre_attack_id: - - T1078 - - T1572 - - T1090 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity \ No newline at end of file + analytic_story: + - Remote Employment Fraud + - Suspicious Okta Activity + asset_type: Identity + mitre_attack_id: + - T1078 + - T1572 + - T1090 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity diff --git a/detections/cloud/risk_rule_for_dev_sec_ops_by_repository.yml b/detections/cloud/risk_rule_for_dev_sec_ops_by_repository.yml index 2835fac254..2581b1d60a 100644 --- a/detections/cloud/risk_rule_for_dev_sec_ops_by_repository.yml +++ b/detections/cloud/risk_rule_for_dev_sec_ops_by_repository.yml @@ -1,61 +1,47 @@ name: Risk Rule for Dev Sec Ops by Repository id: 161bc0ca-4651-4c13-9c27-27770660cf67 -version: 9 -date: '2026-01-22' +version: 10 +date: '2026-02-25' author: Bhavin Patel status: production type: Correlation -description: The following analytic identifies high-risk activities within repositories - by correlating repository data with risk scores. It leverages findings and intermediate findings created by detections from the - Dev Sec Ops analytic stories, summing risk scores and capturing source and user - information. The detection focuses on high-risk scores above 100 and sources with - more than three occurrences. This activity is significant as it highlights repositories - frequently targeted by threats, providing insights into potential vulnerabilities. - If confirmed malicious, attackers could exploit these repositories, leading to data - breaches or infrastructure compromise. +description: The following analytic identifies high-risk activities within repositories by correlating repository data with risk scores. It leverages findings and intermediate findings created by detections from the Dev Sec Ops analytic stories, summing risk scores and capturing source and user information. The detection focuses on high-risk scores above 100 and sources with more than three occurrences. This activity is significant as it highlights repositories frequently targeted by threats, providing insights into potential vulnerabilities. If confirmed malicious, attackers could exploit these repositories, leading to data breaches or infrastructure compromise. data_source: [] -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime sum(All_Risk.calculated_risk_score) as sum_risk_score, values(All_Risk.annotations.mitre_attack.mitre_tactic) - as annotations.mitre_attack.mitre_tactic, values(All_Risk.annotations.mitre_attack.mitre_technique_id) - as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) - as mitre_technique_id_count values(source) as source, dc(source) as source_count - from datamodel=Risk.All_Risk where All_Risk.analyticstories="Dev Sec Ops" All_Risk.risk_object_type - = "other" by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic - | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | where source_count > 3 and sum_risk_score > 100 | `risk_rule_for_dev_sec_ops_by_repository_filter`' -how_to_implement: Ensure that all relevant detections in the Dev Sec Ops analytic - stories are enabled and are configured to create findings or intermediate findings in Enterprise Security. +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as sum_risk_score, values(All_Risk.annotations.mitre_attack.mitre_tactic) as annotations.mitre_attack.mitre_tactic, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count values(source) as source, dc(source) as source_count FROM datamodel=Risk.All_Risk + WHERE All_Risk.analyticstories="Dev Sec Ops" All_Risk.risk_object_type = "other" + BY All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic + | `drop_dm_object_name(All_Risk)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | where source_count > 3 and sum_risk_score > 100 + | `risk_rule_for_dev_sec_ops_by_repository_filter` +how_to_implement: Ensure that all relevant detections in the Dev Sec Ops analytic stories are enabled and are configured to create findings or intermediate findings in Enterprise Security. known_false_positives: No false positives have been identified at this time. references: [] drilldown_searches: -- name: View the detection results for - "$risk_object$" - search: '%original_detection_search% | search risk_object = "$risk_object$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$risk_object$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$risk_object$" + search: '%original_detection_search% | search risk_object = "$risk_object$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$risk_object$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ tags: - analytic_story: - - Dev Sec Ops - asset_type: Amazon Elastic Container Registry - mitre_attack_id: - - T1204.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Dev Sec Ops + asset_type: Amazon Elastic Container Registry + mitre_attack_id: + - T1204.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/risk_dataset/aws_ecr_risk_dataset.log - source: aws_ecr_risk_dataset.log - sourcetype: stash + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/risk_dataset/aws_ecr_risk_dataset.log + source: aws_ecr_risk_dataset.log + sourcetype: stash diff --git a/detections/deprecated/linux_apt_get_privilege_escalation.yml b/detections/deprecated/linux_apt_get_privilege_escalation.yml index 40ddf1d827..23144f3d9e 100644 --- a/detections/deprecated/linux_apt_get_privilege_escalation.yml +++ b/detections/deprecated/linux_apt_get_privilege_escalation.yml @@ -5,87 +5,57 @@ date: '2026-02-10' author: Gowthamaraj Rajendran, Bhavin Patel, Splunk status: deprecated type: Anomaly -description: The following analytic detects the execution of the 'apt-get' command - with elevated privileges using 'sudo' on a Linux system. It leverages data from - Endpoint Detection and Response (EDR) agents, focusing on process execution logs - that include command-line details. This activity is significant because it indicates - a user may be attempting to escalate privileges to root, which could lead to unauthorized - system control. If confirmed malicious, an attacker could gain root access, allowing - them to execute arbitrary commands, install or remove software, and potentially - compromise the entire system. +description: The following analytic detects the execution of the 'apt-get' command with elevated privileges using 'sudo' on a Linux system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because it indicates a user may be attempting to escalate privileges to root, which could lead to unauthorized system control. If confirmed malicious, an attacker could gain root access, allowing them to execute arbitrary commands, install or remove software, and potentially compromise the entire system. data_source: -- Sysmon for Linux EventID 1 -- Cisco Isovalent Process Exec -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*apt-get*" - AND Processes.process="*APT::Update::Pre-Invoke::*" AND Processes.process="*sudo*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_apt_get_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 + - Cisco Isovalent Process Exec +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*apt-get*" AND Processes.process="*APT::Update::Pre-Invoke::*" AND Processes.process="*sudo*" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_apt_get_privilege_escalation_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives may be present, filter as needed. references: -- https://gtfobins.github.io/gtfobins/apt-get/ -- https://phoenixnap.com/kb/how-to-use-apt-get-commands + - https://gtfobins.github.io/gtfobins/apt-get/ + - https://phoenixnap.com/kb/how-to-use-apt-get-commands drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 10 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 10 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - - Cisco Isovalent Suspicious Activity - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + - Cisco Isovalent Suspicious Activity + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/apt_get/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux -- name: True Positive Test - Cisco Isovalent - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/apt_get/cisco_isovalent.log - source: not_applicable - sourcetype: cisco:isovalent:processExec \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/apt_get/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux + - name: True Positive Test - Cisco Isovalent + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/apt_get/cisco_isovalent.log + source: not_applicable + sourcetype: cisco:isovalent:processExec diff --git a/detections/endpoint/7zip_commandline_to_smb_share_path.yml b/detections/endpoint/7zip_commandline_to_smb_share_path.yml index 72eed1230e..2290435a0a 100644 --- a/detections/endpoint/7zip_commandline_to_smb_share_path.yml +++ b/detections/endpoint/7zip_commandline_to_smb_share_path.yml @@ -5,55 +5,30 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects the execution of 7z or 7za processes with - command lines pointing to SMB network shares. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process names and command-line arguments. - This activity is significant as it may indicate an attempt to archive and exfiltrate - sensitive files to a network share, a technique observed in CONTI LEAK tools. If - confirmed malicious, this behavior could lead to data exfiltration, compromising - sensitive information and potentially aiding further attacks. +description: The following analytic detects the execution of 7z or 7za processes with command lines pointing to SMB network shares. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant as it may indicate an attempt to archive and exfiltrate sensitive files to a network share, a technique observed in CONTI LEAK tools. If confirmed malicious, this behavior could lead to data exfiltration, compromising sensitive information and potentially aiding further attacks. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name ="7z.exe" - OR Processes.process_name = "7za.exe" OR Processes.process_name = "7zr.exe" OR Processes.original_file_name - = "7z.exe" OR Processes.original_file_name = "7za.exe" OR Processes.original_file_name - = "7zr.exe") AND (Processes.process="*\\C$\\*" OR Processes.process="*\\Admin$\\*" - OR Processes.process="*\\IPC$\\*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `7zip_commandline_to_smb_share_path_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name ="7z.exe" OR Processes.process_name = "7za.exe" OR Processes.process_name = "7zr.exe" OR Processes.original_file_name = "7z.exe" OR Processes.original_file_name = "7za.exe" OR Processes.original_file_name = "7zr.exe") AND (Processes.process="*\\C$\\*" OR Processes.process="*\\Admin$\\*" OR Processes.process="*\\IPC$\\*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `7zip_commandline_to_smb_share_path_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://threadreaderapp.com/thread/1423361119926816776.html + - https://threadreaderapp.com/thread/1423361119926816776.html tags: - analytic_story: - - Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1560.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1560.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/conti/conti_leak/windows-sysmon_7z.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/conti/conti_leak/windows-sysmon_7z.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/access_lsass_memory_for_dump_creation.yml b/detections/endpoint/access_lsass_memory_for_dump_creation.yml index 660e8d5d04..416daa97fe 100644 --- a/detections/endpoint/access_lsass_memory_for_dump_creation.yml +++ b/detections/endpoint/access_lsass_memory_for_dump_creation.yml @@ -1,84 +1,69 @@ name: Access LSASS Memory for Dump Creation id: fb4c31b0-13e8-4155-8aa5-24de4b8d6717 -version: 12 -date: '2025-10-14' +version: 13 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects attempts to dump the LSASS process - memory, a common technique in credential dumping attacks. It leverages Sysmon - logs, specifically EventCode 10, to identify suspicious call traces to - dbgcore.dll and dbghelp.dll associated with lsass.exe. This activity is - significant as it often precedes the theft of sensitive login credentials, - posing a high risk of unauthorized access to systems and data. If confirmed - malicious, attackers could gain access to critical credentials, enabling - further compromise and lateral movement within the network. +description: The following analytic detects attempts to dump the LSASS process memory, a common technique in credential dumping attacks. It leverages Sysmon logs, specifically EventCode 10, to identify suspicious call traces to dbgcore.dll and dbghelp.dll associated with lsass.exe. This activity is significant as it often precedes the theft of sensitive login credentials, posing a high risk of unauthorized access to systems and data. If confirmed malicious, attackers could gain access to critical credentials, enabling further compromise and lateral movement within the network. data_source: -- Sysmon EventID 10 -search: '`sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR CallTrace=*dbghelp.dll* - | stats count min(_time) as firstTime max(_time) as lastTime by CallTrace EventID - GrantedAccess Guid Opcode ProcessID SecurityID SourceImage SourceProcessGUID SourceProcessId - TargetImage TargetProcessGUID TargetProcessId UserID dest granted_access parent_process_exec - parent_process_guid parent_process_id parent_process_name parent_process_path process_exec - process_guid process_id process_name process_path signature signature_id user_id - vendor_product | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` - | `access_lsass_memory_for_dump_creation_filter`' -how_to_implement: This search requires Sysmon Logs and a Sysmon configuration, - which includes EventCode 10 for lsass.exe. This search uses an input macro - named `sysmon`. We strongly recommend that you specify your - environment-specific configurations (index, source, sourcetype, etc.) for - Windows Sysmon logs. Replace the macro definition with configurations for your - Splunk environment. The search also uses a post-filter macro designed to - filter out known false positives. -known_false_positives: Administrators can create memory dumps for debugging - purposes, but memory dumps of the LSASS process would be unusual. + - Sysmon EventID 10 +search: |- + `sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR CallTrace=*dbghelp.dll* + | stats count min(_time) as firstTime max(_time) as lastTime + BY CallTrace EventID GrantedAccess + Guid Opcode ProcessID + SecurityID SourceImage SourceProcessGUID + SourceProcessId TargetImage TargetProcessGUID + TargetProcessId UserID dest + granted_access parent_process_exec parent_process_guid + parent_process_id parent_process_name parent_process_path + process_exec process_guid process_id + process_name process_path signature + signature_id user_id vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `access_lsass_memory_for_dump_creation_filter` +how_to_implement: This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. +known_false_positives: Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual. references: -- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: process $SourceImage$ injected into $TargetImage$ and was attempted - dump LSASS on $dest$. Adversaries tend to do this when trying to accesss - credential material stored in the process memory of the Local Security - Authority Subsystem Service (LSASS). - risk_objects: - - field: dest - type: system - score: 63 - threat_objects: - - field: TargetImage - type: process + message: process $SourceImage$ injected into $TargetImage$ and was attempted dump LSASS on $dest$. Adversaries tend to do this when trying to accesss credential material stored in the process memory of the Local Security Authority Subsystem Service (LSASS). + risk_objects: + - field: dest + type: system + score: 63 + threat_objects: + - field: TargetImage + type: process tags: - analytic_story: - - CISA AA23-347A - - Credential Dumping - - Cactus Ransomware - - Lokibot - - Scattered Lapsus$ Hunters - asset_type: Windows - mitre_attack_id: - - T1003.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA23-347A + - Credential Dumping + - Cactus Ransomware + - Lokibot + - Scattered Lapsus$ Hunters + asset_type: Windows + mitre_attack_id: + - T1003.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/active_directory_lateral_movement_identified.yml b/detections/endpoint/active_directory_lateral_movement_identified.yml index 607a3e2331..829c9df7c8 100644 --- a/detections/endpoint/active_directory_lateral_movement_identified.yml +++ b/detections/endpoint/active_directory_lateral_movement_identified.yml @@ -1,74 +1,50 @@ name: Active Directory Lateral Movement Identified id: 6aa6f9dd-adfe-45a8-8f74-c4c7a0d7d037 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Correlation data_source: [] -description: The following analytic identifies potential lateral movement activities - within an organization's Active Directory (AD) environment. It detects this activity - by correlating multiple analytics from the Active Directory Lateral Movement analytic - story within a specified time frame. This is significant for a SOC as lateral movement - is a common tactic used by attackers to expand their access within a network, posing - a substantial risk. If confirmed malicious, this activity could allow attackers - to escalate privileges, access sensitive information, and persist within the environment, - leading to severe security breaches. -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) - as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as - annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) - as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) - as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) - as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, - dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories="Active - Directory Lateral Movement" All_Risk.risk_object_type="system" by All_Risk.risk_object - All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where - source_count >= 4 | `active_directory_lateral_movement_identified_filter`' -how_to_implement: Splunk Enterprise Security is required to utilize this correlation. - In addition, modify the source_count value to your environment. In our testing, - a count of 4 or 5 was decent in a lab, but the number may need to be increased as - the analytic story includes over 30 analytics. In addition, based on false positives, - modify any analytics to be anomaly and lower or increase risk based on organization - importance. -known_false_positives: False positives will most likely be present based on risk scoring - and how the organization handles system to system communication. Filter, or modify - as needed. In addition to count by analytics, adding a risk score may be useful. - In our testing, with 22 events over 30 days, the risk scores ranged from 500 to - 80,000. Your organization will be different, monitor and modify as needed. +description: The following analytic identifies potential lateral movement activities within an organization's Active Directory (AD) environment. It detects this activity by correlating multiple analytics from the Active Directory Lateral Movement analytic story within a specified time frame. This is significant for a SOC as lateral movement is a common tactic used by attackers to expand their access within a network, posing a substantial risk. If confirmed malicious, this activity could allow attackers to escalate privileges, access sensitive information, and persist within the environment, leading to severe security breaches. +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count FROM datamodel=Risk.All_Risk + WHERE All_Risk.analyticstories="Active Directory Lateral Movement" All_Risk.risk_object_type="system" + BY All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic + | `drop_dm_object_name(All_Risk)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | where source_count >= 4 + | `active_directory_lateral_movement_identified_filter` +how_to_implement: Splunk Enterprise Security is required to utilize this correlation. In addition, modify the source_count value to your environment. In our testing, a count of 4 or 5 was decent in a lab, but the number may need to be increased as the analytic story includes over 30 analytics. In addition, based on false positives, modify any analytics to be anomaly and lower or increase risk based on organization importance. +known_false_positives: False positives will most likely be present based on risk scoring and how the organization handles system to system communication. Filter, or modify as needed. In addition to count by analytics, adding a risk score may be useful. In our testing, with 22 events over 30 days, the risk scores ranged from 500 to 80,000. Your organization will be different, monitor and modify as needed. references: -- https://attack.mitre.org/tactics/TA0008/ -- https://research.splunk.com/stories/active_directory_lateral_movement/ + - https://attack.mitre.org/tactics/TA0008/ + - https://research.splunk.com/stories/active_directory_lateral_movement/ drilldown_searches: -- name: View the detection results for - "$risk_object$" - search: '%original_detection_search% | search risk_object = "$risk_object$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$risk_object$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$risk_object$" + search: '%original_detection_search% | search risk_object = "$risk_object$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$risk_object$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ tags: - analytic_story: - - Active Directory Lateral Movement - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1210 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1210 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218/living_off_the_land/adlm_risk.log - source: adlm - sourcetype: stash + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218/living_off_the_land/adlm_risk.log + source: adlm + sourcetype: stash diff --git a/detections/endpoint/active_directory_privilege_escalation_identified.yml b/detections/endpoint/active_directory_privilege_escalation_identified.yml index e3b4b406cf..37d19b0b99 100644 --- a/detections/endpoint/active_directory_privilege_escalation_identified.yml +++ b/detections/endpoint/active_directory_privilege_escalation_identified.yml @@ -1,68 +1,44 @@ name: Active Directory Privilege Escalation Identified id: 583e8a68-f2f7-45be-8fc9-bf725f0e22fd -version: 7 -date: '2026-01-13' +version: 8 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: experimental type: Correlation data_source: [] -description: The following analytic identifies potential privilege escalation activities - within an organization's Active Directory (AD) environment. It detects this activity - by correlating multiple analytics from the Active Directory Privilege Escalation - analytic story within a specified time frame. This is significant for a SOC as it - helps identify coordinated attempts to gain elevated privileges, which could indicate - a serious security threat. If confirmed malicious, this activity could allow attackers - to gain unauthorized access to sensitive systems and data, leading to potential - data breaches and further compromise of the network. -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) - as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as - annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) - as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) - as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) - as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, - dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories="Active - Directory Privilege Escalation" All_Risk.risk_object_type="system" by All_Risk.risk_object - All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where - source_count >= 4 | `active_directory_privilege_escalation_identified_filter`' -how_to_implement: Splunk Enterprise Security is required to utilize this correlation. - In addition, modify the source_count value to your environment. In our testing, - a count of 4 or 5 was decent in a lab, but the number may need to be increased as - the analytic story includes over 30 analytics. In addition, based on false positives, - modify any analytics to be anomaly and lower or increase risk based on organization - importance. -known_false_positives: False positives will most likely be present based on risk scoring - and how the organization handles system to system communication. Filter, or modify - as needed. In addition to count by analytics, adding a risk score may be useful. - In our testing, with 22 events over 30 days, the risk scores ranged from 500 to - 80,000. Your organization will be different, monitor and modify as needed. +description: The following analytic identifies potential privilege escalation activities within an organization's Active Directory (AD) environment. It detects this activity by correlating multiple analytics from the Active Directory Privilege Escalation analytic story within a specified time frame. This is significant for a SOC as it helps identify coordinated attempts to gain elevated privileges, which could indicate a serious security threat. If confirmed malicious, this activity could allow attackers to gain unauthorized access to sensitive systems and data, leading to potential data breaches and further compromise of the network. +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count FROM datamodel=Risk.All_Risk + WHERE All_Risk.analyticstories="Active Directory Privilege Escalation" All_Risk.risk_object_type="system" + BY All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic + | `drop_dm_object_name(All_Risk)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | where source_count >= 4 + | `active_directory_privilege_escalation_identified_filter` +how_to_implement: Splunk Enterprise Security is required to utilize this correlation. In addition, modify the source_count value to your environment. In our testing, a count of 4 or 5 was decent in a lab, but the number may need to be increased as the analytic story includes over 30 analytics. In addition, based on false positives, modify any analytics to be anomaly and lower or increase risk based on organization importance. +known_false_positives: False positives will most likely be present based on risk scoring and how the organization handles system to system communication. Filter, or modify as needed. In addition to count by analytics, adding a risk score may be useful. In our testing, with 22 events over 30 days, the risk scores ranged from 500 to 80,000. Your organization will be different, monitor and modify as needed. references: -- https://attack.mitre.org/tactics/TA0004/ -- https://research.splunk.com/stories/active_directory_privilege_escalation/ + - https://attack.mitre.org/tactics/TA0004/ + - https://research.splunk.com/stories/active_directory_privilege_escalation/ drilldown_searches: -- name: View the detection results for - "$risk_object$" - search: '%original_detection_search% | search risk_object = "$risk_object$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$risk_object$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$risk_object$" + search: '%original_detection_search% | search risk_object = "$risk_object$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$risk_object$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ tags: - analytic_story: - - Active Directory Privilege Escalation - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1484 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Privilege Escalation + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1484 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/active_setup_registry_autostart.yml b/detections/endpoint/active_setup_registry_autostart.yml index 5c15ba89ee..7c74e95d4f 100644 --- a/detections/endpoint/active_setup_registry_autostart.yml +++ b/detections/endpoint/active_setup_registry_autostart.yml @@ -5,73 +5,51 @@ date: '2025-05-02' author: Steven Dick, Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects suspicious modifications to the Active - Setup registry for persistence and privilege escalation. It leverages data from - the Endpoint.Registry data model, focusing on changes to the "StubPath" value within - the "SOFTWARE\\Microsoft\\Active Setup\\Installed Components" path. This activity - is significant as it is commonly used by malware, adware, and APTs to maintain persistence - on compromised machines. If confirmed malicious, this could allow attackers to execute - code upon system startup, potentially leading to further system compromise and unauthorized - access. +description: The following analytic detects suspicious modifications to the Active Setup registry for persistence and privilege escalation. It leverages data from the Endpoint.Registry data model, focusing on changes to the "StubPath" value within the "SOFTWARE\\Microsoft\\Active Setup\\Installed Components" path. This activity is significant as it is commonly used by malware, adware, and APTs to maintain persistence on compromised machines. If confirmed malicious, this could allow attackers to execute code upon system startup, potentially leading to further system compromise and unauthorized access. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_value_name= - "StubPath" Registry.registry_path = "*\\SOFTWARE\\Microsoft\\Active Setup\\Installed - Components*") by Registry.action Registry.dest Registry.process_guid Registry.process_id - Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data - Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user - Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`| `active_setup_registry_autostart_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_value_name= "StubPath" Registry.registry_path = "*\\SOFTWARE\\Microsoft\\Active Setup\\Installed Components*") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `active_setup_registry_autostart_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: Active setup installer may add or modify this registry. references: -- https://www.microsoft.com/en-us/wdsi/threats/malware-encyclopedia-description?Name=Backdoor%3AWin32%2FPoisonivy.E -- https://attack.mitre.org/techniques/T1547/014/ + - https://www.microsoft.com/en-us/wdsi/threats/malware-encyclopedia-description?Name=Backdoor%3AWin32%2FPoisonivy.E + - https://attack.mitre.org/techniques/T1547/014/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: modified/added/deleted registry entry $registry_path$ on $dest$ - risk_objects: - - field: dest - type: system - score: 64 - - field: user - type: user - score: 64 - threat_objects: [] + message: modified/added/deleted registry entry $registry_path$ on $dest$ + risk_objects: + - field: dest + type: system + score: 64 + - field: user + type: user + score: 64 + threat_objects: [] tags: - analytic_story: - - Data Destruction - - Windows Privilege Escalation - - Hermetic Wiper - - Windows Persistence Techniques - asset_type: Endpoint - mitre_attack_id: - - T1547.014 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Destruction + - Windows Privilege Escalation + - Hermetic Wiper + - Windows Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1547.014 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/t1547.014/active_setup_stubpath/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/t1547.014/active_setup_stubpath/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/add_defaultuser_and_password_in_registry.yml b/detections/endpoint/add_defaultuser_and_password_in_registry.yml index e4ec57c31b..8bdceab553 100644 --- a/detections/endpoint/add_defaultuser_and_password_in_registry.yml +++ b/detections/endpoint/add_defaultuser_and_password_in_registry.yml @@ -5,70 +5,45 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk, Steven Dick status: production type: Anomaly -description: The following analytic detects suspicious registry modifications that - implement auto admin logon by adding DefaultUserName and DefaultPassword values. - It leverages data from the Endpoint.Registry data model, specifically monitoring - changes to the "SOFTWARE\Microsoft\Windows NT\CurrentVersion\Winlogon" registry - path. This activity is significant because it is associated with BlackMatter ransomware, - which uses this technique to automatically log on to compromised hosts and continue - encryption after a safe mode boot. If confirmed malicious, this could allow attackers - to maintain persistence and further encrypt the network, leading to significant - data loss and operational disruption. +description: The following analytic detects suspicious registry modifications that implement auto admin logon by adding DefaultUserName and DefaultPassword values. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the "SOFTWARE\Microsoft\Windows NT\CurrentVersion\Winlogon" registry path. This activity is significant because it is associated with BlackMatter ransomware, which uses this technique to automatically log on to compromised hosts and continue encryption after a safe mode boot. If confirmed malicious, this could allow attackers to maintain persistence and further encrypt the network, leading to significant data loss and operational disruption. data_source: -- Sysmon EventID 12 -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*SOFTWARE\\Microsoft\\Windows - NT\\CurrentVersion\\Winlogon*" AND Registry.registry_value_name= DefaultPassword - OR Registry.registry_value_name= DefaultUserName) by Registry.action Registry.dest - Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `add_defaultuser_and_password_in_registry_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 12 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\Winlogon*" AND Registry.registry_value_name= DefaultPassword OR Registry.registry_value_name= DefaultUserName) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `add_defaultuser_and_password_in_registry_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: No false positives have been identified at this time. references: -- https://news.sophos.com/en-us/2021/08/09/blackmatter-ransomware-emerges-from-the-shadow-of-darkside/ + - https://news.sophos.com/en-us/2021/08/09/blackmatter-ransomware-emerges-from-the-shadow-of-darkside/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: modified registry key $registry_key_name$ with registry value $registry_value_name$ - to prepare autoadminlogon - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: modified registry key $registry_key_name$ with registry value $registry_value_name$ to prepare autoadminlogon + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - BlackMatter Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1552.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - BlackMatter Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1552.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.002/autoadminlogon/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.002/autoadminlogon/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/add_or_set_windows_defender_exclusion.yml b/detections/endpoint/add_or_set_windows_defender_exclusion.yml index 2af3ea7744..b5b46c89d9 100644 --- a/detections/endpoint/add_or_set_windows_defender_exclusion.yml +++ b/detections/endpoint/add_or_set_windows_defender_exclusion.yml @@ -1,110 +1,105 @@ name: Add or Set Windows Defender Exclusion id: 773b66fe-4dd9-11ec-8289-acde48001122 -version: 12 -date: '2025-11-20' +version: 13 +date: '2026-02-25' author: Teoderick Contreras, Nasreddine Bencherchali, Splunk status: production type: TTP description: | - The following analytic detects the use of commands to add or set exclusions - in Windows Defender. It leverages data from Endpoint Detection and Response (EDR) - agents, focusing on command-line executions involving "Add-MpPreference" or "Set-MpPreference" - with exclusion parameters. This activity is significant because adversaries often - use it to bypass Windows Defender, allowing malicious code to execute undetected. - If confirmed malicious, this behavior could enable attackers to evade antivirus - detection, maintain persistence, and execute further malicious activities without - interference from Windows Defender. + The following analytic detects the use of commands to add or set exclusions + in Windows Defender. It leverages data from Endpoint Detection and Response (EDR) + agents, focusing on command-line executions involving "Add-MpPreference" or "Set-MpPreference" + with exclusion parameters. This activity is significant because adversaries often + use it to bypass Windows Defender, allowing malicious code to execute undetected. + If confirmed malicious, this behavior could enable attackers to evade antivirus + detection, maintain persistence, and execute further malicious activities without + interference from Windows Defender. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - ( - Processes.process = "*Add-MpPreference *" - OR - Processes.process = "*Set-MpPreference *" - ) - Processes.process IN ( - "*-Exclusion*", - "*-ControlledFolderAccessAllowedApplications*", - "*-AttackSurfaceReductionOnlyExclusions*" - ) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `add_or_set_windows_defender_exclusion_filter` + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where + ( + Processes.process = "*Add-MpPreference *" + OR + Processes.process = "*Set-MpPreference *" + ) + Processes.process IN ( + "*-Exclusion*", + "*-ControlledFolderAccessAllowedApplications*", + "*-AttackSurfaceReductionOnlyExclusions*" + ) + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `add_or_set_windows_defender_exclusion_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: | - Admin or user may choose to use this windows features. Filter as needed. + Admin or user may choose to use this windows features. Filter as needed. references: - - https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html - - https://app.any.run/tasks/cf1245de-06a7-4366-8209-8e3006f2bfe5/ - - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ - - https://learn.microsoft.com/en-us/powershell/module/defender/add-mppreference?view=windowsserver2025-ps + - https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html + - https://app.any.run/tasks/cf1245de-06a7-4366-8209-8e3006f2bfe5/ + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://learn.microsoft.com/en-us/powershell/module/defender/add-mppreference?view=windowsserver2025-ps drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: exclusion command $process$ executed on $dest$ - risk_objects: - - field: user - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: [] + message: exclusion command $process$ executed on $dest$ + risk_objects: + - field: user + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - AgentTesla - - Data Destruction - - Remcos - - CISA AA22-320A - - ValleyRAT - - XWorm - - WhisperGate - - Windows Defense Evasion Tactics - - Crypto Stealer - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - AgentTesla + - Data Destruction + - Remcos + - CISA AA22-320A + - ValleyRAT + - XWorm + - WhisperGate + - Windows Defense Evasion Tactics + - Crypto Stealer + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/defender_exclusion_sysmon/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/defender_exclusion_sysmon/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/adsisearcher_account_discovery.yml b/detections/endpoint/adsisearcher_account_discovery.yml index 287de174ed..78d336b802 100644 --- a/detections/endpoint/adsisearcher_account_discovery.yml +++ b/detections/endpoint/adsisearcher_account_discovery.yml @@ -1,75 +1,68 @@ name: AdsiSearcher Account Discovery id: de7fcadc-04f3-11ec-a241-acde48001122 -version: 9 -date: '2025-10-14' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the use of the `[Adsisearcher]` type accelerator - in PowerShell to query Active Directory for domain users. It leverages PowerShell - Script Block Logging (EventCode=4104) to identify script blocks containing `[adsisearcher]`, - `objectcategory=user`, and `.findAll()`. This activity is significant as it may - indicate an attempt by adversaries or Red Teams to enumerate domain users for situational - awareness and Active Directory discovery. If confirmed malicious, this could lead - to further reconnaissance, privilege escalation, or lateral movement within the - network. +description: The following analytic detects the use of the `[Adsisearcher]` type accelerator in PowerShell to query Active Directory for domain users. It leverages PowerShell Script Block Logging (EventCode=4104) to identify script blocks containing `[adsisearcher]`, `objectcategory=user`, and `.findAll()`. This activity is significant as it may indicate an attempt by adversaries or Red Teams to enumerate domain users for situational awareness and Active Directory discovery. If confirmed malicious, this could lead to further reconnaissance, privilege escalation, or lateral movement within the network. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 ScriptBlockText = "*[adsisearcher]*" ScriptBlockText - = "*objectcategory=user*" ScriptBlockText = "*.findAll()*" | fillnull | stats count - min(_time) as firstTime max(_time) as lastTime by dest signature signature_id user_id - vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `adsisearcher_account_discovery_filter`' -how_to_implement: The following Hunting analytic requires PowerShell operational logs - to be imported. Modify the powershell macro as needed to match the sourcetype or - add index. This analytic is specific to 4104, or PowerShell Script Block Logging. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*[adsisearcher]*" ScriptBlockText = "*objectcategory=user*" ScriptBlockText = "*.findAll()*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `adsisearcher_account_discovery_filter` +how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1087/002/ -- https://www.blackhillsinfosec.com/red-blue-purple/ -- https://devblogs.microsoft.com/scripting/use-the-powershell-adsisearcher-type-accelerator-to-search-active-directory/ + - https://attack.mitre.org/techniques/T1087/002/ + - https://www.blackhillsinfosec.com/red-blue-purple/ + - https://devblogs.microsoft.com/scripting/use-the-powershell-adsisearcher-type-accelerator-to-search-active-directory/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user_id$" - search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user_id$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user_id$" + search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Powershell process have been used for user enumeration on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - - field: user_id - type: user - score: 25 - threat_objects: [] + message: Powershell process have been used for user enumeration on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + - field: user_id + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Industroyer2 - - Active Directory Discovery - - CISA AA23-347A - - Data Destruction - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1087.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Industroyer2 + - Active Directory Discovery + - CISA AA23-347A + - Data Destruction + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1087.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/adsisearcher_powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/adsisearcher_powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/advanced_ip_or_port_scanner_execution.yml b/detections/endpoint/advanced_ip_or_port_scanner_execution.yml index c05f840ccf..740c2f81cb 100644 --- a/detections/endpoint/advanced_ip_or_port_scanner_execution.yml +++ b/detections/endpoint/advanced_ip_or_port_scanner_execution.yml @@ -6,96 +6,91 @@ author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - The following analytic detects the execution of network scanning utilities such as Advanced IP Scanner or Advanced Port Scanner. - These legitimate administrative tools are often leveraged by threat actors and ransomware operators during the discovery phase to enumerate active hosts and open ports within a target environment. - Detection is based on process creation telemetry referencing known executable names, original file names, or specific command-line parameters such as "/portable" and "/lng" that are characteristic of these tools. - If confirmed malicious, this activity may indicate internal reconnaissance aimed at identifying reachable systems or services prior to lateral movement or further post-compromise actions. + The following analytic detects the execution of network scanning utilities such as Advanced IP Scanner or Advanced Port Scanner. + These legitimate administrative tools are often leveraged by threat actors and ransomware operators during the discovery phase to enumerate active hosts and open ports within a target environment. + Detection is based on process creation telemetry referencing known executable names, original file names, or specific command-line parameters such as "/portable" and "/lng" that are characteristic of these tools. + If confirmed malicious, this activity may indicate internal reconnaissance aimed at identifying reachable systems or services prior to lateral movement or further post-compromise actions. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime - from datamodel=Endpoint.Processes where - Processes.process_name IN ("advanced_ip_scanner.exe", "advanced_ip_scanner_console.exe", "advanced_port_scanner.exe", "advanced_port_scanner_console.exe") - OR - Processes.original_file_name IN ("advanced_ip_scanner.exe", "advanced_ip_scanner_console.exe", "advanced_port_scanner.exe", "advanced_port_scanner_console.exe") - OR ( - Processes.process = "* /portable *" - Processes.process = "* /lng *" - ) + from datamodel=Endpoint.Processes where + Processes.process_name IN ("advanced_ip_scanner.exe", "advanced_ip_scanner_console.exe", "advanced_port_scanner.exe", "advanced_port_scanner_console.exe") + OR + Processes.original_file_name IN ("advanced_ip_scanner.exe", "advanced_ip_scanner_console.exe", "advanced_port_scanner.exe", "advanced_port_scanner_console.exe") + OR ( + Processes.process = "* /portable *" + Processes.process = "* /lng *" + ) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `advanced_ip_or_port_scanner_execution_filter` + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `advanced_ip_or_port_scanner_execution_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: | - Legitimate administrators or IT staff may use Advanced IP or Port Scanner for authorized - network management or inventory purposes. Validate the context of execution and apply any filters as necessary. + Legitimate administrators or IT staff may use Advanced IP or Port Scanner for authorized + network management or inventory purposes. Validate the context of execution and apply any filters as necessary. references: - - https://news.sophos.com/en-us/2019/12/09/snatch-ransomware-reboots-pcs-into-safe-mode-to-bypass-protection/ - - https://cloud.google.com/blog/topics/threat-intelligence/tactics-techniques-procedures-associated-with-maze-ransomware-incidents/ - - https://assets.documentcloud.org/documents/20444693/fbi-pin-egregor-ransomware-bc-01062021.pdf - - https://thedfirreport.com/2021/01/18/all-that-for-a-coinminer - - https://github.com/3CORESec/MAL-CL/tree/master/Descriptors/Other/Advanced%20IP%20Scanner + - https://news.sophos.com/en-us/2019/12/09/snatch-ransomware-reboots-pcs-into-safe-mode-to-bypass-protection/ + - https://cloud.google.com/blog/topics/threat-intelligence/tactics-techniques-procedures-associated-with-maze-ransomware-incidents/ + - https://assets.documentcloud.org/documents/20444693/fbi-pin-egregor-ransomware-bc-01062021.pdf + - https://thedfirreport.com/2021/01/18/all-that-for-a-coinminer + - https://github.com/3CORESec/MAL-CL/tree/master/Descriptors/Other/Advanced%20IP%20Scanner drilldown_searches: - - name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Execution of Advanced IP or Port Scanner detected via $process$ on $dest$ - risk_objects: - - field: user - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: [] + message: Execution of Advanced IP or Port Scanner detected via $process$ on $dest$ + risk_objects: + - field: user + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - asset_type: Endpoint - mitre_attack_id: - - T1046 - - T1135 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + asset_type: Endpoint + mitre_attack_id: + - T1046 + - T1135 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1046/advanced_ip_port_scanner/advanced_ip_port_scanner.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1046/advanced_ip_port_scanner/advanced_ip_port_scanner.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/allow_file_and_printing_sharing_in_firewall.yml b/detections/endpoint/allow_file_and_printing_sharing_in_firewall.yml index f14d2a0753..bf582c11d0 100644 --- a/detections/endpoint/allow_file_and_printing_sharing_in_firewall.yml +++ b/detections/endpoint/allow_file_and_printing_sharing_in_firewall.yml @@ -1,87 +1,72 @@ name: Allow File And Printing Sharing In Firewall id: ce27646e-d411-11eb-8a00-acde48001122 -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the modification of firewall settings - to allow file and printer sharing. It leverages data from Endpoint Detection and - Response (EDR) agents, focusing on command-line executions involving 'netsh' commands - that enable file and printer sharing. This activity is significant because it can - indicate an attempt by ransomware to discover and encrypt files on additional machines - connected to the compromised host. If confirmed malicious, this could lead to widespread - file encryption across the network, significantly increasing the impact of a ransomware - attack. +description: The following analytic detects the modification of firewall settings to allow file and printer sharing. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving 'netsh' commands that enable file and printer sharing. This activity is significant because it can indicate an attempt by ransomware to discover and encrypt files on additional machines connected to the compromised host. If confirmed malicious, this could lead to widespread file encryption across the network, significantly increasing the impact of a ransomware attack. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_netsh` Processes.process= - "*firewall*" Processes.process= "*group=\"File and Printer Sharing\"*" Processes.process="*enable=Yes*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `allow_file_and_printing_sharing_in_firewall_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: network admin may modify this firewall feature that may cause - this rule to be triggered. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime + from datamodel=Endpoint.Processes where + `process_netsh` + Processes.process= "*firewall*" + Processes.process= "*group=\"File and Printer Sharing\"*" + Processes.process="*enable=Yes*" + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process + Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name + Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `allow_file_and_printing_sharing_in_firewall_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: network admin may modify this firewall feature that may cause this rule to be triggered. references: -- https://community.fortinet.com:443/t5/FortiEDR/How-FortiEDR-detects-and-blocks-Revil-Ransomware-aka-sodinokibi/ta-p/189638?externalID=FD52469 -- https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/ + - https://community.fortinet.com:443/t5/FortiEDR/How-FortiEDR-detects-and-blocks-Revil-Ransomware-aka-sodinokibi/ta-p/189638?externalID=FD52469 + - https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious modification of firewall to allow file and printer sharing - detected on host - $dest$ - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: A suspicious modification of firewall to allow file and printer sharing detected on host - $dest$ + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Ransomware - - BlackByte Ransomware - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1562.007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - BlackByte Ransomware + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1562.007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/allow_inbound_traffic_by_firewall_rule_registry.yml b/detections/endpoint/allow_inbound_traffic_by_firewall_rule_registry.yml index 42398daf80..12317fa542 100644 --- a/detections/endpoint/allow_inbound_traffic_by_firewall_rule_registry.yml +++ b/detections/endpoint/allow_inbound_traffic_by_firewall_rule_registry.yml @@ -5,77 +5,52 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: The following analytic detects suspicious modifications to firewall rule - registry settings that allow inbound traffic on specific ports with a public profile. - It leverages data from the Endpoint.Registry data model, focusing on registry paths - and values indicative of such changes. This activity is significant as it may indicate - an adversary attempting to grant remote access to a machine by modifying firewall - rules. If confirmed malicious, this could enable unauthorized remote access, potentially - leading to further exploitation, data exfiltration, or lateral movement within the - network. +description: The following analytic detects suspicious modifications to firewall rule registry settings that allow inbound traffic on specific ports with a public profile. It leverages data from the Endpoint.Registry data model, focusing on registry paths and values indicative of such changes. This activity is significant as it may indicate an adversary attempting to grant remote access to a machine by modifying firewall rules. If confirmed malicious, this could enable unauthorized remote access, potentially leading to further exploitation, data exfiltration, or lateral movement within the network. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\System\\CurrentControlSet\\Services\\SharedAccess\\Parameters\\FirewallPolicy\\FirewallRules\\*" - Registry.registry_value_data = "*|Action=Allow|*" Registry.registry_value_data = - "*|Dir=In|*" Registry.registry_value_data = "*|LPort=*") by Registry.action Registry.dest - Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `allow_inbound_traffic_by_firewall_rule_registry_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 -known_false_positives: network admin may add/remove/modify public inbound firewall - rule that may cause this rule to be triggered. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\System\\CurrentControlSet\\Services\\SharedAccess\\Parameters\\FirewallPolicy\\FirewallRules\\*" Registry.registry_value_data = "*|Action=Allow|*" Registry.registry_value_data = "*|Dir=In|*" Registry.registry_value_data = "*|LPort=*") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `allow_inbound_traffic_by_firewall_rule_registry_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 +known_false_positives: network admin may add/remove/modify public inbound firewall rule that may cause this rule to be triggered. references: -- https://docs.microsoft.com/en-us/powershell/module/netsecurity/new-netfirewallrule?view=windowsserver2019-ps + - https://docs.microsoft.com/en-us/powershell/module/netsecurity/new-netfirewallrule?view=windowsserver2019-ps drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious firewall allow rule modifications were detected via the registry - on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: Suspicious firewall allow rule modifications were detected via the registry on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Windows Registry Abuse - - NjRAT - - PlugX - - Prohibited Traffic Allowed or Protocol Mismatch - - Medusa Ransomware - - Azorult - asset_type: Endpoint - mitre_attack_id: - - T1021.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Registry Abuse + - NjRAT + - PlugX + - Prohibited Traffic Allowed or Protocol Mismatch + - Medusa Ransomware + - Azorult + asset_type: Endpoint + mitre_attack_id: + - T1021.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/casper/datasets1/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/casper/datasets1/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/allow_inbound_traffic_in_firewall_rule.yml b/detections/endpoint/allow_inbound_traffic_in_firewall_rule.yml index eedfdc407b..84cc58e000 100644 --- a/detections/endpoint/allow_inbound_traffic_in_firewall_rule.yml +++ b/detections/endpoint/allow_inbound_traffic_in_firewall_rule.yml @@ -1,70 +1,63 @@ name: Allow Inbound Traffic In Firewall Rule id: a5d85486-b89c-11eb-8267-acde48001122 -version: 9 -date: '2025-11-20' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects a suspicious PowerShell command that allows - inbound traffic to a specific local port within the public profile. It leverages - PowerShell script block logging (EventCode 4104) to identify commands containing - keywords like "firewall," "Inbound," "Allow," and "-LocalPort." This activity is - significant because it may indicate an attacker attempting to establish remote access - by modifying firewall rules. If confirmed malicious, this could allow unauthorized - access to the machine, potentially leading to further exploitation and data exfiltration. +description: The following analytic detects a suspicious PowerShell command that allows inbound traffic to a specific local port within the public profile. It leverages PowerShell script block logging (EventCode 4104) to identify commands containing keywords like "firewall," "Inbound," "Allow," and "-LocalPort." This activity is significant because it may indicate an attacker attempting to establish remote access by modifying firewall rules. If confirmed malicious, this could allow unauthorized access to the machine, potentially leading to further exploitation and data exfiltration. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 ScriptBlockText = "*firewall*" ScriptBlockText - = "*Inbound*" ScriptBlockText = "*Allow*" ScriptBlockText = "*-LocalPort*" | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `allow_inbound_traffic_in_firewall_rule_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the powershell logs from your endpoints. make sure you enable needed - registry to monitor this event. -known_false_positives: administrator may allow inbound traffic in certain network - or machine. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*firewall*" ScriptBlockText = "*Inbound*" ScriptBlockText = "*Allow*" ScriptBlockText = "*-LocalPort*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `allow_inbound_traffic_in_firewall_rule_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the powershell logs from your endpoints. make sure you enable needed registry to monitor this event. +known_false_positives: administrator may allow inbound traffic in certain network or machine. references: -- https://docs.microsoft.com/en-us/powershell/module/netsecurity/new-netfirewallrule?view=windowsserver2019-ps + - https://docs.microsoft.com/en-us/powershell/module/netsecurity/new-netfirewallrule?view=windowsserver2019-ps drilldown_searches: -- name: View the detection results for - "$user_id$" and "$dest$" - search: '%original_detection_search% | search user_id = "$user_id$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user_id$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user_id$" and "$dest$" + search: '%original_detection_search% | search user_id = "$user_id$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user_id$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious firewall modification detected on endpoint $dest$ by user $user_id$. - risk_objects: - - field: user_id - type: user - score: 3 - - field: dest - type: system - score: 3 - threat_objects: [] + message: Suspicious firewall modification detected on endpoint $dest$ by user $user_id$. + risk_objects: + - field: user_id + type: user + score: 3 + - field: dest + type: system + score: 3 + threat_objects: [] tags: - analytic_story: - - Prohibited Traffic Allowed or Protocol Mismatch - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1021.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Prohibited Traffic Allowed or Protocol Mismatch + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1021.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021/allow_inbound_traffic_in_firewall_rule/windows-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021/allow_inbound_traffic_in_firewall_rule/windows-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/allow_network_discovery_in_firewall.yml b/detections/endpoint/allow_network_discovery_in_firewall.yml index 2ca58a2224..64e82b4904 100644 --- a/detections/endpoint/allow_network_discovery_in_firewall.yml +++ b/detections/endpoint/allow_network_discovery_in_firewall.yml @@ -1,87 +1,69 @@ name: Allow Network Discovery In Firewall id: ccd6a38c-d40b-11eb-85a5-acde48001122 -version: 9 -date: '2025-10-14' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects a suspicious modification to the firewall - to allow network discovery on a machine. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on command-line executions involving the 'netsh' - command to enable network discovery. This activity is significant because it is - commonly used by ransomware, such as REvil and RedDot, to discover and compromise - additional machines on the network. If confirmed malicious, this could lead to widespread - file encryption across multiple hosts, significantly amplifying the impact of the - ransomware attack. +description: The following analytic detects a suspicious modification to the firewall to allow network discovery on a machine. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving the 'netsh' command to enable network discovery. This activity is significant because it is commonly used by ransomware, such as REvil and RedDot, to discover and compromise additional machines on the network. If confirmed malicious, this could lead to widespread file encryption across multiple hosts, significantly amplifying the impact of the ransomware attack. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_netsh` Processes.process= - "*firewall*" Processes.process= "*group=\"Network Discovery\"*" Processes.process="*enable*" - Processes.process="*Yes*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `allow_network_discovery_in_firewall_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: network admin may modify this firewall feature that may cause - this rule to be triggered. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_netsh` Processes.process= "*firewall*" Processes.process= "*group=\"Network Discovery\"*" Processes.process="*enable*" Processes.process="*Yes*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `allow_network_discovery_in_firewall_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: network admin may modify this firewall feature that may cause this rule to be triggered. references: -- https://community.fortinet.com:443/t5/FortiEDR/How-FortiEDR-detects-and-blocks-Revil-Ransomware-aka-sodinokibi/ta-p/189638?externalID=FD52469 -- https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/ + - https://community.fortinet.com:443/t5/FortiEDR/How-FortiEDR-detects-and-blocks-Revil-Ransomware-aka-sodinokibi/ta-p/189638?externalID=FD52469 + - https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious modification to the firewall to allow network discovery detected - on host - $dest$ - risk_objects: - - field: user - type: user - score: 25 - threat_objects: [] + message: Suspicious modification to the firewall to allow network discovery detected on host - $dest$ + risk_objects: + - field: user + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - BlackByte Ransomware - - NjRAT - - Revil Ransomware - - Ransomware - - Medusa Ransomware - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1562.007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - BlackByte Ransomware + - NjRAT + - Revil Ransomware + - Ransomware + - Medusa Ransomware + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1562.007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/allow_operation_with_consent_admin.yml b/detections/endpoint/allow_operation_with_consent_admin.yml index de5fc90593..629b2296e9 100644 --- a/detections/endpoint/allow_operation_with_consent_admin.yml +++ b/detections/endpoint/allow_operation_with_consent_admin.yml @@ -5,75 +5,51 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: The following analytic detects a registry modification that allows the - 'Consent Admin' to perform operations requiring elevation without user consent or - credentials. It leverages data from the Endpoint.Registry data model, specifically - monitoring changes to the 'ConsentPromptBehaviorAdmin' value within the Windows - Policies System registry path. This activity is significant as it indicates a potential - privilege escalation attempt, which could allow an attacker to execute high-privilege - tasks without user approval. If confirmed malicious, this could lead to unauthorized - administrative access and control over the compromised machine, posing a severe - security risk. +description: The following analytic detects a registry modification that allows the 'Consent Admin' to perform operations requiring elevation without user consent or credentials. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the 'ConsentPromptBehaviorAdmin' value within the Windows Policies System registry path. This activity is significant as it indicates a potential privilege escalation attempt, which could allow an attacker to execute high-privilege tasks without user approval. If confirmed malicious, this could lead to unauthorized administrative access and control over the compromised machine, posing a severe security risk. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\Microsoft\\Windows\\CurrentVersion\\Policies\\System*" - Registry.registry_value_name = ConsentPromptBehaviorAdmin Registry.registry_value_data - = "0x00000000") by Registry.action Registry.dest Registry.process_guid Registry.process_id - Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data - Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user - Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `allow_operation_with_consent_admin_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\Microsoft\\Windows\\CurrentVersion\\Policies\\System*" Registry.registry_value_name = ConsentPromptBehaviorAdmin Registry.registry_value_data = "0x00000000") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `allow_operation_with_consent_admin_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: No false positives have been identified at this time. references: -- https://docs.microsoft.com/en-us/openspecs/windows_protocols/ms-gpsb/341747f5-6b5d-4d30-85fc-fa1cc04038d4 -- https://www.trendmicro.com/vinfo/no/threat-encyclopedia/malware/Ransom.Win32.MRDEC.MRA/ + - https://docs.microsoft.com/en-us/openspecs/windows_protocols/ms-gpsb/341747f5-6b5d-4d30-85fc-fa1cc04038d4 + - https://www.trendmicro.com/vinfo/no/threat-encyclopedia/malware/Ransom.Win32.MRDEC.MRA/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious registry modification was performed on endpoint $dest$ by user - $user$. This behavior is indicative of privilege escalation. - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: Suspicious registry modification was performed on endpoint $dest$ by user $user$. This behavior is indicative of privilege escalation. + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Ransomware - - Windows Registry Abuse - - Azorult - - MoonPeak - asset_type: Endpoint - mitre_attack_id: - - T1548 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - Windows Registry Abuse + - Azorult + - MoonPeak + asset_type: Endpoint + mitre_attack_id: + - T1548 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/anomalous_usage_of_7zip.yml b/detections/endpoint/anomalous_usage_of_7zip.yml index 6c44d0dd35..a4b333ee13 100644 --- a/detections/endpoint/anomalous_usage_of_7zip.yml +++ b/detections/endpoint/anomalous_usage_of_7zip.yml @@ -1,94 +1,76 @@ name: Anomalous usage of 7zip id: 9364ee8e-a39a-11eb-8f1d-acde48001122 -version: 10 -date: '2025-05-02' +version: 11 +date: '2026-02-25' author: Michael Haag, Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the execution of 7z.exe, a 7-Zip utility, - spawned from rundll32.exe or dllhost.exe. This behavior is identified using Endpoint - Detection and Response (EDR) telemetry, focusing on process names and parent processes. - This activity is significant as it may indicate an adversary attempting to use 7-Zip - for data exfiltration, often by renaming the executable to evade detection. If confirmed - malicious, this could lead to unauthorized data archiving and exfiltration, compromising - sensitive information and potentially leading to further system exploitation. +description: The following analytic detects the execution of 7z.exe, a 7-Zip utility, spawned from rundll32.exe or dllhost.exe. This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on process names and parent processes. This activity is significant as it may indicate an adversary attempting to use 7-Zip for data exfiltration, often by renaming the executable to evade detection. If confirmed malicious, this could lead to unauthorized data archiving and exfiltration, compromising sensitive information and potentially leading to further system exploitation. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name - IN ("rundll32.exe", "dllhost.exe") Processes.process_name=*7z* by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| - `anomalous_usage_of_7zip_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives should be limited as this behavior is not normal - for `rundll32.exe` or `dllhost.exe` to spawn and run 7zip. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name IN ("rundll32.exe", "dllhost.exe") Processes.process_name=*7z* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `anomalous_usage_of_7zip_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives should be limited as this behavior is not normal for `rundll32.exe` or `dllhost.exe` to spawn and run 7zip. references: -- https://attack.mitre.org/techniques/T1560/001/ -- https://www.microsoft.com/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/ -- https://thedfirreport.com/2021/01/31/bazar-no-ryuk/ + - https://attack.mitre.org/techniques/T1560/001/ + - https://www.microsoft.com/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/ + - https://thedfirreport.com/2021/01/31/bazar-no-ryuk/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$. This behavior is indicative of suspicious loading - of 7zip. - risk_objects: - - field: user - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$. This behavior is indicative of suspicious loading of 7zip. + risk_objects: + - field: user + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - NOBELIUM Group - - BlackByte Ransomware - - Cobalt Strike - - Graceful Wipe Out Attack - - BlackSuit Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1560.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - NOBELIUM Group + - BlackByte Ransomware + - Cobalt Strike + - Graceful Wipe Out Attack + - BlackSuit Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1560.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560.001/archive_utility/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560.001/archive_utility/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/attacker_tools_on_endpoint.yml b/detections/endpoint/attacker_tools_on_endpoint.yml index 024654e10d..3f0a38a56c 100644 --- a/detections/endpoint/attacker_tools_on_endpoint.yml +++ b/detections/endpoint/attacker_tools_on_endpoint.yml @@ -1,109 +1,80 @@ name: Attacker Tools On Endpoint id: a51bfe1a-94f0-48cc-b4e4-16a110145893 -version: 13 -date: '2025-07-29' +version: 14 +date: '2026-02-25' author: Bhavin Patel, Splunk, sventec, Github Community status: production type: TTP -description: - The following analytic detects the execution of tools commonly exploited - by cybercriminals, such as those used for unauthorized access, network scanning, - or data exfiltration. It leverages process activity data from Endpoint Detection - and Response (EDR) agents, focusing on known attacker tool names. This activity - is significant because it serves as an early warning system for potential security - incidents, enabling prompt response. If confirmed malicious, this activity could - lead to unauthorized access, data theft, or further network compromise, posing a - severe threat to the organization's security infrastructure. +description: The following analytic detects the execution of tools commonly exploited by cybercriminals, such as those used for unauthorized access, network scanning, or data exfiltration. It leverages process activity data from Endpoint Detection and Response (EDR) agents, focusing on known attacker tool names. This activity is significant because it serves as an early warning system for potential security incidents, enabling prompt response. If confirmed malicious, this activity could lead to unauthorized access, data theft, or further network compromise, posing a severe threat to the organization's security infrastructure. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 - - Cisco Network Visibility Module Flow Data -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime values(Processes.process) as process values(Processes.parent_process) - as parent_process from datamodel=Endpoint.Processes where - [| inputlookup attacker_tools | rename attacker_tool_names AS Processes.process_name | fields Processes.process_name] AND - Processes.dest!=unknown AND Processes.user!=unknown by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | lookup - attacker_tools attacker_tool_names AS process_name OUTPUT description | search description - !=false| `attacker_tools_on_endpoint_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: - Some administrator activity can be potentially triggered, please - add those users to the filter macro. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 + - Cisco Network Visibility Module Flow Data +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process) as process values(Processes.parent_process) as parent_process FROM datamodel=Endpoint.Processes + WHERE [ + | inputlookup attacker_tools + | rename attacker_tool_names AS Processes.process_name + | fields Processes.process_name] AND Processes.dest!=unknown AND Processes.user!=unknown by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `drop_dm_object_name(Processes)` + | lookup attacker_tools attacker_tool_names AS process_name OUTPUT description + | search description !=false + | `attacker_tools_on_endpoint_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Some administrator activity can be potentially triggered, please add those users to the filter macro. references: [] drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - An attacker tool $process_name$, listed in attacker_tools.csv is executed - on host $dest$ by User $user$. This process $process_name$ is known to do- $description$ - risk_objects: - - field: user - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: - - field: process_name - type: process_name + message: An attacker tool $process_name$, listed in attacker_tools.csv is executed on host $dest$ by User $user$. This process $process_name$ is known to do- $description$ + risk_objects: + - field: user + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - XMRig - - Unusual Processes - - SamSam Ransomware - - CISA AA22-264A - - Compromised Windows Host - - PHP-CGI RCE Attack on Japanese Organizations - - Cisco Network Visibility Module Analytics - - Scattered Spider - asset_type: Endpoint - mitre_attack_id: - - T1003 - - T1036.005 - - T1595 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - XMRig + - Unusual Processes + - SamSam Ransomware + - CISA AA22-264A + - Compromised Windows Host + - PHP-CGI RCE Attack on Japanese Organizations + - Cisco Network Visibility Module Analytics + - Scattered Spider + asset_type: Endpoint + mitre_attack_id: + - T1003 + - T1036.005 + - T1595 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - Sysmon - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1595/attacker_scan_tools/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata + - name: True Positive Test - Sysmon + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1595/attacker_scan_tools/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/attempt_to_add_certificate_to_untrusted_store.yml b/detections/endpoint/attempt_to_add_certificate_to_untrusted_store.yml index f774a0cf45..214b7f6e22 100644 --- a/detections/endpoint/attempt_to_add_certificate_to_untrusted_store.yml +++ b/detections/endpoint/attempt_to_add_certificate_to_untrusted_store.yml @@ -1,87 +1,76 @@ name: Attempt To Add Certificate To Untrusted Store id: 6bc5243e-ef36-45dc-9b12-f4a6be131159 -version: 16 -date: '2025-10-06' +version: 17 +date: '2026-02-25' author: Patrick Bareiss, Rico Valdez, Splunk status: production type: Anomaly description: | - The following analytic detects attempts to add a certificate to the untrusted - certificate store using the 'certutil -addstore' command. - It leverages process activity and command-line arguments from Endpoint Detection and Response (EDR) logs mapped to the Splunk `Processes` data model. - This activity is significant as it may indicate an attacker trying to disable security tools to gain unauthorized access. - If confirmed malicious, this could lead to the compromise of system security, allowing attackers - to bypass defenses and potentially escalate privileges or persist in the environment. + The following analytic detects attempts to add a certificate to the untrusted + certificate store using the 'certutil -addstore' command. + It leverages process activity and command-line arguments from Endpoint Detection and Response (EDR) logs mapped to the Splunk `Processes` data model. + This activity is significant as it may indicate an attacker trying to disable security tools to gain unauthorized access. + If confirmed malicious, this could lead to the compromise of system security, allowing attackers + to bypass defenses and potentially escalate privileges or persist in the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) - as process max(_time) as lastTime from datamodel=Endpoint.Processes where `process_certutil` - (Processes.process=*-addstore*) by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name("Processes")` - | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `attempt_to_add_certificate_to_untrusted_store_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: There may be legitimate reasons for administrators to add a - certificate to the untrusted certificate store. In such cases, this will typically - be done on a large number of systems. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_certutil` (Processes.process=*-addstore*) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `attempt_to_add_certificate_to_untrusted_store_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: There may be legitimate reasons for administrators to add a certificate to the untrusted certificate store. In such cases, this will typically be done on a large number of systems. references: -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1553.004/T1553.004.md + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1553.004/T1553.004.md drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - attempting to add a certificate to the store on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 35 - - field: dest - type: system - score: 35 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified attempting to add a certificate to the store on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 35 + - field: dest + type: system + score: 35 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Disabling Security Tools - asset_type: Endpoint - mitre_attack_id: - - T1553.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Disabling Security Tools + asset_type: Endpoint + mitre_attack_id: + - T1553.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.004/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.004/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/auto_admin_logon_registry_entry.yml b/detections/endpoint/auto_admin_logon_registry_entry.yml index 455c36f102..846f6853b1 100644 --- a/detections/endpoint/auto_admin_logon_registry_entry.yml +++ b/detections/endpoint/auto_admin_logon_registry_entry.yml @@ -5,70 +5,45 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: The following analytic detects a suspicious registry modification that - enables auto admin logon on a host. It leverages data from the Endpoint.Registry - data model, specifically looking for changes to the "AutoAdminLogon" value within - the "SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\Winlogon" registry path. This - activity is significant because it was observed in BlackMatter ransomware attacks - to maintain access after a safe mode reboot, facilitating further encryption. If - confirmed malicious, this could allow attackers to automatically log in and continue - their operations, potentially leading to widespread network encryption and data - loss. +description: The following analytic detects a suspicious registry modification that enables auto admin logon on a host. It leverages data from the Endpoint.Registry data model, specifically looking for changes to the "AutoAdminLogon" value within the "SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\Winlogon" registry path. This activity is significant because it was observed in BlackMatter ransomware attacks to maintain access after a safe mode reboot, facilitating further encryption. If confirmed malicious, this could allow attackers to automatically log in and continue their operations, potentially leading to widespread network encryption and data loss. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*SOFTWARE\\Microsoft\\Windows - NT\\CurrentVersion\\Winlogon*" AND Registry.registry_value_name=AutoAdminLogon AND - Registry.registry_value_data=1) by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `auto_admin_logon_registry_entry_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\Winlogon*" AND Registry.registry_value_name=AutoAdminLogon AND Registry.registry_value_data=1) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `auto_admin_logon_registry_entry_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: No false positives have been identified at this time. references: -- https://news.sophos.com/en-us/2021/08/09/blackmatter-ransomware-emerges-from-the-shadow-of-darkside/ + - https://news.sophos.com/en-us/2021/08/09/blackmatter-ransomware-emerges-from-the-shadow-of-darkside/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: modified registry key $registry_key_name$ with registry value $registry_value_name$ - to prepare autoadminlogon - risk_objects: - - field: dest - type: system - score: 63 - threat_objects: [] + message: modified registry key $registry_key_name$ with registry value $registry_value_name$ to prepare autoadminlogon + risk_objects: + - field: dest + type: system + score: 63 + threat_objects: [] tags: - analytic_story: - - BlackMatter Ransomware - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1552.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - BlackMatter Ransomware + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1552.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.002/autoadminlogon/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.002/autoadminlogon/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/batch_file_write_to_system32.yml b/detections/endpoint/batch_file_write_to_system32.yml index 47b60a6ac6..15bb16c546 100644 --- a/detections/endpoint/batch_file_write_to_system32.yml +++ b/detections/endpoint/batch_file_write_to_system32.yml @@ -5,81 +5,49 @@ date: '2025-05-02' author: Steven Dick, Michael Haag, Rico Valdez, Splunk status: production type: TTP -description: The following analytic detects the creation of a batch file (.bat) within - the Windows system directory tree, specifically in the System32 or SysWOW64 folders. - It leverages data from the Endpoint datamodel, focusing on process and filesystem - events to identify this behavior. This activity is significant because writing batch - files to system directories can be indicative of malicious intent, such as persistence - mechanisms or system manipulation. If confirmed malicious, this could allow an attacker - to execute arbitrary commands with elevated privileges, potentially compromising - the entire system. +description: The following analytic detects the creation of a batch file (.bat) within the Windows system directory tree, specifically in the System32 or SysWOW64 folders. It leverages data from the Endpoint datamodel, focusing on process and filesystem events to identify this behavior. This activity is significant because writing batch files to system directories can be indicative of malicious intent, such as persistence mechanisms or system manipulation. If confirmed malicious, this could allow an attacker to execute arbitrary commands with elevated privileges, potentially compromising the entire system. data_source: -- Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem - where Filesystem.file_path IN ("*\\system32\\*","*\\syswow64\\*") Filesystem.file_name="*.bat" - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time - Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product - | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `batch_file_write_to_system32_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: It is possible for this search to generate a finding event - for a batch file write to a path that includes the string "system32", but is not - the actual Windows system directory. As such, you should confirm the path of the - batch file identified by the search. In addition, a false positive may be generated - by an administrator copying a legitimate batch file in this directory tree. You - should confirm that the activity is legitimate and modify the search to add exclusions, - as necessary. + - Sysmon EventID 11 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\system32\\*","*\\syswow64\\*") Filesystem.file_name="*.bat" by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `batch_file_write_to_system32_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: It is possible for this search to generate a finding event for a batch file write to a path that includes the string "system32", but is not the actual Windows system directory. As such, you should confirm the path of the batch file identified by the search. In addition, a false positive may be generated by an administrator copying a legitimate batch file in this directory tree. You should confirm that the activity is legitimate and modify the search to add exclusions, as necessary. references: [] drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A file - $file_name$ was written to system32 has occurred on endpoint $dest$ - by user $user$. - risk_objects: - - field: user - type: user - score: 63 - - field: dest - type: system - score: 63 - threat_objects: - - field: file_name - type: file_name + message: A file - $file_name$ was written to system32 has occurred on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 63 + - field: dest + type: system + score: 63 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - SamSam Ransomware - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1204.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SamSam Ransomware + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1204.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/batch_file_in_system32/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/batch_file_in_system32/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/bcdedit_command_back_to_normal_mode_boot.yml b/detections/endpoint/bcdedit_command_back_to_normal_mode_boot.yml index 2563fce5b8..83077e243a 100644 --- a/detections/endpoint/bcdedit_command_back_to_normal_mode_boot.yml +++ b/detections/endpoint/bcdedit_command_back_to_normal_mode_boot.yml @@ -1,84 +1,67 @@ name: Bcdedit Command Back To Normal Mode Boot id: dc7a8004-0f18-11ec-8c54-acde48001122 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of a suspicious `bcdedit` - command that reconfigures a host from safe mode back to normal boot. This detection - leverages Endpoint Detection and Response (EDR) data, focusing on command-line executions - involving `bcdedit.exe` with specific parameters. This activity is significant as - it may indicate the presence of ransomware, such as BlackMatter, which manipulates - boot configurations to facilitate encryption processes. If confirmed malicious, - this behavior could allow attackers to maintain control over the boot process, potentially - leading to further system compromise and data encryption. +description: The following analytic detects the execution of a suspicious `bcdedit` command that reconfigures a host from safe mode back to normal boot. This detection leverages Endpoint Detection and Response (EDR) data, focusing on command-line executions involving `bcdedit.exe` with specific parameters. This activity is significant as it may indicate the presence of ransomware, such as BlackMatter, which manipulates boot configurations to facilitate encryption processes. If confirmed malicious, this behavior could allow attackers to maintain control over the boot process, potentially leading to further system compromise and data encryption. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe - Processes.process="*/deletevalue*" Processes.process="*{current}*" Processes.process="*safeboot*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - |`drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `bcdedit_command_back_to_normal_mode_boot_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = bcdedit.exe Processes.process="*/deletevalue*" Processes.process="*{current}*" Processes.process="*safeboot*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `bcdedit_command_back_to_normal_mode_boot_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://news.sophos.com/en-us/2021/08/09/blackmatter-ransomware-emerges-from-the-shadow-of-darkside/ + - https://news.sophos.com/en-us/2021/08/09/blackmatter-ransomware-emerges-from-the-shadow-of-darkside/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: bcdedit process with commandline $process$ to bring back to normal boot - configuration the $dest$ - risk_objects: - - field: user - type: user - score: 35 - - field: dest - type: system - score: 35 - threat_objects: [] + message: bcdedit process with commandline $process$ to bring back to normal boot configuration the $dest$ + risk_objects: + - field: user + type: user + score: 35 + - field: dest + type: system + score: 35 + threat_objects: [] tags: - analytic_story: - - Black Basta Ransomware - - BlackMatter Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1490 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Black Basta Ransomware + - BlackMatter Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1490 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.002/autoadminlogon/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.002/autoadminlogon/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/bcdedit_failure_recovery_modification.yml b/detections/endpoint/bcdedit_failure_recovery_modification.yml index 0bf08a1641..08f922d3a2 100644 --- a/detections/endpoint/bcdedit_failure_recovery_modification.yml +++ b/detections/endpoint/bcdedit_failure_recovery_modification.yml @@ -1,91 +1,73 @@ name: BCDEdit Failure Recovery Modification id: 809b31d2-5462-11eb-ae93-0242ac130002 -version: 10 -date: '2025-05-02' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects modifications to the Windows error recovery - boot configurations using bcdedit.exe with flags such as "recoveryenabled" and "no". - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on - process names, parent processes, and command-line executions. This activity is significant - because ransomware often disables recovery options to prevent system restoration, - making it crucial for SOC analysts to investigate. If confirmed malicious, this - could hinder recovery efforts, allowing ransomware to cause extensive damage and - complicate remediation. +description: The following analytic detects modifications to the Windows error recovery boot configurations using bcdedit.exe with flags such as "recoveryenabled" and "no". It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, parent processes, and command-line executions. This activity is significant because ransomware often disables recovery options to prevent system restoration, making it crucial for SOC analysts to investigate. If confirmed malicious, this could hinder recovery efforts, allowing ransomware to cause extensive damage and complicate remediation. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe - Processes.process="*recoveryenabled*" (Processes.process="* no*") by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `bcdedit_failure_recovery_modification_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = bcdedit.exe Processes.process="*recoveryenabled*" (Processes.process="* no*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `bcdedit_failure_recovery_modification_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators may modify the boot configuration. references: -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md#atomic-test-4---windows---disable-windows-recovery-console-repair + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md#atomic-test-4---windows---disable-windows-recovery-console-repair drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to disable the ability to recover - the endpoint. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to disable the ability to recover the endpoint. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Ransomware - - Compromised Windows Host - - Ryuk Ransomware - - Storm-2460 CLFS Zero Day Exploitation - asset_type: Endpoint - mitre_attack_id: - - T1490 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - Compromised Windows Host + - Ryuk Ransomware + - Storm-2460 CLFS Zero Day Exploitation + asset_type: Endpoint + mitre_attack_id: + - T1490 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/bits_job_persistence.yml b/detections/endpoint/bits_job_persistence.yml index 94df4dd8d5..9640c0f412 100644 --- a/detections/endpoint/bits_job_persistence.yml +++ b/detections/endpoint/bits_job_persistence.yml @@ -1,98 +1,79 @@ name: BITS Job Persistence id: e97a5ffe-90bf-11eb-928a-acde48001122 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the use of `bitsadmin.exe` to schedule - a BITS job for persistence on an endpoint. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on specific command-line parameters such as - `create`, `addfile`, and `resume`. This activity is significant because BITS jobs - can be used by attackers to maintain persistence, download malicious payloads, or - exfiltrate data. If confirmed malicious, this could allow an attacker to persist - in the environment, execute arbitrary code, or transfer sensitive information, necessitating - further investigation and potential remediation. +description: The following analytic detects the use of `bitsadmin.exe` to schedule a BITS job for persistence on an endpoint. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line parameters such as `create`, `addfile`, and `resume`. This activity is significant because BITS jobs can be used by attackers to maintain persistence, download malicious payloads, or exfiltrate data. If confirmed malicious, this could allow an attacker to persist in the environment, execute arbitrary code, or transfer sensitive information, necessitating further investigation and potential remediation. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_bitsadmin` Processes.process - IN (*create*, *addfile*, *setnotifyflags*, *setnotifycmdline*, *setminretrydelay*, - *setcustomheaders*, *resume* ) by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `bits_job_persistence_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Limited false positives will be present. Typically, applications - will use `BitsAdmin.exe`. Any filtering should be done based on command-line arguments - (legitimate applications) or parent process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_bitsadmin` Processes.process IN (*create*, *addfile*, *setnotifyflags*, *setnotifycmdline*, *setminretrydelay*, *setcustomheaders*, *resume* ) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `bits_job_persistence_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Limited false positives will be present. Typically, applications will use `BitsAdmin.exe`. Any filtering should be done based on command-line arguments (legitimate applications) or parent process. references: -- https://attack.mitre.org/techniques/T1197/ -- https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/bitsadmin -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1197/T1197.md#atomic-test-3---persist-download--execute -- https://lolbas-project.github.io/lolbas/Binaries/Bitsadmin/ + - https://attack.mitre.org/techniques/T1197/ + - https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/bitsadmin + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1197/T1197.md#atomic-test-3---persist-download--execute + - https://lolbas-project.github.io/lolbas/Binaries/Bitsadmin/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to persist using BITS. - risk_objects: - - field: user - type: user - score: 56 - - field: dest - type: system - score: 56 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to persist using BITS. + risk_objects: + - field: user + type: user + score: 56 + - field: dest + type: system + score: 56 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - BITS Jobs - - Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1197 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - BITS Jobs + - Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1197 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/crowdstrike_falcon.log - source: crowdstrike - sourcetype: crowdstrike:events:sensor + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/crowdstrike_falcon.log + source: crowdstrike + sourcetype: crowdstrike:events:sensor diff --git a/detections/endpoint/bitsadmin_download_file.yml b/detections/endpoint/bitsadmin_download_file.yml index ffec311b3b..e6e6add417 100644 --- a/detections/endpoint/bitsadmin_download_file.yml +++ b/detections/endpoint/bitsadmin_download_file.yml @@ -1,106 +1,88 @@ name: BITSAdmin Download File id: 80630ff4-8e4c-11eb-aab5-acde48001122 -version: 14 -date: '2025-10-14' +version: 15 +date: '2026-02-25' author: Michael Haag, Sittikorn S status: production type: TTP -description: The following analytic detects the use of `bitsadmin.exe` with the `transfer` - parameter to download a remote object. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process and command-line telemetry. This - activity is significant because `bitsadmin.exe` can be exploited to download and - execute malicious files without immediate detection. If confirmed malicious, an - attacker could use this technique to download and execute payloads, potentially - leading to code execution, privilege escalation, or persistent access within the - environment. Review parallel and child processes, especially `svchost.exe`, for - associated artifacts. +description: The following analytic detects the use of `bitsadmin.exe` with the `transfer` parameter to download a remote object. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and command-line telemetry. This activity is significant because `bitsadmin.exe` can be exploited to download and execute malicious files without immediate detection. If confirmed malicious, an attacker could use this technique to download and execute payloads, potentially leading to code execution, privilege escalation, or persistent access within the environment. Review parallel and child processes, especially `svchost.exe`, for associated artifacts. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_bitsadmin` Processes.process - IN ("*transfer*", "*addfile*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `bitsadmin_download_file_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Limited false positives, however it may be required to filter - based on parent process name or network connection. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_bitsadmin` Processes.process IN ("*transfer*", "*addfile*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `bitsadmin_download_file_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Limited false positives, however it may be required to filter based on parent process name or network connection. references: -- https://github.com/redcanaryco/atomic-red-team/blob/8eb52117b748d378325f7719554a896e37bccec7/atomics/T1105/T1105.md#atomic-test-9---windows---bitsadmin-bits-download -- https://github.com/redcanaryco/atomic-red-team/blob/bc705cb7aaa5f26f2d96585fac8e4c7052df0ff9/atomics/T1197/T1197.md -- https://docs.microsoft.com/en-us/windows/win32/bits/bitsadmin-tool -- https://thedfirreport.com/2021/03/29/sodinokibi-aka-revil-ransomware/ + - https://github.com/redcanaryco/atomic-red-team/blob/8eb52117b748d378325f7719554a896e37bccec7/atomics/T1105/T1105.md#atomic-test-9---windows---bitsadmin-bits-download + - https://github.com/redcanaryco/atomic-red-team/blob/bc705cb7aaa5f26f2d96585fac8e4c7052df0ff9/atomics/T1197/T1197.md + - https://docs.microsoft.com/en-us/windows/win32/bits/bitsadmin-tool + - https://thedfirreport.com/2021/03/29/sodinokibi-aka-revil-ransomware/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to download a file. - risk_objects: - - field: user - type: user - score: 49 - - field: dest - type: system - score: 49 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to download a file. + risk_objects: + - field: user + type: user + score: 49 + - field: dest + type: system + score: 49 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Ingress Tool Transfer - - BITS Jobs - - DarkSide Ransomware - - Living Off The Land - - Flax Typhoon - - Gozi Malware - - Scattered Spider - - APT37 Rustonotto and FadeStealer - - GhostRedirector IIS Module and Rungan Backdoor - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1197 - - T1105 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ingress Tool Transfer + - BITS Jobs + - DarkSide Ransomware + - Living Off The Land + - Flax Typhoon + - Gozi Malware + - Scattered Spider + - APT37 Rustonotto and FadeStealer + - GhostRedirector IIS Module and Rungan Backdoor + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1197 + - T1105 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - Sysmon - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog -- name: True Positive Test - CrowdStrike - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/crowdstrike_falcon.log - source: crowdstrike - sourcetype: crowdstrike:events:sensor + - name: True Positive Test - Sysmon + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test - CrowdStrike + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/crowdstrike_falcon.log + source: crowdstrike + sourcetype: crowdstrike:events:sensor diff --git a/detections/endpoint/certutil_exe_certificate_extraction.yml b/detections/endpoint/certutil_exe_certificate_extraction.yml index 6d95828180..e2326cf7ce 100644 --- a/detections/endpoint/certutil_exe_certificate_extraction.yml +++ b/detections/endpoint/certutil_exe_certificate_extraction.yml @@ -1,92 +1,74 @@ name: Certutil exe certificate extraction id: 337a46be-600f-11eb-ae93-0242ac130002 -version: 11 -date: '2025-05-02' +version: 12 +date: '2026-02-25' author: Rod Soto, Splunk status: production type: TTP -description: The following analytic identifies the use of certutil.exe with arguments - indicating the manipulation or extraction of certificates. It leverages data from - Endpoint Detection and Response (EDR) agents, focusing on process names and command-line - arguments. This activity is significant because extracting certificates can allow - attackers to sign new authentication tokens, particularly in federated environments - like Windows ADFS. If confirmed malicious, this could enable attackers to forge - authentication tokens, potentially leading to unauthorized access and privilege - escalation within the network. +description: The following analytic identifies the use of certutil.exe with arguments indicating the manipulation or extraction of certificates. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because extracting certificates can allow attackers to sign new authentication tokens, particularly in federated environments like Windows ADFS. If confirmed malicious, this could enable attackers to forge authentication tokens, potentially leading to unauthorized access and privilege escalation within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe - Processes.process = "*-exportPFX*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `certutil_exe_certificate_extraction_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Unless there are specific use cases, manipulating or exporting - certificates using certutil is uncommon. Extraction of certificate has been observed - during attacks such as Golden SAML and other campaigns targeting Federated services. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=certutil.exe Processes.process = "*-exportPFX*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `certutil_exe_certificate_extraction_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Unless there are specific use cases, manipulating or exporting certificates using certutil is uncommon. Extraction of certificate has been observed during attacks such as Golden SAML and other campaigns targeting Federated services. references: -- https://blog.sygnia.co/detection-and-hunting-of-golden-saml-attack -- https://strontic.github.io/xcyclopedia/library/certutil.exe-09A8A29BAA3A451713FD3D07943B4A43.html + - https://blog.sygnia.co/detection-and-hunting-of-golden-saml-attack + - https://strontic.github.io/xcyclopedia/library/certutil.exe-09A8A29BAA3A451713FD3D07943B4A43.html drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting export a certificate. - risk_objects: - - field: user - type: user - score: 63 - - field: dest - type: system - score: 63 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting export a certificate. + risk_objects: + - field: user + type: user + score: 63 + - field: dest + type: system + score: 63 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Windows Persistence Techniques - - Living Off The Land - - Cloud Federated Credential Abuse - - Compromised Windows Host - - Windows Certificate Services - - Storm-2460 CLFS Zero Day Exploitation - asset_type: Endpoint - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Persistence Techniques + - Living Off The Land + - Cloud Federated Credential Abuse + - Compromised Windows Host + - Windows Certificate Services + - Storm-2460 CLFS Zero Day Exploitation + asset_type: Endpoint + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/certutil_exe_certificate_extraction/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/certutil_exe_certificate_extraction/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/certutil_with_decode_argument.yml b/detections/endpoint/certutil_with_decode_argument.yml index 701f349102..f546f0fc63 100644 --- a/detections/endpoint/certutil_with_decode_argument.yml +++ b/detections/endpoint/certutil_with_decode_argument.yml @@ -1,100 +1,82 @@ name: CertUtil With Decode Argument id: bfe94226-8c10-11eb-a4b3-acde48001122 -version: 11 -date: '2025-09-16' +version: 12 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the use of CertUtil.exe with the 'decode' - argument, which may indicate an attempt to decode a previously encoded file, potentially - containing malicious payloads. This detection leverages data from Endpoint Detection - and Response (EDR) agents, focusing on command-line executions involving CertUtil.exe. - This activity is significant because attackers often use CertUtil to decode malicious - files downloaded from the internet, which are then executed to compromise the system. - If confirmed malicious, this activity could lead to unauthorized code execution, - further system compromise, and potential data exfiltration. +description: The following analytic detects the use of CertUtil.exe with the 'decode' argument, which may indicate an attempt to decode a previously encoded file, potentially containing malicious payloads. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving CertUtil.exe. This activity is significant because attackers often use CertUtil to decode malicious files downloaded from the internet, which are then executed to compromise the system. If confirmed malicious, this activity could lead to unauthorized code execution, further system compromise, and potential data exfiltration. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_certutil` Processes.process=*decode* - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `certutil_with_decode_argument_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Typically seen used to `encode` files, but it is possible to - see legitimate use of `decode`. Filter based on parent-child relationship, file - paths, endpoint or user. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_certutil` Processes.process=*decode* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `certutil_with_decode_argument_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Typically seen used to `encode` files, but it is possible to see legitimate use of `decode`. Filter based on parent-child relationship, file paths, endpoint or user. references: -- https://attack.mitre.org/techniques/T1140/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1140/T1140.md -- https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/certutil -- https://www.bleepingcomputer.com/news/security/certutilexe-could-allow-attackers-to-download-malware-while-bypassing-av/ + - https://attack.mitre.org/techniques/T1140/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1140/T1140.md + - https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/certutil + - https://www.bleepingcomputer.com/news/security/certutilexe-could-allow-attackers-to-download-malware-while-bypassing-av/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to decode a file. - risk_objects: - - field: user - type: user - score: 40 - - field: dest - type: system - score: 40 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to decode a file. + risk_objects: + - field: user + type: user + score: 40 + - field: dest + type: system + score: 40 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Deobfuscate-Decode Files or Information - - Living Off The Land - - Forest Blizzard - - APT29 Diplomatic Deceptions with WINELOADER - - Storm-2460 CLFS Zero Day Exploitation - - GhostRedirector IIS Module and Rungan Backdoor - group: - - APT29 - - Cozy Bear - - Midnight Blizzard - asset_type: Endpoint - mitre_attack_id: - - T1140 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Deobfuscate-Decode Files or Information + - Living Off The Land + - Forest Blizzard + - APT29 Diplomatic Deceptions with WINELOADER + - Storm-2460 CLFS Zero Day Exploitation + - GhostRedirector IIS Module and Rungan Backdoor + group: + - APT29 + - Cozy Bear + - Midnight Blizzard + asset_type: Endpoint + mitre_attack_id: + - T1140 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1140/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1140/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/change_to_safe_mode_with_network_config.yml b/detections/endpoint/change_to_safe_mode_with_network_config.yml index 4dbf05bdd2..594f7338de 100644 --- a/detections/endpoint/change_to_safe_mode_with_network_config.yml +++ b/detections/endpoint/change_to_safe_mode_with_network_config.yml @@ -1,83 +1,67 @@ name: Change To Safe Mode With Network Config id: 81f1dce0-0f18-11ec-a5d7-acde48001122 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of a suspicious `bcdedit` - command that configures a host to boot in safe mode with network support. It leverages - data from Endpoint Detection and Response (EDR) agents, focusing on command-line - executions involving `bcdedit.exe` with specific parameters. This activity is significant - because it is a known technique used by BlackMatter ransomware to force a compromised - host into safe mode for continued encryption. If confirmed malicious, this could - allow attackers to bypass certain security controls, persist in the environment, - and continue their malicious activities. +description: The following analytic detects the execution of a suspicious `bcdedit` command that configures a host to boot in safe mode with network support. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving `bcdedit.exe` with specific parameters. This activity is significant because it is a known technique used by BlackMatter ransomware to force a compromised host into safe mode for continued encryption. If confirmed malicious, this could allow attackers to bypass certain security controls, persist in the environment, and continue their malicious activities. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe - Processes.process="*/set*" Processes.process="*{current}*" Processes.process="*safeboot*" - Processes.process="*network*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product |`drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `change_to_safe_mode_with_network_config_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = bcdedit.exe Processes.process="*/set*" Processes.process="*{current}*" Processes.process="*safeboot*" Processes.process="*network*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `change_to_safe_mode_with_network_config_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://news.sophos.com/en-us/2021/08/09/blackmatter-ransomware-emerges-from-the-shadow-of-darkside/ + - https://news.sophos.com/en-us/2021/08/09/blackmatter-ransomware-emerges-from-the-shadow-of-darkside/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: bcdedit process with commandline $process$ to force safemode boot the $dest$ - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: bcdedit process with commandline $process$ to force safemode boot the $dest$ + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Black Basta Ransomware - - BlackMatter Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1490 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Black Basta Ransomware + - BlackMatter Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1490 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.002/autoadminlogon/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.002/autoadminlogon/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/chcp_command_execution.yml b/detections/endpoint/chcp_command_execution.yml index 8e265c99f4..11dfd77662 100644 --- a/detections/endpoint/chcp_command_execution.yml +++ b/detections/endpoint/chcp_command_execution.yml @@ -1,87 +1,71 @@ name: CHCP Command Execution id: 21d236ec-eec1-11eb-b23e-acde48001122 -version: 9 -date: '2025-08-07' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the chcp.com utility, - which is used to change the active code page of the console. This detection leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process creation - events. This activity is significant because it can indicate the presence of malware, - such as IcedID, which uses this technique to determine the locale region, language, - or country of the compromised host. If confirmed malicious, this could lead to further - system compromise and data exfiltration. +description: The following analytic detects the execution of the chcp.com utility, which is used to change the active code page of the console. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events. This activity is significant because it can indicate the presence of malware, such as IcedID, which uses this technique to determine the locale region, language, or country of the compromised host. If confirmed malicious, this could lead to further system compromise and data exfiltration. data_source: -- Sysmon EventID 1 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=chcp.com - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `chcp_command_execution_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: other tools or script may used this to change code page to - UTF-* or others + - Sysmon EventID 1 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=chcp.com + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `chcp_command_execution_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: other tools or script may used this to change code page to UTF-* or others references: -- https://ss64.com/nt/chcp.html -- https://twitter.com/tccontre18/status/1419941156633329665?s=20 + - https://ss64.com/nt/chcp.html + - https://twitter.com/tccontre18/status/1419941156633329665?s=20 drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: parent process $parent_process_name$ spawning chcp process $process_name$ - with parent command line $parent_process$ - risk_objects: - - field: dest - type: system - score: 9 - - field: user - type: user - score: 9 - threat_objects: [] + message: parent process $parent_process_name$ spawning chcp process $process_name$ with parent command line $parent_process$ + risk_objects: + - field: dest + type: system + score: 9 + - field: user + type: user + score: 9 + threat_objects: [] tags: - analytic_story: - - IcedID - - Azorult - - Crypto Stealer - - Quasar RAT - - Forest Blizzard - - Interlock Rat - asset_type: Endpoint - mitre_attack_id: - - T1059 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - IcedID + - Azorult + - Crypto Stealer + - Quasar RAT + - Forest Blizzard + - Interlock Rat + asset_type: Endpoint + mitre_attack_id: + - T1059 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/simulated_icedid/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/simulated_icedid/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/check_elevated_cmd_using_whoami.yml b/detections/endpoint/check_elevated_cmd_using_whoami.yml index 1f780019c5..f5b7774a41 100644 --- a/detections/endpoint/check_elevated_cmd_using_whoami.yml +++ b/detections/endpoint/check_elevated_cmd_using_whoami.yml @@ -1,83 +1,65 @@ name: Check Elevated CMD using whoami id: a9079b18-1633-11ec-859c-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic identifies the execution of the "whoami" command - with the "/group" flag, where the results are passed to the "find" command in order - to look for a the string "12288". This string represents the SID of the group - "Mandatory Label\High Mandatory Level" effectively checking if the current process - is running as a "High" integrity process or with Administrator privileges. It leverages data from - Endpoint Detection and Response (EDR) agents, focusing on process and command-line - telemetry. This activity is significant because it is commonly used by attackers, - such as FIN7, to perform reconnaissance on a compromised host. If confirmed malicious, - this behavior could indicate an attacker is assessing their privilege level, potentially - leading to further privilege escalation or persistence within the environment. +description: The following analytic identifies the execution of the "whoami" command with the "/group" flag, where the results are passed to the "find" command in order to look for a the string "12288". This string represents the SID of the group "Mandatory Label\High Mandatory Level" effectively checking if the current process is running as a "High" integrity process or with Administrator privileges. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and command-line telemetry. This activity is significant because it is commonly used by attackers, such as FIN7, to perform reconnaissance on a compromised host. If confirmed malicious, this behavior could indicate an attacker is assessing their privilege level, potentially leading to further privilege escalation or persistence within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process = "*whoami*" - Processes.process = "*/group*" Processes.process = "* find *" Processes.process - = "*12288*" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `check_elevated_cmd_using_whoami_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: The combination of these commands is unlikely to occur in a production environment. Any matches should be investigated. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process = "*whoami*" Processes.process = "*/group*" Processes.process = "* find *" Processes.process = "*12288*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `check_elevated_cmd_using_whoami_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: The combination of these commands is unlikely to occur in a production environment. Any matches should be investigated. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Process name $process_name$ with commandline $process$ on $dest$ - risk_objects: - - field: dest - type: system - score: 56 - - field: user - type: user - score: 56 - threat_objects: [] + message: Process name $process_name$ with commandline $process$ on $dest$ + risk_objects: + - field: dest + type: system + score: 56 + - field: user + type: user + score: 56 + threat_objects: [] tags: - analytic_story: - - FIN7 - asset_type: Endpoint - mitre_attack_id: - - T1033 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - FIN7 + asset_type: Endpoint + mitre_attack_id: + - T1033 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/fin7_js_2/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/fin7_js_2/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/child_processes_of_spoolsv_exe.yml b/detections/endpoint/child_processes_of_spoolsv_exe.yml index db0f64d6db..b9a72c8957 100644 --- a/detections/endpoint/child_processes_of_spoolsv_exe.yml +++ b/detections/endpoint/child_processes_of_spoolsv_exe.yml @@ -1,67 +1,56 @@ name: Child Processes of Spoolsv exe id: aa0c4aeb-5b18-41c4-8c07-f1442d7599df -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Rico Valdez, Splunk status: experimental type: TTP -description: The following analytic identifies child processes spawned by spoolsv.exe, - the Print Spooler service in Windows, which typically runs with SYSTEM privileges. - This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process and parent process relationships. Monitoring this activity is - crucial as it can indicate exploitation attempts, such as those associated with - CVE-2018-8440, which can lead to privilege escalation. If confirmed malicious, attackers - could gain SYSTEM-level access, allowing them to execute arbitrary code, escalate - privileges, and potentially compromise the entire system. +description: The following analytic identifies child processes spawned by spoolsv.exe, the Print Spooler service in Windows, which typically runs with SYSTEM privileges. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process relationships. Monitoring this activity is crucial as it can indicate exploitation attempts, such as those associated with CVE-2018-8440, which can lead to privilege escalation. If confirmed malicious, attackers could gain SYSTEM-level access, allowing them to execute arbitrary code, escalate privileges, and potentially compromise the entire system. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count values(Processes.process_name) - as process_name values(Processes.process) as process min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe - AND Processes.process_name!=regsvr32.exe by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `child_processes_of_spoolsv_exe_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Some legitimate printer-related processes may show up as children - of spoolsv.exe. You should confirm that any activity as legitimate and may be added - as exclusions in the search. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name=spoolsv.exe + AND + Processes.process_name!=regsvr32.exe + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `child_processes_of_spoolsv_exe_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Some legitimate printer-related processes may show up as children of spoolsv.exe. You should confirm that any activity as legitimate and may be added as exclusions in the search. references: [] rba: - message: Potentially suspicious child processes of spoolsv.exe on $dest$ - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: Potentially suspicious child processes of spoolsv.exe on $dest$ + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Data Destruction - - Hermetic Wiper - - Windows Privilege Escalation - asset_type: Endpoint - cve: - - CVE-2018-8440 - mitre_attack_id: - - T1068 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Destruction + - Hermetic Wiper + - Windows Privilege Escalation + asset_type: Endpoint + cve: + - CVE-2018-8440 + mitre_attack_id: + - T1068 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/cisco_isovalent___access_to_cloud_metadata_service.yml b/detections/endpoint/cisco_isovalent___access_to_cloud_metadata_service.yml index 570c543a5b..45af16a8fe 100644 --- a/detections/endpoint/cisco_isovalent___access_to_cloud_metadata_service.yml +++ b/detections/endpoint/cisco_isovalent___access_to_cloud_metadata_service.yml @@ -1,69 +1,63 @@ name: Cisco Isovalent - Access To Cloud Metadata Service -id: 7f2e1a9a-1e8e-4d2e-8b7c-5f2c3d6a9b21 -version: 2 -date: '2026-01-20' +id: 7f2e1a9a-1e8e-4d2e-8b7c-5f2c3d6a9b21 +version: 3 +date: '2026-02-25' author: Bhavin Patel, Splunk type: Anomaly data_source: -- Cisco Isovalent Process Connect + - Cisco Isovalent Process Connect status: production description: The following analytic detects workloads accessing the cloud instance metadata service at 169.254.169.254. This IP is used by AWS, GCP and Azure metadata endpoints and is frequently abused in SSRF or lateral movement scenarios to obtain credentials and sensitive environment details. Monitor unexpected access to this service from application pods or namespaces where such behavior is atypical. search: | - `cisco_isovalent_process_connect` | rename process_connect.parent.binary as binary | `excluded_cloud_binaries` - | stats count - min(_time) as firstTime - max(_time) as lastTime - values(dest_port) as dest_port - values(src_ip) as src_ip - by cluster_name pod_name pod_image_name pod_namespace node_name dest_ip - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_isovalent___access_to_cloud_metadata_service_filter` + `cisco_isovalent_process_connect` | rename process_connect.parent.binary as binary | `excluded_cloud_binaries` + | stats count + min(_time) as firstTime + max(_time) as lastTime + values(dest_port) as dest_port + values(src_ip) as src_ip + by cluster_name pod_name pod_image_name pod_namespace node_name dest_ip + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_isovalent___access_to_cloud_metadata_service_filter` how_to_implement: This detection relies on Cisco Isovalent Runtime Security process_connect telemetry. Deploy Isovalent Runtime Security and the Cisco Security Cloud TA to collect these logs via HEC and normalize them. Optionally, a similar variant can be built with process_exec by looking for command-lines that reference 169.254.169.254 (for example curl or wget invocations from within pods). Please update a macro named `excluded_cloud_binaries` that returns true for binaries that are known to access the cloud metadata service. known_false_positives: Legitimate platform components and node agents may query the metadata service. Validate by namespace, labels and workload identity; suppress expected sources and alert on atypical pods or namespaces. references: -- https://attack.mitre.org/techniques/T1552/005/ -- https://hackerone.com/reports/341876 -- https://docs.isovalent.com/user-guide/sec-ops-visibility/lateral-movement/index.html + - https://attack.mitre.org/techniques/T1552/005/ + - https://hackerone.com/reports/341876 + - https://docs.isovalent.com/user-guide/sec-ops-visibility/lateral-movement/index.html drilldown_searches: -- name: View the detection results for - "$pod_name$" - search: '%original_detection_search% | search pod_name = "$pod_name$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$pod_name$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$pod_name$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$pod_name$" + search: '%original_detection_search% | search pod_name = "$pod_name$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$pod_name$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$pod_name$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Pod [$pod_name$] accessed the cloud metadata service [$dest_ip$] in cluster [$cluster_name$] - risk_objects: - - field: pod_name - type: system - score: 50 - threat_objects: - - field: src_ip - type: ip_address + message: Pod [$pod_name$] accessed the cloud metadata service [$dest_ip$] in cluster [$cluster_name$] + risk_objects: + - field: pod_name + type: system + score: 50 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Cisco Isovalent Suspicious Activity - - VoidLink Cloud-Native Linux Malware - asset_type: Kubernetes - mitre_attack_id: - - T1552.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Cisco Isovalent Suspicious Activity + - VoidLink Cloud-Native Linux Malware + asset_type: Kubernetes + mitre_attack_id: + - T1552.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.005/isovalent_cloud_metadata/process_connect.log - source: not_applicable - sourcetype: cisco:isovalent:processConnect - + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.005/isovalent_cloud_metadata/process_connect.log + source: not_applicable + sourcetype: cisco:isovalent:processConnect diff --git a/detections/endpoint/cisco_isovalent___cron_job_creation.yml b/detections/endpoint/cisco_isovalent___cron_job_creation.yml index b19a4eab85..c60cab06e0 100644 --- a/detections/endpoint/cisco_isovalent___cron_job_creation.yml +++ b/detections/endpoint/cisco_isovalent___cron_job_creation.yml @@ -1,68 +1,63 @@ name: Cisco Isovalent - Cron Job Creation id: 94531a31-a041-4777-909f-cd92ed3b71ad -version: 1 -date: '2026-01-05' +version: 2 +date: '2026-02-25' author: Bhavin Patel, Splunk type: Anomaly data_source: -- Cisco Isovalent Process Exec + - Cisco Isovalent Process Exec status: production description: The following analytic detects the creation of a cron job within the Cisco Isovalent environment. It identifies this activity by monitoring process execution logs for cron job creation events. This behavior is significant for a SOC as it could allow an attacker to execute malicious tasks repeatedly and automatically, posing a threat to the Kubernetes infrastructure. If confirmed malicious, this activity could lead to persistent attacks, service disruptions, or unauthorized access to sensitive information. search: | - `cisco_isovalent_process_exec` process_name IN ("crond","cron","crontab") - | search pod_name!="" - | stats count - min(_time) as firstTime - max(_time) as lastTime - values(process) as process - by cluster_name pod_name parent_process_name process_name process_exec process_id node_name - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_isovalent___cron_job_creation_filter` + `cisco_isovalent_process_exec` process_name IN ("crond","cron","crontab") + | search pod_name!="" + | stats count + min(_time) as firstTime + max(_time) as lastTime + values(process) as process + by cluster_name pod_name parent_process_name process_name process_exec process_id node_name + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_isovalent___cron_job_creation_filter` how_to_implement: The detection is based on process execution data generated by Cisco Isovalent Runtime Security. Ensure that Isovalent Runtime Security is deployed and configured in your Kubernetes environment to emit process_exec events. Configure the Cisco Security Cloud TA to collect these logs via HTTP Event Collector (HEC) and normalize them into the Splunk Common Information Model. This integration ensures that all relevant pod, container, and process activity is captured for monitoring and detection of suspicious behavior. known_false_positives: This activity may be triggered by legitimate administrative scripts, container images, or third-party operators that use cron for scheduled tasks, so please investigate the alert in context to rule out benign operations. references: -- https://attack.mitre.org/techniques/T1053/003/ -- https://medium.com/@bag0zathev2/cronjobs-for-hackers-bugbounty-article-7d51588d0fd5 -- https://kubernetes.io/docs/concepts/workloads/controllers/cron-jobs/ + - https://attack.mitre.org/techniques/T1053/003/ + - https://medium.com/@bag0zathev2/cronjobs-for-hackers-bugbounty-article-7d51588d0fd5 + - https://kubernetes.io/docs/concepts/workloads/controllers/cron-jobs/ drilldown_searches: -- name: View the detection results for - "$pod_name$" - search: '%original_detection_search% | search pod_name = "$pod_name$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$pod_name$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$pod_name$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$pod_name$" + search: '%original_detection_search% | search pod_name = "$pod_name$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$pod_name$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$pod_name$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: cron job creation detected in pod [$pod_name$] in the cluster [$cluster_name$] - risk_objects: - - field: pod_name - type: system - score: 50 - threat_objects: - - field: process_name - type: process_name + message: cron job creation detected in pod [$pod_name$] in the cluster [$cluster_name$] + risk_objects: + - field: pod_name + type: system + score: 50 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Cisco Isovalent Suspicious Activity - asset_type: Kubernetes - mitre_attack_id: - - T1053.003 - - T1053.007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Cisco Isovalent Suspicious Activity + asset_type: Kubernetes + mitre_attack_id: + - T1053.003 + - T1053.007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_isovalent/cisco_isovalent.log - source: not_applicable - sourcetype: cisco:isovalent:processExec + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_isovalent/cisco_isovalent.log + source: not_applicable + sourcetype: cisco:isovalent:processExec diff --git a/detections/endpoint/cisco_isovalent___curl_execution_with_insecure_flags.yml b/detections/endpoint/cisco_isovalent___curl_execution_with_insecure_flags.yml index 3a15dce5af..81d017d78d 100644 --- a/detections/endpoint/cisco_isovalent___curl_execution_with_insecure_flags.yml +++ b/detections/endpoint/cisco_isovalent___curl_execution_with_insecure_flags.yml @@ -1,65 +1,60 @@ name: Cisco Isovalent - Curl Execution With Insecure Flags id: c16c4899-d3f7-461b-92c2-cc0ef5758855 -version: 1 -date: '2026-01-05' +version: 2 +date: '2026-02-25' author: Bhavin Patel, Splunk type: Anomaly data_source: -- Cisco Isovalent Process Exec + - Cisco Isovalent Process Exec status: production -description: The following analytic detects the execution of curl commands with insecure flags within the Cisco Isovalent environment. It identifies this activity by monitoring process execution logs for curl commands that use the -k or --insecure flags. This behavior is significant for a SOC as it could allow an attacker to bypass SSL/TLS verification, potentially exposing the Kubernetes infrastructure to man-in-the-middle attacks. If confirmed malicious, this activity could lead to data interception, service disruptions, or unauthorized access to sensitive information. +description: The following analytic detects the execution of curl commands with insecure flags within the Cisco Isovalent environment. It identifies this activity by monitoring process execution logs for curl commands that use the -k or --insecure flags. This behavior is significant for a SOC as it could allow an attacker to bypass SSL/TLS verification, potentially exposing the Kubernetes infrastructure to man-in-the-middle attacks. If confirmed malicious, this activity could lead to data interception, service disruptions, or unauthorized access to sensitive information. search: | - `cisco_isovalent_process_exec` process_name="curl" - | regex process="(?i)(? 10 | `cisco_isovalent___kprobe_spike_filter` + `cisco_isovalent` process_kprobe.action!="" + | bin _time span=5m | rename process_kprobe.parent.pod.name as pod_name + | stats count as kprobe_count + values(process_kprobe.function_name) as functions + values(process_kprobe.process.binary) as binaries + values(process_kprobe.args{}.string_arg) as args + by pod_name _time + | where kprobe_count > 10 | `cisco_isovalent___kprobe_spike_filter` how_to_implement: | - Requires Cisco Isovalent Runtime Security with kprobe tracing enabled and logs - forwarded into Splunk. Ensure that your Splunk Technology Add-on (TA) for Cisco - Security Cloud parses the kprobe JSON correctly. Tune the threshold based on - your workload baseline. + Requires Cisco Isovalent Runtime Security with kprobe tracing enabled and logs + forwarded into Splunk. Ensure that your Splunk Technology Add-on (TA) for Cisco + Security Cloud parses the kprobe JSON correctly. Tune the threshold based on + your workload baseline. known_false_positives: | - Busy or noisy pods may legitimately produce bursts of kprobe events during normal - operation. Tune thresholds and filter by function_name to reduce false positives. + Busy or noisy pods may legitimately produce bursts of kprobe events during normal + operation. Tune thresholds and filter by function_name to reduce false positives. references: -- https://docs.isovalent.com/user-guide/sec-ops-visibility/process-execution/index.html + - https://docs.isovalent.com/user-guide/sec-ops-visibility/process-execution/index.html tags: - analytic_story: - - Cisco Isovalent Suspicious Activity - - VoidLink Cloud-Native Linux Malware - asset_type: Endpoint - mitre_attack_id: - - T1068 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Cisco Isovalent Suspicious Activity + - VoidLink Cloud-Native Linux Malware + asset_type: Endpoint + mitre_attack_id: + - T1068 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_isovalent/kprobe_spike.log - source: not_applicable - sourcetype: cisco:isovalent \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_isovalent/kprobe_spike.log + source: not_applicable + sourcetype: cisco:isovalent diff --git a/detections/endpoint/cisco_isovalent___late_process_execution.yml b/detections/endpoint/cisco_isovalent___late_process_execution.yml index dabab0f69b..01d79756ad 100644 --- a/detections/endpoint/cisco_isovalent___late_process_execution.yml +++ b/detections/endpoint/cisco_isovalent___late_process_execution.yml @@ -1,69 +1,64 @@ name: Cisco Isovalent - Late Process Execution id: 7f4b9b8e-5d6a-4a21-9e3f-0f1e8f2d1c3a -version: 1 -date: '2026-01-05' +version: 2 +date: '2026-02-25' author: Bhavin Patel, Splunk type: Anomaly data_source: -- Cisco Isovalent Process Exec + - Cisco Isovalent Process Exec status: production description: | - Detects process executions that occur well after a container has initialized, which can indicate - suspicious activity (e.g., interactive shells, injected binaries, or post-compromise tooling). - The analytic compares the process start time to the container start time and flags processes - launched more than 5 minutes (300 seconds) after initialization. + Detects process executions that occur well after a container has initialized, which can indicate + suspicious activity (e.g., interactive shells, injected binaries, or post-compromise tooling). + The analytic compares the process start time to the container start time and flags processes + launched more than 5 minutes (300 seconds) after initialization. search: | - `cisco_isovalent_process_exec` process_name="sh" - | rename process_exec.process.start_time as ProcessStartTime - | rename process_exec.process.pod.container.start_time as ContainerStartTime - | eval ProcessStartTime=strptime(ProcessStartTime, "%Y-%m-%dT%H:%M:%S.%3Q") - | eval ContainerStartTime=strptime(ContainerStartTime, "%Y-%m-%dT%H:%M:%S.%9Q") - | eval ContainerTime5min=relative_time(ContainerStartTime, "+5m") - | where ProcessStartTime > ContainerTime5min - | table node_name cluster_name, pod_name, container_id, process_name, process_exec, process, ProcessStartTime, ContainerTime5min | `security_content_ctime(ProcessStartTime)` - | `security_content_ctime(ContainerTime5min)` - | `cisco_isovalent___late_process_execution_filter` + `cisco_isovalent_process_exec` process_name="sh" + | rename process_exec.process.start_time as ProcessStartTime + | rename process_exec.process.pod.container.start_time as ContainerStartTime + | eval ProcessStartTime=strptime(ProcessStartTime, "%Y-%m-%dT%H:%M:%S.%3Q") + | eval ContainerStartTime=strptime(ContainerStartTime, "%Y-%m-%dT%H:%M:%S.%9Q") + | eval ContainerTime5min=relative_time(ContainerStartTime, "+5m") + | where ProcessStartTime > ContainerTime5min + | table node_name cluster_name, pod_name, container_id, process_name, process_exec, process, ProcessStartTime, ContainerTime5min | `security_content_ctime(ProcessStartTime)` + | `security_content_ctime(ContainerTime5min)` + | `cisco_isovalent___late_process_execution_filter` how_to_implement: The detection is based on process execution data generated by Cisco Isovalent Runtime Security. Ensure that Isovalent Runtime Security is deployed and configured in your Kubernetes environment to emit process_exec events. Configure the Cisco Security Cloud TA to collect these logs via HTTP Event Collector (HEC) and normalize them into the Splunk Common Information Model. This integration ensures that all relevant pod, container, and process activity is captured for monitoring and detection of suspicious behavior. known_false_positives: This activity may be triggered by legitimate administrative scripts, container images, or third-party operators that use cron for scheduled tasks, so please investigate the alert in context to rule out benign operations. references: -- https://docs.isovalent.com/user-guide/sec-ops-visibility/process-execution/index.html + - https://docs.isovalent.com/user-guide/sec-ops-visibility/process-execution/index.html drilldown_searches: -- name: View the detection results for pod - "$pod_name$" - search: '%original_detection_search% | search pod_name = "$pod_name$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$pod_name$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$pod_name$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for pod - "$pod_name$" + search: '%original_detection_search% | search pod_name = "$pod_name$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$pod_name$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$pod_name$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Late process execution [$process_name$] detected in pod [$pod_name$] - risk_objects: - - field: pod_name - type: system - score: 45 - threat_objects: - - field: process_name - type: process_name + message: Late process execution [$process_name$] detected in pod [$pod_name$] + risk_objects: + - field: pod_name + type: system + score: 45 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Cisco Isovalent Suspicious Activity - asset_type: Endpoint - mitre_attack_id: - - T1543 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Cisco Isovalent Suspicious Activity + asset_type: Endpoint + mitre_attack_id: + - T1543 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_isovalent/cisco_isovalent_process_exec_delayed_shell.log - source: not_applicable - sourcetype: cisco:isovalent:processExec \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_isovalent/cisco_isovalent_process_exec_delayed_shell.log + source: not_applicable + sourcetype: cisco:isovalent:processExec diff --git a/detections/endpoint/cisco_isovalent___non_allowlisted_image_use.yml b/detections/endpoint/cisco_isovalent___non_allowlisted_image_use.yml index 076bf9639c..9500b1a65b 100644 --- a/detections/endpoint/cisco_isovalent___non_allowlisted_image_use.yml +++ b/detections/endpoint/cisco_isovalent___non_allowlisted_image_use.yml @@ -1,79 +1,73 @@ name: Cisco Isovalent - Non Allowlisted Image Use id: 9f2b7b1d-6c2f-4f2d-9a8b-8a1d7c5f2e11 -version: 1 -date: '2026-01-05' +version: 2 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: Anomaly description: | - The following analytic detects use of container images that fall outside an approved - allowlist, leveraging Cisco Isovalent/Tetragon runtime telemetry (image name and - workload identity). Adversaries commonly introduce untrusted or newly published - images to deploy tooling, establish persistence, or abuse supply‑chain trust. This - behavior may indicate image pulls from unauthorized registries, execution of - unvetted software, or a drift from established deployment baselines. Extra scrutiny - is warranted for namespaces and workloads that normally source images from restricted - registries, and for pods that suddenly begin running images outside expected - prefixes. - Maintain an environment‑specific allowlist via the macro `cisco_isovalent_allowed_images` - (for example, allow trusted registries/prefixes such as ImageName="gcr.io/org/*", - "registry.local/*", or "myco/*") and keep it updated as new baseline images are - introduced. This analytic alerts on images NOT matching the allowlist. + The following analytic detects use of container images that fall outside an approved + allowlist, leveraging Cisco Isovalent/Tetragon runtime telemetry (image name and + workload identity). Adversaries commonly introduce untrusted or newly published + images to deploy tooling, establish persistence, or abuse supply‑chain trust. This + behavior may indicate image pulls from unauthorized registries, execution of + unvetted software, or a drift from established deployment baselines. Extra scrutiny + is warranted for namespaces and workloads that normally source images from restricted + registries, and for pods that suddenly begin running images outside expected + prefixes. + Maintain an environment‑specific allowlist via the macro `cisco_isovalent_allowed_images` + (for example, allow trusted registries/prefixes such as ImageName="gcr.io/org/*", + "registry.local/*", or "myco/*") and keep it updated as new baseline images are + introduced. This analytic alerts on images NOT matching the allowlist. data_source: -- Cisco Isovalent Process Exec + - Cisco Isovalent Process Exec search: | - `cisco_isovalent_process_exec` pod_name!="" - | search NOT `cisco_isovalent_allowed_images` - | stats count - min(_time) as firstTime - max(_time) as lastTime - by pod_image_name pod_namespace pod_name process_name cluster_name - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_isovalent___non_allowlisted_image_use_filter` + `cisco_isovalent_process_exec` pod_name!="" + | search NOT `cisco_isovalent_allowed_images` + | stats count + min(_time) as firstTime + max(_time) as lastTime + by pod_image_name pod_namespace pod_name process_name cluster_name + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_isovalent___non_allowlisted_image_use_filter` how_to_implement: The detection is based on process execution data generated by Cisco Isovalent Runtime Security. Ensure that Isovalent Runtime Security is deployed and configured in your Kubernetes environment to emit process_exec events. Configure the Cisco Security Cloud TA to collect these logs via HTTP Event Collector (HEC) and normalize them into the Splunk Common Information Model. This integration ensures that all relevant pod, container, and process activity is captured for monitoring and detection of suspicious behavior. Create and maintain an environment‑specific macro named`cisco_isovalent_allowed_images` that returns true for approved images, for example:(ImageName="gcr.io/org/app:*" OR ImageName="registry.local/*" OR ImageName="myco/*"). The search alerts on images NOT matching that allowlist. Tune by namespace or team as needed. known_false_positives: New legitimate images during rollouts or blue/green deployments may appear until the allowlist is updated. Coordinate with platform/DevOps teams to synchronize allowlist changes. references: -- https://dev.to/thenjdevopsguy/attacking-a-kubernetes-cluster-enter-red-team-mode-2onj -- https://www.reddit.com/r/kubernetes/comments/l6e5yr/one_of_our_kubernetes_containers_was_compromised/ + - https://dev.to/thenjdevopsguy/attacking-a-kubernetes-cluster-enter-red-team-mode-2onj + - https://www.reddit.com/r/kubernetes/comments/l6e5yr/one_of_our_kubernetes_containers_was_compromised/ drilldown_searches: -- name: View the detection results for - "$pod_name$" - search: '%original_detection_search% | search pod_name = "$pod_name$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$pod_name$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$pod_name$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$pod_name$" + search: '%original_detection_search% | search pod_name = "$pod_name$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$pod_name$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$pod_name$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Non Allowlisted image [$pod_image_name$] used by pod [$pod_name$] in the cluster [$cluster_name$] - risk_objects: - - field: pod_name - type: system - score: 45 - threat_objects: - - field: process_name - type: process_name + message: Non Allowlisted image [$pod_image_name$] used by pod [$pod_name$] in the cluster [$cluster_name$] + risk_objects: + - field: pod_name + type: system + score: 45 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Cisco Isovalent Suspicious Activity - asset_type: Kubernetes - mitre_attack_id: - - T1204.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Cisco Isovalent Suspicious Activity + asset_type: Kubernetes + mitre_attack_id: + - T1204.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_isovalent/cisco_isovalent.log - source: not_applicable - sourcetype: cisco:isovalent:processExec - + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_isovalent/cisco_isovalent.log + source: not_applicable + sourcetype: cisco:isovalent:processExec diff --git a/detections/endpoint/cisco_isovalent___nsenter_usage_in_kubernetes_pod.yml b/detections/endpoint/cisco_isovalent___nsenter_usage_in_kubernetes_pod.yml index 15123d2e15..f306f043cf 100644 --- a/detections/endpoint/cisco_isovalent___nsenter_usage_in_kubernetes_pod.yml +++ b/detections/endpoint/cisco_isovalent___nsenter_usage_in_kubernetes_pod.yml @@ -1,70 +1,65 @@ name: Cisco Isovalent - Nsenter Usage in Kubernetes Pod id: cd07120d-4265-481a-ba0f-3b91fbc5a02f -version: 1 -date: '2026-01-05' +version: 2 +date: '2026-02-25' author: Bhavin Patel, Splunk type: Anomaly data_source: -- Cisco Isovalent Process Exec + - Cisco Isovalent Process Exec status: production description: | - This analytic detects the execution of the nsenter utility from within a container, a technique often used for exploitation and container escape. Nsenter allows an attacker to enter the namespaces of another process—such as the host's init process (PID 1)—and execute a shell or other binaries with elevated privileges. For example, an attacker may use docker exec to gain a shell in a container, enumerate the PID of a target container or the host, and then use nsenter to access all namespaces (mount, UTS, IPC, net, pid) of the host or another container. Example to escape to the host: `nsenter --target 1 --mount --uts --ipc --net --pid -- bash`. The WorkloadAncestorsBinary field is used to track the ancestry of the process, this is useful to understand the context of the nsenter usage. + This analytic detects the execution of the nsenter utility from within a container, a technique often used for exploitation and container escape. Nsenter allows an attacker to enter the namespaces of another process—such as the host's init process (PID 1)—and execute a shell or other binaries with elevated privileges. For example, an attacker may use docker exec to gain a shell in a container, enumerate the PID of a target container or the host, and then use nsenter to access all namespaces (mount, UTS, IPC, net, pid) of the host or another container. Example to escape to the host: `nsenter --target 1 --mount --uts --ipc --net --pid -- bash`. The WorkloadAncestorsBinary field is used to track the ancestry of the process, this is useful to understand the context of the nsenter usage. - The options -m -u -n -i -p correspond to the various Linux namespaces. Adversaries exploit nsenter when pods are misconfigured with excessive privileges (e.g., privileged, hostPID, or broad hostPath mounts), enabling them to interact with the underlying node filesystem and processes. This can be an indicator of a container escape attempt or privilege escalation. Security teams should pay close attention to any nsenter invocation from within containers, especially outside of normal maintenance activity or in workloads with elevated privileges. -search: | - `cisco_isovalent_process_exec` process_name="nsenter" - | eval WorkloadAncestorsBinary=mvjoin(parent_process_name, " <- ") - | stats count - min(_time) as firstTime - max(_time) as lastTime - values(process) as process - values(WorkloadAncestorsBinary) as WorkloadAncestorsBinary - by cluster_name container_id pod_name pod_namespace pod_image_name parent_process_name process_name process_exec process_id node_name - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_isovalent___nsenter_usage_in_kubernetes_pod_filter` + The options -m -u -n -i -p correspond to the various Linux namespaces. Adversaries exploit nsenter when pods are misconfigured with excessive privileges (e.g., privileged, hostPID, or broad hostPath mounts), enabling them to interact with the underlying node filesystem and processes. This can be an indicator of a container escape attempt or privilege escalation. Security teams should pay close attention to any nsenter invocation from within containers, especially outside of normal maintenance activity or in workloads with elevated privileges. +search: | + `cisco_isovalent_process_exec` process_name="nsenter" + | eval WorkloadAncestorsBinary=mvjoin(parent_process_name, " <- ") + | stats count + min(_time) as firstTime + max(_time) as lastTime + values(process) as process + values(WorkloadAncestorsBinary) as WorkloadAncestorsBinary + by cluster_name container_id pod_name pod_namespace pod_image_name parent_process_name process_name process_exec process_id node_name + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_isovalent___nsenter_usage_in_kubernetes_pod_filter` how_to_implement: The detection is based on process execution data generated by Cisco Isovalent Runtime Security. Ensure that Isovalent Runtime Security is deployed and configured in your Kubernetes environment to emit process_exec events. Configure the Cisco Security Cloud TA to collect these logs via HTTP Event Collector (HEC) and normalize them into the Splunk Common Information Model. This integration ensures that all relevant pod, container, and process activity is captured for monitoring and detection of suspicious behavior. known_false_positives: It is highly unlikely that nsenter will be used in a legitimate way, investigate the alert in context to rule out benign operations. references: -- https://isovalent.com/blog/post/2021-11-container-escape/ -- https://kubehound.io/reference/attacks/CE_NSENTER/ + - https://isovalent.com/blog/post/2021-11-container-escape/ + - https://kubehound.io/reference/attacks/CE_NSENTER/ drilldown_searches: -- name: View the detection results for - "$pod_name$" - search: '%original_detection_search% | search pod_name = "$pod_name$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$pod_name$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$pod_name$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$pod_name$" + search: '%original_detection_search% | search pod_name = "$pod_name$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$pod_name$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$pod_name$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An nsenter escape attempt has been detected by user on container pod - [$pod_name$] - risk_objects: - - field: pod_name - type: system - score: 50 - threat_objects: - - field: process_name - type: process_name + message: An nsenter escape attempt has been detected by user on container pod - [$pod_name$] + risk_objects: + - field: pod_name + type: system + score: 50 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Cisco Isovalent Suspicious Activity - asset_type: Endpoint - mitre_attack_id: - - T1543 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Cisco Isovalent Suspicious Activity + asset_type: Endpoint + mitre_attack_id: + - T1543 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_isovalent/cisco_isovalent.log - source: not_applicable - sourcetype: cisco:isovalent:processExec + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_isovalent/cisco_isovalent.log + source: not_applicable + sourcetype: cisco:isovalent:processExec diff --git a/detections/endpoint/cisco_isovalent___pods_running_offensive_tools.yml b/detections/endpoint/cisco_isovalent___pods_running_offensive_tools.yml index 7a29c2c70d..562de890a8 100644 --- a/detections/endpoint/cisco_isovalent___pods_running_offensive_tools.yml +++ b/detections/endpoint/cisco_isovalent___pods_running_offensive_tools.yml @@ -1,66 +1,60 @@ name: Cisco Isovalent - Pods Running Offensive Tools id: e9d0b9e6-2f3c-4a8a-9d61-2b6f4a9c1c2e -version: 1 -date: '2026-01-05' +version: 2 +date: '2026-02-25' author: Bhavin Patel, Splunk type: Anomaly data_source: -- Cisco Isovalent Process Exec + - Cisco Isovalent Process Exec status: production description: The following analytic detects execution of known offensive tooling from within Kubernetes pods, including network scanners and post-exploitation frameworks (e.g., nmap, masscan, zmap, impacket-*, hashcat, john, SharpHound, kube-hunter, peirates). We have created a macro named `linux_offsec_tool_processes` that contains the list of known offensive tooling found on linux systems. Adversaries commonly introduce these tools into compromised workloads to conduct discovery, lateral movement, credential access, or cluster reconnaissance. This behavior may indicate a compromised container or supply-chain abuse. Extra scrutiny is warranted for namespaces that do not typically run diagnostic scanners and for pods that suddenly begin invoking these binaries outside of normal maintenance activity. search: | - `cisco_isovalent_process_exec` `linux_offsec_tool_processes` - | stats count - min(_time) as firstTime - max(_time) as lastTime - values(process) as process - by cluster_name container_id pod_name pod_namespace pod_image_name parent_process_name process_name process_exec process_id node_name - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_isovalent___pods_running_offensive_tools_filter` + `cisco_isovalent_process_exec` `linux_offsec_tool_processes` + | stats count + min(_time) as firstTime + max(_time) as lastTime + values(process) as process + by cluster_name container_id pod_name pod_namespace pod_image_name parent_process_name process_name process_exec process_id node_name + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_isovalent___pods_running_offensive_tools_filter` how_to_implement: The detection is based on process execution data generated by Cisco Isovalent Runtime Security. Ensure that Isovalent Runtime Security is deployed and configured in your Kubernetes environment to emit process_exec events. Configure the Cisco Security Cloud TA to collect these logs via HTTP Event Collector (HEC) and normalize them into the Splunk Common Information Model. This integration ensures that all relevant pod, container, and process activity is captured for monitoring and detection of suspicious behavior. known_false_positives: Security testing, approved red team exercises, or sanctioned diagnostics can trigger this analytic. Coordinate allowlists and maintenance windows with platform/SecOps teams. Please update a macro named `linux_offsec_tool_processes` that contains the list of known offensive tooling found on linux systems if your environment has additional known offensive tools that are not included in the macro. references: -- https://dev.to/thenjdevopsguy/attacking-a-kubernetes-cluster-enter-red-team-mode-2onj -- https://www.reddit.com/r/kubernetes/comments/l6e5yr/one_of_our_kubernetes_containers_was_compromised/ + - https://dev.to/thenjdevopsguy/attacking-a-kubernetes-cluster-enter-red-team-mode-2onj + - https://www.reddit.com/r/kubernetes/comments/l6e5yr/one_of_our_kubernetes_containers_was_compromised/ drilldown_searches: -- name: View the detection results for - "$pod_name$" - search: '%original_detection_search% | search pod_name = "$pod_name$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$pod_name$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$pod_name$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$pod_name$" + search: '%original_detection_search% | search pod_name = "$pod_name$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$pod_name$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$pod_name$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Offensive tool execution [$process_name$] detected in pod [$pod_name$] on cluster [$cluster_name$] - risk_objects: - - field: pod_name - type: system - score: 48 - threat_objects: - - field: process_name - type: process_name + message: Offensive tool execution [$process_name$] detected in pod [$pod_name$] on cluster [$cluster_name$] + risk_objects: + - field: pod_name + type: system + score: 48 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Cisco Isovalent Suspicious Activity - asset_type: Endpoint - mitre_attack_id: - - T1204.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Cisco Isovalent Suspicious Activity + asset_type: Endpoint + mitre_attack_id: + - T1204.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_isovalent/cisco_isovalent.log - source: not_applicable - sourcetype: cisco:isovalent:processExec - + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_isovalent/cisco_isovalent.log + source: not_applicable + sourcetype: cisco:isovalent:processExec diff --git a/detections/endpoint/cisco_isovalent___potential_escape_to_host.yml b/detections/endpoint/cisco_isovalent___potential_escape_to_host.yml index 51e512783c..9e01be6f89 100644 --- a/detections/endpoint/cisco_isovalent___potential_escape_to_host.yml +++ b/detections/endpoint/cisco_isovalent___potential_escape_to_host.yml @@ -1,89 +1,84 @@ name: Cisco Isovalent - Potential Escape to Host id: 2b8a7a21-bec6-4e1f-84c4-7b319f45d2ab -version: 2 -date: '2026-01-20' +version: 3 +date: '2026-02-25' author: Bhavin Patel, Splunk type: Anomaly data_source: -- Cisco Isovalent Process Exec + - Cisco Isovalent Process Exec status: production description: | - This analytic detects potential container escape or reconnaissance attempts by monitoring for the rapid execution of multiple suspicious Linux commands (nsenter, mount, ps aux, and ls) within a short time window. The search aggregates process execution logs into 5-minute buckets and identifies when two or more distinct commands occur in quick succession. This behavior is noteworthy because attackers often chain these commands together to pivot from a container into the host, enumerate processes, or browse filesystems. For a SOC, catching these clustered command executions is important because it highlights possible adversary activity attempting to break isolation and escalate privileges inside a Kubernetes environment. + This analytic detects potential container escape or reconnaissance attempts by monitoring for the rapid execution of multiple suspicious Linux commands (nsenter, mount, ps aux, and ls) within a short time window. The search aggregates process execution logs into 5-minute buckets and identifies when two or more distinct commands occur in quick succession. This behavior is noteworthy because attackers often chain these commands together to pivot from a container into the host, enumerate processes, or browse filesystems. For a SOC, catching these clustered command executions is important because it highlights possible adversary activity attempting to break isolation and escalate privileges inside a Kubernetes environment. search: | - `cisco_isovalent_process_exec` + `cisco_isovalent_process_exec` - ( - process_name IN ("nsenter","mount","ps","ls") - OR - process IN ("*nsenter*", "*mount*", "*ps aux*", "*ps -ef*") - ) - | bin _time span=5m - | stats - count AS total_events - dc(process_name) AS distinct_cmds - min(_time) AS firstTime - max(_time) AS lastTime - values(process) AS process - values(process_name) AS process_name - BY cluster_name node_name pod_name _time - | eval duration_s = round(lastTime - firstTime, 0) - | where distinct_cmds >= 2 AND duration_s <= 120 - | table _time cluster_name node_name pod_name total_events distinct_cmds duration_s firstTime lastTime process process_name - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_isovalent___potential_escape_to_host_filter` + ( + process_name IN ("nsenter","mount","ps","ls") + OR + process IN ("*nsenter*", "*mount*", "*ps aux*", "*ps -ef*") + ) + | bin _time span=5m + | stats + count AS total_events + dc(process_name) AS distinct_cmds + min(_time) AS firstTime + max(_time) AS lastTime + values(process) AS process + values(process_name) AS process_name + BY cluster_name node_name pod_name _time + | eval duration_s = round(lastTime - firstTime, 0) + | where distinct_cmds >= 2 AND duration_s <= 120 + | table _time cluster_name node_name pod_name total_events distinct_cmds duration_s firstTime lastTime process process_name + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_isovalent___potential_escape_to_host_filter` how_to_implement: | - This detection relies on process execution telemetry from Cisco Isovalent Runtime Security. - Ensure Isovalent Runtime Security is deployed and configured in your Kubernetes environment - to generate process_exec events. Configure the Cisco Security Cloud TA to collect these logs - via HEC and normalize them into Splunk CIM. Privileged pods and hostPID configurations - should be closely monitored as they increase the risk of container escape attempts. + This detection relies on process execution telemetry from Cisco Isovalent Runtime Security. + Ensure Isovalent Runtime Security is deployed and configured in your Kubernetes environment + to generate process_exec events. Configure the Cisco Security Cloud TA to collect these logs + via HEC and normalize them into Splunk CIM. Privileged pods and hostPID configurations + should be closely monitored as they increase the risk of container escape attempts. known_false_positives: | - Some legitimate administrative containers or troubleshooting workflows may use nsenter - or mount commands (e.g., debugging nodes with hostPID pods). Such activity should be - investigated in context to ensure it is not malicious. + Some legitimate administrative containers or troubleshooting workflows may use nsenter + or mount commands (e.g., debugging nodes with hostPID pods). Such activity should be + investigated in context to ensure it is not malicious. references: -- https://attack.mitre.org/techniques/T1611/ -- https://unit42.paloaltonetworks.com/hildegard-malware-teamtnt/ + - https://attack.mitre.org/techniques/T1611/ + - https://unit42.paloaltonetworks.com/hildegard-malware-teamtnt/ drilldown_searches: -- name: View the detection results for pod - "$pod_name$" - search: '%original_detection_search% | search pod_name = "$pod_name$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$pod_name$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$pod_name$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for pod - "$pod_name$" + search: '%original_detection_search% | search pod_name = "$pod_name$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$pod_name$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$pod_name$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Escape-to-host attempt detected in pod $pod_name$ on cluster $cluster_name$ using a command - [$process$] - risk_objects: - - field: pod_name - type: system - score: 70 - threat_objects: - - field: process_name - type: process_name + message: Escape-to-host attempt detected in pod $pod_name$ on cluster $cluster_name$ using a command - [$process$] + risk_objects: + - field: pod_name + type: system + score: 70 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Cisco Isovalent Suspicious Activity - - VoidLink Cloud-Native Linux Malware - asset_type: Endpoint - mitre_attack_id: - - T1611 - atomic_guid: [] - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Cisco Isovalent Suspicious Activity + - VoidLink Cloud-Native Linux Malware + asset_type: Endpoint + mitre_attack_id: + - T1611 + atomic_guid: [] + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1611/cisco_isovalent_k8_escape/cisco_isovalent.log - source: not_applicable - sourcetype: cisco:isovalent:processExec \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1611/cisco_isovalent_k8_escape/cisco_isovalent.log + source: not_applicable + sourcetype: cisco:isovalent:processExec diff --git a/detections/endpoint/cisco_isovalent___shell_execution.yml b/detections/endpoint/cisco_isovalent___shell_execution.yml index 94a26f7d71..2dfc8b952e 100644 --- a/detections/endpoint/cisco_isovalent___shell_execution.yml +++ b/detections/endpoint/cisco_isovalent___shell_execution.yml @@ -5,53 +5,48 @@ date: '2026-01-05' author: Bhavin Patel, Splunk type: Anomaly data_source: -- Cisco Isovalent Process Exec + - Cisco Isovalent Process Exec status: production description: The following analytic detects the execution of a shell inside a container namespace within the Cisco Isovalent environment. It identifies this activity by monitoring process execution logs for the execution of a shell (sh or bash) inside a container namespace. This behavior is significant for a SOC as it could allow an attacker to gain shell access to the container, potentially leading to further compromise of the Kubernetes cluster. If confirmed malicious, this activity could lead to data theft, service disruption, privilege escalation, lateral movement, and further attacks, severely compromising the cluster's security and integrity. search: | - `cisco_isovalent_process_exec` process_name IN ("sh", "ksh", "zsh", "bash", "dash", "rbash", "fish", "csh", "tcsh", "ion", "eshell") - | stats count by cluster_name parent_process_name process_name process_exec process_id node_name | `cisco_isovalent___shell_execution_filter` + `cisco_isovalent_process_exec` process_name IN ("sh", "ksh", "zsh", "bash", "dash", "rbash", "fish", "csh", "tcsh", "ion", "eshell") + | stats count by cluster_name parent_process_name process_name process_exec process_id node_name | `cisco_isovalent___shell_execution_filter` how_to_implement: The detection is based on process execution data generated by Cisco Isovalent Runtime Security. Ensure that Isovalent Runtime Security is deployed and configured in your Kubernetes environment to emit process_exec events. Configure the Cisco Security Cloud TA to collect these logs via HTTP Event Collector (HEC) and normalize them into the Splunk Common Information Model. This integration ensures that all relevant pod, container, and process activity is captured for monitoring and detection of suspicious behavior. known_false_positives: This activity may be triggered by legitimate administrative scripts, container images, or third-party operators that use cron for scheduled tasks, so please investigate the alert in context to rule out benign operations. references: -- https://www.sysdig.com/blog/mitre-attck-framework-for-container-runtime-security-with-sysdig-falco + - https://www.sysdig.com/blog/mitre-attck-framework-for-container-runtime-security-with-sysdig-falco drilldown_searches: -- name: View the detection results for - "$node_name$" - search: '%original_detection_search% | search node_name = "$node_name$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$node_name$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$node_name$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$node_name$" + search: '%original_detection_search% | search node_name = "$node_name$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$node_name$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$node_name$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The shell [$process_name$] was executed on container pod namespace [$node_name$] - risk_objects: - - field: node_name - type: system - score: 49 - threat_objects: - - field: process_name - type: process_name + message: The shell [$process_name$] was executed on container pod namespace [$node_name$] + risk_objects: + - field: node_name + type: system + score: 49 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Cisco Isovalent Suspicious Activity - asset_type: Endpoint - mitre_attack_id: - - T1543 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Cisco Isovalent Suspicious Activity + asset_type: Endpoint + mitre_attack_id: + - T1543 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_isovalent/cisco_isovalent.log - source: not_applicable - sourcetype: cisco:isovalent:processExec + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_isovalent/cisco_isovalent.log + source: not_applicable + sourcetype: cisco:isovalent:processExec diff --git a/detections/endpoint/cisco_nvm___curl_execution_with_insecure_flags.yml b/detections/endpoint/cisco_nvm___curl_execution_with_insecure_flags.yml index edacc0da30..4434c40bf4 100644 --- a/detections/endpoint/cisco_nvm___curl_execution_with_insecure_flags.yml +++ b/detections/endpoint/cisco_nvm___curl_execution_with_insecure_flags.yml @@ -6,96 +6,90 @@ author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - This analytic detects the use of `curl.exe` with insecure flags such as `-k`, `--insecure`, `--proxy-insecure`, or `--doh-insecure` - which disable TLS certificate validation. - It leverages Cisco Network Visibility Module (NVM) flow data and process arguments - to identify outbound connections initiated by curl where TLS checks were explicitly disabled. - This behavior may indicate an attempt to bypass certificate validation to connect to potentially untrusted or malicious endpoints, - a common tactic in red team operations, malware staging, or data exfiltration over HTTPS. + This analytic detects the use of `curl.exe` with insecure flags such as `-k`, `--insecure`, `--proxy-insecure`, or `--doh-insecure` + which disable TLS certificate validation. + It leverages Cisco Network Visibility Module (NVM) flow data and process arguments + to identify outbound connections initiated by curl where TLS checks were explicitly disabled. + This behavior may indicate an attempt to bypass certificate validation to connect to potentially untrusted or malicious endpoints, + a common tactic in red team operations, malware staging, or data exfiltration over HTTPS. data_source: -- Cisco Network Visibility Module Flow Data + - Cisco Network Visibility Module Flow Data search: | - `cisco_network_visibility_module_flowdata` - process_name = "curl.exe" - NOT dest IN ( - "10.0.0.0/8", "172.16.0.0/12", "192.168.0.0/16", "100.64.0.0/10", - "127.0.0.0/8", "169.254.0.0/16", "192.0.0.0/24", "192.0.0.0/29", "192.0.0.8/32", - "192.0.0.9/32", "192.0.0.10/32", "192.0.0.170/32", "192.0.0.171/32", "192.0.2.0/24", - "192.31.196.0/24", "192.52.193.0/24", "192.88.99.0/24", "224.0.0.0/4", "192.175.48.0/24", - "198.18.0.0/15", "198.51.100.0/24", "203.0.113.0/24", "240.0.0.0/4", "::1" - ) - | regex process_arguments="(?i)(?[^\s\"']+)$" - | lookup typo_squatted_python_packages - typosquatted_package_name as package_name - OUTPUTNEW comment package_official_url - | where isnotnull(comment) - | stats count min(_time) as firstTime max(_time) as lastTime - values(parent_process_arguments) as parent_process_arguments - values(process_arguments) as process_arguments - values(parent_process_hash) as parent_process_hash - values(process_hash) as process_hash - values(module_name_list) as module_name_list - values(module_hash_list) as module_hash_list - values(dest_port) as dest_port - values(aliul) as additional_logged_in_users_list - values(dest_hostname) as dest_hostname - by src dest parent_process_path parent_process_integrity_level process_path process_name process_integrity_level process_id transport package_name comment package_official_url - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | table firstTime lastTime src dest_hostname dest dest_port transport package_name comment package_official_url - parent_process_integrity_level parent_process_path parent_process_arguments parent_process_hash - process_integrity_level process_path process_name process_arguments process_hash process_id - additional_logged_in_users_list module_name_list module_hash_list - | `cisco_nvm___installation_of_typosquatted_python_package_filter` + `cisco_network_visibility_module_flowdata` + dest_hostname IN ("*.pythonhosted.org", "*pypi.org", "*python-poetry.org") + ( + (process_arguments = "*pip*" process_arguments = "*install*") + OR + (process_arguments = "*poetry*" process_arguments = "*add*") + ) + | rex field=process_arguments "(?i)(?:pip|poetry)[^|]*?\s+(?:install|add)\s+(?P[^\s\"']+)$" + | lookup typo_squatted_python_packages + typosquatted_package_name as package_name + OUTPUTNEW comment package_official_url + | where isnotnull(comment) + | stats count min(_time) as firstTime max(_time) as lastTime + values(parent_process_arguments) as parent_process_arguments + values(process_arguments) as process_arguments + values(parent_process_hash) as parent_process_hash + values(process_hash) as process_hash + values(module_name_list) as module_name_list + values(module_hash_list) as module_hash_list + values(dest_port) as dest_port + values(aliul) as additional_logged_in_users_list + values(dest_hostname) as dest_hostname + by src dest parent_process_path parent_process_integrity_level process_path process_name process_integrity_level process_id transport package_name comment package_official_url + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table firstTime lastTime src dest_hostname dest dest_port transport package_name comment package_official_url + parent_process_integrity_level parent_process_path parent_process_arguments parent_process_hash + process_integrity_level process_path process_name process_arguments process_hash process_id + additional_logged_in_users_list module_name_list module_hash_list + | `cisco_nvm___installation_of_typosquatted_python_package_filter` how_to_implement: | - This search requires Network Visibility Module logs, which includes the flow data sourcetype. - This search uses an input macro named `cisco_network_visibility_module_flowdata`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. - Replace the macro definition with configurations for your Splunk environment. - The search also uses a post-filter macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). - In addition to this, the search make use of the lookup "typo_squatted_python_packages". Which needs to be configured and tuned. + This search requires Network Visibility Module logs, which includes the flow data sourcetype. + This search uses an input macro named `cisco_network_visibility_module_flowdata`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. + Replace the macro definition with configurations for your Splunk environment. + The search also uses a post-filter macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). + In addition to this, the search make use of the lookup "typo_squatted_python_packages". Which needs to be configured and tuned. known_false_positives: | - False positives should be very minimal to non existent, as the names of the packages in the lookup are all extracted from previously malicious packages. + False positives should be very minimal to non existent, as the names of the packages in the lookup are all extracted from previously malicious packages. references: - - https://securelist.com/two-more-malicious-python-packages-in-the-pypi/107218/ - - https://blog.checkpoint.com/securing-the-cloud/pypi-inundated-by-malicious-typosquatting-campaign/ - - https://rhisac.org/threat-intelligence/typosquatting-campaign-targets-python-developers-with-hundreds-of-malicious-libraries/ + - https://securelist.com/two-more-malicious-python-packages-in-the-pypi/107218/ + - https://blog.checkpoint.com/securing-the-cloud/pypi-inundated-by-malicious-typosquatting-campaign/ + - https://rhisac.org/threat-intelligence/typosquatting-campaign-targets-python-developers-with-hundreds-of-malicious-libraries/ drilldown_searches: - - name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$src$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Host $src$ used pip or poetry to install a likely typosquatted python package $package_name$ from $dest_hostname$ - risk_objects: - - field: src - type: system - score: 60 - threat_objects: - - field: process_name - type: process_name + message: Host $src$ used pip or poetry to install a likely typosquatted python package $package_name$ from $dest_hostname$ + risk_objects: + - field: src + type: system + score: 60 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Cisco Network Visibility Module Analytics - asset_type: Endpoint - mitre_attack_id: - - T1059 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: endpoint + analytic_story: + - Cisco Network Visibility Module Analytics + asset_type: Endpoint + mitre_attack_id: + - T1059 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: endpoint tests: - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/cisco_nvm___mshtml_or_mshta_network_execution_without_url_in_cli.yml b/detections/endpoint/cisco_nvm___mshtml_or_mshta_network_execution_without_url_in_cli.yml index ca6e533e72..286a3e3205 100644 --- a/detections/endpoint/cisco_nvm___mshtml_or_mshta_network_execution_without_url_in_cli.yml +++ b/detections/endpoint/cisco_nvm___mshtml_or_mshta_network_execution_without_url_in_cli.yml @@ -6,99 +6,94 @@ author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - This analytic detects suspicious use of 'mshta.exe' or 'rundll32.exe' invoking 'mshtml.dll' - or the 'RunHTMLApplication' export without including a direct HTTP/HTTPS URL in the command line. - This pattern could be associated with obfuscated script execution used by threat actors during - initial access or payload staging. The absence of a visible URL may indicate attempts to evade static - detections by embedding the URL via string concatenation, encoding (e.g., hex), or indirect script loaders - like 'GetObject()'. + This analytic detects suspicious use of 'mshta.exe' or 'rundll32.exe' invoking 'mshtml.dll' + or the 'RunHTMLApplication' export without including a direct HTTP/HTTPS URL in the command line. + This pattern could be associated with obfuscated script execution used by threat actors during + initial access or payload staging. The absence of a visible URL may indicate attempts to evade static + detections by embedding the URL via string concatenation, encoding (e.g., hex), or indirect script loaders + like 'GetObject()'. data_source: - - Cisco Network Visibility Module Flow Data + - Cisco Network Visibility Module Flow Data search: | - `cisco_network_visibility_module_flowdata` - ( + `cisco_network_visibility_module_flowdata` ( - process_name = "mshta.exe" - process_arguments IN ("*javascript*", "*vbscript*") + ( + process_name = "mshta.exe" + process_arguments IN ("*javascript*", "*vbscript*") + ) + OR + ( process_name = "rundll32.exe" AND + process_arguments = "*mshtml*" AND + process_arguments = "*RunHTMLApplication*" + ) ) - OR - ( process_name = "rundll32.exe" AND - process_arguments = "*mshtml*" AND - process_arguments = "*RunHTMLApplication*" - ) - ) - NOT process_arguments IN ("*http://*", "*https://*") - | stats count min(_time) as firstTime max(_time) as lastTime - values(parent_process_arguments) as parent_process_arguments - values(process_arguments) as process_arguments - values(parent_process_hash) as parent_process_hash - values(process_hash) as process_hash - values(module_name_list) as module_name_list - values(module_hash_list) as module_hash_list - values(dest_port) as dest_port - values(aliul) as additional_logged_in_users_list - values(dest_hostname) as dest_hostname - by src dest parent_process_path parent_process_integrity_level process_path process_name process_integrity_level process_id transport - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | table - parent_process_integrity_level parent_process_path parent_process_arguments parent_process_hash - process_integrity_level process_path process_name process_arguments process_hash process_id - additional_logged_in_users_list module_name_list module_hash_list - src dest_hostname dest dest_port transport firstTime lastTime - | `cisco_nvm___mshtml_or_mshta_network_execution_without_url_in_cli_filter` + NOT process_arguments IN ("*http://*", "*https://*") + | stats count min(_time) as firstTime max(_time) as lastTime + values(parent_process_arguments) as parent_process_arguments + values(process_arguments) as process_arguments + values(parent_process_hash) as parent_process_hash + values(process_hash) as process_hash + values(module_name_list) as module_name_list + values(module_hash_list) as module_hash_list + values(dest_port) as dest_port + values(aliul) as additional_logged_in_users_list + values(dest_hostname) as dest_hostname + by src dest parent_process_path parent_process_integrity_level process_path process_name process_integrity_level process_id transport + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table + parent_process_integrity_level parent_process_path parent_process_arguments parent_process_hash + process_integrity_level process_path process_name process_arguments process_hash process_id + additional_logged_in_users_list module_name_list module_hash_list + src dest_hostname dest dest_port transport firstTime lastTime + | `cisco_nvm___mshtml_or_mshta_network_execution_without_url_in_cli_filter` how_to_implement: | - This search requires Network Visibility Module logs, which includes the flow data sourcetype. - This search uses an input macro named `cisco_network_visibility_module_flowdata`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. - Replace the macro definition with configurations for your Splunk environment. - The search also uses a post-filter macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). + This search requires Network Visibility Module logs, which includes the flow data sourcetype. + This search uses an input macro named `cisco_network_visibility_module_flowdata`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. + Replace the macro definition with configurations for your Splunk environment. + The search also uses a post-filter macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). known_false_positives: | - False positives should be minimal as the presence of a network connection during such executions increases the likelihood of malicious behavior. + False positives should be minimal as the presence of a network connection during such executions increases the likelihood of malicious behavior. references: - - https://attack.mitre.org/techniques/T1218/005/ - - https://redcanary.com/blog/mshta-attack-technique/ - - https://lolbas-project.github.io/lolbas/Binaries/Rundll32/ - - https://learn.microsoft.com/en-us/windows/win32/api/mshtml/nf-mshtml-mshtml_runhtmlapplication + - https://attack.mitre.org/techniques/T1218/005/ + - https://redcanary.com/blog/mshta-attack-technique/ + - https://lolbas-project.github.io/lolbas/Binaries/Rundll32/ + - https://learn.microsoft.com/en-us/windows/win32/api/mshtml/nf-mshtml-mshtml_runhtmlapplication drilldown_searches: - - name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$src$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The host $src$ executed $process_name$ with potential obfuscated logic and initiated a network connection to $dest_hostname$ / $dest$ over $dest_port$. - risk_objects: - - field: src - type: system - score: 40 - threat_objects: - - field: process_name - type: process_name + message: The host $src$ executed $process_name$ with potential obfuscated logic and initiated a network connection to $dest_hostname$ / $dest$ over $dest_port$. + risk_objects: + - field: src + type: system + score: 40 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Cisco Network Visibility Module Analytics - asset_type: Endpoint - mitre_attack_id: - - T1218.005 - - T1059.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: endpoint + analytic_story: + - Cisco Network Visibility Module Analytics + asset_type: Endpoint + mitre_attack_id: + - T1218.005 + - T1059.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: endpoint tests: - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/cisco_nvm___non_network_binary_making_network_connection.yml b/detections/endpoint/cisco_nvm___non_network_binary_making_network_connection.yml index ff85219298..1e1fd004d7 100644 --- a/detections/endpoint/cisco_nvm___non_network_binary_making_network_connection.yml +++ b/detections/endpoint/cisco_nvm___non_network_binary_making_network_connection.yml @@ -6,95 +6,90 @@ author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - This analytic detects network connections initiated by binaries that are not typically associated with network communication, - such as 'notepad.exe', 'calc.exe' or 'write.exe'. - It leverages Cisco Network Visibility Module logs to correlate network flow activity with process context, including command-line arguments, process path, and parent process information. - These applications are normally used for locally and do not require outbound network access. When they do initiate such connections, it may indicate process hollowing, code injection, or proxy execution, where adversaries abuse a trusted process to mask malicious activity. + This analytic detects network connections initiated by binaries that are not typically associated with network communication, + such as 'notepad.exe', 'calc.exe' or 'write.exe'. + It leverages Cisco Network Visibility Module logs to correlate network flow activity with process context, including command-line arguments, process path, and parent process information. + These applications are normally used for locally and do not require outbound network access. When they do initiate such connections, it may indicate process hollowing, code injection, or proxy execution, where adversaries abuse a trusted process to mask malicious activity. data_source: - - Cisco Network Visibility Module Flow Data + - Cisco Network Visibility Module Flow Data search: | - `cisco_network_visibility_module_flowdata` - process_name IN ( - "notepad.exe", "write.exe", "mspaint.exe", "calc.exe", - "addinutil.exe", "cmstp.exe", "dialer.exe", "eqnedt32.exe", "IMEWDBLD.exe" - ) - NOT dest IN ( - "10.0.0.0/8", "172.16.0.0/12", "192.168.0.0/16", "100.64.0.0/10", - "127.0.0.0/8", "169.254.0.0/16", "192.0.0.0/24", "192.0.0.0/29", "192.0.0.8/32", - "192.0.0.9/32", "192.0.0.10/32", "192.0.0.170/32", "192.0.0.171/32", "192.0.2.0/24", - "192.31.196.0/24", "192.52.193.0/24", "192.88.99.0/24", "224.0.0.0/4", "192.175.48.0/24", - "198.18.0.0/15", "198.51.100.0/24", "203.0.113.0/24", "240.0.0.0/4", "::1" - ) - | stats count min(_time) as firstTime max(_time) as lastTime - values(parent_process_arguments) as parent_process_arguments - values(process_arguments) as process_arguments - values(parent_process_hash) as parent_process_hash - values(process_hash) as process_hash - values(module_name_list) as module_name_list - values(module_hash_list) as module_hash_list - values(dest_port) as dest_port - values(aliul) as additional_logged_in_users_list - values(dest_hostname) as dest_hostname - by src dest parent_process_path parent_process_integrity_level process_path process_name process_integrity_level process_id transport - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | table - parent_process_integrity_level parent_process_path parent_process_arguments parent_process_hash - process_integrity_level process_path process_name process_arguments process_hash process_id - additional_logged_in_users_list module_name_list module_hash_list - src dest_hostname dest dest_port transport firstTime lastTime - | `cisco_nvm___non_network_binary_making_network_connection_filter` + `cisco_network_visibility_module_flowdata` + process_name IN ( + "notepad.exe", "write.exe", "mspaint.exe", "calc.exe", + "addinutil.exe", "cmstp.exe", "dialer.exe", "eqnedt32.exe", "IMEWDBLD.exe" + ) + NOT dest IN ( + "10.0.0.0/8", "172.16.0.0/12", "192.168.0.0/16", "100.64.0.0/10", + "127.0.0.0/8", "169.254.0.0/16", "192.0.0.0/24", "192.0.0.0/29", "192.0.0.8/32", + "192.0.0.9/32", "192.0.0.10/32", "192.0.0.170/32", "192.0.0.171/32", "192.0.2.0/24", + "192.31.196.0/24", "192.52.193.0/24", "192.88.99.0/24", "224.0.0.0/4", "192.175.48.0/24", + "198.18.0.0/15", "198.51.100.0/24", "203.0.113.0/24", "240.0.0.0/4", "::1" + ) + | stats count min(_time) as firstTime max(_time) as lastTime + values(parent_process_arguments) as parent_process_arguments + values(process_arguments) as process_arguments + values(parent_process_hash) as parent_process_hash + values(process_hash) as process_hash + values(module_name_list) as module_name_list + values(module_hash_list) as module_hash_list + values(dest_port) as dest_port + values(aliul) as additional_logged_in_users_list + values(dest_hostname) as dest_hostname + by src dest parent_process_path parent_process_integrity_level process_path process_name process_integrity_level process_id transport + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table + parent_process_integrity_level parent_process_path parent_process_arguments parent_process_hash + process_integrity_level process_path process_name process_arguments process_hash process_id + additional_logged_in_users_list module_name_list module_hash_list + src dest_hostname dest dest_port transport firstTime lastTime + | `cisco_nvm___non_network_binary_making_network_connection_filter` how_to_implement: | - This search requires Network Visibility Module logs, which includes the flow data sourcetype. - This search uses an input macro named `cisco_network_visibility_module_flowdata`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. - Replace the macro definition with configurations for your Splunk environment. - The search also uses a post-filter macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). + This search requires Network Visibility Module logs, which includes the flow data sourcetype. + This search uses an input macro named `cisco_network_visibility_module_flowdata`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. + Replace the macro definition with configurations for your Splunk environment. + The search also uses a post-filter macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). known_false_positives: | - Rare cases may exist where these binaries are used by plugins or third-party extensions to initiate outbound communication. - However, such behavior is extremely uncommon and should be investigated for potential injection or abuse. + Rare cases may exist where these binaries are used by plugins or third-party extensions to initiate outbound communication. + However, such behavior is extremely uncommon and should be investigated for potential injection or abuse. references: - - https://redcanary.com/threat-detection-report/techniques/process-injection/ - - https://www.blue-prints.blog/content/blog/posts/lolbin/addinutil-lolbas.html + - https://redcanary.com/threat-detection-report/techniques/process-injection/ + - https://www.blue-prints.blog/content/blog/posts/lolbin/addinutil-lolbas.html drilldown_searches: - - name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$src$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The host $src$ observed $process_path$ initiating a network connection to $dest$ over port $dest_port$, which is highly unusual - risk_objects: - - field: src - type: system - score: 40 - threat_objects: - - field: process_name - type: process_name + message: The host $src$ observed $process_path$ initiating a network connection to $dest$ over port $dest_port$, which is highly unusual + risk_objects: + - field: src + type: system + score: 40 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Cisco Network Visibility Module Analytics - asset_type: Endpoint - mitre_attack_id: - - T1055 - - T1036 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: endpoint + analytic_story: + - Cisco Network Visibility Module Analytics + asset_type: Endpoint + mitre_attack_id: + - T1055 + - T1036 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: endpoint tests: - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/cisco_nvm___outbound_connection_to_suspicious_port.yml b/detections/endpoint/cisco_nvm___outbound_connection_to_suspicious_port.yml index 86e61a3607..1478ee77a8 100644 --- a/detections/endpoint/cisco_nvm___outbound_connection_to_suspicious_port.yml +++ b/detections/endpoint/cisco_nvm___outbound_connection_to_suspicious_port.yml @@ -6,92 +6,87 @@ author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - The following analytic detects any outbound network connection from an endpoint process to a known suspicious or non-standard port. - It leverages Cisco Network Visibility Module flow data logs to identify potentially suspicious behavior by looking at processes - communicating over ports like 4444, 2222, or 51820 are commonly used by tools like Metasploit, SliverC2 or other pentest, red team or malware. - These connections are worth investigating further, especially when initiated by unexpected or non-network-native binaries. + The following analytic detects any outbound network connection from an endpoint process to a known suspicious or non-standard port. + It leverages Cisco Network Visibility Module flow data logs to identify potentially suspicious behavior by looking at processes + communicating over ports like 4444, 2222, or 51820 are commonly used by tools like Metasploit, SliverC2 or other pentest, red team or malware. + These connections are worth investigating further, especially when initiated by unexpected or non-network-native binaries. data_source: - - Cisco Network Visibility Module Flow Data + - Cisco Network Visibility Module Flow Data search: | - `cisco_network_visibility_module_flowdata` - NOT dest IN ( - "10.0.0.0/8", "172.16.0.0/12", "192.168.0.0/16", "100.64.0.0/10", - "127.0.0.0/8", "169.254.0.0/16", "192.0.0.0/24", "192.0.0.0/29", "192.0.0.8/32", - "192.0.0.9/32", "192.0.0.10/32", "192.0.0.170/32", "192.0.0.171/32", "192.0.2.0/24", - "192.31.196.0/24", "192.52.193.0/24", "192.88.99.0/24", "224.0.0.0/4", "192.175.48.0/24", - "198.18.0.0/15", "198.51.100.0/24", "203.0.113.0/24", "240.0.0.0/4", "::1" - ) - | stats count min(_time) as firstTime max(_time) as lastTime - values(parent_process_arguments) as parent_process_arguments - values(process_arguments) as process_arguments - values(parent_process_hash) as parent_process_hash - values(process_hash) as process_hash - values(module_name_list) as module_name_list - values(module_hash_list) as module_hash_list - values(dest_port) as dest_port - values(aliul) as additional_logged_in_users_list - values(dest_hostname) as dest_hostname - by src dest parent_process_path parent_process_integrity_level process_path process_name process_integrity_level process_id transport - | lookup suspicious_ports_list dest_port OUTPUTNEW comment as dest_port_metadata confidence as dest_confidence category as dest_port_category - | where isnotnull(dest_port_metadata) - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | table - parent_process_integrity_level parent_process_path parent_process_arguments parent_process_hash - process_integrity_level process_path process_name process_arguments process_hash process_id - additional_logged_in_users_list module_name_list module_hash_list - src dest_hostname dest dest_port transport firstTime lastTime - | `cisco_nvm___outbound_connection_to_suspicious_port_filter` + `cisco_network_visibility_module_flowdata` + NOT dest IN ( + "10.0.0.0/8", "172.16.0.0/12", "192.168.0.0/16", "100.64.0.0/10", + "127.0.0.0/8", "169.254.0.0/16", "192.0.0.0/24", "192.0.0.0/29", "192.0.0.8/32", + "192.0.0.9/32", "192.0.0.10/32", "192.0.0.170/32", "192.0.0.171/32", "192.0.2.0/24", + "192.31.196.0/24", "192.52.193.0/24", "192.88.99.0/24", "224.0.0.0/4", "192.175.48.0/24", + "198.18.0.0/15", "198.51.100.0/24", "203.0.113.0/24", "240.0.0.0/4", "::1" + ) + | stats count min(_time) as firstTime max(_time) as lastTime + values(parent_process_arguments) as parent_process_arguments + values(process_arguments) as process_arguments + values(parent_process_hash) as parent_process_hash + values(process_hash) as process_hash + values(module_name_list) as module_name_list + values(module_hash_list) as module_hash_list + values(dest_port) as dest_port + values(aliul) as additional_logged_in_users_list + values(dest_hostname) as dest_hostname + by src dest parent_process_path parent_process_integrity_level process_path process_name process_integrity_level process_id transport + | lookup suspicious_ports_list dest_port OUTPUTNEW comment as dest_port_metadata confidence as dest_confidence category as dest_port_category + | where isnotnull(dest_port_metadata) + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table + parent_process_integrity_level parent_process_path parent_process_arguments parent_process_hash + process_integrity_level process_path process_name process_arguments process_hash process_id + additional_logged_in_users_list module_name_list module_hash_list + src dest_hostname dest dest_port transport firstTime lastTime + | `cisco_nvm___outbound_connection_to_suspicious_port_filter` how_to_implement: | - This search requires Network Visibility Module logs, which includes the flow data sourcetype. - This search uses an input macro named `cisco_network_visibility_module_flowdata`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. - Replace the macro definition with configurations for your Splunk environment. - The search also uses a post-filter macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). + This search requires Network Visibility Module logs, which includes the flow data sourcetype. + This search uses an input macro named `cisco_network_visibility_module_flowdata`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. + Replace the macro definition with configurations for your Splunk environment. + The search also uses a post-filter macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). known_false_positives: | - Some legitimate applications may use high or non-standard ports, such as alternate SSH daemons or development tools. - However, many of these ports are commonly used by threat actors for reverse shells or C2 communications. - Review the associated process and command-line context to determine intent. + Some legitimate applications may use high or non-standard ports, such as alternate SSH daemons or development tools. + However, many of these ports are commonly used by threat actors for reverse shells or C2 communications. + Review the associated process and command-line context to determine intent. references: - - https://mthcht.medium.com/hunting-for-suspicious-ports-activities-50ef56d5cef + - https://mthcht.medium.com/hunting-for-suspicious-ports-activities-50ef56d5cef drilldown_searches: - - name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$src$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The host $src$ established an outbound network connection via the process $process_path$ with the commandline arguments $process_arguments$ to $dest$ over suspicious port $dest_port$. - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: - - field: process_name - type: process_name + message: The host $src$ established an outbound network connection via the process $process_path$ with the commandline arguments $process_arguments$ to $dest$ over suspicious port $dest_port$. + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Cisco Network Visibility Module Analytics - asset_type: Endpoint - mitre_attack_id: - - T1571 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: endpoint + analytic_story: + - Cisco Network Visibility Module Analytics + asset_type: Endpoint + mitre_attack_id: + - T1571 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: endpoint tests: - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/cisco_nvm___rclone_execution_with_network_activity.yml b/detections/endpoint/cisco_nvm___rclone_execution_with_network_activity.yml index babc267a82..6a636c00eb 100644 --- a/detections/endpoint/cisco_nvm___rclone_execution_with_network_activity.yml +++ b/detections/endpoint/cisco_nvm___rclone_execution_with_network_activity.yml @@ -6,103 +6,98 @@ author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - This detection identifies execution of the file synchronization utility "rclone". - It leverages Cisco Network Visibility Module logs, specifically flow data in order to capture process executions - initiating network connections. - While rclone is a legitimate command-line tool for syncing data to cloud storage providers, it has been widely abused by threat actors for data exfiltration. - This analytic inspects process name and arguments for rclone and flags usage of suspicious flags. - If matched, this could indicate malicious usage for stealthy data exfiltration or cloud abuse. + This detection identifies execution of the file synchronization utility "rclone". + It leverages Cisco Network Visibility Module logs, specifically flow data in order to capture process executions + initiating network connections. + While rclone is a legitimate command-line tool for syncing data to cloud storage providers, it has been widely abused by threat actors for data exfiltration. + This analytic inspects process name and arguments for rclone and flags usage of suspicious flags. + If matched, this could indicate malicious usage for stealthy data exfiltration or cloud abuse. data_source: - - Cisco Network Visibility Module Flow Data + - Cisco Network Visibility Module Flow Data search: | - `cisco_network_visibility_module_flowdata` - ( - process_name = "rclone.exe" - OR + `cisco_network_visibility_module_flowdata` ( - process_arguments = "* copy *" - process_arguments = "*\\\\*" - process_arguments IN ("*remote:*", "*mega:*", "*ftp:*", "*ftp1:*") + process_name = "rclone.exe" + OR + ( + process_arguments = "* copy *" + process_arguments = "*\\\\*" + process_arguments IN ("*remote:*", "*mega:*", "*ftp:*", "*ftp1:*") + ) + OR + ( + process_arguments IN ("*remote:*", "*mega:*", "*ftp:*", "*ftp1:*") + process_arguments = "*--transfers" + process_arguments = "*--ignore-existing*" + process_arguments = "*--auto-confirm*" + ) ) - OR - ( - process_arguments IN ("*remote:*", "*mega:*", "*ftp:*", "*ftp1:*") - process_arguments = "*--transfers" - process_arguments = "*--ignore-existing*" - process_arguments = "*--auto-confirm*" - ) - ) - | stats count min(_time) as firstTime max(_time) as lastTime - values(parent_process_arguments) as parent_process_arguments - values(process_arguments) as process_arguments - values(parent_process_hash) as parent_process_hash - values(process_hash) as process_hash - values(module_name_list) as module_name_list - values(module_hash_list) as module_hash_list - values(dest_port) as dest_port - values(aliul) as additional_logged_in_users_list - values(dest_hostname) as dest_hostname - by src dest parent_process_path parent_process_integrity_level process_path process_name process_integrity_level process_id transport - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | table - parent_process_integrity_level parent_process_path parent_process_arguments parent_process_hash - process_integrity_level process_path process_name process_arguments process_hash process_id - additional_logged_in_users_list module_name_list module_hash_list - src dest_hostname dest dest_port transport firstTime lastTime - | `cisco_nvm___rclone_execution_with_network_activity_filter` + | stats count min(_time) as firstTime max(_time) as lastTime + values(parent_process_arguments) as parent_process_arguments + values(process_arguments) as process_arguments + values(parent_process_hash) as parent_process_hash + values(process_hash) as process_hash + values(module_name_list) as module_name_list + values(module_hash_list) as module_hash_list + values(dest_port) as dest_port + values(aliul) as additional_logged_in_users_list + values(dest_hostname) as dest_hostname + by src dest parent_process_path parent_process_integrity_level process_path process_name process_integrity_level process_id transport + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table + parent_process_integrity_level parent_process_path parent_process_arguments parent_process_hash + process_integrity_level process_path process_name process_arguments process_hash process_id + additional_logged_in_users_list module_name_list module_hash_list + src dest_hostname dest dest_port transport firstTime lastTime + | `cisco_nvm___rclone_execution_with_network_activity_filter` how_to_implement: | - This search requires Network Visibility Module logs, which includes the flow data sourcetype. - This search uses an input macro named `cisco_network_visibility_module_flowdata`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. - Replace the macro definition with configurations for your Splunk environment. - The search also uses a post-filter macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). + This search requires Network Visibility Module logs, which includes the flow data sourcetype. + This search uses an input macro named `cisco_network_visibility_module_flowdata`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. + Replace the macro definition with configurations for your Splunk environment. + The search also uses a post-filter macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). known_false_positives: | - Rclone is used legitimately in some backup or other workflows. Tune this rule based on known-good operational usage or restrict by known user/service accounts an specific folders or remote names. + Rclone is used legitimately in some backup or other workflows. Tune this rule based on known-good operational usage or restrict by known user/service accounts an specific folders or remote names. references: - - https://thedfirreport.com/2021/03/29/sodinokibi-aka-revil-ransomware/ - - https://thedfirreport.com/2021/10/04/bazarloader-and-the-conti-leaks/ - - https://thedfirreport.com/2021/11/29/continuing-the-bazar-ransomware-story/ - - https://redcanary.com/blog/threat-detection/rclone-mega-extortion/ + - https://thedfirreport.com/2021/03/29/sodinokibi-aka-revil-ransomware/ + - https://thedfirreport.com/2021/10/04/bazarloader-and-the-conti-leaks/ + - https://thedfirreport.com/2021/11/29/continuing-the-bazar-ransomware-story/ + - https://redcanary.com/blog/threat-detection/rclone-mega-extortion/ drilldown_searches: - - name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$src$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Rclone was executed on $src$ using flags $process_arguments$ and connected to $dest_hostname$ over $dest_port$. - risk_objects: - - field: src - type: system - score: 60 - threat_objects: - - field: process_name - type: process_name + message: Rclone was executed on $src$ using flags $process_arguments$ and connected to $dest_hostname$ over $dest_port$. + risk_objects: + - field: src + type: system + score: 60 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Scattered Lapsus$ Hunters - - Cisco Network Visibility Module Analytics - asset_type: Endpoint - mitre_attack_id: - - T1567.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: endpoint + analytic_story: + - Scattered Lapsus$ Hunters + - Cisco Network Visibility Module Analytics + asset_type: Endpoint + mitre_attack_id: + - T1567.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: endpoint tests: - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/cisco_nvm___rundll32_abuse_of_mshtml_dll_for_payload_download.yml b/detections/endpoint/cisco_nvm___rundll32_abuse_of_mshtml_dll_for_payload_download.yml index 0d9401e795..b602733a9f 100644 --- a/detections/endpoint/cisco_nvm___rundll32_abuse_of_mshtml_dll_for_payload_download.yml +++ b/detections/endpoint/cisco_nvm___rundll32_abuse_of_mshtml_dll_for_payload_download.yml @@ -6,90 +6,85 @@ author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - This analytic detects suspicious use of `rundll32.exe` in combination with `mshtml.dll` and the export `RunHTMLApplication`. - This behavior is often observed in malware to execute JavaScript or VBScript in memory, enabling payload staging or - bypassing script execution policies and bypassing the usage of the "mshta.exe" binary. - The detection leverages Cisco Network Visibility Module telemetry which offers network flow activity - along with process information such as command-line arguments - If confirmed malicious, this activity may indicate initial access or payload download. + This analytic detects suspicious use of `rundll32.exe` in combination with `mshtml.dll` and the export `RunHTMLApplication`. + This behavior is often observed in malware to execute JavaScript or VBScript in memory, enabling payload staging or + bypassing script execution policies and bypassing the usage of the "mshta.exe" binary. + The detection leverages Cisco Network Visibility Module telemetry which offers network flow activity + along with process information such as command-line arguments + If confirmed malicious, this activity may indicate initial access or payload download. data_source: - - Cisco Network Visibility Module Flow Data + - Cisco Network Visibility Module Flow Data search: | - `cisco_network_visibility_module_flowdata` - process_name = "rundll32.exe" - process_arguments = "*mshtml*" - process_arguments IN ("*135*", "*RunHTMLApplication*") - | stats count min(_time) as firstTime max(_time) as lastTime - values(parent_process_arguments) as parent_process_arguments - values(process_arguments) as process_arguments - values(parent_process_hash) as parent_process_hash - values(process_hash) as process_hash - values(module_name_list) as module_name_list - values(module_hash_list) as module_hash_list - values(dest_port) as dest_port - values(aliul) as additional_logged_in_users_list - values(dest_hostname) as dest_hostname - by src dest parent_process_path parent_process_integrity_level process_path process_name process_integrity_level process_id transport - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | table - parent_process_integrity_level parent_process_path parent_process_arguments parent_process_hash - process_integrity_level process_path process_name process_arguments process_hash process_id - additional_logged_in_users_list module_name_list module_hash_list - src dest_hostname dest dest_port transport firstTime lastTime - | `cisco_nvm___rundll32_abuse_of_mshtml_dll_for_payload_download_filter` + `cisco_network_visibility_module_flowdata` + process_name = "rundll32.exe" + process_arguments = "*mshtml*" + process_arguments IN ("*135*", "*RunHTMLApplication*") + | stats count min(_time) as firstTime max(_time) as lastTime + values(parent_process_arguments) as parent_process_arguments + values(process_arguments) as process_arguments + values(parent_process_hash) as parent_process_hash + values(process_hash) as process_hash + values(module_name_list) as module_name_list + values(module_hash_list) as module_hash_list + values(dest_port) as dest_port + values(aliul) as additional_logged_in_users_list + values(dest_hostname) as dest_hostname + by src dest parent_process_path parent_process_integrity_level process_path process_name process_integrity_level process_id transport + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table + parent_process_integrity_level parent_process_path parent_process_arguments parent_process_hash + process_integrity_level process_path process_name process_arguments process_hash process_id + additional_logged_in_users_list module_name_list module_hash_list + src dest_hostname dest dest_port transport firstTime lastTime + | `cisco_nvm___rundll32_abuse_of_mshtml_dll_for_payload_download_filter` how_to_implement: | - This search requires Network Visibility Module logs, which includes the flow data sourcetype. - This search uses an input macro named `cisco_network_visibility_module_flowdata`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. - Replace the macro definition with configurations for your Splunk environment. - The search also uses a post-filter macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). + This search requires Network Visibility Module logs, which includes the flow data sourcetype. + This search uses an input macro named `cisco_network_visibility_module_flowdata`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. + Replace the macro definition with configurations for your Splunk environment. + The search also uses a post-filter macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). known_false_positives: | - `rundll32.exe` using `mshtml.dll` is rare in legitimate environments. However, edge cases might exist. Tuning may be needed in environments with custom automation scripts. + `rundll32.exe` using `mshtml.dll` is rare in legitimate environments. However, edge cases might exist. Tuning may be needed in environments with custom automation scripts. references: - - https://lolbas-project.github.io/lolbas/Binaries/Rundll32/ - - https://redcanary.com/blog/threat-detection/threat-research-questions/ - - https://twitter.com/n1nj4sec/status/1421190238081277959 - - https://hyp3rlinx.altervista.org/advisories/MICROSOFT_WINDOWS_DEFENDER_TROJAN.WIN32.POWESSERE.G_MITIGATION_BYPASS_PART2.txt - - http://hyp3rlinx.altervista.org/advisories/MICROSOFT_WINDOWS_DEFENDER_DETECTION_BYPASS.txt + - https://lolbas-project.github.io/lolbas/Binaries/Rundll32/ + - https://redcanary.com/blog/threat-detection/threat-research-questions/ + - https://twitter.com/n1nj4sec/status/1421190238081277959 + - https://hyp3rlinx.altervista.org/advisories/MICROSOFT_WINDOWS_DEFENDER_TROJAN.WIN32.POWESSERE.G_MITIGATION_BYPASS_PART2.txt + - http://hyp3rlinx.altervista.org/advisories/MICROSOFT_WINDOWS_DEFENDER_DETECTION_BYPASS.txt drilldown_searches: - - name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$src$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $process_path$ was executed on $src$ leveraging the mshtml.dll and the RunHTMLApplication export to download a potentially suspicious file from $dest_hostname$. - risk_objects: - - field: src - type: system - score: 40 - threat_objects: - - field: process_name - type: process_name + message: $process_path$ was executed on $src$ leveraging the mshtml.dll and the RunHTMLApplication export to download a potentially suspicious file from $dest_hostname$. + risk_objects: + - field: src + type: system + score: 40 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Cisco Network Visibility Module Analytics - asset_type: Endpoint - mitre_attack_id: - - T1218.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: endpoint + analytic_story: + - Cisco Network Visibility Module Analytics + asset_type: Endpoint + mitre_attack_id: + - T1218.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: endpoint tests: - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/cisco_nvm___susp_script_from_archive_triggering_network_activity.yml b/detections/endpoint/cisco_nvm___susp_script_from_archive_triggering_network_activity.yml index 280763f4c4..dfd5e754cf 100644 --- a/detections/endpoint/cisco_nvm___susp_script_from_archive_triggering_network_activity.yml +++ b/detections/endpoint/cisco_nvm___susp_script_from_archive_triggering_network_activity.yml @@ -6,91 +6,84 @@ author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - This analytic detects script execution (`wscript.exe` or `cscript.exe`) triggered from compressed files opened directly using - `explorer.exe`, `winrar.exe`, or `7zFM.exe`. - When a user double clicks on a ".js" file from within one of these compressed files. Its extracted temporally in the temp directory in folder with certain markers. - It leverages Cisco Network Visibility Module (NVM) flow data, in order to look for a specific parent/child relationship and an initiated network connection. - This behavior is exploited by threat actors such as Scarlet Goldfinch to deliver and run malicious scripts as an initial access technique. + This analytic detects script execution (`wscript.exe` or `cscript.exe`) triggered from compressed files opened directly using + `explorer.exe`, `winrar.exe`, or `7zFM.exe`. + When a user double clicks on a ".js" file from within one of these compressed files. Its extracted temporally in the temp directory in folder with certain markers. + It leverages Cisco Network Visibility Module (NVM) flow data, in order to look for a specific parent/child relationship and an initiated network connection. + This behavior is exploited by threat actors such as Scarlet Goldfinch to deliver and run malicious scripts as an initial access technique. data_source: - - Cisco Network Visibility Module Flow Data + - Cisco Network Visibility Module Flow Data search: | - `cisco_network_visibility_module_flowdata` - parent_process_name IN ("explorer.exe", "winrar.exe", "7zFM.exe") - process_name IN ("wscript.exe", "cscript.exe") - process_arguments = "*\\AppData\\Local\\Temp\\*" - process_arguments IN ("*\\rar*", "*\\7z*", "*.zip*") - | stats count min(_time) as firstTime max(_time) as lastTime - values(parent_process_arguments) as parent_process_arguments - values(process_arguments) as process_arguments - values(parent_process_hash) as parent_process_hash - values(process_hash) as process_hash - values(module_name_list) as module_name_list - values(module_hash_list) as module_hash_list - values(dest_port) as dest_port - values(aliul) as additional_logged_in_users_list - values(dest_hostname) as dest_hostname - by src dest parent_process_path parent_process_name parent_process_integrity_level process_path process_name process_integrity_level process_id transport - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | table - parent_process_integrity_level parent_process_name parent_process_path parent_process_arguments parent_process_hash - process_integrity_level process_path process_name process_arguments process_hash process_id - additional_logged_in_users_list module_name_list module_hash_list - src dest_hostname dest dest_port transport firstTime lastTime - | `cisco_nvm___susp_script_from_archive_triggering_network_activity_filter` + `cisco_network_visibility_module_flowdata` + parent_process_name IN ("explorer.exe", "winrar.exe", "7zFM.exe") + process_name IN ("wscript.exe", "cscript.exe") + process_arguments = "*\\AppData\\Local\\Temp\\*" + process_arguments IN ("*\\rar*", "*\\7z*", "*.zip*") + | stats count min(_time) as firstTime max(_time) as lastTime + values(parent_process_arguments) as parent_process_arguments + values(process_arguments) as process_arguments + values(parent_process_hash) as parent_process_hash + values(process_hash) as process_hash + values(module_name_list) as module_name_list + values(module_hash_list) as module_hash_list + values(dest_port) as dest_port + values(aliul) as additional_logged_in_users_list + values(dest_hostname) as dest_hostname + by src dest parent_process_path parent_process_name parent_process_integrity_level process_path process_name process_integrity_level process_id transport + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table + parent_process_integrity_level parent_process_name parent_process_path parent_process_arguments parent_process_hash + process_integrity_level process_path process_name process_arguments process_hash process_id + additional_logged_in_users_list module_name_list module_hash_list + src dest_hostname dest dest_port transport firstTime lastTime + | `cisco_nvm___susp_script_from_archive_triggering_network_activity_filter` how_to_implement: | - This search requires Network Visibility Module logs, which includes the flow data sourcetype. - This search uses an input macro named `cisco_network_visibility_module_flowdata`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. - Replace the macro definition with configurations for your Splunk environment. - The search also uses a post-filter macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). + This search requires Network Visibility Module logs, which includes the flow data sourcetype. + This search uses an input macro named `cisco_network_visibility_module_flowdata`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. + Replace the macro definition with configurations for your Splunk environment. + The search also uses a post-filter macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). known_false_positives: | - Some software installers or automation scripts may extract and run scripts from archive files in temporary directories. - However, it is uncommon for such scripts to initiate outbound network connections immediately upon extraction. - This behavior should be considered suspicious and investigated, especially in environments where such scripting is not typical. + Some software installers or automation scripts may extract and run scripts from archive files in temporary directories. + However, it is uncommon for such scripts to initiate outbound network connections immediately upon extraction. + This behavior should be considered suspicious and investigated, especially in environments where such scripting is not typical. references: - - https://redcanary.com/threat-detection-report/threats/scarlet-goldfinch/ + - https://redcanary.com/threat-detection-report/threats/scarlet-goldfinch/ drilldown_searches: - - name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$src$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - $process_path$ running from $parent_process_name$ with archive-related execution in Temp was observed from host $src$ - performing network a connection towards $dest$ / $dest_hostname$ over port $dest_port$. - risk_objects: - - field: src - type: system - score: 40 - threat_objects: - - field: process_name - type: process_name + message: $process_path$ running from $parent_process_name$ with archive-related execution in Temp was observed from host $src$ performing network a connection towards $dest$ / $dest_hostname$ over port $dest_port$. + risk_objects: + - field: src + type: system + score: 40 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Cisco Network Visibility Module Analytics - asset_type: Endpoint - mitre_attack_id: - - T1059.005 - - T1204.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: endpoint + analytic_story: + - Cisco Network Visibility Module Analytics + asset_type: Endpoint + mitre_attack_id: + - T1059.005 + - T1204.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: endpoint tests: - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/cisco_nvm___suspicious_download_from_file_sharing_website.yml b/detections/endpoint/cisco_nvm___suspicious_download_from_file_sharing_website.yml index fd1a19227c..cfbc8a958c 100644 --- a/detections/endpoint/cisco_nvm___suspicious_download_from_file_sharing_website.yml +++ b/detections/endpoint/cisco_nvm___suspicious_download_from_file_sharing_website.yml @@ -6,109 +6,104 @@ author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - This analytic detects suspicious downloads from common file sharing and content delivery platforms using known living-off-the-land binaries (LOLBins) - such as 'curl.exe', 'certutil.exe', 'msiexec.exe', 'powershell.exe', 'wmic.exe', and others. - It leverages Cisco Network Visibility Module logs to correlate network flow activity with process context, including command-line arguments, process path, - and parent process information. These tools are often abused by adversaries and malware to retrieve payloads from public hosting platforms - such as GitHub, Discord CDN, Transfer.sh, or Pastebin. - This detection helps identify potential initial access, payload staging, or command and control activity using legitimate services. + This analytic detects suspicious downloads from common file sharing and content delivery platforms using known living-off-the-land binaries (LOLBins) + such as 'curl.exe', 'certutil.exe', 'msiexec.exe', 'powershell.exe', 'wmic.exe', and others. + It leverages Cisco Network Visibility Module logs to correlate network flow activity with process context, including command-line arguments, process path, + and parent process information. These tools are often abused by adversaries and malware to retrieve payloads from public hosting platforms + such as GitHub, Discord CDN, Transfer.sh, or Pastebin. + This detection helps identify potential initial access, payload staging, or command and control activity using legitimate services. data_source: - - Cisco Network Visibility Module Flow Data + - Cisco Network Visibility Module Flow Data search: | - `cisco_network_visibility_module_flowdata` - ( - (process_name = "svchost.exe" process_arguments = "*-s BITS*") - OR - process_name IN ( - "curl.exe", "wmic.exe", "wscript.exe", "cscript.exe", "certutil.exe", - "msiexec.exe", "hh.exe", "powershell.exe", "pwsh.exe", "powershell_ise.exe", - "installutil.exe", "certoc.exe", "bitsadmin.exe" + `cisco_network_visibility_module_flowdata` + ( + (process_name = "svchost.exe" process_arguments = "*-s BITS*") + OR + process_name IN ( + "curl.exe", "wmic.exe", "wscript.exe", "cscript.exe", "certutil.exe", + "msiexec.exe", "hh.exe", "powershell.exe", "pwsh.exe", "powershell_ise.exe", + "installutil.exe", "certoc.exe", "bitsadmin.exe" + ) ) - ) - dest_hostname IN ( - "*.githubusercontent.com*", "*anonfiles.com*", "*cdn.discordapp.com*", "*ddns.net*", - "*dl.dropboxusercontent.com*", "*ghostbin.co*", "*glitch.me*", "*gofile.io*", - "*hastebin.com*", "*mediafire.com*", "*mega.nz*", "*onrender.com*", "*pages.dev*", - "*paste.ee*", "*pastebin.*", "*pastetext.net*", "*privatlab.*", - "*send.exploit.in*", "*sendspace.com*", "*storage.googleapis.com*", - "*storjshare.io*", "*supabase.co*", "*temp.sh*", "*transfer.sh*", "*trycloudflare.com*", - "*ufile.io*", "*w3spaces.com*", "*workers.dev*" - ) - | stats count min(_time) as firstTime max(_time) as lastTime - values(parent_process_arguments) as parent_process_arguments - values(process_arguments) as process_arguments - values(parent_process_hash) as parent_process_hash - values(process_hash) as process_hash - values(module_name_list) as module_name_list - values(module_hash_list) as module_hash_list - values(dest_port) as dest_port - values(aliul) as additional_logged_in_users_list - values(dest_hostname) as dest_hostname - by src dest parent_process_path parent_process_integrity_level process_path process_name process_integrity_level process_id transport - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | table - parent_process_integrity_level parent_process_path parent_process_arguments parent_process_hash - process_integrity_level process_path process_name process_arguments process_hash process_id - additional_logged_in_users_list module_name_list module_hash_list - src dest_hostname dest dest_port transport firstTime lastTime - | `cisco_nvm___suspicious_download_from_file_sharing_website_filter` + dest_hostname IN ( + "*.githubusercontent.com*", "*anonfiles.com*", "*cdn.discordapp.com*", "*ddns.net*", + "*dl.dropboxusercontent.com*", "*ghostbin.co*", "*glitch.me*", "*gofile.io*", + "*hastebin.com*", "*mediafire.com*", "*mega.nz*", "*onrender.com*", "*pages.dev*", + "*paste.ee*", "*pastebin.*", "*pastetext.net*", "*privatlab.*", + "*send.exploit.in*", "*sendspace.com*", "*storage.googleapis.com*", + "*storjshare.io*", "*supabase.co*", "*temp.sh*", "*transfer.sh*", "*trycloudflare.com*", + "*ufile.io*", "*w3spaces.com*", "*workers.dev*" + ) + | stats count min(_time) as firstTime max(_time) as lastTime + values(parent_process_arguments) as parent_process_arguments + values(process_arguments) as process_arguments + values(parent_process_hash) as parent_process_hash + values(process_hash) as process_hash + values(module_name_list) as module_name_list + values(module_hash_list) as module_hash_list + values(dest_port) as dest_port + values(aliul) as additional_logged_in_users_list + values(dest_hostname) as dest_hostname + by src dest parent_process_path parent_process_integrity_level process_path process_name process_integrity_level process_id transport + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table + parent_process_integrity_level parent_process_path parent_process_arguments parent_process_hash + process_integrity_level process_path process_name process_arguments process_hash process_id + additional_logged_in_users_list module_name_list module_hash_list + src dest_hostname dest dest_port transport firstTime lastTime + | `cisco_nvm___suspicious_download_from_file_sharing_website_filter` how_to_implement: | - This search requires Network Visibility Module logs, which includes the flow data sourcetype. - This search uses an input macro named `cisco_network_visibility_module_flowdata`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. - Replace the macro definition with configurations for your Splunk environment. - The search also uses a post-filter macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). + This search requires Network Visibility Module logs, which includes the flow data sourcetype. + This search uses an input macro named `cisco_network_visibility_module_flowdata`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. + Replace the macro definition with configurations for your Splunk environment. + The search also uses a post-filter macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). known_false_positives: | - Some system administrators or development teams may use tools like curl or PowerShell to download files from public services - for legitimate automation or scripting purposes. However, use of these binaries to contact domains commonly associated with file sharing or temporary hosting - should be carefully reviewed, as such services are frequently abused by threat actors for malware delivery and staging. - Tuning by domain allowlisting or internal usage policies is recommended. + Some system administrators or development teams may use tools like curl or PowerShell to download files from public services + for legitimate automation or scripting purposes. However, use of these binaries to contact domains commonly associated with file sharing or temporary hosting + should be carefully reviewed, as such services are frequently abused by threat actors for malware delivery and staging. + Tuning by domain allowlisting or internal usage policies is recommended. references: - - https://twitter.com/jhencinski/status/1102695118455349248 - - https://isc.sans.edu/forums/diary/Investigating+Microsoft+BITS+Activity/23281/ - - https://www.virustotal.com/gui/domain/paste.ee/relations - - https://www.cisa.gov/uscert/ncas/alerts/aa22-321a - - https://www.microsoft.com/en-us/security/blog/2024/01/17/new-ttps-observed-in-mint-sandstorm-campaign-targeting-high-profile-individuals-at-universities-and-research-orgs/ + - https://twitter.com/jhencinski/status/1102695118455349248 + - https://isc.sans.edu/forums/diary/Investigating+Microsoft+BITS+Activity/23281/ + - https://www.virustotal.com/gui/domain/paste.ee/relations + - https://www.cisa.gov/uscert/ncas/alerts/aa22-321a + - https://www.microsoft.com/en-us/security/blog/2024/01/17/new-ttps-observed-in-mint-sandstorm-campaign-targeting-high-profile-individuals-at-universities-and-research-orgs/ drilldown_searches: - - name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$src$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The host $src$ used $process_path$ to download content from the file-sharing domain $dest_hostname$ over port $dest_port$ - risk_objects: - - field: src - type: system - score: 30 - threat_objects: - - field: process_name - type: process_name + message: The host $src$ used $process_path$ to download content from the file-sharing domain $dest_hostname$ over port $dest_port$ + risk_objects: + - field: src + type: system + score: 30 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - APT37 Rustonotto and FadeStealer - - Cisco Network Visibility Module Analytics - asset_type: Endpoint - mitre_attack_id: - - T1197 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: endpoint + analytic_story: + - APT37 Rustonotto and FadeStealer + - Cisco Network Visibility Module Analytics + asset_type: Endpoint + mitre_attack_id: + - T1197 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: endpoint tests: - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/cisco_nvm___suspicious_file_download_via_headless_browser.yml b/detections/endpoint/cisco_nvm___suspicious_file_download_via_headless_browser.yml index 87caf2a5a9..da017c2631 100644 --- a/detections/endpoint/cisco_nvm___suspicious_file_download_via_headless_browser.yml +++ b/detections/endpoint/cisco_nvm___suspicious_file_download_via_headless_browser.yml @@ -6,129 +6,122 @@ author: Nasreddine Bencherchali, Splunk status: production type: TTP description: | - This analytic identifies the use of Chromium-based browsers (like Microsoft Edge) running in headless mode with the `--dump-dom` argument. - This behavior has been observed in attack campaigns such as DUCKTAIL, where browsers are automated to stealthily download content from the internet using direct URLs or suspicious hosting platforms. - The detection focuses on identifying connections to known file-sharing domains or direct IPs extracted from command-line arguments and cross-checks those against the destination of the flow. - Since it leverages Cisco Network Visibility Module telemetry, the rule triggers only if a network connection is made. + This analytic identifies the use of Chromium-based browsers (like Microsoft Edge) running in headless mode with the `--dump-dom` argument. + This behavior has been observed in attack campaigns such as DUCKTAIL, where browsers are automated to stealthily download content from the internet using direct URLs or suspicious hosting platforms. + The detection focuses on identifying connections to known file-sharing domains or direct IPs extracted from command-line arguments and cross-checks those against the destination of the flow. + Since it leverages Cisco Network Visibility Module telemetry, the rule triggers only if a network connection is made. data_source: - - Cisco Network Visibility Module Flow Data + - Cisco Network Visibility Module Flow Data search: | - `cisco_network_visibility_module_flowdata` + `cisco_network_visibility_module_flowdata` - ``` Usually the initiator of the connection is the child process, meaning the parent will contain the suspicious command.``` + ``` Usually the initiator of the connection is the child process, meaning the parent will contain the suspicious command.``` - ( - parent_process_name IN ("brave.exe", "chrome.exe", "msedge.exe", "opera.exe", "vivaldi.exe") - OR - process_name IN ("brave.exe", "chrome.exe", "msedge.exe", "opera.exe", "vivaldi.exe") - ) - ( - (parent_process_arguments="*--headless*" parent_process_arguments="*--dump-dom*") - OR - (process_arguments="*--headless*" process_arguments="*--dump-dom*") - ) - NOT dest IN ( - "10.0.0.0/8", "172.16.0.0/12", "192.168.0.0/16", "100.64.0.0/10", - "127.0.0.0/8", "169.254.0.0/16", "192.0.0.0/24", "192.0.0.0/29", "192.0.0.8/32", - "192.0.0.9/32", "192.0.0.10/32", "192.0.0.170/32", "192.0.0.171/32", "192.0.2.0/24", - "192.31.196.0/24", "192.52.193.0/24", "192.88.99.0/24", "224.0.0.0/4", "192.175.48.0/24", - "198.18.0.0/15", "198.51.100.0/24", "203.0.113.0/24", "240.0.0.0/4", "::1" - ) + ( + parent_process_name IN ("brave.exe", "chrome.exe", "msedge.exe", "opera.exe", "vivaldi.exe") + OR + process_name IN ("brave.exe", "chrome.exe", "msedge.exe", "opera.exe", "vivaldi.exe") + ) + ( + (parent_process_arguments="*--headless*" parent_process_arguments="*--dump-dom*") + OR + (process_arguments="*--headless*" process_arguments="*--dump-dom*") + ) + NOT dest IN ( + "10.0.0.0/8", "172.16.0.0/12", "192.168.0.0/16", "100.64.0.0/10", + "127.0.0.0/8", "169.254.0.0/16", "192.0.0.0/24", "192.0.0.0/29", "192.0.0.8/32", + "192.0.0.9/32", "192.0.0.10/32", "192.0.0.170/32", "192.0.0.171/32", "192.0.2.0/24", + "192.31.196.0/24", "192.52.193.0/24", "192.88.99.0/24", "224.0.0.0/4", "192.175.48.0/24", + "198.18.0.0/15", "198.51.100.0/24", "203.0.113.0/24", "240.0.0.0/4", "::1" + ) - ``` In order to avoid matching with any public IP, we extract the IP value from the CommandLine and filter on it``` + ``` In order to avoid matching with any public IP, we extract the IP value from the CommandLine and filter on it``` - | rex field=parent_process_arguments "(?i)\\b(?:https?|ftp)://(?(?:\\d{1,3}\\.){3}\\d{1,3})" - | rex field=process_arguments "(?i)\\b(?:https?|ftp)://(?(?:\\d{1,3}\\.){3}\\d{1,3})" - | eval direct_ip_match=if(dest == extracted_ip_child, 1, if(dest == extracted_ip_parent, 1, 0)) + | rex field=parent_process_arguments "(?i)\\b(?:https?|ftp)://(?(?:\\d{1,3}\\.){3}\\d{1,3})" + | rex field=process_arguments "(?i)\\b(?:https?|ftp)://(?(?:\\d{1,3}\\.){3}\\d{1,3})" + | eval direct_ip_match=if(dest == extracted_ip_child, 1, if(dest == extracted_ip_parent, 1, 0)) - | where ( - dest_hostname IN ( - "*.githubusercontent.com*", "*anonfiles.com*", "*cdn.discordapp.com*", "*ddns.net*", - "*dl.dropboxusercontent.com*", "*ghostbin.co*", "*glitch.me*", "*gofile.io*", - "*hastebin.com*", "*mediafire.com*", "*mega.nz*", "*onrender.com*", "*pages.dev*", - "*paste.ee*", "*pastebin.*", "*pastetext.net*", "*privatlab.*", - "*send.exploit.in*", "*sendspace.com*", "*storage.googleapis.com*", - "*storjshare.io*", "*supabase.co*", "*temp.sh*", "*transfer.sh*", "*trycloudflare.com*", - "*ufile.io*", "*w3spaces.com*", "*workers.dev*" + | where ( + dest_hostname IN ( + "*.githubusercontent.com*", "*anonfiles.com*", "*cdn.discordapp.com*", "*ddns.net*", + "*dl.dropboxusercontent.com*", "*ghostbin.co*", "*glitch.me*", "*gofile.io*", + "*hastebin.com*", "*mediafire.com*", "*mega.nz*", "*onrender.com*", "*pages.dev*", + "*paste.ee*", "*pastebin.*", "*pastetext.net*", "*privatlab.*", + "*send.exploit.in*", "*sendspace.com*", "*storage.googleapis.com*", + "*storjshare.io*", "*supabase.co*", "*temp.sh*", "*transfer.sh*", "*trycloudflare.com*", + "*ufile.io*", "*w3spaces.com*", "*workers.dev*" + ) + OR direct_ip_match = 1 ) - OR direct_ip_match = 1 - ) - | stats count min(_time) as firstTime max(_time) as lastTime - values(parent_process_arguments) as parent_process_arguments - values(process_arguments) as process_arguments - values(parent_process_hash) as parent_process_hash - values(process_hash) as process_hash - values(module_name_list) as module_name_list - values(module_hash_list) as module_hash_list - values(dest_port) as dest_port - values(aliul) as additional_logged_in_users_list - values(dest_hostname) as dest_hostname - by src dest parent_process_path parent_process_name parent_process_integrity_level process_path process_name process_integrity_level process_id transport - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | table - parent_process_integrity_level parent_process_path parent_process_name parent_process_arguments parent_process_hash - process_integrity_level process_path process_name process_arguments process_hash process_id - additional_logged_in_users_list module_name_list module_hash_list - src dest_hostname dest dest_port transport firstTime lastTime - | `cisco_nvm___suspicious_file_download_via_headless_browser_filter` + | stats count min(_time) as firstTime max(_time) as lastTime + values(parent_process_arguments) as parent_process_arguments + values(process_arguments) as process_arguments + values(parent_process_hash) as parent_process_hash + values(process_hash) as process_hash + values(module_name_list) as module_name_list + values(module_hash_list) as module_hash_list + values(dest_port) as dest_port + values(aliul) as additional_logged_in_users_list + values(dest_hostname) as dest_hostname + by src dest parent_process_path parent_process_name parent_process_integrity_level process_path process_name process_integrity_level process_id transport + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table + parent_process_integrity_level parent_process_path parent_process_name parent_process_arguments parent_process_hash + process_integrity_level process_path process_name process_arguments process_hash process_id + additional_logged_in_users_list module_name_list module_hash_list + src dest_hostname dest dest_port transport firstTime lastTime + | `cisco_nvm___suspicious_file_download_via_headless_browser_filter` how_to_implement: | - This search requires Network Visibility Module logs, which includes the flow data sourcetype. - This search uses an input macro named `cisco_network_visibility_module_flowdata`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. - Replace the macro definition with configurations for your Splunk environment. - The search also uses a post-filter macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). + This search requires Network Visibility Module logs, which includes the flow data sourcetype. + This search uses an input macro named `cisco_network_visibility_module_flowdata`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. + Replace the macro definition with configurations for your Splunk environment. + The search also uses a post-filter macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). known_false_positives: | - Some internal automation frameworks may invoke Chromium browsers in headless mode to programmatically access internal services or webpages. - These tools may occasionally download legitimate resources as part of their normal behavior. - Tuning based on command-line patterns or known dest hostnames may be required to avoid noise. + Some internal automation frameworks may invoke Chromium browsers in headless mode to programmatically access internal services or webpages. + These tools may occasionally download legitimate resources as part of their normal behavior. + Tuning based on command-line patterns or known dest hostnames may be required to avoid noise. references: - - https://labs.withsecure.com/content/dam/labs/docs/WithSecure_Research_DUCKTAIL.pdf - - https://www.trendmicro.com/en_us/research/23/e/managed-xdr-investigation-of-ducktail-in-trend-micro-vision-one.html - - https://x.com/mrd0x/status/1478234484881436672?s=12 - - https://developer.chrome.com/docs/chromium/headless + - https://labs.withsecure.com/content/dam/labs/docs/WithSecure_Research_DUCKTAIL.pdf + - https://www.trendmicro.com/en_us/research/23/e/managed-xdr-investigation-of-ducktail-in-trend-micro-vision-one.html + - https://x.com/mrd0x/status/1478234484881436672?s=12 + - https://developer.chrome.com/docs/chromium/headless drilldown_searches: - - name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$src$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - Suspicious file download using the Chromium-based browser $parent_process_name$ via the commandline $process_arguments$. - Observed on host $src$ communicating with $dest$ / $dest_hostname$ - risk_objects: - - field: src - type: system - score: 40 - threat_objects: - - field: process_name - type: process_name + message: Suspicious file download using the Chromium-based browser $parent_process_name$ via the commandline $process_arguments$. Observed on host $src$ communicating with $dest$ / $dest_hostname$ + risk_objects: + - field: src + type: system + score: 40 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Cisco Network Visibility Module Analytics - asset_type: Endpoint - mitre_attack_id: - - T1105 - - T1059 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: endpoint + analytic_story: + - Cisco Network Visibility Module Analytics + asset_type: Endpoint + mitre_attack_id: + - T1105 + - T1059 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: endpoint tests: - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/cisco_nvm___suspicious_network_connection_from_process_with_no_args.yml b/detections/endpoint/cisco_nvm___suspicious_network_connection_from_process_with_no_args.yml index d0ce493ffb..0d55b56452 100644 --- a/detections/endpoint/cisco_nvm___suspicious_network_connection_from_process_with_no_args.yml +++ b/detections/endpoint/cisco_nvm___suspicious_network_connection_from_process_with_no_args.yml @@ -6,99 +6,94 @@ author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - This analytic detects system binaries that are commonly abused in process injection techniques but are observed without any command-line arguments. - It leverages Cisco Network Visibility Module (NVM) flow data and process arguments - to identify outbound connections initiated by curl where TLS checks were explicitly disabled. - Binaries such as `rundll32.exe`, `regsvr32.exe`, `dllhost.exe`, `svchost.exe`, and others are legitimate Windows processes that are often injected into by malware or post-exploitation frameworks (e.g., Cobalt Strike) to hide execution. - When these processes are seen initiating a network connection with an empty or missing command line, it can indicate - potential injection and communication with a command and control server. + This analytic detects system binaries that are commonly abused in process injection techniques but are observed without any command-line arguments. + It leverages Cisco Network Visibility Module (NVM) flow data and process arguments + to identify outbound connections initiated by curl where TLS checks were explicitly disabled. + Binaries such as `rundll32.exe`, `regsvr32.exe`, `dllhost.exe`, `svchost.exe`, and others are legitimate Windows processes that are often injected into by malware or post-exploitation frameworks (e.g., Cobalt Strike) to hide execution. + When these processes are seen initiating a network connection with an empty or missing command line, it can indicate + potential injection and communication with a command and control server. data_source: - - Cisco Network Visibility Module Flow Data + - Cisco Network Visibility Module Flow Data search: | - `cisco_network_visibility_module_flowdata` - process_name IN ( - "backgroundtaskhost.exe", "svchost.exe", "dllhost.exe", "werfault.exe", - "searchprotocolhost.exe", "wuauclt.exe", "spoolsv.exe", "rundll32.exe", - "regasm.exe", "regsvr32.exe", "regsvcs.exe" - ) - NOT process_arguments="*" - NOT dest IN ( - "10.0.0.0/8", "172.16.0.0/12", "192.168.0.0/16", "100.64.0.0/10", - "127.0.0.0/8", "169.254.0.0/16", "192.0.0.0/24", "192.0.0.0/29", "192.0.0.8/32", - "192.0.0.9/32", "192.0.0.10/32", "192.0.0.170/32", "192.0.0.171/32", "192.0.2.0/24", - "192.31.196.0/24", "192.52.193.0/24", "192.88.99.0/24", "224.0.0.0/4", "192.175.48.0/24", - "198.18.0.0/15", "198.51.100.0/24", "203.0.113.0/24", "240.0.0.0/4", "::1" - ) - | stats count min(_time) as firstTime max(_time) as lastTime - values(parent_process_arguments) as parent_process_arguments - values(process_arguments) as process_arguments - values(parent_process_hash) as parent_process_hash - values(process_hash) as process_hash - values(module_name_list) as module_name_list - values(module_hash_list) as module_hash_list - values(dest_port) as dest_port - values(aliul) as additional_logged_in_users_list - values(dest_hostname) as dest_hostname - by src dest parent_process_path parent_process_integrity_level process_path process_name process_integrity_level process_id transport - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | table - parent_process_integrity_level parent_process_path parent_process_arguments parent_process_hash - process_integrity_level process_path process_name process_arguments process_hash process_id - additional_logged_in_users_list module_name_list module_hash_list - src dest_hostname dest dest_port transport firstTime lastTime - | `cisco_nvm___suspicious_network_connection_from_process_with_no_args_filter` + `cisco_network_visibility_module_flowdata` + process_name IN ( + "backgroundtaskhost.exe", "svchost.exe", "dllhost.exe", "werfault.exe", + "searchprotocolhost.exe", "wuauclt.exe", "spoolsv.exe", "rundll32.exe", + "regasm.exe", "regsvr32.exe", "regsvcs.exe" + ) + NOT process_arguments="*" + NOT dest IN ( + "10.0.0.0/8", "172.16.0.0/12", "192.168.0.0/16", "100.64.0.0/10", + "127.0.0.0/8", "169.254.0.0/16", "192.0.0.0/24", "192.0.0.0/29", "192.0.0.8/32", + "192.0.0.9/32", "192.0.0.10/32", "192.0.0.170/32", "192.0.0.171/32", "192.0.2.0/24", + "192.31.196.0/24", "192.52.193.0/24", "192.88.99.0/24", "224.0.0.0/4", "192.175.48.0/24", + "198.18.0.0/15", "198.51.100.0/24", "203.0.113.0/24", "240.0.0.0/4", "::1" + ) + | stats count min(_time) as firstTime max(_time) as lastTime + values(parent_process_arguments) as parent_process_arguments + values(process_arguments) as process_arguments + values(parent_process_hash) as parent_process_hash + values(process_hash) as process_hash + values(module_name_list) as module_name_list + values(module_hash_list) as module_hash_list + values(dest_port) as dest_port + values(aliul) as additional_logged_in_users_list + values(dest_hostname) as dest_hostname + by src dest parent_process_path parent_process_integrity_level process_path process_name process_integrity_level process_id transport + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table + parent_process_integrity_level parent_process_path parent_process_arguments parent_process_hash + process_integrity_level process_path process_name process_arguments process_hash process_id + additional_logged_in_users_list module_name_list module_hash_list + src dest_hostname dest dest_port transport firstTime lastTime + | `cisco_nvm___suspicious_network_connection_from_process_with_no_args_filter` how_to_implement: | - This search requires Network Visibility Module logs, which includes the flow data sourcetype. - This search uses an input macro named `cisco_network_visibility_module_flowdata`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. - Replace the macro definition with configurations for your Splunk environment. - The search also uses a post-filter macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). + This search requires Network Visibility Module logs, which includes the flow data sourcetype. + This search uses an input macro named `cisco_network_visibility_module_flowdata`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. + Replace the macro definition with configurations for your Splunk environment. + The search also uses a post-filter macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). known_false_positives: | - Some system binaries may execute without arguments in rare legitimate scenarios (e.g., certain service launches), and initiate - a network connection to microsoft servers for telemetry or update purposes. Apply additional filters as needed. - However, binaries such as `rundll32.exe` or `dllhost.exe` running with no command-line context are highly suspicious and warrant investigation. + Some system binaries may execute without arguments in rare legitimate scenarios (e.g., certain service launches), and initiate + a network connection to microsoft servers for telemetry or update purposes. Apply additional filters as needed. + However, binaries such as `rundll32.exe` or `dllhost.exe` running with no command-line context are highly suspicious and warrant investigation. references: - - https://redcanary.com/threat-detection-report/techniques/process-injection/ + - https://redcanary.com/threat-detection-report/techniques/process-injection/ drilldown_searches: - - name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$src$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The $process_name$ was seen on host $src$ executing without any command-line arguments and initiating a network connection towards $dest$. This might indicate a potential communication with a C&C server. - risk_objects: - - field: src - type: system - score: 40 - threat_objects: - - field: process_name - type: process_name + message: The $process_name$ was seen on host $src$ executing without any command-line arguments and initiating a network connection towards $dest$. This might indicate a potential communication with a C&C server. + risk_objects: + - field: src + type: system + score: 40 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Cisco Network Visibility Module Analytics - asset_type: Endpoint - mitre_attack_id: - - T1055 - - T1218 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: endpoint + analytic_story: + - Cisco Network Visibility Module Analytics + asset_type: Endpoint + mitre_attack_id: + - T1055 + - T1218 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: endpoint tests: - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/cisco_nvm___suspicious_network_connection_initiated_via_msxsl.yml b/detections/endpoint/cisco_nvm___suspicious_network_connection_initiated_via_msxsl.yml index 5165ef79e4..1756fbe883 100644 --- a/detections/endpoint/cisco_nvm___suspicious_network_connection_initiated_via_msxsl.yml +++ b/detections/endpoint/cisco_nvm___suspicious_network_connection_initiated_via_msxsl.yml @@ -6,92 +6,87 @@ author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - This analytic identifies the use of `msxsl.exe` initiating a network connection to a non-private IP address. - Although `msxsl.exe` is a legitimate Microsoft utility used to apply XSLT transformations, adversaries can abuse it - to execute arbitrary code or load external resources in an evasive manner. - This detection leverages Cisco NVM telemetry to identify potentially malicious use of `msxsl.exe` making network connections - that may indicate command and control (C2) or data exfiltration activity. + This analytic identifies the use of `msxsl.exe` initiating a network connection to a non-private IP address. + Although `msxsl.exe` is a legitimate Microsoft utility used to apply XSLT transformations, adversaries can abuse it + to execute arbitrary code or load external resources in an evasive manner. + This detection leverages Cisco NVM telemetry to identify potentially malicious use of `msxsl.exe` making network connections + that may indicate command and control (C2) or data exfiltration activity. data_source: - - Cisco Network Visibility Module Flow Data + - Cisco Network Visibility Module Flow Data search: | - `cisco_network_visibility_module_flowdata` - process_name = "msxsl.exe" - NOT dest IN ( - "10.0.0.0/8", "172.16.0.0/12", "192.168.0.0/16", "100.64.0.0/10", - "127.0.0.0/8", "169.254.0.0/16", "192.0.0.0/24", "192.0.0.0/29", "192.0.0.8/32", - "192.0.0.9/32", "192.0.0.10/32", "192.0.0.170/32", "192.0.0.171/32", "192.0.2.0/24", - "192.31.196.0/24", "192.52.193.0/24", "192.88.99.0/24", "224.0.0.0/4", "192.175.48.0/24", - "198.18.0.0/15", "198.51.100.0/24", "203.0.113.0/24", "240.0.0.0/4", "::1" - ) - | stats count min(_time) as firstTime max(_time) as lastTime - values(parent_process_arguments) as parent_process_arguments - values(process_arguments) as process_arguments - values(parent_process_hash) as parent_process_hash - values(process_hash) as process_hash - values(module_name_list) as module_name_list - values(module_hash_list) as module_hash_list - values(dest_port) as dest_port - values(aliul) as additional_logged_in_users_list - values(dest_hostname) as dest_hostname - by src dest parent_process_path parent_process_integrity_level process_path process_name process_integrity_level process_id transport - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | table - parent_process_integrity_level parent_process_path parent_process_arguments parent_process_hash - process_integrity_level process_path process_name process_arguments process_hash process_id - additional_logged_in_users_list module_name_list module_hash_list - src dest_hostname dest dest_port transport firstTime lastTime - | `cisco_nvm___suspicious_network_connection_initiated_via_msxsl_filter` + `cisco_network_visibility_module_flowdata` + process_name = "msxsl.exe" + NOT dest IN ( + "10.0.0.0/8", "172.16.0.0/12", "192.168.0.0/16", "100.64.0.0/10", + "127.0.0.0/8", "169.254.0.0/16", "192.0.0.0/24", "192.0.0.0/29", "192.0.0.8/32", + "192.0.0.9/32", "192.0.0.10/32", "192.0.0.170/32", "192.0.0.171/32", "192.0.2.0/24", + "192.31.196.0/24", "192.52.193.0/24", "192.88.99.0/24", "224.0.0.0/4", "192.175.48.0/24", + "198.18.0.0/15", "198.51.100.0/24", "203.0.113.0/24", "240.0.0.0/4", "::1" + ) + | stats count min(_time) as firstTime max(_time) as lastTime + values(parent_process_arguments) as parent_process_arguments + values(process_arguments) as process_arguments + values(parent_process_hash) as parent_process_hash + values(process_hash) as process_hash + values(module_name_list) as module_name_list + values(module_hash_list) as module_hash_list + values(dest_port) as dest_port + values(aliul) as additional_logged_in_users_list + values(dest_hostname) as dest_hostname + by src dest parent_process_path parent_process_integrity_level process_path process_name process_integrity_level process_id transport + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table + parent_process_integrity_level parent_process_path parent_process_arguments parent_process_hash + process_integrity_level process_path process_name process_arguments process_hash process_id + additional_logged_in_users_list module_name_list module_hash_list + src dest_hostname dest dest_port transport firstTime lastTime + | `cisco_nvm___suspicious_network_connection_initiated_via_msxsl_filter` how_to_implement: | - This search requires Network Visibility Module logs, which includes the flow data sourcetype. - This search uses an input macro named `cisco_network_visibility_module_flowdata`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. - Replace the macro definition with configurations for your Splunk environment. - The search also uses a post-filter macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). + This search requires Network Visibility Module logs, which includes the flow data sourcetype. + This search uses an input macro named `cisco_network_visibility_module_flowdata`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. + Replace the macro definition with configurations for your Splunk environment. + The search also uses a post-filter macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). known_false_positives: | - False positives may occur in development or administrative environments where msxsl.exe is used - for legitimate XML transformations. However, its use is uncommon in standard user activity - and should be reviewed in most environments. + False positives may occur in development or administrative environments where msxsl.exe is used + for legitimate XML transformations. However, its use is uncommon in standard user activity + and should be reviewed in most environments. references: - - https://lolbas-project.github.io/lolbas/OtherMSBinaries/Msxsl/ + - https://lolbas-project.github.io/lolbas/OtherMSBinaries/Msxsl/ drilldown_searches: - - name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$src$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Host $src$ used msxsl.exe to initiate a suspicious network connection to $dest$ - risk_objects: - - field: src - type: system - score: 40 - threat_objects: - - field: process_name - type: process_name + message: Host $src$ used msxsl.exe to initiate a suspicious network connection to $dest$ + risk_objects: + - field: src + type: system + score: 40 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Cisco Network Visibility Module Analytics - asset_type: Endpoint - mitre_attack_id: - - T1220 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: endpoint + analytic_story: + - Cisco Network Visibility Module Analytics + asset_type: Endpoint + mitre_attack_id: + - T1220 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: endpoint tests: - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/cisco_nvm___suspicious_network_connection_to_ip_lookup_service_api.yml b/detections/endpoint/cisco_nvm___suspicious_network_connection_to_ip_lookup_service_api.yml index d73175d5a9..fb14f9c1eb 100644 --- a/detections/endpoint/cisco_nvm___suspicious_network_connection_to_ip_lookup_service_api.yml +++ b/detections/endpoint/cisco_nvm___suspicious_network_connection_to_ip_lookup_service_api.yml @@ -6,106 +6,99 @@ author: Nasreddine Bencherchali, Splunk, Janantha Marasinghe status: production type: Anomaly description: | - This analytic identifies non-browser processes reaching out to public IP lookup or geolocation services, - such as `ipinfo.io`, `icanhazip.com`, `ip-api.com`, and others. - These domains are commonly used by legitimate tools, but their usage outside of browsers may indicate - network reconnaissance, virtual machine detection, or staging by malware. - This activity is observed in post-exploitation frameworks, stealer malware, and advanced threat actor campaigns. - The detection relies on Cisco Network Visibility Module (NVM) telemetry and excludes known browser - processes to reduce noise. + This analytic identifies non-browser processes reaching out to public IP lookup or geolocation services, + such as `ipinfo.io`, `icanhazip.com`, `ip-api.com`, and others. + These domains are commonly used by legitimate tools, but their usage outside of browsers may indicate + network reconnaissance, virtual machine detection, or staging by malware. + This activity is observed in post-exploitation frameworks, stealer malware, and advanced threat actor campaigns. + The detection relies on Cisco Network Visibility Module (NVM) telemetry and excludes known browser + processes to reduce noise. data_source: -- Cisco Network Visibility Module Flow Data + - Cisco Network Visibility Module Flow Data search: | - `cisco_network_visibility_module_flowdata` - dest_hostname IN ( - "*api.2ip.ua*", "*api.bigdatacloud.net*", "*api.ipify.org*", "*whatismyipaddress.com*", - "*canireachthe.net*", "*checkip.amazonaws.com*", "*checkip.dyndns.org*", "*curlmyip.com*", - "*db-ip.com*", "*edns.ip-api.com*", "*eth0.me*", "*freegeoip.app*", "*geoipy.com*", "*getip.pro*", - "*icanhazip.com*", "*ident.me*", "*ifconfig.io*", "*ifconfig.me*", "*ip-api.com*", "*ip.360.cn*", - "*ip.anysrc.net*", "*ip.taobao.com*", "*ip.tyk.nu*", "*ipaddressworld.com*", "*ipapi.co*", - "*ipconfig.io*", "*ipecho.net*", "*ipinfo.io*", "*ipip.net*", "*iplocation.net*", - "*ipof.in*", "*ipv6-test.com*", "*ipwho.is*", "*trackip.net*", "*inet-ip.info*", - "*jsonip.com*", "*myexternalip.com*", "*seeip.org*", "*wgetip.com*", - "*whatismyip.akamai.com*", "*whois.pconline.com.cn*", "*wtfismyip.com*", "*ip.cn" - ) - NOT process_name IN ( - "brave.exe", "chrome.exe", "firefox.exe", "iexplore.exe", "maxthon.exe", - "MicrosoftEdge.exe", "msedge.exe", "msedgewebview2.exe", "opera.exe", "safari.exe", - "seamonkey.exe", "vivaldi.exe", "whale.exe" - ) - | stats count min(_time) as firstTime max(_time) as lastTime - values(parent_process_arguments) as parent_process_arguments - values(process_arguments) as process_arguments - values(parent_process_hash) as parent_process_hash - values(process_hash) as process_hash - values(module_name_list) as module_name_list - values(module_hash_list) as module_hash_list - values(dest_port) as dest_port - values(aliul) as additional_logged_in_users_list - values(dest_hostname) as dest_hostname - by src dest parent_process_path parent_process_integrity_level process_path process_name process_integrity_level process_id transport - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | table - parent_process_integrity_level parent_process_path parent_process_arguments parent_process_hash - process_integrity_level process_path process_name process_arguments process_hash process_id - additional_logged_in_users_list module_name_list module_hash_list - src dest_hostname dest dest_port transport firstTime lastTime - | `cisco_nvm___suspicious_network_connection_to_ip_lookup_service_api_filter` + `cisco_network_visibility_module_flowdata` + dest_hostname IN ( + "*api.2ip.ua*", "*api.bigdatacloud.net*", "*api.ipify.org*", "*whatismyipaddress.com*", + "*canireachthe.net*", "*checkip.amazonaws.com*", "*checkip.dyndns.org*", "*curlmyip.com*", + "*db-ip.com*", "*edns.ip-api.com*", "*eth0.me*", "*freegeoip.app*", "*geoipy.com*", "*getip.pro*", + "*icanhazip.com*", "*ident.me*", "*ifconfig.io*", "*ifconfig.me*", "*ip-api.com*", "*ip.360.cn*", + "*ip.anysrc.net*", "*ip.taobao.com*", "*ip.tyk.nu*", "*ipaddressworld.com*", "*ipapi.co*", + "*ipconfig.io*", "*ipecho.net*", "*ipinfo.io*", "*ipip.net*", "*iplocation.net*", + "*ipof.in*", "*ipv6-test.com*", "*ipwho.is*", "*trackip.net*", "*inet-ip.info*", + "*jsonip.com*", "*myexternalip.com*", "*seeip.org*", "*wgetip.com*", + "*whatismyip.akamai.com*", "*whois.pconline.com.cn*", "*wtfismyip.com*", "*ip.cn" + ) + NOT process_name IN ( + "brave.exe", "chrome.exe", "firefox.exe", "iexplore.exe", "maxthon.exe", + "MicrosoftEdge.exe", "msedge.exe", "msedgewebview2.exe", "opera.exe", "safari.exe", + "seamonkey.exe", "vivaldi.exe", "whale.exe" + ) + | stats count min(_time) as firstTime max(_time) as lastTime + values(parent_process_arguments) as parent_process_arguments + values(process_arguments) as process_arguments + values(parent_process_hash) as parent_process_hash + values(process_hash) as process_hash + values(module_name_list) as module_name_list + values(module_hash_list) as module_hash_list + values(dest_port) as dest_port + values(aliul) as additional_logged_in_users_list + values(dest_hostname) as dest_hostname + by src dest parent_process_path parent_process_integrity_level process_path process_name process_integrity_level process_id transport + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table + parent_process_integrity_level parent_process_path parent_process_arguments parent_process_hash + process_integrity_level process_path process_name process_arguments process_hash process_id + additional_logged_in_users_list module_name_list module_hash_list + src dest_hostname dest dest_port transport firstTime lastTime + | `cisco_nvm___suspicious_network_connection_to_ip_lookup_service_api_filter` how_to_implement: | - This search requires Network Visibility Module logs, which includes the flow data sourcetype. - This search uses an input macro named `cisco_network_visibility_module_flowdata`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. - Replace the macro definition with configurations for your Splunk environment. - The search also uses a post-filter macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). + This search requires Network Visibility Module logs, which includes the flow data sourcetype. + This search uses an input macro named `cisco_network_visibility_module_flowdata`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. + Replace the macro definition with configurations for your Splunk environment. + The search also uses a post-filter macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). known_false_positives: | - Internal scripts or agents performing network checks may query IP geolocation services. - Tune by excluding known tools or adding internal allowlists for destination domains or process names and commandlines. + Internal scripts or agents performing network checks may query IP geolocation services. + Tune by excluding known tools or adding internal allowlists for destination domains or process names and commandlines. references: -- https://github.com/SigmaHQ/sigma/blob/master/rules/windows/network_connection/net_connection_win_domain_external_ip_lookup.yml -- https://www.cisa.gov/news-events/cybersecurity-advisories/aa20-302a + - https://github.com/SigmaHQ/sigma/blob/master/rules/windows/network_connection/net_connection_win_domain_external_ip_lookup.yml + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa20-302a drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The host $src$ made a network request to IP lookup service - $dest_hostname$ using suspicious process $process_path$ - risk_objects: - - field: src - type: system - score: 40 - threat_objects: - - field: process_name - type: process_name + message: The host $src$ made a network request to IP lookup service $dest_hostname$ using suspicious process $process_path$ + risk_objects: + - field: src + type: system + score: 40 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Cisco Network Visibility Module Analytics - - Castle RAT - asset_type: Endpoint - mitre_attack_id: - - T1590.005 - - T1016 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: endpoint + analytic_story: + - Cisco Network Visibility Module Analytics + - Castle RAT + asset_type: Endpoint + mitre_attack_id: + - T1590.005 + - T1016 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: endpoint tests: -- name: True Positive Test - Cisco NVM - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/cisco_nvm___webserver_download_from_file_sharing_website.yml b/detections/endpoint/cisco_nvm___webserver_download_from_file_sharing_website.yml index b2ff76c975..66c2998363 100644 --- a/detections/endpoint/cisco_nvm___webserver_download_from_file_sharing_website.yml +++ b/detections/endpoint/cisco_nvm___webserver_download_from_file_sharing_website.yml @@ -6,99 +6,94 @@ author: Nasreddine Bencherchali, Splunk status: production type: TTP description: | - This analytic detects unexpected outbound network connections initiated by known webserver processes such as `httpd.exe`, `nginx.exe`, or `tomcat.exe` to common file sharing or public content hosting services like GitHub, Discord CDN, Transfer.sh, or Pastebin. - Webservers are rarely expected to perform outbound downloads, especially to dynamic or anonymous file hosting domains. This behavior is often associated with server compromise, - where an attacker uses a reverse shell, webshell, or injected task to fetch malware or tools post-exploitation. - The detection leverages Cisco Network Visibility Module flow data, enriched with process context, to identify this highly suspicious behavior. + This analytic detects unexpected outbound network connections initiated by known webserver processes such as `httpd.exe`, `nginx.exe`, or `tomcat.exe` to common file sharing or public content hosting services like GitHub, Discord CDN, Transfer.sh, or Pastebin. + Webservers are rarely expected to perform outbound downloads, especially to dynamic or anonymous file hosting domains. This behavior is often associated with server compromise, + where an attacker uses a reverse shell, webshell, or injected task to fetch malware or tools post-exploitation. + The detection leverages Cisco Network Visibility Module flow data, enriched with process context, to identify this highly suspicious behavior. data_source: - - Cisco Network Visibility Module Flow Data + - Cisco Network Visibility Module Flow Data search: | - `cisco_network_visibility_module_flowdata` - process_name IN ( - "http*.exe", "nginx*.exe", "php*.exe", "php-cgi*.exe", "tomcat*.exe" - ) - dest_hostname IN ( - "*.githubusercontent.com*", "*anonfiles.com*", "*cdn.discordapp.com*", "*ddns.net*", - "*dl.dropboxusercontent.com*", "*ghostbin.co*", "*glitch.me*", "*gofile.io*", - "*hastebin.com*", "*mediafire.com*", "*mega.nz*", "*onrender.com*", "*pages.dev*", - "*paste.ee*", "*pastebin.*", "*pastetext.net*", "*privatlab.*", - "*send.exploit.in*", "*sendspace.com*", "*storage.googleapis.com*", - "*storjshare.io*", "*supabase.co*", "*temp.sh*", "*transfer.sh*", "*trycloudflare.com*", - "*ufile.io*", "*w3spaces.com*", "*workers.dev*" - ) - | stats count min(_time) as firstTime max(_time) as lastTime - values(parent_process_arguments) as parent_process_arguments - values(process_arguments) as process_arguments - values(parent_process_hash) as parent_process_hash - values(process_hash) as process_hash - values(module_name_list) as module_name_list - values(module_hash_list) as module_hash_list - values(dest_port) as dest_port - values(aliul) as additional_logged_in_users_list - values(dest_hostname) as dest_hostname - by src dest parent_process_path parent_process_integrity_level process_path process_name process_integrity_level process_id transport - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | table - parent_process_integrity_level parent_process_path parent_process_arguments parent_process_hash - process_integrity_level process_path process_name process_arguments process_hash process_id - additional_logged_in_users_list module_name_list module_hash_list - src dest_hostname dest dest_port transport firstTime lastTime - | `cisco_nvm___webserver_download_from_file_sharing_website_filter` + `cisco_network_visibility_module_flowdata` + process_name IN ( + "http*.exe", "nginx*.exe", "php*.exe", "php-cgi*.exe", "tomcat*.exe" + ) + dest_hostname IN ( + "*.githubusercontent.com*", "*anonfiles.com*", "*cdn.discordapp.com*", "*ddns.net*", + "*dl.dropboxusercontent.com*", "*ghostbin.co*", "*glitch.me*", "*gofile.io*", + "*hastebin.com*", "*mediafire.com*", "*mega.nz*", "*onrender.com*", "*pages.dev*", + "*paste.ee*", "*pastebin.*", "*pastetext.net*", "*privatlab.*", + "*send.exploit.in*", "*sendspace.com*", "*storage.googleapis.com*", + "*storjshare.io*", "*supabase.co*", "*temp.sh*", "*transfer.sh*", "*trycloudflare.com*", + "*ufile.io*", "*w3spaces.com*", "*workers.dev*" + ) + | stats count min(_time) as firstTime max(_time) as lastTime + values(parent_process_arguments) as parent_process_arguments + values(process_arguments) as process_arguments + values(parent_process_hash) as parent_process_hash + values(process_hash) as process_hash + values(module_name_list) as module_name_list + values(module_hash_list) as module_hash_list + values(dest_port) as dest_port + values(aliul) as additional_logged_in_users_list + values(dest_hostname) as dest_hostname + by src dest parent_process_path parent_process_integrity_level process_path process_name process_integrity_level process_id transport + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table + parent_process_integrity_level parent_process_path parent_process_arguments parent_process_hash + process_integrity_level process_path process_name process_arguments process_hash process_id + additional_logged_in_users_list module_name_list module_hash_list + src dest_hostname dest dest_port transport firstTime lastTime + | `cisco_nvm___webserver_download_from_file_sharing_website_filter` how_to_implement: | - This search requires Network Visibility Module logs, which includes the flow data sourcetype. - This search uses an input macro named `cisco_network_visibility_module_flowdata`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. - Replace the macro definition with configurations for your Splunk environment. - The search also uses a post-filter macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). + This search requires Network Visibility Module logs, which includes the flow data sourcetype. + This search uses an input macro named `cisco_network_visibility_module_flowdata`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Network Visibility Module logs. + Replace the macro definition with configurations for your Splunk environment. + The search also uses a post-filter macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Endpoint Security Analytics (CESA) (https://splunkbase.splunk.com/app/4221). known_false_positives: | - In rare cases, a web server may make outbound connections to pull content for legitimate purposes (e.g., downloading templates or updates from a trusted source). - However, communication to anonymous file-sharing or temporary content domains is strongly suspicious. - If legitimate use is confirmed, domain- or process-level allowlisting is recommended. + In rare cases, a web server may make outbound connections to pull content for legitimate purposes (e.g., downloading templates or updates from a trusted source). + However, communication to anonymous file-sharing or temporary content domains is strongly suspicious. + If legitimate use is confirmed, domain- or process-level allowlisting is recommended. references: - - https://www.cisa.gov/news-events/alerts/2023/04/13/cisa-adds-3-known-exploited-vulnerabilities-kev-catalog - - https://www.microsoft.com/en-us/security/blog/2024/01/17/new-ttps-observed-in-mint-sandstorm-campaign-targeting-high-profile-individuals-at-universities-and-research-orgs/ - - https://research.splunk.com/endpoint/4e8391eb-527e-4e39-9a17-c5bde2f89158/ + - https://www.cisa.gov/news-events/alerts/2023/04/13/cisa-adds-3-known-exploited-vulnerabilities-kev-catalog + - https://www.microsoft.com/en-us/security/blog/2024/01/17/new-ttps-observed-in-mint-sandstorm-campaign-targeting-high-profile-individuals-at-universities-and-research-orgs/ + - https://research.splunk.com/endpoint/4e8391eb-527e-4e39-9a17-c5bde2f89158/ rba: - message: The host $src$ ran web server process $process_path$ which downloaded content from $dest_hostname$ over port $dest_port$ - risk_objects: - - field: src - type: system - score: 40 - threat_objects: - - field: process_name - type: process_name + message: The host $src$ ran web server process $process_path$ which downloaded content from $dest_hostname$ over port $dest_port$ + risk_objects: + - field: src + type: system + score: 40 + threat_objects: + - field: process_name + type: process_name drilldown_searches: - - name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$src$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ tags: - analytic_story: - - GhostRedirector IIS Module and Rungan Backdoor - - Cisco Network Visibility Module Analytics - asset_type: Endpoint - mitre_attack_id: - - T1105 - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: endpoint + analytic_story: + - GhostRedirector IIS Module and Rungan Backdoor + - Cisco Network Visibility Module Analytics + asset_type: Endpoint + mitre_attack_id: + - T1105 + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: endpoint tests: - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/clear_unallocated_sector_using_cipher_app.yml b/detections/endpoint/clear_unallocated_sector_using_cipher_app.yml index 975f2cefff..3f55885689 100644 --- a/detections/endpoint/clear_unallocated_sector_using_cipher_app.yml +++ b/detections/endpoint/clear_unallocated_sector_using_cipher_app.yml @@ -1,89 +1,73 @@ name: Clear Unallocated Sector Using Cipher App id: cd80a6ac-c9d9-11eb-8839-acde48001122 -version: 11 -date: '2025-07-29' +version: 12 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of `cipher.exe` with the - `/w` flag to clear unallocated sectors on a disk. It leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process names, command-line arguments, - and parent processes. This activity is significant because it is a technique used - by ransomware to prevent forensic recovery of deleted files. If confirmed malicious, - this action could hinder incident response efforts by making it impossible to recover - critical data, thereby complicating the investigation and remediation process. +description: The following analytic detects the execution of `cipher.exe` with the `/w` flag to clear unallocated sectors on a disk. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, command-line arguments, and parent processes. This activity is significant because it is a technique used by ransomware to prevent forensic recovery of deleted files. If confirmed malicious, this action could hinder incident response efforts by making it impossible to recover critical data, thereby complicating the investigation and remediation process. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "cipher.exe" - Processes.process = "*/w:*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `clear_unallocated_sector_using_cipher_app_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "cipher.exe" Processes.process = "*/w:*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `clear_unallocated_sector_using_cipher_app_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: administrator may execute this app to manage disk references: -- https://unit42.paloaltonetworks.com/vatet-pyxie-defray777/3/ -- https://www.sophos.com/en-us/medialibrary/PDFs/technical-papers/sophoslabs-ransomware-behavior-report.pdf + - https://unit42.paloaltonetworks.com/vatet-pyxie-defray777/3/ + - https://www.sophos.com/en-us/medialibrary/PDFs/technical-papers/sophoslabs-ransomware-behavior-report.pdf drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to clear the unallocated sectors - of a specific disk. - risk_objects: - - field: user - type: user - score: 90 - - field: dest - type: system - score: 90 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to clear the unallocated sectors of a specific disk. + risk_objects: + - field: user + type: user + score: 90 + - field: dest + type: system + score: 90 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Ransomware - - Compromised Windows Host - - Scattered Spider - asset_type: Endpoint - mitre_attack_id: - - T1070.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - Compromised Windows Host + - Scattered Spider + asset_type: Endpoint + mitre_attack_id: + - T1070.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/clop_common_exec_parameter.yml b/detections/endpoint/clop_common_exec_parameter.yml index 11abfed324..57058e3114 100644 --- a/detections/endpoint/clop_common_exec_parameter.yml +++ b/detections/endpoint/clop_common_exec_parameter.yml @@ -1,90 +1,74 @@ name: Clop Common Exec Parameter id: 5a8a2a72-8322-11eb-9ee9-acde48001122 -version: 10 -date: '2025-05-02' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic identifies the execution of CLOP ransomware variants - using specific arguments ("runrun" or "temp.dat") to trigger their malicious activities. - This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process names and command-line arguments. Monitoring this activity is - crucial as it indicates potential ransomware behavior, which can lead to file encryption - on network shares or local machines. If confirmed malicious, this activity could - result in significant data loss and operational disruption due to encrypted files, - highlighting the need for immediate investigation and response. +description: The following analytic identifies the execution of CLOP ransomware variants using specific arguments ("runrun" or "temp.dat") to trigger their malicious activities. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. Monitoring this activity is crucial as it indicates potential ransomware behavior, which can lead to file encryption on network shares or local machines. If confirmed malicious, this activity could result in significant data loss and operational disruption due to encrypted files, highlighting the need for immediate investigation and response. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name != "*temp.dat*" - Processes.process = "*runrun*" OR Processes.process = "*temp.dat*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `clop_common_exec_parameter_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name != "*temp.dat*" Processes.process = "*runrun*" + OR + Processes.process = "*temp.dat*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `clop_common_exec_parameter_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Operators can execute third party tools using these parameters. references: -- https://www.mandiant.com/resources/fin11-email-campaigns-precursor-for-ransomware-data-theft -- https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html + - https://www.mandiant.com/resources/fin11-email-campaigns-precursor-for-ransomware-data-theft + - https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting using arguments to execute its main - code or feature of its code related to Clop ransomware. - risk_objects: - - field: user - type: user - score: 100 - - field: dest - type: system - score: 100 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting using arguments to execute its main code or feature of its code related to Clop ransomware. + risk_objects: + - field: user + type: user + score: 100 + - field: dest + type: system + score: 100 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Compromised Windows Host - - Clop Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - Clop Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_b/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_b/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/clop_ransomware_known_service_name.yml b/detections/endpoint/clop_ransomware_known_service_name.yml index 5370ab9a7b..683b6d7cda 100644 --- a/detections/endpoint/clop_ransomware_known_service_name.yml +++ b/detections/endpoint/clop_ransomware_known_service_name.yml @@ -1,68 +1,58 @@ name: Clop Ransomware Known Service Name id: 07e08a12-870c-11eb-b5f9-acde48001122 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Teoderick Contreras status: production type: TTP -description: The following analytic identifies the creation of a service with a known - name used by CLOP ransomware for persistence and high-privilege code execution. - It detects this activity by monitoring Windows Event Logs (EventCode 7045) for specific - service names ("SecurityCenterIBM", "WinCheckDRVs"). This activity is significant - because the creation of such services is a common tactic used by ransomware to maintain - control over infected systems. If confirmed malicious, this could allow attackers - to execute code with elevated privileges, maintain persistence, and potentially - disrupt or encrypt critical data. +description: The following analytic identifies the creation of a service with a known name used by CLOP ransomware for persistence and high-privilege code execution. It detects this activity by monitoring Windows Event Logs (EventCode 7045) for specific service names ("SecurityCenterIBM", "WinCheckDRVs"). This activity is significant because the creation of such services is a common tactic used by ransomware to maintain control over infected systems. If confirmed malicious, this could allow attackers to execute code with elevated privileges, maintain persistence, and potentially disrupt or encrypt critical data. data_source: -- Windows Event Log System 7045 -search: '`wineventlog_system` EventCode=7045 ServiceName IN ("SecurityCenterIBM", - "WinCheckDRVs") | stats count min(_time) as firstTime max(_time) as lastTime by - Computer EventCode ServiceName StartType ServiceType | rename Computer as dest | - `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `clop_ransomware_known_service_name_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the Service name, Service File Name Service Start type, and Service Type - from your endpoints. + - Windows Event Log System 7045 +search: |- + `wineventlog_system` EventCode=7045 ServiceName IN ("SecurityCenterIBM", "WinCheckDRVs") + | stats count min(_time) as firstTime max(_time) as lastTime + BY Computer EventCode ServiceName + StartType ServiceType + | rename Computer as dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `clop_ransomware_known_service_name_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints. known_false_positives: No false positives have been identified at this time. references: -- https://www.mandiant.com/resources/fin11-email-campaigns-precursor-for-ransomware-data-theft -- https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html + - https://www.mandiant.com/resources/fin11-email-campaigns-precursor-for-ransomware-data-theft + - https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of a known Clop Ransomware Service Name detected on $dest$ - risk_objects: - - field: dest - type: system - score: 100 - threat_objects: [] + message: An instance of a known Clop Ransomware Service Name detected on $dest$ + risk_objects: + - field: dest + type: system + score: 100 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - Clop Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1543 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - Clop Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1543 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-xml.log - source: XmlWinEventLog:System - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-xml.log + source: XmlWinEventLog:System + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/cmd_carry_out_string_command_parameter.yml b/detections/endpoint/cmd_carry_out_string_command_parameter.yml index 3dcb00400f..62f12ec6ff 100644 --- a/detections/endpoint/cmd_carry_out_string_command_parameter.yml +++ b/detections/endpoint/cmd_carry_out_string_command_parameter.yml @@ -1,88 +1,77 @@ name: CMD Carry Out String Command Parameter id: 54a6ed00-3256-11ec-b031-acde48001122 -version: 16 -date: '2025-12-16' +version: 17 +date: '2026-02-25' author: Teoderick Contreras, Bhavin Patel, Splunk status: production type: Hunting -description: The following analytic detects the use of `cmd.exe /c` to execute - commands, a technique often employed by adversaries and malware to run batch - commands or invoke other shells like PowerShell. This detection leverages data - from Endpoint Detection and Response (EDR) agents, focusing on command-line - executions and process metadata. Monitoring this activity is crucial as it can - indicate script-based attacks or unauthorized command execution. If confirmed - malicious, this behavior could lead to unauthorized code execution, privilege - escalation, or persistence within the environment. +description: The following analytic detects the use of `cmd.exe /c` to execute commands, a technique often employed by adversaries and malware to run batch commands or invoke other shells like PowerShell. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process metadata. Monitoring this activity is crucial as it can indicate script-based attacks or unauthorized command execution. If confirmed malicious, this behavior could lead to unauthorized code execution, privilege escalation, or persistence within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_cmd` AND Processes.process - IN ("*/c*", "*/k*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `cmd_carry_out_string_command_parameter_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: False positives may be high based on legitimate scripted - code in any environment. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_cmd` + AND + Processes.process IN ("*/c*", "*/k*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cmd_carry_out_string_command_parameter_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be high based on legitimate scripted code in any environment. Filter as needed. references: -- https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ -- https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ tags: - analytic_story: - - StealC Stealer - - PlugX - - Warzone RAT - - Data Destruction - - Winter Vivern - - WhisperGate - - ProxyNotShell - - DarkGate Malware - - Chaos Ransomware - - Hermetic Wiper - - Quasar RAT - - Rhysida Ransomware - - DarkCrystal RAT - - Qakbot - - IcedID - - CISA AA23-347A - - Azorult - - Living Off The Land - - Crypto Stealer - - Malicious Inno Setup Loader - - NjRAT - - AsyncRAT - - RedLine Stealer - - Log4Shell CVE-2021-44228 - - Interlock Rat - - 0bj3ctivity Stealer - asset_type: Endpoint - cve: - - CVE-2021-44228 - mitre_attack_id: - - T1059.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - StealC Stealer + - PlugX + - Warzone RAT + - Data Destruction + - Winter Vivern + - WhisperGate + - ProxyNotShell + - DarkGate Malware + - Chaos Ransomware + - Hermetic Wiper + - Quasar RAT + - Rhysida Ransomware + - DarkCrystal RAT + - Qakbot + - IcedID + - CISA AA23-347A + - Azorult + - Living Off The Land + - Crypto Stealer + - Malicious Inno Setup Loader + - NjRAT + - AsyncRAT + - RedLine Stealer + - Log4Shell CVE-2021-44228 + - Interlock Rat + - 0bj3ctivity Stealer + asset_type: Endpoint + cve: + - CVE-2021-44228 + mitre_attack_id: + - T1059.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/cmd_carry_str_param/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/cmd_carry_str_param/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/cmd_echo_pipe___escalation.yml b/detections/endpoint/cmd_echo_pipe___escalation.yml index cbf9722efb..45fb85dc20 100644 --- a/detections/endpoint/cmd_echo_pipe___escalation.yml +++ b/detections/endpoint/cmd_echo_pipe___escalation.yml @@ -1,94 +1,79 @@ name: CMD Echo Pipe - Escalation id: eb277ba0-b96b-11eb-b00e-acde48001122 -version: 12 -date: '2026-01-14' +version: 13 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies the use of named-pipe impersonation - for privilege escalation, commonly associated with Cobalt Strike and similar frameworks. - It detects command-line executions where `cmd.exe` uses `echo` to write to a named - pipe, such as `cmd.exe /c echo 4sgryt3436 > \\.\Pipe\5erg53`. This detection leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process and - command-line telemetry. This activity is significant as it indicates potential privilege - escalation attempts. If confirmed malicious, attackers could gain elevated privileges, - enabling further compromise and persistence within the environment. +description: The following analytic identifies the use of named-pipe impersonation for privilege escalation, commonly associated with Cobalt Strike and similar frameworks. It detects command-line executions where `cmd.exe` uses `echo` to write to a named pipe, such as `cmd.exe /c echo 4sgryt3436 > \\.\Pipe\5erg53`. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and command-line telemetry. This activity is significant as it indicates potential privilege escalation attempts. If confirmed malicious, attackers could gain elevated privileges, enabling further compromise and persistence within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_cmd` OR Processes.process=*%comspec%* - (Processes.process=*echo* AND Processes.process=*pipe*) by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `cmd_echo_pipe___escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: No false positives have been identified at this time. - fidelity. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_cmd` + OR + Processes.process=*%comspec%* (Processes.process=*echo* + AND + Processes.process=*pipe*) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cmd_echo_pipe___escalation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: No false positives have been identified at this time. fidelity. references: -- https://redcanary.com/threat-detection-report/threats/cobalt-strike/ -- https://github.com/rapid7/meterpreter/blob/master/source/extensions/priv/server/elevate/namedpipe.c + - https://redcanary.com/threat-detection-report/threats/cobalt-strike/ + - https://github.com/rapid7/meterpreter/blob/master/source/extensions/priv/server/elevate/namedpipe.c drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ potentially performing privilege escalation - using named pipes related to Cobalt Strike and other frameworks. - risk_objects: - - field: user - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ potentially performing privilege escalation using named pipes related to Cobalt Strike and other frameworks. + risk_objects: + - field: user + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Graceful Wipe Out Attack - - Cobalt Strike - - Compromised Windows Host - - BlackByte Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1059.003 - - T1543.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Graceful Wipe Out Attack + - Cobalt Strike + - Compromised Windows Host + - BlackByte Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1059.003 + - T1543.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/cmlua_or_cmstplua_uac_bypass.yml b/detections/endpoint/cmlua_or_cmstplua_uac_bypass.yml index e439602d58..ccd1a6396e 100644 --- a/detections/endpoint/cmlua_or_cmstplua_uac_bypass.yml +++ b/detections/endpoint/cmlua_or_cmstplua_uac_bypass.yml @@ -5,69 +5,47 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the use of COM objects like CMLUA or CMSTPLUA - to bypass User Account Control (UAC). It leverages Sysmon EventCode 7 to identify - the loading of specific DLLs (CMLUA.dll, CMSTPLUA.dll, CMLUAUTIL.dll) by processes - not typically associated with these libraries. This activity is significant as it - indicates an attempt to gain elevated privileges, a common tactic used by ransomware - adversaries. If confirmed malicious, this could allow attackers to execute code - with administrative rights, leading to potential system compromise and further malicious - activities. +description: The following analytic detects the use of COM objects like CMLUA or CMSTPLUA to bypass User Account Control (UAC). It leverages Sysmon EventCode 7 to identify the loading of specific DLLs (CMLUA.dll, CMSTPLUA.dll, CMLUAUTIL.dll) by processes not typically associated with these libraries. This activity is significant as it indicates an attempt to gain elevated privileges, a common tactic used by ransomware adversaries. If confirmed malicious, this could allow attackers to execute code with administrative rights, leading to potential system compromise and further malicious activities. data_source: -- Sysmon EventID 7 -search: '`sysmon` EventCode=7 ImageLoaded IN ("*\\CMLUA.dll", "*\\CMSTPLUA.dll", - "*\\CMLUAUTIL.dll") NOT(process_name IN("CMSTP.exe", "CMMGR32.exe")) NOT(Image IN("*\\windows\\*", - "*\\program files*")) | fillnull | stats count min(_time) as firstTime max(_time) - as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name - process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists - service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `cmlua_or_cmstplua_uac_bypass_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name and imageloaded executions from your endpoints. If you - are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -known_false_positives: Legitimate windows application that are not on the list loading - this dll. Filter as needed. + - Sysmon EventID 7 +search: '`sysmon` EventCode=7 ImageLoaded IN ("*\\CMLUA.dll", "*\\CMSTPLUA.dll", "*\\CMLUAUTIL.dll") NOT(process_name IN("CMSTP.exe", "CMMGR32.exe")) NOT(Image IN("*\\windows\\*", "*\\program files*")) | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `cmlua_or_cmstplua_uac_bypass_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: Legitimate windows application that are not on the list loading this dll. Filter as needed. references: -- https://attack.mitre.org/techniques/T1218/003/ + - https://attack.mitre.org/techniques/T1218/003/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The following module $ImageLoaded$ was loaded by a non-standard application - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: [] + message: The following module $ImageLoaded$ was loaded by a non-standard application on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: [] tags: - analytic_story: - - DarkSide Ransomware - - Ransomware - - LockBit Ransomware - - ValleyRAT - asset_type: Endpoint - mitre_attack_id: - - T1218.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - DarkSide Ransomware + - Ransomware + - LockBit Ransomware + - ValleyRAT + asset_type: Endpoint + mitre_attack_id: + - T1218.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/darkside_cmstp_com/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/darkside_cmstp_com/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/common_ransomware_extensions.yml b/detections/endpoint/common_ransomware_extensions.yml index 442d5f335f..5caa9c72e2 100644 --- a/detections/endpoint/common_ransomware_extensions.yml +++ b/detections/endpoint/common_ransomware_extensions.yml @@ -1,149 +1,130 @@ name: Common Ransomware Extensions id: a9e5c5db-db11-43ca-86a8-c852d1b2c0ec -version: 17 -date: '2026-01-19' +version: 18 +date: '2026-02-25' author: David Dorsey, Michael Haag, Splunk, Steven Dick status: production type: TTP -description: The following analytic detects modifications to files with extensions - commonly associated with ransomware. It leverages the Endpoint.Filesystem data model - to identify changes in file extensions that match known ransomware patterns. This - activity is significant because it suggests an attacker is attempting to encrypt - or alter files, potentially leading to severe data loss and operational disruption. - If confirmed malicious, this activity could result in the encryption of critical - data, rendering it inaccessible and causing significant damage to the organization's - data integrity and availability. +description: The following analytic detects modifications to files with extensions commonly associated with ransomware. It leverages the Endpoint.Filesystem data model to identify changes in file extensions that match known ransomware patterns. This activity is significant because it suggests an attacker is attempting to encrypt or alter files, potentially leading to severe data loss and operational disruption. If confirmed malicious, this activity could result in the encryption of critical data, rendering it inaccessible and causing significant damage to the organization's data integrity and availability. data_source: -- Sysmon EventID 11 + - Sysmon EventID 11 search: | - | tstats `security_content_summariesonly` - min(_time) as firstTime - max(_time) as lastTime - count latest(Filesystem.user) as user - values(Filesystem.file_path) as file_path - from datamodel=Endpoint.Filesystem - where NOT Filesystem.file_name IN ( - "*.bat", - "*.cmd", - "*.com", - "*.cpl", - "*.dll", - "*.doc", - "*.docx", - "*.exe", - "*.gif", - "*.jar", - "*.jpeg", - "*.jpg", - "*.js", - "*.lnk", - "*.pif", - "*.png", - "*.ppt", - "*.pptx", - "*.ps1", - "*.psm1", - "*.scr", - "*.sys", - "*.txt", - "*.vbs", - "*.wsf", - "*.xls", - "*.xlsx" - ) - by Filesystem.action Filesystem.dest - Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time - Filesystem.file_name Filesystem.file_path - Filesystem.file_acl Filesystem.file_size - Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product - | `drop_dm_object_name(Filesystem)` - | rex field=file_name "(?(\.[^\.]+){1,2})$" - | lookup update=true ransomware_extensions_lookup Extensions AS file_extension OUTPUT Extensions Name - | search Name !=False - | stats min(firstTime) as firstTime - max(lastTime) as lastTime - dc(file_path) as path_count - dc(file_name) as file_count - values(action) as action - values(file_access_time) as file_access_time - values(file_create_time) as file_create_time - values(file_hash) as file_hash - values(file_modify_time) as file_modify_time - values(file_acl) as file_acl - values(file_size) as file_size - values(file_path) as file_path - values(process_guid) as process_guid - values(process_id) as process_id - values(user) as user - values(vendor_product) as vendor_product - values(file_name) as file_name - values(file_extension) as file_extension - values(Name) as Name - by dest - | where path_count > 1 OR file_count > 20 - | `common_ransomware_extensions_filter` -how_to_implement: You must be ingesting data that records the filesystem activity - from your hosts to populate the Endpoint Filesystem data model node. To see the - additional metadata, add the following fields, if not already present, please review - the detailed documentation on how to create a new field within Mission Control Queue -known_false_positives: It is possible for a legitimate file with these extensions - to be created. If this is a true ransomware attack, there will be a large number - of files created with these extensions. + | tstats `security_content_summariesonly` + min(_time) as firstTime + max(_time) as lastTime + count latest(Filesystem.user) as user + values(Filesystem.file_path) as file_path + from datamodel=Endpoint.Filesystem + where NOT Filesystem.file_name IN ( + "*.bat", + "*.cmd", + "*.com", + "*.cpl", + "*.dll", + "*.doc", + "*.docx", + "*.exe", + "*.gif", + "*.jar", + "*.jpeg", + "*.jpg", + "*.js", + "*.lnk", + "*.pif", + "*.png", + "*.ppt", + "*.pptx", + "*.ps1", + "*.psm1", + "*.scr", + "*.sys", + "*.txt", + "*.vbs", + "*.wsf", + "*.xls", + "*.xlsx" + ) + by Filesystem.action Filesystem.dest + Filesystem.file_access_time Filesystem.file_create_time + Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path + Filesystem.file_acl Filesystem.file_size + Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | rex field=file_name "(?(\.[^\.]+){1,2})$" + | lookup update=true ransomware_extensions_lookup Extensions AS file_extension OUTPUT Extensions Name + | search Name !=False + | stats min(firstTime) as firstTime + max(lastTime) as lastTime + dc(file_path) as path_count + dc(file_name) as file_count + values(action) as action + values(file_access_time) as file_access_time + values(file_create_time) as file_create_time + values(file_hash) as file_hash + values(file_modify_time) as file_modify_time + values(file_acl) as file_acl + values(file_size) as file_size + values(file_path) as file_path + values(process_guid) as process_guid + values(process_id) as process_id + values(user) as user + values(vendor_product) as vendor_product + values(file_name) as file_name + values(file_extension) as file_extension + values(Name) as Name + by dest + | where path_count > 1 OR file_count > 20 + | `common_ransomware_extensions_filter` +how_to_implement: You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint Filesystem data model node. To see the additional metadata, add the following fields, if not already present, please review the detailed documentation on how to create a new field within Mission Control Queue +known_false_positives: It is possible for a legitimate file with these extensions to be created. If this is a true ransomware attack, there will be a large number of files created with these extensions. references: -- https://github.com/splunk/security_content/issues/2448 + - https://github.com/splunk/security_content/issues/2448 drilldown_searches: -- name: View the detection results for "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The device $dest$ wrote $file_count$ files to $path_count$ path(s) with - the $file_extension$ extension. This extension and behavior may indicate a $Name$ - ransomware attack. - risk_objects: - - field: user - type: user - score: 90 - - field: dest - type: system - score: 90 - threat_objects: [] + message: The device $dest$ wrote $file_count$ files to $path_count$ path(s) with the $file_extension$ extension. This extension and behavior may indicate a $Name$ ransomware attack. + risk_objects: + - field: user + type: user + score: 90 + - field: dest + type: system + score: 90 + threat_objects: [] tags: - analytic_story: - - Rhysida Ransomware - - Prestige Ransomware - - Ransomware - - LockBit Ransomware - - Medusa Ransomware - - SamSam Ransomware - - Clop Ransomware - - Ryuk Ransomware - - Black Basta Ransomware - - Termite Ransomware - - Interlock Ransomware - - NailaoLocker Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Rhysida Ransomware + - Prestige Ransomware + - Ransomware + - LockBit Ransomware + - Medusa Ransomware + - SamSam Ransomware + - Clop Ransomware + - Ryuk Ransomware + - Black Basta Ransomware + - Termite Ransomware + - Interlock Ransomware + - NailaoLocker Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/ransomware_notes/ransom-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/ransomware_notes/ransom-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/common_ransomware_notes.yml b/detections/endpoint/common_ransomware_notes.yml index 9918e4869f..9033086c69 100644 --- a/detections/endpoint/common_ransomware_notes.yml +++ b/detections/endpoint/common_ransomware_notes.yml @@ -6,77 +6,73 @@ author: David Dorsey, Splunk status: production type: Hunting description: | - The following analytic detects the creation of files with names commonly associated with ransomware notes. - It leverages file-system activity data from the Endpoint Filesystem data model, typically populated by endpoint detection and response (EDR) tools or Sysmon logs. - This activity is significant because ransomware notes indicate a potential ransomware attack, which can lead to data encryption and extortion. - If confirmed malicious, this activity could result in significant data loss, operational disruption, and financial impact due to ransom demands. - Note that this analytic relies on a lookup table (ransomware_notes_lookup) that contains known ransomware note file names. - Ensure that this lookup table is regularly updated to include new ransomware note file names as they are identified in the threat landscape. - Also this analytic leverages a sub-search to enhance performance. sub-searches have limitations on the amount of data they can return. Keep this in mind if you have an extensive list of ransomware note file names. + The following analytic detects the creation of files with names commonly associated with ransomware notes. + It leverages file-system activity data from the Endpoint Filesystem data model, typically populated by endpoint detection and response (EDR) tools or Sysmon logs. + This activity is significant because ransomware notes indicate a potential ransomware attack, which can lead to data encryption and extortion. + If confirmed malicious, this activity could result in significant data loss, operational disruption, and financial impact due to ransom demands. + Note that this analytic relies on a lookup table (ransomware_notes_lookup) that contains known ransomware note file names. + Ensure that this lookup table is regularly updated to include new ransomware note file names as they are identified in the threat landscape. + Also this analytic leverages a sub-search to enhance performance. sub-searches have limitations on the amount of data they can return. Keep this in mind if you have an extensive list of ransomware note file names. data_source: -- Sysmon EventID 11 + - Sysmon EventID 11 search: | - | tstats `security_content_summariesonly` - count - min(_time) as firstTime - max(_time) as lastTime - values(Filesystem.user) as user - values(Filesystem.dest) as dest - values(Filesystem.file_path) as file_path - from datamodel=Endpoint.Filesystem - where [ - | inputlookup ransomware_notes_lookup - | search status=true - | fields ransomware_notes - | dedup ransomware_notes - | rename ransomware_notes as Filesystem.file_name - ] - by Filesystem.action Filesystem.dest Filesystem.file_access_time - Filesystem.file_create_time Filesystem.file_hash - Filesystem.file_modify_time Filesystem.file_name - Filesystem.file_path Filesystem.file_acl Filesystem.file_size - Filesystem.process_guid Filesystem.process_id Filesystem.user - Filesystem.vendor_product - | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(lastTime)` - | `security_content_ctime(firstTime)` - | `common_ransomware_notes_filter` -how_to_implement: You must be ingesting data that records file-system activity from - your hosts to populate the Endpoint Filesystem data-model node. This is typically - populated via endpoint detection-and-response product, such as Carbon Black, or - via other endpoint data sources, such as Sysmon. The data used for this search is - typically generated via logs that report file-system reads and writes. + | tstats `security_content_summariesonly` + count + min(_time) as firstTime + max(_time) as lastTime + values(Filesystem.user) as user + values(Filesystem.dest) as dest + values(Filesystem.file_path) as file_path + from datamodel=Endpoint.Filesystem + where [ + | inputlookup ransomware_notes_lookup + | search status=true + | fields ransomware_notes + | dedup ransomware_notes + | rename ransomware_notes as Filesystem.file_name + ] + by Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash + Filesystem.file_modify_time Filesystem.file_name + Filesystem.file_path Filesystem.file_acl Filesystem.file_size + Filesystem.process_guid Filesystem.process_id Filesystem.user + Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` + | `common_ransomware_notes_filter` +how_to_implement: You must be ingesting data that records file-system activity from your hosts to populate the Endpoint Filesystem data-model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. known_false_positives: | - There could be cases where a legitimate file coincidentally matches a known ransomware note - name. In such cases, further investigation is required to determine the nature of the file and its context. + There could be cases where a legitimate file coincidentally matches a known ransomware note + name. In such cases, further investigation is required to determine the nature of the file and its context. references: [] tags: - analytic_story: - - Chaos Ransomware - - Rhysida Ransomware - - Ransomware - - LockBit Ransomware - - Medusa Ransomware - - SamSam Ransomware - - Clop Ransomware - - Ryuk Ransomware - - Black Basta Ransomware - - Termite Ransomware - - Interlock Ransomware - - NailaoLocker Ransomware - - Hellcat Ransomware - - Storm-0501 Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Chaos Ransomware + - Rhysida Ransomware + - Ransomware + - LockBit Ransomware + - Medusa Ransomware + - SamSam Ransomware + - Clop Ransomware + - Ryuk Ransomware + - Black Basta Ransomware + - Termite Ransomware + - Interlock Ransomware + - NailaoLocker Ransomware + - Hellcat Ransomware + - Storm-0501 Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/ransomware_notes/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/ransomware_notes/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/connectwise_screenconnect_path_traversal.yml b/detections/endpoint/connectwise_screenconnect_path_traversal.yml index 800c6dcbc0..36de2abb33 100644 --- a/detections/endpoint/connectwise_screenconnect_path_traversal.yml +++ b/detections/endpoint/connectwise_screenconnect_path_traversal.yml @@ -4,78 +4,51 @@ version: 6 date: '2025-05-02' author: Michael Haag, Splunk data_source: -- Sysmon EventID 11 + - Sysmon EventID 11 type: TTP status: production -description: The following analytic detects attempts to exploit the ConnectWise ScreenConnect - CVE-2024-1708 vulnerability, which allows path traversal attacks by manipulating - file_path and file_name parameters in the URL. It leverages the Endpoint datamodel - Filesystem node to identify suspicious file system events, specifically targeting - paths and filenames associated with ScreenConnect. This activity is significant - as it can lead to unauthorized access to sensitive files and directories, potentially - resulting in data exfiltration or arbitrary code execution. If confirmed malicious, - attackers could gain unauthorized access and control over the host system, posing - a severe security risk. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\ScreenConnect\\App_Extensions\\*") - Filesystem.file_name IN ("*.aspx","*.ashx") by Filesystem.action Filesystem.dest - Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time - Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size - Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product - | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `connectwise_screenconnect_path_traversal_filter`' -how_to_implement: This analytic utilizes the Endpoint datamodel Filesystem node to - identify path traversal attempts against ScreenConnect. Note that using SACL auditing - or other file system monitoring tools may also be used to detect path traversal - attempts. Typically the data for this analytic will come from EDR or other properly - CIM mapped data sources. -known_false_positives: False positives are not expected, as the detection is based - on the presence of file system events that indicate path traversal attempts. The - analytic may be modified to look for any file writes to this path as it is not common - for files to write here. +description: The following analytic detects attempts to exploit the ConnectWise ScreenConnect CVE-2024-1708 vulnerability, which allows path traversal attacks by manipulating file_path and file_name parameters in the URL. It leverages the Endpoint datamodel Filesystem node to identify suspicious file system events, specifically targeting paths and filenames associated with ScreenConnect. This activity is significant as it can lead to unauthorized access to sensitive files and directories, potentially resulting in data exfiltration or arbitrary code execution. If confirmed malicious, attackers could gain unauthorized access and control over the host system, posing a severe security risk. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\ScreenConnect\\App_Extensions\\*") Filesystem.file_name IN ("*.aspx","*.ashx") by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `connectwise_screenconnect_path_traversal_filter`' +how_to_implement: This analytic utilizes the Endpoint datamodel Filesystem node to identify path traversal attempts against ScreenConnect. Note that using SACL auditing or other file system monitoring tools may also be used to detect path traversal attempts. Typically the data for this analytic will come from EDR or other properly CIM mapped data sources. +known_false_positives: False positives are not expected, as the detection is based on the presence of file system events that indicate path traversal attempts. The analytic may be modified to look for any file writes to this path as it is not common for files to write here. references: -- https://www.huntress.com/blog/a-catastrophe-for-control-understanding-the-screenconnect-authentication-bypass -- https://www.huntress.com/blog/detection-guidance-for-connectwise-cwe-288-2 -- https://www.connectwise.com/company/trust/security-bulletins/connectwise-screenconnect-23.9.8 + - https://www.huntress.com/blog/a-catastrophe-for-control-understanding-the-screenconnect-authentication-bypass + - https://www.huntress.com/blog/detection-guidance-for-connectwise-cwe-288-2 + - https://www.connectwise.com/company/trust/security-bulletins/connectwise-screenconnect-23.9.8 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A path traversal attack against ScreenConnect has been detected on $dest$. - risk_objects: - - field: dest - type: system - score: 100 - threat_objects: [] + message: A path traversal attack against ScreenConnect has been detected on $dest$. + risk_objects: + - field: dest + type: system + score: 100 + threat_objects: [] tags: - analytic_story: - - ConnectWise ScreenConnect Vulnerabilities - - Seashell Blizzard - asset_type: Endpoint - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: - - CVE-2024-1708 - - CVE-2024-1709 + analytic_story: + - ConnectWise ScreenConnect Vulnerabilities + - Seashell Blizzard + asset_type: Endpoint + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: + - CVE-2024-1708 + - CVE-2024-1709 tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/screenconnect/sysmon_app_extensions.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/screenconnect/sysmon_app_extensions.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/connectwise_screenconnect_path_traversal_windows_sacl.yml b/detections/endpoint/connectwise_screenconnect_path_traversal_windows_sacl.yml index 71bd7449e3..98d5ffc11d 100644 --- a/detections/endpoint/connectwise_screenconnect_path_traversal_windows_sacl.yml +++ b/detections/endpoint/connectwise_screenconnect_path_traversal_windows_sacl.yml @@ -4,75 +4,53 @@ version: 7 date: '2025-05-02' author: Michael Haag, Splunk data_source: -- Windows Event Log Security 4663 + - Windows Event Log Security 4663 type: TTP status: production -description: The following analytic detects attempts to exploit the ConnectWise ScreenConnect - CVE-2024-1708 vulnerability using Windows SACL EventCode 4663. It identifies path - traversal attacks by monitoring file system events related to the ScreenConnect - service. This activity is significant as it allows unauthorized access to sensitive - files and directories, potentially leading to data exfiltration or arbitrary code - execution. If confirmed malicious, attackers could gain unauthorized access to critical - data or execute harmful code, compromising the integrity and security of the affected - system. Immediate remediation by updating to version 23.9.8 or above is recommended. -search: '`wineventlog_security` EventCode=4663 ProcessName=*\\ScreenConnect.Service.exe - file_path IN ("*\\ScreenConnect\\App_Extensions\\*") file_name IN ("*.aspx","*.ashx") - | stats count min(_time) as firstTime max(_time) as lastTime by ObjectName ObjectType - ProcessName AccessMask process_id EventCode Computer Caller_User_Name | rename Computer - as dest Caller_User_Name as user ProcessName as process_name | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `connectwise_screenconnect_path_traversal_windows_sacl_filter`' -how_to_implement: To implement the following query, enable SACL auditing for the ScreenConnect - directory(ies). With this data, the following analytic will work correctly. A GIST - is provided in the references to assist with enabling SACL Auditing. -known_false_positives: False positives should be limited as the analytic is specific - to ScreenConnect path traversal attempts. Tune as needed, or restrict to specific - hosts if false positives are encountered. +description: The following analytic detects attempts to exploit the ConnectWise ScreenConnect CVE-2024-1708 vulnerability using Windows SACL EventCode 4663. It identifies path traversal attacks by monitoring file system events related to the ScreenConnect service. This activity is significant as it allows unauthorized access to sensitive files and directories, potentially leading to data exfiltration or arbitrary code execution. If confirmed malicious, attackers could gain unauthorized access to critical data or execute harmful code, compromising the integrity and security of the affected system. Immediate remediation by updating to version 23.9.8 or above is recommended. +search: '`wineventlog_security` EventCode=4663 ProcessName=*\\ScreenConnect.Service.exe file_path IN ("*\\ScreenConnect\\App_Extensions\\*") file_name IN ("*.aspx","*.ashx") | stats count min(_time) as firstTime max(_time) as lastTime by ObjectName ObjectType ProcessName AccessMask process_id EventCode Computer Caller_User_Name | rename Computer as dest Caller_User_Name as user ProcessName as process_name | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `connectwise_screenconnect_path_traversal_windows_sacl_filter`' +how_to_implement: To implement the following query, enable SACL auditing for the ScreenConnect directory(ies). With this data, the following analytic will work correctly. A GIST is provided in the references to assist with enabling SACL Auditing. +known_false_positives: False positives should be limited as the analytic is specific to ScreenConnect path traversal attempts. Tune as needed, or restrict to specific hosts if false positives are encountered. references: -- https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4663 -- https://www.huntress.com/blog/a-catastrophe-for-control-understanding-the-screenconnect-authentication-bypass -- https://www.huntress.com/blog/detection-guidance-for-connectwise-cwe-288-2 -- https://www.connectwise.com/company/trust/security-bulletins/connectwise-screenconnect-23.9.8 + - https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4663 + - https://www.huntress.com/blog/a-catastrophe-for-control-understanding-the-screenconnect-authentication-bypass + - https://www.huntress.com/blog/detection-guidance-for-connectwise-cwe-288-2 + - https://www.connectwise.com/company/trust/security-bulletins/connectwise-screenconnect-23.9.8 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A path traversal attack against ScreenConnect has been detected on $dest$. - risk_objects: - - field: dest - type: system - score: 100 - threat_objects: [] + message: A path traversal attack against ScreenConnect has been detected on $dest$. + risk_objects: + - field: dest + type: system + score: 100 + threat_objects: [] tags: - analytic_story: - - ConnectWise ScreenConnect Vulnerabilities - - Compromised Windows Host - - Seashell Blizzard - asset_type: Endpoint - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: - - CVE-2024-1708 - - CVE-2024-1709 + analytic_story: + - ConnectWise ScreenConnect Vulnerabilities + - Compromised Windows Host + - Seashell Blizzard + asset_type: Endpoint + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: + - CVE-2024-1708 + - CVE-2024-1709 tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/screenconnect/4663_connectwise_aspx_app_extensions.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Security + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/screenconnect/4663_connectwise_aspx_app_extensions.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Security diff --git a/detections/endpoint/conti_common_exec_parameter.yml b/detections/endpoint/conti_common_exec_parameter.yml index afb22fa701..a88aed102d 100644 --- a/detections/endpoint/conti_common_exec_parameter.yml +++ b/detections/endpoint/conti_common_exec_parameter.yml @@ -1,91 +1,78 @@ name: Conti Common Exec parameter id: 624919bc-c382-11eb-adcc-acde48001122 -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of suspicious command-line - arguments commonly associated with Conti ransomware, specifically targeting local - drives and network shares for encryption. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process execution logs that include command-line - details. This activity is significant because it indicates a potential ransomware - attack, which can lead to widespread data encryption and operational disruption. - If confirmed malicious, the impact could be severe, resulting in data loss, system - downtime, and potential ransom demands. +description: The following analytic detects the execution of suspicious command-line arguments commonly associated with Conti ransomware, specifically targeting local drives and network shares for encryption. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because it indicates a potential ransomware attack, which can lead to widespread data encryption and operational disruption. If confirmed malicious, the impact could be severe, resulting in data loss, system downtime, and potential ransom demands. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process = "*-m local*" - OR Processes.process = "*-m net*" OR Processes.process = "*-m all*" OR Processes.process - = "*-nomutex*" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `conti_common_exec_parameter_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: 3rd party tool may have commandline parameter that can trigger - this detection. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process = "*-m local*" + OR + Processes.process = "*-m net*" + OR + Processes.process = "*-m all*" + OR + Processes.process = "*-nomutex*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `conti_common_exec_parameter_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: 3rd party tool may have commandline parameter that can trigger this detection. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.conti + - https://malpedia.caad.fkie.fraunhofer.de/details/win.conti drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ executing specific Conti Ransomware related - parameters. - risk_objects: - - field: user - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ executing specific Conti Ransomware related parameters. + risk_objects: + - field: user + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Ransomware - - Compromised Windows Host - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - Compromised Windows Host + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/conti/inf1/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/conti/inf1/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/control_loading_from_world_writable_directory.yml b/detections/endpoint/control_loading_from_world_writable_directory.yml index 570553b678..31a71732d9 100644 --- a/detections/endpoint/control_loading_from_world_writable_directory.yml +++ b/detections/endpoint/control_loading_from_world_writable_directory.yml @@ -5,93 +5,61 @@ date: '2025-05-02' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies instances of control.exe loading a - .cpl or .inf file from a writable directory, which is related to CVE-2021-40444. - This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process names and command-line executions mapped to the `Processes` - node of the `Endpoint` data model. This activity is significant as it may indicate - an attempt to exploit a known vulnerability, potentially leading to unauthorized - code execution. If confirmed malicious, this could allow an attacker to gain control - over the affected system, leading to further compromise. +description: The following analytic identifies instances of control.exe loading a .cpl or .inf file from a writable directory, which is related to CVE-2021-40444. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions mapped to the `Processes` node of the `Endpoint` data model. This activity is significant as it may indicate an attempt to exploit a known vulnerability, potentially leading to unauthorized code execution. If confirmed malicious, this could allow an attacker to gain control over the affected system, leading to further compromise. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=control.exe - OR Processes.original_file_name=CONTROL.EXE) AND Processes.process IN ("*\\appdata\\*", - "*\\windows\\temp\\*", "*\\programdata\\*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `control_loading_from_world_writable_directory_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Limited false positives will be present as control.exe does - not natively load from writable paths as defined. One may add .cpl or .inf to the - command-line if there is any false positives. Tune as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=control.exe OR Processes.original_file_name=CONTROL.EXE) AND Processes.process IN ("*\\appdata\\*", "*\\windows\\temp\\*", "*\\programdata\\*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `control_loading_from_world_writable_directory_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Limited false positives will be present as control.exe does not natively load from writable paths as defined. One may add .cpl or .inf to the command-line if there is any false positives. Tune as needed. references: -- https://strontic.github.io/xcyclopedia/library/rundll32.exe-111474C61232202B5B588D2B512CBB25.html -- https://app.any.run/tasks/36c14029-9df8-439c-bba0-45f2643b0c70/ -- https://attack.mitre.org/techniques/T1218/011/ -- https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-40444 -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.002/T1218.002.yaml + - https://strontic.github.io/xcyclopedia/library/rundll32.exe-111474C61232202B5B588D2B512CBB25.html + - https://app.any.run/tasks/36c14029-9df8-439c-bba0-45f2643b0c70/ + - https://attack.mitre.org/techniques/T1218/011/ + - https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-40444 + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.002/T1218.002.yaml drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to load a suspicious file from disk. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to load a suspicious file from disk. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Microsoft MSHTML Remote Code Execution CVE-2021-40444 - - Living Off The Land - - Compromised Windows Host - asset_type: Endpoint - cve: - - CVE-2021-40444 - mitre_attack_id: - - T1218.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Microsoft MSHTML Remote Code Execution CVE-2021-40444 + - Living Off The Land + - Compromised Windows Host + asset_type: Endpoint + cve: + - CVE-2021-40444 + mitre_attack_id: + - T1218.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.002/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.002/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/create_or_delete_windows_shares_using_net_exe.yml b/detections/endpoint/create_or_delete_windows_shares_using_net_exe.yml index b987eef6a4..129e633ea0 100644 --- a/detections/endpoint/create_or_delete_windows_shares_using_net_exe.yml +++ b/detections/endpoint/create_or_delete_windows_shares_using_net_exe.yml @@ -1,94 +1,75 @@ name: Create or delete windows shares using net exe id: 743a322c-9a68-4a0f-9c17-85d9cce2a27c -version: 14 -date: '2025-05-02' +version: 15 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP -description: The following analytic detects the creation or deletion of Windows shares - using the net.exe command. It leverages Endpoint Detection and Response (EDR) data - to identify processes involving net.exe with actions related to share management. - This activity is significant because it may indicate an attacker attempting to manipulate - network shares for malicious purposes, such as data exfiltration, malware distribution, - or establishing persistence. If confirmed malicious, this activity could lead to - unauthorized access to sensitive information, service disruption, or malware introduction. - Immediate investigation is required to determine the intent and mitigate potential - threats. +description: The following analytic detects the creation or deletion of Windows shares using the net.exe command. It leverages Endpoint Detection and Response (EDR) data to identify processes involving net.exe with actions related to share management. This activity is significant because it may indicate an attacker attempting to manipulate network shares for malicious purposes, such as data exfiltration, malware distribution, or establishing persistence. If confirmed malicious, this activity could lead to unauthorized access to sensitive information, service disruption, or malware introduction. Immediate investigation is required to determine the intent and mitigate potential threats. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count values(Processes.user) as - user values(Processes.parent_process) as parent_process min(_time) as firstTime - max(_time) as lastTime from datamodel=Endpoint.Processes where `process_net` by - Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | search process IN ("*share* /delete*", "*share* /REMARK:*", "*share* /CACHE:*") - | `create_or_delete_windows_shares_using_net_exe_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrators often leverage net.exe to create or delete network - shares. You should verify that the activity was intentional and is legitimate. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_net` + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | search process IN ("*share* /delete*", "*share* /REMARK:*", "*share* /CACHE:*") + | `create_or_delete_windows_shares_using_net_exe_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators often leverage net.exe to create or delete network shares. You should verify that the activity was intentional and is legitimate. references: -- https://attack.mitre.org/techniques/T1070/005/ + - https://attack.mitre.org/techniques/T1070/005/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ enumerating Windows file shares. - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ enumerating Windows file shares. + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Hidden Cobra Malware - - CISA AA22-277A - - Windows Post-Exploitation - - Prestige Ransomware - - DarkGate Malware - asset_type: Endpoint - mitre_attack_id: - - T1070.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Hidden Cobra Malware + - CISA AA22-277A + - Windows Post-Exploitation + - Prestige Ransomware + - DarkGate Malware + asset_type: Endpoint + mitre_attack_id: + - T1070.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.005/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.005/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/create_remote_thread_in_shell_application.yml b/detections/endpoint/create_remote_thread_in_shell_application.yml index c9de76e841..18242521b2 100644 --- a/detections/endpoint/create_remote_thread_in_shell_application.yml +++ b/detections/endpoint/create_remote_thread_in_shell_application.yml @@ -5,71 +5,48 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects suspicious process injection in command - shell applications, specifically targeting `cmd.exe` and `powershell.exe`. It leverages - Sysmon EventCode 8 to identify the creation of remote threads within these shell - processes. This activity is significant because it is a common technique used by - malware, such as IcedID, to inject malicious code and execute it within legitimate - processes. If confirmed malicious, this behavior could allow an attacker to execute - arbitrary code, escalate privileges, or maintain persistence within the environment, - posing a severe threat to system security. +description: The following analytic detects suspicious process injection in command shell applications, specifically targeting `cmd.exe` and `powershell.exe`. It leverages Sysmon EventCode 8 to identify the creation of remote threads within these shell processes. This activity is significant because it is a common technique used by malware, such as IcedID, to inject malicious code and execute it within legitimate processes. If confirmed malicious, this behavior could allow an attacker to execute arbitrary code, escalate privileges, or maintain persistence within the environment, posing a severe threat to system security. data_source: -- Sysmon EventID 8 -search: '`sysmon` EventCode=8 TargetImage IN ("*\\cmd.exe", "*\\powershell*", "*\\pwsh.exe") - | stats count min(_time) as firstTime max(_time) as lastTime by EventID Guid NewThreadId - ProcessID SecurityID SourceImage SourceProcessGuid SourceProcessId StartAddress - StartFunction StartModule TargetImage TargetProcessGuid TargetProcessId UserID dest - parent_process_exec parent_process_guid parent_process_id parent_process_name parent_process_path - process_exec process_guid process_id process_name process_path signature signature_id - user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `create_remote_thread_in_shell_application_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. + - Sysmon EventID 8 +search: '`sysmon` EventCode=8 TargetImage IN ("*\\cmd.exe", "*\\powershell*", "*\\pwsh.exe") | stats count min(_time) as firstTime max(_time) as lastTime by EventID Guid NewThreadId ProcessID SecurityID SourceImage SourceProcessGuid SourceProcessId StartAddress StartFunction StartModule TargetImage TargetProcessGuid TargetProcessId UserID dest parent_process_exec parent_process_guid parent_process_id parent_process_name parent_process_path process_exec process_guid process_id process_name process_path signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `create_remote_thread_in_shell_application_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: No false positives have been identified at this time. references: -- https://thedfirreport.com/2021/07/19/icedid-and-cobalt-strike-vs-antivirus/ + - https://thedfirreport.com/2021/07/19/icedid-and-cobalt-strike-vs-antivirus/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: process $process_name$ create a remote thread to shell app process $TargetImage$ - in host $dest$ - risk_objects: - - field: dest - type: system - score: 70 - threat_objects: - - field: process_name - type: process_name + message: process $process_name$ create a remote thread to shell app process $TargetImage$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 70 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - IcedID - - Qakbot - - Warzone RAT - asset_type: Endpoint - mitre_attack_id: - - T1055 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - IcedID + - Qakbot + - Warzone RAT + asset_type: Endpoint + mitre_attack_id: + - T1055 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/simulated_icedid/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/simulated_icedid/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/create_remote_thread_into_lsass.yml b/detections/endpoint/create_remote_thread_into_lsass.yml index 1a45c6f416..dc50fd22db 100644 --- a/detections/endpoint/create_remote_thread_into_lsass.yml +++ b/detections/endpoint/create_remote_thread_into_lsass.yml @@ -1,82 +1,67 @@ name: Create Remote Thread into LSASS id: 67d4dbef-9564-4699-8da8-03a151529edc -version: 10 -date: '2025-09-30' +version: 11 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects the creation of a remote thread in - the Local Security Authority Subsystem Service (LSASS). This behavior is - identified using Sysmon EventID 8 logs, focusing on processes that create - remote threads in lsass.exe. This activity is significant because it is - commonly associated with credential dumping, a tactic used by adversaries to - steal user authentication credentials. If confirmed malicious, this could - allow attackers to gain unauthorized access to sensitive information, leading - to potential compromise of the entire network. Analysts should investigate to - differentiate between legitimate tools and potential threats. +description: The following analytic detects the creation of a remote thread in the Local Security Authority Subsystem Service (LSASS). This behavior is identified using Sysmon EventID 8 logs, focusing on processes that create remote threads in lsass.exe. This activity is significant because it is commonly associated with credential dumping, a tactic used by adversaries to steal user authentication credentials. If confirmed malicious, this could allow attackers to gain unauthorized access to sensitive information, leading to potential compromise of the entire network. Analysts should investigate to differentiate between legitimate tools and potential threats. data_source: -- Sysmon EventID 8 -search: '`sysmon` EventID=8 TargetImage=*lsass.exe | stats count min(_time) as firstTime - max(_time) as lastTime by EventID Guid NewThreadId ProcessID SecurityID SourceImage - SourceProcessGuid SourceProcessId StartAddress StartFunction StartModule TargetImage - TargetProcessGuid TargetProcessId UserID dest parent_process_exec parent_process_guid - parent_process_id parent_process_name parent_process_path process_exec process_guid - process_id process_name process_path signature signature_id user_id vendor_product - | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `create_remote_thread_into_lsass_filter`' -how_to_implement: This search needs Sysmon Logs with a Sysmon configuration, - which includes EventCode 8 with lsass.exe. This search uses an input macro - named `sysmon`. We strongly recommend that you specify your - environment-specific configurations (index, source, sourcetype, etc.) for - Windows Sysmon logs. Replace the macro definition with configurations for your - Splunk environment. The search also uses a post-filter macro designed to - filter out known false positives. -known_false_positives: Other tools can access LSASS for legitimate reasons and - generate an event. In these cases, tweaking the search may help eliminate - noise. + - Sysmon EventID 8 +search: |- + `sysmon` EventID=8 TargetImage=*lsass.exe + | stats count min(_time) as firstTime max(_time) as lastTime + BY EventID Guid NewThreadId + ProcessID SecurityID SourceImage + SourceProcessGuid SourceProcessId StartAddress + StartFunction StartModule TargetImage + TargetProcessGuid TargetProcessId UserID + dest parent_process_exec parent_process_guid + parent_process_id parent_process_name parent_process_path + process_exec process_guid process_id + process_name process_path signature + signature_id user_id vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `create_remote_thread_into_lsass_filter` +how_to_implement: This search needs Sysmon Logs with a Sysmon configuration, which includes EventCode 8 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. +known_false_positives: Other tools can access LSASS for legitimate reasons and generate an event. In these cases, tweaking the search may help eliminate noise. references: -- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process has created a remote thread into $TargetImage$ on $dest$. - This behavior is indicative of credential dumping and should be - investigated. - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: - - field: TargetImage - type: process_name + message: A process has created a remote thread into $TargetImage$ on $dest$. This behavior is indicative of credential dumping and should be investigated. + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: + - field: TargetImage + type: process_name tags: - analytic_story: - - Credential Dumping - - BlackSuit Ransomware - - Lokibot - asset_type: Windows - mitre_attack_id: - - T1003.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Credential Dumping + - BlackSuit Ransomware + - Lokibot + asset_type: Windows + mitre_attack_id: + - T1003.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/creation_of_lsass_dump_with_taskmgr.yml b/detections/endpoint/creation_of_lsass_dump_with_taskmgr.yml index e2bbe1d22a..76657de6d9 100644 --- a/detections/endpoint/creation_of_lsass_dump_with_taskmgr.yml +++ b/detections/endpoint/creation_of_lsass_dump_with_taskmgr.yml @@ -1,75 +1,63 @@ name: Creation of lsass Dump with Taskmgr id: b2fbe95a-9c62-4c12-8a29-24b97e84c0cd -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the creation of an lsass.exe process dump - using Windows Task Manager. It leverages Sysmon EventID 11 to identify file creation - events where the target filename matches *lsass*.dmp. This activity is significant - because creating an lsass dump can be a precursor to credential theft, as the dump - file contains sensitive information such as user passwords. If confirmed malicious, - an attacker could use the lsass dump to extract credentials and escalate privileges, - potentially compromising the entire network. +description: The following analytic detects the creation of an lsass.exe process dump using Windows Task Manager. It leverages Sysmon EventID 11 to identify file creation events where the target filename matches *lsass*.dmp. This activity is significant because creating an lsass dump can be a precursor to credential theft, as the dump file contains sensitive information such as user passwords. If confirmed malicious, an attacker could use the lsass dump to extract credentials and escalate privileges, potentially compromising the entire network. data_source: -- Sysmon EventID 11 -search: '`sysmon` EventID=11 process_name=taskmgr.exe TargetFilename=*lsass*.dmp | - stats count min(_time) as firstTime max(_time) as lastTime by action dest file_name - file_path process_guid process_id user_id vendor_product process_name TargetFilename - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `creation_of_lsass_dump_with_taskmgr_filter`' -how_to_implement: This search requires Sysmon Logs and a Sysmon configuration, which - includes EventCode 11 for detecting file create of lsass.dmp. This search uses an - input macro named `sysmon`. We strongly recommend that you specify your environment-specific - configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace - the macro definition with configurations for your Splunk environment. The search - also uses a post-filter macro designed to filter out known false positives. -known_false_positives: Administrators can create memory dumps for debugging purposes, - but memory dumps of the LSASS process would be unusual. + - Sysmon EventID 11 +search: |- + `sysmon` EventID=11 process_name=taskmgr.exe TargetFilename=*lsass*.dmp + | stats count min(_time) as firstTime max(_time) as lastTime + BY action dest file_name + file_path process_guid process_id + user_id vendor_product process_name + TargetFilename + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `creation_of_lsass_dump_with_taskmgr_filter` +how_to_implement: This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 11 for detecting file create of lsass.dmp. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. +known_false_positives: Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual. references: -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-5---dump-lsassexe-memory-using-windows-task-manager -- https://attack.mitre.org/techniques/T1003/001/ -- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-5---dump-lsassexe-memory-using-windows-task-manager + - https://attack.mitre.org/techniques/T1003/001/ + - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $process_name$ was identified on endpoint $dest$ writing $TargetFilename$ - to disk. This behavior is related to dumping credentials via Task Manager. - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: [] + message: $process_name$ was identified on endpoint $dest$ writing $TargetFilename$ to disk. This behavior is related to dumping credentials via Task Manager. + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: [] tags: - analytic_story: - - Credential Dumping - - CISA AA22-257A - - Cactus Ransomware - - Seashell Blizzard - - Scattered Lapsus$ Hunters - asset_type: Windows - mitre_attack_id: - - T1003.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Credential Dumping + - CISA AA22-257A + - Cactus Ransomware + - Seashell Blizzard + - Scattered Lapsus$ Hunters + asset_type: Windows + mitre_attack_id: + - T1003.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/creation_of_shadow_copy.yml b/detections/endpoint/creation_of_shadow_copy.yml index 346421fa83..6ba536e90a 100644 --- a/detections/endpoint/creation_of_shadow_copy.yml +++ b/detections/endpoint/creation_of_shadow_copy.yml @@ -1,96 +1,79 @@ name: Creation of Shadow Copy id: eb120f5f-b879-4a63-97c1-93352b5df844 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects the creation of shadow copies using Vssadmin - or Wmic. It leverages data from Endpoint Detection and Response (EDR) agents, focusing - on process execution logs that include command-line details. This activity is significant - because creating shadow copies can be a precursor to ransomware attacks or data - exfiltration, allowing attackers to bypass file locks and access sensitive data. - If confirmed malicious, this behavior could enable attackers to maintain persistence, - recover deleted files, or prepare for further malicious activities, posing a significant - risk to the integrity and confidentiality of the system. +description: The following analytic detects the creation of shadow copies using Vssadmin or Wmic. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because creating shadow copies can be a precursor to ransomware attacks or data exfiltration, allowing attackers to bypass file locks and access sensitive data. If confirmed malicious, this behavior could enable attackers to maintain persistence, recover deleted files, or prepare for further malicious activities, posing a significant risk to the integrity and confidentiality of the system. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe - Processes.process=*create* Processes.process=*shadow*) OR (Processes.process_name=wmic.exe - Processes.process=*shadowcopy* Processes.process=*create*) by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `creation_of_shadow_copy_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Legitimate administrator usage of Vssadmin or Wmic will create - false positives. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name=vssadmin.exe Processes.process=*create* Processes.process=*shadow* + ) + OR (Processes.process_name=wmic.exe Processes.process=*shadowcopy* Processes.process=*create*) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `creation_of_shadow_copy_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Legitimate administrator usage of Vssadmin or Wmic will create false positives. references: -- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf -- https://media.defense.gov/2023/May/24/2003229517/-1/-1/0/CSA_Living_off_the_Land.PDF + - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + - https://media.defense.gov/2023/May/24/2003229517/-1/-1/0/CSA_Living_off_the_Land.PDF drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to create a shadow copy to perform - offline password cracking. - risk_objects: - - field: user - type: user - score: 81 - - field: dest - type: system - score: 81 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to create a shadow copy to perform offline password cracking. + risk_objects: + - field: user + type: user + score: 81 + - field: dest + type: system + score: 81 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - Volt Typhoon - - Compromised Windows Host - - Credential Dumping - asset_type: Endpoint - mitre_attack_id: - - T1003.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Volt Typhoon + - Compromised Windows Host + - Credential Dumping + asset_type: Endpoint + mitre_attack_id: + - T1003.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/crowdstrike_falcon.log - source: crowdstrike - sourcetype: crowdstrike:events:sensor + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/crowdstrike_falcon.log + source: crowdstrike + sourcetype: crowdstrike:events:sensor diff --git a/detections/endpoint/creation_of_shadow_copy_with_wmic_and_powershell.yml b/detections/endpoint/creation_of_shadow_copy_with_wmic_and_powershell.yml index caa3f0915f..21a9b9d784 100644 --- a/detections/endpoint/creation_of_shadow_copy_with_wmic_and_powershell.yml +++ b/detections/endpoint/creation_of_shadow_copy_with_wmic_and_powershell.yml @@ -1,88 +1,72 @@ name: Creation of Shadow Copy with wmic and powershell id: 2ed8b538-d284-449a-be1d-82ad1dbd186b -version: 11 -date: '2025-05-02' +version: 12 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects the creation of shadow copies using "wmic" - or "Powershell" commands. It leverages the Endpoint.Processes data model in Splunk - to identify processes where the command includes "shadowcopy" and "create". This - activity is significant because it may indicate an attacker attempting to manipulate - or access data in an unauthorized manner, potentially leading to data theft or manipulation. - If confirmed malicious, this behavior could allow attackers to backup and exfiltrate - sensitive data or hide their tracks by restoring files to a previous state after - an attack. +description: The following analytic detects the creation of shadow copies using "wmic" or "Powershell" commands. It leverages the Endpoint.Processes data model in Splunk to identify processes where the command includes "shadowcopy" and "create". This activity is significant because it may indicate an attacker attempting to manipulate or access data in an unauthorized manner, potentially leading to data theft or manipulation. If confirmed malicious, this behavior could allow attackers to backup and exfiltrate sensitive data or hide their tracks by restoring files to a previous state after an attack. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_wmic` OR `process_powershell` - Processes.process=*shadowcopy* Processes.process=*create* by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `creation_of_shadow_copy_with_wmic_and_powershell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_wmic` + OR + `process_powershell` Processes.process=*shadowcopy* Processes.process=*create* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `creation_of_shadow_copy_with_wmic_and_powershell_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Legitimate administrator usage of wmic to create a shadow copy. references: -- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf -- https://media.defense.gov/2023/May/24/2003229517/-1/-1/0/CSA_Living_off_the_Land.PDF + - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + - https://media.defense.gov/2023/May/24/2003229517/-1/-1/0/CSA_Living_off_the_Land.PDF drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to create a shadow copy to perform - offline password cracking. - risk_objects: - - field: user - type: user - score: 81 - - field: dest - type: system - score: 81 - threat_objects: [] + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to create a shadow copy to perform offline password cracking. + risk_objects: + - field: user + type: user + score: 81 + - field: dest + type: system + score: 81 + threat_objects: [] tags: - analytic_story: - - Volt Typhoon - - Living Off The Land - - Compromised Windows Host - - Credential Dumping - asset_type: Endpoint - mitre_attack_id: - - T1003.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Volt Typhoon + - Living Off The Land + - Compromised Windows Host + - Credential Dumping + asset_type: Endpoint + mitre_attack_id: + - T1003.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/credential_dumping_via_copy_command_from_shadow_copy.yml b/detections/endpoint/credential_dumping_via_copy_command_from_shadow_copy.yml index 2c8a043db7..1796e134cd 100644 --- a/detections/endpoint/credential_dumping_via_copy_command_from_shadow_copy.yml +++ b/detections/endpoint/credential_dumping_via_copy_command_from_shadow_copy.yml @@ -5,82 +5,50 @@ date: '2026-01-14' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects the use of the copy command to dump credentials - from a shadow copy. It leverages Endpoint Detection and Response (EDR) data to identify - processes with command lines referencing critical files like "sam", "security", - "system", and "ntds.dit" in system directories. This activity is significant as - it indicates an attempt to extract credentials, a common technique for unauthorized - access and privilege escalation. If confirmed malicious, this could lead to attackers - gaining sensitive login information, escalating privileges, moving laterally within - the network, or accessing sensitive data. +description: The following analytic detects the use of the copy command to dump credentials from a shadow copy. It leverages Endpoint Detection and Response (EDR) data to identify processes with command lines referencing critical files like "sam", "security", "system", and "ntds.dit" in system directories. This activity is significant as it indicates an attempt to extract credentials, a common technique for unauthorized access and privilege escalation. If confirmed malicious, this could lead to attackers gaining sensitive login information, escalating privileges, moving laterally within the network, or accessing sensitive data. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_cmd` (Processes.process=*\\system32\\config\\sam* - OR Processes.process=*\\system32\\config\\security* OR Processes.process=*\\system32\\config\\system* - OR Processes.process=*\\windows\\ntds\\ntds.dit*) by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `credential_dumping_via_copy_command_from_shadow_copy_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_cmd` (Processes.process=*\\system32\\config\\sam* OR Processes.process=*\\system32\\config\\security* OR Processes.process=*\\system32\\config\\system* OR Processes.process=*\\windows\\ntds\\ntds.dit*) by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `credential_dumping_via_copy_command_from_shadow_copy_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to copy SAM and NTDS.dit for offline - password cracking. - risk_objects: - - field: user - type: user - score: 81 - - field: dest - type: system - score: 81 - threat_objects: [] + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to copy SAM and NTDS.dit for offline password cracking. + risk_objects: + - field: user + type: user + score: 81 + - field: dest + type: system + score: 81 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - Credential Dumping - asset_type: Endpoint - mitre_attack_id: - - T1003.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - Credential Dumping + asset_type: Endpoint + mitre_attack_id: + - T1003.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/credential_dumping_via_symlink_to_shadow_copy.yml b/detections/endpoint/credential_dumping_via_symlink_to_shadow_copy.yml index 74747c5964..6c85966cb3 100644 --- a/detections/endpoint/credential_dumping_via_symlink_to_shadow_copy.yml +++ b/detections/endpoint/credential_dumping_via_symlink_to_shadow_copy.yml @@ -1,86 +1,67 @@ name: Credential Dumping via Symlink to Shadow Copy id: c5eac648-fae0-4263-91a6-773df1f4c903 -version: 10 -date: '2026-01-14' +version: 11 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects the creation of a symlink to a shadow - copy, which may indicate credential dumping attempts. It leverages the Endpoint.Processes - data model in Splunk to identify processes executing commands containing "mklink" - and "HarddiskVolumeShadowCopy". This activity is significant because attackers often - use this technique to manipulate or delete shadow copies, hindering system backup - and recovery efforts. If confirmed malicious, this could prevent data restoration, - complicate incident response, and lead to data loss or compromise. Analysts should - review the process details, user, parent process, and any related artifacts to identify - the attack source. +description: The following analytic detects the creation of a symlink to a shadow copy, which may indicate credential dumping attempts. It leverages the Endpoint.Processes data model in Splunk to identify processes executing commands containing "mklink" and "HarddiskVolumeShadowCopy". This activity is significant because attackers often use this technique to manipulate or delete shadow copies, hindering system backup and recovery efforts. If confirmed malicious, this could prevent data restoration, complicate incident response, and lead to data loss or compromise. Analysts should review the process details, user, parent process, and any related artifacts to identify the attack source. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_cmd` Processes.process=*mklink* - Processes.process=*HarddiskVolumeShadowCopy* by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `credential_dumping_via_symlink_to_shadow_copy_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_cmd` Processes.process=*mklink* Processes.process=*HarddiskVolumeShadowCopy* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `credential_dumping_via_symlink_to_shadow_copy_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to create symlink to a shadow copy - to grab credentials. - risk_objects: - - field: user - type: user - score: 81 - - field: dest - type: system - score: 81 - threat_objects: [] + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to create symlink to a shadow copy to grab credentials. + risk_objects: + - field: user + type: user + score: 81 + - field: dest + type: system + score: 81 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - Credential Dumping - asset_type: Endpoint - mitre_attack_id: - - T1003.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - Credential Dumping + asset_type: Endpoint + mitre_attack_id: + - T1003.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/crowdstrike_admin_weak_password_policy.yml b/detections/endpoint/crowdstrike_admin_weak_password_policy.yml index cea8e0eb47..86d4f21030 100644 --- a/detections/endpoint/crowdstrike_admin_weak_password_policy.yml +++ b/detections/endpoint/crowdstrike_admin_weak_password_policy.yml @@ -1,67 +1,57 @@ name: Crowdstrike Admin Weak Password Policy id: bb1481fd-23c0-4195-b6a0-94d746c9637c -version: 5 -date: '2026-01-14' +version: 6 +date: '2026-02-25' author: Teoderick Contreras, Splunk data_source: [] type: TTP status: production -description: The following analytic detects CrowdStrike alerts for admin weak password - policy violations, identifying instances where administrative passwords do not meet - security standards. These alerts highlight significant vulnerabilities that could - be exploited by attackers to gain unauthorized access. Promptly addressing these - alerts is crucial for maintaining robust security and protecting critical systems - and data from potential threats. -search: '`crowdstrike_identities` primaryDisplayName = "*admin*" | rename riskFactors{}.severity - as severity, riskFactors{}.type as risk_type, roles{}.type as role_type, accounts{}.domain - as domain, accounts{}.dn as dn, accounts{}.samAccountName as user | stats count - min(_time) as firstTime max(_time) as lastTime by domain dn primaryDisplayName - risk_type severity riskScore riskScoreSeverity user role_type | where risk_type - = "WEAK_PASSWORD_POLICY" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `crowdstrike_admin_weak_password_policy_filter`' -how_to_implement: To implement crowdstrike:identities logs, use the Falcon Streaming - API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe - to the "crowdstrike:identities" event stream. Process and store the logs as needed, - integrating them into your logging or SIEM system for monitoring and analysis. +description: The following analytic detects CrowdStrike alerts for admin weak password policy violations, identifying instances where administrative passwords do not meet security standards. These alerts highlight significant vulnerabilities that could be exploited by attackers to gain unauthorized access. Promptly addressing these alerts is crucial for maintaining robust security and protecting critical systems and data from potential threats. +search: |- + `crowdstrike_identities` primaryDisplayName = "*admin*" + | rename riskFactors{}.severity as severity, riskFactors{}.type as risk_type, roles{}.type as role_type, accounts{}.domain as domain, accounts{}.dn as dn, accounts{}.samAccountName as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY domain dn primaryDisplayName + risk_type severity riskScore + riskScoreSeverity user role_type + | where risk_type = "WEAK_PASSWORD_POLICY" + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `crowdstrike_admin_weak_password_policy_filter` +how_to_implement: To implement crowdstrike:identities logs, use the Falcon Streaming API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe to the "crowdstrike:identities" event stream. Process and store the logs as needed, integrating them into your logging or SIEM system for monitoring and analysis. known_false_positives: No false positives have been identified at this time. references: -- https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf + - https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Weak Password for Admin User found on $domain$ - risk_objects: - - field: user - type: user - score: 80 - threat_objects: [] + message: Weak Password for Admin User found on $domain$ + risk_objects: + - field: user + type: user + score: 80 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1110 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1110 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/admin_weak_password_policy/crowdstrike_weak_password_admin_cleaned.log - sourcetype: crowdstrike:identities - source: crowdstrike:identities + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/admin_weak_password_policy/crowdstrike_weak_password_admin_cleaned.log + sourcetype: crowdstrike:identities + source: crowdstrike:identities diff --git a/detections/endpoint/crowdstrike_admin_with_duplicate_password.yml b/detections/endpoint/crowdstrike_admin_with_duplicate_password.yml index 465fab4965..e3ab8f49bd 100644 --- a/detections/endpoint/crowdstrike_admin_with_duplicate_password.yml +++ b/detections/endpoint/crowdstrike_admin_with_duplicate_password.yml @@ -1,67 +1,57 @@ name: Crowdstrike Admin With Duplicate Password id: b8bccfbf-6ac2-40f2-83b6-e72b7efaa7d4 -version: 5 -date: '2026-01-14' +version: 6 +date: '2026-02-25' author: Teoderick Contreras, Splunk data_source: [] type: TTP status: production -description: The following analytic detects CrowdStrike alerts for admin accounts - with duplicate password risk, identifying instances where administrative users share - the same password. This practice significantly increases the risk of unauthorized - access and potential breaches. Addressing these alerts promptly is crucial for maintaining - strong security protocols, ensuring each admin account uses a unique, secure password - to protect critical systems and data. -search: '`crowdstrike_identities` primaryDisplayName = "*admin*" | rename riskFactors{}.severity - as severity, riskFactors{}.type as risk_type, roles{}.type as role_type, accounts{}.domain - as domain, accounts{}.dn as dn, accounts{}.samAccountName as user | stats count - min(_time) as firstTime max(_time) as lastTime by domain dn primaryDisplayName - risk_type severity riskScore riskScoreSeverity user role_type | where risk_type - = "DUPLICATE_PASSWORD" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `crowdstrike_admin_with_duplicate_password_filter`' -how_to_implement: To implement crowdstrike:identities logs, use the Falcon Streaming - API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe - to the "crowdstrike:identities" event stream. Process and store the logs as needed, - integrating them into your logging or SIEM system for monitoring and analysis. +description: The following analytic detects CrowdStrike alerts for admin accounts with duplicate password risk, identifying instances where administrative users share the same password. This practice significantly increases the risk of unauthorized access and potential breaches. Addressing these alerts promptly is crucial for maintaining strong security protocols, ensuring each admin account uses a unique, secure password to protect critical systems and data. +search: |- + `crowdstrike_identities` primaryDisplayName = "*admin*" + | rename riskFactors{}.severity as severity, riskFactors{}.type as risk_type, roles{}.type as role_type, accounts{}.domain as domain, accounts{}.dn as dn, accounts{}.samAccountName as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY domain dn primaryDisplayName + risk_type severity riskScore + riskScoreSeverity user role_type + | where risk_type = "DUPLICATE_PASSWORD" + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `crowdstrike_admin_with_duplicate_password_filter` +how_to_implement: To implement crowdstrike:identities logs, use the Falcon Streaming API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe to the "crowdstrike:identities" event stream. Process and store the logs as needed, integrating them into your logging or SIEM system for monitoring and analysis. known_false_positives: No false positives have been identified at this time. references: -- https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf + - https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Duplicate Password for Admin User found on $domain$ - risk_objects: - - field: user - type: user - score: 80 - threat_objects: [] + message: Duplicate Password for Admin User found on $domain$ + risk_objects: + - field: user + type: user + score: 80 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1110 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1110 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/admin_duplicate_password/crowdstrike_admin_dup_pwd_cleaned.log - sourcetype: crowdstrike:identities - source: crowdstrike:identities + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/admin_duplicate_password/crowdstrike_admin_dup_pwd_cleaned.log + sourcetype: crowdstrike:identities + source: crowdstrike:identities diff --git a/detections/endpoint/crowdstrike_falcon_stream_alerts.yml b/detections/endpoint/crowdstrike_falcon_stream_alerts.yml index 1e8eda68fc..996e74890c 100644 --- a/detections/endpoint/crowdstrike_falcon_stream_alerts.yml +++ b/detections/endpoint/crowdstrike_falcon_stream_alerts.yml @@ -1,95 +1,95 @@ name: CrowdStrike Falcon Stream Alerts id: cb6af2b3-29ab-441c-8d8d-679811c8b014 -version: 2 -date: '2026-02-06' +version: 3 +date: '2026-02-25' author: Bryan Pluta, Teoderick Contreras, Splunk status: production type: Anomaly data_source: -- CrowdStrike Falcon Stream Alert -description: The following analytic is to leverage alerts from CrowdStrike Falcon Event Stream. This query aggregates and summarizes DetectionSummaryEvent and IdpDetectionSummaryEvent alerts from CrowdStrike Falcon Event Stream, providing details such as destination, user, severity, MITRE information, and Crowdstrike id and links. The evals in the search do multiple things to include align the severity, ensure the user, dest, title, description, MITRE fields are set properly, and the drilldowns are defined based on the type of alert. The search is highly dynamic to account for different alert types in which some fields may or may not be populated. Having all these fields properly set ensure the appropriate risk and analyst queue fields are correctly populated. + - CrowdStrike Falcon Stream Alert +description: The following analytic is to leverage alerts from CrowdStrike Falcon Event Stream. This query aggregates and summarizes DetectionSummaryEvent and IdpDetectionSummaryEvent alerts from CrowdStrike Falcon Event Stream, providing details such as destination, user, severity, MITRE information, and Crowdstrike id and links. The evals in the search do multiple things to include align the severity, ensure the user, dest, title, description, MITRE fields are set properly, and the drilldowns are defined based on the type of alert. The search is highly dynamic to account for different alert types in which some fields may or may not be populated. Having all these fields properly set ensure the appropriate risk and analyst queue fields are correctly populated. search: | - `crowdstrike_stream` metadata.eventType IN (XdrDetectionSummaryEvent,IdpDetectionSummaryEvent,EppDetectionSummaryEvent) - | rename event.* as * - | eval risk_score=case(Severity<20, 0, Severity<40 AND Severity>=20, 25, Severity<60 AND Severity>=40, 100, Severity<80 AND Severity>=60, 250, Severity>=80, 500) - | eval user=coalesce(lower(SourceAccountName),lower(UserName)) - | eval dest=coalesce(HostNames,SourceEndpointHostName) - | eval mitre_technique = case(!match(Name, "(NGAV - |Intel Detection)"), Technique) - | join type=left mitre_technique - [| inputlookup append=t mitre_attack_lookup - | fields mitre_technique mitre_technique_id ] - | eval annotations.mitre_attack = mitre_technique_id - | eval drilldown_user = if(NOT isnull(user), if(NOT isnull(SourceAccountName),("event.SourceAccountName=" + $SourceAccountName$),"event.UserName=" + $UserName$ ),"") - | eval drilldown_dest = if(NOT isnull(dest), if(NOT isnull(SourceEndpointHostName),("event.SourceEndpointHostName=" + $SourceEndpointHostName$ +"*"),"event.HostNames=" + $HostNames$ +"*"),"") - | eval drilldown_dest2 = if( NOT isnull(dest) AND NOT isnull(IOARuleInstanceID) AND Tactic=="Custom Intelligence", if(NOT isnull(SourceEndpointHostName),("dest=" + $SourceEndpointHostName$ +"*"),"dest=" + $HostNames$ +"*"),"") - | eval annotations.drilldown_search = if(isnull(IOARuleInstanceID) AND Tactic!="Custom Intelligence", "`crowdstrike_stream` metadata.eventType=" + $metadata.eventType$ + " " + drilldown_user + " " + drilldown_dest, "`crowdstrike_stream` ((metadata.eventType=" + $metadata.eventType$ + " " + drilldown_user + " " + drilldown_dest + ") OR (event_simpleName IN (CustomIOABasicProcessDetectionInfoEvent,CustomIOADomainNameDetectionInfoEvent,CustomIOAFileWrittenDetectionInfoEvent,CustomIOANetworkConnectionDetectionInfoEvent) TemplateInstanceId=" + IOARuleInstanceID + " " + drilldown_dest2 + "))") - | rename "metadata.eventType" as eventType - | eval title = case(Name=="NGAV", ("RR - CS - " + Tactic + " - " + Technique),Name=="Intel Detection", ("RR - CS - " + Name),eventType=="IdpDetectionSummaryEvent", ("RR - CS - Identity Protection"),1==1, ("RR - CS - " + Name + " - " + Technique) ) - | eval user_append = if(NOT isnull(user)," by " + user,"") - | eval dest_append = if(NOT isnull(dest)," on " + dest,"") - | eval description = case(Name=="NGAV", ("CS " + Tactic + " - " + Technique + ": " + FileName),eventType=="IdpDetectionSummaryEvent", ("CS IdP" + " - " + Name),Name=="Intel Detection", ("CS " + Name + " - " + IOCType + ": " + IOCValue),1==1, (Objective + " - " + DetectDescription) ) - | eval description = description + user_append + dest_append - | eval gid=id, display_id=FalconHostLink, file_hash=SHA256String, hash=MD5String, signature=IOCValue, ip='NetworkAccesses{}.RemoteAddress', process=CommandLine, pid=ProcessId - | eval file_name = if(isnull('ExecutablesWritten{}.FileName'), FileName, 'ExecutablesWritten{}.FileName') - | rename id as detection_id, FalconHostLink as detection_url - | table _time source detection_id detection_url title risk_score description Severity severity HostNames dest Tactic Technique user UserName Objective Name DetectDescription gid, display_id, mitre_technique annotations.mitre_attack annotations.drilldown_search file_hash hash signature ip process pid file_name - | `crowdstrike_falcon_stream_alerts_filter` + `crowdstrike_stream` metadata.eventType IN (XdrDetectionSummaryEvent,IdpDetectionSummaryEvent,EppDetectionSummaryEvent) + | rename event.* as * + | eval risk_score=case(Severity<20, 0, Severity<40 AND Severity>=20, 25, Severity<60 AND Severity>=40, 100, Severity<80 AND Severity>=60, 250, Severity>=80, 500) + | eval user=coalesce(lower(SourceAccountName),lower(UserName)) + | eval dest=coalesce(HostNames,SourceEndpointHostName) + | eval mitre_technique = case(!match(Name, "(NGAV + |Intel Detection)"), Technique) + | join type=left mitre_technique + [| inputlookup append=t mitre_attack_lookup + | fields mitre_technique mitre_technique_id ] + | eval annotations.mitre_attack = mitre_technique_id + | eval drilldown_user = if(NOT isnull(user), if(NOT isnull(SourceAccountName),("event.SourceAccountName=" + $SourceAccountName$),"event.UserName=" + $UserName$ ),"") + | eval drilldown_dest = if(NOT isnull(dest), if(NOT isnull(SourceEndpointHostName),("event.SourceEndpointHostName=" + $SourceEndpointHostName$ +"*"),"event.HostNames=" + $HostNames$ +"*"),"") + | eval drilldown_dest2 = if( NOT isnull(dest) AND NOT isnull(IOARuleInstanceID) AND Tactic=="Custom Intelligence", if(NOT isnull(SourceEndpointHostName),("dest=" + $SourceEndpointHostName$ +"*"),"dest=" + $HostNames$ +"*"),"") + | eval annotations.drilldown_search = if(isnull(IOARuleInstanceID) AND Tactic!="Custom Intelligence", "`crowdstrike_stream` metadata.eventType=" + $metadata.eventType$ + " " + drilldown_user + " " + drilldown_dest, "`crowdstrike_stream` ((metadata.eventType=" + $metadata.eventType$ + " " + drilldown_user + " " + drilldown_dest + ") OR (event_simpleName IN (CustomIOABasicProcessDetectionInfoEvent,CustomIOADomainNameDetectionInfoEvent,CustomIOAFileWrittenDetectionInfoEvent,CustomIOANetworkConnectionDetectionInfoEvent) TemplateInstanceId=" + IOARuleInstanceID + " " + drilldown_dest2 + "))") + | rename "metadata.eventType" as eventType + | eval title = case(Name=="NGAV", ("RR - CS - " + Tactic + " - " + Technique),Name=="Intel Detection", ("RR - CS - " + Name),eventType=="IdpDetectionSummaryEvent", ("RR - CS - Identity Protection"),1==1, ("RR - CS - " + Name + " - " + Technique) ) + | eval user_append = if(NOT isnull(user)," by " + user,"") + | eval dest_append = if(NOT isnull(dest)," on " + dest,"") + | eval description = case(Name=="NGAV", ("CS " + Tactic + " - " + Technique + ": " + FileName),eventType=="IdpDetectionSummaryEvent", ("CS IdP" + " - " + Name),Name=="Intel Detection", ("CS " + Name + " - " + IOCType + ": " + IOCValue),1==1, (Objective + " - " + DetectDescription) ) + | eval description = description + user_append + dest_append + | eval gid=id, display_id=FalconHostLink, file_hash=SHA256String, hash=MD5String, signature=IOCValue, ip='NetworkAccesses{}.RemoteAddress', process=CommandLine, pid=ProcessId + | eval file_name = if(isnull('ExecutablesWritten{}.FileName'), FileName, 'ExecutablesWritten{}.FileName') + | rename id as detection_id, FalconHostLink as detection_url + | table _time source detection_id detection_url title risk_score description Severity severity HostNames dest Tactic Technique user UserName Objective Name DetectDescription gid, display_id, mitre_technique annotations.mitre_attack annotations.drilldown_search file_hash hash signature ip process pid file_name + | `crowdstrike_falcon_stream_alerts_filter` how_to_implement: In order to properly run this search, you need to ingest alerts data from CrowdStrike Event Stream, specifcally using the CrowdStrike Falcon Event Streams Technical Add-On. This add-on will collect alerts using the CrowdStrike:Event:Streams:JSON sourcetype. You will need to define the `crowdstrike_stream` macro to point to the proper index that contains the CrowdStrike:Event:Streams:JSON sourcetype. known_false_positives: False positives may vary based on Crowdstrike configuration; monitor and filter out the alerts that are not relevant to your environment. references: -- https://www.crowdstrike.com/en-us/resources/guides/crowdstrike-falcon-event-streams-add-on-for-splunk-guide-v3/ -- https://splunkbase.splunk.com/app/5082 + - https://www.crowdstrike.com/en-us/resources/guides/crowdstrike-falcon-event-streams-add-on-for-splunk-guide-v3/ + - https://splunkbase.splunk.com/app/5082 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View other CrowdStrike events for "$user$ on "$dest$" - search: '$annotations.drilldown_search$' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View other CrowdStrike events for "$user$ on "$dest$" + search: '$annotations.drilldown_search$' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $description$ - risk_objects: - - field: dest - type: system - score: 81 - - field: user - type: user - score: 81 - threat_objects: - - field: file_name - type: file_name - - field: process - type: process_name - - field: ip - type: ip_address - - field: file_hash - type: file_hash - - field: hash - type: file_hash - - field: signature - type: file_hash + message: $description$ + risk_objects: + - field: dest + type: system + score: 81 + - field: user + type: user + score: 81 + threat_objects: + - field: file_name + type: file_name + - field: process + type: process_name + - field: ip + type: ip_address + - field: file_hash + type: file_hash + - field: hash + type: file_hash + - field: signature + type: file_hash tags: - analytic_story: - - Critical Alerts - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: [] - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - manual_test: We are dynamically creating the risk_score field based on the severity of the alert in the SPL and that supersedes the risk score set in the detection. Setting this detection to manual test since otherwise we fail integration testing. This detection is also based on the mitre_attack_lookup lookup table which is not available in the ESCU app + analytic_story: + - Critical Alerts + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: [] + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + manual_test: We are dynamically creating the risk_score field based on the severity of the alert in the SPL and that supersedes the risk score set in the detection. Setting this detection to manual test since otherwise we fail integration testing. This detection is also based on the mitre_attack_lookup lookup table which is not available in the ESCU app tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/event_stream_events/stream_events_2.log - source: CrowdStrike:Event:Streams - sourcetype: CrowdStrike:Event:Streams:JSON + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/event_stream_events/stream_events_2.log + source: CrowdStrike:Event:Streams + sourcetype: CrowdStrike:Event:Streams:JSON diff --git a/detections/endpoint/crowdstrike_high_identity_risk_severity.yml b/detections/endpoint/crowdstrike_high_identity_risk_severity.yml index d1d046008a..3e0645e07a 100644 --- a/detections/endpoint/crowdstrike_high_identity_risk_severity.yml +++ b/detections/endpoint/crowdstrike_high_identity_risk_severity.yml @@ -1,65 +1,56 @@ name: Crowdstrike High Identity Risk Severity id: 0df524ad-6d78-4883-9987-d29418928103 -version: 5 -date: '2026-01-14' +version: 6 +date: '2026-02-25' author: Teoderick Contreras, Splunk data_source: [] type: TTP status: production -description: The following analytic detects CrowdStrike alerts for High Identity Risk - Severity with a risk score of 70 or higher. These alerts indicate significant vulnerabilities - in user identities, such as suspicious behavior or compromised credentials. Promptly - investigating and addressing these alerts is crucial to prevent potential security - breaches and ensure the integrity and protection of sensitive information and systems. -search: '`crowdstrike_identities` riskScoreSeverity="HIGH" OR riskScore >= 0.70 | - rename riskFactors{}.severity as severity, riskFactors{}.type as risk_type, roles{}.type - as role_type, accounts{}.domain as domain, accounts{}.dn as dn, accounts{}.samAccountName - as user | stats count min(_time) as firstTime max(_time) as lastTime by domain - dn primaryDisplayName risk_type severity riskScore riskScoreSeverity user role_type - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `crowdstrike_high_identity_risk_severity_filter`' -how_to_implement: To implement crowdstrike:identities logs, use the Falcon Streaming - API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe - to the "crowdstrike:identities" event stream. Process and store the logs as needed, - integrating them into your logging or SIEM system for monitoring and analysis. +description: The following analytic detects CrowdStrike alerts for High Identity Risk Severity with a risk score of 70 or higher. These alerts indicate significant vulnerabilities in user identities, such as suspicious behavior or compromised credentials. Promptly investigating and addressing these alerts is crucial to prevent potential security breaches and ensure the integrity and protection of sensitive information and systems. +search: |- + `crowdstrike_identities` riskScoreSeverity="HIGH" OR riskScore >= 0.70 + | rename riskFactors{}.severity as severity, riskFactors{}.type as risk_type, roles{}.type as role_type, accounts{}.domain as domain, accounts{}.dn as dn, accounts{}.samAccountName as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY domain dn primaryDisplayName + risk_type severity riskScore + riskScoreSeverity user role_type + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `crowdstrike_high_identity_risk_severity_filter` +how_to_implement: To implement crowdstrike:identities logs, use the Falcon Streaming API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe to the "crowdstrike:identities" event stream. Process and store the logs as needed, integrating them into your logging or SIEM system for monitoring and analysis. known_false_positives: No false positives have been identified at this time. references: -- https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf + - https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: High Identity Risk Score Severity found on $domain$ - risk_objects: - - field: user - type: user - score: 90 - threat_objects: [] + message: High Identity Risk Score Severity found on $domain$ + risk_objects: + - field: user + type: user + score: 90 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1110 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1110 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/high_risk_score/crowdstrike_high_riskscore_cleaned.log - sourcetype: crowdstrike:identities - source: crowdstrike:identities + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/high_risk_score/crowdstrike_high_riskscore_cleaned.log + sourcetype: crowdstrike:identities + source: crowdstrike:identities diff --git a/detections/endpoint/crowdstrike_medium_identity_risk_severity.yml b/detections/endpoint/crowdstrike_medium_identity_risk_severity.yml index 861e6e95ea..9cdfa6ab0d 100644 --- a/detections/endpoint/crowdstrike_medium_identity_risk_severity.yml +++ b/detections/endpoint/crowdstrike_medium_identity_risk_severity.yml @@ -1,67 +1,56 @@ name: Crowdstrike Medium Identity Risk Severity id: c23b425c-9024-4bd7-b526-c18a4a51d93e -version: 5 -date: '2026-01-14' +version: 6 +date: '2026-02-25' author: Teoderick Contreras, Splunk data_source: [] type: TTP status: production -description: The following analytic detects CrowdStrike alerts for Medium Identity - Risk Severity with a risk score of 55 or higher. These alerts indicate significant - vulnerabilities in user identities, such as suspicious behavior or compromised credentials. - Promptly investigating and addressing these alerts is crucial to prevent potential - security breaches and ensure the integrity and protection of sensitive information - and systems. -search: '`crowdstrike_identities` riskScoreSeverity = "MEDIUM" OR riskScore >= 0.55 - AND riskScore < 0.70 | rename riskFactors{}.severity as severity, riskFactors{}.type - as risk_type, roles{}.type as role_type, accounts{}.domain as domain, accounts{}.dn - as dn, accounts{}.samAccountName as user | stats count min(_time) as firstTime max(_time) - as lastTime by domain dn primaryDisplayName risk_type severity riskScore riskScoreSeverity - user role_type | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `crowdstrike_medium_identity_risk_severity_filter`' -how_to_implement: To implement crowdstrike:identities logs, use the Falcon Streaming - API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe - to the "crowdstrike:identities" event stream. Process and store the logs as needed, - integrating them into your logging or SIEM system for monitoring and analysis. +description: The following analytic detects CrowdStrike alerts for Medium Identity Risk Severity with a risk score of 55 or higher. These alerts indicate significant vulnerabilities in user identities, such as suspicious behavior or compromised credentials. Promptly investigating and addressing these alerts is crucial to prevent potential security breaches and ensure the integrity and protection of sensitive information and systems. +search: |- + `crowdstrike_identities` riskScoreSeverity = "MEDIUM" OR riskScore >= 0.55 AND riskScore < 0.70 + | rename riskFactors{}.severity as severity, riskFactors{}.type as risk_type, roles{}.type as role_type, accounts{}.domain as domain, accounts{}.dn as dn, accounts{}.samAccountName as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY domain dn primaryDisplayName + risk_type severity riskScore + riskScoreSeverity user role_type + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `crowdstrike_medium_identity_risk_severity_filter` +how_to_implement: To implement crowdstrike:identities logs, use the Falcon Streaming API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe to the "crowdstrike:identities" event stream. Process and store the logs as needed, integrating them into your logging or SIEM system for monitoring and analysis. known_false_positives: No false positives have been identified at this time. references: -- https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf + - https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Medium Identity Risk Score Severity found on $domain$ - risk_objects: - - field: user - type: user - score: 70 - threat_objects: [] + message: Medium Identity Risk Score Severity found on $domain$ + risk_objects: + - field: user + type: user + score: 70 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1110 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1110 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/riskscore/crowdstrike_riskscore_cleaned.log - sourcetype: crowdstrike:identities - source: crowdstrike:identities + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/riskscore/crowdstrike_riskscore_cleaned.log + sourcetype: crowdstrike:identities + source: crowdstrike:identities diff --git a/detections/endpoint/crowdstrike_medium_severity_alert.yml b/detections/endpoint/crowdstrike_medium_severity_alert.yml index 0f627e20a1..137188ad15 100644 --- a/detections/endpoint/crowdstrike_medium_severity_alert.yml +++ b/detections/endpoint/crowdstrike_medium_severity_alert.yml @@ -1,71 +1,58 @@ name: Crowdstrike Medium Severity Alert id: 7e80d92a-6ec3-4eb1-a444-1480acfe2d14 -version: 5 -date: '2026-01-14' +version: 6 +date: '2026-02-25' author: Teoderick Contreras, Splunk data_source: [] type: Anomaly status: production -description: The following analytic detects a CrowdStrike alert with MEDIUM severity - indicates a potential threat that requires prompt attention. This alert level suggests - suspicious activity that may compromise security but is not immediately critical. - It typically involves detectable but non-imminent risks, such as unusual behavior - or attempted policy violations, which should be investigated further and mitigated - quickly to prevent escalation of attacks. -search: '`crowdstrike_stream` | rename event.EndpointIp as src_ip, event.EndpointName - as src_host, event.UserName as user, event.IncidentDescription as description, event.IncidentType - as type, event.NumbersOfAlerts as count_alerts, event.SeverityName as severity | - stats count min(_time) as firstTime max(_time) as lastTime by src_ip, src_host, - user, description, type, count_alerts, severity | where LIKE (severity, "%MEDIUM%") - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `crowdstrike_medium_severity_alert_filter`' -how_to_implement: To implement CrowdStrike stream JSON logs, use the Falcon Streaming - API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe - to the "CrowdStrike:Event:Streams:JSON" event stream. Process and store the JSON - logs as needed, integrating them into your logging or SIEM system for monitoring - and analysis. +description: The following analytic detects a CrowdStrike alert with MEDIUM severity indicates a potential threat that requires prompt attention. This alert level suggests suspicious activity that may compromise security but is not immediately critical. It typically involves detectable but non-imminent risks, such as unusual behavior or attempted policy violations, which should be investigated further and mitigated quickly to prevent escalation of attacks. +search: |- + `crowdstrike_stream` + | rename event.EndpointIp as src_ip, event.EndpointName as src_host, event.UserName as user, event.IncidentDescription as description, event.IncidentType as type, event.NumbersOfAlerts as count_alerts, event.SeverityName as severity + | stats count min(_time) as firstTime max(_time) as lastTime + BY src_ip, src_host, user, + description, type, count_alerts, + severity + | where LIKE (severity, "%MEDIUM%") + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `crowdstrike_medium_severity_alert_filter` +how_to_implement: To implement CrowdStrike stream JSON logs, use the Falcon Streaming API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe to the "CrowdStrike:Event:Streams:JSON" event stream. Process and store the JSON logs as needed, integrating them into your logging or SIEM system for monitoring and analysis. known_false_positives: No false positives have been identified at this time. references: -- https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf + - https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A MEDIUM Severity Crowdstrike Alert found in $src_host$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: [] + message: A MEDIUM Severity Crowdstrike Alert found in $src_host$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1110 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - manual_test: This detection is marked manual test because the attack_data file and - TA do not provide the event.EndpointIp and event.EndpointName fields. event.EndpointName - is required to be present for the Risk Message Validation Integration Testing. - This will be investigated and is a tracked issue. + analytic_story: + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1110 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + manual_test: This detection is marked manual test because the attack_data file and TA do not provide the event.EndpointIp and event.EndpointName fields. event.EndpointName is required to be present for the Risk Message Validation Integration Testing. This will be investigated and is a tracked issue. tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/medium_alert/crowdstrike_medium_clean.log - sourcetype: CrowdStrike:Event:Streams:JSON - source: CrowdStrike:Event:Streams + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/medium_alert/crowdstrike_medium_clean.log + sourcetype: CrowdStrike:Event:Streams:JSON + source: CrowdStrike:Event:Streams diff --git a/detections/endpoint/crowdstrike_multiple_low_severity_alerts.yml b/detections/endpoint/crowdstrike_multiple_low_severity_alerts.yml index e8457880ef..22dae5c80e 100644 --- a/detections/endpoint/crowdstrike_multiple_low_severity_alerts.yml +++ b/detections/endpoint/crowdstrike_multiple_low_severity_alerts.yml @@ -1,69 +1,55 @@ name: Crowdstrike Multiple LOW Severity Alerts id: 5c2c02d8-bee7-4f5c-9dea-e3e1012daddb -version: 5 -date: '2026-01-14' +version: 6 +date: '2026-02-25' author: Teoderick Contreras, Splunk data_source: [] type: Anomaly status: production -description: The following analytic detects multiple CrowdStrike LOW severity alerts, - indicating a series of minor suspicious activities or policy violations. These alerts - are not immediately critical but should be reviewed to prevent potential threats. - They often highlight unusual behavior or low-level risks that, if left unchecked, - could escalate into more significant security issues. Regular monitoring and analysis - of these alerts are essential for maintaining robust security. -search: '`crowdstrike_stream` tag=alert event.SeverityName= LOW | rename event.EndpointIp - as src_ip, event.EndpointName as src_host, event.UserName as user, event.IncidentDescription - as description, event.IncidentType as type, event.NumbersOfAlerts as count_alerts, - event.SeverityName as severity | stats dc(type) as type_count, values(user) as users, - values(description) as descriptions, values(type) as types, values(severity) count - min(_time) as firstTime max(_time) as lastTime by src_ip src_host | where type_count - >= 3 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `crowdstrike_multiple_low_severity_alerts_filter`' -how_to_implement: To implement CrowdStrike stream JSON logs, use the Falcon Streaming - API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe - to the "CrowdStrike:Event:Streams:JSON" event stream. Process and store the JSON - logs as needed, integrating them into your logging or SIEM system for monitoring - and analysis. +description: The following analytic detects multiple CrowdStrike LOW severity alerts, indicating a series of minor suspicious activities or policy violations. These alerts are not immediately critical but should be reviewed to prevent potential threats. They often highlight unusual behavior or low-level risks that, if left unchecked, could escalate into more significant security issues. Regular monitoring and analysis of these alerts are essential for maintaining robust security. +search: |- + `crowdstrike_stream` tag=alert event.SeverityName= LOW + | rename event.EndpointIp as src_ip, event.EndpointName as src_host, event.UserName as user, event.IncidentDescription as description, event.IncidentType as type, event.NumbersOfAlerts as count_alerts, event.SeverityName as severity + | stats dc(type) as type_count, values(user) as users, values(description) as descriptions, values(type) as types, values(severity) count min(_time) as firstTime max(_time) as lastTime + BY src_ip src_host + | where type_count >= 3 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `crowdstrike_multiple_low_severity_alerts_filter` +how_to_implement: To implement CrowdStrike stream JSON logs, use the Falcon Streaming API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe to the "CrowdStrike:Event:Streams:JSON" event stream. Process and store the JSON logs as needed, integrating them into your logging or SIEM system for monitoring and analysis. known_false_positives: No false positives have been identified at this time. references: -- https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf + - https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf drilldown_searches: -- name: View the detection results for - "$src_host$" - search: '%original_detection_search% | search src_host = "$src_host$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_host$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_host$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_host$" + search: '%original_detection_search% | search src_host = "$src_host$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_host$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_host$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Several LOW severity alerts found in $src_host$ - risk_objects: - - field: src_host - type: system - score: 49 - threat_objects: [] + message: Several LOW severity alerts found in $src_host$ + risk_objects: + - field: src_host + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1110 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1110 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/multiple_low_alert/crowdstrike_multiple_low_cleaned.log - sourcetype: CrowdStrike:Event:Streams:JSON - source: CrowdStrike:Event:Streams + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/multiple_low_alert/crowdstrike_multiple_low_cleaned.log + sourcetype: CrowdStrike:Event:Streams:JSON + source: CrowdStrike:Event:Streams diff --git a/detections/endpoint/crowdstrike_privilege_escalation_for_non_admin_user.yml b/detections/endpoint/crowdstrike_privilege_escalation_for_non_admin_user.yml index 3bd4e0c3c8..8974ca61db 100644 --- a/detections/endpoint/crowdstrike_privilege_escalation_for_non_admin_user.yml +++ b/detections/endpoint/crowdstrike_privilege_escalation_for_non_admin_user.yml @@ -1,72 +1,58 @@ name: Crowdstrike Privilege Escalation For Non-Admin User id: 69e2860c-0e4b-40ae-9dc4-bf9e3bf2a548 -version: 5 -date: '2026-01-14' +version: 6 +date: '2026-02-25' author: Teoderick Contreras, Splunk data_source: [] type: Anomaly status: production -description: The following analytic detects CrowdStrike alerts for privilege escalation - attempts by non-admin users. These alerts indicate unauthorized efforts by regular - users to gain elevated permissions, posing a significant security risk. Detecting - and addressing these attempts promptly helps prevent potential breaches and ensures - that user privileges remain properly managed, maintaining the integrity of the organization's - security protocols. -search: '`crowdstrike_stream` tag=alert | rename event.EndpointIp as src_ip, event.EndpointName - as src_host, event.UserName as user, event.IncidentDescription as description, event.IncidentType - as type, event.NumbersOfAlerts as count_alerts, event.SeverityName as severity | - stats count min(_time) as firstTime max(_time) as lastTime by src_ip, src_host, - user, description, type, count_alerts, severity | where LIKE(type,"%Privilege escalation%") - AND NOT LIKE(user, "%adm%") AND NOT LIKE(user, "%svc%") AND NOT LIKE(user, "%admin%") - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `crowdstrike_privilege_escalation_for_non_admin_user_filter`' -how_to_implement: To implement CrowdStrike stream JSON logs, use the Falcon Streaming - API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe - to the "CrowdStrike:Event:Streams:JSON" event stream. Process and store the JSON - logs as needed, integrating them into your logging or SIEM system for monitoring - and analysis. +description: The following analytic detects CrowdStrike alerts for privilege escalation attempts by non-admin users. These alerts indicate unauthorized efforts by regular users to gain elevated permissions, posing a significant security risk. Detecting and addressing these attempts promptly helps prevent potential breaches and ensures that user privileges remain properly managed, maintaining the integrity of the organization's security protocols. +search: |- + `crowdstrike_stream` tag=alert + | rename event.EndpointIp as src_ip, event.EndpointName as src_host, event.UserName as user, event.IncidentDescription as description, event.IncidentType as type, event.NumbersOfAlerts as count_alerts, event.SeverityName as severity + | stats count min(_time) as firstTime max(_time) as lastTime + BY src_ip, src_host, user, + description, type, count_alerts, + severity + | where LIKE(type,"%Privilege escalation%") AND NOT LIKE(user, "%adm%") AND NOT LIKE(user, "%svc%") AND NOT LIKE(user, "%admin%") + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `crowdstrike_privilege_escalation_for_non_admin_user_filter` +how_to_implement: To implement CrowdStrike stream JSON logs, use the Falcon Streaming API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe to the "CrowdStrike:Event:Streams:JSON" event stream. Process and store the JSON logs as needed, integrating them into your logging or SIEM system for monitoring and analysis. known_false_positives: No false positives have been identified at this time. references: -- https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf + - https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Privilege escalation happened in Non-Admin Account in $src_host$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: [] + message: A Privilege escalation happened in Non-Admin Account in $src_host$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1110 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - manual_test: This detection is marked manual test because the attack_data file and - TA do not provide the event.EndpointIp and event.EndpointName fields. event.EndpointName - is required to be present for the Risk Message Validation Integration Testing. - This will be investigated and is a tracked issue. + analytic_story: + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1110 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + manual_test: This detection is marked manual test because the attack_data file and TA do not provide the event.EndpointIp and event.EndpointName fields. event.EndpointName is required to be present for the Risk Message Validation Integration Testing. This will be investigated and is a tracked issue. tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/privilege_escalation/crowdstrike_priv_esc_cleaned.log - sourcetype: CrowdStrike:Event:Streams:JSON - source: CrowdStrike:Event:Streams + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/privilege_escalation/crowdstrike_priv_esc_cleaned.log + sourcetype: CrowdStrike:Event:Streams:JSON + source: CrowdStrike:Event:Streams diff --git a/detections/endpoint/crowdstrike_user_weak_password_policy.yml b/detections/endpoint/crowdstrike_user_weak_password_policy.yml index ffcfc5a16c..6de6117c03 100644 --- a/detections/endpoint/crowdstrike_user_weak_password_policy.yml +++ b/detections/endpoint/crowdstrike_user_weak_password_policy.yml @@ -1,67 +1,57 @@ name: Crowdstrike User Weak Password Policy id: b49b6ef4-57cd-4d42-bd7e-64e00f11cc87 -version: 5 -date: '2026-01-14' +version: 6 +date: '2026-02-25' author: Teoderick Contreras, Splunk data_source: [] type: Anomaly status: production -description: The following analytic detects CrowdStrike alerts for weak password policy - violations, identifying instances where passwords do not meet the required security - standards. These alerts highlight potential vulnerabilities that could be exploited - by attackers, emphasizing the need for stronger password practices. Addressing these - alerts promptly helps to enhance overall security and protect sensitive information - from unauthorized access. -search: '`crowdstrike_identities` primaryDisplayName != "*admin*" | rename riskFactors{}.severity - as severity, riskFactors{}.type as risk_type, roles{}.type as role_type, accounts{}.domain - as domain, accounts{}.dn as dn, accounts{}.samAccountName as user | stats count - min(_time) as firstTime max(_time) as lastTime by domain dn primaryDisplayName - risk_type severity riskScore riskScoreSeverity user role_type | where risk_type - = "WEAK_PASSWORD_POLICY" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `crowdstrike_user_weak_password_policy_filter`' -how_to_implement: To implement crowdstrike:identities logs, use the Falcon Streaming - API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe - to the "crowdstrike:identities" event stream. Process and store the logs as needed, - integrating them into your logging or SIEM system for monitoring and analysis. +description: The following analytic detects CrowdStrike alerts for weak password policy violations, identifying instances where passwords do not meet the required security standards. These alerts highlight potential vulnerabilities that could be exploited by attackers, emphasizing the need for stronger password practices. Addressing these alerts promptly helps to enhance overall security and protect sensitive information from unauthorized access. +search: |- + `crowdstrike_identities` primaryDisplayName != "*admin*" + | rename riskFactors{}.severity as severity, riskFactors{}.type as risk_type, roles{}.type as role_type, accounts{}.domain as domain, accounts{}.dn as dn, accounts{}.samAccountName as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY domain dn primaryDisplayName + risk_type severity riskScore + riskScoreSeverity user role_type + | where risk_type = "WEAK_PASSWORD_POLICY" + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `crowdstrike_user_weak_password_policy_filter` +how_to_implement: To implement crowdstrike:identities logs, use the Falcon Streaming API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe to the "crowdstrike:identities" event stream. Process and store the logs as needed, integrating them into your logging or SIEM system for monitoring and analysis. known_false_positives: No false positives have been identified at this time. references: -- https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf + - https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User Weak Password found on $domain$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: [] + message: User Weak Password found on $domain$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1110 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1110 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/non_adminweak_password_policy/crowdstrike_user_weak_password_cleaned.log - sourcetype: crowdstrike:identities - source: crowdstrike:identities + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/non_adminweak_password_policy/crowdstrike_user_weak_password_cleaned.log + sourcetype: crowdstrike:identities + source: crowdstrike:identities diff --git a/detections/endpoint/crowdstrike_user_with_duplicate_password.yml b/detections/endpoint/crowdstrike_user_with_duplicate_password.yml index 46225a1c90..e1d57af612 100644 --- a/detections/endpoint/crowdstrike_user_with_duplicate_password.yml +++ b/detections/endpoint/crowdstrike_user_with_duplicate_password.yml @@ -1,67 +1,57 @@ name: Crowdstrike User with Duplicate Password id: 386dd914-16e5-400b-9bf6-25572cc4415a -version: 5 -date: '2026-01-14' +version: 6 +date: '2026-02-25' author: Teoderick Contreras, Splunk data_source: [] type: Anomaly status: production -description: The following analytic detects CrowdStrike alerts for non-admin accounts - with duplicate password risk, identifying instances where multiple non-admin users - share the same password. This practice weakens security and increases the potential - for unauthorized access. Addressing these alerts is essential to ensure each user - account has a unique, strong password, thereby enhancing overall security and protecting - sensitive information. -search: '`crowdstrike_identities` primaryDisplayName != "*admin*" | rename riskFactors{}.severity - as severity, riskFactors{}.type as risk_type, roles{}.type as role_type, accounts{}.domain - as domain, accounts{}.dn as dn, accounts{}.samAccountName as user | stats count - min(_time) as firstTime max(_time) as lastTime by domain dn primaryDisplayName - risk_type severity riskScore riskScoreSeverity user role_type | where risk_type - = "DUPLICATE_PASSWORD" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `crowdstrike_user_with_duplicate_password_filter`' -how_to_implement: To implement crowdstrike:identities logs, use the Falcon Streaming - API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe - to the "crowdstrike:identities" event stream. Process and store the logs as needed, - integrating them into your logging or SIEM system for monitoring and analysis. +description: The following analytic detects CrowdStrike alerts for non-admin accounts with duplicate password risk, identifying instances where multiple non-admin users share the same password. This practice weakens security and increases the potential for unauthorized access. Addressing these alerts is essential to ensure each user account has a unique, strong password, thereby enhancing overall security and protecting sensitive information. +search: |- + `crowdstrike_identities` primaryDisplayName != "*admin*" + | rename riskFactors{}.severity as severity, riskFactors{}.type as risk_type, roles{}.type as role_type, accounts{}.domain as domain, accounts{}.dn as dn, accounts{}.samAccountName as user + | stats count min(_time) as firstTime max(_time) as lastTime + BY domain dn primaryDisplayName + risk_type severity riskScore + riskScoreSeverity user role_type + | where risk_type = "DUPLICATE_PASSWORD" + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `crowdstrike_user_with_duplicate_password_filter` +how_to_implement: To implement crowdstrike:identities logs, use the Falcon Streaming API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe to the "crowdstrike:identities" event stream. Process and store the logs as needed, integrating them into your logging or SIEM system for monitoring and analysis. known_false_positives: No false positives have been identified at this time. references: -- https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf + - https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User with Duplicate Password found on $domain$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: [] + message: User with Duplicate Password found on $domain$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1110 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1110 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/user_duplicate_password/crowdstrike_user_dup_pwd_cleaned.log - sourcetype: crowdstrike:identities - source: crowdstrike:identities + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/user_duplicate_password/crowdstrike_user_dup_pwd_cleaned.log + sourcetype: crowdstrike:identities + source: crowdstrike:identities diff --git a/detections/endpoint/csc_net_on_the_fly_compilation.yml b/detections/endpoint/csc_net_on_the_fly_compilation.yml index 88ec0acc2e..a0a487b1aa 100644 --- a/detections/endpoint/csc_net_on_the_fly_compilation.yml +++ b/detections/endpoint/csc_net_on_the_fly_compilation.yml @@ -1,64 +1,53 @@ name: CSC Net On The Fly Compilation id: ea73128a-43ab-11ec-9753-acde48001122 -version: 8 -date: '2025-12-15' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects the use of the .NET compiler csc.exe for - on-the-fly compilation of potentially malicious .NET code. It leverages data from - Endpoint Detection and Response (EDR) agents, focusing on specific command-line - patterns associated with csc.exe. This activity is significant because adversaries - and malware often use this technique to evade detection by compiling malicious code - at runtime. If confirmed malicious, this could allow attackers to execute arbitrary - code, potentially leading to system compromise, data exfiltration, or further lateral - movement within the network. +description: The following analytic detects the use of the .NET compiler csc.exe for on-the-fly compilation of potentially malicious .NET code. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns associated with csc.exe. This activity is significant because adversaries and malware often use this technique to evade detection by compiling malicious code at runtime. If confirmed malicious, this could allow attackers to execute arbitrary code, potentially leading to system compromise, data exfiltration, or further lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - (Processes.process_name=csc.exe OR Processes.original_file_name=csc.exe) - Processes.process = "*/noconfig*" - Processes.process = "*/fullpaths*" - Processes.process = "*@*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `csc_net_on_the_fly_compilation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: A network operator or systems administrator may utilize an - automated powershell script taht execute .net code that may generate false positive. - filter is needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name=csc.exe + OR + Processes.original_file_name=csc.exe + ) + Processes.process = "*/noconfig*" Processes.process = "*/fullpaths*" Processes.process = "*@*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `csc_net_on_the_fly_compilation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: A network operator or systems administrator may utilize an automated powershell script taht execute .net code that may generate false positive. filter is needed. references: -- https://app.any.run/tasks/ad4c3cda-41f2-4401-8dba-56cc2d245488/ -- https://tccontre.blogspot.com/2019/06/maicious-macro-that-compile-c-code-as.html + - https://app.any.run/tasks/ad4c3cda-41f2-4401-8dba-56cc2d245488/ + - https://tccontre.blogspot.com/2019/06/maicious-macro-that-compile-c-code-as.html tags: - analytic_story: - - Windows Defense Evasion Tactics - asset_type: Endpoint - mitre_attack_id: - - T1027.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + asset_type: Endpoint + mitre_attack_id: + - T1027.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/vilsel/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/vilsel/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/delete_shadowcopy_with_powershell.yml b/detections/endpoint/delete_shadowcopy_with_powershell.yml index 85415c1441..8d2130883c 100644 --- a/detections/endpoint/delete_shadowcopy_with_powershell.yml +++ b/detections/endpoint/delete_shadowcopy_with_powershell.yml @@ -1,75 +1,68 @@ name: Delete ShadowCopy With PowerShell id: 5ee2bcd0-b2ff-11eb-bb34-acde48001122 -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the use of PowerShell to delete shadow - copies via the WMIC PowerShell module. It leverages EventCode 4104 and searches - for specific keywords like "ShadowCopy," "Delete," or "Remove" within the ScriptBlockText. - This activity is significant because deleting shadow copies is a common tactic used - by ransomware, such as DarkSide, to prevent data recovery. If confirmed malicious, - this action could lead to irreversible data loss and hinder recovery efforts, significantly - impacting business continuity and data integrity. +description: The following analytic detects the use of PowerShell to delete shadow copies via the WMIC PowerShell module. It leverages EventCode 4104 and searches for specific keywords like "ShadowCopy," "Delete," or "Remove" within the ScriptBlockText. This activity is significant because deleting shadow copies is a common tactic used by ransomware, such as DarkSide, to prevent data recovery. If confirmed malicious, this action could lead to irreversible data loss and hinder recovery efforts, significantly impacting business continuity and data integrity. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 ScriptBlockText= "*ShadowCopy*" (ScriptBlockText - = "*Delete*" OR ScriptBlockText = "*Remove*") | fillnull | stats count min(_time) - as firstTime max(_time) as lastTime by dest signature signature_id user_id vendor_product - EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `delete_shadowcopy_with_powershell_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the powershell logs from your endpoints. make sure you enable needed - registry to monitor this event. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText= "*ShadowCopy*" (ScriptBlockText = "*Delete*" OR ScriptBlockText = "*Remove*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `delete_shadowcopy_with_powershell_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the powershell logs from your endpoints. make sure you enable needed registry to monitor this event. known_false_positives: No false positives have been identified at this time. references: -- https://www.mandiant.com/resources/shining-a-light-on-darkside-ransomware-operations -- https://www.techtarget.com/searchwindowsserver/tutorial/Set-up-PowerShell-script-block-logging-for-added-security + - https://www.mandiant.com/resources/shining-a-light-on-darkside-ransomware-operations + - https://www.techtarget.com/searchwindowsserver/tutorial/Set-up-PowerShell-script-block-logging-for-added-security drilldown_searches: -- name: View the detection results for - "$user_id$" and "$dest$" - search: '%original_detection_search% | search user_id = "$user_id$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user_id$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user_id$" and "$dest$" + search: '%original_detection_search% | search user_id = "$user_id$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user_id$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An attempt to delete ShadowCopy was performed using PowerShell on $dest$ - by $user_id$. - risk_objects: - - field: user_id - type: user - score: 81 - - field: dest - type: system - score: 81 - threat_objects: [] + message: An attempt to delete ShadowCopy was performed using PowerShell on $dest$ by $user_id$. + risk_objects: + - field: user_id + type: user + score: 81 + - field: dest + type: system + score: 81 + threat_objects: [] tags: - analytic_story: - - DarkSide Ransomware - - Ransomware - - Revil Ransomware - - DarkGate Malware - - Cactus Ransomware - - VanHelsing Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1490 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - DarkSide Ransomware + - Ransomware + - Revil Ransomware + - DarkGate Malware + - Cactus Ransomware + - VanHelsing Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1490 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/single_event_delete_shadowcopy.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/single_event_delete_shadowcopy.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/deleting_shadow_copies.yml b/detections/endpoint/deleting_shadow_copies.yml index 16283c6304..2e513c7db0 100644 --- a/detections/endpoint/deleting_shadow_copies.yml +++ b/detections/endpoint/deleting_shadow_copies.yml @@ -1,106 +1,91 @@ name: Deleting Shadow Copies id: b89919ed-ee5f-492c-b139-95dbb162039e -version: 14 -date: '2025-05-02' +version: 15 +date: '2026-02-25' author: David Dorsey, Splunk status: production type: TTP -description: The following analytic detects the deletion of shadow copies using the - vssadmin.exe or wmic.exe utilities. It leverages data from Endpoint Detection and - Response (EDR) agents, focusing on process names and command-line arguments. This - activity is significant because deleting shadow copies is a common tactic used by - attackers to prevent recovery and hide their tracks. If confirmed malicious, this - action could hinder incident response efforts and allow attackers to maintain persistence - and cover their activities, making it crucial for security teams to investigate - promptly. +description: The following analytic detects the deletion of shadow copies using the vssadmin.exe or wmic.exe utilities. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because deleting shadow copies is a common tactic used by attackers to prevent recovery and hide their tracks. If confirmed malicious, this action could hinder incident response efforts and allow attackers to maintain persistence and cover their activities, making it crucial for security teams to investigate promptly. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count values(Processes.process) - as process values(Processes.parent_process) as parent_process min(_time) as firstTime - max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe - OR Processes.process_name=wmic.exe) Processes.process=*delete* Processes.process=*shadow* - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `deleting_shadow_copies_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: vssadmin.exe and wmic.exe are standard applications shipped - with modern versions of windows. They may be used by administrators to legitimately - delete old backup copies, although this is typically rare. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name=vssadmin.exe + OR + Processes.process_name=wmic.exe + ) + Processes.process=*delete* Processes.process=*shadow* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `deleting_shadow_copies_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: vssadmin.exe and wmic.exe are standard applications shipped with modern versions of windows. They may be used by administrators to legitimately delete old backup copies, although this is typically rare. references: -- https://blogs.vmware.com/security/2022/10/lockbit-3-0-also-known-as-lockbit-black.html + - https://blogs.vmware.com/security/2022/10/lockbit-3-0-also-known-as-lockbit-black.html drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to delete shadow copies. - risk_objects: - - field: user - type: user - score: 81 - - field: dest - type: system - score: 81 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to delete shadow copies. + risk_objects: + - field: user + type: user + score: 81 + - field: dest + type: system + score: 81 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Rhysida Ransomware - - Prestige Ransomware - - CISA AA22-264A - - LockBit Ransomware - - SamSam Ransomware - - Chaos Ransomware - - Black Basta Ransomware - - DarkGate Malware - - Ransomware - - Windows Log Manipulation - - Compromised Windows Host - - Clop Ransomware - - Cactus Ransomware - - Medusa Ransomware - - VanHelsing Ransomware - - Termite Ransomware - - Storm-2460 CLFS Zero Day Exploitation - asset_type: Endpoint - mitre_attack_id: - - T1490 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Rhysida Ransomware + - Prestige Ransomware + - CISA AA22-264A + - LockBit Ransomware + - SamSam Ransomware + - Chaos Ransomware + - Black Basta Ransomware + - DarkGate Malware + - Ransomware + - Windows Log Manipulation + - Compromised Windows Host + - Clop Ransomware + - Cactus Ransomware + - Medusa Ransomware + - VanHelsing Ransomware + - Termite Ransomware + - Storm-2460 CLFS Zero Day Exploitation + asset_type: Endpoint + mitre_attack_id: + - T1490 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_azurehound_command_line_arguments.yml b/detections/endpoint/detect_azurehound_command_line_arguments.yml index 417c1db1e0..73584b4d2b 100644 --- a/detections/endpoint/detect_azurehound_command_line_arguments.yml +++ b/detections/endpoint/detect_azurehound_command_line_arguments.yml @@ -1,93 +1,78 @@ name: Detect AzureHound Command-Line Arguments id: 26f02e96-c300-11eb-b611-acde48001122 -version: 12 -date: '2026-01-14' +version: 13 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of the `Invoke-AzureHound` - command-line argument, commonly used by the AzureHound tool. It leverages data from - Endpoint Detection and Response (EDR) agents, focusing on process names and command-line - executions. This activity is significant because AzureHound is often used for reconnaissance - in Azure environments, potentially exposing sensitive information. If confirmed - malicious, this activity could allow an attacker to map out Azure Active Directory - structures, aiding in further attacks and privilege escalation. +description: The following analytic detects the execution of the `Invoke-AzureHound` command-line argument, commonly used by the AzureHound tool. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because AzureHound is often used for reconnaissance in Azure environments, potentially exposing sensitive information. If confirmed malicious, this activity could allow an attacker to map out Azure Active Directory structures, aiding in further attacks and privilege escalation. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*invoke-azurehound*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `detect_azurehound_command_line_arguments_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process IN ("*invoke-azurehound*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_azurehound_command_line_arguments_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://attack.mitre.org/software/S0521/ -- https://github.com/BloodHoundAD/BloodHound/tree/master/Collectors -- https://posts.specterops.io/introducing-bloodhound-4-0-the-azure-update-9b2b26c5e350 -- https://github.com/BloodHoundAD/Legacy-AzureHound.ps1/blob/master/AzureHound.ps1 + - https://attack.mitre.org/software/S0521/ + - https://github.com/BloodHoundAD/BloodHound/tree/master/Collectors + - https://posts.specterops.io/introducing-bloodhound-4-0-the-azure-update-9b2b26c5e350 + - https://github.com/BloodHoundAD/Legacy-AzureHound.ps1/blob/master/AzureHound.ps1 drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ using AzureHound to enumerate AzureAD. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ using AzureHound to enumerate AzureAD. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Windows Discovery Techniques - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1069.001 - - T1069.002 - - T1087.001 - - T1087.002 - - T1482 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Discovery Techniques + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1069.001 + - T1069.002 + - T1087.001 + - T1087.002 + - T1482 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_azurehound_file_modifications.yml b/detections/endpoint/detect_azurehound_file_modifications.yml index f3c822536f..9ccfd535ac 100644 --- a/detections/endpoint/detect_azurehound_file_modifications.yml +++ b/detections/endpoint/detect_azurehound_file_modifications.yml @@ -1,82 +1,69 @@ name: Detect AzureHound File Modifications id: 1c34549e-c31b-11eb-996b-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the creation of specific AzureHound-related - files, such as `*-azurecollection.zip` and various `.json` files, on disk. It leverages - data from the Endpoint.Filesystem datamodel, focusing on file creation events with - specific filenames. This activity is significant because AzureHound is a tool used - to gather information about Azure environments, similar to SharpHound for on-premises - Active Directory. If confirmed malicious, this activity could indicate an attacker - is collecting sensitive Azure environment data, potentially leading to further exploitation - or privilege escalation within the cloud infrastructure. +description: The following analytic detects the creation of specific AzureHound-related files, such as `*-azurecollection.zip` and various `.json` files, on disk. It leverages data from the Endpoint.Filesystem datamodel, focusing on file creation events with specific filenames. This activity is significant because AzureHound is a tool used to gather information about Azure environments, similar to SharpHound for on-premises Active Directory. If confirmed malicious, this activity could indicate an attacker is collecting sensitive Azure environment data, potentially leading to further exploitation or privilege escalation within the cloud infrastructure. data_source: -- Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*-azurecollection.zip", - "*-azprivroleadminrights.json", "*-azglobaladminrights.json", "*-azcloudappadmins.json", - "*-azapplicationadmins.json") by Filesystem.action Filesystem.dest Filesystem.file_access_time - Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name - Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid - Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_azurehound_file_modifications_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on file modifications that include the name of the process, and file, responsible - for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` - node. -known_false_positives: False positives should be limited as the analytic is specific - to a filename with extension .zip. Filter as needed. + - Sysmon EventID 11 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.file_name IN ("*-azurecollection.zip", "*-azprivroleadminrights.json", "*-azglobaladminrights.json", "*-azcloudappadmins.json", "*-azapplicationadmins.json") + BY Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path Filesystem.file_acl + Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_azurehound_file_modifications_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on file modifications that include the name of the process, and file, responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. +known_false_positives: False positives should be limited as the analytic is specific to a filename with extension .zip. Filter as needed. references: -- https://posts.specterops.io/introducing-bloodhound-4-0-the-azure-update-9b2b26c5e350 -- https://github.com/BloodHoundAD/Legacy-AzureHound.ps1/blob/master/AzureHound.ps1 + - https://posts.specterops.io/introducing-bloodhound-4-0-the-azure-update-9b2b26c5e350 + - https://github.com/BloodHoundAD/Legacy-AzureHound.ps1/blob/master/AzureHound.ps1 drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A file - $file_name$ was written to disk that is related to AzureHound, - a AzureAD enumeration utility, has occurred on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 63 - - field: dest - type: system - score: 63 - threat_objects: - - field: file_name - type: file_name + message: A file - $file_name$ was written to disk that is related to AzureHound, a AzureAD enumeration utility, has occurred on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 63 + - field: dest + type: system + score: 63 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - Windows Discovery Techniques - asset_type: Endpoint - mitre_attack_id: - - T1069.001 - - T1069.002 - - T1087.001 - - T1087.002 - - T1482 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Discovery Techniques + asset_type: Endpoint + mitre_attack_id: + - T1069.001 + - T1069.002 + - T1087.001 + - T1087.002 + - T1482 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_baron_samedit_cve_2021_3156.yml b/detections/endpoint/detect_baron_samedit_cve_2021_3156.yml index b3e89b0f0f..4fb1986df7 100644 --- a/detections/endpoint/detect_baron_samedit_cve_2021_3156.yml +++ b/detections/endpoint/detect_baron_samedit_cve_2021_3156.yml @@ -5,39 +5,29 @@ date: '2026-01-14' author: Shannon Davis, Splunk status: experimental type: TTP -description: The following analytic detects attempts to exploit the Baron Samedit - vulnerability (CVE-2021-3156) by identifying the use of the "sudoedit -s \\" command. - This detection leverages logs from Linux systems, specifically searching for instances - of the sudoedit command with the "-s" flag followed by a double quote. This activity - is significant because it indicates an attempt to exploit a known vulnerability - that allows attackers to gain root privileges. If confirmed malicious, this could - lead to complete system compromise, unauthorized access to sensitive data, and potential - data breaches. +description: The following analytic detects attempts to exploit the Baron Samedit vulnerability (CVE-2021-3156) by identifying the use of the "sudoedit -s \\" command. This detection leverages logs from Linux systems, specifically searching for instances of the sudoedit command with the "-s" flag followed by a double quote. This activity is significant because it indicates an attempt to exploit a known vulnerability that allows attackers to gain root privileges. If confirmed malicious, this could lead to complete system compromise, unauthorized access to sensitive data, and potential data breaches. data_source: [] search: '`linux_hosts` "sudoedit -s \\" | `detect_baron_samedit_cve_2021_3156_filter`' -how_to_implement: Splunk Universal Forwarder running on Linux systems, capturing logs - from the /var/log directory. The vulnerability is exposed when a non privledged - user tries passing in a single \ character at the end of the command while using - the shell and edit flags. +how_to_implement: Splunk Universal Forwarder running on Linux systems, capturing logs from the /var/log directory. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags. known_false_positives: No false positives have been identified at this time. references: [] rba: - message: Potential Baron Samedit behavior on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: Potential Baron Samedit behavior on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Baron Samedit CVE-2021-3156 - asset_type: Endpoint - cve: - - CVE-2021-3156 - mitre_attack_id: - - T1068 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Baron Samedit CVE-2021-3156 + asset_type: Endpoint + cve: + - CVE-2021-3156 + mitre_attack_id: + - T1068 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/detect_baron_samedit_cve_2021_3156_segfault.yml b/detections/endpoint/detect_baron_samedit_cve_2021_3156_segfault.yml index 1c31aa7ce3..0e80edddc3 100644 --- a/detections/endpoint/detect_baron_samedit_cve_2021_3156_segfault.yml +++ b/detections/endpoint/detect_baron_samedit_cve_2021_3156_segfault.yml @@ -1,46 +1,38 @@ name: Detect Baron Samedit CVE-2021-3156 Segfault id: 10f2bae0-bbe6-4984-808c-37dc1c67980d -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Shannon Davis, Splunk status: experimental type: TTP -description: The following analytic identifies a heap-based buffer overflow in sudoedit - by detecting Linux logs containing both "sudoedit" and "segfault" terms. This detection - leverages Splunk to monitor for more than five occurrences of these terms on a single - host within a specified timeframe. This activity is significant because exploiting - this vulnerability (CVE-2021-3156) can allow attackers to gain root privileges, - leading to potential system compromise, unauthorized access, and data breaches. - If confirmed malicious, this could result in elevated privileges and full control - over the affected system, posing a severe security risk. +description: The following analytic identifies a heap-based buffer overflow in sudoedit by detecting Linux logs containing both "sudoedit" and "segfault" terms. This detection leverages Splunk to monitor for more than five occurrences of these terms on a single host within a specified timeframe. This activity is significant because exploiting this vulnerability (CVE-2021-3156) can allow attackers to gain root privileges, leading to potential system compromise, unauthorized access, and data breaches. If confirmed malicious, this could result in elevated privileges and full control over the affected system, posing a severe security risk. data_source: [] -search: '`linux_hosts` TERM(sudoedit) TERM(segfault) | stats count min(_time) as firstTime - max(_time) as lastTime by host | where count > 5 | `detect_baron_samedit_cve_2021_3156_segfault_filter`' -how_to_implement: Splunk Universal Forwarder running on Linux systems (tested on Centos - and Ubuntu), where segfaults are being logged. This also captures instances where - the exploit has been compiled into a binary. The detection looks for greater than - 5 instances of sudoedit combined with segfault over your search time period on a - single host -known_false_positives: If sudoedit is throwing segfaults for other reasons this will - pick those up too. +search: |- + `linux_hosts` TERM(sudoedit) TERM(segfault) + | stats count min(_time) as firstTime max(_time) as lastTime + BY host + | where count > 5 + | `detect_baron_samedit_cve_2021_3156_segfault_filter` +how_to_implement: Splunk Universal Forwarder running on Linux systems (tested on Centos and Ubuntu), where segfaults are being logged. This also captures instances where the exploit has been compiled into a binary. The detection looks for greater than 5 instances of sudoedit combined with segfault over your search time period on a single host +known_false_positives: If sudoedit is throwing segfaults for other reasons this will pick those up too. references: [] rba: - message: Potential Baron Samedit segfault on $host$ - risk_objects: - - field: host - type: system - score: 25 - threat_objects: [] + message: Potential Baron Samedit segfault on $host$ + risk_objects: + - field: host + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Baron Samedit CVE-2021-3156 - asset_type: Endpoint - cve: - - CVE-2021-3156 - mitre_attack_id: - - T1068 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Baron Samedit CVE-2021-3156 + asset_type: Endpoint + cve: + - CVE-2021-3156 + mitre_attack_id: + - T1068 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/detect_baron_samedit_cve_2021_3156_via_osquery.yml b/detections/endpoint/detect_baron_samedit_cve_2021_3156_via_osquery.yml index 95d9312bd3..dcb4b68382 100644 --- a/detections/endpoint/detect_baron_samedit_cve_2021_3156_via_osquery.yml +++ b/detections/endpoint/detect_baron_samedit_cve_2021_3156_via_osquery.yml @@ -5,39 +5,29 @@ date: '2026-01-14' author: Shannon Davis, Splunk status: experimental type: TTP -description: The following analytic detects the execution of the "sudoedit -s *" command, - which is associated with the Baron Samedit CVE-2021-3156 heap-based buffer overflow - vulnerability. This detection leverages the `osquery_process` data source to identify - instances where this specific command is run. This activity is significant because - it indicates an attempt to exploit a known vulnerability that allows privilege escalation. - If confirmed malicious, an attacker could gain full control of the system, execute - arbitrary code, or access sensitive data, leading to potential data breaches and - system disruptions. +description: The following analytic detects the execution of the "sudoedit -s *" command, which is associated with the Baron Samedit CVE-2021-3156 heap-based buffer overflow vulnerability. This detection leverages the `osquery_process` data source to identify instances where this specific command is run. This activity is significant because it indicates an attempt to exploit a known vulnerability that allows privilege escalation. If confirmed malicious, an attacker could gain full control of the system, execute arbitrary code, or access sensitive data, leading to potential data breaches and system disruptions. data_source: [] search: '`osquery_process` | search "columns.cmdline"="sudoedit -s \\*" | `detect_baron_samedit_cve_2021_3156_via_osquery_filter`' -how_to_implement: OSQuery installed and configured to pick up process events (info - at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402. - The vulnerability is exposed when a non privledged user tries passing in a single - \ character at the end of the command while using the shell and edit flags. +how_to_implement: OSQuery installed and configured to pick up process events (info at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags. known_false_positives: No false positives have been identified at this time. references: [] rba: - message: Potential Baron Samedit behavior on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: Potential Baron Samedit behavior on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Baron Samedit CVE-2021-3156 - asset_type: Endpoint - cve: - - CVE-2021-3156 - mitre_attack_id: - - T1068 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Baron Samedit CVE-2021-3156 + asset_type: Endpoint + cve: + - CVE-2021-3156 + mitre_attack_id: + - T1068 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/detect_certify_command_line_arguments.yml b/detections/endpoint/detect_certify_command_line_arguments.yml index d77efe809e..33580c8af3 100644 --- a/detections/endpoint/detect_certify_command_line_arguments.yml +++ b/detections/endpoint/detect_certify_command_line_arguments.yml @@ -1,89 +1,74 @@ name: Detect Certify Command Line Arguments id: e6d2dc61-a8b9-4b03-906c-da0ca75d71b8 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Steven Dick status: production type: TTP -description: The following analytic detects the use of Certify or Certipy tools to - enumerate Active Directory Certificate Services (AD CS) environments. It leverages - Endpoint Detection and Response (EDR) data, focusing on specific command-line arguments - associated with these tools. This activity is significant because it indicates potential - reconnaissance or exploitation attempts targeting AD CS, which could lead to unauthorized - access or privilege escalation. If confirmed malicious, attackers could gain insights - into the AD CS infrastructure, potentially compromising sensitive certificates and - escalating their privileges within the network. +description: The following analytic detects the use of Certify or Certipy tools to enumerate Active Directory Certificate Services (AD CS) environments. It leverages Endpoint Detection and Response (EDR) data, focusing on specific command-line arguments associated with these tools. This activity is significant because it indicates potential reconnaissance or exploitation attempts targeting AD CS, which could lead to unauthorized access or privilege escalation. If confirmed malicious, attackers could gain insights into the AD CS infrastructure, potentially compromising sensitive certificates and escalating their privileges within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("* find - *","* auth *","* request *","* req *","* download *",) AND Processes.process IN - ("* /vulnerable*","* /enrolleeSuppliesSubject *","* /json /outfile*","* /ca*", "* - -username *","* -u *") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `detect_certify_command_line_arguments_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process IN ("* find *","* auth *","* request *","* req *","* download *",) + AND + Processes.process IN ("* /vulnerable*","* /enrolleeSuppliesSubject *","* /json /outfile*","* /ca*", "* -username *","* -u *") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_certify_command_line_arguments_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://github.com/GhostPack/Certify -- https://github.com/ly4k/Certipy -- https://specterops.io/wp-content/uploads/sites/3/2022/06/Certified_Pre-Owned.pdf + - https://github.com/GhostPack/Certify + - https://github.com/ly4k/Certipy + - https://specterops.io/wp-content/uploads/sites/3/2022/06/Certified_Pre-Owned.pdf drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Certify/Certipy arguments detected on $dest$. - risk_objects: - - field: dest - type: system - score: 90 - threat_objects: - - field: process_name - type: process_name - - field: process_name - type: process_name + message: Certify/Certipy arguments detected on $dest$. + risk_objects: + - field: dest + type: system + score: 90 + threat_objects: + - field: process_name + type: process_name + - field: process_name + type: process_name tags: - analytic_story: - - Compromised Windows Host - - Windows Certificate Services - - Ingress Tool Transfer - asset_type: Endpoint - mitre_attack_id: - - T1649 - - T1105 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - Windows Certificate Services + - Ingress Tool Transfer + asset_type: Endpoint + mitre_attack_id: + - T1649 + - T1105 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/certify_abuse/certify_esc1_abuse_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/certify_abuse/certify_esc1_abuse_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_certify_with_powershell_script_block_logging.yml b/detections/endpoint/detect_certify_with_powershell_script_block_logging.yml index d364adb6fb..7d194f9eaf 100644 --- a/detections/endpoint/detect_certify_with_powershell_script_block_logging.yml +++ b/detections/endpoint/detect_certify_with_powershell_script_block_logging.yml @@ -1,80 +1,67 @@ name: Detect Certify With PowerShell Script Block Logging id: f533ca6c-9440-4686-80cb-7f294c07812a -version: 10 -date: '2026-01-14' +version: 11 +date: '2026-02-25' author: Steven Dick status: production type: TTP -description: - The following analytic detects the use of the Certify tool via an in-memory - PowerShell function to enumerate Active Directory Certificate Services (AD CS) environments. - It leverages PowerShell Script Block Logging (EventCode 4104) to identify specific - command patterns associated with Certify's enumeration and exploitation functions. - This activity is significant as it indicates potential reconnaissance or exploitation - attempts against AD CS, which could lead to unauthorized certificate issuance. If - confirmed malicious, attackers could leverage this to escalate privileges, persist - in the environment, or access sensitive information by abusing AD CS. +description: The following analytic detects the use of the Certify tool via an in-memory PowerShell function to enumerate Active Directory Certificate Services (AD CS) environments. It leverages PowerShell Script Block Logging (EventCode 4104) to identify specific command patterns associated with Certify's enumeration and exploitation functions. This activity is significant as it indicates potential reconnaissance or exploitation attempts against AD CS, which could lead to unauthorized certificate issuance. If confirmed malicious, attackers could leverage this to escalate privileges, persist in the environment, or access sensitive information by abusing AD CS. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 (ScriptBlockText IN ("*find *") AND ScriptBlockText - IN ("* /vulnerable*","* -vulnerable*","* /enrolleeSuppliesSubject *","* /json /outfile*")) - OR (ScriptBlockText IN (,"*auth *","*req *",) AND ScriptBlockText IN ("* -ca *","* - -username *","* -u *")) OR (ScriptBlockText IN ("*request *","*download *") AND - ScriptBlockText IN ("* /ca:*")) | fillnull | stats count min(_time) as firstTime - max(_time) as lastTime by dest signature signature_id user_id vendor_product EventID - Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | eval file_name = case(isnotnull(file_name),file_name,true(),"unknown") - | eval signature = substr(command,0,256) | `detect_certify_with_powershell_script_block_logging_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba.. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 (ScriptBlockText IN ("*find *") AND ScriptBlockText IN ("* /vulnerable*","* -vulnerable*","* /enrolleeSuppliesSubject *","* /json /outfile*")) OR (ScriptBlockText IN (,"*auth *","*req *",) AND ScriptBlockText IN ("* -ca *","* -username *","* -u *")) OR (ScriptBlockText IN ("*request *","*download *") AND ScriptBlockText IN ("* /ca:*")) + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | eval file_name = case(isnotnull(file_name),file_name,true(),"unknown") + | eval signature = substr(command,0,256) + | `detect_certify_with_powershell_script_block_logging_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba.. known_false_positives: No false positives have been identified at this time. references: - - https://github.com/GhostPack/Certify - - https://specterops.io/wp-content/uploads/sites/3/2022/06/Certified_Pre-Owned.pdf + - https://github.com/GhostPack/Certify + - https://specterops.io/wp-content/uploads/sites/3/2022/06/Certified_Pre-Owned.pdf drilldown_searches: - - name: View the detection results for - "$dest$" and "$user_id$" - search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user_id$" + search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Certify arguments through PowerShell detected on $dest$. - risk_objects: - - field: dest - type: system - score: 90 - - field: user_id - type: user - score: 90 - threat_objects: [] + message: Certify arguments through PowerShell detected on $dest$. + risk_objects: + - field: dest + type: system + score: 90 + - field: user_id + type: user + score: 90 + threat_objects: [] tags: - analytic_story: - - Windows Certificate Services - - Malicious PowerShell - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - - T1649 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Certificate Services + - Malicious PowerShell + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + - T1649 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/certify_abuse/certify_esc1_abuse_powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/certify_abuse/certify_esc1_abuse_powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_certipy_file_modifications.yml b/detections/endpoint/detect_certipy_file_modifications.yml index e6f9478a75..ab455e2615 100644 --- a/detections/endpoint/detect_certipy_file_modifications.yml +++ b/detections/endpoint/detect_certipy_file_modifications.yml @@ -1,78 +1,67 @@ name: Detect Certipy File Modifications id: 7e3df743-b1d8-4631-8fa8-bd5819688876 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Steven Dick status: production type: TTP -description: The following analytic detects the use of the Certipy tool to enumerate - Active Directory Certificate Services (AD CS) environments by identifying unique - file modifications. It leverages endpoint process and filesystem data to spot the - creation of files with specific names or extensions associated with Certipy's information - gathering and exfiltration activities. This activity is significant as it indicates - potential reconnaissance and data exfiltration efforts by an attacker. If confirmed - malicious, this could lead to unauthorized access to sensitive AD CS information, - enabling further attacks or privilege escalation within the network. +description: The following analytic detects the use of the Certipy tool to enumerate Active Directory Certificate Services (AD CS) environments by identifying unique file modifications. It leverages endpoint process and filesystem data to spot the creation of files with specific names or extensions associated with Certipy's information gathering and exfiltration activities. This activity is significant as it indicates potential reconnaissance and data exfiltration efforts by an attacker. If confirmed malicious, this could lead to unauthorized access to sensitive AD CS information, enabling further attacks or privilege escalation within the network. data_source: -- Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from - datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*_certipy.zip","*_certipy.txt", "*_certipy.json", "*.ccache") - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time - Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product - | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `detect_certipy_file_modifications_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints as well as file creation or deletion events. + - Sysmon EventID 11 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.file_name IN ("*_certipy.zip","*_certipy.txt", "*_certipy.json", "*.ccache") + BY Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path Filesystem.file_acl + Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_certipy_file_modifications_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints as well as file creation or deletion events. known_false_positives: No false positives have been identified at this time. references: -- https://github.com/ly4k/Certipy + - https://github.com/ly4k/Certipy drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious files $file_name$ related to Certipy detected on $dest$ - risk_objects: - - field: dest - type: system - score: 45 - - field: user - type: user - score: 45 - threat_objects: - - field: file_name - type: file_name + message: Suspicious files $file_name$ related to Certipy detected on $dest$ + risk_objects: + - field: dest + type: system + score: 45 + - field: user + type: user + score: 45 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - Windows Certificate Services - - Data Exfiltration - - Ingress Tool Transfer - asset_type: Endpoint - mitre_attack_id: - - T1649 - - T1560 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Certificate Services + - Data Exfiltration + - Ingress Tool Transfer + asset_type: Endpoint + mitre_attack_id: + - T1649 + - T1560 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/certify_abuse/certify_esc1_abuse_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/certify_abuse/certify_esc1_abuse_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_computer_changed_with_anonymous_account.yml b/detections/endpoint/detect_computer_changed_with_anonymous_account.yml index a9b389f22e..d95308af2b 100644 --- a/detections/endpoint/detect_computer_changed_with_anonymous_account.yml +++ b/detections/endpoint/detect_computer_changed_with_anonymous_account.yml @@ -1,44 +1,40 @@ name: Detect Computer Changed with Anonymous Account id: 1400624a-d42d-484d-8843-e6753e6e3645 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Rod Soto, Jose Hernandez, Splunk status: experimental type: Hunting -description: The following analytic detects changes to computer accounts using an - anonymous logon. It leverages Windows Security Event Codes 4742 (Computer Change) - and 4624 (Successful Logon) with the TargetUserName set to "ANONYMOUS LOGON" and - LogonType 3. This activity is significant because anonymous logons should not typically - be modifying computer accounts, indicating potential unauthorized access or misconfiguration. - If confirmed malicious, this could allow an attacker to alter computer accounts, - potentially leading to privilege escalation or persistent access within the network. +description: The following analytic detects changes to computer accounts using an anonymous logon. It leverages Windows Security Event Codes 4742 (Computer Change) and 4624 (Successful Logon) with the TargetUserName set to "ANONYMOUS LOGON" and LogonType 3. This activity is significant because anonymous logons should not typically be modifying computer accounts, indicating potential unauthorized access or misconfiguration. If confirmed malicious, this could allow an attacker to alter computer accounts, potentially leading to privilege escalation or persistent access within the network. data_source: -- Windows Event Log Security 4624 -- Windows Event Log Security 4742 -search: '`wineventlog_security` EventCode=4624 OR EventCode=4742 TargetUserName="ANONYMOUS - LOGON" LogonType=3 | stats count min(_time) as firstTime max(_time) as lastTime - by action app authentication_method dest dvc process process_id process_name process_path - signature signature_id src src_port status subject user user_group vendor_product - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_computer_changed_with_anonymous_account_filter`' -how_to_implement: This search requires audit computer account management to be enabled - on the system in order to generate Event ID 4742. We strongly recommend that you - specify your environment-specific configurations (index, source, sourcetype, etc.) - for Windows Event Logs. Replace the macro definition with configurations for your - Splunk environment. The search also uses a post-filter macro designed to filter - out known false positives. + - Windows Event Log Security 4624 + - Windows Event Log Security 4742 +search: |- + `wineventlog_security` EventCode=4624 OR EventCode=4742 TargetUserName="ANONYMOUS LOGON" LogonType=3 + | stats count min(_time) as firstTime max(_time) as lastTime + BY action app authentication_method + dest dvc process + process_id process_name process_path + signature signature_id src + src_port status subject + user user_group vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_computer_changed_with_anonymous_account_filter` +how_to_implement: This search requires audit computer account management to be enabled on the system in order to generate Event ID 4742. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Event Logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. known_false_positives: No false positives have been identified at this time. references: -- https://www.lares.com/blog/from-lares-labs-defensive-guidance-for-zerologon-cve-2020-1472/ + - https://www.lares.com/blog/from-lares-labs-defensive-guidance-for-zerologon-cve-2020-1472/ tags: - analytic_story: - - Detect Zerologon Attack - asset_type: Windows - cve: - - CVE-2020-1472 - mitre_attack_id: - - T1210 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Detect Zerologon Attack + asset_type: Windows + cve: + - CVE-2020-1472 + mitre_attack_id: + - T1210 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/detect_copy_of_shadowcopy_with_script_block_logging.yml b/detections/endpoint/detect_copy_of_shadowcopy_with_script_block_logging.yml index 831d0c02de..68317bd084 100644 --- a/detections/endpoint/detect_copy_of_shadowcopy_with_script_block_logging.yml +++ b/detections/endpoint/detect_copy_of_shadowcopy_with_script_block_logging.yml @@ -5,78 +5,52 @@ date: '2025-06-24' author: Michael Haag, Splunk status: production type: TTP -description: - The following analytic detects the use of PowerShell commands to copy - the SAM, SYSTEM, or SECURITY hives, which are critical for credential theft. It - leverages PowerShell Script Block Logging (EventCode=4104) to capture and analyze - the full command executed. This activity is significant as it indicates an attempt - to exfiltrate sensitive registry hives for offline password cracking. If confirmed - malicious, this could lead to unauthorized access to credentials, enabling further - compromise of the system and potential lateral movement within the network. +description: The following analytic detects the use of PowerShell commands to copy the SAM, SYSTEM, or SECURITY hives, which are critical for credential theft. It leverages PowerShell Script Block Logging (EventCode=4104) to capture and analyze the full command executed. This activity is significant as it indicates an attempt to exfiltrate sensitive registry hives for offline password cracking. If confirmed malicious, this could lead to unauthorized access to credentials, enabling further compromise of the system and potential lateral movement within the network. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText IN ("*copy*","*[System.IO.File]::Copy*") - AND ScriptBlockText IN ("*System32\\config\\SAM*", "*System32\\config\\SYSTEM*","*System32\\config\\SECURITY*") - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `detect_copy_of_shadowcopy_with_script_block_logging_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - Limited false positives as the scope is limited to SAM, SYSTEM - and SECURITY hives. + - Powershell Script Block Logging 4104 +search: '`powershell` EventCode=4104 ScriptBlockText IN ("*copy*","*[System.IO.File]::Copy*") AND ScriptBlockText IN ("*System32\\config\\SAM*", "*System32\\config\\SYSTEM*","*System32\\config\\SECURITY*") | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_copy_of_shadowcopy_with_script_block_logging_filter`' +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: Limited false positives as the scope is limited to SAM, SYSTEM and SECURITY hives. references: - - https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-36934 - - https://github.com/GossiTheDog/HiveNightmare - - https://github.com/JumpsecLabs/Guidance-Advice/tree/main/SAM_Permissions + - https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-36934 + - https://github.com/GossiTheDog/HiveNightmare + - https://github.com/JumpsecLabs/Guidance-Advice/tree/main/SAM_Permissions drilldown_searches: - - name: View the detection results for - "$user_id$" and "$dest$" - search: '%original_detection_search% | search user_id = "$user_id$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user_id$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user_id$" and "$dest$" + search: '%original_detection_search% | search user_id = "$user_id$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user_id$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - PowerShell was identified running a script to capture the SAM hive on endpoint - $dest$ by user $user_id$. - risk_objects: - - field: user_id - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: [] + message: PowerShell was identified running a script to capture the SAM hive on endpoint $dest$ by user $user_id$. + risk_objects: + - field: user_id + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: [] tags: - analytic_story: - - Credential Dumping - - VanHelsing Ransomware - asset_type: Endpoint - cve: - - CVE-2021-36934 - mitre_attack_id: - - T1003.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Credential Dumping + - VanHelsing Ransomware + asset_type: Endpoint + cve: + - CVE-2021-36934 + mitre_attack_id: + - T1003.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/detect_copy_of_shadowcopy_with_script_block_logging/windows-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/detect_copy_of_shadowcopy_with_script_block_logging/windows-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_credential_dumping_through_lsass_access.yml b/detections/endpoint/detect_credential_dumping_through_lsass_access.yml index 0b019898fb..a5cda82d74 100644 --- a/detections/endpoint/detect_credential_dumping_through_lsass_access.yml +++ b/detections/endpoint/detect_credential_dumping_through_lsass_access.yml @@ -1,84 +1,67 @@ name: Detect Credential Dumping through LSASS access id: 2c365e57-4414-4540-8dc0-73ab10729996 -version: 11 -date: '2025-10-14' +version: 12 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects attempts to read LSASS memory, - indicative of credential dumping. It leverages Sysmon EventCode 10, filtering - for specific access permissions (0x1010 and 0x1410) on the lsass.exe process. - This activity is significant because it suggests an attacker is trying to - extract credentials from LSASS memory, potentially leading to unauthorized - access, data breaches, and compromise of sensitive information. If confirmed - malicious, this could enable attackers to escalate privileges, move laterally - within the network, or exfiltrate data. Extensive triage is necessary to - differentiate between malicious and benign activities. +description: The following analytic detects attempts to read LSASS memory, indicative of credential dumping. It leverages Sysmon EventCode 10, filtering for specific access permissions (0x1010 and 0x1410) on the lsass.exe process. This activity is significant because it suggests an attacker is trying to extract credentials from LSASS memory, potentially leading to unauthorized access, data breaches, and compromise of sensitive information. If confirmed malicious, this could enable attackers to escalate privileges, move laterally within the network, or exfiltrate data. Extensive triage is necessary to differentiate between malicious and benign activities. data_source: -- Sysmon EventID 10 -search: '`sysmon` EventCode=10 TargetImage=*lsass.exe (GrantedAccess=0x1010 OR GrantedAccess=0x1410) - | stats count min(_time) as firstTime max(_time) as lastTime by CallTrace EventID - GrantedAccess Guid Opcode ProcessID SecurityID SourceImage SourceProcessGUID SourceProcessId - TargetImage TargetProcessGUID TargetProcessId UserID dest granted_access parent_process_exec - parent_process_guid parent_process_id parent_process_name parent_process_path process_exec - process_guid process_id process_name process_path signature signature_id user_id - vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `detect_credential_dumping_through_lsass_access_filter`' -how_to_implement: This search needs Sysmon Logs and a sysmon configuration, - which includes EventCode 10 with lsass.exe. This search uses an input macro - named `sysmon`. We strongly recommend that you specify your - environment-specific configurations (index, source, sourcetype, etc.) for - Windows Sysmon logs. Replace the macro definition with configurations for your - Splunk environment. The search also uses a post-filter macro designed to - filter out known false positives. -known_false_positives: The activity may be legitimate. Other tools can access - lsass for legitimate reasons, and it's possible this event could be generated - in those cases. In these cases, false positives should be fairly obvious and - you may need to tweak the search to eliminate noise. + - Sysmon EventID 10 +search: |- + `sysmon` EventCode=10 TargetImage=*lsass.exe (GrantedAccess=0x1010 OR GrantedAccess=0x1410) + | stats count min(_time) as firstTime max(_time) as lastTime + BY CallTrace EventID GrantedAccess + Guid Opcode ProcessID + SecurityID SourceImage SourceProcessGUID + SourceProcessId TargetImage TargetProcessGUID + TargetProcessId UserID dest + granted_access parent_process_exec parent_process_guid + parent_process_id parent_process_name parent_process_path + process_exec process_guid process_id + process_name process_path signature + signature_id user_id vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_credential_dumping_through_lsass_access_filter` +how_to_implement: This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 10 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. +known_false_positives: The activity may be legitimate. Other tools can access lsass for legitimate reasons, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" and "$TargetImage$" - search: '%original_detection_search% | search dest = "$dest$" TargetImage = "$TargetImage$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$TargetImage$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$TargetImage$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$TargetImage$" + search: '%original_detection_search% | search dest = "$dest$" TargetImage = "$TargetImage$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$TargetImage$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$TargetImage$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The $SourceImage$ has attempted access to read $TargetImage$ was - identified on endpoint $dest$, this is indicative of credential dumping and - should be investigated. - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: [] + message: The $SourceImage$ has attempted access to read $TargetImage$ was identified on endpoint $dest$, this is indicative of credential dumping and should be investigated. + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: [] tags: - analytic_story: - - Detect Zerologon Attack - - CISA AA23-347A - - Credential Dumping - - BlackSuit Ransomware - - Lokibot - - Scattered Lapsus$ Hunters - asset_type: Windows - mitre_attack_id: - - T1003.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Detect Zerologon Attack + - CISA AA23-347A + - Credential Dumping + - BlackSuit Ransomware + - Lokibot + - Scattered Lapsus$ Hunters + asset_type: Windows + mitre_attack_id: + - T1003.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_empire_with_powershell_script_block_logging.yml b/detections/endpoint/detect_empire_with_powershell_script_block_logging.yml index 8ab0a49587..0fa9b1c167 100644 --- a/detections/endpoint/detect_empire_with_powershell_script_block_logging.yml +++ b/detections/endpoint/detect_empire_with_powershell_script_block_logging.yml @@ -1,86 +1,70 @@ name: Detect Empire with PowerShell Script Block Logging id: bc1dc6b8-c954-11eb-bade-acde48001122 -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: - The following analytic detects suspicious PowerShell execution indicative - of PowerShell-Empire activity. It leverages PowerShell Script Block Logging (EventCode=4104) - to capture and analyze commands sent to PowerShell, specifically looking for patterns - involving `system.net.webclient` and base64 encoding. This behavior is significant - as it often represents initial stagers used by PowerShell-Empire, a known post-exploitation - framework. If confirmed malicious, this activity could allow attackers to download - and execute additional payloads, leading to potential code execution, data exfiltration, - or further compromise of the affected system. +description: The following analytic detects suspicious PowerShell execution indicative of PowerShell-Empire activity. It leverages PowerShell Script Block Logging (EventCode=4104) to capture and analyze commands sent to PowerShell, specifically looking for patterns involving `system.net.webclient` and base64 encoding. This behavior is significant as it often represents initial stagers used by PowerShell-Empire, a known post-exploitation framework. If confirmed malicious, this activity could allow attackers to download and execute additional payloads, leading to potential code execution, data exfiltration, or further compromise of the affected system. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 (ScriptBlockText=*system.net.webclient* AND - ScriptBlockText=*frombase64string*) | fillnull | stats count min(_time) as firstTime - max(_time) as lastTime by dest signature signature_id user_id vendor_product EventID - Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `detect_empire_with_powershell_script_block_logging_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - False positives may only pertain to it not being related to - Empire, but another framework. Filter as needed if any applications use the same - pattern. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 (ScriptBlockText=*system.net.webclient* AND ScriptBlockText=*frombase64string*) + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_empire_with_powershell_script_block_logging_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: False positives may only pertain to it not being related to Empire, but another framework. Filter as needed if any applications use the same pattern. references: - - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. - - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 - - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf - - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ - - https://github.com/BC-SECURITY/Empire - - https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html + - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 + - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf + - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ + - https://github.com/BC-SECURITY/Empire + - https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html drilldown_searches: - - name: View the detection results for - "$user$" and "$Computer$" - search: '%original_detection_search% | search user = "$user$" Computer = "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$Computer$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$Computer$" + search: '%original_detection_search% | search user = "$user$" Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - The following behavior was identified and typically related to PowerShell-Empire - on $dest$ by $user_id$. - risk_objects: - - field: user_id - type: user - score: 81 - - field: dest - type: system - score: 81 - threat_objects: [] + message: The following behavior was identified and typically related to PowerShell-Empire on $dest$ by $user_id$. + risk_objects: + - field: user_id + type: user + score: 81 + - field: dest + type: system + score: 81 + threat_objects: [] tags: - analytic_story: - - Hellcat Ransomware - - Malicious PowerShell - - Hermetic Wiper - - Data Destruction - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Hellcat Ransomware + - Malicious PowerShell + - Hermetic Wiper + - Data Destruction + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/empire.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/empire.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_excessive_account_lockouts_from_endpoint.yml b/detections/endpoint/detect_excessive_account_lockouts_from_endpoint.yml index c65347b7ae..12940edd11 100644 --- a/detections/endpoint/detect_excessive_account_lockouts_from_endpoint.yml +++ b/detections/endpoint/detect_excessive_account_lockouts_from_endpoint.yml @@ -1,89 +1,64 @@ name: Detect Excessive Account Lockouts From Endpoint id: c026e3dd-7e18-4abb-8f41-929e836efe74 -version: 13 -date: '2025-05-02' +version: 14 +date: '2026-02-25' author: David Dorsey, Splunk status: production type: Anomaly -description: The following analytic detects endpoints causing a high number of account - lockouts within a short period. It leverages the Windows security event logs ingested - into the `Change` datamodel, specifically under the `Account_Management` node, to - identify and count lockout events. This activity is significant as it may indicate - a brute-force attack or misconfigured system causing repeated authentication failures. - If confirmed malicious, this behavior could lead to account lockouts, disrupting - user access and potentially indicating an ongoing attack attempting to compromise - user credentials. +description: The following analytic detects endpoints causing a high number of account lockouts within a short period. It leverages the Windows security event logs ingested into the `Change` datamodel, specifically under the `Account_Management` node, to identify and count lockout events. This activity is significant as it may indicate a brute-force attack or misconfigured system causing repeated authentication failures. If confirmed malicious, this behavior could lead to account lockouts, disrupting user access and potentially indicating an ongoing attack attempting to compromise user credentials. data_source: [] -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime values(All_Changes.user) as user from datamodel=Change.All_Changes where - All_Changes.result="*lock*" by All_Changes.dest All_Changes.result |`drop_dm_object_name("All_Changes")` - |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | search count > 5 | `detect_excessive_account_lockouts_from_endpoint_filter`' -how_to_implement: You must ingest your Windows security event logs in the `Change` - datamodel under the nodename is `Account_Management`, for this search to execute - successfully. Please consider updating the cron schedule and the count of lockouts - you want to monitor, according to your environment.\n**Splunk>Phantom Playbook Integration** - If Splunk>Phantom is also configured in your environment, a Playbook called \"Excessive - Account Lockouts Enrichment and Response\" can be configured to run when any results - are found by this detection search. The Playbook executes the Contextual and Investigative - searches in this Story, conducts additional information gathering on Windows endpoints, - and takes a response action to shut down the affected endpoint. To use this integration, - install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add - the correct hostname to the \"Phantom Instance\" field in the Adaptive Response - Actions when configuring this detection search, and set the corresponding Playbook - to active.\nPlaybook - Link:`https://my.phantom.us/4.1/playbook/excessive-account-lockouts-enrichment-and-response/`) -known_false_positives: It's possible that a widely used system, such as a kiosk, could - cause a large number of account lockouts. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_Changes.user) as user FROM datamodel=Change.All_Changes + WHERE All_Changes.result="*lock*" + BY All_Changes.dest All_Changes.result + | `drop_dm_object_name("All_Changes")` + | `drop_dm_object_name("Account_Management")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | search count > 5 + | `detect_excessive_account_lockouts_from_endpoint_filter` +how_to_implement: You must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment.\n**Splunk>Phantom Playbook Integration** If Splunk>Phantom is also configured in your environment, a Playbook called \"Excessive Account Lockouts Enrichment and Response\" can be configured to run when any results are found by this detection search. The Playbook executes the Contextual and Investigative searches in this Story, conducts additional information gathering on Windows endpoints, and takes a response action to shut down the affected endpoint. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the \"Phantom Instance\" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active.\nPlaybook Link:`https://my.phantom.us/4.1/playbook/excessive-account-lockouts-enrichment-and-response/`) +known_false_positives: It's possible that a widely used system, such as a kiosk, could cause a large number of account lockouts. references: [] drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Multiple accounts have been locked out. Review $dest$ and results related - to $user$. - risk_objects: - - field: user - type: user - score: 36 - - field: dest - type: system - score: 36 - threat_objects: [] + message: Multiple accounts have been locked out. Review $dest$ and results related to $user$. + risk_objects: + - field: user + type: user + score: 36 + - field: dest + type: system + score: 36 + threat_objects: [] tags: - analytic_story: - - Active Directory Password Spraying - asset_type: Windows - mitre_attack_id: - - T1078.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - Active Directory Password Spraying + asset_type: Windows + mitre_attack_id: + - T1078.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-security.log - source: WinEventLog:Security - sourcetype: WinEventLog - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-system.log - source: WinEventLog:System - sourcetype: WinEventLog - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-security.log + source: WinEventLog:Security + sourcetype: WinEventLog + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-system.log + source: WinEventLog:System + sourcetype: WinEventLog + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_excessive_user_account_lockouts.yml b/detections/endpoint/detect_excessive_user_account_lockouts.yml index 6cde70a909..dd962d0cdd 100644 --- a/detections/endpoint/detect_excessive_user_account_lockouts.yml +++ b/detections/endpoint/detect_excessive_user_account_lockouts.yml @@ -1,67 +1,56 @@ name: Detect Excessive User Account Lockouts id: 95a7f9a5-6096-437e-a19e-86f42ac609bd -version: 11 -date: '2025-10-14' +version: 12 +date: '2026-02-25' author: David Dorsey, Splunk status: production type: Anomaly -description: The following analytic identifies user accounts experiencing an excessive - number of lockouts within a short timeframe. It leverages the 'Change' data model, - specifically focusing on events where the result indicates a lockout. This activity - is significant as it may indicate a brute-force attack or misconfiguration, both - of which require immediate attention. If confirmed malicious, this behavior could - lead to account compromise, unauthorized access, and potential lateral movement - within the network. +description: The following analytic identifies user accounts experiencing an excessive number of lockouts within a short timeframe. It leverages the 'Change' data model, specifically focusing on events where the result indicates a lockout. This activity is significant as it may indicate a brute-force attack or misconfiguration, both of which require immediate attention. If confirmed malicious, this behavior could lead to account compromise, unauthorized access, and potential lateral movement within the network. data_source: [] -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Change.All_Changes where All_Changes.result="*lock*" - by All_Changes.user All_Changes.result |`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")`| - `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search - count > 5 | `detect_excessive_user_account_lockouts_filter`' -how_to_implement: ou must ingest your Windows security event logs in the `Change` - datamodel under the nodename is `Account_Management`, for this search to execute - successfully. Please consider updating the cron schedule and the count of lockouts - you want to monitor, according to your environment. -known_false_positives: It is possible that a legitimate user is experiencing an issue - causing multiple account login failures leading to lockouts. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Change.All_Changes + WHERE All_Changes.result="*lock*" + BY All_Changes.user All_Changes.result + | `drop_dm_object_name("All_Changes")` + | `drop_dm_object_name("Account_Management")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | search count > 5 + | `detect_excessive_user_account_lockouts_filter` +how_to_implement: ou must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. +known_false_positives: It is possible that a legitimate user is experiencing an issue causing multiple account login failures leading to lockouts. references: [] drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Excessive user account lockouts for $user$ in a short period of time - risk_objects: - - field: user - type: user - score: 36 - threat_objects: [] + message: Excessive user account lockouts for $user$ in a short period of time + risk_objects: + - field: user + type: user + score: 36 + threat_objects: [] tags: - analytic_story: - - Active Directory Password Spraying - - Scattered Lapsus$ Hunters - asset_type: Windows - mitre_attack_id: - - T1078.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - Active Directory Password Spraying + - Scattered Lapsus$ Hunters + asset_type: Windows + mitre_attack_id: + - T1078.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-xml-1.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-xml-1.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_exchange_web_shell.yml b/detections/endpoint/detect_exchange_web_shell.yml index 17ff3a3ba3..32e62456c9 100644 --- a/detections/endpoint/detect_exchange_web_shell.yml +++ b/detections/endpoint/detect_exchange_web_shell.yml @@ -5,89 +5,61 @@ date: '2025-09-16' author: Michael Haag, Shannon Davis, David Dorsey, Splunk status: production type: TTP -description: The following analytic identifies the creation of suspicious .aspx files - in known drop locations for Exchange exploitation, specifically targeting paths - associated with HAFNIUM group and vulnerabilities like ProxyShell and ProxyNotShell. - It leverages data from the Endpoint datamodel, focusing on process and filesystem - events. This activity is significant as it may indicate a web shell deployment, - a common method for persistent access and remote code execution. If confirmed malicious, - attackers could gain unauthorized access, execute arbitrary commands, and potentially - escalate privileges within the Exchange environment. +description: The following analytic identifies the creation of suspicious .aspx files in known drop locations for Exchange exploitation, specifically targeting paths associated with HAFNIUM group and vulnerabilities like ProxyShell and ProxyNotShell. It leverages data from the Endpoint datamodel, focusing on process and filesystem events. This activity is significant as it may indicate a web shell deployment, a common method for persistent access and remote code execution. If confirmed malicious, attackers could gain unauthorized access, execute arbitrary commands, and potentially escalate privileges within the Exchange environment. data_source: -- Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime - FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\HttpProxy\\owa\\auth\\*", "*\\inetpub\\wwwroot\\aspnet_client\\*", "*\\HttpProxy\\OAB\\*") - Filesystem.file_name IN( "*.aspx", "*.ashx") - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time - Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product - | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `detect_exchange_web_shell_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem` - node. -known_false_positives: The query is structured in a way that `action` (read, create) - is not defined. Review the results of this query, filter, and tune as necessary. - It may be necessary to generate this query specific to your endpoint product. + - Sysmon EventID 11 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\HttpProxy\\owa\\auth\\*", "*\\inetpub\\wwwroot\\aspnet_client\\*", "*\\HttpProxy\\OAB\\*") Filesystem.file_name IN( "*.aspx", "*.ashx") by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_exchange_web_shell_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem` node. +known_false_positives: The query is structured in a way that `action` (read, create) is not defined. Review the results of this query, filter, and tune as necessary. It may be necessary to generate this query specific to your endpoint product. references: -- https://raw.githubusercontent.com/Azure/Azure-Sentinel/master/Sample%20Data/Feeds/MSTICIoCs-ExchangeServerVulnerabilitiesDisclosedMarch2021.csv -- https://www.zerodayinitiative.com/blog/2021/8/17/from-pwn2own-2021-a-new-attack-surface-on-microsoft-exchange-proxyshell -- https://www.youtube.com/watch?v=FC6iHw258RI -- https://www.huntress.com/blog/rapid-response-microsoft-exchange-servers-still-vulnerable-to-proxyshell-exploit#what-should-you-do + - https://raw.githubusercontent.com/Azure/Azure-Sentinel/master/Sample%20Data/Feeds/MSTICIoCs-ExchangeServerVulnerabilitiesDisclosedMarch2021.csv + - https://www.zerodayinitiative.com/blog/2021/8/17/from-pwn2own-2021-a-new-attack-surface-on-microsoft-exchange-proxyshell + - https://www.youtube.com/watch?v=FC6iHw258RI + - https://www.huntress.com/blog/rapid-response-microsoft-exchange-servers-still-vulnerable-to-proxyshell-exploit#what-should-you-do drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A file - $file_name$ was written to disk that is related to IIS exploitation - previously performed by HAFNIUM. Review further file modifications on endpoint - $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 81 - - field: dest - type: system - score: 81 - threat_objects: - - field: file_name - type: file_name + message: A file - $file_name$ was written to disk that is related to IIS exploitation previously performed by HAFNIUM. Review further file modifications on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 81 + - field: dest + type: system + score: 81 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - ProxyNotShell - - CISA AA22-257A - - HAFNIUM Group - - ProxyShell - - Compromised Windows Host - - BlackByte Ransomware - - Seashell Blizzard - - GhostRedirector IIS Module and Rungan Backdoor - asset_type: Endpoint - mitre_attack_id: - - T1133 - - T1190 - - T1505.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ProxyNotShell + - CISA AA22-257A + - HAFNIUM Group + - ProxyShell + - Compromised Windows Host + - BlackByte Ransomware + - Seashell Blizzard + - GhostRedirector IIS Module and Rungan Backdoor + asset_type: Endpoint + mitre_attack_id: + - T1133 + - T1190 + - T1505.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/windows-sysmon_proxylogon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/windows-sysmon_proxylogon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_html_help_renamed.yml b/detections/endpoint/detect_html_help_renamed.yml index f5a823524d..6b96a4d442 100644 --- a/detections/endpoint/detect_html_help_renamed.yml +++ b/detections/endpoint/detect_html_help_renamed.yml @@ -1,62 +1,53 @@ name: Detect HTML Help Renamed id: 62fed254-513b-460e-953d-79771493a9f3 -version: 12 -date: '2025-09-18' +version: 13 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic detects instances where hh.exe (HTML Help) has - been renamed and is executing a Compiled HTML Help (CHM) file. This detection leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process names - and original file names. This activity is significant because attackers can use - renamed hh.exe to execute malicious scripts embedded in CHM files, potentially leading - to code execution. If confirmed malicious, this technique could allow attackers - to run arbitrary scripts, escalate privileges, or persist within the environment, - posing a significant security risk. +description: The following analytic detects instances where hh.exe (HTML Help) has been renamed and is executing a Compiled HTML Help (CHM) file. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and original file names. This activity is significant because attackers can use renamed hh.exe to execute malicious scripts embedded in CHM files, potentially leading to code execution. If confirmed malicious, this technique could allow attackers to run arbitrary scripts, escalate privileges, or persist within the environment, posing a significant security risk. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name!=hh.exe - AND Processes.original_file_name=HH.EXE by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_renamed_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although unlikely a renamed instance of hh.exe will be used - legitimately, filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name!=hh.exe + AND + Processes.original_file_name=HH.EXE + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_html_help_renamed_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although unlikely a renamed instance of hh.exe will be used legitimately, filter as needed. references: -- https://attack.mitre.org/techniques/T1218/001/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md -- https://lolbas-project.github.io/lolbas/Binaries/Hh/ + - https://attack.mitre.org/techniques/T1218/001/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md + - https://lolbas-project.github.io/lolbas/Binaries/Hh/ tags: - analytic_story: - - Suspicious Compiled HTML Activity - - Living Off The Land - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1218.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Compiled HTML Activity + - Living Off The Land + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1218.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_html_help_url_in_command_line.yml b/detections/endpoint/detect_html_help_url_in_command_line.yml index 987241bff9..c6dd4b83c9 100644 --- a/detections/endpoint/detect_html_help_url_in_command_line.yml +++ b/detections/endpoint/detect_html_help_url_in_command_line.yml @@ -1,108 +1,85 @@ name: Detect HTML Help URL in Command Line id: 8c5835b9-39d9-438b-817c-95f14c69a31e -version: 13 -date: '2025-09-18' +version: 14 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: - The following analytic detects the execution of hh.exe (HTML Help) loading - a Compiled HTML Help (CHM) file from a remote URL. This detection leverages data - from Endpoint Detection and Response (EDR) agents, focusing on command-line executions - containing URLs. This activity is significant as it can indicate an attempt to execute - malicious scripts via CHM files, potentially leading to unauthorized code execution. - If confirmed malicious, this could allow an attacker to run scripts using engines - like JScript or VBScript, leading to further system compromise or data exfiltration. +description: The following analytic detects the execution of hh.exe (HTML Help) loading a Compiled HTML Help (CHM) file from a remote URL. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions containing URLs. This activity is significant as it can indicate an attempt to execute malicious scripts via CHM files, potentially leading to unauthorized code execution. If confirmed malicious, this could allow an attacker to run scripts using engines like JScript or VBScript, leading to further system compromise or data exfiltration. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 - - Cisco Network Visibility Module Flow Data -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_hh` Processes.process=*http* - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `detect_html_help_url_in_command_line_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: - Although unlikely, some legitimate applications may retrieve - a CHM remotely, filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 + - Cisco Network Visibility Module Flow Data +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_hh` Processes.process=*http* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_html_help_url_in_command_line_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although unlikely, some legitimate applications may retrieve a CHM remotely, filter as needed. references: - - https://attack.mitre.org/techniques/T1218/001/ - - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md - - https://lolbas-project.github.io/lolbas/Binaries/Hh/ - - https://blog.sevagas.com/?Hacking-around-HTA-files - - https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7 - - https://web.archive.org/web/20220119133748/https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/ + - https://attack.mitre.org/techniques/T1218/001/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md + - https://lolbas-project.github.io/lolbas/Binaries/Hh/ + - https://blog.sevagas.com/?Hacking-around-HTA-files + - https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7 + - https://web.archive.org/web/20220119133748/https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/ drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ contacting a remote destination to potentally - download a malicious payload. - risk_objects: - - field: user - type: user - score: 90 - - field: dest - type: system - score: 90 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ contacting a remote destination to potentally download a malicious payload. + risk_objects: + - field: user + type: user + score: 90 + - field: dest + type: system + score: 90 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - APT37 Rustonotto and FadeStealer - - Suspicious Compiled HTML Activity - - Living Off The Land - - Compromised Windows Host - - Cisco Network Visibility Module Analytics - asset_type: Endpoint - mitre_attack_id: - - T1218.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - APT37 Rustonotto and FadeStealer + - Suspicious Compiled HTML Activity + - Living Off The Land + - Compromised Windows Host + - Cisco Network Visibility Module Analytics + asset_type: Endpoint + mitre_attack_id: + - T1218.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - Sysmon - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata + - name: True Positive Test - Sysmon + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/detect_html_help_using_infotech_storage_handlers.yml b/detections/endpoint/detect_html_help_using_infotech_storage_handlers.yml index 62415a7ddc..4d80d96bd7 100644 --- a/detections/endpoint/detect_html_help_using_infotech_storage_handlers.yml +++ b/detections/endpoint/detect_html_help_using_infotech_storage_handlers.yml @@ -1,93 +1,76 @@ name: Detect HTML Help Using InfoTech Storage Handlers id: 0b2eefa5-5508-450d-b970-3dd2fb761aec -version: 11 -date: '2025-09-18' +version: 12 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of hh.exe (HTML Help) using - InfoTech Storage Handlers to load Windows script code from a Compiled HTML Help - (CHM) file. This detection leverages data from Endpoint Detection and Response (EDR) - agents, focusing on process names and command-line executions. This activity is - significant because it can be used to execute malicious scripts embedded within - CHM files, potentially leading to code execution. If confirmed malicious, this technique - could allow an attacker to execute arbitrary code, escalate privileges, or persist - within the environment. +description: The following analytic detects the execution of hh.exe (HTML Help) using InfoTech Storage Handlers to load Windows script code from a Compiled HTML Help (CHM) file. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because it can be used to execute malicious scripts embedded within CHM files, potentially leading to code execution. If confirmed malicious, this technique could allow an attacker to execute arbitrary code, escalate privileges, or persist within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_hh` Processes.process - IN ("*its:*", "*mk:@MSITStore:*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_using_infotech_storage_handlers_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: It is rare to see instances of InfoTech Storage Handlers being - used, but it does happen in some legitimate instances. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_hh` Processes.process IN ("*its:*", "*mk:@MSITStore:*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_html_help_using_infotech_storage_handlers_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: It is rare to see instances of InfoTech Storage Handlers being used, but it does happen in some legitimate instances. Filter as needed. references: -- https://attack.mitre.org/techniques/T1218/001/ -- https://www.kb.cert.org/vuls/id/851869 -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md -- https://lolbas-project.github.io/lolbas/Binaries/Hh/ -- https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7 -- https://web.archive.org/web/20220119133748/https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/ + - https://attack.mitre.org/techniques/T1218/001/ + - https://www.kb.cert.org/vuls/id/851869 + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md + - https://lolbas-project.github.io/lolbas/Binaries/Hh/ + - https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7 + - https://web.archive.org/web/20220119133748/https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $process_name$ has been identified using Infotech Storage Handlers to load - a specific file within a CHM on $dest$ under user $user$. - risk_objects: - - field: user - type: user - score: 72 - - field: dest - type: system - score: 72 - threat_objects: - - field: process_name - type: process_name + message: $process_name$ has been identified using Infotech Storage Handlers to load a specific file within a CHM on $dest$ under user $user$. + risk_objects: + - field: user + type: user + score: 72 + - field: dest + type: system + score: 72 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Suspicious Compiled HTML Activity - - Living Off The Land - - Compromised Windows Host - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1218.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Compiled HTML Activity + - Living Off The Land + - Compromised Windows Host + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1218.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_mimikatz_with_powershell_script_block_logging.yml b/detections/endpoint/detect_mimikatz_with_powershell_script_block_logging.yml index 5ef33c8cbb..8d54842f4a 100644 --- a/detections/endpoint/detect_mimikatz_with_powershell_script_block_logging.yml +++ b/detections/endpoint/detect_mimikatz_with_powershell_script_block_logging.yml @@ -1,90 +1,75 @@ name: Detect Mimikatz With PowerShell Script Block Logging id: 8148c29c-c952-11eb-9255-acde48001122 -version: 11 -date: '2025-10-14' +version: 12 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: - The following analytic detects the execution of Mimikatz commands via - PowerShell by leveraging PowerShell Script Block Logging (EventCode=4104). This - method captures and logs the full command sent to PowerShell, allowing for the identification - of suspicious activities such as Pass the Ticket, Pass the Hash, and credential - dumping. This activity is significant as Mimikatz is a well-known tool used for - credential theft and lateral movement. If confirmed malicious, this could lead to - unauthorized access, privilege escalation, and potential compromise of sensitive - information within the environment. +description: The following analytic detects the execution of Mimikatz commands via PowerShell by leveraging PowerShell Script Block Logging (EventCode=4104). This method captures and logs the full command sent to PowerShell, allowing for the identification of suspicious activities such as Pass the Ticket, Pass the Hash, and credential dumping. This activity is significant as Mimikatz is a well-known tool used for credential theft and lateral movement. If confirmed malicious, this could lead to unauthorized access, privilege escalation, and potential compromise of sensitive information within the environment. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText IN (*mimikatz*, *-dumpcr*, *sekurlsa::pth*, - *kerberos::ptt*, *kerberos::golden*) | fillnull | stats count min(_time) as firstTime - max(_time) as lastTime by dest signature signature_id user_id vendor_product EventID - Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `detect_mimikatz_with_powershell_script_block_logging_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - False positives should be limited as the commands being identifies - are quite specific to EventCode 4104 and Mimikatz. Filter as needed. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText IN (*mimikatz*, *-dumpcr*, *sekurlsa::pth*, *kerberos::ptt*, *kerberos::golden*) + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_mimikatz_with_powershell_script_block_logging_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: False positives should be limited as the commands being identifies are quite specific to EventCode 4104 and Mimikatz. Filter as needed. references: - - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. - - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 - - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf - - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ - - https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html + - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 + - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf + - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ + - https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html drilldown_searches: - - name: View the detection results for - "$user$" and "$Computer$" - search: '%original_detection_search% | search user = "$user$" Computer = "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$Computer$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$Computer$" + search: '%original_detection_search% | search user = "$user$" Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - The following behavior was identified and typically related to MimiKatz - being loaded within the context of PowerShell on $dest$ by $user_id$. - risk_objects: - - field: user_id - type: user - score: 90 - - field: dest - type: system - score: 90 - threat_objects: [] + message: The following behavior was identified and typically related to MimiKatz being loaded within the context of PowerShell on $dest$ by $user_id$. + risk_objects: + - field: user_id + type: user + score: 90 + - field: dest + type: system + score: 90 + threat_objects: [] tags: - analytic_story: - - Hellcat Ransomware - - Malicious PowerShell - - Hermetic Wiper - - Sandworm Tools - - CISA AA22-264A - - CISA AA22-320A - - CISA AA23-347A - - Data Destruction - - Scattered Spider - asset_type: Endpoint - mitre_attack_id: - - T1003 - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Hellcat Ransomware + - Malicious PowerShell + - Hermetic Wiper + - Sandworm Tools + - CISA AA22-264A + - CISA AA22-320A + - CISA AA23-347A + - Data Destruction + - Scattered Spider + asset_type: Endpoint + mitre_attack_id: + - T1003 + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/credaccess-powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/credaccess-powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_mshta_inline_hta_execution.yml b/detections/endpoint/detect_mshta_inline_hta_execution.yml index c7fefb0733..79dd8301be 100644 --- a/detections/endpoint/detect_mshta_inline_hta_execution.yml +++ b/detections/endpoint/detect_mshta_inline_hta_execution.yml @@ -5,101 +5,78 @@ date: '2026-01-13' author: Bhavin Patel, Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of "mshta.exe" with inline - protocol handlers such as "JavaScript", "VBScript", and "About". It leverages data - from Endpoint Detection and Response (EDR) agents, focusing on command-line arguments - and process details. This activity is significant because mshta.exe can be exploited - to execute malicious scripts, potentially leading to unauthorized code execution. - If confirmed malicious, this could allow an attacker to execute arbitrary code, - escalate privileges, or establish persistence within the environment, posing a severe - security risk. +description: The following analytic detects the execution of "mshta.exe" with inline protocol handlers such as "JavaScript", "VBScript", and "About". It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line arguments and process details. This activity is significant because mshta.exe can be exploited to execute malicious scripts, potentially leading to unauthorized code execution. If confirmed malicious, this could allow an attacker to execute arbitrary code, escalate privileges, or establish persistence within the environment, posing a severe security risk. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count values(Processes.process) - as process values(Processes.parent_process) as parent_process min(_time) as firstTime - max(_time) as lastTime from datamodel=Endpoint.Processes where - - `process_mshta` - Processes.process IN ("*vbscript*", "*javascript*", "*about*") - - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process - Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | tstats `security_content_summariesonly` count values(Processes.process) + as process values(Processes.parent_process) as parent_process min(_time) as firstTime + max(_time) as lastTime from datamodel=Endpoint.Processes where - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `detect_mshta_inline_hta_execution_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although unlikely, some legitimate applications may exhibit - this behavior, triggering a false positive. + `process_mshta` + Processes.process IN ("*vbscript*", "*javascript*", "*about*") + + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process + Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name + Processes.process_path Processes.user Processes.user_id Processes.vendor_product + + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_mshta_inline_hta_execution_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. references: -- https://github.com/redcanaryco/AtomicTestHarnesses -- https://redcanary.com/blog/introducing-atomictestharnesses/ -- https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing + - https://github.com/redcanaryco/AtomicTestHarnesses + - https://redcanary.com/blog/introducing-atomictestharnesses/ + - https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ executing with inline HTA, indicative of defense - evasion. - risk_objects: - - field: user - type: user - score: 90 - - field: dest - type: system - score: 90 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ executing with inline HTA, indicative of defense evasion. + risk_objects: + - field: user + type: user + score: 90 + - field: dest + type: system + score: 90 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Compromised Windows Host - - Gozi Malware - - Living Off The Land - - Suspicious MSHTA Activity - - XWorm - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1218.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - Gozi Malware + - Living Off The Land + - Suspicious MSHTA Activity + - XWorm + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1218.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_mshta_renamed.yml b/detections/endpoint/detect_mshta_renamed.yml index 664f76887c..87a47fe7fa 100644 --- a/detections/endpoint/detect_mshta_renamed.yml +++ b/detections/endpoint/detect_mshta_renamed.yml @@ -1,60 +1,52 @@ name: Detect mshta renamed id: 8f45fcf0-5b68-11eb-ae93-0242ac130002 -version: 11 -date: '2025-09-18' +version: 12 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic identifies instances where mshta.exe has been - renamed and executed. It leverages Endpoint Detection and Response (EDR) data, specifically - focusing on the original file name field to detect discrepancies. This activity - is significant because renaming mshta.exe is a common tactic used by attackers to - evade detection and execute malicious scripts. If confirmed malicious, this could - allow an attacker to execute arbitrary code, potentially leading to system compromise, - data exfiltration, or further lateral movement within the network. +description: The following analytic identifies instances where mshta.exe has been renamed and executed. It leverages Endpoint Detection and Response (EDR) data, specifically focusing on the original file name field to detect discrepancies. This activity is significant because renaming mshta.exe is a common tactic used by attackers to evade detection and execute malicious scripts. If confirmed malicious, this could allow an attacker to execute arbitrary code, potentially leading to system compromise, data exfiltration, or further lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name!=mshta.exe - AND Processes.original_file_name=MSHTA.EXE by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_mshta_renamed_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although unlikely, some legitimate applications may use a moved - copy of mshta.exe, but never renamed, triggering a false positive. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name!=mshta.exe + AND + Processes.original_file_name=MSHTA.EXE + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_mshta_renamed_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although unlikely, some legitimate applications may use a moved copy of mshta.exe, but never renamed, triggering a false positive. references: -- https://github.com/redcanaryco/AtomicTestHarnesses -- https://redcanary.com/blog/introducing-atomictestharnesses/ + - https://github.com/redcanaryco/AtomicTestHarnesses + - https://redcanary.com/blog/introducing-atomictestharnesses/ tags: - analytic_story: - - Suspicious MSHTA Activity - - Living Off The Land - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1218.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious MSHTA Activity + - Living Off The Land + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1218.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_mshta_url_in_command_line.yml b/detections/endpoint/detect_mshta_url_in_command_line.yml index 210f6e6ac4..7e549798f4 100644 --- a/detections/endpoint/detect_mshta_url_in_command_line.yml +++ b/detections/endpoint/detect_mshta_url_in_command_line.yml @@ -1,111 +1,89 @@ name: Detect MSHTA Url in Command Line id: 9b3af1e6-5b68-11eb-ae93-0242ac130002 -version: 16 -date: '2025-11-20' +version: 17 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: - The following analytic detects the use of Microsoft HTML Application - Host (mshta.exe) to make remote HTTP or HTTPS connections. It leverages data from - Endpoint Detection and Response (EDR) agents, focusing on command-line arguments - containing URLs. This activity is significant because adversaries often use mshta.exe - to download and execute remote .hta files, bypassing security controls. If confirmed - malicious, this behavior could allow attackers to execute arbitrary code, potentially - leading to system compromise, data exfiltration, or further network infiltration. +description: The following analytic detects the use of Microsoft HTML Application Host (mshta.exe) to make remote HTTP or HTTPS connections. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line arguments containing URLs. This activity is significant because adversaries often use mshta.exe to download and execute remote .hta files, bypassing security controls. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, potentially leading to system compromise, data exfiltration, or further network infiltration. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 - - Cisco Network Visibility Module Flow Data -search: - '| tstats `security_content_summariesonly` count values(Processes.process) - as process values(Processes.parent_process) as parent_process min(_time) as firstTime - max(_time) as lastTime from datamodel=Endpoint.Processes where `process_mshta` (Processes.process="*http://*" - OR Processes.process="*https://*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mshta_url_in_command_line_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: - It is possible legitimate applications may perform this behavior - and will need to be filtered. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 + - Cisco Network Visibility Module Flow Data +search: |- + | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_mshta` (Processes.process="*http://*" + OR + Processes.process="*https://*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_mshta_url_in_command_line_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: It is possible legitimate applications may perform this behavior and will need to be filtered. references: - - https://github.com/redcanaryco/AtomicTestHarnesses - - https://redcanary.com/blog/introducing-atomictestharnesses/ - - https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing - - https://denwp.com/dissecting-lumma-malware/ - - https://www.mcafee.com/blogs/other-blogs/mcafee-labs/behind-the-captcha-a-clever-gateway-of-malware/ + - https://github.com/redcanaryco/AtomicTestHarnesses + - https://redcanary.com/blog/introducing-atomictestharnesses/ + - https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing + - https://denwp.com/dissecting-lumma-malware/ + - https://www.mcafee.com/blogs/other-blogs/mcafee-labs/behind-the-captcha-a-clever-gateway-of-malware/ drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to access a remote destination to - download an additional payload. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to access a remote destination to download an additional payload. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - APT37 Rustonotto and FadeStealer - - Compromised Windows Host - - Lumma Stealer - - Living Off The Land - - Suspicious MSHTA Activity - - XWorm - - Cisco Network Visibility Module Analytics - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1218.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - APT37 Rustonotto and FadeStealer + - Compromised Windows Host + - Lumma Stealer + - Living Off The Land + - Suspicious MSHTA Activity + - XWorm + - Cisco Network Visibility Module Analytics + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1218.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - Sysmon - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata \ No newline at end of file + - name: True Positive Test - Sysmon + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/detect_new_local_admin_account.yml b/detections/endpoint/detect_new_local_admin_account.yml index d632cfc539..5385c20b87 100644 --- a/detections/endpoint/detect_new_local_admin_account.yml +++ b/detections/endpoint/detect_new_local_admin_account.yml @@ -1,104 +1,84 @@ name: Detect New Local Admin account id: b25f6f62-0712-43c1-b203-083231ffd97d -version: 9 -date: '2025-10-23' +version: 10 +date: '2026-02-25' author: David Dorsey, Splunk status: production type: TTP -description: The following analytic detects the creation of new accounts elevated - to local administrators. It uses Windows event logs, specifically EventCode 4720 - (user account creation) and EventCode 4732 (user added to Administrators group). - This activity is significant as it indicates potential unauthorized privilege escalation, - which is critical for SOC monitoring. If confirmed malicious, this could allow attackers - to gain administrative access, leading to unauthorized data access, system modifications, - and disruption of services. Immediate investigation is required to mitigate risks - and prevent further unauthorized actions. +description: The following analytic detects the creation of new accounts elevated to local administrators. It uses Windows event logs, specifically EventCode 4720 (user account creation) and EventCode 4732 (user added to Administrators group). This activity is significant as it indicates potential unauthorized privilege escalation, which is critical for SOC monitoring. If confirmed malicious, this could allow attackers to gain administrative access, leading to unauthorized data access, system modifications, and disruption of services. Immediate investigation is required to mitigate risks and prevent further unauthorized actions. data_source: -- Windows Event Log Security 4732 -- Windows Event Log Security 4720 + - Windows Event Log Security 4732 + - Windows Event Log Security 4720 search: | - `wineventlog_security` - ( - EventCode=4720 - OR + `wineventlog_security` ( - EventCode=4732 - AND + EventCode=4720 + OR ( - Group_Name=Administrators - OR - TargetUserName=Administrators + EventCode=4732 + AND + ( + Group_Name=Administrators + OR + TargetUserName=Administrators + ) ) ) - ) - | transaction user dest connected=false maxspan=180m - | stats count min(_time) as firstTime - max(_time) as lastTime - dc(EventCode) as distinct_eventcodes - by src_user user dest - | where distinct_eventcodes > 1 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `detect_new_local_admin_account_filter` -how_to_implement: You must be ingesting Windows event logs using the Splunk Windows - TA and collecting event code 4720 and 4732 -known_false_positives: The activity may be legitimate. For this reason, it's best - to verify the account with an administrator and ask whether there was a valid service - request for the account creation. If your local administrator group name is not - "Administrators", this search may generate an excessive number of false positives + | transaction user dest connected=false maxspan=180m + | stats count min(_time) as firstTime + max(_time) as lastTime + dc(EventCode) as distinct_eventcodes + by src_user user dest + | where distinct_eventcodes > 1 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_new_local_admin_account_filter` +how_to_implement: You must be ingesting Windows event logs using the Splunk Windows TA and collecting event code 4720 and 4732 +known_false_positives: The activity may be legitimate. For this reason, it's best to verify the account with an administrator and ask whether there was a valid service request for the account creation. If your local administrator group name is not "Administrators", this search may generate an excessive number of false positives references: [] drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A $user$ on $dest$ was added recently. Identify if this was legitimate - behavior or not. - risk_objects: - - field: user - type: user - score: 42 - - field: dest - type: system - score: 42 - threat_objects: [] + message: A $user$ on $dest$ was added recently. Identify if this was legitimate behavior or not. + risk_objects: + - field: user + type: user + score: 42 + - field: dest + type: system + score: 42 + threat_objects: [] tags: - analytic_story: - - DHS Report TA18-074A - - HAFNIUM Group - - CISA AA22-257A - - CISA AA24-241A - - Scattered Lapsus$ Hunters - asset_type: Windows - mitre_attack_id: - - T1136.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - DHS Report TA18-074A + - HAFNIUM Group + - CISA AA22-257A + - CISA AA24-241A + - Scattered Lapsus$ Hunters + asset_type: Windows + mitre_attack_id: + - T1136.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log - source: WinEventLog:Security - sourcetype: WinEventLog - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log - source: WinEventLog:System - sourcetype: WinEventLog - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log + source: WinEventLog:Security + sourcetype: WinEventLog + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log + source: WinEventLog:System + sourcetype: WinEventLog + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_outlook_exe_writing_a_zip_file.yml b/detections/endpoint/detect_outlook_exe_writing_a_zip_file.yml index 176e406ca2..c1e6d2d99c 100644 --- a/detections/endpoint/detect_outlook_exe_writing_a_zip_file.yml +++ b/detections/endpoint/detect_outlook_exe_writing_a_zip_file.yml @@ -6,132 +6,126 @@ author: Bhavin Patel, Splunk status: production type: Anomaly description: | - The following analytic identifies the execution of `outlook.exe` writing a `.zip` file to the disk. - It leverages data from the Endpoint data model, specifically monitoring process and filesystem activities. - This behavior can be significant as it may indicate the use of Outlook to deliver malicious payloads or exfiltrate data via compressed files. - If confirmed malicious, this activity could lead to unauthorized data access, data exfiltration, or the delivery of malware, potentially compromising the security of the affected system and network. + The following analytic identifies the execution of `outlook.exe` writing a `.zip` file to the disk. + It leverages data from the Endpoint data model, specifically monitoring process and filesystem activities. + This behavior can be significant as it may indicate the use of Outlook to deliver malicious payloads or exfiltrate data via compressed files. + If confirmed malicious, this activity could lead to unauthorized data access, data exfiltration, or the delivery of malware, potentially compromising the security of the affected system and network. data_source: -- Sysmon EventID 1 AND Sysmon EventID 11 + - Sysmon EventID 1 AND Sysmon EventID 11 search: | - | tstats `security_content_summariesonly` - min(_time) as firstTime - max(_time) as lastTime + | tstats `security_content_summariesonly` + min(_time) as firstTime + max(_time) as lastTime - FROM datamodel=Endpoint.Processes where + FROM datamodel=Endpoint.Processes where - Processes.process_name=outlook.exe + Processes.process_name=outlook.exe - by _time span=5m - Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product + by _time span=5m + Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec + Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` - | rename process_guid as malicious_id - | rename parent_process_id as outlook_id + | rename process_guid as malicious_id + | rename parent_process_id as outlook_id - | join malicious_id type=inner - [ - | tstats `security_content_summariesonly` - count values(Filesystem.file_path) as file_path - values(Filesystem.file_name) as file_name - FROM datamodel=Endpoint.Filesystem where + | join malicious_id type=inner + [ + | tstats `security_content_summariesonly` + count values(Filesystem.file_path) as file_path + values(Filesystem.file_name) as file_name + FROM datamodel=Endpoint.Filesystem where - Filesystem.file_path=*.zip - Filesystem.file_path IN ("*:\\Users*", "*\\AppData\\Local\\Temp*") - Filesystem.action=created + Filesystem.file_path=*.zip + Filesystem.file_path IN ("*:\\Users*", "*\\AppData\\Local\\Temp*") + Filesystem.action=created - by _time span=5m - Filesystem.process_guid Filesystem.process_id - Filesystem.file_hash Filesystem.dest Filesystem.dvc - Filesystem.signature Filesystem.signature_id + by _time span=5m + Filesystem.process_guid Filesystem.process_id + Filesystem.file_hash Filesystem.dest Filesystem.dvc + Filesystem.signature Filesystem.signature_id - | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` - | rename process_guid as malicious_id + | rename process_guid as malicious_id - | fields malicious_id outlook_id dest - file_path file_name - file_hash count file_id - ] - | table firstTime lastTime user malicious_id outlook_id - process_name parent_process_name file_name file_path - dest action original_file_name parent_process - parent_process_name parent_process_exec parent_process_guid - parent_process_id parent_process_path process_exec - process_guid process_hash process_id process_integrity_level - process_name process_path user user_id vendor_product + | fields malicious_id outlook_id dest + file_path file_name + file_hash count file_id + ] + | table firstTime lastTime user malicious_id outlook_id + process_name parent_process_name file_name file_path + dest action original_file_name parent_process + parent_process_name parent_process_exec parent_process_guid + parent_process_id parent_process_path process_exec + process_guid process_hash process_id process_integrity_level + process_name process_path user user_id vendor_product - | where file_name != "" - | `detect_outlook_exe_writing_a_zip_file_filter` + | where file_name != "" + | `detect_outlook_exe_writing_a_zip_file_filter` how_to_implement: | - You must be ingesting data that records filesystem and process activity - from your hosts to populate the Endpoint data model. This is typically populated - via endpoint detection-and-response product, such as Carbon Black, or endpoint data - sources, such as Sysmon. -known_false_positives: It is not uncommon for outlook to write legitimate zip files - to the disk. + You must be ingesting data that records filesystem and process activity + from your hosts to populate the Endpoint data model. This is typically populated + via endpoint detection-and-response product, such as Carbon Black, or endpoint data + sources, such as Sysmon. +known_false_positives: It is not uncommon for outlook to write legitimate zip files to the disk. references: - - https://www.paubox.com/news/hackers-exploit-corrupted-zip-and-office-files-to-bypass-email-security - - https://docs.datadoghq.com/security/default_rules/def-000-14w/ - - https://theweborion.com/blog/zip-files/ + - https://www.paubox.com/news/hackers-exploit-corrupted-zip-and-office-files-to-bypass-email-security + - https://docs.datadoghq.com/security/default_rules/def-000-14w/ + - https://theweborion.com/blog/zip-files/ drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: ZIP file - [$file_name$] located in [$file_path$] written by outlook.exe on destination host - [$dest$] by user - [$user$] - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: - - field: file_name - type: file_name - - field: file_path - type: file_path + message: ZIP file - [$file_name$] located in [$file_path$] written by outlook.exe on destination host - [$dest$] by user - [$user$] + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: + - field: file_name + type: file_name + - field: file_path + type: file_path tags: - analytic_story: - - Amadey - - APT37 Rustonotto and FadeStealer - - Meduza Stealer - - PXA Stealer - - Remcos - - Spearphishing Attachments - asset_type: Endpoint - mitre_attack_id: - - T1566.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Amadey + - APT37 Rustonotto and FadeStealer + - Meduza Stealer + - PXA Stealer + - Remcos + - Spearphishing Attachments + asset_type: Endpoint + mitre_attack_id: + - T1566.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/outlook_writing_zip/outlook_writing_zip.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/outlook_writing_zip/outlook_writing_zip.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_password_spray_attack_behavior_from_source.yml b/detections/endpoint/detect_password_spray_attack_behavior_from_source.yml index b969b5c2d5..f0f47e058b 100644 --- a/detections/endpoint/detect_password_spray_attack_behavior_from_source.yml +++ b/detections/endpoint/detect_password_spray_attack_behavior_from_source.yml @@ -1,84 +1,67 @@ name: Detect Password Spray Attack Behavior From Source id: b6391b15-e913-4c2c-8949-9eecc06efacc -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Steven Dick status: production type: TTP -description: The following analytic identifies one source failing to authenticate - with 10 or more unique users. This behavior could represent an adversary performing - a Password Spraying attack to obtain initial access or elevate privileges. This - logic can be used for real time security monitoring as well as threat hunting exercises - and works well against any number of data sources ingested into the CIM datamodel. - Environments can be very different depending on the organization. Test and customize - this detections thresholds if needed. +description: The following analytic identifies one source failing to authenticate with 10 or more unique users. This behavior could represent an adversary performing a Password Spraying attack to obtain initial access or elevate privileges. This logic can be used for real time security monitoring as well as threat hunting exercises and works well against any number of data sources ingested into the CIM datamodel. Environments can be very different depending on the organization. Test and customize this detections thresholds if needed. data_source: -- Windows Event Log Security 4624 -- Windows Event Log Security 4625 -search: '| tstats `security_content_summariesonly` max(_time) as lastTime, min(_time) - as firstTime, values(Authentication.user_category) as user_category values(Authentication.src_category) - as src_category values(Authentication.app) as app count from datamodel=Authentication.Authentication - by Authentication.action Authentication.app Authentication.authentication_method - Authentication.dest Authentication.signature Authentication.signature_id Authentication.src - Authentication.user | `drop_dm_object_name("Authentication")` | eval user=case((match(upper(user),"[a-zA-Z0-9]{3}")),upper(user),true(),null), - src=upper(src), success=if(action="success",count,0),success_user=if(action="success",user,null),failure=if(action="failure",count,0), - failed_user=if(action="failure",user,null) - | stats count min(firstTime) as firstTime max(lastTime) as lastTime values(app) - as app values(src_category) as src_category values(success_user) as user values(failed_user) - as failed_user dc(success_user) as success_dc dc(failed_user) as failed_dc dc(user) - as user_dc ,sum(failure) as failure,sum(success) as success by src | fields - _time - | where user_dc >= 10 AND .25 > (success/failure) AND failed_dc > success_dc | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `detect_password_spray_attack_behavior_from_source_filter`' -how_to_implement: This detection requires ingesting authentication data to the appropriate - accelerated datamodel. Recommend adjusting the search time window for this correlation - to match the number of unique users (user_dc) in hours. i.e. 10 users over 10hrs -known_false_positives: Domain controllers, authentication chokepoints, and vulnerability - scanners. + - Windows Event Log Security 4624 + - Windows Event Log Security 4625 +search: |- + | tstats `security_content_summariesonly` max(_time) as lastTime, min(_time) as firstTime, values(Authentication.user_category) as user_category values(Authentication.src_category) as src_category values(Authentication.app) as app count FROM datamodel=Authentication.Authentication + BY Authentication.action Authentication.app Authentication.authentication_method + Authentication.dest Authentication.signature Authentication.signature_id + Authentication.src Authentication.user + | `drop_dm_object_name("Authentication")` + | eval user=case((match(upper(user),"[a-zA-Z0-9]{3}")),upper(user),true(),null), src=upper(src), success=if(action="success",count,0),success_user=if(action="success",user,null),failure=if(action="failure",count,0), failed_user=if(action="failure",user,null) + | stats count min(firstTime) as firstTime max(lastTime) as lastTime values(app) as app values(src_category) as src_category values(success_user) as user values(failed_user) as failed_user dc(success_user) as success_dc dc(failed_user) as failed_dc dc(user) as user_dc ,sum(failure) as failure,sum(success) as success + BY src + | fields - _time + | where user_dc >= 10 AND .25 > (success/failure) AND failed_dc > success_dc + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_password_spray_attack_behavior_from_source_filter` +how_to_implement: This detection requires ingesting authentication data to the appropriate accelerated datamodel. Recommend adjusting the search time window for this correlation to match the number of unique users (user_dc) in hours. i.e. 10 users over 10hrs +known_false_positives: Domain controllers, authentication chokepoints, and vulnerability scanners. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://www.microsoft.com/en-us/security/blog/2020/04/23/protecting-organization-password-spray-attacks/ -- https://github.com/MarkoH17/Spray365 + - https://attack.mitre.org/techniques/T1110/003/ + - https://www.microsoft.com/en-us/security/blog/2020/04/23/protecting-organization-password-spray-attacks/ + - https://github.com/MarkoH17/Spray365 drilldown_searches: -- name: View the detection results for - "$src$" and "$user$" - search: '%original_detection_search% | search src = "$src$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" and "$user$" + search: '%original_detection_search% | search src = "$src$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The source [$src$] attempted to access $user_dc$ distinct users a total - of $count$ times between [$firstTime$] and [$lastTime$]. $success$ successful - logins detected. - risk_objects: - - field: src - type: system - score: 60 - - field: user - type: user - score: 60 - threat_objects: [] + message: The source [$src$] attempted to access $user_dc$ distinct users a total of $count$ times between [$firstTime$] and [$lastTime$]. $success$ successful logins detected. + risk_objects: + - field: src + type: system + score: 60 + - field: user + type: user + score: 60 + threat_objects: [] tags: - analytic_story: - - Compromised User Account - asset_type: Account - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - Compromised User Account + asset_type: Account + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/generic_password_spray/password_spray_attack.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/generic_password_spray/password_spray_attack.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_password_spray_attack_behavior_on_user.yml b/detections/endpoint/detect_password_spray_attack_behavior_on_user.yml index 343c142b1b..5c98c86e38 100644 --- a/detections/endpoint/detect_password_spray_attack_behavior_on_user.yml +++ b/detections/endpoint/detect_password_spray_attack_behavior_on_user.yml @@ -1,85 +1,68 @@ name: Detect Password Spray Attack Behavior On User id: a7539705-7183-4a12-9b6a-b6eef645a6d7 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Steven Dick status: production type: TTP -description: The following analytic identifies any user failing to authenticate from - 10 or more unique sources. This behavior could represent an adversary performing - a Password Spraying attack to obtain initial access or elevate privileges. This - logic can be used for real time security monitoring as well as threat hunting exercises. - Environments can be very different depending on the organization. Test and customize - this detections thresholds as needed +description: The following analytic identifies any user failing to authenticate from 10 or more unique sources. This behavior could represent an adversary performing a Password Spraying attack to obtain initial access or elevate privileges. This logic can be used for real time security monitoring as well as threat hunting exercises. Environments can be very different depending on the organization. Test and customize this detections thresholds as needed data_source: -- Windows Event Log Security 4624 -- Windows Event Log Security 4625 -search: '| tstats `security_content_summariesonly` max(_time) as lastTime, min(_time) - as firstTime, values(Authentication.user_category) as user_category values(Authentication.src_category) - as src_category values(Authentication.app) as app count from datamodel=Authentication.Authentication - by Authentication.action Authentication.app Authentication.authentication_method - Authentication.dest Authentication.signature Authentication.signature_id Authentication.src - Authentication.user | `drop_dm_object_name("Authentication")` | eval user=case((match(upper(user),"[a-zA-Z0-9]{3}")),upper(user),true(),null), - success=if(action="success",count,0), src=upper(src), success_src=if(action="success",src,null), - failure=if(action="failure",count,0), failed_src=if(action="failure",src,null) - | stats count min(firstTime) - as firstTime max(lastTime) as lastTime values(app) as app values(src_category) as - src_category values(success_src) as src values(failed_src) as failed_src dc(success_src) - as success_dc dc(failed_src) as failed_dc dc(src) as src_dc, sum(failure) as failure, - sum(success) as success by user | fields - _time | where src_dc >= 10 AND .25 > - (success/failure) AND failed_dc > success_dc | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `detect_password_spray_attack_behavior_on_user_filter`' -how_to_implement: This detection requires ingesting authentication data to the appropriate - accelerated datamodel. Recommend adjusting the search time window for this correlation - to match the number of unique users (user_dc) in hours. i.e. 10 users over 10hrs -known_false_positives: Domain controllers, authentication chokepoints, and vulnerability - scanners. + - Windows Event Log Security 4624 + - Windows Event Log Security 4625 +search: |- + | tstats `security_content_summariesonly` max(_time) as lastTime, min(_time) as firstTime, values(Authentication.user_category) as user_category values(Authentication.src_category) as src_category values(Authentication.app) as app count FROM datamodel=Authentication.Authentication + BY Authentication.action Authentication.app Authentication.authentication_method + Authentication.dest Authentication.signature Authentication.signature_id + Authentication.src Authentication.user + | `drop_dm_object_name("Authentication")` + | eval user=case((match(upper(user),"[a-zA-Z0-9]{3}")),upper(user),true(),null), success=if(action="success",count,0), src=upper(src), success_src=if(action="success",src,null), failure=if(action="failure",count,0), failed_src=if(action="failure",src,null) + | stats count min(firstTime) as firstTime max(lastTime) as lastTime values(app) as app values(src_category) as src_category values(success_src) as src values(failed_src) as failed_src dc(success_src) as success_dc dc(failed_src) as failed_dc dc(src) as src_dc, sum(failure) as failure, sum(success) as success + BY user + | fields - _time + | where src_dc >= 10 AND .25 > (success/failure) AND failed_dc > success_dc + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_password_spray_attack_behavior_on_user_filter` +how_to_implement: This detection requires ingesting authentication data to the appropriate accelerated datamodel. Recommend adjusting the search time window for this correlation to match the number of unique users (user_dc) in hours. i.e. 10 users over 10hrs +known_false_positives: Domain controllers, authentication chokepoints, and vulnerability scanners. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://www.microsoft.com/en-us/security/blog/2020/04/23/protecting-organization-password-spray-attacks/ -- https://github.com/MarkoH17/Spray365 + - https://attack.mitre.org/techniques/T1110/003/ + - https://www.microsoft.com/en-us/security/blog/2020/04/23/protecting-organization-password-spray-attacks/ + - https://github.com/MarkoH17/Spray365 drilldown_searches: -- name: View the detection results for - "$src$" and "$user$" - search: '%original_detection_search% | search src = "$src$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" and "$user$" + search: '%original_detection_search% | search src = "$src$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A total of $src_dc$ distinct sources attempted to access the account [$user$], - $count$ times between [$firstTime$] and [$lastTime$]. $success$ successful logins - detected. - risk_objects: - - field: src - type: system - score: 60 - - field: user - type: user - score: 60 - threat_objects: [] + message: A total of $src_dc$ distinct sources attempted to access the account [$user$], $count$ times between [$firstTime$] and [$lastTime$]. $success$ successful logins detected. + risk_objects: + - field: src + type: system + score: 60 + - field: user + type: user + score: 60 + threat_objects: [] tags: - analytic_story: - - Compromised User Account - - Crypto Stealer - asset_type: Account - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - Compromised User Account + - Crypto Stealer + asset_type: Account + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/generic_password_spray/password_spray_attack.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/generic_password_spray/password_spray_attack.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_path_interception_by_creation_of_program_exe.yml b/detections/endpoint/detect_path_interception_by_creation_of_program_exe.yml index 331b9becf3..c5a86df549 100644 --- a/detections/endpoint/detect_path_interception_by_creation_of_program_exe.yml +++ b/detections/endpoint/detect_path_interception_by_creation_of_program_exe.yml @@ -5,86 +5,54 @@ date: '2026-01-14' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic identifies the creation of a program executable - in an unquoted service path, a common technique for privilege escalation. It leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process creation - events where the parent process is 'services.exe'. This activity is significant - because unquoted service paths can be exploited by attackers to execute arbitrary - code with elevated privileges. If confirmed malicious, this could allow an attacker - to gain higher-level access, potentially leading to full system compromise and persistent - control over the affected endpoint. +description: The following analytic identifies the creation of a program executable in an unquoted service path, a common technique for privilege escalation. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where the parent process is 'services.exe'. This activity is significant because unquoted service paths can be exploited by attackers to execute arbitrary code with elevated privileges. If confirmed malicious, this could allow an attacker to gain higher-level access, potentially leading to full system compromise and persistent control over the affected endpoint. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | rex field=process "^.*?\\\\(?[^\\\\]*\.(?:exe|bat|com|ps1))" - | eval process_name = lower(process_name) | eval service_process = lower(service_process) - | where process_name != service_process | `security_content_ctime(firstTime)` | - `security_content_ctime(lastTime)` | `detect_path_interception_by_creation_of_program_exe_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | rex field=process "^.*?\\\\(?[^\\\\]*\.(?:exe|bat|com|ps1))" | eval process_name = lower(process_name) | eval service_process = lower(service_process) | where process_name != service_process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_path_interception_by_creation_of_program_exe_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://medium.com/@SumitVerma101/windows-privilege-escalation-part-1-unquoted-service-path-c7a011a8d8ae + - https://medium.com/@SumitVerma101/windows-privilege-escalation-part-1-unquoted-service-path-c7a011a8d8ae drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to perform privilege escalation by - using unquoted service paths. - risk_objects: - - field: user - type: user - score: 49 - - field: dest - type: system - score: 49 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to perform privilege escalation by using unquoted service paths. + risk_objects: + - field: user + type: user + score: 49 + - field: dest + type: system + score: 49 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Windows Persistence Techniques - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1574.009 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Persistence Techniques + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1574.009 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.009/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.009/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_prohibited_applications_spawning_cmd_exe.yml b/detections/endpoint/detect_prohibited_applications_spawning_cmd_exe.yml index fcff8bbdc6..4587d4aace 100644 --- a/detections/endpoint/detect_prohibited_applications_spawning_cmd_exe.yml +++ b/detections/endpoint/detect_prohibited_applications_spawning_cmd_exe.yml @@ -1,71 +1,54 @@ name: Detect Prohibited Applications Spawning cmd exe id: dcfd6b40-42f9-469d-a433-2e53f7486664 -version: 14 -date: '2025-12-15' +version: 15 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: Hunting -description: The following analytic detects executions of cmd.exe spawned by processes - that are commonly abused by attackers and do not typically launch cmd.exe. It leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process GUID, - process name, parent process, and command-line executions. This activity is significant - because it may indicate an attempt to execute unauthorized commands or scripts, - often a precursor to further malicious actions. If confirmed malicious, this behavior - could lead to unauthorized code execution, privilege escalation, or persistence - within the environment. +description: The following analytic detects executions of cmd.exe spawned by processes that are commonly abused by attackers and do not typically launch cmd.exe. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process GUID, process name, parent process, and command-line executions. This activity is significant because it may indicate an attempt to execute unauthorized commands or scripts, often a precursor to further malicious actions. If confirmed malicious, this behavior could lead to unauthorized code execution, privilege escalation, or persistence within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count values(Processes.process) - as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=cmd.exe OR Processes.original_file_name=Cmd.Exe) - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - |search [ - | inputlookup prohibited_apps_launching_cmd - | rename prohibited_applications as parent_process_name - | eval parent_process_name="*" . parent_process_name - | table parent_process_name - ] - | `detect_prohibited_applications_spawning_cmd_exe_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: There are circumstances where an application may legitimately - execute and interact with the Windows command-line interface. Investigate and modify - the lookup file, as appropriate. + | tstats `security_content_summariesonly` count values(Processes.process) + as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=cmd.exe OR Processes.original_file_name=Cmd.Exe) + by Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + |search [ + | inputlookup prohibited_apps_launching_cmd + | rename prohibited_applications as parent_process_name + | eval parent_process_name="*" . parent_process_name + | table parent_process_name + ] + | `detect_prohibited_applications_spawning_cmd_exe_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate. references: [] tags: - analytic_story: - - Suspicious Command-Line Executions - - Suspicious MSHTA Activity - - Suspicious Zoom Child Processes - - NOBELIUM Group - asset_type: Endpoint - mitre_attack_id: - - T1059.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Command-Line Executions + - Suspicious MSHTA Activity + - Suspicious Zoom Child Processes + - NOBELIUM Group + asset_type: Endpoint + mitre_attack_id: + - T1059.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/powershell_spawn_cmd/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/powershell_spawn_cmd/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_psexec_with_accepteula_flag.yml b/detections/endpoint/detect_psexec_with_accepteula_flag.yml index e1f8477a3e..ba5ce068e5 100644 --- a/detections/endpoint/detect_psexec_with_accepteula_flag.yml +++ b/detections/endpoint/detect_psexec_with_accepteula_flag.yml @@ -1,112 +1,91 @@ name: Detect PsExec With accepteula Flag id: 27c3a83d-cada-47c6-9042-67baf19d2574 -version: 15 -date: '2026-01-22' +version: 16 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP -description: The following analytic identifies the execution of `PsExec.exe` with - the `accepteula` flag in the command line. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process execution logs and command-line arguments. - This activity is significant because PsExec is commonly used by threat actors to - execute code on remote systems, and the `accepteula` flag indicates first-time usage, - which could signify initial compromise. If confirmed malicious, this activity could - allow attackers to gain remote code execution capabilities, potentially leading - to further system compromise and lateral movement within the network. +description: The following analytic identifies the execution of `PsExec.exe` with the `accepteula` flag in the command line. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant because PsExec is commonly used by threat actors to execute code on remote systems, and the `accepteula` flag indicates first-time usage, which could signify initial compromise. If confirmed malicious, this activity could allow attackers to gain remote code execution capabilities, potentially leading to further system compromise and lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where - ( - Processes.process_name IN ("psexec.exe", "psexec64.exe") - OR - Processes.original_file_name="psexec.c" - ) - Processes.process=*accepteula* - by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)`| `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `detect_psexec_with_accepteula_flag_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrators can leverage PsExec for accessing remote systems - and might pass `accepteula` as an argument if they are running this tool for the - first time. However, it is not likely that you'd see multiple occurrences of this - event on a machine + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name IN ("psexec.exe", "psexec64.exe") + OR + Processes.original_file_name="psexec.c" + ) + Processes.process=*accepteula* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_psexec_with_accepteula_flag_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators can leverage PsExec for accessing remote systems and might pass `accepteula` as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine references: -- https://media.defense.gov/2023/May/24/2003229517/-1/-1/0/CSA_Living_off_the_Land.PDF + - https://media.defense.gov/2023/May/24/2003229517/-1/-1/0/CSA_Living_off_the_Land.PDF drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ running the utility for possibly the first time. - risk_objects: - - field: user - type: user - score: 35 - - field: dest - type: system - score: 35 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ running the utility for possibly the first time. + risk_objects: + - field: user + type: user + score: 35 + - field: dest + type: system + score: 35 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - DHS Report TA18-074A - - Active Directory Lateral Movement - - HAFNIUM Group - - Rhysida Ransomware - - Medusa Ransomware - - DarkSide Ransomware - - SamSam Ransomware - - CISA AA22-320A - - Sandworm Tools - - IcedID - - BlackByte Ransomware - - DarkGate Malware - - Cactus Ransomware - - Volt Typhoon - - Seashell Blizzard - - VanHelsing Ransomware - - Storm-0501 Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1021.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - DHS Report TA18-074A + - Active Directory Lateral Movement + - HAFNIUM Group + - Rhysida Ransomware + - Medusa Ransomware + - DarkSide Ransomware + - SamSam Ransomware + - CISA AA22-320A + - Sandworm Tools + - IcedID + - BlackByte Ransomware + - DarkGate Malware + - Cactus Ransomware + - Volt Typhoon + - Seashell Blizzard + - VanHelsing Ransomware + - Storm-0501 Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1021.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.002/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.002/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_rare_executables.yml b/detections/endpoint/detect_rare_executables.yml index d9a74d84f1..f3fdd77ef3 100644 --- a/detections/endpoint/detect_rare_executables.yml +++ b/detections/endpoint/detect_rare_executables.yml @@ -6,102 +6,89 @@ author: Bhavin Patel, Splunk status: production type: Anomaly description: | - The following analytic detects the execution of rare processes that appear only once across the network within a specified timeframe. - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. - This activity is significant for a SOC as it helps identify potentially malicious activities or unauthorized software, which could indicate a security breach or ongoing attack. - If confirmed malicious, such rare processes could lead to data theft, privilege escalation, or complete system compromise, making early detection crucial for minimizing impact. - The search currently identifies processes executed on fewer than 10 hosts, but this threshold can be adjusted based on the organization's environment and risk tolerance. - The search groups results by process name which can lead to blind spots if a malicious process uses a common name. To mitigate this, consider enhancing the detection logic to group by additional attributes such as process hash. + The following analytic detects the execution of rare processes that appear only once across the network within a specified timeframe. + It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. + This activity is significant for a SOC as it helps identify potentially malicious activities or unauthorized software, which could indicate a security breach or ongoing attack. + If confirmed malicious, such rare processes could lead to data theft, privilege escalation, or complete system compromise, making early detection crucial for minimizing impact. + The search currently identifies processes executed on fewer than 10 hosts, but this threshold can be adjusted based on the organization's environment and risk tolerance. + The search groups results by process name which can lead to blind spots if a malicious process uses a common name. To mitigate this, consider enhancing the detection logic to group by additional attributes such as process hash. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` - dc(Processes.dest) as dc_dest - values(Processes.dest) as dest - values(Processes.user) as user - min(_time) as firstTime - max(_time) as lastTime - latest(Processes.action) as action - values(Processes.original_file_name) as original_file_name - values(Processes.parent_process) as parent_process - values(Processes.parent_process_exec) as parent_process_exec - latest(Processes.parent_process_guid) as parent_process_guid - latest(Processes.parent_process_id) as parent_process_id - values(Processes.parent_process_name) as parent_process_name - values(Processes.parent_process_path) as parent_process_path - values(Processes.process) as process - values(Processes.process_exec) as process_exec - latest(Processes.process_guid) as process_guid - values(Processes.process_hash) as process_hash - values(Processes.process_path) as process_path - latest(Processes.process_id) as process_id - latest(Processes.process_integrity_level) as process_integrity_level - latest(Processes.user_id) as user_id - latest(Processes.vendor_product) as vendor_product - from datamodel=Endpoint.Processes - by Processes.process_name - | `drop_dm_object_name(Processes)` - | search dc_dest < 10 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `detect_rare_executables_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + | tstats `security_content_summariesonly` + dc(Processes.dest) as dc_dest + values(Processes.dest) as dest + values(Processes.user) as user + min(_time) as firstTime + max(_time) as lastTime + latest(Processes.action) as action + values(Processes.original_file_name) as original_file_name + values(Processes.parent_process) as parent_process + values(Processes.parent_process_exec) as parent_process_exec + latest(Processes.parent_process_guid) as parent_process_guid + latest(Processes.parent_process_id) as parent_process_id + values(Processes.parent_process_name) as parent_process_name + values(Processes.parent_process_path) as parent_process_path + values(Processes.process) as process + values(Processes.process_exec) as process_exec + latest(Processes.process_guid) as process_guid + values(Processes.process_hash) as process_hash + values(Processes.process_path) as process_path + latest(Processes.process_id) as process_id + latest(Processes.process_integrity_level) as process_integrity_level + latest(Processes.user_id) as user_id + latest(Processes.vendor_product) as vendor_product + from datamodel=Endpoint.Processes + by Processes.process_name + | `drop_dm_object_name(Processes)` + | search dc_dest < 10 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_rare_executables_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: | - Some legitimate processes may be only rarely executed in your environment. - Apply additional filters as needed. + Some legitimate processes may be only rarely executed in your environment. + Apply additional filters as needed. references: [] drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A rare process - [$process_name$] has been detected on less than 10 hosts on $dest$. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: - - field: process_name - type: process_name + message: A rare process - [$process_name$] has been detected on less than 10 hosts on $dest$. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - China-Nexus Threat Activity - - Unusual Processes - - SnappyBee - - Salt Typhoon - - Rhysida Ransomware - - Crypto Stealer - asset_type: Endpoint - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - China-Nexus Threat Activity + - Unusual Processes + - SnappyBee + - Salt Typhoon + - Rhysida Ransomware + - Crypto Stealer + asset_type: Endpoint + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/rare_executables/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/rare_executables/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_rclone_command_line_usage.yml b/detections/endpoint/detect_rclone_command_line_usage.yml index c6e9defa44..ce741eda7f 100644 --- a/detections/endpoint/detect_rclone_command_line_usage.yml +++ b/detections/endpoint/detect_rclone_command_line_usage.yml @@ -5,113 +5,96 @@ date: '2026-01-20' author: Michael Haag, Splunk status: production type: TTP -description: - The following analytic detects the usage of `rclone.exe` with specific - command-line arguments indicative of file transfer activities. It leverages data - from Endpoint Detection and Response (EDR) agents, focusing on command-line executions - and process details. This activity is significant as `rclone.exe` is often used - by adversaries for data exfiltration, especially during ransomware attacks. If confirmed - malicious, this behavior could lead to unauthorized data transfer, resulting in - data breaches and potential loss of sensitive information. Immediate isolation of - the affected endpoint and further investigation are recommended. +description: The following analytic detects the usage of `rclone.exe` with specific command-line arguments indicative of file transfer activities. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process details. This activity is significant as `rclone.exe` is often used by adversaries for data exfiltration, especially during ransomware attacks. If confirmed malicious, this behavior could lead to unauthorized data transfer, resulting in data breaches and potential loss of sensitive information. Immediate isolation of the affected endpoint and further investigation are recommended. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 - - Cisco Network Visibility Module Flow Data + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 + - Cisco Network Visibility Module Flow Data search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - (Processes.original_file_name="rclone.exe" OR Processes.process_name="rclone.exe") - Processes.process IN ( - "*copy*", "*mega*", "*pcloud*", "*ftp*", - "*--config*", "*--progress*", "*--no-check-certificate*", - "*--ignore-existing*", "*--auto-confirm*", "*--transfers*", - "*--multi-thread-streams*" - ) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `detect_rclone_command_line_usage_filter` + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where + (Processes.original_file_name="rclone.exe" OR Processes.process_name="rclone.exe") + Processes.process IN ( + "*copy*", "*mega*", "*pcloud*", "*ftp*", + "*--config*", "*--progress*", "*--no-check-certificate*", + "*--ignore-existing*", "*--auto-confirm*", "*--transfers*", + "*--multi-thread-streams*" + ) + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_rclone_command_line_usage_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: | - False positives should be limited as this is restricted to the Rclone process name. Filter or tune the analytic as needed. + False positives should be limited as this is restricted to the Rclone process name. Filter or tune the analytic as needed. references: - - https://redcanary.com/blog/rclone-mega-extortion/ - - https://www.mandiant.com/resources/shining-a-light-on-darkside-ransomware-operations - - https://thedfirreport.com/2021/03/29/sodinokibi-aka-revil-ransomware/ - - https://thedfirreport.com/2021/11/29/continuing-the-bazar-ransomware-story/ + - https://redcanary.com/blog/rclone-mega-extortion/ + - https://www.mandiant.com/resources/shining-a-light-on-darkside-ransomware-operations + - https://thedfirreport.com/2021/03/29/sodinokibi-aka-revil-ransomware/ + - https://thedfirreport.com/2021/11/29/continuing-the-bazar-ransomware-story/ drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to connect to a remote cloud service - to move files or folders. - risk_objects: - - field: user - type: user - score: 35 - - field: dest - type: system - score: 35 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to connect to a remote cloud service to move files or folders. + risk_objects: + - field: user + type: user + score: 35 + - field: dest + type: system + score: 35 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Storm-0501 Ransomware - - Hellcat Ransomware - - DarkSide Ransomware - - Ransomware - - Black Basta Ransomware - - Cactus Ransomware - - Cisco Network Visibility Module Analytics - asset_type: Endpoint - mitre_attack_id: - - T1020 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Storm-0501 Ransomware + - Hellcat Ransomware + - DarkSide Ransomware + - Ransomware + - Black Basta Ransomware + - Cactus Ransomware + - Cisco Network Visibility Module Analytics + asset_type: Endpoint + mitre_attack_id: + - T1020 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - Sysmon - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1020/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata + - name: True Positive Test - Sysmon + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1020/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/detect_regasm_spawning_a_process.yml b/detections/endpoint/detect_regasm_spawning_a_process.yml index 9505c8ec86..83e56dc197 100644 --- a/detections/endpoint/detect_regasm_spawning_a_process.yml +++ b/detections/endpoint/detect_regasm_spawning_a_process.yml @@ -1,99 +1,78 @@ name: Detect Regasm Spawning a Process id: 72170ec5-f7d2-42f5-aefb-2b8be6aad15f -version: 12 -date: '2025-05-02' +version: 13 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects regasm.exe spawning a child process. This - behavior is identified using data from Endpoint Detection and Response (EDR) agents, - focusing on process creation events where regasm.exe is the parent process. This - activity is significant because regasm.exe spawning a process is rare and can indicate - an attempt to bypass application control mechanisms. If confirmed malicious, this - could allow an attacker to execute arbitrary code, potentially leading to privilege - escalation or persistent access within the environment. Immediate investigation - is recommended to determine the legitimacy of the spawned process and any associated - activities. +description: The following analytic detects regasm.exe spawning a child process. This behavior is identified using data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where regasm.exe is the parent process. This activity is significant because regasm.exe spawning a process is rare and can indicate an attempt to bypass application control mechanisms. If confirmed malicious, this could allow an attacker to execute arbitrary code, potentially leading to privilege escalation or persistent access within the environment. Immediate investigation is recommended to determine the legitimacy of the spawned process and any associated activities. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regasm.exe - NOT (Processes.process_name IN ("conhost.exe")) by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `detect_regasm_spawning_a_process_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although unlikely, limited instances of regasm.exe or regsvcs.exe - may cause a false positive. Filter based endpoint usage, command line arguments, - or process lineage. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name=regasm.exe NOT (Processes.process_name IN ("conhost.exe")) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_regasm_spawning_a_process_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. references: -- https://attack.mitre.org/techniques/T1218/009/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md -- https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ -- https://lolbas-project.github.io/lolbas/Binaries/Regasm/ + - https://attack.mitre.org/techniques/T1218/009/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md + - https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ + - https://lolbas-project.github.io/lolbas/Binaries/Regasm/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ spawning a child process, typically not normal - behavior for $parent_process_name$. - risk_objects: - - field: user - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ spawning a child process, typically not normal behavior for $parent_process_name$. + risk_objects: + - field: user + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Suspicious Regsvcs Regasm Activity - - Living Off The Land - - Handala Wiper - - Compromised Windows Host - - DarkGate Malware - - Snake Keylogger - asset_type: Endpoint - mitre_attack_id: - - T1218.009 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Regsvcs Regasm Activity + - Living Off The Land + - Handala Wiper + - Compromised Windows Host + - DarkGate Malware + - Snake Keylogger + asset_type: Endpoint + mitre_attack_id: + - T1218.009 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_regasm_with_network_connection.yml b/detections/endpoint/detect_regasm_with_network_connection.yml index 1f38318108..a76421774e 100644 --- a/detections/endpoint/detect_regasm_with_network_connection.yml +++ b/detections/endpoint/detect_regasm_with_network_connection.yml @@ -1,92 +1,75 @@ name: Detect Regasm with Network Connection id: 07921114-6db4-4e2e-ae58-3ea8a52ae93f -version: 11 -date: '2025-10-20' +version: 12 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of regasm.exe establishing - a network connection to a public IP address, excluding private IP ranges. This detection - leverages Sysmon EventID 3 logs to identify such behavior. This activity is significant - as regasm.exe is a legitimate Microsoft-signed binary that can be exploited to bypass - application control mechanisms. If confirmed malicious, this behavior could indicate - an adversary's attempt to establish a remote Command and Control (C2) channel, potentially - leading to privilege escalation and further malicious actions within the environment. +description: The following analytic detects the execution of regasm.exe establishing a network connection to a public IP address, excluding private IP ranges. This detection leverages Sysmon EventID 3 logs to identify such behavior. This activity is significant as regasm.exe is a legitimate Microsoft-signed binary that can be exploited to bypass application control mechanisms. If confirmed malicious, this behavior could indicate an adversary's attempt to establish a remote Command and Control (C2) channel, potentially leading to privilege escalation and further malicious actions within the environment. data_source: -- Sysmon EventID 3 + - Sysmon EventID 3 search: | - `sysmon` - EventID=3 - process_name=regasm.exe - NOT dest_ip IN ( - "10.0.0.0/8", "172.16.0.0/12", "192.168.0.0/16", "100.64.0.0/10", - "127.0.0.0/8", "169.254.0.0/16", "192.0.0.0/24", "192.0.0.0/29", "192.0.0.8/32", - "192.0.0.9/32", "192.0.0.10/32", "192.0.0.170/32", "192.0.0.171/32", "192.0.2.0/24", - "192.31.196.0/24", "192.52.193.0/24", "192.88.99.0/24", "224.0.0.0/4", "192.175.48.0/24", - "198.18.0.0/15", "198.51.100.0/24", "203.0.113.0/24", "240.0.0.0/4" - ) - | stats count min(_time) as firstTime max(_time) as lastTime - by action app dest dest_ip dest_port direction dvc protocol protocol_version src - src_ip src_port transport user vendor_product process_name process_exec process_guid - process_id - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `detect_regasm_with_network_connection_filter` -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. -known_false_positives: Although unlikely, limited instances of regasm.exe with a network - connection may cause a false positive. Filter based endpoint usage, command line - arguments, or process lineage. + `sysmon` + EventID=3 + process_name=regasm.exe + NOT dest_ip IN ( + "10.0.0.0/8", "172.16.0.0/12", "192.168.0.0/16", "100.64.0.0/10", + "127.0.0.0/8", "169.254.0.0/16", "192.0.0.0/24", "192.0.0.0/29", "192.0.0.8/32", + "192.0.0.9/32", "192.0.0.10/32", "192.0.0.170/32", "192.0.0.171/32", "192.0.2.0/24", + "192.31.196.0/24", "192.52.193.0/24", "192.88.99.0/24", "224.0.0.0/4", "192.175.48.0/24", + "198.18.0.0/15", "198.51.100.0/24", "203.0.113.0/24", "240.0.0.0/4" + ) + | stats count min(_time) as firstTime max(_time) as lastTime + by action app dest dest_ip dest_port direction dvc protocol protocol_version src + src_ip src_port transport user vendor_product process_name process_exec process_guid + process_id + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_regasm_with_network_connection_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: Although unlikely, limited instances of regasm.exe with a network connection may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. references: -- https://attack.mitre.org/techniques/T1218/009/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md -- https://lolbas-project.github.io/lolbas/Binaries/Regasm/ + - https://attack.mitre.org/techniques/T1218/009/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md + - https://lolbas-project.github.io/lolbas/Binaries/Regasm/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $process_name$ contacting a remote destination was identified - on endpoint $dest$ by user $user$. This behavior is not normal for $process_name$. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: - - field: process_name - type: process_name + message: An instance of $process_name$ contacting a remote destination was identified on endpoint $dest$ by user $user$. This behavior is not normal for $process_name$. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Suspicious Regsvcs Regasm Activity - - Living Off The Land - - Handala Wiper - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1218.009 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Regsvcs Regasm Activity + - Living Off The Land + - Handala Wiper + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1218.009 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_regasm_with_no_command_line_arguments.yml b/detections/endpoint/detect_regasm_with_no_command_line_arguments.yml index 2c566ea428..8226480037 100644 --- a/detections/endpoint/detect_regasm_with_no_command_line_arguments.yml +++ b/detections/endpoint/detect_regasm_with_no_command_line_arguments.yml @@ -5,94 +5,70 @@ date: '2025-12-15' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects instances of regasm.exe running without - command line arguments. This behavior typically indicates process injection, where - another process manipulates regasm.exe. The detection leverages Endpoint Detection - and Response (EDR) data, focusing on process names and command-line executions. - This activity is significant as it may signal an attempt to evade detection or execute - malicious code. If confirmed malicious, attackers could achieve code execution, - potentially leading to privilege escalation, persistence, or access to sensitive - information. Investigate network connections, parallel processes, and suspicious - module loads for further context. +description: The following analytic detects instances of regasm.exe running without command line arguments. This behavior typically indicates process injection, where another process manipulates regasm.exe. The detection leverages Endpoint Detection and Response (EDR) data, focusing on process names and command-line executions. This activity is significant as it may signal an attempt to evade detection or execute malicious code. If confirmed malicious, attackers could achieve code execution, potentially leading to privilege escalation, persistence, or access to sensitive information. Investigate network connections, parallel processes, and suspicious module loads for further context. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where - (Processes.process_name=regasm.exe OR Processes.original_file_name=RegAsm.exe) - Processes.process IN ("*regasm","*regasm.exe", "*regasm.exe\"") - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `detect_regasm_with_no_command_line_arguments_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although unlikely, limited instances of regasm.exe or may cause - a false positive. Filter based endpoint usage, command line arguments, or process - lineage. + | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where + (Processes.process_name=regasm.exe OR Processes.original_file_name=RegAsm.exe) + Processes.process IN ("*regasm","*regasm.exe", "*regasm.exe\"") + by Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_regasm_with_no_command_line_arguments_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although unlikely, limited instances of regasm.exe or may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. references: -- https://attack.mitre.org/techniques/T1218/009/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md -- https://lolbas-project.github.io/lolbas/Binaries/Regasm/ + - https://attack.mitre.org/techniques/T1218/009/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md + - https://lolbas-project.github.io/lolbas/Binaries/Regasm/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The process $process_name$ was spawned by $parent_process_name$ without - any command-line arguments on $dest$ by $user$. - risk_objects: - - field: user - type: user - score: 49 - - field: dest - type: system - score: 49 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: The process $process_name$ was spawned by $parent_process_name$ without any command-line arguments on $dest$ by $user$. + risk_objects: + - field: user + type: user + score: 49 + - field: dest + type: system + score: 49 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Suspicious Regsvcs Regasm Activity - - Living Off The Land - - Handala Wiper - asset_type: Endpoint - mitre_attack_id: - - T1218.009 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Regsvcs Regasm Activity + - Living Off The Land + - Handala Wiper + asset_type: Endpoint + mitre_attack_id: + - T1218.009 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_regsvcs_spawning_a_process.yml b/detections/endpoint/detect_regsvcs_spawning_a_process.yml index 2763669577..589acf1e3a 100644 --- a/detections/endpoint/detect_regsvcs_spawning_a_process.yml +++ b/detections/endpoint/detect_regsvcs_spawning_a_process.yml @@ -1,93 +1,74 @@ name: Detect Regsvcs Spawning a Process id: bc477b57-5c21-4ab6-9c33-668772e7f114 -version: 11 -date: '2025-05-02' +version: 12 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies regsvcs.exe spawning a child process. - This behavior is detected using Endpoint Detection and Response (EDR) telemetry, - focusing on process creation events where the parent process is regsvcs.exe. This - activity is significant because regsvcs.exe rarely spawns child processes, and such - behavior can indicate an attempt to bypass application control mechanisms. If confirmed - malicious, this could allow an attacker to execute arbitrary code, potentially leading - to privilege escalation or persistent access within the environment. Immediate investigation - is recommended to determine the legitimacy of the spawned process and any associated - suspicious activities. +description: The following analytic identifies regsvcs.exe spawning a child process. This behavior is detected using Endpoint Detection and Response (EDR) telemetry, focusing on process creation events where the parent process is regsvcs.exe. This activity is significant because regsvcs.exe rarely spawns child processes, and such behavior can indicate an attempt to bypass application control mechanisms. If confirmed malicious, this could allow an attacker to execute arbitrary code, potentially leading to privilege escalation or persistent access within the environment. Immediate investigation is recommended to determine the legitimacy of the spawned process and any associated suspicious activities. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regsvcs.exe - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `detect_regsvcs_spawning_a_process_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although unlikely, limited instances of regasm.exe or regsvcs.exe - may cause a false positive. Filter based endpoint usage, command line arguments, - or process lineage. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name=regsvcs.exe + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_regsvcs_spawning_a_process_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. references: -- https://attack.mitre.org/techniques/T1218/009/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md -- https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ + - https://attack.mitre.org/techniques/T1218/009/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md + - https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ typically not normal for this process. - risk_objects: - - field: user - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ typically not normal for this process. + risk_objects: + - field: user + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Suspicious Regsvcs Regasm Activity - - Living Off The Land - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1218.009 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Regsvcs Regasm Activity + - Living Off The Land + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1218.009 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_regsvcs_with_network_connection.yml b/detections/endpoint/detect_regsvcs_with_network_connection.yml index 55b6457c86..743700e3c1 100644 --- a/detections/endpoint/detect_regsvcs_with_network_connection.yml +++ b/detections/endpoint/detect_regsvcs_with_network_connection.yml @@ -5,88 +5,70 @@ date: '2025-10-20' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies instances of Regsvcs.exe establishing - a network connection to a public IP address, excluding private IP ranges. This detection - leverages Sysmon EventID 3 logs to monitor network connections initiated by Regsvcs.exe. - This activity is significant as Regsvcs.exe, a legitimate Microsoft-signed binary, - can be exploited to bypass application control mechanisms and establish remote Command - and Control (C2) channels. If confirmed malicious, this behavior could allow an - attacker to escalate privileges, persist in the environment, and exfiltrate sensitive - data. Immediate investigation and remediation are recommended. +description: The following analytic identifies instances of Regsvcs.exe establishing a network connection to a public IP address, excluding private IP ranges. This detection leverages Sysmon EventID 3 logs to monitor network connections initiated by Regsvcs.exe. This activity is significant as Regsvcs.exe, a legitimate Microsoft-signed binary, can be exploited to bypass application control mechanisms and establish remote Command and Control (C2) channels. If confirmed malicious, this behavior could allow an attacker to escalate privileges, persist in the environment, and exfiltrate sensitive data. Immediate investigation and remediation are recommended. data_source: -- Sysmon EventID 3 + - Sysmon EventID 3 search: | - `sysmon` - EventID=3 - NOT dest_ip IN ( - "10.0.0.0/8", "172.16.0.0/12", "192.168.0.0/16", "100.64.0.0/10", - "127.0.0.0/8", "169.254.0.0/16", "192.0.0.0/24", "192.0.0.0/29", "192.0.0.8/32", - "192.0.0.9/32", "192.0.0.10/32", "192.0.0.170/32", "192.0.0.171/32", "192.0.2.0/24", - "192.31.196.0/24", "192.52.193.0/24", "192.88.99.0/24", "224.0.0.0/4", "192.175.48.0/24", - "198.18.0.0/15", "198.51.100.0/24", "203.0.113.0/24", "240.0.0.0/4" - ) - process_name=regsvcs.exe - | stats count min(_time) as firstTime max(_time) as lastTime - by action app dest dest_ip dest_port direction dvc protocol protocol_version src - src_ip src_port transport user vendor_product process_name process_exec process_guid - process_id - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `detect_regsvcs_with_network_connection_filter` -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. -known_false_positives: Although unlikely, limited instances of regsvcs.exe may cause - a false positive. Filter based endpoint usage, command line arguments, or process - lineage. + `sysmon` + EventID=3 + NOT dest_ip IN ( + "10.0.0.0/8", "172.16.0.0/12", "192.168.0.0/16", "100.64.0.0/10", + "127.0.0.0/8", "169.254.0.0/16", "192.0.0.0/24", "192.0.0.0/29", "192.0.0.8/32", + "192.0.0.9/32", "192.0.0.10/32", "192.0.0.170/32", "192.0.0.171/32", "192.0.2.0/24", + "192.31.196.0/24", "192.52.193.0/24", "192.88.99.0/24", "224.0.0.0/4", "192.175.48.0/24", + "198.18.0.0/15", "198.51.100.0/24", "203.0.113.0/24", "240.0.0.0/4" + ) + process_name=regsvcs.exe + | stats count min(_time) as firstTime max(_time) as lastTime + by action app dest dest_ip dest_port direction dvc protocol protocol_version src + src_ip src_port transport user vendor_product process_name process_exec process_guid + process_id + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_regsvcs_with_network_connection_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. references: -- https://attack.mitre.org/techniques/T1218/009/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md -- https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ + - https://attack.mitre.org/techniques/T1218/009/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md + - https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $process_name$ contacting a remote destination was identified - on endpoint $dest$ by user $user$. This behavior is not normal for $process_name$. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: - - field: process_name - type: process_name + message: An instance of $process_name$ contacting a remote destination was identified on endpoint $dest$ by user $user$. This behavior is not normal for $process_name$. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Suspicious Regsvcs Regasm Activity - - Living Off The Land - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1218.009 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Regsvcs Regasm Activity + - Living Off The Land + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1218.009 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_regsvcs_with_no_command_line_arguments.yml b/detections/endpoint/detect_regsvcs_with_no_command_line_arguments.yml index 4f4b1a6e41..811170ed6d 100644 --- a/detections/endpoint/detect_regsvcs_with_no_command_line_arguments.yml +++ b/detections/endpoint/detect_regsvcs_with_no_command_line_arguments.yml @@ -5,92 +5,69 @@ date: '2025-12-15' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects instances of regsvcs.exe running without - command line arguments. This behavior typically indicates process injection, where - another process manipulates regsvcs.exe. The detection leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process names, IDs, and command-line - executions. This activity is significant as it may signal an attempt to evade detection - and execute malicious code. If confirmed malicious, the attacker could achieve code - execution, potentially leading to privilege escalation, persistence, or access to - sensitive information. +description: The following analytic detects instances of regsvcs.exe running without command line arguments. This behavior typically indicates process injection, where another process manipulates regsvcs.exe. The detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, IDs, and command-line executions. This activity is significant as it may signal an attempt to evade detection and execute malicious code. If confirmed malicious, the attacker could achieve code execution, potentially leading to privilege escalation, persistence, or access to sensitive information. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Processes where - (Processes.process_name=regsvcs.exe OR Processes.original_file_name=RegSvcs.exe) - Processes.process IN ("*regsvcs","*regsvcs.exe", "*regsvcs.exe\"") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `detect_regsvcs_with_no_command_line_arguments_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although unlikely, limited instances of regsvcs.exe may cause - a false positive. Filter based endpoint usage, command line arguments, or process - lineage. + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime FROM datamodel=Endpoint.Processes where + (Processes.process_name=regsvcs.exe OR Processes.original_file_name=RegSvcs.exe) + Processes.process IN ("*regsvcs","*regsvcs.exe", "*regsvcs.exe\"") + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec + Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name + Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name + Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_regsvcs_with_no_command_line_arguments_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage. references: -- https://attack.mitre.org/techniques/T1218/009/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md -- https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ + - https://attack.mitre.org/techniques/T1218/009/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md + - https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The process $process_name$ was spawned by $parent_process_name$ without - any command-line arguments on $dest$ by $user$. - risk_objects: - - field: user - type: user - score: 49 - - field: dest - type: system - score: 49 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: The process $process_name$ was spawned by $parent_process_name$ without any command-line arguments on $dest$ by $user$. + risk_objects: + - field: user + type: user + score: 49 + - field: dest + type: system + score: 49 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Suspicious Regsvcs Regasm Activity - - Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1218.009 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Regsvcs Regasm Activity + - Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1218.009 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_regsvr32_application_control_bypass.yml b/detections/endpoint/detect_regsvr32_application_control_bypass.yml index 59b1fc91ca..601e3a9756 100644 --- a/detections/endpoint/detect_regsvr32_application_control_bypass.yml +++ b/detections/endpoint/detect_regsvr32_application_control_bypass.yml @@ -1,97 +1,79 @@ name: Detect Regsvr32 Application Control Bypass id: 070e9b80-6252-11eb-ae93-0242ac130002 -version: 11 -date: '2025-05-02' +version: 12 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies the abuse of Regsvr32.exe to proxy - execution of malicious code, specifically detecting the loading of "scrobj.dll" - by Regsvr32.exe. This detection leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process creation events and command-line executions. This - activity is significant because Regsvr32.exe is a trusted, signed Microsoft binary, - often used in "Squiblydoo" attacks to bypass application control mechanisms. If - confirmed malicious, this technique could allow an attacker to execute arbitrary - code, potentially leading to system compromise and persistent access. +description: The following analytic identifies the abuse of Regsvr32.exe to proxy execution of malicious code, specifically detecting the loading of "scrobj.dll" by Regsvr32.exe. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events and command-line executions. This activity is significant because Regsvr32.exe is a trusted, signed Microsoft binary, often used in "Squiblydoo" attacks to bypass application control mechanisms. If confirmed malicious, this technique could allow an attacker to execute arbitrary code, potentially leading to system compromise and persistent access. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_regsvr32` Processes.process=*scrobj* - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `detect_regsvr32_application_control_bypass_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Limited false positives related to third party software registering - .DLL's. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_regsvr32` Processes.process=*scrobj* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_regsvr32_application_control_bypass_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Limited false positives related to third party software registering .DLL's. references: -- https://attack.mitre.org/techniques/T1218/010/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md -- https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/ -- https://support.microsoft.com/en-us/topic/how-to-use-the-regsvr32-tool-and-troubleshoot-regsvr32-error-messages-a98d960a-7392-e6fe-d90a-3f4e0cb543e5 + - https://attack.mitre.org/techniques/T1218/010/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md + - https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/ + - https://support.microsoft.com/en-us/topic/how-to-use-the-regsvr32-tool-and-troubleshoot-regsvr32-error-messages-a98d960a-7392-e6fe-d90a-3f4e0cb543e5 drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ in an attempt - to bypass detection and preventative controls was identified on endpoint $dest$ - by user $user$. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ in an attempt to bypass detection and preventative controls was identified on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Living Off The Land - - Suspicious Regsvr32 Activity - - Graceful Wipe Out Attack - - Cobalt Strike - - Compromised Windows Host - - BlackByte Ransomware - - PHP-CGI RCE Attack on Japanese Organizations - asset_type: Endpoint - mitre_attack_id: - - T1218.010 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + - Suspicious Regsvr32 Activity + - Graceful Wipe Out Attack + - Cobalt Strike + - Compromised Windows Host + - BlackByte Ransomware + - PHP-CGI RCE Attack on Japanese Organizations + asset_type: Endpoint + mitre_attack_id: + - T1218.010 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_remote_access_software_usage_file.yml b/detections/endpoint/detect_remote_access_software_usage_file.yml index 7ba8cf6e6c..627e4f4a9c 100644 --- a/detections/endpoint/detect_remote_access_software_usage_file.yml +++ b/detections/endpoint/detect_remote_access_software_usage_file.yml @@ -6,123 +6,102 @@ author: Steven Dick status: production type: Anomaly description: | - The following analytic detects the writing of files from known remote access software to disk within the environment. - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on file path, file name, and user information. - This activity is significant as adversaries often use remote access tools like AnyDesk, GoToMyPC, LogMeIn, and TeamViewer to maintain unauthorized access. - If confirmed malicious, this could allow attackers to persist in the environment, potentially leading to data exfiltration, further compromise, or complete control over affected systems. - It is best to update both the remote_access_software_usage_exception.csv lookup and the remote_access_software lookup with any known or approved remote access software to reduce false positives and increase coverage. - In order to enhance performance, the detection filters for specific file names extensions / names that are used in the remote_access_software lookup. - If add additional entries, consider updating the search filters to include those file names / extensions as well, if not alread covered. + The following analytic detects the writing of files from known remote access software to disk within the environment. + It leverages data from Endpoint Detection and Response (EDR) agents, focusing on file path, file name, and user information. + This activity is significant as adversaries often use remote access tools like AnyDesk, GoToMyPC, LogMeIn, and TeamViewer to maintain unauthorized access. + If confirmed malicious, this could allow attackers to persist in the environment, potentially leading to data exfiltration, further compromise, or complete control over affected systems. + It is best to update both the remote_access_software_usage_exception.csv lookup and the remote_access_software lookup with any known or approved remote access software to reduce false positives and increase coverage. + In order to enhance performance, the detection filters for specific file names extensions / names that are used in the remote_access_software lookup. + If add additional entries, consider updating the search filters to include those file names / extensions as well, if not alread covered. data_source: -- Sysmon EventID 11 + - Sysmon EventID 11 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime, - max(_time) as lastTime - values(Filesystem.file_path) as file_path - from datamodel=Endpoint.Filesystem - where Filesystem.file_name IN ( - "*.app", - "*.exe", - "*.msi", - "*.pkg", - "*echoware.dll", - "*Idrive.*", - "*rdp2tcp.py" - ) - by Filesystem.action Filesystem.dest Filesystem.file_access_time - Filesystem.file_create_time Filesystem.file_hash - Filesystem.file_modify_time Filesystem.file_name - Filesystem.file_path Filesystem.file_acl Filesystem.file_size - Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `drop_dm_object_name(Filesystem)` - | lookup remote_access_software remote_utility AS file_name OUTPUT isutility, description as signature, comment_reference as desc, category - | search isutility = TRUE - | `remote_access_software_usage_exceptions` - | `detect_remote_access_software_usage_file_filter` -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the file path, file - name, and the user that created the file. These logs must be processed using - the appropriate Splunk Technology Add-ons that are specific to the EDR - product. The logs must also be mapped to the `Filesystem` node of the - `Endpoint` data model. Use the Splunk Common Information Model (CIM) to - normalize the field names and speed up the data modeling process. The - "exceptions" macro leverages both an Assets and Identities lookup, as well as - a KVStore collection called "remote_software_exceptions" that lets you track - and maintain device-based exceptions for this set of detections. -known_false_positives: Known or approved applications used by the organization - or usage of built-in functions. Known false positives can be added to the - remote_access_software_usage_exception.csv lookup to globally suppress these - situations across all remote access content + | tstats `security_content_summariesonly` + count min(_time) as firstTime, + max(_time) as lastTime + values(Filesystem.file_path) as file_path + from datamodel=Endpoint.Filesystem + where Filesystem.file_name IN ( + "*.app", + "*.exe", + "*.msi", + "*.pkg", + "*echoware.dll", + "*Idrive.*", + "*rdp2tcp.py" + ) + by Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash + Filesystem.file_modify_time Filesystem.file_name + Filesystem.file_path Filesystem.file_acl Filesystem.file_size + Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `drop_dm_object_name(Filesystem)` + | lookup remote_access_software remote_utility AS file_name OUTPUT isutility, description as signature, comment_reference as desc, category + | search isutility = TRUE + | `remote_access_software_usage_exceptions` + | `detect_remote_access_software_usage_file_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the file path, file name, and the user that created the file. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Filesystem` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. The "exceptions" macro leverages both an Assets and Identities lookup, as well as a KVStore collection called "remote_software_exceptions" that lets you track and maintain device-based exceptions for this set of detections. +known_false_positives: Known or approved applications used by the organization or usage of built-in functions. Known false positives can be added to the remote_access_software_usage_exception.csv lookup to globally suppress these situations across all remote access content references: -- https://attack.mitre.org/techniques/T1219/ -- https://thedfirreport.com/2022/08/08/bumblebee-roasts-its-way-to-domain-admin/ -- https://thedfirreport.com/2022/11/28/emotet-strikes-again-lnk-file-leads-to-domain-wide-ransomware/ + - https://attack.mitre.org/techniques/T1219/ + - https://thedfirreport.com/2022/08/08/bumblebee-roasts-its-way-to-domain-admin/ + - https://thedfirreport.com/2022/11/28/emotet-strikes-again-lnk-file-leads-to-domain-wide-ransomware/ drilldown_searches: - - name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: Investigate files on $dest$ - search: '| from datamodel:Endpoint.Filesystem | search dest=$dest$ file_name=$file_name$' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate files on $dest$ + search: '| from datamodel:Endpoint.Filesystem | search dest=$dest$ file_name=$file_name$' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A file for known a remote access software [$file_name$] was created - on $dest$ by $user$. - risk_objects: - - field: dest - type: system - score: 25 - - field: user - type: user - score: 25 - threat_objects: - - field: file_name - type: file_name - - field: signature - type: signature + message: A file for known a remote access software [$file_name$] was created on $dest$ by $user$. + risk_objects: + - field: dest + type: system + score: 25 + - field: user + type: user + score: 25 + threat_objects: + - field: file_name + type: file_name + - field: signature + type: signature tags: - analytic_story: - - Cactus Ransomware - - CISA AA24-241A - - Command And Control - - GhostRedirector IIS Module and Rungan Backdoor - - Gozi Malware - - Insider Threat - - Interlock Ransomware - - Ransomware - - Remote Monitoring and Management Software - - Scattered Lapsus$ Hunters - - Scattered Spider - - Seashell Blizzard - asset_type: Endpoint - mitre_attack_id: - - T1219 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - manual_test: This detection uses A&I lookups from Enterprise Security. + analytic_story: + - Cactus Ransomware + - CISA AA24-241A + - Command And Control + - GhostRedirector IIS Module and Rungan Backdoor + - Gozi Malware + - Insider Threat + - Interlock Ransomware + - Ransomware + - Remote Monitoring and Management Software + - Scattered Lapsus$ Hunters + - Scattered Spider + - Seashell Blizzard + asset_type: Endpoint + mitre_attack_id: + - T1219 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + manual_test: This detection uses A&I lookups from Enterprise Security. tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_remote_access_software_usage_fileinfo.yml b/detections/endpoint/detect_remote_access_software_usage_fileinfo.yml index 0435961e3a..835b980497 100644 --- a/detections/endpoint/detect_remote_access_software_usage_fileinfo.yml +++ b/detections/endpoint/detect_remote_access_software_usage_fileinfo.yml @@ -1,100 +1,84 @@ name: Detect Remote Access Software Usage FileInfo id: ccad96d7-a48c-4f13-8b9c-9f6a31cba454 -version: 11 -date: '2025-10-14' +version: 12 +date: '2026-02-25' author: Steven Dick status: production type: Anomaly -description: The following analytic detects the execution of processes with file - or code signing attributes from known remote access software within the - environment. It leverages Sysmon EventCode 1 data and cross-references a - lookup table of remote access utilities such as AnyDesk, GoToMyPC, LogMeIn, - and TeamViewer. This activity is significant as adversaries often use these - tools to maintain unauthorized remote access. If confirmed malicious, this - could allow attackers to persist in the environment, potentially leading to - data exfiltration or further compromise of the network. +description: The following analytic detects the execution of processes with file or code signing attributes from known remote access software within the environment. It leverages Sysmon EventCode 1 data and cross-references a lookup table of remote access utilities such as AnyDesk, GoToMyPC, LogMeIn, and TeamViewer. This activity is significant as adversaries often use these tools to maintain unauthorized remote access. If confirmed malicious, this could allow attackers to persist in the environment, potentially leading to data exfiltration or further compromise of the network. data_source: -- Sysmon EventID 1 -search: '`sysmon` EventCode=1 | stats count min(_time) as firstTime max(_time) as - lastTime, values(Company) as Company values(Product) as Product by action dest original_file_name - parent_process parent_process_exec parent_process_guid parent_process_id parent_process_name - parent_process_path process process_exec process_guid process_hash process_id process_integrity_level - process_name process_path user user_id vendor_product | lookup remote_access_software - remote_utility_fileinfo AS Product OUTPUT isutility, description as signature, comment_reference - as desc, category | search isutility = True | `remote_access_software_usage_exceptions` - | `detect_remote_access_software_usage_fileinfo_filter`' -how_to_implement: This analytic relies on Sysmon to be properly installed and - utilized in the environment. Ensure that proper logging is setup for Sysmon - and data is being ingested into Splunk. The "exceptions" macro leverages both - an Assets and Identities lookup, as well as a KVStore collection named - "remote_software_exceptions" that lets you track and maintain device-based - exceptions for this set of detections. -known_false_positives: Known or approved applications used by the organization - or usage of built-in functions. Known false positives can be added to the - remote_access_software_usage_exception.csv lookup to globally suppress these - situations across all remote access content + - Sysmon EventID 1 +search: |- + `sysmon` EventCode=1 + | stats count min(_time) as firstTime max(_time) as lastTime, values(Company) as Company values(Product) as Product + BY action dest original_file_name + parent_process parent_process_exec parent_process_guid + parent_process_id parent_process_name parent_process_path + process process_exec process_guid + process_hash process_id process_integrity_level + process_name process_path user + user_id vendor_product + | lookup remote_access_software remote_utility_fileinfo AS Product OUTPUT isutility, description as signature, comment_reference as desc, category + | search isutility = True + | `remote_access_software_usage_exceptions` + | `detect_remote_access_software_usage_fileinfo_filter` +how_to_implement: This analytic relies on Sysmon to be properly installed and utilized in the environment. Ensure that proper logging is setup for Sysmon and data is being ingested into Splunk. The "exceptions" macro leverages both an Assets and Identities lookup, as well as a KVStore collection named "remote_software_exceptions" that lets you track and maintain device-based exceptions for this set of detections. +known_false_positives: Known or approved applications used by the organization or usage of built-in functions. Known false positives can be added to the remote_access_software_usage_exception.csv lookup to globally suppress these situations across all remote access content references: -- https://attack.mitre.org/techniques/T1219/ -- https://thedfirreport.com/2022/08/08/bumblebee-roasts-its-way-to-domain-admin/ -- https://thedfirreport.com/2022/11/28/emotet-strikes-again-lnk-file-leads-to-domain-wide-ransomware/ + - https://attack.mitre.org/techniques/T1219/ + - https://thedfirreport.com/2022/08/08/bumblebee-roasts-its-way-to-domain-admin/ + - https://thedfirreport.com/2022/11/28/emotet-strikes-again-lnk-file-leads-to-domain-wide-ransomware/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate processes on $dest$ - search: '| from datamodel:Endpoint.Processes| search dest=$dest$ process_name=$process_name$' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate processes on $dest$ + search: '| from datamodel:Endpoint.Processes| search dest=$dest$ process_name=$process_name$' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A file attributes for known a remote access software [$process_name$] - was detected on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - - field: user - type: user - score: 25 - threat_objects: - - field: process_name - type: process_name - - field: signature - type: signature + message: A file attributes for known a remote access software [$process_name$] was detected on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + - field: user + type: user + score: 25 + threat_objects: + - field: process_name + type: process_name + - field: signature + type: signature tags: - analytic_story: - - Insider Threat - - Command And Control - - Ransomware - - Gozi Malware - - Remote Monitoring and Management Software - - Cactus Ransomware - - Seashell Blizzard - - Scattered Spider - - Interlock Ransomware - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1219 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - manual_test: This detection uses A&I lookups from Enterprise Security. + analytic_story: + - Insider Threat + - Command And Control + - Ransomware + - Gozi Malware + - Remote Monitoring and Management Software + - Cactus Ransomware + - Seashell Blizzard + - Scattered Spider + - Interlock Ransomware + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1219 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + manual_test: This detection uses A&I lookups from Enterprise Security. tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_remote_access_software_usage_process.yml b/detections/endpoint/detect_remote_access_software_usage_process.yml index ad7fc98312..c1ae9e010f 100644 --- a/detections/endpoint/detect_remote_access_software_usage_process.yml +++ b/detections/endpoint/detect_remote_access_software_usage_process.yml @@ -1,129 +1,95 @@ name: Detect Remote Access Software Usage Process id: ffd5e001-2e34-48f4-97a2-26dc4bb08178 -version: 13 -date: '2026-01-20' +version: 14 +date: '2026-02-25' author: Steven Dick, Sebastian Wurl, Splunk Community status: production type: Anomaly -description: The following analytic detects the execution of known remote access - software within the environment. It leverages data from Endpoint Detection and - Response (EDR) agents, focusing on process names and parent processes mapped - to the Endpoint data model. We then compare with with a list of known remote - access software shipped as a lookup file - remote_access_software. This - activity is significant as adversaries often use remote access tools like - AnyDesk, GoToMyPC, LogMeIn, and TeamViewer to maintain unauthorized access. If - confirmed malicious, this could allow attackers to control systems remotely, - exfiltrate data, or deploy additional malware, posing a severe threat to the - organization's security. +description: The following analytic detects the execution of known remote access software within the environment. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and parent processes mapped to the Endpoint data model. We then compare with with a list of known remote access software shipped as a lookup file - remote_access_software. This activity is significant as adversaries often use remote access tools like AnyDesk, GoToMyPC, LogMeIn, and TeamViewer to maintain unauthorized access. If confirmed malicious, this could allow attackers to control systems remotely, exfiltrate data, or deploy additional malware, posing a severe threat to the organization's security. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.parent_process) as parent_process - from datamodel=Endpoint.Processes - where - [| inputlookup remote_access_software where isutility=TRUE - | rename remote_utility AS Processes.process_name - | fields Processes.process_name] - AND Processes.dest!="unknown" - AND Processes.user!="unknown" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `drop_dm_object_name(Processes)` - | lookup remote_access_software remote_utility AS process_name OUTPUT isutility description AS signature comment_reference AS desc category - | search isutility = TRUE - | `remote_access_software_usage_exceptions` - | `detect_remote_access_software_usage_process_filter` -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. The "exceptions" macro leverages both an Assets - and Identities lookup, as well as a KVStore collection called - "remote_software_exceptions" that lets you track and maintain device- based - exceptions for this set of detections. -known_false_positives: It is possible that legitimate remote access software is - used within the environment. Ensure that the lookup is reviewed and updated - with any additional remote access software that is used within the - environment. Known false positives can be added to the - remote_access_software_usage_exception.csv lookup to globally suppress these - situations across all remote access content + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.parent_process) as parent_process + from datamodel=Endpoint.Processes + where + [| inputlookup remote_access_software where isutility=TRUE + | rename remote_utility AS Processes.process_name + | fields Processes.process_name] + AND Processes.dest!="unknown" + AND Processes.user!="unknown" + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `drop_dm_object_name(Processes)` + | lookup remote_access_software remote_utility AS process_name OUTPUT isutility description AS signature comment_reference AS desc category + | search isutility = TRUE + | `remote_access_software_usage_exceptions` + | `detect_remote_access_software_usage_process_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. The "exceptions" macro leverages both an Assets and Identities lookup, as well as a KVStore collection called "remote_software_exceptions" that lets you track and maintain device- based exceptions for this set of detections. +known_false_positives: It is possible that legitimate remote access software is used within the environment. Ensure that the lookup is reviewed and updated with any additional remote access software that is used within the environment. Known false positives can be added to the remote_access_software_usage_exception.csv lookup to globally suppress these situations across all remote access content references: -- https://attack.mitre.org/techniques/T1219/ -- https://thedfirreport.com/2022/08/08/bumblebee-roasts-its-way-to-domain-admin/ -- https://thedfirreport.com/2022/11/28/emotet-strikes-again-lnk-file-leads-to-domain-wide-ransomware/ + - https://attack.mitre.org/techniques/T1219/ + - https://thedfirreport.com/2022/08/08/bumblebee-roasts-its-way-to-domain-admin/ + - https://thedfirreport.com/2022/11/28/emotet-strikes-again-lnk-file-leads-to-domain-wide-ransomware/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate processes on $dest$ - search: '| from datamodel:Endpoint.Processes| search dest=$dest$ process_name=$process_name$' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate processes on $dest$ + search: '| from datamodel:Endpoint.Processes| search dest=$dest$ process_name=$process_name$' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process for a known remote access software $process_name$ was - identified on $dest$. - risk_objects: - - field: dest - type: system - score: 25 - - field: user - type: user - score: 25 - threat_objects: - - field: process_name - type: process_name - - field: signature - type: signature + message: A process for a known remote access software $process_name$ was identified on $dest$. + risk_objects: + - field: dest + type: system + score: 25 + - field: user + type: user + score: 25 + threat_objects: + - field: process_name + type: process_name + - field: signature + type: signature tags: - analytic_story: - - Insider Threat - - Command And Control - - Ransomware - - Gozi Malware - - CISA AA24-241A - - Remote Monitoring and Management Software - - Cactus Ransomware - - Seashell Blizzard - - Scattered Spider - - Interlock Ransomware - - GhostRedirector IIS Module and Rungan Backdoor - - Scattered Lapsus$ Hunters - - Storm-0501 Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1219 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - manual_test: This detection uses A&I lookups from Enterprise Security. + analytic_story: + - Insider Threat + - Command And Control + - Ransomware + - Gozi Malware + - CISA AA24-241A + - Remote Monitoring and Management Software + - Cactus Ransomware + - Seashell Blizzard + - Scattered Spider + - Interlock Ransomware + - GhostRedirector IIS Module and Rungan Backdoor + - Scattered Lapsus$ Hunters + - Storm-0501 Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1219 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + manual_test: This detection uses A&I lookups from Enterprise Security. tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_remote_access_software_usage_registry.yml b/detections/endpoint/detect_remote_access_software_usage_registry.yml index e22e2d4e1e..f62cf3f9ac 100644 --- a/detections/endpoint/detect_remote_access_software_usage_registry.yml +++ b/detections/endpoint/detect_remote_access_software_usage_registry.yml @@ -5,102 +5,67 @@ date: '2025-10-14' author: Steven Dick status: production type: Anomaly -description: The following analytic detects when a known remote access software is - added to common persistence locations on a device within the environment. Adversaries - use these utilities to retain remote access capabilities to the environment. Utilities - in the lookup include AnyDesk, GoToMyPC, LogMeIn, TeamViewer and much more. Review - the lookup for the entire list and add any others. +description: The following analytic detects when a known remote access software is added to common persistence locations on a device within the environment. Adversaries use these utilities to retain remote access capabilities to the environment. Utilities in the lookup include AnyDesk, GoToMyPC, LogMeIn, TeamViewer and much more. Review the lookup for the entire list and add any others. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` latest(Registry.process_guid) as - process_guid count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry - where (Registry.registry_path="*\\Microsoft\\Windows\\CurrentVersion\\Run*" OR (Registry.registry_path="*\\SYSTEM\\CurrentControlSet\\Services\\*" - AND Registry.registry_value_name="ImagePath")) by Registry.action Registry.dest - Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | rex field=registry_value_data "(\")?.+\\\(?[^\"=]+\.[^\" ]{1,5})(\")?" - | rex field=registry_value_data "(?[^\.]+\.[^\" ]{1,5}$)" | eval file_name - = coalesce(file_name_1,file_name_2) | lookup remote_access_software remote_utility - AS file_name OUTPUT isutility, description as signature, comment_reference as desc, - category | search isutility = TRUE | `remote_access_software_usage_exceptions` | - `detect_remote_access_software_usage_registry_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the file path, file name, and the user that created - the file. These logs must be processed using the appropriate Splunk Technology Add-ons - that are specific to the EDR product. The logs must also be mapped to the `Registry` - node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) - to normalize the field names and speed up the data modeling process. The "exceptions" - macro leverages both an Assets and Identities lookup, as well as a KVStore collection - called "remote_software_exceptions" that lets you track and maintain device-based - exceptions for this set of detections. -known_false_positives: Known or approved applications used by the organization or - usage of built-in functions. Known false positives can be added to the remote_access_software_usage_exception.csv - lookup to globally suppress these situations across all remote access content + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` latest(Registry.process_guid) as process_guid count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*\\Microsoft\\Windows\\CurrentVersion\\Run*" OR (Registry.registry_path="*\\SYSTEM\\CurrentControlSet\\Services\\*" AND Registry.registry_value_name="ImagePath")) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rex field=registry_value_data "(\")?.+\\\(?[^\"=]+\.[^\" ]{1,5})(\")?" | rex field=registry_value_data "(?[^\.]+\.[^\" ]{1,5}$)" | eval file_name = coalesce(file_name_1,file_name_2) | lookup remote_access_software remote_utility AS file_name OUTPUT isutility, description as signature, comment_reference as desc, category | search isutility = TRUE | `remote_access_software_usage_exceptions` | `detect_remote_access_software_usage_registry_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the file path, file name, and the user that created the file. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Registry` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. The "exceptions" macro leverages both an Assets and Identities lookup, as well as a KVStore collection called "remote_software_exceptions" that lets you track and maintain device-based exceptions for this set of detections. +known_false_positives: Known or approved applications used by the organization or usage of built-in functions. Known false positives can be added to the remote_access_software_usage_exception.csv lookup to globally suppress these situations across all remote access content references: -- https://attack.mitre.org/techniques/T1219/ -- https://thedfirreport.com/2022/08/08/bumblebee-roasts-its-way-to-domain-admin/ -- https://thedfirreport.com/2022/11/28/emotet-strikes-again-lnk-file-leads-to-domain-wide-ransomware/ + - https://attack.mitre.org/techniques/T1219/ + - https://thedfirreport.com/2022/08/08/bumblebee-roasts-its-way-to-domain-admin/ + - https://thedfirreport.com/2022/11/28/emotet-strikes-again-lnk-file-leads-to-domain-wide-ransomware/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$","$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate registry changes on $dest$ - search: '| from datamodel:Endpoint.Registry| search dest=$dest$ registry_path=$registry_path$' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$","$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate registry changes on $dest$ + search: '| from datamodel:Endpoint.Registry| search dest=$dest$ registry_path=$registry_path$' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process for a known remote access software [$signature$] was detected - on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - - field: user - type: user - score: 25 - threat_objects: - - field: registry_path - type: registry_path - - field: signature - type: signature + message: A process for a known remote access software [$signature$] was detected on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + - field: user + type: user + score: 25 + threat_objects: + - field: registry_path + type: registry_path + - field: signature + type: signature tags: - analytic_story: - - Insider Threat - - Command And Control - - Ransomware - - Gozi Malware - - CISA AA24-241A - - Remote Monitoring and Management Software - - Seashell Blizzard - - Cactus Ransomware - - Scattered Spider - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1219 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - manual_test: This detection uses A&I lookups from Enterprise Security. + analytic_story: + - Insider Threat + - Command And Control + - Ransomware + - Gozi Malware + - CISA AA24-241A + - Remote Monitoring and Management Software + - Seashell Blizzard + - Cactus Ransomware + - Scattered Spider + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1219 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + manual_test: This detection uses A&I lookups from Enterprise Security. tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_renamed_7_zip.yml b/detections/endpoint/detect_renamed_7_zip.yml index bb5427b755..7f4f8cc896 100644 --- a/detections/endpoint/detect_renamed_7_zip.yml +++ b/detections/endpoint/detect_renamed_7_zip.yml @@ -1,60 +1,52 @@ name: Detect Renamed 7-Zip id: 4057291a-b8cf-11eb-95fe-acde48001122 -version: 10 -date: '2025-06-02' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic detects the usage of a renamed 7-Zip executable - using Sysmon data. It leverages the OriginalFileName field to identify instances - where the 7-Zip process has been renamed. This activity is significant as attackers - often rename legitimate tools to evade detection while staging or exfiltrating data. - If confirmed malicious, this behavior could indicate data exfiltration attempts - or other unauthorized data manipulation, potentially leading to significant data - breaches or loss of sensitive information. Analysts should validate the legitimacy - of the 7-Zip executable and investigate parallel processes for further suspicious - activities. +description: The following analytic detects the usage of a renamed 7-Zip executable using Sysmon data. It leverages the OriginalFileName field to identify instances where the 7-Zip process has been renamed. This activity is significant as attackers often rename legitimate tools to evade detection while staging or exfiltrating data. If confirmed malicious, this behavior could indicate data exfiltration attempts or other unauthorized data manipulation, potentially leading to significant data breaches or loss of sensitive information. Analysts should validate the legitimacy of the 7-Zip executable and investigate parallel processes for further suspicious activities. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.original_file_name=7z*.exe - AND Processes.process_name!=7z*.exe) by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_renamed_7_zip_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Limited false positives, however this analytic will need to - be modified for each environment if Sysmon is not used. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.original_file_name=7z*.exe + AND + Processes.process_name!=7z*.exe + ) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_renamed_7_zip_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Limited false positives, however this analytic will need to be modified for each environment if Sysmon is not used. references: -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1560.001/T1560.001.md + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1560.001/T1560.001.md tags: - analytic_story: - - Collection and Staging - - Malicious Inno Setup Loader - asset_type: Endpoint - mitre_attack_id: - - T1560.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Collection and Staging + - Malicious Inno Setup Loader + asset_type: Endpoint + mitre_attack_id: + - T1560.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560.001/archive_utility/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560.001/archive_utility/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_renamed_psexec.yml b/detections/endpoint/detect_renamed_psexec.yml index a448f340ed..e7be5f919b 100644 --- a/detections/endpoint/detect_renamed_psexec.yml +++ b/detections/endpoint/detect_renamed_psexec.yml @@ -1,73 +1,67 @@ name: Detect Renamed PSExec id: 683e6196-b8e8-11eb-9a79-acde48001122 -version: 15 -date: '2025-05-02' +version: 16 +date: '2026-02-25' author: Michael Haag, Splunk, Alex Oberkircher, Github Community status: production type: Hunting -description: The following analytic identifies instances where `PsExec.exe` has been - renamed and executed on an endpoint. It leverages data from Endpoint Detection and - Response (EDR) agents, focusing on process names and original file names. This activity - is significant because renaming `PsExec.exe` is a common tactic to evade detection. - If confirmed malicious, this could allow an attacker to execute commands remotely, - potentially leading to unauthorized access, lateral movement, or further compromise - of the network. +description: The following analytic identifies instances where `PsExec.exe` has been renamed and executed on an endpoint. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and original file names. This activity is significant because renaming `PsExec.exe` is a common tactic to evade detection. If confirmed malicious, this could allow an attacker to execute commands remotely, potentially leading to unauthorized access, lateral movement, or further compromise of the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name!=psexec.exe - AND Processes.process_name!=psexec64.exe) AND Processes.original_file_name=psexec.c - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `detect_renamed_psexec_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Limited false positives should be present. It is possible some - third party applications may use older versions of PsExec, filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name!=psexec.exe + AND + Processes.process_name!=psexec64.exe + ) + AND Processes.original_file_name=psexec.c + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_renamed_psexec_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Limited false positives should be present. It is possible some third party applications may use older versions of PsExec, filter as needed. references: -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1569.002/T1569.002.yaml -- https://redcanary.com/blog/threat-hunting-psexec-lateral-movement/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1569.002/T1569.002.yaml + - https://redcanary.com/blog/threat-hunting-psexec-lateral-movement/ tags: - analytic_story: - - Active Directory Lateral Movement - - BlackByte Ransomware - - Cactus Ransomware - - China-Nexus Threat Activity - - CISA AA22-320A - - DarkGate Malware - - DarkSide Ransomware - - DHS Report TA18-074A - - HAFNIUM Group - - Medusa Ransomware - - Rhysida Ransomware - - Salt Typhoon - - SamSam Ransomware - - Sandworm Tools - - VanHelsing Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1569.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + - BlackByte Ransomware + - Cactus Ransomware + - China-Nexus Threat Activity + - CISA AA22-320A + - DarkGate Malware + - DarkSide Ransomware + - DHS Report TA18-074A + - HAFNIUM Group + - Medusa Ransomware + - Rhysida Ransomware + - Salt Typhoon + - SamSam Ransomware + - Sandworm Tools + - VanHelsing Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1569.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_renamed_rclone.yml b/detections/endpoint/detect_renamed_rclone.yml index 2033da6451..b56fbf2f43 100644 --- a/detections/endpoint/detect_renamed_rclone.yml +++ b/detections/endpoint/detect_renamed_rclone.yml @@ -1,63 +1,56 @@ name: Detect Renamed RClone id: 6dca1124-b3ec-11eb-9328-acde48001122 -version: 10 -date: '2025-05-02' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic detects the execution of a renamed `rclone.exe` - process, which is commonly used for data exfiltration to remote destinations. This - detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on - process names and original file names that do not match. This activity is significant - because ransomware groups often use RClone to exfiltrate sensitive data. If confirmed - malicious, this behavior could indicate an ongoing data exfiltration attempt, potentially - leading to significant data loss and further compromise of the affected systems. +description: The following analytic detects the execution of a renamed `rclone.exe` process, which is commonly used for data exfiltration to remote destinations. This detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on process names and original file names that do not match. This activity is significant because ransomware groups often use RClone to exfiltrate sensitive data. If confirmed malicious, this behavior could indicate an ongoing data exfiltration attempt, potentially leading to significant data loss and further compromise of the affected systems. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.original_file_name=rclone.exe - AND Processes.process_name!=rclone.exe) by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_renamed_rclone_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives should be limited as this analytic identifies - renamed instances of `rclone.exe`. Filter as needed if there is a legitimate business - use case. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.original_file_name=rclone.exe + AND + Processes.process_name!=rclone.exe + ) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_renamed_rclone_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives should be limited as this analytic identifies renamed instances of `rclone.exe`. Filter as needed if there is a legitimate business use case. references: -- https://redcanary.com/blog/rclone-mega-extortion/ -- https://www.mandiant.com/resources/shining-a-light-on-darkside-ransomware-operations -- https://thedfirreport.com/2021/03/29/sodinokibi-aka-revil-ransomware/ + - https://redcanary.com/blog/rclone-mega-extortion/ + - https://www.mandiant.com/resources/shining-a-light-on-darkside-ransomware-operations + - https://thedfirreport.com/2021/03/29/sodinokibi-aka-revil-ransomware/ tags: - analytic_story: - - DarkSide Ransomware - - Ransomware - - Black Basta Ransomware - - Cactus Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1020 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - DarkSide Ransomware + - Ransomware + - Black Basta Ransomware + - Cactus Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1020 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1020/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1020/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_renamed_winrar.yml b/detections/endpoint/detect_renamed_winrar.yml index 68dcb3998a..c0e5364ab2 100644 --- a/detections/endpoint/detect_renamed_winrar.yml +++ b/detections/endpoint/detect_renamed_winrar.yml @@ -1,63 +1,52 @@ name: Detect Renamed WinRAR id: 1b7bfb2c-b8e6-11eb-99ac-acde48001122 -version: 14 -date: '2026-01-14' +version: 15 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic identifies instances where `WinRAR.exe` has been - renamed and executed. It leverages data from Endpoint Detection and Response (EDR) - agents, focusing on process names and original file names within the Endpoint data - model. This activity is significant because renaming executables is a common tactic - used by attackers to evade detection. If confirmed malicious, this could indicate - an attempt to bypass security controls, potentially leading to unauthorized data - extraction or further system compromise. +description: The following analytic identifies instances where `WinRAR.exe` has been renamed and executed. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and original file names within the Endpoint data model. This activity is significant because renaming executables is a common tactic used by attackers to evade detection. If confirmed malicious, this could indicate an attempt to bypass security controls, potentially leading to unauthorized data extraction or further system compromise. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.original_file_name=WinRAR.exe - (Processes.process_name!=rar.exe AND Processes.process_name!=winrar.exe) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `detect_renamed_winrar_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: No false positives have been identified at this time. - instances of WinRAR. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.original_file_name=WinRAR.exe (Processes.process_name!=rar.exe + AND + Processes.process_name!=winrar.exe) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_renamed_winrar_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: No false positives have been identified at this time. instances of WinRAR. references: -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1560.001/T1560.001.md + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1560.001/T1560.001.md tags: - analytic_story: - - China-Nexus Threat Activity - - Collection and Staging - - CISA AA22-277A - - Salt Typhoon - asset_type: Endpoint - mitre_attack_id: - - T1560.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - China-Nexus Threat Activity + - Collection and Staging + - CISA AA22-277A + - Salt Typhoon + asset_type: Endpoint + mitre_attack_id: + - T1560.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560.001/archive_utility/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560.001/archive_utility/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_rtlo_in_file_name.yml b/detections/endpoint/detect_rtlo_in_file_name.yml index 46596f2573..7cd8f9c3c7 100644 --- a/detections/endpoint/detect_rtlo_in_file_name.yml +++ b/detections/endpoint/detect_rtlo_in_file_name.yml @@ -1,95 +1,90 @@ name: Detect RTLO In File Name id: 468b7e11-d362-43b8-b6ec-7a2d3b246678 -version: 9 -date: '2026-01-10' +version: 10 +date: '2026-02-25' author: Steven Dick status: production type: TTP description: | - The following analytic identifies the use of the right-to-left override - (RTLO) character in file names. It leverages data from the Endpoint.Filesystem datamodel, - specifically focusing on file creation events and file names containing the RTLO - character (U+202E). This activity is significant because adversaries use RTLO to - disguise malicious files as benign by reversing the text that follows the character. - If confirmed malicious, this technique can deceive users and security tools, leading - to the execution of harmful files and potential system compromise. + The following analytic identifies the use of the right-to-left override + (RTLO) character in file names. It leverages data from the Endpoint.Filesystem datamodel, + specifically focusing on file creation events and file names containing the RTLO + character (U+202E). This activity is significant because adversaries use RTLO to + disguise malicious files as benign by reversing the text that follows the character. + If confirmed malicious, this technique can deceive users and security tools, leading + to the execution of harmful files and potential system compromise. data_source: -- Sysmon EventID 11 + - Sysmon EventID 11 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime - values(Filesystem.file_create_time) as file_create_time + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + values(Filesystem.file_create_time) as file_create_time - from datamodel=Endpoint.Filesystem where Filesystem.file_name!=unknown - - by Filesystem.action Filesystem.dest Filesystem.file_access_time - Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time - Filesystem.file_name Filesystem.file_path Filesystem.file_acl - Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product + from datamodel=Endpoint.Filesystem where Filesystem.file_name!=unknown - | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | regex file_name = "\\x{202E}" - | rex field=file_name "(?.+)(?\\x{202E})(?.+)" - | eval file_name_with_RTLO=file_name - | eval file_name=RTLO_file_1.RTLO_file_2 - | fields - RTLO* - | `detect_rtlo_in_file_name_filter` + by Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path Filesystem.file_acl + Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | regex file_name = "\\x{202E}" + | rex field=file_name "(?.+)(?\\x{202E})(?.+)" + | eval file_name_with_RTLO=file_name + | eval file_name=RTLO_file_1.RTLO_file_2 + | fields - RTLO* + | `detect_rtlo_in_file_name_filter` how_to_implement: | - To successfully implement this search you need to be ingesting information - on process that includes the full command line of the process being launched on - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. + To successfully implement this search you need to be ingesting information + on process that includes the full command line of the process being launched on + your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, + confirm the latest CIM App 4.20 or higher is installed and the latest TA for the + endpoint product. known_false_positives: | - Implementation in regions that use right to left in native language. + Implementation in regions that use right to left in native language. references: - - https://attack.mitre.org/techniques/T1036/002/ - - https://resources.infosecinstitute.com/topic/spoof-using-right-to-left-override-rtlo-technique-2/ - - https://www.trendmicro.com/en_us/research/17/f/following-trail-blacktech-cyber-espionage-campaigns.html + - https://attack.mitre.org/techniques/T1036/002/ + - https://resources.infosecinstitute.com/topic/spoof-using-right-to-left-override-rtlo-technique-2/ + - https://www.trendmicro.com/en_us/research/17/f/following-trail-blacktech-cyber-espionage-campaigns.html drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious RTLO detected in $file_name$ on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 40 - - field: dest - type: system - score: 40 - threat_objects: - - field: file_name - type: file_name + message: Suspicious RTLO detected in $file_name$ on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 40 + - field: dest + type: system + score: 40 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - Spearphishing Attachments - asset_type: Endpoint - mitre_attack_id: - - T1036.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Spearphishing Attachments + asset_type: Endpoint + mitre_attack_id: + - T1036.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.002/outlook_attachment/rtlo_events.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.002/outlook_attachment/rtlo_events.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_rtlo_in_process.yml b/detections/endpoint/detect_rtlo_in_process.yml index 49c967efea..ba3dedd883 100644 --- a/detections/endpoint/detect_rtlo_in_process.yml +++ b/detections/endpoint/detect_rtlo_in_process.yml @@ -5,84 +5,53 @@ date: '2025-05-02' author: Steven Dick status: production type: TTP -description: The following analytic identifies the abuse of the right-to-left override - (RTLO) character (U+202E) in process names. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process execution logs and command-line data. - This activity is significant because adversaries use the RTLO character to disguise - malicious files or commands, making them appear benign. If confirmed malicious, - this technique can allow attackers to execute harmful code undetected, potentially - leading to unauthorized access, data exfiltration, or further system compromise. +description: The following analytic identifies the abuse of the right-to-left override (RTLO) character (U+202E) in process names. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line data. This activity is significant because adversaries use the RTLO character to disguise malicious files or commands, making them appear benign. If confirmed malicious, this technique can allow attackers to execute harmful code undetected, potentially leading to unauthorized access, data exfiltration, or further system compromise. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process!=unknown AND - Processes.action=allowed by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | regex - process="\\x{202E}" | rex field=process "(?.+)(?\\x{202E})(?.+)" - | eval process_with_RTLO=process | eval process=RTLO_command_1.RTLO_command_2 | - fields - RTLO* | `detect_rtlo_in_process_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Implementation in regions that use right to left in native - language. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process!=unknown AND Processes.action=allowed by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | regex process="\\x{202E}" | rex field=process "(?.+)(?\\x{202E})(?.+)" | eval process_with_RTLO=process | eval process=RTLO_command_1.RTLO_command_2 | fields - RTLO* | `detect_rtlo_in_process_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Implementation in regions that use right to left in native language. references: -- https://attack.mitre.org/techniques/T1036/002/ -- https://resources.infosecinstitute.com/topic/spoof-using-right-to-left-override-rtlo-technique-2/ -- https://www.trendmicro.com/en_us/research/17/f/following-trail-blacktech-cyber-espionage-campaigns.html + - https://attack.mitre.org/techniques/T1036/002/ + - https://resources.infosecinstitute.com/topic/spoof-using-right-to-left-override-rtlo-technique-2/ + - https://www.trendmicro.com/en_us/research/17/f/following-trail-blacktech-cyber-espionage-campaigns.html drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious RTLO detected in $process_name$ on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 40 - - field: dest - type: system - score: 40 - threat_objects: - - field: process_name - type: process_name + message: Suspicious RTLO detected in $process_name$ on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 40 + - field: dest + type: system + score: 40 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Spearphishing Attachments - asset_type: Endpoint - mitre_attack_id: - - T1036.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Spearphishing Attachments + asset_type: Endpoint + mitre_attack_id: + - T1036.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.002/outlook_attachment/rtlo_events.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.002/outlook_attachment/rtlo_events.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_rundll32_inline_hta_execution.yml b/detections/endpoint/detect_rundll32_inline_hta_execution.yml index 3d21c17e75..d949852584 100644 --- a/detections/endpoint/detect_rundll32_inline_hta_execution.yml +++ b/detections/endpoint/detect_rundll32_inline_hta_execution.yml @@ -1,85 +1,72 @@ name: Detect Rundll32 Inline HTA Execution id: 91c79f14-5b41-11eb-ae93-0242ac130002 -version: 9 -date: '2025-09-18' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of "rundll32.exe" with inline - protocol handlers such as "JavaScript", "VBScript", and "About". This behavior is - identified using Endpoint Detection and Response (EDR) telemetry, focusing on command-line - arguments. This activity is significant as it is often associated with fileless - malware or application whitelisting bypass techniques. If confirmed malicious, this - could allow an attacker to execute arbitrary code, bypass security controls, and - maintain persistence within the environment. +description: The following analytic detects the execution of "rundll32.exe" with inline protocol handlers such as "JavaScript", "VBScript", and "About". This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on command-line arguments. This activity is significant as it is often associated with fileless malware or application whitelisting bypass techniques. If confirmed malicious, this could allow an attacker to execute arbitrary code, bypass security controls, and maintain persistence within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count values(Processes.process) - as process values(Processes.parent_process) as parent_process min(_time) as firstTime - max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` - (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `detect_rundll32_inline_hta_execution_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although unlikely, some legitimate applications may exhibit - this behavior, triggering a false positive. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_rundll32` (Processes.process=*vbscript* + OR + Processes.process=*javascript* + OR + Processes.process=*about*) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_rundll32_inline_hta_execution_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. references: -- https://github.com/redcanaryco/AtomicTestHarnesses -- https://redcanary.com/blog/introducing-atomictestharnesses/ -- https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing + - https://github.com/redcanaryco/AtomicTestHarnesses + - https://redcanary.com/blog/introducing-atomictestharnesses/ + - https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious rundll32.exe inline HTA execution on $dest$ - risk_objects: - - field: dest - type: system - score: 56 - threat_objects: [] + message: Suspicious rundll32.exe inline HTA execution on $dest$ + risk_objects: + - field: dest + type: system + score: 56 + threat_objects: [] tags: - analytic_story: - - Suspicious MSHTA Activity - - NOBELIUM Group - - Living Off The Land - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1218.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious MSHTA Activity + - NOBELIUM Group + - Living Off The Land + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1218.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_sharphound_command_line_arguments.yml b/detections/endpoint/detect_sharphound_command_line_arguments.yml index 298adf1ab7..83ccc74462 100644 --- a/detections/endpoint/detect_sharphound_command_line_arguments.yml +++ b/detections/endpoint/detect_sharphound_command_line_arguments.yml @@ -1,89 +1,73 @@ name: Detect SharpHound Command-Line Arguments id: a0bdd2f6-c2ff-11eb-b918-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of SharpHound command-line - arguments, specifically `-collectionMethod` and `invoke-bloodhound`. It leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process names - and command-line executions. This activity is significant as SharpHound is commonly - used for Active Directory enumeration, which can be a precursor to lateral movement - or privilege escalation. If confirmed malicious, this activity could allow an attacker - to map out the network, identify high-value targets, and plan further attacks, potentially - compromising sensitive information and critical systems. +description: The following analytic detects the execution of SharpHound command-line arguments, specifically `-collectionMethod` and `invoke-bloodhound`. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as SharpHound is commonly used for Active Directory enumeration, which can be a precursor to lateral movement or privilege escalation. If confirmed malicious, this activity could allow an attacker to map out the network, identify high-value targets, and plan further attacks, potentially compromising sensitive information and critical systems. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*-collectionMethod*","*invoke-bloodhound*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `detect_sharphound_command_line_arguments_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives should be limited as the arguments used are - specific to SharpHound. Filter as needed or add more command-line arguments as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process IN ("*-collectionMethod*","*invoke-bloodhound*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_sharphound_command_line_arguments_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives should be limited as the arguments used are specific to SharpHound. Filter as needed or add more command-line arguments as needed. references: -- https://attack.mitre.org/software/S0521/ -- https://thedfirreport.com/?s=bloodhound -- https://github.com/BloodHoundAD/BloodHound/tree/master/Collectors -- https://github.com/BloodHoundAD/SharpHound3 -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md#atomic-test-2---run-bloodhound-from-local-disk + - https://attack.mitre.org/software/S0521/ + - https://thedfirreport.com/?s=bloodhound + - https://github.com/BloodHoundAD/BloodHound/tree/master/Collectors + - https://github.com/BloodHoundAD/SharpHound3 + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md#atomic-test-2---run-bloodhound-from-local-disk drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible SharpHound command-Line arguments identified on $dest$ - risk_objects: - - field: dest - type: system - score: 24 - threat_objects: [] + message: Possible SharpHound command-Line arguments identified on $dest$ + risk_objects: + - field: dest + type: system + score: 24 + threat_objects: [] tags: - analytic_story: - - Windows Discovery Techniques - - Ransomware - - BlackSuit Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1069.001 - - T1069.002 - - T1087.001 - - T1087.002 - - T1482 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Discovery Techniques + - Ransomware + - BlackSuit Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1069.001 + - T1069.002 + - T1087.001 + - T1087.002 + - T1482 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_sharphound_file_modifications.yml b/detections/endpoint/detect_sharphound_file_modifications.yml index b0dbbab8ea..bb983c31ca 100644 --- a/detections/endpoint/detect_sharphound_file_modifications.yml +++ b/detections/endpoint/detect_sharphound_file_modifications.yml @@ -1,84 +1,72 @@ name: Detect SharpHound File Modifications id: 42b4b438-beed-11eb-ba1d-acde48001122 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the creation of files typically associated - with SharpHound, a reconnaissance tool used for gathering domain and trust data. - It leverages file modification events from the Endpoint.Filesystem data model, focusing - on default file naming patterns like `*_BloodHound.zip` and various JSON files. - This activity is significant as it indicates potential domain enumeration, which - is a precursor to more targeted attacks. If confirmed malicious, an attacker could - gain detailed insights into the domain structure, facilitating lateral movement - and privilege escalation. +description: The following analytic detects the creation of files typically associated with SharpHound, a reconnaissance tool used for gathering domain and trust data. It leverages file modification events from the Endpoint.Filesystem data model, focusing on default file naming patterns like `*_BloodHound.zip` and various JSON files. This activity is significant as it indicates potential domain enumeration, which is a precursor to more targeted attacks. If confirmed malicious, an attacker could gain detailed insights into the domain structure, facilitating lateral movement and privilege escalation. data_source: -- Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*bloodhound.zip", - "*_computers.json", "*_gpos.json", "*_domains.json", "*_users.json", "*_groups.json", - "*_ous.json", "*_containers.json") by Filesystem.action Filesystem.dest Filesystem.file_access_time - Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name - Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid - Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_sharphound_file_modifications_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on file modifications that include the name of the process, and file, responsible - for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` - node. -known_false_positives: False positives should be limited as the analytic is specific - to a filename with extension .zip. Filter as needed. + - Sysmon EventID 11 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.file_name IN ("*bloodhound.zip", "*_computers.json", "*_gpos.json", "*_domains.json", "*_users.json", "*_groups.json", "*_ous.json", "*_containers.json") + BY Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path Filesystem.file_acl + Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_sharphound_file_modifications_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on file modifications that include the name of the process, and file, responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. +known_false_positives: False positives should be limited as the analytic is specific to a filename with extension .zip. Filter as needed. references: -- https://attack.mitre.org/software/S0521/ -- https://thedfirreport.com/?s=bloodhound -- https://github.com/BloodHoundAD/BloodHound/tree/master/Collectors -- https://github.com/BloodHoundAD/SharpHound3 -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md#atomic-test-2---run-bloodhound-from-local-disk + - https://attack.mitre.org/software/S0521/ + - https://thedfirreport.com/?s=bloodhound + - https://github.com/BloodHoundAD/BloodHound/tree/master/Collectors + - https://github.com/BloodHoundAD/SharpHound3 + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md#atomic-test-2---run-bloodhound-from-local-disk drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential SharpHound file modifications identified on $dest$ - risk_objects: - - field: dest - type: system - score: 24 - - field: user - type: user - score: 24 - threat_objects: [] + message: Potential SharpHound file modifications identified on $dest$ + risk_objects: + - field: dest + type: system + score: 24 + - field: user + type: user + score: 24 + threat_objects: [] tags: - analytic_story: - - Windows Discovery Techniques - - Ransomware - - BlackSuit Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1069.001 - - T1069.002 - - T1087.001 - - T1087.002 - - T1482 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Discovery Techniques + - Ransomware + - BlackSuit Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1069.001 + - T1069.002 + - T1087.001 + - T1087.002 + - T1482 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_sharphound_usage.yml b/detections/endpoint/detect_sharphound_usage.yml index dc44938536..ad93e180f9 100644 --- a/detections/endpoint/detect_sharphound_usage.yml +++ b/detections/endpoint/detect_sharphound_usage.yml @@ -1,89 +1,76 @@ name: Detect SharpHound Usage id: dd04b29a-beed-11eb-87bc-acde48001122 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the usage of the SharpHound binary by - identifying its original filename, `SharpHound.exe`, and the process name. This - detection leverages data from Endpoint Detection and Response (EDR) agents, focusing - on process metadata and command-line executions. SharpHound is a tool used for Active - Directory enumeration, often by attackers during the reconnaissance phase. If confirmed - malicious, this activity could allow an attacker to map out the network, identify - high-value targets, and plan further attacks, potentially leading to privilege escalation - and lateral movement within the environment. +description: The following analytic detects the usage of the SharpHound binary by identifying its original filename, `SharpHound.exe`, and the process name. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process metadata and command-line executions. SharpHound is a tool used for Active Directory enumeration, often by attackers during the reconnaissance phase. If confirmed malicious, this activity could allow an attacker to map out the network, identify high-value targets, and plan further attacks, potentially leading to privilege escalation and lateral movement within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=sharphound.exe - OR Processes.original_file_name=SharpHound.exe) by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `detect_sharphound_usage_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives should be limited as this is specific to a - file attribute not used by anything else. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name=sharphound.exe + OR + Processes.original_file_name=SharpHound.exe + ) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_sharphound_usage_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives should be limited as this is specific to a file attribute not used by anything else. Filter as needed. references: -- https://attack.mitre.org/software/S0521/ -- https://thedfirreport.com/?s=bloodhound -- https://github.com/BloodHoundAD/BloodHound/tree/master/Collectors -- https://github.com/BloodHoundAD/SharpHound3 -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md#atomic-test-2---run-bloodhound-from-local-disk + - https://attack.mitre.org/software/S0521/ + - https://thedfirreport.com/?s=bloodhound + - https://github.com/BloodHoundAD/BloodHound/tree/master/Collectors + - https://github.com/BloodHoundAD/SharpHound3 + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md#atomic-test-2---run-bloodhound-from-local-disk drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential SharpHound binary identified on $dest$ - risk_objects: - - field: dest - type: system - score: 24 - threat_objects: [] + message: Potential SharpHound binary identified on $dest$ + risk_objects: + - field: dest + type: system + score: 24 + threat_objects: [] tags: - analytic_story: - - Windows Discovery Techniques - - Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1069.001 - - T1069.002 - - T1087.001 - - T1087.002 - - T1482 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Discovery Techniques + - Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1069.001 + - T1069.002 + - T1087.001 + - T1087.002 + - T1482 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_suspicious_processnames_using_pretrained_model_in_dsdl.yml b/detections/endpoint/detect_suspicious_processnames_using_pretrained_model_in_dsdl.yml index cb4be84838..c83cd93331 100644 --- a/detections/endpoint/detect_suspicious_processnames_using_pretrained_model_in_dsdl.yml +++ b/detections/endpoint/detect_suspicious_processnames_using_pretrained_model_in_dsdl.yml @@ -1,65 +1,53 @@ name: Detect suspicious processnames using pretrained model in DSDL id: a15f8977-ad7d-4669-92ef-b59b97219bf5 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Abhinav Mishra, Kumar Sharad and Namratha Sreekanta, Splunk type: Anomaly status: experimental data_source: -- Sysmon EventID 1 -description: The following analytic identifies suspicious process names using a pre-trained - Deep Learning model. It leverages Endpoint Detection and Response (EDR) telemetry - to analyze process names and predict their likelihood of being malicious. The model, - a character-level Recurrent Neural Network (RNN), classifies process names as benign - or suspicious based on a threshold score of 0.5. This detection is significant as - it helps identify malware, such as TrickBot, which often uses randomly generated - filenames to evade detection. If confirmed malicious, this activity could indicate - the presence of malware capable of propagating across the network and executing - harmful actions. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | rename process_name as text | fields text, - parent_process_name, process, user, dest | apply detect_suspicious_processnames_using_pretrained_model_in_dsdl - | rename predicted_label as is_suspicious_score | rename text as process_name | - where is_suspicious_score > 0.5 | `detect_suspicious_processnames_using_pretrained_model_in_dsdl_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present if a suspicious processname - is similar to a benign processname. + - Sysmon EventID 1 +description: The following analytic identifies suspicious process names using a pre-trained Deep Learning model. It leverages Endpoint Detection and Response (EDR) telemetry to analyze process names and predict their likelihood of being malicious. The model, a character-level Recurrent Neural Network (RNN), classifies process names as benign or suspicious based on a threshold score of 0.5. This detection is significant as it helps identify malware, such as TrickBot, which often uses randomly generated filenames to evade detection. If confirmed malicious, this activity could indicate the presence of malware capable of propagating across the network and executing harmful actions. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | rename process_name as text + | fields text, parent_process_name, process, user, dest + | apply detect_suspicious_processnames_using_pretrained_model_in_dsdl + | rename predicted_label as is_suspicious_score + | rename text as process_name + | where is_suspicious_score > 0.5 + | `detect_suspicious_processnames_using_pretrained_model_in_dsdl_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present if a suspicious processname is similar to a benign processname. references: -- https://www.cisa.gov/uscert/ncas/alerts/aa20-302a -- https://www.splunk.com/en_us/blog/security/random-words-on-entropy-and-dns.html + - https://www.cisa.gov/uscert/ncas/alerts/aa20-302a + - https://www.splunk.com/en_us/blog/security/random-words-on-entropy-and-dns.html rba: - message: The process $process$ is running from an unusual place by $user$ on $dest$ - with a processname that appears to be randomly generated. - risk_objects: - - field: dest - type: system - score: 45 - - field: user - type: user - score: 45 - threat_objects: [] + message: The process $process$ is running from an unusual place by $user$ on $dest$ with a processname that appears to be randomly generated. + risk_objects: + - field: dest + type: system + score: 45 + - field: user + type: user + score: 45 + threat_objects: [] tags: - analytic_story: - - Suspicious Command-Line Executions - asset_type: Endpoint - mitre_attack_id: - - T1059 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Command-Line Executions + asset_type: Endpoint + mitre_attack_id: + - T1059 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/detect_use_of_cmd_exe_to_launch_script_interpreters.yml b/detections/endpoint/detect_use_of_cmd_exe_to_launch_script_interpreters.yml index a7fdae3395..43458cc7cb 100644 --- a/detections/endpoint/detect_use_of_cmd_exe_to_launch_script_interpreters.yml +++ b/detections/endpoint/detect_use_of_cmd_exe_to_launch_script_interpreters.yml @@ -1,84 +1,68 @@ name: Detect Use of cmd exe to Launch Script Interpreters id: b89919ed-fe5f-492c-b139-95dbb162039e -version: 11 -date: '2025-05-02' +version: 12 +date: '2026-02-25' author: Bhavin Patel, Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the execution of cscript.exe or wscript.exe - processes initiated by cmd.exe. It leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process names and parent processes within the Endpoint - data model. This activity is significant as it may indicate script-based attacks - or administrative actions that could be leveraged for malicious purposes. If confirmed - malicious, this behavior could allow attackers to execute scripts, potentially leading - to code execution, privilege escalation, or persistence within the environment. +description: The following analytic detects the execution of cscript.exe or wscript.exe processes initiated by cmd.exe. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and parent processes within the Endpoint data model. This activity is significant as it may indicate script-based attacks or administrative actions that could be leveraged for malicious purposes. If confirmed malicious, this behavior could allow attackers to execute scripts, potentially leading to code execution, privilege escalation, or persistence within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="cmd.exe" - (Processes.process_name=cscript.exe OR Processes.process_name =wscript.exe) by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` - | `detect_use_of_cmd_exe_to_launch_script_interpreters_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: This detection may also be triggered by legitimate applications - and numerous service accounts, which often end with a $ sign. To manage this, it's - advised to check the service account's activities and, if they are valid, modify - the filter macro to exclude them. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name="cmd.exe" (Processes.process_name=cscript.exe + OR + Processes.process_name =wscript.exe) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_use_of_cmd_exe_to_launch_script_interpreters_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: This detection may also be triggered by legitimate applications and numerous service accounts, which often end with a $ sign. To manage this, it's advised to check the service account's activities and, if they are valid, modify the filter macro to exclude them. references: -- https://attack.mitre.org/techniques/T1059/ -- https://redcanary.com/threat-detection-report/techniques/windows-command-shell/ + - https://attack.mitre.org/techniques/T1059/ + - https://redcanary.com/threat-detection-report/techniques/windows-command-shell/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: cmd.exe launching script interpreters $process_name$ on $dest$ - risk_objects: - - field: dest - type: system - score: 35 - threat_objects: [] + message: cmd.exe launching script interpreters $process_name$ on $dest$ + risk_objects: + - field: dest + type: system + score: 35 + threat_objects: [] tags: - analytic_story: - - Emotet Malware DHS Report TA18-201A - - Suspicious Command-Line Executions - - Azorult - asset_type: Endpoint - mitre_attack_id: - - T1059.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Emotet Malware DHS Report TA18-201A + - Suspicious Command-Line Executions + - Azorult + asset_type: Endpoint + mitre_attack_id: + - T1059.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/cmd_spawns_cscript/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/cmd_spawns_cscript/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detect_wmi_event_subscription_persistence.yml b/detections/endpoint/detect_wmi_event_subscription_persistence.yml index 1cf30fb53f..4ab778d7e2 100644 --- a/detections/endpoint/detect_wmi_event_subscription_persistence.yml +++ b/detections/endpoint/detect_wmi_event_subscription_persistence.yml @@ -1,72 +1,61 @@ name: Detect WMI Event Subscription Persistence id: 01d9a0c2-cece-11eb-ab46-acde48001122 -version: 8 -date: '2025-10-14' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies the creation of WMI Event Subscriptions, - which can be used to establish persistence or perform privilege escalation. It detects - EventID 19 (EventFilter creation), EventID 20 (EventConsumer creation), and EventID - 21 (FilterToConsumerBinding creation) from Sysmon logs. This activity is significant - because WMI Event Subscriptions can execute code with elevated SYSTEM privileges, - making it a powerful persistence mechanism. If confirmed malicious, an attacker - could maintain long-term access, escalate privileges, and execute arbitrary code, - posing a severe threat to the environment. +description: The following analytic identifies the creation of WMI Event Subscriptions, which can be used to establish persistence or perform privilege escalation. It detects EventID 19 (EventFilter creation), EventID 20 (EventConsumer creation), and EventID 21 (FilterToConsumerBinding creation) from Sysmon logs. This activity is significant because WMI Event Subscriptions can execute code with elevated SYSTEM privileges, making it a powerful persistence mechanism. If confirmed malicious, an attacker could maintain long-term access, escalate privileges, and execute arbitrary code, posing a severe threat to the environment. data_source: -- Sysmon EventID 20 -search: '`sysmon` EventID=20 | stats count min(_time) as firstTime max(_time) as lastTime - by dest dvc object object_category object_path signature signature_id src status - user user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `detect_wmi_event_subscription_persistence_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with that provide WMI Event Subscription from your endpoints. If you are using - Sysmon, you must have at least version 6.0.4 of the Sysmon TA and have enabled EventID - 19, 20 and 21. Tune and filter known good to limit the volume. -known_false_positives: It is possible some applications will create a consumer and - may be required to be filtered. For tuning, add any additional LOLBin's for further - depth of coverage. + - Sysmon EventID 20 +search: |- + `sysmon` EventID=20 + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest dvc object + object_category object_path signature + signature_id src status + user user_id vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_wmi_event_subscription_persistence_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with that provide WMI Event Subscription from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA and have enabled EventID 19, 20 and 21. Tune and filter known good to limit the volume. +known_false_positives: It is possible some applications will create a consumer and may be required to be filtered. For tuning, add any additional LOLBin's for further depth of coverage. references: -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1546.003/T1546.003.md -- https://www.eideon.com/2018-03-02-THL03-WMIBackdoors/ -- https://github.com/trustedsec/SysmonCommunityGuide/blob/master/chapters/WMI-events.md -- https://in.security/2019/04/03/an-intro-into-abusing-and-identifying-wmi-event-subscriptions-for-persistence/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1546.003/T1546.003.md + - https://www.eideon.com/2018-03-02-THL03-WMIBackdoors/ + - https://github.com/trustedsec/SysmonCommunityGuide/blob/master/chapters/WMI-events.md + - https://in.security/2019/04/03/an-intro-into-abusing-and-identifying-wmi-event-subscriptions-for-persistence/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible malicious WMI Subscription created on $dest$ - risk_objects: - - field: dest - type: system - score: 63 - threat_objects: [] + message: Possible malicious WMI Subscription created on $dest$ + risk_objects: + - field: dest + type: system + score: 63 + threat_objects: [] tags: - analytic_story: - - Suspicious WMI Use - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1546.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious WMI Use + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1546.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.003/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.003/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/detection_of_tools_built_by_nirsoft.yml b/detections/endpoint/detection_of_tools_built_by_nirsoft.yml index b218f78a9d..1ec2c2bab5 100644 --- a/detections/endpoint/detection_of_tools_built_by_nirsoft.yml +++ b/detections/endpoint/detection_of_tools_built_by_nirsoft.yml @@ -1,62 +1,54 @@ name: Detection of tools built by NirSoft id: 3d8d201c-aa03-422d-b0ee-2e5ecf9718c0 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Bhavin Patel, Splunk status: experimental type: Anomaly -description: The following analytic identifies the execution of tools built by NirSoft - by detecting specific command-line arguments such as "/stext" and "/scomma". It - leverages data from Endpoint Detection and Response (EDR) agents, focusing on process - names, parent processes, and command-line executions. This activity is significant - because NirSoft tools, while legitimate, can be exploited by attackers for malicious - purposes such as credential theft or system reconnaissance. If confirmed malicious, - this activity could lead to unauthorized access, data exfiltration, or further compromise - of the affected system. +description: The following analytic identifies the execution of tools built by NirSoft by detecting specific command-line arguments such as "/stext" and "/scomma". It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, parent processes, and command-line executions. This activity is significant because NirSoft tools, while legitimate, can be exploited by attackers for malicious purposes such as credential theft or system reconnaissance. If confirmed malicious, this activity could lead to unauthorized access, data exfiltration, or further compromise of the affected system. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) values(Processes.process) - as process max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="* - /stext *" OR Processes.process="* /scomma *" ) by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` - | `detection_of_tools_built_by_nirsoft_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: While legitimate, these NirSoft tools are prone to abuse. You - should verify that the tool was used for a legitimate purpose. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process="* /stext *" + OR + Processes.process="* /scomma *" + ) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detection_of_tools_built_by_nirsoft_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: While legitimate, these NirSoft tools are prone to abuse. You should verify that the tool was used for a legitimate purpose. references: [] rba: - message: NirSoft tool detected on $dest$ - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: NirSoft tool detected on $dest$ + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Emotet Malware DHS Report TA18-201A - asset_type: Endpoint - mitre_attack_id: - - T1072 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Emotet Malware DHS Report TA18-201A + asset_type: Endpoint + mitre_attack_id: + - T1072 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/disable_amsi_through_registry.yml b/detections/endpoint/disable_amsi_through_registry.yml index 245559a534..08223e6f9b 100644 --- a/detections/endpoint/disable_amsi_through_registry.yml +++ b/detections/endpoint/disable_amsi_through_registry.yml @@ -5,71 +5,47 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: The following analytic detects modifications to the Windows registry - that disable the Antimalware Scan Interface (AMSI) by setting the "AmsiEnable" value - to "0x00000000". This detection leverages data from the Endpoint.Registry data model, - specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows - Script\\Settings\\AmsiEnable". Disabling AMSI is significant as it is a common technique - used by ransomware, Remote Access Trojans (RATs), and Advanced Persistent Threats - (APTs) to evade detection and impair defenses. If confirmed malicious, this activity - could allow attackers to execute payloads with minimal alerts, leading to potential - system compromise and data exfiltration. +description: The following analytic detects modifications to the Windows registry that disable the Antimalware Scan Interface (AMSI) by setting the "AmsiEnable" value to "0x00000000". This detection leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows Script\\Settings\\AmsiEnable". Disabling AMSI is significant as it is a common technique used by ransomware, Remote Access Trojans (RATs), and Advanced Persistent Threats (APTs) to evade detection and impair defenses. If confirmed malicious, this activity could allow attackers to execute payloads with minimal alerts, leading to potential system compromise and data exfiltration. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows - Script\\Settings\\AmsiEnable" Registry.registry_value_data = "0x00000000") by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `disable_amsi_through_registry_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 -known_false_positives: network operator may disable this feature of windows but not - so common. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows Script\\Settings\\AmsiEnable" Registry.registry_value_data = "0x00000000") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_amsi_through_registry_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 +known_false_positives: network operator may disable this feature of windows but not so common. references: -- https://blog.f-secure.com/hunting-for-amsi-bypasses/ -- https://gist.github.com/rxwx/8955e5abf18dc258fd6b43a3a7f4dbf9 + - https://blog.f-secure.com/hunting-for-amsi-bypasses/ + - https://gist.github.com/rxwx/8955e5abf18dc258fd6b43a3a7f4dbf9 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Disable AMSI Through Registry on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: Disable AMSI Through Registry on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Ransomware - - CISA AA23-347A - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - CISA AA23-347A + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disable_defender_antivirus_registry.yml b/detections/endpoint/disable_defender_antivirus_registry.yml index 90bc86358a..cfe15c2c7f 100644 --- a/detections/endpoint/disable_defender_antivirus_registry.yml +++ b/detections/endpoint/disable_defender_antivirus_registry.yml @@ -5,76 +5,52 @@ date: '2026-02-09' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: The following analytic detects the modification of Windows Defender registry - settings to disable antivirus and antispyware protections. It leverages data from - the Endpoint.Registry data model, specifically monitoring changes to registry paths - associated with Windows Defender policies. This activity is significant because - disabling antivirus protections is a common tactic used by adversaries to evade - detection and maintain persistence on compromised systems. If confirmed malicious, - this action could allow attackers to execute further malicious activities undetected, - leading to potential data breaches, system compromise, and further propagation of - malware within the network. +description: The following analytic detects the modification of Windows Defender registry settings to disable antivirus and antispyware protections. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to registry paths associated with Windows Defender policies. This activity is significant because disabling antivirus protections is a common tactic used by adversaries to evade detection and maintain persistence on compromised systems. If confirmed malicious, this action could allow attackers to execute further malicious activities undetected, leading to potential data breaches, system compromise, and further propagation of malware within the network. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\Policies\\Microsoft\\Windows - Defender*" Registry.registry_value_name IN ("DisableAntiSpyware","DisableAntiVirus") - Registry.registry_value_data = 0x00000001) by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `disable_defender_antivirus_registry_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\Policies\\Microsoft\\Windows Defender*" Registry.registry_value_name IN ("DisableAntiSpyware","DisableAntiVirus") Registry.registry_value_data = 0x00000001) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_defender_antivirus_registry_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: admin or user may choose to disable windows defender product references: -- https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ + - https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Modified/added/deleted registry entry $registry_path$ on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - - field: user - type: user - score: 49 - threat_objects: [] + message: Modified/added/deleted registry entry $registry_path$ on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - SolarWinds WHD RCE Post Exploitation - - Windows Registry Abuse - - CISA AA24-241A - - IcedID - - Black Basta Ransomware - - Cactus Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SolarWinds WHD RCE Post Exploitation + - Windows Registry Abuse + - CISA AA24-241A + - IcedID + - Black Basta Ransomware + - Cactus Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disable_defender_blockatfirstseen_feature.yml b/detections/endpoint/disable_defender_blockatfirstseen_feature.yml index 540daa2b7a..258c90ef65 100644 --- a/detections/endpoint/disable_defender_blockatfirstseen_feature.yml +++ b/detections/endpoint/disable_defender_blockatfirstseen_feature.yml @@ -6,72 +6,50 @@ author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects the modification of the Windows registry - to disable the Windows Defender BlockAtFirstSeen feature. It leverages data from - the Endpoint.Registry data model, specifically monitoring changes to the registry - path associated with Windows Defender SpyNet and the DisableBlockAtFirstSeen value. - This activity is significant because disabling this feature can allow malicious - files to bypass initial detection by Windows Defender, increasing the risk of malware - infection. If confirmed malicious, this action could enable attackers to execute - malicious code undetected, leading to potential system compromise and data breaches. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\Microsoft\\Windows - Defender\\SpyNet*" Registry.registry_value_name = DisableBlockAtFirstSeen Registry.registry_value_data - = 0x00000001) by Registry.action Registry.dest Registry.process_guid Registry.process_id - Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data - Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user - Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_defender_blockatfirstseen_feature_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +description: The following analytic detects the modification of the Windows registry to disable the Windows Defender BlockAtFirstSeen feature. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path associated with Windows Defender SpyNet and the DisableBlockAtFirstSeen value. This activity is significant because disabling this feature can allow malicious files to bypass initial detection by Windows Defender, increasing the risk of malware infection. If confirmed malicious, this action could enable attackers to execute malicious code undetected, leading to potential system compromise and data breaches. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\Microsoft\\Windows Defender\\SpyNet*" Registry.registry_value_name = DisableBlockAtFirstSeen Registry.registry_value_data = 0x00000001) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_defender_blockatfirstseen_feature_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: admin or user may choose to disable windows defender product references: -- https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ + - https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: modified/added/deleted registry entry $registry_path$ on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - - field: user - type: user - score: 49 - threat_objects: [] + message: modified/added/deleted registry entry $registry_path$ on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - SolarWinds WHD RCE Post Exploitation - - Azorult - - CISA AA23-347A - - IcedID - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SolarWinds WHD RCE Post Exploitation + - Azorult + - CISA AA23-347A + - IcedID + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disable_defender_enhanced_notification.yml b/detections/endpoint/disable_defender_enhanced_notification.yml index bbe298f1b2..3922b0639b 100644 --- a/detections/endpoint/disable_defender_enhanced_notification.yml +++ b/detections/endpoint/disable_defender_enhanced_notification.yml @@ -5,78 +5,50 @@ date: '2025-06-10' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: The following analytic detects the modification of the registry to disable - Windows Defender's Enhanced Notification feature. It leverages data from Endpoint - Detection and Response (EDR) agents, specifically monitoring changes to the registry - path associated with Windows Defender reporting. This activity is significant because - disabling Enhanced Notifications can prevent users and administrators from receiving - critical security alerts, potentially allowing malicious activities to go unnoticed. - If confirmed malicious, this action could enable an attacker to bypass detection - mechanisms, maintain persistence, and escalate their activities without triggering - alerts. +description: The following analytic detects the modification of the registry to disable Windows Defender's Enhanced Notification feature. It leverages data from Endpoint Detection and Response (EDR) agents, specifically monitoring changes to the registry path associated with Windows Defender reporting. This activity is significant because disabling Enhanced Notifications can prevent users and administrators from receiving critical security alerts, potentially allowing malicious activities to go unnoticed. If confirmed malicious, this action could enable an attacker to bypass detection mechanisms, maintain persistence, and escalate their activities without triggering alerts. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry - WHERE (Registry.registry_path = "*Microsoft\\Windows Defender\\Reporting*" Registry.registry_value_name - = DisableEnhancedNotifications Registry.registry_value_data = 0x00000001) by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `disable_defender_enhanced_notification_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*Microsoft\\Windows Defender\\Reporting*" Registry.registry_value_name = DisableEnhancedNotifications Registry.registry_value_data = 0x00000001) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_defender_enhanced_notification_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: user may choose to disable windows defender AV references: -- https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ + - https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: modified/added/deleted registry entry $registry_path$ on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - - field: user - type: user - score: 49 - threat_objects: [] + message: modified/added/deleted registry entry $registry_path$ on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - Azorult - - CISA AA23-347A - - IcedID - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azorult + - CISA AA23-347A + - IcedID + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disable_defender_mpengine_registry.yml b/detections/endpoint/disable_defender_mpengine_registry.yml index e81d03d426..3a78a9861a 100644 --- a/detections/endpoint/disable_defender_mpengine_registry.yml +++ b/detections/endpoint/disable_defender_mpengine_registry.yml @@ -5,70 +5,48 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: The following analytic detects the modification of the Windows Defender - MpEngine registry value, specifically setting MpEnablePus to 0x00000000. This detection - leverages endpoint registry logs, focusing on changes within the path "*\\Policies\\Microsoft\\Windows - Defender\\MpEngine*". This activity is significant as it indicates an attempt to - disable key Windows Defender features, potentially allowing malware to evade detection. - If confirmed malicious, this could lead to undetected malware execution, persistence, - and further system compromise. Immediate investigation and endpoint isolation are - recommended. +description: The following analytic detects the modification of the Windows Defender MpEngine registry value, specifically setting MpEnablePus to 0x00000000. This detection leverages endpoint registry logs, focusing on changes within the path "*\\Policies\\Microsoft\\Windows Defender\\MpEngine*". This activity is significant as it indicates an attempt to disable key Windows Defender features, potentially allowing malware to evade detection. If confirmed malicious, this could lead to undetected malware execution, persistence, and further system compromise. Immediate investigation and endpoint isolation are recommended. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\Policies\\Microsoft\\Windows - Defender\\MpEngine*" Registry.registry_value_name = MpEnablePus Registry.registry_value_data - = 0x00000000) by Registry.action Registry.dest Registry.process_guid Registry.process_id - Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data - Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user - Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_defender_mpengine_registry_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\Policies\\Microsoft\\Windows Defender\\MpEngine*" Registry.registry_value_name = MpEnablePus Registry.registry_value_data = 0x00000000) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_defender_mpengine_registry_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: admin or user may choose to disable windows defender product references: -- https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ + - https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Modified/added/deleted registry entry $registry_path$ on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - - field: user - type: user - score: 49 - threat_objects: [] + message: Modified/added/deleted registry entry $registry_path$ on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - IcedID - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - IcedID + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disable_defender_spynet_reporting.yml b/detections/endpoint/disable_defender_spynet_reporting.yml index 4eaa1ff555..8e65a15d54 100644 --- a/detections/endpoint/disable_defender_spynet_reporting.yml +++ b/detections/endpoint/disable_defender_spynet_reporting.yml @@ -5,73 +5,51 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: The following analytic detects the modification of the registry to disable - Windows Defender SpyNet reporting. It leverages data from the Endpoint.Registry - data model, specifically monitoring changes to the registry path associated with - Windows Defender SpyNet settings. This activity is significant because disabling - SpyNet reporting can prevent Windows Defender from sending telemetry data, potentially - allowing malicious activities to go undetected. If confirmed malicious, this action - could enable an attacker to evade detection, maintain persistence, and carry out - further attacks without being flagged by Windows Defender. +description: The following analytic detects the modification of the registry to disable Windows Defender SpyNet reporting. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path associated with Windows Defender SpyNet settings. This activity is significant because disabling SpyNet reporting can prevent Windows Defender from sending telemetry data, potentially allowing malicious activities to go undetected. If confirmed malicious, this action could enable an attacker to evade detection, maintain persistence, and carry out further attacks without being flagged by Windows Defender. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\Microsoft\\Windows - Defender\\SpyNet*" Registry.registry_value_name = SpynetReporting Registry.registry_value_data - = 0x00000000) by Registry.action Registry.dest Registry.process_guid Registry.process_id - Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data - Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user - Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_defender_spynet_reporting_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\Microsoft\\Windows Defender\\SpyNet*" Registry.registry_value_name = SpynetReporting Registry.registry_value_data = 0x00000000) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_defender_spynet_reporting_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: admin or user may choose to disable windows defender product references: -- https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ + - https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: modified/added/deleted registry entry $registry_path$ on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - - field: user - type: user - score: 49 - threat_objects: [] + message: modified/added/deleted registry entry $registry_path$ on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - Azorult - - Windows Registry Abuse - - Qakbot - - IcedID - - CISA AA23-347A - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azorult + - Windows Registry Abuse + - Qakbot + - IcedID + - CISA AA23-347A + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disable_defender_submit_samples_consent_feature.yml b/detections/endpoint/disable_defender_submit_samples_consent_feature.yml index cd4e6fbc50..feec3fa0a5 100644 --- a/detections/endpoint/disable_defender_submit_samples_consent_feature.yml +++ b/detections/endpoint/disable_defender_submit_samples_consent_feature.yml @@ -5,72 +5,50 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: The following analytic detects the modification of the Windows registry - to disable the Windows Defender Submit Samples Consent feature. It leverages data - from the Endpoint.Registry data model, specifically monitoring changes to the registry - path associated with Windows Defender SpyNet and the SubmitSamplesConsent value - set to 0x00000000. This activity is significant as it indicates an attempt to bypass - or evade detection by preventing Windows Defender from submitting samples for further - analysis. If confirmed malicious, this could allow an attacker to execute malicious - code without being detected by Windows Defender, leading to potential system compromise. +description: The following analytic detects the modification of the Windows registry to disable the Windows Defender Submit Samples Consent feature. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path associated with Windows Defender SpyNet and the SubmitSamplesConsent value set to 0x00000000. This activity is significant as it indicates an attempt to bypass or evade detection by preventing Windows Defender from submitting samples for further analysis. If confirmed malicious, this could allow an attacker to execute malicious code without being detected by Windows Defender, leading to potential system compromise. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\Microsoft\\Windows - Defender\\SpyNet*" Registry.registry_value_name = SubmitSamplesConsent Registry.registry_value_data - = 0x00000000) by Registry.action Registry.dest Registry.process_guid Registry.process_id - Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data - Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user - Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_defender_submit_samples_consent_feature_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\Microsoft\\Windows Defender\\SpyNet*" Registry.registry_value_name = SubmitSamplesConsent Registry.registry_value_data = 0x00000000) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_defender_submit_samples_consent_feature_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: admin or user may choose to disable windows defender product references: -- https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ + - https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: modified/added/deleted registry entry $registry_path$ on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - - field: user - type: user - score: 49 - threat_objects: [] + message: modified/added/deleted registry entry $registry_path$ on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - Azorult - - CISA AA23-347A - - IcedID - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azorult + - CISA AA23-347A + - IcedID + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disable_etw_through_registry.yml b/detections/endpoint/disable_etw_through_registry.yml index 3551d6466b..c08beff8ed 100644 --- a/detections/endpoint/disable_etw_through_registry.yml +++ b/detections/endpoint/disable_etw_through_registry.yml @@ -5,69 +5,46 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: The following analytic detects modifications to the registry that disable - the Event Tracing for Windows (ETW) feature. It leverages data from the Endpoint.Registry - data model, specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\.NETFramework\\ETWEnabled" - with a value set to "0x00000000". This activity is significant because disabling - ETW can allow attackers to evade detection mechanisms, making it harder for security - tools to monitor malicious activities. If confirmed malicious, this could enable - attackers to execute payloads with minimal alerts, impairing defenses and potentially - leading to further compromise of the system. +description: The following analytic detects modifications to the registry that disable the Event Tracing for Windows (ETW) feature. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\.NETFramework\\ETWEnabled" with a value set to "0x00000000". This activity is significant because disabling ETW can allow attackers to evade detection mechanisms, making it harder for security tools to monitor malicious activities. If confirmed malicious, this could enable attackers to execute payloads with minimal alerts, impairing defenses and potentially leading to further compromise of the system. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\.NETFramework\\ETWEnabled" - Registry.registry_value_data = "0x00000000") by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `disable_etw_through_registry_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 -known_false_positives: network operator may disable this feature of windows but not - so common. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\.NETFramework\\ETWEnabled" Registry.registry_value_data = "0x00000000") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_etw_through_registry_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 +known_false_positives: network operator may disable this feature of windows but not so common. references: -- https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/ + - https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Disable ETW Through Registry on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: Disable ETW Through Registry on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Ransomware - - CISA AA23-347A - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - CISA AA23-347A + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disable_logs_using_wevtutil.yml b/detections/endpoint/disable_logs_using_wevtutil.yml index 0005b3b0d7..808ad097bc 100644 --- a/detections/endpoint/disable_logs_using_wevtutil.yml +++ b/detections/endpoint/disable_logs_using_wevtutil.yml @@ -1,81 +1,69 @@ name: Disable Logs Using WevtUtil id: 236e7c8e-c9d9-11eb-a824-acde48001122 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of "wevtutil.exe" with parameters - to disable event logs. It leverages data from Endpoint Detection and Response (EDR) - agents, focusing on process names and command-line arguments. This activity is significant - because disabling event logs is a common tactic used by ransomware to evade detection - and hinder forensic investigations. If confirmed malicious, this action could allow - attackers to operate undetected, making it difficult to trace their activities and - respond effectively to the incident. +description: The following analytic detects the execution of "wevtutil.exe" with parameters to disable event logs. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because disabling event logs is a common tactic used by ransomware to evade detection and hinder forensic investigations. If confirmed malicious, this action could allow attackers to operate undetected, making it difficult to trace their activities and respond effectively to the incident. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "wevtutil.exe" - AND (Processes.process = "*sl*" OR Processes.process = "*set-log*" ) Processes.process - = "*/e:false*" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `disable_logs_using_wevtutil_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: network operator may disable audit event logs for debugging - purposes. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "wevtutil.exe" + AND + (Processes.process = "*sl*" + OR + Processes.process = "*set-log*" ) Processes.process = "*/e:false*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `disable_logs_using_wevtutil_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: network operator may disable audit event logs for debugging purposes. references: -- https://www.bleepingcomputer.com/news/security/new-ransom-x-ransomware-used-in-texas-txdot-cyberattack/ + - https://www.bleepingcomputer.com/news/security/new-ransom-x-ransomware-used-in-texas-txdot-cyberattack/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: WevtUtil.exe used to disable Event Logging on $dest$ - risk_objects: - - field: dest - type: system - score: 24 - threat_objects: [] + message: WevtUtil.exe used to disable Event Logging on $dest$ + risk_objects: + - field: dest + type: system + score: 24 + threat_objects: [] tags: - analytic_story: - - Ransomware - - CISA AA23-347A - - Rhysida Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1070.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - CISA AA23-347A + - Rhysida Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1070.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disable_registry_tool.yml b/detections/endpoint/disable_registry_tool.yml index b2d0aa10e8..5a55098436 100644 --- a/detections/endpoint/disable_registry_tool.yml +++ b/detections/endpoint/disable_registry_tool.yml @@ -5,73 +5,47 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: - The following analytic detects modifications to the Windows registry - aimed at disabling the Registry Editor (regedit). It leverages data from the Endpoint.Registry - data model, specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableRegistryTools" - with a value of "0x00000001". This activity is significant because malware, such - as RATs or trojans, often disable registry tools to prevent the removal of their - entries, aiding in persistence and defense evasion. If confirmed malicious, this - could hinder incident response efforts and allow the attacker to maintain control - over the compromised system. +description: The following analytic detects modifications to the Windows registry aimed at disabling the Registry Editor (regedit). It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableRegistryTools" with a value of "0x00000001". This activity is significant because malware, such as RATs or trojans, often disable registry tools to prevent the removal of their entries, aiding in persistence and defense evasion. If confirmed malicious, this could hinder incident response efforts and allow the attacker to maintain control over the compromised system. data_source: - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableRegistryTools" - Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)`| - where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `disable_registry_tool_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableRegistryTools" Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_registry_tool_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: admin may disable this application for non technical user. references: - - https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry + - https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Disabled Registry Tools on $dest$ - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: [] + message: Disabled Registry Tools on $dest$ + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - - NjRAT - asset_type: Endpoint - mitre_attack_id: - - T1112 - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + - NjRAT + asset_type: Endpoint + mitre_attack_id: + - T1112 + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disable_schedule_task.yml b/detections/endpoint/disable_schedule_task.yml index 717b883e2f..e1e19713d8 100644 --- a/detections/endpoint/disable_schedule_task.yml +++ b/detections/endpoint/disable_schedule_task.yml @@ -1,81 +1,64 @@ name: Disable Schedule Task id: db596056-3019-11ec-a9ff-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of a command to disable - an existing scheduled task using 'schtasks.exe' with the '/change' and '/disable' - parameters. This detection leverages data from Endpoint Detection and Response (EDR) - agents, focusing on process names and command-line arguments. Disabling scheduled - tasks is significant as it is a common tactic used by adversaries, including malware - like IcedID, to disable security applications and evade detection. If confirmed - malicious, this activity could allow attackers to persist undetected, disable critical - security defenses, and further compromise the targeted host. +description: The following analytic detects the execution of a command to disable an existing scheduled task using 'schtasks.exe' with the '/change' and '/disable' parameters. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. Disabling scheduled tasks is significant as it is a common tactic used by adversaries, including malware like IcedID, to disable security applications and evade detection. If confirmed malicious, this activity could allow attackers to persist undetected, disable critical security defenses, and further compromise the targeted host. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime - max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe - Processes.process=*/change* Processes.process=*/disable* by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `disable_schedule_task_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=schtasks.exe Processes.process=*/change* Processes.process=*/disable* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `disable_schedule_task_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: admin may disable problematic schedule task references: -- https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ + - https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: schtask process with commandline $process$ to disable schedule task in - $dest$ - risk_objects: - - field: dest - type: system - score: 56 - threat_objects: [] + message: schtask process with commandline $process$ to disable schedule task in $dest$ + risk_objects: + - field: dest + type: system + score: 56 + threat_objects: [] tags: - analytic_story: - - IcedID - - Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - IcedID + - Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_schtask/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_schtask/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disable_security_logs_using_minint_registry.yml b/detections/endpoint/disable_security_logs_using_minint_registry.yml index da5ceace33..12599b7bcb 100644 --- a/detections/endpoint/disable_security_logs_using_minint_registry.yml +++ b/detections/endpoint/disable_security_logs_using_minint_registry.yml @@ -5,74 +5,49 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: - The following analytic detects a suspicious registry modification aimed - at disabling security audit logs by adding a specific registry entry. It leverages - data from the Endpoint.Registry data model, focusing on changes to the "Control\\MiniNt" - registry path. This activity is significant because it can prevent Windows from - logging any events to the Security Log, effectively blinding security monitoring - efforts. If confirmed malicious, this technique could allow an attacker to operate - undetected, making it difficult to trace their actions and compromising the integrity - of security audits. +description: The following analytic detects a suspicious registry modification aimed at disabling security audit logs by adding a specific registry entry. It leverages data from the Endpoint.Registry data model, focusing on changes to the "Control\\MiniNt" registry path. This activity is significant because it can prevent Windows from logging any events to the Security Log, effectively blinding security monitoring efforts. If confirmed malicious, this technique could allow an attacker to operate undetected, making it difficult to trace their actions and compromising the integrity of security audits. data_source: - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\Control\\MiniNt\\*") - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `disable_security_logs_using_minint_registry_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\Control\\MiniNt\\*") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_security_logs_using_minint_registry_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: No false positives have been identified at this time. references: - - https://twitter.com/0gtweet/status/1182516740955226112 + - https://twitter.com/0gtweet/status/1182516740955226112 drilldown_searches: - - name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Modified/added/deleted registry entry $registry_path$ on $dest$ - risk_objects: - - field: dest - type: system - score: 80 - - field: user - type: user - score: 80 - threat_objects: [] + message: Modified/added/deleted registry entry $registry_path$ on $dest$ + risk_objects: + - field: dest + type: system + score: 80 + - field: user + type: user + score: 80 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - CISA AA23-347A - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - CISA AA23-347A + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/minint_reg/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/minint_reg/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disable_show_hidden_files.yml b/detections/endpoint/disable_show_hidden_files.yml index d723de4c05..700f70ab3f 100644 --- a/detections/endpoint/disable_show_hidden_files.yml +++ b/detections/endpoint/disable_show_hidden_files.yml @@ -5,81 +5,54 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk, Steven Dick status: production type: Anomaly -description: - The following analytic detects modifications to the Windows registry - that disable the display of hidden files. It leverages data from the Endpoint.Registry - data model, specifically monitoring changes to registry paths associated with hidden - file settings. This activity is significant because malware, such as worms and trojan - spyware, often use hidden files to evade detection. If confirmed malicious, this - behavior could allow an attacker to conceal malicious files on the system, making - it harder for security tools and analysts to identify and remove the threat. +description: The following analytic detects modifications to the Windows registry that disable the display of hidden files. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to registry paths associated with hidden file settings. This activity is significant because malware, such as worms and trojan spyware, often use hidden files to evade detection. If confirmed malicious, this behavior could allow an attacker to conceal malicious files on the system, making it harder for security tools and analysts to identify and remove the threat. data_source: - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced\\Hidden" - OR (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced\\HideFileExt" - Registry.registry_value_data = "0x00000001") OR (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced\\ShowSuperHidden" - Registry.registry_value_data = "0x00000000" )) by Registry.action Registry.dest - Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `disable_show_hidden_files_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced\\Hidden" OR (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced\\HideFileExt" Registry.registry_value_data = "0x00000001") OR (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced\\ShowSuperHidden" Registry.registry_value_data = "0x00000000" )) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_show_hidden_files_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: No false positives have been identified at this time. references: - - https://www.sophos.com/en-us/threat-center/threat-analyses/viruses-and-spyware/W32~Tiotua-P/detailed-analysis + - https://www.sophos.com/en-us/threat-center/threat-analyses/viruses-and-spyware/W32~Tiotua-P/detailed-analysis drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Disabled 'Show Hidden Files' on $dest$ - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: [] + message: Disabled 'Show Hidden Files' on $dest$ + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - - Azorult - asset_type: Endpoint - mitre_attack_id: - - T1112 - - T1562.001 - - T1564.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + - Azorult + asset_type: Endpoint + mitre_attack_id: + - T1112 + - T1562.001 + - T1564.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log - source: WinEventLog:Security - sourcetype: WinEventLog - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log - source: WinEventLog:System - sourcetype: WinEventLog - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log + source: WinEventLog:Security + sourcetype: WinEventLog + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log + source: WinEventLog:System + sourcetype: WinEventLog + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disable_uac_remote_restriction.yml b/detections/endpoint/disable_uac_remote_restriction.yml index c8f741dac9..053bfd6a8b 100644 --- a/detections/endpoint/disable_uac_remote_restriction.yml +++ b/detections/endpoint/disable_uac_remote_restriction.yml @@ -5,72 +5,50 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: The following analytic detects the modification of the registry to disable - UAC remote restriction by setting the "LocalAccountTokenFilterPolicy" value to "0x00000001". - It leverages data from the Endpoint.Registry data model, specifically monitoring - changes to the registry path "*\\CurrentVersion\\Policies\\System*". This activity - is significant because disabling UAC remote restriction can allow an attacker to - bypass User Account Control (UAC) protections, potentially leading to privilege - escalation. If confirmed malicious, this could enable an attacker to execute unauthorized - actions with elevated privileges, compromising the security of the affected system. +description: The following analytic detects the modification of the registry to disable UAC remote restriction by setting the "LocalAccountTokenFilterPolicy" value to "0x00000001". It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path "*\\CurrentVersion\\Policies\\System*". This activity is significant because disabling UAC remote restriction can allow an attacker to bypass User Account Control (UAC) protections, potentially leading to privilege escalation. If confirmed malicious, this could enable an attacker to execute unauthorized actions with elevated privileges, compromising the security of the affected system. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\CurrentVersion\\Policies\\System*" - Registry.registry_value_name="LocalAccountTokenFilterPolicy" Registry.registry_value_data="0x00000001" ) - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `disable_uac_remote_restriction_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\CurrentVersion\\Policies\\System*" Registry.registry_value_name="LocalAccountTokenFilterPolicy" Registry.registry_value_data="0x00000001" ) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_uac_remote_restriction_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: admin may set this policy for non-critical machine. references: -- https://docs.microsoft.com/en-us/troubleshoot/windows-server/windows-security/user-account-control-and-remote-restriction + - https://docs.microsoft.com/en-us/troubleshoot/windows-server/windows-security/user-account-control-and-remote-restriction drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Modified/added/deleted registry entry $registry_path$ on $dest$ - risk_objects: - - field: dest - type: system - score: 80 - - field: user - type: user - score: 80 - threat_objects: [] + message: Modified/added/deleted registry entry $registry_path$ on $dest$ + risk_objects: + - field: dest + type: system + score: 80 + - field: user + type: user + score: 80 + threat_objects: [] tags: - analytic_story: - - Suspicious Windows Registry Activities - - Windows Defense Evasion Tactics - - CISA AA23-347A - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1548.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Windows Registry Activities + - Windows Defense Evasion Tactics + - CISA AA23-347A + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1548.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/LocalAccountTokenFilterPolicy/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/LocalAccountTokenFilterPolicy/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disable_windows_app_hotkeys.yml b/detections/endpoint/disable_windows_app_hotkeys.yml index ebf4205b90..a4394b579a 100644 --- a/detections/endpoint/disable_windows_app_hotkeys.yml +++ b/detections/endpoint/disable_windows_app_hotkeys.yml @@ -5,72 +5,46 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: - The following analytic detects a suspicious registry modification aimed - at disabling Windows hotkeys for native applications. It leverages data from the - Endpoint.Registry data model, focusing on specific registry paths and values indicative - of this behavior. This activity is significant as it can impair an analyst's ability - to use essential tools like Task Manager and Command Prompt, hindering incident - response efforts. If confirmed malicious, this technique can allow an attacker to - maintain persistence and evade detection, complicating the remediation process. +description: The following analytic detects a suspicious registry modification aimed at disabling Windows hotkeys for native applications. It leverages data from the Endpoint.Registry data model, focusing on specific registry paths and values indicative of this behavior. This activity is significant as it can impair an analyst's ability to use essential tools like Task Manager and Command Prompt, hindering incident response efforts. If confirmed malicious, this technique can allow an attacker to maintain persistence and evade detection, complicating the remediation process. data_source: - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\Windows - NT\\CurrentVersion\\Image File Execution Options\\*" AND Registry.registry_value_data= - "HotKey Disabled" AND Registry.registry_value_name = "Debugger") by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `disable_windows_app_hotkeys_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\Windows NT\\CurrentVersion\\Image File Execution Options\\*" AND Registry.registry_value_data= "HotKey Disabled" AND Registry.registry_value_name = "Debugger") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_windows_app_hotkeys_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: No false positives have been identified at this time. references: - - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ + - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Disabled 'Windows App Hotkeys' on $dest$ - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: [] + message: Disabled 'Windows App Hotkeys' on $dest$ + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: [] tags: - analytic_story: - - XMRig - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1112 - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - XMRig + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1112 + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/hotkey_disabled_hidden_user/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/hotkey_disabled_hidden_user/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disable_windows_behavior_monitoring.yml b/detections/endpoint/disable_windows_behavior_monitoring.yml index 3b955f1c7a..7b413b74d5 100644 --- a/detections/endpoint/disable_windows_behavior_monitoring.yml +++ b/detections/endpoint/disable_windows_behavior_monitoring.yml @@ -5,84 +5,56 @@ date: '2026-02-09' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: The following analytic identifies modifications in the registry to disable - Windows Defender's real-time behavior monitoring. It leverages data from the Endpoint.Registry - data model, specifically monitoring changes to registry paths associated with Windows - Defender settings. This activity is significant because disabling real-time protection - is a common tactic used by malware such as RATs, bots, or Trojans to evade detection. - If confirmed malicious, this action could allow an attacker to execute code, escalate - privileges, or persist in the environment without being detected by antivirus software. +description: The following analytic identifies modifications in the registry to disable Windows Defender's real-time behavior monitoring. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to registry paths associated with Windows Defender settings. This activity is significant because disabling real-time protection is a common tactic used by malware such as RATs, bots, or Trojans to evade detection. If confirmed malicious, this action could allow an attacker to execute code, escalate privileges, or persist in the environment without being detected by antivirus software. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows - Defender\\Real-Time Protection\\DisableBehaviorMonitoring" OR Registry.registry_path= - "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableOnAccessProtection" - OR Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Real-Time - Protection\\DisableScanOnRealtimeEnable" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows - Defender\\Real-Time Protection\\DisableRealtimeMonitoring" OR Registry.registry_path= - "*\\Real-Time Protection\\DisableIntrusionPreventionSystem" OR Registry.registry_path= - "*\\Real-Time Protection\\DisableIOAVProtection" OR Registry.registry_path= "*\\Real-Time - Protection\\DisableScriptScanning" AND Registry.registry_value_data = "0x00000001") - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `disable_windows_behavior_monitoring_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableBehaviorMonitoring" OR Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableOnAccessProtection" OR Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableScanOnRealtimeEnable" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableRealtimeMonitoring" OR Registry.registry_path= "*\\Real-Time Protection\\DisableIntrusionPreventionSystem" OR Registry.registry_path= "*\\Real-Time Protection\\DisableIOAVProtection" OR Registry.registry_path= "*\\Real-Time Protection\\DisableScriptScanning" AND Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_windows_behavior_monitoring_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: admin or user may choose to disable this windows features. references: -- https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html + - https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Defender real time behavior monitoring disabled on $dest$ - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: [] + message: Windows Defender real time behavior monitoring disabled on $dest$ + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: [] tags: - analytic_story: - - SolarWinds WHD RCE Post Exploitation - - Windows Defense Evasion Tactics - - CISA AA23-347A - - Revil Ransomware - - Azorult - - Windows Registry Abuse - - Black Basta Ransomware - - Ransomware - - RedLine Stealer - - Cactus Ransomware - - Scattered Lapsus$ Hunters - - NetSupport RMM Tool Abuse - - Storm-0501 Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SolarWinds WHD RCE Post Exploitation + - Windows Defense Evasion Tactics + - CISA AA23-347A + - Revil Ransomware + - Azorult + - Windows Registry Abuse + - Black Basta Ransomware + - Ransomware + - RedLine Stealer + - Cactus Ransomware + - Scattered Lapsus$ Hunters + - NetSupport RMM Tool Abuse + - Storm-0501 Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disable_windows_smartscreen_protection.yml b/detections/endpoint/disable_windows_smartscreen_protection.yml index 2b3fcbe7b2..1eafc6f566 100644 --- a/detections/endpoint/disable_windows_smartscreen_protection.yml +++ b/detections/endpoint/disable_windows_smartscreen_protection.yml @@ -5,72 +5,49 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: The following analytic detects modifications to the Windows registry - that disable SmartScreen protection. It leverages data from the Endpoint.Registry - data model, specifically monitoring changes to registry paths associated with SmartScreen - settings. This activity is significant because SmartScreen provides an early warning - system against phishing and malware. Disabling it can indicate malicious intent, - often seen in Remote Access Trojans (RATs) to evade detection while downloading - additional payloads. If confirmed malicious, this action could allow attackers to - bypass security measures, increasing the risk of successful phishing attacks and - malware infections. +description: The following analytic detects modifications to the Windows registry that disable SmartScreen protection. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to registry paths associated with SmartScreen settings. This activity is significant because SmartScreen provides an early warning system against phishing and malware. Disabling it can indicate malicious intent, often seen in Remote Access Trojans (RATs) to evade detection while downloading additional payloads. If confirmed malicious, this action could allow attackers to bypass security measures, increasing the risk of successful phishing attacks and malware infections. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path IN ("*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\SmartScreenEnabled", - "*\\Microsoft\\Windows\\System\\EnableSmartScreen") Registry.registry_value_data IN - ("Off", "0") by Registry.action Registry.dest Registry.process_guid Registry.process_id - Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data - Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user - Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `disable_windows_smartscreen_protection_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path IN ("*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\SmartScreenEnabled", "*\\Microsoft\\Windows\\System\\EnableSmartScreen") Registry.registry_value_data IN ("Off", "0") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_windows_smartscreen_protection_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: admin or user may choose to disable this windows features. references: -- https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html + - https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The Windows Smartscreen was disabled on $dest$ by $user$. - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: The Windows Smartscreen was disabled on $dest$ by $user$. + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - CISA AA23-347A - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - CISA AA23-347A + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disabled_kerberos_pre_authentication_discovery_with_get_aduser.yml b/detections/endpoint/disabled_kerberos_pre_authentication_discovery_with_get_aduser.yml index 707e11b5cc..42e8ee4e8b 100644 --- a/detections/endpoint/disabled_kerberos_pre_authentication_discovery_with_get_aduser.yml +++ b/detections/endpoint/disabled_kerberos_pre_authentication_discovery_with_get_aduser.yml @@ -1,76 +1,64 @@ name: Disabled Kerberos Pre-Authentication Discovery With Get-ADUser id: 114c6bfe-9406-11ec-bcce-acde48001122 -version: 11 -date: '2025-07-28' +version: 12 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the execution of the `Get-ADUser` - PowerShell cmdlet with parameters indicating a search for domain accounts with - Kerberos Pre-Authentication disabled. It leverages PowerShell Script Block - Logging (EventCode=4104) to identify this specific activity. This behavior is - significant because discovering accounts with Kerberos Pre-Authentication - disabled can allow adversaries to perform offline password cracking. If - confirmed malicious, this activity could lead to unauthorized access to user - accounts, potentially compromising sensitive information and escalating - privileges within the network. +description: The following analytic detects the execution of the `Get-ADUser` PowerShell cmdlet with parameters indicating a search for domain accounts with Kerberos Pre-Authentication disabled. It leverages PowerShell Script Block Logging (EventCode=4104) to identify this specific activity. This behavior is significant because discovering accounts with Kerberos Pre-Authentication disabled can allow adversaries to perform offline password cracking. If confirmed malicious, this activity could lead to unauthorized access to user accounts, potentially compromising sensitive information and escalating privileges within the network. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-ADUser*" AND ScriptBlockText="*4194304*") - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `disabled_kerberos_pre_authentication_discovery_with_get_aduser_filter`' -how_to_implement: To successfully implement this analytic, you will need to - enable PowerShell Script Block Logging on some or all endpoints. Additional - setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: Administrators or power users may use search for accounts - with Kerberos Pre Authentication disabled for legitimate purposes. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 (ScriptBlockText = "*Get-ADUser*" AND ScriptBlockText="*4194304*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `disabled_kerberos_pre_authentication_discovery_with_get_aduser_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: Administrators or power users may use search for accounts with Kerberos Pre Authentication disabled for legitimate purposes. references: -- https://attack.mitre.org/techniques/T1558/004/ -- https://m0chan.github.io/2019/07/31/How-To-Attack-Kerberos-101.html -- https://stealthbits.com/blog/cracking-active-directory-passwords-with-as-rep-roasting/ + - https://attack.mitre.org/techniques/T1558/004/ + - https://m0chan.github.io/2019/07/31/How-To-Attack-Kerberos-101.html + - https://stealthbits.com/blog/cracking-active-directory-passwords-with-as-rep-roasting/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Disabled Kerberos Pre-Authentication Discovery With Get-ADUser from - $dest$ - risk_objects: - - field: dest - type: system - score: 54 - threat_objects: [] + message: Disabled Kerberos Pre-Authentication Discovery With Get-ADUser from $dest$ + risk_objects: + - field: dest + type: system + score: 54 + threat_objects: [] tags: - analytic_story: - - CISA AA23-347A - - Active Directory Kerberos Attacks - - BlackSuit Ransomware - - Interlock Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1558.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA23-347A + - Active Directory Kerberos Attacks + - BlackSuit Ransomware + - Interlock Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1558.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.004/getaduser/get-aduser-powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.004/getaduser/get-aduser-powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disabled_kerberos_pre_authentication_discovery_with_powerview.yml b/detections/endpoint/disabled_kerberos_pre_authentication_discovery_with_powerview.yml index 67ebc445ba..3862237438 100644 --- a/detections/endpoint/disabled_kerberos_pre_authentication_discovery_with_powerview.yml +++ b/detections/endpoint/disabled_kerberos_pre_authentication_discovery_with_powerview.yml @@ -1,74 +1,62 @@ name: Disabled Kerberos Pre-Authentication Discovery With PowerView id: b0b34e2c-90de-11ec-baeb-acde48001122 -version: 11 -date: '2025-07-28' +version: 12 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the execution of the - `Get-DomainUser` commandlet with the `-PreauthNotRequired` parameter using - PowerShell Script Block Logging (EventCode=4104). This command is part of - PowerView, a tool used for enumerating Windows Active Directory networks. - Identifying domain accounts with Kerberos Pre-Authentication disabled is - significant because adversaries can leverage this information to attempt - offline password cracking. If confirmed malicious, this activity could lead to - unauthorized access to domain accounts, potentially compromising sensitive - information and escalating privileges within the network. +description: The following analytic detects the execution of the `Get-DomainUser` commandlet with the `-PreauthNotRequired` parameter using PowerShell Script Block Logging (EventCode=4104). This command is part of PowerView, a tool used for enumerating Windows Active Directory networks. Identifying domain accounts with Kerberos Pre-Authentication disabled is significant because adversaries can leverage this information to attempt offline password cracking. If confirmed malicious, this activity could lead to unauthorized access to domain accounts, potentially compromising sensitive information and escalating privileges within the network. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainUser*" AND ScriptBlockText="*PreauthNotRequired*") - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `disabled_kerberos_pre_authentication_discovery_with_powerview_filter`' -how_to_implement: To successfully implement this analytic, you will need to - enable PowerShell Script Block Logging on some or all endpoints. Additional - setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: Administrators or power users may use PowerView for - troubleshooting + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainUser*" AND ScriptBlockText="*PreauthNotRequired*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `disabled_kerberos_pre_authentication_discovery_with_powerview_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: Administrators or power users may use PowerView for troubleshooting references: -- https://attack.mitre.org/techniques/T1558/004/ -- https://m0chan.github.io/2019/07/31/How-To-Attack-Kerberos-101.html -- https://stealthbits.com/blog/cracking-active-directory-passwords-with-as-rep-roasting/ + - https://attack.mitre.org/techniques/T1558/004/ + - https://m0chan.github.io/2019/07/31/How-To-Attack-Kerberos-101.html + - https://stealthbits.com/blog/cracking-active-directory-passwords-with-as-rep-roasting/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Disabled Kerberos Pre-Authentication Discovery With PowerView from - $dest$ - risk_objects: - - field: dest - type: system - score: 54 - threat_objects: [] + message: Disabled Kerberos Pre-Authentication Discovery With PowerView from $dest$ + risk_objects: + - field: dest + type: system + score: 54 + threat_objects: [] tags: - analytic_story: - - Active Directory Kerberos Attacks - - Interlock Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1558.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Kerberos Attacks + - Interlock Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1558.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/getdomainuser.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/getdomainuser.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disabling_cmd_application.yml b/detections/endpoint/disabling_cmd_application.yml index 907d61fadf..971072f3e0 100644 --- a/detections/endpoint/disabling_cmd_application.yml +++ b/detections/endpoint/disabling_cmd_application.yml @@ -5,76 +5,50 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: - The following analytic detects modifications to the registry that disable - the CMD prompt application. It leverages data from the Endpoint.Registry data model, - specifically looking for changes to the "DisableCMD" registry value. This activity - is significant because disabling CMD can hinder an analyst's ability to investigate - and remediate threats, a tactic often used by malware such as RATs, Trojans, or - Worms. If confirmed malicious, this could prevent security teams from using CMD - for directory and file traversal, complicating incident response and allowing the - attacker to maintain persistence. +description: The following analytic detects modifications to the registry that disable the CMD prompt application. It leverages data from the Endpoint.Registry data model, specifically looking for changes to the "DisableCMD" registry value. This activity is significant because disabling CMD can hinder an analyst's ability to investigate and remediate threats, a tactic often used by malware such as RATs, Trojans, or Worms. If confirmed malicious, this could prevent security teams from using CMD for directory and file traversal, complicating incident response and allowing the attacker to maintain persistence. data_source: - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows\\System\\DisableCMD" - Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `disabling_cmd_application_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows\\System\\DisableCMD" Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_cmd_application_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: admin may disable this application for non technical user. references: - - https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry + - https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The Windows command prompt was disabled on $dest$ by $user$. - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: The Windows command prompt was disabled on $dest$ by $user$. + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - - NjRAT - asset_type: Endpoint - mitre_attack_id: - - T1112 - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + - NjRAT + asset_type: Endpoint + mitre_attack_id: + - T1112 + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disabling_controlpanel.yml b/detections/endpoint/disabling_controlpanel.yml index 140aab72db..6ef73824b7 100644 --- a/detections/endpoint/disabling_controlpanel.yml +++ b/detections/endpoint/disabling_controlpanel.yml @@ -5,75 +5,49 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: - The following analytic detects registry modifications that disable the - Control Panel on Windows systems. It leverages data from the Endpoint.Registry data - model, specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoControlPanel" - with a value of "0x00000001". This activity is significant as it is commonly used - by malware to prevent users from accessing the Control Panel, thereby hindering - the removal of malicious artifacts and persistence mechanisms. If confirmed malicious, - this could allow attackers to maintain control over the infected machine and prevent - remediation efforts. +description: The following analytic detects registry modifications that disable the Control Panel on Windows systems. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoControlPanel" with a value of "0x00000001". This activity is significant as it is commonly used by malware to prevent users from accessing the Control Panel, thereby hindering the removal of malicious artifacts and persistence mechanisms. If confirmed malicious, this could allow attackers to maintain control over the infected machine and prevent remediation efforts. data_source: - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoControlPanel" - Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)`| - where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `disabling_controlpanel_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoControlPanel" Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_controlpanel_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: admin may disable this application for non technical user. references: - - https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry + - https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The Windows Control Panel was disabled on $dest$ by $user$. - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: The Windows Control Panel was disabled on $dest$ by $user$. + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1112 - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1112 + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test (XML) - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-xml.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test (XML) + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-xml.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disabling_defender_services.yml b/detections/endpoint/disabling_defender_services.yml index c0efd290dc..fb47d973d1 100644 --- a/detections/endpoint/disabling_defender_services.yml +++ b/detections/endpoint/disabling_defender_services.yml @@ -5,72 +5,49 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: The following analytic detects the disabling of Windows Defender services - by monitoring registry modifications. It leverages registry event data to identify - changes to specific registry paths associated with Defender services, where the - 'Start' value is set to '0x00000004'. This activity is significant because disabling - Defender services can indicate an attempt by an adversary to evade detection and - maintain persistence on the endpoint. If confirmed malicious, this action could - allow attackers to execute further malicious activities undetected, leading to potential - data breaches or system compromise. +description: The following analytic detects the disabling of Windows Defender services by monitoring registry modifications. It leverages registry event data to identify changes to specific registry paths associated with Defender services, where the 'Start' value is set to '0x00000004'. This activity is significant because disabling Defender services can indicate an attempt by an adversary to evade detection and maintain persistence on the endpoint. If confirmed malicious, this action could allow attackers to execute further malicious activities undetected, leading to potential data breaches or system compromise. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\System\\CurrentControlSet\\Services\\*" - AND (Registry.registry_path IN("*WdBoot*", "*WdFilter*", "*WdNisDrv*", "*WdNisSvc*","*WinDefend*", - "*SecurityHealthService*")) AND Registry.registry_value_name = Start Registry.registry_value_data - = 0x00000004) by Registry.action Registry.dest Registry.process_guid Registry.process_id - Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data - Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user - Registry.vendor_product | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data) - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_defender_services_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\System\\CurrentControlSet\\Services\\*" AND (Registry.registry_path IN("*WdBoot*", "*WdFilter*", "*WdNisDrv*", "*WdNisSvc*","*WinDefend*", "*SecurityHealthService*")) AND Registry.registry_value_name = Start Registry.registry_value_data = 0x00000004) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_defender_services_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: admin or user may choose to disable windows defender product references: -- https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ + - https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: modified/added/deleted registry entry $registry_path$ on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - - field: user - type: user - score: 49 - threat_objects: [] + message: modified/added/deleted registry entry $registry_path$ on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - IcedID - - Windows Registry Abuse - - RedLine Stealer - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - IcedID + - Windows Registry Abuse + - RedLine Stealer + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon2.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon2.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disabling_firewall_with_netsh.yml b/detections/endpoint/disabling_firewall_with_netsh.yml index 3c6b6df111..98733d4e11 100644 --- a/detections/endpoint/disabling_firewall_with_netsh.yml +++ b/detections/endpoint/disabling_firewall_with_netsh.yml @@ -1,84 +1,69 @@ name: Disabling Firewall with Netsh id: 6860a62c-9203-11eb-9e05-acde48001122 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies the disabling of the firewall using - the netsh application. It leverages data from Endpoint Detection and Response (EDR) - agents, focusing on command-line executions that include keywords like "firewall," - "off," or "disable." This activity is significant because disabling the firewall - can expose the system to external threats, allowing malware to communicate with - its command and control (C2) server. If confirmed malicious, this action could lead - to unauthorized data exfiltration, further malware downloads, and broader network - compromise. +description: The following analytic identifies the disabling of the firewall using the netsh application. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions that include keywords like "firewall," "off," or "disable." This activity is significant because disabling the firewall can expose the system to external threats, allowing malware to communicate with its command and control (C2) server. If confirmed malicious, this action could lead to unauthorized data exfiltration, further malware downloads, and broader network compromise. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_netsh` Processes.process= - "*firewall*" (Processes.process= "*off*" OR Processes.process= "*disable*") by - Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `disabling_firewall_with_netsh_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: admin may disable firewall during testing or fixing network - problem. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_netsh` Processes.process= "*firewall*" (Processes.process= "*off*" + OR + Processes.process= "*disable*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `disabling_firewall_with_netsh_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: admin may disable firewall during testing or fixing network problem. references: -- https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html + - https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The Windows Firewall was disabled on $dest$ by $user$. - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: The Windows Firewall was disabled on $dest$ by $user$. + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - BlackByte Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - BlackByte Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disabling_folderoptions_windows_feature.yml b/detections/endpoint/disabling_folderoptions_windows_feature.yml index b77b23066c..c0b8088e36 100644 --- a/detections/endpoint/disabling_folderoptions_windows_feature.yml +++ b/detections/endpoint/disabling_folderoptions_windows_feature.yml @@ -5,71 +5,49 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: The following analytic detects the modification of the Windows registry - to disable the Folder Options feature, which prevents users from showing hidden - files and file extensions. It leverages data from the Endpoint.Registry data model, - specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoFolderOptions" - with a value of "0x00000001". This activity is significant as it is commonly used - by malware to conceal malicious files and deceive users with fake file extensions. - If confirmed malicious, this could allow an attacker to hide their presence and - malicious files, making detection and remediation more difficult. +description: The following analytic detects the modification of the Windows registry to disable the Folder Options feature, which prevents users from showing hidden files and file extensions. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoFolderOptions" with a value of "0x00000001". This activity is significant as it is commonly used by malware to conceal malicious files and deceive users with fake file extensions. If confirmed malicious, this could allow an attacker to hide their presence and malicious files, making detection and remediation more difficult. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoFolderOptions" - Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)`| - where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `disabling_folderoptions_windows_feature_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoFolderOptions" Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_folderoptions_windows_feature_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: admin may disable this application for non technical user. references: -- https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry + - https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The Windows Folder Options, to hide files, was disabled on $dest$ by $user$. - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: The Windows Folder Options, to hide files, was disabled on $dest$ by $user$. + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - CISA AA23-347A - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - CISA AA23-347A + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disabling_norun_windows_app.yml b/detections/endpoint/disabling_norun_windows_app.yml index aa34874332..f038ff13d1 100644 --- a/detections/endpoint/disabling_norun_windows_app.yml +++ b/detections/endpoint/disabling_norun_windows_app.yml @@ -5,77 +5,50 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: - The following analytic detects the modification of the Windows registry - to disable the Run application in the Start menu. It leverages data from the Endpoint.Registry - data model, specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoRun" - with a value of "0x00000001". This activity is significant because the Run application - is a useful shortcut for executing known applications and scripts. If confirmed - malicious, this action could hinder system cleaning efforts and make it more difficult - to run essential tools, thereby aiding malware persistence. +description: The following analytic detects the modification of the Windows registry to disable the Run application in the Start menu. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoRun" with a value of "0x00000001". This activity is significant because the Run application is a useful shortcut for executing known applications and scripts. If confirmed malicious, this action could hinder system cleaning efforts and make it more difficult to run essential tools, thereby aiding malware persistence. data_source: - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoRun" - Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)`| - where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `disabling_norun_windows_app_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoRun" Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_norun_windows_app_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: admin may disable this application for non technical user. references: - - https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry - - https://blog.malwarebytes.com/detections/pum-optional-norun/ + - https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry + - https://blog.malwarebytes.com/detections/pum-optional-norun/ drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - The Windows registry was modified to disable run application in window - start menu on $dest$ by $user$. - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: The Windows registry was modified to disable run application in window start menu on $dest$ by $user$. + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1112 - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1112 + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disabling_remote_user_account_control.yml b/detections/endpoint/disabling_remote_user_account_control.yml index fdc55fb649..75d0d0fdc0 100644 --- a/detections/endpoint/disabling_remote_user_account_control.yml +++ b/detections/endpoint/disabling_remote_user_account_control.yml @@ -5,76 +5,51 @@ date: '2025-05-02' author: David Dorsey, Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic identifies modifications to the registry key that - controls the enforcement of Windows User Account Control (UAC). It detects changes - to the registry path `HKLM\SOFTWARE\Microsoft\Windows\CurrentVersion\Policies\System\EnableLUA` - where the value is set to `0x00000000`. This activity is significant because disabling - UAC can allow unauthorized changes to the system without user consent, potentially - leading to privilege escalation. If confirmed malicious, an attacker could gain - elevated privileges, making it easier to execute further attacks or maintain persistence - within the environment. +description: The following analytic identifies modifications to the registry key that controls the enforcement of Windows User Account Control (UAC). It detects changes to the registry path `HKLM\SOFTWARE\Microsoft\Windows\CurrentVersion\Policies\System\EnableLUA` where the value is set to `0x00000000`. This activity is significant because disabling UAC can allow unauthorized changes to the system without user consent, potentially leading to privilege escalation. If confirmed malicious, an attacker could gain elevated privileges, making it easier to execute further attacks or maintain persistence within the environment. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLUA* - Registry.registry_value_data="0x00000000" by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `disabling_remote_user_account_control_filter`' -how_to_implement: To successfully implement this search, you must be ingesting data - that records registry activity from your hosts to populate the endpoint data model - in the registry node. This is typically populated via endpoint detection-and-response - product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. - The data used for this search is typically generated via logs that report registry - modifications. -known_false_positives: This registry key may be modified via administrators to implement - a change in system policy. This type of change should be a very rare occurrence. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLUA* Registry.registry_value_data="0x00000000" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `disabling_remote_user_account_control_filter`' +how_to_implement: To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications. +known_false_positives: This registry key may be modified via administrators to implement a change in system policy. This type of change should be a very rare occurrence. references: [] drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The Windows registry keys that control the enforcement of Windows User - Account Control (UAC) were modified on $dest$ by $user$. - risk_objects: - - field: user - type: user - score: 42 - - field: dest - type: system - score: 42 - threat_objects: [] + message: The Windows registry keys that control the enforcement of Windows User Account Control (UAC) were modified on $dest$ by $user$. + risk_objects: + - field: user + type: user + score: 42 + - field: dest + type: system + score: 42 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Suspicious Windows Registry Activities - - Remcos - - Windows Registry Abuse - - Azorult - - AgentTesla - asset_type: Endpoint - mitre_attack_id: - - T1548.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Suspicious Windows Registry Activities + - Remcos + - Windows Registry Abuse + - Azorult + - AgentTesla + asset_type: Endpoint + mitre_attack_id: + - T1548.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disabling_systemrestore_in_registry.yml b/detections/endpoint/disabling_systemrestore_in_registry.yml index a28213b800..36bc77d9a9 100644 --- a/detections/endpoint/disabling_systemrestore_in_registry.yml +++ b/detections/endpoint/disabling_systemrestore_in_registry.yml @@ -5,84 +5,58 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: The following analytic detects the modification of registry keys to disable - System Restore on a machine. It leverages data from the Endpoint.Registry data model, - specifically monitoring changes to registry paths associated with System Restore - settings. This activity is significant because disabling System Restore can hinder - recovery efforts and is a tactic often used by Remote Access Trojans (RATs) to maintain - persistence on an infected system. If confirmed malicious, this action could prevent - system recovery, allowing the attacker to sustain their foothold and potentially - cause further damage or data loss. +description: The following analytic detects the modification of registry keys to disable System Restore on a machine. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to registry paths associated with System Restore settings. This activity is significant because disabling System Restore can hinder recovery efforts and is a tactic often used by Remote Access Trojans (RATs) to maintain persistence on an infected system. If confirmed malicious, this action could prevent system recovery, allowing the attacker to sustain their foothold and potentially cause further damage or data loss. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows - NT\\CurrentVersion\\SystemRestore\\DisableSR" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows - NT\\CurrentVersion\\SystemRestore\\DisableConfig" OR Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows - NT\\SystemRestore\\DisableSR" OR Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows - NT\\SystemRestore\\DisableConfig" Registry.registry_value_data = "0x00000001") by - Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `disabling_systemrestore_in_registry_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\SystemRestore\\DisableSR" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\SystemRestore\\DisableConfig" OR Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows NT\\SystemRestore\\DisableSR" OR Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows NT\\SystemRestore\\DisableConfig" Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_systemrestore_in_registry_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: in some cases admin can disable systemrestore on a machine. references: -- https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html + - https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The Windows registry was modified to disable system restore on $dest$ by - $user$. - risk_objects: - - field: user - type: user - score: 49 - - field: dest - type: system - score: 49 - threat_objects: [] + message: The Windows registry was modified to disable system restore on $dest$ by $user$. + risk_objects: + - field: user + type: user + score: 49 + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - - NjRAT - asset_type: Endpoint - mitre_attack_id: - - T1490 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + - NjRAT + asset_type: Endpoint + mitre_attack_id: + - T1490 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log - source: WinEventLog:Security - sourcetype: WinEventLog - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log - source: WinEventLog:System - sourcetype: WinEventLog - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-xml.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log + source: WinEventLog:Security + sourcetype: WinEventLog + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log + source: WinEventLog:System + sourcetype: WinEventLog + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-xml.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disabling_task_manager.yml b/detections/endpoint/disabling_task_manager.yml index a8ccb5db79..6142adcabd 100644 --- a/detections/endpoint/disabling_task_manager.yml +++ b/detections/endpoint/disabling_task_manager.yml @@ -5,71 +5,50 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: The following analytic identifies modifications to the Windows registry - that disable Task Manager. It leverages data from the Endpoint.Registry data model, - specifically looking for changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableTaskMgr" - with a value of "0x00000001". This activity is significant as it is commonly associated - with malware such as RATs, Trojans, and worms, which disable Task Manager to prevent - users from terminating malicious processes. If confirmed malicious, this could allow - attackers to maintain persistence and control over the infected system. +description: The following analytic identifies modifications to the Windows registry that disable Task Manager. It leverages data from the Endpoint.Registry data model, specifically looking for changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableTaskMgr" with a value of "0x00000001". This activity is significant as it is commonly associated with malware such as RATs, Trojans, and worms, which disable Task Manager to prevent users from terminating malicious processes. If confirmed malicious, this could allow attackers to maintain persistence and control over the infected system. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableTaskMgr" - Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `disabling_task_manager_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableTaskMgr" Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_task_manager_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: admin may disable this application for non technical user. references: -- https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry -- https://blog.talosintelligence.com/2020/05/threat-roundup-0424-0501.html + - https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry + - https://blog.talosintelligence.com/2020/05/threat-roundup-0424-0501.html drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The Windows Task Manager was disabled on $dest$ by $user$. - risk_objects: - - field: user - type: user - score: 42 - - field: dest - type: system - score: 42 - threat_objects: [] + message: The Windows Task Manager was disabled on $dest$ by $user$. + risk_objects: + - field: user + type: user + score: 42 + - field: dest + type: system + score: 42 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - - NjRAT - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + - NjRAT + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/disabling_windows_local_security_authority_defences_via_registry.yml b/detections/endpoint/disabling_windows_local_security_authority_defences_via_registry.yml index 8d61a2e7d2..bc6dcae6ba 100644 --- a/detections/endpoint/disabling_windows_local_security_authority_defences_via_registry.yml +++ b/detections/endpoint/disabling_windows_local_security_authority_defences_via_registry.yml @@ -6,79 +6,48 @@ author: Dean Luxton,Teoderick Contreras Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic identifies the deletion of registry keys that - disable Local Security Authority (LSA) protection and Microsoft Defender Device - Guard. It leverages data from Endpoint Detection and Response (EDR) agents, focusing - on registry actions and paths associated with LSA and Device Guard settings. This - activity is significant because disabling these defenses can leave a system vulnerable - to various attacks, including credential theft and unauthorized code execution. - If confirmed malicious, this action could allow attackers to bypass critical security - mechanisms, leading to potential system compromise and persistent access. -search: '| tstats `security_content_summariesonly` min(_time) as _time from datamodel=Endpoint.Registry - where Registry.registry_path IN ("*\\Lsa\\LsaCfgFlags", "*\\Lsa\\RunAsPPL", "*\\SOFTWARE\\Policies\\Microsoft\\Windows\\DeviceGuard\\*") - AND ((Registry.action = deleted) - OR (Registry.action = modified AND Registry.registry_value_data IN(0x00000000, 0))) - by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `disabling_windows_local_security_authority_defences_via_registry_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Potential to be triggered by an administrator disabling protections - for troubleshooting purposes. + - Sysmon EventID 13 +description: The following analytic identifies the deletion of registry keys that disable Local Security Authority (LSA) protection and Microsoft Defender Device Guard. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on registry actions and paths associated with LSA and Device Guard settings. This activity is significant because disabling these defenses can leave a system vulnerable to various attacks, including credential theft and unauthorized code execution. If confirmed malicious, this action could allow attackers to bypass critical security mechanisms, leading to potential system compromise and persistent access. +search: '| tstats `security_content_summariesonly` min(_time) as _time from datamodel=Endpoint.Registry where Registry.registry_path IN ("*\\Lsa\\LsaCfgFlags", "*\\Lsa\\RunAsPPL", "*\\SOFTWARE\\Policies\\Microsoft\\Windows\\DeviceGuard\\*") AND ((Registry.action = deleted) OR (Registry.action = modified AND Registry.registry_value_data IN(0x00000000, 0))) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_windows_local_security_authority_defences_via_registry_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Potential to be triggered by an administrator disabling protections for troubleshooting purposes. references: -- https://docs.microsoft.com/en-us/windows-server/security/credentials-protection-and-management/configuring-additional-lsa-protection -- https://docs.microsoft.com/en-us/windows/security/identity-protection/credential-guard/credential-guard-manage + - https://docs.microsoft.com/en-us/windows-server/security/credentials-protection-and-management/configuring-additional-lsa-protection + - https://docs.microsoft.com/en-us/windows/security/identity-protection/credential-guard/credential-guard-manage drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An attempt to disable Windows LSA defences was detected on $dest$. The - reg key $registry_path$ was deleted by $user$. - risk_objects: - - field: user - type: user - score: 60 - - field: dest - type: system - score: 60 - threat_objects: [] + message: An attempt to disable Windows LSA defences was detected on $dest$. The reg key $registry_path$ was deleted by $user$. + risk_objects: + - field: user + type: user + score: 60 + - field: dest + type: system + score: 60 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1556 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1556 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/disable_lsa_protection_new/lsa_reg_deletion_modification.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/disable_lsa_protection_new/lsa_reg_deletion_modification.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/dllhost_with_no_command_line_arguments_with_network.yml b/detections/endpoint/dllhost_with_no_command_line_arguments_with_network.yml index 72a288fd12..b5726d9251 100644 --- a/detections/endpoint/dllhost_with_no_command_line_arguments_with_network.yml +++ b/detections/endpoint/dllhost_with_no_command_line_arguments_with_network.yml @@ -6,121 +6,115 @@ author: Steven Dick, Michael Haag, Splunk status: production type: TTP description: | - The following analytic detects instances of DLLHost.exe running without - command line arguments while establishing a network connection. - This behavior is identified using Endpoint Detection and Response (EDR) telemetry, - focusing on process execution and network activity data. - It is significant because DLLHost.exe typically runs with specific arguments, - and its absence can indicate malicious activity, such as Cobalt Strike usage. - If confirmed malicious, this activity could allow attackers to execute code, - move laterally, or exfiltrate data, posing a severe threat to the network's security. + The following analytic detects instances of DLLHost.exe running without + command line arguments while establishing a network connection. + This behavior is identified using Endpoint Detection and Response (EDR) telemetry, + focusing on process execution and network activity data. + It is significant because DLLHost.exe typically runs with specific arguments, + and its absence can indicate malicious activity, such as Cobalt Strike usage. + If confirmed malicious, this activity could allow attackers to execute code, + move laterally, or exfiltrate data, posing a severe threat to the network's security. data_source: -- Sysmon EventID 1 AND Sysmon EventID 3 + - Sysmon EventID 1 AND Sysmon EventID 3 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Processes where - ( - Processes.process_name=dllhost.exe - OR - Processes.original_file_name=dllhost.exe - ) - Processes.process IN ( - "*dllhost", - "*dllhost.exe", - "*dllhost.exe\"" - ) - by host _time span=1h - Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | rename dest as src - | join host process_id - [ - | tstats `security_content_summariesonly` - count - latest(All_Traffic.dest) as dest - latest(All_Traffic.dest_ip) as dest_ip - latest(All_Traffic.dest_port) as dest_port - FROM datamodel=Network_Traffic.All_Traffic where - All_Traffic.dest_port != 0 - by host All_Traffic.action All_Traffic.app All_Traffic.bytes All_Traffic.bytes_in - All_Traffic.bytes_out All_Traffic.dest All_Traffic.dest_ip All_Traffic.dest_port - All_Traffic.dvc All_Traffic.protocol All_Traffic.protocol_version All_Traffic.src - All_Traffic.src_ip All_Traffic.src_port All_Traffic.transport All_Traffic.user - All_Traffic.vendor_product All_Traffic.direction All_Traffic.process_id - | `drop_dm_object_name(All_Traffic)` - ] - | `dllhost_with_no_command_line_arguments_with_network_filter` + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime FROM datamodel=Endpoint.Processes where + ( + Processes.process_name=dllhost.exe + OR + Processes.original_file_name=dllhost.exe + ) + Processes.process IN ( + "*dllhost", + "*dllhost.exe", + "*dllhost.exe\"" + ) + by host _time span=1h + Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | rename dest as src + | join host process_id + [ + | tstats `security_content_summariesonly` + count + latest(All_Traffic.dest) as dest + latest(All_Traffic.dest_ip) as dest_ip + latest(All_Traffic.dest_port) as dest_port + FROM datamodel=Network_Traffic.All_Traffic where + All_Traffic.dest_port != 0 + by host All_Traffic.action All_Traffic.app All_Traffic.bytes All_Traffic.bytes_in + All_Traffic.bytes_out All_Traffic.dest All_Traffic.dest_ip All_Traffic.dest_port + All_Traffic.dvc All_Traffic.protocol All_Traffic.protocol_version All_Traffic.src + All_Traffic.src_ip All_Traffic.src_port All_Traffic.transport All_Traffic.user + All_Traffic.vendor_product All_Traffic.direction All_Traffic.process_id + | `drop_dm_object_name(All_Traffic)` + ] + | `dllhost_with_no_command_line_arguments_with_network_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: | - Although unlikely, some legitimate third party applications - may use a moved copy of dllhost, triggering a false positive. + Although unlikely, some legitimate third party applications + may use a moved copy of dllhost, triggering a false positive. references: -- https://raw.githubusercontent.com/threatexpress/malleable-c2/c3385e481159a759f79b8acfe11acf240893b830/jquery-c2.4.2.profile -- https://www.cobaltstrike.com/blog/learn-pipe-fitting-for-all-of-your-offense-projects/ + - https://raw.githubusercontent.com/threatexpress/malleable-c2/c3385e481159a759f79b8acfe11acf240893b830/jquery-c2.4.2.profile + - https://www.cobaltstrike.com/blog/learn-pipe-fitting-for-all-of-your-offense-projects/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The process $process_name$ was spawned by $parent_process_name$ without - any command-line arguments on $src$ by $user$. - risk_objects: - - field: user - type: user - score: 49 - - field: dest - type: system - score: 49 - threat_objects: - - field: parent_process_name - type: process - - field: process_name - type: process_name + message: The process $process_name$ was spawned by $parent_process_name$ without any command-line arguments on $src$ by $user$. + risk_objects: + - field: user + type: user + score: 49 + - field: dest + type: system + score: 49 + threat_objects: + - field: parent_process_name + type: process + - field: process_name + type: process_name tags: - analytic_story: - - BlackByte Ransomware - - Cobalt Strike - - Graceful Wipe Out Attack - - Cactus Ransomware - - Storm-2460 CLFS Zero Day Exploitation - - Earth Alux - asset_type: Endpoint - mitre_attack_id: - - T1055 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - BlackByte Ransomware + - Cobalt Strike + - Graceful Wipe Out Attack + - Cactus Ransomware + - Storm-2460 CLFS Zero Day Exploitation + - Earth Alux + asset_type: Endpoint + mitre_attack_id: + - T1055 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon_dllhost.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon_dllhost.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/dns_exfiltration_using_nslookup_app.yml b/detections/endpoint/dns_exfiltration_using_nslookup_app.yml index 53026348f7..cb3b7a782f 100644 --- a/detections/endpoint/dns_exfiltration_using_nslookup_app.yml +++ b/detections/endpoint/dns_exfiltration_using_nslookup_app.yml @@ -1,95 +1,84 @@ name: DNS Exfiltration Using Nslookup App id: 2452e632-9e0d-11eb-bacd-acde48001122 -version: 10 -date: '2025-05-02' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk, Wouter Jansen status: production type: TTP -description: The following analytic identifies potential DNS exfiltration using the - nslookup application. It detects specific command-line parameters such as query - type (TXT, A, AAAA) and retry options, which are commonly used by attackers to exfiltrate - data. The detection leverages Endpoint Detection and Response (EDR) telemetry, focusing - on process execution logs. This activity is significant as it may indicate an attempt - to communicate with a Command and Control (C2) server or exfiltrate sensitive data. - If confirmed malicious, this could lead to data breaches and unauthorized access - to critical information. +description: The following analytic identifies potential DNS exfiltration using the nslookup application. It detects specific command-line parameters such as query type (TXT, A, AAAA) and retry options, which are commonly used by attackers to exfiltrate data. The detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on process execution logs. This activity is significant as it may indicate an attempt to communicate with a Command and Control (C2) server or exfiltrate sensitive data. If confirmed malicious, this could lead to data breaches and unauthorized access to critical information. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - values(Processes.process_id) as process_id values(Processes.parent_process) as parent_process - count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.process_name = "nslookup.exe" Processes.process = "*-querytype=*" - OR Processes.process="*-qt=*" OR Processes.process="*-q=*" OR Processes.process="*-type=*" - OR Processes.process="*-retry=*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dns_exfiltration_using_nslookup_app_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id values(Processes.parent_process) as parent_process count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "nslookup.exe" Processes.process = "*-querytype=*" + OR + Processes.process="*-qt=*" + OR + Processes.process="*-q=*" + OR + Processes.process="*-type=*" + OR + Processes.process="*-retry=*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `dns_exfiltration_using_nslookup_app_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: admin nslookup usage references: -- https://www.mandiant.com/resources/fin7-spear-phishing-campaign-targets-personnel-involved-sec-filings -- https://www.varonis.com/blog/dns-tunneling -- https://www.microsoft.com/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/ + - https://www.mandiant.com/resources/fin7-spear-phishing-campaign-targets-personnel-involved-sec-filings + - https://www.varonis.com/blog/dns-tunneling + - https://www.microsoft.com/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ performing activity related to DNS exfiltration. - risk_objects: - - field: user - type: user - score: 72 - - field: dest - type: system - score: 72 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ performing activity related to DNS exfiltration. + risk_objects: + - field: user + type: user + score: 72 + - field: dest + type: system + score: 72 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Suspicious DNS Traffic - - Dynamic DNS - - Data Exfiltration - - Command And Control - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1048 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious DNS Traffic + - Dynamic DNS + - Data Exfiltration + - Command And Control + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1048 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/nslookup_exfil/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/nslookup_exfil/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/domain_account_discovery_with_dsquery.yml b/detections/endpoint/domain_account_discovery_with_dsquery.yml index a157073bbb..253fe62e30 100644 --- a/detections/endpoint/domain_account_discovery_with_dsquery.yml +++ b/detections/endpoint/domain_account_discovery_with_dsquery.yml @@ -1,92 +1,74 @@ name: Domain Account Discovery with Dsquery id: b1a8ce04-04c2-11ec-bea7-acde48001122 -version: 9 -date: '2025-08-27' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Mauricio Velazco, Splunk status: production type: Anomaly -description: The following analytic identifies the execution of `dsquery.exe` - with command-line arguments used to discover domain users. It leverages data - from Endpoint Detection and Response (EDR) agents, focusing on process names - and command-line executions. This activity is significant as it indicates - potential reconnaissance efforts by adversaries to map out domain users, which - is a common precursor to further attacks. If confirmed malicious, this - behavior could allow attackers to gain insights into user accounts, - facilitating subsequent actions like privilege escalation or lateral movement - within the network. +description: The following analytic identifies the execution of `dsquery.exe` with command-line arguments used to discover domain users. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential reconnaissance efforts by adversaries to map out domain users, which is a common precursor to further attacks. If confirmed malicious, this behavior could allow attackers to gain insights into user accounts, facilitating subsequent actions like privilege escalation or lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name="dsquery.exe" - AND Processes.process = "*user*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_account_discovery_with_dsquery_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: Administrators or power users may use this command for - troubleshooting. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name="dsquery.exe" + AND + Processes.process = "*user*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `domain_account_discovery_with_dsquery_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://jpcertcc.github.io/ToolAnalysisResultSheet/details/dsquery.htm -- https://attack.mitre.org/techniques/T1087/002/ + - https://jpcertcc.github.io/ToolAnalysisResultSheet/details/dsquery.htm + - https://attack.mitre.org/techniques/T1087/002/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 3 - - field: dest - type: system - score: 3 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 3 + - field: dest + type: system + score: 3 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Active Directory Discovery - - LAMEHUG - asset_type: Endpoint - mitre_attack_id: - - T1087.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - LAMEHUG + asset_type: Endpoint + mitre_attack_id: + - T1087.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/domain_account_discovery_with_wmic.yml b/detections/endpoint/domain_account_discovery_with_wmic.yml index f929ad0304..8772e27e01 100644 --- a/detections/endpoint/domain_account_discovery_with_wmic.yml +++ b/detections/endpoint/domain_account_discovery_with_wmic.yml @@ -5,86 +5,52 @@ date: '2025-07-28' author: Teoderick Contreras, Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the execution of `wmic.exe` with - command-line arguments used to query for domain users. It leverages data from - Endpoint Detection and Response (EDR) agents, focusing on specific - command-line patterns indicative of domain account discovery. This activity is - significant as it often precedes lateral movement or privilege escalation - attempts by adversaries. If confirmed malicious, this behavior could allow - attackers to map out user accounts within the domain, facilitating further - attacks and potentially compromising sensitive information. +description: The following analytic detects the execution of `wmic.exe` with command-line arguments used to query for domain users. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns indicative of domain account discovery. This activity is significant as it often precedes lateral movement or privilege escalation attempts by adversaries. If confirmed malicious, this behavior could allow attackers to map out user accounts within the domain, facilitating further attacks and potentially compromising sensitive information. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name="wmic.exe" - AND Processes.process = "*/NAMESPACE:\\\\root\\directory\\ldap*" AND Processes.process - = "*ds_user*" AND Processes.process = "*GET*" AND Processes.process = "*ds_samaccountname*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `domain_account_discovery_with_wmic_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: Administrators or power users may use this command for - troubleshooting. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="wmic.exe" AND Processes.process = "*/NAMESPACE:\\\\root\\directory\\ldap*" AND Processes.process = "*ds_user*" AND Processes.process = "*GET*" AND Processes.process = "*ds_samaccountname*" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_account_discovery_with_wmic_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1087/002/ + - https://attack.mitre.org/techniques/T1087/002/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: an instance of process $process_name$ with commandline $process$ on - $dest$ - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: an instance of process $process_name$ with commandline $process$ on $dest$ + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - Active Directory Discovery - - Interlock Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1087.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - Interlock Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1087.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/domain_controller_discovery_with_nltest.yml b/detections/endpoint/domain_controller_discovery_with_nltest.yml index 51118133b4..427e131350 100644 --- a/detections/endpoint/domain_controller_discovery_with_nltest.yml +++ b/detections/endpoint/domain_controller_discovery_with_nltest.yml @@ -1,85 +1,74 @@ name: Domain Controller Discovery with Nltest id: 41243735-89a7-4c83-bcdd-570aa78f00a1 -version: 9 -date: '2025-12-18' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the execution of `nltest.exe` with command-line - arguments `/dclist:` or `/dsgetdc:` to discover domain controllers. It leverages - Endpoint Detection and Response (EDR) data, focusing on process names and command-line - arguments. This activity is significant because both Red Teams and adversaries use - `nltest.exe` for situational awareness and Active Directory discovery. If confirmed - malicious, this behavior could allow attackers to map out domain controllers, facilitating - further attacks such as privilege escalation or lateral movement within the network. +description: The following analytic detects the execution of `nltest.exe` with command-line arguments `/dclist:` or `/dsgetdc:` to discover domain controllers. It leverages Endpoint Detection and Response (EDR) data, focusing on process names and command-line arguments. This activity is significant because both Red Teams and adversaries use `nltest.exe` for situational awareness and Active Directory discovery. If confirmed malicious, this behavior could allow attackers to map out domain controllers, facilitating further attacks such as privilege escalation or lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - (Processes.process_name=nltest.exe OR Processes.original_file_name=nltestrk.exe) - (Processes.process="*/dclist:*" OR Processes.process="*/dsgetdc:*") - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_controller_discovery_with_nltest_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name=nltest.exe + OR + Processes.original_file_name=nltestrk.exe + ) + (Processes.process="*/dclist:*" OR Processes.process="*/dsgetdc:*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `domain_controller_discovery_with_nltest_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1018/ -- https://learn.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-r2-and-2012/cc731935(v=ws.11) + - https://attack.mitre.org/techniques/T1018/ + - https://learn.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-r2-and-2012/cc731935(v=ws.11) drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Domain controller discovery on $dest$ by $user$ - risk_objects: - - field: dest - type: system - score: 21 - threat_objects: [] + message: Domain controller discovery on $dest$ by $user$ + risk_objects: + - field: dest + type: system + score: 21 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - - CISA AA23-347A - - Medusa Ransomware - - BlackSuit Ransomware - - Rhysida Ransomware - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1018 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - CISA AA23-347A + - Medusa Ransomware + - BlackSuit Ransomware + - Rhysida Ransomware + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1018 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/domain_controller_discovery_with_wmic.yml b/detections/endpoint/domain_controller_discovery_with_wmic.yml index 2c650757f6..0b3b088c55 100644 --- a/detections/endpoint/domain_controller_discovery_with_wmic.yml +++ b/detections/endpoint/domain_controller_discovery_with_wmic.yml @@ -1,58 +1,50 @@ name: Domain Controller Discovery with Wmic id: 64c7adaa-48ee-483c-b0d6-7175bc65e6cc -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting -description: The following analytic identifies the execution of `wmic.exe` with command-line - arguments used to discover domain controllers in a Windows domain. It leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process names - and command-line arguments. This activity is significant because it is commonly - used by adversaries and Red Teams for situational awareness and Active Directory - discovery. If confirmed malicious, this behavior could allow attackers to map out - the network, identify key systems, and plan further attacks, potentially leading - to unauthorized access and data exfiltration. +description: The following analytic identifies the execution of `wmic.exe` with command-line arguments used to discover domain controllers in a Windows domain. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because it is commonly used by adversaries and Red Teams for situational awareness and Active Directory discovery. If confirmed malicious, this behavior could allow attackers to map out the network, identify key systems, and plan further attacks, potentially leading to unauthorized access and data exfiltration. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="wmic.exe") - (Processes.process="" OR Processes.process="*DomainControllerAddress*") by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `domain_controller_discovery_with_wmic_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="wmic.exe" + ) + (Processes.process="" OR Processes.process="*DomainControllerAddress*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `domain_controller_discovery_with_wmic_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1018/ + - https://attack.mitre.org/techniques/T1018/ tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1018 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1018 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/domain_group_discovery_with_adsisearcher.yml b/detections/endpoint/domain_group_discovery_with_adsisearcher.yml index 1aafa72588..f99018a719 100644 --- a/detections/endpoint/domain_group_discovery_with_adsisearcher.yml +++ b/detections/endpoint/domain_group_discovery_with_adsisearcher.yml @@ -1,72 +1,61 @@ name: Domain Group Discovery with Adsisearcher id: 089c862f-5f83-49b5-b1c8-7e4ff66560c7 -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: - The following analytic detects the use of the `[Adsisearcher]` type accelerator - in PowerShell to query Active Directory for domain groups. It leverages PowerShell - Script Block Logging (EventCode=4104) to identify specific script blocks containing - `[adsisearcher]` and group-related queries. This activity is significant as it may - indicate an attempt by adversaries or Red Teams to enumerate domain groups for situational - awareness and Active Directory discovery. If confirmed malicious, this behavior - could lead to further reconnaissance, privilege escalation, or lateral movement - within the network. +description: The following analytic detects the use of the `[Adsisearcher]` type accelerator in PowerShell to query Active Directory for domain groups. It leverages PowerShell Script Block Logging (EventCode=4104) to identify specific script blocks containing `[adsisearcher]` and group-related queries. This activity is significant as it may indicate an attempt by adversaries or Red Teams to enumerate domain groups for situational awareness and Active Directory discovery. If confirmed malicious, this behavior could lead to further reconnaissance, privilege escalation, or lateral movement within the network. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` (ScriptBlockText = "*[adsisearcher]*" AND ScriptBlockText = - "*(objectcategory=group)*" AND ScriptBlockText = "*findAll()*") | fillnull | stats - count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_group_discovery_with_adsisearcher_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - Powershell Script Block Logging 4104 +search: |- + `powershell` (ScriptBlockText = "*[adsisearcher]*" AND ScriptBlockText = "*(objectcategory=group)*" AND ScriptBlockText = "*findAll()*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `domain_group_discovery_with_adsisearcher_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. known_false_positives: Administrators or power users may use Adsisearcher for troubleshooting. references: - - https://attack.mitre.org/techniques/T1069/002/ - - https://devblogs.microsoft.com/scripting/use-the-powershell-adsisearcher-type-accelerator-to-search-active-directory/ + - https://attack.mitre.org/techniques/T1069/002/ + - https://devblogs.microsoft.com/scripting/use-the-powershell-adsisearcher-type-accelerator-to-search-active-directory/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Domain group discovery enumeration using PowerShell on $dest$ by $user_id$ - risk_objects: - - field: dest - type: system - score: 18 - threat_objects: [] + message: Domain group discovery enumeration using PowerShell on $dest$ by $user_id$ + risk_objects: + - field: dest + type: system + score: 18 + threat_objects: [] tags: - analytic_story: - - Scattered Lapsus$ Hunters - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1069.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Scattered Lapsus$ Hunters + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1069.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/domain_group_discovery_with_adsisearcher/windows-powershell-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/domain_group_discovery_with_adsisearcher/windows-powershell-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/domain_group_discovery_with_dsquery.yml b/detections/endpoint/domain_group_discovery_with_dsquery.yml index 1116127c01..e962b08dda 100644 --- a/detections/endpoint/domain_group_discovery_with_dsquery.yml +++ b/detections/endpoint/domain_group_discovery_with_dsquery.yml @@ -1,91 +1,74 @@ name: Domain Group Discovery With Dsquery id: f0c9d62f-a232-4edd-b17e-bc409fb133d4 -version: 8 -date: '2025-08-27' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Anomaly -description: The following analytic identifies the execution of `dsquery.exe` - with command-line arguments used to query for domain groups. It leverages - Endpoint Detection and Response (EDR) data, focusing on process names and - command-line arguments. This activity is significant because both Red Teams - and adversaries use `dsquery.exe` to enumerate domain groups, gaining - situational awareness and facilitating further Active Directory discovery. If - confirmed malicious, this behavior could allow attackers to map out the domain - structure, identify high-value targets, and plan subsequent attacks, - potentially leading to privilege escalation or data exfiltration. +description: The following analytic identifies the execution of `dsquery.exe` with command-line arguments used to query for domain groups. It leverages Endpoint Detection and Response (EDR) data, focusing on process names and command-line arguments. This activity is significant because both Red Teams and adversaries use `dsquery.exe` to enumerate domain groups, gaining situational awareness and facilitating further Active Directory discovery. If confirmed malicious, this behavior could allow attackers to map out the domain structure, identify high-value targets, and plan subsequent attacks, potentially leading to privilege escalation or data exfiltration. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="dsquery.exe") - (Processes.process="*group*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_group_discovery_with_dsquery_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: Administrators or power users may use this command for - troubleshooting. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="dsquery.exe" + ) + (Processes.process="*group*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `domain_group_discovery_with_dsquery_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1069/002/ + - https://attack.mitre.org/techniques/T1069/002/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 3 - - field: dest - type: system - score: 3 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 3 + - field: dest + type: system + score: 3 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Active Directory Discovery - - LAMEHUG - asset_type: Endpoint - mitre_attack_id: - - T1069.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - LAMEHUG + asset_type: Endpoint + mitre_attack_id: + - T1069.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/domain_group_discovery_with_wmic.yml b/detections/endpoint/domain_group_discovery_with_wmic.yml index 15cdd2ffcb..b1fdaae91e 100644 --- a/detections/endpoint/domain_group_discovery_with_wmic.yml +++ b/detections/endpoint/domain_group_discovery_with_wmic.yml @@ -5,54 +5,30 @@ date: '2025-05-02' author: Mauricio Velazco, Splunk status: production type: Hunting -description: The following analytic identifies the execution of `wmic.exe` with command-line - arguments used to query for domain groups. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process names and command-line executions. - This activity is significant as it indicates potential reconnaissance efforts by - adversaries to gain situational awareness and map out Active Directory structures. - If confirmed malicious, this behavior could allow attackers to identify and target - specific domain groups, potentially leading to privilege escalation or lateral movement - within the network. +description: The following analytic identifies the execution of `wmic.exe` with command-line arguments used to query for domain groups. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential reconnaissance efforts by adversaries to gain situational awareness and map out Active Directory structures. If confirmed malicious, this behavior could allow attackers to identify and target specific domain groups, potentially leading to privilege escalation or lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_wmic` (Processes.process=*/NAMESPACE:\\\\root\\directory\\ldap* - AND Processes.process=*ds_group* AND Processes.process="*GET ds_samaccountname*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `domain_group_discovery_with_wmic_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_wmic` (Processes.process=*/NAMESPACE:\\\\root\\directory\\ldap* AND Processes.process=*ds_group* AND Processes.process="*GET ds_samaccountname*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_group_discovery_with_wmic_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1069/002/ + - https://attack.mitre.org/techniques/T1069/002/ tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1069.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1069.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/download_files_using_telegram.yml b/detections/endpoint/download_files_using_telegram.yml index d3ba439230..cde6946942 100644 --- a/detections/endpoint/download_files_using_telegram.yml +++ b/detections/endpoint/download_files_using_telegram.yml @@ -1,76 +1,64 @@ name: Download Files Using Telegram id: 58194e28-ae5e-11eb-8912-acde48001122 -version: 8 -date: '2025-08-22' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects suspicious file downloads by the - Telegram application on a Windows system. It leverages Sysmon EventCode 15 to - identify instances where Telegram.exe creates files with a Zone.Identifier, - indicating a download. This activity is significant as it may indicate an - adversary using Telegram to download malicious tools, such as network - scanners, for further exploitation. If confirmed malicious, this behavior - could lead to network mapping, lateral movement, and potential compromise of - additional systems within the network. +description: The following analytic detects suspicious file downloads by the Telegram application on a Windows system. It leverages Sysmon EventCode 15 to identify instances where Telegram.exe creates files with a Zone.Identifier, indicating a download. This activity is significant as it may indicate an adversary using Telegram to download malicious tools, such as network scanners, for further exploitation. If confirmed malicious, this behavior could lead to network mapping, lateral movement, and potential compromise of additional systems within the network. data_source: -- Sysmon EventID 15 -search: '`sysmon` EventCode= 15 process_name = "telegram.exe" TargetFilename = "*:Zone.Identifier" - | stats count min(_time) as firstTime max(_time) as lastTime by dest dvc file_hash - file_name file_path process_exec process_guid process_id process_name process_path - signature signature_id user_id vendor_product Contents Image | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `download_files_using_telegram_filter`' -how_to_implement: To successfully implement this search, you need to be - ingesting logs with the process name and TargetFilename from your endpoints or - Events that monitor filestream events which is happened when process download - something. (EventCode 15) If you are using Sysmon, you must have at least - version 6.0.4 of the Sysmon TA. -known_false_positives: normal download of file in telegram app. (if it was a - common app in network) + - Sysmon EventID 15 +search: |- + `sysmon` EventCode= 15 process_name = "telegram.exe" TargetFilename = "*:Zone.Identifier" + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest dvc file_hash + file_name file_path process_exec + process_guid process_id process_name + process_path signature signature_id + user_id vendor_product Contents + Image + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `download_files_using_telegram_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and TargetFilename from your endpoints or Events that monitor filestream events which is happened when process download something. (EventCode 15) If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: normal download of file in telegram app. (if it was a common app in network) references: -- https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ + - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious files were downloaded with the Telegram application on - $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Suspicious files were downloaded with the Telegram application on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Phemedrone Stealer - - Crypto Stealer - - Snake Keylogger - - XMRig - - Water Gamayun - - 0bj3ctivity Stealer - asset_type: Endpoint - mitre_attack_id: - - T1105 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Phemedrone Stealer + - Crypto Stealer + - Snake Keylogger + - XMRig + - Water Gamayun + - 0bj3ctivity Stealer + asset_type: Endpoint + mitre_attack_id: + - T1105 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/minergate/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/minergate/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/drop_icedid_license_dat.yml b/detections/endpoint/drop_icedid_license_dat.yml index d5b9b3a5ab..4a5dbff1e7 100644 --- a/detections/endpoint/drop_icedid_license_dat.yml +++ b/detections/endpoint/drop_icedid_license_dat.yml @@ -5,42 +5,28 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects the dropping of a suspicious file named - "license.dat" in %appdata% or %programdata%. This behavior is associated with the - IcedID malware, which uses this file to inject its core bot into other processes - for banking credential theft. The detection leverages Sysmon EventCode 11 to monitor - file creation events in these directories. This activity is significant as it indicates - a potential malware infection aiming to steal sensitive banking information. If - confirmed malicious, the attacker could gain unauthorized access to financial data, - leading to significant financial loss and data breaches. +description: The following analytic detects the dropping of a suspicious file named "license.dat" in %appdata% or %programdata%. This behavior is associated with the IcedID malware, which uses this file to inject its core bot into other processes for banking credential theft. The detection leverages Sysmon EventCode 11 to monitor file creation events in these directories. This activity is significant as it indicates a potential malware infection aiming to steal sensitive banking information. If confirmed malicious, the attacker could gain unauthorized access to financial data, leading to significant financial loss and data breaches. data_source: -- Sysmon EventID 11 -search: '`sysmon` EventCode= 11 TargetFilename = "*\\license.dat" AND (TargetFilename="*\\appdata\\*" - OR TargetFilename="*\\programdata\\*") |stats count min(_time) as firstTime max(_time) - as lastTime by TargetFilename EventCode process_id process_name dest | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `drop_icedid_license_dat_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. + - Sysmon EventID 11 +search: '`sysmon` EventCode= 11 TargetFilename = "*\\license.dat" AND (TargetFilename="*\\appdata\\*" OR TargetFilename="*\\programdata\\*") |stats count min(_time) as firstTime max(_time) as lastTime by TargetFilename EventCode process_id process_name dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_icedid_license_dat_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: No false positives have been identified at this time. references: -- https://www.cisecurity.org/insights/white-papers/security-primer-icedid + - https://www.cisecurity.org/insights/white-papers/security-primer-icedid tags: - analytic_story: - - IcedID - asset_type: Endpoint - mitre_attack_id: - - T1204.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - IcedID + asset_type: Endpoint + mitre_attack_id: + - T1204.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/simulated_icedid/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/simulated_icedid/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/dsquery_domain_discovery.yml b/detections/endpoint/dsquery_domain_discovery.yml index e69dbe8600..e497ce9205 100644 --- a/detections/endpoint/dsquery_domain_discovery.yml +++ b/detections/endpoint/dsquery_domain_discovery.yml @@ -1,92 +1,75 @@ name: DSQuery Domain Discovery id: cc316032-924a-11eb-91a2-acde48001122 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of "dsquery.exe" with arguments - targeting `TrustedDomain` queries directly from the command line. This behavior - is identified using Endpoint Detection and Response (EDR) telemetry, focusing on - process names and command-line arguments. This activity is significant as it often - indicates domain trust discovery, a common step in lateral movement or privilege - escalation by adversaries. If confirmed malicious, this could allow attackers to - map domain trusts, potentially leading to further exploitation and unauthorized - access to trusted domains. +description: The following analytic detects the execution of "dsquery.exe" with arguments targeting `TrustedDomain` queries directly from the command line. This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on process names and command-line arguments. This activity is significant as it often indicates domain trust discovery, a common step in lateral movement or privilege escalation by adversaries. If confirmed malicious, this could allow attackers to map domain trusts, potentially leading to further exploitation and unauthorized access to trusted domains. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=dsquery.exe - Processes.process=*trustedDomain* by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dsquery_domain_discovery_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Limited false positives. If there is a true false positive, - filter based on command-line or parent process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=dsquery.exe Processes.process=*trustedDomain* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `dsquery_domain_discovery_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Limited false positives. If there is a true false positive, filter based on command-line or parent process. references: -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1482/T1482.md -- https://blog.harmj0y.net/redteaming/a-guide-to-attacking-domain-trusts/ -- https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc732952(v=ws.11) -- https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc754232(v=ws.11) + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1482/T1482.md + - https://blog.harmj0y.net/redteaming/a-guide-to-attacking-domain-trusts/ + - https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc732952(v=ws.11) + - https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc754232(v=ws.11) drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - performing domain discovery on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 72 - - field: dest - type: system - score: 72 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified performing domain discovery on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 72 + - field: dest + type: system + score: 72 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Active Directory Discovery - - Domain Trust Discovery - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1482 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - Domain Trust Discovery + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1482 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/dump_lsass_via_comsvcs_dll.yml b/detections/endpoint/dump_lsass_via_comsvcs_dll.yml index bf8d0f4ddf..5b688ec2c4 100644 --- a/detections/endpoint/dump_lsass_via_comsvcs_dll.yml +++ b/detections/endpoint/dump_lsass_via_comsvcs_dll.yml @@ -1,103 +1,85 @@ name: Dump LSASS via comsvcs DLL id: 8943b567-f14d-4ee8-a0bb-2121d4ce3184 -version: 13 -date: '2026-01-14' +version: 14 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects the behavior of dumping credentials from - memory by exploiting the Local Security Authority Subsystem Service (LSASS) using - the comsvcs.dll and MiniDump via rundll32. This detection leverages process information - from Endpoint Detection and Response (EDR) logs, focusing on specific command-line - executions. This activity is significant because it indicates potential credential - theft, which can lead to broader system compromise, persistence, lateral movement, - and privilege escalation. If confirmed malicious, attackers could gain unauthorized - access to sensitive information, leading to data theft, ransomware attacks, or other - damaging outcomes. +description: The following analytic detects the behavior of dumping credentials from memory by exploiting the Local Security Authority Subsystem Service (LSASS) using the comsvcs.dll and MiniDump via rundll32. This detection leverages process information from Endpoint Detection and Response (EDR) logs, focusing on specific command-line executions. This activity is significant because it indicates potential credential theft, which can lead to broader system compromise, persistence, lateral movement, and privilege escalation. If confirmed malicious, attackers could gain unauthorized access to sensitive information, leading to data theft, ransomware attacks, or other damaging outcomes. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*comsvcs.dll* - Processes.process IN ("*MiniDump*", "*#24*") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `dump_lsass_via_comsvcs_dll_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_rundll32` Processes.process=*comsvcs.dll* Processes.process IN ("*MiniDump*", "*#24*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `dump_lsass_via_comsvcs_dll_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://modexp.wordpress.com/2019/08/30/minidumpwritedump-via-com-services-dll/ -- https://twitter.com/SBousseaden/status/1167417096374050817 -- https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ + - https://modexp.wordpress.com/2019/08/30/minidumpwritedump-via-com-services-dll/ + - https://twitter.com/SBousseaden/status/1167417096374050817 + - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - accessing credentials using comsvcs.dll on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified accessing credentials using comsvcs.dll on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Living Off The Land - - CISA AA22-257A - - Volt Typhoon - - HAFNIUM Group - - Prestige Ransomware - - Suspicious Rundll32 Activity - - Industroyer2 - - Data Destruction - - Flax Typhoon - - CISA AA22-264A - - Compromised Windows Host - - Credential Dumping - - Scattered Lapsus$ Hunters - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1003.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + - CISA AA22-257A + - Volt Typhoon + - HAFNIUM Group + - Prestige Ransomware + - Suspicious Rundll32 Activity + - Industroyer2 + - Data Destruction + - Flax Typhoon + - CISA AA22-264A + - Compromised Windows Host + - Credential Dumping + - Scattered Lapsus$ Hunters + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1003.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/dump_lsass_via_procdump.yml b/detections/endpoint/dump_lsass_via_procdump.yml index 7a582ef7c0..f8e2ffed08 100644 --- a/detections/endpoint/dump_lsass_via_procdump.yml +++ b/detections/endpoint/dump_lsass_via_procdump.yml @@ -1,122 +1,116 @@ name: Dump LSASS via procdump id: 3742ebfe-64c2-11eb-ae93-0242ac130002 -version: 16 -date: '2026-01-14' +version: 17 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP description: | - The following analytic detects the use of procdump.exe to dump the LSASS - process, specifically looking for the -mm and -ma command-line arguments. It leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process names, - command-line executions, and parent processes. This activity is significant because - dumping LSASS can expose sensitive credentials, posing a severe security risk. If - confirmed malicious, an attacker could obtain credentials, escalate privileges, - and move laterally within the network, leading to potential data breaches and further - compromise of the environment. + The following analytic detects the use of procdump.exe to dump the LSASS + process, specifically looking for the -mm and -ma command-line arguments. It leverages + data from Endpoint Detection and Response (EDR) agents, focusing on process names, + command-line executions, and parent processes. This activity is significant because + dumping LSASS can expose sensitive credentials, posing a severe security risk. If + confirmed malicious, an attacker could obtain credentials, escalate privileges, + and move laterally within the network, leading to potential data breaches and further + compromise of the environment. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime - - from datamodel=Endpoint.Processes where - - ( - Processes.process_name IN ( - "procdump.exe", - "procdump64.exe", - "procdump64a.exe" + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + + from datamodel=Endpoint.Processes where + + ( + Processes.process_name IN ( + "procdump.exe", + "procdump64.exe", + "procdump64a.exe" + ) + OR + Processes.original_file_name=procdump ) - OR - Processes.original_file_name=procdump - ) - Processes.process IN (*-ma*, *-mm*, "*-mp*", */ma*, */mm*, "*/mp*") - Processes.process IN (* ls*, "* keyiso*", "* samss*") - - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `dump_lsass_via_procdump_filter` + Processes.process IN (*-ma*, *-mm*, "*-mp*", */ma*, */mm*, "*/mp*") + Processes.process IN (* ls*, "* keyiso*", "* samss*") + + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `dump_lsass_via_procdump_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: - - https://attack.mitre.org/techniques/T1003/001/ - - https://docs.microsoft.com/en-us/sysinternals/downloads/procdump - - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-2---dump-lsassexe-memory-using-procdump - - https://thedfirreport.com/2022/08/08/bumblebee-roasts-its-way-to-domain-admin/ - - https://x.com/wietze/status/1958302556033065292?s=12 + - https://attack.mitre.org/techniques/T1003/001/ + - https://docs.microsoft.com/en-us/sysinternals/downloads/procdump + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-2---dump-lsassexe-memory-using-procdump + - https://thedfirreport.com/2022/08/08/bumblebee-roasts-its-way-to-domain-admin/ + - https://x.com/wietze/status/1958302556033065292?s=12 drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - attempting to dump lsass.exe via the command $process$ on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified attempting to dump lsass.exe via the command $process$ on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - CISA AA22-257A - - HAFNIUM Group - - Compromised Windows Host - - Credential Dumping - - Seashell Blizzard - - Storm-2460 CLFS Zero Day Exploitation - asset_type: Endpoint - mitre_attack_id: - - T1003.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA22-257A + - HAFNIUM Group + - Compromised Windows Host + - Credential Dumping + - Seashell Blizzard + - Storm-2460 CLFS Zero Day Exploitation + asset_type: Endpoint + mitre_attack_id: + - T1003.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/crowdstrike_falcon.log - source: crowdstrike - sourcetype: crowdstrike:events:sensor + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/crowdstrike_falcon.log + source: crowdstrike + sourcetype: crowdstrike:events:sensor diff --git a/detections/endpoint/elevated_group_discovery_with_powerview.yml b/detections/endpoint/elevated_group_discovery_with_powerview.yml index e6476e0e09..bcaa009b1e 100644 --- a/detections/endpoint/elevated_group_discovery_with_powerview.yml +++ b/detections/endpoint/elevated_group_discovery_with_powerview.yml @@ -1,56 +1,49 @@ name: Elevated Group Discovery with PowerView id: 10d62950-0de5-4199-a710-cff9ea79b413 -version: 9 -date: '2025-06-24' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting -description: - The following analytic detects the execution of the `Get-DomainGroupMember` - cmdlet from PowerView, identified through PowerShell Script Block Logging (EventCode=4104). - This cmdlet is used to enumerate members of elevated domain groups such as Domain - Admins and Enterprise Admins. Monitoring this activity is crucial as it indicates - potential reconnaissance efforts by adversaries to identify high-privileged users - within the domain. If confirmed malicious, this activity could lead to targeted - attacks on privileged accounts, facilitating further compromise and lateral movement - within the network. +description: The following analytic detects the execution of the `Get-DomainGroupMember` cmdlet from PowerView, identified through PowerShell Script Block Logging (EventCode=4104). This cmdlet is used to enumerate members of elevated domain groups such as Domain Admins and Enterprise Admins. Monitoring this activity is crucial as it indicates potential reconnaissance efforts by adversaries to identify high-privileged users within the domain. If confirmed malicious, this activity could lead to targeted attacks on privileged accounts, facilitating further compromise and lateral movement within the network. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainGroupMember*") - AND ScriptBlockText IN ("*Domain Admins*","*Enterprise Admins*", "*Schema Admins*", - "*Account Operators*" , "*Server Operators*", "*Protected Users*", "*Dns Admins*") - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `elevated_group_discovery_with_powerview_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainGroupMember*") AND ScriptBlockText IN ("*Domain Admins*","*Enterprise Admins*", "*Schema Admins*", "*Account Operators*" , "*Server Operators*", "*Protected Users*", "*Dns Admins*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `elevated_group_discovery_with_powerview_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. known_false_positives: Administrators or power users may use this PowerView for troubleshooting. references: - - https://attack.mitre.org/techniques/T1069/002/ - - https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainGroupMember/ - - https://docs.microsoft.com/en-us/windows-server/identity/ad-ds/plan/security-best-practices/appendix-b--privileged-accounts-and-groups-in-active-directory - - https://attack.mitre.org/techniques/T1069/002/ + - https://attack.mitre.org/techniques/T1069/002/ + - https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainGroupMember/ + - https://docs.microsoft.com/en-us/windows-server/identity/ad-ds/plan/security-best-practices/appendix-b--privileged-accounts-and-groups-in-active-directory + - https://attack.mitre.org/techniques/T1069/002/ tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1069.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1069.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-powershell-xml-powerview.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-xml.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-powershell-xml-powerview.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-xml.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/elevated_group_discovery_with_wmic.yml b/detections/endpoint/elevated_group_discovery_with_wmic.yml index 83914bd9b5..0178406f24 100644 --- a/detections/endpoint/elevated_group_discovery_with_wmic.yml +++ b/detections/endpoint/elevated_group_discovery_with_wmic.yml @@ -5,80 +5,48 @@ date: '2025-05-02' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the execution of `wmic.exe` with command-line - arguments querying specific elevated domain groups. It leverages Endpoint Detection - and Response (EDR) telemetry to identify processes that access the LDAP namespace - and search for groups like "Domain Admins" or "Enterprise Admins." This activity - is significant as it indicates potential reconnaissance efforts by adversaries to - identify high-privilege accounts within Active Directory. If confirmed malicious, - this behavior could lead to privilege escalation, allowing attackers to gain elevated - access and control over critical network resources. +description: The following analytic detects the execution of `wmic.exe` with command-line arguments querying specific elevated domain groups. It leverages Endpoint Detection and Response (EDR) telemetry to identify processes that access the LDAP namespace and search for groups like "Domain Admins" or "Enterprise Admins." This activity is significant as it indicates potential reconnaissance efforts by adversaries to identify high-privilege accounts within Active Directory. If confirmed malicious, this behavior could lead to privilege escalation, allowing attackers to gain elevated access and control over critical network resources. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="wmic.exe") - (Processes.process=*/NAMESPACE:\\\\root\\directory\\ldap*) (Processes.process="*Domain - Admins*" OR Processes.process="*Enterprise Admins*" OR Processes.process="*Schema - Admins*" OR Processes.process="*Account Operators*" OR Processes.process="*Server - Operators*" OR Processes.process="*Protected Users*" OR Processes.process="*Dns - Admins*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `elevated_group_discovery_with_wmic_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="wmic.exe") (Processes.process=*/NAMESPACE:\\\\root\\directory\\ldap*) (Processes.process="*Domain Admins*" OR Processes.process="*Enterprise Admins*" OR Processes.process="*Schema Admins*" OR Processes.process="*Account Operators*" OR Processes.process="*Server Operators*" OR Processes.process="*Protected Users*" OR Processes.process="*Dns Admins*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `elevated_group_discovery_with_wmic_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1069/002/ -- https://docs.microsoft.com/en-us/windows-server/identity/ad-ds/plan/security-best-practices/appendix-b--privileged-accounts-and-groups-in-active-directory -- https://adsecurity.org/?p=3658 + - https://attack.mitre.org/techniques/T1069/002/ + - https://docs.microsoft.com/en-us/windows-server/identity/ad-ds/plan/security-best-practices/appendix-b--privileged-accounts-and-groups-in-active-directory + - https://adsecurity.org/?p=3658 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Elevated domain group discovery enumeration on $dest$ by $user$ - risk_objects: - - field: dest - type: system - score: 21 - threat_objects: [] + message: Elevated domain group discovery enumeration on $dest$ by $user$ + risk_objects: + - field: dest + type: system + score: 21 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1069.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1069.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/enable_rdp_in_other_port_number.yml b/detections/endpoint/enable_rdp_in_other_port_number.yml index 04701de61e..ccc1122247 100644 --- a/detections/endpoint/enable_rdp_in_other_port_number.yml +++ b/detections/endpoint/enable_rdp_in_other_port_number.yml @@ -5,74 +5,50 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: The following analytic detects modifications to the registry that - enable RDP on a machine using a non-default port number. It leverages data - from the Endpoint.Registry data model, specifically monitoring changes to the - registry path "HKLM\SYSTEM\CurrentControlSet\Control\Terminal - Server\WinStations\RDP-Tcp" and the "PortNumber" value. This activity is - significant as attackers often modify RDP settings to facilitate lateral - movement and maintain remote access to compromised systems. If confirmed - malicious, this could allow attackers to bypass network defenses, gain - persistent access, and potentially control the compromised machine. +description: The following analytic detects modifications to the registry that enable RDP on a machine using a non-default port number. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path "HKLM\SYSTEM\CurrentControlSet\Control\Terminal Server\WinStations\RDP-Tcp" and the "PortNumber" value. This activity is significant as attackers often modify RDP settings to facilitate lateral movement and maintain remote access to compromised systems. If confirmed malicious, this could allow attackers to bypass network defenses, gain persistent access, and potentially control the compromised machine. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\CurrentControlSet\\Control\\Terminal - Server\\WinStations\\RDP-Tcp*" Registry.registry_value_name = "PortNumber") by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `enable_rdp_in_other_port_number_filter`' -how_to_implement: To successfully implement this search, you need to be - ingesting logs with the registry value name, registry path, and registry value - data from your endpoints. If you are using Sysmon, you must have at least - version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\CurrentControlSet\\Control\\Terminal Server\\WinStations\\RDP-Tcp*" Registry.registry_value_name = "PortNumber") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `enable_rdp_in_other_port_number_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: No false positives have been identified at this time. references: -- https://www.mvps.net/docs/how-to-secure-remote-desktop-rdp/ + - https://www.mvps.net/docs/how-to-secure-remote-desktop-rdp/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: RDP was moved to a non-standard port on $dest$ by $user$. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: [] + message: RDP was moved to a non-standard port on $dest$ by $user$. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: [] tags: - analytic_story: - - Prohibited Traffic Allowed or Protocol Mismatch - - Windows Registry Abuse - - Windows RDP Artifacts and Defense Evasion - - Interlock Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1021 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Prohibited Traffic Allowed or Protocol Mismatch + - Windows Registry Abuse + - Windows RDP Artifacts and Defense Evasion + - Interlock Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1021 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/casper/datasets1/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/casper/datasets1/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/enable_wdigest_uselogoncredential_registry.yml b/detections/endpoint/enable_wdigest_uselogoncredential_registry.yml index 33a7e3213d..06a2d1e21e 100644 --- a/detections/endpoint/enable_wdigest_uselogoncredential_registry.yml +++ b/detections/endpoint/enable_wdigest_uselogoncredential_registry.yml @@ -5,76 +5,50 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: - The following analytic detects a suspicious registry modification that - enables the plain text credential feature in Windows by setting the "UseLogonCredential" - value to 1 in the WDigest registry path. This detection leverages data from the - Endpoint.Registry data model, focusing on specific registry paths and values. This - activity is significant because it is commonly used by malware and tools like Mimikatz - to dump plain text credentials, indicating a potential credential dumping attempt. - If confirmed malicious, this could allow an attacker to obtain sensitive credentials, - leading to further compromise and lateral movement within the network. +description: The following analytic detects a suspicious registry modification that enables the plain text credential feature in Windows by setting the "UseLogonCredential" value to 1 in the WDigest registry path. This detection leverages data from the Endpoint.Registry data model, focusing on specific registry paths and values. This activity is significant because it is commonly used by malware and tools like Mimikatz to dump plain text credentials, indicating a potential credential dumping attempt. If confirmed malicious, this could allow an attacker to obtain sensitive credentials, leading to further compromise and lateral movement within the network. data_source: - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\System\\CurrentControlSet\\Control\\SecurityProviders\\WDigest\\*" - Registry.registry_value_name = "UseLogonCredential" Registry.registry_value_data=0x00000001) - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `enable_wdigest_uselogoncredential_registry_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\System\\CurrentControlSet\\Control\\SecurityProviders\\WDigest\\*" Registry.registry_value_name = "UseLogonCredential" Registry.registry_value_data=0x00000001) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `enable_wdigest_uselogoncredential_registry_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: No false positives have been identified at this time. references: - - https://www.csoonline.com/article/3438824/how-to-detect-and-halt-credential-theft-via-windows-wdigest.html + - https://www.csoonline.com/article/3438824/how-to-detect-and-halt-credential-theft-via-windows-wdigest.html drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: wdigest registry $registry_path$ was modified on $dest$ - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: [] + message: wdigest registry $registry_path$ was modified on $dest$ + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: [] tags: - analytic_story: - - Credential Dumping - - Windows Registry Abuse - - CISA AA22-320A - asset_type: Endpoint - mitre_attack_id: - - T1112 - - T1003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Credential Dumping + - Windows Registry Abuse + - CISA AA22-320A + asset_type: Endpoint + mitre_attack_id: + - T1112 + - T1003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/atomic_red_team/wdigest_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/atomic_red_team/wdigest_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/enumerate_users_local_group_using_telegram.yml b/detections/endpoint/enumerate_users_local_group_using_telegram.yml index 53fdf91c89..42d19636fa 100644 --- a/detections/endpoint/enumerate_users_local_group_using_telegram.yml +++ b/detections/endpoint/enumerate_users_local_group_using_telegram.yml @@ -5,73 +5,50 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk status: production type: TTP -description: - The following analytic detects a Telegram process enumerating all network - users in a local group. It leverages EventCode 4798, which is generated when a process - enumerates a user's security-enabled local groups on a computer or device. This - activity is significant as it may indicate an attempt to gather information on user - accounts, a common precursor to further malicious actions. If confirmed malicious, - this behavior could allow an attacker to map out user accounts, potentially leading - to privilege escalation or lateral movement within the network. +description: The following analytic detects a Telegram process enumerating all network users in a local group. It leverages EventCode 4798, which is generated when a process enumerates a user's security-enabled local groups on a computer or device. This activity is significant as it may indicate an attempt to gather information on user accounts, a common precursor to further malicious actions. If confirmed malicious, this behavior could allow an attacker to map out user accounts, potentially leading to privilege escalation or lateral movement within the network. data_source: - - Windows Event Log Security 4798 -search: - '`wineventlog_security` EventCode=4798 CallerProcessName = "*\\telegram.exe" - | stats count min(_time) as firstTime max(_time) as lastTime by user Computer EventCode - CallerProcessName ProcessID SubjectUserSid SubjectDomainName SubjectLogonId | - rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `enumerate_users_local_group_using_telegram_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs with the Task Schedule (Exa. Security Log EventCode 4798) endpoints. Tune and - filter known instances of process like logonUI used in your environment. + - Windows Event Log Security 4798 +search: '`wineventlog_security` EventCode=4798 CallerProcessName = "*\\telegram.exe" | stats count min(_time) as firstTime max(_time) as lastTime by user Computer EventCode CallerProcessName ProcessID SubjectUserSid SubjectDomainName SubjectLogonId | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `enumerate_users_local_group_using_telegram_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the Task Schedule (Exa. Security Log EventCode 4798) endpoints. Tune and filter known instances of process like logonUI used in your environment. known_false_positives: No false positives have been identified at this time. references: - - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ - - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4798 + - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4798 drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - The Telegram application has been identified enumerating local groups on - $dest$ by $user$. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: [] + message: The Telegram application has been identified enumerating local groups on $dest$ by $user$. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: [] tags: - analytic_story: - - XMRig - - Compromised Windows Host - - Water Gamayun - asset_type: Endpoint - mitre_attack_id: - - T1087 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - XMRig + - Compromised Windows Host + - Water Gamayun + asset_type: Endpoint + mitre_attack_id: + - T1087 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087/enumerate_users_local_group_using_telegram/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087/enumerate_users_local_group_using_telegram/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/esentutl_sam_copy.yml b/detections/endpoint/esentutl_sam_copy.yml index 06aff9e428..27ad1234a4 100644 --- a/detections/endpoint/esentutl_sam_copy.yml +++ b/detections/endpoint/esentutl_sam_copy.yml @@ -5,61 +5,46 @@ date: '2025-12-15' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic detects the use of `esentutl.exe` to access credentials - stored in the ntds.dit or SAM file. This detection leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process execution logs that include - command-line details. This activity is significant because it may indicate an attempt - to extract sensitive credential information, which is a common tactic in lateral - movement and privilege escalation. If confirmed malicious, this could allow an attacker - to gain unauthorized access to user credentials, potentially compromising the entire - network. +description: The following analytic detects the use of `esentutl.exe` to access credentials stored in the ntds.dit or SAM file. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because it may indicate an attempt to extract sensitive credential information, which is a common tactic in lateral movement and privilege escalation. If confirmed malicious, this could allow an attacker to gain unauthorized access to user credentials, potentially compromising the entire network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - (Processes.process_name=esentutl.exe OR Processes.original_file_name=esentutl.exe) - Processes.process IN ("*ntds*", "*SAM*") - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `esentutl_sam_copy_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where + (Processes.process_name=esentutl.exe OR Processes.original_file_name=esentutl.exe) + Processes.process IN ("*ntds*", "*SAM*") + by Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `esentutl_sam_copy_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives should be limited. Filter as needed. references: -- https://github.com/redcanaryco/atomic-red-team/blob/6a570c2a4630cf0c2bd41a2e8375b5d5ab92f700/atomics/T1003.002/T1003.002.md -- https://attack.mitre.org/software/S0404/ + - https://github.com/redcanaryco/atomic-red-team/blob/6a570c2a4630cf0c2bd41a2e8375b5d5ab92f700/atomics/T1003.002/T1003.002.md + - https://attack.mitre.org/software/S0404/ tags: - analytic_story: - - Credential Dumping - - Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1003.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Credential Dumping + - Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1003.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/etw_registry_disabled.yml b/detections/endpoint/etw_registry_disabled.yml index f5a533be45..172b98ac21 100644 --- a/detections/endpoint/etw_registry_disabled.yml +++ b/detections/endpoint/etw_registry_disabled.yml @@ -5,76 +5,54 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: The following analytic detects a registry modification that disables - the ETW for the .NET Framework. It leverages data from the Endpoint.Registry data - model, specifically monitoring changes to the ETWEnabled registry value under the - .NETFramework path. This activity is significant because disabling ETW can allow - attackers to evade Endpoint Detection and Response (EDR) tools and hide their execution - from audit logs. If confirmed malicious, this action could enable attackers to operate - undetected, potentially leading to further compromise and persistent access within - the environment. +description: The following analytic detects a registry modification that disables the ETW for the .NET Framework. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the ETWEnabled registry value under the .NETFramework path. This activity is significant because disabling ETW can allow attackers to evade Endpoint Detection and Response (EDR) tools and hide their execution from audit logs. If confirmed malicious, this action could enable attackers to operate undetected, potentially leading to further compromise and persistent access within the environment. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\SOFTWARE\\Microsoft\\.NETFramework*" - Registry.registry_value_name = ETWEnabled Registry.registry_value_data=0x00000000) - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `etw_registry_disabled_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\SOFTWARE\\Microsoft\\.NETFramework*" Registry.registry_value_name = ETWEnabled Registry.registry_value_data=0x00000000) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `etw_registry_disabled_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: No false positives have been identified at this time. references: -- https://gist.github.com/Cyb3rWard0g/a4a115fd3ab518a0e593525a379adee3 -- https://blog.xpnsec.com/hiding-your-dotnet-complus-etwenabled/ + - https://gist.github.com/Cyb3rWard0g/a4a115fd3ab518a0e593525a379adee3 + - https://blog.xpnsec.com/hiding-your-dotnet-complus-etwenabled/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Modified/added/deleted registry entry $registry_path$ on $dest$ - risk_objects: - - field: dest - type: system - score: 90 - - field: user - type: user - score: 90 - threat_objects: [] + message: Modified/added/deleted registry entry $registry_path$ on $dest$ + risk_objects: + - field: dest + type: system + score: 90 + - field: user + type: user + score: 90 + threat_objects: [] tags: - analytic_story: - - Hermetic Wiper - - Windows Persistence Techniques - - Windows Privilege Escalation - - Windows Registry Abuse - - CISA AA23-347A - - Data Destruction - asset_type: Endpoint - mitre_attack_id: - - T1127 - - T1562.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Hermetic Wiper + - Windows Persistence Techniques + - Windows Privilege Escalation + - Windows Registry Abuse + - CISA AA23-347A + - Data Destruction + asset_type: Endpoint + mitre_attack_id: + - T1127 + - T1562.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/etw_disable/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/etw_disable/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/eventvwr_uac_bypass.yml b/detections/endpoint/eventvwr_uac_bypass.yml index 2d6def34c6..d87f1c88cc 100644 --- a/detections/endpoint/eventvwr_uac_bypass.yml +++ b/detections/endpoint/eventvwr_uac_bypass.yml @@ -5,81 +5,54 @@ date: '2025-05-02' author: Steven Dick, Michael Haag, Splunk status: production type: TTP -description: The following analytic detects an Eventvwr UAC bypass by identifying - suspicious registry modifications in the path that Eventvwr.msc references upon - execution. This detection leverages data from Endpoint Detection and Response (EDR) - agents, focusing on registry changes and process execution details. This activity - is significant because it indicates a potential privilege escalation attempt, allowing - an attacker to execute arbitrary commands with elevated privileges. If confirmed - malicious, this could lead to unauthorized code execution, persistence, and further - compromise of the affected system. +description: The following analytic detects an Eventvwr UAC bypass by identifying suspicious registry modifications in the path that Eventvwr.msc references upon execution. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on registry changes and process execution details. This activity is significant because it indicates a potential privilege escalation attempt, allowing an attacker to execute arbitrary commands with elevated privileges. If confirmed malicious, this could lead to unauthorized code execution, persistence, and further compromise of the affected system. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry - WHERE (Registry.registry_path="*mscfile\\shell\\open\\command\\*") by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `eventvwr_uac_bypass_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*mscfile\\shell\\open\\command\\*") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `eventvwr_uac_bypass_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Some false positives may be present and will need to be filtered. references: -- https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1548.002/T1548.002.md -- https://attack.mitre.org/techniques/T1548/002/ -- https://enigma0x3.net/2016/08/15/fileless-uac-bypass-using-eventvwr-exe-and-registry-hijacking/ + - https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1548.002/T1548.002.md + - https://attack.mitre.org/techniques/T1548/002/ + - https://enigma0x3.net/2016/08/15/fileless-uac-bypass-using-eventvwr-exe-and-registry-hijacking/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Registry values were modified to bypass UAC using Event Viewer on $dest$ - by $user$. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: [] + message: Registry values were modified to bypass UAC using Event Viewer on $dest$ by $user$. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - IcedID - - Living Off The Land - - Windows Registry Abuse - - ValleyRAT - asset_type: Endpoint - mitre_attack_id: - - T1548.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - IcedID + - Living Off The Land + - Windows Registry Abuse + - ValleyRAT + asset_type: Endpoint + mitre_attack_id: + - T1548.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/excessive_attempt_to_disable_services.yml b/detections/endpoint/excessive_attempt_to_disable_services.yml index 07dcf4c13f..468235f24d 100644 --- a/detections/endpoint/excessive_attempt_to_disable_services.yml +++ b/detections/endpoint/excessive_attempt_to_disable_services.yml @@ -1,88 +1,66 @@ name: Excessive Attempt To Disable Services id: 8fa2a0f0-acd9-11eb-8994-acde48001122 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies a suspicious series of command-line - executions attempting to disable multiple services. It leverages data from Endpoint - Detection and Response (EDR) agents, focusing on processes where "sc.exe" is used - with parameters like "config" or "Disabled" within a short time frame. This activity - is significant as it may indicate an adversary's attempt to disable security or - other critical services to further compromise the system. If confirmed malicious, - this could lead to the attacker achieving persistence, evading detection, or disabling - security mechanisms, thereby increasing the risk of further exploitation. +description: The following analytic identifies a suspicious series of command-line executions attempting to disable multiple services. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on processes where "sc.exe" is used with parameters like "config" or "Disabled" within a short time frame. This activity is significant as it may indicate an adversary's attempt to disable security or other critical services to further compromise the system. If confirmed malicious, this could lead to the attacker achieving persistence, evading detection, or disabling security mechanisms, thereby increasing the risk of further exploitation. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.action) as action - values(Processes.original_file_name) as original_file_name values(Processes.parent_process) - as parent_process values(Processes.parent_process_exec) as parent_process_exec values(Processes.parent_process_guid) - as parent_process_guid values(Processes.parent_process_id) as parent_process_id - values(Processes.parent_process_path) as parent_process_path values(Processes.process) - as process values(Processes.process_exec) as process_exec values(Processes.process_guid) - as process_guid values(Processes.process_hash) as process_hash values(Processes.process_id) - as process_id values(Processes.process_integrity_level) as process_integrity_level - values(Processes.process_path) as process_path values(Processes.user_id) as user_id - values(Processes.vendor_product) as vendor_product count min(_time) as firstTime - max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name - = "sc.exe" AND Processes.process="*config*" OR Processes.process="*Disabled*" by - Processes.process_name Processes.parent_process_name Processes.dest Processes.user - _time span=1m | where count >=4 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `excessive_attempt_to_disable_services_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.action) as action values(Processes.original_file_name) as original_file_name values(Processes.parent_process) as parent_process values(Processes.parent_process_exec) as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid values(Processes.parent_process_id) as parent_process_id values(Processes.parent_process_path) as parent_process_path values(Processes.process) as process values(Processes.process_exec) as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) as process_integrity_level values(Processes.process_path) as process_path values(Processes.user_id) as user_id values(Processes.vendor_product) as vendor_product count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "sc.exe" + AND + Processes.process="*config*" + OR + Processes.process="*Disabled*" + BY Processes.process_name Processes.parent_process_name Processes.dest + Processes.user _time span=1m + | where count >=4 + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `excessive_attempt_to_disable_services_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ + - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An excessive amount of $process_name$ was executed on $dest$ attempting - to disable services. - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: - - field: process_name - type: process_name + message: An excessive amount of $process_name$ was executed on $dest$ attempting to disable services. + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - XMRig - - Azorult - asset_type: Endpoint - mitre_attack_id: - - T1489 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - XMRig + - Azorult + asset_type: Endpoint + mitre_attack_id: + - T1489 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/excessive_distinct_processes_from_windows_temp.yml b/detections/endpoint/excessive_distinct_processes_from_windows_temp.yml index afe7845af4..9522b3a449 100644 --- a/detections/endpoint/excessive_distinct_processes_from_windows_temp.yml +++ b/detections/endpoint/excessive_distinct_processes_from_windows_temp.yml @@ -5,82 +5,46 @@ date: '2025-05-02' author: Michael Hart, Mauricio Velazco, Splunk status: production type: Anomaly -description: The following analytic identifies an excessive number of distinct processes - executing from the Windows\Temp directory. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process paths and counts within a 20-minute - window. This behavior is significant as it often indicates the presence of post-exploit - frameworks like Koadic and Meterpreter, which use this technique to execute malicious - actions. If confirmed malicious, this activity could allow attackers to execute - arbitrary code, escalate privileges, and maintain persistence within the environment, - posing a severe threat to system integrity and security. +description: The following analytic identifies an excessive number of distinct processes executing from the Windows\Temp directory. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process paths and counts within a 20-minute window. This behavior is significant as it often indicates the presence of post-exploit frameworks like Koadic and Meterpreter, which use this technique to execute malicious actions. If confirmed malicious, this activity could allow attackers to execute arbitrary code, escalate privileges, and maintain persistence within the environment, posing a severe threat to system integrity and security. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` distinct_count(Processes.process) - as distinct_process_count min(_time) as firstTime max(_time) as lastTime values(Processes.action) - as action values(Processes.original_file_name) as original_file_name values(Processes.parent_process) - as parent_process values(Processes.parent_process_exec) as parent_process_exec values(Processes.parent_process_guid) - as parent_process_guid values(Processes.parent_process_id) as parent_process_id - values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_path) - as parent_process_path values(Processes.process) as process values(Processes.process_exec) - as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) - as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) - as process_integrity_level values(Processes.process_name) as process_name values(Processes.process_path) - as process_path values(Processes.user_id) as user_id values(Processes.vendor_product) - as vendor_product from datamodel=Endpoint.Processes where Processes.process_path - = "*\\Windows\\Temp\\*" by Processes.dest Processes.user _time span=20m | where - distinct_process_count > 37 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `excessive_distinct_processes_from_windows_temp_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Many benign applications will create processes from executables - in Windows\Temp, although unlikely to exceed the given threshold. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` distinct_count(Processes.process) as distinct_process_count min(_time) as firstTime max(_time) as lastTime values(Processes.action) as action values(Processes.original_file_name) as original_file_name values(Processes.parent_process) as parent_process values(Processes.parent_process_exec) as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid values(Processes.parent_process_id) as parent_process_id values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_path) as parent_process_path values(Processes.process) as process values(Processes.process_exec) as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) as process_integrity_level values(Processes.process_name) as process_name values(Processes.process_path) as process_path values(Processes.user_id) as user_id values(Processes.vendor_product) as vendor_product from datamodel=Endpoint.Processes where Processes.process_path = "*\\Windows\\Temp\\*" by Processes.dest Processes.user _time span=20m | where distinct_process_count > 37 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_distinct_processes_from_windows_temp_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Many benign applications will create processes from executables in Windows\Temp, although unlikely to exceed the given threshold. Filter as needed. references: -- https://www.offensive-security.com/metasploit-unleashed/about-meterpreter/ + - https://www.offensive-security.com/metasploit-unleashed/about-meterpreter/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Multiple processes were executed out of windows\temp within a short amount - of time on $dest$. - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: [] + message: Multiple processes were executed out of windows\temp within a short amount of time on $dest$. + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: [] tags: - analytic_story: - - Meterpreter - asset_type: Endpoint - mitre_attack_id: - - T1059 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Meterpreter + asset_type: Endpoint + mitre_attack_id: + - T1059 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/excessive_distinct_processes_from_windows_temp/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/excessive_distinct_processes_from_windows_temp/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/excessive_file_deletion_in_windefender_folder.yml b/detections/endpoint/excessive_file_deletion_in_windefender_folder.yml index 8dc2536976..41b972431f 100644 --- a/detections/endpoint/excessive_file_deletion_in_windefender_folder.yml +++ b/detections/endpoint/excessive_file_deletion_in_windefender_folder.yml @@ -5,74 +5,52 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: The following analytic detects excessive file deletion events in the - Windows Defender folder. It leverages Sysmon EventCodes 23 and 26 to identify processes - deleting multiple files within this directory. This behavior is significant as it - may indicate an attempt to corrupt or disable Windows Defender, a key security component. - If confirmed malicious, this activity could allow an attacker to disable endpoint - protection, facilitating further malicious actions without detection. +description: The following analytic detects excessive file deletion events in the Windows Defender folder. It leverages Sysmon EventCodes 23 and 26 to identify processes deleting multiple files within this directory. This behavior is significant as it may indicate an attempt to corrupt or disable Windows Defender, a key security component. If confirmed malicious, this activity could allow an attacker to disable endpoint protection, facilitating further malicious actions without detection. data_source: -- Sysmon EventID 23 -- Sysmon EventID 26 -search: '`sysmon` EventCode IN ("23","26") TargetFilename = "*\\ProgramData\\Microsoft\\Windows - Defender\\*" | stats count min(_time) as firstTime, max(_time) as lastTime values(file_path) - as file_path values(file_hash) as file_hash values(file_name) as file_name values(file_modify_time) - as file_modify_time values(process_name) as process_name values(process_path) as - process_path values(process_guid) as process_guid values(process_id) as process_id - values(process_exec) as process_exec by action dest dvc signature signature_id user - user_id vendor_product | where count >=50 | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `excessive_file_deletion_in_windefender_folder_filter`' -how_to_implement: To successfully implement this search, you must ingest logs that - include the process name, TargetFilename, and ProcessID executions from your endpoints. - If you are utilizing Sysmon, ensure you have at least version 2.0 of the Sysmon - TA installed. -known_false_positives: Windows Defender AV updates may trigger this alert. Please - adjust the filter macros to mitigate false positives. + - Sysmon EventID 23 + - Sysmon EventID 26 +search: '`sysmon` EventCode IN ("23","26") TargetFilename = "*\\ProgramData\\Microsoft\\Windows Defender\\*" | stats count min(_time) as firstTime, max(_time) as lastTime values(file_path) as file_path values(file_hash) as file_hash values(file_name) as file_name values(file_modify_time) as file_modify_time values(process_name) as process_name values(process_path) as process_path values(process_guid) as process_guid values(process_id) as process_id values(process_exec) as process_exec by action dest dvc signature signature_id user user_id vendor_product | where count >=50 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_file_deletion_in_windefender_folder_filter`' +how_to_implement: To successfully implement this search, you must ingest logs that include the process name, TargetFilename, and ProcessID executions from your endpoints. If you are utilizing Sysmon, ensure you have at least version 2.0 of the Sysmon TA installed. +known_false_positives: Windows Defender AV updates may trigger this alert. Please adjust the filter macros to mitigate false positives. references: -- https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Excessive file deletion events were detected in the Windows Defender folder - on $dest$ by $user$. Investigate further to determine if this activity is malicious. - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: - - field: file_name - type: file_name + message: Excessive file deletion events were detected in the Windows Defender folder on $dest$ by $user$. Investigate further to determine if this activity is malicious. + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - Data Destruction - - WhisperGate - - BlackByte Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Destruction + - WhisperGate + - BlackByte Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/excessive_file_del_in_windefender_dir/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/excessive_file_del_in_windefender_dir/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/excessive_number_of_service_control_start_as_disabled.yml b/detections/endpoint/excessive_number_of_service_control_start_as_disabled.yml index 662a3189f0..ff5d6826bb 100644 --- a/detections/endpoint/excessive_number_of_service_control_start_as_disabled.yml +++ b/detections/endpoint/excessive_number_of_service_control_start_as_disabled.yml @@ -1,91 +1,65 @@ name: Excessive number of service control start as disabled id: 77592bec-d5cc-11eb-9e60-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Hart, Splunk status: production type: Anomaly -description: The following analytic detects an excessive number of `sc.exe` processes - launched with the command line argument `start= disabled` within a short period. - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on - process names, command-line executions, and process GUIDs. This activity is significant - as it may indicate an attempt to disable critical services, potentially impairing - system defenses. If confirmed malicious, this behavior could allow an attacker to - disrupt security mechanisms, hinder incident response, and maintain control over - the compromised system. +description: The following analytic detects an excessive number of `sc.exe` processes launched with the command line argument `start= disabled` within a short period. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, command-line executions, and process GUIDs. This activity is significant as it may indicate an attempt to disable critical services, potentially impairing system defenses. If confirmed malicious, this behavior could allow an attacker to disrupt security mechanisms, hinder incident response, and maintain control over the compromised system. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` distinct_count(Processes.process) - as distinct_cmdlines values(Processes.action) as action values(Processes.original_file_name) - as original_file_name values(Processes.parent_process_exec) as parent_process_exec - values(Processes.parent_process_guid) as parent_process_guid values(Processes.parent_process_name) - as parent_process_name values(Processes.parent_process_path) as parent_process_path - values(Processes.process) as process values(Processes.process_exec) as process_exec - values(Processes.process_guid) as process_guid values(Processes.process_hash) as - process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) - as process_integrity_level values(Processes.process_path) as process_path values(Processes.user_id) - as user_id values(Processes.vendor_product) as vendor_product min(_time) as firstTime - max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process_name - = "sc.exe" AND Processes.process="*start= disabled*" by Processes.dest Processes.user - Processes.parent_process Processes.process_name Processes.parent_process_id, _time - span=30m | where distinct_cmdlines >= 8 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `excessive_number_of_service_control_start_as_disabled_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Legitimate programs and administrators will execute sc.exe - with the start disabled flag. It is possible, but unlikely from the telemetry of - normal Windows operation we observed, that sc.exe will be called more than seven - times in a short period of time. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` distinct_count(Processes.process) as distinct_cmdlines values(Processes.action) as action values(Processes.original_file_name) as original_file_name values(Processes.parent_process_exec) as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_path) as parent_process_path values(Processes.process) as process values(Processes.process_exec) as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) as process_integrity_level values(Processes.process_path) as process_path values(Processes.user_id) as user_id values(Processes.vendor_product) as vendor_product min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "sc.exe" + AND + Processes.process="*start= disabled*" + BY Processes.dest Processes.user Processes.parent_process + Processes.process_name Processes.parent_process_id, _time + span=30m + | where distinct_cmdlines >= 8 + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `excessive_number_of_service_control_start_as_disabled_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Legitimate programs and administrators will execute sc.exe with the start disabled flag. It is possible, but unlikely from the telemetry of normal Windows operation we observed, that sc.exe will be called more than seven times in a short period of time. references: -- https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/sc-create -- https://attack.mitre.org/techniques/T1562/001/ + - https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/sc-create + - https://attack.mitre.org/techniques/T1562/001/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An excessive amount of $process_name$ was executed on $dest$ attempting - to disable services. - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: - - field: process_name - type: process_name + message: An excessive amount of $process_name$ was executed on $dest$ attempting to disable services. + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Windows Defense Evasion Tactics - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/sc_service_start_disabled/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/sc_service_start_disabled/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/excessive_number_of_taskhost_processes.yml b/detections/endpoint/excessive_number_of_taskhost_processes.yml index ae23d14f40..05c4b23df5 100644 --- a/detections/endpoint/excessive_number_of_taskhost_processes.yml +++ b/detections/endpoint/excessive_number_of_taskhost_processes.yml @@ -1,104 +1,67 @@ name: Excessive number of taskhost processes id: f443dac2-c7cf-11eb-ab51-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Hart status: production type: Anomaly -description: - The following analytic identifies an excessive number of taskhost.exe - and taskhostex.exe processes running within a short time frame. It leverages data - from Endpoint Detection and Response (EDR) agents, focusing on process names and - their counts. This behavior is significant as it is commonly associated with post-exploitation - tools like Meterpreter and Koadic, which use multiple instances of these processes - for actions such as discovery and lateral movement. If confirmed malicious, this - activity could indicate an ongoing attack, allowing attackers to execute code, escalate - privileges, or move laterally within the network. +description: The following analytic identifies an excessive number of taskhost.exe and taskhostex.exe processes running within a short time frame. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and their counts. This behavior is significant as it is commonly associated with post-exploitation tools like Meterpreter and Koadic, which use multiple instances of these processes for actions such as discovery and lateral movement. If confirmed malicious, this activity could indicate an ongoing attack, allowing attackers to execute code, escalate privileges, or move laterally within the network. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 -search: - '| tstats `security_content_summariesonly` values(Processes.action) as action - values(Processes.original_file_name) as original_file_name values(Processes.parent_process) - as parent_process values(Processes.parent_process_exec) as parent_process_exec values(Processes.parent_process_guid) - as parent_process_guid values(Processes.parent_process_id) as parent_process_id - values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_path) - as parent_process_path values(Processes.process) as process values(Processes.process_exec) - as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) - as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) - as process_integrity_level values(Processes.user) as user values(Processes.process_path) - as process_path values(Processes.user_id) as user_id values(Processes.vendor_product) - as vendor_product min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes - WHERE Processes.process_name = "taskhost.exe" OR Processes.process_name = "taskhostex.exe" - BY Processes.dest Processes.process_name _time span=1h | `drop_dm_object_name(Processes)` - | eval pid_count=mvcount(process_id) | eval taskhost_count_=if(process_name == "taskhost.exe", - pid_count, 0) | eval taskhostex_count_=if(process_name == "taskhostex.exe", pid_count, - 0) | stats sum(taskhost_count_) as taskhost_count, sum(taskhostex_count_) as taskhostex_count - values(action) as action values(original_file_name) as original_file_name values(parent_process) - as parent_process values(parent_process_exec) as parent_process_exec values(parent_process_guid) - as parent_process_guid values(parent_process_id) as parent_process_id values(parent_process_name) - as parent_process_name values(parent_process_path) as parent_process_path values(process) - as process values(process_exec) as process_exec values(process_guid) as process_guid - values(process_hash) as process_hash values(process_id) as process_id values(process_integrity_level) - as process_integrity_level values(user) as user values(process_path) as process_path - values(user_id) as user_id values(vendor_product) as vendor_product values(process_name) - as process_name by _time, dest, firstTime, lastTime | where taskhost_count > - 10 or taskhostex_count > 10 | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `excessive_number_of_taskhost_processes_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: - Administrators, administrative actions or certain applications - may run many instances of taskhost and taskhostex concurrently. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.action) as action values(Processes.original_file_name) as original_file_name values(Processes.parent_process) as parent_process values(Processes.parent_process_exec) as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid values(Processes.parent_process_id) as parent_process_id values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_path) as parent_process_path values(Processes.process) as process values(Processes.process_exec) as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) as process_integrity_level values(Processes.user) as user values(Processes.process_path) as process_path values(Processes.user_id) as user_id values(Processes.vendor_product) as vendor_product min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "taskhost.exe" + OR + Processes.process_name = "taskhostex.exe" + BY Processes.dest Processes.process_name _time + span=1h + | `drop_dm_object_name(Processes)` + | eval pid_count=mvcount(process_id) + | eval taskhost_count_=if(process_name == "taskhost.exe", pid_count, 0) + | eval taskhostex_count_=if(process_name == "taskhostex.exe", pid_count, 0) + | stats sum(taskhost_count_) as taskhost_count, sum(taskhostex_count_) as taskhostex_count values(action) as action values(original_file_name) as original_file_name values(parent_process) as parent_process values(parent_process_exec) as parent_process_exec values(parent_process_guid) as parent_process_guid values(parent_process_id) as parent_process_id values(parent_process_name) as parent_process_name values(parent_process_path) as parent_process_path values(process) as process values(process_exec) as process_exec values(process_guid) as process_guid values(process_hash) as process_hash values(process_id) as process_id values(process_integrity_level) as process_integrity_level values(user) as user values(process_path) as process_path values(user_id) as user_id values(vendor_product) as vendor_product values(process_name) as process_name + BY _time, dest, firstTime, + lastTime + | where taskhost_count > 10 or taskhostex_count > 10 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `excessive_number_of_taskhost_processes_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators, administrative actions or certain applications may run many instances of taskhost and taskhostex concurrently. Filter as needed. references: - - https://attack.mitre.org/software/S0250/ + - https://attack.mitre.org/software/S0250/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - An excessive amount of taskhost.exe and taskhostex.exe was executed on - $dest$ indicative of suspicious behavior. - risk_objects: - - field: dest - type: system - score: 56 - threat_objects: [] + message: An excessive amount of taskhost.exe and taskhostex.exe was executed on $dest$ indicative of suspicious behavior. + risk_objects: + - field: dest + type: system + score: 56 + threat_objects: [] tags: - analytic_story: - - Meterpreter - asset_type: Endpoint - mitre_attack_id: - - T1059 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Meterpreter + asset_type: Endpoint + mitre_attack_id: + - T1059 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/excessive_distinct_processes_from_windows_temp/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/excessive_distinct_processes_from_windows_temp/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/excessive_usage_of_cacls_app.yml b/detections/endpoint/excessive_usage_of_cacls_app.yml index 7e62aaafdd..ef7b8bfde1 100644 --- a/detections/endpoint/excessive_usage_of_cacls_app.yml +++ b/detections/endpoint/excessive_usage_of_cacls_app.yml @@ -1,111 +1,104 @@ name: Excessive Usage Of Cacls App id: 0bdf6092-af17-11eb-939a-acde48001122 -version: 9 -date: '2025-06-17' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: | - The following analytic identifies excessive usage of `cacls.exe`, `xcacls.exe`, - or `icacls.exe` to change file or folder permissions. - It looks for 10 or more execution of the aforementioned processes in the span of 1 minute. - It leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process names and command-line executions. - This activity is significant as it may indicate an adversary attempting - to restrict access to malware components or artifacts on a compromised system. - If confirmed malicious, this behavior could prevent users from deleting or accessing - critical files, aiding in the persistence and concealment of malicious activities. + The following analytic identifies excessive usage of `cacls.exe`, `xcacls.exe`, + or `icacls.exe` to change file or folder permissions. + It looks for 10 or more execution of the aforementioned processes in the span of 1 minute. + It leverages data from Endpoint Detection and Response (EDR) agents, + focusing on process names and command-line executions. + This activity is significant as it may indicate an adversary attempting + to restrict access to malware components or artifacts on a compromised system. + If confirmed malicious, this behavior could prevent users from deleting or accessing + critical files, aiding in the persistence and concealment of malicious activities. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` - min(_time) as firstTime - max(_time) as lastTime - values(Processes.dest) as dest - values(Processes.user) as user - values(Processes.action) as action - values(Processes.original_file_name) as original_file_name - values(Processes.parent_process_exec) as parent_process_exec - values(Processes.parent_process_guid) as parent_process_guid - values(Processes.parent_process_id) as parent_process_id - values(Processes.parent_process_path) as parent_process_path - values(Processes.process) as process - values(Processes.process_exec) as process_exec - values(Processes.process_guid) as process_guid - values(Processes.process_hash) as process_hash - values(Processes.process_id) as process_id - values(Processes.process_integrity_level) as process_integrity_level - values(Processes.process_name) as process_name - values(Processes.process_path) as process_path - values(Processes.user_id) as user_id - values(Processes.vendor_product) as vendor_product count - from datamodel=Endpoint.Processes where - Processes.process_name IN( "icacls.exe", "cacls.exe", "xcacls.exe") - by Processes.parent_process_name Processes.parent_process Processes.dest Processes.user _time span=1m - | where count >=10 - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `excessive_usage_of_cacls_app_filter` + | tstats `security_content_summariesonly` + min(_time) as firstTime + max(_time) as lastTime + values(Processes.dest) as dest + values(Processes.user) as user + values(Processes.action) as action + values(Processes.original_file_name) as original_file_name + values(Processes.parent_process_exec) as parent_process_exec + values(Processes.parent_process_guid) as parent_process_guid + values(Processes.parent_process_id) as parent_process_id + values(Processes.parent_process_path) as parent_process_path + values(Processes.process) as process + values(Processes.process_exec) as process_exec + values(Processes.process_guid) as process_guid + values(Processes.process_hash) as process_hash + values(Processes.process_id) as process_id + values(Processes.process_integrity_level) as process_integrity_level + values(Processes.process_name) as process_name + values(Processes.process_path) as process_path + values(Processes.user_id) as user_id + values(Processes.vendor_product) as vendor_product count + from datamodel=Endpoint.Processes where + Processes.process_name IN( "icacls.exe", "cacls.exe", "xcacls.exe") + by Processes.parent_process_name Processes.parent_process Processes.dest Processes.user _time span=1m + | where count >=10 + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `excessive_usage_of_cacls_app_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrators or administrative scripts may use this application. - Filter as needed. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. +known_false_positives: Administrators or administrative scripts may use this application. Filter as needed. references: -- https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ + - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An excessive amount of $process_name$ was executed on $dest$ attempting - to modify permissions. - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: - - field: process_name - type: process_name + message: An excessive amount of $process_name$ was executed on $dest$ attempting to modify permissions. + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Azorult - - Windows Post-Exploitation - - Prestige Ransomware - - XMRig - - Crypto Stealer - - Defense Evasion or Unauthorized Access Via SDDL Tampering - asset_type: Endpoint - mitre_attack_id: - - T1222 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azorult + - Windows Post-Exploitation + - Prestige Ransomware + - XMRig + - Crypto Stealer + - Defense Evasion or Unauthorized Access Via SDDL Tampering + asset_type: Endpoint + mitre_attack_id: + - T1222 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/excessive_usage_of_nslookup_app.yml b/detections/endpoint/excessive_usage_of_nslookup_app.yml index 0eb4517db1..0131bc9eda 100644 --- a/detections/endpoint/excessive_usage_of_nslookup_app.yml +++ b/detections/endpoint/excessive_usage_of_nslookup_app.yml @@ -1,88 +1,67 @@ name: Excessive Usage of NSLOOKUP App id: 0a69fdaa-a2b8-11eb-b16d-acde48001122 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Stanislav Miskovic, Splunk status: production type: Anomaly -description: The following analytic detects excessive usage of the nslookup application, - which may indicate potential DNS exfiltration attempts. It leverages Sysmon EventCode - 1 to monitor process executions, specifically focusing on nslookup.exe. The detection - identifies outliers by comparing the frequency of nslookup executions against a - calculated threshold. This activity is significant as it can reveal attempts by - malware or APT groups to exfiltrate data via DNS queries. If confirmed malicious, - this behavior could allow attackers to stealthily transfer sensitive information - out of the network, bypassing traditional data exfiltration defenses. +description: The following analytic detects excessive usage of the nslookup application, which may indicate potential DNS exfiltration attempts. It leverages Sysmon EventCode 1 to monitor process executions, specifically focusing on nslookup.exe. The detection identifies outliers by comparing the frequency of nslookup executions against a calculated threshold. This activity is significant as it can reveal attempts by malware or APT groups to exfiltrate data via DNS queries. If confirmed malicious, this behavior could allow attackers to stealthily transfer sensitive information out of the network, bypassing traditional data exfiltration defenses. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count as numNsLookup min(_time) - as firstTime max(_time) as lastTime values(Processes.action) as action values(Processes.original_file_name) - as original_file_name values(Processes.parent_process_exec) as parent_process_exec - values(Processes.parent_process_guid) as parent_process_guid values(Processes.parent_process_name) - as parent_process_name values(Processes.parent_process_path) as parent_process_path - values(Processes.process) as process values(Processes.process_exec) as process_exec - values(Processes.process_guid) as process_guid values(Processes.process_hash) as - process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) - as process_integrity_level values(Processes.process_path) as process_path values(Processes.user_id) - as user_id values(Processes.vendor_product) as vendor_product values(Processes.parent_process) - as parent_process values(Processes.process_name) as process_name values(Processes.parent_process_id) - as parent_process_id values(Processes.user) as user from datamodel=Endpoint.Processes - where Processes.process_name = "nslookup.exe" by Processes.dest _time span=1m | - `drop_dm_object_name(Processes)` | eventstats avg(numNsLookup) as avgNsLookup, stdev(numNsLookup) - as stdNsLookup, count as numSlots by dest | eval upperThreshold=(avgNsLookup + stdNsLookup - *3) | eval isOutlier=if(numNsLookup > 20 and numNsLookup >= upperThreshold, 1, 0) - | search isOutlier=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `excessive_usage_of_nslookup_app_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. Tune and filter known instances of nslookup.exe may be used. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count as numNsLookup min(_time) as firstTime max(_time) as lastTime values(Processes.action) as action values(Processes.original_file_name) as original_file_name values(Processes.parent_process_exec) as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_path) as parent_process_path values(Processes.process) as process values(Processes.process_exec) as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) as process_integrity_level values(Processes.process_path) as process_path values(Processes.user_id) as user_id values(Processes.vendor_product) as vendor_product values(Processes.parent_process) as parent_process values(Processes.process_name) as process_name values(Processes.parent_process_id) as parent_process_id values(Processes.user) as user FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "nslookup.exe" + BY Processes.dest _time span=1m + | `drop_dm_object_name(Processes)` + | eventstats avg(numNsLookup) as avgNsLookup, stdev(numNsLookup) as stdNsLookup, count as numSlots + BY dest + | eval upperThreshold=(avgNsLookup + stdNsLookup *3) + | eval isOutlier=if(numNsLookup > 20 and numNsLookup >= upperThreshold, 1, 0) + | search isOutlier=1 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `excessive_usage_of_nslookup_app_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances of nslookup.exe may be used. known_false_positives: No false positives have been identified at this time. references: -- https://www.mandiant.com/resources/fin7-spear-phishing-campaign-targets-personnel-involved-sec-filings -- https://www.varonis.com/blog/dns-tunneling -- https://www.microsoft.com/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/ + - https://www.mandiant.com/resources/fin7-spear-phishing-campaign-targets-personnel-involved-sec-filings + - https://www.varonis.com/blog/dns-tunneling + - https://www.microsoft.com/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Excessive usage of nslookup.exe has been detected on $dest$. This detection - is triggered as as it violates the dynamic threshold - risk_objects: - - field: dest - type: system - score: 28 - threat_objects: [] + message: Excessive usage of nslookup.exe has been detected on $dest$. This detection is triggered as as it violates the dynamic threshold + risk_objects: + - field: dest + type: system + score: 28 + threat_objects: [] tags: - analytic_story: - - Suspicious DNS Traffic - - Dynamic DNS - - Data Exfiltration - - Command And Control - asset_type: Endpoint - mitre_attack_id: - - T1048 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious DNS Traffic + - Dynamic DNS + - Data Exfiltration + - Command And Control + asset_type: Endpoint + mitre_attack_id: + - T1048 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/nslookup_exfil/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/nslookup_exfil/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/excessive_usage_of_sc_service_utility.yml b/detections/endpoint/excessive_usage_of_sc_service_utility.yml index 1b61887707..506c25e559 100644 --- a/detections/endpoint/excessive_usage_of_sc_service_utility.yml +++ b/detections/endpoint/excessive_usage_of_sc_service_utility.yml @@ -1,82 +1,62 @@ name: Excessive Usage Of SC Service Utility id: cb6b339e-d4c6-11eb-a026-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects excessive usage of the `sc.exe` service - utility on a host machine. It leverages Sysmon EventCode 1 logs to identify instances - where `sc.exe` is executed more frequently than normal within a 15-minute window. - This behavior is significant as it is commonly associated with ransomware, cryptocurrency - miners, and other malware attempting to create, modify, delete, or disable services, - potentially related to security applications or for privilege escalation. If confirmed - malicious, this activity could allow attackers to manipulate critical services, - leading to system compromise or disruption of security defenses. +description: The following analytic detects excessive usage of the `sc.exe` service utility on a host machine. It leverages Sysmon EventCode 1 logs to identify instances where `sc.exe` is executed more frequently than normal within a 15-minute window. This behavior is significant as it is commonly associated with ransomware, cryptocurrency miners, and other malware attempting to create, modify, delete, or disable services, potentially related to security applications or for privilege escalation. If confirmed malicious, this activity could allow attackers to manipulate critical services, leading to system compromise or disruption of security defenses. data_source: -- Sysmon EventID 1 -search: '| tstats `security_content_summariesonly` count as numScExe min(_time) as - firstTime max(_time) as lastTime values(Processes.action) as action values(Processes.original_file_name) - as original_file_name values(Processes.parent_process_exec) as parent_process_exec - values(Processes.parent_process_guid) as parent_process_guid values(Processes.parent_process_name) - as parent_process_name values(Processes.parent_process_path) as parent_process_path - values(Processes.process) as process values(Processes.process_exec) as process_exec - values(Processes.process_guid) as process_guid values(Processes.process_hash) as - process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) - as process_integrity_level values(Processes.process_path) as process_path values(Processes.user_id) - as user_id values(Processes.vendor_product) as vendor_product values(Processes.parent_process) - as parent_process values(Processes.process_name) as process_name values(Processes.parent_process_id) - as parent_process_id values(Processes.user) as user from datamodel=Endpoint.Processes - where Processes.process_name = "sc.exe" by Processes.dest _time span=15m | `drop_dm_object_name(Processes)` - | eventstats avg(numScExe) as avgScExe, stdev(numScExe) as stdScExe, count as numSlots - by dest | eval upperThreshold=(avgScExe + stdScExe *3) | eval isOutlier=if(avgScExe - > 5 and avgScExe >= upperThreshold, 1, 0) | search isOutlier=1 | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `excessive_usage_of_sc_service_utility_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. Tune and filter known instances where renamed taskkill.exe may be used. -known_false_positives: excessive execution of sc.exe is quite suspicious since it - can modify or execute app in high privilege permission. + - Sysmon EventID 1 +search: |- + | tstats `security_content_summariesonly` count as numScExe min(_time) as firstTime max(_time) as lastTime values(Processes.action) as action values(Processes.original_file_name) as original_file_name values(Processes.parent_process_exec) as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_path) as parent_process_path values(Processes.process) as process values(Processes.process_exec) as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) as process_integrity_level values(Processes.process_path) as process_path values(Processes.user_id) as user_id values(Processes.vendor_product) as vendor_product values(Processes.parent_process) as parent_process values(Processes.process_name) as process_name values(Processes.parent_process_id) as parent_process_id values(Processes.user) as user FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "sc.exe" + BY Processes.dest _time span=15m + | `drop_dm_object_name(Processes)` + | eventstats avg(numScExe) as avgScExe, stdev(numScExe) as stdScExe, count as numSlots + BY dest + | eval upperThreshold=(avgScExe + stdScExe *3) + | eval isOutlier=if(avgScExe > 5 and avgScExe >= upperThreshold, 1, 0) + | search isOutlier=1 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `excessive_usage_of_sc_service_utility_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed taskkill.exe may be used. +known_false_positives: excessive execution of sc.exe is quite suspicious since it can modify or execute app in high privilege permission. references: -- https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/ + - https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Excessive Usage Of SC Service Utility - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: Excessive Usage Of SC Service Utility + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Azorult - - Ransomware - - Crypto Stealer - asset_type: Endpoint - mitre_attack_id: - - T1569.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azorult + - Ransomware + - Crypto Stealer + asset_type: Endpoint + mitre_attack_id: + - T1569.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/excessive_usage_of_taskkill.yml b/detections/endpoint/excessive_usage_of_taskkill.yml index 84ff71a722..1fdd9e6642 100644 --- a/detections/endpoint/excessive_usage_of_taskkill.yml +++ b/detections/endpoint/excessive_usage_of_taskkill.yml @@ -1,93 +1,68 @@ name: Excessive Usage Of Taskkill id: fe5bca48-accb-11eb-a67c-acde48001122 -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies excessive usage of `taskkill.exe`, - a command-line utility used to terminate processes. The detection leverages data - from Endpoint Detection and Response (EDR) agents, focusing on instances where `taskkill.exe` - is executed ten or more times within a one-minute span. This behavior is significant - as adversaries often use `taskkill.exe` to disable security tools or other critical - processes to evade detection. If confirmed malicious, this activity could allow - attackers to bypass security defenses, maintain persistence, and further compromise - the system. +description: The following analytic identifies excessive usage of `taskkill.exe`, a command-line utility used to terminate processes. The detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on instances where `taskkill.exe` is executed ten or more times within a one-minute span. This behavior is significant as adversaries often use `taskkill.exe` to disable security tools or other critical processes to evade detection. If confirmed malicious, this activity could allow attackers to bypass security defenses, maintain persistence, and further compromise the system. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime values(Processes.action) as action values(Processes.dest) as dest values(Processes.original_file_name) - as original_file_name values(Processes.parent_process) as parent_process values(Processes.parent_process_exec) - as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid - values(Processes.parent_process_id) as parent_process_id values(Processes.parent_process_path) - as parent_process_path values(Processes.process) as process values(Processes.process_exec) - as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) - as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) - as process_integrity_level values(Processes.process_path) as process_path values(Processes.user) - as user values(Processes.user_id) as user_id values(Processes.vendor_product) as - vendor_product from datamodel=Endpoint.Processes where Processes.process_name = - "taskkill.exe" by Processes.parent_process_name Processes.process_name Processes.dest - Processes.user _time span=1m | where count >=10 | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_usage_of_taskkill_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.action) as action values(Processes.dest) as dest values(Processes.original_file_name) as original_file_name values(Processes.parent_process) as parent_process values(Processes.parent_process_exec) as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid values(Processes.parent_process_id) as parent_process_id values(Processes.parent_process_path) as parent_process_path values(Processes.process) as process values(Processes.process_exec) as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) as process_integrity_level values(Processes.process_path) as process_path values(Processes.user) as user values(Processes.user_id) as user_id values(Processes.vendor_product) as vendor_product FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "taskkill.exe" + BY Processes.parent_process_name Processes.process_name Processes.dest + Processes.user _time span=1m + | where count >=10 + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `excessive_usage_of_taskkill_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ -- https://www.joesandbox.com/analysis/702680/0/html + - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ + - https://www.joesandbox.com/analysis/702680/0/html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Excessive usage of taskkill.exe with process id $process_id$ (more than - 10 within 1m) has been detected on $dest$ with a parent process of $parent_process_name$. - risk_objects: - - field: dest - type: system - score: 28 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: Excessive usage of taskkill.exe with process id $process_id$ (more than 10 within 1m) has been detected on $dest$ with a parent process of $parent_process_name$. + risk_objects: + - field: dest + type: system + score: 28 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - Azorult - - AgentTesla - - CISA AA22-277A - - NjRAT - - CISA AA22-264A - - XMRig - - Crypto Stealer - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azorult + - AgentTesla + - CISA AA22-277A + - NjRAT + - CISA AA22-264A + - XMRig + - Crypto Stealer + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/exchange_powershell_abuse_via_ssrf.yml b/detections/endpoint/exchange_powershell_abuse_via_ssrf.yml index 8d47b91409..0232764ac8 100644 --- a/detections/endpoint/exchange_powershell_abuse_via_ssrf.yml +++ b/detections/endpoint/exchange_powershell_abuse_via_ssrf.yml @@ -1,51 +1,45 @@ name: Exchange PowerShell Abuse via SSRF id: 29228ab4-0762-11ec-94aa-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: experimental type: TTP -description: The following analytic detects suspicious behavior indicative of ProxyShell - exploitation against on-premise Microsoft Exchange servers. It identifies HTTP POST - requests to `autodiscover.json` containing `PowerShell` in the URI, leveraging server-side - request forgery (SSRF) to access backend PowerShell. This detection uses Exchange - server logs ingested into Splunk. Monitoring this activity is crucial as it may - indicate an attacker attempting to execute commands or scripts on the Exchange server. - If confirmed malicious, this could lead to unauthorized access, privilege escalation, - or persistent control over the Exchange environment. +description: The following analytic detects suspicious behavior indicative of ProxyShell exploitation against on-premise Microsoft Exchange servers. It identifies HTTP POST requests to `autodiscover.json` containing `PowerShell` in the URI, leveraging server-side request forgery (SSRF) to access backend PowerShell. This detection uses Exchange server logs ingested into Splunk. Monitoring this activity is crucial as it may indicate an attacker attempting to execute commands or scripts on the Exchange server. If confirmed malicious, this could lead to unauthorized access, privilege escalation, or persistent control over the Exchange environment. data_source: [] -search: '`windows_exchange_iis` c_uri="*//autodiscover*" cs_uri_query="*PowerShell*" cs_method="POST" - | stats count min(_time) as firstTime max(_time) as lastTime by dest, cs_uri_query, - cs_method, c_uri | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `exchange_powershell_abuse_via_ssrf_filter`' -how_to_implement: The following analytic requires on-premise Exchange to be logging - to Splunk using the TA - https://splunkbase.splunk.com/app/3225. Ensure logs are - parsed correctly, or tune the analytic for your environment. +search: |- + `windows_exchange_iis` c_uri="*//autodiscover*" cs_uri_query="*PowerShell*" cs_method="POST" + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest, cs_uri_query, cs_method, + c_uri + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `exchange_powershell_abuse_via_ssrf_filter` +how_to_implement: The following analytic requires on-premise Exchange to be logging to Splunk using the TA - https://splunkbase.splunk.com/app/3225. Ensure logs are parsed correctly, or tune the analytic for your environment. known_false_positives: Limited false positives, however, tune as needed. references: -- https://github.com/GossiTheDog/ThreatHunting/blob/master/AzureSentinel/Exchange-Powershell-via-SSRF -- https://blog.orange.tw/2021/08/proxylogon-a-new-attack-surface-on-ms-exchange-part-1.html -- https://peterjson.medium.com/reproducing-the-proxyshell-pwn2own-exploit-49743a4ea9a1 + - https://github.com/GossiTheDog/ThreatHunting/blob/master/AzureSentinel/Exchange-Powershell-via-SSRF + - https://blog.orange.tw/2021/08/proxylogon-a-new-attack-surface-on-ms-exchange-part-1.html + - https://peterjson.medium.com/reproducing-the-proxyshell-pwn2own-exploit-49743a4ea9a1 rba: - message: Activity related to ProxyShell has been identified on $dest$. Review events - and take action accordingly. - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: [] + message: Activity related to ProxyShell has been identified on $dest$. Review events and take action accordingly. + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: [] tags: - analytic_story: - - ProxyShell - - BlackByte Ransomware - - ProxyNotShell - - Seashell Blizzard - asset_type: Endpoint - mitre_attack_id: - - T1190 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ProxyShell + - BlackByte Ransomware + - ProxyNotShell + - Seashell Blizzard + asset_type: Endpoint + mitre_attack_id: + - T1190 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/exchange_powershell_module_usage.yml b/detections/endpoint/exchange_powershell_module_usage.yml index 406f783b62..a008ea0acc 100644 --- a/detections/endpoint/exchange_powershell_module_usage.yml +++ b/detections/endpoint/exchange_powershell_module_usage.yml @@ -1,87 +1,72 @@ name: Exchange PowerShell Module Usage id: 2d10095e-05ae-11ec-8fdf-acde48001122 -version: 13 -date: '2025-07-29' +version: 14 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: - The following analytic detects the usage of specific Exchange PowerShell - modules, such as New-MailboxExportRequest, New-ManagementRoleAssignment, New-MailboxSearch, - and Get-Recipient. It leverages PowerShell Script Block Logging (EventCode 4104) - to identify these commands. This activity is significant because these modules can - be exploited by adversaries who have gained access via ProxyShell or ProxyNotShell - vulnerabilities. If confirmed malicious, attackers could export mailbox contents, - assign management roles, conduct mailbox searches, or view recipient objects, potentially - leading to data exfiltration, privilege escalation, or unauthorized access to sensitive - information. +description: The following analytic detects the usage of specific Exchange PowerShell modules, such as New-MailboxExportRequest, New-ManagementRoleAssignment, New-MailboxSearch, and Get-Recipient. It leverages PowerShell Script Block Logging (EventCode 4104) to identify these commands. This activity is significant because these modules can be exploited by adversaries who have gained access via ProxyShell or ProxyNotShell vulnerabilities. If confirmed malicious, attackers could export mailbox contents, assign management roles, conduct mailbox searches, or view recipient objects, potentially leading to data exfiltration, privilege escalation, or unauthorized access to sensitive information. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText IN ("*New-MailboxExportRequest*", - "*New-ManagementRoleAssignment*", "*New-MailboxSearch*", "*Get-Recipient*", "Search-Mailbox") - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `exchange_powershell_module_usage_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - Administrators or power users may use this PowerShell commandlet - for troubleshooting. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText IN ("*New-MailboxExportRequest*", "*New-ManagementRoleAssignment*", "*New-MailboxSearch*", "*Get-Recipient*", "Search-Mailbox") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `exchange_powershell_module_usage_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting. references: - - https://docs.microsoft.com/en-us/powershell/module/exchange/new-mailboxexportrequest?view=exchange-ps - - https://docs.microsoft.com/en-us/powershell/module/exchange/new-managementroleassignment?view=exchange-ps - - https://blog.orange.tw/2021/08/proxyshell-a-new-attack-surface-on-ms-exchange-part-3.html - - https://www.zerodayinitiative.com/blog/2021/8/17/from-pwn2own-2021-a-new-attack-surface-on-microsoft-exchange-proxyshell - - https://thedfirreport.com/2021/11/15/exchange-exploit-leads-to-domain-wide-ransomware/ - - https://www.cisa.gov/uscert/ncas/alerts/aa22-264a - - https://learn.microsoft.com/en-us/powershell/module/exchange/new-mailboxsearch?view=exchange-ps - - https://learn.microsoft.com/en-us/powershell/module/exchange/get-recipient?view=exchange-ps - - https://thedfirreport.com/2022/03/21/apt35-automates-initial-access-using-proxyshell/ + - https://docs.microsoft.com/en-us/powershell/module/exchange/new-mailboxexportrequest?view=exchange-ps + - https://docs.microsoft.com/en-us/powershell/module/exchange/new-managementroleassignment?view=exchange-ps + - https://blog.orange.tw/2021/08/proxyshell-a-new-attack-surface-on-ms-exchange-part-3.html + - https://www.zerodayinitiative.com/blog/2021/8/17/from-pwn2own-2021-a-new-attack-surface-on-microsoft-exchange-proxyshell + - https://thedfirreport.com/2021/11/15/exchange-exploit-leads-to-domain-wide-ransomware/ + - https://www.cisa.gov/uscert/ncas/alerts/aa22-264a + - https://learn.microsoft.com/en-us/powershell/module/exchange/new-mailboxsearch?view=exchange-ps + - https://learn.microsoft.com/en-us/powershell/module/exchange/get-recipient?view=exchange-ps + - https://thedfirreport.com/2022/03/21/apt35-automates-initial-access-using-proxyshell/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious Exchange PowerShell module usaged was identified on $dest$. - risk_objects: - - field: dest - type: system - score: 32 - threat_objects: [] + message: Suspicious Exchange PowerShell module usaged was identified on $dest$. + risk_objects: + - field: dest + type: system + score: 32 + threat_objects: [] tags: - analytic_story: - - ProxyNotShell - - CISA AA22-277A - - ProxyShell - - BlackByte Ransomware - - CISA AA22-264A - - Scattered Spider - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ProxyNotShell + - CISA AA22-277A + - ProxyShell + - BlackByte Ransomware + - CISA AA22-264A + - Scattered Spider + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/exchange/windows-powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/exchange/windows-powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/executable_file_written_in_administrative_smb_share.yml b/detections/endpoint/executable_file_written_in_administrative_smb_share.yml index 5321eb3fb8..58ee94dd72 100644 --- a/detections/endpoint/executable_file_written_in_administrative_smb_share.yml +++ b/detections/endpoint/executable_file_written_in_administrative_smb_share.yml @@ -5,79 +5,58 @@ date: '2025-05-02' author: Teoderick Contreras, Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects executable files (.exe or .dll) being - written to Windows administrative SMB shares (Admin$, IPC$, C$). It leverages Windows - Security Event Logs with EventCode 5145 to identify this activity. This behavior - is significant as it is commonly used by tools like PsExec/PaExec for staging binaries - before creating and starting services on remote endpoints, a technique often employed - for lateral movement and remote code execution. If confirmed malicious, this activity - could allow an attacker to execute arbitrary code remotely, potentially compromising - additional systems within the network. +description: The following analytic detects executable files (.exe or .dll) being written to Windows administrative SMB shares (Admin$, IPC$, C$). It leverages Windows Security Event Logs with EventCode 5145 to identify this activity. This behavior is significant as it is commonly used by tools like PsExec/PaExec for staging binaries before creating and starting services on remote endpoints, a technique often employed for lateral movement and remote code execution. If confirmed malicious, this activity could allow an attacker to execute arbitrary code remotely, potentially compromising additional systems within the network. data_source: -- Windows Event Log Security 5145 -search: '`wineventlog_security` EventCode=5145 RelativeTargetName IN ("*.exe","*.dll") - ObjectType=File ShareName IN ("\\\\*\\C$","\\\\*\\IPC$","\\\\*\\admin$") AccessMask= - "0x2" | stats min(_time) as firstTime max(_time) as lastTime count by EventCode - ShareName RelativeTargetName ObjectType AccessMask src_user src_port IpAddress dest - | `security_content_ctime(firstTime)` | `executable_file_written_in_administrative_smb_share_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Windows Security Event Logs with 5145 EventCode enabled. The Windows TA is also - required. Also enable the object Audit access success/failure in your group policy. -known_false_positives: System Administrators may use looks like PsExec for troubleshooting - or administrations tasks. However, this will typically come only from certain users - and certain systems that can be added to an allow list. + - Windows Event Log Security 5145 +search: '`wineventlog_security` EventCode=5145 RelativeTargetName IN ("*.exe","*.dll") ObjectType=File ShareName IN ("\\\\*\\C$","\\\\*\\IPC$","\\\\*\\admin$") AccessMask= "0x2" | stats min(_time) as firstTime max(_time) as lastTime count by EventCode ShareName RelativeTargetName ObjectType AccessMask src_user src_port IpAddress dest | `security_content_ctime(firstTime)` | `executable_file_written_in_administrative_smb_share_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 5145 EventCode enabled. The Windows TA is also required. Also enable the object Audit access success/failure in your group policy. +known_false_positives: System Administrators may use looks like PsExec for troubleshooting or administrations tasks. However, this will typically come only from certain users and certain systems that can be added to an allow list. references: -- https://attack.mitre.org/techniques/T1021/002/ -- https://www.rapid7.com/blog/post/2013/03/09/psexec-demystified/ -- https://labs.vipre.com/trickbot-and-its-modules/ -- https://whitehat.eu/incident-response-case-study-featuring-ryuk-and-trickbot-part-2/ -- https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/ + - https://attack.mitre.org/techniques/T1021/002/ + - https://www.rapid7.com/blog/post/2013/03/09/psexec-demystified/ + - https://labs.vipre.com/trickbot-and-its-modules/ + - https://whitehat.eu/incident-response-case-study-featuring-ryuk-and-trickbot-part-2/ + - https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/ drilldown_searches: -- name: View the detection results for - "$src_user$" - search: '%original_detection_search% | search src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_user$" + search: '%original_detection_search% | search src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $src_user$ dropped or created an executable file in known sensitive SMB - share. Share name=$ShareName$, Target name=$RelativeTargetName$, and Access mask=$AccessMask$ - risk_objects: - - field: src_user - type: user - score: 70 - threat_objects: [] + message: $src_user$ dropped or created an executable file in known sensitive SMB share. Share name=$ShareName$, Target name=$RelativeTargetName$, and Access mask=$AccessMask$ + risk_objects: + - field: src_user + type: user + score: 70 + threat_objects: [] tags: - analytic_story: - - Active Directory Lateral Movement - - BlackSuit Ransomware - - IcedID - - Prestige Ransomware - - Industroyer2 - - Data Destruction - - Graceful Wipe Out Attack - - Compromised Windows Host - - Hermetic Wiper - - Trickbot - - VanHelsing Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1021.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + - BlackSuit Ransomware + - IcedID + - Prestige Ransomware + - Industroyer2 + - Data Destruction + - Graceful Wipe Out Attack + - Compromised Windows Host + - Hermetic Wiper + - Trickbot + - VanHelsing Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1021.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/exe_smbshare/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/exe_smbshare/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/executables_or_script_creation_in_suspicious_path.yml b/detections/endpoint/executables_or_script_creation_in_suspicious_path.yml index a89e1b5a6d..aef801b568 100644 --- a/detections/endpoint/executables_or_script_creation_in_suspicious_path.yml +++ b/detections/endpoint/executables_or_script_creation_in_suspicious_path.yml @@ -6,166 +6,158 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly description: | - The following analytic identifies the creation of executables or scripts in suspicious file paths on Windows systems. - It leverages the Endpoint.Filesystem dataset to detect files with specific extensions (e.g., .exe, .dll, .ps1) created in uncommon directories (e.g., \windows\fonts\, \users\public\). - This activity can be significant as adversaries often use these paths to evade detection and maintain persistence. - If confirmed malicious, this behavior could allow attackers to execute unauthorized code, escalate privileges, or persist within the environment, posing a significant security threat. + The following analytic identifies the creation of executables or scripts in suspicious file paths on Windows systems. + It leverages the Endpoint.Filesystem dataset to detect files with specific extensions (e.g., .exe, .dll, .ps1) created in uncommon directories (e.g., \windows\fonts\, \users\public\). + This activity can be significant as adversaries often use these paths to evade detection and maintain persistence. + If confirmed malicious, this behavior could allow attackers to execute unauthorized code, escalate privileges, or persist within the environment, posing a significant security threat. data_source: - - Sysmon EventID 11 + - Sysmon EventID 11 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime - from datamodel=Endpoint.Filesystem where + from datamodel=Endpoint.Filesystem where - Filesystem.file_name IN ( - "*.bat", - "*.cmd", - "*.com", - "*.dll", - "*.exe", - "*.js", - "*.msc", - "*.pif", - "*.ps1", - "*.sys", - "*.vbe", - "*.vbs" - ) - Filesystem.file_path IN ( - "*\\PerfLogs\\*", - "*\\Users\\Administrator\\Music\\*", - "*\\Users\\Default\\*", - "*\\Users\\Public\\*", - "*\\Windows\\debug\\*", - "*\\Windows\\fonts\\*", - "*\\Windows\\Media\\*", - "*\\Windows\\repair\\*", - "*\\Windows\\servicing\\*", - "*Recycle.bin*", - "*:\\inetpub\\*" - ) + Filesystem.file_name IN ( + "*.bat", + "*.cmd", + "*.com", + "*.dll", + "*.exe", + "*.js", + "*.msc", + "*.pif", + "*.ps1", + "*.sys", + "*.vbe", + "*.vbs" + ) + Filesystem.file_path IN ( + "*\\PerfLogs\\*", + "*\\Users\\Administrator\\Music\\*", + "*\\Users\\Default\\*", + "*\\Users\\Public\\*", + "*\\Windows\\debug\\*", + "*\\Windows\\fonts\\*", + "*\\Windows\\Media\\*", + "*\\Windows\\repair\\*", + "*\\Windows\\servicing\\*", + "*Recycle.bin*", + "*:\\inetpub\\*" + ) - by Filesystem.action Filesystem.dest Filesystem.file_access_time - Filesystem.file_create_time Filesystem.file_hash - Filesystem.file_modify_time Filesystem.file_name - Filesystem.file_path Filesystem.file_acl Filesystem.file_size - Filesystem.process_guid Filesystem.process_id Filesystem.user - Filesystem.vendor_product - | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `executables_or_script_creation_in_suspicious_path_filter` + by Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash + Filesystem.file_modify_time Filesystem.file_name + Filesystem.file_path Filesystem.file_acl Filesystem.file_size + Filesystem.process_guid Filesystem.process_id Filesystem.user + Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `executables_or_script_creation_in_suspicious_path_filter` how_to_implement: | - To successfully implement this search you need to be ingesting - information on process that include the name of the Filesystem responsible for - the changes from your endpoints into the `Endpoint` datamodel in the - `Filesystem` node. + To successfully implement this search you need to be ingesting + information on process that include the name of the Filesystem responsible for + the changes from your endpoints into the `Endpoint` datamodel in the + `Filesystem` node. known_false_positives: | - Some false positives may arise from paths like Recycle.bin and \Users\Public. - Other than that executable creation and certain script extensions in these suspicious paths should be less common. + Some false positives may arise from paths like Recycle.bin and \Users\Public. + Other than that executable creation and certain script extensions in these suspicious paths should be less common. references: - - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ - - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ - - https://twitter.com/pr0xylife/status/1590394227758104576 - - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ + - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://twitter.com/pr0xylife/status/1590394227758104576 + - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ drilldown_searches: - - name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious executable or scripts with file name $file_name$, - $file_path$ and process_id $process_id$ executed in suspicious file path in - Windows by $user$ - risk_objects: - - field: user - type: user - score: 30 - threat_objects: - - field: file_name - type: file_name - - field: file_path - type: file_path + message: Suspicious executable or scripts with file name $file_name$, $file_path$ and process_id $process_id$ executed in suspicious file path in Windows by $user$ + risk_objects: + - field: user + type: user + score: 30 + threat_objects: + - field: file_name + type: file_name + - field: file_path + type: file_path tags: - analytic_story: - - PlugX - - Warzone RAT - - Swift Slicer - - Data Destruction - - AgentTesla - - LockBit Ransomware - - Volt Typhoon - - Brute Ratel C4 - - Industroyer2 - - WhisperGate - - DarkGate Malware - - Chaos Ransomware - - ValleyRAT - - XMRig - - Hermetic Wiper - - Remcos - - Quasar RAT - - Rhysida Ransomware - - DarkCrystal RAT - - Qakbot - - Snake Keylogger - - China-Nexus Threat Activity - - IcedID - - CISA AA23-347A - - Azorult - - Handala Wiper - - Crypto Stealer - - Salt Typhoon - - Earth Alux - - Double Zero Destructor - - Trickbot - - Cactus Ransomware - - BlackByte Ransomware - - SystemBC - - AcidPour - - NjRAT - - Graceful Wipe Out Attack - - Amadey - - Derusbi - - AsyncRAT - - RedLine Stealer - - SnappyBee - - Meduza Stealer - - WinDealer RAT - - MoonPeak - - Interlock Ransomware - - Interlock Rat - - NailaoLocker Ransomware - - PromptLock - - GhostRedirector IIS Module and Rungan Backdoor - - Lokibot - - Castle RAT - - SesameOp - - DynoWiper - - XML Runner Loader - asset_type: Endpoint - mitre_attack_id: - - T1036 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - PlugX + - Warzone RAT + - Swift Slicer + - Data Destruction + - AgentTesla + - LockBit Ransomware + - Volt Typhoon + - Brute Ratel C4 + - Industroyer2 + - WhisperGate + - DarkGate Malware + - Chaos Ransomware + - ValleyRAT + - XMRig + - Hermetic Wiper + - Remcos + - Quasar RAT + - Rhysida Ransomware + - DarkCrystal RAT + - Qakbot + - Snake Keylogger + - China-Nexus Threat Activity + - IcedID + - CISA AA23-347A + - Azorult + - Handala Wiper + - Crypto Stealer + - Salt Typhoon + - Earth Alux + - Double Zero Destructor + - Trickbot + - Cactus Ransomware + - BlackByte Ransomware + - SystemBC + - AcidPour + - NjRAT + - Graceful Wipe Out Attack + - Amadey + - Derusbi + - AsyncRAT + - RedLine Stealer + - SnappyBee + - Meduza Stealer + - WinDealer RAT + - MoonPeak + - Interlock Ransomware + - Interlock Rat + - NailaoLocker Ransomware + - PromptLock + - GhostRedirector IIS Module and Rungan Backdoor + - Lokibot + - Castle RAT + - SesameOp + - DynoWiper + - XML Runner Loader + asset_type: Endpoint + mitre_attack_id: + - T1036 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/executables_suspicious_file_path/exec_susp_path2.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/executables_suspicious_file_path/exec_susp_path2.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/executables_or_script_creation_in_temp_path.yml b/detections/endpoint/executables_or_script_creation_in_temp_path.yml index a6751571ef..75ff63eff3 100644 --- a/detections/endpoint/executables_or_script_creation_in_temp_path.yml +++ b/detections/endpoint/executables_or_script_creation_in_temp_path.yml @@ -6,150 +6,141 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly description: | - The following analytic identifies the creation of executables or scripts in temporary file paths on Windows systems. - It leverages the Endpoint.Filesystem data set to detect files with specific extensions (e.g., .exe, .dll, .ps1) created in temporary directories (e.g., \windows\Temp\, \AppData\Local\Temp\). - This activity can be significant as adversaries often use these paths to evade detection and maintain persistence. - If confirmed malicious, this behavior could allow attackers to execute unauthorized code, escalate privileges, or persist within the environment, posing a significant security threat. + The following analytic identifies the creation of executables or scripts in temporary file paths on Windows systems. + It leverages the Endpoint.Filesystem data set to detect files with specific extensions (e.g., .exe, .dll, .ps1) created in temporary directories (e.g., \windows\Temp\, \AppData\Local\Temp\). + This activity can be significant as adversaries often use these paths to evade detection and maintain persistence. + If confirmed malicious, this behavior could allow attackers to execute unauthorized code, escalate privileges, or persist within the environment, posing a significant security threat. data_source: - - Sysmon EventID 11 + - Sysmon EventID 11 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime - from datamodel=Endpoint.Filesystem where + from datamodel=Endpoint.Filesystem where - Filesystem.action="created" - Filesystem.file_name IN ( - "*.bat", - "*.cmd", - "*.com", - "*.dll", - "*.exe", - "*.js", - "*.msc", - "*.pif", - "*.ps1", - "*.sys", - "*.vbe", - "*.vbs" - ) - Filesystem.file_path IN ( - "*:\\Temp\\*", - "*:\\Windows\\Temp\\*", - "*\\AppData\\Local\\Temp\\*", - ) - NOT Filesystem.file_path IN ( - "*\\__PSScriptPolicyTest_*", - ) - by Filesystem.action Filesystem.dest Filesystem.file_access_time - Filesystem.file_create_time Filesystem.file_hash - Filesystem.file_modify_time Filesystem.file_name - Filesystem.file_path Filesystem.file_acl Filesystem.file_size - Filesystem.process_guid Filesystem.process_id Filesystem.user - Filesystem.vendor_product - | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `executables_or_script_creation_in_temp_path_filter` -how_to_implement: To successfully implement this search you need to be ingesting - information on process that include the name of the Filesystem responsible for - the changes from your endpoints into the `Endpoint` datamodel in the - `Filesystem` node. + Filesystem.action="created" + Filesystem.file_name IN ( + "*.bat", + "*.cmd", + "*.com", + "*.dll", + "*.exe", + "*.js", + "*.msc", + "*.pif", + "*.ps1", + "*.sys", + "*.vbe", + "*.vbs" + ) + Filesystem.file_path IN ( + "*:\\Temp\\*", + "*:\\Windows\\Temp\\*", + "*\\AppData\\Local\\Temp\\*", + ) + NOT Filesystem.file_path IN ( + "*\\__PSScriptPolicyTest_*", + ) + by Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash + Filesystem.file_modify_time Filesystem.file_name + Filesystem.file_path Filesystem.file_acl Filesystem.file_size + Filesystem.process_guid Filesystem.process_id Filesystem.user + Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `executables_or_script_creation_in_temp_path_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. known_false_positives: | - Executable creation and certain script extensions in temporary paths can very common in certain environments and legitimate use cases. It is important to review and filter these events based on your organization's normal activity and policies. + Executable creation and certain script extensions in temporary paths can very common in certain environments and legitimate use cases. It is important to review and filter these events based on your organization's normal activity and policies. references: - - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ - - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ - - https://twitter.com/pr0xylife/status/1590394227758104576 - - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ + - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://twitter.com/pr0xylife/status/1590394227758104576 + - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ drilldown_searches: - - name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Executable or script with file name $file_name$ located $file_path$ and process_id $process_id$ was created in temporary folder by $user$ - risk_objects: - - field: user - type: user - score: 5 - threat_objects: - - field: file_name - type: file_name - - field: file_path - type: file_path + message: Executable or script with file name $file_name$ located $file_path$ and process_id $process_id$ was created in temporary folder by $user$ + risk_objects: + - field: user + type: user + score: 5 + threat_objects: + - field: file_name + type: file_name + - field: file_path + type: file_path tags: - analytic_story: - - Snake Keylogger - - China-Nexus Threat Activity - - Remcos - - LockBit Ransomware - - AsyncRAT - - DarkCrystal RAT - - Derusbi - - WinDealer RAT - - DarkGate Malware - - AcidPour - - ValleyRAT - - Crypto Stealer - - PlugX - - Data Destruction - - Qakbot - - CISA AA23-347A - - Hermetic Wiper - - Volt Typhoon - - Double Zero Destructor - - NjRAT - - Trickbot - - Meduza Stealer - - AgentTesla - - SnappyBee - - Azorult - - WhisperGate - - Warzone RAT - - Swift Slicer - - Rhysida Ransomware - - Brute Ratel C4 - - BlackByte Ransomware - - Graceful Wipe Out Attack - - Chaos Ransomware - - Handala Wiper - - RedLine Stealer - - Salt Typhoon - - XMRig - - MoonPeak - - Industroyer2 - - Amadey - - IcedID - - Interlock Rat - - APT37 Rustonotto and FadeStealer - - PromptLock - - Lokibot - - SesameOp - - PromptFlux - - XML Runner Loader - asset_type: Endpoint - mitre_attack_id: - - T1036 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Snake Keylogger + - China-Nexus Threat Activity + - Remcos + - LockBit Ransomware + - AsyncRAT + - DarkCrystal RAT + - Derusbi + - WinDealer RAT + - DarkGate Malware + - AcidPour + - ValleyRAT + - Crypto Stealer + - PlugX + - Data Destruction + - Qakbot + - CISA AA23-347A + - Hermetic Wiper + - Volt Typhoon + - Double Zero Destructor + - NjRAT + - Trickbot + - Meduza Stealer + - AgentTesla + - SnappyBee + - Azorult + - WhisperGate + - Warzone RAT + - Swift Slicer + - Rhysida Ransomware + - Brute Ratel C4 + - BlackByte Ransomware + - Graceful Wipe Out Attack + - Chaos Ransomware + - Handala Wiper + - RedLine Stealer + - Salt Typhoon + - XMRig + - MoonPeak + - Industroyer2 + - Amadey + - IcedID + - Interlock Rat + - APT37 Rustonotto and FadeStealer + - PromptLock + - Lokibot + - SesameOp + - PromptFlux + - XML Runner Loader + asset_type: Endpoint + mitre_attack_id: + - T1036 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/execute_javascript_with_jscript_com_clsid.yml b/detections/endpoint/execute_javascript_with_jscript_com_clsid.yml index 8e1474f5e0..fb677669f2 100644 --- a/detections/endpoint/execute_javascript_with_jscript_com_clsid.yml +++ b/detections/endpoint/execute_javascript_with_jscript_com_clsid.yml @@ -1,86 +1,68 @@ name: Execute Javascript With Jscript COM CLSID id: dc64d064-d346-11eb-8588-acde48001122 -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of JavaScript using the - JScript.Encode CLSID (COM Object) by cscript.exe. It leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process names, command-line executions, - and parent processes. This activity is significant as it is a known technique used - by ransomware, such as Reddot, to execute malicious scripts and potentially disable - AMSI (Antimalware Scan Interface). If confirmed malicious, this behavior could allow - attackers to execute arbitrary code, evade detection, and maintain persistence within - the environment. +description: The following analytic detects the execution of JavaScript using the JScript.Encode CLSID (COM Object) by cscript.exe. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, command-line executions, and parent processes. This activity is significant as it is a known technique used by ransomware, such as Reddot, to execute malicious scripts and potentially disable AMSI (Antimalware Scan Interface). If confirmed malicious, this behavior could allow attackers to execute arbitrary code, evade detection, and maintain persistence within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "cscript.exe" - Processes.process="*-e:{F414C262-6AC0-11CF-B6D1-00AA00BBBB58}*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `execute_javascript_with_jscript_com_clsid_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "cscript.exe" Processes.process="*-e:{F414C262-6AC0-11CF-B6D1-00AA00BBBB58}*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `execute_javascript_with_jscript_com_clsid_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/ + - https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious process of cscript.exe with a parent process $parent_process_name$ - where it tries to execute javascript using jscript.encode CLSID (COM OBJ), detected - on $dest$ by $user$ - risk_objects: - - field: user - type: user - score: 56 - - field: dest - type: system - score: 56 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: Suspicious process of cscript.exe with a parent process $parent_process_name$ where it tries to execute javascript using jscript.encode CLSID (COM OBJ), detected on $dest$ by $user$ + risk_objects: + - field: user + type: user + score: 56 + - field: dest + type: system + score: 56 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1059.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1059.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/execution_of_file_with_multiple_extensions.yml b/detections/endpoint/execution_of_file_with_multiple_extensions.yml index 65d8c488b5..c957c1ed0d 100644 --- a/detections/endpoint/execution_of_file_with_multiple_extensions.yml +++ b/detections/endpoint/execution_of_file_with_multiple_extensions.yml @@ -1,89 +1,71 @@ name: Execution of File with Multiple Extensions id: b06a555e-dce0-417d-a2eb-28a5d8d66ef7 -version: 13 -date: '2026-01-14' +version: 14 +date: '2026-02-25' author: Rico Valdez, Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of files with multiple extensions, - such as ".doc.exe" or ".pdf.exe". This behavior is identified using Endpoint Detection - and Response (EDR) telemetry, focusing on process creation events where the file - name contains double extensions. This activity is significant because attackers - often use double extensions to disguise malicious executables as benign documents, - increasing the likelihood of user execution. If confirmed malicious, this technique - can lead to unauthorized code execution, potentially compromising the endpoint and - allowing further malicious activities. +description: The following analytic detects the execution of files with multiple extensions, such as ".doc.exe" or ".pdf.exe". This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on process creation events where the file name contains double extensions. This activity is significant because attackers often use double extensions to disguise malicious executables as benign documents, increasing the likelihood of user execution. If confirmed malicious, this technique can lead to unauthorized code execution, potentially compromising the endpoint and allowing further malicious activities. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*.doc.exe", - "*.xls.exe","*.ppt.exe", "*.htm.exe", "*.html.exe", "*.txt.exe", "*.pdf.exe", "*.docx.exe", - "*.xlsx.exe", "*.pptx.exe","*.one.exe", "*.bat.exe", "*.rtf.exe") by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` - | `execution_of_file_with_multiple_extensions_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process IN ("*.doc.exe", "*.xls.exe","*.ppt.exe", "*.htm.exe", "*.html.exe", "*.txt.exe", "*.pdf.exe", "*.docx.exe", "*.xlsx.exe", "*.pptx.exe","*.one.exe", "*.bat.exe", "*.rtf.exe") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `drop_dm_object_name(Processes)` + | `execution_of_file_with_multiple_extensions_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.asyncrat + - https://malpedia.caad.fkie.fraunhofer.de/details/win.asyncrat drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: process $process$ have double extensions in the file name is executed on - $dest$ by $user$ - risk_objects: - - field: user - type: user - score: 56 - - field: dest - type: system - score: 56 - threat_objects: - - field: process_name - type: process_name + message: process $process$ have double extensions in the file name is executed on $dest$ by $user$ + risk_objects: + - field: user + type: user + score: 56 + - field: dest + type: system + score: 56 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Windows File Extension and Association Abuse - - Masquerading - Rename System Utilities - - AsyncRAT - - DarkGate Malware - asset_type: Endpoint - mitre_attack_id: - - T1036.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows File Extension and Association Abuse + - Masquerading - Rename System Utilities + - AsyncRAT + - DarkGate Malware + asset_type: Endpoint + mitre_attack_id: + - T1036.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/file_download_or_read_to_pipe_execution.yml b/detections/endpoint/file_download_or_read_to_pipe_execution.yml index 44d6842027..95cc0ffa01 100644 --- a/detections/endpoint/file_download_or_read_to_pipe_execution.yml +++ b/detections/endpoint/file_download_or_read_to_pipe_execution.yml @@ -6,146 +6,140 @@ author: Michael Haag, Nasreddine Bencherchali, Splunk, DipsyTipsy status: production type: TTP description: | - The following analytic detects the use of download or file reading utilities from Windows, Linux or MacOS to download or read the contents of a file from a remote or local source and pipe it directly to a shell for execution. - This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions. - This activity is significant as it is commonly associated with malicious actions like coinminers and exploits such as CVE-2021-44228 in Log4j. - If confirmed malicious, this behavior could allow attackers to execute arbitrary code, potentially leading to system compromise and unauthorized access to sensitive data. + The following analytic detects the use of download or file reading utilities from Windows, Linux or MacOS to download or read the contents of a file from a remote or local source and pipe it directly to a shell for execution. + This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions. + This activity is significant as it is commonly associated with malicious actions like coinminers and exploits such as CVE-2021-44228 in Log4j. + If confirmed malicious, this behavior could allow attackers to execute arbitrary code, potentially leading to system compromise and unauthorized access to sensitive data. data_source: - - Sysmon EventID 1 - - Sysmon for Linux EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Sysmon for Linux EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime - from datamodel=Endpoint.Processes where + from datamodel=Endpoint.Processes where - ``` This aims to cover download utilities and file reading ones ``` + ``` This aims to cover download utilities and file reading ones ``` - Processes.process IN ( - "*.DownloadFile(*", - "*.DownloadString(*", - "*ASCII.GetString*", - "*bitsadmin*", - "*certutil*", - "*curl*", - "*Invoke-RestMethod*", - "*Invoke-WebRequest*", - "*irm*", - "*iwr *", - "*mshta*", - "*wget*" - ) - - Processes.process IN ("*|*") - - ( - ``` Linux / MacOS ``` Processes.process IN ( - "*bash*", - "*csh*", - "*dash*", - "*fish*", - "*ksh*", - "*rbash*", - "*tcsh*", - "*zsh*" + "*.DownloadFile(*", + "*.DownloadString(*", + "*ASCII.GetString*", + "*bitsadmin*", + "*certutil*", + "*curl*", + "*Invoke-RestMethod*", + "*Invoke-WebRequest*", + "*irm*", + "*iwr *", + "*mshta*", + "*wget*" ) - OR - ``` Because the "sh" string can overlap and is a short atom we treat it in a special case ``` - Processes.process IN ( - "*|sh" - "* sh*" + + Processes.process IN ("*|*") + + ( + ``` Linux / MacOS ``` + Processes.process IN ( + "*bash*", + "*csh*", + "*dash*", + "*fish*", + "*ksh*", + "*rbash*", + "*tcsh*", + "*zsh*" + ) + OR + ``` Because the "sh" string can overlap and is a short atom we treat it in a special case ``` + Processes.process IN ( + "*|sh" + "* sh*" + ) + OR + ``` Windows ``` + Processes.process IN ("*IEX*", "*Invoke-Expression*") ) - OR - ``` Windows ``` - Processes.process IN ("*IEX*", "*Invoke-Expression*") - ) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process - Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id - Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user - Processes.user_id Processes.vendor_product + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process + Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id + Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `file_download_or_read_to_pipe_execution_filter` + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `file_download_or_read_to_pipe_execution_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: | - False positives should be limited, however filtering may be required. + False positives should be limited, however filtering may be required. references: - - https://gist.github.com/nathanqthai/01808c569903f41a52e7e7b575caa890 - - https://github.com/MHaggis/notes/blob/master/utilities/warp_pipe_tester.py - - https://www.huntress.com/blog/rapid-response-critical-rce-vulnerability-is-affecting-java - - https://www.lunasec.io/docs/blog/log4j-zero-day/ - - https://securelist.com/bad-magic-apt/109087/ + - https://gist.github.com/nathanqthai/01808c569903f41a52e7e7b575caa890 + - https://github.com/MHaggis/notes/blob/master/utilities/warp_pipe_tester.py + - https://www.huntress.com/blog/rapid-response-critical-rce-vulnerability-is-affecting-java + - https://www.lunasec.io/docs/blog/log4j-zero-day/ + - https://securelist.com/bad-magic-apt/109087/ drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $process_name$ was identified on endpoint $dest$ attempting - to immediately read or download a file and run it via a shell. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: - - field: process_name - type: process_name - - field: process - type: process_name + message: An instance of $process_name$ was identified on endpoint $dest$ attempting to immediately read or download a file and run it via a shell. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: + - field: process_name + type: process_name + - field: process + type: process_name tags: - analytic_story: - - Compromised Windows Host - - Ingress Tool Transfer - - Linux Living Off The Land - - Log4Shell CVE-2021-44228 - - NPM Supply Chain Compromise - asset_type: Endpoint - cve: - - CVE-2021-44228 - mitre_attack_id: - - T1105 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - Ingress Tool Transfer + - Linux Living Off The Land + - Log4Shell CVE-2021-44228 + - NPM Supply Chain Compromise + asset_type: Endpoint + cve: + - CVE-2021-44228 + mitre_attack_id: + - T1105 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - Windows - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/download_to_pipe_exec/download_to_pipe_exec.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - - name: True Positive Test - Linux - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/download_to_pipe_exec/download_to_pipe_exec_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test - Windows + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/download_to_pipe_exec/download_to_pipe_exec.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test - Linux + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/download_to_pipe_exec/download_to_pipe_exec_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/file_with_samsam_extension.yml b/detections/endpoint/file_with_samsam_extension.yml index 944433ddfa..cfe533899c 100644 --- a/detections/endpoint/file_with_samsam_extension.yml +++ b/detections/endpoint/file_with_samsam_extension.yml @@ -6,86 +6,78 @@ author: Rico Valdez, Splunk status: production type: TTP description: | - The following analytic detects file writes with extensions indicative of a SamSam ransomware attack. - It leverages file-system activity data to identify file names ending in .stubbin, .berkshire, .satoshi, .sophos, or .keyxml. - This activity is significant because SamSam ransomware is highly destructive, leading to file encryption and ransom demands. - If confirmed malicious, the impact includes significant financial losses, operational disruptions, and reputational damage. - Immediate actions should include isolating affected systems, restoring files from backups, and investigating the attack source to prevent further incidents. + The following analytic detects file writes with extensions indicative of a SamSam ransomware attack. + It leverages file-system activity data to identify file names ending in .stubbin, .berkshire, .satoshi, .sophos, or .keyxml. + This activity is significant because SamSam ransomware is highly destructive, leading to file encryption and ransom demands. + If confirmed malicious, the impact includes significant financial losses, operational disruptions, and reputational damage. + Immediate actions should include isolating affected systems, restoring files from backups, and investigating the attack source to prevent further incidents. data_source: -- Sysmon EventID 11 + - Sysmon EventID 11 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime - values(Filesystem.user) as user - values(Filesystem.dest) as dest - values(Filesystem.file_path) as file_path - from datamodel=Endpoint.Filesystem where - Filesystem.file_name IN ( - "*.berkshire", - "*.keyxml", - "*.satoshi", - "*.sophos", - "*.stubbin" - ) - by Filesystem.action Filesystem.dest Filesystem.file_access_time - Filesystem.file_create_time Filesystem.file_hash - Filesystem.file_modify_time Filesystem.file_name - Filesystem.file_path Filesystem.file_acl Filesystem.file_size - Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product - | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(lastTime)` - | `security_content_ctime(firstTime)` - | rex field=file_name "(?\.[^\.]+)$" - | search file_extension IN (".berkshire", ".keyxml", ".satoshi", ".sophos", ".stubbin") - | `file_with_samsam_extension_filter` -how_to_implement: You must be ingesting data that records file-system activity from - your hosts to populate the Endpoint file-system data-model node. If you are using - Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you - want to collect data. + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + values(Filesystem.user) as user + values(Filesystem.dest) as dest + values(Filesystem.file_path) as file_path + from datamodel=Endpoint.Filesystem where + Filesystem.file_name IN ( + "*.berkshire", + "*.keyxml", + "*.satoshi", + "*.sophos", + "*.stubbin" + ) + by Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash + Filesystem.file_modify_time Filesystem.file_name + Filesystem.file_path Filesystem.file_acl Filesystem.file_size + Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` + | rex field=file_name "(?\.[^\.]+)$" + | search file_extension IN (".berkshire", ".keyxml", ".satoshi", ".sophos", ".stubbin") + | `file_with_samsam_extension_filter` +how_to_implement: You must be ingesting data that records file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. known_false_positives: | - Because these extensions are not typically used in normal operations, you should investigate all results. + Because these extensions are not typically used in normal operations, you should investigate all results. references: [] drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The $file_name$ with extensions consistent with a SamSam ransomware attack seen on $dest$ - risk_objects: - - field: user - type: user - score: 90 - - field: dest - type: system - score: 90 - threat_objects: - - field: file_name - type: file_name + message: The $file_name$ with extensions consistent with a SamSam ransomware attack seen on $dest$ + risk_objects: + - field: user + type: user + score: 90 + - field: dest + type: system + score: 90 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - SamSam Ransomware - - Hellcat Ransomware - asset_type: Endpoint - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SamSam Ransomware + - Hellcat Ransomware + asset_type: Endpoint + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/samsam_extension/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/samsam_extension/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/firewall_allowed_program_enable.yml b/detections/endpoint/firewall_allowed_program_enable.yml index 09b665e5f9..f69b2c445d 100644 --- a/detections/endpoint/firewall_allowed_program_enable.yml +++ b/detections/endpoint/firewall_allowed_program_enable.yml @@ -1,87 +1,68 @@ name: Firewall Allowed Program Enable id: 9a8f63a8-43ac-11ec-904c-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the modification of a firewall rule to - allow the execution of a specific application. This detection leverages data from - Endpoint Detection and Response (EDR) agents, focusing on process creation events - with command-line arguments related to firewall rule changes. This activity is significant - as it may indicate an attempt to bypass firewall restrictions, potentially allowing - unauthorized applications to communicate over the network. If confirmed malicious, - this could enable an attacker to execute arbitrary code, escalate privileges, or - maintain persistence within the target environment. +description: The following analytic detects the modification of a firewall rule to allow the execution of a specific application. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events with command-line arguments related to firewall rule changes. This activity is significant as it may indicate an attempt to bypass firewall restrictions, potentially allowing unauthorized applications to communicate over the network. If confirmed malicious, this could enable an attacker to execute arbitrary code, escalate privileges, or maintain persistence within the target environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process = "*firewall*" - Processes.process = "*allow*" Processes.process = "*add*" Processes.process = "*ENABLE*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `firewall_allowed_program_enable_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: A network operator or systems administrator may utilize an - automated or manual execution of this firewall rule that may generate false positives. - Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process = "*firewall*" Processes.process = "*allow*" Processes.process = "*add*" Processes.process = "*ENABLE*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `firewall_allowed_program_enable_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: A network operator or systems administrator may utilize an automated or manual execution of this firewall rule that may generate false positives. Filter as needed. references: -- https://app.any.run/tasks/ad4c3cda-41f2-4401-8dba-56cc2d245488/ + - https://app.any.run/tasks/ad4c3cda-41f2-4401-8dba-56cc2d245488/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: firewall allowed program commandline $process$ of $process_name$ on $dest$ - by $user$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: firewall allowed program commandline $process$ of $process_name$ on $dest$ by $user$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - BlackByte Ransomware - - NjRAT - - PlugX - - Windows Defense Evasion Tactics - - Medusa Ransomware - - Azorult - asset_type: Endpoint - mitre_attack_id: - - T1562.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - BlackByte Ransomware + - NjRAT + - PlugX + - Windows Defense Evasion Tactics + - Medusa Ransomware + - Azorult + asset_type: Endpoint + mitre_attack_id: + - T1562.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/vilsel/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/vilsel/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/first_time_seen_child_process_of_zoom.yml b/detections/endpoint/first_time_seen_child_process_of_zoom.yml index ae1b047389..cfbbbfa17c 100644 --- a/detections/endpoint/first_time_seen_child_process_of_zoom.yml +++ b/detections/endpoint/first_time_seen_child_process_of_zoom.yml @@ -1,72 +1,51 @@ name: First Time Seen Child Process of Zoom id: e91bd102-d630-4e76-ab73-7e3ba22c5961 -version: 8 -date: '2025-05-15' +version: 9 +date: '2026-02-25' author: David Dorsey, Splunk status: experimental type: Anomaly -description: The following analytic identifies the first-time execution of child processes - spawned by Zoom (zoom.exe or zoom.us). It leverages Endpoint Detection and Response - (EDR) data, specifically monitoring process creation events and comparing them against - previously seen child processes. This activity is significant because the execution - of unfamiliar child processes by Zoom could indicate malicious exploitation or misuse - of the application. If confirmed malicious, this could lead to unauthorized code - execution, data exfiltration, or further compromise of the endpoint. +description: The following analytic identifies the first-time execution of child processes spawned by Zoom (zoom.exe or zoom.us). It leverages Endpoint Detection and Response (EDR) data, specifically monitoring process creation events and comparing them against previously seen child processes. This activity is significant because the execution of unfamiliar child processes by Zoom could indicate malicious exploitation or misuse of the application. If confirmed malicious, this could lead to unauthorized code execution, data exfiltration, or further compromise of the endpoint. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime values(Processes.user) as user values(Processes.action) as action values(Processes.dest) - as dest values(Processes.original_file_name) as original_file_name values(Processes.parent_process) - as parent_process values(Processes.parent_process_exec) as parent_process_exec values(Processes.parent_process_guid) - as parent_process_guid values(Processes.parent_process_id) as parent_process_id - values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_path) - as parent_process_path values(Processes.process) as process values(Processes.process_exec) - as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) - as process_hash values(Processes.process_integrity_level) as process_integrity_level - values(Processes.process_name) as process_name values(Processes.process_path) as - process_path values(Processes.user_id) as user_id - values(Processes.vendor_product) as vendor_product from datamodel=Endpoint.Processes - where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) - by Processes.process_id Processes.dest | `drop_dm_object_name(Processes)` | lookup - zoom_first_time_child_process dest as dest process_name as process_name OUTPUT firstTimeSeen - | where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_window`") - | `security_content_ctime(firstTime)` | `first_time_seen_child_process_of_zoom_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: A new child process of zoom isn't malicious by that fact alone. - Further investigation of the actions of the child process is needed to verify any - malicious behavior is taken. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime values(Processes.user) as user values(Processes.action) as action values(Processes.dest) as dest values(Processes.original_file_name) as original_file_name values(Processes.parent_process) as parent_process values(Processes.parent_process_exec) as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid values(Processes.parent_process_id) as parent_process_id values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_path) as parent_process_path values(Processes.process) as process values(Processes.process_exec) as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) as process_hash values(Processes.process_integrity_level) as process_integrity_level values(Processes.process_name) as process_name values(Processes.process_path) as process_path values(Processes.user_id) as user_id values(Processes.vendor_product) as vendor_product FROM datamodel=Endpoint.Processes + WHERE ( + Processes.parent_process_name=zoom.exe + OR + Processes.parent_process_name=zoom.us + ) + BY Processes.process_id Processes.dest + | `drop_dm_object_name(Processes)` + | lookup zoom_first_time_child_process dest as dest process_name as process_name OUTPUT firstTimeSeen + | where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_window`") + | `security_content_ctime(firstTime)` + | `first_time_seen_child_process_of_zoom_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: A new child process of zoom isn't malicious by that fact alone. Further investigation of the actions of the child process is needed to verify any malicious behavior is taken. references: [] rba: - message: Child process $process_name$ with $process_id$ spawned by zoom.exe or zoom.us - which has not been previously on host $dest$ - risk_objects: - - field: user - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: - - field: process_name - type: process_name + message: Child process $process_name$ with $process_id$ spawned by zoom.exe or zoom.us which has not been previously on host $dest$ + risk_objects: + - field: user + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Suspicious Zoom Child Processes - asset_type: Endpoint - mitre_attack_id: - - T1068 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Zoom Child Processes + asset_type: Endpoint + mitre_attack_id: + - T1068 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/first_time_seen_running_windows_service.yml b/detections/endpoint/first_time_seen_running_windows_service.yml index 770794af2f..364bb4091d 100644 --- a/detections/endpoint/first_time_seen_running_windows_service.yml +++ b/detections/endpoint/first_time_seen_running_windows_service.yml @@ -1,54 +1,41 @@ name: First Time Seen Running Windows Service id: 823136f2-d755-4b6d-ae04-372b486a5808 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: David Dorsey, Splunk status: experimental type: Anomaly -description: The following analytic detects the first occurrence of a Windows service - running in your environment. It leverages Windows system event logs, specifically - EventCode 7036, to identify services entering the "running" state. This activity - is significant because the appearance of a new or previously unseen service could - indicate the installation of unauthorized or malicious software. If confirmed malicious, - this activity could allow an attacker to execute arbitrary code, maintain persistence, - or escalate privileges within the environment. Monitoring for new services helps - in early detection of potential threats. +description: The following analytic detects the first occurrence of a Windows service running in your environment. It leverages Windows system event logs, specifically EventCode 7036, to identify services entering the "running" state. This activity is significant because the appearance of a new or previously unseen service could indicate the installation of unauthorized or malicious software. If confirmed malicious, this activity could allow an attacker to execute arbitrary code, maintain persistence, or escalate privileges within the environment. Monitoring for new services helps in early detection of potential threats. data_source: -- Windows Event Log System 7036 -search: '`wineventlog_system` EventCode=7036 | rex field=Message "The (?[-\(\)\s\w]+) - service entered the (?\w+) state" | where state="running" | lookup previously_seen_running_windows_services - service as service OUTPUT firstTimeSeen | where isnull(firstTimeSeen) OR firstTimeSeen - > relative_time(now(), `previously_seen_windows_services_window`) | table _time - dest service | `first_time_seen_running_windows_service_filter`' -how_to_implement: While this search does not require you to adhere to Splunk CIM, - you must be ingesting your Windows system event logs in order for this search to - execute successfully. You should run the baseline search `Previously Seen Running - Windows Services - Initial` to build the initial table of child processes and hostnames - for this search to work. You should also schedule at the same interval as this search - the second baseline search `Previously Seen Running Windows Services - Update` to - keep this table up to date and to age out old Windows Services. Please update the - `previously_seen_windows_services_window` macro to adjust the time window. Please - ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above. -known_false_positives: A previously unseen service is not necessarily malicious. Verify - that the service is legitimate and that was installed by a legitimate process. + - Windows Event Log System 7036 +search: |- + `wineventlog_system` EventCode=7036 + | rex field=Message "The (?[-\(\)\s\w]+) service entered the (?\w+) state" + | where state="running" + | lookup previously_seen_running_windows_services service as service OUTPUT firstTimeSeen + | where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), `previously_seen_windows_services_window`) + | table _time dest service + | `first_time_seen_running_windows_service_filter` +how_to_implement: While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows system event logs in order for this search to execute successfully. You should run the baseline search `Previously Seen Running Windows Services - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Running Windows Services - Update` to keep this table up to date and to age out old Windows Services. Please update the `previously_seen_windows_services_window` macro to adjust the time window. Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above. +known_false_positives: A previously unseen service is not necessarily malicious. Verify that the service is legitimate and that was installed by a legitimate process. references: [] rba: - message: Windows Service observed running for first time on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: Windows Service observed running for first time on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Windows Service Abuse - - Orangeworm Attack Group - - NOBELIUM Group - asset_type: Endpoint - mitre_attack_id: - - T1569.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Service Abuse + - Orangeworm Attack Group + - NOBELIUM Group + asset_type: Endpoint + mitre_attack_id: + - T1569.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/fodhelper_uac_bypass.yml b/detections/endpoint/fodhelper_uac_bypass.yml index e1e58c308d..d21d3d1bb3 100644 --- a/detections/endpoint/fodhelper_uac_bypass.yml +++ b/detections/endpoint/fodhelper_uac_bypass.yml @@ -1,96 +1,75 @@ name: FodHelper UAC Bypass id: 909f8fd8-7ac8-11eb-a1f3-acde48001122 -version: 11 -date: '2025-05-02' +version: 12 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: - The following analytic detects the execution of fodhelper.exe, which - is known to exploit a User Account Control (UAC) bypass by leveraging specific registry - keys. The detection method uses Endpoint Detection and Response (EDR) telemetry - to identify when fodhelper.exe spawns a child process and accesses the registry - keys. This activity is significant because it indicates a potential privilege escalation - attempt by an attacker. If confirmed malicious, the attacker could execute commands - with elevated privileges, leading to unauthorized system changes and potential full - system compromise. +description: The following analytic detects the execution of fodhelper.exe, which is known to exploit a User Account Control (UAC) bypass by leveraging specific registry keys. The detection method uses Endpoint Detection and Response (EDR) telemetry to identify when fodhelper.exe spawns a child process and accesses the registry keys. This activity is significant because it indicates a potential privilege escalation attempt by an attacker. If confirmed malicious, the attacker could execute commands with elevated privileges, leading to unauthorized system changes and potential full system compromise. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=fodhelper.exe - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `fodhelper_uac_bypass_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name=fodhelper.exe + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `fodhelper_uac_bypass_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Limited to no false positives are expected. references: - - https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/ - - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1548.002/T1548.002.md - - https://github.com/gushmazuko/WinBypass/blob/master/FodhelperBypass.ps1 - - https://attack.mitre.org/techniques/T1548/002/ + - https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1548.002/T1548.002.md + - https://github.com/gushmazuko/WinBypass/blob/master/FodhelperBypass.ps1 + - https://attack.mitre.org/techniques/T1548/002/ drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - Suspicious registry keys added by process fodhelper.exe with a parent_process - of $parent_process_name$ that has been executed on $dest$ by $user$. - risk_objects: - - field: user - type: user - score: 81 - - field: dest - type: system - score: 81 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: Suspicious registry keys added by process fodhelper.exe with a parent_process of $parent_process_name$ that has been executed on $dest$ by $user$. + risk_objects: + - field: user + type: user + score: 81 + - field: dest + type: system + score: 81 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - IcedID - - ValleyRAT - - Compromised Windows Host - - Windows Defense Evasion Tactics - asset_type: Endpoint - mitre_attack_id: - - T1112 - - T1548.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - IcedID + - ValleyRAT + - Compromised Windows Host + - Windows Defense Evasion Tactics + asset_type: Endpoint + mitre_attack_id: + - T1112 + - T1548.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/fsutil_zeroing_file.yml b/detections/endpoint/fsutil_zeroing_file.yml index b02f427cdf..6b6cc326bc 100644 --- a/detections/endpoint/fsutil_zeroing_file.yml +++ b/detections/endpoint/fsutil_zeroing_file.yml @@ -1,81 +1,65 @@ name: Fsutil Zeroing File id: 4e5e024e-fabb-11eb-8b8f-acde48001122 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of the 'fsutil' command - with the 'setzerodata' parameter, which zeros out a target file. This detection - leverages data from Endpoint Detection and Response (EDR) agents, focusing on process - names and command-line arguments. This activity is significant because it is a technique - used by ransomware, such as LockBit, to evade detection by erasing its malware path - after encrypting the host. If confirmed malicious, this action could hinder forensic - investigations and allow attackers to cover their tracks, complicating incident - response efforts. +description: The following analytic detects the execution of the 'fsutil' command with the 'setzerodata' parameter, which zeros out a target file. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because it is a technique used by ransomware, such as LockBit, to evade detection by erasing its malware path after encrypting the host. If confirmed malicious, this action could hinder forensic investigations and allow attackers to cover their tracks, complicating incident response efforts. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count values(Processes.process) - as process values(Processes.parent_process) as parent_process min(_time) as firstTime - max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe Processes.process="*setzerodata*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `fsutil_zeroing_file_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=fsutil.exe Processes.process="*setzerodata*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `fsutil_zeroing_file_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://app.any.run/tasks/e0ac072d-58c9-4f53-8a3b-3e491c7ac5db/ -- https://news.sophos.com/en-us/2020/04/24/lockbit-ransomware-borrows-tricks-to-keep-up-with-revil-and-maze/ + - https://app.any.run/tasks/e0ac072d-58c9-4f53-8a3b-3e491c7ac5db/ + - https://news.sophos.com/en-us/2020/04/24/lockbit-ransomware-borrows-tricks-to-keep-up-with-revil-and-maze/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible file data deletion on $dest$ using $process$ - risk_objects: - - field: dest - type: system - score: 54 - threat_objects: [] + message: Possible file data deletion on $dest$ using $process$ + risk_objects: + - field: dest + type: system + score: 54 + threat_objects: [] tags: - analytic_story: - - Ransomware - - LockBit Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1070 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - LockBit Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1070 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070/fsutil_file_zero/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070/fsutil_file_zero/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/get_addefaultdomainpasswordpolicy_with_powershell.yml b/detections/endpoint/get_addefaultdomainpasswordpolicy_with_powershell.yml index f19e75d940..0f694c2db0 100644 --- a/detections/endpoint/get_addefaultdomainpasswordpolicy_with_powershell.yml +++ b/detections/endpoint/get_addefaultdomainpasswordpolicy_with_powershell.yml @@ -1,60 +1,54 @@ name: Get ADDefaultDomainPasswordPolicy with Powershell id: 36e46ebe-065a-11ec-b4c7-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects the execution of `powershell.exe` running - the `Get-ADDefaultDomainPasswordPolicy` cmdlet, which is used to retrieve the password - policy in a Windows domain. This detection leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process names and command-line executions. - Monitoring this activity is crucial as it can indicate attempts by adversaries to - gather information about domain policies for situational awareness and Active Directory - discovery. If confirmed malicious, this activity could lead to further reconnaissance - and potential exploitation of domain security settings. +description: The following analytic detects the execution of `powershell.exe` running the `Get-ADDefaultDomainPasswordPolicy` cmdlet, which is used to retrieve the password policy in a Windows domain. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. Monitoring this activity is crucial as it can indicate attempts by adversaries to gather information about domain policies for situational awareness and Active Directory discovery. If confirmed malicious, this activity could lead to further reconnaissance and potential exploitation of domain security settings. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="cmd.exe" - OR Processes.process_name="powershell*") AND Processes.process = "*Get-ADDefaultDomainPasswordPolicy*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `get_addefaultdomainpasswordpolicy_with_powershell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="cmd.exe" + OR + Processes.process_name="powershell*" + ) + AND Processes.process = "*Get-ADDefaultDomainPasswordPolicy*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `get_addefaultdomainpasswordpolicy_with_powershell_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://github.com/S1ckB0y1337/Active-Directory-Exploitation-Cheat-Sheet -- https://attack.mitre.org/techniques/T1201/ -- https://docs.microsoft.com/en-us/powershell/module/activedirectory/get-addefaultdomainpasswordpolicy?view=windowsserver2019-ps + - https://github.com/S1ckB0y1337/Active-Directory-Exploitation-Cheat-Sheet + - https://attack.mitre.org/techniques/T1201/ + - https://docs.microsoft.com/en-us/powershell/module/activedirectory/get-addefaultdomainpasswordpolicy?view=windowsserver2019-ps tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1201 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1201 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/pwd_policy_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/pwd_policy_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/get_addefaultdomainpasswordpolicy_with_powershell_script_block.yml b/detections/endpoint/get_addefaultdomainpasswordpolicy_with_powershell_script_block.yml index 079af90f4a..ab4061b279 100644 --- a/detections/endpoint/get_addefaultdomainpasswordpolicy_with_powershell_script_block.yml +++ b/detections/endpoint/get_addefaultdomainpasswordpolicy_with_powershell_script_block.yml @@ -1,47 +1,45 @@ name: Get ADDefaultDomainPasswordPolicy with Powershell Script Block id: 1ff7ccc8-065a-11ec-91e4-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Mauricio Velazco, Splunk status: production type: Hunting -description: The following analytic detects the execution of the `Get-ADDefaultDomainPasswordPolicy` - PowerShell cmdlet, which is used to retrieve the password policy in a Windows domain. - This detection leverages PowerShell Script Block Logging (EventCode=4104) to identify - the specific command execution. Monitoring this activity is significant as it can - indicate an attempt to gather domain policy information, which is often a precursor - to further malicious actions. If confirmed malicious, this activity could allow - an attacker to understand password policies, aiding in password attacks or further - domain enumeration. +description: The following analytic detects the execution of the `Get-ADDefaultDomainPasswordPolicy` PowerShell cmdlet, which is used to retrieve the password policy in a Windows domain. This detection leverages PowerShell Script Block Logging (EventCode=4104) to identify the specific command execution. Monitoring this activity is significant as it can indicate an attempt to gather domain policy information, which is often a precursor to further malicious actions. If confirmed malicious, this activity could allow an attacker to understand password policies, aiding in password attacks or further domain enumeration. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 ScriptBlockText ="*Get-ADDefaultDomainPasswordPolicy*" - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `get_addefaultdomainpasswordpolicy_with_powershell_script_block_filter`' -how_to_implement: The following Hunting analytic requires PowerShell operational logs - to be imported. Modify the powershell macro as needed to match the sourcetype or - add index. This analytic is specific to 4104, or PowerShell Script Block Logging. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText ="*Get-ADDefaultDomainPasswordPolicy*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `get_addefaultdomainpasswordpolicy_with_powershell_script_block_filter` +how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://github.com/S1ckB0y1337/Active-Directory-Exploitation-Cheat-Sheet -- https://attack.mitre.org/techniques/T1201/ -- https://docs.microsoft.com/en-us/powershell/module/activedirectory/get-addefaultdomainpasswordpolicy?view=windowsserver2019-ps + - https://github.com/S1ckB0y1337/Active-Directory-Exploitation-Cheat-Sheet + - https://attack.mitre.org/techniques/T1201/ + - https://docs.microsoft.com/en-us/powershell/module/activedirectory/get-addefaultdomainpasswordpolicy?view=windowsserver2019-ps tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1201 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1201 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/pwd_policy_discovery/windows-powershell-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/pwd_policy_discovery/windows-powershell-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/get_aduser_with_powershell.yml b/detections/endpoint/get_aduser_with_powershell.yml index 51bab1d9e7..4972fcbab3 100644 --- a/detections/endpoint/get_aduser_with_powershell.yml +++ b/detections/endpoint/get_aduser_with_powershell.yml @@ -1,61 +1,55 @@ name: Get ADUser with PowerShell id: 0b6ee3f4-04e3-11ec-a87d-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Mauricio Velazco, Splunk status: production type: Hunting -description: The following analytic detects the execution of `powershell.exe` with - command-line arguments used to enumerate domain users via the `Get-ADUser` cmdlet. - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on - process names and command-line executions. This activity is significant as it may - indicate an attempt by adversaries to gather information about domain users for - situational awareness and Active Directory discovery. If confirmed malicious, this - behavior could lead to further reconnaissance, enabling attackers to identify high-value - targets and plan subsequent attacks. +description: The following analytic detects the execution of `powershell.exe` with command-line arguments used to enumerate domain users via the `Get-ADUser` cmdlet. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it may indicate an attempt by adversaries to gather information about domain users for situational awareness and Active Directory discovery. If confirmed malicious, this behavior could lead to further reconnaissance, enabling attackers to identify high-value targets and plan subsequent attacks. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="cmd.exe" - OR Processes.process_name="powershell*") AND Processes.process = "*Get-ADUser*" - AND Processes.process = "*-filter*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_aduser_with_powershell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="cmd.exe" + OR + Processes.process_name="powershell*" + ) + AND Processes.process = "*Get-ADUser*" AND Processes.process = "*-filter*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `get_aduser_with_powershell_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://www.blackhillsinfosec.com/red-blue-purple/ -- https://attack.mitre.org/techniques/T1087/002/ -- https://docs.microsoft.com/en-us/powershell/module/activedirectory/get-aduser?view=windowsserver2019-ps + - https://www.blackhillsinfosec.com/red-blue-purple/ + - https://attack.mitre.org/techniques/T1087/002/ + - https://docs.microsoft.com/en-us/powershell/module/activedirectory/get-aduser?view=windowsserver2019-ps tags: - analytic_story: - - Active Directory Discovery - - CISA AA23-347A - asset_type: Endpoint - mitre_attack_id: - - T1087.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - CISA AA23-347A + asset_type: Endpoint + mitre_attack_id: + - T1087.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/get_aduser_with_powershell_script_block.yml b/detections/endpoint/get_aduser_with_powershell_script_block.yml index fb8afbf0b7..08e033388a 100644 --- a/detections/endpoint/get_aduser_with_powershell_script_block.yml +++ b/detections/endpoint/get_aduser_with_powershell_script_block.yml @@ -1,48 +1,46 @@ name: Get ADUser with PowerShell Script Block id: 21432e40-04f4-11ec-b7e6-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Mauricio Velazco, Splunk status: production type: Hunting -description: The following analytic detects the execution of the `Get-AdUser` PowerShell - cmdlet, which is used to enumerate all domain users. It leverages PowerShell Script - Block Logging (EventCode=4104) to identify instances where this command is executed - with a filter. This activity is significant as it may indicate an attempt by adversaries - or Red Teams to gather information about domain users for situational awareness - and Active Directory discovery. If confirmed malicious, this behavior could lead - to further reconnaissance and potential exploitation of user accounts within the - domain. +description: The following analytic detects the execution of the `Get-AdUser` PowerShell cmdlet, which is used to enumerate all domain users. It leverages PowerShell Script Block Logging (EventCode=4104) to identify instances where this command is executed with a filter. This activity is significant as it may indicate an attempt by adversaries or Red Teams to gather information about domain users for situational awareness and Active Directory discovery. If confirmed malicious, this behavior could lead to further reconnaissance and potential exploitation of user accounts within the domain. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 ScriptBlockText = "*get-aduser*" ScriptBlockText - = "*-filter*" | fillnull | stats count min(_time) as firstTime max(_time) as lastTime - by dest signature signature_id user_id vendor_product EventID Guid Opcode Name Path - ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | - `security_content_ctime(lastTime)` | `get_aduser_with_powershell_script_block_filter`' -how_to_implement: The following Hunting analytic requires PowerShell operational logs - to be imported. Modify the powershell macro as needed to match the sourcetype or - add index. This analytic is specific to 4104, or PowerShell Script Block Logging. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*get-aduser*" ScriptBlockText = "*-filter*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `get_aduser_with_powershell_script_block_filter` +how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://www.blackhillsinfosec.com/red-blue-purple/ -- https://attack.mitre.org/techniques/T1087/002/ -- https://docs.microsoft.com/en-us/powershell/module/activedirectory/get-aduser?view=windowsserver2019-ps + - https://www.blackhillsinfosec.com/red-blue-purple/ + - https://attack.mitre.org/techniques/T1087/002/ + - https://docs.microsoft.com/en-us/powershell/module/activedirectory/get-aduser?view=windowsserver2019-ps tags: - analytic_story: - - Active Directory Discovery - - CISA AA23-347A - asset_type: Endpoint - mitre_attack_id: - - T1087.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - CISA AA23-347A + asset_type: Endpoint + mitre_attack_id: + - T1087.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/aduser_powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/aduser_powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/get_aduserresultantpasswordpolicy_with_powershell.yml b/detections/endpoint/get_aduserresultantpasswordpolicy_with_powershell.yml index 18fe2ed880..100a51aacb 100644 --- a/detections/endpoint/get_aduserresultantpasswordpolicy_with_powershell.yml +++ b/detections/endpoint/get_aduserresultantpasswordpolicy_with_powershell.yml @@ -1,87 +1,76 @@ name: Get ADUserResultantPasswordPolicy with Powershell id: 8b5ef342-065a-11ec-b0fc-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the execution of `powershell.exe` running - the `Get-ADUserResultantPasswordPolicy` cmdlet, which is used to obtain the password - policy in a Windows domain. It leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process names and command-line executions. This activity - is significant as it indicates potential enumeration of domain policies, a common - tactic for situational awareness and Active Directory discovery by adversaries. - If confirmed malicious, this could allow attackers to understand password policies, - aiding in further attacks such as password spraying or brute force attempts. +description: The following analytic detects the execution of `powershell.exe` running the `Get-ADUserResultantPasswordPolicy` cmdlet, which is used to obtain the password policy in a Windows domain. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential enumeration of domain policies, a common tactic for situational awareness and Active Directory discovery by adversaries. If confirmed malicious, this could allow attackers to understand password policies, aiding in further attacks such as password spraying or brute force attempts. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="cmd.exe" - OR Processes.process_name="powershell*") AND Processes.process = "*Get-ADUserResultantPasswordPolicy*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `get_aduserresultantpasswordpolicy_with_powershell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="cmd.exe" + OR + Processes.process_name="powershell*" + ) + AND Processes.process = "*Get-ADUserResultantPasswordPolicy*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `get_aduserresultantpasswordpolicy_with_powershell_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://github.com/S1ckB0y1337/Active-Directory-Exploitation-Cheat-Sheet -- https://attack.mitre.org/techniques/T1201/ -- https://docs.microsoft.com/en-us/powershell/module/activedirectory/get-aduserresultantpasswordpolicy?view=windowsserver2019-ps + - https://github.com/S1ckB0y1337/Active-Directory-Exploitation-Cheat-Sheet + - https://attack.mitre.org/techniques/T1201/ + - https://docs.microsoft.com/en-us/powershell/module/activedirectory/get-aduserresultantpasswordpolicy?view=windowsserver2019-ps drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: an instance of process $process_name$ with commandline $process$ on $dest$ - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: an instance of process $process_name$ with commandline $process$ on $dest$ + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - Active Directory Discovery - - CISA AA23-347A - asset_type: Endpoint - mitre_attack_id: - - T1201 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - CISA AA23-347A + asset_type: Endpoint + mitre_attack_id: + - T1201 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/pwd_policy_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/pwd_policy_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/get_aduserresultantpasswordpolicy_with_powershell_script_block.yml b/detections/endpoint/get_aduserresultantpasswordpolicy_with_powershell_script_block.yml index 2a2a4d8ce8..c42b251196 100644 --- a/detections/endpoint/get_aduserresultantpasswordpolicy_with_powershell_script_block.yml +++ b/detections/endpoint/get_aduserresultantpasswordpolicy_with_powershell_script_block.yml @@ -1,73 +1,65 @@ name: Get ADUserResultantPasswordPolicy with Powershell Script Block id: 737e1eb0-065a-11ec-921a-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the execution of the `Get-ADUserResultantPasswordPolicy` - PowerShell cmdlet, which is used to obtain the password policy in a Windows domain. - It leverages PowerShell Script Block Logging (EventCode=4104) to identify this activity. - Monitoring this behavior is significant as it may indicate an attempt to enumerate - domain policies, a common tactic used by adversaries for situational awareness and - Active Directory discovery. If confirmed malicious, this activity could allow attackers - to understand password policies, aiding in further attacks such as password guessing - or policy exploitation. +description: The following analytic detects the execution of the `Get-ADUserResultantPasswordPolicy` PowerShell cmdlet, which is used to obtain the password policy in a Windows domain. It leverages PowerShell Script Block Logging (EventCode=4104) to identify this activity. Monitoring this behavior is significant as it may indicate an attempt to enumerate domain policies, a common tactic used by adversaries for situational awareness and Active Directory discovery. If confirmed malicious, this activity could allow attackers to understand password policies, aiding in further attacks such as password guessing or policy exploitation. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 ScriptBlockText="*Get-ADUserResultantPasswordPolicy*" - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `get_aduserresultantpasswordpolicy_with_powershell_script_block_filter`' -how_to_implement: The following Hunting analytic requires PowerShell operational logs - to be imported. Modify the powershell macro as needed to match the sourcetype or - add index. This analytic is specific to 4104, or PowerShell Script Block Logging. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText="*Get-ADUserResultantPasswordPolicy*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `get_aduserresultantpasswordpolicy_with_powershell_script_block_filter` +how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://github.com/S1ckB0y1337/Active-Directory-Exploitation-Cheat-Sheet -- https://attack.mitre.org/techniques/T1201/ -- https://docs.microsoft.com/en-us/powershell/module/activedirectory/get-aduserresultantpasswordpolicy?view=windowsserver2019-ps + - https://github.com/S1ckB0y1337/Active-Directory-Exploitation-Cheat-Sheet + - https://attack.mitre.org/techniques/T1201/ + - https://docs.microsoft.com/en-us/powershell/module/activedirectory/get-aduserresultantpasswordpolicy?view=windowsserver2019-ps drilldown_searches: -- name: View the detection results for - "$dest$" and "$user_id$" - search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user_id$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user_id$" + search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: powershell process having commandline to query domain user password policy - detected on host - $dest$. - risk_objects: - - field: dest - type: system - score: 9 - - field: user_id - type: user - score: 9 - threat_objects: [] + message: powershell process having commandline to query domain user password policy detected on host - $dest$. + risk_objects: + - field: dest + type: system + score: 9 + - field: user_id + type: user + score: 9 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - - CISA AA23-347A - asset_type: Endpoint - mitre_attack_id: - - T1201 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - CISA AA23-347A + asset_type: Endpoint + mitre_attack_id: + - T1201 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/get_domainpolicy_with_powershell.yml b/detections/endpoint/get_domainpolicy_with_powershell.yml index 8bb26c0751..9ef9d2169d 100644 --- a/detections/endpoint/get_domainpolicy_with_powershell.yml +++ b/detections/endpoint/get_domainpolicy_with_powershell.yml @@ -1,86 +1,75 @@ name: Get DomainPolicy with Powershell id: b8f9947e-065a-11ec-aafb-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the execution of `powershell.exe` running - the `Get-DomainPolicy` cmdlet, which is used to retrieve password policies in a - Windows domain. It leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process names and command-line executions. This activity is significant - as it indicates potential reconnaissance efforts by adversaries to gather domain - policy information, which is crucial for planning further attacks. If confirmed - malicious, this could lead to unauthorized access to sensitive domain configurations, - aiding in privilege escalation and lateral movement within the network. +description: The following analytic detects the execution of `powershell.exe` running the `Get-DomainPolicy` cmdlet, which is used to retrieve password policies in a Windows domain. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential reconnaissance efforts by adversaries to gather domain policy information, which is crucial for planning further attacks. If confirmed malicious, this could lead to unauthorized access to sensitive domain configurations, aiding in privilege escalation and lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="cmd.exe" - OR Processes.process_name="powershell*") AND Processes.process = "*Get-DomainPolicy*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `get_domainpolicy_with_powershell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="cmd.exe" + OR + Processes.process_name="powershell*" + ) + AND Processes.process = "*Get-DomainPolicy*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `get_domainpolicy_with_powershell_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://github.com/S1ckB0y1337/Active-Directory-Exploitation-Cheat-Sheet -- https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainPolicy/ -- https://attack.mitre.org/techniques/T1201/ + - https://github.com/S1ckB0y1337/Active-Directory-Exploitation-Cheat-Sheet + - https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainPolicy/ + - https://attack.mitre.org/techniques/T1201/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: an instance of process $process_name$ with commandline $process$ on $dest$ - risk_objects: - - field: user - type: user - score: 30 - - field: dest - type: system - score: 30 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: an instance of process $process_name$ with commandline $process$ on $dest$ + risk_objects: + - field: user + type: user + score: 30 + - field: dest + type: system + score: 30 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1201 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1201 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/pwd_policy_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/pwd_policy_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/get_domainpolicy_with_powershell_script_block.yml b/detections/endpoint/get_domainpolicy_with_powershell_script_block.yml index 6446e98528..154ab72e65 100644 --- a/detections/endpoint/get_domainpolicy_with_powershell_script_block.yml +++ b/detections/endpoint/get_domainpolicy_with_powershell_script_block.yml @@ -1,70 +1,64 @@ name: Get DomainPolicy with Powershell Script Block id: a360d2b2-065a-11ec-b0bf-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of the `Get-DomainPolicy` - cmdlet using PowerShell Script Block Logging (EventCode=4104). It leverages logs - capturing script block text to identify attempts to obtain the password policy in - a Windows domain. This activity is significant as it indicates potential reconnaissance - efforts by adversaries or Red Teams to gather domain policy information, which is - crucial for planning further attacks. If confirmed malicious, this behavior could - lead to detailed knowledge of domain security settings, aiding in privilege escalation - or lateral movement within the network. +description: The following analytic detects the execution of the `Get-DomainPolicy` cmdlet using PowerShell Script Block Logging (EventCode=4104). It leverages logs capturing script block text to identify attempts to obtain the password policy in a Windows domain. This activity is significant as it indicates potential reconnaissance efforts by adversaries or Red Teams to gather domain policy information, which is crucial for planning further attacks. If confirmed malicious, this behavior could lead to detailed knowledge of domain security settings, aiding in privilege escalation or lateral movement within the network. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 ScriptBlockText ="*Get-DomainPolicy*" | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_domainpolicy_with_powershell_script_block_filter`' -how_to_implement: The following analytic requires PowerShell operational logs to be - imported. Modify the powershell macro as needed to match the sourcetype or add index. - This analytic is specific to 4104, or PowerShell Script Block Logging. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText ="*Get-DomainPolicy*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `get_domainpolicy_with_powershell_script_block_filter` +how_to_implement: The following analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://github.com/S1ckB0y1337/Active-Directory-Exploitation-Cheat-Sheet -- https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainPolicy/ -- https://attack.mitre.org/techniques/T1201/ + - https://github.com/S1ckB0y1337/Active-Directory-Exploitation-Cheat-Sheet + - https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainPolicy/ + - https://attack.mitre.org/techniques/T1201/ drilldown_searches: -- name: View the detection results for - "$Computer$" and "$user$" - search: '%original_detection_search% | search Computer = "$Computer$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$Computer$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" and "$user$" + search: '%original_detection_search% | search Computer = "$Computer$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Powershell process with command line indicative of querying domain policy. - risk_objects: - - field: dest - type: system - score: 30 - - field: user_id - type: user - score: 30 - threat_objects: [] + message: Powershell process with command line indicative of querying domain policy. + risk_objects: + - field: dest + type: system + score: 30 + - field: user_id + type: user + score: 30 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1201 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1201 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/domainpolicy.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/domainpolicy.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/get_domaintrust_with_powershell.yml b/detections/endpoint/get_domaintrust_with_powershell.yml index e63a8ca444..1aed057901 100644 --- a/detections/endpoint/get_domaintrust_with_powershell.yml +++ b/detections/endpoint/get_domaintrust_with_powershell.yml @@ -1,84 +1,66 @@ name: Get-DomainTrust with PowerShell id: 4fa7f846-054a-11ec-a836-acde48001122 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies the execution of the Get-DomainTrust - command from PowerView using PowerShell, which is used to gather domain trust information. - This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process and command-line telemetry. This activity is significant as - it indicates potential reconnaissance efforts by an adversary to understand domain - trust relationships, which can inform lateral movement strategies. If confirmed - malicious, this could allow attackers to map out the network, identify potential - targets, and plan further attacks, potentially compromising additional systems within - the domain. +description: The following analytic identifies the execution of the Get-DomainTrust command from PowerView using PowerShell, which is used to gather domain trust information. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and command-line telemetry. This activity is significant as it indicates potential reconnaissance efforts by an adversary to understand domain trust relationships, which can inform lateral movement strategies. If confirmed malicious, this could allow attackers to map out the network, identify potential targets, and plan further attacks, potentially compromising additional systems within the domain. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process=*get-domaintrust* - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `get_domaintrust_with_powershell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Limited false positives as this requires an active Administrator - or adversary to bring in, import, and execute. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process=*get-domaintrust* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `get_domaintrust_with_powershell_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Limited false positives as this requires an active Administrator or adversary to bring in, import, and execute. references: -- https://blog.harmj0y.net/redteaming/a-guide-to-attacking-domain-trusts/ + - https://blog.harmj0y.net/redteaming/a-guide-to-attacking-domain-trusts/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious PowerShell Get-DomainTrust was identified on endpoint $dest$ - by user $user$. - risk_objects: - - field: user - type: user - score: 12 - - field: dest - type: system - score: 12 - threat_objects: [] + message: Suspicious PowerShell Get-DomainTrust was identified on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 12 + - field: dest + type: system + score: 12 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1482 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1482 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/get_domaintrust_with_powershell_script_block.yml b/detections/endpoint/get_domaintrust_with_powershell_script_block.yml index 4f2401efa6..16883b7370 100644 --- a/detections/endpoint/get_domaintrust_with_powershell_script_block.yml +++ b/detections/endpoint/get_domaintrust_with_powershell_script_block.yml @@ -1,80 +1,66 @@ name: Get-DomainTrust with PowerShell Script Block id: 89275e7e-0548-11ec-bf75-acde48001122 -version: 8 -date: '2025-06-24' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: - The following analytic detects the execution of the Get-DomainTrust command - from PowerView using PowerShell Script Block Logging (EventCode=4104). This method - captures the full command sent to PowerShell, allowing for detailed inspection. - Identifying this activity is significant because it may indicate an attempt to gather - domain trust information, which is often a precursor to lateral movement or privilege - escalation. If confirmed malicious, this activity could enable an attacker to map - trust relationships within the domain, potentially leading to further exploitation - and compromise of additional systems. +description: The following analytic detects the execution of the Get-DomainTrust command from PowerView using PowerShell Script Block Logging (EventCode=4104). This method captures the full command sent to PowerShell, allowing for detailed inspection. Identifying this activity is significant because it may indicate an attempt to gather domain trust information, which is often a precursor to lateral movement or privilege escalation. If confirmed malicious, this activity could enable an attacker to map trust relationships within the domain, potentially leading to further exploitation and compromise of additional systems. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText = "*get-domaintrust*" | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_domaintrust_with_powershell_script_block_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - It is possible certain system management frameworks utilize - this command to gather trust information. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*get-domaintrust*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `get_domaintrust_with_powershell_script_block_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: It is possible certain system management frameworks utilize this command to gather trust information. references: - - https://blog.harmj0y.net/redteaming/a-guide-to-attacking-domain-trusts/ - - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. - - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 - - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf - - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ + - https://blog.harmj0y.net/redteaming/a-guide-to-attacking-domain-trusts/ + - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 + - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf + - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ drilldown_searches: - - name: View the detection results for - "$user_id$" and "$dest$" - search: '%original_detection_search% | search user_id = "$user_id$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user_id$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user_id$" and "$dest$" + search: '%original_detection_search% | search user_id = "$user_id$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user_id$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - Suspicious PowerShell Get-DomainTrust was identified on endpoint $dest$ - by user $user_id$. - risk_objects: - - field: user_id - type: user - score: 12 - - field: dest - type: system - score: 12 - threat_objects: [] + message: Suspicious PowerShell Get-DomainTrust was identified on endpoint $dest$ by user $user_id$. + risk_objects: + - field: user_id + type: user + score: 12 + - field: dest + type: system + score: 12 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1482 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1482 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/domaintrust.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/domaintrust.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/get_domainuser_with_powershell.yml b/detections/endpoint/get_domainuser_with_powershell.yml index a788790e3c..243f4076fe 100644 --- a/detections/endpoint/get_domainuser_with_powershell.yml +++ b/detections/endpoint/get_domainuser_with_powershell.yml @@ -1,86 +1,74 @@ name: Get DomainUser with PowerShell id: 9a5a41d6-04e7-11ec-923c-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the execution of `powershell.exe` with - command-line arguments used to enumerate domain users via the `Get-DomainUser` command. - This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process names and command-line executions mapped to the `Processes` - node of the `Endpoint` data model. This activity is significant as it indicates - potential reconnaissance efforts by adversaries or Red Teams using PowerView for - Active Directory discovery. If confirmed malicious, this could allow attackers to - gain situational awareness and identify valuable targets within the domain, potentially - leading to further exploitation. +description: The following analytic detects the execution of `powershell.exe` with command-line arguments used to enumerate domain users via the `Get-DomainUser` command. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions mapped to the `Processes` node of the `Endpoint` data model. This activity is significant as it indicates potential reconnaissance efforts by adversaries or Red Teams using PowerView for Active Directory discovery. If confirmed malicious, this could allow attackers to gain situational awareness and identify valuable targets within the domain, potentially leading to further exploitation. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="cmd.exe" - OR Processes.process_name="powershell*") AND Processes.process = "*Get-DomainUser*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `get_domainuser_with_powershell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="cmd.exe" + OR + Processes.process_name="powershell*" + ) + AND Processes.process = "*Get-DomainUser*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `get_domainuser_with_powershell_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainUser/ + - https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainUser/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: an instance of process $process_name$ with commandline $process$ on $dest$ - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: an instance of process $process_name$ with commandline $process$ on $dest$ + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - Active Directory Discovery - - CISA AA23-347A - asset_type: Endpoint - mitre_attack_id: - - T1087.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - CISA AA23-347A + asset_type: Endpoint + mitre_attack_id: + - T1087.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/get_domainuser_with_powershell_script_block.yml b/detections/endpoint/get_domainuser_with_powershell_script_block.yml index 98d35ed774..bfe224ff22 100644 --- a/detections/endpoint/get_domainuser_with_powershell_script_block.yml +++ b/detections/endpoint/get_domainuser_with_powershell_script_block.yml @@ -1,70 +1,63 @@ name: Get DomainUser with PowerShell Script Block id: 61994268-04f4-11ec-865c-acde48001122 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the execution of the `Get-DomainUser` - cmdlet using PowerShell Script Block Logging (EventCode=4104). This cmdlet is part - of PowerView, a tool often used for domain enumeration. The detection leverages - PowerShell operational logs to identify instances where this command is executed. - Monitoring this activity is crucial as it may indicate an adversary's attempt to - gather information about domain users, which is a common step in Active Directory - Discovery. If confirmed malicious, this activity could lead to further reconnaissance - and potential exploitation of domain resources. +description: The following analytic detects the execution of the `Get-DomainUser` cmdlet using PowerShell Script Block Logging (EventCode=4104). This cmdlet is part of PowerView, a tool often used for domain enumeration. The detection leverages PowerShell operational logs to identify instances where this command is executed. Monitoring this activity is crucial as it may indicate an adversary's attempt to gather information about domain users, which is a common step in Active Directory Discovery. If confirmed malicious, this activity could lead to further reconnaissance and potential exploitation of domain resources. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 ScriptBlockText = "*Get-DomainUser*" | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_domainuser_with_powershell_script_block_filter`' -how_to_implement: The following Hunting analytic requires PowerShell operational logs - to be imported. Modify the powershell macro as needed to match the sourcetype or - add index. This analytic is specific to 4104, or PowerShell Script Block Logging. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*Get-DomainUser*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `get_domainuser_with_powershell_script_block_filter` +how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainUser/ + - https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainUser/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Powershell process having commandline "*Get-DomainUser*" for user enumeration - on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - - field: user_id - type: user - score: 25 - threat_objects: [] + message: Powershell process having commandline "*Get-DomainUser*" for user enumeration on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + - field: user_id + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - - CISA AA23-347A - asset_type: Endpoint - mitre_attack_id: - - T1087.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - CISA AA23-347A + asset_type: Endpoint + mitre_attack_id: + - T1087.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-powershell-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-powershell-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/get_foresttrust_with_powershell.yml b/detections/endpoint/get_foresttrust_with_powershell.yml index 9abb4dad18..2b3c88027c 100644 --- a/detections/endpoint/get_foresttrust_with_powershell.yml +++ b/detections/endpoint/get_foresttrust_with_powershell.yml @@ -1,84 +1,68 @@ name: Get-ForestTrust with PowerShell id: 584f4884-0bf1-11ec-a5ec-acde48001122 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of the Get-ForestTrust command - via PowerShell, commonly used by adversaries to gather domain trust information. - This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process names and command-line executions. Identifying this activity - is crucial as it indicates potential reconnaissance efforts to map out domain trusts, - which can inform further attacks. If confirmed malicious, this activity could allow - attackers to understand domain relationships, aiding in lateral movement and privilege - escalation within the network. +description: The following analytic detects the execution of the Get-ForestTrust command via PowerShell, commonly used by adversaries to gather domain trust information. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. Identifying this activity is crucial as it indicates potential reconnaissance efforts to map out domain trusts, which can inform further attacks. If confirmed malicious, this activity could allow attackers to understand domain relationships, aiding in lateral movement and privilege escalation within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe - OR Processes.process_name=cmd.exe Processes.process=*get-foresttrust* by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `get_foresttrust_with_powershell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Limited false positives as this requires an active Administrator - or adversary to bring in, import, and execute. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=powershell.exe + OR + Processes.process_name=cmd.exe Processes.process=*get-foresttrust* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `get_foresttrust_with_powershell_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Limited false positives as this requires an active Administrator or adversary to bring in, import, and execute. references: -- https://powersploit.readthedocs.io/en/latest/Recon/Get-ForestTrust/ + - https://powersploit.readthedocs.io/en/latest/Recon/Get-ForestTrust/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious PowerShell Get-ForestTrust was identified on endpoint $dest$ - by user $user$. - risk_objects: - - field: user - type: user - score: 12 - - field: dest - type: system - score: 12 - threat_objects: [] + message: Suspicious PowerShell Get-ForestTrust was identified on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 12 + - field: dest + type: system + score: 12 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1482 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1482 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/get_foresttrust_with_powershell_script_block.yml b/detections/endpoint/get_foresttrust_with_powershell_script_block.yml index 21779c85fe..a740bb36bd 100644 --- a/detections/endpoint/get_foresttrust_with_powershell_script_block.yml +++ b/detections/endpoint/get_foresttrust_with_powershell_script_block.yml @@ -1,76 +1,64 @@ name: Get-ForestTrust with PowerShell Script Block id: 70fac80e-0bf1-11ec-9ba0-acde48001122 -version: 8 -date: '2025-06-24' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: - The following analytic detects the execution of the Get-ForestTrust command - from PowerSploit using PowerShell Script Block Logging (EventCode=4104). This method - captures the full command sent to PowerShell, providing detailed visibility into - potentially suspicious activities. Monitoring this behavior is crucial as it can - indicate an attempt to gather domain trust information, which is often a precursor - to lateral movement or privilege escalation. If confirmed malicious, this activity - could allow an attacker to map trust relationships within the domain, facilitating - further exploitation and access to sensitive resources. +description: The following analytic detects the execution of the Get-ForestTrust command from PowerSploit using PowerShell Script Block Logging (EventCode=4104). This method captures the full command sent to PowerShell, providing detailed visibility into potentially suspicious activities. Monitoring this behavior is crucial as it can indicate an attempt to gather domain trust information, which is often a precursor to lateral movement or privilege escalation. If confirmed malicious, this activity could allow an attacker to map trust relationships within the domain, facilitating further exploitation and access to sensitive resources. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText = "*get-foresttrust*" | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_foresttrust_with_powershell_script_block_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*get-foresttrust*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `get_foresttrust_with_powershell_script_block_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. known_false_positives: False positives may be present. Tune as needed. references: - - https://powersploit.readthedocs.io/en/latest/Recon/Get-ForestTrust/ - - https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html + - https://powersploit.readthedocs.io/en/latest/Recon/Get-ForestTrust/ + - https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html drilldown_searches: - - name: View the detection results for - "$user_id$" and "$dest$" - search: '%original_detection_search% | search user_id = "$user_id$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user_id$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user_id$" and "$dest$" + search: '%original_detection_search% | search user_id = "$user_id$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user_id$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - Suspicious PowerShell Get-ForestTrust was identified on endpoint $dest$ - by user $user_id$. - risk_objects: - - field: user_id - type: user - score: 12 - - field: dest - type: system - score: 12 - threat_objects: [] + message: Suspicious PowerShell Get-ForestTrust was identified on endpoint $dest$ by user $user_id$. + risk_objects: + - field: user_id + type: user + score: 12 + - field: dest + type: system + score: 12 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1482 - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1482 + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/discovery/windows-powershell-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/discovery/windows-powershell-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/get_wmiobject_group_discovery.yml b/detections/endpoint/get_wmiobject_group_discovery.yml index 9f73451b62..85c2cd10e6 100644 --- a/detections/endpoint/get_wmiobject_group_discovery.yml +++ b/detections/endpoint/get_wmiobject_group_discovery.yml @@ -1,58 +1,53 @@ name: Get WMIObject Group Discovery id: 5434f670-155d-11ec-8cca-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic detects the use of the `Get-WMIObject Win32_Group` - command executed via PowerShell to enumerate local groups on an endpoint. This detection - leverages data from Endpoint Detection and Response (EDR) agents, focusing on process - names and command-line executions. Identifying local groups can be a precursor to - privilege escalation or lateral movement. If confirmed malicious, this activity - could allow an attacker to map out group memberships, aiding in further exploitation - or unauthorized access to sensitive resources. +description: The following analytic detects the use of the `Get-WMIObject Win32_Group` command executed via PowerShell to enumerate local groups on an endpoint. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. Identifying local groups can be a precursor to privilege escalation or lateral movement. If confirmed malicious, this activity could allow an attacker to map out group memberships, aiding in further exploitation or unauthorized access to sensitive resources. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=powershell.exe - OR processes.process_name=cmd.exe) (Processes.process="*Get-WMIObject*" AND Processes.process="*Win32_Group*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | - `get_wmiobject_group_discovery_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name=powershell.exe + OR + processes.process_name=cmd.exe + ) + (Processes.process="*Get-WMIObject*" AND Processes.process="*Win32_Group*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `get_wmiobject_group_discovery_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives may be present. Tune as needed. references: -- https://attack.mitre.org/techniques/T1069/001/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1069.001/T1069.001.md + - https://attack.mitre.org/techniques/T1069/001/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1069.001/T1069.001.md tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1069.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1069.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.001/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.001/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/get_wmiobject_group_discovery_with_script_block_logging.yml b/detections/endpoint/get_wmiobject_group_discovery_with_script_block_logging.yml index c08105e3a9..86d4b1cfdc 100644 --- a/detections/endpoint/get_wmiobject_group_discovery_with_script_block_logging.yml +++ b/detections/endpoint/get_wmiobject_group_discovery_with_script_block_logging.yml @@ -1,52 +1,48 @@ name: Get WMIObject Group Discovery with Script Block Logging id: 69df7f7c-155d-11ec-a055-acde48001122 -version: 9 -date: '2025-06-24' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: - The following analytic detects the execution of the `Get-WMIObject Win32_Group` - command using PowerShell Script Block Logging (EventCode=4104). This method captures - the full command sent to PowerShell, allowing for detailed analysis. Identifying - group information on an endpoint is not inherently malicious but can be suspicious - based on context such as time, endpoint, and user. This activity is significant - as it may indicate reconnaissance efforts by an attacker. If confirmed malicious, - it could lead to further enumeration and potential lateral movement within the network. +description: The following analytic detects the execution of the `Get-WMIObject Win32_Group` command using PowerShell Script Block Logging (EventCode=4104). This method captures the full command sent to PowerShell, allowing for detailed analysis. Identifying group information on an endpoint is not inherently malicious but can be suspicious based on context such as time, endpoint, and user. This activity is significant as it may indicate reconnaissance efforts by an attacker. If confirmed malicious, it could lead to further enumeration and potential lateral movement within the network. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText = "*Get-WMIObject*" AND ScriptBlockText - = "*Win32_Group*" | fillnull | stats count min(_time) as firstTime max(_time) as - lastTime by dest signature signature_id user_id vendor_product EventID Guid Opcode - Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `get_wmiobject_group_discovery_with_script_block_logging_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*Get-WMIObject*" AND ScriptBlockText = "*Win32_Group*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `get_wmiobject_group_discovery_with_script_block_logging_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. known_false_positives: False positives may be present. Tune as needed. references: - - https://www.splunk.com/en_us/blog/security/powershell-detections-threat-research-release-august-2021.html - - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1069.001/T1069.001.md - - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. - - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 - - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf - - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ + - https://www.splunk.com/en_us/blog/security/powershell-detections-threat-research-release-august-2021.html + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1069.001/T1069.001.md + - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 + - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf + - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1069.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1069.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.001/atomic_red_team/windows-powershell-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.001/atomic_red_team/windows-powershell-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/getadcomputer_with_powershell.yml b/detections/endpoint/getadcomputer_with_powershell.yml index 1826f611ef..7fbda65ded 100644 --- a/detections/endpoint/getadcomputer_with_powershell.yml +++ b/detections/endpoint/getadcomputer_with_powershell.yml @@ -1,58 +1,51 @@ name: GetAdComputer with PowerShell id: c5a31f80-5888-4d81-9f78-1cc65026316e -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting -description: The following analytic detects the execution of `powershell.exe` with - the `Get-AdComputer` commandlet, which is used to discover remote systems within - a domain. This detection leverages data from Endpoint Detection and Response (EDR) - agents, focusing on process names and command-line arguments. This activity is significant - because it indicates potential reconnaissance efforts by adversaries to map out - domain computers, which is a common step in the attack lifecycle. If confirmed malicious, - this behavior could allow attackers to gain situational awareness and plan further - attacks, potentially leading to unauthorized access and data exfiltration. +description: The following analytic detects the execution of `powershell.exe` with the `Get-AdComputer` commandlet, which is used to discover remote systems within a domain. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because it indicates potential reconnaissance efforts by adversaries to map out domain computers, which is a common step in the attack lifecycle. If confirmed malicious, this behavior could allow attackers to gain situational awareness and plan further attacks, potentially leading to unauthorized access and data exfiltration. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") - (Processes.process=*Get-AdComputer*) by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getadcomputer_with_powershell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="powershell.exe" + ) + (Processes.process=*Get-AdComputer*) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `getadcomputer_with_powershell_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1018/ + - https://attack.mitre.org/techniques/T1018/ tags: - analytic_story: - - Active Directory Discovery - - Medusa Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1018 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - Medusa Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1018 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/getadcomputer_with_powershell_script_block.yml b/detections/endpoint/getadcomputer_with_powershell_script_block.yml index d25e64a754..af602c3b19 100644 --- a/detections/endpoint/getadcomputer_with_powershell_script_block.yml +++ b/detections/endpoint/getadcomputer_with_powershell_script_block.yml @@ -1,53 +1,46 @@ name: GetAdComputer with PowerShell Script Block id: a9a1da02-8e27-4bf7-a348-f4389c9da487 -version: 9 -date: '2025-06-24' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting -description: - The following analytic detects the execution of the `Get-AdComputer` - PowerShell commandlet using PowerShell Script Block Logging (EventCode=4104). This - detection leverages script block text to identify when this commandlet is run. The - `Get-AdComputer` commandlet is significant as it can be used by adversaries to enumerate - all domain computers, aiding in situational awareness and Active Directory discovery. - If confirmed malicious, this activity could allow attackers to map the network, - identify targets, and plan further attacks, potentially leading to unauthorized - access and data exfiltration. +description: The following analytic detects the execution of the `Get-AdComputer` PowerShell commandlet using PowerShell Script Block Logging (EventCode=4104). This detection leverages script block text to identify when this commandlet is run. The `Get-AdComputer` commandlet is significant as it can be used by adversaries to enumerate all domain computers, aiding in situational awareness and Active Directory discovery. If confirmed malicious, this activity could allow attackers to map the network, identify targets, and plan further attacks, potentially leading to unauthorized access and data exfiltration. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 (ScriptBlockText = "*Get-AdComputer*") | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `getadcomputer_with_powershell_script_block_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - Administrators or power users may use this PowerShell commandlet - for troubleshooting. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 (ScriptBlockText = "*Get-AdComputer*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `getadcomputer_with_powershell_script_block_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting. references: - - https://attack.mitre.org/techniques/T1018/ - - https://docs.microsoft.com/en-us/powershell/module/activedirectory/get-adgroup?view=windowsserver2019-ps + - https://attack.mitre.org/techniques/T1018/ + - https://docs.microsoft.com/en-us/powershell/module/activedirectory/get-adgroup?view=windowsserver2019-ps tags: - analytic_story: - - Active Directory Discovery - - CISA AA22-320A - - Medusa Ransomware - - Gozi Malware - asset_type: Endpoint - mitre_attack_id: - - T1018 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - CISA AA22-320A + - Medusa Ransomware + - Gozi Malware + asset_type: Endpoint + mitre_attack_id: + - T1018 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/getadgroup_with_powershell.yml b/detections/endpoint/getadgroup_with_powershell.yml index 9dbd986418..c2e250b276 100644 --- a/detections/endpoint/getadgroup_with_powershell.yml +++ b/detections/endpoint/getadgroup_with_powershell.yml @@ -1,58 +1,51 @@ name: GetAdGroup with PowerShell id: 872e3063-0fc4-4e68-b2f3-f2b99184a708 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting -description: The following analytic detects the execution of `powershell.exe` with - the `Get-AdGroup` commandlet, which is used to query domain groups in a Windows - Domain. This detection leverages data from Endpoint Detection and Response (EDR) - agents, focusing on process names and command-line arguments. Monitoring this activity - is crucial as it may indicate an adversary or Red Team enumerating domain groups - for situational awareness and Active Directory discovery. If confirmed malicious, - this activity could lead to further reconnaissance, privilege escalation, or lateral - movement within the network. +description: The following analytic detects the execution of `powershell.exe` with the `Get-AdGroup` commandlet, which is used to query domain groups in a Windows Domain. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. Monitoring this activity is crucial as it may indicate an adversary or Red Team enumerating domain groups for situational awareness and Active Directory discovery. If confirmed malicious, this activity could lead to further reconnaissance, privilege escalation, or lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") - (Processes.process=*Get-AdGroup*) by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getadgroup_with_powershell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="powershell.exe" + ) + (Processes.process=*Get-AdGroup*) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `getadgroup_with_powershell_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1069/002/ -- https://docs.microsoft.com/en-us/powershell/module/activedirectory/get-adgroup?view=windowsserver2019-ps + - https://attack.mitre.org/techniques/T1069/002/ + - https://docs.microsoft.com/en-us/powershell/module/activedirectory/get-adgroup?view=windowsserver2019-ps tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1069.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1069.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/getadgroup_with_powershell_script_block.yml b/detections/endpoint/getadgroup_with_powershell_script_block.yml index 683b8441a2..af425b9d1c 100644 --- a/detections/endpoint/getadgroup_with_powershell_script_block.yml +++ b/detections/endpoint/getadgroup_with_powershell_script_block.yml @@ -1,50 +1,45 @@ name: GetAdGroup with PowerShell Script Block id: e4c73d68-794b-468d-b4d0-dac1772bbae7 -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting -description: - The following analytic detects the execution of the `Get-AdGroup` PowerShell - cmdlet using PowerShell Script Block Logging (EventCode=4104). This cmdlet is used - to enumerate all domain groups, which adversaries may exploit for situational awareness - and Active Directory discovery. Monitoring this activity is crucial as it can indicate - reconnaissance efforts within the network. If confirmed malicious, this behavior - could lead to further exploitation, such as privilege escalation or lateral movement, - by providing attackers with detailed information about the domain's group structure. +description: The following analytic detects the execution of the `Get-AdGroup` PowerShell cmdlet using PowerShell Script Block Logging (EventCode=4104). This cmdlet is used to enumerate all domain groups, which adversaries may exploit for situational awareness and Active Directory discovery. Monitoring this activity is crucial as it can indicate reconnaissance efforts within the network. If confirmed malicious, this behavior could lead to further exploitation, such as privilege escalation or lateral movement, by providing attackers with detailed information about the domain's group structure. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText = "*Get-ADGroup*" | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getadgroup_with_powershell_script_block_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - Administrators or power users may use this PowerShell commandlet - for troubleshooting. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*Get-ADGroup*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `getadgroup_with_powershell_script_block_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting. references: - - https://attack.mitre.org/techniques/T1069/002/ - - https://docs.microsoft.com/en-us/powershell/module/activedirectory/get-adgroup?view=windowsserver2019-ps + - https://attack.mitre.org/techniques/T1069/002/ + - https://docs.microsoft.com/en-us/powershell/module/activedirectory/get-adgroup?view=windowsserver2019-ps tags: - analytic_story: - - Scattered Lapsus$ Hunters - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1069.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Scattered Lapsus$ Hunters + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1069.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-powershell-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-powershell-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/getcurrent_user_with_powershell.yml b/detections/endpoint/getcurrent_user_with_powershell.yml index 6380c2f61e..0f9beea366 100644 --- a/detections/endpoint/getcurrent_user_with_powershell.yml +++ b/detections/endpoint/getcurrent_user_with_powershell.yml @@ -1,58 +1,50 @@ name: GetCurrent User with PowerShell id: 7eb9c3d5-c98c-4088-acc5-8240bad15379 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting -description: The following analytic detects the execution of `powershell.exe` with - command-line arguments invoking the `GetCurrent` method of the WindowsIdentity .NET - class. This detection leverages data from Endpoint Detection and Response (EDR) - agents, focusing on process names and command-line executions. This activity is - significant as adversaries may use this method to identify the logged-in user on - a compromised endpoint, aiding in situational awareness and Active Directory discovery. - If confirmed malicious, this could allow attackers to gain insights into user context, - potentially facilitating further exploitation and lateral movement within the network. +description: The following analytic detects the execution of `powershell.exe` with command-line arguments invoking the `GetCurrent` method of the WindowsIdentity .NET class. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as adversaries may use this method to identify the logged-in user on a compromised endpoint, aiding in situational awareness and Active Directory discovery. If confirmed malicious, this could allow attackers to gain insights into user context, potentially facilitating further exploitation and lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") - (Processes.process=*System.Security.Principal.WindowsIdentity* OR Processes.process=*GetCurrent()*) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `getcurrent_user_with_powershell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="powershell.exe" + ) + (Processes.process=*System.Security.Principal.WindowsIdentity* OR Processes.process=*GetCurrent()*) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `getcurrent_user_with_powershell_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1033/ + - https://attack.mitre.org/techniques/T1033/ tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1033 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1033 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/getcurrent_user_with_powershell_script_block.yml b/detections/endpoint/getcurrent_user_with_powershell_script_block.yml index 6dad5db06d..fa1c6ba29e 100644 --- a/detections/endpoint/getcurrent_user_with_powershell_script_block.yml +++ b/detections/endpoint/getcurrent_user_with_powershell_script_block.yml @@ -1,51 +1,44 @@ name: GetCurrent User with PowerShell Script Block id: 80879283-c30f-44f7-8471-d1381f6d437a -version: 8 -date: '2025-06-24' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting -description: - The following analytic detects the execution of the `GetCurrent` method - from the WindowsIdentity .NET class using PowerShell Script Block Logging (EventCode=4104). - This method identifies the current Windows user. The detection leverages PowerShell - script block logs to identify when this method is called. This activity is significant - because adversaries and Red Teams may use it to gain situational awareness and perform - Active Directory discovery on compromised endpoints. If confirmed malicious, this - could allow attackers to map out user accounts and potentially escalate privileges - or move laterally within the network. +description: The following analytic detects the execution of the `GetCurrent` method from the WindowsIdentity .NET class using PowerShell Script Block Logging (EventCode=4104). This method identifies the current Windows user. The detection leverages PowerShell script block logs to identify when this method is called. This activity is significant because adversaries and Red Teams may use it to gain situational awareness and perform Active Directory discovery on compromised endpoints. If confirmed malicious, this could allow attackers to map out user accounts and potentially escalate privileges or move laterally within the network. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText = "*[System.Security.Principal.WindowsIdentity]*" ScriptBlockText - = "*GetCurrent()*" | fillnull | stats count min(_time) as firstTime max(_time) as - lastTime by dest signature signature_id user_id vendor_product EventID Guid Opcode - Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `getcurrent_user_with_powershell_script_block_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - Administrators or power users may use this PowerShell commandlet - for troubleshooting. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*[System.Security.Principal.WindowsIdentity]*" ScriptBlockText = "*GetCurrent()*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `getcurrent_user_with_powershell_script_block_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting. references: - - https://attack.mitre.org/techniques/T1033/ - - https://docs.microsoft.com/en-us/dotnet/api/system.security.principal.windowsidentity.getcurrent?view=net-6.0&viewFallbackFrom=net-5.0 + - https://attack.mitre.org/techniques/T1033/ + - https://docs.microsoft.com/en-us/dotnet/api/system.security.principal.windowsidentity.getcurrent?view=net-6.0&viewFallbackFrom=net-5.0 tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1033 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1033 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/AD_discovery/windows-powershell-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/AD_discovery/windows-powershell-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/getdomaincomputer_with_powershell.yml b/detections/endpoint/getdomaincomputer_with_powershell.yml index be9d42d8a0..69b44a1197 100644 --- a/detections/endpoint/getdomaincomputer_with_powershell.yml +++ b/detections/endpoint/getdomaincomputer_with_powershell.yml @@ -1,78 +1,66 @@ name: GetDomainComputer with PowerShell id: ed550c19-712e-43f6-bd19-6f58f61b3a5e -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the execution of `powershell.exe` with - command-line arguments that utilize `Get-DomainComputer` to discover remote systems. - This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process names and command-line executions. This activity is significant - as `Get-DomainComputer` is part of PowerView, a tool often used by adversaries for - domain enumeration and situational awareness. If confirmed malicious, this activity - could allow attackers to map out the network, identify critical systems, and plan - further attacks, potentially leading to unauthorized access and data exfiltration. +description: The following analytic detects the execution of `powershell.exe` with command-line arguments that utilize `Get-DomainComputer` to discover remote systems. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as `Get-DomainComputer` is part of PowerView, a tool often used by adversaries for domain enumeration and situational awareness. If confirmed malicious, this activity could allow attackers to map out the network, identify critical systems, and plan further attacks, potentially leading to unauthorized access and data exfiltration. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") - (Processes.process=*Get-DomainComputer*) by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getdomaincomputer_with_powershell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="powershell.exe" + ) + (Processes.process=*Get-DomainComputer*) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `getdomaincomputer_with_powershell_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use PowerView for troubleshooting. references: -- https://attack.mitre.org/techniques/T1018/ + - https://attack.mitre.org/techniques/T1018/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Remote system discovery enumeration on $dest$ by $user$ - risk_objects: - - field: dest - type: system - score: 24 - threat_objects: [] + message: Remote system discovery enumeration on $dest$ by $user$ + risk_objects: + - field: dest + type: system + score: 24 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1018 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1018 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/getdomaincomputer_with_powershell_script_block.yml b/detections/endpoint/getdomaincomputer_with_powershell_script_block.yml index 075b5fb8a0..b596cdf2f8 100644 --- a/detections/endpoint/getdomaincomputer_with_powershell_script_block.yml +++ b/detections/endpoint/getdomaincomputer_with_powershell_script_block.yml @@ -1,71 +1,60 @@ name: GetDomainComputer with PowerShell Script Block id: f64da023-b988-4775-8d57-38e512beb56e -version: 8 -date: '2025-06-24' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: - The following analytic detects the execution of the `Get-DomainComputer` - commandlet using PowerShell Script Block Logging (EventCode=4104). This commandlet - is part of PowerView, a tool often used for enumerating domain computers within - Windows environments. The detection leverages script block text analysis to identify - this specific command. Monitoring this activity is crucial as it can indicate an - adversary's attempt to gather information about domain computers, which is a common - step in Active Directory reconnaissance. If confirmed malicious, this activity could - lead to further network enumeration and potential lateral movement within the domain. +description: The following analytic detects the execution of the `Get-DomainComputer` commandlet using PowerShell Script Block Logging (EventCode=4104). This commandlet is part of PowerView, a tool often used for enumerating domain computers within Windows environments. The detection leverages script block text analysis to identify this specific command. Monitoring this activity is crucial as it can indicate an adversary's attempt to gather information about domain computers, which is a common step in Active Directory reconnaissance. If confirmed malicious, this activity could lead to further network enumeration and potential lateral movement within the domain. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainComputer*") | - fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest signature - signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId - ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `getdomaincomputer_with_powershell_script_block_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainComputer*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `getdomaincomputer_with_powershell_script_block_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. known_false_positives: Administrators or power users may use PowerView for troubleshooting. references: - - https://attack.mitre.org/techniques/T1018/ - - https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainComputer/ + - https://attack.mitre.org/techniques/T1018/ + - https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainComputer/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Remote system discovery with PowerView on $dest$ by $user_id$ - risk_objects: - - field: dest - type: system - score: 24 - threat_objects: [] + message: Remote system discovery with PowerView on $dest$ by $user_id$ + risk_objects: + - field: dest + type: system + score: 24 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1018 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1018 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/getdomaincontroller_with_powershell.yml b/detections/endpoint/getdomaincontroller_with_powershell.yml index 37b652e008..a3ff9413bd 100644 --- a/detections/endpoint/getdomaincontroller_with_powershell.yml +++ b/detections/endpoint/getdomaincontroller_with_powershell.yml @@ -1,58 +1,51 @@ name: GetDomainController with PowerShell id: 868ee0e4-52ab-484a-833a-6d85b7c028d0 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting -description: The following analytic detects the execution of `powershell.exe` with - the `Get-DomainController` command, which is used to discover remote systems within - a Windows domain. This detection leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process names and command-line arguments. Monitoring this - activity is crucial as it may indicate an attempt to enumerate domain controllers, - a common tactic in Active Directory discovery. If confirmed malicious, this activity - could allow attackers to gain situational awareness, potentially leading to further - exploitation and lateral movement within the network. +description: The following analytic detects the execution of `powershell.exe` with the `Get-DomainController` command, which is used to discover remote systems within a Windows domain. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. Monitoring this activity is crucial as it may indicate an attempt to enumerate domain controllers, a common tactic in Active Directory discovery. If confirmed malicious, this activity could allow attackers to gain situational awareness, potentially leading to further exploitation and lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") - (Processes.process=*Get-DomainController*) by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getdomaincontroller_with_powershell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="powershell.exe" + ) + (Processes.process=*Get-DomainController*) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `getdomaincontroller_with_powershell_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use PowerView for troubleshooting. references: -- https://attack.mitre.org/techniques/T1018/ -- https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainController/ + - https://attack.mitre.org/techniques/T1018/ + - https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainController/ tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1018 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1018 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/getdomaincontroller_with_powershell_script_block.yml b/detections/endpoint/getdomaincontroller_with_powershell_script_block.yml index 7c96c329af..346a688cd8 100644 --- a/detections/endpoint/getdomaincontroller_with_powershell_script_block.yml +++ b/detections/endpoint/getdomaincontroller_with_powershell_script_block.yml @@ -1,72 +1,59 @@ name: GetDomainController with PowerShell Script Block id: 676b600a-a94d-4951-b346-11329431e6c1 -version: 8 -date: '2025-06-24' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: - The following analytic detects the execution of the `Get-DomainController` - commandlet using PowerShell Script Block Logging (EventCode=4104). This commandlet - is part of PowerView, a tool often used for domain enumeration. The detection leverages - script block text to identify this specific activity. Monitoring this behavior is - crucial as it may indicate an adversary or Red Team performing reconnaissance to - map out domain controllers. If confirmed malicious, this activity could lead to - further domain enumeration, potentially exposing sensitive information and aiding - in lateral movement within the network. +description: The following analytic detects the execution of the `Get-DomainController` commandlet using PowerShell Script Block Logging (EventCode=4104). This commandlet is part of PowerView, a tool often used for domain enumeration. The detection leverages script block text to identify this specific activity. Monitoring this behavior is crucial as it may indicate an adversary or Red Team performing reconnaissance to map out domain controllers. If confirmed malicious, this activity could lead to further domain enumeration, potentially exposing sensitive information and aiding in lateral movement within the network. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainController*") - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `getdomaincontroller_with_powershell_script_block_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - Administrators or power users may use this PowerShell commandlet - for troubleshooting. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainController*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `getdomaincontroller_with_powershell_script_block_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting. references: - - https://attack.mitre.org/techniques/T1018/ - - https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainController/ + - https://attack.mitre.org/techniques/T1018/ + - https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainController/ drilldown_searches: - - name: View the detection results for - "$Computer$" - search: '%original_detection_search% | search Computer = "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$Computer$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" + search: '%original_detection_search% | search Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Remote system discovery with PowerView on $dest$ by $user_id$ - risk_objects: - - field: dest - type: system - score: 24 - threat_objects: [] + message: Remote system discovery with PowerView on $dest$ by $user_id$ + risk_objects: + - field: dest + type: system + score: 24 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1018 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1018 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/getdc.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/getdc.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/getdomaingroup_with_powershell.yml b/detections/endpoint/getdomaingroup_with_powershell.yml index 53bcf083a4..0c28b60993 100644 --- a/detections/endpoint/getdomaingroup_with_powershell.yml +++ b/detections/endpoint/getdomaingroup_with_powershell.yml @@ -1,80 +1,67 @@ name: GetDomainGroup with PowerShell id: 93c94be3-bead-4a60-860f-77ca3fe59903 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the execution of `powershell.exe` with - command-line arguments that query for domain groups using `Get-DomainGroup`. This - detection leverages data from Endpoint Detection and Response (EDR) agents, focusing - on process names and command-line executions mapped to the `Processes` node of the - `Endpoint` data model. Monitoring this activity is crucial as `Get-DomainGroup` - is part of PowerView, a tool often used by adversaries for domain enumeration and - situational awareness. If confirmed malicious, this activity could allow attackers - to gain insights into domain group structures, aiding in further exploitation and - privilege escalation. +description: The following analytic detects the execution of `powershell.exe` with command-line arguments that query for domain groups using `Get-DomainGroup`. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions mapped to the `Processes` node of the `Endpoint` data model. Monitoring this activity is crucial as `Get-DomainGroup` is part of PowerView, a tool often used by adversaries for domain enumeration and situational awareness. If confirmed malicious, this activity could allow attackers to gain insights into domain group structures, aiding in further exploitation and privilege escalation. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") - (Processes.process=*Get-DomainGroup*) by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getdomaingroup_with_powershell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="powershell.exe" + ) + (Processes.process=*Get-DomainGroup*) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `getdomaingroup_with_powershell_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1069/002/ -- https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainGroup/ + - https://attack.mitre.org/techniques/T1069/002/ + - https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainGroup/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Domain group discovery with PowerView on $dest$ by $user$ - risk_objects: - - field: dest - type: system - score: 15 - threat_objects: [] + message: Domain group discovery with PowerView on $dest$ by $user$ + risk_objects: + - field: dest + type: system + score: 15 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1069.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1069.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/getdomaingroup_with_powershell_script_block.yml b/detections/endpoint/getdomaingroup_with_powershell_script_block.yml index e0ea137e3b..f6104c60a2 100644 --- a/detections/endpoint/getdomaingroup_with_powershell_script_block.yml +++ b/detections/endpoint/getdomaingroup_with_powershell_script_block.yml @@ -1,72 +1,60 @@ name: GetDomainGroup with PowerShell Script Block id: 09725404-a44f-4ed3-9efa-8ed5d69e4c53 -version: 9 -date: '2025-06-24' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: - The following analytic detects the execution of the `Get-DomainGroup` - cmdlet using PowerShell Script Block Logging (EventCode=4104). This cmdlet, part - of the PowerView tool, is used to enumerate domain groups within a Windows domain. - The detection leverages script block text to identify this specific command. Monitoring - this activity is crucial as it may indicate an adversary or Red Team performing - reconnaissance to gain situational awareness and map out Active Directory structures. - If confirmed malicious, this activity could lead to further exploitation, including - privilege escalation and lateral movement within the network. +description: The following analytic detects the execution of the `Get-DomainGroup` cmdlet using PowerShell Script Block Logging (EventCode=4104). This cmdlet, part of the PowerView tool, is used to enumerate domain groups within a Windows domain. The detection leverages script block text to identify this specific command. Monitoring this activity is crucial as it may indicate an adversary or Red Team performing reconnaissance to gain situational awareness and map out Active Directory structures. If confirmed malicious, this activity could lead to further exploitation, including privilege escalation and lateral movement within the network. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainGroup*") | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getdomaingroup_with_powershell_script_block_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - Administrators or power users may use this PowerView functions - for troubleshooting. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainGroup*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `getdomaingroup_with_powershell_script_block_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: Administrators or power users may use this PowerView functions for troubleshooting. references: - - https://attack.mitre.org/techniques/T1069/002/ - - https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainGroup/ + - https://attack.mitre.org/techniques/T1069/002/ + - https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainGroup/ drilldown_searches: - - name: View the detection results for - "$Computer$" - search: '%original_detection_search% | search Computer = "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$Computer$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" + search: '%original_detection_search% | search Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Domain group discovery enumeration using PowerView on $dest$ by $user_id$ - risk_objects: - - field: dest - type: system - score: 15 - threat_objects: [] + message: Domain group discovery enumeration using PowerView on $dest$ by $user_id$ + risk_objects: + - field: dest + type: system + score: 15 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1069.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1069.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/domaingroup.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/domaingroup.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/getlocaluser_with_powershell.yml b/detections/endpoint/getlocaluser_with_powershell.yml index d3e2c79871..fc5dd2b5fd 100644 --- a/detections/endpoint/getlocaluser_with_powershell.yml +++ b/detections/endpoint/getlocaluser_with_powershell.yml @@ -1,58 +1,50 @@ name: GetLocalUser with PowerShell id: 85fae8fa-0427-11ec-8b78-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting -description: The following analytic detects the execution of `powershell.exe` with - the `Get-LocalUser` commandlet, which is used to query local user accounts. This - detection leverages data from Endpoint Detection and Response (EDR) agents, focusing - on process names and command-line arguments. Monitoring this activity is significant - because adversaries and Red Teams may use it to enumerate local users for situational - awareness and Active Directory discovery. If confirmed malicious, this activity - could allow attackers to identify potential targets for further exploitation or - privilege escalation within the environment. +description: The following analytic detects the execution of `powershell.exe` with the `Get-LocalUser` commandlet, which is used to query local user accounts. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. Monitoring this activity is significant because adversaries and Red Teams may use it to enumerate local users for situational awareness and Active Directory discovery. If confirmed malicious, this activity could allow attackers to identify potential targets for further exploitation or privilege escalation within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") - (Processes.process=*Get-LocalUser*) by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getlocaluser_with_powershell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrators or power users may use this PowerShell commandlet - for troubleshooting. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="powershell.exe" + ) + (Processes.process=*Get-LocalUser*) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `getlocaluser_with_powershell_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting. references: -- https://attack.mitre.org/techniques/T1087/001/ + - https://attack.mitre.org/techniques/T1087/001/ tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1087.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1087.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.001/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.001/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/getlocaluser_with_powershell_script_block.yml b/detections/endpoint/getlocaluser_with_powershell_script_block.yml index b012a9002e..19d1c62d19 100644 --- a/detections/endpoint/getlocaluser_with_powershell_script_block.yml +++ b/detections/endpoint/getlocaluser_with_powershell_script_block.yml @@ -1,52 +1,46 @@ name: GetLocalUser with PowerShell Script Block id: 2e891cbe-0426-11ec-9c9c-acde48001122 -version: 9 -date: '2025-06-24' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting -description: - The following analytic detects the execution of the `Get-LocalUser` PowerShell - commandlet using PowerShell Script Block Logging (EventCode=4104). This commandlet - lists all local users on a system. The detection leverages script block text from - PowerShell logs to identify this activity. Monitoring this behavior is significant - as adversaries and Red Teams may use it to enumerate local users for situational - awareness and Active Directory discovery. If confirmed malicious, this activity - could lead to further reconnaissance, enabling attackers to identify potential targets - for privilege escalation or lateral movement. +description: The following analytic detects the execution of the `Get-LocalUser` PowerShell commandlet using PowerShell Script Block Logging (EventCode=4104). This commandlet lists all local users on a system. The detection leverages script block text from PowerShell logs to identify this activity. Monitoring this behavior is significant as adversaries and Red Teams may use it to enumerate local users for situational awareness and Active Directory discovery. If confirmed malicious, this activity could lead to further reconnaissance, enabling attackers to identify potential targets for privilege escalation or lateral movement. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 (ScriptBlockText = "*Get-LocalUser*") | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getlocaluser_with_powershell_script_block_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - Administrators or power users may use this PowerShell commandlet - for troubleshooting. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 (ScriptBlockText = "*Get-LocalUser*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `getlocaluser_with_powershell_script_block_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting. references: - - https://attack.mitre.org/techniques/T1087/001/ - - https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html + - https://attack.mitre.org/techniques/T1087/001/ + - https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html tags: - analytic_story: - - Active Directory Discovery - - Malicious PowerShell - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - - T1087.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - Malicious PowerShell + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + - T1087.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.001/AD_discovery/windows-powershell-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.001/AD_discovery/windows-powershell-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/getnettcpconnection_with_powershell.yml b/detections/endpoint/getnettcpconnection_with_powershell.yml index 7aa698f32b..88186eefcf 100644 --- a/detections/endpoint/getnettcpconnection_with_powershell.yml +++ b/detections/endpoint/getnettcpconnection_with_powershell.yml @@ -1,58 +1,51 @@ name: GetNetTcpconnection with PowerShell id: e02af35c-1de5-4afe-b4be-f45aba57272b -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting -description: The following analytic identifies the execution of `powershell.exe` with - the `Get-NetTcpConnection` command, which lists current TCP connections on a system. - This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process names and command-line executions. Monitoring this activity - is significant as it may indicate an adversary or Red Team performing network reconnaissance - or situational awareness. If confirmed malicious, this activity could allow attackers - to map network connections, aiding in lateral movement or further exploitation within - the network. +description: The following analytic identifies the execution of `powershell.exe` with the `Get-NetTcpConnection` command, which lists current TCP connections on a system. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. Monitoring this activity is significant as it may indicate an adversary or Red Team performing network reconnaissance or situational awareness. If confirmed malicious, this activity could allow attackers to map network connections, aiding in lateral movement or further exploitation within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") - (Processes.process=*Get-NetTcpConnection*) by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getnettcpconnection_with_powershell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="powershell.exe" + ) + (Processes.process=*Get-NetTcpConnection*) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `getnettcpconnection_with_powershell_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1049/ -- https://docs.microsoft.com/en-us/powershell/module/nettcpip/get-nettcpconnection?view=windowsserver2019-ps + - https://attack.mitre.org/techniques/T1049/ + - https://docs.microsoft.com/en-us/powershell/module/nettcpip/get-nettcpconnection?view=windowsserver2019-ps tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1049 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1049 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1049/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1049/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/getnettcpconnection_with_powershell_script_block.yml b/detections/endpoint/getnettcpconnection_with_powershell_script_block.yml index 7a0a4e7b28..9db10ae6aa 100644 --- a/detections/endpoint/getnettcpconnection_with_powershell_script_block.yml +++ b/detections/endpoint/getnettcpconnection_with_powershell_script_block.yml @@ -1,51 +1,44 @@ name: GetNetTcpconnection with PowerShell Script Block id: 091712ff-b02a-4d43-82ed-34765515d95d -version: 8 -date: '2025-06-24' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting -description: - The following analytic detects the execution of the `Get-NetTcpconnection` - PowerShell cmdlet using PowerShell Script Block Logging (EventCode=4104). This cmdlet - lists network connections on a system, which adversaries may use for situational - awareness and Active Directory discovery. Monitoring this activity is crucial as - it can indicate reconnaissance efforts by an attacker. If confirmed malicious, this - behavior could allow an attacker to map the network, identify critical systems, - and plan further attacks, potentially leading to data exfiltration or lateral movement - within the network. +description: The following analytic detects the execution of the `Get-NetTcpconnection` PowerShell cmdlet using PowerShell Script Block Logging (EventCode=4104). This cmdlet lists network connections on a system, which adversaries may use for situational awareness and Active Directory discovery. Monitoring this activity is crucial as it can indicate reconnaissance efforts by an attacker. If confirmed malicious, this behavior could allow an attacker to map the network, identify critical systems, and plan further attacks, potentially leading to data exfiltration or lateral movement within the network. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 (ScriptBlockText = "*Get-NetTcpconnection*") - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `getnettcpconnection_with_powershell_script_block_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - Administrators or power users may use this PowerShell commandlet - for troubleshooting. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 (ScriptBlockText = "*Get-NetTcpconnection*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `getnettcpconnection_with_powershell_script_block_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting. references: - - https://attack.mitre.org/techniques/T1049/ - - https://docs.microsoft.com/en-us/powershell/module/nettcpip/get-nettcpconnection?view=windowsserver2019-ps + - https://attack.mitre.org/techniques/T1049/ + - https://docs.microsoft.com/en-us/powershell/module/nettcpip/get-nettcpconnection?view=windowsserver2019-ps tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1049 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1049 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/nettcpconnection.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/nettcpconnection.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/getwmiobject_ds_computer_with_powershell.yml b/detections/endpoint/getwmiobject_ds_computer_with_powershell.yml index f151409a45..8ce9fa8e49 100644 --- a/detections/endpoint/getwmiobject_ds_computer_with_powershell.yml +++ b/detections/endpoint/getwmiobject_ds_computer_with_powershell.yml @@ -5,77 +5,46 @@ date: '2025-05-02' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the execution of `powershell.exe` with - command-line arguments that utilize the `Get-WmiObject` cmdlet to discover remote - systems, specifically targeting the `DS_Computer` parameter. This detection leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process names - and command-line executions. This activity is significant as it indicates potential - reconnaissance efforts by adversaries to enumerate domain computers and gather situational - awareness within Active Directory. If confirmed malicious, this behavior could allow - attackers to map the network, identify critical systems, and plan further attacks, - potentially leading to unauthorized access and data exfiltration. +description: The following analytic detects the execution of `powershell.exe` with command-line arguments that utilize the `Get-WmiObject` cmdlet to discover remote systems, specifically targeting the `DS_Computer` parameter. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential reconnaissance efforts by adversaries to enumerate domain computers and gather situational awareness within Active Directory. If confirmed malicious, this behavior could allow attackers to map the network, identify critical systems, and plan further attacks, potentially leading to unauthorized access and data exfiltration. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") - (Processes.process=*Get-WmiObject* AND Processes.process="*namespace root\\directory\\ldap*" - AND Processes.process="*class ds_computer*") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `getwmiobject_ds_computer_with_powershell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process=*Get-WmiObject* AND Processes.process="*namespace root\\directory\\ldap*" AND Processes.process="*class ds_computer*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getwmiobject_ds_computer_with_powershell_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1018/ + - https://attack.mitre.org/techniques/T1018/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Remote system discovery enumeration using WMI on $dest$ by $user$ - risk_objects: - - field: dest - type: system - score: 21 - threat_objects: [] + message: Remote system discovery enumeration using WMI on $dest$ by $user$ + risk_objects: + - field: dest + type: system + score: 21 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1018 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1018 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/getwmiobject_ds_computer_with_powershell_script_block.yml b/detections/endpoint/getwmiobject_ds_computer_with_powershell_script_block.yml index 2ca1905a02..1b699b060f 100644 --- a/detections/endpoint/getwmiobject_ds_computer_with_powershell_script_block.yml +++ b/detections/endpoint/getwmiobject_ds_computer_with_powershell_script_block.yml @@ -5,68 +5,45 @@ date: '2025-06-24' author: Mauricio Velazco, Splunk status: production type: TTP -description: - The following analytic detects the execution of the `Get-WmiObject` cmdlet - with the `DS_Computer` class parameter via PowerShell Script Block Logging (EventCode=4104). - This detection leverages script block text to identify queries targeting domain - computers using WMI. Monitoring this activity is crucial as adversaries and Red - Teams may use it for Active Directory Discovery and situational awareness. If confirmed - malicious, this behavior could allow attackers to map out domain computers, facilitating - further attacks such as lateral movement or privilege escalation. +description: The following analytic detects the execution of the `Get-WmiObject` cmdlet with the `DS_Computer` class parameter via PowerShell Script Block Logging (EventCode=4104). This detection leverages script block text to identify queries targeting domain computers using WMI. Monitoring this activity is crucial as adversaries and Red Teams may use it for Active Directory Discovery and situational awareness. If confirmed malicious, this behavior could allow attackers to map out domain computers, facilitating further attacks such as lateral movement or privilege escalation. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 (ScriptBlockText=*Get-WmiObject* AND ScriptBlockText="*namespace - root\\directory\\ldap*" AND ScriptBlockText="*class ds_computer*") | fillnull | - stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getwmiobject_ds_computer_with_powershell_script_block_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - Administrators or power users may use this PowerShell commandlet - for troubleshooting. + - Powershell Script Block Logging 4104 +search: '`powershell` EventCode=4104 (ScriptBlockText=*Get-WmiObject* AND ScriptBlockText="*namespace root\\directory\\ldap*" AND ScriptBlockText="*class ds_computer*") | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getwmiobject_ds_computer_with_powershell_script_block_filter`' +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting. references: - - https://attack.mitre.org/techniques/T1018/ - - https://docs.microsoft.com/en-us/powershell/module/microsoft.powershell.management/get-wmiobject?view=powershell-5.1 + - https://attack.mitre.org/techniques/T1018/ + - https://docs.microsoft.com/en-us/powershell/module/microsoft.powershell.management/get-wmiobject?view=powershell-5.1 drilldown_searches: - - name: View the detection results for - "$Computer$" - search: '%original_detection_search% | search Computer = "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$Computer$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" + search: '%original_detection_search% | search Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Remote system discovery enumeration on $dest$ by $user_id$ - risk_objects: - - field: dest - type: system - score: 15 - threat_objects: [] + message: Remote system discovery enumeration on $dest$ by $user_id$ + risk_objects: + - field: dest + type: system + score: 15 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1018 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1018 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/getwmiobject_ds_group_with_powershell.yml b/detections/endpoint/getwmiobject_ds_group_with_powershell.yml index 99eacd0cda..4532790b5e 100644 --- a/detections/endpoint/getwmiobject_ds_group_with_powershell.yml +++ b/detections/endpoint/getwmiobject_ds_group_with_powershell.yml @@ -5,76 +5,47 @@ date: '2025-05-02' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic identifies the execution of `powershell.exe` with - command-line arguments used to query domain groups via the `Get-WmiObject` cmdlet - and the `-class ds_group` parameter. This detection leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process names and command-line - executions. This activity is significant as it indicates potential reconnaissance - efforts by adversaries to enumerate domain groups, which is a common step in Active - Directory Discovery. If confirmed malicious, this could allow attackers to gain - insights into the domain structure, aiding in further attacks and privilege escalation. +description: The following analytic identifies the execution of `powershell.exe` with command-line arguments used to query domain groups via the `Get-WmiObject` cmdlet and the `-class ds_group` parameter. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential reconnaissance efforts by adversaries to enumerate domain groups, which is a common step in Active Directory Discovery. If confirmed malicious, this could allow attackers to gain insights into the domain structure, aiding in further attacks and privilege escalation. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") - (Processes.process=*Get-WmiObject* AND Processes.process="*namespace root\\directory\\ldap*" - AND Processes.process="*class ds_group*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getwmiobject_ds_group_with_powershell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process=*Get-WmiObject* AND Processes.process="*namespace root\\directory\\ldap*" AND Processes.process="*class ds_group*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getwmiobject_ds_group_with_powershell_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1069/002/ -- https://docs.microsoft.com/en-us/powershell/module/microsoft.powershell.management/get-wmiobject?view=powershell-5.1 + - https://attack.mitre.org/techniques/T1069/002/ + - https://docs.microsoft.com/en-us/powershell/module/microsoft.powershell.management/get-wmiobject?view=powershell-5.1 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Domain group discovery enumeration on $dest$ by $user$ - risk_objects: - - field: dest - type: system - score: 15 - threat_objects: [] + message: Domain group discovery enumeration on $dest$ by $user$ + risk_objects: + - field: dest + type: system + score: 15 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1069.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1069.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/getwmiobject_ds_group_with_powershell_script_block.yml b/detections/endpoint/getwmiobject_ds_group_with_powershell_script_block.yml index 5dd46d5940..e2514a5cbe 100644 --- a/detections/endpoint/getwmiobject_ds_group_with_powershell_script_block.yml +++ b/detections/endpoint/getwmiobject_ds_group_with_powershell_script_block.yml @@ -5,68 +5,45 @@ date: '2025-06-24' author: Mauricio Velazco, Splunk status: production type: TTP -description: - The following analytic detects the execution of the `Get-WmiObject` commandlet - with the `DS_Group` parameter via PowerShell Script Block Logging (EventCode=4104). - This method leverages WMI to query all domain groups. Monitoring this activity is - crucial as adversaries and Red Teams may use it for domain group enumeration, aiding - in situational awareness and Active Directory discovery. If confirmed malicious, - this activity could allow attackers to map out the domain structure, potentially - leading to further exploitation and privilege escalation within the network. +description: The following analytic detects the execution of the `Get-WmiObject` commandlet with the `DS_Group` parameter via PowerShell Script Block Logging (EventCode=4104). This method leverages WMI to query all domain groups. Monitoring this activity is crucial as adversaries and Red Teams may use it for domain group enumeration, aiding in situational awareness and Active Directory discovery. If confirmed malicious, this activity could allow attackers to map out the domain structure, potentially leading to further exploitation and privilege escalation within the network. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 (ScriptBlockText=*Get-WmiObject* AND ScriptBlockText="*namespace - root\\directory\\ldap*" AND ScriptBlockText="*class ds_group*") | fillnull | stats - count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |`getwmiobject_ds_group_with_powershell_script_block_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - Administrators or power users may use this PowerShell commandlet - for troubleshooting. + - Powershell Script Block Logging 4104 +search: '`powershell` EventCode=4104 (ScriptBlockText=*Get-WmiObject* AND ScriptBlockText="*namespace root\\directory\\ldap*" AND ScriptBlockText="*class ds_group*") | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |`getwmiobject_ds_group_with_powershell_script_block_filter`' +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting. references: - - https://attack.mitre.org/techniques/T1069/002/ - - https://docs.microsoft.com/en-us/powershell/module/microsoft.powershell.management/get-wmiobject?view=powershell-5.1 + - https://attack.mitre.org/techniques/T1069/002/ + - https://docs.microsoft.com/en-us/powershell/module/microsoft.powershell.management/get-wmiobject?view=powershell-5.1 drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Domain group discovery enumeration using PowerShell on $dest$ by $user_id$ - risk_objects: - - field: dest - type: system - score: 15 - threat_objects: [] + message: Domain group discovery enumeration using PowerShell on $dest$ by $user_id$ + risk_objects: + - field: dest + type: system + score: 15 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1069.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1069.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/getwmiobject_ds_user_with_powershell.yml b/detections/endpoint/getwmiobject_ds_user_with_powershell.yml index a7b5f5726e..04de5f7339 100644 --- a/detections/endpoint/getwmiobject_ds_user_with_powershell.yml +++ b/detections/endpoint/getwmiobject_ds_user_with_powershell.yml @@ -5,81 +5,51 @@ date: '2025-05-02' author: Teoderick Contreras, Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the execution of `powershell.exe` with - command-line arguments used to query domain users via the `Get-WmiObject` cmdlet - and `-class ds_user` parameter. This detection leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process names and command-line executions. - This activity is significant as it indicates potential reconnaissance efforts by - adversaries to enumerate domain users, which is a common step in Active Directory - Discovery. If confirmed malicious, this could lead to further attacks, including - privilege escalation and lateral movement within the network. +description: The following analytic detects the execution of `powershell.exe` with command-line arguments used to query domain users via the `Get-WmiObject` cmdlet and `-class ds_user` parameter. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential reconnaissance efforts by adversaries to enumerate domain users, which is a common step in Active Directory Discovery. If confirmed malicious, this could lead to further attacks, including privilege escalation and lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="cmd.exe" - OR Processes.process_name="powershell*") AND Processes.process = "*get-wmiobject*" - AND Processes.process = "*ds_user*" AND Processes.process = "*root\\directory\\ldap*" - AND Processes.process = "*-namespace*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getwmiobject_ds_user_with_powershell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="cmd.exe" OR Processes.process_name="powershell*") AND Processes.process = "*get-wmiobject*" AND Processes.process = "*ds_user*" AND Processes.process = "*root\\directory\\ldap*" AND Processes.process = "*-namespace*" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getwmiobject_ds_user_with_powershell_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://jpcertcc.github.io/ToolAnalysisResultSheet/details/dsquery.htm + - https://jpcertcc.github.io/ToolAnalysisResultSheet/details/dsquery.htm drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: an instance of process $process_name$ with commandline $process$ on $dest$ - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: an instance of process $process_name$ with commandline $process$ on $dest$ + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1087.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1087.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/getwmiobject_ds_user_with_powershell_script_block.yml b/detections/endpoint/getwmiobject_ds_user_with_powershell_script_block.yml index 27ea3e1b74..cdd8ce2b61 100644 --- a/detections/endpoint/getwmiobject_ds_user_with_powershell_script_block.yml +++ b/detections/endpoint/getwmiobject_ds_user_with_powershell_script_block.yml @@ -5,68 +5,48 @@ date: '2025-05-02' author: Teoderick Contreras, Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the execution of the `Get-WmiObject` cmdlet - with the `DS_User` class parameter via PowerShell Script Block Logging (EventCode=4104). - It leverages logs to identify attempts to query all domain users using WMI. This - activity is significant as it may indicate an adversary or Red Team operation attempting - to enumerate domain users for situational awareness and Active Directory discovery. - If confirmed malicious, this behavior could lead to further reconnaissance, enabling - attackers to map out the network and identify potential targets for privilege escalation - or lateral movement. +description: The following analytic detects the execution of the `Get-WmiObject` cmdlet with the `DS_User` class parameter via PowerShell Script Block Logging (EventCode=4104). It leverages logs to identify attempts to query all domain users using WMI. This activity is significant as it may indicate an adversary or Red Team operation attempting to enumerate domain users for situational awareness and Active Directory discovery. If confirmed malicious, this behavior could lead to further reconnaissance, enabling attackers to map out the network and identify potential targets for privilege escalation or lateral movement. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 ScriptBlockText = "*get-wmiobject*" ScriptBlockText - = "*ds_user*" ScriptBlockText = "*-namespace*" ScriptBlockText = "*root\\directory\\ldap*" - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `getwmiobject_ds_user_with_powershell_script_block_filter`' -how_to_implement: The following Hunting analytic requires PowerShell operational logs - to be imported. Modify the powershell macro as needed to match the sourcetype or - add index. This analytic is specific to 4104, or PowerShell Script Block Logging. + - Powershell Script Block Logging 4104 +search: '`powershell` EventCode=4104 ScriptBlockText = "*get-wmiobject*" ScriptBlockText = "*ds_user*" ScriptBlockText = "*-namespace*" ScriptBlockText = "*root\\directory\\ldap*" | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getwmiobject_ds_user_with_powershell_script_block_filter`' +how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://www.blackhillsinfosec.com/red-blue-purple/ -- https://docs.microsoft.com/en-us/windows/win32/wmisdk/describing-the-ldap-namespace + - https://www.blackhillsinfosec.com/red-blue-purple/ + - https://docs.microsoft.com/en-us/windows/win32/wmisdk/describing-the-ldap-namespace drilldown_searches: -- name: View the detection results for - "$dest$" and "$user_id$" - search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user_id$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user_id$" + search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: powershell process having commandline for user enumeration detected on - host - $dest$ - risk_objects: - - field: dest - type: system - score: 25 - - field: user_id - type: user - score: 25 - threat_objects: [] + message: powershell process having commandline for user enumeration detected on host - $dest$ + risk_objects: + - field: dest + type: system + score: 25 + - field: user_id + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1087.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1087.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/getwmiobject_user_account_with_powershell.yml b/detections/endpoint/getwmiobject_user_account_with_powershell.yml index 366186093e..2182f346a9 100644 --- a/detections/endpoint/getwmiobject_user_account_with_powershell.yml +++ b/detections/endpoint/getwmiobject_user_account_with_powershell.yml @@ -1,65 +1,52 @@ name: GetWmiObject User Account with PowerShell id: b44f6ac6-0429-11ec-87e9-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting -description: - The following analytic detects the execution of `powershell.exe` with - command-line arguments that utilize the `Get-WmiObject` cmdlet and the `Win32_UserAccount` - parameter to query local user accounts. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process names and command-line executions. - This activity is significant as it may indicate an attempt by adversaries to enumerate - user accounts for situational awareness or Active Directory discovery. If confirmed - malicious, this behavior could lead to further reconnaissance, privilege escalation, - or lateral movement within the network. +description: The following analytic detects the execution of `powershell.exe` with command-line arguments that utilize the `Get-WmiObject` cmdlet and the `Win32_UserAccount` parameter to query local user accounts. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it may indicate an attempt by adversaries to enumerate user accounts for situational awareness or Active Directory discovery. If confirmed malicious, this behavior could lead to further reconnaissance, privilege escalation, or lateral movement within the network. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") - (Processes.process=*Get-WmiObject* AND Processes.process=*Win32_UserAccount*) by - Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `getwmiobject_user_account_with_powershell_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: - Administrators or power users may use this PowerShell commandlet - for troubleshooting. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="powershell.exe" + ) + (Processes.process=*Get-WmiObject* AND Processes.process=*Win32_UserAccount*) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `getwmiobject_user_account_with_powershell_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting. references: - - https://attack.mitre.org/techniques/T1087/001/ + - https://attack.mitre.org/techniques/T1087/001/ tags: - analytic_story: - - Winter Vivern - - Active Directory Discovery - - Water Gamayun - asset_type: Endpoint - mitre_attack_id: - - T1087.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Winter Vivern + - Active Directory Discovery + - Water Gamayun + asset_type: Endpoint + mitre_attack_id: + - T1087.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.001/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.001/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/getwmiobject_user_account_with_powershell_script_block.yml b/detections/endpoint/getwmiobject_user_account_with_powershell_script_block.yml index 350f8362fa..54c22a609f 100644 --- a/detections/endpoint/getwmiobject_user_account_with_powershell_script_block.yml +++ b/detections/endpoint/getwmiobject_user_account_with_powershell_script_block.yml @@ -1,53 +1,47 @@ name: GetWmiObject User Account with PowerShell Script Block id: 640b0eda-0429-11ec-accd-acde48001122 -version: 9 -date: '2025-06-24' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting -description: - The following analytic detects the execution of the `Get-WmiObject` commandlet - with the `Win32_UserAccount` parameter via PowerShell Script Block Logging (EventCode=4104). - This method leverages script block text to identify when a list of all local users - is being enumerated. This activity is significant as it may indicate an adversary - or Red Team operation attempting to gather user information for situational awareness - and Active Directory discovery. If confirmed malicious, this could lead to further - reconnaissance, privilege escalation, or lateral movement within the network. +description: The following analytic detects the execution of the `Get-WmiObject` commandlet with the `Win32_UserAccount` parameter via PowerShell Script Block Logging (EventCode=4104). This method leverages script block text to identify when a list of all local users is being enumerated. This activity is significant as it may indicate an adversary or Red Team operation attempting to gather user information for situational awareness and Active Directory discovery. If confirmed malicious, this could lead to further reconnaissance, privilege escalation, or lateral movement within the network. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 (ScriptBlockText="*Get-WmiObject*" AND ScriptBlockText="*Win32_UserAccount*") - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `getwmiobject_user_account_with_powershell_script_block_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - Administrators or power users may use this PowerShell commandlet - for troubleshooting. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 (ScriptBlockText="*Get-WmiObject*" AND ScriptBlockText="*Win32_UserAccount*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `getwmiobject_user_account_with_powershell_script_block_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting. references: - - https://attack.mitre.org/techniques/T1087/001/ - - https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html + - https://attack.mitre.org/techniques/T1087/001/ + - https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html tags: - analytic_story: - - Winter Vivern - - Active Directory Discovery - - Malicious PowerShell - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - - T1087.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Winter Vivern + - Active Directory Discovery + - Malicious PowerShell + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + - T1087.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/github_workflow_file_creation_or_modification.yml b/detections/endpoint/github_workflow_file_creation_or_modification.yml index 29b0ec61ef..014929d1c7 100644 --- a/detections/endpoint/github_workflow_file_creation_or_modification.yml +++ b/detections/endpoint/github_workflow_file_creation_or_modification.yml @@ -6,71 +6,71 @@ author: Michael Haag, Splunk status: production type: Hunting description: | - The following analytic hunts for any creations or modifications to GitHub Actions workflow YAML files across the organization's Linux or Windows endpoints. - This hunting query tracks all workflow file activity under .github/workflows directories to help defenders establish baselines of legitimate CI/CD workflow creation patterns, identify unusual or unauthorized changes, and detect anomalies that may indicate supply chain compromise. - GitHub Actions workflows execute with privileged access to secrets and deployment credentials, making them high-value targets for attackers. - By monitoring workflow file modifications over time, defenders can identify suspicious patterns such as unexpected workflow creation on developer workstations, modifications outside normal change windows, or activity in repositories that don't typically contain workflows. - This data is essential for detecting supply chain attacks like Shai-Hulud that inject malicious workflows across multiple repositories. + The following analytic hunts for any creations or modifications to GitHub Actions workflow YAML files across the organization's Linux or Windows endpoints. + This hunting query tracks all workflow file activity under .github/workflows directories to help defenders establish baselines of legitimate CI/CD workflow creation patterns, identify unusual or unauthorized changes, and detect anomalies that may indicate supply chain compromise. + GitHub Actions workflows execute with privileged access to secrets and deployment credentials, making them high-value targets for attackers. + By monitoring workflow file modifications over time, defenders can identify suspicious patterns such as unexpected workflow creation on developer workstations, modifications outside normal change windows, or activity in repositories that don't typically contain workflows. + This data is essential for detecting supply chain attacks like Shai-Hulud that inject malicious workflows across multiple repositories. data_source: -- Sysmon for Linux EventID 11 -- Sysmon EventID 11 + - Sysmon for Linux EventID 11 + - Sysmon EventID 11 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime - from datamodel=Endpoint.Filesystem where + from datamodel=Endpoint.Filesystem where - Filesystem.file_path IN ( - "*/.github/workflows/*.yaml", - "*/.github/workflows/*.yml", - "*\\.github\\workflows\\*.yaml", - "*\\.github\\workflows\\*.yml" - ) + Filesystem.file_path IN ( + "*/.github/workflows/*.yaml", + "*/.github/workflows/*.yml", + "*\\.github\\workflows\\*.yaml", + "*\\.github\\workflows\\*.yml" + ) - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user - Filesystem.vendor_product + by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time + Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user + Filesystem.vendor_product - | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `github_workflow_file_creation_or_modification_filter` + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `github_workflow_file_creation_or_modification_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain filesystem events, specifically file creation - events. These logs must be processed using the appropriate Splunk Technology Add-ons - that are specific to the EDR product. The logs must also be mapped to the `Filesystem` - node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) - to normalize the field names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain filesystem events, specifically file creation + events. These logs must be processed using the appropriate Splunk Technology Add-ons + that are specific to the EDR product. The logs must also be mapped to the `Filesystem` + node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) + to normalize the field names and speed up the data modeling process. known_false_positives: | - Legitimate engineering activity regularly creates workflow YAMLs. Suppress by repository path allowlisting, CI hosts, change windows, or approval timeframes. + Legitimate engineering activity regularly creates workflow YAMLs. Suppress by repository path allowlisting, CI hosts, change windows, or approval timeframes. references: - - https://www.cisa.gov/news-events/alerts/2025/09/23/widespread-supply-chain-compromise-impacting-npm-ecosystem - - https://securelist.com/shai-hulud-worm-infects-500-npm-packages-in-a-supply-chain-attack/117547/ - - https://github.com/SigmaHQ/sigma/pull/5658/files - - https://docs.github.com/en/actions/reference/workflows-and-actions/workflow-syntax + - https://www.cisa.gov/news-events/alerts/2025/09/23/widespread-supply-chain-compromise-impacting-npm-ecosystem + - https://securelist.com/shai-hulud-worm-infects-500-npm-packages-in-a-supply-chain-attack/117547/ + - https://github.com/SigmaHQ/sigma/pull/5658/files + - https://docs.github.com/en/actions/reference/workflows-and-actions/workflow-syntax tags: - analytic_story: - - NPM Supply Chain Compromise - asset_type: Endpoint - mitre_attack_id: - - T1574.006 - - T1554 - - T1195 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - NPM Supply Chain Compromise + asset_type: Endpoint + mitre_attack_id: + - T1574.006 + - T1554 + - T1195 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - Linux - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.001/npm/workflow_yml_sysmon.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux -- name: True Positive Test - Windows - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.001/npm/windows_workflow_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test - Linux + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.001/npm/workflow_yml_sysmon.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux + - name: True Positive Test - Windows + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.001/npm/windows_workflow_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/gpupdate_with_no_command_line_arguments_with_network.yml b/detections/endpoint/gpupdate_with_no_command_line_arguments_with_network.yml index 9d1bc9a7a2..39cbd2a95f 100644 --- a/detections/endpoint/gpupdate_with_no_command_line_arguments_with_network.yml +++ b/detections/endpoint/gpupdate_with_no_command_line_arguments_with_network.yml @@ -1,99 +1,79 @@ name: GPUpdate with no Command Line Arguments with Network id: 2c853856-a140-11eb-a5b5-acde48001122 -version: 12 -date: '2025-10-14' +version: 13 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of gpupdate.exe without - command line arguments and with an active network connection. This behavior is identified - using Endpoint Detection and Response (EDR) telemetry, focusing on process execution - and network traffic data. It is significant because gpupdate.exe typically runs - with specific arguments, and its execution without them, especially with network - activity, is often associated with malicious software like Cobalt Strike. If confirmed - malicious, this activity could indicate an attacker leveraging gpupdate.exe for - lateral movement, command and control, or other nefarious purposes, potentially - leading to system compromise. +description: The following analytic detects the execution of gpupdate.exe without command line arguments and with an active network connection. This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on process execution and network traffic data. It is significant because gpupdate.exe typically runs with specific arguments, and its execution without them, especially with network activity, is often associated with malicious software like Cobalt Strike. If confirmed malicious, this activity could indicate an attacker leveraging gpupdate.exe for lateral movement, command and control, or other nefarious purposes, potentially leading to system compromise. data_source: -- Sysmon EventID 1 AND Sysmon EventID 3 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes - where Processes.process_name=gpupdate.exe - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | regex process="(?i)(gpupdate\.exe.{0,4}$)"| - join process_id [| tstats `security_content_summariesonly` count values(All_Traffic.app) as app values(All_Traffic.dest_ip) as dest_ip - values(All_Traffic.direction) as direction values(All_Traffic.dvc) as dvc values(All_Traffic.protocol) as protocol - values(All_Traffic.protocol_version) as protocol_version values(All_Traffic.src) as src values(All_Traffic.src_ip) as src_ip - values(All_Traffic.src_port) as src_port values(All_Traffic.transport) as transport FROM datamodel=Network_Traffic.All_Traffic - where All_Traffic.dest_port != 0 by All_Traffic.process_id All_Traffic.dest All_Traffic.dest_port - | `drop_dm_object_name(All_Traffic)` | rename dest as C2 ] | table _time user dest - parent_process_name process_name process_path process process_id dest_port C2 app dest_ip direction dvc protocol - protocol_version src src_ip src_port transport | - `gpupdate_with_no_command_line_arguments_with_network_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Limited false positives may be present in small environments. - Tuning may be required based on parent process. + - Sysmon EventID 1 AND Sysmon EventID 3 +search: |- + | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=gpupdate.exe + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | regex process="(?i)(gpupdate\.exe.{0,4}$)" + | join process_id [ + | tstats `security_content_summariesonly` count values(All_Traffic.app) as app values(All_Traffic.dest_ip) as dest_ip values(All_Traffic.direction) as direction values(All_Traffic.dvc) as dvc values(All_Traffic.protocol) as protocol values(All_Traffic.protocol_version) as protocol_version values(All_Traffic.src) as src values(All_Traffic.src_ip) as src_ip values(All_Traffic.src_port) as src_port values(All_Traffic.transport) as transport FROM datamodel=Network_Traffic.All_Traffic + WHERE All_Traffic.dest_port != 0 + BY All_Traffic.process_id All_Traffic.dest All_Traffic.dest_port + | `drop_dm_object_name(All_Traffic)` + | rename dest as C2 ] + | table _time user dest parent_process_name process_name process_path process process_id dest_port C2 app dest_ip direction dvc protocol protocol_version src src_ip src_port transport + | `gpupdate_with_no_command_line_arguments_with_network_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Limited false positives may be present in small environments. Tuning may be required based on parent process. references: -- https://raw.githubusercontent.com/xx0hcd/Malleable-C2-Profiles/0ef8cf4556e26f6d4190c56ba697c2159faa5822/crimeware/trick_ryuk.profile -- https://www.cobaltstrike.com/blog/learn-pipe-fitting-for-all-of-your-offense-projects/ + - https://raw.githubusercontent.com/xx0hcd/Malleable-C2-Profiles/0ef8cf4556e26f6d4190c56ba697c2159faa5822/crimeware/trick_ryuk.profile + - https://www.cobaltstrike.com/blog/learn-pipe-fitting-for-all-of-your-offense-projects/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Process gpupdate.exe with parent_process $parent_process_name$ is executed - on $dest$ by user $user$, followed by an outbound network connection on port $dest_port$. - This behaviour is seen with cobaltstrike. - risk_objects: - - field: user - type: user - score: 81 - - field: dest - type: system - score: 81 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: Process gpupdate.exe with parent_process $parent_process_name$ is executed on $dest$ by user $user$, followed by an outbound network connection on port $dest_port$. This behaviour is seen with cobaltstrike. + risk_objects: + - field: user + type: user + score: 81 + - field: dest + type: system + score: 81 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - Graceful Wipe Out Attack - - Cobalt Strike - - Compromised Windows Host - - BlackByte Ransomware - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1055 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Graceful Wipe Out Attack + - Cobalt Strike + - Compromised Windows Host + - BlackByte Ransomware + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1055 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/headless_browser_mockbin_or_mocky_request.yml b/detections/endpoint/headless_browser_mockbin_or_mocky_request.yml index 7e2559f0e8..baf93b5a3c 100644 --- a/detections/endpoint/headless_browser_mockbin_or_mocky_request.yml +++ b/detections/endpoint/headless_browser_mockbin_or_mocky_request.yml @@ -1,83 +1,77 @@ name: Headless Browser Mockbin or Mocky Request id: 94fc85a1-e55b-4265-95e1-4b66730e05c0 -version: 8 -date: '2025-09-16' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic detects headless browser activity accessing mockbin.org - or mocky.io. It identifies processes with the "--headless" and "--disable-gpu" command - line arguments, along with references to mockbin.org or mocky.io. This behavior - is significant as headless browsers are often used for automated tasks, including - malicious activities like web scraping or automated attacks. If confirmed malicious, - this activity could indicate an attempt to bypass traditional browser security measures, - potentially leading to data exfiltration or further exploitation of web applications. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process="*--headless*" - AND Processes.process="*--disable-gpu*" AND (Processes.process="*mockbin.org/*" - OR Processes.process="*mocky.io/*")) by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `headless_browser_mockbin_or_mocky_request_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: False positives are not expected with this detection, unless - within the organization there is a legitimate need for headless browsing accessing - mockbin.org or mocky.io. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic detects headless browser activity accessing mockbin.org or mocky.io. It identifies processes with the "--headless" and "--disable-gpu" command line arguments, along with references to mockbin.org or mocky.io. This behavior is significant as headless browsers are often used for automated tasks, including malicious activities like web scraping or automated attacks. If confirmed malicious, this activity could indicate an attempt to bypass traditional browser security measures, potentially leading to data exfiltration or further exploitation of web applications. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process="*--headless*" + AND + Processes.process="*--disable-gpu*" + AND + (Processes.process="*mockbin.org/*" + OR + Processes.process="*mocky.io/*") + ) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `headless_browser_mockbin_or_mocky_request_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: False positives are not expected with this detection, unless within the organization there is a legitimate need for headless browsing accessing mockbin.org or mocky.io. references: -- https://mockbin.org/ -- https://www.mocky.io/ + - https://mockbin.org/ + - https://www.mocky.io/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Headless browser activity accessing mockbin.org or mocky.io detected on - $dest$ by $user$. - risk_objects: - - field: user - type: user - score: 56 - - field: dest - type: system - score: 56 - threat_objects: [] + message: Headless browser activity accessing mockbin.org or mocky.io detected on $dest$ by $user$. + risk_objects: + - field: user + type: user + score: 56 + - field: dest + type: system + score: 56 + threat_objects: [] tags: - analytic_story: - - Forest Blizzard - - GhostRedirector IIS Module and Rungan Backdoor - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1564.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Forest Blizzard + - GhostRedirector IIS Module and Rungan Backdoor + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1564.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/headlessbrowser/headless_mockbin.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/headlessbrowser/headless_mockbin.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/headless_browser_usage.yml b/detections/endpoint/headless_browser_usage.yml index a405aaebcc..b2819995ec 100644 --- a/detections/endpoint/headless_browser_usage.yml +++ b/detections/endpoint/headless_browser_usage.yml @@ -6,92 +6,62 @@ author: Michael Haag, Teoderick Contreras, Splunk status: production type: Anomaly data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic detects the usage of headless browsers within - an organization. It identifies processes containing the "--headless" and "--disable-gpu" - command line arguments, which are indicative of headless browsing. This detection - leverages data from the Endpoint.Processes datamodel to identify such processes. - Monitoring headless browser usage is significant as these tools can be exploited - by adversaries for malicious activities like web scraping, automated testing, and - undetected web interactions. If confirmed malicious, this activity could lead to - unauthorized data extraction, automated attacks, or other covert operations on web - applications. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes - where Processes.process_name IN ("Chrome.exe","Brave.exe", "Opera.exe", "Vivaldi.exe", "msedge.exe") - (Processes.process="*--headless*" AND Processes.process="*--disable-gpu*") - - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `headless_browser_usage_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: Administrators may enable or disable this feature for framework testing that may - cause some false positive. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic detects the usage of headless browsers within an organization. It identifies processes containing the "--headless" and "--disable-gpu" command line arguments, which are indicative of headless browsing. This detection leverages data from the Endpoint.Processes datamodel to identify such processes. Monitoring headless browser usage is significant as these tools can be exploited by adversaries for malicious activities like web scraping, automated testing, and undetected web interactions. If confirmed malicious, this activity could lead to unauthorized data extraction, automated attacks, or other covert operations on web applications. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("Chrome.exe","Brave.exe", "Opera.exe", "Vivaldi.exe", "msedge.exe") (Processes.process="*--headless*" AND Processes.process="*--disable-gpu*") + + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `headless_browser_usage_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: Administrators may enable or disable this feature for framework testing that may cause some false positive. references: -- https://www.trendmicro.com/en_us/research/26/a/analysis-of-the-evelyn-stealer-campaign.html -- https://cert.gov.ua/article/5702579 + - https://www.trendmicro.com/en_us/research/26/a/analysis-of-the-evelyn-stealer-campaign.html + - https://cert.gov.ua/article/5702579 drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Chromium-based browser process $process_name$ was launched by $parent_process_name$ on $dest$ by user $user$ with the command-line $process$. - risk_objects: - - field: dest - type: system - score: 30 - - field: user - type: user - score: 30 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process - type: process - - field: parent_process - type: parent_process + message: A Chromium-based browser process $process_name$ was launched by $parent_process_name$ on $dest$ by user $user$ with the command-line $process$. + risk_objects: + - field: dest + type: system + score: 30 + - field: user + type: user + score: 30 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process + type: process + - field: parent_process + type: parent_process tags: - analytic_story: - - Browser Hijacking - - Forest Blizzard - asset_type: Endpoint - mitre_attack_id: - - T1497 - - T1564.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Browser Hijacking + - Forest Blizzard + asset_type: Endpoint + mitre_attack_id: + - T1497 + - T1564.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/headlessbrowser/headless_mockbin.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497/headless_browser/headless_chrome.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/headlessbrowser/headless_mockbin.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497/headless_browser/headless_chrome.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/hide_user_account_from_sign_in_screen.yml b/detections/endpoint/hide_user_account_from_sign_in_screen.yml index 5efe48ed4c..dca56f5e62 100644 --- a/detections/endpoint/hide_user_account_from_sign_in_screen.yml +++ b/detections/endpoint/hide_user_account_from_sign_in_screen.yml @@ -5,76 +5,52 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: The following analytic detects a suspicious registry modification that - hides a user account from the Windows Login screen. It leverages data from the Endpoint.Registry - data model, specifically monitoring changes to the registry path "*\\Windows NT\\CurrentVersion\\Winlogon\\SpecialAccounts\\Userlist*" - with a value of "0x00000000". This activity is significant as it may indicate an - adversary attempting to create a hidden admin account to avoid detection and maintain - persistence on the compromised machine. If confirmed malicious, this could allow - the attacker to maintain undetected access and control over the system, posing a - severe security risk. +description: The following analytic detects a suspicious registry modification that hides a user account from the Windows Login screen. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path "*\\Windows NT\\CurrentVersion\\Winlogon\\SpecialAccounts\\Userlist*" with a value of "0x00000000". This activity is significant as it may indicate an adversary attempting to create a hidden admin account to avoid detection and maintain persistence on the compromised machine. If confirmed malicious, this could allow the attacker to maintain undetected access and control over the system, posing a severe security risk. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\Windows - NT\\CurrentVersion\\Winlogon\\SpecialAccounts\\Userlist*" AND Registry.registry_value_data - = "0x00000000") by Registry.action Registry.dest Registry.process_guid Registry.process_id - Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data - Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user - Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `hide_user_account_from_sign_in_screen_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\Windows NT\\CurrentVersion\\Winlogon\\SpecialAccounts\\Userlist*" AND Registry.registry_value_data = "0x00000000") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `hide_user_account_from_sign_in_screen_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: No false positives have been identified at this time. references: -- https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ + - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious registry modification ($registry_value_name$) which is used - go hide a user account on the Windows Login screen detected on $dest$ executed - by $user$ - risk_objects: - - field: user - type: user - score: 72 - - field: dest - type: system - score: 72 - threat_objects: - - field: registry_value_name - type: registry_value_name + message: Suspicious registry modification ($registry_value_name$) which is used go hide a user account on the Windows Login screen detected on $dest$ executed by $user$ + risk_objects: + - field: user + type: user + score: 72 + - field: dest + type: system + score: 72 + threat_objects: + - field: registry_value_name + type: registry_value_name tags: - analytic_story: - - XMRig - - Windows Registry Abuse - - Azorult - - Warzone RAT - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - XMRig + - Windows Registry Abuse + - Azorult + - Warzone RAT + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/hotkey_disabled_hidden_user/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/hotkey_disabled_hidden_user/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/hiding_files_and_directories_with_attrib_exe.yml b/detections/endpoint/hiding_files_and_directories_with_attrib_exe.yml index 0b01e8825d..0e429bdef0 100644 --- a/detections/endpoint/hiding_files_and_directories_with_attrib_exe.yml +++ b/detections/endpoint/hiding_files_and_directories_with_attrib_exe.yml @@ -1,86 +1,70 @@ name: Hiding Files And Directories With Attrib exe id: 6e5a3ae4-90a3-462d-9aa6-0119f638c0f1 -version: 13 -date: '2025-05-26' +version: 14 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP -description: The following analytic detects the use of the Windows binary attrib.exe - to hide files or directories by marking them with specific flags. It leverages data - from Endpoint Detection and Response (EDR) agents, focusing on command-line arguments - that include the "+h" flag. This activity is significant because hiding files can - be a tactic used by attackers to conceal malicious files or tools from users and - security software. If confirmed malicious, this behavior could allow an attacker - to persist in the environment undetected, potentially leading to further compromise - or data exfiltration. +description: The following analytic detects the use of the Windows binary attrib.exe to hide files or directories by marking them with specific flags. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line arguments that include the "+h" flag. This activity is significant because hiding files can be a tactic used by attackers to conceal malicious files or tools from users and security software. If confirmed malicious, this behavior could allow an attacker to persist in the environment undetected, potentially leading to further compromise or data exfiltration. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) values(Processes.process) - as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=attrib.exe - (Processes.process=*+h*) by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name("Processes")` - | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` |`hiding_files_and_directories_with_attrib_exe_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Some applications and users may legitimately use attrib.exe - to interact with the files. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=attrib.exe (Processes.process=*+h*) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `hiding_files_and_directories_with_attrib_exe_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Some applications and users may legitimately use attrib.exe to interact with the files. references: [] drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Attrib.exe with +h flag to hide files on $dest$ executed by $user$ is detected. - risk_objects: - - field: user - type: user - score: 72 - - field: dest - type: system - score: 72 - threat_objects: [] + message: Attrib.exe with +h flag to hide files on $dest$ executed by $user$ is detected. + risk_objects: + - field: user + type: user + score: 72 + - field: dest + type: system + score: 72 + threat_objects: [] tags: - analytic_story: - - Windows Persistence Techniques - - Malicious Inno Setup Loader - - Azorult - - Compromised Windows Host - - Windows Defense Evasion Tactics - - Crypto Stealer - asset_type: Endpoint - mitre_attack_id: - - T1222.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Persistence Techniques + - Malicious Inno Setup Loader + - Azorult + - Compromised Windows Host + - Windows Defense Evasion Tactics + - Crypto Stealer + asset_type: Endpoint + mitre_attack_id: + - T1222.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/high_frequency_copy_of_files_in_network_share.yml b/detections/endpoint/high_frequency_copy_of_files_in_network_share.yml index ef23a94a69..fc376f03f7 100644 --- a/detections/endpoint/high_frequency_copy_of_files_in_network_share.yml +++ b/detections/endpoint/high_frequency_copy_of_files_in_network_share.yml @@ -5,72 +5,48 @@ date: '2025-10-14' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects a high frequency of file copying or moving - within network shares, which may indicate potential data sabotage or exfiltration - attempts. It leverages Windows Security Event Logs (EventCode 5145) to monitor access - to specific file types and network shares. This activity is significant as it can - reveal insider threats attempting to transfer classified or internal files, potentially - leading to data breaches or evidence tampering. If confirmed malicious, this behavior - could result in unauthorized data access, data loss, or compromised sensitive information. +description: The following analytic detects a high frequency of file copying or moving within network shares, which may indicate potential data sabotage or exfiltration attempts. It leverages Windows Security Event Logs (EventCode 5145) to monitor access to specific file types and network shares. This activity is significant as it can reveal insider threats attempting to transfer classified or internal files, potentially leading to data breaches or evidence tampering. If confirmed malicious, this behavior could result in unauthorized data access, data loss, or compromised sensitive information. data_source: -- Windows Event Log Security 5145 -search: '`wineventlog_security` EventCode=5145 RelativeTargetName IN ("*.doc","*.docx","*.xls","*.xlsx","*.ppt","*.pptx","*.log","*.txt","*.db","*.7z","*.zip","*.rar","*.tar","*.gz","*.jpg","*.gif","*.png","*.bmp","*.pdf","*.rtf","*.key") - ObjectType=File ShareName IN ("\\\\*\\C$","\\\\*\\IPC$","\\\\*\\admin$") AccessMask= - "0x2" | bucket _time span=5m | stats values(RelativeTargetName) as valRelativeTargetName, - values(ShareName) as valShareName, values(ObjectType) as valObjectType, values(AccessMask) - as valAccessmask, values(src_port) as valSrcPort, values(SourceAddress) as valSrcAddress - count as numShareName by dest, _time, EventCode, src_user, src_ip | eventstats avg(numShareName) - as avgShareName, stdev(numShareName) as stdShareName, count as numSlots by dest, - _time, EventCode, src_user | eval upperThreshold=(avgShareName + stdShareName *3) - | eval isOutlier=if(avgShareName > 20 and avgShareName >= upperThreshold, 1, 0) - | search isOutlier=1 | `high_frequency_copy_of_files_in_network_share_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Windows Security Event Logs with 5145 EventCode enabled. The Windows TA is also - required. Also enable the object Audit access success/failure in your group policy. -known_false_positives: This behavior may seen in normal transfer of file within network - if network share is common place for sharing documents. + - Windows Event Log Security 5145 +search: '`wineventlog_security` EventCode=5145 RelativeTargetName IN ("*.doc","*.docx","*.xls","*.xlsx","*.ppt","*.pptx","*.log","*.txt","*.db","*.7z","*.zip","*.rar","*.tar","*.gz","*.jpg","*.gif","*.png","*.bmp","*.pdf","*.rtf","*.key") ObjectType=File ShareName IN ("\\\\*\\C$","\\\\*\\IPC$","\\\\*\\admin$") AccessMask= "0x2" | bucket _time span=5m | stats values(RelativeTargetName) as valRelativeTargetName, values(ShareName) as valShareName, values(ObjectType) as valObjectType, values(AccessMask) as valAccessmask, values(src_port) as valSrcPort, values(SourceAddress) as valSrcAddress count as numShareName by dest, _time, EventCode, src_user, src_ip | eventstats avg(numShareName) as avgShareName, stdev(numShareName) as stdShareName, count as numSlots by dest, _time, EventCode, src_user | eval upperThreshold=(avgShareName + stdShareName *3) | eval isOutlier=if(avgShareName > 20 and avgShareName >= upperThreshold, 1, 0) | search isOutlier=1 | `high_frequency_copy_of_files_in_network_share_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 5145 EventCode enabled. The Windows TA is also required. Also enable the object Audit access success/failure in your group policy. +known_false_positives: This behavior may seen in normal transfer of file within network if network share is common place for sharing documents. references: -- https://attack.mitre.org/techniques/T1537/ + - https://attack.mitre.org/techniques/T1537/ drilldown_searches: -- name: View the detection results for - "$src_user$" - search: '%original_detection_search% | search src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_user$" + search: '%original_detection_search% | search src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: High frequency copy of document into a network share from $src_ip$ by $src_user$ - risk_objects: - - field: src_user - type: user - score: 9 - threat_objects: - - field: src_ip - type: ip_address + message: High frequency copy of document into a network share from $src_ip$ by $src_user$ + risk_objects: + - field: src_user + type: user + score: 9 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Information Sabotage - - Insider Threat - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1537 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Information Sabotage + - Insider Threat + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1537 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1537/high_frequency_copy_of_files_in_network_share/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1537/high_frequency_copy_of_files_in_network_share/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/high_process_termination_frequency.yml b/detections/endpoint/high_process_termination_frequency.yml index f630e31f5a..09f8c29f15 100644 --- a/detections/endpoint/high_process_termination_frequency.yml +++ b/detections/endpoint/high_process_termination_frequency.yml @@ -1,82 +1,69 @@ name: High Process Termination Frequency id: 17cd75b2-8666-11eb-9ab4-acde48001122 -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Teoderick Contreras status: production type: Anomaly -description: The following analytic identifies a high frequency of process - termination events on a computer within a short period. It leverages Sysmon - EventCode 5 logs to detect instances where 15 or more processes are terminated - within a 3-second window. This behavior is significant as it is commonly - associated with ransomware attempting to avoid exceptions during file - encryption. If confirmed malicious, this activity could indicate an active - ransomware attack, potentially leading to widespread file encryption and - significant data loss. +description: The following analytic identifies a high frequency of process termination events on a computer within a short period. It leverages Sysmon EventCode 5 logs to detect instances where 15 or more processes are terminated within a 3-second window. This behavior is significant as it is commonly associated with ransomware attempting to avoid exceptions during file encryption. If confirmed malicious, this activity could indicate an active ransomware attack, potentially leading to widespread file encryption and significant data loss. data_source: -- Sysmon EventID 5 -search: '`sysmon` EventCode=5 | bin _time span=3s | stats values(process) as process - values(process_exec) as process_exec values(process_guid) as process_guid values(process_id) - as process_id values(process_name) as process_name values(process_path) as process_path - values(user_id) as user_id min(_time) as firstTime max(_time) as lastTime count - by _time dest EventCode ProcessID signature signature_id vendor_product | where - count >= 15 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `high_process_termination_frequency_filter`' -how_to_implement: To successfully implement this search, you need to be - ingesting logs with the Image (process full path of terminated process) from - your endpoints. If you are using Sysmon, you must have at least version 6.0.4 - of the Sysmon TA. + - Sysmon EventID 5 +search: |- + `sysmon` EventCode=5 + | bin _time span=3s + | stats values(process) as process values(process_exec) as process_exec values(process_guid) as process_guid values(process_id) as process_id values(process_name) as process_name values(process_path) as process_path values(user_id) as user_id min(_time) as firstTime max(_time) as lastTime count + BY _time dest EventCode + ProcessID signature signature_id + vendor_product + | where count >= 15 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `high_process_termination_frequency_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the Image (process full path of terminated process) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: admin or user tool that can terminate multiple process. references: -- https://www.mandiant.com/resources/fin11-email-campaigns-precursor-for-ransomware-data-theft -- https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html + - https://www.mandiant.com/resources/fin11-email-campaigns-precursor-for-ransomware-data-theft + - https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: High frequency process termination (more than 15 processes within 3s) - detected on host $dest$ - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: [] + message: High frequency process termination (more than 15 processes within 3s) detected on host $dest$ + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: [] tags: - analytic_story: - - BlackByte Ransomware - - Rhysida Ransomware - - LockBit Ransomware - - Medusa Ransomware - - Crypto Stealer - - Snake Keylogger - - Clop Ransomware - - Termite Ransomware - - Interlock Ransomware - - NailaoLocker Ransomware - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1486 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - BlackByte Ransomware + - Rhysida Ransomware + - LockBit Ransomware + - Medusa Ransomware + - Crypto Stealer + - Snake Keylogger + - Clop Ransomware + - Termite Ransomware + - Interlock Ransomware + - NailaoLocker Ransomware + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1486 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/hunting_3cxdesktopapp_software.yml b/detections/endpoint/hunting_3cxdesktopapp_software.yml index 624a5e5b4f..fa5bb48e8a 100644 --- a/detections/endpoint/hunting_3cxdesktopapp_software.yml +++ b/detections/endpoint/hunting_3cxdesktopapp_software.yml @@ -1,64 +1,55 @@ name: Hunting 3CXDesktopApp Software id: 553d0429-1a1c-44bf-b3f5-a8513deb9ee5 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk type: Hunting status: production data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic detects the presence of any version of the 3CXDesktopApp, - also known as the 3CX Desktop App, on Mac or Windows systems. It leverages the Endpoint - data model's Processes node to identify instances of the application running, although - it does not provide file version information. This activity is significant because - 3CX has identified vulnerabilities in versions 18.12.407 and 18.12.416, which could - be exploited by attackers. If confirmed malicious, this could lead to unauthorized - access, data exfiltration, or further compromise of the affected systems. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=3CXDesktopApp.exe - OR Processes.process_name="3CX Desktop App" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `hunting_3cxdesktopapp_software_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: There may be false positives generated due to the reliance - on version numbers for identification purposes. Despite this limitation, the primary - goal of this approach is to aid in the detection of the software within the environment. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic detects the presence of any version of the 3CXDesktopApp, also known as the 3CX Desktop App, on Mac or Windows systems. It leverages the Endpoint data model's Processes node to identify instances of the application running, although it does not provide file version information. This activity is significant because 3CX has identified vulnerabilities in versions 18.12.407 and 18.12.416, which could be exploited by attackers. If confirmed malicious, this could lead to unauthorized access, data exfiltration, or further compromise of the affected systems. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=3CXDesktopApp.exe + OR + Processes.process_name="3CX Desktop App" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `hunting_3cxdesktopapp_software_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: There may be false positives generated due to the reliance on version numbers for identification purposes. Despite this limitation, the primary goal of this approach is to aid in the detection of the software within the environment. references: -- https://www.sentinelone.com/blog/smoothoperator-ongoing-campaign-trojanizes-3cx-software-in-software-supply-chain-attack/ -- https://www.cisa.gov/news-events/alerts/2023/03/30/supply-chain-attack-against-3cxdesktopapp -- https://www.reddit.com/r/crowdstrike/comments/125r3uu/20230329_situational_awareness_crowdstrike/ -- https://www.3cx.com/community/threads/crowdstrike-endpoint-security-detection-re-3cx-desktop-app.119934/page-2#post-558898 -- https://www.3cx.com/community/threads/3cx-desktopapp-security-alert.119951/ + - https://www.sentinelone.com/blog/smoothoperator-ongoing-campaign-trojanizes-3cx-software-in-software-supply-chain-attack/ + - https://www.cisa.gov/news-events/alerts/2023/03/30/supply-chain-attack-against-3cxdesktopapp + - https://www.reddit.com/r/crowdstrike/comments/125r3uu/20230329_situational_awareness_crowdstrike/ + - https://www.3cx.com/community/threads/crowdstrike-endpoint-security-detection-re-3cx-desktop-app.119934/page-2#post-558898 + - https://www.3cx.com/community/threads/3cx-desktopapp-security-alert.119951/ tags: - analytic_story: - - 3CX Supply Chain Attack - asset_type: Endpoint - cve: - - CVE-2023-29059 - mitre_attack_id: - - T1195.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - 3CX Supply Chain Attack + asset_type: Endpoint + cve: + - CVE-2023-29059 + mitre_attack_id: + - T1195.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.002/3CX/3cx_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.002/3CX/3cx_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/icacls_deny_command.yml b/detections/endpoint/icacls_deny_command.yml index 07b362cb20..b2fba8be5b 100644 --- a/detections/endpoint/icacls_deny_command.yml +++ b/detections/endpoint/icacls_deny_command.yml @@ -1,95 +1,88 @@ name: Icacls Deny Command id: cf8d753e-a8fe-11eb-8f58-acde48001122 -version: 10 -date: '2026-01-14' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: | - The following analytic detects instances where an adversary modifies - security permissions of a file or directory using commands like "icacls.exe", "cacls.exe", - or "xcacls.exe" with deny options. It leverages data from Endpoint Detection and - Response (EDR) agents, focusing on process names and command-line executions. This - activity is significant as it is commonly used by Advanced Persistent Threats (APTs) - and coinminer scripts to evade detection and impede access to critical files. If - confirmed malicious, this could allow attackers to maintain persistence and hinder - incident response efforts. + The following analytic detects instances where an adversary modifies + security permissions of a file or directory using commands like "icacls.exe", "cacls.exe", + or "xcacls.exe" with deny options. It leverages data from Endpoint Detection and + Response (EDR) agents, focusing on process names and command-line executions. This + activity is significant as it is commonly used by Advanced Persistent Threats (APTs) + and coinminer scripts to evade detection and impede access to critical files. If + confirmed malicious, this could allow attackers to maintain persistence and hinder + incident response efforts. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - Processes.process_name IN ( "icacls.exe", "cacls.exe", "xcacls.exe") AND - Processes.process IN ("*/deny*", "*/d:*", "*/d ") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `icacls_deny_command_filter` + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where + Processes.process_name IN ( "icacls.exe", "cacls.exe", "xcacls.exe") AND + Processes.process IN ("*/deny*", "*/d:*", "*/d ") + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `icacls_deny_command_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: No false positives have been identified at this time. - Filter as needed. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. +known_false_positives: No false positives have been identified at this time. Filter as needed. references: -- https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ + - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Process name $process_name$ with deny argument executed by $user$ to change - security permission of a specific file or directory on host $dest$ - risk_objects: - - field: dest - type: system - score: 72 - - field: user - type: user - score: 72 - threat_objects: [] + message: Process name $process_name$ with deny argument executed by $user$ to change security permission of a specific file or directory on host $dest$ + risk_objects: + - field: dest + type: system + score: 72 + - field: user + type: user + score: 72 + threat_objects: [] tags: - analytic_story: - - Azorult - - Sandworm Tools - - Compromised Windows Host - - XMRig - - Crypto Stealer - - Defense Evasion or Unauthorized Access Via SDDL Tampering - asset_type: Endpoint - mitre_attack_id: - - T1222 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azorult + - Sandworm Tools + - Compromised Windows Host + - XMRig + - Crypto Stealer + - Defense Evasion or Unauthorized Access Via SDDL Tampering + asset_type: Endpoint + mitre_attack_id: + - T1222 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/icacls_grant_command.yml b/detections/endpoint/icacls_grant_command.yml index d43ef90925..2e263fad5b 100644 --- a/detections/endpoint/icacls_grant_command.yml +++ b/detections/endpoint/icacls_grant_command.yml @@ -1,93 +1,87 @@ name: ICACLS Grant Command id: b1b1e316-accc-11eb-a9b4-acde48001122 -version: 10 -date: '2026-01-14' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: | - The following analytic detects the use of the ICACLS command to grant - additional access permissions to files or directories. It leverages data from Endpoint - Detection and Response (EDR) agents, focusing on specific process names and command-line - arguments. This activity is significant because it is commonly used by Advanced - Persistent Threats (APTs) and coinminer scripts to evade detection and maintain - control over compromised systems. If confirmed malicious, this behavior could allow - attackers to manipulate file permissions, potentially leading to unauthorized access, - data exfiltration, or further system compromise. + The following analytic detects the use of the ICACLS command to grant + additional access permissions to files or directories. It leverages data from Endpoint + Detection and Response (EDR) agents, focusing on specific process names and command-line + arguments. This activity is significant because it is commonly used by Advanced + Persistent Threats (APTs) and coinminer scripts to evade detection and maintain + control over compromised systems. If confirmed malicious, this behavior could allow + attackers to manipulate file permissions, potentially leading to unauthorized access, + data exfiltration, or further system compromise. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - Processes.process_name IN ( "icacls.exe", "cacls.exe", "xcacls.exe") AND - Processes.process IN ("*/grant*", "*/g:*", "*/g *") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `icacls_grant_command_filter` + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where + Processes.process_name IN ( "icacls.exe", "cacls.exe", "xcacls.exe") AND + Processes.process IN ("*/grant*", "*/g:*", "*/g *") + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `icacls_grant_command_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ + - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Process name $process_name$ with grant argument executed by $user$ to change - security permission of a specific file or directory on host $dest$ - risk_objects: - - field: dest - type: system - score: 49 - - field: user - type: user - score: 49 - threat_objects: [] + message: Process name $process_name$ with grant argument executed by $user$ to change security permission of a specific file or directory on host $dest$ + risk_objects: + - field: dest + type: system + score: 49 + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - Ransomware - - Crypto Stealer - - XMRig - - Defense Evasion or Unauthorized Access Via SDDL Tampering - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1222 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - Crypto Stealer + - XMRig + - Defense Evasion or Unauthorized Access Via SDDL Tampering + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1222 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/icedid_exfiltrated_archived_file_creation.yml b/detections/endpoint/icedid_exfiltrated_archived_file_creation.yml index 9e978dc00d..92503f3c5a 100644 --- a/detections/endpoint/icedid_exfiltrated_archived_file_creation.yml +++ b/detections/endpoint/icedid_exfiltrated_archived_file_creation.yml @@ -5,46 +5,29 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects the creation of suspicious files named - passff.tar and cookie.tar, which are indicative of archived stolen browser information - such as history and cookies on a machine compromised with IcedID. It leverages Sysmon - EventCode 11 to identify these specific filenames. This activity is significant - because it suggests that sensitive browser data has been exfiltrated, which could - lead to further exploitation or data breaches. If confirmed malicious, this could - allow attackers to access personal information, conduct further phishing attacks, - or escalate their presence within the network. +description: The following analytic detects the creation of suspicious files named passff.tar and cookie.tar, which are indicative of archived stolen browser information such as history and cookies on a machine compromised with IcedID. It leverages Sysmon EventCode 11 to identify these specific filenames. This activity is significant because it suggests that sensitive browser data has been exfiltrated, which could lead to further exploitation or data breaches. If confirmed malicious, this could allow attackers to access personal information, conduct further phishing attacks, or escalate their presence within the network. data_source: -- Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count values(Filesystem.file_path) - as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem - where Filesystem.file_path="*\\passff.tar" OR Filesystem.file_path="*\\cookie.tar" - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path - Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | - `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `icedid_exfiltrated_archived_file_creation_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. + - Sysmon EventID 11 +search: '| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_path="*\\passff.tar" OR Filesystem.file_path="*\\cookie.tar" by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `icedid_exfiltrated_archived_file_creation_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: No false positives have been identified at this time. references: -- https://www.cisecurity.org/insights/white-papers/security-primer-icedid + - https://www.cisecurity.org/insights/white-papers/security-primer-icedid tags: - analytic_story: - - IcedID - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1560.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - IcedID + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1560.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/simulated_icedid/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/simulated_icedid/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/impacket_lateral_movement_commandline_parameters.yml b/detections/endpoint/impacket_lateral_movement_commandline_parameters.yml index 1f6b633b81..dd9710a5fb 100644 --- a/detections/endpoint/impacket_lateral_movement_commandline_parameters.yml +++ b/detections/endpoint/impacket_lateral_movement_commandline_parameters.yml @@ -5,100 +5,67 @@ date: '2026-01-20' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic identifies the use of suspicious command-line - parameters associated with Impacket tools, such as `wmiexec.py`, `smbexec.py`, `dcomexec.py`, - and `atexec.py`, which are used for lateral movement and remote code execution. - It detects these activities by analyzing process execution logs from Endpoint Detection - and Response (EDR) agents, focusing on specific command-line patterns. This activity - is significant because Impacket tools are commonly used by adversaries and Red Teams - to move laterally within a network. If confirmed malicious, this could allow attackers - to execute commands remotely, potentially leading to further compromise and data - exfiltration. +description: The following analytic identifies the use of suspicious command-line parameters associated with Impacket tools, such as `wmiexec.py`, `smbexec.py`, `dcomexec.py`, and `atexec.py`, which are used for lateral movement and remote code execution. It detects these activities by analyzing process execution logs from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns. This activity is significant because Impacket tools are commonly used by adversaries and Red Teams to move laterally within a network. If confirmed malicious, this could allow attackers to execute commands remotely, potentially leading to further compromise and data exfiltration. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe - (Processes.process = "*/Q /c * \\\\127.0.0.1\\*$*" AND Processes.process IN ("*2>&1*","*2>&1*")) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `impacket_lateral_movement_commandline_parameters_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although uncommon, Administrators may leverage Impackets tools - to start a process on remote systems for system administration or automation use - cases. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe (Processes.process = "*/Q /c * \\\\127.0.0.1\\*$*" AND Processes.process IN ("*2>&1*","*2>&1*")) by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `impacket_lateral_movement_commandline_parameters_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although uncommon, Administrators may leverage Impackets tools to start a process on remote systems for system administration or automation use cases. references: -- https://attack.mitre.org/techniques/T1021/002/ -- https://attack.mitre.org/techniques/T1021/003/ -- https://attack.mitre.org/techniques/T1047/ -- https://attack.mitre.org/techniques/T1053/ -- https://attack.mitre.org/techniques/T1053/005/ -- https://github.com/SecureAuthCorp/impacket -- https://vk9-sec.com/impacket-remote-code-execution-rce-on-windows-from-linux/ -- https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ -- https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ + - https://attack.mitre.org/techniques/T1021/002/ + - https://attack.mitre.org/techniques/T1021/003/ + - https://attack.mitre.org/techniques/T1047/ + - https://attack.mitre.org/techniques/T1053/ + - https://attack.mitre.org/techniques/T1053/005/ + - https://github.com/SecureAuthCorp/impacket + - https://vk9-sec.com/impacket-remote-code-execution-rce-on-windows-from-linux/ + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious command line parameters on $dest$ may represent a lateral movement - attack with Impackets tools - risk_objects: - - field: dest - type: system - score: 63 - threat_objects: [] + message: Suspicious command line parameters on $dest$ may represent a lateral movement attack with Impackets tools + risk_objects: + - field: dest + type: system + score: 63 + threat_objects: [] tags: - analytic_story: - - WhisperGate - - Gozi Malware - - Active Directory Lateral Movement - - Volt Typhoon - - Prestige Ransomware - - Industroyer2 - - Data Destruction - - Graceful Wipe Out Attack - - Compromised Windows Host - - CISA AA22-277A - - Storm-0501 Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1021.002 - - T1021.003 - - T1047 - - T1543.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - WhisperGate + - Gozi Malware + - Active Directory Lateral Movement + - Volt Typhoon + - Prestige Ransomware + - Industroyer2 + - Data Destruction + - Graceful Wipe Out Attack + - Compromised Windows Host + - CISA AA22-277A + - Storm-0501 Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1021.002 + - T1021.003 + - T1047 + - T1543.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.003/impacket/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.003/impacket/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/impacket_lateral_movement_smbexec_commandline_parameters.yml b/detections/endpoint/impacket_lateral_movement_smbexec_commandline_parameters.yml index ccc601e994..148db6dcdf 100644 --- a/detections/endpoint/impacket_lateral_movement_smbexec_commandline_parameters.yml +++ b/detections/endpoint/impacket_lateral_movement_smbexec_commandline_parameters.yml @@ -6,98 +6,65 @@ author: Michael Haag, Splunk status: production type: TTP data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic identifies suspicious command-line parameters - associated with the use of Impacket's smbexec.py for lateral movement. It leverages - data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line - patterns indicative of Impacket tool usage. This activity is significant as both - Red Teams and adversaries use Impacket for remote code execution and lateral movement. - If confirmed malicious, this activity could allow attackers to execute commands - on remote endpoints, potentially leading to unauthorized access, data exfiltration, - or further compromise of the network. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | where match(process, "(?i)cmd\.exe\s+\/Q\s+\/c") - AND match(process,"(?i)echo\s+cd") AND match(process, "(?i)\\__output") AND match(process, - "(?i)C:\\\\Windows\\\\[a-zA-Z]{1,8}\\.bat") AND match(process, "\\\\127\.0\.0\.1\\.*") - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `impacket_lateral_movement_smbexec_commandline_parameters_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although uncommon, Administrators may leverage Impackets tools - to start a process on remote systems for system administration or automation use - cases. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic identifies suspicious command-line parameters associated with the use of Impacket's smbexec.py for lateral movement. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns indicative of Impacket tool usage. This activity is significant as both Red Teams and adversaries use Impacket for remote code execution and lateral movement. If confirmed malicious, this activity could allow attackers to execute commands on remote endpoints, potentially leading to unauthorized access, data exfiltration, or further compromise of the network. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | where match(process, "(?i)cmd\.exe\s+\/Q\s+\/c") AND match(process,"(?i)echo\s+cd") AND match(process, "(?i)\\__output") AND match(process, "(?i)C:\\\\Windows\\\\[a-zA-Z]{1,8}\\.bat") AND match(process, "\\\\127\.0\.0\.1\\.*") | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `impacket_lateral_movement_smbexec_commandline_parameters_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although uncommon, Administrators may leverage Impackets tools to start a process on remote systems for system administration or automation use cases. references: -- https://attack.mitre.org/techniques/T1021/002/ -- https://attack.mitre.org/techniques/T1021/003/ -- https://attack.mitre.org/techniques/T1047/ -- https://attack.mitre.org/techniques/T1053/ -- https://attack.mitre.org/techniques/T1053/005/ -- https://github.com/SecureAuthCorp/impacket -- https://vk9-sec.com/impacket-remote-code-execution-rce-on-windows-from-linux/ -- https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ -- https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ + - https://attack.mitre.org/techniques/T1021/002/ + - https://attack.mitre.org/techniques/T1021/003/ + - https://attack.mitre.org/techniques/T1047/ + - https://attack.mitre.org/techniques/T1053/ + - https://attack.mitre.org/techniques/T1053/005/ + - https://github.com/SecureAuthCorp/impacket + - https://vk9-sec.com/impacket-remote-code-execution-rce-on-windows-from-linux/ + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious command-line parameters on $dest$ may represent lateral movement - using smbexec. - risk_objects: - - field: dest - type: system - score: 63 - threat_objects: [] + message: Suspicious command-line parameters on $dest$ may represent lateral movement using smbexec. + risk_objects: + - field: dest + type: system + score: 63 + threat_objects: [] tags: - analytic_story: - - WhisperGate - - Active Directory Lateral Movement - - Volt Typhoon - - Prestige Ransomware - - Industroyer2 - - Data Destruction - - Graceful Wipe Out Attack - - Compromised Windows Host - - CISA AA22-277A - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1021.002 - - T1021.003 - - T1047 - - T1543.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - WhisperGate + - Active Directory Lateral Movement + - Volt Typhoon + - Prestige Ransomware + - Industroyer2 + - Data Destruction + - Graceful Wipe Out Attack + - Compromised Windows Host + - CISA AA22-277A + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1021.002 + - T1021.003 + - T1047 + - T1543.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.002/atomic_red_team/smbexec_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.002/atomic_red_team/smbexec_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/impacket_lateral_movement_wmiexec_commandline_parameters.yml b/detections/endpoint/impacket_lateral_movement_wmiexec_commandline_parameters.yml index ae4842b8f1..314bade0dc 100644 --- a/detections/endpoint/impacket_lateral_movement_wmiexec_commandline_parameters.yml +++ b/detections/endpoint/impacket_lateral_movement_wmiexec_commandline_parameters.yml @@ -6,99 +6,67 @@ author: Michael Haag, Splunk status: production type: TTP data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic detects the use of Impacket's `wmiexec.py` tool - for lateral movement by identifying specific command-line parameters. It leverages - data from Endpoint Detection and Response (EDR) agents, focusing on processes spawned - by `wmiprvse.exe` with command-line patterns indicative of Impacket usage. This - activity is significant as Impacket tools are commonly used by adversaries for remote - code execution and lateral movement within a network. If confirmed malicious, this - could allow attackers to execute arbitrary commands on remote systems, potentially - leading to further compromise and data exfiltration. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wmiprvse.exe - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | where match(process, "(?i)cmd\.exe\s+\/Q\s+\/c") - AND match(process, "\\\\127\.0\.0\.1\\.*") AND match(process, "__\\d{1,10}\\.\\d{1,10}") - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `impacket_lateral_movement_wmiexec_commandline_parameters_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although uncommon, Administrators may leverage Impackets tools - to start a process on remote systems for system administration or automation use - cases. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic detects the use of Impacket's `wmiexec.py` tool for lateral movement by identifying specific command-line parameters. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on processes spawned by `wmiprvse.exe` with command-line patterns indicative of Impacket usage. This activity is significant as Impacket tools are commonly used by adversaries for remote code execution and lateral movement within a network. If confirmed malicious, this could allow attackers to execute arbitrary commands on remote systems, potentially leading to further compromise and data exfiltration. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wmiprvse.exe by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | where match(process, "(?i)cmd\.exe\s+\/Q\s+\/c") AND match(process, "\\\\127\.0\.0\.1\\.*") AND match(process, "__\\d{1,10}\\.\\d{1,10}") | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `impacket_lateral_movement_wmiexec_commandline_parameters_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although uncommon, Administrators may leverage Impackets tools to start a process on remote systems for system administration or automation use cases. references: -- https://attack.mitre.org/techniques/T1021/002/ -- https://attack.mitre.org/techniques/T1021/003/ -- https://attack.mitre.org/techniques/T1047/ -- https://attack.mitre.org/techniques/T1053/ -- https://attack.mitre.org/techniques/T1053/005/ -- https://github.com/SecureAuthCorp/impacket -- https://vk9-sec.com/impacket-remote-code-execution-rce-on-windows-from-linux/ -- https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ -- https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ + - https://attack.mitre.org/techniques/T1021/002/ + - https://attack.mitre.org/techniques/T1021/003/ + - https://attack.mitre.org/techniques/T1047/ + - https://attack.mitre.org/techniques/T1053/ + - https://attack.mitre.org/techniques/T1053/005/ + - https://github.com/SecureAuthCorp/impacket + - https://vk9-sec.com/impacket-remote-code-execution-rce-on-windows-from-linux/ + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious command-line parameters on $dest$ may represent lateral movement - using wmiexec. - risk_objects: - - field: dest - type: system - score: 63 - threat_objects: [] + message: Suspicious command-line parameters on $dest$ may represent lateral movement using wmiexec. + risk_objects: + - field: dest + type: system + score: 63 + threat_objects: [] tags: - analytic_story: - - WhisperGate - - Gozi Malware - - Active Directory Lateral Movement - - Volt Typhoon - - Prestige Ransomware - - Industroyer2 - - Data Destruction - - Graceful Wipe Out Attack - - Compromised Windows Host - - CISA AA22-277A - - Storm-0501 Ransomware - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1021.002 - - T1021.003 - - T1047 - - T1543.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - WhisperGate + - Gozi Malware + - Active Directory Lateral Movement + - Volt Typhoon + - Prestige Ransomware + - Industroyer2 + - Data Destruction + - Graceful Wipe Out Attack + - Compromised Windows Host + - CISA AA22-277A + - Storm-0501 Ransomware + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1021.002 + - T1021.003 + - T1047 + - T1543.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.002/atomic_red_team/wmiexec_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.002/atomic_red_team/wmiexec_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/interactive_session_on_remote_endpoint_with_powershell.yml b/detections/endpoint/interactive_session_on_remote_endpoint_with_powershell.yml index cac6afccc0..cfc5947efa 100644 --- a/detections/endpoint/interactive_session_on_remote_endpoint_with_powershell.yml +++ b/detections/endpoint/interactive_session_on_remote_endpoint_with_powershell.yml @@ -1,74 +1,60 @@ name: Interactive Session on Remote Endpoint with PowerShell id: a4e8f3a4-48b2-11ec-bcfc-3e22fbd008af -version: 11 -date: '2025-06-24' +version: 12 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: - The following analytic detects the use of the `Enter-PSSession` cmdlet - to establish an interactive session on a remote endpoint via the WinRM protocol. - It leverages PowerShell Script Block Logging (EventCode=4104) to identify this activity - by searching for specific script block text patterns. This behavior is significant - as it may indicate lateral movement or remote code execution attempts by adversaries. - If confirmed malicious, this activity could allow attackers to execute commands - remotely, potentially leading to further compromise of the network and unauthorized - access to sensitive information. +description: The following analytic detects the use of the `Enter-PSSession` cmdlet to establish an interactive session on a remote endpoint via the WinRM protocol. It leverages PowerShell Script Block Logging (EventCode=4104) to identify this activity by searching for specific script block text patterns. This behavior is significant as it may indicate lateral movement or remote code execution attempts by adversaries. If confirmed malicious, this activity could allow attackers to execute commands remotely, potentially leading to further compromise of the network and unauthorized access to sensitive information. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 (ScriptBlockText="*Enter-PSSession*" AND ScriptBlockText="*-ComputerName*") - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `interactive_session_on_remote_endpoint_with_powershell_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup instructions - can be found https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - Administrators may leverage WinRM and `Enter-PSSession` for - administrative and troubleshooting tasks. This activity is usually limited to a - small set of hosts or users. In certain environments, tuning may not be possible. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 (ScriptBlockText="*Enter-PSSession*" AND ScriptBlockText="*-ComputerName*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `interactive_session_on_remote_endpoint_with_powershell_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup instructions can be found https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: Administrators may leverage WinRM and `Enter-PSSession` for administrative and troubleshooting tasks. This activity is usually limited to a small set of hosts or users. In certain environments, tuning may not be possible. references: - - https://attack.mitre.org/techniques/T1021/006/ - - https://docs.microsoft.com/en-us/powershell/module/microsoft.powershell.core/enter-pssession?view=powershell-7.2 + - https://attack.mitre.org/techniques/T1021/006/ + - https://docs.microsoft.com/en-us/powershell/module/microsoft.powershell.core/enter-pssession?view=powershell-7.2 drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An interactive session was opened on a remote endpoint from $dest$ - risk_objects: - - field: dest - type: system - score: 45 - threat_objects: [] + message: An interactive session was opened on a remote endpoint from $dest$ + risk_objects: + - field: dest + type: system + score: 45 + threat_objects: [] tags: - analytic_story: - - Active Directory Lateral Movement - asset_type: Endpoint - mitre_attack_id: - - T1021.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + asset_type: Endpoint + mitre_attack_id: + - T1021.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/lateral_movement_pssession/windows-powershell-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/lateral_movement_pssession/windows-powershell-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/java_writing_jsp_file.yml b/detections/endpoint/java_writing_jsp_file.yml index 9d60a744b2..00de66c165 100644 --- a/detections/endpoint/java_writing_jsp_file.yml +++ b/detections/endpoint/java_writing_jsp_file.yml @@ -1,92 +1,85 @@ name: Java Writing JSP File id: eb65619c-4f8d-4383-a975-d352765d344b -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the Java process writing a .jsp file to - disk, which may indicate a web shell being deployed. It leverages data from the - Endpoint datamodel, specifically monitoring process and filesystem activities. This - activity is significant because web shells can provide attackers with remote control - over the compromised server, leading to further exploitation. If confirmed malicious, - this could allow unauthorized access, data exfiltration, or further compromise of - the affected system, posing a severe security risk. +description: The following analytic detects the Java process writing a .jsp file to disk, which may indicate a web shell being deployed. It leverages data from the Endpoint datamodel, specifically monitoring process and filesystem activities. This activity is significant because web shells can provide attackers with remote control over the compromised server, leading to further exploitation. If confirmed malicious, this could allow unauthorized access, data exfiltration, or further compromise of the affected system, posing a severe security risk. data_source: -- Sysmon for Linux EventID 1 AND Sysmon for Linux EventID 11 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes - where Processes.process_name IN ("java","java.exe", "javaw.exe") by _time Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | join process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem - where Filesystem.file_name="*.jsp*" by _time Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path - Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product - | `drop_dm_object_name(Filesystem)` | fields _time process_guid file_path file_name - file_create_time user dest process_name] | stats count min(_time) as firstTime max(_time) - as lastTime by dest process_name process_guid file_name file_path file_create_time - user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `java_writing_jsp_file_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` - node. In addition, confirm the latest CIM App 4.20 or higher is installed and the - latest TA for the endpoint product. -known_false_positives: False positives are possible and filtering may be required. - Restrict by assets or filter known jsp files that are common for the environment. + - Sysmon for Linux EventID 1 AND Sysmon for Linux EventID 11 +search: |- + | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes + WHERE Processes.process_name IN ("java","java.exe", "javaw.exe") + BY _time Processes.action Processes.dest + Processes.original_file_name Processes.parent_process Processes.parent_process_exec + Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name + Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id + Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | join process_guid [ + | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.file_name="*.jsp*" + BY _time Filesystem.action Filesystem.dest + Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash + Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path + Filesystem.file_acl Filesystem.file_size Filesystem.process_guid + Filesystem.process_id Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | fields _time process_guid file_path file_name file_create_time user dest process_name] + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest process_name process_guid + file_name file_path file_create_time + user + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `java_writing_jsp_file_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: False positives are possible and filtering may be required. Restrict by assets or filter known jsp files that are common for the environment. references: -- https://www.microsoft.com/security/blog/2022/04/04/springshell-rce-vulnerability-guidance-for-protecting-against-and-detecting-cve-2022-22965/ -- https://github.com/TheGejr/SpringShell -- https://www.tenable.com/blog/spring4shell-faq-spring-framework-remote-code-execution-vulnerability + - https://www.microsoft.com/security/blog/2022/04/04/springshell-rce-vulnerability-guidance-for-protecting-against-and-detecting-cve-2022-22965/ + - https://github.com/TheGejr/SpringShell + - https://www.tenable.com/blog/spring4shell-faq-spring-framework-remote-code-execution-vulnerability drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $process_name$ was identified on endpoint $dest$ writing - a jsp file $file_name$ to disk, potentially indicative of exploitation. - risk_objects: - - field: dest - type: system - score: 42 - threat_objects: - - field: process_name - type: process_name + message: An instance of $process_name$ was identified on endpoint $dest$ writing a jsp file $file_name$ to disk, potentially indicative of exploitation. + risk_objects: + - field: dest + type: system + score: 42 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Spring4Shell CVE-2022-22965 - - Atlassian Confluence Server and Data Center CVE-2022-26134 - - SysAid On-Prem Software CVE-2023-47246 Vulnerability - - SAP NetWeaver Exploitation - asset_type: Endpoint - cve: - - CVE-2022-22965 - mitre_attack_id: - - T1190 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Spring4Shell CVE-2022-22965 + - Atlassian Confluence Server and Data Center CVE-2022-26134 + - SysAid On-Prem Software CVE-2023-47246 Vulnerability + - SAP NetWeaver Exploitation + asset_type: Endpoint + cve: + - CVE-2022-22965 + mitre_attack_id: + - T1190 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/spring4shell/java_write_jsp-linux-sysmon.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/spring4shell/java_write_jsp-linux-sysmon.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/jscript_execution_using_cscript_app.yml b/detections/endpoint/jscript_execution_using_cscript_app.yml index 21efc760ab..4485ee27b2 100644 --- a/detections/endpoint/jscript_execution_using_cscript_app.yml +++ b/detections/endpoint/jscript_execution_using_cscript_app.yml @@ -1,84 +1,72 @@ name: Jscript Execution Using Cscript App id: 002f1e24-146e-11ec-a470-acde48001122 -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of JScript using the cscript.exe - process. It leverages data from Endpoint Detection and Response (EDR) agents, focusing - on process and command-line telemetry. This behavior is significant because JScript - files are typically executed by wscript.exe, making cscript.exe execution unusual - and potentially indicative of malicious activity, such as the FIN7 group's tactics. - If confirmed malicious, this activity could allow attackers to execute arbitrary - scripts, leading to code execution, data exfiltration, or further system compromise. +description: The following analytic detects the execution of JScript using the cscript.exe process. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and command-line telemetry. This behavior is significant because JScript files are typically executed by wscript.exe, making cscript.exe execution unusual and potentially indicative of malicious activity, such as the FIN7 group's tactics. If confirmed malicious, this activity could allow attackers to execute arbitrary scripts, leading to code execution, data exfiltration, or further system compromise. data_source: -- Sysmon EventID 1 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name - = "cscript.exe" AND Processes.parent_process = "*//e:jscript*") OR (Processes.process_name - = "cscript.exe" AND Processes.process = "*//e:jscript*") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `jscript_execution_using_cscript_app_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.parent_process_name = "cscript.exe" + AND + Processes.parent_process = "*//e:jscript*" + ) + OR (Processes.process_name = "cscript.exe" AND Processes.process = "*//e:jscript*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `jscript_execution_using_cscript_app_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://www.mandiant.com/resources/fin7-pursuing-an-enigmatic-and-evasive-global-criminal-operation -- https://attack.mitre.org/groups/G0046/ + - https://www.mandiant.com/resources/fin7-pursuing-an-enigmatic-and-evasive-global-criminal-operation + - https://attack.mitre.org/groups/G0046/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Process name $process_name$ with commandline $process$ to execute jscript - on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - - field: user - type: user - score: 49 - threat_objects: [] + message: Process name $process_name$ with commandline $process$ to execute jscript on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - FIN7 - - Remcos - asset_type: Endpoint - mitre_attack_id: - - T1059.007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - FIN7 + - Remcos + asset_type: Endpoint + mitre_attack_id: + - T1059.007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/fin7_macro_js_1/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/fin7_macro_js_1/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/kerberoasting_spn_request_with_rc4_encryption.yml b/detections/endpoint/kerberoasting_spn_request_with_rc4_encryption.yml index ba7457a2ce..e737aef361 100644 --- a/detections/endpoint/kerberoasting_spn_request_with_rc4_encryption.yml +++ b/detections/endpoint/kerberoasting_spn_request_with_rc4_encryption.yml @@ -1,76 +1,61 @@ name: Kerberoasting spn request with RC4 encryption id: 5cc67381-44fa-4111-8a37-7a230943f027 -version: 11 -date: '2025-05-02' +version: 12 +date: '2026-02-25' author: Jose Hernandez, Patrick Bareiss, Mauricio Velazco, Dean Luxton, Splunk status: production type: TTP -description: The following analytic detects potential Kerberoasting attacks by identifying - Kerberos service ticket requests with RC4 encryption through Event ID 4769. It leverages - specific Ticket_Options values commonly used by Kerberoasting tools. This activity - is significant as Kerberoasting allows attackers to request service tickets for - domain accounts, typically service accounts, and crack them offline to gain privileged - access. If confirmed malicious, this could lead to unauthorized access, privilege - escalation, and further compromise of the Active Directory environment. +description: The following analytic detects potential Kerberoasting attacks by identifying Kerberos service ticket requests with RC4 encryption through Event ID 4769. It leverages specific Ticket_Options values commonly used by Kerberoasting tools. This activity is significant as Kerberoasting allows attackers to request service tickets for domain accounts, typically service accounts, and crack them offline to gain privileged access. If confirmed malicious, this could lead to unauthorized access, privilege escalation, and further compromise of the Active Directory environment. data_source: -- Windows Event Log Security 4769 -search: '`wineventlog_security` EventCode=4769 ServiceName!="*$" (TicketOptions=0x40810000 - OR TicketOptions=0x40800000 OR TicketOptions=0x40810010) TicketEncryptionType=0x17 - | stats count min(_time) as firstTime max(_time) as lastTime by Computer, user, - service_id, service, TicketEncryptionType, TicketOptions | rename Computer as dest - | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `kerberoasting_spn_request_with_rc4_encryption_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Domain Controller and Kerberos events. The Advanced Security Audit policy setting - `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. - Whithin environments where this type of communication is common, consider dropping - the risk score and add throttling based on the user and service_id for 30 days. - This will allow RBA to alert when there is an anomalous spike of these kerberoastable - SPN requests within a short period of time. -known_false_positives: Older systems that support kerberos RC4 by default like NetApp - may generate false positives. Filter as needed + - Windows Event Log Security 4769 +search: |- + `wineventlog_security` EventCode=4769 ServiceName!="*$" (TicketOptions=0x40810000 OR TicketOptions=0x40800000 OR TicketOptions=0x40810010) TicketEncryptionType=0x17 + | stats count min(_time) as firstTime max(_time) as lastTime + BY Computer, user, service_id, + service, TicketEncryptionType, TicketOptions + | rename Computer as dest + | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` + | `kerberoasting_spn_request_with_rc4_encryption_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. Whithin environments where this type of communication is common, consider dropping the risk score and add throttling based on the user and service_id for 30 days. This will allow RBA to alert when there is an anomalous spike of these kerberoastable SPN requests within a short period of time. +known_false_positives: Older systems that support kerberos RC4 by default like NetApp may generate false positives. Filter as needed references: -- https://github.com/redcanaryco/atomic-red-team/blob/4e3e9c8096dde00639a6b98845ec349135554ed5/atomics/T1208/T1208.md -- https://www.hub.trimarcsecurity.com/post/trimarc-research-detecting-kerberoasting-activity + - https://github.com/redcanaryco/atomic-red-team/blob/4e3e9c8096dde00639a6b98845ec349135554ed5/atomics/T1208/T1208.md + - https://www.hub.trimarcsecurity.com/post/trimarc-research-detecting-kerberoasting-activity drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ requested a service ticket for SPN $service_id$ with RC4 encryption - risk_objects: - - field: user - type: user - score: 72 - threat_objects: [] + message: User $user$ requested a service ticket for SPN $service_id$ with RC4 encryption + risk_objects: + - field: user + type: user + score: 72 + threat_objects: [] tags: - analytic_story: - - Windows Privilege Escalation - - Data Destruction - - Active Directory Kerberos Attacks - - Compromised Windows Host - - Hermetic Wiper - asset_type: Endpoint - mitre_attack_id: - - T1558.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Privilege Escalation + - Data Destruction + - Active Directory Kerberos Attacks + - Compromised Windows Host + - Hermetic Wiper + asset_type: Endpoint + mitre_attack_id: + - T1558.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/kerberoasting_spn_request_with_rc4_encryption/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/kerberoasting_spn_request_with_rc4_encryption/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/kerberos_pre_authentication_flag_disabled_in_useraccountcontrol.yml b/detections/endpoint/kerberos_pre_authentication_flag_disabled_in_useraccountcontrol.yml index ea101849de..7a530978cf 100644 --- a/detections/endpoint/kerberos_pre_authentication_flag_disabled_in_useraccountcontrol.yml +++ b/detections/endpoint/kerberos_pre_authentication_flag_disabled_in_useraccountcontrol.yml @@ -5,69 +5,52 @@ date: '2026-02-02' author: Mauricio Velazco, Splunk status: production type: TTP -description: - The following analytic detects when the Kerberos Pre-Authentication flag - is disabled in a user account, using Windows Security Event 4738. This event indicates - a change in the UserAccountControl property of a domain user object. Disabling this - flag allows adversaries to perform offline brute force attacks on the user's password - using the AS-REP Roasting technique. This activity is significant as it can be used - by attackers with existing privileges to escalate their access or maintain persistence. - If confirmed malicious, this could lead to unauthorized access and potential compromise - of sensitive information. +description: The following analytic detects when the Kerberos Pre-Authentication flag is disabled in a user account, using Windows Security Event 4738. This event indicates a change in the UserAccountControl property of a domain user object. Disabling this flag allows adversaries to perform offline brute force attacks on the user's password using the AS-REP Roasting technique. This activity is significant as it can be used by attackers with existing privileges to escalate their access or maintain persistence. If confirmed malicious, this could lead to unauthorized access and potential compromise of sensitive information. data_source: - - Windows Event Log Security 4738 + - Windows Event Log Security 4738 search: > - `wineventlog_security` EventCode=4738 UserAccountControl="*%%2096*" - | rename TargetUserName as user, SubjectUserName as actor | stats count earliest(_time) as firstTime latest(_time) as lastTime by actor, user, dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `kerberos_pre_authentication_flag_disabled_in_useraccountcontrol_filter` -how_to_implement: - To successfully implement this search, you need to be ingesting - Domain Controller events. The Advanced Security Audit policy setting `User Account - Management` within `Account Management` needs to be enabled. + `wineventlog_security` EventCode=4738 UserAccountControl="*%%2096*" + | rename TargetUserName as user, SubjectUserName as actor | stats count earliest(_time) as firstTime latest(_time) as lastTime by actor, user, dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `kerberos_pre_authentication_flag_disabled_in_useraccountcontrol_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller events. The Advanced Security Audit policy setting `User Account Management` within `Account Management` needs to be enabled. known_false_positives: No false positives have been identified at this time. references: - - https://docs.microsoft.com/en-us/troubleshoot/windows-server/identity/useraccountcontrol-manipulate-account-properties - - https://m0chan.github.io/2019/07/31/How-To-Attack-Kerberos-101.html - - https://stealthbits.com/blog/cracking-active-directory-passwords-with-as-rep-roasting/ + - https://docs.microsoft.com/en-us/troubleshoot/windows-server/identity/useraccountcontrol-manipulate-account-properties + - https://m0chan.github.io/2019/07/31/How-To-Attack-Kerberos-101.html + - https://stealthbits.com/blog/cracking-active-directory-passwords-with-as-rep-roasting/ drilldown_searches: - - name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Kerberos Pre Authentication was Disabled for $user$ - risk_objects: - - field: user - type: user - score: 45 - threat_objects: [] + message: Kerberos Pre Authentication was Disabled for $user$ + risk_objects: + - field: user + type: user + score: 45 + threat_objects: [] tags: - analytic_story: - - Active Directory Kerberos Attacks - - BlackSuit Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1558.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Kerberos Attacks + - BlackSuit Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1558.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/refs/heads/master/datasets/attack_techniques/T1558.004/powershell/windows-security-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/refs/heads/master/datasets/attack_techniques/T1558.004/powershell/windows-security-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/kerberos_pre_authentication_flag_disabled_with_powershell.yml b/detections/endpoint/kerberos_pre_authentication_flag_disabled_with_powershell.yml index f79d8752e7..e22fbd6f45 100644 --- a/detections/endpoint/kerberos_pre_authentication_flag_disabled_with_powershell.yml +++ b/detections/endpoint/kerberos_pre_authentication_flag_disabled_with_powershell.yml @@ -1,74 +1,61 @@ name: Kerberos Pre-Authentication Flag Disabled with PowerShell id: 59b51620-94c9-11ec-b3d5-acde48001122 -version: 9 -date: '2025-06-24' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: - The following analytic detects the use of the `Set-ADAccountControl` - PowerShell cmdlet with parameters that disable Kerberos Pre-Authentication. It leverages - PowerShell Script Block Logging (EventCode=4104) to identify this specific command - execution. Disabling Kerberos Pre-Authentication is significant because it allows - adversaries to perform offline brute force attacks against user passwords using - the AS-REP Roasting technique. If confirmed malicious, this activity could enable - attackers to escalate privileges or maintain persistence within an Active Directory - environment, posing a severe security risk. +description: The following analytic detects the use of the `Set-ADAccountControl` PowerShell cmdlet with parameters that disable Kerberos Pre-Authentication. It leverages PowerShell Script Block Logging (EventCode=4104) to identify this specific command execution. Disabling Kerberos Pre-Authentication is significant because it allows adversaries to perform offline brute force attacks against user passwords using the AS-REP Roasting technique. If confirmed malicious, this activity could enable attackers to escalate privileges or maintain persistence within an Active Directory environment, posing a severe security risk. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 (ScriptBlockText = "*Set-ADAccountControl*" AND - ScriptBlockText="*DoesNotRequirePreAuth:$true*") | fillnull | stats count min(_time) - as firstTime max(_time) as lastTime by dest signature signature_id user_id vendor_product - EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `kerberos_pre_authentication_flag_disabled_with_powershell_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - Although unlikely, Administrators may need to set this flag - for legitimate purposes. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 (ScriptBlockText = "*Set-ADAccountControl*" AND ScriptBlockText="*DoesNotRequirePreAuth:$true*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `kerberos_pre_authentication_flag_disabled_with_powershell_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: Although unlikely, Administrators may need to set this flag for legitimate purposes. references: - - https://docs.microsoft.com/en-us/troubleshoot/windows-server/identity/useraccountcontrol-manipulate-account-properties - - https://m0chan.github.io/2019/07/31/How-To-Attack-Kerberos-101.html - - https://stealthbits.com/blog/cracking-active-directory-passwords-with-as-rep-roasting/ + - https://docs.microsoft.com/en-us/troubleshoot/windows-server/identity/useraccountcontrol-manipulate-account-properties + - https://m0chan.github.io/2019/07/31/How-To-Attack-Kerberos-101.html + - https://stealthbits.com/blog/cracking-active-directory-passwords-with-as-rep-roasting/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Kerberos Pre Authentication was Disabled using PowerShell on $dest$ - risk_objects: - - field: dest - type: system - score: 45 - threat_objects: [] + message: Kerberos Pre Authentication was Disabled using PowerShell on $dest$ + risk_objects: + - field: dest + type: system + score: 45 + threat_objects: [] tags: - analytic_story: - - Active Directory Kerberos Attacks - asset_type: Endpoint - mitre_attack_id: - - T1558.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Kerberos Attacks + asset_type: Endpoint + mitre_attack_id: + - T1558.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.004/powershell/windows-powershell-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.004/powershell/windows-powershell-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/kerberos_service_ticket_request_using_rc4_encryption.yml b/detections/endpoint/kerberos_service_ticket_request_using_rc4_encryption.yml index e31cf04d61..974da1c730 100644 --- a/detections/endpoint/kerberos_service_ticket_request_using_rc4_encryption.yml +++ b/detections/endpoint/kerberos_service_ticket_request_using_rc4_encryption.yml @@ -1,78 +1,61 @@ name: Kerberos Service Ticket Request Using RC4 Encryption id: 7d90f334-a482-11ec-908c-acde48001122 -version: 9 -date: '2025-10-14' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: 'The following analytic detects Kerberos service ticket requests using - RC4 encryption, leveraging Kerberos Event 4769. This method identifies potential - Golden Ticket attacks, where adversaries forge Kerberos Granting Tickets (TGT) using - the Krbtgt account NTLM password hash to gain unrestricted access to an Active Directory - environment. Monitoring for RC4 encryption usage is significant as it is rare in - modern networks, indicating possible malicious activity. If confirmed malicious, - attackers could move laterally and execute code on remote systems, compromising - the entire network. Note: This detection may be bypassed if attackers use the AES - key instead of the NTLM hash.' +description: 'The following analytic detects Kerberos service ticket requests using RC4 encryption, leveraging Kerberos Event 4769. This method identifies potential Golden Ticket attacks, where adversaries forge Kerberos Granting Tickets (TGT) using the Krbtgt account NTLM password hash to gain unrestricted access to an Active Directory environment. Monitoring for RC4 encryption usage is significant as it is rare in modern networks, indicating possible malicious activity. If confirmed malicious, attackers could move laterally and execute code on remote systems, compromising the entire network. Note: This detection may be bypassed if attackers use the AES key instead of the NTLM hash.' data_source: -- Windows Event Log Security 4769 -search: '`wineventlog_security` EventCode=4769 ServiceName="*$" (TicketOptions=0x40810000 - OR TicketOptions=0x40800000 OR TicketOptions=0x40810010) TicketEncryptionType=0x17 - | stats count min(_time) as firstTime max(_time) as lastTime by dest, service, service_id, - TicketEncryptionType, TicketOptions | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` - | `kerberos_service_ticket_request_using_rc4_encryption_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Domain Controller and Kerberos events. The Advanced Security Audit policy setting - `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. -known_false_positives: Based on Microsoft documentation, legacy systems or applications - will use RC4-HMAC as the default encryption for Kerberos Service Ticket requests. - Specifically, systems before Windows Server 2008 and Windows Vista. Newer systems - will use AES128 or AES256. + - Windows Event Log Security 4769 +search: |- + `wineventlog_security` EventCode=4769 ServiceName="*$" (TicketOptions=0x40810000 OR TicketOptions=0x40800000 OR TicketOptions=0x40810010) TicketEncryptionType=0x17 + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest, service, service_id, + TicketEncryptionType, TicketOptions + | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` + | `kerberos_service_ticket_request_using_rc4_encryption_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. +known_false_positives: Based on Microsoft documentation, legacy systems or applications will use RC4-HMAC as the default encryption for Kerberos Service Ticket requests. Specifically, systems before Windows Server 2008 and Windows Vista. Newer systems will use AES128 or AES256. references: -- https://attack.mitre.org/techniques/T1558/001/ -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4769 -- https://adsecurity.org/?p=1515 -- https://gist.github.com/TarlogicSecurity/2f221924fef8c14a1d8e29f3cb5c5c4a -- https://en.hackndo.com/kerberos-silver-golden-tickets/ + - https://attack.mitre.org/techniques/T1558/001/ + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4769 + - https://adsecurity.org/?p=1515 + - https://gist.github.com/TarlogicSecurity/2f221924fef8c14a1d8e29f3cb5c5c4a + - https://en.hackndo.com/kerberos-silver-golden-tickets/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Kerberos Service TTicket request with RC4 encryption was requested from - $dest$ - risk_objects: - - field: dest - type: system - score: 45 - threat_objects: [] + message: A Kerberos Service TTicket request with RC4 encryption was requested from $dest$ + risk_objects: + - field: dest + type: system + score: 45 + threat_objects: [] tags: - analytic_story: - - Active Directory Kerberos Attacks - - Active Directory Privilege Escalation - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1558.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Kerberos Attacks + - Active Directory Privilege Escalation + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1558.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.001/kerberos_service_ticket_request_using_rc4_encryption/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.001/kerberos_service_ticket_request_using_rc4_encryption/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/kerberos_tgt_request_using_rc4_encryption.yml b/detections/endpoint/kerberos_tgt_request_using_rc4_encryption.yml index 9f11e33733..f00c0da0f3 100644 --- a/detections/endpoint/kerberos_tgt_request_using_rc4_encryption.yml +++ b/detections/endpoint/kerberos_tgt_request_using_rc4_encryption.yml @@ -1,71 +1,57 @@ name: Kerberos TGT Request Using RC4 Encryption id: 18916468-9c04-11ec-bdc6-acde48001122 -version: 8 -date: '2025-10-14' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects a Kerberos Ticket Granting Ticket (TGT) - request using RC4-HMAC encryption (type 0x17) by leveraging Event 4768. This encryption - type is outdated and its presence may indicate an OverPass The Hash attack. Monitoring - this activity is crucial as it can signify credential theft, allowing adversaries - to authenticate to the Kerberos Distribution Center (KDC) using a stolen NTLM hash. - If confirmed malicious, this could enable unauthorized access to systems and resources, - potentially leading to lateral movement and further compromise within the network. +description: The following analytic detects a Kerberos Ticket Granting Ticket (TGT) request using RC4-HMAC encryption (type 0x17) by leveraging Event 4768. This encryption type is outdated and its presence may indicate an OverPass The Hash attack. Monitoring this activity is crucial as it can signify credential theft, allowing adversaries to authenticate to the Kerberos Distribution Center (KDC) using a stolen NTLM hash. If confirmed malicious, this could enable unauthorized access to systems and resources, potentially leading to lateral movement and further compromise within the network. data_source: -- Windows Event Log Security 4768 -search: '`wineventlog_security` EventCode=4768 TicketEncryptionType=0x17 ServiceName!=*$ - | stats count min(_time) as firstTime max(_time) as lastTime by ServiceName src_ip - dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `kerberos_tgt_request_using_rc4_encryption_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Domain Controller and Kerberos events. The Advanced Security Audit policy setting - `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. -known_false_positives: Based on Microsoft documentation, legacy systems or applications - will use RC4-HMAC as the default encryption for TGT requests. Specifically, systems - before Windows Server 2008 and Windows Vista. Newer systems will use AES128 or AES256. + - Windows Event Log Security 4768 +search: |- + `wineventlog_security` EventCode=4768 TicketEncryptionType=0x17 ServiceName!=*$ + | stats count min(_time) as firstTime max(_time) as lastTime + BY ServiceName src_ip dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `kerberos_tgt_request_using_rc4_encryption_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. +known_false_positives: Based on Microsoft documentation, legacy systems or applications will use RC4-HMAC as the default encryption for TGT requests. Specifically, systems before Windows Server 2008 and Windows Vista. Newer systems will use AES128 or AES256. references: -- https://stealthbits.com/blog/how-to-detect-overpass-the-hash-attacks/ -- https://www.thehacker.recipes/ad/movement/kerberos/ptk -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4768 + - https://stealthbits.com/blog/how-to-detect-overpass-the-hash-attacks/ + - https://www.thehacker.recipes/ad/movement/kerberos/ptk + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4768 drilldown_searches: -- name: View the detection results for - "$src_ip$" - search: '%original_detection_search% | search src_ip = "$src_ip$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_ip$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_ip$" + search: '%original_detection_search% | search src_ip = "$src_ip$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_ip$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Kerberos TGT request with RC4 encryption was requested for $ServiceName$ - from $src_ip$ - risk_objects: - - field: src_ip - type: system - score: 25 - threat_objects: [] + message: A Kerberos TGT request with RC4 encryption was requested for $ServiceName$ from $src_ip$ + risk_objects: + - field: src_ip + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Active Directory Kerberos Attacks - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1550 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Kerberos Attacks + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1550 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550/kerberos_tgt_request_using_rc4_encryption/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550/kerberos_tgt_request_using_rc4_encryption/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/kerberos_user_enumeration.yml b/detections/endpoint/kerberos_user_enumeration.yml index 37f40928da..61c7c3aaab 100644 --- a/detections/endpoint/kerberos_user_enumeration.yml +++ b/detections/endpoint/kerberos_user_enumeration.yml @@ -1,69 +1,60 @@ name: Kerberos User Enumeration id: d82d4af4-a0bd-11ec-9445-3e22fbd008af -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Anomaly -description: The following analytic detects an unusual number of Kerberos Ticket Granting - Ticket (TGT) requests for non-existing users from a single source endpoint. It leverages - Event ID 4768 and identifies anomalies using the 3-sigma statistical rule. This - behavior is significant as it may indicate an adversary performing a user enumeration - attack against Active Directory. If confirmed malicious, the attacker could validate - a list of usernames, potentially leading to further attacks such as brute force - or credential stuffing, compromising the security of the environment. +description: The following analytic detects an unusual number of Kerberos Ticket Granting Ticket (TGT) requests for non-existing users from a single source endpoint. It leverages Event ID 4768 and identifies anomalies using the 3-sigma statistical rule. This behavior is significant as it may indicate an adversary performing a user enumeration attack against Active Directory. If confirmed malicious, the attacker could validate a list of usernames, potentially leading to further attacks such as brute force or credential stuffing, compromising the security of the environment. data_source: -- Windows Event Log Security 4768 -search: '`wineventlog_security` EventCode=4768 Status=0x6 TargetUserName!="*$" | bucket - span=2m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) - as tried_accounts values(dest) as dest by _time, src_ip | eventstats avg(unique_accounts) - as comp_avg , stdev(unique_accounts) as comp_std by src_ip | eval upperBound=(comp_avg+comp_std*3) - | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) - | search isOutlier=1| `kerberos_user_enumeration_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Domain Controller and Kerberos events. The Advanced Security Audit policy setting - `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. -known_false_positives: Possible false positive scenarios include but are not limited - to vulnerability scanners and missconfigured systems. + - Windows Event Log Security 4768 +search: |- + `wineventlog_security` EventCode=4768 Status=0x6 TargetUserName!="*$" + | bucket span=2m _time + | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts values(dest) as dest + BY _time, src_ip + | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std + BY src_ip + | eval upperBound=(comp_avg+comp_std*3) + | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) + | search isOutlier=1 + | `kerberos_user_enumeration_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. +known_false_positives: Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. references: -- https://github.com/ropnop/kerbrute -- https://attack.mitre.org/techniques/T1589/002/ -- https://redsiege.com/tools-techniques/2020/04/user-enumeration-part-3-windows/ + - https://github.com/ropnop/kerbrute + - https://attack.mitre.org/techniques/T1589/002/ + - https://redsiege.com/tools-techniques/2020/04/user-enumeration-part-3-windows/ drilldown_searches: -- name: View the detection results for - "$src_ip$" - search: '%original_detection_search% | search src_ip = "$src_ip$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_ip$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_ip$" + search: '%original_detection_search% | search src_ip = "$src_ip$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_ip$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential Kerberos based user enumeration attack $src_ip$ - risk_objects: - - field: src_ip - type: system - score: 24 - threat_objects: [] + message: Potential Kerberos based user enumeration attack $src_ip$ + risk_objects: + - field: src_ip + type: system + score: 24 + threat_objects: [] tags: - analytic_story: - - Active Directory Kerberos Attacks - asset_type: Endpoint - mitre_attack_id: - - T1589.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Kerberos Attacks + asset_type: Endpoint + mitre_attack_id: + - T1589.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1589.002/kerberos_user_enumeration/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1589.002/kerberos_user_enumeration/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/linux_account_manipulation_of_ssh_config_and_keys.yml b/detections/endpoint/linux_account_manipulation_of_ssh_config_and_keys.yml index 3e5058994c..0df4ac8949 100644 --- a/detections/endpoint/linux_account_manipulation_of_ssh_config_and_keys.yml +++ b/detections/endpoint/linux_account_manipulation_of_ssh_config_and_keys.yml @@ -1,73 +1,63 @@ name: Linux Account Manipulation Of SSH Config and Keys id: 73a56508-1cf5-4df7-b8d9-5737fbdc27d2 -version: 9 -date: '2025-10-14' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the deletion of SSH keys on a Linux machine. - It leverages filesystem event logs to identify when files within "/etc/ssh/*" or - "~/.ssh/*" are deleted. This activity is significant because attackers may delete - or modify SSH keys to evade security measures or as part of a destructive payload, - similar to the AcidRain malware. If confirmed malicious, this behavior could lead - to impaired security features, hindered forensic investigations, or further unauthorized - access, necessitating immediate investigation to identify the responsible process - and user. +description: The following analytic detects the deletion of SSH keys on a Linux machine. It leverages filesystem event logs to identify when files within "/etc/ssh/*" or "~/.ssh/*" are deleted. This activity is significant because attackers may delete or modify SSH keys to evade security measures or as part of a destructive payload, similar to the AcidRain malware. If confirmed malicious, this behavior could lead to impaired security features, hindered forensic investigations, or further unauthorized access, necessitating immediate investigation to identify the responsible process and user. data_source: -- Sysmon for Linux EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.action=deleted AND - Filesystem.file_path IN ("/etc/ssh/*", "~/.ssh/*") by Filesystem.action Filesystem.dest - Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time - Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size - Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product - | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_account_manipulation_of_ssh_config_and_keys_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from - Splunkbase. -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 11 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.action=deleted + AND + Filesystem.file_path IN ("/etc/ssh/*", "~/.ssh/*") + BY Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path Filesystem.file_acl + Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_account_manipulation_of_ssh_config_and_keys_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase. +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://www.sentinelone.com/labs/acidrain-a-modem-wiper-rains-down-on-europe/ + - https://www.sentinelone.com/labs/acidrain-a-modem-wiper-rains-down-on-europe/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: SSH Config and keys are deleted on $dest$ by Process GUID - $process_guid$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: SSH Config and keys are deleted on $dest$ by Process GUID - $process_guid$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - AcidRain - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1070.004 - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AcidRain + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1070.004 + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/acidrain/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/acidrain/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_add_files_in_known_crontab_directories.yml b/detections/endpoint/linux_add_files_in_known_crontab_directories.yml index 2b653bd346..2fbc802c4f 100644 --- a/detections/endpoint/linux_add_files_in_known_crontab_directories.yml +++ b/detections/endpoint/linux_add_files_in_known_crontab_directories.yml @@ -1,74 +1,64 @@ name: Linux Add Files In Known Crontab Directories id: 023f3452-5f27-11ec-bf00-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects unauthorized file creation in known crontab - directories on Unix-based systems. It leverages filesystem data to identify new - files in directories such as /etc/cron* and /var/spool/cron/*. This activity is - significant as it may indicate an attempt by threat actors or malware to establish - persistence on a compromised host. If confirmed malicious, this could allow attackers - to execute arbitrary code at scheduled intervals, potentially leading to further - system compromise and unauthorized access to sensitive information. +description: The following analytic detects unauthorized file creation in known crontab directories on Unix-based systems. It leverages filesystem data to identify new files in directories such as /etc/cron* and /var/spool/cron/*. This activity is significant as it may indicate an attempt by threat actors or malware to establish persistence on a compromised host. If confirmed malicious, this could allow attackers to execute arbitrary code at scheduled intervals, potentially leading to further system compromise and unauthorized access to sensitive information. data_source: -- Sysmon for Linux EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/etc/cron*", - "*/var/spool/cron/*") by Filesystem.action Filesystem.dest Filesystem.file_access_time - Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name - Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid - Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_add_files_in_known_crontab_directories_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the file name, file path, and process_guid executions from your endpoints. - If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase. -known_false_positives: Administrator or network operator can create file in crontab - folders for automation purposes. Please update the filter macros to remove false - positives. + - Sysmon for Linux EventID 11 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.file_path IN ("*/etc/cron*", "*/var/spool/cron/*") + BY Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path Filesystem.file_acl + Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` + | `linux_add_files_in_known_crontab_directories_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the file name, file path, and process_guid executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase. +known_false_positives: Administrator or network operator can create file in crontab folders for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.sandflysecurity.com/blog/detecting-cronrat-malware-on-linux-instantly/ -- https://www.cyberciti.biz/faq/how-do-i-add-jobs-to-cron-under-linux-or-unix-oses/ + - https://www.sandflysecurity.com/blog/detecting-cronrat-malware-on-linux-instantly/ + - https://www.cyberciti.biz/faq/how-do-i-add-jobs-to-cron-under-linux-or-unix-oses/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a file $file_name$ is created in $file_path$ on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: a file $file_name$ is created in $file_path$ on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - XorDDos - - Linux Living Off The Land - - Linux Privilege Escalation - - Scheduled Tasks - - Linux Persistence Techniques - asset_type: Endpoint - mitre_attack_id: - - T1053.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - XorDDos + - Linux Living Off The Land + - Linux Privilege Escalation + - Scheduled Tasks + - Linux Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1053.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/cronjobs_entry/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/cronjobs_entry/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_add_user_account.yml b/detections/endpoint/linux_add_user_account.yml index 62d963c655..eb4f846b40 100644 --- a/detections/endpoint/linux_add_user_account.yml +++ b/detections/endpoint/linux_add_user_account.yml @@ -1,63 +1,55 @@ name: Linux Add User Account id: 51fbcaf2-6259-11ec-b0f3-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Bhavin Patel, Splunk status: production type: Hunting -description: The following analytic detects the creation of new user accounts on Linux - systems using commands like "useradd" or "adduser." It leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process names and command-line - executions. This activity is significant as adversaries often create new user accounts - to establish persistence on compromised hosts. If confirmed malicious, this could - allow attackers to maintain access, escalate privileges, and further compromise - the system, posing a severe security risk. +description: The following analytic detects the creation of new user accounts on Linux systems using commands like "useradd" or "adduser." It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as adversaries often create new user accounts to establish persistence on compromised hosts. If confirmed malicious, this could allow attackers to maintain access, escalate privileges, and further compromise the system, posing a severe security risk. data_source: -- Sysmon for Linux EventID 1 -- Cisco Isovalent Process Exec -search: '| tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes - where Processes.process_name IN ("useradd", "adduser") OR Processes.process IN ("*useradd - *", "*adduser *") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_add_user_account_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 + - Cisco Isovalent Process Exec +search: |- + | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes + WHERE Processes.process_name IN ("useradd", "adduser") + OR + Processes.process IN ("*useradd *", "*adduser *") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_add_user_account_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://linuxize.com/post/how-to-create-users-in-linux-using-the-useradd-command/ + - https://linuxize.com/post/how-to-create-users-in-linux-using-the-useradd-command/ tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - - Cisco Isovalent Suspicious Activity - asset_type: Endpoint - mitre_attack_id: - - T1136.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + - Cisco Isovalent Suspicious Activity + asset_type: Endpoint + mitre_attack_id: + - T1136.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/linux_adduser/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux -- name: True Positive Test - Cisco Isovalent - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_isovalent/cisco_isovalent.log - source: not_applicable - sourcetype: cisco:isovalent:processExec \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/linux_adduser/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux + - name: True Positive Test - Cisco Isovalent + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_isovalent/cisco_isovalent.log + source: not_applicable + sourcetype: cisco:isovalent:processExec diff --git a/detections/endpoint/linux_adding_crontab_using_list_parameter.yml b/detections/endpoint/linux_adding_crontab_using_list_parameter.yml index 7db8bbc5af..40112f4c51 100644 --- a/detections/endpoint/linux_adding_crontab_using_list_parameter.yml +++ b/detections/endpoint/linux_adding_crontab_using_list_parameter.yml @@ -1,70 +1,59 @@ name: Linux Adding Crontab Using List Parameter id: 52f6d751-1fd4-4c74-a4c9-777ecfeb5c58 -version: 9 -date: '2026-01-20' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Bhavin Patel, Splunk status: production type: Hunting -description: The following analytic detects suspicious modifications to cron jobs - on Linux systems using the crontab command with list parameters. It leverages data - from Endpoint Detection and Response (EDR) agents, focusing on process names and - command-line executions. This activity is significant as it may indicate an attempt - to establish persistence or execute malicious code on a schedule. If confirmed malicious, - the impact could include unauthorized code execution, data destruction, or other - damaging outcomes. Further investigation should analyze the added cron job, its - associated command, and any related processes. +description: The following analytic detects suspicious modifications to cron jobs on Linux systems using the crontab command with list parameters. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it may indicate an attempt to establish persistence or execute malicious code on a schedule. If confirmed malicious, the impact could include unauthorized code execution, data destruction, or other damaging outcomes. Further investigation should analyze the added cron job, its associated command, and any related processes. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "crontab" - Processes.process= "* -l*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_adding_crontab_using_list_parameter_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "crontab" Processes.process= "* -l*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_adding_crontab_using_list_parameter_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ -- https://cert.gov.ua/article/39518 + - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ + - https://cert.gov.ua/article/39518 tags: - analytic_story: - - Cisco Isovalent Suspicious Activity - - Industroyer2 - - Linux Privilege Escalation - - Linux Living Off The Land - - Data Destruction - - Linux Persistence Techniques - - Scheduled Tasks - - Gomir - - VoidLink Cloud-Native Linux Malware - asset_type: Endpoint - mitre_attack_id: - - T1053.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Cisco Isovalent Suspicious Activity + - Industroyer2 + - Linux Privilege Escalation + - Linux Living Off The Land + - Data Destruction + - Linux Persistence Techniques + - Scheduled Tasks + - Gomir + - VoidLink Cloud-Native Linux Malware + asset_type: Endpoint + mitre_attack_id: + - T1053.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/crontab_list_parameter/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux -- name: True Positive Test - Cisco Isovalent - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_isovalent/cisco_isovalent.log - source: not_applicable - sourcetype: cisco:isovalent:processExec + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/crontab_list_parameter/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux + - name: True Positive Test - Cisco Isovalent + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_isovalent/cisco_isovalent.log + source: not_applicable + sourcetype: cisco:isovalent:processExec diff --git a/detections/endpoint/linux_apt_privilege_escalation.yml b/detections/endpoint/linux_apt_privilege_escalation.yml index d6ed37d74d..d00e7dcda2 100644 --- a/detections/endpoint/linux_apt_privilege_escalation.yml +++ b/detections/endpoint/linux_apt_privilege_escalation.yml @@ -5,86 +5,56 @@ date: '2026-02-10' author: Gowthamaraj Rajendran, Bhavin Patel, Splunk status: production type: Anomaly -description: The following analytic detects the use of the Advanced Package Tool (APT) or apt-get - with elevated privileges via sudo on Linux systems. It leverages Endpoint Detection - and Response (EDR) telemetry to identify processes where APT commands are executed - with sudo rights. This activity is significant because it indicates a user can run - system commands as root, potentially leading to unauthorized root shell access. - If confirmed malicious, this could allow an attacker to escalate privileges, execute - arbitrary commands, and gain full control over the affected system, posing a severe - security risk. +description: The following analytic detects the use of the Advanced Package Tool (APT) or apt-get with elevated privileges via sudo on Linux systems. It leverages Endpoint Detection and Response (EDR) telemetry to identify processes where APT commands are executed with sudo rights. This activity is significant because it indicates a user can run system commands as root, potentially leading to unauthorized root shell access. If confirmed malicious, this could allow an attacker to escalate privileges, execute arbitrary commands, and gain full control over the affected system, posing a severe security risk. data_source: -- Sysmon for Linux EventID 1 -- Cisco Isovalent Process Exec -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*apt*" AND - Processes.process="*APT::Update::Pre-Invoke::*" AND Processes.process="*sudo*" by - Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_apt_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 + - Cisco Isovalent Process Exec +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*apt*" AND Processes.process="*APT::Update::Pre-Invoke::*" AND Processes.process="*sudo*" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_apt_privilege_escalation_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives may be present, filter as needed. references: -- https://gtfobins.github.io/gtfobins/apt/ -- https://www.digitalocean.com/community/tutorials/what-is-apt + - https://gtfobins.github.io/gtfobins/apt/ + - https://www.digitalocean.com/community/tutorials/what-is-apt drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 10 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 10 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/apt/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux -- name: True Positive Test - Cisco Isovalent - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/apt_get/cisco_isovalent.log - source: not_applicable - sourcetype: cisco:isovalent:processExec + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/apt/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux + - name: True Positive Test - Cisco Isovalent + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/apt_get/cisco_isovalent.log + source: not_applicable + sourcetype: cisco:isovalent:processExec diff --git a/detections/endpoint/linux_at_allow_config_file_creation.yml b/detections/endpoint/linux_at_allow_config_file_creation.yml index 0dfdc75e6b..09bbb3f081 100644 --- a/detections/endpoint/linux_at_allow_config_file_creation.yml +++ b/detections/endpoint/linux_at_allow_config_file_creation.yml @@ -1,73 +1,62 @@ name: Linux At Allow Config File Creation id: 977b3082-5f3d-11ec-b954-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the creation of the /etc/at.allow or /etc/at.deny - configuration files in Linux. It leverages file creation events from the Endpoint - datamodel to identify when these files are created. This activity is significant - as these files control user permissions for the "at" scheduling application and - can be abused by attackers to establish persistence. If confirmed malicious, this - could allow unauthorized execution of malicious code, leading to potential data - theft or further system compromise. Analysts should review the file path, creation - time, and associated processes to assess the threat. +description: The following analytic detects the creation of the /etc/at.allow or /etc/at.deny configuration files in Linux. It leverages file creation events from the Endpoint datamodel to identify when these files are created. This activity is significant as these files control user permissions for the "at" scheduling application and can be abused by attackers to establish persistence. If confirmed malicious, this could allow unauthorized execution of malicious code, leading to potential data theft or further system compromise. Analysts should review the file path, creation time, and associated processes to assess the threat. data_source: -- Sysmon for Linux EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/etc/at.allow", - "*/etc/at.deny") by Filesystem.action Filesystem.dest Filesystem.file_access_time - Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name - Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid - Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_at_allow_config_file_creation_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the file name, file path, and process_guid executions from your endpoints - into the Endpoint datamodel. If you are using Sysmon, you can use the Add-on for - Linux Sysmon from Splunkbase. -known_false_positives: Administrator or network operator can create this file for - automation purposes. Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 11 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.file_path IN ("*/etc/at.allow", "*/etc/at.deny") + BY Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path Filesystem.file_acl + Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` + | `linux_at_allow_config_file_creation_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the file name, file path, and process_guid executions from your endpoints into the Endpoint datamodel. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase. +known_false_positives: Administrator or network operator can create this file for automation purposes. Please update the filter macros to remove false positives. references: -- https://linuxize.com/post/at-command-in-linux/ + - https://linuxize.com/post/at-command-in-linux/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A file $file_name$ is created in $file_path$ on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A file $file_name$ is created in $file_path$ on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - - Linux Living Off The Land - - Scheduled Tasks - asset_type: Endpoint - mitre_attack_id: - - T1053.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + - Linux Living Off The Land + - Scheduled Tasks + asset_type: Endpoint + mitre_attack_id: + - T1053.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.002/at_execution/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.002/at_execution/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_at_application_execution.yml b/detections/endpoint/linux_at_application_execution.yml index 13f987046b..fcac10678a 100644 --- a/detections/endpoint/linux_at_application_execution.yml +++ b/detections/endpoint/linux_at_application_execution.yml @@ -1,90 +1,75 @@ name: Linux At Application Execution id: bf0a378e-5f3c-11ec-a6de-acde48001122 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Bhavin Patel, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the "At" application - in Linux, which can be used by attackers to create persistence entries on a compromised - host. This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process names and parent process names associated with "at" or "atd". - This activity is significant because the "At" application can be exploited to maintain - unauthorized access or deliver additional malicious payloads. If confirmed malicious, - this behavior could lead to data theft, ransomware attacks, or other severe consequences. - Immediate investigation is required to determine the legitimacy of the execution - and mitigate potential risks. +description: The following analytic detects the execution of the "At" application in Linux, which can be used by attackers to create persistence entries on a compromised host. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and parent process names associated with "at" or "atd". This activity is significant because the "At" application can be exploited to maintain unauthorized access or deliver additional malicious payloads. If confirmed malicious, this behavior could lead to data theft, ransomware attacks, or other severe consequences. Immediate investigation is required to determine the legitimacy of the execution and mitigate potential risks. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes - where Processes.process_name IN ("at", "atd") OR Processes.parent_process_name - IN ("at", "atd") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_at_application_execution_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes + WHERE Processes.process_name IN ("at", "atd") + OR + Processes.parent_process_name IN ("at", "atd") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_at_application_execution_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://attack.mitre.org/techniques/T1053/001/ -- https://www.linkedin.com/pulse/getting-attacker-ip-address-from-malicious-linux-job-craig-rowland/ + - https://attack.mitre.org/techniques/T1053/001/ + - https://www.linkedin.com/pulse/getting-attacker-ip-address-from-malicious-linux-job-craig-rowland/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: At application was executed on $dest$ - risk_objects: - - field: dest - type: system - score: 9 - threat_objects: [] + message: At application was executed on $dest$ + risk_objects: + - field: dest + type: system + score: 9 + threat_objects: [] tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - - Linux Living Off The Land - - Scheduled Tasks - - Cisco Isovalent Suspicious Activity - asset_type: Endpoint - mitre_attack_id: - - T1053.002 - atomic_guid: - - 9ddf2e5e-7e2c-46c2-9940-3c2ff29c7213 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + - Linux Living Off The Land + - Scheduled Tasks + - Cisco Isovalent Suspicious Activity + asset_type: Endpoint + mitre_attack_id: + - T1053.002 + atomic_guid: + - 9ddf2e5e-7e2c-46c2-9940-3c2ff29c7213 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.002/at_execution/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux -- name: True Positive Test - Cisco Isovalent - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_isovalent/cisco_isovalent.log - source: not_applicable - sourcetype: cisco:isovalent:processExec + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.002/at_execution/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux + - name: True Positive Test - Cisco Isovalent + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_isovalent/cisco_isovalent.log + source: not_applicable + sourcetype: cisco:isovalent:processExec diff --git a/detections/endpoint/linux_auditd_add_user_account.yml b/detections/endpoint/linux_auditd_add_user_account.yml index 0720f6c160..fe8fb04db7 100644 --- a/detections/endpoint/linux_auditd_add_user_account.yml +++ b/detections/endpoint/linux_auditd_add_user_account.yml @@ -1,76 +1,57 @@ name: Linux Auditd Add User Account id: aae66dc0-74b4-4807-b480-b35f8027abb4 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the creation of new user accounts on Linux - systems using commands like "useradd" or "adduser." It leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process names and command-line - executions. This activity is significant as adversaries often create new user accounts - to establish persistence on compromised hosts. If confirmed malicious, this could - allow attackers to maintain access, escalate privileges, and further compromise - the system, posing a severe security risk. +description: The following analytic detects the creation of new user accounts on Linux systems using commands like "useradd" or "adduser." It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as adversaries often create new user accounts to establish persistence on compromised hosts. If confirmed malicious, this could allow attackers to maintain access, escalate privileges, and further compromise the system, posing a severe security risk. data_source: -- Linux Auditd Proctitle -search: '`linux_auditd` proctitle IN ("*useradd*", "*adduser*") - | rename host as dest - | stats count min(_time) as firstTime max(_time) as lastTime by proctitle dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - |`linux_auditd_add_user_account_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Linux Auditd Proctitle +search: |- + `linux_auditd` proctitle IN ("*useradd*", "*adduser*") + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY proctitle dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_add_user_account_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://linuxize.com/post/how-to-create-users-in-linux-using-the-useradd-command/ + - https://linuxize.com/post/how-to-create-users-in-linux-using-the-useradd-command/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$proctitle$] event occurred on host - [$dest$] to add a user account. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A [$proctitle$] event occurred on host - [$dest$] to add a user account. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1136.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1136.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/linux_auditd_add_user/auditd_proctitle_user_add.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/linux_auditd_add_user/auditd_proctitle_user_add.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_add_user_account_type.yml b/detections/endpoint/linux_auditd_add_user_account_type.yml index f1a4dbc5fc..39085890c1 100644 --- a/detections/endpoint/linux_auditd_add_user_account_type.yml +++ b/detections/endpoint/linux_auditd_add_user_account_type.yml @@ -1,74 +1,59 @@ name: Linux Auditd Add User Account Type id: f8c325ea-506e-4105-8ccf-da1492e90115 -version: 8 -date: '2025-06-26' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the suspicious add user account type. - This behavior is critical for a SOC to monitor because it may indicate attempts - to gain unauthorized access or maintain control over a system. Such actions could - be signs of malicious activity. If confirmed, this could lead to serious consequences, - including a compromised system, unauthorized access to sensitive data, or even a - wider breach affecting the entire network. Detecting and responding to these signs - early is essential to prevent potential security incidents. +description: The following analytic detects the suspicious add user account type. This behavior is critical for a SOC to monitor because it may indicate attempts to gain unauthorized access or maintain control over a system. Such actions could be signs of malicious activity. If confirmed, this could lead to serious consequences, including a compromised system, unauthorized access to sensitive data, or even a wider breach affecting the entire network. Detecting and responding to these signs early is essential to prevent potential security incidents. data_source: -- Linux Auditd Add User -search: '`linux_auditd` type=ADD_USER | rename host as dest| stats count min(_time) - as firstTime max(_time) as lastTime by exe pid dest res type | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`| `linux_auditd_add_user_account_type_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Linux Auditd Add User +search: |- + `linux_auditd` type=ADD_USER + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY exe pid dest + res type + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_add_user_account_type_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html + - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: New [$type$] event on host - [$dest$] to add a user account type. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: New [$type$] event on host - [$dest$] to add a user account type. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1136.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1136.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/linux_auditd_add_user_type/linux_auditd_add_user_type.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/linux_auditd_add_user_type/linux_auditd_add_user_type.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_at_application_execution.yml b/detections/endpoint/linux_auditd_at_application_execution.yml index f840941991..314e50f676 100644 --- a/detections/endpoint/linux_auditd_at_application_execution.yml +++ b/detections/endpoint/linux_auditd_at_application_execution.yml @@ -1,83 +1,62 @@ name: Linux Auditd At Application Execution id: 9f306e0a-1c36-469e-8892-968ca12470dd -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the "At" application - in Linux, which can be used by attackers to create persistence entries on a compromised - host. This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process names and parent process names associated with "at" or "atd". - This activity is significant because the "At" application can be exploited to maintain - unauthorized access or deliver additional malicious payloads. If confirmed malicious, - this behavior could lead to data theft, ransomware attacks, or other severe consequences. - Immediate investigation is required to determine the legitimacy of the execution - and mitigate potential risks. +description: The following analytic detects the execution of the "At" application in Linux, which can be used by attackers to create persistence entries on a compromised host. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and parent process names associated with "at" or "atd". This activity is significant because the "At" application can be exploited to maintain unauthorized access or deliver additional malicious payloads. If confirmed malicious, this behavior could lead to data theft, ransomware attacks, or other severe consequences. Immediate investigation is required to determine the legitimacy of the execution and mitigate potential risks. data_source: -- Linux Auditd Syscall -search: '`linux_auditd` type=SYSCALL comm IN ("at", "atd") OR exe IN ("/usr/bin/at","/usr/bin/atd") AND NOT (uid IN ("daemon")) - | rename host as dest - | stats count min(_time) as firstTime max(_time) as lastTime - by comm exe syscall uid ppid pid dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_auditd_at_application_execution_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Linux Auditd Syscall +search: |- + `linux_auditd` type=SYSCALL comm IN ("at", "atd") OR exe IN ("/usr/bin/at","/usr/bin/atd") AND NOT (uid IN ("daemon")) + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY comm exe syscall + uid ppid pid + dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_at_application_execution_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://attack.mitre.org/techniques/T1053/001/ -- https://www.linkedin.com/pulse/getting-attacker-ip-address-from-malicious-linux-job-craig-rowland/ + - https://attack.mitre.org/techniques/T1053/001/ + - https://www.linkedin.com/pulse/getting-attacker-ip-address-from-malicious-linux-job-craig-rowland/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to execute the - "at" application. - risk_objects: - - field: dest - type: system - score: 9 - threat_objects: [] + message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to execute the "at" application. + risk_objects: + - field: dest + type: system + score: 9 + threat_objects: [] tags: - analytic_story: - - Scheduled Tasks - - Linux Privilege Escalation - - Linux Persistence Techniques - - Linux Living Off The Land - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1053.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Scheduled Tasks + - Linux Privilege Escalation + - Linux Persistence Techniques + - Linux Living Off The Land + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1053.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.002/linux_new_auditd_at/linux_auditd_new_at.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.002/linux_new_auditd_at/linux_auditd_new_at.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_auditd_daemon_abort.yml b/detections/endpoint/linux_auditd_auditd_daemon_abort.yml index 0a4ee0eb82..98c077f6fc 100644 --- a/detections/endpoint/linux_auditd_auditd_daemon_abort.yml +++ b/detections/endpoint/linux_auditd_auditd_daemon_abort.yml @@ -1,68 +1,56 @@ name: Linux Auditd Auditd Daemon Abort id: 76d6573f-c4ab-4fa1-8390-c036416d4add -version: 1 -date: '2025-06-06' +version: 2 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: The following analytic detects the abnormal termination of the Linux audit daemon (auditd) by identifying DAEMON_ABORT events in audit logs. These terminations suggest a serious failure of the auditing subsystem, potentially due to resource exhaustion, corruption, or malicious interference. Unlike a clean shutdown, DAEMON_ABORT implies that audit logging may have been disabled without system administrator intent. Alerts should be generated on detection and correlated with DAEMON_START, DAEMON_END, and system logs to determine root cause. If no DAEMON_START follows soon after, or this pattern repeats, it indicates a high-severity issue that impacts log integrity and should be immediately investigated. data_source: -- Linux Auditd Daemon Abort -search: '`linux_auditd` type=DAEMON_ABORT - | rename host as dest - | stats count min(_time) as firstTime max(_time) as lastTime - by type op res uid dest pid - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_auditd_auditd_daemon_abort_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Linux Auditd Daemon Abort +search: |- + `linux_auditd` type=DAEMON_ABORT + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY type op res + uid dest pid + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_auditd_daemon_abort_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/6/html/security_guide/sec-audit_record_types + - https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/6/html/security_guide/sec-audit_record_types drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Auditd service event - [$type$] event occurred on host - [$dest$]. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: Auditd service event - [$type$] event occurred on host - [$dest$]. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1562.012 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1562.012 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.012/auditd_daemon_type/linux_auditd_daemon.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.012/auditd_daemon_type/linux_auditd_daemon.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_auditd_daemon_shutdown.yml b/detections/endpoint/linux_auditd_auditd_daemon_shutdown.yml index b0e86cd4a9..7cece75c29 100644 --- a/detections/endpoint/linux_auditd_auditd_daemon_shutdown.yml +++ b/detections/endpoint/linux_auditd_auditd_daemon_shutdown.yml @@ -1,68 +1,56 @@ name: Linux Auditd Auditd Daemon Shutdown id: 6e2574b3-e24b-4321-ae3c-ba83a75bb714 -version: 1 -date: '2025-06-06' +version: 2 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: The following analytic detects the unexpected termination of the Linux Audit daemon (auditd) by monitoring for log entries of type DAEMON_END. This event signifies that the audit logging service has stopped, either due to a legitimate system shutdown, manual administrative action, or potentially malicious tampering. Since auditd is responsible for recording critical security events, its sudden stoppage may indicate an attempt to disable security monitoring or evade detection during an attack. This detection should be correlated with system logs to determine whether the shutdown was part of routine maintenance or an anomaly. If confirmed as malicious, this could lead to a compromised system where security events are no longer being logged, allowing attackers to operate undetected. Therefore, monitoring and alerting on auditd shutdown events is crucial for maintaining the integrity of system security monitoring. data_source: -- Linux Auditd Daemon End -search: '`linux_auditd` type=DAEMON_END - | rename host as dest - | stats count min(_time) as firstTime max(_time) as lastTime - by type op res auid dest pid - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_auditd_auditd_daemon_shutdown_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Linux Auditd Daemon End +search: |- + `linux_auditd` type=DAEMON_END + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY type op res + auid dest pid + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_auditd_daemon_shutdown_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/6/html/security_guide/sec-audit_record_types + - https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/6/html/security_guide/sec-audit_record_types drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Auditd service event - [$type$] event occurred on host - [$dest$]. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: Auditd service event - [$type$] event occurred on host - [$dest$]. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1562.012 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1562.012 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.012/auditd_daemon_end/linux_daemon_end.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.012/auditd_daemon_end/linux_daemon_end.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_auditd_daemon_start.yml b/detections/endpoint/linux_auditd_auditd_daemon_start.yml index 880b91a4c5..89d0e5129a 100644 --- a/detections/endpoint/linux_auditd_auditd_daemon_start.yml +++ b/detections/endpoint/linux_auditd_auditd_daemon_start.yml @@ -1,68 +1,56 @@ name: Linux Auditd Auditd Daemon Start id: 6b0cb0ff-9a7e-4475-a687-43827fdb31d6 -version: 1 -date: '2025-06-06' +version: 2 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: The following analytic detects the (re)initialization of the Linux audit daemon (auditd) by identifying log entries of type DAEMON_START. This event indicates that the audit subsystem has resumed logging after being stopped or has started during system boot. While DAEMON_START may be expected during reboots or legitimate configuration changes, it can also signal attempts to re-enable audit logging after evasion, or restarts with modified or reduced rule sets. Monitoring this event in correlation with DAEMON_END, DAEMON_ABORT, and auditctl activity provides visibility into the continuity and integrity of audit logs. Frequent or unexplained DAEMON_START events should be investigated, especially if they are not accompanied by valid administrative or system activity. data_source: -- Linux Auditd Daemon Start -search: '`linux_auditd` type=DAEMON_START - | rename host as dest - | stats count min(_time) as firstTime max(_time) as lastTime - by type op res auid dest pid - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_auditd_auditd_daemon_start_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Linux Auditd Daemon Start +search: |- + `linux_auditd` type=DAEMON_START + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY type op res + auid dest pid + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_auditd_daemon_start_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/6/html/security_guide/sec-audit_record_types + - https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/6/html/security_guide/sec-audit_record_types drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Auditd service event - [$type$] event occurred on host - [$dest$]. - risk_objects: - - field: dest - type: system - score: 15 - threat_objects: [] + message: Auditd service event - [$type$] event occurred on host - [$dest$]. + risk_objects: + - field: dest + type: system + score: 15 + threat_objects: [] tags: - analytic_story: - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1562.012 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1562.012 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.012/auditd_daemon_type/linux_auditd_daemon.log - source: auditd - sourcetype: auditd \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.012/auditd_daemon_type/linux_auditd_daemon.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_auditd_service_stop.yml b/detections/endpoint/linux_auditd_auditd_service_stop.yml index 2644e34cc2..0a235efec2 100644 --- a/detections/endpoint/linux_auditd_auditd_service_stop.yml +++ b/detections/endpoint/linux_auditd_auditd_service_stop.yml @@ -1,75 +1,59 @@ name: Linux Auditd Auditd Service Stop id: 6cb9d0e1-eabe-41de-a11a-5efade354e9d -version: 6 -date: '2025-06-10' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the suspicious auditd service stop. This - behavior is critical for a SOC to monitor because it may indicate attempts to gain - unauthorized access or maintain control over a system. Such actions could be signs - of malicious activity. If confirmed, this could lead to serious consequences, including - a compromised system, unauthorized access to sensitive data, or even a wider breach - affecting the entire network. Detecting and responding to these signs early is essential - to prevent potential security incidents. +description: The following analytic detects the suspicious auditd service stop. This behavior is critical for a SOC to monitor because it may indicate attempts to gain unauthorized access or maintain control over a system. Such actions could be signs of malicious activity. If confirmed, this could lead to serious consequences, including a compromised system, unauthorized access to sensitive data, or even a wider breach affecting the entire network. Detecting and responding to these signs early is essential to prevent potential security incidents. data_source: -- Linux Auditd Service Stop -search: '`linux_auditd` type=SERVICE_STOP unit IN ("auditd") | rename host as dest - | stats count min(_time) as firstTime max(_time) as lastTime by type pid comm - exe unit dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| - `linux_auditd_auditd_service_stop_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Linux Auditd Service Stop +search: |- + `linux_auditd` type=SERVICE_STOP unit IN ("auditd") + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY type pid comm + exe unit dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_auditd_service_stop_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html + - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A service event - [$type$] event occurred on host - [$dest$]. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A service event - [$type$] event occurred on host - [$dest$]. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1489 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1489 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1489/linux_auditd_auditd_service_stop/linux_auditd_auditd_service_stop.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1489/linux_auditd_auditd_service_stop/linux_auditd_auditd_service_stop.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_base64_decode_files.yml b/detections/endpoint/linux_auditd_base64_decode_files.yml index 531a41f5fc..11965a0e04 100644 --- a/detections/endpoint/linux_auditd_base64_decode_files.yml +++ b/detections/endpoint/linux_auditd_base64_decode_files.yml @@ -1,82 +1,61 @@ name: Linux Auditd Base64 Decode Files id: 5890ba10-4e48-4dc0-8a40-3e1ebe75e737 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects suspicious Base64 decode operations that - may indicate malicious activity, such as data exfiltration or execution of encoded - commands. Base64 is commonly used to encode data for safe transmission, but attackers - may abuse it to conceal malicious payloads. This detection focuses on identifying - unusual or unexpected Base64 decoding processes, particularly when associated with - critical files or directories. By monitoring these activities, the analytic helps - uncover potential threats, enabling security teams to respond promptly and mitigate - risks associated with encoded malware or unauthorized data access. +description: The following analytic detects suspicious Base64 decode operations that may indicate malicious activity, such as data exfiltration or execution of encoded commands. Base64 is commonly used to encode data for safe transmission, but attackers may abuse it to conceal malicious payloads. This detection focuses on identifying unusual or unexpected Base64 decoding processes, particularly when associated with critical files or directories. By monitoring these activities, the analytic helps uncover potential threats, enabling security teams to respond promptly and mitigate risks associated with encoded malware or unauthorized data access. data_source: -- Linux Auditd Execve -search: '`linux_auditd` execve_command = "*base64*" AND execve_command IN ("*-d*", "* --d*") - | rename host as dest - | rename comm as process_name - | rename exe as process - | stats count min(_time) as firstTime max(_time) as lastTime by argc execve_command dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - |`linux_auditd_base64_decode_files_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Linux Auditd Execve +search: |- + `linux_auditd` execve_command = "*base64*" AND execve_command IN ("*-d*", "* --d*") + | rename host as dest + | rename comm as process_name + | rename exe as process + | stats count min(_time) as firstTime max(_time) as lastTime + BY argc execve_command dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_base64_decode_files_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html -- https://gtfobins.github.io/gtfobins/dd/ + - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html + - https://gtfobins.github.io/gtfobins/dd/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$execve_command$] event occurred on host - [$dest$] to decode a file using - base64. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A [$execve_command$] event occurred on host - [$dest$] to decode a file using base64. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1140 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1140 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1140/linux_auditd_base64/auditd_execve_base64.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1140/linux_auditd_base64/auditd_execve_base64.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_change_file_owner_to_root.yml b/detections/endpoint/linux_auditd_change_file_owner_to_root.yml index 267056dd92..fe16233be8 100644 --- a/detections/endpoint/linux_auditd_change_file_owner_to_root.yml +++ b/detections/endpoint/linux_auditd_change_file_owner_to_root.yml @@ -1,79 +1,59 @@ name: Linux Auditd Change File Owner To Root id: 7b87c556-0ca4-47e0-b84c-6cd62a0a3e90 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the use of the 'chown' command to change - a file owner to 'root' on a Linux system. It leverages Linux Auditd telemetry, specifically - monitoring command-line executions and process details. This activity is significant - as it may indicate an attempt to escalate privileges by adversaries, malware, or - red teamers. If confirmed malicious, this action could allow an attacker to gain - root-level access, leading to full control over the compromised host and potential - persistence within the environment. +description: The following analytic detects the use of the 'chown' command to change a file owner to 'root' on a Linux system. It leverages Linux Auditd telemetry, specifically monitoring command-line executions and process details. This activity is significant as it may indicate an attempt to escalate privileges by adversaries, malware, or red teamers. If confirmed malicious, this action could allow an attacker to gain root-level access, leading to full control over the compromised host and potential persistence within the environment. data_source: -- Linux Auditd Proctitle -search: '`linux_auditd` proctitle = "*chown *root*" - | rename host as dest - | stats count min(_time) as firstTime max(_time) as lastTime by proctitle dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_auditd_change_file_owner_to_root_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures - command-line executions and process details on Unix/Linux systems. These logs should - be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Linux Auditd Proctitle +search: |- + `linux_auditd` proctitle = "*chown *root*" + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY proctitle dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_change_file_owner_to_root_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://unix.stackexchange.com/questions/101073/how-to-change-permissions-from-root-user-to-all-users -- https://askubuntu.com/questions/617850/changing-from-user-to-superuser + - https://unix.stackexchange.com/questions/101073/how-to-change-permissions-from-root-user-to-all-users + - https://askubuntu.com/questions/617850/changing-from-user-to-superuser drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$proctitle$] event occurred on host - [$dest$] to change a file owner - to root. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A [$proctitle$] event occurred on host - [$dest$] to change a file owner to root. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1222.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1222.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.002/linux_auditd_chown_root/auditd_proctitle_chown_root.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.002/linux_auditd_chown_root/auditd_proctitle_chown_root.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_clipboard_data_copy.yml b/detections/endpoint/linux_auditd_clipboard_data_copy.yml index fb9d2ee09b..d7663d7964 100644 --- a/detections/endpoint/linux_auditd_clipboard_data_copy.yml +++ b/detections/endpoint/linux_auditd_clipboard_data_copy.yml @@ -1,69 +1,59 @@ name: Linux Auditd Clipboard Data Copy id: 9ddfe470-c4d0-4e60-8668-7337bd699edd -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the use of the Linux 'xclip' command to - copy data from the clipboard. It leverages Linux Auditd telemetry, focusing on process - names and command-line arguments related to clipboard operations. This activity - is significant because adversaries can exploit clipboard data to capture sensitive - information such as passwords or IP addresses. If confirmed malicious, this technique - could lead to unauthorized data exfiltration, compromising sensitive information - and potentially aiding further attacks within the environment. +description: The following analytic detects the use of the Linux 'xclip' command to copy data from the clipboard. It leverages Linux Auditd telemetry, focusing on process names and command-line arguments related to clipboard operations. This activity is significant because adversaries can exploit clipboard data to capture sensitive information such as passwords or IP addresses. If confirmed malicious, this technique could lead to unauthorized data exfiltration, compromising sensitive information and potentially aiding further attacks within the environment. data_source: -- Linux Auditd Execve -search: '`linux_auditd` execve_command IN ("*xclip*", "*clipboard*") AND execve_command IN ("*-o*", "*-selection *", "*-sel *" ) - | rename host as dest - | rename comm as process_name - | rename exe as process - | stats count min(_time) as firstTime max(_time) as lastTime by argc execve_command dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_auditd_clipboard_data_copy_filter`' + - Linux Auditd Execve +search: |- + `linux_auditd` execve_command IN ("*xclip*", "*clipboard*") AND execve_command IN ("*-o*", "*-selection *", "*-sel *" ) + | rename host as dest + | rename comm as process_name + | rename exe as process + | stats count min(_time) as firstTime max(_time) as lastTime + BY argc execve_command dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_clipboard_data_copy_filter` how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed known_false_positives: False positives may be present on Linux desktop as it may commonly be used by administrators or end users. Filter as needed. references: -- https://attack.mitre.org/techniques/T1115/ -- https://linux.die.net/man/1/xclip + - https://attack.mitre.org/techniques/T1115/ + - https://linux.die.net/man/1/xclip drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$execve_command$] event occurred on host - [$dest$] to copy data from - the clipboard. - risk_objects: - - field: dest - type: system - score: 16 - threat_objects: [] + message: A [$execve_command$] event occurred on host - [$dest$] to copy data from the clipboard. + risk_objects: + - field: dest + type: system + score: 16 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1115 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1115 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1115/linux_auditd_xclip/linux_auditd_xclip2.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1115/linux_auditd_xclip/linux_auditd_xclip2.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_data_destruction_command.yml b/detections/endpoint/linux_auditd_data_destruction_command.yml index 24f48a1f26..c3266ad55a 100644 --- a/detections/endpoint/linux_auditd_data_destruction_command.yml +++ b/detections/endpoint/linux_auditd_data_destruction_command.yml @@ -1,70 +1,60 @@ name: Linux Auditd Data Destruction Command id: 4da5ce1a-f71b-4e71-bb73-c0a3c73f3c3c -version: 6 -date: '2026-01-14' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of a Unix shell command - designed to wipe root directories on a Linux host. It leverages data from Linux - Auditd, focusing on the 'rm' command with force recursive deletion and the '--no-preserve-root' - option. This activity is significant as it indicates potential data destruction - attempts, often associated with malware like Awfulshred. If confirmed malicious, - this behavior could lead to severe data loss, system instability, and compromised - integrity of the affected Linux host. Immediate investigation and response are crucial - to mitigate potential damage. +description: The following analytic detects the execution of a Unix shell command designed to wipe root directories on a Linux host. It leverages data from Linux Auditd, focusing on the 'rm' command with force recursive deletion and the '--no-preserve-root' option. This activity is significant as it indicates potential data destruction attempts, often associated with malware like Awfulshred. If confirmed malicious, this behavior could lead to severe data loss, system instability, and compromised integrity of the affected Linux host. Immediate investigation and response are crucial to mitigate potential damage. data_source: -- Linux Auditd Proctitle -search: '`linux_auditd` (proctitle = "*rm *" AND proctitle = "*-rf *" AND proctitle = "*--no-preserve-root*") - | rename host as dest - | rename comm as process_name - | rename exe as process - | stats count min(_time) as firstTime max(_time) as lastTime by proctitle dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_auditd_data_destruction_command_filter`' + - Linux Auditd Proctitle +search: |- + `linux_auditd` (proctitle = "*rm *" AND proctitle = "*-rf *" AND proctitle = "*--no-preserve-root*") + | rename host as dest + | rename comm as process_name + | rename exe as process + | stats count min(_time) as firstTime max(_time) as lastTime + BY proctitle dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_data_destruction_command_filter` how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed known_false_positives: No false positives have been identified at this time. references: -- https://cert.gov.ua/article/3718487 -- https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/overview-of-the-cyber-weapons-used-in-the-ukraine-russia-war/ + - https://cert.gov.ua/article/3718487 + - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/overview-of-the-cyber-weapons-used-in-the-ukraine-russia-war/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$proctitle$] event occurred on host - [$dest$] to destroy data. - risk_objects: - - field: dest - type: system - score: 90 - threat_objects: [] + message: A [$proctitle$] event occurred on host - [$dest$] to destroy data. + risk_objects: + - field: dest + type: system + score: 90 + threat_objects: [] tags: - analytic_story: - - Data Destruction - - AwfulShred - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Destruction + - AwfulShred + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/linux_auditd_no_preserve_root/auditd_proctitle_rm_rf.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/linux_auditd_no_preserve_root/auditd_proctitle_rm_rf.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_data_transfer_size_limits_via_split.yml b/detections/endpoint/linux_auditd_data_transfer_size_limits_via_split.yml index ceafd38c52..8145d38e71 100644 --- a/detections/endpoint/linux_auditd_data_transfer_size_limits_via_split.yml +++ b/detections/endpoint/linux_auditd_data_transfer_size_limits_via_split.yml @@ -1,71 +1,61 @@ name: Linux Auditd Data Transfer Size Limits Via Split id: 4669561d-3bbd-44e3-857c-0e3c6ef2120c -version: 6 -date: '2025-10-14' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects suspicious data transfer activities that - involve the use of the `split` syscall, potentially indicating an attempt to evade - detection by breaking large files into smaller parts. Attackers may use this technique - to bypass size-based security controls, facilitating the covert exfiltration of - sensitive data. By monitoring for unusual or unauthorized use of the `split` syscall, - this analytic helps identify potential data exfiltration attempts, allowing security - teams to intervene and prevent the unauthorized transfer of critical information - from the network. +description: The following analytic detects suspicious data transfer activities that involve the use of the `split` syscall, potentially indicating an attempt to evade detection by breaking large files into smaller parts. Attackers may use this technique to bypass size-based security controls, facilitating the covert exfiltration of sensitive data. By monitoring for unusual or unauthorized use of the `split` syscall, this analytic helps identify potential data exfiltration attempts, allowing security teams to intervene and prevent the unauthorized transfer of critical information from the network. data_source: -- Linux Auditd Execve -search: '`linux_auditd` execve_command = "*split*" AND execve_command = "*-b *" - | rename host as dest - | rename comm as process_name - | rename exe as process - | stats count min(_time) as firstTime max(_time) as lastTime by argc execve_command dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_auditd_data_transfer_size_limits_via_split_filter`' + - Linux Auditd Execve +search: |- + `linux_auditd` execve_command = "*split*" AND execve_command = "*-b *" + | rename host as dest + | rename comm as process_name + | rename exe as process + | stats count min(_time) as firstTime max(_time) as lastTime + BY argc execve_command dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_data_transfer_size_limits_via_split_filter` how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html + - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$execve_command$] event occurred on host - [$dest$] to split a file. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: A [$execve_command$] event occurred on host - [$dest$] to split a file. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1030 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1030 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1030/linux_auditd_split_b_exec/auditd_execve_split.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1030/linux_auditd_split_b_exec/auditd_execve_split.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_data_transfer_size_limits_via_split_syscall.yml b/detections/endpoint/linux_auditd_data_transfer_size_limits_via_split_syscall.yml index 8eabc73a99..e0fa1a061a 100644 --- a/detections/endpoint/linux_auditd_data_transfer_size_limits_via_split_syscall.yml +++ b/detections/endpoint/linux_auditd_data_transfer_size_limits_via_split_syscall.yml @@ -1,80 +1,60 @@ name: Linux Auditd Data Transfer Size Limits Via Split Syscall id: c03d4a49-cf9d-435b-86e9-c6f8c9b6c42e -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects suspicious data transfer activities that - involve the use of the `split` syscall, potentially indicating an attempt to evade - detection by breaking large files into smaller parts. Attackers may use this technique - to bypass size-based security controls, facilitating the covert exfiltration of - sensitive data. By monitoring for unusual or unauthorized use of the `split` syscall, - this analytic helps identify potential data exfiltration attempts, allowing security - teams to intervene and prevent the unauthorized transfer of critical information - from the network. +description: The following analytic detects suspicious data transfer activities that involve the use of the `split` syscall, potentially indicating an attempt to evade detection by breaking large files into smaller parts. Attackers may use this technique to bypass size-based security controls, facilitating the covert exfiltration of sensitive data. By monitoring for unusual or unauthorized use of the `split` syscall, this analytic helps identify potential data exfiltration attempts, allowing security teams to intervene and prevent the unauthorized transfer of critical information from the network. data_source: -- Linux Auditd Syscall -search: '`linux_auditd` type=SYSCALL comm=split OR exe= "*/split" - | rename host as dest - | stats count min(_time) as firstTime max(_time) as lastTime - by comm exe syscall uid ppid pid success dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_auditd_data_transfer_size_limits_via_split_syscall_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Linux Auditd Syscall +search: |- + `linux_auditd` type=SYSCALL comm=split OR exe= "*/split" + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY comm exe syscall + uid ppid pid + success dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_data_transfer_size_limits_via_split_syscall_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html + - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A SYSCALL - [$comm$] event was executed on host - [$dest$] that limits - the size of data transfer. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A SYSCALL - [$comm$] event was executed on host - [$dest$] that limits the size of data transfer. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1030 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1030 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1030/linux_auditd_split_syscall_new/linux_auditd_new_split.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1030/linux_auditd_split_syscall_new/linux_auditd_new_split.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_database_file_and_directory_discovery.yml b/detections/endpoint/linux_auditd_database_file_and_directory_discovery.yml index ee4aa77404..c6b42cc430 100644 --- a/detections/endpoint/linux_auditd_database_file_and_directory_discovery.yml +++ b/detections/endpoint/linux_auditd_database_file_and_directory_discovery.yml @@ -1,72 +1,61 @@ name: Linux Auditd Database File And Directory Discovery id: f616c4f3-bde9-41cf-856c-019b65f668bb -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects suspicious database file and directory - discovery activities, which may signal an attacker attempt to locate and assess - critical database assets on a compromised system. This behavior is often a precursor - to data theft, unauthorized access, or privilege escalation, as attackers seek to - identify valuable information stored in databases. By monitoring for unusual or - unauthorized attempts to locate database files and directories, this analytic aids - in early detection of potential reconnaissance or data breach efforts, enabling - security teams to respond swiftly and mitigate the risk of further compromise. +description: The following analytic detects suspicious database file and directory discovery activities, which may signal an attacker attempt to locate and assess critical database assets on a compromised system. This behavior is often a precursor to data theft, unauthorized access, or privilege escalation, as attackers seek to identify valuable information stored in databases. By monitoring for unusual or unauthorized attempts to locate database files and directories, this analytic aids in early detection of potential reconnaissance or data breach efforts, enabling security teams to respond swiftly and mitigate the risk of further compromise. data_source: -- Linux Auditd Execve -search: '`linux_auditd` execve_command IN ("*find*", "*grep*") AND execve_command IN("*.db*", "*.sql*", "*.sqlite*", "*.mdb*", "*.accdb*", "*.mdf*", "*.ndf*", "*.ldf*", "*.frm*", "*.myd*", "*.myi*", "*.dbf*", "*.db2*", "*.dbc*", "*.fpt*", "*.ora*") - | rename host as dest - | rename comm as process_name - | rename exe as process - | stats count min(_time) as firstTime max(_time) as lastTime by argc execve_command dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_auditd_database_file_and_directory_discovery_filter`' + - Linux Auditd Execve +search: |- + `linux_auditd` execve_command IN ("*find*", "*grep*") AND execve_command IN("*.db*", "*.sql*", "*.sqlite*", "*.mdb*", "*.accdb*", "*.mdf*", "*.ndf*", "*.ldf*", "*.frm*", "*.myd*", "*.myi*", "*.dbf*", "*.db2*", "*.dbc*", "*.fpt*", "*.ora*") + | rename host as dest + | rename comm as process_name + | rename exe as process + | stats count min(_time) as firstTime max(_time) as lastTime + BY argc execve_command dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_database_file_and_directory_discovery_filter` how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html -- https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS + - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html + - https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$execve_command$] event occurred on host - [$dest$] to discover database - files and directories. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A [$execve_command$] event occurred on host - [$dest$] to discover database files and directories. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1083 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1083 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1083/linux_auditd_find_db/linux_auditd_find_db.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1083/linux_auditd_find_db/linux_auditd_find_db.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_dd_file_overwrite.yml b/detections/endpoint/linux_auditd_dd_file_overwrite.yml index f75042fc1d..6b71df8ba3 100644 --- a/detections/endpoint/linux_auditd_dd_file_overwrite.yml +++ b/detections/endpoint/linux_auditd_dd_file_overwrite.yml @@ -1,76 +1,58 @@ name: Linux Auditd Dd File Overwrite id: d1b74420-4cea-4752-a123-9b40dfcca49a -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the use of the 'dd' command to overwrite - files on a Linux system. It leverages data from Linux Auditd telemetry, focusing - on process execution logs that include command-line details. This activity is significant - because adversaries often use the 'dd' command to destroy or irreversibly overwrite - files, disrupting system availability and services. If confirmed malicious, this - behavior could lead to data destruction, making recovery difficult and potentially - causing significant operational disruptions. +description: The following analytic detects the use of the 'dd' command to overwrite files on a Linux system. It leverages data from Linux Auditd telemetry, focusing on process execution logs that include command-line details. This activity is significant because adversaries often use the 'dd' command to destroy or irreversibly overwrite files, disrupting system availability and services. If confirmed malicious, this behavior could lead to data destruction, making recovery difficult and potentially causing significant operational disruptions. data_source: -- Linux Auditd Proctitle -search: '`linux_auditd` proctitle = "*dd *" AND proctitle = "*of=*" AND proctitle = "*if=/dev/zero*" - | rename host as dest - | stats count min(_time) as firstTime max(_time) as lastTime by proctitle dest - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - |`linux_auditd_dd_file_overwrite_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Linux Auditd Proctitle +search: |- + `linux_auditd` proctitle = "*dd *" AND proctitle = "*of=*" AND proctitle = "*if=/dev/zero*" + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY proctitle dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_dd_file_overwrite_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://gtfobins.github.io/gtfobins/dd/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1485/T1485.md + - https://gtfobins.github.io/gtfobins/dd/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1485/T1485.md drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$proctitle$] event occurred on host - [$dest$]. - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: [] + message: A [$proctitle$] event occurred on host - [$dest$]. + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: [] tags: - analytic_story: - - Industroyer2 - - Data Destruction - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Industroyer2 + - Data Destruction + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/linux_auditd_dd_overwrite/auditd_proctitle_dd_overwrite.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/linux_auditd_dd_overwrite/auditd_proctitle_dd_overwrite.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_disable_or_modify_system_firewall.yml b/detections/endpoint/linux_auditd_disable_or_modify_system_firewall.yml index 422413bfad..37b84049ae 100644 --- a/detections/endpoint/linux_auditd_disable_or_modify_system_firewall.yml +++ b/detections/endpoint/linux_auditd_disable_or_modify_system_firewall.yml @@ -1,76 +1,59 @@ name: Linux Auditd Disable Or Modify System Firewall id: 07052556-d4b5-4bae-89aa-cbdc1bb11250 -version: 7 -date: '2025-06-10' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the suspicious disable or modify system - firewall. This behavior is critical for a SOC to monitor because it may indicate - attempts to gain unauthorized access or maintain control over a system. Such actions - could be signs of malicious activity. If confirmed, this could lead to serious consequences, - including a compromised system, unauthorized access to sensitive data, or even a - wider breach affecting the entire network. Detecting and responding to these signs - early is essential to prevent potential security incidents. +description: The following analytic detects the suspicious disable or modify system firewall. This behavior is critical for a SOC to monitor because it may indicate attempts to gain unauthorized access or maintain control over a system. Such actions could be signs of malicious activity. If confirmed, this could lead to serious consequences, including a compromised system, unauthorized access to sensitive data, or even a wider breach affecting the entire network. Detecting and responding to these signs early is essential to prevent potential security incidents. data_source: -- Linux Auditd Service Stop -search: '`linux_auditd` type=SERVICE_STOP unit IN ("firewalld", "ufw") | rename host - as dest | stats count min(_time) as firstTime max(_time) as lastTime by type pid comm exe unit dest - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| - `linux_auditd_disable_or_modify_system_firewall_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Linux Auditd Service Stop +search: |- + `linux_auditd` type=SERVICE_STOP unit IN ("firewalld", "ufw") + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY type pid comm + exe unit dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_disable_or_modify_system_firewall_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html + - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A service event - [$type$] to disable or modify system firewall occurred - on host - [$dest$] . - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A service event - [$type$] to disable or modify system firewall occurred on host - [$dest$] . + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1562.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1562.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/linux_auditd_disable_firewall/linux_auditd_disable_firewall.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/linux_auditd_disable_firewall/linux_auditd_disable_firewall.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_doas_conf_file_creation.yml b/detections/endpoint/linux_auditd_doas_conf_file_creation.yml index f6f256b02b..1fcfb5e4bd 100644 --- a/detections/endpoint/linux_auditd_doas_conf_file_creation.yml +++ b/detections/endpoint/linux_auditd_doas_conf_file_creation.yml @@ -6,105 +6,99 @@ author: Teoderick Contreras, Nasreddine Bencherchali, Splunk status: production type: TTP description: | - The following analytic detects the creation of the doas.conf file on a Linux host. - This file is used by the doas utility to allow standard users to perform tasks as root, similar to sudo. - The detection leverages Linux Auditd data, focusing on the creation of the doas.conf file. - This activity is significant because it can indicate an attempt to gain elevated privileges, potentially by an adversary. If confirmed malicious, this could allow an attacker to execute commands with root commands with root privileges, leading to full system compromise. + The following analytic detects the creation of the doas.conf file on a Linux host. + This file is used by the doas utility to allow standard users to perform tasks as root, similar to sudo. + The detection leverages Linux Auditd data, focusing on the creation of the doas.conf file. + This activity is significant because it can indicate an attempt to gain elevated privileges, potentially by an adversary. If confirmed malicious, this could allow an attacker to execute commands with root commands with root privileges, leading to full system compromise. data_source: - - Linux Auditd Path - - Linux Auditd Cwd + - Linux Auditd Path + - Linux Auditd Cwd search: | - `linux_auditd` - ( - (type=PATH nametype="CREATE") - OR - type=CWD - ) - | rex "msg=audit\([^)]*:(?\d+)\)" + `linux_auditd` + ( + (type=PATH nametype="CREATE") + OR + type=CWD + ) + | rex "msg=audit\([^)]*:(?\d+)\)" - | stats - values(type) as types - values(name) as names - values(nametype) as nametype - values(cwd) as cwd_list - values(_time) as event_times - by audit_id host + | stats + values(type) as types + values(name) as names + values(nametype) as nametype + values(cwd) as cwd_list + values(_time) as event_times + by audit_id host - | eval current_working_directory = coalesce(mvindex(cwd_list, 0), "N/A") - | eval candidate_paths = mvmap(names, if(match(names, "^/"), names, current_working_directory + "/" + names)) - | eval matched_paths = mvfilter(match(candidate_paths, "/etc/doas.conf.*")) - | eval match_count = mvcount(matched_paths) - | eval reconstructed_path = mvindex(matched_paths, 0) - | eval e_time = mvindex(event_times, 0) - | where match_count > 0 - | rename host as dest + | eval current_working_directory = coalesce(mvindex(cwd_list, 0), "N/A") + | eval candidate_paths = mvmap(names, if(match(names, "^/"), names, current_working_directory + "/" + names)) + | eval matched_paths = mvfilter(match(candidate_paths, "/etc/doas.conf.*")) + | eval match_count = mvcount(matched_paths) + | eval reconstructed_path = mvindex(matched_paths, 0) + | eval e_time = mvindex(event_times, 0) + | where match_count > 0 + | rename host as dest - | stats count min(e_time) as firstTime max(e_time) as lastTime - values(nametype) as nametype - by current_working_directory - reconstructed_path - match_count - dest - audit_id + | stats count min(e_time) as firstTime max(e_time) as lastTime + values(nametype) as nametype + by current_working_directory + reconstructed_path + match_count + dest + audit_id - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | table nametype current_working_directory reconstructed_path dest audit_id match_count firstTime lastTime - | `linux_auditd_doas_conf_file_creation_filter` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table nametype current_working_directory reconstructed_path dest audit_id match_count firstTime lastTime + | `linux_auditd_doas_conf_file_creation_filter` how_to_implement: | - To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling and make sure the type=CWD record type is activate in your auditd configuration. - This approach enables effective monitoring and detection of linux endpoints where auditd is deployed. + To implement this detection, the process begins by ingesting auditd + data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line + executions and process details on Unix/Linux systems. These logs should be ingested + and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), + which is essential for correctly parsing and categorizing the data. The next step + involves normalizing the field names to match the field names set by the Splunk + Common Information Model (CIM) to ensure consistency across different data sources + and enhance the efficiency of data modeling and make sure the type=CWD record type is activate in your auditd configuration. + This approach enables effective monitoring and detection of linux endpoints where auditd is deployed. known_false_positives: | - Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + Administrator or network operator can execute this command. + Please update the filter macros to remove false positives. references: - - https://wiki.gentoo.org/wiki/Doas - - https://www.makeuseof.com/how-to-install-and-use-doas/ + - https://wiki.gentoo.org/wiki/Doas + - https://www.makeuseof.com/how-to-install-and-use-doas/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A $reconstructed_path$ file was created on host - [$dest$] - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A $reconstructed_path$ file was created on host - [$dest$] + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/auditd_path_cwd_doas_conf/path_doas.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/auditd_path_cwd_doas_conf/path_doas.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_doas_tool_execution.yml b/detections/endpoint/linux_auditd_doas_tool_execution.yml index 6f72f46a1f..9be66fa80b 100644 --- a/detections/endpoint/linux_auditd_doas_tool_execution.yml +++ b/detections/endpoint/linux_auditd_doas_tool_execution.yml @@ -1,79 +1,60 @@ name: Linux Auditd Doas Tool Execution id: 91b8ca78-f205-4826-a3ef-cd8d6b24e97b -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the 'doas' tool on a - Linux host. This tool allows standard users to perform tasks with root privileges, - similar to 'sudo'. The detection leverages data from Linux Auditd, focusing on process - names and command-line executions. This activity is significant as 'doas' can be - exploited by adversaries to gain elevated privileges on a compromised host. If confirmed - malicious, this could lead to unauthorized administrative access, potentially compromising - the entire system. +description: The following analytic detects the execution of the 'doas' tool on a Linux host. This tool allows standard users to perform tasks with root privileges, similar to 'sudo'. The detection leverages data from Linux Auditd, focusing on process names and command-line executions. This activity is significant as 'doas' can be exploited by adversaries to gain elevated privileges on a compromised host. If confirmed malicious, this could lead to unauthorized administrative access, potentially compromising the entire system. data_source: -- Linux Auditd Syscall -search: '`linux_auditd` type=SYSCALL comm=doas - | rename host as dest - | stats count min(_time) as firstTime max(_time) as lastTime - by comm exe syscall uid ppid pid success dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_auditd_doas_tool_execution_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Linux Auditd Syscall +search: |- + `linux_auditd` type=SYSCALL comm=doas + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY comm exe syscall + uid ppid pid + success dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_doas_tool_execution_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://wiki.gentoo.org/wiki/Doas -- https://www.makeuseof.com/how-to-install-and-use-doas/ + - https://wiki.gentoo.org/wiki/Doas + - https://www.makeuseof.com/how-to-install-and-use-doas/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to execute the - "doas" tool. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to execute the "doas" tool. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/linux_auditd_doas_new/linux_auditd_new_doas.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/linux_auditd_doas_new/linux_auditd_new_doas.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_edit_cron_table_parameter.yml b/detections/endpoint/linux_auditd_edit_cron_table_parameter.yml index bd43821817..522a16cbbf 100644 --- a/detections/endpoint/linux_auditd_edit_cron_table_parameter.yml +++ b/detections/endpoint/linux_auditd_edit_cron_table_parameter.yml @@ -1,80 +1,61 @@ name: Linux Auditd Edit Cron Table Parameter id: f4bb7321-7e64-4d1e-b1aa-21f8b019a91f -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the suspicious editing of cron jobs in - Linux using the crontab command-line parameter (-e). It identifies this activity - by monitoring command-line executions involving 'crontab' and the edit parameter. - This behavior is significant for a SOC as cron job manipulations can indicate unauthorized - persistence attempts or scheduled malicious actions. If confirmed malicious, this - activity could lead to system compromise, unauthorized access, or broader network - compromise. +description: The following analytic detects the suspicious editing of cron jobs in Linux using the crontab command-line parameter (-e). It identifies this activity by monitoring command-line executions involving 'crontab' and the edit parameter. This behavior is significant for a SOC as cron job manipulations can indicate unauthorized persistence attempts or scheduled malicious actions. If confirmed malicious, this activity could lead to system compromise, unauthorized access, or broader network compromise. data_source: -- Linux Auditd Syscall -search: '`linux_auditd` type=SYSCALL syscall IN ("rename", "execve") (comm IN ("crontab") OR exe IN ("*/crontab")) success=yes AND NOT (UID IN("daemon")) - | rename host as dest - | stats count min(_time) as firstTime max(_time) as lastTime - by comm exe syscall uid ppid pid dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_auditd_edit_cron_table_parameter_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Linux Auditd Syscall +search: |- + `linux_auditd` type=SYSCALL syscall IN ("rename", "execve") (comm IN ("crontab") OR exe IN ("*/crontab")) success=yes AND NOT (UID IN("daemon")) + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY comm exe syscall + uid ppid pid + dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_edit_cron_table_parameter_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://attack.mitre.org/techniques/T1053/003/ + - https://attack.mitre.org/techniques/T1053/003/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to edit the - cron table. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to edit the cron table. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Scheduled Tasks - - Linux Privilege Escalation - - Linux Persistence Techniques - - Linux Living Off The Land - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1053.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Scheduled Tasks + - Linux Privilege Escalation + - Linux Persistence Techniques + - Linux Living Off The Land + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1053.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/linux_auditd_crontab_edit_new/linux_auditd_new_crontab.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/linux_auditd_crontab_edit_new/linux_auditd_new_crontab.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_file_and_directory_discovery.yml b/detections/endpoint/linux_auditd_file_and_directory_discovery.yml index 64ea14403e..397ccd78f1 100644 --- a/detections/endpoint/linux_auditd_file_and_directory_discovery.yml +++ b/detections/endpoint/linux_auditd_file_and_directory_discovery.yml @@ -1,71 +1,61 @@ name: Linux Auditd File And Directory Discovery id: 0bbfb79c-a755-49a5-a38a-1128d0a452f1 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects suspicious file and directory discovery - activities, which may indicate an attacker's effort to locate sensitive documents - and files on a compromised system. This behavior often precedes data exfiltration, - as adversaries seek to identify valuable or confidential information for theft. - By identifying unusual or unauthorized attempts to browse or enumerate files and - directories, this analytic helps security teams detect potential reconnaissance - or preparatory actions by an attacker, enabling timely intervention to prevent data - breaches or unauthorized access. +description: The following analytic detects suspicious file and directory discovery activities, which may indicate an attacker's effort to locate sensitive documents and files on a compromised system. This behavior often precedes data exfiltration, as adversaries seek to identify valuable or confidential information for theft. By identifying unusual or unauthorized attempts to browse or enumerate files and directories, this analytic helps security teams detect potential reconnaissance or preparatory actions by an attacker, enabling timely intervention to prevent data breaches or unauthorized access. data_source: -- Linux Auditd Execve -search: '`linux_auditd` execve_command IN ("*grep*", "*find*") AND execve_command IN ("*.tif*", "*.tiff*", "*.gif*", "*.jpeg*", "*.jpg*", "*.jif*", "*.jfif*", "*.jp2*", "*.jpx*", "*.j2k*", "*.j2c*", "*.fpx*", "*.pcd*", "*.png*", "*.flv*", "*.pdf*", "*.mp4*", "*.mp3*", "*.gifv*", "*.avi*", "*.mov*", "*.mpeg*", "*.wav*", "*.doc*", "*.docx*", "*.xls*", "*.xlsx*", "*.svg*") - | rename host as dest - | rename comm as process_name - | rename exe as process - | stats count min(_time) as firstTime max(_time) as lastTime by argc execve_command dest - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_auditd_file_and_directory_discovery_filter`' + - Linux Auditd Execve +search: |- + `linux_auditd` execve_command IN ("*grep*", "*find*") AND execve_command IN ("*.tif*", "*.tiff*", "*.gif*", "*.jpeg*", "*.jpg*", "*.jif*", "*.jfif*", "*.jp2*", "*.jpx*", "*.j2k*", "*.j2c*", "*.fpx*", "*.pcd*", "*.png*", "*.flv*", "*.pdf*", "*.mp4*", "*.mp3*", "*.gifv*", "*.avi*", "*.mov*", "*.mpeg*", "*.wav*", "*.doc*", "*.docx*", "*.xls*", "*.xlsx*", "*.svg*") + | rename host as dest + | rename comm as process_name + | rename exe as process + | stats count min(_time) as firstTime max(_time) as lastTime + BY argc execve_command dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_file_and_directory_discovery_filter` how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html -- https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS + - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html + - https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$execve_command$] event occurred on host - [$dest$] to discover files - and directories. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A [$execve_command$] event occurred on host - [$dest$] to discover files and directories. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1083 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1083 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1083/linux_auditd_find_document/auditd_execve_file_dir_discovery.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1083/linux_auditd_find_document/auditd_execve_file_dir_discovery.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_file_permission_modification_via_chmod.yml b/detections/endpoint/linux_auditd_file_permission_modification_via_chmod.yml index 6e8c8195dc..c19dc01885 100644 --- a/detections/endpoint/linux_auditd_file_permission_modification_via_chmod.yml +++ b/detections/endpoint/linux_auditd_file_permission_modification_via_chmod.yml @@ -1,79 +1,61 @@ name: Linux Auditd File Permission Modification Via Chmod id: 5f1d2ea7-eec0-4790-8b24-6875312ad492 -version: 11 -date: '2025-05-02' -author: "Teoderick Contreras, Splunk, Ivar Nyg\xE5rd" +version: 12 +date: '2026-02-25' +author: "Teoderick Contreras, Splunk, Ivar Nygård" status: production type: Anomaly -description: The following analytic detects suspicious file permission modifications - using the `chmod` command, which may indicate an attacker attempting to alter access - controls on critical files or directories. Such modifications can be used to grant - unauthorized users elevated privileges or to conceal malicious activities by restricting - legitimate access. By monitoring for unusual or unauthorized `chmod` usage, this - analytic helps identify potential security breaches, allowing security teams to - respond promptly to prevent privilege escalation, data tampering, or other unauthorized - actions on the system. +description: The following analytic detects suspicious file permission modifications using the `chmod` command, which may indicate an attacker attempting to alter access controls on critical files or directories. Such modifications can be used to grant unauthorized users elevated privileges or to conceal malicious activities by restricting legitimate access. By monitoring for unusual or unauthorized `chmod` usage, this analytic helps identify potential security breaches, allowing security teams to respond promptly to prevent privilege escalation, data tampering, or other unauthorized actions on the system. data_source: -- Linux Auditd Proctitle -search: '`linux_auditd` proctitle="*chmod*" AND proctitle IN ("* 777 *", "* 755 *", - "*+*x*", "* 754 *") | rename host as dest | stats count min(_time) as firstTime - max(_time) as lastTime by proctitle dest | `security_content_ctime(firstTime)` | - `security_content_ctime(lastTime)` | `linux_auditd_file_permission_modification_via_chmod_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures - command-line executions and process details on Unix/Linux systems. These logs should - be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Linux Auditd Proctitle +search: |- + `linux_auditd` proctitle="*chmod*" AND proctitle IN ("* 777 *", "* 755 *", "*+*x*", "* 754 *") + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY proctitle dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_file_permission_modification_via_chmod_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html + - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A $proctitle$ event occurred on host $dest$ to modify file permissions - using the "chmod" command. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A $proctitle$ event occurred on host $dest$ to modify file permissions using the "chmod" command. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Linux Persistence Techniques - - Compromised Linux Host - - China-Nexus Threat Activity - - Linux Living Off The Land - - XorDDos - - Salt Typhoon - - Linux Privilege Escalation - asset_type: Endpoint - mitre_attack_id: - - T1222.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Persistence Techniques + - Compromised Linux Host + - China-Nexus Threat Activity + - Linux Living Off The Land + - XorDDos + - Salt Typhoon + - Linux Privilege Escalation + asset_type: Endpoint + mitre_attack_id: + - T1222.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.002/linux_auditd_chmod_exec_attrib/auditd_proctitle_chmod.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.002/linux_auditd_chmod_exec_attrib/auditd_proctitle_chmod.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_file_permissions_modification_via_chattr.yml b/detections/endpoint/linux_auditd_file_permissions_modification_via_chattr.yml index a4ba23c7d6..e6a73849c3 100644 --- a/detections/endpoint/linux_auditd_file_permissions_modification_via_chattr.yml +++ b/detections/endpoint/linux_auditd_file_permissions_modification_via_chattr.yml @@ -1,78 +1,58 @@ name: Linux Auditd File Permissions Modification Via Chattr id: f2d1110d-b01c-4a58-9975-90a9edeb083a -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects suspicious file permissions modifications - using the chattr command, which may indicate an attacker attempting to manipulate - file attributes to evade detection or prevent alteration. The chattr command can - be used to make files immutable or restrict deletion, which can be leveraged to - protect malicious files or disrupt system operations. By monitoring for unusual - or unauthorized chattr usage, this analytic helps identify potential tampering with - critical files, enabling security teams to quickly respond to and mitigate threats - associated with unauthorized file attribute changes. +description: The following analytic detects suspicious file permissions modifications using the chattr command, which may indicate an attacker attempting to manipulate file attributes to evade detection or prevent alteration. The chattr command can be used to make files immutable or restrict deletion, which can be leveraged to protect malicious files or disrupt system operations. By monitoring for unusual or unauthorized chattr usage, this analytic helps identify potential tampering with critical files, enabling security teams to quickly respond to and mitigate threats associated with unauthorized file attribute changes. data_source: -- Linux Auditd Execve -search: '`linux_auditd` proctitle = "*chattr *" AND proctitle = "* -i*" - | rename host as dest - | stats count min(_time) as firstTime max(_time) as lastTime by proctitle dest - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - |`linux_auditd_file_permissions_modification_via_chattr_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures - command-line executions and process details on Unix/Linux systems. These logs should - be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Linux Auditd Execve +search: |- + `linux_auditd` proctitle = "*chattr *" AND proctitle = "* -i*" + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY proctitle dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_file_permissions_modification_via_chattr_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html + - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$proctitle$] event occurred on host - [$dest$] to modify file permissions - using the "chattr" command. - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: [] + message: A [$proctitle$] event occurred on host - [$dest$] to modify file permissions using the "chattr" command. + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1222.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1222.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.002/linux_auditd_chattr_i/auditd_proctitle_chattr.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.002/linux_auditd_chattr_i/auditd_proctitle_chattr.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_find_credentials_from_password_managers.yml b/detections/endpoint/linux_auditd_find_credentials_from_password_managers.yml index 60cdbbc8ae..2280049640 100644 --- a/detections/endpoint/linux_auditd_find_credentials_from_password_managers.yml +++ b/detections/endpoint/linux_auditd_find_credentials_from_password_managers.yml @@ -1,73 +1,62 @@ name: Linux Auditd Find Credentials From Password Managers id: 784241aa-85a5-4782-a503-d071bd3446f9 -version: 7 -date: '2025-10-14' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects suspicious attempts to find credentials - stored in password managers, which may indicate an attacker's effort to retrieve - sensitive login information. Password managers are often targeted by adversaries - seeking to access stored passwords for further compromise or lateral movement within - a network. By monitoring for unusual or unauthorized access to password manager - files or processes, this analytic helps identify potential credential theft attempts, - enabling security teams to respond quickly to protect critical accounts and prevent - further unauthorized access. +description: The following analytic detects suspicious attempts to find credentials stored in password managers, which may indicate an attacker's effort to retrieve sensitive login information. Password managers are often targeted by adversaries seeking to access stored passwords for further compromise or lateral movement within a network. By monitoring for unusual or unauthorized access to password manager files or processes, this analytic helps identify potential credential theft attempts, enabling security teams to respond quickly to protect critical accounts and prevent further unauthorized access. data_source: -- Linux Auditd Execve -search: '`linux_auditd` execve_command IN ("*find*", "*grep*") AND execve_command IN ("*.kdbx*", "*KeePass*", "*.enforced*", "*.lpdb*", "*.opvault*", "*.agilekeychain*", "*.dashlane*", "*.rfx*", "*passbolt*", "*.spdb*", "*StickyPassword*", "*.walletx*", "*enpass*", "*vault*", "*.kdb*") - | rename host as dest - | rename comm as process_name - | rename exe as process - | stats count min(_time) as firstTime max(_time) as lastTime by argc execve_command dest - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_auditd_find_credentials_from_password_managers_filter`' + - Linux Auditd Execve +search: |- + `linux_auditd` execve_command IN ("*find*", "*grep*") AND execve_command IN ("*.kdbx*", "*KeePass*", "*.enforced*", "*.lpdb*", "*.opvault*", "*.agilekeychain*", "*.dashlane*", "*.rfx*", "*passbolt*", "*.spdb*", "*StickyPassword*", "*.walletx*", "*enpass*", "*vault*", "*.kdb*") + | rename host as dest + | rename comm as process_name + | rename exe as process + | stats count min(_time) as firstTime max(_time) as lastTime + BY argc execve_command dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_find_credentials_from_password_managers_filter` how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html -- https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS + - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html + - https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$execve_command$] event occurred on host - [$dest$] to find credentials - stored in password managers. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A [$execve_command$] event occurred on host - [$dest$] to find credentials stored in password managers. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1555.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1555.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555.005/linux_auditd_find_password_db/auditd_execve_pwd_mgr.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555.005/linux_auditd_find_password_db/auditd_execve_pwd_mgr.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_find_credentials_from_password_stores.yml b/detections/endpoint/linux_auditd_find_credentials_from_password_stores.yml index 1053b4d329..358b2a2c33 100644 --- a/detections/endpoint/linux_auditd_find_credentials_from_password_stores.yml +++ b/detections/endpoint/linux_auditd_find_credentials_from_password_stores.yml @@ -1,83 +1,63 @@ name: Linux Auditd Find Credentials From Password Stores id: 4de73044-9a1d-4a51-a1c2-85267d8dcab3 -version: 7 -date: '2025-10-14' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects suspicious attempts to find credentials - stored in password stores, indicating a potential attacker's effort to access sensitive - login information. Password stores are critical repositories that contain valuable - credentials, and unauthorized access to them can lead to significant security breaches. - By monitoring for unusual or unauthorized activities related to password store access, - this analytic helps identify potential credential theft attempts, allowing security - teams to respond promptly and prevent unauthorized access to critical systems and - data. +description: The following analytic detects suspicious attempts to find credentials stored in password stores, indicating a potential attacker's effort to access sensitive login information. Password stores are critical repositories that contain valuable credentials, and unauthorized access to them can lead to significant security breaches. By monitoring for unusual or unauthorized activities related to password store access, this analytic helps identify potential credential theft attempts, allowing security teams to respond promptly and prevent unauthorized access to critical systems and data. data_source: -- Linux Auditd Execve -search: '`linux_auditd` execve_command IN ("*find*", "*grep*") AND execve_command IN ("*password*", "*pass *", "*credential*", "*creds*") - | rename host as dest - | rename comm as process_name - | rename exe as process - | stats count min(_time) as firstTime max(_time) as lastTime by argc execve_command dest - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_auditd_find_credentials_from_password_stores_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures - command-line executions and process details on Unix/Linux systems. These logs should - be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Linux Auditd Execve +search: |- + `linux_auditd` execve_command IN ("*find*", "*grep*") AND execve_command IN ("*password*", "*pass *", "*credential*", "*creds*") + | rename host as dest + | rename comm as process_name + | rename exe as process + | stats count min(_time) as firstTime max(_time) as lastTime + BY argc execve_command dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_find_credentials_from_password_stores_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html -- https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS + - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html + - https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$execve_command$] event occurred on host - [$dest$] to find credentials - stored in password managers. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A [$execve_command$] event occurred on host - [$dest$] to find credentials stored in password managers. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - - Scattered Lapsus$ Hunters - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1555.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + - Scattered Lapsus$ Hunters + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1555.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555.005/linux_auditd_find_credentials/auditd_execve_find_creds.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555.005/linux_auditd_find_credentials/auditd_execve_find_creds.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_find_ssh_private_keys.yml b/detections/endpoint/linux_auditd_find_ssh_private_keys.yml index 1fca47aad3..aa8cfae548 100644 --- a/detections/endpoint/linux_auditd_find_ssh_private_keys.yml +++ b/detections/endpoint/linux_auditd_find_ssh_private_keys.yml @@ -1,82 +1,62 @@ name: Linux Auditd Find Ssh Private Keys id: e2d2bd10-dcd1-4b2f-8a76-0198eab32ba5 -version: 7 -date: '2025-10-14' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects suspicious attempts to find SSH private - keys, which may indicate an attacker's effort to compromise secure access to systems. - SSH private keys are essential for secure authentication, and unauthorized access - to these keys can enable attackers to gain unauthorized access to servers and other - critical infrastructure. By monitoring for unusual or unauthorized searches for - SSH private keys, this analytic helps identify potential threats to network security, - allowing security teams to quickly respond and safeguard against unauthorized access - and potential breaches. +description: The following analytic detects suspicious attempts to find SSH private keys, which may indicate an attacker's effort to compromise secure access to systems. SSH private keys are essential for secure authentication, and unauthorized access to these keys can enable attackers to gain unauthorized access to servers and other critical infrastructure. By monitoring for unusual or unauthorized searches for SSH private keys, this analytic helps identify potential threats to network security, allowing security teams to quickly respond and safeguard against unauthorized access and potential breaches. data_source: -- Linux Auditd Execve -search: '`linux_auditd` execve_command IN ("*find*", "*grep*") AND execve_command IN ("*id_rsa*", "*id_dsa*", "*.key*", "*ssh_key*", "*authorized_keys*") - | rename host as dest - | rename comm as process_name - | rename exe as process - | stats count min(_time) as firstTime max(_time) as lastTime by argc execve_command dest - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_auditd_find_ssh_private_keys_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures - command-line executions and process details on Unix/Linux systems. These logs should - be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Linux Auditd Execve +search: |- + `linux_auditd` execve_command IN ("*find*", "*grep*") AND execve_command IN ("*id_rsa*", "*id_dsa*", "*.key*", "*ssh_key*", "*authorized_keys*") + | rename host as dest + | rename comm as process_name + | rename exe as process + | stats count min(_time) as firstTime max(_time) as lastTime + BY argc execve_command dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_find_ssh_private_keys_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html -- https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS + - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html + - https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$execve_command$] event occurred on host - [$dest$] to find SSH private - keys. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: A [$execve_command$] event occurred on host - [$dest$] to find SSH private keys. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1552.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1552.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.004/linux_auditd_find_ssh_files/auditd_execve_find_ssh.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.004/linux_auditd_find_ssh_files/auditd_execve_find_ssh.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_hardware_addition_swapoff.yml b/detections/endpoint/linux_auditd_hardware_addition_swapoff.yml index b49b768a8e..9d1e6ddebf 100644 --- a/detections/endpoint/linux_auditd_hardware_addition_swapoff.yml +++ b/detections/endpoint/linux_auditd_hardware_addition_swapoff.yml @@ -1,68 +1,58 @@ name: Linux Auditd Hardware Addition Swapoff id: 5728bb16-1a0b-4b66-bce2-0074ac839770 -version: 6 -date: '2025-10-14' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the "swapoff" command, - which disables the swapping of paging devices on a Linux system. It leverages data - from Endpoint Detection and Response (EDR) agents, focusing on process execution - logs. This activity is significant because disabling swap can be a tactic used by - malware, such as Awfulshred, to evade detection and hinder forensic analysis. If - confirmed malicious, this action could allow an attacker to manipulate system memory - management, potentially leading to data corruption, system instability, or evasion - of memory-based detection mechanisms. +description: The following analytic detects the execution of the "swapoff" command, which disables the swapping of paging devices on a Linux system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This activity is significant because disabling swap can be a tactic used by malware, such as Awfulshred, to evade detection and hinder forensic analysis. If confirmed malicious, this action could allow an attacker to manipulate system memory management, potentially leading to data corruption, system instability, or evasion of memory-based detection mechanisms. data_source: -- Linux Auditd Execve -search: '`linux_auditd` proctitle = "*swapoff*" AND proctitle = "*-a*" - | rename host as dest - | stats count min(_time) as firstTime max(_time) as lastTime by proctitle dest - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `linux_auditd_hardware_addition_swapoff_filter`' + - Linux Auditd Execve +search: |- + `linux_auditd` proctitle = "*swapoff*" AND proctitle = "*-a*" + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY proctitle dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_hardware_addition_swapoff_filter` how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed known_false_positives: administrator may disable swapping of devices in a linux host. Filter is needed. references: -- https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/overview-of-the-cyber-weapons-used-in-the-ukraine-russia-war/ + - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/overview-of-the-cyber-weapons-used-in-the-ukraine-russia-war/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$proctitle$] event occurred on host - [$dest$] to disable the swapping - of paging devices on a Linux system. - risk_objects: - - field: dest - type: system - score: 36 - threat_objects: [] + message: A [$proctitle$] event occurred on host - [$dest$] to disable the swapping of paging devices on a Linux system. + risk_objects: + - field: dest + type: system + score: 36 + threat_objects: [] tags: - analytic_story: - - Data Destruction - - AwfulShred - - Compromised Linux Host - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1200 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Destruction + - AwfulShred + - Compromised Linux Host + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1200 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1200/linux_auditd_swapoff/linux_auditd_swapoff2.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1200/linux_auditd_swapoff/linux_auditd_swapoff2.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_hidden_files_and_directories_creation.yml b/detections/endpoint/linux_auditd_hidden_files_and_directories_creation.yml index 6836693f6f..8ddb0cd935 100644 --- a/detections/endpoint/linux_auditd_hidden_files_and_directories_creation.yml +++ b/detections/endpoint/linux_auditd_hidden_files_and_directories_creation.yml @@ -1,71 +1,61 @@ name: Linux Auditd Hidden Files And Directories Creation id: 555cc358-bf16-4e05-9b3a-0f89c73b7261 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects suspicious creation of hidden files and - directories, which may indicate an attacker's attempt to conceal malicious activities - or unauthorized data. Hidden files and directories are often used to evade detection - by security tools and administrators, providing a stealthy means for storing malware, - logs, or sensitive information. By monitoring for unusual or unauthorized creation - of hidden files and directories, this analytic helps identify potential attempts - to hide or unauthorized creation of hidden files and directories, this analytic - helps identify potential attempts to hide malicious operations, enabling security - teams to uncover and address hidden threats effectively. +description: The following analytic detects suspicious creation of hidden files and directories, which may indicate an attacker's attempt to conceal malicious activities or unauthorized data. Hidden files and directories are often used to evade detection by security tools and administrators, providing a stealthy means for storing malware, logs, or sensitive information. By monitoring for unusual or unauthorized creation of hidden files and directories, this analytic helps identify potential attempts to hide or unauthorized creation of hidden files and directories, this analytic helps identify potential attempts to hide malicious operations, enabling security teams to uncover and address hidden threats effectively. data_source: -- Linux Auditd Execve -search: '`linux_auditd` execve_command IN ("*touch *", "*mkdir *", "*vim *", "*vi *", "*nano *") AND execve_command IN ("* ./.*", "* .*", "*/.*") - | rename host as dest - | rename comm as process_name - | rename exe as process - | stats count min(_time) as firstTime max(_time) as lastTime by argc execve_command dest - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_auditd_hidden_files_and_directories_creation_filter`' + - Linux Auditd Execve +search: |- + `linux_auditd` execve_command IN ("*touch *", "*mkdir *", "*vim *", "*vi *", "*nano *") AND execve_command IN ("* ./.*", "* .*", "*/.*") + | rename host as dest + | rename comm as process_name + | rename exe as process + | stats count min(_time) as firstTime max(_time) as lastTime + BY argc execve_command dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_hidden_files_and_directories_creation_filter` how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html -- https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS + - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html + - https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$execve_command$] event occurred on host - [$dest$]. - risk_objects: - - field: dest - type: system - score: 9 - threat_objects: [] + message: A [$execve_command$] event occurred on host - [$dest$]. + risk_objects: + - field: dest + type: system + score: 9 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1083 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1083 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1083/linux_auditd_hidden_file/auditd_execve_hidden_file.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1083/linux_auditd_hidden_file/auditd_execve_hidden_file.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_insert_kernel_module_using_insmod_utility.yml b/detections/endpoint/linux_auditd_insert_kernel_module_using_insmod_utility.yml index ca7b4b1b59..5d656da481 100644 --- a/detections/endpoint/linux_auditd_insert_kernel_module_using_insmod_utility.yml +++ b/detections/endpoint/linux_auditd_insert_kernel_module_using_insmod_utility.yml @@ -1,82 +1,63 @@ name: Linux Auditd Insert Kernel Module Using Insmod Utility id: bc0ca53f-dea6-4906-9b12-09c396fdf1d3 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the insertion of a Linux kernel module - using the insmod utility. It leverages data from Linux Auditd, focusing on process - execution logs that include process names and command-line details. This activity - is significant as it may indicate the installation of a rootkit or malicious kernel - module, potentially allowing an attacker to gain elevated privileges and bypass - security detections. If confirmed malicious, this could lead to unauthorized code - execution, persistent access, and severe compromise of the affected system. +description: The following analytic detects the insertion of a Linux kernel module using the insmod utility. It leverages data from Linux Auditd, focusing on process execution logs that include process names and command-line details. This activity is significant as it may indicate the installation of a rootkit or malicious kernel module, potentially allowing an attacker to gain elevated privileges and bypass security detections. If confirmed malicious, this could lead to unauthorized code execution, persistent access, and severe compromise of the affected system. data_source: -- Linux Auditd Syscall -search: '`linux_auditd` type=SYSCALL comm=insmod - | rename host as dest - | stats count min(_time) as firstTime max(_time) as lastTime - by comm exe syscall uid ppid pid success dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_auditd_insert_kernel_module_using_insmod_utility_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures - command-line executions and process details on Unix/Linux systems. These logs should - be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Linux Auditd Syscall +search: |- + `linux_auditd` type=SYSCALL comm=insmod + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY comm exe syscall + uid ppid pid + success dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_insert_kernel_module_using_insmod_utility_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://docs.fedoraproject.org/en-US/fedora/rawhide/system-administrators-guide/kernel-module-driver-configuration/Working_with_Kernel_Modules/ -- https://security.stackexchange.com/questions/175953/how-to-load-a-malicious-lkm-at-startup -- https://0x00sec.org/t/kernel-rootkits-getting-your-hands-dirty/1485 + - https://docs.fedoraproject.org/en-US/fedora/rawhide/system-administrators-guide/kernel-module-driver-configuration/Working_with_Kernel_Modules/ + - https://security.stackexchange.com/questions/175953/how-to-load-a-malicious-lkm-at-startup + - https://0x00sec.org/t/kernel-rootkits-getting-your-hands-dirty/1485 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to insert a - Linux kernel module using the insmod utility. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to insert a Linux kernel module using the insmod utility. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - XorDDos - - Linux Rootkit - - Compromised Linux Host - - Linux Privilege Escalation - - Linux Persistence Techniques - asset_type: Endpoint - mitre_attack_id: - - T1547.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - XorDDos + - Linux Rootkit + - Compromised Linux Host + - Linux Privilege Escalation + - Linux Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1547.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/linux_auditd_insmod_new/linux_auditd_new_insmod.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/linux_auditd_insmod_new/linux_auditd_new_insmod.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_install_kernel_module_using_modprobe_utility.yml b/detections/endpoint/linux_auditd_install_kernel_module_using_modprobe_utility.yml index b920f28038..3d66bec95c 100644 --- a/detections/endpoint/linux_auditd_install_kernel_module_using_modprobe_utility.yml +++ b/detections/endpoint/linux_auditd_install_kernel_module_using_modprobe_utility.yml @@ -1,82 +1,63 @@ name: Linux Auditd Install Kernel Module Using Modprobe Utility id: 95165985-ace5-4d42-9c42-93a89a5af901 -version: 7 -date: '2025-08-18' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the installation of a Linux kernel module - using the modprobe utility. It leverages data from Linux Auditd, focusing on process - names and command-line executions. This activity is significant because installing - a kernel module can indicate an attempt to deploy a rootkit or other malicious kernel-level - code, potentially leading to elevated privileges and bypassing security detections. - If confirmed malicious, this could allow an attacker to gain persistent, high-level - access to the system, compromising its integrity and security. +description: The following analytic detects the installation of a Linux kernel module using the modprobe utility. It leverages data from Linux Auditd, focusing on process names and command-line executions. This activity is significant because installing a kernel module can indicate an attempt to deploy a rootkit or other malicious kernel-level code, potentially leading to elevated privileges and bypassing security detections. If confirmed malicious, this could allow an attacker to gain persistent, high-level access to the system, compromising its integrity and security. data_source: -- Linux Auditd Syscall -search: '`linux_auditd` type=SYSCALL comm=modprobe - | rename host as dest - | stats count min(_time) as firstTime max(_time) as lastTime - by comm exe syscall uid ppid pid success dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_auditd_install_kernel_module_using_modprobe_utility_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures - command-line executions and process details on Unix/Linux systems. These logs should - be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Linux Auditd Syscall +search: |- + `linux_auditd` type=SYSCALL comm=modprobe + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY comm exe syscall + uid ppid pid + success dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_install_kernel_module_using_modprobe_utility_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://docs.fedoraproject.org/en-US/fedora/rawhide/system-administrators-guide/kernel-module-driver-configuration/Working_with_Kernel_Modules/ -- https://security.stackexchange.com/questions/175953/how-to-load-a-malicious-lkm-at-startup -- https://0x00sec.org/t/kernel-rootkits-getting-your-hands-dirty/1485 + - https://docs.fedoraproject.org/en-US/fedora/rawhide/system-administrators-guide/kernel-module-driver-configuration/Working_with_Kernel_Modules/ + - https://security.stackexchange.com/questions/175953/how-to-load-a-malicious-lkm-at-startup + - https://0x00sec.org/t/kernel-rootkits-getting-your-hands-dirty/1485 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to install a - Linux kernel module using the modprobe utility. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to install a Linux kernel module using the modprobe utility. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Linux Privilege Escalation - - Linux Rootkit - - Linux Persistence Techniques - - Compromised Linux Host - - China-Nexus Threat Activity - asset_type: Endpoint - mitre_attack_id: - - T1547.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Rootkit + - Linux Persistence Techniques + - Compromised Linux Host + - China-Nexus Threat Activity + asset_type: Endpoint + mitre_attack_id: + - T1547.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/linux_auditd_modprobe_new/linux_auditd_new_modprobe.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/linux_auditd_modprobe_new/linux_auditd_new_modprobe.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_kernel_module_enumeration.yml b/detections/endpoint/linux_auditd_kernel_module_enumeration.yml index 527fe37286..7a120b7198 100644 --- a/detections/endpoint/linux_auditd_kernel_module_enumeration.yml +++ b/detections/endpoint/linux_auditd_kernel_module_enumeration.yml @@ -1,79 +1,60 @@ name: Linux Auditd Kernel Module Enumeration id: d1b088de-c47a-4572-9339-bdcc26493b32 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies the use of the 'kmod' process to list - kernel modules on a Linux system. This detection leverages data from Linux Auditd, - focusing on process names and command-line executions. While listing kernel modules - is not inherently malicious, it can be a precursor to loading unauthorized modules - using 'insmod'. If confirmed malicious, this activity could allow an attacker to - load kernel modules, potentially leading to privilege escalation, persistence, or - other malicious actions within the system. +description: The following analytic identifies the use of the 'kmod' process to list kernel modules on a Linux system. This detection leverages data from Linux Auditd, focusing on process names and command-line executions. While listing kernel modules is not inherently malicious, it can be a precursor to loading unauthorized modules using 'insmod'. If confirmed malicious, this activity could allow an attacker to load kernel modules, potentially leading to privilege escalation, persistence, or other malicious actions within the system. data_source: -- Linux Auditd Syscall -search: '`linux_auditd` type=SYSCALL comm=lsmod - | rename host as dest - | stats count min(_time) as firstTime max(_time) as lastTime - by comm exe syscall uid ppid pid success dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_auditd_kernel_module_enumeration_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures - command-line executions and process details on Unix/Linux systems. These logs should - be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: False positives are present based on automated tooling or system - administrative usage. Filter as needed. + - Linux Auditd Syscall +search: |- + `linux_auditd` type=SYSCALL comm=lsmod + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY comm exe syscall + uid ppid pid + success dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_kernel_module_enumeration_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: False positives are present based on automated tooling or system administrative usage. Filter as needed. references: -- https://man7.org/linux/man-pages/man8/kmod.8.html + - https://man7.org/linux/man-pages/man8/kmod.8.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to list kernel - modules. - risk_objects: - - field: dest - type: system - score: 15 - threat_objects: [] + message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to list kernel modules. + risk_objects: + - field: dest + type: system + score: 15 + threat_objects: [] tags: - analytic_story: - - Compromised Linux Host - - XorDDos - - Linux Rootkit - asset_type: Endpoint - mitre_attack_id: - - T1082 - - T1014 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Linux Host + - XorDDos + - Linux Rootkit + asset_type: Endpoint + mitre_attack_id: + - T1082 + - T1014 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1082/linux_auditd_lsmod_new/linux_auditd_new_lsmod.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1082/linux_auditd_lsmod_new/linux_auditd_new_lsmod.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_kernel_module_using_rmmod_utility.yml b/detections/endpoint/linux_auditd_kernel_module_using_rmmod_utility.yml index 5b972a1c54..dc3dd008f4 100644 --- a/detections/endpoint/linux_auditd_kernel_module_using_rmmod_utility.yml +++ b/detections/endpoint/linux_auditd_kernel_module_using_rmmod_utility.yml @@ -1,80 +1,60 @@ name: Linux Auditd Kernel Module Using Rmmod Utility id: 31810b7a-0abe-42be-a210-0dec8106afee -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects suspicious use of the `rmmod` utility - for kernel module removal, which may indicate an attacker attempt to unload critical - or security-related kernel modules. The `rmmod` command is used to remove modules - from the Linux kernel, and unauthorized use can be a tactic to disable security - features, conceal malicious activities, or disrupt system operations. By monitoring - for unusual or unauthorized `rmmod` activity, this analytic helps identify potential - tampering with kernel modules, enabling security teams to take proactive measures - to protect system integrity and security. +description: The following analytic detects suspicious use of the `rmmod` utility for kernel module removal, which may indicate an attacker attempt to unload critical or security-related kernel modules. The `rmmod` command is used to remove modules from the Linux kernel, and unauthorized use can be a tactic to disable security features, conceal malicious activities, or disrupt system operations. By monitoring for unusual or unauthorized `rmmod` activity, this analytic helps identify potential tampering with kernel modules, enabling security teams to take proactive measures to protect system integrity and security. data_source: -- Linux Auditd Syscall -search: '`linux_auditd` type=SYSCALL comm=rmmod - | rename host as dest - | stats count min(_time) as firstTime max(_time) as lastTime - by comm exe syscall uid ppid pid success dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_auditd_kernel_module_using_rmmod_utility_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Linux Auditd Syscall +search: |- + `linux_auditd` type=SYSCALL comm=rmmod + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY comm exe syscall + uid ppid pid + success dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_kernel_module_using_rmmod_utility_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html + - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to remove a - Linux kernel module using the rmmod utility. - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: [] + message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to remove a Linux kernel module using the rmmod utility. + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1547.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1547.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/linux_auditd_rmmod_new/linux_auditd_new_rmmod.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/linux_auditd_rmmod_new/linux_auditd_new_rmmod.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_nopasswd_entry_in_sudoers_file.yml b/detections/endpoint/linux_auditd_nopasswd_entry_in_sudoers_file.yml index d60f18b488..3b8692330c 100644 --- a/detections/endpoint/linux_auditd_nopasswd_entry_in_sudoers_file.yml +++ b/detections/endpoint/linux_auditd_nopasswd_entry_in_sudoers_file.yml @@ -1,76 +1,60 @@ name: Linux Auditd Nopasswd Entry In Sudoers File id: 651df959-ad17-4b73-a323-90cb96d5fa1b -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the addition of NOPASSWD entries to the - /etc/sudoers file on Linux systems. It leverages Linux Auditd data to identify command - lines containing "NOPASSWD:". This activity is significant because it allows users - to execute commands with elevated privileges without requiring a password, which - can be exploited by adversaries to maintain persistent, privileged access. If confirmed - malicious, this could lead to unauthorized privilege escalation, persistent access, - and potential compromise of sensitive data and system integrity. +description: The following analytic detects the addition of NOPASSWD entries to the /etc/sudoers file on Linux systems. It leverages Linux Auditd data to identify command lines containing "NOPASSWD:". This activity is significant because it allows users to execute commands with elevated privileges without requiring a password, which can be exploited by adversaries to maintain persistent, privileged access. If confirmed malicious, this could lead to unauthorized privilege escalation, persistent access, and potential compromise of sensitive data and system integrity. data_source: -- Linux Auditd Proctitle -search: '`linux_auditd` proctitle = "*NOPASSWD*" | rename host as dest | stats count - min(_time) as firstTime max(_time) as lastTime by proctitle dest | `security_content_ctime(firstTime)`| - `security_content_ctime(lastTime)` | `linux_auditd_nopasswd_entry_in_sudoers_file_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Linux Auditd Proctitle +search: |- + `linux_auditd` proctitle = "*NOPASSWD*" + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY proctitle dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_nopasswd_entry_in_sudoers_file_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://askubuntu.com/questions/334318/sudoers-file-enable-nopasswd-for-user-all-commands -- https://help.ubuntu.com/community/Sudoers + - https://askubuntu.com/questions/334318/sudoers-file-enable-nopasswd-for-user-all-commands + - https://help.ubuntu.com/community/Sudoers drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$proctitle$] event occurred on host - [$dest$] to add NOPASSWD entry - in sudoers file. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A [$proctitle$] event occurred on host - [$dest$] to add NOPASSWD entry in sudoers file. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Linux Persistence Techniques - - Compromised Linux Host - - China-Nexus Threat Activity - - Salt Typhoon - - Linux Privilege Escalation - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Persistence Techniques + - Compromised Linux Host + - China-Nexus Threat Activity + - Salt Typhoon + - Linux Privilege Escalation + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/linux_auditd_nopasswd/linux_auditd_nopasswd2.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/linux_auditd_nopasswd/linux_auditd_nopasswd2.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_osquery_service_stop.yml b/detections/endpoint/linux_auditd_osquery_service_stop.yml index 89beb358bc..2ca137cbef 100644 --- a/detections/endpoint/linux_auditd_osquery_service_stop.yml +++ b/detections/endpoint/linux_auditd_osquery_service_stop.yml @@ -1,77 +1,59 @@ name: Linux Auditd Osquery Service Stop id: 0c320fea-6e87-4b99-a884-74d09d4b655d -version: 6 -date: '2025-06-10' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects suspicious stopping of the `osquery` service, - which may indicate an attempt to disable monitoring and evade detection. `Osquery` - is a powerful tool used for querying system information and detecting anomalies, - and stopping its service can be a sign that an attacker is trying to disrupt security - monitoring or hide malicious activities. By monitoring for unusual or unauthorized - stops of the `osquery` service, this analytic helps identify potential efforts to - bypass security controls, enabling security teams to investigate and respond to - possible threats effectively. +description: The following analytic detects suspicious stopping of the `osquery` service, which may indicate an attempt to disable monitoring and evade detection. `Osquery` is a powerful tool used for querying system information and detecting anomalies, and stopping its service can be a sign that an attacker is trying to disrupt security monitoring or hide malicious activities. By monitoring for unusual or unauthorized stops of the `osquery` service, this analytic helps identify potential efforts to bypass security controls, enabling security teams to investigate and respond to possible threats effectively. data_source: -- Linux Auditd Service Stop -search: '`linux_auditd` type=SERVICE_STOP unit IN ("osqueryd") | rename host as dest - | stats count min(_time) as firstTime max(_time) as lastTime by type pid comm - exe unit dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| - `linux_auditd_osquery_service_stop_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Linux Auditd Service Stop +search: |- + `linux_auditd` type=SERVICE_STOP unit IN ("osqueryd") + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY type pid comm + exe unit dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_osquery_service_stop_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html + - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A service event - [$type$] event occurred on host - [$dest$] to stop the - osquery service. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A service event - [$type$] event occurred on host - [$dest$] to stop the osquery service. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1489 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1489 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1489/linux_auditd_osquerd_service_stop/linux_auditd_osquerd_service_stop.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1489/linux_auditd_osquerd_service_stop/linux_auditd_osquerd_service_stop.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_possible_access_or_modification_of_sshd_config_file.yml b/detections/endpoint/linux_auditd_possible_access_or_modification_of_sshd_config_file.yml index 48ae6aad34..b729f0725d 100644 --- a/detections/endpoint/linux_auditd_possible_access_or_modification_of_sshd_config_file.yml +++ b/detections/endpoint/linux_auditd_possible_access_or_modification_of_sshd_config_file.yml @@ -6,106 +6,100 @@ author: Teoderick Contreras, Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - The following analytic detects access, deletion or modification of the ssh_config file on Linux systems. - It leverages data from Linux Auditd, focusing on events of type PATH with a nametype of ("NORMAL", "CREATE", "DELETE"). - This activity could be significant because unauthorized changes to ssh_config can allow threat actors to redirect port connections or use unauthorized keys, potentially compromising the system. - Correlate this with related EXECVE or PROCTITLE events to identify the process or user responsible for the access or modification. - If confirmed malicious, this could lead to unauthorized access, privilege escalation, or persistent backdoor access, posing a severe security risk. + The following analytic detects access, deletion or modification of the ssh_config file on Linux systems. + It leverages data from Linux Auditd, focusing on events of type PATH with a nametype of ("NORMAL", "CREATE", "DELETE"). + This activity could be significant because unauthorized changes to ssh_config can allow threat actors to redirect port connections or use unauthorized keys, potentially compromising the system. + Correlate this with related EXECVE or PROCTITLE events to identify the process or user responsible for the access or modification. + If confirmed malicious, this could lead to unauthorized access, privilege escalation, or persistent backdoor access, posing a severe security risk. data_source: - - Linux Auditd Path - - Linux Auditd Cwd + - Linux Auditd Path + - Linux Auditd Cwd search: | - `linux_auditd` - ( - (type=PATH nametype IN ("NORMAL", "CREATE", "DELETE")) - OR - type=CWD - ) - | rex "msg=audit\([^)]*:(?\d+)\)" + `linux_auditd` + ( + (type=PATH nametype IN ("NORMAL", "CREATE", "DELETE")) + OR + type=CWD + ) + | rex "msg=audit\([^)]*:(?\d+)\)" - | stats - values(type) as types - values(name) as names - values(nametype) as nametype - values(cwd) as cwd_list - values(_time) as event_times - by audit_id, host + | stats + values(type) as types + values(name) as names + values(nametype) as nametype + values(cwd) as cwd_list + values(_time) as event_times + by audit_id, host - | eval current_working_directory = coalesce(mvindex(cwd_list, 0), "N/A") - | eval candidate_paths = mvmap(names, if(match(names, "^/"), names, current_working_directory + "/" + names)) - | eval matched_paths = mvfilter(match(candidate_paths, "/etc/ssh/ssh_config.*")) - | eval match_count = mvcount(matched_paths) - | eval reconstructed_path = mvindex(matched_paths, 0) - | eval e_time = mvindex(event_times, 0) - | where match_count > 0 - | rename host as dest + | eval current_working_directory = coalesce(mvindex(cwd_list, 0), "N/A") + | eval candidate_paths = mvmap(names, if(match(names, "^/"), names, current_working_directory + "/" + names)) + | eval matched_paths = mvfilter(match(candidate_paths, "/etc/ssh/ssh_config.*")) + | eval match_count = mvcount(matched_paths) + | eval reconstructed_path = mvindex(matched_paths, 0) + | eval e_time = mvindex(event_times, 0) + | where match_count > 0 + | rename host as dest - | stats count min(e_time) as firstTime max(e_time) as lastTime - values(nametype) as nametype - by current_working_directory - reconstructed_path - match_count - dest - audit_id + | stats count min(e_time) as firstTime max(e_time) as lastTime + values(nametype) as nametype + by current_working_directory + reconstructed_path + match_count + dest + audit_id - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_auditd_possible_access_or_modification_of_sshd_config_file_filter` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_possible_access_or_modification_of_sshd_config_file_filter` how_to_implement: | - To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling and make sure the type=CWD record type is activate in your auditd configuration. - This approach enables effective monitoring and detection of linux endpoints where auditd is deployed. + To implement this detection, the process begins by ingesting auditd + data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line + executions and process details on Unix/Linux systems. These logs should be ingested + and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), + which is essential for correctly parsing and categorizing the data. The next step + involves normalizing the field names to match the field names set by the Splunk + Common Information Model (CIM) to ensure consistency across different data sources + and enhance the efficiency of data modeling and make sure the type=CWD record type is activate in your auditd configuration. + This approach enables effective monitoring and detection of linux endpoints where auditd is deployed. known_false_positives: | - Administrator or network operator can use this commandline for automation purposes. - Please update the filter macros to remove false positives. + Administrator or network operator can use this commandline for automation purposes. + Please update the filter macros to remove false positives. references: - - https://www.hackingarticles.in/ssh-penetration-testing-port-22/ - - https://attack.mitre.org/techniques/T1098/004/ + - https://www.hackingarticles.in/ssh-penetration-testing-port-22/ + - https://attack.mitre.org/techniques/T1098/004/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $reconstructed_path$ has been accessed with type $nametype$ on host - [$dest$] - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: $reconstructed_path$ has been accessed with type $nametype$ on host - [$dest$] + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1098.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1098.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.004/auditd_path_ssh_config/path_ssh_config.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.004/auditd_path_ssh_config/path_ssh_config.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_possible_access_to_credential_files.yml b/detections/endpoint/linux_auditd_possible_access_to_credential_files.yml index 4a813d80f0..1b7063f026 100644 --- a/detections/endpoint/linux_auditd_possible_access_to_credential_files.yml +++ b/detections/endpoint/linux_auditd_possible_access_to_credential_files.yml @@ -1,77 +1,60 @@ name: Linux Auditd Possible Access To Credential Files id: 0419cb7a-57ea-467b-974f-77c303dfe2a3 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects attempts to access or dump the contents - of /etc/passwd and /etc/shadow files on Linux systems. It leverages data from Linux - Auditd, focusing on processes like 'cat', 'nano', 'vim', and 'vi' accessing these - files. This activity is significant as it may indicate credential dumping, a technique - used by adversaries to gain persistence or escalate privileges. If confirmed malicious, - privileges. If confirmed malicious, attackers could obtain hashed passwords for - offline cracking, leading to unauthorized access and potential system compromise. +description: The following analytic detects attempts to access or dump the contents of /etc/passwd and /etc/shadow files on Linux systems. It leverages data from Linux Auditd, focusing on processes like 'cat', 'nano', 'vim', and 'vi' accessing these files. This activity is significant as it may indicate credential dumping, a technique used by adversaries to gain persistence or escalate privileges. If confirmed malicious, privileges. If confirmed malicious, attackers could obtain hashed passwords for offline cracking, leading to unauthorized access and potential system compromise. data_source: -- Linux Auditd Proctitle -search: '`linux_auditd` proctitle IN ("*shadow*", "*passwd*") AND proctitle IN ("*cat - *", "*nano *", "*vim *", "*vi *") | rename host as dest | stats count min(_time) - as firstTime max(_time) as lastTime by proctitle dest | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `linux_auditd_possible_access_to_credential_files_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Linux Auditd Proctitle +search: |- + `linux_auditd` proctitle IN ("*shadow*", "*passwd*") AND proctitle IN ("*cat *", "*nano *", "*vim *", "*vi *") + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY proctitle dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_possible_access_to_credential_files_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://askubuntu.com/questions/445361/what-is-difference-between-etc-shadow-and-etc-passwd -- https://attack.mitre.org/techniques/T1003/008/ + - https://askubuntu.com/questions/445361/what-is-difference-between-etc-shadow-and-etc-passwd + - https://attack.mitre.org/techniques/T1003/008/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$proctitle$] event occurred on host - [$dest$] to access or dump the - contents of /etc/passwd and /etc/shadow files. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A [$proctitle$] event occurred on host - [$dest$] to access or dump the contents of /etc/passwd and /etc/shadow files. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Linux Persistence Techniques - - Compromised Linux Host - - China-Nexus Threat Activity - - Salt Typhoon - - Linux Privilege Escalation - asset_type: Endpoint - mitre_attack_id: - - T1003.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Persistence Techniques + - Compromised Linux Host + - China-Nexus Threat Activity + - Salt Typhoon + - Linux Privilege Escalation + asset_type: Endpoint + mitre_attack_id: + - T1003.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.008/linux_auditd_access_credential/auditd_proctitle_access_cred.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.008/linux_auditd_access_credential/auditd_proctitle_access_cred.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_possible_access_to_sudoers_file.yml b/detections/endpoint/linux_auditd_possible_access_to_sudoers_file.yml index aca9724538..fdc331489b 100644 --- a/detections/endpoint/linux_auditd_possible_access_to_sudoers_file.yml +++ b/detections/endpoint/linux_auditd_possible_access_to_sudoers_file.yml @@ -6,102 +6,97 @@ author: Teoderick Contreras, Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - The following analytic detects potential access or modification of the /etc/sudoers file on a Linux system. - It leverages data from Linux Auditd, focusing on events of type PATH or CWD. - This activity could be significant because the sudoers file controls user permissions for executing commands with elevated privileges. - Correlate this with related EXECVE or PROCTITLE events to identify the process or user responsible for the access or modification. - If confirmed malicious, an attacker could gain persistence or escalate privileges, compromising the security of the targeted host. + The following analytic detects potential access or modification of the /etc/sudoers file on a Linux system. + It leverages data from Linux Auditd, focusing on events of type PATH or CWD. + This activity could be significant because the sudoers file controls user permissions for executing commands with elevated privileges. + Correlate this with related EXECVE or PROCTITLE events to identify the process or user responsible for the access or modification. + If confirmed malicious, an attacker could gain persistence or escalate privileges, compromising the security of the targeted host. data_source: - - Linux Auditd Path - - Linux Auditd Cwd + - Linux Auditd Path + - Linux Auditd Cwd search: | - `linux_auditd` - (type=PATH OR type=CWD) - | rex "msg=audit\([^)]*:(?\d+)\)" + `linux_auditd` + (type=PATH OR type=CWD) + | rex "msg=audit\([^)]*:(?\d+)\)" - | stats - values(type) as types - values(name) as names - values(nametype) as nametype - values(cwd) as cwd_list - values(_time) as event_times - by audit_id, host + | stats + values(type) as types + values(name) as names + values(nametype) as nametype + values(cwd) as cwd_list + values(_time) as event_times + by audit_id, host - | eval current_working_directory = coalesce(mvindex(cwd_list, 0), "N/A") - | eval candidate_paths = mvmap(names, if(match(names, "^/"), names, current_working_directory + "/" + names)) - | eval matched_paths = mvfilter(match(candidate_paths, "/etc/sudoers.*")) - | eval match_count = mvcount(matched_paths) - | eval reconstructed_path = mvindex(matched_paths, 0) - | eval e_time = mvindex(event_times, 0) - | where match_count > 0 - | rename host as dest + | eval current_working_directory = coalesce(mvindex(cwd_list, 0), "N/A") + | eval candidate_paths = mvmap(names, if(match(names, "^/"), names, current_working_directory + "/" + names)) + | eval matched_paths = mvfilter(match(candidate_paths, "/etc/sudoers.*")) + | eval match_count = mvcount(matched_paths) + | eval reconstructed_path = mvindex(matched_paths, 0) + | eval e_time = mvindex(event_times, 0) + | where match_count > 0 + | rename host as dest - | stats count min(e_time) as firstTime max(e_time) as lastTime - values(nametype) as nametype - by current_working_directory - reconstructed_path - match_count - dest - audit_id + | stats count min(e_time) as firstTime max(e_time) as lastTime + values(nametype) as nametype + by current_working_directory + reconstructed_path + match_count + dest + audit_id - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_auditd_possible_access_to_sudoers_file_filter` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_possible_access_to_sudoers_file_filter` how_to_implement: | - To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling and make sure the type=CWD record type is activate in your auditd configuration. - This approach enables effective monitoring and detection of linux endpoints where auditd is deployed. + To implement this detection, the process begins by ingesting auditd + data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line + executions and process details on Unix/Linux systems. These logs should be ingested + and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), + which is essential for correctly parsing and categorizing the data. The next step + involves normalizing the field names to match the field names set by the Splunk + Common Information Model (CIM) to ensure consistency across different data sources + and enhance the efficiency of data modeling and make sure the type=CWD record type is activate in your auditd configuration. + This approach enables effective monitoring and detection of linux endpoints where auditd is deployed. known_false_positives: | - Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + Administrator or network operator can execute this command. + Please update the filter macros to remove false positives. references: - - https://attack.mitre.org/techniques/T1548/003/ - - https://web.archive.org/web/20210708035426/https://www.cobaltstrike.com/downloads/csmanual43.pdf + - https://attack.mitre.org/techniques/T1548/003/ + - https://web.archive.org/web/20210708035426/https://www.cobaltstrike.com/downloads/csmanual43.pdf drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $reconstructed_path$ has been accessed for potential modification or deletion on host - [$dest$] - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: $reconstructed_path$ has been accessed for potential modification or deletion on host - [$dest$] + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Linux Persistence Techniques - - Compromised Linux Host - - China-Nexus Threat Activity - - Salt Typhoon - - Linux Privilege Escalation - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Persistence Techniques + - Compromised Linux Host + - China-Nexus Threat Activity + - Salt Typhoon + - Linux Privilege Escalation + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/auditd_path_sudoers/path_sudoers.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/auditd_path_sudoers/path_sudoers.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_possible_append_cronjob_entry_on_existing_cronjob_file.yml b/detections/endpoint/linux_auditd_possible_append_cronjob_entry_on_existing_cronjob_file.yml index 60267c4b16..0e380b7455 100644 --- a/detections/endpoint/linux_auditd_possible_append_cronjob_entry_on_existing_cronjob_file.yml +++ b/detections/endpoint/linux_auditd_possible_append_cronjob_entry_on_existing_cronjob_file.yml @@ -6,83 +6,82 @@ author: Teoderick Contreras, Splunk status: production type: Hunting description: | - The following analytic detects potential tampering with cronjob files on a Linux system. - It leverages logs from Linux Auditd, focusing on events of type PATH or CWD. - This activity could be significant because adversaries often use it for persistence or privilege escalation. - Correlate this with related EXECVE or PROCTITLE events to identify the process or user responsible for the access or modification. - If confirmed malicious, this could allow attackers to execute unauthorized code automatically, leading to system compromises and unauthorized data access, thereby impacting business operations and data integrity. + The following analytic detects potential tampering with cronjob files on a Linux system. + It leverages logs from Linux Auditd, focusing on events of type PATH or CWD. + This activity could be significant because adversaries often use it for persistence or privilege escalation. + Correlate this with related EXECVE or PROCTITLE events to identify the process or user responsible for the access or modification. + If confirmed malicious, this could allow attackers to execute unauthorized code automatically, leading to system compromises and unauthorized data access, thereby impacting business operations and data integrity. data_source: - - Linux Auditd Path - - Linux Auditd Cwd + - Linux Auditd Path + - Linux Auditd Cwd search: | - `linux_auditd` (type=PATH OR type=CWD) - | rex "msg=audit\([^)]*:(?\d+)\)" + `linux_auditd` (type=PATH OR type=CWD) + | rex "msg=audit\([^)]*:(?\d+)\)" - | stats - values(type) as types - values(name) as names - values(nametype) as nametype - values(cwd) as cwd_list - values(_time) as event_times - by audit_id, host + | stats + values(type) as types + values(name) as names + values(nametype) as nametype + values(cwd) as cwd_list + values(_time) as event_times + by audit_id, host - | eval current_working_directory = coalesce(mvindex(cwd_list, 0), "N/A") - | eval candidate_paths = mvmap(names, if(match(names, "^/"), names, current_working_directory + "/" + names)) - | eval matched_paths = mvfilter(match(candidate_paths, "/etc/cron.*|.*/cron/.*|/etc/anacrontab.*")) - | eval match_count = mvcount(matched_paths) - | eval reconstructed_path = mvindex(matched_paths, 0) - | eval e_time = mvindex(event_times, 0) - | where match_count > 0 - | rename host as dest + | eval current_working_directory = coalesce(mvindex(cwd_list, 0), "N/A") + | eval candidate_paths = mvmap(names, if(match(names, "^/"), names, current_working_directory + "/" + names)) + | eval matched_paths = mvfilter(match(candidate_paths, "/etc/cron.*|.*/cron/.*|/etc/anacrontab.*")) + | eval match_count = mvcount(matched_paths) + | eval reconstructed_path = mvindex(matched_paths, 0) + | eval e_time = mvindex(event_times, 0) + | where match_count > 0 + | rename host as dest - | stats count min(e_time) as firstTime max(e_time) as lastTime - values(nametype) as nametype - by current_working_directory - reconstructed_path - match_count - dest - audit_id + | stats count min(e_time) as firstTime max(e_time) as lastTime + values(nametype) as nametype + by current_working_directory + reconstructed_path + match_count + dest + audit_id - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_auditd_possible_append_cronjob_entry_on_existing_cronjob_file_filter` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_possible_append_cronjob_entry_on_existing_cronjob_file_filter` how_to_implement: | - To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling and make sure the type=CWD record type is activate in your auditd configuration. - This approach enables effective monitoring and detection of linux endpoints where auditd is deployed. + To implement this detection, the process begins by ingesting auditd + data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line + executions and process details on Unix/Linux systems. These logs should be ingested + and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), + which is essential for correctly parsing and categorizing the data. The next step + involves normalizing the field names to match the field names set by the Splunk + Common Information Model (CIM) to ensure consistency across different data sources + and enhance the efficiency of data modeling and make sure the type=CWD record type is activate in your auditd configuration. + This approach enables effective monitoring and detection of linux endpoints where auditd is deployed. known_false_positives: | - False positives may arise from legitimate actions by administrators or network operators who may use these commands for automation purposes. - Therefore, it's recommended to adjust filter macros to eliminate such false positives. + False positives may arise from legitimate actions by administrators or network operators who may use these commands for automation purposes. + Therefore, it's recommended to adjust filter macros to eliminate such false positives. references: - - https://attack.mitre.org/techniques/T1053/003/ - - https://blog.aquasec.com/threat-alert-kinsing-malware-container-vulnerability - - https://www.intezer.com/blog/research/kaiji-new-chinese-linux-malware-turning-to-golang/ + - https://attack.mitre.org/techniques/T1053/003/ + - https://blog.aquasec.com/threat-alert-kinsing-malware-container-vulnerability + - https://www.intezer.com/blog/research/kaiji-new-chinese-linux-malware-turning-to-golang/ tags: - analytic_story: - - XorDDos - - Linux Living Off The Land - - Compromised Linux Host - - Linux Privilege Escalation - - Scheduled Tasks - - Linux Persistence Techniques - asset_type: Endpoint - mitre_attack_id: - - T1053.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - XorDDos + - Linux Living Off The Land + - Compromised Linux Host + - Linux Privilege Escalation + - Scheduled Tasks + - Linux Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1053.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/auditd_path_cron/path_cron.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/auditd_path_cron/path_cron.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_preload_hijack_library_calls.yml b/detections/endpoint/linux_auditd_preload_hijack_library_calls.yml index 0ac9c9bc86..8f8ad5ea56 100644 --- a/detections/endpoint/linux_auditd_preload_hijack_library_calls.yml +++ b/detections/endpoint/linux_auditd_preload_hijack_library_calls.yml @@ -1,77 +1,61 @@ name: Linux Auditd Preload Hijack Library Calls id: 35c50572-a70b-452f-afa9-bebdf3c3ce36 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the use of the LD_PRELOAD environment - variable to hijack or hook library functions on a Linux platform. It leverages data - from Linux Auditd, focusing on process execution logs that include command-line - details. This activity is significant because adversaries, malware authors, and - red teamers commonly use this technique to gain elevated privileges and establish - persistence on a compromised machine. If confirmed malicious, this behavior could - allow attackers to execute arbitrary code, escalate privileges, and maintain long-term - access to the system. +description: The following analytic detects the use of the LD_PRELOAD environment variable to hijack or hook library functions on a Linux platform. It leverages data from Linux Auditd, focusing on process execution logs that include command-line details. This activity is significant because adversaries, malware authors, and red teamers commonly use this technique to gain elevated privileges and establish persistence on a compromised machine. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, and maintain long-term access to the system. data_source: -- Linux Auditd Execve -search: '`linux_auditd` execve_command = "*LD_PRELOAD*" | rename host as dest | rename - comm as process_name | rename exe as process | stats count min(_time) as firstTime - max(_time) as lastTime by argc execve_command dest | `security_content_ctime(firstTime)`| - `security_content_ctime(lastTime)` | `linux_auditd_preload_hijack_library_calls_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures - command-line executions and process details on Unix/Linux systems. These logs should - be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Linux Auditd Execve +search: |- + `linux_auditd` execve_command = "*LD_PRELOAD*" + | rename host as dest + | rename comm as process_name + | rename exe as process + | stats count min(_time) as firstTime max(_time) as lastTime + BY argc execve_command dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_preload_hijack_library_calls_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://compilepeace.medium.com/memory-malware-part-0x2-writing-userland-rootkits-via-ld-preload-30121c8343d5 + - https://compilepeace.medium.com/memory-malware-part-0x2-writing-userland-rootkits-via-ld-preload-30121c8343d5 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$execve_command$] event occurred on host - [$dest$] to hijack or hook - library functions using the LD_PRELOAD environment variable. - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: [] + message: A [$execve_command$] event occurred on host - [$dest$] to hijack or hook library functions using the LD_PRELOAD environment variable. + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: [] tags: - analytic_story: - - Linux Persistence Techniques - - Compromised Linux Host - - China-Nexus Threat Activity - - Salt Typhoon - - Linux Privilege Escalation - asset_type: Endpoint - mitre_attack_id: - - T1574.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Persistence Techniques + - Compromised Linux Host + - China-Nexus Threat Activity + - Salt Typhoon + - Linux Privilege Escalation + asset_type: Endpoint + mitre_attack_id: + - T1574.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.006/linux_auditd_ldpreload/auditd_execve_ldpreload.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.006/linux_auditd_ldpreload/auditd_execve_ldpreload.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_preload_hijack_via_preload_file.yml b/detections/endpoint/linux_auditd_preload_hijack_via_preload_file.yml index f372c38722..4becb3cd2f 100644 --- a/detections/endpoint/linux_auditd_preload_hijack_via_preload_file.yml +++ b/detections/endpoint/linux_auditd_preload_hijack_via_preload_file.yml @@ -6,103 +6,95 @@ author: Teoderick Contreras, Splunk status: production type: TTP description: | - The following analytic detects suspicious preload hijacking via the `preload` file, which may indicate an attacker's attempt to intercept or manipulate library loading processes. - The `preload` file can be used to force the loading of specific libraries before others, potentially allowing malicious code to execute or alter application behavior. - By monitoring for unusual or unauthorized modifications to the `preload` file, this analytic helps identify attempts to hijack preload mechanisms, enabling security teams to investigate and address potential threats to system integrity and security. - Correlate this with related EXECVE or PROCTITLE events to identify the process or user responsible for the access or modification. + The following analytic detects suspicious preload hijacking via the `preload` file, which may indicate an attacker's attempt to intercept or manipulate library loading processes. + The `preload` file can be used to force the loading of specific libraries before others, potentially allowing malicious code to execute or alter application behavior. + By monitoring for unusual or unauthorized modifications to the `preload` file, this analytic helps identify attempts to hijack preload mechanisms, enabling security teams to investigate and address potential threats to system integrity and security. + Correlate this with related EXECVE or PROCTITLE events to identify the process or user responsible for the access or modification. data_source: - - Linux Auditd Path - - Linux Auditd Cwd + - Linux Auditd Path + - Linux Auditd Cwd search: | - `linux_auditd` - (type=PATH OR type=CWD) - | rex "msg=audit\([^)]*:(?\d+)\)" + `linux_auditd` + (type=PATH OR type=CWD) + | rex "msg=audit\([^)]*:(?\d+)\)" - | stats - values(type) as types - values(name) as names - values(nametype) as nametype - values(cwd) as cwd_list - values(_time) as event_times - by audit_id, host + | stats + values(type) as types + values(name) as names + values(nametype) as nametype + values(cwd) as cwd_list + values(_time) as event_times + by audit_id, host - | eval current_working_directory = coalesce(mvindex(cwd_list, 0), "N/A") - | eval candidate_paths = mvmap(names, if(match(names, "^/"), names, current_working_directory + "/" + names)) - | eval matched_paths = mvfilter(match(candidate_paths, "/etc/ld.so.preload.*")) - | eval match_count = mvcount(matched_paths) - | eval reconstructed_path = mvindex(matched_paths, 0) - | eval e_time = mvindex(event_times, 0) - | where match_count > 0 - | rename host as dest + | eval current_working_directory = coalesce(mvindex(cwd_list, 0), "N/A") + | eval candidate_paths = mvmap(names, if(match(names, "^/"), names, current_working_directory + "/" + names)) + | eval matched_paths = mvfilter(match(candidate_paths, "/etc/ld.so.preload.*")) + | eval match_count = mvcount(matched_paths) + | eval reconstructed_path = mvindex(matched_paths, 0) + | eval e_time = mvindex(event_times, 0) + | where match_count > 0 + | rename host as dest - | stats count min(e_time) as firstTime max(e_time) as lastTime - values(nametype) as nametype - by current_working_directory - reconstructed_path - match_count - dest - audit_id + | stats count min(e_time) as firstTime max(e_time) as lastTime + values(nametype) as nametype + by current_working_directory + reconstructed_path + match_count + dest + audit_id - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_auditd_preload_hijack_via_preload_file_filter` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_preload_hijack_via_preload_file_filter` how_to_implement: | - To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling and make sure the type=CWD record type is activate in your auditd configuration. - This approach enables effective monitoring and detection of linux endpoints where auditd is deployed. + To implement this detection, the process begins by ingesting auditd + data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line + executions and process details on Unix/Linux systems. These logs should be ingested + and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), + which is essential for correctly parsing and categorizing the data. The next step + involves normalizing the field names to match the field names set by the Splunk + Common Information Model (CIM) to ensure consistency across different data sources + and enhance the efficiency of data modeling and make sure the type=CWD record type is activate in your auditd configuration. + This approach enables effective monitoring and detection of linux endpoints where auditd is deployed. known_false_positives: | - Administrator or network operator can use this application for automation purposes. - Please update the filter macros to remove false positives. + Administrator or network operator can use this application for automation purposes. + Please update the filter macros to remove false positives. references: - - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html + - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A [$nametype$] event has occurred on host - [$dest$] to modify the preload - file. - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: [] + message: A [$nametype$] event has occurred on host - [$dest$] to modify the preload file. + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: [] tags: - analytic_story: - - VoidLink Cloud-Native Linux Malware - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1574.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - VoidLink Cloud-Native Linux Malware + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1574.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.006/auditd_path_preload_file/path_preload.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.006/auditd_path_preload_file/path_preload.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_private_keys_and_certificate_enumeration.yml b/detections/endpoint/linux_auditd_private_keys_and_certificate_enumeration.yml index 092e5be2dc..06a2d7e1fe 100644 --- a/detections/endpoint/linux_auditd_private_keys_and_certificate_enumeration.yml +++ b/detections/endpoint/linux_auditd_private_keys_and_certificate_enumeration.yml @@ -1,81 +1,61 @@ name: Linux Auditd Private Keys and Certificate Enumeration id: 892eb674-3344-4143-8e52-4775b1daf3f1 -version: 4 -date: '2025-05-02' +version: 5 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects suspicious attempts to find private keys, - which may indicate an attacker's effort to access sensitive cryptographic information. - Private keys are crucial for securing encrypted communications and data, and unauthorized - access to them can lead to severe security breaches, including data decryption and - identity theft. By monitoring for unusual or unauthorized searches for private keys, - this analytic helps identify potential threats to cryptographic security, enabling - security teams to take swift action to protect the integrity and confidentiality - of encrypted information. +description: The following analytic detects suspicious attempts to find private keys, which may indicate an attacker's effort to access sensitive cryptographic information. Private keys are crucial for securing encrypted communications and data, and unauthorized access to them can lead to severe security breaches, including data decryption and identity theft. By monitoring for unusual or unauthorized searches for private keys, this analytic helps identify potential threats to cryptographic security, enabling security teams to take swift action to protect the integrity and confidentiality of encrypted information. data_source: -- Linux Auditd Execve -search: '`linux_auditd` execve_command IN ("*find*", "*grep*") - AND execve_command IN ("*.pem*", "*.cer*", "*.crt*", "*.pgp*", "*.key*", "*.gpg*", "*.ppk*", "*.p12*", "*.pfx*", "*.p7b*") - | rename host as dest - | rename comm as process_name - | rename exe as process - | stats count min(_time) as firstTime max(_time) as lastTime by argc execve_command dest - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_auditd_private_keys_and_certificate_enumeration_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures - command-line executions and process details on Unix/Linux systems. These logs should - be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Linux Auditd Execve +search: |- + `linux_auditd` execve_command IN ("*find*", "*grep*") AND execve_command IN ("*.pem*", "*.cer*", "*.crt*", "*.pgp*", "*.key*", "*.gpg*", "*.ppk*", "*.p12*", "*.pfx*", "*.p7b*") + | rename host as dest + | rename comm as process_name + | rename exe as process + | stats count min(_time) as firstTime max(_time) as lastTime + BY argc execve_command dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_private_keys_and_certificate_enumeration_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html -- https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS + - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html + - https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$execve_command$] event occurred on host - [$dest$] to find private keys. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A [$execve_command$] event occurred on host - [$dest$] to find private keys. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1552.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1552.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.004/linux_auditd_find_gpg/auditd_execve_find_gpg.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.004/linux_auditd_find_gpg/auditd_execve_find_gpg.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_service_restarted.yml b/detections/endpoint/linux_auditd_service_restarted.yml index db786ebd91..f91cf562dc 100644 --- a/detections/endpoint/linux_auditd_service_restarted.yml +++ b/detections/endpoint/linux_auditd_service_restarted.yml @@ -1,82 +1,62 @@ name: Linux Auditd Service Restarted id: 8eb3e858-18d3-44a4-a514-52cfa39f154a -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the restarting or re-enabling of services - on Linux systems using the `systemctl` or `service` commands. It leverages data - from Linux Auditd, focusing on process and command-line execution logs. This activity - is significant as adversaries may use it to maintain persistence or execute unauthorized - actions. If confirmed malicious, this behavior could lead to repeated execution - of malicious payloads, unauthorized access, or data destruction. Security analysts - should investigate these events to mitigate risks and prevent further compromise. +description: The following analytic detects the restarting or re-enabling of services on Linux systems using the `systemctl` or `service` commands. It leverages data from Linux Auditd, focusing on process and command-line execution logs. This activity is significant as adversaries may use it to maintain persistence or execute unauthorized actions. If confirmed malicious, this behavior could lead to repeated execution of malicious payloads, unauthorized access, or data destruction. Security analysts should investigate these events to mitigate risks and prevent further compromise. data_source: -- Linux Auditd Proctitle -search: '`linux_auditd` proctitle IN ("*systemctl *", "*service *") AND proctitle IN ("*restart*", "*reenable*", "*reload*") - | rename host as dest - | stats count min(_time) as firstTime max(_time) as lastTime by proctitle dest - | `security_content_ctime(firstTime)` - |`security_content_ctime(lastTime)` - | `linux_auditd_service_restarted_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can use this commandline - for automation purposes. Please update the filter macros to remove false positives. + - Linux Auditd Proctitle +search: |- + `linux_auditd` proctitle IN ("*systemctl *", "*service *") AND proctitle IN ("*restart*", "*reenable*", "*reload*") + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY proctitle dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_service_restarted_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can use this commandline for automation purposes. Please update the filter macros to remove false positives. references: -- https://attack.mitre.org/techniques/T1543/003/ + - https://attack.mitre.org/techniques/T1543/003/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$proctitle$] event occurred on host - [$dest$] to restart or re-enable - a service. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A [$proctitle$] event occurred on host - [$dest$] to restart or re-enable a service. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - AwfulShred - - Scheduled Tasks - - Linux Privilege Escalation - - Data Destruction - - Linux Persistence Techniques - - Linux Living Off The Land - - Gomir - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1053.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AwfulShred + - Scheduled Tasks + - Linux Privilege Escalation + - Data Destruction + - Linux Persistence Techniques + - Linux Living Off The Land + - Gomir + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1053.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.006/linux_services_restart/auditd_proctitle_service_restart.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.006/linux_services_restart/auditd_proctitle_service_restart.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_service_started.yml b/detections/endpoint/linux_auditd_service_started.yml index ab3ed5ba45..cdc61ca29b 100644 --- a/detections/endpoint/linux_auditd_service_started.yml +++ b/detections/endpoint/linux_auditd_service_started.yml @@ -1,78 +1,58 @@ name: Linux Auditd Service Started id: b5eed06d-5c97-4092-a3a1-fa4b7e77c71a -version: 7 -date: '2025-09-18' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the suspicious service started. This behavior - is critical for a SOC to monitor because it may indicate attempts to gain unauthorized - access or maintain control over a system. Such actions could be signs of malicious - activity. If confirmed, this could lead to serious consequences, including a compromised - system, unauthorized access to sensitive data, or even a wider breach affecting - the entire network. Detecting and responding to these signs early is essential to - prevent potential security incidents. +description: The following analytic detects the suspicious service started. This behavior is critical for a SOC to monitor because it may indicate attempts to gain unauthorized access or maintain control over a system. Such actions could be signs of malicious activity. If confirmed, this could lead to serious consequences, including a compromised system, unauthorized access to sensitive data, or even a wider breach affecting the entire network. Detecting and responding to these signs early is essential to prevent potential security incidents. data_source: -- Linux Auditd Proctitle -search: '`linux_auditd` proctitle IN ("*systemctl *", "*service *") AND proctitle IN ("* start*", "* enable*") - | rename host as dest - | stats count min(_time) as firstTime max(_time) as lastTime by proctitle dest - | `security_content_ctime(firstTime)` - |`security_content_ctime(lastTime)` - | `linux_auditd_service_started_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Linux Auditd Proctitle +search: |- + `linux_auditd` proctitle IN ("*systemctl *", "*service *") AND proctitle IN ("* start*", "* enable*") + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY proctitle dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_service_started_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html + - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$proctitle$] event occurred on host - [$dest$] to start or enable - a service. - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: [] + message: A [$proctitle$] event occurred on host - [$dest$] to start or enable a service. + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1569.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1569.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/linux_service_start/auditd_proctitle_service_start.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/linux_service_start/auditd_proctitle_service_start.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_setuid_using_chmod_utility.yml b/detections/endpoint/linux_auditd_setuid_using_chmod_utility.yml index fecb0288f8..f56fbc1ec5 100644 --- a/detections/endpoint/linux_auditd_setuid_using_chmod_utility.yml +++ b/detections/endpoint/linux_auditd_setuid_using_chmod_utility.yml @@ -1,78 +1,58 @@ name: Linux Auditd Setuid Using Chmod Utility id: 8230c407-1b47-4d95-ac2e-718bd6381386 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the chmod utility to - set the SUID or SGID bit on files, which can allow users to temporarily gain root - or group-level access. This detection leverages data from Linux Auditd, focusing - on process names and command-line arguments related to chmod. This activity is significant - as it can indicate an attempt to escalate privileges or maintain persistence on - a system. If confirmed malicious, an attacker could gain elevated access, potentially - compromising sensitive data or critical system functions. +description: The following analytic detects the execution of the chmod utility to set the SUID or SGID bit on files, which can allow users to temporarily gain root or group-level access. This detection leverages data from Linux Auditd, focusing on process names and command-line arguments related to chmod. This activity is significant as it can indicate an attempt to escalate privileges or maintain persistence on a system. If confirmed malicious, an attacker could gain elevated access, potentially compromising sensitive data or critical system functions. data_source: -- Linux Auditd Proctitle -search: '`linux_auditd` proctitle IN ("*chmod *") AND proctitle IN ("* u+s *", "* g+s *", "* 4777 *", "* 4577 *") - | rename host as dest - | stats count min(_time) as firstTime max(_time) as lastTime by proctitle dest - | `security_content_ctime(firstTime)` - |`security_content_ctime(lastTime)` - | `linux_auditd_setuid_using_chmod_utility_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Linux Auditd Proctitle +search: |- + `linux_auditd` proctitle IN ("*chmod *") AND proctitle IN ("* u+s *", "* g+s *", "* 4777 *", "* 4577 *") + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY proctitle dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_setuid_using_chmod_utility_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://www.hackingarticles.in/linux-privilege-escalation-using-capabilities/ + - https://www.hackingarticles.in/linux-privilege-escalation-using-capabilities/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$proctitle$] event occurred on host - [$dest$] to set the SUID or - SGID bit on files using the chmod utility. - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: [] + message: A [$proctitle$] event occurred on host - [$dest$] to set the SUID or SGID bit on files using the chmod utility. + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1548.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1548.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.001/linux_auditd_setuid/auditd_proctitle_setuid.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.001/linux_auditd_setuid/auditd_proctitle_setuid.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_setuid_using_setcap_utility.yml b/detections/endpoint/linux_auditd_setuid_using_setcap_utility.yml index 5f56cebd5a..6ad573ae15 100644 --- a/detections/endpoint/linux_auditd_setuid_using_setcap_utility.yml +++ b/detections/endpoint/linux_auditd_setuid_using_setcap_utility.yml @@ -1,78 +1,59 @@ name: Linux Auditd Setuid Using Setcap Utility id: 1474459a-302b-4255-8add-d82f96d14cd9 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of the 'setcap' utility - to enable the SUID bit on Linux systems. It leverages Linux Auditd data, focusing - on process names and command-line arguments that indicate the use of 'setcap' with - specific capabilities. This activity is significant because setting the SUID bit - allows a user to temporarily gain root access, posing a substantial security risk. - If confirmed malicious, an attacker could escalate privileges, execute arbitrary - commands with elevated permissions, and potentially compromise the entire system. +description: The following analytic detects the execution of the 'setcap' utility to enable the SUID bit on Linux systems. It leverages Linux Auditd data, focusing on process names and command-line arguments that indicate the use of 'setcap' with specific capabilities. This activity is significant because setting the SUID bit allows a user to temporarily gain root access, posing a substantial security risk. If confirmed malicious, an attacker could escalate privileges, execute arbitrary commands with elevated permissions, and potentially compromise the entire system. data_source: -- Linux Auditd Execve -search: '`linux_auditd` execve_command IN ("*setcap *") AND execve_command IN ("*cap_setuid+ep*", "*cap_setuid=ep*", "*cap_net_bind_service+p*", "*cap_net_raw+ep*", "*cap_dac_read_search+ep*") - | rename host as dest - | rename comm as process_name - | rename exe as process - | stats count min(_time) as firstTime max(_time) as lastTime by argc execve_command dest - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_auditd_setuid_using_setcap_utility_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures - command-line executions and process details on Unix/Linux systems. These logs should - be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Linux Auditd Execve +search: |- + `linux_auditd` execve_command IN ("*setcap *") AND execve_command IN ("*cap_setuid+ep*", "*cap_setuid=ep*", "*cap_net_bind_service+p*", "*cap_net_raw+ep*", "*cap_dac_read_search+ep*") + | rename host as dest + | rename comm as process_name + | rename exe as process + | stats count min(_time) as firstTime max(_time) as lastTime + BY argc execve_command dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_setuid_using_setcap_utility_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://www.hackingarticles.in/linux-privilege-escalation-using-capabilities/ + - https://www.hackingarticles.in/linux-privilege-escalation-using-capabilities/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$execve_command$] event occurred on host - [$dest$] to set the SUID or - SGID bit on files using the setcap utility. - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: [] + message: A [$execve_command$] event occurred on host - [$dest$] to set the SUID or SGID bit on files using the setcap utility. + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: [] tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1548.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1548.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.001/linux_auditd_setuid/auditd_execve_setcap.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.001/linux_auditd_setuid/auditd_execve_setcap.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_shred_overwrite_command.yml b/detections/endpoint/linux_auditd_shred_overwrite_command.yml index 7bf47dd05d..0d9d3ae0bf 100644 --- a/detections/endpoint/linux_auditd_shred_overwrite_command.yml +++ b/detections/endpoint/linux_auditd_shred_overwrite_command.yml @@ -1,81 +1,61 @@ name: Linux Auditd Shred Overwrite Command id: ce2bde4d-a1d4-4452-8c87-98440e5adfb3 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of the 'shred' command on - a Linux machine, which is used to overwrite files to make them unrecoverable. It - leverages data from Linux Auditd, focusing on process names and command-line arguments. - This activity is significant because the 'shred' command can be used in destructive - attacks, such as those seen in the Industroyer2 malware targeting energy facilities. - If confirmed malicious, this activity could lead to the permanent destruction of - critical files, severely impacting system integrity and data availability. +description: The following analytic detects the execution of the 'shred' command on a Linux machine, which is used to overwrite files to make them unrecoverable. It leverages data from Linux Auditd, focusing on process names and command-line arguments. This activity is significant because the 'shred' command can be used in destructive attacks, such as those seen in the Industroyer2 malware targeting energy facilities. If confirmed malicious, this activity could lead to the permanent destruction of critical files, severely impacting system integrity and data availability. data_source: -- Linux Auditd Proctitle -search: '`linux_auditd` proctitle IN ("*shred*") AND proctitle IN ("*-n*", "*-z*", "*-u*", "*-s*") - | rename host as dest - | stats count min(_time) as firstTime max(_time) as lastTime by proctitle dest - | `security_content_ctime(firstTime)` - |`security_content_ctime(lastTime)` - | `linux_auditd_shred_overwrite_command_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Linux Auditd Proctitle +search: |- + `linux_auditd` proctitle IN ("*shred*") AND proctitle IN ("*-n*", "*-z*", "*-u*", "*-s*") + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY proctitle dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_shred_overwrite_command_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ -- https://cert.gov.ua/article/39518 + - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ + - https://cert.gov.ua/article/39518 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$proctitle$] event occurred on host - [$dest$] to overwrite files - using the shred utility. - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: [] + message: A [$proctitle$] event occurred on host - [$dest$] to overwrite files using the shred utility. + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: [] tags: - analytic_story: - - AwfulShred - - Linux Privilege Escalation - - Data Destruction - - Linux Persistence Techniques - - Industroyer2 - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AwfulShred + - Linux Privilege Escalation + - Data Destruction + - Linux Persistence Techniques + - Industroyer2 + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/linux_auditd_shred/auditd_proctitle_shred.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/linux_auditd_shred/auditd_proctitle_shred.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_stop_services.yml b/detections/endpoint/linux_auditd_stop_services.yml index f62e4f74a7..94fcbe2c1f 100644 --- a/detections/endpoint/linux_auditd_stop_services.yml +++ b/detections/endpoint/linux_auditd_stop_services.yml @@ -1,67 +1,53 @@ name: Linux Auditd Stop Services id: 43bc9281-753b-4743-b4b7-60af84f085f3 -version: 6 -date: '2025-06-10' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects attempts to stop a service on Linux systems. - It leverages data from Linux Auditd. This activity is significant as adversaries - often stop or terminate security or critical services to disable defenses or disrupt - operations, as seen in malware like Industroyer2. If confirmed malicious, this could - lead to the disabling of security mechanisms, allowing attackers to persist, escalate - privileges, or deploy destructive payloads, severely impacting system integrity - and availability. +description: The following analytic detects attempts to stop a service on Linux systems. It leverages data from Linux Auditd. This activity is significant as adversaries often stop or terminate security or critical services to disable defenses or disrupt operations, as seen in malware like Industroyer2. If confirmed malicious, this could lead to the disabling of security mechanisms, allowing attackers to persist, escalate privileges, or deploy destructive payloads, severely impacting system integrity and availability. data_source: -- Linux Auditd Service Stop -search: '`linux_auditd` type=SERVICE_STOP | rename host as dest | stats count min(_time) - as firstTime max(_time) as lastTime by type pid comm exe dest | `security_content_ctime(firstTime)`| - `security_content_ctime(lastTime)`| `linux_auditd_stop_services_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures - command-line executions and process details on Unix/Linux systems. These logs should - be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Linux Auditd Service Stop +search: |- + `linux_auditd` type=SERVICE_STOP + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY type pid comm + exe dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_stop_services_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ -- https://cert.gov.ua/article/39518 + - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ + - https://cert.gov.ua/article/39518 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ tags: - analytic_story: - - Industroyer2 - - Data Destruction - - AwfulShred - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1489 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Industroyer2 + - Data Destruction + - AwfulShred + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1489 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1489/linux_auditd_service_stop/linux_auditd_service_stop.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1489/linux_auditd_service_stop/linux_auditd_service_stop.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_sudo_or_su_execution.yml b/detections/endpoint/linux_auditd_sudo_or_su_execution.yml index eb9efa676c..879fd7f777 100644 --- a/detections/endpoint/linux_auditd_sudo_or_su_execution.yml +++ b/detections/endpoint/linux_auditd_sudo_or_su_execution.yml @@ -1,77 +1,57 @@ name: Linux Auditd Sudo Or Su Execution id: 817a5c89-5b92-4818-a22d-aa35e1361afe -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the "sudo" or "su" command - on a Linux operating system. It leverages data from Linux Auditd, focusing on process - names and parent process names. This activity is significant because "sudo" and - "su" commands are commonly used by adversaries to elevate privileges, potentially - leading to unauthorized access or control over the system. If confirmed malicious, - this activity could allow attackers to execute commands with root privileges, leading - to severe security breaches, data exfiltration, or further system compromise. +description: The following analytic detects the execution of the "sudo" or "su" command on a Linux operating system. It leverages data from Linux Auditd, focusing on process names and parent process names. This activity is significant because "sudo" and "su" commands are commonly used by adversaries to elevate privileges, potentially leading to unauthorized access or control over the system. If confirmed malicious, this activity could allow attackers to execute commands with root privileges, leading to severe security breaches, data exfiltration, or further system compromise. data_source: -- Linux Auditd Proctitle -search: '`linux_auditd` proctitle IN ("*sudo *", "*su *") - | rename host as dest - | stats count min(_time) as firstTime max(_time) as lastTime by proctitle dest - | `security_content_ctime(firstTime)` - |`security_content_ctime(lastTime)` - | `linux_auditd_sudo_or_su_execution_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures - command-line executions and process details on Unix/Linux systems. These logs should - be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Linux Auditd Proctitle +search: |- + `linux_auditd` proctitle IN ("*sudo *", "*su *") + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY proctitle dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_sudo_or_su_execution_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://attack.mitre.org/techniques/T1548/003/ + - https://attack.mitre.org/techniques/T1548/003/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$proctitle$] event occurred on host - [$dest$] to execute the sudo - or su command. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A [$proctitle$] event occurred on host - [$dest$] to execute the sudo or su command. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/linux_auditd_sudo_su/auditd_proctitle_sudo.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/linux_auditd_sudo_su/auditd_proctitle_sudo.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_sysmon_service_stop.yml b/detections/endpoint/linux_auditd_sysmon_service_stop.yml index 2c46b2fbb7..ad3cc1032a 100644 --- a/detections/endpoint/linux_auditd_sysmon_service_stop.yml +++ b/detections/endpoint/linux_auditd_sysmon_service_stop.yml @@ -1,75 +1,59 @@ name: Linux Auditd Sysmon Service Stop id: 20901256-633a-40de-8753-7b88811a460f -version: 6 -date: '2025-06-10' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the suspicious sysmon service stop. This - behavior is critical for a SOC to monitor because it may indicate attempts to gain - unauthorized access or maintain control over a system. Such actions could be signs - of malicious activity. If confirmed, this could lead to serious consequences, including - a compromised system, unauthorized access to sensitive data, or even a wider breach - affecting the entire network. Detecting and responding to these signs early is essential - to prevent potential security incidents. +description: The following analytic detects the suspicious sysmon service stop. This behavior is critical for a SOC to monitor because it may indicate attempts to gain unauthorized access or maintain control over a system. Such actions could be signs of malicious activity. If confirmed, this could lead to serious consequences, including a compromised system, unauthorized access to sensitive data, or even a wider breach affecting the entire network. Detecting and responding to these signs early is essential to prevent potential security incidents. data_source: -- Linux Auditd Service Stop -search: '`linux_auditd` type=SERVICE_STOP unit IN ("sysmon") | rename host as dest - | stats count min(_time) as firstTime max(_time) as lastTime by type pid comm - exe unit dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| - `linux_auditd_sysmon_service_stop_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Linux Auditd Service Stop +search: |- + `linux_auditd` type=SERVICE_STOP unit IN ("sysmon") + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY type pid comm + exe unit dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_sysmon_service_stop_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html + - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A service event - [$type$] event occurred on host - [$dest$] to stop or - disable the sysmon service. - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: [] + message: A service event - [$type$] event occurred on host - [$dest$] to stop or disable the sysmon service. + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1489 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1489 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1489/linux_auditd_sysmon_service_stop/linux_auditd_sysmon_service_stop.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1489/linux_auditd_sysmon_service_stop/linux_auditd_sysmon_service_stop.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_system_network_configuration_discovery.yml b/detections/endpoint/linux_auditd_system_network_configuration_discovery.yml index 3c4c97f9ae..8882aeb1e3 100644 --- a/detections/endpoint/linux_auditd_system_network_configuration_discovery.yml +++ b/detections/endpoint/linux_auditd_system_network_configuration_discovery.yml @@ -1,83 +1,60 @@ name: Linux Auditd System Network Configuration Discovery id: 5db16825-81bd-4923-a8d6-d6a13a59832a -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects suspicious system network configuration - discovery activities, which may indicate an adversary's attempt to gather information - about the network environment. Such actions typically involve commands or tools - used to identify network interfaces, routing tables, and active connections. Detecting - these activities is crucial, as they often precede more targeted attacks like lateral - movement or data exfiltration. By identifying unusual or unauthorized network discovery - efforts, this analytic helps security teams to swiftly detect and respond to potential - reconnaissance operations, mitigating the risk of further compromise. +description: The following analytic detects suspicious system network configuration discovery activities, which may indicate an adversary's attempt to gather information about the network environment. Such actions typically involve commands or tools used to identify network interfaces, routing tables, and active connections. Detecting these activities is crucial, as they often precede more targeted attacks like lateral movement or data exfiltration. By identifying unusual or unauthorized network discovery efforts, this analytic helps security teams to swiftly detect and respond to potential reconnaissance operations, mitigating the risk of further compromise. data_source: -- Linux Auditd Syscall -search: '`linux_auditd` type=SYSCALL comm IN ("arp", "ifconfig", "ip", "netstat", "firewall-cmd", "ufw", "iptables", "ss", "route") - | bucket _time span=15m - | rename host as dest - | stats dc(comm) as unique_commands, values(comm) as comm, values(exe) - as exe, values(syscall) as syscall, values(uid) as uid, values(ppid) as ppid, values(pid) - as pid, count, min(_time) as firstTime, max(_time) as lastTime by success dest - | where unique_commands >= 4 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_auditd_system_network_configuration_discovery_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Linux Auditd Syscall +search: |- + `linux_auditd` type=SYSCALL comm IN ("arp", "ifconfig", "ip", "netstat", "firewall-cmd", "ufw", "iptables", "ss", "route") + | bucket _time span=15m + | rename host as dest + | stats dc(comm) as unique_commands, values(comm) as comm, values(exe) as exe, values(syscall) as syscall, values(uid) as uid, values(ppid) as ppid, values(pid) as pid, count, min(_time) as firstTime, max(_time) as lastTime + BY success dest + | where unique_commands >= 4 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_system_network_configuration_discovery_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html + - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to discover - system network configuration. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to discover system network configuration. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1016 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1016 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1016/linux_auditd_net_tool_new/linux_auditd_net_tool_bucket_new.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1016/linux_auditd_net_tool_new/linux_auditd_net_tool_bucket_new.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_unix_shell_configuration_modification.yml b/detections/endpoint/linux_auditd_unix_shell_configuration_modification.yml index 8248351282..3ab2212527 100644 --- a/detections/endpoint/linux_auditd_unix_shell_configuration_modification.yml +++ b/detections/endpoint/linux_auditd_unix_shell_configuration_modification.yml @@ -6,104 +6,96 @@ author: Teoderick Contreras, Splunk status: production type: TTP description: | - The following analytic detects suspicious access or modifications to Unix shell configuration files, which may indicate an attempt to alter system behavior or gain unauthorized access. - Unix shell configuration files, such as `.bashrc` or `.profile`, control user environment settings and command execution. - Unauthorized changes to these files can be used to execute malicious commands, escalate privileges, or hide malicious activities. - By monitoring for unusual or unauthorized modifications to shell configuration files, this analytic helps identify potential security threats, allowing security teams to respond quickly and mitigate risks. - Correlate this with related EXECVE or PROCTITLE events to identify the process or user responsible for the access or modification. + The following analytic detects suspicious access or modifications to Unix shell configuration files, which may indicate an attempt to alter system behavior or gain unauthorized access. + Unix shell configuration files, such as `.bashrc` or `.profile`, control user environment settings and command execution. + Unauthorized changes to these files can be used to execute malicious commands, escalate privileges, or hide malicious activities. + By monitoring for unusual or unauthorized modifications to shell configuration files, this analytic helps identify potential security threats, allowing security teams to respond quickly and mitigate risks. + Correlate this with related EXECVE or PROCTITLE events to identify the process or user responsible for the access or modification. data_source: - - Linux Auditd Path - - Linux Auditd Cwd + - Linux Auditd Path + - Linux Auditd Cwd search: | - `linux_auditd` - (type=PATH OR type=CWD) - | rex "msg=audit\([^)]*:(?\d+)\)" + `linux_auditd` + (type=PATH OR type=CWD) + | rex "msg=audit\([^)]*:(?\d+)\)" - | stats - values(type) as types - values(name) as names - values(nametype) as nametype - values(cwd) as cwd_list - values(_time) as event_times - by audit_id, host + | stats + values(type) as types + values(name) as names + values(nametype) as nametype + values(cwd) as cwd_list + values(_time) as event_times + by audit_id, host - | eval current_working_directory = coalesce(mvindex(cwd_list, 0), "N/A") - | eval candidate_paths = mvmap(names, if(match(names, "^/"), names, current_working_directory + "/" + names)) - | eval matched_paths = mvfilter(match(candidate_paths, "/etc/profile|/etc/shells|/etc/profile\\.d/.*|/etc/bash\\.bashrc.*|/etc/bashrc|.*/zsh/zprofile|.*/zsh/zshrc|.*/zsh/zlogin|.*/zsh/zlogout|/etc/csh\\.cshrc.*|/etc/csh\\.login.*|/root/\\.bashrc.*|/root/\\.bash_profile.*|/root/\\.profile.*|/root/\\.zshrc.*|/root/\\.zprofile.*|/home/.*/\\.bashrc.*|/home/.*/\\.zshrc.*|/home/.*/\\.bash_profile.*|/home/.*/\\.zprofile.*|/home/.*/\\.profile.*|/home/.*/\\.bash_login.*|/home/.*/\\.bash_logout.*|/home/.*/\\.zlogin.*|/home/.*/\\.zlogout.*")) - | eval match_count = mvcount(matched_paths) - | eval reconstructed_path = mvindex(matched_paths, 0) - | eval e_time = mvindex(event_times, 0) - | where match_count > 0 - | rename host as dest + | eval current_working_directory = coalesce(mvindex(cwd_list, 0), "N/A") + | eval candidate_paths = mvmap(names, if(match(names, "^/"), names, current_working_directory + "/" + names)) + | eval matched_paths = mvfilter(match(candidate_paths, "/etc/profile|/etc/shells|/etc/profile\\.d/.*|/etc/bash\\.bashrc.*|/etc/bashrc|.*/zsh/zprofile|.*/zsh/zshrc|.*/zsh/zlogin|.*/zsh/zlogout|/etc/csh\\.cshrc.*|/etc/csh\\.login.*|/root/\\.bashrc.*|/root/\\.bash_profile.*|/root/\\.profile.*|/root/\\.zshrc.*|/root/\\.zprofile.*|/home/.*/\\.bashrc.*|/home/.*/\\.zshrc.*|/home/.*/\\.bash_profile.*|/home/.*/\\.zprofile.*|/home/.*/\\.profile.*|/home/.*/\\.bash_login.*|/home/.*/\\.bash_logout.*|/home/.*/\\.zlogin.*|/home/.*/\\.zlogout.*")) + | eval match_count = mvcount(matched_paths) + | eval reconstructed_path = mvindex(matched_paths, 0) + | eval e_time = mvindex(event_times, 0) + | where match_count > 0 + | rename host as dest - | stats count min(e_time) as firstTime max(e_time) as lastTime - values(nametype) as nametype - by current_working_directory - reconstructed_path - match_count - dest - audit_id + | stats count min(e_time) as firstTime max(e_time) as lastTime + values(nametype) as nametype + by current_working_directory + reconstructed_path + match_count + dest + audit_id - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_auditd_unix_shell_configuration_modification_filter` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_unix_shell_configuration_modification_filter` how_to_implement: | - To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling and make sure the type=CWD record type is activate in your auditd configuration. - This approach enables effective monitoring and detection of linux endpoints where auditd is deployed. + To implement this detection, the process begins by ingesting auditd + data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line + executions and process details on Unix/Linux systems. These logs should be ingested + and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), + which is essential for correctly parsing and categorizing the data. The next step + involves normalizing the field names to match the field names set by the Splunk + Common Information Model (CIM) to ensure consistency across different data sources + and enhance the efficiency of data modeling and make sure the type=CWD record type is activate in your auditd configuration. + This approach enables effective monitoring and detection of linux endpoints where auditd is deployed. known_false_positives: | - Administrator or network operator can use this application for automation purposes. - Please update the filter macros to remove false positives. + Administrator or network operator can use this application for automation purposes. + Please update the filter macros to remove false positives. references: - - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html - - https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS + - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html + - https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A [$nametype$] event occurred on host - [$dest$] to modify the unix shell configuration - file. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A [$nametype$] event occurred on host - [$dest$] to modify the unix shell configuration file. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1546.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1546.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.004/linux_auditd_unix_shell_mod_config//linux_path_profile_d.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.004/linux_auditd_unix_shell_mod_config//linux_path_profile_d.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_unload_module_via_modprobe.yml b/detections/endpoint/linux_auditd_unload_module_via_modprobe.yml index b83918c943..7c2c4ad265 100644 --- a/detections/endpoint/linux_auditd_unload_module_via_modprobe.yml +++ b/detections/endpoint/linux_auditd_unload_module_via_modprobe.yml @@ -1,80 +1,60 @@ name: Linux Auditd Unload Module Via Modprobe id: 90964d6a-4b5f-409a-85bd-95e261e03fe9 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects suspicious use of the `modprobe` command - to unload kernel modules, which may indicate an attempt to disable critical system - components or evade detection. The `modprobe` utility manages kernel modules, and - unauthorized unloading of modules can disrupt system security features, remove logging - capabilities, or conceal malicious activities. By monitoring for unusual or unauthorized - `modprobe` operations involving module unloading, this analytic helps identify potential - tampering with kernel functionality, enabling security teams to investigate and - address possible threats to system integrity. +description: The following analytic detects suspicious use of the `modprobe` command to unload kernel modules, which may indicate an attempt to disable critical system components or evade detection. The `modprobe` utility manages kernel modules, and unauthorized unloading of modules can disrupt system security features, remove logging capabilities, or conceal malicious activities. By monitoring for unusual or unauthorized `modprobe` operations involving module unloading, this analytic helps identify potential tampering with kernel functionality, enabling security teams to investigate and address possible threats to system integrity. data_source: -- Linux Auditd Execve -search: '`linux_auditd` execve_command = "*modprobe*" AND execve_command = "*-r *" - | rename host as dest - | rename comm as process_name - | rename exe as process - | stats count min(_time) as firstTime max(_time) as lastTime by argc execve_command dest - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `linux_auditd_unload_module_via_modprobe_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures - command-line executions and process details on Unix/Linux systems. These logs should - be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Linux Auditd Execve +search: |- + `linux_auditd` execve_command = "*modprobe*" AND execve_command = "*-r *" + | rename host as dest + | rename comm as process_name + | rename exe as process + | stats count min(_time) as firstTime max(_time) as lastTime + BY argc execve_command dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_unload_module_via_modprobe_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html + - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$execve_command$] event occurred on host - [$dest$] to unload a kernel - module via the modprobe command. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: A [$execve_command$] event occurred on host - [$dest$] to unload a kernel module via the modprobe command. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1547.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1547.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/linux_auditd_modprobe_unload_module/auditd_execve_modprobe.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/linux_auditd_modprobe_unload_module/auditd_execve_modprobe.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_virtual_disk_file_and_directory_discovery.yml b/detections/endpoint/linux_auditd_virtual_disk_file_and_directory_discovery.yml index 4cd4f6f33c..c34d46c872 100644 --- a/detections/endpoint/linux_auditd_virtual_disk_file_and_directory_discovery.yml +++ b/detections/endpoint/linux_auditd_virtual_disk_file_and_directory_discovery.yml @@ -1,71 +1,61 @@ name: Linux Auditd Virtual Disk File And Directory Discovery id: eec78cef-d4c8-4b35-8f5b-6922102a4a41 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects suspicious discovery of virtual disk files - and directories, which may indicate an attacker's attempt to locate and access virtualized - storage environments. Virtual disks can contain sensitive data or critical system - configurations, and unauthorized discovery attempts could signify preparatory actions - for data exfiltration or further compromise. By monitoring for unusual or unauthorized - searches for virtual disk files and directories, this analytic helps identify potential - reconnaissance activities, enabling security teams to respond promptly and safeguard - against unauthorized access and data breaches. +description: The following analytic detects suspicious discovery of virtual disk files and directories, which may indicate an attacker's attempt to locate and access virtualized storage environments. Virtual disks can contain sensitive data or critical system configurations, and unauthorized discovery attempts could signify preparatory actions for data exfiltration or further compromise. By monitoring for unusual or unauthorized searches for virtual disk files and directories, this analytic helps identify potential reconnaissance activities, enabling security teams to respond promptly and safeguard against unauthorized access and data breaches. data_source: -- Linux Auditd Execve -search: '`linux_auditd` execve_command IN ("*find*", "*grep*") AND execve_command IN ("*.vhd*", "*.vhdx*", "*.vmdk*") - | rename host as dest - | rename comm as process_name - | rename exe as process - | stats count min(_time) as firstTime max(_time) as lastTime by argc execve_command dest - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `linux_auditd_virtual_disk_file_and_directory_discovery_filter`' + - Linux Auditd Execve +search: |- + `linux_auditd` execve_command IN ("*find*", "*grep*") AND execve_command IN ("*.vhd*", "*.vhdx*", "*.vmdk*") + | rename host as dest + | rename comm as process_name + | rename exe as process + | stats count min(_time) as firstTime max(_time) as lastTime + BY argc execve_command dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_virtual_disk_file_and_directory_discovery_filter` how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consists of SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html -- https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS + - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html + - https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$execve_command$] event occurred on host - [$dest$] to discover virtual - disk files and directories. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A [$execve_command$] event occurred on host - [$dest$] to discover virtual disk files and directories. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1083 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1083 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1083/linux_auditd_find_virtual_disk/auditd_execve_find_vhd.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1083/linux_auditd_find_virtual_disk/auditd_execve_find_vhd.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_auditd_whoami_user_discovery.yml b/detections/endpoint/linux_auditd_whoami_user_discovery.yml index 5852854c88..8f8fcf583f 100644 --- a/detections/endpoint/linux_auditd_whoami_user_discovery.yml +++ b/detections/endpoint/linux_auditd_whoami_user_discovery.yml @@ -1,81 +1,61 @@ name: Linux Auditd Whoami User Discovery id: d1ff2e22-310d-446a-80b3-faedaa7b3b52 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the suspicious use of the whoami command, - which may indicate an attacker trying to gather information about the current user - account on a compromised system. The whoami command is commonly used to verify user - privileges and identity, especially during initial stages of an attack to assess - the level of access. By monitoring for unusual or unauthorized executions of whoami, - this analytic helps in identifying potential reconnaissance activities, enabling - security teams to take action before the attacker escalates privileges or conducts - further malicious operations. +description: The following analytic detects the suspicious use of the whoami command, which may indicate an attacker trying to gather information about the current user account on a compromised system. The whoami command is commonly used to verify user privileges and identity, especially during initial stages of an attack to assess the level of access. By monitoring for unusual or unauthorized executions of whoami, this analytic helps in identifying potential reconnaissance activities, enabling security teams to take action before the attacker escalates privileges or conducts further malicious operations. data_source: -- Linux Auditd Syscall -search: '`linux_auditd` type=SYSCALL comm=whoami OR exe= "*/whoami" - | rename host as dest - | stats count min(_time) as firstTime max(_time) as lastTime - by comm exe syscall uid ppid pid dest success - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_auditd_whoami_user_discovery_filter`' -how_to_implement: To implement this detection, the process begins by ingesting auditd - data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line - executions and process details on Unix/Linux systems. These logs should be ingested - and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), - which is essential for correctly parsing and categorizing the data. The next step - involves normalizing the field names to match the field names set by the Splunk - Common Information Model (CIM) to ensure consistency across different data sources - and enhance the efficiency of data modeling. This approach enables effective monitoring - and detection of linux endpoints where auditd is deployed -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Linux Auditd Syscall +search: |- + `linux_auditd` type=SYSCALL comm=whoami OR exe= "*/whoami" + | rename host as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY comm exe syscall + uid ppid pid + dest success + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_auditd_whoami_user_discovery_filter` +how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html -- https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS + - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html + - https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to discover - virtual disk files and directories. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to discover virtual disk files and directories. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Linux Living Off The Land - - Linux Privilege Escalation - - Linux Persistence Techniques - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1033 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Linux Privilege Escalation + - Linux Persistence Techniques + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1033 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/linux_auditd_whoami_new/linux_auditd_new_whoami.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/linux_auditd_whoami_new/linux_auditd_new_whoami.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_awk_privilege_escalation.yml b/detections/endpoint/linux_awk_privilege_escalation.yml index 80570709d0..8eaf041142 100644 --- a/detections/endpoint/linux_awk_privilege_escalation.yml +++ b/detections/endpoint/linux_awk_privilege_escalation.yml @@ -1,84 +1,70 @@ name: Linux AWK Privilege Escalation id: 4510cae0-96a2-4840-9919-91d262db210a -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: Anomaly -description: The following analytic detects the use of the AWK command with elevated - privileges to execute system commands. It leverages Endpoint Detection and Response - (EDR) telemetry, specifically monitoring processes that include "sudo," "awk," and - "BEGIN*system" in their command lines. This activity is significant because it indicates - a potential privilege escalation attempt, where a user could gain root access by - executing commands as the root user. If confirmed malicious, this could allow an - attacker to fully compromise the system, execute arbitrary commands, and maintain - persistent control over the affected endpoint. +description: The following analytic detects the use of the AWK command with elevated privileges to execute system commands. It leverages Endpoint Detection and Response (EDR) telemetry, specifically monitoring processes that include "sudo," "awk," and "BEGIN*system" in their command lines. This activity is significant because it indicates a potential privilege escalation attempt, where a user could gain root access by executing commands as the root user. If confirmed malicious, this could allow an attacker to fully compromise the system, execute arbitrary commands, and maintain persistent control over the affected endpoint. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*sudo*" AND - Processes.process="*awk*" AND Processes.process="*BEGIN*system*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| - `linux_awk_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives are present based on automated tooling or system - administrative usage. Filter as needed. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*sudo*" + AND + Processes.process="*awk*" + AND + Processes.process="*BEGIN*system*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_awk_privilege_escalation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives are present based on automated tooling or system administrative usage. Filter as needed. references: -- https://www.hacknos.com/awk-privilege-escalation/ + - https://www.hacknos.com/awk-privilege-escalation/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/awk/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/awk/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_busybox_privilege_escalation.yml b/detections/endpoint/linux_busybox_privilege_escalation.yml index 3ebe39c258..837aa1d790 100644 --- a/detections/endpoint/linux_busybox_privilege_escalation.yml +++ b/detections/endpoint/linux_busybox_privilege_escalation.yml @@ -1,84 +1,71 @@ name: Linux Busybox Privilege Escalation id: 387c4e78-f4a4-413d-ad44-e9f7bc4642c9 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: Anomaly -description: The following analytic detects the execution of BusyBox with sudo privileges, - which can lead to privilege escalation on Linux systems. It leverages data from - Endpoint Detection and Response (EDR) agents, focusing on process creation events - where BusyBox is executed with both 'sh' and 'sudo' commands. This activity is significant - because it indicates a user may be attempting to gain root access, bypassing standard - security controls. If confirmed malicious, this could allow an attacker to execute - arbitrary commands as root, leading to full system compromise and potential persistence - within the environment. +description: The following analytic detects the execution of BusyBox with sudo privileges, which can lead to privilege escalation on Linux systems. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where BusyBox is executed with both 'sh' and 'sudo' commands. This activity is significant because it indicates a user may be attempting to gain root access, bypassing standard security controls. If confirmed malicious, this could allow an attacker to execute arbitrary commands as root, leading to full system compromise and potential persistence within the environment. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*busybox*" - AND Processes.process="*sh*" AND Processes.process="*sudo*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_busybox_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*busybox*" + AND + Processes.process="*sh*" + AND + Processes.process="*sudo*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_busybox_privilege_escalation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives may be present, filter as needed. references: -- https://gtfobins.github.io/gtfobins/busybox/ -- https://man.archlinux.org/man/busybox.1.en + - https://gtfobins.github.io/gtfobins/busybox/ + - https://man.archlinux.org/man/busybox.1.en drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 10 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 10 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/busybox/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/busybox/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_c89_privilege_escalation.yml b/detections/endpoint/linux_c89_privilege_escalation.yml index efab2c508f..c10154875a 100644 --- a/detections/endpoint/linux_c89_privilege_escalation.yml +++ b/detections/endpoint/linux_c89_privilege_escalation.yml @@ -1,84 +1,71 @@ name: Linux c89 Privilege Escalation id: 54c95f4d-3e5d-44be-9521-ea19ba62f7a8 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the 'c89' command with - elevated privileges, which can be used to compile and execute C programs as root. - This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process creation events that include command-line arguments. This activity - is significant because it indicates a potential privilege escalation attempt, allowing - a user to execute arbitrary commands as root. If confirmed malicious, this could - lead to full system compromise, enabling the attacker to gain root access and execute - any command with elevated privileges. +description: The following analytic detects the execution of the 'c89' command with elevated privileges, which can be used to compile and execute C programs as root. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events that include command-line arguments. This activity is significant because it indicates a potential privilege escalation attempt, allowing a user to execute arbitrary commands as root. If confirmed malicious, this could lead to full system compromise, enabling the attacker to gain root access and execute any command with elevated privileges. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*c89*" AND - Processes.process="*-wrapper*" AND Processes.process="*sudo*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_c89_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*c89*" + AND + Processes.process="*-wrapper*" + AND + Processes.process="*sudo*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_c89_privilege_escalation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives may be present, filter as needed. references: -- https://gtfobins.github.io/gtfobins/c89/ -- https://www.ibm.com/docs/en/zos/2.1.0?topic=guide-c89-compiler-invocation-using-host-environment-variables + - https://gtfobins.github.io/gtfobins/c89/ + - https://www.ibm.com/docs/en/zos/2.1.0?topic=guide-c89-compiler-invocation-using-host-environment-variables drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/c89/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/c89/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_c99_privilege_escalation.yml b/detections/endpoint/linux_c99_privilege_escalation.yml index 5ab4e52aa6..99b6246ccb 100644 --- a/detections/endpoint/linux_c99_privilege_escalation.yml +++ b/detections/endpoint/linux_c99_privilege_escalation.yml @@ -1,84 +1,71 @@ name: Linux c99 Privilege Escalation id: e1c6dec5-2249-442d-a1f9-99a4bd228183 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the c99 utility with - sudo privileges, which can lead to privilege escalation on Linux systems. It leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process execution - logs that include command-line details. This activity is significant because it - indicates a potential misuse of the c99 utility to gain root access, which is critical - for maintaining system security. If confirmed malicious, this could allow an attacker - to execute commands as root, potentially compromising the entire system and accessing - sensitive information. +description: The following analytic detects the execution of the c99 utility with sudo privileges, which can lead to privilege escalation on Linux systems. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because it indicates a potential misuse of the c99 utility to gain root access, which is critical for maintaining system security. If confirmed malicious, this could allow an attacker to execute commands as root, potentially compromising the entire system and accessing sensitive information. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*c99*" AND - Processes.process="*-wrapper*" AND Processes.process="*sudo*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_c99_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*c99*" + AND + Processes.process="*-wrapper*" + AND + Processes.process="*sudo*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_c99_privilege_escalation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives may be present, filter as needed. references: -- https://gtfobins.github.io/gtfobins/c99/ -- https://pubs.opengroup.org/onlinepubs/009604499/utilities/c99.html + - https://gtfobins.github.io/gtfobins/c99/ + - https://pubs.opengroup.org/onlinepubs/009604499/utilities/c99.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/c99/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/c99/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_change_file_owner_to_root.yml b/detections/endpoint/linux_change_file_owner_to_root.yml index ee46f688ed..12ca6b56c1 100644 --- a/detections/endpoint/linux_change_file_owner_to_root.yml +++ b/detections/endpoint/linux_change_file_owner_to_root.yml @@ -1,80 +1,69 @@ name: Linux Change File Owner To Root id: c1400ea2-6257-11ec-ad49-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the use of the 'chown' command to change - a file owner to 'root' on a Linux system. It leverages Endpoint Detection and Response - (EDR) telemetry, specifically monitoring command-line executions and process details. - This activity is significant as it may indicate an attempt to escalate privileges - by adversaries, malware, or red teamers. If confirmed malicious, this action could - allow an attacker to gain root-level access, leading to full control over the compromised - host and potential persistence within the environment. +description: The following analytic detects the use of the 'chown' command to change a file owner to 'root' on a Linux system. It leverages Endpoint Detection and Response (EDR) telemetry, specifically monitoring command-line executions and process details. This activity is significant as it may indicate an attempt to escalate privileges by adversaries, malware, or red teamers. If confirmed malicious, this action could allow an attacker to gain root-level access, leading to full control over the compromised host and potential persistence within the environment. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = chown - OR Processes.process = "*chown *") AND Processes.process = "* root *" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_change_file_owner_to_root_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name = chown + OR + Processes.process = "*chown *" + ) + AND Processes.process = "* root *" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_change_file_owner_to_root_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://unix.stackexchange.com/questions/101073/how-to-change-permissions-from-root-user-to-all-users -- https://askubuntu.com/questions/617850/changing-from-user-to-superuser + - https://unix.stackexchange.com/questions/101073/how-to-change-permissions-from-root-user-to-all-users + - https://askubuntu.com/questions/617850/changing-from-user-to-superuser drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A commandline $process$ that may change ownership to root on $dest$ - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A commandline $process$ that may change ownership to root on $dest$ + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1222.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1222.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.001/chmod_uid/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.001/chmod_uid/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_clipboard_data_copy.yml b/detections/endpoint/linux_clipboard_data_copy.yml index ade4a8506a..40c4494f57 100644 --- a/detections/endpoint/linux_clipboard_data_copy.yml +++ b/detections/endpoint/linux_clipboard_data_copy.yml @@ -1,84 +1,67 @@ name: Linux Clipboard Data Copy id: 7173b2ad-6146-418f-85ae-c3479e4515fc -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic detects the use of the Linux 'xclip' command to - copy data from the clipboard. It leverages Endpoint Detection and Response (EDR) - telemetry, focusing on process names and command-line arguments related to clipboard - operations. This activity is significant because adversaries can exploit clipboard - data to capture sensitive information such as passwords or IP addresses. If confirmed - malicious, this technique could lead to unauthorized data exfiltration, compromising - sensitive information and potentially aiding further attacks within the environment. +description: The following analytic detects the use of the Linux 'xclip' command to copy data from the clipboard. It leverages Endpoint Detection and Response (EDR) telemetry, focusing on process names and command-line arguments related to clipboard operations. This activity is significant because adversaries can exploit clipboard data to capture sensitive information such as passwords or IP addresses. If confirmed malicious, this technique could lead to unauthorized data exfiltration, compromising sensitive information and potentially aiding further attacks within the environment. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=xclip - Processes.process IN ("*-o *", "*-sel *", "*-selection *", "*clip *","*clipboard*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_clipboard_data_copy_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present on Linux desktop as it may commonly - be used by administrators or end users. Filter as needed. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=xclip Processes.process IN ("*-o *", "*-sel *", "*-selection *", "*clip *","*clipboard*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_clipboard_data_copy_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present on Linux desktop as it may commonly be used by administrators or end users. Filter as needed. references: -- https://attack.mitre.org/techniques/T1115/ -- https://linux.die.net/man/1/xclip + - https://attack.mitre.org/techniques/T1115/ + - https://linux.die.net/man/1/xclip drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $process_name$ was identified on endpoint $dest$ by user - $user$ adding or removing content from the clipboard. - risk_objects: - - field: user - type: user - score: 16 - - field: dest - type: system - score: 16 - threat_objects: - - field: process_name - type: process_name + message: An instance of $process_name$ was identified on endpoint $dest$ by user $user$ adding or removing content from the clipboard. + risk_objects: + - field: user + type: user + score: 16 + - field: dest + type: system + score: 16 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1115 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1115 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1115/atomic_red_team/linux-sysmon.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1115/atomic_red_team/linux-sysmon.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_common_process_for_elevation_control.yml b/detections/endpoint/linux_common_process_for_elevation_control.yml index a702c4600d..59c6351239 100644 --- a/detections/endpoint/linux_common_process_for_elevation_control.yml +++ b/detections/endpoint/linux_common_process_for_elevation_control.yml @@ -1,69 +1,54 @@ name: Linux Common Process For Elevation Control id: 66ab15c0-63d0-11ec-9e70-acde48001122 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic identifies the execution of common Linux processes - used for elevation control, such as `chmod`, `chown`, and `setuid`. It leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process names - and command-line executions. This activity is significant because these processes - are often abused by adversaries to gain persistence or escalate privileges on compromised - hosts. If confirmed malicious, this behavior could allow attackers to modify file - attributes, change file ownership, or set user IDs, potentially leading to unauthorized - access and control over critical system resources. +description: The following analytic identifies the execution of common Linux processes used for elevation control, such as `chmod`, `chown`, and `setuid`. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because these processes are often abused by adversaries to gain persistence or escalate privileges on compromised hosts. If confirmed malicious, this behavior could allow attackers to modify file attributes, change file ownership, or set user IDs, potentially leading to unauthorized access and control over critical system resources. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("chmod", - "chown", "fchmod", "fchmodat", "fchown", "fchownat", "fremovexattr", "fsetxattr", - "lchown", "lremovexattr", "lsetxattr", "removexattr", "setuid", "setgid", "setreuid", - "setregid", "chattr") OR Processes.process IN ("*chmod *", "*chown *", "*fchmod - *", "*fchmodat *", "*fchown *", "*fchownat *", "*fremovexattr *", "*fsetxattr *", - "*lchown *", "*lremovexattr *", "*lsetxattr *", "*removexattr *", "*setuid *", "*setgid - *", "*setreuid *", "*setregid *", "*setcap *", "*chattr *") by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_common_process_for_elevation_control_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name IN ("chmod", "chown", "fchmod", "fchmodat", "fchown", "fchownat", "fremovexattr", "fsetxattr", "lchown", "lremovexattr", "lsetxattr", "removexattr", "setuid", "setgid", "setreuid", "setregid", "chattr") + OR + Processes.process IN ("*chmod *", "*chown *", "*fchmod *", "*fchmodat *", "*fchown *", "*fchownat *", "*fremovexattr *", "*fsetxattr *", "*lchown *", "*lremovexattr *", "*lsetxattr *", "*removexattr *", "*setuid *", "*setgid *", "*setreuid *", "*setregid *", "*setcap *", "*chattr *") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_common_process_for_elevation_control_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://attack.mitre.org/techniques/T1548/001/ -- https://github.com/Neo23x0/auditd/blob/master/audit.rules#L285-L297 -- https://github.com/bfuzzy1/auditd-attack/blob/master/auditd-attack/auditd-attack.rules#L269-L270 -- https://github.com/microsoft/MSTIC-Sysmon/blob/main/linux/configs/attack-based/privilege_escalation/T1548.001_ElevationControl_CommonProcesses.xml + - https://attack.mitre.org/techniques/T1548/001/ + - https://github.com/Neo23x0/auditd/blob/master/audit.rules#L285-L297 + - https://github.com/bfuzzy1/auditd-attack/blob/master/auditd-attack/auditd-attack.rules#L269-L270 + - https://github.com/microsoft/MSTIC-Sysmon/blob/main/linux/configs/attack-based/privilege_escalation/T1548.001_ElevationControl_CommonProcesses.xml tags: - analytic_story: - - Linux Persistence Techniques - - China-Nexus Threat Activity - - Linux Living Off The Land - - Salt Typhoon - - Linux Privilege Escalation - asset_type: Endpoint - mitre_attack_id: - - T1548.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Persistence Techniques + - China-Nexus Threat Activity + - Linux Living Off The Land + - Salt Typhoon + - Linux Privilege Escalation + asset_type: Endpoint + mitre_attack_id: + - T1548.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.001/chmod_uid/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.001/chmod_uid/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_composer_privilege_escalation.yml b/detections/endpoint/linux_composer_privilege_escalation.yml index 32b4e7bd71..e5f852a8f2 100644 --- a/detections/endpoint/linux_composer_privilege_escalation.yml +++ b/detections/endpoint/linux_composer_privilege_escalation.yml @@ -1,85 +1,71 @@ name: Linux Composer Privilege Escalation id: a3bddf71-6ba3-42ab-a6b2-396929b16d92 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the Composer tool with - elevated privileges on a Linux system. It identifies instances where Composer is - run with the 'sudo' command, allowing the user to execute system commands as root. - This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process execution logs and command-line arguments. This activity is - significant because it can indicate an attempt to escalate privileges, potentially - leading to unauthorized root access. If confirmed malicious, an attacker could gain - full control over the system, execute arbitrary commands, and compromise sensitive - data. +description: The following analytic detects the execution of the Composer tool with elevated privileges on a Linux system. It identifies instances where Composer is run with the 'sudo' command, allowing the user to execute system commands as root. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant because it can indicate an attempt to escalate privileges, potentially leading to unauthorized root access. If confirmed malicious, an attacker could gain full control over the system, execute arbitrary commands, and compromise sensitive data. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*composer*" - AND Processes.process="*run-script*" AND Processes.process="*sudo*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_composer_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*composer*" + AND + Processes.process="*run-script*" + AND + Processes.process="*sudo*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_composer_privilege_escalation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives may be present, filter as needed. references: -- https://gtfobins.github.io/gtfobins/composer/ -- https://getcomposer.org/doc/00-intro.md + - https://gtfobins.github.io/gtfobins/composer/ + - https://getcomposer.org/doc/00-intro.md drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 10 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 10 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/composer/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/composer/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_cpulimit_privilege_escalation.yml b/detections/endpoint/linux_cpulimit_privilege_escalation.yml index 56e92d688f..40d585ee47 100644 --- a/detections/endpoint/linux_cpulimit_privilege_escalation.yml +++ b/detections/endpoint/linux_cpulimit_privilege_escalation.yml @@ -1,83 +1,73 @@ name: Linux Cpulimit Privilege Escalation id: d4e40b7e-aad3-4a7d-aac8-550ea5222be5 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: Anomaly -description: The following analytic detects the use of the 'cpulimit' command with - specific flags ('-l', '-f') executed with 'sudo' privileges. It leverages data from - Endpoint Detection and Response (EDR) agents, focusing on process command-line arguments - and execution details. This activity is significant because if 'cpulimit' is granted - sudo rights, a user can potentially execute system commands as root, leading to - privilege escalation. If confirmed malicious, this could allow an attacker to gain - root access, execute arbitrary commands, and fully compromise the affected system. +description: The following analytic detects the use of the 'cpulimit' command with specific flags ('-l', '-f') executed with 'sudo' privileges. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process command-line arguments and execution details. This activity is significant because if 'cpulimit' is granted sudo rights, a user can potentially execute system commands as root, leading to privilege escalation. If confirmed malicious, this could allow an attacker to gain root access, execute arbitrary commands, and fully compromise the affected system. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*cpulimit*" - AND Processes.process="*-l*" AND Processes.process="*-f*" AND Processes.process="*sudo*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_cpulimit_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*cpulimit*" + AND + Processes.process="*-l*" + AND + Processes.process="*-f*" + AND + Processes.process="*sudo*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_cpulimit_privilege_escalation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives may be present, filter as needed. references: -- https://gtfobins.github.io/gtfobins/cpulimit/ -- http://cpulimit.sourceforge.net/ + - https://gtfobins.github.io/gtfobins/cpulimit/ + - http://cpulimit.sourceforge.net/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 20 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 20 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/cpulimit/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/cpulimit/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_csvtool_privilege_escalation.yml b/detections/endpoint/linux_csvtool_privilege_escalation.yml index c83ff74766..473823befe 100644 --- a/detections/endpoint/linux_csvtool_privilege_escalation.yml +++ b/detections/endpoint/linux_csvtool_privilege_escalation.yml @@ -1,83 +1,70 @@ name: Linux Csvtool Privilege Escalation id: f8384f9e-1a5c-4c3a-96d6-8a7e5a38a8b8 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the 'csvtool' command - with 'sudo' privileges, which can allow a user to run system commands as root. This - detection leverages data from Endpoint Detection and Response (EDR) agents, focusing - on process execution logs that include command-line details. This activity is significant - because it indicates a potential privilege escalation attempt, where a user could - gain unauthorized root access. If confirmed malicious, this could lead to full system - compromise, allowing an attacker to execute arbitrary commands, escalate privileges, - and maintain persistent access. +description: The following analytic detects the execution of the 'csvtool' command with 'sudo' privileges, which can allow a user to run system commands as root. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because it indicates a potential privilege escalation attempt, where a user could gain unauthorized root access. If confirmed malicious, this could lead to full system compromise, allowing an attacker to execute arbitrary commands, escalate privileges, and maintain persistent access. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*csvtool*" - AND Processes.process="*call*" AND Processes.process="*sudo*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_csvtool_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*csvtool*" + AND + Processes.process="*call*" + AND + Processes.process="*sudo*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_csvtool_privilege_escalation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives may be present, filter as needed. references: -- https://gtfobins.github.io/gtfobins/csvtool/ + - https://gtfobins.github.io/gtfobins/csvtool/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 10 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 10 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/csvtool/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/csvtool/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_curl_upload_file.yml b/detections/endpoint/linux_curl_upload_file.yml index 2e568dc177..13285c1771 100644 --- a/detections/endpoint/linux_curl_upload_file.yml +++ b/detections/endpoint/linux_curl_upload_file.yml @@ -1,97 +1,79 @@ name: Linux Curl Upload File id: c1de2d9a-0c02-4bb4-a49a-510c6e9cf2bf -version: 9 -date: '2025-10-27' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the use of the curl command with specific - switches (-F, --form, --upload-file, -T, -d, --data, --data-raw, -I, --head) to - upload AWS credentials or configuration files to a remote destination. This detection - leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line - executions and process details. This activity is significant as it may indicate - an attempt to exfiltrate sensitive AWS credentials, a technique known to be used - by the TeamTNT group. If confirmed malicious, this could lead to unauthorized access - and potential compromise of AWS resources. +description: The following analytic detects the use of the curl command with specific switches (-F, --form, --upload-file, -T, -d, --data, --data-raw, -I, --head) to upload AWS credentials or configuration files to a remote destination. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process details. This activity is significant as it may indicate an attempt to exfiltrate sensitive AWS credentials, a technique known to be used by the TeamTNT group. If confirmed malicious, this could lead to unauthorized access and potential compromise of AWS resources. data_source: -- Sysmon for Linux EventID 1 -- Cisco Isovalent Process Exec -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=curl - Processes.process IN ("*-F *", "*--form *","*--upload-file *","*-T *","*-d *","*--data - *","*--data-raw *", "*-I *", "*--head *") AND Processes.process IN ("*.aws/credentials*". - "*.aws/config*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_curl_upload_file_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Filtering may be required. In addition to AWS credentials, - add other important files and monitor. The inverse would be to look for _all_ -F - behavior and tune from there. + - Sysmon for Linux EventID 1 + - Cisco Isovalent Process Exec +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=curl Processes.process IN ("*-F *", "*--form *","*--upload-file *","*-T *","*-d *","*--data *","*--data-raw *", "*-I *", "*--head *") + AND + Processes.process IN ("*.aws/credentials*". "*.aws/config*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_curl_upload_file_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Filtering may be required. In addition to AWS credentials, add other important files and monitor. The inverse would be to look for _all_ -F behavior and tune from there. references: -- https://curl.se/docs/manpage.html -- https://www.cadosecurity.com/team-tnt-the-first-crypto-mining-worm-to-steal-aws-credentials/ -- https://gtfobins.github.io/gtfobins/curl/ + - https://curl.se/docs/manpage.html + - https://www.cadosecurity.com/team-tnt-the-first-crypto-mining-worm-to-steal-aws-credentials/ + - https://gtfobins.github.io/gtfobins/curl/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $process_name$ was identified on endpoint $dest$ by user - $user$ attempting to upload important files to a remote destination. - risk_objects: - - field: user - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: - - field: process_name - type: process_name + message: An instance of $process_name$ was identified on endpoint $dest$ by user $user$ attempting to upload important files to a remote destination. + risk_objects: + - field: user + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Linux Living Off The Land - - Data Exfiltration - - Ingress Tool Transfer - - NPM Supply Chain Compromise - asset_type: Endpoint - mitre_attack_id: - - T1105 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Data Exfiltration + - Ingress Tool Transfer + - NPM Supply Chain Compromise + asset_type: Endpoint + mitre_attack_id: + - T1105 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/curl-linux-sysmon.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux -- name: True Positive Test - Cisco Isovalent - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_isovalent/cisco_isovalent.log - source: not_applicable - sourcetype: cisco:isovalent:processExec + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/curl-linux-sysmon.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux + - name: True Positive Test - Cisco Isovalent + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_isovalent/cisco_isovalent.log + source: not_applicable + sourcetype: cisco:isovalent:processExec diff --git a/detections/endpoint/linux_data_destruction_command.yml b/detections/endpoint/linux_data_destruction_command.yml index 755382587a..d5bf364aa2 100644 --- a/detections/endpoint/linux_data_destruction_command.yml +++ b/detections/endpoint/linux_data_destruction_command.yml @@ -1,82 +1,69 @@ name: Linux Data Destruction Command id: b11d3979-b2f7-411b-bb1a-bd00e642173b -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of a Unix shell command - designed to wipe root directories on a Linux host. It leverages data from Endpoint - Detection and Response (EDR) agents, focusing on the 'rm' command with force recursive - deletion and the '--no-preserve-root' option. This activity is significant as it - indicates potential data destruction attempts, often associated with malware like - Awfulshred. If confirmed malicious, this behavior could lead to severe data loss, - system instability, and compromised integrity of the affected Linux host. Immediate - investigation and response are crucial to mitigate potential damage. +description: The following analytic detects the execution of a Unix shell command designed to wipe root directories on a Linux host. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on the 'rm' command with force recursive deletion and the '--no-preserve-root' option. This activity is significant as it indicates potential data destruction attempts, often associated with malware like Awfulshred. If confirmed malicious, this behavior could lead to severe data loss, system instability, and compromised integrity of the affected Linux host. Immediate investigation and response are crucial to mitigate potential damage. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "rm" AND - Processes.process IN ("* -rf*", "* -fr*") AND Processes.process = "* --no-preserve-root" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_data_destruction_command_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "rm" + AND + Processes.process IN ("* -rf*", "* -fr*") + AND + Processes.process = "* --no-preserve-root" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `linux_data_destruction_command_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://cert.gov.ua/article/3718487 -- https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/overview-of-the-cyber-weapons-used-in-the-ukraine-russia-war/ + - https://cert.gov.ua/article/3718487 + - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/overview-of-the-cyber-weapons-used-in-the-ukraine-russia-war/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a $process_name$ execute rm command with --no-preserve-root parmeter that - can wipe root files on $dest$ - risk_objects: - - field: dest - type: system - score: 90 - - field: user - type: user - score: 90 - threat_objects: [] + message: a $process_name$ execute rm command with --no-preserve-root parmeter that can wipe root files on $dest$ + risk_objects: + - field: dest + type: system + score: 90 + - field: user + type: user + score: 90 + threat_objects: [] tags: - analytic_story: - - AwfulShred - - Data Destruction - asset_type: Endpoint - mitre_attack_id: - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AwfulShred + - Data Destruction + asset_type: Endpoint + mitre_attack_id: + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test1/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test1/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_dd_file_overwrite.yml b/detections/endpoint/linux_dd_file_overwrite.yml index 7a7bfb926b..c17e61b98a 100644 --- a/detections/endpoint/linux_dd_file_overwrite.yml +++ b/detections/endpoint/linux_dd_file_overwrite.yml @@ -1,78 +1,65 @@ name: Linux DD File Overwrite id: 9b6aae5e-8d85-11ec-b2ae-acde48001122 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the use of the 'dd' command to overwrite - files on a Linux system. It leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process execution logs that include command-line details. - This activity is significant because adversaries often use the 'dd' command to destroy - or irreversibly overwrite files, disrupting system availability and services. If - confirmed malicious, this behavior could lead to data destruction, making recovery - difficult and potentially causing significant operational disruptions. +description: The following analytic detects the use of the 'dd' command to overwrite files on a Linux system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because adversaries often use the 'dd' command to destroy or irreversibly overwrite files, disrupting system availability and services. If confirmed malicious, this behavior could lead to data destruction, making recovery difficult and potentially causing significant operational disruptions. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "dd" - AND Processes.process = "*of=*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_dd_file_overwrite_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "dd" + AND + Processes.process = "*of=*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_dd_file_overwrite_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://gtfobins.github.io/gtfobins/dd/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1485/T1485.md + - https://gtfobins.github.io/gtfobins/dd/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1485/T1485.md drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A commandline $process$ executed on $dest$ - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A commandline $process$ executed on $dest$ + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Data Destruction - - Industroyer2 - asset_type: Endpoint - mitre_attack_id: - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Destruction + - Industroyer2 + asset_type: Endpoint + mitre_attack_id: + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/linux_dd_file_overwrite/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/linux_dd_file_overwrite/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_decode_base64_to_shell.yml b/detections/endpoint/linux_decode_base64_to_shell.yml index 47cf46a587..bdace4d2d4 100644 --- a/detections/endpoint/linux_decode_base64_to_shell.yml +++ b/detections/endpoint/linux_decode_base64_to_shell.yml @@ -1,98 +1,81 @@ name: Linux Decode Base64 to Shell id: 637b603e-1799-40fd-bf87-47ecbd551b66 -version: 10 -date: '2025-10-01' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the behavior of decoding base64-encoded - data and passing it to a Linux shell. Additionally, it mitigates the potential damage - and protects the organization's systems and data.The detection is made by searching - for specific commands in the Splunk query, namely "base64 -d" and "base64 --decode", - within the Endpoint.Processes data model. The analytic also includes a filter for - Linux shells. The detection is important because it indicates the presence of malicious - activity since Base64 encoding is commonly used to obfuscate malicious commands - or payloads, and decoding it can be a step in running those commands. It suggests - that an attacker is attempting to run malicious commands on a Linux system to gain - unauthorized access, for data exfiltration, or perform other malicious actions. +description: The following analytic detects the behavior of decoding base64-encoded data and passing it to a Linux shell. Additionally, it mitigates the potential damage and protects the organization's systems and data.The detection is made by searching for specific commands in the Splunk query, namely "base64 -d" and "base64 --decode", within the Endpoint.Processes data model. The analytic also includes a filter for Linux shells. The detection is important because it indicates the presence of malicious activity since Base64 encoding is commonly used to obfuscate malicious commands or payloads, and decoding it can be a step in running those commands. It suggests that an attacker is attempting to run malicious commands on a Linux system to gain unauthorized access, for data exfiltration, or perform other malicious actions. data_source: -- Sysmon for Linux EventID 1 -- Cisco Isovalent Process Exec -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*|*" `linux_shells` - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | rex field=process "base64\s+(?-{1,2}d\w*)" - | where isnotnull(decode_flag) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_decode_base64_to_shell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present based on legitimate software - being utilized. Filter as needed. + - Sysmon for Linux EventID 1 + - Cisco Isovalent Process Exec +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime + from datamodel=Endpoint.Processes where + Processes.process="*|*" + `linux_shells` + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process + Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name + Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | rex field=process "base64\s+(?-{1,2}d\w*)" + | where isnotnull(decode_flag) + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_decode_base64_to_shell_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present based on legitimate software being utilized. Filter as needed. references: -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1027/T1027.md#atomic-test-1---decode-base64-data-into-script -- https://redcanary.com/blog/lateral-movement-with-secure-shell/ -- https://linux.die.net/man/1/base64 + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1027/T1027.md#atomic-test-1---decode-base64-data-into-script + - https://redcanary.com/blog/lateral-movement-with-secure-shell/ + - https://linux.die.net/man/1/base64 drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ decoding base64 and passing it to a shell. - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ decoding base64 and passing it to a shell. + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Living Off The Land - - Cisco Isovalent Suspicious Activity - asset_type: Endpoint - mitre_attack_id: - - T1027 - - T1059.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Cisco Isovalent Suspicious Activity + asset_type: Endpoint + mitre_attack_id: + - T1027 + - T1059.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1027/atomic_red_team/linux-sysmon.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux -- name: True Positive Test - Cisco Isovalent - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_isovalent/cisco_isovalent.log - source: not_applicable - sourcetype: cisco:isovalent:processExec + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1027/atomic_red_team/linux-sysmon.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux + - name: True Positive Test - Cisco Isovalent + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_isovalent/cisco_isovalent.log + source: not_applicable + sourcetype: cisco:isovalent:processExec diff --git a/detections/endpoint/linux_deleting_critical_directory_using_rm_command.yml b/detections/endpoint/linux_deleting_critical_directory_using_rm_command.yml index 5bceb4ac0d..aeea782f14 100644 --- a/detections/endpoint/linux_deleting_critical_directory_using_rm_command.yml +++ b/detections/endpoint/linux_deleting_critical_directory_using_rm_command.yml @@ -1,82 +1,68 @@ name: Linux Deleting Critical Directory Using RM Command id: 33f89303-cc6f-49ad-921d-2eaea38a6f7a -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the deletion of critical directories on - a Linux machine using the `rm` command with argument rf. It leverages data from - Endpoint Detection and Response (EDR) agents, focusing on command-line executions - targeting directories like /boot, /var/log, /etc, and /dev. This activity is significant - because deleting these directories can severely disrupt system operations and is - often associated with destructive campaigns like Industroyer2. If confirmed malicious, - this action could lead to system instability, data loss, and potential downtime, - making it crucial for immediate investigation and response. +description: The following analytic detects the deletion of critical directories on a Linux machine using the `rm` command with argument rf. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions targeting directories like /boot, /var/log, /etc, and /dev. This activity is significant because deleting these directories can severely disrupt system operations and is often associated with destructive campaigns like Industroyer2. If confirmed malicious, this action could lead to system instability, data loss, and potential downtime, making it crucial for immediate investigation and response. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name =rm AND - Processes.process= "* -rf *" AND Processes.process IN ("*/boot/*", "*/var/log/*", - "*/etc/*", "*/dev/*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_deleting_critical_directory_using_rm_command_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name =rm + AND + Processes.process= "* -rf *" + AND + Processes.process IN ("*/boot/*", "*/var/log/*", "*/etc/*", "*/dev/*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_deleting_critical_directory_using_rm_command_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ -- https://cert.gov.ua/article/39518 + - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ + - https://cert.gov.ua/article/39518 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A deletion in known critical list of folder using rm command $process$ - executed on $dest$ - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A deletion in known critical list of folder using rm command $process$ executed on $dest$ + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - AwfulShred - - Data Destruction - - Industroyer2 - asset_type: Endpoint - mitre_attack_id: - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AwfulShred + - Data Destruction + - Industroyer2 + asset_type: Endpoint + mitre_attack_id: + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/rm_shred_critical_dir/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/rm_shred_critical_dir/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_deletion_of_cron_jobs.yml b/detections/endpoint/linux_deletion_of_cron_jobs.yml index f21babe795..4f279d295e 100644 --- a/detections/endpoint/linux_deletion_of_cron_jobs.yml +++ b/detections/endpoint/linux_deletion_of_cron_jobs.yml @@ -1,74 +1,64 @@ name: Linux Deletion Of Cron Jobs id: 3b132a71-9335-4f33-9932-00bb4f6ac7e8 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the deletion of cron jobs on a Linux machine. - It leverages filesystem event logs to identify when files within the "/etc/cron.*" - directory are deleted. This activity is significant because attackers or malware - may delete cron jobs to disable scheduled security tasks or evade detection mechanisms. - If confirmed malicious, this action could allow an attacker to disrupt system operations, - evade security measures, or facilitate further malicious activities such as data - wiping, as seen with the acidrain malware. +description: The following analytic detects the deletion of cron jobs on a Linux machine. It leverages filesystem event logs to identify when files within the "/etc/cron.*" directory are deleted. This activity is significant because attackers or malware may delete cron jobs to disable scheduled security tasks or evade detection mechanisms. If confirmed malicious, this action could allow an attacker to disrupt system operations, evade security measures, or facilitate further malicious activities such as data wiping, as seen with the acidrain malware. data_source: -- Sysmon for Linux EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.action=deleted Filesystem.file_path="/etc/cron.*" - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path - Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | - `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_deletion_of_cron_jobs_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from - Splunkbase. -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 11 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.action=deleted Filesystem.file_path="/etc/cron.*" + BY Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path Filesystem.file_acl + Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_deletion_of_cron_jobs_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase. +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://www.sentinelone.com/labs/acidrain-a-modem-wiper-rains-down-on-europe/ + - https://www.sentinelone.com/labs/acidrain-a-modem-wiper-rains-down-on-europe/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Linux cron jobs are deleted on host $dest$ by process GUID- $process_guid$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: - - field: file_name - type: file_name + message: Linux cron jobs are deleted on host $dest$ by process GUID- $process_guid$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - AcidRain - - Data Destruction - - AcidPour - asset_type: Endpoint - mitre_attack_id: - - T1070.004 - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AcidRain + - Data Destruction + - AcidPour + asset_type: Endpoint + mitre_attack_id: + - T1070.004 + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/acidrain/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/acidrain/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_deletion_of_init_daemon_script.yml b/detections/endpoint/linux_deletion_of_init_daemon_script.yml index be8bd22c2b..d8d22fd532 100644 --- a/detections/endpoint/linux_deletion_of_init_daemon_script.yml +++ b/detections/endpoint/linux_deletion_of_init_daemon_script.yml @@ -1,74 +1,64 @@ name: Linux Deletion Of Init Daemon Script id: 729aab57-d26f-4156-b97f-ab8dda8f44b1 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the deletion of init daemon scripts on - a Linux machine. It leverages filesystem event logs to identify when files within - the /etc/init.d/ directory are deleted. This activity is significant because init - daemon scripts control the start and stop of critical services, and their deletion - can indicate an attempt to impair security features or evade defenses. If confirmed - malicious, this behavior could allow an attacker to disrupt essential services, - execute destructive payloads, or persist undetected in the environment. +description: The following analytic detects the deletion of init daemon scripts on a Linux machine. It leverages filesystem event logs to identify when files within the /etc/init.d/ directory are deleted. This activity is significant because init daemon scripts control the start and stop of critical services, and their deletion can indicate an attempt to impair security features or evade defenses. If confirmed malicious, this behavior could allow an attacker to disrupt essential services, execute destructive payloads, or persist undetected in the environment. data_source: -- Sysmon for Linux EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.action=deleted Filesystem.file_path - IN ( "/etc/init.d/*") by Filesystem.action Filesystem.dest Filesystem.file_access_time - Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name - Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid - Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_deletion_of_init_daemon_script_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from - Splunkbase. -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 11 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.action=deleted Filesystem.file_path IN ( "/etc/init.d/*") + BY Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path Filesystem.file_acl + Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_deletion_of_init_daemon_script_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase. +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://www.sentinelone.com/labs/acidrain-a-modem-wiper-rains-down-on-europe/ + - https://www.sentinelone.com/labs/acidrain-a-modem-wiper-rains-down-on-europe/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Init daemon script deleted on host $dest$ by process GUID- $process_guid$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: - - field: file_name - type: file_name + message: Init daemon script deleted on host $dest$ by process GUID- $process_guid$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - AcidRain - - Data Destruction - - AcidPour - asset_type: Endpoint - mitre_attack_id: - - T1070.004 - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AcidRain + - Data Destruction + - AcidPour + asset_type: Endpoint + mitre_attack_id: + - T1070.004 + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/acidrain/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/acidrain/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_deletion_of_services.yml b/detections/endpoint/linux_deletion_of_services.yml index f38138272c..f02afb3b00 100644 --- a/detections/endpoint/linux_deletion_of_services.yml +++ b/detections/endpoint/linux_deletion_of_services.yml @@ -1,79 +1,67 @@ name: Linux Deletion Of Services id: b509bbd3-0331-4aaa-8e4a-d2affe100af6 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the deletion of services on a Linux machine. - It leverages filesystem event logs to identify when service files within system - directories (e.g., /etc/systemd/, /lib/systemd/, /run/systemd/) are deleted. This - activity is significant because attackers may delete or modify services to disable - security features or evade defenses. If confirmed malicious, this behavior could - indicate an attempt to impair system functionality or execute a destructive payload, - potentially leading to system instability or data loss. Immediate investigation - is required to determine the responsible process and user. +description: The following analytic detects the deletion of services on a Linux machine. It leverages filesystem event logs to identify when service files within system directories (e.g., /etc/systemd/, /lib/systemd/, /run/systemd/) are deleted. This activity is significant because attackers may delete or modify services to disable security features or evade defenses. If confirmed malicious, this behavior could indicate an attempt to impair system functionality or execute a destructive payload, potentially leading to system instability or data loss. Immediate investigation is required to determine the responsible process and user. data_source: -- Sysmon for Linux EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.action=deleted Filesystem.file_path - IN ( "/etc/systemd/*", "*/lib/systemd/*", "*/run/systemd/*") Filesystem.file_path - = "*.service" by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path - Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | - `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_deletion_of_services_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from - Splunkbase. -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 11 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.action=deleted Filesystem.file_path IN ( "/etc/systemd/*", "*/lib/systemd/*", "*/run/systemd/*") Filesystem.file_path = "*.service" + BY Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path Filesystem.file_acl + Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_deletion_of_services_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase. +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://www.sentinelone.com/labs/acidrain-a-modem-wiper-rains-down-on-europe/ -- https://unix.stackexchange.com/questions/224992/where-do-i-put-my-systemd-unit-file -- https://cert.gov.ua/article/3718487 + - https://www.sentinelone.com/labs/acidrain-a-modem-wiper-rains-down-on-europe/ + - https://unix.stackexchange.com/questions/224992/where-do-i-put-my-systemd-unit-file + - https://cert.gov.ua/article/3718487 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A services file $file_name$ deteted on host $dest$ by process GUID - $process_guid$ - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: - - field: file_name - type: file_name + message: A services file $file_name$ deteted on host $dest$ by process GUID - $process_guid$ + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - AwfulShred - - AcidRain - - Data Destruction - - AcidPour - asset_type: Endpoint - mitre_attack_id: - - T1070.004 - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AwfulShred + - AcidRain + - Data Destruction + - AcidPour + asset_type: Endpoint + mitre_attack_id: + - T1070.004 + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/acidrain/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/acidrain/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_deletion_of_ssl_certificate.yml b/detections/endpoint/linux_deletion_of_ssl_certificate.yml index a06796978a..caf10c668d 100644 --- a/detections/endpoint/linux_deletion_of_ssl_certificate.yml +++ b/detections/endpoint/linux_deletion_of_ssl_certificate.yml @@ -1,75 +1,63 @@ name: Linux Deletion of SSL Certificate id: 839ab790-a60a-4f81-bfb3-02567063f615 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the deletion of SSL certificates on a - Linux machine. It leverages filesystem event logs to identify when files with extensions - .pem or .crt are deleted from the /etc/ssl/certs/ directory. This activity is significant - because attackers may delete or modify SSL certificates to disable security features - or evade defenses on a compromised system. If confirmed malicious, this behavior - could indicate an attempt to disrupt secure communications, evade detection, or - execute a destructive payload, potentially leading to significant security breaches - and data loss. +description: The following analytic detects the deletion of SSL certificates on a Linux machine. It leverages filesystem event logs to identify when files with extensions .pem or .crt are deleted from the /etc/ssl/certs/ directory. This activity is significant because attackers may delete or modify SSL certificates to disable security features or evade defenses on a compromised system. If confirmed malicious, this behavior could indicate an attempt to disrupt secure communications, evade detection, or execute a destructive payload, potentially leading to significant security breaches and data loss. data_source: -- Sysmon for Linux EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.action=deleted Filesystem.file_path - = "/etc/ssl/certs/*" Filesystem.file_path IN ("*.pem", "*.crt") by Filesystem.action - Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash - Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl - Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user - Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`| `linux_deletion_of_ssl_certificate_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from - Splunkbase. -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 11 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.action=deleted Filesystem.file_path = "/etc/ssl/certs/*" Filesystem.file_path IN ("*.pem", "*.crt") + BY Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path Filesystem.file_acl + Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_deletion_of_ssl_certificate_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase. +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://www.sentinelone.com/labs/acidrain-a-modem-wiper-rains-down-on-europe/ + - https://www.sentinelone.com/labs/acidrain-a-modem-wiper-rains-down-on-europe/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: SSL certificate deleted on host $dest$ by process GUID- $process_guid$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: - - field: file_name - type: file_name + message: SSL certificate deleted on host $dest$ by process GUID- $process_guid$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - AcidRain - - AcidPour - asset_type: Endpoint - mitre_attack_id: - - T1070.004 - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AcidRain + - AcidPour + asset_type: Endpoint + mitre_attack_id: + - T1070.004 + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/acidrain/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/acidrain/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_disable_services.yml b/detections/endpoint/linux_disable_services.yml index cca2249b23..b2710c1232 100644 --- a/detections/endpoint/linux_disable_services.yml +++ b/detections/endpoint/linux_disable_services.yml @@ -1,82 +1,64 @@ name: Linux Disable Services id: f2e08a38-6689-4df4-ad8c-b51c16262316 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects attempts to disable a service on a Linux - system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing - on processes like "systemctl," "service," and "svcadm" with commands containing - "disable." This activity is significant as adversaries may disable security or critical - services to evade detection and facilitate further malicious actions, such as deploying - destructive payloads. If confirmed malicious, this could lead to the termination - of essential security services, allowing attackers to persist undetected and potentially - cause significant damage to the system. +description: The following analytic detects attempts to disable a service on a Linux system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on processes like "systemctl," "service," and "svcadm" with commands containing "disable." This activity is significant as adversaries may disable security or critical services to evade detection and facilitate further malicious actions, such as deploying destructive payloads. If confirmed malicious, this could lead to the termination of essential security services, allowing attackers to persist undetected and potentially cause significant damage to the system. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("systemctl", - "service", "svcadm") Processes.process = "* disable*" by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_disable_services_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name IN ("systemctl", "service", "svcadm") Processes.process = "* disable*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_disable_services_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ -- https://cert.gov.ua/article/39518 + - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ + - https://cert.gov.ua/article/39518 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - attempting to disable services on endpoint $dest$ by $user$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: An instance of $parent_process_name$ spawning $process_name$ was identified attempting to disable services on endpoint $dest$ by $user$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - AwfulShred - - Data Destruction - - Industroyer2 - asset_type: Endpoint - mitre_attack_id: - - T1489 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AwfulShred + - Data Destruction + - Industroyer2 + asset_type: Endpoint + mitre_attack_id: + - T1489 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1489/linux_service_stop_disable/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1489/linux_service_stop_disable/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_doas_conf_file_creation.yml b/detections/endpoint/linux_doas_conf_file_creation.yml index 9ab6ec1f4b..f35c5014ef 100644 --- a/detections/endpoint/linux_doas_conf_file_creation.yml +++ b/detections/endpoint/linux_doas_conf_file_creation.yml @@ -1,71 +1,61 @@ name: Linux Doas Conf File Creation id: f6343e86-6e09-11ec-9376-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the creation of the doas.conf file on - a Linux host. This file is used by the doas utility to allow standard users to perform - tasks as root, similar to sudo. The detection leverages filesystem data from the - Endpoint data model, focusing on the creation of the doas.conf file. This activity - is significant because it can indicate an attempt to gain elevated privileges, potentially - by an adversary. If confirmed malicious, this could allow an attacker to execute - commands with root privileges, leading to full system compromise. +description: The following analytic detects the creation of the doas.conf file on a Linux host. This file is used by the doas utility to allow standard users to perform tasks as root, similar to sudo. The detection leverages filesystem data from the Endpoint data model, focusing on the creation of the doas.conf file. This activity is significant because it can indicate an attempt to gain elevated privileges, potentially by an adversary. If confirmed malicious, this could allow an attacker to execute commands with root privileges, leading to full system compromise. data_source: -- Sysmon for Linux EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/etc/doas.conf") - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path - Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | - `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_doas_conf_file_creation_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from - Splunkbase. -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 11 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.file_path IN ("*/etc/doas.conf") + BY Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path Filesystem.file_acl + Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` + | `linux_doas_conf_file_creation_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase. +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://wiki.gentoo.org/wiki/Doas -- https://www.makeuseof.com/how-to-install-and-use-doas/ + - https://wiki.gentoo.org/wiki/Doas + - https://www.makeuseof.com/how-to-install-and-use-doas/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A file $file_name$ is created in $file_path$ on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: A file $file_name$ is created in $file_path$ on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/doas/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/doas/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_doas_tool_execution.yml b/detections/endpoint/linux_doas_tool_execution.yml index f1080db49e..bd3e329005 100644 --- a/detections/endpoint/linux_doas_tool_execution.yml +++ b/detections/endpoint/linux_doas_tool_execution.yml @@ -1,78 +1,63 @@ name: Linux Doas Tool Execution id: d5a62490-6e09-11ec-884e-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the 'doas' tool on a - Linux host. This tool allows standard users to perform tasks with root privileges, - similar to 'sudo'. The detection leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process names and command-line executions. This activity - is significant as 'doas' can be exploited by adversaries to gain elevated privileges - on a compromised host. If confirmed malicious, this could lead to unauthorized administrative - access, potentially compromising the entire system. +description: The following analytic detects the execution of the 'doas' tool on a Linux host. This tool allows standard users to perform tasks with root privileges, similar to 'sudo'. The detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as 'doas' can be exploited by adversaries to gain elevated privileges on a compromised host. If confirmed malicious, this could lead to unauthorized administrative access, potentially compromising the entire system. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "doas" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_doas_tool_execution_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "doas" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_doas_tool_execution_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://wiki.gentoo.org/wiki/Doas -- https://www.makeuseof.com/how-to-install-and-use-doas/ + - https://wiki.gentoo.org/wiki/Doas + - https://www.makeuseof.com/how-to-install-and-use-doas/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A doas $process_name$ with commandline $process$ was executed on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: A doas $process_name$ with commandline $process$ was executed on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/doas_exec/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/doas_exec/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_docker_privilege_escalation.yml b/detections/endpoint/linux_docker_privilege_escalation.yml index c430553461..6af1be1e31 100644 --- a/detections/endpoint/linux_docker_privilege_escalation.yml +++ b/detections/endpoint/linux_docker_privilege_escalation.yml @@ -1,84 +1,68 @@ name: Linux Docker Privilege Escalation id: 2e7bfb78-85f6-47b5-bc2f-15813a4ef2b3 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: Anomaly -description: The following analytic detects attempts to escalate privileges on a Linux - system using Docker. It identifies processes where Docker commands are used to mount - the root directory or execute shell commands within a container. This detection - leverages Endpoint Detection and Response (EDR) telemetry, focusing on process names, - command-line arguments, and parent processes. This activity is significant because - it can allow an attacker with Docker privileges to modify critical system files, - such as /etc/passwd, to create a superuser. If confirmed malicious, this could lead - to full system compromise and persistent unauthorized access. +description: The following analytic detects attempts to escalate privileges on a Linux system using Docker. It identifies processes where Docker commands are used to mount the root directory or execute shell commands within a container. This detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on process names, command-line arguments, and parent processes. This activity is significant because it can allow an attacker with Docker privileges to modify critical system files, such as /etc/passwd, to create a superuser. If confirmed malicious, this could lead to full system compromise and persistent unauthorized access. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process IN("*docker*-v*/*:*","*docker*--volume*/*:*") - OR Processes.process IN("*docker*exec*sh*","*docker*exec*bash*") by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_docker_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives are present based on automated tooling or system - administrative usage. Filter as needed. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process IN("*docker*-v*/*:*","*docker*--volume*/*:*") + OR + Processes.process IN("*docker*exec*sh*","*docker*exec*bash*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_docker_privilege_escalation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives are present based on automated tooling or system administrative usage. Filter as needed. references: -- https://gtfobins.github.io/gtfobins/docker/ + - https://gtfobins.github.io/gtfobins/docker/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 5 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 5 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/docker/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/docker/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_edit_cron_table_parameter.yml b/detections/endpoint/linux_edit_cron_table_parameter.yml index 7b0e9c7de6..8a85484e20 100644 --- a/detections/endpoint/linux_edit_cron_table_parameter.yml +++ b/detections/endpoint/linux_edit_cron_table_parameter.yml @@ -1,59 +1,48 @@ name: Linux Edit Cron Table Parameter id: 0d370304-5f26-11ec-a4bb-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects the suspicious editing of cron jobs in - Linux using the crontab command-line parameter (-e). It identifies this activity - by monitoring command-line executions involving 'crontab' and the edit parameter. - This behavior is significant for a SOC as cron job manipulations can indicate unauthorized - persistence attempts or scheduled malicious actions. If confirmed malicious, this - activity could lead to system compromise, unauthorized access, or broader network - compromise. +description: The following analytic detects the suspicious editing of cron jobs in Linux using the crontab command-line parameter (-e). It identifies this activity by monitoring command-line executions involving 'crontab' and the edit parameter. This behavior is significant for a SOC as cron job manipulations can indicate unauthorized persistence attempts or scheduled malicious actions. If confirmed malicious, this activity could lead to system compromise, unauthorized access, or broader network compromise. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = crontab - Processes.process = "*crontab *" Processes.process = "* -e*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_edit_cron_table_parameter_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = crontab Processes.process = "*crontab *" Processes.process = "* -e*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_edit_cron_table_parameter_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://attack.mitre.org/techniques/T1053/003/ + - https://attack.mitre.org/techniques/T1053/003/ tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - - Linux Living Off The Land - - Scheduled Tasks - asset_type: Endpoint - mitre_attack_id: - - T1053.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + - Linux Living Off The Land + - Scheduled Tasks + asset_type: Endpoint + mitre_attack_id: + - T1053.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/crontab_edit_parameter/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/crontab_edit_parameter/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_emacs_privilege_escalation.yml b/detections/endpoint/linux_emacs_privilege_escalation.yml index 734fd5d61a..3426ffdb5a 100644 --- a/detections/endpoint/linux_emacs_privilege_escalation.yml +++ b/detections/endpoint/linux_emacs_privilege_escalation.yml @@ -1,84 +1,71 @@ name: Linux Emacs Privilege Escalation id: 92033cab-1871-483d-a03b-a7ce98665cfc -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: Anomaly -description: The following analytic detects the execution of Emacs with elevated privileges - using the `sudo` command and the `--eval` option. It leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process execution logs that include - command-line arguments. This activity is significant because it indicates a potential - privilege escalation attempt, where a user could gain root access by running Emacs - with elevated permissions. If confirmed malicious, this could allow an attacker - to execute arbitrary commands as root, leading to full system compromise and unauthorized - access to sensitive information. +description: The following analytic detects the execution of Emacs with elevated privileges using the `sudo` command and the `--eval` option. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line arguments. This activity is significant because it indicates a potential privilege escalation attempt, where a user could gain root access by running Emacs with elevated permissions. If confirmed malicious, this could allow an attacker to execute arbitrary commands as root, leading to full system compromise and unauthorized access to sensitive information. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*emacs*" - AND Processes.process="*--eval*" AND Processes.process="*sudo*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_emacs_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*emacs*" + AND + Processes.process="*--eval*" + AND + Processes.process="*sudo*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_emacs_privilege_escalation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives may be present, filter as needed. references: -- https://gtfobins.github.io/gtfobins/emacs/ -- https://en.wikipedia.org/wiki/Emacs + - https://gtfobins.github.io/gtfobins/emacs/ + - https://en.wikipedia.org/wiki/Emacs drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 20 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 20 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/emacs/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/emacs/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_file_created_in_kernel_driver_directory.yml b/detections/endpoint/linux_file_created_in_kernel_driver_directory.yml index ea1fdcb53c..b3fc6d6254 100644 --- a/detections/endpoint/linux_file_created_in_kernel_driver_directory.yml +++ b/detections/endpoint/linux_file_created_in_kernel_driver_directory.yml @@ -1,71 +1,63 @@ name: Linux File Created In Kernel Driver Directory id: b85bbeec-6326-11ec-9311-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the creation of files in the Linux kernel/driver - directory. It leverages filesystem data to identify new files in this critical directory. - This activity is significant because the kernel/driver directory is typically reserved - for kernel modules, and unauthorized file creation here can indicate a rootkit installation. - If confirmed malicious, this could allow an attacker to gain high-level privileges, - potentially compromising the entire system by executing code at the kernel level. +description: The following analytic detects the creation of files in the Linux kernel/driver directory. It leverages filesystem data to identify new files in this critical directory. This activity is significant because the kernel/driver directory is typically reserved for kernel modules, and unauthorized file creation here can indicate a rootkit installation. If confirmed malicious, this could allow an attacker to gain high-level privileges, potentially compromising the entire system by executing code at the kernel level. data_source: -- Sysmon for Linux EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/kernel/drivers/*") - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path - Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | - `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_file_created_in_kernel_driver_directory_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the file name, file path, and process_guid executions from your endpoints. - If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase. -known_false_positives: Administrator or network operator can create file in this folders - for automation purposes. Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 11 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.file_path IN ("*/kernel/drivers/*") + BY Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path Filesystem.file_acl + Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` + | `linux_file_created_in_kernel_driver_directory_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the file name, file path, and process_guid executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase. +known_false_positives: Administrator or network operator can create file in this folders for automation purposes. Please update the filter macros to remove false positives. references: -- https://docs.fedoraproject.org/en-US/fedora/rawhide/system-administrators-guide/kernel-module-driver-configuration/Working_with_Kernel_Modules/ -- https://security.stackexchange.com/questions/175953/how-to-load-a-malicious-lkm-at-startup -- https://0x00sec.org/t/kernel-rootkits-getting-your-hands-dirty/1485 + - https://docs.fedoraproject.org/en-US/fedora/rawhide/system-administrators-guide/kernel-module-driver-configuration/Working_with_Kernel_Modules/ + - https://security.stackexchange.com/questions/175953/how-to-load-a-malicious-lkm-at-startup + - https://0x00sec.org/t/kernel-rootkits-getting-your-hands-dirty/1485 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A file $file_name$ is created in $file_path$ on $dest$ - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: [] + message: A file $file_name$ is created in $file_path$ on $dest$ + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: [] tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - - Linux Rootkit - asset_type: Endpoint - mitre_attack_id: - - T1547.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + - Linux Rootkit + asset_type: Endpoint + mitre_attack_id: + - T1547.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/loading_linux_kernel_module/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/loading_linux_kernel_module/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_file_creation_in_init_boot_directory.yml b/detections/endpoint/linux_file_creation_in_init_boot_directory.yml index e82727db2d..db9037eecc 100644 --- a/detections/endpoint/linux_file_creation_in_init_boot_directory.yml +++ b/detections/endpoint/linux_file_creation_in_init_boot_directory.yml @@ -1,73 +1,63 @@ name: Linux File Creation In Init Boot Directory id: 97d9cfb2-61ad-11ec-bb2d-acde48001122 -version: 10 -date: '2025-05-02' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the creation of files in Linux init boot - directories, which are used for automatic execution upon system startup. It leverages - file system logs to identify new files in directories such as /etc/init.d/ and /etc/rc.d/. - This activity is significant as it is a common persistence technique used by adversaries, - malware authors, and red teamers. If confirmed malicious, this could allow an attacker - to maintain persistence on the compromised host, potentially leading to further - exploitation and unauthorized control over the system. +description: The following analytic detects the creation of files in Linux init boot directories, which are used for automatic execution upon system startup. It leverages file system logs to identify new files in directories such as /etc/init.d/ and /etc/rc.d/. This activity is significant as it is a common persistence technique used by adversaries, malware authors, and red teamers. If confirmed malicious, this could allow an attacker to maintain persistence on the compromised host, potentially leading to further exploitation and unauthorized control over the system. data_source: -- Sysmon for Linux EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/etc/init.d/*", - "*/etc/rc.d/*", "*/sbin/init.d/*", "*/etc/rc.local*") by Filesystem.action Filesystem.dest - Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time - Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size - Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product - | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` - | `linux_file_creation_in_init_boot_directory_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the file name, file path, and process_guid executions from your endpoints. - If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase -known_false_positives: Administrator or network operator can create file in this folders - for automation purposes. Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 11 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.file_path IN ("*/etc/init.d/*", "*/etc/rc.d/*", "*/sbin/init.d/*", "*/etc/rc.local*") + BY Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path Filesystem.file_acl + Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` + | `linux_file_creation_in_init_boot_directory_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the file name, file path, and process_guid executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase +known_false_positives: Administrator or network operator can create file in this folders for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.intezer.com/blog/research/kaiji-new-chinese-linux-malware-turning-to-golang/ + - https://www.intezer.com/blog/research/kaiji-new-chinese-linux-malware-turning-to-golang/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A file $file_name$ is created in $file_path$ on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: A file $file_name$ is created in $file_path$ on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - China-Nexus Threat Activity - - Backdoor Pingpong - - Linux Persistence Techniques - - XorDDos - - Linux Privilege Escalation - asset_type: Endpoint - mitre_attack_id: - - T1037.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - China-Nexus Threat Activity + - Backdoor Pingpong + - Linux Persistence Techniques + - XorDDos + - Linux Privilege Escalation + asset_type: Endpoint + mitre_attack_id: + - T1037.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.004/linux_init_profile/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.004/linux_init_profile/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_file_creation_in_profile_directory.yml b/detections/endpoint/linux_file_creation_in_profile_directory.yml index 3c763b8adc..c229e9b76a 100644 --- a/detections/endpoint/linux_file_creation_in_profile_directory.yml +++ b/detections/endpoint/linux_file_creation_in_profile_directory.yml @@ -1,71 +1,61 @@ name: Linux File Creation In Profile Directory id: 46ba0082-61af-11ec-9826-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the creation of files in the /etc/profile.d - directory on Linux systems. It leverages filesystem data to identify new files in - this directory, which is often used by adversaries for persistence by executing - scripts upon system boot. This activity is significant as it may indicate an attempt - to maintain long-term access to the compromised host. If confirmed malicious, this - could allow attackers to execute arbitrary code with elevated privileges each time - the system boots, potentially leading to further compromise and data exfiltration. +description: The following analytic detects the creation of files in the /etc/profile.d directory on Linux systems. It leverages filesystem data to identify new files in this directory, which is often used by adversaries for persistence by executing scripts upon system boot. This activity is significant as it may indicate an attempt to maintain long-term access to the compromised host. If confirmed malicious, this could allow attackers to execute arbitrary code with elevated privileges each time the system boots, potentially leading to further compromise and data exfiltration. data_source: -- Sysmon for Linux EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/etc/profile.d/*") - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path - Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | - `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_file_creation_in_profile_directory_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the file name, file path, and process_guid executions from your endpoints. - If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase. -known_false_positives: Administrator or network operator can create file in profile.d - folders for automation purposes. Please update the filter macros to remove false - positives. + - Sysmon for Linux EventID 11 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.file_path IN ("*/etc/profile.d/*") + BY Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path Filesystem.file_acl + Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` + | `linux_file_creation_in_profile_directory_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the file name, file path, and process_guid executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase. +known_false_positives: Administrator or network operator can create file in profile.d folders for automation purposes. Please update the filter macros to remove false positives. references: -- https://attack.mitre.org/techniques/T1546/004/ -- https://www.intezer.com/blog/research/kaiji-new-chinese-linux-malware-turning-to-golang/ + - https://attack.mitre.org/techniques/T1546/004/ + - https://www.intezer.com/blog/research/kaiji-new-chinese-linux-malware-turning-to-golang/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A file $file_name$ is created in $file_path$ on $dest$ - risk_objects: - - field: dest - type: system - score: 56 - threat_objects: [] + message: A file $file_name$ is created in $file_path$ on $dest$ + risk_objects: + - field: dest + type: system + score: 56 + threat_objects: [] tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - asset_type: Endpoint - mitre_attack_id: - - T1546.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1546.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.004/linux_init_profile/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.004/linux_init_profile/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_find_privilege_escalation.yml b/detections/endpoint/linux_find_privilege_escalation.yml index b09a413b0c..ed8e5c4db4 100644 --- a/detections/endpoint/linux_find_privilege_escalation.yml +++ b/detections/endpoint/linux_find_privilege_escalation.yml @@ -1,85 +1,71 @@ name: Linux Find Privilege Escalation id: 2ff4e0c2-8256-4143-9c07-1e39c7231111 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: Anomaly -description: The following analytic detects the use of the 'find' command with 'sudo' - and '-exec' options, which can indicate an attempt to escalate privileges on a Linux - system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing - on process execution logs that include command-line arguments. This activity is - significant because it can allow a user to execute system commands as root, potentially - leading to a root shell. If confirmed malicious, this could enable an attacker to - gain full control over the system, leading to severe security breaches and unauthorized - access to sensitive data. +description: The following analytic detects the use of the 'find' command with 'sudo' and '-exec' options, which can indicate an attempt to escalate privileges on a Linux system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line arguments. This activity is significant because it can allow a user to execute system commands as root, potentially leading to a root shell. If confirmed malicious, this could enable an attacker to gain full control over the system, leading to severe security breaches and unauthorized access to sensitive data. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*find*" AND - Processes.process="*-exec*" AND Processes.process="*sudo*" by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_find_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives are present based on automated tooling or system - administrative usage. Filter as needed. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*find*" + AND + Processes.process="*-exec*" + AND + Processes.process="*sudo*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_find_privilege_escalation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives are present based on automated tooling or system administrative usage. Filter as needed. references: -- https://gtfobins.github.io/gtfobins/find/ -- https://en.wikipedia.org/wiki/Find_(Unix) + - https://gtfobins.github.io/gtfobins/find/ + - https://en.wikipedia.org/wiki/Find_(Unix) drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 5 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 5 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/find/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/find/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_gdb_privilege_escalation.yml b/detections/endpoint/linux_gdb_privilege_escalation.yml index 89651fcc0f..56ba1ec49f 100644 --- a/detections/endpoint/linux_gdb_privilege_escalation.yml +++ b/detections/endpoint/linux_gdb_privilege_escalation.yml @@ -1,82 +1,72 @@ name: Linux GDB Privilege Escalation id: 310b7da2-ab52-437f-b1bf-0bd458674308 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the GNU Debugger (GDB) - with specific flags that indicate an attempt to escalate privileges on a Linux system. - It leverages Endpoint Detection and Response (EDR) telemetry to identify processes - where GDB is run with the `-nx`, `-ex`, and `sudo` flags. This activity is significant - because it can allow a user to execute system commands as root, potentially leading - to a root shell. If confirmed malicious, this could result in full system compromise, - allowing an attacker to gain complete control over the affected endpoint. +description: The following analytic detects the execution of the GNU Debugger (GDB) with specific flags that indicate an attempt to escalate privileges on a Linux system. It leverages Endpoint Detection and Response (EDR) telemetry to identify processes where GDB is run with the `-nx`, `-ex`, and `sudo` flags. This activity is significant because it can allow a user to execute system commands as root, potentially leading to a root shell. If confirmed malicious, this could result in full system compromise, allowing an attacker to gain complete control over the affected endpoint. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*gdb*" AND - Processes.process="*-nx*" AND Processes.process="*-ex*!*" AND Processes.process="*sudo*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_gdb_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*gdb*" + AND + Processes.process="*-nx*" + AND + Processes.process="*-ex*!*" + AND + Processes.process="*sudo*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_gdb_privilege_escalation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives may be present, filter as needed. references: -- https://gtfobins.github.io/gtfobins/gdb/ + - https://gtfobins.github.io/gtfobins/gdb/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 10 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 10 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/gdb/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/gdb/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_gdrive_binary_activity.yml b/detections/endpoint/linux_gdrive_binary_activity.yml index 61a5ab2ba9..382c536d0e 100644 --- a/detections/endpoint/linux_gdrive_binary_activity.yml +++ b/detections/endpoint/linux_gdrive_binary_activity.yml @@ -1,78 +1,61 @@ name: Linux Gdrive Binary Activity id: a42f8029-5472-4c33-8943-bb17bb07466a -version: 1 -date: '2025-08-01' +version: 2 +date: '2026-02-25' author: Raven Tait, Splunk status: production type: TTP -description: The following analytic detects the execution of the 'gdrive' tool on a - Linux host. This tool allows standard users to perform tasks associated with Google Drive - via the command line. This is used by actors to stage tools as well as exfiltrate data. - The detection leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process names and command-line executions. If confirmed malicious, - this could lead to compromise of systems or sensitive data being stolen. +description: The following analytic detects the execution of the 'gdrive' tool on a Linux host. This tool allows standard users to perform tasks associated with Google Drive via the command line. This is used by actors to stage tools as well as exfiltrate data. The detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. If confirmed malicious, this could lead to compromise of systems or sensitive data being stolen. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name="gdrive" - Processes.process IN ("* download *", "* upload *", "* list*", "* update *", - "* sync *", "* share *", "* account add*", "* drives *", "* files *") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_gdrive_binary_activity_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name="gdrive" Processes.process IN ("* download *", "* upload *", "* list*", "* update *", "* sync *", "* share *", "* account add*", "* drives *", "* files *") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_gdrive_binary_activity_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://cloud.google.com/blog/topics/threat-intelligence/uncovering-unc3886-espionage-operations + - https://cloud.google.com/blog/topics/threat-intelligence/uncovering-unc3886-espionage-operations drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $process_name$ was identified - attempting to interact with Google Drive on endpoint $dest$ by $user$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: An instance of $process_name$ was identified attempting to interact with Google Drive on endpoint $dest$ by $user$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - China-Nexus Threat Activity - asset_type: Endpoint - mitre_attack_id: - - T1567 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - China-Nexus Threat Activity + asset_type: Endpoint + mitre_attack_id: + - T1567 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1567/gdrive/gdrive_linux.log - sourcetype: sysmon:linux - source: Syslog:Linux-Sysmon/Operational \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1567/gdrive/gdrive_linux.log + sourcetype: sysmon:linux + source: Syslog:Linux-Sysmon/Operational diff --git a/detections/endpoint/linux_gem_privilege_escalation.yml b/detections/endpoint/linux_gem_privilege_escalation.yml index 78fbeb1409..f6c7781876 100644 --- a/detections/endpoint/linux_gem_privilege_escalation.yml +++ b/detections/endpoint/linux_gem_privilege_escalation.yml @@ -1,84 +1,71 @@ name: Linux Gem Privilege Escalation id: 0115482a-5dcb-4bb0-bcca-5d095d224236 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the RubyGems utility - with elevated privileges, specifically when it is used to run system commands as - root. This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on command-line executions that include "gem open -e" and "sudo". This - activity is significant because it indicates a potential privilege escalation attempt, - allowing a user to execute commands as the root user. If confirmed malicious, this - could lead to full system compromise, enabling the attacker to gain root access - and execute arbitrary commands with elevated privileges. +description: The following analytic detects the execution of the RubyGems utility with elevated privileges, specifically when it is used to run system commands as root. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions that include "gem open -e" and "sudo". This activity is significant because it indicates a potential privilege escalation attempt, allowing a user to execute commands as the root user. If confirmed malicious, this could lead to full system compromise, enabling the attacker to gain root access and execute arbitrary commands with elevated privileges. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*gem*open*-e*" - AND Processes.process="*-c*" AND Processes.process="*sudo*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_gem_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*gem*open*-e*" + AND + Processes.process="*-c*" + AND + Processes.process="*sudo*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_gem_privilege_escalation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives may be present, filter as needed. references: -- https://gtfobins.github.io/gtfobins/gem/ -- https://en.wikipedia.org/wiki/RubyGems + - https://gtfobins.github.io/gtfobins/gem/ + - https://en.wikipedia.org/wiki/RubyGems drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 10 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 10 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/gem/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/gem/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_gnu_awk_privilege_escalation.yml b/detections/endpoint/linux_gnu_awk_privilege_escalation.yml index 03cefa7b09..7c314e9509 100644 --- a/detections/endpoint/linux_gnu_awk_privilege_escalation.yml +++ b/detections/endpoint/linux_gnu_awk_privilege_escalation.yml @@ -1,82 +1,70 @@ name: Linux GNU Awk Privilege Escalation id: 0dcf43b9-50d8-42a6-acd9-d1c9201fe6ae -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the 'gawk' command with - elevated privileges on a Linux system. It leverages Endpoint Detection and Response - (EDR) telemetry to identify command-line executions where 'gawk' is used with 'sudo' - and 'BEGIN{system' patterns. This activity is significant because it indicates a - potential privilege escalation attempt, allowing a user to execute system commands - as root. If confirmed malicious, this could lead to full root access, enabling the - attacker to control the system, modify critical files, and maintain persistent access. +description: The following analytic detects the execution of the 'gawk' command with elevated privileges on a Linux system. It leverages Endpoint Detection and Response (EDR) telemetry to identify command-line executions where 'gawk' is used with 'sudo' and 'BEGIN{system' patterns. This activity is significant because it indicates a potential privilege escalation attempt, allowing a user to execute system commands as root. If confirmed malicious, this could lead to full root access, enabling the attacker to control the system, modify critical files, and maintain persistent access. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*gawk*" AND - Processes.process="*BEGIN*{system*" AND Processes.process="*sudo*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_gnu_awk_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*gawk*" + AND + Processes.process="*BEGIN*{system*" + AND + Processes.process="*sudo*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `linux_gnu_awk_privilege_escalation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives may be present, filter as needed. references: -- https://gtfobins.github.io/gtfobins/gawk/ -- https://www.geeksforgeeks.org/gawk-command-in-linux-with-examples/ + - https://gtfobins.github.io/gtfobins/gawk/ + - https://www.geeksforgeeks.org/gawk-command-in-linux-with-examples/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/gawk/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/gawk/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_hardware_addition_swapoff.yml b/detections/endpoint/linux_hardware_addition_swapoff.yml index d4e3214243..2befe5ccd3 100644 --- a/detections/endpoint/linux_hardware_addition_swapoff.yml +++ b/detections/endpoint/linux_hardware_addition_swapoff.yml @@ -1,81 +1,65 @@ name: Linux Hardware Addition SwapOff id: c1eea697-99ed-44c2-9b70-d8935464c499 -version: 7 -date: '2025-10-14' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the "swapoff" command, - which disables the swapping of paging devices on a Linux system. It leverages data - from Endpoint Detection and Response (EDR) agents, focusing on process execution - logs. This activity is significant because disabling swap can be a tactic used by - malware, such as Awfulshred, to evade detection and hinder forensic analysis. If - confirmed malicious, this action could allow an attacker to manipulate system memory - management, potentially leading to data corruption, system instability, or evasion - of memory-based detection mechanisms. +description: The following analytic detects the execution of the "swapoff" command, which disables the swapping of paging devices on a Linux system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This activity is significant because disabling swap can be a tactic used by malware, such as Awfulshred, to evade detection and hinder forensic analysis. If confirmed malicious, this action could allow an attacker to manipulate system memory management, potentially leading to data corruption, system instability, or evasion of memory-based detection mechanisms. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "swapoff" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_hardware_addition_swapoff_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: administrator may disable swapping of devices in a linux host. - Filter is needed. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "swapoff" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `linux_hardware_addition_swapoff_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: administrator may disable swapping of devices in a linux host. Filter is needed. references: -- https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/overview-of-the-cyber-weapons-used-in-the-ukraine-russia-war/ + - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/overview-of-the-cyber-weapons-used-in-the-ukraine-russia-war/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a $process_name$ swap off paging device on $dest$ - risk_objects: - - field: dest - type: system - score: 36 - - field: user - type: user - score: 36 - threat_objects: [] + message: a $process_name$ swap off paging device on $dest$ + risk_objects: + - field: dest + type: system + score: 36 + - field: user + type: user + score: 36 + threat_objects: [] tags: - analytic_story: - - AwfulShred - - Data Destruction - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1200 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AwfulShred + - Data Destruction + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1200 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test1/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test1/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_high_frequency_of_file_deletion_in_boot_folder.yml b/detections/endpoint/linux_high_frequency_of_file_deletion_in_boot_folder.yml index 5678b7e48e..31d54c686f 100644 --- a/detections/endpoint/linux_high_frequency_of_file_deletion_in_boot_folder.yml +++ b/detections/endpoint/linux_high_frequency_of_file_deletion_in_boot_folder.yml @@ -1,78 +1,61 @@ name: Linux High Frequency Of File Deletion In Boot Folder id: e27fbc5d-0445-4c4a-bc39-87f060d5c602 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects a high frequency of file deletions in - the /boot/ folder on Linux systems. It leverages filesystem event logs to identify - when 200 or more files are deleted within an hour by the same process. This behavior - is significant as it may indicate the presence of wiper malware, such as Industroyer2, - which targets critical system directories. If confirmed malicious, this activity - could lead to system instability or failure, hindering the boot process and potentially - causing a complete system compromise. +description: The following analytic detects a high frequency of file deletions in the /boot/ folder on Linux systems. It leverages filesystem event logs to identify when 200 or more files are deleted within an hour by the same process. This behavior is significant as it may indicate the presence of wiper malware, such as Industroyer2, which targets critical system directories. If confirmed malicious, this activity could lead to system instability or failure, hindering the boot process and potentially causing a complete system compromise. data_source: -- Sysmon for Linux EventID 11 -search: '| tstats `security_content_summariesonly` values(Filesystem.file_access_time) - as file_access_time values(Filesystem.file_create_time) as file_create_time values(Filesystem.file_hash) - as file_hash values(Filesystem.file_modify_time) as file_modify_time values(Filesystem.file_name) - as file_name values(Filesystem.file_path) as file_path values(Filesystem.file_acl) - as file_acl values(Filesystem.file_size) as file_size values(Filesystem.process_id) - as process_id values(Filesystem.user) as user values(Filesystem.vendor_product) - as vendor_product dc(Filesystem.file_path) as numOfDelFilePath count min(_time) - as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.action=deleted - Filesystem.file_path = "/boot/*" by _time span=1h Filesystem.dest Filesystem.process_guid - Filesystem.action | `drop_dm_object_name(Filesystem)` | where numOfDelFilePath - >= 200 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_high_frequency_of_file_deletion_in_boot_folder_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from - Splunkbase. -known_false_positives: linux package installer/uninstaller may cause this event. Please - update you filter macro to remove false positives. + - Sysmon for Linux EventID 11 +search: |- + | tstats `security_content_summariesonly` values(Filesystem.file_access_time) as file_access_time values(Filesystem.file_create_time) as file_create_time values(Filesystem.file_hash) as file_hash values(Filesystem.file_modify_time) as file_modify_time values(Filesystem.file_name) as file_name values(Filesystem.file_path) as file_path values(Filesystem.file_acl) as file_acl values(Filesystem.file_size) as file_size values(Filesystem.process_id) as process_id values(Filesystem.user) as user values(Filesystem.vendor_product) as vendor_product dc(Filesystem.file_path) as numOfDelFilePath count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.action=deleted Filesystem.file_path = "/boot/*" + BY _time span=1h Filesystem.dest + Filesystem.process_guid Filesystem.action + | `drop_dm_object_name(Filesystem)` + | where numOfDelFilePath >= 200 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_high_frequency_of_file_deletion_in_boot_folder_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase. +known_false_positives: linux package installer/uninstaller may cause this event. Please update you filter macro to remove false positives. references: -- https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ -- https://cert.gov.ua/article/39518 + - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ + - https://cert.gov.ua/article/39518 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Multiple files detection in /boot/ folder on $dest$ by process GUID - $process_guid$ - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: [] + message: Multiple files detection in /boot/ folder on $dest$ by process GUID - $process_guid$ + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: [] tags: - analytic_story: - - Data Destruction - - Industroyer2 - - AcidPour - asset_type: Endpoint - mitre_attack_id: - - T1070.004 - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Destruction + - Industroyer2 + - AcidPour + asset_type: Endpoint + mitre_attack_id: + - T1070.004 + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/rm_boot_dir/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/rm_boot_dir/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_high_frequency_of_file_deletion_in_etc_folder.yml b/detections/endpoint/linux_high_frequency_of_file_deletion_in_etc_folder.yml index 23d1870ea5..64d8cd6732 100644 --- a/detections/endpoint/linux_high_frequency_of_file_deletion_in_etc_folder.yml +++ b/detections/endpoint/linux_high_frequency_of_file_deletion_in_etc_folder.yml @@ -1,75 +1,59 @@ name: Linux High Frequency Of File Deletion In Etc Folder id: 9d867448-2aff-4d07-876c-89409a752ff8 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects a high frequency of file deletions in - the /etc/ folder on Linux systems. It leverages the Endpoint.Filesystem data model - to identify instances where 200 or more files are deleted within an hour, grouped - by process name and process ID. This behavior is significant as it may indicate - the presence of wiper malware, such as AcidRain, which aims to delete critical system - files. If confirmed malicious, this activity could lead to severe system instability, - data loss, and potential disruption of services. +description: The following analytic detects a high frequency of file deletions in the /etc/ folder on Linux systems. It leverages the Endpoint.Filesystem data model to identify instances where 200 or more files are deleted within an hour, grouped by process name and process ID. This behavior is significant as it may indicate the presence of wiper malware, such as AcidRain, which aims to delete critical system files. If confirmed malicious, this activity could lead to severe system instability, data loss, and potential disruption of services. data_source: -- Sysmon for Linux EventID 11 -search: '| tstats `security_content_summariesonly` values(Filesystem.file_access_time) - as file_access_time values(Filesystem.file_create_time) as file_create_time values(Filesystem.file_hash) - as file_hash values(Filesystem.file_modify_time) as file_modify_time values(Filesystem.file_name) - as file_name values(Filesystem.file_path) as file_path values(Filesystem.file_acl) - as file_acl values(Filesystem.file_size) as file_size values(Filesystem.process_id) - as process_id values(Filesystem.user) as user dc(Filesystem.file_path) as numOfDelFilePath - count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem - where Filesystem.action=deleted Filesystem.file_path = "/etc/*" by _time span=1h Filesystem.dest - Filesystem.process_guid Filesystem.action Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` - | where numOfDelFilePath >= 200 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_high_frequency_of_file_deletion_in_etc_folder_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from - Splunkbase. -known_false_positives: linux package installer/uninstaller may cause this event. Please - update you filter macro to remove false positives. + - Sysmon for Linux EventID 11 +search: |- + | tstats `security_content_summariesonly` values(Filesystem.file_access_time) as file_access_time values(Filesystem.file_create_time) as file_create_time values(Filesystem.file_hash) as file_hash values(Filesystem.file_modify_time) as file_modify_time values(Filesystem.file_name) as file_name values(Filesystem.file_path) as file_path values(Filesystem.file_acl) as file_acl values(Filesystem.file_size) as file_size values(Filesystem.process_id) as process_id values(Filesystem.user) as user dc(Filesystem.file_path) as numOfDelFilePath count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.action=deleted Filesystem.file_path = "/etc/*" + BY _time span=1h Filesystem.dest + Filesystem.process_guid Filesystem.action Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | where numOfDelFilePath >= 200 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_high_frequency_of_file_deletion_in_etc_folder_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase. +known_false_positives: linux package installer/uninstaller may cause this event. Please update you filter macro to remove false positives. references: -- https://www.sentinelone.com/labs/acidrain-a-modem-wiper-rains-down-on-europe/ + - https://www.sentinelone.com/labs/acidrain-a-modem-wiper-rains-down-on-europe/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Multiple files delted in /etc/ folder on $dest$ by process GUID - $process_guid$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Multiple files delted in /etc/ folder on $dest$ by process GUID - $process_guid$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - AcidRain - - Data Destruction - asset_type: Endpoint - mitre_attack_id: - - T1070.004 - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AcidRain + - Data Destruction + asset_type: Endpoint + mitre_attack_id: + - T1070.004 + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/acidrain/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/acidrain/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_impair_defenses_process_kill.yml b/detections/endpoint/linux_impair_defenses_process_kill.yml index 00c1512877..7b5d662728 100644 --- a/detections/endpoint/linux_impair_defenses_process_kill.yml +++ b/detections/endpoint/linux_impair_defenses_process_kill.yml @@ -1,59 +1,47 @@ name: Linux Impair Defenses Process Kill id: 435c6b33-adf9-47fe-be87-8e29fd6654f5 -version: 8 -date: '2025-10-14' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic identifies the execution of the 'pkill' command, - which is used to terminate processes on a Linux system. It leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process names and command-line - executions. This activity is significant because threat actors often use 'pkill' - to disable security defenses or terminate critical processes, facilitating further - malicious actions. If confirmed malicious, this behavior could lead to the disruption - of security applications, enabling attackers to evade detection and potentially - corrupt or destroy files on the targeted system. +description: The following analytic identifies the execution of the 'pkill' command, which is used to terminate processes on a Linux system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because threat actors often use 'pkill' to disable security defenses or terminate critical processes, facilitating further malicious actions. If confirmed malicious, this behavior could lead to the disruption of security applications, enabling attackers to evade detection and potentially corrupt or destroy files on the targeted system. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ( - "pgrep", "pkill") Processes.process = "*pkill *" by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_impair_defenses_process_kill_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: network admin can terminate a process using this linux command. - Filter is needed. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name IN ( "pgrep", "pkill") Processes.process = "*pkill *" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `linux_impair_defenses_process_kill_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: network admin can terminate a process using this linux command. Filter is needed. references: -- https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/overview-of-the-cyber-weapons-used-in-the-ukraine-russia-war/ -- https://cert.gov.ua/article/3718487 + - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/overview-of-the-cyber-weapons-used-in-the-ukraine-russia-war/ + - https://cert.gov.ua/article/3718487 tags: - analytic_story: - - AwfulShred - - Data Destruction - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AwfulShred + - Data Destruction + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test1/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test1/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_indicator_removal_clear_cache.yml b/detections/endpoint/linux_indicator_removal_clear_cache.yml index 5176f0e763..968437c1c7 100644 --- a/detections/endpoint/linux_indicator_removal_clear_cache.yml +++ b/detections/endpoint/linux_indicator_removal_clear_cache.yml @@ -1,82 +1,69 @@ name: Linux Indicator Removal Clear Cache id: e0940505-0b73-4719-84e6-cb94c44a5245 -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects processes that clear or free page cache - on a Linux system. It leverages Endpoint Detection and Response (EDR) data, focusing - on specific command-line executions involving the kernel system request `drop_caches`. - This activity is significant as it may indicate an attempt to delete forensic evidence - or the presence of wiper malware like Awfulshred. If confirmed malicious, this behavior - could allow an attacker to cover their tracks, making it difficult to investigate - other malicious activities or system compromises. +description: The following analytic detects processes that clear or free page cache on a Linux system. It leverages Endpoint Detection and Response (EDR) data, focusing on specific command-line executions involving the kernel system request `drop_caches`. This activity is significant as it may indicate an attempt to delete forensic evidence or the presence of wiper malware like Awfulshred. If confirmed malicious, this behavior could allow an attacker to cover their tracks, making it difficult to investigate other malicious activities or system compromises. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("dash", - "sudo", "bash") AND Processes.process IN("* echo 3 > *", "* echo 2 > *","* - echo 1 > *") AND Processes.process = "*/proc/sys/vm/drop_caches" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_indicator_removal_clear_cache_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name IN ("dash", "sudo", "bash") + AND + Processes.process IN("* echo 3 > *", "* echo 2 > *","* echo 1 > *") + AND + Processes.process = "*/proc/sys/vm/drop_caches" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `linux_indicator_removal_clear_cache_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/overview-of-the-cyber-weapons-used-in-the-ukraine-russia-war/ -- https://cert.gov.ua/article/3718487 + - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/overview-of-the-cyber-weapons-used-in-the-ukraine-russia-war/ + - https://cert.gov.ua/article/3718487 drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a $process_name$ clear cache using kernel drop cache system request in - $dest$ - risk_objects: - - field: dest - type: system - score: 49 - - field: user - type: user - score: 49 - threat_objects: [] + message: a $process_name$ clear cache using kernel drop cache system request in $dest$ + risk_objects: + - field: dest + type: system + score: 49 + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - AwfulShred - - Data Destruction - asset_type: Endpoint - mitre_attack_id: - - T1070 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AwfulShred + - Data Destruction + asset_type: Endpoint + mitre_attack_id: + - T1070 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test3/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test3/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_indicator_removal_service_file_deletion.yml b/detections/endpoint/linux_indicator_removal_service_file_deletion.yml index ce3ccc3af6..493da60fa2 100644 --- a/detections/endpoint/linux_indicator_removal_service_file_deletion.yml +++ b/detections/endpoint/linux_indicator_removal_service_file_deletion.yml @@ -1,83 +1,69 @@ name: Linux Indicator Removal Service File Deletion id: 6c077f81-2a83-4537-afbc-0e62e3215d55 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the deletion of Linux service unit configuration - files by suspicious processes. It leverages Endpoint Detection and Response (EDR) - telemetry, focusing on processes executing the 'rm' command targeting '.service' - files. This activity is significant as it may indicate malware attempting to disable - critical services or security products, a common defense evasion tactic. If confirmed - malicious, this behavior could lead to service disruption, security tool incapacitation, - or complete system compromise, severely impacting the integrity and availability - of the affected Linux host. +description: The following analytic detects the deletion of Linux service unit configuration files by suspicious processes. It leverages Endpoint Detection and Response (EDR) telemetry, focusing on processes executing the 'rm' command targeting '.service' files. This activity is significant as it may indicate malware attempting to disable critical services or security products, a common defense evasion tactic. If confirmed malicious, this behavior could lead to service disruption, security tool incapacitation, or complete system compromise, severely impacting the integrity and availability of the affected Linux host. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "rm" AND - Processes.process = "*rm *" AND Processes.process = "*.service" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_indicator_removal_service_file_deletion_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: network admin can delete services unit configuration file as - part of normal software installation. Filter is needed. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "rm" + AND + Processes.process = "*rm *" + AND + Processes.process = "*.service" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `linux_indicator_removal_service_file_deletion_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: network admin can delete services unit configuration file as part of normal software installation. Filter is needed. references: -- https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/overview-of-the-cyber-weapons-used-in-the-ukraine-russia-war/ -- https://cert.gov.ua/article/3718487 + - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/overview-of-the-cyber-weapons-used-in-the-ukraine-russia-war/ + - https://cert.gov.ua/article/3718487 drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a $process_name$ has a commandline $process$ to delete service configuration - file on $dest$ - risk_objects: - - field: dest - type: system - score: 36 - - field: user - type: user - score: 36 - threat_objects: [] + message: a $process_name$ has a commandline $process$ to delete service configuration file on $dest$ + risk_objects: + - field: dest + type: system + score: 36 + - field: user + type: user + score: 36 + threat_objects: [] tags: - analytic_story: - - AwfulShred - - Data Destruction - asset_type: Endpoint - mitre_attack_id: - - T1070.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AwfulShred + - Data Destruction + asset_type: Endpoint + mitre_attack_id: + - T1070.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test1/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test1/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_ingress_tool_transfer_hunting.yml b/detections/endpoint/linux_ingress_tool_transfer_hunting.yml index b5394062f9..dabb156c0d 100644 --- a/detections/endpoint/linux_ingress_tool_transfer_hunting.yml +++ b/detections/endpoint/linux_ingress_tool_transfer_hunting.yml @@ -1,62 +1,55 @@ name: Linux Ingress Tool Transfer Hunting id: 52fd468b-cb6d-48f5-b16a-92f1c9bb10cf -version: 9 -date: '2025-10-27' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic detects the use of 'curl' and 'wget' commands - within a Linux environment. It leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process names, user information, and command-line executions. - This activity is significant as 'curl' and 'wget' are commonly used for downloading - files, which can indicate potential ingress of malicious tools. If confirmed malicious, - this activity could lead to unauthorized code execution, data exfiltration, or further - compromise of the system. Monitoring and tuning this detection helps identify and - differentiate between normal and potentially harmful usage. +description: The following analytic detects the use of 'curl' and 'wget' commands within a Linux environment. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, user information, and command-line executions. This activity is significant as 'curl' and 'wget' are commonly used for downloading files, which can indicate potential ingress of malicious tools. If confirmed malicious, this activity could lead to unauthorized code execution, data exfiltration, or further compromise of the system. Monitoring and tuning this detection helps identify and differentiate between normal and potentially harmful usage. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=curl - OR Processes.process_name=wget) by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_ingress_tool_transfer_hunting_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives will be present. This query is meant to help - tune other curl and wget analytics. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name=curl + OR + Processes.process_name=wget + ) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_ingress_tool_transfer_hunting_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives will be present. This query is meant to help tune other curl and wget analytics. references: -- https://gtfobins.github.io/gtfobins/curl/ -- https://curl.se/docs/manpage.html#-I -- https://gtfobins.github.io/gtfobins/curl/ -- https://github.com/rapid7/metasploit-framework/search?q=curl + - https://gtfobins.github.io/gtfobins/curl/ + - https://curl.se/docs/manpage.html#-I + - https://gtfobins.github.io/gtfobins/curl/ + - https://github.com/rapid7/metasploit-framework/search?q=curl tags: - analytic_story: - - Ingress Tool Transfer - - Linux Living Off The Land - - XorDDos - - NPM Supply Chain Compromise - asset_type: Endpoint - mitre_attack_id: - - T1105 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ingress Tool Transfer + - Linux Living Off The Land + - XorDDos + - NPM Supply Chain Compromise + asset_type: Endpoint + mitre_attack_id: + - T1105 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/curl-linux-sysmon.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/curl-linux-sysmon.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_ingress_tool_transfer_with_curl.yml b/detections/endpoint/linux_ingress_tool_transfer_with_curl.yml index 182462a4a6..0a7e773803 100644 --- a/detections/endpoint/linux_ingress_tool_transfer_with_curl.yml +++ b/detections/endpoint/linux_ingress_tool_transfer_with_curl.yml @@ -1,102 +1,81 @@ name: Linux Ingress Tool Transfer with Curl id: 8c1de57d-abc1-4b41-a727-a7a8fc5e0857 -version: 10 -date: '2026-01-20' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic detects the use of the curl command with specific - switches (-O, -sO, -ksO, --output) commonly used to download remote scripts or binaries. - This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process names and command-line arguments. This activity is significant - as it may indicate an attempt to download and execute potentially malicious files, - often used in initial stages of an attack. If confirmed malicious, this could lead - to unauthorized code execution, enabling attackers to compromise the system further. +description: The following analytic detects the use of the curl command with specific switches (-O, -sO, -ksO, --output) commonly used to download remote scripts or binaries. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant as it may indicate an attempt to download and execute potentially malicious files, often used in initial stages of an attack. If confirmed malicious, this could lead to unauthorized code execution, enabling attackers to compromise the system further. data_source: -- Sysmon for Linux EventID 1 + - Sysmon for Linux EventID 1 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime - - from datamodel=Endpoint.Processes where - - Processes.process_name=curl - Processes.process IN ("*-O*","*-sO*","*-ksO*","*--output*") - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id - Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id - Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | where match(process, "(?i)(-O|-sO|-ksO|--output)") - | `linux_ingress_tool_transfer_with_curl_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives will be present. Tune and then change type - to TTP. + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + + from datamodel=Endpoint.Processes where + + Processes.process_name=curl + Processes.process IN ("*-O*","*-sO*","*-ksO*","*--output*") + by Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec + Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id + Processes.process_integrity_level Processes.process_name + Processes.process_path Processes.user Processes.user_id + Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | where match(process, "(?i)(-O|-sO|-ksO|--output)") + | `linux_ingress_tool_transfer_with_curl_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives will be present. Tune and then change type to TTP. references: -- https://gtfobins.github.io/gtfobins/curl/ -- https://curl.se/docs/manpage.html#-I -- https://gtfobins.github.io/gtfobins/curl/ -- https://github.com/rapid7/metasploit-framework/search?q=curl + - https://gtfobins.github.io/gtfobins/curl/ + - https://curl.se/docs/manpage.html#-I + - https://gtfobins.github.io/gtfobins/curl/ + - https://github.com/rapid7/metasploit-framework/search?q=curl drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $process_name$ was identified on endpoint $dest$ by user - $user$ to download a remote file. Review activity for further details. - risk_objects: - - field: user - type: user - score: 12 - - field: dest - type: system - score: 12 - threat_objects: - - field: process_name - type: process_name + message: An instance of $process_name$ was identified on endpoint $dest$ by user $user$ to download a remote file. Review activity for further details. + risk_objects: + - field: user + type: user + score: 12 + - field: dest + type: system + score: 12 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Ingress Tool Transfer - - Linux Living Off The Land - - XorDDos - - NPM Supply Chain Compromise - asset_type: Endpoint - mitre_attack_id: - - T1105 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ingress Tool Transfer + - Linux Living Off The Land + - XorDDos + - NPM Supply Chain Compromise + asset_type: Endpoint + mitre_attack_id: + - T1105 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/curl-linux-sysmon.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/curl-linux-sysmon.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_insert_kernel_module_using_insmod_utility.yml b/detections/endpoint/linux_insert_kernel_module_using_insmod_utility.yml index 88aeabfa64..bd18029193 100644 --- a/detections/endpoint/linux_insert_kernel_module_using_insmod_utility.yml +++ b/detections/endpoint/linux_insert_kernel_module_using_insmod_utility.yml @@ -1,82 +1,68 @@ name: Linux Insert Kernel Module Using Insmod Utility id: 18b5a1a0-6326-11ec-943a-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the insertion of a Linux kernel module - using the insmod utility. It leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process execution logs that include process names and - command-line details. This activity is significant as it may indicate the installation - of a rootkit or malicious kernel module, potentially allowing an attacker to gain - elevated privileges and bypass security detections. If confirmed malicious, this - could lead to unauthorized code execution, persistent access, and severe compromise - of the affected system. +description: The following analytic detects the insertion of a Linux kernel module using the insmod utility. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include process names and command-line details. This activity is significant as it may indicate the installation of a rootkit or malicious kernel module, potentially allowing an attacker to gain elevated privileges and bypass security detections. If confirmed malicious, this could lead to unauthorized code execution, persistent access, and severe compromise of the affected system. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN("kmod", - "sudo") AND Processes.process = *insmod* by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_insert_kernel_module_using_insmod_utility_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name IN("kmod", "sudo") + AND + Processes.process = *insmod* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_insert_kernel_module_using_insmod_utility_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://docs.fedoraproject.org/en-US/fedora/rawhide/system-administrators-guide/kernel-module-driver-configuration/Working_with_Kernel_Modules/ -- https://security.stackexchange.com/questions/175953/how-to-load-a-malicious-lkm-at-startup -- https://0x00sec.org/t/kernel-rootkits-getting-your-hands-dirty/1485 + - https://docs.fedoraproject.org/en-US/fedora/rawhide/system-administrators-guide/kernel-module-driver-configuration/Working_with_Kernel_Modules/ + - https://security.stackexchange.com/questions/175953/how-to-load-a-malicious-lkm-at-startup + - https://0x00sec.org/t/kernel-rootkits-getting-your-hands-dirty/1485 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A commandline $process$ that may install kernel module on $dest$ - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A commandline $process$ that may install kernel module on $dest$ + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Linux Persistence Techniques - - XorDDos - - Linux Rootkit - - Linux Privilege Escalation - asset_type: Endpoint - mitre_attack_id: - - T1547.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Persistence Techniques + - XorDDos + - Linux Rootkit + - Linux Privilege Escalation + asset_type: Endpoint + mitre_attack_id: + - T1547.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/loading_linux_kernel_module/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/loading_linux_kernel_module/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_install_kernel_module_using_modprobe_utility.yml b/detections/endpoint/linux_install_kernel_module_using_modprobe_utility.yml index 34d5c8fc02..d6f0437505 100644 --- a/detections/endpoint/linux_install_kernel_module_using_modprobe_utility.yml +++ b/detections/endpoint/linux_install_kernel_module_using_modprobe_utility.yml @@ -1,83 +1,69 @@ name: Linux Install Kernel Module Using Modprobe Utility id: 387b278a-6326-11ec-aa2c-acde48001122 -version: 9 -date: '2026-01-20' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the installation of a Linux kernel module - using the modprobe utility. It leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process names and command-line executions. This activity - is significant because installing a kernel module can indicate an attempt to deploy - a rootkit or other malicious kernel-level code, potentially leading to elevated - privileges and bypassing security detections. If confirmed malicious, this could - allow an attacker to gain persistent, high-level access to the system, compromising - its integrity and security. +description: The following analytic detects the installation of a Linux kernel module using the modprobe utility. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because installing a kernel module can indicate an attempt to deploy a rootkit or other malicious kernel-level code, potentially leading to elevated privileges and bypassing security detections. If confirmed malicious, this could allow an attacker to gain persistent, high-level access to the system, compromising its integrity and security. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN("kmod", - "sudo") AND Processes.process = *modprobe* by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_install_kernel_module_using_modprobe_utility_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name IN("kmod", "sudo") + AND + Processes.process = *modprobe* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_install_kernel_module_using_modprobe_utility_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://docs.fedoraproject.org/en-US/fedora/rawhide/system-administrators-guide/kernel-module-driver-configuration/Working_with_Kernel_Modules/ -- https://security.stackexchange.com/questions/175953/how-to-load-a-malicious-lkm-at-startup -- https://0x00sec.org/t/kernel-rootkits-getting-your-hands-dirty/1485 + - https://docs.fedoraproject.org/en-US/fedora/rawhide/system-administrators-guide/kernel-module-driver-configuration/Working_with_Kernel_Modules/ + - https://security.stackexchange.com/questions/175953/how-to-load-a-malicious-lkm-at-startup + - https://0x00sec.org/t/kernel-rootkits-getting-your-hands-dirty/1485 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A commandline $process$ that may install kernel module on $dest$ - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A commandline $process$ that may install kernel module on $dest$ + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - - Linux Rootkit - - China-Nexus Threat Activity - - VoidLink Cloud-Native Linux Malware - asset_type: Endpoint - mitre_attack_id: - - T1547.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + - Linux Rootkit + - China-Nexus Threat Activity + - VoidLink Cloud-Native Linux Malware + asset_type: Endpoint + mitre_attack_id: + - T1547.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/loading_linux_kernel_module/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/loading_linux_kernel_module/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_iptables_firewall_modification.yml b/detections/endpoint/linux_iptables_firewall_modification.yml index ec36cf5d5d..9c714fd7e2 100644 --- a/detections/endpoint/linux_iptables_firewall_modification.yml +++ b/detections/endpoint/linux_iptables_firewall_modification.yml @@ -1,88 +1,83 @@ name: Linux Iptables Firewall Modification id: 309d59dc-1e1b-49b2-9800-7cf18d12f7b7 -version: 11 -date: '2025-05-02' +version: 12 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects suspicious command-line activity that - modifies the iptables firewall settings on a Linux machine. It leverages data from - Endpoint Detection and Response (EDR) agents, focusing on specific command patterns - that alter firewall rules to accept traffic on certain TCP ports. This activity - is significant as it can indicate malware, such as CyclopsBlink, modifying firewall - settings to allow communication with a Command and Control (C2) server. If confirmed - malicious, this could enable attackers to maintain persistent access and exfiltrate - data, posing a severe security risk. +description: The following analytic detects suspicious command-line activity that modifies the iptables firewall settings on a Linux machine. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command patterns that alter firewall rules to accept traffic on certain TCP ports. This activity is significant as it can indicate malware, such as CyclopsBlink, modifying firewall settings to allow communication with a Command and Control (C2) server. If confirmed malicious, this could enable attackers to maintain persistent access and exfiltrate data, posing a severe security risk. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process = "*iptables - *" AND Processes.process = "* --dport *" AND Processes.process = "* ACCEPT*" AND - Processes.process = "*&>/dev/null*" AND Processes.process = "* tcp *" AND - NOT(Processes.parent_process_path IN("/bin/*", "/lib/*", "/usr/bin/*", "/sbin/*")) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | rex field=Processes.process "--dport (?3269|636|989|994|995|8443)" | stats - values(Processes.process) as processes_exec values(port) as ports values(Processes.process_guid) - as guids values(Processes.process_id) as pids dc(port) as port_count count by Processes.process_name - Processes.parent_process_name Processes.parent_process_id Processes.dest Processes.user - Processes.parent_process_path Processes.process_path | where port_count >=3 | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_iptables_firewall_modification_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: administrator may do this commandline for auditing and testing - purposes. In this scenario filter is needed. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime + from datamodel=Endpoint.Processes where + + Processes.process = "*iptables *" + Processes.process = "* --dport *" + Processes.process = "* ACCEPT*" + Processes.process = "*&>/dev/null*" + Processes.process = "* tcp *" + NOT Processes.parent_process_path IN("/bin/*", "/lib/*", "/usr/bin/*", "/sbin/*") + + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process + Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id + Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product + + | rex field=Processes.process "--dport (?3269|636|989|994|995|8443)" + | stats values(Processes.process) as processes_exec + values(port) as ports + values(Processes.process_guid) as guids + values(Processes.process_id) as pids + dc(port) as port_count + count by Processes.process_name Processes.parent_process_name + Processes.parent_process_id Processes.dest Processes.user + Processes.parent_process_path Processes.process_path + | where port_count >=3 + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_iptables_firewall_modification_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: administrator may do this commandline for auditing and testing purposes. In this scenario filter is needed. references: -- https://www.ncsc.gov.uk/files/Cyclops-Blink-Malware-Analysis-Report.pdf -- https://www.trendmicro.com/en_us/research/22/c/cyclops-blink-sets-sights-on-asus-routers--.html + - https://www.ncsc.gov.uk/files/Cyclops-Blink-Malware-Analysis-Report.pdf + - https://www.trendmicro.com/en_us/research/22/c/cyclops-blink-sets-sights-on-asus-routers--.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process name - $process_name$ that may modify iptables firewall on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A process name - $process_name$ that may modify iptables firewall on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - China-Nexus Threat Activity - - Backdoor Pingpong - - Cyclops Blink - - Sandworm Tools - asset_type: Endpoint - mitre_attack_id: - - T1562.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - China-Nexus Threat Activity + - Backdoor Pingpong + - Cyclops Blink + - Sandworm Tools + asset_type: Endpoint + mitre_attack_id: + - T1562.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/cyclopsblink/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/cyclopsblink/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_kernel_module_enumeration.yml b/detections/endpoint/linux_kernel_module_enumeration.yml index c99077eb2c..10ab100886 100644 --- a/detections/endpoint/linux_kernel_module_enumeration.yml +++ b/detections/endpoint/linux_kernel_module_enumeration.yml @@ -1,86 +1,70 @@ name: Linux Kernel Module Enumeration id: 6df99886-0e04-4c11-8b88-325747419278 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic identifies the use of the 'kmod' process to list - kernel modules on a Linux system. This detection leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process names and command-line executions. - While listing kernel modules is not inherently malicious, it can be a precursor - to loading unauthorized modules using 'insmod'. If confirmed malicious, this activity - could allow an attacker to load kernel modules, potentially leading to privilege - escalation, persistence, or other malicious actions within the system. +description: The following analytic identifies the use of the 'kmod' process to list kernel modules on a Linux system. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. While listing kernel modules is not inherently malicious, it can be a precursor to loading unauthorized modules using 'insmod'. If confirmed malicious, this activity could allow an attacker to load kernel modules, potentially leading to privilege escalation, persistence, or other malicious actions within the system. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=kmod - Processes.process IN ("*lsmod*", "*list*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_kernel_module_enumeration_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives are present based on automated tooling or system - administrative usage. Filter as needed. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=kmod Processes.process IN ("*lsmod*", "*list*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_kernel_module_enumeration_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives are present based on automated tooling or system administrative usage. Filter as needed. references: -- https://man7.org/linux/man-pages/man8/kmod.8.html + - https://man7.org/linux/man-pages/man8/kmod.8.html drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ enumeration kernel modules. - risk_objects: - - field: user - type: user - score: 15 - - field: dest - type: system - score: 15 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ enumeration kernel modules. + risk_objects: + - field: user + type: user + score: 15 + - field: dest + type: system + score: 15 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - XorDDos - - Linux Rootkit - asset_type: Endpoint - mitre_attack_id: - - T1082 - - T1014 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - XorDDos + - Linux Rootkit + asset_type: Endpoint + mitre_attack_id: + - T1082 + - T1014 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1082/atomic_red_team/linux-sysmon.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1082/atomic_red_team/linux-sysmon.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_kworker_process_in_writable_process_path.yml b/detections/endpoint/linux_kworker_process_in_writable_process_path.yml index 467e1d6bae..f1ec56f0a0 100644 --- a/detections/endpoint/linux_kworker_process_in_writable_process_path.yml +++ b/detections/endpoint/linux_kworker_process_in_writable_process_path.yml @@ -1,58 +1,47 @@ name: Linux Kworker Process In Writable Process Path id: 1cefb270-74a5-4e27-aa0c-2b6fa7c5b4ed -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects the execution of a kworker process with - a command line in writable directories such as /home/, /var/log, and /tmp on a Linux - machine. It leverages data from Endpoint Detection and Response (EDR) agents, focusing - on process and parent process paths. This activity is significant as kworker processes - are typically kernel threads, and their presence in writable directories is unusual - and indicative of potential malware, such as CyclopsBlink. If confirmed malicious, - this could allow attackers to blend malicious processes with legitimate ones, leading - to persistent access and further system compromise. +description: The following analytic detects the execution of a kworker process with a command line in writable directories such as /home/, /var/log, and /tmp on a Linux machine. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process paths. This activity is significant as kworker processes are typically kernel threads, and their presence in writable directories is unusual and indicative of potential malware, such as CyclopsBlink. If confirmed malicious, this could allow attackers to blend malicious processes with legitimate ones, leading to persistent access and further system compromise. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process = - "*[kworker/*" Processes.parent_process_path IN ("/home/*", "/tmp/*", "/var/log/*") - Processes.process="*iptables*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_kworker_process_in_writable_process_path_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process = "*[kworker/*" Processes.parent_process_path IN ("/home/*", "/tmp/*", "/var/log/*") Processes.process="*iptables*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_kworker_process_in_writable_process_path_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://www.ncsc.gov.uk/files/Cyclops-Blink-Malware-Analysis-Report.pdf -- https://www.trendmicro.com/en_us/research/22/c/cyclops-blink-sets-sights-on-asus-routers--.html + - https://www.ncsc.gov.uk/files/Cyclops-Blink-Malware-Analysis-Report.pdf + - https://www.trendmicro.com/en_us/research/22/c/cyclops-blink-sets-sights-on-asus-routers--.html tags: - analytic_story: - - Sandworm Tools - - Cyclops Blink - asset_type: Endpoint - mitre_attack_id: - - T1036.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sandworm Tools + - Cyclops Blink + asset_type: Endpoint + mitre_attack_id: + - T1036.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/cyclopsblink/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/cyclopsblink/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_magic_sysrq_key_abuse.yml b/detections/endpoint/linux_magic_sysrq_key_abuse.yml index 9a38db6094..a72886f774 100644 --- a/detections/endpoint/linux_magic_sysrq_key_abuse.yml +++ b/detections/endpoint/linux_magic_sysrq_key_abuse.yml @@ -6,102 +6,94 @@ author: Milad Cheraghi status: production type: TTP description: | - Detects potential abuse of the Linux Magic SysRq (System Request) key by adversaries with root or sufficient privileges to manipulate or destabilize a system. - Writing to /proc/sysrq-trigger can crash the system, kill processes, or bypass standard logging. - Monitoring SysRq abuse helps detect stealthy post-exploitation activity. - Correlate with related EXECVE or PROCTITLE events to identify the process or user responsible for the access or modification. + Detects potential abuse of the Linux Magic SysRq (System Request) key by adversaries with root or sufficient privileges to manipulate or destabilize a system. + Writing to /proc/sysrq-trigger can crash the system, kill processes, or bypass standard logging. + Monitoring SysRq abuse helps detect stealthy post-exploitation activity. + Correlate with related EXECVE or PROCTITLE events to identify the process or user responsible for the access or modification. data_source: - - Linux Auditd Path - - Linux Auditd Cwd + - Linux Auditd Path + - Linux Auditd Cwd search: | - `linux_auditd` - (type=PATH OR type=CWD) - | rex "msg=audit\([^)]*:(?\d+)\)" + `linux_auditd` + (type=PATH OR type=CWD) + | rex "msg=audit\([^)]*:(?\d+)\)" - | stats - values(type) as types - values(name) as names - values(nametype) as nametype - values(cwd) as cwd_list - values(_time) as event_times - by audit_id, host + | stats + values(type) as types + values(name) as names + values(nametype) as nametype + values(cwd) as cwd_list + values(_time) as event_times + by audit_id, host - | eval current_working_directory = coalesce(mvindex(cwd_list, 0), "N/A") - | eval candidate_paths = mvmap(names, if(match(names, "^/"), names, current_working_directory + "/" + names)) - | eval matched_paths = mvfilter(match(candidate_paths, ".*/proc/sysrq-trigger|.*/proc/sys/kernel/sysrq|.*/etc/sysctl.conf")) - | eval match_count = mvcount(matched_paths) - | eval reconstructed_path = mvindex(matched_paths, 0) - | eval e_time = mvindex(event_times, 0) - | where match_count > 0 - | rename host as dest + | eval current_working_directory = coalesce(mvindex(cwd_list, 0), "N/A") + | eval candidate_paths = mvmap(names, if(match(names, "^/"), names, current_working_directory + "/" + names)) + | eval matched_paths = mvfilter(match(candidate_paths, ".*/proc/sysrq-trigger|.*/proc/sys/kernel/sysrq|.*/etc/sysctl.conf")) + | eval match_count = mvcount(matched_paths) + | eval reconstructed_path = mvindex(matched_paths, 0) + | eval e_time = mvindex(event_times, 0) + | where match_count > 0 + | rename host as dest - | stats count min(e_time) as firstTime max(e_time) as lastTime - values(nametype) as nametype - by current_working_directory - reconstructed_path - match_count - dest - audit_id + | stats count min(e_time) as firstTime max(e_time) as lastTime + values(nametype) as nametype + by current_working_directory + reconstructed_path + match_count + dest + audit_id - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_magic_sysrq_key_abuse_filter` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_magic_sysrq_key_abuse_filter` how_to_implement: | - To implement this detection, ensure auditd is configured to watch: - - /proc/sysrq-trigger - - /proc/sys/kernel/sysrq - - /etc/sysctl.conf - with write and attribute changes (`-p wa`) and key `sysrq`. Make sure the type=CWD record type is activate in your auditd configuration and - Use the Splunk Add-on for Unix and Linux for proper ingestion and CIM normalization. - This enables effective monitoring of Linux endpoints for SysRq abuse. + To implement this detection, ensure auditd is configured to watch: + - /proc/sysrq-trigger + - /proc/sys/kernel/sysrq + - /etc/sysctl.conf + with write and attribute changes (`-p wa`) and key `sysrq`. Make sure the type=CWD record type is activate in your auditd configuration and + Use the Splunk Add-on for Unix and Linux for proper ingestion and CIM normalization. + This enables effective monitoring of Linux endpoints for SysRq abuse. known_false_positives: | - Legitimate administrative activity modifying SysRq for debugging or recovery. - Please update the filter macros to remove false positives. + Legitimate administrative activity modifying SysRq for debugging or recovery. + Please update the filter macros to remove false positives. references: - - https://www.kernel.org/doc/html/v4.10/_sources/admin-guide/sysrq.txt - - https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/4/html/reference_guide/s3-proc-sys-kernel - - https://www.splunk.com/en_us/blog/security/threat-update-awfulshred-script-wiper.html + - https://www.kernel.org/doc/html/v4.10/_sources/admin-guide/sysrq.txt + - https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/4/html/reference_guide/s3-proc-sys-kernel + - https://www.splunk.com/en_us/blog/security/threat-update-awfulshred-script-wiper.html drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest="$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 - | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" - values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" - values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest="$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Abuse of the Linux Magic System Request key detected on host - [$dest$] - risk_objects: - - field: dest - type: system - score: 70 - threat_objects: [] + message: Abuse of the Linux Magic System Request key detected on host - [$dest$] + risk_objects: + - field: dest + type: system + score: 70 + threat_objects: [] tags: - analytic_story: - - Compromised Linux Host - asset_type: Endpoint - mitre_attack_id: - - T1059.004 - - T1529 - - T1489 - - T1499 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Linux Host + asset_type: Endpoint + mitre_attack_id: + - T1059.004 + - T1529 + - T1489 + - T1499 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1529/auditd_path_sysrq/path_sysrq.log - source: auditd - sourcetype: auditd + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1529/auditd_path_sysrq/path_sysrq.log + source: auditd + sourcetype: auditd diff --git a/detections/endpoint/linux_make_privilege_escalation.yml b/detections/endpoint/linux_make_privilege_escalation.yml index e8d5fa158d..329afa9466 100644 --- a/detections/endpoint/linux_make_privilege_escalation.yml +++ b/detections/endpoint/linux_make_privilege_escalation.yml @@ -1,83 +1,71 @@ name: Linux Make Privilege Escalation id: 80b22836-5091-4944-80ee-f733ac443f4f -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: Anomaly -description: The following analytic detects the use of the 'make' command with elevated - privileges to execute system commands as root, potentially leading to a root shell. - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on - command-line executions that include 'make', '--eval', and 'sudo'. This activity - is significant because it indicates a possible privilege escalation attempt, allowing - a user to gain root access. If confirmed malicious, an attacker could achieve full - control over the system, execute arbitrary commands, and compromise the entire environment. +description: The following analytic detects the use of the 'make' command with elevated privileges to execute system commands as root, potentially leading to a root shell. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions that include 'make', '--eval', and 'sudo'. This activity is significant because it indicates a possible privilege escalation attempt, allowing a user to gain root access. If confirmed malicious, an attacker could achieve full control over the system, execute arbitrary commands, and compromise the entire environment. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*make*-s*" - AND Processes.process="*--eval*" AND Processes.process="*sudo*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_make_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*make*-s*" + AND + Processes.process="*--eval*" + AND + Processes.process="*sudo*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_make_privilege_escalation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives may be present, filter as needed. references: -- https://gtfobins.github.io/gtfobins/make/ -- https://www.javatpoint.com/linux-make-command + - https://gtfobins.github.io/gtfobins/make/ + - https://www.javatpoint.com/linux-make-command drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 20 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 20 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/make/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/make/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_medusa_rootkit.yml b/detections/endpoint/linux_medusa_rootkit.yml index a0f051bfab..ff109b3646 100644 --- a/detections/endpoint/linux_medusa_rootkit.yml +++ b/detections/endpoint/linux_medusa_rootkit.yml @@ -1,78 +1,63 @@ name: Linux Medusa Rootkit id: 7add8520-71d5-43aa-b262-ee082b1f0238 -version: 3 -date: '2026-01-20' +version: 4 +date: '2026-02-25' author: Raven Tait, Splunk status: production type: TTP -description: This detection identifies file creation events associated with the installation of the Medusa - rootkit, a userland LD_PRELOAD-based rootkit known for deploying shared objects, loader binaries, and - configuration files into specific system directories. These files typically facilitate process hiding, - credential theft, and backdoor access. Monitoring for such file creation patterns enables early - detection of rootkit deployment before full compromise. +description: This detection identifies file creation events associated with the installation of the Medusa rootkit, a userland LD_PRELOAD-based rootkit known for deploying shared objects, loader binaries, and configuration files into specific system directories. These files typically facilitate process hiding, credential theft, and backdoor access. Monitoring for such file creation patterns enables early detection of rootkit deployment before full compromise. data_source: -- Sysmon for Linux EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/lib/libseconf", - "*.backup_ld.so", "*.boot.sh", "*.logpam", "*sshpass.txt", "*sshpass2.txt", "*/lib/libdsx.so", - "*rkload", "*/lib/libseconf/local.txt", "*/lib/locate/local.txt", "*/var/log/remote.txt", - "*/lib/libseconf/.pts", "*/lib/locate /.pts", "*/libseconf/.ports") - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path - Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | - `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_medusa_rootkit_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 11 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.file_path IN ("*/lib/libseconf", "*.backup_ld.so", "*.boot.sh", "*.logpam", "*sshpass.txt", "*sshpass2.txt", "*/lib/libdsx.so", "*rkload", "*/lib/libseconf/local.txt", "*/lib/locate/local.txt", "*/var/log/remote.txt", "*/lib/libseconf/.pts", "*/lib/locate /.pts", "*/libseconf/.ports") + BY Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path Filesystem.file_acl + Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` + | `linux_medusa_rootkit_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Little to no false positives in most environments. Tune as needed. references: -- https://cloud.google.com/blog/topics/threat-intelligence/uncovering-unc3886-espionage-operations + - https://cloud.google.com/blog/topics/threat-intelligence/uncovering-unc3886-espionage-operations drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Medusa rootkit files were identified on endpoint $dest$. - risk_objects: - - field: dest - type: system - score: 62 - threat_objects: [] + message: Medusa rootkit files were identified on endpoint $dest$. + risk_objects: + - field: dest + type: system + score: 62 + threat_objects: [] tags: - analytic_story: - - China-Nexus Threat Activity - - Medusa Rootkit - - Hellcat Ransomware - - VoidLink Cloud-Native Linux Malware - asset_type: Endpoint - mitre_attack_id: - - T1014 - - T1589.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - China-Nexus Threat Activity + - Medusa Rootkit + - Hellcat Ransomware + - VoidLink Cloud-Native Linux Malware + asset_type: Endpoint + mitre_attack_id: + - T1014 + - T1589.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1014/medusa_rootkit/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1014/medusa_rootkit/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_mysql_privilege_escalation.yml b/detections/endpoint/linux_mysql_privilege_escalation.yml index 89f10fad82..fe514e9137 100644 --- a/detections/endpoint/linux_mysql_privilege_escalation.yml +++ b/detections/endpoint/linux_mysql_privilege_escalation.yml @@ -1,84 +1,70 @@ name: Linux MySQL Privilege Escalation id: c0d810f4-230c-44ea-b703-989da02ff145 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: Anomaly -description: The following analytic detects the execution of MySQL commands with elevated - privileges using sudo, which can lead to privilege escalation. It leverages data - from Endpoint Detection and Response (EDR) agents, focusing on process execution - logs that include command-line details. This activity is significant because it - indicates a potential misuse of MySQL to execute system commands as root, which - could allow an attacker to gain root shell access. If confirmed malicious, this - could result in full control over the affected system, leading to severe security - breaches and unauthorized access to sensitive data. +description: The following analytic detects the execution of MySQL commands with elevated privileges using sudo, which can lead to privilege escalation. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because it indicates a potential misuse of MySQL to execute system commands as root, which could allow an attacker to gain root shell access. If confirmed malicious, this could result in full control over the affected system, leading to severe security breaches and unauthorized access to sensitive data. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*mysql*-e*" - AND Processes.process="*\!**" AND Processes.process="*sudo*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_mysql_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives are present based on automated tooling or system - administrative usage. Filter as needed. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*mysql*-e*" + AND + Processes.process="*\!**" + AND + Processes.process="*sudo*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_mysql_privilege_escalation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives are present based on automated tooling or system administrative usage. Filter as needed. references: -- https://gtfobins.github.io/gtfobins/mysql/ + - https://gtfobins.github.io/gtfobins/mysql/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/mysql/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/mysql/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_ngrok_reverse_proxy_usage.yml b/detections/endpoint/linux_ngrok_reverse_proxy_usage.yml index 522767690e..fc8edac077 100644 --- a/detections/endpoint/linux_ngrok_reverse_proxy_usage.yml +++ b/detections/endpoint/linux_ngrok_reverse_proxy_usage.yml @@ -1,88 +1,71 @@ name: Linux Ngrok Reverse Proxy Usage id: bc84d574-708c-467d-b78a-4c1e20171f97 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic detects the use of Ngrok on a Linux operating - system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing - on process names and command-line arguments associated with Ngrok. This activity - is significant because Ngrok can be used by adversaries to establish reverse proxies, - potentially bypassing network defenses. If confirmed malicious, this could allow - attackers to create persistent, unauthorized access channels, facilitating data - exfiltration or further exploitation of the compromised system. +description: The following analytic detects the use of Ngrok on a Linux operating system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments associated with Ngrok. This activity is significant because Ngrok can be used by adversaries to establish reverse proxies, potentially bypassing network defenses. If confirmed malicious, this could allow attackers to create persistent, unauthorized access channels, facilitating data exfiltration or further exploitation of the compromised system. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=ngrok - Processes.process IN ("*start*", "*--config*","*http*","*authtoken*", "*http*", - "*tcp*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_ngrok_reverse_proxy_usage_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present if Ngrok is an authorized utility. - Filter as needed. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=ngrok Processes.process IN ("*start*", "*--config*","*http*","*authtoken*", "*http*", "*tcp*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_ngrok_reverse_proxy_usage_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present if Ngrok is an authorized utility. Filter as needed. references: -- https://ngrok.com -- https://www.cisa.gov/uscert/sites/default/files/publications/aa22-320a_joint_csa_iranian_government-sponsored_apt_actors_compromise_federal%20network_deploy_crypto%20miner_credential_harvester.pdf + - https://ngrok.com + - https://www.cisa.gov/uscert/sites/default/files/publications/aa22-320a_joint_csa_iranian_government-sponsored_apt_actors_compromise_federal%20network_deploy_crypto%20miner_credential_harvester.pdf drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A reverse proxy was identified spawning from $parent_process_name$ - $process_name$ - on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 50 - - field: dest - type: system - score: 50 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: A reverse proxy was identified spawning from $parent_process_name$ - $process_name$ on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 50 + - field: dest + type: system + score: 50 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Reverse Network Proxy - asset_type: Endpoint - mitre_attack_id: - - T1572 - - T1090 - - T1102 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Reverse Network Proxy + asset_type: Endpoint + mitre_attack_id: + - T1572 + - T1090 + - T1102 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1572/ngrok/ngrok_linux-sysmon.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1572/ngrok/ngrok_linux-sysmon.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_node_privilege_escalation.yml b/detections/endpoint/linux_node_privilege_escalation.yml index dc0344dcb0..7a4358cea0 100644 --- a/detections/endpoint/linux_node_privilege_escalation.yml +++ b/detections/endpoint/linux_node_privilege_escalation.yml @@ -1,85 +1,73 @@ name: Linux Node Privilege Escalation id: 2e58a4ff-398f-42f4-8fd0-e01ebfe2a8ce -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: Anomaly -description: The following analytic identifies the execution of Node.js with elevated - privileges using sudo, specifically when spawning child processes. It leverages - data from Endpoint Detection and Response (EDR) agents, focusing on command-line - executions that include specific Node.js commands. This activity is significant - because running Node.js as a superuser without dropping privileges can allow unauthorized - access to the file system and potential privilege escalation. If confirmed malicious, - this could enable an attacker to maintain privileged access, execute arbitrary code, - and compromise sensitive data within the environment. +description: The following analytic identifies the execution of Node.js with elevated privileges using sudo, specifically when spawning child processes. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions that include specific Node.js commands. This activity is significant because running Node.js as a superuser without dropping privileges can allow unauthorized access to the file system and potential privilege escalation. If confirmed malicious, this could enable an attacker to maintain privileged access, execute arbitrary code, and compromise sensitive data within the environment. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*sudo*node*" - AND Processes.process="*-e*" AND Processes.process="*child_process.spawn*" AND Processes.process="*stdio*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_node_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives are present based on automated tooling or system - administrative usage. Filter as needed. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*sudo*node*" + AND + Processes.process="*-e*" + AND + Processes.process="*child_process.spawn*" + AND + Processes.process="*stdio*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_node_privilege_escalation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives are present based on automated tooling or system administrative usage. Filter as needed. references: -- https://gtfobins.github.io/gtfobins/docker/ -- https://en.wikipedia.org/wiki/Node.js + - https://gtfobins.github.io/gtfobins/docker/ + - https://en.wikipedia.org/wiki/Node.js drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/node/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/node/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_nopasswd_entry_in_sudoers_file.yml b/detections/endpoint/linux_nopasswd_entry_in_sudoers_file.yml index 1920d5c2b3..645262aa1d 100644 --- a/detections/endpoint/linux_nopasswd_entry_in_sudoers_file.yml +++ b/detections/endpoint/linux_nopasswd_entry_in_sudoers_file.yml @@ -1,81 +1,65 @@ name: Linux NOPASSWD Entry In Sudoers File id: ab1e0d52-624a-11ec-8e0b-acde48001122 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the addition of NOPASSWD entries to the - /etc/sudoers file on Linux systems. It leverages Endpoint Detection and Response - (EDR) telemetry to identify command lines containing "NOPASSWD:". This activity - is significant because it allows users to execute commands with elevated privileges - without requiring a password, which can be exploited by adversaries to maintain - persistent, privileged access. If confirmed malicious, this could lead to unauthorized - privilege escalation, persistent access, and potential compromise of sensitive data - and system integrity. +description: The following analytic detects the addition of NOPASSWD entries to the /etc/sudoers file on Linux systems. It leverages Endpoint Detection and Response (EDR) telemetry to identify command lines containing "NOPASSWD:". This activity is significant because it allows users to execute commands with elevated privileges without requiring a password, which can be exploited by adversaries to maintain persistent, privileged access. If confirmed malicious, this could lead to unauthorized privilege escalation, persistent access, and potential compromise of sensitive data and system integrity. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process = "*NOPASSWD:*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_nopasswd_entry_in_sudoers_file_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process = "*NOPASSWD:*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_nopasswd_entry_in_sudoers_file_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://askubuntu.com/questions/334318/sudoers-file-enable-nopasswd-for-user-all-commands -- https://help.ubuntu.com/community/Sudoers + - https://askubuntu.com/questions/334318/sudoers-file-enable-nopasswd-for-user-all-commands + - https://help.ubuntu.com/community/Sudoers drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a commandline $process$ executed on $dest$ - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: a commandline $process$ executed on $dest$ + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Linux Persistence Techniques - - China-Nexus Threat Activity - - Salt Typhoon - - Linux Privilege Escalation - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Persistence Techniques + - China-Nexus Threat Activity + - Salt Typhoon + - Linux Privilege Escalation + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/nopasswd_sudoers/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/nopasswd_sudoers/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_obfuscated_files_or_information_base64_decode.yml b/detections/endpoint/linux_obfuscated_files_or_information_base64_decode.yml index 257bef22b8..469052321e 100644 --- a/detections/endpoint/linux_obfuscated_files_or_information_base64_decode.yml +++ b/detections/endpoint/linux_obfuscated_files_or_information_base64_decode.yml @@ -1,86 +1,70 @@ name: Linux Obfuscated Files or Information Base64 Decode id: 303b38b2-c03f-44e2-8f41-4594606fcfc7 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic detects the use of the base64 decode command on - Linux systems, which is often used to deobfuscate files. It leverages data from - Endpoint Detection and Response (EDR) agents, focusing on command-line executions - that include "base64 -d" or "base64 --decode". This activity is significant as it - may indicate an attempt to hide malicious payloads or scripts. If confirmed malicious, - an attacker could use this technique to execute hidden code, potentially leading - to unauthorized access, data exfiltration, or further system compromise. +description: The following analytic detects the use of the base64 decode command on Linux systems, which is often used to deobfuscate files. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions that include "base64 -d" or "base64 --decode". This activity is significant as it may indicate an attempt to hide malicious payloads or scripts. If confirmed malicious, an attacker could use this technique to execute hidden code, potentially leading to unauthorized access, data exfiltration, or further system compromise. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_path="*/base64" - Processes.process="*-d*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_obfuscated_files_or_information_base64_decode_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present and will require some tuning - based on processes. Filter as needed. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_path="*/base64" Processes.process="*-d*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_obfuscated_files_or_information_base64_decode_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present and will require some tuning based on processes. Filter as needed. references: -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1027/T1027.md#atomic-test-1---decode-base64-data-into-script -- https://redcanary.com/blog/lateral-movement-with-secure-shell/ -- https://linux.die.net/man/1/base64 + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1027/T1027.md#atomic-test-1---decode-base64-data-into-script + - https://redcanary.com/blog/lateral-movement-with-secure-shell/ + - https://linux.die.net/man/1/base64 drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ decoding base64. - risk_objects: - - field: user - type: user - score: 15 - - field: dest - type: system - score: 15 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ decoding base64. + risk_objects: + - field: user + type: user + score: 15 + - field: dest + type: system + score: 15 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1027 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1027 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1027/atomic_red_team/linux-sysmon.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1027/atomic_red_team/linux-sysmon.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_octave_privilege_escalation.yml b/detections/endpoint/linux_octave_privilege_escalation.yml index 7689f50616..0749bdb253 100644 --- a/detections/endpoint/linux_octave_privilege_escalation.yml +++ b/detections/endpoint/linux_octave_privilege_escalation.yml @@ -1,84 +1,73 @@ name: Linux Octave Privilege Escalation id: 78f7487d-42ce-4f7f-8685-2159b25fb477 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: Anomaly -description: The following analytic detects the execution of GNU Octave with elevated - privileges, specifically when it runs system commands via sudo. It leverages data - from Endpoint Detection and Response (EDR) agents, focusing on process command-line - arguments that include "octave-cli," "--eval," "system," and "sudo." This activity - is significant because it indicates a potential privilege escalation attempt, allowing - a user to execute commands as root. If confirmed malicious, this could lead to full - system compromise, enabling an attacker to gain root access and execute arbitrary - commands, severely impacting system security and integrity. +description: The following analytic detects the execution of GNU Octave with elevated privileges, specifically when it runs system commands via sudo. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process command-line arguments that include "octave-cli," "--eval," "system," and "sudo." This activity is significant because it indicates a potential privilege escalation attempt, allowing a user to execute commands as root. If confirmed malicious, this could lead to full system compromise, enabling an attacker to gain root access and execute arbitrary commands, severely impacting system security and integrity. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*octave-cli*" - AND Processes.process="*--eval*" AND Processes.process="*system*" AND Processes.process="*sudo*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_octave_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*octave-cli*" + AND + Processes.process="*--eval*" + AND + Processes.process="*system*" + AND + Processes.process="*sudo*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_octave_privilege_escalation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives may be present, filter as needed. references: -- https://gtfobins.github.io/gtfobins/octave/ -- https://en.wikipedia.org/wiki/GNU_Octave + - https://gtfobins.github.io/gtfobins/octave/ + - https://en.wikipedia.org/wiki/GNU_Octave drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 20 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 20 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/octave/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/octave/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_openvpn_privilege_escalation.yml b/detections/endpoint/linux_openvpn_privilege_escalation.yml index 5b1e6e18c0..cc359d9589 100644 --- a/detections/endpoint/linux_openvpn_privilege_escalation.yml +++ b/detections/endpoint/linux_openvpn_privilege_escalation.yml @@ -1,85 +1,75 @@ name: Linux OpenVPN Privilege Escalation id: d25feebe-fa1c-4754-8a1e-afb03bedc0f2 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: Anomaly -description: The following analytic detects the execution of OpenVPN with elevated - privileges, specifically when combined with the `--dev`, `--script-security`, `--up`, - and `sudo` options. This detection leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process command-line arguments and execution details. - This activity is significant because it indicates a potential privilege escalation - attempt, allowing a user to execute system commands as root. If confirmed malicious, - this could lead to full system compromise, enabling an attacker to gain root access - and execute arbitrary commands with elevated privileges. +description: The following analytic detects the execution of OpenVPN with elevated privileges, specifically when combined with the `--dev`, `--script-security`, `--up`, and `sudo` options. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process command-line arguments and execution details. This activity is significant because it indicates a potential privilege escalation attempt, allowing a user to execute system commands as root. If confirmed malicious, this could lead to full system compromise, enabling an attacker to gain root access and execute arbitrary commands with elevated privileges. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*openvpn*" - AND Processes.process="*--dev*" AND Processes.process="*--script-security*" AND - Processes.process="*--up*" AND Processes.process="*sudo*" by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_openvpn_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*openvpn*" + AND + Processes.process="*--dev*" + AND + Processes.process="*--script-security*" + AND + Processes.process="*--up*" + AND + Processes.process="*sudo*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_openvpn_privilege_escalation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives may be present, filter as needed. references: -- https://gtfobins.github.io/gtfobins/openvpn/ -- https://en.wikipedia.org/wiki/OpenVPN + - https://gtfobins.github.io/gtfobins/openvpn/ + - https://en.wikipedia.org/wiki/OpenVPN drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/openvpn/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/openvpn/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_persistence_and_privilege_escalation_risk_behavior.yml b/detections/endpoint/linux_persistence_and_privilege_escalation_risk_behavior.yml index 15ba268593..f3d788d115 100644 --- a/detections/endpoint/linux_persistence_and_privilege_escalation_risk_behavior.yml +++ b/detections/endpoint/linux_persistence_and_privilege_escalation_risk_behavior.yml @@ -1,71 +1,54 @@ name: Linux Persistence and Privilege Escalation Risk Behavior id: ad5ac21b-3b1e-492c-8e19-ea5d5e8e5cf1 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Correlation -description: The following analytic identifies potential Linux persistence and privilege - escalation activities. It leverages risk scores and event counts from various Linux-related - data sources, focusing on tactics associated with persistence and privilege escalation. - This activity is significant for a SOC because it highlights behaviors that could - allow an attacker to maintain access or gain elevated privileges on a Linux system. - If confirmed malicious, this activity could enable an attacker to execute code with - higher privileges, persist in the environment, and potentially access sensitive - information, posing a severe security risk. +description: The following analytic identifies potential Linux persistence and privilege escalation activities. It leverages risk scores and event counts from various Linux-related data sources, focusing on tactics associated with persistence and privilege escalation. This activity is significant for a SOC because it highlights behaviors that could allow an attacker to maintain access or gain elevated privileges on a Linux system. If confirmed malicious, this activity could enable an attacker to execute code with higher privileges, persist in the environment, and potentially access sensitive information, posing a severe security risk. data_source: [] -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) - as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as - annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) - as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) - as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) - as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, - dc(source) as source_count from datamodel=Risk.All_Risk where (All_Risk.analyticstories - IN ("Linux Privilege Escalation", "Linux Persistence Techniques") OR source = "*Linux*") - All_Risk.annotations.mitre_attack.mitre_tactic IN ("persistence", "privilege-escalation") - All_Risk.risk_object_type="system" by All_Risk.risk_object All_Risk.risk_object_type - All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where - source_count >= 4 | `linux_persistence_and_privilege_escalation_risk_behavior_filter`' -how_to_implement: Ensure Linux anomaly and TTP analytics are enabled. TTP may be set - to finding for point detections, anomaly should not be findings but risk generators. - The correlation relies on more than x amount of distict detection names generated - before generating a finding. Modify the value as needed. Default value is set to - 4. This value may need to be increased based on activity in your environment. -known_false_positives: False positives will be present based on many factors. Tune - the correlation as needed to reduce too many triggers. +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count FROM datamodel=Risk.All_Risk + WHERE ( + All_Risk.analyticstories IN ("Linux Privilege Escalation", "Linux Persistence Techniques") + OR + source = "*Linux*" + ) + All_Risk.annotations.mitre_attack.mitre_tactic IN ("persistence", "privilege-escalation") All_Risk.risk_object_type="system" + BY All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic + | `drop_dm_object_name(All_Risk)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | where source_count >= 4 + | `linux_persistence_and_privilege_escalation_risk_behavior_filter` +how_to_implement: Ensure Linux anomaly and TTP analytics are enabled. TTP may be set to finding for point detections, anomaly should not be findings but risk generators. The correlation relies on more than x amount of distict detection names generated before generating a finding. Modify the value as needed. Default value is set to 4. This value may need to be increased based on activity in your environment. +known_false_positives: False positives will be present based on many factors. Tune the correlation as needed to reduce too many triggers. references: -- https://attack.mitre.org/tactics/TA0004/ + - https://attack.mitre.org/tactics/TA0004/ drilldown_searches: -- name: View the detection results for - "$risk_object$" - search: '%original_detection_search% | search risk_object = "$risk_object$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$risk_object$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$risk_object$" + search: '%original_detection_search% | search risk_object = "$risk_object$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$risk_object$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - asset_type: Endpoint - mitre_attack_id: - - T1548 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: audit + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1548 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: audit tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/linux_risk/linuxrisk.log - source: linuxrisk - sourcetype: stash + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/linux_risk/linuxrisk.log + source: linuxrisk + sourcetype: stash diff --git a/detections/endpoint/linux_php_privilege_escalation.yml b/detections/endpoint/linux_php_privilege_escalation.yml index 3dafdc2b9e..b667de8e7b 100644 --- a/detections/endpoint/linux_php_privilege_escalation.yml +++ b/detections/endpoint/linux_php_privilege_escalation.yml @@ -1,84 +1,71 @@ name: Linux PHP Privilege Escalation id: 4fc4c031-e5be-4cc0-8cf9-49f9f507bcb5 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: Anomaly -description: The following analytic detects the execution of PHP commands with elevated - privileges on a Linux system. It identifies instances where PHP is used in conjunction - with 'sudo' and 'system' commands, indicating an attempt to run system commands - as the root user. This detection leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process command-line arguments. This activity is significant - because it can indicate an attempt to escalate privileges, potentially leading to - full root access. If confirmed malicious, this could allow an attacker to execute - arbitrary commands with root privileges, compromising the entire system. +description: The following analytic detects the execution of PHP commands with elevated privileges on a Linux system. It identifies instances where PHP is used in conjunction with 'sudo' and 'system' commands, indicating an attempt to run system commands as the root user. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process command-line arguments. This activity is significant because it can indicate an attempt to escalate privileges, potentially leading to full root access. If confirmed malicious, this could allow an attacker to execute arbitrary commands with root privileges, compromising the entire system. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*php*-r*" - AND Processes.process="*system*" AND Processes.process="*sudo*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_php_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*php*-r*" + AND + Processes.process="*system*" + AND + Processes.process="*sudo*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_php_privilege_escalation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives may be present, filter as needed. references: -- https://gtfobins.github.io/gtfobins/php/ -- https://en.wikipedia.org/wiki/PHP + - https://gtfobins.github.io/gtfobins/php/ + - https://en.wikipedia.org/wiki/PHP drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/php/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/php/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_pkexec_privilege_escalation.yml b/detections/endpoint/linux_pkexec_privilege_escalation.yml index 7ef872c149..dac1a3c51f 100644 --- a/detections/endpoint/linux_pkexec_privilege_escalation.yml +++ b/detections/endpoint/linux_pkexec_privilege_escalation.yml @@ -1,91 +1,75 @@ name: Linux pkexec Privilege Escalation id: 03e22c1c-8086-11ec-ac2e-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of `pkexec` without any - command-line arguments. This behavior leverages data from Endpoint Detection and - Response (EDR) agents, focusing on process telemetry. The significance lies in the - fact that this pattern is associated with the exploitation of CVE-2021-4034 (PwnKit), - a critical vulnerability in Polkit's pkexec component. If confirmed malicious, this - activity could allow an attacker to gain full root privileges on the affected Linux - system, leading to complete system compromise and potential unauthorized access - to sensitive information. +description: The following analytic detects the execution of `pkexec` without any command-line arguments. This behavior leverages data from Endpoint Detection and Response (EDR) agents, focusing on process telemetry. The significance lies in the fact that this pattern is associated with the exploitation of CVE-2021-4034 (PwnKit), a critical vulnerability in Polkit's pkexec component. If confirmed malicious, this activity could allow an attacker to gain full root privileges on the affected Linux system, leading to complete system compromise and potential unauthorized access to sensitive information. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes - where Processes.process_name=pkexec by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex - process="(^.{1}$)" | `linux_pkexec_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=pkexec + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | regex process="(^.{1}$)" + | `linux_pkexec_privilege_escalation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives may be present, filter as needed. references: -- https://www.reddit.com/r/crowdstrike/comments/sdfeig/20220126_cool_query_friday_hunting_pwnkit_local/ -- https://linux.die.net/man/1/pkexec -- https://www.bleepingcomputer.com/news/security/linux-system-service-bug-gives-root-on-all-major-distros-exploit-released/ -- https://access.redhat.com/security/security-updates/#/?q=polkit&p=1&sort=portal_publication_date%20desc&rows=10&portal_advisory_type=Security%20Advisory&documentKind=PortalProduct + - https://www.reddit.com/r/crowdstrike/comments/sdfeig/20220126_cool_query_friday_hunting_pwnkit_local/ + - https://linux.die.net/man/1/pkexec + - https://www.bleepingcomputer.com/news/security/linux-system-service-bug-gives-root-on-all-major-distros-exploit-released/ + - https://access.redhat.com/security/security-updates/#/?q=polkit&p=1&sort=portal_publication_date%20desc&rows=10&portal_advisory_type=Security%20Advisory&documentKind=PortalProduct drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ related to a local privilege escalation in polkit - pkexec. - risk_objects: - - field: user - type: user - score: 56 - - field: dest - type: system - score: 56 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ related to a local privilege escalation in polkit pkexec. + risk_objects: + - field: user + type: user + score: 56 + - field: dest + type: system + score: 56 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - asset_type: Endpoint - cve: - - CVE-2021-4034 - mitre_attack_id: - - T1068 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + asset_type: Endpoint + cve: + - CVE-2021-4034 + mitre_attack_id: + - T1068 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/pkexec/linux-sysmon.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/pkexec/linux-sysmon.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_possible_access_or_modification_of_sshd_config_file.yml b/detections/endpoint/linux_possible_access_or_modification_of_sshd_config_file.yml index 79bde41308..11d08d6ef0 100644 --- a/detections/endpoint/linux_possible_access_or_modification_of_sshd_config_file.yml +++ b/detections/endpoint/linux_possible_access_or_modification_of_sshd_config_file.yml @@ -1,81 +1,66 @@ name: Linux Possible Access Or Modification Of sshd Config File id: 7a85eb24-72da-11ec-ac76-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects suspicious access or modification of the - sshd_config file on Linux systems. It leverages data from Endpoint Detection and - Response (EDR) agents, focusing on command-line executions involving processes like - "cat," "nano," "vim," and "vi" accessing the sshd_config file. This activity is - significant because unauthorized changes to sshd_config can allow threat actors - to redirect port connections or use unauthorized keys, potentially compromising - the system. If confirmed malicious, this could lead to unauthorized access, privilege - escalation, or persistent backdoor access, posing a severe security risk. +description: The following analytic detects suspicious access or modification of the sshd_config file on Linux systems. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving processes like "cat," "nano," "vim," and "vi" accessing the sshd_config file. This activity is significant because unauthorized changes to sshd_config can allow threat actors to redirect port connections or use unauthorized keys, potentially compromising the system. If confirmed malicious, this could lead to unauthorized access, privilege escalation, or persistent backdoor access, posing a severe security risk. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN("cat", - "nano*","vim*", "vi*") AND Processes.process IN("*/etc/ssh/sshd_config") by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_possible_access_or_modification_of_sshd_config_file_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can use this commandline - for automation purposes. Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name IN("cat", "nano*","vim*", "vi*") + AND + Processes.process IN("*/etc/ssh/sshd_config") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_possible_access_or_modification_of_sshd_config_file_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can use this commandline for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.hackingarticles.in/ssh-penetration-testing-port-22/ -- https://attack.mitre.org/techniques/T1098/004/ + - https://www.hackingarticles.in/ssh-penetration-testing-port-22/ + - https://attack.mitre.org/techniques/T1098/004/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a commandline $process$ executed on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: a commandline $process$ executed on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1098.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1098.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.004/ssh_authorized_keys/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.004/ssh_authorized_keys/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_possible_access_to_credential_files.yml b/detections/endpoint/linux_possible_access_to_credential_files.yml index 9c496e183b..f7f7853112 100644 --- a/detections/endpoint/linux_possible_access_to_credential_files.yml +++ b/detections/endpoint/linux_possible_access_to_credential_files.yml @@ -1,82 +1,68 @@ name: Linux Possible Access To Credential Files id: 16107e0e-71fc-11ec-b862-acde48001122 -version: 10 -date: '2025-05-02' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects attempts to access or dump the contents - of /etc/passwd and /etc/shadow files on Linux systems. It leverages data from Endpoint - Detection and Response (EDR) agents, focusing on processes like 'cat', 'nano', 'vim', - and 'vi' accessing these files. This activity is significant as it may indicate - credential dumping, a technique used by adversaries to gain persistence or escalate - privileges. If confirmed malicious, attackers could obtain hashed passwords for - offline cracking, leading to unauthorized access and potential system compromise. +description: The following analytic detects attempts to access or dump the contents of /etc/passwd and /etc/shadow files on Linux systems. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on processes like 'cat', 'nano', 'vim', and 'vi' accessing these files. This activity is significant as it may indicate credential dumping, a technique used by adversaries to gain persistence or escalate privileges. If confirmed malicious, attackers could obtain hashed passwords for offline cracking, leading to unauthorized access and potential system compromise. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN("cat", - "nano*","vim*", "vi*") AND Processes.process IN("*/etc/shadow*", "*/etc/passwd*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_possible_access_to_credential_files_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name IN("cat", "nano*","vim*", "vi*") + AND + Processes.process IN("*/etc/shadow*", "*/etc/passwd*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_possible_access_to_credential_files_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://askubuntu.com/questions/445361/what-is-difference-between-etc-shadow-and-etc-passwd -- https://attack.mitre.org/techniques/T1003/008/ + - https://askubuntu.com/questions/445361/what-is-difference-between-etc-shadow-and-etc-passwd + - https://attack.mitre.org/techniques/T1003/008/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A commandline $process$ executed on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A commandline $process$ executed on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Linux Persistence Techniques - - China-Nexus Threat Activity - - XorDDos - - Salt Typhoon - - Linux Privilege Escalation - asset_type: Endpoint - mitre_attack_id: - - T1003.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Persistence Techniques + - China-Nexus Threat Activity + - XorDDos + - Salt Typhoon + - Linux Privilege Escalation + asset_type: Endpoint + mitre_attack_id: + - T1003.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.008/copy_file_stdoutpipe/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.008/copy_file_stdoutpipe/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_possible_access_to_sudoers_file.yml b/detections/endpoint/linux_possible_access_to_sudoers_file.yml index 58259d9710..e692947a69 100644 --- a/detections/endpoint/linux_possible_access_to_sudoers_file.yml +++ b/detections/endpoint/linux_possible_access_to_sudoers_file.yml @@ -1,81 +1,67 @@ name: Linux Possible Access To Sudoers File id: 4479539c-71fc-11ec-b2e2-acde48001122 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects potential access or modification of the - /etc/sudoers file on a Linux system. It leverages data from Endpoint Detection and - Response (EDR) agents, focusing on processes like "cat," "nano," "vim," and "vi" - accessing the /etc/sudoers file. This activity is significant because the sudoers - file controls user permissions for executing commands with elevated privileges. - If confirmed malicious, an attacker could gain persistence or escalate privileges, - compromising the security of the targeted host. +description: The following analytic detects potential access or modification of the /etc/sudoers file on a Linux system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on processes like "cat," "nano," "vim," and "vi" accessing the /etc/sudoers file. This activity is significant because the sudoers file controls user permissions for executing commands with elevated privileges. If confirmed malicious, an attacker could gain persistence or escalate privileges, compromising the security of the targeted host. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN("cat", - "nano*","vim*", "vi*") AND Processes.process IN("*/etc/sudoers*") by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_possible_access_to_sudoers_file_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name IN("cat", "nano*","vim*", "vi*") + AND + Processes.process IN("*/etc/sudoers*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_possible_access_to_sudoers_file_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://attack.mitre.org/techniques/T1548/003/ -- https://web.archive.org/web/20210708035426/https://www.cobaltstrike.com/downloads/csmanual43.pdf + - https://attack.mitre.org/techniques/T1548/003/ + - https://web.archive.org/web/20210708035426/https://www.cobaltstrike.com/downloads/csmanual43.pdf drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A commandline $process$ executed on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A commandline $process$ executed on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Linux Persistence Techniques - - China-Nexus Threat Activity - - Salt Typhoon - - Linux Privilege Escalation - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Persistence Techniques + - China-Nexus Threat Activity + - Salt Typhoon + - Linux Privilege Escalation + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.008/copy_file_stdoutpipe/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.008/copy_file_stdoutpipe/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_possible_append_command_to_at_allow_config_file.yml b/detections/endpoint/linux_possible_append_command_to_at_allow_config_file.yml index ca92c3c04a..4b76ae2a70 100644 --- a/detections/endpoint/linux_possible_append_command_to_at_allow_config_file.yml +++ b/detections/endpoint/linux_possible_append_command_to_at_allow_config_file.yml @@ -1,80 +1,66 @@ name: Linux Possible Append Command To At Allow Config File id: 7bc20606-5f40-11ec-a586-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects suspicious command lines that append user - entries to /etc/at.allow or /etc/at.deny files. It leverages data from Endpoint - Detection and Response (EDR) agents, focusing on command-line executions involving - these files. This activity is significant because altering these configuration files - can allow attackers to schedule tasks with elevated permissions, facilitating persistence - on a compromised Linux host. If confirmed malicious, this could enable attackers - to execute arbitrary code at scheduled intervals, potentially leading to further - system compromise and unauthorized access to sensitive information. +description: The following analytic detects suspicious command lines that append user entries to /etc/at.allow or /etc/at.deny files. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving these files. This activity is significant because altering these configuration files can allow attackers to schedule tasks with elevated permissions, facilitating persistence on a compromised Linux host. If confirmed malicious, this could enable attackers to execute arbitrary code at scheduled intervals, potentially leading to further system compromise and unauthorized access to sensitive information. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes - where Processes.process = "*echo*" AND Processes.process IN("*/etc/at.allow", "*/etc/at.deny") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_possible_append_command_to_at_allow_config_file_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can use this commandline - for automation purposes. Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes + WHERE Processes.process = "*echo*" + AND + Processes.process IN("*/etc/at.allow", "*/etc/at.deny") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_possible_append_command_to_at_allow_config_file_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can use this commandline for automation purposes. Please update the filter macros to remove false positives. references: -- https://linuxize.com/post/at-command-in-linux/ -- https://attack.mitre.org/techniques/T1053/001/ + - https://linuxize.com/post/at-command-in-linux/ + - https://attack.mitre.org/techniques/T1053/001/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A commandline $process$ that may modify at allow config file on $dest$ - risk_objects: - - field: dest - type: system - score: 9 - threat_objects: [] + message: A commandline $process$ that may modify at allow config file on $dest$ + risk_objects: + - field: dest + type: system + score: 9 + threat_objects: [] tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - - Scheduled Tasks - asset_type: Endpoint - mitre_attack_id: - - T1053.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + - Scheduled Tasks + asset_type: Endpoint + mitre_attack_id: + - T1053.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.002/at_execution/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.002/at_execution/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_possible_append_command_to_profile_config_file.yml b/detections/endpoint/linux_possible_append_command_to_profile_config_file.yml index 1d3113d10f..f0b139d65e 100644 --- a/detections/endpoint/linux_possible_append_command_to_profile_config_file.yml +++ b/detections/endpoint/linux_possible_append_command_to_profile_config_file.yml @@ -1,80 +1,65 @@ name: Linux Possible Append Command To Profile Config File id: 9c94732a-61af-11ec-91e3-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects suspicious command-lines that modify user - profile files to automatically execute scripts or executables upon system reboot. - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on - command-line executions involving profile files like ~/.bashrc and /etc/profile. - This activity is significant as it indicates potential persistence mechanisms used - by adversaries to maintain access to compromised hosts. If confirmed malicious, - this could allow attackers to execute arbitrary code upon reboot, leading to persistent - control over the system and potential further exploitation. +description: The following analytic detects suspicious command-lines that modify user profile files to automatically execute scripts or executables upon system reboot. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving profile files like ~/.bashrc and /etc/profile. This activity is significant as it indicates potential persistence mechanisms used by adversaries to maintain access to compromised hosts. If confirmed malicious, this could allow attackers to execute arbitrary code upon reboot, leading to persistent control over the system and potential further exploitation. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process = "*echo*" - AND Processes.process IN("*~/.bashrc", "*~/.bash_profile", "*/etc/profile", "~/.bash_login", - "*~/.profile", "~/.bash_logout") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_possible_append_command_to_profile_config_file_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can use this commandline - for automation purposes. Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process = "*echo*" + AND + Processes.process IN("*~/.bashrc", "*~/.bash_profile", "*/etc/profile", "~/.bash_login", "*~/.profile", "~/.bash_logout") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_possible_append_command_to_profile_config_file_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can use this commandline for automation purposes. Please update the filter macros to remove false positives. references: -- https://unix.stackexchange.com/questions/129143/what-is-the-purpose-of-bashrc-and-how-does-it-work -- https://attack.mitre.org/techniques/T1546/004/ + - https://unix.stackexchange.com/questions/129143/what-is-the-purpose-of-bashrc-and-how-does-it-work + - https://attack.mitre.org/techniques/T1546/004/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a commandline $process$ that may modify profile files on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: a commandline $process$ that may modify profile files on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - asset_type: Endpoint - mitre_attack_id: - - T1546.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1546.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.004/linux_init_profile/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.004/linux_init_profile/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_possible_append_cronjob_entry_on_existing_cronjob_file.yml b/detections/endpoint/linux_possible_append_cronjob_entry_on_existing_cronjob_file.yml index 48d704ae91..1885d6d435 100644 --- a/detections/endpoint/linux_possible_append_cronjob_entry_on_existing_cronjob_file.yml +++ b/detections/endpoint/linux_possible_append_cronjob_entry_on_existing_cronjob_file.yml @@ -1,63 +1,53 @@ name: Linux Possible Append Cronjob Entry on Existing Cronjob File id: b5b91200-5f27-11ec-bb4e-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects potential tampering with cronjob files - on a Linux system by identifying 'echo' commands that append code to existing cronjob - files. It leverages logs from Endpoint Detection and Response (EDR) agents, focusing - on process names, parent processes, and command-line executions. This activity is - significant because adversaries often use it for persistence or privilege escalation. - If confirmed malicious, this could allow attackers to execute unauthorized code - automatically, leading to system compromises and unauthorized data access, thereby - impacting business operations and data integrity. +description: The following analytic detects potential tampering with cronjob files on a Linux system by identifying 'echo' commands that append code to existing cronjob files. It leverages logs from Endpoint Detection and Response (EDR) agents, focusing on process names, parent processes, and command-line executions. This activity is significant because adversaries often use it for persistence or privilege escalation. If confirmed malicious, this could allow attackers to execute unauthorized code automatically, leading to system compromises and unauthorized data access, thereby impacting business operations and data integrity. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes - where Processes.process = "*echo*" AND Processes.process IN("*/etc/cron*", "*/var/spool/cron/*", - "*/etc/anacrontab*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_possible_append_cronjob_entry_on_existing_cronjob_file_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may arise from legitimate actions by administrators - or network operators who may use these commands for automation purposes. Therefore, - it's recommended to adjust filter macros to eliminate such false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes + WHERE Processes.process = "*echo*" + AND + Processes.process IN("*/etc/cron*", "*/var/spool/cron/*", "*/etc/anacrontab*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_possible_append_cronjob_entry_on_existing_cronjob_file_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may arise from legitimate actions by administrators or network operators who may use these commands for automation purposes. Therefore, it's recommended to adjust filter macros to eliminate such false positives. references: -- https://attack.mitre.org/techniques/T1053/003/ -- https://blog.aquasec.com/threat-alert-kinsing-malware-container-vulnerability -- https://www.intezer.com/blog/research/kaiji-new-chinese-linux-malware-turning-to-golang/ + - https://attack.mitre.org/techniques/T1053/003/ + - https://blog.aquasec.com/threat-alert-kinsing-malware-container-vulnerability + - https://www.intezer.com/blog/research/kaiji-new-chinese-linux-malware-turning-to-golang/ tags: - analytic_story: - - XorDDos - - Linux Living Off The Land - - Linux Privilege Escalation - - Scheduled Tasks - - Linux Persistence Techniques - asset_type: Endpoint - mitre_attack_id: - - T1053.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - XorDDos + - Linux Living Off The Land + - Linux Privilege Escalation + - Scheduled Tasks + - Linux Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1053.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/cronjobs_entry/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/cronjobs_entry/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_possible_cronjob_modification_with_editor.yml b/detections/endpoint/linux_possible_cronjob_modification_with_editor.yml index 7fba0f6e85..55b47af568 100644 --- a/detections/endpoint/linux_possible_cronjob_modification_with_editor.yml +++ b/detections/endpoint/linux_possible_cronjob_modification_with_editor.yml @@ -1,60 +1,54 @@ name: Linux Possible Cronjob Modification With Editor id: dcc89bde-5f24-11ec-87ca-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects potential unauthorized modifications to - Linux cronjobs using text editors like "nano," "vi," or "vim." It identifies this - activity by monitoring command-line executions that interact with cronjob configuration - paths. This behavior is significant for a SOC as it may indicate attempts at privilege - escalation or establishing persistent access. If confirmed malicious, the impact - could be severe, allowing attackers to execute damaging actions such as data theft, - system sabotage, or further network penetration. +description: The following analytic detects potential unauthorized modifications to Linux cronjobs using text editors like "nano," "vi," or "vim." It identifies this activity by monitoring command-line executions that interact with cronjob configuration paths. This behavior is significant for a SOC as it may indicate attempts at privilege escalation or establishing persistent access. If confirmed malicious, the impact could be severe, allowing attackers to execute damaging actions such as data theft, system sabotage, or further network penetration. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name IN("nano","vim.basic") - OR Processes.process IN ("*nano *", "*vi *", "*vim *")) AND Processes.process IN("*/etc/cron*", - "*/var/spool/cron/*", "*/etc/anacrontab*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_possible_cronjob_modification_with_editor_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can use this commandline - for automation purposes. Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name IN("nano","vim.basic") + OR + Processes.process IN ("*nano *", "*vi *", "*vim *") + ) + AND Processes.process IN("*/etc/cron*", "*/var/spool/cron/*", "*/etc/anacrontab*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_possible_cronjob_modification_with_editor_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can use this commandline for automation purposes. Please update the filter macros to remove false positives. references: -- https://attack.mitre.org/techniques/T1053/003/ + - https://attack.mitre.org/techniques/T1053/003/ tags: - analytic_story: - - XorDDos - - Linux Living Off The Land - - Linux Privilege Escalation - - Scheduled Tasks - - Linux Persistence Techniques - asset_type: Endpoint - mitre_attack_id: - - T1053.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - XorDDos + - Linux Living Off The Land + - Linux Privilege Escalation + - Scheduled Tasks + - Linux Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1053.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/cronjobs_entry/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/cronjobs_entry/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_possible_ssh_key_file_creation.yml b/detections/endpoint/linux_possible_ssh_key_file_creation.yml index f73b697e53..04a7150722 100644 --- a/detections/endpoint/linux_possible_ssh_key_file_creation.yml +++ b/detections/endpoint/linux_possible_ssh_key_file_creation.yml @@ -1,72 +1,63 @@ name: Linux Possible Ssh Key File Creation id: c04ef40c-72da-11ec-8eac-acde48001122 -version: 8 -date: '2025-10-14' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the creation of SSH key files in the ~/.ssh/ - directory. It leverages filesystem data to identify new files in this specific path. - This activity is significant because threat actors often create SSH keys to gain - persistent access and escalate privileges on a compromised host. If confirmed malicious, - this could allow attackers to remotely access the machine using the OpenSSH daemon - service, leading to potential unauthorized control and data exfiltration. +description: The following analytic detects the creation of SSH key files in the ~/.ssh/ directory. It leverages filesystem data to identify new files in this specific path. This activity is significant because threat actors often create SSH keys to gain persistent access and escalate privileges on a compromised host. If confirmed malicious, this could allow attackers to remotely access the machine using the OpenSSH daemon service, leading to potential unauthorized control and data exfiltration. data_source: -- Sysmon for Linux EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/.ssh*") - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path - Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | - `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_possible_ssh_key_file_creation_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the file name, file path, and process_guid executions from your endpoints. - If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase. -known_false_positives: Administrator or network operator can create file in ~/.ssh - folders for automation purposes. Please update the filter macros to remove false - positives. + - Sysmon for Linux EventID 11 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.file_path IN ("*/.ssh*") + BY Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path Filesystem.file_acl + Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` + | `linux_possible_ssh_key_file_creation_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the file name, file path, and process_guid executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase. +known_false_positives: Administrator or network operator can create file in ~/.ssh folders for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.hackingarticles.in/ssh-penetration-testing-port-22/ -- https://attack.mitre.org/techniques/T1098/004/ + - https://www.hackingarticles.in/ssh-penetration-testing-port-22/ + - https://attack.mitre.org/techniques/T1098/004/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A file $file_name$ is created in $file_path$ on $dest$ - risk_objects: - - field: dest - type: system - score: 36 - threat_objects: [] + message: A file $file_name$ is created in $file_path$ on $dest$ + risk_objects: + - field: dest + type: system + score: 36 + threat_objects: [] tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - - Linux Living Off The Land - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1098.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + - Linux Living Off The Land + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1098.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.004/ssh_authorized_keys/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.004/ssh_authorized_keys/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_preload_hijack_library_calls.yml b/detections/endpoint/linux_preload_hijack_library_calls.yml index bf0bf9a962..7ce1f6b21d 100644 --- a/detections/endpoint/linux_preload_hijack_library_calls.yml +++ b/detections/endpoint/linux_preload_hijack_library_calls.yml @@ -1,81 +1,65 @@ name: Linux Preload Hijack Library Calls id: cbe2ca30-631e-11ec-8670-acde48001122 -version: 10 -date: '2026-01-20' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the use of the LD_PRELOAD environment - variable to hijack or hook library functions on a Linux platform. It leverages data - from Endpoint Detection and Response (EDR) agents, focusing on process execution - logs that include command-line details. This activity is significant because adversaries, - malware authors, and red teamers commonly use this technique to gain elevated privileges - and establish persistence on a compromised machine. If confirmed malicious, this - behavior could allow attackers to execute arbitrary code, escalate privileges, and - maintain long-term access to the system. +description: The following analytic detects the use of the LD_PRELOAD environment variable to hijack or hook library functions on a Linux platform. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because adversaries, malware authors, and red teamers commonly use this technique to gain elevated privileges and establish persistence on a compromised machine. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, and maintain long-term access to the system. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process = "*LD_PRELOAD*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_preload_hijack_library_calls_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process = "*LD_PRELOAD*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_preload_hijack_library_calls_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://compilepeace.medium.com/memory-malware-part-0x2-writing-userland-rootkits-via-ld-preload-30121c8343d5 + - https://compilepeace.medium.com/memory-malware-part-0x2-writing-userland-rootkits-via-ld-preload-30121c8343d5 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A commandline $process$ that may hijack library function on $dest$ - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A commandline $process$ that may hijack library function on $dest$ + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Linux Persistence Techniques - - China-Nexus Threat Activity - - Salt Typhoon - - Linux Privilege Escalation - - VoidLink Cloud-Native Linux Malware - asset_type: Endpoint - mitre_attack_id: - - T1574.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Persistence Techniques + - China-Nexus Threat Activity + - Salt Typhoon + - Linux Privilege Escalation + - VoidLink Cloud-Native Linux Malware + asset_type: Endpoint + mitre_attack_id: + - T1574.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.006/lib_hijack/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.006/lib_hijack/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_proxy_socks_curl.yml b/detections/endpoint/linux_proxy_socks_curl.yml index 64c5505948..5d74a39d48 100644 --- a/detections/endpoint/linux_proxy_socks_curl.yml +++ b/detections/endpoint/linux_proxy_socks_curl.yml @@ -1,93 +1,75 @@ name: Linux Proxy Socks Curl id: bd596c22-ad1e-44fc-b242-817253ce8b08 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk, 0xC0FFEEEE, Github Community status: production type: TTP -description: The following analytic detects the use of the `curl` command with proxy-related - arguments such as `-x`, `socks`, `--preproxy`, and `--proxy`. This detection leverages - data from Endpoint Detection and Response (EDR) agents, focusing on command-line - executions and process details. This activity is significant as it may indicate - an adversary attempting to use a proxy to evade network monitoring and obscure their - actions. If confirmed malicious, this behavior could allow attackers to bypass security - controls, making it difficult to track their activities and potentially leading - to unauthorized data access or exfiltration. +description: The following analytic detects the use of the `curl` command with proxy-related arguments such as `-x`, `socks`, `--preproxy`, and `--proxy`. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process details. This activity is significant as it may indicate an adversary attempting to use a proxy to evade network monitoring and obscure their actions. If confirmed malicious, this behavior could allow attackers to bypass security controls, making it difficult to track their activities and potentially leading to unauthorized data access or exfiltration. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=curl - Processes.process IN ("*-x *", "*socks4a://*", "*socks5h://*", "*socks4://*","*socks5://*", - "*--preproxy *", "--proxy*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where - match(process, "-x\s") OR match(process, "(?i)socks\d\w?:\/\/|--(pre)?proxy") | - `linux_proxy_socks_curl_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present based on proxy usage internally. - Filter as needed. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=curl Processes.process IN ("*-x *", "*socks4a://*", "*socks5h://*", "*socks4://*","*socks5://*", "*--preproxy *", "--proxy*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | where match(process, "-x\s") OR match(process, "(?i)socks\d\w?:\/\/ + | --(pre)?proxy") + | `linux_proxy_socks_curl_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present based on proxy usage internally. Filter as needed. references: -- https://www.offensive-security.com/metasploit-unleashed/proxytunnels/ -- https://curl.se/docs/manpage.html -- https://en.wikipedia.org/wiki/SOCKS -- https://oxylabs.io/blog/curl-with-proxy -- https://reqbin.com/req/c-ddxflki5/curl-proxy-server#:~:text=To%20use%20a%20proxy%20with,be%20URL%20decoded%20by%20Curl. -- https://gtfobins.github.io/gtfobins/curl/ + - https://www.offensive-security.com/metasploit-unleashed/proxytunnels/ + - https://curl.se/docs/manpage.html + - https://en.wikipedia.org/wiki/SOCKS + - https://oxylabs.io/blog/curl-with-proxy + - https://reqbin.com/req/c-ddxflki5/curl-proxy-server#:~:text=To%20use%20a%20proxy%20with,be%20URL%20decoded%20by%20Curl. + - https://gtfobins.github.io/gtfobins/curl/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $process_name$ was identified on endpoint $dest$ by user - $user$ utilizing a proxy. Review activity for further details. - risk_objects: - - field: user - type: user - score: 56 - - field: dest - type: system - score: 56 - threat_objects: - - field: process_name - type: process_name + message: An instance of $process_name$ was identified on endpoint $dest$ by user $user$ utilizing a proxy. Review activity for further details. + risk_objects: + - field: user + type: user + score: 56 + - field: dest + type: system + score: 56 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Linux Living Off The Land - - Ingress Tool Transfer - asset_type: Endpoint - mitre_attack_id: - - T1090 - - T1095 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Ingress Tool Transfer + asset_type: Endpoint + mitre_attack_id: + - T1090 + - T1095 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/curl-linux-sysmon.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/curl-linux-sysmon.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_puppet_privilege_escalation.yml b/detections/endpoint/linux_puppet_privilege_escalation.yml index 4a9e15e84c..15d463d43a 100644 --- a/detections/endpoint/linux_puppet_privilege_escalation.yml +++ b/detections/endpoint/linux_puppet_privilege_escalation.yml @@ -1,84 +1,75 @@ name: Linux Puppet Privilege Escalation id: 1d19037f-466e-4d56-8d87-36fafd9aa3ce -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: Anomaly -description: The following analytic detects the execution of Puppet commands with - elevated privileges, specifically when Puppet is used to apply configurations with - sudo rights. This detection leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process execution logs that include command-line details. - This activity is significant because it indicates a potential privilege escalation - attempt, where a user could gain root access and execute system commands as the - root user. If confirmed malicious, this could allow an attacker to fully compromise - the system, execute arbitrary commands, and maintain persistent control. +description: The following analytic detects the execution of Puppet commands with elevated privileges, specifically when Puppet is used to apply configurations with sudo rights. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because it indicates a potential privilege escalation attempt, where a user could gain root access and execute system commands as the root user. If confirmed malicious, this could allow an attacker to fully compromise the system, execute arbitrary commands, and maintain persistent control. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*puppet*" - AND Processes.process="*apply*" AND Processes.process="*-e*" AND Processes.process="*exec*" - AND Processes.process="*sudo*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_puppet_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*puppet*" + AND + Processes.process="*apply*" + AND + Processes.process="*-e*" + AND + Processes.process="*exec*" + AND + Processes.process="*sudo*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_puppet_privilege_escalation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives may be present, filter as needed. references: -- https://gtfobins.github.io/gtfobins/puppet/ -- https://en.wikipedia.org/wiki/Puppet_(software) + - https://gtfobins.github.io/gtfobins/puppet/ + - https://en.wikipedia.org/wiki/Puppet_(software) drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 5 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 5 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/puppet/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/puppet/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_rpm_privilege_escalation.yml b/detections/endpoint/linux_rpm_privilege_escalation.yml index 3abf8c5a04..591854b2e9 100644 --- a/detections/endpoint/linux_rpm_privilege_escalation.yml +++ b/detections/endpoint/linux_rpm_privilege_escalation.yml @@ -1,85 +1,71 @@ name: Linux RPM Privilege Escalation id: f8e58a23-cecd-495f-9c65-6c76b4cb9774 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the RPM Package Manager - with elevated privileges, specifically when it is used to run system commands as - root via the `--eval` and `lua:os.execute` options. This detection leverages data - from Endpoint Detection and Response (EDR) agents, focusing on command-line executions - and process metadata. This activity is significant because it indicates a potential - privilege escalation attempt, allowing a user to gain root access. If confirmed - malicious, this could lead to full system compromise, unauthorized access to sensitive - data, and further exploitation of the environment. +description: The following analytic detects the execution of the RPM Package Manager with elevated privileges, specifically when it is used to run system commands as root via the `--eval` and `lua:os.execute` options. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process metadata. This activity is significant because it indicates a potential privilege escalation attempt, allowing a user to gain root access. If confirmed malicious, this could lead to full system compromise, unauthorized access to sensitive data, and further exploitation of the environment. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*rpm*--eval*" - AND Processes.process="*lua:os.execute*" AND Processes.process="*sudo*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_rpm_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives are present based on automated tooling or system - administrative usage. Filter as needed. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*rpm*--eval*" + AND + Processes.process="*lua:os.execute*" + AND + Processes.process="*sudo*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_rpm_privilege_escalation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives are present based on automated tooling or system administrative usage. Filter as needed. references: -- https://gtfobins.github.io/gtfobins/rpm/ -- https://en.wikipedia.org/wiki/RPM_Package_Manager + - https://gtfobins.github.io/gtfobins/rpm/ + - https://en.wikipedia.org/wiki/RPM_Package_Manager drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/rpm/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/rpm/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_ruby_privilege_escalation.yml b/detections/endpoint/linux_ruby_privilege_escalation.yml index b837365de1..8f7f0552fd 100644 --- a/detections/endpoint/linux_ruby_privilege_escalation.yml +++ b/detections/endpoint/linux_ruby_privilege_escalation.yml @@ -1,83 +1,70 @@ name: Linux Ruby Privilege Escalation id: 097b28b5-7004-4d40-a715-7e390501788b -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: Anomaly -description: The following analytic detects the execution of Ruby commands with elevated - privileges on a Linux system. It identifies processes where Ruby is used with the - `-e` flag to execute commands via `sudo`, leveraging Endpoint Detection and Response - (EDR) telemetry. This activity is significant because it indicates a potential privilege - escalation attempt, allowing a user to execute commands as root. If confirmed malicious, - this could lead to full system compromise, enabling an attacker to gain root access, - execute arbitrary commands, and maintain persistent control over the affected system. +description: The following analytic detects the execution of Ruby commands with elevated privileges on a Linux system. It identifies processes where Ruby is used with the `-e` flag to execute commands via `sudo`, leveraging Endpoint Detection and Response (EDR) telemetry. This activity is significant because it indicates a potential privilege escalation attempt, allowing a user to execute commands as root. If confirmed malicious, this could lead to full system compromise, enabling an attacker to gain root access, execute arbitrary commands, and maintain persistent control over the affected system. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*ruby*-e*" - AND Processes.process="*exec*" AND Processes.process="*sudo*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_ruby_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives are present based on automated tooling or system - administrative usage. Filter as needed. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*ruby*-e*" + AND + Processes.process="*exec*" + AND + Processes.process="*sudo*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_ruby_privilege_escalation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives are present based on automated tooling or system administrative usage. Filter as needed. references: -- https://gtfobins.github.io/gtfobins/ruby/ + - https://gtfobins.github.io/gtfobins/ruby/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/ruby/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/ruby/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_service_file_created_in_systemd_directory.yml b/detections/endpoint/linux_service_file_created_in_systemd_directory.yml index 4abc2ff849..fecfe9f837 100644 --- a/detections/endpoint/linux_service_file_created_in_systemd_directory.yml +++ b/detections/endpoint/linux_service_file_created_in_systemd_directory.yml @@ -1,83 +1,68 @@ name: Linux Service File Created In Systemd Directory id: c7495048-61b6-11ec-9a37-acde48001122 -version: 9 -date: '2026-01-20' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the creation of suspicious service files - within the systemd directories on Linux platforms. It leverages logs containing - file name, file path, and process GUID data from endpoints. This activity is significant - for a SOC as it may indicate an adversary attempting to establish persistence on - a compromised host. If confirmed malicious, this could lead to system compromise - or data exfiltration, allowing attackers to maintain control over the system and - execute further malicious activities. +description: The following analytic detects the creation of suspicious service files within the systemd directories on Linux platforms. It leverages logs containing file name, file path, and process GUID data from endpoints. This activity is significant for a SOC as it may indicate an adversary attempting to establish persistence on a compromised host. If confirmed malicious, this could lead to system compromise or data exfiltration, allowing attackers to maintain control over the system and execute further malicious activities. data_source: -- Sysmon for Linux EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name = *.service - Filesystem.file_path IN ("*/etc/systemd/system*", "*/lib/systemd/system*", "*/usr/lib/systemd/system*", - "*/run/systemd/system*", "*~/.config/systemd/*", "*~/.local/share/systemd/*","*/etc/systemd/user*", - "*/lib/systemd/user*", "*/usr/lib/systemd/user*", "*/run/systemd/user*") by Filesystem.action - Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash - Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl - Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user - Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` - | `security_content_ctime(firstTime)` | `linux_service_file_created_in_systemd_directory_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the file name, file path, and process_guid executions from your endpoints. - If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase. -known_false_positives: False positives may arise when administrators or network operators - create files in systemd directories for legitimate automation tasks. Therefore, - it's important to adjust filter macros to account for valid activities. To implement - this search successfully, it's crucial to ingest appropriate logs, preferably using - the Linux Sysmon Add-on from Splunkbase for those using Sysmon. + - Sysmon for Linux EventID 11 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.file_name = *.service Filesystem.file_path IN ("*/etc/systemd/system*", "*/lib/systemd/system*", "*/usr/lib/systemd/system*", "*/run/systemd/system*", "*~/.config/systemd/*", "*~/.local/share/systemd/*","*/etc/systemd/user*", "*/lib/systemd/user*", "*/usr/lib/systemd/user*", "*/run/systemd/user*") + BY Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path Filesystem.file_acl + Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` + | `linux_service_file_created_in_systemd_directory_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the file name, file path, and process_guid executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase. +known_false_positives: False positives may arise when administrators or network operators create files in systemd directories for legitimate automation tasks. Therefore, it's important to adjust filter macros to account for valid activities. To implement this search successfully, it's crucial to ingest appropriate logs, preferably using the Linux Sysmon Add-on from Splunkbase for those using Sysmon. references: -- https://attack.mitre.org/techniques/T1053/006/ -- https://www.intezer.com/blog/research/kaiji-new-chinese-linux-malware-turning-to-golang/ -- https://redcanary.com/blog/attck-t1501-understanding-systemd-service-persistence/ -- https://github.com/microsoft/MSTIC-Sysmon/blob/main/linux/configs/attack-based/persistence/T1053.003_Cron_Activity.xml + - https://attack.mitre.org/techniques/T1053/006/ + - https://www.intezer.com/blog/research/kaiji-new-chinese-linux-malware-turning-to-golang/ + - https://redcanary.com/blog/attck-t1501-understanding-systemd-service-persistence/ + - https://github.com/microsoft/MSTIC-Sysmon/blob/main/linux/configs/attack-based/persistence/T1053.003_Cron_Activity.xml drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A service file named as $file_path$ is created in systemd folder on $dest$ - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A service file named as $file_path$ is created in systemd folder on $dest$ + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - - Linux Living Off The Land - - Scheduled Tasks - - Gomir - - China-Nexus Threat Activity - - VoidLink Cloud-Native Linux Malware - asset_type: Endpoint - mitre_attack_id: - - T1053.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + - Linux Living Off The Land + - Scheduled Tasks + - Gomir + - China-Nexus Threat Activity + - VoidLink Cloud-Native Linux Malware + asset_type: Endpoint + mitre_attack_id: + - T1053.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.006/service_systemd/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.006/service_systemd/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_service_restarted.yml b/detections/endpoint/linux_service_restarted.yml index 55674db9c4..cbca65a7a2 100644 --- a/detections/endpoint/linux_service_restarted.yml +++ b/detections/endpoint/linux_service_restarted.yml @@ -1,84 +1,72 @@ name: Linux Service Restarted id: 084275ba-61b8-11ec-8d64-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the restarting or re-enabling of services - on Linux systems using the `systemctl` or `service` commands. It leverages data - from Endpoint Detection and Response (EDR) agents, focusing on process and command-line - execution logs. This activity is significant as adversaries may use it to maintain - persistence or execute unauthorized actions. If confirmed malicious, this behavior - could lead to repeated execution of malicious payloads, unauthorized access, or - data destruction. Security analysts should investigate these events to mitigate - risks and prevent further compromise. +description: The following analytic detects the restarting or re-enabling of services on Linux systems using the `systemctl` or `service` commands. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and command-line execution logs. This activity is significant as adversaries may use it to maintain persistence or execute unauthorized actions. If confirmed malicious, this behavior could lead to repeated execution of malicious payloads, unauthorized access, or data destruction. Security analysts should investigate these events to mitigate risks and prevent further compromise. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name IN ("systemctl", - "service") OR Processes.process IN ("*systemctl *", "*service *")) Processes.process - IN ("*restart*", "*reload*", "*reenable*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_service_restarted_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can use this commandline - for automation purposes. Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name IN ("systemctl", "service") + OR + Processes.process IN ("*systemctl *", "*service *") + ) + Processes.process IN ("*restart*", "*reload*", "*reenable*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_service_restarted_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can use this commandline for automation purposes. Please update the filter macros to remove false positives. references: -- https://attack.mitre.org/techniques/T1543/003/ + - https://attack.mitre.org/techniques/T1543/003/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A commandline $process$ that may create or start a service on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A commandline $process$ that may create or start a service on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - AwfulShred - - Linux Privilege Escalation - - Linux Living Off The Land - - Data Destruction - - Linux Persistence Techniques - - Scheduled Tasks - - Gomir - asset_type: Endpoint - mitre_attack_id: - - T1053.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AwfulShred + - Linux Privilege Escalation + - Linux Living Off The Land + - Data Destruction + - Linux Persistence Techniques + - Scheduled Tasks + - Gomir + asset_type: Endpoint + mitre_attack_id: + - T1053.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.006/service_systemd/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.006/service_systemd/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_service_started_or_enabled.yml b/detections/endpoint/linux_service_started_or_enabled.yml index 13c1f28b08..81695205ba 100644 --- a/detections/endpoint/linux_service_started_or_enabled.yml +++ b/detections/endpoint/linux_service_started_or_enabled.yml @@ -1,83 +1,70 @@ name: Linux Service Started Or Enabled id: e0428212-61b7-11ec-88a3-acde48001122 -version: 9 -date: '2025-10-08' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the creation or enabling of services on - Linux platforms using the systemctl or service tools. It leverages Endpoint Detection - and Response (EDR) logs, focusing on process names, parent processes, and command-line - executions. This activity is significant as adversaries may create or modify services - to maintain persistence or execute malicious payloads. If confirmed malicious, this - behavior could lead to persistent access, data theft, ransomware deployment, or - other damaging outcomes. Monitoring and investigating such activities are crucial - for maintaining the security and integrity of the environment. +description: The following analytic detects the creation or enabling of services on Linux platforms using the systemctl or service tools. It leverages Endpoint Detection and Response (EDR) logs, focusing on process names, parent processes, and command-line executions. This activity is significant as adversaries may create or modify services to maintain persistence or execute malicious payloads. If confirmed malicious, this behavior could lead to persistent access, data theft, ransomware deployment, or other damaging outcomes. Monitoring and investigating such activities are crucial for maintaining the security and integrity of the environment. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name IN ("systemctl", - "service") OR Processes.process IN ("*systemctl *", "*service *")) Processes.process - IN ("* start *", "* enable *") AND NOT (Processes.os="Microsoft Windows" OR Processes.vendor_product="Microsoft - Windows") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_service_started_or_enabled_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can use this commandline - for automation purposes. Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name IN ("systemctl", "service") + OR + Processes.process IN ("*systemctl *", "*service *") + ) + Processes.process IN ("* start *", "* enable *") AND NOT (Processes.os="Microsoft Windows" OR Processes.vendor_product="Microsoft Windows") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_service_started_or_enabled_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can use this commandline for automation purposes. Please update the filter macros to remove false positives. references: -- https://attack.mitre.org/techniques/T1543/003/ + - https://attack.mitre.org/techniques/T1543/003/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a commandline $process$ that may create or start a service on $dest$ - risk_objects: - - field: dest - type: system - score: 42 - threat_objects: [] + message: a commandline $process$ that may create or start a service on $dest$ + risk_objects: + - field: dest + type: system + score: 42 + threat_objects: [] tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - - Linux Living Off The Land - - Scheduled Tasks - - Gomir - asset_type: Endpoint - mitre_attack_id: - - T1053.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + - Linux Living Off The Land + - Scheduled Tasks + - Gomir + asset_type: Endpoint + mitre_attack_id: + - T1053.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.006/service_systemd/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.006/service_systemd/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_setuid_using_chmod_utility.yml b/detections/endpoint/linux_setuid_using_chmod_utility.yml index 7a83ca3e28..df25b4e3c4 100644 --- a/detections/endpoint/linux_setuid_using_chmod_utility.yml +++ b/detections/endpoint/linux_setuid_using_chmod_utility.yml @@ -1,80 +1,68 @@ name: Linux Setuid Using Chmod Utility id: bf0304b6-6250-11ec-9d7c-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the chmod utility to - set the SUID or SGID bit on files, which can allow users to temporarily gain root - or group-level access. This detection leverages data from Endpoint Detection and - Response (EDR) agents, focusing on process names and command-line arguments related - to chmod. This activity is significant as it can indicate an attempt to escalate - privileges or maintain persistence on a system. If confirmed malicious, an attacker - could gain elevated access, potentially compromising sensitive data or critical - system functions. +description: The following analytic detects the execution of the chmod utility to set the SUID or SGID bit on files, which can allow users to temporarily gain root or group-level access. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments related to chmod. This activity is significant as it can indicate an attempt to escalate privileges or maintain persistence on a system. If confirmed malicious, an attacker could gain elevated access, potentially compromising sensitive data or critical system functions. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes WHERE (Processes.process_name = chmod - OR Processes.process = "*chmod *") AND Processes.process IN("* g+s *", "* u+s *", - "* 4777 *", "* 4577 *") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_setuid_using_chmod_utility_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name = chmod + OR + Processes.process = "*chmod *" + ) + AND Processes.process IN("* g+s *", "* u+s *", "* 4777 *", "* 4577 *") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_setuid_using_chmod_utility_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://www.hackingarticles.in/linux-privilege-escalation-using-capabilities/ + - https://www.hackingarticles.in/linux-privilege-escalation-using-capabilities/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a commandline $process$ that may set suid or sgid on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: a commandline $process$ that may set suid or sgid on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1548.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1548.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.001/chmod_uid/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.001/chmod_uid/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_setuid_using_setcap_utility.yml b/detections/endpoint/linux_setuid_using_setcap_utility.yml index 4e15597ebc..0667021ce9 100644 --- a/detections/endpoint/linux_setuid_using_setcap_utility.yml +++ b/detections/endpoint/linux_setuid_using_setcap_utility.yml @@ -1,80 +1,67 @@ name: Linux Setuid Using Setcap Utility id: 9d96022e-6250-11ec-9a19-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the 'setcap' utility - to enable the SUID bit on Linux systems. It leverages Endpoint Detection and Response - (EDR) data, focusing on process names and command-line arguments that indicate the - use of 'setcap' with specific capabilities. This activity is significant because - setting the SUID bit allows a user to temporarily gain root access, posing a substantial - security risk. If confirmed malicious, an attacker could escalate privileges, execute - arbitrary commands with elevated permissions, and potentially compromise the entire - system. +description: The following analytic detects the execution of the 'setcap' utility to enable the SUID bit on Linux systems. It leverages Endpoint Detection and Response (EDR) data, focusing on process names and command-line arguments that indicate the use of 'setcap' with specific capabilities. This activity is significant because setting the SUID bit allows a user to temporarily gain root access, posing a substantial security risk. If confirmed malicious, an attacker could escalate privileges, execute arbitrary commands with elevated permissions, and potentially compromise the entire system. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = setcap - OR Processes.process = "*setcap *") AND Processes.process IN ("* cap_setuid=ep *", - "* cap_setuid+ep *", "* cap_net_bind_service+p *", "* cap_net_raw+ep *", "* cap_dac_read_search+ep - *") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_setuid_using_setcap_utility_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name = setcap + OR + Processes.process = "*setcap *" + ) + AND Processes.process IN ("* cap_setuid=ep *", "* cap_setuid+ep *", "* cap_net_bind_service+p *", "* cap_net_raw+ep *", "* cap_dac_read_search+ep *") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_setuid_using_setcap_utility_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://www.hackingarticles.in/linux-privilege-escalation-using-capabilities/ + - https://www.hackingarticles.in/linux-privilege-escalation-using-capabilities/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A commandline $process$ that may set suid or sgid on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: A commandline $process$ that may set suid or sgid on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - asset_type: Endpoint - mitre_attack_id: - - T1548.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1548.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.001/linux_setcap/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.001/linux_setcap/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_shred_overwrite_command.yml b/detections/endpoint/linux_shred_overwrite_command.yml index 549bed10a4..c78109a67b 100644 --- a/detections/endpoint/linux_shred_overwrite_command.yml +++ b/detections/endpoint/linux_shred_overwrite_command.yml @@ -1,83 +1,68 @@ name: Linux Shred Overwrite Command id: c1952cf1-643c-4965-82de-11c067cbae76 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of the 'shred' command on - a Linux machine, which is used to overwrite files to make them unrecoverable. It - leverages data from Endpoint Detection and Response (EDR) agents, focusing on process - names and command-line arguments. This activity is significant because the 'shred' - command can be used in destructive attacks, such as those seen in the Industroyer2 - malware targeting energy facilities. If confirmed malicious, this activity could - lead to the permanent destruction of critical files, severely impacting system integrity - and data availability. +description: The following analytic detects the execution of the 'shred' command on a Linux machine, which is used to overwrite files to make them unrecoverable. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because the 'shred' command can be used in destructive attacks, such as those seen in the Industroyer2 malware targeting energy facilities. If confirmed malicious, this activity could lead to the permanent destruction of critical files, severely impacting system integrity and data availability. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name =shred - AND Processes.process IN ("*-n*", "*-u*", "*-z*", "*-s*") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_shred_overwrite_command_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name =shred + AND + Processes.process IN ("*-n*", "*-u*", "*-z*", "*-s*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_shred_overwrite_command_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ -- https://cert.gov.ua/article/39518 + - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ + - https://cert.gov.ua/article/39518 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A possible shred overwrite command $process$ executed on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: A possible shred overwrite command $process$ executed on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Industroyer2 - - AwfulShred - - Linux Privilege Escalation - - Data Destruction - - Linux Persistence Techniques - asset_type: Endpoint - mitre_attack_id: - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Industroyer2 + - AwfulShred + - Linux Privilege Escalation + - Data Destruction + - Linux Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/rm_shred_critical_dir/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/rm_shred_critical_dir/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_sqlite3_privilege_escalation.yml b/detections/endpoint/linux_sqlite3_privilege_escalation.yml index d660f165b1..c00322581a 100644 --- a/detections/endpoint/linux_sqlite3_privilege_escalation.yml +++ b/detections/endpoint/linux_sqlite3_privilege_escalation.yml @@ -1,84 +1,71 @@ name: Linux Sqlite3 Privilege Escalation id: ab75dbb7-c3ba-4689-9c1b-8d2717bdcba1 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the sqlite3 command with - elevated privileges, which can be exploited for privilege escalation. It leverages - Endpoint Detection and Response (EDR) telemetry to identify instances where sqlite3 - is used in conjunction with shell commands and sudo. This activity is significant - because it indicates a potential attempt to gain root access, which could lead to - full system compromise. If confirmed malicious, an attacker could execute arbitrary - commands as root, leading to unauthorized access, data exfiltration, or further - lateral movement within the network. +description: The following analytic detects the execution of the sqlite3 command with elevated privileges, which can be exploited for privilege escalation. It leverages Endpoint Detection and Response (EDR) telemetry to identify instances where sqlite3 is used in conjunction with shell commands and sudo. This activity is significant because it indicates a potential attempt to gain root access, which could lead to full system compromise. If confirmed malicious, an attacker could execute arbitrary commands as root, leading to unauthorized access, data exfiltration, or further lateral movement within the network. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*sqlite3*" - AND Processes.process="*.shell*" AND Processes.process="*sudo*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_sqlite3_privilege_escalation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*sqlite3*" + AND + Processes.process="*.shell*" + AND + Processes.process="*sudo*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_sqlite3_privilege_escalation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives may be present, filter as needed. references: -- https://gtfobins.github.io/gtfobins/sqlite3/ -- https://manpages.ubuntu.com/manpages/trusty/en/man1/sqlite3.1.html + - https://gtfobins.github.io/gtfobins/sqlite3/ + - https://manpages.ubuntu.com/manpages/trusty/en/man1/sqlite3.1.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Privilege Escalation - - Linux Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/sqlite3/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/sqlite3/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_ssh_authorized_keys_modification.yml b/detections/endpoint/linux_ssh_authorized_keys_modification.yml index 3224572cf9..58afaf8315 100644 --- a/detections/endpoint/linux_ssh_authorized_keys_modification.yml +++ b/detections/endpoint/linux_ssh_authorized_keys_modification.yml @@ -1,88 +1,71 @@ name: Linux SSH Authorized Keys Modification id: f5ab595e-28e5-4327-8077-5008ba97c850 -version: 10 -date: '2026-01-20' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic detects the modification of SSH Authorized Keys - on Linux systems. It leverages process execution data from Endpoint Detection and - Response (EDR) agents, specifically monitoring commands like "bash" and "cat" interacting - with "authorized_keys" files. This activity is significant as adversaries often - modify SSH Authorized Keys to establish persistent access to compromised endpoints. - If confirmed malicious, this behavior could allow attackers to maintain unauthorized - access, bypassing traditional authentication mechanisms and potentially leading - to further exploitation or data exfiltration. +description: The following analytic detects the modification of SSH Authorized Keys on Linux systems. It leverages process execution data from Endpoint Detection and Response (EDR) agents, specifically monitoring commands like "bash" and "cat" interacting with "authorized_keys" files. This activity is significant as adversaries often modify SSH Authorized Keys to establish persistent access to compromised endpoints. If confirmed malicious, this behavior could allow attackers to maintain unauthorized access, bypassing traditional authentication mechanisms and potentially leading to further exploitation or data exfiltration. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("bash","cat") - Processes.process IN ("*/authorized_keys*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_ssh_authorized_keys_modification_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Filtering will be required as system administrators will add - and remove. One way to filter query is to add "echo". + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name IN ("bash","cat") Processes.process IN ("*/authorized_keys*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_ssh_authorized_keys_modification_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Filtering will be required as system administrators will add and remove. One way to filter query is to add "echo". references: -- https://redcanary.com/blog/lateral-movement-with-secure-shell/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1098.004/T1098.004.md + - https://redcanary.com/blog/lateral-movement-with-secure-shell/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1098.004/T1098.004.md drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ modifying SSH Authorized Keys. - risk_objects: - - field: user - type: user - score: 15 - - field: dest - type: system - score: 15 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ modifying SSH Authorized Keys. + risk_objects: + - field: user + type: user + score: 15 + - field: dest + type: system + score: 15 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Linux Living Off The Land - - Hellcat Ransomware - - VoidLink Cloud-Native Linux Malware - asset_type: Endpoint - mitre_attack_id: - - T1098.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Hellcat Ransomware + - VoidLink Cloud-Native Linux Malware + asset_type: Endpoint + mitre_attack_id: + - T1098.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.004/ssh_authorized_keys/authkey_linux-sysmon.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.004/ssh_authorized_keys/authkey_linux-sysmon.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_ssh_remote_services_script_execute.yml b/detections/endpoint/linux_ssh_remote_services_script_execute.yml index df6258f331..d53cf5d731 100644 --- a/detections/endpoint/linux_ssh_remote_services_script_execute.yml +++ b/detections/endpoint/linux_ssh_remote_services_script_execute.yml @@ -1,86 +1,70 @@ name: Linux SSH Remote Services Script Execute id: aa1748dd-4a5c-457a-9cf6-ca7b4eb711b3 -version: 10 -date: '2026-01-20' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the use of SSH to move laterally and execute - a script or file on a remote host. It leverages data from Endpoint Detection and - Response (EDR) agents, focusing on specific SSH command-line parameters and URLs. - This activity is significant as it may indicate an attacker attempting to execute - remote commands or scripts, potentially leading to unauthorized access or control - over additional systems. If confirmed malicious, this could result in lateral movement, - privilege escalation, or the execution of malicious payloads, compromising the security - of the network. +description: The following analytic detects the use of SSH to move laterally and execute a script or file on a remote host. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific SSH command-line parameters and URLs. This activity is significant as it may indicate an attacker attempting to execute remote commands or scripts, potentially leading to unauthorized access or control over additional systems. If confirmed malicious, this could result in lateral movement, privilege escalation, or the execution of malicious payloads, compromising the security of the network. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=ssh - Processes.process IN ("*oStrictHostKeyChecking*", "*oConnectTimeout*", "*oBatchMode*") - AND Processes.process IN ("*http:*","*https:*") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_ssh_remote_services_script_execute_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=ssh Processes.process IN ("*oStrictHostKeyChecking*", "*oConnectTimeout*", "*oBatchMode*") + AND + Processes.process IN ("*http:*","*https:*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_ssh_remote_services_script_execute_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: This is not a common command to be executed. Filter as needed. references: -- https://redcanary.com/blog/lateral-movement-with-secure-shell/ + - https://redcanary.com/blog/lateral-movement-with-secure-shell/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $process_name$ was identified on endpoint $dest$ by user - $user$ attempting to move laterally and download a file. - risk_objects: - - field: user - type: user - score: 56 - - field: dest - type: system - score: 56 - threat_objects: - - field: process_name - type: process_name + message: An instance of $process_name$ was identified on endpoint $dest$ by user $user$ attempting to move laterally and download a file. + risk_objects: + - field: user + type: user + score: 56 + - field: dest + type: system + score: 56 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Linux Living Off The Land - - Hellcat Ransomware - - VoidLink Cloud-Native Linux Malware - asset_type: Endpoint - mitre_attack_id: - - T1021.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Living Off The Land + - Hellcat Ransomware + - VoidLink Cloud-Native Linux Malware + asset_type: Endpoint + mitre_attack_id: + - T1021.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.004/atomic_red_team/linux-sysmon.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.004/atomic_red_team/linux-sysmon.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_stdout_redirection_to_dev_null_file.yml b/detections/endpoint/linux_stdout_redirection_to_dev_null_file.yml index e337dd20f7..931007d973 100644 --- a/detections/endpoint/linux_stdout_redirection_to_dev_null_file.yml +++ b/detections/endpoint/linux_stdout_redirection_to_dev_null_file.yml @@ -1,79 +1,64 @@ name: Linux Stdout Redirection To Dev Null File id: de62b809-a04d-46b5-9a15-8298d330f0c8 -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects command-line activities that redirect - stdout or stderr to the /dev/null file. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process execution logs. This behavior is - significant as it can indicate attempts to hide command outputs, a technique observed - in the CyclopsBlink malware to conceal modifications to iptables firewall settings. - If confirmed malicious, this activity could allow an attacker to stealthily alter - system configurations, potentially leading to unauthorized access or persistent - control over the compromised machine. +description: The following analytic detects command-line activities that redirect stdout or stderr to the /dev/null file. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This behavior is significant as it can indicate attempts to hide command outputs, a technique observed in the CyclopsBlink malware to conceal modifications to iptables firewall settings. If confirmed malicious, this activity could allow an attacker to stealthily alter system configurations, potentially leading to unauthorized access or persistent control over the compromised machine. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process = "*&>/dev/null*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_stdout_redirection_to_dev_null_file_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process = "*&>/dev/null*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_stdout_redirection_to_dev_null_file_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://www.ncsc.gov.uk/files/Cyclops-Blink-Malware-Analysis-Report.pdf -- https://www.trendmicro.com/en_us/research/22/c/cyclops-blink-sets-sights-on-asus-routers--.html + - https://www.ncsc.gov.uk/files/Cyclops-Blink-Malware-Analysis-Report.pdf + - https://www.trendmicro.com/en_us/research/22/c/cyclops-blink-sets-sights-on-asus-routers--.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a commandline $process$ that redirect stdout to dev/null on $dest$ - risk_objects: - - field: dest - type: system - score: 36 - threat_objects: [] + message: a commandline $process$ that redirect stdout to dev/null on $dest$ + risk_objects: + - field: dest + type: system + score: 36 + threat_objects: [] tags: - analytic_story: - - Cyclops Blink - - Data Destruction - - Industroyer2 - asset_type: Endpoint - mitre_attack_id: - - T1562.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Cyclops Blink + - Data Destruction + - Industroyer2 + asset_type: Endpoint + mitre_attack_id: + - T1562.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/cyclopsblink/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/cyclopsblink/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_stop_services.yml b/detections/endpoint/linux_stop_services.yml index 4158969370..1ed0a865e6 100644 --- a/detections/endpoint/linux_stop_services.yml +++ b/detections/endpoint/linux_stop_services.yml @@ -1,82 +1,64 @@ name: Linux Stop Services id: d05204a5-9f1c-4946-a7f3-4fa58d76d5fd -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects attempts to stop or clear a service on - Linux systems. It leverages data from Endpoint Detection and Response (EDR) agents, - focusing on processes like "systemctl," "service," and "svcadm" executing stop commands. - This activity is significant as adversaries often terminate security or critical - services to disable defenses or disrupt operations, as seen in malware like Industroyer2. - If confirmed malicious, this could lead to the disabling of security mechanisms, - allowing attackers to persist, escalate privileges, or deploy destructive payloads, - severely impacting system integrity and availability. +description: The following analytic detects attempts to stop or clear a service on Linux systems. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on processes like "systemctl," "service," and "svcadm" executing stop commands. This activity is significant as adversaries often terminate security or critical services to disable defenses or disrupt operations, as seen in malware like Industroyer2. If confirmed malicious, this could lead to the disabling of security mechanisms, allowing attackers to persist, escalate privileges, or deploy destructive payloads, severely impacting system integrity and availability. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("systemctl", - "service", "svcadm") Processes.process ="*stop*" by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_stop_services_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name IN ("systemctl", "service", "svcadm") Processes.process ="*stop*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_stop_services_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ -- https://cert.gov.ua/article/39518 + - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ + - https://cert.gov.ua/article/39518 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - attempting to stop services on endpoint $dest$ by $user$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: An instance of $parent_process_name$ spawning $process_name$ was identified attempting to stop services on endpoint $dest$ by $user$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - AwfulShred - - Data Destruction - - Industroyer2 - asset_type: Endpoint - mitre_attack_id: - - T1489 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AwfulShred + - Data Destruction + - Industroyer2 + asset_type: Endpoint + mitre_attack_id: + - T1489 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1489/linux_service_stop_disable/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1489/linux_service_stop_disable/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_sudo_or_su_execution.yml b/detections/endpoint/linux_sudo_or_su_execution.yml index dc0eaab423..ed5038fc6f 100644 --- a/detections/endpoint/linux_sudo_or_su_execution.yml +++ b/detections/endpoint/linux_sudo_or_su_execution.yml @@ -1,59 +1,49 @@ name: Linux Sudo OR Su Execution id: 4b00f134-6d6a-11ec-a90c-acde48001122 -version: 8 -date: '2026-01-20' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects the execution of the "sudo" or "su" command - on a Linux operating system. It leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process names and parent process names. This activity - is significant because "sudo" and "su" commands are commonly used by adversaries - to elevate privileges, potentially leading to unauthorized access or control over - the system. If confirmed malicious, this activity could allow attackers to execute - commands with root privileges, leading to severe security breaches, data exfiltration, - or further system compromise. +description: The following analytic detects the execution of the "sudo" or "su" command on a Linux operating system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and parent process names. This activity is significant because "sudo" and "su" commands are commonly used by adversaries to elevate privileges, potentially leading to unauthorized access or control over the system. If confirmed malicious, this activity could allow attackers to execute commands with root privileges, leading to severe security breaches, data exfiltration, or further system compromise. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("sudo", - "su") OR Processes.parent_process_name IN ("sudo", "su") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_sudo_or_su_execution_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name IN ("sudo", "su") + OR + Processes.parent_process_name IN ("sudo", "su") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_sudo_or_su_execution_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://attack.mitre.org/techniques/T1548/003/ + - https://attack.mitre.org/techniques/T1548/003/ tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - - VoidLink Cloud-Native Linux Malware - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + - VoidLink Cloud-Native Linux Malware + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/sudo_su/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/sudo_su/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_sudoers_tmp_file_creation.yml b/detections/endpoint/linux_sudoers_tmp_file_creation.yml index 5144dde1a3..bc956f7370 100644 --- a/detections/endpoint/linux_sudoers_tmp_file_creation.yml +++ b/detections/endpoint/linux_sudoers_tmp_file_creation.yml @@ -1,73 +1,62 @@ name: Linux Sudoers Tmp File Creation id: be254a5c-63e7-11ec-89da-acde48001122 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the creation of the "sudoers.tmp" file, - which occurs when editing the /etc/sudoers file using visudo or another editor on - a Linux platform. This detection leverages filesystem data to identify the presence - of "sudoers.tmp" files. Monitoring this activity is crucial as adversaries may exploit - it to gain elevated privileges on a compromised host. If confirmed malicious, this - activity could allow attackers to modify sudoers configurations, potentially granting - them unauthorized access to execute commands as other users, including root, thereby - compromising the system's security. +description: The following analytic detects the creation of the "sudoers.tmp" file, which occurs when editing the /etc/sudoers file using visudo or another editor on a Linux platform. This detection leverages filesystem data to identify the presence of "sudoers.tmp" files. Monitoring this activity is crucial as adversaries may exploit it to gain elevated privileges on a compromised host. If confirmed malicious, this activity could allow attackers to modify sudoers configurations, potentially granting them unauthorized access to execute commands as other users, including root, thereby compromising the system's security. data_source: -- Sysmon for Linux EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*sudoers.tmp*") - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path - Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | - `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_sudoers_tmp_file_creation_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from - Splunkbase. -known_false_positives: administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 11 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.file_path IN ("*sudoers.tmp*") + BY Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path Filesystem.file_acl + Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` + | `linux_sudoers_tmp_file_creation_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase. +known_false_positives: administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://forum.ubuntuusers.de/topic/sudo-visudo-gibt-etc-sudoers-tmp/ + - https://forum.ubuntuusers.de/topic/sudo-visudo-gibt-etc-sudoers-tmp/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A file $file_name$ is created in $file_path$ on $dest$ - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: [] + message: A file $file_name$ is created in $file_path$ on $dest$ + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: [] tags: - analytic_story: - - Linux Persistence Techniques - - China-Nexus Threat Activity - - Salt Typhoon - - Linux Privilege Escalation - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Persistence Techniques + - China-Nexus Threat Activity + - Salt Typhoon + - Linux Privilege Escalation + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/sudoers_temp/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/sudoers_temp/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_suspicious_react_or_next_js_child_process.yml b/detections/endpoint/linux_suspicious_react_or_next_js_child_process.yml index 94e615173e..d83f006a3e 100644 --- a/detections/endpoint/linux_suspicious_react_or_next_js_child_process.yml +++ b/detections/endpoint/linux_suspicious_react_or_next_js_child_process.yml @@ -6,166 +6,156 @@ author: Nasreddine Bencherchali, Splunk status: production type: TTP description: | - This analytic detects Linux processes such as sh, bash, and common Linux LOLBINs being spawned by React or Next.js application servers. - In the context of CVE-2025-55182 / React2Shell / CVE-2025-66478 for Next.js, successful exploitation can lead to arbitrary JavaScript execution on the server, which in turn is commonly used to invoke Node's child_process APIs (for example child_process.execSync) to run OS-level commands. - Public proof-of-concept payloads and observed in-the-wild exploit traffic show patterns where the vulnerable React Server Components handler triggers process.mainModule.require('child_process').execSync() to execute binaries such as ping, curl, or arbitrary shells on the underlying host. - This detection focuses on suspicious child processes where a Next/React server process spawns an uncommon process. - Such activity might be a strong indicator of exploitation of the aforementioned vulnerability. + This analytic detects Linux processes such as sh, bash, and common Linux LOLBINs being spawned by React or Next.js application servers. + In the context of CVE-2025-55182 / React2Shell / CVE-2025-66478 for Next.js, successful exploitation can lead to arbitrary JavaScript execution on the server, which in turn is commonly used to invoke Node's child_process APIs (for example child_process.execSync) to run OS-level commands. + Public proof-of-concept payloads and observed in-the-wild exploit traffic show patterns where the vulnerable React Server Components handler triggers process.mainModule.require('child_process').execSync() to execute binaries such as ping, curl, or arbitrary shells on the underlying host. + This detection focuses on suspicious child processes where a Next/React server process spawns an uncommon process. + Such activity might be a strong indicator of exploitation of the aforementioned vulnerability. data_source: - - Sysmon for Linux EventID 1 + - Sysmon for Linux EventID 1 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime - from datamodel=Endpoint.Processes - where - Processes.parent_process_name = "node" - Processes.parent_process IN ( - "*--experimental-https*", - "*--experimental-next-config-strip-types*", - "*/node_modules/next*", - "*next dev*", - "*next start*", - "*node_modules/.bin*", - "*react-scripts start*", - "*start-server.js*" - ) - AND ( - Processes.process_name IN ( - "awk", - "gawk", - "ifconfig", - "lua", - "nc", - "ncat", - "netcat", - "openssl", - "perl", - "php", - "python", - "python2", - "python3", - "ruby", - "socat", - "telnet" + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime + from datamodel=Endpoint.Processes + where + Processes.parent_process_name = "node" + Processes.parent_process IN ( + "*--experimental-https*", + "*--experimental-next-config-strip-types*", + "*/node_modules/next*", + "*next dev*", + "*next start*", + "*node_modules/.bin*", + "*react-scripts start*", + "*start-server.js*" ) - OR ( - Processes.process_name IN ("curl", "wget") - Processes.process = "*|*" - ) - OR ( + AND ( Processes.process_name IN ( - "bash", - "dash", - "sh" + "awk", + "gawk", + "ifconfig", + "lua", + "nc", + "ncat", + "netcat", + "openssl", + "perl", + "php", + "python", + "python2", + "python3", + "ruby", + "socat", + "telnet" ) - NOT Processes.process = "*-c*" - ) - OR ( - Processes.process_name IN ( - "bash", - "dash", - "ksh", - "sh", - "zsh" + OR ( + Processes.process_name IN ("curl", "wget") + Processes.process = "*|*" + ) + OR ( + Processes.process_name IN ( + "bash", + "dash", + "sh" + ) + NOT Processes.process = "*-c*" ) - Processes.process IN ( - "*/dev/tcp/*", - "*/dev/udp/*", - "*0>&1*", - "*curl*", - "*exec *>&*", - "*fsockopen*", - "*ifconfig*", - "*mkfifo*", - "*nc *", - "*ncat*", - "*netcat*", - "*proc_open*", - "*s_client*", - "*socat*", - "*socket*", - "*subprocess*", - "*TCPSocket*", - "*wget*" + OR ( + Processes.process_name IN ( + "bash", + "dash", + "ksh", + "sh", + "zsh" + ) + Processes.process IN ( + "*/dev/tcp/*", + "*/dev/udp/*", + "*0>&1*", + "*curl*", + "*exec *>&*", + "*fsockopen*", + "*ifconfig*", + "*mkfifo*", + "*nc *", + "*ncat*", + "*netcat*", + "*proc_open*", + "*s_client*", + "*socat*", + "*socket*", + "*subprocess*", + "*TCPSocket*", + "*wget*" + ) ) ) - ) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process - Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id - Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process + Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id + Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_suspicious_react_or_next_js_child_process_filter` + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_suspicious_react_or_next_js_child_process_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. - These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. - To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. - These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. - Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. + These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. + To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. + These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. + Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: | - Rare false positives might show up from child processes such as sh. Apply additional filters as needed. + Rare false positives might show up from child processes such as sh. Apply additional filters as needed. references: - - https://react.dev/blog/2025/12/03/critical-security-vulnerability-in-react-server-components - - https://nextjs.org/blog/CVE-2025-66478 - - https://nvd.nist.gov/vuln/detail/CVE-2025-55182 - - https://gist.github.com/maple3142/48bc9393f45e068cf8c90ab865c0f5f3 - - https://www.wiz.io/blog/critical-vulnerability-in-react-cve-2025-55182 + - https://react.dev/blog/2025/12/03/critical-security-vulnerability-in-react-server-components + - https://nextjs.org/blog/CVE-2025-66478 + - https://nvd.nist.gov/vuln/detail/CVE-2025-55182 + - https://gist.github.com/maple3142/48bc9393f45e068cf8c90ab865c0f5f3 + - https://www.wiz.io/blog/critical-vulnerability-in-react-cve-2025-55182 drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A Node-based server process ($parent_process_name$) on Linux spawned the - child process $process_name$ with command-line $process$ on host $dest$ by user $user$, which may - indicate remote code execution via React Server Components (CVE-2025-55182 / - React2Shell) or abuse of a similar Node.js RCE vector. - risk_objects: - - field: user - type: user - score: 70 - - field: dest - type: system - score: 70 - threat_objects: - - field: parent_process_name - type: process - - field: process_name - type: process - - field: process - type: process + message: A Node-based server process ($parent_process_name$) on Linux spawned the child process $process_name$ with command-line $process$ on host $dest$ by user $user$, which may indicate remote code execution via React Server Components (CVE-2025-55182 / React2Shell) or abuse of a similar Node.js RCE vector. + risk_objects: + - field: user + type: user + score: 70 + - field: dest + type: system + score: 70 + threat_objects: + - field: parent_process_name + type: process + - field: process_name + type: process + - field: process + type: process tags: - analytic_story: - - React2Shell - asset_type: Endpoint - mitre_attack_id: - - T1190 - - T1059.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - React2Shell + asset_type: Endpoint + mitre_attack_id: + - T1190 + - T1059.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/emerging_threats/react2shell/react2shell_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/emerging_threats/react2shell/react2shell_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_system_network_discovery.yml b/detections/endpoint/linux_system_network_discovery.yml index 3b66186d34..e0b006b286 100644 --- a/detections/endpoint/linux_system_network_discovery.yml +++ b/detections/endpoint/linux_system_network_discovery.yml @@ -1,88 +1,60 @@ name: Linux System Network Discovery id: 535cb214-8b47-11ec-a2c7-acde48001122 -version: 7 -date: '2026-01-20' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies potential enumeration of local network - configuration on Linux systems. It detects this activity by monitoring processes - such as "arp," "ifconfig," "ip," "netstat," "firewall-cmd," "ufw," "iptables," "ss," - and "route" within a 30-minute window. This behavior is significant as it often - indicates reconnaissance efforts by adversaries to gather network information for - subsequent attacks. If confirmed malicious, this activity could enable attackers - to map the network, identify vulnerabilities, and plan further exploitation or lateral - movement within the environment. +description: The following analytic identifies potential enumeration of local network configuration on Linux systems. It detects this activity by monitoring processes such as "arp," "ifconfig," "ip," "netstat," "firewall-cmd," "ufw," "iptables," "ss," and "route" within a 30-minute window. This behavior is significant as it often indicates reconnaissance efforts by adversaries to gather network information for subsequent attacks. If confirmed malicious, this activity could enable attackers to map the network, identify vulnerabilities, and plan further exploitation or lateral movement within the environment. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime values(Processes.action) as action values(Processes.dest) as dest values(Processes.original_file_name) - as original_file_name values(Processes.parent_process) as parent_process values(Processes.parent_process_exec) - as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid - values(Processes.parent_process_id) as parent_process_id values(Processes.parent_process_name) - as parent_process_name values(Processes.parent_process_path) as parent_process_path - values(Processes.process) as process values(Processes.process_exec) as process_exec - values(Processes.process_guid) as process_guid values(Processes.process_hash) as - process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) - as process_integrity_level values(Processes.process_name) as process_name values(Processes.process_path) - as process_path values(Processes.user) as user values(Processes.user_id) as user_id - values(Processes.vendor_product) as vendor_product dc(Processes.process_name) as - process_name_count from datamodel=Endpoint.Processes where Processes.process_name - IN ("arp", "ifconfig", "ip", "netstat", "firewall-cmd", "ufw", "iptables", "ss", - "route") by _time span=30m Processes.dest Processes.user | where process_name_count>=4 - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_system_network_discovery_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.action) as action values(Processes.dest) as dest values(Processes.original_file_name) as original_file_name values(Processes.parent_process) as parent_process values(Processes.parent_process_exec) as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid values(Processes.parent_process_id) as parent_process_id values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_path) as parent_process_path values(Processes.process) as process values(Processes.process_exec) as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) as process_integrity_level values(Processes.process_name) as process_name values(Processes.process_path) as process_path values(Processes.user) as user values(Processes.user_id) as user_id values(Processes.vendor_product) as vendor_product dc(Processes.process_name) as process_name_count FROM datamodel=Endpoint.Processes + WHERE Processes.process_name IN ("arp", "ifconfig", "ip", "netstat", "firewall-cmd", "ufw", "iptables", "ss", "route") + BY _time span=30m Processes.dest + Processes.user + | where process_name_count>=4 + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_system_network_discovery_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1016/T1016.md + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1016/T1016.md drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Network discovery process $process$ executed on $dest$ - risk_objects: - - field: dest - type: system - score: 9 - threat_objects: [] + message: Network discovery process $process$ executed on $dest$ + risk_objects: + - field: dest + type: system + score: 9 + threat_objects: [] tags: - analytic_story: - - Data Destruction - - Network Discovery - - Industroyer2 - - VoidLink Cloud-Native Linux Malware - asset_type: Endpoint - mitre_attack_id: - - T1016 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Destruction + - Network Discovery + - Industroyer2 + - VoidLink Cloud-Native Linux Malware + asset_type: Endpoint + mitre_attack_id: + - T1016 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1016/atomic_red_team/linux_net_discovery/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1016/atomic_red_team/linux_net_discovery/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_system_reboot_via_system_request_key.yml b/detections/endpoint/linux_system_reboot_via_system_request_key.yml index 2ed04a7c8d..4a66b12a9b 100644 --- a/detections/endpoint/linux_system_reboot_via_system_request_key.yml +++ b/detections/endpoint/linux_system_reboot_via_system_request_key.yml @@ -1,78 +1,63 @@ name: Linux System Reboot Via System Request Key id: e1912b58-ed9c-422c-bbb0-2dbc70398345 -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of the SysReq hack to reboot - a Linux system host. It leverages Endpoint Detection and Response (EDR) data to - identify processes executing the command to pipe 'b' to /proc/sysrq-trigger. This - activity is significant as it is an uncommon method to reboot a system and was observed - in the Awfulshred malware wiper. If confirmed malicious, this technique could indicate - the presence of suspicious processes and potential system compromise, leading to - unauthorized reboots and disruption of services. +description: The following analytic detects the execution of the SysReq hack to reboot a Linux system host. It leverages Endpoint Detection and Response (EDR) data to identify processes executing the command to pipe 'b' to /proc/sysrq-trigger. This activity is significant as it is an uncommon method to reboot a system and was observed in the Awfulshred malware wiper. If confirmed malicious, this technique could indicate the presence of suspicious processes and potential system compromise, leading to unauthorized reboots and disruption of services. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("dash", - "sudo", "bash") Processes.process = "* echo b > *" Processes.process = "*/proc/sysrq-trigger" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_system_reboot_via_system_request_key_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name IN ("dash", "sudo", "bash") Processes.process = "* echo b > *" Processes.process = "*/proc/sysrq-trigger" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `linux_system_reboot_via_system_request_key_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://www.kernel.org/doc/html/latest/admin-guide/sysrq.html -- https://cert.gov.ua/article/3718487 -- https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/overview-of-the-cyber-weapons-used-in-the-ukraine-russia-war/ + - https://www.kernel.org/doc/html/latest/admin-guide/sysrq.html + - https://cert.gov.ua/article/3718487 + - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/overview-of-the-cyber-weapons-used-in-the-ukraine-russia-war/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a $process_name$ execute sysrq command $process$ to reboot $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: a $process_name$ execute sysrq command $process$ to reboot $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - AwfulShred - - Data Destruction - asset_type: Endpoint - mitre_attack_id: - - T1529 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AwfulShred + - Data Destruction + asset_type: Endpoint + mitre_attack_id: + - T1529 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test2/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test2/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_telnet_authentication_bypass.yml b/detections/endpoint/linux_telnet_authentication_bypass.yml index 2f8588fb30..6a0ce1c8b1 100644 --- a/detections/endpoint/linux_telnet_authentication_bypass.yml +++ b/detections/endpoint/linux_telnet_authentication_bypass.yml @@ -1,88 +1,72 @@ name: Linux Telnet Authentication Bypass id: 6e0913d4-5461-487c-9dce-6d22ef2c0f03 -version: 1 -date: '2026-01-29' +version: 2 +date: '2026-02-25' author: Raven Tait, Splunk status: production type: TTP -description: Detects an authentication bypass in telnet tracked as CVE-2026-24061. An attacker can - supply a specifically crafted USER environment variable (-f root) that is passed to /usr/bin/login. - Because this input isn't sanitized an attacker can force the system to skip authentication and - login directly as root. +description: Detects an authentication bypass in telnet tracked as CVE-2026-24061. An attacker can supply a specifically crafted USER environment variable (-f root) that is passed to /usr/bin/login. Because this input isn't sanitized an attacker can force the system to skip authentication and login directly as root. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - Processes.process_name = "login" Processes.parent_process_name = "telnetd" - Processes.process = "* -p *" Processes.process = "* -f root*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `linux_telnet_authentication_bypass_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "login" Processes.parent_process_name = "telnetd" Processes.process = "* -p *" Processes.process = "* -f root*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_telnet_authentication_bypass_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: It is rare for the telnetd to spawn login process with these arguments. references: -- https://www.safebreach.com/blog/safebreach-labs-root-cause-analysis-and-poc-exploit-for-cve-2026-24061/ + - https://www.safebreach.com/blog/safebreach-labs-root-cause-analysis-and-poc-exploit-for-cve-2026-24061/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ with CommandLine $process$ was identified - on endpoint $dest$ by user $user$ related to an authentication bypass in telnetd. - risk_objects: - - field: user - type: user - score: 56 - - field: dest - type: system - score: 56 - threat_objects: - - field: parent_process - type: parent_process - - field: process - type: process - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ with CommandLine $process$ was identified on endpoint $dest$ by user $user$ related to an authentication bypass in telnetd. + risk_objects: + - field: user + type: user + score: 56 + - field: dest + type: system + score: 56 + threat_objects: + - field: parent_process + type: parent_process + - field: process + type: process + - field: process_name + type: process_name tags: - analytic_story: - - Telnetd CVE-2026-24061 - asset_type: Endpoint - mitre_attack_id: - - T1548 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: - - CVE-2026-24061 + analytic_story: + - Telnetd CVE-2026-24061 + asset_type: Endpoint + mitre_attack_id: + - T1548 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: + - CVE-2026-24061 tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/telnet/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/telnet/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_unix_shell_enable_all_sysrq_functions.yml b/detections/endpoint/linux_unix_shell_enable_all_sysrq_functions.yml index 77cc6e6423..14ba45b95a 100644 --- a/detections/endpoint/linux_unix_shell_enable_all_sysrq_functions.yml +++ b/detections/endpoint/linux_unix_shell_enable_all_sysrq_functions.yml @@ -1,80 +1,63 @@ name: Linux Unix Shell Enable All SysRq Functions id: e7a96937-3b58-4962-8dce-538e4763cf15 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the execution of a command to enable all - SysRq functions on a Linux system, a technique associated with the AwfulShred malware. - It leverages Endpoint Detection and Response (EDR) data to identify processes executing - the command to pipe bitmask '1' to /proc/sys/kernel/sysrq. This activity is significant - as it can indicate an attempt to manipulate kernel system requests, which is uncommon - and potentially malicious. If confirmed, this could allow an attacker to reboot - the system or perform other critical actions, leading to system instability or further - compromise. +description: The following analytic detects the execution of a command to enable all SysRq functions on a Linux system, a technique associated with the AwfulShred malware. It leverages Endpoint Detection and Response (EDR) data to identify processes executing the command to pipe bitmask '1' to /proc/sys/kernel/sysrq. This activity is significant as it can indicate an attempt to manipulate kernel system requests, which is uncommon and potentially malicious. If confirmed, this could allow an attacker to reboot the system or perform other critical actions, leading to system instability or further compromise. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("dash", - "sudo", "bash") Processes.process = "* echo 1 > *" Processes.process = "*/proc/sys/kernel/sysrq" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_unix_shell_enable_all_sysrq_functions_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name IN ("dash", "sudo", "bash") Processes.process = "* echo 1 > *" Processes.process = "*/proc/sys/kernel/sysrq" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `linux_unix_shell_enable_all_sysrq_functions_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://www.kernel.org/doc/html/latest/admin-guide/sysrq.html -- https://cert.gov.ua/article/3718487 -- https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/overview-of-the-cyber-weapons-used-in-the-ukraine-russia-war/ + - https://www.kernel.org/doc/html/latest/admin-guide/sysrq.html + - https://cert.gov.ua/article/3718487 + - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/overview-of-the-cyber-weapons-used-in-the-ukraine-russia-war/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a $process_name$ execute sysrq command $process$ to enable all function - of system request on $dest$ - risk_objects: - - field: dest - type: system - score: 36 - threat_objects: [] + message: a $process_name$ execute sysrq command $process$ to enable all function of system request on $dest$ + risk_objects: + - field: dest + type: system + score: 36 + threat_objects: [] tags: - analytic_story: - - AwfulShred - - Data Destruction - asset_type: Endpoint - mitre_attack_id: - - T1059.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AwfulShred + - Data Destruction + asset_type: Endpoint + mitre_attack_id: + - T1059.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test2/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test2/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/linux_visudo_utility_execution.yml b/detections/endpoint/linux_visudo_utility_execution.yml index 505e062cdf..f637c1e48b 100644 --- a/detections/endpoint/linux_visudo_utility_execution.yml +++ b/detections/endpoint/linux_visudo_utility_execution.yml @@ -1,78 +1,62 @@ name: Linux Visudo Utility Execution id: 08c41040-624c-11ec-a71f-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the 'visudo' utility - to modify the /etc/sudoers file on a Linux system. It leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process execution logs. This activity - is significant because unauthorized changes to the /etc/sudoers file can grant elevated - privileges to users, potentially allowing adversaries to execute commands as root. - If confirmed malicious, this could lead to full system compromise, privilege escalation, - and persistent unauthorized access, severely impacting the security posture of the - affected host. +description: The following analytic detects the execution of the 'visudo' utility to modify the /etc/sudoers file on a Linux system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This activity is significant because unauthorized changes to the /etc/sudoers file can grant elevated privileges to users, potentially allowing adversaries to execute commands as root. If confirmed malicious, this could lead to full system compromise, privilege escalation, and persistent unauthorized access, severely impacting the security posture of the affected host. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = visudo - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `linux_visudo_utility_execution_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can execute this command. - Please update the filter macros to remove false positives. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = visudo + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `linux_visudo_utility_execution_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives. references: -- https://askubuntu.com/questions/334318/sudoers-file-enable-nopasswd-for-user-all-commands + - https://askubuntu.com/questions/334318/sudoers-file-enable-nopasswd-for-user-all-commands drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A commandline $process$ executed on $dest$ - risk_objects: - - field: dest - type: system - score: 16 - threat_objects: [] + message: A commandline $process$ executed on $dest$ + risk_objects: + - field: dest + type: system + score: 16 + threat_objects: [] tags: - analytic_story: - - Linux Privilege Escalation - - Linux Persistence Techniques - asset_type: Endpoint - mitre_attack_id: - - T1548.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Privilege Escalation + - Linux Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1548.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/visudo/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/visudo/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/living_off_the_land_detection.yml b/detections/endpoint/living_off_the_land_detection.yml index f9b3473e18..a06c2ff9d3 100644 --- a/detections/endpoint/living_off_the_land_detection.yml +++ b/detections/endpoint/living_off_the_land_detection.yml @@ -1,75 +1,53 @@ name: Living Off The Land Detection id: 1be30d80-3a39-4df9-9102-64a467b24abc -version: 9 -date: '2025-10-14' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Correlation -description: The following correlation identifies multiple risk events associated - with the "Living Off The Land" analytic story, indicating potentially suspicious - behavior. It leverages the Risk data model to aggregate and correlate events tagged - under this story, focusing on systems with a high count of distinct sources. This - activity is significant as it often involves the use of legitimate tools for malicious - purposes, making detection challenging. If confirmed malicious, this behavior could - allow attackers to execute code, escalate privileges, or persist within the environment - using trusted system utilities. +description: The following correlation identifies multiple risk events associated with the "Living Off The Land" analytic story, indicating potentially suspicious behavior. It leverages the Risk data model to aggregate and correlate events tagged under this story, focusing on systems with a high count of distinct sources. This activity is significant as it often involves the use of legitimate tools for malicious purposes, making detection challenging. If confirmed malicious, this behavior could allow attackers to execute code, escalate privileges, or persist within the environment using trusted system utilities. data_source: [] -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) - as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as - annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) - as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) - as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) - as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, - dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories="Living - Off The Land" All_Risk.risk_object_type="system" by All_Risk.risk_object All_Risk.risk_object_type - All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where - source_count >= 5 | `living_off_the_land_detection_filter`' -how_to_implement: To implement this correlation search a user needs to enable all - detections in the Living Off The Land Analytic Story and confirm it is generating - risk events. A simple search `index=risk analyticstories="Living Off The Land"` - should contain events. -known_false_positives: There are no known false positive for this search, but it could - contain false positives as multiple detections can trigger and not have successful - exploitation. Modify the static value distinct_detection_name to a higher value. - It is also required to tune analytics that are also tagged to ensure volume is never - too much. +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count FROM datamodel=Risk.All_Risk + WHERE All_Risk.analyticstories="Living Off The Land" All_Risk.risk_object_type="system" + BY All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic + | `drop_dm_object_name(All_Risk)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | where source_count >= 5 + | `living_off_the_land_detection_filter` +how_to_implement: To implement this correlation search a user needs to enable all detections in the Living Off The Land Analytic Story and confirm it is generating risk events. A simple search `index=risk analyticstories="Living Off The Land"` should contain events. +known_false_positives: There are no known false positive for this search, but it could contain false positives as multiple detections can trigger and not have successful exploitation. Modify the static value distinct_detection_name to a higher value. It is also required to tune analytics that are also tagged to ensure volume is never too much. references: -- https://www.splunk.com/en_us/blog/security/living-off-the-land-threat-research-february-2022-release.html -- https://research.splunk.com/stories/living_off_the_land/ + - https://www.splunk.com/en_us/blog/security/living-off-the-land-threat-research-february-2022-release.html + - https://research.splunk.com/stories/living_off_the_land/ drilldown_searches: -- name: View the detection results for - "$risk_object$" - search: '%original_detection_search% | search risk_object = "$risk_object$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$risk_object$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$risk_object$" + search: '%original_detection_search% | search risk_object = "$risk_object$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$risk_object$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ tags: - analytic_story: - - Living Off The Land - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1105 - - T1190 - - T1059 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1105 + - T1190 + - T1059 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218/living_off_the_land/lolbinrisk.log - source: lotl - sourcetype: stash + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218/living_off_the_land/lolbinrisk.log + source: lotl + sourcetype: stash diff --git a/detections/endpoint/llm_model_file_creation.yml b/detections/endpoint/llm_model_file_creation.yml index 77bc087350..3e7cba1f97 100644 --- a/detections/endpoint/llm_model_file_creation.yml +++ b/detections/endpoint/llm_model_file_creation.yml @@ -6,58 +6,58 @@ author: Rod Soto status: production type: Hunting description: | - Detects the creation of Large Language Model (LLM) files on Windows endpoints by monitoring file creation events for specific model file formats and extensions commonly used by local AI frameworks. - This detection identifies potential shadow AI deployments, unauthorized model downloads, and rogue LLM infrastructure by detecting file creation patterns associated with quantized models (.gguf, .ggml), safetensors model format files, and Ollama Modelfiles. - These file types are characteristic of local inference frameworks such as Ollama, llama.cpp, GPT4All, LM Studio, and similar tools that enable running LLMs locally without cloud dependencies. - Organizations can use this detection to identify potential data exfiltration risks, policy violations related to unapproved AI usage, and security blind spots created by decentralized AI deployments that bypass enterprise governance and monitoring. + Detects the creation of Large Language Model (LLM) files on Windows endpoints by monitoring file creation events for specific model file formats and extensions commonly used by local AI frameworks. + This detection identifies potential shadow AI deployments, unauthorized model downloads, and rogue LLM infrastructure by detecting file creation patterns associated with quantized models (.gguf, .ggml), safetensors model format files, and Ollama Modelfiles. + These file types are characteristic of local inference frameworks such as Ollama, llama.cpp, GPT4All, LM Studio, and similar tools that enable running LLMs locally without cloud dependencies. + Organizations can use this detection to identify potential data exfiltration risks, policy violations related to unapproved AI usage, and security blind spots created by decentralized AI deployments that bypass enterprise governance and monitoring. data_source: - - Sysmon EventID 11 + - Sysmon EventID 11 search: | - | tstats `security_content_summariesonly` count - min(_time) as firstTime - max(_time) as lastTime - from datamodel=Endpoint.Filesystem - where Filesystem.file_name IN ( - "*.gguf*", - "*ggml*", - "*Modelfile*", - "*safetensors*" - ) - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path - Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product - | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `llm_model_file_creation_filter` + | tstats `security_content_summariesonly` count + min(_time) as firstTime + max(_time) as lastTime + from datamodel=Endpoint.Filesystem + where Filesystem.file_name IN ( + "*.gguf*", + "*ggml*", + "*Modelfile*", + "*safetensors*" + ) + by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time + Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path + Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `llm_model_file_creation_filter` how_to_implement: | - To successfully implement this search, you need to be ingesting logs with file creation events from your endpoints. - Ensure that the Endpoint data model is properly populated with filesystem events from EDR agents or Sysmon Event ID 11. - The logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. - The logs must also be mapped to the `Filesystem` node of the `Endpoint` data model. - Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. + To successfully implement this search, you need to be ingesting logs with file creation events from your endpoints. + Ensure that the Endpoint data model is properly populated with filesystem events from EDR agents or Sysmon Event ID 11. + The logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. + The logs must also be mapped to the `Filesystem` node of the `Endpoint` data model. + Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: | - Legitimate creation of LLM model files by authorized developers, ML engineers, and researchers during model training, fine-tuning, or experimentation. Approved AI/ML sandboxes and lab environments where model file creation is expected. Automated ML pipelines and workflows that generate or update model files as part of their normal operation. Third-party applications and services that manage or cache LLM model files for legitimate purposes. + Legitimate creation of LLM model files by authorized developers, ML engineers, and researchers during model training, fine-tuning, or experimentation. Approved AI/ML sandboxes and lab environments where model file creation is expected. Automated ML pipelines and workflows that generate or update model files as part of their normal operation. Third-party applications and services that manage or cache LLM model files for legitimate purposes. references: - - https://docs.microsoft.com/en-us/sysinternals/downloads/sysmon - - https://www.ibm.com/think/topics/shadow-ai - - https://www.splunk.com/en_us/blog/artificial-intelligence/splunk-technology-add-on-for-ollama.html - - https://blogs.cisco.com/security/detecting-exposed-llm-servers-shodan-case-study-on-ollama + - https://docs.microsoft.com/en-us/sysinternals/downloads/sysmon + - https://www.ibm.com/think/topics/shadow-ai + - https://www.splunk.com/en_us/blog/artificial-intelligence/splunk-technology-add-on-for-ollama.html + - https://blogs.cisco.com/security/detecting-exposed-llm-servers-shodan-case-study-on-ollama tags: - analytic_story: - - Suspicious Local LLM Frameworks - asset_type: Endpoint - mitre_attack_id: - - T1543 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Local LLM Frameworks + asset_type: Endpoint + mitre_attack_id: + - T1543 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/local_llms/sysmon_local_llms.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/local_llms/sysmon_local_llms.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/loading_of_dynwrapx_module.yml b/detections/endpoint/loading_of_dynwrapx_module.yml index 7acbbeb083..63b0af7abb 100644 --- a/detections/endpoint/loading_of_dynwrapx_module.yml +++ b/detections/endpoint/loading_of_dynwrapx_module.yml @@ -5,75 +5,50 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the loading of the dynwrapx.dll module, - which is associated with the DynamicWrapperX ActiveX component. This detection leverages - Sysmon EventCode 7 to identify processes that load or register dynwrapx.dll. This - activity is significant because DynamicWrapperX can be used to call Windows API - functions in scripts, making it a potential tool for malicious actions. If confirmed - malicious, this could allow an attacker to execute arbitrary code, escalate privileges, - or maintain persistence on the host. Immediate investigation of parallel processes - and registry modifications is recommended. +description: The following analytic detects the loading of the dynwrapx.dll module, which is associated with the DynamicWrapperX ActiveX component. This detection leverages Sysmon EventCode 7 to identify processes that load or register dynwrapx.dll. This activity is significant because DynamicWrapperX can be used to call Windows API functions in scripts, making it a potential tool for malicious actions. If confirmed malicious, this could allow an attacker to execute arbitrary code, escalate privileges, or maintain persistence on the host. Immediate investigation of parallel processes and registry modifications is recommended. data_source: -- Sysmon EventID 7 -search: '`sysmon` EventCode=7 (ImageLoaded = "*\\dynwrapx.dll" OR OriginalFileName - = "dynwrapx.dll" OR Product = "DynamicWrapperX") | fillnull | stats count min(_time) - as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path - original_file_name process_exec process_guid process_hash process_id process_name - process_path service_dll_signature_exists service_dll_signature_verified signature - signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `loading_of_dynwrapx_module_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on processes that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` - node. In addition, confirm the latest CIM App 4.20 or higher is installed and the - latest TA for the endpoint product. -known_false_positives: False positives should be limited, however it is possible to - filter by Processes.process_name and specific processes (ex. wscript.exe). Filter - as needed. This may need modification based on EDR telemetry and how it brings in - registry data. For example, removal of (Default). + - Sysmon EventID 7 +search: '`sysmon` EventCode=7 (ImageLoaded = "*\\dynwrapx.dll" OR OriginalFileName = "dynwrapx.dll" OR Product = "DynamicWrapperX") | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `loading_of_dynwrapx_module_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on processes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: False positives should be limited, however it is possible to filter by Processes.process_name and specific processes (ex. wscript.exe). Filter as needed. This may need modification based on EDR telemetry and how it brings in registry data. For example, removal of (Default). references: -- https://blog.f-secure.com/hunting-for-koadic-a-com-based-rootkit/ -- https://www.script-coding.com/dynwrapx_eng.html -- https://bohops.com/2018/06/28/abusing-com-registry-structure-clsid-localserver32-inprocserver32/ -- https://tria.ge/210929-ap75vsddan -- https://www.virustotal.com/gui/file/cb77b93150cb0f7fe65ce8a7e2a5781e727419451355a7736db84109fa215a89 -- https://malpedia.caad.fkie.fraunhofer.de/details/win.asyncrat + - https://blog.f-secure.com/hunting-for-koadic-a-com-based-rootkit/ + - https://www.script-coding.com/dynwrapx_eng.html + - https://bohops.com/2018/06/28/abusing-com-registry-structure-clsid-localserver32-inprocserver32/ + - https://tria.ge/210929-ap75vsddan + - https://www.virustotal.com/gui/file/cb77b93150cb0f7fe65ce8a7e2a5781e727419451355a7736db84109fa215a89 + - https://malpedia.caad.fkie.fraunhofer.de/details/win.asyncrat drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: dynwrapx.dll loaded by process $process_name$ on $dest$ - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: [] + message: dynwrapx.dll loaded by process $process_name$ on $dest$ + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: [] tags: - analytic_story: - - Remcos - - AsyncRAT - asset_type: Endpoint - mitre_attack_id: - - T1055.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Remcos + - AsyncRAT + asset_type: Endpoint + mitre_attack_id: + - T1055.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos_dynwrapx/sysmon_dynwraper.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos_dynwrapx/sysmon_dynwraper.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/local_account_discovery_with_wmic.yml b/detections/endpoint/local_account_discovery_with_wmic.yml index 6375ce8985..de76a5a814 100644 --- a/detections/endpoint/local_account_discovery_with_wmic.yml +++ b/detections/endpoint/local_account_discovery_with_wmic.yml @@ -1,58 +1,48 @@ name: Local Account Discovery With Wmic id: 4902d7aa-0134-11ec-9d65-acde48001122 -version: 9 -date: '2025-10-14' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting -description: The following analytic detects the execution of `wmic.exe` with command-line - arguments used to query local user accounts, specifically the `useraccount` argument. - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on - process execution logs that include command-line details. This activity is significant - as it indicates potential reconnaissance efforts by adversaries to enumerate local - users, which is a common step in situational awareness and Active Directory discovery. - If confirmed malicious, this behavior could lead to further targeted attacks, privilege - escalation, or lateral movement within the network. +description: The following analytic detects the execution of `wmic.exe` with command-line arguments used to query local user accounts, specifically the `useraccount` argument. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant as it indicates potential reconnaissance efforts by adversaries to enumerate local users, which is a common step in situational awareness and Active Directory discovery. If confirmed malicious, this behavior could lead to further targeted attacks, privilege escalation, or lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_wmic` (Processes.process=*useraccount*) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `local_account_discovery_with_wmic_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_wmic` (Processes.process=*useraccount*) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `local_account_discovery_with_wmic_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1087/001/ + - https://attack.mitre.org/techniques/T1087/001/ tags: - analytic_story: - - Active Directory Discovery - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1087.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1087.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.001/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.001/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/local_llm_framework_dns_query.yml b/detections/endpoint/local_llm_framework_dns_query.yml index 1611732e11..da32e8ba65 100644 --- a/detections/endpoint/local_llm_framework_dns_query.yml +++ b/detections/endpoint/local_llm_framework_dns_query.yml @@ -6,69 +6,69 @@ author: Rod Soto status: production type: Hunting description: | - Detects DNS queries related to local LLM models on endpoints by monitoring Sysmon DNS query events (Event ID 22) for known LLM model domains and services. - Local LLM frameworks like Ollama, LM Studio, and GPT4All make DNS calls to repositories such as huggingface.co and ollama.ai for model downloads, updates, and telemetry. - These queries can reveal unauthorized AI tool usage or data exfiltration risks on corporate networks. + Detects DNS queries related to local LLM models on endpoints by monitoring Sysmon DNS query events (Event ID 22) for known LLM model domains and services. + Local LLM frameworks like Ollama, LM Studio, and GPT4All make DNS calls to repositories such as huggingface.co and ollama.ai for model downloads, updates, and telemetry. + These queries can reveal unauthorized AI tool usage or data exfiltration risks on corporate networks. data_source: - - Sysmon EventID 22 + - Sysmon EventID 22 search: | - `sysmon` - EventCode=22 - QueryName IN ( - "*huggingface*", - "*ollama*", - "*jan.ai*", - "*gpt4all*", - "*nomic*", - "*koboldai*", - "*lmstudio*", - "*modelscope*", - "*civitai*", - "*oobabooga*", - "*replicate*", - "*anthropic*", - "*openai*", - "*openrouter*", - "*api.openrouter*", - "*aliyun*", - "*alibabacloud*", - "*dashscope.aliyuncs*" - ) - NOT Image IN ( - "*\\MsMpEng.exe", - "C:\\ProgramData\\*", - "C:\\Windows\\System32\\*", - "C:\\Windows\\SysWOW64\\*" - ) - | stats count - min(_time) as firstTime - max(_time) as lastTime - by src Image process_name QueryName query_count answer answer_count reply_code_id vendor_product - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `local_llm_framework_dns_query_filter` + `sysmon` + EventCode=22 + QueryName IN ( + "*huggingface*", + "*ollama*", + "*jan.ai*", + "*gpt4all*", + "*nomic*", + "*koboldai*", + "*lmstudio*", + "*modelscope*", + "*civitai*", + "*oobabooga*", + "*replicate*", + "*anthropic*", + "*openai*", + "*openrouter*", + "*api.openrouter*", + "*aliyun*", + "*alibabacloud*", + "*dashscope.aliyuncs*" + ) + NOT Image IN ( + "*\\MsMpEng.exe", + "C:\\ProgramData\\*", + "C:\\Windows\\System32\\*", + "C:\\Windows\\SysWOW64\\*" + ) + | stats count + min(_time) as firstTime + max(_time) as lastTime + by src Image process_name QueryName query_count answer answer_count reply_code_id vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `local_llm_framework_dns_query_filter` how_to_implement: | - Ensure Sysmon is deployed across Windows endpoints and configured to capture DNS query events (Event ID 22). Configure Sysmon's XML configuration file to log detailed command-line arguments, parent process information, and full process image paths. Ingest Sysmon event logs into Splunk via the Splunk Universal Forwarder or Windows Event Log Input, ensuring they are tagged with `sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational`. Verify the `sysmon` macro exists in your Splunk environment and correctly references the Sysmon event logs. Create or update the `unauthorized_local_llm_framework_usage_filter` macro in your detections/filters folder to exclude approved systems, authorized developers, sanctioned ML/AI workstations, or known development/lab environments as needed. Deploy this hunting search to your Splunk Enterprise Security or Splunk Enterprise instance and schedule it to run on a regular cadence to detect unauthorized LLM model DNS queries and shadow AI activities. Correlate findings with endpoint asset inventory and user identity data to prioritize investigation. + Ensure Sysmon is deployed across Windows endpoints and configured to capture DNS query events (Event ID 22). Configure Sysmon's XML configuration file to log detailed command-line arguments, parent process information, and full process image paths. Ingest Sysmon event logs into Splunk via the Splunk Universal Forwarder or Windows Event Log Input, ensuring they are tagged with `sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational`. Verify the `sysmon` macro exists in your Splunk environment and correctly references the Sysmon event logs. Create or update the `unauthorized_local_llm_framework_usage_filter` macro in your detections/filters folder to exclude approved systems, authorized developers, sanctioned ML/AI workstations, or known development/lab environments as needed. Deploy this hunting search to your Splunk Enterprise Security or Splunk Enterprise instance and schedule it to run on a regular cadence to detect unauthorized LLM model DNS queries and shadow AI activities. Correlate findings with endpoint asset inventory and user identity data to prioritize investigation. known_false_positives: | - Legitimate DNS queries to LLM model hosting platforms by authorized developers, ML engineers, and researchers during model training, fine-tuning, or experimentation. Approved AI/ML sandboxes and lab environments where LLM model downloads are expected. Automated ML pipelines and workflows that interact with LLM model hosting services as part of their normal operation. Third-party applications and services that access LLM model platforms for legitimate purposes. + Legitimate DNS queries to LLM model hosting platforms by authorized developers, ML engineers, and researchers during model training, fine-tuning, or experimentation. Approved AI/ML sandboxes and lab environments where LLM model downloads are expected. Automated ML pipelines and workflows that interact with LLM model hosting services as part of their normal operation. Third-party applications and services that access LLM model platforms for legitimate purposes. references: - - https://docs.microsoft.com/en-us/sysinternals/downloads/sysmon - - https://www.splunk.com/en_us/blog/artificial-intelligence/splunk-technology-add-on-for-ollama.html - - https://blogs.cisco.com/security/detecting-exposed-llm-servers-shodan-case-study-on-ollama + - https://docs.microsoft.com/en-us/sysinternals/downloads/sysmon + - https://www.splunk.com/en_us/blog/artificial-intelligence/splunk-technology-add-on-for-ollama.html + - https://blogs.cisco.com/security/detecting-exposed-llm-servers-shodan-case-study-on-ollama tags: - analytic_story: - - Suspicious Local LLM Frameworks - asset_type: Endpoint - mitre_attack_id: - - T1590 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Local LLM Frameworks + asset_type: Endpoint + mitre_attack_id: + - T1590 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/local_llms/sysmon_dns.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/local_llms/sysmon_dns.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/log4shell_cve_2021_44228_exploitation.yml b/detections/endpoint/log4shell_cve_2021_44228_exploitation.yml index bcb0d78d3f..84c489c32f 100644 --- a/detections/endpoint/log4shell_cve_2021_44228_exploitation.yml +++ b/detections/endpoint/log4shell_cve_2021_44228_exploitation.yml @@ -1,75 +1,53 @@ name: Log4Shell CVE-2021-44228 Exploitation id: 9be30d80-3a39-4df9-9102-64a467b24eac -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Jose Hernandez, Splunk status: production type: Correlation -description: The following analytic identifies potential exploitation of Log4Shell - CVE-2021-44228 by correlating multiple MITRE ATT&CK tactics detected in risk events. - It leverages Splunk's risk data model to calculate the distinct count of MITRE ATT&CK - tactics from Log4Shell-related detections. This activity is significant because - it indicates a high probability of exploitation if two or more distinct tactics - are observed. If confirmed malicious, this activity could lead to initial payload - delivery, callback to a malicious server, and post-exploitation activities, potentially - resulting in unauthorized access, lateral movement, and further compromise of the - affected systems. +description: The following analytic identifies potential exploitation of Log4Shell CVE-2021-44228 by correlating multiple MITRE ATT&CK tactics detected in risk events. It leverages Splunk's risk data model to calculate the distinct count of MITRE ATT&CK tactics from Log4Shell-related detections. This activity is significant because it indicates a high probability of exploitation if two or more distinct tactics are observed. If confirmed malicious, this activity could lead to initial payload delivery, callback to a malicious server, and post-exploitation activities, potentially resulting in unauthorized access, lateral movement, and further compromise of the affected systems. data_source: [] -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) - as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as - annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) - as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) - as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) - as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, - dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories="Log4Shell - CVE-2021-44228" All_Risk.risk_object_type="system" by All_Risk.risk_object All_Risk.risk_object_type - All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where - source_count >= 2 | `log4shell_cve_2021_44228_exploitation_filter`' -how_to_implement: To implement this correlation search a user needs to enable all - detections in the Log4Shell Analytic Story and confirm it is generation risk events. - A simple search `index=risk analyticstories="Log4Shell CVE-2021-44228"` should contain - events. -known_false_positives: There are no known false positive for this search, but it could - contain false positives as multiple detections can trigger and not have successful - exploitation. +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count FROM datamodel=Risk.All_Risk + WHERE All_Risk.analyticstories="Log4Shell CVE-2021-44228" All_Risk.risk_object_type="system" + BY All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic + | `drop_dm_object_name(All_Risk)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | where source_count >= 2 + | `log4shell_cve_2021_44228_exploitation_filter` +how_to_implement: To implement this correlation search a user needs to enable all detections in the Log4Shell Analytic Story and confirm it is generation risk events. A simple search `index=risk analyticstories="Log4Shell CVE-2021-44228"` should contain events. +known_false_positives: There are no known false positive for this search, but it could contain false positives as multiple detections can trigger and not have successful exploitation. references: -- https://research.splunk.com/stories/log4shell_cve-2021-44228/ -- https://www.splunk.com/en_us/blog/security/simulating-detecting-and-responding-to-log4shell-with-splunk.html + - https://research.splunk.com/stories/log4shell_cve-2021-44228/ + - https://www.splunk.com/en_us/blog/security/simulating-detecting-and-responding-to-log4shell-with-splunk.html drilldown_searches: -- name: View the detection results for - "$risk_object$" - search: '%original_detection_search% | search risk_object = "$risk_object$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$risk_object$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$risk_object$" + search: '%original_detection_search% | search risk_object = "$risk_object$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$risk_object$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ tags: - analytic_story: - - Log4Shell CVE-2021-44228 - - CISA AA22-320A - asset_type: Endpoint - mitre_attack_id: - - T1105 - - T1190 - - T1059 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Log4Shell CVE-2021-44228 + - CISA AA22-320A + asset_type: Endpoint + mitre_attack_id: + - T1105 + - T1190 + - T1059 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/log4shell_exploitation/log4shell_correlation.log - source: log4shell - sourcetype: stash + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/log4shell_exploitation/log4shell_correlation.log + source: log4shell + sourcetype: stash diff --git a/detections/endpoint/logon_script_event_trigger_execution.yml b/detections/endpoint/logon_script_event_trigger_execution.yml index 8c2a03be13..c71fb2d4d7 100644 --- a/detections/endpoint/logon_script_event_trigger_execution.yml +++ b/detections/endpoint/logon_script_event_trigger_execution.yml @@ -5,73 +5,50 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the modification of the UserInitMprLogonScript - registry entry, which is often used by attackers to establish persistence and gain - privilege escalation upon system boot. It leverages data from the Endpoint.Registry - data model, focusing on changes to the specified registry path. This activity is - significant because it is a common technique used by APT groups and malware to ensure - their payloads execute automatically when the system starts. If confirmed malicious, - this could allow attackers to maintain persistent access and potentially escalate - their privileges on the compromised host. +description: The following analytic detects the modification of the UserInitMprLogonScript registry entry, which is often used by attackers to establish persistence and gain privilege escalation upon system boot. It leverages data from the Endpoint.Registry data model, focusing on changes to the specified registry path. This activity is significant because it is a common technique used by APT groups and malware to ensure their payloads execute automatically when the system starts. If confirmed malicious, this could allow attackers to maintain persistent access and potentially escalate their privileges on the compromised host. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime - max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path - IN ("*\\Environment\\UserInitMprLogonScript") by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `security_content_ctime(lastTime)` - | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `logon_script_event_trigger_execution_filter`' -how_to_implement: To successfully implement this search, you must be ingesting data - that records registry activity from your hosts to populate the endpoint data model - in the registry node. This is typically populated via endpoint detection-and-response - product, such as Carbon Black or endpoint data sources, such as Sysmon. The data - used for this search is typically generated via logs that report reads and writes - to the registry. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path IN ("*\\Environment\\UserInitMprLogonScript") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `logon_script_event_trigger_execution_filter`' +how_to_implement: To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. known_false_positives: No false positives have been identified at this time. references: -- https://attack.mitre.org/techniques/T1037/001/ + - https://attack.mitre.org/techniques/T1037/001/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Registry path $registry_path$ was modified, added, or deleted on $dest$. - risk_objects: - - field: dest - type: system - score: 80 - - field: user - type: user - score: 80 - threat_objects: [] + message: Registry path $registry_path$ was modified, added, or deleted on $dest$. + risk_objects: + - field: dest + type: system + score: 80 + - field: user + type: user + score: 80 + threat_objects: [] tags: - analytic_story: - - Data Destruction - - Windows Privilege Escalation - - Hermetic Wiper - - Windows Persistence Techniques - asset_type: Endpoint - mitre_attack_id: - - T1037.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Destruction + - Windows Privilege Escalation + - Hermetic Wiper + - Windows Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1037.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1037.001/logonscript_reg/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1037.001/logonscript_reg/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/lolbas_with_network_traffic.yml b/detections/endpoint/lolbas_with_network_traffic.yml index 4fbbbd6a2d..2b8b272205 100644 --- a/detections/endpoint/lolbas_with_network_traffic.yml +++ b/detections/endpoint/lolbas_with_network_traffic.yml @@ -1,162 +1,144 @@ name: LOLBAS With Network Traffic id: 2820f032-19eb-497e-8642-25b04a880359 -version: 14 -date: '2025-11-20' +version: 15 +date: '2026-02-25' author: Steven Dick status: production type: TTP -description: The following analytic identifies the use of Living Off the Land Binaries - and Scripts (LOLBAS) with network traffic. It leverages data from the Network Traffic - data model to detect when native Windows binaries, often abused by adversaries, - initiate network connections. This activity is significant as LOLBAS are frequently - used to download malicious payloads, enabling lateral movement, command-and-control, - or data exfiltration. If confirmed malicious, this behavior could allow attackers - to execute arbitrary code, escalate privileges, or maintain persistence within the - environment, posing a severe threat to organizational security. +description: The following analytic identifies the use of Living Off the Land Binaries and Scripts (LOLBAS) with network traffic. It leverages data from the Network Traffic data model to detect when native Windows binaries, often abused by adversaries, initiate network connections. This activity is significant as LOLBAS are frequently used to download malicious payloads, enabling lateral movement, command-and-control, or data exfiltration. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment, posing a severe threat to organizational security. data_source: -- Sysmon EventID 3 + - Sysmon EventID 3 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime - from datamodel=Network_Traffic.All_Traffic where - All_Traffic.app IN ( - "*\\At.exe", - "*\\Atbroker.exe", - "*\\Bash.exe", - "*\\Bitsadmin.exe", - "*\\Certoc.exe", - "*\\certutil.exe", - "*\\cmd.exe", - "*\\Cmstp.exe", - "*\\cscript.exe", - "*\\Diskshadow.exe", - "*\\Dnscmd.exe", - "*\\Extexport.exe", - "*\\Forfiles.exe", - "*\\Ftp.exe", - "*\\Gpscript.exe", - "*\\Hh.exe", - "*\\Ie4uinit.exe", - "*\\Ieexec.exe", - "*\\Infdefaultinstall.exe", - "*\\Installutil.exe", - "*\\makecab.exe", - "*\\Mavinject.exe", - "*\\Microsoft.Workflow.Compiler.exe", - "*\\Msbuild.exe", - "*\\Msconfig.exe", - "*\\Msdt.exe", - "*\\Mshta.exe", - "*\\Msiexec.exe", - "*\\Netsh.exe", - "*\\notepad.exe", - "*\\Odbcconf.exe", - "*\\OfflineScannerShell.exe", - "*\\Pcalua.exe", - "*\\Pcwrun.exe", - "*\\Pnputil.exe", - "*\\powershell_ise.exe", - "*\\powershell.exe", - "*\\Presentationhost.exe", - "*\\pwsh.exe", - "*\\Rasautou.exe", - "*\\Regasm.exe", - "*\\Register-cimprovider.exe", - "*\\Regsvcs.exe", - "*\\Regsvr32.exe", - "*\\Runonce.exe", - "*\\Runscripthelper.exe", - "*\\Schtasks.exe", - "*\\Scriptrunner.exe", - "*\\SettingSyncHost.exe", - "*\\Stordiag.exe", - "*\\Syncappvpublishingserver.exe", - "*\\Ttdinject.exe", - "*\\Tttracer.exe", - "*\\Verclsid.exe", - "*\\Wab.exe", - "*\\Wmic.exe", - "*\\WorkFolders.exe", - "*\\Wuauclt.exe", - "*\\Xwizard.exe" - ) + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + from datamodel=Network_Traffic.All_Traffic where + All_Traffic.app IN ( + "*\\At.exe", + "*\\Atbroker.exe", + "*\\Bash.exe", + "*\\Bitsadmin.exe", + "*\\Certoc.exe", + "*\\certutil.exe", + "*\\cmd.exe", + "*\\Cmstp.exe", + "*\\cscript.exe", + "*\\Diskshadow.exe", + "*\\Dnscmd.exe", + "*\\Extexport.exe", + "*\\Forfiles.exe", + "*\\Ftp.exe", + "*\\Gpscript.exe", + "*\\Hh.exe", + "*\\Ie4uinit.exe", + "*\\Ieexec.exe", + "*\\Infdefaultinstall.exe", + "*\\Installutil.exe", + "*\\makecab.exe", + "*\\Mavinject.exe", + "*\\Microsoft.Workflow.Compiler.exe", + "*\\Msbuild.exe", + "*\\Msconfig.exe", + "*\\Msdt.exe", + "*\\Mshta.exe", + "*\\Msiexec.exe", + "*\\Netsh.exe", + "*\\notepad.exe", + "*\\Odbcconf.exe", + "*\\OfflineScannerShell.exe", + "*\\Pcalua.exe", + "*\\Pcwrun.exe", + "*\\Pnputil.exe", + "*\\powershell_ise.exe", + "*\\powershell.exe", + "*\\Presentationhost.exe", + "*\\pwsh.exe", + "*\\Rasautou.exe", + "*\\Regasm.exe", + "*\\Register-cimprovider.exe", + "*\\Regsvcs.exe", + "*\\Regsvr32.exe", + "*\\Runonce.exe", + "*\\Runscripthelper.exe", + "*\\Schtasks.exe", + "*\\Scriptrunner.exe", + "*\\SettingSyncHost.exe", + "*\\Stordiag.exe", + "*\\Syncappvpublishingserver.exe", + "*\\Ttdinject.exe", + "*\\Tttracer.exe", + "*\\Verclsid.exe", + "*\\Wab.exe", + "*\\Wmic.exe", + "*\\WorkFolders.exe", + "*\\Wuauclt.exe", + "*\\Xwizard.exe" + ) - NOT All_Traffic IN ( - "10.0.0.0/8", "172.16.0.0/12", "192.168.0.0/16", "100.64.0.0/10", - "127.0.0.0/8", "169.254.0.0/16", "192.0.0.0/24", "192.0.0.0/29", "192.0.0.8/32", - "192.0.0.9/32", "192.0.0.10/32", "192.0.0.170/32", "192.0.0.171/32", "192.0.2.0/24", - "192.31.196.0/24", "192.52.193.0/24", "192.88.99.0/24", "224.0.0.0/4", "192.175.48.0/24", - "198.18.0.0/15", "198.51.100.0/24", "203.0.113.0/24", "240.0.0.0/4" - ) + NOT All_Traffic IN ( + "10.0.0.0/8", "172.16.0.0/12", "192.168.0.0/16", "100.64.0.0/10", + "127.0.0.0/8", "169.254.0.0/16", "192.0.0.0/24", "192.0.0.0/29", "192.0.0.8/32", + "192.0.0.9/32", "192.0.0.10/32", "192.0.0.170/32", "192.0.0.171/32", "192.0.2.0/24", + "192.31.196.0/24", "192.52.193.0/24", "192.88.99.0/24", "224.0.0.0/4", "192.175.48.0/24", + "198.18.0.0/15", "198.51.100.0/24", "203.0.113.0/24", "240.0.0.0/4" + ) - by All_Traffic.action All_Traffic.app All_Traffic.dest All_Traffic.dest_ip All_Traffic.dest_port - All_Traffic.direction All_Traffic.dvc All_Traffic.protocol All_Traffic.protocol_version - All_Traffic.src All_Traffic.src_ip All_Traffic.src_port All_Traffic.transport All_Traffic.user - All_Traffic.vendor_product + by All_Traffic.action All_Traffic.app All_Traffic.dest All_Traffic.dest_ip All_Traffic.dest_port + All_Traffic.direction All_Traffic.dvc All_Traffic.protocol All_Traffic.protocol_version + All_Traffic.src All_Traffic.src_ip All_Traffic.src_port All_Traffic.transport All_Traffic.user + All_Traffic.vendor_product - | `drop_dm_object_name(All_Traffic)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | rex field=app ".*\\\(?.*)$" - | `lolbas_with_network_traffic_filter` -how_to_implement: To successfully implement this detection you must ingest events - into the Network traffic data model that contain the source, destination, and communicating - process in the app field. Relevant processes must also be ingested in the Endpoint - data model with matching process_id field. Sysmon EID1 and EID3 are good examples - of this type this data type. -known_false_positives: Legitimate usage of internal automation or scripting, especially - powershell.exe or pwsh.exe, internal to internal or logon scripts. It may be necessary - to omit internal IP ranges if extremely noisy. ie NOT dest_ip IN ("10.0.0.0/8","172.16.0.0/12","192.168.0.0/16","170.98.0.0/16","0:0:0:0:0:0:0:1") + | `drop_dm_object_name(All_Traffic)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | rex field=app ".*\\\(?.*)$" + | `lolbas_with_network_traffic_filter` +how_to_implement: To successfully implement this detection you must ingest events into the Network traffic data model that contain the source, destination, and communicating process in the app field. Relevant processes must also be ingested in the Endpoint data model with matching process_id field. Sysmon EID1 and EID3 are good examples of this type this data type. +known_false_positives: Legitimate usage of internal automation or scripting, especially powershell.exe or pwsh.exe, internal to internal or logon scripts. It may be necessary to omit internal IP ranges if extremely noisy. ie NOT dest_ip IN ("10.0.0.0/8","172.16.0.0/12","192.168.0.0/16","170.98.0.0/16","0:0:0:0:0:0:0:1") references: -- https://lolbas-project.github.io/# -- https://www.sans.org/presentations/lolbin-detection-methods-seven-common-attacks-revealed/ + - https://lolbas-project.github.io/# + - https://www.sans.org/presentations/lolbin-detection-methods-seven-common-attacks-revealed/ drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The LOLBAS $process_name$ on device $src$ was seen communicating with $dest$. - risk_objects: - - field: src - type: system - score: 25 - threat_objects: - - field: dest_ip - type: ip_address + message: The LOLBAS $process_name$ on device $src$ was seen communicating with $dest$. + risk_objects: + - field: src + type: system + score: 25 + threat_objects: + - field: dest_ip + type: ip_address tags: - analytic_story: - - Fake CAPTCHA Campaigns - - Living Off The Land - - Malicious Inno Setup Loader - - Water Gamayun - - APT37 Rustonotto and FadeStealer - - GhostRedirector IIS Module and Rungan Backdoor - - Hellcat Ransomware - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1105 - - T1567 - - T1218 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Fake CAPTCHA Campaigns + - Living Off The Land + - Malicious Inno Setup Loader + - Water Gamayun + - APT37 Rustonotto and FadeStealer + - GhostRedirector IIS Module and Rungan Backdoor + - Hellcat Ransomware + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1105 + - T1567 + - T1218 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218/lolbas_with_network_traffic/lolbas_with_network_traffic.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218/lolbas_with_network_traffic/lolbas_with_network_traffic.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/macos___re_opened_applications.yml b/detections/endpoint/macos___re_opened_applications.yml index 5650e67da5..712af7d3f3 100644 --- a/detections/endpoint/macos___re_opened_applications.yml +++ b/detections/endpoint/macos___re_opened_applications.yml @@ -1,59 +1,41 @@ name: MacOS - Re-opened Applications id: 40bb64f9-f619-4e3d-8732-328d40377c4b -version: 5 -date: '2025-05-05' +version: 6 +date: '2026-02-25' author: Jamie Windley, Splunk status: experimental type: TTP -description: The following analytic identifies processes referencing plist files that - determine which applications are re-opened when a user reboots their MacOS machine. - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on - process names and parent processes related to "com.apple.loginwindow." This activity - is significant because it can indicate attempts to persist across reboots, a common - tactic used by attackers to maintain access. If confirmed malicious, this could - allow an attacker to execute code or maintain persistence on the affected system, - potentially leading to further compromise. +description: The following analytic identifies processes referencing plist files that determine which applications are re-opened when a user reboots their MacOS machine. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and parent processes related to "com.apple.loginwindow." This activity is significant because it can indicate attempts to persist across reboots, a common tactic used by attackers to maintain access. If confirmed malicious, this could allow an attacker to execute code or maintain persistence on the affected system, potentially leading to further compromise. data_source: -- Sysmon EventID 1 -search: '| tstats `security_content_summariesonly` count values(Processes.process) - as process values(Processes.parent_process) as parent_process min(_time) as firstTime - max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*com.apple.loginwindow*" - by Processes.user Processes.process_name Processes.parent_process_name Processes.dest - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `macos___re_opened_applications_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: At this stage, there are no known false positives. During testing, - no process events referring the com.apple.loginwindow.plist files were observed during - normal operation of re-opening applications on reboot. Therefore, it can be assumed - that any occurrences of this in the process events would be worth investigating. - In the event that the legitimate modification by the system of these files is in - fact logged to the process log, then the process_name of that process can be added - to an allow list. + - Sysmon EventID 1 +search: |- + | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*com.apple.loginwindow*" + BY Processes.user Processes.process_name Processes.parent_process_name + Processes.dest + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `macos___re_opened_applications_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: At this stage, there are no known false positives. During testing, no process events referring the com.apple.loginwindow.plist files were observed during normal operation of re-opening applications on reboot. Therefore, it can be assumed that any occurrences of this in the process events would be worth investigating. In the event that the legitimate modification by the system of these files is in fact logged to the process log, then the process_name of that process can be added to an allow list. references: [] rba: - message: Possible persistence mechanism via plists on $dest$ - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: Possible persistence mechanism via plists on $dest$ + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - ColdRoot MacOS RAT - asset_type: Endpoint - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - ColdRoot MacOS RAT + asset_type: Endpoint + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat diff --git a/detections/endpoint/macos_amos_stealer___virtual_machine_check_activity.yml b/detections/endpoint/macos_amos_stealer___virtual_machine_check_activity.yml index 05831102cc..94498e601b 100644 --- a/detections/endpoint/macos_amos_stealer___virtual_machine_check_activity.yml +++ b/detections/endpoint/macos_amos_stealer___virtual_machine_check_activity.yml @@ -1,80 +1,75 @@ name: MacOS AMOS Stealer - Virtual Machine Check Activity id: 4e41ad21-9761-426d-8aa1-083712ff9f30 -version: 3 -date: '2026-01-14' +version: 4 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk, Alex Karkins status: production type: Anomaly description: | - The following analytic detects AMOS Stealer VM check activity on macOS. It leverages osquery to monitor process events and identifies the execution of the "osascript" command along with specific commandline strings. This activity is significant - as AMOS stealer was seen using this pattern in order to check if the host is a Virtual Machine or not. If confirmed malicious, this behavior indicate that the host is already infected by the AMOS stealer, which could allow attackers to execute arbitrary code, escalate privileges, steal information, or persist within the environment, posing a significant security risk. + The following analytic detects AMOS Stealer VM check activity on macOS. It leverages osquery to monitor process events and identifies the execution of the "osascript" command along with specific commandline strings. This activity is significant + as AMOS stealer was seen using this pattern in order to check if the host is a Virtual Machine or not. If confirmed malicious, this behavior indicate that the host is already infected by the AMOS stealer, which could allow attackers to execute arbitrary code, escalate privileges, steal information, or persist within the environment, posing a significant security risk. data_source: - - osquery + - osquery search: | - `osquery_macro` name=es_process_events - columns.cmdline="*osascript*" AND columns.cmdline="* -e *" AND columns.cmdline="*set*" AND columns.cmdline="*system_profiler*" AND columns.cmdline IN ("*VMware*", "*QEMU*") - | rename columns.* as * - | stats min(_time) as firstTime max(_time) as lastTime - values(cmdline) as cmdline, - values(pid) as pid, - values(parent) as parent, - values(path) as path, - values(signing_id) as signing_id, - by username host - | rename - username as user, - cmdline as process, - parent as parent_process, - path as process_path, - host as dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `macos_amos_stealer___virtual_machine_check_activity_filter` + `osquery_macro` name=es_process_events + columns.cmdline="*osascript*" AND columns.cmdline="* -e *" AND columns.cmdline="*set*" AND columns.cmdline="*system_profiler*" AND columns.cmdline IN ("*VMware*", "*QEMU*") + | rename columns.* as * + | stats min(_time) as firstTime max(_time) as lastTime + values(cmdline) as cmdline, + values(pid) as pid, + values(parent) as parent, + values(path) as path, + values(signing_id) as signing_id, + by username host + | rename + username as user, + cmdline as process, + parent as parent_process, + path as process_path, + host as dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `macos_amos_stealer___virtual_machine_check_activity_filter` how_to_implement: | - This detection leverages osquery and endpoint security on MacOS. Follow the link in references, which describes how to setup process auditing in MacOS with endpoint security and osquery. + This detection leverages osquery and endpoint security on MacOS. Follow the link in references, which describes how to setup process auditing in MacOS with endpoint security and osquery. known_false_positives: No false positives have been identified at this time. references: - - https://osquery.readthedocs.io/en/stable/deployment/process-auditing/ - - https://www.virustotal.com/gui/search/behaviour_processes%253A%2522osascript%2520-e%2520set%2522%2520AND%2520behaviour_processes%253A%2522system_profiler%2522%2520AND%2520(behaviour_processes%253A%2522VMware%2522%2520OR%2520behaviour_processes%253A%2522QEMU%2522)?type=files + - https://osquery.readthedocs.io/en/stable/deployment/process-auditing/ + - https://www.virustotal.com/gui/search/behaviour_processes%253A%2522osascript%2520-e%2520set%2522%2520AND%2520behaviour_processes%253A%2522system_profiler%2522%2520AND%2520(behaviour_processes%253A%2522VMware%2522%2520OR%2520behaviour_processes%253A%2522QEMU%2522)?type=files drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: AMOS Stealer activity on host $dest$ by user $user$ - risk_objects: - - field: user - type: user - score: 40 - - field: dest - type: system - score: 40 - threat_objects: [] + message: AMOS Stealer activity on host $dest$ by user $user$ + risk_objects: + - field: user + type: user + score: 40 + - field: dest + type: system + score: 40 + threat_objects: [] tags: - analytic_story: - - AMOS Stealer - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1059.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AMOS Stealer + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1059.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.002/amos_stealer/amos_stealer.log - source: osquery - sourcetype: osquery:results + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.002/amos_stealer/amos_stealer.log + source: osquery + sourcetype: osquery:results diff --git a/detections/endpoint/macos_lolbin.yml b/detections/endpoint/macos_lolbin.yml index 69e2571273..e58f815f2a 100644 --- a/detections/endpoint/macos_lolbin.yml +++ b/detections/endpoint/macos_lolbin.yml @@ -1,74 +1,61 @@ name: MacOS LOLbin id: 58d270fb-5b39-418e-a855-4b8ac046805e -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects multiple executions of Living off the - Land (LOLbin) binaries on macOS within a short period. It leverages osquery to monitor - process events and identifies commands such as "find", "crontab", "screencapture", - "openssl", "curl", "wget", "killall", and "funzip". This activity is significant - as LOLbins are often used by attackers to perform malicious actions while evading - detection. If confirmed malicious, this behavior could allow attackers to execute - arbitrary code, escalate privileges, or persist within the environment, posing a - significant security risk. +description: The following analytic detects multiple executions of Living off the Land (LOLbin) binaries on macOS within a short period. It leverages osquery to monitor process events and identifies commands such as "find", "crontab", "screencapture", "openssl", "curl", "wget", "killall", and "funzip". This activity is significant as LOLbins are often used by attackers to perform malicious actions while evading detection. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or persist within the environment, posing a significant security risk. data_source: -- osquery -search: '`osquery_macro` name=es_process_events columns.cmdline IN ("find*", "crontab*", - "screencapture*", "openssl*", "curl*", "wget*", "killall*", "funzip*") | rename - columns.* as * | stats min(_time) as firstTime max(_time) as lastTime values(cmdline) - as cmdline, values(pid) as pid, values(parent) as parent, values(path) as path, - values(signing_id) as signing_id, dc(path) as dc_path by username host | rename - username as user, cmdline as process, path as process_path, host as dest | where - dc_path > 3 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `macos_lolbin_filter`' -how_to_implement: This detection uses osquery and endpoint security on MacOS. Follow - the link in references, which describes how to setup process auditing in MacOS with - endpoint security and osquery. + - osquery +search: |- + `osquery_macro` name=es_process_events columns.cmdline IN ("find*", "crontab*", "screencapture*", "openssl*", "curl*", "wget*", "killall*", "funzip*") + | rename columns.* as * + | stats min(_time) as firstTime max(_time) as lastTime values(cmdline) as cmdline, values(pid) as pid, values(parent) as parent, values(path) as path, values(signing_id) as signing_id, dc(path) as dc_path + BY username host + | rename username as user, cmdline as process, path as process_path, host as dest + | where dc_path > 3 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `macos_lolbin_filter` +how_to_implement: This detection uses osquery and endpoint security on MacOS. Follow the link in references, which describes how to setup process auditing in MacOS with endpoint security and osquery. known_false_positives: No false positives have been identified at this time. references: -- https://osquery.readthedocs.io/en/stable/deployment/process-auditing/ + - https://osquery.readthedocs.io/en/stable/deployment/process-auditing/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Multiplle LOLbin are executed on host $dest$ by user $user$ - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: Multiplle LOLbin are executed on host $dest$ by user $user$ + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Living Off The Land - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1059.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1059.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.004/macos_lolbin/osquery.log - source: osquery - sourcetype: osquery:results + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.004/macos_lolbin/osquery.log + source: osquery + sourcetype: osquery:results diff --git a/detections/endpoint/macos_plutil.yml b/detections/endpoint/macos_plutil.yml index 03858c402f..2c381f713c 100644 --- a/detections/endpoint/macos_plutil.yml +++ b/detections/endpoint/macos_plutil.yml @@ -1,70 +1,61 @@ name: MacOS plutil id: c11f2b57-92c1-4cd2-b46c-064eafb833ac -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects the usage of the `plutil` command to modify - plist files on macOS systems. It leverages osquery to monitor process events, specifically - looking for executions of `/usr/bin/plutil`. This activity is significant because - adversaries can use `plutil` to alter plist files, potentially adding malicious - binaries or command-line arguments that execute upon user logon or system startup. - If confirmed malicious, this could allow attackers to achieve persistence, execute - arbitrary code, or escalate privileges, posing a significant threat to the system's - security. +description: The following analytic detects the usage of the `plutil` command to modify plist files on macOS systems. It leverages osquery to monitor process events, specifically looking for executions of `/usr/bin/plutil`. This activity is significant because adversaries can use `plutil` to alter plist files, potentially adding malicious binaries or command-line arguments that execute upon user logon or system startup. If confirmed malicious, this could allow attackers to achieve persistence, execute arbitrary code, or escalate privileges, posing a significant threat to the system's security. data_source: -- osquery -search: '`osquery_macro` name=es_process_events columns.path=/usr/bin/plutil | rename - columns.* as * | stats count min(_time) as firstTime max(_time) as lastTime by - username host cmdline pid path parent signing_id | rename username as user, cmdline - as process, path as process_path, host as dest | `security_content_ctime(firstTime)`| - `security_content_ctime(lastTime)` | `macos_plutil_filter`' -how_to_implement: This detection uses osquery and endpoint security on MacOS. Follow - the link in references, which describes how to setup process auditing in MacOS with - endpoint security and osquery. + - osquery +search: |- + `osquery_macro` name=es_process_events columns.path=/usr/bin/plutil + | rename columns.* as * + | stats count min(_time) as firstTime max(_time) as lastTime + BY username host cmdline + pid path parent + signing_id + | rename username as user, cmdline as process, path as process_path, host as dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `macos_plutil_filter` +how_to_implement: This detection uses osquery and endpoint security on MacOS. Follow the link in references, which describes how to setup process auditing in MacOS with endpoint security and osquery. known_false_positives: Administrators using plutil to change plist files. references: -- https://osquery.readthedocs.io/en/stable/deployment/process-auditing/ + - https://osquery.readthedocs.io/en/stable/deployment/process-auditing/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: plutil are executed on $dest$ from $user$ - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: plutil are executed on $dest$ from $user$ + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1647 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1647 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1647/atomic_red_team/osquery.log - source: osquery - sourcetype: osquery:results + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1647/atomic_red_team/osquery.log + source: osquery + sourcetype: osquery:results diff --git a/detections/endpoint/mailsniper_invoke_functions.yml b/detections/endpoint/mailsniper_invoke_functions.yml index 796d5ed2f1..3bad63032a 100644 --- a/detections/endpoint/mailsniper_invoke_functions.yml +++ b/detections/endpoint/mailsniper_invoke_functions.yml @@ -1,71 +1,62 @@ name: Mailsniper Invoke functions id: a36972c8-b894-11eb-9f78-acde48001122 -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of known MailSniper PowerShell - functions on a machine. It leverages PowerShell logs (EventCode 4104) to identify - specific script block text associated with MailSniper activities. This behavior - is significant as MailSniper is often used by attackers to harvest sensitive emails - from compromised Exchange servers. If confirmed malicious, this activity could lead - to unauthorized access to sensitive email data, credential theft, and further compromise - of the email infrastructure. +description: The following analytic detects the execution of known MailSniper PowerShell functions on a machine. It leverages PowerShell logs (EventCode 4104) to identify specific script block text associated with MailSniper activities. This behavior is significant as MailSniper is often used by attackers to harvest sensitive emails from compromised Exchange servers. If confirmed malicious, this activity could lead to unauthorized access to sensitive email data, credential theft, and further compromise of the email infrastructure. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 ScriptBlockText IN ("*Invoke-GlobalO365MailSearch*", - "*Invoke-GlobalMailSearch*", "*Invoke-SelfSearch*", "*Invoke-PasswordSprayOWA*", - "*Invoke-PasswordSprayEWS*","*Invoke-DomainHarvestOWA*", "*Invoke-UsernameHarvestOWA*","*Invoke-OpenInboxFinder*","*Invoke-InjectGEventAPI*","*Invoke-InjectGEvent*","*Invoke-SearchGmail*", - "*Invoke-MonitorCredSniper*", "*Invoke-AddGmailRule*","*Invoke-PasswordSprayEAS*","*Invoke-UsernameHarvestEAS*") - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `mailsniper_invoke_functions_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the powershell logs from your endpoints. make sure you enable needed - registry to monitor this event. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText IN ("*Invoke-GlobalO365MailSearch*", "*Invoke-GlobalMailSearch*", "*Invoke-SelfSearch*", "*Invoke-PasswordSprayOWA*", "*Invoke-PasswordSprayEWS*","*Invoke-DomainHarvestOWA*", "*Invoke-UsernameHarvestOWA*","*Invoke-OpenInboxFinder*","*Invoke-InjectGEventAPI*","*Invoke-InjectGEvent*","*Invoke-SearchGmail*", "*Invoke-MonitorCredSniper*", "*Invoke-AddGmailRule*","*Invoke-PasswordSprayEAS*","*Invoke-UsernameHarvestEAS*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `mailsniper_invoke_functions_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the powershell logs from your endpoints. make sure you enable needed registry to monitor this event. known_false_positives: No false positives have been identified at this time. references: -- https://www.blackhillsinfosec.com/introducing-mailsniper-a-tool-for-searching-every-users-email-for-sensitive-data/ + - https://www.blackhillsinfosec.com/introducing-mailsniper-a-tool-for-searching-every-users-email-for-sensitive-data/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user_id$" - search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user_id$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user_id$" + search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential mailsniper.ps1 functions executed on dest $dest$ by user $user_id$. - risk_objects: - - field: dest - type: system - score: 72 - - field: user_id - type: user - score: 72 - threat_objects: [] + message: Potential mailsniper.ps1 functions executed on dest $dest$ by user $user_id$. + risk_objects: + - field: dest + type: system + score: 72 + - field: user_id + type: user + score: 72 + threat_objects: [] tags: - analytic_story: - - Data Exfiltration - asset_type: Endpoint - mitre_attack_id: - - T1114.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Exfiltration + asset_type: Endpoint + mitre_attack_id: + - T1114.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/malicious_inprocserver32_modification.yml b/detections/endpoint/malicious_inprocserver32_modification.yml index 6ca4d46321..a1996bf34d 100644 --- a/detections/endpoint/malicious_inprocserver32_modification.yml +++ b/detections/endpoint/malicious_inprocserver32_modification.yml @@ -5,84 +5,49 @@ date: '2025-05-02' author: Michael Haag, Splunk status: production type: TTP -description: - The following analytic detects a process modifying the registry with - a known malicious CLSID under InProcServer32. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on registry modifications within the HKLM or - HKCU Software Classes CLSID paths. This activity is significant as it may indicate - an attempt to load a malicious DLL, potentially leading to code execution. If confirmed - malicious, this could allow an attacker to persist in the environment, execute arbitrary - code, or escalate privileges, posing a severe threat to system integrity and security. +description: The following analytic detects a process modifying the registry with a known malicious CLSID under InProcServer32. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on registry modifications within the HKLM or HKCU Software Classes CLSID paths. This activity is significant as it may indicate an attempt to load a malicious DLL, potentially leading to code execution. If confirmed malicious, this could allow an attacker to persist in the environment, execute arbitrary code, or escalate privileges, posing a severe threat to system integrity and security. data_source: - - Sysmon EventID 12 - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry - where Registry.registry_path="*\\CLSID\\{89565275-A714-4a43-912E-978B935EDCCC}\\InProcServer32\\(Default)" - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `malicious_inprocserver32_modification_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: - False positives should be limited, filter as needed. In our - test case, Remcos used regsvr32.exe to modify the registry. It may be required, - dependent upon the EDR tool producing registry events, to remove (Default) from - the command-line. + - Sysmon EventID 12 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\CLSID\\{89565275-A714-4a43-912E-978B935EDCCC}\\InProcServer32\\(Default)" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `malicious_inprocserver32_modification_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives should be limited, filter as needed. In our test case, Remcos used regsvr32.exe to modify the registry. It may be required, dependent upon the EDR tool producing registry events, to remove (Default) from the command-line. references: - - https://bohops.com/2018/06/28/abusing-com-registry-structure-clsid-localserver32-inprocserver32/ - - https://tria.ge/210929-ap75vsddan - - https://www.virustotal.com/gui/file/cb77b93150cb0f7fe65ce8a7e2a5781e727419451355a7736db84109fa215a89 + - https://bohops.com/2018/06/28/abusing-com-registry-structure-clsid-localserver32-inprocserver32/ + - https://tria.ge/210929-ap75vsddan + - https://www.virustotal.com/gui/file/cb77b93150cb0f7fe65ce8a7e2a5781e727419451355a7736db84109fa215a89 drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A process identified on endpoint $dest$ modifying the registry with a known - malicious clsid under InProcServer32. - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: [] + message: A process identified on endpoint $dest$ modifying the registry with a known malicious clsid under InProcServer32. + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: [] tags: - analytic_story: - - Suspicious Regsvr32 Activity - - Remcos - asset_type: Endpoint - mitre_attack_id: - - T1218.010 - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Regsvr32 Activity + - Remcos + asset_type: Endpoint + mitre_attack_id: + - T1218.010 + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/malicious_powershell_executed_as_a_service.yml b/detections/endpoint/malicious_powershell_executed_as_a_service.yml index 105a5ecc07..180a9f029a 100644 --- a/detections/endpoint/malicious_powershell_executed_as_a_service.yml +++ b/detections/endpoint/malicious_powershell_executed_as_a_service.yml @@ -1,77 +1,67 @@ name: Malicious Powershell Executed As A Service id: 8e204dfd-cae0-4ea8-a61d-e972a1ff2ff8 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Ryan Becwar status: production type: TTP -description: The following analytic identifies the execution of malicious PowerShell - commands or payloads via the Windows SC.exe utility. It detects this activity by - analyzing Windows System logs (EventCode 7045) and filtering for specific PowerShell-related - patterns in the ImagePath field. This behavior is significant because it indicates - potential abuse of the Windows Service Control Manager to run unauthorized or harmful - scripts, which could lead to system compromise. If confirmed malicious, this activity - could allow attackers to execute arbitrary code, escalate privileges, or maintain - persistence within the environment. +description: The following analytic identifies the execution of malicious PowerShell commands or payloads via the Windows SC.exe utility. It detects this activity by analyzing Windows System logs (EventCode 7045) and filtering for specific PowerShell-related patterns in the ImagePath field. This behavior is significant because it indicates potential abuse of the Windows Service Control Manager to run unauthorized or harmful scripts, which could lead to system compromise. If confirmed malicious, this activity could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment. data_source: -- Windows Event Log System 7045 -search: '`wineventlog_system` EventCode=7045 | eval l_ImagePath=lower(ImagePath) | - regex l_ImagePath="powershell[.\s]|powershell_ise[.\s]|pwsh[.\s]|psexec[.\s]" | - regex l_ImagePath="-nop[rofile\s]+|-w[indowstyle]*\s+hid[den]*|-noe[xit\s]+|-enc[odedcommand\s]+" - | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ImagePath - ServiceName StartType ServiceType AccountName UserID dest | rename UserID as user| - `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `malicious_powershell_executed_as_a_service_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Windows System logs with the Service name, Service File Name Service Start type, - and Service Type from your endpoints. -known_false_positives: Creating a hidden powershell service is rare and could key - off of those instances. + - Windows Event Log System 7045 +search: |- + `wineventlog_system` + EventCode=7045 + | eval l_ImagePath=lower(ImagePath) + | regex l_ImagePath="powershell[.\s]|powershell_ise[.\s]|pwsh[.\s]|psexec[.\s]" + | regex l_ImagePath="-nop[rofile\s]+|-w[indowstyle]*\s+hid[den]*|-noe[xit\s]+|-enc[odedcommand\s]+" + | stats count min(_time) as firstTime max(_time) as lastTime + by EventCode ImagePath ServiceName StartType + ServiceType AccountName UserID dest + | rename UserID as user + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `malicious_powershell_executed_as_a_service_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Windows System logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints. +known_false_positives: Creating a hidden powershell service is rare and could key off of those instances. references: -- https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/dosfuscation-report.pdf -- http://az4n6.blogspot.com/2017/ -- https://www.danielbohannon.com/blog-1/2017/3/12/powershell-execution-argument-obfuscation-how-it-can-make-detection-easier + - https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/dosfuscation-report.pdf + - http://az4n6.blogspot.com/2017/ + - https://www.danielbohannon.com/blog-1/2017/3/12/powershell-execution-argument-obfuscation-how-it-can-make-detection-easier drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Identifies the abuse the Windows SC.exe to execute malicious powerShell - as a service $ImagePath$ by $user$ on $dest$ - risk_objects: - - field: dest - type: system - score: 72 - - field: user - type: user - score: 72 - threat_objects: [] + message: Identifies the abuse the Windows SC.exe to execute malicious powerShell as a service $ImagePath$ by $user$ on $dest$ + risk_objects: + - field: dest + type: system + score: 72 + - field: user + type: user + score: 72 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - Rhysida Ransomware - - Malicious PowerShell - asset_type: Endpoint - mitre_attack_id: - - T1569.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - Rhysida Ransomware + - Malicious PowerShell + asset_type: Endpoint + mitre_attack_id: + - T1569.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/malicious_powershell_executed_as_a_service/windows-xml.log - source: XmlWinEventLog:System - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/malicious_powershell_executed_as_a_service/windows-xml.log + source: XmlWinEventLog:System + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/malicious_powershell_process___encoded_command.yml b/detections/endpoint/malicious_powershell_process___encoded_command.yml index 61667ec208..ee52923103 100644 --- a/detections/endpoint/malicious_powershell_process___encoded_command.yml +++ b/detections/endpoint/malicious_powershell_process___encoded_command.yml @@ -5,77 +5,51 @@ date: '2026-02-09' author: David Dorsey, Michael Haag, Splunk, SirDuckly, GitHub Community status: production type: Hunting -description: The following analytic detects the use of the EncodedCommand parameter - in PowerShell processes. It leverages Endpoint Detection and Response (EDR) data - to identify variations of the EncodedCommand parameter, including shortened forms - and different command switch types. This activity is significant because adversaries - often use encoded commands to obfuscate malicious scripts, making detection harder. - If confirmed malicious, this behavior could allow attackers to execute hidden code, - potentially leading to unauthorized access, privilege escalation, or persistent - threats within the environment. Review parallel events to determine legitimacy and - tune based on known administrative scripts. +description: The following analytic detects the use of the EncodedCommand parameter in PowerShell processes. It leverages Endpoint Detection and Response (EDR) data to identify variations of the EncodedCommand parameter, including shortened forms and different command switch types. This activity is significant because adversaries often use encoded commands to obfuscate malicious scripts, making detection harder. If confirmed malicious, this behavior could allow attackers to execute hidden code, potentially leading to unauthorized access, privilege escalation, or persistent threats within the environment. Review parallel events to determine legitimacy and tune based on known administrative scripts. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: "| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)\ - \ as lastTime from datamodel=Endpoint.Processes where `process_powershell` by Processes.action\ - \ Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec\ - \ Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name\ - \ Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid\ - \ Processes.process_hash Processes.process_id Processes.process_integrity_level\ - \ Processes.process_name Processes.process_path Processes.user Processes.user_id\ - \ Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`\ - \ | `security_content_ctime(lastTime)` | where match(process,\"(?i)[\\-|\\/|\u2013\ - |\u2014|\u2015][Ee^]{1,2}[NnCcOoDdEeMmAa^]+\\s+[\\\"]?[A-Za-z0-9+/=]{5,}[\\\"]?\"\ - ) | `malicious_powershell_process___encoded_command_filter`" -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: "| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where match(process,\"(?i)[\\-|\\/|–|—|―][Ee^]{1,2}[NnCcOoDdEeMmAa^]+\\s+[\\\"]?[A-Za-z0-9+/=]{5,}[\\\"]?\") | `malicious_powershell_process___encoded_command_filter`" +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: System administrators may use this option, but it's not common. references: -- https://regexr.com/662ov -- https://github.com/redcanaryco/AtomicTestHarnesses/blob/master/Windows/TestHarnesses/T1059.001_PowerShell/OutPowerShellCommandLineParameter.ps1 -- https://ss64.com/ps/powershell.html -- https://twitter.com/M_haggis/status/1440758396534214658?s=20 -- https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ -- https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ + - https://regexr.com/662ov + - https://github.com/redcanaryco/AtomicTestHarnesses/blob/master/Windows/TestHarnesses/T1059.001_PowerShell/OutPowerShellCommandLineParameter.ps1 + - https://ss64.com/ps/powershell.html + - https://twitter.com/M_haggis/status/1440758396534214658?s=20 + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ tags: - analytic_story: - - SolarWinds WHD RCE Post Exploitation - - CISA AA22-320A - - Hermetic Wiper - - Sandworm Tools - - Qakbot - - Volt Typhoon - - NOBELIUM Group - - Data Destruction - - Lumma Stealer - - Malicious PowerShell - - DarkCrystal RAT - - WhisperGate - - Crypto Stealer - - Microsoft SharePoint Vulnerabilities - - Scattered Spider - - GhostRedirector IIS Module and Rungan Backdoor - - Microsoft WSUS CVE-2025-59287 - asset_type: Endpoint - mitre_attack_id: - - T1027 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SolarWinds WHD RCE Post Exploitation + - CISA AA22-320A + - Hermetic Wiper + - Sandworm Tools + - Qakbot + - Volt Typhoon + - NOBELIUM Group + - Data Destruction + - Lumma Stealer + - Malicious PowerShell + - DarkCrystal RAT + - WhisperGate + - Crypto Stealer + - Microsoft SharePoint Vulnerabilities + - Scattered Spider + - GhostRedirector IIS Module and Rungan Backdoor + - Microsoft WSUS CVE-2025-59287 + asset_type: Endpoint + mitre_attack_id: + - T1027 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1027/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1027/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/malicious_powershell_process___execution_policy_bypass.yml b/detections/endpoint/malicious_powershell_process___execution_policy_bypass.yml index c2c100fff4..4a74d3f11c 100644 --- a/detections/endpoint/malicious_powershell_process___execution_policy_bypass.yml +++ b/detections/endpoint/malicious_powershell_process___execution_policy_bypass.yml @@ -1,94 +1,74 @@ name: Malicious PowerShell Process - Execution Policy Bypass id: 9be56c82-b1cc-4318-87eb-d138afaaca39 -version: 16 -date: '2025-09-18' +version: 17 +date: '2026-02-25' author: Rico Valdez, Mauricio Velazco, Splunk status: production type: Anomaly -description: The following analytic detects PowerShell processes initiated with - parameters that bypass the local execution policy for scripts. It leverages - data from Endpoint Detection and Response (EDR) agents, focusing on - command-line executions containing specific flags like "-ex" or "bypass." This - activity is significant because bypassing execution policies is a common - tactic used by attackers to run malicious scripts undetected. If confirmed - malicious, this could allow an attacker to execute arbitrary code, potentially - leading to further system compromise, data exfiltration, or persistent access - within the environment. +description: The following analytic detects PowerShell processes initiated with parameters that bypass the local execution policy for scripts. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions containing specific flags like "-ex" or "bypass." This activity is significant because bypassing execution policies is a common tactic used by attackers to run malicious scripts undetected. If confirmed malicious, this could allow an attacker to execute arbitrary code, potentially leading to further system compromise, data exfiltration, or persistent access within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process_id) as - process_id, values(Processes.parent_process_id) as parent_process_id values(Processes.process) - as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where `process_powershell` (Processes.process="* -ex*" AND Processes.process="* - bypass *") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `malicious_powershell_process___execution_policy_bypass_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: There may be legitimate reasons to bypass the PowerShell - execution policy. The PowerShell script being run with this parameter should - be validated to ensure that it is legitimate. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process_id) as process_id, values(Processes.parent_process_id) as parent_process_id values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_powershell` (Processes.process="* -ex*" + AND + Processes.process="* bypass *") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `malicious_powershell_process___execution_policy_bypass_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: There may be legitimate reasons to bypass the PowerShell execution policy. The PowerShell script being run with this parameter should be validated to ensure that it is legitimate. references: -- https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ + - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: PowerShell local execution policy bypass attempt on $dest$ - risk_objects: - - field: dest - type: system - score: 42 - threat_objects: [] + message: PowerShell local execution policy bypass attempt on $dest$ + risk_objects: + - field: dest + type: system + score: 42 + threat_objects: [] tags: - analytic_story: - - DHS Report TA18-074A - - Volt Typhoon - - China-Nexus Threat Activity - - AsyncRAT - - HAFNIUM Group - - Salt Typhoon - - XWorm - - DarkCrystal RAT - - 0bj3ctivity Stealer - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - DHS Report TA18-074A + - Volt Typhoon + - China-Nexus Threat Activity + - AsyncRAT + - HAFNIUM Group + - Salt Typhoon + - XWorm + - DarkCrystal RAT + - 0bj3ctivity Stealer + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/encoded_powershell/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/encoded_powershell/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/malicious_powershell_process_with_obfuscation_techniques.yml b/detections/endpoint/malicious_powershell_process_with_obfuscation_techniques.yml index 8d6dcfb864..7431f910ea 100644 --- a/detections/endpoint/malicious_powershell_process_with_obfuscation_techniques.yml +++ b/detections/endpoint/malicious_powershell_process_with_obfuscation_techniques.yml @@ -5,80 +5,47 @@ date: '2026-01-14' author: David Dorsey, Splunk status: production type: TTP -description: The following analytic detects PowerShell processes launched with command-line - arguments indicative of obfuscation techniques. It leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process names, parent processes, - and complete command-line executions. This activity is significant because obfuscated - PowerShell commands are often used by attackers to evade detection and execute malicious - scripts. If confirmed malicious, this activity could lead to unauthorized code execution, - privilege escalation, or persistent access within the environment, posing a significant - security risk. +description: The following analytic detects PowerShell processes launched with command-line arguments indicative of obfuscation techniques. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, parent processes, and complete command-line executions. This activity is significant because obfuscated PowerShell commands are often used by attackers to evade detection and execute malicious scripts. If confirmed malicious, this activity could lead to unauthorized code execution, privilege escalation, or persistent access within the environment, posing a significant security risk. data_source: -- Sysmon EventID 1 -search: '| tstats `security_content_summariesonly` count values(Processes.process) - as process values(Processes.parent_process) as parent_process min(_time) as firstTime - max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| - eval num_obfuscation = (mvcount(split(process,"`"))-1) + (mvcount(split(process, - "^"))-1) + (mvcount(split(process, "''"))-1) - | search num_obfuscation > 10 - | `malicious_powershell_process_with_obfuscation_techniques_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: These characters might be legitimately on the command-line, - but it is not common. + - Sysmon EventID 1 +search: '| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval num_obfuscation = (mvcount(split(process,"`"))-1) + (mvcount(split(process, "^"))-1) + (mvcount(split(process, "''"))-1) | search num_obfuscation > 10 | `malicious_powershell_process_with_obfuscation_techniques_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: These characters might be legitimately on the command-line, but it is not common. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Powershell.exe running with potential obfuscated arguments on $dest$ - risk_objects: - - field: dest - type: system - score: 42 - threat_objects: [] + message: Powershell.exe running with potential obfuscated arguments on $dest$ + risk_objects: + - field: dest + type: system + score: 42 + threat_objects: [] tags: - analytic_story: - - Malicious PowerShell - - Hermetic Wiper - - Data Destruction - - GhostRedirector IIS Module and Rungan Backdoor - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Malicious PowerShell + - Hermetic Wiper + - Data Destruction + - GhostRedirector IIS Module and Rungan Backdoor + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/obfuscated_powershell/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/obfuscated_powershell/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/microsoft_defender_atp_alerts.yml b/detections/endpoint/microsoft_defender_atp_alerts.yml index 14208b45b3..7cbe6e904c 100644 --- a/detections/endpoint/microsoft_defender_atp_alerts.yml +++ b/detections/endpoint/microsoft_defender_atp_alerts.yml @@ -1,71 +1,75 @@ name: Microsoft Defender ATP Alerts id: 38f034ed-1598-46c8-95e8-14edf05fdf5d -version: 4 -date: '2025-05-02' +version: 5 +date: '2026-02-25' author: Bryan Pluta, Bhavin Patel, Splunk status: production type: TTP data_source: -- MS Defender ATP Alerts -description: The following analytic is to leverage alerts from Microsoft Defender ATP Alerts. This query aggregates and summarizes all alerts from Microsoft Defender ATP Alerts, providing details such as the source, file name, severity, process command line, ip address, registry key, signature, description, unique id, and timestamps. This detection is not intended to detect new activity from raw data, but leverages Microsoft provided alerts to be correlated with other data as part of risk based alerting. The data contained in the alert is mapped not only to the risk obejct, but also the threat object. This detection filters out evidence that has a verdict of clean from Microsoft. It dynamically maps the MITRE technique at search time to auto populate the annotation field with the value provided in the alert. It also uses a dynamic mapping to set the risk score in Enterprise Security based on the severity of the alert. -search: ' `ms_defender_atp_alerts` (dest=* OR user=*)| eval tmp_evidence=json_extract(_raw, "evidence"), tmp_evidencemv=json_array_to_mv(tmp_evidence), entityType = mvmap(tmp_evidencemv, spath(tmp_evidencemv, "entityType")), filePath = mvmap(tmp_evidencemv, spath(tmp_evidencemv, "filePath")), processCommandLine = mvmap(tmp_evidencemv, spath(tmp_evidencemv, "processCommandLine")), ipAddress = mvmap(tmp_evidencemv, spath(tmp_evidencemv, "ipAddress")), registryKey = mvmap(tmp_evidencemv, spath(tmp_evidencemv, "registryKey")), url = mvmap(tmp_evidencemv, spath(tmp_evidencemv, "url")), fileName = mvmap(tmp_evidencemv, spath(tmp_evidencemv, "fileName")) - | eval tmp_evidencemv=mvfilter(json_extract(tmp_evidencemv, "entityType") = "File"), fileName = mvmap(tmp_evidencemv, spath(tmp_evidencemv, "fileName")) - | eval risk_score=case(severity="informational", 5, severity="low", 15, severity="medium", 25, severity="high", 50 , true(), 2) - | eval processCommandLine=if(processCommandLine="null", "", processCommandLine), ipAddress=if(ipAddress="null", "", ipAddress), registryKey=if(registryKey="null", "", registryKey), url=if(url="null", "", url) - | stats count min(_time) as firstTime max(_time) as lastTime values(fileName) as file_name values(severity) as severity values(processCommandLine) as process values(ipAddress) as ip_address values(registryKey) as registry_key values(url) as url values(mitreTechniques{}) as annotations.mitre_attack.mitre_technique_id values(signature) as signature values(user) as user values(risk_score) as risk_score by id description src - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `microsoft_defender_atp_alerts_filter`' + - MS Defender ATP Alerts +description: The following analytic is to leverage alerts from Microsoft Defender ATP Alerts. This query aggregates and summarizes all alerts from Microsoft Defender ATP Alerts, providing details such as the source, file name, severity, process command line, ip address, registry key, signature, description, unique id, and timestamps. This detection is not intended to detect new activity from raw data, but leverages Microsoft provided alerts to be correlated with other data as part of risk based alerting. The data contained in the alert is mapped not only to the risk obejct, but also the threat object. This detection filters out evidence that has a verdict of clean from Microsoft. It dynamically maps the MITRE technique at search time to auto populate the annotation field with the value provided in the alert. It also uses a dynamic mapping to set the risk score in Enterprise Security based on the severity of the alert. +search: |- + `ms_defender_atp_alerts` (dest=* OR user=*) + | eval tmp_evidence=json_extract(_raw, "evidence"), tmp_evidencemv=json_array_to_mv(tmp_evidence), entityType = mvmap(tmp_evidencemv, spath(tmp_evidencemv, "entityType")), filePath = mvmap(tmp_evidencemv, spath(tmp_evidencemv, "filePath")), processCommandLine = mvmap(tmp_evidencemv, spath(tmp_evidencemv, "processCommandLine")), ipAddress = mvmap(tmp_evidencemv, spath(tmp_evidencemv, "ipAddress")), registryKey = mvmap(tmp_evidencemv, spath(tmp_evidencemv, "registryKey")), url = mvmap(tmp_evidencemv, spath(tmp_evidencemv, "url")), fileName = mvmap(tmp_evidencemv, spath(tmp_evidencemv, "fileName")) + | eval tmp_evidencemv=mvfilter(json_extract(tmp_evidencemv, "entityType") = "File"), fileName = mvmap(tmp_evidencemv, spath(tmp_evidencemv, "fileName")) + | eval risk_score=case(severity="informational", 5, severity="low", 15, severity="medium", 25, severity="high", 50 , true(), 2) + | eval processCommandLine=if(processCommandLine="null", "", processCommandLine), ipAddress=if(ipAddress="null", "", ipAddress), registryKey=if(registryKey="null", "", registryKey), url=if(url="null", "", url) + | stats count min(_time) as firstTime max(_time) as lastTime values(fileName) as file_name values(severity) as severity values(processCommandLine) as process values(ipAddress) as ip_address values(registryKey) as registry_key values(url) as url values(mitreTechniques{}) as annotations.mitre_attack.mitre_technique_id values(signature) as signature values(user) as user values(risk_score) as risk_score + BY id description src + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `microsoft_defender_atp_alerts_filter` how_to_implement: In order to properly run this search, you need to ingest alerts data from Microsoft Defender, specifcally using the Splunk add-on for Microsoft Security. This add-on will collect alerts using the ms:defender:atp:alerts sourcetype. You will need to define the `ms_defender_atp_alerts` macro to point to the proper index that contains the ms:defender:atp:alerts sourcetype. known_false_positives: False positives may vary based on Microsfot Defender configuration; monitor and filter out the alerts that are not relevant to your environment. references: -- https://learn.microsoft.com/en-us/defender-xdr/api-list-incidents?view=o365-worldwide -- https://learn.microsoft.com/en-us/graph/api/resources/security-alert?view=graph-rest-1.0 -- https://splunkbase.splunk.com/app/6207 -- https://jasonconger.com/splunk-azure-gdi/ + - https://learn.microsoft.com/en-us/defender-xdr/api-list-incidents?view=o365-worldwide + - https://learn.microsoft.com/en-us/graph/api/resources/security-alert?view=graph-rest-1.0 + - https://splunkbase.splunk.com/app/6207 + - https://jasonconger.com/splunk-azure-gdi/ drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $severity$ alert for $src$ - $signature$ - risk_objects: - - field: src - type: system - score: 81 - - field: user - type: user - score: 81 - threat_objects: - - field: file_name - type: file_name - - field: process - type: process_name - - field: ip_address - type: ip_address - - field: registry_key - type: registry_path - - field: url - type: url + message: $severity$ alert for $src$ - $signature$ + risk_objects: + - field: src + type: system + score: 81 + - field: user + type: user + score: 81 + threat_objects: + - field: file_name + type: file_name + - field: process + type: process_name + - field: ip_address + type: ip_address + - field: registry_key + type: registry_path + - field: url + type: url tags: - analytic_story: - - Critical Alerts - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: [] - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - manual_test: We are dynamically creating the risk_score field based on the severity of the alert in the SPL and that supersedes the risk score set in the detection. Setting these to manual test since otherwise we fail integration testing. The detection is also failing on unit-testing as some of the fields set in the observables are empty. + analytic_story: + - Critical Alerts + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: [] + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + manual_test: We are dynamically creating the risk_score field based on the severity of the alert in the SPL and that supersedes the risk score set in the detection. Setting these to manual test since otherwise we fail integration testing. The detection is also failing on unit-testing as some of the fields set in the observables are empty. tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/alerts/defender_atp_alerts_single_event.log - source: ms_defender_atp_alerts - sourcetype: ms:defender:atp:alerts + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/alerts/defender_atp_alerts_single_event.log + source: ms_defender_atp_alerts + sourcetype: ms:defender:atp:alerts diff --git a/detections/endpoint/microsoft_defender_incident_alerts.yml b/detections/endpoint/microsoft_defender_incident_alerts.yml index b32ea4fb0e..48a078462b 100644 --- a/detections/endpoint/microsoft_defender_incident_alerts.yml +++ b/detections/endpoint/microsoft_defender_incident_alerts.yml @@ -1,77 +1,74 @@ name: Microsoft Defender Incident Alerts id: 13435b55-afd8-46d4-9045-7d5457f430a5 -version: 5 -date: '2025-11-05' +version: 6 +date: '2026-02-25' author: Bryan Pluta, Bhavin Patel, Splunk, lyonheart14, Github Community status: production type: TTP data_source: -- MS365 Defender Incident Alerts -description: The following analytic is to leverage alerts from Microsoft Defender O365 Incidents. This query aggregates and summarizes all alerts from Microsoft Defender O365 Incidents, providing details such as the destination, file name, severity, process command line, ip address, registry key, signature, description, unique id, and timestamps. This detection is not intended to detect new activity from raw data, but leverages Microsoft provided alerts to be correlated with other data as part of risk based alerting. The data contained in the alert is mapped not only to the risk obejct, but also the threat object. This detection filters out evidence that has a verdict of clean from Microsoft. It dynamically maps the MITRE technique at search time to auto populate the annotation field with the value provided in the alert. It also uses a static mapping to set the risk score based on the severity of the alert. -search: '`ms365_defender_incident_alerts` (dest=* OR user=*) - | eval tmp_entities=json_extract(_raw, "entities"), - tmp_entitymv=json_array_to_mv(tmp_entities), - tmp_filtered_mv=mvfilter(json_extract(tmp_entitymv, "verdict") != "Clean"), - entityType = mvmap(tmp_filtered_mv, spath(tmp_filtered_mv, "entityType")), - filePath = mvmap(tmp_filtered_mv, spath(tmp_filtered_mv, "filePath")), - processCommandLine = mvmap(tmp_filtered_mv, spath(tmp_filtered_mv, "processCommandLine")), - ipAddress = mvmap(tmp_filtered_mv, spath(tmp_filtered_mv, "ipAddress")), - registryKey = mvmap(tmp_filtered_mv, spath(tmp_filtered_mv, "registryKey")), - url = mvmap(tmp_filtered_mv, spath(tmp_filtered_mv, "url")) - | eval tmp_filtered_mv=mvfilter(json_extract(tmp_filtered_mv, "entityType") = "File"), fileName = mvmap(tmp_filtered_mv, spath(tmp_filtered_mv, "fileName")) - | eval risk_score=case(severity="informational", 5, severity="low", 15, severity="medium", 25, severity="high", 50, true(), 2) - | stats count min(_time) as firstTime max(_time) as lastTime values(fileName) as file_name values(severity) as severity values(processCommandLine) as process values(ipAddress) as ip_address values(registryKey) as registry_key values(url) as url values(mitreTechniques{}) as annotations.mitre_attack.mitre_technique_id values(signature) as signature values(dest) as dest values(user) as user values(risk_score) as risk_score by id description | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `microsoft_defender_incident_alerts_filter`' + - MS365 Defender Incident Alerts +description: The following analytic is to leverage alerts from Microsoft Defender O365 Incidents. This query aggregates and summarizes all alerts from Microsoft Defender O365 Incidents, providing details such as the destination, file name, severity, process command line, ip address, registry key, signature, description, unique id, and timestamps. This detection is not intended to detect new activity from raw data, but leverages Microsoft provided alerts to be correlated with other data as part of risk based alerting. The data contained in the alert is mapped not only to the risk obejct, but also the threat object. This detection filters out evidence that has a verdict of clean from Microsoft. It dynamically maps the MITRE technique at search time to auto populate the annotation field with the value provided in the alert. It also uses a static mapping to set the risk score based on the severity of the alert. +search: |- + `ms365_defender_incident_alerts` (dest=* OR user=*) + | eval tmp_entities=json_extract(_raw, "entities"), tmp_entitymv=json_array_to_mv(tmp_entities), tmp_filtered_mv=mvfilter(json_extract(tmp_entitymv, "verdict") != "Clean"), entityType = mvmap(tmp_filtered_mv, spath(tmp_filtered_mv, "entityType")), filePath = mvmap(tmp_filtered_mv, spath(tmp_filtered_mv, "filePath")), processCommandLine = mvmap(tmp_filtered_mv, spath(tmp_filtered_mv, "processCommandLine")), ipAddress = mvmap(tmp_filtered_mv, spath(tmp_filtered_mv, "ipAddress")), registryKey = mvmap(tmp_filtered_mv, spath(tmp_filtered_mv, "registryKey")), url = mvmap(tmp_filtered_mv, spath(tmp_filtered_mv, "url")) + | eval tmp_filtered_mv=mvfilter(json_extract(tmp_filtered_mv, "entityType") = "File"), fileName = mvmap(tmp_filtered_mv, spath(tmp_filtered_mv, "fileName")) + | eval risk_score=case(severity="informational", 5, severity="low", 15, severity="medium", 25, severity="high", 50, true(), 2) + | stats count min(_time) as firstTime max(_time) as lastTime values(fileName) as file_name values(severity) as severity values(processCommandLine) as process values(ipAddress) as ip_address values(registryKey) as registry_key values(url) as url values(mitreTechniques{}) as annotations.mitre_attack.mitre_technique_id values(signature) as signature values(dest) as dest values(user) as user values(risk_score) as risk_score + BY id description + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `microsoft_defender_incident_alerts_filter` how_to_implement: In order to properly run this search, you need to ingest alerts data from Microsoft Defender, specifcally using the Splunk add-on for Microsoft Security. This add-on will collect alerts using the ms365:defender:incident:alerts sourcetype. You will need to define the `ms365_defender_incident_alerts` macro to point to the proper index that contains the ms365:defender:incident:alerts sourcetype. known_false_positives: False positives may vary based on Microsoft Defender configuration; monitor and filter out the alerts that are not relevant to your environment. references: -- https://learn.microsoft.com/en-us/defender-xdr/api-list-incidents?view=o365-worldwide -- https://learn.microsoft.com/en-us/graph/api/resources/security-alert?view=graph-rest-1.0 -- https://splunkbase.splunk.com/app/6207 -- https://jasonconger.com/splunk-azure-gdi/ + - https://learn.microsoft.com/en-us/defender-xdr/api-list-incidents?view=o365-worldwide + - https://learn.microsoft.com/en-us/graph/api/resources/security-alert?view=graph-rest-1.0 + - https://splunkbase.splunk.com/app/6207 + - https://jasonconger.com/splunk-azure-gdi/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$","$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$","$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $severity$ alert for $dest$ - $signature$ - risk_objects: - - field: dest - type: system - score: 81 - - field: user - type: user - score: 81 - threat_objects: - - field: file_name - type: file_name - - field: process - type: process_name - - field: ip_address - type: ip_address - - field: registry_key - type: registry_path - - field: url - type: url + message: $severity$ alert for $dest$ - $signature$ + risk_objects: + - field: dest + type: system + score: 81 + - field: user + type: user + score: 81 + threat_objects: + - field: file_name + type: file_name + - field: process + type: process_name + - field: ip_address + type: ip_address + - field: registry_key + type: registry_path + - field: url + type: url tags: - analytic_story: - - Critical Alerts - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: [] - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - manual_test: We are dynamically creating the risk_score field based on the severity of the alert in the SPL and that supersedes the risk score set in the detection. Setting these to manual test since otherwise we fail integration testing. The detection is also failing on unit-testing as some of the fields set in the observables are empty. + analytic_story: + - Critical Alerts + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: [] + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + manual_test: We are dynamically creating the risk_score field based on the severity of the alert in the SPL and that supersedes the risk score set in the detection. Setting these to manual test since otherwise we fail integration testing. The detection is also failing on unit-testing as some of the fields set in the observables are empty. tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/alerts/defender_incident_alerts_single_event.log - source: m365_defender_incident_alerts - sourcetype: ms365:defender:incident:alerts + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/alerts/defender_incident_alerts_single_event.log + source: m365_defender_incident_alerts + sourcetype: ms365:defender:incident:alerts diff --git a/detections/endpoint/mimikatz_passtheticket_commandline_parameters.yml b/detections/endpoint/mimikatz_passtheticket_commandline_parameters.yml index 41287ff934..5b3160e1a4 100644 --- a/detections/endpoint/mimikatz_passtheticket_commandline_parameters.yml +++ b/detections/endpoint/mimikatz_passtheticket_commandline_parameters.yml @@ -1,91 +1,77 @@ name: Mimikatz PassTheTicket CommandLine Parameters id: 13bbd574-83ac-11ec-99d4-acde48001122 -version: 9 -date: '2025-10-14' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the use of Mimikatz command line parameters - associated with pass-the-ticket attacks. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on specific command-line patterns related to - Kerberos ticket manipulation. This activity is significant because pass-the-ticket - attacks allow adversaries to move laterally within an environment using stolen Kerberos - tickets, bypassing normal access controls. If confirmed malicious, this could enable - attackers to escalate privileges, access sensitive information, and maintain persistence - within the network. +description: The following analytic detects the use of Mimikatz command line parameters associated with pass-the-ticket attacks. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns related to Kerberos ticket manipulation. This activity is significant because pass-the-ticket attacks allow adversaries to move laterally within an environment using stolen Kerberos tickets, bypassing normal access controls. If confirmed malicious, this could enable attackers to escalate privileges, access sensitive information, and maintain persistence within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process = "*sekurlsa::tickets - /export*" OR Processes.process = "*kerberos::ptt*") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `mimikatz_passtheticket_commandline_parameters_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although highly unlikely, legitimate applications may use the - same command line parameters as Mimikatz. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process = "*sekurlsa::tickets /export*" + OR + Processes.process = "*kerberos::ptt*" + ) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `mimikatz_passtheticket_commandline_parameters_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although highly unlikely, legitimate applications may use the same command line parameters as Mimikatz. references: -- https://github.com/gentilkiwi/mimikatz -- https://attack.mitre.org/techniques/T1550/003/ + - https://github.com/gentilkiwi/mimikatz + - https://attack.mitre.org/techniques/T1550/003/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Mimikatz command line parameters for pass the ticket attacks were used - on $dest$ - risk_objects: - - field: user - type: user - score: 36 - - field: dest - type: system - score: 36 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: Mimikatz command line parameters for pass the ticket attacks were used on $dest$ + risk_objects: + - field: user + type: user + score: 36 + - field: dest + type: system + score: 36 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - Sandworm Tools - - CISA AA23-347A - - CISA AA22-320A - - Active Directory Kerberos Attacks - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1550.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sandworm Tools + - CISA AA23-347A + - CISA AA22-320A + - Active Directory Kerberos Attacks + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1550.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550.003/mimikatz/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550.003/mimikatz/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/mmc_lolbas_execution_process_spawn.yml b/detections/endpoint/mmc_lolbas_execution_process_spawn.yml index 9bf7d7f7c1..790fa1b405 100644 --- a/detections/endpoint/mmc_lolbas_execution_process_spawn.yml +++ b/detections/endpoint/mmc_lolbas_execution_process_spawn.yml @@ -1,103 +1,78 @@ name: Mmc LOLBAS Execution Process Spawn id: f6601940-4c74-11ec-b9b7-3e22fbd008af -version: 9 -date: '2026-02-03' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: - The following analytic identifies `mmc.exe` spawning a LOLBAS execution - process. It leverages data from Endpoint Detection and Response (EDR) agents, focusing - on process creation events where `mmc.exe` is the parent process. This activity - is significant because adversaries can abuse the DCOM protocol and MMC20 COM object - to execute malicious code, using Windows native binaries documented by the LOLBAS - project. If confirmed malicious, this behavior could indicate lateral movement, - allowing attackers to execute code remotely, potentially leading to further compromise - and persistence within the environment. +description: The following analytic identifies `mmc.exe` spawning a LOLBAS execution process. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where `mmc.exe` is the parent process. This activity is significant because adversaries can abuse the DCOM protocol and MMC20 COM object to execute malicious code, using Windows native binaries documented by the LOLBAS project. If confirmed malicious, this behavior could indicate lateral movement, allowing attackers to execute code remotely, potentially leading to further compromise and persistence within the environment. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where (Processes.parent_process_name=mmc.exe) (Processes.process_name IN ("Regsvcs.exe", "Ftp.exe", "OfflineScannerShell.exe", - "Rasautou.exe", "Schtasks.exe", "Xwizard.exe", "Dllhost.exe", "Pnputil.exe", "Atbroker.exe", - "Pcwrun.exe", "Ttdinject.exe","Mshta.exe", "Bitsadmin.exe", "Certoc.exe", "Ieexec.exe", - "Microsoft.Workflow.Compiler.exe", "Runscripthelper.exe", "Forfiles.exe", "Msbuild.exe", - "Register-cimprovider.exe", "Tttracer.exe", "Ie4uinit.exe", "Bash.exe", "Hh.exe", - "SettingSyncHost.exe", "Cmstp.exe", "Mmc.exe", "Stordiag.exe", "Scriptrunner.exe", - "Odbcconf.exe", "Extexport.exe", "Msdt.exe", "WorkFolders.exe", "Diskshadow.exe", - "Mavinject.exe", "Regasm.exe", "Gpscript.exe", "Rundll32.exe", "Regsvr32.exe", "Msiexec.exe", - "Wuauclt.exe", "Presentationhost.exe", "Wmic.exe", "Runonce.exe", "Syncappvpublishingserver.exe", - "Verclsid.exe", "Infdefaultinstall.exe", "Explorer.exe", "Installutil.exe", "Netsh.exe", - "Wab.exe", "Dnscmd.exe", "At.exe", "Pcalua.exe", "Msconfig.exe")) - - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `mmc_lolbas_execution_process_spawn_filter` -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: - Legitimate applications may trigger this behavior, filter as - needed. + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes + where (Processes.parent_process_name=mmc.exe) (Processes.process_name IN ("Regsvcs.exe", "Ftp.exe", "OfflineScannerShell.exe", + "Rasautou.exe", "Schtasks.exe", "Xwizard.exe", "Dllhost.exe", "Pnputil.exe", "Atbroker.exe", + "Pcwrun.exe", "Ttdinject.exe","Mshta.exe", "Bitsadmin.exe", "Certoc.exe", "Ieexec.exe", + "Microsoft.Workflow.Compiler.exe", "Runscripthelper.exe", "Forfiles.exe", "Msbuild.exe", + "Register-cimprovider.exe", "Tttracer.exe", "Ie4uinit.exe", "Bash.exe", "Hh.exe", + "SettingSyncHost.exe", "Cmstp.exe", "Mmc.exe", "Stordiag.exe", "Scriptrunner.exe", + "Odbcconf.exe", "Extexport.exe", "Msdt.exe", "WorkFolders.exe", "Diskshadow.exe", + "Mavinject.exe", "Regasm.exe", "Gpscript.exe", "Rundll32.exe", "Regsvr32.exe", "Msiexec.exe", + "Wuauclt.exe", "Presentationhost.exe", "Wmic.exe", "Runonce.exe", "Syncappvpublishingserver.exe", + "Verclsid.exe", "Infdefaultinstall.exe", "Explorer.exe", "Installutil.exe", "Netsh.exe", + "Wab.exe", "Dnscmd.exe", "At.exe", "Pcalua.exe", "Msconfig.exe")) + + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec + Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name + Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name + Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `mmc_lolbas_execution_process_spawn_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Legitimate applications may trigger this behavior, filter as needed. references: - - https://attack.mitre.org/techniques/T1021/003/ - - https://www.cybereason.com/blog/dcom-lateral-movement-techniques - - https://lolbas-project.github.io/ + - https://attack.mitre.org/techniques/T1021/003/ + - https://www.cybereason.com/blog/dcom-lateral-movement-techniques + - https://lolbas-project.github.io/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Mmc.exe spawned a LOLBAS process on $dest$. - risk_objects: - - field: dest - type: system - score: 54 - threat_objects: [] + message: Mmc.exe spawned a LOLBAS process on $dest$. + risk_objects: + - field: dest + type: system + score: 54 + threat_objects: [] tags: - analytic_story: - - Active Directory Lateral Movement - - Living Off The Land - - Water Gamayun - - XML Runner Loader - asset_type: Endpoint - mitre_attack_id: - - T1021.003 - - T1218.014 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + - Living Off The Land + - Water Gamayun + - XML Runner Loader + asset_type: Endpoint + mitre_attack_id: + - T1021.003 + - T1218.014 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.003/lateral_movement_lolbas/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.003/lateral_movement_lolbas/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/modification_of_wallpaper.yml b/detections/endpoint/modification_of_wallpaper.yml index de69837c94..ab824c58cb 100644 --- a/detections/endpoint/modification_of_wallpaper.yml +++ b/detections/endpoint/modification_of_wallpaper.yml @@ -5,76 +5,54 @@ date: '2026-01-12' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the modification of registry keys related - to the desktop wallpaper settings. It leverages Sysmon EventCode 13 to identify - changes to the "Control Panel\\Desktop\\Wallpaper" and "Control Panel\\Desktop\\WallpaperStyle" - registry keys, especially when the modifying process is not explorer.exe or involves - suspicious file paths like temp or public directories. This activity is significant - as it can indicate ransomware behavior, such as the REVIL ransomware, which changes - the wallpaper to display a ransom note. If confirmed malicious, this could signify - a compromised machine and the presence of ransomware, leading to potential data - encryption and extortion. +description: The following analytic detects the modification of registry keys related to the desktop wallpaper settings. It leverages Sysmon EventCode 13 to identify changes to the "Control Panel\\Desktop\\Wallpaper" and "Control Panel\\Desktop\\WallpaperStyle" registry keys, especially when the modifying process is not explorer.exe or involves suspicious file paths like temp or public directories. This activity is significant as it can indicate ransomware behavior, such as the REVIL ransomware, which changes the wallpaper to display a ransom note. If confirmed malicious, this could signify a compromised machine and the presence of ransomware, leading to potential data encryption and extortion. data_source: -- Sysmon EventID 13 -search: '`sysmon` EventCode =13 (TargetObject IN ("*\\Control Panel\\Desktop\\Wallpaper","*\\Control - Panel\\Desktop\\WallpaperStyle") AND Image != "*\\explorer.exe") OR (TargetObject - IN ("*\\Control Panel\\Desktop\\Wallpaper","*\\Control Panel\\Desktop\\WallpaperStyle") - AND Details IN ("*\\temp\\*", "*\\users\\public\\*")) | stats count min(_time) as - firstTime max(_time) as lastTime by action dest process_guid process_id registry_hive - registry_path registry_key_name registry_value_data registry_value_name status user_id - vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `modification_of_wallpaper_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the Image, TargetObject registry key, registry Details from your endpoints. - If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + - Sysmon EventID 13 +search: '`sysmon` EventCode =13 (TargetObject IN ("*\\Control Panel\\Desktop\\Wallpaper","*\\Control Panel\\Desktop\\WallpaperStyle") AND Image != "*\\explorer.exe") OR (TargetObject IN ("*\\Control Panel\\Desktop\\Wallpaper","*\\Control Panel\\Desktop\\WallpaperStyle") AND Details IN ("*\\temp\\*", "*\\users\\public\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by action dest process_guid process_id registry_hive registry_path registry_key_name registry_value_data registry_value_name status user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `modification_of_wallpaper_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the Image, TargetObject registry key, registry Details from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: 3rd party tool may used to changed the wallpaper of the machine references: -- https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/ -- https://www.mcafee.com/blogs/other-blogs/mcafee-labs/mcafee-atr-analyzes-sodinokibi-aka-revil-ransomware-as-a-service-what-the-code-tells-us/ -- https://news.sophos.com/en-us/2020/04/24/lockbit-ransomware-borrows-tricks-to-keep-up-with-revil-and-maze/ + - https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/ + - https://www.mcafee.com/blogs/other-blogs/mcafee-labs/mcafee-atr-analyzes-sodinokibi-aka-revil-ransomware-as-a-service-what-the-code-tells-us/ + - https://news.sophos.com/en-us/2020/04/24/lockbit-ransomware-borrows-tricks-to-keep-up-with-revil-and-maze/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Wallpaper modification on $dest$ - risk_objects: - - field: dest - type: system - score: 54 - threat_objects: [] + message: Wallpaper modification on $dest$ + risk_objects: + - field: dest + type: system + score: 54 + threat_objects: [] tags: - analytic_story: - - Revil Ransomware - - Rhysida Ransomware - - LockBit Ransomware - - BlackMatter Ransomware - - Brute Ratel C4 - - Windows Registry Abuse - - Black Basta Ransomware - - Ransomware - - ZOVWiper - asset_type: Endpoint - mitre_attack_id: - - T1491 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Revil Ransomware + - Rhysida Ransomware + - LockBit Ransomware + - BlackMatter Ransomware + - Brute Ratel C4 + - Windows Registry Abuse + - Black Basta Ransomware + - Ransomware + - ZOVWiper + asset_type: Endpoint + mitre_attack_id: + - T1491 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf1/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf1/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/modify_acl_permission_to_files_or_folder.yml b/detections/endpoint/modify_acl_permission_to_files_or_folder.yml index f7d9ea485d..52c5cb34ac 100644 --- a/detections/endpoint/modify_acl_permission_to_files_or_folder.yml +++ b/detections/endpoint/modify_acl_permission_to_files_or_folder.yml @@ -1,91 +1,79 @@ name: Modify ACL permission To Files Or Folder id: 7e8458cc-acca-11eb-9e3f-acde48001122 -version: 9 -date: '2025-06-17' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the modification of ACL permissions to - files or folders, making them accessible to everyone or to system account. It leverages - data from Endpoint Detection and Response (EDR) agents, focusing on processes like - "cacls.exe," "icacls.exe," and "xcacls.exe" with specific command-line arguments. - This activity is significant as it may indicate an adversary attempting to evade - ACLs or access protected files. If confirmed malicious, this could allow unauthorized - access to sensitive data, potentially leading to data breaches or further system - compromise. +description: The following analytic detects the modification of ACL permissions to files or folders, making them accessible to everyone or to system account. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on processes like "cacls.exe," "icacls.exe," and "xcacls.exe" with specific command-line arguments. This activity is significant as it may indicate an adversary attempting to evade ACLs or access protected files. If confirmed malicious, this could allow unauthorized access to sensitive data, potentially leading to data breaches or further system compromise. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count - min(_time) as firstTime - max(_time) as lastTime - values(Processes.process) as process - values(Processes.process_id) as process_id - from datamodel=Endpoint.Processes where - Processes.process_name IN ("icacls.exe", "cacls.exe", "xcacls.exe") - Processes.process IN ("*/grant*", "*/g:*", "*/g *") - Processes.process IN ("* Everyone:*", "* SYSTEM:*", "* S-1-1-0:*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `modify_acl_permission_to_files_or_folder_filter` + | tstats `security_content_summariesonly` count + min(_time) as firstTime + max(_time) as lastTime + values(Processes.process) as process + values(Processes.process_id) as process_id + from datamodel=Endpoint.Processes where + Processes.process_name IN ("icacls.exe", "cacls.exe", "xcacls.exe") + Processes.process IN ("*/grant*", "*/g:*", "*/g *") + Processes.process IN ("* Everyone:*", "* SYSTEM:*", "* S-1-1-0:*") + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `modify_acl_permission_to_files_or_folder_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: administrators may use this command. Filter as needed. references: -- https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ + - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious ACL permission modification on $dest$ - risk_objects: - - field: dest - type: system - score: 32 - threat_objects: [] + message: Suspicious ACL permission modification on $dest$ + risk_objects: + - field: dest + type: system + score: 32 + threat_objects: [] tags: - analytic_story: - - Crypto Stealer - - XMRig - - Defense Evasion or Unauthorized Access Via SDDL Tampering - asset_type: Endpoint - mitre_attack_id: - - T1222 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Crypto Stealer + - XMRig + - Defense Evasion or Unauthorized Access Via SDDL Tampering + asset_type: Endpoint + mitre_attack_id: + - T1222 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/monitor_registry_keys_for_print_monitors.yml b/detections/endpoint/monitor_registry_keys_for_print_monitors.yml index 36b793aa25..820a02cbcd 100644 --- a/detections/endpoint/monitor_registry_keys_for_print_monitors.yml +++ b/detections/endpoint/monitor_registry_keys_for_print_monitors.yml @@ -5,67 +5,45 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk, Steven Dick, Bhavin Patel status: production type: TTP -description: The following analytic detects modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. - It leverages data from the Endpoint.Registry data model, focusing on events where - the registry path is modified. This activity is significant because attackers can - exploit this registry key to load arbitrary .dll files, which will execute with - elevated SYSTEM permissions and persist after a reboot. If confirmed malicious, - this could allow attackers to maintain persistence, execute code with high privileges, - and potentially compromise the entire system. +description: The following analytic detects modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. It leverages data from the Endpoint.Registry data model, focusing on events where the registry path is modified. This activity is significant because attackers can exploit this registry key to load arbitrary .dll files, which will execute with elevated SYSTEM permissions and persist after a reboot. If confirmed malicious, this could allow attackers to maintain persistence, execute code with high privileges, and potentially compromise the entire system. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.action=modified AND - Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*") by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `monitor_registry_keys_for_print_monitors_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 -known_false_positives: You will encounter noise from legitimate print-monitor registry - entries. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.action=modified AND Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `monitor_registry_keys_for_print_monitors_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 +known_false_positives: You will encounter noise from legitimate print-monitor registry entries. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: New print monitor added on $dest$ - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: New print monitor added on $dest$ + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Suspicious Windows Registry Activities - - Windows Persistence Techniques - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1547.010 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Windows Registry Activities + - Windows Persistence Techniques + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1547.010 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.010/atomic_red_team/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.010/atomic_red_team/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/moveit_certificate_store_access_failure.yml b/detections/endpoint/moveit_certificate_store_access_failure.yml index 30701c61a7..9566a18bec 100644 --- a/detections/endpoint/moveit_certificate_store_access_failure.yml +++ b/detections/endpoint/moveit_certificate_store_access_failure.yml @@ -1,45 +1,39 @@ name: MOVEit Certificate Store Access Failure id: d61292d5-46e4-49ea-b23b-8049ea70b525 -version: 4 -date: '2025-05-02' +version: 5 +date: '2026-02-25' author: Michael Haag, Splunk data_source: [] type: Hunting status: production -description: This detection identifies potential exploitation attempts of the CVE-2024-5806 - vulnerability in Progress MOVEit Transfer. It looks for log entries indicating failures - to access the certificate store, which can occur when an attacker attempts to exploit - the authentication bypass vulnerability. This behavior is a key indicator of attempts - to impersonate valid users without proper credentials. While certificate store access - failures can occur during normal operations, an unusual increase in such events, - especially from unexpected sources, may indicate malicious activity. -search: '`moveit_sftp_logs` "IpWorksKeyService: Caught exception of type IPWorksSSHException: - The certificate store could not be opened"| stats count by source _raw | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `moveit_certificate_store_access_failure_filter`' -how_to_implement: The MOVEit logs must be collected in Splunk. Currently, there is - no TA available for MOVEit. Modify the analytic as needed to match the log format - of your environment. -known_false_positives: False positives may occur, therefore utilize the analytic as - a jump off point to identifiy potential certificate store errors. +description: This detection identifies potential exploitation attempts of the CVE-2024-5806 vulnerability in Progress MOVEit Transfer. It looks for log entries indicating failures to access the certificate store, which can occur when an attacker attempts to exploit the authentication bypass vulnerability. This behavior is a key indicator of attempts to impersonate valid users without proper credentials. While certificate store access failures can occur during normal operations, an unusual increase in such events, especially from unexpected sources, may indicate malicious activity. +search: |- + `moveit_sftp_logs` "IpWorksKeyService: Caught exception of type IPWorksSSHException: The certificate store could not be opened" + | stats count + BY source _raw + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `moveit_certificate_store_access_failure_filter` +how_to_implement: The MOVEit logs must be collected in Splunk. Currently, there is no TA available for MOVEit. Modify the analytic as needed to match the log format of your environment. +known_false_positives: False positives may occur, therefore utilize the analytic as a jump off point to identifiy potential certificate store errors. references: -- https://labs.watchtowr.com/auth-bypass-in-un-limited-scenarios-progress-moveit-transfer-cve-2024-5806/ + - https://labs.watchtowr.com/auth-bypass-in-un-limited-scenarios-progress-moveit-transfer-cve-2024-5806/ tags: - analytic_story: - - MOVEit Transfer Authentication Bypass - asset_type: Web Server - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: - - CVE-2024-5806 + analytic_story: + - MOVEit Transfer Authentication Bypass + asset_type: Web Server + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: + - CVE-2024-5806 tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/moveit/SftpServer.log - sourcetype: sftp_server_logs - source: sftp_server_logs + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/moveit/SftpServer.log + sourcetype: sftp_server_logs + source: sftp_server_logs diff --git a/detections/endpoint/moveit_empty_key_fingerprint_authentication_attempt.yml b/detections/endpoint/moveit_empty_key_fingerprint_authentication_attempt.yml index 6bd049c4d7..1229809461 100644 --- a/detections/endpoint/moveit_empty_key_fingerprint_authentication_attempt.yml +++ b/detections/endpoint/moveit_empty_key_fingerprint_authentication_attempt.yml @@ -1,48 +1,40 @@ name: MOVEit Empty Key Fingerprint Authentication Attempt id: 1a537acc-199f-4713-b5d7-3d98c05ab932 -version: 5 -date: '2025-10-14' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk data_source: [] type: Hunting status: production -description: This detection identifies attempts to authenticate with an empty public - key fingerprint in Progress MOVEit Transfer, which is a key indicator of potential - exploitation of the CVE-2024-5806 vulnerability. Such attempts are characteristic - of the authentication bypass technique used in this vulnerability, where attackers - try to impersonate valid users without providing proper credentials. While occasional - empty key fingerprint authentication attempts might occur due to misconfigurations, - a sudden increase or attempts from unexpected sources could signify malicious activity. - This analytic helps security teams identify and investigate potential exploitation - attempts of the MOVEit Transfer authentication bypass vulnerability. -search: '`moveit_sftp_logs` "UserAuthRequestHandler: SftpPublicKeyAuthenticator: Attempted - to authenticate empty public key fingerprint" | stats count by source _raw | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `moveit_empty_key_fingerprint_authentication_attempt_filter`' -how_to_implement: The MOVEit logs must be collected in Splunk. Currently, there is - no TA available for MOVEit. Modify the analytic as needed to match the log format - of your environment. -known_false_positives: False positives may occur, therefore utilize the analytic as - a jump off point to identify potential empty key fingerprint authentication attempts. +description: This detection identifies attempts to authenticate with an empty public key fingerprint in Progress MOVEit Transfer, which is a key indicator of potential exploitation of the CVE-2024-5806 vulnerability. Such attempts are characteristic of the authentication bypass technique used in this vulnerability, where attackers try to impersonate valid users without providing proper credentials. While occasional empty key fingerprint authentication attempts might occur due to misconfigurations, a sudden increase or attempts from unexpected sources could signify malicious activity. This analytic helps security teams identify and investigate potential exploitation attempts of the MOVEit Transfer authentication bypass vulnerability. +search: |- + `moveit_sftp_logs` "UserAuthRequestHandler: SftpPublicKeyAuthenticator: Attempted to authenticate empty public key fingerprint" + | stats count + BY source _raw + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `moveit_empty_key_fingerprint_authentication_attempt_filter` +how_to_implement: The MOVEit logs must be collected in Splunk. Currently, there is no TA available for MOVEit. Modify the analytic as needed to match the log format of your environment. +known_false_positives: False positives may occur, therefore utilize the analytic as a jump off point to identify potential empty key fingerprint authentication attempts. references: -- https://labs.watchtowr.com/auth-bypass-in-un-limited-scenarios-progress-moveit-transfer-cve-2024-5806/ + - https://labs.watchtowr.com/auth-bypass-in-un-limited-scenarios-progress-moveit-transfer-cve-2024-5806/ tags: - analytic_story: - - MOVEit Transfer Authentication Bypass - - Hellcat Ransomware - asset_type: Web Server - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: - - CVE-2024-5806 + analytic_story: + - MOVEit Transfer Authentication Bypass + - Hellcat Ransomware + asset_type: Web Server + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: + - CVE-2024-5806 tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/moveit/SftpServer.log - sourcetype: sftp_server_logs - source: sftp_server_logs + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/moveit/SftpServer.log + sourcetype: sftp_server_logs + source: sftp_server_logs diff --git a/detections/endpoint/ms_exchange_mailbox_replication_service_writing_active_server_pages.yml b/detections/endpoint/ms_exchange_mailbox_replication_service_writing_active_server_pages.yml index ea2a3c89e8..beb538ca91 100644 --- a/detections/endpoint/ms_exchange_mailbox_replication_service_writing_active_server_pages.yml +++ b/detections/endpoint/ms_exchange_mailbox_replication_service_writing_active_server_pages.yml @@ -5,62 +5,38 @@ date: '2025-05-02' author: Michael Haag, Splunk status: experimental type: TTP -description: The following analytic identifies the creation of suspicious .aspx files - in specific directories associated with Exchange exploitation by the HAFNIUM group - and the ProxyShell vulnerability. It detects this activity by monitoring the MSExchangeMailboxReplication.exe - process, which typically does not write .aspx files. This behavior is significant - as it may indicate an active exploitation attempt on Exchange servers. If confirmed - malicious, attackers could gain unauthorized access, execute arbitrary code, or - maintain persistence within the environment. Immediate investigation and remediation - are crucial to prevent further compromise. +description: The following analytic identifies the creation of suspicious .aspx files in specific directories associated with Exchange exploitation by the HAFNIUM group and the ProxyShell vulnerability. It detects this activity by monitoring the MSExchangeMailboxReplication.exe process, which typically does not write .aspx files. This behavior is significant as it may indicate an active exploitation attempt on Exchange servers. If confirmed malicious, attackers could gain unauthorized access, execute arbitrary code, or maintain persistence within the environment. Immediate investigation and remediation are crucial to prevent further compromise. data_source: -- Sysmon EventID 1 AND Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes - where Processes.process_name=MSExchangeMailboxReplication.exe by _time span=1h - Processes.process_id Processes.process_name Processes.process_guid Processes.dest - | `drop_dm_object_name(Processes)` | join process_guid, _time [| tstats `security_content_summariesonly` - count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem - where Filesystem.file_path IN ("*\\HttpProxy\\owa\\auth\\*", "*\\inetpub\\wwwroot\\aspnet_client\\*", - "*\\HttpProxy\\OAB\\*") Filesystem.file_name="*.aspx" by _time span=1h Filesystem.dest - Filesystem.file_create_time Filesystem.file_name Filesystem.file_path | `drop_dm_object_name(Filesystem)` - | fields _time dest file_create_time file_name file_path process_name process_path - process process_guid] | dedup file_create_time | table dest file_create_time, file_name, - file_path, process_name | `ms_exchange_mailbox_replication_service_writing_active_server_pages_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem` - node. -known_false_positives: The query is structured in a way that `action` (read, create) - is not defined. Review the results of this query, filter, and tune as necessary. - It may be necessary to generate this query specific to your endpoint product. + - Sysmon EventID 1 AND Sysmon EventID 11 +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=MSExchangeMailboxReplication.exe by _time span=1h Processes.process_id Processes.process_name Processes.process_guid Processes.dest | `drop_dm_object_name(Processes)` | join process_guid, _time [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\HttpProxy\\owa\\auth\\*", "*\\inetpub\\wwwroot\\aspnet_client\\*", "*\\HttpProxy\\OAB\\*") Filesystem.file_name="*.aspx" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path | `drop_dm_object_name(Filesystem)` | fields _time dest file_create_time file_name file_path process_name process_path process process_guid] | dedup file_create_time | table dest file_create_time, file_name, file_path, process_name | `ms_exchange_mailbox_replication_service_writing_active_server_pages_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem` node. +known_false_positives: The query is structured in a way that `action` (read, create) is not defined. Review the results of this query, filter, and tune as necessary. It may be necessary to generate this query specific to your endpoint product. references: -- https://redcanary.com/blog/blackbyte-ransomware/ + - https://redcanary.com/blog/blackbyte-ransomware/ rba: - message: A file - $file_name$ was written to disk that is related to IIS exploitation - related to ProxyShell. Review further file modifications on endpoint $dest$ by - user $user$. - risk_objects: - - field: user - type: user - score: 81 - - field: dest - type: system - score: 81 - threat_objects: - - field: file_name - type: file_name + message: A file - $file_name$ was written to disk that is related to IIS exploitation related to ProxyShell. Review further file modifications on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 81 + - field: dest + type: system + score: 81 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - ProxyShell - - Ransomware - - BlackByte Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1133 - - T1190 - - T1505.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ProxyShell + - Ransomware + - BlackByte Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1133 + - T1190 + - T1505.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/ms_scripting_process_loading_ldap_module.yml b/detections/endpoint/ms_scripting_process_loading_ldap_module.yml index 4bca793af9..e40d4b30ca 100644 --- a/detections/endpoint/ms_scripting_process_loading_ldap_module.yml +++ b/detections/endpoint/ms_scripting_process_loading_ldap_module.yml @@ -5,66 +5,45 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the execution of MS scripting processes - (wscript.exe or cscript.exe) loading LDAP-related modules (Wldap32.dll, adsldp.dll, - adsldpc.dll). It leverages Sysmon EventCode 7 to identify these specific DLL loads. - This activity is significant as it may indicate an attempt to query LDAP for host - information, a behavior observed in FIN7 implants. If confirmed malicious, this - could allow attackers to gather detailed Active Directory information, potentially - leading to further exploitation or data exfiltration. +description: The following analytic detects the execution of MS scripting processes (wscript.exe or cscript.exe) loading LDAP-related modules (Wldap32.dll, adsldp.dll, adsldpc.dll). It leverages Sysmon EventCode 7 to identify these specific DLL loads. This activity is significant as it may indicate an attempt to query LDAP for host information, a behavior observed in FIN7 implants. If confirmed malicious, this could allow attackers to gather detailed Active Directory information, potentially leading to further exploitation or data exfiltration. data_source: -- Sysmon EventID 7 -search: '`sysmon` EventCode =7 Image IN ("*\\wscript.exe", "*\\cscript.exe") ImageLoaded - IN ("*\\Wldap32.dll", "*\\adsldp.dll", "*\\adsldpc.dll") | fillnull | stats count - min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file - loaded_file_path original_file_name process_exec process_guid process_hash process_id - process_name process_path service_dll_signature_exists service_dll_signature_verified - signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `ms_scripting_process_loading_ldap_module_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used. -known_false_positives: automation scripting language may used by network operator - to do ldap query. + - Sysmon EventID 7 +search: '`sysmon` EventCode =7 Image IN ("*\\wscript.exe", "*\\cscript.exe") ImageLoaded IN ("*\\Wldap32.dll", "*\\adsldp.dll", "*\\adsldpc.dll") | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ms_scripting_process_loading_ldap_module_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used. +known_false_positives: automation scripting language may used by network operator to do ldap query. references: -- https://www.mandiant.com/resources/fin7-pursuing-an-enigmatic-and-evasive-global-criminal-operation -- https://attack.mitre.org/groups/G0046/ + - https://www.mandiant.com/resources/fin7-pursuing-an-enigmatic-and-evasive-global-criminal-operation + - https://attack.mitre.org/groups/G0046/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $process_name$ loading ldap modules $ImageLoaded$ on $dest$ - risk_objects: - - field: dest - type: system - score: 9 - threat_objects: [] + message: $process_name$ loading ldap modules $ImageLoaded$ on $dest$ + risk_objects: + - field: dest + type: system + score: 9 + threat_objects: [] tags: - analytic_story: - - FIN7 - asset_type: Endpoint - mitre_attack_id: - - T1059.007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - FIN7 + asset_type: Endpoint + mitre_attack_id: + - T1059.007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/fin7_js_2/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/fin7_js_2/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/ms_scripting_process_loading_wmi_module.yml b/detections/endpoint/ms_scripting_process_loading_wmi_module.yml index 0d4c91e0de..4e6b4b7811 100644 --- a/detections/endpoint/ms_scripting_process_loading_wmi_module.yml +++ b/detections/endpoint/ms_scripting_process_loading_wmi_module.yml @@ -5,67 +5,45 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the loading of WMI modules by Microsoft - scripting processes like wscript.exe or cscript.exe. It leverages Sysmon EventCode - 7 to identify instances where these scripting engines load specific WMI-related - DLLs. This activity is significant because it can indicate the presence of malware, - such as the FIN7 implant, which uses JavaScript to execute WMI queries for gathering - host information to send to a C2 server. If confirmed malicious, this behavior could - allow attackers to collect sensitive system information and maintain persistence - within the environment. +description: The following analytic detects the loading of WMI modules by Microsoft scripting processes like wscript.exe or cscript.exe. It leverages Sysmon EventCode 7 to identify instances where these scripting engines load specific WMI-related DLLs. This activity is significant because it can indicate the presence of malware, such as the FIN7 implant, which uses JavaScript to execute WMI queries for gathering host information to send to a C2 server. If confirmed malicious, this behavior could allow attackers to collect sensitive system information and maintain persistence within the environment. data_source: -- Sysmon EventID 7 -search: '`sysmon` EventCode =7 Image IN ("*\\wscript.exe", "*\\cscript.exe") ImageLoaded - IN ("*\\fastprox.dll", "*\\wbemdisp.dll", "*\\wbemprox.dll", "*\\wbemsvc.dll" , - "*\\wmiutils.dll", "*\\wbemcomn.dll") | fillnull | stats count min(_time) as firstTime - max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name - process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists - service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `ms_scripting_process_loading_wmi_module_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used. -known_false_positives: automation scripting language may used by network operator - to do ldap query. + - Sysmon EventID 7 +search: '`sysmon` EventCode =7 Image IN ("*\\wscript.exe", "*\\cscript.exe") ImageLoaded IN ("*\\fastprox.dll", "*\\wbemdisp.dll", "*\\wbemprox.dll", "*\\wbemsvc.dll" , "*\\wmiutils.dll", "*\\wbemcomn.dll") | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ms_scripting_process_loading_wmi_module_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used. +known_false_positives: automation scripting language may used by network operator to do ldap query. references: -- https://www.mandiant.com/resources/fin7-pursuing-an-enigmatic-and-evasive-global-criminal-operation -- https://attack.mitre.org/groups/G0046/ + - https://www.mandiant.com/resources/fin7-pursuing-an-enigmatic-and-evasive-global-criminal-operation + - https://attack.mitre.org/groups/G0046/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $process_name$ loading wmi modules $ImageLoaded$ on $dest$ - risk_objects: - - field: dest - type: system - score: 9 - threat_objects: [] + message: $process_name$ loading wmi modules $ImageLoaded$ on $dest$ + risk_objects: + - field: dest + type: system + score: 9 + threat_objects: [] tags: - analytic_story: - - FIN7 - asset_type: Endpoint - mitre_attack_id: - - T1059.007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - FIN7 + asset_type: Endpoint + mitre_attack_id: + - T1059.007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/fin7_js_2/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/fin7_js_2/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/msbuild_suspicious_spawned_by_script_process.yml b/detections/endpoint/msbuild_suspicious_spawned_by_script_process.yml index 10376c22b9..0cc48fa27e 100644 --- a/detections/endpoint/msbuild_suspicious_spawned_by_script_process.yml +++ b/detections/endpoint/msbuild_suspicious_spawned_by_script_process.yml @@ -1,87 +1,69 @@ name: MSBuild Suspicious Spawned By Script Process id: 213b3148-24ea-11ec-93a2-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the suspicious spawning of MSBuild.exe - by Windows Script Host processes (cscript.exe or wscript.exe). This behavior is - often associated with malware or adversaries executing malicious MSBuild processes - via scripts on compromised hosts. The detection leverages Endpoint Detection and - Response (EDR) telemetry, focusing on process creation events where MSBuild is a - child of script hosts. This activity is significant as it may indicate an attempt - to execute malicious code. If confirmed malicious, it could lead to unauthorized - code execution, potentially compromising the host and allowing further malicious - activities. +description: The following analytic detects the suspicious spawning of MSBuild.exe by Windows Script Host processes (cscript.exe or wscript.exe). This behavior is often associated with malware or adversaries executing malicious MSBuild processes via scripts on compromised hosts. The detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on process creation events where MSBuild is a child of script hosts. This activity is significant as it may indicate an attempt to execute malicious code. If confirmed malicious, it could lead to unauthorized code execution, potentially compromising the host and allowing further malicious activities. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count values(Processes.process_name) - as process_name values(Processes.process) as process min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name - IN ("wscript.exe", "cscript.exe") AND `process_msbuild` by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `msbuild_suspicious_spawned_by_script_process_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives should be limited as developers do not spawn - MSBuild via a WSH. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name IN ("wscript.exe", "cscript.exe") + AND + `process_msbuild` + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `msbuild_suspicious_spawned_by_script_process_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives should be limited as developers do not spawn MSBuild via a WSH. references: -- https://app.any.run/tasks/dc93ee63-050c-4ff8-b07e-8277af9ab939/ + - https://app.any.run/tasks/dc93ee63-050c-4ff8-b07e-8277af9ab939/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Msbuild.exe process spawned by $parent_process_name$ on $dest$ executed - by $user$ - risk_objects: - - field: dest - type: system - score: 49 - - field: user - type: user - score: 49 - threat_objects: [] + message: Msbuild.exe process spawned by $parent_process_name$ on $dest$ executed by $user$ + risk_objects: + - field: dest + type: system + score: 49 + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - Trusted Developer Utilities Proxy Execution MSBuild - - Storm-2460 CLFS Zero Day Exploitation - asset_type: Endpoint - mitre_attack_id: - - T1127.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Trusted Developer Utilities Proxy Execution MSBuild + - Storm-2460 CLFS Zero Day Exploitation + asset_type: Endpoint + mitre_attack_id: + - T1127.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/regsvr32_silent/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/regsvr32_silent/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/mshta_spawning_rundll32_or_regsvr32_process.yml b/detections/endpoint/mshta_spawning_rundll32_or_regsvr32_process.yml index b2007887d4..2c74069b07 100644 --- a/detections/endpoint/mshta_spawning_rundll32_or_regsvr32_process.yml +++ b/detections/endpoint/mshta_spawning_rundll32_or_regsvr32_process.yml @@ -1,86 +1,71 @@ name: Mshta spawning Rundll32 OR Regsvr32 Process id: 4aa5d062-e893-11eb-9eb2-acde48001122 -version: 10 -date: '2025-09-18' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects a suspicious mshta.exe process spawning - rundll32 or regsvr32 child processes. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process GUID, process name, and parent process - fields. This activity is significant as it is a known technique used by malware - like Trickbot to load malicious DLLs and execute payloads. If confirmed malicious, - this behavior could allow attackers to execute arbitrary code, escalate privileges, - or download additional malware, posing a severe threat to the environment. +description: The following analytic detects a suspicious mshta.exe process spawning rundll32 or regsvr32 child processes. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process GUID, process name, and parent process fields. This activity is significant as it is a known technique used by malware like Trickbot to load malicious DLLs and execute payloads. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or download additional malware, posing a severe threat to the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name - = "mshta.exe" `process_rundll32` OR `process_regsvr32` by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` - | `mshta_spawning_rundll32_or_regsvr32_process_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: limitted. this anomaly behavior is not commonly seen in clean - host. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name = "mshta.exe" `process_rundll32` + OR + `process_regsvr32` + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `mshta_spawning_rundll32_or_regsvr32_process_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: limitted. this anomaly behavior is not commonly seen in clean host. references: -- https://twitter.com/cyb3rops/status/1416050325870587910?s=21 + - https://twitter.com/cyb3rops/status/1416050325870587910?s=21 drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a mshta parent process $parent_process_name$ spawn child process $process_name$ - in host $dest$ - risk_objects: - - field: dest - type: system - score: 56 - - field: user - type: user - score: 56 - threat_objects: [] + message: a mshta parent process $parent_process_name$ spawn child process $process_name$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 56 + - field: user + type: user + score: 56 + threat_objects: [] tags: - analytic_story: - - Trickbot - - IcedID - - Living Off The Land - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1218.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Trickbot + - IcedID + - Living Off The Land + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1218.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/spear_phish/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/spear_phish/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/msi_module_loaded_by_non_system_binary.yml b/detections/endpoint/msi_module_loaded_by_non_system_binary.yml index 40ad5cf03f..97f389de97 100644 --- a/detections/endpoint/msi_module_loaded_by_non_system_binary.yml +++ b/detections/endpoint/msi_module_loaded_by_non_system_binary.yml @@ -5,48 +5,34 @@ date: '2025-05-02' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic detects the loading of `msi.dll` by a binary not - located in `system32`, `syswow64`, `winsxs`, or `windows` directories. This is identified - using Sysmon EventCode 7, which logs DLL loads, and filters out legitimate system - paths. This activity is significant as it may indicate exploitation of CVE-2021-41379 - or DLL side-loading attacks, both of which can lead to unauthorized system modifications. - If confirmed malicious, this could allow an attacker to execute arbitrary code, - escalate privileges, or persist within the environment. +description: The following analytic detects the loading of `msi.dll` by a binary not located in `system32`, `syswow64`, `winsxs`, or `windows` directories. This is identified using Sysmon EventCode 7, which logs DLL loads, and filters out legitimate system paths. This activity is significant as it may indicate exploitation of CVE-2021-41379 or DLL side-loading attacks, both of which can lead to unauthorized system modifications. If confirmed malicious, this could allow an attacker to execute arbitrary code, escalate privileges, or persist within the environment. data_source: -- Sysmon EventID 7 -search: '`sysmon` EventCode=7 ImageLoaded="*\\msi.dll" NOT (Image IN ("*\\System32\\*","*\\syswow64\\*","*\\windows\\*", - "*\\winsxs\\*")) | fillnull | stats count min(_time) as firstTime max(_time) as - lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name - process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists - service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `msi_module_loaded_by_non_system_binary_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name and imageloaded executions from your endpoints. If you - are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -known_false_positives: It is possible some Administrative utilities will load msi.dll - outside of normal system paths, filter as needed. + - Sysmon EventID 7 +search: '`sysmon` EventCode=7 ImageLoaded="*\\msi.dll" NOT (Image IN ("*\\System32\\*","*\\syswow64\\*","*\\windows\\*", "*\\winsxs\\*")) | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `msi_module_loaded_by_non_system_binary_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: It is possible some Administrative utilities will load msi.dll outside of normal system paths, filter as needed. references: -- https://attackerkb.com/topics/7LstI2clmF/cve-2021-41379/rapid7-analysis -- https://github.com/AlexandrVIvanov/InstallerFileTakeOver -- https://github.com/mandiant/red_team_tool_countermeasures/blob/master/rules/PGF/supplemental/hxioc/msi.dll%20Hijack%20(Methodology).ioc + - https://attackerkb.com/topics/7LstI2clmF/cve-2021-41379/rapid7-analysis + - https://github.com/AlexandrVIvanov/InstallerFileTakeOver + - https://github.com/mandiant/red_team_tool_countermeasures/blob/master/rules/PGF/supplemental/hxioc/msi.dll%20Hijack%20(Methodology).ioc tags: - analytic_story: - - Data Destruction - - Hermetic Wiper - - Windows Privilege Escalation - asset_type: Endpoint - cve: - - CVE-2021-41379 - mitre_attack_id: - - T1574.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Destruction + - Hermetic Wiper + - Windows Privilege Escalation + asset_type: Endpoint + cve: + - CVE-2021-41379 + mitre_attack_id: + - T1574.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/msi_module_load/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/msi_module_load/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/msmpeng_application_dll_side_loading.yml b/detections/endpoint/msmpeng_application_dll_side_loading.yml index 6f522f2a5d..da08d0f80a 100644 --- a/detections/endpoint/msmpeng_application_dll_side_loading.yml +++ b/detections/endpoint/msmpeng_application_dll_side_loading.yml @@ -5,67 +5,45 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk, Sanjay Govind status: production type: TTP -description: The following analytic detects the suspicious creation of msmpeng.exe - or mpsvc.dll in non-default Windows Defender folders. It leverages the Endpoint.Filesystem - datamodel to identify instances where these files are created outside their expected - directories. This activity is significant because it is associated with the REvil - ransomware, which uses DLL side-loading to execute malicious payloads. If confirmed - malicious, this could lead to ransomware deployment, resulting in data encryption, - system compromise, and potential data loss or extortion. +description: The following analytic detects the suspicious creation of msmpeng.exe or mpsvc.dll in non-default Windows Defender folders. It leverages the Endpoint.Filesystem datamodel to identify instances where these files are created outside their expected directories. This activity is significant because it is associated with the REvil ransomware, which uses DLL side-loading to execute malicious payloads. If confirmed malicious, this could lead to ransomware deployment, resulting in data encryption, system compromise, and potential data loss or extortion. data_source: -- Sysmon EventID 11 -search: '|tstats `security_content_summariesonly` values(Filesystem.file_path) as - file_path count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem - where (Filesystem.file_name = "msmpeng.exe" OR Filesystem.file_name = "mpsvc.dll") AND - NOT (Filesystem.file_path IN ("*\\Program Files\\windows defender\\*","*\\WinSxS\\*defender-service*","*\\WinSxS\\Temp\\*defender-service*")) - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path - Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | - `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `msmpeng_application_dll_side_loading_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the Filesystem responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Filesystem` node. + - Sysmon EventID 11 +search: '|tstats `security_content_summariesonly` values(Filesystem.file_path) as file_path count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name = "msmpeng.exe" OR Filesystem.file_name = "mpsvc.dll") AND NOT (Filesystem.file_path IN ("*\\Program Files\\windows defender\\*","*\\WinSxS\\*defender-service*","*\\WinSxS\\Temp\\*defender-service*")) by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `msmpeng_application_dll_side_loading_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. known_false_positives: quite minimal false positive expected. references: -- https://community.sophos.com/b/security-blog/posts/active-ransomware-attack-on-kaseya-customers + - https://community.sophos.com/b/security-blog/posts/active-ransomware-attack-on-kaseya-customers drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious creation of msmpeng.exe or mpsvc.dll in non default windows - defender folder on host - $dest$ - risk_objects: - - field: user - type: user - score: 25 - threat_objects: [] + message: Suspicious creation of msmpeng.exe or mpsvc.dll in non default windows defender folder on host - $dest$ + risk_objects: + - field: user + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Ransomware - - Revil Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1574.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - Revil Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1574.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets//malware/revil/msmpeng_side/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets//malware/revil/msmpeng_side/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/net_profiler_uac_bypass.yml b/detections/endpoint/net_profiler_uac_bypass.yml index 433a591b4c..9745a5a43c 100644 --- a/detections/endpoint/net_profiler_uac_bypass.yml +++ b/detections/endpoint/net_profiler_uac_bypass.yml @@ -5,67 +5,44 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects modifications to the registry aimed at - bypassing the User Account Control (UAC) feature in Windows. It identifies changes - to the .NET COR_PROFILER_PATH registry key, which can be exploited to load a malicious - DLL via mmc.exe. This detection leverages data from the Endpoint.Registry datamodel, - focusing on specific registry paths and values. Monitoring this activity is crucial - as it can indicate an attempt to escalate privileges or persist within the environment. - If confirmed malicious, this could allow an attacker to execute arbitrary code with - elevated privileges, compromising system integrity. +description: The following analytic detects modifications to the registry aimed at bypassing the User Account Control (UAC) feature in Windows. It identifies changes to the .NET COR_PROFILER_PATH registry key, which can be exploited to load a malicious DLL via mmc.exe. This detection leverages data from the Endpoint.Registry datamodel, focusing on specific registry paths and values. Monitoring this activity is crucial as it can indicate an attempt to escalate privileges or persist within the environment. If confirmed malicious, this could allow an attacker to execute arbitrary code with elevated privileges, compromising system integrity. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\Environment\\COR_PROFILER_PATH" - Registry.registry_value_data = "*.dll" by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `net_profiler_uac_bypass_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure - that this registry was included in your config files ex. sysmon config to be monitored. -known_false_positives: limited false positive. It may trigger by some windows update - that will modify this registry. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\Environment\\COR_PROFILER_PATH" Registry.registry_value_data = "*.dll" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `net_profiler_uac_bypass_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored. +known_false_positives: limited false positive. It may trigger by some windows update that will modify this registry. references: -- https://offsec.almond.consulting/UAC-bypass-dotnet.html + - https://offsec.almond.consulting/UAC-bypass-dotnet.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious modification of registry $registry_path$ with possible payload - path $registry_path$ and key $registry_key_name$ on $dest$ - risk_objects: - - field: dest - type: system - score: 63 - threat_objects: [] + message: Suspicious modification of registry $registry_path$ with possible payload path $registry_path$ and key $registry_key_name$ on $dest$ + risk_objects: + - field: dest + type: system + score: 63 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - asset_type: Endpoint - mitre_attack_id: - - T1548.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + asset_type: Endpoint + mitre_attack_id: + - T1548.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/uac_bypass/windows-sysmon2.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/uac_bypass/windows-sysmon2.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/network_connection_discovery_with_arp.yml b/detections/endpoint/network_connection_discovery_with_arp.yml index 2ddd4c4e31..76cfd0403f 100644 --- a/detections/endpoint/network_connection_discovery_with_arp.yml +++ b/detections/endpoint/network_connection_discovery_with_arp.yml @@ -1,69 +1,58 @@ name: Network Connection Discovery With Arp id: ae008c0f-83bd-4ed4-9350-98d4328e15d2 -version: 7 -date: '2025-07-28' +version: 8 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting -description: The following analytic detects the execution of `arp.exe` with the - `-a` flag, which is used to list network connections on a compromised system. - This detection leverages data from Endpoint Detection and Response (EDR) - agents, focusing on process names, command-line executions, and related - telemetry. Monitoring this activity is significant because both Red Teams and - adversaries use `arp.exe` for situational awareness and Active Directory - discovery. If confirmed malicious, this activity could allow attackers to map - the network, identify active devices, and plan further lateral movement or - attacks. +description: The following analytic detects the execution of `arp.exe` with the `-a` flag, which is used to list network connections on a compromised system. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, command-line executions, and related telemetry. Monitoring this activity is significant because both Red Teams and adversaries use `arp.exe` for situational awareness and Active Directory discovery. If confirmed malicious, this activity could allow attackers to map the network, identify active devices, and plan further lateral movement or attacks. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="arp.exe") - (Processes.process=*-a*) by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `network_connection_discovery_with_arp_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: Administrators or power users may use this command for - troubleshooting. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="arp.exe" + ) + (Processes.process=*-a*) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `network_connection_discovery_with_arp_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1049/ -- https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ -- https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/ + - https://attack.mitre.org/techniques/T1049/ + - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ + - https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/ tags: - analytic_story: - - Active Directory Discovery - - Qakbot - - Windows Post-Exploitation - - Prestige Ransomware - - Volt Typhoon - - IcedID - - Interlock Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1049 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - Qakbot + - Windows Post-Exploitation + - Prestige Ransomware + - Volt Typhoon + - IcedID + - Interlock Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1049 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1049/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1049/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/network_connection_discovery_with_netstat.yml b/detections/endpoint/network_connection_discovery_with_netstat.yml index 771ed99fee..1fc6a1b99a 100644 --- a/detections/endpoint/network_connection_discovery_with_netstat.yml +++ b/detections/endpoint/network_connection_discovery_with_netstat.yml @@ -1,65 +1,59 @@ name: Network Connection Discovery With Netstat id: 2cf5cc25-f39a-436d-a790-4857e5995ede -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting -description: The following analytic detects the execution of `netstat.exe` with command-line - arguments to list network connections on a system. It leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process names, command-line executions, - and parent processes. This activity is significant as both Red Teams and adversaries - use `netstat.exe` for situational awareness and Active Directory discovery. If confirmed - malicious, this behavior could allow attackers to map network connections, identify - critical systems, and plan further lateral movement or data exfiltration. +description: The following analytic detects the execution of `netstat.exe` with command-line arguments to list network connections on a system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, command-line executions, and parent processes. This activity is significant as both Red Teams and adversaries use `netstat.exe` for situational awareness and Active Directory discovery. If confirmed malicious, this behavior could allow attackers to map network connections, identify critical systems, and plan further lateral movement or data exfiltration. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="netstat.exe") - (Processes.process=*-a*) by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `network_connection_discovery_with_netstat_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="netstat.exe" + ) + (Processes.process=*-a*) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `network_connection_discovery_with_netstat_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1049/ -- https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ + - https://attack.mitre.org/techniques/T1049/ + - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ tags: - analytic_story: - - CISA AA22-277A - - Windows Post-Exploitation - - Active Directory Discovery - - CISA AA23-347A - - Prestige Ransomware - - Qakbot - - PlugX - - Medusa Ransomware - - Volt Typhoon - asset_type: Endpoint - mitre_attack_id: - - T1049 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA22-277A + - Windows Post-Exploitation + - Active Directory Discovery + - CISA AA23-347A + - Prestige Ransomware + - Qakbot + - PlugX + - Medusa Ransomware + - Volt Typhoon + asset_type: Endpoint + mitre_attack_id: + - T1049 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1049/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1049/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/network_discovery_using_route_windows_app.yml b/detections/endpoint/network_discovery_using_route_windows_app.yml index 92cbac89b0..119ad059b4 100644 --- a/detections/endpoint/network_discovery_using_route_windows_app.yml +++ b/detections/endpoint/network_discovery_using_route_windows_app.yml @@ -5,63 +5,46 @@ date: '2025-12-15' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects the execution of the `route.exe` Windows - application, commonly used for network discovery. It leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process creation events. This activity - is significant because adversaries often use `route.exe` to map network routes and - identify potential targets within a network. If confirmed malicious, this behavior - could allow attackers to gain insights into network topology, facilitating lateral - movement and further exploitation. Note that false positives may occur due to legitimate - administrative tasks or automated scripts. +description: The following analytic detects the execution of the `route.exe` Windows application, commonly used for network discovery. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events. This activity is significant because adversaries often use `route.exe` to map network routes and identify potential targets within a network. If confirmed malicious, this behavior could allow attackers to gain insights into network topology, facilitating lateral movement and further exploitation. Note that false positives may occur due to legitimate administrative tasks or automated scripts. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - (Processes.process_name=route.exe OR Processes.original_file_name=route.exe) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `network_discovery_using_route_windows_app_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: A network operator or systems administrator may utilize an - automated host discovery application that may generate false positives or an amazon - ec2 script that uses this application. Filter as needed. + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where + (Processes.process_name=route.exe OR Processes.original_file_name=route.exe) + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `network_discovery_using_route_windows_app_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: A network operator or systems administrator may utilize an automated host discovery application that may generate false positives or an amazon ec2 script that uses this application. Filter as needed. references: -- https://app.any.run/tasks/ad4c3cda-41f2-4401-8dba-56cc2d245488/ + - https://app.any.run/tasks/ad4c3cda-41f2-4401-8dba-56cc2d245488/ tags: - analytic_story: - - Active Directory Discovery - - Qakbot - - CISA AA22-277A - - Windows Post-Exploitation - - Prestige Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1016.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - Qakbot + - CISA AA22-277A + - Windows Post-Exploitation + - Prestige Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1016.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/vilsel/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/vilsel/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/network_share_discovery_via_dir_command.yml b/detections/endpoint/network_share_discovery_via_dir_command.yml index 372ba46dc9..3bd856b545 100644 --- a/detections/endpoint/network_share_discovery_via_dir_command.yml +++ b/detections/endpoint/network_share_discovery_via_dir_command.yml @@ -6,45 +6,29 @@ author: Teoderick Contreras, Splunk status: production type: Hunting data_source: -- Windows Event Log Security 5140 -description: The following analytic detects access to Windows administrative SMB shares - (Admin$, IPC$, C$) using the 'dir' command. It leverages Windows Security Event - Logs with EventCode 5140 to identify this activity. This behavior is significant - as it is commonly used by tools like PsExec/PaExec for staging binaries before creating - and starting services on remote endpoints, a technique often employed by adversaries - for lateral movement and remote code execution. If confirmed malicious, this activity - could allow attackers to propagate malware, such as IcedID, across the network, - leading to widespread infection and potential data breaches. -search: '`wineventlog_security` EventCode=5140 ShareName IN("\\\\*\\ADMIN$","\\\\*\\C$","*\\\\*\\IPC$") - AccessMask= 0x1 | stats min(_time) as firstTime max(_time) as lastTime count by - ShareName IpAddress ObjectType SubjectUserName SubjectDomainName IpPort AccessMask - Computer | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `network_share_discovery_via_dir_command_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Windows Security Event Logs with 5140 EventCode enabled. The Windows TA is also - required. Also enable the object Audit access success/failure in your group policy. -known_false_positives: System Administrators may use looks like net.exe or "dir commandline" - for troubleshooting or administrations tasks. However, this will typically come - only from certain users and certain systems that can be added to an allow list. + - Windows Event Log Security 5140 +description: The following analytic detects access to Windows administrative SMB shares (Admin$, IPC$, C$) using the 'dir' command. It leverages Windows Security Event Logs with EventCode 5140 to identify this activity. This behavior is significant as it is commonly used by tools like PsExec/PaExec for staging binaries before creating and starting services on remote endpoints, a technique often employed by adversaries for lateral movement and remote code execution. If confirmed malicious, this activity could allow attackers to propagate malware, such as IcedID, across the network, leading to widespread infection and potential data breaches. +search: '`wineventlog_security` EventCode=5140 ShareName IN("\\\\*\\ADMIN$","\\\\*\\C$","*\\\\*\\IPC$") AccessMask= 0x1 | stats min(_time) as firstTime max(_time) as lastTime count by ShareName IpAddress ObjectType SubjectUserName SubjectDomainName IpPort AccessMask Computer | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `network_share_discovery_via_dir_command_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 5140 EventCode enabled. The Windows TA is also required. Also enable the object Audit access success/failure in your group policy. +known_false_positives: System Administrators may use looks like net.exe or "dir commandline" for troubleshooting or administrations tasks. However, this will typically come only from certain users and certain systems that can be added to an allow list. references: -- https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/ + - https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/ tags: - analytic_story: - - IcedID - asset_type: Endpoint - atomic_guid: - - 13daa2cf-195a-43df-a8bd-7dd5ffb607b5 - mitre_attack_id: - - T1135 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - IcedID + asset_type: Endpoint + atomic_guid: + - 13daa2cf-195a-43df-a8bd-7dd5ffb607b5 + mitre_attack_id: + - T1135 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1135/net_share_discovery_via_dir/smb_access_security_xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1135/net_share_discovery_via_dir/smb_access_security_xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/network_traffic_to_active_directory_web_services_protocol.yml b/detections/endpoint/network_traffic_to_active_directory_web_services_protocol.yml index 1aa6f9ecb9..0373eb344f 100644 --- a/detections/endpoint/network_traffic_to_active_directory_web_services_protocol.yml +++ b/detections/endpoint/network_traffic_to_active_directory_web_services_protocol.yml @@ -1,57 +1,48 @@ name: Network Traffic to Active Directory Web Services Protocol id: 68a0056c-34cb-455f-b03d-df935ea62c4f -version: 9 -date: '2025-06-17' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting data_source: -- Sysmon EventID 3 -description: The following analytic identifies network traffic directed to the Active - Directory Web Services Protocol (ADWS) on port 9389. It leverages network traffic - logs, focusing on source and destination IP addresses, application names, and destination - ports. This activity is significant as ADWS is used to manage Active Directory, - and unauthorized access could indicate malicious intent. If confirmed malicious, - an attacker could manipulate Active Directory, potentially leading to privilege - escalation, unauthorized access, or persistent control over the environment. -search: '| tstats count from datamodel=Network_Traffic where All_Traffic.dest_port=9389 - by All_Traffic.action All_Traffic.app All_Traffic.dest All_Traffic.dest_ip All_Traffic.dest_port - All_Traffic.direction All_Traffic.dvc All_Traffic.protocol All_Traffic.protocol_version - All_Traffic.src All_Traffic.src_ip All_Traffic.src_port All_Traffic.transport All_Traffic.user - All_Traffic.vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `drop_dm_object_name("All_Traffic")` | `network_traffic_to_active_directory_web_services_protocol_filter`' -how_to_implement: The detection is based on data that originates from network traffic - logs. The logs must contain the source and destination IP addresses, the application - name, and the destination port. The logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the network traffic data source. - The logs must also be mapped to the `Network_Traffic` data model. Use the Splunk - Common Information Model (CIM) to normalize the field names and speed up the data - modeling process. -known_false_positives: False positives should be limited as the destination port is - specific to Active Directory Web Services Protocol, however we recommend utilizing - this analytic to hunt for non-standard processes querying the ADWS port. Filter - by App or dest_ip to AD servers and remove known processes querying ADWS. + - Sysmon EventID 3 +description: The following analytic identifies network traffic directed to the Active Directory Web Services Protocol (ADWS) on port 9389. It leverages network traffic logs, focusing on source and destination IP addresses, application names, and destination ports. This activity is significant as ADWS is used to manage Active Directory, and unauthorized access could indicate malicious intent. If confirmed malicious, an attacker could manipulate Active Directory, potentially leading to privilege escalation, unauthorized access, or persistent control over the environment. +search: |- + | tstats count FROM datamodel=Network_Traffic + WHERE All_Traffic.dest_port=9389 + BY All_Traffic.action All_Traffic.app All_Traffic.dest + All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.direction + All_Traffic.dvc All_Traffic.protocol All_Traffic.protocol_version + All_Traffic.src All_Traffic.src_ip All_Traffic.src_port + All_Traffic.transport All_Traffic.user All_Traffic.vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `drop_dm_object_name("All_Traffic")` + | `network_traffic_to_active_directory_web_services_protocol_filter` +how_to_implement: The detection is based on data that originates from network traffic logs. The logs must contain the source and destination IP addresses, the application name, and the destination port. The logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the network traffic data source. The logs must also be mapped to the `Network_Traffic` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives should be limited as the destination port is specific to Active Directory Web Services Protocol, however we recommend utilizing this analytic to hunt for non-standard processes querying the ADWS port. Filter by App or dest_ip to AD servers and remove known processes querying ADWS. references: -- https://github.com/FalconForceTeam/SOAPHound + - https://github.com/FalconForceTeam/SOAPHound tags: - analytic_story: - - Windows Discovery Techniques - asset_type: Network - atomic_guid: [] - mitre_attack_id: - - T1069.001 - - T1069.002 - - T1087.001 - - T1087.002 - - T1482 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Discovery Techniques + asset_type: Network + atomic_guid: [] + mitre_attack_id: + - T1069.001 + - T1069.002 + - T1087.001 + - T1087.002 + - T1482 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/soaphound/sysmon_soaphound.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/soaphound/sysmon_soaphound.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/nishang_powershelltcponeline.yml b/detections/endpoint/nishang_powershelltcponeline.yml index 8cae1a0240..dc0cdad034 100644 --- a/detections/endpoint/nishang_powershelltcponeline.yml +++ b/detections/endpoint/nishang_powershelltcponeline.yml @@ -1,83 +1,69 @@ name: Nishang PowershellTCPOneLine id: 1a382c6c-7c2e-11eb-ac69-acde48001122 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the use of the Nishang Invoke-PowerShellTCPOneLine - utility, which initiates a callback to a remote Command and Control (C2) server. - It leverages Endpoint Detection and Response (EDR) data, focusing on PowerShell - processes that include specific .NET classes like Net.Sockets.TCPClient and System.Text.ASCIIEncoding. - This activity is significant as it indicates potential remote control or data exfiltration - attempts by an attacker. If confirmed malicious, this could lead to unauthorized - remote access, data theft, or further compromise of the affected system. +description: The following analytic detects the use of the Nishang Invoke-PowerShellTCPOneLine utility, which initiates a callback to a remote Command and Control (C2) server. It leverages Endpoint Detection and Response (EDR) data, focusing on PowerShell processes that include specific .NET classes like Net.Sockets.TCPClient and System.Text.ASCIIEncoding. This activity is significant as it indicates potential remote control or data exfiltration attempts by an attacker. If confirmed malicious, this could lead to unauthorized remote access, data theft, or further compromise of the affected system. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_powershell` (Processes.process=*Net.Sockets.TCPClient* - AND Processes.process=*System.Text.ASCIIEncoding*) by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| - `nishang_powershelltcponeline_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Limited false positives may be present. Filter as needed based - on initial analysis. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_powershell` (Processes.process=*Net.Sockets.TCPClient* + AND + Processes.process=*System.Text.ASCIIEncoding*) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `nishang_powershelltcponeline_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Limited false positives may be present. Filter as needed based on initial analysis. references: -- https://github.com/samratashok/nishang/blob/master/Shells/Invoke-PowerShellTcpOneLine.ps1 -- https://www.volexity.com/blog/2021/03/02/active-exploitation-of-microsoft-exchange-zero-day-vulnerabilities/ -- https://www.microsoft.com/security/blog/2021/03/02/hafnium-targeting-exchange-servers/ -- https://www.rapid7.com/blog/post/2021/03/03/rapid7s-insightidr-enables-detection-and-response-to-microsoft-exchange-0-day/ + - https://github.com/samratashok/nishang/blob/master/Shells/Invoke-PowerShellTcpOneLine.ps1 + - https://www.volexity.com/blog/2021/03/02/active-exploitation-of-microsoft-exchange-zero-day-vulnerabilities/ + - https://www.microsoft.com/security/blog/2021/03/02/hafnium-targeting-exchange-servers/ + - https://www.rapid7.com/blog/post/2021/03/03/rapid7s-insightidr-enables-detection-and-response-to-microsoft-exchange-0-day/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible Nishang Invoke-PowerShellTCPOneLine behavior on $dest$ - risk_objects: - - field: dest - type: system - score: 42 - threat_objects: [] + message: Possible Nishang Invoke-PowerShellTCPOneLine behavior on $dest$ + risk_objects: + - field: dest + type: system + score: 42 + threat_objects: [] tags: - analytic_story: - - HAFNIUM Group - - Cleo File Transfer Software - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - HAFNIUM Group + - Cleo File Transfer Software + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/nltest_domain_trust_discovery.yml b/detections/endpoint/nltest_domain_trust_discovery.yml index bbac445965..35f362b21b 100644 --- a/detections/endpoint/nltest_domain_trust_discovery.yml +++ b/detections/endpoint/nltest_domain_trust_discovery.yml @@ -1,95 +1,82 @@ name: NLTest Domain Trust Discovery id: c3e05466-5f22-11eb-ae93-0242ac130002 -version: 10 -date: '2026-01-20' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies the execution of `nltest.exe` with - command-line arguments `/domain_trusts` or `/all_trusts` to query Domain Trust information. - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on - process execution logs and command-line arguments. This activity is significant - as it indicates potential reconnaissance efforts by adversaries to understand domain - trust relationships, which can inform their lateral movement strategies. If confirmed - malicious, this activity could enable attackers to map out trusted domains, facilitating - further compromise and pivoting within the network. +description: The following analytic identifies the execution of `nltest.exe` with command-line arguments `/domain_trusts` or `/all_trusts` to query Domain Trust information. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant as it indicates potential reconnaissance efforts by adversaries to understand domain trust relationships, which can inform their lateral movement strategies. If confirmed malicious, this activity could enable attackers to map out trusted domains, facilitating further compromise and pivoting within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - (Processes.process_name=nltest.exe OR Processes.original_file_name=nltestrk.exe) - (Processes.process=*/domain_trusts* OR Processes.process=*/all_trusts*) - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `nltest_domain_trust_discovery_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrators may use nltest for troubleshooting purposes, - otherwise, rarely used. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name=nltest.exe + OR + Processes.original_file_name=nltestrk.exe + ) + (Processes.process=*/domain_trusts* OR Processes.process=*/all_trusts*) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `nltest_domain_trust_discovery_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators may use nltest for troubleshooting purposes, otherwise, rarely used. references: -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1482/T1482.md -- https://malware.news/t/lets-learn-trickbot-implements-network-collector-module-leveraging-cmd-wmi-ldap/19104 -- https://attack.mitre.org/techniques/T1482/ -- https://owasp.org/www-pdf-archive/Red_Team_Operating_in_a_Modern_Environment.pdf -- https://ss64.com/nt/nltest.html -- https://redcanary.com/threat-detection-report/techniques/domain-trust-discovery/ -- https://thedfirreport.com/2020/10/08/ryuks-return/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1482/T1482.md + - https://malware.news/t/lets-learn-trickbot-implements-network-collector-module-leveraging-cmd-wmi-ldap/19104 + - https://attack.mitre.org/techniques/T1482/ + - https://owasp.org/www-pdf-archive/Red_Team_Operating_in_a_Modern_Environment.pdf + - https://ss64.com/nt/nltest.html + - https://redcanary.com/threat-detection-report/techniques/domain-trust-discovery/ + - https://thedfirreport.com/2020/10/08/ryuks-return/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Domain trust discovery execution on $dest$ - risk_objects: - - field: dest - type: system - score: 15 - threat_objects: [] + message: Domain trust discovery execution on $dest$ + risk_objects: + - field: dest + type: system + score: 15 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - - Qakbot - - Domain Trust Discovery - - Medusa Ransomware - - Cleo File Transfer Software - - Rhysida Ransomware - - IcedID - - Ryuk Ransomware - - Storm-0501 Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1482 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - Qakbot + - Domain Trust Discovery + - Medusa Ransomware + - Cleo File Transfer Software + - Rhysida Ransomware + - IcedID + - Ryuk Ransomware + - Storm-0501 Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1482 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/non_chrome_process_accessing_chrome_default_dir.yml b/detections/endpoint/non_chrome_process_accessing_chrome_default_dir.yml index 28d5d5db5f..10e1c733af 100644 --- a/detections/endpoint/non_chrome_process_accessing_chrome_default_dir.yml +++ b/detections/endpoint/non_chrome_process_accessing_chrome_default_dir.yml @@ -5,82 +5,60 @@ date: '2025-12-16' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects a non-Chrome process accessing files - in the Chrome user default folder. It leverages Windows Security Event logs, - specifically event code 4663, to identify unauthorized access attempts. This - activity is significant because the Chrome default folder contains sensitive - user data such as login credentials, browsing history, and cookies. If - confirmed malicious, this behavior could indicate an attempt to exfiltrate - sensitive information, often associated with RATs, trojans, and advanced - persistent threats like FIN7. Such access could lead to data theft and further - compromise of the affected system. +description: The following analytic detects a non-Chrome process accessing files in the Chrome user default folder. It leverages Windows Security Event logs, specifically event code 4663, to identify unauthorized access attempts. This activity is significant because the Chrome default folder contains sensitive user data such as login credentials, browsing history, and cookies. If confirmed malicious, this behavior could indicate an attempt to exfiltrate sensitive information, often associated with RATs, trojans, and advanced persistent threats like FIN7. Such access could lead to data theft and further compromise of the affected system. data_source: -- Windows Event Log Security 4663 -search: '`wineventlog_security` EventCode=4663 NOT (ProcessName IN ("*\\chrome.exe", - "*\\explorer.exe", "*sql*", "*\\dllhost.exe")) ObjectName="*\\Google\\Chrome\\User - Data\\Default*" | stats count min(_time) as firstTime max(_time) as lastTime by - ObjectName ObjectType ProcessName AccessMask EventCode dest | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `non_chrome_process_accessing_chrome_default_dir_filter`' -how_to_implement: To successfully implement this search, you must ingest Windows - Security Event logs and track event code 4663. For 4663, enable "Audit Object - Access" in Group Policy. Then check the two boxes listed for both "Success" - and "Failure." -known_false_positives: other browser not listed related to chrome may catch by - this rule. + - Windows Event Log Security 4663 +search: '`wineventlog_security` EventCode=4663 NOT (ProcessName IN ("*\\chrome.exe", "*\\explorer.exe", "*sql*", "*\\dllhost.exe")) ObjectName="*\\Google\\Chrome\\User Data\\Default*" | stats count min(_time) as firstTime max(_time) as lastTime by ObjectName ObjectType ProcessName AccessMask EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `non_chrome_process_accessing_chrome_default_dir_filter`' +how_to_implement: To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure." +known_false_positives: other browser not listed related to chrome may catch by this rule. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a non chrome browser process $ProcessName$ accessing $ObjectName$ - risk_objects: - - field: dest - type: system - score: 35 - threat_objects: [] + message: a non chrome browser process $ProcessName$ accessing $ObjectName$ + risk_objects: + - field: dest + type: system + score: 35 + threat_objects: [] tags: - analytic_story: - - StealC Stealer - - CISA AA23-347A - - Phemedrone Stealer - - DarkGate Malware - - NjRAT - - Malicious Inno Setup Loader - - Salt Typhoon - - Remcos - - Warzone RAT - - Quasar RAT - - 3CX Supply Chain Attack - - AgentTesla - - FIN7 - - SnappyBee - - RedLine Stealer - - Snake Keylogger - - China-Nexus Threat Activity - - Lokibot - asset_type: Endpoint - mitre_attack_id: - - T1555.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - StealC Stealer + - CISA AA23-347A + - Phemedrone Stealer + - DarkGate Malware + - NjRAT + - Malicious Inno Setup Loader + - Salt Typhoon + - Remcos + - Warzone RAT + - Quasar RAT + - 3CX Supply Chain Attack + - AgentTesla + - FIN7 + - SnappyBee + - RedLine Stealer + - Snake Keylogger + - China-Nexus Threat Activity + - Lokibot + asset_type: Endpoint + mitre_attack_id: + - T1555.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/non_chrome_process_accessing_chrome_default_dir/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/non_chrome_process_accessing_chrome_default_dir/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/non_firefox_process_access_firefox_profile_dir.yml b/detections/endpoint/non_firefox_process_access_firefox_profile_dir.yml index 62198d4eac..57aadae144 100644 --- a/detections/endpoint/non_firefox_process_access_firefox_profile_dir.yml +++ b/detections/endpoint/non_firefox_process_access_firefox_profile_dir.yml @@ -5,83 +5,62 @@ date: '2025-12-16' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects non-Firefox processes accessing the - Firefox profile directory, which contains sensitive user data such as login - credentials, browsing history, and cookies. It leverages Windows Security - Event logs, specifically event code 4663, to monitor access attempts. This - activity is significant because it may indicate attempts by malware, such as - RATs or trojans, to harvest user information. If confirmed malicious, this - behavior could lead to data exfiltration, unauthorized access to user - accounts, and further compromise of the affected system. +description: The following analytic detects non-Firefox processes accessing the Firefox profile directory, which contains sensitive user data such as login credentials, browsing history, and cookies. It leverages Windows Security Event logs, specifically event code 4663, to monitor access attempts. This activity is significant because it may indicate attempts by malware, such as RATs or trojans, to harvest user information. If confirmed malicious, this behavior could lead to data exfiltration, unauthorized access to user accounts, and further compromise of the affected system. data_source: -- Windows Event Log Security 4663 -search: '`wineventlog_security` EventCode=4663 NOT (ProcessName IN ("*\\firefox.exe", - "*\\explorer.exe", "*sql*")) ObjectName="*\\AppData\\Roaming\\Mozilla\\Firefox\\Profiles*" - | stats count min(_time) as firstTime max(_time) as lastTime by ObjectName ObjectType - ProcessName AccessMask EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `non_firefox_process_access_firefox_profile_dir_filter`' -how_to_implement: To successfully implement this search, you must ingest Windows - Security Event logs and track event code 4663. For 4663, enable "Audit Object - Access" in Group Policy. Then check the two boxes listed for both "Success" - and "Failure." -known_false_positives: other browser not listed related to firefox may catch by - this rule. + - Windows Event Log Security 4663 +search: '`wineventlog_security` EventCode=4663 NOT (ProcessName IN ("*\\firefox.exe", "*\\explorer.exe", "*sql*")) ObjectName="*\\AppData\\Roaming\\Mozilla\\Firefox\\Profiles*" | stats count min(_time) as firstTime max(_time) as lastTime by ObjectName ObjectType ProcessName AccessMask EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `non_firefox_process_access_firefox_profile_dir_filter`' +how_to_implement: To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure." +known_false_positives: other browser not listed related to firefox may catch by this rule. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a non firefox browser process $ProcessName$ accessing $ObjectName$ - risk_objects: - - field: dest - type: system - score: 35 - threat_objects: [] + message: a non firefox browser process $ProcessName$ accessing $ObjectName$ + risk_objects: + - field: dest + type: system + score: 35 + threat_objects: [] tags: - analytic_story: - - StealC Stealer - - DarkGate Malware - - CISA AA23-347A - - NjRAT - - Phemedrone Stealer - - Azorult - - Salt Typhoon - - Remcos - - Warzone RAT - - Quasar RAT - - 3CX Supply Chain Attack - - AgentTesla - - RedLine Stealer - - SnappyBee - - Malicious Inno Setup Loader - - FIN7 - - Snake Keylogger - - China-Nexus Threat Activity - - 0bj3ctivity Stealer - - Lokibot - asset_type: Endpoint - mitre_attack_id: - - T1555.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - StealC Stealer + - DarkGate Malware + - CISA AA23-347A + - NjRAT + - Phemedrone Stealer + - Azorult + - Salt Typhoon + - Remcos + - Warzone RAT + - Quasar RAT + - 3CX Supply Chain Attack + - AgentTesla + - RedLine Stealer + - SnappyBee + - Malicious Inno Setup Loader + - FIN7 + - Snake Keylogger + - China-Nexus Threat Activity + - 0bj3ctivity Stealer + - Lokibot + asset_type: Endpoint + mitre_attack_id: + - T1555.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/non_chrome_process_accessing_chrome_default_dir/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/non_chrome_process_accessing_chrome_default_dir/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/notepad_with_no_command_line_arguments.yml b/detections/endpoint/notepad_with_no_command_line_arguments.yml index 0eacb16c14..27506ef25f 100644 --- a/detections/endpoint/notepad_with_no_command_line_arguments.yml +++ b/detections/endpoint/notepad_with_no_command_line_arguments.yml @@ -1,86 +1,71 @@ name: Notepad with no Command Line Arguments id: 5adbc5f1-9a2f-41c1-a810-f37e015f8179 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk type: TTP status: production data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic identifies instances where Notepad.exe is launched - without any command line arguments, a behavior commonly associated with the SliverC2 - framework. This detection leverages process creation events from Endpoint Detection - and Response (EDR) agents, focusing on processes initiated by Notepad.exe within - a short time frame. This activity is significant as it may indicate an attempt to - inject malicious code into Notepad.exe, a known tactic for evading detection. If - confirmed malicious, this could allow an attacker to execute arbitrary code, potentially - leading to system compromise and unauthorized access. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=notepad.exe - AND Processes.action!="blocked" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex - process="(?i)(notepad\.exe.{0,4}$)" | `notepad_with_no_command_line_arguments_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present and filtering may need to occur - based on organization endpoint behavior. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic identifies instances where Notepad.exe is launched without any command line arguments, a behavior commonly associated with the SliverC2 framework. This detection leverages process creation events from Endpoint Detection and Response (EDR) agents, focusing on processes initiated by Notepad.exe within a short time frame. This activity is significant as it may indicate an attempt to inject malicious code into Notepad.exe, a known tactic for evading detection. If confirmed malicious, this could allow an attacker to execute arbitrary code, potentially leading to system compromise and unauthorized access. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=notepad.exe + AND + Processes.action!="blocked" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | regex process="(?i)(notepad\.exe.{0,4}$)" + | `notepad_with_no_command_line_arguments_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present and filtering may need to occur based on organization endpoint behavior. references: -- https://www.microsoft.com/en-us/security/blog/2022/08/24/looking-for-the-sliver-lining-hunting-for-emerging-command-and-control-frameworks/ -- https://www.cybereason.com/blog/sliver-c2-leveraged-by-many-threat-actors#Purple-Team-Section + - https://www.microsoft.com/en-us/security/blog/2022/08/24/looking-for-the-sliver-lining-hunting-for-emerging-command-and-control-frameworks/ + - https://www.cybereason.com/blog/sliver-c2-leveraged-by-many-threat-actors#Purple-Team-Section drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ with no command line arguments. - risk_objects: - - field: dest - type: system - score: 35 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ with no command line arguments. + risk_objects: + - field: dest + type: system + score: 35 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - BishopFox Sliver Adversary Emulation Framework - asset_type: Endpoint - mitre_attack_id: - - T1055 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - BishopFox Sliver Adversary Emulation Framework + asset_type: Endpoint + mitre_attack_id: + - T1055 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/sliver/notepad_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/sliver/notepad_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/ntdsutil_export_ntds.yml b/detections/endpoint/ntdsutil_export_ntds.yml index c1f7bffde2..a7c0ecfd29 100644 --- a/detections/endpoint/ntdsutil_export_ntds.yml +++ b/detections/endpoint/ntdsutil_export_ntds.yml @@ -1,89 +1,75 @@ name: Ntdsutil Export NTDS id: da63bc76-61ae-11eb-ae93-0242ac130002 -version: 8 -date: '2025-11-20' +version: 9 +date: '2026-02-25' author: Michael Haag, Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects the use of Ntdsutil to export the Active - Directory database (NTDS.dit). It leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process names and command-line arguments. This activity - is significant because exporting NTDS.dit can be a precursor to offline password - cracking, posing a severe security risk. If confirmed malicious, an attacker could - gain access to sensitive credentials, potentially leading to unauthorized access - and privilege escalation within the network. +description: The following analytic detects the use of Ntdsutil to export the Active Directory database (NTDS.dit). It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because exporting NTDS.dit can be a precursor to offline password cracking, posing a severe security risk. If confirmed malicious, an attacker could gain access to sensitive credentials, potentially leading to unauthorized access and privilege escalation within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=ntdsutil.exe - Processes.process=*ntds* Processes.process=*create*) by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `ntdsutil_export_ntds_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Highly possible Server Administrators will troubleshoot with - ntdsutil.exe, generating false positives. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name=ntdsutil.exe Processes.process=*ntds* Processes.process=*create* + ) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `ntdsutil_export_ntds_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Highly possible Server Administrators will troubleshoot with ntdsutil.exe, generating false positives. references: -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.003/T1003.003.md#atomic-test-3---dump-active-directory-database-with-ntdsutil -- https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc753343(v=ws.11) -- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf -- https://strontic.github.io/xcyclopedia/library/vss_ps.dll-97B15BDAE9777F454C9A6BA25E938DB3.html -- https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.003/T1003.003.md#atomic-test-3---dump-active-directory-database-with-ntdsutil + - https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc753343(v=ws.11) + - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf + - https://strontic.github.io/xcyclopedia/library/vss_ps.dll-97B15BDAE9777F454C9A6BA25E938DB3.html + - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Active Directory NTDS export on $dest$ - risk_objects: - - field: dest - type: system - score: 50 - threat_objects: [] + message: Active Directory NTDS export on $dest$ + risk_objects: + - field: dest + type: system + score: 50 + threat_objects: [] tags: - analytic_story: - - Credential Dumping - - HAFNIUM Group - - Living Off The Land - - Prestige Ransomware - - Volt Typhoon - - Rhysida Ransomware - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1003.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Credential Dumping + - HAFNIUM Group + - Living Off The Land + - Prestige Ransomware + - Volt Typhoon + - Rhysida Ransomware + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1003.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/outbound_network_connection_from_java_using_default_ports.yml b/detections/endpoint/outbound_network_connection_from_java_using_default_ports.yml index 0455936908..1e8eef9a09 100644 --- a/detections/endpoint/outbound_network_connection_from_java_using_default_ports.yml +++ b/detections/endpoint/outbound_network_connection_from_java_using_default_ports.yml @@ -1,91 +1,92 @@ name: Outbound Network Connection from Java Using Default Ports id: d2c14d28-5c47-11ec-9892-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Lou Stella, Splunk status: production type: TTP -description: "The following analytic detects outbound network connections from Java\ - \ processes to default ports used by LDAP and RMI protocols, which may indicate\ - \ exploitation of the CVE-2021-44228-Log4j vulnerability. This detection leverages\ - \ data from Endpoint Detection and Response (EDR) agents, focusing on process and\ - \ network traffic logs. Monitoring this activity is crucial as it can signify an\ - \ attacker\u2019s attempt to perform JNDI lookups and retrieve malicious payloads.\ - \ If confirmed malicious, this activity could lead to remote code execution and\ - \ further compromise of the affected server." +description: "The following analytic detects outbound network connections from Java processes to default ports used by LDAP and RMI protocols, which may indicate exploitation of the CVE-2021-44228-Log4j vulnerability. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and network traffic logs. Monitoring this activity is crucial as it can signify an attacker’s attempt to perform JNDI lookups and retrieve malicious payloads. If confirmed malicious, this activity could lead to remote code execution and further compromise of the affected server." data_source: -- Sysmon EventID 1 AND Sysmon EventID 3 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes - where (Processes.process_name="java.exe" OR Processes.process_name=javaw.exe OR - Processes.process_name=javaw.exe) by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | join process_id [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic - where (All_Traffic.dest_port= 389 OR All_Traffic.dest_port= 636 OR All_Traffic.dest_port - = 1389 OR All_Traffic.dest_port = 1099 ) by All_Traffic.action All_Traffic.app All_Traffic.bytes All_Traffic.bytes_in All_Traffic.bytes_out - All_Traffic.dest All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.dvc All_Traffic.protocol - All_Traffic.protocol_version All_Traffic.src All_Traffic.src_ip All_Traffic.src_port - All_Traffic.transport All_Traffic.user All_Traffic.vendor_product All_Traffic.direction All_Traffic.process_id - | `drop_dm_object_name(All_Traffic)` | rename dest as connection_to_CNC] - | table _time dest parent_process_name process_name process_path process dest_port - | `outbound_network_connection_from_java_using_default_ports_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Legitimate Java applications may use perform outbound connections - to these ports. Filter as needed + - Sysmon EventID 1 AND Sysmon EventID 3 +search: |- + | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="java.exe" + OR + Processes.process_name=javaw.exe + OR + Processes.process_name=javaw.exe + ) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | join process_id [ + | tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic + WHERE ( + All_Traffic.dest_port= 389 + OR + All_Traffic.dest_port= 636 + OR + All_Traffic.dest_port = 1389 + OR + All_Traffic.dest_port = 1099 + ) + BY All_Traffic.action All_Traffic.app All_Traffic.bytes + All_Traffic.bytes_in All_Traffic.bytes_out All_Traffic.dest + All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.dvc + All_Traffic.protocol All_Traffic.protocol_version All_Traffic.src + All_Traffic.src_ip All_Traffic.src_port All_Traffic.transport + All_Traffic.user All_Traffic.vendor_product All_Traffic.direction + All_Traffic.process_id + | `drop_dm_object_name(All_Traffic)` + | rename dest as connection_to_CNC] + | table _time dest parent_process_name process_name process_path process dest_port + | `outbound_network_connection_from_java_using_default_ports_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Legitimate Java applications may use perform outbound connections to these ports. Filter as needed references: -- https://www.lunasec.io/docs/blog/log4j-zero-day/ -- https://www.govcert.admin.ch/blog/zero-day-exploit-targeting-popular-java-library-log4j/ + - https://www.lunasec.io/docs/blog/log4j-zero-day/ + - https://www.govcert.admin.ch/blog/zero-day-exploit-targeting-popular-java-library-log4j/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Java performed outbound connections to default ports of LDAP or RMI on - $dest$ - risk_objects: - - field: dest - type: system - score: 54 - threat_objects: [] + message: Java performed outbound connections to default ports of LDAP or RMI on $dest$ + risk_objects: + - field: dest + type: system + score: 54 + threat_objects: [] tags: - analytic_story: - - Log4Shell CVE-2021-44228 - asset_type: Endpoint - cve: - - CVE-2021-44228 - mitre_attack_id: - - T1190 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Log4Shell CVE-2021-44228 + asset_type: Endpoint + cve: + - CVE-2021-44228 + mitre_attack_id: + - T1190 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/outbound_java/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/outbound_java/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/overwriting_accessibility_binaries.yml b/detections/endpoint/overwriting_accessibility_binaries.yml index 8197eaa903..8191ce9470 100644 --- a/detections/endpoint/overwriting_accessibility_binaries.yml +++ b/detections/endpoint/overwriting_accessibility_binaries.yml @@ -5,74 +5,48 @@ date: '2025-05-02' author: David Dorsey, Splunk status: production type: TTP -description: The following analytic detects modifications to Windows accessibility - binaries such as sethc.exe, utilman.exe, osk.exe, Magnify.exe, Narrator.exe, DisplaySwitch.exe, - and AtBroker.exe. It leverages filesystem activity data from the Endpoint.Filesystem - data model to identify changes to these specific files. This activity is significant - because adversaries can exploit these binaries to gain unauthorized access or execute - commands without logging in. If confirmed malicious, this could allow attackers - to bypass authentication mechanisms, potentially leading to unauthorized system - access and further compromise of the environment. +description: The following analytic detects modifications to Windows accessibility binaries such as sethc.exe, utilman.exe, osk.exe, Magnify.exe, Narrator.exe, DisplaySwitch.exe, and AtBroker.exe. It leverages filesystem activity data from the Endpoint.Filesystem data model to identify changes to these specific files. This activity is significant because adversaries can exploit these binaries to gain unauthorized access or execute commands without logging in. If confirmed malicious, this could allow attackers to bypass authentication mechanisms, potentially leading to unauthorized system access and further compromise of the environment. data_source: -- Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) - as file_path from datamodel=Endpoint.Filesystem where (Filesystem.file_path=*\\Windows\\System32\\sethc.exe* - OR Filesystem.file_path=*\\Windows\\System32\\utilman.exe* OR Filesystem.file_path=*\\Windows\\System32\\osk.exe* - OR Filesystem.file_path=*\\Windows\\System32\\Magnify.exe* OR Filesystem.file_path=*\\Windows\\System32\\Narrator.exe* - OR Filesystem.file_path=*\\Windows\\System32\\DisplaySwitch.exe* OR Filesystem.file_path=*\\Windows\\System32\\AtBroker.exe*) - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path - Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | - `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `overwriting_accessibility_binaries_filter`' -how_to_implement: You must be ingesting data that records the filesystem activity - from your hosts to populate the Endpoint file-system data model node. If you are - using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which - you want to collect data. -known_false_positives: Microsoft may provide updates to these binaries. Verify that - these changes do not correspond with your normal software update cycle. + - Sysmon EventID 11 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem where (Filesystem.file_path=*\\Windows\\System32\\sethc.exe* OR Filesystem.file_path=*\\Windows\\System32\\utilman.exe* OR Filesystem.file_path=*\\Windows\\System32\\osk.exe* OR Filesystem.file_path=*\\Windows\\System32\\Magnify.exe* OR Filesystem.file_path=*\\Windows\\System32\\Narrator.exe* OR Filesystem.file_path=*\\Windows\\System32\\DisplaySwitch.exe* OR Filesystem.file_path=*\\Windows\\System32\\AtBroker.exe*) by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `overwriting_accessibility_binaries_filter`' +how_to_implement: You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. +known_false_positives: Microsoft may provide updates to these binaries. Verify that these changes do not correspond with your normal software update cycle. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious file modification or replace in $file_path$ in host $dest$ - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: - - field: file_name - type: file_name + message: A suspicious file modification or replace in $file_path$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - Data Destruction - - Hermetic Wiper - - Windows Privilege Escalation - - Flax Typhoon - asset_type: Endpoint - mitre_attack_id: - - T1546.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Destruction + - Hermetic Wiper + - Windows Privilege Escalation + - Flax Typhoon + asset_type: Endpoint + mitre_attack_id: + - T1546.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.008/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.008/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/papercut_ng_suspicious_behavior_debug_log.yml b/detections/endpoint/papercut_ng_suspicious_behavior_debug_log.yml index c6fb6f7f8e..785701b53f 100644 --- a/detections/endpoint/papercut_ng_suspicious_behavior_debug_log.yml +++ b/detections/endpoint/papercut_ng_suspicious_behavior_debug_log.yml @@ -1,55 +1,49 @@ name: PaperCut NG Suspicious Behavior Debug Log id: 395163b8-689b-444b-86c7-9fe9ad624734 -version: 6 -date: '2025-06-10' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting data_source: [] -description: The following analytic identifies potential exploitation attempts on - a PaperCut NG server by analyzing its debug log data. It detects unauthorized or - suspicious access attempts from public IP addresses and searches for specific URIs - associated with known exploits. The detection leverages regex to parse unstructured - log data, focusing on admin login activities. This activity is significant as it - can indicate an active exploitation attempt on the server. If confirmed malicious, - attackers could gain unauthorized access, potentially leading to data breaches or - further compromise of the server. -search: '`papercutng` (loginType=Admin OR userName=admin) | eval uri_match=if(match(_raw, - "(?i)(\/app\?service=page\/SetupCompleted|\/app|\/app\?service=page\/PrinterList|\/app\?service=direct\/1\/PrinterList\/selectPrinter&sp=l1001|\/app\?service=direct\/1\/PrinterDetails\/printerOptionsTab\.tab)"), - "URI matches", null()) | eval ip_match=if(match(_raw, "(?i)((25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?))") - AND NOT match(_raw, "(?i)(10\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?))|(172\.(1[6-9]|2[0-9]|3[0-1])\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?))|(192\.168\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?))"), - "IP matches", null()) | where (isnotnull(uri_match) OR isnotnull(ip_match)) | stats - sparkline, count, values(uri_match) AS uri_match, values(ip_match) AS ip_match latest(_raw) - BY host, index, sourcetype | `papercut_ng_suspicious_behavior_debug_log_filter`' -how_to_implement: Debug logs must be enabled and shipped to Splunk in order to properly - identify behavior with this analytic. -known_false_positives: False positives may be present, as this is based on the admin - user accessing the Papercut NG instance from a public IP address. Filter as needed. +description: The following analytic identifies potential exploitation attempts on a PaperCut NG server by analyzing its debug log data. It detects unauthorized or suspicious access attempts from public IP addresses and searches for specific URIs associated with known exploits. The detection leverages regex to parse unstructured log data, focusing on admin login activities. This activity is significant as it can indicate an active exploitation attempt on the server. If confirmed malicious, attackers could gain unauthorized access, potentially leading to data breaches or further compromise of the server. +search: |- + `papercutng` + (loginType=Admin OR userName=admin) + + | eval uri_match=if(match(_raw, "(?i)(\/app\?service=page\/SetupCompleted|\/app|\/app\?service=page\/PrinterList|\/app\?service=direct\/1\/PrinterList\/selectPrinter&sp=l1001|\/app\?service=direct\/1\/PrinterDetails\/printerOptionsTab\.tab)"), "URI matches", null()) + + | eval ip_match=if(match(_raw, "(?i)((25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?))") AND NOT match(_raw, "(?i)(10\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?))|(172\.(1[6-9]|2[0-9]|3[0-1])\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?))|(192\.168\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?))"), "IP matches", null()) + + | where (isnotnull(uri_match) OR isnotnull(ip_match)) + | stats sparkline, count, values(uri_match) AS uri_match, values(ip_match) AS ip_match latest(_raw) BY host, index, sourcetype + | `papercut_ng_suspicious_behavior_debug_log_filter` +how_to_implement: Debug logs must be enabled and shipped to Splunk in order to properly identify behavior with this analytic. +known_false_positives: False positives may be present, as this is based on the admin user accessing the Papercut NG instance from a public IP address. Filter as needed. references: -- https://www.papercut.com/kb/Main/HowToCollectApplicationServerDebugLogs -- https://github.com/inodee/threathunting-spl/blob/master/hunt-queries/HAFNIUM.md -- https://www.cisa.gov/news-events/alerts/2023/05/11/cisa-and-fbi-release-joint-advisory-response-active-exploitation-papercut-vulnerability -- https://www.papercut.com/kb/Main/PO-1216-and-PO-1219 -- https://www.horizon3.ai/papercut-cve-2023-27350-deep-dive-and-indicators-of-compromise/ -- https://www.bleepingcomputer.com/news/security/hackers-actively-exploit-critical-rce-bug-in-papercut-servers/ -- https://www.huntress.com/blog/critical-vulnerabilities-in-papercut-print-management-software + - https://www.papercut.com/kb/Main/HowToCollectApplicationServerDebugLogs + - https://github.com/inodee/threathunting-spl/blob/master/hunt-queries/HAFNIUM.md + - https://www.cisa.gov/news-events/alerts/2023/05/11/cisa-and-fbi-release-joint-advisory-response-active-exploitation-papercut-vulnerability + - https://www.papercut.com/kb/Main/PO-1216-and-PO-1219 + - https://www.horizon3.ai/papercut-cve-2023-27350-deep-dive-and-indicators-of-compromise/ + - https://www.bleepingcomputer.com/news/security/hackers-actively-exploit-critical-rce-bug-in-papercut-servers/ + - https://www.huntress.com/blog/critical-vulnerabilities-in-papercut-print-management-software tags: - analytic_story: - - PaperCut MF NG Vulnerability - asset_type: Web Server - atomic_guid: [] - mitre_attack_id: - - T1190 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - PaperCut MF NG Vulnerability + asset_type: Web Server + atomic_guid: [] + mitre_attack_id: + - T1190 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/papercut/server.log - source: papercutng - sourcetype: papercutng + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/papercut/server.log + source: papercutng + sourcetype: papercutng diff --git a/detections/endpoint/permission_modification_using_takeown_app.yml b/detections/endpoint/permission_modification_using_takeown_app.yml index a4d6fd39a8..9a53d5feea 100644 --- a/detections/endpoint/permission_modification_using_takeown_app.yml +++ b/detections/endpoint/permission_modification_using_takeown_app.yml @@ -1,85 +1,68 @@ name: Permission Modification using Takeown App id: fa7ca5c6-c9d8-11eb-bce9-acde48001122 -version: 8 -date: '2025-10-14' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the modification of file or directory - permissions using the takeown.exe Windows application. It leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process execution logs that include - process GUID, process name, and command-line details. This activity is significant - because it is a common technique used by ransomware to take ownership of files or - folders for encryption or deletion. If confirmed malicious, this could lead to unauthorized - access, data encryption, or data destruction, severely impacting the integrity and - availability of critical data. +description: The following analytic detects the modification of file or directory permissions using the takeown.exe Windows application. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include process GUID, process name, and command-line details. This activity is significant because it is a common technique used by ransomware to take ownership of files or folders for encryption or deletion. If confirmed malicious, this could lead to unauthorized access, data encryption, or data destruction, severely impacting the integrity and availability of critical data. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "takeown.exe" - Processes.process = "*/f*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `permission_modification_using_takeown_app_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: takeown.exe is a normal windows application that may used by - network operator. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "takeown.exe" Processes.process = "*/f*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `permission_modification_using_takeown_app_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: takeown.exe is a normal windows application that may used by network operator. references: -- https://research.nccgroup.com/2020/06/23/wastedlocker-a-new-ransomware-variant-developed-by-the-evil-corp-group/ + - https://research.nccgroup.com/2020/06/23/wastedlocker-a-new-ransomware-variant-developed-by-the-evil-corp-group/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious of execution of $process_name$ with process id $process_id$ - and commandline $process$ to modify permission of directory or files in host $dest$ - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: - - field: process_name - type: process_name + message: A suspicious of execution of $process_name$ with process id $process_id$ and commandline $process$ to modify permission of directory or files in host $dest$ + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Sandworm Tools - - Ransomware - - Crypto Stealer - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1222 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sandworm Tools + - Ransomware + - Crypto Stealer + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1222 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/petitpotam_network_share_access_request.yml b/detections/endpoint/petitpotam_network_share_access_request.yml index cc3cac00b3..6f6d931b62 100644 --- a/detections/endpoint/petitpotam_network_share_access_request.yml +++ b/detections/endpoint/petitpotam_network_share_access_request.yml @@ -1,71 +1,59 @@ name: PetitPotam Network Share Access Request id: 95b8061a-0a67-11ec-85ec-acde48001122 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects network share access requests indicative - of the PetitPotam attack (CVE-2021-36942). It leverages Windows Event Code 5145, - which logs attempts to access network share objects. This detection is significant - as PetitPotam can coerce authentication from domain controllers, potentially leading - to unauthorized access. If confirmed malicious, this activity could allow attackers - to escalate privileges or move laterally within the network, posing a severe security - risk. Ensure Event Code 5145 is enabled via Group Policy to utilize this analytic - effectively. +description: The following analytic detects network share access requests indicative of the PetitPotam attack (CVE-2021-36942). It leverages Windows Event Code 5145, which logs attempts to access network share objects. This detection is significant as PetitPotam can coerce authentication from domain controllers, potentially leading to unauthorized access. If confirmed malicious, this activity could allow attackers to escalate privileges or move laterally within the network, posing a severe security risk. Ensure Event Code 5145 is enabled via Group Policy to utilize this analytic effectively. data_source: -- Windows Event Log Security 5145 -search: '`wineventlog_security` SubjectUserName="ANONYMOUS LOGON" EventCode=5145 RelativeTargetName=lsarpc - | stats count min(_time) as firstTime max(_time) as lastTime by dest, SubjectUserSid, - ShareName, src, AccessMask, AccessReason | `security_content_ctime(firstTime)` | - `security_content_ctime(lastTime)` | `petitpotam_network_share_access_request_filter`' -how_to_implement: Windows Event Code 5145 is required to utilize this analytic and - it may not be enabled in most environments. -known_false_positives: False positives have been limited when the Anonymous Logon - is used for Account Name. + - Windows Event Log Security 5145 +search: |- + `wineventlog_security` SubjectUserName="ANONYMOUS LOGON" EventCode=5145 RelativeTargetName=lsarpc + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest, SubjectUserSid, ShareName, + src, AccessMask, AccessReason + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `petitpotam_network_share_access_request_filter` +how_to_implement: Windows Event Code 5145 is required to utilize this analytic and it may not be enabled in most environments. +known_false_positives: False positives have been limited when the Anonymous Logon is used for Account Name. references: -- https://attack.mitre.org/techniques/T1187/ -- https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventid=5145 -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-5145 + - https://attack.mitre.org/techniques/T1187/ + - https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventid=5145 + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-5145 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A remote host is enumerating a $dest$ to identify permissions. This is - a precursor event to CVE-2021-36942, PetitPotam. - risk_objects: - - field: dest - type: system - score: 56 - threat_objects: [] + message: A remote host is enumerating a $dest$ to identify permissions. This is a precursor event to CVE-2021-36942, PetitPotam. + risk_objects: + - field: dest + type: system + score: 56 + threat_objects: [] tags: - analytic_story: - - PetitPotam NTLM Relay on Active Directory Certificate Services - asset_type: Endpoint - cve: - - CVE-2021-36942 - mitre_attack_id: - - T1187 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - PetitPotam NTLM Relay on Active Directory Certificate Services + asset_type: Endpoint + cve: + - CVE-2021-36942 + mitre_attack_id: + - T1187 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1187/petitpotam/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1187/petitpotam/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/petitpotam_suspicious_kerberos_tgt_request.yml b/detections/endpoint/petitpotam_suspicious_kerberos_tgt_request.yml index 3889738e3a..f95c45e718 100644 --- a/detections/endpoint/petitpotam_suspicious_kerberos_tgt_request.yml +++ b/detections/endpoint/petitpotam_suspicious_kerberos_tgt_request.yml @@ -1,71 +1,59 @@ name: PetitPotam Suspicious Kerberos TGT Request id: e3ef244e-0a67-11ec-abf2-acde48001122 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects a suspicious Kerberos Ticket Granting - Ticket (TGT) request, identified by Event Code 4768. This detection leverages Windows - Security Event Logs to identify TGT requests with unusual fields, which may indicate - the use of tools like Rubeus following the exploitation of CVE-2021-36942 (PetitPotam). - This activity is significant as it can signal an attacker leveraging a compromised - certificate to request Kerberos tickets, potentially leading to unauthorized access. - If confirmed malicious, this could allow attackers to escalate privileges and persist - within the environment, posing a severe security risk. +description: The following analytic detects a suspicious Kerberos Ticket Granting Ticket (TGT) request, identified by Event Code 4768. This detection leverages Windows Security Event Logs to identify TGT requests with unusual fields, which may indicate the use of tools like Rubeus following the exploitation of CVE-2021-36942 (PetitPotam). This activity is significant as it can signal an attacker leveraging a compromised certificate to request Kerberos tickets, potentially leading to unauthorized access. If confirmed malicious, this could allow attackers to escalate privileges and persist within the environment, posing a severe security risk. data_source: -- Windows Event Log Security 4768 -search: '`wineventlog_security` EventCode=4768 src!="::1" TargetUserName=*$ CertThumbprint!="" - | stats count min(_time) as firstTime max(_time) as lastTime by dest, TargetUserName, - src, action | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `petitpotam_suspicious_kerberos_tgt_request_filter`' -how_to_implement: The following analytic requires Event Code 4768. Ensure that it - is logging no Domain Controllers and appearing in Splunk. -known_false_positives: False positives are possible if the environment is using certificates - for authentication. + - Windows Event Log Security 4768 +search: |- + `wineventlog_security` EventCode=4768 src!="::1" TargetUserName=*$ CertThumbprint!="" + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest, TargetUserName, src, + action + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `petitpotam_suspicious_kerberos_tgt_request_filter` +how_to_implement: The following analytic requires Event Code 4768. Ensure that it is logging no Domain Controllers and appearing in Splunk. +known_false_positives: False positives are possible if the environment is using certificates for authentication. references: -- https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventid=4768 -- https://isc.sans.edu/forums/diary/Active+Directory+Certificate+Services+ADCS+PKI+domain+admin+vulnerability/27668/ + - https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventid=4768 + - https://isc.sans.edu/forums/diary/Active+Directory+Certificate+Services+ADCS+PKI+domain+admin+vulnerability/27668/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Kerberos TGT was requested in a non-standard manner against $dest$, potentially - related to CVE-2021-36942, PetitPotam. - risk_objects: - - field: dest - type: system - score: 56 - threat_objects: [] + message: A Kerberos TGT was requested in a non-standard manner against $dest$, potentially related to CVE-2021-36942, PetitPotam. + risk_objects: + - field: dest + type: system + score: 56 + threat_objects: [] tags: - analytic_story: - - PetitPotam NTLM Relay on Active Directory Certificate Services - - Active Directory Kerberos Attacks - asset_type: Endpoint - cve: - - CVE-2021-36942 - mitre_attack_id: - - T1003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - PetitPotam NTLM Relay on Active Directory Certificate Services + - Active Directory Kerberos Attacks + asset_type: Endpoint + cve: + - CVE-2021-36942 + mitre_attack_id: + - T1003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1187/petitpotam/windows-xml-1.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1187/petitpotam/windows-xml-1.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/ping_sleep_batch_command.yml b/detections/endpoint/ping_sleep_batch_command.yml index db340f5b20..f10154e37f 100644 --- a/detections/endpoint/ping_sleep_batch_command.yml +++ b/detections/endpoint/ping_sleep_batch_command.yml @@ -1,110 +1,89 @@ name: Ping Sleep Batch Command id: ce058d6c-79f2-11ec-b476-acde48001122 -version: 11 -date: '2025-07-16' +version: 12 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: 'The following analytic identifies the execution of ping sleep batch - commands. +description: 'The following analytic identifies the execution of ping sleep batch commands. - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on + It leverages data from Endpoint Detection and Response (EDR) agents, focusing on - process and parent process command-line details. This activity is significant as + process and parent process command-line details. This activity is significant as - it indicates an attempt to delay malicious code execution, potentially evading detection + it indicates an attempt to delay malicious code execution, potentially evading detection - or sandbox analysis. If confirmed malicious, this technique allows attackers to + or sandbox analysis. If confirmed malicious, this technique allows attackers to - bypass security measures, making it harder to detect and analyze their activities, + bypass security measures, making it harder to detect and analyze their activities, - thereby increasing the risk of prolonged unauthorized access and potential data + thereby increasing the risk of prolonged unauthorized access and potential data - exfiltration. + exfiltration. - ' +' data_source: -- Sysmon EventID 1 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - ( - Processes.parent_process= "*ping*" - Processes.parent_process = *-n* - Processes.parent_process="* Nul*" - Processes.parent_process IN ("*>*", "*>*") - Processes.parent_process IN ("*&*", "*& *") - ) - OR ( - Processes.process = "*ping*" - Processes.process = *-n* - Processes.process="* Nul*" - Processes.process IN ("*>*", "*>*") - Processes.process IN ("*&*", "*& *") - ) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` - | `ping_sleep_batch_command_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator may execute this command. - Please update the filter macros to remove false positives. + - Sysmon EventID 1 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.parent_process= "*ping*" Processes.parent_process = *-n* Processes.parent_process="* Nul*" Processes.parent_process IN ("*>*", "*>*") Processes.parent_process IN ("*&*", "*& *") + ) + OR ( Processes.process = "*ping*" Processes.process = *-n* Processes.process="* Nul*" Processes.process IN ("*>*", "*>*") Processes.process IN ("*&*", "*& *") ) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `ping_sleep_batch_command_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator may execute this command. Please update the filter macros to remove false positives. references: -- https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: suspicious $process$ commandline run on $dest$ - risk_objects: - - field: user - type: user - score: 36 - - field: dest - type: system - score: 36 - threat_objects: [] + message: suspicious $process$ commandline run on $dest$ + risk_objects: + - field: user + type: user + score: 36 + - field: dest + type: system + score: 36 + threat_objects: [] tags: - analytic_story: - - Warzone RAT - - Quasar RAT - - Data Destruction - - Meduza Stealer - - WhisperGate - - BlackByte Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1497.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Warzone RAT + - Quasar RAT + - Data Destruction + - Meduza Stealer + - WhisperGate + - BlackByte Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1497.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497.003/ping_sleep/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497.003/ping_sleep/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/possible_browser_pass_view_parameter.yml b/detections/endpoint/possible_browser_pass_view_parameter.yml index a4f64e4dcd..d33db8f962 100644 --- a/detections/endpoint/possible_browser_pass_view_parameter.yml +++ b/detections/endpoint/possible_browser_pass_view_parameter.yml @@ -5,58 +5,31 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic identifies processes with command-line parameters - associated with web browser credential dumping tools, specifically targeting behaviors - used by Remcos RAT malware. It leverages data from Endpoint Detection and Response - (EDR) agents, focusing on command-line executions and specific file paths. This - activity is significant as it indicates potential credential theft, a common tactic - in broader cyber-espionage campaigns. If confirmed malicious, attackers could gain - unauthorized access to sensitive web credentials, leading to further system compromise - and data breaches. +description: The following analytic identifies processes with command-line parameters associated with web browser credential dumping tools, specifically targeting behaviors used by Remcos RAT malware. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and specific file paths. This activity is significant as it indicates potential credential theft, a common tactic in broader cyber-espionage campaigns. If confirmed malicious, attackers could gain unauthorized access to sensitive web credentials, leading to further system compromise and data breaches. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*/stext - *", "*/shtml *", "*/LoadPasswordsIE*", "*/LoadPasswordsFirefox*", "*/LoadPasswordsChrome*", - "*/LoadPasswordsOpera*", "*/LoadPasswordsSafari*" , "*/UseOperaPasswordFile*", "*/OperaPasswordFile*","*/stab*", - "*/scomma*", "*/stabular*", "*/shtml*", "*/sverhtml*", "*/sxml*", "*/skeepass*" - ) AND Processes.process IN ("*\\temp\\*", "*\\users\\public\\*", "*\\programdata\\*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `possible_browser_pass_view_parameter_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*/stext *", "*/shtml *", "*/LoadPasswordsIE*", "*/LoadPasswordsFirefox*", "*/LoadPasswordsChrome*", "*/LoadPasswordsOpera*", "*/LoadPasswordsSafari*" , "*/UseOperaPasswordFile*", "*/OperaPasswordFile*","*/stab*", "*/scomma*", "*/stabular*", "*/shtml*", "*/sverhtml*", "*/sxml*", "*/skeepass*" ) AND Processes.process IN ("*\\temp\\*", "*\\users\\public\\*", "*\\programdata\\*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `possible_browser_pass_view_parameter_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positive is quite limited. Filter is needed references: -- https://www.nirsoft.net/utils/web_browser_password.html -- https://app.any.run/tasks/df0baf9f-8baf-4c32-a452-16562ecb19be/ + - https://www.nirsoft.net/utils/web_browser_password.html + - https://app.any.run/tasks/df0baf9f-8baf-4c32-a452-16562ecb19be/ tags: - analytic_story: - - Remcos - asset_type: Endpoint - mitre_attack_id: - - T1555.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Remcos + asset_type: Endpoint + mitre_attack_id: + - T1555.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/web_browser_pass_view/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/web_browser_pass_view/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/possible_lateral_movement_powershell_spawn.yml b/detections/endpoint/possible_lateral_movement_powershell_spawn.yml index d4536bdfea..89b71d614a 100644 --- a/detections/endpoint/possible_lateral_movement_powershell_spawn.yml +++ b/detections/endpoint/possible_lateral_movement_powershell_spawn.yml @@ -5,97 +5,62 @@ date: '2025-10-24' author: Mauricio Velazco, Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the spawning of a PowerShell process as - a child or grandchild of commonly abused processes like services.exe, wmiprvse.exe, - svchost.exe, wsmprovhost.exe, and mmc.exe. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process and parent process names, as well - as command-line executions. This activity is significant as it often indicates lateral - movement or remote code execution attempts by adversaries. If confirmed malicious, - this behavior could allow attackers to execute code remotely, escalate privileges, - or persist within the environment. +description: The following analytic detects the spawning of a PowerShell process as a child or grandchild of commonly abused processes like services.exe, wmiprvse.exe, svchost.exe, wsmprovhost.exe, and mmc.exe. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process names, as well as command-line executions. This activity is significant as it often indicates lateral movement or remote code execution attempts by adversaries. If confirmed malicious, this behavior could allow attackers to execute code remotely, escalate privileges, or persist within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=wmiprvse.exe - OR Processes.parent_process_name=services.exe OR Processes.parent_process_name=svchost.exe - OR Processes.parent_process_name=wsmprovhost.exe OR Processes.parent_process_name=mmc.exe) - (Processes.process_name=powershell.exe OR (Processes.process_name=cmd.exe AND Processes.process=*powershell.exe*) - OR Processes.process_name=pwsh.exe OR (Processes.process_name=cmd.exe AND Processes.process=*pwsh.exe*)) - NOT (Processes.process IN ("*c:\\windows\\ccm\\*")) by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `possible_lateral_movement_powershell_spawn_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Legitimate applications may spawn PowerShell as a child process - of the the identified processes. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=wmiprvse.exe OR Processes.parent_process_name=services.exe OR Processes.parent_process_name=svchost.exe OR Processes.parent_process_name=wsmprovhost.exe OR Processes.parent_process_name=mmc.exe) (Processes.process_name=powershell.exe OR (Processes.process_name=cmd.exe AND Processes.process=*powershell.exe*) OR Processes.process_name=pwsh.exe OR (Processes.process_name=cmd.exe AND Processes.process=*pwsh.exe*)) NOT (Processes.process IN ("*c:\\windows\\ccm\\*")) by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `possible_lateral_movement_powershell_spawn_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Legitimate applications may spawn PowerShell as a child process of the the identified processes. Filter as needed. references: -- https://attack.mitre.org/techniques/T1021/003/ -- https://attack.mitre.org/techniques/T1021/006/ -- https://attack.mitre.org/techniques/T1047/ -- https://attack.mitre.org/techniques/T1053/005/ -- https://attack.mitre.org/techniques/T1543/003/ + - https://attack.mitre.org/techniques/T1021/003/ + - https://attack.mitre.org/techniques/T1021/006/ + - https://attack.mitre.org/techniques/T1047/ + - https://attack.mitre.org/techniques/T1053/005/ + - https://attack.mitre.org/techniques/T1543/003/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A PowerShell process was spawned as a child process of typically abused - processes on $dest$ - risk_objects: - - field: dest - type: system - score: 45 - threat_objects: [] + message: A PowerShell process was spawned as a child process of typically abused processes on $dest$ + risk_objects: + - field: dest + type: system + score: 45 + threat_objects: [] tags: - analytic_story: - - Active Directory Lateral Movement - - Malicious PowerShell - - Hermetic Wiper - - Data Destruction - - Scheduled Tasks - - CISA AA24-241A - - Microsoft WSUS CVE-2025-59287 - asset_type: Endpoint - mitre_attack_id: - - T1021.003 - - T1021.006 - - T1047 - - T1053.005 - - T1059.001 - - T1218.014 - - T1543.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + - Malicious PowerShell + - Hermetic Wiper + - Data Destruction + - Scheduled Tasks + - CISA AA24-241A + - Microsoft WSUS CVE-2025-59287 + asset_type: Endpoint + mitre_attack_id: + - T1021.003 + - T1021.006 + - T1047 + - T1053.005 + - T1059.001 + - T1218.014 + - T1543.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/lateral_movement_powershell/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/lateral_movement_powershell/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/potential_password_in_username.yml b/detections/endpoint/potential_password_in_username.yml index 13890de000..a37a5d742f 100644 --- a/detections/endpoint/potential_password_in_username.yml +++ b/detections/endpoint/potential_password_in_username.yml @@ -1,60 +1,54 @@ name: Potential password in username id: 5ced34b4-ab32-4bb0-8f22-3b8f186f0a38 -version: 6 -date: '2025-06-26' +version: 7 +date: '2026-02-25' author: Mikael Bjerkeland, Splunk status: production type: Hunting -description: The following analytic identifies instances where users may have mistakenly - entered their passwords in the username field during authentication attempts. It - detects this by analyzing failed authentication events with usernames longer than - 7 characters and high Shannon entropy, followed by a successful authentication from - the same source to the same destination. This activity is significant as it can - indicate potential security risks, such as password exposure. If confirmed malicious, - attackers could exploit this to gain unauthorized access, leading to potential data - breaches or further compromise of the system. +description: The following analytic identifies instances where users may have mistakenly entered their passwords in the username field during authentication attempts. It detects this by analyzing failed authentication events with usernames longer than 7 characters and high Shannon entropy, followed by a successful authentication from the same source to the same destination. This activity is significant as it can indicate potential security risks, such as password exposure. If confirmed malicious, attackers could exploit this to gain unauthorized access, leading to potential data breaches or further compromise of the system. data_source: -- Linux Secure -search: '| tstats `security_content_summariesonly` earliest(_time) AS starttime latest(_time) - AS endtime latest(sourcetype) AS sourcetype values(Authentication.src) AS src values(Authentication.dest) - AS dest count FROM datamodel=Authentication WHERE nodename=Authentication.Failed_Authentication - BY "Authentication.user" | `drop_dm_object_name(Authentication)` | lookup ut_shannon_lookup - word AS user | where ut_shannon>3 AND len(user)>=8 AND mvcount(src) == 1 | sort - count, - ut_shannon | eval incorrect_cred=user | eval endtime=endtime+1000 | map - maxsearches=70 search="| tstats `security_content_summariesonly` earliest(_time) - AS starttime latest(_time) AS endtime latest(sourcetype) AS sourcetype values(Authentication.src) - AS src values(Authentication.dest) AS dest count FROM datamodel=Authentication WHERE - nodename=Authentication.Successful_Authentication Authentication.src=\"$src$\" Authentication.dest=\"$dest$\" - sourcetype IN (\"$sourcetype$\") earliest=\"$starttime$\" latest=\"$endtime$\" BY - \"Authentication.user\" | `drop_dm_object_name(\"Authentication\")` | `potential_password_in_username_false_positive_reduction` - | eval incorrect_cred=\"$incorrect_cred$\" | eval ut_shannon=\"$ut_shannon$\" | - sort count" | where user!=incorrect_cred | outlier action=RM count | `potential_password_in_username_filter`' -how_to_implement: To successfully implement this search, you need to have relevant - authentication logs mapped to the Authentication data model. You also need to have - the Splunk TA URL Toolbox (https://splunkbase.splunk.com/app/2734/) installed. The - detection must run with a time interval shorter than endtime+1000. -known_false_positives: Valid usernames with high entropy or source/destination system - pairs with multiple authenticating users will make it difficult to identify the - real user authenticating. + - Linux Secure +search: |- + | tstats `security_content_summariesonly` earliest(_time) AS starttime latest(_time) AS endtime latest(sourcetype) AS sourcetype values(Authentication.src) AS src values(Authentication.dest) AS dest count FROM datamodel=Authentication + WHERE nodename=Authentication.Failed_Authentication + BY "Authentication.user" + | `drop_dm_object_name(Authentication)` + | lookup ut_shannon_lookup word AS user + | where ut_shannon>3 AND len(user)>=8 AND mvcount(src) == 1 + | sort count, - ut_shannon + | eval incorrect_cred=user + | eval endtime=endtime+1000 + | map maxsearches=70 search=" + | tstats `security_content_summariesonly` earliest(_time) AS starttime latest(_time) AS endtime latest(sourcetype) AS sourcetype values(Authentication.src) AS src values(Authentication.dest) AS dest count FROM datamodel=Authentication + WHERE nodename=Authentication.Successful_Authentication Authentication.src=\"$src$\" Authentication.dest=\"$dest$\" sourcetype IN (\"$sourcetype$\") earliest=\"$starttime$\" latest=\"$endtime$\" BY \"Authentication.user\" + | `drop_dm_object_name(\"Authentication\")` + | `potential_password_in_username_false_positive_reduction` + | eval incorrect_cred=\"$incorrect_cred$\" + | eval ut_shannon=\"$ut_shannon$\" + | sort count" + | where user!=incorrect_cred + | outlier action=RM count + | `potential_password_in_username_filter` +how_to_implement: To successfully implement this search, you need to have relevant authentication logs mapped to the Authentication data model. You also need to have the Splunk TA URL Toolbox (https://splunkbase.splunk.com/app/2734/) installed. The detection must run with a time interval shorter than endtime+1000. +known_false_positives: Valid usernames with high entropy or source/destination system pairs with multiple authenticating users will make it difficult to identify the real user authenticating. references: -- https://medium.com/@markmotig/search-for-passwords-accidentally-typed-into-the-username-field-975f1a389928 + - https://medium.com/@markmotig/search-for-passwords-accidentally-typed-into-the-username-field-975f1a389928 tags: - analytic_story: - - Credential Dumping - - Insider Threat - asset_type: Endpoint - mitre_attack_id: - - T1078.003 - - T1552.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - Credential Dumping + - Insider Threat + asset_type: Endpoint + mitre_attack_id: + - T1078.003 + - T1552.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.001/password_in_username/linux_secure.log - source: /var/log/secure - sourcetype: linux_secure + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.001/password_in_username/linux_secure.log + source: /var/log/secure + sourcetype: linux_secure diff --git a/detections/endpoint/potential_system_network_configuration_discovery_activity.yml b/detections/endpoint/potential_system_network_configuration_discovery_activity.yml index 67a41aa707..85544632c2 100644 --- a/detections/endpoint/potential_system_network_configuration_discovery_activity.yml +++ b/detections/endpoint/potential_system_network_configuration_discovery_activity.yml @@ -1,120 +1,95 @@ name: Potential System Network Configuration Discovery Activity id: 3f0b95e3-3195-46ac-bea3-84fb59e7fac5 -version: 5 -date: '2025-12-17' +version: 6 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: Anomaly -description: The following analytic identifies the rapid execution of processes used - for system network configuration discovery on an endpoint. It leverages data from - Endpoint Detection and Response (EDR) agents, focusing on process GUIDs, names, - parent processes, and command-line executions. This activity can be significant - as it may indicate an attacker attempting to map the network, which is a common - precursor to lateral movement or further exploitation. If confirmed malicious, this - behavior could allow an attacker to gain insights into the network topology, identify - critical systems, and plan subsequent attacks, potentially leading to data exfiltration - or system compromise. +description: The following analytic identifies the rapid execution of processes used for system network configuration discovery on an endpoint. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process GUIDs, names, parent processes, and command-line executions. This activity can be significant as it may indicate an attacker attempting to map the network, which is a common precursor to lateral movement or further exploitation. If confirmed malicious, this behavior could allow an attacker to gain insights into the network topology, identify critical systems, and plan subsequent attacks, potentially leading to data exfiltration or system compromise. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` - count values(Processes.process) as process - values(Processes.parent_process) as parent_process - min(_time) as firstTime - max(_time) as lastTime - from datamodel=Endpoint.Processes where - - NOT Processes.user IN ("","unknown") - - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product _time - - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `drop_dm_object_name(Processes)` - | search process_name IN ( - "arp.exe", - "dsquery.exe", - "hostname.exe", - "ipconfig.exe", - "nbstat.exe", - "net.exe", - "net1.exe", - "nltest.exe", - "netsh.exe", - "nslookup.exe", - "ping.exe", - "quser.exe", - "qwinsta.exe", - "telnet.exe", - "tracert.exe", - ) - | transaction dest connected=false maxpause=5m - | where eventcount>=5 - | `potential_system_network_configuration_discovery_activity_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: It is uncommon for normal users to execute a series of commands - used for network discovery. System administrators often use scripts to execute these - commands. These can generate false positives. + | tstats `security_content_summariesonly` + count values(Processes.process) as process + values(Processes.parent_process) as parent_process + min(_time) as firstTime + max(_time) as lastTime + from datamodel=Endpoint.Processes where + + NOT Processes.user IN ("","unknown") + + by Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product _time + + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `drop_dm_object_name(Processes)` + | search process_name IN ( + "arp.exe", + "dsquery.exe", + "hostname.exe", + "ipconfig.exe", + "nbstat.exe", + "net.exe", + "net1.exe", + "nltest.exe", + "netsh.exe", + "nslookup.exe", + "ping.exe", + "quser.exe", + "qwinsta.exe", + "telnet.exe", + "tracert.exe", + ) + | transaction dest connected=false maxpause=5m + | where eventcount>=5 + | `potential_system_network_configuration_discovery_activity_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: It is uncommon for normal users to execute a series of commands used for network discovery. System administrators often use scripts to execute these commands. These can generate false positives. references: [] drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning multiple $process_name$ was - identified on endpoint $dest$ by user $user$ typically not a normal behavior of - the process. - risk_objects: - - field: user - type: user - score: 32 - - field: dest - type: system - score: 32 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning multiple $process_name$ was identified on endpoint $dest$ by user $user$ typically not a normal behavior of the process. + risk_objects: + - field: user + type: user + score: 32 + - field: dest + type: system + score: 32 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Unusual Processes - asset_type: Endpoint - mitre_attack_id: - - T1016 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Unusual Processes + asset_type: Endpoint + mitre_attack_id: + - T1016 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1016/discovery_commands/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1016/discovery_commands/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/potential_telegram_api_request_via_commandline.yml b/detections/endpoint/potential_telegram_api_request_via_commandline.yml index e7845f32e3..7e7f9e6a42 100644 --- a/detections/endpoint/potential_telegram_api_request_via_commandline.yml +++ b/detections/endpoint/potential_telegram_api_request_via_commandline.yml @@ -1,92 +1,72 @@ name: Potential Telegram API Request Via CommandLine id: d6b0d627-d0bf-46b1-936f-c48284767d21 -version: 6 -date: '2025-10-14' +version: 7 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk, Zaki Zarkasih Al Mustafa status: production type: Anomaly -description: The following analytic detects the presence of "api.telegram.org" - in the CommandLine of a process. It leverages data from Endpoint Detection and - Response (EDR) agents, focusing on process execution logs that include - command-line details. This activity can be significant as the telegram API has - been used as an exfiltration mechanism or even as a C2 channel. If confirmed - malicious, this could allow an attacker or malware to exfiltrate data or - receive additional C2 instruction, potentially leading to further compromise - and persistence within the network. +description: The following analytic detects the presence of "api.telegram.org" in the CommandLine of a process. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity can be significant as the telegram API has been used as an exfiltration mechanism or even as a C2 channel. If confirmed malicious, this could allow an attacker or malware to exfiltrate data or receive additional C2 instruction, potentially leading to further compromise and persistence within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process= "*api.telegram.org*" - NOT Processes.process IN ("*-osint -url*", "* --single-argument*") by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `potential_telegram_api_request_via_commandline_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: False positive may stem from application or users - requesting the API directly via CommandLine for testing purposes. Investigate - the matches and apply the necessary filters. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process= "*api.telegram.org*" NOT Processes.process IN ("*-osint -url*", "* --single-argument*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `potential_telegram_api_request_via_commandline_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positive may stem from application or users requesting the API directly via CommandLine for testing purposes. Investigate the matches and apply the necessary filters. references: -- https://www.virustotal.com/gui/file/0b3ef5e04329cefb5bb4bf30b3edcb32d1ec6bbcb29d22695a079bfb5b56e8ac/behavior -- https://www.virustotal.com/gui/file/72c59eeb15b5ec1d95e72e4b06a030bc058822bc10e5cb807e78a4624d329666/behavior -- https://www.virustotal.com/gui/file/72c59eeb15b5ec1d95e72e4b06a030bc058822bc10e5cb807e78a4624d329666/content -- https://www.virustotal.com/gui/file/1c4541bf70b6e251ef024ec4dde8dce400539c2368461c0d90e15a81b11ace44/content + - https://www.virustotal.com/gui/file/0b3ef5e04329cefb5bb4bf30b3edcb32d1ec6bbcb29d22695a079bfb5b56e8ac/behavior + - https://www.virustotal.com/gui/file/72c59eeb15b5ec1d95e72e4b06a030bc058822bc10e5cb807e78a4624d329666/behavior + - https://www.virustotal.com/gui/file/72c59eeb15b5ec1d95e72e4b06a030bc058822bc10e5cb807e78a4624d329666/content + - https://www.virustotal.com/gui/file/1c4541bf70b6e251ef024ec4dde8dce400539c2368461c0d90e15a81b11ace44/content drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Process $process_name$ with command line $process$ in $dest$ - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: - - field: process_name - type: process_name + message: Process $process_name$ with command line $process$ in $dest$ + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - XMRig - - Water Gamayun - - 0bj3ctivity Stealer - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1102.002 - - T1041 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - XMRig + - Water Gamayun + - 0bj3ctivity Stealer + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1102.002 + - T1041 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1102.002/telegram_api_cli/telegram_cli.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1102.002/telegram_api_cli/telegram_cli.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/potentially_malicious_code_on_commandline.yml b/detections/endpoint/potentially_malicious_code_on_commandline.yml index a3e8f8d010..e89cece1a1 100644 --- a/detections/endpoint/potentially_malicious_code_on_commandline.yml +++ b/detections/endpoint/potentially_malicious_code_on_commandline.yml @@ -5,90 +5,50 @@ date: '2025-05-02' author: Michael Hart, Splunk status: production type: Anomaly -description: The following analytic detects potentially malicious command lines using - a pretrained machine learning text classifier. It identifies unusual keyword combinations - in command lines, such as "streamreader," "webclient," "mutex," "function," and - "computehash," which are often associated with adversarial PowerShell code execution - for C2 communication. This detection leverages data from Endpoint Detection and - Response (EDR) agents, focusing on command lines longer than 200 characters. This - activity is significant as it can indicate an attempt to execute malicious scripts, - potentially leading to unauthorized code execution, data exfiltration, or further - system compromise. +description: The following analytic detects potentially malicious command lines using a pretrained machine learning text classifier. It identifies unusual keyword combinations in command lines, such as "streamreader," "webclient," "mutex," "function," and "computehash," which are often associated with adversarial PowerShell code execution for C2 communication. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command lines longer than 200 characters. This activity is significant as it can indicate an attempt to execute malicious scripts, potentially leading to unauthorized code execution, data exfiltration, or further system compromise. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime values(Processes.original_file_name) as original_file_name values(Processes.action) - as action values(Processes.parent_process_exec) as parent_process_exec values(Processes.parent_process_guid) - as parent_process_guid values(Processes.parent_process_id) as parent_process_id - values(Processes.parent_process_path) as parent_process_path values(Processes.process_exec) - as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) - as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) - as process_integrity_level values(Processes.process_name) as process_name values(Processes.process_path) - as process_path values(Processes.user) as user values(Processes.user_id) as user_id - values(Processes.vendor_product) as vendor_product from datamodel="Endpoint.Processes" - by Processes.parent_process_name Processes.process_name Processes.process Processes.user - Processes.dest | `drop_dm_object_name(Processes)` | where len(process) > 200 | `potentially_malicious_code_on_cmdline_tokenize_score` - | apply unusual_commandline_detection | eval score=''predicted(unusual_cmdline_logits)'', - process=orig_process | fields - unusual_cmdline* predicted(unusual_cmdline_logits) - orig_process | where score > 0.5 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `potentially_malicious_code_on_commandline_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: This model is an anomaly detector that identifies usage of - APIs and scripting constructs that are correllated with malicious activity. These - APIs and scripting constructs are part of the programming langauge and advanced - scripts may generate false positives. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.original_file_name) as original_file_name values(Processes.action) as action values(Processes.parent_process_exec) as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid values(Processes.parent_process_id) as parent_process_id values(Processes.parent_process_path) as parent_process_path values(Processes.process_exec) as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) as process_integrity_level values(Processes.process_name) as process_name values(Processes.process_path) as process_path values(Processes.user) as user values(Processes.user_id) as user_id values(Processes.vendor_product) as vendor_product from datamodel="Endpoint.Processes" by Processes.parent_process_name Processes.process_name Processes.process Processes.user Processes.dest | `drop_dm_object_name(Processes)` | where len(process) > 200 | `potentially_malicious_code_on_cmdline_tokenize_score` | apply unusual_commandline_detection | eval score=''predicted(unusual_cmdline_logits)'', process=orig_process | fields - unusual_cmdline* predicted(unusual_cmdline_logits) orig_process | where score > 0.5 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `potentially_malicious_code_on_commandline_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: This model is an anomaly detector that identifies usage of APIs and scripting constructs that are correllated with malicious activity. These APIs and scripting constructs are part of the programming langauge and advanced scripts may generate false positives. references: -- https://attack.mitre.org/techniques/T1059/003/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md + - https://attack.mitre.org/techniques/T1059/003/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Unusual command-line execution with command line length greater than 200 - found on $dest$ with commandline value - [$process$] - risk_objects: - - field: dest - type: system - score: 12 - - field: user - type: user - score: 12 - threat_objects: [] + message: Unusual command-line execution with command line length greater than 200 found on $dest$ with commandline value - [$process$] + risk_objects: + - field: dest + type: system + score: 12 + - field: user + type: user + score: 12 + threat_objects: [] tags: - analytic_story: - - Suspicious Command-Line Executions - asset_type: Endpoint - mitre_attack_id: - - T1059.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Command-Line Executions + asset_type: Endpoint + mitre_attack_id: + - T1059.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/malicious_cmd_line_samples/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/malicious_cmd_line_samples/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_4104_hunting.yml b/detections/endpoint/powershell_4104_hunting.yml index bb98aac29a..6247821b89 100644 --- a/detections/endpoint/powershell_4104_hunting.yml +++ b/detections/endpoint/powershell_4104_hunting.yml @@ -1,104 +1,253 @@ name: PowerShell 4104 Hunting id: d6f2b006-0041-11ec-8885-acde48001122 -version: 21 -date: '2025-10-24' +version: 22 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic identifies suspicious PowerShell execution - using Script Block Logging (EventCode 4104). It leverages specific patterns - and keywords within the ScriptBlockText field to detect potentially malicious - activities. This detection is significant for SOC analysts as PowerShell is - commonly used by attackers for various malicious purposes, including code - execution, privilege escalation, and persistence. If confirmed malicious, this - activity could allow attackers to execute arbitrary commands, exfiltrate data, - or maintain long-term access to the compromised system, posing a severe threat - to the organization's security. +description: The following analytic identifies suspicious PowerShell execution using Script Block Logging (EventCode 4104). It leverages specific patterns and keywords within the ScriptBlockText field to detect potentially malicious activities. This detection is significant for SOC analysts as PowerShell is commonly used by attackers for various malicious purposes, including code execution, privilege escalation, and persistence. If confirmed malicious, this activity could allow attackers to execute arbitrary commands, exfiltrate data, or maintain long-term access to the compromised system, posing a severe threat to the organization's security. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 | eval DoIt = if(match(ScriptBlockText,"(?i)(\$doit)"), - "4", 0) | eval enccom=if(match(ScriptBlockText,"[A-Za-z0-9+\/]{44,}([A-Za-z0-9+\/]{4}|[A-Za-z0-9+\/]{3}=|[A-Za-z0-9+\/]{2}==)") - OR match(ScriptBlockText, "(?i)[-]e(nc*o*d*e*d*c*o*m*m*a*n*d*)*\s+[^-]"),4,0) | - eval suspcmdlet=if(match(ScriptBlockText, "(?i)Add-Exfiltration|Add-Persistence|Add-RegBackdoor|Add-ScrnSaveBackdoor|Check-VM|Do-Exfiltration|Enabled-DuplicateToken|Exploit-Jboss|Find-Fruit|Find-GPOLocation|Find-TrustedDocuments|Get-ApplicationHost|Get-ChromeDump|Get-ClipboardContents|Get-FoxDump|Get-GPPPassword|Get-IndexedItem|Get-Keystrokes|LSASecret|Get-PassHash|Get-RegAlwaysInstallElevated|Get-RegAutoLogon|Get-RickAstley|Get-Screenshot|Get-SecurityPackages|Get-ServiceFilePermission|Get-ServicePermission|Get-ServiceUnquoted|Get-SiteListPassword|Get-System|Get-TimedScreenshot|Get-UnattendedInstallFile|Get-Unconstrained|Get-VaultCredential|Get-VulnAutoRun|Get-VulnSchTask|Gupt-Backdoor|HTTP-Login|Install-SSP|Install-ServiceBinary|Invoke-ACLScanner|Invoke-ADSBackdoor|Invoke-ARPScan|Invoke-AllChecks|Invoke-BackdoorLNK|Invoke-BypassUAC|Invoke-CredentialInjection|Invoke-DCSync|Invoke-DllInjection|Invoke-DowngradeAccount|Invoke-EgressCheck|Invoke-Inveigh|Invoke-InveighRelay|Invoke-Mimikittenz|Invoke-NetRipper|Invoke-NinjaCopy|Invoke-PSInject|Invoke-Paranoia|Invoke-PortScan|Invoke-PoshRat|Invoke-PostExfil|Invoke-PowerDump|Invoke-PowerShellTCP|Invoke-PsExec|Invoke-PsUaCme|Invoke-ReflectivePEInjection|Invoke-ReverseDNSLookup|Invoke-RunAs|Invoke-SMBScanner|Invoke-SSHCommand|Invoke-Service|Invoke-Shellcode|Invoke-Tater|Invoke-ThunderStruck|Invoke-Token|Invoke-UserHunter|Invoke-VoiceTroll|Invoke-WScriptBypassUAC|Invoke-WinEnum|MailRaider|New-HoneyHash|Out-Minidump|Port-Scan|PowerBreach|PowerUp|PowerView|Remove-Update|Set-MacAttribute|Set-Wallpaper|Show-TargetScreen|Start-CaptureServer|VolumeShadowCopyTools|NEEEEWWW|(Computer|User)Property|CachedRDPConnection|get-net\S+|invoke-\S+hunter|Install-Service|get-\S+(credent|password)|remoteps|Kerberos.*(policy|ticket)|netfirewall|Uninstall-Windows|Verb\s+Runas|AmsiBypass|nishang|Invoke-Interceptor|EXEonRemote|NetworkRelay|PowerShelludp|PowerShellIcmp|CreateShortcut|copy-vss|invoke-dll|invoke-mass|out-shortcut|Invoke-ShellCommand"),1,0) - | eval base64 = if(match(lower(ScriptBlockText),"frombase64"), "4", 0) | eval empire=if(match(lower(ScriptBlockText),"system.net.webclient") - AND match(lower(ScriptBlockText), "frombase64string") ,5,0) | eval mimikatz=if(match(lower(ScriptBlockText),"mimikatz") - OR match(lower(ScriptBlockText), "-dumpcr") OR match(lower(ScriptBlockText), "SEKURLSA::Pth") - OR match(lower(ScriptBlockText), "kerberos::ptt") OR match(lower(ScriptBlockText), - "kerberos::golden") ,5,0) | eval iex=if(match(ScriptBlockText, "(?i)iex|invoke-expression"),2,0) - | eval webclient=if(match(lower(ScriptBlockText),"http") OR match(lower(ScriptBlockText),"web(client|request)") - OR match(lower(ScriptBlockText),"socket") OR match(lower(ScriptBlockText),"download(file|string)") - OR match(lower(ScriptBlockText),"bitstransfer") OR match(lower(ScriptBlockText),"internetexplorer.application") - OR match(lower(ScriptBlockText),"xmlhttp"),5,0) | eval get = if(match(lower(ScriptBlockText),"get-"), - "1", 0) | eval rundll32 = if(match(lower(ScriptBlockText),"rundll32"), "4", 0) | - eval suspkeywrd=if(match(ScriptBlockText, "(?i)(bitstransfer|mimik|metasp|AssemblyBuilderAccess|Reflection\.Assembly|shellcode|injection|cnvert|shell\.application|start-process|Rc4ByteStream|System\.Security\.Cryptography|lsass\.exe|localadmin|LastLoggedOn|hijack|BackupPrivilege|ngrok|comsvcs|backdoor|brute.?force|Port.?Scan|Exfiltration|exploit|DisableRealtimeMonitoring|beacon)"),1,0) - | eval syswow64 = if(match(lower(ScriptBlockText),"syswow64"), "3", 0) | eval httplocal - = if(match(lower(ScriptBlockText),"http://127.0.0.1"), "4", 0) | eval reflection - = if(match(lower(ScriptBlockText),"reflection"), "1", 0) | eval invokewmi=if(match(lower(ScriptBlockText), - "(?i)(wmiobject|WMIMethod|RemoteWMI|PowerShellWmi|wmicommand)"),5,0) | eval downgrade=if(match(ScriptBlockText, - "(?i)([-]ve*r*s*i*o*n*\s+2)") OR match(lower(ScriptBlockText),"powershell -version"),3,0) - | eval compressed=if(match(ScriptBlockText, "(?i)GZipStream|::Decompress|IO.Compression|write-zip|(expand|compress)-Archive"),5,0) - | eval invokecmd = if(match(lower(ScriptBlockText),"invoke-command"), "4", 0) | - addtotals fieldname=Score DoIt, enccom, suspcmdlet, suspkeywrd, compressed, downgrade, - mimikatz, iex, empire, rundll32, webclient, syswow64, httplocal, reflection, invokewmi, - invokecmd, base64, get | stats values(Score) by UserID, Computer, DoIt, enccom, - compressed, downgrade, iex, mimikatz, rundll32, empire, webclient, syswow64, httplocal, - reflection, invokewmi, invokecmd, base64, get, suspcmdlet, suspkeywrd | rename Computer - as dest, UserID as user | `powershell_4104_hunting_filter`' -how_to_implement: The following Hunting analytic requires PowerShell operational - logs to be imported. Modify the powershell macro as needed to match the - sourcetype or add index. This analytic is specific to 4104, or PowerShell - Script Block Logging. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 + | eval DoIt = if(match(ScriptBlockText,"(?i)(\$doit)"), "4", 0) + | eval enccom=if(match(ScriptBlockText,"[A-Za-z0-9+\/]{44,}([A-Za-z0-9+\/]{4} + | [A-Za-z0-9+\/]{3}= + | [A-Za-z0-9+\/]{2}==)") OR match(ScriptBlockText, "(?i)[-]e(nc*o*d*e*d*c*o*m*m*a*n*d*)*\s+[^-]"),4,0) + | eval suspcmdlet=if(match(ScriptBlockText, "(?i)Add-Exfiltration + | Add-Persistence + | Add-RegBackdoor + | Add-ScrnSaveBackdoor + | Check-VM + | Do-Exfiltration + | Enabled-DuplicateToken + | Exploit-Jboss + | Find-Fruit + | Find-GPOLocation + | Find-TrustedDocuments + | Get-ApplicationHost + | Get-ChromeDump + | Get-ClipboardContents + | Get-FoxDump + | Get-GPPPassword + | Get-IndexedItem + | Get-Keystrokes + | LSASecret + | Get-PassHash + | Get-RegAlwaysInstallElevated + | Get-RegAutoLogon + | Get-RickAstley + | Get-Screenshot + | Get-SecurityPackages + | Get-ServiceFilePermission + | Get-ServicePermission + | Get-ServiceUnquoted + | Get-SiteListPassword + | Get-System + | Get-TimedScreenshot + | Get-UnattendedInstallFile + | Get-Unconstrained + | Get-VaultCredential + | Get-VulnAutoRun + | Get-VulnSchTask + | Gupt-Backdoor + | HTTP-Login + | Install-SSP + | Install-ServiceBinary + | Invoke-ACLScanner + | Invoke-ADSBackdoor + | Invoke-ARPScan + | Invoke-AllChecks + | Invoke-BackdoorLNK + | Invoke-BypassUAC + | Invoke-CredentialInjection + | Invoke-DCSync + | Invoke-DllInjection + | Invoke-DowngradeAccount + | Invoke-EgressCheck + | Invoke-Inveigh + | Invoke-InveighRelay + | Invoke-Mimikittenz + | Invoke-NetRipper + | Invoke-NinjaCopy + | Invoke-PSInject + | Invoke-Paranoia + | Invoke-PortScan + | Invoke-PoshRat + | Invoke-PostExfil + | Invoke-PowerDump + | Invoke-PowerShellTCP + | Invoke-PsExec + | Invoke-PsUaCme + | Invoke-ReflectivePEInjection + | Invoke-ReverseDNSLookup + | Invoke-RunAs + | Invoke-SMBScanner + | Invoke-SSHCommand + | Invoke-Service + | Invoke-Shellcode + | Invoke-Tater + | Invoke-ThunderStruck + | Invoke-Token + | Invoke-UserHunter + | Invoke-VoiceTroll + | Invoke-WScriptBypassUAC + | Invoke-WinEnum + | MailRaider + | New-HoneyHash + | Out-Minidump + | Port-Scan + | PowerBreach + | PowerUp + | PowerView + | Remove-Update + | Set-MacAttribute + | Set-Wallpaper + | Show-TargetScreen + | Start-CaptureServer + | VolumeShadowCopyTools + | NEEEEWWW + | (Computer + | User)Property + | CachedRDPConnection + | get-net\S+ + | invoke-\S+hunter + | Install-Service + | get-\S+(credent + | password) + | remoteps + | Kerberos.*(policy + | ticket) + | netfirewall + | Uninstall-Windows + | Verb\s+Runas + | AmsiBypass + | nishang + | Invoke-Interceptor + | EXEonRemote + | NetworkRelay + | PowerShelludp + | PowerShellIcmp + | CreateShortcut + | copy-vss + | invoke-dll + | invoke-mass + | out-shortcut + | Invoke-ShellCommand"),1,0) + | eval base64 = if(match(lower(ScriptBlockText),"frombase64"), "4", 0) + | eval empire=if(match(lower(ScriptBlockText),"system.net.webclient") AND match(lower(ScriptBlockText), "frombase64string") ,5,0) + | eval mimikatz=if(match(lower(ScriptBlockText),"mimikatz") OR match(lower(ScriptBlockText), "-dumpcr") OR match(lower(ScriptBlockText), "SEKURLSA::Pth") OR match(lower(ScriptBlockText), "kerberos::ptt") OR match(lower(ScriptBlockText), "kerberos::golden") ,5,0) + | eval iex=if(match(ScriptBlockText, "(?i)iex + | invoke-expression"),2,0) + | eval webclient=if(match(lower(ScriptBlockText),"http") OR match(lower(ScriptBlockText),"web(client + | request)") OR match(lower(ScriptBlockText),"socket") OR match(lower(ScriptBlockText),"download(file + | string)") OR match(lower(ScriptBlockText),"bitstransfer") OR match(lower(ScriptBlockText),"internetexplorer.application") OR match(lower(ScriptBlockText),"xmlhttp"),5,0) + | eval get = if(match(lower(ScriptBlockText),"get-"), "1", 0) + | eval rundll32 = if(match(lower(ScriptBlockText),"rundll32"), "4", 0) + | eval suspkeywrd=if(match(ScriptBlockText, "(?i)(bitstransfer + | mimik + | metasp + | AssemblyBuilderAccess + | Reflection\.Assembly + | shellcode + | injection + | cnvert + | shell\.application + | start-process + | Rc4ByteStream + | System\.Security\.Cryptography + | lsass\.exe + | localadmin + | LastLoggedOn + | hijack + | BackupPrivilege + | ngrok + | comsvcs + | backdoor + | brute.?force + | Port.?Scan + | Exfiltration + | exploit + | DisableRealtimeMonitoring + | beacon)"),1,0) + | eval syswow64 = if(match(lower(ScriptBlockText),"syswow64"), "3", 0) + | eval httplocal = if(match(lower(ScriptBlockText),"http://127.0.0.1"), "4", 0) + | eval reflection = if(match(lower(ScriptBlockText),"reflection"), "1", 0) + | eval invokewmi=if(match(lower(ScriptBlockText), "(?i)(wmiobject + | WMIMethod + | RemoteWMI + | PowerShellWmi + | wmicommand)"),5,0) + | eval downgrade=if(match(ScriptBlockText, "(?i)([-]ve*r*s*i*o*n*\s+2)") OR match(lower(ScriptBlockText),"powershell -version"),3,0) + | eval compressed=if(match(ScriptBlockText, "(?i)GZipStream + | ::Decompress + | IO.Compression + | write-zip + | (expand + | compress)-Archive"),5,0) + | eval invokecmd = if(match(lower(ScriptBlockText),"invoke-command"), "4", 0) + | addtotals fieldname=Score DoIt, enccom, suspcmdlet, suspkeywrd, compressed, downgrade, mimikatz, iex, empire, rundll32, webclient, syswow64, httplocal, reflection, invokewmi, invokecmd, base64, get + | stats values(Score) + BY UserID, Computer, DoIt, + enccom, compressed, downgrade, + iex, mimikatz, rundll32, + empire, webclient, syswow64, + httplocal, reflection, invokewmi, + invokecmd, base64, get, + suspcmdlet, suspkeywrd + | rename Computer as dest, UserID as user + | `powershell_4104_hunting_filter` +how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. known_false_positives: Limited false positives. May filter as needed. references: -- https://github.com/inodee/threathunting-spl/blob/master/hunt-queries/powershell_qualifiers.md -- https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba -- https://github.com/marcurdy/dfir-toolset/blob/master/Powershell%20Blueteam.txt -- https://devblogs.microsoft.com/powershell/powershell-the-blue-team/ -- https://docs.microsoft.com/en-us/powershell/module/microsoft.powershell.core/about/about_logging?view=powershell-5.1 -- https://www.mandiant.com/resources/greater-visibilityt -- https://hurricanelabs.com/splunk-tutorials/how-to-use-powershell-transcription-logs-in-splunk/ -- https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html -- https://adlumin.com/post/powerdrop-a-new-insidious-powershell-script-for-command-and-control-attacks-targets-u-s-aerospace-defense-industry/ + - https://github.com/inodee/threathunting-spl/blob/master/hunt-queries/powershell_qualifiers.md + - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba + - https://github.com/marcurdy/dfir-toolset/blob/master/Powershell%20Blueteam.txt + - https://devblogs.microsoft.com/powershell/powershell-the-blue-team/ + - https://docs.microsoft.com/en-us/powershell/module/microsoft.powershell.core/about/about_logging?view=powershell-5.1 + - https://www.mandiant.com/resources/greater-visibilityt + - https://hurricanelabs.com/splunk-tutorials/how-to-use-powershell-transcription-logs-in-splunk/ + - https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html + - https://adlumin.com/post/powerdrop-a-new-insidious-powershell-script-for-command-and-control-attacks-targets-u-s-aerospace-defense-industry/ tags: - analytic_story: - - Braodo Stealer - - Cactus Ransomware - - China-Nexus Threat Activity - - CISA AA23-347A - - CISA AA24-241A - - Cleo File Transfer Software - - DarkGate Malware - - Data Destruction - - Flax Typhoon - - Hermetic Wiper - - Lumma Stealer - - Malicious PowerShell - - Medusa Ransomware - - Rhysida Ransomware - - Salt Typhoon - - SystemBC - - PHP-CGI RCE Attack on Japanese Organizations - - Water Gamayun - - XWorm - - Scattered Spider - - Interlock Ransomware - - 0bj3ctivity Stealer - - APT37 Rustonotto and FadeStealer - - GhostRedirector IIS Module and Rungan Backdoor - - Hellcat Ransomware - - Microsoft WSUS CVE-2025-59287 - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Braodo Stealer + - Cactus Ransomware + - China-Nexus Threat Activity + - CISA AA23-347A + - CISA AA24-241A + - Cleo File Transfer Software + - DarkGate Malware + - Data Destruction + - Flax Typhoon + - Hermetic Wiper + - Lumma Stealer + - Malicious PowerShell + - Medusa Ransomware + - Rhysida Ransomware + - Salt Typhoon + - SystemBC + - PHP-CGI RCE Attack on Japanese Organizations + - Water Gamayun + - XWorm + - Scattered Spider + - Interlock Ransomware + - 0bj3ctivity Stealer + - APT37 Rustonotto and FadeStealer + - GhostRedirector IIS Module and Rungan Backdoor + - Hellcat Ransomware + - Microsoft WSUS CVE-2025-59287 + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell___connect_to_internet_with_hidden_window.yml b/detections/endpoint/powershell___connect_to_internet_with_hidden_window.yml index 1a11c03693..8bf0ef48ee 100644 --- a/detections/endpoint/powershell___connect_to_internet_with_hidden_window.yml +++ b/detections/endpoint/powershell___connect_to_internet_with_hidden_window.yml @@ -1,72 +1,64 @@ name: PowerShell - Connect To Internet With Hidden Window id: ee18ed37-0802-4268-9435-b3b91aaa18db -version: 14 -date: '2025-05-02' +version: 15 +date: '2026-02-25' author: David Dorsey, Michael Haag Splunk status: production type: Hunting -description: The following analytic detects PowerShell commands using the WindowStyle - parameter to hide the window while connecting to the Internet. This behavior is - identified through Endpoint Detection and Response (EDR) telemetry, focusing on - command-line executions that include variations of the WindowStyle parameter. This - activity is significant because it attempts to bypass default PowerShell execution - policies and conceal its actions, which is often indicative of malicious intent. - If confirmed malicious, this could allow an attacker to execute commands stealthily, - potentially leading to unauthorized data exfiltration or further compromise of the - endpoint. +description: The following analytic detects PowerShell commands using the WindowStyle parameter to hide the window while connecting to the Internet. This behavior is identified through Endpoint Detection and Response (EDR) telemetry, focusing on command-line executions that include variations of the WindowStyle parameter. This activity is significant because it attempts to bypass default PowerShell execution policies and conceal its actions, which is often indicative of malicious intent. If confirmed malicious, this could allow an attacker to execute commands stealthily, potentially leading to unauthorized data exfiltration or further compromise of the endpoint. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: "| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)\ - \ as lastTime from datamodel=Endpoint.Processes where `process_powershell` by Processes.action\ - \ Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec\ - \ Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name\ - \ Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid\ - \ Processes.process_hash Processes.process_id Processes.process_integrity_level\ - \ Processes.process_name Processes.process_path Processes.user Processes.user_id\ - \ Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`\ - \ | `security_content_ctime(lastTime)` | where match(process,\"(?i)[\\-|\\/|\u2013\ - \ |\u2014|\u2015]w(in*d*o*w*s*t*y*l*e*)*\\s+[^-]\") | `powershell___connect_to_internet_with_hidden_window_filter`" -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Legitimate process can have this combination of command-line - options, but it's not common. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_powershell` + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | where match(process,"(?i)[\- + | \/ + | – + | — + | ―]w(in*d*o*w*s*t*y*l*e*)*\s+[^-]") + | `powershell___connect_to_internet_with_hidden_window_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Legitimate process can have this combination of command-line options, but it's not common. references: -- https://regexr.com/663rr -- https://github.com/redcanaryco/AtomicTestHarnesses/blob/master/Windows/TestHarnesses/T1059.001_PowerShell/OutPowerShellCommandLineParameter.ps1 -- https://ss64.com/ps/powershell.html -- https://twitter.com/M_haggis/status/1440758396534214658?s=20 -- https://blog.netlab.360.com/ten-families-of-malicious-samples-are-spreading-using-the-log4j2-vulnerability-now/ + - https://regexr.com/663rr + - https://github.com/redcanaryco/AtomicTestHarnesses/blob/master/Windows/TestHarnesses/T1059.001_PowerShell/OutPowerShellCommandLineParameter.ps1 + - https://ss64.com/ps/powershell.html + - https://twitter.com/M_haggis/status/1440758396534214658?s=20 + - https://blog.netlab.360.com/ten-families-of-malicious-samples-are-spreading-using-the-log4j2-vulnerability-now/ tags: - analytic_story: - - AgentTesla - - HAFNIUM Group - - Hermetic Wiper - - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns - - Malicious PowerShell - - Data Destruction - - Log4Shell CVE-2021-44228 - asset_type: Endpoint - cve: - - CVE-2021-44228 - mitre_attack_id: - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AgentTesla + - HAFNIUM Group + - Hermetic Wiper + - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + - Malicious PowerShell + - Data Destruction + - Log4Shell CVE-2021-44228 + asset_type: Endpoint + cve: + - CVE-2021-44228 + mitre_attack_id: + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/hidden_powershell/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/hidden_powershell/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_com_hijacking_inprocserver32_modification.yml b/detections/endpoint/powershell_com_hijacking_inprocserver32_modification.yml index fc0487e6c1..f66cbabb21 100644 --- a/detections/endpoint/powershell_com_hijacking_inprocserver32_modification.yml +++ b/detections/endpoint/powershell_com_hijacking_inprocserver32_modification.yml @@ -5,68 +5,48 @@ date: '2025-05-02' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects attempts to modify or add a Component - Object Model (COM) entry to the InProcServer32 path within the registry using PowerShell. - It leverages PowerShell ScriptBlock Logging (EventCode 4104) to identify suspicious - script blocks that target the InProcServer32 registry path. This activity is significant - because modifying COM objects can be used for persistence or privilege escalation - by attackers. If confirmed malicious, this could allow an attacker to execute arbitrary - code or maintain persistent access to the compromised system, posing a severe security - risk. +description: The following analytic detects attempts to modify or add a Component Object Model (COM) entry to the InProcServer32 path within the registry using PowerShell. It leverages PowerShell ScriptBlock Logging (EventCode 4104) to identify suspicious script blocks that target the InProcServer32 registry path. This activity is significant because modifying COM objects can be used for persistence or privilege escalation by attackers. If confirmed malicious, this could allow an attacker to execute arbitrary code or maintain persistent access to the compromised system, posing a severe security risk. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 ScriptBlockText = "*Software\\Classes\\CLSID\\*\\InProcServer32*" - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `powershell_com_hijacking_inprocserver32_modification_filter`' -how_to_implement: The following analytic requires PowerShell operational logs to be - imported. Modify the PowerShell macro as needed to match the sourcetype or add index. - This analytic is specific to 4104, or PowerShell Script Block Logging. -known_false_positives: False positives will be present if any scripts are adding to - inprocserver32. Filter as needed. + - Powershell Script Block Logging 4104 +search: '`powershell` EventCode=4104 ScriptBlockText = "*Software\\Classes\\CLSID\\*\\InProcServer32*" | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_com_hijacking_inprocserver32_modification_filter`' +how_to_implement: The following analytic requires PowerShell operational logs to be imported. Modify the PowerShell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. +known_false_positives: False positives will be present if any scripts are adding to inprocserver32. Filter as needed. references: -- https://attack.mitre.org/techniques/T1546/015/ -- https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html -- https://blog.cluster25.duskrise.com/2022/09/23/in-the-footsteps-of-the-fancy-bear-powerpoint-graphite/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1546.015/T1546.015.md + - https://attack.mitre.org/techniques/T1546/015/ + - https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html + - https://blog.cluster25.duskrise.com/2022/09/23/in-the-footsteps-of-the-fancy-bear-powerpoint-graphite/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1546.015/T1546.015.md drilldown_searches: -- name: View the detection results for - "$Computer$" - search: '%original_detection_search% | search Computer = "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$Computer$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" + search: '%original_detection_search% | search Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A PowerShell script has been identified with InProcServer32 within the - script code on $dest$. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A PowerShell script has been identified with InProcServer32 within the script code on $dest$. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Malicious PowerShell - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - - T1546.015 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Malicious PowerShell + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + - T1546.015 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.015/atomic_red_team/windows-powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.015/atomic_red_team/windows-powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_creating_thread_mutex.yml b/detections/endpoint/powershell_creating_thread_mutex.yml index 2fa2ec40bb..423a99c49d 100644 --- a/detections/endpoint/powershell_creating_thread_mutex.yml +++ b/detections/endpoint/powershell_creating_thread_mutex.yml @@ -1,80 +1,68 @@ name: Powershell Creating Thread Mutex id: 637557ec-ca08-11eb-bd0a-acde48001122 -version: 11 -date: '2025-06-24' +version: 12 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: - The following analytic detects the execution of PowerShell scripts using - the `mutex` function via EventCode 4104. This detection leverages PowerShell Script - Block Logging to identify scripts that create thread mutexes, a technique often - used in obfuscated scripts to ensure only one instance runs on a compromised machine. - This activity is significant as it may indicate the presence of sophisticated malware - or persistence mechanisms. If confirmed malicious, the attacker could maintain exclusive - control over a process, potentially leading to further exploitation or persistence - within the environment. +description: The following analytic detects the execution of PowerShell scripts using the `mutex` function via EventCode 4104. This detection leverages PowerShell Script Block Logging to identify scripts that create thread mutexes, a technique often used in obfuscated scripts to ensure only one instance runs on a compromised machine. This activity is significant as it may indicate the presence of sophisticated malware or persistence mechanisms. If confirmed malicious, the attacker could maintain exclusive control over a process, potentially leading to further exploitation or persistence within the environment. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText = "*Threading.Mutex*" | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_creating_thread_mutex_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - powershell developer may used this function in their script - for instance checking too. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*Threading.Mutex*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `powershell_creating_thread_mutex_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: powershell developer may used this function in their script for instance checking too. references: - - https://isc.sans.edu/forums/diary/Some+Powershell+Malicious+Code/22988/ - - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. - - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 - - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf - - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ + - https://isc.sans.edu/forums/diary/Some+Powershell+Malicious+Code/22988/ + - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 + - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf + - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ drilldown_searches: - - name: View the detection results for - "$dest$" and "$user_id$" - search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user_id$" + search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious powershell script contains Thread Mutex on host $dest$ - risk_objects: - - field: dest - type: system - score: 40 - - field: user_id - type: user - score: 40 - threat_objects: [] + message: A suspicious powershell script contains Thread Mutex on host $dest$ + risk_objects: + - field: dest + type: system + score: 40 + - field: user_id + type: user + score: 40 + threat_objects: [] tags: - analytic_story: - - Malicious PowerShell - - Water Gamayun - asset_type: Endpoint - mitre_attack_id: - - T1027.005 - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Malicious PowerShell + - Water Gamayun + asset_type: Endpoint + mitre_attack_id: + - T1027.005 + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_disable_security_monitoring.yml b/detections/endpoint/powershell_disable_security_monitoring.yml index af2c121a94..f70fcde506 100644 --- a/detections/endpoint/powershell_disable_security_monitoring.yml +++ b/detections/endpoint/powershell_disable_security_monitoring.yml @@ -1,133 +1,128 @@ name: Powershell Disable Security Monitoring id: c148a894-dd93-11eb-bf2a-acde48001122 -version: 11 -date: '2025-07-10' +version: 12 +date: '2026-02-25' author: Michael Haag, Nasreddine Bencherchali, Splunk status: production type: TTP description: | - The following analytic identifies attempts to disable Windows Defender - real-time behavior monitoring via PowerShell commands. It detects the use of specific - `Set-MpPreference` parameters that disable various security features. This activity - is significant as it is commonly used by malware such as RATs, bots, or Trojans - to evade detection by disabling antivirus protections. If confirmed malicious, this - action could allow an attacker to operate undetected, leading to potential data - exfiltration, further system compromise, or persistent access within the environment. + The following analytic identifies attempts to disable Windows Defender + real-time behavior monitoring via PowerShell commands. It detects the use of specific + `Set-MpPreference` parameters that disable various security features. This activity + is significant as it is commonly used by malware such as RATs, bots, or Trojans + to evade detection by disabling antivirus protections. If confirmed malicious, this + action could allow an attacker to operate undetected, leading to potential data + exfiltration, further system compromise, or persistent access within the environment. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime - from datamodel=Endpoint.Processes where - `process_powershell` - Processes.process="*Set-MpPreference*" - ( - Processes.process IN ( - "*DisableArchiveScanning*", - "*DisableBehaviorMonitoring*", - "*DisableBlockAtFirstSeen*", - "*DisableCatchupFullScan*", - "*DisableCatchupQuickScan*", - "*DisableIOAVProtection*", - "*DisableRealtimeMonitoring*", - "*DisableRemovableDriveScanning*", - "*DisableRestorePoint*", - "*DisableScanningMappedNetworkDrivesForFullScan*", - "*DisableScanningNetworkFiles*", - "*DisableScriptScanning*", - "*drdsc*", - "*dsnf *", - "*drtm *", - "*dioavp *", - "*dscrptsc *", - "*dbaf *", - "*darchsc *", - "*dcfsc *", - "*dbm *" + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + from datamodel=Endpoint.Processes where + `process_powershell` + Processes.process="*Set-MpPreference*" + ( + Processes.process IN ( + "*DisableArchiveScanning*", + "*DisableBehaviorMonitoring*", + "*DisableBlockAtFirstSeen*", + "*DisableCatchupFullScan*", + "*DisableCatchupQuickScan*", + "*DisableIOAVProtection*", + "*DisableRealtimeMonitoring*", + "*DisableRemovableDriveScanning*", + "*DisableRestorePoint*", + "*DisableScanningMappedNetworkDrivesForFullScan*", + "*DisableScanningNetworkFiles*", + "*DisableScriptScanning*", + "*drdsc*", + "*dsnf *", + "*drtm *", + "*dioavp *", + "*dscrptsc *", + "*dbaf *", + "*darchsc *", + "*dcfsc *", + "*dbm *" + ) + Processes.process IN ( + "* $true*", + "* 1*" + ) ) - Processes.process IN ( - "* $true*", - "* 1*" + OR + ( + Processes.process = "*PUAProtection*" + Processes.process = "*disable*" ) - ) - OR - ( - Processes.process = "*PUAProtection*" - Processes.process = "*disable*" - ) - OR - ( - Processes.process = "*CloudBlockLevel*" - Processes.process IN ( - "* $false*", - "* 0*" + OR + ( + Processes.process = "*CloudBlockLevel*" + Processes.process IN ( + "* $false*", + "* 0*" + ) ) - ) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `powershell_disable_security_monitoring_filter` + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `powershell_disable_security_monitoring_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: | - Limited false positives. However, tune based on scripts that may perform this action. + Limited false positives. However, tune based on scripts that may perform this action. references: - - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1562.001/T1562.001.md#atomic-test-15---tamper-with-windows-defender-atp-powershell - - https://docs.microsoft.com/en-us/powershell/module/defender/set-mppreference?view=windowsserver2022-ps + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1562.001/T1562.001.md#atomic-test-15---tamper-with-windows-defender-atp-powershell + - https://docs.microsoft.com/en-us/powershell/module/defender/set-mppreference?view=windowsserver2022-ps drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Defender Real-time Behavior Monitoring disabled on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: Windows Defender Real-time Behavior Monitoring disabled on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Ransomware - - Revil Ransomware - - CISA AA24-241A - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - Revil Ransomware + - CISA AA24-241A + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/pwh_defender_disabling/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/pwh_defender_disabling/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_domain_enumeration.yml b/detections/endpoint/powershell_domain_enumeration.yml index 23d4d52fd9..fafa402871 100644 --- a/detections/endpoint/powershell_domain_enumeration.yml +++ b/detections/endpoint/powershell_domain_enumeration.yml @@ -1,83 +1,71 @@ name: PowerShell Domain Enumeration id: e1866ce2-ca22-11eb-8e44-acde48001122 -version: 11 -date: '2025-10-24' +version: 12 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of PowerShell commands - used for domain enumeration, such as `get-netdomaintrust` and - `get-adgroupmember`. It leverages PowerShell Script Block Logging - (EventCode=4104) to capture and analyze the full command sent to PowerShell. - This activity is significant as it often indicates reconnaissance efforts by - an attacker to map out the domain structure and identify key users and groups. - If confirmed malicious, this behavior could lead to further targeted attacks, - privilege escalation, and unauthorized access to sensitive information within - the domain. +description: The following analytic detects the execution of PowerShell commands used for domain enumeration, such as `get-netdomaintrust` and `get-adgroupmember`. It leverages PowerShell Script Block Logging (EventCode=4104) to capture and analyze the full command sent to PowerShell. This activity is significant as it often indicates reconnaissance efforts by an attacker to map out the domain structure and identify key users and groups. If confirmed malicious, this behavior could lead to further targeted attacks, privilege escalation, and unauthorized access to sensitive information within the domain. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 ScriptBlockText IN (*get-netdomaintrust*, *get-netforesttrust*, - *get-addomain*, *get-adgroupmember*, *get-domainuser*) | fillnull | stats count - min(_time) as firstTime max(_time) as lastTime by dest signature signature_id user_id - vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_domain_enumeration_filter`' -how_to_implement: To successfully implement this analytic, you will need to - enable PowerShell Script Block Logging on some or all endpoints. Additional - setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: It is possible there will be false positives, filter as - needed. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText IN (*get-netdomaintrust*, *get-netforesttrust*, *get-addomain*, *get-adgroupmember*, *get-domainuser*) + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `powershell_domain_enumeration_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: It is possible there will be false positives, filter as needed. references: -- https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -- https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 -- https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf -- https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ -- https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html + - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 + - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf + - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ + - https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html drilldown_searches: -- name: View the detection results for - "$dest$" and "$user_id$" - search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user_id$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user_id$" + search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious powershell script contains domain enumeration command in - $ScriptBlockText$ in host $dest$ - risk_objects: - - field: dest - type: system - score: 42 - - field: user_id - type: user - score: 42 - threat_objects: [] + message: A suspicious powershell script contains domain enumeration command in $ScriptBlockText$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 42 + - field: user_id + type: user + score: 42 + threat_objects: [] tags: - analytic_story: - - Hermetic Wiper - - Malicious PowerShell - - CISA AA23-347A - - Data Destruction - - Interlock Ransomware - - Microsoft WSUS CVE-2025-59287 - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Hermetic Wiper + - Malicious PowerShell + - CISA AA23-347A + - Data Destruction + - Interlock Ransomware + - Microsoft WSUS CVE-2025-59287 + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/enumeration.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/enumeration.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_enable_powershell_remoting.yml b/detections/endpoint/powershell_enable_powershell_remoting.yml index 96d20007bf..0dbcfa88ea 100644 --- a/detections/endpoint/powershell_enable_powershell_remoting.yml +++ b/detections/endpoint/powershell_enable_powershell_remoting.yml @@ -1,72 +1,59 @@ name: PowerShell Enable PowerShell Remoting id: 40e3b299-19a5-4460-96e9-e1467f714f8e -version: 8 -date: '2025-06-24' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk type: Anomaly status: production data_source: - - Powershell Script Block Logging 4104 -description: - The following analytic detects the use of the Enable-PSRemoting cmdlet, - which allows PowerShell remoting on a local or remote computer. This detection leverages - PowerShell Script Block Logging (EventCode 4104) to identify when this cmdlet is - executed. Monitoring this activity is crucial as it can indicate an attacker enabling - remote command execution capabilities on a compromised system. If confirmed malicious, - this activity could allow an attacker to take control of the system remotely, execute - commands, and potentially pivot to other systems within the network, leading to - further compromise and lateral movement. -search: - '`powershell` EventCode=4104 ScriptBlockText="*Enable-PSRemoting*" | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `powershell_enable_powershell_remoting_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - Note that false positives may occur due to the use of the Enable-PSRemoting - cmdlet by legitimate users, such as system administrators. It is recommended to - apply appropriate filters as needed to minimize the number of false positives. + - Powershell Script Block Logging 4104 +description: The following analytic detects the use of the Enable-PSRemoting cmdlet, which allows PowerShell remoting on a local or remote computer. This detection leverages PowerShell Script Block Logging (EventCode 4104) to identify when this cmdlet is executed. Monitoring this activity is crucial as it can indicate an attacker enabling remote command execution capabilities on a compromised system. If confirmed malicious, this activity could allow an attacker to take control of the system remotely, execute commands, and potentially pivot to other systems within the network, leading to further compromise and lateral movement. +search: |- + `powershell` EventCode=4104 ScriptBlockText="*Enable-PSRemoting*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `powershell_enable_powershell_remoting_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: Note that false positives may occur due to the use of the Enable-PSRemoting cmdlet by legitimate users, such as system administrators. It is recommended to apply appropriate filters as needed to minimize the number of false positives. references: - - https://learn.microsoft.com/en-us/powershell/module/microsoft.powershell.core/enable-psremoting?view=powershell-7.3 + - https://learn.microsoft.com/en-us/powershell/module/microsoft.powershell.core/enable-psremoting?view=powershell-7.3 drilldown_searches: - - name: View the detection results for - "$Computer$" - search: '%original_detection_search% | search Computer = "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$Computer$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" + search: '%original_detection_search% | search Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: PowerShell was identified running a Invoke-PSremoting on $dest$. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: PowerShell was identified running a Invoke-PSremoting on $dest$. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Malicious PowerShell - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Malicious PowerShell + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/4104-psremoting-windows-powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/4104-psremoting-windows-powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_enable_smb1protocol_feature.yml b/detections/endpoint/powershell_enable_smb1protocol_feature.yml index 8253882591..88f5a1e589 100644 --- a/detections/endpoint/powershell_enable_smb1protocol_feature.yml +++ b/detections/endpoint/powershell_enable_smb1protocol_feature.yml @@ -1,69 +1,63 @@ name: Powershell Enable SMB1Protocol Feature id: afed80b2-d34b-11eb-a952-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the enabling of the SMB1 protocol via - `powershell.exe`. It leverages PowerShell script block logging (EventCode 4104) - to identify the execution of the `Enable-WindowsOptionalFeature` cmdlet with the - `SMB1Protocol` parameter. This activity is significant because enabling SMB1 can - facilitate lateral movement and file encryption by ransomware, such as RedDot. If - confirmed malicious, this action could allow an attacker to propagate through the - network, encrypt files, and potentially disrupt business operations. +description: The following analytic detects the enabling of the SMB1 protocol via `powershell.exe`. It leverages PowerShell script block logging (EventCode 4104) to identify the execution of the `Enable-WindowsOptionalFeature` cmdlet with the `SMB1Protocol` parameter. This activity is significant because enabling SMB1 can facilitate lateral movement and file encryption by ransomware, such as RedDot. If confirmed malicious, this action could allow an attacker to propagate through the network, encrypt files, and potentially disrupt business operations. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 ScriptBlockText = "*Enable-WindowsOptionalFeature*" - ScriptBlockText = "*SMB1Protocol*" | fillnull | stats count min(_time) as firstTime - max(_time) as lastTime by dest signature signature_id user_id vendor_product EventID - Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `powershell_enable_smb1protocol_feature_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the powershell logs from your endpoints. make sure you enable needed - registry to monitor this event. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*Enable-WindowsOptionalFeature*" ScriptBlockText = "*SMB1Protocol*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `powershell_enable_smb1protocol_feature_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the powershell logs from your endpoints. make sure you enable needed registry to monitor this event. known_false_positives: network operator may enable or disable this windows feature. references: -- https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/ -- https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html + - https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/ + - https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html drilldown_searches: -- name: View the detection results for - "$Computer$" - search: '%original_detection_search% | search Computer = "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$Computer$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" + search: '%original_detection_search% | search Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Powershell Enable SMB1Protocol Feature on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: Powershell Enable SMB1Protocol Feature on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Ransomware - - Malicious PowerShell - - Hermetic Wiper - - Data Destruction - asset_type: Endpoint - mitre_attack_id: - - T1027.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - Malicious PowerShell + - Hermetic Wiper + - Data Destruction + asset_type: Endpoint + mitre_attack_id: + - T1027.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_execute_com_object.yml b/detections/endpoint/powershell_execute_com_object.yml index 07a6ea050d..6da4a509f2 100644 --- a/detections/endpoint/powershell_execute_com_object.yml +++ b/detections/endpoint/powershell_execute_com_object.yml @@ -1,71 +1,64 @@ name: Powershell Execute COM Object id: 65711630-f9bf-11eb-8d72-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of a COM CLSID through PowerShell. - It leverages EventCode 4104 and searches for specific script block text indicating - the creation of a COM object. This activity is significant as it is commonly used - by adversaries and malware, such as the Conti ransomware, to execute commands, potentially - for privilege escalation or bypassing User Account Control (UAC). If confirmed malicious, - this technique could allow attackers to gain elevated privileges or persist within - the environment, posing a significant security risk. +description: The following analytic detects the execution of a COM CLSID through PowerShell. It leverages EventCode 4104 and searches for specific script block text indicating the creation of a COM object. This activity is significant as it is commonly used by adversaries and malware, such as the Conti ransomware, to execute commands, potentially for privilege escalation or bypassing User Account Control (UAC). If confirmed malicious, this technique could allow attackers to gain elevated privileges or persist within the environment, posing a significant security risk. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 ScriptBlockText = "*CreateInstance([type]::GetTypeFromCLSID*" - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `powershell_execute_com_object_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*CreateInstance([type]::GetTypeFromCLSID*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `powershell_execute_com_object_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: network operrator may use this command. references: -- https://threadreaderapp.com/thread/1423361119926816776.html -- https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html + - https://threadreaderapp.com/thread/1423361119926816776.html + - https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious powershell script contains COM CLSID command on host $dest$ - risk_objects: - - field: dest - type: system - score: 5 - threat_objects: [] + message: A suspicious powershell script contains COM CLSID command on host $dest$ + risk_objects: + - field: dest + type: system + score: 5 + threat_objects: [] tags: - analytic_story: - - Ransomware - - Malicious PowerShell - - Hermetic Wiper - - Data Destruction - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - - T1546.015 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - Malicious PowerShell + - Hermetic Wiper + - Data Destruction + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + - T1546.015 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.015/pwh_com_object/windows-powershell-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.015/pwh_com_object/windows-powershell-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_fileless_process_injection_via_getprocaddress.yml b/detections/endpoint/powershell_fileless_process_injection_via_getprocaddress.yml index 66a2b9d149..fa760ae853 100644 --- a/detections/endpoint/powershell_fileless_process_injection_via_getprocaddress.yml +++ b/detections/endpoint/powershell_fileless_process_injection_via_getprocaddress.yml @@ -1,77 +1,67 @@ name: Powershell Fileless Process Injection via GetProcAddress id: a26d9db4-c883-11eb-9d75-acde48001122 -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: - The following analytic detects the use of `GetProcAddress` in PowerShell - script blocks, leveraging PowerShell Script Block Logging (EventCode=4104). This - method captures the full command sent to PowerShell, which is then logged in Windows - event logs. The presence of `GetProcAddress` is unusual for typical PowerShell scripts - and often indicates malicious activity, as many attack toolkits use it to achieve - code execution. If confirmed malicious, this activity could allow an attacker to - execute arbitrary code, potentially leading to system compromise. Analysts should - review parallel processes and the entire logged script block for further investigation. +description: The following analytic detects the use of `GetProcAddress` in PowerShell script blocks, leveraging PowerShell Script Block Logging (EventCode=4104). This method captures the full command sent to PowerShell, which is then logged in Windows event logs. The presence of `GetProcAddress` is unusual for typical PowerShell scripts and often indicates malicious activity, as many attack toolkits use it to achieve code execution. If confirmed malicious, this activity could allow an attacker to execute arbitrary code, potentially leading to system compromise. Analysts should review parallel processes and the entire logged script block for further investigation. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText=*getprocaddress* | fillnull | - stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_fileless_process_injection_via_getprocaddress_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText=*getprocaddress* + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `powershell_fileless_process_injection_via_getprocaddress_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. known_false_positives: Limited false positives. Filter as needed. references: - - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. - - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 - - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf - - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ - - https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html + - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 + - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf + - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ + - https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious powershell script contains GetProcAddress API on host $dest$ - risk_objects: - - field: dest - type: system - score: 48 - threat_objects: [] + message: A suspicious powershell script contains GetProcAddress API on host $dest$ + risk_objects: + - field: dest + type: system + score: 48 + threat_objects: [] tags: - analytic_story: - - Hellcat Ransomware - - Malicious PowerShell - - Hermetic Wiper - - Data Destruction - asset_type: Endpoint - mitre_attack_id: - - T1055 - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Hellcat Ransomware + - Malicious PowerShell + - Hermetic Wiper + - Data Destruction + asset_type: Endpoint + mitre_attack_id: + - T1055 + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_fileless_script_contains_base64_encoded_content.yml b/detections/endpoint/powershell_fileless_script_contains_base64_encoded_content.yml index d8af1336b1..d2232b12a6 100644 --- a/detections/endpoint/powershell_fileless_script_contains_base64_encoded_content.yml +++ b/detections/endpoint/powershell_fileless_script_contains_base64_encoded_content.yml @@ -1,87 +1,78 @@ name: Powershell Fileless Script Contains Base64 Encoded Content id: 8acbc04c-c882-11eb-b060-acde48001122 -version: 15 -date: '2025-11-20' +version: 16 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Powershell Script Block Logging 4104 -description: The following analytic detects the execution of PowerShell scripts - containing Base64 encoded content, specifically identifying the use of - `FromBase64String`. It leverages PowerShell Script Block Logging - (EventCode=4104) to capture and analyze the full command sent to PowerShell. - This activity is significant as Base64 encoding is often used by attackers to - obfuscate malicious payloads, making it harder to detect. If confirmed - malicious, this could lead to code execution, allowing attackers to run - arbitrary commands and potentially compromise the system. -search: '`powershell` EventCode=4104 ScriptBlockText = "*frombase64string*" OR ScriptBlockText - = "*gnirtS46esaBmorF*" | fillnull | stats count min(_time) as firstTime max(_time) - as lastTime by dest signature signature_id user_id vendor_product EventID Guid Opcode - Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `powershell_fileless_script_contains_base64_encoded_content_filter`' -how_to_implement: To successfully implement this analytic, you will need to - enable PowerShell Script Block Logging on some or all endpoints. Additional - setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - Powershell Script Block Logging 4104 +description: The following analytic detects the execution of PowerShell scripts containing Base64 encoded content, specifically identifying the use of `FromBase64String`. It leverages PowerShell Script Block Logging (EventCode=4104) to capture and analyze the full command sent to PowerShell. This activity is significant as Base64 encoding is often used by attackers to obfuscate malicious payloads, making it harder to detect. If confirmed malicious, this could lead to code execution, allowing attackers to run arbitrary commands and potentially compromise the system. +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*frombase64string*" OR ScriptBlockText = "*gnirtS46esaBmorF*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `powershell_fileless_script_contains_base64_encoded_content_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. known_false_positives: False positives should be limited. Filter as needed. references: -- https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -- https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 -- https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf -- https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ -- https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/ + - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 + - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf + - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ + - https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious powershell script contains base64 command on host $dest$ - risk_objects: - - field: dest - type: system - score: 56 - threat_objects: [] + message: A suspicious powershell script contains base64 command on host $dest$ + risk_objects: + - field: dest + type: system + score: 56 + threat_objects: [] tags: - analytic_story: - - Winter Vivern - - Malicious PowerShell - - Medusa Ransomware - - Data Destruction - - NjRAT - - AsyncRAT - - Hermetic Wiper - - IcedID - - XWorm - - 0bj3ctivity Stealer - - APT37 Rustonotto and FadeStealer - - GhostRedirector IIS Module and Rungan Backdoor - - Hellcat Ransomware - - Microsoft WSUS CVE-2025-59287 - - NetSupport RMM Tool Abuse - mitre_attack_id: - - T1027 - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - asset_type: Endpoint + analytic_story: + - Winter Vivern + - Malicious PowerShell + - Medusa Ransomware + - Data Destruction + - NjRAT + - AsyncRAT + - Hermetic Wiper + - IcedID + - XWorm + - 0bj3ctivity Stealer + - APT37 Rustonotto and FadeStealer + - GhostRedirector IIS Module and Rungan Backdoor + - Hellcat Ransomware + - Microsoft WSUS CVE-2025-59287 + - NetSupport RMM Tool Abuse + mitre_attack_id: + - T1027 + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + asset_type: Endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/frombase64string.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/frombase64string.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_get_localgroup_discovery.yml b/detections/endpoint/powershell_get_localgroup_discovery.yml index ec14fa98ed..1b20921622 100644 --- a/detections/endpoint/powershell_get_localgroup_discovery.yml +++ b/detections/endpoint/powershell_get_localgroup_discovery.yml @@ -1,59 +1,53 @@ name: PowerShell Get LocalGroup Discovery id: b71adfcc-155b-11ec-9413-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic identifies the use of the `get-localgroup` command - executed via PowerShell or cmd.exe to enumerate local groups on an endpoint. This - detection leverages data from Endpoint Detection and Response (EDR) agents, focusing - on process names and command-line arguments. Monitoring this activity is significant - as it may indicate an attacker attempting to gather information about local group - memberships, which can be a precursor to privilege escalation. If confirmed malicious, - this activity could allow an attacker to identify and target privileged accounts, - potentially leading to unauthorized access and control over the system. +description: The following analytic identifies the use of the `get-localgroup` command executed via PowerShell or cmd.exe to enumerate local groups on an endpoint. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. Monitoring this activity is significant as it may indicate an attacker attempting to gather information about local group memberships, which can be a precursor to privilege escalation. If confirmed malicious, this activity could allow an attacker to identify and target privileged accounts, potentially leading to unauthorized access and control over the system. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=powershell.exe - OR Processes.process_name=cmd.exe) (Processes.process="*get-localgroup*") by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `powershell_get_localgroup_discovery_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name=powershell.exe + OR + Processes.process_name=cmd.exe + ) + (Processes.process="*get-localgroup*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `powershell_get_localgroup_discovery_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives may be present. Tune as needed. references: -- https://attack.mitre.org/techniques/T1069/001/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1069.001/T1069.001.md + - https://attack.mitre.org/techniques/T1069/001/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1069.001/T1069.001.md tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1069.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1069.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.001/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.001/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_get_localgroup_discovery_with_script_block_logging.yml b/detections/endpoint/powershell_get_localgroup_discovery_with_script_block_logging.yml index 4485768177..81f70bb17f 100644 --- a/detections/endpoint/powershell_get_localgroup_discovery_with_script_block_logging.yml +++ b/detections/endpoint/powershell_get_localgroup_discovery_with_script_block_logging.yml @@ -1,52 +1,48 @@ name: Powershell Get LocalGroup Discovery with Script Block Logging id: d7c6ad22-155c-11ec-bb64-acde48001122 -version: 9 -date: '2025-06-24' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: - The following analytic detects the execution of the PowerShell cmdlet - `get-localgroup` using PowerShell Script Block Logging (EventCode=4104). This method - captures the full command sent to PowerShell, providing detailed visibility into - script execution. Monitoring this activity is significant as it can indicate an - attempt to enumerate local groups, which may be a precursor to privilege escalation - or lateral movement. If confirmed malicious, an attacker could gain insights into - group memberships, potentially leading to unauthorized access or privilege abuse. - Review parallel processes and the entire script block for comprehensive analysis. +description: The following analytic detects the execution of the PowerShell cmdlet `get-localgroup` using PowerShell Script Block Logging (EventCode=4104). This method captures the full command sent to PowerShell, providing detailed visibility into script execution. Monitoring this activity is significant as it can indicate an attempt to enumerate local groups, which may be a precursor to privilege escalation or lateral movement. If confirmed malicious, an attacker could gain insights into group memberships, potentially leading to unauthorized access or privilege abuse. Review parallel processes and the entire script block for comprehensive analysis. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText = "*get-localgroup*" | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `powershell_get_localgroup_discovery_with_script_block_logging_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*get-localgroup*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `powershell_get_localgroup_discovery_with_script_block_logging_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. known_false_positives: False positives may be present. Tune as needed. references: - - https://www.splunk.com/en_us/blog/security/powershell-detections-threat-research-release-august-2021.html - - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1069.001/T1069.001.md - - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba - - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 - - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf - - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ + - https://www.splunk.com/en_us/blog/security/powershell-detections-threat-research-release-august-2021.html + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1069.001/T1069.001.md + - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba + - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 + - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf + - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1069.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1069.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/getlocalgroup.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/getlocalgroup.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_invoke_cimmethod_cimsession.yml b/detections/endpoint/powershell_invoke_cimmethod_cimsession.yml index 15d01beb3b..d740c24d03 100644 --- a/detections/endpoint/powershell_invoke_cimmethod_cimsession.yml +++ b/detections/endpoint/powershell_invoke_cimmethod_cimsession.yml @@ -1,76 +1,61 @@ name: PowerShell Invoke CIMMethod CIMSession id: 651ee958-a433-471c-b264-39725b788b83 -version: 8 -date: '2025-10-14' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk type: Anomaly status: production data_source: - - Powershell Script Block Logging 4104 -description: - The following analytic detects the creation of a New-CIMSession cmdlet - followed by the use of the Invoke-CIMMethod cmdlet within PowerShell. It leverages - PowerShell Script Block Logging to identify these specific cmdlets in the ScriptBlockText - field. This activity is significant because it mirrors the behavior of the Invoke-WMIMethod - cmdlet, often used for remote code execution via NTLMv2 pass-the-hash authentication. - If confirmed malicious, this could allow an attacker to execute commands remotely, - potentially leading to unauthorized access and control over targeted systems. -search: - '`powershell` EventCode=4104 ScriptBlockText IN ("*invoke-CIMMethod*", "*New-CimSession*") - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `powershell_invoke_cimmethod_cimsession_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - False positives may be present based on third-party applications - or administrators using CIM. It is recommended to apply appropriate filters as needed - to minimize the number of false positives. + - Powershell Script Block Logging 4104 +description: The following analytic detects the creation of a New-CIMSession cmdlet followed by the use of the Invoke-CIMMethod cmdlet within PowerShell. It leverages PowerShell Script Block Logging to identify these specific cmdlets in the ScriptBlockText field. This activity is significant because it mirrors the behavior of the Invoke-WMIMethod cmdlet, often used for remote code execution via NTLMv2 pass-the-hash authentication. If confirmed malicious, this could allow an attacker to execute commands remotely, potentially leading to unauthorized access and control over targeted systems. +search: |- + `powershell` EventCode=4104 ScriptBlockText IN ("*invoke-CIMMethod*", "*New-CimSession*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `powershell_invoke_cimmethod_cimsession_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: False positives may be present based on third-party applications or administrators using CIM. It is recommended to apply appropriate filters as needed to minimize the number of false positives. references: - - https://learn.microsoft.com/en-us/powershell/module/cimcmdlets/invoke-cimmethod?view=powershell-7.3 + - https://learn.microsoft.com/en-us/powershell/module/cimcmdlets/invoke-cimmethod?view=powershell-7.3 drilldown_searches: - - name: View the detection results for - "$Computer$" - search: '%original_detection_search% | search Computer = "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$Computer$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" + search: '%original_detection_search% | search Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - PowerShell was identified running a Invoke-CIMMethod Invoke-CIMSession - on $dest$. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: PowerShell was identified running a Invoke-CIMMethod Invoke-CIMSession on $dest$. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Scattered Lapsus$ Hunters - - Malicious PowerShell - - Active Directory Lateral Movement - asset_type: Endpoint - mitre_attack_id: - - T1047 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Scattered Lapsus$ Hunters + - Malicious PowerShell + - Active Directory Lateral Movement + asset_type: Endpoint + mitre_attack_id: + - T1047 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/4104-cimmethod-windows-powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/4104-cimmethod-windows-powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_invoke_wmiexec_usage.yml b/detections/endpoint/powershell_invoke_wmiexec_usage.yml index 6e2a97ffd7..5e03ad8d51 100644 --- a/detections/endpoint/powershell_invoke_wmiexec_usage.yml +++ b/detections/endpoint/powershell_invoke_wmiexec_usage.yml @@ -1,72 +1,60 @@ name: PowerShell Invoke WmiExec Usage id: 0734bd21-2769-4972-a5f1-78bb1e011224 -version: 8 -date: '2025-10-14' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk type: TTP status: production data_source: - - Powershell Script Block Logging 4104 -description: - The following analytic detects the execution of the Invoke-WMIExec utility - within PowerShell Script Block Logging (EventCode 4104). This detection leverages - PowerShell script block logs to identify instances where the Invoke-WMIExec command - is used. Monitoring this activity is crucial as it indicates potential lateral movement - using WMI commands with NTLMv2 pass-the-hash authentication. If confirmed malicious, - this activity could allow an attacker to execute commands remotely on target systems, - potentially leading to further compromise and lateral spread within the network. -search: - '`powershell` EventCode=4104 ScriptBlockText IN ("*invoke-wmiexec*") | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_invoke_wmiexec_usage_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - False positives should be limited as this analytic is designed - to detect a specific utility. It is recommended to apply appropriate filters as - needed to minimize the number of false positives. + - Powershell Script Block Logging 4104 +description: The following analytic detects the execution of the Invoke-WMIExec utility within PowerShell Script Block Logging (EventCode 4104). This detection leverages PowerShell script block logs to identify instances where the Invoke-WMIExec command is used. Monitoring this activity is crucial as it indicates potential lateral movement using WMI commands with NTLMv2 pass-the-hash authentication. If confirmed malicious, this activity could allow an attacker to execute commands remotely on target systems, potentially leading to further compromise and lateral spread within the network. +search: |- + `powershell` EventCode=4104 ScriptBlockText IN ("*invoke-wmiexec*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `powershell_invoke_wmiexec_usage_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: False positives should be limited as this analytic is designed to detect a specific utility. It is recommended to apply appropriate filters as needed to minimize the number of false positives. references: - - https://github.com/Kevin-Robertson/Invoke-TheHash/blob/master/Invoke-WMIExec.ps1 + - https://github.com/Kevin-Robertson/Invoke-TheHash/blob/master/Invoke-WMIExec.ps1 drilldown_searches: - - name: View the detection results for - "$Computer$" - search: '%original_detection_search% | search Computer = "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$Computer$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" + search: '%original_detection_search% | search Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: PowerShell was identified running a Invoke-WmiExec on $dest$. - risk_objects: - - field: dest - type: system - score: 100 - threat_objects: [] + message: PowerShell was identified running a Invoke-WmiExec on $dest$. + risk_objects: + - field: dest + type: system + score: 100 + threat_objects: [] tags: - analytic_story: - - Scattered Lapsus$ Hunters - - Suspicious WMI Use - asset_type: Endpoint - mitre_attack_id: - - T1047 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Scattered Lapsus$ Hunters + - Suspicious WMI Use + asset_type: Endpoint + mitre_attack_id: + - T1047 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/invokewmiexec_windows-powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/invokewmiexec_windows-powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_load_module_in_meterpreter.yml b/detections/endpoint/powershell_load_module_in_meterpreter.yml index 88c714886a..6916b08a2a 100644 --- a/detections/endpoint/powershell_load_module_in_meterpreter.yml +++ b/detections/endpoint/powershell_load_module_in_meterpreter.yml @@ -1,72 +1,62 @@ name: Powershell Load Module in Meterpreter id: d5905da5-d050-48db-9259-018d8f034fcf -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of suspicious PowerShell - commands associated with Meterpreter modules, such as "MSF.Powershell" and "MSF.Powershell.Meterpreter". - It leverages PowerShell Script Block Logging (EventCode=4104) to capture and analyze - the full command sent to PowerShell. This activity is significant as it indicates - potential post-exploitation actions, including credential dumping and persistence - mechanisms. If confirmed malicious, an attacker could gain extensive control over - the compromised system, escalate privileges, and maintain long-term access, posing - a severe threat to the environment. +description: The following analytic detects the execution of suspicious PowerShell commands associated with Meterpreter modules, such as "MSF.Powershell" and "MSF.Powershell.Meterpreter". It leverages PowerShell Script Block Logging (EventCode=4104) to capture and analyze the full command sent to PowerShell. This activity is significant as it indicates potential post-exploitation actions, including credential dumping and persistence mechanisms. If confirmed malicious, an attacker could gain extensive control over the compromised system, escalate privileges, and maintain long-term access, posing a severe threat to the environment. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 ScriptBlockText IN ("*MSF.Powershell*","*MSF.Powershell.Meterpreter*","*MSF.Powershell.Meterpreter.Kiwi*","*MSF.Powershell.Meterpreter.Transport*") - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `powershell_load_module_in_meterpreter_filter`' -how_to_implement: The following analytic requires PowerShell operational logs to be - imported. Modify the powershell macro as needed to match the sourcetype or add index. - This analytic is specific to 4104, or PowerShell Script Block Logging. -known_false_positives: False positives should be very limited as this is strict to - MetaSploit behavior. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText IN ("*MSF.Powershell*","*MSF.Powershell.Meterpreter*","*MSF.Powershell.Meterpreter.Kiwi*","*MSF.Powershell.Meterpreter.Transport*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `powershell_load_module_in_meterpreter_filter` +how_to_implement: The following analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. +known_false_positives: False positives should be very limited as this is strict to MetaSploit behavior. references: -- https://github.com/OJ/metasploit-payloads/blob/master/powershell/MSF.Powershell/Scripts.cs + - https://github.com/OJ/metasploit-payloads/blob/master/powershell/MSF.Powershell/Scripts.cs drilldown_searches: -- name: View the detection results for - "$user_id$" and "$Computer$" - search: '%original_detection_search% | search user_id = "$user_id$" Computer = - "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user_id$" and "$Computer$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$", - "$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user_id$" and "$Computer$" + search: '%original_detection_search% | search user_id = "$user_id$" Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user_id$" and "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$", "$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: PowerShell was identified running a script utilized by Meterpreter from - MetaSploit on endpoint $dest$ by user $user_id$. - risk_objects: - - field: user_id - type: user - score: 100 - - field: dest - type: system - score: 100 - threat_objects: [] + message: PowerShell was identified running a script utilized by Meterpreter from MetaSploit on endpoint $dest$ by user $user_id$. + risk_objects: + - field: user_id + type: user + score: 100 + - field: dest + type: system + score: 100 + threat_objects: [] tags: - analytic_story: - - MetaSploit - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - MetaSploit + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/metasploit/msf.powershell.powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/metasploit/msf.powershell.powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_loading_dotnet_into_memory_via_reflection.yml b/detections/endpoint/powershell_loading_dotnet_into_memory_via_reflection.yml index 02dc1dc564..d8ae2126a2 100644 --- a/detections/endpoint/powershell_loading_dotnet_into_memory_via_reflection.yml +++ b/detections/endpoint/powershell_loading_dotnet_into_memory_via_reflection.yml @@ -6,85 +6,66 @@ author: Michael Haag, Teoderick Contreras Splunk status: production type: Anomaly data_source: -- Powershell Script Block Logging 4104 -description: The following analytic detects the use of PowerShell scripts to - load .NET assemblies into memory via reflection, a technique often used in - malicious activities such as those by Empire and Cobalt Strike. It leverages - PowerShell Script Block Logging (EventCode=4104) to capture and analyze the - full command executed. This behavior is significant as it can indicate - advanced attack techniques aiming to execute code in memory, bypassing - traditional defenses. If confirmed malicious, this activity could lead to - unauthorized code execution, privilege escalation, and persistent access - within the environment. + - Powershell Script Block Logging 4104 +description: The following analytic detects the use of PowerShell scripts to load .NET assemblies into memory via reflection, a technique often used in malicious activities such as those by Empire and Cobalt Strike. It leverages PowerShell Script Block Logging (EventCode=4104) to capture and analyze the full command executed. This behavior is significant as it can indicate advanced attack techniques aiming to execute code in memory, bypassing traditional defenses. If confirmed malicious, this activity could lead to unauthorized code execution, privilege escalation, and persistent access within the environment. search: | - `powershell` EventCode=4104 ScriptBlockText IN ("*Reflection.Assembly]::Load*", - "*Reflection.Assembly.Load*", "*UnsafeLoadFrom*", "*.LoadFrom(*", "*.LoadModule(*", - "*.LoadWithPartialName*", "*ReflectionOnlyLoad*", "*Reflection.Assembly]::('daoL'[-1..-4] -join '')*") - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime - by dest signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `powershell_loading_dotnet_into_memory_via_reflection_filter` -how_to_implement: To successfully implement this analytic, you will need to - enable PowerShell Script Block Logging on some or all endpoints. Additional - setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: False positives should be limited as day to day scripts - do not use this method. + `powershell` EventCode=4104 ScriptBlockText IN ("*Reflection.Assembly]::Load*", + "*Reflection.Assembly.Load*", "*UnsafeLoadFrom*", "*.LoadFrom(*", "*.LoadModule(*", + "*.LoadWithPartialName*", "*ReflectionOnlyLoad*", "*Reflection.Assembly]::('daoL'[-1..-4] -join '')*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + by dest signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `powershell_loading_dotnet_into_memory_via_reflection_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: False positives should be limited as day to day scripts do not use this method. references: -- https://docs.microsoft.com/en-us/dotnet/api/system.reflection.assembly?view=net-5.0 -- https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -- https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 -- https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf -- https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ + - https://docs.microsoft.com/en-us/dotnet/api/system.reflection.assembly?view=net-5.0 + - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 + - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf + - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user_id$" - search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user_id$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user_id$" + search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious powershell script contains reflective class assembly - command in $ScriptBlockText$ to load .net code in memory in host $dest$ - risk_objects: - - field: dest - type: system - score: 56 - - field: user_id - type: user - score: 56 - threat_objects: [] + message: A suspicious powershell script contains reflective class assembly command in $ScriptBlockText$ to load .net code in memory in host $dest$ + risk_objects: + - field: dest + type: system + score: 56 + - field: user_id + type: user + score: 56 + threat_objects: [] tags: - analytic_story: - - Winter Vivern - - AgentTesla - - AsyncRAT - - Hermetic Wiper - - Malicious PowerShell - - Data Destruction - - 0bj3ctivity Stealer - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Winter Vivern + - AgentTesla + - AsyncRAT + - Hermetic Wiper + - Malicious PowerShell + - Data Destruction + - 0bj3ctivity Stealer + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/reflection.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/reflection.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_processing_stream_of_data.yml b/detections/endpoint/powershell_processing_stream_of_data.yml index 239e81b696..99cdabc2cb 100644 --- a/detections/endpoint/powershell_processing_stream_of_data.yml +++ b/detections/endpoint/powershell_processing_stream_of_data.yml @@ -1,91 +1,78 @@ name: Powershell Processing Stream Of Data id: 0d718b52-c9f1-11eb-bc61-acde48001122 -version: 13 -date: '2025-10-14' +version: 14 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: - The following analytic detects suspicious PowerShell script execution - involving compressed stream data processing, identified via EventCode 4104. It leverages - PowerShell Script Block Logging to flag scripts using `IO.Compression`, `IO.StreamReader`, - or decompression methods. This activity is significant as it often indicates obfuscated - PowerShell or embedded .NET/binary execution, which are common tactics for evading - detection. If confirmed malicious, this behavior could allow attackers to execute - hidden code, escalate privileges, or maintain persistence within the environment. +description: The following analytic detects suspicious PowerShell script execution involving compressed stream data processing, identified via EventCode 4104. It leverages PowerShell Script Block Logging to flag scripts using `IO.Compression`, `IO.StreamReader`, or decompression methods. This activity is significant as it often indicates obfuscated PowerShell or embedded .NET/binary execution, which are common tactics for evading detection. If confirmed malicious, this behavior could allow attackers to execute hidden code, escalate privileges, or maintain persistence within the environment. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText = "*IO.Compression.*" OR ScriptBlockText - = "*IO.StreamReader*" OR ScriptBlockText = "*]::Decompress*" | fillnull | stats - count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_processing_stream_of_data_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*IO.Compression.*" OR ScriptBlockText = "*IO.StreamReader*" OR ScriptBlockText = "*]::Decompress*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `powershell_processing_stream_of_data_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. known_false_positives: powershell may used this function to process compressed data. references: - - https://medium.com/@ahmedjouini99/deobfuscating-emotets-powershell-payload-e39fb116f7b9 - - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba - - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 - - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf - - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ - - https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html - - https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/ + - https://medium.com/@ahmedjouini99/deobfuscating-emotets-powershell-payload-e39fb116f7b9 + - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba + - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 + - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf + - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ + - https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html + - https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/ drilldown_searches: - - name: View the detection results for - "$Computer$" and "$user$" - search: '%original_detection_search% | search Computer = "$Computer$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$Computer$" and "$user$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" and "$user$" + search: '%original_detection_search% | search Computer = "$Computer$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A suspicious powershell script contains stream command in $ScriptBlockText$ - commonly for processing compressed or to decompressed binary file with EventCode - $EventID$ in host $dest$ - risk_objects: - - field: dest - type: system - score: 40 - - field: user_id - type: user - score: 40 - threat_objects: [] + message: A suspicious powershell script contains stream command in $ScriptBlockText$ commonly for processing compressed or to decompressed binary file with EventCode $EventID$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 40 + - field: user_id + type: user + score: 40 + threat_objects: [] tags: - analytic_story: - - Hellcat Ransomware - - Malicious PowerShell - - Medusa Ransomware - - PXA Stealer - - Data Destruction - - Braodo Stealer - - AsyncRAT - - Hermetic Wiper - - IcedID - - XWorm - - MoonPeak - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Hellcat Ransomware + - Malicious PowerShell + - Medusa Ransomware + - PXA Stealer + - Data Destruction + - Braodo Stealer + - AsyncRAT + - Hermetic Wiper + - IcedID + - XWorm + - MoonPeak + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/streamreader.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/streamreader.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_remote_services_add_trustedhost.yml b/detections/endpoint/powershell_remote_services_add_trustedhost.yml index 8966bebfbc..0f4ccea4e8 100644 --- a/detections/endpoint/powershell_remote_services_add_trustedhost.yml +++ b/detections/endpoint/powershell_remote_services_add_trustedhost.yml @@ -6,70 +6,46 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: - - Powershell Script Block Logging 4104 -description: - The following analytic detects the execution of a PowerShell script that - modifies the 'TrustedHosts' configuration via EventCode 4104. It leverages PowerShell - Script Block Logging to identify commands targeting WSMan settings, specifically - those altering or concatenating trusted hosts. This activity is significant as it - can indicate attempts to manipulate remote connection settings, potentially allowing - unauthorized remote access. If confirmed malicious, this could enable attackers - to establish persistent remote connections, bypass security protocols, and gain - unauthorized access to sensitive systems and data. -search: - '`powershell` EventCode=4104 ScriptBlockText = "*WSMan:\\localhost\\Client\\TrustedHosts*" - ScriptBlockText IN ("* -Value *", "* -Concatenate *") | fillnull | stats count min(_time) - as firstTime max(_time) as lastTime by dest signature signature_id user_id vendor_product - EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `powershell_remote_services_add_trustedhost_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - user and network administrator may used this function to add - trusted host. + - Powershell Script Block Logging 4104 +description: The following analytic detects the execution of a PowerShell script that modifies the 'TrustedHosts' configuration via EventCode 4104. It leverages PowerShell Script Block Logging to identify commands targeting WSMan settings, specifically those altering or concatenating trusted hosts. This activity is significant as it can indicate attempts to manipulate remote connection settings, potentially allowing unauthorized remote access. If confirmed malicious, this could enable attackers to establish persistent remote connections, bypass security protocols, and gain unauthorized access to sensitive systems and data. +search: '`powershell` EventCode=4104 ScriptBlockText = "*WSMan:\\localhost\\Client\\TrustedHosts*" ScriptBlockText IN ("* -Value *", "* -Concatenate *") | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_remote_services_add_trustedhost_filter`' +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: user and network administrator may used this function to add trusted host. references: - - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate + - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate drilldown_searches: - - name: View the detection results for - "$dest$" and "$user_id$" - search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user_id$" + search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a powershell script adding a remote trustedhost on $dest$ . - risk_objects: - - field: dest - type: system - score: 64 - - field: user_id - type: user - score: 64 - threat_objects: [] + message: a powershell script adding a remote trustedhost on $dest$ . + risk_objects: + - field: dest + type: system + score: 64 + - field: user_id + type: user + score: 64 + threat_objects: [] tags: - analytic_story: - - DarkGate Malware - asset_type: Endpoint - mitre_attack_id: - - T1021.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - DarkGate Malware + asset_type: Endpoint + mitre_attack_id: + - T1021.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/wsman_trustedhost/wsman_pwh.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/wsman_trustedhost/wsman_pwh.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_remote_thread_to_known_windows_process.yml b/detections/endpoint/powershell_remote_thread_to_known_windows_process.yml index 8ac3918f29..9088b47b2c 100644 --- a/detections/endpoint/powershell_remote_thread_to_known_windows_process.yml +++ b/detections/endpoint/powershell_remote_thread_to_known_windows_process.yml @@ -5,70 +5,46 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects suspicious PowerShell processes attempting - to inject code into critical Windows processes using CreateRemoteThread. It leverages - Sysmon EventCode 8 to identify instances where PowerShell spawns threads in processes - like svchost.exe, csrss.exe, and others. This activity is significant as it is commonly - used by malware such as TrickBot and offensive tools like Cobalt Strike to execute - malicious payloads, establish reverse shells, or download additional malware. If - confirmed malicious, this behavior could lead to unauthorized code execution, privilege - escalation, and persistent access within the environment. +description: The following analytic detects suspicious PowerShell processes attempting to inject code into critical Windows processes using CreateRemoteThread. It leverages Sysmon EventCode 8 to identify instances where PowerShell spawns threads in processes like svchost.exe, csrss.exe, and others. This activity is significant as it is commonly used by malware such as TrickBot and offensive tools like Cobalt Strike to execute malicious payloads, establish reverse shells, or download additional malware. If confirmed malicious, this behavior could lead to unauthorized code execution, privilege escalation, and persistent access within the environment. data_source: -- Sysmon EventID 8 -search: '`sysmon` EventCode = 8 parent_process_name IN ("powershell_ise.exe", "powershell.exe") - TargetImage IN ("*\\svchost.exe","*\\csrss.exe" "*\\gpupdate.exe", "*\\explorer.exe","*\\services.exe","*\\winlogon.exe","*\\smss.exe","*\\wininit.exe","*\\userinit.exe","*\\spoolsv.exe","*\\taskhost.exe") - | stats count min(_time) as firstTime max(_time) as lastTime by EventID Guid NewThreadId - ProcessID SecurityID SourceImage SourceProcessGuid SourceProcessId StartAddress - StartFunction StartModule TargetImage TargetProcessGuid TargetProcessId UserID dest - parent_process_exec parent_process_guid parent_process_id parent_process_name parent_process_path - process_exec process_guid process_id process_name process_path signature signature_id - user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `powershell_remote_thread_to_known_windows_process_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, Create Remote thread from your endpoints. If you are - using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter - known instances of create remote thread may be used. + - Sysmon EventID 8 +search: '`sysmon` EventCode = 8 parent_process_name IN ("powershell_ise.exe", "powershell.exe") TargetImage IN ("*\\svchost.exe","*\\csrss.exe" "*\\gpupdate.exe", "*\\explorer.exe","*\\services.exe","*\\winlogon.exe","*\\smss.exe","*\\wininit.exe","*\\userinit.exe","*\\spoolsv.exe","*\\taskhost.exe") | stats count min(_time) as firstTime max(_time) as lastTime by EventID Guid NewThreadId ProcessID SecurityID SourceImage SourceProcessGuid SourceProcessId StartAddress StartFunction StartModule TargetImage TargetProcessGuid TargetProcessId UserID dest parent_process_exec parent_process_guid parent_process_id parent_process_name parent_process_path process_exec process_guid process_id process_name process_path signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_remote_thread_to_known_windows_process_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, Create Remote thread from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances of create remote thread may be used. known_false_positives: No false positives have been identified at this time. references: -- https://thedfirreport.com/2021/01/11/trickbot-still-alive-and-well/ + - https://thedfirreport.com/2021/01/11/trickbot-still-alive-and-well/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious powershell process $process_name$ that tries to create a remote - thread on target process $TargetImage$ on host $dest$ - risk_objects: - - field: dest - type: system - score: 63 - threat_objects: - - field: process_name - type: process_name + message: A suspicious powershell process $process_name$ that tries to create a remote thread on target process $TargetImage$ on host $dest$ + risk_objects: + - field: dest + type: system + score: 63 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Trickbot - asset_type: Endpoint - mitre_attack_id: - - T1055 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Trickbot + asset_type: Endpoint + mitre_attack_id: + - T1055 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_remove_windows_defender_directory.yml b/detections/endpoint/powershell_remove_windows_defender_directory.yml index ba65ed32c5..29fd83ef72 100644 --- a/detections/endpoint/powershell_remove_windows_defender_directory.yml +++ b/detections/endpoint/powershell_remove_windows_defender_directory.yml @@ -5,69 +5,48 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk status: production type: TTP -description: - The following analytic detects a suspicious PowerShell command attempting - to delete the Windows Defender directory. It leverages PowerShell Script Block Logging - to identify commands containing "rmdir" and targeting the Windows Defender path. - This activity is significant as it may indicate an attempt to disable or corrupt - Windows Defender, a key security component. If confirmed malicious, this action - could allow an attacker to bypass endpoint protection, facilitating further malicious - activities without detection. +description: The following analytic detects a suspicious PowerShell command attempting to delete the Windows Defender directory. It leverages PowerShell Script Block Logging to identify commands containing "rmdir" and targeting the Windows Defender path. This activity is significant as it may indicate an attempt to disable or corrupt Windows Defender, a key security component. If confirmed malicious, this action could allow an attacker to bypass endpoint protection, facilitating further malicious activities without detection. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText = "*rmdir *" AND ScriptBlockText - = "*\\Microsoft\\Windows Defender*" | fillnull | stats count min(_time) as firstTime - max(_time) as lastTime by dest signature signature_id user_id vendor_product EventID - Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `powershell_remove_windows_defender_directory_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - Powershell Script Block Logging 4104 +search: '`powershell` EventCode=4104 ScriptBlockText = "*rmdir *" AND ScriptBlockText = "*\\Microsoft\\Windows Defender*" | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_remove_windows_defender_directory_filter`' +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. known_false_positives: No false positives have been identified at this time. references: - - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ drilldown_searches: - - name: View the detection results for - "$Computer$" and "$user$" - search: '%original_detection_search% | search Computer = "$Computer$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$Computer$" and "$user$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" and "$user$" + search: '%original_detection_search% | search Computer = "$Computer$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: suspicious powershell script $ScriptBlockText$ was executed on the $dest$ - risk_objects: - - field: dest - type: system - score: 90 - - field: user_id - type: user - score: 90 - threat_objects: [] + message: suspicious powershell script $ScriptBlockText$ was executed on the $dest$ + risk_objects: + - field: dest + type: system + score: 90 + - field: user_id + type: user + score: 90 + threat_objects: [] tags: - analytic_story: - - Data Destruction - - WhisperGate - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Destruction + - WhisperGate + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_script_block_with_url_chain.yml b/detections/endpoint/powershell_script_block_with_url_chain.yml index c8a7e40279..bc279b78d7 100644 --- a/detections/endpoint/powershell_script_block_with_url_chain.yml +++ b/detections/endpoint/powershell_script_block_with_url_chain.yml @@ -1,76 +1,55 @@ name: PowerShell Script Block With URL Chain id: 4a3f2a7d-6402-4e64-a76a-869588ec3b57 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Steven Dick status: production type: TTP -description: The following analytic identifies suspicious PowerShell script execution - via EventCode 4104 that contains multiple URLs within a function or array. It leverages - PowerShell operational logs to detect script blocks with embedded URLs, often indicative - of obfuscated scripts or those attempting to download secondary payloads. This activity - is significant as it may signal an attempt to execute malicious code or download - additional malware. If confirmed malicious, this could lead to code execution, further - system compromise, or data exfiltration. Review parallel processes and the full - script block for additional context and related artifacts. +description: The following analytic identifies suspicious PowerShell script execution via EventCode 4104 that contains multiple URLs within a function or array. It leverages PowerShell operational logs to detect script blocks with embedded URLs, often indicative of obfuscated scripts or those attempting to download secondary payloads. This activity is significant as it may signal an attempt to execute malicious code or download additional malware. If confirmed malicious, this could lead to code execution, further system compromise, or data exfiltration. Review parallel processes and the full script block for additional context and related artifacts. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 ScriptBlockText IN ("*http:*","*https:*") | regex - ScriptBlockText="(\"?(https?:\/\/(?:www\.)?[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b(?:[-a-zA-Z0-9()@:%_\+.~#?&\/=]*))\"?(?:,|\))?){2,}" - | rex max_match=20 field=ScriptBlockText "(?https?:\/\/(?:www\.)?[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b(?:[-a-zA-Z0-9()@:%_\+.~#?&\/=]*))" - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `powershell_script_block_with_url_chain_filter`' -how_to_implement: The following analytic requires PowerShell operational logs to be - imported. Modify the powershell macro as needed to match the sourcetype or add index. - This analytic is specific to 4104, or PowerShell Script Block Logging. + - Powershell Script Block Logging 4104 +search: '`powershell` EventCode=4104 ScriptBlockText IN ("*http:*","*https:*") | regex ScriptBlockText="(\"?(https?:\/\/(?:www\.)?[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b(?:[-a-zA-Z0-9()@:%_\+.~#?&\/=]*))\"?(?:,|\))?){2,}" | rex max_match=20 field=ScriptBlockText "(?https?:\/\/(?:www\.)?[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b(?:[-a-zA-Z0-9()@:%_\+.~#?&\/=]*))" | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_script_block_with_url_chain_filter`' +how_to_implement: The following analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. known_false_positives: No false positives have been identified at this time. references: -- https://www.mandiant.com/resources/blog/tracking-evolution-gootloader-operations -- https://thedfirreport.com/2022/05/09/seo-poisoning-a-gootloader-story/ -- https://attack.mitre.org/techniques/T1059/001/ + - https://www.mandiant.com/resources/blog/tracking-evolution-gootloader-operations + - https://thedfirreport.com/2022/05/09/seo-poisoning-a-gootloader-story/ + - https://attack.mitre.org/techniques/T1059/001/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious powershell script used by $user_id$ on host $dest$ contains - URLs in an array, this is commonly used for malware. - risk_objects: - - field: dest - type: system - score: 80 - - field: user_id - type: user - score: 80 - threat_objects: [] + message: A suspicious powershell script used by $user_id$ on host $dest$ contains URLs in an array, this is commonly used for malware. + risk_objects: + - field: dest + type: system + score: 80 + - field: user_id + type: user + score: 80 + threat_objects: [] tags: - analytic_story: - - Malicious PowerShell - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - - T1105 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Malicious PowerShell + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + - T1105 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/gootloader/partial_ttps/windows-powershell-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/gootloader/partial_ttps/windows-powershell-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_start_bitstransfer.yml b/detections/endpoint/powershell_start_bitstransfer.yml index 37353c7279..10a2aa57b3 100644 --- a/detections/endpoint/powershell_start_bitstransfer.yml +++ b/detections/endpoint/powershell_start_bitstransfer.yml @@ -1,86 +1,68 @@ name: PowerShell Start-BitsTransfer id: 39e2605a-90d8-11eb-899e-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of the PowerShell command - `Start-BitsTransfer`, which can be used for file transfers, including potential - data exfiltration. It leverages data from Endpoint Detection and Response (EDR) - agents, focusing on process creation events and command-line arguments. This activity - is significant because `Start-BitsTransfer` can be abused by adversaries to upload - sensitive files to remote locations, posing a risk of data loss. If confirmed malicious, - this could lead to unauthorized data exfiltration, compromising sensitive information - and potentially leading to further exploitation of the network. +description: The following analytic detects the execution of the PowerShell command `Start-BitsTransfer`, which can be used for file transfers, including potential data exfiltration. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events and command-line arguments. This activity is significant because `Start-BitsTransfer` can be abused by adversaries to upload sensitive files to remote locations, posing a risk of data loss. If confirmed malicious, this could lead to unauthorized data exfiltration, compromising sensitive information and potentially leading to further exploitation of the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_powershell` Processes.process=*start-bitstransfer* - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `powershell_start_bitstransfer_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Limited false positives. It is possible administrators will - utilize Start-BitsTransfer for administrative tasks, otherwise filter based parent - process or command-line arguments. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_powershell` Processes.process=*start-bitstransfer* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `powershell_start_bitstransfer_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Limited false positives. It is possible administrators will utilize Start-BitsTransfer for administrative tasks, otherwise filter based parent process or command-line arguments. references: -- https://isc.sans.edu/diary/Investigating+Microsoft+BITS+Activity/23281 -- https://docs.microsoft.com/en-us/windows/win32/bits/using-windows-powershell-to-create-bits-transfer-jobs + - https://isc.sans.edu/diary/Investigating+Microsoft+BITS+Activity/23281 + - https://docs.microsoft.com/en-us/windows/win32/bits/using-windows-powershell-to-create-bits-transfer-jobs drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious process $process_name$ with commandline $process$ that are - related to bittransfer functionality in host $dest$ - risk_objects: - - field: dest - type: system - score: 56 - - field: user - type: user - score: 56 - threat_objects: [] + message: A suspicious process $process_name$ with commandline $process$ that are related to bittransfer functionality in host $dest$ + risk_objects: + - field: dest + type: system + score: 56 + - field: user + type: user + score: 56 + threat_objects: [] tags: - analytic_story: - - BITS Jobs - - Gozi Malware - asset_type: Endpoint - mitre_attack_id: - - T1197 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - BITS Jobs + - Gozi Malware + asset_type: Endpoint + mitre_attack_id: + - T1197 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_start_or_stop_service.yml b/detections/endpoint/powershell_start_or_stop_service.yml index f93f4323ce..9b0219a072 100644 --- a/detections/endpoint/powershell_start_or_stop_service.yml +++ b/detections/endpoint/powershell_start_or_stop_service.yml @@ -1,76 +1,61 @@ name: PowerShell Start or Stop Service id: 04207f8a-e08d-4ee6-be26-1e0c4488b04a -version: 8 -date: '2025-10-14' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk type: Anomaly status: production data_source: - - Powershell Script Block Logging 4104 -description: - The following analytic identifies the use of PowerShell's Start-Service - or Stop-Service cmdlets on an endpoint. It leverages PowerShell Script Block Logging - to detect these commands. This activity is significant because attackers can manipulate - services to disable or stop critical functions, causing system instability or disrupting - business operations. If confirmed malicious, this behavior could allow attackers - to disable security services, evade detection, or disrupt essential services, leading - to potential system downtime and compromised security. -search: - '`powershell` EventCode=4104 ScriptBlockText IN ("*start-service*", "*stop-service*") - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `powershell_start_or_stop_service_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - This behavior may be noisy, as these cmdlets are commonly used - by system administrators or other legitimate users to manage services. Therefore, - it is recommended not to enable this analytic as a direct finding Instead, it should - be used as part of a broader set of security controls to detect and investigate - potential threats. + - Powershell Script Block Logging 4104 +description: The following analytic identifies the use of PowerShell's Start-Service or Stop-Service cmdlets on an endpoint. It leverages PowerShell Script Block Logging to detect these commands. This activity is significant because attackers can manipulate services to disable or stop critical functions, causing system instability or disrupting business operations. If confirmed malicious, this behavior could allow attackers to disable security services, evade detection, or disrupt essential services, leading to potential system downtime and compromised security. +search: |- + `powershell` EventCode=4104 ScriptBlockText IN ("*start-service*", "*stop-service*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `powershell_start_or_stop_service_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: This behavior may be noisy, as these cmdlets are commonly used by system administrators or other legitimate users to manage services. Therefore, it is recommended not to enable this analytic as a direct finding Instead, it should be used as part of a broader set of security controls to detect and investigate potential threats. references: - - https://learn-powershell.net/2012/01/15/startingstopping-and-restarting-remote-services-with-powershell/ - - https://learn.microsoft.com/en-us/powershell/module/microsoft.powershell.management/start-service?view=powershell-7.3 + - https://learn-powershell.net/2012/01/15/startingstopping-and-restarting-remote-services-with-powershell/ + - https://learn.microsoft.com/en-us/powershell/module/microsoft.powershell.management/start-service?view=powershell-7.3 drilldown_searches: - - name: View the detection results for - "$Computer$" - search: '%original_detection_search% | search Computer = "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$Computer$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" + search: '%original_detection_search% | search Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: PowerShell was identified attempting to start or stop a service on $dest$. - risk_objects: - - field: dest - type: system - score: 10 - threat_objects: [] + message: PowerShell was identified attempting to start or stop a service on $dest$. + risk_objects: + - field: dest + type: system + score: 10 + threat_objects: [] tags: - analytic_story: - - Scattered Lapsus$ Hunters - - Active Directory Lateral Movement - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Scattered Lapsus$ Hunters + - Active Directory Lateral Movement + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/start_stop_service_windows-powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/start_stop_service_windows-powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_using_memory_as_backing_store.yml b/detections/endpoint/powershell_using_memory_as_backing_store.yml index 395fe9043f..78abdd08b9 100644 --- a/detections/endpoint/powershell_using_memory_as_backing_store.yml +++ b/detections/endpoint/powershell_using_memory_as_backing_store.yml @@ -1,85 +1,72 @@ name: Powershell Using memory As Backing Store id: c396a0c4-c9f2-11eb-b4f5-acde48001122 -version: 9 -date: '2025-06-24' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: - The following analytic detects suspicious PowerShell script execution - using memory streams as a backing store, identified via EventCode 4104. It leverages - PowerShell Script Block Logging to capture scripts that create new objects with - memory streams, often used to decompress and execute payloads in memory. This activity - is significant as it indicates potential in-memory execution of malicious code, - bypassing traditional file-based detection. If confirmed malicious, this technique - could allow attackers to execute arbitrary code, maintain persistence, or escalate - privileges without leaving a trace on the disk. +description: The following analytic detects suspicious PowerShell script execution using memory streams as a backing store, identified via EventCode 4104. It leverages PowerShell Script Block Logging to capture scripts that create new objects with memory streams, often used to decompress and execute payloads in memory. This activity is significant as it indicates potential in-memory execution of malicious code, bypassing traditional file-based detection. If confirmed malicious, this technique could allow attackers to execute arbitrary code, maintain persistence, or escalate privileges without leaving a trace on the disk. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText = *New-Object* ScriptBlockText - = *IO.MemoryStream* | fillnull | stats count min(_time) as firstTime max(_time) - as lastTime by dest signature signature_id user_id vendor_product EventID Guid Opcode - Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `powershell_using_memory_as_backing_store_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - powershell may used this function to store out object into - memory. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText = *New-Object* ScriptBlockText = *IO.MemoryStream* + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `powershell_using_memory_as_backing_store_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: powershell may used this function to store out object into memory. references: - - https://web.archive.org/web/20201112031711/https://www.carbonblack.com/blog/decoding-malicious-powershell-streams/ - - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. - - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 - - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf - - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ - - https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/ + - https://web.archive.org/web/20201112031711/https://www.carbonblack.com/blog/decoding-malicious-powershell-streams/ + - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 + - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf + - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ + - https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/ drilldown_searches: - - name: View the detection results for - "$dest$" and "$user_id$" - search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user_id$" + search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A PowerShell script contains memorystream command on host $dest$. - risk_objects: - - field: dest - type: system - score: 40 - - field: user_id - type: user - score: 40 - threat_objects: [] + message: A PowerShell script contains memorystream command on host $dest$. + risk_objects: + - field: dest + type: system + score: 40 + - field: user_id + type: user + score: 40 + threat_objects: [] tags: - analytic_story: - - Data Destruction - - MoonPeak - - Medusa Ransomware - - Hermetic Wiper - - IcedID - - Malicious PowerShell - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Destruction + - MoonPeak + - Medusa Ransomware + - Hermetic Wiper + - IcedID + - Malicious PowerShell + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/pwsh/windows-powershell-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/pwsh/windows-powershell-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_webrequest_using_memory_stream.yml b/detections/endpoint/powershell_webrequest_using_memory_stream.yml index 9da38cdbe8..ab7519a24f 100644 --- a/detections/endpoint/powershell_webrequest_using_memory_stream.yml +++ b/detections/endpoint/powershell_webrequest_using_memory_stream.yml @@ -1,77 +1,69 @@ name: PowerShell WebRequest Using Memory Stream id: 103affa6-924a-4b53-aff4-1d5075342aab -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Steven Dick status: production type: TTP -description: The following analytic detects the use of .NET classes in PowerShell - to download a URL payload directly into memory, a common fileless malware staging - technique. It leverages PowerShell Script Block Logging (EventCode=4104) to identify - suspicious PowerShell commands involving `system.net.webclient`, `system.net.webrequest`, - and `IO.MemoryStream`. This activity is significant as it indicates potential fileless - malware execution, which is harder to detect and can bypass traditional file-based - defenses. If confirmed malicious, this technique could allow attackers to execute - code in memory, evade detection, and maintain persistence in the environment. +description: The following analytic detects the use of .NET classes in PowerShell to download a URL payload directly into memory, a common fileless malware staging technique. It leverages PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell commands involving `system.net.webclient`, `system.net.webrequest`, and `IO.MemoryStream`. This activity is significant as it indicates potential fileless malware execution, which is harder to detect and can bypass traditional file-based defenses. If confirmed malicious, this technique could allow attackers to execute code in memory, evade detection, and maintain persistence in the environment. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 ScriptBlockText IN ("*system.net.webclient*","*system.net.webrequest*") - AND ScriptBlockText="*IO.MemoryStream*" | fillnull | stats count min(_time) as firstTime - max(_time) as lastTime by dest signature signature_id user_id vendor_product EventID - Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `powershell_webrequest_using_memory_stream_filter`' -how_to_implement: The following analytic requires PowerShell operational logs to be - imported. Modify the powershell macro as needed to match the sourcetype or add index. - This analytic is specific to 4104, or PowerShell Script Block Logging. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText IN ("*system.net.webclient*","*system.net.webrequest*") AND ScriptBlockText="*IO.MemoryStream*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `powershell_webrequest_using_memory_stream_filter` +how_to_implement: The following analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. known_false_positives: No false positives have been identified at this time. references: -- https://www.mandiant.com/resources/blog/tracking-evolution-gootloader-operations -- https://thedfirreport.com/2022/05/09/seo-poisoning-a-gootloader-story/ -- https://attack.mitre.org/techniques/T1059/001/ + - https://www.mandiant.com/resources/blog/tracking-evolution-gootloader-operations + - https://thedfirreport.com/2022/05/09/seo-poisoning-a-gootloader-story/ + - https://attack.mitre.org/techniques/T1059/001/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Powershell webrequest to memory stream behavior. Possible fileless malware - staging on $dest$ by $user_id$. - risk_objects: - - field: dest - type: system - score: 80 - - field: user_id - type: user - score: 80 - threat_objects: [] + message: Powershell webrequest to memory stream behavior. Possible fileless malware staging on $dest$ by $user_id$. + risk_objects: + - field: dest + type: system + score: 80 + - field: user_id + type: user + score: 80 + threat_objects: [] tags: - analytic_story: - - MoonPeak - - Medusa Ransomware - - Malicious PowerShell - - PHP-CGI RCE Attack on Japanese Organizations - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - - T1105 - - T1027.011 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - MoonPeak + - Medusa Ransomware + - Malicious PowerShell + - PHP-CGI RCE Attack on Japanese Organizations + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + - T1105 + - T1027.011 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/gootloader/partial_ttps/windows-powershell-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/gootloader/partial_ttps/windows-powershell-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/powershell_windows_defender_exclusion_commands.yml b/detections/endpoint/powershell_windows_defender_exclusion_commands.yml index d04bb38d72..43140b78d4 100644 --- a/detections/endpoint/powershell_windows_defender_exclusion_commands.yml +++ b/detections/endpoint/powershell_windows_defender_exclusion_commands.yml @@ -1,78 +1,71 @@ name: Powershell Windows Defender Exclusion Commands id: 907ac95c-4dd9-11ec-ba2c-acde48001122 -version: 10 -date: '2025-11-20' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the use of PowerShell commands to add - or set Windows Defender exclusions. It leverages EventCode 4104 to identify suspicious - `Add-MpPreference` or `Set-MpPreference` commands with exclusion parameters. This - activity is significant because adversaries often use it to bypass Windows Defender, - allowing malicious code to execute without detection. If confirmed malicious, this - behavior could enable attackers to evade antivirus defenses, maintain persistence, - and execute further malicious activities undetected. +description: The following analytic detects the use of PowerShell commands to add or set Windows Defender exclusions. It leverages EventCode 4104 to identify suspicious `Add-MpPreference` or `Set-MpPreference` commands with exclusion parameters. This activity is significant because adversaries often use it to bypass Windows Defender, allowing malicious code to execute without detection. If confirmed malicious, this behavior could enable attackers to evade antivirus defenses, maintain persistence, and execute further malicious activities undetected. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 (ScriptBlockText = "*Add-MpPreference *" OR ScriptBlockText - = "*Set-MpPreference *") AND ScriptBlockText = "*-exclusion*" | fillnull | stats - count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_windows_defender_exclusion_commands_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure - that this registry was included in your config files ex. sysmon config to be monitored. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 (ScriptBlockText = "*Add-MpPreference *" OR ScriptBlockText = "*Set-MpPreference *") AND ScriptBlockText = "*-exclusion*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `powershell_windows_defender_exclusion_commands_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored. known_false_positives: admin or user may choose to use this windows features. references: -- https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html -- https://app.any.run/tasks/cf1245de-06a7-4366-8209-8e3006f2bfe5/ -- https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html + - https://app.any.run/tasks/cf1245de-06a7-4366-8209-8e3006f2bfe5/ + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ drilldown_searches: -- name: View the detection results for - "$user_id$" and "$dest$" - search: '%original_detection_search% | search user_id = "$user_id$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user_id$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user_id$" and "$dest$" + search: '%original_detection_search% | search user_id = "$user_id$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user_id$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Exclusion command $ScriptBlockText$ executed on $dest$ - risk_objects: - - field: user_id - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: [] + message: Exclusion command $ScriptBlockText$ executed on $dest$ + risk_objects: + - field: user_id + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - CISA AA22-320A - - AgentTesla - - Remcos - - Windows Defense Evasion Tactics - - Data Destruction - - WhisperGate - - Warzone RAT - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA22-320A + - AgentTesla + - Remcos + - Windows Defense Evasion Tactics + - Data Destruction + - WhisperGate + - Warzone RAT + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/powershell_windows_defender_exclusion_commands/windows-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/powershell_windows_defender_exclusion_commands/windows-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/prevent_automatic_repair_mode_using_bcdedit.yml b/detections/endpoint/prevent_automatic_repair_mode_using_bcdedit.yml index 0defddddef..46a67fba62 100644 --- a/detections/endpoint/prevent_automatic_repair_mode_using_bcdedit.yml +++ b/detections/endpoint/prevent_automatic_repair_mode_using_bcdedit.yml @@ -1,85 +1,67 @@ name: Prevent Automatic Repair Mode using Bcdedit id: 7742aa92-c9d9-11eb-bbfc-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of "bcdedit.exe" with parameters - to set the boot status policy to ignore all failures. It leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process names and command-line - arguments. This activity is significant because it can indicate an attempt by ransomware - to prevent a compromised machine from booting into automatic repair mode, thereby - hindering recovery efforts. If confirmed malicious, this action could allow attackers - to maintain control over the infected system, complicating remediation and potentially - leading to further damage. +description: The following analytic detects the execution of "bcdedit.exe" with parameters to set the boot status policy to ignore all failures. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because it can indicate an attempt by ransomware to prevent a compromised machine from booting into automatic repair mode, thereby hindering recovery efforts. If confirmed malicious, this action could allow attackers to maintain control over the infected system, complicating remediation and potentially leading to further damage. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "bcdedit.exe" - Processes.process = "*bootstatuspolicy*" Processes.process = "*ignoreallfailures*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| - `prevent_automatic_repair_mode_using_bcdedit_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrators may modify the boot configuration ignore failure - during testing and debugging. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "bcdedit.exe" Processes.process = "*bootstatuspolicy*" Processes.process = "*ignoreallfailures*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `prevent_automatic_repair_mode_using_bcdedit_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators may modify the boot configuration ignore failure during testing and debugging. references: -- https://jsac.jpcert.or.jp/archive/2020/pdf/JSAC2020_1_tamada-yamazaki-nakatsuru_en.pdf + - https://jsac.jpcert.or.jp/archive/2020/pdf/JSAC2020_1_tamada-yamazaki-nakatsuru_en.pdf drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious process $process_name$ with process id $process_id$ contains - commandline $process$ to ignore all bcdedit execution failure in host $dest$ - risk_objects: - - field: dest - type: system - score: 56 - - field: user - type: user - score: 56 - threat_objects: [] + message: A suspicious process $process_name$ with process id $process_id$ contains commandline $process$ to ignore all bcdedit execution failure in host $dest$ + risk_objects: + - field: dest + type: system + score: 56 + - field: user + type: user + score: 56 + threat_objects: [] tags: - analytic_story: - - Ransomware - - Chaos Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1490 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - Chaos Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1490 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/print_processor_registry_autostart.yml b/detections/endpoint/print_processor_registry_autostart.yml index 6e1b378c86..ceeeebce06 100644 --- a/detections/endpoint/print_processor_registry_autostart.yml +++ b/detections/endpoint/print_processor_registry_autostart.yml @@ -5,75 +5,51 @@ date: '2025-06-10' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects suspicious modifications or new entries - in the Print Processor registry path. It leverages registry activity data from the - Endpoint data model to identify changes in the specified registry path. This activity - is significant because the Print Processor registry is known to be exploited by - APT groups like Turla for persistence and privilege escalation. If confirmed malicious, - this could allow an attacker to execute a malicious DLL payload by restarting the - spoolsv.exe process, leading to potential control over the compromised machine. +description: The following analytic detects suspicious modifications or new entries in the Print Processor registry path. It leverages registry activity data from the Endpoint data model to identify changes in the specified registry path. This activity is significant because the Print Processor registry is known to be exploited by APT groups like Turla for persistence and privilege escalation. If confirmed malicious, this could allow an attacker to execute a malicious DLL payload by restarting the spoolsv.exe process, leading to potential control over the compromised machine. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime - max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path - ="*\\Control\\Print\\Environments\\Windows x64\\Print Processors*" by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` - | `print_processor_registry_autostart_filter`' -how_to_implement: To successfully implement this search, you must be ingesting data - that records registry activity from your hosts to populate the endpoint data model - in the registry node. This is typically populated via endpoint detection-and-response - product, such as Carbon Black or endpoint data sources, such as Sysmon. The data - used for this search is typically generated via logs that report reads and writes - to the registry. -known_false_positives: possible new printer installation may add driver component - on this registry. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path ="*\\Control\\Print\\Environments\\Windows x64\\Print Processors*" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `print_processor_registry_autostart_filter`' +how_to_implement: To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. +known_false_positives: possible new printer installation may add driver component on this registry. references: -- https://attack.mitre.org/techniques/T1547/012/ -- https://www.welivesecurity.com/2020/05/21/no-game-over-winnti-group/ + - https://attack.mitre.org/techniques/T1547/012/ + - https://www.welivesecurity.com/2020/05/21/no-game-over-winnti-group/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: modified/added/deleted registry entry $registry_path$ on $dest$ - risk_objects: - - field: dest - type: system - score: 80 - - field: user - type: user - score: 80 - threat_objects: [] + message: modified/added/deleted registry entry $registry_path$ on $dest$ + risk_objects: + - field: dest + type: system + score: 80 + - field: user + type: user + score: 80 + threat_objects: [] tags: - analytic_story: - - Data Destruction - - Windows Privilege Escalation - - Hermetic Wiper - - Windows Persistence Techniques - asset_type: Endpoint - mitre_attack_id: - - T1547.012 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Destruction + - Windows Privilege Escalation + - Hermetic Wiper + - Windows Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1547.012 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/print_reg/sysmon_print.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/print_reg/sysmon_print.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/print_spooler_adding_a_printer_driver.yml b/detections/endpoint/print_spooler_adding_a_printer_driver.yml index 5583667544..1fbf2d3dbe 100644 --- a/detections/endpoint/print_spooler_adding_a_printer_driver.yml +++ b/detections/endpoint/print_spooler_adding_a_printer_driver.yml @@ -1,72 +1,62 @@ name: Print Spooler Adding A Printer Driver id: 313681a2-da8e-11eb-adad-acde48001122 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Michael Haag, Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the addition of new printer drivers by - monitoring Windows PrintService operational logs, specifically EventCode 316. This - detection leverages log data to identify messages indicating the addition or update - of printer drivers, such as "kernelbase.dll" and "UNIDRV.DLL." This activity is - significant as it may indicate exploitation attempts related to vulnerabilities - like CVE-2021-34527 (PrintNightmare). If confirmed malicious, attackers could gain - code execution or escalate privileges, potentially compromising the affected system. - Immediate isolation and investigation of the endpoint are recommended. +description: The following analytic detects the addition of new printer drivers by monitoring Windows PrintService operational logs, specifically EventCode 316. This detection leverages log data to identify messages indicating the addition or update of printer drivers, such as "kernelbase.dll" and "UNIDRV.DLL." This activity is significant as it may indicate exploitation attempts related to vulnerabilities like CVE-2021-34527 (PrintNightmare). If confirmed malicious, attackers could gain code execution or escalate privileges, potentially compromising the affected system. Immediate isolation and investigation of the endpoint are recommended. data_source: -- Windows Event Log Printservice 316 -search: '`printservice` EventCode=316 category = "Adding a printer driver" Message - = "*kernelbase.dll,*" Message = "*UNIDRV.DLL,*" Message = "*.DLL.*" | stats count - min(_time) as firstTime max(_time) as lastTime by OpCode EventCode ComputerName - Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `print_spooler_adding_a_printer_driver_filter`' -how_to_implement: You will need to ensure PrintService Admin and Operational logs - are being logged to Splunk from critical or all systems. + - Windows Event Log Printservice 316 +search: |- + `printservice` EventCode=316 category = "Adding a printer driver" Message = "*kernelbase.dll,*" Message = "*UNIDRV.DLL,*" Message = "*.DLL.*" + | stats count min(_time) as firstTime max(_time) as lastTime + BY OpCode EventCode ComputerName + Message + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `print_spooler_adding_a_printer_driver_filter` +how_to_implement: You will need to ensure PrintService Admin and Operational logs are being logged to Splunk from critical or all systems. known_false_positives: No false positives have been identified at this time. references: -- https://twitter.com/MalwareJake/status/1410421445608476679?s=20 -- https://www.truesec.com/hub/blog/fix-for-printnightmare-cve-2021-1675-exploit-to-keep-your-print-servers-running-while-a-patch-is-not-available -- https://www.truesec.com/hub/blog/exploitable-critical-rce-vulnerability-allows-regular-users-to-fully-compromise-active-directory-printnightmare-cve-2021-1675 -- https://www.reddit.com/r/msp/comments/ob6y02/critical_vulnerability_printnightmare_exposes + - https://twitter.com/MalwareJake/status/1410421445608476679?s=20 + - https://www.truesec.com/hub/blog/fix-for-printnightmare-cve-2021-1675-exploit-to-keep-your-print-servers-running-while-a-patch-is-not-available + - https://www.truesec.com/hub/blog/exploitable-critical-rce-vulnerability-allows-regular-users-to-fully-compromise-active-directory-printnightmare-cve-2021-1675 + - https://www.reddit.com/r/msp/comments/ob6y02/critical_vulnerability_printnightmare_exposes drilldown_searches: -- name: View the detection results for - "$ComputerName$" - search: '%original_detection_search% | search ComputerName = "$ComputerName$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$ComputerName$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$ComputerName$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$ComputerName$" + search: '%original_detection_search% | search ComputerName = "$ComputerName$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$ComputerName$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$ComputerName$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious print driver was loaded on endpoint $ComputerName$. - risk_objects: - - field: ComputerName - type: system - score: 72 - threat_objects: [] + message: Suspicious print driver was loaded on endpoint $ComputerName$. + risk_objects: + - field: ComputerName + type: system + score: 72 + threat_objects: [] tags: - analytic_story: - - PrintNightmare CVE-2021-34527 - - Black Basta Ransomware - asset_type: Endpoint - cve: - - CVE-2021-34527 - - CVE-2021-1675 - mitre_attack_id: - - T1547.012 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - PrintNightmare CVE-2021-34527 + - Black Basta Ransomware + asset_type: Endpoint + cve: + - CVE-2021-34527 + - CVE-2021-1675 + mitre_attack_id: + - T1547.012 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-printservice_operational.log - source: WinEventLog:Microsoft-Windows-PrintService/Operational - sourcetype: WinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-printservice_operational.log + source: WinEventLog:Microsoft-Windows-PrintService/Operational + sourcetype: WinEventLog diff --git a/detections/endpoint/print_spooler_failed_to_load_a_plug_in.yml b/detections/endpoint/print_spooler_failed_to_load_a_plug_in.yml index 924db0db59..5388488eb2 100644 --- a/detections/endpoint/print_spooler_failed_to_load_a_plug_in.yml +++ b/detections/endpoint/print_spooler_failed_to_load_a_plug_in.yml @@ -5,69 +5,51 @@ date: '2025-05-02' author: Mauricio Velazco, Michael Haag, Splunk status: production type: TTP -description: The following analytic detects driver load errors in the Windows PrintService - Admin logs, specifically identifying issues related to CVE-2021-34527 (PrintNightmare). - It triggers on error messages indicating the print spooler failed to load a plug-in - module, such as "meterpreter.dll," with error code 0x45A. This detection method - leverages specific event codes and error messages. This activity is significant - as it may indicate an exploitation attempt of a known vulnerability. If confirmed - malicious, an attacker could gain unauthorized code execution on the affected system, - leading to potential system compromise. +description: The following analytic detects driver load errors in the Windows PrintService Admin logs, specifically identifying issues related to CVE-2021-34527 (PrintNightmare). It triggers on error messages indicating the print spooler failed to load a plug-in module, such as "meterpreter.dll," with error code 0x45A. This detection method leverages specific event codes and error messages. This activity is significant as it may indicate an exploitation attempt of a known vulnerability. If confirmed malicious, an attacker could gain unauthorized code execution on the affected system, leading to potential system compromise. data_source: -- Windows Event Log Printservice 808 -- Windows Event Log Printservice 4909 -search: '`printservice` ((ErrorCode="0x45A" (EventCode="808" OR EventCode="4909")) - OR ("The print spooler failed to load a plug-in module" OR "\\drivers\\x64\\")) - | stats count min(_time) as firstTime max(_time) as lastTime by OpCode EventCode - ComputerName Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `print_spooler_failed_to_load_a_plug_in_filter`' -how_to_implement: You will need to ensure PrintService Admin and Operational logs - are being logged to Splunk from critical or all systems. + - Windows Event Log Printservice 808 + - Windows Event Log Printservice 4909 +search: '`printservice` ((ErrorCode="0x45A" (EventCode="808" OR EventCode="4909")) OR ("The print spooler failed to load a plug-in module" OR "\\drivers\\x64\\")) | stats count min(_time) as firstTime max(_time) as lastTime by OpCode EventCode ComputerName Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `print_spooler_failed_to_load_a_plug_in_filter`' +how_to_implement: You will need to ensure PrintService Admin and Operational logs are being logged to Splunk from critical or all systems. known_false_positives: False positives are unknown and filtering may be required. references: -- https://www.truesec.com/hub/blog/fix-for-printnightmare-cve-2021-1675-exploit-to-keep-your-print-servers-running-while-a-patch-is-not-available -- https://www.truesec.com/hub/blog/exploitable-critical-rce-vulnerability-allows-regular-users-to-fully-compromise-active-directory-printnightmare-cve-2021-1675 -- https://www.reddit.com/r/msp/comments/ob6y02/critical_vulnerability_printnightmare_exposes + - https://www.truesec.com/hub/blog/fix-for-printnightmare-cve-2021-1675-exploit-to-keep-your-print-servers-running-while-a-patch-is-not-available + - https://www.truesec.com/hub/blog/exploitable-critical-rce-vulnerability-allows-regular-users-to-fully-compromise-active-directory-printnightmare-cve-2021-1675 + - https://www.reddit.com/r/msp/comments/ob6y02/critical_vulnerability_printnightmare_exposes drilldown_searches: -- name: View the detection results for - "$ComputerName$" - search: '%original_detection_search% | search ComputerName = "$ComputerName$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$ComputerName$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$ComputerName$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$ComputerName$" + search: '%original_detection_search% | search ComputerName = "$ComputerName$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$ComputerName$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$ComputerName$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious printer spooler errors have occurred on endpoint $ComputerName$ - with EventCode $EventCode$. - risk_objects: - - field: ComputerName - type: system - score: 72 - threat_objects: [] + message: Suspicious printer spooler errors have occurred on endpoint $ComputerName$ with EventCode $EventCode$. + risk_objects: + - field: ComputerName + type: system + score: 72 + threat_objects: [] tags: - analytic_story: - - PrintNightmare CVE-2021-34527 - - Black Basta Ransomware - asset_type: Endpoint - cve: - - CVE-2021-34527 - - CVE-2021-1675 - mitre_attack_id: - - T1547.012 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - PrintNightmare CVE-2021-34527 + - Black Basta Ransomware + asset_type: Endpoint + cve: + - CVE-2021-34527 + - CVE-2021-1675 + mitre_attack_id: + - T1547.012 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-printservice_admin.log - source: WinEventLog:Microsoft-Windows-PrintService/Admin - sourcetype: WinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-printservice_admin.log + source: WinEventLog:Microsoft-Windows-PrintService/Admin + sourcetype: WinEventLog diff --git a/detections/endpoint/process_creating_lnk_file_in_suspicious_location.yml b/detections/endpoint/process_creating_lnk_file_in_suspicious_location.yml index d936d09c48..abfea48eb4 100644 --- a/detections/endpoint/process_creating_lnk_file_in_suspicious_location.yml +++ b/detections/endpoint/process_creating_lnk_file_in_suspicious_location.yml @@ -6,109 +6,104 @@ author: Jose Hernandez, Michael Haag, Splunk status: production type: Anomaly description: | - The following analytic detects a process creating a `.lnk` file in suspicious locations such as `C:\User*` or `*\Local\Temp\*`. - It leverages filesystem and process activity data from the Endpoint data model to identify this behavior. - This activity can be significant because creating `.lnk` files in these directories is a common indicator of spear phishing tools to establish persistence or execute malicious payloads. - If confirmed malicious, this could allow an attacker to maintain persistence, execute arbitrary code, or further compromise the system. + The following analytic detects a process creating a `.lnk` file in suspicious locations such as `C:\User*` or `*\Local\Temp\*`. + It leverages filesystem and process activity data from the Endpoint data model to identify this behavior. + This activity can be significant because creating `.lnk` files in these directories is a common indicator of spear phishing tools to establish persistence or execute malicious payloads. + If confirmed malicious, this could allow an attacker to maintain persistence, execute arbitrary code, or further compromise the system. data_source: - - Sysmon EventID 11 + - Sysmon EventID 11 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime - FROM datamodel=Endpoint.Filesystem where + FROM datamodel=Endpoint.Filesystem where - Filesystem.action="created" - Filesystem.file_name="*.lnk" - Filesystem.file_path IN ( - "*:\\AppData\\Local\\Temp\\*", - "*:\\Temp\\*", - "*:\\Users\\*", - "*:\\Windows\\Temp\\*" - ) - NOT Filesystem.file_path IN ( - "*\\AppData\\Local\\Microsoft\\Windows\\WinX\\*", - "*\\AppData\\Roaming\\Microsoft\\Excel\\*", - "*\\AppData\\Roaming\\Microsoft\\Internet Explorer\\Quick Launch\\*", - "*\\AppData\\Roaming\\Microsoft\\Office\\Recent\\*", - "*\\AppData\\Roaming\\Microsoft\\Windows\\Recent\\*", - "*\\AppData\\Roaming\\Microsoft\\Windows\\Start Menu\\*", - "*\\AppData\\Roaming\\Microsoft\\Word\\*", - "*\\Links\\*", - "*\\OneDrive *" - ) + Filesystem.action="created" + Filesystem.file_name="*.lnk" + Filesystem.file_path IN ( + "*:\\AppData\\Local\\Temp\\*", + "*:\\Temp\\*", + "*:\\Users\\*", + "*:\\Windows\\Temp\\*" + ) + NOT Filesystem.file_path IN ( + "*\\AppData\\Local\\Microsoft\\Windows\\WinX\\*", + "*\\AppData\\Roaming\\Microsoft\\Excel\\*", + "*\\AppData\\Roaming\\Microsoft\\Internet Explorer\\Quick Launch\\*", + "*\\AppData\\Roaming\\Microsoft\\Office\\Recent\\*", + "*\\AppData\\Roaming\\Microsoft\\Windows\\Recent\\*", + "*\\AppData\\Roaming\\Microsoft\\Windows\\Start Menu\\*", + "*\\AppData\\Roaming\\Microsoft\\Word\\*", + "*\\Links\\*", + "*\\OneDrive *" + ) - by Filesystem.action Filesystem.dest Filesystem.file_access_time - Filesystem.file_create_time Filesystem.file_hash - Filesystem.file_modify_time Filesystem.file_name - Filesystem.file_path Filesystem.file_acl Filesystem.file_size - Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product + by Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash + Filesystem.file_modify_time Filesystem.file_name + Filesystem.file_path Filesystem.file_acl Filesystem.file_size + Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product - | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `process_creating_lnk_file_in_suspicious_location_filter` + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `process_creating_lnk_file_in_suspicious_location_filter` how_to_implement: | - You must be ingesting data that records filesystem and process activity - from your hosts to populate the Endpoint data model. This is typically populated - via endpoint detection-and-response product, such as Carbon Black, or endpoint data - sources, such as Sysmon. + You must be ingesting data that records filesystem and process activity + from your hosts to populate the Endpoint data model. This is typically populated + via endpoint detection-and-response product, such as Carbon Black, or endpoint data + sources, such as Sysmon. known_false_positives: | - False positives are expected to occur, since `.lnk` files can be created legitimately - by users or applications. To reduce false positives. To reduce noise, think of joining this to the process that created the LNK file and see if it's a known good process. + False positives are expected to occur, since `.lnk` files can be created legitimately + by users or applications. To reduce false positives. To reduce noise, think of joining this to the process that created the LNK file and see if it's a known good process. references: - - https://attack.mitre.org/techniques/T1566/001/ - - https://www.trendmicro.com/en_us/research/17/e/rising-trend-attackers-using-lnk-files-download-malware.html - - https://twitter.com/pr0xylife/status/1590394227758104576 + - https://attack.mitre.org/techniques/T1566/001/ + - https://www.trendmicro.com/en_us/research/17/e/rising-trend-attackers-using-lnk-files-download-malware.html + - https://twitter.com/pr0xylife/status/1590394227758104576 drilldown_searches: - - name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A shortcut file [$file_name$] was created in $file_path$ on the host $dest$ - risk_objects: - - field: dest - type: system - score: 30 - - field: user - type: user - score: 30 - threat_objects: - - field: file_name - type: file_name - - field: file_path - type: file_path + message: A shortcut file [$file_name$] was created in $file_path$ on the host $dest$ + risk_objects: + - field: dest + type: system + score: 30 + - field: user + type: user + score: 30 + threat_objects: + - field: file_name + type: file_name + - field: file_path + type: file_path tags: - analytic_story: - - Spearphishing Attachments - - Qakbot - - IcedID - - Amadey - - Gozi Malware - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1566.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Spearphishing Attachments + - Qakbot + - IcedID + - Amadey + - Gozi Malware + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1566.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.002/lnk_file_temp_folder/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.002/lnk_file_temp_folder/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/process_deleting_its_process_file_path.yml b/detections/endpoint/process_deleting_its_process_file_path.yml index 8fbbb10f3c..781202513a 100644 --- a/detections/endpoint/process_deleting_its_process_file_path.yml +++ b/detections/endpoint/process_deleting_its_process_file_path.yml @@ -5,80 +5,52 @@ date: '2026-01-14' author: Teoderick Contreras status: production type: TTP -description: The following analytic identifies a process attempting to delete its - own file path, a behavior often associated with defense evasion techniques. This - detection leverages Sysmon EventCode 1 logs, focusing on command lines executed - via cmd.exe that include deletion commands. This activity is significant as it may - indicate malware, such as Clop ransomware, trying to evade detection by removing - its executable file if certain conditions are met. If confirmed malicious, this - could allow the attacker to persist undetected, complicating incident response and - remediation efforts. +description: The following analytic identifies a process attempting to delete its own file path, a behavior often associated with defense evasion techniques. This detection leverages Sysmon EventCode 1 logs, focusing on command lines executed via cmd.exe that include deletion commands. This activity is significant as it may indicate malware, such as Clop ransomware, trying to evade detection by removing its executable file if certain conditions are met. If confirmed malicious, this could allow the attacker to persist undetected, complicating incident response and remediation efforts. data_source: -- Sysmon EventID 1 -search: '`sysmon` EventCode=1 CommandLine = "* /c *" CommandLine = "* del*" Image - = "*\\cmd.exe" | eval result = if(like(process,"%".parent_process."%"), "Found", - "Not Found") | stats min(_time) as firstTime max(_time) as lastTime count by action - dest original_file_name parent_process parent_process_exec parent_process_guid parent_process_id - parent_process_name parent_process_path process process_exec process_guid process_hash - process_id process_integrity_level process_name process_path user user_id vendor_product - result | where result = "Found" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `process_deleting_its_process_file_path_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 +search: '`sysmon` EventCode=1 CommandLine = "* /c *" CommandLine = "* del*" Image = "*\\cmd.exe" | eval result = if(like(process,"%".parent_process."%"), "Found", "Not Found") | stats min(_time) as firstTime max(_time) as lastTime count by action dest original_file_name parent_process parent_process_exec parent_process_guid parent_process_id parent_process_name parent_process_path process process_exec process_guid process_hash process_id process_integrity_level process_name process_path user user_id vendor_product result | where result = "Found" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `process_deleting_its_process_file_path_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://www.mandiant.com/resources/fin11-email-campaigns-precursor-for-ransomware-data-theft -- https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html -- https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://www.mandiant.com/resources/fin11-email-campaigns-precursor-for-ransomware-data-theft + - https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process $process_name$ tries to delete its process path in commandline $process$ - as part of defense evasion in host $dest$ by user $user$ - risk_objects: - - field: dest - type: system - score: 60 - - field: user - type: user - score: 60 - threat_objects: [] + message: A process $process_name$ tries to delete its process path in commandline $process$ as part of defense evasion in host $dest$ by user $user$ + risk_objects: + - field: dest + type: system + score: 60 + - field: user + type: user + score: 60 + threat_objects: [] tags: - analytic_story: - - Clop Ransomware - - Data Destruction - - WhisperGate - - Remcos - asset_type: Endpoint - mitre_attack_id: - - T1070 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Clop Ransomware + - Data Destruction + - WhisperGate + - Remcos + asset_type: Endpoint + mitre_attack_id: + - T1070 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/process_execution_via_wmi.yml b/detections/endpoint/process_execution_via_wmi.yml index df4aca73ef..287bd76260 100644 --- a/detections/endpoint/process_execution_via_wmi.yml +++ b/detections/endpoint/process_execution_via_wmi.yml @@ -5,79 +5,48 @@ date: '2025-05-02' author: Rico Valdez, Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of a process by `WmiPrvSE.exe`, - indicating potential use of WMI (Windows Management Instrumentation) for process - creation. This detection leverages data from Endpoint Detection and Response (EDR) - agents, focusing on process and parent process relationships. This activity is significant - as WMI can be used for lateral movement, remote code execution, or persistence by - attackers. If confirmed malicious, this could allow an attacker to execute arbitrary - commands or scripts, potentially leading to further compromise of the affected system - or network. +description: The following analytic detects the execution of a process by `WmiPrvSE.exe`, indicating potential use of WMI (Windows Management Instrumentation) for process creation. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process relationships. This activity is significant as WMI can be used for lateral movement, remote code execution, or persistence by attackers. If confirmed malicious, this could allow an attacker to execute arbitrary commands or scripts, potentially leading to further compromise of the affected system or network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=WmiPrvSE.exe - NOT (Processes.process IN ("*\\dismhost.exe*")) by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `process_execution_via_wmi_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although unlikely, administrators may use wmi to execute commands - for legitimate purposes. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=WmiPrvSE.exe NOT (Processes.process IN ("*\\dismhost.exe*")) by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `process_execution_via_wmi_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although unlikely, administrators may use wmi to execute commands for legitimate purposes. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A remote instance execution of wmic.exe by WmiPrvSE.exe detected on host - - $dest$ - risk_objects: - - field: dest - type: system - score: 49 - - field: user - type: user - score: 49 - threat_objects: [] + message: A remote instance execution of wmic.exe by WmiPrvSE.exe detected on host - $dest$ + risk_objects: + - field: dest + type: system + score: 49 + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - Suspicious WMI Use - asset_type: Endpoint - mitre_attack_id: - - T1047 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious WMI Use + asset_type: Endpoint + mitre_attack_id: + - T1047 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/process_kill_base_on_file_path.yml b/detections/endpoint/process_kill_base_on_file_path.yml index 01cef30391..4a6ef67164 100644 --- a/detections/endpoint/process_kill_base_on_file_path.yml +++ b/detections/endpoint/process_kill_base_on_file_path.yml @@ -1,84 +1,72 @@ name: Process Kill Base On File Path id: 5ffaa42c-acdb-11eb-9ad3-acde48001122 -version: 10 -date: '2026-01-14' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the use of `wmic.exe` with the `delete` - command to remove an executable path. This detection leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process names, parent processes, - and command-line executions. This activity is significant because it often indicates - the initial stages of an adversary setting up malicious activities, such as cryptocurrency - mining, on an endpoint. If confirmed malicious, this behavior could allow an attacker - to disable security tools or other critical processes, facilitating further compromise - and persistence within the environment. +description: The following analytic detects the use of `wmic.exe` with the `delete` command to remove an executable path. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, parent processes, and command-line executions. This activity is significant because it often indicates the initial stages of an adversary setting up malicious activities, such as cryptocurrency mining, on an endpoint. If confirmed malicious, this behavior could allow an attacker to disable security tools or other critical processes, facilitating further compromise and persistence within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_wmic` AND Processes.process="*process*" - AND Processes.process="*executablepath*" AND Processes.process="*delete*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `process_kill_base_on_file_path_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_wmic` + AND + Processes.process="*process*" + AND + Processes.process="*executablepath*" + AND + Processes.process="*delete*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `process_kill_base_on_file_path_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ + - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process $process_name$ attempt to kill process by its file path using - commandline $process$ in host $dest$ - risk_objects: - - field: dest - type: system - score: 56 - - field: user - type: user - score: 56 - threat_objects: [] + message: A process $process_name$ attempt to kill process by its file path using commandline $process$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 56 + - field: user + type: user + score: 56 + threat_objects: [] tags: - analytic_story: - - XMRig - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - XMRig + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/process_writing_dynamicwrapperx.yml b/detections/endpoint/process_writing_dynamicwrapperx.yml index 84460c5bfb..5bc9acc408 100644 --- a/detections/endpoint/process_writing_dynamicwrapperx.yml +++ b/detections/endpoint/process_writing_dynamicwrapperx.yml @@ -1,56 +1,48 @@ name: Process Writing DynamicWrapperX id: b0a078e4-2601-11ec-9aec-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic detects a process writing the dynwrapx.dll file - to disk and registering it in the registry. It leverages data from the Endpoint - datamodel, specifically monitoring process and filesystem events. This activity - is significant because DynamicWrapperX is an ActiveX component often used in scripts - to call Windows API functions, and its presence in non-standard locations is highly - suspicious. If confirmed malicious, this could allow an attacker to execute arbitrary - code, escalate privileges, or maintain persistence within the environment. Immediate - investigation of parallel processes and registry modifications is recommended. +description: The following analytic detects a process writing the dynwrapx.dll file to disk and registering it in the registry. It leverages data from the Endpoint datamodel, specifically monitoring process and filesystem events. This activity is significant because DynamicWrapperX is an ActiveX component often used in scripts to call Windows API functions, and its presence in non-standard locations is highly suspicious. If confirmed malicious, this could allow an attacker to execute arbitrary code, escalate privileges, or maintain persistence within the environment. Immediate investigation of parallel processes and registry modifications is recommended. data_source: -- Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem - where Filesystem.file_name="dynwrapx.dll" by Filesystem.action Filesystem.dest Filesystem.file_access_time - Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name - Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid - Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `process_writing_dynamicwrapperx_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` - node. In addition, confirm the latest CIM App 4.20 or higher is installed and the - latest TA for the endpoint product. -known_false_positives: False positives should be limited, however it is possible to - filter by Processes.process_name and specific processes (ex. wscript.exe). Filter - as needed. This may need modification based on EDR telemetry and how it brings in - registry data. For example, removal of (Default). + - Sysmon EventID 11 +search: |- + | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.file_name="dynwrapx.dll" + BY Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path Filesystem.file_acl + Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `process_writing_dynamicwrapperx_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: False positives should be limited, however it is possible to filter by Processes.process_name and specific processes (ex. wscript.exe). Filter as needed. This may need modification based on EDR telemetry and how it brings in registry data. For example, removal of (Default). references: -- https://blog.f-secure.com/hunting-for-koadic-a-com-based-rootkit/ -- https://www.script-coding.com/dynwrapx_eng.html -- https://bohops.com/2018/06/28/abusing-com-registry-structure-clsid-localserver32-inprocserver32/ -- https://tria.ge/210929-ap75vsddan -- https://www.virustotal.com/gui/file/cb77b93150cb0f7fe65ce8a7e2a5781e727419451355a7736db84109fa215a89 + - https://blog.f-secure.com/hunting-for-koadic-a-com-based-rootkit/ + - https://www.script-coding.com/dynwrapx_eng.html + - https://bohops.com/2018/06/28/abusing-com-registry-structure-clsid-localserver32-inprocserver32/ + - https://tria.ge/210929-ap75vsddan + - https://www.virustotal.com/gui/file/cb77b93150cb0f7fe65ce8a7e2a5781e727419451355a7736db84109fa215a89 tags: - analytic_story: - - Remcos - asset_type: Endpoint - mitre_attack_id: - - T1059 - - T1559.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Remcos + asset_type: Endpoint + mitre_attack_id: + - T1059 + - T1559.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/processes_launching_netsh.yml b/detections/endpoint/processes_launching_netsh.yml index e547384228..eb2561ad47 100644 --- a/detections/endpoint/processes_launching_netsh.yml +++ b/detections/endpoint/processes_launching_netsh.yml @@ -1,91 +1,73 @@ name: Processes launching netsh id: b89919ed-fe5f-492c-b139-95dbb162040e -version: 11 -date: '2025-10-14' +version: 12 +date: '2026-02-25' author: Michael Haag, Josef Kuepker, Splunk status: production type: Anomaly -description: The following analytic identifies processes launching netsh.exe, a command-line - utility used to modify network configurations. It detects this activity by analyzing - data from Endpoint Detection and Response (EDR) agents, focusing on process GUIDs, - names, parent processes, and command-line executions. This behavior is significant - because netsh.exe can be exploited to execute malicious helper DLLs, serving as - a persistence mechanism. If confirmed malicious, an attacker could gain persistent - access, modify network settings, and potentially escalate privileges, posing a severe - threat to the network's integrity and security. +description: The following analytic identifies processes launching netsh.exe, a command-line utility used to modify network configurations. It detects this activity by analyzing data from Endpoint Detection and Response (EDR) agents, focusing on process GUIDs, names, parent processes, and command-line executions. This behavior is significant because netsh.exe can be exploited to execute malicious helper DLLs, serving as a persistence mechanism. If confirmed malicious, an attacker could gain persistent access, modify network settings, and potentially escalate privileges, posing a severe threat to the network's integrity and security. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_netsh` by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - |`drop_dm_object_name("Processes")` |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` - |`processes_launching_netsh_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Some VPN applications are known to launch netsh.exe. Outside - of these instances, it is unusual for an executable to launch netsh.exe and run - commands. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_netsh` + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `processes_launching_netsh_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Some VPN applications are known to launch netsh.exe. Outside of these instances, it is unusual for an executable to launch netsh.exe and run commands. references: -- https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ + - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process $process_name$ has launched netsh with command-line $process$ - on $dest$. - risk_objects: - - field: dest - type: system - score: 14 - - field: user - type: user - score: 14 - threat_objects: [] + message: A process $process_name$ has launched netsh with command-line $process$ on $dest$. + risk_objects: + - field: dest + type: system + score: 14 + - field: user + type: user + score: 14 + threat_objects: [] tags: - analytic_story: - - Netsh Abuse - - Disabling Security Tools - - DHS Report TA18-074A - - Azorult - - Volt Typhoon - - Snake Keylogger - - ShrinkLocker - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1562.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Netsh Abuse + - Disabling Security Tools + - DHS Report TA18-074A + - Azorult + - Volt Typhoon + - Snake Keylogger + - ShrinkLocker + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1562.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/processes_tapping_keyboard_events.yml b/detections/endpoint/processes_tapping_keyboard_events.yml index 6725649f2c..cdf5ff9cd6 100644 --- a/detections/endpoint/processes_tapping_keyboard_events.yml +++ b/detections/endpoint/processes_tapping_keyboard_events.yml @@ -1,48 +1,37 @@ name: Processes Tapping Keyboard Events id: 2a371608-331d-4034-ae2c-21dda8f1d0ec -version: 8 -date: '2025-10-21' +version: 9 +date: '2026-02-25' author: Jose Hernandez, Splunk status: experimental type: TTP -description: The following analytic detects processes on macOS systems that are tapping - keyboard events, potentially monitoring all keystrokes made by a user. It leverages - data from osquery results within the Alerts data model, focusing on specific process - names and command lines. This activity is significant as it is a common technique - used by Remote Access Trojans (RATs) to log keystrokes, posing a serious security - risk. If confirmed malicious, this could lead to unauthorized access to sensitive - information, including passwords and personal data, compromising the integrity and - confidentiality of the system. +description: The following analytic detects processes on macOS systems that are tapping keyboard events, potentially monitoring all keystrokes made by a user. It leverages data from osquery results within the Alerts data model, focusing on specific process names and command lines. This activity is significant as it is a common technique used by Remote Access Trojans (RATs) to log keystrokes, posing a serious security risk. If confirmed malicious, this could lead to unauthorized access to sensitive information, including passwords and personal data, compromising the integrity and confidentiality of the system. data_source: - - osquery -search: '| from datamodel Alerts.Alerts | search app=osquery:results name=pack_osx-attacks_Keyboard_Event_Taps - | rename columns.cmdline as cmd, columns.name as process_name, columns.pid as process_id| - dedup host,process_name | table host,process_name, cmd, process_id | `processes_tapping_keyboard_events_filter`' -how_to_implement: In order to properly run this search, Splunk needs to ingest data - from your osquery deployed agents with the - [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) - pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must - be deployed across your indexers and universal forwarders in order to have the osquery - data populate the Alerts data model. -known_false_positives: There might be some false positives as keyboard event taps - are used by processes like Siri and Zoom video chat, for some good examples of processes - to exclude please see [this](https://github.com/facebook/osquery/pull/5345#issuecomment-454639161) - comment. + - osquery +search: |- + | from datamodel Alerts.Alerts + | search app=osquery:results name=pack_osx-attacks_Keyboard_Event_Taps + | rename columns.cmdline as cmd, columns.name as process_name, columns.pid as process_id + | dedup host,process_name + | table host,process_name, cmd, process_id + | `processes_tapping_keyboard_events_filter` +how_to_implement: In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model. +known_false_positives: There might be some false positives as keyboard event taps are used by processes like Siri and Zoom video chat, for some good examples of processes to exclude please see [this](https://github.com/facebook/osquery/pull/5345#issuecomment-454639161) comment. references: [] rba: - message: Keyboard Event Tapping observed on $host$ - risk_objects: - - field: host - type: system - score: 25 - threat_objects: [] + message: Keyboard Event Tapping observed on $host$ + risk_objects: + - field: host + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - ColdRoot MacOS RAT - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - ColdRoot MacOS RAT + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat diff --git a/detections/endpoint/randomly_generated_scheduled_task_name.yml b/detections/endpoint/randomly_generated_scheduled_task_name.yml index ea8a37376a..4be6392526 100644 --- a/detections/endpoint/randomly_generated_scheduled_task_name.yml +++ b/detections/endpoint/randomly_generated_scheduled_task_name.yml @@ -1,44 +1,37 @@ name: Randomly Generated Scheduled Task Name id: 9d22a780-5165-11ec-ad4f-3e22fbd008af -version: 8 -date: '2025-08-22' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: experimental type: Hunting -description: The following analytic detects the creation of a Scheduled Task - with a high entropy, randomly generated name, leveraging Event ID 4698. It - uses the `ut_shannon` function from the URL ToolBox Splunk application to - measure the entropy of the Task Name. This activity is significant as - adversaries often use randomly named Scheduled Tasks for lateral movement and - remote code execution, employing tools like Impacket or CrackMapExec. If - confirmed malicious, this could allow attackers to execute arbitrary code - remotely, potentially leading to further compromise and persistence within the - network. +description: The following analytic detects the creation of a Scheduled Task with a high entropy, randomly generated name, leveraging Event ID 4698. It uses the `ut_shannon` function from the URL ToolBox Splunk application to measure the entropy of the Task Name. This activity is significant as adversaries often use randomly named Scheduled Tasks for lateral movement and remote code execution, employing tools like Impacket or CrackMapExec. If confirmed malicious, this could allow attackers to execute arbitrary code remotely, potentially leading to further compromise and persistence within the network. data_source: -- Windows Event Log Security 4698 -search: '`wineventlog_security` EventCode=4698 | xmlkv Message | lookup ut_shannon_lookup - word as Task_Name | where ut_shannon > 3 | table _time, dest, Task_Name, ut_shannon, - Command, Author, Enabled, Hidden | `randomly_generated_scheduled_task_name_filter`' -how_to_implement: To successfully implement this search, you need to be - ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows - TA as well as the URL ToolBox application are also required. -known_false_positives: Legitimate applications may use random Scheduled Task - names. + - Windows Event Log Security 4698 +search: |- + `wineventlog_security` EventCode=4698 + | xmlkv Message + | lookup ut_shannon_lookup word as Task_Name + | where ut_shannon > 3 + | table _time, dest, Task_Name, ut_shannon, Command, Author, Enabled, Hidden + | `randomly_generated_scheduled_task_name_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA as well as the URL ToolBox application are also required. +known_false_positives: Legitimate applications may use random Scheduled Task names. references: -- https://attack.mitre.org/techniques/T1053/005/ -- https://splunkbase.splunk.com/app/2734/ -- https://en.wikipedia.org/wiki/Entropy_(information_theory) + - https://attack.mitre.org/techniques/T1053/005/ + - https://splunkbase.splunk.com/app/2734/ + - https://en.wikipedia.org/wiki/Entropy_(information_theory) tags: - analytic_story: - - Active Directory Lateral Movement - - CISA AA22-257A - - Scheduled Tasks - - 0bj3ctivity Stealer - asset_type: Endpoint - mitre_attack_id: - - T1053.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + - CISA AA22-257A + - Scheduled Tasks + - 0bj3ctivity Stealer + asset_type: Endpoint + mitre_attack_id: + - T1053.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/randomly_generated_windows_service_name.yml b/detections/endpoint/randomly_generated_windows_service_name.yml index fd7305e0a3..6071b1077b 100644 --- a/detections/endpoint/randomly_generated_windows_service_name.yml +++ b/detections/endpoint/randomly_generated_windows_service_name.yml @@ -1,38 +1,32 @@ name: Randomly Generated Windows Service Name id: 2032a95a-5165-11ec-a2c3-3e22fbd008af -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: experimental type: Hunting -description: The following analytic detects the installation of a Windows Service - with a suspicious, high-entropy name, indicating potential malicious activity. It - leverages Event ID 7045 and the `ut_shannon` function from the URL ToolBox Splunk - application to identify services with random names. This behavior is significant - as adversaries often use randomly named services for lateral movement and remote - code execution. If confirmed malicious, this activity could allow attackers to execute - arbitrary code, escalate privileges, or maintain persistence within the environment. +description: The following analytic detects the installation of a Windows Service with a suspicious, high-entropy name, indicating potential malicious activity. It leverages Event ID 7045 and the `ut_shannon` function from the URL ToolBox Splunk application to identify services with random names. This behavior is significant as adversaries often use randomly named services for lateral movement and remote code execution. If confirmed malicious, this activity could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment. data_source: -- Windows Event Log System 7045 -search: '`wineventlog_system` EventCode=7045 | lookup ut_shannon_lookup word as Service_Name - | where ut_shannon > 3 | table EventCode ComputerName Service_Name ut_shannon Service_Start_Type - Service_Type Service_File_Name | `randomly_generated_windows_service_name_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the Service name, Service File Name Service Start type, and Service Type - from your endpoints. The Windows TA as well as the URL ToolBox application are also - required. + - Windows Event Log System 7045 +search: |- + `wineventlog_system` EventCode=7045 + | lookup ut_shannon_lookup word as Service_Name + | where ut_shannon > 3 + | table EventCode ComputerName Service_Name ut_shannon Service_Start_Type Service_Type Service_File_Name + | `randomly_generated_windows_service_name_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints. The Windows TA as well as the URL ToolBox application are also required. known_false_positives: Legitimate applications may use random Windows Service names. references: -- https://attack.mitre.org/techniques/T1543/003/ + - https://attack.mitre.org/techniques/T1543/003/ tags: - analytic_story: - - Active Directory Lateral Movement - - BlackSuit Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1543.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + - BlackSuit Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1543.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/ransomware_notes_bulk_creation.yml b/detections/endpoint/ransomware_notes_bulk_creation.yml index 1b1ac5c20b..d4cfa9cbb3 100644 --- a/detections/endpoint/ransomware_notes_bulk_creation.yml +++ b/detections/endpoint/ransomware_notes_bulk_creation.yml @@ -1,87 +1,69 @@ name: Ransomware Notes bulk creation id: eff7919a-8330-11eb-83f8-acde48001122 -version: 12 -date: '2026-01-14' +version: 13 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies the bulk creation of ransomware - notes (e.g., .txt, .html, .hta files) on an infected machine. It leverages - Sysmon EventCode 11 to detect multiple instances of these file types being - created within a short time frame. This activity is significant as it often - indicates an active ransomware attack, where the attacker is notifying the - victim of the encryption. If confirmed malicious, this behavior could lead to - widespread data encryption, rendering critical files inaccessible and - potentially causing significant operational disruption. +description: The following analytic identifies the bulk creation of ransomware notes (e.g., .txt, .html, .hta files) on an infected machine. It leverages Sysmon EventCode 11 to detect multiple instances of these file types being created within a short time frame. This activity is significant as it often indicates an active ransomware attack, where the attacker is notifying the victim of the encryption. If confirmed malicious, this behavior could lead to widespread data encryption, rendering critical files inaccessible and potentially causing significant operational disruption. data_source: -- Sysmon EventID 11 -search: '`sysmon` EventCode=11 file_name IN ("*\.txt","*\.html","*\.hta") | bin _time - span=10s | stats min(_time) as firstTime max(_time) as lastTime dc(TargetFilename) - as unique_readme_path_count values(TargetFilename) as list_of_readme_path values(action) - as action values(file_access_time) as file_access_time values(file_create_time) - as file_create_time values(file_hash) as file_hash values(file_modify_time) as file_modify_time - values(file_path) as file_path values(file_acl) as file_acl values(file_size) as - file_size values(process_guid) as process_guid values(process_id) as process_id - values(user) as user values(vendor_product) as vendor_product by dest file_name - | where unique_readme_path_count >= 15 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `ransomware_notes_bulk_creation_filter`' -how_to_implement: You must be ingesting data that records the filesystem - activity from your hosts to populate the Endpoint file-system data model node. - If you are using Sysmon, you will need a Splunk Universal Forwarder on each - endpoint from which you want to collect data. + - Sysmon EventID 11 +search: |- + `sysmon` EventCode=11 file_name IN ("*\.txt","*\.html","*\.hta") + | bin _time span=10s + | stats min(_time) as firstTime max(_time) as lastTime dc(TargetFilename) as unique_readme_path_count values(TargetFilename) as list_of_readme_path values(action) as action values(file_access_time) as file_access_time values(file_create_time) as file_create_time values(file_hash) as file_hash values(file_modify_time) as file_modify_time values(file_path) as file_path values(file_acl) as file_acl values(file_size) as file_size values(process_guid) as process_guid values(process_id) as process_id values(user) as user values(vendor_product) as vendor_product + BY dest file_name + | where unique_readme_path_count >= 15 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `ransomware_notes_bulk_creation_filter` +how_to_implement: You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. known_false_positives: No false positives have been identified at this time. references: -- https://www.mandiant.com/resources/fin11-email-campaigns-precursor-for-ransomware-data-theft -- https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html + - https://www.mandiant.com/resources/fin11-email-campaigns-precursor-for-ransomware-data-theft + - https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A high frequency file creation of $file_name$ in different file path - in host $dest$ - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: [] + message: A high frequency file creation of $file_name$ in different file path in host $dest$ + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: [] tags: - analytic_story: - - BlackMatter Ransomware - - DarkSide Ransomware - - Chaos Ransomware - - Rhysida Ransomware - - LockBit Ransomware - - Medusa Ransomware - - Black Basta Ransomware - - Clop Ransomware - - Cactus Ransomware - - Termite Ransomware - - Interlock Ransomware - - NailaoLocker Ransomware - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1486 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - BlackMatter Ransomware + - DarkSide Ransomware + - Chaos Ransomware + - Rhysida Ransomware + - LockBit Ransomware + - Medusa Ransomware + - Black Basta Ransomware + - Clop Ransomware + - Cactus Ransomware + - Termite Ransomware + - Interlock Ransomware + - NailaoLocker Ransomware + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1486 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/recon_avproduct_through_pwh_or_wmi.yml b/detections/endpoint/recon_avproduct_through_pwh_or_wmi.yml index 69c83321f3..27467c66c3 100644 --- a/detections/endpoint/recon_avproduct_through_pwh_or_wmi.yml +++ b/detections/endpoint/recon_avproduct_through_pwh_or_wmi.yml @@ -1,84 +1,76 @@ name: Recon AVProduct Through Pwh or WMI id: 28077620-c9f6-11eb-8785-acde48001122 -version: 11 -date: '2025-07-16' +version: 12 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects suspicious PowerShell script execution - via EventCode 4104, specifically targeting checks for installed anti-virus products - using WMI or PowerShell commands. This detection leverages PowerShell Script Block - Logging to identify scripts containing keywords like "SELECT," "WMIC," "AntiVirusProduct," - or "AntiSpywareProduct." This activity is significant as it is commonly used by - malware and APT actors to map running security applications or services, potentially - aiding in evasion techniques. If confirmed malicious, this could allow attackers - to disable or bypass security measures, leading to further compromise of the endpoint. +description: The following analytic detects suspicious PowerShell script execution via EventCode 4104, specifically targeting checks for installed anti-virus products using WMI or PowerShell commands. This detection leverages PowerShell Script Block Logging to identify scripts containing keywords like "SELECT," "WMIC," "AntiVirusProduct," or "AntiSpywareProduct." This activity is significant as it is commonly used by malware and APT actors to map running security applications or services, potentially aiding in evasion techniques. If confirmed malicious, this could allow attackers to disable or bypass security measures, leading to further compromise of the endpoint. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 (ScriptBlockText = "*SELECT*" OR ScriptBlockText - = "*WMIC*") AND (ScriptBlockText = "*AntiVirusProduct*" OR ScriptBlockText = "*AntiSpywareProduct*") - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `recon_avproduct_through_pwh_or_wmi_filter`' -how_to_implement: To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 (ScriptBlockText = "*SELECT*" OR ScriptBlockText = "*WMIC*") AND (ScriptBlockText = "*AntiVirusProduct*" OR ScriptBlockText = "*AntiSpywareProduct*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `recon_avproduct_through_pwh_or_wmi_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. known_false_positives: network administrator may used this command for checking purposes references: -- https://news.sophos.com/en-us/2020/05/12/maze-ransomware-1-year-counting/ -- https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -- https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 -- https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf -- https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ -- https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html + - https://news.sophos.com/en-us/2020/05/12/maze-ransomware-1-year-counting/ + - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 + - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf + - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ + - https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html drilldown_searches: -- name: View the detection results for - "$dest$" and "$user_id$" - search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user_id$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user_id$" + search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious powershell script contains AV recon command on host $dest$ - risk_objects: - - field: dest - type: system - score: 56 - - field: user_id - type: user - score: 56 - threat_objects: [] + message: A suspicious powershell script contains AV recon command on host $dest$ + risk_objects: + - field: dest + type: system + score: 56 + - field: user_id + type: user + score: 56 + threat_objects: [] tags: - analytic_story: - - XWorm - - Ransomware - - Hermetic Wiper - - Prestige Ransomware - - Quasar RAT - - Malicious PowerShell - - Data Destruction - - MoonPeak - - Qakbot - - Windows Post-Exploitation - asset_type: Endpoint - mitre_attack_id: - - T1592 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - XWorm + - Ransomware + - Hermetic Wiper + - Prestige Ransomware + - Quasar RAT + - Malicious PowerShell + - Data Destruction + - MoonPeak + - Qakbot + - Windows Post-Exploitation + asset_type: Endpoint + mitre_attack_id: + - T1592 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/t1592/pwh_av_recon/windows-powershell-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/t1592/pwh_av_recon/windows-powershell-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/recon_using_wmi_class.yml b/detections/endpoint/recon_using_wmi_class.yml index 6bedce8ad2..2c22a88b9f 100644 --- a/detections/endpoint/recon_using_wmi_class.yml +++ b/detections/endpoint/recon_using_wmi_class.yml @@ -1,91 +1,80 @@ name: Recon Using WMI Class id: 018c1972-ca07-11eb-9473-acde48001122 -version: 11 -date: '2025-07-29' +version: 12 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects suspicious PowerShell activity via EventCode - 4104, where WMI performs event queries to gather information on running processes - or services. This detection leverages PowerShell Script Block Logging to identify - specific WMI queries targeting system information classes like Win32_Bios and Win32_OperatingSystem. - This activity is significant as it often indicates reconnaissance efforts by an - adversary to profile the compromised machine. If confirmed malicious, the attacker - could gain detailed system information, aiding in further exploitation or lateral - movement within the network. +description: The following analytic detects suspicious PowerShell activity via EventCode 4104, where WMI performs event queries to gather information on running processes or services. This detection leverages PowerShell Script Block Logging to identify specific WMI queries targeting system information classes like Win32_Bios and Win32_OperatingSystem. This activity is significant as it often indicates reconnaissance efforts by an adversary to profile the compromised machine. If confirmed malicious, the attacker could gain detailed system information, aiding in further exploitation or lateral movement within the network. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 AND ScriptBlockText IN ("*SELECT*", "*Get-WmiObject*") - AND ScriptBlockText IN ("*Win32_Bios*", "*Win32_OperatingSystem*", "*Win32_Processor*", - "*Win32_ComputerSystem*", "*Win32_PnPEntity*", "*Win32_ShadowCopy*", "*Win32_DiskDrive*", - "*Win32_PhysicalMemory*", "*Win32_BaseBoard*", "*Win32_DisplayConfiguration*") | - fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest signature - signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId - ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `recon_using_wmi_class_filter`' -how_to_implement: To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 AND ScriptBlockText IN ("*SELECT*", "*Get-WmiObject*") AND ScriptBlockText IN ("*Win32_Bios*", "*Win32_OperatingSystem*", "*Win32_Processor*", "*Win32_ComputerSystem*", "*Win32_PnPEntity*", "*Win32_ShadowCopy*", "*Win32_DiskDrive*", "*Win32_PhysicalMemory*", "*Win32_BaseBoard*", "*Win32_DisplayConfiguration*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `recon_using_wmi_class_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. known_false_positives: network administrator may used this command for checking purposes references: -- https://news.sophos.com/en-us/2020/05/12/maze-ransomware-1-year-counting/ -- https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -- https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 -- https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf -- https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ -- https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html -- https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ -- https://blogs.vmware.com/security/2022/10/lockbit-3-0-also-known-as-lockbit-black.html + - https://news.sophos.com/en-us/2020/05/12/maze-ransomware-1-year-counting/ + - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 + - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf + - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ + - https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html + - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ + - https://blogs.vmware.com/security/2022/10/lockbit-3-0-also-known-as-lockbit-black.html drilldown_searches: -- name: View the detection results for - "$dest$" and "$user_id$" - search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user_id$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user_id$" + search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious powershell script contains host recon commands detected on - host $dest$ - risk_objects: - - field: dest - type: system - score: 60 - - field: user_id - type: user - score: 60 - threat_objects: [] + message: A suspicious powershell script contains host recon commands detected on host $dest$ + risk_objects: + - field: dest + type: system + score: 60 + - field: user_id + type: user + score: 60 + threat_objects: [] tags: - analytic_story: - - Hermetic Wiper - - Quasar RAT - - Malicious PowerShell - - Data Destruction - - AsyncRAT - - MoonPeak - - LockBit Ransomware - - Malicious Inno Setup Loader - - Qakbot - - Industroyer2 - - Scattered Spider - asset_type: Endpoint - mitre_attack_id: - - T1592 - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Hermetic Wiper + - Quasar RAT + - Malicious PowerShell + - Data Destruction + - AsyncRAT + - MoonPeak + - LockBit Ransomware + - Malicious Inno Setup Loader + - Qakbot + - Industroyer2 + - Scattered Spider + asset_type: Endpoint + mitre_attack_id: + - T1592 + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/reconusingwmi.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/reconusingwmi.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/recursive_delete_of_directory_in_batch_cmd.yml b/detections/endpoint/recursive_delete_of_directory_in_batch_cmd.yml index 498cad0bb7..03ced158e6 100644 --- a/detections/endpoint/recursive_delete_of_directory_in_batch_cmd.yml +++ b/detections/endpoint/recursive_delete_of_directory_in_batch_cmd.yml @@ -1,81 +1,64 @@ name: Recursive Delete of Directory In Batch CMD id: ba570b3a-d356-11eb-8358-acde48001122 -version: 10 -date: '2025-09-18' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of a batch command designed - to recursively delete files or directories, a technique often used by ransomware - like Reddot to delete files in the recycle bin and prevent recovery. It leverages - data from Endpoint Detection and Response (EDR) agents, focusing on command-line - executions that include specific flags for recursive and quiet deletions. This activity - is significant as it indicates potential ransomware behavior aimed at data destruction. - If confirmed malicious, it could lead to significant data loss and hinder recovery - efforts, severely impacting business operations. +description: The following analytic detects the execution of a batch command designed to recursively delete files or directories, a technique often used by ransomware like Reddot to delete files in the recycle bin and prevent recovery. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions that include specific flags for recursive and quiet deletions. This activity is significant as it indicates potential ransomware behavior aimed at data destruction. If confirmed malicious, it could lead to significant data loss and hinder recovery efforts, severely impacting business operations. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_cmd` Processes.process=*/c* Processes.process="* - rd *" Processes.process="*/s*" Processes.process="*/q*" by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - |`drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `recursive_delete_of_directory_in_batch_cmd_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: network operator may use this batch command to delete recursively - a directory or files within directory + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_cmd` Processes.process=*/c* Processes.process="* rd *" Processes.process="*/s*" Processes.process="*/q*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `recursive_delete_of_directory_in_batch_cmd_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: network operator may use this batch command to delete recursively a directory or files within directory references: -- https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/ + - https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Recursive Delete of Directory In Batch CMD by $user$ on $dest$ - risk_objects: - - field: user - type: user - score: 25 - threat_objects: [] + message: Recursive Delete of Directory In Batch CMD by $user$ on $dest$ + risk_objects: + - field: user + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Ransomware - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1070.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1070.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/reg_exe_manipulating_windows_services_registry_keys.yml b/detections/endpoint/reg_exe_manipulating_windows_services_registry_keys.yml index c4c3428891..2f15c3d7ed 100644 --- a/detections/endpoint/reg_exe_manipulating_windows_services_registry_keys.yml +++ b/detections/endpoint/reg_exe_manipulating_windows_services_registry_keys.yml @@ -1,86 +1,67 @@ name: Reg exe Manipulating Windows Services Registry Keys id: 8470d755-0c13-45b3-bd63-387a373c10cf -version: 12 -date: '2025-05-02' +version: 13 +date: '2026-02-25' author: Rico Valdez, Splunk status: production type: TTP -description: The following analytic detects the use of reg.exe to modify registry - keys associated with Windows services and their configurations. It leverages data - from Endpoint Detection and Response (EDR) agents, focusing on process names, parent - processes, and command-line executions. This activity is significant because unauthorized - changes to service registry keys can indicate an attempt to establish persistence - or escalate privileges. If confirmed malicious, this could allow an attacker to - control service behavior, potentially leading to unauthorized code execution or - system compromise. +description: The following analytic detects the use of reg.exe to modify registry keys associated with Windows services and their configurations. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, parent processes, and command-line executions. This activity is significant because unauthorized changes to service registry keys can indicate an attempt to establish persistence or escalate privileges. If confirmed malicious, this could allow an attacker to control service behavior, potentially leading to unauthorized code execution or system compromise. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) - as parent_process_name values(Processes.user) as user FROM datamodel=Endpoint.Processes - where Processes.process_name=reg.exe Processes.process=*reg* Processes.process=*add* - Processes.process=*Services* by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name("Processes")` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `reg_exe_manipulating_windows_services_registry_keys_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: It is unusual for a service to be created or modified by directly - manipulating the registry. However, there may be legitimate instances of this behavior. - It is important to validate and investigate, as appropriate. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name values(Processes.user) as user FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=reg.exe Processes.process=*reg* Processes.process=*add* Processes.process=*Services* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `reg_exe_manipulating_windows_services_registry_keys_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: It is unusual for a service to be created or modified by directly manipulating the registry. However, there may be legitimate instances of this behavior. It is important to validate and investigate, as appropriate. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A reg.exe process $process_name$ with commandline $process$ in host $dest$ - risk_objects: - - field: dest - type: system - score: 45 - - field: user - type: user - score: 45 - threat_objects: [] + message: A reg.exe process $process_name$ with commandline $process$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 45 + - field: user + type: user + score: 45 + threat_objects: [] tags: - analytic_story: - - Windows Service Abuse - - Windows Persistence Techniques - - Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1574.011 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Service Abuse + - Windows Persistence Techniques + - Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1574.011 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.011/change_registry_path_service/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.011/change_registry_path_service/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/registry_keys_for_creating_shim_databases.yml b/detections/endpoint/registry_keys_for_creating_shim_databases.yml index eb215c7959..8623457f75 100644 --- a/detections/endpoint/registry_keys_for_creating_shim_databases.yml +++ b/detections/endpoint/registry_keys_for_creating_shim_databases.yml @@ -5,72 +5,48 @@ date: '2025-05-02' author: Patrick Bareiss, Teoderick Contreras, Splunk, Steven Dick, Bhavin Patel status: production type: TTP -description: The following analytic detects registry activity related to the creation - of application compatibility shims. It leverages data from the Endpoint.Registry - data model, specifically monitoring registry paths associated with AppCompatFlags. - This activity is significant because attackers can use shims to bypass security - controls, achieve persistence, or escalate privileges. If confirmed malicious, this - could allow an attacker to maintain long-term access, execute arbitrary code, or - manipulate application behavior, posing a severe risk to the integrity and security - of the affected systems. +description: The following analytic detects registry activity related to the creation of application compatibility shims. It leverages data from the Endpoint.Registry data model, specifically monitoring registry paths associated with AppCompatFlags. This activity is significant because attackers can use shims to bypass security controls, achieve persistence, or escalate privileges. If confirmed malicious, this could allow an attacker to maintain long-term access, execute arbitrary code, or manipulate application behavior, posing a severe risk to the integrity and security of the affected systems. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path=*CurrentVersion\\AppCompatFlags\\Custom* - OR Registry.registry_path=*CurrentVersion\\AppCompatFlags\\InstalledSDB*) by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `registry_keys_for_creating_shim_databases_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 -known_false_positives: There are many legitimate applications that leverage shim databases - for compatibility purposes for legacy applications + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path=*CurrentVersion\\AppCompatFlags\\Custom* OR Registry.registry_path=*CurrentVersion\\AppCompatFlags\\InstalledSDB*) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `registry_keys_for_creating_shim_databases_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 +known_false_positives: There are many legitimate applications that leverage shim databases for compatibility purposes for legacy applications references: [] drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A registry activity in $registry_path$ related to shim modication in host - $dest$ - risk_objects: - - field: dest - type: system - score: 56 - - field: user - type: user - score: 56 - threat_objects: [] + message: A registry activity in $registry_path$ related to shim modication in host $dest$ + risk_objects: + - field: dest + type: system + score: 56 + - field: user + type: user + score: 56 + threat_objects: [] tags: - analytic_story: - - Suspicious Windows Registry Activities - - Windows Persistence Techniques - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1546.011 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Windows Registry Activities + - Windows Persistence Techniques + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1546.011 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/registry_keys_used_for_persistence.yml b/detections/endpoint/registry_keys_used_for_persistence.yml index 2c1d04a6e9..35ad1b60d3 100644 --- a/detections/endpoint/registry_keys_used_for_persistence.yml +++ b/detections/endpoint/registry_keys_used_for_persistence.yml @@ -5,135 +5,88 @@ date: '2025-12-10' author: Jose Hernandez, David Dorsey, Teoderick Contreras, Rod Soto, Splunk status: production type: TTP -description: The following analytic identifies modifications to registry keys - commonly used for persistence mechanisms. It leverages data from endpoint - detection sources like Sysmon or Carbon Black, focusing on specific registry - paths known to initiate applications or services during system startup. This - activity is significant as unauthorized changes to these keys can indicate - attempts to maintain persistence or execute malicious actions upon system - boot. If confirmed malicious, this could allow attackers to achieve persistent - access, execute arbitrary code, or maintain control over compromised systems, - posing a severe threat to system integrity and security. +description: The following analytic identifies modifications to registry keys commonly used for persistence mechanisms. It leverages data from endpoint detection sources like Sysmon or Carbon Black, focusing on specific registry paths known to initiate applications or services during system startup. This activity is significant as unauthorized changes to these keys can indicate attempts to maintain persistence or execute malicious actions upon system boot. If confirmed malicious, this could allow attackers to achieve persistent access, execute arbitrary code, or maintain control over compromised systems, posing a severe threat to system integrity and security. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path=*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\RunOnce - OR Registry.registry_path=*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\StartupApproved\\Run - OR Registry.registry_path= "*\\Software\\Microsoft\\Windows\\CurrentVersion\\Explorer\\User - Shell Folders\\*" OR Registry.registry_path= "*\\Software\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Shell - Folders\\*" OR Registry.registry_path=*\\currentversion\\run* OR Registry.registry_path=*\\currentVersion\\Windows\\Appinit_Dlls* - OR Registry.registry_path=*\\CurrentVersion\\Winlogon\\Shell* OR Registry.registry_path=*\\CurrentVersion\\Winlogon\\Notify* - OR Registry.registry_path=*\\CurrentVersion\\Winlogon\\Userinit* OR Registry.registry_path=*\\CurrentVersion\\Winlogon\\VmApplet* - OR Registry.registry_path=*\\currentversion\\policies\\explorer\\run* OR Registry.registry_path=*\\currentversion\\runservices* - OR Registry.registry_path=*\\SOFTWARE\\Microsoft\\Netsh\\* OR Registry.registry_path= - "*\\Software\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Shell Folders\\Common - Startup" OR Registry.registry_path= *\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\SharedTaskScheduler - OR Registry.registry_path= *\\Classes\\htmlfile\\shell\\open\\command OR (Registry.registry_path="*Microsoft\\Windows - NT\\CurrentVersion\\Image File Execution Options*" AND Registry.registry_key_name=Debugger) - OR (Registry.registry_path="*\\CurrentControlSet\\Control\\Lsa" AND Registry.registry_key_name="Security - Packages") OR (Registry.registry_path="*\\CurrentControlSet\\Control\\Lsa\\OSConfig" - AND Registry.registry_key_name="Security Packages") OR (Registry.registry_path="*\\Microsoft\\Windows - NT\\CurrentVersion\\SilentProcessExit\\*") OR (Registry.registry_path="*currentVersion\\Windows" - AND Registry.registry_key_name="Load") OR (Registry.registry_path="*\\CurrentVersion" - AND Registry.registry_key_name="Svchost") OR (Registry.registry_path="*\\CurrentControlSet\Control\Session - Manager"AND Registry.registry_key_name="BootExecute") OR (Registry.registry_path="*\\Software\\Run" - AND Registry.registry_key_name="auto_update")) by Registry.action Registry.dest - Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `registry_keys_used_for_persistence_filter`' -how_to_implement: To successfully implement this search, you must be ingesting - data that records registry activity from your hosts to populate the endpoint - data model in the registry node. This is typically populated via endpoint - detection-and-response product, such as Carbon Black or endpoint data sources, - such as Sysmon. The data used for this search is typically generated via logs - that report reads and writes to the registry. -known_false_positives: There are many legitimate applications that must execute - on system startup and will use these registry keys to accomplish that task. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path=*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\RunOnce OR Registry.registry_path=*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\StartupApproved\\Run OR Registry.registry_path= "*\\Software\\Microsoft\\Windows\\CurrentVersion\\Explorer\\User Shell Folders\\*" OR Registry.registry_path= "*\\Software\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Shell Folders\\*" OR Registry.registry_path=*\\currentversion\\run* OR Registry.registry_path=*\\currentVersion\\Windows\\Appinit_Dlls* OR Registry.registry_path=*\\CurrentVersion\\Winlogon\\Shell* OR Registry.registry_path=*\\CurrentVersion\\Winlogon\\Notify* OR Registry.registry_path=*\\CurrentVersion\\Winlogon\\Userinit* OR Registry.registry_path=*\\CurrentVersion\\Winlogon\\VmApplet* OR Registry.registry_path=*\\currentversion\\policies\\explorer\\run* OR Registry.registry_path=*\\currentversion\\runservices* OR Registry.registry_path=*\\SOFTWARE\\Microsoft\\Netsh\\* OR Registry.registry_path= "*\\Software\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Shell Folders\\Common Startup" OR Registry.registry_path= *\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\SharedTaskScheduler OR Registry.registry_path= *\\Classes\\htmlfile\\shell\\open\\command OR (Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*" AND Registry.registry_key_name=Debugger) OR (Registry.registry_path="*\\CurrentControlSet\\Control\\Lsa" AND Registry.registry_key_name="Security Packages") OR (Registry.registry_path="*\\CurrentControlSet\\Control\\Lsa\\OSConfig" AND Registry.registry_key_name="Security Packages") OR (Registry.registry_path="*\\Microsoft\\Windows NT\\CurrentVersion\\SilentProcessExit\\*") OR (Registry.registry_path="*currentVersion\\Windows" AND Registry.registry_key_name="Load") OR (Registry.registry_path="*\\CurrentVersion" AND Registry.registry_key_name="Svchost") OR (Registry.registry_path="*\\CurrentControlSet\Control\Session Manager"AND Registry.registry_key_name="BootExecute") OR (Registry.registry_path="*\\Software\\Run" AND Registry.registry_key_name="auto_update")) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `registry_keys_used_for_persistence_filter`' +how_to_implement: To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. +known_false_positives: There are many legitimate applications that must execute on system startup and will use these registry keys to accomplish that task. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A registry activity in $registry_path$ related to persistence in host - $dest$ - risk_objects: - - field: dest - type: system - score: 76 - - field: user - type: user - score: 76 - threat_objects: [] + message: A registry activity in $registry_path$ related to persistence in host $dest$ + risk_objects: + - field: dest + type: system + score: 76 + - field: user + type: user + score: 76 + threat_objects: [] tags: - analytic_story: - - Warzone RAT - - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns - - Sneaky Active Directory Persistence Tricks - - Windows Registry Abuse - - Chaos Ransomware - - DarkGate Malware - - Remcos - - Quasar RAT - - Braodo Stealer - - Qakbot - - Snake Keylogger - - China-Nexus Threat Activity - - IcedID - - CISA AA23-347A - - Ransomware - - XWorm - - Azorult - - Salt Typhoon - - Cactus Ransomware - - BlackSuit Ransomware - - BlackByte Ransomware - - SystemBC - - NjRAT - - DHS Report TA18-074A - - Derusbi - - Amadey - - Suspicious MSHTA Activity - - Suspicious Windows Registry Activities - - Emotet Malware DHS Report TA18-201A - - WinDealer RAT - - AsyncRAT - - RedLine Stealer - - SnappyBee - - Windows Persistence Techniques - - MoonPeak - - Interlock Ransomware - - 0bj3ctivity Stealer - - APT37 Rustonotto and FadeStealer - - NetSupport RMM Tool Abuse - - DarkCrystal RAT - - Lokibot - - ValleyRAT - - Castle RAT - asset_type: Endpoint - mitre_attack_id: - - T1547.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Warzone RAT + - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + - Sneaky Active Directory Persistence Tricks + - Windows Registry Abuse + - Chaos Ransomware + - DarkGate Malware + - Remcos + - Quasar RAT + - Braodo Stealer + - Qakbot + - Snake Keylogger + - China-Nexus Threat Activity + - IcedID + - CISA AA23-347A + - Ransomware + - XWorm + - Azorult + - Salt Typhoon + - Cactus Ransomware + - BlackSuit Ransomware + - BlackByte Ransomware + - SystemBC + - NjRAT + - DHS Report TA18-074A + - Derusbi + - Amadey + - Suspicious MSHTA Activity + - Suspicious Windows Registry Activities + - Emotet Malware DHS Report TA18-201A + - WinDealer RAT + - AsyncRAT + - RedLine Stealer + - SnappyBee + - Windows Persistence Techniques + - MoonPeak + - Interlock Ransomware + - 0bj3ctivity Stealer + - APT37 Rustonotto and FadeStealer + - NetSupport RMM Tool Abuse + - DarkCrystal RAT + - Lokibot + - ValleyRAT + - Castle RAT + asset_type: Endpoint + mitre_attack_id: + - T1547.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.001/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.001/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/registry_keys_used_for_privilege_escalation.yml b/detections/endpoint/registry_keys_used_for_privilege_escalation.yml index 10a7127f31..c315bb9f82 100644 --- a/detections/endpoint/registry_keys_used_for_privilege_escalation.yml +++ b/detections/endpoint/registry_keys_used_for_privilege_escalation.yml @@ -5,77 +5,52 @@ date: '2025-05-02' author: David Dorsey, Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: The following analytic detects modifications to registry keys under "Image - File Execution Options" that can be used for privilege escalation. It leverages - data from the Endpoint.Registry data model, specifically monitoring changes to registry - paths and values like GlobalFlag and Debugger. This activity is significant because - attackers can use these modifications to intercept executable calls and attach malicious - binaries to legitimate system binaries. If confirmed malicious, this could allow - attackers to execute arbitrary code with elevated privileges, leading to potential - system compromise and persistent access. +description: The following analytic detects modifications to registry keys under "Image File Execution Options" that can be used for privilege escalation. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to registry paths and values like GlobalFlag and Debugger. This activity is significant because attackers can use these modifications to intercept executable calls and attach malicious binaries to legitimate system binaries. If confirmed malicious, this could allow attackers to execute arbitrary code with elevated privileges, leading to potential system compromise and persistent access. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE ((Registry.registry_path="*Microsoft\\Windows - NT\\CurrentVersion\\Image File Execution Options*") AND (Registry.registry_value_name=GlobalFlag - OR Registry.registry_value_name=Debugger)) by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `registry_keys_used_for_privilege_escalation_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 -known_false_positives: There are many legitimate applications that must execute upon - system startup and will use these registry keys to accomplish that task. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE ((Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*") AND (Registry.registry_value_name=GlobalFlag OR Registry.registry_value_name=Debugger)) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `registry_keys_used_for_privilege_escalation_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 +known_false_positives: There are many legitimate applications that must execute upon system startup and will use these registry keys to accomplish that task. references: -- https://blog.malwarebytes.com/101/2015/12/an-introduction-to-image-file-execution-options/ + - https://blog.malwarebytes.com/101/2015/12/an-introduction-to-image-file-execution-options/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A registry activity in $registry_path$ related to privilege escalation - in host $dest$ - risk_objects: - - field: dest - type: system - score: 76 - - field: user - type: user - score: 76 - threat_objects: [] + message: A registry activity in $registry_path$ related to privilege escalation in host $dest$ + risk_objects: + - field: dest + type: system + score: 76 + - field: user + type: user + score: 76 + threat_objects: [] tags: - analytic_story: - - Cloud Federated Credential Abuse - - Hermetic Wiper - - Windows Privilege Escalation - - Windows Registry Abuse - - Data Destruction - - Suspicious Windows Registry Activities - asset_type: Endpoint - mitre_attack_id: - - T1546.012 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Cloud Federated Credential Abuse + - Hermetic Wiper + - Windows Privilege Escalation + - Windows Registry Abuse + - Data Destruction + - Suspicious Windows Registry Activities + asset_type: Endpoint + mitre_attack_id: + - T1546.012 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.012/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.012/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/regsvr32_silent_and_install_param_dll_loading.yml b/detections/endpoint/regsvr32_silent_and_install_param_dll_loading.yml index b3f1c94f55..4521338408 100644 --- a/detections/endpoint/regsvr32_silent_and_install_param_dll_loading.yml +++ b/detections/endpoint/regsvr32_silent_and_install_param_dll_loading.yml @@ -1,93 +1,80 @@ name: Regsvr32 Silent and Install Param Dll Loading id: f421c250-24e7-11ec-bc43-acde48001122 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the loading of a DLL using the regsvr32 - application with the silent parameter and DLLInstall execution. It leverages data - from Endpoint Detection and Response (EDR) agents, focusing on process command-line - arguments and parent process details. This activity is significant as it is commonly - used by RAT malware like Remcos and njRAT to load malicious DLLs on compromised - machines. If confirmed malicious, this technique could allow attackers to execute - arbitrary code, maintain persistence, and further compromise the system. +description: The following analytic detects the loading of a DLL using the regsvr32 application with the silent parameter and DLLInstall execution. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process command-line arguments and parent process details. This activity is significant as it is commonly used by RAT malware like Remcos and njRAT to load malicious DLLs on compromised machines. If confirmed malicious, this technique could allow attackers to execute arbitrary code, maintain persistence, and further compromise the system. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_regsvr32` AND Processes.process="*/i*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | where match(process,"(?i)[\-|\/][Ss]{1}") | `regsvr32_silent_and_install_param_dll_loading_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Other third part application may used this parameter but not - so common in base windows environment. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_regsvr32` + AND + Processes.process="*/i*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | where match(process,"(?i)[\- + | \/][Ss]{1}") + | `regsvr32_silent_and_install_param_dll_loading_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Other third part application may used this parameter but not so common in base windows environment. references: -- https://app.any.run/tasks/dc93ee63-050c-4ff8-b07e-8277af9ab939/ -- https://attack.mitre.org/techniques/T1218/010/ + - https://app.any.run/tasks/dc93ee63-050c-4ff8-b07e-8277af9ab939/ + - https://attack.mitre.org/techniques/T1218/010/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to load a DLL using the silent and - dllinstall parameter. - risk_objects: - - field: user - type: user - score: 36 - - field: dest - type: system - score: 36 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to load a DLL using the silent and dllinstall parameter. + risk_objects: + - field: user + type: user + score: 36 + - field: dest + type: system + score: 36 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - AsyncRAT - - Hermetic Wiper - - Living Off The Land - - Data Destruction - - Remcos - - Suspicious Regsvr32 Activity - asset_type: Endpoint - mitre_attack_id: - - T1218.010 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AsyncRAT + - Hermetic Wiper + - Living Off The Land + - Data Destruction + - Remcos + - Suspicious Regsvr32 Activity + asset_type: Endpoint + mitre_attack_id: + - T1218.010 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.005/vbs_wscript/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.005/vbs_wscript/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/regsvr32_with_known_silent_switch_cmdline.yml b/detections/endpoint/regsvr32_with_known_silent_switch_cmdline.yml index 4a31c9dcd1..e216195bdd 100644 --- a/detections/endpoint/regsvr32_with_known_silent_switch_cmdline.yml +++ b/detections/endpoint/regsvr32_with_known_silent_switch_cmdline.yml @@ -1,93 +1,78 @@ name: Regsvr32 with Known Silent Switch Cmdline id: c9ef7dc4-eeaf-11eb-b2b6-acde48001122 -version: 10 -date: '2025-05-02' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the execution of Regsvr32.exe with the - silent switch to load DLLs. This behavior is identified using Endpoint Detection - and Response (EDR) telemetry, focusing on command-line executions containing the - `-s` or `/s` switches. This activity is significant as it is commonly used in malware - campaigns, such as IcedID, to stealthily load malicious DLLs. If confirmed malicious, - this could allow an attacker to execute arbitrary code, download additional payloads, - and potentially compromise the system further. Immediate investigation and endpoint - isolation are recommended. +description: The following analytic detects the execution of Regsvr32.exe with the silent switch to load DLLs. This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on command-line executions containing the `-s` or `/s` switches. This activity is significant as it is commonly used in malware campaigns, such as IcedID, to stealthily load malicious DLLs. If confirmed malicious, this could allow an attacker to execute arbitrary code, download additional payloads, and potentially compromise the system further. Immediate investigation and endpoint isolation are recommended. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_regsvr32` by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | where match(process,"(?i)[\-|\/][Ss]{1}") | `regsvr32_with_known_silent_switch_cmdline_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: minimal. but network operator can use this application to load - dll. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_regsvr32` + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | where match(process,"(?i)[\- + | \/][Ss]{1}") + | `regsvr32_with_known_silent_switch_cmdline_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: minimal. but network operator can use this application to load dll. references: -- https://app.any.run/tasks/56680cba-2bbc-4b34-8633-5f7878ddf858/ -- https://regexr.com/699e2 + - https://app.any.run/tasks/56680cba-2bbc-4b34-8633-5f7878ddf858/ + - https://regexr.com/699e2 drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to load a DLL using the silent parameter. - risk_objects: - - field: user - type: user - score: 56 - - field: dest - type: system - score: 56 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to load a DLL using the silent parameter. + risk_objects: + - field: user + type: user + score: 56 + - field: dest + type: system + score: 56 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - IcedID - - Suspicious Regsvr32 Activity - - Remcos - - Living Off The Land - - Qakbot - - AsyncRAT - asset_type: Endpoint - mitre_attack_id: - - T1218.010 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - IcedID + - Suspicious Regsvr32 Activity + - Remcos + - Living Off The Land + - Qakbot + - AsyncRAT + asset_type: Endpoint + mitre_attack_id: + - T1218.010 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/remcos_client_registry_install_entry.yml b/detections/endpoint/remcos_client_registry_install_entry.yml index 37b150766c..9e4b666409 100644 --- a/detections/endpoint/remcos_client_registry_install_entry.yml +++ b/detections/endpoint/remcos_client_registry_install_entry.yml @@ -5,78 +5,46 @@ date: '2026-01-14' author: Steven Dick, Bhavin Patel, Rod Soto, Teoderick Contreras, Splunk status: production type: TTP -description: - The following analytic detects the presence of a registry key associated - with the Remcos RAT agent on a host. It leverages data from the Endpoint.Processes - and Endpoint.Registry data models in Splunk, focusing on instances where the "license" - key is found in the "Software\Remcos" path. This behavior is significant as it indicates - potential compromise by the Remcos RAT, a remote access Trojan used for unauthorized - access and data exfiltration. If confirmed malicious, the attacker could gain control - over the system, steal sensitive information, or use the compromised host for further - attacks. Immediate investigation and remediation are required. +description: The following analytic detects the presence of a registry key associated with the Remcos RAT agent on a host. It leverages data from the Endpoint.Processes and Endpoint.Registry data models in Splunk, focusing on instances where the "license" key is found in the "Software\Remcos" path. This behavior is significant as it indicates potential compromise by the Remcos RAT, a remote access Trojan used for unauthorized access and data exfiltration. If confirmed malicious, the attacker could gain control over the system, steal sensitive information, or use the compromised host for further attacks. Immediate investigation and remediation are required. data_source: - - Sysmon EventID 12 - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry - WHERE (Registry.registry_path=*\\Software\\Remcos*) by Registry.action Registry.dest - Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - |`remcos_client_registry_install_entry_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 12 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path=*\\Software\\Remcos*) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |`remcos_client_registry_install_entry_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: - - https://attack.mitre.org/software/S0332/ + - https://attack.mitre.org/software/S0332/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A registry entry $registry_path$ with registry keyname $registry_key_name$ - related to Remcos RAT in host $dest$ - risk_objects: - - field: dest - type: system - score: 90 - threat_objects: [] + message: A registry entry $registry_path$ with registry keyname $registry_key_name$ related to Remcos RAT in host $dest$ + risk_objects: + - field: dest + type: system + score: 90 + threat_objects: [] tags: - analytic_story: - - Remcos - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Remcos + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos_registry/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos_registry/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/remcos_rat_file_creation_in_remcos_folder.yml b/detections/endpoint/remcos_rat_file_creation_in_remcos_folder.yml index a29b3ef657..874da456f9 100644 --- a/detections/endpoint/remcos_rat_file_creation_in_remcos_folder.yml +++ b/detections/endpoint/remcos_rat_file_creation_in_remcos_folder.yml @@ -5,65 +5,45 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk, Sanjay Govind status: production type: TTP -description: The following analytic detects the creation of files in the Remcos folder - within the AppData directory, specifically targeting keylog and clipboard log files. - It leverages the Endpoint.Filesystem data model to identify .dat files created in - paths containing "remcos." This activity is significant as it indicates the presence - of the Remcos RAT, which performs keylogging, clipboard capturing, and audio recording. - If confirmed malicious, this could lead to unauthorized data exfiltration and extensive - surveillance capabilities for the attacker. +description: The following analytic detects the creation of files in the Remcos folder within the AppData directory, specifically targeting keylog and clipboard log files. It leverages the Endpoint.Filesystem data model to identify .dat files created in paths containing "remcos." This activity is significant as it indicates the presence of the Remcos RAT, which performs keylogging, clipboard capturing, and audio recording. If confirmed malicious, this could lead to unauthorized data exfiltration and extensive surveillance capabilities for the attacker. data_source: -- Sysmon EventID 11 -search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.dat") - Filesystem.file_path = "*\\remcos\\*" by Filesystem.action Filesystem.dest Filesystem.file_access_time - Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name - Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid - Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remcos_rat_file_creation_in_remcos_folder_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. + - Sysmon EventID 11 +search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.dat") Filesystem.file_path = "*\\remcos\\*" by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remcos_rat_file_creation_in_remcos_folder_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: No false positives have been identified at this time. references: -- https://success.trendmicro.com/dcx/s/solution/1123281-remcos-malware-information?language=en_US -- https://blog.malwarebytes.com/threat-intelligence/2021/07/remcos-rat-delivered-via-visual-basic/ + - https://success.trendmicro.com/dcx/s/solution/1123281-remcos-malware-information?language=en_US + - https://blog.malwarebytes.com/threat-intelligence/2021/07/remcos-rat-delivered-via-visual-basic/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: file $file_name$ created in $file_path$ of $dest$ - risk_objects: - - field: dest - type: system - score: 100 - threat_objects: [] + message: file $file_name$ created in $file_path$ of $dest$ + risk_objects: + - field: dest + type: system + score: 100 + threat_objects: [] tags: - analytic_story: - - Remcos - asset_type: Endpoint - mitre_attack_id: - - T1113 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Remcos + asset_type: Endpoint + mitre_attack_id: + - T1113 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos_agent/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos_agent/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/remote_desktop_process_running_on_system.yml b/detections/endpoint/remote_desktop_process_running_on_system.yml index 6edf3fd8ca..b0557dfb71 100644 --- a/detections/endpoint/remote_desktop_process_running_on_system.yml +++ b/detections/endpoint/remote_desktop_process_running_on_system.yml @@ -1,55 +1,44 @@ name: Remote Desktop Process Running On System id: f5939373-8054-40ad-8c64-cec478a22a4a -version: 12 -date: '2025-08-07' +version: 13 +date: '2026-02-25' author: David Dorsey, Splunk status: experimental type: Hunting -description: The following analytic detects the execution of the remote desktop - process (mstsc.exe) on systems where it is not typically run. This detection - leverages data from Endpoint Detection and Response (EDR) agents, filtering - out systems categorized as common RDP sources. This activity is significant - because unauthorized use of mstsc.exe can indicate lateral movement or - unauthorized remote access attempts. If confirmed malicious, this could allow - an attacker to gain remote control of a system, potentially leading to data - exfiltration, privilege escalation, or further network compromise. +description: The following analytic detects the execution of the remote desktop process (mstsc.exe) on systems where it is not typically run. This detection leverages data from Endpoint Detection and Response (EDR) agents, filtering out systems categorized as common RDP sources. This activity is significant because unauthorized use of mstsc.exe can indicate lateral movement or unauthorized remote access attempts. If confirmed malicious, this could allow an attacker to gain remote control of a system, potentially leading to data exfiltration, privilege escalation, or further network compromise. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process=*mstsc.exe - AND Processes.dest_category!=common_rdp_source by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` - | `remote_desktop_process_running_on_system_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: Remote Desktop may be used legitimately by users on the - network. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process=*mstsc.exe + AND + Processes.dest_category!=common_rdp_source + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `drop_dm_object_name(Processes)` + | `remote_desktop_process_running_on_system_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Remote Desktop may be used legitimately by users on the network. references: [] tags: - analytic_story: - - Hidden Cobra Malware - - Active Directory Lateral Movement - - Windows RDP Artifacts and Defense Evasion - asset_type: Endpoint - mitre_attack_id: - - T1021.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Hidden Cobra Malware + - Active Directory Lateral Movement + - Windows RDP Artifacts and Defense Evasion + asset_type: Endpoint + mitre_attack_id: + - T1021.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/remote_process_instantiation_via_dcom_and_powershell.yml b/detections/endpoint/remote_process_instantiation_via_dcom_and_powershell.yml index 12aa7b0d31..5a64f161c9 100644 --- a/detections/endpoint/remote_process_instantiation_via_dcom_and_powershell.yml +++ b/detections/endpoint/remote_process_instantiation_via_dcom_and_powershell.yml @@ -1,84 +1,67 @@ name: Remote Process Instantiation via DCOM and PowerShell id: d4f42098-4680-11ec-ad07-3e22fbd008af -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the execution of `powershell.exe` with - arguments used to start a process on a remote endpoint by abusing the DCOM protocol, - specifically targeting ShellExecute and ExecuteShellCommand. It leverages data from - Endpoint Detection and Response (EDR) agents, focusing on process names, parent - processes, and command-line executions. This activity is significant as it indicates - potential lateral movement and remote code execution attempts by adversaries. If - confirmed malicious, this could allow attackers to execute arbitrary code remotely, - escalate privileges, and move laterally within the network, posing a severe security - risk. +description: The following analytic detects the execution of `powershell.exe` with arguments used to start a process on a remote endpoint by abusing the DCOM protocol, specifically targeting ShellExecute and ExecuteShellCommand. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, parent processes, and command-line executions. This activity is significant as it indicates potential lateral movement and remote code execution attempts by adversaries. If confirmed malicious, this could allow attackers to execute arbitrary code remotely, escalate privileges, and move laterally within the network, posing a severe security risk. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_powershell` (Processes.process="*Document.ActiveView.ExecuteShellCommand*" - OR Processes.process="*Document.Application.ShellExecute*") by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `remote_process_instantiation_via_dcom_and_powershell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrators may leverage DCOM to start a process on remote - systems, but this activity is usually limited to a small set of hosts or users. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_powershell` (Processes.process="*Document.ActiveView.ExecuteShellCommand*" + OR + Processes.process="*Document.Application.ShellExecute*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `remote_process_instantiation_via_dcom_and_powershell_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators may leverage DCOM to start a process on remote systems, but this activity is usually limited to a small set of hosts or users. references: -- https://attack.mitre.org/techniques/T1021/003/ -- https://www.cybereason.com/blog/dcom-lateral-movement-techniques + - https://attack.mitre.org/techniques/T1021/003/ + - https://www.cybereason.com/blog/dcom-lateral-movement-techniques drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process was started on a remote endpoint from $dest$ by abusing DCOM - using PowerShell.exe - risk_objects: - - field: dest - type: system - score: 63 - threat_objects: [] + message: A process was started on a remote endpoint from $dest$ by abusing DCOM using PowerShell.exe + risk_objects: + - field: dest + type: system + score: 63 + threat_objects: [] tags: - analytic_story: - - Active Directory Lateral Movement - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1021.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1021.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.003/lateral_movement/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.003/lateral_movement/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/remote_process_instantiation_via_dcom_and_powershell_script_block.yml b/detections/endpoint/remote_process_instantiation_via_dcom_and_powershell_script_block.yml index e195039c18..5d8aa15e51 100644 --- a/detections/endpoint/remote_process_instantiation_via_dcom_and_powershell_script_block.yml +++ b/detections/endpoint/remote_process_instantiation_via_dcom_and_powershell_script_block.yml @@ -1,74 +1,60 @@ name: Remote Process Instantiation via DCOM and PowerShell Script Block id: fa1c3040-4680-11ec-a618-3e22fbd008af -version: 9 -date: '2025-06-24' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: - The following analytic detects the execution of PowerShell commands that - initiate a process on a remote endpoint via the DCOM protocol. It leverages PowerShell - Script Block Logging (EventCode=4104) to identify the use of ShellExecute and ExecuteShellCommand. - This activity is significant as it may indicate lateral movement or remote code - execution attempts by adversaries. If confirmed malicious, this behavior could allow - attackers to execute arbitrary code on remote systems, potentially leading to further - compromise and persistence within the network. +description: The following analytic detects the execution of PowerShell commands that initiate a process on a remote endpoint via the DCOM protocol. It leverages PowerShell Script Block Logging (EventCode=4104) to identify the use of ShellExecute and ExecuteShellCommand. This activity is significant as it may indicate lateral movement or remote code execution attempts by adversaries. If confirmed malicious, this behavior could allow attackers to execute arbitrary code on remote systems, potentially leading to further compromise and persistence within the network. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 (ScriptBlockText="*Document.Application.ShellExecute*" - OR ScriptBlockText="*Document.ActiveView.ExecuteShellCommand*") | fillnull | stats - count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remote_process_instantiation_via_dcom_and_powershell_script_block_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup instructions - can be found https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - Administrators may leverage DCOM to start a process on remote - systems, but this activity is usually limited to a small set of hosts or users. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 (ScriptBlockText="*Document.Application.ShellExecute*" OR ScriptBlockText="*Document.ActiveView.ExecuteShellCommand*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `remote_process_instantiation_via_dcom_and_powershell_script_block_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup instructions can be found https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: Administrators may leverage DCOM to start a process on remote systems, but this activity is usually limited to a small set of hosts or users. references: - - https://attack.mitre.org/techniques/T1021/003/ - - https://www.cybereason.com/blog/dcom-lateral-movement-techniques + - https://attack.mitre.org/techniques/T1021/003/ + - https://www.cybereason.com/blog/dcom-lateral-movement-techniques drilldown_searches: - - name: View the detection results for - "$Computer$" - search: '%original_detection_search% | search Computer = "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$Computer$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" + search: '%original_detection_search% | search Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A process was started on a remote endpoint from $dest$ by abusing WMI using - PowerShell.exe - risk_objects: - - field: dest - type: system - score: 63 - threat_objects: [] + message: A process was started on a remote endpoint from $dest$ by abusing WMI using PowerShell.exe + risk_objects: + - field: dest + type: system + score: 63 + threat_objects: [] tags: - analytic_story: - - Active Directory Lateral Movement - asset_type: Endpoint - mitre_attack_id: - - T1021.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + asset_type: Endpoint + mitre_attack_id: + - T1021.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/lateral_movement_psh/windows-powershell-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/lateral_movement_psh/windows-powershell-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/remote_process_instantiation_via_winrm_and_powershell.yml b/detections/endpoint/remote_process_instantiation_via_winrm_and_powershell.yml index 2dc346685b..32871f20b2 100644 --- a/detections/endpoint/remote_process_instantiation_via_winrm_and_powershell.yml +++ b/detections/endpoint/remote_process_instantiation_via_winrm_and_powershell.yml @@ -1,82 +1,66 @@ name: Remote Process Instantiation via WinRM and PowerShell id: ba24cda8-4716-11ec-8009-3e22fbd008af -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the execution of `powershell.exe` with - arguments used to start a process on a remote endpoint via the WinRM protocol, specifically - targeting the `Invoke-Command` cmdlet. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on command-line executions and process telemetry. - This activity is significant as it may indicate lateral movement or remote code - execution attempts by adversaries. If confirmed malicious, this could allow attackers - to execute arbitrary code on remote systems, potentially leading to further compromise - and lateral spread within the network. +description: The following analytic detects the execution of `powershell.exe` with arguments used to start a process on a remote endpoint via the WinRM protocol, specifically targeting the `Invoke-Command` cmdlet. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process telemetry. This activity is significant as it may indicate lateral movement or remote code execution attempts by adversaries. If confirmed malicious, this could allow attackers to execute arbitrary code on remote systems, potentially leading to further compromise and lateral spread within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_powershell` (Processes.process="*Invoke-Command*" - AND Processes.process="*-ComputerName*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_process_instantiation_via_winrm_and_powershell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrators may leverage WinRM and `Invoke-Command` to start - a process on remote systems for system administration or automation use cases. However, - this activity is usually limited to a small set of hosts or users. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_powershell` (Processes.process="*Invoke-Command*" + AND + Processes.process="*-ComputerName*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `remote_process_instantiation_via_winrm_and_powershell_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators may leverage WinRM and `Invoke-Command` to start a process on remote systems for system administration or automation use cases. However, this activity is usually limited to a small set of hosts or users. references: -- https://attack.mitre.org/techniques/T1021/006/ -- https://pentestlab.blog/2018/05/15/lateral-movement-winrm/ + - https://attack.mitre.org/techniques/T1021/006/ + - https://pentestlab.blog/2018/05/15/lateral-movement-winrm/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process was started on a remote endpoint from $dest$ by abusing WinRM - using PowerShell.exe - risk_objects: - - field: dest - type: system - score: 45 - threat_objects: [] + message: A process was started on a remote endpoint from $dest$ by abusing WinRM using PowerShell.exe + risk_objects: + - field: dest + type: system + score: 45 + threat_objects: [] tags: - analytic_story: - - Active Directory Lateral Movement - asset_type: Endpoint - mitre_attack_id: - - T1021.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + asset_type: Endpoint + mitre_attack_id: + - T1021.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/lateral_movement_psh/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/lateral_movement_psh/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/remote_process_instantiation_via_winrm_and_powershell_script_block.yml b/detections/endpoint/remote_process_instantiation_via_winrm_and_powershell_script_block.yml index 2876bb9945..3138a43a76 100644 --- a/detections/endpoint/remote_process_instantiation_via_winrm_and_powershell_script_block.yml +++ b/detections/endpoint/remote_process_instantiation_via_winrm_and_powershell_script_block.yml @@ -1,76 +1,60 @@ name: Remote Process Instantiation via WinRM and PowerShell Script Block id: 7d4c618e-4716-11ec-951c-3e22fbd008af -version: 9 -date: '2025-06-24' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: - The following analytic detects the execution of PowerShell commands that - use the `Invoke-Command` cmdlet to start a process on a remote endpoint via the - WinRM protocol. It leverages PowerShell Script Block Logging (EventCode=4104) to - identify such activities. This behavior is significant as it may indicate lateral - movement or remote code execution attempts by adversaries. If confirmed malicious, - this activity could allow attackers to execute arbitrary code on remote systems, - potentially leading to further compromise and persistence within the network. +description: The following analytic detects the execution of PowerShell commands that use the `Invoke-Command` cmdlet to start a process on a remote endpoint via the WinRM protocol. It leverages PowerShell Script Block Logging (EventCode=4104) to identify such activities. This behavior is significant as it may indicate lateral movement or remote code execution attempts by adversaries. If confirmed malicious, this activity could allow attackers to execute arbitrary code on remote systems, potentially leading to further compromise and persistence within the network. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 (ScriptBlockText="*Invoke-Command*" AND ScriptBlockText="*-ComputerName*") - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `remote_process_instantiation_via_winrm_and_powershell_script_block_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup instructions - can be found https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - Administrators may leverage WinRM and `Invoke-Command` to start - a process on remote systems for system administration or automation use cases. This - activity is usually limited to a small set of hosts or users. In certain environments, - tuning may not be possible. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 (ScriptBlockText="*Invoke-Command*" AND ScriptBlockText="*-ComputerName*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `remote_process_instantiation_via_winrm_and_powershell_script_block_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup instructions can be found https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: Administrators may leverage WinRM and `Invoke-Command` to start a process on remote systems for system administration or automation use cases. This activity is usually limited to a small set of hosts or users. In certain environments, tuning may not be possible. references: - - https://attack.mitre.org/techniques/T1021/006/ - - https://pentestlab.blog/2018/05/15/lateral-movement-winrm/ + - https://attack.mitre.org/techniques/T1021/006/ + - https://pentestlab.blog/2018/05/15/lateral-movement-winrm/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A process was started on a remote endpoint from $dest$ by abusing WinRM - using PowerShell.exe - risk_objects: - - field: dest - type: system - score: 45 - threat_objects: [] + message: A process was started on a remote endpoint from $dest$ by abusing WinRM using PowerShell.exe + risk_objects: + - field: dest + type: system + score: 45 + threat_objects: [] tags: - analytic_story: - - Active Directory Lateral Movement - asset_type: Endpoint - mitre_attack_id: - - T1021.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + asset_type: Endpoint + mitre_attack_id: + - T1021.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/lateral_movement_psh/windows-powershell-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/lateral_movement_psh/windows-powershell-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/remote_process_instantiation_via_winrm_and_winrs.yml b/detections/endpoint/remote_process_instantiation_via_winrm_and_winrs.yml index 13c0ad00b1..7f57f2ec68 100644 --- a/detections/endpoint/remote_process_instantiation_via_winrm_and_winrs.yml +++ b/detections/endpoint/remote_process_instantiation_via_winrm_and_winrs.yml @@ -1,82 +1,69 @@ name: Remote Process Instantiation via WinRM and Winrs id: 0dd296a2-4338-11ec-ba02-3e22fbd008af -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the execution of `winrs.exe` with command-line - arguments used to start a process on a remote endpoint. It leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process names and command-line - executions mapped to the `Processes` node of the `Endpoint` data model. This activity - is significant as it may indicate lateral movement or remote code execution attempts - by adversaries. If confirmed malicious, this could allow attackers to execute arbitrary - code on remote systems, potentially leading to further compromise and lateral spread - within the network. +description: The following analytic detects the execution of `winrs.exe` with command-line arguments used to start a process on a remote endpoint. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions mapped to the `Processes` node of the `Endpoint` data model. This activity is significant as it may indicate lateral movement or remote code execution attempts by adversaries. If confirmed malicious, this could allow attackers to execute arbitrary code on remote systems, potentially leading to further compromise and lateral spread within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=winrs.exe - OR Processes.original_file_name=winrs.exe) (Processes.process="*-r:*" OR Processes.process="*-remote:*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `remote_process_instantiation_via_winrm_and_winrs_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrators may leverage WinRM and WinRs to start a process - on remote systems, but this activity is usually limited to a small set of hosts - or users. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name=winrs.exe + OR + Processes.original_file_name=winrs.exe + ) + (Processes.process="*-r:*" OR Processes.process="*-remote:*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `remote_process_instantiation_via_winrm_and_winrs_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators may leverage WinRM and WinRs to start a process on remote systems, but this activity is usually limited to a small set of hosts or users. references: -- https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/winrs -- https://attack.mitre.org/techniques/T1021/006/ + - https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/winrs + - https://attack.mitre.org/techniques/T1021/006/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process was started on a remote endpoint from $dest$ - risk_objects: - - field: dest - type: system - score: 54 - threat_objects: [] + message: A process was started on a remote endpoint from $dest$ + risk_objects: + - field: dest + type: system + score: 54 + threat_objects: [] tags: - analytic_story: - - Active Directory Lateral Movement - asset_type: Endpoint - mitre_attack_id: - - T1021.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + asset_type: Endpoint + mitre_attack_id: + - T1021.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/lateral_movement/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/lateral_movement/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/remote_process_instantiation_via_wmi.yml b/detections/endpoint/remote_process_instantiation_via_wmi.yml index 3f5e6866ee..16f48c850c 100644 --- a/detections/endpoint/remote_process_instantiation_via_wmi.yml +++ b/detections/endpoint/remote_process_instantiation_via_wmi.yml @@ -1,91 +1,78 @@ name: Remote Process Instantiation via WMI id: d25d2c3d-d9d8-40ec-8fdf-e86fe155a3da -version: 14 -date: '2025-05-02' +version: 15 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the execution of wmic.exe with parameters - to spawn a process on a remote system. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on command-line executions and process telemetry - mapped to the `Processes` node of the `Endpoint` data model. This activity is significant - as WMI can be abused for lateral movement and remote code execution, often used - by adversaries and Red Teams. If confirmed malicious, this could allow attackers - to execute arbitrary code on remote systems, facilitating further compromise and - lateral spread within the network. +description: The following analytic detects the execution of wmic.exe with parameters to spawn a process on a remote system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process telemetry mapped to the `Processes` node of the `Endpoint` data model. This activity is significant as WMI can be abused for lateral movement and remote code execution, often used by adversaries and Red Teams. If confirmed malicious, this could allow attackers to execute arbitrary code on remote systems, facilitating further compromise and lateral spread within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_wmic` (Processes.process="*/node:*" - AND Processes.process="*process*" AND Processes.process="*call*" AND Processes.process="*create*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_process_instantiation_via_wmi_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: The wmic.exe utility is a benign Windows application. It may - be used legitimately by Administrators with these parameters for remote system administration, - but it's relatively uncommon. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_wmic` (Processes.process="*/node:*" + AND + Processes.process="*process*" + AND + Processes.process="*call*" + AND + Processes.process="*create*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `remote_process_instantiation_via_wmi_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: The wmic.exe utility is a benign Windows application. It may be used legitimately by Administrators with these parameters for remote system administration, but it's relatively uncommon. references: -- https://attack.mitre.org/techniques/T1047/ -- https://docs.microsoft.com/en-us/windows/win32/cimwin32prov/create-method-in-class-win32-process + - https://attack.mitre.org/techniques/T1047/ + - https://docs.microsoft.com/en-us/windows/win32/cimwin32prov/create-method-in-class-win32-process drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A wmic.exe process $process$ contain process spawn commandline $process$ - in host $dest$ - risk_objects: - - field: dest - type: system - score: 49 - - field: user - type: user - score: 49 - threat_objects: [] + message: A wmic.exe process $process$ contain process spawn commandline $process$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 49 + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - CISA AA23-347A - - China-Nexus Threat Activity - - Ransomware - - Suspicious WMI Use - - Salt Typhoon - - Active Directory Lateral Movement - asset_type: Endpoint - mitre_attack_id: - - T1047 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA23-347A + - China-Nexus Threat Activity + - Ransomware + - Suspicious WMI Use + - Salt Typhoon + - Active Directory Lateral Movement + asset_type: Endpoint + mitre_attack_id: + - T1047 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/remote_process_instantiation_via_wmi_and_powershell.yml b/detections/endpoint/remote_process_instantiation_via_wmi_and_powershell.yml index f901eaedb7..655f9ffd9a 100644 --- a/detections/endpoint/remote_process_instantiation_via_wmi_and_powershell.yml +++ b/detections/endpoint/remote_process_instantiation_via_wmi_and_powershell.yml @@ -1,83 +1,71 @@ name: Remote Process Instantiation via WMI and PowerShell id: 112638b4-4634-11ec-b9ab-3e22fbd008af -version: 16 -date: '2025-05-02' +version: 17 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the execution of `powershell.exe` using - the `Invoke-WmiMethod` cmdlet to start a process on a remote endpoint via WMI. It - leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line - executions and process telemetry. This activity is significant as it indicates potential - lateral movement or remote code execution attempts by adversaries. If confirmed - malicious, this could allow attackers to execute arbitrary code on remote systems, - leading to further compromise and persistence within the network. +description: The following analytic detects the execution of `powershell.exe` using the `Invoke-WmiMethod` cmdlet to start a process on a remote endpoint via WMI. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process telemetry. This activity is significant as it indicates potential lateral movement or remote code execution attempts by adversaries. If confirmed malicious, this could allow attackers to execute arbitrary code on remote systems, leading to further compromise and persistence within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_powershell` (Processes.process="*Invoke-WmiMethod*" - AND Processes.process="*-CN*" AND Processes.process="*-Class Win32_Process*" AND Processes.process="*-Name - create*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `remote_process_instantiation_via_wmi_and_powershell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrators may leverage WWMI and powershell.exe to start - a process on remote systems, but this activity is usually limited to a small set - of hosts or users. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_powershell` (Processes.process="*Invoke-WmiMethod*" + AND + Processes.process="*-CN*" + AND + Processes.process="*-Class Win32_Process*" + AND + Processes.process="*-Name create*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `remote_process_instantiation_via_wmi_and_powershell_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators may leverage WWMI and powershell.exe to start a process on remote systems, but this activity is usually limited to a small set of hosts or users. references: -- https://attack.mitre.org/techniques/T1047/ -- https://docs.microsoft.com/en-us/powershell/module/microsoft.powershell.management/invoke-wmimethod?view=powershell-5.1 + - https://attack.mitre.org/techniques/T1047/ + - https://docs.microsoft.com/en-us/powershell/module/microsoft.powershell.management/invoke-wmimethod?view=powershell-5.1 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process was started on a remote endpoint from $dest$ by abusing WMI using - PowerShell.exe - risk_objects: - - field: dest - type: system - score: 63 - threat_objects: [] + message: A process was started on a remote endpoint from $dest$ by abusing WMI using PowerShell.exe + risk_objects: + - field: dest + type: system + score: 63 + threat_objects: [] tags: - analytic_story: - - Active Directory Lateral Movement - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1047 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1047 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/lateral_movement/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/lateral_movement/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/remote_process_instantiation_via_wmi_and_powershell_script_block.yml b/detections/endpoint/remote_process_instantiation_via_wmi_and_powershell_script_block.yml index a104e1cd71..841c8d0d82 100644 --- a/detections/endpoint/remote_process_instantiation_via_wmi_and_powershell_script_block.yml +++ b/detections/endpoint/remote_process_instantiation_via_wmi_and_powershell_script_block.yml @@ -1,77 +1,60 @@ name: Remote Process Instantiation via WMI and PowerShell Script Block id: 2a048c14-4634-11ec-a618-3e22fbd008af -version: 8 -date: '2025-06-24' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: - The following analytic detects the execution of the `Invoke-WmiMethod` - commandlet with parameters used to start a process on a remote endpoint via WMI, - leveraging PowerShell Script Block Logging (EventCode=4104). This method identifies - specific script block text patterns associated with remote process instantiation. - This activity is significant as it may indicate lateral movement or remote code - execution attempts by adversaries. If confirmed malicious, this could allow attackers - to execute arbitrary code on remote systems, potentially leading to further compromise - and persistence within the network. +description: The following analytic detects the execution of the `Invoke-WmiMethod` commandlet with parameters used to start a process on a remote endpoint via WMI, leveraging PowerShell Script Block Logging (EventCode=4104). This method identifies specific script block text patterns associated with remote process instantiation. This activity is significant as it may indicate lateral movement or remote code execution attempts by adversaries. If confirmed malicious, this could allow attackers to execute arbitrary code on remote systems, potentially leading to further compromise and persistence within the network. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText="*Invoke-WmiMethod*" AND (ScriptBlockText="*-CN*" - OR ScriptBlockText="*-ComputerName*") AND ScriptBlockText="*-Class Win32_Process*" - AND ScriptBlockText="*-Name create*" | fillnull | stats count min(_time) as firstTime - max(_time) as lastTime by dest signature signature_id user_id vendor_product EventID - Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `remote_process_instantiation_via_wmi_and_powershell_script_block_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup instructions - can be found https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - Administrators may leverage WWMI and powershell.exe to start - a process on remote systems, but this activity is usually limited to a small set - of hosts or users. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText="*Invoke-WmiMethod*" AND (ScriptBlockText="*-CN*" OR ScriptBlockText="*-ComputerName*") AND ScriptBlockText="*-Class Win32_Process*" AND ScriptBlockText="*-Name create*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `remote_process_instantiation_via_wmi_and_powershell_script_block_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup instructions can be found https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: Administrators may leverage WWMI and powershell.exe to start a process on remote systems, but this activity is usually limited to a small set of hosts or users. references: - - https://attack.mitre.org/techniques/T1047/ - - https://docs.microsoft.com/en-us/powershell/module/microsoft.powershell.management/invoke-wmimethod?view=powershell-5.1 + - https://attack.mitre.org/techniques/T1047/ + - https://docs.microsoft.com/en-us/powershell/module/microsoft.powershell.management/invoke-wmimethod?view=powershell-5.1 drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A process was started on a remote endpoint from $dest$ by abusing WMI using - PowerShell.exe - risk_objects: - - field: dest - type: system - score: 63 - threat_objects: [] + message: A process was started on a remote endpoint from $dest$ by abusing WMI using PowerShell.exe + risk_objects: + - field: dest + type: system + score: 63 + threat_objects: [] tags: - analytic_story: - - Active Directory Lateral Movement - asset_type: Endpoint - mitre_attack_id: - - T1047 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + asset_type: Endpoint + mitre_attack_id: + - T1047 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/lateral_movement/wmi_remote_process_powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/lateral_movement/wmi_remote_process_powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/remote_system_discovery_with_adsisearcher.yml b/detections/endpoint/remote_system_discovery_with_adsisearcher.yml index 43ec3399aa..e1829a5f16 100644 --- a/detections/endpoint/remote_system_discovery_with_adsisearcher.yml +++ b/detections/endpoint/remote_system_discovery_with_adsisearcher.yml @@ -1,71 +1,59 @@ name: Remote System Discovery with Adsisearcher id: 70803451-0047-4e12-9d63-77fa7eb8649c -version: 8 -date: '2025-06-24' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: - The following analytic detects the use of the `[Adsisearcher]` type accelerator - in PowerShell scripts to query Active Directory for domain computers. It leverages - PowerShell Script Block Logging (EventCode=4104) to identify specific script blocks - containing `adsisearcher` and `objectcategory=computer` with methods like `findAll()` - or `findOne()`. This activity is significant as it may indicate an attempt by adversaries - or Red Teams to perform Active Directory discovery and gain situational awareness. - If confirmed malicious, this could lead to further reconnaissance and potential - lateral movement within the network. +description: The following analytic detects the use of the `[Adsisearcher]` type accelerator in PowerShell scripts to query Active Directory for domain computers. It leverages PowerShell Script Block Logging (EventCode=4104) to identify specific script blocks containing `adsisearcher` and `objectcategory=computer` with methods like `findAll()` or `findOne()`. This activity is significant as it may indicate an attempt by adversaries or Red Teams to perform Active Directory discovery and gain situational awareness. If confirmed malicious, this could lead to further reconnaissance and potential lateral movement within the network. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText = "*adsisearcher*" AND ScriptBlockText - = "*objectcategory=computer*" AND ScriptBlockText IN ("*findAll()*","*findOne()*") - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `remote_system_discovery_with_adsisearcher_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*adsisearcher*" AND ScriptBlockText = "*objectcategory=computer*" AND ScriptBlockText IN ("*findAll()*","*findOne()*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `remote_system_discovery_with_adsisearcher_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. known_false_positives: Administrators or power users may use Adsisearcher for troubleshooting. references: - - https://attack.mitre.org/techniques/T1018/ - - https://devblogs.microsoft.com/scripting/use-the-powershell-adsisearcher-type-accelerator-to-search-active-directory/ + - https://attack.mitre.org/techniques/T1018/ + - https://devblogs.microsoft.com/scripting/use-the-powershell-adsisearcher-type-accelerator-to-search-active-directory/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Remote system discovery enumeration with adsisearcher on $dest$ by $user_id$ - risk_objects: - - field: dest - type: system - score: 15 - threat_objects: [] + message: Remote system discovery enumeration with adsisearcher on $dest$ by $user_id$ + risk_objects: + - field: dest + type: system + score: 15 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1018 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1018 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/adsisearcher-powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/adsisearcher-powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/remote_system_discovery_with_dsquery.yml b/detections/endpoint/remote_system_discovery_with_dsquery.yml index 3f06aa781c..41d8696dc9 100644 --- a/detections/endpoint/remote_system_discovery_with_dsquery.yml +++ b/detections/endpoint/remote_system_discovery_with_dsquery.yml @@ -1,92 +1,75 @@ name: Remote System Discovery with Dsquery id: 9fb562f4-42f8-4139-8e11-a82edf7ed718 -version: 7 -date: '2025-08-27' +version: 8 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Anomaly -description: The following analytic detects the execution of `dsquery.exe` with - the `computer` argument, which is used to discover remote systems within a - domain. This detection leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process names and command-line arguments. Remote - system discovery is significant as it indicates potential reconnaissance - activities by adversaries or Red Teams to map out network resources and Active - Directory structures. If confirmed malicious, this activity could lead to - further exploitation, lateral movement, and unauthorized access to critical - systems within the network. +description: The following analytic detects the execution of `dsquery.exe` with the `computer` argument, which is used to discover remote systems within a domain. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. Remote system discovery is significant as it indicates potential reconnaissance activities by adversaries or Red Teams to map out network resources and Active Directory structures. If confirmed malicious, this activity could lead to further exploitation, lateral movement, and unauthorized access to critical systems within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="dsquery.exe") - (Processes.process="*computer*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remote_system_discovery_with_dsquery_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: Administrators or power users may use this command for - troubleshooting. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="dsquery.exe" + ) + (Processes.process="*computer*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `remote_system_discovery_with_dsquery_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1018/ -- https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-r2-and-2012/cc732952(v=ws.11) + - https://attack.mitre.org/techniques/T1018/ + - https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-r2-and-2012/cc732952(v=ws.11) drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 3 - - field: dest - type: system - score: 3 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 3 + - field: dest + type: system + score: 3 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Active Directory Discovery - - LAMEHUG - asset_type: Endpoint - mitre_attack_id: - - T1018 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - LAMEHUG + asset_type: Endpoint + mitre_attack_id: + - T1018 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/remote_system_discovery_with_wmic.yml b/detections/endpoint/remote_system_discovery_with_wmic.yml index a37a42e73d..c5bbe1b4fe 100644 --- a/detections/endpoint/remote_system_discovery_with_wmic.yml +++ b/detections/endpoint/remote_system_discovery_with_wmic.yml @@ -5,77 +5,47 @@ date: '2025-05-02' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the execution of `wmic.exe` with specific - command-line arguments used to discover remote systems within a domain. It leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process names - and command-line executions. This activity is significant as it indicates potential - reconnaissance efforts by adversaries to map out network resources and Active Directory - structures. If confirmed malicious, this behavior could allow attackers to gain - situational awareness, identify critical systems, and plan further attacks, potentially - leading to unauthorized access and data exfiltration. +description: The following analytic detects the execution of `wmic.exe` with specific command-line arguments used to discover remote systems within a domain. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential reconnaissance efforts by adversaries to map out network resources and Active Directory structures. If confirmed malicious, this behavior could allow attackers to gain situational awareness, identify critical systems, and plan further attacks, potentially leading to unauthorized access and data exfiltration. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="wmic.exe") - (Processes.process=*/NAMESPACE:\\\\root\\directory\\ldap* AND Processes.process=*ds_computer* - AND Processes.process="*GET ds_samaccountname*") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `remote_system_discovery_with_wmic_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="wmic.exe") (Processes.process=*/NAMESPACE:\\\\root\\directory\\ldap* AND Processes.process=*ds_computer* AND Processes.process="*GET ds_samaccountname*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remote_system_discovery_with_wmic_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1018/ -- https://docs.microsoft.com/en-us/windows/win32/wmisdk/wmic + - https://attack.mitre.org/techniques/T1018/ + - https://docs.microsoft.com/en-us/windows/win32/wmisdk/wmic drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Remote system discovery enumeration on $dest$ by $user$ - risk_objects: - - field: dest - type: system - score: 15 - threat_objects: [] + message: Remote system discovery enumeration on $dest$ by $user$ + risk_objects: + - field: dest + type: system + score: 15 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1018 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1018 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/remote_wmi_command_attempt.yml b/detections/endpoint/remote_wmi_command_attempt.yml index 02a821ea67..41b134404e 100644 --- a/detections/endpoint/remote_wmi_command_attempt.yml +++ b/detections/endpoint/remote_wmi_command_attempt.yml @@ -1,89 +1,73 @@ name: Remote WMI Command Attempt id: 272df6de-61f1-4784-877c-1fbc3e2d0838 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Rico Valdez, Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of `wmic.exe` with the `node` - switch, indicating an attempt to spawn a local or remote process. This detection - leverages data from Endpoint Detection and Response (EDR) agents, focusing on process - creation events and command-line arguments. This activity is significant as it may - indicate lateral movement or remote code execution attempts by an attacker. If confirmed - malicious, the attacker could gain remote control over the targeted system, execute - arbitrary commands, and potentially escalate privileges or persist within the environment. +description: The following analytic detects the execution of `wmic.exe` with the `node` switch, indicating an attempt to spawn a local or remote process. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events and command-line arguments. This activity is significant as it may indicate lateral movement or remote code execution attempts by an attacker. If confirmed malicious, the attacker could gain remote control over the targeted system, execute arbitrary commands, and potentially escalate privileges or persist within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_wmic` Processes.process=*node* - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `remote_wmi_command_attempt_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrators may use this legitimately to gather info from - remote systems. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_wmic` Processes.process=*node* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `remote_wmi_command_attempt_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators may use this legitimately to gather info from remote systems. Filter as needed. references: -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1047/T1047.yaml -- https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ -- https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1047/T1047.yaml + - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ + - https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A wmic.exe process $process$ contain node commandline $process$ in host - $dest$ - risk_objects: - - field: dest - type: system - score: 36 - - field: user - type: user - score: 36 - threat_objects: [] + message: A wmic.exe process $process$ contain node commandline $process$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 36 + - field: user + type: user + score: 36 + threat_objects: [] tags: - analytic_story: - - Graceful Wipe Out Attack - - Volt Typhoon - - Living Off The Land - - IcedID - - Suspicious WMI Use - - CISA AA23-347A - asset_type: Endpoint - mitre_attack_id: - - T1047 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Graceful Wipe Out Attack + - Volt Typhoon + - Living Off The Land + - IcedID + - Suspicious WMI Use + - CISA AA23-347A + asset_type: Endpoint + mitre_attack_id: + - T1047 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/resize_shadowstorage_volume.yml b/detections/endpoint/resize_shadowstorage_volume.yml index 69bb68bb7b..348b48a499 100644 --- a/detections/endpoint/resize_shadowstorage_volume.yml +++ b/detections/endpoint/resize_shadowstorage_volume.yml @@ -1,95 +1,80 @@ name: Resize ShadowStorage volume id: bc760ca6-8336-11eb-bcbb-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras status: production type: TTP -description: The following analytic identifies the resizing of shadow storage volumes, - a technique used by ransomware like CLOP to prevent the recreation of shadow volumes. - This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on command-line executions involving "vssadmin.exe" with parameters related - to resizing shadow storage. This activity is significant as it indicates an attempt - to hinder recovery efforts by manipulating shadow copies. If confirmed malicious, - this could lead to successful ransomware deployment, making data recovery difficult - and increasing the potential for data loss. +description: The following analytic identifies the resizing of shadow storage volumes, a technique used by ransomware like CLOP to prevent the recreation of shadow volumes. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving "vssadmin.exe" with parameters related to resizing shadow storage. This activity is significant as it indicates an attempt to hinder recovery efforts by manipulating shadow copies. If confirmed malicious, this could lead to successful ransomware deployment, making data recovery difficult and increasing the potential for data loss. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as cmdline - values(Processes.parent_process_name) as parent_process values(Processes.process_name) - as process_name min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.parent_process_name = "cmd.exe" OR Processes.parent_process_name - = "powershell.exe" OR Processes.parent_process_name = "powershell_ise.exe" OR Processes.parent_process_name - = "wmic.exe" Processes.process_name = "vssadmin.exe" Processes.process="*resize*" - Processes.process="*shadowstorage*" Processes.process="*/maxsize*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` - | `resize_shadowstorage_volume_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as cmdline values(Processes.parent_process_name) as parent_process values(Processes.process_name) as process_name min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name = "cmd.exe" + OR + Processes.parent_process_name = "powershell.exe" + OR + Processes.parent_process_name = "powershell_ise.exe" + OR + Processes.parent_process_name = "wmic.exe" Processes.process_name = "vssadmin.exe" Processes.process="*resize*" Processes.process="*shadowstorage*" Processes.process="*/maxsize*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `resize_shadowstorage_volume_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: network admin can resize the shadowstorage for valid purposes. references: -- https://www.mandiant.com/resources/fin11-email-campaigns-precursor-for-ransomware-data-theft -- https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md -- https://redcanary.com/blog/blackbyte-ransomware/ -- https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/vssadmin-resize-shadowstorage + - https://www.mandiant.com/resources/fin11-email-campaigns-precursor-for-ransomware-data-theft + - https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md + - https://redcanary.com/blog/blackbyte-ransomware/ + - https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/vssadmin-resize-shadowstorage drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process $parent_process_name$ attempted to resize shadow copy with commandline - $process$ in host $dest$ - risk_objects: - - field: dest - type: system - score: 72 - - field: user - type: user - score: 72 - threat_objects: [] + message: A process $parent_process_name$ attempted to resize shadow copy with commandline $process$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 72 + - field: user + type: user + score: 72 + threat_objects: [] tags: - analytic_story: - - Medusa Ransomware - - Clop Ransomware - - Compromised Windows Host - - BlackByte Ransomware - - VanHelsing Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1490 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Medusa Ransomware + - Clop Ransomware + - Compromised Windows Host + - BlackByte Ransomware + - VanHelsing Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1490 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/revil_common_exec_parameter.yml b/detections/endpoint/revil_common_exec_parameter.yml index 4eec16b613..5b1a93d0d3 100644 --- a/detections/endpoint/revil_common_exec_parameter.yml +++ b/detections/endpoint/revil_common_exec_parameter.yml @@ -1,86 +1,74 @@ name: Revil Common Exec Parameter id: 85facebe-c382-11eb-9c3e-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of command-line parameters - commonly associated with REVIL ransomware, such as "-nolan", "-nolocal", "-fast", - and "-full". It leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process execution logs mapped to the `Processes` node of the `Endpoint` - data model. This activity is significant because these parameters are indicative - of ransomware attempting to encrypt files on a compromised machine. If confirmed - malicious, this could lead to widespread data encryption, rendering critical files - inaccessible and potentially causing significant operational disruption. +description: The following analytic detects the execution of command-line parameters commonly associated with REVIL ransomware, such as "-nolan", "-nolocal", "-fast", and "-full". It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs mapped to the `Processes` node of the `Endpoint` data model. This activity is significant because these parameters are indicative of ransomware attempting to encrypt files on a compromised machine. If confirmed malicious, this could lead to widespread data encryption, rendering critical files inaccessible and potentially causing significant operational disruption. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process = "* -nolan - *" OR Processes.process = "* -nolocal *" OR Processes.process = "* -fast *" OR Processes.process - = "* -full *" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `revil_common_exec_parameter_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: third party tool may have same command line parameters as revil - ransomware. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process = "* -nolan *" + OR + Processes.process = "* -nolocal *" + OR + Processes.process = "* -fast *" + OR + Processes.process = "* -full *" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `revil_common_exec_parameter_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: third party tool may have same command line parameters as revil ransomware. references: -- https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/ -- https://www.mcafee.com/blogs/other-blogs/mcafee-labs/mcafee-atr-analyzes-sodinokibi-aka-revil-ransomware-as-a-service-what-the-code-tells-us/ + - https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/ + - https://www.mcafee.com/blogs/other-blogs/mcafee-labs/mcafee-atr-analyzes-sodinokibi-aka-revil-ransomware-as-a-service-what-the-code-tells-us/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process $process_name$ with commandline $process$ related to revil ransomware - in host $dest$ - risk_objects: - - field: dest - type: system - score: 54 - - field: user - type: user - score: 54 - threat_objects: [] + message: A process $process_name$ with commandline $process$ related to revil ransomware in host $dest$ + risk_objects: + - field: dest + type: system + score: 54 + - field: user + type: user + score: 54 + threat_objects: [] tags: - analytic_story: - - Ransomware - - Revil Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1204 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - Revil Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1204 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf1/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf1/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/revil_registry_entry.yml b/detections/endpoint/revil_registry_entry.yml index 1901ccf223..bcc090f3b0 100644 --- a/detections/endpoint/revil_registry_entry.yml +++ b/detections/endpoint/revil_registry_entry.yml @@ -5,85 +5,51 @@ date: '2026-01-14' author: Steven Dick, Teoderick Contreras, Splunk status: production type: TTP -description: - The following analytic identifies suspicious modifications in the registry - entry, specifically targeting paths used by malware like REVIL. It detects changes - in registry paths such as `SOFTWARE\\WOW6432Node\\Facebook_Assistant` and `SOFTWARE\\WOW6432Node\\BlackLivesMatter`. - This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on registry modifications linked to process GUIDs. This activity is significant - as it indicates potential malware persistence mechanisms, often used by advanced - persistent threats (APTs) and ransomware. If confirmed malicious, this could allow - attackers to maintain persistence, encrypt files, and store critical ransomware-related - information on compromised hosts. +description: The following analytic identifies suspicious modifications in the registry entry, specifically targeting paths used by malware like REVIL. It detects changes in registry paths such as `SOFTWARE\\WOW6432Node\\Facebook_Assistant` and `SOFTWARE\\WOW6432Node\\BlackLivesMatter`. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on registry modifications linked to process GUIDs. This activity is significant as it indicates potential malware persistence mechanisms, often used by advanced persistent threats (APTs) and ransomware. If confirmed malicious, this could allow attackers to maintain persistence, encrypt files, and store critical ransomware-related information on compromised hosts. data_source: - - Sysmon EventID 12 - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry - WHERE (Registry.registry_path="*\\SOFTWARE\\WOW6432Node\\Facebook_Assistant\\*" - OR Registry.registry_path="*\\SOFTWARE\\WOW6432Node\\BlackLivesMatter*") by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `revil_registry_entry_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 12 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\SOFTWARE\\WOW6432Node\\Facebook_Assistant\\*" OR Registry.registry_path="*\\SOFTWARE\\WOW6432Node\\BlackLivesMatter*") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `revil_registry_entry_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: - - https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/ - - https://www.mcafee.com/blogs/other-blogs/mcafee-labs/mcafee-atr-analyzes-sodinokibi-aka-revil-ransomware-as-a-service-what-the-code-tells-us/ + - https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/ + - https://www.mcafee.com/blogs/other-blogs/mcafee-labs/mcafee-atr-analyzes-sodinokibi-aka-revil-ransomware-as-a-service-what-the-code-tells-us/ drilldown_searches: - - name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A registry entry $registry_path$ with registry value $registry_value_name$ - and $registry_value_name$ related to revil ransomware in host $dest$ - risk_objects: - - field: dest - type: system - score: 60 - - field: user - type: user - score: 60 - threat_objects: [] + message: A registry entry $registry_path$ with registry value $registry_value_name$ and $registry_value_name$ related to revil ransomware in host $dest$ + risk_objects: + - field: dest + type: system + score: 60 + - field: user + type: user + score: 60 + threat_objects: [] tags: - analytic_story: - - Ransomware - - Revil Ransomware - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - Revil Ransomware + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf1/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf1/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/rubeus_command_line_parameters.yml b/detections/endpoint/rubeus_command_line_parameters.yml index cef4523f07..28aae63e09 100644 --- a/detections/endpoint/rubeus_command_line_parameters.yml +++ b/detections/endpoint/rubeus_command_line_parameters.yml @@ -5,95 +5,61 @@ date: '2026-02-12' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the use of Rubeus command line parameters, - a toolset for Kerberos attacks within Active Directory environments. It leverages - Endpoint Detection and Response (EDR) data to identify specific command-line arguments - associated with actions like ticket manipulation, kerberoasting, and password spraying. - This activity is significant as Rubeus is commonly used by adversaries to exploit - Kerberos for privilege escalation and lateral movement. If confirmed malicious, - this could lead to unauthorized access, persistence, and potential compromise of - sensitive information within the network. +description: The following analytic detects the use of Rubeus command line parameters, a toolset for Kerberos attacks within Active Directory environments. It leverages Endpoint Detection and Response (EDR) data to identify specific command-line arguments associated with actions like ticket manipulation, kerberoasting, and password spraying. This activity is significant as Rubeus is commonly used by adversaries to exploit Kerberos for privilege escalation and lateral movement. If confirmed malicious, this could lead to unauthorized access, persistence, and potential compromise of sensitive information within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process = "*ptt /ticket*" - OR Processes.process = "* monitor /interval*" OR Processes.process ="* asktgt* /user:*" - OR Processes.process ="* asktgs* /service:*" OR Processes.process ="* golden* /user:*" - OR Processes.process ="* silver* /service:*" OR Processes.process ="* kerberoast*" - OR Processes.process ="* asreproast*" OR Processes.process = "* renew* /ticket:*" - OR Processes.process = "* brute* /password:*" OR Processes.process = "* brute* /passwords:*" - OR Processes.process ="* harvest*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rubeus_command_line_parameters_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although unlikely, legitimate applications may use the same - command line parameters as Rubeus. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process = "*ptt /ticket*" OR Processes.process = "* monitor /interval*" OR Processes.process ="* asktgt* /user:*" OR Processes.process ="* asktgs* /service:*" OR Processes.process ="* golden* /user:*" OR Processes.process ="* silver* /service:*" OR Processes.process ="* kerberoast*" OR Processes.process ="* asreproast*" OR Processes.process = "* renew* /ticket:*" OR Processes.process = "* brute* /password:*" OR Processes.process = "* brute* /passwords:*" OR Processes.process ="* harvest*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rubeus_command_line_parameters_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although unlikely, legitimate applications may use the same command line parameters as Rubeus. Filter as needed. references: -- https://github.com/GhostPack/Rubeus -- https://web.archive.org/web/20210725005734/http://www.harmj0y.net/blog/redteaming/from-kekeo-to-rubeus/ -- https://attack.mitre.org/techniques/T1550/003/ -- https://en.hackndo.com/kerberos-silver-golden-tickets/ + - https://github.com/GhostPack/Rubeus + - https://web.archive.org/web/20210725005734/http://www.harmj0y.net/blog/redteaming/from-kekeo-to-rubeus/ + - https://attack.mitre.org/techniques/T1550/003/ + - https://en.hackndo.com/kerberos-silver-golden-tickets/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Rubeus command line parameters were used on $dest$ - risk_objects: - - field: user - type: user - score: 36 - - field: dest - type: system - score: 36 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: Rubeus command line parameters were used on $dest$ + risk_objects: + - field: user + type: user + score: 36 + - field: dest + type: system + score: 36 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - Active Directory Privilege Escalation - - CISA AA23-347A - - Active Directory Kerberos Attacks - - BlackSuit Ransomware - - Scattered Lapsus$ Hunters - - ZOVWiper - asset_type: Endpoint - mitre_attack_id: - - T1550.003 - - T1558.003 - - T1558.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Privilege Escalation + - CISA AA23-347A + - Active Directory Kerberos Attacks + - BlackSuit Ransomware + - Scattered Lapsus$ Hunters + - ZOVWiper + asset_type: Endpoint + mitre_attack_id: + - T1550.003 + - T1558.003 + - T1558.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550.003/rubeus/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550.003/rubeus/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/rubeus_kerberos_ticket_exports_through_winlogon_access.yml b/detections/endpoint/rubeus_kerberos_ticket_exports_through_winlogon_access.yml index 75a00da16e..985c2f517d 100644 --- a/detections/endpoint/rubeus_kerberos_ticket_exports_through_winlogon_access.yml +++ b/detections/endpoint/rubeus_kerberos_ticket_exports_through_winlogon_access.yml @@ -5,78 +5,52 @@ date: '2026-02-12' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects a process accessing the winlogon.exe system - process, indicative of the Rubeus tool attempting to export Kerberos tickets from - memory. This detection leverages Sysmon EventCode 10 logs, focusing on processes - obtaining a handle to winlogon.exe with specific access rights. This activity is - significant as it often precedes pass-the-ticket attacks, where adversaries use - stolen Kerberos tickets to move laterally within an environment. If confirmed malicious, - this could allow attackers to bypass normal access controls, escalate privileges, - and persist within the network, posing a severe security risk. +description: The following analytic detects a process accessing the winlogon.exe system process, indicative of the Rubeus tool attempting to export Kerberos tickets from memory. This detection leverages Sysmon EventCode 10 logs, focusing on processes obtaining a handle to winlogon.exe with specific access rights. This activity is significant as it often precedes pass-the-ticket attacks, where adversaries use stolen Kerberos tickets to move laterally within an environment. If confirmed malicious, this could allow attackers to bypass normal access controls, escalate privileges, and persist within the network, posing a severe security risk. data_source: -- Sysmon EventID 10 -search: '`sysmon` EventCode=10 TargetImage=C:\\Windows\\system32\\winlogon.exe (GrantedAccess=0x1f3fff) - (SourceImage!=C:\\Windows\\system32\\svchost.exe AND SourceImage!=C:\\Windows\\system32\\lsass.exe - AND SourceImage!=C:\\Windows\\system32\\LogonUI.exe AND SourceImage!=C:\\Windows\\system32\\smss.exe - AND SourceImage!=C:\\Windows\\system32\\wbem\\wmiprvse.exe) | stats count min(_time) - as firstTime max(_time) as lastTime by CallTrace EventID GrantedAccess Guid Opcode - ProcessID SecurityID SourceImage SourceProcessGUID SourceProcessId TargetImage TargetProcessGUID - TargetProcessId UserID dest granted_access parent_process_exec parent_process_guid - parent_process_id parent_process_name parent_process_path process_exec process_guid - process_id process_name process_path signature signature_id user_id vendor_product - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `rubeus_kerberos_ticket_exports_through_winlogon_access_filter`' -how_to_implement: This search needs Sysmon Logs and a sysmon configuration, which - includes EventCode 10. This search uses an input macro named `sysmon`. We strongly - recommend that you specify your environment-specific configurations (index, source, - sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations - for your Splunk environment. -known_false_positives: Legitimate applications may obtain a handle for winlogon.exe. - Filter as needed + - Sysmon EventID 10 +search: '`sysmon` EventCode=10 TargetImage=C:\\Windows\\system32\\winlogon.exe (GrantedAccess=0x1f3fff) (SourceImage!=C:\\Windows\\system32\\svchost.exe AND SourceImage!=C:\\Windows\\system32\\lsass.exe AND SourceImage!=C:\\Windows\\system32\\LogonUI.exe AND SourceImage!=C:\\Windows\\system32\\smss.exe AND SourceImage!=C:\\Windows\\system32\\wbem\\wmiprvse.exe) | stats count min(_time) as firstTime max(_time) as lastTime by CallTrace EventID GrantedAccess Guid Opcode ProcessID SecurityID SourceImage SourceProcessGUID SourceProcessId TargetImage TargetProcessGUID TargetProcessId UserID dest granted_access parent_process_exec parent_process_guid parent_process_id parent_process_name parent_process_path process_exec process_guid process_id process_name process_path signature signature_id user_id vendor_product | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `rubeus_kerberos_ticket_exports_through_winlogon_access_filter`' +how_to_implement: This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 10. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. +known_false_positives: Legitimate applications may obtain a handle for winlogon.exe. Filter as needed references: -- https://github.com/GhostPack/Rubeus -- https://web.archive.org/web/20210725005734/http://www.harmj0y.net/blog/redteaming/from-kekeo-to-rubeus/ -- https://attack.mitre.org/techniques/T1550/003/ + - https://github.com/GhostPack/Rubeus + - https://web.archive.org/web/20210725005734/http://www.harmj0y.net/blog/redteaming/from-kekeo-to-rubeus/ + - https://attack.mitre.org/techniques/T1550/003/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Winlogon.exe was accessed by $SourceImage$ on $dest$ - risk_objects: - - field: dest - type: system - score: 36 - threat_objects: - - field: TargetImage - type: process + message: Winlogon.exe was accessed by $SourceImage$ on $dest$ + risk_objects: + - field: dest + type: system + score: 36 + threat_objects: + - field: TargetImage + type: process tags: - analytic_story: - - CISA AA23-347A - - Active Directory Kerberos Attacks - - BlackSuit Ransomware - - Scattered Lapsus$ Hunters - - ZOVWiper - asset_type: Endpoint - mitre_attack_id: - - T1550.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA23-347A + - Active Directory Kerberos Attacks + - BlackSuit Ransomware + - Scattered Lapsus$ Hunters + - ZOVWiper + asset_type: Endpoint + mitre_attack_id: + - T1550.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550.003/rubeus/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550.003/rubeus/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/runas_execution_in_commandline.yml b/detections/endpoint/runas_execution_in_commandline.yml index 06e773a3f6..b2f5f82297 100644 --- a/detections/endpoint/runas_execution_in_commandline.yml +++ b/detections/endpoint/runas_execution_in_commandline.yml @@ -5,65 +5,48 @@ date: '2025-12-15' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects the execution of the runas.exe process - with administrator user options. It leverages data from Endpoint Detection and Response - (EDR) agents, focusing on command-line executions and process details. This activity - is significant as it may indicate an attempt to gain elevated privileges, a common - tactic in privilege escalation and lateral movement. If confirmed malicious, this - could allow an attacker to execute commands with higher privileges, potentially - leading to unauthorized access, data exfiltration, or further compromise of the - target host. +description: The following analytic detects the execution of the runas.exe process with administrator user options. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process details. This activity is significant as it may indicate an attempt to gain elevated privileges, a common tactic in privilege escalation and lateral movement. If confirmed malicious, this could allow an attacker to execute commands with higher privileges, potentially leading to unauthorized access, data exfiltration, or further compromise of the target host. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - (Processes.process_name="runas.exe" OR Processes.original_file_name="runas.exe") - Processes.process ="*/user:*" - Processes.process = "*admin*" - by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `runas_execution_in_commandline_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: A network operator or systems administrator may utilize an - automated or manual execute this command that may generate false positives. filter - is needed. + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where + (Processes.process_name="runas.exe" OR Processes.original_file_name="runas.exe") + Processes.process ="*/user:*" + Processes.process = "*admin*" + by Processes.action Processes.dest + Processes.original_file_name Processes.parent_process Processes.parent_process_exec + Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name + Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name + Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `runas_execution_in_commandline_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: A network operator or systems administrator may utilize an automated or manual execute this command that may generate false positives. filter is needed. references: -- https://app.any.run/tasks/ad4c3cda-41f2-4401-8dba-56cc2d245488/ + - https://app.any.run/tasks/ad4c3cda-41f2-4401-8dba-56cc2d245488/ tags: - analytic_story: - - Quasar RAT - - Data Destruction - - Windows Privilege Escalation - - Hermetic Wiper - asset_type: Endpoint - mitre_attack_id: - - T1134.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Quasar RAT + - Data Destruction + - Windows Privilege Escalation + - Hermetic Wiper + asset_type: Endpoint + mitre_attack_id: + - T1134.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/vilsel/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/vilsel/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/rundll32_control_rundll_hunt.yml b/detections/endpoint/rundll32_control_rundll_hunt.yml index 73c22a3d57..7e7152f7b9 100644 --- a/detections/endpoint/rundll32_control_rundll_hunt.yml +++ b/detections/endpoint/rundll32_control_rundll_hunt.yml @@ -1,67 +1,56 @@ name: Rundll32 Control RunDLL Hunt id: c8e7ced0-10c5-11ec-8b03-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic identifies instances of rundll32.exe executing - with `Control_RunDLL` in the command line, which is indicative of loading a .cpl - or other file types. This detection leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process execution logs and command-line arguments. This - activity is significant as rundll32.exe can be exploited to execute malicious Control - Panel Item files, potentially linked to CVE-2021-40444. If confirmed malicious, - this could allow attackers to execute arbitrary code, escalate privileges, or maintain - persistence within the environment. +description: The following analytic identifies instances of rundll32.exe executing with `Control_RunDLL` in the command line, which is indicative of loading a .cpl or other file types. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant as rundll32.exe can be exploited to execute malicious Control Panel Item files, potentially linked to CVE-2021-40444. If confirmed malicious, this could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*Control_RunDLL* - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `rundll32_control_rundll_hunt_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: This is a hunting detection, meant to provide a understanding - of how voluminous control_rundll is within the environment. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_rundll32` Processes.process=*Control_RunDLL* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `rundll32_control_rundll_hunt_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: This is a hunting detection, meant to provide a understanding of how voluminous control_rundll is within the environment. references: -- https://strontic.github.io/xcyclopedia/library/rundll32.exe-111474C61232202B5B588D2B512CBB25.html -- https://app.any.run/tasks/36c14029-9df8-439c-bba0-45f2643b0c70/ -- https://attack.mitre.org/techniques/T1218/011/ -- https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-40444 -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.002/T1218.002.yaml -- https://redcanary.com/blog/intelligence-insights-december-2021/ + - https://strontic.github.io/xcyclopedia/library/rundll32.exe-111474C61232202B5B588D2B512CBB25.html + - https://app.any.run/tasks/36c14029-9df8-439c-bba0-45f2643b0c70/ + - https://attack.mitre.org/techniques/T1218/011/ + - https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-40444 + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.002/T1218.002.yaml + - https://redcanary.com/blog/intelligence-insights-december-2021/ tags: - analytic_story: - - Suspicious Rundll32 Activity - - Microsoft MSHTML Remote Code Execution CVE-2021-40444 - - Living Off The Land - asset_type: Endpoint - cve: - - CVE-2021-40444 - mitre_attack_id: - - T1218.011 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Rundll32 Activity + - Microsoft MSHTML Remote Code Execution CVE-2021-40444 + - Living Off The Land + asset_type: Endpoint + cve: + - CVE-2021-40444 + mitre_attack_id: + - T1218.011 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.002/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.002/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/rundll32_control_rundll_world_writable_directory.yml b/detections/endpoint/rundll32_control_rundll_world_writable_directory.yml index 697cad5f8a..3be58b76ba 100644 --- a/detections/endpoint/rundll32_control_rundll_world_writable_directory.yml +++ b/detections/endpoint/rundll32_control_rundll_world_writable_directory.yml @@ -5,94 +5,63 @@ date: '2025-05-02' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of rundll32.exe with the - `Control_RunDLL` command, loading files from world-writable directories such as - windows\temp, programdata, or appdata. This detection leverages Endpoint Detection - and Response (EDR) telemetry, focusing on process command-line data and specific - directory paths. This activity is significant as it may indicate an attempt to exploit - CVE-2021-40444 or similar vulnerabilities, allowing attackers to execute arbitrary - code. If confirmed malicious, this could lead to unauthorized code execution, privilege - escalation, or persistent access within the environment. +description: The following analytic detects the execution of rundll32.exe with the `Control_RunDLL` command, loading files from world-writable directories such as windows\temp, programdata, or appdata. This detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on process command-line data and specific directory paths. This activity is significant as it may indicate an attempt to exploit CVE-2021-40444 or similar vulnerabilities, allowing attackers to execute arbitrary code. If confirmed malicious, this could lead to unauthorized code execution, privilege escalation, or persistent access within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*Control_RunDLL* - AND Processes.process IN ("*\\appdata\\*", "*\\windows\\temp\\*", "*\\programdata\\*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `rundll32_control_rundll_world_writable_directory_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: This may be tuned, or a new one related, by adding .cpl to - command-line. However, it's important to look for both. Tune/filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*Control_RunDLL* AND Processes.process IN ("*\\appdata\\*", "*\\windows\\temp\\*", "*\\programdata\\*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_control_rundll_world_writable_directory_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: This may be tuned, or a new one related, by adding .cpl to command-line. However, it's important to look for both. Tune/filter as needed. references: -- https://strontic.github.io/xcyclopedia/library/rundll32.exe-111474C61232202B5B588D2B512CBB25.html -- https://app.any.run/tasks/36c14029-9df8-439c-bba0-45f2643b0c70/ -- https://attack.mitre.org/techniques/T1218/011/ -- https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-40444 -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.002/T1218.002.yaml -- https://redcanary.com/blog/intelligence-insights-december-2021/ + - https://strontic.github.io/xcyclopedia/library/rundll32.exe-111474C61232202B5B588D2B512CBB25.html + - https://app.any.run/tasks/36c14029-9df8-439c-bba0-45f2643b0c70/ + - https://attack.mitre.org/techniques/T1218/011/ + - https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-40444 + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.002/T1218.002.yaml + - https://redcanary.com/blog/intelligence-insights-december-2021/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to load a suspicious file from disk. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to load a suspicious file from disk. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Microsoft MSHTML Remote Code Execution CVE-2021-40444 - - Suspicious Rundll32 Activity - - Living Off The Land - - Compromised Windows Host - asset_type: Endpoint - cve: - - CVE-2021-40444 - mitre_attack_id: - - T1218.011 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Microsoft MSHTML Remote Code Execution CVE-2021-40444 + - Suspicious Rundll32 Activity + - Living Off The Land + - Compromised Windows Host + asset_type: Endpoint + cve: + - CVE-2021-40444 + mitre_attack_id: + - T1218.011 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.002/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.002/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/rundll32_create_remote_thread_to_a_process.yml b/detections/endpoint/rundll32_create_remote_thread_to_a_process.yml index 2a76ebc542..2ab8f2c792 100644 --- a/detections/endpoint/rundll32_create_remote_thread_to_a_process.yml +++ b/detections/endpoint/rundll32_create_remote_thread_to_a_process.yml @@ -5,69 +5,47 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the creation of a remote thread by rundll32.exe - into another process. It leverages Sysmon EventCode 8 logs, specifically monitoring - SourceImage and TargetImage fields. This activity is significant as it is a common - technique used by malware, such as IcedID, to execute malicious code within legitimate - processes, aiding in defense evasion and data theft. If confirmed malicious, this - behavior could allow an attacker to execute arbitrary code, escalate privileges, - and exfiltrate sensitive information from the compromised host. +description: The following analytic detects the creation of a remote thread by rundll32.exe into another process. It leverages Sysmon EventCode 8 logs, specifically monitoring SourceImage and TargetImage fields. This activity is significant as it is a common technique used by malware, such as IcedID, to execute malicious code within legitimate processes, aiding in defense evasion and data theft. If confirmed malicious, this behavior could allow an attacker to execute arbitrary code, escalate privileges, and exfiltrate sensitive information from the compromised host. data_source: -- Sysmon EventID 8 -search: '`sysmon` EventCode=8 SourceImage = "*\\rundll32.exe" TargetImage = "*.exe" - | stats count min(_time) as firstTime max(_time) as lastTime by EventID Guid NewThreadId - ProcessID SecurityID SourceImage SourceProcessGuid SourceProcessId StartAddress - StartFunction StartModule TargetImage TargetProcessGuid TargetProcessId UserID dest - parent_process_exec parent_process_guid parent_process_id parent_process_name parent_process_path - process_exec process_guid process_id process_name process_path signature signature_id - user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `rundll32_create_remote_thread_to_a_process_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the SourceImage, TargetImage, and EventCode executions from your endpoints - related to create remote thread or injecting codes. If you are using Sysmon, you - must have at least version 6.0.4 of the Sysmon TA. + - Sysmon EventID 8 +search: '`sysmon` EventCode=8 SourceImage = "*\\rundll32.exe" TargetImage = "*.exe" | stats count min(_time) as firstTime max(_time) as lastTime by EventID Guid NewThreadId ProcessID SecurityID SourceImage SourceProcessGuid SourceProcessId StartAddress StartFunction StartModule TargetImage TargetProcessGuid TargetProcessId UserID dest parent_process_exec parent_process_guid parent_process_id parent_process_name parent_process_path process_exec process_guid process_id process_name process_path signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_create_remote_thread_to_a_process_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the SourceImage, TargetImage, and EventCode executions from your endpoints related to create remote thread or injecting codes. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: No false positives have been identified at this time. references: -- https://www.joesandbox.com/analysis/380662/0/html + - https://www.joesandbox.com/analysis/380662/0/html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: rundl32 process $SourceImage$ create a remote thread to process $TargetImage$ - in host $dest$ - risk_objects: - - field: dest - type: system - score: 56 - threat_objects: - - field: SourceImage - type: process + message: rundl32 process $SourceImage$ create a remote thread to process $TargetImage$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 56 + threat_objects: + - field: SourceImage + type: process tags: - analytic_story: - - IcedID - - Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1055 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - IcedID + - Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1055 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/rundll32_createremotethread_in_browser.yml b/detections/endpoint/rundll32_createremotethread_in_browser.yml index 5ee2b5ab15..179be30d0b 100644 --- a/detections/endpoint/rundll32_createremotethread_in_browser.yml +++ b/detections/endpoint/rundll32_createremotethread_in_browser.yml @@ -5,70 +5,47 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the suspicious creation of a remote thread - by rundll32.exe targeting browser processes such as firefox.exe, chrome.exe, iexplore.exe, - and microsoftedgecp.exe. This detection leverages Sysmon EventCode 8, focusing on - SourceImage and TargetImage fields to identify the behavior. This activity is significant - as it is commonly associated with malware like IcedID, which hooks browsers to steal - sensitive information such as banking details. If confirmed malicious, this could - allow attackers to intercept and exfiltrate sensitive user data, leading to potential - financial loss and privacy breaches. +description: The following analytic detects the suspicious creation of a remote thread by rundll32.exe targeting browser processes such as firefox.exe, chrome.exe, iexplore.exe, and microsoftedgecp.exe. This detection leverages Sysmon EventCode 8, focusing on SourceImage and TargetImage fields to identify the behavior. This activity is significant as it is commonly associated with malware like IcedID, which hooks browsers to steal sensitive information such as banking details. If confirmed malicious, this could allow attackers to intercept and exfiltrate sensitive user data, leading to potential financial loss and privacy breaches. data_source: -- Sysmon EventID 8 -search: '`sysmon` EventCode=8 SourceImage = "*\\rundll32.exe" TargetImage IN ("*\\firefox.exe", - "*\\chrome.exe", "*\\iexplore.exe","*\\microsoftedgecp.exe") | stats count min(_time) - as firstTime max(_time) as lastTime by EventID Guid NewThreadId ProcessID SecurityID - SourceImage SourceProcessGuid SourceProcessId StartAddress StartFunction StartModule - TargetImage TargetProcessGuid TargetProcessId UserID dest parent_process_exec parent_process_guid - parent_process_id parent_process_name parent_process_path process_exec process_guid - process_id process_name process_path signature signature_id user_id vendor_product - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_createremotethread_in_browser_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the SourceImage, TargetImage, and EventCode executions from your endpoints - related to create remote thread or injecting codes. If you are using Sysmon, you - must have at least version 6.0.4 of the Sysmon TA. + - Sysmon EventID 8 +search: '`sysmon` EventCode=8 SourceImage = "*\\rundll32.exe" TargetImage IN ("*\\firefox.exe", "*\\chrome.exe", "*\\iexplore.exe","*\\microsoftedgecp.exe") | stats count min(_time) as firstTime max(_time) as lastTime by EventID Guid NewThreadId ProcessID SecurityID SourceImage SourceProcessGuid SourceProcessId StartAddress StartFunction StartModule TargetImage TargetProcessGuid TargetProcessId UserID dest parent_process_exec parent_process_guid parent_process_id parent_process_name parent_process_path process_exec process_guid process_id process_name process_path signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_createremotethread_in_browser_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the SourceImage, TargetImage, and EventCode executions from your endpoints related to create remote thread or injecting codes. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: No false positives have been identified at this time. references: -- https://www.joesandbox.com/analysis/380662/0/html + - https://www.joesandbox.com/analysis/380662/0/html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: rundl32 process $SourceImage$ create a remote thread to browser process - $TargetImage$ in host $dest$ - risk_objects: - - field: dest - type: system - score: 70 - threat_objects: - - field: SourceImage - type: process + message: rundl32 process $SourceImage$ create a remote thread to browser process $TargetImage$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 70 + threat_objects: + - field: SourceImage + type: process tags: - analytic_story: - - IcedID - - Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1055 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - IcedID + - Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1055 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/rundll32_lockworkstation.yml b/detections/endpoint/rundll32_lockworkstation.yml index 6872c8d762..81accaf2b4 100644 --- a/detections/endpoint/rundll32_lockworkstation.yml +++ b/detections/endpoint/rundll32_lockworkstation.yml @@ -1,80 +1,65 @@ name: Rundll32 LockWorkStation id: fa90f372-f91d-11eb-816c-acde48001122 -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the rundll32.exe command - with the user32.dll,LockWorkStation parameter, which is used to lock the workstation - via command line. This detection leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process names and command-line executions. This activity - is significant as it is an uncommon method to lock a screen and has been observed - in CONTI ransomware tooling for defense evasion. If confirmed malicious, this technique - could indicate an attempt to evade detection and hinder incident response efforts. +description: The following analytic detects the execution of the rundll32.exe command with the user32.dll,LockWorkStation parameter, which is used to lock the workstation via command line. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it is an uncommon method to lock a screen and has been observed in CONTI ransomware tooling for defense evasion. If confirmed malicious, this technique could indicate an attempt to evade detection and hinder incident response efforts. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe - Processes.process= "*user32.dll,LockWorkStation*" by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `rundll32_lockworkstation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=rundll32.exe Processes.process= "*user32.dll,LockWorkStation*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `rundll32_lockworkstation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://threadreaderapp.com/thread/1423361119926816776.html + - https://threadreaderapp.com/thread/1423361119926816776.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Process $process_name$ with cmdline $process$ in host $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: - - field: process_name - type: process_name + message: Process $process_name$ with cmdline $process$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1218.011 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1218.011 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/conti/conti_leak/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/conti/conti_leak/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/rundll32_process_creating_exe_dll_files.yml b/detections/endpoint/rundll32_process_creating_exe_dll_files.yml index 603ca6c21c..6b80a500ee 100644 --- a/detections/endpoint/rundll32_process_creating_exe_dll_files.yml +++ b/detections/endpoint/rundll32_process_creating_exe_dll_files.yml @@ -1,68 +1,59 @@ name: Rundll32 Process Creating Exe Dll Files id: 6338266a-ee2a-11eb-bf68-acde48001122 -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects a rundll32 process creating executable - (.exe) or dynamic link library (.dll) files. It leverages Sysmon EventCode 11 to - identify instances where rundll32.exe generates these file types. This activity - is significant because rundll32 is often exploited by malware, such as IcedID, to - drop malicious payloads in directories like Temp, AppData, or ProgramData. If confirmed - malicious, this behavior could allow an attacker to execute arbitrary code, establish - persistence, or escalate privileges within the environment. +description: The following analytic detects a rundll32 process creating executable (.exe) or dynamic link library (.dll) files. It leverages Sysmon EventCode 11 to identify instances where rundll32.exe generates these file types. This activity is significant because rundll32 is often exploited by malware, such as IcedID, to drop malicious payloads in directories like Temp, AppData, or ProgramData. If confirmed malicious, this behavior could allow an attacker to execute arbitrary code, establish persistence, or escalate privileges within the environment. data_source: -- Sysmon EventID 11 -search: '`sysmon` EventCode=11 Image="*rundll32.exe" TargetFilename IN ("*.exe", "*.dll") - | stats count min(_time) as firstTime max(_time) as lastTime by action dest file_name - file_path process_guid process_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `rundll32_process_creating_exe_dll_files_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, TargetFilename, and eventcode 11 executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used. + - Sysmon EventID 11 +search: |- + `sysmon` EventCode=11 Image="*rundll32.exe" TargetFilename IN ("*.exe", "*.dll") + | stats count min(_time) as firstTime max(_time) as lastTime + BY action dest file_name + file_path process_guid process_id + user_id vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `rundll32_process_creating_exe_dll_files_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, TargetFilename, and eventcode 11 executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used. known_false_positives: No false positives have been identified at this time. references: -- https://any.run/malware-trends/icedid + - https://any.run/malware-trends/icedid drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: rundll32 process drops a file $file_name$ on host $dest$ - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: - - field: file_name - type: file_name + message: rundll32 process drops a file $file_name$ on host $dest$ + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - IcedID - - Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1218.011 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - IcedID + - Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1218.011 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/rundll32_shimcache_flush.yml b/detections/endpoint/rundll32_shimcache_flush.yml index 72023f9608..6396d1e494 100644 --- a/detections/endpoint/rundll32_shimcache_flush.yml +++ b/detections/endpoint/rundll32_shimcache_flush.yml @@ -1,86 +1,70 @@ name: Rundll32 Shimcache Flush id: a913718a-25b6-11ec-96d3-acde48001122 -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: - The following analytic detects the execution of a suspicious rundll32 - command line used to clear the shim cache. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process execution logs and command-line arguments. - This activity is significant because clearing the shim cache is an anti-forensic - technique aimed at evading detection and removing forensic artifacts. If confirmed - malicious, this action could hinder incident response efforts, allowing an attacker - to cover their tracks and maintain persistence on the compromised machine. +description: The following analytic detects the execution of a suspicious rundll32 command line used to clear the shim cache. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant because clearing the shim cache is an anti-forensic technique aimed at evading detection and removing forensic artifacts. If confirmed malicious, this action could hinder incident response efforts, allowing an attacker to cover their tracks and maintain persistence on the compromised machine. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_rundll32` AND Processes.process - = "*apphelp.dll,ShimFlushCache*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_shimcache_flush_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_rundll32` + AND + Processes.process = "*apphelp.dll,ShimFlushCache*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `rundll32_shimcache_flush_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: - - https://blueteamops.medium.com/shimcache-flush-89daff28d15e + - https://blueteamops.medium.com/shimcache-flush-89daff28d15e drilldown_searches: - - name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: rundll32 process execute $process$ to clear shim cache on $dest$ - risk_objects: - - field: dest - type: system - score: 80 - - field: user - type: user - score: 80 - threat_objects: [] + message: rundll32 process execute $process$ to clear shim cache on $dest$ + risk_objects: + - field: dest + type: system + score: 80 + - field: user + type: user + score: 80 + threat_objects: [] tags: - analytic_story: - - Unusual Processes - - Living Off The Land - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Unusual Processes + - Living Off The Land + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/shimcache_flush/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/shimcache_flush/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/rundll32_with_no_command_line_arguments_with_network.yml b/detections/endpoint/rundll32_with_no_command_line_arguments_with_network.yml index b14e881432..18dc3d7741 100644 --- a/detections/endpoint/rundll32_with_no_command_line_arguments_with_network.yml +++ b/detections/endpoint/rundll32_with_no_command_line_arguments_with_network.yml @@ -5,111 +5,89 @@ date: '2026-01-01' author: Steven Dick, Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of rundll32.exe without - command line arguments, followed by a network connection. This behavior is identified - using Endpoint Detection and Response (EDR) telemetry and network traffic data. - It is significant because rundll32.exe typically requires arguments to function, - and its absence is often associated with malicious activity, such as Cobalt Strike. - If confirmed malicious, this activity could indicate an attempt to establish unauthorized - network connections, potentially leading to data exfiltration or further compromise - of the system. +description: The following analytic detects the execution of rundll32.exe without command line arguments, followed by a network connection. This behavior is identified using Endpoint Detection and Response (EDR) telemetry and network traffic data. It is significant because rundll32.exe typically requires arguments to function, and its absence is often associated with malicious activity, such as Cobalt Strike. If confirmed malicious, this activity could indicate an attempt to establish unauthorized network connections, potentially leading to data exfiltration or further compromise of the system. data_source: -- Sysmon EventID 1 AND Sysmon EventID 3 + - Sysmon EventID 1 AND Sysmon EventID 3 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Processes where - `process_rundll32` - Processes.process IN ( - "*rundll32", - "*rundll32.exe", - "*rundll32.exe\"" - ) - by host _time span=1h Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | rename dest as src - | join host process_id - [ - | tstats `security_content_summariesonly` count - FROM datamodel=Network_Traffic.All_Traffic where - All_Traffic.dest_port != 0 - by host All_Traffic.action All_Traffic.app All_Traffic.bytes All_Traffic.bytes_in All_Traffic.bytes_out - All_Traffic.dest All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.dvc All_Traffic.protocol - All_Traffic.protocol_version All_Traffic.src All_Traffic.src_ip All_Traffic.src_port - All_Traffic.transport All_Traffic.user All_Traffic.vendor_product All_Traffic.direction - All_Traffic.process_id - | `drop_dm_object_name(All_Traffic)` - ] - | `rundll32_with_no_command_line_arguments_with_network_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although unlikely, some legitimate applications may use a moved - copy of rundll32, triggering a false positive. + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime FROM datamodel=Endpoint.Processes where + `process_rundll32` + Processes.process IN ( + "*rundll32", + "*rundll32.exe", + "*rundll32.exe\"" + ) + by host _time span=1h Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | rename dest as src + | join host process_id + [ + | tstats `security_content_summariesonly` count + FROM datamodel=Network_Traffic.All_Traffic where + All_Traffic.dest_port != 0 + by host All_Traffic.action All_Traffic.app All_Traffic.bytes All_Traffic.bytes_in All_Traffic.bytes_out + All_Traffic.dest All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.dvc All_Traffic.protocol + All_Traffic.protocol_version All_Traffic.src All_Traffic.src_ip All_Traffic.src_port + All_Traffic.transport All_Traffic.user All_Traffic.vendor_product All_Traffic.direction + All_Traffic.process_id + | `drop_dm_object_name(All_Traffic)` + ] + | `rundll32_with_no_command_line_arguments_with_network_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive. references: -- https://attack.mitre.org/techniques/T1218/011/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md -- https://lolbas-project.github.io/lolbas/Binaries/Rundll32/ -- https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ + - https://attack.mitre.org/techniques/T1218/011/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md + - https://lolbas-project.github.io/lolbas/Binaries/Rundll32/ + - https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A rundll32 process $process_name$ with no commandline argument like this - process commandline $process$ in host $src$ - risk_objects: - - field: dest - type: system - score: 70 - threat_objects: - - field: process_name - type: process_name + message: A rundll32 process $process_name$ with no commandline argument like this process commandline $process$ in host $src$ + risk_objects: + - field: dest + type: system + score: 70 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - BlackSuit Ransomware - - Suspicious Rundll32 Activity - - Graceful Wipe Out Attack - - Cobalt Strike - - Compromised Windows Host - - PrintNightmare CVE-2021-34527 - - BlackByte Ransomware - - Cactus Ransomware - asset_type: Endpoint - cve: - - CVE-2021-34527 - mitre_attack_id: - - T1218.011 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - BlackSuit Ransomware + - Suspicious Rundll32 Activity + - Graceful Wipe Out Attack + - Cobalt Strike + - Compromised Windows Host + - PrintNightmare CVE-2021-34527 + - BlackByte Ransomware + - Cactus Ransomware + asset_type: Endpoint + cve: + - CVE-2021-34527 + mitre_attack_id: + - T1218.011 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/rundll_loading_dll_by_ordinal.yml b/detections/endpoint/rundll_loading_dll_by_ordinal.yml index 4196484d9d..a01ab47109 100644 --- a/detections/endpoint/rundll_loading_dll_by_ordinal.yml +++ b/detections/endpoint/rundll_loading_dll_by_ordinal.yml @@ -1,89 +1,72 @@ name: RunDLL Loading DLL By Ordinal id: 6c135f8d-5e60-454e-80b7-c56eed739833 -version: 12 -date: '2025-05-02' +version: 13 +date: '2026-02-25' author: Michael Haag, David Dorsey, Splunk status: production type: TTP -description: The following analytic detects rundll32.exe loading a DLL export function - by ordinal value. It leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process command-line executions. This behavior is significant because - adversaries may use rundll32.exe to execute malicious code while evading security - tools that do not monitor this process. If confirmed malicious, this activity could - allow attackers to execute arbitrary code, potentially leading to system compromise, - privilege escalation, or persistent access within the environment. +description: The following analytic detects rundll32.exe loading a DLL export function by ordinal value. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process command-line executions. This behavior is significant because adversaries may use rundll32.exe to execute malicious code while evading security tools that do not monitor this process. If confirmed malicious, this activity could allow attackers to execute arbitrary code, potentially leading to system compromise, privilege escalation, or persistent access within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where `process_rundll32` by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where - match(process,"rundll32.+\#\d+") | `rundll_loading_dll_by_ordinal_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives are possible with native utilities and third - party applications. Filtering may be needed based on command-line, or add world - writeable paths to restrict query. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_rundll32` + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | where match(process,"rundll32.+\#\d+") + | `rundll_loading_dll_by_ordinal_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives are possible with native utilities and third party applications. Filtering may be needed based on command-line, or add world writeable paths to restrict query. references: -- https://thedfirreport.com/2022/02/07/qbot-likes-to-move-it-move-it/ -- https://twitter.com/M_haggis/status/1491109262428635136 -- https://twitter.com/pr0xylife/status/1590394227758104576 + - https://thedfirreport.com/2022/02/07/qbot-likes-to-move-it-move-it/ + - https://twitter.com/M_haggis/status/1491109262428635136 + - https://twitter.com/pr0xylife/status/1590394227758104576 drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A rundll32 process $process_name$ with ordinal parameter like this process - commandline $process$ on host $dest$. - risk_objects: - - field: dest - type: system - score: 49 - - field: user - type: user - score: 49 - threat_objects: [] + message: A rundll32 process $process_name$ with ordinal parameter like this process commandline $process$ on host $dest$. + risk_objects: + - field: dest + type: system + score: 49 + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - Unusual Processes - - Suspicious Rundll32 Activity - - Living Off The Land - - IcedID - asset_type: Endpoint - mitre_attack_id: - - T1218.011 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Unusual Processes + - Suspicious Rundll32 Activity + - Living Off The Land + - IcedID + asset_type: Endpoint + mitre_attack_id: + - T1218.011 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/ordinal_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/ordinal_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/ryuk_test_files_detected.yml b/detections/endpoint/ryuk_test_files_detected.yml index 059aa43047..e8d8fffef9 100644 --- a/detections/endpoint/ryuk_test_files_detected.yml +++ b/detections/endpoint/ryuk_test_files_detected.yml @@ -5,68 +5,46 @@ date: '2025-05-02' author: Rod Soto, Jose Hernandez, Splunk status: production type: TTP -description: The following analytic identifies the presence of files containing the - keyword "Ryuk" in any folder on the C drive, indicative of Ryuk ransomware activity. - It leverages the Endpoint Filesystem data model to detect file paths matching this - pattern. This activity is significant as Ryuk ransomware is known for its destructive - impact, encrypting critical files and demanding ransom. If confirmed malicious, - this could lead to significant data loss, operational disruption, and financial - damage due to ransom payments and recovery efforts. Immediate investigation and - response are crucial to mitigate potential damage. +description: The following analytic identifies the presence of files containing the keyword "Ryuk" in any folder on the C drive, indicative of Ryuk ransomware activity. It leverages the Endpoint Filesystem data model to detect file paths matching this pattern. This activity is significant as Ryuk ransomware is known for its destructive impact, encrypting critical files and demanding ransom. If confirmed malicious, this could lead to significant data loss, operational disruption, and financial damage due to ransom payments and recovery efforts. Immediate investigation and response are crucial to mitigate potential damage. data_source: -- Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem WHERE "Filesystem.file_path"=C:\\*Ryuk* - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path - Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | - `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `ryuk_test_files_detected_filter`' -how_to_implement: You must be ingesting data that records the filesystem activity - from your hosts to populate the Endpoint Filesystem data-model object. If you are - using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which - you want to collect data. -known_false_positives: If there are files with this keywoord as file names it might - trigger false possitives, please make use of our filters to tune out potential FPs. + - Sysmon EventID 11 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem WHERE "Filesystem.file_path"=C:\\*Ryuk* by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `ryuk_test_files_detected_filter`' +how_to_implement: You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint Filesystem data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. +known_false_positives: If there are files with this keywoord as file names it might trigger false possitives, please make use of our filters to tune out potential FPs. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A creation of ryuk test file $file_path$ in host $dest$ - risk_objects: - - field: dest - type: system - score: 70 - - field: user - type: user - score: 70 - threat_objects: [] + message: A creation of ryuk test file $file_path$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 70 + - field: user + type: user + score: 70 + threat_objects: [] tags: - analytic_story: - - Ryuk Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1486 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ryuk Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1486 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/ryuk_wake_on_lan_command.yml b/detections/endpoint/ryuk_wake_on_lan_command.yml index 6d05603ff2..9b44a553e6 100644 --- a/detections/endpoint/ryuk_wake_on_lan_command.yml +++ b/detections/endpoint/ryuk_wake_on_lan_command.yml @@ -1,86 +1,74 @@ name: Ryuk Wake on LAN Command id: 538d0152-7aaa-11eb-beaa-acde48001122 -version: 9 -date: '2025-10-14' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the use of Wake-on-LAN commands associated - with Ryuk ransomware. It leverages data from Endpoint Detection and Response (EDR) - agents, focusing on specific process and command-line activities. This behavior - is significant as Ryuk ransomware uses Wake-on-LAN to power on devices in a compromised - network, increasing its encryption success rate. If confirmed malicious, this activity - could lead to widespread ransomware encryption across multiple endpoints, causing - significant operational disruption and data loss. Immediate isolation and thorough - investigation of the affected endpoints are crucial to mitigate the impact. +description: The following analytic detects the use of Wake-on-LAN commands associated with Ryuk ransomware. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific process and command-line activities. This behavior is significant as Ryuk ransomware uses Wake-on-LAN to power on devices in a compromised network, increasing its encryption success rate. If confirmed malicious, this activity could lead to widespread ransomware encryption across multiple endpoints, causing significant operational disruption and data loss. Immediate isolation and thorough investigation of the affected endpoints are crucial to mitigate the impact. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process="*8 LAN*" - OR Processes.process="*9 REP*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `ryuk_wake_on_lan_command_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process="*8 LAN*" + OR + Processes.process="*9 REP*" + ) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `ryuk_wake_on_lan_command_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Limited to no known false positives. references: -- https://www.bleepingcomputer.com/news/security/ryuk-ransomware-uses-wake-on-lan-to-encrypt-offline-devices/ -- https://www.bleepingcomputer.com/news/security/ryuk-ransomware-now-self-spreads-to-other-windows-lan-devices/ -- https://www.cert.ssi.gouv.fr/uploads/CERTFR-2021-CTI-006.pdf + - https://www.bleepingcomputer.com/news/security/ryuk-ransomware-uses-wake-on-lan-to-encrypt-offline-devices/ + - https://www.bleepingcomputer.com/news/security/ryuk-ransomware-now-self-spreads-to-other-windows-lan-devices/ + - https://www.cert.ssi.gouv.fr/uploads/CERTFR-2021-CTI-006.pdf drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process $process_name$ with wake on LAN commandline $process$ on host - $dest$ - risk_objects: - - field: dest - type: system - score: 63 - - field: user - type: user - score: 63 - threat_objects: [] + message: A process $process_name$ with wake on LAN commandline $process$ on host $dest$ + risk_objects: + - field: dest + type: system + score: 63 + - field: user + type: user + score: 63 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - Ryuk Ransomware - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1059.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - Ryuk Ransomware + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1059.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/ryuk/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/ryuk/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/sam_database_file_access_attempt.yml b/detections/endpoint/sam_database_file_access_attempt.yml index 76526b9687..e0f82440af 100644 --- a/detections/endpoint/sam_database_file_access_attempt.yml +++ b/detections/endpoint/sam_database_file_access_attempt.yml @@ -5,52 +5,37 @@ date: '2025-05-02' author: Michael Haag, Mauricio Velazco, Splunk status: production type: Hunting -description: The following analytic detects attempts to access the SAM, SYSTEM, or - SECURITY database files within the `windows\system32\config` directory using Windows - Security EventCode 4663. This detection leverages Windows Security Event logs to - identify unauthorized access attempts. Monitoring this activity is crucial as it - indicates potential credential access attempts, possibly exploiting vulnerabilities - like CVE-2021-36934. If confirmed malicious, an attacker could extract user passwords, - leading to unauthorized access, privilege escalation, and further compromise of - the system. +description: The following analytic detects attempts to access the SAM, SYSTEM, or SECURITY database files within the `windows\system32\config` directory using Windows Security EventCode 4663. This detection leverages Windows Security Event logs to identify unauthorized access attempts. Monitoring this activity is crucial as it indicates potential credential access attempts, possibly exploiting vulnerabilities like CVE-2021-36934. If confirmed malicious, an attacker could extract user passwords, leading to unauthorized access, privilege escalation, and further compromise of the system. data_source: -- Windows Event Log Security 4663 -search: '`wineventlog_security` (EventCode=4663) ProcessName!=*\\dllhost.exe ObjectName - IN ("*\\Windows\\System32\\config\\SAM*","*\\Windows\\System32\\config\\SYSTEM*","*\\Windows\\System32\\config\\SECURITY*") - | stats values(AccessList) count by ProcessName ObjectName dest src_user | rename - ProcessName as process_name | `sam_database_file_access_attempt_filter`' -how_to_implement: To successfully implement this search, you must ingest Windows Security - Event logs and track event code 4663. For 4663, enable "Audit Object Access" in - Group Policy. Then check the two boxes listed for both "Success" and "Failure." -known_false_positives: Natively, `dllhost.exe` will access the files. Every environment - will have additional native processes that do as well. Filter by process_name. As - an aside, one can remove process_name entirely and add `Object_Name=*ShadowCopy*`. + - Windows Event Log Security 4663 +search: '`wineventlog_security` (EventCode=4663) ProcessName!=*\\dllhost.exe ObjectName IN ("*\\Windows\\System32\\config\\SAM*","*\\Windows\\System32\\config\\SYSTEM*","*\\Windows\\System32\\config\\SECURITY*") | stats values(AccessList) count by ProcessName ObjectName dest src_user | rename ProcessName as process_name | `sam_database_file_access_attempt_filter`' +how_to_implement: To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure." +known_false_positives: Natively, `dllhost.exe` will access the files. Every environment will have additional native processes that do as well. Filter by process_name. As an aside, one can remove process_name entirely and add `Object_Name=*ShadowCopy*`. references: -- https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4663 -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4663 -- https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-36934 -- https://github.com/GossiTheDog/HiveNightmare -- https://github.com/JumpsecLabs/Guidance-Advice/tree/main/SAM_Permissions -- https://en.wikipedia.org/wiki/Security_Account_Manager + - https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4663 + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4663 + - https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-36934 + - https://github.com/GossiTheDog/HiveNightmare + - https://github.com/JumpsecLabs/Guidance-Advice/tree/main/SAM_Permissions + - https://en.wikipedia.org/wiki/Security_Account_Manager tags: - analytic_story: - - Credential Dumping - - Graceful Wipe Out Attack - - Rhysida Ransomware - asset_type: Endpoint - cve: - - CVE-2021-36934 - mitre_attack_id: - - T1003.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Credential Dumping + - Graceful Wipe Out Attack + - Rhysida Ransomware + asset_type: Endpoint + cve: + - CVE-2021-36934 + mitre_attack_id: + - T1003.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/serioussam/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/serioussam/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/samsam_test_file_write.yml b/detections/endpoint/samsam_test_file_write.yml index ec2c9f3aac..12fce5c2e0 100644 --- a/detections/endpoint/samsam_test_file_write.yml +++ b/detections/endpoint/samsam_test_file_write.yml @@ -5,68 +5,46 @@ date: '2025-05-02' author: Rico Valdez, Splunk status: production type: TTP -description: The following analytic detects the creation of a file named "test.txt" - within the Windows system directory, indicative of Samsam ransomware propagation. - It leverages file-system activity data from the Endpoint data model, specifically - monitoring file paths within the Windows System32 directory. This activity is significant - as it aligns with known Samsam ransomware behavior, which uses such files for propagation - and execution. If confirmed malicious, this could lead to ransomware deployment, - resulting in data encryption, system disruption, and potential data loss. Immediate - investigation and remediation are crucial to prevent further damage. +description: The following analytic detects the creation of a file named "test.txt" within the Windows system directory, indicative of Samsam ransomware propagation. It leverages file-system activity data from the Endpoint data model, specifically monitoring file paths within the Windows System32 directory. This activity is significant as it aligns with known Samsam ransomware behavior, which uses such files for propagation and execution. If confirmed malicious, this could lead to ransomware deployment, resulting in data encryption, system disruption, and potential data loss. Immediate investigation and remediation are crucial to prevent further damage. data_source: -- Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_name) - as file_name from datamodel=Endpoint.Filesystem where Filesystem.file_path=*\\windows\\system32\\test.txt - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path - Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | - `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `samsam_test_file_write_filter`' -how_to_implement: You must be ingesting data that records the file-system activity - from your hosts to populate the Endpoint file-system data-model node. If you are - using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which - you want to collect data. + - Sysmon EventID 11 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name from datamodel=Endpoint.Filesystem where Filesystem.file_path=*\\windows\\system32\\test.txt by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `samsam_test_file_write_filter`' +how_to_implement: You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. known_false_positives: No false positives have been identified. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A samsam ransomware test file creation in $file_path$ in host $dest$ - risk_objects: - - field: dest - type: system - score: 12 - - field: user - type: user - score: 12 - threat_objects: [] + message: A samsam ransomware test file creation in $file_path$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 12 + - field: user + type: user + score: 12 + threat_objects: [] tags: - analytic_story: - - SamSam Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1486 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SamSam Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1486 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/sam_sam_note/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/sam_sam_note/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/sc_exe_manipulating_windows_services.yml b/detections/endpoint/sc_exe_manipulating_windows_services.yml index 05c471af1d..23713edd08 100644 --- a/detections/endpoint/sc_exe_manipulating_windows_services.yml +++ b/detections/endpoint/sc_exe_manipulating_windows_services.yml @@ -1,94 +1,77 @@ name: Sc exe Manipulating Windows Services id: f0c693d8-2a89-4ce7-80b4-98fea4c3ea6d -version: 12 -date: '2025-07-29' +version: 13 +date: '2026-02-25' author: Rico Valdez, Splunk status: production type: TTP -description: The following analytic detects the creation or modification of Windows - services using the sc.exe command. It leverages data from Endpoint Detection and - Response (EDR) agents, focusing on process names and command-line arguments. This - activity is significant because manipulating Windows services can be a method for - attackers to establish persistence, escalate privileges, or execute arbitrary code. - If confirmed malicious, this behavior could allow an attacker to maintain long-term - access, disrupt services, or gain control over critical system functions, posing - a severe threat to the environment. +description: The following analytic detects the creation or modification of Windows services using the sc.exe command. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because manipulating Windows services can be a method for attackers to establish persistence, escalate privileges, or execute arbitrary code. If confirmed malicious, this behavior could allow an attacker to maintain long-term access, disrupt services, or gain control over critical system functions, posing a severe threat to the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.process_name = sc.exe (Processes.process="* create *" OR Processes.process="* - config *") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `sc_exe_manipulating_windows_services_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Using sc.exe to manipulate Windows services is uncommon. However, - there may be legitimate instances of this behavior. It is important to validate - and investigate as appropriate. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = sc.exe (Processes.process="* create *" + OR + Processes.process="* config *") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `sc_exe_manipulating_windows_services_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Using sc.exe to manipulate Windows services is uncommon. However, there may be legitimate instances of this behavior. It is important to validate and investigate as appropriate. references: -- https://www.secureworks.com/blog/drokbk-malware-uses-github-as-dead-drop-resolver + - https://www.secureworks.com/blog/drokbk-malware-uses-github-as-dead-drop-resolver drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A sc process $process_name$ with commandline $process$ to create of configure - services in host $dest$ - risk_objects: - - field: dest - type: system - score: 56 - - field: user - type: user - score: 56 - threat_objects: [] + message: A sc process $process_name$ with commandline $process$ to create of configure services in host $dest$ + risk_objects: + - field: dest + type: system + score: 56 + - field: user + type: user + score: 56 + threat_objects: [] tags: - analytic_story: - - Azorult - - Orangeworm Attack Group - - Windows Drivers - - NOBELIUM Group - - Windows Persistence Techniques - - Disabling Security Tools - - Windows Service Abuse - - DHS Report TA18-074A - - Crypto Stealer - - Scattered Spider - asset_type: Endpoint - mitre_attack_id: - - T1543.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azorult + - Orangeworm Attack Group + - Windows Drivers + - NOBELIUM Group + - Windows Persistence Techniques + - Disabling Security Tools + - Windows Service Abuse + - DHS Report TA18-074A + - Crypto Stealer + - Scattered Spider + asset_type: Endpoint + mitre_attack_id: + - T1543.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/schcache_change_by_app_connect_and_create_adsi_object.yml b/detections/endpoint/schcache_change_by_app_connect_and_create_adsi_object.yml index fafd94f12e..34f2d03da7 100644 --- a/detections/endpoint/schcache_change_by_app_connect_and_create_adsi_object.yml +++ b/detections/endpoint/schcache_change_by_app_connect_and_create_adsi_object.yml @@ -1,73 +1,57 @@ name: SchCache Change By App Connect And Create ADSI Object id: 991eb510-0fc6-11ec-82d3-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects an application attempting to connect and - create an ADSI object to perform an LDAP query. It leverages Sysmon EventCode 11 - to identify changes in the Active Directory Schema cache files located in %LOCALAPPDATA%\Microsoft\Windows\SchCache - or %systemroot%\SchCache. This activity is significant as it can indicate the presence - of suspicious applications, such as ransomware, using ADSI object APIs for LDAP - queries. If confirmed malicious, this behavior could allow attackers to gather sensitive - directory information, potentially leading to further exploitation or lateral movement - within the network. +description: The following analytic detects an application attempting to connect and create an ADSI object to perform an LDAP query. It leverages Sysmon EventCode 11 to identify changes in the Active Directory Schema cache files located in %LOCALAPPDATA%\Microsoft\Windows\SchCache or %systemroot%\SchCache. This activity is significant as it can indicate the presence of suspicious applications, such as ransomware, using ADSI object APIs for LDAP queries. If confirmed malicious, this behavior could allow attackers to gather sensitive directory information, potentially leading to further exploitation or lateral movement within the network. data_source: -- Sysmon EventID 11 + - Sysmon EventID 11 search: |- - `sysmon` EventCode=11 TargetFilename = "*\\Windows\\SchCache\\*" TargetFilename - = "*.sch*" NOT (Image IN ("*\\Windows\\system32\\mmc.exe")) - | stats count min(_time) - as firstTime max(_time) as lastTime by action dest file_name file_path process_guid - process_id user_id vendor_product process_name - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `schcache_change_by_app_connect_and_create_adsi_object_filter` -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. -known_false_positives: normal application like mmc.exe and other ldap query tool may - trigger this detections. + `sysmon` EventCode=11 TargetFilename = "*\\Windows\\SchCache\\*" TargetFilename + = "*.sch*" NOT (Image IN ("*\\Windows\\system32\\mmc.exe")) + | stats count min(_time) + as firstTime max(_time) as lastTime by action dest file_name file_path process_guid + process_id user_id vendor_product process_name + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `schcache_change_by_app_connect_and_create_adsi_object_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: normal application like mmc.exe and other ldap query tool may trigger this detections. references: -- https://docs.microsoft.com/en-us/windows/win32/adsi/adsi-and-uac -- https://news.sophos.com/en-us/2021/08/09/blackmatter-ransomware-emerges-from-the-shadow-of-darkside/ + - https://docs.microsoft.com/en-us/windows/win32/adsi/adsi-and-uac + - https://news.sophos.com/en-us/2021/08/09/blackmatter-ransomware-emerges-from-the-shadow-of-darkside/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Process $process_name$ created a file $file_name$ on host $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: Process $process_name$ created a file $file_name$ on host $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - BlackMatter Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1087.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - BlackMatter Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1087.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/blackmatter_schcache/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/blackmatter_schcache/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/schedule_task_with_http_command_arguments.yml b/detections/endpoint/schedule_task_with_http_command_arguments.yml index 5eed10e00c..43ba67b252 100644 --- a/detections/endpoint/schedule_task_with_http_command_arguments.yml +++ b/detections/endpoint/schedule_task_with_http_command_arguments.yml @@ -1,72 +1,63 @@ name: Schedule Task with HTTP Command Arguments id: 523c2684-a101-11eb-916b-acde48001122 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the creation of scheduled tasks on Windows - systems that include HTTP command arguments, using Windows Security EventCode 4698. - It identifies tasks registered via schtasks.exe or TaskService with HTTP in their - command arguments. This behavior is significant as it often indicates malware activity - or the use of Living off the Land binaries (lolbins) to download additional payloads. - If confirmed malicious, this activity could lead to data exfiltration, malware propagation, - or unauthorized access to sensitive information, necessitating immediate investigation - and mitigation. +description: The following analytic detects the creation of scheduled tasks on Windows systems that include HTTP command arguments, using Windows Security EventCode 4698. It identifies tasks registered via schtasks.exe or TaskService with HTTP in their command arguments. This behavior is significant as it often indicates malware activity or the use of Living off the Land binaries (lolbins) to download additional payloads. If confirmed malicious, this activity could lead to data exfiltration, malware propagation, or unauthorized access to sensitive information, necessitating immediate investigation and mitigation. data_source: -- Windows Event Log Security 4698 -search: '`wineventlog_security` EventCode=4698 | xmlkv Message| search Arguments IN - ("*http*") | stats count min(_time) as firstTime max(_time) as lastTime by dest, - Task_Name, Command, Author, Enabled, Hidden, Arguments | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `schedule_task_with_http_command_arguments_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the task schedule (Exa. Security Log EventCode 4698) endpoints. Tune and - filter known instances of Task schedule used in your environment. + - Windows Event Log Security 4698 +search: |- + `wineventlog_security` EventCode=4698 + | xmlkv Message + | search Arguments IN ("*http*") + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest, Task_Name, Command, + Author, Enabled, Hidden, + Arguments + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `schedule_task_with_http_command_arguments_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the task schedule (Exa. Security Log EventCode 4698) endpoints. Tune and filter known instances of Task schedule used in your environment. known_false_positives: No false positives have been identified at this time. references: -- https://app.any.run/tasks/92d7ef61-bfd7-4c92-bc15-322172b4ebec/ + - https://app.any.run/tasks/92d7ef61-bfd7-4c92-bc15-322172b4ebec/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A scheduled task process commandline arguments $Arguments$ with http string - in it on host $dest$ - risk_objects: - - field: dest - type: system - score: 63 - threat_objects: [] + message: A scheduled task process commandline arguments $Arguments$ with http string in it on host $dest$ + risk_objects: + - field: dest + type: system + score: 63 + threat_objects: [] tags: - analytic_story: - - Windows Persistence Techniques - - Living Off The Land - - Compromised Windows Host - - Scheduled Tasks - - Winter Vivern - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1053 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Persistence Techniques + - Living Off The Land + - Compromised Windows Host + - Scheduled Tasks + - Winter Vivern + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1053 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/tasksched/windows-security.log - source: WinEventLog:Security - sourcetype: WinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/tasksched/windows-security.log + source: WinEventLog:Security + sourcetype: WinEventLog diff --git a/detections/endpoint/schedule_task_with_rundll32_command_trigger.yml b/detections/endpoint/schedule_task_with_rundll32_command_trigger.yml index 52974cb7a0..fe4f1ca3ba 100644 --- a/detections/endpoint/schedule_task_with_rundll32_command_trigger.yml +++ b/detections/endpoint/schedule_task_with_rundll32_command_trigger.yml @@ -1,75 +1,65 @@ name: Schedule Task with Rundll32 Command Trigger id: 75b00fd8-a0ff-11eb-8b31-acde48001122 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the creation of scheduled tasks in - Windows that use the rundll32 command. It leverages Windows Security EventCode - 4698, which logs the creation of scheduled tasks, and filters for tasks - executed via rundll32. This activity is significant as it is a common - technique used by malware, such as TrickBot, to persist in an environment or - deliver additional payloads. If confirmed malicious, this could lead to data - theft, ransomware deployment, or other damaging outcomes. Immediate - investigation and mitigation are crucial to prevent further compromise. +description: The following analytic detects the creation of scheduled tasks in Windows that use the rundll32 command. It leverages Windows Security EventCode 4698, which logs the creation of scheduled tasks, and filters for tasks executed via rundll32. This activity is significant as it is a common technique used by malware, such as TrickBot, to persist in an environment or deliver additional payloads. If confirmed malicious, this could lead to data theft, ransomware deployment, or other damaging outcomes. Immediate investigation and mitigation are crucial to prevent further compromise. data_source: -- Windows Event Log Security 4698 -search: '`wineventlog_security` EventCode=4698 | xmlkv Message | search Command IN - ("*rundll32*") | stats count min(_time) as firstTime max(_time) as lastTime by dest, - Task_Name, Command, Author, Enabled, Hidden, Arguments | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `schedule_task_with_rundll32_command_trigger_filter`' -how_to_implement: To successfully implement this search, you need to be - ingesting logs with the task schedule (Exa. Security Log EventCode 4698) - endpoints. Tune and filter known instances of Task schedule used in your - environment. + - Windows Event Log Security 4698 +search: |- + `wineventlog_security` EventCode=4698 + | xmlkv Message + | search Command IN ("*rundll32*") + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest, Task_Name, Command, + Author, Enabled, Hidden, + Arguments + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `schedule_task_with_rundll32_command_trigger_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the task schedule (Exa. Security Log EventCode 4698) endpoints. Tune and filter known instances of Task schedule used in your environment. known_false_positives: No false positives have been identified at this time. references: -- https://labs.vipre.com/trickbot-and-its-modules/ -- https://whitehat.eu/incident-response-case-study-featuring-ryuk-and-trickbot-part-2/ + - https://labs.vipre.com/trickbot-and-its-modules/ + - https://whitehat.eu/incident-response-case-study-featuring-ryuk-and-trickbot-part-2/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A scheduled task process commandline rundll32 arguments $Arguments$ - on host $dest$ - risk_objects: - - field: dest - type: system - score: 70 - threat_objects: [] + message: A scheduled task process commandline rundll32 arguments $Arguments$ on host $dest$ + risk_objects: + - field: dest + type: system + score: 70 + threat_objects: [] tags: - analytic_story: - - Windows Persistence Techniques - - Living Off The Land - - IcedID - - Scheduled Tasks - - Compromised Windows Host - - Trickbot - - Castle RAT - asset_type: Endpoint - mitre_attack_id: - - T1053 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Persistence Techniques + - Living Off The Land + - IcedID + - Scheduled Tasks + - Compromised Windows Host + - Trickbot + - Castle RAT + asset_type: Endpoint + mitre_attack_id: + - T1053 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/tasksched/windows-security.log - source: WinEventLog:Security - sourcetype: WinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/tasksched/windows-security.log + source: WinEventLog:Security + sourcetype: WinEventLog diff --git a/detections/endpoint/scheduled_task_creation_on_remote_endpoint_using_at.yml b/detections/endpoint/scheduled_task_creation_on_remote_endpoint_using_at.yml index a92b9f7d71..ee58fe2062 100644 --- a/detections/endpoint/scheduled_task_creation_on_remote_endpoint_using_at.yml +++ b/detections/endpoint/scheduled_task_creation_on_remote_endpoint_using_at.yml @@ -5,84 +5,50 @@ date: '2025-08-22' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the creation of scheduled tasks on - remote Windows endpoints using the at.exe command. This detection leverages - Endpoint Detection and Response (EDR) telemetry, focusing on process creation - events involving at.exe with remote command-line arguments. Identifying this - activity is significant for a SOC as it may indicate lateral movement or - remote code execution attempts by an attacker. If confirmed malicious, this - activity could lead to unauthorized access, persistence, or execution of - malicious code, potentially resulting in data theft or further compromise of - the network. +description: The following analytic detects the creation of scheduled tasks on remote Windows endpoints using the at.exe command. This detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on process creation events involving at.exe with remote command-line arguments. Identifying this activity is significant for a SOC as it may indicate lateral movement or remote code execution attempts by an attacker. If confirmed malicious, this activity could lead to unauthorized access, persistence, or execution of malicious code, potentially resulting in data theft or further compromise of the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=at.exe - OR Processes.original_file_name=at.exe) (Processes.process=*\\\\*) by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `scheduled_task_creation_on_remote_endpoint_using_at_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: Administrators may create scheduled tasks on remote - systems, but this activity is usually limited to a small set of hosts or - users. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=at.exe OR Processes.original_file_name=at.exe) (Processes.process=*\\\\*) by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `scheduled_task_creation_on_remote_endpoint_using_at_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators may create scheduled tasks on remote systems, but this activity is usually limited to a small set of hosts or users. references: -- https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/at -- https://docs.microsoft.com/en-us/windows/win32/cimwin32prov/win32-scheduledjob?redirectedfrom=MSDN + - https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/at + - https://docs.microsoft.com/en-us/windows/win32/cimwin32prov/win32-scheduledjob?redirectedfrom=MSDN drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Windows Scheduled Task was created on a remote endpoint from $dest$ - risk_objects: - - field: dest - type: system - score: 54 - threat_objects: [] + message: A Windows Scheduled Task was created on a remote endpoint from $dest$ + risk_objects: + - field: dest + type: system + score: 54 + threat_objects: [] tags: - analytic_story: - - Active Directory Lateral Movement - - Living Off The Land - - Scheduled Tasks - - 0bj3ctivity Stealer - asset_type: Endpoint - mitre_attack_id: - - T1053.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + - Living Off The Land + - Scheduled Tasks + - 0bj3ctivity Stealer + asset_type: Endpoint + mitre_attack_id: + - T1053.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.002/lateral_movement/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.002/lateral_movement/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/scheduled_task_deleted_or_created_via_cmd.yml b/detections/endpoint/scheduled_task_deleted_or_created_via_cmd.yml index 9ede625ea7..ffb14a9e8d 100644 --- a/detections/endpoint/scheduled_task_deleted_or_created_via_cmd.yml +++ b/detections/endpoint/scheduled_task_deleted_or_created_via_cmd.yml @@ -5,125 +5,87 @@ date: '2026-02-09' author: Bhavin Patel, Splunk status: production type: TTP -description: The following analytic identifies the creation or deletion of - scheduled tasks using the schtasks.exe utility with the -create or -delete - flags. It leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process names and command-line executions. This activity is - significant as it can indicate unauthorized system manipulation or malicious - intent, often associated with threat actors like Dragonfly and incidents such - as the SUNBURST attack. If confirmed malicious, this activity could allow - attackers to execute code, escalate privileges, or persist within the - environment, posing a significant security risk. +description: The following analytic identifies the creation or deletion of scheduled tasks using the schtasks.exe utility with the -create or -delete flags. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it can indicate unauthorized system manipulation or malicious intent, often associated with threat actors like Dragonfly and incidents such as the SUNBURST attack. If confirmed malicious, this activity could allow attackers to execute code, escalate privileges, or persist within the environment, posing a significant security risk. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count values(Processes.process) - as process values(Processes.parent_process) as parent_process min(_time) as firstTime - max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe - (Processes.process=*delete* OR Processes.process=*create*) by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `scheduled_task_deleted_or_created_via_cmd_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: While it is possible for legitimate scripts or - administrators to trigger this behavior, filtering can be applied based on the - parent process and application to reduce false positives. Analysts should - reference the provided references to understand the context and threat - landscape associated with this activity. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process=*delete* OR Processes.process=*create*) by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `scheduled_task_deleted_or_created_via_cmd_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: While it is possible for legitimate scripts or administrators to trigger this behavior, filtering can be applied based on the parent process and application to reduce false positives. Analysts should reference the provided references to understand the context and threat landscape associated with this activity. references: -- https://thedfirreport.com/2022/02/21/qbot-and-zerologon-lead-to-full-domain-compromise/ -- https://www.joesandbox.com/analysis/691823/0/html + - https://thedfirreport.com/2022/02/21/qbot-and-zerologon-lead-to-full-domain-compromise/ + - https://www.joesandbox.com/analysis/691823/0/html drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A schedule task process $process_name$ with create or delete - commandline $process$ in host $dest$ - risk_objects: - - field: dest - type: system - score: 56 - - field: user - type: user - score: 56 - threat_objects: [] + message: A schedule task process $process_name$ with create or delete commandline $process$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 56 + - field: user + type: user + score: 56 + threat_objects: [] tags: - analytic_story: - - SolarWinds WHD RCE Post Exploitation - - ShrinkLocker - - AgentTesla - - CISA AA24-241A - - Winter Vivern - - Quasar RAT - - Rhysida Ransomware - - Sandworm Tools - - DarkCrystal RAT - - Qakbot - - China-Nexus Threat Activity - - XWorm - - CISA AA23-347A - - Azorult - - Living Off The Land - - Salt Typhoon - - Trickbot - - NOBELIUM Group - - CISA AA22-257A - - Medusa Ransomware - - Phemedrone Stealer - - NjRAT - - DHS Report TA18-074A - - Scheduled Tasks - - Prestige Ransomware - - Amadey - - AsyncRAT - - RedLine Stealer - - Windows Persistence Techniques - - MoonPeak - - Scattered Spider - - 0bj3ctivity Stealer - - APT37 Rustonotto and FadeStealer - - Lokibot - - NetSupport RMM Tool Abuse - - ValleyRAT - - PlugX - - Remcos - asset_type: Endpoint - mitre_attack_id: - - T1053.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SolarWinds WHD RCE Post Exploitation + - ShrinkLocker + - AgentTesla + - CISA AA24-241A + - Winter Vivern + - Quasar RAT + - Rhysida Ransomware + - Sandworm Tools + - DarkCrystal RAT + - Qakbot + - China-Nexus Threat Activity + - XWorm + - CISA AA23-347A + - Azorult + - Living Off The Land + - Salt Typhoon + - Trickbot + - NOBELIUM Group + - CISA AA22-257A + - Medusa Ransomware + - Phemedrone Stealer + - NjRAT + - DHS Report TA18-074A + - Scheduled Tasks + - Prestige Ransomware + - Amadey + - AsyncRAT + - RedLine Stealer + - Windows Persistence Techniques + - MoonPeak + - Scattered Spider + - 0bj3ctivity Stealer + - APT37 Rustonotto and FadeStealer + - Lokibot + - NetSupport RMM Tool Abuse + - ValleyRAT + - PlugX + - Remcos + asset_type: Endpoint + mitre_attack_id: + - T1053.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/scheduled_task_initiation_on_remote_endpoint.yml b/detections/endpoint/scheduled_task_initiation_on_remote_endpoint.yml index 8fc82e6f18..a24b31fd71 100644 --- a/detections/endpoint/scheduled_task_initiation_on_remote_endpoint.yml +++ b/detections/endpoint/scheduled_task_initiation_on_remote_endpoint.yml @@ -1,84 +1,73 @@ name: Scheduled Task Initiation on Remote Endpoint id: 95cf4608-4302-11ec-8194-3e22fbd008af -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk, Badoodish, Github Community status: production type: TTP -description: The following analytic detects the use of 'schtasks.exe' to start a Scheduled - Task on a remote endpoint. This detection leverages Endpoint Detection and Response - (EDR) data, focusing on process details such as process name, parent process, and - command-line executions. This activity is significant as adversaries often abuse - Task Scheduler for lateral movement and remote code execution. If confirmed malicious, - this behavior could allow attackers to execute arbitrary code remotely, potentially - leading to further compromise of the network. +description: The following analytic detects the use of 'schtasks.exe' to start a Scheduled Task on a remote endpoint. This detection leverages Endpoint Detection and Response (EDR) data, focusing on process details such as process name, parent process, and command-line executions. This activity is significant as adversaries often abuse Task Scheduler for lateral movement and remote code execution. If confirmed malicious, this behavior could allow attackers to execute arbitrary code remotely, potentially leading to further compromise of the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=schtasks.exe - OR Processes.original_file_name=schtasks.exe) (Processes.process= "* /S *" AND Processes.process=*/run*) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `scheduled_task_initiation_on_remote_endpoint_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrators may start scheduled tasks on remote systems, - but this activity is usually limited to a small set of hosts or users. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name=schtasks.exe + OR + Processes.original_file_name=schtasks.exe + ) + (Processes.process= "* /S *" AND Processes.process=*/run*) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `scheduled_task_initiation_on_remote_endpoint_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators may start scheduled tasks on remote systems, but this activity is usually limited to a small set of hosts or users. references: -- https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/schtasks -- https://attack.mitre.org/techniques/T1053/005/ + - https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/schtasks + - https://attack.mitre.org/techniques/T1053/005/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Windows Scheduled Task was ran on a remote endpoint from $dest$ - risk_objects: - - field: dest - type: system - score: 54 - threat_objects: [] + message: A Windows Scheduled Task was ran on a remote endpoint from $dest$ + risk_objects: + - field: dest + type: system + score: 54 + threat_objects: [] tags: - analytic_story: - - Living Off The Land - - Active Directory Lateral Movement - - Scheduled Tasks - - Medusa Ransomware - - Seashell Blizzard - asset_type: Endpoint - mitre_attack_id: - - T1053.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + - Active Directory Lateral Movement + - Scheduled Tasks + - Medusa Ransomware + - Seashell Blizzard + asset_type: Endpoint + mitre_attack_id: + - T1053.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/lateral_movement/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/lateral_movement/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/schtasks_run_task_on_demand.yml b/detections/endpoint/schtasks_run_task_on_demand.yml index a42bf1849b..97a906d67e 100644 --- a/detections/endpoint/schtasks_run_task_on_demand.yml +++ b/detections/endpoint/schtasks_run_task_on_demand.yml @@ -1,92 +1,72 @@ name: Schtasks Run Task On Demand id: bb37061e-af1f-11eb-a159-acde48001122 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of a Windows Scheduled Task - on demand via the shell or command line. It leverages process-related data, including - process name, parent process, and command-line executions, sourced from endpoint - logs. The detection focuses on 'schtasks.exe' with an associated 'run' command. - This activity is significant as adversaries often use it to force the execution - of their created Scheduled Tasks for persistent access or lateral movement within - a compromised machine. If confirmed malicious, this could allow attackers to maintain - persistence or move laterally within the network, potentially leading to further - compromise. +description: The following analytic detects the execution of a Windows Scheduled Task on demand via the shell or command line. It leverages process-related data, including process name, parent process, and command-line executions, sourced from endpoint logs. The detection focuses on 'schtasks.exe' with an associated 'run' command. This activity is significant as adversaries often use it to force the execution of their created Scheduled Tasks for persistent access or lateral movement within a compromised machine. If confirmed malicious, this could allow attackers to maintain persistence or move laterally within the network, potentially leading to further compromise. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "schtasks.exe" - Processes.process = "*/run*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schtasks_run_task_on_demand_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Bear in mind, administrators debugging Scheduled Task entries - may trigger this analytic, necessitating fine-tuning and filtering to distinguish - between legitimate and potentially malicious use of 'schtasks.exe'. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "schtasks.exe" Processes.process = "*/run*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `schtasks_run_task_on_demand_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Bear in mind, administrators debugging Scheduled Task entries may trigger this analytic, necessitating fine-tuning and filtering to distinguish between legitimate and potentially malicious use of 'schtasks.exe'. references: -- https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ + - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A "on demand" execution of schedule task process $process_name$ using - commandline $process$ in host $dest$ - risk_objects: - - field: dest - type: system - score: 48 - - field: user - type: user - score: 48 - threat_objects: [] + message: A "on demand" execution of schedule task process $process_name$ using commandline $process$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 48 + - field: user + type: user + score: 48 + threat_objects: [] tags: - analytic_story: - - Industroyer2 - - CISA AA22-257A - - Data Destruction - - Qakbot - - XMRig - - Medusa Ransomware - - Scheduled Tasks - asset_type: Endpoint - mitre_attack_id: - - T1053 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Industroyer2 + - CISA AA22-257A + - Data Destruction + - Qakbot + - XMRig + - Medusa Ransomware + - Scheduled Tasks + asset_type: Endpoint + mitre_attack_id: + - T1053 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/schtasks_scheduling_job_on_remote_system.yml b/detections/endpoint/schtasks_scheduling_job_on_remote_system.yml index 6b43578d71..c8afd2c8b4 100644 --- a/detections/endpoint/schtasks_scheduling_job_on_remote_system.yml +++ b/detections/endpoint/schtasks_scheduling_job_on_remote_system.yml @@ -1,94 +1,80 @@ name: Schtasks scheduling job on remote system id: 1297fb80-f42a-4b4a-9c8a-88c066237cf6 -version: 16 -date: '2025-07-16' +version: 17 +date: '2026-02-25' author: David Dorsey, Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the use of 'schtasks.exe' to create a - scheduled task on a remote system, indicating potential lateral movement or remote - code execution. It leverages process data from Endpoint Detection and Response (EDR) - agents, focusing on specific command-line arguments and flags. This activity is - significant as it may signify an adversary's attempt to persist or execute code - remotely. If confirmed malicious, this could allow attackers to maintain access, - execute arbitrary commands, or further infiltrate the network, posing a severe security - risk. +description: The following analytic detects the use of 'schtasks.exe' to create a scheduled task on a remote system, indicating potential lateral movement or remote code execution. It leverages process data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line arguments and flags. This activity is significant as it may signify an adversary's attempt to persist or execute code remotely. If confirmed malicious, this could allow attackers to maintain access, execute arbitrary commands, or further infiltrate the network, posing a severe security risk. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = schtasks.exe - OR Processes.original_file_name=schtasks.exe) (Processes.process="*/create*" AND - Processes.process="*/s *") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schtasks_scheduling_job_on_remote_system_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: While it is possible to have false positives, due to legitimate - administrative tasks, these are usually limited and should still be validated and - investigated as appropriate. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name = schtasks.exe + OR + Processes.original_file_name=schtasks.exe + ) + (Processes.process="*/create*" AND Processes.process="*/s *") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `schtasks_scheduling_job_on_remote_system_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: While it is possible to have false positives, due to legitimate administrative tasks, these are usually limited and should still be validated and investigated as appropriate. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A scheduled task process $process_name$ with remote job command-line $process$ - on host $dest$ by $user$. - risk_objects: - - field: dest - type: system - score: 63 - - field: user - type: user - score: 63 - threat_objects: - - field: process_name - type: process_name + message: A scheduled task process $process_name$ with remote job command-line $process$ on host $dest$ by $user$. + risk_objects: + - field: dest + type: system + score: 63 + - field: user + type: user + score: 63 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Scheduled Tasks - - Phemedrone Stealer - - Living Off The Land - - Prestige Ransomware - - Quasar RAT - - RedLine Stealer - - Active Directory Lateral Movement - - NOBELIUM Group - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1053.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Scheduled Tasks + - Phemedrone Stealer + - Living Off The Land + - Prestige Ransomware + - Quasar RAT + - RedLine Stealer + - Active Directory Lateral Movement + - NOBELIUM Group + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1053.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/schtasks_used_for_forcing_a_reboot.yml b/detections/endpoint/schtasks_used_for_forcing_a_reboot.yml index f7482bb916..2a0cfb28a6 100644 --- a/detections/endpoint/schtasks_used_for_forcing_a_reboot.yml +++ b/detections/endpoint/schtasks_used_for_forcing_a_reboot.yml @@ -1,86 +1,67 @@ name: Schtasks used for forcing a reboot id: 1297fb80-f42a-4b4a-9c8a-88c066437cf6 -version: 11 -date: '2025-05-02' +version: 12 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP -description: The following analytic detects the use of 'schtasks.exe' to schedule - forced system reboots using the 'shutdown' and '/create' flags. It leverages endpoint - process data to identify instances where these specific command-line arguments are - used. This activity is significant because it may indicate an adversary attempting - to disrupt operations or force a reboot to execute further malicious actions. If - confirmed malicious, this could lead to system downtime, potential data loss, and - provide an attacker with an opportunity to execute additional payloads or evade - detection. +description: The following analytic detects the use of 'schtasks.exe' to schedule forced system reboots using the 'shutdown' and '/create' flags. It leverages endpoint process data to identify instances where these specific command-line arguments are used. This activity is significant because it may indicate an adversary attempting to disrupt operations or force a reboot to execute further malicious actions. If confirmed malicious, this could lead to system downtime, potential data loss, and provide an attacker with an opportunity to execute additional payloads or evade detection. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.process_name=schtasks.exe Processes.process="*shutdown*" Processes.process="*/create - *" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `schtasks_used_for_forcing_a_reboot_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: This analytic may also capture legitimate administrative activities - such as system updates or maintenance tasks, which can be classified as false positives. - Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=schtasks.exe Processes.process="*shutdown*" Processes.process="*/create *" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `schtasks_used_for_forcing_a_reboot_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: This analytic may also capture legitimate administrative activities such as system updates or maintenance tasks, which can be classified as false positives. Filter as needed. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A schedule task process $process_name$ with force reboot commandline $process$ - in host $dest$ - risk_objects: - - field: dest - type: system - score: 56 - - field: user - type: user - score: 56 - threat_objects: [] + message: A schedule task process $process_name$ with force reboot commandline $process$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 56 + - field: user + type: user + score: 56 + threat_objects: [] tags: - analytic_story: - - Windows Persistence Techniques - - Ransomware - - Scheduled Tasks - asset_type: Endpoint - mitre_attack_id: - - T1053.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Persistence Techniques + - Ransomware + - Scheduled Tasks + asset_type: Endpoint + mitre_attack_id: + - T1053.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/schtask_shutdown/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/schtask_shutdown/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/screensaver_event_trigger_execution.yml b/detections/endpoint/screensaver_event_trigger_execution.yml index 08da4cb41c..9e3dc0919a 100644 --- a/detections/endpoint/screensaver_event_trigger_execution.yml +++ b/detections/endpoint/screensaver_event_trigger_execution.yml @@ -5,75 +5,52 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects modifications to the SCRNSAVE.EXE registry - entry, indicating potential event trigger execution via screensaver settings for - persistence or privilege escalation. It leverages registry activity data from the - Endpoint data model to identify changes to the specified registry path. This activity - is significant as it is a known technique used by APT groups and malware to maintain - persistence or escalate privileges. If confirmed malicious, this could allow an - attacker to execute arbitrary code with elevated privileges, leading to further - system compromise and persistent access. +description: The following analytic detects modifications to the SCRNSAVE.EXE registry entry, indicating potential event trigger execution via screensaver settings for persistence or privilege escalation. It leverages registry activity data from the Endpoint data model to identify changes to the specified registry path. This activity is significant as it is a known technique used by APT groups and malware to maintain persistence or escalate privileges. If confirmed malicious, this could allow an attacker to execute arbitrary code with elevated privileges, leading to further system compromise and persistent access. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime - max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*\\Control - Panel\\Desktop\\SCRNSAVE.EXE*") by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `security_content_ctime(lastTime)` - | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `screensaver_event_trigger_execution_filter`' -how_to_implement: To successfully implement this search, you must be ingesting data - that records registry activity from your hosts to populate the endpoint data model - in the registry node. This is typically populated via endpoint detection-and-response - product, such as Carbon Black or endpoint data sources, such as Sysmon. The data - used for this search is typically generated via logs that report reads and writes - to the registry. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*\\Control Panel\\Desktop\\SCRNSAVE.EXE*") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `screensaver_event_trigger_execution_filter`' +how_to_implement: To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. known_false_positives: No false positives have been identified at this time. references: -- https://attack.mitre.org/techniques/T1546/002/ -- https://dmcxblue.gitbook.io/red-team-notes-2-0/red-team-techniques/privilege-escalation/untitled-3/screensaver + - https://attack.mitre.org/techniques/T1546/002/ + - https://dmcxblue.gitbook.io/red-team-notes-2-0/red-team-techniques/privilege-escalation/untitled-3/screensaver drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Registry path $registry_path$ was modified, added, or deleted on $dest$. - risk_objects: - - field: dest - type: system - score: 72 - - field: user - type: user - score: 72 - threat_objects: [] + message: Registry path $registry_path$ was modified, added, or deleted on $dest$. + risk_objects: + - field: dest + type: system + score: 72 + - field: user + type: user + score: 72 + threat_objects: [] tags: - analytic_story: - - Hermetic Wiper - - Windows Privilege Escalation - - Windows Persistence Techniques - - Windows Registry Abuse - - Data Destruction - asset_type: Endpoint - mitre_attack_id: - - T1546.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Hermetic Wiper + - Windows Privilege Escalation + - Windows Persistence Techniques + - Windows Registry Abuse + - Data Destruction + asset_type: Endpoint + mitre_attack_id: + - T1546.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.002/scrnsave_reg/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.002/scrnsave_reg/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/script_execution_via_wmi.yml b/detections/endpoint/script_execution_via_wmi.yml index e6b0fc4425..d7c13ffd17 100644 --- a/detections/endpoint/script_execution_via_wmi.yml +++ b/detections/endpoint/script_execution_via_wmi.yml @@ -1,84 +1,67 @@ name: Script Execution via WMI id: aa73f80d-d728-4077-b226-81ea0c8be589 -version: 10 -date: '2025-07-29' +version: 11 +date: '2026-02-25' author: Rico Valdez, Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of scripts via Windows Management - Instrumentation (WMI) by monitoring the process 'scrcons.exe'. This detection leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process creation - events. WMI-based script execution is significant because adversaries often use - it to perform malicious activities stealthily, such as system compromise, data exfiltration, - or establishing persistence. If confirmed malicious, this activity could allow attackers - to execute arbitrary code, escalate privileges, or maintain long-term access to - the environment. Analysts should differentiate between legitimate administrative - use and potential threats. +description: The following analytic detects the execution of scripts via Windows Management Instrumentation (WMI) by monitoring the process 'scrcons.exe'. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events. WMI-based script execution is significant because adversaries often use it to perform malicious activities stealthily, such as system compromise, data exfiltration, or establishing persistence. If confirmed malicious, this activity could allow attackers to execute arbitrary code, escalate privileges, or maintain long-term access to the environment. Analysts should differentiate between legitimate administrative use and potential threats. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=scrcons.exe - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `script_execution_via_wmi_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although unlikely, administrators may use wmi to launch scripts - for legitimate purposes. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=scrcons.exe + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `script_execution_via_wmi_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although unlikely, administrators may use wmi to launch scripts for legitimate purposes. Filter as needed. references: -- https://redcanary.com/blog/child-processes/ + - https://redcanary.com/blog/child-processes/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A wmic.exe process $process_name$ that execute script in host $dest$ - risk_objects: - - field: dest - type: system - score: 36 - - field: user - type: user - score: 36 - threat_objects: [] + message: A wmic.exe process $process_name$ that execute script in host $dest$ + risk_objects: + - field: dest + type: system + score: 36 + - field: user + type: user + score: 36 + threat_objects: [] tags: - analytic_story: - - Suspicious WMI Use - - Scattered Spider - asset_type: Endpoint - mitre_attack_id: - - T1047 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious WMI Use + - Scattered Spider + asset_type: Endpoint + mitre_attack_id: + - T1047 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/execution_scrcons/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/execution_scrcons/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/sdclt_uac_bypass.yml b/detections/endpoint/sdclt_uac_bypass.yml index 42788ab3dd..3b3769fd98 100644 --- a/detections/endpoint/sdclt_uac_bypass.yml +++ b/detections/endpoint/sdclt_uac_bypass.yml @@ -5,77 +5,48 @@ date: '2025-05-02' author: Steven Dick, Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects suspicious modifications to the sdclt.exe - registry, a technique often used to bypass User Account Control (UAC). It leverages - data from Endpoint Detection and Response (EDR) agents, focusing on specific registry - paths and values associated with sdclt.exe. This activity is significant because - UAC bypasses can allow attackers to execute payloads with elevated privileges without - user consent. If confirmed malicious, this could lead to unauthorized code execution, - privilege escalation, and potential persistence within the environment, posing a - severe security risk. +description: The following analytic detects suspicious modifications to the sdclt.exe registry, a technique often used to bypass User Account Control (UAC). It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific registry paths and values associated with sdclt.exe. This activity is significant because UAC bypasses can allow attackers to execute payloads with elevated privileges without user consent. If confirmed malicious, this could lead to unauthorized code execution, privilege escalation, and potential persistence within the environment, posing a severe security risk. data_source: -- Sysmon EventID 12 -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry - WHERE ((Registry.registry_path= "*\\Windows\\CurrentVersion\\App Paths\\control.exe*" - OR Registry.registry_path= "*\\exefile\\shell\\runas\\command\\*") (Registry.registry_value_name - = "(Default)" OR Registry.registry_value_name = "IsolatedCommand")) by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `sdclt_uac_bypass_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 12 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE ((Registry.registry_path= "*\\Windows\\CurrentVersion\\App Paths\\control.exe*" OR Registry.registry_path= "*\\exefile\\shell\\runas\\command\\*") (Registry.registry_value_name = "(Default)" OR Registry.registry_value_name = "IsolatedCommand")) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `sdclt_uac_bypass_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Limited to no false positives are expected. references: -- https://enigma0x3.net/2017/03/17/fileless-uac-bypass-using-sdclt-exe/ -- https://github.com/hfiref0x/UACME -- https://www.cyborgsecurity.com/cyborg-labs/threat-hunt-deep-dives-user-account-control-bypass-via-registry-modification/ + - https://enigma0x3.net/2017/03/17/fileless-uac-bypass-using-sdclt-exe/ + - https://github.com/hfiref0x/UACME + - https://www.cyborgsecurity.com/cyborg-labs/threat-hunt-deep-dives-user-account-control-bypass-via-registry-modification/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious modification of registry $registry_path$ with possible payload - path $registry_value_name$ on $dest$ - risk_objects: - - field: dest - type: system - score: 63 - threat_objects: [] + message: Suspicious modification of registry $registry_path$ with possible payload path $registry_value_name$ on $dest$ + risk_objects: + - field: dest + type: system + score: 63 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1548.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1548.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/uac_bypass/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/uac_bypass/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/sdelete_application_execution.yml b/detections/endpoint/sdelete_application_execution.yml index 606b1fd694..afd54be961 100644 --- a/detections/endpoint/sdelete_application_execution.yml +++ b/detections/endpoint/sdelete_application_execution.yml @@ -1,85 +1,72 @@ name: Sdelete Application Execution id: 31702fc0-2682-11ec-85c3-acde48001122 -version: 9 -date: '2025-12-15' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of the sdelete.exe application, - a Sysinternals tool often used by adversaries to securely delete files and remove - forensic evidence from a targeted host. This detection leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process execution logs. Monitoring - this activity is crucial as sdelete.exe is not commonly used in regular operations - and its presence may indicate an attempt to cover malicious activities. If confirmed - malicious, this could lead to the loss of critical forensic data, hindering incident - response and investigation efforts. +description: The following analytic detects the execution of the sdelete.exe application, a Sysinternals tool often used by adversaries to securely delete files and remove forensic evidence from a targeted host. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. Monitoring this activity is crucial as sdelete.exe is not commonly used in regular operations and its presence may indicate an attempt to cover malicious activities. If confirmed malicious, this could lead to the loss of critical forensic data, hindering incident response and investigation efforts. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - values(Processes.parent_process) as parent_process values(Processes.process_id) - as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where (Processes.process_name="sdelete.exe" OR Processes.original_file_name="sdelete.exe") - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `sdelete_application_execution_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="sdelete.exe" + OR + Processes.original_file_name="sdelete.exe" + ) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `sdelete_application_execution_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: user may execute and use this application references: -- https://app.any.run/tasks/956f50be-2c13-465a-ac00-6224c14c5f89/ + - https://app.any.run/tasks/956f50be-2c13-465a-ac00-6224c14c5f89/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: sdelete process $process_name$ executed on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - - field: user - type: user - score: 49 - threat_objects: [] + message: sdelete process $process_name$ executed on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - Masquerading - Rename System Utilities - - Scattered Spider - asset_type: Endpoint - mitre_attack_id: - - T1070.004 - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Masquerading - Rename System Utilities + - Scattered Spider + asset_type: Endpoint + mitre_attack_id: + - T1070.004 + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/sdelete/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/sdelete/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/searchprotocolhost_with_no_command_line_with_network.yml b/detections/endpoint/searchprotocolhost_with_no_command_line_with_network.yml index 73806b79c2..5e2373ae8a 100644 --- a/detections/endpoint/searchprotocolhost_with_no_command_line_with_network.yml +++ b/detections/endpoint/searchprotocolhost_with_no_command_line_with_network.yml @@ -5,108 +5,86 @@ date: '2026-01-01' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects instances of searchprotocolhost.exe running - without command line arguments but with an active network connection. This behavior - is identified using Endpoint Detection and Response (EDR) telemetry, focusing on - process execution and network traffic data. It is significant because searchprotocolhost.exe - typically runs with specific command line arguments, and deviations from this norm - can indicate malicious activity, such as Cobalt Strike usage. If confirmed malicious, - this activity could allow attackers to establish network connections for command - and control, potentially leading to data exfiltration or further system compromise. +description: The following analytic detects instances of searchprotocolhost.exe running without command line arguments but with an active network connection. This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on process execution and network traffic data. It is significant because searchprotocolhost.exe typically runs with specific command line arguments, and deviations from this norm can indicate malicious activity, such as Cobalt Strike usage. If confirmed malicious, this activity could allow attackers to establish network connections for command and control, potentially leading to data exfiltration or further system compromise. data_source: -- Sysmon EventID 1 AND Sysmon EventID 3 + - Sysmon EventID 1 AND Sysmon EventID 3 search: | - | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where - ( - Processes.process_name=searchprotocolhost.exe - OR - Processes.original_file_name=searchprotocolhost.exe - ) - Processes.process IN ( - "*searchprotocolhost", - "*searchprotocolhost.exe", - "*searchprotocolhost.exe\"" - ) - by _time span=1h Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | join process_id - [ - | tstats `security_content_summariesonly` count - FROM datamodel=Network_Traffic.All_Traffic where - All_Traffic.dest_port != 0 - by All_Traffic.action All_Traffic.app All_Traffic.bytes All_Traffic.bytes_in All_Traffic.bytes_out - All_Traffic.dest All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.dvc All_Traffic.protocol - All_Traffic.protocol_version All_Traffic.src All_Traffic.src_ip All_Traffic.src_port - All_Traffic.transport All_Traffic.user All_Traffic.vendor_product All_Traffic.direction - All_Traffic.process_id - | `drop_dm_object_name(All_Traffic)` - | rename dest as C2 - ] - | table _time dest parent_process_name process_name process_path process process_id dest_port C2 - | `searchprotocolhost_with_no_command_line_with_network_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Limited false positives may be present in small environments. - Tuning may be required based on parent process. + | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where + ( + Processes.process_name=searchprotocolhost.exe + OR + Processes.original_file_name=searchprotocolhost.exe + ) + Processes.process IN ( + "*searchprotocolhost", + "*searchprotocolhost.exe", + "*searchprotocolhost.exe\"" + ) + by _time span=1h Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | join process_id + [ + | tstats `security_content_summariesonly` count + FROM datamodel=Network_Traffic.All_Traffic where + All_Traffic.dest_port != 0 + by All_Traffic.action All_Traffic.app All_Traffic.bytes All_Traffic.bytes_in All_Traffic.bytes_out + All_Traffic.dest All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.dvc All_Traffic.protocol + All_Traffic.protocol_version All_Traffic.src All_Traffic.src_ip All_Traffic.src_port + All_Traffic.transport All_Traffic.user All_Traffic.vendor_product All_Traffic.direction + All_Traffic.process_id + | `drop_dm_object_name(All_Traffic)` + | rename dest as C2 + ] + | table _time dest parent_process_name process_name process_path process process_id dest_port C2 + | `searchprotocolhost_with_no_command_line_with_network_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Limited false positives may be present in small environments. Tuning may be required based on parent process. references: -- https://github.com/mandiant/red_team_tool_countermeasures/blob/master/rules/PGF/supplemental/hxioc/SUSPICIOUS%20EXECUTION%20OF%20SEARCHPROTOCOLHOST%20(METHODOLOGY).ioc + - https://github.com/mandiant/red_team_tool_countermeasures/blob/master/rules/PGF/supplemental/hxioc/SUSPICIOUS%20EXECUTION%20OF%20SEARCHPROTOCOLHOST%20(METHODOLOGY).ioc drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A searchprotocolhost.exe process $process_name$ with no commandline on - host $dest$ - risk_objects: - - field: dest - type: system - score: 70 - threat_objects: - - field: process_name - type: process_name + message: A searchprotocolhost.exe process $process_name$ with no commandline on host $dest$ + risk_objects: + - field: dest + type: system + score: 70 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Graceful Wipe Out Attack - - Cobalt Strike - - Compromised Windows Host - - BlackByte Ransomware - - Cactus Ransomware - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1055 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Graceful Wipe Out Attack + - Cobalt Strike + - Compromised Windows Host + - BlackByte Ransomware + - Cactus Ransomware + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1055 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon_searchprotocolhost.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon_searchprotocolhost.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/secretdumps_offline_ntds_dumping_tool.yml b/detections/endpoint/secretdumps_offline_ntds_dumping_tool.yml index cb00d2b771..7487dd5594 100644 --- a/detections/endpoint/secretdumps_offline_ntds_dumping_tool.yml +++ b/detections/endpoint/secretdumps_offline_ntds_dumping_tool.yml @@ -1,88 +1,76 @@ name: SecretDumps Offline NTDS Dumping Tool id: 5672819c-be09-11eb-bbfb-acde48001122 -version: 9 -date: '2026-01-20' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the potential use of the secretsdump.py - tool to dump NTLM hashes from a copy of ntds.dit and the SAM, SYSTEM, and SECURITY - registry hives. It leverages data from Endpoint Detection and Response (EDR) agents, - focusing on specific command-line patterns and process names associated with secretsdump.py. - This activity is significant because it indicates an attempt to extract sensitive - credential information offline, which is a common post-exploitation technique. If - confirmed malicious, this could allow an attacker to obtain NTLM hashes, facilitating - further lateral movement and potential privilege escalation within the network. +description: The following analytic detects the potential use of the secretsdump.py tool to dump NTLM hashes from a copy of ntds.dit and the SAM, SYSTEM, and SECURITY registry hives. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns and process names associated with secretsdump.py. This activity is significant because it indicates an attempt to extract sensitive credential information offline, which is a common post-exploitation technique. If confirmed malicious, this could allow an attacker to obtain NTLM hashes, facilitating further lateral movement and potential privilege escalation within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "python*.exe" - Processes.process = "*.py*" Processes.process = "*-ntds*" (Processes.process = "*-system*" - OR Processes.process = "*-sam*" OR Processes.process = "*-security*" OR Processes.process - = "*-bootkey*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `secretdumps_offline_ntds_dumping_tool_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "python*.exe" Processes.process = "*.py*" Processes.process = "*-ntds*" (Processes.process = "*-system*" + OR + Processes.process = "*-sam*" + OR + Processes.process = "*-security*" + OR + Processes.process = "*-bootkey*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `secretdumps_offline_ntds_dumping_tool_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://github.com/SecureAuthCorp/impacket/blob/master/examples/secretsdump.py + - https://github.com/SecureAuthCorp/impacket/blob/master/examples/secretsdump.py drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A secretdump process $process_name$ with secretdump commandline $process$ - to dump credentials on host $dest$ - risk_objects: - - field: dest - type: system - score: 80 - - field: user - type: user - score: 80 - threat_objects: [] + message: A secretdump process $process_name$ with secretdump commandline $process$ to dump credentials on host $dest$ + risk_objects: + - field: dest + type: system + score: 80 + - field: user + type: user + score: 80 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - Graceful Wipe Out Attack - - Rhysida Ransomware - - Credential Dumping - - Storm-0501 Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1003.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - Graceful Wipe Out Attack + - Rhysida Ransomware + - Credential Dumping + - Storm-0501 Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1003.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/casper/datasets1/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/casper/datasets1/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/serviceprincipalnames_discovery_with_powershell.yml b/detections/endpoint/serviceprincipalnames_discovery_with_powershell.yml index 514e6ff796..7aed6c3893 100644 --- a/detections/endpoint/serviceprincipalnames_discovery_with_powershell.yml +++ b/detections/endpoint/serviceprincipalnames_discovery_with_powershell.yml @@ -1,91 +1,79 @@ name: ServicePrincipalNames Discovery with PowerShell id: 13243068-2d38-11ec-8908-acde48001122 -version: 9 -date: '2025-10-14' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: - The following analytic detects the use of `powershell.exe` to query the - domain for Service Principal Names (SPNs) using Script Block Logging EventCode 4104. - It identifies the use of the KerberosRequestorSecurityToken class within the script - block, which is equivalent to using setspn.exe. This activity is significant as - it often precedes kerberoasting or silver ticket attacks, which can lead to credential - theft. If confirmed malicious, attackers could leverage this information to escalate - privileges or persist within the environment. +description: The following analytic detects the use of `powershell.exe` to query the domain for Service Principal Names (SPNs) using Script Block Logging EventCode 4104. It identifies the use of the KerberosRequestorSecurityToken class within the script block, which is equivalent to using setspn.exe. This activity is significant as it often precedes kerberoasting or silver ticket attacks, which can lead to credential theft. If confirmed malicious, attackers could leverage this information to escalate privileges or persist within the environment. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText="*KerberosRequestorSecurityToken*" - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `serviceprincipalnames_discovery_with_powershell_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText="*KerberosRequestorSecurityToken*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `serviceprincipalnames_discovery_with_powershell_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. known_false_positives: False positives should be limited, however filter as needed. references: - - https://docs.microsoft.com/en-us/windows/win32/ad/service-principal-names - - https://docs.microsoft.com/en-us/dotnet/api/system.identitymodel.tokens.kerberosrequestorsecuritytoken?view=netframework-4.8 - - https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/t1208-kerberoasting - - https://strontic.github.io/xcyclopedia/library/setspn.exe-5C184D581524245DAD7A0A02B51FD2C2.html - - https://attack.mitre.org/techniques/T1558/003/ - - https://social.technet.microsoft.com/wiki/contents/articles/717.service-principal-names-spn-setspn-syntax.aspx - - https://web.archive.org/web/20220212163642/https://www.harmj0y.net/blog/powershell/kerberoasting-without-mimikatz/ - - https://blog.zsec.uk/paving-2-da-wholeset/ - - https://msitpros.com/?p=3113 - - https://adsecurity.org/?p=3466 - - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. - - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 - - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf - - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ + - https://docs.microsoft.com/en-us/windows/win32/ad/service-principal-names + - https://docs.microsoft.com/en-us/dotnet/api/system.identitymodel.tokens.kerberosrequestorsecuritytoken?view=netframework-4.8 + - https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/t1208-kerberoasting + - https://strontic.github.io/xcyclopedia/library/setspn.exe-5C184D581524245DAD7A0A02B51FD2C2.html + - https://attack.mitre.org/techniques/T1558/003/ + - https://social.technet.microsoft.com/wiki/contents/articles/717.service-principal-names-spn-setspn-syntax.aspx + - https://web.archive.org/web/20220212163642/https://www.harmj0y.net/blog/powershell/kerberoasting-without-mimikatz/ + - https://blog.zsec.uk/paving-2-da-wholeset/ + - https://msitpros.com/?p=3113 + - https://adsecurity.org/?p=3466 + - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 + - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf + - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ drilldown_searches: - - name: View the detection results for - "$user_id$" and "$dest$" - search: '%original_detection_search% | search user_id = "$user_id$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user_id$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user_id$" and "$dest$" + search: '%original_detection_search% | search user_id = "$user_id$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user_id$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - An instance of attempting to identify service principle detected on $dest$ - names. - risk_objects: - - field: user_id - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: [] + message: An instance of attempting to identify service principle detected on $dest$ names. + risk_objects: + - field: user_id + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: [] tags: - analytic_story: - - Hellcat Ransomware - - Active Directory Discovery - - Active Directory Kerberos Attacks - - Malicious PowerShell - - Active Directory Privilege Escalation - asset_type: Endpoint - mitre_attack_id: - - T1558.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Hellcat Ransomware + - Active Directory Discovery + - Active Directory Kerberos Attacks + - Malicious PowerShell + - Active Directory Privilege Escalation + asset_type: Endpoint + mitre_attack_id: + - T1558.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/serviceprincipalnames_discovery_with_setspn.yml b/detections/endpoint/serviceprincipalnames_discovery_with_setspn.yml index ced6e87c44..822272ca60 100644 --- a/detections/endpoint/serviceprincipalnames_discovery_with_setspn.yml +++ b/detections/endpoint/serviceprincipalnames_discovery_with_setspn.yml @@ -1,99 +1,91 @@ name: ServicePrincipalNames Discovery with SetSPN id: ae8b3efc-2d2e-11ec-8b57-acde48001122 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the use of `setspn.exe` to query the domain - for Service Principal Names (SPNs). This detection leverages Endpoint Detection - and Response (EDR) data, focusing on specific command-line arguments associated - with `setspn.exe`. Monitoring this activity is crucial as it often precedes Kerberoasting - or Silver Ticket attacks, which can lead to credential theft. If confirmed malicious, - an attacker could use the gathered SPNs to escalate privileges or persist within - the environment, posing a significant security risk. +description: The following analytic detects the use of `setspn.exe` to query the domain for Service Principal Names (SPNs). This detection leverages Endpoint Detection and Response (EDR) data, focusing on specific command-line arguments associated with `setspn.exe`. Monitoring this activity is crucial as it often precedes Kerberoasting or Silver Ticket attacks, which can lead to credential theft. If confirmed malicious, an attacker could use the gathered SPNs to escalate privileges or persist within the environment, posing a significant security risk. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_setspn` (Processes.process="*-t*" - AND Processes.process="*-f*") OR (Processes.process="*-q*" AND Processes.process="**/**") - OR (Processes.process="*-q*") OR (Processes.process="*-s*") by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| - `serviceprincipalnames_discovery_with_setspn_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be caused by Administrators resetting SPNs - or querying for SPNs. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_setspn` (Processes.process="*-t*" + AND + Processes.process="*-f*") + OR + (Processes.process="*-q*" + AND + Processes.process="**/**") + OR + (Processes.process="*-q*") + OR + (Processes.process="*-s*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `serviceprincipalnames_discovery_with_setspn_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be caused by Administrators resetting SPNs or querying for SPNs. Filter as needed. references: -- https://docs.microsoft.com/en-us/windows/win32/ad/service-principal-names -- https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/t1208-kerberoasting -- https://strontic.github.io/xcyclopedia/library/setspn.exe-5C184D581524245DAD7A0A02B51FD2C2.html -- https://attack.mitre.org/techniques/T1558/003/ -- https://social.technet.microsoft.com/wiki/contents/articles/717.service-principal-names-spn-setspn-syntax.aspx -- https://web.archive.org/web/20220212163642/https://www.harmj0y.net/blog/powershell/kerberoasting-without-mimikatz/ -- https://blog.zsec.uk/paving-2-da-wholeset/ -- https://msitpros.com/?p=3113 -- https://adsecurity.org/?p=3466 + - https://docs.microsoft.com/en-us/windows/win32/ad/service-principal-names + - https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/t1208-kerberoasting + - https://strontic.github.io/xcyclopedia/library/setspn.exe-5C184D581524245DAD7A0A02B51FD2C2.html + - https://attack.mitre.org/techniques/T1558/003/ + - https://social.technet.microsoft.com/wiki/contents/articles/717.service-principal-names-spn-setspn-syntax.aspx + - https://web.archive.org/web/20220212163642/https://www.harmj0y.net/blog/powershell/kerberoasting-without-mimikatz/ + - https://blog.zsec.uk/paving-2-da-wholeset/ + - https://msitpros.com/?p=3113 + - https://adsecurity.org/?p=3466 drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to identify service principal names. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to identify service principal names. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Active Directory Discovery - - Active Directory Privilege Escalation - - Compromised Windows Host - - Active Directory Kerberos Attacks - asset_type: Endpoint - mitre_attack_id: - - T1558.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - Active Directory Privilege Escalation + - Compromised Windows Host + - Active Directory Kerberos Attacks + asset_type: Endpoint + mitre_attack_id: + - T1558.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/atomic_red_team/windows-sysmon_setspn.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/atomic_red_team/windows-sysmon_setspn.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/services_escalate_exe.yml b/detections/endpoint/services_escalate_exe.yml index 1f7d0ab4da..f1bb216a0d 100644 --- a/detections/endpoint/services_escalate_exe.yml +++ b/detections/endpoint/services_escalate_exe.yml @@ -1,89 +1,72 @@ name: Services Escalate Exe id: c448488c-b7ec-11eb-8253-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies the execution of a randomly named binary - via `services.exe`, indicative of privilege escalation using Cobalt Strike's `svc-exe`. - This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process lineage and command-line executions. This activity is significant - as it often follows initial access, allowing adversaries to escalate privileges - and establish persistence. If confirmed malicious, this behavior could enable attackers - to execute arbitrary code, maintain long-term access, and potentially move laterally - within the network, posing a severe threat to the organization's security. +description: The following analytic identifies the execution of a randomly named binary via `services.exe`, indicative of privilege escalation using Cobalt Strike's `svc-exe`. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process lineage and command-line executions. This activity is significant as it often follows initial access, allowing adversaries to escalate privileges and establish persistence. If confirmed malicious, this behavior could enable attackers to execute arbitrary code, maintain long-term access, and potentially move laterally within the network, posing a severe threat to the organization's security. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe - Processes.process_path=*admin$* by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `services_escalate_exe_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives should be limited as `services.exe` should - never spawn a process from `ADMIN$`. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name=services.exe Processes.process_path=*admin$* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `services_escalate_exe_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives should be limited as `services.exe` should never spawn a process from `ADMIN$`. Filter as needed. references: -- https://thedfirreport.com/2021/03/29/sodinokibi-aka-revil-ransomware/ -- https://attack.mitre.org/techniques/T1548/ -- https://hstechdocs.helpsystems.com/manuals/cobaltstrike/current/userguide/index.htm#cshid=1085 + - https://thedfirreport.com/2021/03/29/sodinokibi-aka-revil-ransomware/ + - https://attack.mitre.org/techniques/T1548/ + - https://hstechdocs.helpsystems.com/manuals/cobaltstrike/current/userguide/index.htm#cshid=1085 drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A service process $parent_process_name$ with process path $process_path$ - on host $dest$ - risk_objects: - - field: dest - type: system - score: 76 - - field: user - type: user - score: 76 - threat_objects: [] + message: A service process $parent_process_name$ with process path $process_path$ on host $dest$ + risk_objects: + - field: dest + type: system + score: 76 + - field: user + type: user + score: 76 + threat_objects: [] tags: - analytic_story: - - Graceful Wipe Out Attack - - Cobalt Strike - - CISA AA23-347A - - Compromised Windows Host - - BlackByte Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1548 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Graceful Wipe Out Attack + - Cobalt Strike + - CISA AA23-347A + - Compromised Windows Host + - BlackByte Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1548 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/services_lolbas_execution_process_spawn.yml b/detections/endpoint/services_lolbas_execution_process_spawn.yml index 2c831c009a..3e578be859 100644 --- a/detections/endpoint/services_lolbas_execution_process_spawn.yml +++ b/detections/endpoint/services_lolbas_execution_process_spawn.yml @@ -1,98 +1,74 @@ name: Services LOLBAS Execution Process Spawn id: ba9e1954-4c04-11ec-8b74-3e22fbd008af -version: 9 -date: '2026-01-21' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic identifies `services.exe` spawning a LOLBAS (Living - Off the Land Binaries and Scripts) execution process. It leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process creation events where `services.exe` - is the parent process. This activity is significant because adversaries often abuse - the Service Control Manager to execute malicious code via native Windows binaries, - facilitating lateral movement. If confirmed malicious, this behavior could allow - attackers to execute arbitrary code, escalate privileges, or maintain persistence - within the environment, posing a severe security risk. +description: The following analytic identifies `services.exe` spawning a LOLBAS (Living Off the Land Binaries and Scripts) execution process. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where `services.exe` is the parent process. This activity is significant because adversaries often abuse the Service Control Manager to execute malicious code via native Windows binaries, facilitating lateral movement. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment, posing a severe security risk. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=services.exe) - (Processes.process_name IN ("Regsvcs.exe", "Ftp.exe", "OfflineScannerShell.exe", - "Rasautou.exe", "Schtasks.exe", "Xwizard.exe", "Dllhost.exe", "Pnputil.exe", "Atbroker.exe", - "Pcwrun.exe", "Ttdinject.exe","Mshta.exe", "Bitsadmin.exe", "Certoc.exe", "Ieexec.exe", - "Microsoft.Workflow.Compiler.exe", "Runscripthelper.exe", "Forfiles.exe", "Msbuild.exe", - "Register-cimprovider.exe", "Tttracer.exe", "Ie4uinit.exe", "Bash.exe", "Hh.exe", - "SettingSyncHost.exe", "Cmstp.exe", "Mmc.exe", "Stordiag.exe", "Scriptrunner.exe", - "Odbcconf.exe", "Extexport.exe", "Msdt.exe", "WorkFolders.exe", "Diskshadow.exe", - "Mavinject.exe", "Regasm.exe", "Gpscript.exe", "Rundll32.exe", "Regsvr32.exe", "Msiexec.exe", - "Wuauclt.exe", "Presentationhost.exe", "Wmic.exe", "Runonce.exe", "Syncappvpublishingserver.exe", - "Verclsid.exe", "Infdefaultinstall.exe", "Explorer.exe", "Installutil.exe", "Netsh.exe", - "Wab.exe", "Dnscmd.exe", "At.exe", "Pcalua.exe", "Msconfig.exe")) by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `services_lolbas_execution_process_spawn_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Legitimate applications may trigger this behavior, filter as - needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.parent_process_name=services.exe + ) + (Processes.process_name IN ("Regsvcs.exe", "Ftp.exe", "OfflineScannerShell.exe", "Rasautou.exe", "Schtasks.exe", "Xwizard.exe", "Dllhost.exe", "Pnputil.exe", "Atbroker.exe", "Pcwrun.exe", "Ttdinject.exe","Mshta.exe", "Bitsadmin.exe", "Certoc.exe", "Ieexec.exe", "Microsoft.Workflow.Compiler.exe", "Runscripthelper.exe", "Forfiles.exe", "Msbuild.exe", "Register-cimprovider.exe", "Tttracer.exe", "Ie4uinit.exe", "Bash.exe", "Hh.exe", "SettingSyncHost.exe", "Cmstp.exe", "Mmc.exe", "Stordiag.exe", "Scriptrunner.exe", "Odbcconf.exe", "Extexport.exe", "Msdt.exe", "WorkFolders.exe", "Diskshadow.exe", "Mavinject.exe", "Regasm.exe", "Gpscript.exe", "Rundll32.exe", "Regsvr32.exe", "Msiexec.exe", "Wuauclt.exe", "Presentationhost.exe", "Wmic.exe", "Runonce.exe", "Syncappvpublishingserver.exe", "Verclsid.exe", "Infdefaultinstall.exe", "Explorer.exe", "Installutil.exe", "Netsh.exe", "Wab.exe", "Dnscmd.exe", "At.exe", "Pcalua.exe", "Msconfig.exe")) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `services_lolbas_execution_process_spawn_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Legitimate applications may trigger this behavior, filter as needed. references: -- https://attack.mitre.org/techniques/T1543/003/ -- https://pentestlab.blog/2020/07/21/lateral-movement-services/ -- https://lolbas-project.github.io/ + - https://attack.mitre.org/techniques/T1543/003/ + - https://pentestlab.blog/2020/07/21/lateral-movement-services/ + - https://lolbas-project.github.io/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: "Services.exe spawned LOLBAS: $process_name$ located in $process_path$ on $dest$" - risk_objects: - - field: dest - type: system - score: 54 - threat_objects: - - field: process - type: process + message: "Services.exe spawned LOLBAS: $process_name$ located in $process_path$ on $dest$" + risk_objects: + - field: dest + type: system + score: 54 + threat_objects: + - field: process + type: process tags: - analytic_story: - - Active Directory Lateral Movement - - Living Off The Land - - Qakbot - - CISA AA23-347A - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1543.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + - Living Off The Land + - Qakbot + - CISA AA23-347A + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1543.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/lateral_movement_lolbas/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/lateral_movement_lolbas/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/set_default_powershell_execution_policy_to_unrestricted_or_bypass.yml b/detections/endpoint/set_default_powershell_execution_policy_to_unrestricted_or_bypass.yml index 41f9882e68..81386fd884 100644 --- a/detections/endpoint/set_default_powershell_execution_policy_to_unrestricted_or_bypass.yml +++ b/detections/endpoint/set_default_powershell_execution_policy_to_unrestricted_or_bypass.yml @@ -5,82 +5,52 @@ date: '2026-02-09' author: Steven Dick, Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects changes to the PowerShell ExecutionPolicy - in the registry to "Unrestricted" or "Bypass." It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on registry modifications under the path *Software\Microsoft\Powershell\1\ShellIds\Microsoft.PowerShell*. - This activity is significant because setting the ExecutionPolicy to these values - can allow the execution of potentially malicious scripts without restriction. If - confirmed malicious, this could enable an attacker to execute arbitrary code, leading - to further compromise of the system and potential escalation of privileges. +description: The following analytic detects changes to the PowerShell ExecutionPolicy in the registry to "Unrestricted" or "Bypass." It leverages data from Endpoint Detection and Response (EDR) agents, focusing on registry modifications under the path *Software\Microsoft\Powershell\1\ShellIds\Microsoft.PowerShell*. This activity is significant because setting the ExecutionPolicy to these values can allow the execution of potentially malicious scripts without restriction. If confirmed malicious, this could enable an attacker to execute arbitrary code, leading to further compromise of the system and potential escalation of privileges. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry - WHERE (Registry.registry_path=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell* - Registry.registry_value_name=ExecutionPolicy (Registry.registry_value_data=Unrestricted - OR Registry.registry_value_data=Bypass)) by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrators may attempt to change the default execution - policy on a system for a variety of reasons. However, setting the policy to "unrestricted" - or "bypass" as this search is designed to identify, would be unusual. Hits should - be reviewed and investigated as appropriate. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell* Registry.registry_value_name=ExecutionPolicy (Registry.registry_value_data=Unrestricted OR Registry.registry_value_data=Bypass)) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators may attempt to change the default execution policy on a system for a variety of reasons. However, setting the policy to "unrestricted" or "bypass" as this search is designed to identify, would be unusual. Hits should be reviewed and investigated as appropriate. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A registry modification in $registry_path$ with reg key $registry_key_name$ - and reg value $registry_value_name$ in host $dest$ - risk_objects: - - field: dest - type: system - score: 48 - threat_objects: - - field: registry_path - type: registry_path + message: A registry modification in $registry_path$ with reg key $registry_key_name$ and reg value $registry_value_name$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 48 + threat_objects: + - field: registry_path + type: registry_path tags: - analytic_story: - - SolarWinds WHD RCE Post Exploitation - - HAFNIUM Group - - Hermetic Wiper - - Credential Dumping - - Malicious PowerShell - - Data Destruction - - DarkGate Malware - - SystemBC - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SolarWinds WHD RCE Post Exploitation + - HAFNIUM Group + - Hermetic Wiper + - Credential Dumping + - Malicious PowerShell + - Data Destruction + - DarkGate Malware + - SystemBC + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_execution_policy/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_execution_policy/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/shai_hulud_2_exfiltration_artifact_files.yml b/detections/endpoint/shai_hulud_2_exfiltration_artifact_files.yml index 9aa5934d44..ee65d49936 100644 --- a/detections/endpoint/shai_hulud_2_exfiltration_artifact_files.yml +++ b/detections/endpoint/shai_hulud_2_exfiltration_artifact_files.yml @@ -6,93 +6,88 @@ author: Michael Haag, Splunk status: production type: TTP description: | - Detects creation of exfiltration artifact files associated with Shai-Hulud 2.0 npm supply - chain malware. The malware creates cloud.json, contents.json, environment.json, truffleSecrets.json, - and actionsSecrets.json files containing harvested credentials from AWS, Azure, GCP, GitHub secrets, - and environment variables. These files are staged before being pushed to attacker-controlled repositories. + Detects creation of exfiltration artifact files associated with Shai-Hulud 2.0 npm supply + chain malware. The malware creates cloud.json, contents.json, environment.json, truffleSecrets.json, + and actionsSecrets.json files containing harvested credentials from AWS, Azure, GCP, GitHub secrets, + and environment variables. These files are staged before being pushed to attacker-controlled repositories. data_source: -- Sysmon for Linux EventID 11 -- Sysmon EventID 11 + - Sysmon for Linux EventID 11 + - Sysmon EventID 11 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime - - from datamodel=Endpoint.Filesystem where - - Filesystem.file_name IN ( - "cloud.json", - "contents.json", - "environment.json", - "truffleSecrets.json", - "actionsSecrets.json" - ) + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user - Filesystem.vendor_product - - | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `shai_hulud_2_exfiltration_artifact_files_filter` + from datamodel=Endpoint.Filesystem where + + Filesystem.file_name IN ( + "cloud.json", + "contents.json", + "environment.json", + "truffleSecrets.json", + "actionsSecrets.json" + ) + + by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time + Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user + Filesystem.vendor_product + + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `shai_hulud_2_exfiltration_artifact_files_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain filesystem events, specifically file creation - events. These logs must be processed using the appropriate Splunk Technology Add-ons - that are specific to the EDR product. The logs must also be mapped to the `Filesystem` - node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) - to normalize the field names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain filesystem events, specifically file creation + events. These logs must be processed using the appropriate Splunk Technology Add-ons + that are specific to the EDR product. The logs must also be mapped to the `Filesystem` + node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) + to normalize the field names and speed up the data modeling process. known_false_positives: | - Low but possible. Generic filenames like cloud.json or environment.json may appear in legitimate contexts. Correlate with npm install activity or suspicious parent processes. + Low but possible. Generic filenames like cloud.json or environment.json may appear in legitimate contexts. Correlate with npm install activity or suspicious parent processes. references: - - https://www.wiz.io/blog/shai-hulud-2-0-ongoing-supply-chain-attack - - https://securelist.com/shai-hulud-worm-infects-500-npm-packages-in-a-supply-chain-attack/117547/ + - https://www.wiz.io/blog/shai-hulud-2-0-ongoing-supply-chain-attack + - https://securelist.com/shai-hulud-worm-infects-500-npm-packages-in-a-supply-chain-attack/117547/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Shai-Hulud 2.0 exfiltration artifact $file_name$ created on $dest$ - risk_objects: - - field: dest - type: system - score: 35 - threat_objects: - - field: file_name - type: file_name - score: 25 + message: Shai-Hulud 2.0 exfiltration artifact $file_name$ created on $dest$ + risk_objects: + - field: dest + type: system + score: 35 + threat_objects: + - field: file_name + type: file_name + score: 25 tags: - analytic_story: - - NPM Supply Chain Compromise - asset_type: Endpoint - mitre_attack_id: - - T1074.001 - - T1552.001 - - T1195.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - NPM Supply Chain Compromise + asset_type: Endpoint + mitre_attack_id: + - T1074.001 + - T1552.001 + - T1195.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - Linux - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.001/npm/shai_hulud_workflow_sysmon.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux -- name: True Positive Test - Windows - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.001/npm/windows_workflow_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test - Linux + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.001/npm/shai_hulud_workflow_sysmon.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux + - name: True Positive Test - Windows + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.001/npm/windows_workflow_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/shai_hulud_workflow_file_creation_or_modification.yml b/detections/endpoint/shai_hulud_workflow_file_creation_or_modification.yml index b7a24570a8..6cd0ed195e 100644 --- a/detections/endpoint/shai_hulud_workflow_file_creation_or_modification.yml +++ b/detections/endpoint/shai_hulud_workflow_file_creation_or_modification.yml @@ -6,109 +6,103 @@ author: Michael Haag, Splunk status: production type: TTP description: | - Detects creation or deletion of malicious GitHub Actions workflow files associated with - Shai-Hulud worm variants on Linux or Windows endpoints. This includes the original shai-hulud-workflow.yml, - the 2.0 backdoor discussion.yaml (enables command injection via GitHub Discussions on self-hosted - runners named SHA1HULUD), and the secrets exfiltration workflow formatter_*.yml pattern. These - files are used to exfiltrate credentials and propagate across repositories. + Detects creation or deletion of malicious GitHub Actions workflow files associated with + Shai-Hulud worm variants on Linux or Windows endpoints. This includes the original shai-hulud-workflow.yml, + the 2.0 backdoor discussion.yaml (enables command injection via GitHub Discussions on self-hosted + runners named SHA1HULUD), and the secrets exfiltration workflow formatter_*.yml pattern. These + files are used to exfiltrate credentials and propagate across repositories. data_source: -- Sysmon for Linux EventID 11 -- Sysmon EventID 11 + - Sysmon for Linux EventID 11 + - Sysmon EventID 11 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime - - from datamodel=Endpoint.Filesystem where - - Filesystem.file_path IN ( - "*/.github/workflows/discussion.yaml", - "*/.github/workflows/discussion.yml", - "*/.github/workflows/formatter_*.yaml", - "*/.github/workflows/formatter_*.yml", - "*/.github/workflows/shai-hulud-workflow.yaml", - "*/.github/workflows/shai-hulud-workflow.yml", - "*/.github/workflows/shai-hulud.yaml", - "*/.github/workflows/shai-hulud.yml", - "*\\.github\\workflows\\discussion.yaml", - "*\\.github\\workflows\\discussion.yml", - "*\\.github\\workflows\\formatter_*.yaml", - "*\\.github\\workflows\\formatter_*.yml", - "*\\.github\\workflows\\shai-hulud-workflow.yaml", - "*\\.github\\workflows\\shai-hulud-workflow.yml", - "*\\.github\\workflows\\shai-hulud.yaml", - "*\\.github\\workflows\\shai-hulud.yml" - ) - - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user - Filesystem.vendor_product + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime - | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `shai_hulud_workflow_file_creation_or_modification_filter` + from datamodel=Endpoint.Filesystem where + + Filesystem.file_path IN ( + "*/.github/workflows/discussion.yaml", + "*/.github/workflows/discussion.yml", + "*/.github/workflows/formatter_*.yaml", + "*/.github/workflows/formatter_*.yml", + "*/.github/workflows/shai-hulud-workflow.yaml", + "*/.github/workflows/shai-hulud-workflow.yml", + "*/.github/workflows/shai-hulud.yaml", + "*/.github/workflows/shai-hulud.yml", + "*\\.github\\workflows\\discussion.yaml", + "*\\.github\\workflows\\discussion.yml", + "*\\.github\\workflows\\formatter_*.yaml", + "*\\.github\\workflows\\formatter_*.yml", + "*\\.github\\workflows\\shai-hulud-workflow.yaml", + "*\\.github\\workflows\\shai-hulud-workflow.yml", + "*\\.github\\workflows\\shai-hulud.yaml", + "*\\.github\\workflows\\shai-hulud.yml" + ) + + by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time + Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user + Filesystem.vendor_product + + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `shai_hulud_workflow_file_creation_or_modification_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain filesystem events, specifically file creation - and deletion events. These logs must be processed using the appropriate Splunk - Technology Add-ons that are specific to the EDR product. The logs must also be - mapped to the `Filesystem` node of the `Endpoint` data model. Use the Splunk Common - Information Model (CIM) to normalize the field names and speed up the data modeling - process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain filesystem events, specifically file creation + and deletion events. These logs must be processed using the appropriate Splunk + Technology Add-ons that are specific to the EDR product. The logs must also be + mapped to the `Filesystem` node of the `Endpoint` data model. Use the Splunk Common + Information Model (CIM) to normalize the field names and speed up the data modeling + process. known_false_positives: | - Very low. Legitimate usage of a file with this exact name is unlikely; validate with repository owners. + Very low. Legitimate usage of a file with this exact name is unlikely; validate with repository owners. references: -- https://www.wiz.io/blog/shai-hulud-2-0-ongoing-supply-chain-attack -- https://securelist.com/shai-hulud-worm-infects-500-npm-packages-in-a-supply-chain-attack/117547/ -- https://github.com/SigmaHQ/sigma/pull/5658/files -- https://www.cisa.gov/news-events/alerts/2025/09/23/widespread-supply-chain-compromise-impacting-npm-ecosystem + - https://www.wiz.io/blog/shai-hulud-2-0-ongoing-supply-chain-attack + - https://securelist.com/shai-hulud-worm-infects-500-npm-packages-in-a-supply-chain-attack/117547/ + - https://github.com/SigmaHQ/sigma/pull/5658/files + - https://www.cisa.gov/news-events/alerts/2025/09/23/widespread-supply-chain-compromise-impacting-npm-ecosystem drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Shai-Hulud malicious workflow file detected on endpoint $dest$ at $file_path$. - Immediate investigation required. - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: - - field: file_path - type: file_path - score: 20 + message: Shai-Hulud malicious workflow file detected on endpoint $dest$ at $file_path$. Immediate investigation required. + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: + - field: file_path + type: file_path + score: 20 tags: - analytic_story: - - NPM Supply Chain Compromise - asset_type: Endpoint - mitre_attack_id: - - T1574.006 - - T1554 - - T1195 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - NPM Supply Chain Compromise + asset_type: Endpoint + mitre_attack_id: + - T1574.006 + - T1554 + - T1195 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - Linux - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.001/npm/shai_hulud_workflow_sysmon.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux -- name: True Positive Test - Windows - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.001/npm/windows_workflow_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test - Linux + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.001/npm/shai_hulud_workflow_sysmon.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux + - name: True Positive Test - Windows + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.001/npm/windows_workflow_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/shim_database_file_creation.yml b/detections/endpoint/shim_database_file_creation.yml index caa8b0e8d6..536d03297d 100644 --- a/detections/endpoint/shim_database_file_creation.yml +++ b/detections/endpoint/shim_database_file_creation.yml @@ -5,68 +5,45 @@ date: '2025-05-02' author: David Dorsey, Splunk status: production type: TTP -description: The following analytic detects the creation of shim database files (.sdb) - in default directories using the sdbinst.exe application. It leverages filesystem - activity data from the Endpoint.Filesystem data model to identify file writes to - the Windows\AppPatch\Custom directory. This activity is significant because shims - can intercept and alter API calls, potentially allowing attackers to bypass security - controls or execute malicious code. If confirmed malicious, this could lead to unauthorized - code execution, privilege escalation, or persistent access within the environment. +description: The following analytic detects the creation of shim database files (.sdb) in default directories using the sdbinst.exe application. It leverages filesystem activity data from the Endpoint.Filesystem data model to identify file writes to the Windows\AppPatch\Custom directory. This activity is significant because shims can intercept and alter API calls, potentially allowing attackers to bypass security controls or execute malicious code. If confirmed malicious, this could lead to unauthorized code execution, privilege escalation, or persistent access within the environment. data_source: -- Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count values(Filesystem.action) - values(Filesystem.file_hash) as file_hash values(Filesystem.file_path) as file_path min(_time) - as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path=*Windows\\AppPatch\\Custom* - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path - Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product | `security_content_ctime(lastTime)` | - `security_content_ctime(firstTime)` |`drop_dm_object_name(Filesystem)` | `shim_database_file_creation_filter`' -how_to_implement: You must be ingesting data that records the filesystem activity - from your hosts to populate the Endpoint file-system data model node. If you are - using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which - you want to collect data. -known_false_positives: Because legitimate shim files are created and used all the - time, this event, in itself, is not suspicious. However, if there are other correlating - events, it may warrant further investigation. + - Sysmon EventID 11 +search: '| tstats `security_content_summariesonly` count values(Filesystem.action) values(Filesystem.file_hash) as file_hash values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path=*Windows\\AppPatch\\Custom* by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` |`drop_dm_object_name(Filesystem)` | `shim_database_file_creation_filter`' +how_to_implement: You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. +known_false_positives: Because legitimate shim files are created and used all the time, this event, in itself, is not suspicious. However, if there are other correlating events, it may warrant further investigation. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process that possibly write shim database in $file_path$ in host $dest$ - risk_objects: - - field: dest - type: system - score: 56 - threat_objects: - - field: file_path - type: file_path + message: A process that possibly write shim database in $file_path$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 56 + threat_objects: + - field: file_path + type: file_path tags: - analytic_story: - - Windows Persistence Techniques - asset_type: Endpoint - mitre_attack_id: - - T1546.011 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1546.011 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/shim_database_installation_with_suspicious_parameters.yml b/detections/endpoint/shim_database_installation_with_suspicious_parameters.yml index bb3dde4f74..bae841a56d 100644 --- a/detections/endpoint/shim_database_installation_with_suspicious_parameters.yml +++ b/detections/endpoint/shim_database_installation_with_suspicious_parameters.yml @@ -5,80 +5,49 @@ date: '2026-01-14' author: David Dorsey, Splunk status: production type: TTP -description: The following analytic detects the execution of sdbinst.exe with parameters - indicative of silently creating a shim database. It leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process names, parent processes, - and command-line arguments. This activity is significant because shim databases - can be used to intercept and manipulate API calls, potentially allowing attackers - to bypass security controls or achieve persistence. If confirmed malicious, this - could enable unauthorized code execution, privilege escalation, or persistent access - to the compromised system. +description: The following analytic detects the execution of sdbinst.exe with parameters indicative of silently creating a shim database. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, parent processes, and command-line arguments. This activity is significant because shim databases can be used to intercept and manipulate API calls, potentially allowing attackers to bypass security controls or achieve persistence. If confirmed malicious, this could enable unauthorized code execution, privilege escalation, or persistent access to the compromised system. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.process_name = sdbinst.exe NOT Processes.process IN ("\"C:\\Windows\\System32\\sdbinst.exe\"", - "C:\\Windows\\System32\\sdbinst.exe", "*-mm", "*-?", "*-m -bg") by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `shim_database_installation_with_suspicious_parameters_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sdbinst.exe NOT Processes.process IN ("\"C:\\Windows\\System32\\sdbinst.exe\"", "C:\\Windows\\System32\\sdbinst.exe", "*-mm", "*-?", "*-m -bg") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `shim_database_installation_with_suspicious_parameters_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process $process_name$ that possibly creates a shim db silently in host - $dest$ - risk_objects: - - field: dest - type: system - score: 63 - - field: user - type: user - score: 63 - threat_objects: [] + message: A process $process_name$ that possibly creates a shim db silently in host $dest$ + risk_objects: + - field: dest + type: system + score: 63 + - field: user + type: user + score: 63 + threat_objects: [] tags: - analytic_story: - - Windows Persistence Techniques - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1546.011 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Persistence Techniques + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1546.011 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/short_lived_scheduled_task.yml b/detections/endpoint/short_lived_scheduled_task.yml index 3ce1222190..9854c645c3 100644 --- a/detections/endpoint/short_lived_scheduled_task.yml +++ b/detections/endpoint/short_lived_scheduled_task.yml @@ -1,73 +1,62 @@ name: Short Lived Scheduled Task id: 6fa31414-546e-11ec-adfa-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the creation and deletion of scheduled - tasks within a short time frame (less than 30 seconds) using Windows Security EventCodes - 4698 and 4699. This behavior is identified by analyzing Windows Security Event Logs - and leveraging the Windows TA for parsing. Such activity is significant as it may - indicate lateral movement or remote code execution attempts by adversaries. If confirmed - malicious, this could lead to unauthorized access, data exfiltration, or execution - of malicious payloads, necessitating prompt investigation and response by security - analysts. +description: The following analytic detects the creation and deletion of scheduled tasks within a short time frame (less than 30 seconds) using Windows Security EventCodes 4698 and 4699. This behavior is identified by analyzing Windows Security Event Logs and leveraging the Windows TA for parsing. Such activity is significant as it may indicate lateral movement or remote code execution attempts by adversaries. If confirmed malicious, this could lead to unauthorized access, data exfiltration, or execution of malicious payloads, necessitating prompt investigation and response by security analysts. data_source: -- Windows Event Log Security 4698 -- Windows Event Log Security 4699 -search: '`wineventlog_security` EventCode=4698 OR EventCode=4699 | xmlkv Message | - transaction Task_Name startswith=(EventCode=4698) endswith=(EventCode=4699) | eval - short_lived=case((duration<30),"TRUE") | search short_lived = TRUE | rename ComputerName - as dest| table _time, dest, Account_Name, Command, Task_Name, short_lived | `short_lived_scheduled_task_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also - required. -known_false_positives: Although uncommon, legitimate applications may create and delete - a Scheduled Task within 30 seconds. Filter as needed. + - Windows Event Log Security 4698 + - Windows Event Log Security 4699 +search: |- + `wineventlog_security` EventCode=4698 OR EventCode=4699 + | xmlkv Message + | transaction Task_Name startswith=(EventCode=4698) endswith=(EventCode=4699) + | eval short_lived=case((duration<30),"TRUE") + | search short_lived = TRUE + | rename ComputerName as dest + | table _time, dest, Account_Name, Command, Task_Name, short_lived + | `short_lived_scheduled_task_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also required. +known_false_positives: Although uncommon, legitimate applications may create and delete a Scheduled Task within 30 seconds. Filter as needed. references: -- https://attack.mitre.org/techniques/T1053/005/ -- https://docs.microsoft.com/en-us/windows/win32/taskschd/about-the-task-scheduler + - https://attack.mitre.org/techniques/T1053/005/ + - https://docs.microsoft.com/en-us/windows/win32/taskschd/about-the-task-scheduler drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A windows scheduled task was created and deleted in 30 seconds on $dest$ - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: [] + message: A windows scheduled task was created and deleted in 30 seconds on $dest$ + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: [] tags: - analytic_story: - - Active Directory Lateral Movement - - CISA AA22-257A - - CISA AA23-347A - - Compromised Windows Host - - Scheduled Tasks - asset_type: Endpoint - mitre_attack_id: - - T1053.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + - CISA AA22-257A + - CISA AA23-347A + - Compromised Windows Host + - Scheduled Tasks + asset_type: Endpoint + mitre_attack_id: + - T1053.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/lateral_movement/windows-security.log - source: WinEventLog:Security - sourcetype: WinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/lateral_movement/windows-security.log + source: WinEventLog:Security + sourcetype: WinEventLog diff --git a/detections/endpoint/short_lived_windows_accounts.yml b/detections/endpoint/short_lived_windows_accounts.yml index bfe675e3c2..fd6e871826 100644 --- a/detections/endpoint/short_lived_windows_accounts.yml +++ b/detections/endpoint/short_lived_windows_accounts.yml @@ -1,89 +1,77 @@ name: Short Lived Windows Accounts id: b25f6f62-0782-43c1-b403-083231ffd97d -version: 10 -date: '2025-09-16' +version: 11 +date: '2026-02-25' author: David Dorsey, Bhavin Patel, Splunk status: production type: TTP -description: The following analytic detects the rapid creation and deletion of Windows - accounts within a short time frame of 1 hour. It leverages the "Change" data model - in Splunk, specifically monitoring events with result IDs 4720 (account creation) - and 4726 (account deletion). This behavior is significant as it may indicate an - attacker attempting to create and remove accounts quickly to evade detection or - gain unauthorized access. If confirmed malicious, this activity could lead to unauthorized - access, privilege escalation, or further malicious actions within the environment. - Immediate investigation of flagged events is crucial to mitigate potential damage. +description: The following analytic detects the rapid creation and deletion of Windows accounts within a short time frame of 1 hour. It leverages the "Change" data model in Splunk, specifically monitoring events with result IDs 4720 (account creation) and 4726 (account deletion). This behavior is significant as it may indicate an attacker attempting to create and remove accounts quickly to evade detection or gain unauthorized access. If confirmed malicious, this activity could lead to unauthorized access, privilege escalation, or further malicious actions within the environment. Immediate investigation of flagged events is crucial to mitigate potential damage. data_source: -- Windows Event Log System 4720 -- Windows Event Log System 4726 -search: '| tstats `security_content_summariesonly` values(All_Changes.result_id) as - result_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Change - where All_Changes.result_id=4720 OR All_Changes.result_id=4726 by _time span=1h - All_Changes.user All_Changes.dest All_Changes.Account_Management.src All_Changes.Account_Management.src_user - | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name("All_Changes")` - | `drop_dm_object_name("Account_Management")` | transaction user connected=false - maxspan=60m | eval create_result_id=mvindex(result_id, 0) | eval delete_result_id=mvindex(result_id, - 1) | search create_result_id = 4720 delete_result_id=4726 | table firstTime lastTime - count user src src_user dest create_result_id delete_result_id | `short_lived_windows_accounts_filter`' -how_to_implement: 'This search requires you to have enabled your Group Management - Audit Logs in your Local Windows Security Policy and be ingesting those logs. More - information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. - We also recommend adjsuting the maxspan and _time parameter to better fit your environment.' -known_false_positives: It is possible that an administrator created and deleted an - account in a short time period. Verifying activity with an administrator is advised. + - Windows Event Log System 4720 + - Windows Event Log System 4726 +search: |- + | tstats `security_content_summariesonly` values(All_Changes.result_id) as result_id count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Change + WHERE All_Changes.result_id=4720 + OR + All_Changes.result_id=4726 + BY _time span=1h All_Changes.user + All_Changes.dest All_Changes.Account_Management.src All_Changes.Account_Management.src_user + | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` + | `drop_dm_object_name("All_Changes")` + | `drop_dm_object_name("Account_Management")` + | transaction user connected=false maxspan=60m + | eval create_result_id=mvindex(result_id, 0) + | eval delete_result_id=mvindex(result_id, 1) + | search create_result_id = 4720 delete_result_id=4726 + | table firstTime lastTime count user src src_user dest create_result_id delete_result_id + | `short_lived_windows_accounts_filter` +how_to_implement: 'This search requires you to have enabled your Group Management Audit Logs in your Local Windows Security Policy and be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. We also recommend adjsuting the maxspan and _time parameter to better fit your environment.' +known_false_positives: It is possible that an administrator created and deleted an account in a short time period. Verifying activity with an administrator is advised. references: -- https://www.youtube.com/watch?v=D4Cd-KK4ctk -- https://attack.mitre.org/techniques/T1078/ + - https://www.youtube.com/watch?v=D4Cd-KK4ctk + - https://attack.mitre.org/techniques/T1078/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$src_user$" - search: '%original_detection_search% | search dest = "$dest$" src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$src_user$" + search: '%original_detection_search% | search dest = "$dest$" src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A user account $user$ is created and deleted within a short time period - on host $dest$ by user $src_user$ - risk_objects: - - field: dest - type: system - score: 63 - - field: src_user - type: user - score: 63 - threat_objects: [] + message: A user account $user$ is created and deleted within a short time period on host $dest$ by user $src_user$ + risk_objects: + - field: dest + type: system + score: 63 + - field: src_user + type: user + score: 63 + threat_objects: [] tags: - analytic_story: - - Active Directory Lateral Movement - - GhostRedirector IIS Module and Rungan Backdoor - asset_type: Windows - mitre_attack_id: - - T1078.003 - - T1136.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - Active Directory Lateral Movement + - GhostRedirector IIS Module and Rungan Backdoor + asset_type: Windows + mitre_attack_id: + - T1078.003 + - T1136.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log - source: WinEventLog:Security - sourcetype: WinEventLog - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log - source: WinEventLog:System - sourcetype: WinEventLog - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log + source: WinEventLog:Security + sourcetype: WinEventLog + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log + source: WinEventLog:System + sourcetype: WinEventLog + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/silentcleanup_uac_bypass.yml b/detections/endpoint/silentcleanup_uac_bypass.yml index 75b2138ce1..8de08730ce 100644 --- a/detections/endpoint/silentcleanup_uac_bypass.yml +++ b/detections/endpoint/silentcleanup_uac_bypass.yml @@ -5,74 +5,47 @@ date: '2026-01-14' author: Steven Dick, Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects suspicious modifications to the registry - that may indicate a UAC (User Account Control) bypass attempt via the SilentCleanup - task. It leverages data from Endpoint Detection and Response (EDR) agents, focusing - on registry changes in the path "*\\Environment\\windir" with executable values. - This activity is significant as it can allow an attacker to gain high-privilege - execution without user consent, bypassing UAC protections. If confirmed malicious, - this could lead to unauthorized administrative access, enabling further system compromise - and persistence. +description: The following analytic detects suspicious modifications to the registry that may indicate a UAC (User Account Control) bypass attempt via the SilentCleanup task. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on registry changes in the path "*\\Environment\\windir" with executable values. This activity is significant as it can allow an attacker to gain high-privilege execution without user consent, bypassing UAC protections. If confirmed malicious, this could lead to unauthorized administrative access, enabling further system compromise and persistence. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry - WHERE (Registry.registry_path= "*\\Environment\\windir" Registry.registry_value_data - = "*.exe*") by Registry.action Registry.dest Registry.process_guid Registry.process_id - Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data - Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user - Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `silentcleanup_uac_bypass_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\Environment\\windir" Registry.registry_value_data = "*.exe*") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `silentcleanup_uac_bypass_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://github.com/hfiref0x/UACME -- https://www.intezer.com/blog/malware-analysis/klingon-rat-holding-on-for-dear-life/ + - https://github.com/hfiref0x/UACME + - https://www.intezer.com/blog/malware-analysis/klingon-rat-holding-on-for-dear-life/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious modification of registry $registry_path$ with possible payload - path $registry_value_name$ on $dest$ - risk_objects: - - field: dest - type: system - score: 63 - threat_objects: [] + message: Suspicious modification of registry $registry_path$ with possible payload path $registry_value_name$ on $dest$ + risk_objects: + - field: dest + type: system + score: 63 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - - MoonPeak - asset_type: Endpoint - mitre_attack_id: - - T1548.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + - MoonPeak + asset_type: Endpoint + mitre_attack_id: + - T1548.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/uac_bypass/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/uac_bypass/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/single_letter_process_on_endpoint.yml b/detections/endpoint/single_letter_process_on_endpoint.yml index d342f2a02c..bca767d9c9 100644 --- a/detections/endpoint/single_letter_process_on_endpoint.yml +++ b/detections/endpoint/single_letter_process_on_endpoint.yml @@ -5,143 +5,121 @@ date: '2025-12-29' author: David Dorsey, Splunk status: production type: TTP -description: The following analytic detects processes with names consisting of a single - letter, which is often indicative of malware or an attacker attempting to evade - detection. This detection leverages data from Endpoint Detection and Response (EDR) - agents, focusing on process names and command-line executions. This activity is - significant because attackers use such techniques to obscure their presence and - carry out malicious activities like data theft or ransomware attacks. If confirmed - malicious, this behavior could lead to unauthorized access, data exfiltration, or - system compromise. Immediate investigation is required to determine the legitimacy - of the process. +description: The following analytic detects processes with names consisting of a single letter, which is often indicative of malware or an attacker attempting to evade detection. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because attackers use such techniques to obscure their presence and carry out malicious activities like data theft or ransomware attacks. If confirmed malicious, this behavior could lead to unauthorized access, data exfiltration, or system compromise. Immediate investigation is required to determine the legitimacy of the process. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime - from datamodel=Endpoint.Processes where - Processes.process_name IN ( - "_.exe", - "-.exe", - ",.exe", - ";.exe", - "!.exe", - "'.exe" - "(.exe", - "(.exe", - ").exe", - ").exe", - "@.exe", - "&.exe", - "#.exe", - "%.exe", - "`.exe", - "^.exe", - "+.exe", - "=.exe", - "~.exe", - "$.exe", - "0.exe", - "1.exe", - "2.exe", - "3.exe", - "4.exe", - "5.exe", - "6.exe", - "7.exe", - "8.exe", - "9.exe", - "a.exe", - "b.exe", - "c.exe", - "d.exe", - "e.exe", - "f.exe", - "g.exe", - "h.exe", - "i.exe", - "j.exe", - "k.exe", - "l.exe", - "m.exe", - "N.exe", - "o.exe", - "p.exe", - "q.exe", - "r.exe", - "s.exe", - "t.exe", - "u.exe", - "v.exe", - "w.exe", - "x.exe", - "y.exe", - "z.exe", - ) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process - Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id - Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user - Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(lastTime)` - | `security_content_ctime(firstTime)` - | `single_letter_process_on_endpoint_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Single-letter executables are not always malicious. Investigate - this activity with your normal incident-response process. + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + from datamodel=Endpoint.Processes where + Processes.process_name IN ( + "_.exe", + "-.exe", + ",.exe", + ";.exe", + "!.exe", + "'.exe" + "(.exe", + "(.exe", + ").exe", + ").exe", + "@.exe", + "&.exe", + "#.exe", + "%.exe", + "`.exe", + "^.exe", + "+.exe", + "=.exe", + "~.exe", + "$.exe", + "0.exe", + "1.exe", + "2.exe", + "3.exe", + "4.exe", + "5.exe", + "6.exe", + "7.exe", + "8.exe", + "9.exe", + "a.exe", + "b.exe", + "c.exe", + "d.exe", + "e.exe", + "f.exe", + "g.exe", + "h.exe", + "i.exe", + "j.exe", + "k.exe", + "l.exe", + "m.exe", + "N.exe", + "o.exe", + "p.exe", + "q.exe", + "r.exe", + "s.exe", + "t.exe", + "u.exe", + "v.exe", + "w.exe", + "x.exe", + "y.exe", + "z.exe", + ) + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process + Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id + Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` + | `single_letter_process_on_endpoint_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Single-letter executables are not always malicious. Investigate this activity with your normal incident-response process. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious process $process_name$ with single letter on host $dest$ - risk_objects: - - field: dest - type: system - score: 63 - - field: user - type: user - score: 63 - threat_objects: [] + message: A suspicious process $process_name$ with single letter on host $dest$ + risk_objects: + - field: dest + type: system + score: 63 + - field: user + type: user + score: 63 + threat_objects: [] tags: - analytic_story: - - DHS Report TA18-074A - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1204.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - DHS Report TA18-074A + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1204.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/single_letter_exe/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/single_letter_exe/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/slui_runas_elevated.yml b/detections/endpoint/slui_runas_elevated.yml index 005967b8e6..e89dfd92b2 100644 --- a/detections/endpoint/slui_runas_elevated.yml +++ b/detections/endpoint/slui_runas_elevated.yml @@ -1,90 +1,72 @@ name: SLUI RunAs Elevated id: 8d124810-b3e4-11eb-96c7-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of the Microsoft Software - Licensing User Interface Tool (`slui.exe`) with elevated privileges using the `-verb - runas` function. This activity is identified through logs from Endpoint Detection - and Response (EDR) agents, focusing on specific registry keys and command-line parameters. - This behavior is significant as it indicates a potential privilege escalation attempt, - which could allow an attacker to gain elevated access and execute malicious actions - with higher privileges. If confirmed malicious, this could lead to unauthorized - system changes, data exfiltration, or further compromise of the affected endpoint. +description: The following analytic detects the execution of the Microsoft Software Licensing User Interface Tool (`slui.exe`) with elevated privileges using the `-verb runas` function. This activity is identified through logs from Endpoint Detection and Response (EDR) agents, focusing on specific registry keys and command-line parameters. This behavior is significant as it indicates a potential privilege escalation attempt, which could allow an attacker to gain elevated access and execute malicious actions with higher privileges. If confirmed malicious, this could lead to unauthorized system changes, data exfiltration, or further compromise of the affected endpoint. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=slui.exe - (Processes.process=*-verb* Processes.process=*runas*) by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `slui_runas_elevated_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Limited false positives should be present as this is not commonly - used by legitimate applications. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=slui.exe (Processes.process=*-verb* Processes.process=*runas*) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `slui_runas_elevated_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Limited false positives should be present as this is not commonly used by legitimate applications. references: -- https://www.exploit-db.com/exploits/46998 -- https://mattharr0ey.medium.com/privilege-escalation-uac-bypass-in-changepk-c40b92818d1b -- https://gist.github.com/r00t-3xp10it/0c92cd554d3156fd74f6c25660ccc466 -- https://www.rapid7.com/db/modules/exploit/windows/local/bypassuac_sluihijack/ -- https://www.mandiant.com/resources/shining-a-light-on-darkside-ransomware-operations + - https://www.exploit-db.com/exploits/46998 + - https://mattharr0ey.medium.com/privilege-escalation-uac-bypass-in-changepk-c40b92818d1b + - https://gist.github.com/r00t-3xp10it/0c92cd554d3156fd74f6c25660ccc466 + - https://www.rapid7.com/db/modules/exploit/windows/local/bypassuac_sluihijack/ + - https://www.mandiant.com/resources/shining-a-light-on-darkside-ransomware-operations drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A slui process $process_name$ with elevated commandline $process$ on host - $dest$ - risk_objects: - - field: dest - type: system - score: 63 - - field: user - type: system - score: 63 - threat_objects: [] + message: A slui process $process_name$ with elevated commandline $process$ on host $dest$ + risk_objects: + - field: dest + type: system + score: 63 + - field: user + type: system + score: 63 + threat_objects: [] tags: - analytic_story: - - DarkSide Ransomware - - Compromised Windows Host - - Windows Defense Evasion Tactics - asset_type: Endpoint - mitre_attack_id: - - T1548.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - DarkSide Ransomware + - Compromised Windows Host + - Windows Defense Evasion Tactics + asset_type: Endpoint + mitre_attack_id: + - T1548.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/slui/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/slui/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/slui_spawning_a_process.yml b/detections/endpoint/slui_spawning_a_process.yml index 41b7a17a00..8c423e2ba5 100644 --- a/detections/endpoint/slui_spawning_a_process.yml +++ b/detections/endpoint/slui_spawning_a_process.yml @@ -1,87 +1,70 @@ name: SLUI Spawning a Process id: 879c4330-b3e0-11eb-b1b1-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the Microsoft Software Licensing User - Interface Tool (`slui.exe`) spawning a child process. This behavior is identified - using Endpoint Detection and Response (EDR) telemetry, focusing on process creation - events where `slui.exe` is the parent process. This activity is significant because - `slui.exe` should not typically spawn child processes, and doing so may indicate - a UAC bypass attempt, leading to elevated privileges. If confirmed malicious, an - attacker could leverage this to execute code with elevated privileges, potentially - compromising the system's security and gaining unauthorized access. +description: The following analytic detects the Microsoft Software Licensing User Interface Tool (`slui.exe`) spawning a child process. This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on process creation events where `slui.exe` is the parent process. This activity is significant because `slui.exe` should not typically spawn child processes, and doing so may indicate a UAC bypass attempt, leading to elevated privileges. If confirmed malicious, an attacker could leverage this to execute code with elevated privileges, potentially compromising the system's security and gaining unauthorized access. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=slui.exe - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `slui_spawning_a_process_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Certain applications may spawn from `slui.exe` that are legitimate. - Filtering will be needed to ensure proper monitoring. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name=slui.exe + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `slui_spawning_a_process_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Certain applications may spawn from `slui.exe` that are legitimate. Filtering will be needed to ensure proper monitoring. references: -- https://www.exploit-db.com/exploits/46998 -- https://www.rapid7.com/db/modules/exploit/windows/local/bypassuac_sluihijack/ -- https://www.mandiant.com/resources/shining-a-light-on-darkside-ransomware-operations + - https://www.exploit-db.com/exploits/46998 + - https://www.rapid7.com/db/modules/exploit/windows/local/bypassuac_sluihijack/ + - https://www.mandiant.com/resources/shining-a-light-on-darkside-ransomware-operations drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A slui process $parent_process_name$ spawning child process $process_name$ - on host $dest$ - risk_objects: - - field: dest - type: system - score: 63 - - field: user - type: user - score: 63 - threat_objects: [] + message: A slui process $parent_process_name$ spawning child process $process_name$ on host $dest$ + risk_objects: + - field: dest + type: system + score: 63 + - field: user + type: user + score: 63 + threat_objects: [] tags: - analytic_story: - - DarkSide Ransomware - - Compromised Windows Host - - Windows Defense Evasion Tactics - asset_type: Endpoint - mitre_attack_id: - - T1548.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - DarkSide Ransomware + - Compromised Windows Host + - Windows Defense Evasion Tactics + asset_type: Endpoint + mitre_attack_id: + - T1548.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/slui/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/slui/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/spike_in_file_writes.yml b/detections/endpoint/spike_in_file_writes.yml index caaa8b1731..4d82498770 100644 --- a/detections/endpoint/spike_in_file_writes.yml +++ b/detections/endpoint/spike_in_file_writes.yml @@ -1,51 +1,43 @@ name: Spike in File Writes id: fdb0f805-74e4-4539-8c00-618927333aae -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: David Dorsey, Splunk status: experimental type: Anomaly -description: The following analytic detects a sharp increase in the number of files - written to a specific host. It leverages the Endpoint.Filesystem data model, focusing - on 'created' actions and comparing current file write counts against historical - averages and standard deviations. This activity is significant as a sudden spike - in file writes can indicate malicious activities such as ransomware encryption or - data exfiltration. If confirmed malicious, this behavior could lead to significant - data loss, system compromise, or further propagation of malware within the network. +description: The following analytic detects a sharp increase in the number of files written to a specific host. It leverages the Endpoint.Filesystem data model, focusing on 'created' actions and comparing current file write counts against historical averages and standard deviations. This activity is significant as a sudden spike in file writes can indicate malicious activities such as ransomware encryption or data exfiltration. If confirmed malicious, this behavior could lead to significant data loss, system compromise, or further propagation of malware within the network. data_source: -- Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem - where Filesystem.action=created by _time span=1h, Filesystem.dest | `drop_dm_object_name(Filesystem)` - | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time - >= relative_time(maxtime, "-1d@d"), count, null))) as "count" avg(eval(if(_time upperBound) AND num_data_samples >=20, 1, 0) | search isOutlier=1 | `spike_in_file_writes_filter`' -how_to_implement: In order to implement this search, you must populate the Endpoint - file-system data model node. This is typically populated via endpoint detection - and response product, such as Carbon Black or endpoint data sources such as Sysmon. - The data used for this search is typically generated via logs that report reads - and writes to the file system. -known_false_positives: It is important to understand that if you happen to install - any new applications on your hosts or are copying a large number of files, you can - expect to see a large increase of file modifications. + - Sysmon EventID 11 +search: |- + | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.action=created + BY _time span=1h, Filesystem.dest + | `drop_dm_object_name(Filesystem)` + | eventstats max(_time) as maxtime + | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-1d@d"), count, null))) as "count" avg(eval(if(_time upperBound) AND num_data_samples >=20, 1, 0) + | search isOutlier=1 + | `spike_in_file_writes_filter` +how_to_implement: In order to implement this search, you must populate the Endpoint file-system data model node. This is typically populated via endpoint detection and response product, such as Carbon Black or endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the file system. +known_false_positives: It is important to understand that if you happen to install any new applications on your hosts or are copying a large number of files, you can expect to see a large increase of file modifications. references: [] rba: - message: Spike in File Writes observed on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: Spike in File Writes observed on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - SamSam Ransomware - - Ryuk Ransomware - - Ransomware - - Rhysida Ransomware - asset_type: Endpoint - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SamSam Ransomware + - Ryuk Ransomware + - Ransomware + - Rhysida Ransomware + asset_type: Endpoint + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/spoolsv_spawning_rundll32.yml b/detections/endpoint/spoolsv_spawning_rundll32.yml index 0f52aee5dc..b3233cb8b2 100644 --- a/detections/endpoint/spoolsv_spawning_rundll32.yml +++ b/detections/endpoint/spoolsv_spawning_rundll32.yml @@ -1,89 +1,71 @@ name: Spoolsv Spawning Rundll32 id: 15d905f6-da6b-11eb-ab82-acde48001122 -version: 10 -date: '2025-05-02' +version: 11 +date: '2026-02-25' author: Mauricio Velazco, Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the spawning of `rundll32.exe` without - command-line arguments by `spoolsv.exe`, which is unusual and potentially indicative - of exploitation attempts like CVE-2021-34527 (PrintNightmare). This detection leverages - Endpoint Detection and Response (EDR) telemetry, focusing on process creation events - where `spoolsv.exe` is the parent process. This activity is significant as `spoolsv.exe` - typically does not spawn other processes, and such behavior could indicate an active - exploitation attempt. If confirmed malicious, this could allow an attacker to execute - arbitrary code, escalate privileges, or maintain persistence on the compromised - endpoint. +description: The following analytic detects the spawning of `rundll32.exe` without command-line arguments by `spoolsv.exe`, which is unusual and potentially indicative of exploitation attempts like CVE-2021-34527 (PrintNightmare). This detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on process creation events where `spoolsv.exe` is the parent process. This activity is significant as `spoolsv.exe` typically does not spawn other processes, and such behavior could indicate an active exploitation attempt. If confirmed malicious, this could allow an attacker to execute arbitrary code, escalate privileges, or maintain persistence on the compromised endpoint. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe - `process_rundll32` by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spoolsv_spawning_rundll32_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Limited false positives have been identified. There are limited - instances where `rundll32.exe` may be spawned by a legitimate print driver. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name=spoolsv.exe `process_rundll32` + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `spoolsv_spawning_rundll32_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Limited false positives have been identified. There are limited instances where `rundll32.exe` may be spawned by a legitimate print driver. references: -- https://www.truesec.com/hub/blog/fix-for-printnightmare-cve-2021-1675-exploit-to-keep-your-print-servers-running-while-a-patch-is-not-available -- https://www.truesec.com/hub/blog/exploitable-critical-rce-vulnerability-allows-regular-users-to-fully-compromise-active-directory-printnightmare-cve-2021-1675 -- https://www.reddit.com/r/msp/comments/ob6y02/critical_vulnerability_printnightmare_exposes + - https://www.truesec.com/hub/blog/fix-for-printnightmare-cve-2021-1675-exploit-to-keep-your-print-servers-running-while-a-patch-is-not-available + - https://www.truesec.com/hub/blog/exploitable-critical-rce-vulnerability-allows-regular-users-to-fully-compromise-active-directory-printnightmare-cve-2021-1675 + - https://www.reddit.com/r/msp/comments/ob6y02/critical_vulnerability_printnightmare_exposes drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $parent_process_name$ has spawned $process_name$ on endpoint $dest$. This - behavior is suspicious and related to PrintNightmare. - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: - - field: process_name - type: process_name + message: $parent_process_name$ has spawned $process_name$ on endpoint $dest$. This behavior is suspicious and related to PrintNightmare. + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - PrintNightmare CVE-2021-34527 - - Compromised Windows Host - - Black Basta Ransomware - asset_type: Endpoint - cve: - - CVE-2021-34527 - mitre_attack_id: - - T1547.012 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - PrintNightmare CVE-2021-34527 + - Compromised Windows Host + - Black Basta Ransomware + asset_type: Endpoint + cve: + - CVE-2021-34527 + mitre_attack_id: + - T1547.012 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/spoolsv_suspicious_loaded_modules.yml b/detections/endpoint/spoolsv_suspicious_loaded_modules.yml index c1527830ac..9de488e475 100644 --- a/detections/endpoint/spoolsv_suspicious_loaded_modules.yml +++ b/detections/endpoint/spoolsv_suspicious_loaded_modules.yml @@ -5,71 +5,47 @@ date: '2026-01-14' author: Mauricio Velazco, Michael Haag, Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the suspicious loading of DLLs by spoolsv.exe, - potentially indicating PrintNightmare exploitation. It leverages Sysmon EventCode - 7 to identify instances where spoolsv.exe loads multiple DLLs from the Windows System32 - spool drivers x64 directory. This activity is significant as it may signify an attacker - exploiting the PrintNightmare vulnerability to execute arbitrary code. If confirmed - malicious, this could lead to unauthorized code execution, privilege escalation, - and persistent access within the environment, posing a severe security risk. +description: The following analytic detects the suspicious loading of DLLs by spoolsv.exe, potentially indicating PrintNightmare exploitation. It leverages Sysmon EventCode 7 to identify instances where spoolsv.exe loads multiple DLLs from the Windows System32 spool drivers x64 directory. This activity is significant as it may signify an attacker exploiting the PrintNightmare vulnerability to execute arbitrary code. If confirmed malicious, this could lead to unauthorized code execution, privilege escalation, and persistent access within the environment, posing a severe security risk. data_source: -- Sysmon EventID 7 -search: '`sysmon` EventCode=7 Image ="*\\spoolsv.exe" ImageLoaded="*\\Windows\\System32\\spool\\drivers\\x64\\*" - ImageLoaded = "*.dll" | stats dc(ImageLoaded) as countImgloaded values(ImageLoaded) - as ImageLoaded values(loaded_file) as loaded_file values(loaded_file_path) as loaded_file_path - values(original_file_name) as original_file_name values(process_exec) as process_exec - values(process_guid) as process_guid values(process_hash) as process_hash values(process_name) - as process_name values(service_dll_signature_exists) as service_dll_signature_exists - values(service_dll_signature_verified) as service_dll_signature_verified values(signature) - as signature values(signature_id) as signature_id values(user_id) as user_id values(vendor_product) - as vendor_product values(Image) as Image count min(_time) as firstTime max(_time) as lastTime by process_path - dest process_id | where countImgloaded >= 3 | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `spoolsv_suspicious_loaded_modules_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name and imageloaded executions from your endpoints. If you - are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + - Sysmon EventID 7 +search: '`sysmon` EventCode=7 Image ="*\\spoolsv.exe" ImageLoaded="*\\Windows\\System32\\spool\\drivers\\x64\\*" ImageLoaded = "*.dll" | stats dc(ImageLoaded) as countImgloaded values(ImageLoaded) as ImageLoaded values(loaded_file) as loaded_file values(loaded_file_path) as loaded_file_path values(original_file_name) as original_file_name values(process_exec) as process_exec values(process_guid) as process_guid values(process_hash) as process_hash values(process_name) as process_name values(service_dll_signature_exists) as service_dll_signature_exists values(service_dll_signature_verified) as service_dll_signature_verified values(signature) as signature values(signature_id) as signature_id values(user_id) as user_id values(vendor_product) as vendor_product values(Image) as Image count min(_time) as firstTime max(_time) as lastTime by process_path dest process_id | where countImgloaded >= 3 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spoolsv_suspicious_loaded_modules_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: No false positives have been identified at this time. references: -- https://raw.githubusercontent.com/hieuttmmo/sigma/dceb13fe3f1821b119ae495b41e24438bd97e3d0/rules/windows/image_load/sysmon_cve_2021_1675_print_nightmare.yml + - https://raw.githubusercontent.com/hieuttmmo/sigma/dceb13fe3f1821b119ae495b41e24438bd97e3d0/rules/windows/image_load/sysmon_cve_2021_1675_print_nightmare.yml drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $Image$ with process id $process_id$ has loaded a driver from $ImageLoaded$ - on endpoint $dest$. This behavior is suspicious and related to PrintNightmare. - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: [] + message: $Image$ with process id $process_id$ has loaded a driver from $ImageLoaded$ on endpoint $dest$. This behavior is suspicious and related to PrintNightmare. + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: [] tags: - analytic_story: - - PrintNightmare CVE-2021-34527 - - Black Basta Ransomware - asset_type: Endpoint - cve: - - CVE-2021-34527 - mitre_attack_id: - - T1547.012 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - PrintNightmare CVE-2021-34527 + - Black Basta Ransomware + asset_type: Endpoint + cve: + - CVE-2021-34527 + mitre_attack_id: + - T1547.012 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/spoolsv_suspicious_process_access.yml b/detections/endpoint/spoolsv_suspicious_process_access.yml index ba02024fba..0f807d37c9 100644 --- a/detections/endpoint/spoolsv_suspicious_process_access.yml +++ b/detections/endpoint/spoolsv_suspicious_process_access.yml @@ -5,78 +5,54 @@ date: '2026-01-14' author: Mauricio Velazco, Michael Haag, Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects suspicious process access by spoolsv.exe, - potentially indicating exploitation of the PrintNightmare vulnerability (CVE-2021-34527). - It leverages Sysmon EventCode 10 to identify when spoolsv.exe accesses critical - system files or processes like rundll32.exe with elevated privileges. This activity - is significant as it may signal an attempt to gain unauthorized privilege escalation - on a vulnerable machine. If confirmed malicious, an attacker could achieve elevated - privileges, leading to further system compromise, persistent access, or unauthorized - control over the affected environment. +description: The following analytic detects suspicious process access by spoolsv.exe, potentially indicating exploitation of the PrintNightmare vulnerability (CVE-2021-34527). It leverages Sysmon EventCode 10 to identify when spoolsv.exe accesses critical system files or processes like rundll32.exe with elevated privileges. This activity is significant as it may signal an attempt to gain unauthorized privilege escalation on a vulnerable machine. If confirmed malicious, an attacker could achieve elevated privileges, leading to further system compromise, persistent access, or unauthorized control over the affected environment. data_source: -- Sysmon EventID 10 -search: '`sysmon` EventCode=10 SourceImage = "*\\spoolsv.exe" CallTrace = "*\\Windows\\system32\\spool\\DRIVERS\\x64\\*" - TargetImage IN ("*\\rundll32.exe", "*\\spoolsv.exe") GrantedAccess = 0x1fffff | - stats count min(_time) as firstTime max(_time) as lastTime by CallTrace EventID - GrantedAccess Guid Opcode ProcessID SecurityID SourceImage SourceProcessGUID SourceProcessId - TargetImage TargetProcessGUID TargetProcessId UserID dest granted_access parent_process_exec - parent_process_guid parent_process_id parent_process_name parent_process_path process_exec - process_guid process_id process_name process_path signature signature_id user_id - vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `spoolsv_suspicious_process_access_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with process access event where SourceImage, TargetImage, GrantedAccess and - CallTrace executions from your endpoints. If you are using Sysmon, you must have - at least version 6.0.4 of the Sysmon TA. Tune and filter known instances of spoolsv.exe. + - Sysmon EventID 10 +search: '`sysmon` EventCode=10 SourceImage = "*\\spoolsv.exe" CallTrace = "*\\Windows\\system32\\spool\\DRIVERS\\x64\\*" TargetImage IN ("*\\rundll32.exe", "*\\spoolsv.exe") GrantedAccess = 0x1fffff | stats count min(_time) as firstTime max(_time) as lastTime by CallTrace EventID GrantedAccess Guid Opcode ProcessID SecurityID SourceImage SourceProcessGUID SourceProcessId TargetImage TargetProcessGUID TargetProcessId UserID dest granted_access parent_process_exec parent_process_guid parent_process_id parent_process_name parent_process_path process_exec process_guid process_id process_name process_path signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spoolsv_suspicious_process_access_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with process access event where SourceImage, TargetImage, GrantedAccess and CallTrace executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances of spoolsv.exe. known_false_positives: No false positives have been identified at this time. references: -- https://github.com/cube0x0/impacket/commit/73b9466c17761384ece11e1028ec6689abad6818 -- https://www.truesec.com/hub/blog/fix-for-printnightmare-cve-2021-1675-exploit-to-keep-your-print-servers-running-while-a-patch-is-not-available -- https://www.truesec.com/hub/blog/exploitable-critical-rce-vulnerability-allows-regular-users-to-fully-compromise-active-directory-printnightmare-cve-2021-1675 -- https://www.reddit.com/r/msp/comments/ob6y02/critical_vulnerability_printnightmare_exposes + - https://github.com/cube0x0/impacket/commit/73b9466c17761384ece11e1028ec6689abad6818 + - https://www.truesec.com/hub/blog/fix-for-printnightmare-cve-2021-1675-exploit-to-keep-your-print-servers-running-while-a-patch-is-not-available + - https://www.truesec.com/hub/blog/exploitable-critical-rce-vulnerability-allows-regular-users-to-fully-compromise-active-directory-printnightmare-cve-2021-1675 + - https://www.reddit.com/r/msp/comments/ob6y02/critical_vulnerability_printnightmare_exposes drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $SourceImage$ was GrantedAccess open access to $TargetImage$ on endpoint - $dest$. This behavior is suspicious and related to PrintNightmare. - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: - - field: ProcessID - type: process - - field: TargetImage - type: process_name + message: $SourceImage$ was GrantedAccess open access to $TargetImage$ on endpoint $dest$. This behavior is suspicious and related to PrintNightmare. + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: + - field: ProcessID + type: process + - field: TargetImage + type: process_name tags: - analytic_story: - - PrintNightmare CVE-2021-34527 - - Black Basta Ransomware - asset_type: Endpoint - cve: - - CVE-2021-34527 - mitre_attack_id: - - T1068 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - PrintNightmare CVE-2021-34527 + - Black Basta Ransomware + asset_type: Endpoint + cve: + - CVE-2021-34527 + mitre_attack_id: + - T1068 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/spoolsv_writing_a_dll.yml b/detections/endpoint/spoolsv_writing_a_dll.yml index aa39829f72..b3ae3e89fd 100644 --- a/detections/endpoint/spoolsv_writing_a_dll.yml +++ b/detections/endpoint/spoolsv_writing_a_dll.yml @@ -5,82 +5,53 @@ date: '2026-01-14' author: Mauricio Velazco, Michael Haag, Splunk status: production type: TTP -description: The following analytic detects `spoolsv.exe` writing a `.dll` file, which - is unusual behavior and may indicate exploitation of vulnerabilities like CVE-2021-34527 - (PrintNightmare). This detection leverages the Endpoint datamodel, specifically - monitoring process and filesystem events to identify `.dll` file creation within - the `\spool\drivers\x64\` path. This activity is significant as it may signify an - attacker attempting to execute malicious code via the Print Spooler service. If - confirmed malicious, this could lead to unauthorized code execution and potential - system compromise. Immediate endpoint isolation and further investigation are recommended. +description: The following analytic detects `spoolsv.exe` writing a `.dll` file, which is unusual behavior and may indicate exploitation of vulnerabilities like CVE-2021-34527 (PrintNightmare). This detection leverages the Endpoint datamodel, specifically monitoring process and filesystem events to identify `.dll` file creation within the `\spool\drivers\x64\` path. This activity is significant as it may signify an attacker attempting to execute malicious code via the Print Spooler service. If confirmed malicious, this could lead to unauthorized code execution and potential system compromise. Immediate endpoint isolation and further investigation are recommended. data_source: -- Sysmon EventID 1 AND Sysmon EventID 11 -- Windows Event Log Security 4688 AND Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes - where Processes.process_name=spoolsv.exe by _time Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | join process_guid, _time [| - tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path="*\\spool\\drivers\\x64\\*" - Filesystem.file_name="*.dll" by _time Filesystem.dest Filesystem.process_guid Filesystem.file_create_time - Filesystem.file_name Filesystem.file_path | `drop_dm_object_name(Filesystem)` | - fields _time dest file_create_time file_name file_path process_name process_path - process_guid process] | dedup file_create_time | table dest file_create_time, file_name, - file_path, process_name process_guid | `spoolsv_writing_a_dll_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem` - node. + - Sysmon EventID 1 AND Sysmon EventID 11 + - Windows Event Log Security 4688 AND Sysmon EventID 11 +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=spoolsv.exe by _time Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | join process_guid, _time [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path="*\\spool\\drivers\\x64\\*" Filesystem.file_name="*.dll" by _time Filesystem.dest Filesystem.process_guid Filesystem.file_create_time Filesystem.file_name Filesystem.file_path | `drop_dm_object_name(Filesystem)` | fields _time dest file_create_time file_name file_path process_name process_path process_guid process] | dedup file_create_time | table dest file_create_time, file_name, file_path, process_name process_guid | `spoolsv_writing_a_dll_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem` node. known_false_positives: No false positives have been identified at this time. references: -- https://www.truesec.com/hub/blog/fix-for-printnightmare-cve-2021-1675-exploit-to-keep-your-print-servers-running-while-a-patch-is-not-available -- https://www.truesec.com/hub/blog/exploitable-critical-rce-vulnerability-allows-regular-users-to-fully-compromise-active-directory-printnightmare-cve-2021-1675 -- https://www.reddit.com/r/msp/comments/ob6y02/critical_vulnerability_printnightmare_exposes + - https://www.truesec.com/hub/blog/fix-for-printnightmare-cve-2021-1675-exploit-to-keep-your-print-servers-running-while-a-patch-is-not-available + - https://www.truesec.com/hub/blog/exploitable-critical-rce-vulnerability-allows-regular-users-to-fully-compromise-active-directory-printnightmare-cve-2021-1675 + - https://www.reddit.com/r/msp/comments/ob6y02/critical_vulnerability_printnightmare_exposes drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $process_name$ has been identified writing dll's to $file_path$ on endpoint - $dest$. This behavior is suspicious and related to PrintNightmare. - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: - - field: process_name - type: process_name + message: $process_name$ has been identified writing dll's to $file_path$ on endpoint $dest$. This behavior is suspicious and related to PrintNightmare. + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - PrintNightmare CVE-2021-34527 - - Compromised Windows Host - - Black Basta Ransomware - asset_type: Endpoint - cve: - - CVE-2021-34527 - mitre_attack_id: - - T1547.012 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - PrintNightmare CVE-2021-34527 + - Compromised Windows Host + - Black Basta Ransomware + asset_type: Endpoint + cve: + - CVE-2021-34527 + mitre_attack_id: + - T1547.012 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/spoolsv_writing_a_dll___sysmon.yml b/detections/endpoint/spoolsv_writing_a_dll___sysmon.yml index 3d5cf9b194..193ccfe776 100644 --- a/detections/endpoint/spoolsv_writing_a_dll___sysmon.yml +++ b/detections/endpoint/spoolsv_writing_a_dll___sysmon.yml @@ -5,71 +5,52 @@ date: '2025-05-02' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects `spoolsv.exe` writing a `.dll` file, which - is unusual behavior and may indicate exploitation of vulnerabilities like CVE-2021-34527 - (PrintNightmare). This detection leverages Sysmon EventID 11 to monitor file creation - events in the `\spool\drivers\x64\` directory. This activity is significant because - `spoolsv.exe` typically does not write DLL files, and such behavior could signify - an ongoing attack. If confirmed malicious, this could allow an attacker to execute - arbitrary code, escalate privileges, or maintain persistence on the compromised - system. +description: The following analytic detects `spoolsv.exe` writing a `.dll` file, which is unusual behavior and may indicate exploitation of vulnerabilities like CVE-2021-34527 (PrintNightmare). This detection leverages Sysmon EventID 11 to monitor file creation events in the `\spool\drivers\x64\` directory. This activity is significant because `spoolsv.exe` typically does not write DLL files, and such behavior could signify an ongoing attack. If confirmed malicious, this could allow an attacker to execute arbitrary code, escalate privileges, or maintain persistence on the compromised system. data_source: -- Sysmon EventID 11 -search: '`sysmon` EventID=11 process_name=spoolsv.exe file_path="*\\spool\\drivers\\x64\\*" - file_name=*.dll | stats count min(_time) as firstTime max(_time) as lastTime by - action dest file_name file_path process_guid process_id user_id vendor_product - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spoolsv_writing_a_dll___sysmon_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used. + - Sysmon EventID 11 +search: '`sysmon` EventID=11 process_name=spoolsv.exe file_path="*\\spool\\drivers\\x64\\*" file_name=*.dll | stats count min(_time) as firstTime max(_time) as lastTime by action dest file_name file_path process_guid process_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spoolsv_writing_a_dll___sysmon_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used. known_false_positives: Limited false positives. Filter as needed. references: -- https://github.com/cube0x0/impacket/commit/73b9466c17761384ece11e1028ec6689abad6818 -- https://www.truesec.com/hub/blog/fix-for-printnightmare-cve-2021-1675-exploit-to-keep-your-print-servers-running-while-a-patch-is-not-available -- https://www.truesec.com/hub/blog/exploitable-critical-rce-vulnerability-allows-regular-users-to-fully-compromise-active-directory-printnightmare-cve-2021-1675 -- https://www.reddit.com/r/msp/comments/ob6y02/critical_vulnerability_printnightmare_exposes + - https://github.com/cube0x0/impacket/commit/73b9466c17761384ece11e1028ec6689abad6818 + - https://www.truesec.com/hub/blog/fix-for-printnightmare-cve-2021-1675-exploit-to-keep-your-print-servers-running-while-a-patch-is-not-available + - https://www.truesec.com/hub/blog/exploitable-critical-rce-vulnerability-allows-regular-users-to-fully-compromise-active-directory-printnightmare-cve-2021-1675 + - https://www.reddit.com/r/msp/comments/ob6y02/critical_vulnerability_printnightmare_exposes drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process has been identified writing dll's to $file_path$ on endpoint - $dest$. This behavior is suspicious and related to PrintNightmare. - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: - - field: file_name - type: file_name + message: A process has been identified writing dll's to $file_path$ on endpoint $dest$. This behavior is suspicious and related to PrintNightmare. + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - PrintNightmare CVE-2021-34527 - - Black Basta Ransomware - asset_type: Endpoint - cve: - - CVE-2021-34527 - mitre_attack_id: - - T1547.012 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - PrintNightmare CVE-2021-34527 + - Black Basta Ransomware + asset_type: Endpoint + cve: + - CVE-2021-34527 + mitre_attack_id: + - T1547.012 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/sqlite_module_in_temp_folder.yml b/detections/endpoint/sqlite_module_in_temp_folder.yml index 67fc3ee2c5..7ecd53d3ce 100644 --- a/detections/endpoint/sqlite_module_in_temp_folder.yml +++ b/detections/endpoint/sqlite_module_in_temp_folder.yml @@ -5,65 +5,45 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the creation of sqlite3.dll files in - the %temp% folder. It leverages Sysmon EventCode 11 to identify when these - files are written to the temporary directory. This activity is significant - because it is associated with IcedID malware, which uses the sqlite3 module to - parse browser databases and steal sensitive information such as banking - details, credit card information, and credentials. If confirmed malicious, - this behavior could lead to significant data theft and compromise of user - accounts. +description: The following analytic detects the creation of sqlite3.dll files in the %temp% folder. It leverages Sysmon EventCode 11 to identify when these files are written to the temporary directory. This activity is significant because it is associated with IcedID malware, which uses the sqlite3 module to parse browser databases and steal sensitive information such as banking details, credit card information, and credentials. If confirmed malicious, this behavior could lead to significant data theft and compromise of user accounts. data_source: -- Sysmon EventID 11 -search: '`sysmon` EventCode=11 (TargetFilename = "*\\sqlite32.dll" OR TargetFilename - = "*\\sqlite64.dll") (TargetFilename = "*\\temp\\*") | stats count min(_time) as - firstTime max(_time) as lastTime by action dest file_name file_path process_guid - process_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `sqlite_module_in_temp_folder_filter`' -how_to_implement: To successfully implement this search, you need to be - ingesting logs with the process name, parent process, and command-line - executions from your endpoints. If you are using Sysmon, you must have at - least version 6.0.4 of the Sysmon TA. + - Sysmon EventID 11 +search: '`sysmon` EventCode=11 (TargetFilename = "*\\sqlite32.dll" OR TargetFilename = "*\\sqlite64.dll") (TargetFilename = "*\\temp\\*") | stats count min(_time) as firstTime max(_time) as lastTime by action dest file_name file_path process_guid process_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `sqlite_module_in_temp_folder_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: No false positives have been identified at this time. references: -- https://www.cisecurity.org/insights/white-papers/security-primer-icedid + - https://www.cisecurity.org/insights/white-papers/security-primer-icedid drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Process creates a file $file_name$ in host $dest$ - risk_objects: - - field: dest - type: system - score: 9 - threat_objects: [] + message: Process creates a file $file_name$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 9 + threat_objects: [] tags: - analytic_story: - - IcedID - - Lokibot - asset_type: Endpoint - mitre_attack_id: - - T1005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - IcedID + - Lokibot + asset_type: Endpoint + mitre_attack_id: + - T1005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/simulated_icedid/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/simulated_icedid/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/steal_or_forge_authentication_certificates_behavior_identified.yml b/detections/endpoint/steal_or_forge_authentication_certificates_behavior_identified.yml index 9fe57583aa..fe886b74e6 100644 --- a/detections/endpoint/steal_or_forge_authentication_certificates_behavior_identified.yml +++ b/detections/endpoint/steal_or_forge_authentication_certificates_behavior_identified.yml @@ -1,71 +1,53 @@ name: Steal or Forge Authentication Certificates Behavior Identified id: 87ac670e-bbfd-44ca-b566-44e9f835518d -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Correlation data_source: [] -description: The following analytic identifies potential threats related to the theft - or forgery of authentication certificates. It detects when five or more analytics - from the Windows Certificate Services story trigger within a specified timeframe. - This detection leverages aggregated risk scores and event counts from the Risk data - model. This activity is significant as it may indicate an ongoing attack aimed at - compromising authentication mechanisms. If confirmed malicious, attackers could - gain unauthorized access to sensitive systems and data, potentially leading to severe - security breaches. -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) - as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as - annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) - as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) - as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) - as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, - dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories="Windows - Certificate Services" All_Risk.risk_object_type="system" by All_Risk.risk_object - All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where - source_count >= 5 | `steal_or_forge_authentication_certificates_behavior_identified_filter`' -how_to_implement: The Windows Certificate Services analytic story must have 5 or more - analytics enabled. In addition, ensure data is being logged that is required. Modify - the correlation as needed based on volume of noise related to the other analytics. -known_false_positives: False positives may be present based on automated tooling or - system administrators. Filter as needed. +description: The following analytic identifies potential threats related to the theft or forgery of authentication certificates. It detects when five or more analytics from the Windows Certificate Services story trigger within a specified timeframe. This detection leverages aggregated risk scores and event counts from the Risk data model. This activity is significant as it may indicate an ongoing attack aimed at compromising authentication mechanisms. If confirmed malicious, attackers could gain unauthorized access to sensitive systems and data, potentially leading to severe security breaches. +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count FROM datamodel=Risk.All_Risk + WHERE All_Risk.analyticstories="Windows Certificate Services" All_Risk.risk_object_type="system" + BY All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic + | `drop_dm_object_name(All_Risk)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | where source_count >= 5 + | `steal_or_forge_authentication_certificates_behavior_identified_filter` +how_to_implement: The Windows Certificate Services analytic story must have 5 or more analytics enabled. In addition, ensure data is being logged that is required. Modify the correlation as needed based on volume of noise related to the other analytics. +known_false_positives: False positives may be present based on automated tooling or system administrators. Filter as needed. references: -- https://research.splunk.com/stories/windows_certificate_services/ -- https://attack.mitre.org/techniques/T1649/ + - https://research.splunk.com/stories/windows_certificate_services/ + - https://attack.mitre.org/techniques/T1649/ drilldown_searches: -- name: View the detection results for - "$risk_object$" - search: '%original_detection_search% | search risk_object = "$risk_object$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$risk_object$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$risk_object$" + search: '%original_detection_search% | search risk_object = "$risk_object$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$risk_object$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ tags: - analytic_story: - - Windows Certificate Services - asset_type: Endpoint - atomic_guid: - - 290df60e-4b5d-4a5e-b0c7-dc5348ea0c86 - - 78b274f8-acb0-428b-b1f7-7b0d0e73330a - - 7617f689-bbd8-44bc-adcd-6f8968897848 - mitre_attack_id: - - T1649 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Certificate Services + asset_type: Endpoint + atomic_guid: + - 290df60e-4b5d-4a5e-b0c7-dc5348ea0c86 + - 78b274f8-acb0-428b-b1f7-7b0d0e73330a + - 7617f689-bbd8-44bc-adcd-6f8968897848 + mitre_attack_id: + - T1649 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/risk_certificate_services.log - source: certs - sourcetype: stash + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/risk_certificate_services.log + source: certs + sourcetype: stash diff --git a/detections/endpoint/sunburst_correlation_dll_and_network_event.yml b/detections/endpoint/sunburst_correlation_dll_and_network_event.yml index 8492c01032..347146028f 100644 --- a/detections/endpoint/sunburst_correlation_dll_and_network_event.yml +++ b/detections/endpoint/sunburst_correlation_dll_and_network_event.yml @@ -1,49 +1,47 @@ name: Sunburst Correlation DLL and Network Event id: 701a8740-e8db-40df-9190-5516d3819787 -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Patrick Bareiss, Splunk status: experimental type: TTP -description: The following analytic identifies the loading of the malicious SolarWinds.Orion.Core.BusinessLayer.dll - by SolarWinds.BusinessLayerHost.exe and subsequent DNS queries to avsvmcloud.com. - It uses Sysmon EventID 7 for DLL loading and Event ID 22 for DNS queries, correlating - these events within a 12-14 day period. This activity is significant as it indicates - potential Sunburst malware infection, a known supply chain attack. If confirmed - malicious, this could lead to unauthorized network access, data exfiltration, and - further compromise of the affected systems. +description: The following analytic identifies the loading of the malicious SolarWinds.Orion.Core.BusinessLayer.dll by SolarWinds.BusinessLayerHost.exe and subsequent DNS queries to avsvmcloud.com. It uses Sysmon EventID 7 for DLL loading and Event ID 22 for DNS queries, correlating these events within a 12-14 day period. This activity is significant as it indicates potential Sunburst malware infection, a known supply chain attack. If confirmed malicious, this could lead to unauthorized network access, data exfiltration, and further compromise of the affected systems. data_source: -- Sysmon EventID 7 -- Sysmon EventID 22 -search: (`sysmon` EventCode=7 ImageLoaded=*SolarWinds.Orion.Core.BusinessLayer.dll) - OR (`sysmon` EventCode=22 QueryName=*avsvmcloud.com) | eventstats dc(EventCode) - AS dc_events | where dc_events=2 | stats count min(_time) as firstTime max(_time) - as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name - process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists - service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)`| - `security_content_ctime(lastTime)` | `sunburst_correlation_dll_and_network_event_filter` -how_to_implement: This detection relies on sysmon logs with the Event ID 7, Driver - loaded. Please tune your sysmon config that you DriverLoad event for SolarWinds.Orion.Core.BusinessLayer.dll - is captured by Sysmon. Additionally, you need sysmon logs for Event ID 22, DNS Query. - We suggest to run this detection at least once a day over the last 14 days. + - Sysmon EventID 7 + - Sysmon EventID 22 +search: |- + | (`sysmon` EventCode=7 ImageLoaded=*SolarWinds.Orion.Core.BusinessLayer.dll) OR (`sysmon` EventCode=22 QueryName=*avsvmcloud.com) + | eventstats dc(EventCode) AS dc_events + | where dc_events=2 + | stats count min(_time) as firstTime max(_time) as lastTime + BY Image ImageLoaded dest + loaded_file loaded_file_path original_file_name + process_exec process_guid process_hash + process_id process_name process_path + service_dll_signature_exists service_dll_signature_verified signature + signature_id user_id vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `sunburst_correlation_dll_and_network_event_filter` +how_to_implement: This detection relies on sysmon logs with the Event ID 7, Driver loaded. Please tune your sysmon config that you DriverLoad event for SolarWinds.Orion.Core.BusinessLayer.dll is captured by Sysmon. Additionally, you need sysmon logs for Event ID 22, DNS Query. We suggest to run this detection at least once a day over the last 14 days. known_false_positives: No false positives have been identified at this time. references: -- https://www.mandiant.com/resources/evasive-attacker-leverages-solarwinds-supply-chain-compromises-with-sunburst-backdoor + - https://www.mandiant.com/resources/evasive-attacker-leverages-solarwinds-supply-chain-compromises-with-sunburst-backdoor rba: - message: Possible Sunburst activity on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: Possible Sunburst activity on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - NOBELIUM Group - asset_type: Windows - mitre_attack_id: - - T1203 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - NOBELIUM Group + asset_type: Windows + mitre_attack_id: + - T1203 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/suspicious_computer_account_name_change.yml b/detections/endpoint/suspicious_computer_account_name_change.yml index 52214478c8..1a7dce2e81 100644 --- a/detections/endpoint/suspicious_computer_account_name_change.yml +++ b/detections/endpoint/suspicious_computer_account_name_change.yml @@ -1,77 +1,63 @@ name: Suspicious Computer Account Name Change id: 35a61ed8-61c4-11ec-bc1e-acde48001122 -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects a suspicious computer account name change - in Active Directory. It leverages Event ID 4781, which logs account name changes, - to identify instances where a computer account name is changed to one that does - not end with a `$`. This behavior is significant as it may indicate an attempt to - exploit CVE-2021-42278 and CVE-2021-42287, which can lead to domain controller impersonation - and privilege escalation. If confirmed malicious, this activity could allow an attacker - to gain elevated privileges and potentially control the domain. +description: The following analytic detects a suspicious computer account name change in Active Directory. It leverages Event ID 4781, which logs account name changes, to identify instances where a computer account name is changed to one that does not end with a `$`. This behavior is significant as it may indicate an attempt to exploit CVE-2021-42278 and CVE-2021-42287, which can lead to domain controller impersonation and privilege escalation. If confirmed malicious, this activity could allow an attacker to gain elevated privileges and potentially control the domain. data_source: -- Windows Event Log Security 4781 -search: '`wineventlog_security` EventCode=4781 OldTargetUserName="*$" NewTargetUserName!="*$" - | table _time, Computer, Caller_User_Name, OldTargetUserName, NewTargetUserName - | rename Computer as dest | `suspicious_computer_account_name_change_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Windows event logs from your hosts. In addition, the Splunk Windows TA is needed. -known_false_positives: Renaming a computer account name to a name that not end with - '$' is highly unsual and may not have any legitimate scenarios. + - Windows Event Log Security 4781 +search: |- + `wineventlog_security` EventCode=4781 OldTargetUserName="*$" NewTargetUserName!="*$" + | table _time, Computer, Caller_User_Name, OldTargetUserName, NewTargetUserName + | rename Computer as dest + | `suspicious_computer_account_name_change_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Windows event logs from your hosts. In addition, the Splunk Windows TA is needed. +known_false_positives: Renaming a computer account name to a name that not end with '$' is highly unsual and may not have any legitimate scenarios. references: -- https://exploit.ph/cve-2021-42287-cve-2021-42278-weaponisation.html -- https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-42278 -- https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-42287 + - https://exploit.ph/cve-2021-42287-cve-2021-42278-weaponisation.html + - https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-42278 + - https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-42287 drilldown_searches: -- name: View the detection results for - "$dest$" and "$OldTargetUserName$" - search: '%original_detection_search% | search dest = "$dest$" OldTargetUserName - = "$OldTargetUserName$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$OldTargetUserName$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$OldTargetUserName$") starthoursago=168 | stats count min(_time) as firstTime - max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$OldTargetUserName$" + search: '%original_detection_search% | search dest = "$dest$" OldTargetUserName = "$OldTargetUserName$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$OldTargetUserName$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$OldTargetUserName$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A computer account $OldTargetUserName$ was renamed with a suspicious computer - name on $dest$ - risk_objects: - - field: dest - type: system - score: 70 - - field: OldTargetUserName - type: user - score: 70 - threat_objects: [] + message: A computer account $OldTargetUserName$ was renamed with a suspicious computer name on $dest$ + risk_objects: + - field: dest + type: system + score: 70 + - field: OldTargetUserName + type: user + score: 70 + threat_objects: [] tags: - analytic_story: - - Active Directory Privilege Escalation - - Compromised Windows Host - - sAMAccountName Spoofing and Domain Controller Impersonation - - Scattered Lapsus$ Hunters - asset_type: Endpoint - cve: - - CVE-2021-42287 - - CVE-2021-42278 - mitre_attack_id: - - T1078.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Privilege Escalation + - Compromised Windows Host + - sAMAccountName Spoofing and Domain Controller Impersonation + - Scattered Lapsus$ Hunters + asset_type: Endpoint + cve: + - CVE-2021-42287 + - CVE-2021-42278 + mitre_attack_id: + - T1078.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/suspicious_computer_account_name_change/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/suspicious_computer_account_name_change/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_copy_on_system32.yml b/detections/endpoint/suspicious_copy_on_system32.yml index b7fee3e8be..992feb183c 100644 --- a/detections/endpoint/suspicious_copy_on_system32.yml +++ b/detections/endpoint/suspicious_copy_on_system32.yml @@ -1,117 +1,111 @@ name: Suspicious Copy on System32 id: ce633e56-25b2-11ec-9e76-acde48001122 -version: 12 -date: '2025-12-18' +version: 13 +date: '2026-02-25' author: Teoderick Contreras, Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - The following analytic detects potentially suspicious file copy operations targeting the - System32 or SysWow64 directories as source, often indicative of malicious activity. - It leverages data from Endpoint Detection and Response (EDR) agents, - focusing on activity initiated by command-line tools like cmd.exe or PowerShell. - This behavior is significant as it may indicate an attempt to evade defenses by copying - an existing binary from the system directory and renaming it. - If confirmed malicious, this activity could allow an attacker to execute - code undetected and potentially leading to system compromise or further lateral movement - within the network. + The following analytic detects potentially suspicious file copy operations targeting the + System32 or SysWow64 directories as source, often indicative of malicious activity. + It leverages data from Endpoint Detection and Response (EDR) agents, + focusing on activity initiated by command-line tools like cmd.exe or PowerShell. + This behavior is significant as it may indicate an attempt to evade defenses by copying + an existing binary from the system directory and renaming it. + If confirmed malicious, this activity could allow an attacker to execute + code undetected and potentially leading to system compromise or further lateral movement + within the network. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - Processes.parent_process_name IN ( - "cmd.exe", - "powershell.exe", - "pwsh.exe", - "sqlps.exe", - "sqltoolsps.exe" - ) - ( - Processes.process_name IN ("copy.exe", "xcopy.exe") - OR - Processes.original_file_name IN ("copy.exe", "xcopy.exe") - ) - Processes.process IN ( - "* \"C:\\Windows\\System32\\*", - "* \'C:\\Windows\\System32\\*", - "* C:\\Windows\\System32\\*", - "* \"C:\\Windows\\SysWow64\\*", - "* \'C:\\Windows\\SysWow64\\*", - "* C:\\Windows\\SysWow64\\*" - ) - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `suspicious_copy_on_system32_filter` + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where + Processes.parent_process_name IN ( + "cmd.exe", + "powershell.exe", + "pwsh.exe", + "sqlps.exe", + "sqltoolsps.exe" + ) + ( + Processes.process_name IN ("copy.exe", "xcopy.exe") + OR + Processes.original_file_name IN ("copy.exe", "xcopy.exe") + ) + Processes.process IN ( + "* \"C:\\Windows\\System32\\*", + "* \'C:\\Windows\\System32\\*", + "* C:\\Windows\\System32\\*", + "* \"C:\\Windows\\SysWow64\\*", + "* \'C:\\Windows\\SysWow64\\*", + "* C:\\Windows\\SysWow64\\*" + ) + by Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `suspicious_copy_on_system32_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: Copying files from System directories can happen for multiple admin reasons, allbeit rare without approval. Apply additional filters where needed. references: - - https://www.hybrid-analysis.com/sample/8da5b75b6380a41eee3a399c43dfe0d99eeefaa1fd21027a07b1ecaa4cd96fdd?environmentId=120 - - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ + - https://www.hybrid-analysis.com/sample/8da5b75b6380a41eee3a399c43dfe0d99eeefaa1fd21027a07b1ecaa4cd96fdd?environmentId=120 + - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ drilldown_searches: - - name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Execution of copy exe to copy file from $process$ on $dest$ - risk_objects: - - field: dest - type: system - score: 63 - - field: user - type: user - score: 63 - threat_objects: [] + message: Execution of copy exe to copy file from $process$ on $dest$ + risk_objects: + - field: dest + type: system + score: 63 + - field: user + type: user + score: 63 + threat_objects: [] tags: - analytic_story: - - Qakbot - - Sandworm Tools - - IcedID - - Volt Typhoon - - AsyncRAT - - Unusual Processes - - Compromised Windows Host - - Water Gamayun - asset_type: Endpoint - mitre_attack_id: - - T1036.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Qakbot + - Sandworm Tools + - IcedID + - Volt Typhoon + - AsyncRAT + - Unusual Processes + - Compromised Windows Host + - Water Gamayun + asset_type: Endpoint + mitre_attack_id: + - T1036.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/copy_sysmon/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/copy_sysmon/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_curl_network_connection.yml b/detections/endpoint/suspicious_curl_network_connection.yml index 6dd274ac18..7f112a950a 100644 --- a/detections/endpoint/suspicious_curl_network_connection.yml +++ b/detections/endpoint/suspicious_curl_network_connection.yml @@ -5,71 +5,56 @@ date: '2026-02-25' author: Michael Haag, Splunk status: experimental type: TTP -description: The following analytic detects the use of the curl command contacting - suspicious remote domains, such as s3.amazonaws.com, which is indicative of Command - and Control (C2) activity or downloading further implants. This detection leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process execution - logs and command-line arguments. This activity is significant as it may indicate - the presence of MacOS adware or other malicious software attempting to establish - persistence or exfiltrate data. If confirmed malicious, this could allow attackers - to maintain control over the compromised system and deploy additional payloads. +description: The following analytic detects the use of the curl command contacting suspicious remote domains, such as s3.amazonaws.com, which is indicative of Command and Control (C2) activity or downloading further implants. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant as it may indicate the presence of MacOS adware or other malicious software attempting to establish persistence or exfiltrate data. If confirmed malicious, this could allow attackers to maintain control over the compromised system and deploy additional payloads. data_source: -- Sysmon EventID 1 -- Sysmon for Linux EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Sysmon for Linux EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("curl", "curl.exe") - Processes.process IN ("*s3.amazonaws.com*") - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id - Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id - Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `suspicious_curl_network_connection_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("curl", "curl.exe") + Processes.process IN ("*s3.amazonaws.com*") + by Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec + Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id + Processes.process_integrity_level Processes.process_name + Processes.process_path Processes.user Processes.user_id + Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `suspicious_curl_network_connection_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://redcanary.com/blog/clipping-silver-sparrows-wings/ + - https://redcanary.com/blog/clipping-silver-sparrows-wings/ rba: - message: Suspicious usage of curl on $dest$ - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: Suspicious usage of curl on $dest$ + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Silver Sparrow - - Ingress Tool Transfer - - Linux Living Off The Land - - APT37 Rustonotto and FadeStealer - - GhostRedirector IIS Module and Rungan Backdoor - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1105 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Silver Sparrow + - Ingress Tool Transfer + - Linux Living Off The Land + - APT37 Rustonotto and FadeStealer + - GhostRedirector IIS Module and Rungan Backdoor + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1105 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/suspicious_dllhost_no_command_line_arguments.yml b/detections/endpoint/suspicious_dllhost_no_command_line_arguments.yml index 8082caf920..b24cf4a415 100644 --- a/detections/endpoint/suspicious_dllhost_no_command_line_arguments.yml +++ b/detections/endpoint/suspicious_dllhost_no_command_line_arguments.yml @@ -5,87 +5,65 @@ date: '2025-12-15' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects instances of DLLHost.exe executing without - command line arguments. This behavior is unusual and often associated with malicious - activities, such as those performed by Cobalt Strike. The detection leverages data - from Endpoint Detection and Response (EDR) agents, focusing on process execution - logs. This activity is significant because DLLHost.exe typically requires arguments - to function correctly, and its absence may indicate an attempt to evade detection. - If confirmed malicious, this could lead to unauthorized actions like credential - dumping or file manipulation, posing a severe threat to the environment. +description: The following analytic detects instances of DLLHost.exe executing without command line arguments. This behavior is unusual and often associated with malicious activities, such as those performed by Cobalt Strike. The detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This activity is significant because DLLHost.exe typically requires arguments to function correctly, and its absence may indicate an attempt to evade detection. If confirmed malicious, this could lead to unauthorized actions like credential dumping or file manipulation, posing a severe threat to the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where - (Processes.process_name=dllhost.exe OR Processes.original_file_name=dllhost.exe) - Processes.process IN ("*dllhost","*dllhost.exe", "*dllhost.exe\"") - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `suspicious_dllhost_no_command_line_arguments_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Limited false positives may be present in small environments. - Tuning may be required based on parent process. + | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where + (Processes.process_name=dllhost.exe OR Processes.original_file_name=dllhost.exe) + Processes.process IN ("*dllhost","*dllhost.exe", "*dllhost.exe\"") + by Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `suspicious_dllhost_no_command_line_arguments_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Limited false positives may be present in small environments. Tuning may be required based on parent process. references: -- https://raw.githubusercontent.com/threatexpress/malleable-c2/c3385e481159a759f79b8acfe11acf240893b830/jquery-c2.4.2.profile -- https://www.cobaltstrike.com/blog/learn-pipe-fitting-for-all-of-your-offense-projects/ + - https://raw.githubusercontent.com/threatexpress/malleable-c2/c3385e481159a759f79b8acfe11acf240893b830/jquery-c2.4.2.profile + - https://www.cobaltstrike.com/blog/learn-pipe-fitting-for-all-of-your-offense-projects/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious dllhost.exe process with no command line arguments executed - on $dest$ by $user$ - risk_objects: - - field: dest - type: system - score: 49 - - field: user - type: user - score: 49 - threat_objects: [] + message: Suspicious dllhost.exe process with no command line arguments executed on $dest$ by $user$ + risk_objects: + - field: dest + type: system + score: 49 + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - BlackByte Ransomware - - Cobalt Strike - - Graceful Wipe Out Attack - - Cactus Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1055 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - BlackByte Ransomware + - Cobalt Strike + - Graceful Wipe Out Attack + - Cactus Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1055 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_gpupdate_no_command_line_arguments.yml b/detections/endpoint/suspicious_gpupdate_no_command_line_arguments.yml index e366b9337a..5576be3247 100644 --- a/detections/endpoint/suspicious_gpupdate_no_command_line_arguments.yml +++ b/detections/endpoint/suspicious_gpupdate_no_command_line_arguments.yml @@ -5,87 +5,65 @@ date: '2025-12-15' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of gpupdate.exe without - any command line arguments. This behavior is identified using data from Endpoint - Detection and Response (EDR) agents, focusing on process execution logs. It is significant - because gpupdate.exe typically runs with specific arguments, and its execution without - them is often associated with malicious activities, such as those performed by Cobalt - Strike. If confirmed malicious, this activity could indicate an attempt to execute - unauthorized commands or scripts, potentially leading to further system compromise - or lateral movement within the network. +description: The following analytic detects the execution of gpupdate.exe without any command line arguments. This behavior is identified using data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. It is significant because gpupdate.exe typically runs with specific arguments, and its execution without them is often associated with malicious activities, such as those performed by Cobalt Strike. If confirmed malicious, this activity could indicate an attempt to execute unauthorized commands or scripts, potentially leading to further system compromise or lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where - (Processes.process_name=gpupdate.exe OR Processes.original_file_name=GPUpdate.exe) - Processes.process IN ("*gpupdate","*gpupdate.exe", "*gpupdate.exe\"") - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `suspicious_gpupdate_no_command_line_arguments_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Limited false positives may be present in small environments. - Tuning may be required based on parent process. + | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where + (Processes.process_name=gpupdate.exe OR Processes.original_file_name=GPUpdate.exe) + Processes.process IN ("*gpupdate","*gpupdate.exe", "*gpupdate.exe\"") + by Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `suspicious_gpupdate_no_command_line_arguments_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Limited false positives may be present in small environments. Tuning may be required based on parent process. references: -- https://raw.githubusercontent.com/xx0hcd/Malleable-C2-Profiles/0ef8cf4556e26f6d4190c56ba697c2159faa5822/crimeware/trick_ryuk.profile -- https://www.cobaltstrike.com/blog/learn-pipe-fitting-for-all-of-your-offense-projects/ + - https://raw.githubusercontent.com/xx0hcd/Malleable-C2-Profiles/0ef8cf4556e26f6d4190c56ba697c2159faa5822/crimeware/trick_ryuk.profile + - https://www.cobaltstrike.com/blog/learn-pipe-fitting-for-all-of-your-offense-projects/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious gpupdate.exe process with no command line arguments executed - on $dest$ by $user$ - risk_objects: - - field: dest - type: system - score: 49 - - field: user - type: user - score: 49 - threat_objects: [] + message: Suspicious gpupdate.exe process with no command line arguments executed on $dest$ by $user$ + risk_objects: + - field: dest + type: system + score: 49 + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - BlackByte Ransomware - - Cobalt Strike - - Graceful Wipe Out Attack - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1055 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - BlackByte Ransomware + - Cobalt Strike + - Graceful Wipe Out Attack + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1055 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_icedid_rundll32_cmdline.yml b/detections/endpoint/suspicious_icedid_rundll32_cmdline.yml index 05c6f1336b..5e93f2ca50 100644 --- a/detections/endpoint/suspicious_icedid_rundll32_cmdline.yml +++ b/detections/endpoint/suspicious_icedid_rundll32_cmdline.yml @@ -1,81 +1,66 @@ name: Suspicious IcedID Rundll32 Cmdline id: bed761f8-ee29-11eb-8bf3-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects a suspicious `rundll32.exe` command line - used to execute a DLL file, a technique associated with IcedID malware. It leverages - data from Endpoint Detection and Response (EDR) agents, focusing on command-line - executions containing the pattern `*/i:*`. This activity is significant as it indicates - potential malware attempting to load an encrypted DLL payload, often named `license.dat`. - If confirmed malicious, this could allow attackers to execute arbitrary code, leading - to further system compromise and potential data exfiltration. +description: The following analytic detects a suspicious `rundll32.exe` command line used to execute a DLL file, a technique associated with IcedID malware. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions containing the pattern `*/i:*`. This activity is significant as it indicates potential malware attempting to load an encrypted DLL payload, often named `license.dat`. If confirmed malicious, this could allow attackers to execute arbitrary code, leading to further system compromise and potential data exfiltration. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*/i:* - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `suspicious_icedid_rundll32_cmdline_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: limitted. this parameter is not commonly used by windows application - but can be used by the network operator. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_rundll32` Processes.process=*/i:* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `suspicious_icedid_rundll32_cmdline_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: limitted. this parameter is not commonly used by windows application but can be used by the network operator. references: -- https://threatpost.com/icedid-banking-trojan-surges-emotet/165314/ + - https://threatpost.com/icedid-banking-trojan-surges-emotet/165314/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: rundll32 process $process_name$ with commandline $process$ in host $dest$ - risk_objects: - - field: dest - type: system - score: 56 - threat_objects: - - field: process_name - type: process_name + message: rundll32 process $process_name$ with commandline $process$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 56 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - IcedID - - Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1218.011 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - IcedID + - Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1218.011 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_image_creation_in_appdata_folder.yml b/detections/endpoint/suspicious_image_creation_in_appdata_folder.yml index 50ac4818e9..fbe4ece6d3 100644 --- a/detections/endpoint/suspicious_image_creation_in_appdata_folder.yml +++ b/detections/endpoint/suspicious_image_creation_in_appdata_folder.yml @@ -5,77 +5,48 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the creation of image files in the AppData - folder by processes that also have a file reference in the same folder. It leverages - data from the Endpoint.Processes and Endpoint.Filesystem datamodels to identify - this behavior. This activity is significant because it is commonly associated with - malware, such as the Remcos RAT, which captures screenshots and stores them in the - AppData folder before exfiltrating them to a command-and-control server. If confirmed - malicious, this activity could indicate unauthorized data capture and exfiltration, - compromising sensitive information and user privacy. +description: The following analytic detects the creation of image files in the AppData folder by processes that also have a file reference in the same folder. It leverages data from the Endpoint.Processes and Endpoint.Filesystem datamodels to identify this behavior. This activity is significant because it is commonly associated with malware, such as the Remcos RAT, which captures screenshots and stores them in the AppData folder before exfiltrating them to a command-and-control server. If confirmed malicious, this activity could indicate unauthorized data capture and exfiltration, compromising sensitive information and user privacy. data_source: -- Sysmon EventID 1 AND Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes - where Processes.process_name=*.exe Processes.process_path="*\\appdata\\Roaming\\*" - by _time span=1h Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` |rename process_guid as - proc_guid |join proc_guid, _time [| tstats `security_content_summariesonly` count - min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem - where Filesystem.file_name IN ("*.png","*.jpg","*.bmp","*.gif","*.tiff") Filesystem.file_path= - "*\\appdata\\Roaming\\*" by _time span=1h Filesystem.dest Filesystem.file_create_time - Filesystem.file_name Filesystem.file_path Filesystem.process_guid | `drop_dm_object_name(Filesystem)` - |rename process_guid as proc_guid | fields _time dest file_create_time file_name - file_path process_name process_path process proc_guid] | `suspicious_image_creation_in_appdata_folder_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. + - Sysmon EventID 1 AND Sysmon EventID 11 +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=*.exe Processes.process_path="*\\appdata\\Roaming\\*" by _time span=1h Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` |rename process_guid as proc_guid |join proc_guid, _time [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.png","*.jpg","*.bmp","*.gif","*.tiff") Filesystem.file_path= "*\\appdata\\Roaming\\*" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.process_guid | `drop_dm_object_name(Filesystem)` |rename process_guid as proc_guid | fields _time dest file_create_time file_name file_path process_name process_path process proc_guid] | `suspicious_image_creation_in_appdata_folder_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: No false positives have been identified at this time. references: -- https://success.trendmicro.com/dcx/s/solution/1123281-remcos-malware-information?language=en_US -- https://blog.malwarebytes.com/threat-intelligence/2021/07/remcos-rat-delivered-via-visual-basic/ + - https://success.trendmicro.com/dcx/s/solution/1123281-remcos-malware-information?language=en_US + - https://blog.malwarebytes.com/threat-intelligence/2021/07/remcos-rat-delivered-via-visual-basic/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Process $process_name$ creating image file $file_path$ on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: - - field: process_name - type: process_name + message: Process $process_name$ creating image file $file_path$ on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Remcos - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1113 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Remcos + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1113 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos_agent/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos_agent/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_kerberos_service_ticket_request.yml b/detections/endpoint/suspicious_kerberos_service_ticket_request.yml index f2c7b17d1c..f9cdad3cf5 100644 --- a/detections/endpoint/suspicious_kerberos_service_ticket_request.yml +++ b/detections/endpoint/suspicious_kerberos_service_ticket_request.yml @@ -1,76 +1,63 @@ name: Suspicious Kerberos Service Ticket Request id: 8b1297bc-6204-11ec-b7c4-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects suspicious Kerberos Service Ticket (TGS) - requests where the requesting account name matches the service name, potentially - indicating an exploitation attempt of CVE-2021-42278 and CVE-2021-42287. This detection - leverages Event ID 4769 from Domain Controller and Kerberos events. Such activity - is significant as it may represent an adversary attempting to escalate privileges - by impersonating a domain controller. If confirmed malicious, this could allow an - attacker to take control of the domain controller, leading to complete domain compromise - and unauthorized access to sensitive information. +description: The following analytic detects suspicious Kerberos Service Ticket (TGS) requests where the requesting account name matches the service name, potentially indicating an exploitation attempt of CVE-2021-42278 and CVE-2021-42287. This detection leverages Event ID 4769 from Domain Controller and Kerberos events. Such activity is significant as it may represent an adversary attempting to escalate privileges by impersonating a domain controller. If confirmed malicious, this could allow an attacker to take control of the domain controller, leading to complete domain compromise and unauthorized access to sensitive information. data_source: -- Windows Event Log Security 4769 -search: '`wineventlog_security` EventCode=4769 | eval isSuspicious = if(lower(ServiceName) - = lower(mvindex(split(TargetUserName,"@"),0)),1,0) | where isSuspicious = 1 | rename - Computer as dest| rename TargetUserName as user | table _time, dest, src_ip, user, - ServiceName, Error_Code, isSuspicious | `suspicious_kerberos_service_ticket_request_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Domain Controller and Kerberos events. The Advanced Security Audit policy setting - `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. -known_false_positives: We have tested this detection logic with ~2 million 4769 events - and did not identify false positives. However, they may be possible in certain environments. - Filter as needed. + - Windows Event Log Security 4769 +search: |- + `wineventlog_security` EventCode=4769 + | eval isSuspicious = if(lower(ServiceName) = lower(mvindex(split(TargetUserName,"@"),0)),1,0) + | where isSuspicious = 1 + | rename Computer as dest + | rename TargetUserName as user + | table _time, dest, src_ip, user, ServiceName, Error_Code, isSuspicious + | `suspicious_kerberos_service_ticket_request_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. +known_false_positives: We have tested this detection logic with ~2 million 4769 events and did not identify false positives. However, they may be possible in certain environments. Filter as needed. references: -- https://exploit.ph/cve-2021-42287-cve-2021-42278-weaponisation.html -- https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-42278 -- https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-42287 -- https://docs.microsoft.com/en-us/openspecs/windows_protocols/ms-sfu/02636893-7a1f-4357-af9a-b672e3e3de13 + - https://exploit.ph/cve-2021-42287-cve-2021-42278-weaponisation.html + - https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-42278 + - https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-42287 + - https://docs.microsoft.com/en-us/openspecs/windows_protocols/ms-sfu/02636893-7a1f-4357-af9a-b672e3e3de13 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious Kerberos Service Ticket was requested by $user$ on host $dest$ - risk_objects: - - field: dest - type: system - score: 60 - threat_objects: [] + message: A suspicious Kerberos Service Ticket was requested by $user$ on host $dest$ + risk_objects: + - field: dest + type: system + score: 60 + threat_objects: [] tags: - analytic_story: - - sAMAccountName Spoofing and Domain Controller Impersonation - - Active Directory Kerberos Attacks - - Active Directory Privilege Escalation - asset_type: Endpoint - cve: - - CVE-2021-42287 - - CVE-2021-42278 - mitre_attack_id: - - T1078.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - sAMAccountName Spoofing and Domain Controller Impersonation + - Active Directory Kerberos Attacks + - Active Directory Privilege Escalation + asset_type: Endpoint + cve: + - CVE-2021-42287 + - CVE-2021-42278 + mitre_attack_id: + - T1078.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/suspicious_kerberos_service_ticket_request/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/suspicious_kerberos_service_ticket_request/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_linux_discovery_commands.yml b/detections/endpoint/suspicious_linux_discovery_commands.yml index 80bc90d253..41f2c9f47e 100644 --- a/detections/endpoint/suspicious_linux_discovery_commands.yml +++ b/detections/endpoint/suspicious_linux_discovery_commands.yml @@ -1,89 +1,63 @@ name: Suspicious Linux Discovery Commands id: 0edd5112-56c9-11ec-b990-acde48001122 -version: 7 -date: '2026-01-20' +version: 8 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP -description: The following analytic detects the execution of suspicious bash commands - commonly used in scripts like AutoSUID, LinEnum, and LinPeas for system discovery - on a Linux host. It leverages Endpoint Detection and Response (EDR) data, specifically - looking for a high number of distinct commands executed within a short time frame. - This activity is significant as it often precedes privilege escalation or other - malicious actions. If confirmed malicious, an attacker could gain detailed system - information, identify vulnerabilities, and potentially escalate privileges, posing - a severe threat to the environment. +description: The following analytic detects the execution of suspicious bash commands commonly used in scripts like AutoSUID, LinEnum, and LinPeas for system discovery on a Linux host. It leverages Endpoint Detection and Response (EDR) data, specifically looking for a high number of distinct commands executed within a short time frame. This activity is significant as it often precedes privilege escalation or other malicious actions. If confirmed malicious, an attacker could gain detailed system information, identify vulnerabilities, and potentially escalate privileges, posing a severe threat to the environment. data_source: -- Sysmon for Linux EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime values(Processes.action) as action values(Processes.original_file_name) - as original_file_name values(Processes.parent_process) as parent_process values(Processes.parent_process_exec) - as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid - values(Processes.parent_process_id) as parent_process_id values(Processes.parent_process_name) - as parent_process_name values(Processes.parent_process_path) as parent_process_path - values(Processes.process) as process values(Processes.process_exec) as process_exec - values(Processes.process_guid) as process_guid values(Processes.process_hash) as - process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) - as process_integrity_level values(Processes.process_path) as process_path values(Processes.user_id) - as user_id values(Processes.vendor_product) as vendor_product dc(Processes.process) - as distinct_commands dc(Processes.process_name) as distinct_process_names from datamodel=Endpoint.Processes - where [|inputlookup linux_tool_discovery_process | rename process as Processes.process - |table Processes.process] by _time span=5m Processes.user Processes.dest | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where - distinct_commands > 40 AND distinct_process_names > 3 | `suspicious_linux_discovery_commands_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Unless an administrator is using these commands to troubleshoot - or audit a system, the execution of these commands should be monitored. + - Sysmon for Linux EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.action) as action values(Processes.original_file_name) as original_file_name values(Processes.parent_process) as parent_process values(Processes.parent_process_exec) as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid values(Processes.parent_process_id) as parent_process_id values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_path) as parent_process_path values(Processes.process) as process values(Processes.process_exec) as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) as process_integrity_level values(Processes.process_path) as process_path values(Processes.user_id) as user_id values(Processes.vendor_product) as vendor_product dc(Processes.process) as distinct_commands dc(Processes.process_name) as distinct_process_names FROM datamodel=Endpoint.Processes + WHERE [ + | inputlookup linux_tool_discovery_process + | rename process as Processes.process + | table Processes.process] by _time span=5m Processes.user Processes.dest + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | where distinct_commands > 40 AND distinct_process_names > 3 + | `suspicious_linux_discovery_commands_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Unless an administrator is using these commands to troubleshoot or audit a system, the execution of these commands should be monitored. references: -- https://attack.mitre.org/matrices/enterprise/linux/ -- https://attack.mitre.org/techniques/T1059/004/ -- https://github.com/IvanGlinkin/AutoSUID -- https://github.com/carlospolop/PEASS-ng/tree/master/linPEAS -- https://github.com/rebootuser/LinEnum + - https://attack.mitre.org/matrices/enterprise/linux/ + - https://attack.mitre.org/techniques/T1059/004/ + - https://github.com/IvanGlinkin/AutoSUID + - https://github.com/carlospolop/PEASS-ng/tree/master/linPEAS + - https://github.com/rebootuser/LinEnum drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious Linux Discovery Commands detected on $dest$ - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: [] + message: Suspicious Linux Discovery Commands detected on $dest$ + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: [] tags: - analytic_story: - - Linux Post-Exploitation - - VoidLink Cloud-Native Linux Malware - asset_type: Endpoint - mitre_attack_id: - - T1059.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Linux Post-Exploitation + - VoidLink Cloud-Native Linux Malware + asset_type: Endpoint + mitre_attack_id: + - T1059.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.004/linux_discovery_tools/sysmon_linux.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.004/linux_discovery_tools/sysmon_linux.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/suspicious_microsoft_workflow_compiler_rename.yml b/detections/endpoint/suspicious_microsoft_workflow_compiler_rename.yml index 7d4411e178..f2096c85e6 100644 --- a/detections/endpoint/suspicious_microsoft_workflow_compiler_rename.yml +++ b/detections/endpoint/suspicious_microsoft_workflow_compiler_rename.yml @@ -1,65 +1,56 @@ name: Suspicious microsoft workflow compiler rename id: f0db4464-55d9-11eb-ae93-0242ac130002 -version: 11 -date: '2025-05-02' +version: 12 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic detects the renaming of microsoft.workflow.compiler.exe, - a rarely used executable typically located in C:\Windows\Microsoft.NET\Framework64\v4.0.30319. - This detection leverages Endpoint Detection and Response (EDR) data, focusing on - process names and original file names. This activity is significant because renaming - this executable can indicate an attempt to evade security controls. If confirmed - malicious, an attacker could use this renamed executable to execute arbitrary code, - potentially leading to privilege escalation or persistent access within the environment. +description: The following analytic detects the renaming of microsoft.workflow.compiler.exe, a rarely used executable typically located in C:\Windows\Microsoft.NET\Framework64\v4.0.30319. This detection leverages Endpoint Detection and Response (EDR) data, focusing on process names and original file names. This activity is significant because renaming this executable can indicate an attempt to evade security controls. If confirmed malicious, an attacker could use this renamed executable to execute arbitrary code, potentially leading to privilege escalation or persistent access within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name!=microsoft.workflow.compiler.exe - AND Processes.original_file_name=Microsoft.Workflow.Compiler.exe by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `suspicious_microsoft_workflow_compiler_rename_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although unlikely, some legitimate applications may use a moved - copy of microsoft.workflow.compiler.exe, triggering a false positive. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name!=microsoft.workflow.compiler.exe + AND + Processes.original_file_name=Microsoft.Workflow.Compiler.exe + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `suspicious_microsoft_workflow_compiler_rename_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although unlikely, some legitimate applications may use a moved copy of microsoft.workflow.compiler.exe, triggering a false positive. references: -- https://lolbas-project.github.io/lolbas/Binaries/Microsoft.Workflow.Compiler/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution + - https://lolbas-project.github.io/lolbas/Binaries/Microsoft.Workflow.Compiler/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution tags: - analytic_story: - - Masquerading - Rename System Utilities - - Living Off The Land - - Cobalt Strike - - Trusted Developer Utilities Proxy Execution - - BlackByte Ransomware - - Graceful Wipe Out Attack - asset_type: Endpoint - mitre_attack_id: - - T1036.003 - - T1127 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Masquerading - Rename System Utilities + - Living Off The Land + - Cobalt Strike + - Trusted Developer Utilities Proxy Execution + - BlackByte Ransomware + - Graceful Wipe Out Attack + asset_type: Endpoint + mitre_attack_id: + - T1036.003 + - T1127 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_microsoft_workflow_compiler_usage.yml b/detections/endpoint/suspicious_microsoft_workflow_compiler_usage.yml index a2d3fa240e..b2f95f61ad 100644 --- a/detections/endpoint/suspicious_microsoft_workflow_compiler_usage.yml +++ b/detections/endpoint/suspicious_microsoft_workflow_compiler_usage.yml @@ -1,84 +1,72 @@ name: Suspicious microsoft workflow compiler usage id: 9bbc62e8-55d8-11eb-ae93-0242ac130002 -version: 8 -date: '2025-12-15' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies the usage of microsoft.workflow.compiler.exe, - a rarely utilized executable typically found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319. - This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process execution telemetry. The significance of this activity lies - in its uncommon usage, which may indicate malicious intent such as code execution - or persistence mechanisms. If confirmed malicious, an attacker could leverage this - process to execute arbitrary code, potentially leading to unauthorized access or - further compromise of the system. +description: The following analytic identifies the usage of microsoft.workflow.compiler.exe, a rarely utilized executable typically found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution telemetry. The significance of this activity lies in its uncommon usage, which may indicate malicious intent such as code execution or persistence mechanisms. If confirmed malicious, an attacker could leverage this process to execute arbitrary code, potentially leading to unauthorized access or further compromise of the system. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=microsoft.workflow.compiler.exe OR Processes.original_file_name=Microsoft.Workflow.Compiler.exe) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `suspicious_microsoft_workflow_compiler_usage_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although unlikely, limited instances have been identified coming - from native Microsoft utilities similar to SCCM. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name=microsoft.workflow.compiler.exe + OR + Processes.original_file_name=Microsoft.Workflow.Compiler.exe + ) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `suspicious_microsoft_workflow_compiler_usage_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although unlikely, limited instances have been identified coming from native Microsoft utilities similar to SCCM. references: -- https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution + - https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious microsoft.workflow.compiler.exe process ran on $dest$ by $user$ - risk_objects: - - field: dest - type: system - score: 35 - - field: user - type: user - score: 35 - threat_objects: [] + message: Suspicious microsoft.workflow.compiler.exe process ran on $dest$ by $user$ + risk_objects: + - field: dest + type: system + score: 35 + - field: user + type: user + score: 35 + threat_objects: [] tags: - analytic_story: - - Trusted Developer Utilities Proxy Execution - - Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1127 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Trusted Developer Utilities Proxy Execution + - Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1127 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_msbuild_path.yml b/detections/endpoint/suspicious_msbuild_path.yml index f44b5341cd..33c9f1ca2c 100644 --- a/detections/endpoint/suspicious_msbuild_path.yml +++ b/detections/endpoint/suspicious_msbuild_path.yml @@ -5,88 +5,57 @@ date: '2025-05-02' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of msbuild.exe from a non-standard - path. It leverages data from Endpoint Detection and Response (EDR) agents, focusing - on process execution logs that deviate from typical msbuild.exe locations. This - activity is significant because msbuild.exe is commonly abused by attackers to execute - malicious code, and running it from an unusual path can indicate an attempt to evade - detection. If confirmed malicious, this behavior could allow an attacker to execute - arbitrary code, potentially leading to system compromise and further malicious activities. +description: The following analytic detects the execution of msbuild.exe from a non-standard path. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that deviate from typical msbuild.exe locations. This activity is significant because msbuild.exe is commonly abused by attackers to execute malicious code, and running it from an unusual path can indicate an attempt to evade detection. If confirmed malicious, this behavior could allow an attacker to execute arbitrary code, potentially leading to system compromise and further malicious activities. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count values(Processes.process_name) - as process_name values(Processes.process) as process min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_msbuild` AND (Processes.process_path!=*\\framework*\\v*\\*) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| - `suspicious_msbuild_path_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Some legitimate applications may use a moved copy of msbuild.exe, - triggering a false positive. Baselining of MSBuild.exe usage is recommended to better - understand it's path usage. Visual Studio runs an instance out of a path that will - need to be filtered on. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_msbuild` AND (Processes.process_path!=*\\framework*\\v*\\*) by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `suspicious_msbuild_path_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Some legitimate applications may use a moved copy of msbuild.exe, triggering a false positive. Baselining of MSBuild.exe usage is recommended to better understand it's path usage. Visual Studio runs an instance out of a path that will need to be filtered on. references: -- https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md + - https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Msbuild.exe ran from an uncommon path on $dest$ execyted by $user$ - risk_objects: - - field: dest - type: system - score: 49 - - field: user - type: user - score: 49 - threat_objects: [] + message: Msbuild.exe ran from an uncommon path on $dest$ execyted by $user$ + risk_objects: + - field: dest + type: system + score: 49 + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - Trusted Developer Utilities Proxy Execution MSBuild - - Masquerading - Rename System Utilities - - Living Off The Land - - Cobalt Strike - - BlackByte Ransomware - - Graceful Wipe Out Attack - - Storm-2460 CLFS Zero Day Exploitation - asset_type: Endpoint - mitre_attack_id: - - T1036.003 - - T1127.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Trusted Developer Utilities Proxy Execution MSBuild + - Masquerading - Rename System Utilities + - Living Off The Land + - Cobalt Strike + - BlackByte Ransomware + - Graceful Wipe Out Attack + - Storm-2460 CLFS Zero Day Exploitation + asset_type: Endpoint + mitre_attack_id: + - T1036.003 + - T1127.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_msbuild_rename.yml b/detections/endpoint/suspicious_msbuild_rename.yml index 7327ab5435..1b3d06fecd 100644 --- a/detections/endpoint/suspicious_msbuild_rename.yml +++ b/detections/endpoint/suspicious_msbuild_rename.yml @@ -1,68 +1,58 @@ name: Suspicious MSBuild Rename id: 4006adac-5937-11eb-ae93-0242ac130002 -version: 11 -date: '2025-05-02' +version: 12 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic detects the execution of renamed instances of - msbuild.exe. It leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process names and original file names within the Endpoint data model. - This activity is significant because msbuild.exe is a legitimate tool often abused - by attackers to execute malicious code while evading detection. If confirmed malicious, - this behavior could allow an attacker to execute arbitrary code, potentially leading - to system compromise, data exfiltration, or further lateral movement within the - network. +description: The following analytic detects the execution of renamed instances of msbuild.exe. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and original file names within the Endpoint data model. This activity is significant because msbuild.exe is a legitimate tool often abused by attackers to execute malicious code while evading detection. If confirmed malicious, this behavior could allow an attacker to execute arbitrary code, potentially leading to system compromise, data exfiltration, or further lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name!=msbuild.exe - AND Processes.original_file_name=MSBuild.exe by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `suspicious_msbuild_rename_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although unlikely, some legitimate applications may use a moved - copy of msbuild, triggering a false positive. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name!=msbuild.exe + AND + Processes.original_file_name=MSBuild.exe + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `suspicious_msbuild_rename_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although unlikely, some legitimate applications may use a moved copy of msbuild, triggering a false positive. references: -- https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md -- https://github.com/infosecn1nja/MaliciousMacroMSBuild/ + - https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md + - https://github.com/infosecn1nja/MaliciousMacroMSBuild/ tags: - analytic_story: - - Trusted Developer Utilities Proxy Execution MSBuild - - Masquerading - Rename System Utilities - - Living Off The Land - - Cobalt Strike - - BlackByte Ransomware - - Graceful Wipe Out Attack - - Storm-2460 CLFS Zero Day Exploitation - asset_type: Endpoint - mitre_attack_id: - - T1036.003 - - T1127.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Trusted Developer Utilities Proxy Execution MSBuild + - Masquerading - Rename System Utilities + - Living Off The Land + - Cobalt Strike + - BlackByte Ransomware + - Graceful Wipe Out Attack + - Storm-2460 CLFS Zero Day Exploitation + asset_type: Endpoint + mitre_attack_id: + - T1036.003 + - T1127.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_msbuild_spawn.yml b/detections/endpoint/suspicious_msbuild_spawn.yml index 3d1a886f2c..0bee5f19ec 100644 --- a/detections/endpoint/suspicious_msbuild_spawn.yml +++ b/detections/endpoint/suspicious_msbuild_spawn.yml @@ -1,86 +1,71 @@ name: Suspicious MSBuild Spawn id: a115fba6-5514-11eb-ae93-0242ac130002 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies instances where wmiprvse.exe spawns - msbuild.exe, which is unusual and indicative of potential misuse of a COM object. - This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process relationships and command-line executions. This activity is - significant because msbuild.exe is typically spawned by devenv.exe during legitimate - Visual Studio use, not by wmiprvse.exe. If confirmed malicious, this behavior could - indicate an attacker executing arbitrary code or scripts, potentially leading to - system compromise or further malicious activities. +description: The following analytic identifies instances where wmiprvse.exe spawns msbuild.exe, which is unusual and indicative of potential misuse of a COM object. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process relationships and command-line executions. This activity is significant because msbuild.exe is typically spawned by devenv.exe during legitimate Visual Studio use, not by wmiprvse.exe. If confirmed malicious, this behavior could indicate an attacker executing arbitrary code or scripts, potentially leading to system compromise or further malicious activities. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count values(Processes.process_name) - as process_name values(Processes.process) as process min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wmiprvse.exe - AND `process_msbuild` by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_msbuild_spawn_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although unlikely, some legitimate applications may exhibit - this behavior, triggering a false positive. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name=wmiprvse.exe + AND + `process_msbuild` + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `suspicious_msbuild_spawn_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. references: -- https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md + - https://lolbas-project.github.io/lolbas/Binaries/Msbuild/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious msbuild.exe process executed on $dest$ by $user$ - risk_objects: - - field: dest - type: system - score: 42 - - field: user - type: user - score: 42 - threat_objects: [] + message: Suspicious msbuild.exe process executed on $dest$ by $user$ + risk_objects: + - field: dest + type: system + score: 42 + - field: user + type: user + score: 42 + threat_objects: [] tags: - analytic_story: - - Trusted Developer Utilities Proxy Execution MSBuild - - Living Off The Land - - Storm-2460 CLFS Zero Day Exploitation - asset_type: Endpoint - mitre_attack_id: - - T1127.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Trusted Developer Utilities Proxy Execution MSBuild + - Living Off The Land + - Storm-2460 CLFS Zero Day Exploitation + asset_type: Endpoint + mitre_attack_id: + - T1127.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_mshta_child_process.yml b/detections/endpoint/suspicious_mshta_child_process.yml index 94e5ca7953..d32403a757 100644 --- a/detections/endpoint/suspicious_mshta_child_process.yml +++ b/detections/endpoint/suspicious_mshta_child_process.yml @@ -1,89 +1,73 @@ name: Suspicious mshta child process id: 60023bb6-5500-11eb-ae93-0242ac130002 -version: 11 -date: '2025-11-14' +version: 12 +date: '2026-02-25' author: Michael Haag, Teoderick Contreras Splunk status: production type: TTP -description: The following analytic identifies child processes spawned from "mshta.exe". - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on - specific child processes like "powershell.exe" and "cmd.exe". This activity is significant - because "mshta.exe" is often exploited by attackers to execute malicious scripts - or commands. If confirmed malicious, this behavior could allow an attacker to execute - arbitrary code, escalate privileges, or maintain persistence within the environment. - Monitoring this activity helps in early detection of potential threats leveraging - "mshta.exe" for malicious purposes. +description: The following analytic identifies child processes spawned from "mshta.exe". It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific child processes like "powershell.exe" and "cmd.exe". This activity is significant because "mshta.exe" is often exploited by attackers to execute malicious scripts or commands. If confirmed malicious, this behavior could allow an attacker to execute arbitrary code, escalate privileges, or maintain persistence within the environment. Monitoring this activity helps in early detection of potential threats leveraging "mshta.exe" for malicious purposes. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=mshta.exe - AND Processes.process_name IN ("powershell.exe","colorcpl.exe", "msbuild.exe", "microsoft.workflow.compiler.exe", - "searchprotocolhost.exe", "scrcons.exe", "cscript.exe", "wscript.exe", "cmd.exe", "bitsadmin.exe") - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_mshta_child_process_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although unlikely, some legitimate applications may exhibit - this behavior, triggering a false positive. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name=mshta.exe + AND + Processes.process_name IN ("powershell.exe","colorcpl.exe", "msbuild.exe", "microsoft.workflow.compiler.exe", "searchprotocolhost.exe", "scrcons.exe", "cscript.exe", "wscript.exe", "cmd.exe", "bitsadmin.exe") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `suspicious_mshta_child_process_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. references: -- https://github.com/redcanaryco/AtomicTestHarnesses -- https://redcanary.com/blog/introducing-atomictestharnesses/ + - https://github.com/redcanaryco/AtomicTestHarnesses + - https://redcanary.com/blog/introducing-atomictestharnesses/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious mshta child process $process_name$ detected on host $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 40 - - field: dest - type: system - score: 40 - threat_objects: - - field: process_name - type: process_name + message: Suspicious mshta child process $process_name$ detected on host $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 40 + - field: dest + type: system + score: 40 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Suspicious MSHTA Activity - - Living Off The Land - - Lumma Stealer - asset_type: Endpoint - mitre_attack_id: - - T1218.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious MSHTA Activity + - Living Off The Land + - Lumma Stealer + asset_type: Endpoint + mitre_attack_id: + - T1218.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_mshta_spawn.yml b/detections/endpoint/suspicious_mshta_spawn.yml index 5452cf030d..9f61c500e4 100644 --- a/detections/endpoint/suspicious_mshta_spawn.yml +++ b/detections/endpoint/suspicious_mshta_spawn.yml @@ -1,85 +1,72 @@ name: Suspicious mshta spawn id: 4d33a488-5b5f-11eb-ae93-0242ac130002 -version: 9 -date: '2025-09-18' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the spawning of mshta.exe by wmiprvse.exe - or svchost.exe. This behavior is identified using Endpoint Detection and Response - (EDR) data, focusing on process creation events where the parent process is either - wmiprvse.exe or svchost.exe. This activity is significant as it may indicate the - use of a DCOM object to execute malicious scripts via mshta.exe, a common tactic - in sophisticated attacks. If confirmed malicious, this could allow an attacker to - execute arbitrary code, potentially leading to system compromise and further malicious - activities. +description: The following analytic detects the spawning of mshta.exe by wmiprvse.exe or svchost.exe. This behavior is identified using Endpoint Detection and Response (EDR) data, focusing on process creation events where the parent process is either wmiprvse.exe or svchost.exe. This activity is significant as it may indicate the use of a DCOM object to execute malicious scripts via mshta.exe, a common tactic in sophisticated attacks. If confirmed malicious, this could allow an attacker to execute arbitrary code, potentially leading to system compromise and further malicious activities. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count values(Processes.process_name) - as process_name values(Processes.process) as process min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=svchost.exe - OR Processes.parent_process_name=wmiprvse.exe) AND `process_mshta` by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `suspicious_mshta_spawn_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although unlikely, some legitimate applications may exhibit - this behavior, triggering a false positive. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.parent_process_name=svchost.exe + OR + Processes.parent_process_name=wmiprvse.exe + ) + AND `process_mshta` + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `suspicious_mshta_spawn_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. references: -- https://codewhitesec.blogspot.com/2018/07/lethalhta.html -- https://github.com/redcanaryco/AtomicTestHarnesses -- https://redcanary.com/blog/introducing-atomictestharnesses/ + - https://codewhitesec.blogspot.com/2018/07/lethalhta.html + - https://github.com/redcanaryco/AtomicTestHarnesses + - https://redcanary.com/blog/introducing-atomictestharnesses/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: mshta.exe spawned by wmiprvse.exe on $dest$ - risk_objects: - - field: dest - type: system - score: 42 - threat_objects: [] + message: mshta.exe spawned by wmiprvse.exe on $dest$ + risk_objects: + - field: dest + type: system + score: 42 + threat_objects: [] tags: - analytic_story: - - Suspicious MSHTA Activity - - Living Off The Land - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1218.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious MSHTA Activity + - Living Off The Land + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1218.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_plistbuddy_usage.yml b/detections/endpoint/suspicious_plistbuddy_usage.yml index 943cd45dc7..b5d542f33a 100644 --- a/detections/endpoint/suspicious_plistbuddy_usage.yml +++ b/detections/endpoint/suspicious_plistbuddy_usage.yml @@ -1,60 +1,51 @@ name: Suspicious PlistBuddy Usage id: c3194009-e0eb-4f84-87a9-4070f8688f00 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: experimental type: TTP -description: The following analytic identifies the use of the native macOS utility, - PlistBuddy, to create or modify property list (.plist) files. This detection leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process names - and command-line executions involving PlistBuddy. This activity is significant because - PlistBuddy can be used to establish persistence by modifying LaunchAgents, as seen - in the Silver Sparrow malware. If confirmed malicious, this could allow an attacker - to maintain persistence, execute arbitrary commands, and potentially escalate privileges - on the compromised macOS system. +description: The following analytic identifies the use of the native macOS utility, PlistBuddy, to create or modify property list (.plist) files. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions involving PlistBuddy. This activity is significant because PlistBuddy can be used to establish persistence by modifying LaunchAgents, as seen in the Silver Sparrow malware. If confirmed malicious, this could allow an attacker to maintain persistence, execute arbitrary commands, and potentially escalate privileges on the compromised macOS system. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=PlistBuddy - (Processes.process=*LaunchAgents* OR Processes.process=*RunAtLoad* OR Processes.process=*true*) - by Processes.dest Processes.user Processes.parent_process Processes.process_name - Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_plistbuddy_usage_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Some legitimate applications may use PlistBuddy to create or - modify property lists and possibly generate false positives. Review the property - list being modified or created to confirm. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=PlistBuddy (Processes.process=*LaunchAgents* + OR + Processes.process=*RunAtLoad* + OR + Processes.process=*true*) + BY Processes.dest Processes.user Processes.parent_process + Processes.process_name Processes.process Processes.process_id + Processes.parent_process_id + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `suspicious_plistbuddy_usage_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Some legitimate applications may use PlistBuddy to create or modify property lists and possibly generate false positives. Review the property list being modified or created to confirm. references: -- https://www.marcosantadev.com/manage-plist-files-plistbuddy/ + - https://www.marcosantadev.com/manage-plist-files-plistbuddy/ rba: - message: Suspicious usage of plistbuddy on $dest$ - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: Suspicious usage of plistbuddy on $dest$ + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Silver Sparrow - asset_type: Endpoint - mitre_attack_id: - - T1543.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Silver Sparrow + asset_type: Endpoint + mitre_attack_id: + - T1543.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/suspicious_plistbuddy_usage_via_osquery.yml b/detections/endpoint/suspicious_plistbuddy_usage_via_osquery.yml index f24d478a4e..0eeb1d21b8 100644 --- a/detections/endpoint/suspicious_plistbuddy_usage_via_osquery.yml +++ b/detections/endpoint/suspicious_plistbuddy_usage_via_osquery.yml @@ -1,45 +1,35 @@ name: Suspicious PlistBuddy Usage via OSquery id: 20ba6c32-c733-4a32-b64e-2688cf231399 -version: 8 -date: '2025-10-21' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: experimental type: TTP -description: The following analytic detects the use of the PlistBuddy utility on macOS - to create or modify property list (.plist) files. It leverages OSQuery to monitor - process events, specifically looking for commands that interact with LaunchAgents - and set properties like RunAtLoad. This activity is significant because PlistBuddy - can be used to establish persistence mechanisms, as seen in malware like Silver - Sparrow. If confirmed malicious, this could allow an attacker to maintain persistence, - execute arbitrary commands, and potentially escalate privileges on the compromised - system. +description: The following analytic detects the use of the PlistBuddy utility on macOS to create or modify property list (.plist) files. It leverages OSQuery to monitor process events, specifically looking for commands that interact with LaunchAgents and set properties like RunAtLoad. This activity is significant because PlistBuddy can be used to establish persistence mechanisms, as seen in malware like Silver Sparrow. If confirmed malicious, this could allow an attacker to maintain persistence, execute arbitrary commands, and potentially escalate privileges on the compromised system. data_source: - - osquery -search: '`osquery_process` "columns.cmdline"="*LaunchAgents*" OR "columns.cmdline"="*RunAtLoad*" - OR "columns.cmdline"="*true*" | `suspicious_plistbuddy_usage_via_osquery_filter`' -how_to_implement: OSQuery must be installed and configured to pick up process events - (info at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402. - Modify the macro and validate fields are correct. -known_false_positives: Some legitimate applications may use PlistBuddy to create or - modify property lists and possibly generate false positives. Review the property - list being modified or created to confirm. + - osquery +search: |- + `osquery_process` "columns.cmdline"="*LaunchAgents*" OR "columns.cmdline"="*RunAtLoad*" OR "columns.cmdline"="*true*" + | `suspicious_plistbuddy_usage_via_osquery_filter` +how_to_implement: OSQuery must be installed and configured to pick up process events (info at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402. Modify the macro and validate fields are correct. +known_false_positives: Some legitimate applications may use PlistBuddy to create or modify property lists and possibly generate false positives. Review the property list being modified or created to confirm. references: -- https://www.marcosantadev.com/manage-plist-files-plistbuddy/ + - https://www.marcosantadev.com/manage-plist-files-plistbuddy/ rba: - message: Suspicious usage of plistbuddy on $host$ - risk_objects: - - field: host - type: system - score: 25 - threat_objects: [] + message: Suspicious usage of plistbuddy on $host$ + risk_objects: + - field: host + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Silver Sparrow - asset_type: Endpoint - mitre_attack_id: - - T1543.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Silver Sparrow + asset_type: Endpoint + mitre_attack_id: + - T1543.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/suspicious_process_executed_from_container_file.yml b/detections/endpoint/suspicious_process_executed_from_container_file.yml index c33a5b22d9..df48bfbcd2 100644 --- a/detections/endpoint/suspicious_process_executed_from_container_file.yml +++ b/detections/endpoint/suspicious_process_executed_from_container_file.yml @@ -5,94 +5,60 @@ date: '2025-09-16' author: Steven Dick status: production type: TTP -description: - The following analytic identifies a suspicious process executed from - within common container/archive file types such as ZIP, ISO, IMG, and others. It - leverages data from Endpoint Detection and Response (EDR) agents, focusing on process - names and command-line executions. This activity is significant as it is a common - technique used by adversaries to execute scripts or evade defenses. If confirmed - malicious, this behavior could allow attackers to execute arbitrary code, escalate - privileges, or persist within the environment, posing a significant security risk. +description: The following analytic identifies a suspicious process executed from within common container/archive file types such as ZIP, ISO, IMG, and others. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it is a common technique used by adversaries to execute scripts or evade defenses. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or persist within the environment, posing a significant security risk. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 -search: - '| tstats `security_content_summariesonly` count values(Processes.process_name) - as process_name min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.process IN ("*.ZIP\\*","*.ISO\\*","*.IMG\\*","*.CAB\\*","*.TAR\\*","*.GZ\\*","*.RAR\\*","*.7Z\\*") - AND Processes.action="allowed" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | regex process="(?i).*(ZIP|ISO|IMG|CAB|TAR|GZ|RAR|7Z)\\\\.+\.(BAT|BIN|CAB|CMD|COM|CPL|EX_|EXE|GADGET|INF1|INS|INX||HTM|HTML|ISU|JAR|JOB|JS|JSE|LNK|MSC|MSI|MSP|MST|PAF|PIF|PS1|REG|RGS|SCR|SCT|SHB|SHS|U3P|VB|VBE|VBS|VBSCRIPT|WS|WSF|WSH)\"?$" - | rex field=process "(?i).+\\\\(?[^\\\]+\.(ZIP|ISO|IMG|CAB|TAR|GZ|RAR|7Z))\\\\((.+\\\\)+)?(?.+\.(BAT|BIN|CAB|CMD|COM|CPL|EX_|EXE|GADGET|INF1|INS|INX||HTM|HTML|ISU|JAR|JOB|JS|JSE|LNK|MSC|MSI|MSP|MST|PAF|PIF|PS1|REG|RGS|SCR|SCT|SHB|SHS|U3P|VB|VBE|VBS|VBSCRIPT|WS|WSF|WSH))\"?$"| - `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_process_executed_from_container_file_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*.ZIP\\*","*.ISO\\*","*.IMG\\*","*.CAB\\*","*.TAR\\*","*.GZ\\*","*.RAR\\*","*.7Z\\*") AND Processes.action="allowed" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | regex process="(?i).*(ZIP|ISO|IMG|CAB|TAR|GZ|RAR|7Z)\\\\.+\.(BAT|BIN|CAB|CMD|COM|CPL|EX_|EXE|GADGET|INF1|INS|INX||HTM|HTML|ISU|JAR|JOB|JS|JSE|LNK|MSC|MSI|MSP|MST|PAF|PIF|PS1|REG|RGS|SCR|SCT|SHB|SHS|U3P|VB|VBE|VBS|VBSCRIPT|WS|WSF|WSH)\"?$" | rex field=process "(?i).+\\\\(?[^\\\]+\.(ZIP|ISO|IMG|CAB|TAR|GZ|RAR|7Z))\\\\((.+\\\\)+)?(?.+\.(BAT|BIN|CAB|CMD|COM|CPL|EX_|EXE|GADGET|INF1|INS|INX||HTM|HTML|ISU|JAR|JOB|JS|JSE|LNK|MSC|MSI|MSP|MST|PAF|PIF|PS1|REG|RGS|SCR|SCT|SHB|SHS|U3P|VB|VBE|VBS|VBSCRIPT|WS|WSF|WSH))\"?$"| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_process_executed_from_container_file_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Various business process or userland applications and behavior. references: - - https://www.mandiant.com/resources/blog/tracking-evolution-gootloader-operations - - https://www.crowdstrike.com/blog/weaponizing-disk-image-files-analysis/ - - https://attack.mitre.org/techniques/T1204/002/ + - https://www.mandiant.com/resources/blog/tracking-evolution-gootloader-operations + - https://www.crowdstrike.com/blog/weaponizing-disk-image-files-analysis/ + - https://attack.mitre.org/techniques/T1204/002/ drilldown_searches: - - name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious process $process_name$ was launched from $file_name$ on $dest$. - risk_objects: - - field: dest - type: system - score: 16 - - field: user - type: user - score: 16 - threat_objects: - - field: file_name - type: file_name + message: A suspicious process $process_name$ was launched from $file_name$ on $dest$. + risk_objects: + - field: dest + type: system + score: 16 + - field: user + type: user + score: 16 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - APT37 Rustonotto and FadeStealer - - GhostRedirector IIS Module and Rungan Backdoor - - Unusual Processes - - Amadey - - Remcos - - Snake Keylogger - - Water Gamayun - asset_type: Endpoint - mitre_attack_id: - - T1204.002 - - T1036.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - APT37 Rustonotto and FadeStealer + - GhostRedirector IIS Module and Rungan Backdoor + - Unusual Processes + - Amadey + - Remcos + - Snake Keylogger + - Water Gamayun + asset_type: Endpoint + mitre_attack_id: + - T1204.002 + - T1036.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/gootloader/partial_ttps/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/gootloader/partial_ttps/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_reg_exe_process.yml b/detections/endpoint/suspicious_reg_exe_process.yml index da5eab3e4f..9922de2ef9 100644 --- a/detections/endpoint/suspicious_reg_exe_process.yml +++ b/detections/endpoint/suspicious_reg_exe_process.yml @@ -1,102 +1,82 @@ name: Suspicious Reg exe Process id: a6b3ab4e-dd77-4213-95fa-fc94701995e0 -version: 12 -date: '2025-05-02' +version: 13 +date: '2026-02-25' author: David Dorsey, Splunk status: production type: Anomaly -description: - The following analytic identifies instances of reg.exe being launched - from a command prompt (cmd.exe) that was not initiated by the user, as indicated - by a parent process other than explorer.exe. This detection leverages data from - Endpoint Detection and Response (EDR) agents, focusing on process and parent process - names. This activity is significant because reg.exe is often used in registry manipulation, - which can be indicative of malicious behavior such as persistence mechanisms or - system configuration changes. If confirmed malicious, this could allow an attacker - to modify critical system settings, potentially leading to privilege escalation - or persistent access. +description: The following analytic identifies instances of reg.exe being launched from a command prompt (cmd.exe) that was not initiated by the user, as indicated by a parent process other than explorer.exe. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process names. This activity is significant because reg.exe is often used in registry manipulation, which can be indicative of malicious behavior such as persistence mechanisms or system configuration changes. If confirmed malicious, this could allow an attacker to modify critical system settings, potentially leading to privilege escalation or persistent access. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name - != explorer.exe Processes.process_name =cmd.exe by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | search [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes - where Processes.parent_process_name=cmd.exe Processes.process_name= reg.exe by Processes.parent_process_id - Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | rename parent_process_id as process_id |dedup - process_id| table process_id dest] | `suspicious_reg_exe_process_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: - It's possible for system administrators to write scripts that - exhibit this behavior. If this is the case, the search will need to be modified - to filter them out. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name != explorer.exe Processes.process_name =cmd.exe + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | search [ + | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name=cmd.exe Processes.process_name= reg.exe + BY Processes.parent_process_id Processes.dest Processes.process_name + | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | rename parent_process_id as process_id + | dedup process_id + | table process_id dest] + | `suspicious_reg_exe_process_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: It's possible for system administrators to write scripts that exhibit this behavior. If this is the case, the search will need to be modified to filter them out. references: - - https://car.mitre.org/wiki/CAR-2013-03-001/ + - https://car.mitre.org/wiki/CAR-2013-03-001/ drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to add a registry entry. - risk_objects: - - field: user - type: user - score: 35 - - field: dest - type: system - score: 35 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to add a registry entry. + risk_objects: + - field: user + type: user + score: 35 + - field: dest + type: system + score: 35 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Windows Defense Evasion Tactics - - Disabling Security Tools - - DHS Report TA18-074A - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Disabling Security Tools + - DHS Report TA18-074A + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_regsvr32_register_suspicious_path.yml b/detections/endpoint/suspicious_regsvr32_register_suspicious_path.yml index 71aaf11a20..a477312600 100644 --- a/detections/endpoint/suspicious_regsvr32_register_suspicious_path.yml +++ b/detections/endpoint/suspicious_regsvr32_register_suspicious_path.yml @@ -5,95 +5,63 @@ date: '2025-05-02' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the use of Regsvr32.exe to register DLLs - from suspicious paths such as AppData, ProgramData, or Windows Temp directories. - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on - process execution logs and command-line arguments. This activity is significant - because Regsvr32.exe can be abused to proxy execution of malicious code, bypassing - traditional security controls. If confirmed malicious, this could allow an attacker - to execute arbitrary code, potentially leading to system compromise, data exfiltration, - or further lateral movement within the network. +description: The following analytic detects the use of Regsvr32.exe to register DLLs from suspicious paths such as AppData, ProgramData, or Windows Temp directories. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant because Regsvr32.exe can be abused to proxy execution of malicious code, bypassing traditional security controls. If confirmed malicious, this could allow an attacker to execute arbitrary code, potentially leading to system compromise, data exfiltration, or further lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_regsvr32` Processes.process - IN ("*\\appdata\\*", "*\\programdata\\*","*\\windows\\temp\\*") NOT (Processes.process - IN ("*.dll*", "*.ax*", "*.ocx*")) by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_regsvr32_register_suspicious_path_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Limited false positives with the query restricted to specified - paths. Add more world writeable paths as tuning continues. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_regsvr32` Processes.process IN ("*\\appdata\\*", "*\\programdata\\*","*\\windows\\temp\\*") NOT (Processes.process IN ("*.dll*", "*.ax*", "*.ocx*")) by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_regsvr32_register_suspicious_path_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Limited false positives with the query restricted to specified paths. Add more world writeable paths as tuning continues. references: -- https://attack.mitre.org/techniques/T1218/010/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md -- https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/ -- https://support.microsoft.com/en-us/topic/how-to-use-the-regsvr32-tool-and-troubleshoot-regsvr32-error-messages-a98d960a-7392-e6fe-d90a-3f4e0cb543e5 -- https://any.run/report/f29a7d2ecd3585e1e4208e44bcc7156ab5388725f1d29d03e7699da0d4598e7c/0826458b-5367-45cf-b841-c95a33a01718 + - https://attack.mitre.org/techniques/T1218/010/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md + - https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/ + - https://support.microsoft.com/en-us/topic/how-to-use-the-regsvr32-tool-and-troubleshoot-regsvr32-error-messages-a98d960a-7392-e6fe-d90a-3f4e0cb543e5 + - https://any.run/report/f29a7d2ecd3585e1e4208e44bcc7156ab5388725f1d29d03e7699da0d4598e7c/0826458b-5367-45cf-b841-c95a33a01718 drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to evade detection by using a non-standard - file extension. - risk_objects: - - field: user - type: user - score: 35 - - field: dest - type: system - score: 35 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to evade detection by using a non-standard file extension. + risk_objects: + - field: user + type: user + score: 35 + - field: dest + type: system + score: 35 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Living Off The Land - - Qakbot - - China-Nexus Threat Activity - - Derusbi - - Salt Typhoon - - Suspicious Regsvr32 Activity - - IcedID - asset_type: Endpoint - mitre_attack_id: - - T1218.010 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + - Qakbot + - China-Nexus Threat Activity + - Derusbi + - Salt Typhoon + - Suspicious Regsvr32 Activity + - IcedID + asset_type: Endpoint + mitre_attack_id: + - T1218.010 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_rundll32_dllregisterserver.yml b/detections/endpoint/suspicious_rundll32_dllregisterserver.yml index 3491b522ac..28f5fb91d3 100644 --- a/detections/endpoint/suspicious_rundll32_dllregisterserver.yml +++ b/detections/endpoint/suspicious_rundll32_dllregisterserver.yml @@ -1,95 +1,78 @@ name: Suspicious Rundll32 dllregisterserver id: 8c00a385-9b86-4ac0-8932-c9ec3713b159 -version: 10 -date: '2025-05-02' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of rundll32.exe with the - DllRegisterServer command to load a DLL. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on command-line executions and process details. - This activity is significant as it may indicate an attempt to register a malicious - DLL, which can be a method for code execution or persistence. If confirmed malicious, - an attacker could gain unauthorized code execution, escalate privileges, or maintain - persistence within the environment, posing a severe security risk. +description: The following analytic detects the execution of rundll32.exe with the DllRegisterServer command to load a DLL. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process details. This activity is significant as it may indicate an attempt to register a malicious DLL, which can be a method for code execution or persistence. If confirmed malicious, an attacker could gain unauthorized code execution, escalate privileges, or maintain persistence within the environment, posing a severe security risk. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*dllregisterserver* - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `suspicious_rundll32_dllregisterserver_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: This is likely to produce false positives and will require - some filtering. Tune the query by adding command line paths to known good DLLs, - or filtering based on parent process names. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_rundll32` Processes.process=*dllregisterserver* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `suspicious_rundll32_dllregisterserver_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: This is likely to produce false positives and will require some filtering. Tune the query by adding command line paths to known good DLLs, or filtering based on parent process names. references: -- https://attack.mitre.org/techniques/T1218/011/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md -- https://lolbas-project.github.io/lolbas/Binaries/Rundll32/ -- https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/seedworm-apt-iran-middle-east -- https://github.com/pan-unit42/tweets/blob/master/2020-12-10-IOCs-from-Ursnif-infection-with-Delf-variant.txt -- https://www.crowdstrike.com/blog/duck-hunting-with-falcon-complete-qakbot-zip-based-campaign/ -- https://docs.microsoft.com/en-us/windows/win32/api/olectl/nf-olectl-dllregisterserver?redirectedfrom=MSDN + - https://attack.mitre.org/techniques/T1218/011/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md + - https://lolbas-project.github.io/lolbas/Binaries/Rundll32/ + - https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/seedworm-apt-iran-middle-east + - https://github.com/pan-unit42/tweets/blob/master/2020-12-10-IOCs-from-Ursnif-infection-with-Delf-variant.txt + - https://www.crowdstrike.com/blog/duck-hunting-with-falcon-complete-qakbot-zip-based-campaign/ + - https://docs.microsoft.com/en-us/windows/win32/api/olectl/nf-olectl-dllregisterserver?redirectedfrom=MSDN drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to register a DLL. code - risk_objects: - - field: user - type: user - score: 35 - - field: dest - type: system - score: 35 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to register a DLL. code + risk_objects: + - field: user + type: user + score: 35 + - field: dest + type: system + score: 35 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Suspicious Rundll32 Activity - - Living Off The Land - - IcedID - asset_type: Endpoint - mitre_attack_id: - - T1218.011 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Rundll32 Activity + - Living Off The Land + - IcedID + asset_type: Endpoint + mitre_attack_id: + - T1218.011 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_rundll32_no_command_line_arguments.yml b/detections/endpoint/suspicious_rundll32_no_command_line_arguments.yml index 65cf45f6f7..dfb30b2146 100644 --- a/detections/endpoint/suspicious_rundll32_no_command_line_arguments.yml +++ b/detections/endpoint/suspicious_rundll32_no_command_line_arguments.yml @@ -1,93 +1,77 @@ name: Suspicious Rundll32 no Command Line Arguments id: e451bd16-e4c5-4109-8eb1-c4c6ecf048b4 -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of rundll32.exe without - any command line arguments. This behavior is identified using Endpoint Detection - and Response (EDR) telemetry, focusing on process execution logs. It is significant - because rundll32.exe typically requires command line arguments to function properly, - and its absence is often associated with malicious activities, such as those performed - by Cobalt Strike. If confirmed malicious, this activity could indicate an attempt - to execute arbitrary code, potentially leading to credential dumping, unauthorized - file writes, or other malicious actions. +description: The following analytic detects the execution of rundll32.exe without any command line arguments. This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on process execution logs. It is significant because rundll32.exe typically requires command line arguments to function properly, and its absence is often associated with malicious activities, such as those performed by Cobalt Strike. If confirmed malicious, this activity could indicate an attempt to execute arbitrary code, potentially leading to credential dumping, unauthorized file writes, or other malicious actions. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes - where `process_rundll32` by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex - process="(?i)(rundll32\.exe.{0,4}$)" | `suspicious_rundll32_no_command_line_arguments_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although unlikely, some legitimate applications may use a moved - copy of rundll32, triggering a false positive. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes + WHERE `process_rundll32` + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | regex process="(?i)(rundll32\.exe.{0,4}$)" + | `suspicious_rundll32_no_command_line_arguments_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive. references: -- https://attack.mitre.org/techniques/T1218/011/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md -- https://lolbas-project.github.io/lolbas/Binaries/Rundll32/ -- https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ + - https://attack.mitre.org/techniques/T1218/011/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md + - https://lolbas-project.github.io/lolbas/Binaries/Rundll32/ + - https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious rundll32.exe process with no command line arguments executed - on $dest$ by $user$ - risk_objects: - - field: dest - type: system - score: 49 - - field: user - type: user - score: 49 - threat_objects: [] + message: Suspicious rundll32.exe process with no command line arguments executed on $dest$ by $user$ + risk_objects: + - field: dest + type: system + score: 49 + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - Suspicious Rundll32 Activity - - Cobalt Strike - - BlackByte Ransomware - - PrintNightmare CVE-2021-34527 - - Graceful Wipe Out Attack - - Hellcat Ransomware - asset_type: Endpoint - cve: - - CVE-2021-34527 - mitre_attack_id: - - T1218.011 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Rundll32 Activity + - Cobalt Strike + - BlackByte Ransomware + - PrintNightmare CVE-2021-34527 + - Graceful Wipe Out Attack + - Hellcat Ransomware + asset_type: Endpoint + cve: + - CVE-2021-34527 + mitre_attack_id: + - T1218.011 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_rundll32_plugininit.yml b/detections/endpoint/suspicious_rundll32_plugininit.yml index 106a60db5b..093bd7709e 100644 --- a/detections/endpoint/suspicious_rundll32_plugininit.yml +++ b/detections/endpoint/suspicious_rundll32_plugininit.yml @@ -1,80 +1,65 @@ name: Suspicious Rundll32 PluginInit id: 92d51712-ee29-11eb-b1ae-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic identifies the execution of the rundll32.exe process - with the "plugininit" parameter. This detection leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process creation events and command-line - arguments. This activity is significant because the "plugininit" parameter is commonly - associated with IcedID malware, which uses it to execute an initial DLL stager to - download additional payloads. If confirmed malicious, this behavior could lead to - further malware infections, data exfiltration, or complete system compromise. +description: The following analytic identifies the execution of the rundll32.exe process with the "plugininit" parameter. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events and command-line arguments. This activity is significant because the "plugininit" parameter is commonly associated with IcedID malware, which uses it to execute an initial DLL stager to download additional payloads. If confirmed malicious, this behavior could lead to further malware infections, data exfiltration, or complete system compromise. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*PluginInit* - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `suspicious_rundll32_plugininit_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: third party application may used this dll export name to execute - function. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_rundll32` Processes.process=*PluginInit* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `suspicious_rundll32_plugininit_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: third party application may used this dll export name to execute function. references: -- https://threatpost.com/icedid-banking-trojan-surges-emotet/165314/ + - https://threatpost.com/icedid-banking-trojan-surges-emotet/165314/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: rundll32 process $process_name$ with commandline $process$ in host $dest$ - risk_objects: - - field: dest - type: system - score: 42 - threat_objects: - - field: process_name - type: process_name + message: rundll32 process $process_name$ with commandline $process$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 42 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - IcedID - asset_type: Endpoint - mitre_attack_id: - - T1218.011 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - IcedID + asset_type: Endpoint + mitre_attack_id: + - T1218.011 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_rundll32_startw.yml b/detections/endpoint/suspicious_rundll32_startw.yml index ee0c5384f3..eeebfd92e1 100644 --- a/detections/endpoint/suspicious_rundll32_startw.yml +++ b/detections/endpoint/suspicious_rundll32_startw.yml @@ -1,91 +1,75 @@ name: Suspicious Rundll32 StartW id: 9319dda5-73f2-4d43-a85a-67ce961bddb7 -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies the execution of rundll32.exe with - the DLL function names "Start" and "StartW," commonly associated with Cobalt Strike - payloads. This detection leverages data from Endpoint Detection and Response (EDR) - agents, focusing on command-line executions and process metadata. This activity - is significant as it often indicates the presence of malicious payloads, such as - Cobalt Strike, which can lead to unauthorized code execution. If confirmed malicious, - this activity could allow attackers to inject shellcode, escalate privileges, and - maintain persistence within the environment. +description: The following analytic identifies the execution of rundll32.exe with the DLL function names "Start" and "StartW," commonly associated with Cobalt Strike payloads. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process metadata. This activity is significant as it often indicates the presence of malicious payloads, such as Cobalt Strike, which can lead to unauthorized code execution. If confirmed malicious, this activity could allow attackers to inject shellcode, escalate privileges, and maintain persistence within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*start* - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `suspicious_rundll32_startw_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although unlikely, some legitimate applications may use Start - as a function and call it via the command line. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_rundll32` Processes.process=*start* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `suspicious_rundll32_startw_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although unlikely, some legitimate applications may use Start as a function and call it via the command line. Filter as needed. references: -- https://attack.mitre.org/techniques/T1218/011/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md -- https://hstechdocs.helpsystems.com/manuals/cobaltstrike/current/userguide/index.htm#cshid=1036 -- https://lolbas-project.github.io/lolbas/Binaries/Rundll32/ -- https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ + - https://attack.mitre.org/techniques/T1218/011/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md + - https://hstechdocs.helpsystems.com/manuals/cobaltstrike/current/userguide/index.htm#cshid=1036 + - https://lolbas-project.github.io/lolbas/Binaries/Rundll32/ + - https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: rundll32.exe running with suspicious StartW parameters on $dest$ - risk_objects: - - field: dest - type: system - score: 35 - - field: user - type: user - score: 35 - threat_objects: [] + message: rundll32.exe running with suspicious StartW parameters on $dest$ + risk_objects: + - field: dest + type: system + score: 35 + - field: user + type: user + score: 35 + threat_objects: [] tags: - analytic_story: - - Trickbot - - Suspicious Rundll32 Activity - - Cobalt Strike - - BlackByte Ransomware - - Graceful Wipe Out Attack - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1218.011 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Trickbot + - Suspicious Rundll32 Activity + - Cobalt Strike + - BlackByte Ransomware + - Graceful Wipe Out Attack + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1218.011 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_scheduled_task_from_public_directory.yml b/detections/endpoint/suspicious_scheduled_task_from_public_directory.yml index e18f4fe2a2..f4433a4980 100644 --- a/detections/endpoint/suspicious_scheduled_task_from_public_directory.yml +++ b/detections/endpoint/suspicious_scheduled_task_from_public_directory.yml @@ -5,105 +5,70 @@ date: '2026-02-09' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic identifies the creation of scheduled tasks - that execute binaries or scripts from public directories, such as - users\public, \programdata\, or \windows\temp, using schtasks.exe with the - /create command. It leverages Sysmon Event ID 1 data to detect this behavior. - This activity is significant because it often indicates an attempt to maintain - persistence or execute malicious scripts, which are common tactics in malware - deployment. If confirmed as malicious, this could lead to data compromise, - unauthorized access, and potential lateral movement within the network. +description: The following analytic identifies the creation of scheduled tasks that execute binaries or scripts from public directories, such as users\public, \programdata\, or \windows\temp, using schtasks.exe with the /create command. It leverages Sysmon Event ID 1 data to detect this behavior. This activity is significant because it often indicates an attempt to maintain persistence or execute malicious scripts, which are common tactics in malware deployment. If confirmed as malicious, this could lead to data compromise, unauthorized access, and potential lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe - (Processes.process=*\\users\\public\\* OR Processes.process=*\\programdata\\* OR - Processes.process=*windows\\temp*) Processes.process=*/create* by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| - `suspicious_scheduled_task_from_public_directory_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: The main source of false positives could be the - legitimate use of scheduled tasks from these directories. Careful tuning of - this search may be necessary to suit the specifics of your environment, - reducing the rate of false positives. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process=*\\users\\public\\* OR Processes.process=*\\programdata\\* OR Processes.process=*windows\\temp*) Processes.process=*/create* by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `suspicious_scheduled_task_from_public_directory_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: The main source of false positives could be the legitimate use of scheduled tasks from these directories. Careful tuning of this search may be necessary to suit the specifics of your environment, reducing the rate of false positives. references: -- https://attack.mitre.org/techniques/T1053/005/ + - https://attack.mitre.org/techniques/T1053/005/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious scheduled task registered on $dest$ from Public Directory - risk_objects: - - field: dest - type: system - score: 35 - - field: user - type: user - score: 35 - threat_objects: [] + message: Suspicious scheduled task registered on $dest$ from Public Directory + risk_objects: + - field: dest + type: system + score: 35 + - field: user + type: user + score: 35 + threat_objects: [] tags: - analytic_story: - - SolarWinds WHD RCE Post Exploitation - - XWorm - - Medusa Ransomware - - CISA AA23-347A - - Azorult - - Scheduled Tasks - - Living Off The Land - - Ransomware - - Crypto Stealer - - Salt Typhoon - - Quasar RAT - - DarkCrystal RAT - - Ryuk Ransomware - - CISA AA24-241A - - Malicious Inno Setup Loader - - Windows Persistence Techniques - - MoonPeak - - China-Nexus Threat Activity - - Scattered Spider - - APT37 Rustonotto and FadeStealer - - Lokibot - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1053.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SolarWinds WHD RCE Post Exploitation + - XWorm + - Medusa Ransomware + - CISA AA23-347A + - Azorult + - Scheduled Tasks + - Living Off The Land + - Ransomware + - Crypto Stealer + - Salt Typhoon + - Quasar RAT + - DarkCrystal RAT + - Ryuk Ransomware + - CISA AA24-241A + - Malicious Inno Setup Loader + - Windows Persistence Techniques + - MoonPeak + - China-Nexus Threat Activity + - Scattered Spider + - APT37 Rustonotto and FadeStealer + - Lokibot + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1053.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/schtasks/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/schtasks/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_searchprotocolhost_no_command_line_arguments.yml b/detections/endpoint/suspicious_searchprotocolhost_no_command_line_arguments.yml index c802b4cd4d..3f08c7d4ca 100644 --- a/detections/endpoint/suspicious_searchprotocolhost_no_command_line_arguments.yml +++ b/detections/endpoint/suspicious_searchprotocolhost_no_command_line_arguments.yml @@ -1,87 +1,71 @@ name: Suspicious SearchProtocolHost no Command Line Arguments id: f52d2db8-31f9-4aa7-a176-25779effe55c -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects instances of searchprotocolhost.exe running - without command line arguments. This behavior is unusual and often associated with - malicious activities, such as those performed by Cobalt Strike. The detection leverages - Endpoint Detection and Response (EDR) telemetry, focusing on process execution data. - This activity is significant because searchprotocolhost.exe typically runs with - specific arguments, and its absence may indicate an attempt to evade detection. - If confirmed malicious, this could lead to unauthorized code execution, potential - credential dumping, or other malicious actions within the environment. +description: The following analytic detects instances of searchprotocolhost.exe running without command line arguments. This behavior is unusual and often associated with malicious activities, such as those performed by Cobalt Strike. The detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on process execution data. This activity is significant because searchprotocolhost.exe typically runs with specific arguments, and its absence may indicate an attempt to evade detection. If confirmed malicious, this could lead to unauthorized code execution, potential credential dumping, or other malicious actions within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes - where Processes.process_name=searchprotocolhost.exe by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | regex process="(?i)(searchprotocolhost\.exe.{0,4}$)" | `suspicious_searchprotocolhost_no_command_line_arguments_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Limited false positives may be present in small environments. - Tuning may be required based on parent process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=searchprotocolhost.exe + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | regex process="(?i)(searchprotocolhost\.exe.{0,4}$)" + | `suspicious_searchprotocolhost_no_command_line_arguments_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Limited false positives may be present in small environments. Tuning may be required based on parent process. references: -- https://github.com/mandiant/red_team_tool_countermeasures/blob/master/rules/PGF/supplemental/hxioc/SUSPICIOUS%20EXECUTION%20OF%20SEARCHPROTOCOLHOST%20(METHODOLOGY).ioc + - https://github.com/mandiant/red_team_tool_countermeasures/blob/master/rules/PGF/supplemental/hxioc/SUSPICIOUS%20EXECUTION%20OF%20SEARCHPROTOCOLHOST%20(METHODOLOGY).ioc drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious searchprotocolhost.exe process with no command line arguments - executed on $dest$ by $user$ - risk_objects: - - field: dest - type: system - score: 49 - - field: user - type: user - score: 49 - threat_objects: [] + message: Suspicious searchprotocolhost.exe process with no command line arguments executed on $dest$ by $user$ + risk_objects: + - field: dest + type: system + score: 49 + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - BlackByte Ransomware - - Cobalt Strike - - Graceful Wipe Out Attack - - Cactus Ransomware - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1055 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - BlackByte Ransomware + - Cobalt Strike + - Graceful Wipe Out Attack + - Cactus Ransomware + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1055 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_sqlite3_lsquarantine_behavior.yml b/detections/endpoint/suspicious_sqlite3_lsquarantine_behavior.yml index 15a4736eb6..a4d8945020 100644 --- a/detections/endpoint/suspicious_sqlite3_lsquarantine_behavior.yml +++ b/detections/endpoint/suspicious_sqlite3_lsquarantine_behavior.yml @@ -1,62 +1,52 @@ name: Suspicious SQLite3 LSQuarantine Behavior id: e1997b2e-655f-4561-82fd-aeba8e1c1a86 -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk status: experimental type: TTP -description: The following analytic identifies the use of SQLite3 querying the MacOS - preferences to determine the original URL from which a package was downloaded. This - detection leverages data from Endpoint Detection and Response (EDR) agents, focusing - on process names and command-line executions involving LSQuarantine. This activity - is significant as it is commonly associated with MacOS adware and other malicious - software. If confirmed malicious, this behavior could indicate an attempt to track - or manipulate downloaded packages, potentially leading to further system compromise - or persistent adware infections. +description: The following analytic identifies the use of SQLite3 querying the MacOS preferences to determine the original URL from which a package was downloaded. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions involving LSQuarantine. This activity is significant as it is commonly associated with MacOS adware and other malicious software. If confirmed malicious, this behavior could indicate an attempt to track or manipulate downloaded packages, potentially leading to further system compromise or persistent adware infections. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=sqlite3 - Processes.process=*LSQuarantine* by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_sqlite3_lsquarantine_behavior_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=sqlite3 Processes.process=*LSQuarantine* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `suspicious_sqlite3_lsquarantine_behavior_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://redcanary.com/blog/clipping-silver-sparrows-wings/ -- https://www.marcosantadev.com/manage-plist-files-plistbuddy/ + - https://redcanary.com/blog/clipping-silver-sparrows-wings/ + - https://www.marcosantadev.com/manage-plist-files-plistbuddy/ rba: - message: Suspicious sqlite LSQuarantine activity on $dest$ - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: Suspicious sqlite LSQuarantine activity on $dest$ + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Silver Sparrow - asset_type: Endpoint - mitre_attack_id: - - T1074 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Silver Sparrow + asset_type: Endpoint + mitre_attack_id: + - T1074 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/suspicious_ticket_granting_ticket_request.yml b/detections/endpoint/suspicious_ticket_granting_ticket_request.yml index daa8be9f35..f7bdd12a7e 100644 --- a/detections/endpoint/suspicious_ticket_granting_ticket_request.yml +++ b/detections/endpoint/suspicious_ticket_granting_ticket_request.yml @@ -1,54 +1,45 @@ name: Suspicious Ticket Granting Ticket Request id: d77d349e-6269-11ec-9cfe-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting -description: The following analytic detects suspicious Kerberos Ticket Granting Ticket - (TGT) requests that may indicate exploitation of CVE-2021-42278 and CVE-2021-42287. - It leverages Event ID 4781 (account name change) and Event ID 4768 (TGT request) - to identify sequences where a newly renamed computer account requests a TGT. This - behavior is significant as it could represent an attempt to escalate privileges - by impersonating a Domain Controller. If confirmed malicious, this activity could - allow attackers to gain elevated access and potentially control over the domain - environment. +description: The following analytic detects suspicious Kerberos Ticket Granting Ticket (TGT) requests that may indicate exploitation of CVE-2021-42278 and CVE-2021-42287. It leverages Event ID 4781 (account name change) and Event ID 4768 (TGT request) to identify sequences where a newly renamed computer account requests a TGT. This behavior is significant as it could represent an attempt to escalate privileges by impersonating a Domain Controller. If confirmed malicious, this activity could allow attackers to gain elevated access and potentially control over the domain environment. data_source: -- Windows Event Log Security 4768 -- Windows Event Log Security 4781 -search: '`wineventlog_security` (EventCode=4781 OldTargetUserName="*$" NewTargetUserName!="*$") - OR (EventCode=4768 TargetUserName!="*$") | eval RenamedComputerAccount = coalesce(NewTargetUserName, - TargetUserName) | transaction RenamedComputerAccount startswith=(EventCode=4781) - endswith=(EventCode=4768) | eval short_lived=case((duration<2),"TRUE") | search - short_lived = TRUE | table _time, Computer, EventCode, TargetUserName, RenamedComputerAccount, - short_lived | rename Computer as dest | `suspicious_ticket_granting_ticket_request_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Domain Controller and Kerberos events. The Advanced Security Audit policy setting - `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. -known_false_positives: A computer account name change event inmediately followed by - a kerberos TGT request with matching fields is unsual. However, legitimate behavior - may trigger it. Filter as needed. + - Windows Event Log Security 4768 + - Windows Event Log Security 4781 +search: |- + `wineventlog_security` (EventCode=4781 OldTargetUserName="*$" NewTargetUserName!="*$") OR (EventCode=4768 TargetUserName!="*$") + | eval RenamedComputerAccount = coalesce(NewTargetUserName, TargetUserName) + | transaction RenamedComputerAccount startswith=(EventCode=4781) endswith=(EventCode=4768) + | eval short_lived=case((duration<2),"TRUE") + | search short_lived = TRUE + | table _time, Computer, EventCode, TargetUserName, RenamedComputerAccount, short_lived + | rename Computer as dest + | `suspicious_ticket_granting_ticket_request_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. +known_false_positives: A computer account name change event inmediately followed by a kerberos TGT request with matching fields is unsual. However, legitimate behavior may trigger it. Filter as needed. references: -- https://exploit.ph/cve-2021-42287-cve-2021-42278-weaponisation.html -- https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-42278 -- https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-42287 + - https://exploit.ph/cve-2021-42287-cve-2021-42278-weaponisation.html + - https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-42278 + - https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-42287 tags: - analytic_story: - - sAMAccountName Spoofing and Domain Controller Impersonation - - Active Directory Kerberos Attacks - - Active Directory Privilege Escalation - asset_type: Endpoint - mitre_attack_id: - - T1078.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - sAMAccountName Spoofing and Domain Controller Impersonation + - Active Directory Kerberos Attacks + - Active Directory Privilege Escalation + asset_type: Endpoint + mitre_attack_id: + - T1078.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/suspicious_ticket_granting_ticket_request/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/suspicious_ticket_granting_ticket_request/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_wav_file_in_appdata_folder.yml b/detections/endpoint/suspicious_wav_file_in_appdata_folder.yml index ba90465de0..08d1d2fc86 100644 --- a/detections/endpoint/suspicious_wav_file_in_appdata_folder.yml +++ b/detections/endpoint/suspicious_wav_file_in_appdata_folder.yml @@ -5,77 +5,48 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the creation of .wav files in the AppData - folder, a behavior associated with Remcos RAT malware, which stores audio recordings - in this location for data exfiltration. The detection leverages endpoint process - and filesystem data to identify .wav file creation within the AppData\Roaming directory. - This activity is significant as it indicates potential unauthorized data collection - and exfiltration by malware. If confirmed malicious, this could lead to sensitive - information being sent to an attacker's command and control server, compromising - the affected system's confidentiality. +description: The following analytic detects the creation of .wav files in the AppData folder, a behavior associated with Remcos RAT malware, which stores audio recordings in this location for data exfiltration. The detection leverages endpoint process and filesystem data to identify .wav file creation within the AppData\Roaming directory. This activity is significant as it indicates potential unauthorized data collection and exfiltration by malware. If confirmed malicious, this could lead to sensitive information being sent to an attacker's command and control server, compromising the affected system's confidentiality. data_source: -- Sysmon EventID 1 AND Sysmon EventID 11 -- Windows Event Log Security 4688 AND Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes - where Processes.process_name=*.exe Processes.process_path="*\\appdata\\Roaming\\*" - by _time span=1h Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` |rename process_guid as - proc_guid | join proc_guid, _time [ | tstats `security_content_summariesonly` count - min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem - where Filesystem.file_name IN ("*.wav") Filesystem.file_path = "*\\appdata\\Roaming\\*" - by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name - Filesystem.file_path Filesystem.process_guid | `drop_dm_object_name(Filesystem)` - |rename process_guid as proc_guid | fields file_name file_path process_name process_path - process dest file_create_time _time proc_guid] | `suspicious_wav_file_in_appdata_folder_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, file_name, file_path and command-line - executions from your endpoints. If you are using Sysmon, you must have at least - version 6.0.4 of the Sysmon TA. + - Sysmon EventID 1 AND Sysmon EventID 11 + - Windows Event Log Security 4688 AND Sysmon EventID 11 +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=*.exe Processes.process_path="*\\appdata\\Roaming\\*" by _time span=1h Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` |rename process_guid as proc_guid | join proc_guid, _time [ | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.wav") Filesystem.file_path = "*\\appdata\\Roaming\\*" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.process_guid | `drop_dm_object_name(Filesystem)` |rename process_guid as proc_guid | fields file_name file_path process_name process_path process dest file_create_time _time proc_guid] | `suspicious_wav_file_in_appdata_folder_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, file_name, file_path and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: No false positives have been identified at this time. references: -- https://success.trendmicro.com/dcx/s/solution/1123281-remcos-malware-information?language=en_US -- https://blog.malwarebytes.com/threat-intelligence/2021/07/remcos-rat-delivered-via-visual-basic/ + - https://success.trendmicro.com/dcx/s/solution/1123281-remcos-malware-information?language=en_US + - https://blog.malwarebytes.com/threat-intelligence/2021/07/remcos-rat-delivered-via-visual-basic/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: process $process_name$ creating image file $file_path$ on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: - - field: process_name - type: process_name + message: process $process_name$ creating image file $file_path$ on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Remcos - asset_type: Endpoint - mitre_attack_id: - - T1113 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Remcos + asset_type: Endpoint + mitre_attack_id: + - T1113 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos_agent/sysmon_wav.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos_agent/sysmon_wav.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_wevtutil_usage.yml b/detections/endpoint/suspicious_wevtutil_usage.yml index e52fc080b4..ff6134c73b 100644 --- a/detections/endpoint/suspicious_wevtutil_usage.yml +++ b/detections/endpoint/suspicious_wevtutil_usage.yml @@ -1,91 +1,75 @@ name: Suspicious wevtutil Usage id: 2827c0fd-e1be-4868-ae25-59d28e0f9d4f -version: 15 -date: '2026-01-20' +version: 16 +date: '2026-02-25' author: David Dorsey, Michael Haag, Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the usage of wevtutil.exe with parameters - for clearing event logs such as Application, Security, Setup, Trace, or System. - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on - process names and command-line arguments. This activity is significant because clearing - event logs can be an attempt to cover tracks after malicious actions, hindering - forensic investigations. If confirmed malicious, this behavior could allow an attacker - to erase evidence of their activities, making it difficult to trace their actions - and understand the full scope of the compromise. +description: The following analytic detects the usage of wevtutil.exe with parameters for clearing event logs such as Application, Security, Setup, Trace, or System. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because clearing event logs can be an attempt to cover tracks after malicious actions, hindering forensic investigations. If confirmed malicious, this behavior could allow an attacker to erase evidence of their activities, making it difficult to trace their actions and understand the full scope of the compromise. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.process_name=wevtutil.exe Processes.process IN ("* cl *", "*clear-log*") Processes.process IN ("*System*", "*Security*", "*Setup*", "*Application*", "*trace*", "*powershell*", "Sysmon") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `suspicious_wevtutil_usage_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: The wevtutil.exe application is a legitimate Windows event - log utility. Administrators may use it to manage Windows event logs. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=wevtutil.exe Processes.process IN ("* cl *", "*clear-log*") Processes.process IN ("*System*", "*Security*", "*Setup*", "*Application*", "*trace*", "*powershell*", "Sysmon") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `suspicious_wevtutil_usage_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: The wevtutil.exe application is a legitimate Windows event log utility. Administrators may use it to manage Windows event logs. references: -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1070.001/T1070.001.md + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1070.001/T1070.001.md drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Wevtutil.exe being used to clear Event Logs on $dest$ by $user$ - risk_objects: - - field: dest - type: system - score: 28 - - field: user - type: user - score: 28 - threat_objects: [] + message: Wevtutil.exe being used to clear Event Logs on $dest$ by $user$ + risk_objects: + - field: dest + type: system + score: 28 + - field: user + type: user + score: 28 + threat_objects: [] tags: - analytic_story: - - Windows Log Manipulation - - Ransomware - - Rhysida Ransomware - - Clop Ransomware - - CISA AA23-347A - - ShrinkLocker - - Storm-2460 CLFS Zero Day Exploitation - - Scattered Spider - - Storm-0501 Ransomware - - VoidLink Cloud-Native Linux Malware - asset_type: Endpoint - mitre_attack_id: - - T1070.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Log Manipulation + - Ransomware + - Rhysida Ransomware + - Clop Ransomware + - CISA AA23-347A + - ShrinkLocker + - Storm-2460 CLFS Zero Day Exploitation + - Scattered Spider + - Storm-0501 Ransomware + - VoidLink Cloud-Native Linux Malware + asset_type: Endpoint + mitre_attack_id: + - T1070.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/windows_pwh_log_cleared/wevtutil_clear_log.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/windows_pwh_log_cleared/wevtutil_clear_log.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/suspicious_writes_to_windows_recycle_bin.yml b/detections/endpoint/suspicious_writes_to_windows_recycle_bin.yml index 64ab765444..9f541c06a0 100644 --- a/detections/endpoint/suspicious_writes_to_windows_recycle_bin.yml +++ b/detections/endpoint/suspicious_writes_to_windows_recycle_bin.yml @@ -1,79 +1,67 @@ name: Suspicious writes to windows Recycle Bin id: b5541828-8ffd-4070-9d95-b3da4de924cb -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Rico Valdez, Splunk status: production type: TTP -description: The following analytic detects when a process other than explorer.exe - writes to the Windows Recycle Bin. It leverages the Endpoint.Filesystem and Endpoint.Processes - data models in Splunk to identify any process writing to the "*$Recycle.Bin*" file - path, excluding explorer.exe. This activity is significant because it may indicate - an attacker attempting to hide their actions, potentially leading to data theft, - ransomware, or other malicious outcomes. If confirmed malicious, this behavior could - allow an attacker to persist in the environment and evade detection by security - tools. +description: The following analytic detects when a process other than explorer.exe writes to the Windows Recycle Bin. It leverages the Endpoint.Filesystem and Endpoint.Processes data models in Splunk to identify any process writing to the "*$Recycle.Bin*" file path, excluding explorer.exe. This activity is significant because it may indicate an attacker attempting to hide their actions, potentially leading to data theft, ransomware, or other malicious outcomes. If confirmed malicious, this behavior could allow an attacker to persist in the environment and evade detection by security tools. data_source: -- Sysmon EventID 1 AND Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime values(Filesystem.file_path) as file_path values(Filesystem.file_name) - as file_name FROM datamodel=Endpoint.Filesystem where Filesystem.file_path = "*$Recycle.Bin*" - by Filesystem.process_name Filesystem.process_id Filesystem.dest | `drop_dm_object_name("Filesystem")` - | join process_id [| tstats `security_content_summariesonly` values(Processes.user) - as user values(Processes.process_name) as process_name values(Processes.parent_process_name) - as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name - != "explorer.exe" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name("Processes")` - | table user process_name process_id dest] | `suspicious_writes_to_windows_recycle_bin_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on filesystem and process logs responsible for the changes from your endpoints into - the `Endpoint` datamodel in the `Processes` and `Filesystem` nodes. -known_false_positives: Because the Recycle Bin is a hidden folder in modern versions - of Windows, it would be unusual for a process other than explorer.exe to write to - it. Incidents should be investigated as appropriate. + - Sysmon EventID 1 AND Sysmon EventID 11 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.file_path = "*$Recycle.Bin*" + BY Filesystem.process_name Filesystem.process_id Filesystem.dest + | `drop_dm_object_name("Filesystem")` + | join process_id [ + | tstats `security_content_summariesonly` values(Processes.user) as user values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes + WHERE Processes.process_name != "explorer.exe" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name("Processes")` + | table user process_name process_id dest] + | `suspicious_writes_to_windows_recycle_bin_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on filesystem and process logs responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` nodes. +known_false_positives: Because the Recycle Bin is a hidden folder in modern versions of Windows, it would be unusual for a process other than explorer.exe to write to it. Incidents should be investigated as appropriate. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious writes to windows Recycle Bin process $process_name$ on $dest$ - risk_objects: - - field: dest - type: system - score: 28 - threat_objects: - - field: process_name - type: process_name + message: Suspicious writes to windows Recycle Bin process $process_name$ on $dest$ + risk_objects: + - field: dest + type: system + score: 28 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Collection and Staging - - PlugX - asset_type: Windows - mitre_attack_id: - - T1036 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Collection and Staging + - PlugX + asset_type: Windows + mitre_attack_id: + - T1036 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/write_to_recycle_bin/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/write_to_recycle_bin/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/svchost_lolbas_execution_process_spawn.yml b/detections/endpoint/svchost_lolbas_execution_process_spawn.yml index 0d04ef7a3e..a455c29a89 100644 --- a/detections/endpoint/svchost_lolbas_execution_process_spawn.yml +++ b/detections/endpoint/svchost_lolbas_execution_process_spawn.yml @@ -1,94 +1,71 @@ name: Svchost LOLBAS Execution Process Spawn id: 09e5c72a-4c0d-11ec-aa29-3e22fbd008af -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects instances of 'svchost.exe' spawning Living - Off The Land Binaries and Scripts (LOLBAS) processes. It leverages Endpoint Detection - and Response (EDR) data to monitor child processes of 'svchost.exe' that match known - LOLBAS executables. This activity is significant as adversaries often use LOLBAS - techniques to execute malicious code stealthily, potentially indicating lateral - movement or code execution attempts. If confirmed malicious, this behavior could - allow attackers to execute arbitrary commands, escalate privileges, or maintain - persistence within the environment, posing a significant security risk. +description: The following analytic detects instances of 'svchost.exe' spawning Living Off The Land Binaries and Scripts (LOLBAS) processes. It leverages Endpoint Detection and Response (EDR) data to monitor child processes of 'svchost.exe' that match known LOLBAS executables. This activity is significant as adversaries often use LOLBAS techniques to execute malicious code stealthily, potentially indicating lateral movement or code execution attempts. If confirmed malicious, this behavior could allow attackers to execute arbitrary commands, escalate privileges, or maintain persistence within the environment, posing a significant security risk. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=svchost.exe) - (Processes.process_name IN ("Regsvcs.exe", "Ftp.exe", "OfflineScannerShell.exe", - "Rasautou.exe", "Schtasks.exe", "Xwizard.exe", "Pnputil.exe", "Atbroker.exe", "Pcwrun.exe", - "Ttdinject.exe","Mshta.exe", "Bitsadmin.exe", "Certoc.exe", "Ieexec.exe", "Microsoft.Workflow.Compiler.exe", - "Runscripthelper.exe", "Forfiles.exe", "Msbuild.exe", "Register-cimprovider.exe", - "Tttracer.exe", "Ie4uinit.exe", "Bash.exe", "Hh.exe", "SettingSyncHost.exe", "Cmstp.exe", - "Stordiag.exe", "Scriptrunner.exe", "Odbcconf.exe", "Extexport.exe", "Msdt.exe", - "WorkFolders.exe", "Diskshadow.exe", "Mavinject.exe", "Regasm.exe", "Gpscript.exe", - "Regsvr32.exe", "Msiexec.exe", "Wuauclt.exe", "Presentationhost.exe", "Wmic.exe", - "Runonce.exe", "Syncappvpublishingserver.exe", "Verclsid.exe", "Infdefaultinstall.exe", - "Installutil.exe", "Netsh.exe", "Wab.exe", "Dnscmd.exe", "At.exe", "Pcalua.exe", - "Msconfig.exe")) by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `svchost_lolbas_execution_process_spawn_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Legitimate applications may trigger this behavior, filter as - needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.parent_process_name=svchost.exe + ) + (Processes.process_name IN ("Regsvcs.exe", "Ftp.exe", "OfflineScannerShell.exe", "Rasautou.exe", "Schtasks.exe", "Xwizard.exe", "Pnputil.exe", "Atbroker.exe", "Pcwrun.exe", "Ttdinject.exe","Mshta.exe", "Bitsadmin.exe", "Certoc.exe", "Ieexec.exe", "Microsoft.Workflow.Compiler.exe", "Runscripthelper.exe", "Forfiles.exe", "Msbuild.exe", "Register-cimprovider.exe", "Tttracer.exe", "Ie4uinit.exe", "Bash.exe", "Hh.exe", "SettingSyncHost.exe", "Cmstp.exe", "Stordiag.exe", "Scriptrunner.exe", "Odbcconf.exe", "Extexport.exe", "Msdt.exe", "WorkFolders.exe", "Diskshadow.exe", "Mavinject.exe", "Regasm.exe", "Gpscript.exe", "Regsvr32.exe", "Msiexec.exe", "Wuauclt.exe", "Presentationhost.exe", "Wmic.exe", "Runonce.exe", "Syncappvpublishingserver.exe", "Verclsid.exe", "Infdefaultinstall.exe", "Installutil.exe", "Netsh.exe", "Wab.exe", "Dnscmd.exe", "At.exe", "Pcalua.exe", "Msconfig.exe")) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `svchost_lolbas_execution_process_spawn_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Legitimate applications may trigger this behavior, filter as needed. references: -- https://attack.mitre.org/techniques/T1053/005/ -- https://www.ired.team/offensive-security/persistence/t1053-schtask -- https://lolbas-project.github.io/ + - https://attack.mitre.org/techniques/T1053/005/ + - https://www.ired.team/offensive-security/persistence/t1053-schtask + - https://lolbas-project.github.io/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Svchost.exe spawned a LOLBAS process on $dest$ - risk_objects: - - field: dest - type: system - score: 54 - threat_objects: [] + message: Svchost.exe spawned a LOLBAS process on $dest$ + risk_objects: + - field: dest + type: system + score: 54 + threat_objects: [] tags: - analytic_story: - - Active Directory Lateral Movement - - Living Off The Land - - Scheduled Tasks - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1053.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + - Living Off The Land + - Scheduled Tasks + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1053.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/svchost_lolbas_execution_process_spawn/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/svchost_lolbas_execution_process_spawn/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/system_info_gathering_using_dxdiag_application.yml b/detections/endpoint/system_info_gathering_using_dxdiag_application.yml index 59d77d2a1e..f8de57f577 100644 --- a/detections/endpoint/system_info_gathering_using_dxdiag_application.yml +++ b/detections/endpoint/system_info_gathering_using_dxdiag_application.yml @@ -5,57 +5,41 @@ date: '2025-12-15' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic identifies the execution of the dxdiag.exe process - with specific command-line arguments, which is used to gather system information. - This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process creation events and command-line details. This activity is significant - because dxdiag.exe is rarely used in corporate environments and its execution may - indicate reconnaissance efforts by malicious actors. If confirmed malicious, this - activity could allow attackers to collect detailed system information, aiding in - further exploitation or lateral movement within the network. +description: The following analytic identifies the execution of the dxdiag.exe process with specific command-line arguments, which is used to gather system information. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events and command-line details. This activity is significant because dxdiag.exe is rarely used in corporate environments and its execution may indicate reconnaissance efforts by malicious actors. If confirmed malicious, this activity could allow attackers to collect detailed system information, aiding in further exploitation or lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - (Processes.process_name=dxdiag.exe OR Processes.original_file_name=dxdiag.exe) - Processes.process = "* /t *" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `system_info_gathering_using_dxdiag_application_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: This commandline can be used by a network administrator to - audit host machine specifications. Thus, a filter is needed. + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where + (Processes.process_name=dxdiag.exe OR Processes.original_file_name=dxdiag.exe) + Processes.process = "* /t *" + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` + | `system_info_gathering_using_dxdiag_application_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: This commandline can be used by a network administrator to audit host machine specifications. Thus, a filter is needed. references: -- https://app.any.run/tasks/df0baf9f-8baf-4c32-a452-16562ecb19be/ + - https://app.any.run/tasks/df0baf9f-8baf-4c32-a452-16562ecb19be/ tags: - analytic_story: - - Remcos - asset_type: Endpoint - mitre_attack_id: - - T1592 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Remcos + asset_type: Endpoint + mitre_attack_id: + - T1592 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/t1592/host_info_dxdiag/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/t1592/host_info_dxdiag/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/system_information_discovery_detection.yml b/detections/endpoint/system_information_discovery_detection.yml index 1b302b927c..12f467f99b 100644 --- a/detections/endpoint/system_information_discovery_detection.yml +++ b/detections/endpoint/system_information_discovery_detection.yml @@ -5,98 +5,57 @@ date: '2026-02-09' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic identifies system information discovery - techniques, such as the execution of commands like `wmic qfe`, `systeminfo`, - and `hostname`. It leverages data from Endpoint Detection and Response (EDR) - agents, focusing on process execution logs. This activity is significant - because attackers often use these commands to gather system configuration - details, which can aid in further exploitation. If confirmed malicious, this - behavior could allow attackers to tailor their attacks based on the discovered - system information, potentially leading to privilege escalation, persistence, - or data exfiltration. +description: The following analytic identifies system information discovery techniques, such as the execution of commands like `wmic qfe`, `systeminfo`, and `hostname`. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This activity is significant because attackers often use these commands to gather system configuration details, which can aid in further exploitation. If confirmed malicious, this behavior could allow attackers to tailor their attacks based on the discovered system information, potentially leading to privilege escalation, persistence, or data exfiltration. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process="*wmic* qfe*" - OR Processes.process=*systeminfo* OR Processes.process=*hostname*) by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | eventstats dc(process) as dc_processes_by_dest - by dest | where dc_processes_by_dest > 2 | stats values(process) as process values(action) - as action values(original_file_name) as original_file_name values(parent_process) - as parent_process values(parent_process_exec) as parent_process_exec values(parent_process_guid) - as parent_process_guid values(parent_process_id) as parent_process_id values(parent_process_path) - as parent_process_path values(process_exec) as process_exec values(process_guid) - as process_guid values(.process_hash) as process_hash values(process_id) as process_id - values(process_integrity_level) as process_integrity_level values(process_path) - as process_path values(user_id) as user_id values(vendor_product) as vendor_product min(firstTime) - as firstTime max(lastTime) as lastTime by user, dest parent_process_name | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `system_information_discovery_detection_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*wmic* qfe*" OR Processes.process=*systeminfo* OR Processes.process=*hostname*) by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | eventstats dc(process) as dc_processes_by_dest by dest | where dc_processes_by_dest > 2 | stats values(process) as process values(action) as action values(original_file_name) as original_file_name values(parent_process) as parent_process values(parent_process_exec) as parent_process_exec values(parent_process_guid) as parent_process_guid values(parent_process_id) as parent_process_id values(parent_process_path) as parent_process_path values(process_exec) as process_exec values(process_guid) as process_guid values(.process_hash) as process_hash values(process_id) as process_id values(process_integrity_level) as process_integrity_level values(process_path) as process_path values(user_id) as user_id values(vendor_product) as vendor_product min(firstTime) as firstTime max(lastTime) as lastTime by user, dest parent_process_name | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `system_information_discovery_detection_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators debugging servers references: -- https://web.archive.org/web/20210119205146/https://oscp.infosecsanyam.in/priv-escalation/windows-priv-escalation + - https://web.archive.org/web/20210119205146/https://oscp.infosecsanyam.in/priv-escalation/windows-priv-escalation drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential system information discovery behavior on $dest$ by $user$ - risk_objects: - - field: dest - type: system - score: 15 - - field: user - type: user - score: 15 - threat_objects: [] + message: Potential system information discovery behavior on $dest$ by $user$ + risk_objects: + - field: dest + type: system + score: 15 + - field: user + type: user + score: 15 + threat_objects: [] tags: - analytic_story: - - SolarWinds WHD RCE Post Exploitation - - Windows Discovery Techniques - - Gozi Malware - - Medusa Ransomware - - BlackSuit Ransomware - - Cleo File Transfer Software - - Interlock Ransomware - - LAMEHUG - - NetSupport RMM Tool Abuse - asset_type: Windows - mitre_attack_id: - - T1082 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SolarWinds WHD RCE Post Exploitation + - Windows Discovery Techniques + - Gozi Malware + - Medusa Ransomware + - BlackSuit Ransomware + - Cleo File Transfer Software + - Interlock Ransomware + - LAMEHUG + - NetSupport RMM Tool Abuse + asset_type: Windows + mitre_attack_id: + - T1082 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1082/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1082/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/system_processes_run_from_unexpected_locations.yml b/detections/endpoint/system_processes_run_from_unexpected_locations.yml index 2e3f2de426..0b75b23062 100644 --- a/detections/endpoint/system_processes_run_from_unexpected_locations.yml +++ b/detections/endpoint/system_processes_run_from_unexpected_locations.yml @@ -1,106 +1,85 @@ name: System Processes Run From Unexpected Locations id: a34aae96-ccf8-4aef-952c-3ea21444444d -version: 13 -date: '2025-12-31' +version: 14 +date: '2026-02-25' author: David Dorsey, Michael Haag, Nasreddine Bencherchali, Splunk status: production type: Anomaly -description: The following analytic identifies system processes running from unexpected - locations outside of paths such as `C:\Windows\System32\` or `C:\Windows\SysWOW64`. It leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process paths, - names, and hashes. This activity is significant as it may indicate a malicious process - attempting to masquerade as a legitimate system process. If confirmed malicious, - this behavior could allow an attacker to execute code, escalate privileges, or maintain - persistence within the environment, posing a significant security risk. +description: The following analytic identifies system processes running from unexpected locations outside of paths such as `C:\Windows\System32\` or `C:\Windows\SysWOW64`. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process paths, names, and hashes. This activity is significant as it may indicate a malicious process attempting to masquerade as a legitimate system process. If confirmed malicious, this behavior could allow an attacker to execute code, escalate privileges, or maintain persistence within the environment, posing a significant security risk. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime max(_time) as lastTime - FROM datamodel=Endpoint.Processes where - NOT Processes.process_path IN ( - "*:\\$WINDOWS.~BT\\*", - "*:\\$WinREAgent\\*", - "*:\\Program Files \(x86\)\\Windows Kits\\10\\App Certification Kit\\*", - "*:\\Windows\\SoftwareDistribution\\*", - "*:\\Windows\\System32\\*", - "*:\\Windows\\SystemTemp\\*", - "*:\\Windows\\SysWOW64\\*", - "*:\\Windows\\uus\\*", - "*:\\Windows\\WinSxS\\*" - ) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process - Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name("Processes")` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | lookup update=true is_windows_system_file filename as process_name OUTPUT systemFile - | search systemFile=true - | `system_processes_run_from_unexpected_locations_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: This detection may require tuning based on third party applications - utilizing native Windows binaries in non-standard paths. + | tstats `security_content_summariesonly` + count min(_time) as firstTime max(_time) as lastTime + FROM datamodel=Endpoint.Processes where + NOT Processes.process_path IN ( + "*:\\$WINDOWS.~BT\\*", + "*:\\$WinREAgent\\*", + "*:\\Program Files \(x86\)\\Windows Kits\\10\\App Certification Kit\\*", + "*:\\Windows\\SoftwareDistribution\\*", + "*:\\Windows\\System32\\*", + "*:\\Windows\\SystemTemp\\*", + "*:\\Windows\\SysWOW64\\*", + "*:\\Windows\\uus\\*", + "*:\\Windows\\WinSxS\\*" + ) + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process + Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name + Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | lookup update=true is_windows_system_file filename as process_name OUTPUT systemFile + | search systemFile=true + | `system_processes_run_from_unexpected_locations_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: This detection may require tuning based on third party applications utilizing native Windows binaries in non-standard paths. references: -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1036.003/T1036.003.yaml -- https://attack.mitre.org/techniques/T1036/003/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1036.003/T1036.003.yaml + - https://attack.mitre.org/techniques/T1036/003/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A System process $process_name$ is running from $process_path$ on $dest$, - potentially non-standard. - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: - - field: process_name - type: process_name + message: A System process $process_name$ is running from $process_path$ on $dest$, potentially non-standard. + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Suspicious Command-Line Executions - - Unusual Processes - - Ransomware - - Masquerading - Rename System Utilities - - Qakbot - - Windows Error Reporting Service Elevation of Privilege Vulnerability - - DarkGate Malware - asset_type: Endpoint - mitre_attack_id: - - T1036.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Command-Line Executions + - Unusual Processes + - Ransomware + - Masquerading - Rename System Utilities + - Qakbot + - Windows Error Reporting Service Elevation of Privilege Vulnerability + - DarkGate Malware + asset_type: Endpoint + mitre_attack_id: + - T1036.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/system_user_discovery_with_query.yml b/detections/endpoint/system_user_discovery_with_query.yml index 0d015aa124..ff1077208f 100644 --- a/detections/endpoint/system_user_discovery_with_query.yml +++ b/detections/endpoint/system_user_discovery_with_query.yml @@ -1,59 +1,53 @@ name: System User Discovery With Query id: ad03bfcf-8a91-4bc2-a500-112993deba87 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting -description: The following analytic detects the execution of `query.exe` with command-line - arguments aimed at discovering logged-in users. It leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process names and command-line - executions. This activity is significant as adversaries may use `query.exe` to gain - situational awareness and perform Active Directory discovery on compromised endpoints. - If confirmed malicious, this behavior could allow attackers to identify active users, - aiding in further lateral movement and privilege escalation within the network. +description: The following analytic detects the execution of `query.exe` with command-line arguments aimed at discovering logged-in users. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as adversaries may use `query.exe` to gain situational awareness and perform Active Directory discovery on compromised endpoints. If confirmed malicious, this behavior could allow attackers to identify active users, aiding in further lateral movement and privilege escalation within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="query.exe" - OR Processes.original_file_name="query.exe") AND Processes.process="*user*" AND - ((NOT Processes.process="*/server*") OR Processes.process IN ("*/server:localhost*", - "*/server:127.0.0.1*")) by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `system_user_discovery_with_query_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="query.exe" + OR + Processes.original_file_name="query.exe" + ) + AND Processes.process="*user*" AND ((NOT Processes.process="*/server*") OR Processes.process IN ("*/server:localhost*", "*/server:127.0.0.1*")) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `system_user_discovery_with_query_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1033/ + - https://attack.mitre.org/techniques/T1033/ tags: - analytic_story: - - Active Directory Discovery - - Medusa Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1033 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - Medusa Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1033 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/system_user_discovery_with_whoami.yml b/detections/endpoint/system_user_discovery_with_whoami.yml index 936b45887c..57bc831df6 100644 --- a/detections/endpoint/system_user_discovery_with_whoami.yml +++ b/detections/endpoint/system_user_discovery_with_whoami.yml @@ -1,95 +1,78 @@ name: System User Discovery With Whoami id: 894fc43e-6f50-47d5-a68b-ee9ee23e18f4 -version: 7 -date: '2025-08-27' +version: 8 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Anomaly -description: The following analytic detects the execution of `whoami.exe` - without any arguments. It leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process execution logs. This activity is significant - because both Red Teams and adversaries use `whoami.exe` to identify the - current logged-in user, aiding in situational awareness and Active Directory - discovery. If confirmed malicious, this behavior could indicate an attacker is - gathering information to further compromise the system, potentially leading to - privilege escalation or lateral movement within the network. +description: The following analytic detects the execution of `whoami.exe` without any arguments. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This activity is significant because both Red Teams and adversaries use `whoami.exe` to identify the current logged-in user, aiding in situational awareness and Active Directory discovery. If confirmed malicious, this behavior could indicate an attacker is gathering information to further compromise the system, potentially leading to privilege escalation or lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="whoami.exe") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `system_user_discovery_with_whoami_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: Administrators or power users may use this command for - troubleshooting. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="whoami.exe" + ) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `system_user_discovery_with_whoami_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1033/ + - https://attack.mitre.org/techniques/T1033/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 3 - - field: dest - type: system - score: 3 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 3 + - field: dest + type: system + score: 3 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Winter Vivern - - Active Directory Discovery - - Rhysida Ransomware - - Qakbot - - CISA AA23-347A - - PHP-CGI RCE Attack on Japanese Organizations - - LAMEHUG - asset_type: Endpoint - mitre_attack_id: - - T1033 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Winter Vivern + - Active Directory Discovery + - Rhysida Ransomware + - Qakbot + - CISA AA23-347A + - PHP-CGI RCE Attack on Japanese Organizations + - LAMEHUG + asset_type: Endpoint + mitre_attack_id: + - T1033 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/time_provider_persistence_registry.yml b/detections/endpoint/time_provider_persistence_registry.yml index 889c0d6f92..0ea5b9ca05 100644 --- a/detections/endpoint/time_provider_persistence_registry.yml +++ b/detections/endpoint/time_provider_persistence_registry.yml @@ -5,73 +5,52 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: The following analytic detects suspicious modifications to the time provider - registry for persistence and autostart. It leverages data from the Endpoint.Registry - data model, focusing on changes to the "CurrentControlSet\\Services\\W32Time\\TimeProviders" - registry path. This activity is significant because such modifications are uncommon - and can indicate an attempt to establish persistence on a compromised host. If confirmed - malicious, this technique allows an attacker to maintain access and execute code - automatically upon system boot, potentially leading to further exploitation and - control over the affected system. +description: The following analytic detects suspicious modifications to the time provider registry for persistence and autostart. It leverages data from the Endpoint.Registry data model, focusing on changes to the "CurrentControlSet\\Services\\W32Time\\TimeProviders" registry path. This activity is significant because such modifications are uncommon and can indicate an attempt to establish persistence on a compromised host. If confirmed malicious, this technique allows an attacker to maintain access and execute code automatically upon system boot, potentially leading to further exploitation and control over the affected system. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\CurrentControlSet\\Services\\W32Time\\TimeProviders*") - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `time_provider_persistence_registry_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\CurrentControlSet\\Services\\W32Time\\TimeProviders*") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `time_provider_persistence_registry_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: No false positives have been identified at this time. references: -- https://pentestlab.blog/2019/10/22/persistence-time-providers/ -- https://attack.mitre.org/techniques/T1547/003/ + - https://pentestlab.blog/2019/10/22/persistence-time-providers/ + - https://attack.mitre.org/techniques/T1547/003/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: modified/added/deleted registry entry $registry_path$ on $dest$ - risk_objects: - - field: dest - type: system - score: 80 - - field: user - type: user - score: 80 - threat_objects: [] + message: modified/added/deleted registry entry $registry_path$ on $dest$ + risk_objects: + - field: dest + type: system + score: 80 + - field: user + type: user + score: 80 + threat_objects: [] tags: - analytic_story: - - Hermetic Wiper - - Windows Privilege Escalation - - Windows Persistence Techniques - - Windows Registry Abuse - - Data Destruction - asset_type: Endpoint - mitre_attack_id: - - T1547.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Hermetic Wiper + - Windows Privilege Escalation + - Windows Persistence Techniques + - Windows Registry Abuse + - Data Destruction + asset_type: Endpoint + mitre_attack_id: + - T1547.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.003/timeprovider_reg/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.003/timeprovider_reg/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/trickbot_named_pipe.yml b/detections/endpoint/trickbot_named_pipe.yml index 5f2c3b02b7..6304c56f59 100644 --- a/detections/endpoint/trickbot_named_pipe.yml +++ b/detections/endpoint/trickbot_named_pipe.yml @@ -5,66 +5,49 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the creation or connection to a named - pipe associated with Trickbot malware. It leverages Sysmon EventCodes 17 and 18 - to identify named pipes with the pattern "\\pipe\\*lacesomepipe". This activity - is significant as Trickbot uses named pipes for communication with its command and - control (C2) servers, facilitating data exfiltration and command execution. If confirmed - malicious, this behavior could allow attackers to maintain persistence, execute - arbitrary commands, and exfiltrate sensitive information from the compromised system. +description: The following analytic detects the creation or connection to a named pipe associated with Trickbot malware. It leverages Sysmon EventCodes 17 and 18 to identify named pipes with the pattern "\\pipe\\*lacesomepipe". This activity is significant as Trickbot uses named pipes for communication with its command and control (C2) servers, facilitating data exfiltration and command execution. If confirmed malicious, this behavior could allow attackers to maintain persistence, execute arbitrary commands, and exfiltrate sensitive information from the compromised system. data_source: -- Sysmon EventID 17 -- Sysmon EventID 18 -search: '`sysmon` EventCode IN (17,18) PipeName="\\pipe\\*lacesomepipe" | stats min(_time) - as firstTime max(_time) as lastTime count by dest dvc pipe_name process_exec process_guid - process_id process_name process_path signature signature_id user_id vendor_product - Image PipeName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `trickbot_named_pipe_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name and pipename from your endpoints. If you are using Sysmon, - you must have at least version 6.0.4 of the Sysmon TA. . + - Sysmon EventID 17 + - Sysmon EventID 18 +search: '`sysmon` EventCode IN (17,18) PipeName="\\pipe\\*lacesomepipe" | stats min(_time) as firstTime max(_time) as lastTime count by dest dvc pipe_name process_exec process_guid process_id process_name process_path signature signature_id user_id vendor_product Image PipeName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `trickbot_named_pipe_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and pipename from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. . known_false_positives: No false positives have been identified at this time. references: -- https://labs.vipre.com/trickbot-and-its-modules/ -- https://whitehat.eu/incident-response-case-study-featuring-ryuk-and-trickbot-part-2/ + - https://labs.vipre.com/trickbot-and-its-modules/ + - https://whitehat.eu/incident-response-case-study-featuring-ryuk-and-trickbot-part-2/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible Trickbot namedpipe created on $dest$ by $process_name$ - risk_objects: - - field: dest - type: system - score: 42 - threat_objects: - - field: process_name - type: process_name + message: Possible Trickbot namedpipe created on $dest$ by $process_name$ + risk_objects: + - field: dest + type: system + score: 42 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Trickbot - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1055 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Trickbot + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1055 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/namedpipe/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/namedpipe/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/uac_bypass_mmc_load_unsigned_dll.yml b/detections/endpoint/uac_bypass_mmc_load_unsigned_dll.yml index 21d22daf72..535379a946 100644 --- a/detections/endpoint/uac_bypass_mmc_load_unsigned_dll.yml +++ b/detections/endpoint/uac_bypass_mmc_load_unsigned_dll.yml @@ -5,66 +5,45 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the loading of an unsigned DLL by the - MMC.exe application, which is indicative of a potential UAC bypass or privilege - escalation attempt. It leverages Sysmon EventCode 7 to identify instances where - MMC.exe loads a non-Microsoft, unsigned DLL. This activity is significant because - attackers often use this technique to modify CLSID registry entries, causing MMC.exe - to load malicious DLLs, thereby bypassing User Account Control (UAC) and gaining - elevated privileges. If confirmed malicious, this could allow an attacker to execute - arbitrary code with higher privileges, leading to further system compromise and - persistence. +description: The following analytic detects the loading of an unsigned DLL by the MMC.exe application, which is indicative of a potential UAC bypass or privilege escalation attempt. It leverages Sysmon EventCode 7 to identify instances where MMC.exe loads a non-Microsoft, unsigned DLL. This activity is significant because attackers often use this technique to modify CLSID registry entries, causing MMC.exe to load malicious DLLs, thereby bypassing User Account Control (UAC) and gaining elevated privileges. If confirmed malicious, this could allow an attacker to execute arbitrary code with higher privileges, leading to further system compromise and persistence. data_source: -- Sysmon EventID 7 -search: '`sysmon` EventCode=7 ImageLoaded = "*.dll" Image = "*\\mmc.exe" Signed=false - Company != "Microsoft Corporation" | fillnull | stats count min(_time) as firstTime - max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name - process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists - service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `uac_bypass_mmc_load_unsigned_dll_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name and imageloaded executions from your endpoints. If you - are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -known_false_positives: No false positives have been identified at this time. - dll. + - Sysmon EventID 7 +search: '`sysmon` EventCode=7 ImageLoaded = "*.dll" Image = "*\\mmc.exe" Signed=false Company != "Microsoft Corporation" | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `uac_bypass_mmc_load_unsigned_dll_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: No false positives have been identified at this time. dll. references: -- https://offsec.almond.consulting/UAC-bypass-dotnet.html + - https://offsec.almond.consulting/UAC-bypass-dotnet.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious unsigned $ImageLoaded$ loaded by $Image$ on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 63 - threat_objects: [] + message: Suspicious unsigned $ImageLoaded$ loaded by $Image$ on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 63 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - asset_type: Endpoint - mitre_attack_id: - - T1218.014 - - T1548.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + asset_type: Endpoint + mitre_attack_id: + - T1218.014 + - T1548.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/uac_bypass/windows-sysmon2.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/uac_bypass/windows-sysmon2.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/uac_bypass_with_colorui_com_object.yml b/detections/endpoint/uac_bypass_with_colorui_com_object.yml index 24e9386794..afd23019d5 100644 --- a/detections/endpoint/uac_bypass_with_colorui_com_object.yml +++ b/detections/endpoint/uac_bypass_with_colorui_com_object.yml @@ -5,66 +5,45 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects a potential UAC bypass using the colorui.dll - COM Object. It leverages Sysmon EventCode 7 to identify instances where colorui.dll - is loaded by a process other than colorcpl.exe, excluding common system directories. - This activity is significant because UAC bypass techniques are often used by malware, - such as LockBit ransomware, to gain elevated privileges without user consent. If - confirmed malicious, this could allow an attacker to execute code with higher privileges, - leading to further system compromise and persistence within the environment. +description: The following analytic detects a potential UAC bypass using the colorui.dll COM Object. It leverages Sysmon EventCode 7 to identify instances where colorui.dll is loaded by a process other than colorcpl.exe, excluding common system directories. This activity is significant because UAC bypass techniques are often used by malware, such as LockBit ransomware, to gain elevated privileges without user consent. If confirmed malicious, this could allow an attacker to execute code with higher privileges, leading to further system compromise and persistence within the environment. data_source: -- Sysmon EventID 7 -search: '`sysmon` EventCode=7 ImageLoaded="*\\colorui.dll" process_name != "colorcpl.exe" - NOT(Image IN("*\\windows\\*", "*\\program files*")) | fillnull | stats count min(_time) - as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path - original_file_name process_exec process_guid process_hash process_id process_name - process_path service_dll_signature_exists service_dll_signature_verified signature - signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `uac_bypass_with_colorui_com_object_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. + - Sysmon EventID 7 +search: '`sysmon` EventCode=7 ImageLoaded="*\\colorui.dll" process_name != "colorcpl.exe" NOT(Image IN("*\\windows\\*", "*\\program files*")) | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `uac_bypass_with_colorui_com_object_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: not so common. but 3rd part app may load this dll. references: -- https://news.sophos.com/en-us/2020/04/24/lockbit-ransomware-borrows-tricks-to-keep-up-with-revil-and-maze/ + - https://news.sophos.com/en-us/2020/04/24/lockbit-ransomware-borrows-tricks-to-keep-up-with-revil-and-maze/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The following module $ImageLoaded$ was loaded by a non-standard application - on endpoint $dest$. - risk_objects: - - field: dest - type: system - score: 48 - threat_objects: [] + message: The following module $ImageLoaded$ was loaded by a non-standard application on endpoint $dest$. + risk_objects: + - field: dest + type: system + score: 48 + threat_objects: [] tags: - analytic_story: - - Ransomware - - LockBit Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1218.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - LockBit Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1218.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.015/uac_colorui/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.015/uac_colorui/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/uninstall_app_using_msiexec.yml b/detections/endpoint/uninstall_app_using_msiexec.yml index 621a2f05e8..46a763bb95 100644 --- a/detections/endpoint/uninstall_app_using_msiexec.yml +++ b/detections/endpoint/uninstall_app_using_msiexec.yml @@ -1,81 +1,65 @@ name: Uninstall App Using MsiExec id: 1fca2b28-f922-11eb-b2dd-acde48001122 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the uninstallation of applications using - msiexec with specific command-line arguments. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process execution logs that include command-line - details. This activity is significant because it is an uncommon practice in enterprise - environments and has been associated with malicious behavior, such as disabling - antivirus software. If confirmed malicious, this could allow an attacker to remove - security software, potentially leading to further compromise and persistence within - the network. +description: The following analytic detects the uninstallation of applications using msiexec with specific command-line arguments. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because it is an uncommon practice in enterprise environments and has been associated with malicious behavior, such as disabling antivirus software. If confirmed malicious, this could allow an attacker to remove security software, potentially leading to further compromise and persistence within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=msiexec.exe - Processes.process= "* /qn *" Processes.process= "*/X*" Processes.process= "*REBOOT=*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `uninstall_app_using_msiexec_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=msiexec.exe Processes.process= "* /qn *" Processes.process= "*/X*" Processes.process= "*REBOOT=*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `uninstall_app_using_msiexec_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://threadreaderapp.com/thread/1423361119926816776.html + - https://threadreaderapp.com/thread/1423361119926816776.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: process $process_name$ with a cmdline $process$ in host $dest$ - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: - - field: process_name - type: process_name + message: process $process_name$ with a cmdline $process$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1218.007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1218.007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/conti/conti_leak/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/conti/conti_leak/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/unknown_process_using_the_kerberos_protocol.yml b/detections/endpoint/unknown_process_using_the_kerberos_protocol.yml index 0ea5773741..af5a8affcc 100644 --- a/detections/endpoint/unknown_process_using_the_kerberos_protocol.yml +++ b/detections/endpoint/unknown_process_using_the_kerberos_protocol.yml @@ -1,90 +1,79 @@ name: Unknown Process Using The Kerberos Protocol id: c91a0852-9fbb-11ec-af44-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic identifies a non-lsass.exe process making an outbound - connection on port 88, which is typically used by the Kerberos authentication protocol. - This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process and network traffic logs. This activity is significant because, - under normal circumstances, only the lsass.exe process should interact with the - Kerberos Distribution Center. If confirmed malicious, this behavior could indicate - an adversary attempting to abuse the Kerberos protocol, potentially leading to unauthorized - access or lateral movement within the network. +description: The following analytic identifies a non-lsass.exe process making an outbound connection on port 88, which is typically used by the Kerberos authentication protocol. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and network traffic logs. This activity is significant because, under normal circumstances, only the lsass.exe process should interact with the Kerberos Distribution Center. If confirmed malicious, this behavior could indicate an adversary attempting to abuse the Kerberos protocol, potentially leading to unauthorized access or lateral movement within the network. data_source: -- Sysmon EventID 1 AND Sysmon EventID 3 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes - where Processes.process_name!=lsass.exe by _time Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | join process_id dest [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic - where All_Traffic.dest_port = 88 by All_Traffic.action All_Traffic.app All_Traffic.bytes All_Traffic.bytes_in All_Traffic.bytes_out - All_Traffic.dest All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.dvc All_Traffic.protocol - All_Traffic.protocol_version All_Traffic.src All_Traffic.src_ip All_Traffic.src_port - All_Traffic.transport All_Traffic.user All_Traffic.vendor_product All_Traffic.direction - All_Traffic.process_id - | `drop_dm_object_name(All_Traffic)` | rename src as dest ] | table _time dest - parent_process_name process_name process_path process process_id dest_port | `unknown_process_using_the_kerberos_protocol_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Custom applications may leverage the Kerberos protocol. Filter - as needed. + - Sysmon EventID 1 AND Sysmon EventID 3 +search: |- + | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes + WHERE Processes.process_name!=lsass.exe + BY _time Processes.action Processes.dest + Processes.original_file_name Processes.parent_process Processes.parent_process_exec + Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name + Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id + Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | join process_id dest [ + | tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic + WHERE All_Traffic.dest_port = 88 + BY All_Traffic.action All_Traffic.app All_Traffic.bytes + All_Traffic.bytes_in All_Traffic.bytes_out All_Traffic.dest + All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.dvc + All_Traffic.protocol All_Traffic.protocol_version All_Traffic.src + All_Traffic.src_ip All_Traffic.src_port All_Traffic.transport + All_Traffic.user All_Traffic.vendor_product All_Traffic.direction + All_Traffic.process_id + | `drop_dm_object_name(All_Traffic)` + | rename src as dest ] + | table _time dest parent_process_name process_name process_path process process_id dest_port + | `unknown_process_using_the_kerberos_protocol_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Custom applications may leverage the Kerberos protocol. Filter as needed. references: -- https://stealthbits.com/blog/how-to-detect-overpass-the-hash-attacks/ -- https://www.thehacker.recipes/ad/movement/kerberos/ptk + - https://stealthbits.com/blog/how-to-detect-overpass-the-hash-attacks/ + - https://www.thehacker.recipes/ad/movement/kerberos/ptk drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Unknown process $process_name$ using the kerberos protocol detected on - host $dest$ - risk_objects: - - field: dest - type: system - score: 36 - threat_objects: [] + message: Unknown process $process_name$ using the kerberos protocol detected on host $dest$ + risk_objects: + - field: dest + type: system + score: 36 + threat_objects: [] tags: - analytic_story: - - Active Directory Kerberos Attacks - - BlackSuit Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1550 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Kerberos Attacks + - BlackSuit Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1550 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550/rubeus/windows-security.log - source: WinEventLog:Security - sourcetype: WinEventLog - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550/rubeus/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550/rubeus/windows-security.log + source: WinEventLog:Security + sourcetype: WinEventLog + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550/rubeus/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/unload_sysmon_filter_driver.yml b/detections/endpoint/unload_sysmon_filter_driver.yml index b324e9f2be..568b1e4317 100644 --- a/detections/endpoint/unload_sysmon_filter_driver.yml +++ b/detections/endpoint/unload_sysmon_filter_driver.yml @@ -1,80 +1,69 @@ name: Unload Sysmon Filter Driver id: e5928ff3-23eb-4d8b-b8a4-dcbc844fdfbe -version: 11 -date: '2026-01-14' +version: 12 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: TTP -description: The following analytic detects the use of `fltMC.exe` to unload the Sysmon - driver, which stops Sysmon from collecting data. It leverages Endpoint Detection - and Response (EDR) logs, focusing on process names and command-line executions. - This activity is significant because disabling Sysmon can blind security monitoring, - allowing malicious actions to go undetected. If confirmed malicious, this could - enable attackers to execute further attacks without being logged, leading to potential - data breaches, privilege escalation, or persistent access within the environment. +description: The following analytic detects the use of `fltMC.exe` to unload the Sysmon driver, which stops Sysmon from collecting data. It leverages Endpoint Detection and Response (EDR) logs, focusing on process names and command-line executions. This activity is significant because disabling Sysmon can blind security monitoring, allowing malicious actions to go undetected. If confirmed malicious, this could enable attackers to execute further attacks without being logged, leading to potential data breaches, privilege escalation, or persistent access within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) - as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fltMC.exe - AND Processes.process=*unload* AND Processes.process=*SysmonDrv* by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` - | table firstTime lastTime dest user count process_name process_id parent_process_name - process | `unload_sysmon_filter_driver_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=fltMC.exe + AND + Processes.process=*unload* + AND + Processes.process=*SysmonDrv* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table firstTime lastTime dest user count process_name process_id parent_process_name process + | `unload_sysmon_filter_driver_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://www.ired.team/offensive-security/defense-evasion/unloading-sysmon-driver + - https://www.ired.team/offensive-security/defense-evasion/unloading-sysmon-driver drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible Sysmon filter driver unloading on $dest$ - risk_objects: - - field: dest - type: system - score: 45 - threat_objects: [] + message: Possible Sysmon filter driver unloading on $dest$ + risk_objects: + - field: dest + type: system + score: 45 + threat_objects: [] tags: - analytic_story: - - CISA AA23-347A - - Disabling Security Tools - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA23-347A + - Disabling Security Tools + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/unload_sysmon/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/unload_sysmon/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/unloading_amsi_via_reflection.yml b/detections/endpoint/unloading_amsi_via_reflection.yml index e99eb1f314..49e391b8c9 100644 --- a/detections/endpoint/unloading_amsi_via_reflection.yml +++ b/detections/endpoint/unloading_amsi_via_reflection.yml @@ -1,78 +1,65 @@ name: Unloading AMSI via Reflection id: a21e3484-c94d-11eb-b55b-acde48001122 -version: 8 -date: '2025-06-24' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: - The following analytic detects the tampering of AMSI (Antimalware Scan - Interface) via PowerShell reflection. It leverages PowerShell Script Block Logging - (EventCode=4104) to capture and analyze suspicious PowerShell commands, specifically - those involving `system.management.automation.amsi`. This activity is significant - as it indicates an attempt to bypass AMSI, a critical security feature that helps - detect and block malicious scripts. If confirmed malicious, this could allow an - attacker to execute harmful code undetected, leading to potential system compromise - and data exfiltration. +description: The following analytic detects the tampering of AMSI (Antimalware Scan Interface) via PowerShell reflection. It leverages PowerShell Script Block Logging (EventCode=4104) to capture and analyze suspicious PowerShell commands, specifically those involving `system.management.automation.amsi`. This activity is significant as it indicates an attempt to bypass AMSI, a critical security feature that helps detect and block malicious scripts. If confirmed malicious, this could allow an attacker to execute harmful code undetected, leading to potential system compromise and data exfiltration. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText = *system.management.automation.amsi* - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `unloading_amsi_via_reflection_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - Potential for some third party applications to disable AMSI - upon invocation. Filter as needed. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText = *system.management.automation.amsi* + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `unloading_amsi_via_reflection_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: Potential for some third party applications to disable AMSI upon invocation. Filter as needed. references: - - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. - - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 - - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf - - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ + - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63 + - https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf + - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/ drilldown_searches: - - name: View the detection results for - "$Computer$" - search: '%original_detection_search% | search Computer = "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$Computer$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" + search: '%original_detection_search% | search Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible AMSI Unloading via Reflection using PowerShell on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Possible AMSI Unloading via Reflection using PowerShell on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Malicious PowerShell - - Hermetic Wiper - - Data Destruction - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - - T1562 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Malicious PowerShell + - Hermetic Wiper + - Data Destruction + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + - T1562 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/windows-powershell-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/windows-powershell-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/unusual_number_of_computer_service_tickets_requested.yml b/detections/endpoint/unusual_number_of_computer_service_tickets_requested.yml index fc837873f7..b583d76764 100644 --- a/detections/endpoint/unusual_number_of_computer_service_tickets_requested.yml +++ b/detections/endpoint/unusual_number_of_computer_service_tickets_requested.yml @@ -1,46 +1,38 @@ name: Unusual Number of Computer Service Tickets Requested id: ac3b81c0-52f4-11ec-ac44-acde48001122 -version: 7 -date: '2025-10-14' +version: 8 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: experimental type: Hunting -description: The following analytic identifies an unusual number of computer service - ticket requests from a single source, leveraging Event ID 4769, "A Kerberos service - ticket was requested." It uses statistical analysis, including standard deviation - and the 3-sigma rule, to detect anomalies in service ticket requests. This activity - is significant as it may indicate malicious behavior such as lateral movement, malware - staging, or reconnaissance. If confirmed malicious, an attacker could gain unauthorized - access to multiple endpoints, facilitating further compromise and potential data - exfiltration. +description: The following analytic identifies an unusual number of computer service ticket requests from a single source, leveraging Event ID 4769, "A Kerberos service ticket was requested." It uses statistical analysis, including standard deviation and the 3-sigma rule, to detect anomalies in service ticket requests. This activity is significant as it may indicate malicious behavior such as lateral movement, malware staging, or reconnaissance. If confirmed malicious, an attacker could gain unauthorized access to multiple endpoints, facilitating further compromise and potential data exfiltration. data_source: -- Windows Event Log Security 4769 -search: '`wineventlog_security` EventCode=4769 Service_Name="*$" Account_Name!="*$*" - | bucket span=2m _time | stats dc(Service_Name) AS unique_targets values(Service_Name) - as host_targets by _time, Client_Address, Account_Name | eventstats avg(unique_targets) - as comp_avg , stdev(unique_targets) as comp_std by Client_Address, Account_Name - | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_targets >10 and - unique_targets >= upperBound, 1, 0) | `unusual_number_of_computer_service_tickets_requested_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Domain Controller and Kerberos events. The Advanced Security Audit policy setting - `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. -known_false_positives: An single endpoint requesting a large number of computer service - tickets is not common behavior. Possible false positive scenarios include but are - not limited to vulnerability scanners, administration systeams and missconfigured - systems. + - Windows Event Log Security 4769 +search: |- + `wineventlog_security` EventCode=4769 Service_Name="*$" Account_Name!="*$*" + | bucket span=2m _time + | stats dc(Service_Name) AS unique_targets values(Service_Name) as host_targets + BY _time, Client_Address, Account_Name + | eventstats avg(unique_targets) as comp_avg , stdev(unique_targets) as comp_std + BY Client_Address, Account_Name + | eval upperBound=(comp_avg+comp_std*3) + | eval isOutlier=if(unique_targets >10 and unique_targets >= upperBound, 1, 0) + | `unusual_number_of_computer_service_tickets_requested_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. +known_false_positives: An single endpoint requesting a large number of computer service tickets is not common behavior. Possible false positive scenarios include but are not limited to vulnerability scanners, administration systeams and missconfigured systems. references: -- https://attack.mitre.org/techniques/T1078/ + - https://attack.mitre.org/techniques/T1078/ tags: - analytic_story: - - Active Directory Lateral Movement - - Active Directory Kerberos Attacks - - Active Directory Privilege Escalation - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + - Active Directory Kerberos Attacks + - Active Directory Privilege Escalation + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/unusual_number_of_kerberos_service_tickets_requested.yml b/detections/endpoint/unusual_number_of_kerberos_service_tickets_requested.yml index 302f18dc3b..23185204f4 100644 --- a/detections/endpoint/unusual_number_of_kerberos_service_tickets_requested.yml +++ b/detections/endpoint/unusual_number_of_kerberos_service_tickets_requested.yml @@ -1,75 +1,62 @@ name: Unusual Number of Kerberos Service Tickets Requested id: eb3e6702-8936-11ec-98fe-acde48001122 -version: 10 -date: '2025-05-02' +version: 11 +date: '2026-02-25' author: Mauricio Velazco, Dean Luxton, Splunk status: production type: Anomaly -description: The following analytic identifies an unusual number of Kerberos service - ticket requests, potentially indicating a kerberoasting attack. It leverages Kerberos - Event 4769 and calculates the standard deviation for each host, using the 3-sigma - rule to detect anomalies. This activity is significant as kerberoasting allows adversaries - to request service tickets and crack them offline, potentially gaining privileged - access to the domain. If confirmed malicious, this could lead to unauthorized access - to sensitive accounts and escalation of privileges within the Active Directory environment. +description: The following analytic identifies an unusual number of Kerberos service ticket requests, potentially indicating a kerberoasting attack. It leverages Kerberos Event 4769 and calculates the standard deviation for each host, using the 3-sigma rule to detect anomalies. This activity is significant as kerberoasting allows adversaries to request service tickets and crack them offline, potentially gaining privileged access to the domain. If confirmed malicious, this could lead to unauthorized access to sensitive accounts and escalation of privileges within the Active Directory environment. data_source: -- Windows Event Log Security 4769 -search: '`wineventlog_security` EventCode=4769 ServiceName!="*$" TicketEncryptionType=0x17 - | bucket span=2m _time | stats dc(ServiceName) AS unique_services values(ServiceName) - as requested_services values(user_category) as user_category values(src_category) - as src_category values(dest) as dest by _time, user, src | eventstats avg(unique_services) - as comp_avg , stdev(unique_services) as comp_std by user, src | eval upperBound=(comp_avg+comp_std*3) | - eval isOutlier=if(unique_services > 2 and unique_services >= upperBound, 1, 0) | - search isOutlier=1 | `unusual_number_of_kerberos_service_tickets_requested_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Domain Controller and Kerberos events. The Advanced Security Audit policy setting - `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. -known_false_positives: An single endpoint requesting a large number of kerberos service - tickets is not common behavior. Possible false positive scenarios include but are - not limited to vulnerability scanners, administration systems and missconfigured - systems. + - Windows Event Log Security 4769 +search: |- + `wineventlog_security` EventCode=4769 ServiceName!="*$" TicketEncryptionType=0x17 + | bucket span=2m _time + | stats dc(ServiceName) AS unique_services values(ServiceName) as requested_services values(user_category) as user_category values(src_category) as src_category values(dest) as dest + BY _time, user, src + | eventstats avg(unique_services) as comp_avg , stdev(unique_services) as comp_std + BY user, src + | eval upperBound=(comp_avg+comp_std*3) + | eval isOutlier=if(unique_services > 2 and unique_services >= upperBound, 1, 0) + | search isOutlier=1 + | `unusual_number_of_kerberos_service_tickets_requested_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. +known_false_positives: An single endpoint requesting a large number of kerberos service tickets is not common behavior. Possible false positive scenarios include but are not limited to vulnerability scanners, administration systems and missconfigured systems. references: -- https://attack.mitre.org/techniques/T1558/003/ -- https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/t1208-kerberoasting + - https://attack.mitre.org/techniques/T1558/003/ + - https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/t1208-kerberoasting drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ requested a service ticket for $unique_services$ services indicating - a potential kerberoasting attack - risk_objects: - - field: src - type: system - score: 64 - - field: user - type: user - score: 64 - threat_objects: [] + message: User $user$ requested a service ticket for $unique_services$ services indicating a potential kerberoasting attack + risk_objects: + - field: src + type: system + score: 64 + - field: user + type: user + score: 64 + threat_objects: [] tags: - analytic_story: - - Active Directory Kerberos Attacks - asset_type: Endpoint - mitre_attack_id: - - T1558.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Kerberos Attacks + asset_type: Endpoint + mitre_attack_id: + - T1558.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/unusual_number_of_kerberos_service_tickets_requested/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/unusual_number_of_kerberos_service_tickets_requested/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/unusual_number_of_remote_endpoint_authentication_events.yml b/detections/endpoint/unusual_number_of_remote_endpoint_authentication_events.yml index b8297fec67..cb56461f29 100644 --- a/detections/endpoint/unusual_number_of_remote_endpoint_authentication_events.yml +++ b/detections/endpoint/unusual_number_of_remote_endpoint_authentication_events.yml @@ -1,44 +1,37 @@ name: Unusual Number of Remote Endpoint Authentication Events id: acb5dc74-5324-11ec-a36d-acde48001122 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: experimental type: Hunting -description: The following analytic identifies an unusual number of remote authentication - attempts from a single source by leveraging Windows Event ID 4624, which logs successful - account logons. It uses statistical analysis, specifically the 3-sigma rule, to - detect deviations from normal behavior. This activity is significant for a SOC as - it may indicate lateral movement, malware staging, or reconnaissance. If confirmed - malicious, this behavior could allow an attacker to move laterally within the network, - escalate privileges, or gather information for further attacks. +description: The following analytic identifies an unusual number of remote authentication attempts from a single source by leveraging Windows Event ID 4624, which logs successful account logons. It uses statistical analysis, specifically the 3-sigma rule, to detect deviations from normal behavior. This activity is significant for a SOC as it may indicate lateral movement, malware staging, or reconnaissance. If confirmed malicious, this behavior could allow an attacker to move laterally within the network, escalate privileges, or gather information for further attacks. data_source: -- Windows Event Log Security 4624 -search: '`wineventlog_security` EventCode=4624 Logon_Type=3 Account_Name!="*$" | eval - Source_Account = mvindex(Account_Name, 1) | bucket span=2m _time | stats dc(ComputerName) - AS unique_targets values(ComputerName) as target_hosts by _time, Source_Network_Address, - Source_Account | eventstats avg(unique_targets) as comp_avg , stdev(unique_targets) - as comp_std by Source_Network_Address, Source_Account | eval upperBound=(comp_avg+comp_std*3) - | eval isOutlier=if(unique_targets >10 and unique_targets >= upperBound, 1, 0) | - `unusual_number_of_remote_endpoint_authentication_events_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Windows Event Logs from domain controllers as well as member servers and workstations. - The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs - to be enabled. -known_false_positives: An single endpoint authenticating to a large number of hosts - is not common behavior. Possible false positive scenarios include but are not limited - to vulnerability scanners, jump servers and missconfigured systems. + - Windows Event Log Security 4624 +search: |- + `wineventlog_security` EventCode=4624 Logon_Type=3 Account_Name!="*$" + | eval Source_Account = mvindex(Account_Name, 1) + | bucket span=2m _time + | stats dc(ComputerName) AS unique_targets values(ComputerName) as target_hosts + BY _time, Source_Network_Address, Source_Account + | eventstats avg(unique_targets) as comp_avg , stdev(unique_targets) as comp_std + BY Source_Network_Address, Source_Account + | eval upperBound=(comp_avg+comp_std*3) + | eval isOutlier=if(unique_targets >10 and unique_targets >= upperBound, 1, 0) + | `unusual_number_of_remote_endpoint_authentication_events_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled. +known_false_positives: An single endpoint authenticating to a large number of hosts is not common behavior. Possible false positive scenarios include but are not limited to vulnerability scanners, jump servers and missconfigured systems. references: -- https://attack.mitre.org/techniques/T1078/ + - https://attack.mitre.org/techniques/T1078/ tags: - analytic_story: - - Active Directory Lateral Movement - - Active Directory Privilege Escalation - asset_type: Endpoint - mitre_attack_id: - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + - Active Directory Privilege Escalation + asset_type: Endpoint + mitre_attack_id: + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/unusually_long_command_line.yml b/detections/endpoint/unusually_long_command_line.yml index 0b8958ff5e..38a374acee 100644 --- a/detections/endpoint/unusually_long_command_line.yml +++ b/detections/endpoint/unusually_long_command_line.yml @@ -1,64 +1,57 @@ name: Unusually Long Command Line id: c77162d3-f93c-45cc-80c8-22f6a4264e7f -version: 11 -date: '2026-01-14' +version: 12 +date: '2026-02-25' author: David Dorsey, Splunk status: experimental type: Anomaly -description: The following analytic detects unusually long command lines, which may - indicate malicious activity. It leverages data from Endpoint Detection and Response - (EDR) agents, focusing on the length of command lines executed on hosts. This behavior - is significant because attackers often use obfuscated or complex command lines to - evade detection and execute malicious payloads. If confirmed malicious, this activity - could lead to data theft, ransomware deployment, or further system compromise. Analysts - should investigate the source and content of the command line, inspect relevant - artifacts, and review concurrent processes to identify potential threats. +description: The following analytic detects unusually long command lines, which may indicate malicious activity. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on the length of command lines executed on hosts. This behavior is significant because attackers often use obfuscated or complex command lines to evade detection and execute malicious payloads. If confirmed malicious, this activity could lead to data theft, ransomware deployment, or further system compromise. Analysts should investigate the source and content of the command line, inspect relevant artifacts, and review concurrent processes to identify potential threats. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Processes by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval - processlen=len(process) | eventstats stdev(processlen) as stdev, avg(processlen) - as avg by dest | stats max(processlen) as maxlen, values(stdev) as stdevperhost, - values(avg) as avgperhost by dest, user, process_name, process - |eval threshold = 3 | where maxlen > ((threshold*stdevperhost) + avgperhost) - | `unusually_long_command_line_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | eval processlen=len(process) + | eventstats stdev(processlen) as stdev, avg(processlen) as avg + BY dest + | stats max(processlen) as maxlen, values(stdev) as stdevperhost, values(avg) as avgperhost + BY dest, user, process_name, + process + | eval threshold = 3 + | where maxlen > ((threshold*stdevperhost) + avgperhost) + | `unusually_long_command_line_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Some legitimate applications start with long command lines. references: [] rba: - message: Unusually long command line $process_name$ on $dest$ - risk_objects: - - field: dest - type: system - score: 42 - threat_objects: - - field: process_name - type: process_name + message: Unusually long command line $process_name$ on $dest$ + risk_objects: + - field: dest + type: system + score: 42 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Suspicious Command-Line Executions - - Unusual Processes - - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns - - Ransomware - asset_type: Endpoint - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Command-Line Executions + - Unusual Processes + - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + - Ransomware + asset_type: Endpoint + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/unusually_long_command_line___mltk.yml b/detections/endpoint/unusually_long_command_line___mltk.yml index 76bf6deeb6..4aace61db3 100644 --- a/detections/endpoint/unusually_long_command_line___mltk.yml +++ b/detections/endpoint/unusually_long_command_line___mltk.yml @@ -1,77 +1,56 @@ name: Unusually Long Command Line - MLTK id: 57edaefa-a73b-45e5-bbae-f39c1473f941 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Rico Valdez, Splunk status: experimental type: Anomaly -description: The following analytic identifies unusually long command lines executed - on hosts, which may indicate malicious activity. It leverages the Machine Learning - Toolkit (MLTK) to detect command lines with lengths that deviate from the norm for - a given user. This is significant for a SOC as unusually long command lines can - be a sign of obfuscation or complex malicious scripts. If confirmed malicious, this - activity could allow attackers to execute sophisticated commands, potentially leading - to unauthorized access, data exfiltration, or further compromise of the system. +description: The following analytic identifies unusually long command lines executed on hosts, which may indicate malicious activity. It leverages the Machine Learning Toolkit (MLTK) to detect command lines with lengths that deviate from the norm for a given user. This is significant for a SOC as unusually long command lines can be a sign of obfuscation or complex malicious scripts. If confirmed malicious, this activity could allow attackers to execute sophisticated commands, potentially leading to unauthorized access, data exfiltration, or further compromise of the system. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Processes by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| - eval processlen=len(process) | search user!=unknown | apply cmdline_pdfmodel threshold=0.01 - | rename "IsOutlier(processlen)" as isOutlier | search isOutlier > 0 | table firstTime - lastTime user dest process_name process processlen count | `unusually_long_command_line___mltk_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. In addition, the Machine Learning - Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along - with any required dependencies. Finally, the support search "ESCU - Baseline of - Command Line Length - MLTK" must be executed before this detection search, because - it builds a machine-learning (ML) model over the historical data used by this search. - It is important that this search is run in the same app context as the associated - support search, so that the model created by the support search is available for - use. You should periodically re-run the support search to rebuild the model with - the latest data available in your environment. -known_false_positives: Some legitimate applications use long command lines for installs - or updates. You should review identified command lines for legitimacy. You may modify - the first part of the search to omit legitimate command lines from consideration. - If you are seeing more results than desired, you may consider changing the value - of threshold in the search to a smaller value. You should also periodically re-run - the support search to re-build the ML model on the latest data. You may get unexpected - results if the user identified in the results is not present in the data used to - build the associated model. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | eval processlen=len(process) + | search user!=unknown + | apply cmdline_pdfmodel threshold=0.01 + | rename "IsOutlier(processlen)" as isOutlier + | search isOutlier > 0 + | table firstTime lastTime user dest process_name process processlen count + | `unusually_long_command_line___mltk_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "ESCU - Baseline of Command Line Length - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment. +known_false_positives: Some legitimate applications use long command lines for installs or updates. You should review identified command lines for legitimacy. You may modify the first part of the search to omit legitimate command lines from consideration. If you are seeing more results than desired, you may consider changing the value of threshold in the search to a smaller value. You should also periodically re-run the support search to re-build the ML model on the latest data. You may get unexpected results if the user identified in the results is not present in the data used to build the associated model. references: [] rba: - message: Unusually long command line usage on $dest$ - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: Unusually long command line usage on $dest$ + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Suspicious Command-Line Executions - - Unusual Processes - - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns - - Ransomware - asset_type: Endpoint - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Command-Line Executions + - Unusual Processes + - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + - Ransomware + asset_type: Endpoint + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/user_discovery_with_env_vars_powershell.yml b/detections/endpoint/user_discovery_with_env_vars_powershell.yml index bb912a694d..487468e04f 100644 --- a/detections/endpoint/user_discovery_with_env_vars_powershell.yml +++ b/detections/endpoint/user_discovery_with_env_vars_powershell.yml @@ -1,58 +1,50 @@ name: User Discovery With Env Vars PowerShell id: 0cdf318b-a0dd-47d7-b257-c621c0247de8 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting -description: The following analytic detects the execution of `powershell.exe` with - command-line arguments that use PowerShell environment variables to identify the - current logged user. It leverages data from Endpoint Detection and Response (EDR) - agents, focusing on process names and command-line executions. This activity is - significant as adversaries may use it for situational awareness and Active Directory - discovery on compromised endpoints. If confirmed malicious, this behavior could - allow attackers to gather critical user information, aiding in further exploitation - and lateral movement within the network. +description: The following analytic detects the execution of `powershell.exe` with command-line arguments that use PowerShell environment variables to identify the current logged user. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as adversaries may use it for situational awareness and Active Directory discovery on compromised endpoints. If confirmed malicious, this behavior could allow attackers to gather critical user information, aiding in further exploitation and lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") - (Processes.process="*$env:UserName*" OR Processes.process="*[System.Environment]::UserName*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `user_discovery_with_env_vars_powershell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="powershell.exe" + ) + (Processes.process="*$env:UserName*" OR Processes.process="*[System.Environment]::UserName*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `user_discovery_with_env_vars_powershell_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1033/ + - https://attack.mitre.org/techniques/T1033/ tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1033 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1033 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/user_discovery_with_env_vars_powershell_script_block.yml b/detections/endpoint/user_discovery_with_env_vars_powershell_script_block.yml index fe1d7e3829..ee4c205213 100644 --- a/detections/endpoint/user_discovery_with_env_vars_powershell_script_block.yml +++ b/detections/endpoint/user_discovery_with_env_vars_powershell_script_block.yml @@ -1,50 +1,43 @@ name: User Discovery With Env Vars PowerShell Script Block id: 77f41d9e-b8be-47e3-ab35-5776f5ec1d20 -version: 8 -date: '2025-06-24' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting -description: - The following analytic detects the use of PowerShell environment variables - to identify the current logged user by leveraging PowerShell Script Block Logging - (EventCode=4104). This method monitors script blocks containing `$env:UserName` - or `[System.Environment]::UserName`. Identifying this activity is significant as - adversaries and Red Teams may use it for situational awareness and Active Directory - discovery on compromised endpoints. If confirmed malicious, this activity could - allow attackers to gain insights into user context, aiding in further exploitation - and lateral movement within the network. +description: The following analytic detects the use of PowerShell environment variables to identify the current logged user by leveraging PowerShell Script Block Logging (EventCode=4104). This method monitors script blocks containing `$env:UserName` or `[System.Environment]::UserName`. Identifying this activity is significant as adversaries and Red Teams may use it for situational awareness and Active Directory discovery on compromised endpoints. If confirmed malicious, this activity could allow attackers to gain insights into user context, aiding in further exploitation and lateral movement within the network. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 (ScriptBlockText = "*$env:UserName*" OR ScriptBlockText - = "*[System.Environment]::UserName*") | fillnull | stats count min(_time) as firstTime - max(_time) as lastTime by dest signature signature_id user_id vendor_product EventID - Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `user_discovery_with_env_vars_powershell_script_block_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - Administrators or power users may use this PowerShell commandlet - for troubleshooting. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 (ScriptBlockText = "*$env:UserName*" OR ScriptBlockText = "*[System.Environment]::UserName*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `user_discovery_with_env_vars_powershell_script_block_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting. references: - - https://attack.mitre.org/techniques/T1033/ + - https://attack.mitre.org/techniques/T1033/ tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1033 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1033 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/AD_discovery/windows-powershell-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/AD_discovery/windows-powershell-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/usn_journal_deletion.yml b/detections/endpoint/usn_journal_deletion.yml index 859f4dd600..1bbdd51694 100644 --- a/detections/endpoint/usn_journal_deletion.yml +++ b/detections/endpoint/usn_journal_deletion.yml @@ -1,93 +1,73 @@ name: USN Journal Deletion id: b6e0ff70-b122-4227-9368-4cf322ab43c3 -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: David Dorsey, Splunk status: production type: TTP -description: The following analytic detects the deletion of the USN Journal using - the fsutil.exe utility. It leverages data from Endpoint Detection and Response (EDR) - agents, focusing on process execution logs that include command-line details. This - activity is significant because the USN Journal maintains a log of all changes made - to files on the disk, and its deletion can be an indicator of an attempt to cover - tracks or hinder forensic investigations. If confirmed malicious, this action could - allow an attacker to obscure their activities, making it difficult to trace file - modifications and potentially compromising incident response efforts. +description: The following analytic detects the deletion of the USN Journal using the fsutil.exe utility. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because the USN Journal maintains a log of all changes made to files on the disk, and its deletion can be an indicator of an attempt to cover tracks or hinder forensic investigations. If confirmed malicious, this action could allow an attacker to obscure their activities, making it difficult to trace file modifications and potentially compromising incident response efforts. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` - count values(Processes.process) as process - values(Processes.parent_process) as parent_process - min(_time) as firstTime - max(_time) as lastTime - - from datamodel=Endpoint.Processes where - - Processes.process_name=fsutil.exe - Processes.process = "*usn*" - Processes.process = "*deletejournal*" - - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `usn_journal_deletion_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + | tstats `security_content_summariesonly` + count values(Processes.process) as process + values(Processes.parent_process) as parent_process + min(_time) as firstTime + max(_time) as lastTime + + from datamodel=Endpoint.Processes where + + Processes.process_name=fsutil.exe + Processes.process = "*usn*" + Processes.process = "*deletejournal*" + + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `usn_journal_deletion_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. -references: -- https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/fsutil-usn +references: + - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/fsutil-usn drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible USN journal deletion on $dest$ via $process$ - risk_objects: - - field: dest - type: system - score: 45 - threat_objects: [] + message: Possible USN journal deletion on $dest$ via $process$ + risk_objects: + - field: dest + type: system + score: 45 + threat_objects: [] tags: - analytic_story: - - Windows Log Manipulation - - Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1070 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Log Manipulation + - Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1070 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/vbscript_execution_using_wscript_app.yml b/detections/endpoint/vbscript_execution_using_wscript_app.yml index 0627e28c11..741d4b999f 100644 --- a/detections/endpoint/vbscript_execution_using_wscript_app.yml +++ b/detections/endpoint/vbscript_execution_using_wscript_app.yml @@ -1,85 +1,73 @@ name: Vbscript Execution Using Wscript App id: 35159940-228f-11ec-8a49-acde48001122 -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of VBScript using the wscript.exe - application. It leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process and command-line telemetry. This activity is significant because - wscript.exe is typically not used to execute VBScript, which is usually associated - with cscript.exe. This deviation can indicate an attempt to evade traditional process - monitoring and antivirus defenses. If confirmed malicious, this technique could - allow attackers to execute arbitrary code, potentially leading to system compromise, - data exfiltration, or further lateral movement within the network. +description: The following analytic detects the execution of VBScript using the wscript.exe application. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and command-line telemetry. This activity is significant because wscript.exe is typically not used to execute VBScript, which is usually associated with cscript.exe. This deviation can indicate an attempt to evade traditional process monitoring and antivirus defenses. If confirmed malicious, this technique could allow attackers to execute arbitrary code, potentially leading to system compromise, data exfiltration, or further lateral movement within the network. data_source: -- Sysmon EventID 1 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name - = "wscript.exe" AND Processes.parent_process = "*//e:vbscript*") OR (Processes.process_name - = "wscript.exe" AND Processes.process = "*//e:vbscript*") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `vbscript_execution_using_wscript_app_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.parent_process_name = "wscript.exe" + AND + Processes.parent_process = "*//e:vbscript*" + ) + OR (Processes.process_name = "wscript.exe" AND Processes.process = "*//e:vbscript*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `vbscript_execution_using_wscript_app_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://www.joesandbox.com/analysis/369332/0/html -- https://malpedia.caad.fkie.fraunhofer.de/details/win.asyncrat + - https://www.joesandbox.com/analysis/369332/0/html + - https://malpedia.caad.fkie.fraunhofer.de/details/win.asyncrat drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Process name $process_name$ with commandline $process$ to execute vbsscript - risk_objects: - - field: dest - type: system - score: 49 - - field: user - type: user - score: 49 - threat_objects: [] + message: Process name $process_name$ with commandline $process$ to execute vbsscript + risk_objects: + - field: dest + type: system + score: 49 + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - FIN7 - - Remcos - - AsyncRAT - asset_type: Endpoint - mitre_attack_id: - - T1059.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - FIN7 + - Remcos + - AsyncRAT + asset_type: Endpoint + mitre_attack_id: + - T1059.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.005/vbs_wscript/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.005/vbs_wscript/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/verclsid_clsid_execution.yml b/detections/endpoint/verclsid_clsid_execution.yml index 8a7c86ed1c..ef6e240e68 100644 --- a/detections/endpoint/verclsid_clsid_execution.yml +++ b/detections/endpoint/verclsid_clsid_execution.yml @@ -5,65 +5,49 @@ date: '2025-12-15' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects the potential abuse of the verclsid.exe - utility to execute malicious files via generated CLSIDs. It leverages data from - Endpoint Detection and Response (EDR) agents, focusing on specific command-line - patterns associated with verclsid.exe. This activity is significant because verclsid.exe - is a legitimate Windows application used to verify CLSID COM objects, and its misuse - can indicate an attempt to bypass security controls. If confirmed malicious, this - technique could allow an attacker to execute arbitrary code, potentially leading - to system compromise or further malicious activities. +description: The following analytic detects the potential abuse of the verclsid.exe utility to execute malicious files via generated CLSIDs. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns associated with verclsid.exe. This activity is significant because verclsid.exe is a legitimate Windows application used to verify CLSID COM objects, and its misuse can indicate an attempt to bypass security controls. If confirmed malicious, this technique could allow an attacker to execute arbitrary code, potentially leading to system compromise or further malicious activities. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` values(Processes.process) as process - values(Processes.parent_process) as parent_process values(Processes.process_id) - as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where - (Processes.process_name="verclsid.exe" OR Processes.original_file_name="verclsid.exe") - Processes.process="*/S*" - Processes.process="*/C*" - Processes.process="*{*" - Processes.process="*}*" - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `verclsid_clsid_execution_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: windows can used this application for its normal COM object - validation. + | tstats `security_content_summariesonly` values(Processes.process) as process + values(Processes.parent_process) as parent_process values(Processes.process_id) + as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where + (Processes.process_name="verclsid.exe" OR Processes.original_file_name="verclsid.exe") + Processes.process="*/S*" + Processes.process="*/C*" + Processes.process="*{*" + Processes.process="*}*" + by Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `verclsid_clsid_execution_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: windows can used this application for its normal COM object validation. references: -- https://gist.github.com/NickTyrer/0598b60112eaafe6d07789f7964290d5 -- https://bohops.com/2018/08/18/abusing-the-com-registry-structure-part-2-loading-techniques-for-evasion-and-persistence/ + - https://gist.github.com/NickTyrer/0598b60112eaafe6d07789f7964290d5 + - https://bohops.com/2018/08/18/abusing-the-com-registry-structure-part-2-loading-techniques-for-evasion-and-persistence/ tags: - analytic_story: - - Unusual Processes - asset_type: Endpoint - mitre_attack_id: - - T1218.012 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Unusual Processes + asset_type: Endpoint + mitre_attack_id: + - T1218.012 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.012/verclsid_exec/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.012/verclsid_exec/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/wbadmin_delete_system_backups.yml b/detections/endpoint/wbadmin_delete_system_backups.yml index f67003edde..105a75d728 100644 --- a/detections/endpoint/wbadmin_delete_system_backups.yml +++ b/detections/endpoint/wbadmin_delete_system_backups.yml @@ -1,97 +1,77 @@ name: WBAdmin Delete System Backups id: cd5aed7e-5cea-11eb-ae93-0242ac130002 -version: 10 -date: '2026-01-20' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of wbadmin.exe with flags - that delete backup files, specifically targeting catalog or system state backups. - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on - process names and command-line arguments. This activity is significant because it - is commonly used by ransomware to prevent recovery by deleting system backups. If - confirmed malicious, this action could severely hinder recovery efforts, leading - to prolonged downtime and potential data loss. +description: The following analytic detects the execution of wbadmin.exe with flags that delete backup files, specifically targeting catalog or system state backups. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because it is commonly used by ransomware to prevent recovery by deleting system backups. If confirmed malicious, this action could severely hinder recovery efforts, leading to prolonged downtime and potential data loss. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - ( - Processes.process_name=wbadmin.exe - OR - Processes.original_file_name=WBADMIN.EXE - ) - Processes.process="*delete*" - ( - Processes.process="*catalog*" - OR - Processes.process="*backup*" - ) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| - `wbadmin_delete_system_backups_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name=wbadmin.exe + OR + Processes.original_file_name=WBADMIN.EXE + ) + Processes.process="*delete*" ( Processes.process="*catalog*" OR Processes.process="*backup*" ) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `wbadmin_delete_system_backups_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators may modify the boot configuration. references: -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md -- https://thedfirreport.com/2020/10/08/ryuks-return/ -- https://attack.mitre.org/techniques/T1490/ -- https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/wbadmin -- https://www.welivesecurity.com/2019/04/30/buhtrap-backdoor-ransomware-advertising-platform/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md + - https://thedfirreport.com/2020/10/08/ryuks-return/ + - https://attack.mitre.org/techniques/T1490/ + - https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/wbadmin + - https://www.welivesecurity.com/2019/04/30/buhtrap-backdoor-ransomware-advertising-platform/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: System backups deletion on $dest$ - risk_objects: - - field: dest - type: system - score: 15 - threat_objects: [] + message: System backups deletion on $dest$ + risk_objects: + - field: dest + type: system + score: 15 + threat_objects: [] tags: - analytic_story: - - Ryuk Ransomware - - Ransomware - - Prestige Ransomware - - Chaos Ransomware - - Storm-2460 CLFS Zero Day Exploitation - - Storm-0501 Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1490 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ryuk Ransomware + - Ransomware + - Prestige Ransomware + - Chaos Ransomware + - Storm-2460 CLFS Zero Day Exploitation + - Storm-0501 Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1490 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/wbemprox_com_object_execution.yml b/detections/endpoint/wbemprox_com_object_execution.yml index 8aafb13551..5e80cd89a6 100644 --- a/detections/endpoint/wbemprox_com_object_execution.yml +++ b/detections/endpoint/wbemprox_com_object_execution.yml @@ -5,69 +5,47 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects a suspicious process loading a COM object - from wbemprox.dll, fastprox.dll, or wbemcomn.dll. It leverages Sysmon EventCode - 7 to identify instances where these DLLs are loaded by processes not typically associated - with them, excluding known legitimate processes and directories. This activity is - significant as it may indicate an attempt by threat actors to abuse COM objects - for privilege escalation or evasion of detection mechanisms. If confirmed malicious, - this could allow attackers to gain elevated privileges or maintain persistence within - the environment, posing a significant security risk. +description: The following analytic detects a suspicious process loading a COM object from wbemprox.dll, fastprox.dll, or wbemcomn.dll. It leverages Sysmon EventCode 7 to identify instances where these DLLs are loaded by processes not typically associated with them, excluding known legitimate processes and directories. This activity is significant as it may indicate an attempt by threat actors to abuse COM objects for privilege escalation or evasion of detection mechanisms. If confirmed malicious, this could allow attackers to gain elevated privileges or maintain persistence within the environment, posing a significant security risk. data_source: -- Sysmon EventID 7 -search: '`sysmon` EventCode=7 ImageLoaded IN ("*\\fastprox.dll", "*\\wbemprox.dll", - "*\\wbemcomn.dll") NOT (process_name IN ("wmiprvse.exe", "WmiApSrv.exe", "unsecapp.exe")) - NOT(Image IN("*\\windows\\*","*\\program files*", "*\\wbem\\*")) | fillnull | stats - count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file - loaded_file_path original_file_name process_exec process_guid process_hash process_id - process_name process_path service_dll_signature_exists service_dll_signature_verified - signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `wbemprox_com_object_execution_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name and imageloaded executions from your endpoints. If you - are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -known_false_positives: legitimate process that are not in the exception list may trigger - this event. + - Sysmon EventID 7 +search: '`sysmon` EventCode=7 ImageLoaded IN ("*\\fastprox.dll", "*\\wbemprox.dll", "*\\wbemcomn.dll") NOT (process_name IN ("wmiprvse.exe", "WmiApSrv.exe", "unsecapp.exe")) NOT(Image IN("*\\windows\\*","*\\program files*", "*\\wbem\\*")) | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wbemprox_com_object_execution_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: legitimate process that are not in the exception list may trigger this event. references: -- https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/ -- https://www.mcafee.com/blogs/other-blogs/mcafee-labs/mcafee-atr-analyzes-sodinokibi-aka-revil-ransomware-as-a-service-what-the-code-tells-us/ + - https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/ + - https://www.mcafee.com/blogs/other-blogs/mcafee-labs/mcafee-atr-analyzes-sodinokibi-aka-revil-ransomware-as-a-service-what-the-code-tells-us/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious COM Object Execution on $dest$ - risk_objects: - - field: dest - type: system - score: 35 - threat_objects: [] + message: Suspicious COM Object Execution on $dest$ + risk_objects: + - field: dest + type: system + score: 35 + threat_objects: [] tags: - analytic_story: - - Ransomware - - Revil Ransomware - - LockBit Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1218.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - Revil Ransomware + - LockBit Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1218.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf2/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf2/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/web_or_application_server_spawning_a_shell.yml b/detections/endpoint/web_or_application_server_spawning_a_shell.yml index 61b7a1f9fa..5f43b0474a 100644 --- a/detections/endpoint/web_or_application_server_spawning_a_shell.yml +++ b/detections/endpoint/web_or_application_server_spawning_a_shell.yml @@ -6,118 +6,112 @@ author: Michael Haag, Nasreddine Bencherchali, Splunk status: production type: TTP description: | - The following analytic detects instances where Java, or Tomcat - processes spawn a Linux shell, which may indicate exploitation attempts, such as - those related to CVE-2021-44228 (Log4Shell). This detection leverages Endpoint Detection - and Response (EDR) telemetry, focusing on process names and parent-child process - relationships. This activity is significant as it can signify a compromised Java - application, potentially leading to unauthorized shell access. If confirmed malicious, - attackers could execute arbitrary commands, escalate privileges, or maintain persistent - access, posing a severe threat to the environment. + The following analytic detects instances where Java, or Tomcat + processes spawn a Linux shell, which may indicate exploitation attempts, such as + those related to CVE-2021-44228 (Log4Shell). This detection leverages Endpoint Detection + and Response (EDR) telemetry, focusing on process names and parent-child process + relationships. This activity is significant as it can signify a compromised Java + application, potentially leading to unauthorized shell access. If confirmed malicious, + attackers could execute arbitrary commands, escalate privileges, or maintain persistent + access, posing a severe threat to the environment. data_source: -- Sysmon for Linux EventID 1 -- Sysmon EventID 1 + - Sysmon for Linux EventID 1 + - Sysmon EventID 1 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime - from datamodel=Endpoint.Processes where + from datamodel=Endpoint.Processes where - ( - Processes.parent_process_name IN ("java", "tomcat*", "httpd", "lighttpd", "apache2", "nginx", "node", "caddy") - `linux_shells` - ) - OR - ( - Processes.parent_process_name IN ("httpd.exe", "nginx.exe", "php*.exe", "php-cgi.exe", "tomcat*.exe", "caddy.exe", "UMWorkerProcess.exe", "w3wp.exe", "ws_TomcatService.exe", "node.exe", "java.exe") - `windows_shells` - ) + ( + Processes.parent_process_name IN ("java", "tomcat*", "httpd", "lighttpd", "apache2", "nginx", "node", "caddy") + `linux_shells` + ) + OR + ( + Processes.parent_process_name IN ("httpd.exe", "nginx.exe", "php*.exe", "php-cgi.exe", "tomcat*.exe", "caddy.exe", "UMWorkerProcess.exe", "w3wp.exe", "ws_TomcatService.exe", "node.exe", "java.exe") + `windows_shells` + ) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `web_or_application_server_spawning_a_shell_filter` + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `web_or_application_server_spawning_a_shell_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: | - Filtering may be required on internal developer build systems or classify assets as web facing and restrict the analytic based on asset type. + Filtering may be required on internal developer build systems or classify assets as web facing and restrict the analytic based on asset type. references: -- https://blog.netlab.360.com/ten-families-of-malicious-samples-are-spreading-using-the-log4j2-vulnerability-now/ -- https://gist.github.com/olafhartong/916ebc673ba066537740164f7e7e1d72 + - https://blog.netlab.360.com/ten-families-of-malicious-samples-are-spreading-using-the-log4j2-vulnerability-now/ + - https://gist.github.com/olafhartong/916ebc673ba066537740164f7e7e1d72 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ spawning a Linux shell, potentially indicative of exploitation. - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ spawning a Linux shell, potentially indicative of exploitation. + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - BlackByte Ransomware - - CISA AA22-257A - - CISA AA22-264A - - Cleo File Transfer Software - - Data Destruction - - Flax Typhoon - - GhostRedirector IIS Module and Rungan Backdoor - - HAFNIUM Group - - Hermetic Wiper - - Log4Shell CVE-2021-44228 - - Microsoft SharePoint Vulnerabilities - - Microsoft WSUS CVE-2025-59287 - - PHP-CGI RCE Attack on Japanese Organizations - - ProxyNotShell - - ProxyShell - - SAP NetWeaver Exploitation - - Spring4Shell CVE-2022-22965 - - SysAid On-Prem Software CVE-2023-47246 Vulnerability - - WS FTP Server Critical Vulnerabilities - asset_type: Endpoint - mitre_attack_id: - - T1190 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - BlackByte Ransomware + - CISA AA22-257A + - CISA AA22-264A + - Cleo File Transfer Software + - Data Destruction + - Flax Typhoon + - GhostRedirector IIS Module and Rungan Backdoor + - HAFNIUM Group + - Hermetic Wiper + - Log4Shell CVE-2021-44228 + - Microsoft SharePoint Vulnerabilities + - Microsoft WSUS CVE-2025-59287 + - PHP-CGI RCE Attack on Japanese Organizations + - ProxyNotShell + - ProxyShell + - SAP NetWeaver Exploitation + - Spring4Shell CVE-2022-22965 + - SysAid On-Prem Software CVE-2023-47246 Vulnerability + - WS FTP Server Critical Vulnerabilities + asset_type: Endpoint + mitre_attack_id: + - T1190 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/java/java_spawn_shell_nix.log - source: Syslog:Linux-Sysmon/Operational - sourcetype: sysmon:linux + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/java/java_spawn_shell_nix.log + source: Syslog:Linux-Sysmon/Operational + sourcetype: sysmon:linux diff --git a/detections/endpoint/web_servers_executing_suspicious_processes.yml b/detections/endpoint/web_servers_executing_suspicious_processes.yml index ed459dbe45..a665045406 100644 --- a/detections/endpoint/web_servers_executing_suspicious_processes.yml +++ b/detections/endpoint/web_servers_executing_suspicious_processes.yml @@ -1,61 +1,60 @@ name: Web Servers Executing Suspicious Processes id: ec3b7601-689a-4463-94e0-c9f45638efb9 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: David Dorsey, Splunk status: experimental type: TTP -description: The following analytic detects the execution of suspicious processes - on systems identified as web servers. It leverages the Splunk data model "Endpoint.Processes" - to search for specific process names such as "whoami", "ping", "iptables", "wget", - "service", and "curl". This activity is significant because these processes are - often used by attackers for reconnaissance, persistence, or data exfiltration. If - confirmed malicious, this could lead to data theft, deployment of additional malware, - or even ransomware attacks. Immediate investigation is required to determine the - legitimacy of the activity and mitigate potential threats. +description: The following analytic detects the execution of suspicious processes on systems identified as web servers. It leverages the Splunk data model "Endpoint.Processes" to search for specific process names such as "whoami", "ping", "iptables", "wget", "service", and "curl". This activity is significant because these processes are often used by attackers for reconnaissance, persistence, or data exfiltration. If confirmed malicious, this could lead to data theft, deployment of additional malware, or even ransomware attacks. Immediate investigation is required to determine the legitimacy of the activity and mitigate potential threats. data_source: -- Sysmon EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.dest_category="web_server" - AND (Processes.process="*whoami*" OR Processes.process="*ping*" OR Processes.process="*iptables*" - OR Processes.process="*wget*" OR Processes.process="*service*" OR Processes.process="*curl*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `web_servers_executing_suspicious_processes_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Some of these processes may be used legitimately on web servers - during maintenance or other administrative tasks. + - Sysmon EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.dest_category="web_server" + AND + (Processes.process="*whoami*" + OR + Processes.process="*ping*" + OR + Processes.process="*iptables*" + OR + Processes.process="*wget*" + OR + Processes.process="*service*" + OR + Processes.process="*curl*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `web_servers_executing_suspicious_processes_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Some of these processes may be used legitimately on web servers during maintenance or other administrative tasks. references: [] rba: - message: Suspicious Processes observed on web server $dest$ - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: Suspicious Processes observed on web server $dest$ + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Apache Struts Vulnerability - asset_type: Web Server - mitre_attack_id: - - T1082 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Apache Struts Vulnerability + asset_type: Web Server + mitre_attack_id: + - T1082 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/wermgr_process_create_executable_file.yml b/detections/endpoint/wermgr_process_create_executable_file.yml index 9786c052dd..16a1197058 100644 --- a/detections/endpoint/wermgr_process_create_executable_file.yml +++ b/detections/endpoint/wermgr_process_create_executable_file.yml @@ -1,66 +1,57 @@ name: Wermgr Process Create Executable File id: ab3bcce0-a105-11eb-973c-acde48001122 -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the wermgr.exe process creating an executable - file. It leverages Sysmon EventCode 11 to identify instances where wermgr.exe generates - a .exe file. This behavior is unusual because wermgr.exe is typically associated - with error reporting, not file creation. Such activity is significant as it may - indicate TrickBot malware, which injects code into wermgr.exe to execute malicious - actions like downloading additional payloads. If confirmed malicious, this could - lead to further malware infections, data exfiltration, or system compromise. +description: The following analytic detects the wermgr.exe process creating an executable file. It leverages Sysmon EventCode 11 to identify instances where wermgr.exe generates a .exe file. This behavior is unusual because wermgr.exe is typically associated with error reporting, not file creation. Such activity is significant as it may indicate TrickBot malware, which injects code into wermgr.exe to execute malicious actions like downloading additional payloads. If confirmed malicious, this could lead to further malware infections, data exfiltration, or system compromise. data_source: -- Sysmon EventID 11 -search: '`sysmon` EventCode=11 process_name = "wermgr.exe" TargetFilename = "*.exe" - | stats min(_time) as firstTime max(_time) as lastTime count by action dest file_name - file_path process_guid process_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `wermgr_process_create_executable_file_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. Tune and filter known instances of wermgr.exe may be used. + - Sysmon EventID 11 +search: |- + `sysmon` EventCode=11 process_name = "wermgr.exe" TargetFilename = "*.exe" + | stats min(_time) as firstTime max(_time) as lastTime count + BY action dest file_name + file_path process_guid process_id + user_id vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `wermgr_process_create_executable_file_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances of wermgr.exe may be used. known_false_positives: No false positives have been identified at this time. references: -- https://labs.vipre.com/trickbot-and-its-modules/ -- https://whitehat.eu/incident-response-case-study-featuring-ryuk-and-trickbot-part-2/ + - https://labs.vipre.com/trickbot-and-its-modules/ + - https://whitehat.eu/incident-response-case-study-featuring-ryuk-and-trickbot-part-2/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Wermgr.exe writing executable files on $dest$ - risk_objects: - - field: dest - type: system - score: 56 - threat_objects: [] + message: Wermgr.exe writing executable files on $dest$ + risk_objects: + - field: dest + type: system + score: 56 + threat_objects: [] tags: - analytic_story: - - Trickbot - asset_type: Endpoint - mitre_attack_id: - - T1027 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Trickbot + asset_type: Endpoint + mitre_attack_id: + - T1027 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/wermgr_process_spawned_cmd_or_powershell_process.yml b/detections/endpoint/wermgr_process_spawned_cmd_or_powershell_process.yml index 2bb04e51f1..f45b49e76a 100644 --- a/detections/endpoint/wermgr_process_spawned_cmd_or_powershell_process.yml +++ b/detections/endpoint/wermgr_process_spawned_cmd_or_powershell_process.yml @@ -1,81 +1,67 @@ name: Wermgr Process Spawned CMD Or Powershell Process id: e8fc95bc-a107-11eb-a978-acde48001122 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the spawning of cmd or PowerShell processes - by the wermgr.exe process. It leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process telemetry, including parent-child process relationships - and command-line executions. This behavior is significant as it is commonly associated - with code injection techniques used by malware like TrickBot to execute shellcode - or malicious DLL modules. If confirmed malicious, this activity could allow attackers - to execute arbitrary code, escalate privileges, or maintain persistence within the - environment, posing a severe threat to system security. +description: The following analytic detects the spawning of cmd or PowerShell processes by the wermgr.exe process. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process telemetry, including parent-child process relationships and command-line executions. This behavior is significant as it is commonly associated with code injection techniques used by malware like TrickBot to execute shellcode or malicious DLL modules. If confirmed malicious, this activity could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment, posing a severe threat to system security. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as cmdline - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.parent_process_name = "wermgr.exe" `process_cmd` OR `process_powershell` - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `wermgr_process_spawned_cmd_or_powershell_process_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as cmdline min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name = "wermgr.exe" `process_cmd` + OR + `process_powershell` + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `wermgr_process_spawned_cmd_or_powershell_process_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://labs.vipre.com/trickbot-and-its-modules/ -- https://whitehat.eu/incident-response-case-study-featuring-ryuk-and-trickbot-part-2/ + - https://labs.vipre.com/trickbot-and-its-modules/ + - https://whitehat.eu/incident-response-case-study-featuring-ryuk-and-trickbot-part-2/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Wermgr.exe spawning suspicious processes on $dest$ - risk_objects: - - field: dest - type: system - score: 56 - threat_objects: [] + message: Wermgr.exe spawning suspicious processes on $dest$ + risk_objects: + - field: dest + type: system + score: 56 + threat_objects: [] tags: - analytic_story: - - Trickbot - - Qakbot - asset_type: Endpoint - mitre_attack_id: - - T1059 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Trickbot + - Qakbot + asset_type: Endpoint + mitre_attack_id: + - T1059 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_access_token_manipulation_sedebugprivilege.yml b/detections/endpoint/windows_access_token_manipulation_sedebugprivilege.yml index 9f84c06f05..31a8b6a4e7 100644 --- a/detections/endpoint/windows_access_token_manipulation_sedebugprivilege.yml +++ b/detections/endpoint/windows_access_token_manipulation_sedebugprivilege.yml @@ -5,87 +5,64 @@ date: '2025-12-04' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects a process enabling the - "SeDebugPrivilege" privilege token. It leverages Windows Security Event Logs - with EventCode 4703, filtering out common legitimate processes. This activity - is significant because SeDebugPrivilege allows a process to inspect and modify - the memory of other processes, potentially leading to credential dumping or - code injection. If confirmed malicious, an attacker could gain extensive - control over system processes, enabling them to escalate privileges, persist - in the environment, or access sensitive information. +description: The following analytic detects a process enabling the "SeDebugPrivilege" privilege token. It leverages Windows Security Event Logs with EventCode 4703, filtering out common legitimate processes. This activity is significant because SeDebugPrivilege allows a process to inspect and modify the memory of other processes, potentially leading to credential dumping or code injection. If confirmed malicious, an attacker could gain extensive control over system processes, enabling them to escalate privileges, persist in the environment, or access sensitive information. data_source: -- Windows Event Log Security 4703 -search: '`wineventlog_security` EventCode=4703 EnabledPrivilegeList = "*SeDebugPrivilege*" - AND NOT(ProcessName IN ("*\\Program File*", "*\\System32\\lsass.exe*", "*\\SysWOW64\\lsass.exe*", - "*\\SysWOW64\\svchost.exe*", "*\\System32\\svchost.exe*")) | stats count min(_time) - as firstTime max(_time) as lastTime by Computer ProcessName ProcessId SubjectDomainName - SubjectUserName SubjectUserSid TargetUserName TargetLogonId TargetDomainName EnabledPrivilegeList - action dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_access_token_manipulation_sedebugprivilege_filter`' -how_to_implement: To successfully implement this search, you need to be - ingesting Windows Security Event Logs with 4703 EventCode enabled. The Windows - TA is also required. -known_false_positives: Some native binaries and browser applications may request - SeDebugPrivilege. Filter as needed. + - Windows Event Log Security 4703 +search: '`wineventlog_security` EventCode=4703 EnabledPrivilegeList = "*SeDebugPrivilege*" AND NOT(ProcessName IN ("*\\Program File*", "*\\System32\\lsass.exe*", "*\\SysWOW64\\lsass.exe*", "*\\SysWOW64\\svchost.exe*", "*\\System32\\svchost.exe*")) | stats count min(_time) as firstTime max(_time) as lastTime by Computer ProcessName ProcessId SubjectDomainName SubjectUserName SubjectUserSid TargetUserName TargetLogonId TargetDomainName EnabledPrivilegeList action dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_access_token_manipulation_sedebugprivilege_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4703 EventCode enabled. The Windows TA is also required. +known_false_positives: Some native binaries and browser applications may request SeDebugPrivilege. Filter as needed. references: -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4703 -- https://devblogs.microsoft.com/oldnewthing/20080314-00/?p=23113 -- https://blog.palantir.com/windows-privilege-abuse-auditing-detection-and-defense-3078a403d74e -- https://atomicredteam.io/privilege-escalation/T1134.001/#atomic-test-2---%60sedebugprivilege%60-token-duplication -- https://malpedia.caad.fkie.fraunhofer.de/details/win.asyncrat + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4703 + - https://devblogs.microsoft.com/oldnewthing/20080314-00/?p=23113 + - https://blog.palantir.com/windows-privilege-abuse-auditing-detection-and-defense-3078a403d74e + - https://atomicredteam.io/privilege-escalation/T1134.001/#atomic-test-2---%60sedebugprivilege%60-token-duplication + - https://malpedia.caad.fkie.fraunhofer.de/details/win.asyncrat drilldown_searches: -- name: View the detection results for - "$Computer$" - search: '%original_detection_search% | search Computer = "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$Computer$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" + search: '%original_detection_search% | search Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process $ProcessName$ adjust its privileges with SeDebugPrivilege - on $Computer$. - risk_objects: - - field: Computer - type: system - score: 36 - threat_objects: [] + message: A process $ProcessName$ adjust its privileges with SeDebugPrivilege on $Computer$. + risk_objects: + - field: Computer + type: system + score: 36 + threat_objects: [] tags: - analytic_story: - - Meduza Stealer - - PlugX - - CISA AA23-347A - - China-Nexus Threat Activity - - AsyncRAT - - SnappyBee - - Derusbi - - WinDealer RAT - - Salt Typhoon - - DarkGate Malware - - ValleyRAT - - Brute Ratel C4 - - PathWiper - - GhostRedirector IIS Module and Rungan Backdoor - - Lokibot - - Scattered Lapsus$ Hunters - - Tuoni - asset_type: Endpoint - mitre_attack_id: - - T1134.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Meduza Stealer + - PlugX + - CISA AA23-347A + - China-Nexus Threat Activity + - AsyncRAT + - SnappyBee + - Derusbi + - WinDealer RAT + - Salt Typhoon + - DarkGate Malware + - ValleyRAT + - Brute Ratel C4 + - PathWiper + - GhostRedirector IIS Module and Rungan Backdoor + - Lokibot + - Scattered Lapsus$ Hunters + - Tuoni + asset_type: Endpoint + mitre_attack_id: + - T1134.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/sedebugprivilege_token/security-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/sedebugprivilege_token/security-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_access_token_manipulation_winlogon_duplicate_token_handle.yml b/detections/endpoint/windows_access_token_manipulation_winlogon_duplicate_token_handle.yml index a335a5de6d..ced1dca836 100644 --- a/detections/endpoint/windows_access_token_manipulation_winlogon_duplicate_token_handle.yml +++ b/detections/endpoint/windows_access_token_manipulation_winlogon_duplicate_token_handle.yml @@ -5,47 +5,29 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects a process attempting to access winlogon.exe - to duplicate its handle. This is identified using Sysmon EventCode 10, focusing - on processes targeting winlogon.exe with specific access rights. This activity is - significant because it is a common technique used by adversaries to escalate privileges - by leveraging the high privileges and security tokens associated with winlogon.exe. - If confirmed malicious, this could allow an attacker to gain elevated privileges, - potentially leading to full system compromise and unauthorized access to sensitive - information. +description: The following analytic detects a process attempting to access winlogon.exe to duplicate its handle. This is identified using Sysmon EventCode 10, focusing on processes targeting winlogon.exe with specific access rights. This activity is significant because it is a common technique used by adversaries to escalate privileges by leveraging the high privileges and security tokens associated with winlogon.exe. If confirmed malicious, this could allow an attacker to gain elevated privileges, potentially leading to full system compromise and unauthorized access to sensitive information. data_source: -- Sysmon EventID 10 -search: '`sysmon` EventCode=10 TargetImage IN("*\\system32\\winlogon.exe*", "*\\SysWOW64\\winlogon.exe*") - GrantedAccess = 0x1040 | stats count min(_time) as firstTime max(_time) as lastTime - by CallTrace EventID GrantedAccess Guid Opcode ProcessID SecurityID SourceImage - SourceProcessGUID SourceProcessId TargetImage TargetProcessGUID TargetProcessId - UserID dest granted_access parent_process_exec parent_process_guid parent_process_id - parent_process_name parent_process_path process_exec process_guid process_id process_name - process_path signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_access_token_manipulation_winlogon_duplicate_token_handle_filter`' -how_to_implement: To successfully implement this search, you must be ingesting data - that records process activity from your hosts to populate the endpoint data model - in the processes node. If you are using Sysmon, you must have at least version 6.0.4 - of the Sysmon TA. -known_false_positives: It is possible legitimate applications will request access - to winlogon, filter as needed. + - Sysmon EventID 10 +search: '`sysmon` EventCode=10 TargetImage IN("*\\system32\\winlogon.exe*", "*\\SysWOW64\\winlogon.exe*") GrantedAccess = 0x1040 | stats count min(_time) as firstTime max(_time) as lastTime by CallTrace EventID GrantedAccess Guid Opcode ProcessID SecurityID SourceImage SourceProcessGUID SourceProcessId TargetImage TargetProcessGUID TargetProcessId UserID dest granted_access parent_process_exec parent_process_guid parent_process_id parent_process_name parent_process_path process_exec process_guid process_id process_name process_path signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_access_token_manipulation_winlogon_duplicate_token_handle_filter`' +how_to_implement: To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: It is possible legitimate applications will request access to winlogon, filter as needed. references: -- https://docs.microsoft.com/en-us/windows/win32/api/handleapi/nf-handleapi-duplicatehandle -- https://attack.mitre.org/techniques/T1134/001/ + - https://docs.microsoft.com/en-us/windows/win32/api/handleapi/nf-handleapi-duplicatehandle + - https://attack.mitre.org/techniques/T1134/001/ tags: - analytic_story: - - Brute Ratel C4 - asset_type: Endpoint - mitre_attack_id: - - T1134.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Brute Ratel C4 + asset_type: Endpoint + mitre_attack_id: + - T1134.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/brute_duplicate_token/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/brute_duplicate_token/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_access_token_winlogon_duplicate_handle_in_uncommon_path.yml b/detections/endpoint/windows_access_token_winlogon_duplicate_handle_in_uncommon_path.yml index 88b1d20f1e..8c355ee466 100644 --- a/detections/endpoint/windows_access_token_winlogon_duplicate_handle_in_uncommon_path.yml +++ b/detections/endpoint/windows_access_token_winlogon_duplicate_handle_in_uncommon_path.yml @@ -5,75 +5,48 @@ date: '2025-08-20' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects a process attempting to duplicate - the handle of winlogon.exe from an uncommon or public source path. This is - identified using Sysmon EventCode 10, focusing on processes targeting - winlogon.exe with specific access rights and excluding common system paths. - This activity is significant because it may indicate an adversary trying to - escalate privileges by leveraging the high-privilege tokens associated with - winlogon.exe. If confirmed malicious, this could allow the attacker to gain - elevated access, potentially leading to full system compromise and persistent - control over the affected host. +description: The following analytic detects a process attempting to duplicate the handle of winlogon.exe from an uncommon or public source path. This is identified using Sysmon EventCode 10, focusing on processes targeting winlogon.exe with specific access rights and excluding common system paths. This activity is significant because it may indicate an adversary trying to escalate privileges by leveraging the high-privilege tokens associated with winlogon.exe. If confirmed malicious, this could allow the attacker to gain elevated access, potentially leading to full system compromise and persistent control over the affected host. data_source: -- Sysmon EventID 10 -search: '`sysmon` EventCode=10 TargetImage IN("*\\system32\\winlogon.exe*", "*\\SysWOW64\\winlogon.exe*") - AND GrantedAccess = 0x1040 AND NOT (SourceImage IN("C:\\Windows\\*", "C:\\Program - File*", "%systemroot%\\*")) | stats count min(_time) as firstTime max(_time) as - lastTime by CallTrace EventID GrantedAccess Guid Opcode ProcessID SecurityID SourceImage - SourceProcessGUID SourceProcessId TargetImage TargetProcessGUID TargetProcessId - UserID dest granted_access parent_process_exec parent_process_guid parent_process_id - parent_process_name parent_process_path process_exec process_guid process_id process_name - process_path signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_access_token_winlogon_duplicate_handle_in_uncommon_path_filter`' -how_to_implement: To successfully implement this search, you must be ingesting - data that records process activity from your hosts to populate the endpoint - data model in the processes node. If you are using Sysmon, you must have at - least version 6.0.4 of the Sysmon TA. -known_false_positives: It is possible legitimate applications will request - access to winlogon, filter as needed. + - Sysmon EventID 10 +search: '`sysmon` EventCode=10 TargetImage IN("*\\system32\\winlogon.exe*", "*\\SysWOW64\\winlogon.exe*") AND GrantedAccess = 0x1040 AND NOT (SourceImage IN("C:\\Windows\\*", "C:\\Program File*", "%systemroot%\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by CallTrace EventID GrantedAccess Guid Opcode ProcessID SecurityID SourceImage SourceProcessGUID SourceProcessId TargetImage TargetProcessGUID TargetProcessId UserID dest granted_access parent_process_exec parent_process_guid parent_process_id parent_process_name parent_process_path process_exec process_guid process_id process_name process_path signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_access_token_winlogon_duplicate_handle_in_uncommon_path_filter`' +how_to_implement: To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: It is possible legitimate applications will request access to winlogon, filter as needed. references: -- https://docs.microsoft.com/en-us/windows/win32/api/handleapi/nf-handleapi-duplicatehandle -- https://attack.mitre.org/techniques/T1134/001/ + - https://docs.microsoft.com/en-us/windows/win32/api/handleapi/nf-handleapi-duplicatehandle + - https://attack.mitre.org/techniques/T1134/001/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process $SourceImage$ is duplicating the handle token of - winlogon.exe on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: - - field: SourceImage - type: process_name + message: A process $SourceImage$ is duplicating the handle token of winlogon.exe on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: + - field: SourceImage + type: process_name tags: - analytic_story: - - Brute Ratel C4 - - PathWiper - asset_type: Endpoint - mitre_attack_id: - - T1134.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Brute Ratel C4 + - PathWiper + asset_type: Endpoint + mitre_attack_id: + - T1134.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/brute_duplicate_token/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/brute_duplicate_token/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_account_access_removal_via_logoff_exec.yml b/detections/endpoint/windows_account_access_removal_via_logoff_exec.yml index 74517ba93a..96965f23b9 100644 --- a/detections/endpoint/windows_account_access_removal_via_logoff_exec.yml +++ b/detections/endpoint/windows_account_access_removal_via_logoff_exec.yml @@ -1,73 +1,65 @@ name: Windows Account Access Removal via Logoff Exec id: 223572ab-8768-4e20-9b39-c38707af80dc -version: 4 -date: '2025-05-02' +version: 5 +date: '2026-02-25' author: Teoderick Contreras, Splunk data_source: -- Sysmon EventID 1 + - Sysmon EventID 1 type: Anomaly status: production -description: "The following analytic detects the process of logging off a user through\ - \ the use of the quser and logoff commands. By monitoring for these commands, the\ - \ analytic identifies actions where a user session is forcibly terminated, which\ - \ could be part of an administrative task or a potentially unauthorized access attempt.\ - \ This detection helps identify potential misuse or malicious activity where a user\u2019\ - s access is revoked without proper authorization, providing insight into potential\ - \ security incidents involving account management or session manipulation." -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = logoff.exe - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_account_access_removal_via_logoff_exec_filter`' -how_to_implement: The following Hunting analytic requires PowerShell operational logs - to be imported. Modify the powershell macro as needed to match the sourcetype or - add index. This analytic is specific to 4104, or PowerShell Script Block Logging. +description: "The following analytic detects the process of logging off a user through the use of the quser and logoff commands. By monitoring for these commands, the analytic identifies actions where a user session is forcibly terminated, which could be part of an administrative task or a potentially unauthorized access attempt. This detection helps identify potential misuse or malicious activity where a user’s access is revoked without proper authorization, providing insight into potential security incidents involving account management or session manipulation." +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = logoff.exe + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_account_access_removal_via_logoff_exec_filter` +how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. known_false_positives: Administrators or power users may use this command. references: -- https://devblogs.microsoft.com/scripting/automating-quser-through-powershell/ + - https://devblogs.microsoft.com/scripting/automating-quser-through-powershell/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Process having child process [$process_name$] used to logoff user on [$dest$]. - risk_objects: - - field: dest - type: system - score: 36 - - field: user - type: user - score: 36 - threat_objects: [] + message: Process having child process [$process_name$] used to logoff user on [$dest$]. + risk_objects: + - field: dest + type: system + score: 36 + - field: user + type: user + score: 36 + threat_objects: [] tags: - analytic_story: - - Crypto Stealer - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - - T1531 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Crypto Stealer + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + - T1531 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1531/powershell_log_process_tree/powershell_logoff.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1531/powershell_log_process_tree/powershell_logoff.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_account_discovery_for_none_disable_user_account.yml b/detections/endpoint/windows_account_discovery_for_none_disable_user_account.yml index 89e3da3c6d..ecce2ef5fd 100644 --- a/detections/endpoint/windows_account_discovery_for_none_disable_user_account.yml +++ b/detections/endpoint/windows_account_discovery_for_none_disable_user_account.yml @@ -1,53 +1,46 @@ name: Windows Account Discovery for None Disable User Account id: eddbf5ba-b89e-47ca-995e-2d259804e55e -version: 9 -date: '2025-06-24' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Hunting data_source: - - Powershell Script Block Logging 4104 -description: - The following analytic detects the execution of the PowerView PowerShell - cmdlet Get-NetUser with the UACFilter parameter set to NOT_ACCOUNTDISABLE, indicating - an attempt to enumerate Active Directory user accounts that are not disabled. This - detection leverages PowerShell Script Block Logging (EventCode 4104) to identify - the specific script block text. Monitoring this activity is significant as it may - indicate reconnaissance efforts by an attacker to identify active user accounts - for further exploitation. If confirmed malicious, this activity could lead to unauthorized - access, privilege escalation, or lateral movement within the network. -search: - '`powershell` EventCode=4104 ScriptBlockText = "*Get-NetUser*" ScriptBlockText - = "*NOT_ACCOUNTDISABLE*" ScriptBlockText = "*-UACFilter*" | fillnull | stats count - min(_time) as firstTime max(_time) as lastTime by dest signature signature_id user_id - vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_account_discovery_for_none_disable_user_account_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba.= -known_false_positives: - Administrators may leverage PowerView for legitimate purposes, - filter as needed. + - Powershell Script Block Logging 4104 +description: The following analytic detects the execution of the PowerView PowerShell cmdlet Get-NetUser with the UACFilter parameter set to NOT_ACCOUNTDISABLE, indicating an attempt to enumerate Active Directory user accounts that are not disabled. This detection leverages PowerShell Script Block Logging (EventCode 4104) to identify the specific script block text. Monitoring this activity is significant as it may indicate reconnaissance efforts by an attacker to identify active user accounts for further exploitation. If confirmed malicious, this activity could lead to unauthorized access, privilege escalation, or lateral movement within the network. +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*Get-NetUser*" ScriptBlockText = "*NOT_ACCOUNTDISABLE*" ScriptBlockText = "*-UACFilter*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_account_discovery_for_none_disable_user_account_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba.= +known_false_positives: Administrators may leverage PowerView for legitimate purposes, filter as needed. references: - - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a - - https://powersploit.readthedocs.io/en/stable/Recon/README/ - - https://book.hacktricks.xyz/windows-hardening/basic-powershell-for-pentesters/powerview - - https://atomicredteam.io/discovery/T1087.001/ + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a + - https://powersploit.readthedocs.io/en/stable/Recon/README/ + - https://book.hacktricks.xyz/windows-hardening/basic-powershell-for-pentesters/powerview + - https://atomicredteam.io/discovery/T1087.001/ tags: - analytic_story: - - CISA AA23-347A - asset_type: Endpoint - mitre_attack_id: - - T1087.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA23-347A + asset_type: Endpoint + mitre_attack_id: + - T1087.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087/powerview_get_netuser_preauthnotrequire/get-netuser-not-require-pwh.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087/powerview_get_netuser_preauthnotrequire/get-netuser-not-require-pwh.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_account_discovery_for_sam_account_name.yml b/detections/endpoint/windows_account_discovery_for_sam_account_name.yml index 1facb765b4..bd1de6cb6a 100644 --- a/detections/endpoint/windows_account_discovery_for_sam_account_name.yml +++ b/detections/endpoint/windows_account_discovery_for_sam_account_name.yml @@ -1,72 +1,59 @@ name: Windows Account Discovery for Sam Account Name id: 69934363-e1dd-4c49-8651-9d7663dd4d2f -version: 7 -date: '2025-06-24' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: - - Powershell Script Block Logging 4104 -description: - The following analytic detects the execution of the PowerView PowerShell - cmdlet Get-NetUser, specifically querying for "samaccountname" and "pwdlastset" - attributes. It leverages Event ID 4104 from PowerShell Script Block Logging to identify - this activity. This behavior is significant as it may indicate an attempt to gather - user account information from Active Directory, which is a common reconnaissance - step in lateral movement or privilege escalation attacks. If confirmed malicious, - this activity could allow an attacker to map out user accounts, potentially leading - to further exploitation and unauthorized access within the network. -search: - '`powershell` EventCode=4104 ScriptBlockText = "*Get-NetUser*" ScriptBlockText - IN ("*samaccountname*", "*pwdlastset*") | fillnull | stats count min(_time) as firstTime - max(_time) as lastTime by dest signature signature_id user_id vendor_product EventID - Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_account_discovery_for_sam_account_name_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba.= -known_false_positives: - Administrators may leverage PowerView for legitimate purposes, - filter as needed. + - Powershell Script Block Logging 4104 +description: The following analytic detects the execution of the PowerView PowerShell cmdlet Get-NetUser, specifically querying for "samaccountname" and "pwdlastset" attributes. It leverages Event ID 4104 from PowerShell Script Block Logging to identify this activity. This behavior is significant as it may indicate an attempt to gather user account information from Active Directory, which is a common reconnaissance step in lateral movement or privilege escalation attacks. If confirmed malicious, this activity could allow an attacker to map out user accounts, potentially leading to further exploitation and unauthorized access within the network. +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*Get-NetUser*" ScriptBlockText IN ("*samaccountname*", "*pwdlastset*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_account_discovery_for_sam_account_name_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba.= +known_false_positives: Administrators may leverage PowerView for legitimate purposes, filter as needed. references: - - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Account Discovery for Sam Account Name on $dest$. - risk_objects: - - field: dest - type: system - score: 15 - threat_objects: [] + message: Windows Account Discovery for Sam Account Name on $dest$. + risk_objects: + - field: dest + type: system + score: 15 + threat_objects: [] tags: - analytic_story: - - CISA AA23-347A - asset_type: Endpoint - mitre_attack_id: - - T1087 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA23-347A + asset_type: Endpoint + mitre_attack_id: + - T1087 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087/powerview_get_netuser_preauthnotrequire/get-netuser-not-require-pwh.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087/powerview_get_netuser_preauthnotrequire/get-netuser-not-require-pwh.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_account_discovery_with_netuser_preauthnotrequire.yml b/detections/endpoint/windows_account_discovery_with_netuser_preauthnotrequire.yml index 01a3317339..b4a87d6596 100644 --- a/detections/endpoint/windows_account_discovery_with_netuser_preauthnotrequire.yml +++ b/detections/endpoint/windows_account_discovery_with_netuser_preauthnotrequire.yml @@ -1,49 +1,43 @@ name: Windows Account Discovery With NetUser PreauthNotRequire id: cf056b65-44b2-4d32-9172-d6b6f081a376 -version: 7 -date: '2025-06-24' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Hunting data_source: - - Powershell Script Block Logging 4104 -description: - The following analytic detects the execution of the PowerView PowerShell - cmdlet Get-NetUser with the -PreauthNotRequire parameter, leveraging Event ID 4104. - This method identifies attempts to query Active Directory user accounts that do - not require Kerberos preauthentication. Monitoring this activity is crucial as it - can indicate reconnaissance efforts by an attacker to identify potentially vulnerable - accounts. If confirmed malicious, this behavior could lead to further exploitation, - such as unauthorized access or privilege escalation within the network. -search: - '`powershell` EventCode=4104 ScriptBlockText = "*Get-NetUser*" ScriptBlockText - = "*-PreauthNotRequire*" | fillnull | stats count min(_time) as firstTime max(_time) - as lastTime by dest signature signature_id user_id vendor_product EventID Guid Opcode - Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_account_discovery_with_netuser_preauthnotrequire_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba.= -known_false_positives: - Administrators may leverage PowerView for legitimate purposes, - filter as needed. + - Powershell Script Block Logging 4104 +description: The following analytic detects the execution of the PowerView PowerShell cmdlet Get-NetUser with the -PreauthNotRequire parameter, leveraging Event ID 4104. This method identifies attempts to query Active Directory user accounts that do not require Kerberos preauthentication. Monitoring this activity is crucial as it can indicate reconnaissance efforts by an attacker to identify potentially vulnerable accounts. If confirmed malicious, this behavior could lead to further exploitation, such as unauthorized access or privilege escalation within the network. +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*Get-NetUser*" ScriptBlockText = "*-PreauthNotRequire*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_account_discovery_with_netuser_preauthnotrequire_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba.= +known_false_positives: Administrators may leverage PowerView for legitimate purposes, filter as needed. references: - - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a tags: - analytic_story: - - CISA AA23-347A - asset_type: Endpoint - mitre_attack_id: - - T1087 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA23-347A + asset_type: Endpoint + mitre_attack_id: + - T1087 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087/powerview_get_netuser_preauthnotrequire/get-netuser-not-require-pwh.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087/powerview_get_netuser_preauthnotrequire/get-netuser-not-require-pwh.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_abnormal_object_access_activity.yml b/detections/endpoint/windows_ad_abnormal_object_access_activity.yml index 8bb6f9b1cc..f21d66eb95 100644 --- a/detections/endpoint/windows_ad_abnormal_object_access_activity.yml +++ b/detections/endpoint/windows_ad_abnormal_object_access_activity.yml @@ -1,75 +1,60 @@ name: Windows AD Abnormal Object Access Activity id: 71b289db-5f2c-4c43-8256-8bf26ae7324a -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Steven Dick status: production type: Anomaly -description: The following analytic identifies a statistically significant increase - in access to Active Directory objects, which may indicate attacker enumeration. - It leverages Windows Security Event Code 4662 to monitor and analyze access patterns, - comparing them against historical averages to detect anomalies. This activity is - significant for a SOC because abnormal access to AD objects can be an early indicator - of reconnaissance efforts by an attacker. If confirmed malicious, this behavior - could lead to unauthorized access, privilege escalation, or further compromise of - the Active Directory environment. +description: The following analytic identifies a statistically significant increase in access to Active Directory objects, which may indicate attacker enumeration. It leverages Windows Security Event Code 4662 to monitor and analyze access patterns, comparing them against historical averages to detect anomalies. This activity is significant for a SOC because abnormal access to AD objects can be an early indicator of reconnaissance efforts by an attacker. If confirmed malicious, this behavior could lead to unauthorized access, privilege escalation, or further compromise of the Active Directory environment. data_source: -- Windows Event Log Security 4662 -search: '`wineventlog_security` EventCode=4662 | stats min(_time) AS firstTime, max(_time) - AS lastTime, dc(ObjectName) AS ObjectName_count, values(ObjectType) AS ObjectType, - latest(Computer) AS dest count BY SubjectUserName | eventstats avg(ObjectName_count) - AS average stdev(ObjectName_count) AS standarddev | eval limit = round((average+(standarddev*3)),0), - user = SubjectUserName | where ObjectName_count > limit | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`| `windows_ad_abnormal_object_access_activity_filter`' -how_to_implement: Enable Audit Directory Service Access via GPO and collect event - code 4662. The required SACLs need to be created for the relevant objects. Be aware - Splunk filters this event by default on the Windows TA. Recommend pre-filtering - any known service accounts that frequently query AD to make detection more accurate. - Setting wide search window of 48~72hr may smooth out misfires. -known_false_positives: Service accounts or applications that routinely query Active - Directory for information. + - Windows Event Log Security 4662 +search: |- + `wineventlog_security` EventCode=4662 + | stats min(_time) AS firstTime, max(_time) AS lastTime, dc(ObjectName) AS ObjectName_count, values(ObjectType) AS ObjectType, latest(Computer) AS dest count + BY SubjectUserName + | eventstats avg(ObjectName_count) AS average stdev(ObjectName_count) AS standarddev + | eval limit = round((average+(standarddev*3)),0), user = SubjectUserName + | where ObjectName_count > limit + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_ad_abnormal_object_access_activity_filter` +how_to_implement: Enable Audit Directory Service Access via GPO and collect event code 4662. The required SACLs need to be created for the relevant objects. Be aware Splunk filters this event by default on the Windows TA. Recommend pre-filtering any known service accounts that frequently query AD to make detection more accurate. Setting wide search window of 48~72hr may smooth out misfires. +known_false_positives: Service accounts or applications that routinely query Active Directory for information. references: -- https://medium.com/securonix-tech-blog/detecting-ldap-enumeration-and-bloodhound-s-sharphound-collector-using-active-directory-decoys-dfc840f2f644 -- https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4662 -- https://attack.mitre.org/tactics/TA0007/ + - https://medium.com/securonix-tech-blog/detecting-ldap-enumeration-and-bloodhound-s-sharphound-collector-using-active-directory-decoys-dfc840f2f644 + - https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4662 + - https://attack.mitre.org/tactics/TA0007/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The account $user$ accessed an abnormal amount ($ObjectName_count$) of - [$ObjectType$] AD object(s) between $firstTime$ and $lastTime$. - risk_objects: - - field: user - type: user - score: 25 - threat_objects: [] + message: The account $user$ accessed an abnormal amount ($ObjectName_count$) of [$ObjectType$] AD object(s) between $firstTime$ and $lastTime$. + risk_objects: + - field: user + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - - BlackSuit Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1087.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - BlackSuit Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1087.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/4662_ad_enum/4662_priv_events.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/4662_ad_enum/4662_priv_events.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_add_self_to_group.yml b/detections/endpoint/windows_ad_add_self_to_group.yml index 66fbeb8c27..034ff5baa7 100644 --- a/detections/endpoint/windows_ad_add_self_to_group.yml +++ b/detections/endpoint/windows_ad_add_self_to_group.yml @@ -1,62 +1,55 @@ name: Windows AD add Self to Group id: 065f2701-b7ea-42f5-9ec4-fbc2261165f9 -version: 6 -date: '2026-01-14' +version: 7 +date: '2026-02-25' author: Dean Luxton status: production type: TTP data_source: -- Windows Event Log Security 4728 -description: This analytic detects instances where a user adds themselves to an Active - Directory (AD) group. This activity is a common indicator of privilege escalation, - where a user attempts to gain unauthorized access to higher privileges or sensitive - resources. By monitoring AD logs, this detection identifies such suspicious behavior, - which could be part of a larger attack strategy aimed at compromising critical systems - and data. -search: '`wineventlog_security` EventCode IN (4728) | where user=src_user | stats - min(_time) as _time dc(user) as usercount, values(user) as user values(user_category) - as user_category values(src_user_category) as src_user_category values(dvc) as dvc - by signature, Group_Name, src_user, dest | `windows_ad_add_self_to_group_filter`' + - Windows Event Log Security 4728 +description: This analytic detects instances where a user adds themselves to an Active Directory (AD) group. This activity is a common indicator of privilege escalation, where a user attempts to gain unauthorized access to higher privileges or sensitive resources. By monitoring AD logs, this detection identifies such suspicious behavior, which could be part of a larger attack strategy aimed at compromising critical systems and data. +search: |- + `wineventlog_security` EventCode IN (4728) + | where user=src_user + | stats min(_time) as _time dc(user) as usercount, values(user) as user values(user_category) as user_category values(src_user_category) as src_user_category values(dvc) as dvc + BY signature, Group_Name, src_user, + dest + | `windows_ad_add_self_to_group_filter` how_to_implement: This analytic requires eventCode 4728 to be ingested. known_false_positives: No false positives have been identified at this time. references: [] drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $user$ added themselves to AD Group $Group_Name$ - risk_objects: - - field: user - type: user - score: 50 - threat_objects: [] + message: $user$ added themselves to AD Group $Group_Name$ + risk_objects: + - field: user + type: user + score: 50 + threat_objects: [] tags: - analytic_story: - - Sneaky Active Directory Persistence Tricks - - Medusa Ransomware - - Active Directory Privilege Escalation - asset_type: Endpoint - mitre_attack_id: - - T1098 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: audit + analytic_story: + - Sneaky Active Directory Persistence Tricks + - Medusa Ransomware + - Active Directory Privilege Escalation + asset_type: Endpoint + mitre_attack_id: + - T1098 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: audit tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/account_manipulation/xml-windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/account_manipulation/xml-windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_adminsdholder_acl_modified.yml b/detections/endpoint/windows_ad_adminsdholder_acl_modified.yml index ca485847df..59ad1a94dc 100644 --- a/detections/endpoint/windows_ad_adminsdholder_acl_modified.yml +++ b/detections/endpoint/windows_ad_adminsdholder_acl_modified.yml @@ -6,97 +6,53 @@ author: Mauricio Velazco, Dean Luxton, Splunk type: TTP status: production data_source: -- Windows Event Log Security 5136 -description: The following analytic detects modifications to the Access Control List - (ACL) of the AdminSDHolder object in a Windows domain, specifically the addition - of new rules. It leverages EventCode 5136 from the Security Event Log, focusing - on changes to the nTSecurityDescriptor attribute. This activity is significant because - the AdminSDHolder object secures privileged group members, and unauthorized changes - can allow attackers to establish persistence and escalate privileges. If confirmed - malicious, this could enable an attacker to control domain-level permissions, compromising - the entire Active Directory environment. -search: '`wineventlog_security` EventCode=5136 ObjectClass=container ObjectDN="CN=AdminSDHolder,CN=System*" | - stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) - as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value - values(OperationType) as OperationType values(dest) as dest by ObjectClass ObjectDN - OpCorrelationID src_user SubjectLogonId | rex field=old_value max_match=10000 "\((?P.*?)\)" | - rex field=new_value max_match=10000 "\((?P.*?)\)" | mvexpand new_ace | - where NOT new_ace IN (old_values) | rex field=new_ace "(?P.*?);(?P.*?);(?P.*?);(?P.*?);(?P.*?);(?P.*?)$" - | rex max_match=100 field=aceAccessRights "(?P[A-Z]{2})" | rex max_match=100 - field=aceFlags "(?P[A-Z]{2})" | lookup msad_guid_lookup guid as aceObjectGuid - OUTPUT displayName as ControlAccessRights | lookup ace_access_rights_lookup access_rights_string - as AccessRights OUTPUT access_rights_value | lookup ace_type_lookup ace_type_string - as aceType OUTPUT ace_type_value | lookup ace_flag_lookup flag_string as aceFlags - OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups | lookup - identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user | - lookup admon_groups_def objectSid as aceSid OUTPUT cn as group``` | lookup builtin_groups_lookup - builtin_group_string as aceSid OUTPUTNEW builtin_group_name as builtin_group | - eval aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,"This - object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full - control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid), - user=coalesce(user, group, builtin_group, aceSid) | stats min(_time) as _time values(aceType) - as aceType values(aceFlags) as aceFlags(inheritance) values(aceControlAccessRights) - as aceControlAccessRights values(aceAccessRights) as aceAccessRights values(new_ace) - as new_ace values(SubjectLogonId) as SubjectLogonId by ObjectClass ObjectDN src_user - user | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1 AND aceControlAccessRights="","All - rights",''aceControlAccessRights'') | search NOT aceType IN (*denied*,D,OD,XD) AND - aceAccessRights IN ("Full control","All extended rights","All validated writes","Create - all child objects","Delete all child objects","Delete subtree","Delete","Modify - permissions","Modify owner","Write all properties",CC,CR,DC,DT,SD,SW,WD,WO,WP) | - `windows_ad_adminsdholder_acl_modified_filter`' -how_to_implement: To successfully implement this search, you ned to be ingesting eventcode - `5136`. The Advanced Security Audit policy setting `Audit Directory Services Changes` - within `DS Access` needs to be enabled. Additionally, a SACL needs to be created - for the AdminSDHolder object in order to log modifications. -known_false_positives: Adding new users or groups to the AdminSDHolder ACL is not - usual. Filter as needed + - Windows Event Log Security 5136 +description: The following analytic detects modifications to the Access Control List (ACL) of the AdminSDHolder object in a Windows domain, specifically the addition of new rules. It leverages EventCode 5136 from the Security Event Log, focusing on changes to the nTSecurityDescriptor attribute. This activity is significant because the AdminSDHolder object secures privileged group members, and unauthorized changes can allow attackers to establish persistence and escalate privileges. If confirmed malicious, this could enable an attacker to control domain-level permissions, compromising the entire Active Directory environment. +search: '`wineventlog_security` EventCode=5136 ObjectClass=container ObjectDN="CN=AdminSDHolder,CN=System*" | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) as OperationType values(dest) as dest by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId | rex field=old_value max_match=10000 "\((?P.*?)\)" | rex field=new_value max_match=10000 "\((?P.*?)\)" | mvexpand new_ace | where NOT new_ace IN (old_values) | rex field=new_ace "(?P.*?);(?P.*?);(?P.*?);(?P.*?);(?P.*?);(?P.*?)$" | rex max_match=100 field=aceAccessRights "(?P[A-Z]{2})" | rex max_match=100 field=aceFlags "(?P[A-Z]{2})" | lookup msad_guid_lookup guid as aceObjectGuid OUTPUT displayName as ControlAccessRights | lookup ace_access_rights_lookup access_rights_string as AccessRights OUTPUT access_rights_value | lookup ace_type_lookup ace_type_string as aceType OUTPUT ace_type_value | lookup ace_flag_lookup flag_string as aceFlags OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group``` | lookup builtin_groups_lookup builtin_group_string as aceSid OUTPUTNEW builtin_group_name as builtin_group | eval aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,"This object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid), user=coalesce(user, group, builtin_group, aceSid) | stats min(_time) as _time values(aceType) as aceType values(aceFlags) as aceFlags(inheritance) values(aceControlAccessRights) as aceControlAccessRights values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace values(SubjectLogonId) as SubjectLogonId by ObjectClass ObjectDN src_user user | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1 AND aceControlAccessRights="","All rights",''aceControlAccessRights'') | search NOT aceType IN (*denied*,D,OD,XD) AND aceAccessRights IN ("Full control","All extended rights","All validated writes","Create all child objects","Delete all child objects","Delete subtree","Delete","Modify permissions","Modify owner","Write all properties",CC,CR,DC,DT,SD,SW,WD,WO,WP) | `windows_ad_adminsdholder_acl_modified_filter`' +how_to_implement: To successfully implement this search, you ned to be ingesting eventcode `5136`. The Advanced Security Audit policy setting `Audit Directory Services Changes` within `DS Access` needs to be enabled. Additionally, a SACL needs to be created for the AdminSDHolder object in order to log modifications. +known_false_positives: Adding new users or groups to the AdminSDHolder ACL is not usual. Filter as needed references: -- https://learn.microsoft.com/en-us/windows-server/identity/ad-ds/plan/security-best-practices/appendix-c--protected-accounts-and-groups-in-active-directory -- https://social.technet.microsoft.com/wiki/contents/articles/22331.adminsdholder-protected-groups-and-security-descriptor-propagator.aspx -- https://adsecurity.org/?p=1906 -- https://pentestlab.blog/2022/01/04/domain-persistence-adminsdholder/ -- https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-5136 -- https://learn.microsoft.com/en-us/windows/win32/secauthz/access-control-lists -- https://medium.com/@cryps1s/detecting-windows-endpoint-compromise-with-sacls-cd748e10950 -- https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory + - https://learn.microsoft.com/en-us/windows-server/identity/ad-ds/plan/security-best-practices/appendix-c--protected-accounts-and-groups-in-active-directory + - https://social.technet.microsoft.com/wiki/contents/articles/22331.adminsdholder-protected-groups-and-security-descriptor-propagator.aspx + - https://adsecurity.org/?p=1906 + - https://pentestlab.blog/2022/01/04/domain-persistence-adminsdholder/ + - https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-5136 + - https://learn.microsoft.com/en-us/windows/win32/secauthz/access-control-lists + - https://medium.com/@cryps1s/detecting-windows-endpoint-compromise-with-sacls-cd748e10950 + - https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The AdminSDHolder domain object $ObjectDN$ has been modified by $src_user$ - risk_objects: - - field: user - type: user - score: 56 - - field: src_user - type: user - score: 56 - threat_objects: [] + message: The AdminSDHolder domain object $ObjectDN$ has been modified by $src_user$ + risk_objects: + - field: user + type: user + score: 56 + - field: src_user + type: user + score: 56 + threat_objects: [] tags: - analytic_story: - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1546 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1546 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546/adminsdholder_modified/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546/adminsdholder_modified/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_cross_domain_sid_history_addition.yml b/detections/endpoint/windows_ad_cross_domain_sid_history_addition.yml index 99e8e46277..ac61b0e810 100644 --- a/detections/endpoint/windows_ad_cross_domain_sid_history_addition.yml +++ b/detections/endpoint/windows_ad_cross_domain_sid_history_addition.yml @@ -6,70 +6,50 @@ author: Dean Luxton type: TTP status: production data_source: -- Windows Event Log Security 4742 -- Windows Event Log Security 4738 -description: The following analytic detects changes to the sIDHistory attribute of - user or computer objects across different domains. It leverages Windows Security - Event Codes 4738 and 4742 to identify when the sIDHistory attribute is modified. - This activity is significant because the sIDHistory attribute allows users to inherit - permissions from other AD accounts, which can be exploited by adversaries for inter-domain - privilege escalation and persistence. If confirmed malicious, this could enable - attackers to gain unauthorized access to resources, maintain persistence, and escalate - privileges across domain boundaries. -search: '`wineventlog_security` (EventCode=4742 OR EventCode=4738) NOT SidHistory - IN ("%%1793", -) | rex field=SidHistory "(^%{|^)(?P.*)(\-|\\\)" - | rex field=TargetSid "^(?P.*)(\-|\\\)" | where SidHistoryMatch!=TargetSidmatch - AND SidHistoryMatch!=TargetDomainName | rename TargetSid as userSid | table _time - action status host user userSid SidHistory Logon_ID src_user dest | `windows_ad_cross_domain_sid_history_addition_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - eventcodes `4738` and `4742`. The Advanced Security Audit policy settings `Audit - User Account Management` and `Audit Computer Account Management` within `Account - Management` all need to be enabled. -known_false_positives: Domain mergers and migrations may generate large volumes of - false positives for this analytic. + - Windows Event Log Security 4742 + - Windows Event Log Security 4738 +description: The following analytic detects changes to the sIDHistory attribute of user or computer objects across different domains. It leverages Windows Security Event Codes 4738 and 4742 to identify when the sIDHistory attribute is modified. This activity is significant because the sIDHistory attribute allows users to inherit permissions from other AD accounts, which can be exploited by adversaries for inter-domain privilege escalation and persistence. If confirmed malicious, this could enable attackers to gain unauthorized access to resources, maintain persistence, and escalate privileges across domain boundaries. +search: '`wineventlog_security` (EventCode=4742 OR EventCode=4738) NOT SidHistory IN ("%%1793", -) | rex field=SidHistory "(^%{|^)(?P.*)(\-|\\\)" | rex field=TargetSid "^(?P.*)(\-|\\\)" | where SidHistoryMatch!=TargetSidmatch AND SidHistoryMatch!=TargetDomainName | rename TargetSid as userSid | table _time action status host user userSid SidHistory Logon_ID src_user dest | `windows_ad_cross_domain_sid_history_addition_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting eventcodes `4738` and `4742`. The Advanced Security Audit policy settings `Audit User Account Management` and `Audit Computer Account Management` within `Account Management` all need to be enabled. +known_false_positives: Domain mergers and migrations may generate large volumes of false positives for this analytic. references: -- https://adsecurity.org/?p=1772 -- https://learn.microsoft.com/en-us/windows/win32/adschema/a-sidhistory?redirectedfrom=MSDN -- https://learn.microsoft.com/en-us/defender-for-identity/security-assessment-unsecure-sid-history-attribute + - https://adsecurity.org/?p=1772 + - https://learn.microsoft.com/en-us/windows/win32/adschema/a-sidhistory?redirectedfrom=MSDN + - https://learn.microsoft.com/en-us/defender-for-identity/security-assessment-unsecure-sid-history-attribute drilldown_searches: -- name: View the detection results for - "$src_user$" and "$user$" - search: '%original_detection_search% | search src_user = "$src_user$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_user$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_user$" and "$user$" + search: '%original_detection_search% | search src_user = "$src_user$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_user$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Active Directory SID History Attribute was added to $user$ by $src_user$ - risk_objects: - - field: src_user - type: user - score: 80 - - field: user - type: user - score: 80 - threat_objects: [] + message: Active Directory SID History Attribute was added to $user$ by $src_user$ + risk_objects: + - field: src_user + type: user + score: 80 + - field: user + type: user + score: 80 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1134.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1134.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1134.005/mimikatz/windows-security-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1134.005/mimikatz/windows-security-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_dangerous_deny_acl_modification.yml b/detections/endpoint/windows_ad_dangerous_deny_acl_modification.yml index 5e3dbf5da5..163031927b 100644 --- a/detections/endpoint/windows_ad_dangerous_deny_acl_modification.yml +++ b/detections/endpoint/windows_ad_dangerous_deny_acl_modification.yml @@ -6,85 +6,49 @@ author: Dean Luxton status: production type: TTP data_source: -- Windows Event Log Security 5136 -description: This detection identifies an Active Directory access-control list (ACL) - modification event, which applies permissions that deny the ability to enumerate - permissions of the object. -search: '`wineventlog_security` EventCode=5136 | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) - as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value - values(OperationType) as OperationType values(dest) as dest by ObjectClass ObjectDN - OpCorrelationID src_user SubjectLogonId | rex field=old_value max_match=10000 "\((?P.*?)\)" | - rex field=new_value max_match=10000 "\((?P.*?)\)" | mvexpand new_ace | - where NOT new_ace IN (old_values) | rex field=new_ace "(?P.*?);(?P.*?);(?P.*?);(?P.*?);(?P.*?);(?P.*?)$" | - rex max_match=100 field=aceAccessRights "(?P[A-Z]{2})" | rex max_match=100 - field=aceFlags "(?P[A-Z]{2})" | lookup msad_guid_lookup guid as aceObjectGuid - OUTPUT displayName as ControlAccessRights | lookup ace_access_rights_lookup access_rights_string - as AccessRights OUTPUT access_rights_value | lookup ace_type_lookup ace_type_string - as aceType OUTPUT ace_type_value as aceType | lookup ace_flag_lookup flag_string - as aceFlags OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups - | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName - as user | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` | - lookup builtin_groups_lookup builtin_group_string as aceSid OUTPUT builtin_group_name - as builtin_group | eval aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,"This - object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full - control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid), - user=coalesce(user, group, builtin_group, aceSid) | stats values(aceType) as aceType - values(aceFlags) as aceFlags values(aceControlAccessRights) as aceControlAccessRights - values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace values(aceInheritedTypeGuid) - as aceInheritedTypeGuid by _time ObjectClass ObjectDN src_user SubjectLogonId user - OpCorrelationID | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1 - AND aceControlAccessRights="","All rights",''aceControlAccessRights'') | search - aceType IN ("Access denied",D) AND aceAccessRights IN ("Full control","Read permissions",RC) - | `windows_ad_dangerous_deny_acl_modification_filter`' -how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically - event 5136. See lantern article in references for further on how to onboard AD audit - data. Ensure the wineventlog_security macro is configured with the correct indexes - and include lookups for SID resolution if evt_resolve_ad_obj is set to 0. + - Windows Event Log Security 5136 +description: This detection identifies an Active Directory access-control list (ACL) modification event, which applies permissions that deny the ability to enumerate permissions of the object. +search: '`wineventlog_security` EventCode=5136 | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) as OperationType values(dest) as dest by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId | rex field=old_value max_match=10000 "\((?P.*?)\)" | rex field=new_value max_match=10000 "\((?P.*?)\)" | mvexpand new_ace | where NOT new_ace IN (old_values) | rex field=new_ace "(?P.*?);(?P.*?);(?P.*?);(?P.*?);(?P.*?);(?P.*?)$" | rex max_match=100 field=aceAccessRights "(?P[A-Z]{2})" | rex max_match=100 field=aceFlags "(?P[A-Z]{2})" | lookup msad_guid_lookup guid as aceObjectGuid OUTPUT displayName as ControlAccessRights | lookup ace_access_rights_lookup access_rights_string as AccessRights OUTPUT access_rights_value | lookup ace_type_lookup ace_type_string as aceType OUTPUT ace_type_value as aceType | lookup ace_flag_lookup flag_string as aceFlags OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` | lookup builtin_groups_lookup builtin_group_string as aceSid OUTPUT builtin_group_name as builtin_group | eval aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,"This object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid), user=coalesce(user, group, builtin_group, aceSid) | stats values(aceType) as aceType values(aceFlags) as aceFlags values(aceControlAccessRights) as aceControlAccessRights values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace values(aceInheritedTypeGuid) as aceInheritedTypeGuid by _time ObjectClass ObjectDN src_user SubjectLogonId user OpCorrelationID | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1 AND aceControlAccessRights="","All rights",''aceControlAccessRights'') | search aceType IN ("Access denied",D) AND aceAccessRights IN ("Full control","Read permissions",RC) | `windows_ad_dangerous_deny_acl_modification_filter`' +how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security macro is configured with the correct indexes and include lookups for SID resolution if evt_resolve_ad_obj is set to 0. known_false_positives: No false positives have been identified at this time. references: -- https://happycamper84.medium.com/sneaky-persistence-via-hidden-objects-in-ad-1c91fc37bf54 -- https://www.youtube.com/watch?v=_nGpZ1ydzS8 -- https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory + - https://happycamper84.medium.com/sneaky-persistence-via-hidden-objects-in-ad-1c91fc37bf54 + - https://www.youtube.com/watch?v=_nGpZ1ydzS8 + - https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory drilldown_searches: -- name: View the detection results for - "$user$" and "$src_user$" - search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$src_user$" + search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $src_user$ has added ACL rights to deny $user$ $aceControlAccessRights$ - $aceAccessRights$ to $ObjectDN$ - risk_objects: - - field: user - type: user - score: 100 - - field: src_user - type: user - score: 100 - threat_objects: [] + message: $src_user$ has added ACL rights to deny $user$ $aceControlAccessRights$ $aceAccessRights$ to $ObjectDN$ + risk_objects: + - field: user + type: user + score: 100 + - field: src_user + type: user + score: 100 + threat_objects: [] tags: - analytic_story: - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1222.001 - - T1484 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1222.001 + - T1484 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/hidden_object_windows-security-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/hidden_object_windows-security-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_dangerous_group_acl_modification.yml b/detections/endpoint/windows_ad_dangerous_group_acl_modification.yml index a9b0242957..95b17c515b 100644 --- a/detections/endpoint/windows_ad_dangerous_group_acl_modification.yml +++ b/detections/endpoint/windows_ad_dangerous_group_acl_modification.yml @@ -6,94 +6,50 @@ author: Dean Luxton status: production type: TTP data_source: -- Windows Event Log Security 5136 -description: 'This detection monitors the addition of the following ACLs to an Active - Directory group object: "Full control", "All extended rights", "All validated writes", "Create - all child objects", "Delete all child objects", "Delete subtree", "Delete", "Modify - permissions", "Modify owner", and "Write all properties". Such modifications can - indicate potential privilege escalation or malicious activity. Immediate investigation - is recommended upon alert.' -search: '`wineventlog_security` EventCode=5136 ObjectClass=group | stats min(_time) - as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value - values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) - as OperationType values(dest) as dest by ObjectClass ObjectDN OpCorrelationID src_user - SubjectLogonId | rex field=old_value max_match=10000 "\((?P.*?)\)" | - rex field=new_value max_match=10000 "\((?P.*?)\)" | mvexpand new_ace | - where NOT new_ace IN (old_values) | rex field=new_ace "(?P.*?);(?P.*?);(?P.*?);(?P.*?);(?P.*?);(?P.*?)$" | - rex max_match=100 field=aceAccessRights "(?P[A-Z]{2})" | rex max_match=100 - field=aceFlags "(?P[A-Z]{2})" | lookup ace_type_lookup ace_type_string - as aceType OUTPUT ace_type_value as aceType | lookup ace_flag_lookup flag_string - as aceFlags OUTPUT flag_value as ace_flag_value | lookup ace_access_rights_lookup - access_rights_string as AccessRights OUTPUT access_rights_value | lookup msad_guid_lookup - guid as aceObjectGuid OUTPUT displayName as ControlAccessRights ``` Optional SID - resolution lookups | lookup identity_lookup_expanded objectSid as aceSid OUTPUT - downLevelDomainName as user | lookup admon_groups_def objectSid as aceSid OUTPUT - cn as group ``` | lookup builtin_groups_lookup builtin_group_string as aceSid OUTPUT - builtin_group_name as builtin_group | eval aceType=coalesce(ace_type_value,aceType), - aceInheritance=coalesce(ace_flag_value,"This object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full - control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=if((ControlAccessRights="Write - member" OR aceObjectGuid="bf9679c0-0de6-11d0-a285-00aa003049e2") AND (aceAccessRights="All - validated writes" OR AccessRights="SW"),"Add/remove self as member",coalesce(ControlAccessRights,aceObjectGuid)), - user=coalesce(user, group, builtin_group, aceSid) | stats values(aceType) as aceType - values(aceInheritance) as aceInheritance values(aceControlAccessRights) as aceControlAccessRights - values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace values(aceInheritedTypeGuid) - as aceInheritedTypeGuid by _time ObjectClass ObjectDN src_user SubjectLogonId user - OpCorrelationID | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1 - AND aceControlAccessRights="","All rights",''aceControlAccessRights'') | search - NOT aceType IN ("*denied*","D","OD","XD") AND aceAccessRights IN ("Full control","All - extended rights","All validated writes","Create all child objects","Delete all child - objects","Delete subtree","Delete","Modify permissions","Modify owner","Write all - properties",CC,CR,DC,DT,SD,SW,WD,WO,WP) | `windows_ad_dangerous_group_acl_modification_filter`' -how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically - event 5136. See lantern article in references for further on how to onboard AD audit - data. Ensure the wineventlog_security macro is configured with the correct indexes - and include lookups for SID resolution if evt_resolve_ad_obj is set to 0. + - Windows Event Log Security 5136 +description: 'This detection monitors the addition of the following ACLs to an Active Directory group object: "Full control", "All extended rights", "All validated writes", "Create all child objects", "Delete all child objects", "Delete subtree", "Delete", "Modify permissions", "Modify owner", and "Write all properties". Such modifications can indicate potential privilege escalation or malicious activity. Immediate investigation is recommended upon alert.' +search: '`wineventlog_security` EventCode=5136 ObjectClass=group | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) as OperationType values(dest) as dest by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId | rex field=old_value max_match=10000 "\((?P.*?)\)" | rex field=new_value max_match=10000 "\((?P.*?)\)" | mvexpand new_ace | where NOT new_ace IN (old_values) | rex field=new_ace "(?P.*?);(?P.*?);(?P.*?);(?P.*?);(?P.*?);(?P.*?)$" | rex max_match=100 field=aceAccessRights "(?P[A-Z]{2})" | rex max_match=100 field=aceFlags "(?P[A-Z]{2})" | lookup ace_type_lookup ace_type_string as aceType OUTPUT ace_type_value as aceType | lookup ace_flag_lookup flag_string as aceFlags OUTPUT flag_value as ace_flag_value | lookup ace_access_rights_lookup access_rights_string as AccessRights OUTPUT access_rights_value | lookup msad_guid_lookup guid as aceObjectGuid OUTPUT displayName as ControlAccessRights ``` Optional SID resolution lookups | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` | lookup builtin_groups_lookup builtin_group_string as aceSid OUTPUT builtin_group_name as builtin_group | eval aceType=coalesce(ace_type_value,aceType), aceInheritance=coalesce(ace_flag_value,"This object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=if((ControlAccessRights="Write member" OR aceObjectGuid="bf9679c0-0de6-11d0-a285-00aa003049e2") AND (aceAccessRights="All validated writes" OR AccessRights="SW"),"Add/remove self as member",coalesce(ControlAccessRights,aceObjectGuid)), user=coalesce(user, group, builtin_group, aceSid) | stats values(aceType) as aceType values(aceInheritance) as aceInheritance values(aceControlAccessRights) as aceControlAccessRights values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace values(aceInheritedTypeGuid) as aceInheritedTypeGuid by _time ObjectClass ObjectDN src_user SubjectLogonId user OpCorrelationID | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1 AND aceControlAccessRights="","All rights",''aceControlAccessRights'') | search NOT aceType IN ("*denied*","D","OD","XD") AND aceAccessRights IN ("Full control","All extended rights","All validated writes","Create all child objects","Delete all child objects","Delete subtree","Delete","Modify permissions","Modify owner","Write all properties",CC,CR,DC,DT,SD,SW,WD,WO,WP) | `windows_ad_dangerous_group_acl_modification_filter`' +how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security macro is configured with the correct indexes and include lookups for SID resolution if evt_resolve_ad_obj is set to 0. known_false_positives: No false positives have been identified at this time. references: -- https://learn.microsoft.com/en-us/windows/win32/secauthz/ace-strings -- https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-adts/1522b774-6464-41a3-87a5-1e5633c3fbbb -- https://trustedsec.com/blog/a-hitchhackers-guide-to-dacl-based-detections-part-1-a -- https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory + - https://learn.microsoft.com/en-us/windows/win32/secauthz/ace-strings + - https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-adts/1522b774-6464-41a3-87a5-1e5633c3fbbb + - https://trustedsec.com/blog/a-hitchhackers-guide-to-dacl-based-detections-part-1-a + - https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory drilldown_searches: -- name: View the detection results for - "$user$" and "$src_user$" - search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$src_user$" + search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $src_user$ has added ACL rights to grant $user$ $aceControlAccessRights$ - $aceAccessRights$ to group $ObjectDN$ - risk_objects: - - field: user - type: user - score: 100 - - field: src_user - type: user - score: 100 - threat_objects: [] + message: $src_user$ has added ACL rights to grant $user$ $aceControlAccessRights$ $aceAccessRights$ to group $ObjectDN$ + risk_objects: + - field: user + type: user + score: 100 + - field: src_user + type: user + score: 100 + threat_objects: [] tags: - analytic_story: - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1222.001 - - T1484 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1222.001 + - T1484 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/group_dacl_mod_windows-security-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/group_dacl_mod_windows-security-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_dangerous_user_acl_modification.yml b/detections/endpoint/windows_ad_dangerous_user_acl_modification.yml index 5eb83d478f..aa7e392c22 100644 --- a/detections/endpoint/windows_ad_dangerous_user_acl_modification.yml +++ b/detections/endpoint/windows_ad_dangerous_user_acl_modification.yml @@ -6,92 +6,50 @@ author: Dean Luxton status: production type: TTP data_source: -- Windows Event Log Security 5136 -description: 'This detection monitors the addition of the following ACLs to an Active - Directory user object: "Full control","All extended rights","All validated writes", - "Create all child objects","Delete all child objects","Delete subtree","Delete","Modify - permissions","Modify owner","Write all properties". Such modifications can indicate - potential privilege escalation or malicious activity. Immediate investigation is - recommended upon alert.' -search: '`wineventlog_security` EventCode=5136 ObjectClass=user | stats min(_time) - as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value - values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) - as OperationType values(dest) as dest by ObjectClass ObjectDN OpCorrelationID src_user - SubjectLogonId | rex field=old_value max_match=10000 "\((?P.*?)\)" | - rex field=new_value max_match=10000 "\((?P.*?)\)" | mvexpand new_ace | - where NOT new_ace IN (old_values) | rex field=new_ace "(?P.*?);(?P.*?);(?P.*?);(?P.*?);(?P.*?);(?P.*?)$" | - rex max_match=100 field=aceAccessRights "(?P[A-Z]{2})" | rex max_match=100 - field=aceFlags "(?P[A-Z]{2})" | lookup msad_guid_lookup guid as aceObjectGuid - OUTPUT displayName as ControlAccessRights | lookup ace_access_rights_lookup access_rights_string - as AccessRights OUTPUT access_rights_value | lookup ace_type_lookup ace_type_string - as aceType OUTPUT ace_type_value as aceType | lookup ace_flag_lookup flag_string - as aceFlags OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups - | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName - as user | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` | - lookup builtin_groups_lookup builtin_group_string as aceSid OUTPUT builtin_group_name - as builtin_group | eval aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,"This - object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full - control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid), - user=coalesce(user, group, builtin_group, aceSid) | stats values(aceType) as aceType - values(aceFlags) as aceFlags values(aceControlAccessRights) as aceControlAccessRights - values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace values(aceInheritedTypeGuid) - as aceInheritedTypeGuid by _time ObjectClass ObjectDN src_user SubjectLogonId user - OpCorrelationID | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1 - AND aceControlAccessRights="","All rights",''aceControlAccessRights'') | search - NOT aceType IN (*denied*,D,OD,XD) AND aceAccessRights IN ("Full control","All extended - rights","All validated writes","Create all child objects","Delete all child objects","Delete - subtree","Delete","Modify permissions","Modify owner","Write all properties",CC,CR,DC,DT,SD,SW,WD,WO,WP) - | `windows_ad_dangerous_user_acl_modification_filter`' -how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically - event 5136. See lantern article in references for further on how to onboard AD audit - data. Ensure the wineventlog_security macro is configured with the correct indexes - and include lookups for SID resolution if evt_resolve_ad_obj is set to 0. + - Windows Event Log Security 5136 +description: 'This detection monitors the addition of the following ACLs to an Active Directory user object: "Full control","All extended rights","All validated writes", "Create all child objects","Delete all child objects","Delete subtree","Delete","Modify permissions","Modify owner","Write all properties". Such modifications can indicate potential privilege escalation or malicious activity. Immediate investigation is recommended upon alert.' +search: '`wineventlog_security` EventCode=5136 ObjectClass=user | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) as OperationType values(dest) as dest by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId | rex field=old_value max_match=10000 "\((?P.*?)\)" | rex field=new_value max_match=10000 "\((?P.*?)\)" | mvexpand new_ace | where NOT new_ace IN (old_values) | rex field=new_ace "(?P.*?);(?P.*?);(?P.*?);(?P.*?);(?P.*?);(?P.*?)$" | rex max_match=100 field=aceAccessRights "(?P[A-Z]{2})" | rex max_match=100 field=aceFlags "(?P[A-Z]{2})" | lookup msad_guid_lookup guid as aceObjectGuid OUTPUT displayName as ControlAccessRights | lookup ace_access_rights_lookup access_rights_string as AccessRights OUTPUT access_rights_value | lookup ace_type_lookup ace_type_string as aceType OUTPUT ace_type_value as aceType | lookup ace_flag_lookup flag_string as aceFlags OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` | lookup builtin_groups_lookup builtin_group_string as aceSid OUTPUT builtin_group_name as builtin_group | eval aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,"This object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid), user=coalesce(user, group, builtin_group, aceSid) | stats values(aceType) as aceType values(aceFlags) as aceFlags values(aceControlAccessRights) as aceControlAccessRights values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace values(aceInheritedTypeGuid) as aceInheritedTypeGuid by _time ObjectClass ObjectDN src_user SubjectLogonId user OpCorrelationID | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1 AND aceControlAccessRights="","All rights",''aceControlAccessRights'') | search NOT aceType IN (*denied*,D,OD,XD) AND aceAccessRights IN ("Full control","All extended rights","All validated writes","Create all child objects","Delete all child objects","Delete subtree","Delete","Modify permissions","Modify owner","Write all properties",CC,CR,DC,DT,SD,SW,WD,WO,WP) | `windows_ad_dangerous_user_acl_modification_filter`' +how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security macro is configured with the correct indexes and include lookups for SID resolution if evt_resolve_ad_obj is set to 0. known_false_positives: No false positives have been identified at this time. references: -- https://learn.microsoft.com/en-us/windows/win32/secauthz/ace-strings -- https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-adts/1522b774-6464-41a3-87a5-1e5633c3fbbb -- https://trustedsec.com/blog/a-hitchhackers-guide-to-dacl-based-detections-part-1-a -- https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory + - https://learn.microsoft.com/en-us/windows/win32/secauthz/ace-strings + - https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-adts/1522b774-6464-41a3-87a5-1e5633c3fbbb + - https://trustedsec.com/blog/a-hitchhackers-guide-to-dacl-based-detections-part-1-a + - https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory drilldown_searches: -- name: View the detection results for - "$user$" and "$src_user$" - search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$src_user$" + search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $src_user$ has added ACL rights to grant $user$ $aceControlAccessRights$ - $aceAccessRights$ to user $ObjectDN$ - risk_objects: - - field: user - type: user - score: 100 - - field: src_user - type: user - score: 100 - threat_objects: [] + message: $src_user$ has added ACL rights to grant $user$ $aceControlAccessRights$ $aceAccessRights$ to user $ObjectDN$ + risk_objects: + - field: user + type: user + score: 100 + - field: src_user + type: user + score: 100 + threat_objects: [] tags: - analytic_story: - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1222.001 - - T1484 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1222.001 + - T1484 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/user_dacl_mod_windows-security-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/user_dacl_mod_windows-security-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_dcshadow_privileges_acl_addition.yml b/detections/endpoint/windows_ad_dcshadow_privileges_acl_addition.yml index b6077ef266..925ed0cb69 100644 --- a/detections/endpoint/windows_ad_dcshadow_privileges_acl_addition.yml +++ b/detections/endpoint/windows_ad_dcshadow_privileges_acl_addition.yml @@ -1,94 +1,81 @@ name: Windows AD DCShadow Privileges ACL Addition id: ae915743-1aa8-4a94-975c-8062ebc8b723 -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Dean Luxton status: production type: TTP data_source: -- Windows Event Log Security 5136 -description: This detection identifies an Active Directory access-control list (ACL) - modification event, which applies the minimum required extended rights to perform - the DCShadow attack. -search: '`wineventlog_security` EventCode=5136 ObjectClass=domainDNS | stats min(_time) - as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value - values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) - as OperationType values(dest) as dest by ObjectClass ObjectDN OpCorrelationID src_user - SubjectLogonId | rex field=old_value max_match=10000 "\((?P.*?)\)" | - rex field=new_value max_match=10000 "\((?P.*?)\)" | mvexpand new_ace | - where NOT new_ace IN (old_values) | rex field=new_ace "(?P.*?);(?P.*?);(?P.*?);(?P.*?);;(?P.*?)$" - | search aceObjectGuid IN ("9923a32a-3607-11d2-b9be-0000f87a36b2","1131f6ab-9c07-11d1-f79f-00c04fc2dcd2","1131f6ac-9c07-11d1-f79f-00c04fc2dcd2") - | rex max_match=100 field=aceAccessRights "(?P[A-Z]{2})" | rex max_match=100 - field=aceFlags "(?P[A-Z]{2})" | lookup msad_guid_lookup guid as aceObjectGuid - OUTPUT displayName as ControlAccessRights | lookup ace_access_rights_lookup access_rights_string - as AccessRights OUTPUT access_rights_value | lookup ace_type_lookup ace_type_string - as aceType OUTPUT ace_type_value | lookup ace_flag_lookup flag_string as aceFlags - OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups | lookup - identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user | - lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` | lookup builtin_groups_lookup - builtin_group_string as aceSid OUTPUT builtin_group_name as builtin_group | eval - aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,"This - object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full - control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid), - user=coalesce(user, group, builtin_group, aceSid) | stats min(_time) as _time values(aceType) - as aceType values(aceFlags) as aceFlags(inheritance) values(aceControlAccessRights) - as aceControlAccessRights values(aceAccessRights) as aceAccessRights values(new_ace) - as new_ace values(SubjectLogonId) as SubjectLogonId by ObjectClass ObjectDN src_user - user | search (aceControlAccessRights="Add/Remove Replica In Domain" AND aceControlAccessRights="Manage - Replication Topology" AND aceControlAccessRights="Replication Synchronization") - OR (aceControlAccessRights="9923a32a-3607-11d2-b9be-0000f87a36b2" AND aceControlAccessRights="1131f6ab-9c07-11d1-f79f-00c04fc2dcd2" - AND aceControlAccessRights="1131f6ac-9c07-11d1-f79f-00c04fc2dcd2") | `windows_ad_dcshadow_privileges_acl_addition_filter`' -how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically - event 5136. See lantern article in references for further on how to onboard AD audit - data. Ensure the wineventlog_security macro is configured with the correct indexes - and include lookups for SID resolution if evt_resolve_ad_obj is set to 0. + - Windows Event Log Security 5136 +description: This detection identifies an Active Directory access-control list (ACL) modification event, which applies the minimum required extended rights to perform the DCShadow attack. +search: |- + `wineventlog_security` EventCode=5136 ObjectClass=domainDNS + | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) as OperationType values(dest) as dest + BY ObjectClass ObjectDN OpCorrelationID + src_user SubjectLogonId + | rex field=old_value max_match=10000 "\((?P.*?)\)" + | rex field=new_value max_match=10000 "\((?P.*?)\)" + | mvexpand new_ace + | where NOT new_ace IN (old_values) + | rex field=new_ace "(?P.*?);(?P.*?);(?P.*?);(?P.*?);;(?P.*?)$" + | search aceObjectGuid IN ("9923a32a-3607-11d2-b9be-0000f87a36b2","1131f6ab-9c07-11d1-f79f-00c04fc2dcd2","1131f6ac-9c07-11d1-f79f-00c04fc2dcd2") + | rex max_match=100 field=aceAccessRights "(?P[A-Z]{2})" + | rex max_match=100 field=aceFlags "(?P[A-Z]{2})" + | lookup msad_guid_lookup guid as aceObjectGuid OUTPUT displayName as ControlAccessRights + | lookup ace_access_rights_lookup access_rights_string as AccessRights OUTPUT access_rights_value + | lookup ace_type_lookup ace_type_string as aceType OUTPUT ace_type_value + | lookup ace_flag_lookup flag_string as aceFlags OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups + | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user + | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` + | lookup builtin_groups_lookup builtin_group_string as aceSid OUTPUT builtin_group_name as builtin_group + | eval aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,"This object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid), user=coalesce(user, group, builtin_group, aceSid) + | stats min(_time) as _time values(aceType) as aceType values(aceFlags) as aceFlags(inheritance) values(aceControlAccessRights) as aceControlAccessRights values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace values(SubjectLogonId) as SubjectLogonId + BY ObjectClass ObjectDN src_user + user + | search (aceControlAccessRights="Add/Remove Replica In Domain" AND aceControlAccessRights="Manage Replication Topology" AND aceControlAccessRights="Replication Synchronization") OR (aceControlAccessRights="9923a32a-3607-11d2-b9be-0000f87a36b2" AND aceControlAccessRights="1131f6ab-9c07-11d1-f79f-00c04fc2dcd2" AND aceControlAccessRights="1131f6ac-9c07-11d1-f79f-00c04fc2dcd2") + | `windows_ad_dcshadow_privileges_acl_addition_filter` +how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security macro is configured with the correct indexes and include lookups for SID resolution if evt_resolve_ad_obj is set to 0. known_false_positives: No false positives have been identified at this time. references: -- https://www.labofapenetrationtester.com/2018/04/dcshadow.html -- https://github.com/samratashok/nishang/blob/master/ActiveDirectory/Set-DCShadowPermissions.ps1 -- https://trustedsec.com/blog/a-hitchhackers-guide-to-dacl-based-detections-part-1-a -- https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory + - https://www.labofapenetrationtester.com/2018/04/dcshadow.html + - https://github.com/samratashok/nishang/blob/master/ActiveDirectory/Set-DCShadowPermissions.ps1 + - https://trustedsec.com/blog/a-hitchhackers-guide-to-dacl-based-detections-part-1-a + - https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory drilldown_searches: -- name: View the detection results for - "$user$" and "$src_user$" - search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$src_user$" + search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: ACL modification Event Initiated by $src_user$ applying $user$ the minimum - required extended rights to perform a DCShadow attack. - risk_objects: - - field: user - type: user - score: 100 - - field: src_user - type: user - score: 100 - threat_objects: [] + message: ACL modification Event Initiated by $src_user$ applying $user$ the minimum required extended rights to perform a DCShadow attack. + risk_objects: + - field: user + type: user + score: 100 + - field: src_user + type: user + score: 100 + threat_objects: [] tags: - analytic_story: - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1484 - - T1207 - - T1222.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1484 + - T1207 + - T1222.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484/DCShadowPermissions/windows-security-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484/DCShadowPermissions/windows-security-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_domain_controller_audit_policy_disabled.yml b/detections/endpoint/windows_ad_domain_controller_audit_policy_disabled.yml index 322e0ab1a7..3a7ea3f957 100644 --- a/detections/endpoint/windows_ad_domain_controller_audit_policy_disabled.yml +++ b/detections/endpoint/windows_ad_domain_controller_audit_policy_disabled.yml @@ -1,73 +1,56 @@ name: Windows AD Domain Controller Audit Policy Disabled id: fc3ccef1-60a4-4239-bd66-b279511b4d14 -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Dean Luxton type: TTP status: production data_source: -- Windows Event Log Security 4719 -description: The following analytic detects the disabling of audit policies on a domain - controller. It leverages EventCode 4719 from Windows Security Event Logs to identify - changes where success or failure auditing is removed. This activity is significant - as it suggests an attacker may have gained access to the domain controller and is - attempting to evade detection by tampering with audit policies. If confirmed malicious, - this could lead to severe consequences, including data theft, privilege escalation, - and full network compromise. Immediate investigation is required to determine the - source and intent of the change. -search: '`wineventlog_security` EventCode=4719 (AuditPolicyChanges IN ("%%8448","%%8450","%%8448, - %%8450") OR Changes IN ("Failure removed","Success removed","Success removed, Failure - removed")) dest_category="domain_controller"| replace "%%8448" with "Success removed", - "%%8450" with "Failure removed", "%%8448, %%8450" with "Success removed, Failure - removed" in AuditPolicyChanges | eval AuditPolicyChanges=coalesce(AuditPolicyChanges,Changes), - SubcategoryGuid=coalesce(SubcategoryGuid,Subcategory_GUID) | stats min(_time) as - _time values(host) as dest by AuditPolicyChanges SubcategoryGuid | lookup advanced_audit_policy_guids - GUID as SubcategoryGuid OUTPUT Category SubCategory | `windows_ad_domain_controller_audit_policy_disabled_filter`' -how_to_implement: Ensure you are ingesting EventCode `4719` from your domain controllers, - the category domain_controller exists in assets and identities, and that assets - and identities is enabled. If A&I is not configured, you will need to manually filter - the results within the base search. + - Windows Event Log Security 4719 +description: The following analytic detects the disabling of audit policies on a domain controller. It leverages EventCode 4719 from Windows Security Event Logs to identify changes where success or failure auditing is removed. This activity is significant as it suggests an attacker may have gained access to the domain controller and is attempting to evade detection by tampering with audit policies. If confirmed malicious, this could lead to severe consequences, including data theft, privilege escalation, and full network compromise. Immediate investigation is required to determine the source and intent of the change. +search: |- + `wineventlog_security` EventCode=4719 (AuditPolicyChanges IN ("%%8448","%%8450","%%8448, %%8450") OR Changes IN ("Failure removed","Success removed","Success removed, Failure removed")) dest_category="domain_controller" + | replace "%%8448" with "Success removed", "%%8450" with "Failure removed", "%%8448, %%8450" with "Success removed, Failure removed" in AuditPolicyChanges + | eval AuditPolicyChanges=coalesce(AuditPolicyChanges,Changes), SubcategoryGuid=coalesce(SubcategoryGuid,Subcategory_GUID) + | stats min(_time) as _time values(host) as dest + BY AuditPolicyChanges SubcategoryGuid + | lookup advanced_audit_policy_guids GUID as SubcategoryGuid OUTPUT Category SubCategory + | `windows_ad_domain_controller_audit_policy_disabled_filter` +how_to_implement: Ensure you are ingesting EventCode `4719` from your domain controllers, the category domain_controller exists in assets and identities, and that assets and identities is enabled. If A&I is not configured, you will need to manually filter the results within the base search. known_false_positives: No false positives have been identified at this time. references: -- https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4719 + - https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4719 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: GPO $SubCategory$ of $Category$ was disabled on $dest$ - risk_objects: - - field: dest - type: system - score: 60 - threat_objects: [] + message: GPO $SubCategory$ of $Category$ was disabled on $dest$ + risk_objects: + - field: dest + type: system + score: 60 + threat_objects: [] tags: - analytic_story: - - Windows Audit Policy Tampering - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - manual_test: This search uses a lookup provided by Enterprise Security and needs - to be manually tested + analytic_story: + - Windows Audit Policy Tampering + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + manual_test: This search uses a lookup provided by Enterprise Security and needs to be manually tested tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable_gpo/windows-security-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable_gpo/windows-security-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_domain_controller_promotion.yml b/detections/endpoint/windows_ad_domain_controller_promotion.yml index 7a08b5745e..ed3a7be01a 100644 --- a/detections/endpoint/windows_ad_domain_controller_promotion.yml +++ b/detections/endpoint/windows_ad_domain_controller_promotion.yml @@ -1,74 +1,61 @@ name: Windows AD Domain Controller Promotion id: e633a0ef-2a6e-4ed7-b925-5ff999e5d1f0 -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Dean Luxton type: TTP status: production data_source: -- Windows Event Log Security 4742 -description: The following analytic identifies a genuine Domain Controller (DC) promotion - event by detecting when a computer assigns itself the necessary Service Principal - Names (SPNs) to function as a domain controller. It leverages Windows Security Event - Code 4742 to monitor existing domain controllers for these changes. This activity - is significant as it can help identify rogue DCs added to the network, which could - indicate a DCShadow attack. If confirmed malicious, this could allow an attacker - to manipulate Active Directory, leading to potential privilege escalation and persistent - access within the environment. -search: '`wineventlog_security` EventCode=4742 ServicePrincipalNames IN ("*E3514235-4B06-11D1-AB04-00C04FC2DCD2/*","*GC/*")| - stats min(_time) as _time latest(ServicePrincipalNames) as ServicePrincipalNames,values(signature) - as signature, values(src_user) as src_user, values(user) as user by Logon_ID, dvc| - where src_user=user| rename Logon_ID as TargetLogonId, user as dest | appendpipe - [| map search="search `wineventlog_security` EventCode=4624 TargetLogonId=$TargetLogonId$" - | fields - dest, dvc, signature]| stats min(_time) as _time, values(TargetUserSid) - as TargetUserSid, values(Target_Domain) as Target_Domain, values(user) as user, - values(status) as status, values(src_category) as src_category, values(src_ip) as - src_ip values(ServicePrincipalNames) as ServicePrincipalNames values(signature) - as signature values(dest) as dest values(dvc) as dvc by TargetLogonId | eval dest=trim(dest,"$") - | `windows_ad_domain_controller_promotion_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - eventcode `4742`. The Advanced Security Audit policy setting `Audit Computer Account - Management` within `Account Management` needs to be enabled. + - Windows Event Log Security 4742 +description: The following analytic identifies a genuine Domain Controller (DC) promotion event by detecting when a computer assigns itself the necessary Service Principal Names (SPNs) to function as a domain controller. It leverages Windows Security Event Code 4742 to monitor existing domain controllers for these changes. This activity is significant as it can help identify rogue DCs added to the network, which could indicate a DCShadow attack. If confirmed malicious, this could allow an attacker to manipulate Active Directory, leading to potential privilege escalation and persistent access within the environment. +search: |- + `wineventlog_security` EventCode=4742 ServicePrincipalNames IN ("*E3514235-4B06-11D1-AB04-00C04FC2DCD2/*","*GC/*") + | stats min(_time) as _time latest(ServicePrincipalNames) as ServicePrincipalNames,values(signature) as signature, values(src_user) as src_user, values(user) as user + BY Logon_ID, dvc + | where src_user=user + | rename Logon_ID as TargetLogonId, user as dest + | appendpipe [ + | map search="search `wineventlog_security` EventCode=4624 TargetLogonId=$TargetLogonId$" + | fields - dest, dvc, signature] + | stats min(_time) as _time, values(TargetUserSid) as TargetUserSid, values(Target_Domain) as Target_Domain, values(user) as user, values(status) as status, values(src_category) as src_category, values(src_ip) as src_ip values(ServicePrincipalNames) as ServicePrincipalNames values(signature) as signature values(dest) as dest values(dvc) as dvc + BY TargetLogonId + | eval dest=trim(dest,"$") + | `windows_ad_domain_controller_promotion_filter` +how_to_implement: To successfully implement this search, you need to be ingesting eventcode `4742`. The Advanced Security Audit policy setting `Audit Computer Account Management` within `Account Management` needs to be enabled. known_false_positives: No false positives have been identified at this time. references: -- https://attack.mitre.org/techniques/T1207/ + - https://attack.mitre.org/techniques/T1207/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: AD Domain Controller Promotion Event Detected for $dest$ - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: [] + message: AD Domain Controller Promotion Event Detected for $dest$ + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1207 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1207 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1207/dc_promo/windows-security-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1207/dc_promo/windows-security-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_domain_replication_acl_addition.yml b/detections/endpoint/windows_ad_domain_replication_acl_addition.yml index d1a90a89dd..e9f432a6c8 100644 --- a/detections/endpoint/windows_ad_domain_replication_acl_addition.yml +++ b/detections/endpoint/windows_ad_domain_replication_acl_addition.yml @@ -1,105 +1,80 @@ name: Windows AD Domain Replication ACL Addition id: 8c372853-f459-4995-afdc-280c114d33ab -version: 10 -date: '2025-05-02' +version: 11 +date: '2026-02-25' author: Dean Luxton type: TTP status: production data_source: -- Windows Event Log Security 5136 -description: The following analytic detects the addition of permissions required for - a DCSync attack, specifically DS-Replication-Get-Changes, DS-Replication-Get-Changes-All, - and DS-Replication-Get-Changes-In-Filtered-Set. It leverages EventCode 5136 from - the Windows Security Event Log to identify when these permissions are granted. This - activity is significant because it indicates potential preparation for a DCSync - attack, which can be used to replicate AD objects and exfiltrate sensitive data. - If confirmed malicious, an attacker could gain extensive access to Active Directory, - leading to severe data breaches and privilege escalation. -search: '`wineventlog_security` EventCode=5136 ObjectClass=domainDNS | stats min(_time) - as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value - values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) - as OperationType by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId | - rex field=old_value max_match=10000 "\((?P.*?)\)" | rex field=new_value - max_match=10000 "\((?P.*?)\)" | mvexpand new_ace | where NOT new_ace IN - (old_values) | rex field=new_ace "(?P.*?);(?P.*?);(?P.*?);(?P.*?);;(?P.*?)$" - | search aceObjectGuid IN ("1131f6aa-9c07-11d1-f79f-00c04fc2dcd2","1131f6ad-9c07-11d1-f79f-00c04fc2dcd2","89e95b76-444d-4c62-991a-0facbeda640c") - | rex max_match=100 field=aceAccessRights "(?P[A-Z]{2})" | rex max_match=100 - field=aceFlags "(?P[A-Z]{2})" | lookup msad_guid_lookup guid as aceObjectGuid - OUTPUT displayName as ControlAccessRights | lookup ace_access_rights_lookup access_rights_string - as AccessRights OUTPUT access_rights_value | lookup ace_type_lookup ace_type_string - as aceType OUTPUT ace_type_value | lookup ace_flag_lookup flag_string as aceFlags - OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups | lookup - identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user | - lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` | lookup builtin_groups_lookup - builtin_group_string as aceSid OUTPUT builtin_group_name as builtin_group | eval - aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,"This - object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full - control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid), - user=coalesce(user, group, builtin_group, aceSid) | stats min(_time) as _time values(aceType) - as aceType values(aceFlags) as aceFlags(inheritance) values(aceControlAccessRights) - as aceControlAccessRights values(aceAccessRights) as aceAccessRights values(new_ace) - as new_ace values(SubjectLogonId) as SubjectLogonId by ObjectClass ObjectDN src_user - user dest | search (aceControlAccessRights="DS-Replication-Get-Changes" AND aceControlAccessRights="DS-Replication-Get-Changes-All") - OR (aceControlAccessRights="1131f6aa-9c07-11d1-f79f-00c04fc2dcd2" AND aceControlAccessRights="1131f6ad-9c07-11d1-f79f-00c04fc2dcd2") - | `windows_ad_domain_replication_acl_addition_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - the eventcode 5136. The Advanced Security Audit policy setting `Audit Directory - Services Changes` within `DS Access` needs to be enabled, alongside a SACL for `everybody` - to `Write All Properties` applied to the domain root and all descendant objects. - Once the necessary logging has been enabled, enumerate the domain policy to verify - if existing accounts with access need to be whitelisted, or revoked. Assets and - Identities is also leveraged to automatically translate the objectSid into username. - Ensure your identities lookup is configured with the sAMAccountName and objectSid - of all AD user and computer objects. -known_false_positives: When there is a change to nTSecurityDescriptor, Windows logs - the entire ACL with the newly added components. If existing accounts are present - with this permission, they will raise an alert each time the nTSecurityDescriptor - is updated unless whitelisted. + - Windows Event Log Security 5136 +description: The following analytic detects the addition of permissions required for a DCSync attack, specifically DS-Replication-Get-Changes, DS-Replication-Get-Changes-All, and DS-Replication-Get-Changes-In-Filtered-Set. It leverages EventCode 5136 from the Windows Security Event Log to identify when these permissions are granted. This activity is significant because it indicates potential preparation for a DCSync attack, which can be used to replicate AD objects and exfiltrate sensitive data. If confirmed malicious, an attacker could gain extensive access to Active Directory, leading to severe data breaches and privilege escalation. +search: |- + `wineventlog_security` EventCode=5136 ObjectClass=domainDNS + | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) as OperationType + BY ObjectClass ObjectDN OpCorrelationID + src_user SubjectLogonId + | rex field=old_value max_match=10000 "\((?P.*?)\)" + | rex field=new_value max_match=10000 "\((?P.*?)\)" + | mvexpand new_ace + | where NOT new_ace IN (old_values) + | rex field=new_ace "(?P.*?);(?P.*?);(?P.*?);(?P.*?);;(?P.*?)$" + | search aceObjectGuid IN ("1131f6aa-9c07-11d1-f79f-00c04fc2dcd2","1131f6ad-9c07-11d1-f79f-00c04fc2dcd2","89e95b76-444d-4c62-991a-0facbeda640c") + | rex max_match=100 field=aceAccessRights "(?P[A-Z]{2})" + | rex max_match=100 field=aceFlags "(?P[A-Z]{2})" + | lookup msad_guid_lookup guid as aceObjectGuid OUTPUT displayName as ControlAccessRights + | lookup ace_access_rights_lookup access_rights_string as AccessRights OUTPUT access_rights_value + | lookup ace_type_lookup ace_type_string as aceType OUTPUT ace_type_value + | lookup ace_flag_lookup flag_string as aceFlags OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups + | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user + | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` + | lookup builtin_groups_lookup builtin_group_string as aceSid OUTPUT builtin_group_name as builtin_group + | eval aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,"This object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid), user=coalesce(user, group, builtin_group, aceSid) + | stats min(_time) as _time values(aceType) as aceType values(aceFlags) as aceFlags(inheritance) values(aceControlAccessRights) as aceControlAccessRights values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace values(SubjectLogonId) as SubjectLogonId + BY ObjectClass ObjectDN src_user + user dest + | search (aceControlAccessRights="DS-Replication-Get-Changes" AND aceControlAccessRights="DS-Replication-Get-Changes-All") OR (aceControlAccessRights="1131f6aa-9c07-11d1-f79f-00c04fc2dcd2" AND aceControlAccessRights="1131f6ad-9c07-11d1-f79f-00c04fc2dcd2") + | `windows_ad_domain_replication_acl_addition_filter` +how_to_implement: To successfully implement this search, you need to be ingesting the eventcode 5136. The Advanced Security Audit policy setting `Audit Directory Services Changes` within `DS Access` needs to be enabled, alongside a SACL for `everybody` to `Write All Properties` applied to the domain root and all descendant objects. Once the necessary logging has been enabled, enumerate the domain policy to verify if existing accounts with access need to be whitelisted, or revoked. Assets and Identities is also leveraged to automatically translate the objectSid into username. Ensure your identities lookup is configured with the sAMAccountName and objectSid of all AD user and computer objects. +known_false_positives: When there is a change to nTSecurityDescriptor, Windows logs the entire ACL with the newly added components. If existing accounts are present with this permission, they will raise an alert each time the nTSecurityDescriptor is updated unless whitelisted. references: -- https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-adts/1522b774-6464-41a3-87a5-1e5633c3fbbb -- https://github.com/SigmaHQ/sigma/blob/29a5c62784faf986dc03952ae3e90e3df3294284/rules/windows/builtin/security/win_security_account_backdoor_dcsync_rights.yml -- https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory + - https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-adts/1522b774-6464-41a3-87a5-1e5633c3fbbb + - https://github.com/SigmaHQ/sigma/blob/29a5c62784faf986dc03952ae3e90e3df3294284/rules/windows/builtin/security/win_security_account_backdoor_dcsync_rights.yml + - https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory drilldown_searches: -- name: View the detection results for - "$user$" and "$src_user$" - search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$src_user$" + search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $src_user$ has granted $user$ permission to replicate AD objects - risk_objects: - - field: user - type: user - score: 80 - - field: src_user - type: user - score: 80 - threat_objects: [] + message: $src_user$ has granted $user$ permission to replicate AD objects + risk_objects: + - field: user + type: user + score: 80 + - field: src_user + type: user + score: 80 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1484 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - manual_test: This search uses a lookup provided by Enterprise Security and needs - to be manually tested. + analytic_story: + - Compromised Windows Host + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1484 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + manual_test: This search uses a lookup provided by Enterprise Security and needs to be manually tested. tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484/aclmodification/windows-security-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484/aclmodification/windows-security-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_domain_root_acl_deletion.yml b/detections/endpoint/windows_ad_domain_root_acl_deletion.yml index aba1cbdae3..0165c96a1e 100644 --- a/detections/endpoint/windows_ad_domain_root_acl_deletion.yml +++ b/detections/endpoint/windows_ad_domain_root_acl_deletion.yml @@ -6,85 +6,50 @@ author: Dean Luxton status: production type: TTP data_source: -- Windows Event Log Security 5136 -description: ACL deletion performed on the domain root object, significant AD change - with high impact. Following MS guidance all changes at this level should be reviewed. - Drill into the logonID within EventCode 4624 for information on the source device - during triage. -search: '`wineventlog_security` EventCode=5136 ObjectClass=domainDNS | stats min(_time) - as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value - values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) - as OperationType values(dest) as dest by ObjectClass ObjectDN OpCorrelationID src_user - SubjectLogonId | rex field=old_value max_match=10000 "\((?P.*?)\)" | - rex field=new_value max_match=10000 "\((?P.*?)\)" | mvexpand old_values | - where NOT old_values IN (new_values) | rex field=old_values "(?P.*?);(?P.*?);(?P.*?);(?P.*?);;(?P.*?)$" | - rex max_match=100 field=aceAccessRights "(?P[A-Z]{2})" | rex max_match=100 - field=aceFlags "(?P[A-Z]{2})" | lookup msad_guid_lookup guid as aceObjectGuid - OUTPUT displayName as ControlAccessRights | lookup ace_access_rights_lookup access_rights_string - as AccessRights OUTPUT access_rights_value | lookup ace_type_lookup ace_type_string - as aceType OUTPUT ace_type_value | lookup ace_flag_lookup flag_string as aceFlags - OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups | lookup - identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user | - lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` | lookup builtin_groups_lookup - builtin_group_string as aceSid OUTPUT builtin_group_name as builtin_group | eval - aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,"This - object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full - control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid), - user=coalesce(user, group, builtin_group, aceSid) | stats values(aceType) as aceType - values(aceFlags) as aceFlags(inheritance) values(aceControlAccessRights) as aceControlAccessRights - values(aceAccessRights) as aceAccessRights values(old_values) as old_values by _time - ObjectClass ObjectDN src_user SubjectLogonId user OpCorrelationID | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1 - AND aceControlAccessRights="","All rights",''aceControlAccessRights'') | `windows_ad_domain_root_acl_deletion_filter`' -how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically - event 5136. See lantern article in references for further on how to onboard AD audit - data. Ensure the wineventlog_security macro is configured with the correct indexes - and include lookups for SID resolution if evt_resolve_ad_obj is set to 0. + - Windows Event Log Security 5136 +description: ACL deletion performed on the domain root object, significant AD change with high impact. Following MS guidance all changes at this level should be reviewed. Drill into the logonID within EventCode 4624 for information on the source device during triage. +search: '`wineventlog_security` EventCode=5136 ObjectClass=domainDNS | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) as OperationType values(dest) as dest by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId | rex field=old_value max_match=10000 "\((?P.*?)\)" | rex field=new_value max_match=10000 "\((?P.*?)\)" | mvexpand old_values | where NOT old_values IN (new_values) | rex field=old_values "(?P.*?);(?P.*?);(?P.*?);(?P.*?);;(?P.*?)$" | rex max_match=100 field=aceAccessRights "(?P[A-Z]{2})" | rex max_match=100 field=aceFlags "(?P[A-Z]{2})" | lookup msad_guid_lookup guid as aceObjectGuid OUTPUT displayName as ControlAccessRights | lookup ace_access_rights_lookup access_rights_string as AccessRights OUTPUT access_rights_value | lookup ace_type_lookup ace_type_string as aceType OUTPUT ace_type_value | lookup ace_flag_lookup flag_string as aceFlags OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` | lookup builtin_groups_lookup builtin_group_string as aceSid OUTPUT builtin_group_name as builtin_group | eval aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,"This object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid), user=coalesce(user, group, builtin_group, aceSid) | stats values(aceType) as aceType values(aceFlags) as aceFlags(inheritance) values(aceControlAccessRights) as aceControlAccessRights values(aceAccessRights) as aceAccessRights values(old_values) as old_values by _time ObjectClass ObjectDN src_user SubjectLogonId user OpCorrelationID | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1 AND aceControlAccessRights="","All rights",''aceControlAccessRights'') | `windows_ad_domain_root_acl_deletion_filter`' +how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security macro is configured with the correct indexes and include lookups for SID resolution if evt_resolve_ad_obj is set to 0. known_false_positives: No false positives have been identified at this time. references: -- https://learn.microsoft.com/en-us/windows/win32/secauthz/ace-strings -- https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-adts/1522b774-6464-41a3-87a5-1e5633c3fbbb -- https://trustedsec.com/blog/a-hitchhackers-guide-to-dacl-based-detections-part-1-a -- https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory + - https://learn.microsoft.com/en-us/windows/win32/secauthz/ace-strings + - https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-adts/1522b774-6464-41a3-87a5-1e5633c3fbbb + - https://trustedsec.com/blog/a-hitchhackers-guide-to-dacl-based-detections-part-1-a + - https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory drilldown_searches: -- name: View the detection results for - "$user$" and "$src_user$" - search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$src_user$" + search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $src_user$ has removed $user$ $aceAccessRights$ ACL rights to domain root - $ObjectDN$ - risk_objects: - - field: user - type: user - score: 100 - - field: src_user - type: user - score: 100 - threat_objects: [] + message: $src_user$ has removed $user$ $aceAccessRights$ ACL rights to domain root $ObjectDN$ + risk_objects: + - field: user + type: user + score: 100 + - field: src_user + type: user + score: 100 + threat_objects: [] tags: - analytic_story: - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1222.001 - - T1484 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1222.001 + - T1484 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/domain_root_acl_deletion_windows-security-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/domain_root_acl_deletion_windows-security-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_domain_root_acl_modification.yml b/detections/endpoint/windows_ad_domain_root_acl_modification.yml index 23331f1bc9..5ad1354330 100644 --- a/detections/endpoint/windows_ad_domain_root_acl_modification.yml +++ b/detections/endpoint/windows_ad_domain_root_acl_modification.yml @@ -6,84 +6,50 @@ author: Dean Luxton status: production type: TTP data_source: -- Windows Event Log Security 5136 -description: ACL modification performed on the domain root object, significant AD - change with high impact. Following MS guidance all changes at this level should - be reviewed. Drill into the logonID within EventCode 4624 for information on the - source device during triage. -search: '`wineventlog_security` EventCode=5136 ObjectClass=domainDNS | stats min(_time) - as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value - values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) - as OperationType values(dest) as dest by ObjectClass ObjectDN OpCorrelationID src_user - SubjectLogonId | rex field=old_value max_match=10000 "\((?P.*?)\)" | - rex field=new_value max_match=10000 "\((?P.*?)\)" | mvexpand new_ace | - where NOT new_ace IN (old_values) | rex field=new_ace "(?P.*?);(?P.*?);(?P.*?);(?P.*?);;(?P.*?)$" - | rex max_match=100 field=aceAccessRights "(?P[A-Z]{2})" | rex max_match=100 - field=aceFlags "(?P[A-Z]{2})" | lookup msad_guid_lookup guid as aceObjectGuid - OUTPUT displayName as ControlAccessRights | lookup ace_access_rights_lookup access_rights_string - as AccessRights OUTPUT access_rights_value | lookup ace_type_lookup ace_type_string - as aceType OUTPUT ace_type_value | lookup ace_flag_lookup flag_string as aceFlags - OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups | lookup - identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user | - lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` | lookup builtin_groups_lookup - builtin_group_string as aceSid OUTPUT builtin_group_name as builtin_group | eval - aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full control",''access_rights_value''), - aceType=ace_type_value, aceFlags=coalesce(ace_flag_value,"This object only"), aceControlAccessRights=ControlAccessRights, - user=coalesce(user, group, builtin_group, aceSid) | stats values(aceType) as aceType - values(aceFlags) as aceFlags(inheritance) values(aceControlAccessRights) as aceControlAccessRights - values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace by _time ObjectClass - ObjectDN src_user SubjectLogonId user OpCorrelationID | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1 - AND aceControlAccessRights="","All rights",''aceControlAccessRights'') | `windows_ad_domain_root_acl_modification_filter`' -how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically - event 5136. See lantern article in references for further on how to onboard AD audit - data. Ensure the wineventlog_security macro is configured with the correct indexes - and include lookups for SID resolution if evt_resolve_ad_obj is set to 0. + - Windows Event Log Security 5136 +description: ACL modification performed on the domain root object, significant AD change with high impact. Following MS guidance all changes at this level should be reviewed. Drill into the logonID within EventCode 4624 for information on the source device during triage. +search: '`wineventlog_security` EventCode=5136 ObjectClass=domainDNS | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) as OperationType values(dest) as dest by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId | rex field=old_value max_match=10000 "\((?P.*?)\)" | rex field=new_value max_match=10000 "\((?P.*?)\)" | mvexpand new_ace | where NOT new_ace IN (old_values) | rex field=new_ace "(?P.*?);(?P.*?);(?P.*?);(?P.*?);;(?P.*?)$" | rex max_match=100 field=aceAccessRights "(?P[A-Z]{2})" | rex max_match=100 field=aceFlags "(?P[A-Z]{2})" | lookup msad_guid_lookup guid as aceObjectGuid OUTPUT displayName as ControlAccessRights | lookup ace_access_rights_lookup access_rights_string as AccessRights OUTPUT access_rights_value | lookup ace_type_lookup ace_type_string as aceType OUTPUT ace_type_value | lookup ace_flag_lookup flag_string as aceFlags OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` | lookup builtin_groups_lookup builtin_group_string as aceSid OUTPUT builtin_group_name as builtin_group | eval aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full control",''access_rights_value''), aceType=ace_type_value, aceFlags=coalesce(ace_flag_value,"This object only"), aceControlAccessRights=ControlAccessRights, user=coalesce(user, group, builtin_group, aceSid) | stats values(aceType) as aceType values(aceFlags) as aceFlags(inheritance) values(aceControlAccessRights) as aceControlAccessRights values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace by _time ObjectClass ObjectDN src_user SubjectLogonId user OpCorrelationID | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1 AND aceControlAccessRights="","All rights",''aceControlAccessRights'') | `windows_ad_domain_root_acl_modification_filter`' +how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security macro is configured with the correct indexes and include lookups for SID resolution if evt_resolve_ad_obj is set to 0. known_false_positives: No false positives have been identified at this time. references: -- https://learn.microsoft.com/en-us/windows/win32/secauthz/ace-strings -- https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-adts/1522b774-6464-41a3-87a5-1e5633c3fbbb -- https://trustedsec.com/blog/a-hitchhackers-guide-to-dacl-based-detections-part-1-a -- https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory + - https://learn.microsoft.com/en-us/windows/win32/secauthz/ace-strings + - https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-adts/1522b774-6464-41a3-87a5-1e5633c3fbbb + - https://trustedsec.com/blog/a-hitchhackers-guide-to-dacl-based-detections-part-1-a + - https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory drilldown_searches: -- name: View the detection results for - "$user$" and "$src_user$" - search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$src_user$" + search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $src_user$ has granted $user$ $aceAccessRights$ ACL rights to domain root - $ObjectDN$ - risk_objects: - - field: user - type: user - score: 100 - - field: src_user - type: user - score: 100 - threat_objects: [] + message: $src_user$ has granted $user$ $aceAccessRights$ ACL rights to domain root $ObjectDN$ + risk_objects: + - field: user + type: user + score: 100 + - field: src_user + type: user + score: 100 + threat_objects: [] tags: - analytic_story: - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1222.001 - - T1484 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1222.001 + - T1484 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/domain_root_acl_mod_windows-security-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/domain_root_acl_mod_windows-security-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_dsrm_account_changes.yml b/detections/endpoint/windows_ad_dsrm_account_changes.yml index 6cfe0aa841..a6e8a8cfb5 100644 --- a/detections/endpoint/windows_ad_dsrm_account_changes.yml +++ b/detections/endpoint/windows_ad_dsrm_account_changes.yml @@ -6,75 +6,49 @@ author: Dean Luxton type: TTP status: production data_source: -- Sysmon EventID 13 -description: The following analytic identifies changes to the Directory Services Restore - Mode (DSRM) account behavior via registry modifications. It detects alterations - in the registry path "*\\System\\CurrentControlSet\\Control\\Lsa\\DSRMAdminLogonBehavior" - with specific values indicating potential misuse. This activity is significant because - the DSRM account, if misconfigured, can be exploited to persist within a domain, - similar to a local administrator account. If confirmed malicious, an attacker could - gain persistent administrative access to a Domain Controller, leading to potential - domain-wide compromise and unauthorized access to sensitive information. -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\System\\CurrentControlSet\\Control\\Lsa\\DSRMAdminLogonBehavior" - Registry.registry_value_data IN ("*1","*2") by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_ad_dsrm_account_changes_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 13 +description: The following analytic identifies changes to the Directory Services Restore Mode (DSRM) account behavior via registry modifications. It detects alterations in the registry path "*\\System\\CurrentControlSet\\Control\\Lsa\\DSRMAdminLogonBehavior" with specific values indicating potential misuse. This activity is significant because the DSRM account, if misconfigured, can be exploited to persist within a domain, similar to a local administrator account. If confirmed malicious, an attacker could gain persistent administrative access to a Domain Controller, leading to potential domain-wide compromise and unauthorized access to sensitive information. +search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\System\\CurrentControlSet\\Control\\Lsa\\DSRMAdminLogonBehavior" Registry.registry_value_data IN ("*1","*2") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_ad_dsrm_account_changes_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Disaster recovery events. references: -- https://adsecurity.org/?p=1714 + - https://adsecurity.org/?p=1714 drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: DSRM Account Changes Initiated on $dest$ by $user$ - risk_objects: - - field: user - type: user - score: 100 - - field: dest - type: system - score: 100 - threat_objects: [] + message: DSRM Account Changes Initiated on $dest$ by $user$ + risk_objects: + - field: user + type: user + score: 100 + - field: dest + type: system + score: 100 + threat_objects: [] tags: - analytic_story: - - Sneaky Active Directory Persistence Tricks - - Windows Registry Abuse - - Windows Persistence Techniques - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1098 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sneaky Active Directory Persistence Tricks + - Windows Registry Abuse + - Windows Persistence Techniques + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1098 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/dsrm_account/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/dsrm_account/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_dsrm_password_reset.yml b/detections/endpoint/windows_ad_dsrm_password_reset.yml index ef70aa92b6..5a146067c6 100644 --- a/detections/endpoint/windows_ad_dsrm_password_reset.yml +++ b/detections/endpoint/windows_ad_dsrm_password_reset.yml @@ -1,71 +1,60 @@ name: Windows AD DSRM Password Reset id: d1ab841c-36a6-46cf-b50f-b2b04b31182a -version: 6 -date: '2025-10-14' +version: 7 +date: '2026-02-25' author: Dean Luxton type: TTP status: production data_source: -- Windows Event Log Security 4794 -description: The following analytic detects attempts to reset the Directory Services - Restore Mode (DSRM) administrator password on a Domain Controller. It leverages - event code 4794 from the Windows Security Event Log, specifically looking for events - where the DSRM password reset is attempted. This activity is significant because - the DSRM account can be used similarly to a local administrator account, providing - potential persistence for an attacker. If confirmed malicious, this could allow - an attacker to maintain administrative access to the Domain Controller, posing a - severe risk to the domain's security. -search: '| tstats `security_content_summariesonly` min(_time) as _time from datamodel=Change - where All_Changes.result_id="4794" AND All_Changes.result="set the Directory Services - Restore Mode administrator password" by All_Changes.action, All_Changes.dest, All_Changes.src, - All_Changes.user | `drop_dm_object_name(All_Changes)` | `windows_ad_dsrm_password_reset_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - eventcode `4794` and have the Advanced Security Audit policy `Audit User Account - Management` within `Account Management` enabled. -known_false_positives: Resetting the DSRM password for legitamate reasons, i.e. forgot - the password. Disaster recovery. Deploying AD backdoor deliberately. + - Windows Event Log Security 4794 +description: The following analytic detects attempts to reset the Directory Services Restore Mode (DSRM) administrator password on a Domain Controller. It leverages event code 4794 from the Windows Security Event Log, specifically looking for events where the DSRM password reset is attempted. This activity is significant because the DSRM account can be used similarly to a local administrator account, providing potential persistence for an attacker. If confirmed malicious, this could allow an attacker to maintain administrative access to the Domain Controller, posing a severe risk to the domain's security. +search: |- + | tstats `security_content_summariesonly` min(_time) as _time FROM datamodel=Change + WHERE All_Changes.result_id="4794" + AND + All_Changes.result="set the Directory Services Restore Mode administrator password" + BY All_Changes.action, All_Changes.dest, All_Changes.src, + All_Changes.user + | `drop_dm_object_name(All_Changes)` + | `windows_ad_dsrm_password_reset_filter` +how_to_implement: To successfully implement this search, you need to be ingesting eventcode `4794` and have the Advanced Security Audit policy `Audit User Account Management` within `Account Management` enabled. +known_false_positives: Resetting the DSRM password for legitamate reasons, i.e. forgot the password. Disaster recovery. Deploying AD backdoor deliberately. references: -- https://adsecurity.org/?p=1714 + - https://adsecurity.org/?p=1714 drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: DSRM Account Password was reset on $dest$ by $user$ - risk_objects: - - field: user - type: user - score: 100 - - field: dest - type: system - score: 100 - threat_objects: [] + message: DSRM Account Password was reset on $dest$ by $user$ + risk_objects: + - field: user + type: user + score: 100 + - field: dest + type: system + score: 100 + threat_objects: [] tags: - analytic_story: - - Sneaky Active Directory Persistence Tricks - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1098 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sneaky Active Directory Persistence Tricks + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1098 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/dsrm_account/windows-security-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/dsrm_account/windows-security-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_gpo_deleted.yml b/detections/endpoint/windows_ad_gpo_deleted.yml index 5dd1ced7eb..5f370598b8 100644 --- a/detections/endpoint/windows_ad_gpo_deleted.yml +++ b/detections/endpoint/windows_ad_gpo_deleted.yml @@ -1,73 +1,69 @@ name: Windows AD GPO Deleted id: 0d41772b-35ab-4e1c-a2ba-d0b455481aee -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Dean Luxton status: production type: TTP data_source: -- Windows Event Log Security 5136 -description: This detection identifies when an Active Directory Group Policy is deleted - using the Group Policy Management Console. -search: '`wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=gpLink | eval ObjectDN=upper(ObjectDN) - | stats min(_time) as eventTime values(eval(if(OperationType=="%%14675",AttributeValue,null))) - as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value - values(OperationType) as OperationType values(src_user) as src_user values(dest) - as dest by OpCorrelationID ObjectDN SubjectLogonId | rex field=old_value max_match=10000 - "(?i)LDAP://(?Pcn.*?);(?P\d)\]" | rex field=new_value max_match=10000 - "(?i)LDAP://(?Pcn.*?);(?P\d)\]" | mvexpand old_dn | where NOT - old_dn IN (new_dn) | eval ObjectDN=upper(old_dn) | join ObjectDN type=outer [| search - `admon` objectCategory="CN=Group-Policy-Container*" admonEventType=Update | eval - ObjectDN=upper(distinguishedName) | stats latest(displayName) as displayName by - ObjectDN ] | stats min(eventTime) as _time values(OpCorrelationID) as OpCorrelationID - values(displayName) as policyName values(src_user) as src_user by ObjectDN SubjectLogonId - | `windows_ad_gpo_deleted_filter`' -how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically - event 5136, admon data is also used to display the display name of the GPO. See - lantern article in references for further on how to onboard AD audit data. Ensure - the wineventlog_security and admon macros are configured with the correct indexes. + - Windows Event Log Security 5136 +description: This detection identifies when an Active Directory Group Policy is deleted using the Group Policy Management Console. +search: |- + `wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=gpLink + | eval ObjectDN=upper(ObjectDN) + | stats min(_time) as eventTime values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) as OperationType values(src_user) as src_user values(dest) as dest + BY OpCorrelationID ObjectDN SubjectLogonId + | rex field=old_value max_match=10000 "(?i)LDAP://(?Pcn.*?);(?P\d)\]" + | rex field=new_value max_match=10000 "(?i)LDAP://(?Pcn.*?);(?P\d)\]" + | mvexpand old_dn + | where NOT old_dn IN (new_dn) + | eval ObjectDN=upper(old_dn) + | join ObjectDN type=outer [ + | search `admon` objectCategory="CN=Group-Policy-Container*" admonEventType=Update + | eval ObjectDN=upper(distinguishedName) + | stats latest(displayName) as displayName + BY ObjectDN ] + | stats min(eventTime) as _time values(OpCorrelationID) as OpCorrelationID values(displayName) as policyName values(src_user) as src_user + BY ObjectDN SubjectLogonId + | `windows_ad_gpo_deleted_filter` +how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136, admon data is also used to display the display name of the GPO. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security and admon macros are configured with the correct indexes. known_false_positives: No false positives have been identified at this time. references: -- https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory + - https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory drilldown_searches: -- name: View the detection results for - "$src_user$" - search: '%original_detection_search% | search src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_user$" + search: '%original_detection_search% | search src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: GPO $policyName$ was deleted by $src_user$ - risk_objects: - - field: src_user - type: user - score: 64 - threat_objects: [] + message: GPO $policyName$ was deleted by $src_user$ + risk_objects: + - field: src_user + type: user + score: 64 + threat_objects: [] tags: - analytic_story: - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - - T1484.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + - T1484.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_deleted/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_deleted/windows-admon.log - source: ActiveDirectory - sourcetype: ActiveDirectory + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_deleted/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_deleted/windows-admon.log + source: ActiveDirectory + sourcetype: ActiveDirectory diff --git a/detections/endpoint/windows_ad_gpo_disabled.yml b/detections/endpoint/windows_ad_gpo_disabled.yml index 466c9317ab..f3eedabbf8 100644 --- a/detections/endpoint/windows_ad_gpo_disabled.yml +++ b/detections/endpoint/windows_ad_gpo_disabled.yml @@ -1,70 +1,62 @@ name: Windows AD GPO Disabled id: 72793bc0-c0cd-400e-9e60-fdf36f278917 -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Dean Luxton status: production type: TTP data_source: -- Windows Event Log Security 5136 -description: This detection identifies when an Active Directory Group Policy is disabled - using the Group Policy Management Console. -search: '`wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=flags OperationType="%%14674" - AttributeValue!=0 | eval AttributeValueExp=case(AttributeValue==0,"Enabled",AttributeValue==1,"User - configuration settings disabled",AttributeValue==2,"Computer configuration settings - disabled",AttributeValue==3,"Disabled"), ObjectDN=upper(ObjectDN) | join ObjectDN - type=inner [| search `admon` objectCategory="CN=Group-Policy-Container*" admonEventType=Update - | eval ObjectDN=upper(distinguishedName) | stats latest(displayName) as displayName - by ObjectDN ] | stats min(_time) as _time values(AttributeValue) as AttributeValue - values(AttributeValueExp) as AttributeValueExp values(OpCorrelationID) as OpCorrelationID - values(displayName) as policyName values(src_user) as src_user by ObjectDN SubjectLogonId - dest | `windows_ad_gpo_disabled_filter`' -how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically - event 5136, admon data is also used to display the display name of the GPO. See - lantern article in references for further on how to onboard AD audit data. Ensure - the wineventlog_security and admon macros are configured with the correct indexes. + - Windows Event Log Security 5136 +description: This detection identifies when an Active Directory Group Policy is disabled using the Group Policy Management Console. +search: |- + `wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=flags OperationType="%%14674" AttributeValue!=0 + | eval AttributeValueExp=case(AttributeValue==0,"Enabled",AttributeValue==1,"User configuration settings disabled",AttributeValue==2,"Computer configuration settings disabled",AttributeValue==3,"Disabled"), ObjectDN=upper(ObjectDN) + | join ObjectDN type=inner [ + | search `admon` objectCategory="CN=Group-Policy-Container*" admonEventType=Update + | eval ObjectDN=upper(distinguishedName) + | stats latest(displayName) as displayName + BY ObjectDN ] + | stats min(_time) as _time values(AttributeValue) as AttributeValue values(AttributeValueExp) as AttributeValueExp values(OpCorrelationID) as OpCorrelationID values(displayName) as policyName values(src_user) as src_user + BY ObjectDN SubjectLogonId dest + | `windows_ad_gpo_disabled_filter` +how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136, admon data is also used to display the display name of the GPO. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security and admon macros are configured with the correct indexes. known_false_positives: No false positives have been identified at this time. references: -- https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory + - https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory drilldown_searches: -- name: View the detection results for - "$src_user$" - search: '%original_detection_search% | search src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_user$" + search: '%original_detection_search% | search src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $src_user$ has disabled GPO $policyName$ - risk_objects: - - field: src_user - type: user - score: 64 - threat_objects: [] + message: $src_user$ has disabled GPO $policyName$ + risk_objects: + - field: src_user + type: user + score: 64 + threat_objects: [] tags: - analytic_story: - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - - T1484.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + - T1484.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_disabled/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_disabled/windows-admon.log - source: ActiveDirectory - sourcetype: ActiveDirectory + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_disabled/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_disabled/windows-admon.log + source: ActiveDirectory + sourcetype: ActiveDirectory diff --git a/detections/endpoint/windows_ad_gpo_new_cse_addition.yml b/detections/endpoint/windows_ad_gpo_new_cse_addition.yml index 7632593231..19c1f3d5b9 100644 --- a/detections/endpoint/windows_ad_gpo_new_cse_addition.yml +++ b/detections/endpoint/windows_ad_gpo_new_cse_addition.yml @@ -1,82 +1,79 @@ name: Windows AD GPO New CSE Addition id: 700c11d1-da09-47b2-81aa-358c143c7986 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Dean Luxton status: production type: TTP data_source: -- Windows Event Log Security 5136 -description: This detection identifies when a a new client side extension is added - to an Active Directory Group Policy using the Group Policy Management Console. -search: '`wineventlog_security` EventCode=5136 ObjectClass=groupPolicyContainer AttributeLDAPDisplayName=gPCMachineExtensionNames - | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) - as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value - values(OperationType) as OperationType values(dest) as dest by ObjectClass ObjectDN - OpCorrelationID src_user SubjectLogonId | rex field=old_value max_match=10000 "(?P\{.*?\})" - | rex field=new_value max_match=10000 "(?P\{.*?\})" | rex field=ObjectDN - max_match=10000 "CN=(?P\{.*?\})" | mvexpand new_values | where NOT - new_values IN (old_values,"{00000000-0000-0000-0000-000000000000}",policy_guid) - AND match(new_values, "^\{[A-Z|\d]+\-[A-Z|\d]+\-[A-Z|\d]+\-[A-Z|\d]+\-[A-Z|\d]+\}") - | lookup msad_guid_lookup guid as new_values OUTPUTNEW displayName as policyType - | eval newPolicy=if(policyType like "%",policyType,new_values) | join ObjectDN [| - search `admon` objectCategory="CN=Group-Policy-Container*" admonEventType=Update - | stats latest(displayName) as displayName by distinguishedName | eval ObjectDN=upper(distinguishedName)] - | stats values(OpCorrelationID) as OpCorrelationID values(src_user) as src_user - values(SubjectLogonId) as SubjectLogonId values(newPolicy) as newPolicy values(displayName) - as policyName by ObjectDN | `windows_ad_gpo_new_cse_addition_filter`' -how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically - event 5136, admon data is also used to display the display name of the GPO. See - lantern article in references for further on how to onboard AD audit data. Ensure - the wineventlog_security and admon macros are configured with the correct indexes. -known_false_positives: General usage of group policy will trigger this detection, - also please not GPOs modified using tools such as SharpGPOAbuse will not generate - the AD audit events which enable this detection. + - Windows Event Log Security 5136 +description: This detection identifies when a a new client side extension is added to an Active Directory Group Policy using the Group Policy Management Console. +search: |- + `wineventlog_security` EventCode=5136 ObjectClass=groupPolicyContainer AttributeLDAPDisplayName=gPCMachineExtensionNames + | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) as OperationType values(dest) as dest + BY ObjectClass ObjectDN OpCorrelationID + src_user SubjectLogonId + | rex field=old_value max_match=10000 "(?P\{.*?\})" + | rex field=new_value max_match=10000 "(?P\{.*?\})" + | rex field=ObjectDN max_match=10000 "CN=(?P\{.*?\})" + | mvexpand new_values + | where NOT new_values IN (old_values,"{00000000-0000-0000-0000-000000000000}",policy_guid) AND match(new_values, "^\{[A-Z + | \d]+\-[A-Z + | \d]+\-[A-Z + | \d]+\-[A-Z + | \d]+\-[A-Z + | \d]+\}") + | lookup msad_guid_lookup guid as new_values OUTPUTNEW displayName as policyType + | eval newPolicy=if(policyType like "%",policyType,new_values) + | join ObjectDN [ + | search `admon` objectCategory="CN=Group-Policy-Container*" admonEventType=Update + | stats latest(displayName) as displayName + BY distinguishedName + | eval ObjectDN=upper(distinguishedName)] + | stats values(OpCorrelationID) as OpCorrelationID values(src_user) as src_user values(SubjectLogonId) as SubjectLogonId values(newPolicy) as newPolicy values(displayName) as policyName + BY ObjectDN + | `windows_ad_gpo_new_cse_addition_filter` +how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136, admon data is also used to display the display name of the GPO. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security and admon macros are configured with the correct indexes. +known_false_positives: General usage of group policy will trigger this detection, also please not GPOs modified using tools such as SharpGPOAbuse will not generate the AD audit events which enable this detection. references: -- https://wald0.com/?p=179 -- https://learn.microsoft.com/en-gb/archive/blogs/mempson/group-policy-client-side-extension-list -- https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory -- https://github.com/FSecureLABS/SharpGPOAbuse + - https://wald0.com/?p=179 + - https://learn.microsoft.com/en-gb/archive/blogs/mempson/group-policy-client-side-extension-list + - https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory + - https://github.com/FSecureLABS/SharpGPOAbuse drilldown_searches: -- name: View the detection results for - "$src_user$" - search: '%original_detection_search% | search src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_user$" + search: '%original_detection_search% | search src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $src_user$ has added new GPO Client Side Extensions $newPolicy$ to the - policy $policyName$ - risk_objects: - - field: src_user - type: user - score: 100 - threat_objects: [] + message: $src_user$ has added new GPO Client Side Extensions $newPolicy$ to the policy $policyName$ + risk_objects: + - field: src_user + type: user + score: 100 + threat_objects: [] tags: - analytic_story: - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1222.001 - - T1484.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1222.001 + - T1484.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_new_cse/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_new_cse/windows-admon.log - source: ActiveDirectory - sourcetype: ActiveDirectory + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_new_cse/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_new_cse/windows-admon.log + source: ActiveDirectory + sourcetype: ActiveDirectory diff --git a/detections/endpoint/windows_ad_hidden_ou_creation.yml b/detections/endpoint/windows_ad_hidden_ou_creation.yml index f344f48798..869ff94805 100644 --- a/detections/endpoint/windows_ad_hidden_ou_creation.yml +++ b/detections/endpoint/windows_ad_hidden_ou_creation.yml @@ -6,84 +6,48 @@ author: Dean Luxton status: production type: TTP data_source: -- Windows Event Log Security 5136 -description: This analytic is looking for when an ACL is applied to an OU which denies - listing the objects residing in the OU. This activity combined with modifying the - owner of the OU will hide AD objects even from domain administrators. -search: '`wineventlog_security` EventCode=5136 ObjectClass=organizationalUnit | stats - min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) - as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value - values(OperationType) as OperationType values(dest) as dest by ObjectClass ObjectDN - OpCorrelationID src_user SubjectLogonId | rex field=old_value max_match=10000 "\((?P.*?)\)" | - rex field=new_value max_match=10000 "\((?P.*?)\)" | mvexpand new_ace | - where NOT new_ace IN (old_values) | rex field=new_ace "(?P.*?);(?P.*?);(?P.*?);(?P.*?);(?P.*?);(?P.*?)$" | - rex max_match=100 field=aceAccessRights "(?P[A-Z]{2})" | rex max_match=100 - field=aceFlags "(?P[A-Z]{2})" | lookup msad_guid_lookup guid as aceObjectGuid - OUTPUT displayName as ControlAccessRights | lookup ace_access_rights_lookup access_rights_string - as AccessRights OUTPUT access_rights_value | lookup ace_type_lookup ace_type_string - as aceType OUTPUT ace_type_value as aceType | lookup ace_flag_lookup flag_string - as aceFlags OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups - | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName - as user | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` | - lookup builtin_groups_lookup builtin_group_string as aceSid OUTPUT builtin_group_name - as builtin_group | eval aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,"This - object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full - control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid), - user=coalesce(user, group, builtin_group, aceSid) | stats values(aceType) as aceType - values(aceFlags) as aceFlags values(aceControlAccessRights) as aceControlAccessRights - values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace values(aceInheritedTypeGuid) - as aceInheritedTypeGuid by _time ObjectClass ObjectDN src_user SubjectLogonId user - OpCorrelationID | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1 - AND aceControlAccessRights="","All rights",''aceControlAccessRights'') | search - aceType IN ("Access denied",D) AND aceAccessRights IN ("List contents","List objects",LC,LO) - | `windows_ad_hidden_ou_creation_filter`' -how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically - event 5136. See lantern article in references for further on how to onboard AD audit - data. Ensure the wineventlog_security macro is configured with the correct indexes - and include lookups for SID resolution if evt_resolve_ad_obj is set to 0. + - Windows Event Log Security 5136 +description: This analytic is looking for when an ACL is applied to an OU which denies listing the objects residing in the OU. This activity combined with modifying the owner of the OU will hide AD objects even from domain administrators. +search: '`wineventlog_security` EventCode=5136 ObjectClass=organizationalUnit | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) as OperationType values(dest) as dest by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId | rex field=old_value max_match=10000 "\((?P.*?)\)" | rex field=new_value max_match=10000 "\((?P.*?)\)" | mvexpand new_ace | where NOT new_ace IN (old_values) | rex field=new_ace "(?P.*?);(?P.*?);(?P.*?);(?P.*?);(?P.*?);(?P.*?)$" | rex max_match=100 field=aceAccessRights "(?P[A-Z]{2})" | rex max_match=100 field=aceFlags "(?P[A-Z]{2})" | lookup msad_guid_lookup guid as aceObjectGuid OUTPUT displayName as ControlAccessRights | lookup ace_access_rights_lookup access_rights_string as AccessRights OUTPUT access_rights_value | lookup ace_type_lookup ace_type_string as aceType OUTPUT ace_type_value as aceType | lookup ace_flag_lookup flag_string as aceFlags OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` | lookup builtin_groups_lookup builtin_group_string as aceSid OUTPUT builtin_group_name as builtin_group | eval aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,"This object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid), user=coalesce(user, group, builtin_group, aceSid) | stats values(aceType) as aceType values(aceFlags) as aceFlags values(aceControlAccessRights) as aceControlAccessRights values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace values(aceInheritedTypeGuid) as aceInheritedTypeGuid by _time ObjectClass ObjectDN src_user SubjectLogonId user OpCorrelationID | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1 AND aceControlAccessRights="","All rights",''aceControlAccessRights'') | search aceType IN ("Access denied",D) AND aceAccessRights IN ("List contents","List objects",LC,LO) | `windows_ad_hidden_ou_creation_filter`' +how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security macro is configured with the correct indexes and include lookups for SID resolution if evt_resolve_ad_obj is set to 0. known_false_positives: No false positives have been identified at this time. references: -- https://happycamper84.medium.com/sneaky-persistence-via-hidden-objects-in-ad-1c91fc37bf54 -- https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory + - https://happycamper84.medium.com/sneaky-persistence-via-hidden-objects-in-ad-1c91fc37bf54 + - https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory drilldown_searches: -- name: View the detection results for - "$user$" and "$src_user$" - search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$src_user$" + search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $src_user$ has hidden the contents of OU $ObjectDN$ from $user$ - risk_objects: - - field: user - type: user - score: 100 - - field: src_user - type: user - score: 100 - threat_objects: [] + message: $src_user$ has hidden the contents of OU $ObjectDN$ from $user$ + risk_objects: + - field: user + type: user + score: 100 + - field: src_user + type: user + score: 100 + threat_objects: [] tags: - analytic_story: - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1222.001 - - T1484 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1222.001 + - T1484 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/hidden_ou_windows-security-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/hidden_ou_windows-security-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_object_owner_updated.yml b/detections/endpoint/windows_ad_object_owner_updated.yml index ead9e5530d..9f955ed78d 100644 --- a/detections/endpoint/windows_ad_object_owner_updated.yml +++ b/detections/endpoint/windows_ad_object_owner_updated.yml @@ -1,81 +1,73 @@ name: Windows AD Object Owner Updated id: 4af01f6b-d8d4-4f96-8635-758a01557130 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Dean Luxton status: production type: TTP data_source: -- Windows Event Log Security 5136 -description: AD Object Owner Updated. The owner provides Full control level privileges - over the target AD Object. This event has significant impact alone and is also a - precursor activity for hiding an AD object. -search: '`wineventlog_security` EventCode=5136 | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) - as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value - values(OperationType) as OperationType values(dest) as dest by ObjectClass ObjectDN - OpCorrelationID src_user SubjectLogonId DSName | rex field=old_value "O:(?P.*?)G:" - | rex field=new_value "O:(?P.*?)G:" | where old_owner!=new_owner ``` - optional SID resolution lookups | lookup identity_lookup_expanded objectSid as new_owner - OUTPUT downLevelDomainName as new_owner_user | lookup admon_groups_def objectSid - as new_owner OUTPUT cn as new_owner_group | lookup identity_lookup_expanded objectSid - as old_owner OUTPUT downLevelDomainName as old_owner_user | lookup admon_groups_def - objectSid as old_owner OUTPUT cn as old_owner_group ``` | lookup builtin_groups_lookup - builtin_group_string as new_owner_group OUTPUT builtin_group_name as new_owner_group_builtin_group - | lookup builtin_groups_lookup builtin_group_string as old_owner OUTPUT builtin_group_name - as old_owner_group_builtin_group | eval user=coalesce(new_owner_user, new_owner_group, - new_owner_group_builtin_group, new_owner), previousOwner=coalesce(old_owner_user, - old_owner_group, old_owner_group_builtin_group, old_owner) | stats values(previousOwner) - as previousOwner values(user) as user values(SubjectLogonId) as SubjectLogonId by - _time ObjectClass ObjectDN src_user OpCorrelationID DSName | `windows_ad_object_owner_updated_filter`' -how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically - event 5136. See lantern article in references for further on how to onboard AD audit - data. Ensure the wineventlog_security macro is configured with the correct indexes - and include lookups for SID resolution if evt_resolve_ad_obj is set to 0. + - Windows Event Log Security 5136 +description: AD Object Owner Updated. The owner provides Full control level privileges over the target AD Object. This event has significant impact alone and is also a precursor activity for hiding an AD object. +search: |- + `wineventlog_security` EventCode=5136 + | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) as OperationType values(dest) as dest + BY ObjectClass ObjectDN OpCorrelationID + src_user SubjectLogonId DSName + | rex field=old_value "O:(?P.*?)G:" + | rex field=new_value "O:(?P.*?)G:" + | where old_owner!=new_owner ``` optional SID resolution lookups + | lookup identity_lookup_expanded objectSid as new_owner OUTPUT downLevelDomainName as new_owner_user + | lookup admon_groups_def objectSid as new_owner OUTPUT cn as new_owner_group + | lookup identity_lookup_expanded objectSid as old_owner OUTPUT downLevelDomainName as old_owner_user + | lookup admon_groups_def objectSid as old_owner OUTPUT cn as old_owner_group ``` + | lookup builtin_groups_lookup builtin_group_string as new_owner_group OUTPUT builtin_group_name as new_owner_group_builtin_group + | lookup builtin_groups_lookup builtin_group_string as old_owner OUTPUT builtin_group_name as old_owner_group_builtin_group + | eval user=coalesce(new_owner_user, new_owner_group, new_owner_group_builtin_group, new_owner), previousOwner=coalesce(old_owner_user, old_owner_group, old_owner_group_builtin_group, old_owner) + | stats values(previousOwner) as previousOwner values(user) as user values(SubjectLogonId) as SubjectLogonId + BY _time ObjectClass ObjectDN + src_user OpCorrelationID DSName + | `windows_ad_object_owner_updated_filter` +how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security macro is configured with the correct indexes and include lookups for SID resolution if evt_resolve_ad_obj is set to 0. known_false_positives: No false positives have been identified at this time. references: -- https://learn.microsoft.com/en-us/windows/win32/secauthz/ace-strings -- https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-adts/1522b774-6464-41a3-87a5-1e5633c3fbbb -- https://trustedsec.com/blog/a-hitchhackers-guide-to-dacl-based-detections-part-1-a -- https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory + - https://learn.microsoft.com/en-us/windows/win32/secauthz/ace-strings + - https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-adts/1522b774-6464-41a3-87a5-1e5633c3fbbb + - https://trustedsec.com/blog/a-hitchhackers-guide-to-dacl-based-detections-part-1-a + - https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory drilldown_searches: -- name: View the detection results for - "$user$" and "$src_user$" - search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$src_user$" + search: '%original_detection_search% | search user = "$user$" src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $src_user$ has made $user$ the owner of AD object $ObjectDN$ - risk_objects: - - field: user - type: user - score: 100 - - field: src_user - type: user - score: 100 - threat_objects: [] + message: $src_user$ has made $user$ the owner of AD object $ObjectDN$ + risk_objects: + - field: user + type: user + score: 100 + - field: src_user + type: user + score: 100 + threat_objects: [] tags: - analytic_story: - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1222.001 - - T1484 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1222.001 + - T1484 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/owner_updated_windows-security-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/owner_updated_windows-security-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_privileged_account_sid_history_addition.yml b/detections/endpoint/windows_ad_privileged_account_sid_history_addition.yml index 5c216caeb2..c90a711482 100644 --- a/detections/endpoint/windows_ad_privileged_account_sid_history_addition.yml +++ b/detections/endpoint/windows_ad_privileged_account_sid_history_addition.yml @@ -1,74 +1,61 @@ name: Windows AD Privileged Account SID History Addition id: 6b521149-b91c-43aa-ba97-c2cac59ec830 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Dean Luxton type: TTP status: production data_source: -- Windows Event Log Security 4742 -- Windows Event Log Security 4738 -description: The following analytic identifies when the SID of a privileged user is - added to the SID History attribute of another user. It leverages Windows Security - Event Codes 4742 and 4738, combined with identity lookups, to detect this activity. - This behavior is significant as it may indicate an attempt to abuse SID history - for unauthorized access across multiple domains. If confirmed malicious, this activity - could allow an attacker to escalate privileges or maintain persistent access within - the environment, posing a significant security risk. -search: '`wineventlog_security` (EventCode=4742 OR EventCode=4738) NOT SidHistory - IN ("%%1793", -) | rex field=SidHistory "(^%{|^)(?P.*?)(}$|$)" | eval - category="privileged" | lookup identity_lookup_expanded category, identity as SidHistory - OUTPUT identity_tag as match | where isnotnull(match) | rename TargetSid as userSid - | table _time action status host user userSid SidHistory Logon_ID src_user dest - | `windows_ad_privileged_account_sid_history_addition_filter`' -how_to_implement: Ensure you have objectSid and the Down Level Logon Name `DOMAIN\sAMACountName` - added to the identity field of your Asset and Identities lookup, along with the - category of privileged for the applicable users. Ensure you are ingesting eventcodes - 4742 and 4738. Two advanced audit policies `Audit User Account Management` and `Audit - Computer Account Management` under `Account Management` are required to generate - these event codes. + - Windows Event Log Security 4742 + - Windows Event Log Security 4738 +description: The following analytic identifies when the SID of a privileged user is added to the SID History attribute of another user. It leverages Windows Security Event Codes 4742 and 4738, combined with identity lookups, to detect this activity. This behavior is significant as it may indicate an attempt to abuse SID history for unauthorized access across multiple domains. If confirmed malicious, this activity could allow an attacker to escalate privileges or maintain persistent access within the environment, posing a significant security risk. +search: |- + `wineventlog_security` (EventCode=4742 OR EventCode=4738) NOT SidHistory IN ("%%1793", -) + | rex field=SidHistory "(^%{ + | ^)(?P.*?)(}$ + | $)" + | eval category="privileged" + | lookup identity_lookup_expanded category, identity as SidHistory OUTPUT identity_tag as match + | where isnotnull(match) + | rename TargetSid as userSid + | table _time action status host user userSid SidHistory Logon_ID src_user dest + | `windows_ad_privileged_account_sid_history_addition_filter` +how_to_implement: Ensure you have objectSid and the Down Level Logon Name `DOMAIN\sAMACountName` added to the identity field of your Asset and Identities lookup, along with the category of privileged for the applicable users. Ensure you are ingesting eventcodes 4742 and 4738. Two advanced audit policies `Audit User Account Management` and `Audit Computer Account Management` under `Account Management` are required to generate these event codes. known_false_positives: Migration of privileged accounts. references: -- https://adsecurity.org/?p=1772 + - https://adsecurity.org/?p=1772 drilldown_searches: -- name: View the detection results for - "$src_user$" - search: '%original_detection_search% | search src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_user$" + search: '%original_detection_search% | search src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Privileged User Account SID History Attribute was added to $userSid$ - by $src_user$ - risk_objects: - - field: src_user - type: user - score: 90 - threat_objects: [] + message: A Privileged User Account SID History Attribute was added to $userSid$ by $src_user$ + risk_objects: + - field: src_user + type: user + score: 90 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1134.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - manual_test: This search uses a lookup provided by Enterprise Security and needs - to be manually tested. + analytic_story: + - Compromised Windows Host + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1134.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + manual_test: This search uses a lookup provided by Enterprise Security and needs to be manually tested. tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1134.005/mimikatz/windows-security-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1134.005/mimikatz/windows-security-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_privileged_group_modification.yml b/detections/endpoint/windows_ad_privileged_group_modification.yml index abd8977a10..2c45f04768 100644 --- a/detections/endpoint/windows_ad_privileged_group_modification.yml +++ b/detections/endpoint/windows_ad_privileged_group_modification.yml @@ -1,71 +1,63 @@ name: Windows AD Privileged Group Modification id: 187bf937-c436-4c65-bbcb-7539ffe02da1 -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Dean Luxton status: production type: TTP data_source: -- Windows Event Log Security 4728 + - Windows Event Log Security 4728 description: | - This detection identifies when users are added to privileged Active Directory - groups by leveraging the Windows Security Event Code 4728 along with a lookup - of privileged AD groups provided by Splunk Enterprise Security. - Attackers often add user accounts to privileged AD groups to escalate privileges - or maintain persistence within an Active Directory environment. - Monitoring for modifications to privileged groups can help identify potential security breaches - and unauthorized access attempts. -search: '`wineventlog_security` EventCode IN (4728) | stats min(_time) as _time dc(user) - as usercount, values(user) as user values(user_category) as user_category values(src_user_category) - as src_user_category values(dvc) as dvc by signature, Group_Name,src_user dest | lookup - admon_groups_def cn as Group_Name OUTPUT category | where category="privileged" - | `windows_ad_privileged_group_modification_filter`' -how_to_implement: This analytic requires eventCode 4728 to be ingested along with - the admon_groups_def lookup being configured to include a list of AD groups along - with a category to identify privileged groups. See splunkbase app listed in the - references for further details. + This detection identifies when users are added to privileged Active Directory + groups by leveraging the Windows Security Event Code 4728 along with a lookup + of privileged AD groups provided by Splunk Enterprise Security. + Attackers often add user accounts to privileged AD groups to escalate privileges + or maintain persistence within an Active Directory environment. + Monitoring for modifications to privileged groups can help identify potential security breaches + and unauthorized access attempts. +search: |- + `wineventlog_security` EventCode IN (4728) + | stats min(_time) as _time dc(user) as usercount, values(user) as user values(user_category) as user_category values(src_user_category) as src_user_category values(dvc) as dvc + BY signature, Group_Name,src_user dest + | lookup admon_groups_def cn as Group_Name OUTPUT category + | where category="privileged" + | `windows_ad_privileged_group_modification_filter` +how_to_implement: This analytic requires eventCode 4728 to be ingested along with the admon_groups_def lookup being configured to include a list of AD groups along with a category to identify privileged groups. See splunkbase app listed in the references for further details. known_false_positives: No false positives have been identified at this time. references: -- https://splunkbase.splunk.com/app/6853 + - https://splunkbase.splunk.com/app/6853 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $user$ was added to privileged AD Group $Group_Name$ by $src_user$ - risk_objects: - - field: user - type: user - score: 50 - threat_objects: [] + message: $user$ was added to privileged AD Group $Group_Name$ by $src_user$ + risk_objects: + - field: user + type: user + score: 50 + threat_objects: [] tags: - analytic_story: - - Active Directory Privilege Escalation - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1098 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: identity - manual_test: This search uses a lookup provided by Enterprise Security and needs - to be manually tested. + analytic_story: + - Active Directory Privilege Escalation + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1098 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: identity + manual_test: This search uses a lookup provided by Enterprise Security and needs to be manually tested. tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/account_manipulation/xml-windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/account_manipulation/xml-windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_privileged_object_access_activity.yml b/detections/endpoint/windows_ad_privileged_object_access_activity.yml index c370362c32..da4fe2440f 100644 --- a/detections/endpoint/windows_ad_privileged_object_access_activity.yml +++ b/detections/endpoint/windows_ad_privileged_object_access_activity.yml @@ -1,79 +1,59 @@ name: Windows AD Privileged Object Access Activity id: dc2f58bc-8cd2-4e51-962a-694b963acde0 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Steven Dick status: production type: TTP -description: The following analytic detects access attempts to privileged Active Directory - objects, such as Domain Admins or Enterprise Admins. It leverages Windows Security - Event Code 4662 to identify when these sensitive objects are accessed. This activity - is significant because such objects should rarely be accessed by normal users or - processes, and unauthorized access attempts may indicate attacker enumeration or - lateral movement within the domain. If confirmed malicious, this activity could - allow attackers to escalate privileges, persist in the environment, or gain control - over critical domain resources. +description: The following analytic detects access attempts to privileged Active Directory objects, such as Domain Admins or Enterprise Admins. It leverages Windows Security Event Code 4662 to identify when these sensitive objects are accessed. This activity is significant because such objects should rarely be accessed by normal users or processes, and unauthorized access attempts may indicate attacker enumeration or lateral movement within the domain. If confirmed malicious, this activity could allow attackers to escalate privileges, persist in the environment, or gain control over critical domain resources. data_source: -- Windows Event Log Security 4662 -search: '`wineventlog_security` EventCode=4662 ObjectName IN ( "CN=Account Operators,*", - "CN=Administrators,*", "CN=Backup Operators,*", "CN=Cert Publishers,*", "CN=Certificate - Service DCOM Access,*", "CN=Domain Admins,*", "CN=Domain Controllers,*", "CN=Enterprise - Admins,*", "CN=Enterprise Read-only Domain Controllers,*", "CN=Group Policy Creator - Owners,*", "CN=Incoming Forest Trust Builders,*", "CN=Microsoft Exchange Servers,*", - "CN=Network Configuration Operators,*", "CN=Power Users,*", "CN=Print Operators,*", - "CN=Read-only Domain Controllers,*", "CN=Replicators,*", "CN=Schema Admins,*", "CN=Server - Operators,*", "CN=Exchange Trusted Subsystem,*", "CN=Exchange Windows Permission,*", - "CN=Organization Management,*") | rex field=ObjectName "CN\=(?[^,]+)" - | stats values(Computer) as dest, values(object_name) as object_name, dc(ObjectName) - as object_count, min(_time) as firstTime, max(_time) as lastTime, count by SubjectUserName - | rename SubjectUserName as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_ad_privileged_object_access_activity_filter`' -how_to_implement: Enable Audit Directory Service Access via GPO and collect event - code 4662. The required SACLs need to be created for the relevant objects. Be aware - Splunk filters this event by default on the Windows TA. -known_false_positives: Service accounts or applications that routinely query Active - Directory for information. + - Windows Event Log Security 4662 +search: |- + `wineventlog_security` EventCode=4662 ObjectName IN ( "CN=Account Operators,*", "CN=Administrators,*", "CN=Backup Operators,*", "CN=Cert Publishers,*", "CN=Certificate Service DCOM Access,*", "CN=Domain Admins,*", "CN=Domain Controllers,*", "CN=Enterprise Admins,*", "CN=Enterprise Read-only Domain Controllers,*", "CN=Group Policy Creator Owners,*", "CN=Incoming Forest Trust Builders,*", "CN=Microsoft Exchange Servers,*", "CN=Network Configuration Operators,*", "CN=Power Users,*", "CN=Print Operators,*", "CN=Read-only Domain Controllers,*", "CN=Replicators,*", "CN=Schema Admins,*", "CN=Server Operators,*", "CN=Exchange Trusted Subsystem,*", "CN=Exchange Windows Permission,*", "CN=Organization Management,*") + | rex field=ObjectName "CN\=(?[^,]+)" + | stats values(Computer) as dest, values(object_name) as object_name, dc(ObjectName) as object_count, min(_time) as firstTime, max(_time) as lastTime, count + BY SubjectUserName + | rename SubjectUserName as user + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_ad_privileged_object_access_activity_filter` +how_to_implement: Enable Audit Directory Service Access via GPO and collect event code 4662. The required SACLs need to be created for the relevant objects. Be aware Splunk filters this event by default on the Windows TA. +known_false_positives: Service accounts or applications that routinely query Active Directory for information. references: -- https://medium.com/securonix-tech-blog/detecting-ldap-enumeration-and-bloodhound-s-sharphound-collector-using-active-directory-decoys-dfc840f2f644 -- https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4662 -- https://attack.mitre.org/tactics/TA0007/ + - https://medium.com/securonix-tech-blog/detecting-ldap-enumeration-and-bloodhound-s-sharphound-collector-using-active-directory-decoys-dfc840f2f644 + - https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4662 + - https://attack.mitre.org/tactics/TA0007/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The account $user$ accessed $object_count$ privileged AD object(s). - risk_objects: - - field: user - type: user - score: 40 - threat_objects: [] + message: The account $user$ accessed $object_count$ privileged AD object(s). + risk_objects: + - field: user + type: user + score: 40 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - - BlackSuit Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1087.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - BlackSuit Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1087.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/4662_ad_enum/4662_priv_events.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/4662_ad_enum/4662_priv_events.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_replication_request_initiated_by_user_account.yml b/detections/endpoint/windows_ad_replication_request_initiated_by_user_account.yml index 12de51f54c..f8018f3828 100644 --- a/detections/endpoint/windows_ad_replication_request_initiated_by_user_account.yml +++ b/detections/endpoint/windows_ad_replication_request_initiated_by_user_account.yml @@ -1,86 +1,67 @@ name: Windows AD Replication Request Initiated by User Account id: 51307514-1236-49f6-8686-d46d93cc2821 -version: 11 -date: '2025-08-11' +version: 12 +date: '2026-02-25' author: Dean Luxton type: TTP status: production data_source: -- Windows Event Log Security 4662 -- Windows Event Log Security 4624 -description: The following analytic detects a user account initiating an Active Directory - replication request, indicative of a DCSync attack. It leverages EventCode 4662 - from the Windows Security Event Log, focusing on specific object types and replication - permissions. This activity is significant because it can allow an attacker with - sufficient privileges to request password hashes for any or all users within the - domain. If confirmed malicious, this could lead to unauthorized access, privilege - escalation, and potential compromise of the entire domain. + - Windows Event Log Security 4662 + - Windows Event Log Security 4624 +description: The following analytic detects a user account initiating an Active Directory replication request, indicative of a DCSync attack. It leverages EventCode 4662 from the Windows Security Event Log, focusing on specific object types and replication permissions. This activity is significant because it can allow an attacker with sufficient privileges to request password hashes for any or all users within the domain. If confirmed malicious, this could lead to unauthorized access, privilege escalation, and potential compromise of the entire domain. search: |- - `wineventlog_security` EventCode=4662 ObjectType IN ("%{19195a5b-6da0-11d0-afd3-00c04fd930c9}","domainDNS") - AND Properties IN ("*Replicating Directory Changes All*","*Manage Replication Topology*","*Remove Replica In Domain*","*{1131f6ad-9c07-11d1-f79f-00c04fc2dcd2}*","*{9923a32a-3607-11d2-b9be-0000f87a36b2}*","*{1131f6ac-9c07-11d1-f79f-00c04fc2dcd2}*") - AND AccessMask="0x100" AND NOT (SubjectUserSid="NT AUT*" OR SubjectUserSid="S-1-5-18" OR SubjectDomainName="Window Manager" OR SubjectUserName="*$") - | stats min(_time) as _time, count by SubjectDomainName, SubjectUserName, Computer, Logon_ID, ObjectName, ObjectServer, ObjectType, OperationType, status dest - | rename SubjectDomainName as Target_Domain, SubjectUserName as user, Logon_ID as TargetLogonId, _time as attack_time - | appendpipe - [| map search="search `wineventlog_security` EventCode=4624 TargetLogonId=$TargetLogonId$" - | fields - status] - | stats min(attack_time) as _time values(TargetUserSid) as TargetUserSid, values(Target_Domain) as Target_Domain, values(user) as user, values(Computer) as Computer, values(status) as status, values(src_category) as - src_category, values(src_ip) as src_ip values(action) as action values(authentication_method) as authentication_method values(dest) as dest values(signature) as signature values(signature_id) as signature_id by TargetLogonId - | `windows_ad_replication_request_initiated_by_user_account_filter` -how_to_implement: To successfully implement this search, you need to be ingesting - eventcode `4662`. The Advanced Security Audit policy settings `Audit Directory Services - Access` within `DS Access` needs to be enabled, as well as the following SACLs applied - to the domain root and all descendant objects. The principals `everybody`, `Domain - Computers`, and `Domain Controllers` auditing the permissions `Replicating Directory - Changes`, `Replicating Directory Changes All`, and `Replicating Directory Changes - In Filtered Set` -known_false_positives: Azure AD Connect syncing operations and the dcdiag.exe /Test:Replications - command. + `wineventlog_security` EventCode=4662 ObjectType IN ("%{19195a5b-6da0-11d0-afd3-00c04fd930c9}","domainDNS") + AND Properties IN ("*Replicating Directory Changes All*","*Manage Replication Topology*","*Remove Replica In Domain*","*{1131f6ad-9c07-11d1-f79f-00c04fc2dcd2}*","*{9923a32a-3607-11d2-b9be-0000f87a36b2}*","*{1131f6ac-9c07-11d1-f79f-00c04fc2dcd2}*") + AND AccessMask="0x100" AND NOT (SubjectUserSid="NT AUT*" OR SubjectUserSid="S-1-5-18" OR SubjectDomainName="Window Manager" OR SubjectUserName="*$") + | stats min(_time) as _time, count by SubjectDomainName, SubjectUserName, Computer, Logon_ID, ObjectName, ObjectServer, ObjectType, OperationType, status dest + | rename SubjectDomainName as Target_Domain, SubjectUserName as user, Logon_ID as TargetLogonId, _time as attack_time + | appendpipe + [| map search="search `wineventlog_security` EventCode=4624 TargetLogonId=$TargetLogonId$" + | fields - status] + | stats min(attack_time) as _time values(TargetUserSid) as TargetUserSid, values(Target_Domain) as Target_Domain, values(user) as user, values(Computer) as Computer, values(status) as status, values(src_category) as + src_category, values(src_ip) as src_ip values(action) as action values(authentication_method) as authentication_method values(dest) as dest values(signature) as signature values(signature_id) as signature_id by TargetLogonId + | `windows_ad_replication_request_initiated_by_user_account_filter` +how_to_implement: To successfully implement this search, you need to be ingesting eventcode `4662`. The Advanced Security Audit policy settings `Audit Directory Services Access` within `DS Access` needs to be enabled, as well as the following SACLs applied to the domain root and all descendant objects. The principals `everybody`, `Domain Computers`, and `Domain Controllers` auditing the permissions `Replicating Directory Changes`, `Replicating Directory Changes All`, and `Replicating Directory Changes In Filtered Set` +known_false_positives: Azure AD Connect syncing operations and the dcdiag.exe /Test:Replications command. references: -- https://adsecurity.org/?p=1729 -- https://www.linkedin.com/pulse/mimikatz-dcsync-event-log-detections-john-dwyer -- https://github.com/SigmaHQ/sigma/blob/0.22-699-g29a5c6278/rules/windows/builtin/security/win_security_dcsync.yml + - https://adsecurity.org/?p=1729 + - https://www.linkedin.com/pulse/mimikatz-dcsync-event-log-detections-john-dwyer + - https://github.com/SigmaHQ/sigma/blob/0.22-699-g29a5c6278/rules/windows/builtin/security/win_security_dcsync.yml drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Active Directory Replication Request Initiated by User Account - $user$ from $src_ip$ - risk_objects: - - field: user - type: user - score: 100 - - field: src_ip - type: system - score: 100 - threat_objects: [] + message: Windows Active Directory Replication Request Initiated by User Account $user$ from $src_ip$ + risk_objects: + - field: user + type: user + score: 100 + - field: src_ip + type: system + score: 100 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - Sneaky Active Directory Persistence Tricks - - Credential Dumping - asset_type: Endpoint - mitre_attack_id: - - T1003.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - Sneaky Active Directory Persistence Tricks + - Credential Dumping + asset_type: Endpoint + mitre_attack_id: + - T1003.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.006/mimikatz/xml-windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.006/mimikatz/xml-windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_replication_request_initiated_from_unsanctioned_location.yml b/detections/endpoint/windows_ad_replication_request_initiated_from_unsanctioned_location.yml index aa37aa55ab..e9f9ced8cc 100644 --- a/detections/endpoint/windows_ad_replication_request_initiated_from_unsanctioned_location.yml +++ b/detections/endpoint/windows_ad_replication_request_initiated_from_unsanctioned_location.yml @@ -1,96 +1,72 @@ name: Windows AD Replication Request Initiated from Unsanctioned Location id: 50998483-bb15-457b-a870-965080d9e3d3 -version: 12 -date: '2025-08-11' +version: 13 +date: '2026-02-25' author: Dean Luxton type: TTP status: production data_source: - - Windows Event Log Security 4662 - - Windows Event Log Security 4624 -description: - The following analytic identifies unauthorized Active Directory replication - requests initiated from non-domain controller locations. It leverages EventCode - 4662 to detect when a computer account with replication permissions creates a handle - to domainDNS, filtering out known domain controller IP addresses. This activity - is significant as it may indicate a DCSync attack, where an attacker with privileged - access can request password hashes for any or all users within the domain. If confirmed - malicious, this could lead to unauthorized access to sensitive information and potential - full domain compromise. + - Windows Event Log Security 4662 + - Windows Event Log Security 4624 +description: The following analytic identifies unauthorized Active Directory replication requests initiated from non-domain controller locations. It leverages EventCode 4662 to detect when a computer account with replication permissions creates a handle to domainDNS, filtering out known domain controller IP addresses. This activity is significant as it may indicate a DCSync attack, where an attacker with privileged access can request password hashes for any or all users within the domain. If confirmed malicious, this could lead to unauthorized access to sensitive information and potential full domain compromise. search: |- - `wineventlog_security` EventCode=4662 ObjectType IN ("%{19195a5b-6da0-11d0-afd3-00c04fd930c9}", - "domainDNS") AND Properties IN ("*Replicating Directory Changes All*","*Manage Replication Topology*","*Remove Replica In Domain*","*{1131f6ad-9c07-11d1-f79f-00c04fc2dcd2}*","*{9923a32a-3607-11d2-b9be-0000f87a36b2}*","*{1131f6ac-9c07-11d1-f79f-00c04fc2dcd2}*") - AND AccessMask="0x100" AND (SubjectUserSid="NT AUT*" OR SubjectUserSid="S-1-5-18" - OR SubjectDomainName="Window Manager" OR SubjectUserName="*$") - | stats min(_time) - as attack_time, count by SubjectDomainName, SubjectUserName, Computer, Logon_ID, ObjectName, ObjectServer, ObjectType, OperationType, status - | rename SubjectDomainName - as Target_Domain, SubjectUserName as user, Logon_ID as TargetLogonId - | appendpipe - [| map search="search `wineventlog_security` EventCode=4624 TargetLogonId=$TargetLogonId$"] - | stats min(attack_time) as _time, values(TargetUserSid) - as TargetUserSid, values(Target_Domain) as Target_Domain, values(user) as user, - values(Computer) as Computer, values(status) as status, values(src_category) as - src_category, values(src_ip) as src_ip values(action) as action values(authentication_method) as authentication_method values(dest) as dest values(signature) as signature values(signature_id) as signature_id by TargetLogonId - | search NOT src_category="domain_controller" - | `windows_ad_replication_request_initiated_from_unsanctioned_location_filter` -how_to_implement: - To successfully implement this search, you need to be ingesting - eventcode `4662`. The Advanced Security Audit policy settings `Audit Directory Services - Access` within `DS Access` needs to be enabled, as well as the following SACLs applied - to the domain root and all descendant objects. The principals `everybody`, `Domain - Computers`, and `Domain Controllers` auditing the permissions `Replicating Directory - Changes`, `Replicating Directory Changes All`, and `Replicating Directory Changes - In Filtered Set` Assets and Identities will also need to be configured, with the - category of domain_controller added for domain controllers. -known_false_positives: Genuine DC promotion may trigger this alert. + `wineventlog_security` EventCode=4662 ObjectType IN ("%{19195a5b-6da0-11d0-afd3-00c04fd930c9}", + "domainDNS") AND Properties IN ("*Replicating Directory Changes All*","*Manage Replication Topology*","*Remove Replica In Domain*","*{1131f6ad-9c07-11d1-f79f-00c04fc2dcd2}*","*{9923a32a-3607-11d2-b9be-0000f87a36b2}*","*{1131f6ac-9c07-11d1-f79f-00c04fc2dcd2}*") + AND AccessMask="0x100" AND (SubjectUserSid="NT AUT*" OR SubjectUserSid="S-1-5-18" + OR SubjectDomainName="Window Manager" OR SubjectUserName="*$") + | stats min(_time) + as attack_time, count by SubjectDomainName, SubjectUserName, Computer, Logon_ID, ObjectName, ObjectServer, ObjectType, OperationType, status + | rename SubjectDomainName + as Target_Domain, SubjectUserName as user, Logon_ID as TargetLogonId + | appendpipe + [| map search="search `wineventlog_security` EventCode=4624 TargetLogonId=$TargetLogonId$"] + | stats min(attack_time) as _time, values(TargetUserSid) + as TargetUserSid, values(Target_Domain) as Target_Domain, values(user) as user, + values(Computer) as Computer, values(status) as status, values(src_category) as + src_category, values(src_ip) as src_ip values(action) as action values(authentication_method) as authentication_method values(dest) as dest values(signature) as signature values(signature_id) as signature_id by TargetLogonId + | search NOT src_category="domain_controller" + | `windows_ad_replication_request_initiated_from_unsanctioned_location_filter` +how_to_implement: To successfully implement this search, you need to be ingesting eventcode `4662`. The Advanced Security Audit policy settings `Audit Directory Services Access` within `DS Access` needs to be enabled, as well as the following SACLs applied to the domain root and all descendant objects. The principals `everybody`, `Domain Computers`, and `Domain Controllers` auditing the permissions `Replicating Directory Changes`, `Replicating Directory Changes All`, and `Replicating Directory Changes In Filtered Set` Assets and Identities will also need to be configured, with the category of domain_controller added for domain controllers. +known_false_positives: Genuine DC promotion may trigger this alert. references: - - https://adsecurity.org/?p=1729 - - https://www.linkedin.com/pulse/mimikatz-dcsync-event-log-detections-john-dwyer - - https://github.com/SigmaHQ/sigma/blob/0.22-699-g29a5c6278/rules/windows/builtin/security/win_security_dcsync.yml + - https://adsecurity.org/?p=1729 + - https://www.linkedin.com/pulse/mimikatz-dcsync-event-log-detections-john-dwyer + - https://github.com/SigmaHQ/sigma/blob/0.22-699-g29a5c6278/rules/windows/builtin/security/win_security_dcsync.yml drilldown_searches: - - name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - Windows Active Directory Replication Request Initiated from Unsanctioned - Location $src_ip$ by $user$ - risk_objects: - - field: user - type: user - score: 100 - - field: src_ip - type: system - score: 100 - threat_objects: [] + message: Windows Active Directory Replication Request Initiated from Unsanctioned Location $src_ip$ by $user$ + risk_objects: + - field: user + type: user + score: 100 + - field: src_ip + type: system + score: 100 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - Sneaky Active Directory Persistence Tricks - - Credential Dumping - asset_type: Endpoint - mitre_attack_id: - - T1003.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - Sneaky Active Directory Persistence Tricks + - Credential Dumping + asset_type: Endpoint + mitre_attack_id: + - T1003.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.006/impacket/windows-security-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.006/impacket/windows-security-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_same_domain_sid_history_addition.yml b/detections/endpoint/windows_ad_same_domain_sid_history_addition.yml index 031ab20055..71c023b1d6 100644 --- a/detections/endpoint/windows_ad_same_domain_sid_history_addition.yml +++ b/detections/endpoint/windows_ad_same_domain_sid_history_addition.yml @@ -6,71 +6,52 @@ author: Dean Luxton type: TTP status: production data_source: -- Windows Event Log Security 4742 -- Windows Event Log Security 4738 -description: The following analytic detects changes to the sIDHistory attribute of - user or computer objects within the same domain. It leverages Windows Security Event - Codes 4738 and 4742 to identify when the sIDHistory attribute is modified. This - activity is significant because the sIDHistory attribute can be abused by adversaries - to grant unauthorized access by inheriting permissions from another account. If - confirmed malicious, this could allow attackers to maintain persistent access or - escalate privileges within the domain, posing a severe security risk. -search: '`wineventlog_security` (EventCode=4742 OR EventCode=4738) NOT SidHistory - IN ("%%1793", -) | rex field=SidHistory "(^%{|^)(?P.*)(\-|\\\)" - | rex field=TargetSid "^(?P.*)(\-|\\\)" | where SidHistoryMatch=TargetSidmatch - OR SidHistoryMatch=TargetDomainName | rename TargetSid as userSid, TargetDomainName - as userDomainName | table _time action status host user userSid userDomainName SidHistory - Logon_ID src_user dest | `windows_ad_same_domain_sid_history_addition_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - eventcodes `4738` and `4742`. The Advanced Security Audit policy settings `Audit - User Account Management` and `Audit Computer Account Management` within `Account - Management` all need to be enabled. SID resolution is not required.. + - Windows Event Log Security 4742 + - Windows Event Log Security 4738 +description: The following analytic detects changes to the sIDHistory attribute of user or computer objects within the same domain. It leverages Windows Security Event Codes 4738 and 4742 to identify when the sIDHistory attribute is modified. This activity is significant because the sIDHistory attribute can be abused by adversaries to grant unauthorized access by inheriting permissions from another account. If confirmed malicious, this could allow attackers to maintain persistent access or escalate privileges within the domain, posing a severe security risk. +search: '`wineventlog_security` (EventCode=4742 OR EventCode=4738) NOT SidHistory IN ("%%1793", -) | rex field=SidHistory "(^%{|^)(?P.*)(\-|\\\)" | rex field=TargetSid "^(?P.*)(\-|\\\)" | where SidHistoryMatch=TargetSidmatch OR SidHistoryMatch=TargetDomainName | rename TargetSid as userSid, TargetDomainName as userDomainName | table _time action status host user userSid userDomainName SidHistory Logon_ID src_user dest | `windows_ad_same_domain_sid_history_addition_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting eventcodes `4738` and `4742`. The Advanced Security Audit policy settings `Audit User Account Management` and `Audit Computer Account Management` within `Account Management` all need to be enabled. SID resolution is not required.. known_false_positives: No false positives have been identified at this time. references: -- https://adsecurity.org/?p=1772 -- https://learn.microsoft.com/en-us/windows/win32/adschema/a-sidhistory?redirectedfrom=MSDN -- https://learn.microsoft.com/en-us/defender-for-identity/security-assessment-unsecure-sid-history-attribute -- https://book.hacktricks.xyz/windows-hardening/active-directory-methodology/sid-history-injection + - https://adsecurity.org/?p=1772 + - https://learn.microsoft.com/en-us/windows/win32/adschema/a-sidhistory?redirectedfrom=MSDN + - https://learn.microsoft.com/en-us/defender-for-identity/security-assessment-unsecure-sid-history-attribute + - https://book.hacktricks.xyz/windows-hardening/active-directory-methodology/sid-history-injection drilldown_searches: -- name: View the detection results for - "$src_user$" and "$user$" - search: '%original_detection_search% | search src_user = "$src_user$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_user$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_user$" and "$user$" + search: '%original_detection_search% | search src_user = "$src_user$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_user$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Active Directory SID History Attribute was added to $user$ by $src_user$ - risk_objects: - - field: src_user - type: user - score: 100 - - field: user - type: user - score: 100 - threat_objects: [] + message: Active Directory SID History Attribute was added to $user$ by $src_user$ + risk_objects: + - field: src_user + type: user + score: 100 + - field: user + type: user + score: 100 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - Windows Persistence Techniques - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1134.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - Windows Persistence Techniques + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1134.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1134.005/mimikatz/windows-security-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1134.005/mimikatz/windows-security-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_self_dacl_assignment.yml b/detections/endpoint/windows_ad_self_dacl_assignment.yml index d6b98003c6..bad2b8380c 100644 --- a/detections/endpoint/windows_ad_self_dacl_assignment.yml +++ b/detections/endpoint/windows_ad_self_dacl_assignment.yml @@ -1,134 +1,125 @@ name: Windows AD Self DACL Assignment id: 16132445-da9f-4d03-ad44-56d717dcd67d -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Dean Luxton status: production type: TTP data_source: -- Windows Event Log Security 5136 + - Windows Event Log Security 5136 description: Detect when a user creates a new DACL in AD for their own AD object. search: | - `wineventlog_security` - EventCode=5136 - | stats min(_time) as _time - values( - eval( - if(OperationType=="%%14675",AttributeValue,null) + `wineventlog_security` + EventCode=5136 + | stats min(_time) as _time + values( + eval( + if(OperationType=="%%14675",AttributeValue,null) + ) + ) as old_value + + values( + eval( + if(OperationType=="%%14674" ,AttributeValue,null) ) - ) as old_value - - values( - eval( - if(OperationType=="%%14674" ,AttributeValue,null) - ) - ) as new_value - - values(OperationType) as OperationType - by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId dest - - | rex field=old_value max_match=10000 "\((?P.*?)\)" - | rex field=new_value max_match=10000 "\((?P.*?)\)" - | mvexpand new_ace - | where NOT new_ace IN (old_values) - | rex field=new_ace "(?P.*?);(?P.*?);(?P.*?);(?P.*?);(?P.*?);(?P.*?)$" - | rex max_match=100 field=aceAccessRights "(?P[A-Z]{2})" - | rex max_match=100 field=aceFlags "(?P[A-Z]{2})" - - | lookup ace_type_lookup ace_type_string as aceType OUTPUT ace_type_value as aceType - | lookup ace_flag_lookup flag_string as aceFlags OUTPUT flag_value as ace_flag_value - | lookup ace_access_rights_lookup access_rights_string as AccessRights OUTPUT access_rights_value - | lookup msad_guid_lookup guid as aceObjectGuid OUTPUT displayName as ControlAccessRights - - ``` Optional SID resolution lookups - | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user - | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group - ``` - - | lookup builtin_groups_lookup builtin_group_string as aceSid OUTPUT builtin_group_name as builtin_group - - | eval aceType = coalesce(ace_type_value, aceType), - aceInheritance = coalesce(ace_flag_value, "This object only"), - aceAccessRights = if( - aceAccessRights = "CCDCLCSWRPWPDTLOCRSDRCWDWO", "Full control", coalesce(access_rights_value,AccessRights) - ), - aceControlAccessRights = if( - ( - ControlAccessRights = "Write member" - OR - aceObjectGuid = "bf9679c0-0de6-11d0-a285-00aa003049e2" - ) AND - ( - aceAccessRights = "All validated writes" - OR - AccessRights = "SW" - ), - "Add/remove self as member", - coalesce(ControlAccessRights,aceObjectGuid) - ), - user=coalesce(user, group, builtin_group, aceSid) - - | stats values(aceType) as aceType - values(aceInheritance) as aceInheritance - values(aceControlAccessRights) as aceControlAccessRights - values(aceAccessRights) as aceAccessRights - values(new_ace) as new_ace - values(aceInheritedTypeGuid) as aceInheritedTypeGuid - - by _time ObjectClass ObjectDN src_user SubjectLogonId user OpCorrelationID dest - - | eval aceControlAccessRights = if( - mvcount(aceControlAccessRights) = 1 - AND - aceControlAccessRights = "", "All rights", "aceControlAccessRights" - ) - | rex field=user "\\\\(?P.*?)$" - | where lower(src_user)=lower(nt_user) - | `windows_ad_self_dacl_assignment_filter` -how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically - event 5136. See lantern article in references for further on how to onboard AD audit - data. Ensure the wineventlog_security macro is configured with the correct indexes - and include lookups for SID resolution if evt_resolve_ad_obj is set to 0. + ) as new_value + + values(OperationType) as OperationType + by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId dest + + | rex field=old_value max_match=10000 "\((?P.*?)\)" + | rex field=new_value max_match=10000 "\((?P.*?)\)" + | mvexpand new_ace + | where NOT new_ace IN (old_values) + | rex field=new_ace "(?P.*?);(?P.*?);(?P.*?);(?P.*?);(?P.*?);(?P.*?)$" + | rex max_match=100 field=aceAccessRights "(?P[A-Z]{2})" + | rex max_match=100 field=aceFlags "(?P[A-Z]{2})" + + | lookup ace_type_lookup ace_type_string as aceType OUTPUT ace_type_value as aceType + | lookup ace_flag_lookup flag_string as aceFlags OUTPUT flag_value as ace_flag_value + | lookup ace_access_rights_lookup access_rights_string as AccessRights OUTPUT access_rights_value + | lookup msad_guid_lookup guid as aceObjectGuid OUTPUT displayName as ControlAccessRights + + ``` Optional SID resolution lookups + | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user + | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group + ``` + + | lookup builtin_groups_lookup builtin_group_string as aceSid OUTPUT builtin_group_name as builtin_group + + | eval aceType = coalesce(ace_type_value, aceType), + aceInheritance = coalesce(ace_flag_value, "This object only"), + aceAccessRights = if( + aceAccessRights = "CCDCLCSWRPWPDTLOCRSDRCWDWO", "Full control", coalesce(access_rights_value,AccessRights) + ), + aceControlAccessRights = if( + ( + ControlAccessRights = "Write member" + OR + aceObjectGuid = "bf9679c0-0de6-11d0-a285-00aa003049e2" + ) AND + ( + aceAccessRights = "All validated writes" + OR + AccessRights = "SW" + ), + "Add/remove self as member", + coalesce(ControlAccessRights,aceObjectGuid) + ), + user=coalesce(user, group, builtin_group, aceSid) + + | stats values(aceType) as aceType + values(aceInheritance) as aceInheritance + values(aceControlAccessRights) as aceControlAccessRights + values(aceAccessRights) as aceAccessRights + values(new_ace) as new_ace + values(aceInheritedTypeGuid) as aceInheritedTypeGuid + + by _time ObjectClass ObjectDN src_user SubjectLogonId user OpCorrelationID dest + + | eval aceControlAccessRights = if( + mvcount(aceControlAccessRights) = 1 + AND + aceControlAccessRights = "", "All rights", "aceControlAccessRights" + ) + | rex field=user "\\\\(?P.*?)$" + | where lower(src_user)=lower(nt_user) + | `windows_ad_self_dacl_assignment_filter` +how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security macro is configured with the correct indexes and include lookups for SID resolution if evt_resolve_ad_obj is set to 0. known_false_positives: No false positives have been identified at this time. references: -- https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory + - https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $user$ has created a DACL on $ObjectDN$ to grant themselves $aceControlAccessRights$ - across $aceAccessRights$ - risk_objects: - - field: user - type: user - score: 80 - threat_objects: [] + message: $user$ has created a DACL on $ObjectDN$ to grant themselves $aceControlAccessRights$ across $aceAccessRights$ + risk_objects: + - field: user + type: user + score: 80 + threat_objects: [] tags: - analytic_story: - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1484 - - T1098 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1484 + - T1098 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484/aclmodification/windows-security-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484/aclmodification/windows-security-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_serviceprincipalname_added_to_domain_account.yml b/detections/endpoint/windows_ad_serviceprincipalname_added_to_domain_account.yml index fae08ace0f..df8810526d 100644 --- a/detections/endpoint/windows_ad_serviceprincipalname_added_to_domain_account.yml +++ b/detections/endpoint/windows_ad_serviceprincipalname_added_to_domain_account.yml @@ -6,73 +6,55 @@ author: Mauricio Velazco, Splunk type: TTP status: production data_source: -- Windows Event Log Security 5136 -description: The following analytic detects the addition of a Service Principal - Name (SPN) to a domain account. It leverages Windows Event Code 5136 and - monitors changes to the servicePrincipalName attribute. This activity is - significant because it may indicate an attempt to perform Kerberoasting, a - technique where attackers extract and crack service account passwords offline. - If confirmed malicious, this could allow an attacker to obtain cleartext - passwords, leading to unauthorized access and potential lateral movement - within the domain environment. + - Windows Event Log Security 5136 +description: The following analytic detects the addition of a Service Principal Name (SPN) to a domain account. It leverages Windows Event Code 5136 and monitors changes to the servicePrincipalName attribute. This activity is significant because it may indicate an attempt to perform Kerberoasting, a technique where attackers extract and crack service account passwords offline. If confirmed malicious, this could allow an attacker to obtain cleartext passwords, leading to unauthorized access and potential lateral movement within the domain environment. search: >- - `wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=servicePrincipalName - OperationType="%%14674" ObjectClass=user - | stats values(ObjectDN) as ObjectDN by _time, Computer, SubjectUserName, AttributeValue - | rex field=ObjectDN "^CN=(?P[a-zA-Z0-9!#$%&'@^_{}~.-]+)," - | rename Computer as dest, SubjectUserName as src_user | `windows_ad_serviceprincipalname_added_to_domain_account_filter` -how_to_implement: To successfully implement this search, you ned to be ingesting - eventcode `5136`. The Advanced Security Audit policy setting `Audit Directory - Services Changes` within `DS Access` needs to be enabled. Additionally, a SACL - needs to be created for AD objects in order to ingest attribute modifications. -known_false_positives: A Service Principal Name should only be added to an - account when an application requires it. While infrequent, this detection may - trigger on legitimate actions. Filter as needed. + `wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=servicePrincipalName + OperationType="%%14674" ObjectClass=user + | stats values(ObjectDN) as ObjectDN by _time, Computer, SubjectUserName, AttributeValue + | rex field=ObjectDN "^CN=(?P[a-zA-Z0-9!#$%&'@^_{}~.-]+)," + | rename Computer as dest, SubjectUserName as src_user | `windows_ad_serviceprincipalname_added_to_domain_account_filter` +how_to_implement: To successfully implement this search, you ned to be ingesting eventcode `5136`. The Advanced Security Audit policy setting `Audit Directory Services Changes` within `DS Access` needs to be enabled. Additionally, a SACL needs to be created for AD objects in order to ingest attribute modifications. +known_false_positives: A Service Principal Name should only be added to an account when an application requires it. While infrequent, this detection may trigger on legitimate actions. Filter as needed. references: -- https://adsecurity.org/?p=3466 -- https://www.thehacker.recipes/ad/movement/dacl/targeted-kerberoasting -- https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-5136 -- https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/t1208-kerberoasting + - https://adsecurity.org/?p=3466 + - https://www.thehacker.recipes/ad/movement/dacl/targeted-kerberoasting + - https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-5136 + - https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/t1208-kerberoasting drilldown_searches: -- name: View the detection results for - "$ObjectDN$" - search: '%original_detection_search% | search ObjectDN = "$ObjectDN$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$ObjectDN$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$ObjectDN$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$ObjectDN$" + search: '%original_detection_search% | search ObjectDN = "$ObjectDN$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$ObjectDN$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$ObjectDN$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Servince Principal Name for $ObjectDN$ was set by $user$ - risk_objects: - - field: user - type: user - score: 30 - - field: src_user - type: user - score: 30 - threat_objects: [] + message: A Servince Principal Name for $ObjectDN$ was set by $user$ + risk_objects: + - field: user + type: user + score: 30 + - field: src_user + type: user + score: 30 + threat_objects: [] tags: - analytic_story: - - Sneaky Active Directory Persistence Tricks - - Interlock Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1098 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sneaky Active Directory Persistence Tricks + - Interlock Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1098 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/service_principal_name_added/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/service_principal_name_added/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_short_lived_domain_account_serviceprincipalname.yml b/detections/endpoint/windows_ad_short_lived_domain_account_serviceprincipalname.yml index 3895564fbd..1988ac846b 100644 --- a/detections/endpoint/windows_ad_short_lived_domain_account_serviceprincipalname.yml +++ b/detections/endpoint/windows_ad_short_lived_domain_account_serviceprincipalname.yml @@ -1,74 +1,59 @@ name: Windows AD Short Lived Domain Account ServicePrincipalName id: b681977c-d90c-4efc-81a5-c58f945fb541 -version: 8 -date: '2025-07-28' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk type: TTP status: production data_source: -- Windows Event Log Security 5136 -description: The following analytic identifies the addition and quick deletion - of a Service Principal Name (SPN) to a domain account within 5 minutes. This - detection leverages EventCode 5136 from the Windows Security Event Log, - focusing on changes to the servicePrincipalName attribute. This activity is - significant as it may indicate an attempt to perform Kerberoasting, a - technique used to crack the cleartext password of a domain account offline. If - confirmed malicious, this could allow an attacker to gain unauthorized access - to sensitive information or escalate privileges within the domain environment. -search: '`wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=servicePrincipalName - | transaction ObjectDN AttributeValue startswith=(EventCode=5136 OperationType="%%14674") - endswith=(EventCode=5136 OperationType="%%14675") | eval short_lived=case((duration<300),"TRUE") - | search short_lived = TRUE | rename ObjectDN as user | rename Computer as dest - | `windows_ad_short_lived_domain_account_serviceprincipalname_filter`' -how_to_implement: To successfully implement this search, you ned to be ingesting - eventcode `5136`. The Advanced Security Audit policy setting `Audit Directory - Services Changes` within `DS Access` needs to be enabled. Additionally, a SACL - needs to be created for AD objects in order to ingest attribute modifications. -known_false_positives: A Service Principal Name should only be added to an - account when an application requires it. Adding an SPN and quickly deleting it - is less common but may be part of legitimate action. Filter as needed. + - Windows Event Log Security 5136 +description: The following analytic identifies the addition and quick deletion of a Service Principal Name (SPN) to a domain account within 5 minutes. This detection leverages EventCode 5136 from the Windows Security Event Log, focusing on changes to the servicePrincipalName attribute. This activity is significant as it may indicate an attempt to perform Kerberoasting, a technique used to crack the cleartext password of a domain account offline. If confirmed malicious, this could allow an attacker to gain unauthorized access to sensitive information or escalate privileges within the domain environment. +search: |- + `wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=servicePrincipalName + | transaction ObjectDN AttributeValue startswith=(EventCode=5136 OperationType="%%14674") endswith=(EventCode=5136 OperationType="%%14675") + | eval short_lived=case((duration<300),"TRUE") + | search short_lived = TRUE + | rename ObjectDN as user + | rename Computer as dest + | `windows_ad_short_lived_domain_account_serviceprincipalname_filter` +how_to_implement: To successfully implement this search, you ned to be ingesting eventcode `5136`. The Advanced Security Audit policy setting `Audit Directory Services Changes` within `DS Access` needs to be enabled. Additionally, a SACL needs to be created for AD objects in order to ingest attribute modifications. +known_false_positives: A Service Principal Name should only be added to an account when an application requires it. Adding an SPN and quickly deleting it is less common but may be part of legitimate action. Filter as needed. references: -- https://adsecurity.org/?p=3466 -- https://www.thehacker.recipes/ad/movement/dacl/targeted-kerberoasting -- https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-5136 -- https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/t1208-kerberoasting + - https://adsecurity.org/?p=3466 + - https://www.thehacker.recipes/ad/movement/dacl/targeted-kerberoasting + - https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-5136 + - https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/t1208-kerberoasting drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Servince Principal Name for $user$ was set and shortly deleted - risk_objects: - - field: user - type: user - score: 40 - threat_objects: [] + message: A Servince Principal Name for $user$ was set and shortly deleted + risk_objects: + - field: user + type: user + score: 40 + threat_objects: [] tags: - analytic_story: - - Sneaky Active Directory Persistence Tricks - - Interlock Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1098 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sneaky Active Directory Persistence Tricks + - Interlock Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1098 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/short_lived_service_principal_name/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/short_lived_service_principal_name/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_short_lived_domain_controller_spn_attribute.yml b/detections/endpoint/windows_ad_short_lived_domain_controller_spn_attribute.yml index 3a99b0de5a..77022b66ae 100644 --- a/detections/endpoint/windows_ad_short_lived_domain_controller_spn_attribute.yml +++ b/detections/endpoint/windows_ad_short_lived_domain_controller_spn_attribute.yml @@ -1,80 +1,64 @@ name: Windows AD Short Lived Domain Controller SPN Attribute id: 57e27f27-369c-4df8-af08-e8c7ee8373d4 -version: 10 -date: '2026-01-14' +version: 11 +date: '2026-02-25' author: Dean Luxton type: TTP status: production data_source: -- Windows Event Log Security 5136 -- Windows Event Log Security 4624 -description: The following analytic detects the temporary addition of a global catalog - SPN or a DRS RPC SPN to an Active Directory computer object, indicative of a potential - DCShadow attack. This detection leverages EventCode 5136 from the `wineventlog_security` - data source, focusing on specific SPN attribute changes. This activity is significant - as DCShadow attacks allow attackers with privileged access to register rogue Domain - Controllers, enabling unauthorized changes to the AD infrastructure. If confirmed - malicious, this could lead to unauthorized replication of changes, including credentials - and keys, compromising the entire domain's security. -search: '`wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=servicePrincipalName - (AttributeValue="GC/*" OR AttributeValue="E3514235-4B06-11D1-AB04-00C04FC2DCD2/*") - | stats min(_time) as _time range(_time) as duration values(OperationType) as OperationType - values(user) as user values(src_ip) as src_ip values(src_nt_domain) as src_nt_domain - values(src_user) as src_user values(Computer) as dest, values(ObjectDN) as ObjectDN - values(action) as action values(app) as app values(authentication_method) as authentication_method - values(signature) as signature values(signature_id) as signature_id values(src) - as src by Logon_ID | eval short_lived=case((duration<30),"TRUE") | where short_lived="TRUE" - AND mvcount(OperationType)>1 | replace "%%14674" with "Value Added", "%%14675" with - "Value Deleted" in OperationType | rename Logon_ID as TargetLogonId | appendpipe - [| map search="search `wineventlog_security` EventCode=4624 TargetLogonId=$TargetLogonId$"] - | `windows_ad_short_lived_domain_controller_spn_attribute_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - eventcode `5136`. The Advanced Security Audit policy setting `Audit Directory Services - Changes` within `DS Access` needs to be enabled, alongside a SACL for `everybody` - to `Write All Properties` applied to the domain root and all descendant objects. + - Windows Event Log Security 5136 + - Windows Event Log Security 4624 +description: The following analytic detects the temporary addition of a global catalog SPN or a DRS RPC SPN to an Active Directory computer object, indicative of a potential DCShadow attack. This detection leverages EventCode 5136 from the `wineventlog_security` data source, focusing on specific SPN attribute changes. This activity is significant as DCShadow attacks allow attackers with privileged access to register rogue Domain Controllers, enabling unauthorized changes to the AD infrastructure. If confirmed malicious, this could lead to unauthorized replication of changes, including credentials and keys, compromising the entire domain's security. +search: |- + `wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=servicePrincipalName (AttributeValue="GC/*" OR AttributeValue="E3514235-4B06-11D1-AB04-00C04FC2DCD2/*") + | stats min(_time) as _time range(_time) as duration values(OperationType) as OperationType values(user) as user values(src_ip) as src_ip values(src_nt_domain) as src_nt_domain values(src_user) as src_user values(Computer) as dest, values(ObjectDN) as ObjectDN values(action) as action values(app) as app values(authentication_method) as authentication_method values(signature) as signature values(signature_id) as signature_id values(src) as src + BY Logon_ID + | eval short_lived=case((duration<30),"TRUE") + | where short_lived="TRUE" AND mvcount(OperationType)>1 + | replace "%%14674" with "Value Added", "%%14675" with "Value Deleted" in OperationType + | rename Logon_ID as TargetLogonId + | appendpipe [ + | map search="search `wineventlog_security` EventCode=4624 TargetLogonId=$TargetLogonId$"] + | `windows_ad_short_lived_domain_controller_spn_attribute_filter` +how_to_implement: To successfully implement this search, you need to be ingesting eventcode `5136`. The Advanced Security Audit policy setting `Audit Directory Services Changes` within `DS Access` needs to be enabled, alongside a SACL for `everybody` to `Write All Properties` applied to the domain root and all descendant objects. known_false_positives: No false positives have been identified at this time. references: -- https://www.dcshadow.com/ -- https://blog.netwrix.com/2022/09/28/dcshadow_attack/ -- https://gist.github.com/gentilkiwi/dcc132457408cf11ad2061340dcb53c2 -- https://attack.mitre.org/techniques/T1207/ -- https://blog.alsid.eu/dcshadow-explained-4510f52fc19d + - https://www.dcshadow.com/ + - https://blog.netwrix.com/2022/09/28/dcshadow_attack/ + - https://gist.github.com/gentilkiwi/dcc132457408cf11ad2061340dcb53c2 + - https://attack.mitre.org/techniques/T1207/ + - https://blog.alsid.eu/dcshadow-explained-4510f52fc19d drilldown_searches: -- name: View the detection results for - "$src_user$" - search: '%original_detection_search% | search src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_user$" + search: '%original_detection_search% | search src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Short Lived Domain Controller SPN AD Attribute Triggered by $src_user$ - risk_objects: - - field: src_user - type: user - score: 100 - threat_objects: [] + message: Short Lived Domain Controller SPN AD Attribute Triggered by $src_user$ + risk_objects: + - field: src_user + type: user + score: 100 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1207 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1207 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1207/mimikatz/windows-security-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1207/mimikatz/windows-security-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_short_lived_server_object.yml b/detections/endpoint/windows_ad_short_lived_server_object.yml index 7139b8db12..834de9c8a3 100644 --- a/detections/endpoint/windows_ad_short_lived_server_object.yml +++ b/detections/endpoint/windows_ad_short_lived_server_object.yml @@ -1,78 +1,66 @@ name: Windows AD Short Lived Server Object id: 193769d3-1e33-43a9-970e-ad4a88256cdb -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk type: TTP status: production data_source: -- Windows Event Log Security 5137 -- Windows Event Log Security 5141 -description: The following analytic identifies the creation and quick deletion of - a Domain Controller (DC) object within 30 seconds in an Active Directory environment, - indicative of a potential DCShadow attack. This detection leverages Windows Security - Event Codes 5137 and 5141, analyzing the duration between these events. This activity - is significant as DCShadow allows attackers with privileged access to register a - rogue DC, enabling unauthorized changes to AD objects, including credentials. If - confirmed malicious, this could lead to unauthorized AD modifications, compromising - the integrity and security of the entire domain. -search: '`wineventlog_security` EventCode=5137 OR EventCode=5141 ObjectDN="*CN=Servers,CN=Default-First-Site-Name,CN=Sites,CN=Configuration*" - | transaction ObjectDN startswith=(EventCode=5137) endswith=(EventCode=5141) | eval - short_lived=case((duration<30),"TRUE") | search short_lived = TRUE | stats values(ObjectDN) - values(signature) values(EventCode) by _time, Computer, SubjectUserName, dest | - `windows_ad_short_lived_server_object_filter`' -how_to_implement: To successfully implement this search, you ned to be ingesting Event - codes `5137` and `5141`. The Advanced Security Audit policy setting `Audit Directory - Services Changes` within `DS Access` needs to be enabled. For these event codes - to be generated, specific SACLs are required. -known_false_positives: Creating and deleting a server object within 30 seconds or - less is unusual but not impossible in a production environment. Filter as needed. + - Windows Event Log Security 5137 + - Windows Event Log Security 5141 +description: The following analytic identifies the creation and quick deletion of a Domain Controller (DC) object within 30 seconds in an Active Directory environment, indicative of a potential DCShadow attack. This detection leverages Windows Security Event Codes 5137 and 5141, analyzing the duration between these events. This activity is significant as DCShadow allows attackers with privileged access to register a rogue DC, enabling unauthorized changes to AD objects, including credentials. If confirmed malicious, this could lead to unauthorized AD modifications, compromising the integrity and security of the entire domain. +search: |- + `wineventlog_security` EventCode=5137 OR EventCode=5141 ObjectDN="*CN=Servers,CN=Default-First-Site-Name,CN=Sites,CN=Configuration*" + | transaction ObjectDN startswith=(EventCode=5137) endswith=(EventCode=5141) + | eval short_lived=case((duration<30),"TRUE") + | search short_lived = TRUE + | stats values(ObjectDN) values(signature) values(EventCode) + BY _time, Computer, SubjectUserName, + dest + | `windows_ad_short_lived_server_object_filter` +how_to_implement: To successfully implement this search, you ned to be ingesting Event codes `5137` and `5141`. The Advanced Security Audit policy setting `Audit Directory Services Changes` within `DS Access` needs to be enabled. For these event codes to be generated, specific SACLs are required. +known_false_positives: Creating and deleting a server object within 30 seconds or less is unusual but not impossible in a production environment. Filter as needed. references: -- https://www.dcshadow.com/ -- https://attack.mitre.org/techniques/T1207/ -- https://stealthbits.com/blog/detecting-dcshadow-with-event-logs/ -- https://pentestlab.blog/2018/04/16/dcshadow/ -- https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-5137 -- https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-5141 + - https://www.dcshadow.com/ + - https://attack.mitre.org/techniques/T1207/ + - https://stealthbits.com/blog/detecting-dcshadow-with-event-logs/ + - https://pentestlab.blog/2018/04/16/dcshadow/ + - https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-5137 + - https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-5141 drilldown_searches: -- name: View the detection results for - "$Computer$" - search: '%original_detection_search% | search Computer = "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$Computer$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" + search: '%original_detection_search% | search Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A short-lived server object was created and deleted on $Computer$ - risk_objects: - - field: Computer - type: system - score: 64 - - field: SubjectUserName - type: user - score: 64 - threat_objects: [] + message: A short-lived server object was created and deleted on $Computer$ + risk_objects: + - field: Computer + type: system + score: 64 + - field: SubjectUserName + type: user + score: 64 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1207 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1207 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1207/short_lived_server_object/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1207/short_lived_server_object/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_sid_history_attribute_modified.yml b/detections/endpoint/windows_ad_sid_history_attribute_modified.yml index 4fc35801b0..ce5c864be1 100644 --- a/detections/endpoint/windows_ad_sid_history_attribute_modified.yml +++ b/detections/endpoint/windows_ad_sid_history_attribute_modified.yml @@ -1,71 +1,57 @@ name: Windows AD SID History Attribute Modified id: 1155e47d-307f-4247-beab-71071e3a458c -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Mauricio Velazco, Splunk type: TTP status: production data_source: -- Windows Event Log Security 5136 -description: The following analytic detects modifications to the SID History attribute - in Active Directory by leveraging event code 5136. This detection uses logs from - the `wineventlog_security` data source to identify changes to the sIDHistory attribute. - Monitoring this activity is crucial as the SID History attribute can be exploited - by adversaries to inherit permissions from other accounts, potentially granting - unauthorized access. If confirmed malicious, this activity could allow attackers - to maintain persistent access and escalate privileges within the domain, posing - a significant security risk. -search: '`wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=sIDHistory - OperationType="%%14674" | stats values(ObjectDN) as ObjectDN by _time, Computer, - SubjectUserName, AttributeValue | rename Computer as dest | `windows_ad_sid_history_attribute_modified_filter`' -how_to_implement: To successfully implement this search, you ned to be ingesting eventcode - `5136`. The Advanced Security Audit policy setting `Audit Directory Services Changes` - within `DS Access` needs to be enabled. Additionally, a SACL needs to be created - for AD objects in order to ingest attribute modifications. -known_false_positives: Domain mergers and migrations may generate large volumes of - false positives for this analytic. + - Windows Event Log Security 5136 +description: The following analytic detects modifications to the SID History attribute in Active Directory by leveraging event code 5136. This detection uses logs from the `wineventlog_security` data source to identify changes to the sIDHistory attribute. Monitoring this activity is crucial as the SID History attribute can be exploited by adversaries to inherit permissions from other accounts, potentially granting unauthorized access. If confirmed malicious, this activity could allow attackers to maintain persistent access and escalate privileges within the domain, posing a significant security risk. +search: |- + `wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=sIDHistory OperationType="%%14674" + | stats values(ObjectDN) as ObjectDN + BY _time, Computer, SubjectUserName, + AttributeValue + | rename Computer as dest + | `windows_ad_sid_history_attribute_modified_filter` +how_to_implement: To successfully implement this search, you ned to be ingesting eventcode `5136`. The Advanced Security Audit policy setting `Audit Directory Services Changes` within `DS Access` needs to be enabled. Additionally, a SACL needs to be created for AD objects in order to ingest attribute modifications. +known_false_positives: Domain mergers and migrations may generate large volumes of false positives for this analytic. references: -- https://adsecurity.org/?p=1772 -- https://learn.microsoft.com/en-us/windows/win32/adschema/a-sidhistory?redirectedfrom=MSDN -- https://learn.microsoft.com/en-us/defender-for-identity/security-assessment-unsecure-sid-history-attribute -- https://book.hacktricks.xyz/windows-hardening/active-directory-methodology/sid-history-injection + - https://adsecurity.org/?p=1772 + - https://learn.microsoft.com/en-us/windows/win32/adschema/a-sidhistory?redirectedfrom=MSDN + - https://learn.microsoft.com/en-us/defender-for-identity/security-assessment-unsecure-sid-history-attribute + - https://book.hacktricks.xyz/windows-hardening/active-directory-methodology/sid-history-injection drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: SID History AD attribute modified by $SubjectUserName$ for $ObjectDN$ on - $dest$ - risk_objects: - - field: dest - type: system - score: 56 - threat_objects: [] + message: SID History AD attribute modified by $SubjectUserName$ for $ObjectDN$ on $dest$ + risk_objects: + - field: dest + type: system + score: 56 + threat_objects: [] tags: - analytic_story: - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1134.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1134.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1134.005/sid_history2/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1134.005/sid_history2/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ad_suspicious_attribute_modification.yml b/detections/endpoint/windows_ad_suspicious_attribute_modification.yml index be0ad41b19..dbb5f2b626 100644 --- a/detections/endpoint/windows_ad_suspicious_attribute_modification.yml +++ b/detections/endpoint/windows_ad_suspicious_attribute_modification.yml @@ -1,78 +1,64 @@ name: Windows AD Suspicious Attribute Modification id: 5682052e-ce55-4f9f-8d28-59191420b7e0 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Dean Luxton status: production type: TTP data_source: -- Windows Event Log Security 5136 -description: 'This detection monitors changes to the following Active Directory attributes: - "msDS-AllowedToDelegateTo", "msDS-AllowedToActOnBehalfOfOtherIdentity", "msDS-KeyCredentialLink", - "scriptPath", and "msTSInitialProgram". Modifications to these attributes can indicate - potential malicious activity or privilege escalation attempts. Immediate investigation - is recommended upon alert.' -search: '`wineventlog_security` EventCode=5136 AttributeLDAPDisplayName IN ("msDS-AllowedToDelegateTo","msDS-AllowedToActOnBehalfOfOtherIdentity","scriptPath","msTSInitialProgram") - OperationType=%%14674 ```Changes to the attribute "msDS-KeyCredentialLink" are - also worth moniroting, however tuning will need to be applied``` | table _time ObjectClass - ObjectDN OpCorrelationID src_user SubjectLogonId DSName AttributeValue AttributeLDAPDisplayName | - rename SubjectLogonId as TargetLogonId, src_user as initiator, _time as eventTime | - appendpipe [| map search="search `wineventlog_security` EventCode=4624 TargetLogonId=$TargetLogonId$"] | - stats min(eventTime) as _time values(initiator) as src_user, values(DSName) as targetDomain, - values(ObjectDN) as ObjectDN, values(ObjectClass) as ObjectClass, values(src_category) - as src_category, values(src_ip) as src_ip values(LogonType) as LogonType values(AttributeValue) - as AttributeValue values(AttributeLDAPDisplayName) as AttributeLDAPDisplayName by - TargetLogonId | rex field=ObjectDN "^CN=(?P.*?),[A-Z]{2}\=" | eval dest=if(ObjectClass="computer",cn,null), - user=if(ObjectClass="user",cn,null) | fields - cn | `windows_ad_suspicious_attribute_modification_filter`' -how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically - event 5136. See lantern article in references for further on how to onboard AD audit - data. Ensure the wineventlog_security macro is configured with the correct indexes. -known_false_positives: If key credentials are regularly assigned to users, these events - will need to be tuned out. + - Windows Event Log Security 5136 +description: 'This detection monitors changes to the following Active Directory attributes: "msDS-AllowedToDelegateTo", "msDS-AllowedToActOnBehalfOfOtherIdentity", "msDS-KeyCredentialLink", "scriptPath", and "msTSInitialProgram". Modifications to these attributes can indicate potential malicious activity or privilege escalation attempts. Immediate investigation is recommended upon alert.' +search: |- + `wineventlog_security` EventCode=5136 AttributeLDAPDisplayName IN ("msDS-AllowedToDelegateTo","msDS-AllowedToActOnBehalfOfOtherIdentity","scriptPath","msTSInitialProgram") OperationType=%%14674 ```Changes to the attribute "msDS-KeyCredentialLink" are also worth moniroting, however tuning will need to be applied``` + | table _time ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId DSName AttributeValue AttributeLDAPDisplayName + | rename SubjectLogonId as TargetLogonId, src_user as initiator, _time as eventTime + | appendpipe [ + | map search="search `wineventlog_security` EventCode=4624 TargetLogonId=$TargetLogonId$"] + | stats min(eventTime) as _time values(initiator) as src_user, values(DSName) as targetDomain, values(ObjectDN) as ObjectDN, values(ObjectClass) as ObjectClass, values(src_category) as src_category, values(src_ip) as src_ip values(LogonType) as LogonType values(AttributeValue) as AttributeValue values(AttributeLDAPDisplayName) as AttributeLDAPDisplayName + BY TargetLogonId + | rex field=ObjectDN "^CN=(?P.*?),[A-Z]{2}\=" + | eval dest=if(ObjectClass="computer",cn,null), user=if(ObjectClass="user",cn,null) + | fields - cn + | `windows_ad_suspicious_attribute_modification_filter` +how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security macro is configured with the correct indexes. +known_false_positives: If key credentials are regularly assigned to users, these events will need to be tuned out. references: -- https://trustedsec.com/blog/a-hitchhackers-guide-to-dacl-based-detections-part-1-a -- https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory + - https://trustedsec.com/blog/a-hitchhackers-guide-to-dacl-based-detections-part-1-a + - https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory drilldown_searches: -- name: View the detection results for - "$src_user$" and "$dest$" - search: '%original_detection_search% | search src_user = "$src_user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_user$" and "$dest$" + search: '%original_detection_search% | search src_user = "$src_user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $src_user$ has added $AttributeLDAPDisplayName$ ACL rights to $ObjectClass$ - $ObjectDN$ - risk_objects: - - field: src_user - type: user - score: 100 - - field: dest - type: system - score: 100 - threat_objects: [] + message: $src_user$ has added $AttributeLDAPDisplayName$ ACL rights to $ObjectClass$ $ObjectDN$ + risk_objects: + - field: src_user + type: user + score: 100 + - field: dest + type: system + score: 100 + threat_objects: [] tags: - analytic_story: - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1222.001 - - T1550 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1222.001 + - T1550 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/suspicious_acl_modification-windows-security-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/suspicious_acl_modification-windows-security-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_adfind_exe.yml b/detections/endpoint/windows_adfind_exe.yml index a2171f77a1..18c258bed3 100644 --- a/detections/endpoint/windows_adfind_exe.yml +++ b/detections/endpoint/windows_adfind_exe.yml @@ -1,135 +1,111 @@ name: Windows AdFind Exe id: bd3b0187-189b-46c0-be45-f52da2bae67f -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Jose Hernandez, Bhavin Patel, Nasreddine Bencherchali, Splunk status: production type: TTP -description: - The following analytic identifies the execution of `adfind.exe` standalone or with specific command-line arguments related to Active Directory queries. - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, command-line arguments, and parent Processes. - This activity is significant because `adfind.exe` is a powerful tool often used by threat actors like Wizard Spider and FIN6 to gather sensitive AD information. - If confirmed malicious, this activity could allow attackers to map the AD environment, facilitating further attacks such as privilege escalation or lateral movement. +description: The following analytic identifies the execution of `adfind.exe` standalone or with specific command-line arguments related to Active Directory queries. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, command-line arguments, and parent Processes. This activity is significant because `adfind.exe` is a powerful tool often used by threat actors like Wizard Spider and FIN6 to gather sensitive AD information. If confirmed malicious, this activity could allow attackers to map the AD environment, facilitating further attacks such as privilege escalation or lateral movement. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime - from datamodel=Endpoint.Processes where + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + from datamodel=Endpoint.Processes where - ( - Processes.process_name = "AdFind.exe" - OR - Processes.original_file_name = "AdFind.exe" - ) - OR - ( - Processes.process IN ("* -f *", "* /f*") - Processes.process = "*objectcategory=*" - ) - OR - ( - Processes.process IN ("* -sc *", "* /sc *") - Processes.process IN ("* -gcb *", "* /gcb *") - ) - OR - ( - Processes.process IN ("* -sc *", "* /sc *") - Processes.process IN ( - "* trustdmp*", - "* dclist*", - "* dcdmp*", - "* adobjcnt*", - "* adamobjcnt*", - "* sdump*", - "* exchaddresses*", - "* getacl*", - "* domainlist*", - "* export_user*", - "* export_group*", - "* admincountdmp*" + ( + Processes.process_name = "AdFind.exe" + OR + Processes.original_file_name = "AdFind.exe" ) - ) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_adfind_exe_filter` -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: - ADfind is a command-line tool for AD administration and management - that is seen to be leveraged by various adversaries. Filter out legitimate administrator - usage using the filter macro. + OR + ( + Processes.process IN ("* -f *", "* /f*") + Processes.process = "*objectcategory=*" + ) + OR + ( + Processes.process IN ("* -sc *", "* /sc *") + Processes.process IN ("* -gcb *", "* /gcb *") + ) + OR + ( + Processes.process IN ("* -sc *", "* /sc *") + Processes.process IN ( + "* trustdmp*", + "* dclist*", + "* dcdmp*", + "* adobjcnt*", + "* adamobjcnt*", + "* sdump*", + "* exchaddresses*", + "* getacl*", + "* domainlist*", + "* export_user*", + "* export_group*", + "* admincountdmp*" + ) + ) + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_adfind_exe_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: ADfind is a command-line tool for AD administration and management that is seen to be leveraged by various adversaries. Filter out legitimate administrator usage using the filter macro. references: - - https://www.volexity.com/blog/2020/12/14/dark-halo-leverages-solarwinds-compromise-to-breach-organizations/ - - https://www.mandiant.com/resources/a-nasty-trick-from-credential-theft-malware-to-business-disruption - - https://www.joeware.net/freetools/tools/adfind/index.htm - - https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/ + - https://www.volexity.com/blog/2020/12/14/dark-halo-leverages-solarwinds-compromise-to-breach-organizations/ + - https://www.mandiant.com/resources/a-nasty-trick-from-credential-theft-malware-to-business-disruption + - https://www.joeware.net/freetools/tools/adfind/index.htm + - https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/ drilldown_searches: - - name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - Windows AdFind Exe detected with command-line arguments associated with - Active Directory queries on machine - [dest] - risk_objects: - - field: user - type: user - score: 25 - threat_objects: [] + message: Windows AdFind Exe detected with command-line arguments associated with Active Directory queries on machine - [dest] + risk_objects: + - field: user + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Domain Trust Discovery - - IcedID - - NOBELIUM Group - - Graceful Wipe Out Attack - - BlackSuit Ransomware - asset_type: Endpoint - atomic_guid: - - 736b4f53-f400-4c22-855d-1a6b5a551600 - - b95fd967-4e62-4109-b48d-265edfd28c3a - - e1ec8d20-509a-4b9a-b820-06c9b2da8eb7 - - 5e2938fb-f919-47b6-8b29-2f6a1f718e99 - - abf00f6c-9983-4d9a-afbc-6b1c6c6448e1 - - 51a98f96-0269-4e09-a10f-e307779a8b05 - mitre_attack_id: - - T1018 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Domain Trust Discovery + - IcedID + - NOBELIUM Group + - Graceful Wipe Out Attack + - BlackSuit Ransomware + asset_type: Endpoint + atomic_guid: + - 736b4f53-f400-4c22-855d-1a6b5a551600 + - b95fd967-4e62-4109-b48d-265edfd28c3a + - e1ec8d20-509a-4b9a-b820-06c9b2da8eb7 + - 5e2938fb-f919-47b6-8b29-2f6a1f718e99 + - abf00f6c-9983-4d9a-afbc-6b1c6c6448e1 + - 51a98f96-0269-4e09-a10f-e307779a8b05 + mitre_attack_id: + - T1018 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_admin_permission_discovery.yml b/detections/endpoint/windows_admin_permission_discovery.yml index 5c4e3a6ba7..a9822ce0f1 100644 --- a/detections/endpoint/windows_admin_permission_discovery.yml +++ b/detections/endpoint/windows_admin_permission_discovery.yml @@ -6,70 +6,45 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: -- Sysmon EventID 11 -description: The following analytic identifies the creation of a suspicious file named - 'win.dat' in the root directory (C:\). It leverages data from the Endpoint.Filesystem - datamodel to detect this activity. This behavior is significant as it is commonly - used by malware like NjRAT to check for administrative privileges on a compromised - host. If confirmed malicious, this activity could indicate that the malware has - administrative access, allowing it to perform high-privilege actions, potentially - leading to further system compromise and persistence. -search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.exe", - "*.dll", "*.sys", "*.com", "*.vbs", "*.vbe", "*.js", "*.bat", "*.cmd", "*.pif", - "*.lnk", "*.dat") by Filesystem.action Filesystem.dest Filesystem.file_access_time - Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name - Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid - Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` - | eval dropped_file_path = split(file_path, "\\") | eval dropped_file_path_split_count - = mvcount(dropped_file_path) | eval root_drive = mvindex(dropped_file_path,0) | - where LIKE(root_drive, "C:") AND dropped_file_path_split_count = 2 | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_admin_permission_discovery_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the Filesystem responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Filesystem` node. -known_false_positives: False positives may occur if there are legitimate accounts - with the privilege to drop files in the root of the C drive. It's recommended to - verify the legitimacy of such actions and the accounts involved. + - Sysmon EventID 11 +description: The following analytic identifies the creation of a suspicious file named 'win.dat' in the root directory (C:\). It leverages data from the Endpoint.Filesystem datamodel to detect this activity. This behavior is significant as it is commonly used by malware like NjRAT to check for administrative privileges on a compromised host. If confirmed malicious, this activity could indicate that the malware has administrative access, allowing it to perform high-privilege actions, potentially leading to further system compromise and persistence. +search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.exe", "*.dll", "*.sys", "*.com", "*.vbs", "*.vbe", "*.js", "*.bat", "*.cmd", "*.pif", "*.lnk", "*.dat") by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | eval dropped_file_path = split(file_path, "\\") | eval dropped_file_path_split_count = mvcount(dropped_file_path) | eval root_drive = mvindex(dropped_file_path,0) | where LIKE(root_drive, "C:") AND dropped_file_path_split_count = 2 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_admin_permission_discovery_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. +known_false_positives: False positives may occur if there are legitimate accounts with the privilege to drop files in the root of the C drive. It's recommended to verify the legitimacy of such actions and the accounts involved. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.njrat + - https://malpedia.caad.fkie.fraunhofer.de/details/win.njrat drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A file was created in root drive C:/ on host - $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: - - field: file_name - type: file_name + message: A file was created in root drive C:/ on host - $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - NjRAT - asset_type: Endpoint - mitre_attack_id: - - T1069.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - NjRAT + asset_type: Endpoint + mitre_attack_id: + - T1069.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.001/njrat_admin_check/win_dat.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.001/njrat_admin_check/win_dat.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_administrative_shares_accessed_on_multiple_hosts.yml b/detections/endpoint/windows_administrative_shares_accessed_on_multiple_hosts.yml index af6b4720b9..5ca3445893 100644 --- a/detections/endpoint/windows_administrative_shares_accessed_on_multiple_hosts.yml +++ b/detections/endpoint/windows_administrative_shares_accessed_on_multiple_hosts.yml @@ -6,72 +6,51 @@ author: Mauricio Velazco, Splunk type: TTP status: production data_source: -- Windows Event Log Security 5140 -- Windows Event Log Security 5145 -description: The following analytic detects a source computer accessing Windows administrative - shares (C$, Admin$, IPC$) on 30 or more remote endpoints within a 5-minute window. - It leverages Event IDs 5140 and 5145 from file share events. This behavior is significant - as it may indicate an adversary enumerating network shares to locate sensitive files, - a common tactic used by threat actors. If confirmed malicious, this activity could - lead to unauthorized access to critical data, lateral movement, and potential compromise - of multiple systems within the network. -search: '`wineventlog_security` EventCode=5140 OR EventCode=5145 (ShareName="\\\\*\\ADMIN$" - OR ShareName="\\\\*\\IPC$" OR ShareName="\\\\*\\C$") | bucket span=5m _time | stats - dc(Computer) AS unique_targets values(Computer) as host_targets values(ShareName) - as shares values(dest) as dest by _time, IpAddress, SubjectUserName, EventCode | - where unique_targets > 30 | `windows_administrative_shares_accessed_on_multiple_hosts_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - file share events. The Advanced Security Audit policy setting `Audit Detailed File - Share` or `Audit File Share` within `Object Access` need to be enabled. -known_false_positives: An single endpoint accessing windows administrative shares - across a large number of endpoints is not common behavior. Possible false positive - scenarios include but are not limited to vulnerability scanners, administration - systems and missconfigured systems. + - Windows Event Log Security 5140 + - Windows Event Log Security 5145 +description: The following analytic detects a source computer accessing Windows administrative shares (C$, Admin$, IPC$) on 30 or more remote endpoints within a 5-minute window. It leverages Event IDs 5140 and 5145 from file share events. This behavior is significant as it may indicate an adversary enumerating network shares to locate sensitive files, a common tactic used by threat actors. If confirmed malicious, this activity could lead to unauthorized access to critical data, lateral movement, and potential compromise of multiple systems within the network. +search: '`wineventlog_security` EventCode=5140 OR EventCode=5145 (ShareName="\\\\*\\ADMIN$" OR ShareName="\\\\*\\IPC$" OR ShareName="\\\\*\\C$") | bucket span=5m _time | stats dc(Computer) AS unique_targets values(Computer) as host_targets values(ShareName) as shares values(dest) as dest by _time, IpAddress, SubjectUserName, EventCode | where unique_targets > 30 | `windows_administrative_shares_accessed_on_multiple_hosts_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting file share events. The Advanced Security Audit policy setting `Audit Detailed File Share` or `Audit File Share` within `Object Access` need to be enabled. +known_false_positives: An single endpoint accessing windows administrative shares across a large number of endpoints is not common behavior. Possible false positive scenarios include but are not limited to vulnerability scanners, administration systems and missconfigured systems. references: -- https://attack.mitre.org/techniques/T1135/ -- https://en.wikipedia.org/wiki/Administrative_share -- https://thedfirreport.com/2023/01/23/sharefinder-how-threat-actors-discover-file-shares/ -- https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-5140 -- https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-5145 + - https://attack.mitre.org/techniques/T1135/ + - https://en.wikipedia.org/wiki/Administrative_share + - https://thedfirreport.com/2023/01/23/sharefinder-how-threat-actors-discover-file-shares/ + - https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-5140 + - https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-5145 drilldown_searches: -- name: View the detection results for - "$host_targets$" - search: '%original_detection_search% | search host_targets = "$host_targets$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$host_targets$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host_targets$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$host_targets$" + search: '%original_detection_search% | search host_targets = "$host_targets$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$host_targets$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host_targets$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $IpAddress$ accessed the IPC share on more than 30 endpoints in a timespan - of 5 minutes. - risk_objects: - - field: host_targets - type: system - score: 56 - threat_objects: - - field: IpAddress - type: ip_address + message: $IpAddress$ accessed the IPC share on more than 30 endpoints in a timespan of 5 minutes. + risk_objects: + - field: host_targets + type: system + score: 56 + threat_objects: + - field: IpAddress + type: ip_address tags: - analytic_story: - - Active Directory Privilege Escalation - - Active Directory Lateral Movement - asset_type: Endpoint - mitre_attack_id: - - T1135 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Privilege Escalation + - Active Directory Lateral Movement + asset_type: Endpoint + mitre_attack_id: + - T1135 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1135/ipc_share_accessed/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1135/ipc_share_accessed/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_admon_default_group_policy_object_modified.yml b/detections/endpoint/windows_admon_default_group_policy_object_modified.yml index fed0080731..0022df4994 100644 --- a/detections/endpoint/windows_admon_default_group_policy_object_modified.yml +++ b/detections/endpoint/windows_admon_default_group_policy_object_modified.yml @@ -1,77 +1,59 @@ name: Windows Admon Default Group Policy Object Modified id: 83458004-db60-4170-857d-8572f16f070b -version: 8 -date: '2025-06-24' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP data_source: - - Windows Active Directory Admon -description: - The following analytic detects modifications to the default Group Policy - Objects (GPOs) in an Active Directory environment. It leverages Splunk's Admon to - monitor updates to the "Default Domain Policy" and "Default Domain Controllers Policy." - This activity is significant because changes to these default GPOs can indicate - an adversary with privileged access attempting to gain further control, establish - persistence, or deploy malware across multiple hosts. If confirmed malicious, such - modifications could lead to widespread policy enforcement changes, unauthorized - access, and potential compromise of the entire domain environment. -search: - '`admon` admonEventType=Update objectCategory="CN=Group-Policy-Container,CN=Schema,CN=Configuration,DC=*" - (displayName="Default Domain Policy" OR displayName="Default Domain Controllers - Policy") | stats min(_time) as firstTime max(_time) as lastTime values(gPCFileSysPath) - by dcName, displayName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_admon_default_group_policy_object_modified_filter`' -how_to_implement: - To successfully implement this search, you need to be monitoring - Active Directory logs using Admon. Details can be found here - https://help.splunk.com/en/splunk-cloud-platform/get-started/get-data-in/9.3.2411/get-windows-data/monitor-active-directory -known_false_positives: - The default Group Policy Objects within an AD network may be - legitimately updated for administrative operations, filter as needed. + - Windows Active Directory Admon +description: The following analytic detects modifications to the default Group Policy Objects (GPOs) in an Active Directory environment. It leverages Splunk's Admon to monitor updates to the "Default Domain Policy" and "Default Domain Controllers Policy." This activity is significant because changes to these default GPOs can indicate an adversary with privileged access attempting to gain further control, establish persistence, or deploy malware across multiple hosts. If confirmed malicious, such modifications could lead to widespread policy enforcement changes, unauthorized access, and potential compromise of the entire domain environment. +search: |- + `admon` admonEventType=Update objectCategory="CN=Group-Policy-Container,CN=Schema,CN=Configuration,DC=*" (displayName="Default Domain Policy" OR displayName="Default Domain Controllers Policy") + | stats min(_time) as firstTime max(_time) as lastTime values(gPCFileSysPath) + BY dcName, displayName + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_admon_default_group_policy_object_modified_filter` +how_to_implement: To successfully implement this search, you need to be monitoring Active Directory logs using Admon. Details can be found here https://help.splunk.com/en/splunk-cloud-platform/get-started/get-data-in/9.3.2411/get-windows-data/monitor-active-directory +known_false_positives: The default Group Policy Objects within an AD network may be legitimately updated for administrative operations, filter as needed. references: - - https://attack.mitre.org/techniques/T1484/ - - https://attack.mitre.org/techniques/T1484/001 - - https://www.trustedsec.com/blog/weaponizing-group-policy-objects-access/ - - https://adsecurity.org/?p=2716 - - https://help.splunk.com/en/splunk-cloud-platform/get-started/get-data-in/9.3.2411/get-windows-data/monitor-active-directory + - https://attack.mitre.org/techniques/T1484/ + - https://attack.mitre.org/techniques/T1484/001 + - https://www.trustedsec.com/blog/weaponizing-group-policy-objects-access/ + - https://adsecurity.org/?p=2716 + - https://help.splunk.com/en/splunk-cloud-platform/get-started/get-data-in/9.3.2411/get-windows-data/monitor-active-directory drilldown_searches: - - name: View the detection results for - "$dcName$" - search: '%original_detection_search% | search dcName = "$dcName$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dcName$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dcName$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dcName$" + search: '%original_detection_search% | search dcName = "$dcName$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dcName$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dcName$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A default domain group policy was updated on $dcName$ - risk_objects: - - field: dcName - type: system - score: 50 - threat_objects: [] + message: A default domain group policy was updated on $dcName$ + risk_objects: + - field: dcName + type: system + score: 50 + threat_objects: [] tags: - analytic_story: - - Active Directory Privilege Escalation - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1484.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Privilege Escalation + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1484.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/default_domain_policy_modified/windows-security.log - source: ActiveDirectory - sourcetype: ActiveDirectory + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/default_domain_policy_modified/windows-security.log + source: ActiveDirectory + sourcetype: ActiveDirectory diff --git a/detections/endpoint/windows_admon_group_policy_object_created.yml b/detections/endpoint/windows_admon_group_policy_object_created.yml index 83a92a8aa9..f0cddcdec6 100644 --- a/detections/endpoint/windows_admon_group_policy_object_created.yml +++ b/detections/endpoint/windows_admon_group_policy_object_created.yml @@ -1,75 +1,59 @@ name: Windows Admon Group Policy Object Created id: 69201633-30d9-48ef-b1b6-e680805f0582 -version: 8 -date: '2025-06-24' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP data_source: - - Windows Active Directory Admon -description: - The following analytic detects the creation of a new Group Policy Object - (GPO) using Splunk's Admon data. It identifies events where a new GPO is created, - excluding default "New Group Policy Object" entries. Monitoring GPO creation is - crucial as adversaries can exploit GPOs to escalate privileges or deploy malware - across an Active Directory network. If confirmed malicious, this activity could - allow attackers to control system configurations, deploy ransomware, or propagate - malware, significantly compromising the network's security. -search: - '`admon` admonEventType=Update objectCategory="CN=Group-Policy-Container,CN=Schema,CN=Configuration,DC=*" - versionNumber=0 displayName!="New Group Policy Object" | stats min(_time) as firstTime - max(_time) as lastTime values(gPCFileSysPath) by dcName, displayName | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_admon_group_policy_object_created_filter`' -how_to_implement: - To successfully implement this search, you need to be monitoring - Active Directory logs using Admon. Details can be found here - https://help.splunk.com/en/splunk-cloud-platform/get-started/get-data-in/9.3.2411/get-windows-data/monitor-active-directory -known_false_positives: - Group Policy Objects are created as part of regular administrative - operations, filter as needed. + - Windows Active Directory Admon +description: The following analytic detects the creation of a new Group Policy Object (GPO) using Splunk's Admon data. It identifies events where a new GPO is created, excluding default "New Group Policy Object" entries. Monitoring GPO creation is crucial as adversaries can exploit GPOs to escalate privileges or deploy malware across an Active Directory network. If confirmed malicious, this activity could allow attackers to control system configurations, deploy ransomware, or propagate malware, significantly compromising the network's security. +search: |- + `admon` admonEventType=Update objectCategory="CN=Group-Policy-Container,CN=Schema,CN=Configuration,DC=*" versionNumber=0 displayName!="New Group Policy Object" + | stats min(_time) as firstTime max(_time) as lastTime values(gPCFileSysPath) + BY dcName, displayName + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_admon_group_policy_object_created_filter` +how_to_implement: To successfully implement this search, you need to be monitoring Active Directory logs using Admon. Details can be found here https://help.splunk.com/en/splunk-cloud-platform/get-started/get-data-in/9.3.2411/get-windows-data/monitor-active-directory +known_false_positives: Group Policy Objects are created as part of regular administrative operations, filter as needed. references: - - https://attack.mitre.org/techniques/T1484/ - - https://attack.mitre.org/techniques/T1484/001 - - https://www.trustedsec.com/blog/weaponizing-group-policy-objects-access/ - - https://adsecurity.org/?p=2716 - - https://help.splunk.com/en/splunk-cloud-platform/get-started/get-data-in/9.3.2411/get-windows-data/monitor-active-directory + - https://attack.mitre.org/techniques/T1484/ + - https://attack.mitre.org/techniques/T1484/001 + - https://www.trustedsec.com/blog/weaponizing-group-policy-objects-access/ + - https://adsecurity.org/?p=2716 + - https://help.splunk.com/en/splunk-cloud-platform/get-started/get-data-in/9.3.2411/get-windows-data/monitor-active-directory drilldown_searches: - - name: View the detection results for - "$dcName$" - search: '%original_detection_search% | search dcName = "$dcName$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dcName$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dcName$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dcName$" + search: '%original_detection_search% | search dcName = "$dcName$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dcName$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dcName$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A new group policy objected was created on $dcName$ - risk_objects: - - field: dcName - type: system - score: 50 - threat_objects: [] + message: A new group policy objected was created on $dcName$ + risk_objects: + - field: dcName + type: system + score: 50 + threat_objects: [] tags: - analytic_story: - - Active Directory Privilege Escalation - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1484.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Privilege Escalation + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1484.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_created/windows-admon.log - source: ActiveDirectory - sourcetype: ActiveDirectory + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_created/windows-admon.log + source: ActiveDirectory + sourcetype: ActiveDirectory diff --git a/detections/endpoint/windows_advanced_installer_msix_with_ai_stubs_execution.yml b/detections/endpoint/windows_advanced_installer_msix_with_ai_stubs_execution.yml index 6d45ba01be..03eded7ca6 100644 --- a/detections/endpoint/windows_advanced_installer_msix_with_ai_stubs_execution.yml +++ b/detections/endpoint/windows_advanced_installer_msix_with_ai_stubs_execution.yml @@ -7,72 +7,53 @@ status: production type: TTP description: The following analytic identifies the execution of Advanced Installer MSIX Package Support Framework (PSF) components, specifically the AI_STUBS executables with the original filename 'popupwrapper.exe'. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process paths and original filenames. This activity is significant as adversaries have been observed packaging malicious content within MSIX files built with Advanced Installer to bypass security controls. These AI_STUBS executables (with original filename 'popupwrapper.exe') are hallmark artifacts of potentially malicious MSIX packages. If confirmed malicious, this could allow attackers to execute arbitrary code, establish persistence, or deliver malware while evading traditional detection mechanisms. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Processes where Processes.process_path IN ("*\\AI_STUBS\\AiStubX64Elevated.exe", - "*\\AI_STUBS\\AiStubX86Elevated.exe", "*\\AI_STUBS\\AiStubX64.exe", "*\\AI_STUBS\\AiStubX86.exe") AND - Processes.original_file_name="popupwrapper.exe" by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name("Processes")` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_advanced_installer_msix_with_ai_stubs_execution_filter`' + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_path IN ("*\\AI_STUBS\\AiStubX64Elevated.exe", "*\\AI_STUBS\\AiStubX86Elevated.exe", "*\\AI_STUBS\\AiStubX64.exe", "*\\AI_STUBS\\AiStubX86.exe") AND Processes.original_file_name="popupwrapper.exe" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_advanced_installer_msix_with_ai_stubs_execution_filter`' how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain process execution information, including process paths. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Legitimate applications packaged with Advanced Installer using the Package Support Framework may trigger this detection. Verify if the MSIX package is from a trusted source and signed by a trusted publisher before taking action. Organizations that use Advanced Installer for legitimate software packaging may see false positives. references: - - https://redcanary.com/blog/threat-intelligence/msix-installers/ - - https://redcanary.com/threat-detection-report/techniques/installer-packages/ - - https://learn.microsoft.com/en-us/windows/msix/package/package-support-framework - - https://learn.microsoft.com/en-us/windows/msix/desktop/desktop-to-uwp-behind-the-scenes - - https://attack.mitre.org/techniques/T1218/ + - https://redcanary.com/blog/threat-intelligence/msix-installers/ + - https://redcanary.com/threat-detection-report/techniques/installer-packages/ + - https://learn.microsoft.com/en-us/windows/msix/package/package-support-framework + - https://learn.microsoft.com/en-us/windows/msix/desktop/desktop-to-uwp-behind-the-scenes + - https://attack.mitre.org/techniques/T1218/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Advanced Installer MSIX package with AI_STUBS execution detected on $dest$ by user $user$ - risk_objects: - - field: dest - type: system - score: 60 - threat_objects: - - field: process_path - type: file_path + message: Advanced Installer MSIX package with AI_STUBS execution detected on $dest$ by user $user$ + risk_objects: + - field: dest + type: system + score: 60 + threat_objects: + - field: process_path + type: file_path tags: - analytic_story: - - MSIX Package Abuse - asset_type: Endpoint - mitre_attack_id: - - T1218 - - T1553.005 - - T1204.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - MSIX Package Abuse + asset_type: Endpoint + mitre_attack_id: + - T1218 + - T1553.005 + - T1204.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218/msix_ai_stubs/windows_sysmon.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218/msix_ai_stubs/windows_sysmon.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational diff --git a/detections/endpoint/windows_ai_platform_dns_query.yml b/detections/endpoint/windows_ai_platform_dns_query.yml index c018bc247a..012885e6f2 100644 --- a/detections/endpoint/windows_ai_platform_dns_query.yml +++ b/detections/endpoint/windows_ai_platform_dns_query.yml @@ -1,70 +1,63 @@ name: Windows AI Platform DNS Query id: 1ad89d24-c856-4a0e-8fdf-c20c7b9febe1 -version: 2 -date: '2025-12-17' +version: 3 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: | - The following analytic detects DNS queries initiated by the Windows AI Platform to domains associated with Hugging Face, OpenAI, and other popular providers of machine learning models and services. Monitoring these DNS requests is important because it can reveal when systems are accessing external AI platforms, which may indicate the use of third-party AI resources or the transfer of sensitive data outside the organization’s environment. Detecting such activity enables organizations to enforce data governance policies, prevent unapproved use of external AI services, and maintain visibility into potential data exfiltration risks. Proactive monitoring provides better control over AI model usage and helps safeguard organizational data flows. +description: | + The following analytic detects DNS queries initiated by the Windows AI Platform to domains associated with Hugging Face, OpenAI, and other popular providers of machine learning models and services. Monitoring these DNS requests is important because it can reveal when systems are accessing external AI platforms, which may indicate the use of third-party AI resources or the transfer of sensitive data outside the organization’s environment. Detecting such activity enables organizations to enforce data governance policies, prevent unapproved use of external AI services, and maintain visibility into potential data exfiltration risks. Proactive monitoring provides better control over AI model usage and helps safeguard organizational data flows. data_source: - - Sysmon EventID 22 + - Sysmon EventID 22 search: | - `sysmon` EventCode=22 QueryName IN ("router.huggingface.co", "api.openai.com") - | lookup update=true browser_app_list browser_process_name AS process_name OUTPUT isAllowed | search isAllowed!=true - | rename dvc as dest - | stats count min(_time) as firstTime max(_time) as lastTime - by answer answer_count dest process_exec process_guid process_name query query_count reply_code_id signature signature_id src user_id Image - vendor_product QueryName QueryResults QueryStatus - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_ai_platform_dns_query_filter` -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name and eventcode = 22 dnsquery executions from your endpoints. - If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + `sysmon` EventCode=22 QueryName IN ("router.huggingface.co", "api.openai.com") + | lookup update=true browser_app_list browser_process_name AS process_name OUTPUT isAllowed | search isAllowed!=true + | rename dvc as dest + | stats count min(_time) as firstTime max(_time) as lastTime + by answer answer_count dest process_exec process_guid process_name query query_count reply_code_id signature signature_id src user_id Image + vendor_product QueryName QueryResults QueryStatus + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_ai_platform_dns_query_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and eventcode = 22 dnsquery executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: researcher, engineering and administrator may create a automation that queries huggingface ai platform hub for accomplishing task. references: -- https://cert.gov.ua/article/6284730 -- https://www.microsoft.com/en-us/security/blog/2025/11/03/sesameop-novel-backdoor-uses-openai-assistants-api-for-command-and-control/ + - https://cert.gov.ua/article/6284730 + - https://www.microsoft.com/en-us/security/blog/2025/11/03/sesameop-novel-backdoor-uses-openai-assistants-api-for-command-and-control/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a process $process_name$ made a DNS query for $query$ from host $dest$. - risk_objects: - - field: dest - type: system - score: 15 - threat_objects: - - field: process_name - type: process_name + message: a process $process_name$ made a DNS query for $query$ from host $dest$. + risk_objects: + - field: dest + type: system + score: 15 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - LAMEHUG - - SesameOp - - PromptFlux - asset_type: Endpoint - mitre_attack_id: - - T1071.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - LAMEHUG + - SesameOp + - PromptFlux + asset_type: Endpoint + mitre_attack_id: + - T1071.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/lamehug/T1071.004/hugging_face/huggingface.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/lamehug/T1071.004/hugging_face/huggingface.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_alternate_datastream___base64_content.yml b/detections/endpoint/windows_alternate_datastream___base64_content.yml index 35de5e4bb7..addf0e182b 100644 --- a/detections/endpoint/windows_alternate_datastream___base64_content.yml +++ b/detections/endpoint/windows_alternate_datastream___base64_content.yml @@ -5,73 +5,51 @@ date: '2026-01-14' author: Steven Dick, Teoderick Contreras, Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the creation of Alternate Data Streams - (ADS) with Base64 content on Windows systems. It leverages Sysmon EventID 15, which - captures file creation events, including the content of named streams. ADS can conceal - malicious payloads, making them significant for SOC monitoring. This detection identifies - hidden streams that may contain executables, scripts, or configuration data, often - used by malware to evade detection. If confirmed malicious, this activity could - allow attackers to hide and execute payloads, persist in the environment, or access - sensitive information without being easily detected. +description: The following analytic detects the creation of Alternate Data Streams (ADS) with Base64 content on Windows systems. It leverages Sysmon EventID 15, which captures file creation events, including the content of named streams. ADS can conceal malicious payloads, making them significant for SOC monitoring. This detection identifies hidden streams that may contain executables, scripts, or configuration data, often used by malware to evade detection. If confirmed malicious, this activity could allow attackers to hide and execute payloads, persist in the environment, or access sensitive information without being easily detected. data_source: -- Sysmon EventID 15 -search: '`sysmon` EventCode=15 NOT Contents IN ("-","[ZoneTransfer]*") | regex TargetFilename="(? upperBound, "Yes", "No") | where anomaly="Yes" | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_applocker_execution_from_uncommon_locations_filter`' -how_to_implement: The analytic is designed to be run against Windows AppLocker event - logs collected from endpoints with AppLocker enabled. If using Microsoft Defender - for Endpoint (MDE), modify the analytic to use EventTypes/ActionTypes that match - the block events for AppLocker. The analytic requires the AppLocker event logs to - be ingested into Splunk. Note that, an additional method to reduce any false positives - would be to add the specific EventCodes - 8003 or 8004 and filter from there. Upon - tuning, modify to Anomaly or TTP. -known_false_positives: False positives are possible if legitimate users are executing - applications from file paths that are not permitted by AppLocker. It is recommended - to investigate the context of the application execution to determine if it is malicious - or not. Modify the threshold as needed to reduce false positives. +description: The following analytic identifies the execution of applications or scripts from uncommon or suspicious file paths, potentially indicating malware or unauthorized activity. It leverages Windows AppLocker event logs and uses statistical analysis to detect anomalies. By calculating the average and standard deviation of execution counts per file path, it flags paths with execution counts significantly higher than expected. This behavior is significant as it can uncover malicious activities or policy violations. If confirmed malicious, this activity could allow attackers to execute unauthorized code, leading to potential system compromise or data breaches. +search: |- + `applocker` + | spath input=UserData_Xml + | rename RuleAndFileData.* as *, Computer as dest, TargetUser AS user + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest, PolicyName, RuleId, + user, TargetProcessId, FilePath, + FullFilePath + | eventstats avg(count) as avg, stdev(count) as stdev + | eval upperBound=(avg+stdev*2), anomaly=if(count > upperBound, "Yes", "No") + | where anomaly="Yes" + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_applocker_execution_from_uncommon_locations_filter` +how_to_implement: The analytic is designed to be run against Windows AppLocker event logs collected from endpoints with AppLocker enabled. If using Microsoft Defender for Endpoint (MDE), modify the analytic to use EventTypes/ActionTypes that match the block events for AppLocker. The analytic requires the AppLocker event logs to be ingested into Splunk. Note that, an additional method to reduce any false positives would be to add the specific EventCodes - 8003 or 8004 and filter from there. Upon tuning, modify to Anomaly or TTP. +known_false_positives: False positives are possible if legitimate users are executing applications from file paths that are not permitted by AppLocker. It is recommended to investigate the context of the application execution to determine if it is malicious or not. Modify the threshold as needed to reduce false positives. references: -- https://learn.microsoft.com/en-us/windows/security/application-security/application-control/windows-defender-application-control/operations/querying-application-control-events-centrally-using-advanced-hunting -- https://learn.microsoft.com/en-us/windows/security/application-security/application-control/windows-defender-application-control/applocker/using-event-viewer-with-applocker + - https://learn.microsoft.com/en-us/windows/security/application-security/application-control/windows-defender-application-control/operations/querying-application-control-events-centrally-using-advanced-hunting + - https://learn.microsoft.com/en-us/windows/security/application-security/application-control/windows-defender-application-control/applocker/using-event-viewer-with-applocker tags: - analytic_story: - - Windows AppLocker - asset_type: Endpoint - mitre_attack_id: - - T1218 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - Windows AppLocker + asset_type: Endpoint + mitre_attack_id: + - T1218 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/applocker/applocker.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Microsoft-Windows-AppLocker/MSI and Script + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/applocker/applocker.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Microsoft-Windows-AppLocker/MSI and Script diff --git a/detections/endpoint/windows_applocker_privilege_escalation_via_unauthorized_bypass.yml b/detections/endpoint/windows_applocker_privilege_escalation_via_unauthorized_bypass.yml index cfad624385..998f6a404e 100644 --- a/detections/endpoint/windows_applocker_privilege_escalation_via_unauthorized_bypass.yml +++ b/detections/endpoint/windows_applocker_privilege_escalation_via_unauthorized_bypass.yml @@ -1,76 +1,60 @@ name: Windows AppLocker Privilege Escalation via Unauthorized Bypass id: bca48629-7fa2-40d3-9e5d-807564504e28 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk data_source: [] type: TTP status: production -description: The following analytic utilizes Windows AppLocker event logs to identify - attempts to bypass application restrictions. AppLocker is a feature that allows - administrators to specify which applications are permitted to run on a system. This - analytic is designed to identify attempts to bypass these restrictions, which could - be indicative of an attacker attempting to escalate privileges. The analytic uses - EventCodes 8007, 8004, 8022, 8025, 8029, and 8040 to identify these attempts. The - analytic will identify the host, full file path, and target user associated with - the bypass attempt. These EventCodes are related to block events and focus on 5 - attempts or more. -search: '`applocker` EventCode IN (8007, 8004, 8022, 8025, 8029, 8040) | spath input=UserData_Xml - | rename RuleAndFileData.* as *, Computer as dest, TargetUser AS user | stats count - AS attempt_count min(_time) as firstTime max(_time) as lastTime by dest, PolicyName, - RuleId, user, TargetProcessId, FilePath, FullFilePath, EventCode | where attempt_count - > 5 | sort - attempt_count | lookup applockereventcodes EventCode OUTPUT Description - | `windows_applocker_privilege_escalation_via_unauthorized_bypass_filter`' -how_to_implement: The analytic is designed to be run against Windows AppLocker event - logs collected from endpoints with AppLocker enabled. If using Microsoft Defender - for Endpoint (MDE), modify the analytic to use EventTypes/ActionTypes that match - the block events for AppLocker. The analytic requires the AppLocker event logs to - be ingested into Splunk. -known_false_positives: False positives are possible if legitimate users are attempting - to bypass application restrictions. This could occur if a user is attempting to - run an application that is not permitted by AppLocker. It is recommended to investigate - the context of the bypass attempt to determine if it is malicious or not. Modify - the threshold as needed to reduce false positives. +description: The following analytic utilizes Windows AppLocker event logs to identify attempts to bypass application restrictions. AppLocker is a feature that allows administrators to specify which applications are permitted to run on a system. This analytic is designed to identify attempts to bypass these restrictions, which could be indicative of an attacker attempting to escalate privileges. The analytic uses EventCodes 8007, 8004, 8022, 8025, 8029, and 8040 to identify these attempts. The analytic will identify the host, full file path, and target user associated with the bypass attempt. These EventCodes are related to block events and focus on 5 attempts or more. +search: |- + `applocker` EventCode IN (8007, 8004, 8022, 8025, 8029, 8040) + | spath input=UserData_Xml + | rename RuleAndFileData.* as *, Computer as dest, TargetUser AS user + | stats count AS attempt_count min(_time) as firstTime max(_time) as lastTime + BY dest, PolicyName, RuleId, + user, TargetProcessId, FilePath, + FullFilePath, EventCode + | where attempt_count > 5 + | sort - attempt_count + | lookup applockereventcodes EventCode OUTPUT Description + | `windows_applocker_privilege_escalation_via_unauthorized_bypass_filter` +how_to_implement: The analytic is designed to be run against Windows AppLocker event logs collected from endpoints with AppLocker enabled. If using Microsoft Defender for Endpoint (MDE), modify the analytic to use EventTypes/ActionTypes that match the block events for AppLocker. The analytic requires the AppLocker event logs to be ingested into Splunk. +known_false_positives: False positives are possible if legitimate users are attempting to bypass application restrictions. This could occur if a user is attempting to run an application that is not permitted by AppLocker. It is recommended to investigate the context of the bypass attempt to determine if it is malicious or not. Modify the threshold as needed to reduce false positives. references: -- https://learn.microsoft.com/en-us/windows/security/application-security/application-control/windows-defender-application-control/operations/querying-application-control-events-centrally-using-advanced-hunting -- https://learn.microsoft.com/en-us/windows/security/application-security/application-control/windows-defender-application-control/applocker/using-event-viewer-with-applocker + - https://learn.microsoft.com/en-us/windows/security/application-security/application-control/windows-defender-application-control/operations/querying-application-control-events-centrally-using-advanced-hunting + - https://learn.microsoft.com/en-us/windows/security/application-security/application-control/windows-defender-application-control/applocker/using-event-viewer-with-applocker drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An attempt to bypass application restrictions was detected on a host $dest$. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: An attempt to bypass application restrictions was detected on a host $dest$. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Windows AppLocker - asset_type: Endpoint - mitre_attack_id: - - T1218 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - Windows AppLocker + asset_type: Endpoint + mitre_attack_id: + - T1218 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/applocker/applocker.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Microsoft-Windows-AppLocker/MSI and Script + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/applocker/applocker.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Microsoft-Windows-AppLocker/MSI and Script diff --git a/detections/endpoint/windows_applocker_rare_application_launch_detection.yml b/detections/endpoint/windows_applocker_rare_application_launch_detection.yml index 39b2ea2f8f..95c9d9f0de 100644 --- a/detections/endpoint/windows_applocker_rare_application_launch_detection.yml +++ b/detections/endpoint/windows_applocker_rare_application_launch_detection.yml @@ -1,52 +1,42 @@ name: Windows AppLocker Rare Application Launch Detection id: 9556f7b7-285f-4f18-8eeb-963d989f9d27 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk data_source: [] type: Hunting status: production -description: The following analytic detects the launch of rarely used applications - within the environment, which may indicate the use of potentially malicious software - or tools by attackers. It leverages Windows AppLocker event logs, aggregating application - launch counts over time and flagging those that significantly deviate from the norm. - This behavior is significant as it helps identify unusual application activity that - could signal a security threat. If confirmed malicious, this activity could allow - attackers to execute unauthorized code, potentially leading to further compromise - of the system. -search: '`applocker` | spath input=UserData_Xml | rename RuleAndFileData.* as *, Computer - as dest, TargetUser AS user | stats dc(_time) as days, count by FullFilePath dest - user | eventstats avg(count) as avg, stdev(count) as stdev | eval upperBound=(avg+stdev*3), - lowerBound=(avg-stdev*3) | where count > upperBound OR count < lowerBound | `windows_applocker_rare_application_launch_detection_filter`' -how_to_implement: The analytic is designed to be run against Windows AppLocker event - logs collected from endpoints with AppLocker enabled. If using Microsoft Defender - for Endpoint (MDE), modify the analytic to use EventTypes/ActionTypes that match - the block events for AppLocker. The analytic requires the AppLocker event logs to - be ingested into Splunk. Note that, an additional method to reduce any false positives - would be to add the specific EventCodes - 8003 or 8004 and filter from there. -known_false_positives: False positives are possible if legitimate users are launching - applications that are not permitted by AppLocker. It is recommended to investigate - the context of the application launch to determine if it is malicious or not. Modify - the threshold as needed to reduce false positives. +description: The following analytic detects the launch of rarely used applications within the environment, which may indicate the use of potentially malicious software or tools by attackers. It leverages Windows AppLocker event logs, aggregating application launch counts over time and flagging those that significantly deviate from the norm. This behavior is significant as it helps identify unusual application activity that could signal a security threat. If confirmed malicious, this activity could allow attackers to execute unauthorized code, potentially leading to further compromise of the system. +search: |- + `applocker` + | spath input=UserData_Xml + | rename RuleAndFileData.* as *, Computer as dest, TargetUser AS user + | stats dc(_time) as days, count + BY FullFilePath dest user + | eventstats avg(count) as avg, stdev(count) as stdev + | eval upperBound=(avg+stdev*3), lowerBound=(avg-stdev*3) + | where count > upperBound OR count < lowerBound + | `windows_applocker_rare_application_launch_detection_filter` +how_to_implement: The analytic is designed to be run against Windows AppLocker event logs collected from endpoints with AppLocker enabled. If using Microsoft Defender for Endpoint (MDE), modify the analytic to use EventTypes/ActionTypes that match the block events for AppLocker. The analytic requires the AppLocker event logs to be ingested into Splunk. Note that, an additional method to reduce any false positives would be to add the specific EventCodes - 8003 or 8004 and filter from there. +known_false_positives: False positives are possible if legitimate users are launching applications that are not permitted by AppLocker. It is recommended to investigate the context of the application launch to determine if it is malicious or not. Modify the threshold as needed to reduce false positives. references: -- https://learn.microsoft.com/en-us/windows/security/application-security/application-control/windows-defender-application-control/applocker/using-event-viewer-with-applocker -- https://learn.microsoft.com/en-us/windows/security/application-security/application-control/windows-defender-application-control/operations/querying-application-control-events-centrally-using-advanced-hunting + - https://learn.microsoft.com/en-us/windows/security/application-security/application-control/windows-defender-application-control/applocker/using-event-viewer-with-applocker + - https://learn.microsoft.com/en-us/windows/security/application-security/application-control/windows-defender-application-control/operations/querying-application-control-events-centrally-using-advanced-hunting tags: - analytic_story: - - Windows AppLocker - asset_type: Endpoint - mitre_attack_id: - - T1218 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - Windows AppLocker + asset_type: Endpoint + mitre_attack_id: + - T1218 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/applocker/applocker.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Microsoft-Windows-AppLocker/MSI and Script + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/applocker/applocker.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Microsoft-Windows-AppLocker/MSI and Script diff --git a/detections/endpoint/windows_appx_deployment_full_trust_package_installation.yml b/detections/endpoint/windows_appx_deployment_full_trust_package_installation.yml index 3a5ae9691c..f0642268cc 100644 --- a/detections/endpoint/windows_appx_deployment_full_trust_package_installation.yml +++ b/detections/endpoint/windows_appx_deployment_full_trust_package_installation.yml @@ -1,60 +1,59 @@ name: Windows AppX Deployment Full Trust Package Installation id: 8560de46-ea2d-4c69-8ca3-5b78b90f1338 -version: 1 -date: '2025-08-05' +version: 2 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting description: The following analytic detects the installation of MSIX/AppX packages with full trust privileges. This detection leverages Windows event logs from the AppXDeployment-Server, specifically focusing on EventCode 400 which indicates a package deployment operation. Full trust packages are significant as they run with elevated privileges outside the normal AppX container restrictions, allowing them to access system resources that regular AppX packages cannot. Adversaries have been observed leveraging full trust MSIX packages to deliver malware, as documented in recent threat intelligence reports. If confirmed malicious, these packages could allow attackers to execute arbitrary code with elevated privileges, establish persistence, or deliver malware while evading traditional detection mechanisms. data_source: -- Windows Event Log AppXDeployment-Server 400 -search: '`wineventlog_appxdeploymentserver` EventCode=400 HasFullTrust="true" - | stats count min(_time) as firstTime max(_time) as lastTime values(PackageFullName) as PackageFullName values(Path) as PackagePath values(PackageSourceUri) as PackageSourceUri values(PackageDisplayName) as PackageDisplayName values(CallingProcess) as CallingProcess values(IsCentennial) as IsCentennial by dvc EventCode HasFullTrust user_id | rename dvc as dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_appx_deployment_full_trust_package_installation_filter`' + - Windows Event Log AppXDeployment-Server 400 +search: |- + `wineventlog_appxdeploymentserver` EventCode=400 HasFullTrust="true" + | stats count min(_time) as firstTime max(_time) as lastTime values(PackageFullName) as PackageFullName values(Path) as PackagePath values(PackageSourceUri) as PackageSourceUri values(PackageDisplayName) as PackageDisplayName values(CallingProcess) as CallingProcess values(IsCentennial) as IsCentennial + BY dvc EventCode HasFullTrust + user_id + | rename dvc as dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_appx_deployment_full_trust_package_installation_filter` how_to_implement: To implement this search, you need to configure Windows event log collection for the Microsoft-Windows-AppXDeploymentServer/Operational channel. This can be done through Windows Event Forwarding, Splunk Universal Forwarders, or other log collection methods. Ensure that the log collection method preserves the full XML structure of the events. The sourcetype should be set to XmlWinEventLog or WinEventLog depending on your environment configuration. This detection specifically looks for EventCode 400, which indicates package deployment operations, and filters for packages with full trust privileges. known_false_positives: Legitimate applications may be deployed as full trust MSIX packages, especially line-of-business applications that require access to system resources. Microsoft Store applications, development tools, and enterprise applications may legitimately use full trust packages. Verify if the package is from a trusted source and signed by a trusted publisher before taking action. Review the package source URI and calling process to determine if the installation is expected in your environment. references: -- https://redcanary.com/blog/threat-intelligence/msix-installers/ -- https://redcanary.com/threat-detection-report/techniques/installer-packages/ -- https://learn.microsoft.com/en-us/windows/msix/desktop/desktop-to-uwp-behind-the-scenes -- https://learn.microsoft.com/en-us/windows/msix/package/package-identity -- https://attack.mitre.org/techniques/T1553/005/ + - https://redcanary.com/blog/threat-intelligence/msix-installers/ + - https://redcanary.com/threat-detection-report/techniques/installer-packages/ + - https://learn.microsoft.com/en-us/windows/msix/desktop/desktop-to-uwp-behind-the-scenes + - https://learn.microsoft.com/en-us/windows/msix/package/package-identity + - https://attack.mitre.org/techniques/T1553/005/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Look for related PowerShell activity from the same dest - search: '`powershell` EventCode=4104 dest="$dest$" | stats count by ScriptBlockText' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Look for related PowerShell activity from the same dest + search: '`powershell` EventCode=4104 dest="$dest$" | stats count by ScriptBlockText' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ tags: - analytic_story: - - MSIX Package Abuse - asset_type: Endpoint - mitre_attack_id: - - T1553.005 - - T1204.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - MSIX Package Abuse + asset_type: Endpoint + mitre_attack_id: + - T1553.005 + - T1204.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/appx/windows_appxdeploymentserver.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Microsoft-Windows-AppXDeploymentServer/Operational + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/appx/windows_appxdeploymentserver.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Microsoft-Windows-AppXDeploymentServer/Operational diff --git a/detections/endpoint/windows_appx_deployment_package_installation_success.yml b/detections/endpoint/windows_appx_deployment_package_installation_success.yml index f2dfb52518..d8f2e8026d 100644 --- a/detections/endpoint/windows_appx_deployment_package_installation_success.yml +++ b/detections/endpoint/windows_appx_deployment_package_installation_success.yml @@ -1,61 +1,64 @@ name: Windows AppX Deployment Package Installation Success id: 1234abcd-5678-90ef-1234-56789abcdef0 -version: 1 -date: '2025-08-05' +version: 2 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly description: This analytic detects successful MSIX/AppX package installations on Windows systems by monitoring EventID 854 in the Microsoft-Windows-AppXDeployment-Server/Operational log. This event is generated when an MSIX/AppX package has been successfully installed on a system. While most package installations are legitimate, monitoring these events can help identify unauthorized or suspicious package installations, especially when correlated with other events such as unsigned package installations (EventID 603 with Flags=8388608) or full trust package installations (EventID 400 with HasFullTrust=true). data_source: -- Windows Event Log AppXDeployment-Server 854 -search: '`wineventlog_appxdeploymentserver` EventCode=854 - | stats count min(_time) as firstTime max(_time) as lastTime values(Path) as PackagePath by dvc EventCode user_id | rename dvc as dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_appx_deployment_package_installation_success_filter`' + - Windows Event Log AppXDeployment-Server 854 +search: |- + `wineventlog_appxdeploymentserver` EventCode=854 + | stats count min(_time) as firstTime max(_time) as lastTime values(Path) as PackagePath + BY dvc EventCode user_id + | rename dvc as dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_appx_deployment_package_installation_success_filter` how_to_implement: To implement this detection, you need to be collecting Windows Event Logs from your endpoints, specifically the Microsoft-Windows-AppXDeploymentServer/Operational log. Ensure that your Splunk deployment is ingesting these logs with the source type "XmlWinEventLog:Microsoft-Windows-AppXDeploymentServer/Operational". This detection works best when used in conjunction with other MSIX package abuse detections such as unsigned package installations (EventID 603) and full trust package installations (EventID 400). known_false_positives: Legitimate MSIX/AppX package installations will trigger this detection. This is expected behavior and not necessarily indicative of malicious activity. This analytic is designed to provide visibility into package installations and should be used as part of a broader detection strategy. Consider correlating these events with other suspicious indicators such as unsigned packages or packages from unusual sources. references: -- https://learn.microsoft.com/en-us/windows/win32/appxpkg/troubleshooting -- https://www.appdeploynews.com/packaging-types/msix/troubleshooting-an-msix-package/ -- https://www.advancedinstaller.com/msix-installation-or-launching-errors-and-fixes.html + - https://learn.microsoft.com/en-us/windows/win32/appxpkg/troubleshooting + - https://www.appdeploynews.com/packaging-types/msix/troubleshooting-an-msix-package/ + - https://www.advancedinstaller.com/msix-installation-or-launching-errors-and-fixes.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View related unsigned package installations for - "$dest$" - search: 'source="XmlWinEventLog:Microsoft-Windows-AppXDeploymentServer/Operational" EventCode=603 Flags="8388608" host="$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View related full trust package installations for - "$dest$" - search: 'source="XmlWinEventLog:Microsoft-Windows-AppXDeploymentServer/Operational" EventCode=400 HasFullTrust="true" host="$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View related unsigned package installations for - "$dest$" + search: 'source="XmlWinEventLog:Microsoft-Windows-AppXDeploymentServer/Operational" EventCode=603 Flags="8388608" host="$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View related full trust package installations for - "$dest$" + search: 'source="XmlWinEventLog:Microsoft-Windows-AppXDeploymentServer/Operational" EventCode=400 HasFullTrust="true" host="$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A MSIX/AppX package $PackagePath$ was successfully installed on $dest$ by user $user_id$. - risk_objects: - - field: dest - type: system - score: 10 - threat_objects: - - field: PackagePath - type: file_path + message: A MSIX/AppX package $PackagePath$ was successfully installed on $dest$ by user $user_id$. + risk_objects: + - field: dest + type: system + score: 10 + threat_objects: + - field: PackagePath + type: file_path tags: - analytic_story: - - MSIX Package Abuse - asset_type: Endpoint - mitre_attack_id: - - T1204.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - MSIX Package Abuse + asset_type: Endpoint + mitre_attack_id: + - T1204.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/appx/windows_appxdeploymentserver.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Microsoft-Windows-AppXDeploymentServer/Operational \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/appx/windows_appxdeploymentserver.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Microsoft-Windows-AppXDeploymentServer/Operational diff --git a/detections/endpoint/windows_appx_deployment_unsigned_package_installation.yml b/detections/endpoint/windows_appx_deployment_unsigned_package_installation.yml index 2599cbe0f7..e174858160 100644 --- a/detections/endpoint/windows_appx_deployment_unsigned_package_installation.yml +++ b/detections/endpoint/windows_appx_deployment_unsigned_package_installation.yml @@ -1,68 +1,67 @@ name: Windows AppX Deployment Unsigned Package Installation id: 9b5e7c14-f8d2-4a3b-b1a7-e5c9f2a8d123 -version: 1 -date: '2025-08-05' +version: 2 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP description: The following analytic detects attempts to install unsigned MSIX/AppX packages using the -AllowUnsigned parameter. This detection leverages Windows event logs from the AppXDeployment-Server, specifically focusing on EventID 603 which indicates the start of a deployment operation with specific deployment flags. The flag value 8388608 corresponds to the -AllowUnsigned option in PowerShell's Add-AppxPackage cmdlet. This activity is significant as adversaries have been observed leveraging unsigned MSIX packages to deliver malware, bypassing signature verification that would normally protect users from malicious packages. If confirmed malicious, this could allow attackers to execute arbitrary code, establish persistence, or deliver malware while evading traditional detection mechanisms. data_source: -- Windows Event Log AppXDeployment-Server 855 -search: '`wineventlog_appxdeploymentserver` EventCode=603 Flags="8388608" - | stats count min(_time) as firstTime max(_time) as lastTime values(Path) as file_name values(CallingProcess) as CallingProcess by dvc EventCode Flags user_id | rename dvc as dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_appx_deployment_unsigned_package_installation_filter`' + - Windows Event Log AppXDeployment-Server 855 +search: |- + `wineventlog_appxdeploymentserver` EventCode=603 Flags="8388608" + | stats count min(_time) as firstTime max(_time) as lastTime values(Path) as file_name values(CallingProcess) as CallingProcess + BY dvc EventCode Flags + user_id + | rename dvc as dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_appx_deployment_unsigned_package_installation_filter` how_to_implement: To implement this search, you need to configure Windows event log collection for the Microsoft-Windows-AppXDeploymentServer/Operational channel. This can be done through Windows Event Forwarding, Splunk Universal Forwarders, or other log collection methods. Ensure that the log collection method preserves the full XML structure of the events. The sourcetype should be set to XmlWinEventLog or WinEventLog depending on your environment configuration. This detection specifically looks for EventID 603 with a Flags value of 8388608, which indicates the use of the -AllowUnsigned parameter when installing MSIX packages. known_false_positives: Legitimate software development and testing activities may trigger this detection. Internal application development teams testing MSIX packages before signing or system administrators installing custom unsigned applications for business purposes may use the -AllowUnsigned parameter. Note that the -AllowUnsigned flag is only available on Windows 11 and later versions. Verify if the package installation is expected in your environment and if the calling process and user are authorized to install unsigned packages. references: -- https://learn.microsoft.com/en-us/powershell/module/appx/add-appxpackage -- https://learn.microsoft.com/en-us/windows/msix/package/unsigned-package -- https://redcanary.com/blog/threat-intelligence/msix-installers/ -- https://attack.mitre.org/techniques/T1553/005/ + - https://learn.microsoft.com/en-us/powershell/module/appx/add-appxpackage + - https://learn.microsoft.com/en-us/windows/msix/package/unsigned-package + - https://redcanary.com/blog/threat-intelligence/msix-installers/ + - https://attack.mitre.org/techniques/T1553/005/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Look for related PowerShell activity from the same dest - search: '`powershell` EventCode=4104 dest="$dest$" ScriptBlockText="*Add-AppxPackage*" OR ScriptBlockText="*Add-AppPackage*" | stats count by ScriptBlockText' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Look for related PowerShell activity from the same dest + search: '`powershell` EventCode=4104 dest="$dest$" ScriptBlockText="*Add-AppxPackage*" OR ScriptBlockText="*Add-AppPackage*" | stats count by ScriptBlockText' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Unsigned MSIX/AppX package $file_name$ installation attempted on $dest$ by user $user_id$ using $CallingProcess$ - risk_objects: - - field: dest - type: system - score: 65 - threat_objects: - - field: file_name - type: file_name + message: Unsigned MSIX/AppX package $file_name$ installation attempted on $dest$ by user $user_id$ using $CallingProcess$ + risk_objects: + - field: dest + type: system + score: 65 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - MSIX Package Abuse - asset_type: Endpoint - mitre_attack_id: - - T1553.005 - - T1204.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - MSIX Package Abuse + asset_type: Endpoint + mitre_attack_id: + - T1553.005 + - T1204.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/appx/windows_appxdeploymentserver.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Microsoft-Windows-AppXDeploymentServer/Operational \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/appx/windows_appxdeploymentserver.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Microsoft-Windows-AppXDeploymentServer/Operational diff --git a/detections/endpoint/windows_archive_collected_data_via_powershell.yml b/detections/endpoint/windows_archive_collected_data_via_powershell.yml index d5305ed119..c274df0203 100644 --- a/detections/endpoint/windows_archive_collected_data_via_powershell.yml +++ b/detections/endpoint/windows_archive_collected_data_via_powershell.yml @@ -6,65 +6,44 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: - - Powershell Script Block Logging 4104 -description: - The following analytic detects the use of PowerShell scripts to archive - files into a temporary folder. It leverages PowerShell Script Block Logging, specifically - monitoring for the `Compress-Archive` command targeting the `Temp` directory. This - activity is significant as it may indicate an adversary's attempt to collect and - compress data for exfiltration. If confirmed malicious, this behavior could lead - to unauthorized data access and exfiltration, posing a severe risk to sensitive - information and overall network security. -search: - '`powershell` EventCode=4104 ScriptBlockText = "*Compress-Archive*" ScriptBlockText - = "*\\Temp\\*" | fillnull | stats count min(_time) as firstTime max(_time) as lastTime - by dest signature signature_id user_id vendor_product EventID Guid Opcode Name Path - ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | - `security_content_ctime(lastTime)` | `windows_archive_collected_data_via_powershell_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - Powershell Script Block Logging 4104 +description: The following analytic detects the use of PowerShell scripts to archive files into a temporary folder. It leverages PowerShell Script Block Logging, specifically monitoring for the `Compress-Archive` command targeting the `Temp` directory. This activity is significant as it may indicate an adversary's attempt to collect and compress data for exfiltration. If confirmed malicious, this behavior could lead to unauthorized data access and exfiltration, posing a severe risk to sensitive information and overall network security. +search: '`powershell` EventCode=4104 ScriptBlockText = "*Compress-Archive*" ScriptBlockText = "*\\Temp\\*" | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_archive_collected_data_via_powershell_filter`' +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. known_false_positives: powershell may used this function to archive data. references: - - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Archive Collected Data via Powershell on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Windows Archive Collected Data via Powershell on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - APT37 Rustonotto and FadeStealer - - CISA AA23-347A - asset_type: Endpoint - mitre_attack_id: - - T1560 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - APT37 Rustonotto and FadeStealer + - CISA AA23-347A + asset_type: Endpoint + mitre_attack_id: + - T1560 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560/powershell_archive/powershell_archive.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560/powershell_archive/powershell_archive.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_archive_collected_data_via_rar.yml b/detections/endpoint/windows_archive_collected_data_via_rar.yml index 7f61bc4191..1a99f83c9f 100644 --- a/detections/endpoint/windows_archive_collected_data_via_rar.yml +++ b/detections/endpoint/windows_archive_collected_data_via_rar.yml @@ -1,83 +1,70 @@ name: Windows Archive Collected Data via Rar id: 2015de95-fe91-413d-9d62-2fe011b67e82 -version: 10 -date: '2025-09-18' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic identifies the execution of RAR utilities to archive - files on a system. It leverages data from Endpoint Detection and Response (EDR) - agents, focusing on process names, GUIDs, and command-line arguments. This activity - is significant as threat actors, including red-teamers and malware like DarkGate, - use RAR archiving to compress and exfiltrate collected data from compromised hosts. - If confirmed malicious, this behavior could lead to the unauthorized transfer of - sensitive information to command and control servers, posing a severe risk to data - confidentiality and integrity. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name="Rar.exe" - OR Processes.original_file_name = "Rar.exe" AND Processes.process = "*a*" Processes.process - = "* -ep1*" Processes.process = "* -r*" Processes.process = "* -y*" Processes.process - = "* -v5m*" Processes.process = "* -m1*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_archive_collected_data_via_rar_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic identifies the execution of RAR utilities to archive files on a system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, GUIDs, and command-line arguments. This activity is significant as threat actors, including red-teamers and malware like DarkGate, use RAR archiving to compress and exfiltrate collected data from compromised hosts. If confirmed malicious, this behavior could lead to the unauthorized transfer of sensitive information to command and control servers, posing a severe risk to data confidentiality and integrity. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name="Rar.exe" + OR + Processes.original_file_name = "Rar.exe" + AND + Processes.process = "*a*" Processes.process = "* -ep1*" Processes.process = "* -r*" Processes.process = "* -y*" Processes.process = "* -v5m*" Processes.process = "* -m1*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_archive_collected_data_via_rar_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: user and network administrator can execute this command. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate + - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a Rar.exe commandline used in archiving collected data on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: a Rar.exe commandline used in archiving collected data on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - DarkGate Malware - - Salt Typhoon - - China-Nexus Threat Activity - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1560.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - DarkGate Malware + - Salt Typhoon + - China-Nexus Threat Activity + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1560.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560.001/archive_utility_darkgate/rar_sys.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560.001/archive_utility_darkgate/rar_sys.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_archived_collected_data_in_temp_folder.yml b/detections/endpoint/windows_archived_collected_data_in_temp_folder.yml index b8bbdf1f12..de17dfcd10 100644 --- a/detections/endpoint/windows_archived_collected_data_in_temp_folder.yml +++ b/detections/endpoint/windows_archived_collected_data_in_temp_folder.yml @@ -1,83 +1,67 @@ name: Windows Archived Collected Data In TEMP Folder id: cb56a1ea-e0b1-46d5-913f-e024cba40cbe -version: 6 -date: '2025-10-06' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: -- Sysmon EventID 11 -description: The following analytic detects the creation of archived files in a temporary - folder, which may contain collected data. This behavior is often associated with - malicious activity, where attackers compress sensitive information before exfiltration. - The detection focuses on monitoring specific directories, such as temp folders, - for the presence of newly created archive files (e.g., .zip, .rar, .tar). By identifying - this pattern, security teams can quickly respond to potential data collection and - exfiltration attempts, minimizing the risk of data breaches and improving overall - threat detection. + - Sysmon EventID 11 +description: The following analytic detects the creation of archived files in a temporary folder, which may contain collected data. This behavior is often associated with malicious activity, where attackers compress sensitive information before exfiltration. The detection focuses on monitoring specific directories, such as temp folders, for the presence of newly created archive files (e.g., .zip, .rar, .tar). By identifying this pattern, security teams can quickly respond to potential data collection and exfiltration attempts, minimizing the risk of data breaches and improving overall threat detection. search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime - FROM datamodel=Endpoint.Filesystem where - Filesystem.file_name IN ("*.zip", "*.rar", "*.tar", "*.7z") - Filesystem.file_path IN ("*\\AppData\\Local\\Temp\\*", "*\\Windows\\Temp\\*") - by Filesystem.action Filesystem.dest Filesystem.file_access_time - Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time - Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size - Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product - | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_archived_collected_data_in_temp_folder_filter` -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + FROM datamodel=Endpoint.Filesystem where + Filesystem.file_name IN ("*.zip", "*.rar", "*.tar", "*.7z") + Filesystem.file_path IN ("*\\AppData\\Local\\Temp\\*", "*\\Windows\\Temp\\*") + by Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size + Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_archived_collected_data_in_temp_folder_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. known_false_positives: | - Some installers, debugging or support tools may create archive files in the temp folder. - Legitimate software may also use temporary folders for archiving purposes. - Review and apply filters as needed. + Some installers, debugging or support tools may create archive files in the temp folder. + Legitimate software may also use temporary folders for archiving purposes. + Review and apply filters as needed. references: -- https://x.com/suyog41/status/1825869470323056748 -- https://g0njxa.medium.com/from-vietnam-to-united-states-malware-fraud-and-dropshipping-98b7a7b2c36d + - https://x.com/suyog41/status/1825869470323056748 + - https://g0njxa.medium.com/from-vietnam-to-united-states-malware-fraud-and-dropshipping-98b7a7b2c36d drilldown_searches: -- name: View the detection results for "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An archive file [$file_name$] was created in a temporary folder on [$dest$]. - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: [] + message: An archive file [$file_name$] was created in a temporary folder on [$dest$]. + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: [] tags: - analytic_story: - - Braodo Stealer - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1560 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Braodo Stealer + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1560 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560/archived_in_temp_dir/braodo_zip_temp.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560/archived_in_temp_dir/braodo_zip_temp.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_attempt_to_stop_security_service.yml b/detections/endpoint/windows_attempt_to_stop_security_service.yml index 45a37d72c7..b6044d3492 100644 --- a/detections/endpoint/windows_attempt_to_stop_security_service.yml +++ b/detections/endpoint/windows_attempt_to_stop_security_service.yml @@ -1,96 +1,83 @@ name: Windows Attempt To Stop Security Service id: 9ed27cea-4e27-4eff-b2c6-aac9e78a7517 -version: 6 -date: '2026-01-14' +version: 7 +date: '2026-02-25' author: Rico Valdez, Nasreddine Bencherchali, Splunk status: production type: TTP -description: The following analytic detects attempts to stop security-related services - on an endpoint, which may indicate malicious activity. It leverages data from Endpoint - Detection and Response (EDR) agents, specifically searching for processes involving - the "sc.exe" or "net.exe" command with the "stop" parameter or the PowerShell "Stop-Service" - cmdlet. This activity is significant because disabling security services can undermine - the organization's security posture, potentially leading to unauthorized access, - data exfiltration, or further attacks like malware installation or privilege escalation. - If confirmed malicious, this behavior could compromise the endpoint and the entire - network, necessitating immediate investigation and response. +description: The following analytic detects attempts to stop security-related services on an endpoint, which may indicate malicious activity. It leverages data from Endpoint Detection and Response (EDR) agents, specifically searching for processes involving the "sc.exe" or "net.exe" command with the "stop" parameter or the PowerShell "Stop-Service" cmdlet. This activity is significant because disabling security services can undermine the organization's security posture, potentially leading to unauthorized access, data exfiltration, or further attacks like malware installation or privilege escalation. If confirmed malicious, this behavior could compromise the endpoint and the entire network, necessitating immediate investigation and response. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where ((`process_net` OR `process_sc`) Processes.process="* stop *") OR Processes.process="*Stop-Service - *" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | lookup security_services_lookup service as process OUTPUTNEW category, description - | search category=security | `windows_attempt_to_stop_security_service_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: No false positives have been identified at this time. - should be identified and understood. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + (`process_net` + OR + `process_sc`) Processes.process="* stop *" + ) + OR Processes.process="*Stop-Service *" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | lookup security_services_lookup service as process OUTPUTNEW category, description + | search category=security + | `windows_attempt_to_stop_security_service_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: No false positives have been identified at this time. should be identified and understood. references: -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1562.001/T1562.001.md#atomic-test-14---disable-arbitrary-security-windows-service -- https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1562.001/T1562.001.md#atomic-test-14---disable-arbitrary-security-windows-service + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - attempting to disable security services on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 20 - - field: dest - type: system - score: 20 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified attempting to disable security services on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 20 + - field: dest + type: system + score: 20 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - WhisperGate - - Graceful Wipe Out Attack - - Disabling Security Tools - - Data Destruction - - Azorult - - Trickbot - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - WhisperGate + - Graceful Wipe Out Attack + - Disabling Security Tools + - Data Destruction + - Azorult + - Trickbot + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_defend_service_stop/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_defend_service_stop/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_audit_policy_auditing_option_disabled_via_auditpol.yml b/detections/endpoint/windows_audit_policy_auditing_option_disabled_via_auditpol.yml index b4419bbf3e..d718bb8198 100644 --- a/detections/endpoint/windows_audit_policy_auditing_option_disabled_via_auditpol.yml +++ b/detections/endpoint/windows_audit_policy_auditing_option_disabled_via_auditpol.yml @@ -1,91 +1,69 @@ name: Windows Audit Policy Auditing Option Disabled via Auditpol id: 663a7a50-b752-4c84-975b-8325ca3f6f9e -version: 4 -date: '2026-01-14' +version: 5 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: TTP -description: The following analytic identifies the execution of `auditpol.exe` with - the "/set", "/option" and "/value:disable" command-line arguments used to disable - specific auditing options of the audit policy. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process names and command-line executions. - This activity can be significant as it indicates potential defense evasion by adversaries - or Red Teams, aiming to limit data that can be leveraged for detections and audits. - If confirmed malicious, this behavior could allow attackers to bypass defenses, - and plan further attacks, potentially leading to full machine compromise or lateral - movement. +description: The following analytic identifies the execution of `auditpol.exe` with the "/set", "/option" and "/value:disable" command-line arguments used to disable specific auditing options of the audit policy. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity can be significant as it indicates potential defense evasion by adversaries or Red Teams, aiming to limit data that can be leveraged for detections and audits. If confirmed malicious, this behavior could allow attackers to bypass defenses, and plan further attacks, potentially leading to full machine compromise or lateral movement. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where `process_auditpol` Processes.process="*/set*" Processes.process="*/option:*" - Processes.process="*/value:disable*" Processes.process IN ("*FullPrivilegeAuditing*", - "*AuditBaseObjects*", "*AuditBaseDirectories*") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_audit_policy_auditing_option_disabled_via_auditpol_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process name, and process original file name. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: No false positives have been identified at this time. - and understood. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_auditpol` Processes.process="*/set*" Processes.process="*/option:*" Processes.process="*/value:disable*" Processes.process IN ("*FullPrivilegeAuditing*", "*AuditBaseObjects*", "*AuditBaseDirectories*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_audit_policy_auditing_option_disabled_via_auditpol_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process name, and process original file name. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: No false positives have been identified at this time. and understood. references: -- https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-gpac/262a2bed-93d4-4c04-abec-cf06e9ec72fd -- https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/auditpol-set + - https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-gpac/262a2bed-93d4-4c04-abec-cf06e9ec72fd + - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/auditpol-set drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - attempting to disable an audit policy auditing option on endpoint $dest$ by user - $user$. - risk_objects: - - field: user - type: user - score: 60 - - field: dest - type: system - score: 60 - threat_objects: - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified attempting to disable an audit policy auditing option on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 60 + - field: dest + type: system + score: 60 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Windows Audit Policy Tampering - asset_type: Endpoint - mitre_attack_id: - - T1562.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Audit Policy Tampering + asset_type: Endpoint + mitre_attack_id: + - T1562.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - Sysmon - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test - Sysmon + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_audit_policy_auditing_option_modified___registry.yml b/detections/endpoint/windows_audit_policy_auditing_option_modified___registry.yml index 68a00f6c19..1bf0067c4c 100644 --- a/detections/endpoint/windows_audit_policy_auditing_option_modified___registry.yml +++ b/detections/endpoint/windows_audit_policy_auditing_option_modified___registry.yml @@ -5,72 +5,48 @@ date: '2025-07-30' author: Nasreddine Bencherchali, Bhavin Patel, Splunk status: production type: Anomaly -description: The following analytic detects potentially suspicious modifications to - the Audit Policy auditing options registry values. It leverages data from the Endpoint.Registry - data model, focusing on changes to one of the following auditing option values "CrashOnAuditFail", - "FullPrivilegeAuditing", "AuditBaseObjects" and "AuditBaseDirectories" within the - "HKLM\\System\\CurrentControlSet\\Control\\Lsa\\" registry key. This activity is - significant as it could be a sign of a threat actor trying to tamper with the audit - policy configuration, and disabling SACLs configuration. If confirmed malicious, - this behavior could allow attackers to bypass defenses, and plan further attacks, - potentially leading to full machine compromise or lateral movement. +description: The following analytic detects potentially suspicious modifications to the Audit Policy auditing options registry values. It leverages data from the Endpoint.Registry data model, focusing on changes to one of the following auditing option values "CrashOnAuditFail", "FullPrivilegeAuditing", "AuditBaseObjects" and "AuditBaseDirectories" within the "HKLM\\System\\CurrentControlSet\\Control\\Lsa\\" registry key. This activity is significant as it could be a sign of a threat actor trying to tamper with the audit policy configuration, and disabling SACLs configuration. If confirmed malicious, this behavior could allow attackers to bypass defenses, and plan further attacks, potentially leading to full machine compromise or lateral movement. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path="*\\Control\\Lsa*" - Registry.registry_value_name IN ("CrashOnAuditFail", "FullPrivilegeAuditing", "AuditBaseObjects", - "AuditBaseDirectories") by Registry.action Registry.dest Registry.process_guid Registry.process_id - Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data - Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user - Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_audit_policy_auditing_option_modified___registry_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path="*\\Control\\Lsa*" Registry.registry_value_name IN ("CrashOnAuditFail", "FullPrivilegeAuditing", "AuditBaseObjects", "AuditBaseDirectories") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_audit_policy_auditing_option_modified___registry_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: Active setup installer may add or modify this registry. references: -- https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-gpac/262a2bed-93d4-4c04-abec-cf06e9ec72fd -- https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/auditpol-set + - https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-gpac/262a2bed-93d4-4c04-abec-cf06e9ec72fd + - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/auditpol-set drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The auditing option $registry_value_name$ from the configured Audit Policy - was modified on $dest$. - risk_objects: - - field: dest - type: system - score: 64 - - field: user - type: user - score: 64 - threat_objects: [] + message: The auditing option $registry_value_name$ from the configured Audit Policy was modified on $dest$. + risk_objects: + - field: dest + type: system + score: 64 + - field: user + type: user + score: 64 + threat_objects: [] tags: - analytic_story: - - Windows Audit Policy Tampering - asset_type: Endpoint - mitre_attack_id: - - T1547.014 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Audit Policy Tampering + asset_type: Endpoint + mitre_attack_id: + - T1547.014 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - Sysmon - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test - Sysmon + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_audit_policy_cleared_via_auditpol.yml b/detections/endpoint/windows_audit_policy_cleared_via_auditpol.yml index d9c4aa4241..524b21e366 100644 --- a/detections/endpoint/windows_audit_policy_cleared_via_auditpol.yml +++ b/detections/endpoint/windows_audit_policy_cleared_via_auditpol.yml @@ -1,96 +1,79 @@ name: Windows Audit Policy Cleared via Auditpol id: f067f7cf-f41b-4a60-985e-c23e268a13cb -version: 4 -date: '2026-01-14' +version: 5 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: TTP -description: The following analytic identifies the execution of `auditpol.exe` with - the "/clear" command-line argument used to clears the audit policy. It leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process names - and command-line executions. This activity can be significant as it indicates potential - defense evasion by adversaries or Red Teams, aiming to limit data that can be leveraged - for detections and audits. If confirmed malicious, this behavior could allow attackers - to bypass defenses, and plan further attacks, potentially leading to full machine - compromise or lateral movement. +description: The following analytic identifies the execution of `auditpol.exe` with the "/clear" command-line argument used to clears the audit policy. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity can be significant as it indicates potential defense evasion by adversaries or Red Teams, aiming to limit data that can be leveraged for detections and audits. If confirmed malicious, this behavior could allow attackers to bypass defenses, and plan further attacks, potentially leading to full machine compromise or lateral movement. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where `process_auditpol` Processes.process IN ("*/clear*", "*/remove*") AND NOT - Processes.process IN ("*/resourceSACL*", "*/?*") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_audit_policy_cleared_via_auditpol_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process name, and process original file name. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: No false positives have been identified at this time. - and understood. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_auditpol` Processes.process IN ("*/clear*", "*/remove*") + AND + NOT Processes.process IN ("*/resourceSACL*", "*/?*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_audit_policy_cleared_via_auditpol_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process name, and process original file name. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: No false positives have been identified at this time. and understood. references: -- https://www.microsoft.com/en-us/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/ -- https://www.cybereason.com/blog/research/prometei-botnet-exploiting-microsoft-exchange-vulnerabilities -- https://attack.mitre.org/techniques/T1562/002/ -- https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/auditpol-clear -- https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/auditpol-remove + - https://www.microsoft.com/en-us/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/ + - https://www.cybereason.com/blog/research/prometei-botnet-exploiting-microsoft-exchange-vulnerabilities + - https://attack.mitre.org/techniques/T1562/002/ + - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/auditpol-clear + - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/auditpol-remove drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - attempting to clear logging on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 16 - - field: dest - type: system - score: 16 - threat_objects: - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified attempting to clear logging on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 16 + - field: dest + type: system + score: 16 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Windows Audit Policy Tampering - asset_type: Endpoint - mitre_attack_id: - - T1562.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Audit Policy Tampering + asset_type: Endpoint + mitre_attack_id: + - T1562.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - Sysmon - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog -- name: True Positive Test - Security - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test - Sysmon + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test - Security + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_audit_policy_disabled_via_auditpol.yml b/detections/endpoint/windows_audit_policy_disabled_via_auditpol.yml index ad3946bfd3..8b310efcf8 100644 --- a/detections/endpoint/windows_audit_policy_disabled_via_auditpol.yml +++ b/detections/endpoint/windows_audit_policy_disabled_via_auditpol.yml @@ -1,91 +1,73 @@ name: Windows Audit Policy Disabled via Auditpol id: 14e008e5-6723-4298-b0d4-e95b24e10c18 -version: 3 -date: '2025-05-02' +version: 4 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly -description: The following analytic identifies the execution of `auditpol.exe` with - the "/set" command-line argument in order to disable a specific category or sub-category - from the audit policy. It leverages data from Endpoint Detection and Response (EDR) - agents, focusing on process names and command-line executions. This activity can - be significant as it indicates potential defense evasion by adversaries or Red Teams, - aiming to limit data that can be leveraged for detections and audits. If confirmed - malicious, this behavior could allow attackers to bypass defenses, and plan further - attacks, potentially leading to full machine compromise or lateral movement. +description: The following analytic identifies the execution of `auditpol.exe` with the "/set" command-line argument in order to disable a specific category or sub-category from the audit policy. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity can be significant as it indicates potential defense evasion by adversaries or Red Teams, aiming to limit data that can be leveraged for detections and audits. If confirmed malicious, this behavior could allow attackers to bypass defenses, and plan further attacks, potentially leading to full machine compromise or lateral movement. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where `process_auditpol` Processes.process="*/set*" Processes.process IN ("*/success:*", - "*/failure:*") Processes.process="*disable*" AND NOT Processes.process IN ("*/?*", - "*/exclude*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_audit_policy_disabled_via_auditpol_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process name, and process original file name. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives should be rare, investigate the activity, and - apply additional filters when necessary. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_auditpol` Processes.process="*/set*" Processes.process IN ("*/success:*", "*/failure:*") Processes.process="*disable*" + AND + NOT Processes.process IN ("*/?*", "*/exclude*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_audit_policy_disabled_via_auditpol_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process name, and process original file name. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives should be rare, investigate the activity, and apply additional filters when necessary. references: -- https://www.microsoft.com/en-us/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/ -- https://www.cybereason.com/blog/research/prometei-botnet-exploiting-microsoft-exchange-vulnerabilities -- https://attack.mitre.org/techniques/T1562/002/ -- https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/auditpol-set + - https://www.microsoft.com/en-us/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/ + - https://www.cybereason.com/blog/research/prometei-botnet-exploiting-microsoft-exchange-vulnerabilities + - https://attack.mitre.org/techniques/T1562/002/ + - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/auditpol-set drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ with CommandLine - $process$ was identified attempting to disable and audit policy category/sub-category - on $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ with CommandLine $process$ was identified attempting to disable and audit policy category/sub-category on $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Windows Audit Policy Tampering - asset_type: Endpoint - mitre_attack_id: - - T1562.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Audit Policy Tampering + asset_type: Endpoint + mitre_attack_id: + - T1562.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - Sysmon - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test - Sysmon + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_audit_policy_disabled_via_legacy_auditpol.yml b/detections/endpoint/windows_audit_policy_disabled_via_legacy_auditpol.yml index ab101e4419..a0694fb548 100644 --- a/detections/endpoint/windows_audit_policy_disabled_via_legacy_auditpol.yml +++ b/detections/endpoint/windows_audit_policy_disabled_via_legacy_auditpol.yml @@ -1,93 +1,74 @@ name: Windows Audit Policy Disabled via Legacy Auditpol id: d2cef287-c2b7-4496-a609-7a548c1e27f9 -version: 3 -date: '2025-05-02' +version: 4 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly -description: The following analytic identifies the execution of the legacy `auditpol.exe` - included with the Windows 2000 Resource Kit Tools, with the "/disable" command-line - argument or one of the allowed category flags and the "none" option, in order to - disable a specific logging category from the audit policy. It leverages data from - Endpoint Detection and Response (EDR) agents, focusing on process names and command-line - executions. This activity can be significant as it indicates potential defense evasion - by adversaries or Red Teams, aiming to limit data that can be leveraged for detections - and audits. If confirmed malicious, this behavior could allow attackers to bypass - defenses, and plan further attacks, potentially leading to full machine compromise - or lateral movement. +description: The following analytic identifies the execution of the legacy `auditpol.exe` included with the Windows 2000 Resource Kit Tools, with the "/disable" command-line argument or one of the allowed category flags and the "none" option, in order to disable a specific logging category from the audit policy. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity can be significant as it indicates potential defense evasion by adversaries or Red Teams, aiming to limit data that can be leveraged for detections and audits. If confirmed malicious, this behavior could allow attackers to bypass defenses, and plan further attacks, potentially leading to full machine compromise or lateral movement. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where (`process_auditpol` Processes.process="*/disable") OR Processes.process IN - ("*/system:none*", "*/logon:none*", "*/object:none*", "*/privilege:none*", "*/process:none*", - "*/policy:none*", "*/sam:none*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_audit_policy_disabled_via_legacy_auditpol_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process name, and process original file name. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives should be rare, investigate the activity, and - apply additional filters when necessary. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + `process_auditpol` Processes.process="*/disable" + ) + OR Processes.process IN ("*/system:none*", "*/logon:none*", "*/object:none*", "*/privilege:none*", "*/process:none*", "*/policy:none*", "*/sam:none*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_audit_policy_disabled_via_legacy_auditpol_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process name, and process original file name. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives should be rare, investigate the activity, and apply additional filters when necessary. references: -- https://www.microsoft.com/en-us/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/ -- https://www.cybereason.com/blog/research/prometei-botnet-exploiting-microsoft-exchange-vulnerabilities -- https://attack.mitre.org/techniques/T1562/002/ -- https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/auditpol-set + - https://www.microsoft.com/en-us/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/ + - https://www.cybereason.com/blog/research/prometei-botnet-exploiting-microsoft-exchange-vulnerabilities + - https://attack.mitre.org/techniques/T1562/002/ + - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/auditpol-set drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ with CommandLine - $process$ was identified attempting to disable and audit policy category/sub-category - on $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ with CommandLine $process$ was identified attempting to disable and audit policy category/sub-category on $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Windows Audit Policy Tampering - asset_type: Endpoint - mitre_attack_id: - - T1562.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Audit Policy Tampering + asset_type: Endpoint + mitre_attack_id: + - T1562.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - Sysmon - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test - Sysmon + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_audit_policy_excluded_category_via_auditpol.yml b/detections/endpoint/windows_audit_policy_excluded_category_via_auditpol.yml index af286cb839..f94da716ab 100644 --- a/detections/endpoint/windows_audit_policy_excluded_category_via_auditpol.yml +++ b/detections/endpoint/windows_audit_policy_excluded_category_via_auditpol.yml @@ -1,96 +1,78 @@ name: Windows Audit Policy Excluded Category via Auditpol id: 083708d4-d763-4ba2-87ac-105b526de81a -version: 3 -date: '2025-05-02' +version: 4 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly -description: The following analytic identifies the execution of `auditpol.exe` with - the "/set" and "/exclude" command-line arguments which indicates that the user's - per-user policy will cause audit to be suppressed regardless of the system audit - policy. It leverages data from Endpoint Detection and Response (EDR) agents, focusing - on process names and command-line executions. This activity can be significant as - it indicates potential defense evasion by adversaries or Red Teams, aiming to exclude - specific users events from log data. If confirmed malicious, this behavior could - allow attackers to bypass defenses, and plan further attacks, potentially leading - to full machine compromise or lateral movement. +description: The following analytic identifies the execution of `auditpol.exe` with the "/set" and "/exclude" command-line arguments which indicates that the user's per-user policy will cause audit to be suppressed regardless of the system audit policy. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity can be significant as it indicates potential defense evasion by adversaries or Red Teams, aiming to exclude specific users events from log data. If confirmed malicious, this behavior could allow attackers to bypass defenses, and plan further attacks, potentially leading to full machine compromise or lateral movement. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where `process_auditpol` Processes.process="*/set*" Processes.process="*/exclude*" - AND NOT Processes.process="*/?*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_audit_policy_excluded_category_via_auditpol_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process name, and process original file name. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives should be rare, investigate the activity, and - apply additional filters when necessary. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_auditpol` Processes.process="*/set*" Processes.process="*/exclude*" + AND + NOT Processes.process="*/?*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_audit_policy_excluded_category_via_auditpol_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process name, and process original file name. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives should be rare, investigate the activity, and apply additional filters when necessary. references: -- https://www.microsoft.com/en-us/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/ -- https://www.cybereason.com/blog/research/prometei-botnet-exploiting-microsoft-exchange-vulnerabilities -- https://attack.mitre.org/techniques/T1562/002/ -- https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/auditpol-set + - https://www.microsoft.com/en-us/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/ + - https://www.cybereason.com/blog/research/prometei-botnet-exploiting-microsoft-exchange-vulnerabilities + - https://attack.mitre.org/techniques/T1562/002/ + - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/auditpol-set drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ with CommandLine - $process$ was identified attempting to exclude a specific user events on $dest$ - by user $user$. - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ with CommandLine $process$ was identified attempting to exclude a specific user events on $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Windows Audit Policy Tampering - asset_type: Endpoint - mitre_attack_id: - - T1562.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Audit Policy Tampering + asset_type: Endpoint + mitre_attack_id: + - T1562.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - Sysmon - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog -- name: True Positive Test - Security - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test - Sysmon + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test - Security + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_audit_policy_restored_via_auditpol.yml b/detections/endpoint/windows_audit_policy_restored_via_auditpol.yml index b794f70395..4bac07a1fc 100644 --- a/detections/endpoint/windows_audit_policy_restored_via_auditpol.yml +++ b/detections/endpoint/windows_audit_policy_restored_via_auditpol.yml @@ -1,91 +1,73 @@ name: Windows Audit Policy Restored via Auditpol id: d7d1795b-ea18-47e5-9ca6-2c330d052d21 -version: 3 -date: '2025-05-02' +version: 4 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly -description: The following analytic identifies the execution of `auditpol.exe` with - the "/restore" command-line argument used to restore the audit policy from a file. - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on - process names and command-line executions. This activity can be significant as it - indicates potential defense evasion by adversaries or Red Teams, aiming to limit - data that can be leveraged for detections and audits. Attackers can provide an audit - policy file that disables certain or all audit policy configuration. If confirmed - malicious, this behavior could allow attackers to bypass defenses, and plan further - attacks, potentially leading to full machine compromise or lateral movement. +description: The following analytic identifies the execution of `auditpol.exe` with the "/restore" command-line argument used to restore the audit policy from a file. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity can be significant as it indicates potential defense evasion by adversaries or Red Teams, aiming to limit data that can be leveraged for detections and audits. Attackers can provide an audit policy file that disables certain or all audit policy configuration. If confirmed malicious, this behavior could allow attackers to bypass defenses, and plan further attacks, potentially leading to full machine compromise or lateral movement. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where `process_auditpol` Processes.process="*/restore*" Processes.process="*/file*" - AND NOT Processes.process="*/?*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_audit_policy_restored_via_auditpol_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process name, and process original file name. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives could arise from administrative activity such - as audit policy setup. Apply additional filters to known scripts and parent processes - performing this action where necessary. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_auditpol` Processes.process="*/restore*" Processes.process="*/file*" + AND + NOT Processes.process="*/?*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_audit_policy_restored_via_auditpol_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process name, and process original file name. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives could arise from administrative activity such as audit policy setup. Apply additional filters to known scripts and parent processes performing this action where necessary. references: -- https://www.microsoft.com/en-us/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/ -- https://www.cybereason.com/blog/research/prometei-botnet-exploiting-microsoft-exchange-vulnerabilities -- https://attack.mitre.org/techniques/T1562/002/ -- https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/auditpol-restore + - https://www.microsoft.com/en-us/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/ + - https://www.cybereason.com/blog/research/prometei-botnet-exploiting-microsoft-exchange-vulnerabilities + - https://attack.mitre.org/techniques/T1562/002/ + - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/auditpol-restore drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - attempting to restore and audit policy on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 16 - - field: dest - type: system - score: 16 - threat_objects: - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified attempting to restore and audit policy on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 16 + - field: dest + type: system + score: 16 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Windows Audit Policy Tampering - asset_type: Endpoint - mitre_attack_id: - - T1562.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Audit Policy Tampering + asset_type: Endpoint + mitre_attack_id: + - T1562.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - Sysmon - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test - Sysmon + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_audit_policy_security_descriptor_tampering_via_auditpol.yml b/detections/endpoint/windows_audit_policy_security_descriptor_tampering_via_auditpol.yml index fe685af9f5..6b42c666e7 100644 --- a/detections/endpoint/windows_audit_policy_security_descriptor_tampering_via_auditpol.yml +++ b/detections/endpoint/windows_audit_policy_security_descriptor_tampering_via_auditpol.yml @@ -1,90 +1,70 @@ name: Windows Audit Policy Security Descriptor Tampering via Auditpol id: 5628e0b7-73dc-4f1b-b37a-6e68efc2225f -version: 3 -date: '2025-05-02' +version: 4 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly -description: The following analytic identifies the execution of `auditpol.exe` with - the "/set" flag, and "/sd" command-line arguments used to modify the security descriptor - of the audit policy. It leverages data from Endpoint Detection and Response (EDR) - agents, focusing on process names and command-line executions. This activity can - be significant as it indicates potential defense evasion by adversaries or Red Teams, - aiming to limit data that can be leveraged for detections and audits. An attacker, - can disable certain policy categories from logging and then change the security - descriptor in order to restrict access to certain users or application from reverting - their changes. If confirmed malicious, this behavior could allow attackers to bypass - defenses, and plan further attacks, potentially leading to full machine compromise - or lateral movement. +description: The following analytic identifies the execution of `auditpol.exe` with the "/set" flag, and "/sd" command-line arguments used to modify the security descriptor of the audit policy. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity can be significant as it indicates potential defense evasion by adversaries or Red Teams, aiming to limit data that can be leveraged for detections and audits. An attacker, can disable certain policy categories from logging and then change the security descriptor in order to restrict access to certain users or application from reverting their changes. If confirmed malicious, this behavior could allow attackers to bypass defenses, and plan further attacks, potentially leading to full machine compromise or lateral movement. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where `process_auditpol` Processes.process="*/set*" Processes.process="*/sd:*" AND - NOT Processes.process="*/?*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_audit_policy_security_descriptor_tampering_via_auditpol_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process name, and process original file name. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives should be rare to non existent. Any activity - detected by this analytic should be investigated and approved or denied. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_auditpol` Processes.process="*/set*" Processes.process="*/sd:*" + AND + NOT Processes.process="*/?*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_audit_policy_security_descriptor_tampering_via_auditpol_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process name, and process original file name. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives should be rare to non existent. Any activity detected by this analytic should be investigated and approved or denied. references: -- https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/auditpol-set + - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/auditpol-set drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ with commandline - $process$ was identified attempting to modify the audit policy security descriptor - on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 16 - - field: dest - type: system - score: 16 - threat_objects: - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ with commandline $process$ was identified attempting to modify the audit policy security descriptor on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 16 + - field: dest + type: system + score: 16 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Windows Audit Policy Tampering - asset_type: Endpoint - mitre_attack_id: - - T1562.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Audit Policy Tampering + asset_type: Endpoint + mitre_attack_id: + - T1562.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - Sysmon - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test - Sysmon + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_autoit3_execution.yml b/detections/endpoint/windows_autoit3_execution.yml index 0f2766b14c..c034bde01b 100644 --- a/detections/endpoint/windows_autoit3_execution.yml +++ b/detections/endpoint/windows_autoit3_execution.yml @@ -1,103 +1,88 @@ name: Windows AutoIt3 Execution id: 0ecb40d9-492b-4a57-9f87-515dd742794c -version: 9 -date: '2025-10-06' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 description: | - The following analytic detects the execution of AutoIt3, a scripting - language often used for automating Windows GUI tasks and general scripting. - It identifies instances where AutoIt3 or its variants are executed by searching for process names - or original file names matching 'autoit3.exe'. - This activity is significant because attackers frequently use AutoIt3 to automate malicious actions, such as executing malware. - If confirmed malicious, this activity could lead to unauthorized code execution, - system compromise, or further propagation of malware within the environment. + The following analytic detects the execution of AutoIt3, a scripting + language often used for automating Windows GUI tasks and general scripting. + It identifies instances where AutoIt3 or its variants are executed by searching for process names + or original file names matching 'autoit3.exe'. + This activity is significant because attackers frequently use AutoIt3 to automate malicious actions, such as executing malware. + If confirmed malicious, this activity could lead to unauthorized code execution, + system compromise, or further propagation of malware within the environment. search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime - from datamodel=Endpoint.Processes where - ( - Processes.process_name = "autoit*.exe" - OR - Processes.original_file_name = "autoit*.exe" - ) - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user - Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_autoit3_execution_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present if the application is legitimately - used, filter by user or endpoint as needed. + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + from datamodel=Endpoint.Processes where + ( + Processes.process_name = "autoit*.exe" + OR + Processes.original_file_name = "autoit*.exe" + ) + by Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec + Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_autoit3_execution_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present if the application is legitimately used, filter by user or endpoint as needed. references: -- https://github.com/PaloAltoNetworks/Unit42-timely-threat-intel/blob/main/2023-10-25-IOCs-from-DarkGate-activity.txt + - https://github.com/PaloAltoNetworks/Unit42-timely-threat-intel/blob/main/2023-10-25-IOCs-from-DarkGate-activity.txt drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Execution of AutoIt3 detected. The source process is $parent_process_name$ - and the destination process is $process_name$ on $dest$ by - risk_objects: - - field: dest - type: system - score: 50 - - field: user - type: user - score: 50 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: Execution of AutoIt3 detected. The source process is $parent_process_name$ and the destination process is $process_name$ on $dest$ by + risk_objects: + - field: dest + type: system + score: 50 + - field: user + type: user + score: 50 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Crypto Stealer - - Handala Wiper - - DarkGate Malware - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1059 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Crypto Stealer + - Handala Wiper + - DarkGate Malware + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1059 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/autoit/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/autoit/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_autostart_execution_lsass_driver_registry_modification.yml b/detections/endpoint/windows_autostart_execution_lsass_driver_registry_modification.yml index 674ef1bc5e..78b9105c88 100644 --- a/detections/endpoint/windows_autostart_execution_lsass_driver_registry_modification.yml +++ b/detections/endpoint/windows_autostart_execution_lsass_driver_registry_modification.yml @@ -5,72 +5,45 @@ date: '2025-05-02' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects modifications to undocumented registry - keys that allow a DLL to load into lsass.exe, potentially capturing credentials. - It leverages the Endpoint.Registry data model to identify changes to \CurrentControlSet\Services\NTDS\DirectoryServiceExtPt - or \CurrentControlSet\Services\NTDS\LsaDbExtPt. This activity is significant as - it indicates a possible attempt to inject malicious code into the Local Security - Authority Subsystem Service (LSASS), which can lead to credential theft. If confirmed - malicious, this could allow attackers to gain unauthorized access to sensitive information - and escalate privileges within the environment. +description: The following analytic detects modifications to undocumented registry keys that allow a DLL to load into lsass.exe, potentially capturing credentials. It leverages the Endpoint.Registry data model to identify changes to \CurrentControlSet\Services\NTDS\DirectoryServiceExtPt or \CurrentControlSet\Services\NTDS\LsaDbExtPt. This activity is significant as it indicates a possible attempt to inject malicious code into the Local Security Authority Subsystem Service (LSASS), which can lead to credential theft. If confirmed malicious, this could allow attackers to gain unauthorized access to sensitive information and escalate privileges within the environment. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path IN ("*\\CurrentControlSet\\Services\\NTDS\\DirectoryServiceExtPt","*\\CurrentControlSet\\Services\\NTDS\\LsaDbExtPt") - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_autostart_execution_lsass_driver_registry_modification_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: False positives may be present on recent Windows Operating - Systems. Filtering may be required based on process_name. In addition, look for - non-standard, unsigned, module loads into LSASS. If query is too noisy, modify by - adding Endpoint.processes process_name to query to identify the process making the - modification. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path IN ("*\\CurrentControlSet\\Services\\NTDS\\DirectoryServiceExtPt","*\\CurrentControlSet\\Services\\NTDS\\LsaDbExtPt") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_autostart_execution_lsass_driver_registry_modification_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: False positives may be present on recent Windows Operating Systems. Filtering may be required based on process_name. In addition, look for non-standard, unsigned, module loads into LSASS. If query is too noisy, modify by adding Endpoint.processes process_name to query to identify the process making the modification. references: -- https://blog.xpnsec.com/exploring-mimikatz-part-1/ -- https://github.com/oxfemale/LogonCredentialsSteal/tree/master/lsass_lib + - https://blog.xpnsec.com/exploring-mimikatz-part-1/ + - https://github.com/oxfemale/LogonCredentialsSteal/tree/master/lsass_lib drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The registry values for DirectoryServiceExtPt or LsaDbExtPt were modified - on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: The registry values for DirectoryServiceExtPt or LsaDbExtPt were modified on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1547.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1547.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.008/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.008/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_binary_proxy_execution_mavinject_dll_injection.yml b/detections/endpoint/windows_binary_proxy_execution_mavinject_dll_injection.yml index 7d3a666b4d..a92eb72a13 100644 --- a/detections/endpoint/windows_binary_proxy_execution_mavinject_dll_injection.yml +++ b/detections/endpoint/windows_binary_proxy_execution_mavinject_dll_injection.yml @@ -1,90 +1,72 @@ name: Windows Binary Proxy Execution Mavinject DLL Injection id: ccf4b61b-1b26-4f2e-a089-f2009c569c57 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the use of mavinject.exe for DLL injection - into running processes, identified by specific command-line parameters such as /INJECTRUNNING - and /HMODULE. This detection leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process names and command-line executions. This activity - is significant because it indicates potential arbitrary code execution, a common - tactic for malware deployment and persistence. If confirmed malicious, this could - allow attackers to execute unauthorized code, escalate privileges, and maintain - persistence within the environment, posing a severe security risk. +description: The following analytic detects the use of mavinject.exe for DLL injection into running processes, identified by specific command-line parameters such as /INJECTRUNNING and /HMODULE. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because it indicates potential arbitrary code execution, a common tactic for malware deployment and persistence. If confirmed malicious, this could allow attackers to execute unauthorized code, escalate privileges, and maintain persistence within the environment, posing a severe security risk. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mavinject.exe - Processes.process IN ("*injectrunning*", "*hmodule=0x*") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_binary_proxy_execution_mavinject_dll_injection_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present, filter on DLL name or parent - process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=mavinject.exe Processes.process IN ("*injectrunning*", "*hmodule=0x*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_binary_proxy_execution_mavinject_dll_injection_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present, filter on DLL name or parent process. references: -- https://attack.mitre.org/techniques/T1218/013/ -- https://posts.specterops.io/mavinject-exe-functionality-deconstructed-c29ab2cf5c0e -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-1---mavinject---inject-dll-into-running-process + - https://attack.mitre.org/techniques/T1218/013/ + - https://posts.specterops.io/mavinject-exe-functionality-deconstructed-c29ab2cf5c0e + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-1---mavinject---inject-dll-into-running-process drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting load a DLL. - risk_objects: - - field: user - type: user - score: 49 - - field: dest - type: system - score: 49 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting load a DLL. + risk_objects: + - field: user + type: user + score: 49 + - field: dest + type: system + score: 49 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1218.013 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1218.013 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.013/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.013/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_bitlocker_suspicious_command_usage.yml b/detections/endpoint/windows_bitlocker_suspicious_command_usage.yml index bde1e79e6c..fc46479de1 100644 --- a/detections/endpoint/windows_bitlocker_suspicious_command_usage.yml +++ b/detections/endpoint/windows_bitlocker_suspicious_command_usage.yml @@ -1,90 +1,78 @@ name: Windows BitLocker Suspicious Command Usage id: d0e6ec70-6e40-41a2-8b93-8d9ff077a746 -version: 3 -date: '2025-05-02' +version: 4 +date: '2026-02-25' author: Steven Dick status: production type: TTP -description: This analytic is developed to detect the usage of BitLocker commands - used to disable or impact boot settings. The malware ShrinkLocker uses various commands - change how BitLocker handles encryption, potentially bypassing TPM requirements, - enabling BitLocker without TPM, and enforcing specific startup key and PIN configurations. - Such modifications can weaken system security, making it easier for unauthorized - access and data breaches. Detecting these changes is crucial for maintaining robust - encryption and data protection. +description: This analytic is developed to detect the usage of BitLocker commands used to disable or impact boot settings. The malware ShrinkLocker uses various commands change how BitLocker handles encryption, potentially bypassing TPM requirements, enabling BitLocker without TPM, and enforcing specific startup key and PIN configurations. Such modifications can weaken system security, making it easier for unauthorized access and data breaches. Detecting these changes is crucial for maintaining robust encryption and data protection. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: "| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)\ - \ as lastTime from datamodel=Endpoint.Processes \nwhere Processes.process_name =\ - \ manage-bde.exe AND Processes.process IN (\"* -protectors -disable *\",\"* -protectors\ - \ -delete *\",\"* -forcerecovery *\",\"* -lock *\") \nby Processes.action Processes.dest\ - \ Processes.original_file_name Processes.parent_process Processes.parent_process_exec\ - \ \nProcesses.parent_process_guid Processes.parent_process_id Processes.parent_process_name\ - \ \nProcesses.parent_process_path Processes.process Processes.process_exec Processes.process_guid\ - \ Processes.process_hash \nProcesses.process_id Processes.process_integrity_level\ - \ Processes.process_name Processes.process_path \nProcesses.user Processes.user_id\ - \ Processes.vendor_product \n| `drop_dm_object_name(Processes)` \n| `security_content_ctime(firstTime)`\ - \ \n| `security_content_ctime(lastTime)`\n| `windows_bitlocker_suspicious_command_usage_filter`" -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: Administrators may enable or disable this feature that may - cause some false positive. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = manage-bde.exe + AND + Processes.process IN ("* -protectors -disable *","* -protectors -delete *","* -forcerecovery *","* -lock *") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_bitlocker_suspicious_command_usage_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: Administrators may enable or disable this feature that may cause some false positive. references: -- https://attack.mitre.org/techniques/T1486/ -- https://www.nccgroup.com/us/research-blog/nameless-and-shameless-ransomware-encryption-via-bitlocker/ -- https://www.bitdefender.com/en-us/blog/businessinsights/shrinklocker-decryptor-from-friend-to-foe-and-back-again -- https://www.bleepingcomputer.com/news/security/new-shrinklocker-ransomware-uses-bitlocker-to-encrypt-your-files/ + - https://attack.mitre.org/techniques/T1486/ + - https://www.nccgroup.com/us/research-blog/nameless-and-shameless-ransomware-encryption-via-bitlocker/ + - https://www.bitdefender.com/en-us/blog/businessinsights/shrinklocker-decryptor-from-friend-to-foe-and-back-again + - https://www.bleepingcomputer.com/news/security/new-shrinklocker-ransomware-uses-bitlocker-to-encrypt-your-files/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$","$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate processes on $dest$ - search: '| from datamodel Endpoint.Processes | search process_name = $process_name$ - AND dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$","$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate processes on $dest$ + search: '| from datamodel Endpoint.Processes | search process_name = $process_name$ AND dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious Windows BitLocker command was run by $user$ detected on $dest$ - risk_objects: - - field: dest - type: system - score: 60 - - field: user - type: user - score: 60 - threat_objects: - - field: parent_process - type: process + message: A suspicious Windows BitLocker command was run by $user$ detected on $dest$ + risk_objects: + - field: dest + type: system + score: 60 + - field: user + type: user + score: 60 + threat_objects: + - field: parent_process + type: process tags: - analytic_story: - - ShrinkLocker - asset_type: Endpoint - mitre_attack_id: - - T1486 - - T1490 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ShrinkLocker + asset_type: Endpoint + mitre_attack_id: + - T1486 + - T1490 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/bitlocker_sus_commands/bitlocker_sus_commands.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/bitlocker_sus_commands/bitlocker_sus_commands.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_bitlockertogo_process_execution.yml b/detections/endpoint/windows_bitlockertogo_process_execution.yml index 88150c4e8c..3348817fed 100644 --- a/detections/endpoint/windows_bitlockertogo_process_execution.yml +++ b/detections/endpoint/windows_bitlockertogo_process_execution.yml @@ -1,59 +1,46 @@ name: Windows BitLockerToGo Process Execution id: 68cbc9e9-2882-46f2-b636-3b5080589d58 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Nasreddine Bencherchali, Splunk data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 + - Sysmon EventID 1 + - Windows Event Log Security 4688 type: Hunting status: production -description: The following analytic detects BitLockerToGo.exe execution, which has - been observed being abused by Lumma stealer malware. The malware leverages this - legitimate Windows utility to manipulate registry keys, search for cryptocurrency - wallets and credentials, and exfiltrate sensitive data. This activity is significant - because BitLockerToGo.exe provides functionality for viewing, copying, and writing - files as well as modifying registry branches - capabilities that the Lumma stealer - exploits. However, note that if legitimate use of BitLockerToGo.exe is in the organization, - this detection will -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=bitlockertogo.exe - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_bitlockertogo_process_execution_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives are likely, as BitLockerToGo.exe is a legitimate - Windows utility used for managing BitLocker encryption. However, monitor for usage - of BitLockerToGo.exe in your environment, tune as needed. If BitLockerToGo.exe is - not used in your environment, move to TTP. +description: The following analytic detects BitLockerToGo.exe execution, which has been observed being abused by Lumma stealer malware. The malware leverages this legitimate Windows utility to manipulate registry keys, search for cryptocurrency wallets and credentials, and exfiltrate sensitive data. This activity is significant because BitLockerToGo.exe provides functionality for viewing, copying, and writing files as well as modifying registry branches - capabilities that the Lumma stealer exploits. However, note that if legitimate use of BitLockerToGo.exe is in the organization, this detection will +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=bitlockertogo.exe + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_bitlockertogo_process_execution_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives are likely, as BitLockerToGo.exe is a legitimate Windows utility used for managing BitLocker encryption. However, monitor for usage of BitLockerToGo.exe in your environment, tune as needed. If BitLockerToGo.exe is not used in your environment, move to TTP. references: -- https://securelist.com/fake-captcha-delivers-lumma-amadey/114312/ + - https://securelist.com/fake-captcha-delivers-lumma-amadey/114312/ tags: - analytic_story: - - Lumma Stealer - asset_type: Endpoint - mitre_attack_id: - - T1218 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Lumma Stealer + asset_type: Endpoint + mitre_attack_id: + - T1218 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218/bitlockertogo/4688_bitlockertogo_windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218/bitlockertogo/4688_bitlockertogo_windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_bitlockertogo_with_network_activity.yml b/detections/endpoint/windows_bitlockertogo_with_network_activity.yml index 22ba9efc10..e87c679ce7 100644 --- a/detections/endpoint/windows_bitlockertogo_with_network_activity.yml +++ b/detections/endpoint/windows_bitlockertogo_with_network_activity.yml @@ -1,52 +1,45 @@ name: Windows BitLockerToGo with Network Activity id: 14e3a089-cc23-4f4d-a770-26e44a31fbac -version: 6 -date: '2025-10-14' +version: 7 +date: '2026-02-25' author: Michael Haag, Nasreddine Bencherchali, Splunk data_source: -- Sysmon EventID 22 + - Sysmon EventID 22 type: Hunting status: production -description: The following analytic detects suspicious usage of BitLockerToGo.exe, - which has been observed being abused by Lumma stealer malware. The malware leverages - this legitimate Windows utility to manipulate registry keys, search for cryptocurrency - wallets and credentials, and exfiltrate sensitive data. This activity is significant - because BitLockerToGo.exe provides functionality for viewing, copying, and writing - files as well as modifying registry branches - capabilities that the Lumma stealer - exploits for malicious purposes. If confirmed malicious, this could indicate an - active data theft campaign targeting cryptocurrency wallets, browser credentials, - and password manager archives. The detection focuses on identifying BitLockerToGo.exe - execution patterns that deviate from normal system behavior. -search: '`sysmon` EventCode=22 process_name="bitlockertogo.exe" | stats count min(_time) - as firstTime max(_time) as lastTime by answer answer_count dvc process_exec process_guid - process_name query query_count reply_code_id signature signature_id src user_id - vendor_product QueryName QueryResults QueryStatus | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_bitlockertogo_with_network_activity_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name and eventcode = 22 dnsquery executions from your endpoints. - If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -known_false_positives: False positives are likely, as BitLockerToGo.exe is a legitimate - Windows utility used for managing BitLocker encryption. However, the detection is - designed to flag unusual execution patterns that deviate from standard usage. Filtering - may be required to reduce false positives, once confirmed - move to TTP. +description: The following analytic detects suspicious usage of BitLockerToGo.exe, which has been observed being abused by Lumma stealer malware. The malware leverages this legitimate Windows utility to manipulate registry keys, search for cryptocurrency wallets and credentials, and exfiltrate sensitive data. This activity is significant because BitLockerToGo.exe provides functionality for viewing, copying, and writing files as well as modifying registry branches - capabilities that the Lumma stealer exploits for malicious purposes. If confirmed malicious, this could indicate an active data theft campaign targeting cryptocurrency wallets, browser credentials, and password manager archives. The detection focuses on identifying BitLockerToGo.exe execution patterns that deviate from normal system behavior. +search: |- + `sysmon` EventCode=22 process_name="bitlockertogo.exe" + | stats count min(_time) as firstTime max(_time) as lastTime + BY answer answer_count dvc + process_exec process_guid process_name + query query_count reply_code_id + signature signature_id src + user_id vendor_product QueryName + QueryResults QueryStatus + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_bitlockertogo_with_network_activity_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and eventcode = 22 dnsquery executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: False positives are likely, as BitLockerToGo.exe is a legitimate Windows utility used for managing BitLocker encryption. However, the detection is designed to flag unusual execution patterns that deviate from standard usage. Filtering may be required to reduce false positives, once confirmed - move to TTP. references: -- https://any.run/report/5e9ba24639f70787e56f10a241271ae819ef9c573edb22b9eeade7cb40a2df2a/66f16c7b-2cfc-40c5-91cc-f1cbe9743fa3 -- https://securelist.com/fake-captcha-delivers-lumma-amadey/114312/ + - https://any.run/report/5e9ba24639f70787e56f10a241271ae819ef9c573edb22b9eeade7cb40a2df2a/66f16c7b-2cfc-40c5-91cc-f1cbe9743fa3 + - https://securelist.com/fake-captcha-delivers-lumma-amadey/114312/ tags: - analytic_story: - - Lumma Stealer - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1218 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Lumma Stealer + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1218 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218/bitlockertogo/bitlockertogo_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218/bitlockertogo/bitlockertogo_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_boot_or_logon_autostart_execution_in_startup_folder.yml b/detections/endpoint/windows_boot_or_logon_autostart_execution_in_startup_folder.yml index 7f7b611666..9dd10c4e50 100644 --- a/detections/endpoint/windows_boot_or_logon_autostart_execution_in_startup_folder.yml +++ b/detections/endpoint/windows_boot_or_logon_autostart_execution_in_startup_folder.yml @@ -5,82 +5,59 @@ date: '2025-12-17' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the creation of files in the Windows - %startup% folder, a common persistence technique. It leverages the - Endpoint.Filesystem data model to identify file creation events in this - specific directory. This activity is significant because adversaries often use - the startup folder to ensure their malicious code executes automatically upon - system boot or user logon. If confirmed malicious, this could allow attackers - to maintain persistence on the host, potentially leading to further system - compromise and unauthorized access to sensitive information. +description: The following analytic detects the creation of files in the Windows %startup% folder, a common persistence technique. It leverages the Endpoint.Filesystem data model to identify file creation events in this specific directory. This activity is significant because adversaries often use the startup folder to ensure their malicious code executes automatically upon system boot or user logon. If confirmed malicious, this could allow attackers to maintain persistence on the host, potentially leading to further system compromise and unauthorized access to sensitive information. data_source: -- Sysmon EventID 11 -search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_path = "*\\Microsoft\\Windows\\Start - Menu\\Programs\\Startup\\*" by Filesystem.action Filesystem.dest Filesystem.file_access_time - Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name - Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid - Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_boot_or_logon_autostart_execution_in_startup_folder_filter`' -how_to_implement: To successfully implement this search you need to be ingesting - information on process that include the name of the Filesystem responsible for - the changes from your endpoints into the `Endpoint` datamodel in the - `Filesystem` node. -known_false_positives: Administrators may allow creation of script or exe in - this path. + - Sysmon EventID 11 +search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_path = "*\\Microsoft\\Windows\\Start Menu\\Programs\\Startup\\*" by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_boot_or_logon_autostart_execution_in_startup_folder_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. +known_false_positives: Administrators may allow creation of script or exe in this path. references: -- https://attack.mitre.org/techniques/T1204/002/ -- https://www.fortinet.com/blog/threat-research/chaos-ransomware-variant-sides-with-russia + - https://attack.mitre.org/techniques/T1204/002/ + - https://www.fortinet.com/blog/threat-research/chaos-ransomware-variant-sides-with-russia drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a process dropped a file in %startup% folder on $dest$ - risk_objects: - - field: user - type: user - score: 81 - - field: dest - type: system - score: 81 - threat_objects: - - field: file_name - type: file_name + message: a process dropped a file in %startup% folder on $dest$ + risk_objects: + - field: user + type: user + score: 81 + - field: dest + type: system + score: 81 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - XWorm - - Chaos Ransomware - - NjRAT - - Crypto Stealer - - Gozi Malware - - Quasar RAT - - RedLine Stealer - - Interlock Ransomware - - APT37 Rustonotto and FadeStealer - - PromptFlux - asset_type: Endpoint - mitre_attack_id: - - T1547.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - XWorm + - Chaos Ransomware + - NjRAT + - Crypto Stealer + - Gozi Malware + - Quasar RAT + - RedLine Stealer + - Interlock Ransomware + - APT37 Rustonotto and FadeStealer + - PromptFlux + asset_type: Endpoint + mitre_attack_id: + - T1547.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/chaos_ransomware/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/chaos_ransomware/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_bootloader_inventory.yml b/detections/endpoint/windows_bootloader_inventory.yml index 0c33ed21cd..c56c2c6c8e 100644 --- a/detections/endpoint/windows_bootloader_inventory.yml +++ b/detections/endpoint/windows_bootloader_inventory.yml @@ -1,40 +1,34 @@ name: Windows BootLoader Inventory id: 4f7e3913-4db3-4ccd-afe4-31198982305d -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: experimental type: Hunting data_source: [] -description: The following analytic identifies the bootloader paths on Windows endpoints. - It leverages a PowerShell Scripted input to capture this data, which is then processed - and aggregated using Splunk. Monitoring bootloader paths is significant for a SOC - as it helps detect unauthorized modifications that could indicate bootkits or other - persistent threats. If confirmed malicious, such activity could allow attackers - to maintain persistence, bypass security controls, and potentially control the boot - process, leading to full system compromise. -search: '`bootloader_inventory` | stats count min(_time) as firstTime max(_time) as - lastTime values(_raw) by host | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_bootloader_inventory_filter`' -how_to_implement: To implement this analytic, a new stanza will need to be added to - a inputs.conf and deployed to all or some Windows endpoints. https://gist.github.com/MHaggis/26518cd2844b0e03de6126660bb45707 - provides the stanza. If modifying the sourcetype, be sure to update the Macro for - this analytic. Recommend running it daily, or weekly, depending on threat model. -known_false_positives: No false positives here, only bootloaders. Filter as needed - or create a lookup as a baseline. +description: The following analytic identifies the bootloader paths on Windows endpoints. It leverages a PowerShell Scripted input to capture this data, which is then processed and aggregated using Splunk. Monitoring bootloader paths is significant for a SOC as it helps detect unauthorized modifications that could indicate bootkits or other persistent threats. If confirmed malicious, such activity could allow attackers to maintain persistence, bypass security controls, and potentially control the boot process, leading to full system compromise. +search: |- + `bootloader_inventory` + | stats count min(_time) as firstTime max(_time) as lastTime values(_raw) + BY host + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_bootloader_inventory_filter` +how_to_implement: To implement this analytic, a new stanza will need to be added to a inputs.conf and deployed to all or some Windows endpoints. https://gist.github.com/MHaggis/26518cd2844b0e03de6126660bb45707 provides the stanza. If modifying the sourcetype, be sure to update the Macro for this analytic. Recommend running it daily, or weekly, depending on threat model. +known_false_positives: No false positives here, only bootloaders. Filter as needed or create a lookup as a baseline. references: -- https://gist.github.com/MHaggis/26518cd2844b0e03de6126660bb45707 -- https://www.microsoft.com/en-us/security/blog/2023/04/11/guidance-for-investigating-attacks-using-cve-2022-21894-the-blacklotus-campaign/ + - https://gist.github.com/MHaggis/26518cd2844b0e03de6126660bb45707 + - https://www.microsoft.com/en-us/security/blog/2023/04/11/guidance-for-investigating-attacks-using-cve-2022-21894-the-blacklotus-campaign/ tags: - analytic_story: - - BlackLotus Campaign - - Windows BootKits - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1542.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - BlackLotus Campaign + - Windows BootKits + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1542.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/windows_browser_process_launched_with_unusual_flags.yml b/detections/endpoint/windows_browser_process_launched_with_unusual_flags.yml index 8a99c060ec..3747394d2e 100644 --- a/detections/endpoint/windows_browser_process_launched_with_unusual_flags.yml +++ b/detections/endpoint/windows_browser_process_launched_with_unusual_flags.yml @@ -7,72 +7,45 @@ status: production type: Anomaly description: The following analytic detects the use of unusual browser flags, specifically --mute-audio and --do-not-elevate, which deviate from standard browser launch behavior. These flags may indicate automated scripts, testing environments, or attempts to modify browser functionality for silent operation or restricted privilege execution. Detection focuses on non-standard launch parameters, unexpected process behavior, or deviations from baseline configurations. Monitoring such flag usage helps identify potentially suspicious activity, misconfigurations, or policy violations, enabling security teams to investigate anomalies, ensure system compliance, and differentiate legitimate administrative or testing uses from unusual or unauthorized operations. data_source: -- Sysmon EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes - where NOT (Processes.parent_process_name IN ("chrome.exe", "msedge.exe", "brave.exe", "firefox.exe", "explorer.exe")) AND - NOT (Processes.parent_process_path IN("C:\\Program Files*", "C:\\Windows\\System32\\*", "C:\\Windows\\SysWow64\\*",)) AND - Processes.process_name IN ("chrome.exe", "msedge.exe", "brave.exe", "firefox.exe") AND - Processes.process IN ("*--mute-audio*","*--no-de-elevate*", "*--do-not-de-elevate*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_browser_process_launched_with_unusual_flags_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: It is possible false positives will be present based on third - party applications. Filtering may be needed. + - Sysmon EventID 1 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where NOT (Processes.parent_process_name IN ("chrome.exe", "msedge.exe", "brave.exe", "firefox.exe", "explorer.exe")) AND NOT (Processes.parent_process_path IN("C:\\Program Files*", "C:\\Windows\\System32\\*", "C:\\Windows\\SysWow64\\*",)) AND Processes.process_name IN ("chrome.exe", "msedge.exe", "brave.exe", "firefox.exe") AND Processes.process IN ("*--mute-audio*","*--no-de-elevate*", "*--do-not-de-elevate*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_browser_process_launched_with_unusual_flags_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: It is possible false positives will be present based on third party applications. Filtering may be needed. references: -- https://www.recordedfuture.com/research/from-castleloader-to-castlerat-tag-150-advances-operations -- https://peter.sh/experiments/chromium-command-line-switches/ + - https://www.recordedfuture.com/research/from-castleloader-to-castlerat-tag-150-advances-operations + - https://peter.sh/experiments/chromium-command-line-switches/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest="$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest="$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: chromium browser that has unusual flags for muting or audio and prevent de-elevation of the current process in $dest$. - risk_objects: - - field: dest - type: system - score: 15 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: chromium browser that has unusual flags for muting or audio and prevent de-elevation of the current process in $dest$. + risk_objects: + - field: dest + type: system + score: 15 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - Castle RAT - asset_type: Endpoint - mitre_attack_id: - - T1185 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Castle RAT + asset_type: Endpoint + mitre_attack_id: + - T1185 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/browser_unusual_flag/castle_chrome_shell32.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/browser_unusual_flag/castle_chrome_shell32.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_bypass_uac_via_pkgmgr_tool.yml b/detections/endpoint/windows_bypass_uac_via_pkgmgr_tool.yml index f5541c2b4a..705681bece 100644 --- a/detections/endpoint/windows_bypass_uac_via_pkgmgr_tool.yml +++ b/detections/endpoint/windows_bypass_uac_via_pkgmgr_tool.yml @@ -6,83 +6,49 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic detects the execution of the deprecated 'pkgmgr.exe' - process with an XML input file, which is unusual and potentially suspicious. This - detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on - process execution details and command-line arguments. The significance lies in the - deprecated status of 'pkgmgr.exe' and the use of XML files, which could indicate - an attempt to bypass User Account Control (UAC). If confirmed malicious, this activity - could allow an attacker to execute commands with elevated privileges, leading to - potential system compromise and unauthorized changes. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = pkgmgr.exe - Processes.process = "*.xml*" NOT(Processes.parent_process_path IN("*:\\windows\\system32\\*", - "*:\\windows\\syswow64\\*", "*:\\Program Files*")) by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_bypass_uac_via_pkgmgr_tool_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present on recent Windows Operating - Systems. Filtering may be required based on process_name. In addition, look for - non-standard, unsigned, module loads into LSASS. If query is too noisy, modify by - adding Endpoint.processes process_name to query to identify the process making the - modification. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic detects the execution of the deprecated 'pkgmgr.exe' process with an XML input file, which is unusual and potentially suspicious. This detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on process execution details and command-line arguments. The significance lies in the deprecated status of 'pkgmgr.exe' and the use of XML files, which could indicate an attempt to bypass User Account Control (UAC). If confirmed malicious, this activity could allow an attacker to execute commands with elevated privileges, leading to potential system compromise and unauthorized changes. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = pkgmgr.exe Processes.process = "*.xml*" NOT(Processes.parent_process_path IN("*:\\windows\\system32\\*", "*:\\windows\\syswow64\\*", "*:\\Program Files*")) by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_bypass_uac_via_pkgmgr_tool_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present on recent Windows Operating Systems. Filtering may be required based on process_name. In addition, look for non-standard, unsigned, module loads into LSASS. If query is too noisy, modify by adding Endpoint.processes process_name to query to identify the process making the modification. references: -- https://asec.ahnlab.com/en/17692/ -- https://www.blackberry.com/us/en/solutions/endpoint-security/ransomware-protection/warzone#:~:text=Warzone%20RAT%20(AKA%20Ave%20Maria)%20is%20a%20remote%20access%20trojan,is%20as%20an%20information%20stealer. + - https://asec.ahnlab.com/en/17692/ + - https://www.blackberry.com/us/en/solutions/endpoint-security/ransomware-protection/warzone#:~:text=Warzone%20RAT%20(AKA%20Ave%20Maria)%20is%20a%20remote%20access%20trojan,is%20as%20an%20information%20stealer. drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A pkgmgr.exe executed with package manager xml input file on $dest$ - risk_objects: - - field: user - type: user - score: 9 - - field: dest - type: system - score: 9 - threat_objects: [] + message: A pkgmgr.exe executed with package manager xml input file on $dest$ + risk_objects: + - field: user + type: user + score: 9 + - field: dest + type: system + score: 9 + threat_objects: [] tags: - analytic_story: - - Warzone RAT - asset_type: Endpoint - mitre_attack_id: - - T1548.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Warzone RAT + asset_type: Endpoint + mitre_attack_id: + - T1548.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/warzone_rat/pkgmgr_uac_bypass/pkgmgr_create_file.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/warzone_rat/pkgmgr_uac_bypass/pkgmgr_create_file.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_cab_file_on_disk.yml b/detections/endpoint/windows_cab_file_on_disk.yml index 26af81cfa7..d39919093c 100644 --- a/detections/endpoint/windows_cab_file_on_disk.yml +++ b/detections/endpoint/windows_cab_file_on_disk.yml @@ -1,77 +1,63 @@ name: Windows CAB File on Disk id: 622f08d0-69ef-42c2-8139-66088bc25acd -version: 7 -date: '2025-09-18' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly data_source: -- Sysmon EventID 11 -description: The following analytic detects .cab files being written to disk. It leverages - data from Endpoint Detection and Response (EDR) agents, focusing on events where - the file name is '*.cab' and the action is 'write'. This activity is significant - as .cab files can be used to deliver malicious payloads, including embedded .url - files that execute harmful code. If confirmed malicious, this behavior could lead - to unauthorized code execution and potential system compromise. Analysts should - review the file path and associated artifacts for further investigation. -search: '| tstats `security_content_summariesonly` count values(Filesystem.file_path) - as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem - where (Filesystem.file_name=*.cab) by Filesystem.action Filesystem.dest Filesystem.file_access_time - Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name - Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid - Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name("Filesystem")` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_cab_file_on_disk_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives will only be present if a process legitimately - writes a .cab file to disk. Modify the analytic as needed by file path. Filter as - needed. + - Sysmon EventID 11 +description: The following analytic detects .cab files being written to disk. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on events where the file name is '*.cab' and the action is 'write'. This activity is significant as .cab files can be used to deliver malicious payloads, including embedded .url files that execute harmful code. If confirmed malicious, this behavior could lead to unauthorized code execution and potential system compromise. Analysts should review the file path and associated artifacts for further investigation. +search: |- + | tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE ( + Filesystem.file_name=*.cab + ) + BY Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path Filesystem.file_acl + Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name("Filesystem")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_cab_file_on_disk_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives will only be present if a process legitimately writes a .cab file to disk. Modify the analytic as needed by file path. Filter as needed. references: -- https://github.com/PaloAltoNetworks/Unit42-timely-threat-intel/blob/main/2023-10-25-IOCs-from-DarkGate-activity.txt + - https://github.com/PaloAltoNetworks/Unit42-timely-threat-intel/blob/main/2023-10-25-IOCs-from-DarkGate-activity.txt drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A .cab file was written to disk on endpoint $dest$. - risk_objects: - - field: dest - type: system - score: 5 - threat_objects: [] + message: A .cab file was written to disk on endpoint $dest$. + risk_objects: + - field: dest + type: system + score: 5 + threat_objects: [] tags: - analytic_story: - - DarkGate Malware - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1566.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - DarkGate Malware + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1566.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/autoit/cab_files.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/autoit/cab_files.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_cabinet_file_extraction_via_expand.yml b/detections/endpoint/windows_cabinet_file_extraction_via_expand.yml index 3b528302de..e1dd207b2a 100644 --- a/detections/endpoint/windows_cabinet_file_extraction_via_expand.yml +++ b/detections/endpoint/windows_cabinet_file_extraction_via_expand.yml @@ -6,81 +6,79 @@ author: Michael Haag, Splunk status: production type: TTP description: | - Detects usage of expand.exe to extract Microsoft Cabinet (CAB) archives, with - emphasis on extractions into `C:\\ProgramData` or similar staging locations. In - recent APT37 activity, a CAB payload (e.g., wonder.cab) was expanded into - ProgramData prior to persistence and execution. This behavior is a strong signal - for ingress tool transfer and staging of payloads. + Detects usage of expand.exe to extract Microsoft Cabinet (CAB) archives, with + emphasis on extractions into `C:\\ProgramData` or similar staging locations. In + recent APT37 activity, a CAB payload (e.g., wonder.cab) was expanded into + ProgramData prior to persistence and execution. This behavior is a strong signal + for ingress tool transfer and staging of payloads. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime - from datamodel=Endpoint.Processes - where Processes.process_name="expand.exe" - (Processes.process="*-F:*" OR Processes.process="*/F:*") - Processes.process="*\\ProgramData\\*" - by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.original_file_name Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_path Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_path Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_cabinet_file_extraction_via_expand_filter` + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime + from datamodel=Endpoint.Processes + where Processes.process_name="expand.exe" + (Processes.process="*-F:*" OR Processes.process="*/F:*") + Processes.process="*\\ProgramData\\*" + by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.original_file_name Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_path Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_path Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_cabinet_file_extraction_via_expand_filter` how_to_implement: | - This analytic relies on process creation telemetry mapped to the Endpoint.Processes - datamodel (e.g., Sysmon EID 1 or EDR). Ensure full command-line logging is enabled - to capture expand.exe arguments, including `/F:*` or `-F:*` and destination paths. + This analytic relies on process creation telemetry mapped to the Endpoint.Processes + datamodel (e.g., Sysmon EID 1 or EDR). Ensure full command-line logging is enabled + to capture expand.exe arguments, including `/F:*` or `-F:*` and destination paths. known_false_positives: | - Legitimate software deployment or administrators may use expand.exe for local - file extraction. Filter by approved deployment tools, signed parent processes, - and sanctioned paths. + Legitimate software deployment or administrators may use expand.exe for local + file extraction. Filter by approved deployment tools, signed parent processes, + and sanctioned paths. references: -- https://www.zscaler.com/blogs/security-research/apt37-targets-windows-rust-backdoor-and-python-loader + - https://www.zscaler.com/blogs/security-research/apt37-targets-windows-rust-backdoor-and-python-loader drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: | - | from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$","$dest$") starthoursago=168 - | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" - values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" - values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: | + | from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$","$dest$") starthoursago=168 + | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" + values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" + values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" + by normalized_risk_object + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: expand.exe extracted cabinet contents on $dest$ executed by $user$. - risk_objects: - - field: dest - type: system - score: 30 - - field: user - type: system - score: 30 - threat_objects: - - field: process_name - type: process_name + message: expand.exe extracted cabinet contents on $dest$ executed by $user$. + risk_objects: + - field: dest + type: system + score: 30 + - field: user + type: system + score: 30 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - APT37 Rustonotto and FadeStealer - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1105 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - APT37 Rustonotto and FadeStealer + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1105 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1140/atomic_red_team/expand_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1140/atomic_red_team/expand_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_cached_domain_credentials_reg_query.yml b/detections/endpoint/windows_cached_domain_credentials_reg_query.yml index 9d87c3d30d..123644b231 100644 --- a/detections/endpoint/windows_cached_domain_credentials_reg_query.yml +++ b/detections/endpoint/windows_cached_domain_credentials_reg_query.yml @@ -5,80 +5,49 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies a process command line querying the - CachedLogonsCount registry value in the Winlogon registry. This detection leverages - data from Endpoint Detection and Response (EDR) agents, focusing on command-line - executions and registry queries. Monitoring this activity is significant as it can - indicate the use of post-exploitation tools like Winpeas, which gather information - about login caching settings. If confirmed malicious, this activity could help attackers - understand login caching configurations, potentially aiding in credential theft - or lateral movement within the network. +description: The following analytic identifies a process command line querying the CachedLogonsCount registry value in the Winlogon registry. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and registry queries. Monitoring this activity is significant as it can indicate the use of post-exploitation tools like Winpeas, which gather information about login caching settings. If confirmed malicious, this activity could help attackers understand login caching configurations, potentially aiding in credential theft or lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_reg` AND Processes.process - = "* query *" AND Processes.process = "*\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\Winlogon*" - AND Processes.process = "*CACHEDLOGONSCOUNT*" by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_cached_domain_credentials_reg_query_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_reg` AND Processes.process = "* query *" AND Processes.process = "*\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\Winlogon*" AND Processes.process = "*CACHEDLOGONSCOUNT*" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_cached_domain_credentials_reg_query_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ -- https://learn.microsoft.com/de-de/troubleshoot/windows-server/user-profiles-and-logon/cached-domain-logon-information -- https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS + - https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ + - https://learn.microsoft.com/de-de/troubleshoot/windows-server/user-profiles-and-logon/cached-domain-logon-information + - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a process with commandline $process$ tries to retrieve cache domain credential - logon count on $dest$ - risk_objects: - - field: dest - type: system - score: 9 - threat_objects: [] + message: a process with commandline $process$ tries to retrieve cache domain credential logon count on $dest$ + risk_objects: + - field: dest + type: system + score: 9 + threat_objects: [] tags: - analytic_story: - - Windows Post-Exploitation - - Prestige Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1003.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Post-Exploitation + - Prestige Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1003.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_certutil_root_certificate_addition.yml b/detections/endpoint/windows_certutil_root_certificate_addition.yml index d7db97d6db..483c5c9c1b 100644 --- a/detections/endpoint/windows_certutil_root_certificate_addition.yml +++ b/detections/endpoint/windows_certutil_root_certificate_addition.yml @@ -1,108 +1,103 @@ name: Windows Certutil Root Certificate Addition id: e9926391-ec0c-4bad-8a95-e450dbf6aae4 -version: 2 -date: '2025-10-06' +version: 3 +date: '2026-02-25' author: Teoderick Contreras, Nasreddine Bencherchali, Splunk status: production type: TTP description: | - The following analytic detects the use of certutil.exe to add a certificate to the Root certificate store using the "-addstore" flag. - In this case, the certificate is loaded from a temporary file path (e.g., %TEMP%) or other uncommon locations (e.g. C:\\Users\\Public\\), which is highly suspicious and uncommon in legitimate administrative activity. - This behavior may indicate an adversary is installing a malicious root certificate to intercept HTTPS traffic, impersonate trusted entities, or bypass security controls. - The use of flags such as -f (force) and -Enterprise, combined with loading .tmp files from user-writable locations, is consistent with post-exploitation activity seen in credential theft and adversary-in-the-middle (AiTM) attacks. - This should be investigated immediately, especially if correlated with unauthorized privilege use or prior certificate modifications. - You should monitor when new certificates are added to the root store because this store is what your system uses to decide which websites, apps, and software can be trusted. - If an attacker manages to add their own certificate there, they can silently intercept encrypted traffic, impersonate trusted websites, or make malicious programs look safe. - This means they could steal sensitive data, bypass security tools, and keep access to your system even after other malware is removed. + The following analytic detects the use of certutil.exe to add a certificate to the Root certificate store using the "-addstore" flag. + In this case, the certificate is loaded from a temporary file path (e.g., %TEMP%) or other uncommon locations (e.g. C:\\Users\\Public\\), which is highly suspicious and uncommon in legitimate administrative activity. + This behavior may indicate an adversary is installing a malicious root certificate to intercept HTTPS traffic, impersonate trusted entities, or bypass security controls. + The use of flags such as -f (force) and -Enterprise, combined with loading .tmp files from user-writable locations, is consistent with post-exploitation activity seen in credential theft and adversary-in-the-middle (AiTM) attacks. + This should be investigated immediately, especially if correlated with unauthorized privilege use or prior certificate modifications. + You should monitor when new certificates are added to the root store because this store is what your system uses to decide which websites, apps, and software can be trusted. + If an attacker manages to add their own certificate there, they can silently intercept encrypted traffic, impersonate trusted websites, or make malicious programs look safe. + This means they could steal sensitive data, bypass security tools, and keep access to your system even after other malware is removed. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime - values(Processes.process) as process - from datamodel=Endpoint.Processes where - `process_certutil` - Processes.process=*-addstore* - Processes.process=*root* - Processes.process IN ( - "*:\\PerfLogs\\*", - "*:\\Windows\\Temp\\*", - "*\\AppData\\Local\\Temp\\*", - "*\\ProgramData\\*", - "*\\Users\\Public\\*", - "*%AppData%*", - "*%Public%*", - "*%Temp%*", - "*%tmp%*" - ) - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user - Processes.user_id Processes.vendor_product - | `drop_dm_object_name("Processes")` - | `security_content_ctime(firstTime)` - |`security_content_ctime(lastTime)` - | `windows_certutil_root_certificate_addition_filter` + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + values(Processes.process) as process + from datamodel=Endpoint.Processes where + `process_certutil` + Processes.process=*-addstore* + Processes.process=*root* + Processes.process IN ( + "*:\\PerfLogs\\*", + "*:\\Windows\\Temp\\*", + "*\\AppData\\Local\\Temp\\*", + "*\\ProgramData\\*", + "*\\Users\\Public\\*", + "*%AppData%*", + "*%Public%*", + "*%Temp%*", + "*%tmp%*" + ) + by Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec + Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` + |`security_content_ctime(lastTime)` + | `windows_certutil_root_certificate_addition_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. - To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. - These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. - The logs must also be mapped to the `Processes` node of the `Endpoint` data model. - Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. + To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. + These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. + The logs must also be mapped to the `Processes` node of the `Endpoint` data model. + Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: | - Administrators or third party utilities may use leverage certutil in order to add a root certificate to the store. Filter as needed or restrict to critical assets on the perimeter. + Administrators or third party utilities may use leverage certutil in order to add a root certificate to the store. Filter as needed or restrict to critical assets on the perimeter. references: -- https://www.microsoft.com/en-us/security/blog/2025/07/31/frozen-in-transit-secret-blizzards-aitm-campaign-against-diplomats/ -- https://www.deepinstinct.com/blog/iranian-threat-actor-continues-to-develop-mass-exploitation-tools -- https://unit42.paloaltonetworks.com/retefe-banking-trojan-targets-sweden-switzerland-and-japan/ + - https://www.microsoft.com/en-us/security/blog/2025/07/31/frozen-in-transit-secret-blizzards-aitm-campaign-against-diplomats/ + - https://www.deepinstinct.com/blog/iranian-threat-actor-continues-to-develop-mass-exploitation-tools + - https://unit42.paloaltonetworks.com/retefe-banking-trojan-targets-sweden-switzerland-and-japan/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A potentially suspicious certificate was added to the Root certificate store via Certutil on $dest$. - risk_objects: - - field: dest - type: system - score: 60 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: A potentially suspicious certificate was added to the Root certificate store via Certutil on $dest$. + risk_objects: + - field: dest + type: system + score: 60 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - Secret Blizzard - asset_type: Endpoint - mitre_attack_id: - - T1587.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Secret Blizzard + asset_type: Endpoint + mitre_attack_id: + - T1587.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1587.003/add_store_cert/addstore_cert.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1587.003/add_store_cert/addstore_cert.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_change_file_association_command_to_notepad.yml b/detections/endpoint/windows_change_file_association_command_to_notepad.yml index 284266e234..43ff3da401 100644 --- a/detections/endpoint/windows_change_file_association_command_to_notepad.yml +++ b/detections/endpoint/windows_change_file_association_command_to_notepad.yml @@ -1,102 +1,97 @@ name: Windows Change File Association Command To Notepad id: 339155d6-34cb-4788-9d00-e67f190af93a -version: 2 -date: '2026-01-14' +version: 3 +date: '2026-02-25' author: Teoderick Contreras, Nasreddine Bencherchali, Splunk status: production type: TTP description: | - The following analytic detects attempts to change the command value of a file association of an extension to open with Notepad.exe. - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns and registry modifications. - This activity is significant as it can indicate an attempt to manipulate file handling behavior, a technique observed in APT and ransomware attacks like Prestige. - After changing the extension of all encrypted files to a new one, Prestige ransomware modifies the file association for that extension to open with Notepad.exe in order to display a ransom note. + The following analytic detects attempts to change the command value of a file association of an extension to open with Notepad.exe. + It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns and registry modifications. + This activity is significant as it can indicate an attempt to manipulate file handling behavior, a technique observed in APT and ransomware attacks like Prestige. + After changing the extension of all encrypted files to a new one, Prestige ransomware modifies the file association for that extension to open with Notepad.exe in order to display a ransom note. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime - from datamodel=Endpoint.Processes where - - ( - (`process_reg` AND Processes.process="* add *") - OR - (`process_powershell` AND Processes.process IN ("*New-ItemProperty*", "*Set-ItemProperty*", "* sp *")) - ) - - Processes.process IN ("*HKCR\\*", "*HKEY_CLASSES_ROOT\\*") - Processes.process = "*\\shell\\open\\command*" - Processes.process = "*Notepad.exe*" + from datamodel=Endpoint.Processes where - ``` - The exclusion below aims to filter the default notepad association as well as links to the notepad package from the Microsoft Store. - ``` + ( + (`process_reg` AND Processes.process="* add *") + OR + (`process_powershell` AND Processes.process IN ("*New-ItemProperty*", "*Set-ItemProperty*", "* sp *")) + ) - NOT Processes.process IN ("*\\Applications\\notepad.exe\\*", "*\\WindowsApps\\Microsoft.WindowsNotepad*") + Processes.process IN ("*HKCR\\*", "*HKEY_CLASSES_ROOT\\*") + Processes.process = "*\\shell\\open\\command*" + Processes.process = "*Notepad.exe*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process - Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id - Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user - Processes.user_id Processes.vendor_product + ``` + The exclusion below aims to filter the default notepad association as well as links to the notepad package from the Microsoft Store. + ``` - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_change_file_association_command_to_notepad_filter` + NOT Processes.process IN ("*\\Applications\\notepad.exe\\*", "*\\WindowsApps\\Microsoft.WindowsNotepad*") + + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process + Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id + Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_change_file_association_command_to_notepad_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ + - https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Process with commandline $process$ set the execution command of a file association to notepad.exe on $dest$ - risk_objects: - - field: dest - type: system - score: 70 - threat_objects: [] + message: Process with commandline $process$ set the execution command of a file association to notepad.exe on $dest$ + risk_objects: + - field: dest + type: system + score: 70 + threat_objects: [] tags: - analytic_story: - - Prestige Ransomware - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1546.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Prestige Ransomware + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1546.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/prestige_ransomware/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/prestige_ransomware/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_chrome_auto_update_disabled_via_registry.yml b/detections/endpoint/windows_chrome_auto_update_disabled_via_registry.yml index d49d0e0050..6a95d8e3ef 100644 --- a/detections/endpoint/windows_chrome_auto_update_disabled_via_registry.yml +++ b/detections/endpoint/windows_chrome_auto_update_disabled_via_registry.yml @@ -6,85 +6,79 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly description: | - The following analytic detects modifications to Windows registry values that disable Google Chrome auto-updates. - Changes to values such as DisableAutoUpdateChecksCheckboxValue = 1, Update{8A69D345-D564-463C-AFF1-A69D9E530F96} = 0, UpdateDefault = 0, and AutoUpdateCheckPeriodMinutes = 0 can prevent Chrome from receiving security updates. - This behavior may indicate attempts to bypass update policies, maintain unauthorized extensions, or facilitate malware persistence. - Monitoring these registry changes helps identify potential policy violations or malicious activity targeting browser security. + The following analytic detects modifications to Windows registry values that disable Google Chrome auto-updates. + Changes to values such as DisableAutoUpdateChecksCheckboxValue = 1, Update{8A69D345-D564-463C-AFF1-A69D9E530F96} = 0, UpdateDefault = 0, and AutoUpdateCheckPeriodMinutes = 0 can prevent Chrome from receiving security updates. + This behavior may indicate attempts to bypass update policies, maintain unauthorized extensions, or facilitate malware persistence. + Monitoring these registry changes helps identify potential policy violations or malicious activity targeting browser security. data_source: - - Sysmon EventID 13 + - Sysmon EventID 13 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry - where Registry.registry_path = "*\\Google\\Update*" - AND - ( - Registry.registry_value_name = "DisableAutoUpdateChecksCheckboxValue" - Registry.registry_value_data = 0x00000001 - ) - OR - ( - Registry.registry_value_name IN ( - "AutoUpdateCheckPeriodMinutes", - "Update{8A69D345-D564-463C-AFF1-A69D9E530F96}", - "UpdateDefault" + where Registry.registry_path = "*\\Google\\Update*" + AND + ( + Registry.registry_value_name = "DisableAutoUpdateChecksCheckboxValue" + Registry.registry_value_data = 0x00000001 + ) + OR + ( + Registry.registry_value_name IN ( + "AutoUpdateCheckPeriodMinutes", + "Update{8A69D345-D564-463C-AFF1-A69D9E530F96}", + "UpdateDefault" + ) + Registry.registry_value_data = 0x00000000 ) - Registry.registry_value_data = 0x00000000 - ) - by Registry.action Registry.dest Registry.process_guid Registry.process_id - Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product + by Registry.action Registry.dest Registry.process_guid Registry.process_id + Registry.registry_hive Registry.registry_path Registry.registry_key_name + Registry.registry_value_data Registry.registry_value_name + Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_chrome_auto_update_disabled_via_registry_filter` + | `drop_dm_object_name(Registry)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_chrome_auto_update_disabled_via_registry_filter` how_to_implement: | - To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + To successfully implement this search, you need to be ingesting + logs with the registry value name, registry path, and registry value data from your + endpoints. If you are using Sysmon, you must have at least version 2.0 of the official + Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: | - IT administrators intentionally disabling auto-updates in managed environments for testing, compatibility, or deployment purposes. + IT administrators intentionally disabling auto-updates in managed environments for testing, compatibility, or deployment purposes. references: - - https://www.gdatasoftware.com/blog/2025/11/38298-learning-about-browser-hijacking + - https://www.gdatasoftware.com/blog/2025/11/38298-learning-about-browser-hijacking drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Chrome Auto-update in $registry_path$ was disabled on $dest$ - risk_objects: - - field: dest - type: system - score: 20 - threat_objects: [] + message: Chrome Auto-update in $registry_path$ was disabled on $dest$ + risk_objects: + - field: dest + type: system + score: 20 + threat_objects: [] tags: - analytic_story: - - Browser Hijacking - asset_type: Endpoint - mitre_attack_id: - - T1185 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Browser Hijacking + asset_type: Endpoint + mitre_attack_id: + - T1185 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/disable_chrome_update/disable_chrome_update.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/disable_chrome_update/disable_chrome_update.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_chrome_enable_extension_loading_via_command_line.yml b/detections/endpoint/windows_chrome_enable_extension_loading_via_command_line.yml index 6ccc04f19c..5580fe55ab 100644 --- a/detections/endpoint/windows_chrome_enable_extension_loading_via_command_line.yml +++ b/detections/endpoint/windows_chrome_enable_extension_loading_via_command_line.yml @@ -6,77 +6,68 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly description: | - The following analytic detects instances where Google Chrome is started with the --disable-features=DisableLoadExtensionCommandLineSwitch flag, effectively enabling the loading of extensions via the command line. - This may indicate attempts to bypass enterprise extension policies, load unauthorized or malicious extensions, or manipulate browser behavior. - Monitoring this activity helps identify potential security policy violations, malware persistence techniques, or other suspicious Chrome modifications. + The following analytic detects instances where Google Chrome is started with the --disable-features=DisableLoadExtensionCommandLineSwitch flag, effectively enabling the loading of extensions via the command line. + This may indicate attempts to bypass enterprise extension policies, load unauthorized or malicious extensions, or manipulate browser behavior. + Monitoring this activity helps identify potential security policy violations, malware persistence techniques, or other suspicious Chrome modifications. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime - from datamodel=Endpoint.Processes where - Processes.process_name = "Chrome.exe" - Processes.process= "*--disable-features*" - Processes.process= "*DisableLoadExtensionCommandLineSwitch*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_chrome_enable_extension_loading_via_command_line_filter` -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime + from datamodel=Endpoint.Processes where + Processes.process_name = "Chrome.exe" + Processes.process= "*--disable-features*" + Processes.process= "*DisableLoadExtensionCommandLineSwitch*" + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_chrome_enable_extension_loading_via_command_line_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. known_false_positives: Developers or IT admins loading unpacked extensions for testing or deployment purposes. references: -- https://www.gdatasoftware.com/blog/2025/11/38298-learning-about-browser-hijacking + - https://www.gdatasoftware.com/blog/2025/11/38298-learning-about-browser-hijacking drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A $process_name$ process attempted to enable browser extension loading via command line $process$ on $dest$. - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name - - field: process - type: process + message: A $process_name$ process attempted to enable browser extension loading via command line $process$ on $dest$. + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name + - field: process + type: process tags: - analytic_story: - - Browser Hijacking - asset_type: Endpoint - mitre_attack_id: - - T1185 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Browser Hijacking + asset_type: Endpoint + mitre_attack_id: + - T1185 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/chrome_load_extensions/chrome_load_extension.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/chrome_load_extensions/chrome_load_extension.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_chrome_extension_allowed_registry_modification.yml b/detections/endpoint/windows_chrome_extension_allowed_registry_modification.yml index 397bfc4d74..cb47959524 100644 --- a/detections/endpoint/windows_chrome_extension_allowed_registry_modification.yml +++ b/detections/endpoint/windows_chrome_extension_allowed_registry_modification.yml @@ -7,59 +7,42 @@ status: production type: Anomaly description: The following analytic detects modifications to the Windows registry keys that control the Chrome Extension Install Allowlist. Unauthorized changes to these keys may indicate attempts to bypass Chrome extension restrictions or install unapproved extensions. This detection helps identify potential security policy violations or malicious activity targeting Chrome extension settings. data_source: - - Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry - where Registry.registry_path = "*\\Google\\Chrome\\ExtensionInstallAllowlist*" - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_chrome_extension_allowed_registry_modification_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path = "*\\Google\\Chrome\\ExtensionInstallAllowlist*" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_chrome_extension_allowed_registry_modification_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: Legitimate IT admin updates to Chrome extension allowlist via Group Policy or enterprise management tools. Filtering is needed. references: -- https://www.gdatasoftware.com/blog/2025/11/38298-learning-about-browser-hijacking + - https://www.gdatasoftware.com/blog/2025/11/38298-learning-about-browser-hijacking drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Chrome ExtensionInstallAllowlist Policy in $registry_path$ was modified on $dest$ - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: [] + message: Chrome ExtensionInstallAllowlist Policy in $registry_path$ was modified on $dest$ + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: [] tags: - analytic_story: - - Browser Hijacking - asset_type: Endpoint - mitre_attack_id: - - T1185 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Browser Hijacking + asset_type: Endpoint + mitre_attack_id: + - T1185 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/chrome_allow_list/chrome_extension_allow_list.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/chrome_allow_list/chrome_extension_allow_list.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_chromium_browser_launched_with_small_window_size.yml b/detections/endpoint/windows_chromium_browser_launched_with_small_window_size.yml index 04090fa31b..c2b197822d 100644 --- a/detections/endpoint/windows_chromium_browser_launched_with_small_window_size.yml +++ b/detections/endpoint/windows_chromium_browser_launched_with_small_window_size.yml @@ -1,85 +1,76 @@ name: Windows Chromium Browser Launched with Small Window Size id: 88103f56-8f5c-411f-a87f-71bee776f140 -version: 1 -date: '2026-01-23' +version: 2 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP description: The following analytic detects instances where a Chromium-based browser process, including Chrome, Edge, Brave, Opera, or Vivaldi, is launched with an unusually small window size, typically less than 100 pixels in width or height. Such configurations render the browser effectively invisible to the user and are uncommon in normal user activity. When observed on endpoints, especially in combination with automation, off-screen positioning, or suppression flags, this behavior may indicate attempts to execute web content or automated actions stealthily, bypassing user interaction and security controls. This analytic highlights potential malicious automation or covert browser-based activity. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 -search: | - | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.process_name IN ("Chrome.exe","Brave.exe", "Opera.exe", "Vivaldi.exe", "msedge.exe") - Processes.process = "*--window-size*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | rex field=Processes.process "--window-size=(?\d+)\s*,\s*(?\d+)" - | eval window_width=tonumber(window_width), window_height=tonumber(window_height) - | where window_height < 100 AND window_width < 100 - | `drop_dm_object_name(Processes)` - | fields * window_width window_height - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_chromium_browser_launched_with_small_window_size_filter` -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: | + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes + where Processes.process_name IN ("Chrome.exe","Brave.exe", "Opera.exe", "Vivaldi.exe", "msedge.exe") + Processes.process = "*--window-size*" + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | rex field=Processes.process "--window-size=(?\d+)\s*,\s*(?\d+)" + | eval window_width=tonumber(window_width), window_height=tonumber(window_height) + | where window_height < 100 AND window_width < 100 + | `drop_dm_object_name(Processes)` + | fields * window_width window_height + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_chromium_browser_launched_with_small_window_size_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. known_false_positives: No false positives have been identified at this time. references: -- https://www.trendmicro.com/en_us/research/26/a/analysis-of-the-evelyn-stealer-campaign.html -- https://peter.sh/experiments/chromium-command-line-switches/ + - https://www.trendmicro.com/en_us/research/26/a/analysis-of-the-evelyn-stealer-campaign.html + - https://peter.sh/experiments/chromium-command-line-switches/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Chromium-based browser process was launched on $dest$ by user $user$ with an unusually small window size ($window_width$ x $window_height$ pixels). The process was spawned by $parent_process_name$ and included the following command-line parameters $process$. - risk_objects: - - field: dest - type: system - score: 50 - - field: user - type: user - score: 50 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process - type: process - - field: parent_process - type: parent_process + message: A Chromium-based browser process was launched on $dest$ by user $user$ with an unusually small window size ($window_width$ x $window_height$ pixels). The process was spawned by $parent_process_name$ and included the following command-line parameters $process$. + risk_objects: + - field: dest + type: system + score: 50 + - field: user + type: user + score: 50 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process + type: process + - field: parent_process + type: parent_process tags: - analytic_story: - - Browser Hijacking - asset_type: Endpoint - mitre_attack_id: - - T1497 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Browser Hijacking + asset_type: Endpoint + mitre_attack_id: + - T1497 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497/chrome_disable_popup/chrome_disable_popup.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497/chrome_disable_popup/chrome_disable_popup.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_chromium_browser_no_security_sandbox_process.yml b/detections/endpoint/windows_chromium_browser_no_security_sandbox_process.yml index 226d40a9d9..f71f665590 100644 --- a/detections/endpoint/windows_chromium_browser_no_security_sandbox_process.yml +++ b/detections/endpoint/windows_chromium_browser_no_security_sandbox_process.yml @@ -1,77 +1,71 @@ name: Windows Chromium Browser No Security Sandbox Process id: 314cb263-7eeb-4d45-b693-bb21699c73d2 -version: 1 -date: '2025-05-26' +version: 2 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP description: | - The following analytic detects instances where a Chrome or Chromium-based browser is launched with the --no-sandbox flag, a known indicator of potentially malicious or suspicious behavior. While this flag is occasionally used during software development or testing, it is rarely seen in normal user activity. Threat actors often abuse this setting to disable Chrome's built-in security sandbox, making it easier to execute malicious code or escape browser isolation. This behavior is commonly observed in malware droppers or loaders that embed Chromium components for command and control, credential theft, or UI spoofing. Analysts should investigate such events, especially if they originate from unusual parent processes (e.g., powershell.exe, cmd.exe, or unknown binaries), or if accompanied by other indicators such as file drops, process injection, or outbound network activity. Filtering by command-line arguments and process ancestry can help reduce false positives and surface high-fidelity detections. + The following analytic detects instances where a Chrome or Chromium-based browser is launched with the --no-sandbox flag, a known indicator of potentially malicious or suspicious behavior. While this flag is occasionally used during software development or testing, it is rarely seen in normal user activity. Threat actors often abuse this setting to disable Chrome's built-in security sandbox, making it easier to execute malicious code or escape browser isolation. This behavior is commonly observed in malware droppers or loaders that embed Chromium components for command and control, credential theft, or UI spoofing. Analysts should investigate such events, especially if they originate from unusual parent processes (e.g., powershell.exe, cmd.exe, or unknown binaries), or if accompanied by other indicators such as file drops, process injection, or outbound network activity. Filtering by command-line arguments and process ancestry can help reduce false positives and surface high-fidelity detections. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - Processes.process_name IN ("Chrome.exe","Brave.exe", "Opera.exe", "Vivaldi.exe", "msedge.exe") - Processes.process = "*--no-sandbox*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_chromium_browser_no_security_sandbox_process_filter` + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where + Processes.process_name IN ("Chrome.exe","Brave.exe", "Opera.exe", "Vivaldi.exe", "msedge.exe") + Processes.process = "*--no-sandbox*" + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_chromium_browser_no_security_sandbox_process_filter` how_to_implement: | - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: Administrators may enable or disable this feature that may - cause some false positive. + To successfully implement this search you need to be ingesting information + on process that include the name of the process responsible for the changes from + your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, + confirm the latest CIM App 4.20 or higher is installed and the latest TA for the + endpoint product. +known_false_positives: Administrators may enable or disable this feature that may cause some false positive. references: -- https://unix.stackexchange.com/questions/68832/what-does-the-chromium-option-no-sandbox-mean + - https://unix.stackexchange.com/questions/68832/what-does-the-chromium-option-no-sandbox-mean drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A chromium process with the --no-sandbox flag was launched on $dest$ by user $user$. - risk_objects: - - field: dest - type: system - score: 60 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: A chromium process with the --no-sandbox flag was launched on $dest$ by user $user$. + risk_objects: + - field: dest + type: system + score: 60 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - Malicious Inno Setup Loader - asset_type: Endpoint - mitre_attack_id: - - T1497 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Malicious Inno Setup Loader + asset_type: Endpoint + mitre_attack_id: + - T1497 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497/chrom_no_sandbox/chrome-no_sandbox.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497/chrom_no_sandbox/chrome-no_sandbox.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_chromium_browser_with_custom_user_data_directory.yml b/detections/endpoint/windows_chromium_browser_with_custom_user_data_directory.yml index 8e18b9aee1..633c4c1cb3 100644 --- a/detections/endpoint/windows_chromium_browser_with_custom_user_data_directory.yml +++ b/detections/endpoint/windows_chromium_browser_with_custom_user_data_directory.yml @@ -1,82 +1,74 @@ name: Windows Chromium Browser with Custom User Data Directory id: 4f546cf4-15aa-4368-80f7-940e92bc551e -version: 3 -date: '2025-12-16' +version: 4 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: | - The following analytic detects instances where the Chromium-based browser (e.g., Google Chrome, Microsoft Edge) is launched with the --user-data-dir command-line argument. While this flag is legitimate and used for multi-profile support or automation, it is frequently leveraged by malware and adversaries to run Chrome in an isolated environment for stealth operations, credential harvesting, phishing delivery, or evasion of user session artifacts. + The following analytic detects instances where the Chromium-based browser (e.g., Google Chrome, Microsoft Edge) is launched with the --user-data-dir command-line argument. While this flag is legitimate and used for multi-profile support or automation, it is frequently leveraged by malware and adversaries to run Chrome in an isolated environment for stealth operations, credential harvesting, phishing delivery, or evasion of user session artifacts. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - Processes.process_name IN ("Chrome.exe","Brave.exe", "Opera.exe", "Vivaldi.exe", "msedge.exe") - Processes.process = "*--user-data-dir*" - Processes.process IN ("*--disable-gpu*", "*--disable-3d-apis*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_chromium_browser_with_custom_user_data_directory_filter` + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where + Processes.process_name IN ("Chrome.exe","Brave.exe", "Opera.exe", "Vivaldi.exe", "msedge.exe") + Processes.process = "*--user-data-dir*" + Processes.process IN ("*--disable-gpu*", "*--disable-3d-apis*") + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_chromium_browser_with_custom_user_data_directory_filter` how_to_implement: | - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: Administrators may enable or disable this feature that - may cause some false positive. + To successfully implement this search you need to be ingesting information + on process that include the name of the process responsible for the changes from + your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, + confirm the latest CIM App 4.20 or higher is installed and the latest TA for the + endpoint product. +known_false_positives: Administrators may enable or disable this feature that may cause some false positive. references: -- https://chromium.googlesource.com/chromium/src/+/main/docs/user_data_dir.md + - https://chromium.googlesource.com/chromium/src/+/main/docs/user_data_dir.md drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A chromium process with the --user-data-dir flag was launched on - $dest$ by user $user$. - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: A chromium process with the --user-data-dir flag was launched on $dest$ by user $user$. + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - StealC Stealer - - Malicious Inno Setup Loader - - Lokibot - asset_type: Endpoint - mitre_attack_id: - - T1497 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - StealC Stealer + - Malicious Inno Setup Loader + - Lokibot + asset_type: Endpoint + mitre_attack_id: + - T1497 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497/chrom_no_sandbox/chrome-no_sandbox.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497/chrom_no_sandbox/chrome-no_sandbox.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_chromium_process_launched_with_disable_popup_blocking.yml b/detections/endpoint/windows_chromium_process_launched_with_disable_popup_blocking.yml index fce4c31f2d..1938367020 100644 --- a/detections/endpoint/windows_chromium_process_launched_with_disable_popup_blocking.yml +++ b/detections/endpoint/windows_chromium_process_launched_with_disable_popup_blocking.yml @@ -1,83 +1,72 @@ name: Windows Chromium process Launched with Disable Popup Blocking id: 95f8acd6-978e-42d6-99c1-85baacdd2b46 -version: 1 -date: '2026-01-23' +version: 2 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: The following analytic detects instances where a Windows Chromium-based browser process is launched with the `--disable-popup-blocking` flag. This flag is typically used to bypass the browser’s built-in pop-up protections, allowing automatic execution of pop-ups or redirects without user interaction. While legitimate in some testing or automation scenarios, its presence on endpoints, particularly when combined with other automation or concealment flags, may indicate attempts by malicious actors to execute web-based content stealthily or evade user interaction controls, representing a potential security risk that warrants investigation. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 -search: | - | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes - where Processes.process_name IN ("Chrome.exe","Brave.exe", "Opera.exe", "Vivaldi.exe", "msedge.exe") - Processes.process = "*--disable-popup-blocking*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`| `windows_chromium_process_launched_with_disable_popup_blocking_filter` -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: Administrators may enable or disable this feature for framework testing that may - cause some false positive. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: | + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes + where Processes.process_name IN ("Chrome.exe","Brave.exe", "Opera.exe", "Vivaldi.exe", "msedge.exe") + Processes.process = "*--disable-popup-blocking*" + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)`| `windows_chromium_process_launched_with_disable_popup_blocking_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: Administrators may enable or disable this feature for framework testing that may cause some false positive. references: -- https://www.trendmicro.com/en_us/research/26/a/analysis-of-the-evelyn-stealer-campaign.html -- https://peter.sh/experiments/chromium-command-line-switches/ + - https://www.trendmicro.com/en_us/research/26/a/analysis-of-the-evelyn-stealer-campaign.html + - https://peter.sh/experiments/chromium-command-line-switches/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Chromium-based browser process $process_name$ was launched by $parent_process_name$ on $dest$ by the user $user$ with the command-line $process$. - risk_objects: - - field: dest - type: system - score: 30 - - field: user - type: user - score: 30 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process - type: process - - field: parent_process - type: parent_process + message: A Chromium-based browser process $process_name$ was launched by $parent_process_name$ on $dest$ by the user $user$ with the command-line $process$. + risk_objects: + - field: dest + type: system + score: 30 + - field: user + type: user + score: 30 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process + type: process + - field: parent_process + type: parent_process tags: - analytic_story: - - Browser Hijacking - asset_type: Endpoint - mitre_attack_id: - - T1497 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Browser Hijacking + asset_type: Endpoint + mitre_attack_id: + - T1497 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497/chrome_disable_popup/chrome_disable_popup.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497/chrome_disable_popup/chrome_disable_popup.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_chromium_process_launched_with_logging_disabled.yml b/detections/endpoint/windows_chromium_process_launched_with_logging_disabled.yml index d7dc53578b..d3b11344e5 100644 --- a/detections/endpoint/windows_chromium_process_launched_with_logging_disabled.yml +++ b/detections/endpoint/windows_chromium_process_launched_with_logging_disabled.yml @@ -1,87 +1,78 @@ name: Windows Chromium Process Launched with Logging Disabled id: d31de944-4e61-468f-9154-e50690f0e99e -version: 1 -date: '2026-01-23' +version: 2 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: | - The following analytic detects instances of Chromium-based browser processes on Windows launched with logging disabled via command-line arguments such as --disable-logging and --disable-logging-redirect. - The --disable-logging flag forces browser logging to be disabled, while --disable-logging-redirect disables log redirection and is commonly used for testing or debugging scenarios. - Logging is enabled by default in Chromium debug builds, making these flags more likely to appear in debug or development environments. - While these options may be legitimately used by automation frameworks, debugging workflows, or isolated testing environments, they are also leveraged by malware and malicious scripts to evade security monitoring. - Analysts should review the parent process, full command-line parameters, and execution context to determine whether the behavior is expected or potentially suspicious. + The following analytic detects instances of Chromium-based browser processes on Windows launched with logging disabled via command-line arguments such as --disable-logging and --disable-logging-redirect. + The --disable-logging flag forces browser logging to be disabled, while --disable-logging-redirect disables log redirection and is commonly used for testing or debugging scenarios. + Logging is enabled by default in Chromium debug builds, making these flags more likely to appear in debug or development environments. + While these options may be legitimately used by automation frameworks, debugging workflows, or isolated testing environments, they are also leveraged by malware and malicious scripts to evade security monitoring. + Analysts should review the parent process, full command-line parameters, and execution context to determine whether the behavior is expected or potentially suspicious. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 -search: | - | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes - where Processes.process_name IN ("Chrome.exe","Brave.exe", "Opera.exe", "Vivaldi.exe", "msedge.exe") - Processes.process = "*--disable-logging*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_chromium_process_launched_with_logging_disabled_filter` -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: | + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes + where Processes.process_name IN ("Chrome.exe","Brave.exe", "Opera.exe", "Vivaldi.exe", "msedge.exe") + Processes.process = "*--disable-logging*" + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_chromium_process_launched_with_logging_disabled_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. known_false_positives: No false positives have been identified at this time. references: -- https://www.trendmicro.com/en_us/research/26/a/analysis-of-the-evelyn-stealer-campaign.html -- https://peter.sh/experiments/chromium-command-line-switches/ + - https://www.trendmicro.com/en_us/research/26/a/analysis-of-the-evelyn-stealer-campaign.html + - https://peter.sh/experiments/chromium-command-line-switches/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Chromium-based browser process $process_name$ was launched by $parent_process_name$ on $dest$ by the user $user$ with the command-line $process$. - risk_objects: - - field: dest - type: system - score: 40 - - field: user - type: user - score: 40 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process - type: process - - field: parent_process - type: parent_process + message: A Chromium-based browser process $process_name$ was launched by $parent_process_name$ on $dest$ by the user $user$ with the command-line $process$. + risk_objects: + - field: dest + type: system + score: 40 + - field: user + type: user + score: 40 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process + type: process + - field: parent_process + type: parent_process tags: - analytic_story: - - Browser Hijacking - asset_type: Endpoint - mitre_attack_id: - - T1497 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Browser Hijacking + asset_type: Endpoint + mitre_attack_id: + - T1497 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497/browser_disable_logs/chrome_disable_log.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497/browser_disable_logs/chrome_disable_log.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_chromium_process_loaded_extension_via_command_line.yml b/detections/endpoint/windows_chromium_process_loaded_extension_via_command_line.yml index 4de629fbef..fc931d69af 100644 --- a/detections/endpoint/windows_chromium_process_loaded_extension_via_command_line.yml +++ b/detections/endpoint/windows_chromium_process_loaded_extension_via_command_line.yml @@ -6,76 +6,71 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly description: | - The following analytic detects instances where Google Chrome is started with the --load-extension command-line flag, which allows loading unpacked or non-standard extensions. This behavior can indicate attempts to bypass enterprise extension policies, install malicious extensions, or load potentially harmful browser components. Monitoring such activity helps identify unauthorized extension usage, potential malware persistence mechanisms, or policy violations that could compromise browser security. + The following analytic detects instances where Google Chrome is started with the --load-extension command-line flag, which allows loading unpacked or non-standard extensions. This behavior can indicate attempts to bypass enterprise extension policies, install malicious extensions, or load potentially harmful browser components. Monitoring such activity helps identify unauthorized extension usage, potential malware persistence mechanisms, or policy violations that could compromise browser security. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where - Processes.process_name = "Chrome.exe" - Processes.process= "*--load-extension*" + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where + Processes.process_name = "Chrome.exe" + Processes.process= "*--load-extension*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_chromium_process_loaded_extension_via_command_line_filter` + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_chromium_process_loaded_extension_via_command_line_filter` how_to_implement: | - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. + To successfully implement this search you need to be ingesting information + on process that include the name of the process responsible for the changes from + your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, + confirm the latest CIM App 4.20 or higher is installed and the latest TA for the + endpoint product. known_false_positives: Developers or IT admins loading unpacked extensions for testing or deployment purposes. references: - - https://www.gdatasoftware.com/blog/2025/11/38298-learning-about-browser-hijacking - - https://peter.sh/experiments/chromium-command-line-switches/ + - https://www.gdatasoftware.com/blog/2025/11/38298-learning-about-browser-hijacking + - https://peter.sh/experiments/chromium-command-line-switches/ drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $process_name$ was launched by $parent_process_name$ on $dest$ by user $user$ and attempted to load a browser extension via command-line $process$. - risk_objects: - - field: dest - type: system - score: 20 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name - - field: process - type: process + message: $process_name$ was launched by $parent_process_name$ on $dest$ by user $user$ and attempted to load a browser extension via command-line $process$. + risk_objects: + - field: dest + type: system + score: 20 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name + - field: process + type: process tags: - analytic_story: - - Browser Hijacking - asset_type: Endpoint - mitre_attack_id: - - T1185 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Browser Hijacking + asset_type: Endpoint + mitre_attack_id: + - T1185 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/chrome_load_extensions/chrome_load_extension.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/chrome_load_extensions/chrome_load_extension.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_chromium_process_with_disabled_extensions.yml b/detections/endpoint/windows_chromium_process_with_disabled_extensions.yml index d7dd6e6c41..657ac0f27a 100644 --- a/detections/endpoint/windows_chromium_process_with_disabled_extensions.yml +++ b/detections/endpoint/windows_chromium_process_with_disabled_extensions.yml @@ -1,85 +1,75 @@ name: Windows Chromium Process with Disabled Extensions id: ce245717-779b-483b-bc52-fc7a94729973 -version: 1 -date: '2026-01-23' +version: 2 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: The following analytic detects instances of Chromium-based browser processes on Windows launched with extensions explicitly disabled via command-line arguments. Disabling extensions can be used by automation frameworks, testing tools, or headless browser activity, but may also indicate defense evasion or abuse of browser functionality by malicious scripts or malware. This behavior reduces browser visibility and bypasses user-installed security extensions, making it relevant for detecting non-interactive execution, suspicious automation, or living-off-the-land techniques. Analysts should validate execution context, parent process, and command-line parameters to determine legitimacy. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 -search: | - | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes - where Processes.process_name IN ("Chrome.exe","Brave.exe", "Opera.exe", "Vivaldi.exe", "msedge.exe") - Processes.process = "*--disable-extensions*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_chromium_process_with_disabled_extensions_filter` -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: Administrators may enable or disable this feature for framework testing that may - cause some false positive. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: | + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes + where Processes.process_name IN ("Chrome.exe","Brave.exe", "Opera.exe", "Vivaldi.exe", "msedge.exe") + Processes.process = "*--disable-extensions*" + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_chromium_process_with_disabled_extensions_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: Administrators may enable or disable this feature for framework testing that may cause some false positive. references: -- https://www.trendmicro.com/en_us/research/26/a/analysis-of-the-evelyn-stealer-campaign.html -- https://peter.sh/experiments/chromium-command-line-switches/ + - https://www.trendmicro.com/en_us/research/26/a/analysis-of-the-evelyn-stealer-campaign.html + - https://peter.sh/experiments/chromium-command-line-switches/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ launched a Chromium-based browser on $dest$ with the --disable-extensions flag. Parent process $parent_process_name$. Command line $process$. - risk_objects: - - field: dest - type: system - score: 20 - - field: user - type: user - score: 20 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name - - field: process - type: process - - field: parent_process - type: parent_process + message: User $user$ launched a Chromium-based browser on $dest$ with the --disable-extensions flag. Parent process $parent_process_name$. Command line $process$. + risk_objects: + - field: dest + type: system + score: 20 + - field: user + type: user + score: 20 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name + - field: process + type: process + - field: parent_process + type: parent_process tags: - analytic_story: - - Browser Hijacking - asset_type: Endpoint - mitre_attack_id: - - T1497 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Browser Hijacking + asset_type: Endpoint + mitre_attack_id: + - T1497 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497/browser_disable_extension/chrome_disable_ext.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497/browser_disable_extension/chrome_disable_ext.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_cisco_secure_endpoint_related_service_stopped.yml b/detections/endpoint/windows_cisco_secure_endpoint_related_service_stopped.yml index d0bb3d578c..3ed4b20cbc 100644 --- a/detections/endpoint/windows_cisco_secure_endpoint_related_service_stopped.yml +++ b/detections/endpoint/windows_cisco_secure_endpoint_related_service_stopped.yml @@ -1,60 +1,65 @@ name: Windows Cisco Secure Endpoint Related Service Stopped id: df74f45f-01c8-4fd6-bcb8-f6a9ea58307a -version: 3 -date: '2025-10-14' +version: 4 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: The following analytic detects the suspicious termination of known services commonly targeted by ransomware before file encryption. It leverages Windows System Event Logs (EventCode 7036) to identify when critical services such as Volume Shadow Copy, backup, and antivirus services are stopped. This activity is significant because ransomware often disables these services to avoid errors and ensure successful file encryption. If confirmed malicious, this behavior could lead to widespread data encryption, rendering files inaccessible and potentially causing significant operational disruption and data loss. data_source: -- Windows Event Log System 7036 -search: '`wineventlog_system` `normalized_service_binary_field` | rename param1 as display_name | rename param2 as status | search EventCode=7036 display_name IN ("Cisco AMP Orbital", "*Cisco Secure Endpoint*", "*Cisco Security Connector Monitoring*", "CiscoSAM", "CiscoAMPHeurDriver", "CiscoAMPELAMDriver", "CiscoAMPCEFWDriver", "ImmunetNetworkMonitorDriver", "ImmunetProtectDriver", "ImmunetSelfProtectDriver") status IN ("stopped", "arrêté") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode display_name normalized_service_name status dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_cisco_secure_endpoint_related_service_stopped_filter`' + - Windows Event Log System 7036 +search: |- + `wineventlog_system` `normalized_service_binary_field` + | rename param1 as display_name + | rename param2 as status + | search EventCode=7036 display_name IN ("Cisco AMP Orbital", "*Cisco Secure Endpoint*", "*Cisco Security Connector Monitoring*", "CiscoSAM", "CiscoAMPHeurDriver", "CiscoAMPELAMDriver", "CiscoAMPCEFWDriver", "ImmunetNetworkMonitorDriver", "ImmunetProtectDriver", "ImmunetSelfProtectDriver") status IN ("stopped", "arrêté") + | stats count min(_time) as firstTime max(_time) as lastTime + BY EventCode display_name normalized_service_name + status dest + | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` + | `windows_cisco_secure_endpoint_related_service_stopped_filter` how_to_implement: To successfully implement this search, you need to be ingesting logs with the 7036 EventCode ScManager in System audit Logs from your endpoints. known_false_positives: Administrator or troubleshooting activities may trigger this alert. Investigate the process performing this action to determine if its a legitimate activity. references: -- https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/ -- https://www.mcafee.com/blogs/other-blogs/mcafee-labs/mcafee-atr-analyzes-sodinokibi-aka-revil-ransomware-as-a-service-what-the-code-tells-us/ -- https://news.sophos.com/en-us/2020/04/24/lockbit-ransomware-borrows-tricks-to-keep-up-with-revil-and-maze/ -- https://blogs.vmware.com/security/2022/10/lockbit-3-0-also-known-as-lockbit-black.html + - https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/ + - https://www.mcafee.com/blogs/other-blogs/mcafee-labs/mcafee-atr-analyzes-sodinokibi-aka-revil-ransomware-as-a-service-what-the-code-tells-us/ + - https://news.sophos.com/en-us/2020/04/24/lockbit-ransomware-borrows-tricks-to-keep-up-with-revil-and-maze/ + - https://blogs.vmware.com/security/2022/10/lockbit-3-0-also-known-as-lockbit-black.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Cisco Secure Endpoint Service $display_name$ stopped on $dest$ - risk_objects: - - field: dest - type: system - score: 60 - threat_objects: - - field: display_name - type: service + message: Cisco Secure Endpoint Service $display_name$ stopped on $dest$ + risk_objects: + - field: dest + type: system + score: 60 + threat_objects: + - field: display_name + type: service tags: - analytic_story: - - Security Solution Tampering - - Scattered Lapsus$ Hunters - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1490 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Security Solution Tampering + - Scattered Lapsus$ Hunters + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1490 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/cisco_secure_endpoint_tampering/service_stop.log - source: XmlWinEventLog:System - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/cisco_secure_endpoint_tampering/service_stop.log + source: XmlWinEventLog:System + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_cisco_secure_endpoint_stop_immunet_service_via_sfc.yml b/detections/endpoint/windows_cisco_secure_endpoint_stop_immunet_service_via_sfc.yml index fc1a208bb5..b881116e5c 100644 --- a/detections/endpoint/windows_cisco_secure_endpoint_stop_immunet_service_via_sfc.yml +++ b/detections/endpoint/windows_cisco_secure_endpoint_stop_immunet_service_via_sfc.yml @@ -5,84 +5,53 @@ date: '2025-05-02' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly -description: The following analytic detects the use of the `sfc.exe` utility, in order - to stop the Immunet Protect service. The Sfc.exe utility is part of Cisco Secure - Endpoint installation. This detection leverages telemetry from the endpoint, focusing - on command-line executions involving the `-k` parameter. This activity is significant - as it indicates potential tampering with defensive mechanisms. If confirmed malicious, - attackers could partially blind the EDR, enabling further compromise and lateral - movement within the network. +description: The following analytic detects the use of the `sfc.exe` utility, in order to stop the Immunet Protect service. The Sfc.exe utility is part of Cisco Secure Endpoint installation. This detection leverages telemetry from the endpoint, focusing on command-line executions involving the `-k` parameter. This activity is significant as it indicates potential tampering with defensive mechanisms. If confirmed malicious, attackers could partially blind the EDR, enabling further compromise and lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name="sfc.exe" - Processes.process="* -k*" AND NOT Processes.process_path IN ("*:\\Windows\\System32\\*", - "*:\\Windows\\SysWOW64\\*", ":\\Windows\\WinSxS\\*") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_cisco_secure_endpoint_stop_immunet_service_via_sfc_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: It is possible that this action is executed during troubleshooting - activity. Activity needs to be confirmed on a case by case basis. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="sfc.exe" Processes.process="* -k*" AND NOT Processes.process_path IN ("*:\\Windows\\System32\\*", "*:\\Windows\\SysWOW64\\*", ":\\Windows\\WinSxS\\*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_cisco_secure_endpoint_stop_immunet_service_via_sfc_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: It is possible that this action is executed during troubleshooting activity. Activity needs to be confirmed on a case by case basis. references: -- https://www.cisco.com/c/en/us/support/docs/security/amp-endpoints/213690-amp-for-endpoint-command-line-switches.html + - https://www.cisco.com/c/en/us/support/docs/security/amp-endpoints/213690-amp-for-endpoint-command-line-switches.html drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious use of `sfc.exe` stopping the Immunet Protect service on $dest$ - by user $user$. - risk_objects: - - field: user - type: user - score: 56 - - field: dest - type: system - score: 56 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: Suspicious use of `sfc.exe` stopping the Immunet Protect service on $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 56 + - field: dest + type: system + score: 56 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Security Solution Tampering - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Security Solution Tampering + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/cisco_secure_endpoint_tampering/sfc_tampering.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/cisco_secure_endpoint_tampering/sfc_tampering.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_cisco_secure_endpoint_unblock_file_via_sfc.yml b/detections/endpoint/windows_cisco_secure_endpoint_unblock_file_via_sfc.yml index 1ccb1da072..9ea9c2c054 100644 --- a/detections/endpoint/windows_cisco_secure_endpoint_unblock_file_via_sfc.yml +++ b/detections/endpoint/windows_cisco_secure_endpoint_unblock_file_via_sfc.yml @@ -5,85 +5,53 @@ date: '2025-05-02' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly -description: The following analytic detects the use of the sfc.exe utility with the - "-unblock" parameter, a feature within Cisco Secure Endpoint. The "-unblock" flag - is used to remove system blocks imposed by the endpoint protection. This detection - focuses on command-line activity that includes the "-unblock" parameter, as it may - indicate an attempt to restore access to files or processes previously blocked by - the security software. While this action could be legitimate in troubleshooting - scenarios, malicious actors might use it to override protective measures, enabling - execution of blocked malicious payloads or bypassing other security mechanisms. +description: The following analytic detects the use of the sfc.exe utility with the "-unblock" parameter, a feature within Cisco Secure Endpoint. The "-unblock" flag is used to remove system blocks imposed by the endpoint protection. This detection focuses on command-line activity that includes the "-unblock" parameter, as it may indicate an attempt to restore access to files or processes previously blocked by the security software. While this action could be legitimate in troubleshooting scenarios, malicious actors might use it to override protective measures, enabling execution of blocked malicious payloads or bypassing other security mechanisms. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name="sfc.exe" - Processes.process="* -unblock *" AND NOT Processes.process_path IN ("*:\\Windows\\System32\\*", - "*:\\Windows\\SysWOW64\\*", ":\\Windows\\WinSxS\\*") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_cisco_secure_endpoint_unblock_file_via_sfc_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: It is possible that this action is executed during troubleshooting - activity. Activity needs to be confirmed on a case by case basis. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="sfc.exe" Processes.process="* -unblock *" AND NOT Processes.process_path IN ("*:\\Windows\\System32\\*", "*:\\Windows\\SysWOW64\\*", ":\\Windows\\WinSxS\\*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_cisco_secure_endpoint_unblock_file_via_sfc_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: It is possible that this action is executed during troubleshooting activity. Activity needs to be confirmed on a case by case basis. references: -- https://www.cisco.com/c/en/us/support/docs/security/amp-endpoints/213690-amp-for-endpoint-command-line-switches.html + - https://www.cisco.com/c/en/us/support/docs/security/amp-endpoints/213690-amp-for-endpoint-command-line-switches.html drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious use of `sfc.exe` unblocking a potentially harmful file on $dest$ - by user $user$ - risk_objects: - - field: user - type: user - score: 56 - - field: dest - type: system - score: 56 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: Suspicious use of `sfc.exe` unblocking a potentially harmful file on $dest$ by user $user$ + risk_objects: + - field: user + type: user + score: 56 + - field: dest + type: system + score: 56 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Security Solution Tampering - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Security Solution Tampering + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/cisco_secure_endpoint_tampering/sfc_tampering.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/cisco_secure_endpoint_tampering/sfc_tampering.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_cisco_secure_endpoint_uninstall_immunet_service_via_sfc.yml b/detections/endpoint/windows_cisco_secure_endpoint_uninstall_immunet_service_via_sfc.yml index 1a652df42b..84892e7761 100644 --- a/detections/endpoint/windows_cisco_secure_endpoint_uninstall_immunet_service_via_sfc.yml +++ b/detections/endpoint/windows_cisco_secure_endpoint_uninstall_immunet_service_via_sfc.yml @@ -5,85 +5,53 @@ date: '2025-05-02' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly -description: The following analytic detects the use of the sfc.exe utility with the - "-u" parameter, which is part of the Cisco Secure Endpoint installation. The "-u" - flag allows the uninstallation of Cisco Secure Endpoint components. This detection - leverages endpoint telemetry to monitor command-line executions that include the - "-u" parameter. The use of this flag is significant as it could indicate an attempt - to disable or remove endpoint protection, potentially leaving the system vulnerable - to further exploitation. If identified as malicious, this action may be part of - a broader effort to disable security mechanisms and avoid detection. +description: The following analytic detects the use of the sfc.exe utility with the "-u" parameter, which is part of the Cisco Secure Endpoint installation. The "-u" flag allows the uninstallation of Cisco Secure Endpoint components. This detection leverages endpoint telemetry to monitor command-line executions that include the "-u" parameter. The use of this flag is significant as it could indicate an attempt to disable or remove endpoint protection, potentially leaving the system vulnerable to further exploitation. If identified as malicious, this action may be part of a broader effort to disable security mechanisms and avoid detection. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name="sfc.exe" - Processes.process="* -u*" NOT Processes.process="* -unblock *" AND NOT Processes.process_path - IN ("*:\\Windows\\System32\\*", "*:\\Windows\\SysWOW64\\*", ":\\Windows\\WinSxS\\*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_cisco_secure_endpoint_uninstall_immunet_service_via_sfc_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: It is possible that this action is executed during troubleshooting - activity. Activity needs to be confirmed on a case by case basis. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="sfc.exe" Processes.process="* -u*" NOT Processes.process="* -unblock *" AND NOT Processes.process_path IN ("*:\\Windows\\System32\\*", "*:\\Windows\\SysWOW64\\*", ":\\Windows\\WinSxS\\*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_cisco_secure_endpoint_uninstall_immunet_service_via_sfc_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: It is possible that this action is executed during troubleshooting activity. Activity needs to be confirmed on a case by case basis. references: -- https://www.cisco.com/c/en/us/support/docs/security/amp-endpoints/213690-amp-for-endpoint-command-line-switches.html + - https://www.cisco.com/c/en/us/support/docs/security/amp-endpoints/213690-amp-for-endpoint-command-line-switches.html drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious use of `sfc.exe` to uninstall the Immunet Protect service on - $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 56 - - field: dest - type: system - score: 56 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: Suspicious use of `sfc.exe` to uninstall the Immunet Protect service on $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 56 + - field: dest + type: system + score: 56 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Security Solution Tampering - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Security Solution Tampering + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/cisco_secure_endpoint_tampering/sfc_tampering.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/cisco_secure_endpoint_tampering/sfc_tampering.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_clipboard_data_via_get_clipboard.yml b/detections/endpoint/windows_clipboard_data_via_get_clipboard.yml index c69002785f..e1475d2776 100644 --- a/detections/endpoint/windows_clipboard_data_via_get_clipboard.yml +++ b/detections/endpoint/windows_clipboard_data_via_get_clipboard.yml @@ -1,76 +1,65 @@ name: Windows ClipBoard Data via Get-ClipBoard id: ab73289e-2246-4de0-a14b-67006c72a893 -version: 7 -date: '2025-06-24' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: - The following analytic detects the execution of the PowerShell command - 'Get-Clipboard' to retrieve clipboard data. It leverages PowerShell Script Block - Logging (EventCode 4104) to identify instances where this command is used. This - activity is significant because it can indicate an attempt to steal sensitive information - such as usernames, passwords, or other confidential data copied to the clipboard. - If confirmed malicious, this behavior could lead to unauthorized access to sensitive - information, potentially compromising user accounts and other critical assets. +description: The following analytic detects the execution of the PowerShell command 'Get-Clipboard' to retrieve clipboard data. It leverages PowerShell Script Block Logging (EventCode 4104) to identify instances where this command is used. This activity is significant because it can indicate an attempt to steal sensitive information such as usernames, passwords, or other confidential data copied to the clipboard. If confirmed malicious, this behavior could lead to unauthorized access to sensitive information, potentially compromising user accounts and other critical assets. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText = "*Get-Clipboard*" | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_clipboard_data_via_get_clipboard_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*Get-Clipboard*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_clipboard_data_via_get_clipboard_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. known_false_positives: It is possible there will be false positives, filter as needed. references: - - https://attack.mitre.org/techniques/T1115/ - - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS - - https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ + - https://attack.mitre.org/techniques/T1115/ + - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS + - https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ drilldown_searches: - - name: View the detection results for - "$dest$" and "$user_id$" - search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user_id$" + search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - Powershell script $ScriptBlockText$ execute Get-Clipboard commandlet on - $dest$ - risk_objects: - - field: dest - type: system - score: 25 - - field: user_id - type: user - score: 25 - threat_objects: [] + message: Powershell script $ScriptBlockText$ execute Get-Clipboard commandlet on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + - field: user_id + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Windows Post-Exploitation - - Prestige Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1115 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Post-Exploitation + - Prestige Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1115 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/powershell/windows-powershell-xml2.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/powershell/windows-powershell-xml2.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_cmdline_tool_execution_from_non_shell_process.yml b/detections/endpoint/windows_cmdline_tool_execution_from_non_shell_process.yml index 861042b6a3..1ff003d758 100644 --- a/detections/endpoint/windows_cmdline_tool_execution_from_non_shell_process.yml +++ b/detections/endpoint/windows_cmdline_tool_execution_from_non_shell_process.yml @@ -5,105 +5,66 @@ date: '2026-02-09' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: - The following analytic identifies instances where `ipconfig.exe`, `systeminfo.exe`, - or similar tools are executed by a non-standard shell parent process, excluding - CMD, PowerShell, or Explorer. This detection leverages Endpoint Detection and Response - (EDR) telemetry to monitor process creation events. Such behavior is significant - as it may indicate adversaries using injected processes to perform system discovery, - a tactic observed in FIN7's JSSLoader. If confirmed malicious, this activity could - allow attackers to gather critical host information, aiding in further exploitation - or lateral movement within the network. +description: The following analytic identifies instances where `ipconfig.exe`, `systeminfo.exe`, or similar tools are executed by a non-standard shell parent process, excluding CMD, PowerShell, or Explorer. This detection leverages Endpoint Detection and Response (EDR) telemetry to monitor process creation events. Such behavior is significant as it may indicate adversaries using injected processes to perform system discovery, a tactic observed in FIN7's JSSLoader. If confirmed malicious, this activity could allow attackers to gather critical host information, aiding in further exploitation or lateral movement within the network. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("ipconfig.exe", - "systeminfo.exe", "net1.exe", "arp.exe", "nslookup.exe", "route.exe", "netstat.exe", "hostname.exe", "whoami.exe") - AND NOT Processes.parent_process_name IN ("cmd.exe", "powershell.exe", "powershell_ise.exe", "pwsh.exe", "explorer.exe", "-", "unknown") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_cmdline_tool_execution_from_non_shell_process_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: - A network operator or systems administrator may utilize an - automated host discovery application that may generate false positives. Filter as - needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("ipconfig.exe", "systeminfo.exe", "net1.exe", "arp.exe", "nslookup.exe", "route.exe", "netstat.exe", "hostname.exe", "whoami.exe") AND NOT Processes.parent_process_name IN ("cmd.exe", "powershell.exe", "powershell_ise.exe", "pwsh.exe", "explorer.exe", "-", "unknown") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_cmdline_tool_execution_from_non_shell_process_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: A network operator or systems administrator may utilize an automated host discovery application that may generate false positives. Filter as needed. references: - - https://www.mandiant.com/resources/fin7-pursuing-an-enigmatic-and-evasive-global-criminal-operation - - https://attack.mitre.org/groups/G0046/ - - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ + - https://www.mandiant.com/resources/fin7-pursuing-an-enigmatic-and-evasive-global-criminal-operation + - https://attack.mitre.org/groups/G0046/ + - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ drilldown_searches: - - name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A non-standard parent process $parent_process_name$ spawned child process - $process_name$ to execute command-line tool on $dest$. - risk_objects: - - field: dest - type: system - score: 56 - - field: user - type: user - score: 56 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: A non-standard parent process $parent_process_name$ spawned child process $process_name$ to execute command-line tool on $dest$. + risk_objects: + - field: dest + type: system + score: 56 + - field: user + type: user + score: 56 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - CISA AA22-277A - - Gozi Malware - - CISA AA23-347A - - Qakbot - - Medusa Ransomware - - DarkGate Malware - - Rhysida Ransomware - - Volt Typhoon - - FIN7 - - Water Gamayun - - Tuoni - - SolarWinds WHD RCE Post Exploitation - asset_type: Endpoint - mitre_attack_id: - - T1059.007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA22-277A + - Gozi Malware + - CISA AA23-347A + - Qakbot + - Medusa Ransomware + - DarkGate Malware + - Rhysida Ransomware + - Volt Typhoon + - FIN7 + - Water Gamayun + - Tuoni + - SolarWinds WHD RCE Post Exploitation + asset_type: Endpoint + mitre_attack_id: + - T1059.007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/jssloader/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/jssloader/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_com_hijacking_inprocserver32_modification.yml b/detections/endpoint/windows_com_hijacking_inprocserver32_modification.yml index b6f93ffca8..ba54467720 100644 --- a/detections/endpoint/windows_com_hijacking_inprocserver32_modification.yml +++ b/detections/endpoint/windows_com_hijacking_inprocserver32_modification.yml @@ -1,90 +1,73 @@ name: Windows COM Hijacking InprocServer32 Modification id: b7bd83c0-92b5-4fc7-b286-23eccfa2c561 -version: 10 -date: '2025-05-02' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the modification of the InProcServer32 - registry key by reg.exe, indicative of potential COM hijacking. This detection leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process and - command-line execution logs. COM hijacking is significant as it allows adversaries - to insert malicious code that executes in place of legitimate software, providing - a means for persistence. If confirmed malicious, this activity could enable attackers - to execute arbitrary code, disrupt legitimate system components, and maintain long-term - access to the compromised environment. +description: The following analytic detects the modification of the InProcServer32 registry key by reg.exe, indicative of potential COM hijacking. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and command-line execution logs. COM hijacking is significant as it allows adversaries to insert malicious code that executes in place of legitimate software, providing a means for persistence. If confirmed malicious, this activity could enable attackers to execute arbitrary code, disrupt legitimate system components, and maintain long-term access to the compromised environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_reg` Processes.process=*inprocserver32* - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_com_hijacking_inprocserver32_modification_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_reg` Processes.process=*inprocserver32* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_com_hijacking_inprocserver32_modification_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives may be present and some filtering may be required. references: -- https://attack.mitre.org/techniques/T1546/015/ -- https://blog.cluster25.duskrise.com/2022/09/23/in-the-footsteps-of-the-fancy-bear-powerpoint-graphite/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1546.015/T1546.015.md + - https://attack.mitre.org/techniques/T1546/015/ + - https://blog.cluster25.duskrise.com/2022/09/23/in-the-footsteps-of-the-fancy-bear-powerpoint-graphite/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1546.015/T1546.015.md drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to modify InProcServer32 within the - registry. - risk_objects: - - field: user - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to modify InProcServer32 within the registry. + risk_objects: + - field: user + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Living Off The Land - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1546.015 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1546.015 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.015/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.015/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_command_and_scripting_interpreter_hunting_path_traversal.yml b/detections/endpoint/windows_command_and_scripting_interpreter_hunting_path_traversal.yml index 2afbe98ff4..8b41c9fadd 100644 --- a/detections/endpoint/windows_command_and_scripting_interpreter_hunting_path_traversal.yml +++ b/detections/endpoint/windows_command_and_scripting_interpreter_hunting_path_traversal.yml @@ -6,74 +6,74 @@ author: Teoderick Contreras, Michael Haag, Splunk status: production type: Hunting description: | - The following analytic identifies path traversal command-line executions, - leveraging data from Endpoint Detection and Response (EDR) agents. It detects patterns - in command-line arguments indicative of path traversal techniques, such as multiple - instances of "/..", "\..", or "\\..". This activity is significant as it often indicates - attempts to evade defenses by executing malicious code, such as through msdt.exe. - If confirmed malicious, this behavior could allow attackers to execute arbitrary - code, potentially leading to system compromise, data exfiltration, or further lateral - movement within the network. + The following analytic identifies path traversal command-line executions, + leveraging data from Endpoint Detection and Response (EDR) agents. It detects patterns + in command-line arguments indicative of path traversal techniques, such as multiple + instances of "/..", "\..", or "\\..". This activity is significant as it often indicates + attempts to evade defenses by executing malicious code, such as through msdt.exe. + If confirmed malicious, this behavior could allow attackers to execute arbitrary + code, potentially leading to system compromise, data exfiltration, or further lateral + movement within the network. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Processes where - Processes.process IN ("*\\..*", "*//..*", "*\..*", "*/..*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process - Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id - Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime FROM datamodel=Endpoint.Processes where + Processes.process IN ("*\\..*", "*//..*", "*\..*", "*/..*") + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process + Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id + Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name("Processes")` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | eval count_of_pattern1 = (mvcount(split(process,"/.."))-1) - | eval count_of_pattern2 = (mvcount(split(process,"\.."))-1) - | eval count_of_pattern3 = (mvcount(split(process,"\\.."))-1) - | eval count_of_pattern4 = (mvcount(split(process,"//.."))-1) + | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | eval count_of_pattern1 = (mvcount(split(process,"/.."))-1) + | eval count_of_pattern2 = (mvcount(split(process,"\.."))-1) + | eval count_of_pattern3 = (mvcount(split(process,"\\.."))-1) + | eval count_of_pattern4 = (mvcount(split(process,"//.."))-1) - | search count_of_pattern1 > 1 - OR - count_of_pattern2 > 1 - OR - count_of_pattern3 > 1 - OR - count_of_pattern4 > 1 - | `windows_command_and_scripting_interpreter_hunting_path_traversal_filter` + | search count_of_pattern1 > 1 + OR + count_of_pattern2 > 1 + OR + count_of_pattern3 > 1 + OR + count_of_pattern4 > 1 + | `windows_command_and_scripting_interpreter_hunting_path_traversal_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: | - False positives may vary depending on the score you want to check. + False positives may vary depending on the score you want to check. references: - - https://app.any.run/tasks/713f05d2-fe78-4b9d-a744-f7c133e3fafb/ + - https://app.any.run/tasks/713f05d2-fe78-4b9d-a744-f7c133e3fafb/ tags: - analytic_story: - - Windows Defense Evasion Tactics - - Microsoft Support Diagnostic Tool Vulnerability CVE-2022-30190 - asset_type: Endpoint - mitre_attack_id: - - T1059 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Microsoft Support Diagnostic Tool Vulnerability CVE-2022-30190 + asset_type: Endpoint + mitre_attack_id: + - T1059 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/path_traversal/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/path_traversal/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_command_and_scripting_interpreter_path_traversal_exec.yml b/detections/endpoint/windows_command_and_scripting_interpreter_path_traversal_exec.yml index d4af422bc3..31c38f45a1 100644 --- a/detections/endpoint/windows_command_and_scripting_interpreter_path_traversal_exec.yml +++ b/detections/endpoint/windows_command_and_scripting_interpreter_path_traversal_exec.yml @@ -5,78 +5,48 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects path traversal command-line execution, - often used in malicious documents to execute code via msdt.exe for defense evasion. - It leverages Endpoint Detection and Response (EDR) data, focusing on specific patterns - in process paths. This activity is significant as it can indicate an attempt to - bypass security controls and execute unauthorized code. If confirmed malicious, - this behavior could lead to code execution, privilege escalation, or persistence - within the environment, potentially allowing attackers to deploy malware or leverage - other living-off-the-land binaries (LOLBins). +description: The following analytic detects path traversal command-line execution, often used in malicious documents to execute code via msdt.exe for defense evasion. It leverages Endpoint Detection and Response (EDR) data, focusing on specific patterns in process paths. This activity is significant as it can indicate an attempt to bypass security controls and execute unauthorized code. If confirmed malicious, this behavior could lead to code execution, privilege escalation, or persistence within the environment, potentially allowing attackers to deploy malware or leverage other living-off-the-land binaries (LOLBins). data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Processes where NOT Processes.os="Linux" Processes.process="*\/..\/..\/..\/*" - OR Processes.process="*\\..\\..\\..\\*" OR Processes.process="*\/\/..\/\/..\/\/..\/\/*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_command_and_scripting_interpreter_path_traversal_exec_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where NOT Processes.os="Linux" Processes.process="*\/..\/..\/..\/*" OR Processes.process="*\\..\\..\\..\\*" OR Processes.process="*\/\/..\/\/..\/\/..\/\/*" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_command_and_scripting_interpreter_path_traversal_exec_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Not known at this moment. references: -- https://app.any.run/tasks/713f05d2-fe78-4b9d-a744-f7c133e3fafb/ + - https://app.any.run/tasks/713f05d2-fe78-4b9d-a744-f7c133e3fafb/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process $parent_process_name$ has spawned a child $process_name$ with - path traversal commandline $process$ on $dest$ - risk_objects: - - field: dest - type: system - score: 90 - threat_objects: [] + message: A process $parent_process_name$ has spawned a child $process_name$ with path traversal commandline $process$ on $dest$ + risk_objects: + - field: dest + type: system + score: 90 + threat_objects: [] tags: - analytic_story: - - Microsoft Support Diagnostic Tool Vulnerability CVE-2022-30190 - - Compromised Windows Host - - Windows Defense Evasion Tactics - asset_type: Endpoint - mitre_attack_id: - - T1059 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Microsoft Support Diagnostic Tool Vulnerability CVE-2022-30190 + - Compromised Windows Host + - Windows Defense Evasion Tactics + asset_type: Endpoint + mitre_attack_id: + - T1059 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/path_traversal/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/path_traversal/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_command_shell_dcrat_forkbomb_payload.yml b/detections/endpoint/windows_command_shell_dcrat_forkbomb_payload.yml index 15d46d5f1c..50d1ea753f 100644 --- a/detections/endpoint/windows_command_shell_dcrat_forkbomb_payload.yml +++ b/detections/endpoint/windows_command_shell_dcrat_forkbomb_payload.yml @@ -1,89 +1,64 @@ name: Windows Command Shell DCRat ForkBomb Payload id: 2bb1a362-7aa8-444a-92ed-1987e8da83e1 -version: 10 -date: '2026-01-14' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of a DCRat "forkbomb" payload, - which spawns multiple cmd.exe processes that launch notepad.exe instances in quick - succession. This detection leverages Endpoint Detection and Response (EDR) data, - focusing on the rapid creation of cmd.exe and notepad.exe processes within a 30-second - window. This activity is significant as it indicates a potential DCRat infection, - a known Remote Access Trojan (RAT) with destructive capabilities. If confirmed malicious, - this behavior could lead to system instability, resource exhaustion, and potential - disruption of services. +description: The following analytic detects the execution of a DCRat "forkbomb" payload, which spawns multiple cmd.exe processes that launch notepad.exe instances in quick succession. This detection leverages Endpoint Detection and Response (EDR) data, focusing on the rapid creation of cmd.exe and notepad.exe processes within a 30-second window. This activity is significant as it indicates a potential DCRat infection, a known Remote Access Trojan (RAT) with destructive capabilities. If confirmed malicious, this behavior could lead to system instability, resource exhaustion, and potential disruption of services. data_source: -- Sysmon EventID 1 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.user) as user - values(Processes.action) as action values(Processes.parent_process_exec) as parent_process_exec - values(Processes.parent_process_guid) as parent_process_guid values(Processes.parent_process_id) - as parent_process_id values(Processes.parent_process_path) as parent_process_path - values(Processes.process) as process values(Processes.process_exec) as process_exec - values(Processes.process_guid) as process_guid values(Processes.process_hash) as - process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) - as process_integrity_level values(Processes.process_path) as process_path values(Processes.user_id) - as user_id values(Processes.vendor_product) as vendor_product dc(Processes.parent_process_id) - as parent_process_id_count dc(Processes.process_id) as process_id_count min(_time) - as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name= - "cmd.exe" (Processes.process_name = "notepad.exe" OR Processes.original_file_name= - "notepad.exe") Processes.parent_process = "*.bat*" by Processes.parent_process_name - Processes.process_name Processes.original_file_name Processes.parent_process Processes.dest - Processes.user _time span=30s | where parent_process_id_count>= 10 AND process_id_count - >=10 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | - `security_content_ctime(lastTime)` | `windows_command_shell_dcrat_forkbomb_payload_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.user) as user values(Processes.action) as action values(Processes.parent_process_exec) as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid values(Processes.parent_process_id) as parent_process_id values(Processes.parent_process_path) as parent_process_path values(Processes.process) as process values(Processes.process_exec) as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) as process_integrity_level values(Processes.process_path) as process_path values(Processes.user_id) as user_id values(Processes.vendor_product) as vendor_product dc(Processes.parent_process_id) as parent_process_id_count dc(Processes.process_id) as process_id_count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name= "cmd.exe" (Processes.process_name = "notepad.exe" + OR + Processes.original_file_name= "notepad.exe") Processes.parent_process = "*.bat*" + BY Processes.parent_process_name Processes.process_name Processes.original_file_name + Processes.parent_process Processes.dest Processes.user + _time span=30s + | where parent_process_id_count>= 10 AND process_id_count >=10 + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_command_shell_dcrat_forkbomb_payload_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://cert.gov.ua/article/405538 -- https://malpedia.caad.fkie.fraunhofer.de/details/win.dcrat -- https://www.mandiant.com/resources/analyzing-dark-crystal-rat-backdoor + - https://cert.gov.ua/article/405538 + - https://malpedia.caad.fkie.fraunhofer.de/details/win.dcrat + - https://www.mandiant.com/resources/analyzing-dark-crystal-rat-backdoor drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Multiple cmd.exe processes with child process of notepad.exe executed on - $dest$ - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: [] + message: Multiple cmd.exe processes with child process of notepad.exe executed on $dest$ + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - DarkCrystal RAT - asset_type: Endpoint - mitre_attack_id: - - T1059.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - DarkCrystal RAT + asset_type: Endpoint + mitre_attack_id: + - T1059.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/dcrat_forkbomb/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/dcrat_forkbomb/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_common_abused_cmd_shell_risk_behavior.yml b/detections/endpoint/windows_common_abused_cmd_shell_risk_behavior.yml index 1e5250d39c..12bf73ccbc 100644 --- a/detections/endpoint/windows_common_abused_cmd_shell_risk_behavior.yml +++ b/detections/endpoint/windows_common_abused_cmd_shell_risk_behavior.yml @@ -1,91 +1,65 @@ name: Windows Common Abused Cmd Shell Risk Behavior id: e99fcc4f-c6b0-4443-aa2a-e3c85126ec9a -version: 7 -date: '2025-10-24' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Correlation data_source: [] -description: The following analytic identifies instances where four or more distinct - detection analytics are associated with malicious command line behavior on a specific - host. This detection leverages the Command Line Interface (CLI) data from various - sources to identify suspicious activities. This behavior is significant as it often - indicates attempts to execute malicious commands, access sensitive data, install - backdoors, or perform other nefarious actions. If confirmed malicious, attackers - could gain unauthorized control, exfiltrate information, escalate privileges, or - launch further attacks within the network, leading to severe compromise. -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) - as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as - annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) - as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) - as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) - as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, - dc(source) as source_count from datamodel=Risk.All_Risk where source IN ("*Windows - Cmdline Tool Execution From Non-Shell Process*", "*Windows System Network Config - Discovery Display DNS*", "*Local Account Discovery With Wmic*", "*Windows Group - Discovery Via Net*", "*Windows Create Local Administrator Account Via Net*", "*Windows - User Discovery Via Net*", "*Icacls Deny Command*", "*ICACLS Grant Command*", "*Windows - Proxy Via Netsh*", "*Processes launching netsh*", "*Disabling Firewall with Netsh*", - "*Windows System Network Connections Discovery Netsh*", "*Network Connection Discovery - With Arp*", "*Windows System Discovery Using ldap Nslookup*", "*Windows System Shutdown - CommandLine*") by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic - | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | where source_count >= 4 | `windows_common_abused_cmd_shell_risk_behavior_filter`' -how_to_implement: Splunk Enterprise Security is required to utilize this correlation. - In addition, modify the source_count value to your environment. In our testing, - a count of 4 or 5 was decent in a lab, but the number may need to be increased base - on internal testing. In addition, based on false positives, modify any analytics - to be anomaly and lower or increase risk based on organization importance. -known_false_positives: False positives will be present based on many factors. Tune - the correlation as needed to reduce too many triggers. +description: The following analytic identifies instances where four or more distinct detection analytics are associated with malicious command line behavior on a specific host. This detection leverages the Command Line Interface (CLI) data from various sources to identify suspicious activities. This behavior is significant as it often indicates attempts to execute malicious commands, access sensitive data, install backdoors, or perform other nefarious actions. If confirmed malicious, attackers could gain unauthorized control, exfiltrate information, escalate privileges, or launch further attacks within the network, leading to severe compromise. +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count FROM datamodel=Risk.All_Risk + WHERE source IN ("*Windows Cmdline Tool Execution From Non-Shell Process*", "*Windows System Network Config Discovery Display DNS*", "*Local Account Discovery With Wmic*", "*Windows Group Discovery Via Net*", "*Windows Create Local Administrator Account Via Net*", "*Windows User Discovery Via Net*", "*Icacls Deny Command*", "*ICACLS Grant Command*", "*Windows Proxy Via Netsh*", "*Processes launching netsh*", "*Disabling Firewall with Netsh*", "*Windows System Network Connections Discovery Netsh*", "*Network Connection Discovery With Arp*", "*Windows System Discovery Using ldap Nslookup*", "*Windows System Shutdown CommandLine*") + BY All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic + | `drop_dm_object_name(All_Risk)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | where source_count >= 4 + | `windows_common_abused_cmd_shell_risk_behavior_filter` +how_to_implement: Splunk Enterprise Security is required to utilize this correlation. In addition, modify the source_count value to your environment. In our testing, a count of 4 or 5 was decent in a lab, but the number may need to be increased base on internal testing. In addition, based on false positives, modify any analytics to be anomaly and lower or increase risk based on organization importance. +known_false_positives: False positives will be present based on many factors. Tune the correlation as needed to reduce too many triggers. references: -- https://www.splunk.com/en_us/blog/security/from-macros-to-no-macros-continuous-malware-improvements-by-qakbot.html -- https://www.splunk.com/en_us/blog/security/dark-crystal-rat-agent-deep-dive.html + - https://www.splunk.com/en_us/blog/security/from-macros-to-no-macros-continuous-malware-improvements-by-qakbot.html + - https://www.splunk.com/en_us/blog/security/dark-crystal-rat-agent-deep-dive.html drilldown_searches: -- name: View the detection results for - "$risk_object$" - search: '%original_detection_search% | search risk_object = "$risk_object$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$risk_object$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$risk_object$" + search: '%original_detection_search% | search risk_object = "$risk_object$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$risk_object$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ tags: - analytic_story: - - Azorult - - Volt Typhoon - - Sandworm Tools - - Windows Post-Exploitation - - FIN7 - - Qakbot - - Netsh Abuse - - DarkCrystal RAT - - Windows Defense Evasion Tactics - - CISA AA23-347A - - Disabling Security Tools - - Microsoft WSUS CVE-2025-59287 - asset_type: Endpoint - mitre_attack_id: - - T1222 - - T1049 - - T1033 - - T1529 - - T1016 - - T1059 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azorult + - Volt Typhoon + - Sandworm Tools + - Windows Post-Exploitation + - FIN7 + - Qakbot + - Netsh Abuse + - DarkCrystal RAT + - Windows Defense Evasion Tactics + - CISA AA23-347A + - Disabling Security Tools + - Microsoft WSUS CVE-2025-59287 + asset_type: Endpoint + mitre_attack_id: + - T1222 + - T1049 + - T1033 + - T1529 + - T1016 + - T1059 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/risk_behavior/abused_commandline/risk_recon.log - source: risk - sourcetype: stash + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/risk_behavior/abused_commandline/risk_recon.log + source: risk + sourcetype: stash diff --git a/detections/endpoint/windows_compatibility_telemetry_suspicious_child_process.yml b/detections/endpoint/windows_compatibility_telemetry_suspicious_child_process.yml index 03642af832..71b3e4506a 100644 --- a/detections/endpoint/windows_compatibility_telemetry_suspicious_child_process.yml +++ b/detections/endpoint/windows_compatibility_telemetry_suspicious_child_process.yml @@ -1,92 +1,74 @@ name: Windows Compatibility Telemetry Suspicious Child Process id: 56fe46ca-ffef-46fe-8f0e-5cd4b7b4cc0c -version: 5 -date: '2026-01-14' +version: 6 +date: '2026-02-25' author: Steven Dick status: production type: TTP -description: The following analytic detects the execution of CompatTelRunner.exe with - parameters indicative of a process not part of the normal "Microsoft Compatibility - Appraiser" telemetry collection. It leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process names, parent processes, and command-line arguments. - This activity is significant because CompatTelRunner.exe and the "Microsoft Compatibility - Appraiser" task always run as System and can be used to elevate privileges or establish - a highly privileged persistence mechanism. If confirmed malicious, this could enable - unauthorized code execution, privilege escalation, or persistent access to the compromised - system. +description: The following analytic detects the execution of CompatTelRunner.exe with parameters indicative of a process not part of the normal "Microsoft Compatibility Appraiser" telemetry collection. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, parent processes, and command-line arguments. This activity is significant because CompatTelRunner.exe and the "Microsoft Compatibility Appraiser" task always run as System and can be used to elevate privileges or establish a highly privileged persistence mechanism. If confirmed malicious, this could enable unauthorized code execution, privilege escalation, or persistent access to the compromised system. data_source: -- Windows Event Log Security 4688 -- Sysmon EventID 1 -- CrowdStrike ProcessRollup2 -search: "| tstats `security_content_summariesonly` count min(_time) AS firstTime,\ - \ max(_time) AS lastTime FROM datamodel=Endpoint.Processes \nwhere Processes.parent_process_name\ - \ = \"CompatTelRunner.exe\" AND Processes.process=\"* -cv:*\" NOT Processes.process\ - \ IN (\"* -m:*\") \nby Processes.action Processes.dest Processes.original_file_name\ - \ Processes.parent_process Processes.parent_process_exec \nProcesses.parent_process_guid\ - \ Processes.parent_process_id Processes.parent_process_name \nProcesses.parent_process_path\ - \ Processes.process Processes.process_exec Processes.process_guid Processes.process_hash\ - \ \nProcesses.process_id Processes.process_integrity_level Processes.process_name\ - \ Processes.process_path \nProcesses.user Processes.user_id Processes.vendor_product\ - \ \n|`drop_dm_object_name(Processes)`\n| `security_content_ctime(firstTime)` \n\ - | `security_content_ctime(lastTime)`\n| `windows_compatibility_telemetry_suspicious_child_process_filter`" -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Windows Event Log Security 4688 + - Sysmon EventID 1 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) AS firstTime, max(_time) AS lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name = "CompatTelRunner.exe" + AND + Processes.process="* -cv:*" NOT Processes.process IN ("* -m:*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_compatibility_telemetry_suspicious_child_process_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://attack.mitre.org/techniques/T1546/ -- https://scythe.io/threat-thursday/windows-telemetry-persistence -- https://www.trustedsec.com/blog/abusing-windows-telemetry-for-persistence + - https://attack.mitre.org/techniques/T1546/ + - https://scythe.io/threat-thursday/windows-telemetry-persistence + - https://www.trustedsec.com/blog/abusing-windows-telemetry-for-persistence drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate processes on $dest$ - search: '| from datamodel Endpoint.Processes | search dest = "$dest$" AND process_name - = "$process_name$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate processes on $dest$ + search: '| from datamodel Endpoint.Processes | search dest = "$dest$" AND process_name = "$process_name$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The process $process_name$ was launched in a suspicious manner by $parent_process_name$ - on host $dest$ - risk_objects: - - field: dest - type: system - score: 70 - threat_objects: - - field: process_name - type: process + message: The process $process_name$ was launched in a suspicious manner by $parent_process_name$ on host $dest$ + risk_objects: + - field: dest + type: system + score: 70 + threat_objects: + - field: process_name + type: process tags: - analytic_story: - - Windows Persistence Techniques - asset_type: Endpoint - mitre_attack_id: - - T1546 - - T1053.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1546 + - T1053.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546/compattelrunner_abuse/compattelrunner_abuse.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546/compattelrunner_abuse/compattelrunner_abuse.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_compatibility_telemetry_tampering_through_registry.yml b/detections/endpoint/windows_compatibility_telemetry_tampering_through_registry.yml index 28cd364e2a..fd0838530a 100644 --- a/detections/endpoint/windows_compatibility_telemetry_tampering_through_registry.yml +++ b/detections/endpoint/windows_compatibility_telemetry_tampering_through_registry.yml @@ -1,91 +1,76 @@ name: Windows Compatibility Telemetry Tampering Through Registry id: 43834687-cc48-4878-a2fa-f76e4271791f -version: 5 -date: '2026-01-14' +version: 6 +date: '2026-02-25' author: Steven Dick status: production type: TTP -description: This detection identifies suspicious modifications to the Windows Compatibility - Telemetry registry settings, specifically within the "TelemetryController" registry - key and "Command" registry value. It leverages data from the Endpoint.Registry data - model, focusing on registry paths and values indicative of such changes. This activity - is significant because CompatTelRunner.exe and the "Microsoft Compatibility Appraiser" - task always run as System and can be used to elevate privileges or establish a highly - privileged persistence mechanism. If confirmed malicious, this could enable unauthorized - code execution, privilege escalation, or persistent access to the compromised system. +description: This detection identifies suspicious modifications to the Windows Compatibility Telemetry registry settings, specifically within the "TelemetryController" registry key and "Command" registry value. It leverages data from the Endpoint.Registry data model, focusing on registry paths and values indicative of such changes. This activity is significant because CompatTelRunner.exe and the "Microsoft Compatibility Appraiser" task always run as System and can be used to elevate privileges or establish a highly privileged persistence mechanism. If confirmed malicious, this could enable unauthorized code execution, privilege escalation, or persistent access to the compromised system. data_source: -- Sysmon EventID 13 -search: "| tstats `security_content_summariesonly` min(_time) as firstTime, max(_time)\ - \ as lastTime, count FROM datamodel=Endpoint.Registry \nWHERE (Registry.registry_path\ - \ = \"*\\\\SOFTWARE\\\\Microsoft\\\\Windows NT\\\\CurrentVersion\\\\AppCompatFlags\\\ - \\TelemetryController*\" \nAND Registry.registry_value_name=\"Command\" NOT Registry.registry_value_data\ - \ IN (\"(empty)\")) \nby Registry.action Registry.dest Registry.process_guid Registry.process_id\ - \ Registry.registry_hive Registry.registry_path \nRegistry.registry_key_name Registry.registry_value_data\ - \ Registry.registry_value_name \nRegistry.registry_value_type Registry.status Registry.user\ - \ Registry.vendor_product \n| `drop_dm_object_name(Registry)`\n| eval process =\ - \ registry_value_data \n| `security_content_ctime(firstTime)` \n| `security_content_ctime(lastTime)`\n\ - | `windows_compatibility_telemetry_tampering_through_registry_filter`" -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 13 +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime, max(_time) as lastTime, count FROM datamodel=Endpoint.Registry + WHERE ( + Registry.registry_path = "*\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\AppCompatFlags\\TelemetryController*" + AND + Registry.registry_value_name="Command" NOT Registry.registry_value_data IN ("(empty)") + ) + BY Registry.action Registry.dest Registry.process_guid + Registry.process_id Registry.registry_hive Registry.registry_path + Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name + Registry.registry_value_type Registry.status Registry.user + Registry.vendor_product + | `drop_dm_object_name(Registry)` + | eval process = registry_value_data + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_compatibility_telemetry_tampering_through_registry_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://attack.mitre.org/techniques/T1546/ -- https://scythe.io/threat-thursday/windows-telemetry-persistence -- https://www.trustedsec.com/blog/abusing-windows-telemetry-for-persistence + - https://attack.mitre.org/techniques/T1546/ + - https://scythe.io/threat-thursday/windows-telemetry-persistence + - https://www.trustedsec.com/blog/abusing-windows-telemetry-for-persistence drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$","$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate registry changes on $dest$ - search: '| from datamodel Endpoint.Registry | search registry_path = "*\\SOFTWARE\\Microsoft\\Windows - NT\\CurrentVersion\\AppCompatFlags\\TelemetryController*" AND dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$","$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate registry changes on $dest$ + search: '| from datamodel Endpoint.Registry | search registry_path = "*\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\AppCompatFlags\\TelemetryController*" AND dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The process $process$ was added to registry settings for the Compatibility - Appraiser by $user$ on host $dest$ - risk_objects: - - field: dest - type: system - score: 70 - - field: user - type: user - score: 70 - threat_objects: - - field: process - type: process + message: The process $process$ was added to registry settings for the Compatibility Appraiser by $user$ on host $dest$ + risk_objects: + - field: dest + type: system + score: 70 + - field: user + type: user + score: 70 + threat_objects: + - field: process + type: process tags: - analytic_story: - - Windows Persistence Techniques - asset_type: Endpoint - mitre_attack_id: - - T1546 - - T1053.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1546 + - T1053.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546/compattelrunner_abuse/compattelrunner_abuse.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546/compattelrunner_abuse/compattelrunner_abuse.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_computer_account_created_by_computer_account.yml b/detections/endpoint/windows_computer_account_created_by_computer_account.yml index a5d7a93660..4272231828 100644 --- a/detections/endpoint/windows_computer_account_created_by_computer_account.yml +++ b/detections/endpoint/windows_computer_account_created_by_computer_account.yml @@ -1,71 +1,58 @@ name: Windows Computer Account Created by Computer Account id: 97a8dc5f-8a7c-4fed-9e3e-ec407fd0268a -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies a computer account creating a new computer - account with a specific Service Principal Name (SPN) "RestrictedKrbHost". This detection - leverages Windows Security Event Logs, specifically EventCode 4741, to identify - such activities. This behavior is significant as it may indicate an attempt to establish - unauthorized Kerberos authentication channels, potentially leading to lateral movement - or privilege escalation. If confirmed malicious, this activity could allow an attacker - to impersonate services, access sensitive information, or maintain persistence within - the network. +description: The following analytic identifies a computer account creating a new computer account with a specific Service Principal Name (SPN) "RestrictedKrbHost". This detection leverages Windows Security Event Logs, specifically EventCode 4741, to identify such activities. This behavior is significant as it may indicate an attempt to establish unauthorized Kerberos authentication channels, potentially leading to lateral movement or privilege escalation. If confirmed malicious, this activity could allow an attacker to impersonate services, access sensitive information, or maintain persistence within the network. data_source: -- Windows Event Log Security 4741 -search: '`wineventlog_security` EventCode=4741 user_type=computer SubjectDomainName!="NT - AUTHORITY" ServicePrincipalNames=*RestrictedKrbHost* | stats count min(_time) as - firstTime max(_time) as lastTime by dest, subject, action ,src_user, user, user_type, - SubjectUserName,SubjectDomainName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_computer_account_created_by_computer_account_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Windows Security Event Logs with 4741 EventCode enabled. The Windows TA is also - required. -known_false_positives: It is possible third party applications may have a computer - account that adds computer accounts, filtering may be required. + - Windows Event Log Security 4741 +search: |- + `wineventlog_security` EventCode=4741 user_type=computer SubjectDomainName!="NT AUTHORITY" ServicePrincipalNames=*RestrictedKrbHost* + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest, subject, action + ,src_user, user, user_type, + SubjectUserName,SubjectDomainName + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_computer_account_created_by_computer_account_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4741 EventCode enabled. The Windows TA is also required. +known_false_positives: It is possible third party applications may have a computer account that adds computer accounts, filtering may be required. references: -- https://docs.microsoft.com/en-us/openspecs/windows_protocols/ms-kile/445e4499-7e49-4f2a-8d82-aaf2d1ee3c47 -- https://github.com/Dec0ne/KrbRelayUp + - https://docs.microsoft.com/en-us/openspecs/windows_protocols/ms-kile/445e4499-7e49-4f2a-8d82-aaf2d1ee3c47 + - https://github.com/Dec0ne/KrbRelayUp drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Computer Account on $dest$ created by a computer account (possibly indicative - of Kerberos relay attack). - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: [] + message: A Computer Account on $dest$ created by a computer account (possibly indicative of Kerberos relay attack). + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: [] tags: - analytic_story: - - Active Directory Kerberos Attacks - - Local Privilege Escalation With KrbRelayUp - asset_type: Endpoint - mitre_attack_id: - - T1558 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Kerberos Attacks + - Local Privilege Escalation With KrbRelayUp + asset_type: Endpoint + mitre_attack_id: + - T1558 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558/windows_computer_account_created_by_computer_account/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558/windows_computer_account_created_by_computer_account/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_computer_account_requesting_kerberos_ticket.yml b/detections/endpoint/windows_computer_account_requesting_kerberos_ticket.yml index 07b8316a2b..076d1e1bc8 100644 --- a/detections/endpoint/windows_computer_account_requesting_kerberos_ticket.yml +++ b/detections/endpoint/windows_computer_account_requesting_kerberos_ticket.yml @@ -1,69 +1,56 @@ name: Windows Computer Account Requesting Kerberos Ticket id: fb3b2bb3-75a4-4279-848a-165b42624770 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects a computer account requesting a Kerberos - ticket, which is unusual as typically user accounts request these tickets. This - detection leverages Windows Security Event Logs, specifically EventCode 4768, to - identify instances where the TargetUserName ends with a dollar sign ($), indicating - a computer account. This activity is significant because it may indicate the use - of tools like KrbUpRelay or other Kerberos-based attacks. If confirmed malicious, - this could allow attackers to impersonate computer accounts, potentially leading - to unauthorized access and lateral movement within the network. +description: The following analytic detects a computer account requesting a Kerberos ticket, which is unusual as typically user accounts request these tickets. This detection leverages Windows Security Event Logs, specifically EventCode 4768, to identify instances where the TargetUserName ends with a dollar sign ($), indicating a computer account. This activity is significant because it may indicate the use of tools like KrbUpRelay or other Kerberos-based attacks. If confirmed malicious, this could allow attackers to impersonate computer accounts, potentially leading to unauthorized access and lateral movement within the network. data_source: -- Windows Event Log Security 4768 -search: '`wineventlog_security` EventCode=4768 TargetUserName="*$" src_ip!="::1" - | stats count min(_time) as firstTime max(_time) as lastTime by dest, subject, - action, user, TargetUserName, src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_computer_account_requesting_kerberos_ticket_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Windows Security Event Logs with 4768 EventCode enabled. The Windows TA is also - required. -known_false_positives: It is possible false positives will be present based on third - party applications. Filtering may be needed. + - Windows Event Log Security 4768 +search: |- + `wineventlog_security` EventCode=4768 TargetUserName="*$" src_ip!="::1" + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest, subject, action, + user, TargetUserName, src_ip + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_computer_account_requesting_kerberos_ticket_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4768 EventCode enabled. The Windows TA is also required. +known_false_positives: It is possible false positives will be present based on third party applications. Filtering may be needed. references: -- https://github.com/Dec0ne/KrbRelayUp + - https://github.com/Dec0ne/KrbRelayUp drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Computer Account requested a Kerberos ticket on $dest$, possibly indicative - of Kerberos relay attack. - risk_objects: - - field: dest - type: system - score: 35 - threat_objects: [] + message: A Computer Account requested a Kerberos ticket on $dest$, possibly indicative of Kerberos relay attack. + risk_objects: + - field: dest + type: system + score: 35 + threat_objects: [] tags: - analytic_story: - - Active Directory Kerberos Attacks - - Local Privilege Escalation With KrbRelayUp - asset_type: Endpoint - mitre_attack_id: - - T1558 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Kerberos Attacks + - Local Privilege Escalation With KrbRelayUp + asset_type: Endpoint + mitre_attack_id: + - T1558 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558/windows_computer_account_requesting_kerberos_ticket/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558/windows_computer_account_requesting_kerberos_ticket/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_computer_account_with_spn.yml b/detections/endpoint/windows_computer_account_with_spn.yml index f0692fb796..5e54d0aa4c 100644 --- a/detections/endpoint/windows_computer_account_with_spn.yml +++ b/detections/endpoint/windows_computer_account_with_spn.yml @@ -1,73 +1,57 @@ name: Windows Computer Account With SPN id: 9a3e57e7-33f4-470e-b25d-165baa6e8357 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the addition of Service Principal Names - (SPNs) HOST and RestrictedKrbHost to a computer account, indicative of KrbRelayUp - behavior. This detection leverages Windows Security Event Logs, specifically EventCode - 4741, to identify changes in SPNs. This activity is significant as it is commonly - associated with Kerberos-based attacks, which can be used to escalate privileges - or perform lateral movement within a network. If confirmed malicious, this behavior - could allow an attacker to impersonate services, potentially leading to unauthorized - access to sensitive resources. +description: The following analytic detects the addition of Service Principal Names (SPNs) HOST and RestrictedKrbHost to a computer account, indicative of KrbRelayUp behavior. This detection leverages Windows Security Event Logs, specifically EventCode 4741, to identify changes in SPNs. This activity is significant as it is commonly associated with Kerberos-based attacks, which can be used to escalate privileges or perform lateral movement within a network. If confirmed malicious, this behavior could allow an attacker to impersonate services, potentially leading to unauthorized access to sensitive resources. data_source: -- Windows Event Log Security 4741 -search: '`wineventlog_security` EventCode=4741 NewUacValue="0x80" ServicePrincipalNames - IN ("*HOST/*","*RestrictedKrbHost/*") | stats count min(_time) as firstTime max(_time) - as lastTime values(EventCode),values(TargetDomainName),values(PrimaryGroupId), values(OldUacValue), - values(NewUacValue),values(SamAccountName),values(DnsHostName),values(ServicePrincipalNames) - by dest Logon_ID subject | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_computer_account_with_spn_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Windows Security Event Logs with 4741 EventCode enabled. The Windows TA is also - required. -known_false_positives: It is possible third party applications may add these SPNs - to Computer Accounts, filtering may be needed. + - Windows Event Log Security 4741 +search: |- + `wineventlog_security` EventCode=4741 NewUacValue="0x80" ServicePrincipalNames IN ("*HOST/*","*RestrictedKrbHost/*") + | stats count min(_time) as firstTime max(_time) as lastTime values(EventCode),values(TargetDomainName),values(PrimaryGroupId), values(OldUacValue), values(NewUacValue),values(SamAccountName),values(DnsHostName),values(ServicePrincipalNames) + BY dest Logon_ID subject + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_computer_account_with_spn_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4741 EventCode enabled. The Windows TA is also required. +known_false_positives: It is possible third party applications may add these SPNs to Computer Accounts, filtering may be needed. references: -- https://www.trustedsec.com/blog/an-attack-path-mapping-approach-to-cves-2021-42287-and-2021-42278 -- https://github.com/Dec0ne/KrbRelayUp + - https://www.trustedsec.com/blog/an-attack-path-mapping-approach-to-cves-2021-42287-and-2021-42278 + - https://github.com/Dec0ne/KrbRelayUp drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Computer Account was created with SPNs related to Kerberos on $dest$, - possibly indicative of Kerberos relay attack. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A Computer Account was created with SPNs related to Kerberos on $dest$, possibly indicative of Kerberos relay attack. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Local Privilege Escalation With KrbRelayUp - - Active Directory Kerberos Attacks - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1558 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Local Privilege Escalation With KrbRelayUp + - Active Directory Kerberos Attacks + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1558 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558/windows_computer_account_with_spn/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558/windows_computer_account_with_spn/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_computerdefaults_spawning_a_process.yml b/detections/endpoint/windows_computerdefaults_spawning_a_process.yml index 19247ffe38..d68cfe4b85 100644 --- a/detections/endpoint/windows_computerdefaults_spawning_a_process.yml +++ b/detections/endpoint/windows_computerdefaults_spawning_a_process.yml @@ -1,74 +1,63 @@ name: Windows ComputerDefaults Spawning a Process id: 697eb4c0-1008-4c3c-b5ae-7bd9b39adbd6 -version: 2 -date: '2026-01-14' +version: 3 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP description: The following analytic detects the spawning of ComputerDefaults.exe, a Windows system process used to manage default application associations. While normally legitimate, this process can be exploited by attackers to bypass User Account Control (UAC) and execute unauthorized code with elevated privileges. Detection focuses on abnormal execution patterns, unusual parent-child process relationships, or deviations from standard paths. Such behavior may indicate attempts to modify system defaults or run malicious scripts undetected. Monitoring ComputerDefaults.exe is critical to identify potential security threats, prevent privilege escalation, and maintain system integrity by distinguishing normal operations from suspicious activity. data_source: -- Sysmon EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=ComputerDefaults.exe - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_computerdefaults_spawning_a_process_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name=ComputerDefaults.exe + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_computerdefaults_spawning_a_process_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://www.recordedfuture.com/research/from-castleloader-to-castlerat-tag-150-advances-operations + - https://www.recordedfuture.com/research/from-castleloader-to-castlerat-tag-150-advances-operations drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A ComputerDefaults.exe process $parent_process_name$ spawning child process $process_name$ - on host $dest$ - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: A ComputerDefaults.exe process $parent_process_name$ spawning child process $process_name$ on host $dest$ + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - Castle RAT - asset_type: Endpoint - mitre_attack_id: - - T1548.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Castle RAT + asset_type: Endpoint + mitre_attack_id: + - T1548.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/computerdefaults_spawn_proc/computerdefaults_process.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/computerdefaults_spawn_proc/computerdefaults_process.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_conhost_with_headless_argument.yml b/detections/endpoint/windows_conhost_with_headless_argument.yml index 7a6407e8e3..1c94e8d72b 100644 --- a/detections/endpoint/windows_conhost_with_headless_argument.yml +++ b/detections/endpoint/windows_conhost_with_headless_argument.yml @@ -1,87 +1,70 @@ name: Windows ConHost with Headless Argument id: d5039508-998d-4cfc-8b5e-9dcd679d9a62 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic detects the unusual invocation of the Windows - Console Host process (conhost.exe) with the undocumented --headless parameter. This - detection leverages Endpoint Detection and Response (EDR) telemetry, specifically - monitoring for command-line executions where conhost.exe is executed with the --headless - argument. This activity is significant for a SOC as it is not commonly used in legitimate - operations and may indicate an attacker's attempt to execute commands stealthily. - If confirmed malicious, this behavior could lead to persistence, lateral movement, - or other malicious activities, potentially resulting in data exfiltration or system - compromise. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=conhost.exe - Processes.process="*--headless *" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_conhost_with_headless_argument_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present if the application is legitimately - used, filter by user or endpoint as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic detects the unusual invocation of the Windows Console Host process (conhost.exe) with the undocumented --headless parameter. This detection leverages Endpoint Detection and Response (EDR) telemetry, specifically monitoring for command-line executions where conhost.exe is executed with the --headless argument. This activity is significant for a SOC as it is not commonly used in legitimate operations and may indicate an attacker's attempt to execute commands stealthily. If confirmed malicious, this behavior could lead to persistence, lateral movement, or other malicious activities, potentially resulting in data exfiltration or system compromise. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=conhost.exe Processes.process="*--headless *" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_conhost_with_headless_argument_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present if the application is legitimately used, filter by user or endpoint as needed. references: -- https://x.com/embee_research/status/1559410767564181504?s=20 -- https://x.com/GroupIB_TI/status/1719675754886131959?s=20 + - https://x.com/embee_research/status/1559410767564181504?s=20 + - https://x.com/GroupIB_TI/status/1719675754886131959?s=20 drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows ConHost with Headless Argument detected on $dest$ by $user$. - risk_objects: - - field: user - type: user - score: 70 - - field: dest - type: system - score: 70 - threat_objects: [] + message: Windows ConHost with Headless Argument detected on $dest$ by $user$. + risk_objects: + - field: user + type: user + score: 70 + - field: dest + type: system + score: 70 + threat_objects: [] tags: - analytic_story: - - Spearphishing Attachments - - Compromised Windows Host - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1564.003 - - T1564.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Spearphishing Attachments + - Compromised Windows Host + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1564.003 + - T1564.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1564.003/headless/4688_conhost_headless.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1564.003/headless/4688_conhost_headless.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_consolehost_history_file_deletion.yml b/detections/endpoint/windows_consolehost_history_file_deletion.yml index 9e5e8355b7..9fc025999c 100644 --- a/detections/endpoint/windows_consolehost_history_file_deletion.yml +++ b/detections/endpoint/windows_consolehost_history_file_deletion.yml @@ -7,59 +7,45 @@ status: production type: Anomaly description: The following analytic detects the deletion of the ConsoleHost_history.txt file, which stores command history for PowerShell sessions. Attackers may attempt to remove this file to cover their tracks and evade detection during post-exploitation activities. This detection focuses on file deletion commands executed via PowerShell, Command Prompt, or scripting languages that specifically target ConsoleHost_history.txt, typically located at %APPDATA%\Microsoft\Windows\PowerShell\PSReadline\ConsoleHost_history.txt. Identifying such activity can help uncover potential anti-forensic behavior and suspicious administrative actions. data_source: -- Sysmon EventID 23 -- Sysmon EventID 26 -search: '`sysmon` EventCode IN ("23","26") TargetFilename = "*\\Microsoft\\Windows\\PowerShell\\PSReadline\\ConsoleHost_history.txt" - | stats count min(_time) as firstTime, max(_time) as lastTime - by action dest dvc file_path file_hash file_name file_modify_time process_name process_exec process_id process_path user_id vendor_product process_guid signature signature_id user - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_consolehost_history_file_deletion_filter`' -how_to_implement: To successfully implement this search, you need to ingest logs that - include the deleted target file name, process name, and process ID from your endpoints. - If you are using Sysmon, ensure you have at least version 2.0 of the Sysmon TA installed. -known_false_positives: An administrator may delete the ConsoleHost history file on a specific machine, - potentially triggering this detection. However, this action is uncommon for regular users who are - not accustomed to using the PowerShell command line + - Sysmon EventID 23 + - Sysmon EventID 26 +search: '`sysmon` EventCode IN ("23","26") TargetFilename = "*\\Microsoft\\Windows\\PowerShell\\PSReadline\\ConsoleHost_history.txt" | stats count min(_time) as firstTime, max(_time) as lastTime by action dest dvc file_path file_hash file_name file_modify_time process_name process_exec process_id process_path user_id vendor_product process_guid signature signature_id user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_consolehost_history_file_deletion_filter`' +how_to_implement: To successfully implement this search, you need to ingest logs that include the deleted target file name, process name, and process ID from your endpoints. If you are using Sysmon, ensure you have at least version 2.0 of the Sysmon TA installed. +known_false_positives: An administrator may delete the ConsoleHost history file on a specific machine, potentially triggering this detection. However, this action is uncommon for regular users who are not accustomed to using the PowerShell command line references: -- https://www.cisa.gov/news-events/cybersecurity-advisories/aa25-071a + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa25-071a drilldown_searches: -- name: View the detection results for - "$user_id$" and "$dest$" - search: '%original_detection_search% | search user_id = "$user_id$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user_id$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user_id$" and "$dest$" + search: '%original_detection_search% | search user_id = "$user_id$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user_id$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a process $process_name$ delete ConsoleHost_History.txt on $dest$. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: - - field: process_name - type: process_name + message: a process $process_name$ delete ConsoleHost_History.txt on $dest$. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Medusa Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1070.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Medusa Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1070.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.003/ConsoleHost_History_deletion/delete_pwh_history_file.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.003/ConsoleHost_History_deletion/delete_pwh_history_file.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_create_local_account.yml b/detections/endpoint/windows_create_local_account.yml index 77b4dd4142..437ad03ae8 100644 --- a/detections/endpoint/windows_create_local_account.yml +++ b/detections/endpoint/windows_create_local_account.yml @@ -1,83 +1,69 @@ name: Windows Create Local Account id: 3fb2e8e3-7bc0-4567-9722-c5ab9f8595eb -version: 9 -date: '2025-10-21' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic detects the creation of a new local user account - on a Windows system. It leverages Windows Security Audit logs, specifically event - ID 4720, to identify this activity. Monitoring the creation of local accounts is - crucial for a SOC as it can indicate unauthorized access or lateral movement within - the network. If confirmed malicious, this activity could allow an attacker to establish - persistence, escalate privileges, or gain unauthorized access to sensitive systems - and data. +description: The following analytic detects the creation of a new local user account on a Windows system. It leverages Windows Security Audit logs, specifically event ID 4720, to identify this activity. Monitoring the creation of local accounts is crucial for a SOC as it can indicate unauthorized access or lateral movement within the network. If confirmed malicious, this activity could allow an attacker to establish persistence, escalate privileges, or gain unauthorized access to sensitive systems and data. data_source: - - Windows Event Log Security 4720 + - Windows Event Log Security 4720 search: | - | tstats `security_content_summariesonly` - values(All_Changes.result_id) as result_id - count min(_time) as firstTime - max(_time) as lastTime - from datamodel=Change where - All_Changes.result_id=4720 - by All_Changes.user All_Changes.dest All_Changes.result All_Changes.action - | `drop_dm_object_name("All_Changes")` - | `security_content_ctime(lastTime)` - | `security_content_ctime(firstTime)` - | `windows_create_local_account_filter` -how_to_implement: 'This search requires you to have enabled your Group Management - Audit Logs in your Local Windows Security Policy and be ingesting those logs. More - information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/' + | tstats `security_content_summariesonly` + values(All_Changes.result_id) as result_id + count min(_time) as firstTime + max(_time) as lastTime + from datamodel=Change where + All_Changes.result_id=4720 + by All_Changes.user All_Changes.dest All_Changes.result All_Changes.action + | `drop_dm_object_name("All_Changes")` + | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` + | `windows_create_local_account_filter` +how_to_implement: 'This search requires you to have enabled your Group Management Audit Logs in your Local Windows Security Policy and be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/' known_false_positives: | - It is possible that an administrator created the account. Verifying activity with an administrator is advised. - Accounts created on Domain Controllers will trigger this too. Exclude if it becomes too noisy. - This analytic is set to anomaly to allow for risk to be added. Filter and tune as needed. - Restrict to critical infrastructure to reduce any volume. + It is possible that an administrator created the account. Verifying activity with an administrator is advised. + Accounts created on Domain Controllers will trigger this too. Exclude if it becomes too noisy. + This analytic is set to anomaly to allow for risk to be added. Filter and tune as needed. + Restrict to critical infrastructure to reduce any volume. references: -- https://thedfirreport.com/2022/03/21/apt35-automates-initial-access-using-proxyshell/ + - https://thedfirreport.com/2022/03/21/apt35-automates-initial-access-using-proxyshell/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The following $user$ was added to $dest$ as a local account. - risk_objects: - - field: user - type: user - score: 18 - - field: dest - type: system - score: 18 - threat_objects: [] + message: The following $user$ was added to $dest$ as a local account. + risk_objects: + - field: user + type: user + score: 18 + - field: dest + type: system + score: 18 + threat_objects: [] tags: - analytic_story: - - Active Directory Password Spraying - - CISA AA24-241A - - GhostRedirector IIS Module and Rungan Backdoor - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1136.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Password Spraying + - CISA AA24-241A + - GhostRedirector IIS Module and Rungan Backdoor + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1136.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/4720.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/4720.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_create_local_administrator_account_via_net.yml b/detections/endpoint/windows_create_local_administrator_account_via_net.yml index 6b89ba5c42..8cd561495c 100644 --- a/detections/endpoint/windows_create_local_administrator_account_via_net.yml +++ b/detections/endpoint/windows_create_local_administrator_account_via_net.yml @@ -1,97 +1,80 @@ name: Windows Create Local Administrator Account Via Net id: 2c568c34-bb57-4b43-9d75-19c605b98e70 -version: 7 -date: '2025-10-14' +version: 8 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: Anomaly -description: The following analytic detects the creation of a local administrator - account using the "net.exe" command. It leverages Endpoint Detection and Response - (EDR) data to identify processes named "net.exe" with the "/add" parameter and keywords - related to administrator accounts. This activity is significant as it may indicate - an attacker attempting to gain persistent access or escalate privileges. If confirmed - malicious, this could lead to unauthorized access, data theft, or further system - compromise. Review the process details, user context, and related artifacts to determine - the legitimacy of the activity. +description: The following analytic detects the creation of a local administrator account using the "net.exe" command. It leverages Endpoint Detection and Response (EDR) data to identify processes named "net.exe" with the "/add" parameter and keywords related to administrator accounts. This activity is significant as it may indicate an attacker attempting to gain persistent access or escalate privileges. If confirmed malicious, this could lead to unauthorized access, data theft, or further system compromise. Review the process details, user context, and related artifacts to determine the legitimacy of the activity. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: "| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)\ - \ as lastTime from datamodel=Endpoint.Processes where `process_net` AND Processes.process=*/add*\ - \ AND Processes.process IN (\"*administrators*\", \"*administratoren*\", \"*administrateurs*\"\ - , \"*administrador*\", \"*amministratori*\", \"*administratorer*\", \"*Rendszergazda*\"\ - , \"*\u0410\u0434\u043C\u0438\u043D\u0438\u0441\u0442\u0440\u0430\u0442\u043E\u0440\ - *\", \"*Administrat\xF6r*\") by Processes.action Processes.dest Processes.original_file_name\ - \ Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid\ - \ Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path\ - \ Processes.process Processes.process_exec Processes.process_guid Processes.process_hash\ - \ Processes.process_id Processes.process_integrity_level Processes.process_name\ - \ Processes.process_path Processes.user Processes.user_id Processes.vendor_product\ - \ | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`\ - \ | `windows_create_local_administrator_account_via_net_filter`" -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_net` + AND + Processes.process=*/add* + AND + Processes.process IN ("*administrators*", "*administratoren*", "*administrateurs*", "*administrador*", "*amministratori*", "*administratorer*", "*Rendszergazda*", "*Администратор*", "*Administratör*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_create_local_administrator_account_via_net_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators often leverage net.exe to create admin accounts. references: [] drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to add a user to the local Administrators - group. - risk_objects: - - field: user - type: user - score: 30 - - field: dest - type: system - score: 30 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to add a user to the local Administrators group. + risk_objects: + - field: user + type: user + score: 30 + - field: dest + type: system + score: 30 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - DHS Report TA18-074A - - CISA AA22-257A - - Medusa Ransomware - - CISA AA24-241A - - Azorult - - DarkGate Malware - - GhostRedirector IIS Module and Rungan Backdoor - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1136.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - DHS Report TA18-074A + - CISA AA22-257A + - Medusa Ransomware + - CISA AA24-241A + - Azorult + - DarkGate Malware + - GhostRedirector IIS Module and Rungan Backdoor + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1136.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_credential_access_from_browser_password_store.yml b/detections/endpoint/windows_credential_access_from_browser_password_store.yml index 79d210749c..3947d83da2 100644 --- a/detections/endpoint/windows_credential_access_from_browser_password_store.yml +++ b/detections/endpoint/windows_credential_access_from_browser_password_store.yml @@ -4,90 +4,60 @@ version: 17 date: '2025-12-16' author: Teoderick Contreras, Bhavin Patel Splunk data_source: -- Windows Event Log Security 4663 + - Windows Event Log Security 4663 type: Anomaly status: production -description: The following analytic identifies a possible non-common browser - process accessing its browser user data profile. This tactic/technique has - been observed in various Trojan Stealers, such as SnakeKeylogger, which - attempt to gather sensitive browser information and credentials as part of - their exfiltration strategy. Detecting this anomaly can serve as a valuable - pivot for identifying processes that access lists of browser user data - profiles unexpectedly. This detection uses a lookup file `browser_app_list` - that maintains a list of well known browser applications and the browser paths - that are allowed to access the browser user data profiles. -search: '`wineventlog_security` EventCode=4663 | stats count by _time object_file_path - object_file_name dest process_name process_path process_id EventCode | lookup browser_app_list - browser_object_path as object_file_path OUTPUT browser_process_name isAllowed | - stats count min(_time) as firstTime max(_time) as lastTime values(object_file_name) - values(object_file_path) values(browser_process_name) as browser_process_name by - dest process_name process_path process_id EventCode isAllowed | rex field=process_name - "(?[^\\\\]+)$" | eval isMalicious=if(match(browser_process_name, - extracted_process_name), "0", "1") | where isMalicious=1 and isAllowed="false" | - `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_credential_access_from_browser_password_store_filter`' -how_to_implement: To successfully implement this search, you must ingest Windows - Security Event logs and track event code 4663. For 4663, enable "Audit Object - Access" in Group Policy. Then check the two boxes listed for both "Success" - and "Failure." This search may trigger on a browser application that is not - included in the browser_app_list lookup file. -known_false_positives: The lookup file `browser_app_list` may not contain all - the browser applications that are allowed to access the browser user data - profiles. Consider updating the lookup files to add allowed object paths for - the browser applications that are not included in the lookup file. +description: The following analytic identifies a possible non-common browser process accessing its browser user data profile. This tactic/technique has been observed in various Trojan Stealers, such as SnakeKeylogger, which attempt to gather sensitive browser information and credentials as part of their exfiltration strategy. Detecting this anomaly can serve as a valuable pivot for identifying processes that access lists of browser user data profiles unexpectedly. This detection uses a lookup file `browser_app_list` that maintains a list of well known browser applications and the browser paths that are allowed to access the browser user data profiles. +search: '`wineventlog_security` EventCode=4663 | stats count by _time object_file_path object_file_name dest process_name process_path process_id EventCode | lookup browser_app_list browser_object_path as object_file_path OUTPUT browser_process_name isAllowed | stats count min(_time) as firstTime max(_time) as lastTime values(object_file_name) values(object_file_path) values(browser_process_name) as browser_process_name by dest process_name process_path process_id EventCode isAllowed | rex field=process_name "(?[^\\\\]+)$" | eval isMalicious=if(match(browser_process_name, extracted_process_name), "0", "1") | where isMalicious=1 and isAllowed="false" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_credential_access_from_browser_password_store_filter`' +how_to_implement: To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure." This search may trigger on a browser application that is not included in the browser_app_list lookup file. +known_false_positives: The lookup file `browser_app_list` may not contain all the browser applications that are allowed to access the browser user data profiles. Consider updating the lookup files to add allowed object paths for the browser applications that are not included in the lookup file. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.404keylogger -- https://www.checkpoint.com/cyber-hub/threat-prevention/what-is-malware/snake-keylogger-malware/ + - https://malpedia.caad.fkie.fraunhofer.de/details/win.404keylogger + - https://www.checkpoint.com/cyber-hub/threat-prevention/what-is-malware/snake-keylogger-malware/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A non-common browser process $process_name$ accessing browser user - data folder on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A non-common browser process $process_name$ accessing browser user data folder on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - StealC Stealer - - Salt Typhoon - - Earth Alux - - Quasar RAT - - PXA Stealer - - SnappyBee - - Malicious Inno Setup Loader - - Braodo Stealer - - MoonPeak - - Snake Keylogger - - China-Nexus Threat Activity - - Meduza Stealer - - Scattered Spider - - 0bj3ctivity Stealer - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1012 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - StealC Stealer + - Salt Typhoon + - Earth Alux + - Quasar RAT + - PXA Stealer + - SnappyBee + - Malicious Inno Setup Loader + - Braodo Stealer + - MoonPeak + - Snake Keylogger + - China-Nexus Threat Activity + - Meduza Stealer + - Scattered Spider + - 0bj3ctivity Stealer + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1012 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552/snakey_keylogger_outlook_reg_access/snakekeylogger_4663.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552/snakey_keylogger_outlook_reg_access/snakekeylogger_4663.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_credential_dumping_lsass_memory_createdump.yml b/detections/endpoint/windows_credential_dumping_lsass_memory_createdump.yml index 5177e41a61..feb50724f0 100644 --- a/detections/endpoint/windows_credential_dumping_lsass_memory_createdump.yml +++ b/detections/endpoint/windows_credential_dumping_lsass_memory_createdump.yml @@ -1,90 +1,76 @@ name: Windows Credential Dumping LSASS Memory Createdump id: b3b7ce35-fce5-4c73-85f4-700aeada81a9 -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the use of CreateDump.exe to perform a - process dump. This binary is not native to Windows and is often introduced by third-party - applications, including PowerShell 7. The detection leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process names, GUIDs, and complete - command-line executions. This activity is significant as it may indicate an attempt - to dump LSASS memory, which can be used to extract credentials. If confirmed malicious, - this could lead to unauthorized access and lateral movement within the network. +description: The following analytic detects the use of CreateDump.exe to perform a process dump. This binary is not native to Windows and is often introduced by third-party applications, including PowerShell 7. The detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, GUIDs, and complete command-line executions. This activity is significant as it may indicate an attempt to dump LSASS memory, which can be used to extract credentials. If confirmed malicious, this could lead to unauthorized access and lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=createdump.exe - OR Processes.original_file_name="FX_VER_INTERNALNAME_STR" Processes.process="*-u - *" AND Processes.process="*-f *" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_credential_dumping_lsass_memory_createdump_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present if an application is dumping - processes, filter as needed. Recommend reviewing createdump.exe usage across the - fleet to better understand all usage and by what. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=createdump.exe + OR + Processes.original_file_name="FX_VER_INTERNALNAME_STR" Processes.process="*-u *" + AND + Processes.process="*-f *" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_credential_dumping_lsass_memory_createdump_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present if an application is dumping processes, filter as needed. Recommend reviewing createdump.exe usage across the fleet to better understand all usage and by what. references: -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-11---dump-lsass-with-createdumpexe-from-net-v5 + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-11---dump-lsass-with-createdumpexe-from-net-v5 drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to dump a process. - risk_objects: - - field: user - type: user - score: 70 - - field: dest - type: system - score: 70 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to dump a process. + risk_objects: + - field: user + type: user + score: 70 + - field: dest + type: system + score: 70 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Compromised Windows Host - - Credential Dumping - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1003.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - Credential Dumping + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1003.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/createdump_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/createdump_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_credential_target_information_structure_in_commandline.yml b/detections/endpoint/windows_credential_target_information_structure_in_commandline.yml index 702f22d12b..dabea47edd 100644 --- a/detections/endpoint/windows_credential_target_information_structure_in_commandline.yml +++ b/detections/endpoint/windows_credential_target_information_structure_in_commandline.yml @@ -1,88 +1,73 @@ name: Windows Credential Target Information Structure in Commandline id: f79c5d7a-dd99-4263-93e1-49ace5634c82 -version: 1 -date: '2025-11-13' +version: 2 +date: '2026-02-25' author: Raven Tait, Splunk status: production type: TTP -description: Detects DNS-based Kerberos coercion attacks where adversaries - inject marshaled credential structures into DNS records to spoof SPNs and - redirect authentication such as in CVE-2025-33073. This detection leverages - process creation events looking for specific CREDENTIAL_TARGET_INFORMATION structures. +description: Detects DNS-based Kerberos coercion attacks where adversaries inject marshaled credential structures into DNS records to spoof SPNs and redirect authentication such as in CVE-2025-33073. This detection leverages process creation events looking for specific CREDENTIAL_TARGET_INFORMATION structures. data_source: -- Sysmon EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*1UWhRCA*" - Processes.process="*AAAAA*" Processes.process="*YBAAAA*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_credential_target_information_structure_in_commandline_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Commands with all of these base64 encoded values are unusual in production - environments. Filter as needed. + - Sysmon EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*1UWhRCA*" Processes.process="*AAAAA*" Processes.process="*YBAAAA*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_credential_target_information_structure_in_commandline_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Commands with all of these base64 encoded values are unusual in production environments. Filter as needed. references: -- https://web.archive.org/web/20250617122747/https://www.synacktiv.com/publications/ntlm-reflection-is-dead-long-live-ntlm-reflection-an-in-depth-analysis-of-cve-2025 -- https://www.synacktiv.com/publications/relaying-kerberos-over-smb-using-krbrelayx -- https://www.guidepointsecurity.com/blog/the-birth-and-death-of-loopyticket/ + - https://web.archive.org/web/20250617122747/https://www.synacktiv.com/publications/ntlm-reflection-is-dead-long-live-ntlm-reflection-an-in-depth-analysis-of-cve-2025 + - https://www.synacktiv.com/publications/relaying-kerberos-over-smb-using-krbrelayx + - https://www.guidepointsecurity.com/blog/the-birth-and-death-of-loopyticket/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of CREDENTIAL_TARGET_INFORMATION magic string was identified - in a command on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 44 - - field: dest - type: system - score: 44 - threat_objects: [] + message: An instance of CREDENTIAL_TARGET_INFORMATION magic string was identified in a command on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 44 + - field: dest + type: system + score: 44 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - Suspicious DNS Traffic - - Local Privilege Escalation With KrbRelayUp - - Kerberos Coercion with DNS - asset_type: Endpoint - mitre_attack_id: - - T1557.001 - - T1187 - - T1071.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: - - CVE-2025-33073 + analytic_story: + - Compromised Windows Host + - Suspicious DNS Traffic + - Local Privilege Escalation With KrbRelayUp + - Kerberos Coercion with DNS + asset_type: Endpoint + mitre_attack_id: + - T1557.001 + - T1187 + - T1071.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: + - CVE-2025-33073 tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1071.004/kerberos_coercion/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1071.004/kerberos_coercion/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_credentials_access_via_vaultcli_module.yml b/detections/endpoint/windows_credentials_access_via_vaultcli_module.yml index 0264df5f8e..14d3e607cc 100644 --- a/detections/endpoint/windows_credentials_access_via_vaultcli_module.yml +++ b/detections/endpoint/windows_credentials_access_via_vaultcli_module.yml @@ -4,79 +4,52 @@ version: 6 date: '2025-10-14' author: Teoderick Contreras, Splunk data_source: -- Sysmon EventID 7 + - Sysmon EventID 7 type: Anomaly status: production -description: The following analytic detects potentially abnormal interactions with - VaultCLI.dll, particularly those initiated by processes located in publicly writable - Windows folder paths. The VaultCLI.dll module allows processes to extract credentials - from the Windows Credential Vault. It was seen being abused by information stealers - such as Meduza. The analytic monitors suspicious API calls, unauthorized credential - access patterns, and anomalous process behaviors indicative of malicious activity. - By leveraging a combination of signature-based detection and behavioral analysis, - it effectively flags attempts to misuse the vault for credential theft, enabling - swift response to protect sensitive user data and ensure system security. -search: '`sysmon` EventCode=7 ImageLoaded ="*\\vaultcli.dll" process_path IN("*\\windows\\fonts\\*", - "*\\windows\\temp\\*", "*\\users\\public\\*", "*\\windows\\debug\\*", "*\\Users\\Administrator\\Music\\*", - "*\\Windows\\servicing\\*", "*\\Users\\Default\\*", "*Recycle.bin*", "*\\Windows\\Media\\*", - "\\Windows\\repair\\*", "*\\appdata\\local\\temp\\*", "*\\PerfLogs\\*", "*:\\temp\\*") - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image - ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid - process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified - signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_credentials_access_via_vaultcli_module_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name and imageloaded executions from your endpoints. If you - are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -known_false_positives: Third party software might leverage this DLL in order to make - use of the Credential Manager feature via the provided exports. Typically the vaultcli.dll - module is loaded by the vaultcmd.exe Windows Utility to interact with the Windows - Credential Manager for secure storage and retrieval of credentials. +description: The following analytic detects potentially abnormal interactions with VaultCLI.dll, particularly those initiated by processes located in publicly writable Windows folder paths. The VaultCLI.dll module allows processes to extract credentials from the Windows Credential Vault. It was seen being abused by information stealers such as Meduza. The analytic monitors suspicious API calls, unauthorized credential access patterns, and anomalous process behaviors indicative of malicious activity. By leveraging a combination of signature-based detection and behavioral analysis, it effectively flags attempts to misuse the vault for credential theft, enabling swift response to protect sensitive user data and ensure system security. +search: '`sysmon` EventCode=7 ImageLoaded ="*\\vaultcli.dll" process_path IN("*\\windows\\fonts\\*", "*\\windows\\temp\\*", "*\\users\\public\\*", "*\\windows\\debug\\*", "*\\Users\\Administrator\\Music\\*", "*\\Windows\\servicing\\*", "*\\Users\\Default\\*", "*Recycle.bin*", "*\\Windows\\Media\\*", "\\Windows\\repair\\*", "*\\appdata\\local\\temp\\*", "*\\PerfLogs\\*", "*:\\temp\\*") | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_credentials_access_via_vaultcli_module_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: Third party software might leverage this DLL in order to make use of the Credential Manager feature via the provided exports. Typically the vaultcli.dll module is loaded by the vaultcmd.exe Windows Utility to interact with the Windows Credential Manager for secure storage and retrieval of credentials. references: -- https://hijacklibs.net/entries/microsoft/built-in/vaultcli.html -- https://www.fortinet.com/blog/threat-research/exploiting-cve-2024-21412-stealer-campaign-unleashed -- https://cert.gov.ua/article/6276652 -- https://cert.gov.ua/article/6281018 -- https://g0njxa.medium.com/approaching-stealers-devs-a-brief-interview-with-meduza-f1bbd2efb84f + - https://hijacklibs.net/entries/microsoft/built-in/vaultcli.html + - https://www.fortinet.com/blog/threat-research/exploiting-cve-2024-21412-stealer-campaign-unleashed + - https://cert.gov.ua/article/6276652 + - https://cert.gov.ua/article/6281018 + - https://g0njxa.medium.com/approaching-stealers-devs-a-brief-interview-with-meduza-f1bbd2efb84f drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of process $process_name$ loading the file $ImageLoaded$ was - identified on endpoint $dest$ to potentially capture credentials in memory. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: - - field: process_name - type: process_name + message: An instance of process $process_name$ loading the file $ImageLoaded$ was identified on endpoint $dest$ to potentially capture credentials in memory. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Meduza Stealer - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1555.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Meduza Stealer + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1555.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555.004/vaultcli_creds/vaultcli.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555.004/vaultcli_creds/vaultcli.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_credentials_from_password_stores_chrome_copied_in_temp_dir.yml b/detections/endpoint/windows_credentials_from_password_stores_chrome_copied_in_temp_dir.yml index 990ea4bf41..15bf991066 100644 --- a/detections/endpoint/windows_credentials_from_password_stores_chrome_copied_in_temp_dir.yml +++ b/detections/endpoint/windows_credentials_from_password_stores_chrome_copied_in_temp_dir.yml @@ -4,71 +4,47 @@ version: 7 date: '2026-01-14' author: Teoderick Contreras, Splunk data_source: -- Sysmon EventID 11 + - Sysmon EventID 11 type: TTP status: production -description: The following analytic detects the copying of Chrome's Local State and - Login Data files into temporary folders, a tactic often used by the Braodo stealer - malware. These files contain encrypted user credentials, including saved passwords - and login session details. The detection monitors for suspicious copying activity - involving these specific Chrome files, particularly in temp directories where malware - typically processes the stolen data. Identifying this behavior enables security - teams to act quickly, preventing attackers from decrypting and exfiltrating sensitive - browser credentials and mitigating the risk of unauthorized access. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("Local - State", "Login Data") Filesystem.file_path = "*\\temp\\*" by Filesystem.action Filesystem.dest - Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time - Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size - Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product - | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_credentials_from_password_stores_chrome_copied_in_temp_dir_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. +description: The following analytic detects the copying of Chrome's Local State and Login Data files into temporary folders, a tactic often used by the Braodo stealer malware. These files contain encrypted user credentials, including saved passwords and login session details. The detection monitors for suspicious copying activity involving these specific Chrome files, particularly in temp directories where malware typically processes the stolen data. Identifying this behavior enables security teams to act quickly, preventing attackers from decrypting and exfiltrating sensitive browser credentials and mitigating the risk of unauthorized access. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("Local State", "Login Data") Filesystem.file_path = "*\\temp\\*" by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_credentials_from_password_stores_chrome_copied_in_temp_dir_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. known_false_positives: No false positives have been identified at this time. references: -- https://x.com/suyog41/status/1825869470323056748 -- https://g0njxa.medium.com/from-vietnam-to-united-states-malware-fraud-and-dropshipping-98b7a7b2c36d + - https://x.com/suyog41/status/1825869470323056748 + - https://g0njxa.medium.com/from-vietnam-to-united-states-malware-fraud-and-dropshipping-98b7a7b2c36d drilldown_searches: -- name: View the detection results for "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Chrome Password Store File [$file_name$] was copied in %temp% folder on - [$dest$]. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: Chrome Password Store File [$file_name$] was copied in %temp% folder on [$dest$]. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Braodo Stealer - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1555.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Braodo Stealer + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1555.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555.003/browser_credential_info_temp/braodo_browser_info.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555.003/browser_credential_info_temp/braodo_browser_info.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_credentials_from_password_stores_chrome_extension_access.yml b/detections/endpoint/windows_credentials_from_password_stores_chrome_extension_access.yml index 4c3927cfa7..ff66cac751 100644 --- a/detections/endpoint/windows_credentials_from_password_stores_chrome_extension_access.yml +++ b/detections/endpoint/windows_credentials_from_password_stores_chrome_extension_access.yml @@ -6,77 +6,53 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: -- Windows Event Log Security 4663 -description: The following analytic detects non-Chrome processes attempting to - access the Chrome extensions file. It leverages Windows Security Event logs, - specifically event code 4663, to identify this behavior. This activity is - significant because adversaries may exploit this file to extract sensitive - information from the Chrome browser, posing a security risk. If confirmed - malicious, this could lead to unauthorized access to stored credentials and - other sensitive data, potentially compromising the security of the affected - system and broader network. -search: '`wineventlog_security` EventCode=4663 object_file_path="*\\AppData\\Local\\Google\\Chrome\\User - Data\\Default\\Local Extension Settings\\*" AND NOT (process_path IN ("*:\\Windows\\explorer.exe", - "*\\chrome.exe")) | stats count min(_time) as firstTime max(_time) as lastTime by - object_file_name object_file_path process_name process_path process_id EventCode - dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_credentials_from_password_stores_chrome_extension_access_filter`' -how_to_implement: To successfully implement this search, you must ingest Windows - Security Event logs and track event code 4663. For 4663, enable "Audit Object - Access" in Group Policy. Then check the two boxes listed for both "Success" - and "Failure." -known_false_positives: Uninstall chrome browser extension application may access - this file and folder path to removed chrome installation in the target host. - Filter is needed. + - Windows Event Log Security 4663 +description: The following analytic detects non-Chrome processes attempting to access the Chrome extensions file. It leverages Windows Security Event logs, specifically event code 4663, to identify this behavior. This activity is significant because adversaries may exploit this file to extract sensitive information from the Chrome browser, posing a security risk. If confirmed malicious, this could lead to unauthorized access to stored credentials and other sensitive data, potentially compromising the security of the affected system and broader network. +search: '`wineventlog_security` EventCode=4663 object_file_path="*\\AppData\\Local\\Google\\Chrome\\User Data\\Default\\Local Extension Settings\\*" AND NOT (process_path IN ("*:\\Windows\\explorer.exe", "*\\chrome.exe")) | stats count min(_time) as firstTime max(_time) as lastTime by object_file_name object_file_path process_name process_path process_id EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_credentials_from_password_stores_chrome_extension_access_filter`' +how_to_implement: To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure." +known_false_positives: Uninstall chrome browser extension application may access this file and folder path to removed chrome installation in the target host. Filter is needed. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.redline_stealer + - https://malpedia.caad.fkie.fraunhofer.de/details/win.redline_stealer drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A non-chrome process $process_name$ accessing chrome browser - extension folder files on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A non-chrome process $process_name$ accessing chrome browser extension folder files on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - StealC Stealer - - DarkGate Malware - - Amadey - - Meduza Stealer - - Malicious Inno Setup Loader - - Phemedrone Stealer - - CISA AA23-347A - - RedLine Stealer - - Braodo Stealer - - MoonPeak - - 0bj3ctivity Stealer - asset_type: Endpoint - mitre_attack_id: - - T1012 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - StealC Stealer + - DarkGate Malware + - Amadey + - Meduza Stealer + - Malicious Inno Setup Loader + - Phemedrone Stealer + - CISA AA23-347A + - RedLine Stealer + - Braodo Stealer + - MoonPeak + - 0bj3ctivity Stealer + asset_type: Endpoint + mitre_attack_id: + - T1012 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/browser_ext_access/security-ext-raw.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/browser_ext_access/security-ext-raw.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_credentials_from_password_stores_chrome_localstate_access.yml b/detections/endpoint/windows_credentials_from_password_stores_chrome_localstate_access.yml index 4b40c06594..c7d8e4516a 100644 --- a/detections/endpoint/windows_credentials_from_password_stores_chrome_localstate_access.yml +++ b/detections/endpoint/windows_credentials_from_password_stores_chrome_localstate_access.yml @@ -6,86 +6,63 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: -- Windows Event Log Security 4663 -description: The following analytic detects non-Chrome processes accessing the - Chrome "Local State" file, which contains critical settings and information. - It leverages Windows Security Event logs, specifically event code 4663, to - identify this behavior. This activity is significant because threat actors can - exploit this file to extract the encrypted master key used for decrypting - saved passwords in Chrome. If confirmed malicious, this could lead to - unauthorized access to sensitive information, posing a severe security risk. - Monitoring this anomaly helps identify potential threats and safeguard - browser-stored data. -search: '`wineventlog_security` EventCode=4663 object_file_path="*\\AppData\\Local\\Google\\Chrome\\User - Data\\Local State" NOT (process_name IN ("*\\chrome.exe","*:\\Windows\\explorer.exe")) - | stats count min(_time) as firstTime max(_time) as lastTime by object_file_name - object_file_path process_name process_path process_id EventCode dest | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_credentials_from_password_stores_chrome_localstate_access_filter`' -how_to_implement: To successfully implement this search, you must ingest Windows - Security Event logs and track event code 4663. For 4663, enable "Audit Object - Access" in Group Policy. Then check the two boxes listed for both "Success" - and "Failure." -known_false_positives: Uninstall chrome application may access this file and - folder path to removed chrome installation in target host. Filter is needed. + - Windows Event Log Security 4663 +description: The following analytic detects non-Chrome processes accessing the Chrome "Local State" file, which contains critical settings and information. It leverages Windows Security Event logs, specifically event code 4663, to identify this behavior. This activity is significant because threat actors can exploit this file to extract the encrypted master key used for decrypting saved passwords in Chrome. If confirmed malicious, this could lead to unauthorized access to sensitive information, posing a severe security risk. Monitoring this anomaly helps identify potential threats and safeguard browser-stored data. +search: '`wineventlog_security` EventCode=4663 object_file_path="*\\AppData\\Local\\Google\\Chrome\\User Data\\Local State" NOT (process_name IN ("*\\chrome.exe","*:\\Windows\\explorer.exe")) | stats count min(_time) as firstTime max(_time) as lastTime by object_file_name object_file_path process_name process_path process_id EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_credentials_from_password_stores_chrome_localstate_access_filter`' +how_to_implement: To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure." +known_false_positives: Uninstall chrome application may access this file and folder path to removed chrome installation in target host. Filter is needed. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.redline_stealer + - https://malpedia.caad.fkie.fraunhofer.de/details/win.redline_stealer drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A non-chrome process $process_name$ accessing "Chrome\\User - Data\\Local State" file on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A non-chrome process $process_name$ accessing "Chrome\\User Data\\Local State" file on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - StealC Stealer - - DarkGate Malware - - Malicious Inno Setup Loader - - NjRAT - - Phemedrone Stealer - - Salt Typhoon - - Amadey - - Earth Alux - - Warzone RAT - - Quasar RAT - - PXA Stealer - - RedLine Stealer - - SnappyBee - - Meduza Stealer - - Braodo Stealer - - MoonPeak - - Snake Keylogger - - China-Nexus Threat Activity - - 0bj3ctivity Stealer - - Lokibot - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1012 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - StealC Stealer + - DarkGate Malware + - Malicious Inno Setup Loader + - NjRAT + - Phemedrone Stealer + - Salt Typhoon + - Amadey + - Earth Alux + - Warzone RAT + - Quasar RAT + - PXA Stealer + - RedLine Stealer + - SnappyBee + - Meduza Stealer + - Braodo Stealer + - MoonPeak + - Snake Keylogger + - China-Nexus Threat Activity + - 0bj3ctivity Stealer + - Lokibot + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1012 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/chrome_local_state_simulate_access/redline-localstate-smalldata-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/chrome_local_state_simulate_access/redline-localstate-smalldata-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_credentials_from_password_stores_chrome_login_data_access.yml b/detections/endpoint/windows_credentials_from_password_stores_chrome_login_data_access.yml index 2e9fd571fc..eacb740dbe 100644 --- a/detections/endpoint/windows_credentials_from_password_stores_chrome_login_data_access.yml +++ b/detections/endpoint/windows_credentials_from_password_stores_chrome_login_data_access.yml @@ -6,87 +6,63 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: -- Windows Event Log Security 4663 -description: The following analytic identifies non-Chrome processes accessing - the Chrome user data file "login data." This file is an SQLite database - containing sensitive information, including saved passwords. The detection - leverages Windows Security Event logs, specifically event code 4663, to - monitor access attempts. This activity is significant as it may indicate - attempts by threat actors to extract and decrypt stored passwords, posing a - risk to user credentials. If confirmed malicious, attackers could gain - unauthorized access to sensitive accounts and escalate their privileges within - the environment. -search: '`wineventlog_security` EventCode=4663 object_file_path="*\\AppData\\Local\\Google\\Chrome\\User - Data\\Default\\Login Data" AND NOT (process_path IN ("*:\\Windows\\explorer.exe", - "*:\\Windows\\System32\\dllhost.exe", "*\\chrome.exe")) | stats count min(_time) - as firstTime max(_time) as lastTime by object_file_name object_file_path process_name - process_path process_id EventCode dest | `security_content_ctime(firstTime)` | - `security_content_ctime(lastTime)` | `windows_credentials_from_password_stores_chrome_login_data_access_filter`' -how_to_implement: To successfully implement this search, you must ingest Windows - Security Event logs and track event code 4663. For 4663, enable "Audit Object - Access" in Group Policy. Then check the two boxes listed for both "Success" - and "Failure." -known_false_positives: Uninstall application may access this registry to remove - the entry of the target application. filter is needed. + - Windows Event Log Security 4663 +description: The following analytic identifies non-Chrome processes accessing the Chrome user data file "login data." This file is an SQLite database containing sensitive information, including saved passwords. The detection leverages Windows Security Event logs, specifically event code 4663, to monitor access attempts. This activity is significant as it may indicate attempts by threat actors to extract and decrypt stored passwords, posing a risk to user credentials. If confirmed malicious, attackers could gain unauthorized access to sensitive accounts and escalate their privileges within the environment. +search: '`wineventlog_security` EventCode=4663 object_file_path="*\\AppData\\Local\\Google\\Chrome\\User Data\\Default\\Login Data" AND NOT (process_path IN ("*:\\Windows\\explorer.exe", "*:\\Windows\\System32\\dllhost.exe", "*\\chrome.exe")) | stats count min(_time) as firstTime max(_time) as lastTime by object_file_name object_file_path process_name process_path process_id EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_credentials_from_password_stores_chrome_login_data_access_filter`' +how_to_implement: To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure." +known_false_positives: Uninstall application may access this registry to remove the entry of the target application. filter is needed. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.redline_stealer + - https://malpedia.caad.fkie.fraunhofer.de/details/win.redline_stealer drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A non-chrome process $process_name$ accessing Chrome "Login Data" - file on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: A non-chrome process $process_name$ accessing Chrome "Login Data" file on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - StealC Stealer - - DarkGate Malware - - Malicious Inno Setup Loader - - NjRAT - - Phemedrone Stealer - - Salt Typhoon - - Amadey - - Earth Alux - - Warzone RAT - - Quasar RAT - - PXA Stealer - - RedLine Stealer - - SnappyBee - - Meduza Stealer - - Braodo Stealer - - MoonPeak - - Snake Keylogger - - China-Nexus Threat Activity - - 0bj3ctivity Stealer - - Lokibot - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1012 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - StealC Stealer + - DarkGate Malware + - Malicious Inno Setup Loader + - NjRAT + - Phemedrone Stealer + - Salt Typhoon + - Amadey + - Earth Alux + - Warzone RAT + - Quasar RAT + - PXA Stealer + - RedLine Stealer + - SnappyBee + - Meduza Stealer + - Braodo Stealer + - MoonPeak + - Snake Keylogger + - China-Nexus Threat Activity + - 0bj3ctivity Stealer + - Lokibot + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1012 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/chrome_login_data_simulate_access/redline-login-data-security-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/chrome_login_data_simulate_access/redline-login-data-security-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_credentials_from_password_stores_creation.yml b/detections/endpoint/windows_credentials_from_password_stores_creation.yml index 55b0c9e7fa..35fced753a 100644 --- a/detections/endpoint/windows_credentials_from_password_stores_creation.yml +++ b/detections/endpoint/windows_credentials_from_password_stores_creation.yml @@ -1,82 +1,69 @@ name: Windows Credentials from Password Stores Creation id: c0c5a479-bf57-4ca0-af3a-4c7081e5ba05 -version: 8 -date: '2025-11-20' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic detects the execution of the Windows OS tool cmdkey.exe, - which is used to create stored usernames, passwords, or credentials. This detection - leverages data from Endpoint Detection and Response (EDR) agents, focusing on process - execution logs and command-line arguments. This activity is significant because - cmdkey.exe is often abused by post-exploitation tools and malware, such as Darkgate, - to gain unauthorized access. If confirmed malicious, this behavior could allow attackers - to escalate privileges and maintain persistence on the targeted host, facilitating - further attacks and potential data breaches. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name="cmdkey.exe" - OR Processes.original_file_name = "cmdkey.exe" AND Processes.process = "*/generic*" - Processes.process IN ("*/user*", "*/password*") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_credentials_from_password_stores_creation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic detects the execution of the Windows OS tool cmdkey.exe, which is used to create stored usernames, passwords, or credentials. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant because cmdkey.exe is often abused by post-exploitation tools and malware, such as Darkgate, to gain unauthorized access. If confirmed malicious, this behavior could allow attackers to escalate privileges and maintain persistence on the targeted host, facilitating further attacks and potential data breaches. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name="cmdkey.exe" + OR + Processes.original_file_name = "cmdkey.exe" + AND + Processes.process = "*/generic*" Processes.process IN ("*/user*", "*/password*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_credentials_from_password_stores_creation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: network administrator can use this tool for auditing process. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate + - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a process $process_name$ was executed on $dest$ to create stored credentials - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: a process $process_name$ was executed on $dest$ to create stored credentials + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - DarkGate Malware - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1555 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - DarkGate Malware + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1555 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/cmdkey_create_credential_store/cmdkey_gen_sys.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/cmdkey_create_credential_store/cmdkey_gen_sys.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_credentials_from_password_stores_deletion.yml b/detections/endpoint/windows_credentials_from_password_stores_deletion.yml index b2d7e2d8b9..ae6c05cc2c 100644 --- a/detections/endpoint/windows_credentials_from_password_stores_deletion.yml +++ b/detections/endpoint/windows_credentials_from_password_stores_deletion.yml @@ -1,81 +1,69 @@ name: Windows Credentials from Password Stores Deletion id: 46d676aa-40c6-4fe6-b917-d23b621f0f89 -version: 8 -date: '2025-11-20' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic detects the execution of the Windows OS tool cmdkey.exe - with the /delete parameter. This detection leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process execution logs and command-line arguments. - The activity is significant because cmdkey.exe can be used by attackers to delete - stored credentials, potentially leading to privilege escalation and persistence. - If confirmed malicious, this behavior could allow attackers to remove stored user - credentials, hindering incident response efforts and enabling further unauthorized - access to the compromised system. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name="cmdkey.exe" - OR Processes.original_file_name = "cmdkey.exe" AND Processes.process = "*/delete*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_credentials_from_password_stores_deletion_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic detects the execution of the Windows OS tool cmdkey.exe with the /delete parameter. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. The activity is significant because cmdkey.exe can be used by attackers to delete stored credentials, potentially leading to privilege escalation and persistence. If confirmed malicious, this behavior could allow attackers to remove stored user credentials, hindering incident response efforts and enabling further unauthorized access to the compromised system. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name="cmdkey.exe" + OR + Processes.original_file_name = "cmdkey.exe" + AND + Processes.process = "*/delete*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_credentials_from_password_stores_deletion_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: network administrator can use this tool for auditing process. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate + - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a process $process_name$ was executed on $dest$ to delete stored credentials - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: a process $process_name$ was executed on $dest$ to delete stored credentials + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - DarkGate Malware - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1555 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - DarkGate Malware + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1555 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/cmdkey_delete_credentials_store/cmdkey_del_sys.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/cmdkey_delete_credentials_store/cmdkey_del_sys.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_credentials_from_password_stores_query.yml b/detections/endpoint/windows_credentials_from_password_stores_query.yml index 4c09031f0e..9c63fd81cd 100644 --- a/detections/endpoint/windows_credentials_from_password_stores_query.yml +++ b/detections/endpoint/windows_credentials_from_password_stores_query.yml @@ -1,85 +1,72 @@ name: Windows Credentials from Password Stores Query id: db02d6b4-5d5b-4c33-8d8f-f0577516a8c7 -version: 7 -date: '2025-11-20' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the Windows OS tool cmdkey.exe, - which is often abused by post-exploitation tools like winpeas, commonly used in - ransomware attacks to list stored usernames, passwords, or credentials. This detection - leverages data from Endpoint Detection and Response (EDR) agents, focusing on process - execution logs. This activity is significant as it indicates potential credential - harvesting, which can lead to privilege escalation and persistence. If confirmed - malicious, attackers could gain unauthorized access to sensitive information and - maintain control over compromised systems for further exploitation. +description: The following analytic detects the execution of the Windows OS tool cmdkey.exe, which is often abused by post-exploitation tools like winpeas, commonly used in ransomware attacks to list stored usernames, passwords, or credentials. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This activity is significant as it indicates potential credential harvesting, which can lead to privilege escalation and persistence. If confirmed malicious, attackers could gain unauthorized access to sensitive information and maintain control over compromised systems for further exploitation. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name="cmdkey.exe" - OR Processes.original_file_name = "cmdkey.exe" AND Processes.process = "*/list*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_credentials_from_password_stores_query_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name="cmdkey.exe" + OR + Processes.original_file_name = "cmdkey.exe" + AND + Processes.process = "*/list*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_credentials_from_password_stores_query_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: network administrator can use this tool for auditing process. references: -- https://ss64.com/nt/cmdkey.html -- https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS -- https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ + - https://ss64.com/nt/cmdkey.html + - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS + - https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a process $process_name$ was executed on $dest$ to display stored username - and credentials. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: a process $process_name$ was executed on $dest$ to display stored username and credentials. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Windows Post-Exploitation - - Prestige Ransomware - - DarkGate Malware - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1555 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Post-Exploitation + - Prestige Ransomware + - DarkGate Malware + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1555 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/winpeas_cmdkeylist/cmdkey-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/winpeas_cmdkeylist/cmdkey-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_credentials_from_web_browsers_saved_in_temp_folder.yml b/detections/endpoint/windows_credentials_from_web_browsers_saved_in_temp_folder.yml index 0d44caffa3..271455da4c 100644 --- a/detections/endpoint/windows_credentials_from_web_browsers_saved_in_temp_folder.yml +++ b/detections/endpoint/windows_credentials_from_web_browsers_saved_in_temp_folder.yml @@ -4,71 +4,47 @@ version: 7 date: '2026-01-14' author: Teoderick Contreras, Splunk data_source: -- Sysmon EventID 11 + - Sysmon EventID 11 type: TTP status: production -description: The following analytic detects the creation of files containing passwords, - cookies, and saved login account information by the Braodo stealer malware in temporary - folders. Braodo often collects these credentials from browsers and applications, - storing them in temp directories before exfiltration. This detection focuses on - monitoring for the creation of files with patterns or formats commonly associated - with stolen credentials. By identifying these activities, security teams can take - needed action to prevent sensitive login data from being leaked, reducing the risk - of unauthorized access to user accounts and systems. -search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("login*", - "pass*","cookie*","master_key*") Filesystem.file_path = "*\\temp\\*" by Filesystem.action - Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash - Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl - Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user - Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_credentials_from_web_browsers_saved_in_temp_folder_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. +description: The following analytic detects the creation of files containing passwords, cookies, and saved login account information by the Braodo stealer malware in temporary folders. Braodo often collects these credentials from browsers and applications, storing them in temp directories before exfiltration. This detection focuses on monitoring for the creation of files with patterns or formats commonly associated with stolen credentials. By identifying these activities, security teams can take needed action to prevent sensitive login data from being leaked, reducing the risk of unauthorized access to user accounts and systems. +search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("login*", "pass*","cookie*","master_key*") Filesystem.file_path = "*\\temp\\*" by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_credentials_from_web_browsers_saved_in_temp_folder_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. known_false_positives: No false positives have been identified at this time. references: -- https://x.com/suyog41/status/1825869470323056748 -- https://g0njxa.medium.com/from-vietnam-to-united-states-malware-fraud-and-dropshipping-98b7a7b2c36d + - https://x.com/suyog41/status/1825869470323056748 + - https://g0njxa.medium.com/from-vietnam-to-united-states-malware-fraud-and-dropshipping-98b7a7b2c36d drilldown_searches: -- name: View the detection results for "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A known credential file name - [$file_name$] was saved in %temp% folder - of [$dest$]. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A known credential file name - [$file_name$] was saved in %temp% folder of [$dest$]. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Braodo Stealer - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1555.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Braodo Stealer + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1555.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555.003/browser_credential_info_temp/braodo_browser_info.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555.003/browser_credential_info_temp/braodo_browser_info.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_credentials_in_registry_reg_query.yml b/detections/endpoint/windows_credentials_in_registry_reg_query.yml index 221814615b..484d3c1d0a 100644 --- a/detections/endpoint/windows_credentials_in_registry_reg_query.yml +++ b/detections/endpoint/windows_credentials_in_registry_reg_query.yml @@ -5,81 +5,49 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies processes querying the registry for - potential passwords or credentials. It leverages data from Endpoint Detection and - Response (EDR) agents, focusing on command-line executions that access specific - registry paths known to store sensitive information. This activity is significant - as it may indicate credential theft attempts, often used by adversaries or post-exploitation - tools like winPEAS. If confirmed malicious, this behavior could lead to privilege - escalation, persistence, or lateral movement within the network, posing a severe - security risk. +description: The following analytic identifies processes querying the registry for potential passwords or credentials. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions that access specific registry paths known to store sensitive information. This activity is significant as it may indicate credential theft attempts, often used by adversaries or post-exploitation tools like winPEAS. If confirmed malicious, this behavior could lead to privilege escalation, persistence, or lateral movement within the network, posing a severe security risk. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_reg` AND Processes.process - = "* query *" AND Processes.process IN ("*\\Software\\ORL\\WinVNC3\\Password*", - "*\\SOFTWARE\\RealVNC\\WinVNC4 /v password*", "*\\CurrentControlSet\\Services\\SNMP*", - "*\\Software\\TightVNC\\Server*", "*\\Software\\SimonTatham\\PuTTY\\Sessions*", - "*\\Software\\OpenSSH\\Agent\\Keys*", "*password*") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_credentials_in_registry_reg_query_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_reg` AND Processes.process = "* query *" AND Processes.process IN ("*\\Software\\ORL\\WinVNC3\\Password*", "*\\SOFTWARE\\RealVNC\\WinVNC4 /v password*", "*\\CurrentControlSet\\Services\\SNMP*", "*\\Software\\TightVNC\\Server*", "*\\Software\\SimonTatham\\PuTTY\\Sessions*", "*\\Software\\OpenSSH\\Agent\\Keys*", "*password*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_credentials_in_registry_reg_query_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://attack.mitre.org/techniques/T1552/002/ -- https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS -- https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ + - https://attack.mitre.org/techniques/T1552/002/ + - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS + - https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: reg query commandline $process$ on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: reg query commandline $process$ on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Windows Post-Exploitation - - Prestige Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1552.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Post-Exploitation + - Prestige Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1552.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/winpeas_search_pwd/query-putty-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/winpeas_search_pwd/query-putty-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_curl_download_to_suspicious_path.yml b/detections/endpoint/windows_curl_download_to_suspicious_path.yml index b3a047e33f..8f19db964e 100644 --- a/detections/endpoint/windows_curl_download_to_suspicious_path.yml +++ b/detections/endpoint/windows_curl_download_to_suspicious_path.yml @@ -6,124 +6,116 @@ author: Michael Haag, Nasreddine Bencherchali, Splunk status: production type: TTP description: | - The following analytic detects the use of Windows Curl.exe to download - a file to a suspicious location, such as AppData, ProgramData, or Public directories. - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on - command-line executions that include the -O or --output options. This activity is - significant because downloading files to these locations can indicate an attempt - to bypass security controls or establish persistence. If confirmed malicious, this - behavior could lead to unauthorized code execution, data exfiltration, or further - compromise of the system. + The following analytic detects the use of Windows Curl.exe to download + a file to a suspicious location, such as AppData, ProgramData, or Public directories. + It leverages data from Endpoint Detection and Response (EDR) agents, focusing on + command-line executions that include the -O or --output options. This activity is + significant because downloading files to these locations can indicate an attempt + to bypass security controls or establish persistence. If confirmed malicious, this + behavior could lead to unauthorized code execution, data exfiltration, or further + compromise of the system. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 - - Cisco Network Visibility Module Flow Data + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 + - Cisco Network Visibility Module Flow Data search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - ( - Processes.process_name=curl.exe - OR - Processes.original_file_name=Curl.exe - ) - Processes.process IN ("*-O *","*--output*", "*--output-dir*") - Processes.process IN ( - "*:\\PerfLogs\\*", - "*:\\Windows\\Temp\\*", - "*\\AppData\\*", - "*\\ProgramData\\*", - "*\\Users\\Public\\*", - "*%AppData%*", - "*%Public%*", - "*%Temp%*", - "*%tmp%*" - ) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_curl_download_to_suspicious_path_filter` + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where + ( + Processes.process_name=curl.exe + OR + Processes.original_file_name=Curl.exe + ) + Processes.process IN ("*-O *","*--output*", "*--output-dir*") + Processes.process IN ( + "*:\\PerfLogs\\*", + "*:\\Windows\\Temp\\*", + "*\\AppData\\*", + "*\\ProgramData\\*", + "*\\Users\\Public\\*", + "*%AppData%*", + "*%Public%*", + "*%Temp%*", + "*%tmp%*" + ) + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_curl_download_to_suspicious_path_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: | - It is possible Administrators or super users will use Curl for legitimate purposes. Filter as needed. + It is possible Administrators or super users will use Curl for legitimate purposes. Filter as needed. references: - - https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ - - https://attack.mitre.org/techniques/T1105/ - - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1105/T1105.md + - https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ + - https://attack.mitre.org/techniques/T1105/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1105/T1105.md drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ to download a file to a suspicious directory. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ to download a file to a suspicious directory. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - APT37 Rustonotto and FadeStealer - - Black Basta Ransomware - - China-Nexus Threat Activity - - Cisco Network Visibility Module Analytics - - Compromised Windows Host - - Forest Blizzard - - GhostRedirector IIS Module and Rungan Backdoor - - IcedID - - Ingress Tool Transfer - - NPM Supply Chain Compromise - - Salt Typhoon - asset_type: Endpoint - mitre_attack_id: - - T1105 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - APT37 Rustonotto and FadeStealer + - Black Basta Ransomware + - China-Nexus Threat Activity + - Cisco Network Visibility Module Analytics + - Compromised Windows Host + - Forest Blizzard + - GhostRedirector IIS Module and Rungan Backdoor + - IcedID + - Ingress Tool Transfer + - NPM Supply Chain Compromise + - Salt Typhoon + asset_type: Endpoint + mitre_attack_id: + - T1105 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - Sysmon - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/windows-sysmon_curl.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata + - name: True Positive Test - Sysmon + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/windows-sysmon_curl.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/windows_curl_upload_to_remote_destination.yml b/detections/endpoint/windows_curl_upload_to_remote_destination.yml index f3a88743f8..2f6ea9d855 100644 --- a/detections/endpoint/windows_curl_upload_to_remote_destination.yml +++ b/detections/endpoint/windows_curl_upload_to_remote_destination.yml @@ -5,111 +5,85 @@ date: '2025-12-18' author: Michael Haag, Splunk status: production type: TTP -description: - The following analytic detects the use of Windows Curl.exe to upload - a file to a remote destination. It identifies command-line arguments such as `-T`, - `--upload-file`, `-d`, `--data`, and `-F` in process execution logs. This activity - is significant because adversaries may use Curl to exfiltrate data or upload malicious - payloads. If confirmed malicious, this could lead to data breaches or further compromise - of the system. Analysts should review parallel processes and network logs to determine - if the upload was successful and isolate the endpoint if necessary. +description: The following analytic detects the use of Windows Curl.exe to upload a file to a remote destination. It identifies command-line arguments such as `-T`, `--upload-file`, `-d`, `--data`, and `-F` in process execution logs. This activity is significant because adversaries may use Curl to exfiltrate data or upload malicious payloads. If confirmed malicious, this could lead to data breaches or further compromise of the system. Analysts should review parallel processes and network logs to determine if the upload was successful and isolate the endpoint if necessary. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 - - Cisco Network Visibility Module Flow Data + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 + - Cisco Network Visibility Module Flow Data search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - (Processes.process_name=curl.exe OR Processes.original_file_name=Curl.exe) - Processes.process IN ( - "*-T *", - "*--upload-file *", - "*-d *", - "*--data *", - "*-F *" - ) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id - Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id - Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_curl_upload_to_remote_destination_filter` -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: - False positives may be limited to source control applications - and may be required to be filtered out. + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where + (Processes.process_name=curl.exe OR Processes.original_file_name=Curl.exe) + Processes.process IN ( + "*-T *", + "*--upload-file *", + "*-d *", + "*--data *", + "*-F *" + ) + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec + Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id + Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id + Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_curl_upload_to_remote_destination_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be limited to source control applications and may be required to be filtered out. references: - - https://everything.curl.dev/usingcurl/uploads - - https://techcommunity.microsoft.com/t5/containers/tar-and-curl-come-to-windows/ba-p/382409 - - https://twitter.com/d1r4c/status/1279042657508081664?s=20 + - https://everything.curl.dev/usingcurl/uploads + - https://techcommunity.microsoft.com/t5/containers/tar-and-curl-come-to-windows/ba-p/382409 + - https://twitter.com/d1r4c/status/1279042657508081664?s=20 drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ uploading a file to a remote destination. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ uploading a file to a remote destination. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Cisco Network Visibility Module Analytics - - Compromised Windows Host - - Ingress Tool Transfer - - Microsoft WSUS CVE-2025-59287 - - NPM Supply Chain Compromise - - PromptLock - asset_type: Endpoint - mitre_attack_id: - - T1105 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Cisco Network Visibility Module Analytics + - Compromised Windows Host + - Ingress Tool Transfer + - Microsoft WSUS CVE-2025-59287 + - NPM Supply Chain Compromise + - PromptLock + asset_type: Endpoint + mitre_attack_id: + - T1105 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - Sysmon - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/windows-sysmon_curl_upload.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata + - name: True Positive Test - Sysmon + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/windows-sysmon_curl_upload.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/windows_data_destruction_recursive_exec_files_deletion.yml b/detections/endpoint/windows_data_destruction_recursive_exec_files_deletion.yml index 1e7f0be111..95df494752 100644 --- a/detections/endpoint/windows_data_destruction_recursive_exec_files_deletion.yml +++ b/detections/endpoint/windows_data_destruction_recursive_exec_files_deletion.yml @@ -1,79 +1,65 @@ name: Windows Data Destruction Recursive Exec Files Deletion id: 3596a799-6320-4a2f-8772-a9e98ddb2960 -version: 8 -date: '2025-06-27' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: The following analytic identifies a suspicious process that is recursively - deleting executable files on a compromised host. It leverages Sysmon Event Codes - 23 and 26 to detect this activity by monitoring for a high volume of deletions or - overwrites of files with extensions like .exe, .sys, and .dll. This behavior is - significant as it is commonly associated with destructive malware such as CaddyWiper, - DoubleZero, and SwiftSlicer, which aim to make file recovery impossible. If confirmed - malicious, this activity could lead to significant data loss and system instability, - severely impacting business operations. +description: The following analytic identifies a suspicious process that is recursively deleting executable files on a compromised host. It leverages Sysmon Event Codes 23 and 26 to detect this activity by monitoring for a high volume of deletions or overwrites of files with extensions like .exe, .sys, and .dll. This behavior is significant as it is commonly associated with destructive malware such as CaddyWiper, DoubleZero, and SwiftSlicer, which aim to make file recovery impossible. If confirmed malicious, this activity could lead to significant data loss and system instability, severely impacting business operations. data_source: -- Sysmon EventID 23 -- Sysmon EventID 26 -search: '`sysmon` EventCode IN ("23","26") TargetFilename IN ("*.exe", "*.sys", "*.dll") - | bin _time span=2m | stats count min(_time) as firstTime, max(_time) as lastTime - values(file_path) as file_path values(file_hash) as file_hash values(file_name) - as file_name values(file_modify_time) as file_modify_time values(process_name) as - process_name values(process_path) as process_path values(process_guid) as process_guid - values(process_id) as process_id values(process_exec) as process_exec by action - dest dvc signature signature_id user user_id vendor_product | where count >=100 - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_data_destruction_recursive_exec_files_deletion_filter`' -how_to_implement: To successfully implement this search, you need to ingest logs that - include the process name, TargetFilename, and ProcessID executions from your endpoints. - If you are using Sysmon, ensure you have at least version 2.0 of the Sysmon TA installed. -known_false_positives: The uninstallation of a large software application or the use - of cleanmgr.exe may trigger this detection. A filter is necessary to reduce false - positives. + - Sysmon EventID 23 + - Sysmon EventID 26 +search: |- + `sysmon` EventCode IN ("23","26") TargetFilename IN ("*.exe", "*.sys", "*.dll") + | bin _time span=2m + | stats count min(_time) as firstTime, max(_time) as lastTime values(file_path) as file_path values(file_hash) as file_hash values(file_name) as file_name values(file_modify_time) as file_modify_time values(process_name) as process_name values(process_path) as process_path values(process_guid) as process_guid values(process_id) as process_id values(process_exec) as process_exec + BY action dest dvc + signature signature_id user + user_id vendor_product + | where count >=100 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_data_destruction_recursive_exec_files_deletion_filter` +how_to_implement: To successfully implement this search, you need to ingest logs that include the process name, TargetFilename, and ProcessID executions from your endpoints. If you are using Sysmon, ensure you have at least version 2.0 of the Sysmon TA installed. +known_false_positives: The uninstallation of a large software application or the use of cleanmgr.exe may trigger this detection. A filter is necessary to reduce false positives. references: -- https://www.welivesecurity.com/2023/01/27/swiftslicer-new-destructive-wiper-malware-ukraine/ + - https://www.welivesecurity.com/2023/01/27/swiftslicer-new-destructive-wiper-malware-ukraine/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The process $process_name$ has removed a significant quantity of executable - files, totaling [$count$], from the destination $dest$. - risk_objects: - - field: user - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: [] + message: The process $process_name$ has removed a significant quantity of executable files, totaling [$count$], from the destination $dest$. + risk_objects: + - field: user + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Swift Slicer - - Data Destruction - - Handala Wiper - - Disk Wiper - asset_type: Endpoint - mitre_attack_id: - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Swift Slicer + - Data Destruction + - Handala Wiper + - Disk Wiper + asset_type: Endpoint + mitre_attack_id: + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/swift_slicer/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/swift_slicer/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_debugger_tool_execution.yml b/detections/endpoint/windows_debugger_tool_execution.yml index 52b9f9b79b..51992968ff 100644 --- a/detections/endpoint/windows_debugger_tool_execution.yml +++ b/detections/endpoint/windows_debugger_tool_execution.yml @@ -1,58 +1,53 @@ name: Windows Debugger Tool Execution id: e14d94a3-07fb-4b47-8406-f5e37180d422 -version: 6 -date: '2025-10-21' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 type: Hunting status: production -description: This analysis detects the use of debugger tools within a production environment. - While these tools are legitimate for file analysis and debugging, they are abused - by malware like PlugX and DarkGate for malicious DLL side-loading. The hunting query - aids Security Operations Centers (SOCs) in identifying potentially suspicious tool - executions, particularly for non-technical users in the production network. -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "x32dbg.exe" - OR Processes.process_name = "x64dbg.exe" OR Processes.process_name = "windbg.exe" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_debugger_tool_execution_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: administrator or IT professional may execute this application - for verifying files or debugging application. +description: This analysis detects the use of debugger tools within a production environment. While these tools are legitimate for file analysis and debugging, they are abused by malware like PlugX and DarkGate for malicious DLL side-loading. The hunting query aids Security Operations Centers (SOCs) in identifying potentially suspicious tool executions, particularly for non-technical users in the production network. +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "x32dbg.exe" + OR + Processes.process_name = "x64dbg.exe" + OR + Processes.process_name = "windbg.exe" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_debugger_tool_execution_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: administrator or IT professional may execute this application for verifying files or debugging application. references: -- https://www.splunk.com/en_us/blog/security/enter-the-gates-an-analysis-of-the-darkgate-autoit-loader.html -- https://www.trendmicro.com/en_us/research/23/b/investigating-the-plugx-trojan-disguised-as-a-legitimate-windows.html + - https://www.splunk.com/en_us/blog/security/enter-the-gates-an-analysis-of-the-darkgate-autoit-loader.html + - https://www.trendmicro.com/en_us/research/23/b/investigating-the-plugx-trojan-disguised-as-a-legitimate-windows.html tags: - analytic_story: - - DarkGate Malware - - PlugX - asset_type: Endpoint - mitre_attack_id: - - T1036 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - DarkGate Malware + - PlugX + asset_type: Endpoint + mitre_attack_id: + - T1036 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/debugger_execution/debugger.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/debugger_execution/debugger.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_defacement_modify_transcodedwallpaper_file.yml b/detections/endpoint/windows_defacement_modify_transcodedwallpaper_file.yml index 2171e58d2a..25a446b08f 100644 --- a/detections/endpoint/windows_defacement_modify_transcodedwallpaper_file.yml +++ b/detections/endpoint/windows_defacement_modify_transcodedwallpaper_file.yml @@ -5,79 +5,47 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies modifications to the TranscodedWallpaper - file in the wallpaper theme directory, excluding changes made by explorer.exe. This - detection leverages the Endpoint.Processes and Endpoint.Filesystem data models to - correlate process activity with file modifications. This activity is significant - as it may indicate an adversary attempting to deface or change the desktop wallpaper - of a targeted host, a tactic often used to signal compromise or deliver a message. - If confirmed malicious, this could be a sign of unauthorized access and tampering, - potentially leading to further system compromise or data exfiltration. +description: The following analytic identifies modifications to the TranscodedWallpaper file in the wallpaper theme directory, excluding changes made by explorer.exe. This detection leverages the Endpoint.Processes and Endpoint.Filesystem data models to correlate process activity with file modifications. This activity is significant as it may indicate an adversary attempting to deface or change the desktop wallpaper of a targeted host, a tactic often used to signal compromise or deliver a message. If confirmed malicious, this could be a sign of unauthorized access and tampering, potentially leading to further system compromise or data exfiltration. data_source: -- Sysmon EventID 1 AND Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes - where Processes.process_path !="*\\Windows\\Explorer.EXE" - by _time span=1h Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` |rename process_guid as proc_guid | join proc_guid, - _time [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem - where Filesystem.file_path = "*\\AppData\\Roaming\\Microsoft\\Windows\\Themes\\TranscodedWallpaper" - by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name - Filesystem.file_path Filesystem.process_guid | `drop_dm_object_name(Filesystem)` - |rename process_guid as proc_guid | fields file_name file_path process_name process_path - process dest file_create_time _time proc_guid] | `windows_defacement_modify_transcodedwallpaper_file_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: 3rd part software application can change the wallpaper. Filter - is needed. + - Sysmon EventID 1 AND Sysmon EventID 11 +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_path !="*\\Windows\\Explorer.EXE" by _time span=1h Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` |rename process_guid as proc_guid | join proc_guid, _time [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.file_path = "*\\AppData\\Roaming\\Microsoft\\Windows\\Themes\\TranscodedWallpaper" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.process_guid | `drop_dm_object_name(Filesystem)` |rename process_guid as proc_guid | fields file_name file_path process_name process_path process dest file_create_time _time proc_guid] | `windows_defacement_modify_transcodedwallpaper_file_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: 3rd part software application can change the wallpaper. Filter is needed. references: -- https://forums.ivanti.com/s/article/Wallpaper-Windows-Settings-Desktop-Settings-and-the-transcodedwallpaper-jpg?language=en_US -- https://www.trendmicro.com/vinfo/us/threat-encyclopedia/malware/ransom_sifreli.a + - https://forums.ivanti.com/s/article/Wallpaper-Windows-Settings-Desktop-Settings-and-the-transcodedwallpaper-jpg?language=en_US + - https://www.trendmicro.com/vinfo/us/threat-encyclopedia/malware/ransom_sifreli.a drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: modification or creation of transcodedwallpaper file by $process_name$ - on $dest$ - risk_objects: - - field: dest - type: system - score: 9 - threat_objects: - - field: process_name - type: process_name + message: modification or creation of transcodedwallpaper file by $process_name$ on $dest$ + risk_objects: + - field: dest + type: system + score: 9 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Brute Ratel C4 - asset_type: Endpoint - mitre_attack_id: - - T1491 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Brute Ratel C4 + asset_type: Endpoint + mitre_attack_id: + - T1491 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/wallpaper_via_transcodedwallpaper/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/wallpaper_via_transcodedwallpaper/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_default_group_policy_object_modified.yml b/detections/endpoint/windows_default_group_policy_object_modified.yml index ff63d981fb..f4da760772 100644 --- a/detections/endpoint/windows_default_group_policy_object_modified.yml +++ b/detections/endpoint/windows_default_group_policy_object_modified.yml @@ -1,78 +1,63 @@ name: Windows Default Group Policy Object Modified id: fe6a6cc4-9e0d-4d66-bcf4-2c7f44860876 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- Windows Event Log Security 5136 -description: The following analytic detects modifications to default Group Policy - Objects (GPOs) using Event ID 5136. It monitors changes to the `Default Domain Controllers - Policy` and `Default Domain Policy`, which are critical for enforcing security settings - across domain controllers and all users/computers, respectively. This activity is - significant because unauthorized changes to these GPOs can indicate an adversary - with privileged access attempting to deploy persistence mechanisms or execute malware - across the network. If confirmed malicious, such modifications could lead to widespread - compromise, allowing attackers to maintain control and execute arbitrary code on - numerous hosts. -search: '`wineventlog_security` EventCode=5136 ObjectClass=groupPolicyContainer AttributeLDAPDisplayName=versionNumber - (ObjectDN="CN={31B2F340-016D-11D2-945F-00C04FB984F9},CN=POLICIES,CN=SYSTEM,DC=*" - OR ObjectDN="CN={6AC1786C-016F-11D2-945F-00C04fB984F9},CN=POLICIES,CN=SYSTEM,DC=*") - | stats min(_time) as firstTime max(_time) as lastTime by ObjectDN SubjectUserSid - AttributeValue Computer DSName dest | rename AttributeValue as versionNumber | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_default_group_policy_object_modified_filter`' -how_to_implement: To successfully implement this search, the Advanced Security Audit - policy setting `Audit Directory Service Changes` within `DS Access` needs to be - enabled. Furthermore, the appropriate system access control lists (SACL) need to - be created as the used events are not logged by default. A good guide to accomplish - this can be found here https://jgspiers.com/audit-group-policy-changes/. -known_false_positives: The default Group Policy Objects within an AD network may be - legitimately updated for administrative operations, filter as needed. + - Windows Event Log Security 5136 +description: The following analytic detects modifications to default Group Policy Objects (GPOs) using Event ID 5136. It monitors changes to the `Default Domain Controllers Policy` and `Default Domain Policy`, which are critical for enforcing security settings across domain controllers and all users/computers, respectively. This activity is significant because unauthorized changes to these GPOs can indicate an adversary with privileged access attempting to deploy persistence mechanisms or execute malware across the network. If confirmed malicious, such modifications could lead to widespread compromise, allowing attackers to maintain control and execute arbitrary code on numerous hosts. +search: |- + `wineventlog_security` EventCode=5136 ObjectClass=groupPolicyContainer AttributeLDAPDisplayName=versionNumber (ObjectDN="CN={31B2F340-016D-11D2-945F-00C04FB984F9},CN=POLICIES,CN=SYSTEM,DC=*" OR ObjectDN="CN={6AC1786C-016F-11D2-945F-00C04fB984F9},CN=POLICIES,CN=SYSTEM,DC=*") + | stats min(_time) as firstTime max(_time) as lastTime + BY ObjectDN SubjectUserSid AttributeValue + Computer DSName dest + | rename AttributeValue as versionNumber + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_default_group_policy_object_modified_filter` +how_to_implement: To successfully implement this search, the Advanced Security Audit policy setting `Audit Directory Service Changes` within `DS Access` needs to be enabled. Furthermore, the appropriate system access control lists (SACL) need to be created as the used events are not logged by default. A good guide to accomplish this can be found here https://jgspiers.com/audit-group-policy-changes/. +known_false_positives: The default Group Policy Objects within an AD network may be legitimately updated for administrative operations, filter as needed. references: -- https://attack.mitre.org/techniques/T1484/ -- https://attack.mitre.org/techniques/T1484/001 -- https://www.trustedsec.com/blog/weaponizing-group-policy-objects-access/ -- https://adsecurity.org/?p=2716 + - https://attack.mitre.org/techniques/T1484/ + - https://attack.mitre.org/techniques/T1484/001 + - https://www.trustedsec.com/blog/weaponizing-group-policy-objects-access/ + - https://adsecurity.org/?p=2716 drilldown_searches: -- name: View the detection results for - "$Computer$" - search: '%original_detection_search% | search Computer = "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$Computer$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" + search: '%original_detection_search% | search Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A default group policy object was modified on $Computer$ by $SubjectUserSid$ - risk_objects: - - field: Computer - type: system - score: 50 - - field: SubjectUserSid - type: user - score: 50 - threat_objects: [] + message: A default group policy object was modified on $Computer$ by $SubjectUserSid$ + risk_objects: + - field: Computer + type: system + score: 50 + - field: SubjectUserSid + type: user + score: 50 + threat_objects: [] tags: - analytic_story: - - Active Directory Privilege Escalation - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1484.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Privilege Escalation + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1484.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/default_domain_policy_modified/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/default_domain_policy_modified/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_default_group_policy_object_modified_with_gpme.yml b/detections/endpoint/windows_default_group_policy_object_modified_with_gpme.yml index 72ffef79c0..61cd1af910 100644 --- a/detections/endpoint/windows_default_group_policy_object_modified_with_gpme.yml +++ b/detections/endpoint/windows_default_group_policy_object_modified_with_gpme.yml @@ -1,91 +1,76 @@ name: Windows Default Group Policy Object Modified with GPME id: eaf688b3-bb8f-454d-b105-920a862cd8cb -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic detects modifications to default Group Policy - Objects (GPOs) using the Group Policy Management Editor (GPME). It leverages the - Endpoint data model to identify processes where `mmc.exe` executes `gpme.msc` with - specific GUIDs related to default GPOs. This activity is significant because default - GPOs, such as the `Default Domain Controllers Policy` and `Default Domain Policy`, - are critical for enforcing security policies across the domain. If malicious, such - modifications could allow an attacker to gain further access, establish persistence, - or deploy malware across numerous hosts, severely compromising the network's security. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mmc.exe - (Processes.process =*gpme.msc*) AND (Processes.process = "*31B2F340-016D-11D2-945F-00C04FB984F9*" - OR Processes.process = "*6AC1786C-016F-11D2-945F-00C04fB984F9*" ) by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_default_group_policy_object_modified_with_gpme_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: The default Group Policy Objects within an AD network may be - legitimately updated for administrative operations, filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic detects modifications to default Group Policy Objects (GPOs) using the Group Policy Management Editor (GPME). It leverages the Endpoint data model to identify processes where `mmc.exe` executes `gpme.msc` with specific GUIDs related to default GPOs. This activity is significant because default GPOs, such as the `Default Domain Controllers Policy` and `Default Domain Policy`, are critical for enforcing security policies across the domain. If malicious, such modifications could allow an attacker to gain further access, establish persistence, or deploy malware across numerous hosts, severely compromising the network's security. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=mmc.exe (Processes.process =*gpme.msc*) + AND + (Processes.process = "*31B2F340-016D-11D2-945F-00C04FB984F9*" + OR + Processes.process = "*6AC1786C-016F-11D2-945F-00C04fB984F9*" ) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_default_group_policy_object_modified_with_gpme_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: The default Group Policy Objects within an AD network may be legitimately updated for administrative operations, filter as needed. references: -- https://attack.mitre.org/techniques/T1484/ -- https://attack.mitre.org/techniques/T1484/001 -- https://www.trustedsec.com/blog/weaponizing-group-policy-objects-access/ -- https://adsecurity.org/?p=2716 -- https://learn.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-r2-and-2012/dn265969(v=ws.11) + - https://attack.mitre.org/techniques/T1484/ + - https://attack.mitre.org/techniques/T1484/001 + - https://www.trustedsec.com/blog/weaponizing-group-policy-objects-access/ + - https://adsecurity.org/?p=2716 + - https://learn.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-r2-and-2012/dn265969(v=ws.11) drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A default group policy object was opened with Group Policy Manage Editor - on $dest$ - risk_objects: - - field: dest - type: system - score: 50 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: A default group policy object was opened with Group Policy Manage Editor on $dest$ + risk_objects: + - field: dest + type: system + score: 50 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Active Directory Privilege Escalation - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1484.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Privilege Escalation + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1484.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/default_domain_policy_modified/windows-security.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/default_domain_policy_modified/windows-security.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_default_rdp_file_creation_by_non_mstsc_process.yml b/detections/endpoint/windows_default_rdp_file_creation_by_non_mstsc_process.yml index c3c2596f59..301cc475be 100644 --- a/detections/endpoint/windows_default_rdp_file_creation_by_non_mstsc_process.yml +++ b/detections/endpoint/windows_default_rdp_file_creation_by_non_mstsc_process.yml @@ -1,77 +1,74 @@ name: Windows Default RDP File Creation By Non MSTSC Process id: 692226f1-84e3-4f63-a747-d53e65699608 -version: 1 -date: '2025-10-27' +version: 2 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: This detection monitors the creation or modification of the Default.rdp file by non mstsc.exe process, typically found in the user's Documents folder. This file is automatically generated or updated by the Remote Desktop Connection client (mstsc.exe) when a user initiates an RDP session. It stores connection settings such as the last-used hostname, screen size, and other preferences. The presence or update of this file strongly suggests that an RDP session has been launched from the system. Since this file is commonly overlooked, it can serve as a valuable artifact in identifying remote access activity, including potential lateral movement or attacker-controlled sessions. data_source: -- Sysmon EventID 1 AND Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes - where Processes.process_name != mstsc.exe - by _time span=1h Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - |rename process_guid as proc_guid - | join proc_guid, _time [ | tstats `security_content_summariesonly` - count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem - where Filesystem.file_name=default.rdp - by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.process_guid - | `drop_dm_object_name(Filesystem)` - |rename process_guid as proc_guid | fields _time dest file_create_time file_name file_path process_name process_path process proc_guid] - | dedup file_create_time | table dest, process_name, process, file_create_time, file_name, file_path, proc_guid - | `windows_default_rdp_file_creation_by_non_mstsc_process_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: False positives will be present, filter as needed or restrict - to critical assets on the perimeter. + - Sysmon EventID 1 AND Sysmon EventID 11 +search: |- + | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes + WHERE Processes.process_name != mstsc.exe + BY _time span=1h Processes.action + Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process + Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name + Processes.process_path Processes.user Processes.user_id + Processes.vendor_product + | `drop_dm_object_name(Processes)` + | rename process_guid as proc_guid + | join proc_guid, _time [ + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.file_name=default.rdp + BY _time span=1h Filesystem.dest + Filesystem.file_create_time Filesystem.file_name Filesystem.file_path + Filesystem.process_guid + | `drop_dm_object_name(Filesystem)` + | rename process_guid as proc_guid + | fields _time dest file_create_time file_name file_path process_name process_path process proc_guid] + | dedup file_create_time + | table dest, process_name, process, file_create_time, file_name, file_path, proc_guid + | `windows_default_rdp_file_creation_by_non_mstsc_process_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: False positives will be present, filter as needed or restrict to critical assets on the perimeter. references: -- https://medium.com/@bonguides25/how-to-clear-rdp-connections-history-in-windows-cf0ffb67f344 -- https://thelocalh0st.github.io/posts/rdp/ -- https://iam0xc4t.medium.com/rogue-rdp-via-spear-phishing-initial-access-tactic-d7be328a0b13 + - https://medium.com/@bonguides25/how-to-clear-rdp-connections-history-in-windows-cf0ffb67f344 + - https://thelocalh0st.github.io/posts/rdp/ + - https://iam0xc4t.medium.com/rogue-rdp-via-spear-phishing-initial-access-tactic-d7be328a0b13 drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a file related to rdp connection named as default.rdp has been identified on $dest$. - risk_objects: - - field: dest - type: system - score: 20 - threat_objects: [] + message: a file related to rdp connection named as default.rdp has been identified on $dest$. + risk_objects: + - field: dest + type: system + score: 20 + threat_objects: [] tags: - analytic_story: - - Windows RDP Artifacts and Defense Evasion - asset_type: Endpoint - mitre_attack_id: - - T1021.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows RDP Artifacts and Defense Evasion + asset_type: Endpoint + mitre_attack_id: + - T1021.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.001/susp_default_rdp_creation/default_rdp_dropped.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.001/susp_default_rdp_creation/default_rdp_dropped.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_default_rdp_file_deletion.yml b/detections/endpoint/windows_default_rdp_file_deletion.yml index 88e3239f0a..0db5ef2eb5 100644 --- a/detections/endpoint/windows_default_rdp_file_deletion.yml +++ b/detections/endpoint/windows_default_rdp_file_deletion.yml @@ -7,56 +7,44 @@ status: production type: Anomaly description: This detection identifies the deletion of the Default.rdp file from a user’s Documents folder. This file is automatically created or updated by the Remote Desktop Connection client (mstsc.exe) whenever a user initiates an RDP session. It contains session configuration data, such as the remote hostname and display settings. While the presence of this file is normal during legitimate RDP usage, its deletion may indicate an attempt to conceal evidence of remote access activity. Threat actors and red team operators often remove Default.rdp as part of post-access cleanup to evade forensic detection. Detecting this action—especially when correlated with recent RDP activity—can help identify defense evasion techniques and uncover potentially malicious use of remote desktop connections. Monitoring for this file's deletion adds an important layer of visibility into user behavior and can serve as an early indicator of interactive attacker presence. data_source: -- Sysmon EventID 23 -- Sysmon EventID 26 -search: '`sysmon` EventCode IN ("23", "26") TargetFilename = "*\\default.rdp" - | stats count min(_time) as firstTime, max(_time) as lastTime - by action dest dvc file_path file_hash file_name file_modify_time process_exec process_guid process_id process_name process_path signature signature_id user user_id vendor_product - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_default_rdp_file_deletion_filter`' -how_to_implement: To successfully implement this search, you need to ingest logs that - include the deleted target file name, process name, and process ID from your endpoints. - If you are using Sysmon, ensure you have at least version 2.0 of the Sysmon TA installed. + - Sysmon EventID 23 + - Sysmon EventID 26 +search: '`sysmon` EventCode IN ("23", "26") TargetFilename = "*\\default.rdp" | stats count min(_time) as firstTime, max(_time) as lastTime by action dest dvc file_path file_hash file_name file_modify_time process_exec process_guid process_id process_name process_path signature signature_id user user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_default_rdp_file_deletion_filter`' +how_to_implement: To successfully implement this search, you need to ingest logs that include the deleted target file name, process name, and process ID from your endpoints. If you are using Sysmon, ensure you have at least version 2.0 of the Sysmon TA installed. known_false_positives: No false positives have been identified at this time. references: -- https://medium.com/@bonguides25/how-to-clear-rdp-connections-history-in-windows-cf0ffb67f344 -- https://thelocalh0st.github.io/posts/rdp/ + - https://medium.com/@bonguides25/how-to-clear-rdp-connections-history-in-windows-cf0ffb67f344 + - https://thelocalh0st.github.io/posts/rdp/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a file related to rdp connection named as default.rdp has been deleted on $dest$. - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: [] + message: a file related to rdp connection named as default.rdp has been deleted on $dest$. + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: [] tags: - analytic_story: - - Windows RDP Artifacts and Defense Evasion - asset_type: Endpoint - mitre_attack_id: - - T1070.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows RDP Artifacts and Defense Evasion + asset_type: Endpoint + mitre_attack_id: + - T1070.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.004/rdp_deletion/rdp_file_deleted.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.004/rdp_deletion/rdp_file_deleted.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_default_rdp_file_unhidden.yml b/detections/endpoint/windows_default_rdp_file_unhidden.yml index 44a46ad38d..098307eaed 100644 --- a/detections/endpoint/windows_default_rdp_file_unhidden.yml +++ b/detections/endpoint/windows_default_rdp_file_unhidden.yml @@ -1,75 +1,64 @@ name: Windows Default Rdp File Unhidden id: f5c1f64b-db59-4913-991e-3dac8adff288 -version: 2 -date: '2026-01-14' +version: 3 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: This detection identifies the use of attrib.exe to remove hidden (-h) or system (-s) attributes from the Default.rdp file, which is automatically created in a user's Documents folder when a Remote Desktop Protocol (RDP) session is initiated using mstsc.exe. The Default.rdp file stores session configuration details such as the remote host address and screen settings. Unhiding this file is uncommon in normal user behavior and may indicate that an attacker or red team operator is attempting to access or manipulate RDP connection history that was previously hidden—either by default or as part of an earlier anti-forensics effort. This activity may represent part of a broader pattern of reconnaissance or staging for credential reuse, lateral movement, or forensic analysis evasion. Monitoring for this behavior can help uncover suspicious manipulation of user artifacts and highlight interactive attacker activity on a compromised host. data_source: -- Sysmon EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.process_name = "attrib.exe" Processes.process IN("*-s*", "*-h*") Processes.process = "*default.rdp*" - by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_default_rdp_file_unhidden_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "attrib.exe" Processes.process IN("*-s*", "*-h*") Processes.process = "*default.rdp*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_default_rdp_file_unhidden_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://medium.com/@bonguides25/how-to-clear-rdp-connections-history-in-windows-cf0ffb67f344 -- https://thelocalh0st.github.io/posts/rdp/ + - https://medium.com/@bonguides25/how-to-clear-rdp-connections-history-in-windows-cf0ffb67f344 + - https://thelocalh0st.github.io/posts/rdp/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process unhiding default.rdp on $dest$. - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: A process unhiding default.rdp on $dest$. + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - Windows RDP Artifacts and Defense Evasion - asset_type: Endpoint - mitre_attack_id: - - T1021.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows RDP Artifacts and Defense Evasion + asset_type: Endpoint + mitre_attack_id: + - T1021.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.001/unhide_file/unhide_file.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.001/unhide_file/unhide_file.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_defender_asr_audit_events.yml b/detections/endpoint/windows_defender_asr_audit_events.yml index 99c3c58516..0de6715dba 100644 --- a/detections/endpoint/windows_defender_asr_audit_events.yml +++ b/detections/endpoint/windows_defender_asr_audit_events.yml @@ -1,79 +1,65 @@ name: Windows Defender ASR Audit Events id: 0e4d46b1-22bd-4f0e-8337-ca6f60ad4bea -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly data_source: -- Windows Event Log Defender 1122 -- Windows Event Log Defender 1125 -- Windows Event Log Defender 1126 -- Windows Event Log Defender 1132 -- Windows Event Log Defender 1134 -description: This detection searches for Windows Defender ASR audit events. ASR is - a feature of Windows Defender Exploit Guard that prevents actions and apps that - are typically used by exploit-seeking malware to infect machines. ASR rules are - applied to processes and applications. When a process or application attempts to - perform an action that is blocked by an ASR rule, an event is generated. This detection - searches for ASR audit events that are generated when a process or application attempts - to perform an action that would be blocked by an ASR rule, but is allowed to proceed - for auditing purposes. -search: '`ms_defender` EventCode IN (1122, 1125, 1126, 1132, 1134) | stats count min(_time) - as firstTime max(_time) as lastTime by host, Process_Name, Target_Commandline, Path, - ID, EventCode | lookup asr_rules ID OUTPUT ASR_Rule | fillnull value=NULL | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`| rename host as dest | `windows_defender_asr_audit_events_filter`' -how_to_implement: The following analytic requires collection of Windows Defender Operational - logs in either XML or multi-line. To collect, setup a new input for the Windows - Defender Operational logs. In addition, it does require a lookup that maps the ID - to ASR Rule name. Note that Audit and block Event IDs have different fields, therefore - the analytic will need to be modified for each type of event. -known_false_positives: False positives are expected from legitimate applications generating - events that are similar to those generated by malicious activity. For example, Event - ID 1122 is generated when a process attempts to load a DLL that is blocked by an - ASR rule. This can be triggered by legitimate applications that attempt to load - DLLs that are not blocked by ASR rules. This is audit only. + - Windows Event Log Defender 1122 + - Windows Event Log Defender 1125 + - Windows Event Log Defender 1126 + - Windows Event Log Defender 1132 + - Windows Event Log Defender 1134 +description: This detection searches for Windows Defender ASR audit events. ASR is a feature of Windows Defender Exploit Guard that prevents actions and apps that are typically used by exploit-seeking malware to infect machines. ASR rules are applied to processes and applications. When a process or application attempts to perform an action that is blocked by an ASR rule, an event is generated. This detection searches for ASR audit events that are generated when a process or application attempts to perform an action that would be blocked by an ASR rule, but is allowed to proceed for auditing purposes. +search: |- + `ms_defender` EventCode IN (1122, 1125, 1126, 1132, 1134) + | stats count min(_time) as firstTime max(_time) as lastTime + BY host, Process_Name, Target_Commandline, + Path, ID, EventCode + | lookup asr_rules ID OUTPUT ASR_Rule + | fillnull value=NULL + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | rename host as dest + | `windows_defender_asr_audit_events_filter` +how_to_implement: The following analytic requires collection of Windows Defender Operational logs in either XML or multi-line. To collect, setup a new input for the Windows Defender Operational logs. In addition, it does require a lookup that maps the ID to ASR Rule name. Note that Audit and block Event IDs have different fields, therefore the analytic will need to be modified for each type of event. +known_false_positives: False positives are expected from legitimate applications generating events that are similar to those generated by malicious activity. For example, Event ID 1122 is generated when a process attempts to load a DLL that is blocked by an ASR rule. This can be triggered by legitimate applications that attempt to load DLLs that are not blocked by ASR rules. This is audit only. references: -- https://asrgen.streamlit.app/ + - https://asrgen.streamlit.app/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: ASR audit event, $ASR_Rule$, was triggered on $dest$. - risk_objects: - - field: dest - type: system - score: 5 - threat_objects: [] + message: ASR audit event, $ASR_Rule$, was triggered on $dest$. + risk_objects: + - field: dest + type: system + score: 5 + threat_objects: [] tags: - analytic_story: - - Windows Attack Surface Reduction - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1059 - - T1566.001 - - T1566.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Attack Surface Reduction + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1059 + - T1566.001 + - T1566.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/defender/asr_audit.log - source: WinEventLog:Microsoft-Windows-Windows Defender/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/defender/asr_audit.log + source: WinEventLog:Microsoft-Windows-Windows Defender/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_defender_asr_block_events.yml b/detections/endpoint/windows_defender_asr_block_events.yml index b7a402d0e4..0c97a5e6e8 100644 --- a/detections/endpoint/windows_defender_asr_block_events.yml +++ b/detections/endpoint/windows_defender_asr_block_events.yml @@ -1,80 +1,65 @@ name: Windows Defender ASR Block Events id: 026f5f4e-e99f-4155-9e63-911ba587300b -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly data_source: -- Windows Event Log Defender 1121 -- Windows Event Log Defender 1126 -- Windows Event Log Defender 1129 -- Windows Event Log Defender 1131 -- Windows Event Log Defender 1133 -description: This detection searches for Windows Defender ASR block events. ASR is - a feature of Windows Defender Exploit Guard that prevents actions and apps that - are typically used by exploit-seeking malware to infect machines. ASR rules are - applied to processes and applications. When a process or application attempts to - perform an action that is blocked by an ASR rule, an event is generated. This detection - searches for ASR block events that are generated when a process or application attempts - to perform an action that is blocked by an ASR rule. Typically, these will be enabled - in block most after auditing and tuning the ASR rules themselves. Set to TTP once - tuned. -search: '`ms_defender` EventCode IN (1121, 1126, 1129, 1131, 1133) | stats count min(_time) - as firstTime max(_time) as lastTime by host, Path, Parent_Commandline, Process_Name, - ID, EventCode | lookup asr_rules ID OUTPUT ASR_Rule | fillnull value=NULL | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`| rename host as dest | `windows_defender_asr_block_events_filter`' -how_to_implement: The following analytic requires collection of Windows Defender Operational - logs in either XML or multi-line. To collect, setup a new input for the Windows - Defender Operational logs. In addition, it does require a lookup that maps the ID - to ASR Rule name. Note that Audit and block Event IDs have different fields, therefore - the analytic will need to be modified for each type of event. -known_false_positives: False positives are expected from legitimate applications generating - events that are similar to those generated by malicious activity. For example, Event - ID 1122 is generated when a process attempts to load a DLL that is blocked by an - ASR rule. This can be triggered by legitimate applications that attempt to load - DLLs that are not blocked by ASR rules. This is block only. + - Windows Event Log Defender 1121 + - Windows Event Log Defender 1126 + - Windows Event Log Defender 1129 + - Windows Event Log Defender 1131 + - Windows Event Log Defender 1133 +description: This detection searches for Windows Defender ASR block events. ASR is a feature of Windows Defender Exploit Guard that prevents actions and apps that are typically used by exploit-seeking malware to infect machines. ASR rules are applied to processes and applications. When a process or application attempts to perform an action that is blocked by an ASR rule, an event is generated. This detection searches for ASR block events that are generated when a process or application attempts to perform an action that is blocked by an ASR rule. Typically, these will be enabled in block most after auditing and tuning the ASR rules themselves. Set to TTP once tuned. +search: |- + `ms_defender` EventCode IN (1121, 1126, 1129, 1131, 1133) + | stats count min(_time) as firstTime max(_time) as lastTime + BY host, Path, Parent_Commandline, + Process_Name, ID, EventCode + | lookup asr_rules ID OUTPUT ASR_Rule + | fillnull value=NULL + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | rename host as dest + | `windows_defender_asr_block_events_filter` +how_to_implement: The following analytic requires collection of Windows Defender Operational logs in either XML or multi-line. To collect, setup a new input for the Windows Defender Operational logs. In addition, it does require a lookup that maps the ID to ASR Rule name. Note that Audit and block Event IDs have different fields, therefore the analytic will need to be modified for each type of event. +known_false_positives: False positives are expected from legitimate applications generating events that are similar to those generated by malicious activity. For example, Event ID 1122 is generated when a process attempts to load a DLL that is blocked by an ASR rule. This can be triggered by legitimate applications that attempt to load DLLs that are not blocked by ASR rules. This is block only. references: -- https://asrgen.streamlit.app/ + - https://asrgen.streamlit.app/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: ASR block event, $ASR_Rule$, was triggered on $dest$. - risk_objects: - - field: dest - type: system - score: 45 - threat_objects: [] + message: ASR block event, $ASR_Rule$, was triggered on $dest$. + risk_objects: + - field: dest + type: system + score: 45 + threat_objects: [] tags: - analytic_story: - - Windows Attack Surface Reduction - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1059 - - T1566.001 - - T1566.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Attack Surface Reduction + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1059 + - T1566.001 + - T1566.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/defender/asr_block.log - source: XmlWinEventLog:Microsoft-Windows-Windows Defender/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/defender/asr_block.log + source: XmlWinEventLog:Microsoft-Windows-Windows Defender/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_defender_asr_or_threat_configuration_tamper.yml b/detections/endpoint/windows_defender_asr_or_threat_configuration_tamper.yml index a6cb1f6a23..3304bb3ec4 100644 --- a/detections/endpoint/windows_defender_asr_or_threat_configuration_tamper.yml +++ b/detections/endpoint/windows_defender_asr_or_threat_configuration_tamper.yml @@ -1,104 +1,99 @@ name: Windows Defender ASR or Threat Configuration Tamper id: d0c07718-19d1-4de2-aea9-e0ffff0ed986 -version: 1 -date: '2025-10-13' +version: 2 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: TTP description: | - The following analytic detects the use of commands to disable Attack Surface Reduction (ASR) rules or change threat default actions in Windows Defender. - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving "Add-MpPreference" or "Set-MpPreference". - This activity is significant because adversaries often use it to bypass Windows Defender, allowing malicious code to execute undetected. - If confirmed malicious, this behavior could enable attackers to evade antivirus detection, maintain persistence, and execute further malicious activities without interference from Windows Defender. + The following analytic detects the use of commands to disable Attack Surface Reduction (ASR) rules or change threat default actions in Windows Defender. + It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving "Add-MpPreference" or "Set-MpPreference". + This activity is significant because adversaries often use it to bypass Windows Defender, allowing malicious code to execute undetected. + If confirmed malicious, this behavior could enable attackers to evade antivirus detection, maintain persistence, and execute further malicious activities without interference from Windows Defender. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime - - from datamodel=Endpoint.Processes where - - Processes.process IN ("*Add-MpPreference *", "*Set-MpPreference *") - - Processes.process IN ( - "*-AttackSurfaceReductionRules_Actions*", - "*-ThreatIDDefaultAction_Actions*" - ) - - Processes.process IN ( - "*Allow*", - "*NoAction*", - "*Disabled*", - "*_Actions 6*", - "*_Actions 9*", - "*_Actions 0*") + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + from datamodel=Endpoint.Processes where - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_defender_asr_or_threat_configuration_tamper_filter` + Processes.process IN ("*Add-MpPreference *", "*Set-MpPreference *") + + Processes.process IN ( + "*-AttackSurfaceReductionRules_Actions*", + "*-ThreatIDDefaultAction_Actions*" + ) + + Processes.process IN ( + "*Allow*", + "*NoAction*", + "*Disabled*", + "*_Actions 6*", + "*_Actions 9*", + "*_Actions 0*") + + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_defender_asr_or_threat_configuration_tamper_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: | - Certain administrative tasks may require the modification of ASR rules or threat actions due to FPs being generated. Investigate all attempts and filter as needed. + Certain administrative tasks may require the modification of ASR rules or threat actions due to FPs being generated. Investigate all attempts and filter as needed. references: - - https://learn.microsoft.com/en-us/powershell/module/defender/set-mppreference?view=windowsserver2025-ps#-attacksurfacereductionrules-actions - - https://www.virustotal.com/gui/file/7e805617c313ec2fb59d86719c827074cb7dfbf8f0aa18194ac1ffe6c21c8967/behavior + - https://learn.microsoft.com/en-us/powershell/module/defender/set-mppreference?view=windowsserver2025-ps#-attacksurfacereductionrules-actions + - https://www.virustotal.com/gui/file/7e805617c313ec2fb59d86719c827074cb7dfbf8f0aa18194ac1ffe6c21c8967/behavior drilldown_searches: - - name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: ASR or Threat detection tamper activity executed via $process$ on $dest$ - risk_objects: - - field: user - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: [] + message: ASR or Threat detection tamper activity executed via $process$ on $dest$ + risk_objects: + - field: user + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable_defender_asr_or_threats/disable_defender_asr_or_threats.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable_defender_asr_or_threats/disable_defender_asr_or_threats.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_defender_asr_registry_modification.yml b/detections/endpoint/windows_defender_asr_registry_modification.yml index 1eab1bea26..13b65ecadc 100644 --- a/detections/endpoint/windows_defender_asr_registry_modification.yml +++ b/detections/endpoint/windows_defender_asr_registry_modification.yml @@ -6,55 +6,28 @@ author: Michael Haag, Splunk status: production type: Hunting data_source: - - Windows Event Log Defender 5007 -description: - The following analytic detects modifications to Windows Defender Attack - Surface Reduction (ASR) registry settings. It leverages Windows Defender Operational - logs, specifically EventCode 5007, to identify changes in ASR rules. This activity - is significant because ASR rules are designed to block actions commonly used by - malware to exploit systems. Unauthorized modifications to these settings could indicate - an attempt to weaken system defenses. If confirmed malicious, this could allow an - attacker to bypass security measures, leading to potential system compromise and - data breaches. -search: - '`ms_defender` EventCode IN (5007) | rex field=New_Value "0x(?\\d+)$" - | rex field=Old_Value "0x(?\\d+)$" | rex field=New_Value "Rules\\\\(?[A-Fa-f0-9\\-]+)\\s*=" - | eval New_Registry_Value=case(New_Registry_Value=="0", "Disabled", New_Registry_Value=="1", - "Block", New_Registry_Value=="2", "Audit", New_Registry_Value=="6", "Warn") | eval - Old_Registry_Value=case(Old_Registry_Value=="0", "Disabled", Old_Registry_Value=="1", - "Block", Old_Registry_Value=="2", "Audit", Old_Registry_Value=="6", "Warn") | stats - count min(_time) as firstTime max(_time) as lastTime by host, New_Value, Old_Value, - Old_Registry_Value, New_Registry_Value, ASR_ID | lookup asr_rules ID AS ASR_ID OUTPUT - ASR_Rule | `security_content_ctime(firstTime)`| rename host as dest | `security_content_ctime(lastTime)` - | `windows_defender_asr_registry_modification_filter`' -how_to_implement: - The following analytic requires collection of Windows Defender Operational - logs in either XML or multi-line. To collect, setup a new input for the Windows - Defender Operational logs. In addition, it does require a lookup that maps the ID - to ASR Rule name. -known_false_positives: - False positives are expected from legitimate applications generating - events that are similar to those generated by malicious activity. For example, Event - ID 5007 is generated when a process attempts to modify a registry key that is related - to ASR rules. This can be triggered by legitimate applications that attempt to modify - registry keys that are not blocked by ASR rules. + - Windows Event Log Defender 5007 +description: The following analytic detects modifications to Windows Defender Attack Surface Reduction (ASR) registry settings. It leverages Windows Defender Operational logs, specifically EventCode 5007, to identify changes in ASR rules. This activity is significant because ASR rules are designed to block actions commonly used by malware to exploit systems. Unauthorized modifications to these settings could indicate an attempt to weaken system defenses. If confirmed malicious, this could allow an attacker to bypass security measures, leading to potential system compromise and data breaches. +search: '`ms_defender` EventCode IN (5007) | rex field=New_Value "0x(?\\d+)$" | rex field=Old_Value "0x(?\\d+)$" | rex field=New_Value "Rules\\\\(?[A-Fa-f0-9\\-]+)\\s*=" | eval New_Registry_Value=case(New_Registry_Value=="0", "Disabled", New_Registry_Value=="1", "Block", New_Registry_Value=="2", "Audit", New_Registry_Value=="6", "Warn") | eval Old_Registry_Value=case(Old_Registry_Value=="0", "Disabled", Old_Registry_Value=="1", "Block", Old_Registry_Value=="2", "Audit", Old_Registry_Value=="6", "Warn") | stats count min(_time) as firstTime max(_time) as lastTime by host, New_Value, Old_Value, Old_Registry_Value, New_Registry_Value, ASR_ID | lookup asr_rules ID AS ASR_ID OUTPUT ASR_Rule | `security_content_ctime(firstTime)`| rename host as dest | `security_content_ctime(lastTime)` | `windows_defender_asr_registry_modification_filter`' +how_to_implement: The following analytic requires collection of Windows Defender Operational logs in either XML or multi-line. To collect, setup a new input for the Windows Defender Operational logs. In addition, it does require a lookup that maps the ID to ASR Rule name. +known_false_positives: False positives are expected from legitimate applications generating events that are similar to those generated by malicious activity. For example, Event ID 5007 is generated when a process attempts to modify a registry key that is related to ASR rules. This can be triggered by legitimate applications that attempt to modify registry keys that are not blocked by ASR rules. references: - - https://asrgen.streamlit.app/ + - https://asrgen.streamlit.app/ tags: - analytic_story: - - Windows Attack Surface Reduction - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Attack Surface Reduction + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/defender/asr_registry.log - source: WinEventLog:Microsoft-Windows-Windows Defender/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/defender/asr_registry.log + source: WinEventLog:Microsoft-Windows-Windows Defender/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_defender_asr_rule_disabled.yml b/detections/endpoint/windows_defender_asr_rule_disabled.yml index 03cf5f313b..b49c9987e4 100644 --- a/detections/endpoint/windows_defender_asr_rule_disabled.yml +++ b/detections/endpoint/windows_defender_asr_rule_disabled.yml @@ -6,74 +6,44 @@ author: Michael Haag, Splunk status: production type: TTP data_source: - - Windows Event Log Defender 5007 -description: - The following analytic identifies when a Windows Defender ASR rule disabled - events. ASR is a feature of Windows Defender Exploit Guard that prevents actions - and apps that are typically used by exploit-seeking malware to infect machines. - ASR rules are applied to processes and applications. When a process or application - attempts to perform an action that is blocked by an ASR rule, an event is generated. - This detection searches for ASR rule disabled events that are generated when an - ASR rule is disabled. -search: - '`ms_defender` EventCode IN (5007) | rex field=New_Value "0x(?\\d+)$" - | rex field=Old_Value "0x(?\\d+)$" | rex field=New_Value "Rules\\\\(?[A-Fa-f0-9\\-]+)\\s*=" - | eval New_Registry_Value=case(New_Registry_Value=="0", "Disabled", New_Registry_Value=="1", - "Block", New_Registry_Value=="2", "Audit", New_Registry_Value=="6", "Warn") | eval - Old_Registry_Value=case(Old_Registry_Value=="0", "Disabled", Old_Registry_Value=="1", - "Block", Old_Registry_Value=="2", "Audit", Old_Registry_Value=="6", "Warn") | search - New_Registry_Value="Disabled" | stats count min(_time) as firstTime max(_time) as - lastTime by host, New_Value, Old_Value, Old_Registry_Value, New_Registry_Value, - ASR_ID | lookup asr_rules ID AS ASR_ID OUTPUT ASR_Rule | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`| rename host as dest | `windows_defender_asr_rule_disabled_filter`' -how_to_implement: - The following analytic requires collection of Windows Defender Operational - logs in either XML or multi-line. To collect, setup a new input for the Windows - Defender Operational logs. In addition, it does require a lookup that maps the ID - to ASR Rule name. -known_false_positives: - False positives may occur if applications are typically disabling - ASR rules in the environment. Monitor for changes to ASR rules to determine if this - is a false positive. + - Windows Event Log Defender 5007 +description: The following analytic identifies when a Windows Defender ASR rule disabled events. ASR is a feature of Windows Defender Exploit Guard that prevents actions and apps that are typically used by exploit-seeking malware to infect machines. ASR rules are applied to processes and applications. When a process or application attempts to perform an action that is blocked by an ASR rule, an event is generated. This detection searches for ASR rule disabled events that are generated when an ASR rule is disabled. +search: '`ms_defender` EventCode IN (5007) | rex field=New_Value "0x(?\\d+)$" | rex field=Old_Value "0x(?\\d+)$" | rex field=New_Value "Rules\\\\(?[A-Fa-f0-9\\-]+)\\s*=" | eval New_Registry_Value=case(New_Registry_Value=="0", "Disabled", New_Registry_Value=="1", "Block", New_Registry_Value=="2", "Audit", New_Registry_Value=="6", "Warn") | eval Old_Registry_Value=case(Old_Registry_Value=="0", "Disabled", Old_Registry_Value=="1", "Block", Old_Registry_Value=="2", "Audit", Old_Registry_Value=="6", "Warn") | search New_Registry_Value="Disabled" | stats count min(_time) as firstTime max(_time) as lastTime by host, New_Value, Old_Value, Old_Registry_Value, New_Registry_Value, ASR_ID | lookup asr_rules ID AS ASR_ID OUTPUT ASR_Rule | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| rename host as dest | `windows_defender_asr_rule_disabled_filter`' +how_to_implement: The following analytic requires collection of Windows Defender Operational logs in either XML or multi-line. To collect, setup a new input for the Windows Defender Operational logs. In addition, it does require a lookup that maps the ID to ASR Rule name. +known_false_positives: False positives may occur if applications are typically disabling ASR rules in the environment. Monitor for changes to ASR rules to determine if this is a false positive. references: - - https://asrgen.streamlit.app/ + - https://asrgen.streamlit.app/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: ASR rule disabled event, $ASR_Rule$, was triggered on $dest$. - risk_objects: - - field: dest - type: system - score: 100 - threat_objects: [] + message: ASR rule disabled event, $ASR_Rule$, was triggered on $dest$. + risk_objects: + - field: dest + type: system + score: 100 + threat_objects: [] tags: - analytic_story: - - Windows Attack Surface Reduction - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Attack Surface Reduction + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/defender/asr_disabled_registry.log - source: WinEventLog:Microsoft-Windows-Windows Defender/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/defender/asr_disabled_registry.log + source: WinEventLog:Microsoft-Windows-Windows Defender/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_defender_asr_rules_stacking.yml b/detections/endpoint/windows_defender_asr_rules_stacking.yml index 0562d75af3..c754da54e1 100644 --- a/detections/endpoint/windows_defender_asr_rules_stacking.yml +++ b/detections/endpoint/windows_defender_asr_rules_stacking.yml @@ -1,66 +1,54 @@ name: Windows Defender ASR Rules Stacking id: 425a6657-c5e4-4cbb-909e-fc9e5d326f01 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting data_source: -- Windows Event Log Defender 1121 -- Windows Event Log Defender 1122 -- Windows Event Log Defender 1125 -- Windows Event Log Defender 1126 -- Windows Event Log Defender 1129 -- Windows Event Log Defender 1131 -- Windows Event Log Defender 1133 -- Windows Event Log Defender 1134 -- Windows Event Log Defender 5007 -description: The following analytic identifies security events from Microsoft Defender, - focusing on Exploit Guard and Attack Surface Reduction (ASR) features. It detects - Event IDs 1121, 1126, 1131, and 1133 for blocked operations, and Event IDs 1122, - 1125, 1132, and 1134 for audit logs. Event ID 1129 indicates user overrides, while - Event ID 5007 signals configuration changes. This detection uses a lookup to correlate - ASR rule GUIDs with descriptive names. Monitoring these events is crucial for identifying - unauthorized operations, potential security breaches, and policy enforcement issues. - If confirmed malicious, attackers could bypass security measures, execute unauthorized - actions, or alter system configurations. -search: '`ms_defender` EventCode IN (1121, 1122, 1125, 1126, 1129, 1131, 1132, 1133, - 1134, 5007) | stats count min(_time) as firstTime max(_time) as lastTime by host - Parent_Commandline, Process_Name, Path, ID, EventCode | lookup asr_rules ID OUTPUT - ASR_Rule | fillnull value=NULL | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| - rename host as dest | `windows_defender_asr_rules_stacking_filter`' -how_to_implement: The following analytic requires collection of Windows Defender Operational - logs in either XML or multi-line. To collect, setup a new input for the Windows - Defender Operational logs. In addition, it does require a lookup that maps the ID - to ASR Rule name. Note that Audit and block Event IDs have different fields, therefore - the analytic will need to be modified for each type of event. The analytic can be - modified to look for specific ASR rules, or to look for specific Event IDs. EventID - 5007 is a change in the registry, and may be a false positive. This can be removed - from the search if desired. -known_false_positives: False positives are not expected with this analytic, since - it is a hunting analytic. It is meant to show the use of ASR rules and how they - can be used to detect malicious activity. + - Windows Event Log Defender 1121 + - Windows Event Log Defender 1122 + - Windows Event Log Defender 1125 + - Windows Event Log Defender 1126 + - Windows Event Log Defender 1129 + - Windows Event Log Defender 1131 + - Windows Event Log Defender 1133 + - Windows Event Log Defender 1134 + - Windows Event Log Defender 5007 +description: The following analytic identifies security events from Microsoft Defender, focusing on Exploit Guard and Attack Surface Reduction (ASR) features. It detects Event IDs 1121, 1126, 1131, and 1133 for blocked operations, and Event IDs 1122, 1125, 1132, and 1134 for audit logs. Event ID 1129 indicates user overrides, while Event ID 5007 signals configuration changes. This detection uses a lookup to correlate ASR rule GUIDs with descriptive names. Monitoring these events is crucial for identifying unauthorized operations, potential security breaches, and policy enforcement issues. If confirmed malicious, attackers could bypass security measures, execute unauthorized actions, or alter system configurations. +search: |- + `ms_defender` EventCode IN (1121, 1122, 1125, 1126, 1129, 1131, 1132, 1133, 1134, 5007) + | stats count min(_time) as firstTime max(_time) as lastTime + BY host Parent_Commandline, Process_Name, + Path, ID, EventCode + | lookup asr_rules ID OUTPUT ASR_Rule + | fillnull value=NULL + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | rename host as dest + | `windows_defender_asr_rules_stacking_filter` +how_to_implement: The following analytic requires collection of Windows Defender Operational logs in either XML or multi-line. To collect, setup a new input for the Windows Defender Operational logs. In addition, it does require a lookup that maps the ID to ASR Rule name. Note that Audit and block Event IDs have different fields, therefore the analytic will need to be modified for each type of event. The analytic can be modified to look for specific ASR rules, or to look for specific Event IDs. EventID 5007 is a change in the registry, and may be a false positive. This can be removed from the search if desired. +known_false_positives: False positives are not expected with this analytic, since it is a hunting analytic. It is meant to show the use of ASR rules and how they can be used to detect malicious activity. references: -- https://asrgen.streamlit.app/ -- https://learn.microsoft.com/en-us/microsoft-365/security/defender-endpoint/attack-surface-reduction?view=o365-worldwide + - https://asrgen.streamlit.app/ + - https://learn.microsoft.com/en-us/microsoft-365/security/defender-endpoint/attack-surface-reduction?view=o365-worldwide tags: - analytic_story: - - Windows Attack Surface Reduction - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1566.001 - - T1566.002 - - T1059 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Attack Surface Reduction + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1566.001 + - T1566.002 + - T1059 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/defender/asr_defender_operational.log - source: WinEventLog:Microsoft-Windows-Windows Defender/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/defender/asr_defender_operational.log + source: WinEventLog:Microsoft-Windows-Windows Defender/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_defender_exclusion_registry_entry.yml b/detections/endpoint/windows_defender_exclusion_registry_entry.yml index 808f565d25..1626f21df9 100644 --- a/detections/endpoint/windows_defender_exclusion_registry_entry.yml +++ b/detections/endpoint/windows_defender_exclusion_registry_entry.yml @@ -5,78 +5,56 @@ date: '2025-11-20' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: The following analytic detects modifications to the Windows Defender - exclusion registry entries. It leverages endpoint registry data to identify changes - in the registry path "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Exclusions\\*". - This activity is significant because adversaries often modify these entries to bypass - Windows Defender, allowing malicious code to execute without detection. If confirmed - malicious, this behavior could enable attackers to evade antivirus defenses, maintain - persistence, and execute further malicious activities undetected. +description: The following analytic detects modifications to the Windows Defender exclusion registry entries. It leverages endpoint registry data to identify changes in the registry path "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Exclusions\\*". This activity is significant because adversaries often modify these entries to bypass Windows Defender, allowing malicious code to execute without detection. If confirmed malicious, this behavior could enable attackers to evade antivirus defenses, maintain persistence, and execute further malicious activities undetected. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\SOFTWARE\\Policies\\Microsoft\\Windows - Defender\\Exclusions\\*") by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_defender_exclusion_registry_entry_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Exclusions\\*") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_defender_exclusion_registry_entry_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: admin or user may choose to use this windows features. references: -- https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html -- https://app.any.run/tasks/cf1245de-06a7-4366-8209-8e3006f2bfe5/ -- https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html + - https://app.any.run/tasks/cf1245de-06a7-4366-8209-8e3006f2bfe5/ + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Exclusion registry $registry_path$ modified or added on $dest$ for Windows - Defender - risk_objects: - - field: user - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: [] + message: Exclusion registry $registry_path$ modified or added on $dest$ for Windows Defender + risk_objects: + - field: user + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Qakbot - - Remcos - - ValleyRAT - - XWorm - - Azorult - - Warzone RAT - - Windows Defense Evasion Tactics - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Qakbot + - Remcos + - ValleyRAT + - XWorm + - Azorult + - Warzone RAT + - Windows Defense Evasion Tactics + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/defender_exclusion_sysmon/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/defender_exclusion_sysmon/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_delete_or_modify_system_firewall.yml b/detections/endpoint/windows_delete_or_modify_system_firewall.yml index 2f1a633f92..a0de7134fa 100644 --- a/detections/endpoint/windows_delete_or_modify_system_firewall.yml +++ b/detections/endpoint/windows_delete_or_modify_system_firewall.yml @@ -1,79 +1,64 @@ name: Windows Delete or Modify System Firewall id: b188d11a-eba7-419d-b8b6-cc265b4f2c4f -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic identifies 'netsh' processes that delete or modify - firewall configurations. It leverages data from Endpoint Detection and Response - (EDR) agents, focusing on command-line executions containing specific keywords. - This activity is significant because it can indicate malware, such as NJRAT, attempting - to alter firewall settings to evade detection or remove traces. If confirmed malicious, - this behavior could allow an attacker to disable security measures, facilitating - further compromise and persistence within the network. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_netsh` Processes.process - = "* firewall *" Processes.process = "* del*" by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_delete_or_modify_system_firewall_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic identifies 'netsh' processes that delete or modify firewall configurations. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions containing specific keywords. This activity is significant because it can indicate malware, such as NJRAT, attempting to alter firewall settings to evade detection or remove traces. If confirmed malicious, this behavior could allow an attacker to disable security measures, facilitating further compromise and persistence within the network. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_netsh` Processes.process = "* firewall *" Processes.process = "* del*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_delete_or_modify_system_firewall_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrator may modify or delete firewall configuration. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.njrat + - https://malpedia.caad.fkie.fraunhofer.de/details/win.njrat drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A $process_name$ deleted a firewall configuration on $dest$ - risk_objects: - - field: dest - type: system - score: 36 - threat_objects: [] + message: A $process_name$ deleted a firewall configuration on $dest$ + risk_objects: + - field: dest + type: system + score: 36 + threat_objects: [] tags: - analytic_story: - - NjRAT - - ShrinkLocker - asset_type: Endpoint - mitre_attack_id: - - T1562.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - NjRAT + - ShrinkLocker + asset_type: Endpoint + mitre_attack_id: + - T1562.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/njrat_delete_firewall/njrat_delete_firewall.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/njrat_delete_firewall/njrat_delete_firewall.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_deleted_registry_by_a_non_critical_process_file_path.yml b/detections/endpoint/windows_deleted_registry_by_a_non_critical_process_file_path.yml index e41a9d5141..9fb740ed72 100644 --- a/detections/endpoint/windows_deleted_registry_by_a_non_critical_process_file_path.yml +++ b/detections/endpoint/windows_deleted_registry_by_a_non_critical_process_file_path.yml @@ -5,85 +5,45 @@ date: '2025-05-02' author: Steven Dick, Teoderick Contreras, Splunk status: production type: Anomaly -description: - The following analytic detects the deletion of registry keys by non-critical - processes. It leverages Endpoint Detection and Response (EDR) data, focusing on - registry deletion events and correlating them with processes not typically associated - with system or program files. This activity is significant as it may indicate malware, - such as the Double Zero wiper, attempting to evade defenses or cause destructive - payload impacts. If confirmed malicious, this behavior could lead to significant - system damage, loss of critical configurations, and potential disruption of services. +description: The following analytic detects the deletion of registry keys by non-critical processes. It leverages Endpoint Detection and Response (EDR) data, focusing on registry deletion events and correlating them with processes not typically associated with system or program files. This activity is significant as it may indicate malware, such as the Double Zero wiper, attempting to evade defenses or cause destructive payload impacts. If confirmed malicious, this behavior could lead to significant system damage, loss of critical configurations, and potential disruption of services. data_source: -- Sysmon EventID 1 AND Sysmon EventID 12 -search: '| tstats `security_content_summariesonly` count from datamodel=Endpoint.Registry - WHERE Registry.action=deleted BY _time span=1h Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | join process_guid [| tstats - `security_content_summariesonly` count FROM datamodel=Endpoint.Processes WHERE NOT - (Processes.process_path IN ("*\\windows\\*", "*\\program files*")) by _time span=1h - Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)`] - | fields _time parent_process_name parent_process process_name process_path process - process_guid registry_path registry_value_name registry_value_data registry_key_name - action dest user | `windows_deleted_registry_by_a_non_critical_process_file_path_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: - This detection can catch for third party application updates - or installation. In this scenario false positive filter is needed. + - Sysmon EventID 1 AND Sysmon EventID 12 +search: '| tstats `security_content_summariesonly` count from datamodel=Endpoint.Registry WHERE Registry.action=deleted BY _time span=1h Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | join process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes WHERE NOT (Processes.process_path IN ("*\\windows\\*", "*\\program files*")) by _time span=1h Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)`] | fields _time parent_process_name parent_process process_name process_path process process_guid registry_path registry_value_name registry_value_data registry_key_name action dest user | `windows_deleted_registry_by_a_non_critical_process_file_path_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: This detection can catch for third party application updates or installation. In this scenario false positive filter is needed. references: - - https://blog.talosintelligence.com/2022/03/threat-advisory-doublezero.html + - https://blog.talosintelligence.com/2022/03/threat-advisory-doublezero.html drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - The registry was deleted by a suspicious process named $process_name$ with - the process path $process_path$ on dest $dest$. - risk_objects: - - field: dest - type: system - score: 36 - threat_objects: [] + message: The registry was deleted by a suspicious process named $process_name$ with the process path $process_path$ on dest $dest$. + risk_objects: + - field: dest + type: system + score: 36 + threat_objects: [] tags: - analytic_story: - - Data Destruction - - Double Zero Destructor - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Destruction + - Double Zero Destructor + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/doublezero_wiper/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/doublezero_wiper/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_detect_network_scanner_behavior.yml b/detections/endpoint/windows_detect_network_scanner_behavior.yml index 5766e54cd4..a5f31c9200 100644 --- a/detections/endpoint/windows_detect_network_scanner_behavior.yml +++ b/detections/endpoint/windows_detect_network_scanner_behavior.yml @@ -5,79 +5,51 @@ date: '2025-05-02' author: Steven Dick status: production type: Anomaly -description: The following analytic detects when an application is used to connect - a large number of unique ports/targets within a short time frame. Network enumeration - may be used by adversaries as a method of discovery, lateral movement, or remote - execution. This analytic may require significant tuning depending on the organization - and applications being actively used, highly recommended to pre-populate the filter - macro prior to activation. +description: The following analytic detects when an application is used to connect a large number of unique ports/targets within a short time frame. Network enumeration may be used by adversaries as a method of discovery, lateral movement, or remote execution. This analytic may require significant tuning depending on the organization and applications being actively used, highly recommended to pre-populate the filter macro prior to activation. data_source: -- Sysmon EventID 3 -search: '| tstats `security_content_summariesonly` count values(All_Traffic.action) - as action values(All_Traffic.dest) as dest values(All_Traffic.dest_port) as dest_port - values(All_Traffic.dest_ip) as dest_ip values(All_Traffic.dvc) as dvc values(All_Traffic.direction) - as direction values(All_Traffic.protocol) as protocol values(All_Traffic.protocol_version) - as protocol_version values(All_Traffic.src_port) as src_port values(All_Traffic.transport) - as transport dc(All_Traffic.dest_port) as port_count dc(All_Traffic.dest) as dest_count - min(_time) as firstTime max(_time) as lastTime values(All_Traffic.process_id) as - process_id from datamodel=Network_Traffic.All_Traffic where sourcetype=XmlWinEventLog - All_Traffic.app = "*\\*" All_Traffic.dest_port < 32000 NOT All_Traffic.dest_port - IN (8443,8080,5353,3268,443,389,88,80,53,25) by All_Traffic.app All_Traffic.src - All_Traffic.src_ip All_Traffic.user All_Traffic.vendor_product _time span=5m | `drop_dm_object_name(All_Traffic)` - | rex field=app ".*\\\(?.*)$" | where port_count > 10 OR dest_count - > 10 | stats latest(src) as src, latest(src_ip) as src_ip, max(dest_count) as dest_count, - max(port_count) as port_count, latest(dest_port) as dest_port, min(firstTime) as - firstTime, max(lastTime) as lastTime, max(count) as count by user,app,process_name - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_detect_network_scanner_behavior_filter`' -how_to_implement: This detection relies on Sysmon EventID 3 events being ingested - AND tagged into the Network_Traffic datamodel. -known_false_positives: Various, could be noisy depending on processes in the organization - and sysmon configuration used. Adjusted port/dest count thresholds as needed. + - Sysmon EventID 3 +search: '| tstats `security_content_summariesonly` count values(All_Traffic.action) as action values(All_Traffic.dest) as dest values(All_Traffic.dest_port) as dest_port values(All_Traffic.dest_ip) as dest_ip values(All_Traffic.dvc) as dvc values(All_Traffic.direction) as direction values(All_Traffic.protocol) as protocol values(All_Traffic.protocol_version) as protocol_version values(All_Traffic.src_port) as src_port values(All_Traffic.transport) as transport dc(All_Traffic.dest_port) as port_count dc(All_Traffic.dest) as dest_count min(_time) as firstTime max(_time) as lastTime values(All_Traffic.process_id) as process_id from datamodel=Network_Traffic.All_Traffic where sourcetype=XmlWinEventLog All_Traffic.app = "*\\*" All_Traffic.dest_port < 32000 NOT All_Traffic.dest_port IN (8443,8080,5353,3268,443,389,88,80,53,25) by All_Traffic.app All_Traffic.src All_Traffic.src_ip All_Traffic.user All_Traffic.vendor_product _time span=5m | `drop_dm_object_name(All_Traffic)` | rex field=app ".*\\\(?.*)$" | where port_count > 10 OR dest_count > 10 | stats latest(src) as src, latest(src_ip) as src_ip, max(dest_count) as dest_count, max(port_count) as port_count, latest(dest_port) as dest_port, min(firstTime) as firstTime, max(lastTime) as lastTime, max(count) as count by user,app,process_name | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_detect_network_scanner_behavior_filter`' +how_to_implement: This detection relies on Sysmon EventID 3 events being ingested AND tagged into the Network_Traffic datamodel. +known_false_positives: Various, could be noisy depending on processes in the organization and sysmon configuration used. Adjusted port/dest count thresholds as needed. references: -- https://attack.mitre.org/techniques/T1595 + - https://attack.mitre.org/techniques/T1595 drilldown_searches: -- name: View the detection results for - "$src$" and "$user$" - search: '%original_detection_search% | search src = "$src$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" and "$user$" + search: '%original_detection_search% | search src = "$src$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process exhibiting network scanning behavior [$process_name$] was detected - on $src$ - risk_objects: - - field: src - type: system - score: 25 - - field: user - type: user - score: 25 - threat_objects: - - field: process_name - type: process_name + message: A process exhibiting network scanning behavior [$process_name$] was detected on $src$ + risk_objects: + - field: src + type: system + score: 25 + - field: user + type: user + score: 25 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Network Discovery - - Windows Discovery Techniques - asset_type: Endpoint - mitre_attack_id: - - T1595.001 - - T1595.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Network Discovery + - Windows Discovery Techniques + asset_type: Endpoint + mitre_attack_id: + - T1595.001 + - T1595.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1595/sysmon_scanning_events/sysmon_scanning_events.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1595/sysmon_scanning_events/sysmon_scanning_events.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_developer_signed_msix_package_installation.yml b/detections/endpoint/windows_developer_signed_msix_package_installation.yml index a5bd4e069c..b836186b24 100644 --- a/detections/endpoint/windows_developer_signed_msix_package_installation.yml +++ b/detections/endpoint/windows_developer_signed_msix_package_installation.yml @@ -1,65 +1,62 @@ name: Windows Developer-Signed MSIX Package Installation id: 2c0427aa-982c-4e97-bc33-bddeda4fd095 -version: 1 -date: '2025-08-05' +version: 2 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly description: This detection identifies the installation of developer-signed MSIX packages that lack Microsoft Store signatures. All malicious MSIX packages observed in recent threat campaigns (including those from FIN7, Zloader/Storm-0569, and FakeBat/Storm-1113) were developer-signed rather than Microsoft Store signed. Microsoft Store apps have specific publisher IDs containing '8wekyb3d8bbwe' or 'cw5n1h2txyewy', while developer-signed packages lack these identifiers. This detection focuses on EventID 855 from the Microsoft-Windows-AppXDeployment-Server/Operational logs, which indicates a completed package installation. data_source: -- Windows Event Log AppXDeployment-Server 855 -search: '`wineventlog_appxdeploymentserver` EventCode=855 - NOT PackageMoniker IN ("*8wekyb3d8bbwe*","*cw5n1h2txyewy*") - | stats count min(_time) as firstTime max(_time) as lastTime values(PackageMoniker) as PackageMoniker by dvc EventCode user_id | rename dvc as dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_developer_signed_msix_package_installation_filter`' + - Windows Event Log AppXDeployment-Server 855 +search: |- + `wineventlog_appxdeploymentserver` EventCode=855 NOT PackageMoniker IN ("*8wekyb3d8bbwe*","*cw5n1h2txyewy*") + | stats count min(_time) as firstTime max(_time) as lastTime values(PackageMoniker) as PackageMoniker + BY dvc EventCode user_id + | rename dvc as dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_developer_signed_msix_package_installation_filter` how_to_implement: To implement this detection, you need to be collecting Windows Event logs from the Microsoft-Windows-AppXDeploymentServer/Operational channel. In Splunk, this typically requires the Windows TA and configuration to collect from this specific channel. Ensure your Windows event collection is properly configured to capture EventCode 855 from the Microsoft-Windows-AppXDeploymentServer/Operational log. known_false_positives: Legitimate developer-signed applications that are not from the Microsoft Store will trigger this detection. Organizations should maintain a baseline of expected developer-signed applications in their environment and tune the detection accordingly. Common legitimate developer-signed applications include in-house developed applications and some third-party applications that are not distributed through the Microsoft Store. references: -- https://redcanary.com/blog/threat-intelligence/msix-installers/ -- https://learn.microsoft.com/en-us/windows/win32/appxpkg/troubleshooting -- https://www.advancedinstaller.com/msix-installation-or-launching-errors-and-fixes.html -- https://redcanary.com/blog/threat-detection/code-signing-certificates/ + - https://redcanary.com/blog/threat-intelligence/msix-installers/ + - https://learn.microsoft.com/en-us/windows/win32/appxpkg/troubleshooting + - https://www.advancedinstaller.com/msix-installation-or-launching-errors-and-fixes.html + - https://redcanary.com/blog/threat-detection/code-signing-certificates/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A developer-signed MSIX package "$PackageMoniker$" was installed on $dest$ by user $user_id$. - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: - - field: PackageMoniker - type: file_name + message: A developer-signed MSIX package "$PackageMoniker$" was installed on $dest$ by user $user_id$. + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: + - field: PackageMoniker + type: file_name tags: - analytic_story: - - MSIX Package Abuse - asset_type: Endpoint - mitre_attack_id: - - T1553.005 - - T1204.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - MSIX Package Abuse + asset_type: Endpoint + mitre_attack_id: + - T1553.005 + - T1204.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/appx/windows_appxdeploymentserver.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Microsoft-Windows-AppXDeploymentServer/Operational + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/appx/windows_appxdeploymentserver.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Microsoft-Windows-AppXDeploymentServer/Operational diff --git a/detections/endpoint/windows_disable_change_password_through_registry.yml b/detections/endpoint/windows_disable_change_password_through_registry.yml index 22be86ed96..c882ed5cbf 100644 --- a/detections/endpoint/windows_disable_change_password_through_registry.yml +++ b/detections/endpoint/windows_disable_change_password_through_registry.yml @@ -5,74 +5,45 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk, Steven Dick status: production type: Anomaly -description: - The following analytic detects a suspicious registry modification that - disables the Change Password feature on a Windows host. It identifies changes to - the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableChangePassword" - with a value of "0x00000001". This activity is significant as it can prevent users - from changing their passwords, a tactic often used by ransomware to maintain control - over compromised systems. If confirmed malicious, this could hinder user response - to an attack, allowing the attacker to persist and potentially escalate their access - within the network. +description: The following analytic detects a suspicious registry modification that disables the Change Password feature on a Windows host. It identifies changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableChangePassword" with a value of "0x00000001". This activity is significant as it can prevent users from changing their passwords, a tactic often used by ransomware to maintain control over compromised systems. If confirmed malicious, this could hinder user response to an attack, allowing the attacker to persist and potentially escalate their access within the network. data_source: - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableChangePassword" - Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_disable_change_password_through_registry_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 -known_false_positives: - This windows feature may implemented by administrator to prevent - normal user to change the password of a critical host or server, In this type of - scenario filter is needed to minimized false positive. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableChangePassword" Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_change_password_through_registry_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 +known_false_positives: This windows feature may implemented by administrator to prevent normal user to change the password of a critical host or server, In this type of scenario filter is needed to minimized false positive. references: - - https://www.trendmicro.com/vinfo/us/threat-encyclopedia/malware/ransom_heartbleed.thdobah + - https://www.trendmicro.com/vinfo/us/threat-encyclopedia/malware/ransom_heartbleed.thdobah drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Registry modification in "DisableChangePassword" on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Registry modification in "DisableChangePassword" on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Ransomware - - Windows Defense Evasion Tactics - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - Windows Defense Evasion Tactics + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/ransomware_disable_reg/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/ransomware_disable_reg/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_disable_internet_explorer_addons.yml b/detections/endpoint/windows_disable_internet_explorer_addons.yml index 810bfbd2c8..008a1a484a 100644 --- a/detections/endpoint/windows_disable_internet_explorer_addons.yml +++ b/detections/endpoint/windows_disable_internet_explorer_addons.yml @@ -1,77 +1,67 @@ name: Windows Disable Internet Explorer Addons id: 65224d8b-b95d-44ec-bb44-408d830c1258 -version: 1 -date: '2025-05-26' +version: 2 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: | - The following analytic detects the execution of iexplore.exe (Internet Explorer) with the -extoff command-line flag, which disables all browser extensions. This flag is commonly abused by adversaries to launch a clean browser session that bypasses security controls such as antivirus browser extensions, toolbars, or group policy-enforced add-ons. - Malicious documents or scripts may leverage iexplore.exe -extoff to open phishing pages, command-and-control interfaces, or download additional payloads in an environment free from security monitoring plugins. While this flag may be used legitimately by IT administrators for troubleshooting purposes, its use in modern enterprise environments is rare and should be considered suspicious—particularly when launched by Office applications, scripting engines (e.g., PowerShell, WScript), or scheduled tasks. + The following analytic detects the execution of iexplore.exe (Internet Explorer) with the -extoff command-line flag, which disables all browser extensions. This flag is commonly abused by adversaries to launch a clean browser session that bypasses security controls such as antivirus browser extensions, toolbars, or group policy-enforced add-ons. + Malicious documents or scripts may leverage iexplore.exe -extoff to open phishing pages, command-and-control interfaces, or download additional payloads in an environment free from security monitoring plugins. While this flag may be used legitimately by IT administrators for troubleshooting purposes, its use in modern enterprise environments is rare and should be considered suspicious—particularly when launched by Office applications, scripting engines (e.g., PowerShell, WScript), or scheduled tasks. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - (Processes.process_name = "iexplore.exe" OR Processes.original_file_name="IEXPLORE.EXE") - Processes.process = "*-extoff*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_disable_internet_explorer_addons_filter` -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: Administrators may enable or disable this feature that may - cause some false positive. + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where + (Processes.process_name = "iexplore.exe" OR Processes.original_file_name="IEXPLORE.EXE") + Processes.process = "*-extoff*" + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_disable_internet_explorer_addons_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: Administrators may enable or disable this feature that may cause some false positive. references: -- https://www.hybrid-analysis.com/sample/e285feeca968b3ca22017a64363eea5e69ccd519696671df523291b089597875/588175f1aac2edf92bbed32f + - https://www.hybrid-analysis.com/sample/e285feeca968b3ca22017a64363eea5e69ccd519696671df523291b089597875/588175f1aac2edf92bbed32f drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An iexplore.exe process with the -extoff flag was launched on $dest$ by user $user$. - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: An iexplore.exe process with the -extoff flag was launched on $dest$ by user $user$. + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - Malicious Inno Setup Loader - asset_type: Endpoint - mitre_attack_id: - - T1176.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Malicious Inno Setup Loader + asset_type: Endpoint + mitre_attack_id: + - T1176.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1176.001/disable_extension/iexplore-extoff.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1176.001/disable_extension/iexplore-extoff.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_disable_lock_workstation_feature_through_registry.yml b/detections/endpoint/windows_disable_lock_workstation_feature_through_registry.yml index 3a733dfbe6..a982c781c1 100644 --- a/detections/endpoint/windows_disable_lock_workstation_feature_through_registry.yml +++ b/detections/endpoint/windows_disable_lock_workstation_feature_through_registry.yml @@ -5,73 +5,47 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk, Steven Dick status: production type: Anomaly -description: - The following analytic detects a suspicious registry modification that - disables the Lock Computer feature in Windows. It leverages data from the Endpoint.Registry - data model, specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableLockWorkstation" - with a value of "0x00000001". This activity is significant because it prevents users - from locking their screens, a tactic often used by malware, including ransomware, - to maintain control over compromised systems. If confirmed malicious, this could - allow attackers to sustain their presence and execute further malicious actions - without user interruption. +description: The following analytic detects a suspicious registry modification that disables the Lock Computer feature in Windows. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableLockWorkstation" with a value of "0x00000001". This activity is significant because it prevents users from locking their screens, a tactic often used by malware, including ransomware, to maintain control over compromised systems. If confirmed malicious, this could allow attackers to sustain their presence and execute further malicious actions without user interruption. data_source: - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableLockWorkstation" - Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_disable_lock_workstation_feature_through_registry_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableLockWorkstation" Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_lock_workstation_feature_through_registry_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: No false positives have been identified at this time. references: - - https://www.bleepingcomputer.com/news/security/in-dev-ransomware-forces-you-do-to-survey-before-unlocking-computer/ - - https://heimdalsecurity.com/blog/fatalrat-targets-telegram/ + - https://www.bleepingcomputer.com/news/security/in-dev-ransomware-forces-you-do-to-survey-before-unlocking-computer/ + - https://heimdalsecurity.com/blog/fatalrat-targets-telegram/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Registry modification in "DisableLockWorkstation" on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Registry modification in "DisableLockWorkstation" on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Ransomware - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/ransomware_disable_reg/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/ransomware_disable_reg/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_disable_logoff_button_through_registry.yml b/detections/endpoint/windows_disable_logoff_button_through_registry.yml index e820e6a341..72e9369089 100644 --- a/detections/endpoint/windows_disable_logoff_button_through_registry.yml +++ b/detections/endpoint/windows_disable_logoff_button_through_registry.yml @@ -5,76 +5,47 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk, Steven Dick status: production type: Anomaly -description: - The following analytic detects a suspicious registry modification that - disables the logoff feature on a Windows host. It leverages data from the Endpoint.Registry - data model to identify changes to specific registry values associated with logoff - functionality. This activity is significant because it can indicate ransomware attempting - to make the compromised host unusable and hinder remediation efforts. If confirmed - malicious, this action could prevent users from logging off, complicate incident - response, and allow attackers to maintain persistence and control over the affected - system. +description: The following analytic detects a suspicious registry modification that disables the logoff feature on a Windows host. It leverages data from the Endpoint.Registry data model to identify changes to specific registry values associated with logoff functionality. This activity is significant because it can indicate ransomware attempting to make the compromised host unusable and hinder remediation efforts. If confirmed malicious, this action could prevent users from logging off, complicate incident response, and allow attackers to maintain persistence and control over the affected system. data_source: - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\*" - Registry.registry_value_name IN ("NoLogOff", "StartMenuLogOff") Registry.registry_value_data - = "0x00000001") by Registry.action Registry.dest Registry.process_guid Registry.process_id - Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data - Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user - Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_logoff_button_through_registry_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 -known_false_positives: - This windows feature may implement by administrator in some - server where shutdown is critical. In that scenario filter of machine and users - that can modify this registry is needed. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\*" Registry.registry_value_name IN ("NoLogOff", "StartMenuLogOff") Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_logoff_button_through_registry_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 +known_false_positives: This windows feature may implement by administrator in some server where shutdown is critical. In that scenario filter of machine and users that can modify this registry is needed. references: - - https://www.hybrid-analysis.com/sample/e2d4018fd3bd541c153af98ef7c25b2bf4a66bc3bfb89e437cde89fd08a9dd7b/5b1f4d947ca3e10f22714774 - - https://malwiki.org/index.php?title=DigiPop.xp - - https://www.trendmicro.com/vinfo/be/threat-encyclopedia/search/js_noclose.e/2 + - https://www.hybrid-analysis.com/sample/e2d4018fd3bd541c153af98ef7c25b2bf4a66bc3bfb89e437cde89fd08a9dd7b/5b1f4d947ca3e10f22714774 + - https://malwiki.org/index.php?title=DigiPop.xp + - https://www.trendmicro.com/vinfo/be/threat-encyclopedia/search/js_noclose.e/2 drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Registry modification in "NoLogOff" on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Registry modification in "NoLogOff" on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Ransomware - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/ransomware_disable_reg/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/ransomware_disable_reg/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_disable_memory_crash_dump.yml b/detections/endpoint/windows_disable_memory_crash_dump.yml index 50ed62e4cd..9a4b27cce3 100644 --- a/detections/endpoint/windows_disable_memory_crash_dump.yml +++ b/detections/endpoint/windows_disable_memory_crash_dump.yml @@ -5,72 +5,51 @@ date: '2026-01-14' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects attempts to disable the memory crash dump - feature on Windows systems by setting the registry value to 0. It leverages data - from the Endpoint.Registry datamodel, specifically monitoring changes to the CrashDumpEnabled - registry key. This activity is significant because disabling crash dumps can hinder - forensic analysis and incident response efforts. If confirmed malicious, this action - could be part of a broader attack strategy, such as data destruction or system destabilization, - as seen with HermeticWiper, potentially leading to significant operational disruptions - and data loss. +description: The following analytic detects attempts to disable the memory crash dump feature on Windows systems by setting the registry value to 0. It leverages data from the Endpoint.Registry datamodel, specifically monitoring changes to the CrashDumpEnabled registry key. This activity is significant because disabling crash dumps can hinder forensic analysis and incident response efforts. If confirmed malicious, this action could be part of a broader attack strategy, such as data destruction or system destabilization, as seen with HermeticWiper, potentially leading to significant operational disruptions and data loss. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry - where (Registry.registry_path="*\\CurrentControlSet\\Control\\CrashControl\\CrashDumpEnabled") - AND Registry.registry_value_data="0x00000000" by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_memory_crash_dump_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the Filesystem responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Filesystem` and `Registry` - node. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry where (Registry.registry_path="*\\CurrentControlSet\\Control\\CrashControl\\CrashDumpEnabled") AND Registry.registry_value_data="0x00000000" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_memory_crash_dump_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` and `Registry` node. known_false_positives: No false positives have been identified at this time. references: -- https://blog.talosintelligence.com/2022/02/threat-advisory-hermeticwiper.html -- https://docs.microsoft.com/en-us/troubleshoot/windows-server/performance/memory-dump-file-options + - https://blog.talosintelligence.com/2022/02/threat-advisory-hermeticwiper.html + - https://docs.microsoft.com/en-us/troubleshoot/windows-server/performance/memory-dump-file-options drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process was identified attempting to disable memory crash dumps on $dest$. - risk_objects: - - field: user - type: user - score: 90 - - field: dest - type: system - score: 90 - threat_objects: [] + message: A process was identified attempting to disable memory crash dumps on $dest$. + risk_objects: + - field: user + type: user + score: 90 + - field: dest + type: system + score: 90 + threat_objects: [] tags: - analytic_story: - - Ransomware - - Data Destruction - - Windows Registry Abuse - - Hermetic Wiper - asset_type: Endpoint - mitre_attack_id: - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - Data Destruction + - Windows Registry Abuse + - Hermetic Wiper + asset_type: Endpoint + mitre_attack_id: + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/hermetic_wiper/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/hermetic_wiper/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_disable_notification_center.yml b/detections/endpoint/windows_disable_notification_center.yml index 4aeedf6c0e..e0f9d4f7d0 100644 --- a/detections/endpoint/windows_disable_notification_center.yml +++ b/detections/endpoint/windows_disable_notification_center.yml @@ -1,78 +1,67 @@ name: Windows Disable Notification Center id: 1cd983c8-8fd6-11ec-a09d-acde48001122 -version: 11 -date: '2025-05-02' +version: 12 +date: '2026-02-25' author: Teoderick Contreras, Splunk, Steven Dick status: production type: Anomaly -description: - The following analytic detects the modification of the Windows registry - to disable the Notification Center on a host machine. It leverages data from the - Endpoint.Registry data model, specifically looking for changes to the "DisableNotificationCenter" - registry value set to "0x00000001." This activity is significant because disabling - the Notification Center can be a tactic used by RAT malware to hide its presence - and subsequent actions. If confirmed malicious, this could allow an attacker to - operate stealthily, potentially leading to further system compromise and data exfiltration. +description: The following analytic detects the modification of the Windows registry to disable the Notification Center on a host machine. It leverages data from the Endpoint.Registry data model, specifically looking for changes to the "DisableNotificationCenter" registry value set to "0x00000001." This activity is significant because disabling the Notification Center can be a tactic used by RAT malware to hide its presence and subsequent actions. If confirmed malicious, this could allow an attacker to operate stealthily, potentially leading to further system compromise and data exfiltration. data_source: - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_value_name= - "DisableNotificationCenter" Registry.registry_value_data = "0x00000001") by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_disable_notification_center_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry + WHERE ( + Registry.registry_value_name= "DisableNotificationCenter" Registry.registry_value_data = "0x00000001" + ) + BY Registry.action Registry.dest Registry.process_guid + Registry.process_id Registry.registry_hive Registry.registry_path + Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name + Registry.registry_value_type Registry.status Registry.user + Registry.vendor_product + | `drop_dm_object_name(Registry)` + | where isnotnull(registry_value_data) + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_disable_notification_center_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: admin or user may choose to disable this windows features. references: - - https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html + - https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The Windows notification center was disabled on $dest$ by $user$. - risk_objects: - - field: user - type: user - score: 48 - - field: dest - type: system - score: 48 - threat_objects: [] + message: The Windows notification center was disabled on $dest$ by $user$. + risk_objects: + - field: user + type: user + score: 48 + - field: dest + type: system + score: 48 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - CISA AA23-347A - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - CISA AA23-347A + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/disable_notif_center/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/disable_notif_center/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_disable_or_modify_tools_via_taskkill.yml b/detections/endpoint/windows_disable_or_modify_tools_via_taskkill.yml index 009311577b..499066a79b 100644 --- a/detections/endpoint/windows_disable_or_modify_tools_via_taskkill.yml +++ b/detections/endpoint/windows_disable_or_modify_tools_via_taskkill.yml @@ -1,84 +1,67 @@ name: Windows Disable or Modify Tools Via Taskkill id: a43ae66f-c410-4b3d-8741-9ce1ad17ddb0 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic identifies the use of taskkill.exe to forcibly - terminate processes. It leverages data from Endpoint Detection and Response (EDR) - agents, focusing on command-line executions that include specific taskkill parameters. - This activity is significant because it can indicate attempts to disable security - tools or disrupt legitimate applications, a common tactic in malware operations. - If confirmed malicious, this behavior could allow attackers to evade detection, - disrupt system stability, and potentially gain further control over the compromised - system. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "taskkill.exe" - Processes.process IN ("* /f*", "* /t*") Processes.process IN ("* /im*", "* /pid*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_disable_or_modify_tools_via_taskkill_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Network administrator can use this application to kill process - during audit or investigation. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic identifies the use of taskkill.exe to forcibly terminate processes. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions that include specific taskkill parameters. This activity is significant because it can indicate attempts to disable security tools or disrupt legitimate applications, a common tactic in malware operations. If confirmed malicious, this behavior could allow attackers to evade detection, disrupt system stability, and potentially gain further control over the compromised system. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "taskkill.exe" Processes.process IN ("* /f*", "* /t*") Processes.process IN ("* /im*", "* /pid*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_disable_or_modify_tools_via_taskkill_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Network administrator can use this application to kill process during audit or investigation. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.njrat + - https://malpedia.caad.fkie.fraunhofer.de/details/win.njrat drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A taskkill process to terminate process is executed on host- $dest$ - risk_objects: - - field: dest - type: system - score: 36 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: A taskkill process to terminate process is executed on host- $dest$ + risk_objects: + - field: dest + type: system + score: 36 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - PXA Stealer - - NjRAT - - Crypto Stealer - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - PXA Stealer + - NjRAT + - Crypto Stealer + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/taskkill/taskkill_im.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/taskkill/taskkill_im.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_disable_or_stop_browser_process.yml b/detections/endpoint/windows_disable_or_stop_browser_process.yml index a32dbdd744..15809f1e43 100644 --- a/detections/endpoint/windows_disable_or_stop_browser_process.yml +++ b/detections/endpoint/windows_disable_or_stop_browser_process.yml @@ -1,88 +1,68 @@ name: Windows Disable or Stop Browser Process id: 220d34b7-b6c7-45fe-8dbb-c35cdd9fe6d5 -version: 7 -date: '2025-10-31' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk data_source: -- Sysmon EventID 1 + - Sysmon EventID 1 type: TTP status: production -description: The following analytic detects the use of the taskkill command in a - process command line to terminate several known browser processes, a technique - commonly employed by the Braodo stealer malware to steal credentials. By - forcefully closing browsers like Chrome, Edge, and Firefox, the malware can - unlock files that store sensitive information, such as passwords and login - data. This detection focuses on identifying taskkill commands targeting these - browsers, signaling malicious intent. Early detection allows security teams to - investigate and prevent further credential theft and system compromise. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process = "*taskkill*" - Processes.process IN("*chrome.exe","*firefox.exe","*brave.exe","*opera.exe","*msedge.exe","*chromium.exe") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_disable_or_stop_browser_process_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: Admin or user may choose to terminate browser via - taskkill.exe. Filter as needed. +description: The following analytic detects the use of the taskkill command in a process command line to terminate several known browser processes, a technique commonly employed by the Braodo stealer malware to steal credentials. By forcefully closing browsers like Chrome, Edge, and Firefox, the malware can unlock files that store sensitive information, such as passwords and login data. This detection focuses on identifying taskkill commands targeting these browsers, signaling malicious intent. Early detection allows security teams to investigate and prevent further credential theft and system compromise. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process = "*taskkill*" Processes.process IN("*chrome.exe","*firefox.exe","*brave.exe","*opera.exe","*msedge.exe","*chromium.exe") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_disable_or_stop_browser_process_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Admin or user may choose to terminate browser via taskkill.exe. Filter as needed. references: -- https://x.com/suyog41/status/1825869470323056748 -- https://g0njxa.medium.com/from-vietnam-to-united-states-malware-fraud-and-dropshipping-98b7a7b2c36d + - https://x.com/suyog41/status/1825869470323056748 + - https://g0njxa.medium.com/from-vietnam-to-united-states-malware-fraud-and-dropshipping-98b7a7b2c36d drilldown_searches: -- name: View the detection results for "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process commandline- [$process$] that tries to kill browser on - [$dest$]. - risk_objects: - - field: user - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: [] + message: A process commandline- [$process$] that tries to kill browser on [$dest$]. + risk_objects: + - field: user + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Braodo Stealer - - Scattered Lapsus$ Hunters - - Hellcat Ransomware - - Castle RAT - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Braodo Stealer + - Scattered Lapsus$ Hunters + - Hellcat Ransomware + - Castle RAT + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/taskkill_browser/braodo_taskkill.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/taskkill_browser/braodo_taskkill.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_disable_shutdown_button_through_registry.yml b/detections/endpoint/windows_disable_shutdown_button_through_registry.yml index 966dab733c..0ab0bcd8c1 100644 --- a/detections/endpoint/windows_disable_shutdown_button_through_registry.yml +++ b/detections/endpoint/windows_disable_shutdown_button_through_registry.yml @@ -5,75 +5,45 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk, Steven Dick status: production type: Anomaly -description: - The following analytic detects suspicious registry modifications that - disable the shutdown button on a user's logon screen. It leverages data from the - Endpoint.Registry data model, specifically monitoring changes to registry paths - associated with shutdown policies. This activity is significant because it is a - tactic used by malware, particularly ransomware like KillDisk, to hinder system - usability and prevent the removal of malicious changes. If confirmed malicious, - this could impede system recovery efforts, making it difficult to restart the machine - and remove other harmful modifications. +description: The following analytic detects suspicious registry modifications that disable the shutdown button on a user's logon screen. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to registry paths associated with shutdown policies. This activity is significant because it is a tactic used by malware, particularly ransomware like KillDisk, to hinder system usability and prevent the removal of malicious changes. If confirmed malicious, this could impede system recovery efforts, making it difficult to restart the machine and remove other harmful modifications. data_source: - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE ((Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\shutdownwithoutlogon" - Registry.registry_value_data = "0x00000000") OR (Registry.registry_path="*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoClose" - Registry.registry_value_data = "0x00000001")) by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_disable_shutdown_button_through_registry_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 -known_false_positives: - This windows feature may implement by administrator in some - server where shutdown is critical. In that scenario filter of machine and users - that can modify this registry is needed. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE ((Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\shutdownwithoutlogon" Registry.registry_value_data = "0x00000000") OR (Registry.registry_path="*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoClose" Registry.registry_value_data = "0x00000001")) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_shutdown_button_through_registry_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 +known_false_positives: This windows feature may implement by administrator in some server where shutdown is critical. In that scenario filter of machine and users that can modify this registry is needed. references: - - https://www.trendmicro.com/vinfo/us/threat-encyclopedia/malware/ransom.msil.screenlocker.a/ + - https://www.trendmicro.com/vinfo/us/threat-encyclopedia/malware/ransom.msil.screenlocker.a/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Registry modification in "shutdownwithoutlogon" on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Registry modification in "shutdownwithoutlogon" on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Ransomware - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/ransomware_disable_reg/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/ransomware_disable_reg/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_disable_windows_event_logging_disable_http_logging.yml b/detections/endpoint/windows_disable_windows_event_logging_disable_http_logging.yml index 2dd6003632..e06d11103d 100644 --- a/detections/endpoint/windows_disable_windows_event_logging_disable_http_logging.yml +++ b/detections/endpoint/windows_disable_windows_event_logging_disable_http_logging.yml @@ -1,96 +1,77 @@ name: Windows Disable Windows Event Logging Disable HTTP Logging id: 23fb6787-255f-4d5b-9a66-9fd7504032b5 -version: 10 -date: '2025-05-02' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the use of AppCmd.exe to disable HTTP - logging on IIS servers. It leverages data from Endpoint Detection and Response (EDR) - agents, focusing on process execution events where AppCmd.exe is used with specific - parameters to alter logging settings. This activity is significant because disabling - HTTP logging can help adversaries hide their tracks and avoid detection by removing - evidence of their actions. If confirmed malicious, this could allow attackers to - operate undetected, making it difficult to trace their activities and respond to - the intrusion effectively. +description: The following analytic detects the use of AppCmd.exe to disable HTTP logging on IIS servers. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution events where AppCmd.exe is used with specific parameters to alter logging settings. This activity is significant because disabling HTTP logging can help adversaries hide their tracks and avoid detection by removing evidence of their actions. If confirmed malicious, this could allow attackers to operate undetected, making it difficult to trace their activities and respond to the intrusion effectively. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where NOT (Processes.parent_process_name IN - ("msiexec.exe", "iissetup.exe")) Processes.process_name=appcmd.exe Processes.process - IN ("*set config*", "*httplogging*","*dontlog:true*") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_disable_windows_event_logging_disable_http_logging_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present only if scripts or Administrators - are disabling logging. Filter as needed by parent process or other. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE NOT (Processes.parent_process_name IN ("msiexec.exe", "iissetup.exe")) Processes.process_name=appcmd.exe Processes.process IN ("*set config*", "*httplogging*","*dontlog:true*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_disable_windows_event_logging_disable_http_logging_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present only if scripts or Administrators are disabling logging. Filter as needed by parent process or other. references: -- https://www.crowdstrike.com/wp-content/uploads/2022/05/crowdstrike-iceapple-a-novel-internet-information-services-post-exploitation-framework-1.pdf -- https://unit42.paloaltonetworks.com/unit42-oilrig-uses-rgdoor-iis-backdoor-targets-middle-east/ -- https://www.secureworks.com/research/bronze-union -- https://strontic.github.io/xcyclopedia/library/appcmd.exe-055B2B09409F980BF9B5A3969D01E5B2.html + - https://www.crowdstrike.com/wp-content/uploads/2022/05/crowdstrike-iceapple-a-novel-internet-information-services-post-exploitation-framework-1.pdf + - https://unit42.paloaltonetworks.com/unit42-oilrig-uses-rgdoor-iis-backdoor-targets-middle-east/ + - https://www.secureworks.com/research/bronze-union + - https://strontic.github.io/xcyclopedia/library/appcmd.exe-055B2B09409F980BF9B5A3969D01E5B2.html drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to disable IIS HTTP Logging. - risk_objects: - - field: user - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to disable IIS HTTP Logging. + risk_objects: + - field: user + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - IIS Components - - CISA AA23-347A - - Compromised Windows Host - - Windows Defense Evasion Tactics - asset_type: Endpoint - mitre_attack_id: - - T1505.004 - - T1562.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - IIS Components + - CISA AA23-347A + - Compromised Windows Host + - Windows Defense Evasion Tactics + asset_type: Endpoint + mitre_attack_id: + - T1505.004 + - T1562.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/disable_http_logging_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/disable_http_logging_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_disable_windows_group_policy_features_through_registry.yml b/detections/endpoint/windows_disable_windows_group_policy_features_through_registry.yml index 35a132b0eb..c9de1e7d8d 100644 --- a/detections/endpoint/windows_disable_windows_group_policy_features_through_registry.yml +++ b/detections/endpoint/windows_disable_windows_group_policy_features_through_registry.yml @@ -5,80 +5,49 @@ date: '2025-05-02' author: Steven Dick, Teoderick Contreras, Splunk status: production type: Anomaly -description: - The following analytic detects suspicious registry modifications aimed - at disabling Windows Group Policy features. It leverages data from the Endpoint.Registry - data model, focusing on specific registry paths and values associated with disabling - key Windows functionalities. This activity is significant because it is commonly - used by ransomware to hinder mitigation and forensic response efforts. If confirmed - malicious, this behavior could severely impair the ability of security teams to - analyze and respond to the attack, allowing the attacker to maintain control and - persist within the compromised environment. +description: The following analytic detects suspicious registry modifications aimed at disabling Windows Group Policy features. It leverages data from the Endpoint.Registry data model, focusing on specific registry paths and values associated with disabling key Windows functionalities. This activity is significant because it is commonly used by ransomware to hinder mitigation and forensic response efforts. If confirmed malicious, this behavior could severely impair the ability of security teams to analyze and respond to the attack, allowing the attacker to maintain control and persist within the compromised environment. data_source: - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry - WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\*" - OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\*" - Registry.registry_value_name IN ("NoDesktop", "NoFind", "NoControlPanel", "NoFileMenu", - "NoSetTaskbar", "NoTrayContextMenu", "TaskbarLockAll", "NoThemesTab","NoPropertiesMyDocuments","NoVisualStyleChoice","NoColorChoice","NoPropertiesMyDocuments") - Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_disable_windows_group_policy_features_through_registry_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 -known_false_positives: - Disabling these features for legitimate purposes is not a common - use case but can still be implemented by the administrators. Filter as needed. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\*" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\*" Registry.registry_value_name IN ("NoDesktop", "NoFind", "NoControlPanel", "NoFileMenu", "NoSetTaskbar", "NoTrayContextMenu", "TaskbarLockAll", "NoThemesTab","NoPropertiesMyDocuments","NoVisualStyleChoice","NoColorChoice","NoPropertiesMyDocuments") Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_windows_group_policy_features_through_registry_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 +known_false_positives: Disabling these features for legitimate purposes is not a common use case but can still be implemented by the administrators. Filter as needed. references: - - https://hybrid-analysis.com/sample/ef1c427394c205580576d18ba68d5911089c7da0386f19d1ca126929d3e671ab?environmentId=120&lang=en - - https://www.sophos.com/en-us/threat-center/threat-analyses/viruses-and-spyware/Troj~Krotten-N/detailed-analysis - - https://www.virustotal.com/gui/file/2d7855bf6470aa323edf2949b54ce2a04d9e38770f1322c3d0420c2303178d91/details + - https://hybrid-analysis.com/sample/ef1c427394c205580576d18ba68d5911089c7da0386f19d1ca126929d3e671ab?environmentId=120&lang=en + - https://www.sophos.com/en-us/threat-center/threat-analyses/viruses-and-spyware/Troj~Krotten-N/detailed-analysis + - https://www.virustotal.com/gui/file/2d7855bf6470aa323edf2949b54ce2a04d9e38770f1322c3d0420c2303178d91/details drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Registry modification to disable windows group policy features on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Registry modification to disable windows group policy features on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Ransomware - - CISA AA23-347A - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - CISA AA23-347A + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/ransomware_disable_reg/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/ransomware_disable_reg/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_disableantispyware_registry.yml b/detections/endpoint/windows_disableantispyware_registry.yml index 57ea2c9e91..1295502e59 100644 --- a/detections/endpoint/windows_disableantispyware_registry.yml +++ b/detections/endpoint/windows_disableantispyware_registry.yml @@ -5,75 +5,51 @@ date: '2026-02-09' author: Rod Soto, Jose Hernandez, Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the modification of the Windows Registry - key "DisableAntiSpyware" being set to disable. This detection leverages data from - the Endpoint.Registry datamodel, specifically looking for the registry value name - "DisableAntiSpyware" with a value of "0x00000001". This activity is significant - as it is commonly associated with Ryuk ransomware infections, indicating potential - malicious intent to disable Windows Defender. If confirmed malicious, this action - could allow attackers to disable critical security defenses, facilitating further - malicious activities such as data encryption, exfiltration, or additional system - compromise. +description: The following analytic detects the modification of the Windows Registry key "DisableAntiSpyware" being set to disable. This detection leverages data from the Endpoint.Registry datamodel, specifically looking for the registry value name "DisableAntiSpyware" with a value of "0x00000001". This activity is significant as it is commonly associated with Ryuk ransomware infections, indicating potential malicious intent to disable Windows Defender. If confirmed malicious, this action could allow attackers to disable critical security defenses, facilitating further malicious activities such as data encryption, exfiltration, or additional system compromise. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where Registry.registry_value_name="DisableAntiSpyware" - AND Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_disableantispyware_registry_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_value_name="DisableAntiSpyware" AND Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_disableantispyware_registry_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/ + - https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows DisableAntiSpyware registry key set to 'disabled' on $dest$ - risk_objects: - - field: dest - type: system - score: 24 - threat_objects: [] + message: Windows DisableAntiSpyware registry key set to 'disabled' on $dest$ + risk_objects: + - field: dest + type: system + score: 24 + threat_objects: [] tags: - analytic_story: - - SolarWinds WHD RCE Post Exploitation - - Azorult - - Ryuk Ransomware - - Windows Registry Abuse - - RedLine Stealer - - CISA AA22-264A - - Windows Defense Evasion Tactics - - CISA AA23-347A - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SolarWinds WHD RCE Post Exploitation + - Azorult + - Ryuk Ransomware + - Windows Registry Abuse + - RedLine Stealer + - CISA AA22-264A + - Windows Defense Evasion Tactics + - CISA AA23-347A + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_diskcryptor_usage.yml b/detections/endpoint/windows_diskcryptor_usage.yml index 77dfc4fd83..5a62475c09 100644 --- a/detections/endpoint/windows_diskcryptor_usage.yml +++ b/detections/endpoint/windows_diskcryptor_usage.yml @@ -1,60 +1,52 @@ name: Windows DiskCryptor Usage id: d56fe0c8-4650-11ec-a8fa-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic detects the execution of DiskCryptor, identified - by the process names "dcrypt.exe" or "dcinst.exe". This detection leverages data - from Endpoint Detection and Response (EDR) agents, focusing on process names and - original file names. DiskCryptor is significant because adversaries use it to manually - encrypt disks during an operation, potentially leading to data inaccessibility. - If confirmed malicious, this activity could result in complete disk encryption, - causing data loss and operational disruption. Immediate investigation is required - to mitigate potential ransomware attacks. +description: The following analytic detects the execution of DiskCryptor, identified by the process names "dcrypt.exe" or "dcinst.exe". This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and original file names. DiskCryptor is significant because adversaries use it to manually encrypt disks during an operation, potentially leading to data inaccessibility. If confirmed malicious, this activity could result in complete disk encryption, causing data loss and operational disruption. Immediate investigation is required to mitigate potential ransomware attacks. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="dcrypt.exe" - OR Processes.original_file_name=dcinst.exe) by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_diskcryptor_usage_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: It is possible false positives may be present based on the - internal name dcinst.exe, filter as needed. It may be worthy to alert on the service - name. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="dcrypt.exe" + OR + Processes.original_file_name=dcinst.exe + ) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_diskcryptor_usage_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: It is possible false positives may be present based on the internal name dcinst.exe, filter as needed. It may be worthy to alert on the service name. references: -- https://thedfirreport.com/2021/11/15/exchange-exploit-leads-to-domain-wide-ransomware/ -- https://github.com/DavidXanatos/DiskCryptor + - https://thedfirreport.com/2021/11/15/exchange-exploit-leads-to-domain-wide-ransomware/ + - https://github.com/DavidXanatos/DiskCryptor tags: - analytic_story: - - Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1486 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1486 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/dcrypt/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/dcrypt/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_diskshadow_proxy_execution.yml b/detections/endpoint/windows_diskshadow_proxy_execution.yml index a972daf2ea..c1c90d2e0b 100644 --- a/detections/endpoint/windows_diskshadow_proxy_execution.yml +++ b/detections/endpoint/windows_diskshadow_proxy_execution.yml @@ -5,80 +5,59 @@ date: '2025-12-15' author: Lou Stella, Splunk status: production type: TTP -description: The following analytic detects the use of DiskShadow.exe in scripting - mode, which can execute arbitrary unsigned code. It leverages data from Endpoint - Detection and Response (EDR) agents, focusing on command-line executions with scripting - mode flags. This activity is significant because DiskShadow.exe is typically used - for legitimate backup operations, but its misuse can indicate an attempt to execute - unauthorized code. If confirmed malicious, this could lead to unauthorized code - execution, potentially compromising the system and allowing further malicious activities. +description: The following analytic detects the use of DiskShadow.exe in scripting mode, which can execute arbitrary unsigned code. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions with scripting mode flags. This activity is significant because DiskShadow.exe is typically used for legitimate backup operations, but its misuse can indicate an attempt to execute unauthorized code. If confirmed malicious, this could lead to unauthorized code execution, potentially compromising the system and allowing further malicious activities. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - (Processes.process_name=diskshadow.exe OR Processes.original_file_name=diskshadow.exe) - Processes.process IN (*-s*, */s*) - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_diskshadow_proxy_execution_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrators using the DiskShadow tool in their infrastructure - as a main backup tool with scripts will cause false positives that can be filtered - with `windows_diskshadow_proxy_execution_filter` + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where + (Processes.process_name=diskshadow.exe OR Processes.original_file_name=diskshadow.exe) + Processes.process IN (*-s*, */s*) + by Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_diskshadow_proxy_execution_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators using the DiskShadow tool in their infrastructure as a main backup tool with scripts will cause false positives that can be filtered with `windows_diskshadow_proxy_execution_filter` references: -- https://bohops.com/2018/03/26/diskshadow-the-return-of-vss-evasion-persistence-and-active-directory-database-extraction/ + - https://bohops.com/2018/03/26/diskshadow-the-return-of-vss-evasion-persistence-and-active-directory-database-extraction/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible Signed Binary Proxy Execution on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Possible Signed Binary Proxy Execution on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1218 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1218 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218/diskshadow/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218/diskshadow/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_dism_install_powershell_web_access.yml b/detections/endpoint/windows_dism_install_powershell_web_access.yml index f2d9409642..ad08a58d85 100644 --- a/detections/endpoint/windows_dism_install_powershell_web_access.yml +++ b/detections/endpoint/windows_dism_install_powershell_web_access.yml @@ -1,81 +1,68 @@ name: Windows DISM Install PowerShell Web Access id: fa6142a7-c364-4d11-9954-895dd9efb2d4 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk data_source: -- Windows Event Log Security 4688 -- Sysmon EventID 1 + - Windows Event Log Security 4688 + - Sysmon EventID 1 type: TTP status: production -description: The following analytic detects the installation of PowerShell Web Access - using the Deployment Image Servicing and Management (DISM) tool. It leverages Sysmon - EventID 1 to identify the execution of `dism.exe` with specific parameters related - to enabling the WindowsPowerShellWebAccess feature. This activity is significant - because enabling PowerShell Web Access can facilitate remote execution of PowerShell - commands, potentially allowing an attacker to gain unauthorized access to systems - and networks. If confirmed malicious, this action could lead to further exploitation - and compromise of the affected system. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=dism.exe - (Processes.process="*WindowsPowerShellWebAccess*" AND Processes.process="*/online*" - AND Processes.process="*/enable-feature*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_dism_install_powershell_web_access_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrators using the DISM tool to update and install Windows - features may cause false positives that can be filtered with `windows_dism_install_powershell_web_access_filter`. +description: The following analytic detects the installation of PowerShell Web Access using the Deployment Image Servicing and Management (DISM) tool. It leverages Sysmon EventID 1 to identify the execution of `dism.exe` with specific parameters related to enabling the WindowsPowerShellWebAccess feature. This activity is significant because enabling PowerShell Web Access can facilitate remote execution of PowerShell commands, potentially allowing an attacker to gain unauthorized access to systems and networks. If confirmed malicious, this action could lead to further exploitation and compromise of the affected system. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=dism.exe (Processes.process="*WindowsPowerShellWebAccess*" + AND + Processes.process="*/online*" + AND + Processes.process="*/enable-feature*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_dism_install_powershell_web_access_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators using the DISM tool to update and install Windows features may cause false positives that can be filtered with `windows_dism_install_powershell_web_access_filter`. references: -- https://www.cisa.gov/news-events/cybersecurity-advisories/aa24-241a -- https://gist.github.com/MHaggis/7e67b659af9148fa593cf2402edebb41 + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa24-241a + - https://gist.github.com/MHaggis/7e67b659af9148fa593cf2402edebb41 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: PowerShell Web Access has been installed on $dest$. - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: [] + message: PowerShell Web Access has been installed on $dest$. + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: [] tags: - analytic_story: - - CISA AA24-241A - asset_type: Endpoint - mitre_attack_id: - - T1548.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - CISA AA24-241A + asset_type: Endpoint + mitre_attack_id: + - T1548.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/dism_pswa_4688_windows-security.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Security + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/dism_pswa_4688_windows-security.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Security diff --git a/detections/endpoint/windows_dism_remove_defender.yml b/detections/endpoint/windows_dism_remove_defender.yml index aff2dd9bb5..7f4445a80f 100644 --- a/detections/endpoint/windows_dism_remove_defender.yml +++ b/detections/endpoint/windows_dism_remove_defender.yml @@ -1,90 +1,78 @@ name: Windows DISM Remove Defender id: 8567da9e-47f0-11ec-99a9-acde48001122 -version: 10 -date: '2025-05-02' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the use of `dism.exe` to remove Windows - Defender. It leverages data from Endpoint Detection and Response (EDR) agents, focusing - on command-line executions that include specific parameters for disabling and removing - Windows Defender. This activity is significant because adversaries may disable Defender - to evade detection and carry out further malicious actions undetected. If confirmed - malicious, this could lead to the attacker gaining persistent access, executing - additional payloads, or exfiltrating sensitive data without being intercepted by - Windows Defender. +description: The following analytic detects the use of `dism.exe` to remove Windows Defender. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions that include specific parameters for disabling and removing Windows Defender. This activity is significant because adversaries may disable Defender to evade detection and carry out further malicious actions undetected. If confirmed malicious, this could lead to the attacker gaining persistent access, executing additional payloads, or exfiltrating sensitive data without being intercepted by Windows Defender. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=dism.exe - (Processes.process="*/online*" AND Processes.process="*/disable-feature*" AND Processes.process="*Windows-Defender*" - AND Processes.process="*/remove*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_dism_remove_defender_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Some legitimate administrative tools leverage `dism.exe` to - manipulate packages and features of the operating system. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=dism.exe (Processes.process="*/online*" + AND + Processes.process="*/disable-feature*" + AND + Processes.process="*Windows-Defender*" + AND + Processes.process="*/remove*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_dism_remove_defender_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Some legitimate administrative tools leverage `dism.exe` to manipulate packages and features of the operating system. Filter as needed. references: -- https://thedfirreport.com/2020/11/23/pysa-mespinoza-ransomware/ + - https://thedfirreport.com/2020/11/23/pysa-mespinoza-ransomware/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to disable Windows Defender. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to disable Windows Defender. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - CISA AA23-347A - - Compromised Windows Host - - Windows Defense Evasion Tactics - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: access + analytic_story: + - CISA AA23-347A + - Compromised Windows Host + - Windows Defense Evasion Tactics + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: access tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon_dism.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon_dism.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_dll_module_loaded_in_temp_dir.yml b/detections/endpoint/windows_dll_module_loaded_in_temp_dir.yml index aea482045f..6e52568d1a 100644 --- a/detections/endpoint/windows_dll_module_loaded_in_temp_dir.yml +++ b/detections/endpoint/windows_dll_module_loaded_in_temp_dir.yml @@ -5,53 +5,31 @@ date: '2026-02-09' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects instances where a Dynamic Link - Library (DLL) is loaded from a temporary directory on a Windows system. - Loading DLLs from non-standard paths such as %TEMP% is uncommon for legitimate - applications and is often associated with adversary tradecraft, including DLL - search order hijacking, side-loading, or execution of malicious payloads - staged in temporary folders. Adversaries frequently leverage these directories - because they are writable by standard users and often overlooked by security - controls, making them convenient locations to drop and execute malicious - files. This behavior may indicate attempts to evade detection, execute - unauthorized code, or maintain persistence through hijacked execution flows. - Detection of DLL loads from %TEMP% can help surface early signs of compromise - and should be investigated in the context of the originating process, user - account, and potential file creation or modification activity within the same - directory. +description: The following analytic detects instances where a Dynamic Link Library (DLL) is loaded from a temporary directory on a Windows system. Loading DLLs from non-standard paths such as %TEMP% is uncommon for legitimate applications and is often associated with adversary tradecraft, including DLL search order hijacking, side-loading, or execution of malicious payloads staged in temporary folders. Adversaries frequently leverage these directories because they are writable by standard users and often overlooked by security controls, making them convenient locations to drop and execute malicious files. This behavior may indicate attempts to evade detection, execute unauthorized code, or maintain persistence through hijacked execution flows. Detection of DLL loads from %TEMP% can help surface early signs of compromise and should be investigated in the context of the originating process, user account, and potential file creation or modification activity within the same directory. data_source: -- Sysmon EventID 7 -search: '`sysmon` EventCode=7 NOT (ImageLoaded IN("C:\\Program Files*")) AND ImageLoaded="*\\temp\\*" - AND ImageLoaded="*.dll" | fillnull | stats count min(_time) as firstTime max(_time) - as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name - process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists - service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_dll_module_loaded_in_temp_dir_filter`' -how_to_implement: To successfully implement this search, you need to be - ingesting logs with the process name and imageloaded executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of - the Sysmon TA. + - Sysmon EventID 7 +search: '`sysmon` EventCode=7 NOT (ImageLoaded IN("C:\\Program Files*")) AND ImageLoaded="*\\temp\\*" AND ImageLoaded="*.dll" | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_dll_module_loaded_in_temp_dir_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: No false positives have been identified at this time. references: -- https://www.cisa.gov/news-events/cybersecurity-advisories/aa25-203a -- https://blog.sekoia.io/interlock-ransomware-evolving-under-the-radar/ + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa25-203a + - https://blog.sekoia.io/interlock-ransomware-evolving-under-the-radar/ tags: - analytic_story: - - SolarWinds WHD RCE Post Exploitation - - Interlock Rat - - Lokibot - asset_type: Endpoint - mitre_attack_id: - - T1105 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SolarWinds WHD RCE Post Exploitation + - Interlock Rat + - Lokibot + asset_type: Endpoint + mitre_attack_id: + - T1105 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/dll_loaded_in_temp/module_loaded_in_temp.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/dll_loaded_in_temp/module_loaded_in_temp.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_dll_search_order_hijacking_hunt_with_sysmon.yml b/detections/endpoint/windows_dll_search_order_hijacking_hunt_with_sysmon.yml index bc3b81d62a..334a44e734 100644 --- a/detections/endpoint/windows_dll_search_order_hijacking_hunt_with_sysmon.yml +++ b/detections/endpoint/windows_dll_search_order_hijacking_hunt_with_sysmon.yml @@ -5,48 +5,31 @@ date: '2025-05-26' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic identifies potential DLL search order hijacking - or DLL sideloading by detecting known Windows libraries loaded from non-standard - directories. It leverages Sysmon EventCode 7 to monitor DLL loads and cross-references - them with a lookup of known hijackable libraries. This activity is significant as - it may indicate an attempt to execute malicious code by exploiting DLL search order - vulnerabilities. If confirmed malicious, this could allow attackers to gain code - execution, escalate privileges, or maintain persistence within the environment. +description: The following analytic identifies potential DLL search order hijacking or DLL sideloading by detecting known Windows libraries loaded from non-standard directories. It leverages Sysmon EventCode 7 to monitor DLL loads and cross-references them with a lookup of known hijackable libraries. This activity is significant as it may indicate an attempt to execute malicious code by exploiting DLL search order vulnerabilities. If confirmed malicious, this could allow attackers to gain code execution, escalate privileges, or maintain persistence within the environment. data_source: -- Sysmon EventID 7 -search: '`sysmon` EventCode=7 NOT (process_path IN ("*\\system32\\*", "*\\syswow64\\*","*\\winsxs\\*","*\\wbem\\*")) - | lookup hijacklibs library AS loaded_file OUTPUT islibrary | search islibrary = - True | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded - dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash - process_id process_name process_path service_dll_signature_exists service_dll_signature_verified - signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_dll_search_order_hijacking_hunt_with_sysmon_filter`' -how_to_implement: The search is written against the latest Sysmon TA 4.0 https://splunkbase.splunk.com/app/5709. - For this specific event ID 7, the sysmon TA will extract the ImageLoaded name to - the loaded_file field which is used in the search to compare against the hijacklibs - lookup. -known_false_positives: False positives will be present based on paths. Filter or add - other paths to the exclusion as needed. Some applications may legitimately load - libraries from non-standard paths. + - Sysmon EventID 7 +search: '`sysmon` EventCode=7 NOT (process_path IN ("*\\system32\\*", "*\\syswow64\\*","*\\winsxs\\*","*\\wbem\\*")) | lookup hijacklibs library AS loaded_file OUTPUT islibrary | search islibrary = True | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_dll_search_order_hijacking_hunt_with_sysmon_filter`' +how_to_implement: The search is written against the latest Sysmon TA 4.0 https://splunkbase.splunk.com/app/5709. For this specific event ID 7, the sysmon TA will extract the ImageLoaded name to the loaded_file field which is used in the search to compare against the hijacklibs lookup. +known_false_positives: False positives will be present based on paths. Filter or add other paths to the exclusion as needed. Some applications may legitimately load libraries from non-standard paths. references: -- https://hijacklibs.net + - https://hijacklibs.net tags: - analytic_story: - - Qakbot - - Windows Defense Evasion Tactics - - Living Off The Land - - Malicious Inno Setup Loader - asset_type: Endpoint - mitre_attack_id: - - T1574.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Qakbot + - Windows Defense Evasion Tactics + - Living Off The Land + - Malicious Inno Setup Loader + asset_type: Endpoint + mitre_attack_id: + - T1574.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.001/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.001/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_dll_search_order_hijacking_with_iscsicpl.yml b/detections/endpoint/windows_dll_search_order_hijacking_with_iscsicpl.yml index 8cfb585ece..d78b878c0b 100644 --- a/detections/endpoint/windows_dll_search_order_hijacking_with_iscsicpl.yml +++ b/detections/endpoint/windows_dll_search_order_hijacking_with_iscsicpl.yml @@ -1,90 +1,73 @@ name: Windows DLL Search Order Hijacking with iscsicpl id: f39ee679-3b1e-4f47-841c-5c3c580acda2 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects DLL search order hijacking involving iscsicpl.exe. - It identifies when iscsicpl.exe loads a malicious DLL from a new path, triggering - the payload execution. This detection leverages data from Endpoint Detection and - Response (EDR) agents, focusing on child processes spawned by iscsicpl.exe. This - activity is significant as it indicates a potential attempt to execute unauthorized - code via DLL hijacking. If confirmed malicious, this could allow an attacker to - execute arbitrary code, escalate privileges, or maintain persistence within the - environment. +description: The following analytic detects DLL search order hijacking involving iscsicpl.exe. It identifies when iscsicpl.exe loads a malicious DLL from a new path, triggering the payload execution. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on child processes spawned by iscsicpl.exe. This activity is significant as it indicates a potential attempt to execute unauthorized code via DLL hijacking. If confirmed malicious, this could allow an attacker to execute arbitrary code, escalate privileges, or maintain persistence within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=iscsicpl.exe - `windows_shells` by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_dll_search_order_hijacking_with_iscsicpl_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present, filtering may be required. - Remove the Windows Shells macro to determine if other utilities are using iscsicpl.exe. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name=iscsicpl.exe `windows_shells` + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_dll_search_order_hijacking_with_iscsicpl_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present, filtering may be required. Remove the Windows Shells macro to determine if other utilities are using iscsicpl.exe. references: -- https://github.com/hackerhouse-opensource/iscsicpl_bypassUAC -- https://github.com/422926799/csplugin/tree/master/bypassUAC + - https://github.com/hackerhouse-opensource/iscsicpl_bypassUAC + - https://github.com/422926799/csplugin/tree/master/bypassUAC drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to elevate access. - risk_objects: - - field: user - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to elevate access. + risk_objects: + - field: user + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Living Off The Land - - Compromised Windows Host - - Windows Defense Evasion Tactics - asset_type: Endpoint - mitre_attack_id: - - T1574.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + - Compromised Windows Host + - Windows Defense Evasion Tactics + asset_type: Endpoint + mitre_attack_id: + - T1574.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.001/iscsicpl/iscsicpl-windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.001/iscsicpl/iscsicpl-windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_dll_side_loading_in_calc.yml b/detections/endpoint/windows_dll_side_loading_in_calc.yml index 7df1d56b18..584fb4669f 100644 --- a/detections/endpoint/windows_dll_side_loading_in_calc.yml +++ b/detections/endpoint/windows_dll_side_loading_in_calc.yml @@ -5,74 +5,45 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk status: production type: TTP -description: - The following analytic detects suspicious DLL modules loaded by calc.exe - that are not located in the %systemroot%\system32 or %systemroot%\sysWoW64 directories. - This detection leverages Sysmon EventCode 7 to identify DLL side-loading, a technique - often used by Qakbot malware to execute malicious DLLs. This activity is significant - as it indicates potential malware execution through a trusted process, which can - bypass security controls. If confirmed malicious, this could allow attackers to - execute arbitrary code, maintain persistence, and escalate privileges within the - environment. +description: The following analytic detects suspicious DLL modules loaded by calc.exe that are not located in the %systemroot%\system32 or %systemroot%\sysWoW64 directories. This detection leverages Sysmon EventCode 7 to identify DLL side-loading, a technique often used by Qakbot malware to execute malicious DLLs. This activity is significant as it indicates potential malware execution through a trusted process, which can bypass security controls. If confirmed malicious, this could allow attackers to execute arbitrary code, maintain persistence, and escalate privileges within the environment. data_source: - - Sysmon EventID 7 -search: - '`sysmon` EventCode=7 Image = "*\calc.exe" AND NOT (Image IN ("*:\\windows\\system32\\*", - "*:\\windows\\sysWow64\\*")) AND NOT(ImageLoaded IN("*:\\windows\\system32\\*", - "*:\\windows\\sysWow64\\*", "*:\\windows\\WinSXS\\*")) | fillnull | stats count - min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file - loaded_file_path original_file_name process_exec process_guid process_hash process_id - process_name process_path service_dll_signature_exists service_dll_signature_verified - signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_dll_side_loading_in_calc_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on processes that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` - node. In addition, confirm the latest CIM App 4.20 or higher is installed and the - latest TA for the endpoint product. + - Sysmon EventID 7 +search: '`sysmon` EventCode=7 Image = "*\calc.exe" AND NOT (Image IN ("*:\\windows\\system32\\*", "*:\\windows\\sysWow64\\*")) AND NOT(ImageLoaded IN("*:\\windows\\system32\\*", "*:\\windows\\sysWow64\\*", "*:\\windows\\WinSXS\\*")) | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_dll_side_loading_in_calc_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on processes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. known_false_positives: No false positives have been identified at this time. references: - - https://www.bitdefender.com/blog/hotforsecurity/new-qakbot-malware-strain-replaces-windows-calculator-dll-to-infected-pcs/ + - https://www.bitdefender.com/blog/hotforsecurity/new-qakbot-malware-strain-replaces-windows-calculator-dll-to-infected-pcs/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - a dll modules is loaded by calc.exe in $ImageLoaded$ that are not in common - windows OS installation folder on $dest$ - risk_objects: - - field: dest - type: system - score: 90 - threat_objects: [] + message: a dll modules is loaded by calc.exe in $ImageLoaded$ that are not in common windows OS installation folder on $dest$ + risk_objects: + - field: dest + type: system + score: 90 + threat_objects: [] tags: - analytic_story: - - Qakbot - - Earth Alux - asset_type: Endpoint - mitre_attack_id: - - T1574.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Qakbot + - Earth Alux + asset_type: Endpoint + mitre_attack_id: + - T1574.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot2/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot2/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_dll_side_loading_process_child_of_calc.yml b/detections/endpoint/windows_dll_side_loading_process_child_of_calc.yml index ad3519c682..0f8252f9f7 100644 --- a/detections/endpoint/windows_dll_side_loading_process_child_of_calc.yml +++ b/detections/endpoint/windows_dll_side_loading_process_child_of_calc.yml @@ -1,84 +1,67 @@ name: Windows DLL Side-Loading Process Child Of Calc id: 295ca9ed-e97b-4520-90f7-dfb6469902e1 -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 -description: - The following analytic identifies suspicious child processes spawned - by calc.exe, indicative of DLL side-loading techniques. This detection leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process GUIDs, - names, and parent processes. This activity is significant as it is commonly associated - with Qakbot malware, which uses calc.exe to load malicious DLLs via regsvr32.exe. - If confirmed malicious, this behavior could allow attackers to execute arbitrary - code, maintain persistence, and escalate privileges, posing a severe threat to the - environment. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name - = "calc.exe") AND Processes.process_name != "win32calc.exe" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_dll_side_loading_process_child_of_calc_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic identifies suspicious child processes spawned by calc.exe, indicative of DLL side-loading techniques. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process GUIDs, names, and parent processes. This activity is significant as it is commonly associated with Qakbot malware, which uses calc.exe to load malicious DLLs via regsvr32.exe. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, maintain persistence, and escalate privileges, posing a severe threat to the environment. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.parent_process_name = "calc.exe" + ) + AND Processes.process_name != "win32calc.exe" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_dll_side_loading_process_child_of_calc_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: - - https://malpedia.caad.fkie.fraunhofer.de/details/win.qakbot + - https://malpedia.caad.fkie.fraunhofer.de/details/win.qakbot drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: calc.exe has a child process $process_name$ on $dest$ - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: [] + message: calc.exe has a child process $process_name$ on $dest$ + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: [] tags: - analytic_story: - - Qakbot - - Earth Alux - asset_type: Endpoint - mitre_attack_id: - - T1574.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Qakbot + - Earth Alux + asset_type: Endpoint + mitre_attack_id: + - T1574.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_dns_gather_network_info.yml b/detections/endpoint/windows_dns_gather_network_info.yml index e21706365b..0bb48d90a8 100644 --- a/detections/endpoint/windows_dns_gather_network_info.yml +++ b/detections/endpoint/windows_dns_gather_network_info.yml @@ -1,80 +1,65 @@ name: Windows DNS Gather Network Info id: 347e0892-e8f3-4512-afda-dc0e3fa996f3 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk type: Anomaly status: production data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic detects the use of the dnscmd.exe command to enumerate - DNS records. It leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process command-line executions. This activity is significant as it - may indicate an adversary gathering network information, a common precursor to more - targeted attacks. If confirmed malicious, this behavior could enable attackers to - map the network, identify critical assets, and plan subsequent actions, potentially - leading to data exfiltration or further compromise of the network. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "dnscmd.exe" - Processes.process = "* /enumrecords *" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name("Processes")` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_dns_gather_network_info_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: network administrator can execute this command to enumerate - DNS record. Filter or add other paths to the exclusion as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic detects the use of the dnscmd.exe command to enumerate DNS records. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process command-line executions. This activity is significant as it may indicate an adversary gathering network information, a common precursor to more targeted attacks. If confirmed malicious, this behavior could enable attackers to map the network, identify critical assets, and plan subsequent actions, potentially leading to data exfiltration or further compromise of the network. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "dnscmd.exe" Processes.process = "* /enumrecords *" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_dns_gather_network_info_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: network administrator can execute this command to enumerate DNS record. Filter or add other paths to the exclusion as needed. references: -- https://cert.gov.ua/article/3718487 -- https://media.defense.gov/2023/May/24/2003229517/-1/-1/0/CSA_Living_off_the_Land.PDF + - https://cert.gov.ua/article/3718487 + - https://media.defense.gov/2023/May/24/2003229517/-1/-1/0/CSA_Living_off_the_Land.PDF drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process commandline $process$ to enumerate dns record on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A process commandline $process$ to enumerate dns record on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Sandworm Tools - - Volt Typhoon - asset_type: Endpoint - mitre_attack_id: - - T1590.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sandworm Tools + - Volt Typhoon + asset_type: Endpoint + mitre_attack_id: + - T1590.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1590.002/enum_dns_record/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1590.002/enum_dns_record/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_dns_query_request_to_tinyurl.yml b/detections/endpoint/windows_dns_query_request_to_tinyurl.yml index 22ab8d8cb9..6b7b96d3a6 100644 --- a/detections/endpoint/windows_dns_query_request_to_tinyurl.yml +++ b/detections/endpoint/windows_dns_query_request_to_tinyurl.yml @@ -1,80 +1,74 @@ name: Windows DNS Query Request To TinyUrl id: b1ea79da-719c-437c-acaf-5c93f838f425 -version: 1 -date: '2025-06-02' +version: 2 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: | - The following analytic detects a process located in a potentially suspicious location making DNS queries to known URL shortening services, specifically tinyurl. - URL shorteners are frequently used by threat actors to obfuscate malicious destinations, including phishing pages, malware distribution sites, or command-and-control (C2) endpoints. - While tinyurl.com is a legitimate service, its use in enterprise environments—particularly by non-browser processes or scripts—should be considered suspicious, especially if correlated with subsequent outbound connections, file downloads, process file path or credential prompts. Analysts should investigate the source process, execution context, and destination domain to determine intent and risk. + The following analytic detects a process located in a potentially suspicious location making DNS queries to known URL shortening services, specifically tinyurl. + URL shorteners are frequently used by threat actors to obfuscate malicious destinations, including phishing pages, malware distribution sites, or command-and-control (C2) endpoints. + While tinyurl.com is a legitimate service, its use in enterprise environments—particularly by non-browser processes or scripts—should be considered suspicious, especially if correlated with subsequent outbound connections, file downloads, process file path or credential prompts. Analysts should investigate the source process, execution context, and destination domain to determine intent and risk. data_source: -- Sysmon EventID 22 + - Sysmon EventID 22 search: | - `sysmon` - EventCode=22 - QueryName = "tinyurl.com" - Image IN ( - "*\\AppData\\*", - "*\\Perflogs\\*", - "*\\ProgramData\\*", - "*\\Temp\\*", - "*\\Users\\Public\\*", - "*\\Windows\\Tasks\\*" - ) - | stats count min(_time) as firstTime max(_time) as lastTime - by answer answer_count dvc process_exec process_guid process_name query query_count - reply_code_id signature signature_id src user_id vendor_product QueryName QueryResults QueryStatus - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_dns_query_request_to_tinyurl_filter` + `sysmon` + EventCode=22 + QueryName = "tinyurl.com" + Image IN ( + "*\\AppData\\*", + "*\\Perflogs\\*", + "*\\ProgramData\\*", + "*\\Temp\\*", + "*\\Users\\Public\\*", + "*\\Windows\\Tasks\\*" + ) + | stats count min(_time) as firstTime max(_time) as lastTime + by answer answer_count dvc process_exec process_guid process_name query query_count + reply_code_id signature signature_id src user_id vendor_product QueryName QueryResults QueryStatus + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_dns_query_request_to_tinyurl_filter` how_to_implement: | - This detection relies on sysmon logs with the Event ID 22, DNS Query. - We suggest you run this detection at least once a day over the last 14 days. + This detection relies on sysmon logs with the Event ID 22, DNS Query. + We suggest you run this detection at least once a day over the last 14 days. known_false_positives: | - Noise and false positive can be seen if the following instant - messaging is allowed to use within corporate network. In this case, a filter is needed. + Noise and false positive can be seen if the following instant + messaging is allowed to use within corporate network. In this case, a filter is needed. references: -- https://x.com/Unit42_Intel/status/1919418143476199869 + - https://x.com/Unit42_Intel/status/1919418143476199869 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious process $process_name$ made a DNS query for $QueryName$ on $dvc$ - risk_objects: - - field: dvc - type: system - score: 40 - threat_objects: - - field: process_name - type: process_name - + message: Suspicious process $process_name$ made a DNS query for $QueryName$ on $dvc$ + risk_objects: + - field: dvc + type: system + score: 40 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Malicious Inno Setup Loader - asset_type: Endpoint - mitre_attack_id: - - T1105 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Malicious Inno Setup Loader + asset_type: Endpoint + mitre_attack_id: + - T1105 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/tinyurl_dns_query/tinyurl.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/tinyurl_dns_query/tinyurl.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_dnsadmins_new_member_added.yml b/detections/endpoint/windows_dnsadmins_new_member_added.yml index 2b44f3d5f2..07f96e21e0 100644 --- a/detections/endpoint/windows_dnsadmins_new_member_added.yml +++ b/detections/endpoint/windows_dnsadmins_new_member_added.yml @@ -1,70 +1,57 @@ name: Windows DnsAdmins New Member Added id: 27e600aa-77f8-4614-bc80-2662a67e2f48 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- Windows Event Log Security 4732 -description: The following analytic detects the addition of a new member to the DnsAdmins - group in Active Directory by leveraging Event ID 4732. This detection uses security - event logs to identify changes to this high-privilege group. Monitoring this activity - is crucial because members of the DnsAdmins group can manage the DNS service, often - running on Domain Controllers, and potentially execute malicious code with SYSTEM - privileges. If confirmed malicious, this activity could allow an attacker to escalate - privileges and gain control over critical domain services, posing a significant - security risk. -search: '`wineventlog_security` EventCode=4732 TargetUserName=DnsAdmins | stats min(_time) - as firstTime max(_time) as lastTime values(TargetUserName) as target_users_added - values(user) as user by dest src_user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_dnsadmins_new_member_added_filter`' -how_to_implement: To successfully implement this search, Domain Controller events - need to be ingested. The Advanced Security Audit policy setting `Audit Security - Group Management` within `Account Management` needs to be enabled. -known_false_positives: New members can be added to the DnsAdmins group as part of - legitimate administrative tasks. Filter as needed. + - Windows Event Log Security 4732 +description: The following analytic detects the addition of a new member to the DnsAdmins group in Active Directory by leveraging Event ID 4732. This detection uses security event logs to identify changes to this high-privilege group. Monitoring this activity is crucial because members of the DnsAdmins group can manage the DNS service, often running on Domain Controllers, and potentially execute malicious code with SYSTEM privileges. If confirmed malicious, this activity could allow an attacker to escalate privileges and gain control over critical domain services, posing a significant security risk. +search: |- + `wineventlog_security` EventCode=4732 TargetUserName=DnsAdmins + | stats min(_time) as firstTime max(_time) as lastTime values(TargetUserName) as target_users_added values(user) as user + BY dest src_user + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_dnsadmins_new_member_added_filter` +how_to_implement: To successfully implement this search, Domain Controller events need to be ingested. The Advanced Security Audit policy setting `Audit Security Group Management` within `Account Management` needs to be enabled. +known_false_positives: New members can be added to the DnsAdmins group as part of legitimate administrative tasks. Filter as needed. references: -- https://attack.mitre.org/techniques/T1098/ -- https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/from-dnsadmins-to-system-to-domain-compromise -- https://www.hackingarticles.in/windows-privilege-escalation-dnsadmins-to-domainadmin/ -- https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4732 + - https://attack.mitre.org/techniques/T1098/ + - https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/from-dnsadmins-to-system-to-domain-compromise + - https://www.hackingarticles.in/windows-privilege-escalation-dnsadmins-to-domainadmin/ + - https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4732 drilldown_searches: -- name: View the detection results for - "$src_user$" - search: '%original_detection_search% | search src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_user$" + search: '%original_detection_search% | search src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A new member $user$ added to the DnsAdmins group by $src_user$ - risk_objects: - - field: src_user - type: user - score: 40 - threat_objects: [] + message: A new member $user$ added to the DnsAdmins group by $src_user$ + risk_objects: + - field: src_user + type: user + score: 40 + threat_objects: [] tags: - analytic_story: - - Active Directory Privilege Escalation - asset_type: Endpoint - mitre_attack_id: - - T1098 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Privilege Escalation + asset_type: Endpoint + mitre_attack_id: + - T1098 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/dnsadmins_member_added/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/dnsadmins_member_added/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_domain_account_discovery_via_get_netcomputer.yml b/detections/endpoint/windows_domain_account_discovery_via_get_netcomputer.yml index a74dc79137..42c56ee755 100644 --- a/detections/endpoint/windows_domain_account_discovery_via_get_netcomputer.yml +++ b/detections/endpoint/windows_domain_account_discovery_via_get_netcomputer.yml @@ -1,73 +1,59 @@ name: Windows Domain Account Discovery Via Get-NetComputer id: a7fbbc4e-4571-424a-b627-6968e1c939e4 -version: 8 -date: '2025-06-24' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: - - Powershell Script Block Logging 4104 -description: - The following analytic detects the execution of the PowerView PowerShell - cmdlet Get-NetComputer, which is used to query Active Directory for user account - details such as "samaccountname," "accountexpires," "lastlogon," and more. It leverages - Event ID 4104 from PowerShell Script Block Logging to identify this activity. This - behavior is significant as it may indicate an attempt to gather user account information, - which is often a precursor to further malicious actions. If confirmed malicious, - this activity could lead to unauthorized access, privilege escalation, or lateral - movement within the network. -search: - '`powershell` EventCode=4104 ScriptBlockText = "*Get-NetComputer*" ScriptBlockText - IN ("*samaccountname*", "*accountexpires*", "*lastlogon*", "*lastlogoff*", "*pwdlastset*", - "*logoncount*") | fillnull | stats count min(_time) as firstTime max(_time) as lastTime - by dest signature signature_id user_id vendor_product EventID Guid Opcode Name Path - ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | - `security_content_ctime(lastTime)` | `windows_domain_account_discovery_via_get_netcomputer_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba.= -known_false_positives: - Administrators may leverage PowerView for legitimate purposes, - filter as needed. + - Powershell Script Block Logging 4104 +description: The following analytic detects the execution of the PowerView PowerShell cmdlet Get-NetComputer, which is used to query Active Directory for user account details such as "samaccountname," "accountexpires," "lastlogon," and more. It leverages Event ID 4104 from PowerShell Script Block Logging to identify this activity. This behavior is significant as it may indicate an attempt to gather user account information, which is often a precursor to further malicious actions. If confirmed malicious, this activity could lead to unauthorized access, privilege escalation, or lateral movement within the network. +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*Get-NetComputer*" ScriptBlockText IN ("*samaccountname*", "*accountexpires*", "*lastlogon*", "*lastlogoff*", "*pwdlastset*", "*logoncount*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_domain_account_discovery_via_get_netcomputer_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba.= +known_false_positives: Administrators may leverage PowerView for legitimate purposes, filter as needed. references: - - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Domain Account Discovery Via Get-NetComputer on $dest$. - risk_objects: - - field: dest - type: system - score: 15 - threat_objects: [] + message: Windows Domain Account Discovery Via Get-NetComputer on $dest$. + risk_objects: + - field: dest + type: system + score: 15 + threat_objects: [] tags: - analytic_story: - - CISA AA23-347A - asset_type: Endpoint - mitre_attack_id: - - T1087.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA23-347A + asset_type: Endpoint + mitre_attack_id: + - T1087.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087/powerview_get_netuser_preauthnotrequire/get-netuser-not-require-pwh.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087/powerview_get_netuser_preauthnotrequire/get-netuser-not-require-pwh.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_domain_admin_impersonation_indicator.yml b/detections/endpoint/windows_domain_admin_impersonation_indicator.yml index 669b6d6805..b10bf2e3fd 100644 --- a/detections/endpoint/windows_domain_admin_impersonation_indicator.yml +++ b/detections/endpoint/windows_domain_admin_impersonation_indicator.yml @@ -1,89 +1,64 @@ name: Windows Domain Admin Impersonation Indicator id: 10381f93-6d38-470a-9c30-d25478e3bd3f -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP data_source: -- Windows Event Log Security 4627 -description: The following analytic identifies potential Kerberos ticket forging attacks, - specifically the Diamond Ticket attack. This is detected when a user logs into a - host and the GroupMembership field in event 4627 indicates a privileged group (e.g., - Domain Admins), but the user does not actually belong to that group in the directory - service. The detection leverages Windows Security Event Log 4627, which logs account - logon events. The analytic cross-references the GroupMembership field from the event - against a pre-populated lookup of actual group memberships. Its crucial to note - that the accuracy and effectiveness of this detection heavily rely on the users - diligence in populating and regularly updating this lookup table. Any discrepancies - between the events GroupMembership and the lookup indicate potential ticket forging. - Kerberos ticket forging, especially the Diamond Ticket attack, allows attackers - to impersonate any user and potentially gain unauthorized access to resources. By - forging a ticket that indicates membership in a privileged group, an attacker can - bypass security controls and gain elevated privileges. Detecting such discrepancies - in group memberships during logon events can be a strong indicator of this attack - in progress, making it crucial for security teams to monitor and investigate. If - validated as a true positive, this indicates that an attacker has successfully forged - a Kerberos ticket and may have gained unauthorized access to critical resources, - potentially with elevated privileges. -search: '`wineventlog_security` EventCode=4627 LogonType=3 NOT TargetUserName IN ("*$", - "SYSTEM", "DWM-*","LOCAL SERVICE","NETWORK SERVICE", "ANONYMOUS LOGON", "UMFD-*") - | where match(GroupMembership, "Domain Admins") | stats count by _time TargetUserName - GroupMembership action app dest signature_id user vendor_product | lookup domain_admins - username as TargetUserName OUTPUT username | fillnull value=NotDA username | search - username = "NotDA" | `windows_domain_admin_impersonation_indicator_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Authentication events across all endpoints and ingest Event Id 4627. Specifically, - the Audit Group Membership subcategory within the Logon Logoff category needs to - be enabled. Its crucial to note that the accuracy and effectiveness of this detection - heavily rely on the users diligence in populating and regularly updating this lookup - table. -known_false_positives: False positives may trigger the detections certain scenarios - like directory service delays or out of date lookups. Filter as needed. + - Windows Event Log Security 4627 +description: The following analytic identifies potential Kerberos ticket forging attacks, specifically the Diamond Ticket attack. This is detected when a user logs into a host and the GroupMembership field in event 4627 indicates a privileged group (e.g., Domain Admins), but the user does not actually belong to that group in the directory service. The detection leverages Windows Security Event Log 4627, which logs account logon events. The analytic cross-references the GroupMembership field from the event against a pre-populated lookup of actual group memberships. Its crucial to note that the accuracy and effectiveness of this detection heavily rely on the users diligence in populating and regularly updating this lookup table. Any discrepancies between the events GroupMembership and the lookup indicate potential ticket forging. Kerberos ticket forging, especially the Diamond Ticket attack, allows attackers to impersonate any user and potentially gain unauthorized access to resources. By forging a ticket that indicates membership in a privileged group, an attacker can bypass security controls and gain elevated privileges. Detecting such discrepancies in group memberships during logon events can be a strong indicator of this attack in progress, making it crucial for security teams to monitor and investigate. If validated as a true positive, this indicates that an attacker has successfully forged a Kerberos ticket and may have gained unauthorized access to critical resources, potentially with elevated privileges. +search: |- + `wineventlog_security` EventCode=4627 LogonType=3 NOT TargetUserName IN ("*$", "SYSTEM", "DWM-*","LOCAL SERVICE","NETWORK SERVICE", "ANONYMOUS LOGON", "UMFD-*") + | where match(GroupMembership, "Domain Admins") + | stats count + BY _time TargetUserName GroupMembership + action app dest + signature_id user vendor_product + | lookup domain_admins username as TargetUserName OUTPUT username + | fillnull value=NotDA username + | search username = "NotDA" + | `windows_domain_admin_impersonation_indicator_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Authentication events across all endpoints and ingest Event Id 4627. Specifically, the Audit Group Membership subcategory within the Logon Logoff category needs to be enabled. Its crucial to note that the accuracy and effectiveness of this detection heavily rely on the users diligence in populating and regularly updating this lookup table. +known_false_positives: False positives may trigger the detections certain scenarios like directory service delays or out of date lookups. Filter as needed. references: -- https://trustedsec.com/blog/a-diamond-in-the-ruff -- https://unit42.paloaltonetworks.com/next-gen-kerberos-attacks -- https://github.com/GhostPack/Rubeus/pull/136 -- https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4627 + - https://trustedsec.com/blog/a-diamond-in-the-ruff + - https://unit42.paloaltonetworks.com/next-gen-kerberos-attacks + - https://github.com/GhostPack/Rubeus/pull/136 + - https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4627 drilldown_searches: -- name: View the detection results for - "$TargetUserName$" - search: '%original_detection_search% | search TargetUserName = "$TargetUserName$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$TargetUserName$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$TargetUserName$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$TargetUserName$" + search: '%original_detection_search% | search TargetUserName = "$TargetUserName$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$TargetUserName$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$TargetUserName$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $TargetUserName$ may be impersonating a Domain Administrator through a - forged Kerberos ticket. - risk_objects: - - field: TargetUserName - type: user - score: 80 - threat_objects: [] + message: $TargetUserName$ may be impersonating a Domain Administrator through a forged Kerberos ticket. + risk_objects: + - field: TargetUserName + type: user + score: 80 + threat_objects: [] tags: - analytic_story: - - Active Directory Kerberos Attacks - - Gozi Malware - - Compromised Windows Host - - Active Directory Privilege Escalation - asset_type: Endpoint - mitre_attack_id: - - T1558 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Kerberos Attacks + - Gozi Malware + - Compromised Windows Host + - Active Directory Privilege Escalation + asset_type: Endpoint + mitre_attack_id: + - T1558 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558/diamond_ticket/security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558/diamond_ticket/security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_dotnet_binary_in_non_standard_path.yml b/detections/endpoint/windows_dotnet_binary_in_non_standard_path.yml index d2c2bb16d5..39c620069f 100644 --- a/detections/endpoint/windows_dotnet_binary_in_non_standard_path.yml +++ b/detections/endpoint/windows_dotnet_binary_in_non_standard_path.yml @@ -6,115 +6,107 @@ author: Michael Haag, Splunk status: production type: TTP description: | - The following analytic detects the execution of native .NET binaries from non-standard directories within the Windows operating system. - It leverages Endpoint Detection and Response (EDR) telemetry, comparing process names and original file names against a predefined lookup "is_net_windows_file". - This activity is significant because adversaries may move .NET binaries to unconventional paths to evade detection and execute malicious code. - If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment, posing a significant security risk. - Also this analytic leverages a sub-search to enhance performance. sub-searches have limitations on the amount of data they can return. Keep this in mind if you have an extensive list of ransomware note file names. + The following analytic detects the execution of native .NET binaries from non-standard directories within the Windows operating system. + It leverages Endpoint Detection and Response (EDR) telemetry, comparing process names and original file names against a predefined lookup "is_net_windows_file". + This activity is significant because adversaries may move .NET binaries to unconventional paths to evade detection and execute malicious code. + If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment, posing a significant security risk. + Also this analytic leverages a sub-search to enhance performance. sub-searches have limitations on the amount of data they can return. Keep this in mind if you have an extensive list of ransomware note file names. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime - FROM datamodel=Endpoint.Processes where - NOT Processes.process_path IN ( - "*:\\Windows\\ADWS\\*", - "*:\\Windows\\Microsoft.NET\\*", - "*:\\Windows\\NetworkController\\*", - "*:\\Windows\\System32\\*", - "*:\\Windows\\SystemApps\\*", - "*:\\Windows\\SysWOW64\\*", - "*:\\Windows\\WinSxS\\*" - ) - ( - [ | inputlookup is_net_windows_file - | search netFile=true - | fields originalFileName - | rename originalFileName as Processes.original_file_name - | format - ] - ) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name("Processes")` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | lookup update=true is_net_windows_file filename as process_name OUTPUT netFile - | lookup update=true is_net_windows_file originalFileName as original_file_name OUTPUT netFile - | search netFile=true - | `windows_dotnet_binary_in_non_standard_path_filter` + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime + FROM datamodel=Endpoint.Processes where + NOT Processes.process_path IN ( + "*:\\Windows\\ADWS\\*", + "*:\\Windows\\Microsoft.NET\\*", + "*:\\Windows\\NetworkController\\*", + "*:\\Windows\\System32\\*", + "*:\\Windows\\SystemApps\\*", + "*:\\Windows\\SysWOW64\\*", + "*:\\Windows\\WinSxS\\*" + ) + ( + [ | inputlookup is_net_windows_file + | search netFile=true + | fields originalFileName + | rename originalFileName as Processes.original_file_name + | format + ] + ) + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | lookup update=true is_net_windows_file filename as process_name OUTPUT netFile + | lookup update=true is_net_windows_file originalFileName as original_file_name OUTPUT netFile + | search netFile=true + | `windows_dotnet_binary_in_non_standard_path_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present and filtering may be required. - Certain utilities will run from non-standard paths based on the third-party application - in use. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. +known_false_positives: False positives may be present and filtering may be required. Certain utilities will run from non-standard paths based on the third-party application in use. references: - - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1036.003/T1036.003.yaml - - https://attack.mitre.org/techniques/T1036/003/ - - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ - - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.004/T1218.004.md + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1036.003/T1036.003.yaml + - https://attack.mitre.org/techniques/T1036/003/ + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.004/T1218.004.md drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ from a non-standard - path was identified on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 49 - - field: dest - type: system - score: 49 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ from a non-standard path was identified on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 49 + - field: dest + type: system + score: 49 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Masquerading - Rename System Utilities - - Ransomware - - Unusual Processes - - Signed Binary Proxy Execution InstallUtil - - Data Destruction - - WhisperGate - asset_type: Endpoint - mitre_attack_id: - - T1036.003 - - T1218.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Masquerading - Rename System Utilities + - Ransomware + - Unusual Processes + - Signed Binary Proxy Execution InstallUtil + - Data Destruction + - WhisperGate + asset_type: Endpoint + mitre_attack_id: + - T1036.003 + - T1218.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon_installutil_path.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon_installutil_path.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_driver_inventory.yml b/detections/endpoint/windows_driver_inventory.yml index dd59197b1e..c1f1308c69 100644 --- a/detections/endpoint/windows_driver_inventory.yml +++ b/detections/endpoint/windows_driver_inventory.yml @@ -1,42 +1,39 @@ name: Windows Driver Inventory id: f87aa96b-369b-4a3e-9021-1bbacbfcb8fb -version: 6 -date: '2025-06-10' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic identifies drivers being loaded across the fleet. - It leverages a PowerShell script input deployed to critical systems to capture driver - data. This detection is significant as it helps monitor for unauthorized or malicious - drivers that could compromise system integrity. If confirmed malicious, such drivers - could allow attackers to execute arbitrary code, escalate privileges, or maintain - persistence within the environment. +description: The following analytic identifies drivers being loaded across the fleet. It leverages a PowerShell script input deployed to critical systems to capture driver data. This detection is significant as it helps monitor for unauthorized or malicious drivers that could compromise system integrity. If confirmed malicious, such drivers could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment. data_source: [] -search: '`driverinventory` | stats values(Path) min(_time) as firstTime max(_time) - as lastTime count by host DriverType | rename host as dest | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_driver_inventory_filter`' -how_to_implement: To capture the drivers by host, utilize the referenced Gist to create - the inputs, props and transforms. Otherwise, this hunt query will not work. -known_false_positives: Filter and modify the analytic as you'd like. Filter based - on path. Remove the system32\drivers and look for non-standard paths. +search: |- + `driverinventory` + | stats values(Path) min(_time) as firstTime max(_time) as lastTime count + BY host DriverType + | rename host as dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_driver_inventory_filter` +how_to_implement: To capture the drivers by host, utilize the referenced Gist to create the inputs, props and transforms. Otherwise, this hunt query will not work. +known_false_positives: Filter and modify the analytic as you'd like. Filter based on path. Remove the system32\drivers and look for non-standard paths. references: -- https://gist.github.com/MHaggis/3e4dc85c69b3f7a4595a06c8a692f244 + - https://gist.github.com/MHaggis/3e4dc85c69b3f7a4595a06c8a692f244 tags: - analytic_story: - - Windows Drivers - asset_type: Endpoint - mitre_attack_id: - - T1068 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - manual_test: Cannot be tested automatically, as it needs additional transforms and props to make the data ready. + analytic_story: + - Windows Drivers + asset_type: Endpoint + mitre_attack_id: + - T1068 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + manual_test: Cannot be tested automatically, as it needs additional transforms and props to make the data ready. tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/drivers/driver_inventory.log - source: PwSh:DriverInventory - sourcetype: PwSh:DriverInventory + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/drivers/driver_inventory.log + source: PwSh:DriverInventory + sourcetype: PwSh:DriverInventory diff --git a/detections/endpoint/windows_driver_load_non_standard_path.yml b/detections/endpoint/windows_driver_load_non_standard_path.yml index cfa658b0b1..c5b599500d 100644 --- a/detections/endpoint/windows_driver_load_non_standard_path.yml +++ b/detections/endpoint/windows_driver_load_non_standard_path.yml @@ -1,82 +1,69 @@ name: Windows Driver Load Non-Standard Path id: 9216ef3d-066a-4958-8f27-c84589465e62 -version: 8 -date: '2025-09-23' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the loading of new Kernel Mode Drivers - from non-standard paths using Windows EventCode 7045. It identifies drivers not - located in typical directories like Windows, Program Files, or SystemRoot. This - activity is significant because adversaries may use these non-standard paths to - load malicious or vulnerable drivers, potentially bypassing security controls. If - confirmed malicious, this could allow attackers to execute code at the kernel level, - escalate privileges, or maintain persistence within the environment, posing a severe - threat to system integrity and security. +description: The following analytic detects the loading of new Kernel Mode Drivers from non-standard paths using Windows EventCode 7045. It identifies drivers not located in typical directories like Windows, Program Files, or SystemRoot. This activity is significant because adversaries may use these non-standard paths to load malicious or vulnerable drivers, potentially bypassing security controls. If confirmed malicious, this could allow attackers to execute code at the kernel level, escalate privileges, or maintain persistence within the environment, posing a severe threat to system integrity and security. data_source: - - Windows Event Log System 7045 + - Windows Event Log System 7045 search: >- - `wineventlog_system` - EventCode = 7045 - ServiceType = "kernel mode driver" - | regex ImagePath != "(?i)^(\w:\\\\Program Files\\\\|\w:\\\\Program Files \(x86\)\\\\|\w:\\\\Windows\\\\System32\\\\|\w:\\\\Windows\\\\SysWOW64\\\\|\w:\\\\ProgramData\\\\Microsoft\\\\Windows Defender\\\\Definition Updates\\\\|\w:\\\\ProgramData\\\\Microsoft\\\\Windows Defender\\\\Platform\\\\|%SystemRoot%|\\\\SystemRoot\\\\|SystemRoot\\\\)" - | stats count min(_time) as firstTime max(_time) as lastTime by - Computer EventCode ImagePath ServiceName ServiceType - | rename Computer as dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_driver_load_non_standard_path_filter` + `wineventlog_system` + EventCode = 7045 + ServiceType = "kernel mode driver" + | regex ImagePath != "(?i)^(\w:\\\\Program Files\\\\|\w:\\\\Program Files \(x86\)\\\\|\w:\\\\Windows\\\\System32\\\\|\w:\\\\Windows\\\\SysWOW64\\\\|\w:\\\\ProgramData\\\\Microsoft\\\\Windows Defender\\\\Definition Updates\\\\|\w:\\\\ProgramData\\\\Microsoft\\\\Windows Defender\\\\Platform\\\\|%SystemRoot%|\\\\SystemRoot\\\\|SystemRoot\\\\)" + | stats count min(_time) as firstTime max(_time) as lastTime by + Computer EventCode ImagePath ServiceName ServiceType + | rename Computer as dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_driver_load_non_standard_path_filter` how_to_implement: | - To implement this analytic, the Windows EventCode 7045 will need to be logged. - The Windows TA for Splunk is also recommended. + To implement this analytic, the Windows EventCode 7045 will need to be logged. + The Windows TA for Splunk is also recommended. known_false_positives: | - False positives may be present based on legitimate third party applications needing to install drivers. - Filter, or allow list known good drivers consistently being installed in these paths. + False positives may be present based on legitimate third party applications needing to install drivers. + Filter, or allow list known good drivers consistently being installed in these paths. references: - - https://redcanary.com/blog/tracking-driver-inventory-to-expose-rootkits/ - - https://attack.mitre.org/techniques/T1014/ - - https://www.fuzzysecurity.com/tutorials/28.html + - https://redcanary.com/blog/tracking-driver-inventory-to-expose-rootkits/ + - https://attack.mitre.org/techniques/T1014/ + - https://www.fuzzysecurity.com/tutorials/28.html drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A kernel mode driver was loaded from a non-standard path on $dest$. - risk_objects: - - field: dest - type: system - score: 36 - threat_objects: [] + message: A kernel mode driver was loaded from a non-standard path on $dest$. + risk_objects: + - field: dest + type: system + score: 36 + threat_objects: [] tags: - analytic_story: - - Windows Drivers - - CISA AA22-320A - - AgentTesla - - BlackByte Ransomware - - BlackSuit Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1014 - - T1068 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Drivers + - CISA AA22-320A + - AgentTesla + - BlackByte Ransomware + - BlackSuit Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1014 + - T1068 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/drivers/xml7045_windows-system.log - source: XmlWinEventLog:System - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/drivers/xml7045_windows-system.log + source: XmlWinEventLog:System + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_drivers_loaded_by_signature.yml b/detections/endpoint/windows_drivers_loaded_by_signature.yml index 4385b31f51..5f03355233 100644 --- a/detections/endpoint/windows_drivers_loaded_by_signature.yml +++ b/detections/endpoint/windows_drivers_loaded_by_signature.yml @@ -1,52 +1,46 @@ name: Windows Drivers Loaded by Signature id: d2d4af6a-6c2b-4d79-80c5-fc2cf12a2f68 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic identifies all drivers being loaded on Windows - systems using Sysmon EventCode 6 (Driver Load). It leverages fields such as driver - path, signature status, and hash to detect potentially suspicious drivers. This - activity is significant for a SOC as malicious drivers can be used to gain kernel-level - access, bypass security controls, or persist in the environment. If confirmed malicious, - this activity could allow an attacker to execute arbitrary code with high privileges, - leading to severe system compromise and potential data exfiltration. +description: The following analytic identifies all drivers being loaded on Windows systems using Sysmon EventCode 6 (Driver Load). It leverages fields such as driver path, signature status, and hash to detect potentially suspicious drivers. This activity is significant for a SOC as malicious drivers can be used to gain kernel-level access, bypass security controls, or persist in the environment. If confirmed malicious, this activity could allow an attacker to execute arbitrary code with high privileges, leading to severe system compromise and potential data exfiltration. data_source: -- Sysmon EventID 6 -search: '`sysmon` EventCode=6 | stats count min(_time) as firstTime max(_time) as - lastTime by ImageLoaded dest dvc process_hash process_path signature signature_id - user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_drivers_loaded_by_signature_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you must have the latest version of the Sysmon - TA. Most EDR products provide the ability to review driver loads, or module loads, - and using a query as such help with hunting for malicious drivers. -known_false_positives: This analytic is meant to assist with identifying and hunting - drivers loaded in the environment. + - Sysmon EventID 6 +search: |- + `sysmon` EventCode=6 + | stats count min(_time) as firstTime max(_time) as lastTime + BY ImageLoaded dest dvc + process_hash process_path signature + signature_id user_id vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_drivers_loaded_by_signature_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have the latest version of the Sysmon TA. Most EDR products provide the ability to review driver loads, or module loads, and using a query as such help with hunting for malicious drivers. +known_false_positives: This analytic is meant to assist with identifying and hunting drivers loaded in the environment. references: -- https://redcanary.com/blog/tracking-driver-inventory-to-expose-rootkits/ -- https://attack.mitre.org/techniques/T1014/ -- https://www.fuzzysecurity.com/tutorials/28.html + - https://redcanary.com/blog/tracking-driver-inventory-to-expose-rootkits/ + - https://attack.mitre.org/techniques/T1014/ + - https://www.fuzzysecurity.com/tutorials/28.html tags: - analytic_story: - - Windows Drivers - - CISA AA22-320A - - AgentTesla - - BlackByte Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1014 - - T1068 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Drivers + - CISA AA22-320A + - AgentTesla + - BlackByte Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1014 + - T1068 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1014/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1014/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_enable_powershell_web_access.yml b/detections/endpoint/windows_enable_powershell_web_access.yml index 8627e229bc..e3d330f59a 100644 --- a/detections/endpoint/windows_enable_powershell_web_access.yml +++ b/detections/endpoint/windows_enable_powershell_web_access.yml @@ -1,73 +1,62 @@ name: Windows Enable PowerShell Web Access id: 175bb2de-6227-416b-9678-9b61999cd21f -version: 7 -date: '2025-06-24' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk data_source: - - Powershell Script Block Logging 4104 + - Powershell Script Block Logging 4104 type: TTP status: production -description: - The following analytic detects the enabling of PowerShell Web Access - via PowerShell commands. It leverages PowerShell script block logging (EventCode - 4104) to identify the execution of the `Install-WindowsFeature` cmdlet with the - `WindowsPowerShellWebAccess` parameter. This activity is significant because enabling - PowerShell Web Access can facilitate remote execution of PowerShell commands, potentially - allowing an attacker to gain unauthorized access to systems and networks. -search: - '`powershell` EventCode=4104 ScriptBlockText IN ("*Install-WindowsFeature*WindowsPowerShellWebAccess*","*Install-PswaWebApplication*","*Add-PswaAuthorizationRule*UserName - *ComputerName *") | fillnull | stats count min(_time) as firstTime max(_time) as - lastTime by dest signature signature_id user_id vendor_product EventID Guid Opcode - Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_enable_powershell_web_access_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - It is possible that legitimate scripts or network administrators - may enable PowerShell Web Access. Monitor and escalate as needed. +description: The following analytic detects the enabling of PowerShell Web Access via PowerShell commands. It leverages PowerShell script block logging (EventCode 4104) to identify the execution of the `Install-WindowsFeature` cmdlet with the `WindowsPowerShellWebAccess` parameter. This activity is significant because enabling PowerShell Web Access can facilitate remote execution of PowerShell commands, potentially allowing an attacker to gain unauthorized access to systems and networks. +search: |- + `powershell` EventCode=4104 ScriptBlockText IN ("*Install-WindowsFeature*WindowsPowerShellWebAccess*","*Install-PswaWebApplication*","*Add-PswaAuthorizationRule*UserName *ComputerName *") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_enable_powershell_web_access_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: It is possible that legitimate scripts or network administrators may enable PowerShell Web Access. Monitor and escalate as needed. references: - - https://www.cisa.gov/news-events/cybersecurity-advisories/aa24-241a - - https://gist.github.com/MHaggis/7e67b659af9148fa593cf2402edebb41 + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa24-241a + - https://gist.github.com/MHaggis/7e67b659af9148fa593cf2402edebb41 drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: PowerShell Web Access has been enabled on $dest$. - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: [] + message: PowerShell Web Access has been enabled on $dest$. + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: [] tags: - analytic_story: - - CISA AA24-241A - - Malicious PowerShell - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - CISA AA24-241A + - Malicious PowerShell + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/pswa_powershell.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/pswa_powershell.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational diff --git a/detections/endpoint/windows_enable_win32_scheduledjob_via_registry.yml b/detections/endpoint/windows_enable_win32_scheduledjob_via_registry.yml index 7cf544c5b6..e6624915b8 100644 --- a/detections/endpoint/windows_enable_win32_scheduledjob_via_registry.yml +++ b/detections/endpoint/windows_enable_win32_scheduledjob_via_registry.yml @@ -6,79 +6,48 @@ author: Michael Haag, Splunk type: Anomaly status: production data_source: -- Sysmon EventID 13 -description: The following analytic detects the creation of a new DWORD value named - "EnableAt" in the registry path "HKLM:\SOFTWARE\Microsoft\Windows NT\CurrentVersion\Schedule\Configuration". - This modification enables the use of the at.exe or wmi Win32_ScheduledJob commands - to add scheduled tasks on a Windows endpoint. The detection leverages registry event - data from the Endpoint datamodel. This activity is significant because it may indicate - that an attacker is enabling the ability to schedule tasks, potentially to execute - malicious code at specific times or intervals. If confirmed malicious, this could - allow persistent code execution on the system. -search: '| tstats `security_content_summariesonly` count values(Registry.registry_key_name) - as registry_key_name values(Registry.registry_path) as registry_path min(_time) - as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\CurrentVersion\\Schedule\\Configuration*" - Registry.registry_value_name=EnableAt by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `security_content_ctime(lastTime)` - | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `windows_enable_win32_scheduledjob_via_registry_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: In some cases, an automated script or system may enable this - setting continuously, leading to false positives. To avoid such situations, it is - recommended to monitor the frequency and context of the registry modification and - modify or filter the detection rules as needed. This can help to reduce the number - of false positives and ensure that only genuine threats are identified. Additionally, - it is important to investigate any detected instances of this modification and analyze - them in the broader context of the system and network to determine if further action - is necessary. + - Sysmon EventID 13 +description: The following analytic detects the creation of a new DWORD value named "EnableAt" in the registry path "HKLM:\SOFTWARE\Microsoft\Windows NT\CurrentVersion\Schedule\Configuration". This modification enables the use of the at.exe or wmi Win32_ScheduledJob commands to add scheduled tasks on a Windows endpoint. The detection leverages registry event data from the Endpoint datamodel. This activity is significant because it may indicate that an attacker is enabling the ability to schedule tasks, potentially to execute malicious code at specific times or intervals. If confirmed malicious, this could allow persistent code execution on the system. +search: '| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\CurrentVersion\\Schedule\\Configuration*" Registry.registry_value_name=EnableAt by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `windows_enable_win32_scheduledjob_via_registry_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: In some cases, an automated script or system may enable this setting continuously, leading to false positives. To avoid such situations, it is recommended to monitor the frequency and context of the registry modification and modify or filter the detection rules as needed. This can help to reduce the number of false positives and ensure that only genuine threats are identified. Additionally, it is important to investigate any detected instances of this modification and analyze them in the broader context of the system and network to determine if further action is necessary. references: -- https://securityonline.info/wmiexec-regout-get-outputdata-response-from-registry/ -- https://learn.microsoft.com/en-us/windows/win32/cimwin32prov/win32-scheduledjob + - https://securityonline.info/wmiexec-regout-get-outputdata-response-from-registry/ + - https://learn.microsoft.com/en-us/windows/win32/cimwin32prov/win32-scheduledjob drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process has modified the schedule task registry value - EnableAt - on - endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: A process has modified the schedule task registry value - EnableAt - on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Active Directory Lateral Movement - - Scheduled Tasks - asset_type: Endpoint - mitre_attack_id: - - T1053.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + - Scheduled Tasks + asset_type: Endpoint + mitre_attack_id: + - T1053.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/enableat_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/enableat_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_esx_admins_group_creation_security_event.yml b/detections/endpoint/windows_esx_admins_group_creation_security_event.yml index 92c88c46fc..c1e6104c0b 100644 --- a/detections/endpoint/windows_esx_admins_group_creation_security_event.yml +++ b/detections/endpoint/windows_esx_admins_group_creation_security_event.yml @@ -1,79 +1,67 @@ name: Windows ESX Admins Group Creation Security Event id: 53b4c927-5ec4-47cd-8aed-d4b303304f87 -version: 5 -date: '2025-05-02' -author: Michael Haag, Splunk +version: 6 +date: '2026-02-25' +author: Michael Haag, Splunk data_source: -- Windows Event Log Security 4727 -- Windows Event Log Security 4730 -- Windows Event Log Security 4737 + - Windows Event Log Security 4727 + - Windows Event Log Security 4730 + - Windows Event Log Security 4737 type: TTP status: production -description: This analytic detects creation, deletion, or modification of the "ESX - Admins" group in Active Directory. These events may indicate attempts to exploit - the VMware ESXi Active Directory Integration Authentication Bypass vulnerability - (CVE-2024-37085). -search: '`wineventlog_security` EventCode IN (4727, 4730, 4737) (TargetUserName="ESX - Admins" OR TargetUserName="*ESX Admins*") | stats count min(_time) as firstTime - max(_time) as lastTime by EventCode TargetUserName TargetDomainName SubjectUserName - SubjectDomainName Computer | rename Computer as dest | eval EventCodeDescription=case( - EventCode=4727, "Security Enabled Global Group Created", EventCode=4730, "Security - Enabled Global Group Deleted", EventCode=4737, "Security Enabled Global Group Modified" - ) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_esx_admins_group_creation_security_event_filter`' -how_to_implement: To successfully implement this search, ensure that Windows Security - Event logging is enabled and being ingested into Splunk, particularly for event - codes 4727, 4730, and 4737. Configure Group Policy settings to audit these specific - events. -known_false_positives: Legitimate administrators might create, delete, or modify an - "ESX Admins" group for valid reasons. Verify that the group changes are authorized - and part of normal administrative tasks. Consider the context of the action, such - as the user performing it and any related activities. +description: This analytic detects creation, deletion, or modification of the "ESX Admins" group in Active Directory. These events may indicate attempts to exploit the VMware ESXi Active Directory Integration Authentication Bypass vulnerability (CVE-2024-37085). +search: |- + `wineventlog_security` EventCode IN (4727, 4730, 4737) (TargetUserName="ESX Admins" OR TargetUserName="*ESX Admins*") + | stats count min(_time) as firstTime max(_time) as lastTime + BY EventCode TargetUserName TargetDomainName + SubjectUserName SubjectDomainName Computer + | rename Computer as dest + | eval EventCodeDescription=case( EventCode=4727, "Security Enabled Global Group Created", EventCode=4730, "Security Enabled Global Group Deleted", EventCode=4737, "Security Enabled Global Group Modified" ) + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_esx_admins_group_creation_security_event_filter` +how_to_implement: To successfully implement this search, ensure that Windows Security Event logging is enabled and being ingested into Splunk, particularly for event codes 4727, 4730, and 4737. Configure Group Policy settings to audit these specific events. +known_false_positives: Legitimate administrators might create, delete, or modify an "ESX Admins" group for valid reasons. Verify that the group changes are authorized and part of normal administrative tasks. Consider the context of the action, such as the user performing it and any related activities. references: -- https://support.broadcom.com/web/ecx/support-content-notification/-/external/content/SecurityAdvisories/0/24505 -- https://www.microsoft.com/en-us/security/blog/2024/07/29/ransomware-operators-exploit-esxi-hypervisor-vulnerability-for-mass-encryption/ -- https://www.securityweek.com/microsoft-says-ransomware-gangs-exploiting-just-patched-vmware-esxi-flaw/ + - https://support.broadcom.com/web/ecx/support-content-notification/-/external/content/SecurityAdvisories/0/24505 + - https://www.microsoft.com/en-us/security/blog/2024/07/29/ransomware-operators-exploit-esxi-hypervisor-vulnerability-for-mass-encryption/ + - https://www.securityweek.com/microsoft-says-ransomware-gangs-exploiting-just-patched-vmware-esxi-flaw/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: ESX Admins group $EventCodeDescription$ on $dest$ by user $SubjectUserName$. - risk_objects: - - field: dest - type: system - score: 25 - - field: SubjectUserName - type: user - score: 25 - threat_objects: [] + message: ESX Admins group $EventCodeDescription$ on $dest$ by user $SubjectUserName$. + risk_objects: + - field: dest + type: system + score: 25 + - field: SubjectUserName + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - VMware ESXi AD Integration Authentication Bypass CVE-2024-37085 - asset_type: Endpoint - mitre_attack_id: - - T1136.001 - - T1136.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: - - CVE-2024-37085 + analytic_story: + - VMware ESXi AD Integration Authentication Bypass CVE-2024-37085 + asset_type: Endpoint + mitre_attack_id: + - T1136.001 + - T1136.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: + - CVE-2024-37085 tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security-esxadmins.log - sourcetype: XmlWinEventLog - source: Security + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security-esxadmins.log + sourcetype: XmlWinEventLog + source: Security diff --git a/detections/endpoint/windows_esx_admins_group_creation_via_net.yml b/detections/endpoint/windows_esx_admins_group_creation_via_net.yml index e68cbfeac7..d491e4bcf7 100644 --- a/detections/endpoint/windows_esx_admins_group_creation_via_net.yml +++ b/detections/endpoint/windows_esx_admins_group_creation_via_net.yml @@ -1,83 +1,73 @@ name: Windows ESX Admins Group Creation via Net id: 3d7df60b-3332-4667-8090-afe03e08dce0 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: This analytic detects attempts to create an "ESX Admins" group using - the Windows net.exe or net1.exe commands. This activity may indicate an attempt - to exploit the VMware ESXi Active Directory Integration Authentication Bypass vulnerability - (CVE-2024-37085). Attackers can use this method to gain unauthorized access to ESXi - hosts by recreating the "ESX Admins" group after its deletion from Active Directory. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_net` Processes.process="*group*" - Processes.process="*ESX Admins*" AND Processes.process="*/add*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_esx_admins_group_creation_via_net_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - data that records process activity from your hosts to populate the Endpoint data - model in the Processes node. If you are using Sysmon, you must have at least version - 6.0.4 of the Sysmon TA. -known_false_positives: Legitimate administrators might create an "ESX Admins" group - for valid reasons. Verify that the group creation is authorized and part of normal - administrative tasks. Consider the context of the action, such as the user performing - it and any related activities. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: This analytic detects attempts to create an "ESX Admins" group using the Windows net.exe or net1.exe commands. This activity may indicate an attempt to exploit the VMware ESXi Active Directory Integration Authentication Bypass vulnerability (CVE-2024-37085). Attackers can use this method to gain unauthorized access to ESXi hosts by recreating the "ESX Admins" group after its deletion from Active Directory. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_net` Processes.process="*group*" Processes.process="*ESX Admins*" + AND + Processes.process="*/add*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_esx_admins_group_creation_via_net_filter` +how_to_implement: To successfully implement this search, you need to be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: Legitimate administrators might create an "ESX Admins" group for valid reasons. Verify that the group creation is authorized and part of normal administrative tasks. Consider the context of the action, such as the user performing it and any related activities. references: -- https://support.broadcom.com/web/ecx/support-content-notification/-/external/content/SecurityAdvisories/0/24505 -- https://www.microsoft.com/en-us/security/blog/2024/07/29/ransomware-operators-exploit-esxi-hypervisor-vulnerability-for-mass-encryption/ -- https://www.securityweek.com/microsoft-says-ransomware-gangs-exploiting-just-patched-vmware-esxi-flaw/ + - https://support.broadcom.com/web/ecx/support-content-notification/-/external/content/SecurityAdvisories/0/24505 + - https://www.microsoft.com/en-us/security/blog/2024/07/29/ransomware-operators-exploit-esxi-hypervisor-vulnerability-for-mass-encryption/ + - https://www.securityweek.com/microsoft-says-ransomware-gangs-exploiting-just-patched-vmware-esxi-flaw/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An attempt to create an "ESX Admins" group was detected on $dest$ by user - $user$. - risk_objects: - - field: user - type: user - score: 56 - - field: dest - type: system - score: 56 - threat_objects: [] + message: An attempt to create an "ESX Admins" group was detected on $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 56 + - field: dest + type: system + score: 56 + threat_objects: [] tags: - analytic_story: - - VMware ESXi AD Integration Authentication Bypass CVE-2024-37085 - asset_type: Endpoint - mitre_attack_id: - - T1136.002 - - T1136.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: - - CVE-2024-37085 + analytic_story: + - VMware ESXi AD Integration Authentication Bypass CVE-2024-37085 + asset_type: Endpoint + mitre_attack_id: + - T1136.002 + - T1136.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: + - CVE-2024-37085 tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon-esxadmins.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon-esxadmins.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_esx_admins_group_creation_via_powershell.yml b/detections/endpoint/windows_esx_admins_group_creation_via_powershell.yml index c7a1887c8a..a6a56e1fce 100644 --- a/detections/endpoint/windows_esx_admins_group_creation_via_powershell.yml +++ b/detections/endpoint/windows_esx_admins_group_creation_via_powershell.yml @@ -1,73 +1,65 @@ name: Windows ESX Admins Group Creation via PowerShell id: f48a5557-be06-4b96-b8e8-be563e387620 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk data_source: -- Powershell Script Block Logging 4104 + - Powershell Script Block Logging 4104 type: TTP status: production -description: This analytic detects attempts to create an "ESX Admins" group using - PowerShell commands. This activity may indicate an attempt to exploit the VMware - ESXi Active Directory Integration Authentication Bypass vulnerability (CVE-2024-37085). - Attackers can use this method to gain unauthorized access to ESXi hosts by recreating - the 'ESX Admins' group after its deletion from Active Directory. -search: '`powershell` EventCode=4104 (ScriptBlockText="*New-ADGroup*" OR ScriptBlockText="*New-LocalGroup*") - ScriptBlockText="*ESX Admins*" | fillnull | stats count min(_time) as firstTime - max(_time) as lastTime by dest signature signature_id user_id vendor_product EventID - Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_esx_admins_group_creation_via_powershell_filter`' -how_to_implement: To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. You can find additional - setup instructions in the Splunk documentation for configuring PowerShell logging. -known_false_positives: Legitimate administrators might create an "ESX Admins" group - for valid reasons. Verify that the group creation is authorized and part of normal - administrative tasks. Consider the context of the action, such as the user performing - it and any related activities. +description: This analytic detects attempts to create an "ESX Admins" group using PowerShell commands. This activity may indicate an attempt to exploit the VMware ESXi Active Directory Integration Authentication Bypass vulnerability (CVE-2024-37085). Attackers can use this method to gain unauthorized access to ESXi hosts by recreating the 'ESX Admins' group after its deletion from Active Directory. +search: |- + `powershell` EventCode=4104 (ScriptBlockText="*New-ADGroup*" OR ScriptBlockText="*New-LocalGroup*") ScriptBlockText="*ESX Admins*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_esx_admins_group_creation_via_powershell_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. You can find additional setup instructions in the Splunk documentation for configuring PowerShell logging. +known_false_positives: Legitimate administrators might create an "ESX Admins" group for valid reasons. Verify that the group creation is authorized and part of normal administrative tasks. Consider the context of the action, such as the user performing it and any related activities. references: -- https://support.broadcom.com/web/ecx/support-content-notification/-/external/content/SecurityAdvisories/0/24505 -- https://www.microsoft.com/en-us/security/blog/2024/07/29/ransomware-operators-exploit-esxi-hypervisor-vulnerability-for-mass-encryption/ -- https://www.securityweek.com/microsoft-says-ransomware-gangs-exploiting-just-patched-vmware-esxi-flaw/ + - https://support.broadcom.com/web/ecx/support-content-notification/-/external/content/SecurityAdvisories/0/24505 + - https://www.microsoft.com/en-us/security/blog/2024/07/29/ransomware-operators-exploit-esxi-hypervisor-vulnerability-for-mass-encryption/ + - https://www.securityweek.com/microsoft-says-ransomware-gangs-exploiting-just-patched-vmware-esxi-flaw/ drilldown_searches: -- name: View the detection results for - "$user_id$" and "$dest$" - search: '%original_detection_search% | search user_id = "$user_id$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user_id$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user_id$" and "$dest$" + search: '%original_detection_search% | search user_id = "$user_id$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user_id$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: PowerShell command to create "ESX Admins" group detected on host $dest$ - by user $user_id$. - risk_objects: - - field: user_id - type: user - score: 56 - - field: dest - type: system - score: 56 - threat_objects: [] + message: PowerShell command to create "ESX Admins" group detected on host $dest$ by user $user_id$. + risk_objects: + - field: user_id + type: user + score: 56 + - field: dest + type: system + score: 56 + threat_objects: [] tags: - analytic_story: - - VMware ESXi AD Integration Authentication Bypass CVE-2024-37085 - asset_type: Endpoint - mitre_attack_id: - - T1136.002 - - T1136.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - VMware ESXi AD Integration Authentication Bypass CVE-2024-37085 + asset_type: Endpoint + mitre_attack_id: + - T1136.002 + - T1136.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-powershell-esxadmins.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-powershell-esxadmins.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_event_for_service_disabled.yml b/detections/endpoint/windows_event_for_service_disabled.yml index d80702582e..274b2bd68c 100644 --- a/detections/endpoint/windows_event_for_service_disabled.yml +++ b/detections/endpoint/windows_event_for_service_disabled.yml @@ -1,46 +1,41 @@ name: Windows Event For Service Disabled id: 9c2620a8-94a1-11ec-b40c-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects when a Windows service is modified from - a start type to disabled. It leverages system event logs, specifically EventCode - 7040, to identify this change. This activity is significant because adversaries - often disable security or other critical services to evade detection and maintain - control over a compromised host. If confirmed malicious, this action could allow - attackers to bypass security defenses, leading to further exploitation and persistence - within the environment. +description: The following analytic detects when a Windows service is modified from a start type to disabled. It leverages system event logs, specifically EventCode 7040, to identify this change. This activity is significant because adversaries often disable security or other critical services to evade detection and maintain control over a compromised host. If confirmed malicious, this action could allow attackers to bypass security defenses, leading to further exploitation and persistence within the environment. data_source: -- Windows Event Log System 7040 -search: '`wineventlog_system` EventCode=7040 EventData_Xml="*disabled*" | stats count - min(_time) as firstTime max(_time) as lastTime by Computer EventCode Name UserID - service ServiceName | rename Computer as dest | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_event_for_service_disabled_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the Service name, Service File Name Service Start type, and Service Type - from your endpoints. -known_false_positives: Windows service update may cause this event. In that scenario, - filtering is needed. + - Windows Event Log System 7040 +search: |- + `wineventlog_system` EventCode=7040 EventData_Xml="*disabled*" + | stats count min(_time) as firstTime max(_time) as lastTime + BY Computer EventCode Name + UserID service ServiceName + | rename Computer as dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_event_for_service_disabled_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints. +known_false_positives: Windows service update may cause this event. In that scenario, filtering is needed. references: -- https://blog.talosintelligence.com/2018/02/olympic-destroyer.html + - https://blog.talosintelligence.com/2018/02/olympic-destroyer.html tags: - analytic_story: - - Windows Defense Evasion Tactics - - RedLine Stealer - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - RedLine Stealer + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/windows_excessive_disabled_services_event/windows-xml.log - source: XmlWinEventLog:System - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/windows_excessive_disabled_services_event/windows-xml.log + source: XmlWinEventLog:System + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_event_log_cleared.yml b/detections/endpoint/windows_event_log_cleared.yml index c212ae2fef..3b32751ad7 100644 --- a/detections/endpoint/windows_event_log_cleared.yml +++ b/detections/endpoint/windows_event_log_cleared.yml @@ -1,74 +1,67 @@ name: Windows Event Log Cleared id: ad517544-aff9-4c96-bd99-d6eb43bfbb6a -version: 15 -date: '2025-05-02' +version: 16 +date: '2026-02-25' author: Rico Valdez, Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the clearing of Windows event logs by - identifying Windows Security Event ID 1102 or System log event 104. This detection - leverages Windows event logs to monitor for log clearing activities. Such behavior - is significant as it may indicate an attempt to cover tracks after malicious activities. - If confirmed malicious, this action could hinder forensic investigations and allow - attackers to persist undetected, making it crucial to investigate further and correlate - with other alerts and data sources. +description: The following analytic detects the clearing of Windows event logs by identifying Windows Security Event ID 1102 or System log event 104. This detection leverages Windows event logs to monitor for log clearing activities. Such behavior is significant as it may indicate an attempt to cover tracks after malicious activities. If confirmed malicious, this action could hinder forensic investigations and allow attackers to persist undetected, making it crucial to investigate further and correlate with other alerts and data sources. data_source: -- Windows Event Log Security 1102 -- Windows Event Log System 104 -search: (`wineventlog_security` EventCode=1102) OR (`wineventlog_system` EventCode=104) - | stats count min(_time) as firstTime max(_time) as lastTime by action app change_type - dest dvc name object_attrs object_category signature signature_id src_user status - subject user vendor_product object EventCode | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_event_log_cleared_filter` -how_to_implement: To successfully implement this search, you need to be ingesting - Windows event logs from your hosts. In addition, the Splunk Windows TA is needed. -known_false_positives: It is possible that these logs may be legitimately cleared - by Administrators. Filter as needed. + - Windows Event Log Security 1102 + - Windows Event Log System 104 +search: |- + (`wineventlog_security` EventCode=1102) + OR + (`wineventlog_system` EventCode=104) + | stats count min(_time) as firstTime max(_time) as lastTime + by action app change_type dest dvc name object_attrs object_category + signature signature_id src_user status subject user + vendor_product object EventCode + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_event_log_cleared_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Windows event logs from your hosts. In addition, the Splunk Windows TA is needed. +known_false_positives: It is possible that these logs may be legitimately cleared by Administrators. Filter as needed. references: -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-1102 -- https://www.ired.team/offensive-security/defense-evasion/disabling-windows-event-logs-by-suspending-eventlog-service-threads -- https://attack.mitre.org/techniques/T1070/001/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1070.001/T1070.001.md + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-1102 + - https://www.ired.team/offensive-security/defense-evasion/disabling-windows-event-logs-by-suspending-eventlog-service-threads + - https://attack.mitre.org/techniques/T1070/001/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1070.001/T1070.001.md drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows $object$ cleared on $dest$ via EventCode $EventCode$ - risk_objects: - - field: dest - type: system - score: 70 - threat_objects: [] + message: Windows $object$ cleared on $dest$ via EventCode $EventCode$ + risk_objects: + - field: dest + type: system + score: 70 + threat_objects: [] tags: - analytic_story: - - ShrinkLocker - - Windows Log Manipulation - - Ransomware - - CISA AA22-264A - - Compromised Windows Host - - Clop Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1070.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ShrinkLocker + - Windows Log Manipulation + - Ransomware + - CISA AA22-264A + - Compromised Windows Host + - Clop Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1070.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/windows_event_log_cleared/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/windows_event_log_cleared/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_event_logging_service_has_shutdown.yml b/detections/endpoint/windows_event_logging_service_has_shutdown.yml index 510e85d235..f5e051f054 100644 --- a/detections/endpoint/windows_event_logging_service_has_shutdown.yml +++ b/detections/endpoint/windows_event_logging_service_has_shutdown.yml @@ -1,50 +1,48 @@ name: Windows Event Logging Service Has Shutdown id: d696f622-6b08-4336-b456-696cb5b43ba0 -version: 4 -date: '2025-10-14' +version: 5 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting -description: The following analytic detects the shutdown of the Windows Event Log - service by leveraging Windows Event ID 1100. This event is logged every time the - service stops, including during normal system shutdowns. Monitoring this activity - is crucial as it can indicate attempts to cover tracks or disable logging. If confirmed - malicious, an attacker could hide their activities, making it difficult to trace - their actions and investigate further incidents. Analysts should verify if the shutdown - was planned and review other alerts and data sources for additional suspicious behavior. +description: The following analytic detects the shutdown of the Windows Event Log service by leveraging Windows Event ID 1100. This event is logged every time the service stops, including during normal system shutdowns. Monitoring this activity is crucial as it can indicate attempts to cover tracks or disable logging. If confirmed malicious, an attacker could hide their activities, making it difficult to trace their actions and investigate further incidents. Analysts should verify if the shutdown was planned and review other alerts and data sources for additional suspicious behavior. data_source: -- Windows Event Log Security 1100 -search: '`wineventlog_security` EventCode=1100 | stats count min(_time) as firstTime - max(_time) as lastTime by action app change_type dest dvc name object_attrs object_category - service service_name signature signature_id status subject vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_event_logging_service_has_shutdown_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Windows event logs from your hosts. In addition, the Splunk Windows TA is needed. -known_false_positives: It is possible the Event Logging service gets shut down due - to system errors or legitimate administration tasks. Investigate the cause of this - issue and apply additional filters as needed. + - Windows Event Log Security 1100 +search: |- + `wineventlog_security` EventCode=1100 + | stats count min(_time) as firstTime max(_time) as lastTime + BY action app change_type + dest dvc name + object_attrs object_category service + service_name signature signature_id + status subject vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_event_logging_service_has_shutdown_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Windows event logs from your hosts. In addition, the Splunk Windows TA is needed. +known_false_positives: It is possible the Event Logging service gets shut down due to system errors or legitimate administration tasks. Investigate the cause of this issue and apply additional filters as needed. references: -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-1100 -- https://www.ired.team/offensive-security/defense-evasion/disabling-windows-event-logs-by-suspending-eventlog-service-threads -- https://attack.mitre.org/techniques/T1070/001/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1070.001/T1070.001.md + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-1100 + - https://www.ired.team/offensive-security/defense-evasion/disabling-windows-event-logs-by-suspending-eventlog-service-threads + - https://attack.mitre.org/techniques/T1070/001/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1070.001/T1070.001.md tags: - analytic_story: - - Windows Log Manipulation - - Ransomware - - Clop Ransomware - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1070.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Log Manipulation + - Ransomware + - Clop Ransomware + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1070.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/suspicious_event_log_service_behavior/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/suspicious_event_log_service_behavior/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_event_triggered_image_file_execution_options_injection.yml b/detections/endpoint/windows_event_triggered_image_file_execution_options_injection.yml index f0595c5e56..cbd8487c1d 100644 --- a/detections/endpoint/windows_event_triggered_image_file_execution_options_injection.yml +++ b/detections/endpoint/windows_event_triggered_image_file_execution_options_injection.yml @@ -1,46 +1,40 @@ name: Windows Event Triggered Image File Execution Options Injection id: f7abfab9-12ea-44e8-8745-475f9ca6e0a4 -version: 6 -date: '2026-01-22' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic identifies the creation or modification of Image - File Execution Options (IFEO) registry keys, detected via EventCode 3000 in the - Application channel. This detection leverages Windows Event Logs to monitor for - process names added to IFEO under specific registry paths. This activity is significant - as it can indicate attempts to set traps for process monitoring or debugging, often - used by attackers for persistence or evasion. If confirmed malicious, this could - allow an attacker to execute arbitrary code or manipulate process behavior, leading - to potential system compromise. +description: The following analytic identifies the creation or modification of Image File Execution Options (IFEO) registry keys, detected via EventCode 3000 in the Application channel. This detection leverages Windows Event Logs to monitor for process names added to IFEO under specific registry paths. This activity is significant as it can indicate attempts to set traps for process monitoring or debugging, often used by attackers for persistence or evasion. If confirmed malicious, this could allow an attacker to execute arbitrary code or manipulate process behavior, leading to potential system compromise. data_source: -- Windows Event Log Application 3000 -search: '`wineventlog_application` EventCode=3000 | rename param1 AS "Process" param2 - AS "Exit_Code" | stats count min(_time) as firstTime max(_time) as lastTime by Process - Exit_Code dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_event_triggered_image_file_execution_options_injection_filter`' -how_to_implement: This analytic requires capturing the Windows Event Log Application - channel in XML. -known_false_positives: False positives may be present and tuning will be required - before turning into a finding or intermediate finding. + - Windows Event Log Application 3000 +search: |- + `wineventlog_application` EventCode=3000 + | rename param1 AS "Process" param2 AS "Exit_Code" + | stats count min(_time) as firstTime max(_time) as lastTime + BY Process Exit_Code dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_event_triggered_image_file_execution_options_injection_filter` +how_to_implement: This analytic requires capturing the Windows Event Log Application channel in XML. +known_false_positives: False positives may be present and tuning will be required before turning into a finding or intermediate finding. references: -- https://blog.thinkst.com/2022/09/sensitive-command-token-so-much-offense.html -- https://docs.microsoft.com/en-us/windows-hardware/drivers/debugger/registry-entries-for-silent-process-exit + - https://blog.thinkst.com/2022/09/sensitive-command-token-so-much-offense.html + - https://docs.microsoft.com/en-us/windows-hardware/drivers/debugger/registry-entries-for-silent-process-exit tags: - analytic_story: - - Windows Persistence Techniques - asset_type: Endpoint - mitre_attack_id: - - T1546.012 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1546.012 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.012/atomic_red_team/windows-application.log - source: XmlWinEventLog:Application - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.012/atomic_red_team/windows-application.log + source: XmlWinEventLog:Application + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_eventlog_cleared_via_wevtutil.yml b/detections/endpoint/windows_eventlog_cleared_via_wevtutil.yml index 432849087d..893a6f0870 100644 --- a/detections/endpoint/windows_eventlog_cleared_via_wevtutil.yml +++ b/detections/endpoint/windows_eventlog_cleared_via_wevtutil.yml @@ -1,81 +1,72 @@ name: Windows Eventlog Cleared Via Wevtutil id: fdb829a8-db84-4832-b64b-3e964cd44f01 -version: 2 -date: '2025-05-02' +version: 3 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - The following analytic detects the usage of wevtutil.exe with the "clear-log" parameter in order to clear the contents of logs. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because clearing event logs can be an attempt to cover tracks after malicious actions, hindering forensic investigations. If confirmed malicious, this behavior could allow an attacker to erase evidence of their activities, making it difficult to trace their actions and understand the full scope of the compromise. + The following analytic detects the usage of wevtutil.exe with the "clear-log" parameter in order to clear the contents of logs. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because clearing event logs can be an attempt to cover tracks after malicious actions, hindering forensic investigations. If confirmed malicious, this behavior could allow an attacker to erase evidence of their activities, making it difficult to trace their actions and understand the full scope of the compromise. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.process_name=wevtutil.exe Processes.process IN ("* cl *", "*clear-log*") - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `windows_eventlog_cleared_via_wevtutil_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=wevtutil.exe Processes.process IN ("* cl *", "*clear-log*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_eventlog_cleared_via_wevtutil_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: The wevtutil.exe application is a legitimate Windows event log utility. Administrators may use it to manage Windows event logs. references: -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1070.001/T1070.001.md + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1070.001/T1070.001.md drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Eventlog was cleared using the Wevtutil.exe utility on $dest$ by $user$ - risk_objects: - - field: dest - type: system - score: 28 - - field: user - type: user - score: 28 - threat_objects: [] + message: Eventlog was cleared using the Wevtutil.exe utility on $dest$ by $user$ + risk_objects: + - field: dest + type: system + score: 28 + - field: user + type: user + score: 28 + threat_objects: [] tags: - analytic_story: - - Windows Log Manipulation - - Ransomware - - Rhysida Ransomware - - Clop Ransomware - - CISA AA23-347A - - ShrinkLocker - asset_type: Endpoint - mitre_attack_id: - - T1070.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Log Manipulation + - Ransomware + - Rhysida Ransomware + - Clop Ransomware + - CISA AA23-347A + - ShrinkLocker + asset_type: Endpoint + mitre_attack_id: + - T1070.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/windows_pwh_log_cleared/wevtutil_clear_log.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/windows_pwh_log_cleared/wevtutil_clear_log.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_eventlog_recon_activity_using_log_query_utilities.yml b/detections/endpoint/windows_eventlog_recon_activity_using_log_query_utilities.yml index 5ec2ddb8d5..8ffd116758 100644 --- a/detections/endpoint/windows_eventlog_recon_activity_using_log_query_utilities.yml +++ b/detections/endpoint/windows_eventlog_recon_activity_using_log_query_utilities.yml @@ -1,127 +1,122 @@ name: Windows EventLog Recon Activity Using Log Query Utilities id: dc167f8b-3f9d-4460-9c98-8b6e703fd628 -version: 3 -date: '2025-10-14' +version: 4 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - This analytic detects EventLog reconnaissance activity using utilities such as `wevtutil.exe`, `wmic.exe`, PowerShell cmdlets like `Get-WinEvent`, or WMI queries targeting `Win32_NTLogEvent`. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. These tools are often used by adversaries to extract usernames, IP addresses, session data, and event information for credential access or situational awareness during lateral movement. While these utilities are legitimate, execution with specific arguments or targeting sensitive logs like `Security`, `PowerShell`, or specific EventIDs (e.g., 4624, 4778) can indicate malicious intent. If confirmed malicious, this behavior could allow an attacker to extract sensitive info and potentially have leveraged access or move laterally. + This analytic detects EventLog reconnaissance activity using utilities such as `wevtutil.exe`, `wmic.exe`, PowerShell cmdlets like `Get-WinEvent`, or WMI queries targeting `Win32_NTLogEvent`. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. These tools are often used by adversaries to extract usernames, IP addresses, session data, and event information for credential access or situational awareness during lateral movement. While these utilities are legitimate, execution with specific arguments or targeting sensitive logs like `Security`, `PowerShell`, or specific EventIDs (e.g., 4624, 4778) can indicate malicious intent. If confirmed malicious, this behavior could allow an attacker to extract sensitive info and potentially have leveraged access or move laterally. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime - from datamodel=Endpoint.Processes - where ( - ( + | tstats `security_content_summariesonly` values(Processes.process) as process + min(_time) as firstTime max(_time) as lastTime + from datamodel=Endpoint.Processes + where ( ( - Processes.process_name IN ("powershell.exe", "pwsh.exe", "powershell_ise.exe") - OR - Processes.original_file_name IN ("PowerShell.EXE", "pwsh.dll", "powershell_ise.EXE") + ( + Processes.process_name IN ("powershell.exe", "pwsh.exe", "powershell_ise.exe") + OR + Processes.original_file_name IN ("PowerShell.EXE", "pwsh.dll", "powershell_ise.EXE") + ) + Processes.process IN ("*Get-WinEvent*", "*Get-EventLog*", "*EventLogQuery*", "*.ReadEvent(*") ) - Processes.process IN ("*Get-WinEvent*", "*Get-EventLog*", "*EventLogQuery*", "*.ReadEvent(*") - ) - OR - ( + OR ( - Processes.process_name = wevtutil.exe - OR - Processes.original_file_name = wevtutil.exe + ( + Processes.process_name = wevtutil.exe + OR + Processes.original_file_name = wevtutil.exe + ) + Processes.process IN ("* qe *", "* query-events *") ) - Processes.process IN ("* qe *", "* query-events *") - ) - OR - ( + OR ( - Processes.process_name = wmic.exe - OR - Processes.original_file_name = wmic.exe + ( + Processes.process_name = wmic.exe + OR + Processes.original_file_name = wmic.exe + ) + Processes.process IN ("*ntevent*") + ) + OR + ( + Processes.process="*Win32_NTLogEvent*" + Processes.process="*EventCode*" + ) + OR + ( + Processes.process IN ("*PsLogList*", "*Eventquery*") ) - Processes.process IN ("*ntevent*") - ) - OR - ( - Processes.process="*Win32_NTLogEvent*" - Processes.process="*EventCode*" - ) - OR - ( - Processes.process IN ("*PsLogList*", "*Eventquery*") ) - ) - by - Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_eventlog_recon_activity_using_log_query_utilities_filter` + by + Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec + Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name + Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name + Processes.process_path Processes.user Processes.user_id Processes.vendor_product + + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_eventlog_recon_activity_using_log_query_utilities_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: | - System administrators or monitoring tools may legitimately use these utilities to gather logs for troubleshooting or auditing. Filter known admin behavior or monitoring solutions as needed. + System administrators or monitoring tools may legitimately use these utilities to gather logs for troubleshooting or auditing. Filter known admin behavior or monitoring solutions as needed. references: - - http://blog.talosintelligence.com/2022/09/lazarus-three-rats.html - - https://thedfirreport.com/2023/10/30/netsupport-intrusion-results-in-domain-compromise/ - - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-144a - - https://www.group-ib.com/blog/apt41-world-tour-2021/ - - https://labs.withsecure.com/content/dam/labs/docs/f-secureLABS-tlp-white-lazarus-threat-intel-report2.pdf - - https://learn.microsoft.com/en-us/powershell/module/microsoft.powershell.diagnostics/get-winevent - - https://learn.microsoft.com/en-us/powershell/module/microsoft.powershell.management/get-eventlog - - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/wevtutil + - http://blog.talosintelligence.com/2022/09/lazarus-three-rats.html + - https://thedfirreport.com/2023/10/30/netsupport-intrusion-results-in-domain-compromise/ + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-144a + - https://www.group-ib.com/blog/apt41-world-tour-2021/ + - https://labs.withsecure.com/content/dam/labs/docs/f-secureLABS-tlp-white-lazarus-threat-intel-report2.pdf + - https://learn.microsoft.com/en-us/powershell/module/microsoft.powershell.diagnostics/get-winevent + - https://learn.microsoft.com/en-us/powershell/module/microsoft.powershell.management/get-eventlog + - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/wevtutil drilldown_searches: - - name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious log query $process$ command was run on $dest$ by $user$ - risk_objects: - - field: dest - type: system - score: 30 - - field: user - type: user - score: 30 - threat_objects: [] + message: Suspicious log query $process$ command was run on $dest$ by $user$ + risk_objects: + - field: dest + type: system + score: 30 + - field: user + type: user + score: 30 + threat_objects: [] tags: - analytic_story: - - Windows Discovery Techniques - asset_type: Endpoint - mitre_attack_id: - - T1654 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Discovery Techniques + asset_type: Endpoint + mitre_attack_id: + - T1654 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1654/eventlog_enumeration/eventlog_enumeration.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1654/eventlog_enumeration/eventlog_enumeration.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_excel_activemicrosoftapp_child_process.yml b/detections/endpoint/windows_excel_activemicrosoftapp_child_process.yml index 2ed93ce3dc..59b893081a 100644 --- a/detections/endpoint/windows_excel_activemicrosoftapp_child_process.yml +++ b/detections/endpoint/windows_excel_activemicrosoftapp_child_process.yml @@ -1,75 +1,65 @@ name: Windows Excel ActiveMicrosoftApp Child Process id: 4dfd6a58-93b2-4012-bb33-038bb63652b3 -version: 1 -date: '2025-08-20' +version: 2 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: The following analytic identifies the execution of the ActiveMicrosoftApp process as a child of Microsoft Excel. Under normal conditions, Excel primarily spawns internal Office-related processes, and the creation of ActiveMicrosoftApp is uncommon in day-to-day business workflows. Adversaries may abuse this behavior to blend malicious activity within trusted applications, execute unauthorized code, or bypass application control mechanisms. This technique aligns with common tradecraft where Office applications are leveraged as initial access or execution vectors due to their prevalence in enterprise environments. Detecting this relationship helps defenders spot suspicious child processes that may indicate malware execution, persistence mechanisms, or attempts to establish command-and-control. Security teams should investigate the parent Excel process, the context of the ActiveMicrosoftApp execution, and any subsequent network or file activity. While certain legitimate Office features could trigger this process in specific environments, its occurrence generally warrants further scrutiny to validate intent and rule out compromise. data_source: -- Sysmon EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.parent_process_name = "EXCEL.EXE" Processes.process_name IN ("WINPROJ.EXE", "FOXPROW.exe","SCHDPLUS.exe") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_excel_activemicrosoftapp_child_process_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name = "EXCEL.EXE" Processes.process_name IN ("WINPROJ.EXE", "FOXPROW.exe","SCHDPLUS.exe") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_excel_activemicrosoftapp_child_process_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Microsoft Project has been discontinued since January 2010, so its presence is unlikely in modern environments. If a related child process is observed, verify its legitimacy to rule out potential misuse. references: -- https://specterops.io/blog/2023/10/30/lateral-movement-abuse-the-power-of-dcom-excel-application/ -- https://blog.talosintelligence.com/pathwiper-targets-ukraine/ -- https://www.trellix.com/blogs/research/dcom-abuse-and-network-erasure-with-trellix-ndr/ + - https://specterops.io/blog/2023/10/30/lateral-movement-abuse-the-power-of-dcom-excel-application/ + - https://blog.talosintelligence.com/pathwiper-targets-ukraine/ + - https://www.trellix.com/blogs/research/dcom-abuse-and-network-erasure-with-trellix-ndr/ drilldown_searches: - - name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Risk Message goes here - risk_objects: - - field: dest - type: system - score: 10 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: Risk Message goes here + risk_objects: + - field: dest + type: system + score: 10 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - PathWiper - asset_type: Endpoint - mitre_attack_id: - - T1021.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - PathWiper + asset_type: Endpoint + mitre_attack_id: + - T1021.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.003/excel_activemicrosoftapp/sysmon_winprojexe.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.003/excel_activemicrosoftapp/sysmon_winprojexe.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_excessive_disabled_services_event.yml b/detections/endpoint/windows_excessive_disabled_services_event.yml index efb3c085fe..425c4c5758 100644 --- a/detections/endpoint/windows_excessive_disabled_services_event.yml +++ b/detections/endpoint/windows_excessive_disabled_services_event.yml @@ -1,70 +1,58 @@ name: Windows Excessive Disabled Services Event id: c3f85976-94a5-11ec-9a58-acde48001122 -version: 10 -date: '2026-01-14' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic identifies an excessive number of system events - where services are modified from start to disabled. It leverages Windows Event Logs - (EventCode 7040) to detect multiple service state changes on a single host. This - activity is significant as it may indicate an adversary attempting to disable security - applications or other critical services, potentially leading to defense evasion - or destructive actions. If confirmed malicious, this behavior could allow attackers - to disable security defenses, disrupt system operations, and achieve their objectives - on the compromised system. +description: The following analytic identifies an excessive number of system events where services are modified from start to disabled. It leverages Windows Event Logs (EventCode 7040) to detect multiple service state changes on a single host. This activity is significant as it may indicate an adversary attempting to disable security applications or other critical services, potentially leading to defense evasion or destructive actions. If confirmed malicious, this behavior could allow attackers to disable security defenses, disrupt system operations, and achieve their objectives on the compromised system. data_source: -- Windows Event Log System 7040 -search: '`wineventlog_system` EventCode=7040 "disabled" | stats count values(EventData_Xml) - as MessageList dc(EventData_Xml) as MessageCount min(_time) as firstTime max(_time) - as lastTime by Computer EventCode UserID | rename Computer as dest | where count - >=10 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_excessive_disabled_services_event_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the Service name, Service File Name Service Start type, and Service Type - from your endpoints. + - Windows Event Log System 7040 +search: |- + `wineventlog_system` EventCode=7040 "disabled" + | stats count values(EventData_Xml) as MessageList dc(EventData_Xml) as MessageCount min(_time) as firstTime max(_time) as lastTime + BY Computer EventCode UserID + | rename Computer as dest + | where count >=10 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_excessive_disabled_services_event_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints. known_false_positives: No false positives have been identified at this time. references: -- https://blog.talosintelligence.com/2018/02/olympic-destroyer.html + - https://blog.talosintelligence.com/2018/02/olympic-destroyer.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An excessive number (Count - $MessageCount$) of Windows services were disabled - on dest - $dest$. - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: [] + message: An excessive number (Count - $MessageCount$) of Windows services were disabled on dest - $dest$. + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: [] tags: - analytic_story: - - CISA AA23-347A - - Compromised Windows Host - - Windows Defense Evasion Tactics - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA23-347A + - Compromised Windows Host + - Windows Defense Evasion Tactics + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/windows_excessive_disabled_services_event/windows-xml.log - source: XmlWinEventLog:System - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/windows_excessive_disabled_services_event/windows-xml.log + source: XmlWinEventLog:System + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_excessive_service_stop_attempt.yml b/detections/endpoint/windows_excessive_service_stop_attempt.yml index 18d3d88c81..c0a68ae06a 100644 --- a/detections/endpoint/windows_excessive_service_stop_attempt.yml +++ b/detections/endpoint/windows_excessive_service_stop_attempt.yml @@ -1,88 +1,69 @@ name: Windows Excessive Service Stop Attempt id: 8f3a614f-6b98-4f7d-82dd-d0df38452a8b -version: 4 -date: '2026-01-14' +version: 5 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects multiple attempts to stop or delete services - on a system using `net.exe` or `sc.exe`. It leverages Endpoint Detection and Response - (EDR) telemetry, focusing on process names and command-line executions within a - one-minute window. This activity is significant as it may indicate an adversary - attempting to disable security or critical services to evade detection and further - their objectives. If confirmed malicious, this could lead to the attacker gaining - persistence, escalating privileges, or disrupting essential services, thereby compromising - the system's security posture. +description: The following analytic detects multiple attempts to stop or delete services on a system using `net.exe` or `sc.exe`. It leverages Endpoint Detection and Response (EDR) telemetry, focusing on process names and command-line executions within a one-minute window. This activity is significant as it may indicate an adversary attempting to disable security or critical services to evade detection and further their objectives. If confirmed malicious, this could lead to the attacker gaining persistence, escalating privileges, or disrupting essential services, thereby compromising the system's security posture. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.action) as action - values(Processes.parent_process) as parent_process values(Processes.parent_process_exec) - as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid - values(Processes.parent_process_id) as parent_process_id values(Processes.parent_process_path) - as parent_process_path values(Processes.process) as process values(Processes.process_exec) - as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) - as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) - as process_integrity_level values(Processes.process_path) as process_path values(Processes.user) - as user values(Processes.user_id) as user_id values(Processes.vendor_product) as - vendor_product count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where (`process_net` OR `process_sc`) AND Processes.process="*stop*" OR Processes.process="*delete*" - by Processes.process_name Processes.original_file_name Processes.parent_process_name - Processes.dest Processes.user _time span=1m | where count >=5 | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_excessive_service_stop_attempt_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.action) as action values(Processes.parent_process) as parent_process values(Processes.parent_process_exec) as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid values(Processes.parent_process_id) as parent_process_id values(Processes.parent_process_path) as parent_process_path values(Processes.process) as process values(Processes.process_exec) as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) as process_integrity_level values(Processes.process_path) as process_path values(Processes.user) as user values(Processes.user_id) as user_id values(Processes.vendor_product) as vendor_product count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + `process_net` + OR + `process_sc` + ) + AND Processes.process="*stop*" OR Processes.process="*delete*" + BY Processes.process_name Processes.original_file_name Processes.parent_process_name + Processes.dest Processes.user _time + span=1m + | where count >=5 + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_excessive_service_stop_attempt_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ + - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An excessive amount of $process_name$ was executed on $dest$ attempting - to disable services. - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: - - field: process_name - type: process_name + message: An excessive amount of $process_name$ was executed on $dest$ attempting to disable services. + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - XMRig - - Ransomware - - BlackByte Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1489 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - XMRig + - Ransomware + - BlackByte Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1489 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_excessive_usage_of_net_app.yml b/detections/endpoint/windows_excessive_usage_of_net_app.yml index dfc098ccd9..1b31a3fcff 100644 --- a/detections/endpoint/windows_excessive_usage_of_net_app.yml +++ b/detections/endpoint/windows_excessive_usage_of_net_app.yml @@ -1,94 +1,71 @@ name: Windows Excessive Usage Of Net App id: 355ba810-0a20-4215-8485-9ce3f87f2e38 -version: 5 -date: '2026-01-14' +version: 6 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects excessive usage of `net.exe` within a - one-minute interval. It leverages data from Endpoint Detection and Response (EDR) - agents, focusing on process names, parent processes, and command-line executions. - This behavior is significant as it may indicate an adversary attempting to create, - delete, or disable multiple user accounts rapidly, a tactic observed in Monero mining - incidents. If confirmed malicious, this activity could lead to unauthorized user - account manipulation, potentially compromising system integrity and enabling further - malicious actions. +description: The following analytic detects excessive usage of `net.exe` within a one-minute interval. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, parent processes, and command-line executions. This behavior is significant as it may indicate an adversary attempting to create, delete, or disable multiple user accounts rapidly, a tactic observed in Monero mining incidents. If confirmed malicious, this activity could lead to unauthorized user account manipulation, potentially compromising system integrity and enabling further malicious actions. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.action) as action - values(Processes.parent_process) as parent_process values(Processes.parent_process_exec) - as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid - values(Processes.parent_process_id) as parent_process_id values(Processes.parent_process_path) - as parent_process_path values(Processes.process) as process values(Processes.process_exec) - as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) - as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) - as process_integrity_level values(Processes.process_path) as process_path values(Processes.user) - as user values(Processes.user_id) as user_id values(Processes.vendor_product) as - vendor_product count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where `process_net` by Processes.process_name Processes.parent_process_name Processes.original_file_name - Processes.dest Processes.user _time span=1m | where count >=10 | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_excessive_usage_of_net_app_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.action) as action values(Processes.parent_process) as parent_process values(Processes.parent_process_exec) as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid values(Processes.parent_process_id) as parent_process_id values(Processes.parent_process_path) as parent_process_path values(Processes.process) as process values(Processes.process_exec) as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) as process_integrity_level values(Processes.process_path) as process_path values(Processes.user) as user values(Processes.user_id) as user_id values(Processes.vendor_product) as vendor_product count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_net` + BY Processes.process_name Processes.parent_process_name Processes.original_file_name + Processes.dest Processes.user _time + span=1m + | where count >=10 + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_excessive_usage_of_net_app_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ + - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Excessive usage of net1.exe or net.exe within 1m, with command line $process$ - has been detected on $dest$ by $user$ - risk_objects: - - field: user - type: user - score: 28 - - field: dest - type: system - score: 28 - threat_objects: - - field: process_name - type: process_name + message: Excessive usage of net1.exe or net.exe within 1m, with command line $process$ has been detected on $dest$ by $user$ + risk_objects: + - field: user + type: user + score: 28 + - field: dest + type: system + score: 28 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Prestige Ransomware - - Graceful Wipe Out Attack - - XMRig - - Windows Post-Exploitation - - Azorult - - Ransomware - - Rhysida Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1531 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Prestige Ransomware + - Graceful Wipe Out Attack + - XMRig + - Windows Post-Exploitation + - Azorult + - Ransomware + - Rhysida Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1531 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_executable_in_loaded_modules.yml b/detections/endpoint/windows_executable_in_loaded_modules.yml index 0b74585771..955ebe6e79 100644 --- a/detections/endpoint/windows_executable_in_loaded_modules.yml +++ b/detections/endpoint/windows_executable_in_loaded_modules.yml @@ -1,71 +1,62 @@ name: Windows Executable in Loaded Modules id: 3e27af56-fcf0-4113-988d-24969b062be7 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 7 -description: The following analytic identifies instances where executable files (.exe) - are loaded as modules, detected through 'ImageLoaded' events in Sysmon logs. This - method leverages Sysmon EventCode 7 to track unusual module loading behavior, which - is significant as it deviates from the norm of loading .dll files. This activity - is crucial for SOC monitoring because it can indicate the presence of malware like - NjRAT, which uses this technique to load malicious modules. If confirmed malicious, - this behavior could allow attackers to execute arbitrary code, maintain persistence, - and further compromise the host system. -search: '`sysmon` EventCode=7 ImageLoaded != *.dll AND Signed != true - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime - by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name - process_path service_dll_signature_exists service_dll_signature_verified signature Signed - signature_id user_id vendor_product - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_executable_in_loaded_modules_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name and imageloaded executions from your endpoints. If you - are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + - Sysmon EventID 7 +description: The following analytic identifies instances where executable files (.exe) are loaded as modules, detected through 'ImageLoaded' events in Sysmon logs. This method leverages Sysmon EventCode 7 to track unusual module loading behavior, which is significant as it deviates from the norm of loading .dll files. This activity is crucial for SOC monitoring because it can indicate the presence of malware like NjRAT, which uses this technique to load malicious modules. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, maintain persistence, and further compromise the host system. +search: |- + `sysmon` EventCode=7 ImageLoaded != *.dll AND Signed != true + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY Image ImageLoaded dest + loaded_file loaded_file_path original_file_name + process_exec process_guid process_hash + process_id process_name process_path + service_dll_signature_exists service_dll_signature_verified signature + Signed signature_id user_id + vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_executable_in_loaded_modules_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: No false positives have been identified at this time. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.njrat + - https://malpedia.caad.fkie.fraunhofer.de/details/win.njrat drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An executable $ImageLoaded$ loaded by $Image$ on $dest$ - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: An executable $ImageLoaded$ loaded by $Image$ on $dest$ + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - NjRAT - - Lokibot - asset_type: Endpoint - mitre_attack_id: - - T1129 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - NjRAT + - Lokibot + asset_type: Endpoint + mitre_attack_id: + - T1129 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1129/executable_shared_modules/image_loaded_exe.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1129/executable_shared_modules/image_loaded_exe.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_executable_masquerading_as_benign_file_types.yml b/detections/endpoint/windows_executable_masquerading_as_benign_file_types.yml index 776149946c..5c74732f13 100644 --- a/detections/endpoint/windows_executable_masquerading_as_benign_file_types.yml +++ b/detections/endpoint/windows_executable_masquerading_as_benign_file_types.yml @@ -1,73 +1,68 @@ name: Windows Executable Masquerading as Benign File Types id: 0470c8e7-dd8d-420f-8302-073e8a2b66f0 -version: 1 -date: '2025-11-20' +version: 2 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: | - The following analytic detects the presence of executable files masquerading as benign file types on Windows systems. Adversaries employ this technique to evade defenses and trick users into executing malicious code by renaming executables with extensions commonly associated with documents, images, or other non-executable formats (e.g., .pdf, .jpg, .doc, .png). + The following analytic detects the presence of executable files masquerading as benign file types on Windows systems. Adversaries employ this technique to evade defenses and trick users into executing malicious code by renaming executables with extensions commonly associated with documents, images, or other non-executable formats (e.g., .pdf, .jpg, .doc, .png). data_source: -- Sysmon EventID 29 + - Sysmon EventID 29 search: | - `sysmon` - EventCode=29 - NOT `executable_extensions` - | stats count min(_time) as firstTime max(_time) as lastTime - by Image file_name file_path process_guid file_hash process_id dest user EventCode - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_executable_masquerading_as_benign_file_types_filter` + `sysmon` + EventCode=29 + NOT `executable_extensions` + | stats count min(_time) as firstTime max(_time) as lastTime + by Image file_name file_path process_guid file_hash process_id dest user EventCode + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_executable_masquerading_as_benign_file_types_filter` how_to_implement: | - To implement Sysmon EventCode 29 (File Block Executable), you must ensure that your Sysmon deployment is configured to log instances where executable file creation is blocked based on your organization's FileBlockExecutable rules. Once enabled, the corresponding Splunk search requires Sysmon operational logs and an input macro named sysmon, which should be customized to match your environment-specific index, source, and sourcetype settings for Windows Sysmon data. We strongly recommend replacing this macro with values appropriate to your Splunk environment so the search scopes correctly. The search also uses a post-filter macro designed to filter out known false positives. + To implement Sysmon EventCode 29 (File Block Executable), you must ensure that your Sysmon deployment is configured to log instances where executable file creation is blocked based on your organization's FileBlockExecutable rules. Once enabled, the corresponding Splunk search requires Sysmon operational logs and an input macro named sysmon, which should be customized to match your environment-specific index, source, and sourcetype settings for Windows Sysmon data. We strongly recommend replacing this macro with values appropriate to your Splunk environment so the search scopes correctly. The search also uses a post-filter macro designed to filter out known false positives. known_false_positives: | - File types that are not included in the filter for this detection may generate false positives, so proper filtering is required. + File types that are not included in the filter for this detection may generate false positives, so proper filtering is required. references: - - https://www.linkedin.com/posts/mauricefielenbach_cybersecurity-incidentresponse-dfir-activity-7394805779448418304-g0gZ?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAuFTjIB5weY_kcyu4qp3kHbI4v49tO0zEk - - https://www.blackhillsinfosec.com/a-sysmon-event-id-breakdown/ - - https://thedfirreport.com/2023/10/30/netsupport-intrusion-results-in-domain-compromise/ - - https://www.esentire.com/blog/evalusion-campaign-delivers-amatera-stealer-and-netsupport-rat + - https://www.linkedin.com/posts/mauricefielenbach_cybersecurity-incidentresponse-dfir-activity-7394805779448418304-g0gZ?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAuFTjIB5weY_kcyu4qp3kHbI4v49tO0zEk + - https://www.blackhillsinfosec.com/a-sysmon-event-id-breakdown/ + - https://thedfirreport.com/2023/10/30/netsupport-intrusion-results-in-domain-compromise/ + - https://www.esentire.com/blog/evalusion-campaign-delivers-amatera-stealer-and-netsupport-rat drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A valid Windows PE executable $file_name$ located in $file_path$ was dropped on $dest$, disguised as a non-executable file type. - risk_objects: - - field: dest - type: system - score: 50 - threat_objects: - - field: Image - type: process - - field: file_name - type: file_name - - field: file_path - type: file_path + message: A valid Windows PE executable $file_name$ located in $file_path$ was dropped on $dest$, disguised as a non-executable file type. + risk_objects: + - field: dest + type: system + score: 50 + threat_objects: + - field: Image + type: process + - field: file_name + type: file_name + - field: file_path + type: file_path tags: - analytic_story: - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1036.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1036.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.008/masquerading_executable_as_non_exec_file_type/non_exec_ext_but_exec_detected.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.008/masquerading_executable_as_non_exec_file_type/non_exec_ext_but_exec_detected.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_execute_arbitrary_commands_with_msdt.yml b/detections/endpoint/windows_execute_arbitrary_commands_with_msdt.yml index 9492e75c6b..f484fc9184 100644 --- a/detections/endpoint/windows_execute_arbitrary_commands_with_msdt.yml +++ b/detections/endpoint/windows_execute_arbitrary_commands_with_msdt.yml @@ -1,98 +1,86 @@ name: Windows Execute Arbitrary Commands with MSDT id: e1d5145f-38fe-42b9-a5d5-457796715f97 -version: 11 -date: '2025-05-02' +version: 12 +date: '2026-02-25' author: Michael Haag, Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects arbitrary command execution using Windows - msdt.exe, a Diagnostics Troubleshooting Wizard. It leverages Endpoint Detection - and Response (EDR) data to identify instances where msdt.exe is invoked via the - ms-msdt:/ protocol handler to retrieve a remote payload. This activity is significant - as it can indicate an exploitation attempt leveraging msdt.exe to execute arbitrary - commands, potentially leading to unauthorized code execution. If confirmed malicious, - this could allow an attacker to execute arbitrary code, escalate privileges, or - persist within the environment, posing a severe security risk. +description: The following analytic detects arbitrary command execution using Windows msdt.exe, a Diagnostics Troubleshooting Wizard. It leverages Endpoint Detection and Response (EDR) data to identify instances where msdt.exe is invoked via the ms-msdt:/ protocol handler to retrieve a remote payload. This activity is significant as it can indicate an exploitation attempt leveraging msdt.exe to execute arbitrary commands, potentially leading to unauthorized code execution. If confirmed malicious, this could allow an attacker to execute arbitrary code, escalate privileges, or persist within the environment, posing a severe security risk. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=msdt.exe - Processes.process IN ("*msdt*","*ms-msdt:*","*ms-msdt:/id*","*ms-msdt:-id*","*/id*") - AND (Processes.process="*IT_BrowseForFile=*" OR Processes.process="*IT_RebrowseForFile=*" - OR Processes.process="*.xml*") AND Processes.process="*PCWDiagnostic*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| - `windows_execute_arbitrary_commands_with_msdt_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present, filter as needed. Added .xml - to potentially capture any answer file usage. Remove as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=msdt.exe Processes.process IN ("*msdt*","*ms-msdt:*","*ms-msdt:/id*","*ms-msdt:-id*","*/id*") + AND + (Processes.process="*IT_BrowseForFile=*" + OR + Processes.process="*IT_RebrowseForFile=*" + OR + Processes.process="*.xml*") + AND + Processes.process="*PCWDiagnostic*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_execute_arbitrary_commands_with_msdt_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present, filter as needed. Added .xml to potentially capture any answer file usage. Remove as needed. references: -- https://isc.sans.edu/diary/rss/28694 -- https://doublepulsar.com/follina-a-microsoft-office-code-execution-vulnerability-1a47fce5629e -- https://twitter.com/nao_sec/status/1530196847679401984?s=20&t=ZiXYI4dQuA-0_dzQzSUb3A -- https://app.any.run/tasks/713f05d2-fe78-4b9d-a744-f7c133e3fafb/ -- https://www.virustotal.com/gui/file/4a24048f81afbe9fb62e7a6a49adbd1faf41f266b5f9feecdceb567aec096784/detection -- https://strontic.github.io/xcyclopedia/library/msdt.exe-152D4C9F63EFB332CCB134C6953C0104.html + - https://isc.sans.edu/diary/rss/28694 + - https://doublepulsar.com/follina-a-microsoft-office-code-execution-vulnerability-1a47fce5629e + - https://twitter.com/nao_sec/status/1530196847679401984?s=20&t=ZiXYI4dQuA-0_dzQzSUb3A + - https://app.any.run/tasks/713f05d2-fe78-4b9d-a744-f7c133e3fafb/ + - https://www.virustotal.com/gui/file/4a24048f81afbe9fb62e7a6a49adbd1faf41f266b5f9feecdceb567aec096784/detection + - https://strontic.github.io/xcyclopedia/library/msdt.exe-152D4C9F63EFB332CCB134C6953C0104.html drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A parent process $parent_process_name$ has spawned a child process $process_name$ - on host $dest$ possibly indicative of indirect command execution. - risk_objects: - - field: user - type: user - score: 100 - - field: dest - type: system - score: 100 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: A parent process $parent_process_name$ has spawned a child process $process_name$ on host $dest$ possibly indicative of indirect command execution. + risk_objects: + - field: user + type: user + score: 100 + - field: dest + type: system + score: 100 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Compromised Windows Host - - Microsoft Support Diagnostic Tool Vulnerability CVE-2022-30190 - asset_type: Endpoint - cve: - - CVE-2022-30190 - mitre_attack_id: - - T1218 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - Microsoft Support Diagnostic Tool Vulnerability CVE-2022-30190 + asset_type: Endpoint + cve: + - CVE-2022-30190 + mitre_attack_id: + - T1218 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/msdt.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/msdt.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_execution_of_microsoft_msc_file_in_suspicious_path.yml b/detections/endpoint/windows_execution_of_microsoft_msc_file_in_suspicious_path.yml index 1b27c2de79..c5dbcd4e78 100644 --- a/detections/endpoint/windows_execution_of_microsoft_msc_file_in_suspicious_path.yml +++ b/detections/endpoint/windows_execution_of_microsoft_msc_file_in_suspicious_path.yml @@ -1,105 +1,92 @@ name: Windows Execution of Microsoft MSC File In Suspicious Path id: ac30858b-7c25-4f0a-a7fa-bef036e49dc3 -version: 1 -date: '2026-02-03' +version: 2 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: The following analytic detects when a Microsoft Management Console (MMC) process executes an .msc file in a suspicious path on a Windows system. While .msc files are legitimate components used for system administration, unexpected execution of these files by non-administrative processes or in unusual contexts can indicate malicious activity, such as living-off-the-land attacks, persistence mechanisms, or automated administrative abuse. This detection monitors process creation events, command-line arguments, and parent process relationships to help distinguish normal administrative usage from potential threats. Alerts should be investigated in the context of the process initiating the .msc file, the target system, and any subsequent network or system activity, as routine administrative tasks may also trigger this behavior. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes - where (Processes.process_name=mmc.exe) - AND Processes.process = "*.msc*" - AND Processes.process IN ( - "*\\PerfLogs\\*", - "*\\programdata\\*" - "*Recycle.bin*", - "*\\Download*", - "*\\temp\\*", - "*\\Users\\Administrator\\Music\\*", - "*\\Users\\Default\\*", - "*\\Users\\Public\\*", - "*\\Users\\Administrator\\Music\\*", - "*:\\Windows\\Prefetch\\*", - "*:\\Windows\\Cursors\\*", - "*:\\Windows\\INF\\*" - "*:\\Windows\\debug\\*", - "*:\\Windows\\fonts\\*", - "*:\\Windows\\Media\\*", - "*:\\Windows\\repair\\*", - "*:\\Windows\\servicing\\*", - ) - AND NOT (Processes.process IN ("*C:\\Windows\\System32\\eventvwr.msc*", "*C:\\Windows\\System32\\certmgr.msc*")) + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: | + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes + where (Processes.process_name=mmc.exe) + AND Processes.process = "*.msc*" + AND Processes.process IN ( + "*\\PerfLogs\\*", + "*\\programdata\\*" + "*Recycle.bin*", + "*\\Download*", + "*\\temp\\*", + "*\\Users\\Administrator\\Music\\*", + "*\\Users\\Default\\*", + "*\\Users\\Public\\*", + "*\\Users\\Administrator\\Music\\*", + "*:\\Windows\\Prefetch\\*", + "*:\\Windows\\Cursors\\*", + "*:\\Windows\\INF\\*" + "*:\\Windows\\debug\\*", + "*:\\Windows\\fonts\\*", + "*:\\Windows\\Media\\*", + "*:\\Windows\\repair\\*", + "*:\\Windows\\servicing\\*", + ) + AND NOT (Processes.process IN ("*C:\\Windows\\System32\\eventvwr.msc*", "*C:\\Windows\\System32\\certmgr.msc*")) - by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_execution_of_microsoft_msc_file_in_suspicious_path_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + by Processes.action + Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec + Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name + Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name + Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_execution_of_microsoft_msc_file_in_suspicious_path_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: A possible false positive (FP) for the execution of .msc files is legitimate administrative activity, since .msc files are standard Microsoft Management Console snap-ins used for system administration. references: -- https://www.securonix.com/blog/analyzing-fluxconsole-using-tax-themed-lures-threat-actors-exploit-windows-management-console-to-deliver-backdoor-payloads/ -- https://research.checkpoint.com/2019/microsoft-management-console-mmc-vulnerabilities/ + - https://www.securonix.com/blog/analyzing-fluxconsole-using-tax-themed-lures-threat-actors-exploit-windows-management-console-to-deliver-backdoor-payloads/ + - https://research.checkpoint.com/2019/microsoft-management-console-mmc-vulnerabilities/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Microsoft Management Console process [ $process_name$ ] launched an .msc file [ $process$ ] on the target system [ $dest$ ]. - risk_objects: - - field: dest - type: system - score: 20 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name - - field: process - type: process + message: A Microsoft Management Console process [ $process_name$ ] launched an .msc file [ $process$ ] on the target system [ $dest$ ]. + risk_objects: + - field: dest + type: system + score: 20 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name + - field: process + type: process tags: - analytic_story: - - XML Runner Loader - asset_type: Endpoint - mitre_attack_id: - - T1218.014 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - XML Runner Loader + asset_type: Endpoint + mitre_attack_id: + - T1218.014 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.014/msc_execution/loaded_msc_mmc.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.014/msc_execution/loaded_msc_mmc.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_exfiltration_over_c2_via_invoke_restmethod.yml b/detections/endpoint/windows_exfiltration_over_c2_via_invoke_restmethod.yml index b7fdca3b2f..0b7b8c393c 100644 --- a/detections/endpoint/windows_exfiltration_over_c2_via_invoke_restmethod.yml +++ b/detections/endpoint/windows_exfiltration_over_c2_via_invoke_restmethod.yml @@ -1,78 +1,64 @@ name: Windows Exfiltration Over C2 Via Invoke RestMethod id: 06ade821-f6fa-40d0-80af-15bc1d45b3ba -version: 10 -date: '2025-10-24' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP data_source: - - Powershell Script Block Logging 4104 -description: - The following analytic detects potential data exfiltration using PowerShell's - Invoke-RestMethod. It leverages PowerShell Script Block Logging to identify scripts - that attempt to upload files via HTTP POST requests. This activity is significant - as it may indicate an attacker is exfiltrating sensitive data, such as desktop screenshots - or files, to an external command and control (C2) server. If confirmed malicious, - this could lead to data breaches, loss of sensitive information, and further compromise - of the affected systems. Immediate investigation is recommended to determine the - intent and scope of the activity. -search: - '`powershell` EventCode=4104 ScriptBlockText = "*Invoke-RestMethod *" AND - ScriptBlockText = "* -Uri *" AND ScriptBlockText = "* -Method *" AND ScriptBlockText - = "* Post *" AND ScriptBlockText = "* -InFile *" | fillnull | stats count min(_time) - as firstTime max(_time) as lastTime by dest signature signature_id user_id vendor_product - EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_exfiltration_over_c2_via_invoke_restmethod_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - Powershell Script Block Logging 4104 +description: The following analytic detects potential data exfiltration using PowerShell's Invoke-RestMethod. It leverages PowerShell Script Block Logging to identify scripts that attempt to upload files via HTTP POST requests. This activity is significant as it may indicate an attacker is exfiltrating sensitive data, such as desktop screenshots or files, to an external command and control (C2) server. If confirmed malicious, this could lead to data breaches, loss of sensitive information, and further compromise of the affected systems. Immediate investigation is recommended to determine the intent and scope of the activity. +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*Invoke-RestMethod *" AND ScriptBlockText = "* -Uri *" AND ScriptBlockText = "* -Method *" AND ScriptBlockText = "* Post *" AND ScriptBlockText = "* -InFile *" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_exfiltration_over_c2_via_invoke_restmethod_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. known_false_positives: False positives should be limited. Filter as needed. references: - - https://twitter.com/_CERT_UA/status/1620781684257091584 - - https://cert.gov.ua/article/3761104 + - https://twitter.com/_CERT_UA/status/1620781684257091584 + - https://cert.gov.ua/article/3761104 drilldown_searches: - - name: View the detection results for - "$Computer$" - search: '%original_detection_search% | search Computer = "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$Computer$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" + search: '%original_detection_search% | search Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A PowerShell script on $dest$ is attempting to transfer files to a remote - URL. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: A PowerShell script on $dest$ is attempting to transfer files to a remote URL. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Microsoft WSUS CVE-2025-59287 - - Hellcat Ransomware - - APT37 Rustonotto and FadeStealer - - Winter Vivern - - Water Gamayun - asset_type: Endpoint - mitre_attack_id: - - T1041 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Microsoft WSUS CVE-2025-59287 + - Hellcat Ransomware + - APT37 Rustonotto and FadeStealer + - Winter Vivern + - Water Gamayun + asset_type: Endpoint + mitre_attack_id: + - T1041 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winter-vivern/pwh_exfiltration/windows-powershell-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winter-vivern/pwh_exfiltration/windows-powershell-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_exfiltration_over_c2_via_powershell_uploadstring.yml b/detections/endpoint/windows_exfiltration_over_c2_via_powershell_uploadstring.yml index 04a49cbdb8..10e2d10891 100644 --- a/detections/endpoint/windows_exfiltration_over_c2_via_powershell_uploadstring.yml +++ b/detections/endpoint/windows_exfiltration_over_c2_via_powershell_uploadstring.yml @@ -1,74 +1,61 @@ name: Windows Exfiltration Over C2 Via Powershell UploadString id: 59e8bf41-7472-412a-90d3-00f3afa452e9 -version: 8 -date: '2025-09-18' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP data_source: - - Powershell Script Block Logging 4104 -description: - The following analytic identifies potential data exfiltration using the - PowerShell `net.webclient` command with the `UploadString` method. It leverages - PowerShell Script Block Logging to detect instances where this command is executed. - This activity is significant as it may indicate an attempt to upload sensitive data, - such as desktop screenshots or files, to an external or internal URI, often associated - with malware like Winter-Vivern. If confirmed malicious, this could lead to unauthorized - data transfer, compromising sensitive information and potentially leading to further - exploitation of the compromised host. -search: - '`powershell` EventCode=4104 ScriptBlockText = "*Net.webclient*" AND ScriptBlockText - = "*.UploadString*" | fillnull | stats count min(_time) as firstTime max(_time) - as lastTime by dest signature signature_id user_id vendor_product EventID Guid Opcode - Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_exfiltration_over_c2_via_powershell_uploadstring_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - Powershell Script Block Logging 4104 +description: The following analytic identifies potential data exfiltration using the PowerShell `net.webclient` command with the `UploadString` method. It leverages PowerShell Script Block Logging to detect instances where this command is executed. This activity is significant as it may indicate an attempt to upload sensitive data, such as desktop screenshots or files, to an external or internal URI, often associated with malware like Winter-Vivern. If confirmed malicious, this could lead to unauthorized data transfer, compromising sensitive information and potentially leading to further exploitation of the compromised host. +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*Net.webclient*" AND ScriptBlockText = "*.UploadString*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_exfiltration_over_c2_via_powershell_uploadstring_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. known_false_positives: False positives should be limited. Filter as needed. references: - - https://twitter.com/_CERT_UA/status/1620781684257091584 - - https://cert.gov.ua/article/3761104 + - https://twitter.com/_CERT_UA/status/1620781684257091584 + - https://cert.gov.ua/article/3761104 drilldown_searches: - - name: View the detection results for - "$Computer$" - search: '%original_detection_search% | search Computer = "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$Computer$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" + search: '%original_detection_search% | search Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A PowerShell script on $dest$ is attempting to transfer files to a remote - URL. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: A PowerShell script on $dest$ is attempting to transfer files to a remote URL. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - APT37 Rustonotto and FadeStealer - - Winter Vivern - asset_type: Endpoint - mitre_attack_id: - - T1041 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - APT37 Rustonotto and FadeStealer + - Winter Vivern + asset_type: Endpoint + mitre_attack_id: + - T1041 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winter-vivern/pwh_uploadstring/windows-powershell-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winter-vivern/pwh_uploadstring/windows-powershell-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_explorer_exe_spawning_powershell_or_cmd.yml b/detections/endpoint/windows_explorer_exe_spawning_powershell_or_cmd.yml index 813290f5b0..5b4bad318f 100644 --- a/detections/endpoint/windows_explorer_exe_spawning_powershell_or_cmd.yml +++ b/detections/endpoint/windows_explorer_exe_spawning_powershell_or_cmd.yml @@ -7,29 +7,29 @@ status: production type: Hunting description: This detection identifies instances where Windows Explorer.exe spawns PowerShell or cmd.exe processes, particularly focusing on executions initiated by LNK files. This behavior is associated with the ZDI-CAN-25373 Windows shortcut zero-day vulnerability, where specially crafted LNK files are used to trigger malicious code execution through cmd.exe or powershell.exe. This technique has been actively exploited by multiple APT groups in targeted attacks through both HTTP and SMB delivery methods. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 + - Sysmon EventID 1 + - Windows Event Log Security 4688 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_path="*\\explorer.exe" `process_powershell` OR `process_cmd` by Processes.dest Processes.process_current_directory Processes.process_path Processes.process Processes.original_file_name Processes.parent_process Processes.parent_process_name Processes.parent_process_path Processes.parent_process_guid Processes.parent_process_id Processes.process_guid Processes.process_id Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_explorer_exe_spawning_powershell_or_cmd_filter`' how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Some legitimate user actions may trigger Explorer.exe to spawn PowerShell or cmd.exe, such as right-clicking and selecting "Open PowerShell window here" or similar options. Filter as needed based on your environment's normal behavior patterns. references: -- https://www.zerodayinitiative.com/advisories/ZDI-CAN-25373/ -- https://www.trendmicro.com/en_us/research/25/c/windows-shortcut-zero-day-exploit.html + - https://www.zerodayinitiative.com/advisories/ZDI-CAN-25373/ + - https://www.trendmicro.com/en_us/research/25/c/windows-shortcut-zero-day-exploit.html tags: - analytic_story: - - ZDI-CAN-25373 Windows Shortcut Exploit Abused as Zero-Day - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - - T1204.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ZDI-CAN-25373 Windows Shortcut Exploit Abused as Zero-Day + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + - T1204.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/encoded_powershell/explorer_spawns_windows-sysmon.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/encoded_powershell/explorer_spawns_windows-sysmon.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational diff --git a/detections/endpoint/windows_explorer_lnk_exploit_process_launch_with_padding.yml b/detections/endpoint/windows_explorer_lnk_exploit_process_launch_with_padding.yml index 2d1333e1d2..652d1dd99f 100644 --- a/detections/endpoint/windows_explorer_lnk_exploit_process_launch_with_padding.yml +++ b/detections/endpoint/windows_explorer_lnk_exploit_process_launch_with_padding.yml @@ -7,60 +7,49 @@ status: production type: TTP description: This detection identifies instances where Windows Explorer.exe spawns PowerShell or cmd.exe processes with abnormally large padding (50 or more spaces) in the command line. This specific pattern is a key indicator of the ZDI-CAN-25373 Windows shortcut zero-day vulnerability exploitation, where threat actors craft malicious LNK files containing padded content to trigger code execution. The excessive spacing in the command line is used to manipulate the way Windows processes the shortcut file, enabling arbitrary code execution. This technique has been actively exploited by multiple APT groups in targeted attacks, with malicious LNK files being delivered through both HTTP and SMB protocols. The presence of significant command line padding when Explorer.exe launches command shells is highly suspicious and warrants immediate investigation. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.parent_process_path="*\\explorer.exe" - (Processes.process_path="*\\cmd.exe" OR Processes.process_path="*\\powershell.exe") - by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | regex process=".*?\s{50,}.*" - | `windows_explorer_lnk_exploit_process_launch_with_padding_filter`' + - Sysmon EventID 1 + - Windows Event Log Security 4688 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_path="*\\explorer.exe" (Processes.process_path="*\\cmd.exe" OR Processes.process_path="*\\powershell.exe") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | regex process=".*?\s{50,}.*" | `windows_explorer_lnk_exploit_process_launch_with_padding_filter`' how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Some legitimate user actions may trigger Explorer.exe to spawn PowerShell or cmd.exe, such as right-clicking and selecting "Open PowerShell window here" or similar options. Filter as needed based on your environment's normal behavior patterns. Reduce or increase the padding threshold based on observed false positives. references: -- https://www.trendmicro.com/en_us/research/25/c/windows-shortcut-zero-day-exploit.html + - https://www.trendmicro.com/en_us/research/25/c/windows-shortcut-zero-day-exploit.html drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Explorer.exe spawning PowerShell or cmd.exe with excessive padding (50+ spaces) on $dest$ by $user$. - risk_objects: - - field: dest - type: system - score: 80 - - field: user - type: user - score: 80 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: Windows Explorer.exe spawning PowerShell or cmd.exe with excessive padding (50+ spaces) on $dest$ by $user$. + risk_objects: + - field: dest + type: system + score: 80 + - field: user + type: user + score: 80 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - ZDI-CAN-25373 Windows Shortcut Exploit Abused as Zero-Day - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - - T1204.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ZDI-CAN-25373 Windows Shortcut Exploit Abused as Zero-Day + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + - T1204.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/encoded_powershell/padded_windows-sysmon.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/encoded_powershell/padded_windows-sysmon.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational diff --git a/detections/endpoint/windows_export_certificate.yml b/detections/endpoint/windows_export_certificate.yml index 06a30c125b..28fb3c2229 100644 --- a/detections/endpoint/windows_export_certificate.yml +++ b/detections/endpoint/windows_export_certificate.yml @@ -1,67 +1,57 @@ name: Windows Export Certificate id: d8ddfa9b-b724-4df9-9dbe-f34cc0936714 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic detects the export of a certificate from the Windows - Certificate Store. It leverages the Certificates Lifecycle log channel, specifically - event ID 1007, to identify this activity. Monitoring certificate exports is crucial - as certificates can be used for authentication to VPNs or private resources. If - malicious actors export certificates, they could potentially gain unauthorized access - to sensitive systems or data, leading to significant security breaches. +description: The following analytic detects the export of a certificate from the Windows Certificate Store. It leverages the Certificates Lifecycle log channel, specifically event ID 1007, to identify this activity. Monitoring certificate exports is crucial as certificates can be used for authentication to VPNs or private resources. If malicious actors export certificates, they could potentially gain unauthorized access to sensitive systems or data, leading to significant security breaches. data_source: -- Windows Event Log CertificateServicesClient 1007 -search: '`certificateservices_lifecycle` EventCode=1007 | xmlkv UserData_Xml | stats - count min(_time) as firstTime max(_time) as lastTime by Computer, SubjectName, UserData_Xml - | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `windows_export_certificate_filter`' -how_to_implement: To implement this analytic, you must collect Microsoft-Windows-CertificateServicesClient-Lifecycle-System/Operational - or Microsoft-Windows-CertificateServicesClient-Lifecycle-User/Operational. -known_false_positives: False positives may be generated based on an automated process - or service that exports certificates on the regular. Review is required before setting - to alert. Monitor for abnormal processes performing an export. + - Windows Event Log CertificateServicesClient 1007 +search: |- + `certificateservices_lifecycle` EventCode=1007 + | xmlkv UserData_Xml + | stats count min(_time) as firstTime max(_time) as lastTime + BY Computer, SubjectName, UserData_Xml + | rename Computer as dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_export_certificate_filter` +how_to_implement: To implement this analytic, you must collect Microsoft-Windows-CertificateServicesClient-Lifecycle-System/Operational or Microsoft-Windows-CertificateServicesClient-Lifecycle-User/Operational. +known_false_positives: False positives may be generated based on an automated process or service that exports certificates on the regular. Review is required before setting to alert. Monitor for abnormal processes performing an export. references: -- https://atomicredteam.io/defense-evasion/T1553.004/#atomic-test-4---install-root-ca-on-windows + - https://atomicredteam.io/defense-evasion/T1553.004/#atomic-test-4---install-root-ca-on-windows drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An certificate was exported on $dest$ from the Windows Certificate Store. - risk_objects: - - field: dest - type: system - score: 36 - threat_objects: [] + message: An certificate was exported on $dest$ from the Windows Certificate Store. + risk_objects: + - field: dest + type: system + score: 36 + threat_objects: [] tags: - analytic_story: - - Windows Certificate Services - asset_type: Endpoint - mitre_attack_id: - - T1552.004 - - T1649 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Certificate Services + asset_type: Endpoint + mitre_attack_id: + - T1552.004 + - T1649 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/certificateservices-lifecycle.log - source: - XmlWinEventLog:Microsoft-Windows-CertificateServicesClient-Lifecycle-System/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/certificateservices-lifecycle.log + source: XmlWinEventLog:Microsoft-Windows-CertificateServicesClient-Lifecycle-System/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_file_and_directory_enable_readonly_permissions.yml b/detections/endpoint/windows_file_and_directory_enable_readonly_permissions.yml index f9dca6c7d7..26e08c17c6 100644 --- a/detections/endpoint/windows_file_and_directory_enable_readonly_permissions.yml +++ b/detections/endpoint/windows_file_and_directory_enable_readonly_permissions.yml @@ -4,83 +4,49 @@ version: 4 date: '2025-11-20' author: Teoderick Contreras, Splunk data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 + - Sysmon EventID 1 + - Windows Event Log Security 4688 type: TTP status: production -description: The following analytic detects instances where file or folder permissions - are modified to grant read-only access. Such changes are characterized by the presence - of read-related permissions (e.g., R, REA, RA, RD) and the absence of write (W) - or execute (E) permissions. Monitoring these events is crucial for tracking access - control changes that could be intentional for restricting access or indicative of - malicious behavior. Alerts generated by this detection help ensure that legitimate - security measures are enforced while unauthorized changes are promptly investigated. -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN( "icacls.exe", - "cacls.exe", "xcacls.exe") AND Processes.process IN ("*/grant*", "*/G*") AND Processes.process - IN ("*SYSTEM*", "*admin*", "*S-1-1-0*", "*EVERYONE*") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | rex field=process ":\\((?[^)]+)\\)" - | eval has_read_attribute=if(match(permission, "R"), "true", "false") | eval has_write_execute=if(match(permission, - "(W|GA|X|M|F|AD|DC|DE)"), "true", "false") | where has_write_execute="false" and - has_read_attribute = "true" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_file_and_directory_enable_readonly_permissions_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrators or administrative scripts may use this application. - Filter as needed. +description: The following analytic detects instances where file or folder permissions are modified to grant read-only access. Such changes are characterized by the presence of read-related permissions (e.g., R, REA, RA, RD) and the absence of write (W) or execute (E) permissions. Monitoring these events is crucial for tracking access control changes that could be intentional for restricting access or indicative of malicious behavior. Alerts generated by this detection help ensure that legitimate security measures are enforced while unauthorized changes are promptly investigated. +search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN( "icacls.exe", "cacls.exe", "xcacls.exe") AND Processes.process IN ("*/grant*", "*/G*") AND Processes.process IN ("*SYSTEM*", "*admin*", "*S-1-1-0*", "*EVERYONE*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | rex field=process ":\\((?[^)]+)\\)" | eval has_read_attribute=if(match(permission, "R"), "true", "false") | eval has_write_execute=if(match(permission, "(W|GA|X|M|F|AD|DC|DE)"), "true", "false") | where has_write_execute="false" and has_read_attribute = "true" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_file_and_directory_enable_readonly_permissions_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators or administrative scripts may use this application. Filter as needed. references: -- https://www.splunk.com/en_us/blog/security/-applocker-rules-as-defense-evasion-complete-analysis.html + - https://www.splunk.com/en_us/blog/security/-applocker-rules-as-defense-evasion-complete-analysis.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$process_name$] was executed on [$dest$] attempting to change the access - to a file or directory into readonly permissions. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: - - field: process_name - type: process_name + message: A [$process_name$] was executed on [$dest$] attempting to change the access to a file or directory into readonly permissions. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Crypto Stealer - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1222.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Crypto Stealer + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1222.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/icacls_inheritance/icacls_process_1.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/icacls_inheritance/icacls_process_1.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_file_and_directory_permissions_enable_inheritance.yml b/detections/endpoint/windows_file_and_directory_permissions_enable_inheritance.yml index 4ade529f48..25c2c725b8 100644 --- a/detections/endpoint/windows_file_and_directory_permissions_enable_inheritance.yml +++ b/detections/endpoint/windows_file_and_directory_permissions_enable_inheritance.yml @@ -1,73 +1,58 @@ name: Windows File and Directory Permissions Enable Inheritance id: 0247f90a-aca4-47b2-a94d-e30f445d7b41 -version: 4 -date: '2025-11-20' +version: 5 +date: '2026-02-25' author: Teoderick Contreras, Splunk type: Hunting status: production -description: The following analytic detects the enabling of permission inheritance - using ICACLS. This analytic identifies instances where ICACLS commands are used - to enable permission inheritance on files or directories. The /inheritance:e flag, - which restores inherited permissions from a parent directory, is monitored to detect - changes that might reapply broader access control settings. Enabling inheritance - can indicate legitimate administrative actions but may also signal attempts to override - restrictive custom permissions, potentially exposing sensitive files to unauthorized - access. +description: The following analytic detects the enabling of permission inheritance using ICACLS. This analytic identifies instances where ICACLS commands are used to enable permission inheritance on files or directories. The /inheritance:e flag, which restores inherited permissions from a parent directory, is monitored to detect changes that might reapply broader access control settings. Enabling inheritance can indicate legitimate administrative actions but may also signal attempts to override restrictive custom permissions, potentially exposing sensitive files to unauthorized access. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN( "icacls.exe", - "cacls.exe", "xcacls.exe") AND Processes.process = "*/inheritance:e*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_file_and_directory_permissions_enable_inheritance_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrators or administrative scripts may use this application. - Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name IN( "icacls.exe", "cacls.exe", "xcacls.exe") + AND + Processes.process = "*/inheritance:e*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_file_and_directory_permissions_enable_inheritance_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators or administrative scripts may use this application. Filter as needed. references: -- https://www.splunk.com/en_us/blog/security/-applocker-rules-as-defense-evasion-complete-analysis.html + - https://www.splunk.com/en_us/blog/security/-applocker-rules-as-defense-evasion-complete-analysis.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ tags: - analytic_story: - - Crypto Stealer - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1222.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Crypto Stealer + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1222.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/icacls_inheritance/icacls_process_1.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/icacls_inheritance/icacls_process_1.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_file_and_directory_permissions_remove_inheritance.yml b/detections/endpoint/windows_file_and_directory_permissions_remove_inheritance.yml index 0cebb9baec..90a5d2c9ac 100644 --- a/detections/endpoint/windows_file_and_directory_permissions_remove_inheritance.yml +++ b/detections/endpoint/windows_file_and_directory_permissions_remove_inheritance.yml @@ -1,82 +1,66 @@ name: Windows File and Directory Permissions Remove Inheritance id: 9b62da2c-e442-474f-83ca-fac4dabab1b3 -version: 3 -date: '2025-05-02' +version: 4 +date: '2026-02-25' author: Teoderick Contreras, Splunk data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 + - Sysmon EventID 1 + - Windows Event Log Security 4688 type: Anomaly status: production -description: The following analytic detects the removal of permission inheritance - using ICACLS. This analytic identifies instances where ICACLS is used to remove - permission inheritance from files or directories. The /inheritance:r flag, which - strips inherited permissions while optionally preserving or altering explicit permissions, - is monitored to detect changes that may restrict access or establish isolated permission - configurations. Removing inheritance can be a legitimate administrative action but - may also indicate an attempt to conceal malicious activity or bypass inherited security - controls. -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN( "icacls.exe", - "cacls.exe", "xcacls.exe") AND Processes.process = "*/inheritance:r*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_file_and_directory_permissions_remove_inheritance_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrators or administrative scripts may use this application. - Filter as needed. +description: The following analytic detects the removal of permission inheritance using ICACLS. This analytic identifies instances where ICACLS is used to remove permission inheritance from files or directories. The /inheritance:r flag, which strips inherited permissions while optionally preserving or altering explicit permissions, is monitored to detect changes that may restrict access or establish isolated permission configurations. Removing inheritance can be a legitimate administrative action but may also indicate an attempt to conceal malicious activity or bypass inherited security controls. +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name IN( "icacls.exe", "cacls.exe", "xcacls.exe") + AND + Processes.process = "*/inheritance:r*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_file_and_directory_permissions_remove_inheritance_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators or administrative scripts may use this application. Filter as needed. references: -- https://www.splunk.com/en_us/blog/security/-applocker-rules-as-defense-evasion-complete-analysis.html + - https://www.splunk.com/en_us/blog/security/-applocker-rules-as-defense-evasion-complete-analysis.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A [$process_name$] was executed on [$dest$] attempting to remove inheritance - permissions. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: - - field: process_name - type: process_name + message: A [$process_name$] was executed on [$dest$] attempting to remove inheritance permissions. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Crypto Stealer - asset_type: Endpoint - mitre_attack_id: - - T1222.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Crypto Stealer + asset_type: Endpoint + mitre_attack_id: + - T1222.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/icacls_inheritance/icacls_process_1.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/icacls_inheritance/icacls_process_1.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_file_collection_via_copy_utilities.yml b/detections/endpoint/windows_file_collection_via_copy_utilities.yml index fb97d9f5cb..0c74e5b6d1 100644 --- a/detections/endpoint/windows_file_collection_via_copy_utilities.yml +++ b/detections/endpoint/windows_file_collection_via_copy_utilities.yml @@ -1,107 +1,93 @@ name: Windows File Collection Via Copy Utilities id: dbdd556d-9da8-4c42-9980-8a3ffe25a758 -version: 2 -date: '2025-12-18' +version: 3 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: The following analytic detects the use of Windows command-line copy utilities, such as xcopy, to systematically collect files from user directories and consolidate them into a centralized location on the system. This activity is often indicative of malicious behavior, as threat actors frequently use such commands to gather sensitive information, including documents with .doc, .docx, and .pdf extensions. The detection focuses on identifying recursive copy operations targeting user folders, such as Documents, Desktop, or other directories that commonly store personal or organizational files. Malware that performs this behavior typically attempts to evade detection by using legitimate Windows utilities, executing commands through cmd.exe or other scripting hosts, and writing the collected files to directories like C:\ProgramData or temporary storage locations. Once collected, the information may be staged for exfiltration, used for lateral movement, or leveraged for further compromise of the environment. By monitoring for these types of file collection patterns, security teams can identify suspicious activity early, differentiate between normal administrative tasks and potentially malicious scripts, and prevent sensitive data from being exfiltrated. This analytic is particularly relevant for environments where confidential documents are present and attackers may attempt to harvest them using built-in Windows tools. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime - from datamodel=Endpoint.Processes where - ( - Processes.process_name IN ("copy.exe", "xcopy.exe") - OR - Processes.original_file_name IN ("copy.exe", "xcopy.exe") - ) - Processes.process IN ( - "*.7z*", - "*.bmp*", - "*.db*", - "*.doc*", - "*.gif*", - "*.gz*", - "*.jpg*", - "*.log*", - "*.pdf*", - "*.png*", - "*.ppt*", - "*.rar*", - "*.rtf*", - "*.tar*", - "*.txt*", - "*.xls*", - "*.zip*" - ) - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_file_collection_via_copy_utilities_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime + from datamodel=Endpoint.Processes where + ( + Processes.process_name IN ("copy.exe", "xcopy.exe") + OR + Processes.original_file_name IN ("copy.exe", "xcopy.exe") + ) + Processes.process IN ( + "*.7z*", + "*.bmp*", + "*.db*", + "*.doc*", + "*.gif*", + "*.gz*", + "*.jpg*", + "*.log*", + "*.pdf*", + "*.png*", + "*.ppt*", + "*.rar*", + "*.rtf*", + "*.tar*", + "*.txt*", + "*.xls*", + "*.zip*" + ) + by Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_file_collection_via_copy_utilities_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators may execute this command for testing or auditing. references: -- https://cert.gov.ua/article/6284730 + - https://cert.gov.ua/article/6284730 drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to collect documents.. - risk_objects: - - field: user - type: user - score: 5 - - field: dest - type: system - score: 5 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to collect documents.. + risk_objects: + - field: user + type: user + score: 5 + - field: dest + type: system + score: 5 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - LAMEHUG - asset_type: Endpoint - mitre_attack_id: - - T1119 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - LAMEHUG + asset_type: Endpoint + mitre_attack_id: + - T1119 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/lamehug/T1119/doc_collection/xcopy_event.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/lamehug/T1119/doc_collection/xcopy_event.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_file_download_via_certutil.yml b/detections/endpoint/windows_file_download_via_certutil.yml index b20964b9de..37113bcaa4 100644 --- a/detections/endpoint/windows_file_download_via_certutil.yml +++ b/detections/endpoint/windows_file_download_via_certutil.yml @@ -1,107 +1,95 @@ name: Windows File Download Via CertUtil id: 7fac8d40-e370-45ea-a4a3-031bbcc18b02 -version: 3 -date: '2025-06-30' +version: 4 +date: '2026-02-25' author: Nasreddine Bencherchali, Michael Haag, Splunk status: production type: TTP description: The following analytic detects the use of `certutil.exe` to download files using the `-URL`, `-urlcache` or '-verifyctl' arguments. This behavior is identified by monitoring command-line executions for these specific arguments via Endpoint Detection and Response (EDR) telemetry. This activity is significant because `certutil.exe` is a legitimate tool often abused by attackers to download and execute malicious payloads. If confirmed malicious, this could allow an attacker to download and execute arbitrary files, potentially leading to code execution, data exfiltration, or further compromise of the system. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 - - Cisco Network Visibility Module Flow Data -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_certutil` AND ((Processes.process IN ("*-URL *", "*/URL *")) OR (Processes.process IN ("*urlcache*", "*verifyctl*") AND Processes.process IN ("*/f *", "*-f *"))) - by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_file_download_via_certutil_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: - Limited false positives in most environments, however tune - as needed based on parent-child relationship or network connection. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 + - Cisco Network Visibility Module Flow Data +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_certutil` + AND + ((Processes.process IN ("*-URL *", "*/URL *")) + OR + (Processes.process IN ("*urlcache*", "*verifyctl*") + AND + Processes.process IN ("*/f *", "*-f *"))) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_file_download_via_certutil_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Limited false positives in most environments, however tune as needed based on parent-child relationship or network connection. references: - - https://attack.mitre.org/techniques/T1105/ - - https://www.hexacorn.com/blog/2020/08/23/certutil-one-more-gui-lolbin/ - - https://www.avira.com/en/blog/certutil-abused-by-attackers-to-spread-threats - - https://web.archive.org/web/20210921110637/https://www.fireeye.com/blog/threat-research/2019/10/certutil-qualms-they-came-to-drop-fombs.html - - https://lolbas-project.github.io/lolbas/Binaries/Certutil/ - - https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc732443(v=ws.11)#-verifyctl + - https://attack.mitre.org/techniques/T1105/ + - https://www.hexacorn.com/blog/2020/08/23/certutil-one-more-gui-lolbin/ + - https://www.avira.com/en/blog/certutil-abused-by-attackers-to-spread-threats + - https://web.archive.org/web/20210921110637/https://www.fireeye.com/blog/threat-research/2019/10/certutil-qualms-they-came-to-drop-fombs.html + - https://lolbas-project.github.io/lolbas/Binaries/Certutil/ + - https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc732443(v=ws.11)#-verifyctl drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to download a file. - risk_objects: - - field: user - type: user - score: 90 - - field: dest - type: system - score: 90 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to download a file. + risk_objects: + - field: user + type: user + score: 90 + - field: dest + type: system + score: 90 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Living Off The Land - - Ingress Tool Transfer - - ProxyNotShell - - DarkSide Ransomware - - Forest Blizzard - - Flax Typhoon - - Compromised Windows Host - - CISA AA22-277A - - Cisco Network Visibility Module Analytics - asset_type: Endpoint - mitre_attack_id: - - T1105 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + - Ingress Tool Transfer + - ProxyNotShell + - DarkSide Ransomware + - Forest Blizzard + - Flax Typhoon + - Compromised Windows Host + - CISA AA22-277A + - Cisco Network Visibility Module Analytics + asset_type: Endpoint + mitre_attack_id: + - T1105 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - Sysmon - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata + - name: True Positive Test - Sysmon + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/windows_file_download_via_powershell.yml b/detections/endpoint/windows_file_download_via_powershell.yml index 4189dda48a..14df012cef 100644 --- a/detections/endpoint/windows_file_download_via_powershell.yml +++ b/detections/endpoint/windows_file_download_via_powershell.yml @@ -6,127 +6,103 @@ author: Michael Haag, Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - The following analytic detects the use of PowerShell's download methods such as - "DownloadString" and "DownloadData" from the WebClient class or Invoke-WebRequest - and it's aliases "IWR" or "Curl". - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on - process execution logs that include command-line details. - This activity can be significant such methods and functions are commonly used in malicious - PowerShell scripts to fetch and execute remote code. - If confirmed malicious, this behavior could allow an attacker to download and run - arbitrary code, potentially leading to unauthorized access, data exfiltration, - or further compromise of the affected system. + The following analytic detects the use of PowerShell's download methods such as + "DownloadString" and "DownloadData" from the WebClient class or Invoke-WebRequest + and it's aliases "IWR" or "Curl". + It leverages data from Endpoint Detection and Response (EDR) agents, focusing on + process execution logs that include command-line details. + This activity can be significant such methods and functions are commonly used in malicious + PowerShell scripts to fetch and execute remote code. + If confirmed malicious, this behavior could allow an attacker to download and run + arbitrary code, potentially leading to unauthorized access, data exfiltration, + or further compromise of the affected system. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 - - Cisco Network Visibility Module Flow Data -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - `process_powershell` - Processes.process IN ( - "*iwr *", "*Invoke-WebRequest*", "*wget *", - "curl", "*.DownloadData*", "*.DownloadFile*", - "*.DownloadString*" - ) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_file_download_via_powershell_filter`' + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 + - Cisco Network Visibility Module Flow Data +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` Processes.process IN ( "*iwr *", "*Invoke-WebRequest*", "*wget *", "curl", "*.DownloadData*", "*.DownloadFile*", "*.DownloadString*" ) by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_file_download_via_powershell_filter`' how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: | - False positives may be present and filtering will need to occur - by parent process or command line argument. It may be required to modify this query - to an EDR product for more granular coverage. + False positives may be present and filtering will need to occur + by parent process or command line argument. It may be required to modify this query + to an EDR product for more granular coverage. references: - - https://learn.microsoft.com/en-us/dotnet/api/system.net.webclient?view=net-9.0#methods - - https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/ - - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md - - https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/ + - https://learn.microsoft.com/en-us/dotnet/api/system.net.webclient?view=net-9.0#methods + - https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md + - https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/ drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: File download activity initiated on $dest$ by user $user$. - $process_name$ was identified calling a download function $process$ - risk_objects: - - field: user - type: user - score: 56 - - field: dest - type: system - score: 56 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: File download activity initiated on $dest$ by user $user$. $process_name$ was identified calling a download function $process$ + risk_objects: + - field: user + type: user + score: 56 + - field: dest + type: system + score: 56 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - APT37 Rustonotto and FadeStealer - - Cisco Network Visibility Module Analytics - - Data Destruction - - GhostRedirector IIS Module and Rungan Backdoor - - HAFNIUM Group - - Hermetic Wiper - - IcedID - - Ingress Tool Transfer - - Malicious PowerShell - - Microsoft WSUS CVE-2025-59287 - - NetSupport RMM Tool Abuse - - NPM Supply Chain Compromise - - Phemedrone Stealer - - PHP-CGI RCE Attack on Japanese Organizations - - SysAid On-Prem Software CVE-2023-47246 Vulnerability - - Winter Vivern - - XWorm - - Tuoni - - StealC Stealer - - SolarWinds WHD RCE Post Exploitation - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - - T1105 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - APT37 Rustonotto and FadeStealer + - Cisco Network Visibility Module Analytics + - Data Destruction + - GhostRedirector IIS Module and Rungan Backdoor + - HAFNIUM Group + - Hermetic Wiper + - IcedID + - Ingress Tool Transfer + - Malicious PowerShell + - Microsoft WSUS CVE-2025-59287 + - NetSupport RMM Tool Abuse + - NPM Supply Chain Compromise + - Phemedrone Stealer + - PHP-CGI RCE Attack on Japanese Organizations + - SysAid On-Prem Software CVE-2023-47246 Vulnerability + - Winter Vivern + - XWorm + - Tuoni + - StealC Stealer + - SolarWinds WHD RCE Post Exploitation + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + - T1105 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - Sysmon - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata + - name: True Positive Test - Sysmon + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/windows_file_share_discovery_with_powerview.yml b/detections/endpoint/windows_file_share_discovery_with_powerview.yml index 3ddd68bd35..d0da55cafc 100644 --- a/detections/endpoint/windows_file_share_discovery_with_powerview.yml +++ b/detections/endpoint/windows_file_share_discovery_with_powerview.yml @@ -1,77 +1,65 @@ name: Windows File Share Discovery With Powerview id: a44c0be1-d7ab-41e4-92fd-aa9af4fe232c -version: 7 -date: '2025-06-24' +version: 8 +date: '2026-02-25' author: Mauricio Velazco, Splunk type: TTP status: production data_source: - - Powershell Script Block Logging 4104 -description: - The following analytic detects the execution of the Invoke-ShareFinder - PowerShell cmdlet from PowerView. This detection leverages PowerShell Script Block - Logging to identify instances where this specific command is executed. Monitoring - this activity is crucial as it indicates an attempt to enumerate network file shares, - which may contain sensitive information such as backups, scripts, and credentials. - If confirmed malicious, this activity could enable an attacker to escalate privileges - or move laterally within the network, potentially compromising additional systems - and sensitive data. -search: - '`powershell` EventCode=4104 (ScriptBlockText=Invoke-ShareFinder*) | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_file_share_discovery_with_powerview_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba.= -known_false_positives: - Security teams may leverage PowerView proactively to identify - and remediate sensitive file shares. Filter as needed. + - Powershell Script Block Logging 4104 +description: The following analytic detects the execution of the Invoke-ShareFinder PowerShell cmdlet from PowerView. This detection leverages PowerShell Script Block Logging to identify instances where this specific command is executed. Monitoring this activity is crucial as it indicates an attempt to enumerate network file shares, which may contain sensitive information such as backups, scripts, and credentials. If confirmed malicious, this activity could enable an attacker to escalate privileges or move laterally within the network, potentially compromising additional systems and sensitive data. +search: |- + `powershell` EventCode=4104 (ScriptBlockText=Invoke-ShareFinder*) + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_file_share_discovery_with_powerview_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba.= +known_false_positives: Security teams may leverage PowerView proactively to identify and remediate sensitive file shares. Filter as needed. references: - - https://github.com/PowerShellEmpire/PowerTools/blob/master/PowerView/powerview.ps1 - - https://thedfirreport.com/2023/01/23/sharefinder-how-threat-actors-discover-file-shares/ - - https://attack.mitre.org/techniques/T1135/ + - https://github.com/PowerShellEmpire/PowerTools/blob/master/PowerView/powerview.ps1 + - https://thedfirreport.com/2023/01/23/sharefinder-how-threat-actors-discover-file-shares/ + - https://attack.mitre.org/techniques/T1135/ drilldown_searches: - - name: View the detection results for - "$dest$" and "$user_id$" - search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user_id$" + search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Invoke-ShareFinder commandlet was executed on $dest$ - risk_objects: - - field: dest - type: system - score: 48 - - field: user_id - type: user - score: 48 - threat_objects: [] + message: Invoke-ShareFinder commandlet was executed on $dest$ + risk_objects: + - field: dest + type: system + score: 48 + - field: user_id + type: user + score: 48 + threat_objects: [] tags: - analytic_story: - - Active Directory Privilege Escalation - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1135 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Privilege Escalation + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1135 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1135/powerview_sharefinder/windows-powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1135/powerview_sharefinder/windows-powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_file_transfer_protocol_in_non_common_process_path.yml b/detections/endpoint/windows_file_transfer_protocol_in_non_common_process_path.yml index 4f60855c10..477254216a 100644 --- a/detections/endpoint/windows_file_transfer_protocol_in_non_common_process_path.yml +++ b/detections/endpoint/windows_file_transfer_protocol_in_non_common_process_path.yml @@ -5,83 +5,68 @@ date: '2025-10-20' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects FTP connections initiated by processes - located in non-standard installation paths on Windows systems. It leverages Sysmon - EventCode 3 to identify network connections where the process image path does not - match common directories like "Program Files" or "Windows\System32". This activity - is significant as FTP is often used by adversaries and malware, such as AgentTesla, - for Command and Control (C2) communications to exfiltrate stolen data. If confirmed - malicious, this could lead to unauthorized data transfer, exposing sensitive information - and compromising the integrity of the affected host. +description: The following analytic detects FTP connections initiated by processes located in non-standard installation paths on Windows systems. It leverages Sysmon EventCode 3 to identify network connections where the process image path does not match common directories like "Program Files" or "Windows\System32". This activity is significant as FTP is often used by adversaries and malware, such as AgentTesla, for Command and Control (C2) communications to exfiltrate stolen data. If confirmed malicious, this could lead to unauthorized data transfer, exposing sensitive information and compromising the integrity of the affected host. data_source: -- Sysmon EventID 3 + - Sysmon EventID 3 search: | - `sysmon` - EventCode=3 - NOT Image IN( - "C:\\Program Files \(x86\)\\*", - "C:\\Program Files\\*", - "C:\\Windows\\System32\\*", - "C:\\Windows\\SysWOW64\\*" - ) - ( - DestinationPortName="ftp" - OR - DestinationPort=21 - ) - | stats count min(_time) as firstTime - max(_time) as lastTime + `sysmon` + EventCode=3 + NOT Image IN( + "C:\\Program Files \(x86\)\\*", + "C:\\Program Files\\*", + "C:\\Windows\\System32\\*", + "C:\\Windows\\SysWOW64\\*" + ) + ( + DestinationPortName="ftp" + OR + DestinationPort=21 + ) + | stats count min(_time) as firstTime + max(_time) as lastTime - by action app dest dest_ip dest_port direction dvc protocol protocol_version - src src_ip src_port transport user vendor_product process_name - process_exec process_guid process_id - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_file_transfer_protocol_in_non_common_process_path_filter` -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name and sysmon eventcode = 3 connection events from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. + by action app dest dest_ip dest_port direction dvc protocol protocol_version + src src_ip src_port transport user vendor_product process_name + process_exec process_guid process_id + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_file_transfer_protocol_in_non_common_process_path_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and sysmon eventcode = 3 connection events from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: Third party FTP based applications will trigger this. Apply additional filters as needed. Also consider excluding known FTP based clients installed outside of the Program Files and Windows directories. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.agent_tesla + - https://malpedia.caad.fkie.fraunhofer.de/details/win.agent_tesla drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a process $process_name$ is having a FTP connection to $dest$ in $dest_ip$ - risk_objects: - - field: dest - type: system - score: 9 - threat_objects: [] + message: a process $process_name$ is having a FTP connection to $dest$ in $dest_ip$ + risk_objects: + - field: dest + type: system + score: 9 + threat_objects: [] tags: - analytic_story: - - AgentTesla - - Snake Keylogger - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1071.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AgentTesla + - Snake Keylogger + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1071.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/agent_tesla/agent_tesla_ftp/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/agent_tesla/agent_tesla_ftp/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_file_without_extension_in_critical_folder.yml b/detections/endpoint/windows_file_without_extension_in_critical_folder.yml index e07c0e2403..872155dcde 100644 --- a/detections/endpoint/windows_file_without_extension_in_critical_folder.yml +++ b/detections/endpoint/windows_file_without_extension_in_critical_folder.yml @@ -1,72 +1,65 @@ name: Windows File Without Extension In Critical Folder id: 0dbcac64-963c-11ec-bf04-acde48001122 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Bhavin Patel, Splunk status: production type: TTP description: This analytic detects the creation of files without extensions in critical Windows system and driver-related directories, including but not limited to System32\Drivers, Windows\WinSxS, and other known Windows driver storage and loading paths. The detection has been expanded to comprehensively cover all commonly abused and legitimate Windows driver folder locations, increasing visibility into attempts to stage or deploy kernel-mode components. The analytic leverages telemetry from the Endpoint.Filesystem data model, with a focus on file creation events and file path analysis. File creation activity in these directories—particularly involving extensionless files—is highly suspicious, as it may indicate the presence of destructive or stealthy malware. This behavior is consistent with malware families such as HermeticWiper, which deploy kernel driver components into trusted Windows driver directories to obtain low-level access and execute destructive payloads. If confirmed malicious, this activity can result in severe system compromise, including the deployment of malicious drivers, boot-sector or filesystem destruction, and ultimately system inoperability and irreversible data loss. data_source: -- Sysmon EventID 11 + - Sysmon EventID 11 search: | - | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem - where Filesystem.file_path IN("*\\System32\\drivers\\*", "*\\syswow64\\drivers\\*", "*\\WINDOWS\\inf\\*","*\\Program Files*", "*\\WINDOWS\\System32\\DriverStore\\*","*:\\Windows\\WinSxS\\*","*\\ProgramData\\Microsoft\\Windows Defender\\Definition Updates\\*","*\\ProgramData\\Microsoft\\Windows Defender\\Platform\\*","*\\Windows\\servicing\\*", "*\\Windows\\ELAMBKUP\\*","*\\Windows\\Boot\\*","*\\System32\\Boot\\*","*\\System32\\Recovery\\*", "C:\\AMD\\*", "C:\\OEM\\*") - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path - Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product - | `drop_dm_object_name(Filesystem)` - | rex field="file_name" "\.(?[^\.]*$)" - | where isnull(extension) - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_file_without_extension_in_critical_folder_filter` -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the Filesystem responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Filesystem` node. + | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem + where Filesystem.file_path IN("*\\System32\\drivers\\*", "*\\syswow64\\drivers\\*", "*\\WINDOWS\\inf\\*","*\\Program Files*", "*\\WINDOWS\\System32\\DriverStore\\*","*:\\Windows\\WinSxS\\*","*\\ProgramData\\Microsoft\\Windows Defender\\Definition Updates\\*","*\\ProgramData\\Microsoft\\Windows Defender\\Platform\\*","*\\Windows\\servicing\\*", "*\\Windows\\ELAMBKUP\\*","*\\Windows\\Boot\\*","*\\System32\\Boot\\*","*\\System32\\Recovery\\*", "C:\\AMD\\*", "C:\\OEM\\*") + by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time + Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path + Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | rex field="file_name" "\.(?[^\.]*$)" + | where isnull(extension) + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_file_without_extension_in_critical_folder_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. known_false_positives: No false positives have been identified at this time. references: -- https://blog.talosintelligence.com/2022/02/threat-advisory-hermeticwiper.html -- https://www.splunk.com/en_us/blog/security/detecting-hermeticwiper.html -- https://learn.microsoft.com/en-us/answers/questions/2184241/where-does-windows-installation-search-for-drivers + - https://blog.talosintelligence.com/2022/02/threat-advisory-hermeticwiper.html + - https://www.splunk.com/en_us/blog/security/detecting-hermeticwiper.html + - https://learn.microsoft.com/en-us/answers/questions/2184241/where-does-windows-installation-search-for-drivers drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Driver file with out file extension drop in $file_path$ on $dest$ - risk_objects: - - field: user - type: user - score: 90 - threat_objects: - - field: file_name - type: file_name + message: Driver file with out file extension drop in $file_path$ on $dest$ + risk_objects: + - field: user + type: user + score: 90 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - Data Destruction - - Hermetic Wiper - asset_type: Endpoint - mitre_attack_id: - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Destruction + - Hermetic Wiper + asset_type: Endpoint + mitre_attack_id: + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/hermetic_wiper/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/hermetic_wiper/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_files_and_dirs_access_rights_modification_via_icacls.yml b/detections/endpoint/windows_files_and_dirs_access_rights_modification_via_icacls.yml index cff1b33441..6ef409ef57 100644 --- a/detections/endpoint/windows_files_and_dirs_access_rights_modification_via_icacls.yml +++ b/detections/endpoint/windows_files_and_dirs_access_rights_modification_via_icacls.yml @@ -1,93 +1,77 @@ name: Windows Files and Dirs Access Rights Modification Via Icacls id: c76b796c-27e1-4520-91c4-4a58695c749e -version: 10 -date: '2026-01-14' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP description: | - The following analytic identifies the modification of security permissions - on files or directories using tools like icacls.exe, cacls.exe, or xcacls.exe. It - leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific - command-line executions. This activity is significant as it is commonly used by - Advanced Persistent Threats (APTs) and coinminer scripts to evade detection and - maintain control over compromised systems. If confirmed malicious, this behavior - could allow attackers to hinder investigation, impede remediation efforts, and maintain - persistent access to the compromised environment. + The following analytic identifies the modification of security permissions + on files or directories using tools like icacls.exe, cacls.exe, or xcacls.exe. It + leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific + command-line executions. This activity is significant as it is commonly used by + Advanced Persistent Threats (APTs) and coinminer scripts to evade detection and + maintain control over compromised systems. If confirmed malicious, this behavior + could allow attackers to hinder investigation, impede remediation efforts, and maintain + persistent access to the compromised environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - Processes.process_name IN ("icacls.exe", "cacls.exe","xcacls.exe") AND - Processes.process IN ("*:R*", "*:W*", "*:F*", "*:C*", "*:N*", "*/P*", "*/E*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_files_and_dirs_access_rights_modification_via_icacls_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: No false positives have been identified at this time. - Filter as needed. + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where + Processes.process_name IN ("icacls.exe", "cacls.exe","xcacls.exe") AND + Processes.process IN ("*:R*", "*:W*", "*:F*", "*:C*", "*:N*", "*/P*", "*/E*") + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_files_and_dirs_access_rights_modification_via_icacls_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: No false positives have been identified at this time. Filter as needed. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.amadey + - https://malpedia.caad.fkie.fraunhofer.de/details/win.amadey drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Process name $process_name$ with access right modification argument executed - by $user$ to change security permission of a specific file or directory on host - $dest$ - risk_objects: - - field: dest - type: system - score: 49 - - field: user - type: user - score: 49 - threat_objects: [] + message: Process name $process_name$ with access right modification argument executed by $user$ to change security permission of a specific file or directory on host $dest$ + risk_objects: + - field: dest + type: system + score: 49 + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - Amadey - - Defense Evasion or Unauthorized Access Via SDDL Tampering - asset_type: Endpoint - atomic_guid: - - 3309f53e-b22b-4eb6-8fd2-a6cf58b355a9 - mitre_attack_id: - - T1222.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Amadey + - Defense Evasion or Unauthorized Access Via SDDL Tampering + asset_type: Endpoint + atomic_guid: + - 3309f53e-b22b-4eb6-8fd2-a6cf58b355a9 + mitre_attack_id: + - T1222.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/amadey/access_permission/amadey_sysmon2.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/amadey/access_permission/amadey_sysmon2.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_find_domain_organizational_units_with_getdomainou.yml b/detections/endpoint/windows_find_domain_organizational_units_with_getdomainou.yml index 224a70bc40..7ca64cf253 100644 --- a/detections/endpoint/windows_find_domain_organizational_units_with_getdomainou.yml +++ b/detections/endpoint/windows_find_domain_organizational_units_with_getdomainou.yml @@ -1,72 +1,64 @@ name: Windows Find Domain Organizational Units with GetDomainOU id: 0ada2f82-b7af-40cc-b1d7-1e5985afcb4e -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Gowthamaraj Rajendran, Mauricio Velazco, Splunk status: production type: TTP data_source: -- Powershell Script Block Logging 4104 -description: The following analytic detects the execution of the `Get-DomainOU` cmdlet, - a part of the PowerView toolkit used for Windows domain enumeration. It leverages - PowerShell Script Block Logging (EventCode=4104) to identify this activity. Detecting - `Get-DomainOU` usage is significant as adversaries may use it to gather information - about organizational units within Active Directory, which can facilitate lateral - movement or privilege escalation. If confirmed malicious, this activity could allow - attackers to map the domain structure, aiding in further exploitation and persistence - within the network. -search: '`powershell` EventCode=4104 ScriptBlockText = "*Get-DomainOU*" | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_find_domain_organizational_units_with_getdomainou_filter`' -how_to_implement: The following Hunting analytic requires PowerShell operational logs - to be imported. Modify the powershell macro as needed to match the sourcetype or - add index. This analytic is specific to 4104, or PowerShell Script Block Logging. -known_false_positives: Administrators may leverage PowerSploit tools for legitimate - reasons, filter as needed. + - Powershell Script Block Logging 4104 +description: The following analytic detects the execution of the `Get-DomainOU` cmdlet, a part of the PowerView toolkit used for Windows domain enumeration. It leverages PowerShell Script Block Logging (EventCode=4104) to identify this activity. Detecting `Get-DomainOU` usage is significant as adversaries may use it to gather information about organizational units within Active Directory, which can facilitate lateral movement or privilege escalation. If confirmed malicious, this activity could allow attackers to map the domain structure, aiding in further exploitation and persistence within the network. +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*Get-DomainOU*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_find_domain_organizational_units_with_getdomainou_filter` +how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. +known_false_positives: Administrators may leverage PowerSploit tools for legitimate reasons, filter as needed. references: -- https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainOU/ -- https://attack.mitre.org/techniques/T1087/002/ -- https://book.hacktricks.xyz/windows-hardening/basic-powershell-for-pentesters/powerview + - https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainOU/ + - https://attack.mitre.org/techniques/T1087/002/ + - https://book.hacktricks.xyz/windows-hardening/basic-powershell-for-pentesters/powerview drilldown_searches: -- name: View the detection results for - "$dest$" and "$user_id$" - search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user_id$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user_id$" + search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious PowerShell Get-DomainOU was identified on endpoint $dest$ by - user $user_id$. - risk_objects: - - field: dest - type: system - score: 25 - - field: user_id - type: user - score: 25 - threat_objects: [] + message: Suspicious PowerShell Get-DomainOU was identified on endpoint $dest$ by user $user_id$. + risk_objects: + - field: dest + type: system + score: 25 + - field: user_id + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1087.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1087.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-powershell-DomainOU-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-powershell-DomainOU-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_find_interesting_acl_with_findinterestingdomainacl.yml b/detections/endpoint/windows_find_interesting_acl_with_findinterestingdomainacl.yml index 8dd3bea83e..8f57efddee 100644 --- a/detections/endpoint/windows_find_interesting_acl_with_findinterestingdomainacl.yml +++ b/detections/endpoint/windows_find_interesting_acl_with_findinterestingdomainacl.yml @@ -1,72 +1,64 @@ name: Windows Find Interesting ACL with FindInterestingDomainAcl id: e4a96dfd-667a-4487-b942-ccef5a1e81e8 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Gowthamaraj Rajendran, Mauricio Velazco, Splunk status: production type: TTP data_source: -- Powershell Script Block Logging 4104 -description: The following analytic detects the execution of the `Find-InterestingDomainAcl` - cmdlet, part of the PowerView toolkit, using PowerShell Script Block Logging (EventCode=4104). - This detection leverages logs to identify when this command is run, which is significant - as adversaries may use it to find misconfigured or unusual Access Control Lists - (ACLs) within a domain. If confirmed malicious, this activity could allow attackers - to identify privilege escalation opportunities or weak security configurations in - Active Directory, potentially leading to unauthorized access or further exploitation. -search: '`powershell` EventCode=4104 ScriptBlockText = "*Find-InterestingDomainAcl*" - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_find_interesting_acl_with_findinterestingdomainacl_filter`' -how_to_implement: The following Hunting analytic requires PowerShell operational logs - to be imported. Modify the powershell macro as needed to match the sourcetype or - add index. This analytic is specific to 4104, or PowerShell Script Block Logging. -known_false_positives: Administrators may leverage PowerSploit tools for legitimate - reasons, filter as needed. + - Powershell Script Block Logging 4104 +description: The following analytic detects the execution of the `Find-InterestingDomainAcl` cmdlet, part of the PowerView toolkit, using PowerShell Script Block Logging (EventCode=4104). This detection leverages logs to identify when this command is run, which is significant as adversaries may use it to find misconfigured or unusual Access Control Lists (ACLs) within a domain. If confirmed malicious, this activity could allow attackers to identify privilege escalation opportunities or weak security configurations in Active Directory, potentially leading to unauthorized access or further exploitation. +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*Find-InterestingDomainAcl*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_find_interesting_acl_with_findinterestingdomainacl_filter` +how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. +known_false_positives: Administrators may leverage PowerSploit tools for legitimate reasons, filter as needed. references: -- https://powersploit.readthedocs.io/en/latest/Recon/Find-InterestingDomainAcl/ -- https://attack.mitre.org/techniques/T1087/002/ -- https://book.hacktricks.xyz/windows-hardening/basic-powershell-for-pentesters/powerview + - https://powersploit.readthedocs.io/en/latest/Recon/Find-InterestingDomainAcl/ + - https://attack.mitre.org/techniques/T1087/002/ + - https://book.hacktricks.xyz/windows-hardening/basic-powershell-for-pentesters/powerview drilldown_searches: -- name: View the detection results for - "$dest$" and "$user_id$" - search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user_id$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user_id$" + search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious PowerShell Find-InterestingDomainAcl was identified on endpoint - $dest$ by user $user_id$. - risk_objects: - - field: dest - type: system - score: 25 - - field: user_id - type: user - score: 25 - threat_objects: [] + message: Suspicious PowerShell Find-InterestingDomainAcl was identified on endpoint $dest$ by user $user_id$. + risk_objects: + - field: dest + type: system + score: 25 + - field: user_id + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1087.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1087.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-powershell-interestingACL-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-powershell-interestingACL-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_findstr_gpp_discovery.yml b/detections/endpoint/windows_findstr_gpp_discovery.yml index 4a9316962d..2a47798fab 100644 --- a/detections/endpoint/windows_findstr_gpp_discovery.yml +++ b/detections/endpoint/windows_findstr_gpp_discovery.yml @@ -1,87 +1,76 @@ name: Windows Findstr GPP Discovery id: 1631ac2d-f2a9-42fa-8a59-d6e210d472f5 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk type: TTP status: production data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic detects the use of the findstr command to search - for unsecured credentials in Group Policy Preferences (GPP). It leverages data from - Endpoint Detection and Response (EDR) agents, focusing on command-line executions - involving findstr.exe with references to SYSVOL and cpassword. This activity is - significant because it indicates an attempt to locate and potentially decrypt embedded - credentials in GPP, which could lead to unauthorized access. If confirmed malicious, - this could allow an attacker to escalate privileges or gain access to sensitive - systems and data within the domain. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=findstr.exe - AND Processes.process=*sysvol* AND Processes.process=*cpassword*) by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_findstr_gpp_discovery_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrators may leverage findstr to find passwords in GPO - to validate exposure. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic detects the use of the findstr command to search for unsecured credentials in Group Policy Preferences (GPP). It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving findstr.exe with references to SYSVOL and cpassword. This activity is significant because it indicates an attempt to locate and potentially decrypt embedded credentials in GPP, which could lead to unauthorized access. If confirmed malicious, this could allow an attacker to escalate privileges or gain access to sensitive systems and data within the domain. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name=findstr.exe + AND + Processes.process=*sysvol* + AND + Processes.process=*cpassword* + ) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_findstr_gpp_discovery_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators may leverage findstr to find passwords in GPO to validate exposure. Filter as needed. references: -- https://attack.mitre.org/techniques/T1552/006/ -- https://pentestlab.blog/2017/03/20/group-policy-preferences/ -- https://adsecurity.org/?p=2288 -- https://www.hackingarticles.in/credential-dumping-group-policy-preferences-gpp/ -- https://support.microsoft.com/en-us/topic/ms14-025-vulnerability-in-group-policy-preferences-could-allow-elevation-of-privilege-may-13-2014-60734e15-af79-26ca-ea53-8cd617073c30 + - https://attack.mitre.org/techniques/T1552/006/ + - https://pentestlab.blog/2017/03/20/group-policy-preferences/ + - https://adsecurity.org/?p=2288 + - https://www.hackingarticles.in/credential-dumping-group-policy-preferences-gpp/ + - https://support.microsoft.com/en-us/topic/ms14-025-vulnerability-in-group-policy-preferences-could-allow-elevation-of-privilege-may-13-2014-60734e15-af79-26ca-ea53-8cd617073c30 drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Findstr was executed to discover GPP credentials on $dest$ - risk_objects: - - field: dest - type: system - score: 56 - - field: user - type: user - score: 56 - threat_objects: [] + message: Findstr was executed to discover GPP credentials on $dest$ + risk_objects: + - field: dest + type: system + score: 56 + - field: user + type: user + score: 56 + threat_objects: [] tags: - analytic_story: - - Active Directory Privilege Escalation - asset_type: Endpoint - mitre_attack_id: - - T1552.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Privilege Escalation + asset_type: Endpoint + mitre_attack_id: + - T1552.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.006/findstr_gpp_discovery/windows-security.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.006/findstr_gpp_discovery/windows-security.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_firewall_rule_added.yml b/detections/endpoint/windows_firewall_rule_added.yml index 6f9020278a..0c91cf0b68 100644 --- a/detections/endpoint/windows_firewall_rule_added.yml +++ b/detections/endpoint/windows_firewall_rule_added.yml @@ -1,58 +1,57 @@ name: Windows Firewall Rule Added id: efc25501-4e75-4075-8cc5-ac80f2847d80 -version: 3 -date: '2025-11-20' +version: 4 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: This detection identifies instances where a Windows Firewall rule is added by monitoring Event ID 4946 in the Windows Security Event Log. Firewall rule modifications can indicate legitimate administrative actions, but they may also signal unauthorized changes, misconfigurations, or malicious activity such as attackers allowing traffic for backdoors or persistence mechanisms. By analyzing fields like RuleName, RuleId, Computer, and ProfileChanged, security teams can determine whether the change aligns with expected behavior. Correlating with user activity and process execution can help distinguish false positives from real threats, ensuring better visibility into potential security risks. data_source: -- Windows Event Log Security 4946 -search: '`wineventlog_security` EventCode=4946 - | stats count min(_time) as firstTime max(_time) as lastTime by RuleName signature subject status dest ProcessID - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_firewall_rule_added_filter`' + - Windows Event Log Security 4946 +search: |- + `wineventlog_security` EventCode=4946 + | stats count min(_time) as firstTime max(_time) as lastTime + BY RuleName signature subject + status dest ProcessID + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_firewall_rule_added_filter` how_to_implement: This detection is based on data collected from Endpoint Detection and Response (EDR) agents, which provide security-related telemetry from monitored endpoints. Specifically, it focuses on Windows Security Event Log entries with EventID 4946, which indicates that a new Windows Firewall rule has been added. To implement this detection in Splunk, you must ingest Windows Security Event Logs that capture EventID 4946, ensuring that critical fields such as _time, EventRecordID, ProcessID, ThreadID, Computer, ProfileChanged, RuleName, and RuleId are available for analysis. These logs must be processed using the appropriate Splunk Technology Add-ons (TAs) to ensure compatibility with the EDR product and proper field extraction. Additionally, mapping these logs to the appropriate data model, such as the Endpoint data model, enhances structured analysis. Leveraging the Splunk Common Information Model (CIM) helps normalize field names, ensuring consistency across different data sources. By implementing this approach, you can effectively detect and monitor Windows Firewall rule modifications, providing visibility into potential security risks or unauthorized changes. known_false_positives: Legitimate admin changes, Group Policy updates, software installs, security tools, and automated scripts can trigger false positives for Event ID 4946. references: -- https://learn.microsoft.com/en-us/previous-versions/windows/it-pro/windows-10/security/threat-protection/auditing/event-4946 + - https://learn.microsoft.com/en-us/previous-versions/windows/it-pro/windows-10/security/threat-protection/auditing/event-4946 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a new firewall rule $RuleName$ added on $dest$. - risk_objects: - - field: dest - type: system - score: 20 - threat_objects: [] + message: a new firewall rule $RuleName$ added on $dest$. + risk_objects: + - field: dest + type: system + score: 20 + threat_objects: [] tags: - analytic_story: - - ShrinkLocker - - Medusa Ransomware - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ShrinkLocker + - Medusa Ransomware + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/firewall_win_event/added_rule/MPSSVC_Rule-Level_Policy_Change-4946.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/firewall_win_event/added_rule/MPSSVC_Rule-Level_Policy_Change-4946.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_firewall_rule_deletion.yml b/detections/endpoint/windows_firewall_rule_deletion.yml index 2fd0be6049..737d37a600 100644 --- a/detections/endpoint/windows_firewall_rule_deletion.yml +++ b/detections/endpoint/windows_firewall_rule_deletion.yml @@ -1,58 +1,57 @@ name: Windows Firewall Rule Deletion id: ca5327e1-0a91-4e23-bbd4-8901806c00e1 -version: 3 -date: '2025-11-20' +version: 4 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: This detection identifies instances where a Windows Firewall rule has been deleted, potentially exposing the system to security risks. Unauthorized removal of firewall rules can indicate an attacker attempting to bypass security controls or malware disabling protections for persistence and command-and-control communication. The event logs details such as the deleted rule name, protocol, port, and the user responsible for the action. Security teams should monitor for unexpected deletions, correlate with related events, and investigate anomalies to prevent unauthorized access and maintain network security posture. data_source: -- Windows Event Log Security 4948 -search: '`wineventlog_security` EventCode=4948 - | stats count min(_time) as firstTime max(_time) as lastTime by RuleName signature subject status dest ProcessID - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_firewall_rule_deletion_filter`' + - Windows Event Log Security 4948 +search: |- + `wineventlog_security` EventCode=4948 + | stats count min(_time) as firstTime max(_time) as lastTime + BY RuleName signature subject + status dest ProcessID + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_firewall_rule_deletion_filter` how_to_implement: This detection is based on data collected from Endpoint Detection and Response (EDR) agents, which provide security-related telemetry from monitored endpoints. Specifically, it focuses on Windows Security Event Log entries with EventID 4948, which indicates that a Windows Firewall rule has been delted. To implement this detection in Splunk, you must ingest Windows Security Event Logs that capture EventID 4948, ensuring that critical fields such as _time, EventRecordID, ProcessID, ThreadID, Computer, ProfileChanged, RuleName, and RuleId are available for analysis. These logs must be processed using the appropriate Splunk Technology Add-ons (TAs) to ensure compatibility with the EDR product and proper field extraction. Additionally, mapping these logs to the appropriate data model, such as the Endpoint data model, enhances structured analysis. Leveraging the Splunk Common Information Model (CIM) helps normalize field names, ensuring consistency across different data sources. By implementing this approach, you can effectively detect and monitor Windows Firewall rule modifications, providing visibility into potential security risks or unauthorized changes. known_false_positives: Legitimate admin delete, Group Policy updates, software installs, security tools, and automated scripts can trigger false positives for Event ID 4948. references: -- https://learn.microsoft.com/en-us/previous-versions/windows/it-pro/windows-10/security/threat-protection/auditing/event-4948 + - https://learn.microsoft.com/en-us/previous-versions/windows/it-pro/windows-10/security/threat-protection/auditing/event-4948 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a firewall rule $RuleName$ has been modified on $dest$. - risk_objects: - - field: dest - type: system - score: 20 - threat_objects: [] + message: a firewall rule $RuleName$ has been modified on $dest$. + risk_objects: + - field: dest + type: system + score: 20 + threat_objects: [] tags: - analytic_story: - - ShrinkLocker - - Medusa Ransomware - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ShrinkLocker + - Medusa Ransomware + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/firewall_win_event/delete_rule/MPSSVC_Rule-Level_Policy_Change-4948.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/firewall_win_event/delete_rule/MPSSVC_Rule-Level_Policy_Change-4948.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_firewall_rule_modification.yml b/detections/endpoint/windows_firewall_rule_modification.yml index b0cf532310..0fd6795ee7 100644 --- a/detections/endpoint/windows_firewall_rule_modification.yml +++ b/detections/endpoint/windows_firewall_rule_modification.yml @@ -1,58 +1,57 @@ name: Windows Firewall Rule Modification id: fe7efbf7-5f82-44b9-8c33-316189ab2393 -version: 3 -date: '2025-11-20' +version: 4 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: This detection identifies instances where a Windows Firewall rule has been modified, which may indicate an attempt to alter security policies. Unauthorized modifications can weaken firewall protections, allowing malicious traffic or preventing legitimate communications. The event logs details such as the modified rule name, protocol, ports, application path, and the user responsible for the change. Security teams should monitor unexpected modifications, correlate them with related events, and investigate anomalies to prevent unauthorized access and maintain network security integrity. data_source: -- Windows Event Log Security 4947 -search: '`wineventlog_security` EventCode=4947 - | stats count min(_time) as firstTime max(_time) as lastTime by RuleName signature subject status dest ProcessID - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_firewall_rule_modification_filter`' + - Windows Event Log Security 4947 +search: |- + `wineventlog_security` EventCode=4947 + | stats count min(_time) as firstTime max(_time) as lastTime + BY RuleName signature subject + status dest ProcessID + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_firewall_rule_modification_filter` how_to_implement: This detection is based on data collected from Endpoint Detection and Response (EDR) agents, which provide security-related telemetry from monitored endpoints. Specifically, it focuses on Windows Security Event Log entries with EventID 4947, which indicates that a Windows Firewall rule has been modified. To implement this detection in Splunk, you must ingest Windows Security Event Logs that capture EventID 4947, ensuring that critical fields such as _time, EventRecordID, ProcessID, ThreadID, Computer, ProfileChanged, RuleName, and RuleId are available for analysis. These logs must be processed using the appropriate Splunk Technology Add-ons (TAs) to ensure compatibility with the EDR product and proper field extraction. Additionally, mapping these logs to the appropriate data model, such as the Endpoint data model, enhances structured analysis. Leveraging the Splunk Common Information Model (CIM) helps normalize field names, ensuring consistency across different data sources. By implementing this approach, you can effectively detect and monitor Windows Firewall rule modifications, providing visibility into potential security risks or unauthorized changes. known_false_positives: Legitimate admin changes, Group Policy updates, software installs, security tools, and automated scripts can trigger false positives for Event ID 4947. references: -- https://learn.microsoft.com/en-us/previous-versions/windows/it-pro/windows-10/security/threat-protection/auditing/event-4947 + - https://learn.microsoft.com/en-us/previous-versions/windows/it-pro/windows-10/security/threat-protection/auditing/event-4947 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a firewall rule $RuleName$ has been modified on $dest$. - risk_objects: - - field: dest - type: system - score: 20 - threat_objects: [] + message: a firewall rule $RuleName$ has been modified on $dest$. + risk_objects: + - field: dest + type: system + score: 20 + threat_objects: [] tags: - analytic_story: - - ShrinkLocker - - Medusa Ransomware - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ShrinkLocker + - Medusa Ransomware + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/firewall_win_event/modify_rule/MPSSVC_Rule-Level_Policy_Change-4947.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/firewall_win_event/modify_rule/MPSSVC_Rule-Level_Policy_Change-4947.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_forest_discovery_with_getforestdomain.yml b/detections/endpoint/windows_forest_discovery_with_getforestdomain.yml index 5d417deb96..5ca8e82f2e 100644 --- a/detections/endpoint/windows_forest_discovery_with_getforestdomain.yml +++ b/detections/endpoint/windows_forest_discovery_with_getforestdomain.yml @@ -1,71 +1,64 @@ name: Windows Forest Discovery with GetForestDomain id: a14803b2-4bd9-4c08-8b57-c37980edebe8 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Gowthamaraj Rajendran, Mauricio Velazco, Splunk status: production type: TTP data_source: -- Powershell Script Block Logging 4104 -description: The following analytic detects the execution of the `Get-ForestDomain` - cmdlet, a component of the PowerView toolkit used for Windows domain enumeration. - It leverages PowerShell Script Block Logging (EventCode=4104) to identify this activity. - Detecting `Get-ForestDomain` is significant because adversaries and Red Teams use - it to gather detailed information about Active Directory forest and domain configurations. - If confirmed malicious, this activity could enable attackers to understand the domain - structure, facilitating lateral movement or privilege escalation within the environment. -search: '`powershell` EventCode=4104 ScriptBlockText = "*Get-ForestDomain*" | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_forest_discovery_with_getforestdomain_filter`' -how_to_implement: The following Hunting analytic requires PowerShell operational logs - to be imported. Modify the powershell macro as needed to match the sourcetype or - add index. This analytic is specific to 4104, or PowerShell Script Block Logging. -known_false_positives: Administrators may leverage PowerSploit tools for legitimate - reasons, filter as needed. + - Powershell Script Block Logging 4104 +description: The following analytic detects the execution of the `Get-ForestDomain` cmdlet, a component of the PowerView toolkit used for Windows domain enumeration. It leverages PowerShell Script Block Logging (EventCode=4104) to identify this activity. Detecting `Get-ForestDomain` is significant because adversaries and Red Teams use it to gather detailed information about Active Directory forest and domain configurations. If confirmed malicious, this activity could enable attackers to understand the domain structure, facilitating lateral movement or privilege escalation within the environment. +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*Get-ForestDomain*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_forest_discovery_with_getforestdomain_filter` +how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. +known_false_positives: Administrators may leverage PowerSploit tools for legitimate reasons, filter as needed. references: -- https://powersploit.readthedocs.io/en/latest/Recon/Get-ForestDomain/ -- https://attack.mitre.org/techniques/T1087/002/ -- https://book.hacktricks.xyz/windows-hardening/basic-powershell-for-pentesters/powerview + - https://powersploit.readthedocs.io/en/latest/Recon/Get-ForestDomain/ + - https://attack.mitre.org/techniques/T1087/002/ + - https://book.hacktricks.xyz/windows-hardening/basic-powershell-for-pentesters/powerview drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious PowerShell Get-ForestDomain was identified on endpoint $dest$ - by user $user_id$. - risk_objects: - - field: dest - type: system - score: 25 - - field: user_id - type: user - score: 25 - threat_objects: [] + message: Suspicious PowerShell Get-ForestDomain was identified on endpoint $dest$ by user $user_id$. + risk_objects: + - field: dest + type: system + score: 25 + - field: user_id + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1087.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1087.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-powershell-ForestDomain-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-powershell-ForestDomain-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_gather_victim_host_information_camera.yml b/detections/endpoint/windows_gather_victim_host_information_camera.yml index c4faa59971..3615fe55b7 100644 --- a/detections/endpoint/windows_gather_victim_host_information_camera.yml +++ b/detections/endpoint/windows_gather_victim_host_information_camera.yml @@ -1,77 +1,64 @@ name: Windows Gather Victim Host Information Camera id: e4df4676-ea41-4397-b160-3ee0140dc332 -version: 9 -date: '2025-06-24' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: - The following analytic detects a PowerShell script that enumerates camera - devices on the targeted host. This detection leverages PowerShell Script Block Logging, - specifically looking for commands querying Win32_PnPEntity for camera-related information. - This activity is significant as it is commonly observed in DCRat malware, which - collects camera data to send to its command-and-control server. If confirmed malicious, - this behavior could indicate an attempt to gather sensitive visual information from - the host, potentially leading to privacy breaches or further exploitation. +description: The following analytic detects a PowerShell script that enumerates camera devices on the targeted host. This detection leverages PowerShell Script Block Logging, specifically looking for commands querying Win32_PnPEntity for camera-related information. This activity is significant as it is commonly observed in DCRat malware, which collects camera data to send to its command-and-control server. If confirmed malicious, this behavior could indicate an attempt to gather sensitive visual information from the host, potentially leading to privacy breaches or further exploitation. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText= "* Win32_PnPEntity *" ScriptBlockText= - "*SELECT*" ScriptBlockText= "*WHERE*" ScriptBlockText = "*PNPClass*" ScriptBlockText - IN ("*Image*", "*Camera*") | fillnull | stats count min(_time) as firstTime max(_time) - as lastTime by dest signature signature_id user_id vendor_product EventID Guid Opcode - Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_gather_victim_host_information_camera_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - Administrators may execute this powershell command to get hardware - information related to camera on $dest$. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText= "* Win32_PnPEntity *" ScriptBlockText= "*SELECT*" ScriptBlockText= "*WHERE*" ScriptBlockText = "*PNPClass*" ScriptBlockText IN ("*Image*", "*Camera*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_gather_victim_host_information_camera_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: Administrators may execute this powershell command to get hardware information related to camera on $dest$. references: - - https://cert.gov.ua/article/405538 - - https://malpedia.caad.fkie.fraunhofer.de/details/win.dcrat - - https://www.mandiant.com/resources/analyzing-dark-crystal-rat-backdoor + - https://cert.gov.ua/article/405538 + - https://malpedia.caad.fkie.fraunhofer.de/details/win.dcrat + - https://www.mandiant.com/resources/analyzing-dark-crystal-rat-backdoor drilldown_searches: - - name: View the detection results for - "$dest$" and "$user_id$" - search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user_id$" + search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Powershell script to enumerate camera detected on host - $dest$ - risk_objects: - - field: dest - type: system - score: 42 - - field: user_id - type: user - score: 42 - threat_objects: [] + message: A Powershell script to enumerate camera detected on host - $dest$ + risk_objects: + - field: dest + type: system + score: 42 + - field: user_id + type: user + score: 42 + threat_objects: [] tags: - analytic_story: - - DarkCrystal RAT - asset_type: Endpoint - mitre_attack_id: - - T1592.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - DarkCrystal RAT + asset_type: Endpoint + mitre_attack_id: + - T1592.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/dcrat_enum_camera/windows-powershell-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/dcrat_enum_camera/windows-powershell-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_gather_victim_identity_sam_info.yml b/detections/endpoint/windows_gather_victim_identity_sam_info.yml index fa2f26084b..89ee1f6a9b 100644 --- a/detections/endpoint/windows_gather_victim_identity_sam_info.yml +++ b/detections/endpoint/windows_gather_victim_identity_sam_info.yml @@ -5,45 +5,29 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects processes loading the samlib.dll or samcli.dll - modules, which are often abused to access Security Account Manager (SAM) objects - or credentials on domain controllers. This detection leverages Sysmon EventCode - 7 to identify these DLLs being loaded outside typical system directories. Monitoring - this activity is crucial as it may indicate attempts to gather sensitive identity - information. If confirmed malicious, this behavior could allow attackers to obtain - credentials, escalate privileges, or further infiltrate the network. +description: The following analytic detects processes loading the samlib.dll or samcli.dll modules, which are often abused to access Security Account Manager (SAM) objects or credentials on domain controllers. This detection leverages Sysmon EventCode 7 to identify these DLLs being loaded outside typical system directories. Monitoring this activity is crucial as it may indicate attempts to gather sensitive identity information. If confirmed malicious, this behavior could allow attackers to obtain credentials, escalate privileges, or further infiltrate the network. data_source: -- Sysmon EventID 7 -search: '`sysmon` EventCode=7 (ImageLoaded = "*\\samlib.dll" AND OriginalFileName - = "samlib.dll") OR (ImageLoaded = "*\\samcli.dll" AND OriginalFileName = "SAMCLI.DLL") - AND NOT (Image IN("C:\\Windows\\*", "C:\\Program File*", "%systemroot%\\*")) | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded - dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash - process_id process_name process_path service_dll_signature_exists service_dll_signature_verified - signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_gather_victim_identity_sam_info_filter`' -how_to_implement: The latest Sysmon TA 3.0 https://splunkbase.splunk.com/app/5709 - will add the ImageLoaded name to the process_name field, allowing this query to - work. Use as an example and implement for other products. -known_false_positives: this module can be loaded by a third party application. Filter - is needed. + - Sysmon EventID 7 +search: '`sysmon` EventCode=7 (ImageLoaded = "*\\samlib.dll" AND OriginalFileName = "samlib.dll") OR (ImageLoaded = "*\\samcli.dll" AND OriginalFileName = "SAMCLI.DLL") AND NOT (Image IN("C:\\Windows\\*", "C:\\Program File*", "%systemroot%\\*")) | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_gather_victim_identity_sam_info_filter`' +how_to_implement: The latest Sysmon TA 3.0 https://splunkbase.splunk.com/app/5709 will add the ImageLoaded name to the process_name field, allowing this query to work. Use as an example and implement for other products. +known_false_positives: this module can be loaded by a third party application. Filter is needed. references: -- https://redcanary.com/blog/active-breach-evading-defenses/ -- https://strontic.github.io/xcyclopedia/library/samlib.dll-0BDF6351009F6EBA5BA7E886F23263B1.html + - https://redcanary.com/blog/active-breach-evading-defenses/ + - https://strontic.github.io/xcyclopedia/library/samlib.dll-0BDF6351009F6EBA5BA7E886F23263B1.html tags: - analytic_story: - - Brute Ratel C4 - asset_type: Endpoint - mitre_attack_id: - - T1589.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Brute Ratel C4 + asset_type: Endpoint + mitre_attack_id: + - T1589.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/loading_samlib/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/loading_samlib/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_gdrive_binary_activity.yml b/detections/endpoint/windows_gdrive_binary_activity.yml index 8a68804bee..8680cb3259 100644 --- a/detections/endpoint/windows_gdrive_binary_activity.yml +++ b/detections/endpoint/windows_gdrive_binary_activity.yml @@ -1,81 +1,68 @@ name: Windows Gdrive Binary Activity id: 9e7bd7c8-1c08-496e-9ffe-fd84ceb322e7 -version: 1 -date: '2025-08-01' +version: 2 +date: '2026-02-25' author: Raven Tait, Splunk status: production type: TTP -description: The following analytic detects the execution of the 'gdrive' tool on a - Windows host. This tool allows standard users to perform tasks associated with Google Drive - via the command line. This is used by actors to stage tools as well as exfiltrate data. - The detection leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process names and command-line executions. If confirmed malicious, - this could lead to compromise of systems or sensitive data being stolen. +description: The following analytic detects the execution of the 'gdrive' tool on a Windows host. This tool allows standard users to perform tasks associated with Google Drive via the command line. This is used by actors to stage tools as well as exfiltrate data. The detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. If confirmed malicious, this could lead to compromise of systems or sensitive data being stolen. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="gdrive.exe" OR - Processes.original_file_name="*gdrive.exe") Processes.process IN ("* download *", "* upload *", - "* list*", "* update *", "* sync *", "* share *", "* account add*", "* drives *", "* files *") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process - Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id - Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_gdrive_binary_activity_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator or network operator can use this application - for automation purposes. Please update the filter macros to remove false positives. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="gdrive.exe" + OR + Processes.original_file_name="*gdrive.exe" + ) + Processes.process IN ("* download *", "* upload *", "* list*", "* update *", "* sync *", "* share *", "* account add*", "* drives *", "* files *") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_gdrive_binary_activity_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. references: -- https://cloud.google.com/blog/topics/threat-intelligence/uncovering-unc3886-espionage-operations + - https://cloud.google.com/blog/topics/threat-intelligence/uncovering-unc3886-espionage-operations drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $process_name$ was identified - attempting to interact with Google Drive on endpoint $dest$ by $user$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: An instance of $process_name$ was identified attempting to interact with Google Drive on endpoint $dest$ by $user$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - China-Nexus Threat Activity - asset_type: Endpoint - mitre_attack_id: - - T1567 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - China-Nexus Threat Activity + asset_type: Endpoint + mitre_attack_id: + - T1567 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1567/gdrive/gdrive_windows.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1567/gdrive/gdrive_windows.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_get_adcomputer_unconstrained_delegation_discovery.yml b/detections/endpoint/windows_get_adcomputer_unconstrained_delegation_discovery.yml index 1288c17714..4a9bb54946 100644 --- a/detections/endpoint/windows_get_adcomputer_unconstrained_delegation_discovery.yml +++ b/detections/endpoint/windows_get_adcomputer_unconstrained_delegation_discovery.yml @@ -1,75 +1,67 @@ name: Windows Get-AdComputer Unconstrained Delegation Discovery id: c8640777-469f-4638-ab44-c34a3233ffac -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the use of the Get-ADComputer cmdlet with - parameters indicating a search for Windows endpoints with Kerberos Unconstrained - Delegation. It leverages PowerShell Script Block Logging (EventCode=4104) to identify - this specific activity. This behavior is significant as it may indicate an attempt - by adversaries or Red Teams to gain situational awareness and perform Active Directory - discovery. If confirmed malicious, this activity could allow attackers to identify - high-value targets for further exploitation, potentially leading to privilege escalation - or lateral movement within the network. +description: The following analytic detects the use of the Get-ADComputer cmdlet with parameters indicating a search for Windows endpoints with Kerberos Unconstrained Delegation. It leverages PowerShell Script Block Logging (EventCode=4104) to identify this specific activity. This behavior is significant as it may indicate an attempt by adversaries or Red Teams to gain situational awareness and perform Active Directory discovery. If confirmed malicious, this activity could allow attackers to identify high-value targets for further exploitation, potentially leading to privilege escalation or lateral movement within the network. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-ADComputer*" AND ScriptBlockText - = "*TrustedForDelegation*") | fillnull | stats count min(_time) as firstTime max(_time) - as lastTime by dest signature signature_id user_id vendor_product EventID Guid Opcode - Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_get_adcomputer_unconstrained_delegation_discovery_filter`' -how_to_implement: The following analytic requires PowerShell operational logs to - be imported. Modify the powershell macro as needed to match the sourcetype or add - index. This analytic is specific to 4104, or PowerShell Script Block Logging. -known_false_positives: Administrators or power users may leverage PowerView for system - management or troubleshooting. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 (ScriptBlockText = "*Get-ADComputer*" AND ScriptBlockText = "*TrustedForDelegation*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_get_adcomputer_unconstrained_delegation_discovery_filter` +how_to_implement: The following analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. +known_false_positives: Administrators or power users may leverage PowerView for system management or troubleshooting. references: -- https://attack.mitre.org/techniques/T1018/ -- https://adsecurity.org/?p=1667 -- https://docs.microsoft.com/en-us/defender-for-identity/cas-isp-unconstrained-kerberos -- https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/domain-compromise-via-unrestricted-kerberos-delegation -- https://www.cyberark.com/resources/threat-research-blog/weakness-within-kerberos-delegation + - https://attack.mitre.org/techniques/T1018/ + - https://adsecurity.org/?p=1667 + - https://docs.microsoft.com/en-us/defender-for-identity/cas-isp-unconstrained-kerberos + - https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/domain-compromise-via-unrestricted-kerberos-delegation + - https://www.cyberark.com/resources/threat-research-blog/weakness-within-kerberos-delegation drilldown_searches: -- name: View the detection results for - "$dest$" and "$user_id$" - search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user_id$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user_id$" + search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious PowerShell Get-ADComputer was identified on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 35 - - field: user_id - type: user - score: 35 - threat_objects: [] + message: Suspicious PowerShell Get-ADComputer was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 35 + - field: user_id + type: user + score: 35 + threat_objects: [] tags: - analytic_story: - - Medusa Ransomware - - Active Directory Kerberos Attacks - asset_type: Endpoint - mitre_attack_id: - - T1018 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Medusa Ransomware + - Active Directory Kerberos Attacks + asset_type: Endpoint + mitre_attack_id: + - T1018 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/windows_get_adcomputer_unconstrained_delegation_discovery/windows-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/windows_get_adcomputer_unconstrained_delegation_discovery/windows-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_get_local_admin_with_findlocaladminaccess.yml b/detections/endpoint/windows_get_local_admin_with_findlocaladminaccess.yml index c355d373f6..3be7ac2c38 100644 --- a/detections/endpoint/windows_get_local_admin_with_findlocaladminaccess.yml +++ b/detections/endpoint/windows_get_local_admin_with_findlocaladminaccess.yml @@ -1,73 +1,64 @@ name: Windows Get Local Admin with FindLocalAdminAccess id: d2988160-3ce9-4310-b59d-905334920cdd -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Gowthamaraj Rajendran, Mauricio Velazco, Splunk status: production type: TTP data_source: -- Powershell Script Block Logging 4104 -description: The following analytic detects the execution of the `Find-LocalAdminAccess` - cmdlet using PowerShell Script Block Logging (EventCode=4104). This cmdlet is part - of PowerView, a toolkit for Windows domain enumeration. Identifying the use of `Find-LocalAdminAccess` - is crucial as adversaries may use it to find machines where the current user has - local administrator access, facilitating lateral movement or privilege escalation. - If confirmed malicious, this activity could allow attackers to target and compromise - additional systems within the network, significantly increasing their control and - access to sensitive information. -search: '`powershell` EventCode=4104 ScriptBlockText = "*Find-LocalAdminAccess*" | - fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest signature - signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId - ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_get_local_admin_with_findlocaladminaccess_filter`' -how_to_implement: The following Hunting analytic requires PowerShell operational logs - to be imported. Modify the powershell macro as needed to match the sourcetype or - add index. This analytic is specific to 4104, or PowerShell Script Block Logging. -known_false_positives: Administrators may leverage PowerSploit tools for legitimate - reasons, filter as needed. + - Powershell Script Block Logging 4104 +description: The following analytic detects the execution of the `Find-LocalAdminAccess` cmdlet using PowerShell Script Block Logging (EventCode=4104). This cmdlet is part of PowerView, a toolkit for Windows domain enumeration. Identifying the use of `Find-LocalAdminAccess` is crucial as adversaries may use it to find machines where the current user has local administrator access, facilitating lateral movement or privilege escalation. If confirmed malicious, this activity could allow attackers to target and compromise additional systems within the network, significantly increasing their control and access to sensitive information. +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*Find-LocalAdminAccess*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_get_local_admin_with_findlocaladminaccess_filter` +how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. +known_false_positives: Administrators may leverage PowerSploit tools for legitimate reasons, filter as needed. references: -- https://powersploit.readthedocs.io/en/latest/Recon/Find-LocalAdminAccess/ -- https://attack.mitre.org/techniques/T1087/002/ -- https://book.hacktricks.xyz/windows-hardening/basic-powershell-for-pentesters/powerview + - https://powersploit.readthedocs.io/en/latest/Recon/Find-LocalAdminAccess/ + - https://attack.mitre.org/techniques/T1087/002/ + - https://book.hacktricks.xyz/windows-hardening/basic-powershell-for-pentesters/powerview drilldown_searches: -- name: View the detection results for - "$dest$" and "$user_id$" - search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user_id$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user_id$" + search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious PowerShell Find-LocalAdminAccess was identified on endpoint - $dest$ by user $user_id$. - risk_objects: - - field: dest - type: system - score: 25 - - field: user_id - type: user - score: 25 - threat_objects: [] + message: Suspicious PowerShell Find-LocalAdminAccess was identified on endpoint $dest$ by user $user_id$. + risk_objects: + - field: dest + type: system + score: 25 + - field: user_id + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1087.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1087.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-powershell-LocalAdminAccess-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-powershell-LocalAdminAccess-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_global_object_access_audit_list_cleared_via_auditpol.yml b/detections/endpoint/windows_global_object_access_audit_list_cleared_via_auditpol.yml index f21687e8a8..5bf599a016 100644 --- a/detections/endpoint/windows_global_object_access_audit_list_cleared_via_auditpol.yml +++ b/detections/endpoint/windows_global_object_access_audit_list_cleared_via_auditpol.yml @@ -1,93 +1,75 @@ name: Windows Global Object Access Audit List Cleared Via Auditpol id: 802a0930-0a4a-4451-bf6c-6366c6b6d9e7 -version: 3 -date: '2025-05-02' +version: 4 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: TTP -description: The following analytic identifies the execution of `auditpol.exe` with - the "/resourceSACL" flag, and either the "/clear" or "/remove" command-line arguments - used to remove or clear the global object access audit policy. It leverages data - from Endpoint Detection and Response (EDR) agents, focusing on process names and - command-line executions. This activity can be significant as it indicates potential - defense evasion by adversaries or Red Teams, aiming to limit data that can be leveraged - for detections and audits. If confirmed malicious, this behavior could allow attackers - to bypass defenses, and plan further attacks, potentially leading to full machine - compromise or lateral movement. +description: The following analytic identifies the execution of `auditpol.exe` with the "/resourceSACL" flag, and either the "/clear" or "/remove" command-line arguments used to remove or clear the global object access audit policy. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity can be significant as it indicates potential defense evasion by adversaries or Red Teams, aiming to limit data that can be leveraged for detections and audits. If confirmed malicious, this behavior could allow attackers to bypass defenses, and plan further attacks, potentially leading to full machine compromise or lateral movement. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_auditpol` Processes.process="*/resourceSACL*" - Processes.process IN ("*/clear*", "*/remove*") AND NOT Processes.process="*/?*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_global_object_access_audit_list_cleared_via_auditpol_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process name, and process original file name. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives should be rare to non existent. Any activity - detected by this analytic should be investigated and approved or denied. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_auditpol` Processes.process="*/resourceSACL*" Processes.process IN ("*/clear*", "*/remove*") + AND + NOT Processes.process="*/?*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_global_object_access_audit_list_cleared_via_auditpol_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process name, and process original file name. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives should be rare to non existent. Any activity detected by this analytic should be investigated and approved or denied. references: -- https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/auditpol-resourcesacl + - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/auditpol-resourcesacl drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - attempting to clear the global object access audit policy on endpoint $dest$ by - user $user$. - risk_objects: - - field: user - type: user - score: 16 - - field: dest - type: system - score: 16 - threat_objects: - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified attempting to clear the global object access audit policy on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 16 + - field: dest + type: system + score: 16 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Windows Audit Policy Tampering - asset_type: Endpoint - mitre_attack_id: - - T1562.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Audit Policy Tampering + asset_type: Endpoint + mitre_attack_id: + - T1562.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - Sysmon - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog -- name: True Positive Test - Security - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test - Sysmon + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test - Security + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_group_discovery_via_net.yml b/detections/endpoint/windows_group_discovery_via_net.yml index ecd51509a9..8a9eeea418 100644 --- a/detections/endpoint/windows_group_discovery_via_net.yml +++ b/detections/endpoint/windows_group_discovery_via_net.yml @@ -5,76 +5,52 @@ date: '2026-02-09' author: Michael Haag, Mauricio Velazco, Splunk status: production type: Hunting -description: The following analytic identifies the execution of `net.exe` with command-line - arguments used to query global, local and domain groups. It leverages data from - Endpoint Detection and Response (EDR) agents, focusing on process names and command-line - arguments. This activity is significant as it indicates potential reconnaissance - efforts by adversaries to enumerate local or domain groups, which is a common step - in Active Directory or privileged accounts discovery. If confirmed malicious, this - behavior could allow attackers to gain insights into the domain structure, aiding - in further attacks such as privilege escalation or lateral movement. +description: The following analytic identifies the execution of `net.exe` with command-line arguments used to query global, local and domain groups. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant as it indicates potential reconnaissance efforts by adversaries to enumerate local or domain groups, which is a common step in Active Directory or privileged accounts discovery. If confirmed malicious, this behavior could allow attackers to gain insights into the domain structure, aiding in further attacks such as privilege escalation or lateral movement. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_net` Processes.process="*group*" - AND NOT (Processes.process="*/add" OR Processes.process="*/delete") by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_group_discovery_via_net_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_net` Processes.process="*group*" AND NOT (Processes.process="*/add" OR Processes.process="*/delete") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_group_discovery_via_net_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1069/002/ -- https://attack.mitre.org/techniques/T1069/001/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1069.001/T1069.001.md -- https://media.defense.gov/2023/May/24/2003229517/-1/-1/0/CSA_Living_off_the_Land.PDF -- https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/ + - https://attack.mitre.org/techniques/T1069/002/ + - https://attack.mitre.org/techniques/T1069/001/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1069.001/T1069.001.md + - https://media.defense.gov/2023/May/24/2003229517/-1/-1/0/CSA_Living_off_the_Land.PDF + - https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/ tags: - analytic_story: - - SolarWinds WHD RCE Post Exploitation - - Windows Discovery Techniques - - Windows Post-Exploitation - - Graceful Wipe Out Attack - - Active Directory Discovery - - Prestige Ransomware - - Medusa Ransomware - - Azorult - - Cleo File Transfer Software - - Rhysida Ransomware - - IcedID - - Volt Typhoon - - Microsoft WSUS CVE-2025-59287 - asset_type: Endpoint - mitre_attack_id: - - T1069.001 - - T1069.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SolarWinds WHD RCE Post Exploitation + - Windows Discovery Techniques + - Windows Post-Exploitation + - Graceful Wipe Out Attack + - Active Directory Discovery + - Prestige Ransomware + - Medusa Ransomware + - Azorult + - Cleo File Transfer Software + - Rhysida Ransomware + - IcedID + - Volt Typhoon + - Microsoft WSUS CVE-2025-59287 + asset_type: Endpoint + mitre_attack_id: + - T1069.001 + - T1069.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.001/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.001/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_group_policy_object_created.yml b/detections/endpoint/windows_group_policy_object_created.yml index 5003734536..5592f5d008 100644 --- a/detections/endpoint/windows_group_policy_object_created.yml +++ b/detections/endpoint/windows_group_policy_object_created.yml @@ -1,77 +1,63 @@ name: Windows Group Policy Object Created id: 23add2a8-ea22-4fd4-8bc0-8c0b822373a1 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Mauricio Velazco status: production type: TTP data_source: -- Windows Event Log Security 5136 -- Windows Event Log Security 5137 -description: The following analytic detects the creation of a new Group Policy Object - (GPO) by leveraging Event IDs 5136 and 5137. This detection uses directory service - change events to identify when a new GPO is created. Monitoring GPO creation is - crucial as adversaries can exploit GPOs to escalate privileges or deploy malware - across an Active Directory network. If confirmed malicious, this activity could - allow attackers to control system configurations, deploy ransomware, or propagate - malware, leading to widespread compromise and significant operational disruption. -search: '`wineventlog_security` EventCode=5137 OR (EventCode=5136 AttributeValue!="New - Group Policy Object" AND (AttributeLDAPDisplayName=displayName OR AttributeLDAPDisplayName=gPCFileSysPath) - ) ObjectClass=groupPolicyContainer | stats values(AttributeValue) as details values(SubjectUserSid) - as User values(ObjectDN) as ObjectDN by ObjectGUID Computer dest | eval GPO_Name - = mvindex(details, 0) | eval GPO_Path = mvindex(details, 1) | fields - details | - `windows_group_policy_object_created_filter`' -how_to_implement: To successfully implement this search, the Advanced Security Audit - policy setting `Audit Directory Service Changes` within `DS Access` needs to be - enabled. Furthermore, the appropriate system access control lists (SACL) need to - be created as the used events are not logged by default. A good guide to accomplish - this can be found here https://jgspiers.com/audit-group-policy-changes/. -known_false_positives: Group Policy Objects are created as part of regular administrative - operations, filter as needed. + - Windows Event Log Security 5136 + - Windows Event Log Security 5137 +description: The following analytic detects the creation of a new Group Policy Object (GPO) by leveraging Event IDs 5136 and 5137. This detection uses directory service change events to identify when a new GPO is created. Monitoring GPO creation is crucial as adversaries can exploit GPOs to escalate privileges or deploy malware across an Active Directory network. If confirmed malicious, this activity could allow attackers to control system configurations, deploy ransomware, or propagate malware, leading to widespread compromise and significant operational disruption. +search: |- + `wineventlog_security` EventCode=5137 OR (EventCode=5136 AttributeValue!="New Group Policy Object" AND (AttributeLDAPDisplayName=displayName OR AttributeLDAPDisplayName=gPCFileSysPath) ) ObjectClass=groupPolicyContainer + | stats values(AttributeValue) as details values(SubjectUserSid) as User values(ObjectDN) as ObjectDN + BY ObjectGUID Computer dest + | eval GPO_Name = mvindex(details, 0) + | eval GPO_Path = mvindex(details, 1) + | fields - details + | `windows_group_policy_object_created_filter` +how_to_implement: To successfully implement this search, the Advanced Security Audit policy setting `Audit Directory Service Changes` within `DS Access` needs to be enabled. Furthermore, the appropriate system access control lists (SACL) need to be created as the used events are not logged by default. A good guide to accomplish this can be found here https://jgspiers.com/audit-group-policy-changes/. +known_false_positives: Group Policy Objects are created as part of regular administrative operations, filter as needed. references: -- https://attack.mitre.org/techniques/T1484/ -- https://attack.mitre.org/techniques/T1484/001 -- https://www.trustedsec.com/blog/weaponizing-group-policy-objects-access/ -- https://adsecurity.org/?p=2716 -- https://www.bleepingcomputer.com/news/security/lockbit-ransomware-now-encrypts-windows-domains-using-group-policies/ -- https://www.varonis.com/blog/group-policy-objects + - https://attack.mitre.org/techniques/T1484/ + - https://attack.mitre.org/techniques/T1484/001 + - https://www.trustedsec.com/blog/weaponizing-group-policy-objects-access/ + - https://adsecurity.org/?p=2716 + - https://www.bleepingcomputer.com/news/security/lockbit-ransomware-now-encrypts-windows-domains-using-group-policies/ + - https://www.varonis.com/blog/group-policy-objects drilldown_searches: -- name: View the detection results for - "$User$" - search: '%original_detection_search% | search User = "$User$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$User$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$User$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$User$" + search: '%original_detection_search% | search User = "$User$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$User$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$User$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A new group policy objected was created by $User$ - risk_objects: - - field: User - type: user - score: 40 - threat_objects: [] + message: A new group policy objected was created by $User$ + risk_objects: + - field: User + type: user + score: 40 + threat_objects: [] tags: - analytic_story: - - Active Directory Privilege Escalation - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1078.002 - - T1484.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Privilege Escalation + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1078.002 + - T1484.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_created/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_created/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_handle_duplication_in_known_uac_bypass_binaries.yml b/detections/endpoint/windows_handle_duplication_in_known_uac_bypass_binaries.yml index 3f7167add5..069d29e456 100644 --- a/detections/endpoint/windows_handle_duplication_in_known_uac_bypass_binaries.yml +++ b/detections/endpoint/windows_handle_duplication_in_known_uac_bypass_binaries.yml @@ -7,68 +7,44 @@ status: production type: Anomaly description: The following analytic detects suspicious handle duplication activity targeting known Windows utilities such as ComputerDefaults.exe, Eventvwr.exe, and others. This technique is commonly used to escalate privileges or bypass UAC by inheriting or injecting elevated tokens or handles. The detection focuses on non-standard use of DuplicateHandle or token duplication where process, thread, or token handles are copied into the context of trusted, signed utilities. Such behavior may indicate attempts to execute with elevated rights without user consent. Alerts enable rapid triage using process trees, handle data, token attributes, command-lines, and binary hashes. data_source: -- Sysmon EventID 10 -search: '`sysmon` EventCode=10 - TargetImage IN("*\\ComputerDefaults.exe", "*\\eventvwr.exe*", "*\\fodhelper.exe","*\\slui.exe","*\\sdclt.exe","*\\mmc.exe", "*\\colorcpl.exe","*\\wsreset.exe","*\\esentutl.exe", "*\PkgMgr.exe") - AND NOT (SourceImage IN ("*C:\\Windows\\system32\\*","*C:\\Windows\\syswow64\\*","*C:\\Program Files\\*", "*C:\\Program Files (x86)\\*","%systemroot%\\*")) - | eval g_access_decimal = tonumber(replace(GrantedAccess,"0x",""),16) - | eval PROCESS_DUP_HANDLE = 64 - | eval dup_handle_set = bit_and (g_access_decimal, PROCESS_DUP_HANDLE) - | where dup_handle_set == PROCESS_DUP_HANDLE - | stats count min(_time) as firstTime max(_time) as lastTime - by SourceImage TargetImage GrantedAccess PROCESS_DUP_HANDLE g_access_decimal dup_handle_set Guid Opcode ProcessID SecurityID - SourceProcessGUID SourceProcessId TargetProcessGUID TargetProcessId - UserID dest granted_access parent_process_exec parent_process_guid parent_process_id - parent_process_name parent_process_path process_exec process_guid process_id process_name - process_path signature signature_id user_id vendor_product CallTrace EventID - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_handle_duplication_in_known_uac_bypass_binaries_filter`' -how_to_implement: To successfully implement this search, you must be ingesting data - that records process activity from your hosts to populate the endpoint data model - in the processes node. If you are using Sysmon, you must have at least version 6.0.4 - of the Sysmon TA. -known_false_positives: It is possible legitimate applications will request access - to list of know abused Windows UAC binaries process, filter as needed. + - Sysmon EventID 10 +search: '`sysmon` EventCode=10 TargetImage IN("*\\ComputerDefaults.exe", "*\\eventvwr.exe*", "*\\fodhelper.exe","*\\slui.exe","*\\sdclt.exe","*\\mmc.exe", "*\\colorcpl.exe","*\\wsreset.exe","*\\esentutl.exe", "*\PkgMgr.exe") AND NOT (SourceImage IN ("*C:\\Windows\\system32\\*","*C:\\Windows\\syswow64\\*","*C:\\Program Files\\*", "*C:\\Program Files (x86)\\*","%systemroot%\\*")) | eval g_access_decimal = tonumber(replace(GrantedAccess,"0x",""),16) | eval PROCESS_DUP_HANDLE = 64 | eval dup_handle_set = bit_and (g_access_decimal, PROCESS_DUP_HANDLE) | where dup_handle_set == PROCESS_DUP_HANDLE | stats count min(_time) as firstTime max(_time) as lastTime by SourceImage TargetImage GrantedAccess PROCESS_DUP_HANDLE g_access_decimal dup_handle_set Guid Opcode ProcessID SecurityID SourceProcessGUID SourceProcessId TargetProcessGUID TargetProcessId UserID dest granted_access parent_process_exec parent_process_guid parent_process_id parent_process_name parent_process_path process_exec process_guid process_id process_name process_path signature signature_id user_id vendor_product CallTrace EventID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_handle_duplication_in_known_uac_bypass_binaries_filter`' +how_to_implement: To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: It is possible legitimate applications will request access to list of know abused Windows UAC binaries process, filter as needed. references: -- https://www.recordedfuture.com/research/from-castleloader-to-castlerat-tag-150-advances-operations + - https://www.recordedfuture.com/research/from-castleloader-to-castlerat-tag-150-advances-operations drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process $SourceImage$ is duplicating the handle token of $TargetImage$ on $dest$ - risk_objects: - - field: dest - type: system - score: 20 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: A process $SourceImage$ is duplicating the handle token of $TargetImage$ on $dest$ + risk_objects: + - field: dest + type: system + score: 20 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - Castle RAT - asset_type: Endpoint - mitre_attack_id: - - T1134.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Castle RAT + asset_type: Endpoint + mitre_attack_id: + - T1134.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1134.001/uac_process_handle_dup/Computerdefaults_access.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1134.001/uac_process_handle_dup/Computerdefaults_access.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_hidden_schedule_task_settings.yml b/detections/endpoint/windows_hidden_schedule_task_settings.yml index 98278c68b6..e02e0babfa 100644 --- a/detections/endpoint/windows_hidden_schedule_task_settings.yml +++ b/detections/endpoint/windows_hidden_schedule_task_settings.yml @@ -1,79 +1,65 @@ name: Windows Hidden Schedule Task Settings id: 0b730470-5fe8-4b13-93a7-fe0ad014d0cc -version: 10 -date: '2026-01-14' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the creation of hidden scheduled tasks - on Windows systems, which are not visible in the UI. It leverages Windows Security - EventCode 4698 to identify tasks where the 'Hidden' setting is enabled. This behavior - is significant as it may indicate malware activity, such as Industroyer2, or the - use of living-off-the-land binaries (LOLBINs) to download additional payloads. If - confirmed malicious, this activity could allow attackers to execute code stealthily, - maintain persistence, or further compromise the system by downloading additional - malicious payloads. +description: The following analytic detects the creation of hidden scheduled tasks on Windows systems, which are not visible in the UI. It leverages Windows Security EventCode 4698 to identify tasks where the 'Hidden' setting is enabled. This behavior is significant as it may indicate malware activity, such as Industroyer2, or the use of living-off-the-land binaries (LOLBINs) to download additional payloads. If confirmed malicious, this activity could allow attackers to execute code stealthily, maintain persistence, or further compromise the system by downloading additional malicious payloads. data_source: -- Windows Event Log Security 4698 + - Windows Event Log Security 4698 search: | - `wineventlog_security` - EventCode=4698 - TaskContent = "*<Hidden>true</Hidden>*" - | stats count min(_time) as firstTime max(_time) as lastTime - by TaskName TaskContent action signature status dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_hidden_schedule_task_settings_filter` -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the task schedule (Exa. Security Log EventCode 4698) endpoints. Tune and - filter known instances of Task schedule used in your environment. + `wineventlog_security` + EventCode=4698 + TaskContent = "*<Hidden>true</Hidden>*" + | stats count min(_time) as firstTime max(_time) as lastTime + by TaskName TaskContent action signature status dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_hidden_schedule_task_settings_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the task schedule (Exa. Security Log EventCode 4698) endpoints. Tune and filter known instances of Task schedule used in your environment. known_false_positives: No false positives have been identified at this time. references: -- https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ -- https://cert.gov.ua/article/39518 + - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ + - https://cert.gov.ua/article/39518 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A schedule task with hidden setting enable in host $dest$ - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A schedule task with hidden setting enable in host $dest$ + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - CISA AA22-257A - - Active Directory Discovery - - Malicious Inno Setup Loader - - Compromised Windows Host - - Data Destruction - - Industroyer2 - - Cactus Ransomware - - Scheduled Tasks - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1053 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA22-257A + - Active Directory Discovery + - Malicious Inno Setup Loader + - Compromised Windows Host + - Data Destruction + - Industroyer2 + - Cactus Ransomware + - Scheduled Tasks + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1053 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053/hidden_schedule_task/inno_schtask.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053/hidden_schedule_task/inno_schtask.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_hide_notification_features_through_registry.yml b/detections/endpoint/windows_hide_notification_features_through_registry.yml index 752720ee10..5db6037941 100644 --- a/detections/endpoint/windows_hide_notification_features_through_registry.yml +++ b/detections/endpoint/windows_hide_notification_features_through_registry.yml @@ -5,72 +5,46 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk, Steven Dick status: production type: Anomaly -description: - The following analytic detects suspicious registry modifications aimed - at hiding common Windows notification features on a compromised host. It leverages - data from the Endpoint.Registry data model, focusing on specific registry paths - and values. This activity is significant as it is often used by ransomware to obscure - visual indicators, increasing the impact of the attack. If confirmed malicious, - this could prevent users from noticing critical system alerts, thereby aiding the - attacker in maintaining persistence and furthering their malicious activities undetected. +description: The following analytic detects suspicious registry modifications aimed at hiding common Windows notification features on a compromised host. It leverages data from the Endpoint.Registry data model, focusing on specific registry paths and values. This activity is significant as it is often used by ransomware to obscure visual indicators, increasing the impact of the attack. If confirmed malicious, this could prevent users from noticing critical system alerts, thereby aiding the attacker in maintaining persistence and furthering their malicious activities undetected. data_source: - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\*" - Registry.registry_value_name IN ("HideClock", "HideSCAHealth", "HideSCANetwork", - "HideSCAPower", "HideSCAVolume") Registry.registry_value_data = "0x00000001") by - Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_hide_notification_features_through_registry_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\*" Registry.registry_value_name IN ("HideClock", "HideSCAHealth", "HideSCANetwork", "HideSCAPower", "HideSCAVolume") Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_hide_notification_features_through_registry_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: No false positives have been identified at this time. references: - - https://www.trendmicro.com/vinfo/us/threat-encyclopedia/malware/Ransom.Win32.ONALOCKER.A/ + - https://www.trendmicro.com/vinfo/us/threat-encyclopedia/malware/Ransom.Win32.ONALOCKER.A/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Registry modification to hide windows notification on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Registry modification to hide windows notification on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Ransomware - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/ransomware_disable_reg/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/ransomware_disable_reg/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_high_file_deletion_frequency.yml b/detections/endpoint/windows_high_file_deletion_frequency.yml index bccb732fd4..b30caa5ab8 100644 --- a/detections/endpoint/windows_high_file_deletion_frequency.yml +++ b/detections/endpoint/windows_high_file_deletion_frequency.yml @@ -5,95 +5,65 @@ date: '2026-02-12' author: Teoderick Contreras, Splunk, Steven Dick status: production type: Anomaly -description: The following analytic identifies a high frequency of file - deletions by monitoring Sysmon EventCodes 23 and 26 for specific file - extensions. This detection leverages Sysmon logs to track deleted target - filenames, process names, and process IDs. Such activity is significant as it - often indicates ransomware behavior, where files are encrypted and the - originals are deleted. If confirmed malicious, this activity could lead to - extensive data loss and operational disruption, as ransomware can render - critical files inaccessible, demanding a ransom for their recovery. +description: The following analytic identifies a high frequency of file deletions by monitoring Sysmon EventCodes 23 and 26 for specific file extensions. This detection leverages Sysmon logs to track deleted target filenames, process names, and process IDs. Such activity is significant as it often indicates ransomware behavior, where files are encrypted and the originals are deleted. If confirmed malicious, this activity could lead to extensive data loss and operational disruption, as ransomware can render critical files inaccessible, demanding a ransom for their recovery. data_source: -- Sysmon EventID 23 -- Sysmon EventID 26 -search: '`sysmon` EventCode IN ("23","26") TargetFilename IN ("*.cmd", "*.ini","*.gif", - "*.jpg", "*.jpeg", "*.db", "*.ps1", "*.doc", "*.docx", "*.xls", "*.xlsx", "*.ppt", - "*.pptx", "*.bmp","*.zip", "*.rar", "*.7z", "*.chm", "*.png", "*.log", "*.vbs", - "*.js", "*.vhd", "*.bak", "*.wbcat", "*.bkf" , "*.backup*", "*.dsk", "*.win") NOT - TargetFilename IN ("*\\INetCache\\Content.Outlook\\*") | stats count min(_time) - as firstTime, max(_time) as lastTime values(file_path) as file_path values(file_hash) - as file_hash values(file_name) as file_name values(file_modify_time) as file_modify_time - values(process_name) as process_name values(process_path) as process_path values(process_guid) - as process_guid values(process_id) as process_id values(process_exec) as process_exec - by action dest dvc signature signature_id user user_id vendor_product | where count - >=100 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_high_file_deletion_frequency_filter`' -how_to_implement: To successfully implement this search, you need to ingest logs - that include the deleted target file name, process name, and process ID from - your endpoints. If you are using Sysmon, ensure you have at least version 2.0 - of the Sysmon TA installed. -known_false_positives: Users may delete a large number of pictures or files in a - folder, which could trigger this detection. Additionally, heavy usage of - PowerBI and Outlook may also result in false positives. + - Sysmon EventID 23 + - Sysmon EventID 26 +search: '`sysmon` EventCode IN ("23","26") TargetFilename IN ("*.cmd", "*.ini","*.gif", "*.jpg", "*.jpeg", "*.db", "*.ps1", "*.doc", "*.docx", "*.xls", "*.xlsx", "*.ppt", "*.pptx", "*.bmp","*.zip", "*.rar", "*.7z", "*.chm", "*.png", "*.log", "*.vbs", "*.js", "*.vhd", "*.bak", "*.wbcat", "*.bkf" , "*.backup*", "*.dsk", "*.win") NOT TargetFilename IN ("*\\INetCache\\Content.Outlook\\*") | stats count min(_time) as firstTime, max(_time) as lastTime values(file_path) as file_path values(file_hash) as file_hash values(file_name) as file_name values(file_modify_time) as file_modify_time values(process_name) as process_name values(process_path) as process_path values(process_guid) as process_guid values(process_id) as process_id values(process_exec) as process_exec by action dest dvc signature signature_id user user_id vendor_product | where count >=100 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_high_file_deletion_frequency_filter`' +how_to_implement: To successfully implement this search, you need to ingest logs that include the deleted target file name, process name, and process ID from your endpoints. If you are using Sysmon, ensure you have at least version 2.0 of the Sysmon TA installed. +known_false_positives: Users may delete a large number of pictures or files in a folder, which could trigger this detection. Additionally, heavy usage of PowerBI and Outlook may also result in false positives. references: -- https://www.mandiant.com/resources/fin11-email-campaigns-precursor-for-ransomware-data-theft -- https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html -- https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://www.mandiant.com/resources/fin11-email-campaigns-precursor-for-ransomware-data-theft + - https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Elevated file deletion rate observed from process [$process_name$] on - machine $dest$ - risk_objects: - - field: user - type: user - score: 72 - - field: dest - type: system - score: 72 - threat_objects: - - field: process_name - type: process_name + message: Elevated file deletion rate observed from process [$process_name$] on machine $dest$ + risk_objects: + - field: user + type: user + score: 72 + - field: dest + type: system + score: 72 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Sandworm Tools - - Handala Wiper - - Data Destruction - - WhisperGate - - Swift Slicer - - Medusa Ransomware - - DarkCrystal RAT - - Black Basta Ransomware - - Clop Ransomware - - Interlock Ransomware - - NailaoLocker Ransomware - - APT37 Rustonotto and FadeStealer - - DynoWiper - - ZOVWiper - asset_type: Endpoint - mitre_attack_id: - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sandworm Tools + - Handala Wiper + - Data Destruction + - WhisperGate + - Swift Slicer + - Medusa Ransomware + - DarkCrystal RAT + - Black Basta Ransomware + - Clop Ransomware + - Interlock Ransomware + - NailaoLocker Ransomware + - APT37 Rustonotto and FadeStealer + - DynoWiper + - ZOVWiper + asset_type: Endpoint + mitre_attack_id: + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_hijack_execution_flow_version_dll_side_load.yml b/detections/endpoint/windows_hijack_execution_flow_version_dll_side_load.yml index 1013a76937..6285db31aa 100644 --- a/detections/endpoint/windows_hijack_execution_flow_version_dll_side_load.yml +++ b/detections/endpoint/windows_hijack_execution_flow_version_dll_side_load.yml @@ -5,67 +5,47 @@ date: '2026-02-09' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects a process loading a version.dll file from - a directory other than %windir%\system32 or %windir%\syswow64. This detection leverages - Sysmon EventCode 7 to identify instances where an unsigned or improperly located - version.dll is loaded. This activity is significant as it is a common technique - used in ransomware and APT malware campaigns, including Brute Ratel C4, to execute - malicious code via DLL side loading. If confirmed malicious, this could allow attackers - to execute arbitrary code, maintain persistence, and potentially compromise the - target host. +description: The following analytic detects a process loading a version.dll file from a directory other than %windir%\system32 or %windir%\syswow64. This detection leverages Sysmon EventCode 7 to identify instances where an unsigned or improperly located version.dll is loaded. This activity is significant as it is a common technique used in ransomware and APT malware campaigns, including Brute Ratel C4, to execute malicious code via DLL side loading. If confirmed malicious, this could allow attackers to execute arbitrary code, maintain persistence, and potentially compromise the target host. data_source: -- Sysmon EventID 7 -search: '`sysmon` EventCode=7 ImageLoaded = "*\\version.dll" AND (Signed = "false" - OR NOT(ImageLoaded IN("*\\windows\\system32*", "*\\windows\\syswow64\\*"))) | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded - dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash - process_id process_name process_path service_dll_signature_exists service_dll_signature_verified - signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_hijack_execution_flow_version_dll_side_load_filter`' -how_to_implement: The latest Sysmon TA 3.0 https://splunkbase.splunk.com/app/5709 - will add the ImageLoaded name to the process_name field, allowing this query to - work. Use as an example and implement for other products. + - Sysmon EventID 7 +search: '`sysmon` EventCode=7 ImageLoaded = "*\\version.dll" AND (Signed = "false" OR NOT(ImageLoaded IN("*\\windows\\system32*", "*\\windows\\syswow64\\*"))) | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_hijack_execution_flow_version_dll_side_load_filter`' +how_to_implement: The latest Sysmon TA 3.0 https://splunkbase.splunk.com/app/5709 will add the ImageLoaded name to the process_name field, allowing this query to work. Use as an example and implement for other products. known_false_positives: No false positives have been identified at this time. references: -- https://www.mdsec.co.uk/2022/08/part-3-how-i-met-your-beacon-brute-ratel/ + - https://www.mdsec.co.uk/2022/08/part-3-how-i-met-your-beacon-brute-ratel/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a process $Image$ loading $ImageLoaded$ as a side load dll on $dest$ - risk_objects: - - field: dest - type: system - score: 35 - threat_objects: [] + message: a process $Image$ loading $ImageLoaded$ as a side load dll on $dest$ + risk_objects: + - field: dest + type: system + score: 35 + threat_objects: [] tags: - analytic_story: - - SolarWinds WHD RCE Post Exploitation - - Brute Ratel C4 - - XWorm - - Malicious Inno Setup Loader - asset_type: Endpoint - mitre_attack_id: - - T1574.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SolarWinds WHD RCE Post Exploitation + - Brute Ratel C4 + - XWorm + - Malicious Inno Setup Loader + asset_type: Endpoint + mitre_attack_id: + - T1574.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/iso_version_dll_campaign/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/iso_version_dll_campaign/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_http_network_communication_from_msiexec.yml b/detections/endpoint/windows_http_network_communication_from_msiexec.yml index 8490e9666e..16875789c4 100644 --- a/detections/endpoint/windows_http_network_communication_from_msiexec.yml +++ b/detections/endpoint/windows_http_network_communication_from_msiexec.yml @@ -5,104 +5,63 @@ date: '2026-02-09' author: Michael Haag, Splunk status: production type: Anomaly -description: - The following analytic detects MSIExec making network connections over - ports 443 or 80. This behavior is identified by correlating process creation events - from Endpoint Detection and Response (EDR) agents with network traffic logs. Typically, - MSIExec does not perform network communication to the internet, making this activity - unusual and potentially indicative of malicious behavior. If confirmed malicious, - an attacker could be using MSIExec to download or communicate with external servers, - potentially leading to data exfiltration, command and control (C2) communication, - or further malware deployment. +description: The following analytic detects MSIExec making network connections over ports 443 or 80. This behavior is identified by correlating process creation events from Endpoint Detection and Response (EDR) agents with network traffic logs. Typically, MSIExec does not perform network communication to the internet, making this activity unusual and potentially indicative of malicious behavior. If confirmed malicious, an attacker could be using MSIExec to download or communicate with external servers, potentially leading to data exfiltration, command and control (C2) communication, or further malware deployment. data_source: - - Sysmon EventID 1 AND Sysmon EventID 3 - - Cisco Network Visibility Module Flow Data -search: - '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where `process_msiexec` - by _time Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | join process_id [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic where - All_Traffic.dest_port IN ("80","443") by All_Traffic.action All_Traffic.app All_Traffic.bytes All_Traffic.bytes_in All_Traffic.bytes_out - All_Traffic.dest All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.dvc All_Traffic.protocol - All_Traffic.protocol_version All_Traffic.src All_Traffic.src_ip All_Traffic.src_port - All_Traffic.transport All_Traffic.user All_Traffic.vendor_product All_Traffic.direction All_Traffic.process_id - | `drop_dm_object_name(All_Traffic)` ] - | table _time user dest parent_process_name process_name process_path process process_id dest_port dest_ip - | `windows_http_network_communication_from_msiexec_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 AND Sysmon EventID 3 + - Cisco Network Visibility Module Flow Data +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where `process_msiexec` by _time Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join process_id [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port IN ("80","443") by All_Traffic.action All_Traffic.app All_Traffic.bytes All_Traffic.bytes_in All_Traffic.bytes_out All_Traffic.dest All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.dvc All_Traffic.protocol All_Traffic.protocol_version All_Traffic.src All_Traffic.src_ip All_Traffic.src_port All_Traffic.transport All_Traffic.user All_Traffic.vendor_product All_Traffic.direction All_Traffic.process_id | `drop_dm_object_name(All_Traffic)` ] | table _time user dest parent_process_name process_name process_path process process_id dest_port dest_ip | `windows_http_network_communication_from_msiexec_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives will be present and filtering is required. references: - - https://thedfirreport.com/2022/06/06/will-the-real-msiexec-please-stand-up-exploit-leads-to-data-exfiltration/ - - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.007/T1218.007.md + - https://thedfirreport.com/2022/06/06/will-the-real-msiexec-please-stand-up-exploit-leads-to-data-exfiltration/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.007/T1218.007.md drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - An instance of $process_name$ was identified on endpoint $dest$ contacting - a remote destination $dest_ip$ - risk_objects: - - field: user - type: user - score: 35 - - field: dest - type: system - score: 35 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $process_name$ was identified on endpoint $dest$ contacting a remote destination $dest_ip$ + risk_objects: + - field: user + type: user + score: 35 + - field: dest + type: system + score: 35 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - APT37 Rustonotto and FadeStealer - - GhostRedirector IIS Module and Rungan Backdoor - - Windows System Binary Proxy Execution MSIExec - - Water Gamayun - - Cisco Network Visibility Module Analytics - - SolarWinds WHD RCE Post Exploitation - asset_type: Endpoint - mitre_attack_id: - - T1218.007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - APT37 Rustonotto and FadeStealer + - GhostRedirector IIS Module and Rungan Backdoor + - Windows System Binary Proxy Execution MSIExec + - Water Gamayun + - Cisco Network Visibility Module Analytics + - SolarWinds WHD RCE Post Exploitation + asset_type: Endpoint + mitre_attack_id: + - T1218.007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - Sysmon - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata + - name: True Positive Test - Sysmon + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/windows_hunting_system_account_targeting_lsass.yml b/detections/endpoint/windows_hunting_system_account_targeting_lsass.yml index b50a10148d..ac71f3c739 100644 --- a/detections/endpoint/windows_hunting_system_account_targeting_lsass.yml +++ b/detections/endpoint/windows_hunting_system_account_targeting_lsass.yml @@ -1,59 +1,54 @@ name: Windows Hunting System Account Targeting Lsass id: 1c6abb08-73d1-11ec-9ca0-acde48001122 -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic identifies processes attempting to access - Lsass.exe, which may indicate credential dumping or applications needing - credential access. It leverages Sysmon EventCode 10 to detect such activities - by analyzing fields like TargetImage, GrantedAccess, and SourceImage. This - behavior is significant as unauthorized access to Lsass.exe can lead to - credential theft, posing a severe security risk. If confirmed malicious, - attackers could gain access to sensitive credentials, potentially leading to - privilege escalation and further compromise of the environment. +description: The following analytic identifies processes attempting to access Lsass.exe, which may indicate credential dumping or applications needing credential access. It leverages Sysmon EventCode 10 to detect such activities by analyzing fields like TargetImage, GrantedAccess, and SourceImage. This behavior is significant as unauthorized access to Lsass.exe can lead to credential theft, posing a severe security risk. If confirmed malicious, attackers could gain access to sensitive credentials, potentially leading to privilege escalation and further compromise of the environment. data_source: -- Sysmon EventID 10 -search: '`sysmon` EventCode=10 TargetImage=*lsass.exe | stats count min(_time) as - firstTime max(_time) as lastTime by CallTrace EventID GrantedAccess Guid Opcode - ProcessID SecurityID SourceImage SourceProcessGUID SourceProcessId TargetImage TargetProcessGUID - TargetProcessId UserID dest granted_access parent_process_exec parent_process_guid - parent_process_id parent_process_name parent_process_path process_exec process_guid - process_id process_name process_path signature signature_id user_id vendor_product - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_hunting_system_account_targeting_lsass_filter`' -how_to_implement: To successfully implement this search, you need to be - ingesting logs with the process name, parent process, and command-line - executions from your endpoints. If you are using Sysmon, you must have at - least version 6.0.4 of the Sysmon TA. Enabling EventCode 10 TargetProcess - lsass.exe is required. -known_false_positives: False positives will occur based on GrantedAccess and - SourceUser, filter based on source image as needed. Utilize this hunting - analytic to tune out false positives in TTP or anomaly analytics. + - Sysmon EventID 10 +search: |- + `sysmon` EventCode=10 TargetImage=*lsass.exe + | stats count min(_time) as firstTime max(_time) as lastTime + BY CallTrace EventID GrantedAccess + Guid Opcode ProcessID + SecurityID SourceImage SourceProcessGUID + SourceProcessId TargetImage TargetProcessGUID + TargetProcessId UserID dest + granted_access parent_process_exec parent_process_guid + parent_process_id parent_process_name parent_process_path + process_exec process_guid process_id + process_name process_path signature + signature_id user_id vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_hunting_system_account_targeting_lsass_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Enabling EventCode 10 TargetProcess lsass.exe is required. +known_false_positives: False positives will occur based on GrantedAccess and SourceUser, filter based on source image as needed. Utilize this hunting analytic to tune out false positives in TTP or anomaly analytics. references: -- https://en.wikipedia.org/wiki/Local_Security_Authority_Subsystem_Service -- https://docs.microsoft.com/en-us/windows/win32/api/minidumpapiset/nf-minidumpapiset-minidumpwritedump -- https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for_22.html -- https://raw.githubusercontent.com/PowerShellMafia/PowerSploit/master/Exfiltration/Invoke-Mimikatz.ps1 -- https://docs.microsoft.com/en-us/windows/win32/procthread/process-security-and-access-rights?redirectedfrom=MSDN + - https://en.wikipedia.org/wiki/Local_Security_Authority_Subsystem_Service + - https://docs.microsoft.com/en-us/windows/win32/api/minidumpapiset/nf-minidumpapiset-minidumpwritedump + - https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for_22.html + - https://raw.githubusercontent.com/PowerShellMafia/PowerSploit/master/Exfiltration/Invoke-Mimikatz.ps1 + - https://docs.microsoft.com/en-us/windows/win32/procthread/process-security-and-access-rights?redirectedfrom=MSDN tags: - analytic_story: - - CISA AA23-347A - - Credential Dumping - - Lokibot - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1003.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA23-347A + - Credential Dumping + - Lokibot + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1003.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon_creddump.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon_creddump.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_identify_powershell_web_access_iis_pool.yml b/detections/endpoint/windows_identify_powershell_web_access_iis_pool.yml index 3ac784ec3d..63fd27a417 100644 --- a/detections/endpoint/windows_identify_powershell_web_access_iis_pool.yml +++ b/detections/endpoint/windows_identify_powershell_web_access_iis_pool.yml @@ -4,62 +4,31 @@ version: 4 date: '2025-05-02' author: Michael Haag, Splunk data_source: -- Windows Event Log Security 4648 + - Windows Event Log Security 4648 type: Hunting status: production -description: This analytic detects and analyzes PowerShell Web Access (PSWA) usage - in Windows environments. It tracks both connection attempts (EventID 4648) and successful - logons (EventID 4624) associated with PSWA, providing a comprehensive view of access - patterns. The analytic identifies PSWA's operational status, host servers, processes, - and connection metrics. It highlights unique target accounts, domains accessed, - and verifies logon types. This information is crucial for detecting potential misuse, - such as lateral movement, brute force attempts, or unusual access patterns. By offering - insights into PSWA activity, it enables security teams to quickly assess and investigate - potential security incidents involving this powerful administrative tool. -search: '`wineventlog_security` (EventCode=4648 OR EventCode=4624 OR EventCode=4625) - SubjectUserName="pswa_pool" | fields EventCode, SubjectUserName, TargetUserName, - Computer, TargetDomainName, ProcessName, LogonType | rename Computer as dest | stats - count(eval(EventCode=4648)) as "Connection Attempts", count(eval(EventCode=4624)) - as "Successful Logons", count(eval(EventCode=4625)) as "Unsuccessful Logons", dc(TargetUserName) - as "Unique Target Accounts", values(dest) as "PSWA Host", dc(TargetDomainName) as - "Unique Target Domains", values(ProcessName) as "PSWA Process", values(TargetUserName) - as "Target Users List", values(TargetServerName) as "Target Servers List", values(LogonType) - as "Logon Types" | eval PSWA_Running = "Yes", "PSWA Process" = mvindex(split(mvindex("PSWA - Process", 0), "\\"), -1) | fields PSWA_Running, "PSWA Host", "PSWA Process", "Connection - Attempts", "Successful Logons","Unsuccessful Logons", "Unique Target Accounts", - "Unique Target Domains", "Target Users List","Target Servers List", "Logon Types" - | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `windows_identify_powershell_web_access_iis_pool_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Windows Security Event logs, specifically Event ID 4648 (A logon was attempted using - explicit credentials). Ensure that your Windows systems are configured to audit - logon events and that these logs are being forwarded to your SIEM or log management - solution. You may need to enable advanced audit policy settings in Windows to capture - these events. Additionally, make sure that your environment is set up to capture - the necessary fields such as SubjectUserName, TargetUserName, Computer, TargetServerName, - and ProcessName from these events. If you're using Splunk, ensure that you have - the appropriate Windows TA installed and configured to collect these security logs. -known_false_positives: False positives may occur if legitimate PSWA processes are - used for administrative tasks. Careful review of the logs is recommended to distinguish - between legitimate and malicious activity. +description: This analytic detects and analyzes PowerShell Web Access (PSWA) usage in Windows environments. It tracks both connection attempts (EventID 4648) and successful logons (EventID 4624) associated with PSWA, providing a comprehensive view of access patterns. The analytic identifies PSWA's operational status, host servers, processes, and connection metrics. It highlights unique target accounts, domains accessed, and verifies logon types. This information is crucial for detecting potential misuse, such as lateral movement, brute force attempts, or unusual access patterns. By offering insights into PSWA activity, it enables security teams to quickly assess and investigate potential security incidents involving this powerful administrative tool. +search: '`wineventlog_security` (EventCode=4648 OR EventCode=4624 OR EventCode=4625) SubjectUserName="pswa_pool" | fields EventCode, SubjectUserName, TargetUserName, Computer, TargetDomainName, ProcessName, LogonType | rename Computer as dest | stats count(eval(EventCode=4648)) as "Connection Attempts", count(eval(EventCode=4624)) as "Successful Logons", count(eval(EventCode=4625)) as "Unsuccessful Logons", dc(TargetUserName) as "Unique Target Accounts", values(dest) as "PSWA Host", dc(TargetDomainName) as "Unique Target Domains", values(ProcessName) as "PSWA Process", values(TargetUserName) as "Target Users List", values(TargetServerName) as "Target Servers List", values(LogonType) as "Logon Types" | eval PSWA_Running = "Yes", "PSWA Process" = mvindex(split(mvindex("PSWA Process", 0), "\\"), -1) | fields PSWA_Running, "PSWA Host", "PSWA Process", "Connection Attempts", "Successful Logons","Unsuccessful Logons", "Unique Target Accounts", "Unique Target Domains", "Target Users List","Target Servers List", "Logon Types" | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `windows_identify_powershell_web_access_iis_pool_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event logs, specifically Event ID 4648 (A logon was attempted using explicit credentials). Ensure that your Windows systems are configured to audit logon events and that these logs are being forwarded to your SIEM or log management solution. You may need to enable advanced audit policy settings in Windows to capture these events. Additionally, make sure that your environment is set up to capture the necessary fields such as SubjectUserName, TargetUserName, Computer, TargetServerName, and ProcessName from these events. If you're using Splunk, ensure that you have the appropriate Windows TA installed and configured to collect these security logs. +known_false_positives: False positives may occur if legitimate PSWA processes are used for administrative tasks. Careful review of the logs is recommended to distinguish between legitimate and malicious activity. references: -- https://www.cisa.gov/news-events/cybersecurity-advisories/aa24-241a -- https://gist.github.com/MHaggis/7e67b659af9148fa593cf2402edebb41 + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa24-241a + - https://gist.github.com/MHaggis/7e67b659af9148fa593cf2402edebb41 tags: - analytic_story: - - CISA AA24-241A - asset_type: Endpoint - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - CISA AA24-241A + asset_type: Endpoint + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/pswa/4648_4624_pswa_pool.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Security + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/pswa/4648_4624_pswa_pool.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Security diff --git a/detections/endpoint/windows_identify_protocol_handlers.yml b/detections/endpoint/windows_identify_protocol_handlers.yml index 7d170b5682..02c39f8db5 100644 --- a/detections/endpoint/windows_identify_protocol_handlers.yml +++ b/detections/endpoint/windows_identify_protocol_handlers.yml @@ -1,67 +1,56 @@ name: Windows Identify Protocol Handlers id: bd5c311e-a6ea-48ae-a289-19a3398e3648 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic identifies the use of protocol handlers executed - via the command line. It leverages data from Endpoint Detection and Response (EDR) - agents, focusing on process and command-line telemetry. This activity is significant - because protocol handlers can be exploited to execute arbitrary commands or launch - applications, potentially leading to unauthorized actions. If confirmed malicious, - an attacker could use this technique to gain code execution, escalate privileges, - or maintain persistence within the environment, posing a significant security risk. +description: The following analytic identifies the use of protocol handlers executed via the command line. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and command-line telemetry. This activity is significant because protocol handlers can be exploited to execute arbitrary commands or launch applications, potentially leading to unauthorized actions. If confirmed malicious, an attacker could use this technique to gain code execution, escalate privileges, or maintain persistence within the environment, posing a significant security risk. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime values(Processes.process) as process values(Processes.parent_process) - as parent_process from datamodel=Endpoint.Processes by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` - | lookup windows_protocol_handlers handler AS process OUTPUT handler ishandler | - where ishandler="TRUE" | `windows_identify_protocol_handlers_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives will be found. https and http is a URL Protocol - handler that will trigger this analytic. Tune based on process or command-line. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process) as process values(Processes.parent_process) as parent_process FROM datamodel=Endpoint.Processes + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `drop_dm_object_name(Processes)` + | lookup windows_protocol_handlers handler AS process OUTPUT handler ishandler + | where ishandler="TRUE" + | `windows_identify_protocol_handlers_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives will be found. https and http is a URL Protocol handler that will trigger this analytic. Tune based on process or command-line. references: -- https://gist.github.com/MHaggis/a0d3edb57d36e0916c94c0a464b2722e -- https://www.oreilly.com/library/view/learning-java/1565927184/apas02.html -- https://blogs.windows.com/msedgedev/2022/01/20/getting-started-url-protocol-handlers-microsoft-edge/ -- https://github.com/Mr-Un1k0d3r/PoisonHandler -- https://www.mdsec.co.uk/2021/03/phishing-users-to-take-a-test/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-5---protocolhandlerexe-downloaded-a-suspicious-file -- https://techcommunity.microsoft.com/t5/windows-it-pro-blog/disabling-the-msix-ms-appinstaller-protocol-handler/ba-p/3119479 -- https://www.huntress.com/blog/microsoft-office-remote-code-execution-follina-msdt-bug -- https://parsiya.net/blog/2021-03-17-attack-surface-analysis-part-2-custom-protocol-handlers/ + - https://gist.github.com/MHaggis/a0d3edb57d36e0916c94c0a464b2722e + - https://www.oreilly.com/library/view/learning-java/1565927184/apas02.html + - https://blogs.windows.com/msedgedev/2022/01/20/getting-started-url-protocol-handlers-microsoft-edge/ + - https://github.com/Mr-Un1k0d3r/PoisonHandler + - https://www.mdsec.co.uk/2021/03/phishing-users-to-take-a-test/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-5---protocolhandlerexe-downloaded-a-suspicious-file + - https://techcommunity.microsoft.com/t5/windows-it-pro-blog/disabling-the-msix-ms-appinstaller-protocol-handler/ba-p/3119479 + - https://www.huntress.com/blog/microsoft-office-remote-code-execution-follina-msdt-bug + - https://parsiya.net/blog/2021-03-17-attack-surface-analysis-part-2-custom-protocol-handlers/ tags: - analytic_story: - - Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1059 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1059 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/protocol_handlers/protocolhandlers.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/protocol_handlers/protocolhandlers.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_iis_components_add_new_module.yml b/detections/endpoint/windows_iis_components_add_new_module.yml index 7a15afbea1..15cd512376 100644 --- a/detections/endpoint/windows_iis_components_add_new_module.yml +++ b/detections/endpoint/windows_iis_components_add_new_module.yml @@ -1,95 +1,78 @@ name: Windows IIS Components Add New Module id: 38fe731c-1f13-43d4-b878-a5bbe44807e3 -version: 10 -date: '2025-09-16' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic detects the execution of AppCmd.exe to install - a new module in IIS. This detection leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process names and command-line executions. This activity - is significant as adversaries may use it to install webshells or backdoors, leading - to credit card scraping, persistence, and further post-exploitation. If confirmed - malicious, this could allow attackers to maintain persistent access, execute arbitrary - code, and potentially exfiltrate sensitive information from the compromised web - server. +description: The following analytic detects the execution of AppCmd.exe to install a new module in IIS. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as adversaries may use it to install webshells or backdoors, leading to credit card scraping, persistence, and further post-exploitation. If confirmed malicious, this could allow attackers to maintain persistent access, execute arbitrary code, and potentially exfiltrate sensitive information from the compromised web server. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where NOT (Processes.parent_process_name IN - ("msiexec.exe", "iissetup.exe")) Processes.process_name=appcmd.exe Processes.process - IN ("*install *", "*module *") AND Processes.process="*image*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_iis_components_add_new_module_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present until properly tuned. Filter - as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE NOT (Processes.parent_process_name IN ("msiexec.exe", "iissetup.exe")) Processes.process_name=appcmd.exe Processes.process IN ("*install *", "*module *") + AND + Processes.process="*image*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_iis_components_add_new_module_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present until properly tuned. Filter as needed. references: -- https://www.microsoft.com/en-us/security/blog/2022/12/12/iis-modules-the-evolution-of-web-shells-and-how-to-detect-them/ -- https://www.crowdstrike.com/wp-content/uploads/2022/05/crowdstrike-iceapple-a-novel-internet-information-services-post-exploitation-framework-1.pdf -- https://unit42.paloaltonetworks.com/unit42-oilrig-uses-rgdoor-iis-backdoor-targets-middle-east/ -- https://www.secureworks.com/research/bronze-union -- https://github.com/redcanaryco/atomic-red-team/tree/master/atomics/T1505.004 -- https://strontic.github.io/xcyclopedia/library/appcmd.exe-055B2B09409F980BF9B5A3969D01E5B2.html + - https://www.microsoft.com/en-us/security/blog/2022/12/12/iis-modules-the-evolution-of-web-shells-and-how-to-detect-them/ + - https://www.crowdstrike.com/wp-content/uploads/2022/05/crowdstrike-iceapple-a-novel-internet-information-services-post-exploitation-framework-1.pdf + - https://unit42.paloaltonetworks.com/unit42-oilrig-uses-rgdoor-iis-backdoor-targets-middle-east/ + - https://www.secureworks.com/research/bronze-union + - https://github.com/redcanaryco/atomic-red-team/tree/master/atomics/T1505.004 + - https://strontic.github.io/xcyclopedia/library/appcmd.exe-055B2B09409F980BF9B5A3969D01E5B2.html drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to install a new IIS module. - risk_objects: - - field: user - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to install a new IIS module. + risk_objects: + - field: user + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - IIS Components - - GhostRedirector IIS Module and Rungan Backdoor - asset_type: Endpoint - mitre_attack_id: - - T1505.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - IIS Components + - GhostRedirector IIS Module and Rungan Backdoor + asset_type: Endpoint + mitre_attack_id: + - T1505.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/appcmd_install-windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/appcmd_install-windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_iis_components_get_webglobalmodule_module_query.yml b/detections/endpoint/windows_iis_components_get_webglobalmodule_module_query.yml index 1111113e3b..55285609a8 100644 --- a/detections/endpoint/windows_iis_components_get_webglobalmodule_module_query.yml +++ b/detections/endpoint/windows_iis_components_get_webglobalmodule_module_query.yml @@ -1,51 +1,43 @@ name: Windows IIS Components Get-WebGlobalModule Module Query id: 20db5f70-34b4-4e83-8926-fa26119de173 -version: 7 -date: '2025-09-16' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: - The following analytic identifies the execution of the PowerShell cmdlet - Get-WebGlobalModule, which lists all IIS Modules installed on a system. It leverages - PowerShell input data to detect this activity by capturing the module names and - the image paths of the DLLs. This activity is significant for a SOC because it can - indicate an attempt to enumerate installed IIS modules, which could be a precursor - to exploiting vulnerabilities or misconfigurations. If confirmed malicious, this - could allow an attacker to gain insights into the web server's configuration, potentially - leading to further exploitation or privilege escalation. +description: The following analytic identifies the execution of the PowerShell cmdlet Get-WebGlobalModule, which lists all IIS Modules installed on a system. It leverages PowerShell input data to detect this activity by capturing the module names and the image paths of the DLLs. This activity is significant for a SOC because it can indicate an attempt to enumerate installed IIS modules, which could be a precursor to exploiting vulnerabilities or misconfigurations. If confirmed malicious, this could allow an attacker to gain insights into the web server's configuration, potentially leading to further exploitation or privilege escalation. data_source: - - Powershell Installed IIS Modules -search: - '`iis_get_webglobalmodule` | stats count min(_time) as firstTime max(_time) - as lastTime by host name image | rename host as dest | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_iis_components_get_webglobalmodule_module_query_filter`' -how_to_implement: - You must ingest the PwSh cmdlet Get-WebGlobalModule in order to - utilize this analytic. Follow https://gist.github.com/MHaggis/64396dfd9fc3734e1d1901a8f2f07040 -known_false_positives: - This analytic is meant to assist with hunting modules across - a fleet of IIS servers. Filter and modify as needed. + - Powershell Installed IIS Modules +search: |- + `iis_get_webglobalmodule` + | stats count min(_time) as firstTime max(_time) as lastTime + BY host name image + | rename host as dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_iis_components_get_webglobalmodule_module_query_filter` +how_to_implement: You must ingest the PwSh cmdlet Get-WebGlobalModule in order to utilize this analytic. Follow https://gist.github.com/MHaggis/64396dfd9fc3734e1d1901a8f2f07040 +known_false_positives: This analytic is meant to assist with hunting modules across a fleet of IIS servers. Filter and modify as needed. references: - - https://help.splunk.com/en/splunk-cloud-platform/get-started/get-data-in/9.3.2411/get-windows-data/monitor-windows-data-with-powershell-scripts - - https://gist.github.com/MHaggis/64396dfd9fc3734e1d1901a8f2f07040 - - https://github.com/redcanaryco/atomic-red-team/tree/master/atomics/T1505.004 + - https://help.splunk.com/en/splunk-cloud-platform/get-started/get-data-in/9.3.2411/get-windows-data/monitor-windows-data-with-powershell-scripts + - https://gist.github.com/MHaggis/64396dfd9fc3734e1d1901a8f2f07040 + - https://github.com/redcanaryco/atomic-red-team/tree/master/atomics/T1505.004 tags: - analytic_story: - - GhostRedirector IIS Module and Rungan Backdoor - - IIS Components - - WS FTP Server Critical Vulnerabilities - asset_type: Endpoint - mitre_attack_id: - - T1505.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - GhostRedirector IIS Module and Rungan Backdoor + - IIS Components + - WS FTP Server Critical Vulnerabilities + asset_type: Endpoint + mitre_attack_id: + - T1505.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/pwsh_installediismodules.log - source: powershell://AppCmdModules - sourcetype: Pwsh:InstalledIISModules + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/pwsh_installediismodules.log + source: powershell://AppCmdModules + sourcetype: Pwsh:InstalledIISModules diff --git a/detections/endpoint/windows_iis_components_module_failed_to_load.yml b/detections/endpoint/windows_iis_components_module_failed_to_load.yml index 3199eee210..e76b2c2672 100644 --- a/detections/endpoint/windows_iis_components_module_failed_to_load.yml +++ b/detections/endpoint/windows_iis_components_module_failed_to_load.yml @@ -1,71 +1,61 @@ name: Windows IIS Components Module Failed to Load id: 40c2ba5b-dd6a-496b-9e6e-c9524d0be167 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic detects when an IIS Module DLL fails to load due - to a configuration problem, identified by EventCode 2282. This detection leverages - Windows Application event logs to identify repeated failures in loading IIS modules. - Such failures can indicate misconfigurations or potential tampering with IIS components. - If confirmed malicious, this activity could lead to service disruptions or provide - an attacker with opportunities to exploit vulnerabilities within the IIS environment. - Immediate investigation is required to determine the legitimacy of the failing module - and to mitigate any potential security risks. +description: The following analytic detects when an IIS Module DLL fails to load due to a configuration problem, identified by EventCode 2282. This detection leverages Windows Application event logs to identify repeated failures in loading IIS modules. Such failures can indicate misconfigurations or potential tampering with IIS components. If confirmed malicious, this activity could lead to service disruptions or provide an attacker with opportunities to exploit vulnerabilities within the IIS environment. Immediate investigation is required to determine the legitimacy of the failing module and to mitigate any potential security risks. data_source: -- Windows Event Log Application 2282 -search: '`wineventlog_application` EventCode=2282 | stats count min(_time) as firstTime - max(_time) as lastTime by EventCode dest Name ModuleDll | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_iis_components_module_failed_to_load_filter`' -how_to_implement: IIS must be installed and Application event logs must be collected - in order to utilize this analytic. -known_false_positives: False positives will be present until all module failures are - resolved or reviewed. + - Windows Event Log Application 2282 +search: |- + `wineventlog_application` EventCode=2282 + | stats count min(_time) as firstTime max(_time) as lastTime + BY EventCode dest Name + ModuleDll + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_iis_components_module_failed_to_load_filter` +how_to_implement: IIS must be installed and Application event logs must be collected in order to utilize this analytic. +known_false_positives: False positives will be present until all module failures are resolved or reviewed. references: -- https://social.technet.microsoft.com/wiki/contents/articles/21757.event-id-2282-iis-worker-process-availability.aspx -- https://www.microsoft.com/en-us/security/blog/2022/12/12/iis-modules-the-evolution-of-web-shells-and-how-to-detect-them/ -- https://www.crowdstrike.com/wp-content/uploads/2022/05/crowdstrike-iceapple-a-novel-internet-information-services-post-exploitation-framework-1.pdf -- https://unit42.paloaltonetworks.com/unit42-oilrig-uses-rgdoor-iis-backdoor-targets-middle-east/ -- https://www.secureworks.com/research/bronze-union -- https://github.com/redcanaryco/atomic-red-team/tree/master/atomics/T1505.004 -- https://strontic.github.io/xcyclopedia/library/appcmd.exe-055B2B09409F980BF9B5A3969D01E5B2.html + - https://social.technet.microsoft.com/wiki/contents/articles/21757.event-id-2282-iis-worker-process-availability.aspx + - https://www.microsoft.com/en-us/security/blog/2022/12/12/iis-modules-the-evolution-of-web-shells-and-how-to-detect-them/ + - https://www.crowdstrike.com/wp-content/uploads/2022/05/crowdstrike-iceapple-a-novel-internet-information-services-post-exploitation-framework-1.pdf + - https://unit42.paloaltonetworks.com/unit42-oilrig-uses-rgdoor-iis-backdoor-targets-middle-east/ + - https://www.secureworks.com/research/bronze-union + - https://github.com/redcanaryco/atomic-red-team/tree/master/atomics/T1505.004 + - https://strontic.github.io/xcyclopedia/library/appcmd.exe-055B2B09409F980BF9B5A3969D01E5B2.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A new IIS Module has been loaded and should be reviewed on $dest$. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A new IIS Module has been loaded and should be reviewed on $dest$. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - IIS Components - asset_type: Endpoint - mitre_attack_id: - - T1505.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - IIS Components + asset_type: Endpoint + mitre_attack_id: + - T1505.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/2282_windows-application.log - source: XmlWinEventLog:Application - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/2282_windows-application.log + source: XmlWinEventLog:Application + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_iis_components_new_module_added.yml b/detections/endpoint/windows_iis_components_new_module_added.yml index 362b4342f8..5e4d4b0d9e 100644 --- a/detections/endpoint/windows_iis_components_new_module_added.yml +++ b/detections/endpoint/windows_iis_components_new_module_added.yml @@ -1,72 +1,63 @@ name: Windows IIS Components New Module Added id: 55f22929-cfd3-4388-ba5c-4d01fac7ee7e -version: 7 -date: '2025-09-16' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the addition of new IIS modules on a Windows - IIS server. It leverages the Windows Event log - Microsoft-IIS-Configuration/Operational, - specifically EventCode 29, to identify this activity. This behavior is significant - because IIS modules are rarely added to production servers, and unauthorized modules - could indicate malicious activity. If confirmed malicious, an attacker could use - these modules to execute arbitrary code, escalate privileges, or maintain persistence - within the environment, potentially compromising the server and sensitive data. +description: The following analytic detects the addition of new IIS modules on a Windows IIS server. It leverages the Windows Event log - Microsoft-IIS-Configuration/Operational, specifically EventCode 29, to identify this activity. This behavior is significant because IIS modules are rarely added to production servers, and unauthorized modules could indicate malicious activity. If confirmed malicious, an attacker could use these modules to execute arbitrary code, escalate privileges, or maintain persistence within the environment, potentially compromising the server and sensitive data. data_source: -- Windows IIS 29 -search: '`iis_operational_logs` EventCode=29 | stats count min(_time) as firstTime - max(_time) as lastTime by OpCode EventCode ComputerName Message | rename ComputerName - AS dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_iis_components_new_module_added_filter`' -how_to_implement: You must enabled the IIS Configuration Operational log before ingesting - in Splunk. Setup and inputs may be found here https://gist.github.com/MHaggis/64396dfd9fc3734e1d1901a8f2f07040. -known_false_positives: False positives may be present when updates or an administrator - adds a new module to IIS. Monitor and filter as needed. + - Windows IIS 29 +search: |- + `iis_operational_logs` EventCode=29 + | stats count min(_time) as firstTime max(_time) as lastTime + BY OpCode EventCode ComputerName + Message + | rename ComputerName AS dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_iis_components_new_module_added_filter` +how_to_implement: You must enabled the IIS Configuration Operational log before ingesting in Splunk. Setup and inputs may be found here https://gist.github.com/MHaggis/64396dfd9fc3734e1d1901a8f2f07040. +known_false_positives: False positives may be present when updates or an administrator adds a new module to IIS. Monitor and filter as needed. references: -- https://gist.github.com/MHaggis/64396dfd9fc3734e1d1901a8f2f07040 -- https://www.microsoft.com/en-us/security/blog/2022/12/12/iis-modules-the-evolution-of-web-shells-and-how-to-detect-them/ -- https://www.crowdstrike.com/wp-content/uploads/2022/05/crowdstrike-iceapple-a-novel-internet-information-services-post-exploitation-framework-1.pdf -- https://unit42.paloaltonetworks.com/unit42-oilrig-uses-rgdoor-iis-backdoor-targets-middle-east/ -- https://www.secureworks.com/research/bronze-union -- https://github.com/redcanaryco/atomic-red-team/tree/master/atomics/T1505.004 -- https://strontic.github.io/xcyclopedia/library/appcmd.exe-055B2B09409F980BF9B5A3969D01E5B2.html + - https://gist.github.com/MHaggis/64396dfd9fc3734e1d1901a8f2f07040 + - https://www.microsoft.com/en-us/security/blog/2022/12/12/iis-modules-the-evolution-of-web-shells-and-how-to-detect-them/ + - https://www.crowdstrike.com/wp-content/uploads/2022/05/crowdstrike-iceapple-a-novel-internet-information-services-post-exploitation-framework-1.pdf + - https://unit42.paloaltonetworks.com/unit42-oilrig-uses-rgdoor-iis-backdoor-targets-middle-east/ + - https://www.secureworks.com/research/bronze-union + - https://github.com/redcanaryco/atomic-red-team/tree/master/atomics/T1505.004 + - https://strontic.github.io/xcyclopedia/library/appcmd.exe-055B2B09409F980BF9B5A3969D01E5B2.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A new IIS Module has been loaded and should be reviewed on $dest$. - risk_objects: - - field: dest - type: system - score: 48 - threat_objects: [] + message: A new IIS Module has been loaded and should be reviewed on $dest$. + risk_objects: + - field: dest + type: system + score: 48 + threat_objects: [] tags: - analytic_story: - - IIS Components - - GhostRedirector IIS Module and Rungan Backdoor - asset_type: Endpoint - mitre_attack_id: - - T1505.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - IIS Components + - GhostRedirector IIS Module and Rungan Backdoor + asset_type: Endpoint + mitre_attack_id: + - T1505.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/IIS-Configuration-Operational.log - source: IIS:Configuration:Operational - sourcetype: IIS:Configuration:Operational + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/IIS-Configuration-Operational.log + source: IIS:Configuration:Operational + sourcetype: IIS:Configuration:Operational diff --git a/detections/endpoint/windows_impair_defense_add_xml_applocker_rules.yml b/detections/endpoint/windows_impair_defense_add_xml_applocker_rules.yml index 29c367e205..2463308a44 100644 --- a/detections/endpoint/windows_impair_defense_add_xml_applocker_rules.yml +++ b/detections/endpoint/windows_impair_defense_add_xml_applocker_rules.yml @@ -1,59 +1,53 @@ name: Windows Impair Defense Add Xml Applocker Rules id: 467ed9d9-8035-470e-ad5e-ae5189283033 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects the use of a PowerShell commandlet to - import an AppLocker XML policy. This behavior is identified by monitoring processes - that execute the "Import-Module Applocker" and "Set-AppLockerPolicy" commands with - the "-XMLPolicy" parameter. This activity is significant because it can indicate - an attempt to disable or bypass security controls, as seen in the Azorult malware. - If confirmed malicious, this could allow an attacker to disable antivirus products, - leading to further compromise and persistence within the environment. +description: The following analytic detects the use of a PowerShell commandlet to import an AppLocker XML policy. This behavior is identified by monitoring processes that execute the "Import-Module Applocker" and "Set-AppLockerPolicy" commands with the "-XMLPolicy" parameter. This activity is significant because it can indicate an attempt to disable or bypass security controls, as seen in the Azorult malware. If confirmed malicious, this could allow an attacker to disable antivirus products, leading to further compromise and persistence within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where `process_powershell` AND Processes.process="*Import-Module Applocker*" AND - Processes.process="*Set-AppLockerPolicy *" AND Processes.process="* -XMLPolicy - *" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_impair_defense_add_xml_applocker_rules_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrators may execute this command that may cause some - false positive. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_powershell` + AND + Processes.process="*Import-Module Applocker*" + AND + Processes.process="*Set-AppLockerPolicy *" + AND + Processes.process="* -XMLPolicy *" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_impair_defense_add_xml_applocker_rules_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators may execute this command that may cause some false positive. references: -- https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ + - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ tags: - analytic_story: - - Azorult - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azorult + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defense_change_win_defender_health_check_intervals.yml b/detections/endpoint/windows_impair_defense_change_win_defender_health_check_intervals.yml index da61e55571..9b91981206 100644 --- a/detections/endpoint/windows_impair_defense_change_win_defender_health_check_intervals.yml +++ b/detections/endpoint/windows_impair_defense_change_win_defender_health_check_intervals.yml @@ -6,69 +6,45 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects modifications to the Windows registry - that change the health check interval of Windows Defender. It leverages data from - the Endpoint datamodel, specifically monitoring changes to the "ServiceKeepAlive" - registry path with a value of "0x00000001". This activity is significant because - altering Windows Defender settings can impair its ability to perform timely health - checks, potentially leaving the system vulnerable. If confirmed malicious, this - could allow an attacker to disable or delay security scans, increasing the risk - of undetected malware or other malicious activities. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows - Defender\\ServiceKeepAlive" Registry.registry_value_data="0x00000001" by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_impair_defense_change_win_defender_health_check_intervals_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows registry that change the health check interval of Windows Defender. It leverages data from the Endpoint datamodel, specifically monitoring changes to the "ServiceKeepAlive" registry path with a value of "0x00000001". This activity is significant because altering Windows Defender settings can impair its ability to perform timely health checks, potentially leaving the system vulnerable. If confirmed malicious, this could allow an attacker to disable or delay security scans, increasing the risk of undetected malware or other malicious activities. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\ServiceKeepAlive" Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_change_win_defender_health_check_intervals_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://x.com/malmoeb/status/1742604217989415386?s=20 -- https://github.com/undergroundwires/privacy.sexy + - https://x.com/malmoeb/status/1742604217989415386?s=20 + - https://github.com/undergroundwires/privacy.sexy drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: change in the health check interval of Windows Defender on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: change in the health check interval of Windows Defender on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defense_change_win_defender_quick_scan_interval.yml b/detections/endpoint/windows_impair_defense_change_win_defender_quick_scan_interval.yml index 5efd2ed8b2..81cd13416b 100644 --- a/detections/endpoint/windows_impair_defense_change_win_defender_quick_scan_interval.yml +++ b/detections/endpoint/windows_impair_defense_change_win_defender_quick_scan_interval.yml @@ -6,68 +6,45 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects modifications to the Windows registry - that change the Windows Defender Quick Scan Interval. It leverages data from the - Endpoint.Registry data model, focusing on changes to the "QuickScanInterval" registry - path. This activity is significant because altering the scan interval can impair - Windows Defender's ability to detect malware promptly, potentially allowing threats - to persist undetected. If confirmed malicious, this modification could enable attackers - to bypass security measures, maintain persistence, and execute further malicious - activities without being detected by quick scans. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows - Defender\\Scan\\QuickScanInterval" by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_change_win_defender_quick_scan_interval_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows registry that change the Windows Defender Quick Scan Interval. It leverages data from the Endpoint.Registry data model, focusing on changes to the "QuickScanInterval" registry path. This activity is significant because altering the scan interval can impair Windows Defender's ability to detect malware promptly, potentially allowing threats to persist undetected. If confirmed malicious, this modification could enable attackers to bypass security measures, maintain persistence, and execute further malicious activities without being detected by quick scans. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\Scan\\QuickScanInterval" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_change_win_defender_quick_scan_interval_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://x.com/malmoeb/status/1742604217989415386?s=20 -- https://github.com/undergroundwires/privacy.sexy + - https://x.com/malmoeb/status/1742604217989415386?s=20 + - https://github.com/undergroundwires/privacy.sexy drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Defender QuickScanInterval feature was modified on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Windows Defender QuickScanInterval feature was modified on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defense_change_win_defender_throttle_rate.yml b/detections/endpoint/windows_impair_defense_change_win_defender_throttle_rate.yml index 5d614b2d76..de1ab919b0 100644 --- a/detections/endpoint/windows_impair_defense_change_win_defender_throttle_rate.yml +++ b/detections/endpoint/windows_impair_defense_change_win_defender_throttle_rate.yml @@ -6,69 +6,45 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects modifications to the ThrottleDetectionEventsRate - registry setting in Windows Defender. It leverages data from the Endpoint.Registry - datamodel to identify changes in the registry path related to Windows Defender's - event logging rate. This activity is significant because altering the ThrottleDetectionEventsRate - can reduce the frequency of logged detection events, potentially masking malicious - activities. If confirmed malicious, this could allow an attacker to evade detection - by decreasing the visibility of security events, thereby hindering incident response - and forensic investigations. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows - Defender\\NIS\\Consumers\\IPS\\ThrottleDetectionEventsRate" by Registry.action Registry.dest - Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_impair_defense_change_win_defender_throttle_rate_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +description: The following analytic detects modifications to the ThrottleDetectionEventsRate registry setting in Windows Defender. It leverages data from the Endpoint.Registry datamodel to identify changes in the registry path related to Windows Defender's event logging rate. This activity is significant because altering the ThrottleDetectionEventsRate can reduce the frequency of logged detection events, potentially masking malicious activities. If confirmed malicious, this could allow an attacker to evade detection by decreasing the visibility of security events, thereby hindering incident response and forensic investigations. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\NIS\\Consumers\\IPS\\ThrottleDetectionEventsRate" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_change_win_defender_throttle_rate_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://x.com/malmoeb/status/1742604217989415386?s=20 -- https://github.com/undergroundwires/privacy.sexy + - https://x.com/malmoeb/status/1742604217989415386?s=20 + - https://github.com/undergroundwires/privacy.sexy drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Defender ThrottleDetectionEventsRate feature was modified on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Windows Defender ThrottleDetectionEventsRate feature was modified on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defense_change_win_defender_tracing_level.yml b/detections/endpoint/windows_impair_defense_change_win_defender_tracing_level.yml index 109e391a65..b0d244b8d9 100644 --- a/detections/endpoint/windows_impair_defense_change_win_defender_tracing_level.yml +++ b/detections/endpoint/windows_impair_defense_change_win_defender_tracing_level.yml @@ -6,69 +6,45 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects modifications to the Windows registry - specifically targeting the "WppTracingLevel" setting within Windows Defender. This - detection leverages data from the Endpoint.Registry data model to identify changes - in the registry path associated with Windows Defender tracing levels. Such modifications - are significant as they can impair the diagnostic capabilities of Windows Defender, - potentially hiding malicious activities. If confirmed malicious, this activity could - allow an attacker to evade detection and maintain persistence within the environment, - leading to further compromise and data exfiltration. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows - Defender\\Reporting\\WppTracingLevel" Registry.registry_value_data="0x00000001" - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_impair_defense_change_win_defender_tracing_level_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows registry specifically targeting the "WppTracingLevel" setting within Windows Defender. This detection leverages data from the Endpoint.Registry data model to identify changes in the registry path associated with Windows Defender tracing levels. Such modifications are significant as they can impair the diagnostic capabilities of Windows Defender, potentially hiding malicious activities. If confirmed malicious, this activity could allow an attacker to evade detection and maintain persistence within the environment, leading to further compromise and data exfiltration. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\Reporting\\WppTracingLevel" Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_change_win_defender_tracing_level_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://x.com/malmoeb/status/1742604217989415386?s=20 -- https://github.com/undergroundwires/privacy.sexy + - https://x.com/malmoeb/status/1742604217989415386?s=20 + - https://github.com/undergroundwires/privacy.sexy drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Defender WppTracingLevel registry was modified on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Windows Defender WppTracingLevel registry was modified on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defense_configure_app_install_control.yml b/detections/endpoint/windows_impair_defense_configure_app_install_control.yml index d9054d77f1..9e7d4fa352 100644 --- a/detections/endpoint/windows_impair_defense_configure_app_install_control.yml +++ b/detections/endpoint/windows_impair_defense_configure_app_install_control.yml @@ -6,71 +6,45 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects modifications to the Windows registry - that disable the Windows Defender SmartScreen App Install Control feature. It leverages - data from the Endpoint.Registry data model to identify changes to specific registry - values. This activity is significant because disabling App Install Control can allow - users to install potentially malicious web-based applications without restrictions, - increasing the risk of security vulnerabilities. If confirmed malicious, this action - could lead to the installation of harmful applications, potentially compromising - the system and exposing sensitive information. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\Microsoft\\Windows - Defender\\SmartScreen\\ConfigureAppInstallControl" Registry.registry_value_data= - "Anywhere") OR (Registry.registry_path= "*\\Microsoft\\Windows Defender\\SmartScreen\\ConfigureAppInstallControlEnabled" - Registry.registry_value_data= "0x00000000") by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_configure_app_install_control_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows registry that disable the Windows Defender SmartScreen App Install Control feature. It leverages data from the Endpoint.Registry data model to identify changes to specific registry values. This activity is significant because disabling App Install Control can allow users to install potentially malicious web-based applications without restrictions, increasing the risk of security vulnerabilities. If confirmed malicious, this action could lead to the installation of harmful applications, potentially compromising the system and exposing sensitive information. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\Microsoft\\Windows Defender\\SmartScreen\\ConfigureAppInstallControl" Registry.registry_value_data= "Anywhere") OR (Registry.registry_path= "*\\Microsoft\\Windows Defender\\SmartScreen\\ConfigureAppInstallControlEnabled" Registry.registry_value_data= "0x00000000") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_configure_app_install_control_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://x.com/malmoeb/status/1742604217989415386?s=20 -- https://github.com/undergroundwires/privacy.sexy + - https://x.com/malmoeb/status/1742604217989415386?s=20 + - https://github.com/undergroundwires/privacy.sexy drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Define Windows Defender App Install Control registry set to disable on - $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Define Windows Defender App Install Control registry set to disable on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defense_define_win_defender_threat_action.yml b/detections/endpoint/windows_impair_defense_define_win_defender_threat_action.yml index 34250ab8a3..57238a2bd8 100644 --- a/detections/endpoint/windows_impair_defense_define_win_defender_threat_action.yml +++ b/detections/endpoint/windows_impair_defense_define_win_defender_threat_action.yml @@ -6,69 +6,45 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects modifications to the Windows Defender - ThreatSeverityDefaultAction registry setting. It leverages data from the Endpoint.Registry - datamodel to identify changes in registry values that define how Windows Defender - responds to threats. This activity is significant because altering these settings - can impair the system's defense mechanisms, potentially allowing threats to go unaddressed. - If confirmed malicious, this could enable attackers to bypass antivirus protections, - leading to persistent threats and increased risk of data compromise or further system - exploitation. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows - Defender\\Threats\\ThreatSeverityDefaultAction*" Registry.registry_value_data IN - ("0x00000001", "9") by Registry.action Registry.dest Registry.process_guid Registry.process_id - Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data - Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user - Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_impair_defense_define_win_defender_threat_action_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows Defender ThreatSeverityDefaultAction registry setting. It leverages data from the Endpoint.Registry datamodel to identify changes in registry values that define how Windows Defender responds to threats. This activity is significant because altering these settings can impair the system's defense mechanisms, potentially allowing threats to go unaddressed. If confirmed malicious, this could enable attackers to bypass antivirus protections, leading to persistent threats and increased risk of data compromise or further system exploitation. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\Threats\\ThreatSeverityDefaultAction*" Registry.registry_value_data IN ("0x00000001", "9") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_define_win_defender_threat_action_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://x.com/malmoeb/status/1742604217989415386?s=20 -- https://github.com/undergroundwires/privacy.sexy + - https://x.com/malmoeb/status/1742604217989415386?s=20 + - https://github.com/undergroundwires/privacy.sexy drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Define Windows Defender threat action through registry on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Define Windows Defender threat action through registry on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defense_delete_win_defender_context_menu.yml b/detections/endpoint/windows_impair_defense_delete_win_defender_context_menu.yml index 982c929f07..7d9250ef7a 100644 --- a/detections/endpoint/windows_impair_defense_delete_win_defender_context_menu.yml +++ b/detections/endpoint/windows_impair_defense_delete_win_defender_context_menu.yml @@ -5,48 +5,30 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects the deletion of the Windows Defender context - menu entry from the registry. It leverages data from the Endpoint datamodel, specifically - monitoring registry actions where the path includes "*\\shellex\\ContextMenuHandlers\\EPP" - and the action is 'deleted'. This activity is significant as it is commonly associated - with Remote Access Trojan (RAT) malware attempting to disable security features. - If confirmed malicious, this could allow an attacker to impair defenses, facilitating - further malicious activities such as unauthorized access, persistence, and data - exfiltration. +description: The following analytic detects the deletion of the Windows Defender context menu entry from the registry. It leverages data from the Endpoint datamodel, specifically monitoring registry actions where the path includes "*\\shellex\\ContextMenuHandlers\\EPP" and the action is 'deleted'. This activity is significant as it is commonly associated with Remote Access Trojan (RAT) malware attempting to disable security features. If confirmed malicious, this could allow an attacker to impair defenses, facilitating further malicious activities such as unauthorized access, persistence, and data exfiltration. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\shellex\\ContextMenuHandlers\\EPP" - Registry.action = deleted by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_delete_win_defender_context_menu_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\shellex\\ContextMenuHandlers\\EPP" Registry.action = deleted by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_delete_win_defender_context_menu_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/ -- https://app.any.run/tasks/45f5d114-91ea-486c-ab01-41c4093d2861/ + - https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/ + - https://app.any.run/tasks/45f5d114-91ea-486c-ab01-41c4093d2861/ tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/delete_win_defender_context_menu/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/delete_win_defender_context_menu/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defense_delete_win_defender_profile_registry.yml b/detections/endpoint/windows_impair_defense_delete_win_defender_profile_registry.yml index 9528894fd1..7d023a24ec 100644 --- a/detections/endpoint/windows_impair_defense_delete_win_defender_profile_registry.yml +++ b/detections/endpoint/windows_impair_defense_delete_win_defender_profile_registry.yml @@ -5,69 +5,46 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the deletion of the Windows Defender main - profile registry key. It leverages data from the Endpoint.Registry datamodel, specifically - monitoring for deleted actions within the Windows Defender registry path. This activity - is significant as it indicates potential tampering with security defenses, often - associated with Remote Access Trojans (RATs) and other malware. If confirmed malicious, - this action could allow an attacker to disable Windows Defender, reducing the system's - ability to detect and respond to further malicious activities, thereby compromising - endpoint security. +description: The following analytic detects the deletion of the Windows Defender main profile registry key. It leverages data from the Endpoint.Registry datamodel, specifically monitoring for deleted actions within the Windows Defender registry path. This activity is significant as it indicates potential tampering with security defenses, often associated with Remote Access Trojans (RATs) and other malware. If confirmed malicious, this action could allow an attacker to disable Windows Defender, reducing the system's ability to detect and respond to further malicious activities, thereby compromising endpoint security. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Policies\\Microsoft\\Windows - Defender" Registry.action = deleted by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_delete_win_defender_profile_registry_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Policies\\Microsoft\\Windows Defender" Registry.action = deleted by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_delete_win_defender_profile_registry_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/ -- https://app.any.run/tasks/45f5d114-91ea-486c-ab01-41c4093d2861/ + - https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/ + - https://app.any.run/tasks/45f5d114-91ea-486c-ab01-41c4093d2861/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Defender Logger registry key set to 'disabled' on $dest$. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: Windows Defender Logger registry key set to 'disabled' on $dest$. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/delete_win_defender_context_menu/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/delete_win_defender_context_menu/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defense_deny_security_software_with_applocker.yml b/detections/endpoint/windows_impair_defense_deny_security_software_with_applocker.yml index eaa74e4019..5f7160c09a 100644 --- a/detections/endpoint/windows_impair_defense_deny_security_software_with_applocker.yml +++ b/detections/endpoint/windows_impair_defense_deny_security_software_with_applocker.yml @@ -5,76 +5,46 @@ date: '2025-10-14' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects modifications in the Windows registry - by the Applocker utility that deny the execution of various security products. This - detection leverages data from the Endpoint.Registry datamodel, focusing on specific - registry paths and values indicating a "Deny" action against known antivirus and - security software. This activity is significant as it may indicate an attempt to - disable security defenses, a tactic observed in malware like Azorult. If confirmed - malicious, this could allow attackers to bypass security measures, facilitating - further malicious activities and persistence within the environment. +description: The following analytic detects modifications in the Windows registry by the Applocker utility that deny the execution of various security products. This detection leverages data from the Endpoint.Registry datamodel, focusing on specific registry paths and values indicating a "Deny" action against known antivirus and security software. This activity is significant as it may indicate an attempt to disable security defenses, a tactic observed in malware like Azorult. If confirmed malicious, this could allow attackers to bypass security measures, facilitating further malicious activities and persistence within the environment. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Group - Policy Objects\\*" AND Registry.registry_path= "*}Machine\\Software\\Policies\\Microsoft\\Windows\\SrpV2*") - OR Registry.registry_path="*\\Software\\Policies\\Microsoft\\Windows\\SrpV2*" AND - Registry.registry_value_data = "*Action\=\"Deny\"*" AND Registry.registry_value_data - IN("*O=SYMANTEC*","*O=MCAFEE*","*O=KASPERSKY*","*O=BLEEPING COMPUTER*", "*O=PANDA - SECURITY*","*O=SYSTWEAK SOFTWARE*", "*O=TREND MICRO*", "*O=AVAST*", "*O=GRIDINSOFT*", - "*O=MICROSOFT*", "*O=NANO SECURITY*", "*O=SUPERANTISPYWARE.COM*", "*O=DOCTOR WEB*", - "*O=MALWAREBYTES*", "*O=ESET*", "*O=AVIRA*", "*O=WEBROOT*") by Registry.action Registry.dest - Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_impair_defense_deny_security_software_with_applocker_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure - that this registry was included in your config files ex. sysmon config to be monitored. -known_false_positives: False positives may be present based on organization use of - Applocker. Filter as needed. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Group Policy Objects\\*" AND Registry.registry_path= "*}Machine\\Software\\Policies\\Microsoft\\Windows\\SrpV2*") OR Registry.registry_path="*\\Software\\Policies\\Microsoft\\Windows\\SrpV2*" AND Registry.registry_value_data = "*Action\=\"Deny\"*" AND Registry.registry_value_data IN("*O=SYMANTEC*","*O=MCAFEE*","*O=KASPERSKY*","*O=BLEEPING COMPUTER*", "*O=PANDA SECURITY*","*O=SYSTWEAK SOFTWARE*", "*O=TREND MICRO*", "*O=AVAST*", "*O=GRIDINSOFT*", "*O=MICROSOFT*", "*O=NANO SECURITY*", "*O=SUPERANTISPYWARE.COM*", "*O=DOCTOR WEB*", "*O=MALWAREBYTES*", "*O=ESET*", "*O=AVIRA*", "*O=WEBROOT*") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_deny_security_software_with_applocker_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored. +known_false_positives: False positives may be present based on organization use of Applocker. Filter as needed. references: -- https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ -- https://www.microsoftpressstore.com/articles/article.aspx?p=2228450&seqNum=11 + - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ + - https://www.microsoftpressstore.com/articles/article.aspx?p=2228450&seqNum=11 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Applocker registry modification to deny the action of several AV products - on $dest$. - risk_objects: - - field: dest - type: system - score: 100 - threat_objects: [] + message: Applocker registry modification to deny the action of several AV products on $dest$. + risk_objects: + - field: dest + type: system + score: 100 + threat_objects: [] tags: - analytic_story: - - Azorult - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azorult + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defense_disable_controlled_folder_access.yml b/detections/endpoint/windows_impair_defense_disable_controlled_folder_access.yml index f013aebd08..a2adc8965a 100644 --- a/detections/endpoint/windows_impair_defense_disable_controlled_folder_access.yml +++ b/detections/endpoint/windows_impair_defense_disable_controlled_folder_access.yml @@ -6,69 +6,45 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects a modification in the Windows registry - that disables the Windows Defender Controlled Folder Access feature. It leverages - data from the Endpoint.Registry data model, specifically monitoring changes to the - EnableControlledFolderAccess registry setting. This activity is significant because - Controlled Folder Access is designed to protect critical folders from unauthorized - access, including ransomware attacks. If this activity is confirmed malicious, it - could allow attackers to bypass a key security feature, potentially leading to unauthorized - access or modification of sensitive files. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows - Defender\\Windows Defender Exploit Guard\\Controlled Folder Access\\EnableControlledFolderAccess" - Registry.registry_value_data="0x00000000" by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_controlled_folder_access_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +description: The following analytic detects a modification in the Windows registry that disables the Windows Defender Controlled Folder Access feature. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the EnableControlledFolderAccess registry setting. This activity is significant because Controlled Folder Access is designed to protect critical folders from unauthorized access, including ransomware attacks. If this activity is confirmed malicious, it could allow attackers to bypass a key security feature, potentially leading to unauthorized access or modification of sensitive files. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\Windows Defender Exploit Guard\\Controlled Folder Access\\EnableControlledFolderAccess" Registry.registry_value_data="0x00000000" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_controlled_folder_access_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://x.com/malmoeb/status/1742604217989415386?s=20 -- https://github.com/undergroundwires/privacy.sexy + - https://x.com/malmoeb/status/1742604217989415386?s=20 + - https://github.com/undergroundwires/privacy.sexy drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Defender ControlledFolderAccess feature set to disable on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Windows Defender ControlledFolderAccess feature set to disable on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defense_disable_defender_firewall_and_network.yml b/detections/endpoint/windows_impair_defense_disable_defender_firewall_and_network.yml index acf6d03c19..33714ceda8 100644 --- a/detections/endpoint/windows_impair_defense_disable_defender_firewall_and_network.yml +++ b/detections/endpoint/windows_impair_defense_disable_defender_firewall_and_network.yml @@ -6,71 +6,46 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects modifications in the Windows registry - to disable firewall and network protection settings within Windows Defender Security - Center. It leverages data from the Endpoint.Registry data model, specifically monitoring - changes to the UILockdown registry value. This activity is significant as it may - indicate an attempt to impair system defenses, potentially restricting users from - modifying firewall or network protection settings. If confirmed malicious, this - could allow an attacker to weaken the system's security posture, making it more - vulnerable to further attacks and unauthorized access. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows - Defender Security Center\\Firewall and network protection\\UILockdown" Registry.registry_value_data="0x00000001" - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_impair_defense_disable_defender_firewall_and_network_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +description: The following analytic detects modifications in the Windows registry to disable firewall and network protection settings within Windows Defender Security Center. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the UILockdown registry value. This activity is significant as it may indicate an attempt to impair system defenses, potentially restricting users from modifying firewall or network protection settings. If confirmed malicious, this could allow an attacker to weaken the system's security posture, making it more vulnerable to further attacks and unauthorized access. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender Security Center\\Firewall and network protection\\UILockdown" Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_defender_firewall_and_network_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://x.com/malmoeb/status/1742604217989415386?s=20 -- https://github.com/undergroundwires/privacy.sexy + - https://x.com/malmoeb/status/1742604217989415386?s=20 + - https://github.com/undergroundwires/privacy.sexy drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Defender firewall and network protection section feature set to - disable on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Windows Defender firewall and network protection section feature set to disable on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defense_disable_defender_protocol_recognition.yml b/detections/endpoint/windows_impair_defense_disable_defender_protocol_recognition.yml index b4e2127aa0..494f1f511e 100644 --- a/detections/endpoint/windows_impair_defense_disable_defender_protocol_recognition.yml +++ b/detections/endpoint/windows_impair_defense_disable_defender_protocol_recognition.yml @@ -6,70 +6,46 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects modifications to the Windows registry - that disable the Windows Defender protocol recognition feature. It leverages data - from the Endpoint.Registry data model, specifically looking for changes to the "DisableProtocolRecognition" - setting. This activity is significant because disabling protocol recognition can - hinder Windows Defender's ability to detect and respond to malware or suspicious - software. If confirmed malicious, this action could allow an attacker to bypass - antivirus defenses, facilitating further malicious activities such as data exfiltration - or system compromise. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows - Defender\\NIS\\DisableProtocolRecognition" Registry.registry_value_data="0x00000001" - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_impair_defense_disable_defender_protocol_recognition_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows registry that disable the Windows Defender protocol recognition feature. It leverages data from the Endpoint.Registry data model, specifically looking for changes to the "DisableProtocolRecognition" setting. This activity is significant because disabling protocol recognition can hinder Windows Defender's ability to detect and respond to malware or suspicious software. If confirmed malicious, this action could allow an attacker to bypass antivirus defenses, facilitating further malicious activities such as data exfiltration or system compromise. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\NIS\\DisableProtocolRecognition" Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_defender_protocol_recognition_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://x.com/malmoeb/status/1742604217989415386?s=20 -- https://github.com/undergroundwires/privacy.sexy + - https://x.com/malmoeb/status/1742604217989415386?s=20 + - https://github.com/undergroundwires/privacy.sexy drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Defender Protocol Recognition set to disable on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Windows Defender Protocol Recognition set to disable on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defense_disable_pua_protection.yml b/detections/endpoint/windows_impair_defense_disable_pua_protection.yml index f7b29fbdea..91760a07c0 100644 --- a/detections/endpoint/windows_impair_defense_disable_pua_protection.yml +++ b/detections/endpoint/windows_impair_defense_disable_pua_protection.yml @@ -6,70 +6,46 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects a modification in the Windows registry - to disable Windows Defender PUA protection by setting PUAProtection to 0. This detection - leverages data from the Endpoint.Registry datamodel, focusing on registry path changes - related to Windows Defender. Disabling PUA protection is significant as it reduces - defenses against Potentially Unwanted Applications (PUAs), which, while not always - malicious, can negatively impact user experience and security. If confirmed malicious, - this activity could allow an attacker to introduce adware, browser toolbars, or - other unwanted software, potentially compromising system integrity and user productivity. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows - Defender\\PUAProtection" Registry.registry_value_data="0x00000000" by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_impair_defense_disable_pua_protection_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +description: The following analytic detects a modification in the Windows registry to disable Windows Defender PUA protection by setting PUAProtection to 0. This detection leverages data from the Endpoint.Registry datamodel, focusing on registry path changes related to Windows Defender. Disabling PUA protection is significant as it reduces defenses against Potentially Unwanted Applications (PUAs), which, while not always malicious, can negatively impact user experience and security. If confirmed malicious, this activity could allow an attacker to introduce adware, browser toolbars, or other unwanted software, potentially compromising system integrity and user productivity. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\PUAProtection" Registry.registry_value_data="0x00000000" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_pua_protection_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://x.com/malmoeb/status/1742604217989415386?s=20 -- https://github.com/undergroundwires/privacy.sexy + - https://x.com/malmoeb/status/1742604217989415386?s=20 + - https://github.com/undergroundwires/privacy.sexy drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Defender PUA protection set to disable on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Windows Defender PUA protection set to disable on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defense_disable_realtime_signature_delivery.yml b/detections/endpoint/windows_impair_defense_disable_realtime_signature_delivery.yml index 691e33245e..a87aca7337 100644 --- a/detections/endpoint/windows_impair_defense_disable_realtime_signature_delivery.yml +++ b/detections/endpoint/windows_impair_defense_disable_realtime_signature_delivery.yml @@ -6,69 +6,45 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects modifications to the Windows registry - that disable the Windows Defender real-time signature delivery feature. It leverages - data from the Endpoint.Registry data model, specifically monitoring changes to the - registry path associated with Windows Defender signature updates. This activity - is significant because disabling real-time signature delivery can prevent Windows - Defender from receiving timely malware definitions, reducing its effectiveness. - If confirmed malicious, this action could allow attackers to bypass malware detection, - leading to potential system compromise and persistent threats. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows - Defender\\Signature Updates\\RealtimeSignatureDelivery" Registry.registry_value_data="0x00000000" - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_impair_defense_disable_realtime_signature_delivery_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows registry that disable the Windows Defender real-time signature delivery feature. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path associated with Windows Defender signature updates. This activity is significant because disabling real-time signature delivery can prevent Windows Defender from receiving timely malware definitions, reducing its effectiveness. If confirmed malicious, this action could allow attackers to bypass malware detection, leading to potential system compromise and persistent threats. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\Signature Updates\\RealtimeSignatureDelivery" Registry.registry_value_data="0x00000000" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_realtime_signature_delivery_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://x.com/malmoeb/status/1742604217989415386?s=20 -- https://github.com/undergroundwires/privacy.sexy + - https://x.com/malmoeb/status/1742604217989415386?s=20 + - https://github.com/undergroundwires/privacy.sexy drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Defender File realtime signature delivery set to disable on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Windows Defender File realtime signature delivery set to disable on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defense_disable_web_evaluation.yml b/detections/endpoint/windows_impair_defense_disable_web_evaluation.yml index 260703fdb6..f4572de718 100644 --- a/detections/endpoint/windows_impair_defense_disable_web_evaluation.yml +++ b/detections/endpoint/windows_impair_defense_disable_web_evaluation.yml @@ -6,68 +6,45 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects modifications to the Windows registry - entry "EnableWebContentEvaluation" to disable Windows Defender web content evaluation. - It leverages data from the Endpoint.Registry datamodel, specifically monitoring - changes where the registry value is set to "0x00000000". This activity is significant - as it indicates an attempt to impair browser security features, potentially allowing - malicious web content to bypass security checks. If confirmed malicious, this could - lead to users interacting with harmful scripts or unsafe web elements, increasing - the risk of system exploitation and security breaches. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path= "*\\Windows\\CurrentVersion\\AppHost\\EnableWebContentEvaluation" Registry.registry_value_data= - "0x00000000" by Registry.action Registry.dest Registry.process_guid Registry.process_id - Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data - Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user - Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_web_evaluation_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows registry entry "EnableWebContentEvaluation" to disable Windows Defender web content evaluation. It leverages data from the Endpoint.Registry datamodel, specifically monitoring changes where the registry value is set to "0x00000000". This activity is significant as it indicates an attempt to impair browser security features, potentially allowing malicious web content to bypass security checks. If confirmed malicious, this could lead to users interacting with harmful scripts or unsafe web elements, increasing the risk of system exploitation and security breaches. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path= "*\\Windows\\CurrentVersion\\AppHost\\EnableWebContentEvaluation" Registry.registry_value_data= "0x00000000" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_web_evaluation_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://x.com/malmoeb/status/1742604217989415386?s=20 -- https://github.com/undergroundwires/privacy.sexy + - https://x.com/malmoeb/status/1742604217989415386?s=20 + - https://github.com/undergroundwires/privacy.sexy drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Defender web content evaluation feature set to disable on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Windows Defender web content evaluation feature set to disable on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defense_disable_win_defender_app_guard.yml b/detections/endpoint/windows_impair_defense_disable_win_defender_app_guard.yml index 801002c88b..314612521c 100644 --- a/detections/endpoint/windows_impair_defense_disable_win_defender_app_guard.yml +++ b/detections/endpoint/windows_impair_defense_disable_win_defender_app_guard.yml @@ -6,68 +6,45 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects modifications to the Windows registry - that disable Windows Defender Application Guard auditing. It leverages data from - the Endpoint.Registry data model, focusing on specific registry paths and values. - This activity is significant because disabling auditing can hinder security monitoring - and threat detection within the isolated environment, making it easier for malicious - activities to go unnoticed. If confirmed malicious, this action could allow attackers - to bypass Windows Defender protections, potentially leading to unauthorized access, - data exfiltration, or further system compromise. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Policies\\Microsoft\\AppHVSI\\AuditApplicationGuard" - Registry.registry_value_data="0x00000000" by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_win_defender_app_guard_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows registry that disable Windows Defender Application Guard auditing. It leverages data from the Endpoint.Registry data model, focusing on specific registry paths and values. This activity is significant because disabling auditing can hinder security monitoring and threat detection within the isolated environment, making it easier for malicious activities to go unnoticed. If confirmed malicious, this action could allow attackers to bypass Windows Defender protections, potentially leading to unauthorized access, data exfiltration, or further system compromise. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Policies\\Microsoft\\AppHVSI\\AuditApplicationGuard" Registry.registry_value_data="0x00000000" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_win_defender_app_guard_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://x.com/malmoeb/status/1742604217989415386?s=20 -- https://github.com/undergroundwires/privacy.sexy + - https://x.com/malmoeb/status/1742604217989415386?s=20 + - https://github.com/undergroundwires/privacy.sexy drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Defender AuditApplicationGuard feature set to disable on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Windows Defender AuditApplicationGuard feature set to disable on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defense_disable_win_defender_compute_file_hashes.yml b/detections/endpoint/windows_impair_defense_disable_win_defender_compute_file_hashes.yml index a061337e56..a96c8842d6 100644 --- a/detections/endpoint/windows_impair_defense_disable_win_defender_compute_file_hashes.yml +++ b/detections/endpoint/windows_impair_defense_disable_win_defender_compute_file_hashes.yml @@ -6,69 +6,45 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects modifications to the Windows registry - that disable Windows Defender's file hash computation by setting the EnableFileHashComputation - value to 0. This detection leverages data from the Endpoint.Registry data model, - focusing on changes to the specific registry path associated with Windows Defender. - Disabling file hash computation can significantly impair Windows Defender's ability - to detect and scan for malware, making it a critical behavior to monitor. If confirmed - malicious, this activity could allow attackers to bypass Windows Defender, facilitating - undetected malware execution and persistence in the environment. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows - Defender\\MpEngine\\EnableFileHashComputation" Registry.registry_value_data="0x00000000" - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_impair_defense_disable_win_defender_compute_file_hashes_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows registry that disable Windows Defender's file hash computation by setting the EnableFileHashComputation value to 0. This detection leverages data from the Endpoint.Registry data model, focusing on changes to the specific registry path associated with Windows Defender. Disabling file hash computation can significantly impair Windows Defender's ability to detect and scan for malware, making it a critical behavior to monitor. If confirmed malicious, this activity could allow attackers to bypass Windows Defender, facilitating undetected malware execution and persistence in the environment. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\MpEngine\\EnableFileHashComputation" Registry.registry_value_data="0x00000000" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_win_defender_compute_file_hashes_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://x.com/malmoeb/status/1742604217989415386?s=20 -- https://github.com/undergroundwires/privacy.sexy + - https://x.com/malmoeb/status/1742604217989415386?s=20 + - https://github.com/undergroundwires/privacy.sexy drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Defender File hashes computation set to disable on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Windows Defender File hashes computation set to disable on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defense_disable_win_defender_gen_reports.yml b/detections/endpoint/windows_impair_defense_disable_win_defender_gen_reports.yml index b266b50642..d6a15e0de9 100644 --- a/detections/endpoint/windows_impair_defense_disable_win_defender_gen_reports.yml +++ b/detections/endpoint/windows_impair_defense_disable_win_defender_gen_reports.yml @@ -6,69 +6,45 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects modifications in the Windows registry - to disable Windows Defender generic reports. It leverages data from the Endpoint.Registry - data model, specifically monitoring changes to the "DisableGenericRePorts" registry - value. This activity is significant as it can prevent the transmission of error - reports to Microsoft's Windows Error Reporting service, potentially hiding malicious - activities. If confirmed malicious, this action could allow attackers to bypass - Windows Defender detections, reducing the visibility of their activities and increasing - the risk of undetected system compromise. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows - Defender\\Reporting\\DisableGenericRePorts" Registry.registry_value_data="0x00000001" - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_impair_defense_disable_win_defender_gen_reports_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +description: The following analytic detects modifications in the Windows registry to disable Windows Defender generic reports. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the "DisableGenericRePorts" registry value. This activity is significant as it can prevent the transmission of error reports to Microsoft's Windows Error Reporting service, potentially hiding malicious activities. If confirmed malicious, this action could allow attackers to bypass Windows Defender detections, reducing the visibility of their activities and increasing the risk of undetected system compromise. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\Reporting\\DisableGenericRePorts" Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_win_defender_gen_reports_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://x.com/malmoeb/status/1742604217989415386?s=20 -- https://github.com/undergroundwires/privacy.sexy + - https://x.com/malmoeb/status/1742604217989415386?s=20 + - https://github.com/undergroundwires/privacy.sexy drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Defender DisableGenericRePorts registry is set to enable on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Windows Defender DisableGenericRePorts registry is set to enable on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defense_disable_win_defender_network_protection.yml b/detections/endpoint/windows_impair_defense_disable_win_defender_network_protection.yml index 1c02b6926c..01c702fd40 100644 --- a/detections/endpoint/windows_impair_defense_disable_win_defender_network_protection.yml +++ b/detections/endpoint/windows_impair_defense_disable_win_defender_network_protection.yml @@ -6,70 +6,46 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects modifications to the Windows registry - that disable Windows Defender Network Protection. It leverages data from the Endpoint.Registry - data model, specifically monitoring changes to the EnableNetworkProtection registry - entry. This activity is significant because disabling Network Protection can leave - the system vulnerable to network-based threats by preventing Windows Defender from - analyzing and blocking malicious network activity. If confirmed malicious, this - action could allow attackers to bypass security measures, potentially leading to - unauthorized access, data exfiltration, or further compromise of the network. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows - Defender\\Windows Defender Exploit Guard\\Network Protection\\EnableNetworkProtection" - Registry.registry_value_data="0x00000000" by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_win_defender_network_protection_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows registry that disable Windows Defender Network Protection. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the EnableNetworkProtection registry entry. This activity is significant because disabling Network Protection can leave the system vulnerable to network-based threats by preventing Windows Defender from analyzing and blocking malicious network activity. If confirmed malicious, this action could allow attackers to bypass security measures, potentially leading to unauthorized access, data exfiltration, or further compromise of the network. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\Windows Defender Exploit Guard\\Network Protection\\EnableNetworkProtection" Registry.registry_value_data="0x00000000" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_win_defender_network_protection_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://x.com/malmoeb/status/1742604217989415386?s=20 -- https://github.com/undergroundwires/privacy.sexy + - https://x.com/malmoeb/status/1742604217989415386?s=20 + - https://github.com/undergroundwires/privacy.sexy drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Defender Exploit Guard network protection set to disable on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Windows Defender Exploit Guard network protection set to disable on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defense_disable_win_defender_report_infection.yml b/detections/endpoint/windows_impair_defense_disable_win_defender_report_infection.yml index 77d78bbfa0..dd1933f37c 100644 --- a/detections/endpoint/windows_impair_defense_disable_win_defender_report_infection.yml +++ b/detections/endpoint/windows_impair_defense_disable_win_defender_report_infection.yml @@ -6,69 +6,45 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects modifications to the Windows registry - that disable Windows Defender's infection reporting. It leverages data from the - Endpoint.Registry datamodel, specifically monitoring changes to the "DontReportInfectionInformation" - registry key. This activity is significant because it can prevent Windows Defender - from reporting detailed threat information to Microsoft, potentially allowing malware - to evade detection. If confirmed malicious, this action could enable attackers to - bypass security measures, maintain persistence, and avoid detection, leading to - prolonged unauthorized access and potential data breaches. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Microsoft\\MRT\\DontReportInfectionInformation" - Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_win_defender_report_infection_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows registry that disable Windows Defender's infection reporting. It leverages data from the Endpoint.Registry datamodel, specifically monitoring changes to the "DontReportInfectionInformation" registry key. This activity is significant because it can prevent Windows Defender from reporting detailed threat information to Microsoft, potentially allowing malware to evade detection. If confirmed malicious, this action could enable attackers to bypass security measures, maintain persistence, and avoid detection, leading to prolonged unauthorized access and potential data breaches. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Microsoft\\MRT\\DontReportInfectionInformation" Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_win_defender_report_infection_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://x.com/malmoeb/status/1742604217989415386?s=20 -- https://github.com/undergroundwires/privacy.sexy + - https://x.com/malmoeb/status/1742604217989415386?s=20 + - https://github.com/undergroundwires/privacy.sexy drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Defender DontReportInfectionInformation registry is enabled on - $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Windows Defender DontReportInfectionInformation registry is enabled on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defense_disable_win_defender_scan_on_update.yml b/detections/endpoint/windows_impair_defense_disable_win_defender_scan_on_update.yml index f03d2e14f8..fb923861f7 100644 --- a/detections/endpoint/windows_impair_defense_disable_win_defender_scan_on_update.yml +++ b/detections/endpoint/windows_impair_defense_disable_win_defender_scan_on_update.yml @@ -6,68 +6,45 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects modifications to the Windows registry - that disable the Windows Defender Scan On Update feature. It leverages data from - the Endpoint.Registry datamodel, specifically looking for changes to the "DisableScanOnUpdate" - registry setting with a value of "0x00000001". This activity is significant because - disabling automatic scans can leave systems vulnerable to malware and other threats. - If confirmed malicious, this action could allow attackers to bypass Windows Defender, - facilitating further compromise and persistence within the environment. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows - Defender\\Signature Updates\\DisableScanOnUpdate" Registry.registry_value_data="0x00000001" - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_impair_defense_disable_win_defender_scan_on_update_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows registry that disable the Windows Defender Scan On Update feature. It leverages data from the Endpoint.Registry datamodel, specifically looking for changes to the "DisableScanOnUpdate" registry setting with a value of "0x00000001". This activity is significant because disabling automatic scans can leave systems vulnerable to malware and other threats. If confirmed malicious, this action could allow attackers to bypass Windows Defender, facilitating further compromise and persistence within the environment. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\Signature Updates\\DisableScanOnUpdate" Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_win_defender_scan_on_update_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://x.com/malmoeb/status/1742604217989415386?s=20 -- https://github.com/undergroundwires/privacy.sexy + - https://x.com/malmoeb/status/1742604217989415386?s=20 + - https://github.com/undergroundwires/privacy.sexy drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Defender DisableScanOnUpdate feature set to enable on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Windows Defender DisableScanOnUpdate feature set to enable on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defense_disable_win_defender_signature_retirement.yml b/detections/endpoint/windows_impair_defense_disable_win_defender_signature_retirement.yml index a00af55662..bf72c164de 100644 --- a/detections/endpoint/windows_impair_defense_disable_win_defender_signature_retirement.yml +++ b/detections/endpoint/windows_impair_defense_disable_win_defender_signature_retirement.yml @@ -6,71 +6,46 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects modifications to the Windows registry - that disable Windows Defender Signature Retirement. It leverages data from the Endpoint.Registry - data model, specifically monitoring changes to the DisableSignatureRetirement registry - setting. This activity is significant because disabling signature retirement can - prevent Windows Defender from removing outdated antivirus signatures, potentially - reducing its effectiveness in detecting threats. If confirmed malicious, this action - could allow an attacker to evade detection by using older, less relevant signatures, - thereby compromising the system's security posture. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows - Defender\\NIS\\Consumers\\IPS\\DisableSignatureRetirement" Registry.registry_value_data="0x00000001" - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_impair_defense_disable_win_defender_signature_retirement_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows registry that disable Windows Defender Signature Retirement. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the DisableSignatureRetirement registry setting. This activity is significant because disabling signature retirement can prevent Windows Defender from removing outdated antivirus signatures, potentially reducing its effectiveness in detecting threats. If confirmed malicious, this action could allow an attacker to evade detection by using older, less relevant signatures, thereby compromising the system's security posture. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\NIS\\Consumers\\IPS\\DisableSignatureRetirement" Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_win_defender_signature_retirement_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://x.com/malmoeb/status/1742604217989415386?s=20 -- https://github.com/undergroundwires/privacy.sexy + - https://x.com/malmoeb/status/1742604217989415386?s=20 + - https://github.com/undergroundwires/privacy.sexy drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Defender DisableSignatureRetirement registry is set to enable on - $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Windows Defender DisableSignatureRetirement registry is set to enable on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defense_overide_win_defender_phishing_filter.yml b/detections/endpoint/windows_impair_defense_overide_win_defender_phishing_filter.yml index 916c47d38f..c87170f93b 100644 --- a/detections/endpoint/windows_impair_defense_overide_win_defender_phishing_filter.yml +++ b/detections/endpoint/windows_impair_defense_overide_win_defender_phishing_filter.yml @@ -6,70 +6,45 @@ author: Teoderick Contreras, Bhavin Patel, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects modifications to the Windows registry - that disable the Windows Defender phishing filter. It leverages data from the Endpoint.Registry - data model, focusing on changes to specific registry values related to Microsoft - Edge's phishing filter settings. This activity is significant because disabling - the phishing filter can allow attackers to deceive users into visiting malicious - websites without triggering browser warnings. If confirmed malicious, this could - lead to users unknowingly accessing harmful sites, resulting in potential security - incidents or data compromises. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path = - "*\\MicrosoftEdge\\PhishingFilter*" Registry.registry_value_name IN ("EnabledV9", - "PreventOverride") Registry.registry_value_data="0x00000000" by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_impair_defense_overide_win_defender_phishing_filter_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows registry that disable the Windows Defender phishing filter. It leverages data from the Endpoint.Registry data model, focusing on changes to specific registry values related to Microsoft Edge's phishing filter settings. This activity is significant because disabling the phishing filter can allow attackers to deceive users into visiting malicious websites without triggering browser warnings. If confirmed malicious, this could lead to users unknowingly accessing harmful sites, resulting in potential security incidents or data compromises. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path = "*\\MicrosoftEdge\\PhishingFilter*" Registry.registry_value_name IN ("EnabledV9", "PreventOverride") Registry.registry_value_data="0x00000000" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_overide_win_defender_phishing_filter_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://x.com/malmoeb/status/1742604217989415386?s=20 -- https://github.com/undergroundwires/privacy.sexy + - https://x.com/malmoeb/status/1742604217989415386?s=20 + - https://github.com/undergroundwires/privacy.sexy drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Defender Phishing Filter registry was modified on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Windows Defender Phishing Filter registry was modified on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defense_override_smartscreen_prompt.yml b/detections/endpoint/windows_impair_defense_override_smartscreen_prompt.yml index 64eff872b2..67057c6279 100644 --- a/detections/endpoint/windows_impair_defense_override_smartscreen_prompt.yml +++ b/detections/endpoint/windows_impair_defense_override_smartscreen_prompt.yml @@ -6,68 +6,45 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects modifications to the Windows registry - that override the Windows Defender SmartScreen prompt. It leverages data from the - Endpoint.Registry data model, specifically monitoring changes to the "PreventSmartScreenPromptOverride" - registry setting. This activity is significant because it indicates an attempt to - disable the prevention of user overrides for SmartScreen prompts, potentially allowing - users to bypass security warnings. If confirmed malicious, this could lead to users - inadvertently executing or accessing harmful content, increasing the risk of security - incidents or system compromises. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path= "*\\Microsoft\\Edge\\PreventSmartScreenPromptOverride" - Registry.registry_value_data= "0x00000000" by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_override_smartscreen_prompt_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows registry that override the Windows Defender SmartScreen prompt. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the "PreventSmartScreenPromptOverride" registry setting. This activity is significant because it indicates an attempt to disable the prevention of user overrides for SmartScreen prompts, potentially allowing users to bypass security warnings. If confirmed malicious, this could lead to users inadvertently executing or accessing harmful content, increasing the risk of security incidents or system compromises. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path= "*\\Microsoft\\Edge\\PreventSmartScreenPromptOverride" Registry.registry_value_data= "0x00000000" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_override_smartscreen_prompt_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://x.com/malmoeb/status/1742604217989415386?s=20 -- https://github.com/undergroundwires/privacy.sexy + - https://x.com/malmoeb/status/1742604217989415386?s=20 + - https://github.com/undergroundwires/privacy.sexy drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Defender SmartScreen prompt was override on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Windows Defender SmartScreen prompt was override on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defense_set_win_defender_smart_screen_level_to_warn.yml b/detections/endpoint/windows_impair_defense_set_win_defender_smart_screen_level_to_warn.yml index 7caa746aff..94c3641fb4 100644 --- a/detections/endpoint/windows_impair_defense_set_win_defender_smart_screen_level_to_warn.yml +++ b/detections/endpoint/windows_impair_defense_set_win_defender_smart_screen_level_to_warn.yml @@ -6,68 +6,45 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects modifications to the Windows registry - that set the Windows Defender SmartScreen level to "warn." This detection leverages - data from the Endpoint.Registry data model, specifically monitoring changes to the - ShellSmartScreenLevel registry value. This activity is significant because altering - SmartScreen settings to "warn" can reduce immediate suspicion from users, allowing - potentially malicious executables to run with just a warning prompt. If confirmed - malicious, this could enable attackers to execute harmful files, increasing the - risk of successful malware deployment and subsequent system compromise. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Microsoft\\Windows\\System\\ShellSmartScreenLevel" - Registry.registry_value_data="Warn" by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_set_win_defender_smart_screen_level_to_warn_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows registry that set the Windows Defender SmartScreen level to "warn." This detection leverages data from the Endpoint.Registry data model, specifically monitoring changes to the ShellSmartScreenLevel registry value. This activity is significant because altering SmartScreen settings to "warn" can reduce immediate suspicion from users, allowing potentially malicious executables to run with just a warning prompt. If confirmed malicious, this could enable attackers to execute harmful files, increasing the risk of successful malware deployment and subsequent system compromise. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Microsoft\\Windows\\System\\ShellSmartScreenLevel" Registry.registry_value_data="Warn" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_set_win_defender_smart_screen_level_to_warn_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://x.com/malmoeb/status/1742604217989415386?s=20 -- https://github.com/undergroundwires/privacy.sexy + - https://x.com/malmoeb/status/1742604217989415386?s=20 + - https://github.com/undergroundwires/privacy.sexy drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Defender SmartScreen Level to Warn on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Windows Defender SmartScreen Level to Warn on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defenses_disable_auto_logger_session.yml b/detections/endpoint/windows_impair_defenses_disable_auto_logger_session.yml index dee7ca713a..91b330b3eb 100644 --- a/detections/endpoint/windows_impair_defenses_disable_auto_logger_session.yml +++ b/detections/endpoint/windows_impair_defenses_disable_auto_logger_session.yml @@ -5,74 +5,49 @@ date: '2025-05-02' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly -description: The following analytic detects the disabling of an AutoLogger session - or one of its providers, by identifying changes to the Registry values "Start" and - "Enabled" part of the "\WMI\Autologger\" key path. It leverages data from the Endpoint.Registry - datamodel to monitor specific registry paths and values. This activity is significant - as attackers and adversaries can leverage this in order to evade defense and blind - EDRs and log ingest tooling. If confirmed malicious, this action could allow an - attacker to conceal their activities, making it harder to detect further malicious - actions and maintain persistence on the compromised endpoint. +description: The following analytic detects the disabling of an AutoLogger session or one of its providers, by identifying changes to the Registry values "Start" and "Enabled" part of the "\WMI\Autologger\" key path. It leverages data from the Endpoint.Registry datamodel to monitor specific registry paths and values. This activity is significant as attackers and adversaries can leverage this in order to evade defense and blind EDRs and log ingest tooling. If confirmed malicious, this action could allow an attacker to conceal their activities, making it harder to detect further malicious actions and maintain persistence on the compromised endpoint. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\WMI\\Autologger\\*" - (Registry.registry_value_name="Start" OR Registry.registry_value_name="Enabled") - Registry.registry_value_data ="0x00000000" by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defenses_disable_auto_logger_session_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\WMI\\Autologger\\*" (Registry.registry_value_name="Start" OR Registry.registry_value_name="Enabled") Registry.registry_value_data ="0x00000000" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defenses_disable_auto_logger_session_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/ -- https://app.any.run/tasks/45f5d114-91ea-486c-ab01-41c4093d2861/ -- https://isc.sans.edu/diary/rss/28628 -- https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ -- https://learn.microsoft.com/en-us/windows/win32/etw/configuring-and-starting-an-autologger-session + - https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/ + - https://app.any.run/tasks/45f5d114-91ea-486c-ab01-41c4093d2861/ + - https://isc.sans.edu/diary/rss/28628 + - https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ + - https://learn.microsoft.com/en-us/windows/win32/etw/configuring-and-starting-an-autologger-session drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Auto Logger Session or Provider registry value set to 'disabled' - on $dest$ - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: [] + message: Windows Auto Logger Session or Provider registry value set to 'disabled' on $dest$ + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable_defender_logging/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable_defender_logging/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defenses_disable_av_autostart_via_registry.yml b/detections/endpoint/windows_impair_defenses_disable_av_autostart_via_registry.yml index 84ab65bbaf..69dfbddad7 100644 --- a/detections/endpoint/windows_impair_defenses_disable_av_autostart_via_registry.yml +++ b/detections/endpoint/windows_impair_defenses_disable_av_autostart_via_registry.yml @@ -4,79 +4,50 @@ version: 10 date: '2026-01-14' author: Teoderick Contreras, Splunk data_source: - - Sysmon EventID 13 + - Sysmon EventID 13 type: TTP status: production -description: - The following analytic detects modifications to the registry related - to the disabling of autostart functionality for certain antivirus products, such - as Kingsoft and Tencent. Malware like ValleyRAT may alter specific registry keys - to prevent these security tools from launching automatically at startup, thereby - weakening system defenses. By monitoring changes in the registry entries associated - with antivirus autostart settings, this detection enables security analysts to identify - attempts to disable protective software. Detecting these modifications early is - critical for maintaining system integrity and preventing further compromise by malicious - actors. -search: - '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry - WHERE Registry.registry_path IN("*\\kingsoft\\antivirus\\KAVReport\\*" , "*\\kingsoft\\antivirus\\KSetting\\*", - "*\\kingsoft\\antivirus\\Windhunter\\*" ,"*\\Tencent\\QQPCMgr\\*") AND ((Registry.registry_value_name - IN("autostart","kxesc", "WindhunterSwitch") AND Registry.registry_value_data = "0x00000000") - OR (Registry.registry_value_name = "WindhunterLevel" AND Registry.registry_value_data - = "0x00000004")) by Registry.action Registry.dest Registry.process_guid Registry.process_id - Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data - Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user - Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_impair_defenses_disable_av_autostart_via_registry_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 +description: The following analytic detects modifications to the registry related to the disabling of autostart functionality for certain antivirus products, such as Kingsoft and Tencent. Malware like ValleyRAT may alter specific registry keys to prevent these security tools from launching automatically at startup, thereby weakening system defenses. By monitoring changes in the registry entries associated with antivirus autostart settings, this detection enables security analysts to identify attempts to disable protective software. Detecting these modifications early is critical for maintaining system integrity and preventing further compromise by malicious actors. +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE Registry.registry_path IN("*\\kingsoft\\antivirus\\KAVReport\\*" , "*\\kingsoft\\antivirus\\KSetting\\*", "*\\kingsoft\\antivirus\\Windhunter\\*" ,"*\\Tencent\\QQPCMgr\\*") AND ((Registry.registry_value_name IN("autostart","kxesc", "WindhunterSwitch") AND Registry.registry_value_data = "0x00000000") OR (Registry.registry_value_name = "WindhunterLevel" AND Registry.registry_value_data = "0x00000004")) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defenses_disable_av_autostart_via_registry_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: No false positives have been identified at this time. references: - - https://www.proofpoint.com/us/blog/threat-insight/chinese-malware-appears-earnest-across-cybercrime-threat-landscape - - https://www.fortinet.com/blog/threat-research/valleyrat-campaign-targeting-chinese-speakers + - https://www.proofpoint.com/us/blog/threat-insight/chinese-malware-appears-earnest-across-cybercrime-threat-landscape + - https://www.fortinet.com/blog/threat-research/valleyrat-campaign-targeting-chinese-speakers drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: disable anti-virus autostart via registry on [$dest$]. - risk_objects: - - field: user - type: user - score: 90 - - field: dest - type: system - score: 90 - threat_objects: [] + message: disable anti-virus autostart via registry on [$dest$]. + risk_objects: + - field: user + type: user + score: 90 + - field: dest + type: system + score: 90 + threat_objects: [] tags: - analytic_story: - - Scattered Lapsus$ Hunters - - ValleyRAT - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Scattered Lapsus$ Hunters + - ValleyRAT + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/kingsoft_reg/kingsoft_reg.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/kingsoft_reg/kingsoft_reg.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defenses_disable_hvci.yml b/detections/endpoint/windows_impair_defenses_disable_hvci.yml index a586c79957..d92ff8639a 100644 --- a/detections/endpoint/windows_impair_defenses_disable_hvci.yml +++ b/detections/endpoint/windows_impair_defenses_disable_hvci.yml @@ -6,69 +6,47 @@ author: Michael Haag, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects the disabling of Hypervisor-protected - Code Integrity (HVCI) by monitoring changes in the Windows registry. It leverages - data from the Endpoint datamodel, specifically focusing on registry paths and values - related to HVCI settings. This activity is significant because HVCI helps protect - the kernel and system processes from tampering by malicious code. If confirmed malicious, - disabling HVCI could allow attackers to execute unsigned kernel-mode code, potentially - leading to kernel-level rootkits or other severe security breaches. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\CurrentControlSet\\Control\\DeviceGuard\\Scenarios\\HypervisorEnforcedCodeIntegrity\\Enabled" - Registry.registry_value_data="0x00000000" by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defenses_disable_hvci_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: False positives will be limited to administrative scripts disabling - HVCI. Filter as needed. + - Sysmon EventID 13 +description: The following analytic detects the disabling of Hypervisor-protected Code Integrity (HVCI) by monitoring changes in the Windows registry. It leverages data from the Endpoint datamodel, specifically focusing on registry paths and values related to HVCI settings. This activity is significant because HVCI helps protect the kernel and system processes from tampering by malicious code. If confirmed malicious, disabling HVCI could allow attackers to execute unsigned kernel-mode code, potentially leading to kernel-level rootkits or other severe security breaches. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\CurrentControlSet\\Control\\DeviceGuard\\Scenarios\\HypervisorEnforcedCodeIntegrity\\Enabled" Registry.registry_value_data="0x00000000" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defenses_disable_hvci_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: False positives will be limited to administrative scripts disabling HVCI. Filter as needed. references: -- https://www.microsoft.com/en-us/security/blog/2023/04/11/guidance-for-investigating-attacks-using-cve-2022-21894-the-blacklotus-campaign/ + - https://www.microsoft.com/en-us/security/blog/2023/04/11/guidance-for-investigating-attacks-using-cve-2022-21894-the-blacklotus-campaign/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: HVCI has been disabled on $dest$. - risk_objects: - - field: dest - type: system - score: 70 - threat_objects: [] + message: HVCI has been disabled on $dest$. + risk_objects: + - field: dest + type: system + score: 70 + threat_objects: [] tags: - analytic_story: - - BlackLotus Campaign - - Windows Defense Evasion Tactics - - Windows Registry Abuse - asset_type: Endpoint - atomic_guid: - - 70bd71e6-eba4-4e00-92f7-617911dbe020 - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - BlackLotus Campaign + - Windows Defense Evasion Tactics + - Windows Registry Abuse + asset_type: Endpoint + atomic_guid: + - 70bd71e6-eba4-4e00-92f7-617911dbe020 + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/hvci_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/hvci_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_impair_defenses_disable_win_defender_auto_logging.yml b/detections/endpoint/windows_impair_defenses_disable_win_defender_auto_logging.yml index d4ab58702c..62bec83eb3 100644 --- a/detections/endpoint/windows_impair_defenses_disable_win_defender_auto_logging.yml +++ b/detections/endpoint/windows_impair_defenses_disable_win_defender_auto_logging.yml @@ -5,73 +5,49 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the disabling of Windows Defender logging - by identifying changes to the Registry keys DefenderApiLogger or DefenderAuditLogger - set to disable. It leverages data from the Endpoint.Registry datamodel to monitor - specific registry paths and values. This activity is significant as it is commonly - associated with Remote Access Trojan (RAT) malware attempting to evade detection. - If confirmed malicious, this action could allow an attacker to conceal their activities, - making it harder to detect further malicious actions and maintain persistence on - the compromised endpoint. +description: The following analytic detects the disabling of Windows Defender logging by identifying changes to the Registry keys DefenderApiLogger or DefenderAuditLogger set to disable. It leverages data from the Endpoint.Registry datamodel to monitor specific registry paths and values. This activity is significant as it is commonly associated with Remote Access Trojan (RAT) malware attempting to evade detection. If confirmed malicious, this action could allow an attacker to conceal their activities, making it harder to detect further malicious actions and maintain persistence on the compromised endpoint. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where (Registry.registry_path = "*WMI\\Autologger\\DefenderApiLogger\\Start" - OR Registry.registry_path = "*WMI\\Autologger\\DefenderAuditLogger\\Start") Registry.registry_value_data - ="0x00000000" by Registry.action Registry.dest Registry.process_guid Registry.process_id - Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data - Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user - Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_impair_defenses_disable_win_defender_auto_logging_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: It is unusual to turn this feature off a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where (Registry.registry_path = "*WMI\\Autologger\\DefenderApiLogger\\Start" OR Registry.registry_path = "*WMI\\Autologger\\DefenderAuditLogger\\Start") Registry.registry_value_data ="0x00000000" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defenses_disable_win_defender_auto_logging_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/ -- https://app.any.run/tasks/45f5d114-91ea-486c-ab01-41c4093d2861/ -- https://isc.sans.edu/diary/rss/28628 -- https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ + - https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/ + - https://app.any.run/tasks/45f5d114-91ea-486c-ab01-41c4093d2861/ + - https://isc.sans.edu/diary/rss/28628 + - https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Defender Logger registry key set to 'disabled' on $dest$. - risk_objects: - - field: dest - type: system - score: 24 - threat_objects: [] + message: Windows Defender Logger registry key set to 'disabled' on $dest$. + risk_objects: + - field: dest + type: system + score: 24 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - CISA AA23-347A - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - CISA AA23-347A + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable_defender_logging/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable_defender_logging/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_important_audit_policy_disabled.yml b/detections/endpoint/windows_important_audit_policy_disabled.yml index 60210231a6..9470d08aa6 100644 --- a/detections/endpoint/windows_important_audit_policy_disabled.yml +++ b/detections/endpoint/windows_important_audit_policy_disabled.yml @@ -1,61 +1,62 @@ name: Windows Important Audit Policy Disabled id: 1bf500e5-1226-41d9-af5d-ed1f577929f2 -version: 3 -date: '2026-01-14' +version: 4 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk type: TTP status: production data_source: -- Windows Event Log Security 4719 + - Windows Event Log Security 4719 description: The following analytic detects the disabling of important audit policies. It leverages EventCode 4719 from Windows Security Event Logs to identify changes where success or failure auditing is removed. This activity is significant as it suggests an attacker may have gained access to the domain controller and is attempting to evade detection by tampering with audit policies. If confirmed malicious, this could lead to severe consequences, including data theft, privilege escalation, and full network compromise. Immediate investigation is required to determine the source and intent of the change. -search: '`wineventlog_security` EventCode=4719 (AuditPolicyChanges IN ("%%8448","%%8450","%%8448, %%8450") OR Changes IN ("Failure removed","Success removed","Success removed, Failure removed")) `important_audit_policy_subcategory_guids` | replace "%%8448" with "Success removed", "%%8450" with "Failure removed", "%%8448, %%8450" with "Success removed, Failure removed" in AuditPolicyChanges | eval AuditPolicyChanges=coalesce(AuditPolicyChanges,Changes), SubcategoryGuid=coalesce(SubcategoryGuid,Subcategory_GUID) | rename ClientProcessId as process_id | stats min(_time) as _time values(host) as dest by AuditPolicyChanges SubcategoryGuid, process_id | lookup advanced_audit_policy_guids GUID as SubcategoryGuid OUTPUT Category SubCategory | `windows_important_audit_policy_disabled_filter`' +search: |- + `wineventlog_security` EventCode=4719 (AuditPolicyChanges IN ("%%8448","%%8450","%%8448, %%8450") OR Changes IN ("Failure removed","Success removed","Success removed, Failure removed")) `important_audit_policy_subcategory_guids` + | replace "%%8448" with "Success removed", "%%8450" with "Failure removed", "%%8448, %%8450" with "Success removed, Failure removed" in AuditPolicyChanges + | eval AuditPolicyChanges=coalesce(AuditPolicyChanges,Changes), SubcategoryGuid=coalesce(SubcategoryGuid,Subcategory_GUID) + | rename ClientProcessId as process_id + | stats min(_time) as _time values(host) as dest + BY AuditPolicyChanges SubcategoryGuid, process_id + | lookup advanced_audit_policy_guids GUID as SubcategoryGuid OUTPUT Category SubCategory + | `windows_important_audit_policy_disabled_filter` how_to_implement: To implement the following query, enable the audit policy sub category "Audit Audit Policy Change", and, ensure you are ingesting EventCode `4719` from your endpoints via the appropriate Splunk Add-on for Microsoft Windows. Update the macro definition with the an accurate list of Audit sub categories that you consider important for your environment. known_false_positives: No false positives have been identified at this time. references: -- https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4719 + - https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4719 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Important audit policy "$SubCategory$" of category "$Category$" was disabled on $dest$ - risk_objects: - - field: dest - type: system - score: 60 - threat_objects: [] + message: Important audit policy "$SubCategory$" of category "$Category$" was disabled on $dest$ + risk_objects: + - field: dest + type: system + score: 60 + threat_objects: [] tags: - analytic_story: - - Windows Audit Policy Tampering - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - manual_test: This search uses a lookup provided by Enterprise Security and needs to be manually tested + analytic_story: + - Windows Audit Policy Tampering + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + manual_test: This search uses a lookup provided by Enterprise Security and needs to be manually tested tests: -- name: True Positive Test - Security 1 - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable_gpo/windows-security-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog -- name: True Positive Test - Security 2 - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test - Security 1 + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable_gpo/windows-security-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog + - name: True Positive Test - Security 2 + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/auditpol_tampering/auditpol_tampering_security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_increase_in_group_or_object_modification_activity.yml b/detections/endpoint/windows_increase_in_group_or_object_modification_activity.yml index a6d59b627e..c76567e941 100644 --- a/detections/endpoint/windows_increase_in_group_or_object_modification_activity.yml +++ b/detections/endpoint/windows_increase_in_group_or_object_modification_activity.yml @@ -1,64 +1,59 @@ name: Windows Increase in Group or Object Modification Activity id: 4f9564dd-a204-4f22-b375-4dfca3a68731 -version: 5 -date: '2026-01-14' +version: 6 +date: '2026-02-25' author: Dean Luxton status: production type: TTP data_source: -- Windows Event Log Security 4663 -description: This analytic detects an increase in modifications to AD groups or objects. - Frequent changes to AD groups or objects can indicate potential security risks, - such as unauthorized access attempts, impairing defences or establishing persistence. - By monitoring AD logs for unusual modification patterns, this detection helps identify - suspicious behavior that could compromise the integrity and security of the AD environment. -search: '`wineventlog_security` EventCode IN (4670,4727,4731,4734,4735,4764) | bucket - span=5m _time | stats values(object) as object, dc(object) as objectCount, values(src_user_category) - as src_user_category, values(dest) as dest, values(dest_category) as dest_category - by _time, src_user, signature, status | eventstats avg(objectCount) as comp_avg, - stdev(objectCount) as comp_std by src_user, signature | eval upperBound=(comp_avg+comp_std) - | eval isOutlier=if(objectCount > 10 and (objectCount >= upperBound), 1, 0) | search - isOutlier=1 | `windows_increase_in_group_or_object_modification_activity_filter`' + - Windows Event Log Security 4663 +description: This analytic detects an increase in modifications to AD groups or objects. Frequent changes to AD groups or objects can indicate potential security risks, such as unauthorized access attempts, impairing defences or establishing persistence. By monitoring AD logs for unusual modification patterns, this detection helps identify suspicious behavior that could compromise the integrity and security of the AD environment. +search: |- + `wineventlog_security` EventCode IN (4670,4727,4731,4734,4735,4764) + | bucket span=5m _time + | stats values(object) as object, dc(object) as objectCount, values(src_user_category) as src_user_category, values(dest) as dest, values(dest_category) as dest_category + BY _time, src_user, signature, + status + | eventstats avg(objectCount) as comp_avg, stdev(objectCount) as comp_std + BY src_user, signature + | eval upperBound=(comp_avg+comp_std) + | eval isOutlier=if(objectCount > 10 and (objectCount >= upperBound), 1, 0) + | search isOutlier=1 + | `windows_increase_in_group_or_object_modification_activity_filter` how_to_implement: Run this detection looking over a 7 day timeframe for best results. known_false_positives: No false positives have been identified at this time. references: [] drilldown_searches: -- name: View the detection results for - "$src_user$" - search: '%original_detection_search% | search src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_user$" + search: '%original_detection_search% | search src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Spike in Group or Object Modifications performed by $src_user$ - risk_objects: - - field: src_user - type: user - score: 8 - threat_objects: [] + message: Spike in Group or Object Modifications performed by $src_user$ + risk_objects: + - field: src_user + type: user + score: 8 + threat_objects: [] tags: - analytic_story: - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1098 - - T1562 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: audit + analytic_story: + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1098 + - T1562 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: audit tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/account_manipulation/xml-windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/account_manipulation/xml-windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_increase_in_user_modification_activity.yml b/detections/endpoint/windows_increase_in_user_modification_activity.yml index 3545b00660..2f4a0ca1a6 100644 --- a/detections/endpoint/windows_increase_in_user_modification_activity.yml +++ b/detections/endpoint/windows_increase_in_user_modification_activity.yml @@ -1,68 +1,61 @@ name: Windows Increase in User Modification Activity id: 0995fca1-f346-432f-b0bf-a66d14e6b428 -version: 4 -date: '2025-05-02' +version: 5 +date: '2026-02-25' author: Dean Luxton status: production type: TTP data_source: -- Windows Event Log Security 4720 -description: This analytic detects an increase in modifications to AD user objects. - A large volume of changes to user objects can indicate potential security risks, - such as unauthorized access attempts, impairing defences or establishing persistence. - By monitoring AD logs for unusual modification patterns, this detection helps identify - suspicious behavior that could compromise the integrity and security of the AD environment. -search: '`wineventlog_security` EventCode IN (4720,4722,4723,4724,4725,4726,4728,4732,4733,4738,4743,4780) - | bucket span=5m _time | stats values(TargetDomainName) as TargetDomainName, values(user) - as user, dc(user) as userCount, values(user_category) as user_category, values(src_user_category) - as src_user_category, values(dest) as dest, values(dest_category) as dest_category - by _time, src_user, signature, status | eventstats avg(userCount) as comp_avg , - stdev(userCount) as comp_std by src_user, signature | eval upperBound=(comp_avg+comp_std*3) - | eval isOutlier=if(userCount > 10 and userCount >= upperBound, 1, 0) | search - isOutlier=1 | stats values(TargetDomainName) as TargetDomainName, values(user) as - user, dc(user) as userCount, values(user_category) as user_category, values(src_user_category) - as src_user_category, values(dest) as dest, values(dest_category) as dest_category - values(signature) as signature by _time, src_user, status | `windows_increase_in_user_modification_activity_filter`' + - Windows Event Log Security 4720 +description: This analytic detects an increase in modifications to AD user objects. A large volume of changes to user objects can indicate potential security risks, such as unauthorized access attempts, impairing defences or establishing persistence. By monitoring AD logs for unusual modification patterns, this detection helps identify suspicious behavior that could compromise the integrity and security of the AD environment. +search: |- + `wineventlog_security` EventCode IN (4720,4722,4723,4724,4725,4726,4728,4732,4733,4738,4743,4780) + | bucket span=5m _time + | stats values(TargetDomainName) as TargetDomainName, values(user) as user, dc(user) as userCount, values(user_category) as user_category, values(src_user_category) as src_user_category, values(dest) as dest, values(dest_category) as dest_category + BY _time, src_user, signature, + status + | eventstats avg(userCount) as comp_avg , stdev(userCount) as comp_std + BY src_user, signature + | eval upperBound=(comp_avg+comp_std*3) + | eval isOutlier=if(userCount > 10 and userCount >= upperBound, 1, 0) + | search isOutlier=1 + | stats values(TargetDomainName) as TargetDomainName, values(user) as user, dc(user) as userCount, values(user_category) as user_category, values(src_user_category) as src_user_category, values(dest) as dest, values(dest_category) as dest_category values(signature) as signature + BY _time, src_user, status + | `windows_increase_in_user_modification_activity_filter` how_to_implement: Run this detection looking over a 7 day timeframe for best results. known_false_positives: Genuine activity references: [] drilldown_searches: -- name: View the detection results for - "$src_user$" - search: '%original_detection_search% | search src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_user$" + search: '%original_detection_search% | search src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Spike in User Modification actions performed by $src_user$ - risk_objects: - - field: src_user - type: user - score: 8 - threat_objects: [] + message: Spike in User Modification actions performed by $src_user$ + risk_objects: + - field: src_user + type: user + score: 8 + threat_objects: [] tags: - analytic_story: - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1098 - - T1562 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: audit + analytic_story: + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1098 + - T1562 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: audit tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/account_manipulation/xml-windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/account_manipulation/xml-windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_indicator_removal_via_rmdir.yml b/detections/endpoint/windows_indicator_removal_via_rmdir.yml index 0ee5e5068c..1ccd118d07 100644 --- a/detections/endpoint/windows_indicator_removal_via_rmdir.yml +++ b/detections/endpoint/windows_indicator_removal_via_rmdir.yml @@ -6,76 +6,47 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic detects the execution of the 'rmdir' command with - '/s' and '/q' options to delete files and directory trees. This detection leverages - data from Endpoint Detection and Response (EDR) agents, focusing on command-line - executions and process metadata. This activity is significant as it may indicate - malware attempting to remove traces or components during cleanup operations. If - confirmed malicious, this behavior could allow attackers to eliminate forensic evidence, - hinder incident response efforts, and maintain persistence by removing indicators - of compromise. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process = "*rmdir*" - Processes.process = "* /s *" Processes.process = "* /q *" by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_indicator_removal_via_rmdir_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic detects the execution of the 'rmdir' command with '/s' and '/q' options to delete files and directory trees. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process metadata. This activity is significant as it may indicate malware attempting to remove traces or components during cleanup operations. If confirmed malicious, this behavior could allow attackers to eliminate forensic evidence, hinder incident response efforts, and maintain persistence by removing indicators of compromise. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*rmdir*" Processes.process = "* /s *" Processes.process = "* /q *" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_indicator_removal_via_rmdir_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: user and network administrator can execute this command. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate + - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a process execute rmdir command to delete files and directory tree on $dest$. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: a process execute rmdir command to delete files and directory tree on $dest$. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - DarkGate Malware - - APT37 Rustonotto and FadeStealer - - ZOVWiper - asset_type: Endpoint - mitre_attack_id: - - T1070 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - DarkGate Malware + - APT37 Rustonotto and FadeStealer + - ZOVWiper + asset_type: Endpoint + mitre_attack_id: + - T1070 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070/rmdir_delete_files_and_dir/rmdir.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070/rmdir_delete_files_and_dir/rmdir.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_indirect_command_execution_via_forfiles.yml b/detections/endpoint/windows_indirect_command_execution_via_forfiles.yml index 30deb7e956..55b1e77737 100644 --- a/detections/endpoint/windows_indirect_command_execution_via_forfiles.yml +++ b/detections/endpoint/windows_indirect_command_execution_via_forfiles.yml @@ -1,81 +1,64 @@ name: Windows Indirect Command Execution Via forfiles id: 1fdf31c9-ff4d-4c48-b799-0e8666e08787 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Eric McGinnis, Splunk status: production type: TTP -description: The following analytic detects the execution of programs initiated by - forfiles.exe. This command is typically used to run commands on multiple files, - often within batch scripts. The detection leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process creation events where forfiles.exe - is the parent process. This activity is significant because forfiles.exe can be - exploited to bypass command line execution protections, making it a potential vector - for malicious activity. If confirmed malicious, this could allow attackers to execute - arbitrary commands, potentially leading to unauthorized access or further system - compromise. +description: The following analytic detects the execution of programs initiated by forfiles.exe. This command is typically used to run commands on multiple files, often within batch scripts. The detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where forfiles.exe is the parent process. This activity is significant because forfiles.exe can be exploited to bypass command line execution protections, making it a potential vector for malicious activity. If confirmed malicious, this could allow attackers to execute arbitrary commands, potentially leading to unauthorized access or further system compromise. data_source: -- Sysmon EventID 1 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process="*forfiles* /c *" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_indirect_command_execution_via_forfiles_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Some legacy applications may be run using pcalua.exe. Similarly, - forfiles.exe may be used in legitimate batch scripts. Filter these results as needed. + - Sysmon EventID 1 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process="*forfiles* /c *" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_indirect_command_execution_via_forfiles_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Some legacy applications may be run using pcalua.exe. Similarly, forfiles.exe may be used in legitimate batch scripts. Filter these results as needed. references: -- https://twitter.com/KyleHanslovan/status/912659279806640128 -- https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/forfiles + - https://twitter.com/KyleHanslovan/status/912659279806640128 + - https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/forfiles drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The forfiles command (forfiles.exe) launched the process name - $process_name$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: The forfiles command (forfiles.exe) launched the process name - $process_name$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Living Off The Land - - Windows Post-Exploitation - asset_type: Endpoint - mitre_attack_id: - - T1202 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + - Windows Post-Exploitation + asset_type: Endpoint + mitre_attack_id: + - T1202 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1202/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1202/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_indirect_command_execution_via_pcalua.yml b/detections/endpoint/windows_indirect_command_execution_via_pcalua.yml index f352001fc5..6856fdad19 100644 --- a/detections/endpoint/windows_indirect_command_execution_via_pcalua.yml +++ b/detections/endpoint/windows_indirect_command_execution_via_pcalua.yml @@ -1,79 +1,63 @@ name: Windows Indirect Command Execution Via pcalua id: 3428ac18-a410-4823-816c-ce697d26f7a8 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Eric McGinnis, Splunk status: production type: TTP -description: The following analytic detects programs initiated by pcalua.exe, the - Microsoft Windows Program Compatibility Assistant. This detection leverages data - from Endpoint Detection and Response (EDR) agents, focusing on process and parent - process information. While pcalua.exe can start legitimate programs, it is significant - because attackers may use it to bypass command line execution protections. If confirmed - malicious, this activity could allow attackers to execute arbitrary commands, potentially - leading to unauthorized actions, privilege escalation, or persistence within the - environment. +description: The following analytic detects programs initiated by pcalua.exe, the Microsoft Windows Program Compatibility Assistant. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process information. While pcalua.exe can start legitimate programs, it is significant because attackers may use it to bypass command line execution protections. If confirmed malicious, this activity could allow attackers to execute arbitrary commands, potentially leading to unauthorized actions, privilege escalation, or persistence within the environment. data_source: -- Sysmon EventID 1 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process="*pcalua* - -a*" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_indirect_command_execution_via_pcalua_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Some legacy applications may be run using pcalua.exe. Filter - these results as needed. + - Sysmon EventID 1 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process="*pcalua* -a*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_indirect_command_execution_via_pcalua_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Some legacy applications may be run using pcalua.exe. Filter these results as needed. references: -- https://twitter.com/KyleHanslovan/status/912659279806640128 -- https://lolbas-project.github.io/lolbas/Binaries/Pcalua/ + - https://twitter.com/KyleHanslovan/status/912659279806640128 + - https://lolbas-project.github.io/lolbas/Binaries/Pcalua/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The Program Compatability Assistant (pcalua.exe) launched the process $process_name$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: The Program Compatability Assistant (pcalua.exe) launched the process $process_name$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1202 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1202 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1202/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1202/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_indirect_command_execution_via_series_of_forfiles.yml b/detections/endpoint/windows_indirect_command_execution_via_series_of_forfiles.yml index 0c69a8ca0f..9fdbc0f1cf 100644 --- a/detections/endpoint/windows_indirect_command_execution_via_series_of_forfiles.yml +++ b/detections/endpoint/windows_indirect_command_execution_via_series_of_forfiles.yml @@ -1,87 +1,64 @@ name: Windows Indirect Command Execution Via Series Of Forfiles id: bfdaabe7-3db8-48c5-80c1-220f9b8f22be -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects excessive usage of the forfiles.exe process, - which is often indicative of post-exploitation activities. The detection leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process execution - logs that include process GUID, process name, and parent process. This activity - is significant because forfiles.exe can be abused to execute commands on multiple - files, a technique used by ransomware like Prestige. If confirmed malicious, this - behavior could allow attackers to enumerate files, potentially leading to data exfiltration - or further malicious actions. +description: The following analytic detects excessive usage of the forfiles.exe process, which is often indicative of post-exploitation activities. The detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include process GUID, process name, and parent process. This activity is significant because forfiles.exe can be abused to execute commands on multiple files, a technique used by ransomware like Prestige. If confirmed malicious, this behavior could allow attackers to enumerate files, potentially leading to data exfiltration or further malicious actions. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.action) as action - values(Processes.original_file_name) as original_file_name values(Processes.parent_process_exec) - as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid - values(Processes.parent_process_id) as parent_process_id values(Processes.parent_process_path) - as parent_process_path values(Processes.process) as process values(Processes.process_exec) - as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) - as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) - as process_integrity_level values(Processes.process_name) as process_name values(Processes.process_path) - as process_path values(Processes.user) as user values(Processes.user_id) as user_id - values(Processes.vendor_product) as vendor_product count min(_time) as firstTime - max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name - = "forfiles.exe" OR Processes.original_file_name = "forfiles.exe" by Processes.parent_process_name - Processes.parent_process Processes.dest Processes.user _time span=1m | where count - >=20 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | - `security_content_ctime(lastTime)` | `windows_indirect_command_execution_via_series_of_forfiles_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.action) as action values(Processes.original_file_name) as original_file_name values(Processes.parent_process_exec) as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid values(Processes.parent_process_id) as parent_process_id values(Processes.parent_process_path) as parent_process_path values(Processes.process) as process values(Processes.process_exec) as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) as process_integrity_level values(Processes.process_name) as process_name values(Processes.process_path) as process_path values(Processes.user) as user values(Processes.user_id) as user_id values(Processes.vendor_product) as vendor_product count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "forfiles.exe" + OR + Processes.original_file_name = "forfiles.exe" + BY Processes.parent_process_name Processes.parent_process Processes.dest + Processes.user _time span=1m + | where count >=20 + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_indirect_command_execution_via_series_of_forfiles_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/forfiles -- https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS -- https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ + - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/forfiles + - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS + - https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: excessive forfiles process execution on $dest$ - risk_objects: - - field: dest - type: system - score: 9 - threat_objects: [] + message: excessive forfiles process execution on $dest$ + risk_objects: + - field: dest + type: system + score: 9 + threat_objects: [] tags: - analytic_story: - - Windows Post-Exploitation - - Prestige Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1202 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Post-Exploitation + - Prestige Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1202 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_information_discovery_fsutil.yml b/detections/endpoint/windows_information_discovery_fsutil.yml index a65eebc40c..10612c235f 100644 --- a/detections/endpoint/windows_information_discovery_fsutil.yml +++ b/detections/endpoint/windows_information_discovery_fsutil.yml @@ -1,100 +1,87 @@ name: Windows Information Discovery Fsutil id: 2181f261-93e6-4166-a5a9-47deac58feff -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - The following analytic identifies the execution of the Windows built-in tool FSUTIL with the "FSINFO" or "Volume" parameters, in order to discover file system and disk information. - This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. - Monitoring this activity is significant because FSUTIL can be abused by adversaries to gather detailed information about the file system, aiding in further exploitation. - If confirmed malicious, this activity could enable attackers to map the file system, identify valuable data, and plan subsequent actions such as privilege escalation or persistence. + The following analytic identifies the execution of the Windows built-in tool FSUTIL with the "FSINFO" or "Volume" parameters, in order to discover file system and disk information. + This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. + Monitoring this activity is significant because FSUTIL can be abused by adversaries to gather detailed information about the file system, aiding in further exploitation. + If confirmed malicious, this activity could enable attackers to map the file system, identify valuable data, and plan subsequent actions such as privilege escalation or persistence. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime - from datamodel=Endpoint.Processes where + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + from datamodel=Endpoint.Processes where - ( - Processes.process_name="fsutil.exe" - OR - Processes.original_file_name = "fsutil.exe" - ) - ( - Processes.process = "*fsinfo*" - OR ( - Processes.process = "*volume*" - AND - Processes.process IN ("*diskfree*", "*list*") + Processes.process_name="fsutil.exe" + OR + Processes.original_file_name = "fsutil.exe" ) - ) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_information_discovery_fsutil_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + ( + Processes.process = "*fsinfo*" + OR + ( + Processes.process = "*volume*" + AND + Processes.process IN ("*diskfree*", "*list*") + ) + ) + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_information_discovery_fsutil_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/fsutil -- https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/fsutil-volume -- https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS -- https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ -- https://www.microsoft.com/en-us/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/ + - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/fsutil + - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/fsutil-volume + - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS + - https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ + - https://www.microsoft.com/en-us/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: process $process_name$ with commandline $process$ is executed on $dest$ - risk_objects: - - field: dest - type: system - score: 9 - threat_objects: [] + message: process $process_name$ with commandline $process$ is executed on $dest$ + risk_objects: + - field: dest + type: system + score: 9 + threat_objects: [] tags: - analytic_story: - - Windows Post-Exploitation - - Prestige Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1082 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Post-Exploitation + - Prestige Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1082 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/winpeas_fsutil/fsutil-fsinfo-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/winpeas_fsutil/fsutil-fsinfo-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ingress_tool_transfer_using_explorer.yml b/detections/endpoint/windows_ingress_tool_transfer_using_explorer.yml index 4a5685d7dd..41daa48178 100644 --- a/detections/endpoint/windows_ingress_tool_transfer_using_explorer.yml +++ b/detections/endpoint/windows_ingress_tool_transfer_using_explorer.yml @@ -1,91 +1,69 @@ name: Windows Ingress Tool Transfer Using Explorer id: 76753bab-f116-4ea3-8fb9-89b638be58a9 -version: 10 -date: '2025-07-01' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies instances where the Windows Explorer - process (explorer.exe) is executed with a URL in its command line. This detection - leverages data from Endpoint Detection and Response (EDR) agents, focusing on process - execution logs. This activity is significant because adversaries, such as those - using DCRat malware, may abuse explorer.exe to open URLs with the default browser, - which is an uncommon and suspicious behavior. If confirmed malicious, this technique - could allow attackers to download and execute malicious payloads, leading to potential - system compromise and further malicious activities. +description: The following analytic identifies instances where the Windows Explorer process (explorer.exe) is executed with a URL in its command line. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This activity is significant because adversaries, such as those using DCRat malware, may abuse explorer.exe to open URLs with the default browser, which is an uncommon and suspicious behavior. If confirmed malicious, this technique could allow attackers to download and execute malicious payloads, leading to potential system compromise and further malicious activities. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - (Processes.process_name = explorer.exe OR Processes.original_file_name = explorer.exe) - AND NOT (Processes.parent_process_name IN("userinit.exe", "svchost.exe")) - Processes.process IN ("* http://*", "* https://*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_ingress_tool_transfer_using_explorer_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present based on legitimate applications - or third party utilities. Filter out any additional parent process names. + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where + (Processes.process_name = explorer.exe OR Processes.original_file_name = explorer.exe) + AND NOT (Processes.parent_process_name IN("userinit.exe", "svchost.exe")) + Processes.process IN ("* http://*", "* https://*") + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` + | `windows_ingress_tool_transfer_using_explorer_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present based on legitimate applications or third party utilities. Filter out any additional parent process names. references: -- https://www.mandiant.com/resources/analyzing-dark-crystal-rat-backdoor + - https://www.mandiant.com/resources/analyzing-dark-crystal-rat-backdoor drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to download a remote payload. - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to download a remote payload. + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - DarkCrystal RAT - asset_type: Endpoint - mitre_attack_id: - - T1105 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - DarkCrystal RAT + asset_type: Endpoint + mitre_attack_id: + - T1105 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/dcrat_explorer_url/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/dcrat_explorer_url/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_inprocserver32_new_outlook_form.yml b/detections/endpoint/windows_inprocserver32_new_outlook_form.yml index 39e9192256..350fc32b85 100644 --- a/detections/endpoint/windows_inprocserver32_new_outlook_form.yml +++ b/detections/endpoint/windows_inprocserver32_new_outlook_form.yml @@ -4,76 +4,48 @@ version: 7 date: '2025-05-02' author: Michael Haag, Splunk data_source: - - Sysmon EventID 13 + - Sysmon EventID 13 type: Anomaly status: production -description: - The following analytic detects the creation or modification of registry - keys associated with new Outlook form installations, potentially indicating exploitation - of CVE-2024-21378. It leverages data from the Endpoint.Registry datamodel, focusing - on registry paths involving InProcServer32 keys linked to Outlook forms. This activity - is significant as it may signify an attempt to achieve authenticated remote code - execution via malicious form objects. If confirmed malicious, this could allow an - attacker to create arbitrary files and registry keys, leading to remote code execution - and potential full system compromise. -search: - '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry - where Registry.registry_path="*\\InProcServer32\\*" Registry.registry_value_data=*\\FORMS\\* - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_inprocserver32_new_outlook_form_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: - False positives are possible if the organization adds new forms - to Outlook via an automated method. Filter by name or path to reduce false positives. +description: The following analytic detects the creation or modification of registry keys associated with new Outlook form installations, potentially indicating exploitation of CVE-2024-21378. It leverages data from the Endpoint.Registry datamodel, focusing on registry paths involving InProcServer32 keys linked to Outlook forms. This activity is significant as it may signify an attempt to achieve authenticated remote code execution via malicious form objects. If confirmed malicious, this could allow an attacker to create arbitrary files and registry keys, leading to remote code execution and potential full system compromise. +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\InProcServer32\\*" Registry.registry_value_data=*\\FORMS\\* by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_inprocserver32_new_outlook_form_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: False positives are possible if the organization adds new forms to Outlook via an automated method. Filter by name or path to reduce false positives. references: - - https://www.netspi.com/blog/technical/red-team-operations/microsoft-outlook-remote-code-execution-cve-2024-21378/ + - https://www.netspi.com/blog/technical/red-team-operations/microsoft-outlook-remote-code-execution-cve-2024-21378/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A registry key associated with a new Outlook form installation was created - or modified. This could indicate exploitation of CVE-2024-21378 on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: A registry key associated with a new Outlook form installation was created or modified. This could indicate exploitation of CVE-2024-21378 on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Outlook RCE CVE-2024-21378 - cve: - - CVE-2024-21378 - asset_type: Endpoint - mitre_attack_id: - - T1566 - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Outlook RCE CVE-2024-21378 + cve: + - CVE-2024-21378 + asset_type: Endpoint + mitre_attack_id: + - T1566 + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/cve-2024-21378/inprocserver32_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/cve-2024-21378/inprocserver32_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_input_capture_using_credential_ui_dll.yml b/detections/endpoint/windows_input_capture_using_credential_ui_dll.yml index b84feae810..9ad400b3b4 100644 --- a/detections/endpoint/windows_input_capture_using_credential_ui_dll.yml +++ b/detections/endpoint/windows_input_capture_using_credential_ui_dll.yml @@ -5,46 +5,30 @@ date: '2025-09-18' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects a process loading the credui.dll or wincredui.dll - module. This detection leverages Sysmon EventCode 7 to identify instances where - these DLLs are loaded by processes outside typical system directories. This activity - is significant because adversaries often abuse these modules to create fake credential - prompts or dump credentials, posing a risk of credential theft. If confirmed malicious, - this activity could allow attackers to harvest user credentials, leading to unauthorized - access and potential lateral movement within the network. +description: The following analytic detects a process loading the credui.dll or wincredui.dll module. This detection leverages Sysmon EventCode 7 to identify instances where these DLLs are loaded by processes outside typical system directories. This activity is significant because adversaries often abuse these modules to create fake credential prompts or dump credentials, posing a risk of credential theft. If confirmed malicious, this activity could allow attackers to harvest user credentials, leading to unauthorized access and potential lateral movement within the network. data_source: -- Sysmon EventID 7 -search: '`sysmon` EventCode=7 (ImageLoaded = "*\\credui.dll" AND OriginalFileName - = "credui.dll") OR (ImageLoaded = "*\\wincredui.dll" AND OriginalFileName = "wincredui.dll") - AND NOT(Image IN("*\\windows\\explorer.exe", "*\\windows\\system32\\*", "*\\windows\\sysWow64\\*", - "*:\\program files*")) | fillnull | stats count min(_time) as firstTime max(_time) - as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name - process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists - service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_input_capture_using_credential_ui_dll_filter`' -how_to_implement: The latest Sysmon TA 3.0 https://splunkbase.splunk.com/app/5709 - will add the ImageLoaded name to the process_name field, allowing this query to - work. Use as an example and implement for other products. -known_false_positives: this module can be loaded by a third party application. Filter - is needed. + - Sysmon EventID 7 +search: '`sysmon` EventCode=7 (ImageLoaded = "*\\credui.dll" AND OriginalFileName = "credui.dll") OR (ImageLoaded = "*\\wincredui.dll" AND OriginalFileName = "wincredui.dll") AND NOT(Image IN("*\\windows\\explorer.exe", "*\\windows\\system32\\*", "*\\windows\\sysWow64\\*", "*:\\program files*")) | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_input_capture_using_credential_ui_dll_filter`' +how_to_implement: The latest Sysmon TA 3.0 https://splunkbase.splunk.com/app/5709 will add the ImageLoaded name to the process_name field, allowing this query to work. Use as an example and implement for other products. +known_false_positives: this module can be loaded by a third party application. Filter is needed. references: -- https://docs.microsoft.com/en-us/windows/win32/api/wincred/nf-wincred-creduipromptforcredentialsa -- https://github.com/redcanaryco/atomic-red-team/blob/f339e7da7d05f6057fdfcdd3742bfcf365fee2a9/atomics/T1056.002/T1056.002.md#atomic-test-2---powershell---prompt-user-for-password + - https://docs.microsoft.com/en-us/windows/win32/api/wincred/nf-wincred-creduipromptforcredentialsa + - https://github.com/redcanaryco/atomic-red-team/blob/f339e7da7d05f6057fdfcdd3742bfcf365fee2a9/atomics/T1056.002/T1056.002.md#atomic-test-2---powershell---prompt-user-for-password tags: - analytic_story: - - Brute Ratel C4 - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1056.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Brute Ratel C4 + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1056.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/iso_version_dll_campaign/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/iso_version_dll_campaign/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_installutil_credential_theft.yml b/detections/endpoint/windows_installutil_credential_theft.yml index 74a46a1f99..486b358efa 100644 --- a/detections/endpoint/windows_installutil_credential_theft.yml +++ b/detections/endpoint/windows_installutil_credential_theft.yml @@ -5,66 +5,46 @@ date: '2025-05-02' author: Michael Haag, Mauricio Velazo, Splunk status: production type: TTP -description: The following analytic detects instances where the Windows InstallUtil.exe - binary loads `vaultcli.dll` and `Samlib.dll`. This detection leverages Sysmon EventCode - 7 to identify these specific DLL loads. This activity is significant because it - can indicate an attempt to execute code that bypasses application control and captures - credentials using tools like Mimikatz. If confirmed malicious, this behavior could - allow an attacker to steal credentials, potentially leading to unauthorized access - and further compromise of the system. +description: The following analytic detects instances where the Windows InstallUtil.exe binary loads `vaultcli.dll` and `Samlib.dll`. This detection leverages Sysmon EventCode 7 to identify these specific DLL loads. This activity is significant because it can indicate an attempt to execute code that bypasses application control and captures credentials using tools like Mimikatz. If confirmed malicious, this behavior could allow an attacker to steal credentials, potentially leading to unauthorized access and further compromise of the system. data_source: -- Sysmon EventID 7 -search: '`sysmon` EventCode=7 process_name=installutil.exe loaded_file_path IN ("*\\samlib.dll", - "*\\vaultcli.dll") | fillnull | stats count min(_time) as firstTime max(_time) as - lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name - process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists - service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_installutil_credential_theft_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and module loads from your endpoints. - If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -known_false_positives: Typically, this will not trigger because, by its very nature, - InstallUtil does not require credentials. Filter as needed. + - Sysmon EventID 7 +search: '`sysmon` EventCode=7 process_name=installutil.exe loaded_file_path IN ("*\\samlib.dll", "*\\vaultcli.dll") | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_installutil_credential_theft_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and module loads from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: Typically, this will not trigger because, by its very nature, InstallUtil does not require credentials. Filter as needed. references: -- https://gist.github.com/xorrior/bbac3919ca2aef8d924bdf3b16cce3d0 + - https://gist.github.com/xorrior/bbac3919ca2aef8d924bdf3b16cce3d0 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of process name [$process_name$] loading a file [$loaded_file$] - was identified on endpoint- [$dest$] to potentially capture credentials in memory. - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: - - field: process_name - type: process_name + message: An instance of process name [$process_name$] loading a file [$loaded_file$] was identified on endpoint- [$dest$] to potentially capture credentials in memory. + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Signed Binary Proxy Execution InstallUtil - asset_type: Endpoint - mitre_attack_id: - - T1218.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Signed Binary Proxy Execution InstallUtil + asset_type: Endpoint + mitre_attack_id: + - T1218.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_installutil_in_non_standard_path.yml b/detections/endpoint/windows_installutil_in_non_standard_path.yml index 0d00fd00a3..7246e9814a 100644 --- a/detections/endpoint/windows_installutil_in_non_standard_path.yml +++ b/detections/endpoint/windows_installutil_in_non_standard_path.yml @@ -5,95 +5,63 @@ date: '2025-05-02' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of InstallUtil.exe from - non-standard paths. It leverages Endpoint Detection and Response (EDR) data, focusing - on process names and original file names outside typical directories. This activity - is significant because InstallUtil.exe is often used by attackers to execute malicious - code or scripts. If confirmed malicious, this behavior could allow an attacker to - bypass security controls, execute arbitrary code, and potentially gain unauthorized - access or persist within the environment. +description: The following analytic detects the execution of InstallUtil.exe from non-standard paths. It leverages Endpoint Detection and Response (EDR) data, focusing on process names and original file names outside typical directories. This activity is significant because InstallUtil.exe is often used by attackers to execute malicious code or scripts. If confirmed malicious, this behavior could allow an attacker to bypass security controls, execute arbitrary code, and potentially gain unauthorized access or persist within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Processes where `process_installutil` NOT (Processes.process_path - IN ("*\\Windows\\ADWS\\*","*\\Windows\\SysWOW64*", "*\\Windows\\system32*", "*\\Windows\\NetworkController\\*", - "*\\Windows\\SystemApps\\*", "*\\WinSxS\\*", "*\\Windows\\Microsoft.NET\\*")) by - Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_installutil_in_non_standard_path_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present and filtering may be required. - Certain utilities will run from non-standard paths based on the third-party application - in use. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where `process_installutil` NOT (Processes.process_path IN ("*\\Windows\\ADWS\\*","*\\Windows\\SysWOW64*", "*\\Windows\\system32*", "*\\Windows\\NetworkController\\*", "*\\Windows\\SystemApps\\*", "*\\WinSxS\\*", "*\\Windows\\Microsoft.NET\\*")) by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_installutil_in_non_standard_path_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present and filtering may be required. Certain utilities will run from non-standard paths based on the third-party application in use. references: -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1036.003/T1036.003.yaml -- https://attack.mitre.org/techniques/T1036/003/ -- https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.004/T1218.004.md + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1036.003/T1036.003.yaml + - https://attack.mitre.org/techniques/T1036/003/ + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.004/T1218.004.md drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ from a non-standard - path was identified on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 49 - - field: dest - type: system - score: 49 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ from a non-standard path was identified on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 49 + - field: dest + type: system + score: 49 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Masquerading - Rename System Utilities - - Ransomware - - Unusual Processes - - Signed Binary Proxy Execution InstallUtil - - Living Off The Land - - Data Destruction - - WhisperGate - asset_type: Endpoint - mitre_attack_id: - - T1036.003 - - T1218.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Masquerading - Rename System Utilities + - Ransomware + - Unusual Processes + - Signed Binary Proxy Execution InstallUtil + - Living Off The Land + - Data Destruction + - WhisperGate + asset_type: Endpoint + mitre_attack_id: + - T1036.003 + - T1218.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon_installutil_path.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon_installutil_path.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_installutil_remote_network_connection.yml b/detections/endpoint/windows_installutil_remote_network_connection.yml index 1310af2a4f..9220dbe8d7 100644 --- a/detections/endpoint/windows_installutil_remote_network_connection.yml +++ b/detections/endpoint/windows_installutil_remote_network_connection.yml @@ -5,121 +5,92 @@ date: '2025-09-09' author: Michael Haag, Splunk status: production type: Anomaly -description: - The following analytic detects the Windows InstallUtil.exe binary making - a remote network connection. It leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process and network telemetry. This activity is significant - because InstallUtil.exe can be exploited to download and execute malicious code, - bypassing application control mechanisms. If confirmed malicious, an attacker could - achieve code execution, potentially leading to further system compromise, data exfiltration, - or lateral movement within the network. Analysts should review the parent process, - network connections, and any associated file modifications to determine the legitimacy - of this activity. +description: The following analytic detects the Windows InstallUtil.exe binary making a remote network connection. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and network telemetry. This activity is significant because InstallUtil.exe can be exploited to download and execute malicious code, bypassing application control mechanisms. If confirmed malicious, an attacker could achieve code execution, potentially leading to further system compromise, data exfiltration, or lateral movement within the network. Analysts should review the parent process, network connections, and any associated file modifications to determine the legitimacy of this activity. data_source: - - Sysmon EventID 1 AND Sysmon EventID 3 - - Cisco Network Visibility Module Flow Data + - Sysmon EventID 1 AND Sysmon EventID 3 + - Cisco Network Visibility Module Flow Data search: | - | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes - where `process_installutil` - by _time span=1h - Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | join process_id dest - [| tstats `security_content_summariesonly` - count FROM datamodel=Network_Traffic.All_Traffic where - All_Traffic.dest_port != 0 - by All_Traffic.action All_Traffic.app All_Traffic.bytes All_Traffic.bytes_in All_Traffic.bytes_out - All_Traffic.dest All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.dvc All_Traffic.protocol - All_Traffic.protocol_version All_Traffic.src All_Traffic.src_ip All_Traffic.src_port - All_Traffic.transport All_Traffic.user All_Traffic.vendor_product All_Traffic.direction All_Traffic.process_id - | `drop_dm_object_name(All_Traffic)` - | rename dest as command_and_control - | rename src as dest] - | table _time user src dest parent_process_name process_name process_path process process_id dest_port command_and_control - | stats count min(_time) as firstTime - max(_time) as lastTime - values(process) as process - values(command_and_control) as command_and_control - by user dest process_name process_id dest_port parent_process_name - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_installutil_remote_network_connection_filter` -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: - Limited false positives should be present as InstallUtil is - not typically used to download remote files. Filter as needed based on Developers - requirements. + | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes + where `process_installutil` + by _time span=1h + Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | join process_id dest + [| tstats `security_content_summariesonly` + count FROM datamodel=Network_Traffic.All_Traffic where + All_Traffic.dest_port != 0 + by All_Traffic.action All_Traffic.app All_Traffic.bytes All_Traffic.bytes_in All_Traffic.bytes_out + All_Traffic.dest All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.dvc All_Traffic.protocol + All_Traffic.protocol_version All_Traffic.src All_Traffic.src_ip All_Traffic.src_port + All_Traffic.transport All_Traffic.user All_Traffic.vendor_product All_Traffic.direction All_Traffic.process_id + | `drop_dm_object_name(All_Traffic)` + | rename dest as command_and_control + | rename src as dest] + | table _time user src dest parent_process_name process_name process_path process process_id dest_port command_and_control + | stats count min(_time) as firstTime + max(_time) as lastTime + values(process) as process + values(command_and_control) as command_and_control + by user dest process_name process_id dest_port parent_process_name + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_installutil_remote_network_connection_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Limited false positives should be present as InstallUtil is not typically used to download remote files. Filter as needed based on Developers requirements. references: - - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.004/T1218.004.md + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.004/T1218.004.md drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ generating a remote download. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ generating a remote download. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Living Off The Land - - Compromised Windows Host - - Signed Binary Proxy Execution InstallUtil - - Cisco Network Visibility Module Analytics - asset_type: Endpoint - mitre_attack_id: - - T1218.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + - Compromised Windows Host + - Signed Binary Proxy Execution InstallUtil + - Cisco Network Visibility Module Analytics + asset_type: Endpoint + mitre_attack_id: + - T1218.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - Sysmon - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata + - name: True Positive Test - Sysmon + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/windows_installutil_uninstall_option.yml b/detections/endpoint/windows_installutil_uninstall_option.yml index ba2ef9c12b..e362b4b5b6 100644 --- a/detections/endpoint/windows_installutil_uninstall_option.yml +++ b/detections/endpoint/windows_installutil_uninstall_option.yml @@ -5,89 +5,57 @@ date: '2025-05-02' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the use of the Windows InstallUtil.exe - binary with the `/u` (uninstall) switch, which can execute code while bypassing - application control. This detection leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process names, parent processes, and command-line executions. - This activity is significant because it can indicate an attempt to execute malicious - code without administrative privileges. If confirmed malicious, an attacker could - achieve code execution, potentially leading to further system compromise or persistence - within the environment. +description: The following analytic detects the use of the Windows InstallUtil.exe binary with the `/u` (uninstall) switch, which can execute code while bypassing application control. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, parent processes, and command-line executions. This activity is significant because it can indicate an attempt to execute malicious code without administrative privileges. If confirmed malicious, an attacker could achieve code execution, potentially leading to further system compromise or persistence within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_installutil` Processes.process - IN ("*/u*", "*uninstall*") NOT (Processes.process IN ("*C:\\WINDOWS\\CCM\\*")) NOT - (Processes.parent_process_name IN ("Microsoft.SharePoint.Migration.ClientInstaller.exe")) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| - `windows_installutil_uninstall_option_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Limited false positives should be present. Filter as needed - by parent process or application. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_installutil` Processes.process IN ("*/u*", "*uninstall*") NOT (Processes.process IN ("*C:\\WINDOWS\\CCM\\*")) NOT (Processes.parent_process_name IN ("Microsoft.SharePoint.Migration.ClientInstaller.exe")) by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_installutil_uninstall_option_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Limited false positives should be present. Filter as needed by parent process or application. references: -- https://evi1cg.me/archives/AppLocker_Bypass_Techniques.html#menu_index_12 -- https://github.com/api0cradle/UltimateAppLockerByPassList/blob/master/md/Installutil.exe.md -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.004/T1218.004.md + - https://evi1cg.me/archives/AppLocker_Bypass_Techniques.html#menu_index_12 + - https://github.com/api0cradle/UltimateAppLockerByPassList/blob/master/md/Installutil.exe.md + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.004/T1218.004.md drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ performing an uninstall. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ performing an uninstall. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Living Off The Land - - Compromised Windows Host - - Signed Binary Proxy Execution InstallUtil - asset_type: Endpoint - mitre_attack_id: - - T1218.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + - Compromised Windows Host + - Signed Binary Proxy Execution InstallUtil + asset_type: Endpoint + mitre_attack_id: + - T1218.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_installutil_url_in_command_line.yml b/detections/endpoint/windows_installutil_url_in_command_line.yml index 8156b18f79..758a533f15 100644 --- a/detections/endpoint/windows_installutil_url_in_command_line.yml +++ b/detections/endpoint/windows_installutil_url_in_command_line.yml @@ -5,104 +5,76 @@ date: '2025-09-09' author: Michael Haag, Splunk status: production type: TTP -description: - The following analytic detects the use of Windows InstallUtil.exe with - an HTTP or HTTPS URL in the command line. This is identified through Endpoint Detection - and Response (EDR) telemetry, focusing on command-line executions containing URLs. - This activity is significant as it may indicate an attempt to download and execute - malicious code, potentially bypassing application control mechanisms. If confirmed - malicious, this could lead to unauthorized code execution, privilege escalation, - or persistent access within the environment. Analysts should review the parent process, - network connections, file modifications, and related processes for further investigation. +description: The following analytic detects the use of Windows InstallUtil.exe with an HTTP or HTTPS URL in the command line. This is identified through Endpoint Detection and Response (EDR) telemetry, focusing on command-line executions containing URLs. This activity is significant as it may indicate an attempt to download and execute malicious code, potentially bypassing application control mechanisms. If confirmed malicious, this could lead to unauthorized code execution, privilege escalation, or persistent access within the environment. Analysts should review the parent process, network connections, file modifications, and related processes for further investigation. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 - - Cisco Network Visibility Module Flow Data + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 + - Cisco Network Visibility Module Flow Data search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - `process_installutil` - Processes.process IN ("*http://*","*https://*") - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_installutil_url_in_command_line_filter` -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: - Limited false positives should be present as InstallUtil is - not typically used to download remote files. Filter as needed based on Developers - requirements. + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where + `process_installutil` + Processes.process IN ("*http://*","*https://*") + by Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_installutil_url_in_command_line_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Limited false positives should be present as InstallUtil is not typically used to download remote files. Filter as needed based on Developers requirements. references: - - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.004/T1218.004.md - - https://gist.github.com/DanielRTeixeira/0fd06ec8f041f34a32bf5623c6dd479d + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.004/T1218.004.md + - https://gist.github.com/DanielRTeixeira/0fd06ec8f041f34a32bf5623c6dd479d drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ passing a URL on the command-line. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ passing a URL on the command-line. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Living Off The Land - - Compromised Windows Host - - Signed Binary Proxy Execution InstallUtil - - Cisco Network Visibility Module Analytics - asset_type: Endpoint - mitre_attack_id: - - T1218.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + - Compromised Windows Host + - Signed Binary Proxy Execution InstallUtil + - Cisco Network Visibility Module Analytics + asset_type: Endpoint + mitre_attack_id: + - T1218.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - Sysmon - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata + - name: True Positive Test - Sysmon + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/windows_iso_lnk_file_creation.yml b/detections/endpoint/windows_iso_lnk_file_creation.yml index 0208a03802..ef880bfd8e 100644 --- a/detections/endpoint/windows_iso_lnk_file_creation.yml +++ b/detections/endpoint/windows_iso_lnk_file_creation.yml @@ -5,61 +5,42 @@ date: '2025-09-18' author: Michael Haag, Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects the creation of .iso.lnk files in the - %USER%\AppData\Local\Temp\\ path, indicating that an ISO file - has been mounted and accessed. This detection leverages the Endpoint.Filesystem - data model, specifically monitoring file creation events in the Windows Recent folder. - This activity is significant as it may indicate the delivery and execution of potentially - malicious payloads via ISO files. If confirmed malicious, this could lead to unauthorized - code execution, data exfiltration, or further system compromise. +description: The following analytic detects the creation of .iso.lnk files in the %USER%\AppData\Local\Temp\\ path, indicating that an ISO file has been mounted and accessed. This detection leverages the Endpoint.Filesystem data model, specifically monitoring file creation events in the Windows Recent folder. This activity is significant as it may indicate the delivery and execution of potentially malicious payloads via ISO files. If confirmed malicious, this could lead to unauthorized code execution, data exfiltration, or further system compromise. data_source: -- Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\Microsoft\\Windows\\Recent\\*") - Filesystem.file_name IN ("*.iso.lnk", "*.img.lnk", "*.vhd.lnk", "*vhdx.lnk") by - Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path - Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | - `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_iso_lnk_file_creation_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: False positives may be high depending on the environment and - consistent use of ISOs mounting. Restrict to servers, or filter out based on commonly - used ISO names. Filter as needed. + - Sysmon EventID 11 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\Microsoft\\Windows\\Recent\\*") Filesystem.file_name IN ("*.iso.lnk", "*.img.lnk", "*.vhd.lnk", "*vhdx.lnk") by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_iso_lnk_file_creation_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: False positives may be high depending on the environment and consistent use of ISOs mounting. Restrict to servers, or filter out based on commonly used ISO names. Filter as needed. references: -- https://www.microsoft.com/security/blog/2021/05/27/new-sophisticated-email-based-attack-from-nobelium/ -- https://github.com/MHaggis/notes/blob/master/utilities/ISOBuilder.ps1 -- https://isc.sans.edu/diary/Recent+AZORult+activity/25120 -- https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html + - https://www.microsoft.com/security/blog/2021/05/27/new-sophisticated-email-based-attack-from-nobelium/ + - https://github.com/MHaggis/notes/blob/master/utilities/ISOBuilder.ps1 + - https://isc.sans.edu/diary/Recent+AZORult+activity/25120 + - https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html tags: - analytic_story: - - Spearphishing Attachments - - Brute Ratel C4 - - AgentTesla - - Qakbot - - IcedID - - Azorult - - Remcos - - Warzone RAT - - Amadey - - Gozi Malware - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1204.001 - - T1566.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Spearphishing Attachments + - Brute Ratel C4 + - AgentTesla + - Qakbot + - IcedID + - Azorult + - Remcos + - Warzone RAT + - Amadey + - Gozi Malware + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1204.001 + - T1566.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556.001/atomic_red_team/iso_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556.001/atomic_red_team/iso_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_kerberos_coercion_via_dns.yml b/detections/endpoint/windows_kerberos_coercion_via_dns.yml index 0b47ecdc7e..99e10b6249 100644 --- a/detections/endpoint/windows_kerberos_coercion_via_dns.yml +++ b/detections/endpoint/windows_kerberos_coercion_via_dns.yml @@ -1,87 +1,70 @@ name: Windows Kerberos Coercion via DNS id: 9029b575-6f6b-4ab1-b660-67b24b7e9c3d -version: 1 -date: '2025-11-12' +version: 2 +date: '2026-02-25' author: Raven Tait, Splunk status: production type: TTP -description: Detects DNS-based Kerberos coercion attacks where adversaries - inject marshaled credential structures into DNS records to spoof SPNs and - redirect authentication such as in CVE-2025-33073. This detection leverages - Windows Security Event Codes 5136, 5137, 4662, looking for DNS events with - specific CREDENTIAL_TARGET_INFORMATION entries. +description: Detects DNS-based Kerberos coercion attacks where adversaries inject marshaled credential structures into DNS records to spoof SPNs and redirect authentication such as in CVE-2025-33073. This detection leverages Windows Security Event Codes 5136, 5137, 4662, looking for DNS events with specific CREDENTIAL_TARGET_INFORMATION entries. data_source: -- Windows Event Log Security 4662 -- Windows Event Log Security 5136 -- Windows Event Log Security 5137 -search: '`wineventlog_security` (((EventCode="5136" OR EventCode="5137") ObjectClass="dnsNode" - ObjectDN="*1UWhRCA*" ObjectDN="*AAAAA*" ObjectDN="*YBAAAA*") OR (EventCode="4662" - AdditionalInfo="*1UWhRCA*" AdditionalInfo="*AAAAA*" AdditionalInfo="*YBAAAA*")) - | eval Object=coalesce(lower(ObjectGUID), trim(AdditionalInfo2, "%{}")) - | eval user=coalesce(SubjectUserName, Caller_User_Name) - | stats min(_time) as firstTime, max(_time) as lastTime - values(EventCode) as event_codes - values(ObjectDN) as dns_record - values(user) as user - values(Computer) as dest - by Object - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_kerberos_coercion_via_dns_filter`' -how_to_implement: To successfully implement this search, you ned to be ingesting Event - codes `4662`, `5136`, `5137`. The Advanced Security Audit policy setting `Audit Directory - Services Changes` within `DS Access` needs to be enabled. For these event codes - to be generated, specific SACLs are required. -known_false_positives: Creating a DNS entry matching this pattern is very unusual in a - production environment. Filter as needed. + - Windows Event Log Security 4662 + - Windows Event Log Security 5136 + - Windows Event Log Security 5137 +search: |- + `wineventlog_security` (((EventCode="5136" OR EventCode="5137") ObjectClass="dnsNode" ObjectDN="*1UWhRCA*" ObjectDN="*AAAAA*" ObjectDN="*YBAAAA*") OR (EventCode="4662" AdditionalInfo="*1UWhRCA*" AdditionalInfo="*AAAAA*" AdditionalInfo="*YBAAAA*")) + | eval Object=coalesce(lower(ObjectGUID), trim(AdditionalInfo2, "%{}")) + | eval user=coalesce(SubjectUserName, Caller_User_Name) + | stats min(_time) as firstTime, max(_time) as lastTime values(EventCode) as event_codes values(ObjectDN) as dns_record values(user) as user values(Computer) as dest + BY Object + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_kerberos_coercion_via_dns_filter` +how_to_implement: To successfully implement this search, you ned to be ingesting Event codes `4662`, `5136`, `5137`. The Advanced Security Audit policy setting `Audit Directory Services Changes` within `DS Access` needs to be enabled. For these event codes to be generated, specific SACLs are required. +known_false_positives: Creating a DNS entry matching this pattern is very unusual in a production environment. Filter as needed. references: -- https://web.archive.org/web/20250617122747/https://www.synacktiv.com/publications/ntlm-reflection-is-dead-long-live-ntlm-reflection-an-in-depth-analysis-of-cve-2025 -- https://www.synacktiv.com/publications/relaying-kerberos-over-smb-using-krbrelayx -- https://www.guidepointsecurity.com/blog/the-birth-and-death-of-loopyticket/ + - https://web.archive.org/web/20250617122747/https://www.synacktiv.com/publications/ntlm-reflection-is-dead-long-live-ntlm-reflection-an-in-depth-analysis-of-cve-2025 + - https://www.synacktiv.com/publications/relaying-kerberos-over-smb-using-krbrelayx + - https://www.guidepointsecurity.com/blog/the-birth-and-death-of-loopyticket/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search Computer = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search Computer = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A possible Kerberos coercion DNS object was created $dest$ - risk_objects: - - field: dest - type: system - score: 74 - - field: user - type: user - score: 74 - threat_objects: [] + message: A possible Kerberos coercion DNS object was created $dest$ + risk_objects: + - field: dest + type: system + score: 74 + - field: user + type: user + score: 74 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - Suspicious DNS Traffic - - Local Privilege Escalation With KrbRelayUp - - Kerberos Coercion with DNS - asset_type: Endpoint - mitre_attack_id: - - T1071.004 - - T1557.001 - - T1187 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: - - CVE-2025-33073 + analytic_story: + - Compromised Windows Host + - Suspicious DNS Traffic + - Local Privilege Escalation With KrbRelayUp + - Kerberos Coercion with DNS + asset_type: Endpoint + mitre_attack_id: + - T1071.004 + - T1557.001 + - T1187 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: + - CVE-2025-33073 tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1071.004/kerberos_coercion/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1071.004/kerberos_coercion/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_kerberos_local_successful_logon.yml b/detections/endpoint/windows_kerberos_local_successful_logon.yml index 6a4067ff06..8c1bd6ad2d 100644 --- a/detections/endpoint/windows_kerberos_local_successful_logon.yml +++ b/detections/endpoint/windows_kerberos_local_successful_logon.yml @@ -1,72 +1,63 @@ name: Windows Kerberos Local Successful Logon id: 8309c3a8-4d34-48ae-ad66-631658214653 -version: 10 -date: '2025-11-06' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies a local successful authentication event - on a Windows endpoint using the Kerberos package. It detects EventCode 4624 with - LogonType 3 and source address 127.0.0.1, indicating a login to the built-in local - Administrator account. This activity is significant as it may suggest a Kerberos - relay attack, a method attackers use to escalate privileges. If confirmed malicious, - this could allow an attacker to gain unauthorized access to sensitive systems, execute - arbitrary code, or create new accounts in Active Directory, leading to potential - system compromise. +description: The following analytic identifies a local successful authentication event on a Windows endpoint using the Kerberos package. It detects EventCode 4624 with LogonType 3 and source address 127.0.0.1, indicating a login to the built-in local Administrator account. This activity is significant as it may suggest a Kerberos relay attack, a method attackers use to escalate privileges. If confirmed malicious, this could allow an attacker to gain unauthorized access to sensitive systems, execute arbitrary code, or create new accounts in Active Directory, leading to potential system compromise. data_source: -- Windows Event Log Security 4624 -search: '`wineventlog_security` EventCode=4624 LogonType=3 AuthenticationPackageName=Kerberos - action=success src=127.0.0.1 | fillnull | stats count min(_time) as firstTime max(_time) as - lastTime by action app authentication_method dest dvc process process_id process_name - process_path signature signature_id src src_port status subject user user_group - vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_kerberos_local_successful_logon_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Windows Security Event Logs with 4624 EventCode enabled. The Windows TA is also - required. -known_false_positives: False positives are possible, filtering may be required to - restrict to workstations vs domain controllers. Filter as needed. + - Windows Event Log Security 4624 +search: |- + `wineventlog_security` EventCode=4624 LogonType=3 AuthenticationPackageName=Kerberos action=success src=127.0.0.1 + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY action app authentication_method + dest dvc process + process_id process_name process_path + signature signature_id src + src_port status subject + user user_group vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_kerberos_local_successful_logon_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4624 EventCode enabled. The Windows TA is also required. +known_false_positives: False positives are possible, filtering may be required to restrict to workstations vs domain controllers. Filter as needed. references: -- https://github.com/Dec0ne/KrbRelayUp + - https://github.com/Dec0ne/KrbRelayUp drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A successful localhost Kerberos authentication event occurred on $dest$, - possibly indicative of Kerberos relay attack. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A successful localhost Kerberos authentication event occurred on $dest$, possibly indicative of Kerberos relay attack. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Local Privilege Escalation With KrbRelayUp - - Active Directory Kerberos Attacks - - Compromised Windows Host - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1558 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Local Privilege Escalation With KrbRelayUp + - Active Directory Kerberos Attacks + - Compromised Windows Host + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1558 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558/windows_kerberos_local_successful_logon/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558/windows_kerberos_local_successful_logon/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_known_abused_dll_created.yml b/detections/endpoint/windows_known_abused_dll_created.yml index c0fd1aac52..129d886de1 100644 --- a/detections/endpoint/windows_known_abused_dll_created.yml +++ b/detections/endpoint/windows_known_abused_dll_created.yml @@ -5,88 +5,53 @@ date: '2025-05-02' author: Steven Dick status: production type: Anomaly -description: The following analytic identifies the creation of Dynamic Link Libraries - (DLLs) with a known history of exploitation in atypical locations. It leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process and - filesystem events. This activity is significant as it may indicate DLL search order - hijacking or sideloading, techniques used by attackers to execute arbitrary code, - maintain persistence, or escalate privileges. If confirmed malicious, this activity - could allow attackers to blend in with legitimate operations, posing a severe threat - to system integrity and security. +description: The following analytic identifies the creation of Dynamic Link Libraries (DLLs) with a known history of exploitation in atypical locations. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and filesystem events. This activity is significant as it may indicate DLL search order hijacking or sideloading, techniques used by attackers to execute arbitrary code, maintain persistence, or escalate privileges. If confirmed malicious, this activity could allow attackers to blend in with legitimate operations, posing a severe threat to system integrity and security. data_source: -- Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem - where Filesystem.file_path IN ("*\\users\\*","*\\Windows\Temp\\*","*\\programdata\\*") - Filesystem.file_name="*.dll" by Filesystem.action Filesystem.dest Filesystem.file_access_time - Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name - Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid - Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` - | lookup hijacklibs_loaded library AS file_name OUTPUT islibrary, ttp, comment as - desc | lookup hijacklibs_loaded library AS file_name excludes as file_path OUTPUT - islibrary as excluded | search islibrary = TRUE AND excluded != TRUE | where isnotnull(file_name) - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_known_abused_dll_created_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` and `Filesystem` - nodes of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) - to normalize the field names and speed up the data modeling process. -known_false_positives: This analytic may flag instances where DLLs are loaded by user - mode programs for entirely legitimate and benign purposes. It is important for users - to be aware that false positives are not only possible but likely, and that careful - tuning of this analytic is necessary to distinguish between malicious activity and - normal, everyday operations of applications. This may involve adjusting thresholds, - whitelisting known good software, or incorporating additional context from other - security tools and logs to reduce the rate of false positives. + - Sysmon EventID 11 +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\users\\*","*\\Windows\Temp\\*","*\\programdata\\*") Filesystem.file_name="*.dll" by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | lookup hijacklibs_loaded library AS file_name OUTPUT islibrary, ttp, comment as desc | lookup hijacklibs_loaded library AS file_name excludes as file_path OUTPUT islibrary as excluded | search islibrary = TRUE AND excluded != TRUE | where isnotnull(file_name) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_known_abused_dll_created_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` and `Filesystem` nodes of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: This analytic may flag instances where DLLs are loaded by user mode programs for entirely legitimate and benign purposes. It is important for users to be aware that false positives are not only possible but likely, and that careful tuning of this analytic is necessary to distinguish between malicious activity and normal, everyday operations of applications. This may involve adjusting thresholds, whitelisting known good software, or incorporating additional context from other security tools and logs to reduce the rate of false positives. references: -- https://attack.mitre.org/techniques/T1574/002/ -- https://hijacklibs.net/api/ -- https://wietze.github.io/blog/hijacking-dlls-in-windows -- https://github.com/olafhartong/sysmon-modular/pull/195/files + - https://attack.mitre.org/techniques/T1574/002/ + - https://hijacklibs.net/api/ + - https://wietze.github.io/blog/hijacking-dlls-in-windows + - https://github.com/olafhartong/sysmon-modular/pull/195/files drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The file [$file_name$] was written to an unusual location on [$dest$]. - risk_objects: - - field: dest - type: system - score: 10 - - field: user - type: user - score: 10 - threat_objects: - - field: file_name - type: file_name + message: The file [$file_name$] was written to an unusual location on [$dest$]. + risk_objects: + - field: dest + type: system + score: 10 + - field: user + type: user + score: 10 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - Windows Defense Evasion Tactics - - Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1574.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1574.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/hijacklibs/hijacklibs_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/hijacklibs/hijacklibs_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_known_abused_dll_loaded_suspiciously.yml b/detections/endpoint/windows_known_abused_dll_loaded_suspiciously.yml index acd66a47b5..a8339bdd71 100644 --- a/detections/endpoint/windows_known_abused_dll_loaded_suspiciously.yml +++ b/detections/endpoint/windows_known_abused_dll_loaded_suspiciously.yml @@ -5,75 +5,49 @@ date: '2026-02-09' author: Steven Dick status: production type: TTP -description: The following analytic detects when DLLs with known abuse history are - loaded from an unusual location. This activity may represent an attacker performing - a DLL search order or sideload hijacking technique. These techniques are used to - gain persistence as well as elevate privileges on the target system. This detection - relies on Sysmon EID7 and is compatible with all Officla Sysmon TA versions. +description: The following analytic detects when DLLs with known abuse history are loaded from an unusual location. This activity may represent an attacker performing a DLL search order or sideload hijacking technique. These techniques are used to gain persistence as well as elevate privileges on the target system. This detection relies on Sysmon EID7 and is compatible with all Officla Sysmon TA versions. data_source: -- Sysmon EventID 7 -search: '`sysmon` ImageLoaded EventCode=7 NOT ImageLoaded IN ("*\\Program Files*","*\\system32\\*", - "*\\syswow64\\*","*\\winsxs\\*","*\\wbem\\*") | stats count min(_time) as firstTime - max(_time) as lastTime by Image ImageLoaded dest process_exec process_guid process_hash - process_id process_path service_dll_signature_exists service_dll_signature_verified - signature signature_id user_id vendor_product loaded_file | rename Image as process - | eval process_name = case(isnotnull(process),replace(process,"(.*\\\)(?=.*(\.\w*)$|(\w+)$)","")), - loaded_file_path = case(isnotnull(loaded_file), replace(loaded_file, "(:[\w\. ]+)", - "")), loaded_file = case(isnotnull(loaded_file),replace(loaded_file,"(.*\\\)(?=.*(\.\w*)$|(\w+)$)","")), - user = case(NOT user IN ("-"), replace(user, "(.*)\\\(.+)$","\2")) | lookup hijacklibs_loaded - library AS loaded_file OUTPUT islibrary comment as desc | lookup hijacklibs_loaded - library AS loaded_file excludes as loaded_file_path OUTPUT islibrary as excluded - | search islibrary = TRUE AND excluded = false | stats count min(_time) as firstTime - max(_time) as lastTime by dest loaded_file loaded_file_path process process_exec - process_guid process_hash process_id process_name process_path service_dll_signature_exists - service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_known_abused_dll_loaded_suspiciously_filter`' -how_to_implement: The following analytic requires Sysmon operational logs to be imported, - with EID7 being mapped to the process_name field. Modify the sysmon macro as needed - to match the sourcetype or add index. + - Sysmon EventID 7 +search: '`sysmon` ImageLoaded EventCode=7 NOT ImageLoaded IN ("*\\Program Files*","*\\system32\\*", "*\\syswow64\\*","*\\winsxs\\*","*\\wbem\\*") | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest process_exec process_guid process_hash process_id process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product loaded_file | rename Image as process | eval process_name = case(isnotnull(process),replace(process,"(.*\\\)(?=.*(\.\w*)$|(\w+)$)","")), loaded_file_path = case(isnotnull(loaded_file), replace(loaded_file, "(:[\w\. ]+)", "")), loaded_file = case(isnotnull(loaded_file),replace(loaded_file,"(.*\\\)(?=.*(\.\w*)$|(\w+)$)","")), user = case(NOT user IN ("-"), replace(user, "(.*)\\\(.+)$","\2")) | lookup hijacklibs_loaded library AS loaded_file OUTPUT islibrary comment as desc | lookup hijacklibs_loaded library AS loaded_file excludes as loaded_file_path OUTPUT islibrary as excluded | search islibrary = TRUE AND excluded = false | stats count min(_time) as firstTime max(_time) as lastTime by dest loaded_file loaded_file_path process process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_known_abused_dll_loaded_suspiciously_filter`' +how_to_implement: The following analytic requires Sysmon operational logs to be imported, with EID7 being mapped to the process_name field. Modify the sysmon macro as needed to match the sourcetype or add index. known_false_positives: DLLs being loaded by user mode programs for legitimate reasons. references: -- https://attack.mitre.org/techniques/T1574/002/ -- https://hijacklibs.net/api/ -- https://wietze.github.io/blog/hijacking-dlls-in-windows -- https://github.com/olafhartong/sysmon-modular/pull/195/files + - https://attack.mitre.org/techniques/T1574/002/ + - https://hijacklibs.net/api/ + - https://wietze.github.io/blog/hijacking-dlls-in-windows + - https://github.com/olafhartong/sysmon-modular/pull/195/files drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The module [$loaded_file$] was loaded from an unusual location. - risk_objects: - - field: dest - type: system - score: 10 - threat_objects: [] + message: The module [$loaded_file$] was loaded from an unusual location. + risk_objects: + - field: dest + type: system + score: 10 + threat_objects: [] tags: - analytic_story: - - SolarWinds WHD RCE Post Exploitation - - Windows Defense Evasion Tactics - - Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1574.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SolarWinds WHD RCE Post Exploitation + - Windows Defense Evasion Tactics + - Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1574.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/hijacklibs/hijacklibs_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/hijacklibs/hijacklibs_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_known_graphicalproton_loaded_modules.yml b/detections/endpoint/windows_known_graphicalproton_loaded_modules.yml index 8aff91f5c6..bcac9378a2 100644 --- a/detections/endpoint/windows_known_graphicalproton_loaded_modules.yml +++ b/detections/endpoint/windows_known_graphicalproton_loaded_modules.yml @@ -6,71 +6,45 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: - - Sysmon EventID 7 -description: - The following analytic detects the loading of DLL modules associated - with the GraphicalProton backdoor implant, commonly used by SVR in targeted attacks. - It leverages Sysmon EventCode 7 to identify specific DLLs loaded by processes. This - activity is significant as it may indicate the presence of a sophisticated backdoor, - warranting immediate investigation. If confirmed malicious, the attacker could gain - persistent access to the compromised host, potentially leading to further exploitation - and data exfiltration. -search: - '`sysmon` EventCode=7 ImageLoaded IN ("*\\AclNumsInvertHost.dll", "*\\ModeBitmapNumericAnimate.dll", - "*\\UnregisterAncestorAppendAuto.dll", "*\\DeregisterSeekUsers.dll", "*\\ScrollbarHandleGet.dll", - "*\\PerformanceCaptionApi.dll", "*\\WowIcmpRemoveReg.dll", "*\\BlendMonitorStringBuild.dll", - "*\\HandleFrequencyAll.dll", "*\\HardSwapColor.dll", "*\\LengthInMemoryActivate.dll", - "*\\ParametersNamesPopup.dll", "*\\ModeFolderSignMove.dll", "*\\ChildPaletteConnected.dll", - "*\\AddressResourcesSpec.dll") | fillnull | stats count min(_time) as firstTime - max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name - process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists - service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_known_graphicalproton_loaded_modules_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs with the process name and imageloaded executions from your endpoints. If you - are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + - Sysmon EventID 7 +description: The following analytic detects the loading of DLL modules associated with the GraphicalProton backdoor implant, commonly used by SVR in targeted attacks. It leverages Sysmon EventCode 7 to identify specific DLLs loaded by processes. This activity is significant as it may indicate the presence of a sophisticated backdoor, warranting immediate investigation. If confirmed malicious, the attacker could gain persistent access to the compromised host, potentially leading to further exploitation and data exfiltration. +search: '`sysmon` EventCode=7 ImageLoaded IN ("*\\AclNumsInvertHost.dll", "*\\ModeBitmapNumericAnimate.dll", "*\\UnregisterAncestorAppendAuto.dll", "*\\DeregisterSeekUsers.dll", "*\\ScrollbarHandleGet.dll", "*\\PerformanceCaptionApi.dll", "*\\WowIcmpRemoveReg.dll", "*\\BlendMonitorStringBuild.dll", "*\\HandleFrequencyAll.dll", "*\\HardSwapColor.dll", "*\\LengthInMemoryActivate.dll", "*\\ParametersNamesPopup.dll", "*\\ModeFolderSignMove.dll", "*\\ChildPaletteConnected.dll", "*\\AddressResourcesSpec.dll") | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_known_graphicalproton_loaded_modules_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: No false positives have been identified at this time. references: - - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Known GraphicalProton backdoor Loaded Modules on $dest$. - risk_objects: - - field: dest - type: system - score: 36 - threat_objects: [] + message: Windows Known GraphicalProton backdoor Loaded Modules on $dest$. + risk_objects: + - field: dest + type: system + score: 36 + threat_objects: [] tags: - analytic_story: - - Hellcat Ransomware - - CISA AA23-347A - - Water Gamayun - asset_type: Endpoint - mitre_attack_id: - - T1574.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Hellcat Ransomware + - CISA AA23-347A + - Water Gamayun + asset_type: Endpoint + mitre_attack_id: + - T1574.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/svr_loaded_modules/loaded_module_svr.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/svr_loaded_modules/loaded_module_svr.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_krbrelayup_service_creation.yml b/detections/endpoint/windows_krbrelayup_service_creation.yml index be25ef418d..0d505ad94c 100644 --- a/detections/endpoint/windows_krbrelayup_service_creation.yml +++ b/detections/endpoint/windows_krbrelayup_service_creation.yml @@ -1,65 +1,56 @@ name: Windows KrbRelayUp Service Creation id: e40ef542-8241-4419-9af4-6324582ea60a -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the creation of a service with the default - name "KrbSCM" associated with the KrbRelayUp tool. It leverages Windows System Event - Logs, specifically EventCode 7045, to identify this activity. This behavior is significant - as KrbRelayUp is a known tool used for privilege escalation attacks. If confirmed - malicious, this activity could allow an attacker to escalate privileges, potentially - gaining unauthorized access to sensitive systems and data. +description: The following analytic detects the creation of a service with the default name "KrbSCM" associated with the KrbRelayUp tool. It leverages Windows System Event Logs, specifically EventCode 7045, to identify this activity. This behavior is significant as KrbRelayUp is a known tool used for privilege escalation attacks. If confirmed malicious, this activity could allow an attacker to escalate privileges, potentially gaining unauthorized access to sensitive systems and data. data_source: -- Windows Event Log System 7045 -search: '`wineventlog_system` EventCode=7045 ServiceName IN ("KrbSCM") | stats count - min(_time) as firstTime max(_time) as lastTime by dest EventCode ImagePath ServiceName - StartType ServiceType | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_krbrelayup_service_creation_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Windows System Event Logs with 7045 EventCode enabled. The Windows TA is also required. -known_false_positives: False positives should be limited as this is specific to KrbRelayUp - based attack. Filter as needed. + - Windows Event Log System 7045 +search: |- + `wineventlog_system` EventCode=7045 ServiceName IN ("KrbSCM") + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest EventCode ImagePath + ServiceName StartType ServiceType + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_krbrelayup_service_creation_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Windows System Event Logs with 7045 EventCode enabled. The Windows TA is also required. +known_false_positives: False positives should be limited as this is specific to KrbRelayUp based attack. Filter as needed. references: -- https://github.com/Dec0ne/KrbRelayUp + - https://github.com/Dec0ne/KrbRelayUp drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A service was created on $dest$, related to KrbRelayUp. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A service was created on $dest$, related to KrbRelayUp. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Local Privilege Escalation With KrbRelayUp - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1543.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Local Privilege Escalation With KrbRelayUp + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1543.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/windows_krbrelayup_service_creation/windows-xml.log - source: XmlWinEventLog:System - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/windows_krbrelayup_service_creation/windows-xml.log + source: XmlWinEventLog:System + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_large_number_of_computer_service_tickets_requested.yml b/detections/endpoint/windows_large_number_of_computer_service_tickets_requested.yml index b8b311928b..08bcd2770b 100644 --- a/detections/endpoint/windows_large_number_of_computer_service_tickets_requested.yml +++ b/detections/endpoint/windows_large_number_of_computer_service_tickets_requested.yml @@ -1,72 +1,58 @@ name: Windows Large Number of Computer Service Tickets Requested id: 386ad394-c9a7-4b4f-b66f-586252de20f0 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Mauricio Velazco, Splunk type: Anomaly status: production data_source: -- Windows Event Log Security 4769 -description: The following analytic detects a high volume of Kerberos service ticket - requests, specifically more than 30, from a single source within a 5-minute window. - It leverages Event ID 4769, which logs when a Kerberos service ticket is requested, - focusing on requests with computer names as the Service Name. This behavior is significant - as it may indicate malicious activities such as lateral movement, malware staging, - or reconnaissance. If confirmed malicious, an attacker could gain unauthorized access - to multiple endpoints, potentially compromising the entire network. -search: '`wineventlog_security` EventCode=4769 ServiceName="*$" TargetUserName!="*$" - | bucket span=5m _time | stats dc(ServiceName) AS unique_targets values(ServiceName) - as host_targets values(dest) as dest by _time, IpAddress, TargetUserName | where - unique_targets > 30 | `windows_large_number_of_computer_service_tickets_requested_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Domain Controller and Kerberos events. The Advanced Security Audit policy setting - `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. -known_false_positives: An single endpoint requesting a large number of kerberos service - tickets is not common behavior. Possible false positive scenarios include but are - not limited to vulnerability scanners, administration systems and missconfigured - systems. + - Windows Event Log Security 4769 +description: The following analytic detects a high volume of Kerberos service ticket requests, specifically more than 30, from a single source within a 5-minute window. It leverages Event ID 4769, which logs when a Kerberos service ticket is requested, focusing on requests with computer names as the Service Name. This behavior is significant as it may indicate malicious activities such as lateral movement, malware staging, or reconnaissance. If confirmed malicious, an attacker could gain unauthorized access to multiple endpoints, potentially compromising the entire network. +search: |- + `wineventlog_security` EventCode=4769 ServiceName="*$" TargetUserName!="*$" + | bucket span=5m _time + | stats dc(ServiceName) AS unique_targets values(ServiceName) as host_targets values(dest) as dest + BY _time, IpAddress, TargetUserName + | where unique_targets > 30 + | `windows_large_number_of_computer_service_tickets_requested_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. +known_false_positives: An single endpoint requesting a large number of kerberos service tickets is not common behavior. Possible false positive scenarios include but are not limited to vulnerability scanners, administration systems and missconfigured systems. references: -- https://thedfirreport.com/2023/01/23/sharefinder-how-threat-actors-discover-file-shares/ -- https://attack.mitre.org/techniques/T1135/ -- https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4769 + - https://thedfirreport.com/2023/01/23/sharefinder-how-threat-actors-discover-file-shares/ + - https://attack.mitre.org/techniques/T1135/ + - https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4769 drilldown_searches: -- name: View the detection results for - "$IpAddress$" - search: '%original_detection_search% | search IpAddress = "$IpAddress$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$IpAddress$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$IpAddress$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$IpAddress$" + search: '%original_detection_search% | search IpAddress = "$IpAddress$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$IpAddress$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$IpAddress$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A large number of kerberos computer service tickets were requested by $IpAddress$ - within 5 minutes. - risk_objects: - - field: IpAddress - type: system - score: 30 - threat_objects: [] + message: A large number of kerberos computer service tickets were requested by $IpAddress$ within 5 minutes. + risk_objects: + - field: IpAddress + type: system + score: 30 + threat_objects: [] tags: - analytic_story: - - Active Directory Privilege Escalation - - Active Directory Lateral Movement - asset_type: Endpoint - mitre_attack_id: - - T1135 - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Privilege Escalation + - Active Directory Lateral Movement + asset_type: Endpoint + mitre_attack_id: + - T1135 + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1135/large_number_computer_service_tickets/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1135/large_number_computer_service_tickets/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ldifde_directory_object_behavior.yml b/detections/endpoint/windows_ldifde_directory_object_behavior.yml index 791529f2b4..8f0a6f8d7b 100644 --- a/detections/endpoint/windows_ldifde_directory_object_behavior.yml +++ b/detections/endpoint/windows_ldifde_directory_object_behavior.yml @@ -1,92 +1,76 @@ name: Windows Ldifde Directory Object Behavior id: 35cd29ca-f08c-4489-8815-f715c45460d3 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic identifies the use of Ldifde.exe, a command-line - utility for creating, modifying, or deleting LDAP directory objects. This detection - leverages data from Endpoint Detection and Response (EDR) agents, focusing on process - execution and command-line arguments. Monitoring Ldifde.exe is significant because - it can be used by attackers to manipulate directory objects, potentially leading - to unauthorized changes or data exfiltration. If confirmed malicious, this activity - could allow an attacker to gain control over directory services, escalate privileges, - or access sensitive information within the network. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=ldifde.exe - Processes.process IN ("*-i *", "*-f *") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_ldifde_directory_object_behavior_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic identifies the use of Ldifde.exe, a command-line utility for creating, modifying, or deleting LDAP directory objects. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution and command-line arguments. Monitoring Ldifde.exe is significant because it can be used by attackers to manipulate directory objects, potentially leading to unauthorized changes or data exfiltration. If confirmed malicious, this activity could allow an attacker to gain control over directory services, escalate privileges, or access sensitive information within the network. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=ldifde.exe Processes.process IN ("*-i *", "*-f *") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_ldifde_directory_object_behavior_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives may be present, filter as needed. references: -- https://lolbas-project.github.io/lolbas/Binaries/Ldifde/ -- https://media.defense.gov/2023/May/24/2003229517/-1/-1/0/CSA_Living_off_the_Land.PDF -- https://twitter.com/0gtweet/status/1564968845726580736?s=20 -- https://strontic.github.io/xcyclopedia/library/ldifde.exe-45D28FB47E9B6ACC5DCA9FDA3E790210.html + - https://lolbas-project.github.io/lolbas/Binaries/Ldifde/ + - https://media.defense.gov/2023/May/24/2003229517/-1/-1/0/CSA_Living_off_the_Land.PDF + - https://twitter.com/0gtweet/status/1564968845726580736?s=20 + - https://strontic.github.io/xcyclopedia/library/ldifde.exe-45D28FB47E9B6ACC5DCA9FDA3E790210.html drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ utilizing ldifde on a domain controller. - risk_objects: - - field: user - type: user - score: 40 - - field: dest - type: system - score: 40 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ utilizing ldifde on a domain controller. + risk_objects: + - field: user + type: user + score: 40 + - field: dest + type: system + score: 40 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Volt Typhoon - asset_type: Endpoint - atomic_guid: - - 22cf8cb9-adb1-4e8c-80ca-7c723dfc8784 - mitre_attack_id: - - T1105 - - T1069.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Volt Typhoon + asset_type: Endpoint + atomic_guid: + - 22cf8cb9-adb1-4e8c-80ca-7c723dfc8784 + mitre_attack_id: + - T1105 + - T1069.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/ldifde_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/ldifde_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_linked_policies_in_adsi_discovery.yml b/detections/endpoint/windows_linked_policies_in_adsi_discovery.yml index af3a265d83..865cbdcfc9 100644 --- a/detections/endpoint/windows_linked_policies_in_adsi_discovery.yml +++ b/detections/endpoint/windows_linked_policies_in_adsi_discovery.yml @@ -1,69 +1,62 @@ name: Windows Linked Policies In ADSI Discovery id: 510ea428-4731-4d2f-8829-a28293e427aa -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the use of the `[Adsisearcher]` type accelerator - in PowerShell Script Block Logging (EventCode=4104) to query Active Directory for - domain organizational units. This detection leverages PowerShell operational logs - to identify script blocks containing `[adsisearcher]`, `objectcategory=organizationalunit`, - and `findAll()`. This activity is significant as it indicates potential reconnaissance - efforts by adversaries to gain situational awareness of the domain structure. If - confirmed malicious, this could lead to further exploitation, such as privilege - escalation or lateral movement within the network. +description: The following analytic detects the use of the `[Adsisearcher]` type accelerator in PowerShell Script Block Logging (EventCode=4104) to query Active Directory for domain organizational units. This detection leverages PowerShell operational logs to identify script blocks containing `[adsisearcher]`, `objectcategory=organizationalunit`, and `findAll()`. This activity is significant as it indicates potential reconnaissance efforts by adversaries to gain situational awareness of the domain structure. If confirmed malicious, this could lead to further exploitation, such as privilege escalation or lateral movement within the network. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 ScriptBlockText = "*[adsisearcher]*" ScriptBlockText - = "*objectcategory=organizationalunit*" ScriptBlockText = "*findAll()*" | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_linked_policies_in_adsi_discovery_filter`' -how_to_implement: The following Hunting analytic requires PowerShell operational logs - to be imported. Modify the powershell macro as needed to match the sourcetype or - add index. This analytic is specific to 4104, or PowerShell Script Block Logging. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*[adsisearcher]*" ScriptBlockText = "*objectcategory=organizationalunit*" ScriptBlockText = "*findAll()*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_linked_policies_in_adsi_discovery_filter` +how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ -- https://medium.com/@pentesttas/discover-hidden-gpo-s-on-active-directory-using-ps-adsi-a284b6814c81 + - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ + - https://medium.com/@pentesttas/discover-hidden-gpo-s-on-active-directory-using-ps-adsi-a284b6814c81 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows PowerShell [Adsisearcher] was used user enumeration on $user_id$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: Windows PowerShell [Adsisearcher] was used user enumeration on $user_id$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Data Destruction - - Active Directory Discovery - - Industroyer2 - asset_type: Endpoint - mitre_attack_id: - - T1087.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Destruction + - Active Directory Discovery + - Industroyer2 + asset_type: Endpoint + mitre_attack_id: + - T1087.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/adsi_discovery/windows-powershell-xml2.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/adsi_discovery/windows-powershell-xml2.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_list_env_variables_via_set_command_from_uncommon_parent.yml b/detections/endpoint/windows_list_env_variables_via_set_command_from_uncommon_parent.yml index 338d8e790e..a6d882b75e 100644 --- a/detections/endpoint/windows_list_env_variables_via_set_command_from_uncommon_parent.yml +++ b/detections/endpoint/windows_list_env_variables_via_set_command_from_uncommon_parent.yml @@ -1,82 +1,65 @@ name: Windows List ENV Variables Via SET Command From Uncommon Parent id: aec157f4-8783-4584-aca6-754c4dc7fba9 -version: 3 -date: '2025-05-02' +version: 4 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies a suspicious process command line fetching - environment variables using the cmd.exe "set" command, with a non-shell parent process. - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on - command-line executions and parent process names. This activity could be significant - as it is commonly associated with malware like Qakbot, which uses this technique - to gather system information. If confirmed malicious, this behavior could indicate - that the parent process has been compromised, potentially allowing attackers to - execute arbitrary commands, escalate privileges, or persist within the environment. +description: The following analytic identifies a suspicious process command line fetching environment variables using the cmd.exe "set" command, with a non-shell parent process. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and parent process names. This activity could be significant as it is commonly associated with malware like Qakbot, which uses this technique to gather system information. If confirmed malicious, this behavior could indicate that the parent process has been compromised, potentially allowing attackers to execute arbitrary commands, escalate privileges, or persist within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name="cmd.exe" - Processes.process IN ("*/c set", "*/c \"set") AND NOT Processes.parent_process_name - IN ("cmd.exe", "explorer.exe", "powershell*" "pwsh.exe") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_list_env_variables_via_set_command_from_uncommon_parent_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: shell process that are not included in this search may cause - False positive. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name="cmd.exe" Processes.process IN ("*/c set", "*/c \"set") + AND + NOT Processes.parent_process_name IN ("cmd.exe", "explorer.exe", "powershell*" "pwsh.exe") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_list_env_variables_via_set_command_from_uncommon_parent_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: shell process that are not included in this search may cause False positive. Filter as needed. references: -- https://twitter.com/pr0xylife/status/1585612370441031680?s=46&t=Dc3CJi4AnM-8rNoacLbScg + - https://twitter.com/pr0xylife/status/1585612370441031680?s=46&t=Dc3CJi4AnM-8rNoacLbScg drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: non-shell parent process has a child process $process_name$ with a commandline - $process$ to fetch env variables on $dest$ - risk_objects: - - field: dest - type: system - score: 56 - threat_objects: [] + message: non-shell parent process has a child process $process_name$ with a commandline $process$ to fetch env variables on $dest$ + risk_objects: + - field: dest + type: system + score: 56 + threat_objects: [] tags: - analytic_story: - - Qakbot - asset_type: Endpoint - mitre_attack_id: - - T1055 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Qakbot + asset_type: Endpoint + mitre_attack_id: + - T1055 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot_wermgr/sysmon_wermgr.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot_wermgr/sysmon_wermgr.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_local_administrator_credential_stuffing.yml b/detections/endpoint/windows_local_administrator_credential_stuffing.yml index b155fe8494..91a4221610 100644 --- a/detections/endpoint/windows_local_administrator_credential_stuffing.yml +++ b/detections/endpoint/windows_local_administrator_credential_stuffing.yml @@ -1,78 +1,66 @@ name: Windows Local Administrator Credential Stuffing id: 09555511-aca6-484a-b6ab-72cd03d73c34 -version: 9 -date: '2025-10-14' +version: 10 +date: '2026-02-25' author: Mauricio Velazco, Splunk type: TTP status: production data_source: -- Windows Event Log Security 4624 -- Windows Event Log Security 4625 -description: The following analytic detects attempts to authenticate using the built-in - local Administrator account across more than 30 endpoints within a 5-minute window. - It leverages Windows Event Logs, specifically events 4625 and 4624, to identify - this behavior. This activity is significant as it may indicate an adversary attempting - to validate stolen local credentials across multiple hosts, potentially leading - to privilege escalation. If confirmed malicious, this could allow the attacker to - gain widespread access and control over numerous systems within the network, posing - a severe security risk. -search: '`wineventlog_security` EventCode=4625 OR EventCode=4624 Logon_Type=3 TargetUserName=Administrator - | bucket span=5m _time | stats dc(Computer) AS unique_targets values(Computer) as - host_targets values(dest) as dest values(src) as src values(user) as user by _time, - IpAddress, TargetUserName, EventCode, action, app, authentication_method, signature, - signature_id | where unique_targets > 30 | `windows_local_administrator_credential_stuffing_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Windows Event Logs from domain controllers as well as member servers and workstations. - The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs - to be enabled. -known_false_positives: Vulnerability scanners or system administration tools may also - trigger this detection. Filter as needed. + - Windows Event Log Security 4624 + - Windows Event Log Security 4625 +description: The following analytic detects attempts to authenticate using the built-in local Administrator account across more than 30 endpoints within a 5-minute window. It leverages Windows Event Logs, specifically events 4625 and 4624, to identify this behavior. This activity is significant as it may indicate an adversary attempting to validate stolen local credentials across multiple hosts, potentially leading to privilege escalation. If confirmed malicious, this could allow the attacker to gain widespread access and control over numerous systems within the network, posing a severe security risk. +search: |- + `wineventlog_security` EventCode=4625 OR EventCode=4624 Logon_Type=3 TargetUserName=Administrator + | bucket span=5m _time + | stats dc(Computer) AS unique_targets values(Computer) as host_targets values(dest) as dest values(src) as src values(user) as user + BY _time, IpAddress, TargetUserName, + EventCode, action, app, + authentication_method, signature, signature_id + | where unique_targets > 30 + | `windows_local_administrator_credential_stuffing_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled. +known_false_positives: Vulnerability scanners or system administration tools may also trigger this detection. Filter as needed. references: -- https://attack.mitre.org/techniques/T1110/004/ -- https://attack.mitre.org/techniques/T1110/ -- https://www.blackhillsinfosec.com/wide-spread-local-admin-testing/ -- https://www.pentestpartners.com/security-blog/admin-password-re-use-dont-do-it/ -- https://www.praetorian.com/blog/microsofts-local-administrator-password-solution-laps/ -- https://wiki.porchetta.industries/smb-protocol/password-spraying + - https://attack.mitre.org/techniques/T1110/004/ + - https://attack.mitre.org/techniques/T1110/ + - https://www.blackhillsinfosec.com/wide-spread-local-admin-testing/ + - https://www.pentestpartners.com/security-blog/admin-password-re-use-dont-do-it/ + - https://www.praetorian.com/blog/microsofts-local-administrator-password-solution-laps/ + - https://wiki.porchetta.industries/smb-protocol/password-spraying drilldown_searches: -- name: View the detection results for - "$host_targets$" - search: '%original_detection_search% | search host_targets = "$host_targets$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$host_targets$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host_targets$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$host_targets$" + search: '%original_detection_search% | search host_targets = "$host_targets$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$host_targets$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host_targets$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Local Administrator credential stuffing attack coming from $IpAddress$ - risk_objects: - - field: host_targets - type: system - score: 56 - threat_objects: - - field: IpAddress - type: ip_address + message: Local Administrator credential stuffing attack coming from $IpAddress$ + risk_objects: + - field: host_targets + type: system + score: 56 + threat_objects: + - field: IpAddress + type: ip_address tags: - analytic_story: - - Active Directory Privilege Escalation - - Active Directory Lateral Movement - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1110.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Privilege Escalation + - Active Directory Lateral Movement + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1110.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.004/local_administrator_cred_stuffing/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.004/local_administrator_cred_stuffing/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_local_llm_framework_execution.yml b/detections/endpoint/windows_local_llm_framework_execution.yml index f4f12f6847..216cc39676 100644 --- a/detections/endpoint/windows_local_llm_framework_execution.yml +++ b/detections/endpoint/windows_local_llm_framework_execution.yml @@ -6,145 +6,132 @@ author: Rod Soto, Splunk status: production type: Hunting description: | - The following analytic detects execution of unauthorized local LLM frameworks (Ollama, LM Studio, GPT4All, Jan, llama.cpp, KoboldCPP, Oobabooga, NutStudio) and Python-based AI/ML libraries (HuggingFace Transformers, LangChain) on Windows endpoints by leveraging process creation events. - It identifies cases where known LLM framework executables are launched or command-line arguments reference AI/ML libraries. - This activity is significant as it may indicate shadow AI deployments, unauthorized model inference operations, or potential data exfiltration through local AI systems. - If confirmed malicious, this could lead to unauthorized access to sensitive data, intellectual property theft, or circumvention of organizational AI governance policies. + The following analytic detects execution of unauthorized local LLM frameworks (Ollama, LM Studio, GPT4All, Jan, llama.cpp, KoboldCPP, Oobabooga, NutStudio) and Python-based AI/ML libraries (HuggingFace Transformers, LangChain) on Windows endpoints by leveraging process creation events. + It identifies cases where known LLM framework executables are launched or command-line arguments reference AI/ML libraries. + This activity is significant as it may indicate shadow AI deployments, unauthorized model inference operations, or potential data exfiltration through local AI systems. + If confirmed malicious, this could lead to unauthorized access to sensitive data, intellectual property theft, or circumvention of organizational AI governance policies. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count - min(_time) as firstTime - max(_time) as lastTime - from datamodel=Endpoint.Processes - where - ( - Processes.process_name IN ( - "gpt4all.exe", - "jan.exe", - "kobold.exe", - "koboldcpp.exe", - "llama-run.exe", - "llama.cpp.exe", - "lmstudio.exe", - "nutstudio.exe", - "ollama.exe", - "oobabooga.exe", - "text-generation-webui.exe" + | tstats `security_content_summariesonly` count + min(_time) as firstTime + max(_time) as lastTime + from datamodel=Endpoint.Processes + where + ( + Processes.process_name IN ( + "gpt4all.exe", + "jan.exe", + "kobold.exe", + "koboldcpp.exe", + "llama-run.exe", + "llama.cpp.exe", + "lmstudio.exe", + "nutstudio.exe", + "ollama.exe", + "oobabooga.exe", + "text-generation-webui.exe" + ) + OR + Processes.original_file_name IN ( + "ollama.exe", + "lmstudio.exe", + "gpt4all.exe", + "jan.exe", + "llama-run.exe", + "koboldcpp.exe", + "nutstudio.exe" + ) + OR + Processes.process IN ( + "*\\gpt4all\\*", + "*\\jan\\*", + "*\\koboldcpp\\*", + "*\\llama.cpp\\*", + "*\\lmstudio\\*", + "*\\nutstudio\\*", + "*\\ollama\\*", + "*\\oobabooga\\*", + "*huggingface*", + "*langchain*", + "*llama-run*", + "*transformers*" + ) + OR + Processes.parent_process_name IN ( + "gpt4all.exe", + "jan.exe", + "kobold.exe", + "koboldcpp.exe", + "llama-run.exe", + "llama.cpp.exe", + "lmstudio.exe", + "nutstudio.exe", + "ollama.exe", + "oobabooga.exe", + "text-generation-webui.exe" + ) ) - OR - Processes.original_file_name IN ( - "ollama.exe", - "lmstudio.exe", - "gpt4all.exe", - "jan.exe", - "llama-run.exe", - "koboldcpp.exe", - "nutstudio.exe" - ) - OR - Processes.process IN ( - "*\\gpt4all\\*", - "*\\jan\\*", - "*\\koboldcpp\\*", - "*\\llama.cpp\\*", - "*\\lmstudio\\*", - "*\\nutstudio\\*", - "*\\ollama\\*", - "*\\oobabooga\\*", - "*huggingface*", - "*langchain*", - "*llama-run*", - "*transformers*" - ) - OR - Processes.parent_process_name IN ( - "gpt4all.exe", - "jan.exe", - "kobold.exe", - "koboldcpp.exe", - "llama-run.exe", - "llama.cpp.exe", - "lmstudio.exe", - "nutstudio.exe", - "ollama.exe", - "oobabooga.exe", - "text-generation-webui.exe" - ) - ) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process - Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id - Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user - Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | eval Framework=case( - match(process_name, "(?i)ollama") OR match(process, "(?i)ollama"), "Ollama", - match(process_name, "(?i)lmstudio") OR match(process, "(?i)lmstudio") OR match(process, "(?i)lm-studio"), "LM Studio", - match(process_name, "(?i)gpt4all") OR match(process, "(?i)gpt4all"), "GPT4All", - match(process_name, "(?i)kobold") OR match(process, "(?i)kobold"), "KoboldCPP", - match(process_name, "(?i)jan") OR match(process, "(?i)jan"), "Jan AI", - match(process_name, "(?i)nutstudio") OR match(process, "(?i)nutstudio"), "NutStudio", - match(process_name, "(?i)llama") OR match(process, "(?i)llama"), "llama.cpp", - match(process_name, "(?i)oobabooga") OR match(process, "(?i)oobabooga") OR match(process, "(?i)text-generation-webui"), "Oobabooga", - match(process, "(?i)transformers") OR match(process, "(?i)huggingface"), "HuggingFace/Transformers", - match(process, "(?i)langchain"), "LangChain", - 1=1, "Other" - ) - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | table action dest Framework original_file_name parent_process parent_process_exec - parent_process_guid parent_process_id parent_process_name parent_process_path - process process_exec process_guid process_hash process_id process_integrity_level - process_name process_path user user_id vendor_product - | `windows_local_llm_framework_execution_filter` + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process + Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id + Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | eval Framework=case( + match(process_name, "(?i)ollama") OR match(process, "(?i)ollama"), "Ollama", + match(process_name, "(?i)lmstudio") OR match(process, "(?i)lmstudio") OR match(process, "(?i)lm-studio"), "LM Studio", + match(process_name, "(?i)gpt4all") OR match(process, "(?i)gpt4all"), "GPT4All", + match(process_name, "(?i)kobold") OR match(process, "(?i)kobold"), "KoboldCPP", + match(process_name, "(?i)jan") OR match(process, "(?i)jan"), "Jan AI", + match(process_name, "(?i)nutstudio") OR match(process, "(?i)nutstudio"), "NutStudio", + match(process_name, "(?i)llama") OR match(process, "(?i)llama"), "llama.cpp", + match(process_name, "(?i)oobabooga") OR match(process, "(?i)oobabooga") OR match(process, "(?i)text-generation-webui"), "Oobabooga", + match(process, "(?i)transformers") OR match(process, "(?i)huggingface"), "HuggingFace/Transformers", + match(process, "(?i)langchain"), "LangChain", + 1=1, "Other" + ) + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table action dest Framework original_file_name parent_process parent_process_exec + parent_process_guid parent_process_id parent_process_name parent_process_path + process process_exec process_guid process_hash process_id process_integrity_level + process_name process_path user user_id vendor_product + | `windows_local_llm_framework_execution_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Legitimate development, data science, and AI/ML workflows where - authorized developers, researchers, or engineers intentionally execute local LLM - frameworks (Ollama, LM Studio, GPT4All, Jan, NutStudio) for model experimentation, - fine-tuning, or prototyping. Python developers using HuggingFace Transformers or - LangChain for legitimate AI/ML projects. Approved sandbox and lab environments where - framework testing is authorized. Open-source contributors and hobbyists running - frameworks for educational purposes. Third-party applications that bundle or invoke - LLM frameworks as dependencies (e.g., IDE plugins, analytics tools, chatbot integrations). - System administrators deploying frameworks as part of containerized services or - orchestrated ML workloads. Process name keyword overlap with unrelated utilities - (e.g., "llama-backup", "janimation"). Recommended tuning — baseline approved frameworks - and users by role/department, exclude sanctioned dev/lab systems via the filter - macro, correlate with user identity and peer group anomalies before escalating to - incident response. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. +known_false_positives: Legitimate development, data science, and AI/ML workflows where authorized developers, researchers, or engineers intentionally execute local LLM frameworks (Ollama, LM Studio, GPT4All, Jan, NutStudio) for model experimentation, fine-tuning, or prototyping. Python developers using HuggingFace Transformers or LangChain for legitimate AI/ML projects. Approved sandbox and lab environments where framework testing is authorized. Open-source contributors and hobbyists running frameworks for educational purposes. Third-party applications that bundle or invoke LLM frameworks as dependencies (e.g., IDE plugins, analytics tools, chatbot integrations). System administrators deploying frameworks as part of containerized services or orchestrated ML workloads. Process name keyword overlap with unrelated utilities (e.g., "llama-backup", "janimation"). Recommended tuning — baseline approved frameworks and users by role/department, exclude sanctioned dev/lab systems via the filter macro, correlate with user identity and peer group anomalies before escalating to incident response. references: -- https://splunkbase.splunk.com/app/8024 -- https://www.ibm.com/think/topics/shadow-ai -- https://www.splunk.com/en_us/blog/artificial-intelligence/splunk-technology-add-on-for-ollama.html -- https://blogs.cisco.com/security/detecting-exposed-llm-servers-shodan-case-study-on-ollama -- https://docs.microsoft.com/en-us/sysinternals/downloads/sysmon + - https://splunkbase.splunk.com/app/8024 + - https://www.ibm.com/think/topics/shadow-ai + - https://www.splunk.com/en_us/blog/artificial-intelligence/splunk-technology-add-on-for-ollama.html + - https://blogs.cisco.com/security/detecting-exposed-llm-servers-shodan-case-study-on-ollama + - https://docs.microsoft.com/en-us/sysinternals/downloads/sysmon tags: - analytic_story: - - Suspicious Local LLM Frameworks - asset_type: Endpoint - mitre_attack_id: - - T1543 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Local LLM Frameworks + asset_type: Endpoint + mitre_attack_id: + - T1543 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - Sysmon - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/local_llms/sysmon_local_llms.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test - Sysmon + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/local_llms/sysmon_local_llms.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_lolbas_executed_as_renamed_file.yml b/detections/endpoint/windows_lolbas_executed_as_renamed_file.yml index 527b30c5d3..4ffdc73dda 100644 --- a/detections/endpoint/windows_lolbas_executed_as_renamed_file.yml +++ b/detections/endpoint/windows_lolbas_executed_as_renamed_file.yml @@ -5,96 +5,56 @@ date: '2025-05-02' author: Steven Dick status: production type: TTP -description: - The following analytic identifies a LOLBAS process being executed where - it's process name does not match it's original file name attribute. Processes that - have been renamed and executed may be an indicator that an adversary is attempting - to evade defenses or execute malicious code. The LOLBAS project documents Windows - native binaries that can be abused by threat actors to perform tasks like executing - malicious code. +description: The following analytic identifies a LOLBAS process being executed where it's process name does not match it's original file name attribute. Processes that have been renamed and executed may be an indicator that an adversary is attempting to evade defenses or execute malicious code. The LOLBAS project documents Windows native binaries that can be abused by threat actors to perform tasks like executing malicious code. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 -search: - '| tstats `security_content_summariesonly` latest(Processes.parent_process) - as parent_process, latest(Processes.process) as process, latest(Processes.process_guid) - as process_guid count, min(_time) AS firstTime, max(_time) AS lastTime FROM datamodel=Endpoint.Processes - where NOT Processes.original_file_name IN("-","unknown") AND NOT Processes.process_path - IN ("*\\Program Files*","*\\PROGRA~*","*\\Windows\\System32\\*","*\\Windows\\Syswow64\\*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - |`drop_dm_object_name(Processes)` | where NOT match(process_name, "(?i)".original_file_name) - | lookup lolbas_file_path lolbas_file_name as original_file_name OUTPUT description - as desc | search desc!="false" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_lolbas_executed_as_renamed_file_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: - A certain amount of false positives are likely with this detection. - MSI based installers often trigger for SETUPAPL.dll and vendors will often copy - system exectables to a different path for application usage. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` latest(Processes.parent_process) as parent_process, latest(Processes.process) as process, latest(Processes.process_guid) as process_guid count, min(_time) AS firstTime, max(_time) AS lastTime FROM datamodel=Endpoint.Processes where NOT Processes.original_file_name IN("-","unknown") AND NOT Processes.process_path IN ("*\\Program Files*","*\\PROGRA~*","*\\Windows\\System32\\*","*\\Windows\\Syswow64\\*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product |`drop_dm_object_name(Processes)` | where NOT match(process_name, "(?i)".original_file_name) | lookup lolbas_file_path lolbas_file_name as original_file_name OUTPUT description as desc | search desc!="false" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_lolbas_executed_as_renamed_file_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: A certain amount of false positives are likely with this detection. MSI based installers often trigger for SETUPAPL.dll and vendors will often copy system exectables to a different path for application usage. references: - - https://attack.mitre.org/techniques/T1036/ - - https://attack.mitre.org/techniques/T1036/003/ + - https://attack.mitre.org/techniques/T1036/ + - https://attack.mitre.org/techniques/T1036/003/ drilldown_searches: - - name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - The file originally named $original_file_name$ was executed as $process_name$ - on $dest$ - risk_objects: - - field: dest - type: system - score: 40 - - field: user - type: user - score: 40 - threat_objects: - - field: process_name - type: process_name + message: The file originally named $original_file_name$ was executed as $process_name$ on $dest$ + risk_objects: + - field: dest + type: system + score: 40 + - field: user + type: user + score: 40 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Living Off The Land - - Masquerading - Rename System Utilities - - Windows Defense Evasion Tactics - - Water Gamayun - asset_type: Endpoint - mitre_attack_id: - - T1036.003 - - T1218.011 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + - Masquerading - Rename System Utilities + - Windows Defense Evasion Tactics + - Water Gamayun + asset_type: Endpoint + mitre_attack_id: + - T1036.003 + - T1218.011 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/cmd_lolbas_usage/cmd_lolbas_usage.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/cmd_lolbas_usage/cmd_lolbas_usage.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_lolbas_executed_outside_expected_path.yml b/detections/endpoint/windows_lolbas_executed_outside_expected_path.yml index 417f60b96f..6fa6bcef7d 100644 --- a/detections/endpoint/windows_lolbas_executed_outside_expected_path.yml +++ b/detections/endpoint/windows_lolbas_executed_outside_expected_path.yml @@ -6,92 +6,86 @@ author: Steven Dick status: production type: Anomaly description: | - The following analytic identifies a LOLBAS process being executed outside of it's expected location. - Processes being executed outside of expected locations may be an indicator that an adversary is attempting to evade defenses or execute malicious code. - The LOLBAS project documents Windows native binaries that can be abused by threat actors to perform tasks like executing malicious code. + The following analytic identifies a LOLBAS process being executed outside of it's expected location. + Processes being executed outside of expected locations may be an indicator that an adversary is attempting to evade defenses or execute malicious code. + The LOLBAS project documents Windows native binaries that can be abused by threat actors to perform tasks like executing malicious code. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 + - Sysmon EventID 1 + - Windows Event Log Security 4688 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime - FROM datamodel=Endpoint.Processes where + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + FROM datamodel=Endpoint.Processes where - NOT Processes.process_path IN ( - "*\\PROGRA~*", - "*\\Program Files \(x86\)\\", - "*\\Program Files\\", - "*:\\Windows\\System32\\*", - "*:\\Windows\\SysWOW64\\*", - "*:\\Windows\\WinSxS\\*" - ) + NOT Processes.process_path IN ( + "*\\PROGRA~*", + "*\\Program Files \(x86\)\\", + "*\\Program Files\\", + "*:\\Windows\\System32\\*", + "*:\\Windows\\SysWOW64\\*", + "*:\\Windows\\WinSxS\\*" + ) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process - Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process + Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name + Processes.process_path Processes.user Processes.user_id Processes.vendor_product - |`drop_dm_object_name(Processes)` - | lookup lolbas_file_path lolbas_file_name as process_name OUTPUT description as desc - | lookup lolbas_file_path lolbas_file_name as process_name lolbas_file_path as process_path OUTPUT description as is_lolbas_path - | search desc!="false" AND is_lolbas_path="false" - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_lolbas_executed_outside_expected_path_filter` -how_to_implement: To implement this search, you must ingest logs that contain the - process name and process path, such as with Sysmon EID 1. + |`drop_dm_object_name(Processes)` + | lookup lolbas_file_path lolbas_file_name as process_name OUTPUT description as desc + | lookup lolbas_file_path lolbas_file_name as process_name lolbas_file_path as process_path OUTPUT description as is_lolbas_path + | search desc!="false" AND is_lolbas_path="false" + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_lolbas_executed_outside_expected_path_filter` +how_to_implement: To implement this search, you must ingest logs that contain the process name and process path, such as with Sysmon EID 1. known_false_positives: | - Vendors, third party software or update processes may use versions of the binaries listed in the lookup table from non-standard paths. - It is recommended to tune this analytic to exclude any known legitimate software or paths in your environment + Vendors, third party software or update processes may use versions of the binaries listed in the lookup table from non-standard paths. + It is recommended to tune this analytic to exclude any known legitimate software or paths in your environment references: -- https://attack.mitre.org/techniques/T1036/ -- https://attack.mitre.org/techniques/T1036/005/ + - https://attack.mitre.org/techniques/T1036/ + - https://attack.mitre.org/techniques/T1036/005/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The user $user$ executed a LOLBAS [$process_name$] from an unexpected location [$process_path$] with CommandLine [$process$] on $dest$ - risk_objects: - - field: user - type: user - score: 40 - - field: dest - type: system - score: 40 - threat_objects: - - field: process_name - type: process_name + message: The user $user$ executed a LOLBAS [$process_name$] from an unexpected location [$process_path$] with CommandLine [$process$] on $dest$ + risk_objects: + - field: user + type: user + score: 40 + - field: dest + type: system + score: 40 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Living Off The Land - - Masquerading - Rename System Utilities - - Windows Defense Evasion Tactics - asset_type: Endpoint - mitre_attack_id: - - T1036.005 - - T1218.011 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + - Masquerading - Rename System Utilities + - Windows Defense Evasion Tactics + asset_type: Endpoint + mitre_attack_id: + - T1036.005 + - T1218.011 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/cmd_lolbas_usage/cmd_lolbas_usage.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/cmd_lolbas_usage/cmd_lolbas_usage.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_lsa_secrets_nolmhash_registry.yml b/detections/endpoint/windows_lsa_secrets_nolmhash_registry.yml index 8fbf0ae20c..21059ed8f1 100644 --- a/detections/endpoint/windows_lsa_secrets_nolmhash_registry.yml +++ b/detections/endpoint/windows_lsa_secrets_nolmhash_registry.yml @@ -6,71 +6,47 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects modifications to the Windows registry - related to the Local Security Authority (LSA) NoLMHash setting. It identifies when - the registry value is set to 0, indicating that the system will store passwords - in the weaker Lan Manager (LM) hash format. This detection leverages registry activity - logs from endpoint data sources like Sysmon or EDR tools. Monitoring this activity - is crucial as it can indicate attempts to weaken password storage security. If confirmed - malicious, this could allow attackers to exploit weaker LM hashes, potentially leading - to unauthorized access and credential theft. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\System\\CurrentControlSet\\Control\\Lsa\\NoLMHash" - Registry.registry_value_data = 0x00000000) by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_lsa_secrets_nolmhash_registry_filter`' -how_to_implement: To successfully implement this search, you must be ingesting data - that records registry activity from your hosts to populate the endpoint data model - in the registry node. This is typically populated via endpoint detection-and-response - product, such as Carbon Black or endpoint data sources, such as Sysmon. The data - used for this search is typically generated via logs that report reads and writes - to the registry. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows registry related to the Local Security Authority (LSA) NoLMHash setting. It identifies when the registry value is set to 0, indicating that the system will store passwords in the weaker Lan Manager (LM) hash format. This detection leverages registry activity logs from endpoint data sources like Sysmon or EDR tools. Monitoring this activity is crucial as it can indicate attempts to weaken password storage security. If confirmed malicious, this could allow attackers to exploit weaker LM hashes, potentially leading to unauthorized access and credential theft. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\System\\CurrentControlSet\\Control\\Lsa\\NoLMHash" Registry.registry_value_data = 0x00000000) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_lsa_secrets_nolmhash_registry_filter`' +how_to_implement: To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. known_false_positives: Administrator may change this registry setting. references: -- https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows LSA Secrets NoLMhash Registry on $dest$ by $user$. - risk_objects: - - field: dest - type: system - score: 64 - - field: user - type: user - score: 64 - threat_objects: [] + message: Windows LSA Secrets NoLMhash Registry on $dest$ by $user$. + risk_objects: + - field: dest + type: system + score: 64 + - field: user + type: user + score: 64 + threat_objects: [] tags: - analytic_story: - - CISA AA23-347A - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1003.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA23-347A + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1003.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.004/NoLMHash/lsa-reg-settings-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.004/NoLMHash/lsa-reg-settings-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_mail_protocol_in_non_common_process_path.yml b/detections/endpoint/windows_mail_protocol_in_non_common_process_path.yml index 89b9ebee6c..becb4795d4 100644 --- a/detections/endpoint/windows_mail_protocol_in_non_common_process_path.yml +++ b/detections/endpoint/windows_mail_protocol_in_non_common_process_path.yml @@ -1,84 +1,69 @@ name: Windows Mail Protocol In Non-Common Process Path id: ac3311f5-661d-4e99-bd1f-3ec665b05441 -version: 8 -date: '2025-10-20' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects a Windows application establishing an - SMTP connection from a non-common installation path. It leverages Sysmon EventCode - 3 to identify processes not typically associated with email clients (e.g., Thunderbird, - Outlook) making SMTP connections. This activity is significant as adversaries, including - malware like AgentTesla, use such connections for Command and Control (C2) communication - to exfiltrate stolen data. If confirmed malicious, this behavior could lead to unauthorized - data exfiltration, including sensitive information like desktop screenshots, browser - data, and system details, compromising the affected host. +description: The following analytic detects a Windows application establishing an SMTP connection from a non-common installation path. It leverages Sysmon EventCode 3 to identify processes not typically associated with email clients (e.g., Thunderbird, Outlook) making SMTP connections. This activity is significant as adversaries, including malware like AgentTesla, use such connections for Command and Control (C2) communication to exfiltrate stolen data. If confirmed malicious, this behavior could lead to unauthorized data exfiltration, including sensitive information like desktop screenshots, browser data, and system details, compromising the affected host. data_source: -- Sysmon EventID 3 + - Sysmon EventID 3 search: | - `sysmon` - EventCode=3 - NOT Image IN( - "C:\\Program Files \(x86\)\\*", - "C:\\Program Files\\*", - "C:\\Windows\\System32\\*", - "C:\\Windows\\SysWOW64\\*" - ) - ( - DestinationPortName="smtp" - OR - DestinationPort IN (25, 587) - ) - | stats count min(_time) as firstTime - max(_time) as lastTime - by action app dest dest_ip dest_port direction dvc protocol protocol_version - src src_ip src_port transport user vendor_product process_name - process_exec process_guid process_id - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_mail_protocol_in_non_common_process_path_filter` -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name and sysmon eventcode = 3 connection events from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. + `sysmon` + EventCode=3 + NOT Image IN( + "C:\\Program Files \(x86\)\\*", + "C:\\Program Files\\*", + "C:\\Windows\\System32\\*", + "C:\\Windows\\SysWOW64\\*" + ) + ( + DestinationPortName="smtp" + OR + DestinationPort IN (25, 587) + ) + | stats count min(_time) as firstTime + max(_time) as lastTime + by action app dest dest_ip dest_port direction dvc protocol protocol_version + src src_ip src_port transport user vendor_product process_name + process_exec process_guid process_id + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_mail_protocol_in_non_common_process_path_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and sysmon eventcode = 3 connection events from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: Third party email or SMTP based applications will trigger this. Apply additional filters as needed. Also consider excluding known email or any SMTP based clients installed outside of the Program Files and Windows directories. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.agent_tesla + - https://malpedia.caad.fkie.fraunhofer.de/details/win.agent_tesla drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a process $process_name$ is having a SMTP connection to $dest$ in $dest_ip$ - risk_objects: - - field: dest - type: system - score: 9 - threat_objects: [] + message: a process $process_name$ is having a SMTP connection to $dest$ in $dest_ip$ + risk_objects: + - field: dest + type: system + score: 9 + threat_objects: [] tags: - analytic_story: - - AgentTesla - asset_type: Endpoint - mitre_attack_id: - - T1071.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AgentTesla + asset_type: Endpoint + mitre_attack_id: + - T1071.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/agent_tesla/agent_tesla_smtp/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/agent_tesla/agent_tesla_smtp/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_mark_of_the_web_bypass.yml b/detections/endpoint/windows_mark_of_the_web_bypass.yml index 24b7d77b3e..059cccb6a4 100644 --- a/detections/endpoint/windows_mark_of_the_web_bypass.yml +++ b/detections/endpoint/windows_mark_of_the_web_bypass.yml @@ -1,70 +1,64 @@ name: Windows Mark Of The Web Bypass id: 8ca13343-7405-4916-a2d1-ae34ce0c28ae -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 23 -description: The following analytic identifies a suspicious process that deletes the - Mark-of-the-Web (MOTW) data stream. It leverages Sysmon EventCode 23 to detect when - a file's Zone.Identifier stream is removed. This activity is significant because - it is a common technique used by malware, such as Ave Maria RAT, to bypass security - restrictions on files downloaded from the internet. If confirmed malicious, this - behavior could allow an attacker to execute potentially harmful files without triggering - security warnings, leading to further compromise of the system. -search: '`sysmon` EventCode=23 TargetFilename = "*:Zone.Identifier" | stats count - min(_time) as firstTime, max(_time) as lastTime by action dest dvc file_path file_hash - file_name file_modify_time process_exec process_guid process_id process_name process_path - signature signature_id user user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_mark_of_the_web_bypass_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the deleted target file name, process name and process id from your endpoints. - If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + - Sysmon EventID 23 +description: The following analytic identifies a suspicious process that deletes the Mark-of-the-Web (MOTW) data stream. It leverages Sysmon EventCode 23 to detect when a file's Zone.Identifier stream is removed. This activity is significant because it is a common technique used by malware, such as Ave Maria RAT, to bypass security restrictions on files downloaded from the internet. If confirmed malicious, this behavior could allow an attacker to execute potentially harmful files without triggering security warnings, leading to further compromise of the system. +search: |- + `sysmon` EventCode=23 TargetFilename = "*:Zone.Identifier" + | stats count min(_time) as firstTime, max(_time) as lastTime + BY action dest dvc + file_path file_hash file_name + file_modify_time process_exec process_guid + process_id process_name process_path + signature signature_id user + user_id vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_mark_of_the_web_bypass_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the deleted target file name, process name and process id from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: No false positives have been identified at this time. references: -- https://attack.mitre.org/techniques/T1553/005/ -- https://github.com/nmantani/PS-MOTW#remove-motwps1 + - https://attack.mitre.org/techniques/T1553/005/ + - https://github.com/nmantani/PS-MOTW#remove-motwps1 drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A mark-of-the-web data stream is deleted on $dest$ - risk_objects: - - field: user - type: user - score: 49 - - field: dest - type: system - score: 49 - threat_objects: [] + message: A mark-of-the-web data stream is deleted on $dest$ + risk_objects: + - field: user + type: user + score: 49 + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Quasar RAT - - Warzone RAT - asset_type: Endpoint - mitre_attack_id: - - T1553.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Quasar RAT + - Warzone RAT + asset_type: Endpoint + mitre_attack_id: + - T1553.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.005/mark_of_the_web_bypass/possible-motw-deletion.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.005/mark_of_the_web_bypass/possible-motw-deletion.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_masquerading_explorer_as_child_process.yml b/detections/endpoint/windows_masquerading_explorer_as_child_process.yml index d090d10e1e..9ea2490220 100644 --- a/detections/endpoint/windows_masquerading_explorer_as_child_process.yml +++ b/detections/endpoint/windows_masquerading_explorer_as_child_process.yml @@ -5,82 +5,48 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk status: production type: TTP -description: - The following analytic identifies instances where explorer.exe is spawned - by unusual parent processes such as cmd.exe, powershell.exe, or regsvr32.exe. This - detection leverages data from Endpoint Detection and Response (EDR) agents, focusing - on process and parent process relationships. This activity is significant because - explorer.exe is typically initiated by userinit.exe, and deviations from this norm - can indicate code injection or process masquerading attempts by malware like Qakbot. - If confirmed malicious, this behavior could allow attackers to execute arbitrary - code, evade detection, and maintain persistence within the environment. +description: The following analytic identifies instances where explorer.exe is spawned by unusual parent processes such as cmd.exe, powershell.exe, or regsvr32.exe. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process relationships. This activity is significant because explorer.exe is typically initiated by userinit.exe, and deviations from this norm can indicate code injection or process masquerading attempts by malware like Qakbot. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, evade detection, and maintain persistence within the environment. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name - IN("cmd.exe", "powershell.exe", "regsvr32.exe") AND Processes.process_name = "explorer.exe" - AND Processes.process IN ("*\\explorer.exe") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` - | `windows_masquerading_explorer_as_child_process_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN("cmd.exe", "powershell.exe", "regsvr32.exe") AND Processes.process_name = "explorer.exe" AND Processes.process IN ("*\\explorer.exe") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `windows_masquerading_explorer_as_child_process_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: - - https://malpedia.caad.fkie.fraunhofer.de/details/win.qakbot + - https://malpedia.caad.fkie.fraunhofer.de/details/win.qakbot drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: explorer.exe has a suspicious parent process $parent_process_name$ on $dest$ - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: [] + message: explorer.exe has a suspicious parent process $parent_process_name$ on $dest$ + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: [] tags: - analytic_story: - - Qakbot - - Compromised Windows Host - - Water Gamayun - asset_type: Endpoint - mitre_attack_id: - - T1574.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Qakbot + - Compromised Windows Host + - Water Gamayun + asset_type: Endpoint + mitre_attack_id: + - T1574.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_masquerading_msdtc_process.yml b/detections/endpoint/windows_masquerading_msdtc_process.yml index 29f23f0052..9b9a01e62e 100644 --- a/detections/endpoint/windows_masquerading_msdtc_process.yml +++ b/detections/endpoint/windows_masquerading_msdtc_process.yml @@ -1,80 +1,64 @@ name: Windows Masquerading Msdtc Process id: 238f3a07-8440-480b-b26f-462f41d9a47c -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic identifies the execution of msdtc.exe with specific - command-line parameters (-a or -b), which are indicative of the PlugX malware. This - detection leverages data from Endpoint Detection and Response (EDR) agents, focusing - on process names and command-line arguments. This activity is significant because - PlugX uses these parameters to masquerade its malicious operations within legitimate - processes, making it harder to detect. If confirmed malicious, this behavior could - allow attackers to gain unauthorized access, exfiltrate data, and conduct espionage, - severely compromising the affected system. -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "msdtc.exe" - Processes.process = "*msdtc.exe*" Processes.process IN ("* -a*", "* -b*") by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_masquerading_msdtc_process_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic identifies the execution of msdtc.exe with specific command-line parameters (-a or -b), which are indicative of the PlugX malware. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because PlugX uses these parameters to masquerade its malicious operations within legitimate processes, making it harder to detect. If confirmed malicious, this behavior could allow attackers to gain unauthorized access, exfiltrate data, and conduct espionage, severely compromising the affected system. +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "msdtc.exe" Processes.process = "*msdtc.exe*" Processes.process IN ("* -a*", "* -b*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_masquerading_msdtc_process_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.plugx + - https://malpedia.caad.fkie.fraunhofer.de/details/win.plugx drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: msdtc.exe process with process commandline used by PlugX malware on $dest$. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: msdtc.exe process with process commandline used by PlugX malware on $dest$. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - PlugX - asset_type: Endpoint - mitre_attack_id: - - T1036 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - PlugX + asset_type: Endpoint + mitre_attack_id: + - T1036 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/msdtc_process_param/msdtc_a_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/msdtc_process_param/msdtc_a_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_mimikatz_binary_execution.yml b/detections/endpoint/windows_mimikatz_binary_execution.yml index 696b8b645d..e9b0e67933 100644 --- a/detections/endpoint/windows_mimikatz_binary_execution.yml +++ b/detections/endpoint/windows_mimikatz_binary_execution.yml @@ -1,97 +1,83 @@ name: Windows Mimikatz Binary Execution id: a9e0d6d3-9676-4e26-994d-4e0406bb4467 -version: 10 -date: '2025-07-29' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies the execution of the native mimikatz.exe - binary on Windows systems, including instances where the binary is renamed. It leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process names - and original file names. This activity is significant because Mimikatz is a widely - used tool for extracting authentication credentials, posing a severe security risk. - If confirmed malicious, this activity could allow attackers to obtain sensitive - credentials, escalate privileges, and move laterally within the network, leading - to potential data breaches and system compromise. +description: The following analytic identifies the execution of the native mimikatz.exe binary on Windows systems, including instances where the binary is renamed. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and original file names. This activity is significant because Mimikatz is a widely used tool for extracting authentication credentials, posing a severe security risk. If confirmed malicious, this activity could allow attackers to obtain sensitive credentials, escalate privileges, and move laterally within the network, leading to potential data breaches and system compromise. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=mimikatz.exe - OR Processes.original_file_name=mimikatz.exe) by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_mimikatz_binary_execution_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives should be limited as this is directly looking - for Mimikatz, the credential dumping utility. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name=mimikatz.exe + OR + Processes.original_file_name=mimikatz.exe + ) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_mimikatz_binary_execution_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives should be limited as this is directly looking for Mimikatz, the credential dumping utility. references: -- https://www.cisa.gov/uscert/sites/default/files/publications/aa22-320a_joint_csa_iranian_government-sponsored_apt_actors_compromise_federal%20network_deploy_crypto%20miner_credential_harvester.pdf -- https://www.varonis.com/blog/what-is-mimikatz -- https://media.defense.gov/2023/May/24/2003229517/-1/-1/0/CSA_Living_off_the_Land.PDF + - https://www.cisa.gov/uscert/sites/default/files/publications/aa22-320a_joint_csa_iranian_government-sponsored_apt_actors_compromise_federal%20network_deploy_crypto%20miner_credential_harvester.pdf + - https://www.varonis.com/blog/what-is-mimikatz + - https://media.defense.gov/2023/May/24/2003229517/-1/-1/0/CSA_Living_off_the_Land.PDF drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting dump credentials. - risk_objects: - - field: user - type: user - score: 100 - - field: dest - type: system - score: 100 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting dump credentials. + risk_objects: + - field: user + type: user + score: 100 + - field: dest + type: system + score: 100 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Sandworm Tools - - Volt Typhoon - - Flax Typhoon - - CISA AA22-320A - - CISA AA23-347A - - Compromised Windows Host - - Credential Dumping - - Scattered Spider - asset_type: Endpoint - mitre_attack_id: - - T1003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sandworm Tools + - Volt Typhoon + - Flax Typhoon + - CISA AA22-320A + - CISA AA23-347A + - Compromised Windows Host + - Credential Dumping + - Scattered Spider + asset_type: Endpoint + mitre_attack_id: + - T1003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/mimikatzwindows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/mimikatzwindows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_mimikatz_crypto_export_file_extensions.yml b/detections/endpoint/windows_mimikatz_crypto_export_file_extensions.yml index abc0e34e6b..0a81c43a54 100644 --- a/detections/endpoint/windows_mimikatz_crypto_export_file_extensions.yml +++ b/detections/endpoint/windows_mimikatz_crypto_export_file_extensions.yml @@ -1,74 +1,61 @@ name: Windows Mimikatz Crypto Export File Extensions id: 3a9a6806-16a8-4cda-8d73-b49d10a05b16 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic detects the creation of files with extensions - commonly associated with the Mimikatz Crypto module. It leverages the Endpoint.Filesystem - data model to identify specific file names indicative of certificate export activities. - This behavior is significant as it may indicate the use of Mimikatz to export cryptographic - keys, which is a common tactic for credential theft. If confirmed malicious, this - activity could allow an attacker to exfiltrate sensitive cryptographic material, - potentially leading to unauthorized access and further compromise of the environment. +description: The following analytic detects the creation of files with extensions commonly associated with the Mimikatz Crypto module. It leverages the Endpoint.Filesystem data model to identify specific file names indicative of certificate export activities. This behavior is significant as it may indicate the use of Mimikatz to export cryptographic keys, which is a common tactic for credential theft. If confirmed malicious, this activity could allow an attacker to exfiltrate sensitive cryptographic material, potentially leading to unauthorized access and further compromise of the environment. data_source: -- Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.keyx.rsa.pvk","*sign.rsa.pvk","*sign.dsa.pvk","*dsa.ec.p8k","*dh.ec.p8k", - "*.pfx", "*.der") by Filesystem.action Filesystem.dest Filesystem.file_access_time - Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name - Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid - Filesystem.process_id Filesystem.user Filesystem.vendor_product | `security_content_ctime(lastTime)` - | `security_content_ctime(firstTime)` | `drop_dm_object_name(Filesystem)` | `windows_mimikatz_crypto_export_file_extensions_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: False positives may be present and may need to be reviewed - before this can be turned into a TTP. In addition, remove .pfx (standalone) if it's - too much volume. + - Sysmon EventID 11 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.file_name IN ("*.keyx.rsa.pvk","*sign.rsa.pvk","*sign.dsa.pvk","*dsa.ec.p8k","*dh.ec.p8k", "*.pfx", "*.der") + BY Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path Filesystem.file_acl + Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` + | `drop_dm_object_name(Filesystem)` + | `windows_mimikatz_crypto_export_file_extensions_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: False positives may be present and may need to be reviewed before this can be turned into a TTP. In addition, remove .pfx (standalone) if it's too much volume. references: -- https://github.com/gentilkiwi/mimikatz/blob/master/mimikatz/modules/kuhl_m_crypto.c#L628-L645 + - https://github.com/gentilkiwi/mimikatz/blob/master/mimikatz/modules/kuhl_m_crypto.c#L628-L645 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Certificate file extensions realted to Mimikatz were identified on disk - on $dest$. - risk_objects: - - field: dest - type: system - score: 28 - threat_objects: [] + message: Certificate file extensions realted to Mimikatz were identified on disk on $dest$. + risk_objects: + - field: dest + type: system + score: 28 + threat_objects: [] tags: - analytic_story: - - Sandworm Tools - - CISA AA23-347A - - Windows Certificate Services - asset_type: Endpoint - mitre_attack_id: - - T1649 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Sandworm Tools + - CISA AA23-347A + - Windows Certificate Services + asset_type: Endpoint + mitre_attack_id: + - T1649 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/certwrite_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/certwrite_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_mmc_loaded_script_engine_dll.yml b/detections/endpoint/windows_mmc_loaded_script_engine_dll.yml index bc205fc136..df0fefa8de 100644 --- a/detections/endpoint/windows_mmc_loaded_script_engine_dll.yml +++ b/detections/endpoint/windows_mmc_loaded_script_engine_dll.yml @@ -7,61 +7,45 @@ status: production type: Anomaly description: The following analytic identifies when a Windows process loads scripting libraries like jscript.dll or vbscript.dll to execute script code on a target system. While these DLLs are legitimate parts of the operating system, their use by unexpected processes or in unusual contexts can indicate malicious activity, such as script-based malware, living-off-the-land techniques, or automated attacks. This detection monitors which processes load these libraries, along with their command-line arguments and parent processes, to help distinguish normal administrative behavior from potential threats. Alerts should be investigated with attention to the process context and any subsequent network or system activity, as legitimate tools like MMC snap-ins may also trigger this behavior under routine administrative tasks. data_source: -- Sysmon EventID 7 -search: '`sysmon` EventCode=7 process_name = mmc.exe ImageLoaded IN ("*\\jscript.dll", "*\\vbscript.dll", "*\\jscript9.dll") - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime - by Image ImageLoaded dest loaded_file loaded_file_path original_file_name - process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists - service_dll_signature_verified signature signature_id user_id vendor_product - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_mmc_loaded_script_engine_dll_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name and ImageLoaded (Like sysmon EventCode 7) from your endpoints. - If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. - Also be sure to include those monitored dll to your own sysmon config. + - Sysmon EventID 7 +search: '`sysmon` EventCode=7 process_name = mmc.exe ImageLoaded IN ("*\\jscript.dll", "*\\vbscript.dll", "*\\jscript9.dll") | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_mmc_loaded_script_engine_dll_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and ImageLoaded (Like sysmon EventCode 7) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Also be sure to include those monitored dll to your own sysmon config. known_false_positives: built in Windows tools such as Group Policy Management, Task Scheduler, Event Viewer, or custom MMC snap-ins may load vbscript.dll or jscript.dll to support scripted extensions, automation, or legacy management components. Filter as needed. references: -- https://www.securonix.com/blog/analyzing-fluxconsole-using-tax-themed-lures-threat-actors-exploit-windows-management-console-to-deliver-backdoor-payloads/ -- https://research.checkpoint.com/2019/microsoft-management-console-mmc-vulnerabilities/ + - https://www.securonix.com/blog/analyzing-fluxconsole-using-tax-themed-lures-threat-actors-exploit-windows-management-console-to-deliver-backdoor-payloads/ + - https://research.checkpoint.com/2019/microsoft-management-console-mmc-vulnerabilities/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Process [ $process_name$ ] loaded [ $ImageLoaded$ ] on [ $dest$ ]. - risk_objects: - - field: dest - type: system - score: 20 - threat_objects: - - field: process_name - type: process_name + message: Process [ $process_name$ ] loaded [ $ImageLoaded$ ] on [ $dest$ ]. + risk_objects: + - field: dest + type: system + score: 20 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - XML Runner Loader - asset_type: Endpoint - mitre_attack_id: - - T1620 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - XML Runner Loader + asset_type: Endpoint + mitre_attack_id: + - T1620 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1620/mmc_script_modules/loaded_module_mmc.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1620/mmc_script_modules/loaded_module_mmc.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_authenticationleveloverride.yml b/detections/endpoint/windows_modify_registry_authenticationleveloverride.yml index cde24f4744..1d0c7616b8 100644 --- a/detections/endpoint/windows_modify_registry_authenticationleveloverride.yml +++ b/detections/endpoint/windows_modify_registry_authenticationleveloverride.yml @@ -6,71 +6,43 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: - - Sysmon EventID 13 -description: - The following analytic detects modifications to the Windows registry - key "AuthenticationLevelOverride" within the Terminal Server Client settings. It - leverages data from the Endpoint.Registry datamodel to identify changes where the - registry value is set to 0x00000000. This activity is significant as it may indicate - an attempt to override authentication levels for remote connections, a tactic used - by DarkGate malware for malicious installations. If confirmed malicious, this could - allow attackers to gain unauthorized remote access, potentially leading to data - exfiltration or further system compromise. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Terminal - Server Client\\AuthenticationLevelOverride" Registry.registry_value_data = 0x00000000 - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_modify_registry_authenticationleveloverride_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure - that this registry was included in your config files ex. sysmon config to be monitored. -known_false_positives: - Administrators may enable or disable this feature that may - cause some false positive, however is not common. Filter as needed. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows registry key "AuthenticationLevelOverride" within the Terminal Server Client settings. It leverages data from the Endpoint.Registry datamodel to identify changes where the registry value is set to 0x00000000. This activity is significant as it may indicate an attempt to override authentication levels for remote connections, a tactic used by DarkGate malware for malicious installations. If confirmed malicious, this could allow attackers to gain unauthorized remote access, potentially leading to data exfiltration or further system compromise. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Terminal Server Client\\AuthenticationLevelOverride" Registry.registry_value_data = 0x00000000 by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_authenticationleveloverride_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored. +known_false_positives: Administrators may enable or disable this feature that may cause some false positive, however is not common. Filter as needed. references: - - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate + - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: the registry for authentication level settings was modified on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: the registry for authentication level settings was modified on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - DarkGate Malware - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - DarkGate Malware + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/AuthenticationLevelOverride/auth_sys.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/AuthenticationLevelOverride/auth_sys.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_auto_minor_updates.yml b/detections/endpoint/windows_modify_registry_auto_minor_updates.yml index 1f4d732b2a..5adb0372a5 100644 --- a/detections/endpoint/windows_modify_registry_auto_minor_updates.yml +++ b/detections/endpoint/windows_modify_registry_auto_minor_updates.yml @@ -6,48 +6,27 @@ author: Teoderick Contreras, Splunk status: production type: Hunting data_source: - - Sysmon EventID 13 -description: - The following analytic identifies a suspicious modification to the Windows - auto update configuration registry. It detects changes to the registry path "*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\AutoInstallMinorUpdates" - with a value of "0x00000000". This activity is significant as it is commonly used - by adversaries, including malware like RedLine Stealer, to bypass detection and - deploy additional payloads. If confirmed malicious, this modification could allow - attackers to evade defenses, potentially leading to further system compromise and - exploitation of zero-day vulnerabilities. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\AutoInstallMinorUpdates" - AND Registry.registry_value_data="0x00000000" by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_auto_minor_updates_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: - administrators may enable or disable this feature that may - cause some false positive. + - Sysmon EventID 13 +description: The following analytic identifies a suspicious modification to the Windows auto update configuration registry. It detects changes to the registry path "*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\AutoInstallMinorUpdates" with a value of "0x00000000". This activity is significant as it is commonly used by adversaries, including malware like RedLine Stealer, to bypass detection and deploy additional payloads. If confirmed malicious, this modification could allow attackers to evade defenses, potentially leading to further system compromise and exploitation of zero-day vulnerabilities. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\AutoInstallMinorUpdates" AND Registry.registry_value_data="0x00000000" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_auto_minor_updates_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: administrators may enable or disable this feature that may cause some false positive. references: - - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499 + - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499 tags: - analytic_story: - - RedLine Stealer - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - RedLine Stealer + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_auto_update_notif.yml b/detections/endpoint/windows_modify_registry_auto_update_notif.yml index 2a9823c95d..16584036a7 100644 --- a/detections/endpoint/windows_modify_registry_auto_update_notif.yml +++ b/detections/endpoint/windows_modify_registry_auto_update_notif.yml @@ -6,73 +6,45 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: - - Sysmon EventID 13 -description: - The following analytic detects a suspicious modification to the Windows - registry that changes the auto-update notification setting to "Notify before download." - This detection leverages data from the Endpoint.Registry data model, focusing on - specific registry paths and values. This activity is significant because it is a - known technique used by adversaries, including malware like RedLine Stealer, to - evade detection and potentially deploy additional payloads. If confirmed malicious, - this modification could allow attackers to bypass security measures, maintain persistence, - and exploit vulnerabilities on the target host. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\AUOptions" - AND Registry.registry_value_data="0x00000002" by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_auto_update_notif_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: - administrators may enable or disable this feature that may - cause some false positive. + - Sysmon EventID 13 +description: The following analytic detects a suspicious modification to the Windows registry that changes the auto-update notification setting to "Notify before download." This detection leverages data from the Endpoint.Registry data model, focusing on specific registry paths and values. This activity is significant because it is a known technique used by adversaries, including malware like RedLine Stealer, to evade detection and potentially deploy additional payloads. If confirmed malicious, this modification could allow attackers to bypass security measures, maintain persistence, and exploit vulnerabilities on the target host. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\AUOptions" AND Registry.registry_value_data="0x00000002" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_auto_update_notif_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: administrators may enable or disable this feature that may cause some false positive. references: - - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499 + - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499 drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A registry modification in Windows auto update notification on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A registry modification in Windows auto update notification on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - RedLine Stealer - asset_type: Endpoint - atomic_guid: - - 12e03af7-79f9-4f95-af48-d3f12f28a260 - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - RedLine Stealer + asset_type: Endpoint + atomic_guid: + - 12e03af7-79f9-4f95-af48-d3f12f28a260 + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_configure_bitlocker.yml b/detections/endpoint/windows_modify_registry_configure_bitlocker.yml index eba2ff2b1f..387a543cd8 100644 --- a/detections/endpoint/windows_modify_registry_configure_bitlocker.yml +++ b/detections/endpoint/windows_modify_registry_configure_bitlocker.yml @@ -4,76 +4,45 @@ version: 6 date: '2025-05-02' author: Teoderick Contreras, Splunk data_source: - - Sysmon EventID 13 + - Sysmon EventID 13 type: TTP status: production -description: - This analytic is developed to detect suspicious registry modifications - targeting BitLocker settings. The malware ShrinkLocker alters various registry keys - to change how BitLocker handles encryption, potentially bypassing TPM requirements, - enabling BitLocker without TPM, and enforcing specific startup key and PIN configurations. - Such modifications can weaken system security, making it easier for unauthorized - access and data breaches. Detecting these changes is crucial for maintaining robust - encryption and data protection. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path= "*\\Policies\\Microsoft\\FVE\\*" - Registry.registry_value_name IN("EnableBDEWithNoTPM", "EnableNonTPM", "UseAdvancedStartup") - Registry.registry_value_data = 0x00000001) OR (Registry.registry_path= "*\\Policies\\Microsoft\\FVE\\*" - Registry.registry_value_name IN("UsePIN", "UsePartialEncryptionKey", "UseTPM", "UseTPMKey", - "UseTPMKeyPIN", "UseTPMPIN") Registry.registry_value_data = 0x00000002) by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_modify_registry_configure_bitlocker_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: - administrators may enable or disable this feature that may - cause some false positive. +description: This analytic is developed to detect suspicious registry modifications targeting BitLocker settings. The malware ShrinkLocker alters various registry keys to change how BitLocker handles encryption, potentially bypassing TPM requirements, enabling BitLocker without TPM, and enforcing specific startup key and PIN configurations. Such modifications can weaken system security, making it easier for unauthorized access and data breaches. Detecting these changes is crucial for maintaining robust encryption and data protection. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path= "*\\Policies\\Microsoft\\FVE\\*" Registry.registry_value_name IN("EnableBDEWithNoTPM", "EnableNonTPM", "UseAdvancedStartup") Registry.registry_value_data = 0x00000001) OR (Registry.registry_path= "*\\Policies\\Microsoft\\FVE\\*" Registry.registry_value_name IN("UsePIN", "UsePartialEncryptionKey", "UseTPM", "UseTPMKey", "UseTPMKeyPIN", "UseTPMPIN") Registry.registry_value_data = 0x00000002) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_configure_bitlocker_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: administrators may enable or disable this feature that may cause some false positive. references: - - https://www.bleepingcomputer.com/news/security/new-shrinklocker-ransomware-uses-bitlocker-to-encrypt-your-files/ + - https://www.bleepingcomputer.com/news/security/new-shrinklocker-ransomware-uses-bitlocker-to-encrypt-your-files/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A registry modification in Windows bitlocker registry settings on $dest$ - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A registry modification in Windows bitlocker registry settings on $dest$ + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - ShrinkLocker - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ShrinkLocker + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/bitlocker_registry_setting//fve-reg.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/bitlocker_registry_setting//fve-reg.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_default_icon_setting.yml b/detections/endpoint/windows_modify_registry_default_icon_setting.yml index dc0b47c648..1388aeb95b 100644 --- a/detections/endpoint/windows_modify_registry_default_icon_setting.yml +++ b/detections/endpoint/windows_modify_registry_default_icon_setting.yml @@ -5,78 +5,48 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: - The following analytic detects suspicious modifications to the Windows - registry's default icon settings, a technique associated with Lockbit ransomware. - It leverages data from the Endpoint Registry data model, focusing on changes to - registry paths under "*HKCR\\*\\defaultIcon\\(Default)*". This activity is significant - as it is uncommon for normal users to modify these settings, and such changes can - indicate ransomware infection or other malware. If confirmed malicious, this could - lead to system defacement and signal a broader ransomware attack, potentially compromising - sensitive data and system integrity. +description: The following analytic detects suspicious modifications to the Windows registry's default icon settings, a technique associated with Lockbit ransomware. It leverages data from the Endpoint Registry data model, focusing on changes to registry paths under "*HKCR\\*\\defaultIcon\\(Default)*". This activity is significant as it is uncommon for normal users to modify these settings, and such changes can indicate ransomware infection or other malware. If confirmed malicious, this could lead to system defacement and signal a broader ransomware attack, potentially compromising sensitive data and system integrity. data_source: - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime - max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path - ="*\\defaultIcon\\(Default)*" Registry.registry_path = "*HKCR\\*" by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` - | `windows_modify_registry_default_icon_setting_filter`' -how_to_implement: - To successfully implement this search, you must be ingesting data - that records registry activity from your hosts to populate the endpoint data model - in the registry node. This is typically populated via endpoint detection-and-response - product, such as Carbon Black or endpoint data sources, such as Sysmon. The data - used for this search is typically generated via logs that report reads and writes - to the registry. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path ="*\\defaultIcon\\(Default)*" Registry.registry_path = "*HKCR\\*" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `windows_modify_registry_default_icon_setting_filter`' +how_to_implement: To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. known_false_positives: No false positives have been identified at this time. references: - - https://blogs.vmware.com/security/2022/10/lockbit-3-0-also-known-as-lockbit-black.html - - https://news.sophos.com/en-us/2020/04/24/lockbit-ransomware-borrows-tricks-to-keep-up-with-revil-and-maze/ + - https://blogs.vmware.com/security/2022/10/lockbit-3-0-also-known-as-lockbit-black.html + - https://news.sophos.com/en-us/2020/04/24/lockbit-ransomware-borrows-tricks-to-keep-up-with-revil-and-maze/ drilldown_searches: - - name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A suspicious registry modification to change the default icon association - of windows to ransomware was detected on endpoint $dest$ by user $user$. - risk_objects: - - field: dest - type: system - score: 64 - - field: user - type: user - score: 64 - threat_objects: [] + message: A suspicious registry modification to change the default icon association of windows to ransomware was detected on endpoint $dest$ by user $user$. + risk_objects: + - field: dest + type: system + score: 64 + - field: user + type: user + score: 64 + threat_objects: [] tags: - analytic_story: - - LockBit Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - LockBit Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/lockbit_ransomware/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/lockbit_ransomware/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_delete_firewall_rules.yml b/detections/endpoint/windows_modify_registry_delete_firewall_rules.yml index ef9671bf14..604e41c20d 100644 --- a/detections/endpoint/windows_modify_registry_delete_firewall_rules.yml +++ b/detections/endpoint/windows_modify_registry_delete_firewall_rules.yml @@ -4,73 +4,50 @@ version: 8 date: '2025-11-20' author: Teoderick Contreras, Splunk data_source: - - Sysmon EventID 12 + - Sysmon EventID 12 type: TTP status: production -description: - The following analytic detects a potential deletion of firewall rules, - indicating a possible security breach or unauthorized access attempt. It identifies - actions where firewall rules are removed using commands like netsh advfirewall firewall - delete rule, which can expose the network to external threats by disabling critical - security measures. Monitoring these activities helps maintain network integrity - and prevent malicious attacks. -search: - '`sysmon` EventCode=12 TargetObject = "*\\System\\CurrentControlSet\\Services\\SharedAccess\\Parameters\\FirewallPolicy\\FirewallRules\\*" - EventType=DeleteValue | stats count min(_time) as firstTime max(_time) as lastTime - by action dest process_guid process_id registry_hive registry_path registry_key_name - status user vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_modify_registry_delete_firewall_rules_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 -known_false_positives: - network admin may add/remove/modify public inbound firewall - rule that may cause this rule to be triggered. +description: The following analytic detects a potential deletion of firewall rules, indicating a possible security breach or unauthorized access attempt. It identifies actions where firewall rules are removed using commands like netsh advfirewall firewall delete rule, which can expose the network to external threats by disabling critical security measures. Monitoring these activities helps maintain network integrity and prevent malicious attacks. +search: '`sysmon` EventCode=12 TargetObject = "*\\System\\CurrentControlSet\\Services\\SharedAccess\\Parameters\\FirewallPolicy\\FirewallRules\\*" EventType=DeleteValue | stats count min(_time) as firstTime max(_time) as lastTime by action dest process_guid process_id registry_hive registry_path registry_key_name status user vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_delete_firewall_rules_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 +known_false_positives: network admin may add/remove/modify public inbound firewall rule that may cause this rule to be triggered. references: - - https://www.bleepingcomputer.com/news/security/new-shrinklocker-ransomware-uses-bitlocker-to-encrypt-your-files/ + - https://www.bleepingcomputer.com/news/security/new-shrinklocker-ransomware-uses-bitlocker-to-encrypt-your-files/ drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: firewall deletion found in registry on $dest$ - risk_objects: - - field: user - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: [] + message: firewall deletion found in registry on $dest$ + risk_objects: + - field: user + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - ShrinkLocker - - CISA AA24-241A - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ShrinkLocker + - CISA AA24-241A + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/firewall_modify_delete/firewall_mod_delete.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/firewall_modify_delete/firewall_mod_delete.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_disable_rdp.yml b/detections/endpoint/windows_modify_registry_disable_rdp.yml index 0455888de4..41d18426c9 100644 --- a/detections/endpoint/windows_modify_registry_disable_rdp.yml +++ b/detections/endpoint/windows_modify_registry_disable_rdp.yml @@ -4,70 +4,46 @@ version: 7 date: '2025-08-01' author: Teoderick Contreras, Splunk data_source: -- Sysmon EventID 13 + - Sysmon EventID 13 type: Anomaly status: production -description: This analytic is developed to detect suspicious registry - modifications that disable Remote Desktop Protocol (RDP) by altering the - "fDenyTSConnections" key. Changing this key's value to 1 prevents remote - connections, which can disrupt remote management and access. Such - modifications could indicate an attempt to hinder remote administration or - isolate the system from remote intervention, potentially signifying malicious - activity. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Control\\Terminal - Server\\fDenyTSConnections*" Registry.registry_value_data="0x00000001" by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_modify_registry_disable_rdp_filter`' -how_to_implement: To successfully implement this search you need to be ingesting - information on process that include the name of the process responsible for - the changes from your endpoints into the `Endpoint` datamodel in the - `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is - installed and the latest TA for the endpoint product. -known_false_positives: administrators may enable or disable this feature that - may cause some false positive. +description: This analytic is developed to detect suspicious registry modifications that disable Remote Desktop Protocol (RDP) by altering the "fDenyTSConnections" key. Changing this key's value to 1 prevents remote connections, which can disrupt remote management and access. Such modifications could indicate an attempt to hinder remote administration or isolate the system from remote intervention, potentially signifying malicious activity. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Control\\Terminal Server\\fDenyTSConnections*" Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disable_rdp_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: administrators may enable or disable this feature that may cause some false positive. references: -- https://www.bleepingcomputer.com/news/security/new-shrinklocker-ransomware-uses-bitlocker-to-encrypt-your-files/ + - https://www.bleepingcomputer.com/news/security/new-shrinklocker-ransomware-uses-bitlocker-to-encrypt-your-files/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A registry modification in Windows RDP registry settings on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A registry modification in Windows RDP registry settings on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - ShrinkLocker - - Windows RDP Artifacts and Defense Evasion - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ShrinkLocker + - Windows RDP Artifacts and Defense Evasion + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/disable_rdp//fdenytsconnection-reg.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/disable_rdp//fdenytsconnection-reg.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_disable_restricted_admin.yml b/detections/endpoint/windows_modify_registry_disable_restricted_admin.yml index 064c940929..32b937eae0 100644 --- a/detections/endpoint/windows_modify_registry_disable_restricted_admin.yml +++ b/detections/endpoint/windows_modify_registry_disable_restricted_admin.yml @@ -6,75 +6,48 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: - - Sysmon EventID 13 -description: - The following analytic detects modifications to the Windows registry - entry "DisableRestrictedAdmin," which controls the Restricted Admin mode behavior. - This detection leverages registry activity logs from endpoint data sources like - Sysmon or Carbon Black. Monitoring this activity is crucial as changes to this setting - can disable a security feature that limits credential exposure during remote connections. - If confirmed malicious, an attacker could weaken security controls, increasing the - risk of credential theft and unauthorized access to sensitive systems. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\System\\CurrentControlSet\\Control\\Lsa\\DisableRestrictedAdmin" - Registry.registry_value_data = 0x00000000) by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_modify_registry_disable_restricted_admin_filter`' -how_to_implement: - To successfully implement this search, you must be ingesting data - that records registry activity from your hosts to populate the endpoint data model - in the registry node. This is typically populated via endpoint detection-and-response - product, such as Carbon Black or endpoint data sources, such as Sysmon. The data - used for this search is typically generated via logs that report reads and writes - to the registry. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows registry entry "DisableRestrictedAdmin," which controls the Restricted Admin mode behavior. This detection leverages registry activity logs from endpoint data sources like Sysmon or Carbon Black. Monitoring this activity is crucial as changes to this setting can disable a security feature that limits credential exposure during remote connections. If confirmed malicious, an attacker could weaken security controls, increasing the risk of credential theft and unauthorized access to sensitive systems. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\System\\CurrentControlSet\\Control\\Lsa\\DisableRestrictedAdmin" Registry.registry_value_data = 0x00000000) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disable_restricted_admin_filter`' +how_to_implement: To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. known_false_positives: Administrator may change this registry setting. Filter as needed. references: - - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a drilldown_searches: - - name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Modify Registry Disable Restricted Admin on $dest$ by $user$. - risk_objects: - - field: dest - type: system - score: 64 - - field: user - type: user - score: 64 - threat_objects: [] + message: Windows Modify Registry Disable Restricted Admin on $dest$ by $user$. + risk_objects: + - field: dest + type: system + score: 64 + - field: user + type: user + score: 64 + threat_objects: [] tags: - analytic_story: - - GhostRedirector IIS Module and Rungan Backdoor - - Medusa Ransomware - - CISA AA23-347A - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - GhostRedirector IIS Module and Rungan Backdoor + - Medusa Ransomware + - CISA AA23-347A + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.004/NoLMHash/lsa-reg-settings-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.004/NoLMHash/lsa-reg-settings-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_disable_toast_notifications.yml b/detections/endpoint/windows_modify_registry_disable_toast_notifications.yml index 9a6ebb5b8c..5ce071a797 100644 --- a/detections/endpoint/windows_modify_registry_disable_toast_notifications.yml +++ b/detections/endpoint/windows_modify_registry_disable_toast_notifications.yml @@ -5,72 +5,45 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: - The following analytic detects modifications to the Windows registry - that disable toast notifications. It leverages data from the Endpoint.Registry datamodel, - specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\PushNotifications\\ToastEnabled*" - with a value set to "0x00000000". This activity is significant because disabling - toast notifications can prevent users from receiving critical system and application - updates, which adversaries like Azorult exploit for defense evasion. If confirmed - malicious, this action could allow attackers to operate undetected, leading to prolonged - persistence and potential further compromise of the system. +description: The following analytic detects modifications to the Windows registry that disable toast notifications. It leverages data from the Endpoint.Registry datamodel, specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\PushNotifications\\ToastEnabled*" with a value set to "0x00000000". This activity is significant because disabling toast notifications can prevent users from receiving critical system and application updates, which adversaries like Azorult exploit for defense evasion. If confirmed malicious, this action could allow attackers to operate undetected, leading to prolonged persistence and potential further compromise of the system. data_source: - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\PushNotifications\\ToastEnabled*" - Registry.registry_value_data="0x00000000" by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disable_toast_notifications_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure - that this registry was included in your config files ex. sysmon config to be monitored. -known_false_positives: - administrators may enable or disable this feature that may - cause some false positive. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\PushNotifications\\ToastEnabled*" Registry.registry_value_data="0x00000000" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disable_toast_notifications_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored. +known_false_positives: administrators may enable or disable this feature that may cause some false positive. references: - - https://docs.microsoft.com/en-us/windows-hardware/customize/desktop/unattend/microsoft-windows-remoteassistance-exe-fallowtogethelp - - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ + - https://docs.microsoft.com/en-us/windows-hardware/customize/desktop/unattend/microsoft-windows-remoteassistance-exe-fallowtogethelp + - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: the registry for DisallowRun settings was modified to enable on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: the registry for DisallowRun settings was modified to enable on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Azorult - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azorult + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_disable_win_defender_raw_write_notif.yml b/detections/endpoint/windows_modify_registry_disable_win_defender_raw_write_notif.yml index cccc97d72b..cf4644b7c8 100644 --- a/detections/endpoint/windows_modify_registry_disable_win_defender_raw_write_notif.yml +++ b/detections/endpoint/windows_modify_registry_disable_win_defender_raw_write_notif.yml @@ -5,77 +5,46 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: - The following analytic detects modifications to the Windows registry - that disable the Windows Defender raw write notification feature. It leverages data - from the Endpoint.Registry datamodel, specifically monitoring changes to the registry - path associated with Windows Defender's real-time protection settings. This activity - is significant because disabling raw write notifications can allow malware, such - as Azorult, to bypass Windows Defender's behavior monitoring, potentially leading - to undetected malicious activities. If confirmed malicious, this could enable attackers - to execute code, persist in the environment, and access sensitive information without - detection. +description: The following analytic detects modifications to the Windows registry that disable the Windows Defender raw write notification feature. It leverages data from the Endpoint.Registry datamodel, specifically monitoring changes to the registry path associated with Windows Defender's real-time protection settings. This activity is significant because disabling raw write notifications can allow malware, such as Azorult, to bypass Windows Defender's behavior monitoring, potentially leading to undetected malicious activities. If confirmed malicious, this could enable attackers to execute code, persist in the environment, and access sensitive information without detection. data_source: - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows - Defender\\Real-Time Protection\\DisableRawWriteNotification*" Registry.registry_value_data="0x00000001" - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_modify_registry_disable_win_defender_raw_write_notif_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure - that this registry was included in your config files ex. sysmon config to be monitored. -known_false_positives: - Administrators may enable or disable this feature that may - cause some false positive. Filter as needed. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\Real-Time Protection\\DisableRawWriteNotification*" Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disable_win_defender_raw_write_notif_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored. +known_false_positives: Administrators may enable or disable this feature that may cause some false positive. Filter as needed. references: - - https://admx.help/?Category=SystemCenterEndpointProtection&Policy=Microsoft.Policies.Antimalware::real-time_protection_disablerawwritenotification - - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ + - https://admx.help/?Category=SystemCenterEndpointProtection&Policy=Microsoft.Policies.Antimalware::real-time_protection_disablerawwritenotification + - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - The registry for raw write notification settings was modified to disable - on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: The registry for raw write notification settings was modified to disable on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Azorult - - CISA AA23-347A - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azorult + - CISA AA23-347A + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_disable_windefender_notifications.yml b/detections/endpoint/windows_modify_registry_disable_windefender_notifications.yml index 409bb33434..92fa9c00ae 100644 --- a/detections/endpoint/windows_modify_registry_disable_windefender_notifications.yml +++ b/detections/endpoint/windows_modify_registry_disable_windefender_notifications.yml @@ -6,76 +6,47 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: - - Sysmon EventID 13 -description: - The following analytic detects a suspicious registry modification aimed - at disabling Windows Defender notifications. It leverages data from the Endpoint.Registry - data model, specifically looking for changes to the registry path "*\\SOFTWARE\\Policies\\Microsoft\\Windows - Defender Security Center\\Notifications\\DisableNotifications" with a value of "0x00000001". - This activity is significant as it indicates an attempt to evade detection by disabling - security alerts, a technique used by adversaries and malware like RedLine Stealer. - If confirmed malicious, this could allow attackers to operate undetected, increasing - the risk of further compromise and data exfiltration. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows - Defender Security Center\\Notifications\\DisableNotifications" AND Registry.registry_value_data="0x00000001" - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` - | `windows_modify_registry_disable_windefender_notifications_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: - administrators may enable or disable this feature that may - cause some false positive. + - Sysmon EventID 13 +description: The following analytic detects a suspicious registry modification aimed at disabling Windows Defender notifications. It leverages data from the Endpoint.Registry data model, specifically looking for changes to the registry path "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender Security Center\\Notifications\\DisableNotifications" with a value of "0x00000001". This activity is significant as it indicates an attempt to evade detection by disabling security alerts, a technique used by adversaries and malware like RedLine Stealer. If confirmed malicious, this could allow attackers to operate undetected, increasing the risk of further compromise and data exfiltration. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender Security Center\\Notifications\\DisableNotifications" AND Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_disable_windefender_notifications_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: administrators may enable or disable this feature that may cause some false positive. references: - - https://malpedia.caad.fkie.fraunhofer.de/details/win.redline_stealer + - https://malpedia.caad.fkie.fraunhofer.de/details/win.redline_stealer drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A registry modification to disable Windows Defender notification on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: A registry modification to disable Windows Defender notification on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - CISA AA23-347A - - RedLine Stealer - - SolarWinds WHD RCE Post Exploitation - asset_type: Endpoint - atomic_guid: - - 12e03af7-79f9-4f95-af48-d3f12f28a260 - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA23-347A + - RedLine Stealer + - SolarWinds WHD RCE Post Exploitation + asset_type: Endpoint + atomic_guid: + - 12e03af7-79f9-4f95-af48-d3f12f28a260 + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_disable_windows_security_center_notif.yml b/detections/endpoint/windows_modify_registry_disable_windows_security_center_notif.yml index 4f4e41ba9f..56f3096d7d 100644 --- a/detections/endpoint/windows_modify_registry_disable_windows_security_center_notif.yml +++ b/detections/endpoint/windows_modify_registry_disable_windows_security_center_notif.yml @@ -5,76 +5,46 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: - The following analytic detects modifications to the Windows registry - aimed at disabling Windows Security Center notifications. It leverages data from - the Endpoint.Registry datamodel, specifically monitoring changes to the registry - path "*\\Windows\\CurrentVersion\\ImmersiveShell\\UseActionCenterExperience*" with - a value of "0x00000000". This activity is significant as it can indicate an attempt - by adversaries or malware, such as Azorult, to evade defenses by suppressing critical - update notifications. If confirmed malicious, this could allow attackers to persist - undetected, potentially leading to further exploitation and compromise of the host - system. +description: The following analytic detects modifications to the Windows registry aimed at disabling Windows Security Center notifications. It leverages data from the Endpoint.Registry datamodel, specifically monitoring changes to the registry path "*\\Windows\\CurrentVersion\\ImmersiveShell\\UseActionCenterExperience*" with a value of "0x00000000". This activity is significant as it can indicate an attempt by adversaries or malware, such as Azorult, to evade defenses by suppressing critical update notifications. If confirmed malicious, this could allow attackers to persist undetected, potentially leading to further exploitation and compromise of the host system. data_source: - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows\\CurrentVersion\\ImmersiveShell\\UseActionCenterExperience*" - Registry.registry_value_data="0x00000000" by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disable_windows_security_center_notif_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure - that this registry was included in your config files ex. sysmon config to be monitored. -known_false_positives: - administrators may enable or disable this feature that may - cause some false positive. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows\\CurrentVersion\\ImmersiveShell\\UseActionCenterExperience*" Registry.registry_value_data="0x00000000" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disable_windows_security_center_notif_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored. +known_false_positives: administrators may enable or disable this feature that may cause some false positive. references: - - https://docs.microsoft.com/en-us/windows-hardware/customize/desktop/unattend/microsoft-windows-remoteassistance-exe-fallowtogethelp - - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ + - https://docs.microsoft.com/en-us/windows-hardware/customize/desktop/unattend/microsoft-windows-remoteassistance-exe-fallowtogethelp + - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - the registry for security center notification settings was modified to - disable mode on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: the registry for security center notification settings was modified to disable mode on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Azorult - - CISA AA23-347A - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azorult + - CISA AA23-347A + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_disableremotedesktopantialias.yml b/detections/endpoint/windows_modify_registry_disableremotedesktopantialias.yml index c476fe6693..5c4fe2b4d0 100644 --- a/detections/endpoint/windows_modify_registry_disableremotedesktopantialias.yml +++ b/detections/endpoint/windows_modify_registry_disableremotedesktopantialias.yml @@ -6,73 +6,43 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: - - Sysmon EventID 13 -description: - The following analytic detects modifications to the Windows registry - key "DisableRemoteDesktopAntiAlias" with a value set to 0x00000001. This detection - leverages data from the Endpoint datamodel, specifically monitoring changes in the - Registry node. This activity is significant as it may indicate the presence of DarkGate - malware, which alters this registry setting to enhance its remote desktop capabilities. - If confirmed malicious, this modification could allow an attacker to maintain persistence - and control over the compromised host, potentially leading to further exploitation - and data exfiltration. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Terminal - Services\\DisableRemoteDesktopAntiAlias" Registry.registry_value_data = 0x00000001 - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_modify_registry_disableremotedesktopantialias_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure - that this registry was included in your config files ex. sysmon config to be monitored. -known_false_positives: - Administrators may enable or disable this feature that may - cause some false positive, however is not common. Filter as needed. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows registry key "DisableRemoteDesktopAntiAlias" with a value set to 0x00000001. This detection leverages data from the Endpoint datamodel, specifically monitoring changes in the Registry node. This activity is significant as it may indicate the presence of DarkGate malware, which alters this registry setting to enhance its remote desktop capabilities. If confirmed malicious, this modification could allow an attacker to maintain persistence and control over the compromised host, potentially leading to further exploitation and data exfiltration. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Terminal Services\\DisableRemoteDesktopAntiAlias" Registry.registry_value_data = 0x00000001 by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disableremotedesktopantialias_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored. +known_false_positives: Administrators may enable or disable this feature that may cause some false positive, however is not common. Filter as needed. references: - - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate + - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - the registry for remote desktop settings was modified to be DisableRemoteDesktopAntiAlias - on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: the registry for remote desktop settings was modified to be DisableRemoteDesktopAntiAlias on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - DarkGate Malware - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - DarkGate Malware + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/DisableRemoteDesktopAntiAlias/disable_remote_alias.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/DisableRemoteDesktopAntiAlias/disable_remote_alias.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_disablesecuritysettings.yml b/detections/endpoint/windows_modify_registry_disablesecuritysettings.yml index 32e9c8f56f..f05a562165 100644 --- a/detections/endpoint/windows_modify_registry_disablesecuritysettings.yml +++ b/detections/endpoint/windows_modify_registry_disablesecuritysettings.yml @@ -6,74 +6,44 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: - - Sysmon EventID 13 -description: - The following analytic detects modifications to the Windows registry - that disable security settings for Terminal Services. It leverages the Endpoint - data model, specifically monitoring changes to the registry path associated with - Terminal Services security settings. This activity is significant because altering - these settings can weaken the security posture of Remote Desktop Services, potentially - allowing unauthorized remote access. If confirmed malicious, such modifications - could enable attackers to gain persistent remote access to the system, facilitating - further exploitation and data exfiltration. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Terminal - Services\\DisableSecuritySettings" Registry.registry_value_data = 0x00000001 by - Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_modify_registry_disablesecuritysettings_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure - that this registry was included in your config files ex. sysmon config to be monitored. -known_false_positives: - Administrators may enable or disable this feature that may - cause some false positive, however is not common. Filter as needed. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows registry that disable security settings for Terminal Services. It leverages the Endpoint data model, specifically monitoring changes to the registry path associated with Terminal Services security settings. This activity is significant because altering these settings can weaken the security posture of Remote Desktop Services, potentially allowing unauthorized remote access. If confirmed malicious, such modifications could enable attackers to gain persistent remote access to the system, facilitating further exploitation and data exfiltration. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Terminal Services\\DisableSecuritySettings" Registry.registry_value_data = 0x00000001 by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disablesecuritysettings_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored. +known_false_positives: Administrators may enable or disable this feature that may cause some false positive, however is not common. Filter as needed. references: - - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate + - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - the registry for terminal services settings was modified to disable security - settings on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: the registry for terminal services settings was modified to disable security settings on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - DarkGate Malware - - CISA AA23-347A - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - DarkGate Malware + - CISA AA23-347A + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/disablesecuritysetting.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/disablesecuritysetting.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_disabling_wer_settings.yml b/detections/endpoint/windows_modify_registry_disabling_wer_settings.yml index 8981dfe601..a43d5c4dfa 100644 --- a/detections/endpoint/windows_modify_registry_disabling_wer_settings.yml +++ b/detections/endpoint/windows_modify_registry_disabling_wer_settings.yml @@ -5,73 +5,46 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: TTP -description: - The following analytic detects modifications in the Windows registry - to disable Windows Error Reporting (WER) settings. It leverages data from the Endpoint.Registry - datamodel, specifically monitoring changes to registry paths related to WER with - a value set to "0x00000001". This activity is significant as adversaries may disable - WER to suppress error notifications, hiding the presence of malicious activities. - If confirmed malicious, this could allow attackers to operate undetected, potentially - leading to prolonged persistence and further exploitation within the environment. +description: The following analytic detects modifications in the Windows registry to disable Windows Error Reporting (WER) settings. It leverages data from the Endpoint.Registry datamodel, specifically monitoring changes to registry paths related to WER with a value set to "0x00000001". This activity is significant as adversaries may disable WER to suppress error notifications, hiding the presence of malicious activities. If confirmed malicious, this could allow attackers to operate undetected, potentially leading to prolonged persistence and further exploitation within the environment. data_source: - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\Windows - Error Reporting\\disable*" Registry.registry_value_data="0x00000001" by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_modify_registry_disabling_wer_settings_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure - that this registry was included in your config files ex. sysmon config to be monitored. -known_false_positives: - Administrators may enable or disable this feature that may - cause some false positive, however is not common. Filter as needed. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\Windows Error Reporting\\disable*" Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disabling_wer_settings_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored. +known_false_positives: Administrators may enable or disable this feature that may cause some false positive, however is not common. Filter as needed. references: - - https://docs.microsoft.com/en-us/windows-hardware/customize/desktop/unattend/microsoft-windows-remoteassistance-exe-fallowtogethelp - - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ + - https://docs.microsoft.com/en-us/windows-hardware/customize/desktop/unattend/microsoft-windows-remoteassistance-exe-fallowtogethelp + - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: the registry for WER settings was modified to be disabled on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: the registry for WER settings was modified to be disabled on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Azorult - - CISA AA23-347A - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azorult + - CISA AA23-347A + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_disallow_windows_app.yml b/detections/endpoint/windows_modify_registry_disallow_windows_app.yml index 1dde3b81c8..9ae1ad7011 100644 --- a/detections/endpoint/windows_modify_registry_disallow_windows_app.yml +++ b/detections/endpoint/windows_modify_registry_disallow_windows_app.yml @@ -5,70 +5,44 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: TTP -description: - The following analytic detects modifications to the Windows registry - aimed at preventing the execution of specific computer programs. It leverages data - from the Endpoint.Registry datamodel, focusing on changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\DisallowRun*" - with a value of "0x00000001". This activity is significant as it can indicate an - attempt to disable security tools, a tactic used by malware like Azorult. If confirmed - malicious, this could allow an attacker to evade detection and maintain persistence - on the compromised host. +description: The following analytic detects modifications to the Windows registry aimed at preventing the execution of specific computer programs. It leverages data from the Endpoint.Registry datamodel, focusing on changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\DisallowRun*" with a value of "0x00000001". This activity is significant as it can indicate an attempt to disable security tools, a tactic used by malware like Azorult. If confirmed malicious, this could allow an attacker to evade detection and maintain persistence on the compromised host. data_source: - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\DisallowRun*" - Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disallow_windows_app_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure - that this registry was included in your config files ex. sysmon config to be monitored. -known_false_positives: - Administrators may enable or disable this feature that may - cause some false positive. Filter as needed. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\DisallowRun*" Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disallow_windows_app_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored. +known_false_positives: Administrators may enable or disable this feature that may cause some false positive. Filter as needed. references: - - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ + - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The registry for DisallowRun settings was modified to enable on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: The registry for DisallowRun settings was modified to enable on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Azorult - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azorult + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_do_not_connect_to_win_update.yml b/detections/endpoint/windows_modify_registry_do_not_connect_to_win_update.yml index ebcbb2f88a..a9b3509d96 100644 --- a/detections/endpoint/windows_modify_registry_do_not_connect_to_win_update.yml +++ b/detections/endpoint/windows_modify_registry_do_not_connect_to_win_update.yml @@ -6,74 +6,46 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: - - Sysmon EventID 13 -description: - The following analytic detects a suspicious modification to the Windows - registry that disables automatic updates. It leverages data from the Endpoint datamodel, - specifically monitoring changes to the registry path "*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\DoNotConnectToWindowsUpdateInternetLocations" - with a value of "0x00000001". This activity is significant as it can be used by - adversaries, including malware like RedLine Stealer, to evade detection and prevent - the system from receiving critical updates. If confirmed malicious, this could allow - attackers to exploit vulnerabilities, persist in the environment, and potentially - deploy additional payloads. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\DoNotConnectToWindowsUpdateInternetLocations" - AND Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_do_not_connect_to_win_update_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: - administrators may enable or disable this feature that may - cause some false positive. + - Sysmon EventID 13 +description: The following analytic detects a suspicious modification to the Windows registry that disables automatic updates. It leverages data from the Endpoint datamodel, specifically monitoring changes to the registry path "*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\DoNotConnectToWindowsUpdateInternetLocations" with a value of "0x00000001". This activity is significant as it can be used by adversaries, including malware like RedLine Stealer, to evade detection and prevent the system from receiving critical updates. If confirmed malicious, this could allow attackers to exploit vulnerabilities, persist in the environment, and potentially deploy additional payloads. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\DoNotConnectToWindowsUpdateInternetLocations" AND Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_do_not_connect_to_win_update_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: administrators may enable or disable this feature that may cause some false positive. references: - - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499 - - https://admx.help/?Category=Windows_10_2016&Policy=Microsoft.Policies.WindowsUpdate::DoNotConnectToWindowsUpdateInternetLocations + - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499 + - https://admx.help/?Category=Windows_10_2016&Policy=Microsoft.Policies.WindowsUpdate::DoNotConnectToWindowsUpdateInternetLocations drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a registry modification in Windows auto update configuration on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: a registry modification in Windows auto update configuration on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - RedLine Stealer - asset_type: Endpoint - atomic_guid: - - 12e03af7-79f9-4f95-af48-d3f12f28a260 - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - RedLine Stealer + asset_type: Endpoint + atomic_guid: + - 12e03af7-79f9-4f95-af48-d3f12f28a260 + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_dontshowui.yml b/detections/endpoint/windows_modify_registry_dontshowui.yml index 7e7cab8c7d..eab6854a48 100644 --- a/detections/endpoint/windows_modify_registry_dontshowui.yml +++ b/detections/endpoint/windows_modify_registry_dontshowui.yml @@ -6,71 +6,43 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: - - Sysmon EventID 13 -description: - The following analytic detects modifications to the Windows Error Reporting - registry key "DontShowUI" to suppress error reporting dialogs. It leverages data - from the Endpoint datamodel's Registry node to identify changes where the registry - value is set to 0x00000001. This activity is significant as it is commonly associated - with DarkGate malware, which uses this modification to avoid detection during its - installation. If confirmed malicious, this behavior could allow attackers to maintain - a low profile, avoiding user alerts and potentially enabling further malicious activities - without user intervention. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\SOFTWARE\\Microsoft\\Windows\\Windows - Error Reporting\\DontShowUI" Registry.registry_value_data = 0x00000001 by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_modify_registry_dontshowui_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure - that this registry was included in your config files ex. sysmon config to be monitored. -known_false_positives: - Administrators may enable or disable this feature that may - cause some false positive, however is not common. Filter as needed. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows Error Reporting registry key "DontShowUI" to suppress error reporting dialogs. It leverages data from the Endpoint datamodel's Registry node to identify changes where the registry value is set to 0x00000001. This activity is significant as it is commonly associated with DarkGate malware, which uses this modification to avoid detection during its installation. If confirmed malicious, this behavior could allow attackers to maintain a low profile, avoiding user alerts and potentially enabling further malicious activities without user intervention. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\SOFTWARE\\Microsoft\\Windows\\Windows Error Reporting\\DontShowUI" Registry.registry_value_data = 0x00000001 by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_dontshowui_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored. +known_false_positives: Administrators may enable or disable this feature that may cause some false positive, however is not common. Filter as needed. references: - - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate + - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: the registry for WER settings was modified to be disable show UI on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: the registry for WER settings was modified to be disable show UI on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - DarkGate Malware - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - DarkGate Malware + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/wer_dontshowui/dontshowui_sys.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/wer_dontshowui/dontshowui_sys.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_enablelinkedconnections.yml b/detections/endpoint/windows_modify_registry_enablelinkedconnections.yml index a03626a1d1..79409af943 100644 --- a/detections/endpoint/windows_modify_registry_enablelinkedconnections.yml +++ b/detections/endpoint/windows_modify_registry_enablelinkedconnections.yml @@ -6,74 +6,45 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: - - Sysmon EventID 13 -description: - The following analytic detects a suspicious modification to the Windows - registry setting for EnableLinkedConnections. It leverages data from the Endpoint.Registry - datamodel to identify changes where the registry path is "*\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLinkedConnections" - and the value is set to "0x00000001". This activity is significant because enabling - linked connections can allow network shares to be accessed with both standard and - administrator-level privileges, a technique often abused by malware like BlackByte - ransomware. If confirmed malicious, this could lead to unauthorized access to sensitive - network resources, escalating the attacker's privileges. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLinkedConnections" - Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_enablelinkedconnections_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure - that this registry was included in your config files ex. sysmon config to be monitored. -known_false_positives: - Administrators may enable or disable this feature that may - cause some false positive. + - Sysmon EventID 13 +description: The following analytic detects a suspicious modification to the Windows registry setting for EnableLinkedConnections. It leverages data from the Endpoint.Registry datamodel to identify changes where the registry path is "*\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLinkedConnections" and the value is set to "0x00000001". This activity is significant because enabling linked connections can allow network shares to be accessed with both standard and administrator-level privileges, a technique often abused by malware like BlackByte ransomware. If confirmed malicious, this could lead to unauthorized access to sensitive network resources, escalating the attacker's privileges. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLinkedConnections" Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_enablelinkedconnections_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored. +known_false_positives: Administrators may enable or disable this feature that may cause some false positive. references: - - https://www.microsoft.com/en-us/security/blog/2023/07/06/the-five-day-job-a-blackbyte-ransomware-intrusion-case-study/ + - https://www.microsoft.com/en-us/security/blog/2023/07/06/the-five-day-job-a-blackbyte-ransomware-intrusion-case-study/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A registry modification in Windows EnableLinkedConnections configuration - on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: A registry modification in Windows EnableLinkedConnections configuration on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - BlackByte Ransomware - asset_type: Endpoint - atomic_guid: - - 4f4e2f9f-6209-4fcf-9b15-3b7455706f5b - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - BlackByte Ransomware + asset_type: Endpoint + atomic_guid: + - 4f4e2f9f-6209-4fcf-9b15-3b7455706f5b + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/blackbyte/enablelinkedconnections/blackbyte_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/blackbyte/enablelinkedconnections/blackbyte_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_longpathsenabled.yml b/detections/endpoint/windows_modify_registry_longpathsenabled.yml index 02f737464e..4a20a06528 100644 --- a/detections/endpoint/windows_modify_registry_longpathsenabled.yml +++ b/detections/endpoint/windows_modify_registry_longpathsenabled.yml @@ -6,72 +6,45 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: - - Sysmon EventID 13 -description: - The following analytic detects a modification to the Windows registry - setting "LongPathsEnabled," which allows file paths longer than 260 characters. - This detection leverages data from the Endpoint.Registry datamodel, focusing on - changes to the specific registry path and value. This activity is significant because - adversaries, including malware like BlackByte, exploit this setting to bypass file - path limitations, potentially aiding in evasion techniques. If confirmed malicious, - this modification could facilitate the execution of long-path payloads, aiding in - persistence and further system compromise. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\CurrentControlSet\\Control\\FileSystem\\LongPathsEnabled" - Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_longpathsenabled_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure - that this registry was included in your config files ex. sysmon config to be monitored. -known_false_positives: - Administrators may enable or disable this feature that may - cause some false positive. + - Sysmon EventID 13 +description: The following analytic detects a modification to the Windows registry setting "LongPathsEnabled," which allows file paths longer than 260 characters. This detection leverages data from the Endpoint.Registry datamodel, focusing on changes to the specific registry path and value. This activity is significant because adversaries, including malware like BlackByte, exploit this setting to bypass file path limitations, potentially aiding in evasion techniques. If confirmed malicious, this modification could facilitate the execution of long-path payloads, aiding in persistence and further system compromise. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\CurrentControlSet\\Control\\FileSystem\\LongPathsEnabled" Registry.registry_value_data = "0x00000001") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_longpathsenabled_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored. +known_false_positives: Administrators may enable or disable this feature that may cause some false positive. references: - - https://www.microsoft.com/en-us/security/blog/2023/07/06/the-five-day-job-a-blackbyte-ransomware-intrusion-case-study/ + - https://www.microsoft.com/en-us/security/blog/2023/07/06/the-five-day-job-a-blackbyte-ransomware-intrusion-case-study/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A registry modification in Windows LongPathEnable configuration on $dest$ - risk_objects: - - field: dest - type: system - score: 16 - threat_objects: [] + message: A registry modification in Windows LongPathEnable configuration on $dest$ + risk_objects: + - field: dest + type: system + score: 16 + threat_objects: [] tags: - analytic_story: - - BlackByte Ransomware - asset_type: Endpoint - atomic_guid: - - 4f4e2f9f-6209-4fcf-9b15-3b7455706f5b - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - BlackByte Ransomware + asset_type: Endpoint + atomic_guid: + - 4f4e2f9f-6209-4fcf-9b15-3b7455706f5b + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/blackbyte/longpathsenabled/longpath_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/blackbyte/longpathsenabled/longpath_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_maxconnectionperserver.yml b/detections/endpoint/windows_modify_registry_maxconnectionperserver.yml index 5b7e23de80..908910be37 100644 --- a/detections/endpoint/windows_modify_registry_maxconnectionperserver.yml +++ b/detections/endpoint/windows_modify_registry_maxconnectionperserver.yml @@ -6,73 +6,44 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: - - Sysmon EventID 13 -description: - The following analytic identifies a suspicious modification of the Windows - registry setting for max connections per server. It detects changes to specific - registry paths using data from the Endpoint.Registry datamodel. This activity is - significant because altering this setting can be exploited by attackers to increase - the number of concurrent connections to a remote server, potentially facilitating - DDoS attacks or enabling more effective lateral movement within a compromised network. - If confirmed malicious, this could lead to network disruption or further compromise - of additional systems. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Internet - Settings\\MaxConnectionsPerServer*" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Internet - Settings\\MaxConnectionsPer1_0Server*") Registry.registry_value_data = "0x0000000a" - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_modify_registry_maxconnectionperserver_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure - that this registry was included in your config files ex. sysmon config to be monitored. -known_false_positives: - Administrators may enable or disable this feature that may - cause some false positive. + - Sysmon EventID 13 +description: The following analytic identifies a suspicious modification of the Windows registry setting for max connections per server. It detects changes to specific registry paths using data from the Endpoint.Registry datamodel. This activity is significant because altering this setting can be exploited by attackers to increase the number of concurrent connections to a remote server, potentially facilitating DDoS attacks or enabling more effective lateral movement within a compromised network. If confirmed malicious, this could lead to network disruption or further compromise of additional systems. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Internet Settings\\MaxConnectionsPerServer*" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Internet Settings\\MaxConnectionsPer1_0Server*") Registry.registry_value_data = "0x0000000a" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_maxconnectionperserver_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored. +known_false_positives: Administrators may enable or disable this feature that may cause some false positive. references: - - https://asec.ahnlab.com/en/17692/ - - https://www.blackberry.com/us/en/solutions/endpoint-security/ransomware-protection/warzone#:~:text=Warzone%20RAT%20(AKA%20Ave%20Maria)%20is%20a%20remote%20access%20trojan,is%20as%20an%20information%20stealer. + - https://asec.ahnlab.com/en/17692/ + - https://www.blackberry.com/us/en/solutions/endpoint-security/ransomware-protection/warzone#:~:text=Warzone%20RAT%20(AKA%20Ave%20Maria)%20is%20a%20remote%20access%20trojan,is%20as%20an%20information%20stealer. drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A registry modification in max connection per server configuration on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A registry modification in max connection per server configuration on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Warzone RAT - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Warzone RAT + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/warzone_rat/maxconnectionperserver/registry_event.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/warzone_rat/maxconnectionperserver/registry_event.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_no_auto_reboot_with_logon_user.yml b/detections/endpoint/windows_modify_registry_no_auto_reboot_with_logon_user.yml index b33163745a..57934939eb 100644 --- a/detections/endpoint/windows_modify_registry_no_auto_reboot_with_logon_user.yml +++ b/detections/endpoint/windows_modify_registry_no_auto_reboot_with_logon_user.yml @@ -6,72 +6,45 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: - - Sysmon EventID 13 -description: - The following analytic detects a suspicious modification to the Windows - registry that disables automatic reboot with a logged-on user. This detection leverages - the Endpoint data model to identify changes to the registry path `SOFTWARE\Policies\Microsoft\Windows\WindowsUpdate\AU\NoAutoRebootWithLoggedOnUsers` - with a value of `0x00000001`. This activity is significant as it is commonly used - by adversaries, including malware like RedLine Stealer, to evade detection and maintain - persistence. If confirmed malicious, this could allow attackers to bypass security - measures and deploy additional payloads without interruption. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\NoAutoRebootWithLoggedOnUsers" - AND Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_no_auto_reboot_with_logon_user_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: - Administrators may enable or disable this feature that may - cause some false positive. + - Sysmon EventID 13 +description: The following analytic detects a suspicious modification to the Windows registry that disables automatic reboot with a logged-on user. This detection leverages the Endpoint data model to identify changes to the registry path `SOFTWARE\Policies\Microsoft\Windows\WindowsUpdate\AU\NoAutoRebootWithLoggedOnUsers` with a value of `0x00000001`. This activity is significant as it is commonly used by adversaries, including malware like RedLine Stealer, to evade detection and maintain persistence. If confirmed malicious, this could allow attackers to bypass security measures and deploy additional payloads without interruption. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\NoAutoRebootWithLoggedOnUsers" AND Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_no_auto_reboot_with_logon_user_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: Administrators may enable or disable this feature that may cause some false positive. references: - - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499 + - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499 drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A registry modification in Windows auto update configuration on $dest$ - risk_objects: - - field: dest - type: system - score: 9 - threat_objects: [] + message: A registry modification in Windows auto update configuration on $dest$ + risk_objects: + - field: dest + type: system + score: 9 + threat_objects: [] tags: - analytic_story: - - RedLine Stealer - asset_type: Endpoint - atomic_guid: - - 12e03af7-79f9-4f95-af48-d3f12f28a260 - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - RedLine Stealer + asset_type: Endpoint + atomic_guid: + - 12e03af7-79f9-4f95-af48-d3f12f28a260 + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_no_auto_update.yml b/detections/endpoint/windows_modify_registry_no_auto_update.yml index 1f324c5428..d89959f618 100644 --- a/detections/endpoint/windows_modify_registry_no_auto_update.yml +++ b/detections/endpoint/windows_modify_registry_no_auto_update.yml @@ -6,73 +6,46 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: - - Sysmon EventID 13 -description: - The following analytic identifies a suspicious modification to the Windows - registry that disables automatic updates. It detects changes to the registry path - `SOFTWARE\Policies\Microsoft\Windows\WindowsUpdate\AU\NoAutoUpdate` with a value - of `0x00000001`. This activity is significant as it is commonly used by adversaries, - including malware like RedLine Stealer, to evade detection and maintain persistence. - If confirmed malicious, this could allow attackers to bypass security updates, leaving - the system vulnerable to further exploitation and potential zero-day attacks. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\NoAutoUpdate" - AND Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_no_auto_update_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: - Administrators may enable or disable this feature that may - cause some false positive. + - Sysmon EventID 13 +description: The following analytic identifies a suspicious modification to the Windows registry that disables automatic updates. It detects changes to the registry path `SOFTWARE\Policies\Microsoft\Windows\WindowsUpdate\AU\NoAutoUpdate` with a value of `0x00000001`. This activity is significant as it is commonly used by adversaries, including malware like RedLine Stealer, to evade detection and maintain persistence. If confirmed malicious, this could allow attackers to bypass security updates, leaving the system vulnerable to further exploitation and potential zero-day attacks. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\NoAutoUpdate" AND Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_no_auto_update_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: Administrators may enable or disable this feature that may cause some false positive. references: - - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499 + - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499 drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A registry modification in Windows auto update configuration on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: A registry modification in Windows auto update configuration on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - CISA AA23-347A - - RedLine Stealer - asset_type: Endpoint - atomic_guid: - - 12e03af7-79f9-4f95-af48-d3f12f28a260 - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA23-347A + - RedLine Stealer + asset_type: Endpoint + atomic_guid: + - 12e03af7-79f9-4f95-af48-d3f12f28a260 + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_nochangingwallpaper.yml b/detections/endpoint/windows_modify_registry_nochangingwallpaper.yml index 5a57617659..ec0fc40a1a 100644 --- a/detections/endpoint/windows_modify_registry_nochangingwallpaper.yml +++ b/detections/endpoint/windows_modify_registry_nochangingwallpaper.yml @@ -6,72 +6,43 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: - - Sysmon EventID 13 -description: - The following analytic detects modifications to the Windows registry - aimed at preventing wallpaper changes. It leverages data from the Endpoint.Registry - datamodel, specifically monitoring changes to the "NoChangingWallPaper" registry - value. This activity is significant as it is a known tactic used by Rhysida ransomware - to enforce a malicious wallpaper, thereby limiting user control over system settings. - If confirmed malicious, this registry change could indicate a ransomware infection, - leading to further system compromise and user disruption. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\Windows\\CurrentVersion\\Policies\\ActiveDesktop\\NoChangingWallPaper" - Registry.registry_value_data = 1) by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_modify_registry_nochangingwallpaper_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure - that this registry was included in your config files ex. sysmon config to be monitored. -known_false_positives: - Administrators may enable or disable this feature that may - cause some false positive, however is not common. Filter as needed. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows registry aimed at preventing wallpaper changes. It leverages data from the Endpoint.Registry datamodel, specifically monitoring changes to the "NoChangingWallPaper" registry value. This activity is significant as it is a known tactic used by Rhysida ransomware to enforce a malicious wallpaper, thereby limiting user control over system settings. If confirmed malicious, this registry change could indicate a ransomware infection, leading to further system compromise and user disruption. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\Windows\\CurrentVersion\\Policies\\ActiveDesktop\\NoChangingWallPaper" Registry.registry_value_data = 1) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_nochangingwallpaper_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored. +known_false_positives: Administrators may enable or disable this feature that may cause some false positive, however is not common. Filter as needed. references: - - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-319a + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-319a drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - the registry settings was modified to disable changing of wallpaper on - $dest$. - risk_objects: - - field: dest - type: system - score: 36 - threat_objects: [] + message: the registry settings was modified to disable changing of wallpaper on $dest$. + risk_objects: + - field: dest + type: system + score: 36 + threat_objects: [] tags: - analytic_story: - - Rhysida Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Rhysida Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/no_changing_wallpaper/NoChangingWallPaper.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/no_changing_wallpaper/NoChangingWallPaper.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_on_smart_card_group_policy.yml b/detections/endpoint/windows_modify_registry_on_smart_card_group_policy.yml index d1d6fbb4a9..ff7ce48a3d 100644 --- a/detections/endpoint/windows_modify_registry_on_smart_card_group_policy.yml +++ b/detections/endpoint/windows_modify_registry_on_smart_card_group_policy.yml @@ -4,72 +4,45 @@ version: 6 date: '2025-05-02' author: Teoderick Contreras, Splunk data_source: - - Sysmon EventID 13 + - Sysmon EventID 13 type: Anomaly status: production -description: - This analytic is developed to detect suspicious registry modifications - targeting the "scforceoption" key. Altering this key enforces smart card login for - all users, potentially disrupting normal access methods. Unauthorized changes to - this setting could indicate an attempt to restrict access or force a specific authentication - method, possibly signifying malicious intent to manipulate system security protocols. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows\\CurrentVersion\\Policies\\System\\scforceoption*" - Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_on_smart_card_group_policy_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: - administrators may enable or disable this feature that may - cause some false positive. +description: This analytic is developed to detect suspicious registry modifications targeting the "scforceoption" key. Altering this key enforces smart card login for all users, potentially disrupting normal access methods. Unauthorized changes to this setting could indicate an attempt to restrict access or force a specific authentication method, possibly signifying malicious intent to manipulate system security protocols. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows\\CurrentVersion\\Policies\\System\\scforceoption*" Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_on_smart_card_group_policy_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: administrators may enable or disable this feature that may cause some false positive. references: - - https://www.bleepingcomputer.com/news/security/new-shrinklocker-ransomware-uses-bitlocker-to-encrypt-your-files/ + - https://www.bleepingcomputer.com/news/security/new-shrinklocker-ransomware-uses-bitlocker-to-encrypt-your-files/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A registry modification in Windows Smart Card Group Policy registry settings - on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A registry modification in Windows Smart Card Group Policy registry settings on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - ShrinkLocker - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ShrinkLocker + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/smart_card_group_policy/scforceoption-reg.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/smart_card_group_policy/scforceoption-reg.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_proxyenable.yml b/detections/endpoint/windows_modify_registry_proxyenable.yml index fb886685f5..4e7ec02b93 100644 --- a/detections/endpoint/windows_modify_registry_proxyenable.yml +++ b/detections/endpoint/windows_modify_registry_proxyenable.yml @@ -6,71 +6,43 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: - - Sysmon EventID 13 -description: - The following analytic detects modifications to the Windows registry - key "ProxyEnable" to enable proxy settings. It leverages data from the Endpoint.Registry - datamodel, specifically monitoring changes to the "Internet Settings\ProxyEnable" - registry path. This activity is significant as it is commonly exploited by malware - and adversaries to establish proxy communication, potentially connecting to malicious - Command and Control (C2) servers. If confirmed malicious, this could allow attackers - to redirect network traffic through a proxy, facilitating unauthorized communication - and data exfiltration, thereby compromising the security of the affected host. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Internet - Settings\\ProxyEnable" Registry.registry_value_data = 0x00000001 by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_modify_registry_proxyenable_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure - that this registry was included in your config files ex. sysmon config to be monitored. -known_false_positives: - Administrators may enable or disable this feature that may - cause some false positive, however is not common. Filter as needed. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows registry key "ProxyEnable" to enable proxy settings. It leverages data from the Endpoint.Registry datamodel, specifically monitoring changes to the "Internet Settings\ProxyEnable" registry path. This activity is significant as it is commonly exploited by malware and adversaries to establish proxy communication, potentially connecting to malicious Command and Control (C2) servers. If confirmed malicious, this could allow attackers to redirect network traffic through a proxy, facilitating unauthorized communication and data exfiltration, thereby compromising the security of the affected host. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Internet Settings\\ProxyEnable" Registry.registry_value_data = 0x00000001 by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_proxyenable_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored. +known_false_positives: Administrators may enable or disable this feature that may cause some false positive, however is not common. Filter as needed. references: - - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate + - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: the registry settings was modified to enable proxy on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: the registry settings was modified to enable proxy on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - DarkGate Malware - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - DarkGate Malware + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/proxy_enable/proxyenable.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/proxy_enable/proxyenable.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_proxyserver.yml b/detections/endpoint/windows_modify_registry_proxyserver.yml index ed6af1061c..cfc5f1f6b9 100644 --- a/detections/endpoint/windows_modify_registry_proxyserver.yml +++ b/detections/endpoint/windows_modify_registry_proxyserver.yml @@ -6,69 +6,43 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: - - Sysmon EventID 13 -description: - The following analytic detects modifications to the Windows registry - key for setting up a proxy server. It leverages data from the Endpoint.Registry - datamodel, focusing on changes to the "Internet Settings\\ProxyServer" registry - path. This activity is significant as it can indicate malware or adversaries configuring - a proxy to facilitate unauthorized communication with Command and Control (C2) servers. - If confirmed malicious, this could allow attackers to establish persistent, covert - channels for data exfiltration or further exploitation of the compromised host. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Internet - Settings\\ProxyServer" by Registry.action Registry.dest Registry.process_guid Registry.process_id - Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data - Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user - Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_proxyserver_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure - that this registry was included in your config files ex. sysmon config to be monitored. -known_false_positives: - Administrators may enable or disable this feature that may - cause some false positive, however is not common. Filter as needed. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows registry key for setting up a proxy server. It leverages data from the Endpoint.Registry datamodel, focusing on changes to the "Internet Settings\\ProxyServer" registry path. This activity is significant as it can indicate malware or adversaries configuring a proxy to facilitate unauthorized communication with Command and Control (C2) servers. If confirmed malicious, this could allow attackers to establish persistent, covert channels for data exfiltration or further exploitation of the compromised host. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Internet Settings\\ProxyServer" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_proxyserver_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored. +known_false_positives: Administrators may enable or disable this feature that may cause some false positive, however is not common. Filter as needed. references: - - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate + - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: the registry settings was modified to setup proxy server on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: the registry settings was modified to setup proxy server on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - DarkGate Malware - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - DarkGate Malware + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/proxy_server/ProxyServer_sys.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/proxy_server/ProxyServer_sys.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_qakbot_binary_data_registry.yml b/detections/endpoint/windows_modify_registry_qakbot_binary_data_registry.yml index 73d4c21f2c..643260c94e 100644 --- a/detections/endpoint/windows_modify_registry_qakbot_binary_data_registry.yml +++ b/detections/endpoint/windows_modify_registry_qakbot_binary_data_registry.yml @@ -5,89 +5,44 @@ date: '2026-01-14' author: Teoderick Contreras, Bhavin Patel, Splunk status: production type: Anomaly -description: - The following analytic detects the creation of a suspicious registry - entry by Qakbot malware, characterized by 8 random registry value names with encrypted - binary data. This detection leverages data from Endpoint Detection and Response - (EDR) agents, focusing on registry modifications under the "SOFTWARE\\Microsoft\\" - path by processes like explorer.exe. This activity is significant as it indicates - potential Qakbot infection, which uses the registry to store malicious code or configuration - data. If confirmed malicious, this could allow attackers to maintain persistence - and execute arbitrary code on the compromised system. +description: The following analytic detects the creation of a suspicious registry entry by Qakbot malware, characterized by 8 random registry value names with encrypted binary data. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on registry modifications under the "SOFTWARE\\Microsoft\\" path by processes like explorer.exe. This activity is significant as it indicates potential Qakbot infection, which uses the registry to store malicious code or configuration data. If confirmed malicious, this could allow attackers to maintain persistence and execute arbitrary code on the compromised system. data_source: - - Sysmon EventID 1 AND Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count dc(registry_value_name) as - registry_value_name_count FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Microsoft\\*" - AND Registry.registry_value_data = "Binary Data" by _time span=1m Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | eval registry_key_name_len = len(registry_key_name) - | eval registry_value_name_len = len(registry_value_name) | regex registry_value_name="^[0-9a-fA-F]{8}" - | where registry_key_name_len < 80 AND registry_value_name_len == 8 | join process_guid, - _time [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes - where Processes.process_name IN ("explorer.exe", "wermgr.exe","dxdiag.exe", "OneDriveSetup.exe", - "mobsync.exe", "msra.exe", "xwizard.exe") by _time span=1m Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` ] | stats min(_time) as firstTime max(_time) - as lastTime values(registry_value_name) as registry_value_name dc(registry_value_name) - as registry_value_name_count values(registry_key_name) by dest process_guid process_name - parent_process_name | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | where registry_value_name_count >= 5 | `windows_modify_registry_qakbot_binary_data_registry_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 AND Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count dc(registry_value_name) as registry_value_name_count FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Microsoft\\*" AND Registry.registry_value_data = "Binary Data" by _time span=1m Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | eval registry_key_name_len = len(registry_key_name) | eval registry_value_name_len = len(registry_value_name) | regex registry_value_name="^[0-9a-fA-F]{8}" | where registry_key_name_len < 80 AND registry_value_name_len == 8 | join process_guid, _time [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name IN ("explorer.exe", "wermgr.exe","dxdiag.exe", "OneDriveSetup.exe", "mobsync.exe", "msra.exe", "xwizard.exe") by _time span=1m Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` ] | stats min(_time) as firstTime max(_time) as lastTime values(registry_value_name) as registry_value_name dc(registry_value_name) as registry_value_name_count values(registry_key_name) by dest process_guid process_name parent_process_name | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where registry_value_name_count >= 5 | `windows_modify_registry_qakbot_binary_data_registry_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: - - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/decrypting-qakbots-encrypted-registry-keys/ + - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/decrypting-qakbots-encrypted-registry-keys/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Registry with binary data created by $process_name$ on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Registry with binary data created by $process_name$ on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Qakbot - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Qakbot + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot2/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot2/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_regedit_silent_reg_import.yml b/detections/endpoint/windows_modify_registry_regedit_silent_reg_import.yml index dff0dd83f7..7b9419f4ab 100644 --- a/detections/endpoint/windows_modify_registry_regedit_silent_reg_import.yml +++ b/detections/endpoint/windows_modify_registry_regedit_silent_reg_import.yml @@ -1,89 +1,69 @@ name: Windows Modify Registry Regedit Silent Reg Import id: 824dd598-71be-4203-bc3b-024f4cda340e -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: - The following analytic detects the modification of the Windows registry - using the regedit.exe application with the silent mode parameter. It leverages data - from Endpoint Detection and Response (EDR) agents, focusing on process names and - command-line executions. This activity is significant because the silent mode allows - registry changes without user confirmation, which can be exploited by adversaries - to import malicious registry settings. If confirmed malicious, this could enable - attackers to persist in the environment, escalate privileges, or manipulate system - configurations, leading to potential system compromise. +description: The following analytic detects the modification of the Windows registry using the regedit.exe application with the silent mode parameter. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because the silent mode allows registry changes without user confirmation, which can be exploited by adversaries to import malicious registry settings. If confirmed malicious, this could enable attackers to persist in the environment, escalate privileges, or manipulate system configurations, leading to potential system compromise. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 -search: - '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where (Processes.process_name="regedit.exe" OR Processes.original_file_name="regedit.exe") - AND Processes.process="* /s *" AND Processes.process="*.reg*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_modify_registry_regedit_silent_reg_import_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: - Administrators may execute this command that may cause some - false positive. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="regedit.exe" + OR + Processes.original_file_name="regedit.exe" + ) + AND Processes.process="* /s *" AND Processes.process="*.reg*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_modify_registry_regedit_silent_reg_import_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators may execute this command that may cause some false positive. Filter as needed. references: - - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ - - https://www.techtarget.com/searchwindowsserver/tip/Command-line-options-for-Regeditexe + - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ + - https://www.techtarget.com/searchwindowsserver/tip/Command-line-options-for-Regeditexe drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - The regedit app was executed with silet mode parameter to import .reg file - on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: The regedit app was executed with silet mode parameter to import .reg file on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Azorult - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azorult + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_risk_behavior.yml b/detections/endpoint/windows_modify_registry_risk_behavior.yml index 1832ecd823..03f8338e76 100644 --- a/detections/endpoint/windows_modify_registry_risk_behavior.yml +++ b/detections/endpoint/windows_modify_registry_risk_behavior.yml @@ -1,71 +1,51 @@ name: Windows Modify Registry Risk Behavior id: 5eb479b1-a5ea-4e01-8365-780078613776 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Correlation data_source: [] -description: The following analytic identifies instances where three or more distinct - registry modification events associated with MITRE ATT&CK Technique T1112 are detected. - It leverages data from the Risk data model in Splunk, focusing on registry-related - sources and MITRE technique annotations. This activity is significant because multiple - registry modifications can indicate an attempt to persist, hide malicious configurations, - or erase forensic evidence. If confirmed malicious, this behavior could allow attackers - to maintain persistent access, execute malicious code, and evade detection, posing - a severe threat to the integrity and security of the affected host. -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) - as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as - annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) - as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) - as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) - as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, - dc(source) as source_count from datamodel=Risk.All_Risk where source IN ("*registry*") - All_Risk.annotations.mitre_attack.mitre_technique_id IN ("*T1112*") by All_Risk.risk_object - All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where - source_count >= 3 | `windows_modify_registry_risk_behavior_filter`' -how_to_implement: Splunk Enterprise Security is required to utilize this correlation. - In addition, modify the source_count value to your environment. In our testing, - a count of 4 or 5 was decent in a lab, but the number may need to be increased base - on internal testing. In addition, based on false positives, modify any analytics - to be anomaly and lower or increase risk based on organization importance. -known_false_positives: False positives will be present based on many factors. Tune - the correlation as needed to reduce too many triggers. +description: The following analytic identifies instances where three or more distinct registry modification events associated with MITRE ATT&CK Technique T1112 are detected. It leverages data from the Risk data model in Splunk, focusing on registry-related sources and MITRE technique annotations. This activity is significant because multiple registry modifications can indicate an attempt to persist, hide malicious configurations, or erase forensic evidence. If confirmed malicious, this behavior could allow attackers to maintain persistent access, execute malicious code, and evade detection, posing a severe threat to the integrity and security of the affected host. +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count FROM datamodel=Risk.All_Risk + WHERE source IN ("*registry*") All_Risk.annotations.mitre_attack.mitre_technique_id IN ("*T1112*") + BY All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic + | `drop_dm_object_name(All_Risk)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | where source_count >= 3 + | `windows_modify_registry_risk_behavior_filter` +how_to_implement: Splunk Enterprise Security is required to utilize this correlation. In addition, modify the source_count value to your environment. In our testing, a count of 4 or 5 was decent in a lab, but the number may need to be increased base on internal testing. In addition, based on false positives, modify any analytics to be anomaly and lower or increase risk based on organization importance. +known_false_positives: False positives will be present based on many factors. Tune the correlation as needed to reduce too many triggers. references: -- https://www.splunk.com/en_us/blog/security/do-not-cross-the-redline-stealer-detections-and-analysis.html -- https://www.splunk.com/en_us/blog/security/asyncrat-crusade-detections-and-defense.html -- https://www.splunk.com/en_us/blog/security/from-registry-with-love-malware-registry-abuses.html -- https://www.splunk.com/en_us/blog/security/-applocker-rules-as-defense-evasion-complete-analysis.html + - https://www.splunk.com/en_us/blog/security/do-not-cross-the-redline-stealer-detections-and-analysis.html + - https://www.splunk.com/en_us/blog/security/asyncrat-crusade-detections-and-defense.html + - https://www.splunk.com/en_us/blog/security/from-registry-with-love-malware-registry-abuses.html + - https://www.splunk.com/en_us/blog/security/-applocker-rules-as-defense-evasion-complete-analysis.html drilldown_searches: -- name: View the detection results for - "$risk_object$" - search: '%original_detection_search% | search risk_object = "$risk_object$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$risk_object$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$risk_object$" + search: '%original_detection_search% | search risk_object = "$risk_object$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$risk_object$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ tags: - analytic_story: - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/windows_mod_reg_risk_behavior/modify_reg_risk.log - source: mod_reg - sourcetype: stash + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/windows_mod_reg_risk_behavior/modify_reg_risk.log + source: mod_reg + sourcetype: stash diff --git a/detections/endpoint/windows_modify_registry_suppress_win_defender_notif.yml b/detections/endpoint/windows_modify_registry_suppress_win_defender_notif.yml index eb76fc9c36..6333217ed8 100644 --- a/detections/endpoint/windows_modify_registry_suppress_win_defender_notif.yml +++ b/detections/endpoint/windows_modify_registry_suppress_win_defender_notif.yml @@ -5,76 +5,46 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: - The following analytic detects modifications in the Windows registry - to suppress Windows Defender notifications. It leverages data from the Endpoint.Registry - datamodel, specifically targeting changes to the "Notification_Suppress" registry - value. This activity is significant because adversaries, including those deploying - Azorult malware, use this technique to bypass Windows Defender and disable critical - notifications. If confirmed malicious, this behavior could allow attackers to evade - detection, maintain persistence, and execute further malicious activities without - alerting the user or security tools. +description: The following analytic detects modifications in the Windows registry to suppress Windows Defender notifications. It leverages data from the Endpoint.Registry datamodel, specifically targeting changes to the "Notification_Suppress" registry value. This activity is significant because adversaries, including those deploying Azorult malware, use this technique to bypass Windows Defender and disable critical notifications. If confirmed malicious, this behavior could allow attackers to evade detection, maintain persistence, and execute further malicious activities without alerting the user or security tools. data_source: - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows - Defender\\UX Configuration\\Notification_Suppress*" Registry.registry_value_data="0x00000001" - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_modify_registry_suppress_win_defender_notif_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure - that this registry was included in your config files ex. sysmon config to be monitored. -known_false_positives: - administrators may enable or disable this feature that may - cause some false positive. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\UX Configuration\\Notification_Suppress*" Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_suppress_win_defender_notif_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored. +known_false_positives: administrators may enable or disable this feature that may cause some false positive. references: - - https://docs.microsoft.com/en-us/windows-hardware/customize/desktop/unattend/microsoft-windows-remoteassistance-exe-fallowtogethelp - - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ + - https://docs.microsoft.com/en-us/windows-hardware/customize/desktop/unattend/microsoft-windows-remoteassistance-exe-fallowtogethelp + - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - the registry for suppresing windows fdefender notification settings was - modified to disabled on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: the registry for suppresing windows fdefender notification settings was modified to disabled on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Azorult - - CISA AA23-347A - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azorult + - CISA AA23-347A + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_tamper_protection.yml b/detections/endpoint/windows_modify_registry_tamper_protection.yml index d638ee72a7..74caf2270b 100644 --- a/detections/endpoint/windows_modify_registry_tamper_protection.yml +++ b/detections/endpoint/windows_modify_registry_tamper_protection.yml @@ -6,75 +6,46 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: - - Sysmon EventID 13 -description: - The following analytic detects a suspicious modification to the Windows - Defender Tamper Protection registry setting. It leverages data from the Endpoint - datamodel, specifically targeting changes where the registry path is set to disable - Tamper Protection. This activity is significant because disabling Tamper Protection - can allow adversaries to make further undetected changes to Windows Defender settings, - potentially leading to reduced security on the system. If confirmed malicious, this - could enable attackers to evade detection, persist in the environment, and execute - further malicious activities without interference from Windows Defender. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Microsoft\\Windows - Defender\\Features\\TamperProtection" AND Registry.registry_value_data="0x00000000" - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` - | `windows_modify_registry_tamper_protection_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: - Administrators may enable or disable this feature that may - cause some false positive. + - Sysmon EventID 13 +description: The following analytic detects a suspicious modification to the Windows Defender Tamper Protection registry setting. It leverages data from the Endpoint datamodel, specifically targeting changes where the registry path is set to disable Tamper Protection. This activity is significant because disabling Tamper Protection can allow adversaries to make further undetected changes to Windows Defender settings, potentially leading to reduced security on the system. If confirmed malicious, this could enable attackers to evade detection, persist in the environment, and execute further malicious activities without interference from Windows Defender. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Microsoft\\Windows Defender\\Features\\TamperProtection" AND Registry.registry_value_data="0x00000000" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_tamper_protection_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: Administrators may enable or disable this feature that may cause some false positive. references: - - https://malpedia.caad.fkie.fraunhofer.de/details/win.redline_stealer + - https://malpedia.caad.fkie.fraunhofer.de/details/win.redline_stealer drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A registry modification to tamper Windows Defender protection on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: A registry modification to tamper Windows Defender protection on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Scattered Lapsus$ Hunters - - RedLine Stealer - asset_type: Endpoint - atomic_guid: - - 12e03af7-79f9-4f95-af48-d3f12f28a260 - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Scattered Lapsus$ Hunters + - RedLine Stealer + asset_type: Endpoint + atomic_guid: + - 12e03af7-79f9-4f95-af48-d3f12f28a260 + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_to_add_or_modify_firewall_rule.yml b/detections/endpoint/windows_modify_registry_to_add_or_modify_firewall_rule.yml index f246eea266..f1dc848696 100644 --- a/detections/endpoint/windows_modify_registry_to_add_or_modify_firewall_rule.yml +++ b/detections/endpoint/windows_modify_registry_to_add_or_modify_firewall_rule.yml @@ -4,76 +4,51 @@ version: 9 date: '2025-11-20' author: Teoderick Contreras, Splunk data_source: - - Sysmon EventID 13 - - Sysmon EventID 14 + - Sysmon EventID 13 + - Sysmon EventID 14 type: Anomaly status: production -description: - The following analytic detects a potential addition or modification of - firewall rules, signaling possible configuration changes or security policy adjustments. - It tracks commands such as netsh advfirewall firewall add rule and netsh advfirewall - firewall set rule, which may indicate attempts to alter network access controls. - Monitoring these actions ensures the integrity of firewall settings and helps prevent - unauthorized network access. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path= "*\\System\\CurrentControlSet\\Services\\SharedAccess\\Parameters\\FirewallPolicy\\FirewallRules\\*" Registry.action - = modified by Registry.action Registry.dest Registry.process_guid Registry.process_id - Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data - Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user - Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_modify_registry_to_add_or_modify_firewall_rule_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 -known_false_positives: - network admin may add/remove/modify public inbound firewall - rule that may cause this rule to be triggered. +description: The following analytic detects a potential addition or modification of firewall rules, signaling possible configuration changes or security policy adjustments. It tracks commands such as netsh advfirewall firewall add rule and netsh advfirewall firewall set rule, which may indicate attempts to alter network access controls. Monitoring these actions ensures the integrity of firewall settings and helps prevent unauthorized network access. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path= "*\\System\\CurrentControlSet\\Services\\SharedAccess\\Parameters\\FirewallPolicy\\FirewallRules\\*" Registry.action = modified by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_to_add_or_modify_firewall_rule_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 +known_false_positives: network admin may add/remove/modify public inbound firewall rule that may cause this rule to be triggered. references: - - https://www.bleepingcomputer.com/news/security/new-shrinklocker-ransomware-uses-bitlocker-to-encrypt-your-files/ + - https://www.bleepingcomputer.com/news/security/new-shrinklocker-ransomware-uses-bitlocker-to-encrypt-your-files/ drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: firewall deletion found in registry on $dest$ - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: firewall deletion found in registry on $dest$ + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - ShrinkLocker - - CISA AA24-241A - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ShrinkLocker + - CISA AA24-241A + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/firewall_modify_delete/firewall_mod_delete.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/firewall_modify_delete/firewall_mod_delete.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_updateserviceurlalternate.yml b/detections/endpoint/windows_modify_registry_updateserviceurlalternate.yml index cfdae8439d..701246f10f 100644 --- a/detections/endpoint/windows_modify_registry_updateserviceurlalternate.yml +++ b/detections/endpoint/windows_modify_registry_updateserviceurlalternate.yml @@ -6,71 +6,43 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: - - Sysmon EventID 13 -description: - The following analytic detects a suspicious modification to the Windows - Update configuration registry key, specifically targeting the UpdateServiceUrlAlternate - setting. It leverages data from the Endpoint.Registry datamodel to identify changes - to this registry path. This activity is significant because adversaries, including - malware like RedLine Stealer, exploit this technique to bypass detection and deploy - additional payloads. If confirmed malicious, this modification could allow attackers - to redirect update services, potentially leading to the execution of malicious code, - further system compromise, and persistent evasion of security defenses. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\UpdateServiceUrlAlternate" - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` - | `windows_modify_registry_updateserviceurlalternate_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: - Administrators may enable or disable this feature that may - cause some false positive. + - Sysmon EventID 13 +description: The following analytic detects a suspicious modification to the Windows Update configuration registry key, specifically targeting the UpdateServiceUrlAlternate setting. It leverages data from the Endpoint.Registry datamodel to identify changes to this registry path. This activity is significant because adversaries, including malware like RedLine Stealer, exploit this technique to bypass detection and deploy additional payloads. If confirmed malicious, this modification could allow attackers to redirect update services, potentially leading to the execution of malicious code, further system compromise, and persistent evasion of security defenses. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\UpdateServiceUrlAlternate" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_updateserviceurlalternate_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: Administrators may enable or disable this feature that may cause some false positive. references: - - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499 + - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499 drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A registry modification in Windows auto update configuration on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A registry modification in Windows auto update configuration on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - RedLine Stealer - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - RedLine Stealer + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_usewuserver.yml b/detections/endpoint/windows_modify_registry_usewuserver.yml index 68ac61ac49..538669a6f8 100644 --- a/detections/endpoint/windows_modify_registry_usewuserver.yml +++ b/detections/endpoint/windows_modify_registry_usewuserver.yml @@ -6,49 +6,27 @@ author: Teoderick Contreras, Splunk status: production type: Hunting data_source: - - Sysmon EventID 13 -description: - The following analytic detects a suspicious modification to the Windows - Update configuration registry key "UseWUServer." It leverages data from the Endpoint.Registry - data model to identify changes where the registry value is set to "0x00000001." - This activity is significant because it is commonly used by adversaries, including - malware like RedLine Stealer, to bypass detection mechanisms and potentially exploit - zero-day vulnerabilities. If confirmed malicious, this modification could allow - attackers to evade defenses, persist on the target host, and deploy additional malicious - payloads. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\UseWUServer" - AND Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_usewuserver_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: - administrators may enable or disable this feature that may - cause some false positive. + - Sysmon EventID 13 +description: The following analytic detects a suspicious modification to the Windows Update configuration registry key "UseWUServer." It leverages data from the Endpoint.Registry data model to identify changes where the registry value is set to "0x00000001." This activity is significant because it is commonly used by adversaries, including malware like RedLine Stealer, to bypass detection mechanisms and potentially exploit zero-day vulnerabilities. If confirmed malicious, this modification could allow attackers to evade defenses, persist on the target host, and deploy additional malicious payloads. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\UseWUServer" AND Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_usewuserver_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: administrators may enable or disable this feature that may cause some false positive. references: - - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499 + - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499 tags: - analytic_story: - - RedLine Stealer - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - RedLine Stealer + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_utilize_progids.yml b/detections/endpoint/windows_modify_registry_utilize_progids.yml index 28084ad58a..56869c5f80 100644 --- a/detections/endpoint/windows_modify_registry_utilize_progids.yml +++ b/detections/endpoint/windows_modify_registry_utilize_progids.yml @@ -4,76 +4,50 @@ version: 8 date: '2026-01-14' author: Teoderick Contreras, Splunk data_source: - - Sysmon EventID 13 + - Sysmon EventID 13 type: Anomaly status: production -description: - The following analytic detects modifications to the Windows Registry - specifically targeting Programmatic Identifier associations to bypass User Account - Control (UAC) Windows OS feature. ValleyRAT may create or alter registry entries - to targetted progIDs like `.pwn` files with malicious processes, allowing it to - execute harmful scripts or commands when these files are opened. By monitoring for - unusual changes in registry keys linked to ProgIDs, this detection enables security - analysts to identify potential threats like ValleyRAT execution attempts. Early - detection of these modifications helps mitigate unauthorized execution and prevents - further exploitation of the system. -search: - '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry - WHERE Registry.registry_path= "*\\ms-settings\\CurVer\\(Default)" by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_modify_registry_utilize_progids_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 +description: The following analytic detects modifications to the Windows Registry specifically targeting Programmatic Identifier associations to bypass User Account Control (UAC) Windows OS feature. ValleyRAT may create or alter registry entries to targetted progIDs like `.pwn` files with malicious processes, allowing it to execute harmful scripts or commands when these files are opened. By monitoring for unusual changes in registry keys linked to ProgIDs, this detection enables security analysts to identify potential threats like ValleyRAT execution attempts. Early detection of these modifications helps mitigate unauthorized execution and prevents further exploitation of the system. +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE Registry.registry_path= "*\\ms-settings\\CurVer\\(Default)" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_utilize_progids_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: No false positives have been identified at this time. references: - - https://www.proofpoint.com/us/blog/threat-insight/chinese-malware-appears-earnest-across-cybercrime-threat-landscape - - https://www.fortinet.com/blog/threat-research/valleyrat-campaign-targeting-chinese-speakers - - https://v3ded.github.io/redteam/utilizing-programmatic-identifiers-progids-for-uac-bypasses + - https://www.proofpoint.com/us/blog/threat-insight/chinese-malware-appears-earnest-across-cybercrime-threat-landscape + - https://www.fortinet.com/blog/threat-research/valleyrat-campaign-targeting-chinese-speakers + - https://v3ded.github.io/redteam/utilizing-programmatic-identifiers-progids-for-uac-bypasses drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A possible ValleyRAT Registry modification in [$dest$]. - risk_objects: - - field: user - type: user - score: 49 - - field: dest - type: system - score: 49 - threat_objects: [] + message: A possible ValleyRAT Registry modification in [$dest$]. + risk_objects: + - field: user + type: user + score: 49 + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - ValleyRAT - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ValleyRAT + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/pwn_reg/pwn_reg.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/pwn_reg/pwn_reg.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_valleyrat_c2_config.yml b/detections/endpoint/windows_modify_registry_valleyrat_c2_config.yml index a12a906e84..9ca34ad8b4 100644 --- a/detections/endpoint/windows_modify_registry_valleyrat_c2_config.yml +++ b/detections/endpoint/windows_modify_registry_valleyrat_c2_config.yml @@ -4,76 +4,49 @@ version: 9 date: '2026-01-14' author: Teoderick Contreras, Splunk data_source: - - Sysmon EventID 13 + - Sysmon EventID 13 type: TTP status: production -description: - "The following analytic detects modifications to theregistry related\ - \ to ValleyRAT C2 configuration. Specifically, it monitors changes in registry\ - \ keys where ValleyRAT saves the IP address and port information of its command-and-control\ - \ (C2) server. This activity is a key indicator of ValleyRAT attempting to establish\ - \ persistent communication with its C2 infrastructure. By identifying these unauthorized\ - \ registry modifications, security analysts can quickly detect malicious configurations\ - \ and investigate the associated threats. Early detection of these changes helps\ - \ prevent further exploitation and limits the malware\u2019s ability to exfiltrate\ - \ data or control infected systems." -search: - '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry - WHERE (Registry.registry_path= "*\\Console\\IpDateInfo" AND Registry.registry_value_data="Binary - Data") OR (Registry.registry_path= "*\\Console\\SelfPath" AND Registry.registry_value_data="*.exe") - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_modify_registry_valleyrat_c2_config_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 +description: "The following analytic detects modifications to theregistry related to ValleyRAT C2 configuration. Specifically, it monitors changes in registry keys where ValleyRAT saves the IP address and port information of its command-and-control (C2) server. This activity is a key indicator of ValleyRAT attempting to establish persistent communication with its C2 infrastructure. By identifying these unauthorized registry modifications, security analysts can quickly detect malicious configurations and investigate the associated threats. Early detection of these changes helps prevent further exploitation and limits the malware’s ability to exfiltrate data or control infected systems." +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\Console\\IpDateInfo" AND Registry.registry_value_data="Binary Data") OR (Registry.registry_path= "*\\Console\\SelfPath" AND Registry.registry_value_data="*.exe") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_valleyrat_c2_config_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: No false positives have been identified at this time. references: - - https://www.proofpoint.com/us/blog/threat-insight/chinese-malware-appears-earnest-across-cybercrime-threat-landscape - - https://www.fortinet.com/blog/threat-research/valleyrat-campaign-targeting-chinese-speakers + - https://www.proofpoint.com/us/blog/threat-insight/chinese-malware-appears-earnest-across-cybercrime-threat-landscape + - https://www.fortinet.com/blog/threat-research/valleyrat-campaign-targeting-chinese-speakers drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A registry modification related to ValleyRAT on [$dest$] - risk_objects: - - field: user - type: user - score: 90 - - field: dest - type: system - score: 90 - threat_objects: [] + message: A registry modification related to ValleyRAT on [$dest$] + risk_objects: + - field: user + type: user + score: 90 + - field: dest + type: system + score: 90 + threat_objects: [] tags: - analytic_story: - - ValleyRAT - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ValleyRAT + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/valleyrat_c2_reg2/valleyrat_c2_reg2.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/valleyrat_c2_reg2/valleyrat_c2_reg2.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_valleyrat_pwn_reg_entry.yml b/detections/endpoint/windows_modify_registry_valleyrat_pwn_reg_entry.yml index 6ffa7d3f1d..448b6de633 100644 --- a/detections/endpoint/windows_modify_registry_valleyrat_pwn_reg_entry.yml +++ b/detections/endpoint/windows_modify_registry_valleyrat_pwn_reg_entry.yml @@ -4,74 +4,49 @@ version: 10 date: '2026-01-14' author: Teoderick Contreras, Splunk data_source: - - Sysmon EventID 13 + - Sysmon EventID 13 type: TTP status: production -description: - The following analytic detects modifications to the Windows Registry - specifically targeting `.pwn` file associations related to the ValleyRAT malware. - ValleyRAT may create or alter registry entries to associate `.pwn` files with malicious - processes, allowing it to execute harmful scripts or commands when these files are - opened. By monitoring for unusual changes in registry keys linked to `.pwn` extensions, - this detection enables security analysts to identify potential ValleyRAT infection - attempts. Early detection of these modifications helps mitigate unauthorized execution - and prevents further exploitation of the system. -search: - '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry - WHERE (Registry.registry_path= "*.pwn\\Shell\\Open\\command" OR Registry.registry_value_data - = ".pwn") by Registry.action Registry.dest Registry.process_guid Registry.process_id - Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data - Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user - Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_modify_registry_valleyrat_pwn_reg_entry_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 +description: The following analytic detects modifications to the Windows Registry specifically targeting `.pwn` file associations related to the ValleyRAT malware. ValleyRAT may create or alter registry entries to associate `.pwn` files with malicious processes, allowing it to execute harmful scripts or commands when these files are opened. By monitoring for unusual changes in registry keys linked to `.pwn` extensions, this detection enables security analysts to identify potential ValleyRAT infection attempts. Early detection of these modifications helps mitigate unauthorized execution and prevents further exploitation of the system. +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*.pwn\\Shell\\Open\\command" OR Registry.registry_value_data = ".pwn") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_valleyrat_pwn_reg_entry_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: No false positives have been identified at this time. references: - - https://www.proofpoint.com/us/blog/threat-insight/chinese-malware-appears-earnest-across-cybercrime-threat-landscape - - https://www.fortinet.com/blog/threat-research/valleyrat-campaign-targeting-chinese-speakers + - https://www.proofpoint.com/us/blog/threat-insight/chinese-malware-appears-earnest-across-cybercrime-threat-landscape + - https://www.fortinet.com/blog/threat-research/valleyrat-campaign-targeting-chinese-speakers drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A possible ValleyRAT Registry modification in [$dest$]. - risk_objects: - - field: user - type: user - score: 90 - - field: dest - type: system - score: 90 - threat_objects: [] + message: A possible ValleyRAT Registry modification in [$dest$]. + risk_objects: + - field: user + type: user + score: 90 + - field: dest + type: system + score: 90 + threat_objects: [] tags: - analytic_story: - - ValleyRAT - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ValleyRAT + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/pwn_reg/pwn_reg.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/pwn_reg/pwn_reg.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_with_md5_reg_key_name.yml b/detections/endpoint/windows_modify_registry_with_md5_reg_key_name.yml index 6faa6656fd..3d45cb1387 100644 --- a/detections/endpoint/windows_modify_registry_with_md5_reg_key_name.yml +++ b/detections/endpoint/windows_modify_registry_with_md5_reg_key_name.yml @@ -6,73 +6,43 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: - - Sysmon EventID 13 -description: - The following analytic detects potentially malicious registry modifications - characterized by MD5-like registry key names. It leverages the Endpoint data model - to identify registry entries under the SOFTWARE path with 32-character hexadecimal - names, a technique often used by NjRAT malware for fileless storage of keylogs and - .DLL plugins. This activity is significant as it can indicate the presence of NjRAT - or similar malware, which can lead to unauthorized data access and persistent threats - within the environment. If confirmed malicious, attackers could maintain persistence - and exfiltrate sensitive information. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime - max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path - = "*\\SOFTWARE\\*" Registry.registry_value_data = "Binary Data" by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | eval dropped_reg_path = split(registry_path, - "\\") | eval dropped_reg_path_split_count = mvcount(dropped_reg_path) | eval validation_result= - if(match(registry_value_name,"^[0-9a-fA-F]{32}$"),"md5","nonmd5") | where validation_result - = "md5" AND dropped_reg_path_split_count <= 5 | table dest user registry_path registry_value_name - registry_value_data registry_key_name reg_key_name dropped_reg_path_split_count - validation_result | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` - | `windows_modify_registry_with_md5_reg_key_name_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the Filesystem responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Filesystem` node. + - Sysmon EventID 13 +description: The following analytic detects potentially malicious registry modifications characterized by MD5-like registry key names. It leverages the Endpoint data model to identify registry entries under the SOFTWARE path with 32-character hexadecimal names, a technique often used by NjRAT malware for fileless storage of keylogs and .DLL plugins. This activity is significant as it can indicate the presence of NjRAT or similar malware, which can lead to unauthorized data access and persistent threats within the environment. If confirmed malicious, attackers could maintain persistence and exfiltrate sensitive information. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\SOFTWARE\\*" Registry.registry_value_data = "Binary Data" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | eval dropped_reg_path = split(registry_path, "\\") | eval dropped_reg_path_split_count = mvcount(dropped_reg_path) | eval validation_result= if(match(registry_value_name,"^[0-9a-fA-F]{32}$"),"md5","nonmd5") | where validation_result = "md5" AND dropped_reg_path_split_count <= 5 | table dest user registry_path registry_value_name registry_value_data registry_key_name reg_key_name dropped_reg_path_split_count validation_result | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_with_md5_reg_key_name_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. known_false_positives: No false positives have been identified at this time. references: - - https://malpedia.caad.fkie.fraunhofer.de/details/win.njrat + - https://malpedia.caad.fkie.fraunhofer.de/details/win.njrat drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A md5 registry value name $registry_value_name$ is created on $dest$ - risk_objects: - - field: dest - type: system - score: 36 - threat_objects: [] + message: A md5 registry value name $registry_value_name$ is created on $dest$ + risk_objects: + - field: dest + type: system + score: 36 + threat_objects: [] tags: - analytic_story: - - NjRAT - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - NjRAT + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/njrat_md5_registry_entry/njrat_reg_binary.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/njrat_md5_registry_entry/njrat_reg_binary.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_wuserver.yml b/detections/endpoint/windows_modify_registry_wuserver.yml index f8005b5412..ce86fe4569 100644 --- a/detections/endpoint/windows_modify_registry_wuserver.yml +++ b/detections/endpoint/windows_modify_registry_wuserver.yml @@ -6,49 +6,27 @@ author: Teoderick Contreras, Splunk status: production type: Hunting data_source: - - Sysmon EventID 13 -description: - The following analytic detects suspicious modifications to the Windows - Update Server (WUServer) registry settings. It leverages data from the Endpoint.Registry - data model to identify changes in the registry path associated with Windows Update - configurations. This activity is significant because adversaries, including malware - like RedLine Stealer, exploit this technique to bypass detection and deploy additional - payloads. If confirmed malicious, this registry modification could allow attackers - to evade defenses, potentially leading to further system compromise and persistent - unauthorized access. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\WUServer" - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` - | `windows_modify_registry_wuserver_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: - Administrators may enable or disable this feature that may - cause some false positive. + - Sysmon EventID 13 +description: The following analytic detects suspicious modifications to the Windows Update Server (WUServer) registry settings. It leverages data from the Endpoint.Registry data model to identify changes in the registry path associated with Windows Update configurations. This activity is significant because adversaries, including malware like RedLine Stealer, exploit this technique to bypass detection and deploy additional payloads. If confirmed malicious, this registry modification could allow attackers to evade defenses, potentially leading to further system compromise and persistent unauthorized access. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\WUServer" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_wuserver_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: Administrators may enable or disable this feature that may cause some false positive. references: - - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499 + - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499 tags: - analytic_story: - - RedLine Stealer - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - RedLine Stealer + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_registry_wustatusserver.yml b/detections/endpoint/windows_modify_registry_wustatusserver.yml index 54cbfc73e7..45b9154756 100644 --- a/detections/endpoint/windows_modify_registry_wustatusserver.yml +++ b/detections/endpoint/windows_modify_registry_wustatusserver.yml @@ -6,49 +6,27 @@ author: Teoderick Contreras, Splunk status: production type: Hunting data_source: - - Sysmon EventID 13 -description: - The following analytic identifies suspicious modifications to the Windows - Update configuration registry, specifically targeting the WUStatusServer key. It - leverages data from the Endpoint datamodel to detect changes in the registry path - associated with Windows Update settings. This activity is significant as it is commonly - used by adversaries, including malware like RedLine Stealer, to bypass detection - and deploy additional payloads. If confirmed malicious, this modification could - allow attackers to evade defenses, potentially leading to further system compromise - and persistent unauthorized access. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\WUStatusServer" - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` - | `windows_modify_registry_wustatusserver_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: - administrators may enable or disable this feature that may - cause some false positive. + - Sysmon EventID 13 +description: The following analytic identifies suspicious modifications to the Windows Update configuration registry, specifically targeting the WUStatusServer key. It leverages data from the Endpoint datamodel to detect changes in the registry path associated with Windows Update settings. This activity is significant as it is commonly used by adversaries, including malware like RedLine Stealer, to bypass detection and deploy additional payloads. If confirmed malicious, this modification could allow attackers to evade defenses, potentially leading to further system compromise and persistent unauthorized access. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\WUStatusServer" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_wustatusserver_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: administrators may enable or disable this feature that may cause some false positive. references: - - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499 + - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499 tags: - analytic_story: - - RedLine Stealer - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - RedLine Stealer + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_show_compress_color_and_info_tip_registry.yml b/detections/endpoint/windows_modify_show_compress_color_and_info_tip_registry.yml index 4f0383be2b..fb6d760140 100644 --- a/detections/endpoint/windows_modify_show_compress_color_and_info_tip_registry.yml +++ b/detections/endpoint/windows_modify_show_compress_color_and_info_tip_registry.yml @@ -5,73 +5,47 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk, Steven Dick status: production type: TTP -description: - The following analytic detects suspicious modifications to the Windows - registry keys related to file compression color and information tips. It leverages - data from the Endpoint.Registry data model, specifically monitoring changes to the - "ShowCompColor" and "ShowInfoTip" values under the "Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced" - path. This activity is significant as it was observed in the Hermetic Wiper malware, - indicating potential malicious intent to alter file attributes and user interface - elements. If confirmed malicious, this could signify an attempt to manipulate file - visibility and deceive users, potentially aiding in further malicious activities. +description: The following analytic detects suspicious modifications to the Windows registry keys related to file compression color and information tips. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the "ShowCompColor" and "ShowInfoTip" values under the "Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced" path. This activity is significant as it was observed in the Hermetic Wiper malware, indicating potential malicious intent to alter file attributes and user interface elements. If confirmed malicious, this could signify an attempt to manipulate file visibility and deceive users, potentially aiding in further malicious activities. data_source: - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced*" - AND Registry.registry_value_name IN("ShowCompColor", "ShowInfoTip")) by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_modify_show_compress_color_and_info_tip_registry_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced*" AND Registry.registry_value_name IN("ShowCompColor", "ShowInfoTip")) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_show_compress_color_and_info_tip_registry_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: No false positives have been identified at this time. references: - - https://blog.talosintelligence.com/2022/02/threat-advisory-hermeticwiper.html + - https://blog.talosintelligence.com/2022/02/threat-advisory-hermeticwiper.html drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Registry modification in "ShowCompColor" and "ShowInfoTips" on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: Registry modification in "ShowCompColor" and "ShowInfoTips" on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Data Destruction - - Windows Defense Evasion Tactics - - Windows Registry Abuse - - Hermetic Wiper - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Destruction + - Windows Defense Evasion Tactics + - Windows Registry Abuse + - Hermetic Wiper + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/hermetic_wiper/globalfolderoptions_reg/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/hermetic_wiper/globalfolderoptions_reg/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_modify_system_firewall_with_notable_process_path.yml b/detections/endpoint/windows_modify_system_firewall_with_notable_process_path.yml index f659d3f695..76bf46f84e 100644 --- a/detections/endpoint/windows_modify_system_firewall_with_notable_process_path.yml +++ b/detections/endpoint/windows_modify_system_firewall_with_notable_process_path.yml @@ -6,81 +6,47 @@ author: Teoderick Contreras, Will Metcalf, Splunk status: production type: TTP data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic detects suspicious modifications to system firewall - rules, specifically allowing execution of applications from notable and potentially - malicious file paths. This detection leverages data from Endpoint Detection and - Response (EDR) agents, focusing on command-line executions involving firewall rule - changes. This activity is significant as it may indicate an adversary attempting - to bypass firewall restrictions to execute malicious files. If confirmed malicious, - this could allow attackers to execute unauthorized code, potentially leading to - further system compromise, data exfiltration, or persistence within the environment. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process = "*firewall*" - Processes.process = "*allow*" Processes.process = "*add*" Processes.process = "*ENABLE*" - Processes.process IN ("*\\windows\\fonts\\*", "*\\windows\\temp\\*", "*\\users\\public\\*", - "*\\windows\\debug\\*", "*\\Users\\Administrator\\Music\\*", "*\\Windows\\servicing\\*", - "*\\Users\\Default\\*","*Recycle.bin*", "*\\Windows\\Media\\*", "\\Windows\\repair\\*", - "*\\temp\\*", "*\\PerfLogs\\*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_system_firewall_with_notable_process_path_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: A network operator or systems administrator may utilize an - automated or manual execution of this firewall rule that may generate false positives. - Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic detects suspicious modifications to system firewall rules, specifically allowing execution of applications from notable and potentially malicious file paths. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving firewall rule changes. This activity is significant as it may indicate an adversary attempting to bypass firewall restrictions to execute malicious files. If confirmed malicious, this could allow attackers to execute unauthorized code, potentially leading to further system compromise, data exfiltration, or persistence within the environment. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*firewall*" Processes.process = "*allow*" Processes.process = "*add*" Processes.process = "*ENABLE*" Processes.process IN ("*\\windows\\fonts\\*", "*\\windows\\temp\\*", "*\\users\\public\\*", "*\\windows\\debug\\*", "*\\Users\\Administrator\\Music\\*", "*\\Windows\\servicing\\*", "*\\Users\\Default\\*","*Recycle.bin*", "*\\Windows\\Media\\*", "\\Windows\\repair\\*", "*\\temp\\*", "*\\PerfLogs\\*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_system_firewall_with_notable_process_path_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: A network operator or systems administrator may utilize an automated or manual execution of this firewall rule that may generate false positives. Filter as needed. references: -- https://www.splunk.com/en_us/blog/security/more-than-just-a-rat-unveiling-njrat-s-mbr-wiping-capabilities.html + - https://www.splunk.com/en_us/blog/security/more-than-just-a-rat-unveiling-njrat-s-mbr-wiping-capabilities.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: firewall allowed program commandline $process$ of $process_name$ on $dest$ - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: firewall allowed program commandline $process$ of $process_name$ on $dest$ + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Medusa Ransomware - - NjRAT - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1562.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Medusa Ransomware + - NjRAT + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1562.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/njrat_add_firewall_rule/njrat_firewall_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/njrat_add_firewall_rule/njrat_firewall_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_mof_event_triggered_execution_via_wmi.yml b/detections/endpoint/windows_mof_event_triggered_execution_via_wmi.yml index 2102b1be9e..9c85e7baf6 100644 --- a/detections/endpoint/windows_mof_event_triggered_execution_via_wmi.yml +++ b/detections/endpoint/windows_mof_event_triggered_execution_via_wmi.yml @@ -5,90 +5,58 @@ date: '2025-05-02' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of MOFComp.exe loading a - MOF file, often triggered by cmd.exe or powershell.exe, or from unusual paths like - User Profile directories. It leverages Endpoint Detection and Response (EDR) data, - focusing on process names, parent processes, and command-line executions. This activity - is significant as it may indicate an attacker using WMI for persistence or lateral - movement. If confirmed malicious, it could allow the attacker to execute arbitrary - code, maintain persistence, or escalate privileges within the environment. +description: The following analytic detects the execution of MOFComp.exe loading a MOF file, often triggered by cmd.exe or powershell.exe, or from unusual paths like User Profile directories. It leverages Endpoint Detection and Response (EDR) data, focusing on process names, parent processes, and command-line executions. This activity is significant as it may indicate an attacker using WMI for persistence or lateral movement. If confirmed malicious, it could allow the attacker to execute arbitrary code, maintain persistence, or escalate privileges within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name - IN ("cmd.exe", "powershell.exe") Processes.process_name=mofcomp.exe) OR (Processes.process_name=mofcomp.exe - Processes.process IN ("*\\AppData\\Local\\*","*\\Users\\Public\\*")) by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_mof_event_triggered_execution_via_wmi_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present from automation based applications - (SCCM), filtering may be required. In addition, break the query out based on volume - of usage. Filter process names or file paths. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name IN ("cmd.exe", "powershell.exe") Processes.process_name=mofcomp.exe) OR (Processes.process_name=mofcomp.exe Processes.process IN ("*\\AppData\\Local\\*","*\\Users\\Public\\*")) by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_mof_event_triggered_execution_via_wmi_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present from automation based applications (SCCM), filtering may be required. In addition, break the query out based on volume of usage. Filter process names or file paths. references: -- https://attack.mitre.org/techniques/T1546/003/ -- https://thedfirreport.com/2022/07/11/select-xmrig-from-sqlserver/ -- https://docs.microsoft.com/en-us/windows/win32/wmisdk/mofcomp -- https://pentestlab.blog/2020/01/21/persistence-wmi-event-subscription/ -- https://www.sakshamdixit.com/wmi-events/ + - https://attack.mitre.org/techniques/T1546/003/ + - https://thedfirreport.com/2022/07/11/select-xmrig-from-sqlserver/ + - https://docs.microsoft.com/en-us/windows/win32/wmisdk/mofcomp + - https://pentestlab.blog/2020/01/21/persistence-wmi-event-subscription/ + - https://www.sakshamdixit.com/wmi-events/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ loading a MOF file. - risk_objects: - - field: user - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ loading a MOF file. + risk_objects: + - field: user + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Living Off The Land - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1546.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1546.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.003/atomic_red_team/mofcomp.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.003/atomic_red_team/mofcomp.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_moveit_transfer_writing_aspx.yml b/detections/endpoint/windows_moveit_transfer_writing_aspx.yml index 751b45fda2..62f64ce81b 100644 --- a/detections/endpoint/windows_moveit_transfer_writing_aspx.yml +++ b/detections/endpoint/windows_moveit_transfer_writing_aspx.yml @@ -1,88 +1,76 @@ name: Windows MOVEit Transfer Writing ASPX id: c0ed2aca-5666-45b3-813f-ddfac3f3eda0 -version: 8 -date: '2025-10-14' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Sysmon EventID 11 -description: The following analytic detects the creation of new ASPX files in the - MOVEit Transfer application's "wwwroot" directory. It leverages endpoint data on - process and filesystem activity to identify processes responsible for creating these - files. This activity is significant as it may indicate exploitation of a critical - zero-day vulnerability in MOVEit Transfer, used by threat actors to install malicious - ASPX files. If confirmed malicious, this could lead to exfiltration of sensitive - data, including user credentials and file metadata, posing a severe risk to the - organization's security. + - Sysmon EventID 11 +description: The following analytic detects the creation of new ASPX files in the MOVEit Transfer application's "wwwroot" directory. It leverages endpoint data on process and filesystem activity to identify processes responsible for creating these files. This activity is significant as it may indicate exploitation of a critical zero-day vulnerability in MOVEit Transfer, used by threat actors to install malicious ASPX files. If confirmed malicious, this could lead to exfiltration of sensitive data, including user credentials and file metadata, posing a severe risk to the organization's security. search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem where - Filesystem.file_path IN ("*\\MOVEitTransfer\\wwwroot\\*") AND - Filesystem.file_name IN("*.ashx", "*.asp*") - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path - Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product - | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_moveit_transfer_writing_aspx_filter` + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime FROM datamodel=Endpoint.Filesystem where + Filesystem.file_path IN ("*\\MOVEitTransfer\\wwwroot\\*") AND + Filesystem.file_name IN("*.ashx", "*.asp*") + by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time + Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path + Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_moveit_transfer_writing_aspx_filter` how_to_implement: | - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem` - node. + To successfully implement this search you need to be ingesting information + on process that include the name of the process responsible for the changes from + your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem` + node. known_false_positives: | - The query is structured in a way that `action` (read, create) - is not defined. Review the results of this query, filter, and tune as necessary. - It may be necessary to generate this query specific to your endpoint product. + The query is structured in a way that `action` (read, create) + is not defined. Review the results of this query, filter, and tune as necessary. + It may be necessary to generate this query specific to your endpoint product. references: -- https://community.progress.com/s/article/MOVEit-Transfer-Critical-Vulnerability-31May2023 -- https://www.reddit.com/r/sysadmin/comments/13wxuej/critical_vulnerability_moveit_file_transfer/ -- https://www.bleepingcomputer.com/news/security/new-moveit-transfer-zero-day-mass-exploited-in-data-theft-attacks/ -- https://www.reddit.com/r/sysadmin/comments/13wxuej/critical_vulnerability_moveit_file_transfer/ -- https://www.mandiant.com/resources/blog/zero-day-moveit-data-theft + - https://community.progress.com/s/article/MOVEit-Transfer-Critical-Vulnerability-31May2023 + - https://www.reddit.com/r/sysadmin/comments/13wxuej/critical_vulnerability_moveit_file_transfer/ + - https://www.bleepingcomputer.com/news/security/new-moveit-transfer-zero-day-mass-exploited-in-data-theft-attacks/ + - https://www.reddit.com/r/sysadmin/comments/13wxuej/critical_vulnerability_moveit_file_transfer/ + - https://www.mandiant.com/resources/blog/zero-day-moveit-data-theft drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The MOVEit application on $dest$ has written a new ASPX file $file_name$ to disk. - risk_objects: - - field: dest - type: system - score: 100 - threat_objects: - - field: file_name - type: file_name + message: The MOVEit application on $dest$ has written a new ASPX file $file_name$ to disk. + risk_objects: + - field: dest + type: system + score: 100 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - MOVEit Transfer Critical Vulnerability - - Hellcat Ransomware - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1190 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - MOVEit Transfer Critical Vulnerability + - Hellcat Ransomware + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1190 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/moveit_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/moveit_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_msc_eviltwin_directory_path_manipulation.yml b/detections/endpoint/windows_msc_eviltwin_directory_path_manipulation.yml index e0fceff752..f3c324c37a 100644 --- a/detections/endpoint/windows_msc_eviltwin_directory_path_manipulation.yml +++ b/detections/endpoint/windows_msc_eviltwin_directory_path_manipulation.yml @@ -5,89 +5,55 @@ date: '2025-05-02' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects potential MSC EvilTwin loader exploitation, - which manipulates directory paths with spaces to bypass security controls. The technique, - described as CVE-2025-26633, involves crafting malicious MSC files that leverage - MUIPath parameter manipulation. This detection focuses on suspicious MSC file execution - patterns with unconventional command-line parameters, particularly those containing - unusual spaces in Windows System32 paths or suspicious additional parameters after - the MSC file. If confirmed malicious, this behavior could allow an attacker to execute - arbitrary code with elevated privileges through DLL side-loading or path traversal - techniques. +description: The following analytic detects potential MSC EvilTwin loader exploitation, which manipulates directory paths with spaces to bypass security controls. The technique, described as CVE-2025-26633, involves crafting malicious MSC files that leverage MUIPath parameter manipulation. This detection focuses on suspicious MSC file execution patterns with unconventional command-line parameters, particularly those containing unusual spaces in Windows System32 paths or suspicious additional parameters after the MSC file. If confirmed malicious, this behavior could allow an attacker to execute arbitrary code with elevated privileges through DLL side-loading or path traversal techniques. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime - from datamodel=Endpoint.Processes where Processes.process_name="mmc.exe" - by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_id Processes.process_name Processes.process_path Processes.action - Processes.original_file_name Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_integrity_level Processes.user_id Processes.vendor_product -| `drop_dm_object_name(Processes)` -| regex process="(?i).*mmc\.exe.*((Windows\s+\\\\System32)|(Windows\s+System32)|(\\\\Windows\s+\\\\System32)|(Program\s+Files\s+\\\\)|(Program\s+Files\s+\(\\w+\)\s+\\\\)|(Progra~1\s+\\\\))" -| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_msc_eviltwin_directory_path_manipulation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Some legitimate system maintenance tools might use MSC files - with unusual parameters. Filter for specific known maintenance activities in your - environment. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="mmc.exe" by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_id Processes.process_name Processes.process_path Processes.action Processes.original_file_name Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_integrity_level Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | regex process="(?i).*mmc\.exe.*((Windows\s+\\\\System32)|(Windows\s+System32)|(\\\\Windows\s+\\\\System32)|(Program\s+Files\s+\\\\)|(Program\s+Files\s+\(\\w+\)\s+\\\\)|(Progra~1\s+\\\\))" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_msc_eviltwin_directory_path_manipulation_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Some legitimate system maintenance tools might use MSC files with unusual parameters. Filter for specific known maintenance activities in your environment. references: -- https://securityintelligence.com/posts/new-threat-actor-water-gamayun-targets-telecom-finance/ -- https://www.ncsc.gov.uk/report/weekly-threat-report-12th-april-2024 + - https://securityintelligence.com/posts/new-threat-actor-water-gamayun-targets-telecom-finance/ + - https://www.ncsc.gov.uk/report/weekly-threat-report-12th-april-2024 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Process $process_name$ executed an MSC file with suspicious directory path - manipulation on $dest$ - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: - - field: process_name - type: process + message: Process $process_name$ executed an MSC file with suspicious directory path manipulation on $dest$ + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: + - field: process_name + type: process tags: - analytic_story: - - Water Gamayun - - Windows Defense Evasion Tactics - - Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1218 - - T1036.005 - - T1203 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: - - CVE-2025-26633 + analytic_story: + - Water Gamayun + - Windows Defense Evasion Tactics + - Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1218 + - T1036.005 + - T1203 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: + - CVE-2025-26633 tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218/eviltwin/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218/eviltwin/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_msexchange_management_mailbox_cmdlet_usage.yml b/detections/endpoint/windows_msexchange_management_mailbox_cmdlet_usage.yml index 8b0ae8afcb..4cbd08a859 100644 --- a/detections/endpoint/windows_msexchange_management_mailbox_cmdlet_usage.yml +++ b/detections/endpoint/windows_msexchange_management_mailbox_cmdlet_usage.yml @@ -1,70 +1,57 @@ name: Windows MSExchange Management Mailbox Cmdlet Usage id: 396de86f-25e7-4b0e-be09-a330be35249d -version: 8 -date: '2025-07-29' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic identifies suspicious Cmdlet usage in Exchange - Management logs, focusing on commands like New-MailboxExportRequest and New-ManagementRoleAssignment. - It leverages EventCode 1 and specific Message patterns to detect potential ProxyShell - and ProxyNotShell abuse. This activity is significant as it may indicate unauthorized - access or manipulation of mailboxes and roles, which are critical for maintaining - email security. If confirmed malicious, attackers could export mailbox data, assign - new roles, or search mailboxes, leading to data breaches and privilege escalation. +description: The following analytic identifies suspicious Cmdlet usage in Exchange Management logs, focusing on commands like New-MailboxExportRequest and New-ManagementRoleAssignment. It leverages EventCode 1 and specific Message patterns to detect potential ProxyShell and ProxyNotShell abuse. This activity is significant as it may indicate unauthorized access or manipulation of mailboxes and roles, which are critical for maintaining email security. If confirmed malicious, attackers could export mailbox data, assign new roles, or search mailboxes, leading to data breaches and privilege escalation. data_source: [] -search: '`msexchange_management` EventCode=1 Message IN ("*New-MailboxExportRequest*", - "*New-ManagementRoleAssignment*", "*New-MailboxSearch*", "*Get-Recipient*", "*Search-Mailbox*") - | stats count min(_time) as firstTime max(_time) as lastTime by host Message | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | rename host AS dest | `windows_msexchange_management_mailbox_cmdlet_usage_filter`' -how_to_implement: The following analytic requires collecting the Exchange Management - logs via a input. An example inputs is here https://gist.github.com/MHaggis/f66f1d608ea046efb9157020cd34c178. - We used multiline as the XML format of the logs will require props/transforms. Multiline - gives us everything we need in Message for now. Update the macro with your correct - sourcetype. -known_false_positives: False positives may be present when an Administrator utilizes - the cmdlets in the query. Filter or monitor as needed. +search: |- + `msexchange_management` EventCode=1 Message IN ("*New-MailboxExportRequest*", "*New-ManagementRoleAssignment*", "*New-MailboxSearch*", "*Get-Recipient*", "*Search-Mailbox*") + | stats count min(_time) as firstTime max(_time) as lastTime + BY host Message + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | rename host AS dest + | `windows_msexchange_management_mailbox_cmdlet_usage_filter` +how_to_implement: The following analytic requires collecting the Exchange Management logs via a input. An example inputs is here https://gist.github.com/MHaggis/f66f1d608ea046efb9157020cd34c178. We used multiline as the XML format of the logs will require props/transforms. Multiline gives us everything we need in Message for now. Update the macro with your correct sourcetype. +known_false_positives: False positives may be present when an Administrator utilizes the cmdlets in the query. Filter or monitor as needed. references: -- https://gist.github.com/MHaggis/f66f1d608ea046efb9157020cd34c178 + - https://gist.github.com/MHaggis/f66f1d608ea046efb9157020cd34c178 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Cmdlets related to ProxyShell and ProxyNotShell have been identified on - $dest$. - risk_objects: - - field: dest - type: system - score: 32 - threat_objects: [] + message: Cmdlets related to ProxyShell and ProxyNotShell have been identified on $dest$. + risk_objects: + - field: dest + type: system + score: 32 + threat_objects: [] tags: - analytic_story: - - ProxyShell - - BlackByte Ransomware - - ProxyNotShell - - Scattered Spider - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ProxyShell + - BlackByte Ransomware + - ProxyNotShell + - Scattered Spider + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/exchange/msexchangemanagement.log - source: WinEventLog:MSExchange Management - sourcetype: MSExchange:management + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/exchange/msexchangemanagement.log + source: WinEventLog:MSExchange Management + sourcetype: MSExchange:management diff --git a/detections/endpoint/windows_mshta_execution_in_registry.yml b/detections/endpoint/windows_mshta_execution_in_registry.yml index 7445237a74..b619f50a36 100644 --- a/detections/endpoint/windows_mshta_execution_in_registry.yml +++ b/detections/endpoint/windows_mshta_execution_in_registry.yml @@ -1,77 +1,66 @@ name: Windows Mshta Execution In Registry id: e13ceade-b673-4d34-adc4-4d9c01729753 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of mshta.exe via registry - entries to run malicious scripts. It leverages registry activity logs to identify - entries containing "mshta," "javascript," "vbscript," or "WScript.Shell." This behavior - is significant as it indicates potential fileless malware, such as Kovter, which - uses encoded scripts in the registry to persist and execute without files. If confirmed - malicious, this activity could allow attackers to maintain persistence, execute - arbitrary code, and evade traditional file-based detection methods, posing a significant - threat to system integrity and security. +description: The following analytic detects the execution of mshta.exe via registry entries to run malicious scripts. It leverages registry activity logs to identify entries containing "mshta," "javascript," "vbscript," or "WScript.Shell." This behavior is significant as it indicates potential fileless malware, such as Kovter, which uses encoded scripts in the registry to persist and execute without files. If confirmed malicious, this activity could allow attackers to maintain persistence, execute arbitrary code, and evade traditional file-based detection methods, posing a significant threat to system integrity and security. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_value_data - = "*mshta*" OR Registry.registry_value_data IN ("*javascript:*", "*vbscript:*","*WScript.Shell*") - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_mshta_execution_in_registry_filter`' -how_to_implement: To successfully implement this search, you must be ingesting data - that records registry activity from your hosts to populate the endpoint data model - in the registry node. This is typically populated via endpoint detection-and-response - product, such as Carbon Black or endpoint data sources, such as Sysmon. The data - used for this search is typically generated via logs that report reads and writes - to the registry. + - Sysmon EventID 13 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry + WHERE Registry.registry_value_data = "*mshta*" + OR + Registry.registry_value_data IN ("*javascript:*", "*vbscript:*","*WScript.Shell*") + BY Registry.action Registry.dest Registry.process_guid + Registry.process_id Registry.registry_hive Registry.registry_path + Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name + Registry.registry_value_type Registry.status Registry.user + Registry.vendor_product + | `drop_dm_object_name(Registry)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_mshta_execution_in_registry_filter` +how_to_implement: To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. known_false_positives: No false positives have been identified at this time. references: -- https://redcanary.com/threat-detection-report/techniques/mshta/ -- https://learn.microsoft.com/en-us/microsoft-365/security/intelligence/fileless-threats?view=o365-worldwide + - https://redcanary.com/threat-detection-report/techniques/mshta/ + - https://learn.microsoft.com/en-us/microsoft-365/security/intelligence/fileless-threats?view=o365-worldwide drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A registry $registry_path$ contains mshta $registry_value_data$ on $dest$ - risk_objects: - - field: dest - type: system - score: 72 - - field: user - type: user - score: 72 - threat_objects: [] + message: A registry $registry_path$ contains mshta $registry_value_data$ on $dest$ + risk_objects: + - field: dest + type: system + score: 72 + - field: user + type: user + score: 72 + threat_objects: [] tags: - analytic_story: - - Suspicious Windows Registry Activities - - Windows Persistence Techniques - asset_type: Endpoint - mitre_attack_id: - - T1218.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Windows Registry Activities + - Windows Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1218.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/mshta_in_registry/sysmon3.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/mshta_in_registry/sysmon3.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_mshta_writing_to_world_writable_path.yml b/detections/endpoint/windows_mshta_writing_to_world_writable_path.yml index 34af82b497..6eda50c7c3 100644 --- a/detections/endpoint/windows_mshta_writing_to_world_writable_path.yml +++ b/detections/endpoint/windows_mshta_writing_to_world_writable_path.yml @@ -4,106 +4,85 @@ version: 8 date: '2026-01-13' author: Michael Haag, Splunk data_source: - - Sysmon EventID 11 + - Sysmon EventID 11 type: TTP status: production -description: The following analytic identifies instances of `mshta.exe` writing files - to world-writable directories. It leverages Sysmon EventCode 11 logs to detect file - write operations by `mshta.exe` to directories like `C:\Windows\Tasks` and `C:\Windows\Temp`. - This activity is significant as it often indicates an attempt to establish persistence - or execute malicious code, deviating from the utility's legitimate use. If confirmed - malicious, this behavior could lead to the execution of multi-stage payloads, potentially - resulting in full system compromise and unauthorized access to sensitive information. +description: The following analytic identifies instances of `mshta.exe` writing files to world-writable directories. It leverages Sysmon EventCode 11 logs to detect file write operations by `mshta.exe` to directories like `C:\Windows\Tasks` and `C:\Windows\Temp`. This activity is significant as it often indicates an attempt to establish persistence or execute malicious code, deviating from the utility's legitimate use. If confirmed malicious, this behavior could lead to the execution of multi-stage payloads, potentially resulting in full system compromise and unauthorized access to sensitive information. search: | - `sysmon` - EventCode=11 - ( - Image="*\\mshta.exe" - OR - OriginalFileName="mshta.exe" - ) - TargetFilename IN ( - "*\\Windows\\PLA\\Reports\\*", - "*\\Windows\\PLA\\Rules\\*", - "*\\Windows\\PLA\\Templates\\*", - "*\\Windows\\Registration\\CRMLog\\*", - "*\\Windows\\System32\\Com\\dmp\\*", - "*\\Windows\\System32\\LogFiles\\WMI\\*", - "*\\Windows\\System32\\Microsoft\\Crypto\\RSA\\MachineKeys\\*", - "*\\Windows\\System32\\spool\\drivers\\color\\*", - "*\\Windows\\System32\\spool\\PRINTERS\\*", - "*\\Windows\\System32\\spool\\SERVERS\\*", - "*\\Windows\\System32\\Tasks\\*", - "*\\Windows\\SysWOW64\\Com\\dmp\\*", - "*\\Windows\\SysWOW64\\Tasks\\*", - "*\\Windows\\Tasks\\*", - "*\\Windows\\Temp\\*", - "*\\Windows\\tracing\\*" - ) - | stats count min(_time) as firstTime max(_time) as lastTime by action dest file_name - file_path process_guid process_id user user_id vendor_product Image TargetFilename - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_mshta_writing_to_world_writable_path_filter` -how_to_implement: The analytic is designed to be run against Sysmon event logs collected - from endpoints. The analytic requires the Sysmon event logs to be ingested into - Splunk. The search focuses on EventCode 11 where the Image is `mshta.exe` and the - TargetFilename is within world-writable directories such as `C:\Windows\Tasks`, - `C:\Windows\Temp`, and others. The detection is designed to catch the initial file - write operation by `mshta.exe` to these locations, which is indicative of an attempt - to establish persistence or execute malicious code. The analytic can be modified - to include additional world-writable directories as needed. -known_false_positives: False positives may occur if legitimate processes are writing - to world-writable directories. It is recommended to investigate the context of the - file write operation to determine if it is malicious or not. Modify the search to - include additional known good paths for `mshta.exe` to reduce false positives. + `sysmon` + EventCode=11 + ( + Image="*\\mshta.exe" + OR + OriginalFileName="mshta.exe" + ) + TargetFilename IN ( + "*\\Windows\\PLA\\Reports\\*", + "*\\Windows\\PLA\\Rules\\*", + "*\\Windows\\PLA\\Templates\\*", + "*\\Windows\\Registration\\CRMLog\\*", + "*\\Windows\\System32\\Com\\dmp\\*", + "*\\Windows\\System32\\LogFiles\\WMI\\*", + "*\\Windows\\System32\\Microsoft\\Crypto\\RSA\\MachineKeys\\*", + "*\\Windows\\System32\\spool\\drivers\\color\\*", + "*\\Windows\\System32\\spool\\PRINTERS\\*", + "*\\Windows\\System32\\spool\\SERVERS\\*", + "*\\Windows\\System32\\Tasks\\*", + "*\\Windows\\SysWOW64\\Com\\dmp\\*", + "*\\Windows\\SysWOW64\\Tasks\\*", + "*\\Windows\\Tasks\\*", + "*\\Windows\\Temp\\*", + "*\\Windows\\tracing\\*" + ) + | stats count min(_time) as firstTime max(_time) as lastTime by action dest file_name + file_path process_guid process_id user user_id vendor_product Image TargetFilename + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_mshta_writing_to_world_writable_path_filter` +how_to_implement: The analytic is designed to be run against Sysmon event logs collected from endpoints. The analytic requires the Sysmon event logs to be ingested into Splunk. The search focuses on EventCode 11 where the Image is `mshta.exe` and the TargetFilename is within world-writable directories such as `C:\Windows\Tasks`, `C:\Windows\Temp`, and others. The detection is designed to catch the initial file write operation by `mshta.exe` to these locations, which is indicative of an attempt to establish persistence or execute malicious code. The analytic can be modified to include additional world-writable directories as needed. +known_false_positives: False positives may occur if legitimate processes are writing to world-writable directories. It is recommended to investigate the context of the file write operation to determine if it is malicious or not. Modify the search to include additional known good paths for `mshta.exe` to reduce false positives. references: -- https://www.mandiant.com/resources/blog/apt29-wineloader-german-political-parties -- https://www.zscaler.com/blogs/security-research/european-diplomats-targeted-spikedwine-wineloader + - https://www.mandiant.com/resources/blog/apt29-wineloader-german-political-parties + - https://www.zscaler.com/blogs/security-research/european-diplomats-targeted-spikedwine-wineloader drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $Image$ writing to $TargetFilename$ was detected on $dest$. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: - - field: Image - type: file_name + message: An instance of $Image$ writing to $TargetFilename$ was detected on $dest$. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: + - field: Image + type: file_name tags: - analytic_story: - - APT29 Diplomatic Deceptions with WINELOADER - - Suspicious MSHTA Activity - - XWorm - group: - - APT29 - - Cozy Bear - - Midnight Blizzard - asset_type: Endpoint - mitre_attack_id: - - T1218.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - APT29 Diplomatic Deceptions with WINELOADER + - Suspicious MSHTA Activity + - XWorm + group: + - APT29 + - Cozy Bear + - Midnight Blizzard + asset_type: Endpoint + mitre_attack_id: + - T1218.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/mshta_tasks_windows-sysmon.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/mshta_tasks_windows-sysmon.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational diff --git a/detections/endpoint/windows_msiexec_dllregisterserver.yml b/detections/endpoint/windows_msiexec_dllregisterserver.yml index 9c7f4ba334..14f73645e5 100644 --- a/detections/endpoint/windows_msiexec_dllregisterserver.yml +++ b/detections/endpoint/windows_msiexec_dllregisterserver.yml @@ -1,95 +1,72 @@ name: Windows MSIExec DLLRegisterServer id: fdb59aef-d88f-4909-8369-ec2afbd2c398 -version: 10 -date: '2025-05-19' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: - The following analytic detects the execution of msiexec.exe with the - /y switch parameter, which enables the loading of DLLRegisterServer. This detection - leverages data from Endpoint Detection and Response (EDR) agents, focusing on process - command-line arguments and parent-child process relationships. This activity is - significant because it can indicate an attempt to register malicious DLLs, potentially - leading to code execution or persistence on the system. If confirmed malicious, - this could allow an attacker to execute arbitrary code, escalate privileges, or - maintain persistence within the environment. +description: The following analytic detects the execution of msiexec.exe with the /y switch parameter, which enables the loading of DLLRegisterServer. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process command-line arguments and parent-child process relationships. This activity is significant because it can indicate an attempt to register malicious DLLs, potentially leading to code execution or persistence on the system. If confirmed malicious, this could allow an attacker to execute arbitrary code, escalate privileges, or maintain persistence within the environment. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_msiexec` Processes.process - IN ("* /y*", "* -y*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_msiexec_dllregisterserver_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: - This analytic will need to be tuned for your environment based - on legitimate usage of msiexec.exe. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_msiexec` Processes.process IN ("* /y*", "* -y*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_msiexec_dllregisterserver_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: This analytic will need to be tuned for your environment based on legitimate usage of msiexec.exe. Filter as needed. references: - - https://thedfirreport.com/2022/06/06/will-the-real-msiexec-please-stand-up-exploit-leads-to-data-exfiltration/ - - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.007/T1218.007.md + - https://thedfirreport.com/2022/06/06/will-the-real-msiexec-please-stand-up-exploit-leads-to-data-exfiltration/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.007/T1218.007.md drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to register a file. - risk_objects: - - field: user - type: user - score: 35 - - field: dest - type: system - score: 35 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to register a file. + risk_objects: + - field: user + type: user + score: 35 + - field: dest + type: system + score: 35 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Windows System Binary Proxy Execution MSIExec - - Water Gamayun - asset_type: Endpoint - mitre_attack_id: - - T1218.007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows System Binary Proxy Execution MSIExec + - Water Gamayun + asset_type: Endpoint + mitre_attack_id: + - T1218.007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_msiexec_hidewindow_rundll32_execution.yml b/detections/endpoint/windows_msiexec_hidewindow_rundll32_execution.yml index 96ec53c0f6..67d78774c3 100644 --- a/detections/endpoint/windows_msiexec_hidewindow_rundll32_execution.yml +++ b/detections/endpoint/windows_msiexec_hidewindow_rundll32_execution.yml @@ -1,89 +1,65 @@ name: Windows MsiExec HideWindow Rundll32 Execution id: 9683271d-92e4-43b5-a907-1983bfb9f7fd -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 -description: - The following analytic detects the execution of the msiexec.exe process - with the /HideWindow and rundll32 command-line parameters. It leverages data from - Endpoint Detection and Response (EDR) agents, focusing on process creation events - and command-line arguments. This activity is significant because it is a known tactic - used by malware like QakBot to mask malicious operations under legitimate system - processes. If confirmed malicious, this behavior could allow an attacker to download - additional payloads, execute malicious code, or establish communication with remote - servers, thereby evading detection and maintaining persistence. -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name - = msiexec.exe Processes.process = "* /HideWindow *" Processes.process = "* rundll32*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_msiexec_hidewindow_rundll32_execution_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: - Other possible 3rd party msi software installers use this technique - as part of its installation process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic detects the execution of the msiexec.exe process with the /HideWindow and rundll32 command-line parameters. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events and command-line arguments. This activity is significant because it is a known tactic used by malware like QakBot to mask malicious operations under legitimate system processes. If confirmed malicious, this behavior could allow an attacker to download additional payloads, execute malicious code, or establish communication with remote servers, thereby evading detection and maintaining persistence. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name = msiexec.exe Processes.process = "* /HideWindow *" Processes.process = "* rundll32*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_msiexec_hidewindow_rundll32_execution_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Other possible 3rd party msi software installers use this technique as part of its installation process. references: - - https://twitter.com/Max_Mal_/status/1736392741758611607 - - https://twitter.com/1ZRR4H/status/1735944522075386332 + - https://twitter.com/Max_Mal_/status/1736392741758611607 + - https://twitter.com/1ZRR4H/status/1735944522075386332 drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - a msiexec parent process with /hidewindow rundll32 process commandline - on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: a msiexec parent process with /hidewindow rundll32 process commandline on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Qakbot - - Water Gamayun - asset_type: Endpoint - mitre_attack_id: - - T1218.007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Qakbot + - Water Gamayun + asset_type: Endpoint + mitre_attack_id: + - T1218.007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/msiexec-hidewindow-rundll32/hidewndw-rundll32.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/msiexec-hidewindow-rundll32/hidewndw-rundll32.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_msiexec_remote_download.yml b/detections/endpoint/windows_msiexec_remote_download.yml index e83ae004f1..a4f9665217 100644 --- a/detections/endpoint/windows_msiexec_remote_download.yml +++ b/detections/endpoint/windows_msiexec_remote_download.yml @@ -6,101 +6,93 @@ author: Michael Haag, Splunk status: production type: TTP description: | - The following analytic detects the use of msiexec.exe with an HTTP or - HTTPS URL in the command line, indicating a remote file download attempt. This detection - leverages data from Endpoint Detection and Response (EDR) agents, focusing on process - execution logs that include command-line details. This activity is significant as - it may indicate an attempt to download and execute potentially malicious software - from a remote server. If confirmed malicious, this could lead to unauthorized code - execution, system compromise, or further malware deployment within the network. + The following analytic detects the use of msiexec.exe with an HTTP or + HTTPS URL in the command line, indicating a remote file download attempt. This detection + leverages data from Endpoint Detection and Response (EDR) agents, focusing on process + execution logs that include command-line details. This activity is significant as + it may indicate an attempt to download and execute potentially malicious software + from a remote server. If confirmed malicious, this could lead to unauthorized code + execution, system compromise, or further malware deployment within the network. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 - - Cisco Network Visibility Module Flow Data + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 + - Cisco Network Visibility Module Flow Data search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - `process_msiexec` - Processes.process IN ("*http://*", "*https://*") - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_msiexec_remote_download_filter` + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where + `process_msiexec` + Processes.process IN ("*http://*", "*https://*") + by Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_msiexec_remote_download_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: | - False positives may be present, filter by destination or parent process as needed. + False positives may be present, filter by destination or parent process as needed. references: - - https://thedfirreport.com/2022/06/06/will-the-real-msiexec-please-stand-up-exploit-leads-to-data-exfiltration/ - - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.007/T1218.007.md + - https://thedfirreport.com/2022/06/06/will-the-real-msiexec-please-stand-up-exploit-leads-to-data-exfiltration/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.007/T1218.007.md drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to download a remote file. - risk_objects: - - field: user - type: user - score: 35 - - field: dest - type: system - score: 35 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to download a remote file. + risk_objects: + - field: user + type: user + score: 35 + - field: dest + type: system + score: 35 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Windows System Binary Proxy Execution MSIExec - - Water Gamayun - - Cisco Network Visibility Module Analytics - - StealC Stealer - - SolarWinds WHD RCE Post Exploitation - asset_type: Endpoint - mitre_attack_id: - - T1218.007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows System Binary Proxy Execution MSIExec + - Water Gamayun + - Cisco Network Visibility Module Analytics + - StealC Stealer + - SolarWinds WHD RCE Post Exploitation + asset_type: Endpoint + mitre_attack_id: + - T1218.007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - Sysmon - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata + - name: True Positive Test - Sysmon + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/windows_msiexec_spawn_discovery_command.yml b/detections/endpoint/windows_msiexec_spawn_discovery_command.yml index f744235e19..c122247391 100644 --- a/detections/endpoint/windows_msiexec_spawn_discovery_command.yml +++ b/detections/endpoint/windows_msiexec_spawn_discovery_command.yml @@ -1,99 +1,74 @@ name: Windows MSIExec Spawn Discovery Command id: e9d05aa2-32f0-411b-930c-5b8ca5c4fcee -version: 11 -date: '2025-12-16' +version: 12 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: - The following analytic detects MSIExec spawning multiple discovery commands, - such as Cmd.exe or PowerShell.exe. This behavior is identified using data from Endpoint - Detection and Response (EDR) agents, focusing on process creation events where MSIExec - is the parent process. This activity is significant because MSIExec typically does - not spawn child processes other than itself, making this behavior highly suspicious. - If confirmed malicious, an attacker could use these discovery commands to gather - system information, potentially leading to further exploitation or lateral movement - within the network. +description: The following analytic detects MSIExec spawning multiple discovery commands, such as Cmd.exe or PowerShell.exe. This behavior is identified using data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where MSIExec is the parent process. This activity is significant because MSIExec typically does not spawn child processes other than itself, making this behavior highly suspicious. If confirmed malicious, an attacker could use these discovery commands to gather system information, potentially leading to further exploitation or lateral movement within the network. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=msiexec.exe - Processes.process_name IN ("powershell.exe", "pwsh.exe","cmd.exe", "nltest.exe","ipconfig.exe","systeminfo.exe") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_msiexec_spawn_discovery_command_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: - False positives will be present with MSIExec spawning Cmd or - PowerShell. Filtering will be needed. In addition, add other known discovery processes - to enhance query. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name=msiexec.exe Processes.process_name IN ("powershell.exe", "pwsh.exe","cmd.exe", "nltest.exe","ipconfig.exe","systeminfo.exe") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_msiexec_spawn_discovery_command_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives will be present with MSIExec spawning Cmd or PowerShell. Filtering will be needed. In addition, add other known discovery processes to enhance query. references: - - https://thedfirreport.com/2022/06/06/will-the-real-msiexec-please-stand-up-exploit-leads-to-data-exfiltration/ - - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.007/T1218.007.md + - https://thedfirreport.com/2022/06/06/will-the-real-msiexec-please-stand-up-exploit-leads-to-data-exfiltration/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.007/T1218.007.md drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ running different discovery commands. - risk_objects: - - field: user - type: user - score: 35 - - field: dest - type: system - score: 35 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ running different discovery commands. + risk_objects: + - field: user + type: user + score: 35 + - field: dest + type: system + score: 35 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Windows System Binary Proxy Execution MSIExec - - Medusa Ransomware - - Water Gamayun - - StealC Stealer - asset_type: Endpoint - mitre_attack_id: - - T1218.007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows System Binary Proxy Execution MSIExec + - Medusa Ransomware + - Water Gamayun + - StealC Stealer + asset_type: Endpoint + mitre_attack_id: + - T1218.007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_msiexec_spawn_windbg.yml b/detections/endpoint/windows_msiexec_spawn_windbg.yml index d52ba9f0be..ecfce7299e 100644 --- a/detections/endpoint/windows_msiexec_spawn_windbg.yml +++ b/detections/endpoint/windows_msiexec_spawn_windbg.yml @@ -1,89 +1,72 @@ name: Windows MSIExec Spawn WinDBG id: 9a18f7c2-1fe3-47b8-9467-8b3976770a30 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic identifies the unusual behavior of MSIExec spawning - WinDBG. It detects this activity by analyzing endpoint telemetry data, specifically - looking for instances where 'msiexec.exe' is the parent process of 'windbg.exe'. - This behavior is significant as it may indicate an attempt to debug or tamper with - system processes, which is uncommon in typical user activity and could signify malicious - intent. If confirmed malicious, this activity could allow an attacker to manipulate - or inspect running processes, potentially leading to privilege escalation or persistence - within the environment. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=msiexec.exe - Processes.process_name=windbg.exe by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_msiexec_spawn_windbg_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives will only be present if the MSIExec process - legitimately spawns WinDBG. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic identifies the unusual behavior of MSIExec spawning WinDBG. It detects this activity by analyzing endpoint telemetry data, specifically looking for instances where 'msiexec.exe' is the parent process of 'windbg.exe'. This behavior is significant as it may indicate an attempt to debug or tamper with system processes, which is uncommon in typical user activity and could signify malicious intent. If confirmed malicious, this activity could allow an attacker to manipulate or inspect running processes, potentially leading to privilege escalation or persistence within the environment. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name=msiexec.exe Processes.process_name=windbg.exe + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_msiexec_spawn_windbg_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives will only be present if the MSIExec process legitimately spawns WinDBG. Filter as needed. references: -- https://github.com/PaloAltoNetworks/Unit42-timely-threat-intel/blob/main/2023-10-25-IOCs-from-DarkGate-activity.txt + - https://github.com/PaloAltoNetworks/Unit42-timely-threat-intel/blob/main/2023-10-25-IOCs-from-DarkGate-activity.txt drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 100 - - field: dest - type: system - score: 100 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 100 + - field: dest + type: system + score: 100 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Compromised Windows Host - - DarkGate Malware - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1218.007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - DarkGate Malware + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1218.007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/atomic_red_team/windbg_msiexec.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/atomic_red_team/windbg_msiexec.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_msiexec_unregister_dllregisterserver.yml b/detections/endpoint/windows_msiexec_unregister_dllregisterserver.yml index e0ef89ee94..ddba326b20 100644 --- a/detections/endpoint/windows_msiexec_unregister_dllregisterserver.yml +++ b/detections/endpoint/windows_msiexec_unregister_dllregisterserver.yml @@ -1,88 +1,71 @@ name: Windows MSIExec Unregister DLLRegisterServer id: a27db3c5-1a9a-46df-a577-765d3f1a3c24 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the use of msiexec.exe with the /z switch - parameter, which is used to unload DLLRegisterServer. This detection leverages data - from Endpoint Detection and Response (EDR) agents, focusing on process execution - logs, including command-line arguments. This activity is significant because unloading - DLLRegisterServer can be indicative of an attempt to deregister a DLL, potentially - disrupting legitimate services or hiding malicious activity. If confirmed malicious, - this could allow an attacker to disable security controls, evade detection, or disrupt - system functionality, leading to further compromise of the environment. +description: The following analytic detects the use of msiexec.exe with the /z switch parameter, which is used to unload DLLRegisterServer. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs, including command-line arguments. This activity is significant because unloading DLLRegisterServer can be indicative of an attempt to deregister a DLL, potentially disrupting legitimate services or hiding malicious activity. If confirmed malicious, this could allow an attacker to disable security controls, evade detection, or disrupt system functionality, leading to further compromise of the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_msiexec` Processes.process - IN ("*/z*", "*-z*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_msiexec_unregister_dllregisterserver_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: This analytic will need to be tuned for your environment based - on legitimate usage of msiexec.exe. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_msiexec` Processes.process IN ("*/z*", "*-z*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_msiexec_unregister_dllregisterserver_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: This analytic will need to be tuned for your environment based on legitimate usage of msiexec.exe. Filter as needed. references: -- https://thedfirreport.com/2022/06/06/will-the-real-msiexec-please-stand-up-exploit-leads-to-data-exfiltration/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.007/T1218.007.md + - https://thedfirreport.com/2022/06/06/will-the-real-msiexec-please-stand-up-exploit-leads-to-data-exfiltration/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.007/T1218.007.md drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to unregister a file. - risk_objects: - - field: user - type: user - score: 35 - - field: dest - type: system - score: 35 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to unregister a file. + risk_objects: + - field: user + type: user + score: 35 + - field: dest + type: system + score: 35 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Windows System Binary Proxy Execution MSIExec - asset_type: Endpoint - mitre_attack_id: - - T1218.007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows System Binary Proxy Execution MSIExec + asset_type: Endpoint + mitre_attack_id: + - T1218.007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_msix_package_interaction.yml b/detections/endpoint/windows_msix_package_interaction.yml index 7797d63850..d400ef93fb 100644 --- a/detections/endpoint/windows_msix_package_interaction.yml +++ b/detections/endpoint/windows_msix_package_interaction.yml @@ -1,40 +1,42 @@ name: Windows MSIX Package Interaction id: 1a06689d-814e-4db2-b2c7-5a174f8c2d6d -version: 1 -date: '2025-08-05' +version: 2 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting description: This hunting query detects user interactions with MSIX packages by monitoring EventCode 171 in the Microsoft-Windows-AppXPackaging/Operational logs. These events are generated when a user clicks on or attempts to interact with an MSIX package, even if the package is not fully installed. This information can be valuable for security teams to identify what MSIX packages users are attempting to open in their environment, which may help detect malicious MSIX packages before they're fully installed. Monitoring these interactions can provide early warning of potential MSIX package abuse, which has been leveraged by threat actors such as FIN7, Zloader (Storm-0569), and FakeBat (Storm-1113). data_source: -- Windows Event Log AppXPackaging 171 -search: '`wineventlog_appxpackaging` EventCode=171 - | stats count min(_time) as firstTime max(_time) as lastTime values(packageFullName) as packageFullName values(user_id) as user_id by host EventCode - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_msix_package_interaction_filter`' + - Windows Event Log AppXPackaging 171 +search: |- + `wineventlog_appxpackaging` EventCode=171 + | stats count min(_time) as firstTime max(_time) as lastTime values(packageFullName) as packageFullName values(user_id) as user_id + BY host EventCode + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_msix_package_interaction_filter` how_to_implement: To implement this detection, you need to be collecting Windows Event logs from the Microsoft-Windows-AppXPackaging/Operational channel. In Splunk, this typically requires the Windows TA and configuration to collect from this specific channel. Ensure your Windows event collection is properly configured to capture EventCode 171 from the Microsoft-Windows-AppXPackaging/Operational log. This query is designed as a hunting query to help identify MSIX package interactions across your environment. known_false_positives: This hunting query will detect legitimate MSIX package interactions from normal users. It is not designed to specifically identify malicious activity but rather to provide visibility into all MSIX package interactions. Security teams should review the results and look for unusual patterns, unexpected packages, or suspicious file paths. references: -- https://www.appdeploynews.com/packaging-types/msix/troubleshooting-an-msix-package/ -- https://learn.microsoft.com/en-us/windows/win32/appxpkg/troubleshooting -- https://www.advancedinstaller.com/msix-installation-or-launching-errors-and-fixes.html -- https://redcanary.com/blog/msix-installers/ + - https://www.appdeploynews.com/packaging-types/msix/troubleshooting-an-msix-package/ + - https://learn.microsoft.com/en-us/windows/win32/appxpkg/troubleshooting + - https://www.advancedinstaller.com/msix-installation-or-launching-errors-and-fixes.html + - https://redcanary.com/blog/msix-installers/ tags: - analytic_story: - - MSIX Package Abuse - asset_type: Endpoint - mitre_attack_id: - - T1204.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - MSIX Package Abuse + asset_type: Endpoint + mitre_attack_id: + - T1204.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/appx/windows-appxpackaging.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Microsoft-Windows-AppxPackaging/Operational + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/appx/windows-appxpackaging.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Microsoft-Windows-AppxPackaging/Operational diff --git a/detections/endpoint/windows_mstsc_rdp_commandline.yml b/detections/endpoint/windows_mstsc_rdp_commandline.yml index 0b43f92806..c084c44b59 100644 --- a/detections/endpoint/windows_mstsc_rdp_commandline.yml +++ b/detections/endpoint/windows_mstsc_rdp_commandline.yml @@ -1,82 +1,66 @@ name: Windows MSTSC RDP Commandline id: 3718549b-867e-4084-b770-790e8dab6ab8 -version: 3 -date: '2025-08-01' +version: 4 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the use of the mstsc.exe - command-line, which is commonly used to initiate Remote Desktop Protocol (RDP) - connections. This detection focuses on instances where mstsc.exe is executed - with specific parameters that may indicate suspicious or unauthorized remote - access attempts. Monitoring command-line arguments such as /v: for - direct connections or /admin for administrative sessions can help identify - potential misuse or lateral movement within a network. +description: The following analytic detects the use of the mstsc.exe command-line, which is commonly used to initiate Remote Desktop Protocol (RDP) connections. This detection focuses on instances where mstsc.exe is executed with specific parameters that may indicate suspicious or unauthorized remote access attempts. Monitoring command-line arguments such as /v: for direct connections or /admin for administrative sessions can help identify potential misuse or lateral movement within a network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "mstsc.exe" - Processes.process = "*/v:*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_mstsc_rdp_commandline_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "mstsc.exe" Processes.process = "*/v:*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_mstsc_rdp_commandline_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: administrator may remote desktop a spe references: -- https://www.cisa.gov/news-events/cybersecurity-advisories/aa25-071a + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa25-071a drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a mstsc.exe process commandline $process$ executed on $dest$. - risk_objects: - - field: dest - type: system - score: 20 - threat_objects: - - field: process_name - type: process_name + message: a mstsc.exe process commandline $process$ executed on $dest$. + risk_objects: + - field: dest + type: system + score: 20 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Medusa Ransomware - - Windows RDP Artifacts and Defense Evasion - asset_type: Endpoint - mitre_attack_id: - - T1021.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Medusa Ransomware + - Windows RDP Artifacts and Defense Evasion + asset_type: Endpoint + mitre_attack_id: + - T1021.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.001/mstsc_rdp_cmd/mstsc_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.001/mstsc_rdp_cmd/mstsc_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_multiple_account_passwords_changed.yml b/detections/endpoint/windows_multiple_account_passwords_changed.yml index 53a4814b78..10e249a5a3 100644 --- a/detections/endpoint/windows_multiple_account_passwords_changed.yml +++ b/detections/endpoint/windows_multiple_account_passwords_changed.yml @@ -1,69 +1,57 @@ name: Windows Multiple Account Passwords Changed id: faefb681-14be-4f0d-9cac-0bc0160c7280 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Mauricio Velazco, Splunk data_source: -- Windows Event Log Security 4724 + - Windows Event Log Security 4724 type: TTP status: production -description: The following analytic detects instances where more than five unique - Windows account passwords are changed within a 10-minute interval. It leverages - Event Code 4724 from the Windows Security Event Log, using the wineventlog_security - dataset to monitor and count distinct TargetUserName values. This behavior is significant - as rapid password changes across multiple accounts are unusual and may indicate - unauthorized access or internal compromise. If confirmed malicious, this activity - could lead to widespread account compromise, unauthorized access to sensitive information, - and potential disruption of services. -search: '`wineventlog_security` EventCode=4724 status=success | bucket span=10m _time - | stats count dc(user) as unique_users values(user) as user values(dest) as dest - by EventCode signature _time src_user SubjectDomainName TargetDomainName Logon_ID - | where unique_users > 5 | `windows_multiple_account_passwords_changed_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Domain Controller events with the Windows TA. The Advanced Security Audit policy - setting `Audit User Account Management` within `Account Management` needs to be - enabled. -known_false_positives: Service accounts may be responsible for the creation, deletion - or modification of accounts for legitimate purposes. Filter as needed. +description: The following analytic detects instances where more than five unique Windows account passwords are changed within a 10-minute interval. It leverages Event Code 4724 from the Windows Security Event Log, using the wineventlog_security dataset to monitor and count distinct TargetUserName values. This behavior is significant as rapid password changes across multiple accounts are unusual and may indicate unauthorized access or internal compromise. If confirmed malicious, this activity could lead to widespread account compromise, unauthorized access to sensitive information, and potential disruption of services. +search: |- + `wineventlog_security` EventCode=4724 status=success + | bucket span=10m _time + | stats count dc(user) as unique_users values(user) as user values(dest) as dest + BY EventCode signature _time + src_user SubjectDomainName TargetDomainName + Logon_ID + | where unique_users > 5 + | `windows_multiple_account_passwords_changed_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller events with the Windows TA. The Advanced Security Audit policy setting `Audit User Account Management` within `Account Management` needs to be enabled. +known_false_positives: Service accounts may be responsible for the creation, deletion or modification of accounts for legitimate purposes. Filter as needed. references: -- https://attack.mitre.org/techniques/T1098/ + - https://attack.mitre.org/techniques/T1098/ drilldown_searches: -- name: View the detection results for - "$src_user$" - search: '%original_detection_search% | search src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_user$" + search: '%original_detection_search% | search src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $src_user$ changed the passwords of multiple accounts in a short period - of time. - risk_objects: - - field: src_user - type: user - score: 24 - threat_objects: [] + message: User $src_user$ changed the passwords of multiple accounts in a short period of time. + risk_objects: + - field: src_user + type: user + score: 24 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - asset_type: Endpoint - mitre_attack_id: - - T1098 - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azure Active Directory Persistence + asset_type: Endpoint + mitre_attack_id: + - T1098 + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/windows_multiple_passwords_changed/windows_multiple_passwords_changed.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/windows_multiple_passwords_changed/windows_multiple_passwords_changed.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_multiple_accounts_deleted.yml b/detections/endpoint/windows_multiple_accounts_deleted.yml index 6e643209e1..595798c939 100644 --- a/detections/endpoint/windows_multiple_accounts_deleted.yml +++ b/detections/endpoint/windows_multiple_accounts_deleted.yml @@ -1,67 +1,57 @@ name: Windows Multiple Accounts Deleted id: 49c0d4d6-c55d-4d3a-b3d5-7709fafed70d -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Mauricio Velazco, Splunk data_source: -- Windows Event Log Security 4726 + - Windows Event Log Security 4726 type: TTP status: production -description: The following analytic detects the deletion of more than five unique - Windows accounts within a 10-minute period, using Event Code 4726 from the Windows - Security Event Log. It leverages the `wineventlog_security` dataset, segmenting - data into 10-minute intervals to identify suspicious account deletions. This activity - is significant as it may indicate an attacker attempting to erase traces of their - actions. If confirmed malicious, this could lead to unauthorized access removal, - hindering incident response and forensic investigations. -search: '`wineventlog_security` EventCode=4726 status=success | bucket span=10m _time - | stats count dc(user) as unique_users values(user) as user values(dest) as dest - by EventCode signature _time src_user SubjectDomainName TargetDomainName Logon_ID - | where unique_users > 5 | `windows_multiple_accounts_deleted_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Domain Controller events with the Windows TA. The Advanced Security Audit policy - setting `Audit User Account Management` within `Account Management` needs to be - enabled. -known_false_positives: Service accounts may be responsible for the creation, deletion - or modification of accounts for legitimate purposes. Filter as needed. +description: The following analytic detects the deletion of more than five unique Windows accounts within a 10-minute period, using Event Code 4726 from the Windows Security Event Log. It leverages the `wineventlog_security` dataset, segmenting data into 10-minute intervals to identify suspicious account deletions. This activity is significant as it may indicate an attacker attempting to erase traces of their actions. If confirmed malicious, this could lead to unauthorized access removal, hindering incident response and forensic investigations. +search: |- + `wineventlog_security` EventCode=4726 status=success + | bucket span=10m _time + | stats count dc(user) as unique_users values(user) as user values(dest) as dest + BY EventCode signature _time + src_user SubjectDomainName TargetDomainName + Logon_ID + | where unique_users > 5 + | `windows_multiple_accounts_deleted_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller events with the Windows TA. The Advanced Security Audit policy setting `Audit User Account Management` within `Account Management` needs to be enabled. +known_false_positives: Service accounts may be responsible for the creation, deletion or modification of accounts for legitimate purposes. Filter as needed. references: -- https://attack.mitre.org/techniques/T1098/ + - https://attack.mitre.org/techniques/T1098/ drilldown_searches: -- name: View the detection results for - "$src_user$" - search: '%original_detection_search% | search src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_user$" + search: '%original_detection_search% | search src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $src_user$ deleted multiple accounts in a short period of time. - risk_objects: - - field: src_user - type: user - score: 18 - threat_objects: [] + message: User $src_user$ deleted multiple accounts in a short period of time. + risk_objects: + - field: src_user + type: user + score: 18 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - asset_type: Endpoint - mitre_attack_id: - - T1098 - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azure Active Directory Persistence + asset_type: Endpoint + mitre_attack_id: + - T1098 + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/windows_multiple_accounts_deleted/windows_multiple_accounts_deleted.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/windows_multiple_accounts_deleted/windows_multiple_accounts_deleted.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_multiple_accounts_disabled.yml b/detections/endpoint/windows_multiple_accounts_disabled.yml index 4ca6180eda..8eec4ed3f2 100644 --- a/detections/endpoint/windows_multiple_accounts_disabled.yml +++ b/detections/endpoint/windows_multiple_accounts_disabled.yml @@ -1,68 +1,57 @@ name: Windows Multiple Accounts Disabled id: 5d93894e-befa-4429-abde-7fc541020b7b -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Mauricio Velazco, Splunk data_source: -- Windows Event Log Security 4725 + - Windows Event Log Security 4725 type: TTP status: production -description: The following analytic identifies instances where more than five unique - Windows accounts are disabled within a 10-minute window, as indicated by Event Code - 4725 in the Windows Security Event Log. It leverages the wineventlog_security dataset, - grouping data into 10-minute segments and tracking the count and distinct count - of TargetUserName. This behavior is significant as it may indicate internal policy - breaches or an external attacker's attempt to disrupt operations. If confirmed malicious, - this activity could lead to widespread account lockouts, hindering user access and - potentially disrupting business operations. -search: '`wineventlog_security` EventCode=4725 status=success | bucket span=10m _time - | stats count dc(user) as unique_users values(user) as user values(dest) as dest - by EventCode signature _time src_user SubjectDomainName TargetDomainName Logon_ID - | where unique_users > 5 | `windows_multiple_accounts_disabled_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Domain Controller events with the Windows TA. The Advanced Security Audit policy - setting `Audit User Account Management` within `Account Management` needs to be - enabled. -known_false_positives: Service accounts may be responsible for the creation, deletion - or modification of accounts for legitimate purposes. Filter as needed. +description: The following analytic identifies instances where more than five unique Windows accounts are disabled within a 10-minute window, as indicated by Event Code 4725 in the Windows Security Event Log. It leverages the wineventlog_security dataset, grouping data into 10-minute segments and tracking the count and distinct count of TargetUserName. This behavior is significant as it may indicate internal policy breaches or an external attacker's attempt to disrupt operations. If confirmed malicious, this activity could lead to widespread account lockouts, hindering user access and potentially disrupting business operations. +search: |- + `wineventlog_security` EventCode=4725 status=success + | bucket span=10m _time + | stats count dc(user) as unique_users values(user) as user values(dest) as dest + BY EventCode signature _time + src_user SubjectDomainName TargetDomainName + Logon_ID + | where unique_users > 5 + | `windows_multiple_accounts_disabled_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller events with the Windows TA. The Advanced Security Audit policy setting `Audit User Account Management` within `Account Management` needs to be enabled. +known_false_positives: Service accounts may be responsible for the creation, deletion or modification of accounts for legitimate purposes. Filter as needed. references: -- https://attack.mitre.org/techniques/T1098/ + - https://attack.mitre.org/techniques/T1098/ drilldown_searches: -- name: View the detection results for - "$src_user$" - search: '%original_detection_search% | search src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_user$" + search: '%original_detection_search% | search src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $src_user$ disabled multiple accounts in a short period of time. - risk_objects: - - field: src_user - type: user - score: 18 - threat_objects: [] + message: User $src_user$ disabled multiple accounts in a short period of time. + risk_objects: + - field: src_user + type: user + score: 18 + threat_objects: [] tags: - analytic_story: - - Azure Active Directory Persistence - asset_type: Endpoint - mitre_attack_id: - - T1098 - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azure Active Directory Persistence + asset_type: Endpoint + mitre_attack_id: + - T1098 + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/windows_multiple_accounts_disabled/windows_multiple_accounts_disabled.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/windows_multiple_accounts_disabled/windows_multiple_accounts_disabled.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_multiple_disabled_users_failed_to_authenticate_wth_kerberos.yml b/detections/endpoint/windows_multiple_disabled_users_failed_to_authenticate_wth_kerberos.yml index a43d49f16c..5c03677ff7 100644 --- a/detections/endpoint/windows_multiple_disabled_users_failed_to_authenticate_wth_kerberos.yml +++ b/detections/endpoint/windows_multiple_disabled_users_failed_to_authenticate_wth_kerberos.yml @@ -1,72 +1,58 @@ name: Windows Multiple Disabled Users Failed To Authenticate Wth Kerberos id: 98f22d82-9d62-11eb-9fcf-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk type: TTP status: production data_source: -- Windows Event Log Security 4768 -description: The following analytic detects a single source endpoint failing to authenticate - with 30 unique disabled domain users using the Kerberos protocol within 5 minutes. - It leverages Windows Security Event 4768, focusing on failure code `0x12`, indicating - revoked credentials. This activity is significant as it may indicate a Password - Spraying attack targeting disabled accounts, a tactic used by adversaries to gain - initial access or elevate privileges. If confirmed malicious, this could lead to - unauthorized access or privilege escalation within the Active Directory environment, - posing a severe security risk. -how_to_implement: To successfully implement this search, you need to be ingesting - Domain Controller and Kerberos events. The Advanced Security Audit policy setting - `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. -known_false_positives: A host failing to authenticate with multiple disabled domain - users is not a common behavior for legitimate systems. Possible false positive scenarios - include but are not limited to vulnerability scanners, multi-user systems missconfigured - systems. + - Windows Event Log Security 4768 +description: The following analytic detects a single source endpoint failing to authenticate with 30 unique disabled domain users using the Kerberos protocol within 5 minutes. It leverages Windows Security Event 4768, focusing on failure code `0x12`, indicating revoked credentials. This activity is significant as it may indicate a Password Spraying attack targeting disabled accounts, a tactic used by adversaries to gain initial access or elevate privileges. If confirmed malicious, this could lead to unauthorized access or privilege escalation within the Active Directory environment, posing a severe security risk. +how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. +known_false_positives: A host failing to authenticate with multiple disabled domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, multi-user systems missconfigured systems. references: -- https://attack.mitre.org/techniques/T1110/003/ + - https://attack.mitre.org/techniques/T1110/003/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -search: '`wineventlog_security` EventCode=4768 TargetUserName!=*$ Status=0x12 | bucket - span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) - as user values(dest) as dest by _time, IpAddress | where unique_accounts > 30 | - `windows_multiple_disabled_users_failed_to_authenticate_wth_kerberos_filter`' + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ +search: |- + `wineventlog_security` EventCode=4768 TargetUserName!=*$ Status=0x12 + | bucket span=5m _time + | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as user values(dest) as dest + BY _time, IpAddress + | where unique_accounts > 30 + | `windows_multiple_disabled_users_failed_to_authenticate_wth_kerberos_filter` rba: - message: Potential Kerberos based password spraying attack from $IpAddress$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: IpAddress - type: ip_address + message: Potential Kerberos based password spraying attack from $IpAddress$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: IpAddress + type: ip_address tags: - analytic_story: - - Active Directory Password Spraying - - Active Directory Kerberos Attacks - - Volt Typhoon - asset_type: Endpoint - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Password Spraying + - Active Directory Kerberos Attacks + - Volt Typhoon + asset_type: Endpoint + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_disabled_users_kerberos_xml/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog - name: True Positive Test + - attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_disabled_users_kerberos_xml/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog + name: True Positive Test diff --git a/detections/endpoint/windows_multiple_invalid_users_fail_to_authenticate_using_kerberos.yml b/detections/endpoint/windows_multiple_invalid_users_fail_to_authenticate_using_kerberos.yml index e20c5565a7..f40c55c8f6 100644 --- a/detections/endpoint/windows_multiple_invalid_users_fail_to_authenticate_using_kerberos.yml +++ b/detections/endpoint/windows_multiple_invalid_users_fail_to_authenticate_using_kerberos.yml @@ -1,72 +1,58 @@ name: Windows Multiple Invalid Users Fail To Authenticate Using Kerberos id: 001266a6-9d5b-11eb-829b-acde48001122 -date: '2025-05-02' -version: 8 +date: '2026-02-25' +version: 9 type: TTP status: production author: Mauricio Velazco, Splunk data_source: -- Windows Event Log Security 4768 -description: The following analytic identifies a source endpoint failing to authenticate - with 30 unique invalid domain users using the Kerberos protocol. This detection - leverages EventCode 4768, specifically looking for failure code 0x6, indicating - the user is not found in the Kerberos database. This activity is significant as - it may indicate a Password Spraying attack, where an adversary attempts to gain - initial access or elevate privileges. If confirmed malicious, this could lead to - unauthorized access or privilege escalation within the Active Directory environment, - posing a significant security risk. -how_to_implement: To successfully implement this search, you need to be ingesting - Domain Controller and Kerberos events. The Advanced Security Audit policy setting - `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. -known_false_positives: A host failing to authenticate with multiple invalid domain - users is not a common behavior for legitimate systems. Possible false positive scenarios - include but are not limited to vulnerability scanners, multi-user systems and missconfigured - systems. + - Windows Event Log Security 4768 +description: The following analytic identifies a source endpoint failing to authenticate with 30 unique invalid domain users using the Kerberos protocol. This detection leverages EventCode 4768, specifically looking for failure code 0x6, indicating the user is not found in the Kerberos database. This activity is significant as it may indicate a Password Spraying attack, where an adversary attempts to gain initial access or elevate privileges. If confirmed malicious, this could lead to unauthorized access or privilege escalation within the Active Directory environment, posing a significant security risk. +how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. +known_false_positives: A host failing to authenticate with multiple invalid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, multi-user systems and missconfigured systems. references: -- https://attack.mitre.org/techniques/T1110/003/ + - https://attack.mitre.org/techniques/T1110/003/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -search: '`wineventlog_security` EventCode=4768 TargetUserName!=*$ Status=0x6 | bucket - span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) - as user values(dest) as dest by _time, IpAddress | where unique_accounts > 30 | - `windows_multiple_invalid_users_fail_to_authenticate_using_kerberos_filter`' + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ +search: |- + `wineventlog_security` EventCode=4768 TargetUserName!=*$ Status=0x6 + | bucket span=5m _time + | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as user values(dest) as dest + BY _time, IpAddress + | where unique_accounts > 30 + | `windows_multiple_invalid_users_fail_to_authenticate_using_kerberos_filter` rba: - message: Potential Kerberos based password spraying attack from $IpAddress$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: IpAddress - type: ip_address + message: Potential Kerberos based password spraying attack from $IpAddress$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: IpAddress + type: ip_address tags: - analytic_story: - - Active Directory Password Spraying - - Active Directory Kerberos Attacks - - Volt Typhoon - asset_type: Endpoint - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Password Spraying + - Active Directory Kerberos Attacks + - Volt Typhoon + asset_type: Endpoint + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_invalid_users_kerberos_xml/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog - name: True Positive Test + - attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_invalid_users_kerberos_xml/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog + name: True Positive Test diff --git a/detections/endpoint/windows_multiple_invalid_users_failed_to_authenticate_using_ntlm.yml b/detections/endpoint/windows_multiple_invalid_users_failed_to_authenticate_using_ntlm.yml index c3fda034ae..8a68a8d083 100644 --- a/detections/endpoint/windows_multiple_invalid_users_failed_to_authenticate_using_ntlm.yml +++ b/detections/endpoint/windows_multiple_invalid_users_failed_to_authenticate_using_ntlm.yml @@ -1,72 +1,57 @@ name: Windows Multiple Invalid Users Failed To Authenticate Using NTLM id: 57ad5a64-9df7-11eb-a290-acde48001122 type: TTP -version: 9 +version: 10 author: Mauricio Velazco, Splunk status: production data_source: -- Windows Event Log Security 4776 -date: '2025-05-02' -description: The following analytic detects a single source endpoint failing to authenticate - with 30 unique invalid users using the NTLM protocol. It leverages EventCode 4776 - from Domain Controller logs, focusing on error code 0xC0000064, which indicates - non-existent usernames. This behavior is significant as it may indicate a Password - Spraying attack, where an adversary attempts to gain initial access or elevate privileges. - If confirmed malicious, this activity could lead to unauthorized access, privilege - escalation, and potential compromise of sensitive information within the Active - Directory environment. -how_to_implement: To successfully implement this search, you need to be ingesting - Domain Controller events. The Advanced Security Audit policy setting `Audit Credential - Validation' within `Account Logon` needs to be enabled. -known_false_positives: A host failing to authenticate with multiple invalid domain - users is not a common behavior for legitimate systems. Possible false positive scenarios - include but are not limited to vulnerability scanners and missconfigured systems. - If this detection triggers on a host other than a Domain Controller, the behavior - could represent a password spraying attack against the host's local accounts. + - Windows Event Log Security 4776 +date: '2026-02-25' +description: The following analytic detects a single source endpoint failing to authenticate with 30 unique invalid users using the NTLM protocol. It leverages EventCode 4776 from Domain Controller logs, focusing on error code 0xC0000064, which indicates non-existent usernames. This behavior is significant as it may indicate a Password Spraying attack, where an adversary attempts to gain initial access or elevate privileges. If confirmed malicious, this activity could lead to unauthorized access, privilege escalation, and potential compromise of sensitive information within the Active Directory environment. +how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller events. The Advanced Security Audit policy setting `Audit Credential Validation' within `Account Logon` needs to be enabled. +known_false_positives: A host failing to authenticate with multiple invalid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. If this detection triggers on a host other than a Domain Controller, the behavior could represent a password spraying attack against the host's local accounts. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/audit-credential-validation -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4776 + - https://attack.mitre.org/techniques/T1110/003/ + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/audit-credential-validation + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4776 drilldown_searches: -- name: View the detection results for - "$Workstation$" - search: '%original_detection_search% | search Workstation = "$Workstation$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$Workstation$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Workstation$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -search: '`wineventlog_security` EventCode=4776 TargetUserName!=*$ Status=0xc0000064 - | bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) - as tried_accounts values(dest) as dest by _time, Workstation | where unique_accounts - > 30 | `windows_multiple_invalid_users_failed_to_authenticate_using_ntlm_filter`' + - name: View the detection results for - "$Workstation$" + search: '%original_detection_search% | search Workstation = "$Workstation$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Workstation$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Workstation$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ +search: |- + `wineventlog_security` EventCode=4776 TargetUserName!=*$ Status=0xc0000064 + | bucket span=5m _time + | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts values(dest) as dest + BY _time, Workstation + | where unique_accounts > 30 + | `windows_multiple_invalid_users_failed_to_authenticate_using_ntlm_filter` rba: - message: Potential NTLM based password spraying attack from $Workstation$ - risk_objects: - - field: Workstation - type: system - score: 49 - threat_objects: [] + message: Potential NTLM based password spraying attack from $Workstation$ + risk_objects: + - field: Workstation + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Active Directory Password Spraying - - Volt Typhoon - asset_type: Endpoint - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Password Spraying + - Volt Typhoon + asset_type: Endpoint + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_invalid_users_ntlm_xml/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog - name: True Positive Test + - attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_invalid_users_ntlm_xml/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog + name: True Positive Test diff --git a/detections/endpoint/windows_multiple_ntlm_null_domain_authentications.yml b/detections/endpoint/windows_multiple_ntlm_null_domain_authentications.yml index 3245f7e0ae..4c9675daba 100644 --- a/detections/endpoint/windows_multiple_ntlm_null_domain_authentications.yml +++ b/detections/endpoint/windows_multiple_ntlm_null_domain_authentications.yml @@ -5,79 +5,50 @@ date: '2026-01-14' author: Steven Dick status: production type: TTP -description: The following analytic detects when a device is the target of numerous - NTLM authentications using a null domain. This activity generally results when an - attacker attempts to brute force, password spray, or otherwise authenticate to a - domain joined Windows device from a non-domain device. This activity may also generate - a large number of EventID 4776 events in tandem, however these events will not indicate - the attacker or target device +description: The following analytic detects when a device is the target of numerous NTLM authentications using a null domain. This activity generally results when an attacker attempts to brute force, password spray, or otherwise authenticate to a domain joined Windows device from a non-domain device. This activity may also generate a large number of EventID 4776 events in tandem, however these events will not indicate the attacker or target device data_source: -- NTLM Operational 8004 -- NTLM Operational 8005 -- NTLM Operational 8006 -search: '`ntlm_audit` EventCode IN (8004,8005,8006) DomainName=NULL UserName!=NULL - | eval src = replace(WorkstationName,"\\\\","") ```CIM alignment, remove leading - \\ from some auth attempts ``` | eval dest = SChannelName, user = UserName ``` CIM - alignment``` | where SChannelName!=src ``` Remove NTLM auths to self, improves accuracy - for certain applications``` - | stats count min(_time) as firstTime max(_time) as lastTime dc(eval(upper(user))) - as unique_count dc(eval(upper(src))) as src_count by dest | eventstats avg(unique_count) - as unique_avg , stdev(unique_count) as unique_std | eval upperBound_unique=(1+unique_avg+unique_std*3) - ``` adjust formula for sensitivity``` | eval isOutlier=CASE(unique_count > upperBound_unique, - 1, true(), 0) | where isOutlier==1 | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_multiple_ntlm_null_domain_authentications_filter`' -how_to_implement: The following analytic requires that NTLM Operational logs to be - imported from the environment Domain Controllers. This requires configuration of - specific auditing settings, see Microsoft references for further guidance. This - analytic is specific to EventID 8004~8006. -known_false_positives: Applications that deal with non-domain joined authentications. - Recommend adjusting the upperBound_unique eval for tailoring the correlation to - your environment, running with a 24hr search window will smooth out some statistical - noise. + - NTLM Operational 8004 + - NTLM Operational 8005 + - NTLM Operational 8006 +search: '`ntlm_audit` EventCode IN (8004,8005,8006) DomainName=NULL UserName!=NULL | eval src = replace(WorkstationName,"\\\\","") ```CIM alignment, remove leading \\ from some auth attempts ``` | eval dest = SChannelName, user = UserName ``` CIM alignment``` | where SChannelName!=src ``` Remove NTLM auths to self, improves accuracy for certain applications``` | stats count min(_time) as firstTime max(_time) as lastTime dc(eval(upper(user))) as unique_count dc(eval(upper(src))) as src_count by dest | eventstats avg(unique_count) as unique_avg , stdev(unique_count) as unique_std | eval upperBound_unique=(1+unique_avg+unique_std*3) ``` adjust formula for sensitivity``` | eval isOutlier=CASE(unique_count > upperBound_unique, 1, true(), 0) | where isOutlier==1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_multiple_ntlm_null_domain_authentications_filter`' +how_to_implement: The following analytic requires that NTLM Operational logs to be imported from the environment Domain Controllers. This requires configuration of specific auditing settings, see Microsoft references for further guidance. This analytic is specific to EventID 8004~8006. +known_false_positives: Applications that deal with non-domain joined authentications. Recommend adjusting the upperBound_unique eval for tailoring the correlation to your environment, running with a 24hr search window will smooth out some statistical noise. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://techcommunity.microsoft.com/t5/ask-the-directory-services-team/ntlm-blocking-and-you-application-analysis-and-auditing/ba-p/397191 -- https://techcommunity.microsoft.com/t5/microsoft-defender-for-identity/enriched-ntlm-authentication-data-using-windows-event-8004/m-p/871827 -- https://www.varonis.com/blog/investigate-ntlm-brute-force -- https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-nrpc/4d1235e3-2c96-4e9f-a147-3cb338a0d09f + - https://attack.mitre.org/techniques/T1110/003/ + - https://techcommunity.microsoft.com/t5/ask-the-directory-services-team/ntlm-blocking-and-you-application-analysis-and-auditing/ba-p/397191 + - https://techcommunity.microsoft.com/t5/microsoft-defender-for-identity/enriched-ntlm-authentication-data-using-windows-event-8004/m-p/871827 + - https://www.varonis.com/blog/investigate-ntlm-brute-force + - https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-nrpc/4d1235e3-2c96-4e9f-a147-3cb338a0d09f drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The device [$dest$] was the target of $count$ NTLM authentications from - $src_count$ sources using $unique_count$ unique user accounts. - risk_objects: - - field: dest - type: system - score: 75 - threat_objects: [] + message: The device [$dest$] was the target of $count$ NTLM authentications from $src_count$ sources using $unique_count$ unique user accounts. + risk_objects: + - field: dest + type: system + score: 75 + threat_objects: [] tags: - analytic_story: - - Active Directory Password Spraying - asset_type: Endpoint - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Password Spraying + asset_type: Endpoint + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/ntlm_bruteforce/ntlm_bruteforce.log - source: XmlWinEventLog:Microsoft-Windows-NTLM/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/ntlm_bruteforce/ntlm_bruteforce.log + source: XmlWinEventLog:Microsoft-Windows-NTLM/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_multiple_users_fail_to_authenticate_wth_explicitcredentials.yml b/detections/endpoint/windows_multiple_users_fail_to_authenticate_wth_explicitcredentials.yml index 9f4822f1c9..2b90ea6581 100644 --- a/detections/endpoint/windows_multiple_users_fail_to_authenticate_wth_explicitcredentials.yml +++ b/detections/endpoint/windows_multiple_users_fail_to_authenticate_wth_explicitcredentials.yml @@ -1,74 +1,58 @@ name: Windows Multiple Users Fail To Authenticate Wth ExplicitCredentials id: e61918fa-9ca4-11eb-836c-acde48001122 type: TTP -version: 9 +version: 10 status: production author: Mauricio Velazco, Splunk data_source: -- Windows Event Log Security 4648 -date: '2025-05-02' -description: The following analytic identifies a source user failing to authenticate - with 30 unique users using explicit credentials on a host. It leverages Windows - Event 4648, which is generated when a process attempts an account logon by explicitly - specifying account credentials. This detection is significant as it may indicate - a Password Spraying attack, where an adversary attempts to gain initial access or - elevate privileges within an Active Directory environment. If confirmed malicious, - this activity could lead to unauthorized access, privilege escalation, and potential - compromise of sensitive information. -how_to_implement: To successfully implement this search, you need to be ingesting - Windows Event Logs from domain controllers as well as member servers and workstations. - The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs - to be enabled. -known_false_positives: A source user failing attempting to authenticate multiple users - on a host is not a common behavior for regular systems. Some applications, however, - may exhibit this behavior in which case sets of users hosts can be added to an allow - list. Possible false positive scenarios include systems where several users connect - to like Mail servers, identity providers, remote desktop services, Citrix, etc. + - Windows Event Log Security 4648 +date: '2026-02-25' +description: The following analytic identifies a source user failing to authenticate with 30 unique users using explicit credentials on a host. It leverages Windows Event 4648, which is generated when a process attempts an account logon by explicitly specifying account credentials. This detection is significant as it may indicate a Password Spraying attack, where an adversary attempts to gain initial access or elevate privileges within an Active Directory environment. If confirmed malicious, this activity could lead to unauthorized access, privilege escalation, and potential compromise of sensitive information. +how_to_implement: To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled. +known_false_positives: A source user failing attempting to authenticate multiple users on a host is not a common behavior for regular systems. Some applications, however, may exhibit this behavior in which case sets of users hosts can be added to an allow list. Possible false positive scenarios include systems where several users connect to like Mail servers, identity providers, remote desktop services, Citrix, etc. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4648 -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/basic-audit-logon-events + - https://attack.mitre.org/techniques/T1110/003/ + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4648 + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/basic-audit-logon-events drilldown_searches: -- name: View the detection results for - "$Computer$" - search: '%original_detection_search% | search Computer = "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$Computer$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -search: '`wineventlog_security` EventCode=4648 Caller_User_Name!=*$ Target_User_Name!=*$ - | bucket span=5m _time | stats dc(Target_User_Name) AS unique_accounts values(Target_User_Name) - as tried_account values(dest) as dest values(src_ip) as src_ip values(user) as user - by _time, Computer, Caller_User_Name | where unique_accounts > 30 | `windows_multiple_users_fail_to_authenticate_wth_explicitcredentials_filter`' + - name: View the detection results for - "$Computer$" + search: '%original_detection_search% | search Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ +search: |- + `wineventlog_security` EventCode=4648 Caller_User_Name!=*$ Target_User_Name!=*$ + | bucket span=5m _time + | stats dc(Target_User_Name) AS unique_accounts values(Target_User_Name) as tried_account values(dest) as dest values(src_ip) as src_ip values(user) as user + BY _time, Computer, Caller_User_Name + | where unique_accounts > 30 + | `windows_multiple_users_fail_to_authenticate_wth_explicitcredentials_filter` rba: - message: Potential password spraying attack from $Computer$ - risk_objects: - - field: Computer - type: system - score: 49 - threat_objects: [] + message: Potential password spraying attack from $Computer$ + risk_objects: + - field: Computer + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Active Directory Password Spraying - - Insider Threat - - Volt Typhoon - asset_type: Endpoint - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Password Spraying + - Insider Threat + - Volt Typhoon + asset_type: Endpoint + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_explicit_credential_spray_xml/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog - name: True Positive Test + - attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_explicit_credential_spray_xml/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog + name: True Positive Test diff --git a/detections/endpoint/windows_multiple_users_failed_to_authenticate_from_host_using_ntlm.yml b/detections/endpoint/windows_multiple_users_failed_to_authenticate_from_host_using_ntlm.yml index ca06632bd1..2c47de93d5 100644 --- a/detections/endpoint/windows_multiple_users_failed_to_authenticate_from_host_using_ntlm.yml +++ b/detections/endpoint/windows_multiple_users_failed_to_authenticate_from_host_using_ntlm.yml @@ -3,69 +3,55 @@ id: 7ed272a4-9c77-11eb-af22-acde48001122 author: Mauricio Velazco, Splunk type: TTP status: production -version: 9 +version: 10 data_source: -- Windows Event Log Security 4776 -date: '2025-05-02' -description: The following analytic identifies a single source endpoint failing to - authenticate with 30 unique valid users using the NTLM protocol. It leverages EventCode - 4776 from Domain Controller logs, focusing on error code 0xC000006A, which indicates - a bad password. This behavior is significant as it may indicate a Password Spraying - attack, where an adversary attempts to gain initial access or elevate privileges. - If confirmed malicious, this activity could lead to unauthorized access to sensitive - information or further compromise of the Active Directory environment. -how_to_implement: To successfully implement this search, you need to be ingesting - Domain Controller events. The Advanced Security Audit policy setting `Audit Credential - Validation` within `Account Logon` needs to be enabled. -known_false_positives: A host failing to authenticate with multiple valid domain users - is not a common behavior for legitimate systems. Possible false positive scenarios - include but are not limited to vulnerability scanners and missconfigured systems. - If this detection triggers on a host other than a Domain Controller, the behavior - could represent a password spraying attack against the host's local accounts. + - Windows Event Log Security 4776 +date: '2026-02-25' +description: The following analytic identifies a single source endpoint failing to authenticate with 30 unique valid users using the NTLM protocol. It leverages EventCode 4776 from Domain Controller logs, focusing on error code 0xC000006A, which indicates a bad password. This behavior is significant as it may indicate a Password Spraying attack, where an adversary attempts to gain initial access or elevate privileges. If confirmed malicious, this activity could lead to unauthorized access to sensitive information or further compromise of the Active Directory environment. +how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller events. The Advanced Security Audit policy setting `Audit Credential Validation` within `Account Logon` needs to be enabled. +known_false_positives: A host failing to authenticate with multiple valid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. If this detection triggers on a host other than a Domain Controller, the behavior could represent a password spraying attack against the host's local accounts. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/audit-credential-validation -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4776 + - https://attack.mitre.org/techniques/T1110/003/ + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/audit-credential-validation + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4776 drilldown_searches: -- name: View the detection results for - "$Workstation$" - search: '%original_detection_search% | search Workstation = "$Workstation$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$Workstation$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Workstation$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -search: '`wineventlog_security` EventCode=4776 TargetUserName!=*$ Status=0xC000006A - | bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) - as tried_accounts values(dest) as dest by _time, Workstation | where unique_accounts - > 30 | `windows_multiple_users_failed_to_authenticate_from_host_using_ntlm_filter`' + - name: View the detection results for - "$Workstation$" + search: '%original_detection_search% | search Workstation = "$Workstation$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Workstation$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Workstation$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ +search: |- + `wineventlog_security` EventCode=4776 TargetUserName!=*$ Status=0xC000006A + | bucket span=5m _time + | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts values(dest) as dest + BY _time, Workstation + | where unique_accounts > 30 + | `windows_multiple_users_failed_to_authenticate_from_host_using_ntlm_filter` rba: - message: Potential NTLM based password spraying attack from $Workstation$ - risk_objects: - - field: Workstation - type: system - score: 49 - threat_objects: [] + message: Potential NTLM based password spraying attack from $Workstation$ + risk_objects: + - field: Workstation + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Active Directory Password Spraying - - Volt Typhoon - asset_type: Endpoint - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Password Spraying + - Volt Typhoon + asset_type: Endpoint + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_valid_users_ntlm_xml/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog - name: True Positive Test + - attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_valid_users_ntlm_xml/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog + name: True Positive Test diff --git a/detections/endpoint/windows_multiple_users_failed_to_authenticate_from_process.yml b/detections/endpoint/windows_multiple_users_failed_to_authenticate_from_process.yml index 2d442731ae..ffa65b1773 100644 --- a/detections/endpoint/windows_multiple_users_failed_to_authenticate_from_process.yml +++ b/detections/endpoint/windows_multiple_users_failed_to_authenticate_from_process.yml @@ -1,74 +1,62 @@ name: Windows Multiple Users Failed To Authenticate From Process id: 9015385a-9c84-11eb-bef2-acde48001122 type: TTP -version: 9 +version: 10 status: production author: Mauricio Velazco, Splunk data_source: -- Windows Event Log Security 4625 -date: '2025-05-02' -description: The following analytic detects a source process failing to authenticate - with 30 unique users, indicating a potential Password Spraying attack. It leverages - Windows Event 4625 with Logon Type 2, collected from domain controllers, member - servers, and workstations. This activity is significant as it may represent an adversary - attempting to gain initial access or elevate privileges within an Active Directory - environment. If confirmed malicious, this could lead to unauthorized access, privilege - escalation, or further compromise of the network, posing a severe security risk. -how_to_implement: To successfully implement this search, you need to be ingesting - Windows Event Logs from domain controllers aas well as member servers and workstations. - The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs - to be enabled. -known_false_positives: A process failing to authenticate with multiple users is not - a common behavior for legitimate user sessions. Possible false positive scenarios - include but are not limited to vulnerability scanners and missconfigured systems. + - Windows Event Log Security 4625 +date: '2026-02-25' +description: The following analytic detects a source process failing to authenticate with 30 unique users, indicating a potential Password Spraying attack. It leverages Windows Event 4625 with Logon Type 2, collected from domain controllers, member servers, and workstations. This activity is significant as it may represent an adversary attempting to gain initial access or elevate privileges within an Active Directory environment. If confirmed malicious, this could lead to unauthorized access, privilege escalation, or further compromise of the network, posing a severe security risk. +how_to_implement: To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers aas well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled. +known_false_positives: A process failing to authenticate with multiple users is not a common behavior for legitimate user sessions. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4625 -- https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4625 -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/basic-audit-logon-events + - https://attack.mitre.org/techniques/T1110/003/ + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4625 + - https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4625 + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/basic-audit-logon-events drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -search: '`wineventlog_security` EventCode=4625 Logon_Type=2 ProcessName!="-" | bucket - span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) - as tried_accounts values(dest) as dest values(src) as src values(user) as user by - _time, ProcessName, SubjectUserName, Computer, action, app, authentication_method, - signature, signature_id | rename Computer as dest | where unique_accounts > 30 | - `windows_multiple_users_failed_to_authenticate_from_process_filter`' + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ +search: |- + `wineventlog_security` EventCode=4625 Logon_Type=2 ProcessName!="-" + | bucket span=5m _time + | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts values(dest) as dest values(src) as src values(user) as user + BY _time, ProcessName, SubjectUserName, + Computer, action, app, + authentication_method, signature, signature_id + | rename Computer as dest + | where unique_accounts > 30 + | `windows_multiple_users_failed_to_authenticate_from_process_filter` rba: - message: Potential password spraying attack from $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Potential password spraying attack from $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Active Directory Password Spraying - - Insider Threat - - Volt Typhoon - asset_type: Endpoint - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Password Spraying + - Insider Threat + - Volt Typhoon + asset_type: Endpoint + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_multiple_users_from_process_xml/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog - name: True Positive Test + - attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_multiple_users_from_process_xml/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog + name: True Positive Test diff --git a/detections/endpoint/windows_multiple_users_failed_to_authenticate_using_kerberos.yml b/detections/endpoint/windows_multiple_users_failed_to_authenticate_using_kerberos.yml index d2d3eeaf41..3747c1f367 100644 --- a/detections/endpoint/windows_multiple_users_failed_to_authenticate_using_kerberos.yml +++ b/detections/endpoint/windows_multiple_users_failed_to_authenticate_using_kerberos.yml @@ -1,74 +1,60 @@ name: Windows Multiple Users Failed To Authenticate Using Kerberos id: 3a91a212-98a9-11eb-b86a-acde48001122 type: TTP -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' status: production author: Mauricio Velazco, Splunk data_source: -- Windows Event Log Security 4771 -description: The following analytic identifies a single source endpoint failing to - authenticate with 30 unique users using the Kerberos protocol. It leverages EventCode - 4771 with Status 0x18, indicating wrong password attempts, and aggregates these - events over a 5-minute window. This behavior is significant as it may indicate a - Password Spraying attack, where an adversary attempts to gain initial access or - elevate privileges in an Active Directory environment. If confirmed malicious, this - activity could lead to unauthorized access, privilege escalation, and potential - compromise of sensitive information. -how_to_implement: To successfully implement this search, you need to be ingesting - Domain Controller and Kerberos events. The Advanced Security Audit policy setting - `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. -known_false_positives: A host failing to authenticate with multiple valid domain users - is not a common behavior for legitimate systems. Possible false positive scenarios - include but are not limited to vulnerability scanners, missconfigured systems and - multi-user systems like Citrix farms. + - Windows Event Log Security 4771 +description: The following analytic identifies a single source endpoint failing to authenticate with 30 unique users using the Kerberos protocol. It leverages EventCode 4771 with Status 0x18, indicating wrong password attempts, and aggregates these events over a 5-minute window. This behavior is significant as it may indicate a Password Spraying attack, where an adversary attempts to gain initial access or elevate privileges in an Active Directory environment. If confirmed malicious, this activity could lead to unauthorized access, privilege escalation, and potential compromise of sensitive information. +how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. +known_false_positives: A host failing to authenticate with multiple valid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, missconfigured systems and multi-user systems like Citrix farms. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-r2-and-2012/dn319109(v=ws.11) -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4771 + - https://attack.mitre.org/techniques/T1110/003/ + - https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-r2-and-2012/dn319109(v=ws.11) + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4771 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -search: '`wineventlog_security` EventCode=4771 TargetUserName!="*$" Status=0x18 | - bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) - as user values(dest) as dest by _time, IpAddress | where unique_accounts > 30 | - `windows_multiple_users_failed_to_authenticate_using_kerberos_filter`' + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ +search: |- + `wineventlog_security` EventCode=4771 TargetUserName!="*$" Status=0x18 + | bucket span=5m _time + | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as user values(dest) as dest + BY _time, IpAddress + | where unique_accounts > 30 + | `windows_multiple_users_failed_to_authenticate_using_kerberos_filter` rba: - message: Potential Kerberos based password spraying attack from $IpAddress$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: IpAddress - type: ip_address + message: Potential Kerberos based password spraying attack from $IpAddress$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: IpAddress + type: ip_address tags: - analytic_story: - - Active Directory Password Spraying - - Active Directory Kerberos Attacks - - Volt Typhoon - asset_type: Endpoint - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Password Spraying + - Active Directory Kerberos Attacks + - Volt Typhoon + asset_type: Endpoint + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_valid_users_kerberos_xml/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog - name: True Positive Test + - attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_valid_users_kerberos_xml/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog + name: True Positive Test diff --git a/detections/endpoint/windows_multiple_users_remotely_failed_to_authenticate_from_host.yml b/detections/endpoint/windows_multiple_users_remotely_failed_to_authenticate_from_host.yml index 54ab842db6..00055ec4b3 100644 --- a/detections/endpoint/windows_multiple_users_remotely_failed_to_authenticate_from_host.yml +++ b/detections/endpoint/windows_multiple_users_remotely_failed_to_authenticate_from_host.yml @@ -3,72 +3,59 @@ id: 80f9d53e-9ca1-11eb-b0d6-acde48001122 author: Mauricio Velazco, Splunk type: TTP status: production -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' data_source: -- Windows Event Log Security 4625 -description: The following analytic identifies a source host failing to authenticate - against a remote host with 30 unique users. It leverages Windows Event 4625 with - Logon Type 3, indicating remote authentication attempts. This behavior is significant - as it may indicate a Password Spraying attack, where an adversary attempts to gain - initial access or elevate privileges in an Active Directory environment. If confirmed - malicious, this activity could lead to unauthorized access, privilege escalation, - and potential compromise of sensitive information. This detection is crucial for - real-time security monitoring and threat hunting. -how_to_implement: To successfully implement this search, you need to be ingesting - Windows Event Logs from domain controllers as as well as member servers and workstations. - The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs - to be enabled. -known_false_positives: A host failing to authenticate with multiple valid users against - a remote host is not a common behavior for legitimate systems. Possible false positive - scenarios include but are not limited to vulnerability scanners, remote administration - tools, missconfigyred systems, etc. + - Windows Event Log Security 4625 +description: The following analytic identifies a source host failing to authenticate against a remote host with 30 unique users. It leverages Windows Event 4625 with Logon Type 3, indicating remote authentication attempts. This behavior is significant as it may indicate a Password Spraying attack, where an adversary attempts to gain initial access or elevate privileges in an Active Directory environment. If confirmed malicious, this activity could lead to unauthorized access, privilege escalation, and potential compromise of sensitive information. This detection is crucial for real-time security monitoring and threat hunting. +how_to_implement: To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as as well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled. +known_false_positives: A host failing to authenticate with multiple valid users against a remote host is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, remote administration tools, missconfigyred systems, etc. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4625 -- https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4625 -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/basic-audit-logon-events + - https://attack.mitre.org/techniques/T1110/003/ + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4625 + - https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4625 + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/basic-audit-logon-events drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -search: '`wineventlog_security` EventCode=4625 Logon_Type=3 IpAddress!="-" | bucket - span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) - as tried_accounts values(dest) as dest values(src) as src values(user) as user by - _time, IpAddress, Computer, action, app, authentication_method, signature, signature_id - | rename Computer as dest | where unique_accounts > 30 | `windows_multiple_users_remotely_failed_to_authenticate_from_host_filter`' + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ +search: |- + `wineventlog_security` EventCode=4625 Logon_Type=3 IpAddress!="-" + | bucket span=5m _time + | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts values(dest) as dest values(src) as src values(user) as user + BY _time, IpAddress, Computer, + action, app, authentication_method, + signature, signature_id + | rename Computer as dest + | where unique_accounts > 30 + | `windows_multiple_users_remotely_failed_to_authenticate_from_host_filter` rba: - message: Potential password spraying attack on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Potential password spraying attack on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Active Directory Password Spraying - - Volt Typhoon - asset_type: Endpoint - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Password Spraying + - Volt Typhoon + asset_type: Endpoint + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_remote_spray_xml/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog - name: True Positive Test + - attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_remote_spray_xml/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog + name: True Positive Test diff --git a/detections/endpoint/windows_net_system_service_discovery.yml b/detections/endpoint/windows_net_system_service_discovery.yml index fee28e0e11..795f6a5192 100644 --- a/detections/endpoint/windows_net_system_service_discovery.yml +++ b/detections/endpoint/windows_net_system_service_discovery.yml @@ -1,82 +1,72 @@ name: Windows Net System Service Discovery id: dd7da098-83b8-4c48-b09d-e51aeb621e81 -version: 1 -date: '2025-08-25' +version: 2 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: The following analytic detects the enumeration of Windows services using the net start command, which is a built-in utility that lists all running services on a system. Adversaries, system administrators, or automated tools may use this command to gain situational awareness of what services are active, identify potential security software, or discover opportunities for privilege escalation and lateral movement. The execution of net start is often associated with reconnaissance activity during the early stages of an intrusion, as attackers attempt to map out the system’s defense mechanisms and operational services. By monitoring process execution for instances of cmd.exe /c net start or similar command-line usage, defenders can detect potentially suspicious activity. Correlating this behavior with other reconnaissance commands, such as tasklist or sc query, strengthens detection fidelity. While net start is not inherently malicious, unusual or repeated use in non-administrative contexts should be flagged for further investigation. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where `process_net` AND Processes.process="* start*" - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_net_system_service_discovery_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_net` + AND + Processes.process="* start*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_net_system_service_discovery_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://cert.gov.ua/article/6284730 + - https://cert.gov.ua/article/6284730 drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to enumerate list of running services. - risk_objects: - - field: user - type: user - score: 3 - - field: dest - type: system - score: 3 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to enumerate list of running services. + risk_objects: + - field: user + type: user + score: 3 + - field: dest + type: system + score: 3 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - LAMEHUG - asset_type: Endpoint - mitre_attack_id: - - T1007 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - LAMEHUG + asset_type: Endpoint + mitre_attack_id: + - T1007 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/lamehug/T1007/net_start/net_start.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/lamehug/T1007/net_start/net_start.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_netsupport_rmm_dll_loaded_by_uncommon_process.yml b/detections/endpoint/windows_netsupport_rmm_dll_loaded_by_uncommon_process.yml index be3ce8eec2..1c4dafc7ba 100644 --- a/detections/endpoint/windows_netsupport_rmm_dll_loaded_by_uncommon_process.yml +++ b/detections/endpoint/windows_netsupport_rmm_dll_loaded_by_uncommon_process.yml @@ -6,81 +6,75 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly description: | - The following analytic detects the loading of specific dynamic-link libraries (DLLs) associated with the NetSupport Remote Manager (RMM) tool by any process on a Windows system. - Modules such as CryptPak.dll, HTCTL32.DLL, IPCTL32.DLL, keyshowhook.dll, pcicapi.DLL, PCICL32.DLL, and TCCTL32.DLL, are integral to NetSupport's functionality. - This detection is particularly valuable when these modules are loaded by processes running from unusual directories (e.g., Downloads, ProgramData, or user-specific folders) rather than the legitimate Program Files installation path, or by executables that have been renamed but retain the internal "client32" identifier. - This helps to identify instances where the legitimate NetSupport tool is being misused by adversaries as a Remote Access Trojan (RAT). + The following analytic detects the loading of specific dynamic-link libraries (DLLs) associated with the NetSupport Remote Manager (RMM) tool by any process on a Windows system. + Modules such as CryptPak.dll, HTCTL32.DLL, IPCTL32.DLL, keyshowhook.dll, pcicapi.DLL, PCICL32.DLL, and TCCTL32.DLL, are integral to NetSupport's functionality. + This detection is particularly valuable when these modules are loaded by processes running from unusual directories (e.g., Downloads, ProgramData, or user-specific folders) rather than the legitimate Program Files installation path, or by executables that have been renamed but retain the internal "client32" identifier. + This helps to identify instances where the legitimate NetSupport tool is being misused by adversaries as a Remote Access Trojan (RAT). data_source: -- Sysmon EventID 7 + - Sysmon EventID 7 search: | - `sysmon` - EventCode=7 - ImageLoaded IN ( - "*\\CryptPak.dll", - "*\\HTCTL32.DLL", - "*\\pcicapi.dll", - "*\\pcichek.dll", - "*\\PCICL32.DLL", - "*\\TCCTL32.DLL" - ) - NOT Image IN ("C:\\Program Files\\*", "C:\\Program Files (x86)\\*") - Signature = "NetSupport Ltd*" - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime - by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec - process_guid process_hash process_id process_name process_path service_dll_signature_exists - service_dll_signature_verified signature signature_id user_id vendor_product - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_netsupport_rmm_dll_loaded_by_uncommon_process_filter` + `sysmon` + EventCode=7 + ImageLoaded IN ( + "*\\CryptPak.dll", + "*\\HTCTL32.DLL", + "*\\pcicapi.dll", + "*\\pcichek.dll", + "*\\PCICL32.DLL", + "*\\TCCTL32.DLL" + ) + NOT Image IN ("C:\\Program Files\\*", "C:\\Program Files (x86)\\*") + Signature = "NetSupport Ltd*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec + process_guid process_hash process_id process_name process_path service_dll_signature_exists + service_dll_signature_verified signature signature_id user_id vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_netsupport_rmm_dll_loaded_by_uncommon_process_filter` how_to_implement: | - To successfully implement this search, you need to be ingesting logs with the process name and ImageLoaded executions from your endpoints. - If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + To successfully implement this search, you need to be ingesting logs with the process name and ImageLoaded executions from your endpoints. + If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: | - NetSupport RMM installations outside of the standard Program Files directory will trigger this detection. - Apply appropriate filters to exclude known legitimate installations. + NetSupport RMM installations outside of the standard Program Files directory will trigger this detection. + Apply appropriate filters to exclude known legitimate installations. references: -- https://www.linkedin.com/posts/mauricefielenbach_cybersecurity-incidentresponse-dfir-activity-7394805779448418304-g0gZ?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAuFTjIB5weY_kcyu4qp3kHbI4v49tO0zEk -- https://thedfirreport.com/2023/10/30/netsupport-intrusion-results-in-domain-compromise/ -- https://www.esentire.com/blog/evalusion-campaign-delivers-amatera-stealer-and-netsupport-rat + - https://www.linkedin.com/posts/mauricefielenbach_cybersecurity-incidentresponse-dfir-activity-7394805779448418304-g0gZ?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAuFTjIB5weY_kcyu4qp3kHbI4v49tO0zEk + - https://thedfirreport.com/2023/10/30/netsupport-intrusion-results-in-domain-compromise/ + - https://www.esentire.com/blog/evalusion-campaign-delivers-amatera-stealer-and-netsupport-rat drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The following module $ImageLoaded$ was loaded by a non-standard application - on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: - - field: Image - type: process_name + message: The following module $ImageLoaded$ was loaded by a non-standard application on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: + - field: Image + type: process_name tags: - analytic_story: - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1036 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1036 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/netsupport_modules/net_support_module.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/netsupport_modules/net_support_module.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_network_connection_discovery_via_net.yml b/detections/endpoint/windows_network_connection_discovery_via_net.yml index c3ad01baf9..7dc366aafe 100644 --- a/detections/endpoint/windows_network_connection_discovery_via_net.yml +++ b/detections/endpoint/windows_network_connection_discovery_via_net.yml @@ -1,61 +1,57 @@ name: Windows Network Connection Discovery Via Net id: 86a5b949-679b-4197-8d4c-9c180a818c45 -version: 3 -date: '2025-05-02' +version: 4 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Hunting -description: The following analytic identifies the execution of `net.exe` with command-line - arguments used to list or display information about computer connections. It leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process names - and command-line executions. This activity can be significant as it indicates potential - network reconnaissance by adversaries or Red Teams, aiming to gather situational - awareness and Active Directory information. If confirmed malicious, this behavior - could allow attackers to map the network, identify critical assets, and plan further - attacks, potentially leading to data exfiltration or lateral movement. +description: The following analytic identifies the execution of `net.exe` with command-line arguments used to list or display information about computer connections. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity can be significant as it indicates potential network reconnaissance by adversaries or Red Teams, aiming to gather situational awareness and Active Directory information. If confirmed malicious, this behavior could allow attackers to map the network, identify critical assets, and plan further attacks, potentially leading to data exfiltration or lateral movement. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (`process_net` OR (Processes.process_name="net.exe" - OR Processes.original_file_name="net.exe")) AND (Processes.process=*use) by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_network_connection_discovery_via_net_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + `process_net` + OR + (Processes.process_name="net.exe" + OR + Processes.original_file_name="net.exe") + ) + AND (Processes.process=*use) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_network_connection_discovery_via_net_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1049/ + - https://attack.mitre.org/techniques/T1049/ tags: - analytic_story: - - Active Directory Discovery - - Azorult - - Windows Post-Exploitation - - Prestige Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1049 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - Azorult + - Windows Post-Exploitation + - Prestige Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1049 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1049/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1049/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_network_share_interaction_via_net.yml b/detections/endpoint/windows_network_share_interaction_via_net.yml index 2d93a17f21..b27eebbe4b 100644 --- a/detections/endpoint/windows_network_share_interaction_via_net.yml +++ b/detections/endpoint/windows_network_share_interaction_via_net.yml @@ -1,85 +1,78 @@ name: Windows Network Share Interaction Via Net id: e51fbdb0-0be0-474f-92ea-d289f71a695e -version: 3 -date: '2025-05-02' +version: 4 +date: '2026-02-25' author: Dean Luxton status: production type: Anomaly data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic identifies network share discovery and collection - activities performed on Windows systems using the Net command. Attackers often use - network share discovery to identify accessible shared resources within a network, - which can be a precursor to privilege escalation or data exfiltration. By monitoring - Windows Event Logs for the usage of the Net command to list and interact with network - shares, this detection helps identify potential reconnaissance and collection activities. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes WHERE (`process_net` OR (Processes.process_name="net.exe" - OR Processes.original_file_name="net.exe")) AND Processes.process IN ("*use *", - "*view*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_network_share_interaction_via_net_filter`' -how_to_implement: The detection is based on data originating from either Endpoint - Detection and Response (EDR) telemetry or EventCode 4688 with process command line - logging enabled. These sources provide security-related telemetry from the endpoints. - To implement this search, you must ingest logs that contain the process name, parent - process, and complete command-line executions. These logs must be mapped to the - Splunk Common Information Model (CIM) to normalize the field names capture the data - within the datamodel schema. -known_false_positives: Administrators or power users may use this command. Additional - filters needs to be applied. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic identifies network share discovery and collection activities performed on Windows systems using the Net command. Attackers often use network share discovery to identify accessible shared resources within a network, which can be a precursor to privilege escalation or data exfiltration. By monitoring Windows Event Logs for the usage of the Net command to list and interact with network shares, this detection helps identify potential reconnaissance and collection activities. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + `process_net` + OR + (Processes.process_name="net.exe" + OR + Processes.original_file_name="net.exe") + ) + AND Processes.process IN ("*use *", "*view*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_network_share_interaction_via_net_filter` +how_to_implement: The detection is based on data originating from either Endpoint Detection and Response (EDR) telemetry or EventCode 4688 with process command line logging enabled. These sources provide security-related telemetry from the endpoints. To implement this search, you must ingest logs that contain the process name, parent process, and complete command-line executions. These logs must be mapped to the Splunk Common Information Model (CIM) to normalize the field names capture the data within the datamodel schema. +known_false_positives: Administrators or power users may use this command. Additional filters needs to be applied. references: -- https://attack.mitre.org/techniques/T1135/ + - https://attack.mitre.org/techniques/T1135/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ leveraged net.exe on $dest$ to interact with network shares, - executed by parent process $parent_process$ - risk_objects: - - field: dest - type: system - score: 20 - - field: user - type: user - score: 20 - threat_objects: [] + message: User $user$ leveraged net.exe on $dest$ to interact with network shares, executed by parent process $parent_process$ + risk_objects: + - field: dest + type: system + score: 20 + - field: user + type: user + score: 20 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - - Active Directory Privilege Escalation - - Network Discovery - asset_type: Endpoint - atomic_guid: - - ab39a04f-0c93-4540-9ff2-83f862c385ae - mitre_attack_id: - - T1135 - - T1039 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - Active Directory Privilege Escalation + - Network Discovery + asset_type: Endpoint + atomic_guid: + - ab39a04f-0c93-4540-9ff2-83f862c385ae + mitre_attack_id: + - T1135 + - T1039 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1135/net_share/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1135/net_share/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_new_custom_security_descriptor_set_on_eventlog_channel.yml b/detections/endpoint/windows_new_custom_security_descriptor_set_on_eventlog_channel.yml index 05da361c10..7a10500128 100644 --- a/detections/endpoint/windows_new_custom_security_descriptor_set_on_eventlog_channel.yml +++ b/detections/endpoint/windows_new_custom_security_descriptor_set_on_eventlog_channel.yml @@ -5,75 +5,49 @@ date: '2026-01-14' author: Nasreddine Bencherchali, Michael Haag, Splunk status: production type: Anomaly -description: The following analytic detects suspicious modifications to the EventLog - security descriptor registry value for defense evasion. It leverages data from the - Endpoint.Registry data model, focusing on changes to the "CustomSD" value within - the "HKEY_LOCAL_MACHINE\System\CurrentControlSet\Services\Eventlog\\CustomSD" - path. This activity is significant as changes to the access permissions of the event - log could blind security products and help attackers evade defenses. If confirmed - malicious, this could allow attackers to block users and security products from - viewing, ingesting and interacting event logs. +description: The following analytic detects suspicious modifications to the EventLog security descriptor registry value for defense evasion. It leverages data from the Endpoint.Registry data model, focusing on changes to the "CustomSD" value within the "HKEY_LOCAL_MACHINE\System\CurrentControlSet\Services\Eventlog\\CustomSD" path. This activity is significant as changes to the access permissions of the event log could blind security products and help attackers evade defenses. If confirmed malicious, this could allow attackers to block users and security products from viewing, ingesting and interacting event logs. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path= "*\\Services\\Eventlog\\*" - AND Registry.registry_value_name=CustomSD by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_new_custom_security_descriptor_set_on_eventlog_channel_filter`' -how_to_implement: To successfully implement this search, you must be ingesting data - that records registry activity from your hosts to populate the endpoint data model - in the registry node. This is typically populated via endpoint detection-and-response - product, such as Carbon Black or endpoint data sources, such as Sysmon. The data - used for this search is typically generated via logs that report reads and writes - to the registry. If you are using Sysmon, you must have at least version 2.0 of - the official Sysmon TA. https://splunkbase.splunk.com/app/5709 -known_false_positives: No false positives have been identified at this time. - a legacy option and shouldn't be a common activity. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path= "*\\Services\\Eventlog\\*" AND Registry.registry_value_name=CustomSD by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_new_custom_security_descriptor_set_on_eventlog_channel_filter`' +how_to_implement: To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 +known_false_positives: No false positives have been identified at this time. a legacy option and shouldn't be a common activity. references: -- https://learn.microsoft.com/en-us/troubleshoot/windows-server/group-policy/set-event-log-security-locally-or-via-group-policy -- https://attack.mitre.org/techniques/T1562/002/ + - https://learn.microsoft.com/en-us/troubleshoot/windows-server/group-policy/set-event-log-security-locally-or-via-group-policy + - https://attack.mitre.org/techniques/T1562/002/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: modified/added/deleted registry entry $registry_path$ in $dest$ - risk_objects: - - field: dest - type: system - score: 64 - - field: user - type: user - score: 64 - threat_objects: [] + message: modified/added/deleted registry entry $registry_path$ in $dest$ + risk_objects: + - field: dest + type: system + score: 64 + - field: user + type: user + score: 64 + threat_objects: [] tags: - analytic_story: - - LockBit Ransomware - - Defense Evasion or Unauthorized Access Via SDDL Tampering - asset_type: Endpoint - mitre_attack_id: - - T1562.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - LockBit Ransomware + - Defense Evasion or Unauthorized Access Via SDDL Tampering + asset_type: Endpoint + mitre_attack_id: + - T1562.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/eventlog_sddl_tampering/eventlog_sddl_tampering_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/eventlog_sddl_tampering/eventlog_sddl_tampering_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_new_default_file_association_value_set.yml b/detections/endpoint/windows_new_default_file_association_value_set.yml index faf94da092..694e477aee 100644 --- a/detections/endpoint/windows_new_default_file_association_value_set.yml +++ b/detections/endpoint/windows_new_default_file_association_value_set.yml @@ -5,67 +5,42 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects registry changes to the default file association - value. It leverages data from the Endpoint data model, specifically monitoring registry - paths under "HKCR\\*\\shell\\open\\command\\*". This activity can be significant - because, attackers might alter the default file associations in order to execute - arbitrary scripts or payloads when a user opens a file, leading to potential code - execution. If confirmed malicious, this technique can enable attackers to persist - on the compromised host and execute further malicious commands, posing a severe - threat to the environment. +description: The following analytic detects registry changes to the default file association value. It leverages data from the Endpoint data model, specifically monitoring registry paths under "HKCR\\*\\shell\\open\\command\\*". This activity can be significant because, attackers might alter the default file associations in order to execute arbitrary scripts or payloads when a user opens a file, leading to potential code execution. If confirmed malicious, this technique can enable attackers to persist on the compromised host and execute further malicious commands, posing a severe threat to the environment. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime - max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\shell\\open\\command\\*" - Registry.registry_path IN ("*HKCR\\*", "*HKEY_CLASSES_ROOT\\*") by Registry.dest Registry.user - Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data - | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` - | `windows_new_default_file_association_value_set_filter`' -how_to_implement: To successfully implement this search, you must be ingesting data - that records registry activity from your hosts to populate the endpoint data model - in the registry node. This is typically populated via endpoint detection-and-response - product, such as Carbon Black or endpoint data sources, such as Sysmon. The data - used for this search is typically generated via logs that report reads and writes - to the registry. -known_false_positives: Windows and third party software will create and modify these - file associations during installation or upgrades. Additional filters needs to be - applied to tune environment specific false positives. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\shell\\open\\command\\*" Registry.registry_path IN ("*HKCR\\*", "*HKEY_CLASSES_ROOT\\*") by Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `windows_new_default_file_association_value_set_filter`' +how_to_implement: To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. +known_false_positives: Windows and third party software will create and modify these file associations during installation or upgrades. Additional filters needs to be applied to tune environment specific false positives. references: -- https://dmcxblue.gitbook.io/red-team-notes-2-0/red-team-techniques/privilege-escalation/untitled-3/accessibility-features + - https://dmcxblue.gitbook.io/red-team-notes-2-0/red-team-techniques/privilege-escalation/untitled-3/accessibility-features drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ tags: - analytic_story: - - Hermetic Wiper - - Windows Registry Abuse - - Prestige Ransomware - - Windows Privilege Escalation - - Windows Persistence Techniques - - Data Destruction - asset_type: Endpoint - mitre_attack_id: - - T1546.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Hermetic Wiper + - Windows Registry Abuse + - Prestige Ransomware + - Windows Privilege Escalation + - Windows Persistence Techniques + - Data Destruction + asset_type: Endpoint + mitre_attack_id: + - T1546.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.001/txtfile_reg/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.001/txtfile_reg/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_new_deny_permission_set_on_service_sd_via_sc_exe.yml b/detections/endpoint/windows_new_deny_permission_set_on_service_sd_via_sc_exe.yml index cdfcd82e5b..2d2a029b7f 100644 --- a/detections/endpoint/windows_new_deny_permission_set_on_service_sd_via_sc_exe.yml +++ b/detections/endpoint/windows_new_deny_permission_set_on_service_sd_via_sc_exe.yml @@ -1,89 +1,75 @@ name: Windows New Deny Permission Set On Service SD Via Sc.EXE id: d0f6a5e5-dbfd-46e1-8bd5-2e2905947c33 -version: 5 -date: '2026-01-14' +version: 6 +date: '2026-02-25' author: Nasreddine Bencherchali, Michael Haag, Splunk status: production type: Anomaly -description: The following analytic detects changes in a service security descriptor - where a new deny ace has been added. It leverages data from Endpoint Detection and - Response (EDR) agents, specifically searching for any process execution involving - the "sc.exe" binary with the "sdset" flag targeting any service and adding a dedicated - deny ace. If confirmed malicious, this could allow an attacker to escalate their - privileges, blind defenses and more. +description: The following analytic detects changes in a service security descriptor where a new deny ace has been added. It leverages data from Endpoint Detection and Response (EDR) agents, specifically searching for any process execution involving the "sc.exe" binary with the "sdset" flag targeting any service and adding a dedicated deny ace. If confirmed malicious, this could allow an attacker to escalate their privileges, blind defenses and more. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where (Processes.process_name=sc.exe OR Processes.original_file_name=sc.exe) Processes.process="*sdset - *" Processes.process="*(D;*" Processes.process IN ("*;IU*", "*;S-1-5-4*", "*;SU*", - "*;S-1-5-6*", "*;BA*", "*;S-1-5-32-544*", "*;SY*", "*;S-1-5-18*", "*;WD*", "*;S-1-1-0*", - "*;AU*", "*;S-1-5-11*", "*;LS*", "*;S-1-5-19*") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_new_deny_permission_set_on_service_sd_via_sc_exe_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process name, and process original file name. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: No false positives have been identified at this time. - security-related services should be immediately investigated. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name=sc.exe + OR + Processes.original_file_name=sc.exe + ) + Processes.process="*sdset *" Processes.process="*(D;*" Processes.process IN ("*;IU*", "*;S-1-5-4*", "*;SU*", "*;S-1-5-6*", "*;BA*", "*;S-1-5-32-544*", "*;SY*", "*;S-1-5-18*", "*;WD*", "*;S-1-1-0*", "*;AU*", "*;S-1-5-11*", "*;LS*", "*;S-1-5-19*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_new_deny_permission_set_on_service_sd_via_sc_exe_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process name, and process original file name. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: No false positives have been identified at this time. security-related services should be immediately investigated. references: -- https://www.sans.org/blog/red-team-tactics-hiding-windows-services/ -- https://news.sophos.com/wp-content/uploads/2020/06/glupteba_final-1.pdf -- https://attack.mitre.org/techniques/T1564/ + - https://www.sans.org/blog/red-team-tactics-hiding-windows-services/ + - https://news.sophos.com/wp-content/uploads/2020/06/glupteba_final-1.pdf + - https://attack.mitre.org/techniques/T1564/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - attempting to disable security services on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 30 - - field: dest - type: system - score: 30 - threat_objects: - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified attempting to disable security services on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 30 + - field: dest + type: system + score: 30 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Defense Evasion or Unauthorized Access Via SDDL Tampering - asset_type: Endpoint - mitre_attack_id: - - T1564 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Defense Evasion or Unauthorized Access Via SDDL Tampering + asset_type: Endpoint + mitre_attack_id: + - T1564 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1564/sc_sdset_tampering/sc_sdset_tampering_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1564/sc_sdset_tampering/sc_sdset_tampering_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_new_eventlog_channelaccess_registry_value_set.yml b/detections/endpoint/windows_new_eventlog_channelaccess_registry_value_set.yml index 1559ae23f1..d562d0234a 100644 --- a/detections/endpoint/windows_new_eventlog_channelaccess_registry_value_set.yml +++ b/detections/endpoint/windows_new_eventlog_channelaccess_registry_value_set.yml @@ -5,75 +5,49 @@ date: '2025-05-02' author: Nasreddine Bencherchali, Michael Haag, Splunk status: production type: Anomaly -description: The following analytic detects suspicious modifications to the EventLog - security descriptor registry value for defense evasion. It leverages data from the - Endpoint.Registry data model, focusing on changes to the "CustomSD" value within - the "HKEY_LOCAL_MACHINE\System\CurrentControlSet\Services\Eventlog\\CustomSD" - path. This activity is significant as changes to the access permissions of the event - log could blind security products and help attackers evade defenses. If confirmed - malicious, this could allow attackers to block users and security products from - viewing, ingesting and interacting event logs. +description: The following analytic detects suspicious modifications to the EventLog security descriptor registry value for defense evasion. It leverages data from the Endpoint.Registry data model, focusing on changes to the "CustomSD" value within the "HKEY_LOCAL_MACHINE\System\CurrentControlSet\Services\Eventlog\\CustomSD" path. This activity is significant as changes to the access permissions of the event log could blind security products and help attackers evade defenses. If confirmed malicious, this could allow attackers to block users and security products from viewing, ingesting and interacting event logs. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path IN ("*\\Microsoft\\Windows\\CurrentVersion\\WINEVT\\Channels\\*", - "*\Microsoft\Windows\EventLog\*") AND Registry.registry_value_name=ChannelAccess - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_new_eventlog_channelaccess_registry_value_set_filter`' -how_to_implement: To successfully implement this search, you must be ingesting data - that records registry activity from your hosts to populate the endpoint data model - in the registry node. This is typically populated via endpoint detection-and-response - product, such as Carbon Black or endpoint data sources, such as Sysmon. The data - used for this search is typically generated via logs that report reads and writes - to the registry. If you are using Sysmon, you must have at least version 2.0 of - the official Sysmon TA. https://splunkbase.splunk.com/app/5709 -known_false_positives: False positives may be triggered from newly installed event - providers or windows updates, new "ChannelAccess" values must be investigated. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path IN ("*\\Microsoft\\Windows\\CurrentVersion\\WINEVT\\Channels\\*", "*\Microsoft\Windows\EventLog\*") AND Registry.registry_value_name=ChannelAccess by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_new_eventlog_channelaccess_registry_value_set_filter`' +how_to_implement: To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 +known_false_positives: False positives may be triggered from newly installed event providers or windows updates, new "ChannelAccess" values must be investigated. references: -- https://web.archive.org/web/20220710181255/https://blog.minerva-labs.com/lockbit-3.0-aka-lockbit-black-is-here-with-a-new-icon-new-ransom-note-new-wallpaper-but-less-evasiveness -- https://attack.mitre.org/techniques/T1562/002/ + - https://web.archive.org/web/20220710181255/https://blog.minerva-labs.com/lockbit-3.0-aka-lockbit-black-is-here-with-a-new-icon-new-ransom-note-new-wallpaper-but-less-evasiveness + - https://attack.mitre.org/techniques/T1562/002/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: modified/added/deleted registry entry $registry_path$ in $dest$ - risk_objects: - - field: dest - type: system - score: 30 - - field: user - type: user - score: 30 - threat_objects: [] + message: modified/added/deleted registry entry $registry_path$ in $dest$ + risk_objects: + - field: dest + type: system + score: 30 + - field: user + type: user + score: 30 + threat_objects: [] tags: - analytic_story: - - LockBit Ransomware - - Defense Evasion or Unauthorized Access Via SDDL Tampering - asset_type: Endpoint - mitre_attack_id: - - T1562.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - LockBit Ransomware + - Defense Evasion or Unauthorized Access Via SDDL Tampering + asset_type: Endpoint + mitre_attack_id: + - T1562.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/eventlog_sddl_tampering/eventlog_sddl_tampering_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.002/eventlog_sddl_tampering/eventlog_sddl_tampering_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_new_inprocserver32_added.yml b/detections/endpoint/windows_new_inprocserver32_added.yml index 9ef7249fba..7914bee7e7 100644 --- a/detections/endpoint/windows_new_inprocserver32_added.yml +++ b/detections/endpoint/windows_new_inprocserver32_added.yml @@ -4,50 +4,32 @@ version: 8 date: '2025-10-14' author: Michael Haag, Splunk data_source: - - Sysmon EventID 13 + - Sysmon EventID 13 type: Hunting status: production -description: - The following analytic detects the addition of new InProcServer32 registry - keys on Windows endpoints. It leverages data from the Endpoint.Registry datamodel - to identify changes in registry paths associated with InProcServer32. This activity - is significant because malware often uses this mechanism to achieve persistence - or execute malicious code by registering a new InProcServer32 key pointing to a - harmful DLL. If confirmed malicious, this could allow an attacker to persist in - the environment or execute arbitrary code, posing a significant threat to system - integrity and security. -search: - '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry - where Registry.registry_path="*\\InProcServer32\\*" by Registry.registry_path Registry.registry_key_name - Registry.registry_value_name Registry.registry_value_data Registry.dest Registry.process_guid - Registry.user | `drop_dm_object_name(Registry)` |`security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_new_inprocserver32_added_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. -known_false_positives: - False positives are expected. Filtering will be needed to properly - reduce legitimate applications from the results. +description: The following analytic detects the addition of new InProcServer32 registry keys on Windows endpoints. It leverages data from the Endpoint.Registry datamodel to identify changes in registry paths associated with InProcServer32. This activity is significant because malware often uses this mechanism to achieve persistence or execute malicious code by registering a new InProcServer32 key pointing to a harmful DLL. If confirmed malicious, this could allow an attacker to persist in the environment or execute arbitrary code, posing a significant threat to system integrity and security. +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\InProcServer32\\*" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.dest Registry.process_guid Registry.user | `drop_dm_object_name(Registry)` |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_new_inprocserver32_added_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. +known_false_positives: False positives are expected. Filtering will be needed to properly reduce legitimate applications from the results. references: - - https://www.netspi.com/blog/technical/red-team-operations/microsoft-outlook-remote-code-execution-cve-2024-21378/ + - https://www.netspi.com/blog/technical/red-team-operations/microsoft-outlook-remote-code-execution-cve-2024-21378/ tags: - analytic_story: - - Hellcat Ransomware - - Outlook RCE CVE-2024-21378 - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: - - CVE-2024-21378 + analytic_story: + - Hellcat Ransomware + - Outlook RCE CVE-2024-21378 + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: + - CVE-2024-21378 tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/cve-2024-21378/inprocserver32_windows-sysmon.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/cve-2024-21378/inprocserver32_windows-sysmon.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational diff --git a/detections/endpoint/windows_new_service_security_descriptor_set_via_sc_exe.yml b/detections/endpoint/windows_new_service_security_descriptor_set_via_sc_exe.yml index 30364c2a56..2e68c7ca38 100644 --- a/detections/endpoint/windows_new_service_security_descriptor_set_via_sc_exe.yml +++ b/detections/endpoint/windows_new_service_security_descriptor_set_via_sc_exe.yml @@ -1,86 +1,75 @@ name: Windows New Service Security Descriptor Set Via Sc.EXE id: cde00c31-042a-4307-bf70-25e471da56e9 -version: 5 -date: '2026-01-14' +version: 6 +date: '2026-02-25' author: Nasreddine Bencherchali, Michael Haag, Splunk status: production type: Anomaly -description: The following analytic detects changes in a service security descriptor - where a new deny ace has been added. It leverages data from Endpoint Detection and - Response (EDR) agents, specifically searching for any process execution involving - the "sc.exe" binary with the "sdset" flag targeting any service and adding a dedicated - deny ace. If confirmed malicious, this could allow an attacker to escalate their - privileges, blind defenses and more. +description: The following analytic detects changes in a service security descriptor where a new deny ace has been added. It leverages data from Endpoint Detection and Response (EDR) agents, specifically searching for any process execution involving the "sc.exe" binary with the "sdset" flag targeting any service and adding a dedicated deny ace. If confirmed malicious, this could allow an attacker to escalate their privileges, blind defenses and more. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where (Processes.process_name=sc.exe OR Processes.original_file_name=sc.exe) Processes.process="*sdset - *" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_new_service_security_descriptor_set_via_sc_exe_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process name, and process original file name. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: No false positives have been identified at this time. - should be identified and understood. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name=sc.exe + OR + Processes.original_file_name=sc.exe + ) + Processes.process="*sdset *" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_new_service_security_descriptor_set_via_sc_exe_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process name, and process original file name. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: No false positives have been identified at this time. should be identified and understood. references: -- https://www.sans.org/blog/red-team-tactics-hiding-windows-services/ -- https://news.sophos.com/wp-content/uploads/2020/06/glupteba_final-1.pdf -- https://attack.mitre.org/techniques/T1564/ + - https://www.sans.org/blog/red-team-tactics-hiding-windows-services/ + - https://news.sophos.com/wp-content/uploads/2020/06/glupteba_final-1.pdf + - https://attack.mitre.org/techniques/T1564/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - attempting to disable security services on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 36 - - field: dest - type: system - score: 36 - threat_objects: - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified attempting to disable security services on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 36 + - field: dest + type: system + score: 36 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Defense Evasion or Unauthorized Access Via SDDL Tampering - asset_type: Endpoint - mitre_attack_id: - - T1564 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Defense Evasion or Unauthorized Access Via SDDL Tampering + asset_type: Endpoint + mitre_attack_id: + - T1564 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1564/sc_sdset_tampering/sc_sdset_tampering_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1564/sc_sdset_tampering/sc_sdset_tampering_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_ngrok_reverse_proxy_usage.yml b/detections/endpoint/windows_ngrok_reverse_proxy_usage.yml index 657d0e8af9..3868c2ee97 100644 --- a/detections/endpoint/windows_ngrok_reverse_proxy_usage.yml +++ b/detections/endpoint/windows_ngrok_reverse_proxy_usage.yml @@ -1,91 +1,74 @@ name: Windows Ngrok Reverse Proxy Usage id: e2549f2c-0aef-408a-b0c1-e0f270623436 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic detects the execution of ngrok.exe on a Windows - operating system. It leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process names and command-line arguments. This activity is significant - because while ngrok is a legitimate tool for creating secure tunnels, it is increasingly - used by adversaries to bypass network defenses and establish reverse proxies. If - confirmed malicious, this could allow attackers to exfiltrate data, maintain persistence, - or facilitate further attacks by tunneling traffic through the compromised system. +description: The following analytic detects the execution of ngrok.exe on a Windows operating system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because while ngrok is a legitimate tool for creating secure tunnels, it is increasingly used by adversaries to bypass network defenses and establish reverse proxies. If confirmed malicious, this could allow attackers to exfiltrate data, maintain persistence, or facilitate further attacks by tunneling traffic through the compromised system. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=ngrok.exe - Processes.process IN ("*start*", "*--config*","*http*","*authtoken*", "*http*", - "*tcp*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| - `windows_ngrok_reverse_proxy_usage_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives will be present based on organizations that - allow the use of Ngrok. Filter or monitor as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=ngrok.exe Processes.process IN ("*start*", "*--config*","*http*","*authtoken*", "*http*", "*tcp*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_ngrok_reverse_proxy_usage_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives will be present based on organizations that allow the use of Ngrok. Filter or monitor as needed. references: -- https://www.cisa.gov/uscert/sites/default/files/publications/aa22-320a_joint_csa_iranian_government-sponsored_apt_actors_compromise_federal%20network_deploy_crypto%20miner_credential_harvester.pdf + - https://www.cisa.gov/uscert/sites/default/files/publications/aa22-320a_joint_csa_iranian_government-sponsored_apt_actors_compromise_federal%20network_deploy_crypto%20miner_credential_harvester.pdf drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A reverse proxy was identified spawning from $parent_process_name$ - $process_name$ - on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 50 - - field: dest - type: system - score: 50 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: A reverse proxy was identified spawning from $parent_process_name$ - $process_name$ on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 50 + - field: dest + type: system + score: 50 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Reverse Network Proxy - - CISA AA22-320A - - CISA AA24-241A - asset_type: Endpoint - mitre_attack_id: - - T1572 - - T1090 - - T1102 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Reverse Network Proxy + - CISA AA22-320A + - CISA AA24-241A + asset_type: Endpoint + mitre_attack_id: + - T1572 + - T1090 + - T1102 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1572/ngrok/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1572/ngrok/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_nirsoft_advancedrun.yml b/detections/endpoint/windows_nirsoft_advancedrun.yml index c663010cb0..adb9fea5d2 100644 --- a/detections/endpoint/windows_nirsoft_advancedrun.yml +++ b/detections/endpoint/windows_nirsoft_advancedrun.yml @@ -1,92 +1,79 @@ name: Windows NirSoft AdvancedRun id: bb4f3090-7ae4-11ec-897f-acde48001122 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of AdvancedRun.exe, a tool - with capabilities similar to remote administration programs like PsExec. It identifies - the process by its name or original file name and flags common command-line arguments. - This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process and command-line telemetry. Monitoring this activity is crucial - as AdvancedRun can be used for remote code execution and configuration-based automation. - If malicious, this could allow attackers to execute arbitrary commands, escalate - privileges, or maintain persistence within the environment. +description: The following analytic detects the execution of AdvancedRun.exe, a tool with capabilities similar to remote administration programs like PsExec. It identifies the process by its name or original file name and flags common command-line arguments. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and command-line telemetry. Monitoring this activity is crucial as AdvancedRun can be used for remote code execution and configuration-based automation. If malicious, this could allow attackers to execute arbitrary commands, escalate privileges, or maintain persistence within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=advancedrun.exe - OR Processes.original_file_name=advancedrun.exe) Processes.process IN ("*EXEFilename*","*/cfg*","*RunAs*", - "*WindowState*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_nirsoft_advancedrun_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives should be limited as it is specific to AdvancedRun. - Filter as needed based on legitimate usage. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name=advancedrun.exe + OR + Processes.original_file_name=advancedrun.exe + ) + Processes.process IN ("*EXEFilename*","*/cfg*","*RunAs*", "*WindowState*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_nirsoft_advancedrun_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives should be limited as it is specific to AdvancedRun. Filter as needed based on legitimate usage. references: -- http://www.nirsoft.net/utils/advanced_run.html -- https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - http://www.nirsoft.net/utils/advanced_run.html + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of advancedrun.exe, $process_name$, was spawned by $parent_process_name$ - on $dest$ by $user$. - risk_objects: - - field: user - type: user - score: 60 - - field: dest - type: system - score: 60 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of advancedrun.exe, $process_name$, was spawned by $parent_process_name$ on $dest$ by $user$. + risk_objects: + - field: user + type: user + score: 60 + - field: dest + type: system + score: 60 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Ransomware - - Unusual Processes - - Data Destruction - - WhisperGate - asset_type: Endpoint - mitre_attack_id: - - T1588.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - Unusual Processes + - Data Destruction + - WhisperGate + asset_type: Endpoint + mitre_attack_id: + - T1588.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1588.002/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1588.002/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_nirsoft_tool_bundle_file_created.yml b/detections/endpoint/windows_nirsoft_tool_bundle_file_created.yml index 83743a3a84..a06a148442 100644 --- a/detections/endpoint/windows_nirsoft_tool_bundle_file_created.yml +++ b/detections/endpoint/windows_nirsoft_tool_bundle_file_created.yml @@ -6,93 +6,88 @@ author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - The following analytic detects the creation of files associated with the NirSoft - tool bundles on Windows endpoints. - NirSoft is a well-known provider of free, portable utilities that can be used for various system and network tasks. However, threat actors often leverage these tools for malicious purposes, such as credential harvesting, network reconnaissance, and data exfiltration. - The detection focuses on the creation of specific NirSoft tool bundle files, which may indicate that an attacker is preparing to use these utilities on a compromised system. - Security teams should investigate any instances of these files being created, especially if they are found in unexpected locations or on systems that should not be using such tools. + The following analytic detects the creation of files associated with the NirSoft + tool bundles on Windows endpoints. + NirSoft is a well-known provider of free, portable utilities that can be used for various system and network tasks. However, threat actors often leverage these tools for malicious purposes, such as credential harvesting, network reconnaissance, and data exfiltration. + The detection focuses on the creation of specific NirSoft tool bundle files, which may indicate that an attacker is preparing to use these utilities on a compromised system. + Security teams should investigate any instances of these files being created, especially if they are found in unexpected locations or on systems that should not be using such tools. data_source: -- Sysmon EventID 11 + - Sysmon EventID 11 search: | - | tstats `security_content_summariesonly` - count values(Filesystem.file_path) as file_path - min(_time) as firstTime - max(_time) as lastTime + | tstats `security_content_summariesonly` + count values(Filesystem.file_path) as file_path + min(_time) as firstTime + max(_time) as lastTime - from datamodel=Endpoint.Filesystem where + from datamodel=Endpoint.Filesystem where - ``` Increase coverage by adding additional Nirsoft tool bundle or tool filenames ``` + ``` Increase coverage by adding additional Nirsoft tool bundle or tool filenames ``` - Filesystem.file_name IN ( - "brtools.zip", - "mailpv.zip", - "networktools.zip", - "passreccommandline.zip", - "passrecenc.zip", - "progtools.zip", - "rdpv.zip", - "systools.zip", - "webbrowserpassview.zip" - ) + Filesystem.file_name IN ( + "brtools.zip", + "mailpv.zip", + "networktools.zip", + "passreccommandline.zip", + "passrecenc.zip", + "progtools.zip", + "rdpv.zip", + "systools.zip", + "webbrowserpassview.zip" + ) - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path - Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product + by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time + Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path + Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product - | `drop_dm_object_name("Filesystem")` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_nirsoft_tool_bundle_file_created_filter` + | `drop_dm_object_name("Filesystem")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_nirsoft_tool_bundle_file_created_filter` how_to_implement: | - To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. - This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. - The data used for this search is typically generated via logs that report file-system reads and writes. + To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. + This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. + The data used for this search is typically generated via logs that report file-system reads and writes. known_false_positives: | - Administrators or users may download NirSoft tools for legitimate purposes, such as system maintenance or troubleshooting. - These instances should be reviewed to determine if the activity is authorized. + Administrators or users may download NirSoft tools for legitimate purposes, such as system maintenance or troubleshooting. + These instances should be reviewed to determine if the activity is authorized. references: - - https://thedfirreport.com/2020/04/04/gogoogle-ransomware/ - - https://asec.ahnlab.com/en/48940/ - - https://www.trendmicro.com/en_gb/research/23/c/emotet-returns-now-adopts-binary-padding-for-evasion.html - - https://www.welivesecurity.com/2022/06/16/how-emotet-is-changing-tactics-microsoft-tightening-office-macro-security/ + - https://thedfirreport.com/2020/04/04/gogoogle-ransomware/ + - https://asec.ahnlab.com/en/48940/ + - https://www.trendmicro.com/en_gb/research/23/c/emotet-returns-now-adopts-binary-padding-for-evasion.html + - https://www.welivesecurity.com/2022/06/16/how-emotet-is-changing-tactics-microsoft-tightening-office-macro-security/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: NirSoft tool bundle file $file_name$ created on host $dest$ - risk_objects: - - field: dest - type: system - score: 20 - threat_objects: [] + message: NirSoft tool bundle file $file_name$ created on host $dest$ + risk_objects: + - field: dest + type: system + score: 20 + threat_objects: [] tags: - analytic_story: - - Unusual Processes - - Data Destruction - - WhisperGate - asset_type: Endpoint - mitre_attack_id: - - T1588.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Unusual Processes + - Data Destruction + - WhisperGate + asset_type: Endpoint + mitre_attack_id: + - T1588.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1588.002/nirsoft_tooling/nirsoft_file_bundle_created.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1588.002/nirsoft_tooling/nirsoft_file_bundle_created.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_nirsoft_utilities.yml b/detections/endpoint/windows_nirsoft_utilities.yml index cce307ebb4..a5f31bcf1b 100644 --- a/detections/endpoint/windows_nirsoft_utilities.yml +++ b/detections/endpoint/windows_nirsoft_utilities.yml @@ -6,61 +6,52 @@ author: Michael Haag, Splunk status: production type: Hunting description: | - The following analytic identifies the execution of commonly used NirSoft utilities on Windows systems. - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution details such as process name, parent process, and command-line arguments. - This activity is significant for a SOC because NirSoft utilities, while legitimate, can be used by adversaries for malicious purposes like credential theft or system reconnaissance. - If confirmed malicious, this activity could lead to unauthorized access, data exfiltration, or further system compromise. - Note that this search does not use a where clause to filter out known benign paths, as NirSoft utilities can be executed from various locations. This might hinder performance in environments with high data volumes. - Apply additional filtering as necessary to enhance this. + The following analytic identifies the execution of commonly used NirSoft utilities on Windows systems. + It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution details such as process name, parent process, and command-line arguments. + This activity is significant for a SOC because NirSoft utilities, while legitimate, can be used by adversaries for malicious purposes like credential theft or system reconnaissance. + If confirmed malicious, this activity could lead to unauthorized access, data exfiltration, or further system compromise. + Note that this search does not use a where clause to filter out known benign paths, as NirSoft utilities can be executed from various locations. This might hinder performance in environments with high data volumes. + Apply additional filtering as necessary to enhance this. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Processes - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process - Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id - Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name("Processes")` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | lookup update=true is_nirsoft_software filename as process_name OUTPUT nirsoftFile - | search nirsoftFile=true - | `windows_nirsoft_utilities_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present. Filtering may be required before - setting to alert. + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime FROM datamodel=Endpoint.Processes + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process + Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id + Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | lookup update=true is_nirsoft_software filename as process_name OUTPUT nirsoftFile + | search nirsoftFile=true + | `windows_nirsoft_utilities_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present. Filtering may be required before setting to alert. references: -- https://www.cisa.gov/uscert/ncas/alerts/TA18-201A -- http://www.nirsoft.net/ -- https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://www.cisa.gov/uscert/ncas/alerts/TA18-201A + - http://www.nirsoft.net/ + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ tags: - analytic_story: - - Data Destruction - - WhisperGate - asset_type: Endpoint - mitre_attack_id: - - T1588.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Destruction + - WhisperGate + asset_type: Endpoint + mitre_attack_id: + - T1588.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1588.002/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1588.002/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_njrat_fileless_storage_via_registry.yml b/detections/endpoint/windows_njrat_fileless_storage_via_registry.yml index d06bee3f5a..bae35a7e21 100644 --- a/detections/endpoint/windows_njrat_fileless_storage_via_registry.yml +++ b/detections/endpoint/windows_njrat_fileless_storage_via_registry.yml @@ -6,66 +6,43 @@ author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects suspicious registry modifications indicative - of NjRat's fileless storage technique. It leverages the Endpoint.Registry data model - to identify specific registry paths and values commonly used by NjRat for keylogging - and executing DLL plugins. This activity is significant as it helps evade traditional - file-based detection systems, making it crucial for SOC analysts to monitor. If - confirmed malicious, this behavior could allow attackers to persist on the host, - execute arbitrary code, and capture sensitive keystrokes, leading to potential data - breaches and further system compromise. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\[kl]" - OR Registry.registry_value_data IN ("*[ENTER]*", "*[TAP]*", "*[Back]*") by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` - | `windows_njrat_fileless_storage_via_registry_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. + - Sysmon EventID 13 +description: The following analytic detects suspicious registry modifications indicative of NjRat's fileless storage technique. It leverages the Endpoint.Registry data model to identify specific registry paths and values commonly used by NjRat for keylogging and executing DLL plugins. This activity is significant as it helps evade traditional file-based detection systems, making it crucial for SOC analysts to monitor. If confirmed malicious, this behavior could allow attackers to persist on the host, execute arbitrary code, and capture sensitive keystrokes, leading to potential data breaches and further system compromise. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\[kl]" OR Registry.registry_value_data IN ("*[ENTER]*", "*[TAP]*", "*[Back]*") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_njrat_fileless_storage_via_registry_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. known_false_positives: No false positives have been identified at this time. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.njrat + - https://malpedia.caad.fkie.fraunhofer.de/details/win.njrat drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a suspicious registry entry related to NjRAT keylloging registry on $dest$ - risk_objects: - - field: dest - type: system - score: 100 - threat_objects: [] + message: a suspicious registry entry related to NjRAT keylloging registry on $dest$ + risk_objects: + - field: dest + type: system + score: 100 + threat_objects: [] tags: - analytic_story: - - NjRAT - asset_type: Endpoint - mitre_attack_id: - - T1027.011 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - NjRAT + asset_type: Endpoint + mitre_attack_id: + - T1027.011 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1027.011/njrat_fileless_registry_entry/njrat_registry.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1027.011/njrat_fileless_registry_entry/njrat_registry.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_non_discord_app_access_discord_leveldb.yml b/detections/endpoint/windows_non_discord_app_access_discord_leveldb.yml index 10f9e3fc93..3f0df57613 100644 --- a/detections/endpoint/windows_non_discord_app_access_discord_leveldb.yml +++ b/detections/endpoint/windows_non_discord_app_access_discord_leveldb.yml @@ -4,67 +4,47 @@ version: 7 date: '2026-01-14' author: Teoderick Contreras, Splunk data_source: -- Windows Event Log Security 4663 + - Windows Event Log Security 4663 type: Anomaly status: production -description: The following analytic detects non-Discord applications accessing the - Discord LevelDB database. It leverages Windows Security Event logs, specifically - event code 4663, to identify file access attempts to the LevelDB directory by processes - other than Discord. This activity is significant as it may indicate attempts to - steal Discord credentials or access sensitive user data. If confirmed malicious, - this could lead to unauthorized access to user profiles, messages, and other critical - information, potentially compromising the security and privacy of the affected users. -search: '`wineventlog_security` EventCode=4663 object_file_path IN ("*\\discord\\Local - Storage\\leveldb*") AND process_name != *\\discord.exe AND NOT (process_path IN - ("*:\\Windows\\System32\\*", "*:\\Windows\\SysWow64\\*", "*:\\Program Files*", "*:\\Windows\\*")) - | stats count min(_time) as firstTime max(_time) as lastTime by object_file_name - object_file_path process_name process_path process_id EventCode dest | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_non_discord_app_access_discord_leveldb_filter`' -how_to_implement: To successfully implement this search, you must ingest Windows Security - Event logs and track event code 4663. For 4663, enable "Audit Object Access" in - Group Policy. Then check the two boxes listed for both "Success" and "Failure." +description: The following analytic detects non-Discord applications accessing the Discord LevelDB database. It leverages Windows Security Event logs, specifically event code 4663, to identify file access attempts to the LevelDB directory by processes other than Discord. This activity is significant as it may indicate attempts to steal Discord credentials or access sensitive user data. If confirmed malicious, this could lead to unauthorized access to user profiles, messages, and other critical information, potentially compromising the security and privacy of the affected users. +search: '`wineventlog_security` EventCode=4663 object_file_path IN ("*\\discord\\Local Storage\\leveldb*") AND process_name != *\\discord.exe AND NOT (process_path IN ("*:\\Windows\\System32\\*", "*:\\Windows\\SysWow64\\*", "*:\\Program Files*", "*:\\Windows\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by object_file_name object_file_path process_name process_path process_id EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_non_discord_app_access_discord_leveldb_filter`' +how_to_implement: To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure." known_false_positives: No false positives have been identified at this time. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.404keylogger + - https://malpedia.caad.fkie.fraunhofer.de/details/win.404keylogger drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A non-discord process $process_name$ accessing discord "leveldb" file on - $dest$ - risk_objects: - - field: dest - type: system - score: 9 - threat_objects: [] + message: A non-discord process $process_name$ accessing discord "leveldb" file on $dest$ + risk_objects: + - field: dest + type: system + score: 9 + threat_objects: [] tags: - analytic_story: - - StealC Stealer - - Snake Keylogger - - PXA Stealer - asset_type: Endpoint - mitre_attack_id: - - T1012 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - StealC Stealer + - Snake Keylogger + - PXA Stealer + asset_type: Endpoint + mitre_attack_id: + - T1012 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552/snakey_keylogger_outlook_reg_access/snakekeylogger_4663.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552/snakey_keylogger_outlook_reg_access/snakekeylogger_4663.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_non_system_account_targeting_lsass.yml b/detections/endpoint/windows_non_system_account_targeting_lsass.yml index 40a75b23a3..163540140a 100644 --- a/detections/endpoint/windows_non_system_account_targeting_lsass.yml +++ b/detections/endpoint/windows_non_system_account_targeting_lsass.yml @@ -5,84 +5,56 @@ date: '2025-10-14' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies non-SYSTEM accounts requesting - access to lsass.exe. This detection leverages Sysmon EventCode 10 logs to - monitor access attempts to the Local Security Authority Subsystem Service - (lsass.exe) by non-SYSTEM users. This activity is significant as it may - indicate credential dumping attempts or unauthorized access to sensitive - credentials. If confirmed malicious, an attacker could potentially extract - credentials from memory, leading to privilege escalation or lateral movement - within the network. Immediate investigation is required to determine the - legitimacy of the access request and to mitigate any potential threats. +description: The following analytic identifies non-SYSTEM accounts requesting access to lsass.exe. This detection leverages Sysmon EventCode 10 logs to monitor access attempts to the Local Security Authority Subsystem Service (lsass.exe) by non-SYSTEM users. This activity is significant as it may indicate credential dumping attempts or unauthorized access to sensitive credentials. If confirmed malicious, an attacker could potentially extract credentials from memory, leading to privilege escalation or lateral movement within the network. Immediate investigation is required to determine the legitimacy of the access request and to mitigate any potential threats. data_source: -- Sysmon EventID 10 -search: '`sysmon` EventCode=10 TargetImage=*lsass.exe NOT (SourceUser="NT AUTHORITY\\*") - | stats count min(_time) as firstTime max(_time) as lastTime by CallTrace EventID - GrantedAccess Guid Opcode ProcessID SecurityID SourceImage SourceProcessGUID SourceProcessId - TargetImage TargetProcessGUID TargetProcessId UserID dest granted_access parent_process_exec - parent_process_guid parent_process_id parent_process_name parent_process_path process_exec - process_guid process_id process_name process_path signature signature_id user_id - vendor_product | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `windows_non_system_account_targeting_lsass_filter`' -how_to_implement: To successfully implement this search, you need to be - ingesting logs with the process name, parent process, and command-line - executions from your endpoints. If you are using Sysmon, you must have at - least version 6.0.4 of the Sysmon TA. Enabling EventCode 10 TargetProcess - lsass.exe is required. -known_false_positives: False positives will occur based on legitimate - application requests, filter based on source image as needed. + - Sysmon EventID 10 +search: '`sysmon` EventCode=10 TargetImage=*lsass.exe NOT (SourceUser="NT AUTHORITY\\*") | stats count min(_time) as firstTime max(_time) as lastTime by CallTrace EventID GrantedAccess Guid Opcode ProcessID SecurityID SourceImage SourceProcessGUID SourceProcessId TargetImage TargetProcessGUID TargetProcessId UserID dest granted_access parent_process_exec parent_process_guid parent_process_id parent_process_name parent_process_path process_exec process_guid process_id process_name process_path signature signature_id user_id vendor_product | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_non_system_account_targeting_lsass_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Enabling EventCode 10 TargetProcess lsass.exe is required. +known_false_positives: False positives will occur based on legitimate application requests, filter based on source image as needed. references: -- https://en.wikipedia.org/wiki/Local_Security_Authority_Subsystem_Service -- https://docs.microsoft.com/en-us/windows/win32/api/minidumpapiset/nf-minidumpapiset-minidumpwritedump -- https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for_22.html -- https://raw.githubusercontent.com/PowerShellMafia/PowerSploit/master/Exfiltration/Invoke-Mimikatz.ps1 -- https://docs.microsoft.com/en-us/windows/win32/procthread/process-security-and-access-rights?redirectedfrom=MSDN + - https://en.wikipedia.org/wiki/Local_Security_Authority_Subsystem_Service + - https://docs.microsoft.com/en-us/windows/win32/api/minidumpapiset/nf-minidumpapiset-minidumpwritedump + - https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for_22.html + - https://raw.githubusercontent.com/PowerShellMafia/PowerSploit/master/Exfiltration/Invoke-Mimikatz.ps1 + - https://docs.microsoft.com/en-us/windows/win32/procthread/process-security-and-access-rights?redirectedfrom=MSDN drilldown_searches: -- name: View the detection results for - "$user_id$" and "$dest$" - search: '%original_detection_search% | search user_id = "$user_id$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user_id$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user_id$" and "$dest$" + search: '%original_detection_search% | search user_id = "$user_id$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user_id$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process, $parent_process_path$, has loaded $TargetImage$ that are - typically related to credential dumping on $dest$. Review for further - details. - risk_objects: - - field: user_id - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: - - field: parent_process_path - type: process + message: A process, $parent_process_path$, has loaded $TargetImage$ that are typically related to credential dumping on $dest$. Review for further details. + risk_objects: + - field: user_id + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: + - field: parent_process_path + type: process tags: - analytic_story: - - CISA AA23-347A - - Credential Dumping - - Lokibot - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1003.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA23-347A + - Credential Dumping + - Lokibot + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1003.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon_creddump.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon_creddump.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_obfuscated_files_or_information_via_rar_sfx.yml b/detections/endpoint/windows_obfuscated_files_or_information_via_rar_sfx.yml index 83b156eb2f..8cc85c8f6c 100644 --- a/detections/endpoint/windows_obfuscated_files_or_information_via_rar_sfx.yml +++ b/detections/endpoint/windows_obfuscated_files_or_information_via_rar_sfx.yml @@ -1,71 +1,60 @@ name: Windows Obfuscated Files or Information via RAR SFX id: 4ab6862b-ce88-4223-96c0-f6da2cffb898 -version: 5 -date: '2025-09-16' +version: 6 +date: '2026-02-25' author: Teoderick Contreras, Splunk data_source: -- Sysmon EventID 11 + - Sysmon EventID 11 type: Anomaly status: production -description: The following analytic detects the creation of RAR Self-Extracting (SFX) - files by monitoring the generation of file related to rar sfx .tmp file creation - during sfx installation. This method leverages a heuristic to identify RAR SFX archives - based on specific markers that indicate a combination of executable code and compressed - RAR data. By tracking such activity, the analytic helps pinpoint potentially unauthorized - or suspicious file creation events, which are often associated with malware packaging - or data exfiltration. Legitimate usage may include custom installers or compressed - file delivery. -search: '`sysmon` EventCode=11 TargetFilename IN ("*__tmp_rar_sfx_access_check*") - | stats count min(_time) as firstTime max(_time) as lastTime by action dest file_name - file_path process_guid process_id user user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_obfuscated_files_or_information_via_rar_sfx_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, TargetFilename, and eventcode 11 executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. Tune and filter known instances where rar sfx executable may be used. -known_false_positives: It can detect a third part utility software tool compiled to - rar sfx. +description: The following analytic detects the creation of RAR Self-Extracting (SFX) files by monitoring the generation of file related to rar sfx .tmp file creation during sfx installation. This method leverages a heuristic to identify RAR SFX archives based on specific markers that indicate a combination of executable code and compressed RAR data. By tracking such activity, the analytic helps pinpoint potentially unauthorized or suspicious file creation events, which are often associated with malware packaging or data exfiltration. Legitimate usage may include custom installers or compressed file delivery. +search: |- + `sysmon` EventCode=11 TargetFilename IN ("*__tmp_rar_sfx_access_check*") + | stats count min(_time) as firstTime max(_time) as lastTime + BY action dest file_name + file_path process_guid process_id + user user_id vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_obfuscated_files_or_information_via_rar_sfx_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, TargetFilename, and eventcode 11 executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where rar sfx executable may be used. +known_false_positives: It can detect a third part utility software tool compiled to rar sfx. references: -- https://www.splunk.com/en_us/blog/security/-applocker-rules-as-defense-evasion-complete-analysis.html + - https://www.splunk.com/en_us/blog/security/-applocker-rules-as-defense-evasion-complete-analysis.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process drops [$file_name$] on [$dest$]. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: - - field: file_name - type: file_name + message: A process drops [$file_name$] on [$dest$]. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - Crypto Stealer - - APT37 Rustonotto and FadeStealer - - GhostRedirector IIS Module and Rungan Backdoor - asset_type: Endpoint - mitre_attack_id: - - T1027.013 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Crypto Stealer + - APT37 Rustonotto and FadeStealer + - GhostRedirector IIS Module and Rungan Backdoor + asset_type: Endpoint + mitre_attack_id: + - T1027.013 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1027.013/rar_sfx_execution/rar_sfx.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1027.013/rar_sfx_execution/rar_sfx.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_odbcconf_hunting.yml b/detections/endpoint/windows_odbcconf_hunting.yml index 57c8e45c24..4e10a84994 100644 --- a/detections/endpoint/windows_odbcconf_hunting.yml +++ b/detections/endpoint/windows_odbcconf_hunting.yml @@ -1,59 +1,48 @@ name: Windows Odbcconf Hunting id: 0562ad4b-fdaa-4882-b12f-7b8e0034cd72 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic identifies the execution of Odbcconf.exe within - the environment. It leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process creation events where the process name is Odbcconf.exe. This - activity is significant because Odbcconf.exe can be used by attackers to execute - arbitrary commands or load malicious DLLs, potentially leading to code execution - or persistence. If confirmed malicious, this behavior could allow an attacker to - maintain access to the system, execute further malicious activities, or escalate - privileges, posing a significant threat to the environment. +description: The following analytic identifies the execution of Odbcconf.exe within the environment. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where the process name is Odbcconf.exe. This activity is significant because Odbcconf.exe can be used by attackers to execute arbitrary commands or load malicious DLLs, potentially leading to code execution or persistence. If confirmed malicious, this behavior could allow an attacker to maintain access to the system, execute further malicious activities, or escalate privileges, posing a significant threat to the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=odbcconf.exe - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_odbcconf_hunting_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives will be present as this is meant to assist - with filtering and tuning. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=odbcconf.exe + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_odbcconf_hunting_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives will be present as this is meant to assist with filtering and tuning. references: -- https://strontic.github.io/xcyclopedia/library/odbcconf.exe-07FBA12552331355C103999806627314.html -- https://twitter.com/redcanary/status/1541838407894171650?s=20&t=kp3WBPtfnyA3xW7D7wx0uw + - https://strontic.github.io/xcyclopedia/library/odbcconf.exe-07FBA12552331355C103999806627314.html + - https://twitter.com/redcanary/status/1541838407894171650?s=20&t=kp3WBPtfnyA3xW7D7wx0uw tags: - analytic_story: - - Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1218.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1218.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.008/atomic_red_team/windows-sysmon-odbc-regsvr.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.008/atomic_red_team/windows-sysmon-odbc-regsvr.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_odbcconf_load_dll.yml b/detections/endpoint/windows_odbcconf_load_dll.yml index 72e838e32d..a74877ae6c 100644 --- a/detections/endpoint/windows_odbcconf_load_dll.yml +++ b/detections/endpoint/windows_odbcconf_load_dll.yml @@ -1,89 +1,71 @@ name: Windows Odbcconf Load DLL id: 141e7fca-a9f0-40fd-a539-9aac8be41f1b -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of odbcconf.exe with the - regsvr action to load a DLL. This is identified by monitoring command-line arguments - in process creation logs from Endpoint Detection and Response (EDR) agents. This - activity is significant as it may indicate an attempt to execute arbitrary code - via DLL loading, a common technique used in various attack vectors. If confirmed - malicious, this could allow an attacker to execute code with the privileges of the - odbcconf.exe process, potentially leading to system compromise or further lateral - movement. +description: The following analytic detects the execution of odbcconf.exe with the regsvr action to load a DLL. This is identified by monitoring command-line arguments in process creation logs from Endpoint Detection and Response (EDR) agents. This activity is significant as it may indicate an attempt to execute arbitrary code via DLL loading, a common technique used in various attack vectors. If confirmed malicious, this could allow an attacker to execute code with the privileges of the odbcconf.exe process, potentially leading to system compromise or further lateral movement. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=odbcconf.exe - Processes.process IN ("*/a *", "*-a*") Processes.process="*regsvr*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_odbcconf_load_dll_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present and filtering may need to occur - based on legitimate application usage. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=odbcconf.exe Processes.process IN ("*/a *", "*-a*") Processes.process="*regsvr*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_odbcconf_load_dll_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present and filtering may need to occur based on legitimate application usage. Filter as needed. references: -- https://strontic.github.io/xcyclopedia/library/odbcconf.exe-07FBA12552331355C103999806627314.html -- https://twitter.com/redcanary/status/1541838407894171650?s=20&t=kp3WBPtfnyA3xW7D7wx0uw + - https://strontic.github.io/xcyclopedia/library/odbcconf.exe-07FBA12552331355C103999806627314.html + - https://twitter.com/redcanary/status/1541838407894171650?s=20&t=kp3WBPtfnyA3xW7D7wx0uw drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to circumvent controls. - risk_objects: - - field: user - type: user - score: 42 - - field: dest - type: system - score: 42 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to circumvent controls. + risk_objects: + - field: user + type: user + score: 42 + - field: dest + type: system + score: 42 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1218.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1218.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.008/atomic_red_team/windows-sysmon-odbc-regsvr.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.008/atomic_red_team/windows-sysmon-odbc-regsvr.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_odbcconf_load_response_file.yml b/detections/endpoint/windows_odbcconf_load_response_file.yml index 63014e123b..d42e793aae 100644 --- a/detections/endpoint/windows_odbcconf_load_response_file.yml +++ b/detections/endpoint/windows_odbcconf_load_response_file.yml @@ -1,89 +1,71 @@ name: Windows Odbcconf Load Response File id: 1acafff9-1347-4b40-abae-f35aa4ba85c1 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of odbcconf.exe with a response - file, which may contain commands to load a DLL (REGSVR) or other instructions. This - detection leverages data from Endpoint Detection and Response (EDR) agents, focusing - on process names and command-line arguments. This activity is significant as it - may indicate an attempt to execute arbitrary code or load malicious DLLs, potentially - leading to unauthorized actions. If confirmed malicious, this could allow an attacker - to gain code execution, escalate privileges, or establish persistence within the - environment. +description: The following analytic detects the execution of odbcconf.exe with a response file, which may contain commands to load a DLL (REGSVR) or other instructions. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant as it may indicate an attempt to execute arbitrary code or load malicious DLLs, potentially leading to unauthorized actions. If confirmed malicious, this could allow an attacker to gain code execution, escalate privileges, or establish persistence within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=odbcconf.exe - Processes.process IN ("*-f *","*/f *") Processes.process="*.rsp*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_odbcconf_load_response_file_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present and filtering may need to occur - based on legitimate application usage. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=odbcconf.exe Processes.process IN ("*-f *","*/f *") Processes.process="*.rsp*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_odbcconf_load_response_file_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present and filtering may need to occur based on legitimate application usage. Filter as needed. references: -- https://strontic.github.io/xcyclopedia/library/odbcconf.exe-07FBA12552331355C103999806627314.html -- https://twitter.com/redcanary/status/1541838407894171650?s=20&t=kp3WBPtfnyA3xW7D7wx0uw + - https://strontic.github.io/xcyclopedia/library/odbcconf.exe-07FBA12552331355C103999806627314.html + - https://twitter.com/redcanary/status/1541838407894171650?s=20&t=kp3WBPtfnyA3xW7D7wx0uw drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to circumvent controls. - risk_objects: - - field: user - type: user - score: 42 - - field: dest - type: system - score: 42 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to circumvent controls. + risk_objects: + - field: user + type: user + score: 42 + - field: dest + type: system + score: 42 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1218.008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1218.008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.008/atomic_red_team/windows-sysmon-odbc-rsp.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.008/atomic_red_team/windows-sysmon-odbc-rsp.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_office_product_dropped_cab_or_inf_file.yml b/detections/endpoint/windows_office_product_dropped_cab_or_inf_file.yml index 9954492a60..7cbb0748bd 100644 --- a/detections/endpoint/windows_office_product_dropped_cab_or_inf_file.yml +++ b/detections/endpoint/windows_office_product_dropped_cab_or_inf_file.yml @@ -1,91 +1,84 @@ name: Windows Office Product Dropped Cab or Inf File id: dbdd251e-dd45-4ec9-a555-f5e151391746 -version: 6 -date: '2025-09-18' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects Office products writing .cab or .inf files, - indicative of CVE-2021-40444 exploitation. It leverages the Endpoint.Processes and - Endpoint.Filesystem data models to identify Office applications creating these file - types. This activity is significant as it may signal an attempt to load malicious - ActiveX controls and download remote payloads, a known attack vector. If confirmed - malicious, this could lead to remote code execution, allowing attackers to gain - control over the affected system and potentially compromise sensitive data. +description: The following analytic detects Office products writing .cab or .inf files, indicative of CVE-2021-40444 exploitation. It leverages the Endpoint.Processes and Endpoint.Filesystem data models to identify Office applications creating these file types. This activity is significant as it may signal an attempt to load malicious ActiveX controls and download remote payloads, a known attack vector. If confirmed malicious, this could lead to remote code execution, allowing attackers to gain control over the affected system and potentially compromise sensitive data. data_source: -- Sysmon EventID 1 AND Sysmon EventID 11 -- Windows Event Log Security 4688 AND Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes - where `process_office_products` by _time span=1h Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - |rename process_guid as proc_guid | join proc_guid, _time [ | tstats `security_content_summariesonly` - count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem - where Filesystem.file_name IN ("*.cab", "*.inf") by _time span=1h Filesystem.dest - Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.process_guid - | `drop_dm_object_name(Filesystem)` |rename process_guid as proc_guid | fields _time - dest file_create_time file_name file_path process_name process_path process proc_guid] - | dedup file_create_time | table dest, process_name, process, file_create_time, - file_name, file_path, proc_guid | `windows_office_product_dropped_cab_or_inf_file_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem` - node. -known_false_positives: The query is structured in a way that `action` (read, create) - is not defined. Review the results of this query, filter, and tune as necessary. - It may be necessary to generate this query specific to your endpoint product. + - Sysmon EventID 1 AND Sysmon EventID 11 + - Windows Event Log Security 4688 AND Sysmon EventID 11 +search: |- + | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes + WHERE `process_office_products` + BY _time span=1h Processes.action + Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process + Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name + Processes.process_path Processes.user Processes.user_id + Processes.vendor_product + | `drop_dm_object_name(Processes)` + | rename process_guid as proc_guid + | join proc_guid, _time [ + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.file_name IN ("*.cab", "*.inf") + BY _time span=1h Filesystem.dest + Filesystem.file_create_time Filesystem.file_name Filesystem.file_path + Filesystem.process_guid + | `drop_dm_object_name(Filesystem)` + | rename process_guid as proc_guid + | fields _time dest file_create_time file_name file_path process_name process_path process proc_guid] + | dedup file_create_time + | table dest, process_name, process, file_create_time, file_name, file_path, proc_guid + | `windows_office_product_dropped_cab_or_inf_file_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem` node. +known_false_positives: The query is structured in a way that `action` (read, create) is not defined. Review the results of this query, filter, and tune as necessary. It may be necessary to generate this query specific to your endpoint product. references: -- https://twitter.com/vxunderground/status/1436326057179860992?s=20 -- https://app.any.run/tasks/36c14029-9df8-439c-bba0-45f2643b0c70/ -- https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-40444 -- https://twitter.com/RonnyTNL/status/1436334640617373699?s=20 -- https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/trojanized-onenote-document-leads-to-formbook-malware/ + - https://twitter.com/vxunderground/status/1436326057179860992?s=20 + - https://app.any.run/tasks/36c14029-9df8-439c-bba0-45f2643b0c70/ + - https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-40444 + - https://twitter.com/RonnyTNL/status/1436334640617373699?s=20 + - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/trojanized-onenote-document-leads-to-formbook-malware/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $process_name$ was identified on $dest$ writing an inf or - cab file to this. This is not typical of $process_name$. - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: - - field: process_name - type: process_name + message: An instance of $process_name$ was identified on $dest$ writing an inf or cab file to this. This is not typical of $process_name$. + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Spearphishing Attachments - - Microsoft MSHTML Remote Code Execution CVE-2021-40444 - - Compromised Windows Host - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - cve: - - CVE-2021-40444 - mitre_attack_id: - - T1566.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Spearphishing Attachments + - Microsoft MSHTML Remote Code Execution CVE-2021-40444 + - Compromised Windows Host + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + cve: + - CVE-2021-40444 + mitre_attack_id: + - T1566.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_cabinf.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_cabinf.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_office_product_dropped_uncommon_file.yml b/detections/endpoint/windows_office_product_dropped_uncommon_file.yml index 0d983435ee..3bcd60254e 100644 --- a/detections/endpoint/windows_office_product_dropped_uncommon_file.yml +++ b/detections/endpoint/windows_office_product_dropped_uncommon_file.yml @@ -1,87 +1,80 @@ name: Windows Office Product Dropped Uncommon File id: 7ac0fced-9eae-4381-a748-90dcd1aa9393 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Teoderick Contreras, Michael Haag, Splunk, TheLawsOfChaos, Github status: production type: Anomaly -description: The following analytic detects Microsoft Office applications dropping - or creating executables or scripts on a Windows OS. It leverages process creation - and file system events from the Endpoint data model to identify Office applications - like Word or Excel generating files with extensions such as ".exe", ".dll", or ".ps1". - This behavior is significant as it is often associated with spear-phishing attacks - where malicious files are dropped to compromise the host. If confirmed malicious, - this activity could lead to code execution, privilege escalation, or persistent - access, posing a severe threat to the environment. +description: The following analytic detects Microsoft Office applications dropping or creating executables or scripts on a Windows OS. It leverages process creation and file system events from the Endpoint data model to identify Office applications like Word or Excel generating files with extensions such as ".exe", ".dll", or ".ps1". This behavior is significant as it is often associated with spear-phishing attacks where malicious files are dropped to compromise the host. If confirmed malicious, this activity could lead to code execution, privilege escalation, or persistent access, posing a severe threat to the environment. data_source: -- Sysmon EventID 1 AND Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes - where `process_office_products` by _time span=1h Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | join process_guid, _time [| tstats `security_content_summariesonly` count min(_time) - as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name - IN ("*.dll", "*.exe", "*.js", "*.pif", "*.ps1", "*.scr", "*.vbe", "*.vbs") by _time - span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.process_guid - Filesystem.file_path | `drop_dm_object_name(Filesystem)` | fields _time dest file_create_time - file_name file_path process_name process_path process process_guid] | dedup file_create_time - | table dest, process_name, process, file_create_time, file_name, file_path, process_guid - | `windows_office_product_dropped_uncommon_file_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used. + - Sysmon EventID 1 AND Sysmon EventID 11 +search: |- + | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes + WHERE `process_office_products` + BY _time span=1h Processes.action + Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process + Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name + Processes.process_path Processes.user Processes.user_id + Processes.vendor_product + | `drop_dm_object_name(Processes)` + | join process_guid, _time [ + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.file_name IN ("*.dll", "*.exe", "*.js", "*.pif", "*.ps1", "*.scr", "*.vbe", "*.vbs") + BY _time span=1h Filesystem.dest + Filesystem.file_create_time Filesystem.file_name Filesystem.process_guid + Filesystem.file_path + | `drop_dm_object_name(Filesystem)` + | fields _time dest file_create_time file_name file_path process_name process_path process process_guid] + | dedup file_create_time + | table dest, process_name, process, file_create_time, file_name, file_path, process_guid + | `windows_office_product_dropped_uncommon_file_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used. known_false_positives: office macro for automation may do this behavior references: -- https://www.mandiant.com/resources/fin7-pursuing-an-enigmatic-and-evasive-global-criminal-operation -- https://attack.mitre.org/groups/G0046/ -- https://www.joesandbox.com/analysis/702680/0/html -- https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/trojanized-onenote-document-leads-to-formbook-malware/ + - https://www.mandiant.com/resources/fin7-pursuing-an-enigmatic-and-evasive-global-criminal-operation + - https://attack.mitre.org/groups/G0046/ + - https://www.joesandbox.com/analysis/702680/0/html + - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/trojanized-onenote-document-leads-to-formbook-malware/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: process $process_name$ drops a file $file_name$ in host $dest$ - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: - - field: process_name - type: process_name + message: process $process_name$ drops a file $file_name$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - CVE-2023-21716 Word RTF Heap Corruption - - Warzone RAT - - FIN7 - - Compromised Windows Host - - AgentTesla - - PlugX - asset_type: Endpoint - mitre_attack_id: - - T1566.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CVE-2023-21716 Word RTF Heap Corruption + - Warzone RAT + - FIN7 + - Compromised Windows Host + - AgentTesla + - PlugX + asset_type: Endpoint + mitre_attack_id: + - T1566.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/fin7_macro_js_1/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/fin7_macro_js_1/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_office_product_loaded_mshtml_module.yml b/detections/endpoint/windows_office_product_loaded_mshtml_module.yml index a14df84000..915499f7b7 100644 --- a/detections/endpoint/windows_office_product_loaded_mshtml_module.yml +++ b/detections/endpoint/windows_office_product_loaded_mshtml_module.yml @@ -5,76 +5,53 @@ date: '2025-05-02' author: Michael Haag, Mauricio Velazco, Splunk status: production type: Anomaly -description: The following analytic detects the loading of the mshtml.dll module into - an Office product, which is indicative of CVE-2021-40444 exploitation. It leverages - Sysmon EventID 7 to monitor image loads by specific Office processes. This activity - is significant because it can indicate an attempt to exploit a vulnerability in - the MSHTML component via a malicious document. If confirmed malicious, this could - allow an attacker to execute arbitrary code, potentially leading to system compromise, - data exfiltration, or further network penetration. +description: The following analytic detects the loading of the mshtml.dll module into an Office product, which is indicative of CVE-2021-40444 exploitation. It leverages Sysmon EventID 7 to monitor image loads by specific Office processes. This activity is significant because it can indicate an attempt to exploit a vulnerability in the MSHTML component via a malicious document. If confirmed malicious, this could allow an attacker to execute arbitrary code, potentially leading to system compromise, data exfiltration, or further network penetration. data_source: -- Sysmon EventID 7 -search: '`sysmon` EventID=7 process_name IN ("EQNEDT32.exe", "excel.exe", "Graph.exe", - "msaccess.exe", "mspub.exe", "onenote.exe", "onenoteim.exe", "onenotem.exe", "outlook.exe", - "powerpnt.exe", "visio.exe", "winproj.exe", "winword.exe", "wordpad.exe", "wordview.exe") - loaded_file_path IN ("*\\mshtml.dll", "*\\Microsoft.mshtml.dll","*\\IE.Interop.MSHTML.dll","*\\MshtmlDac.dll","*\\MshtmlDed.dll","*\\MshtmlDer.dll") - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image - ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid - process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified - signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_office_product_loaded_mshtml_module_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process names and image loads from your endpoints. If you are using - Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -known_false_positives: Limited false positives will be present, however, tune as necessary. - Some applications may legitimately load mshtml.dll. + - Sysmon EventID 7 +search: '`sysmon` EventID=7 process_name IN ("EQNEDT32.exe", "excel.exe", "Graph.exe", "msaccess.exe", "mspub.exe", "onenote.exe", "onenoteim.exe", "onenotem.exe", "outlook.exe", "powerpnt.exe", "visio.exe", "winproj.exe", "winword.exe", "wordpad.exe", "wordview.exe") loaded_file_path IN ("*\\mshtml.dll", "*\\Microsoft.mshtml.dll","*\\IE.Interop.MSHTML.dll","*\\MshtmlDac.dll","*\\MshtmlDed.dll","*\\MshtmlDer.dll") | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_office_product_loaded_mshtml_module_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process names and image loads from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: Limited false positives will be present, however, tune as necessary. Some applications may legitimately load mshtml.dll. references: -- https://app.any.run/tasks/36c14029-9df8-439c-bba0-45f2643b0c70/ -- https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-40444 -- https://strontic.github.io/xcyclopedia/index-dll -- https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/trojanized-onenote-document-leads-to-formbook-malware/ + - https://app.any.run/tasks/36c14029-9df8-439c-bba0-45f2643b0c70/ + - https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-40444 + - https://strontic.github.io/xcyclopedia/index-dll + - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/trojanized-onenote-document-leads-to-formbook-malware/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $process_name$ was identified on endpoint $dest$ loading - mshtml.dll. - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: - - field: process_name - type: process_name + message: An instance of $process_name$ was identified on endpoint $dest$ loading mshtml.dll. + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Spearphishing Attachments - - Microsoft MSHTML Remote Code Execution CVE-2021-40444 - - CVE-2023-36884 Office and Windows HTML RCE Vulnerability - asset_type: Endpoint - cve: - - CVE-2021-40444 - mitre_attack_id: - - T1566.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Spearphishing Attachments + - Microsoft MSHTML Remote Code Execution CVE-2021-40444 + - CVE-2023-36884 Office and Windows HTML RCE Vulnerability + asset_type: Endpoint + cve: + - CVE-2021-40444 + mitre_attack_id: + - T1566.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_mshtml.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_mshtml.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_office_product_loading_taskschd_dll.yml b/detections/endpoint/windows_office_product_loading_taskschd_dll.yml index 7caf75fbb3..2fa057b705 100644 --- a/detections/endpoint/windows_office_product_loading_taskschd_dll.yml +++ b/detections/endpoint/windows_office_product_loading_taskschd_dll.yml @@ -5,71 +5,46 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects an Office document creating a scheduled - task, either through a macro VBA API or by loading `taskschd.dll`. This detection - leverages Sysmon EventCode 7 to identify when Office applications load the `taskschd.dll` - file. This activity is significant as it is a common technique used by malicious - macro malware to establish persistence or initiate beaconing. If confirmed malicious, - this could allow an attacker to maintain persistence, execute arbitrary commands, - or schedule future malicious activities, posing a significant threat to the environment. +description: The following analytic detects an Office document creating a scheduled task, either through a macro VBA API or by loading `taskschd.dll`. This detection leverages Sysmon EventCode 7 to identify when Office applications load the `taskschd.dll` file. This activity is significant as it is a common technique used by malicious macro malware to establish persistence or initiate beaconing. If confirmed malicious, this could allow an attacker to maintain persistence, execute arbitrary commands, or schedule future malicious activities, posing a significant threat to the environment. data_source: -- Sysmon EventID 7 -search: '`sysmon` EventCode=7 process_name IN ("EQNEDT32.exe", "excel.exe", "Graph.exe", - "msaccess.exe", "mspub.exe", "onenote.exe", "onenoteim.exe", "onenotem.exe", "outlook.exe", - "powerpnt.exe", "visio.exe", "winproj.exe", "winword.exe") loaded_file_path = "*\\taskschd.dll" - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image - ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid - process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified - signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_office_product_loading_taskschd_dll_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name and ImageLoaded (Like sysmon EventCode 7) from your endpoints. - If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. - Also be sure to include those monitored dll to your own sysmon config. -known_false_positives: False positives may occur if legitimate office documents are - creating scheduled tasks. Ensure to investigate the scheduled task and the command - to be executed. If the task is benign, add the task name to the exclusion list. - Some applications may legitimately load taskschd.dll. + - Sysmon EventID 7 +search: '`sysmon` EventCode=7 process_name IN ("EQNEDT32.exe", "excel.exe", "Graph.exe", "msaccess.exe", "mspub.exe", "onenote.exe", "onenoteim.exe", "onenotem.exe", "outlook.exe", "powerpnt.exe", "visio.exe", "winproj.exe", "winword.exe") loaded_file_path = "*\\taskschd.dll" | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_office_product_loading_taskschd_dll_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and ImageLoaded (Like sysmon EventCode 7) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Also be sure to include those monitored dll to your own sysmon config. +known_false_positives: False positives may occur if legitimate office documents are creating scheduled tasks. Ensure to investigate the scheduled task and the command to be executed. If the task is benign, add the task name to the exclusion list. Some applications may legitimately load taskschd.dll. references: -- https://research.checkpoint.com/2021/irans-apt34-returns-with-an-updated-arsenal/ -- https://redcanary.com/threat-detection-report/techniques/scheduled-task-job/ -- https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/trojanized-onenote-document-leads-to-formbook-malware/ + - https://research.checkpoint.com/2021/irans-apt34-returns-with-an-updated-arsenal/ + - https://redcanary.com/threat-detection-report/techniques/scheduled-task-job/ + - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/trojanized-onenote-document-leads-to-formbook-malware/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An Office document was identified creating a scheduled task on $dest$. - Investigate further. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: An Office document was identified creating a scheduled task on $dest$. Investigate further. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Spearphishing Attachments - asset_type: Endpoint - mitre_attack_id: - - T1566.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Spearphishing Attachments + asset_type: Endpoint + mitre_attack_id: + - T1566.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_office_product_loading_vbe7_dll.yml b/detections/endpoint/windows_office_product_loading_vbe7_dll.yml index 886522ae25..56c4295bd1 100644 --- a/detections/endpoint/windows_office_product_loading_vbe7_dll.yml +++ b/detections/endpoint/windows_office_product_loading_vbe7_dll.yml @@ -5,82 +5,58 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies office documents executing macro code. - It leverages Sysmon EventCode 7 to detect when processes like WINWORD.EXE or EXCEL.EXE - load specific DLLs associated with macros (e.g., VBE7.DLL). This activity is significant - because macros are a common attack vector for delivering malicious payloads, such - as malware. If confirmed malicious, this could lead to unauthorized code execution, - data exfiltration, or further compromise of the system. Disabling macros by default - is recommended to mitigate this risk. +description: The following analytic identifies office documents executing macro code. It leverages Sysmon EventCode 7 to detect when processes like WINWORD.EXE or EXCEL.EXE load specific DLLs associated with macros (e.g., VBE7.DLL). This activity is significant because macros are a common attack vector for delivering malicious payloads, such as malware. If confirmed malicious, this could lead to unauthorized code execution, data exfiltration, or further compromise of the system. Disabling macros by default is recommended to mitigate this risk. data_source: -- Sysmon EventID 7 -search: '`sysmon` EventCode=7 process_name IN ("EQNEDT32.exe", "excel.exe", "Graph.exe", - "msaccess.exe", "mspub.exe", "onenote.exe", "onenoteim.exe", "onenotem.exe", "outlook.exe", - "powerpnt.exe", "visio.exe", "winproj.exe", "winword.exe") loaded_file_path IN ("*\\VBE7INTL.DLL", - "*\\VBE7.DLL", "*\\VBEUI.DLL") | fillnull | stats count min(_time) as firstTime - max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name - process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists - service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_office_product_loading_vbe7_dll_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name and ImageLoaded (Like sysmon EventCode 7) from your endpoints. - If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. - Also be sure to include those monitored dll to your own sysmon config. -known_false_positives: False positives may occur if legitimate office documents are - executing macro code. Ensure to investigate the macro code and the command to be - executed. If the macro code is benign, add the document name to the exclusion list. - Some applications may legitimately load VBE7INTL.DLL, VBE7.DLL, or VBEUI.DLL. + - Sysmon EventID 7 +search: '`sysmon` EventCode=7 process_name IN ("EQNEDT32.exe", "excel.exe", "Graph.exe", "msaccess.exe", "mspub.exe", "onenote.exe", "onenoteim.exe", "onenotem.exe", "outlook.exe", "powerpnt.exe", "visio.exe", "winproj.exe", "winword.exe") loaded_file_path IN ("*\\VBE7INTL.DLL", "*\\VBE7.DLL", "*\\VBEUI.DLL") | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_office_product_loading_vbe7_dll_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and ImageLoaded (Like sysmon EventCode 7) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Also be sure to include those monitored dll to your own sysmon config. +known_false_positives: False positives may occur if legitimate office documents are executing macro code. Ensure to investigate the macro code and the command to be executed. If the macro code is benign, add the document name to the exclusion list. Some applications may legitimately load VBE7INTL.DLL, VBE7.DLL, or VBEUI.DLL. references: -- https://www.joesandbox.com/analysis/386500/0/html -- https://www.joesandbox.com/analysis/702680/0/html -- https://bazaar.abuse.ch/sample/02cbc1ab80695fc12ff8822b926957c3a600247b9ca412a137f69cb5716c8781/ -- https://www.fortinet.com/blog/threat-research/latest-remcos-rat-phishing -- https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/trojanized-onenote-document-leads-to-formbook-malware/ -- https://www.fortinet.com/blog/threat-research/leveraging-microsoft-office-documents-to-deliver-agent-tesla-and-njrat + - https://www.joesandbox.com/analysis/386500/0/html + - https://www.joesandbox.com/analysis/702680/0/html + - https://bazaar.abuse.ch/sample/02cbc1ab80695fc12ff8822b926957c3a600247b9ca412a137f69cb5716c8781/ + - https://www.fortinet.com/blog/threat-research/latest-remcos-rat-phishing + - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/trojanized-onenote-document-leads-to-formbook-malware/ + - https://www.fortinet.com/blog/threat-research/leveraging-microsoft-office-documents-to-deliver-agent-tesla-and-njrat drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Office document executing a macro on $dest$ - risk_objects: - - field: dest - type: system - score: 35 - threat_objects: [] + message: Office document executing a macro on $dest$ + risk_objects: + - field: dest + type: system + score: 35 + threat_objects: [] tags: - analytic_story: - - Spearphishing Attachments - - Trickbot - - IcedID - - DarkCrystal RAT - - AgentTesla - - Qakbot - - Azorult - - Remcos - - PlugX - - NjRAT - asset_type: Endpoint - mitre_attack_id: - - T1566.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Spearphishing Attachments + - Trickbot + - IcedID + - DarkCrystal RAT + - AgentTesla + - Qakbot + - Azorult + - Remcos + - PlugX + - NjRAT + asset_type: Endpoint + mitre_attack_id: + - T1566.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_office_product_spawned_child_process_for_download.yml b/detections/endpoint/windows_office_product_spawned_child_process_for_download.yml index b6b8a1d809..6500c46e63 100644 --- a/detections/endpoint/windows_office_product_spawned_child_process_for_download.yml +++ b/detections/endpoint/windows_office_product_spawned_child_process_for_download.yml @@ -1,94 +1,74 @@ name: Windows Office Product Spawned Child Process For Download id: f02b64b8-cbea-4f75-bf77-7a05111566b1 -version: 6 -date: '2025-09-18' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic identifies Office applications spawning child - processes to download content via HTTP/HTTPS. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process creation events where Office applications - like Word or Excel initiate network connections, excluding common browsers. This - activity is significant as it often indicates the use of malicious documents to - execute living-off-the-land binaries (LOLBins) for payload delivery. If confirmed - malicious, this behavior could lead to unauthorized code execution, data exfiltration, - or further malware deployment, posing a severe threat to the organization's security. +description: The following analytic identifies Office applications spawning child processes to download content via HTTP/HTTPS. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where Office applications like Word or Excel initiate network connections, excluding common browsers. This activity is significant as it often indicates the use of malicious documents to execute living-off-the-land binaries (LOLBins) for payload delivery. If confirmed malicious, this behavior could lead to unauthorized code execution, data exfiltration, or further malware deployment, posing a severe threat to the organization's security. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - `process_office_products_parent` - Processes.process IN ("*http:*","*https:*") - NOT ( - Processes.original_file_name IN ("firefox.exe", "chrome.exe","iexplore.exe","msedge.exe") - OR - Processes.process_name IN ("firefox.exe", "chrome.exe","iexplore.exe","msedge.exe") - ) - by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_office_product_spawned_child_process_for_download_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where + `process_office_products_parent` + Processes.process IN ("*http:*","*https:*") + NOT ( + Processes.original_file_name IN ("firefox.exe", "chrome.exe","iexplore.exe","msedge.exe") + OR + Processes.process_name IN ("firefox.exe", "chrome.exe","iexplore.exe","msedge.exe") + ) + by Processes.action Processes.dest + Processes.original_file_name Processes.parent_process Processes.parent_process_exec + Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name + Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name + Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_office_product_spawned_child_process_for_download_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Default browser not in the filter list. references: -- https://app.any.run/tasks/92d7ef61-bfd7-4c92-bc15-322172b4ebec/ -- https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/trojanized-onenote-document-leads-to-formbook-malware/ + - https://app.any.run/tasks/92d7ef61-bfd7-4c92-bc15-322172b4ebec/ + - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/trojanized-onenote-document-leads-to-formbook-malware/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Office document spawning suspicious child process on $dest$ - risk_objects: - - field: dest - type: system - score: 35 - threat_objects: [] + message: Office document spawning suspicious child process on $dest$ + risk_objects: + - field: dest + type: system + score: 35 + threat_objects: [] tags: - analytic_story: - - Spearphishing Attachments - - CVE-2023-36884 Office and Windows HTML RCE Vulnerability - - PlugX - - NjRAT - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1566.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Spearphishing Attachments + - CVE-2023-36884 Office and Windows HTML RCE Vulnerability + - PlugX + - NjRAT + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1566.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets2/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets2/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_office_product_spawned_control.yml b/detections/endpoint/windows_office_product_spawned_control.yml index 701f8f8762..0b06874120 100644 --- a/detections/endpoint/windows_office_product_spawned_control.yml +++ b/detections/endpoint/windows_office_product_spawned_control.yml @@ -1,93 +1,77 @@ name: Windows Office Product Spawned Control id: 081c485d-ac8d-4bee-ad4c-525772fead4d -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies instances where `control.exe` is spawned - by a Microsoft Office product. It leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process and parent process relationships. This activity - is significant because it can indicate exploitation attempts related to CVE-2021-40444, - where `control.exe` is used to execute malicious .cpl or .inf files. If confirmed - malicious, this behavior could allow an attacker to execute arbitrary code, potentially - leading to system compromise, data exfiltration, or further lateral movement within - the network. +description: The following analytic identifies instances where `control.exe` is spawned by a Microsoft Office product. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process relationships. This activity is significant because it can indicate exploitation attempts related to CVE-2021-40444, where `control.exe` is used to execute malicious .cpl or .inf files. If confirmed malicious, this behavior could allow an attacker to execute arbitrary code, potentially leading to system compromise, data exfiltration, or further lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_office_products_parent` - Processes.process_name=control.exe by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_office_product_spawned_control_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_office_products_parent` Processes.process_name=control.exe + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_office_product_spawned_control_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Limited false positives should be present. references: -- https://strontic.github.io/xcyclopedia/library/control.exe-1F13E714A0FEA8887707DFF49287996F.html -- https://app.any.run/tasks/36c14029-9df8-439c-bba0-45f2643b0c70/ -- https://attack.mitre.org/techniques/T1218/011/ -- https://www.echotrail.io/insights/search/control.exe/ -- https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-40444 -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.002/T1218.002.yaml -- https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/trojanized-onenote-document-leads-to-formbook-malware/ + - https://strontic.github.io/xcyclopedia/library/control.exe-1F13E714A0FEA8887707DFF49287996F.html + - https://app.any.run/tasks/36c14029-9df8-439c-bba0-45f2643b0c70/ + - https://attack.mitre.org/techniques/T1218/011/ + - https://www.echotrail.io/insights/search/control.exe/ + - https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-40444 + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.002/T1218.002.yaml + - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/trojanized-onenote-document-leads-to-formbook-malware/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ clicking a suspicious attachment. - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ clicking a suspicious attachment. + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Spearphishing Attachments - - Microsoft MSHTML Remote Code Execution CVE-2021-40444 - - Compromised Windows Host - asset_type: Endpoint - cve: - - CVE-2021-40444 - mitre_attack_id: - - T1566.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Spearphishing Attachments + - Microsoft MSHTML Remote Code Execution CVE-2021-40444 + - Compromised Windows Host + asset_type: Endpoint + cve: + - CVE-2021-40444 + mitre_attack_id: + - T1566.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_control.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_control.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_office_product_spawned_msdt.yml b/detections/endpoint/windows_office_product_spawned_msdt.yml index b8734a5526..bccce5211b 100644 --- a/detections/endpoint/windows_office_product_spawned_msdt.yml +++ b/detections/endpoint/windows_office_product_spawned_msdt.yml @@ -1,96 +1,80 @@ name: Windows Office Product Spawned MSDT id: a3148fad-3734-4b7f-9a71-62f08d39fab1 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects a Microsoft Office product spawning the - Windows msdt.exe process. This detection leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process creation events where Office applications - are the parent process. This activity is significant as it may indicate an attempt - to exploit protocol handlers to bypass security controls, even if macros are disabled. - If confirmed malicious, this behavior could allow an attacker to execute arbitrary - code, potentially leading to system compromise, data exfiltration, or further lateral - movement within the network. +description: The following analytic detects a Microsoft Office product spawning the Windows msdt.exe process. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where Office applications are the parent process. This activity is significant as it may indicate an attempt to exploit protocol handlers to bypass security controls, even if macros are disabled. If confirmed malicious, this behavior could allow an attacker to execute arbitrary code, potentially leading to system compromise, data exfiltration, or further lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_office_products_parent` - Processes.process_name=msdt.exe by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_office_product_spawned_msdt_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_office_products_parent` Processes.process_name=msdt.exe + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_office_product_spawned_msdt_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives should be limited, however filter as needed. references: -- https://isc.sans.edu/diary/rss/28694 -- https://doublepulsar.com/follina-a-microsoft-office-code-execution-vulnerability-1a47fce5629e -- https://twitter.com/nao_sec/status/1530196847679401984?s=20&t=ZiXYI4dQuA-0_dzQzSUb3A -- https://app.any.run/tasks/713f05d2-fe78-4b9d-a744-f7c133e3fafb/ -- https://www.virustotal.com/gui/file/4a24048f81afbe9fb62e7a6a49adbd1faf41f266b5f9feecdceb567aec096784/detection -- https://strontic.github.io/xcyclopedia/library/msdt.exe-152D4C9F63EFB332CCB134C6953C0104.html -- https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/trojanized-onenote-document-leads-to-formbook-malware/ + - https://isc.sans.edu/diary/rss/28694 + - https://doublepulsar.com/follina-a-microsoft-office-code-execution-vulnerability-1a47fce5629e + - https://twitter.com/nao_sec/status/1530196847679401984?s=20&t=ZiXYI4dQuA-0_dzQzSUb3A + - https://app.any.run/tasks/713f05d2-fe78-4b9d-a744-f7c133e3fafb/ + - https://www.virustotal.com/gui/file/4a24048f81afbe9fb62e7a6a49adbd1faf41f266b5f9feecdceb567aec096784/detection + - https://strontic.github.io/xcyclopedia/library/msdt.exe-152D4C9F63EFB332CCB134C6953C0104.html + - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/trojanized-onenote-document-leads-to-formbook-malware/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Office process $parent_process_name$ has spawned a child process $process_name$ - on host $dest$. - risk_objects: - - field: user - type: user - score: 100 - - field: dest - type: system - score: 100 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: Office process $parent_process_name$ has spawned a child process $process_name$ on host $dest$. + risk_objects: + - field: user + type: user + score: 100 + - field: dest + type: system + score: 100 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Spearphishing Attachments - - Compromised Windows Host - - Microsoft Support Diagnostic Tool Vulnerability CVE-2022-30190 - asset_type: Endpoint - cve: - - CVE-2022-30190 - mitre_attack_id: - - T1566.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Spearphishing Attachments + - Compromised Windows Host + - Microsoft Support Diagnostic Tool Vulnerability CVE-2022-30190 + asset_type: Endpoint + cve: + - CVE-2022-30190 + mitre_attack_id: + - T1566.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/msdt.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/msdt.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_office_product_spawned_rundll32_with_no_dll.yml b/detections/endpoint/windows_office_product_spawned_rundll32_with_no_dll.yml index 45d8d4934a..73cb0e1623 100644 --- a/detections/endpoint/windows_office_product_spawned_rundll32_with_no_dll.yml +++ b/detections/endpoint/windows_office_product_spawned_rundll32_with_no_dll.yml @@ -1,91 +1,72 @@ name: Windows Office Product Spawned Rundll32 With No DLL id: f28e787e-69ca-480e-9f98-ab970e6d4bcc -version: 4 -date: '2025-05-02' +version: 5 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects any Windows Office Product spawning `rundll32.exe` - without a `.dll` file extension. This behavior is identified using Endpoint Detection - and Response (EDR) telemetry, focusing on process and parent process relationships. - This activity is significant as it is a known tactic of the IcedID malware family, - which can lead to unauthorized code execution. If confirmed malicious, this could - allow attackers to execute arbitrary code, potentially leading to data exfiltration, - system compromise, or further malware deployment. Immediate investigation and containment - are recommended. +description: The following analytic detects any Windows Office Product spawning `rundll32.exe` without a `.dll` file extension. This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on process and parent process relationships. This activity is significant as it is a known tactic of the IcedID malware family, which can lead to unauthorized code execution. If confirmed malicious, this could allow attackers to execute arbitrary code, potentially leading to data exfiltration, system compromise, or further malware deployment. Immediate investigation and containment are recommended. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_office_products_parent` - `process_rundll32` (Processes.process!=*.dll*) by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` - | `windows_office_product_spawned_rundll32_with_no_dll_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives should be limited, but if any are present, - filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_office_products_parent` `process_rundll32` (Processes.process!=*.dll*) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_office_product_spawned_rundll32_with_no_dll_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives should be limited, but if any are present, filter as needed. references: -- https://www.joesandbox.com/analysis/395471/0/html -- https://app.any.run/tasks/cef4b8ba-023c-4b3b-b2ef-6486a44f6ed9/ -- https://any.run/malware-trends/icedid + - https://www.joesandbox.com/analysis/395471/0/html + - https://app.any.run/tasks/cef4b8ba-023c-4b3b-b2ef-6486a44f6ed9/ + - https://any.run/malware-trends/icedid drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Office process $parent_process_name$ observed executing a suspicious child - process $process_name$ with process id $process_id$ and no dll commandline $process$ - on host $dest$ - risk_objects: - - field: dest - type: system - score: 63 - threat_objects: - - field: process_name - type: process_name + message: Office process $parent_process_name$ observed executing a suspicious child process $process_name$ with process id $process_id$ and no dll commandline $process$ on host $dest$ + risk_objects: + - field: dest + type: system + score: 63 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Spearphishing Attachments - - CVE-2023-36884 Office and Windows HTML RCE Vulnerability - - Compromised Windows Host - - Prestige Ransomware - - Graceful Wipe Out Attack - - Crypto Stealer - asset_type: Endpoint - mitre_attack_id: - - T1566.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Spearphishing Attachments + - CVE-2023-36884 Office and Windows HTML RCE Vulnerability + - Compromised Windows Host + - Prestige Ransomware + - Graceful Wipe Out Attack + - Crypto Stealer + asset_type: Endpoint + mitre_attack_id: + - T1566.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_icedid.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_icedid.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_office_product_spawned_uncommon_process.yml b/detections/endpoint/windows_office_product_spawned_uncommon_process.yml index 9682976688..f390a22ae2 100644 --- a/detections/endpoint/windows_office_product_spawned_uncommon_process.yml +++ b/detections/endpoint/windows_office_product_spawned_uncommon_process.yml @@ -5,162 +5,142 @@ date: '2025-12-15' author: Michael Haag, Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects a Microsoft Office product spawning uncommon - processes. This detection leverages data from Endpoint Detection and Response (EDR) - agents, focusing on process creation events where Office applications are the parent - process. This activity is significant as it may indicate an attempt of a malicious - macro execution or exploitation of an unknown vulnerability in an office product, - in order to bypass security controls. If confirmed malicious, this behavior could - allow an attacker to execute arbitrary code, potentially leading to system compromise, - data exfiltration, or further lateral movement within the network. +description: The following analytic detects a Microsoft Office product spawning uncommon processes. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where Office applications are the parent process. This activity is significant as it may indicate an attempt of a malicious macro execution or exploitation of an unknown vulnerability in an office product, in order to bypass security controls. If confirmed malicious, this behavior could allow an attacker to execute arbitrary code, potentially leading to system compromise, data exfiltration, or further lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - `process_office_products_parent` - AND - ( - Processes.process_name IN ( - bitsadmin.exe, - certutil.exe, - cmd.exe, - cscript.exe, - mshta.exe, - powershell.exe, - pwsh.exe, - regsvr32.exe, - rundll32.exe, - wmic.exe, - wscript.exe + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where + `process_office_products_parent` + AND + ( + Processes.process_name IN ( + bitsadmin.exe, + certutil.exe, + cmd.exe, + cscript.exe, + mshta.exe, + powershell.exe, + pwsh.exe, + regsvr32.exe, + rundll32.exe, + wmic.exe, + wscript.exe + ) + OR + Processes.original_file_name IN ( + bitsadmin.exe, + CertUtil.exe, + Cmd.Exe, + cscript.exe, + MSHTA.EXE, + PowerShell.EXE, + pwsh.dll, + REGSVR32.EXE, + RUNDLL32.EXE, + wmic.exe, + wscript.exe + ) ) - OR - Processes.original_file_name IN ( - bitsadmin.exe, - CertUtil.exe, - Cmd.Exe, - cscript.exe, - MSHTA.EXE, - PowerShell.EXE, - pwsh.dll, - REGSVR32.EXE, - RUNDLL32.EXE, - wmic.exe, - wscript.exe - ) - ) - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_office_product_spawned_uncommon_process_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + by Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_office_product_spawned_uncommon_process_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives should be limited, however filter as needed. references: -- https://any.run/malware-trends/trickbot -- https://any.run/report/47561b4e949041eff0a0f4693c59c81726591779fe21183ae9185b5eb6a69847/aba3722a-b373-4dae-8273-8730fb40cdbe -- https://app.any.run/tasks/fb894ab8-a966-4b72-920b-935f41756afd/ -- https://attack.mitre.org/techniques/T1047/ -- https://bazaar.abuse.ch/sample/02cbc1ab80695fc12ff8822b926957c3a600247b9ca412a137f69cb5716c8781/ -- https://blog.cluster25.duskrise.com/2022/09/23/in-the-footsteps-of-the-fancy-bear-powerpoint-graphite/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1047/T1047.md -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1105/T1105.md -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1197/T1197.md -- https://redcanary.com/threat-detection-report/threats/TA551/ -- https://twitter.com/cyb3rops/status/1416050325870587910?s=21 -- https://www.fortinet.com/blog/threat-research/latest-remcos-rat-phishing -- https://www.joesandbox.com/analysis/380662/0/html -- https://www.joesandbox.com/analysis/702680/0/html -- https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/trojanized-onenote-document-leads-to-formbook-malware/ + - https://any.run/malware-trends/trickbot + - https://any.run/report/47561b4e949041eff0a0f4693c59c81726591779fe21183ae9185b5eb6a69847/aba3722a-b373-4dae-8273-8730fb40cdbe + - https://app.any.run/tasks/fb894ab8-a966-4b72-920b-935f41756afd/ + - https://attack.mitre.org/techniques/T1047/ + - https://bazaar.abuse.ch/sample/02cbc1ab80695fc12ff8822b926957c3a600247b9ca412a137f69cb5716c8781/ + - https://blog.cluster25.duskrise.com/2022/09/23/in-the-footsteps-of-the-fancy-bear-powerpoint-graphite/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1047/T1047.md + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1105/T1105.md + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1197/T1197.md + - https://redcanary.com/threat-detection-report/threats/TA551/ + - https://twitter.com/cyb3rops/status/1416050325870587910?s=21 + - https://www.fortinet.com/blog/threat-research/latest-remcos-rat-phishing + - https://www.joesandbox.com/analysis/380662/0/html + - https://www.joesandbox.com/analysis/702680/0/html + - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/trojanized-onenote-document-leads-to-formbook-malware/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: User $user$ on $dest$ spawned Windows Script Host from Winword.exe - risk_objects: - - field: dest - type: system - score: 70 - - field: user - type: user - score: 70 - threat_objects: - - field: process_name - type: process_name + message: User $user$ on $dest$ spawned Windows Script Host from Winword.exe + risk_objects: + - field: dest + type: system + score: 70 + - field: user + type: user + score: 70 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - AgentTesla - - Azorult - - Compromised Windows Host - - CVE-2023-21716 Word RTF Heap Corruption - - CVE-2023-36884 Office and Windows HTML RCE Vulnerability - - DarkCrystal RAT - - FIN7 - - IcedID - - NjRAT - - PlugX - - Qakbot - - Remcos - - Spearphishing Attachments - - Trickbot - - Warzone RAT - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1566.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AgentTesla + - Azorult + - Compromised Windows Host + - CVE-2023-21716 Word RTF Heap Corruption + - CVE-2023-36884 Office and Windows HTML RCE Vulnerability + - DarkCrystal RAT + - FIN7 + - IcedID + - NjRAT + - PlugX + - Qakbot + - Remcos + - Spearphishing Attachments + - Trickbot + - Warzone RAT + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1566.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - Macro - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_macros.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog -- name: True Positive Test - IcedId - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/phish_icedid/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.002/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog -- name: True Positive Test - TrickBot - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/spear_phish/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test - Macro + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_macros.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test - IcedId + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/phish_icedid/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.002/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test - TrickBot + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/spear_phish/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_outlook_dialogs_disabled_from_unusual_process.yml b/detections/endpoint/windows_outlook_dialogs_disabled_from_unusual_process.yml index 1f153c9abd..ce63361ba3 100644 --- a/detections/endpoint/windows_outlook_dialogs_disabled_from_unusual_process.yml +++ b/detections/endpoint/windows_outlook_dialogs_disabled_from_unusual_process.yml @@ -5,80 +5,47 @@ date: '2025-09-08' author: Raven Tait, Splunk status: production type: TTP -description: The following analytic detects the modification of the Windows Registry - key "PONT_STRING" under Outlook Options. This disables certain dialog popups, - which could allow malicious scripts to run without notice. This detection leverages data from - the Endpoint.Registry datamodel to search for this key changing from an unusual process. - This activity is significant as it is commonly associated with some malware - infections, indicating potential malicious intent to harvest email information. +description: The following analytic detects the modification of the Windows Registry key "PONT_STRING" under Outlook Options. This disables certain dialog popups, which could allow malicious scripts to run without notice. This detection leverages data from the Endpoint.Registry datamodel to search for this key changing from an unusual process. This activity is significant as it is commonly associated with some malware infections, indicating potential malicious intent to harvest email information. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry - WHERE Registry.registry_path="*\\Outlook\\Options\\General*" Registry.registry_value_name="PONT_STRING" - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data - Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user - Registry.vendor_product | `drop_dm_object_name(Registry)`| join process_guid [| tstats - `security_content_summariesonly` count FROM datamodel=Endpoint.Processes WHERE NOT - (Processes.process_name = "Outlook.exe") by _time span=1h - Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)`] - | fields _time parent_process_name parent_process process_name process_path process - process_guid registry_path registry_value_name registry_value_data registry_key_name - action dest user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_outlook_dialogs_disabled_from_unusual_process_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 -known_false_positives: It is unusual for processes other than Outlook to modify this - feature on a Windows system since it is a default Outlook functionality. Although no - false positives have been identified, use the provided filter macro to tune the search. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE Registry.registry_path="*\\Outlook\\Options\\General*" Registry.registry_value_name="PONT_STRING" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)`| join process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes WHERE NOT (Processes.process_name = "Outlook.exe") by _time span=1h Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)`] | fields _time parent_process_name parent_process process_name process_path process process_guid registry_path registry_value_name registry_value_data registry_key_name action dest user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_outlook_dialogs_disabled_from_unusual_process_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 +known_false_positives: It is unusual for processes other than Outlook to modify this feature on a Windows system since it is a default Outlook functionality. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://lab52.io/blog/analyzing-notdoor-inside-apt28s-expanding-arsenal/ -- https://hackread.com/russian-apt28-notdoor-backdoor-microsoft-outlook/ + - https://lab52.io/blog/analyzing-notdoor-inside-apt28s-expanding-arsenal/ + - https://hackread.com/russian-apt28-notdoor-backdoor-microsoft-outlook/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Outlook Dialog registry key modified on $dest$ by unusual process - risk_objects: - - field: dest - type: system - score: 44 - threat_objects: [] + message: Outlook Dialog registry key modified on $dest$ by unusual process + risk_objects: + - field: dest + type: system + score: 44 + threat_objects: [] tags: - analytic_story: - - NotDoor Malware - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1112 - - T1562 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - NotDoor Malware + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1112 + - T1562 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/notdoor/disable_dialogs/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/notdoor/disable_dialogs/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_outlook_loadmacroprovideronboot_persistence.yml b/detections/endpoint/windows_outlook_loadmacroprovideronboot_persistence.yml index 855ec0db03..f1ebbcb72c 100644 --- a/detections/endpoint/windows_outlook_loadmacroprovideronboot_persistence.yml +++ b/detections/endpoint/windows_outlook_loadmacroprovideronboot_persistence.yml @@ -5,70 +5,47 @@ date: '2025-09-09' author: Raven Tait, Splunk status: production type: TTP -description: The following analytic detects the modification of the Windows Registry - key "LoadMacroProviderOnBoot" under Outlook. This enables automatic loading of macros, - which could allow malicious scripts to run without notice. This detection leverages data from - the Endpoint.Registry datamodel to search for this key being enabled. - This activity is significant as it is commonly associated with some malware - infections, indicating potential malicious intent to harvest email information. +description: The following analytic detects the modification of the Windows Registry key "LoadMacroProviderOnBoot" under Outlook. This enables automatic loading of macros, which could allow malicious scripts to run without notice. This detection leverages data from the Endpoint.Registry datamodel to search for this key being enabled. This activity is significant as it is commonly associated with some malware infections, indicating potential malicious intent to harvest email information. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry - WHERE Registry.registry_path="*\\Outlook\\*" Registry.registry_value_name="LoadMacroProviderOnBoot" - Registry.registry_value_data="0x00000001" by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_outlook_loadmacroprovideronboot_persistence_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 -known_false_positives: It is unusual to modify this feature on a Windows system. - Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE Registry.registry_path="*\\Outlook\\*" Registry.registry_value_name="LoadMacroProviderOnBoot" Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_outlook_loadmacroprovideronboot_persistence_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 +known_false_positives: It is unusual to modify this feature on a Windows system. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://lab52.io/blog/analyzing-notdoor-inside-apt28s-expanding-arsenal/ -- https://hackread.com/russian-apt28-notdoor-backdoor-microsoft-outlook/ + - https://lab52.io/blog/analyzing-notdoor-inside-apt28s-expanding-arsenal/ + - https://hackread.com/russian-apt28-notdoor-backdoor-microsoft-outlook/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Outlook LoadMacroProviderOnBoot registry key modified on $dest$ - risk_objects: - - field: dest - type: system - score: 54 - threat_objects: [] + message: Outlook LoadMacroProviderOnBoot registry key modified on $dest$ + risk_objects: + - field: dest + type: system + score: 54 + threat_objects: [] tags: - analytic_story: - - NotDoor Malware - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1112 - - T1137 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - NotDoor Malware + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1112 + - T1137 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/notdoor/loadmacroprovideronboot/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/notdoor/loadmacroprovideronboot/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_outlook_macro_created_by_suspicious_process.yml b/detections/endpoint/windows_outlook_macro_created_by_suspicious_process.yml index 47a7e090f2..ae17ef0271 100644 --- a/detections/endpoint/windows_outlook_macro_created_by_suspicious_process.yml +++ b/detections/endpoint/windows_outlook_macro_created_by_suspicious_process.yml @@ -5,74 +5,51 @@ date: '2025-09-09' author: Raven Tait, Splunk status: production type: TTP -description: The following analytic detects the creation of an Outlook Macro - (VbaProject.OTM) by a suspicious process. This file is normally created when you - create a macro from within Outlook. If this file is created by a process other than - Outlook.exe it may be maliciously created. This detection leverages data from - the Filesystem datamodel, specifically looking for the file creation event for - VbaProject.OTM. This activity is significant as it is commonly associated with - some malware infections, indicating potential malicious intent to harvest email information. +description: The following analytic detects the creation of an Outlook Macro (VbaProject.OTM) by a suspicious process. This file is normally created when you create a macro from within Outlook. If this file is created by a process other than Outlook.exe it may be maliciously created. This detection leverages data from the Filesystem datamodel, specifically looking for the file creation event for VbaProject.OTM. This activity is significant as it is commonly associated with some malware infections, indicating potential malicious intent to harvest email information. data_source: -- Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime values(Filesystem.file_create_time) as file_create_time from datamodel=Endpoint.Filesystem - where Filesystem.file_path="*Appdata\\Roaming\\Microsoft\\Outlook\\VbaProject.OTM" - by Filesystem.action Filesystem.dest Filesystem.file_access_time - Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name - Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid - Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_outlook_macro_created_by_suspicious_process_filter`' -how_to_implement: You must be ingesting data that records file-system activity from - your hosts to populate the Endpoint file-system data-model node. If you are using - Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you - want to collect data. -known_false_positives: Because this file are always created by Outlook in normal operations, - you should investigate all results. + - Sysmon EventID 11 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.file_create_time) as file_create_time from datamodel=Endpoint.Filesystem where Filesystem.file_path="*Appdata\\Roaming\\Microsoft\\Outlook\\VbaProject.OTM" by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_outlook_macro_created_by_suspicious_process_filter`' +how_to_implement: You must be ingesting data that records file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. +known_false_positives: Because this file are always created by Outlook in normal operations, you should investigate all results. references: -- https://lab52.io/blog/analyzing-notdoor-inside-apt28s-expanding-arsenal/ -- https://hackread.com/russian-apt28-notdoor-backdoor-microsoft-outlook/ + - https://lab52.io/blog/analyzing-notdoor-inside-apt28s-expanding-arsenal/ + - https://hackread.com/russian-apt28-notdoor-backdoor-microsoft-outlook/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious Outlook macro $file_name$ created on $dest$ - risk_objects: - - field: user - type: user - score: 70 - - field: dest - type: system - score: 70 - threat_objects: - - field: file_name - type: file_name + message: Suspicious Outlook macro $file_name$ created on $dest$ + risk_objects: + - field: user + type: user + score: 70 + - field: dest + type: system + score: 70 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - NotDoor Malware - asset_type: Endpoint - mitre_attack_id: - - T1137 - - T1059.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - NotDoor Malware + asset_type: Endpoint + mitre_attack_id: + - T1137 + - T1059.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/notdoor/outlook_macro/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/notdoor/outlook_macro/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_outlook_macro_security_modified.yml b/detections/endpoint/windows_outlook_macro_security_modified.yml index 2b065b8867..c621a0e665 100644 --- a/detections/endpoint/windows_outlook_macro_security_modified.yml +++ b/detections/endpoint/windows_outlook_macro_security_modified.yml @@ -5,71 +5,47 @@ date: '2025-09-08' author: Raven Tait, Splunk status: production type: TTP -description: The following analytic detects the modification of the Windows Registry - key "Level" under Outlook Security. This allows macros to execute without warning, - which could allow malicious scripts to run without notice. This detection leverages data from - the Endpoint.Registry datamodel, specifically looking for the registry value name - "Level" with a value of "0x00000001". This activity is significant - as it is commonly associated with some malware infections, indicating potential - malicious intent to harvest email information. +description: The following analytic detects the modification of the Windows Registry key "Level" under Outlook Security. This allows macros to execute without warning, which could allow malicious scripts to run without notice. This detection leverages data from the Endpoint.Registry datamodel, specifically looking for the registry value name "Level" with a value of "0x00000001". This activity is significant as it is commonly associated with some malware infections, indicating potential malicious intent to harvest email information. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry - WHERE Registry.registry_path="*\\Outlook\\Security*" Registry.registry_value_name="Level" - Registry.registry_value_data="0x00000001" by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_outlook_macro_security_modified_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 -known_false_positives: It is unusual to modify this feature on a Windows system since - it is a default security control, although it is not rare for some policies to disable - it. Although no false positives have been identified, use the provided filter macro - to tune the search. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE Registry.registry_path="*\\Outlook\\Security*" Registry.registry_value_name="Level" Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_outlook_macro_security_modified_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 +known_false_positives: It is unusual to modify this feature on a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search. references: -- https://lab52.io/blog/analyzing-notdoor-inside-apt28s-expanding-arsenal/ -- https://hackread.com/russian-apt28-notdoor-backdoor-microsoft-outlook/ + - https://lab52.io/blog/analyzing-notdoor-inside-apt28s-expanding-arsenal/ + - https://hackread.com/russian-apt28-notdoor-backdoor-microsoft-outlook/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Outlook Macro Security Level registry modified on $dest$ - risk_objects: - - field: dest - type: system - score: 44 - threat_objects: [] + message: Outlook Macro Security Level registry modified on $dest$ + risk_objects: + - field: dest + type: system + score: 44 + threat_objects: [] tags: - analytic_story: - - NotDoor Malware - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1137 - - T1008 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - NotDoor Malware + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1137 + - T1008 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/notdoor/macro_security_level/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/notdoor/macro_security_level/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_outlook_webview_registry_modification.yml b/detections/endpoint/windows_outlook_webview_registry_modification.yml index 41e1ed389a..7e702b5b63 100644 --- a/detections/endpoint/windows_outlook_webview_registry_modification.yml +++ b/detections/endpoint/windows_outlook_webview_registry_modification.yml @@ -4,83 +4,47 @@ version: 6 date: '2025-05-02' author: Michael Haag, Splunk data_source: - - Sysmon EventID 13 + - Sysmon EventID 13 type: Anomaly status: production -description: - The following analytic identifies modifications to specific Outlook registry - values related to WebView and Today features. It detects when a URL is set in these - registry locations, which could indicate attempts to manipulate Outlook's web-based - components. The analytic focuses on changes to the "URL" value within Outlook's - WebView and Today registry paths. This activity is significant as it may represent - an attacker's effort to redirect Outlook's web content or inject malicious URLs. - If successful, this technique could lead to phishing attempts, data theft, or serve - as a stepping stone for further compromise of the user's email client and potentially - sensitive information. -search: - '| tstats `security_content_summariesonly` count values(Registry.registry_value_name) - as registry_value_name values(Registry.registry_value_data) as registry_value_data - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry - where (Registry.registry_path="*\\Software\\Microsoft\\Office\\*\\Outlook\\WebView\\*" - OR Registry.registry_path="*\\Software\\Microsoft\\Office\\*\\Outlook\\Today") AND - Registry.registry_value_name="URL" by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `security_content_ctime(lastTime)` - | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `windows_outlook_webview_registry_modification_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: - False positives may occur if legitimate Outlook processes are - modified. +description: The following analytic identifies modifications to specific Outlook registry values related to WebView and Today features. It detects when a URL is set in these registry locations, which could indicate attempts to manipulate Outlook's web-based components. The analytic focuses on changes to the "URL" value within Outlook's WebView and Today registry paths. This activity is significant as it may represent an attacker's effort to redirect Outlook's web content or inject malicious URLs. If successful, this technique could lead to phishing attempts, data theft, or serve as a stepping stone for further compromise of the user's email client and potentially sensitive information. +search: '| tstats `security_content_summariesonly` count values(Registry.registry_value_name) as registry_value_name values(Registry.registry_value_data) as registry_value_data min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where (Registry.registry_path="*\\Software\\Microsoft\\Office\\*\\Outlook\\WebView\\*" OR Registry.registry_path="*\\Software\\Microsoft\\Office\\*\\Outlook\\Today") AND Registry.registry_value_name="URL" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `windows_outlook_webview_registry_modification_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may occur if legitimate Outlook processes are modified. references: - - https://gist.github.com/MHaggis/c6318acde2e2f691b550e3a491f49ff1 - - https://github.com/trustedsec/specula/wiki + - https://gist.github.com/MHaggis/c6318acde2e2f691b550e3a491f49ff1 + - https://github.com/trustedsec/specula/wiki drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Modification of Outlook WebView registry values on $dest$. - risk_objects: - - field: dest - type: system - score: 100 - threat_objects: [] + message: Modification of Outlook WebView registry values on $dest$. + risk_objects: + - field: dest + type: system + score: 100 + threat_objects: [] tags: - analytic_story: - - Suspicious Windows Registry Activities - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - Suspicious Windows Registry Activities + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/atomic_red_team/windows-sysmon-webview.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/atomic_red_team/windows-sysmon-webview.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_papercut_ng_spawn_shell.yml b/detections/endpoint/windows_papercut_ng_spawn_shell.yml index 9b6c90d90b..f1d1d68b00 100644 --- a/detections/endpoint/windows_papercut_ng_spawn_shell.yml +++ b/detections/endpoint/windows_papercut_ng_spawn_shell.yml @@ -1,91 +1,77 @@ name: Windows PaperCut NG Spawn Shell id: a602d9a2-aaea-45f8-bf0f-d851168d61ca -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic detects instances where the PaperCut NG application - (pc-app.exe) spawns a Windows shell, such as cmd.exe or PowerShell. This behavior - is identified using Endpoint Detection and Response (EDR) telemetry, focusing on - process creation events where the parent process is pc-app.exe. This activity is - significant as it may indicate an attacker attempting to gain unauthorized access - or execute malicious commands on the system. If confirmed malicious, this could - lead to unauthorized code execution, privilege escalation, or further compromise - of the affected environment. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=pc-app.exe - `process_cmd` OR `process_powershell` OR Processes.process_name=java.exe by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_papercut_ng_spawn_shell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present, but most likely not. Filter - as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic detects instances where the PaperCut NG application (pc-app.exe) spawns a Windows shell, such as cmd.exe or PowerShell. This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on process creation events where the parent process is pc-app.exe. This activity is significant as it may indicate an attacker attempting to gain unauthorized access or execute malicious commands on the system. If confirmed malicious, this could lead to unauthorized code execution, privilege escalation, or further compromise of the affected environment. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name=pc-app.exe `process_cmd` + OR + `process_powershell` + OR + Processes.process_name=java.exe + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_papercut_ng_spawn_shell_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present, but most likely not. Filter as needed. references: -- https://www.cisa.gov/news-events/alerts/2023/05/11/cisa-and-fbi-release-joint-advisory-response-active-exploitation-papercut-vulnerability -- https://www.papercut.com/kb/Main/PO-1216-and-PO-1219 + - https://www.cisa.gov/news-events/alerts/2023/05/11/cisa-and-fbi-release-joint-advisory-response-active-exploitation-papercut-vulnerability + - https://www.papercut.com/kb/Main/PO-1216-and-PO-1219 drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The PaperCut NG application has spawned a shell $process_name$ on endpoint - $dest$ by $user$. - risk_objects: - - field: user - type: user - score: 90 - - field: dest - type: system - score: 90 - threat_objects: - - field: process_name - type: process_name + message: The PaperCut NG application has spawned a shell $process_name$ on endpoint $dest$ by $user$. + risk_objects: + - field: user + type: user + score: 90 + - field: dest + type: system + score: 90 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - PaperCut MF NG Vulnerability - - Compromised Windows Host - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1059 - - T1190 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - PaperCut MF NG Vulnerability + - Compromised Windows Host + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1059 + - T1190 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/papercut/papercutng-app-spawn_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/papercut/papercutng-app-spawn_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_parent_pid_spoofing_with_explorer.yml b/detections/endpoint/windows_parent_pid_spoofing_with_explorer.yml index 1c5c08723f..4e552a8032 100644 --- a/detections/endpoint/windows_parent_pid_spoofing_with_explorer.yml +++ b/detections/endpoint/windows_parent_pid_spoofing_with_explorer.yml @@ -1,78 +1,64 @@ name: Windows Parent PID Spoofing with Explorer id: 17f8f69c-5d00-4c88-9c6f-493bbdef20a1 -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic identifies a suspicious `explorer.exe` process - with the `/root` command-line parameter. This detection leverages Endpoint Detection - and Response (EDR) telemetry, focusing on process and command-line data. The presence - of `/root` in `explorer.exe` is significant as it may indicate parent process spoofing, - a technique used by malware to evade detection. If confirmed malicious, this activity - could allow an attacker to operate undetected, potentially leading to unauthorized - access, privilege escalation, or persistent threats within the environment. -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*explorer.exe*" - Processes.process="*/root,*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_parent_pid_spoofing_with_explorer_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic identifies a suspicious `explorer.exe` process with the `/root` command-line parameter. This detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on process and command-line data. The presence of `/root` in `explorer.exe` is significant as it may indicate parent process spoofing, a technique used by malware to evade detection. If confirmed malicious, this activity could allow an attacker to operate undetected, potentially leading to unauthorized access, privilege escalation, or persistent threats within the environment. +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*explorer.exe*" Processes.process="*/root,*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_parent_pid_spoofing_with_explorer_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://x.com/CyberRaiju/status/1273597319322058752?s=20 + - https://x.com/CyberRaiju/status/1273597319322058752?s=20 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An explorer.exe process with process commandline $process$ on dest $dest$ - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: An explorer.exe process with process commandline $process$ on dest $dest$ + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - Windows Defense Evasion Tactics - asset_type: Endpoint - mitre_attack_id: - - T1134.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - Windows Defense Evasion Tactics + asset_type: Endpoint + mitre_attack_id: + - T1134.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1134/explorer_root_proc_cmdline/explorer_root.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1134/explorer_root_proc_cmdline/explorer_root.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_password_managers_discovery.yml b/detections/endpoint/windows_password_managers_discovery.yml index 74c2bd5284..5951122543 100644 --- a/detections/endpoint/windows_password_managers_discovery.yml +++ b/detections/endpoint/windows_password_managers_discovery.yml @@ -1,86 +1,72 @@ name: Windows Password Managers Discovery id: a3b3bc96-1c4f-4eba-8218-027cac739a48 -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies command-line activity that searches - for files related to password manager software, such as "*.kdbx*" and "*credential*". - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on - process execution logs. This activity is significant because attackers often target - password manager databases to extract stored credentials, which can be used for - further exploitation. If confirmed malicious, this behavior could lead to unauthorized - access to sensitive information, enabling attackers to escalate privileges, move - laterally, or exfiltrate critical data. +description: The following analytic identifies command-line activity that searches for files related to password manager software, such as "*.kdbx*" and "*credential*". It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This activity is significant because attackers often target password manager databases to extract stored credentials, which can be used for further exploitation. If confirmed malicious, this behavior could lead to unauthorized access to sensitive information, enabling attackers to escalate privileges, move laterally, or exfiltrate critical data. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process = "*dir *" - OR Processes.process = "*findstr*" AND Processes.process IN ( "*.kdbx*", "*credential*", - "*key3.db*","*pass*", "*cred*", "*key4.db*", "*accessTokens*", "*access_tokens*", - "*.htpasswd*", "*Ntds.dit*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_password_managers_discovery_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process = "*dir *" + OR + Processes.process = "*findstr*" + AND + Processes.process IN ( "*.kdbx*", "*credential*", "*key3.db*","*pass*", "*cred*", "*key4.db*", "*accessTokens*", "*access_tokens*", "*.htpasswd*", "*Ntds.dit*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_password_managers_discovery_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://attack.mitre.org/techniques/T1555/005/ -- https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS -- https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ + - https://attack.mitre.org/techniques/T1555/005/ + - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS + - https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a process with commandline $process$ that can retrieve information related - to password manager databases on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: a process with commandline $process$ that can retrieve information related to password manager databases on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Windows Post-Exploitation - - Prestige Ransomware - - Scattered Spider - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1555.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Post-Exploitation + - Prestige Ransomware + - Scattered Spider + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1555.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/winpeas_search_pwd_db/dir-db-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/winpeas_search_pwd_db/dir-db-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_password_policy_discovery_with_net.yml b/detections/endpoint/windows_password_policy_discovery_with_net.yml index f509f97584..9828f84d56 100644 --- a/detections/endpoint/windows_password_policy_discovery_with_net.yml +++ b/detections/endpoint/windows_password_policy_discovery_with_net.yml @@ -1,59 +1,51 @@ name: Windows Password Policy Discovery with Net id: e52f7865-be78-46bf-b7ed-150fbe447613 -version: 3 -date: '2025-05-02' +version: 4 +date: '2026-02-25' author: Teoderick Contreras, Mauricio Velazco, Nasreddine Bencherchali, Splunk status: production type: Hunting -description: The following analytic identifies the execution of `net.exe` with command - line arguments aimed at obtaining the computer or domain password policy. It leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process names - and command-line executions. This activity is significant as it indicates potential - reconnaissance efforts by adversaries to gather information about Active Directory - password policies. If confirmed malicious, this behavior could allow attackers to - understand password complexity requirements, aiding in brute-force or password-guessing - attacks, ultimately compromising user accounts and gaining unauthorized access to - the network. +description: The following analytic identifies the execution of `net.exe` with command line arguments aimed at obtaining the computer or domain password policy. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential reconnaissance efforts by adversaries to gather information about Active Directory password policies. If confirmed malicious, this behavior could allow attackers to understand password complexity requirements, aiding in brute-force or password-guessing attacks, ultimately compromising user accounts and gaining unauthorized access to the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_net` AND Processes.process - = "*accounts*" AND NOT Processes.process IN ("*/FORCELOGOFF*", "*/MINPWLEN*", "*/MAXPWAGE*", - "*/MINPWAGE*", "*/UNIQUEPW*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_password_policy_discovery_with_net_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_net` + AND + Processes.process = "*accounts*" + AND + NOT Processes.process IN ("*/FORCELOGOFF*", "*/MINPWLEN*", "*/MAXPWAGE*", "*/MINPWAGE*", "*/UNIQUEPW*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_password_policy_discovery_with_net_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://github.com/S1ckB0y1337/Active-Directory-Exploitation-Cheat-Sheet + - https://github.com/S1ckB0y1337/Active-Directory-Exploitation-Cheat-Sheet tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1201 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1201 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/pwd_policy_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/pwd_policy_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_phishing_outlook_drop_dll_in_form_dir.yml b/detections/endpoint/windows_phishing_outlook_drop_dll_in_form_dir.yml index 41f68fb076..ab39994940 100644 --- a/detections/endpoint/windows_phishing_outlook_drop_dll_in_form_dir.yml +++ b/detections/endpoint/windows_phishing_outlook_drop_dll_in_form_dir.yml @@ -4,74 +4,47 @@ version: 8 date: '2026-01-14' author: Teoderick Contreras, Splunk data_source: -- Sysmon EventID 1 AND Sysmon EventID 11 + - Sysmon EventID 1 AND Sysmon EventID 11 type: TTP status: production -description: The following analytic detects the creation of a DLL file by an outlook.exe - process in the AppData\Local\Microsoft\FORMS directory. This detection leverages - data from the Endpoint.Processes and Endpoint.Filesystem datamodels, focusing on - process and file creation events. This activity is significant as it may indicate - an attempt to exploit CVE-2024-21378, where a custom MAPI form loads a potentially - malicious DLL. If confirmed malicious, this could allow an attacker to execute arbitrary - code, leading to further system compromise or data exfiltration. -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes - where Processes.process_name=outlook.exe by _time span=1h Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | join process_guid, _time [ | tstats `security_content_summariesonly` count min(_time) - as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name - ="*.dll" Filesystem.file_path = "*\\AppData\\Local\\Microsoft\\FORMS\\IPM*" by _time - span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path - Filesystem.process_guid | `drop_dm_object_name(Filesystem)` | fields file_name file_path - process_name process_path process dest file_create_time _time process_guid] | `windows_phishing_outlook_drop_dll_in_form_dir_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. +description: The following analytic detects the creation of a DLL file by an outlook.exe process in the AppData\Local\Microsoft\FORMS directory. This detection leverages data from the Endpoint.Processes and Endpoint.Filesystem datamodels, focusing on process and file creation events. This activity is significant as it may indicate an attempt to exploit CVE-2024-21378, where a custom MAPI form loads a potentially malicious DLL. If confirmed malicious, this could allow an attacker to execute arbitrary code, leading to further system compromise or data exfiltration. +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=outlook.exe by _time span=1h Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | join process_guid, _time [ | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name ="*.dll" Filesystem.file_path = "*\\AppData\\Local\\Microsoft\\FORMS\\IPM*" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.process_guid | `drop_dm_object_name(Filesystem)` | fields file_name file_path process_name process_path process dest file_create_time _time process_guid] | `windows_phishing_outlook_drop_dll_in_form_dir_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. known_false_positives: No false positives have been identified at this time. references: -- https://www.netspi.com/blog/technical/red-team-operations/microsoft-outlook-remote-code-execution-cve-2024-21378/ + - https://www.netspi.com/blog/technical/red-team-operations/microsoft-outlook-remote-code-execution-cve-2024-21378/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: an outlook process dropped dll file into $file_path$ on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: an outlook process dropped dll file into $file_path$ on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Outlook RCE CVE-2024-21378 - asset_type: Endpoint - mitre_attack_id: - - T1566 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: - - CVE-2024-21378 + analytic_story: + - Outlook RCE CVE-2024-21378 + asset_type: Endpoint + mitre_attack_id: + - T1566 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: + - CVE-2024-21378 tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/outlook_dropped_dll/outlook_phishing_form_dll.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/outlook_dropped_dll/outlook_phishing_form_dll.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_phishing_pdf_file_executes_url_link.yml b/detections/endpoint/windows_phishing_pdf_file_executes_url_link.yml index e6bdd1bbdf..6575e709da 100644 --- a/detections/endpoint/windows_phishing_pdf_file_executes_url_link.yml +++ b/detections/endpoint/windows_phishing_pdf_file_executes_url_link.yml @@ -1,81 +1,64 @@ name: Windows Phishing PDF File Executes URL Link id: 2fa9dec8-9d8e-46d3-96c1-202c06f0e6e1 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects suspicious PDF viewer processes spawning - browser application child processes. It leverages data from Endpoint Detection and - Response (EDR) agents, focusing on process and parent process names. This activity - is significant as it may indicate a PDF spear-phishing attempt where a malicious - URL link is executed, leading to potential payload download. If confirmed malicious, - this could allow attackers to execute code, escalate privileges, or persist in the - environment by exploiting the user's browser to connect to a malicious site. +description: The following analytic detects suspicious PDF viewer processes spawning browser application child processes. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process names. This activity is significant as it may indicate a PDF spear-phishing attempt where a malicious URL link is executed, leading to potential payload download. If confirmed malicious, this could allow attackers to execute code, escalate privileges, or persist in the environment by exploiting the user's browser to connect to a malicious site. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN - ("AcroRd32.exe", "FoxitPDFReader.exe") Processes.process_name IN ("firefox.exe", - "chrome.exe", "iexplore.exe") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product |`drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_phishing_pdf_file_executes_url_link_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives in PDF file opened PDF Viewer having legitimate - URL link, however filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name IN ("AcroRd32.exe", "FoxitPDFReader.exe") Processes.process_name IN ("firefox.exe", "chrome.exe", "iexplore.exe") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_phishing_pdf_file_executes_url_link_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives in PDF file opened PDF Viewer having legitimate URL link, however filter as needed. references: -- https://twitter.com/pr0xylife/status/1615382907446767616?s=20 + - https://twitter.com/pr0xylife/status/1615382907446767616?s=20 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a pdf file opened in pdf viewer process $parent_process_name$ has a child - process of a browser $process_name$ on $dest$ - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: a pdf file opened in pdf viewer process $parent_process_name$ has a child process of a browser $process_name$ on $dest$ + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Spearphishing Attachments - - Snake Keylogger - asset_type: Endpoint - mitre_attack_id: - - T1566.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Spearphishing Attachments + - Snake Keylogger + asset_type: Endpoint + mitre_attack_id: + - T1566.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/phishing_pdf_uri/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/phishing_pdf_uri/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_phishing_recent_iso_exec_registry.yml b/detections/endpoint/windows_phishing_recent_iso_exec_registry.yml index f27d79343c..7bd146f6df 100644 --- a/detections/endpoint/windows_phishing_recent_iso_exec_registry.yml +++ b/detections/endpoint/windows_phishing_recent_iso_exec_registry.yml @@ -5,58 +5,38 @@ date: '2025-07-30' author: Teoderick Contreras, Bhavin Patel, Splunk status: production type: Hunting -description: The following analytic detects the creation of registry artifacts when - an ISO container is opened, clicked, or mounted on a Windows operating system. It - leverages data from the Endpoint.Registry data model, specifically monitoring registry - keys related to recent ISO or IMG file executions. This activity is significant - as adversaries increasingly use container-based phishing campaigns to bypass macro-based - document execution controls. If confirmed malicious, this behavior could indicate - an initial access attempt, potentially leading to further exploitation, persistence, - or data exfiltration within the environment. +description: The following analytic detects the creation of registry artifacts when an ISO container is opened, clicked, or mounted on a Windows operating system. It leverages data from the Endpoint.Registry data model, specifically monitoring registry keys related to recent ISO or IMG file executions. This activity is significant as adversaries increasingly use container-based phishing campaigns to bypass macro-based document execution controls. If confirmed malicious, this behavior could indicate an initial access attempt, potentially leading to further exploitation, persistence, or data exfiltration within the environment. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\RecentDocs\\.iso*" - OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\RecentDocs\\.img*" - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_phishing_recent_iso_exec_registry_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: False positives may be high depending on the environment and - consistent use of ISOs. Restrict to servers, or filter out based on commonly used - ISO names. Filter as needed. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\RecentDocs\\.iso*" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\RecentDocs\\.img*" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_phishing_recent_iso_exec_registry_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: False positives may be high depending on the environment and consistent use of ISOs. Restrict to servers, or filter out based on commonly used ISO names. Filter as needed. references: -- https://www.microsoft.com/security/blog/2021/05/27/new-sophisticated-email-based-attack-from-nobelium/ -- https://unit42.paloaltonetworks.com/brute-ratel-c4-tool/ -- https://isc.sans.edu/diary/Recent+AZORult+activity/25120 -- https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html + - https://www.microsoft.com/security/blog/2021/05/27/new-sophisticated-email-based-attack-from-nobelium/ + - https://unit42.paloaltonetworks.com/brute-ratel-c4-tool/ + - https://isc.sans.edu/diary/Recent+AZORult+activity/25120 + - https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html tags: - analytic_story: - - Brute Ratel C4 - - AgentTesla - - Qakbot - - IcedID - - Azorult - - Remcos - - Warzone RAT - - Gozi Malware - asset_type: Endpoint - mitre_attack_id: - - T1566.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Brute Ratel C4 + - AgentTesla + - Qakbot + - IcedID + - Azorult + - Remcos + - Warzone RAT + - Gozi Malware + asset_type: Endpoint + mitre_attack_id: + - T1566.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/iso_version_dll_campaign/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/iso_version_dll_campaign/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_possible_credential_dumping.yml b/detections/endpoint/windows_possible_credential_dumping.yml index bdb81be8ae..64fed81108 100644 --- a/detections/endpoint/windows_possible_credential_dumping.yml +++ b/detections/endpoint/windows_possible_credential_dumping.yml @@ -5,87 +5,60 @@ date: '2025-10-14' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects potential credential dumping by identifying - specific GrantedAccess permission requests and CallTrace DLLs targeting the LSASS - process. It leverages Sysmon EventCode 10 logs, focusing on access requests to lsass.exe - and call traces involving debug and native API DLLs like dbgcore.dll, dbghelp.dll, - and ntdll.dll. This activity is significant as credential dumping can lead to unauthorized - access to sensitive credentials. If confirmed malicious, attackers could gain elevated - privileges and persist within the environment, posing a severe security risk. +description: The following analytic detects potential credential dumping by identifying specific GrantedAccess permission requests and CallTrace DLLs targeting the LSASS process. It leverages Sysmon EventCode 10 logs, focusing on access requests to lsass.exe and call traces involving debug and native API DLLs like dbgcore.dll, dbghelp.dll, and ntdll.dll. This activity is significant as credential dumping can lead to unauthorized access to sensitive credentials. If confirmed malicious, attackers could gain elevated privileges and persist within the environment, posing a severe security risk. data_source: -- Sysmon EventID 10 -search: '`sysmon` EventCode=10 TargetImage=*\\lsass.exe granted_access IN ("0x01000", - "0x1010", "0x1038", "0x40", "0x1400", "0x1fffff", "0x1410", "0x143a", "0x1438", - "0x1000") CallTrace IN ("*dbgcore.dll*", "*dbghelp.dll*", "*ntdll.dll*", "*kernelbase.dll*", - "*kernel32.dll*") NOT SourceUser IN ("NT AUTHORITY\\SYSTEM", "NT AUTHORITY\\NETWORK - SERVICE") | stats count min(_time) as firstTime max(_time) as lastTime by CallTrace - EventID GrantedAccess Guid Opcode ProcessID SecurityID SourceImage SourceProcessGUID - SourceProcessId TargetImage TargetProcessGUID TargetProcessId UserID dest granted_access - parent_process_exec parent_process_guid parent_process_id parent_process_name parent_process_path - process_exec process_guid process_id process_name process_path signature signature_id - user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_possible_credential_dumping_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. Enabling EventCode 10 TargetProcess lsass.exe is required. -known_false_positives: False positives will occur based on GrantedAccess 0x1010 and - 0x1400, filter based on source image as needed or remove them. Concern is Cobalt - Strike usage of Mimikatz will generate 0x1010 initially, but later be caught. + - Sysmon EventID 10 +search: '`sysmon` EventCode=10 TargetImage=*\\lsass.exe granted_access IN ("0x01000", "0x1010", "0x1038", "0x40", "0x1400", "0x1fffff", "0x1410", "0x143a", "0x1438", "0x1000") CallTrace IN ("*dbgcore.dll*", "*dbghelp.dll*", "*ntdll.dll*", "*kernelbase.dll*", "*kernel32.dll*") NOT SourceUser IN ("NT AUTHORITY\\SYSTEM", "NT AUTHORITY\\NETWORK SERVICE") | stats count min(_time) as firstTime max(_time) as lastTime by CallTrace EventID GrantedAccess Guid Opcode ProcessID SecurityID SourceImage SourceProcessGUID SourceProcessId TargetImage TargetProcessGUID TargetProcessId UserID dest granted_access parent_process_exec parent_process_guid parent_process_id parent_process_name parent_process_path process_exec process_guid process_id process_name process_path signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_possible_credential_dumping_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Enabling EventCode 10 TargetProcess lsass.exe is required. +known_false_positives: False positives will occur based on GrantedAccess 0x1010 and 0x1400, filter based on source image as needed or remove them. Concern is Cobalt Strike usage of Mimikatz will generate 0x1010 initially, but later be caught. references: -- https://en.wikipedia.org/wiki/Local_Security_Authority_Subsystem_Service -- https://docs.microsoft.com/en-us/windows/win32/api/minidumpapiset/nf-minidumpapiset-minidumpwritedump -- https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for_22.html -- https://raw.githubusercontent.com/PowerShellMafia/PowerSploit/master/Exfiltration/Invoke-Mimikatz.ps1 -- https://docs.microsoft.com/en-us/windows/win32/procthread/process-security-and-access-rights?redirectedfrom=MSDN -- https://github.com/redcanaryco/AtomicTestHarnesses/blob/master/Windows/TestHarnesses/T1003.001_DumpLSASS/DumpLSASS.ps1 + - https://en.wikipedia.org/wiki/Local_Security_Authority_Subsystem_Service + - https://docs.microsoft.com/en-us/windows/win32/api/minidumpapiset/nf-minidumpapiset-minidumpwritedump + - https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for_22.html + - https://raw.githubusercontent.com/PowerShellMafia/PowerSploit/master/Exfiltration/Invoke-Mimikatz.ps1 + - https://docs.microsoft.com/en-us/windows/win32/procthread/process-security-and-access-rights?redirectedfrom=MSDN + - https://github.com/redcanaryco/AtomicTestHarnesses/blob/master/Windows/TestHarnesses/T1003.001_DumpLSASS/DumpLSASS.ps1 drilldown_searches: -- name: View the detection results for - "$user_id$" and "$dest$" - search: '%original_detection_search% | search user_id = "$user_id$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user_id$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user_id$" and "$dest$" + search: '%original_detection_search% | search user_id = "$user_id$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user_id$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process, $SourceImage$, has loaded $TargetImage$ that are typically related - to credential dumping on $dest$. Review for further details. - risk_objects: - - field: user_id - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: - - field: SourceImage - type: process + message: A process, $SourceImage$, has loaded $TargetImage$ that are typically related to credential dumping on $dest$. Review for further details. + risk_objects: + - field: user_id + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: + - field: SourceImage + type: process tags: - analytic_story: - - Detect Zerologon Attack - - CISA AA22-264A - - Credential Dumping - - CISA AA23-347A - - DarkSide Ransomware - - CISA AA22-257A - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1003.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Detect Zerologon Attack + - CISA AA22-264A + - Credential Dumping + - CISA AA23-347A + - DarkSide Ransomware + - CISA AA22-257A + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1003.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon_creddump.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon_creddump.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_post_exploitation_risk_behavior.yml b/detections/endpoint/windows_post_exploitation_risk_behavior.yml index d017c6c13d..a9231a1c00 100644 --- a/detections/endpoint/windows_post_exploitation_risk_behavior.yml +++ b/detections/endpoint/windows_post_exploitation_risk_behavior.yml @@ -1,75 +1,55 @@ name: Windows Post Exploitation Risk Behavior id: edb930df-64c2-4bb7-9b5c-889ed53fb973 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Correlation data_source: [] -description: The following analytic identifies four or more distinct post-exploitation - behaviors on a Windows system. It leverages data from the Risk data model in Splunk - Enterprise Security, focusing on multiple risk events and their associated MITRE - ATT&CK tactics and techniques. This activity is significant as it indicates potential - malicious actions following an initial compromise, such as persistence, privilege - escalation, or data exfiltration. If confirmed malicious, this behavior could allow - attackers to maintain control, escalate privileges, and further exploit the compromised - environment, leading to significant security breaches and data loss. -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) - as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as - annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) - as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) - as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) - as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, - dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories - IN ("*Windows Post-Exploitation*") by All_Risk.risk_object All_Risk.risk_object_type - All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where - source_count >= 4 | `windows_post_exploitation_risk_behavior_filter`' -how_to_implement: Splunk Enterprise Security is required to utilize this correlation. - In addition, modify the source_count value to your environment. In our testing, - a count of 4 or 5 was decent in a lab, but the number may need to be increased base - on internal testing. In addition, based on false positives, modify any analytics - to be anomaly and lower or increase risk based on organization importance. -known_false_positives: False positives will be present based on many factors. Tune - the correlation as needed to reduce too many triggers. +description: The following analytic identifies four or more distinct post-exploitation behaviors on a Windows system. It leverages data from the Risk data model in Splunk Enterprise Security, focusing on multiple risk events and their associated MITRE ATT&CK tactics and techniques. This activity is significant as it indicates potential malicious actions following an initial compromise, such as persistence, privilege escalation, or data exfiltration. If confirmed malicious, this behavior could allow attackers to maintain control, escalate privileges, and further exploit the compromised environment, leading to significant security breaches and data loss. +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count FROM datamodel=Risk.All_Risk + WHERE All_Risk.analyticstories IN ("*Windows Post-Exploitation*") + BY All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic + | `drop_dm_object_name(All_Risk)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | where source_count >= 4 + | `windows_post_exploitation_risk_behavior_filter` +how_to_implement: Splunk Enterprise Security is required to utilize this correlation. In addition, modify the source_count value to your environment. In our testing, a count of 4 or 5 was decent in a lab, but the number may need to be increased base on internal testing. In addition, based on false positives, modify any analytics to be anomaly and lower or increase risk based on organization importance. +known_false_positives: False positives will be present based on many factors. Tune the correlation as needed to reduce too many triggers. references: -- https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS/winPEASbat + - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS/winPEASbat drilldown_searches: -- name: View the detection results for - "$risk_object$" - search: '%original_detection_search% | search risk_object = "$risk_object$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$risk_object$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$risk_object$" + search: '%original_detection_search% | search risk_object = "$risk_object$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$risk_object$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ tags: - analytic_story: - - Windows Post-Exploitation - asset_type: Endpoint - mitre_attack_id: - - T1012 - - T1049 - - T1069 - - T1016 - - T1003 - - T1082 - - T1115 - - T1552 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Post-Exploitation + asset_type: Endpoint + mitre_attack_id: + - T1012 + - T1049 + - T1069 + - T1016 + - T1003 + - T1082 + - T1115 + - T1552 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552/windows_post_exploitation/windows_post_exploitation_risk.log - source: wpe - sourcetype: stash + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552/windows_post_exploitation/windows_post_exploitation_risk.log + source: wpe + sourcetype: stash diff --git a/detections/endpoint/windows_potential_appdomainmanager_hijack_artifacts_creation.yml b/detections/endpoint/windows_potential_appdomainmanager_hijack_artifacts_creation.yml index fe7ff383cb..b69d9df315 100644 --- a/detections/endpoint/windows_potential_appdomainmanager_hijack_artifacts_creation.yml +++ b/detections/endpoint/windows_potential_appdomainmanager_hijack_artifacts_creation.yml @@ -1,94 +1,85 @@ name: Windows Potential AppDomainManager Hijack Artifacts Creation id: be19b369-fd0c-42be-ae97-c10b6c01638f -version: 1 -date: '2025-12-10' +version: 2 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: The following analytic detects the creation of an .exe file along with its corresponding .exe.config and a .dll in the same directory, which is a common pattern indicative of potential AppDomain hijacking or CLR code injection attempts. This behavior may signal that a malicious actor is attempting to load a rogue assembly into a legitimate application's AppDomain, allowing code execution under the context of a trusted process. data_source: -- Sysmon EventID 11 + - Sysmon EventID 11 search: | - | tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time) AS lastTime from datamodel=Endpoint.Filesystem - where Filesystem.file_name IN ("*.exe", "*.exe.config", "*.dll") AND Filesystem.file_path IN - ("*\\windows\\fonts\\*", "*\\temp\\*", "*\\users\\public\\*", "*\\windows\\debug\\*","*\\Users\\Administrator\\Music\\*", "*\\Windows\\servicing\\*", "*\\Users\\Default\\*", "*Recycle.bin*", "*\\Windows\\Media\\*", "*\\Windows\\repair\\*", "*\\PerfLogs\\*") - AND Filesystem.action = "created" - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product - | `drop_dm_object_name("Filesystem")` - | stats values(file_name) AS file_name - values(file_path) AS file_path - values(user) AS user - min(firstTime) AS firstTime max(lastTime) AS lastTime - BY dest process_guid - | eval exe_present = if(mvcount(mvfilter(match(file_name, "\.exe$"))) > 0, 1, 0) - | eval config_present = if(mvcount(mvfilter(match(file_name, "\.exe\.config$"))) > 0, 1, 0) - | eval dll_present = if(mvcount(mvfilter(match(file_name, "\.dll$"))) > 0, 1, 0) + | tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time) AS lastTime from datamodel=Endpoint.Filesystem + where Filesystem.file_name IN ("*.exe", "*.exe.config", "*.dll") AND Filesystem.file_path IN + ("*\\windows\\fonts\\*", "*\\temp\\*", "*\\users\\public\\*", "*\\windows\\debug\\*","*\\Users\\Administrator\\Music\\*", "*\\Windows\\servicing\\*", "*\\Users\\Default\\*", "*Recycle.bin*", "*\\Windows\\Media\\*", "*\\Windows\\repair\\*", "*\\PerfLogs\\*") + AND Filesystem.action = "created" + by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name("Filesystem")` + | stats values(file_name) AS file_name + values(file_path) AS file_path + values(user) AS user + min(firstTime) AS firstTime max(lastTime) AS lastTime + BY dest process_guid + | eval exe_present = if(mvcount(mvfilter(match(file_name, "\.exe$"))) > 0, 1, 0) + | eval config_present = if(mvcount(mvfilter(match(file_name, "\.exe\.config$"))) > 0, 1, 0) + | eval dll_present = if(mvcount(mvfilter(match(file_name, "\.dll$"))) > 0, 1, 0) - | eval exe_files = mvfilter(match(file_name, "\.exe$") AND NOT match(file_name, "\.exe\.config$")) - | eval config_files = mvfilter(match(file_name, "\.exe\.config$")) - | eval exe_base_names = mvmap(exe_files, replace(exe_files, "\.exe$", "")) - | eval config_base_names = mvmap(config_files, replace(config_files, "\.exe\.config$", "")) - - | mvexpand exe_base_names - | mvexpand config_base_names + | eval exe_files = mvfilter(match(file_name, "\.exe$") AND NOT match(file_name, "\.exe\.config$")) + | eval config_files = mvfilter(match(file_name, "\.exe\.config$")) + | eval exe_base_names = mvmap(exe_files, replace(exe_files, "\.exe$", "")) + | eval config_base_names = mvmap(config_files, replace(config_files, "\.exe\.config$", "")) - | eval file_count = mvcount(file_name) - - | where file_count >= 3 AND exe_present = 1 AND config_present = 1 AND dll_present = 1 AND exe_base_names = config_base_names - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_potential_appdomainmanager_hijack_artifacts_creation_filter` -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. + | mvexpand exe_base_names + | mvexpand config_base_names + + | eval file_count = mvcount(file_name) + + | where file_count >= 3 AND exe_present = 1 AND config_present = 1 AND dll_present = 1 AND exe_base_names = config_base_names + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_potential_appdomainmanager_hijack_artifacts_creation_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. known_false_positives: This detection may still produce false positives, so additional filtering is recommended. To validate potential alerts, verify that the executable’s original file name matches its current file name, and also review the associated .config file to confirm which DLLs are expected to load during execution. This helps distinguish legitimate activity from suspicious behavior. references: -- https://www.microsoft.com/en-us/security/blog/2025/11/03/sesameop-novel-backdoor-uses-openai-assistants-api-for-command-and-control/ -- https://attack.mitre.org/techniques/T1574/014/ -- https://gist.github.com/djhohnstein/afb93a114b848e16facf0b98cd7cb57b -- https://www.scworld.com/brief/appdomain-manager-injection-exploited-for-cobalt-strike-beacon-delivery -- https://jp.security.ntt/insights_resources/tech_blog/appdomainmanager-injection-en/ + - https://www.microsoft.com/en-us/security/blog/2025/11/03/sesameop-novel-backdoor-uses-openai-assistants-api-for-command-and-control/ + - https://attack.mitre.org/techniques/T1574/014/ + - https://gist.github.com/djhohnstein/afb93a114b848e16facf0b98cd7cb57b + - https://www.scworld.com/brief/appdomain-manager-injection-exploited-for-cobalt-strike-beacon-delivery + - https://jp.security.ntt/insights_resources/tech_blog/appdomainmanager-injection-en/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential Windows AppDomainManager hijack artifact files created on [$dest$] - risk_objects: - - field: dest - type: system - score: 20 - threat_objects: - - field: file_name - type: file_name - - field: file_path - type: file_path + message: Potential Windows AppDomainManager hijack artifact files created on [$dest$] + risk_objects: + - field: dest + type: system + score: 20 + threat_objects: + - field: file_name + type: file_name + - field: file_path + type: file_path tags: - analytic_story: - - SesameOp - asset_type: Endpoint - mitre_attack_id: - - T1574.014 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SesameOp + asset_type: Endpoint + mitre_attack_id: + - T1574.014 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.014/appdomain_hijack_artifacts/appdomain_hijack.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.014/appdomain_hijack_artifacts/appdomain_hijack.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_powershell_add_module_to_global_assembly_cache.yml b/detections/endpoint/windows_powershell_add_module_to_global_assembly_cache.yml index 440351935d..9bb992d663 100644 --- a/detections/endpoint/windows_powershell_add_module_to_global_assembly_cache.yml +++ b/detections/endpoint/windows_powershell_add_module_to_global_assembly_cache.yml @@ -1,75 +1,60 @@ name: Windows PowerShell Add Module to Global Assembly Cache id: 3fc16961-97e5-4a5b-a079-e4ab0d9763eb -version: 8 -date: '2025-06-24' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: - The following analytic detects the addition of a DLL to the Windows Global - Assembly Cache (GAC) using PowerShell. It leverages PowerShell Script Block Logging - to identify commands containing "system.enterpriseservices.internal.publish". This - activity is significant because adding a DLL to the GAC allows it to be shared across - multiple applications, potentially enabling an adversary to execute malicious code - system-wide. If confirmed malicious, this could lead to widespread code execution, - privilege escalation, and persistent access across the operating system, posing - a severe security risk. +description: The following analytic detects the addition of a DLL to the Windows Global Assembly Cache (GAC) using PowerShell. It leverages PowerShell Script Block Logging to identify commands containing "system.enterpriseservices.internal.publish". This activity is significant because adding a DLL to the GAC allows it to be shared across multiple applications, potentially enabling an adversary to execute malicious code system-wide. If confirmed malicious, this could lead to widespread code execution, privilege escalation, and persistent access across the operating system, posing a severe security risk. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText IN("*system.enterpriseservices.internal.publish*") - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_powershell_add_module_to_global_assembly_cache_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - False positives may be present based on developers or third - party utilities adding items to the GAC. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText IN("*system.enterpriseservices.internal.publish*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_powershell_add_module_to_global_assembly_cache_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: False positives may be present based on developers or third party utilities adding items to the GAC. references: - - https://www.microsoft.com/en-us/security/blog/2022/12/12/iis-modules-the-evolution-of-web-shells-and-how-to-detect-them/ - - https://www.microsoft.com/en-us/security/blog/2022/07/26/malicious-iis-extensions-quietly-open-persistent-backdoors-into-servers/ + - https://www.microsoft.com/en-us/security/blog/2022/12/12/iis-modules-the-evolution-of-web-shells-and-how-to-detect-them/ + - https://www.microsoft.com/en-us/security/blog/2022/07/26/malicious-iis-extensions-quietly-open-persistent-backdoors-into-servers/ drilldown_searches: - - name: View the detection results for - "$Computer$" - search: '%original_detection_search% | search Computer = "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$Computer$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" + search: '%original_detection_search% | search Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - PowerShell was used to install a module to the Global Assembly Cache on - $dest$. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: PowerShell was used to install a module to the Global Assembly Cache on $dest$. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - IIS Components - asset_type: Endpoint - mitre_attack_id: - - T1505.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - IIS Components + asset_type: Endpoint + mitre_attack_id: + - T1505.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/pwsh_publish_powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/pwsh_publish_powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_powershell_cryptography_namespace.yml b/detections/endpoint/windows_powershell_cryptography_namespace.yml index b280136545..b6901fb583 100644 --- a/detections/endpoint/windows_powershell_cryptography_namespace.yml +++ b/detections/endpoint/windows_powershell_cryptography_namespace.yml @@ -1,77 +1,63 @@ name: Windows Powershell Cryptography Namespace id: f8b482f4-6d62-49fa-a905-dfa15698317b -version: 10 -date: '2025-06-24' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: - The following analytic detects suspicious PowerShell script execution - involving the cryptography namespace via EventCode 4104. It leverages PowerShell - Script Block Logging to identify scripts using cryptographic functions, excluding - common hashes like SHA and MD5. This activity is significant as it is often associated - with malware that decrypts or decodes additional malicious payloads. If confirmed - malicious, this could allow an attacker to execute further code, escalate privileges, - or establish persistence within the environment. Analysts should investigate the - parent process, decrypted data, network connections, and the user executing the - script. +description: The following analytic detects suspicious PowerShell script execution involving the cryptography namespace via EventCode 4104. It leverages PowerShell Script Block Logging to identify scripts using cryptographic functions, excluding common hashes like SHA and MD5. This activity is significant as it is often associated with malware that decrypts or decodes additional malicious payloads. If confirmed malicious, this could allow an attacker to execute further code, escalate privileges, or establish persistence within the environment. Analysts should investigate the parent process, decrypted data, network connections, and the user executing the script. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText = "*System.Security.Cryptography*" - AND NOT(ScriptBlockText IN ("*SHA*", "*MD5*", "*DeriveBytes*")) | fillnull | stats - count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_cryptography_namespace_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*System.Security.Cryptography*" AND NOT(ScriptBlockText IN ("*SHA*", "*MD5*", "*DeriveBytes*")) + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_powershell_cryptography_namespace_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. known_false_positives: False positives should be limited. Filter as needed. references: - - https://malpedia.caad.fkie.fraunhofer.de/details/win.asyncrat + - https://malpedia.caad.fkie.fraunhofer.de/details/win.asyncrat drilldown_searches: - - name: View the detection results for - "$dest$" and "$user_id$" - search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user_id$" + search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A suspicious powershell script contains cryptography command detected on - host $dest$ - risk_objects: - - field: dest - type: system - score: 25 - - field: user_id - type: user - score: 25 - threat_objects: [] + message: A suspicious powershell script contains cryptography command detected on host $dest$ + risk_objects: + - field: dest + type: system + score: 25 + - field: user_id + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - AsyncRAT - - XWorm - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AsyncRAT + - XWorm + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/asyncrat_crypto_pwh_namespace/windows-powershell-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/asyncrat_crypto_pwh_namespace/windows-powershell-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_powershell_disable_http_logging.yml b/detections/endpoint/windows_powershell_disable_http_logging.yml index ff733a9141..05e760c555 100644 --- a/detections/endpoint/windows_powershell_disable_http_logging.yml +++ b/detections/endpoint/windows_powershell_disable_http_logging.yml @@ -1,80 +1,64 @@ name: Windows PowerShell Disable HTTP Logging id: 27958de0-2857-43ca-9d4c-b255cf59dcab -version: 8 -date: '2025-06-24' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: - The following analytic detects the use of `get-WebConfigurationProperty` - and `Set-ItemProperty` commands in PowerShell to disable HTTP logging on Windows - systems. This detection leverages PowerShell Script Block Logging, specifically - looking for script blocks that reference HTTP logging properties and attempt to - set them to "false" or "dontLog". Disabling HTTP logging is significant as it can - be used by adversaries to cover their tracks and delete logs, hindering forensic - investigations. If confirmed malicious, this activity could allow attackers to evade - detection and persist in the environment undetected. +description: The following analytic detects the use of `get-WebConfigurationProperty` and `Set-ItemProperty` commands in PowerShell to disable HTTP logging on Windows systems. This detection leverages PowerShell Script Block Logging, specifically looking for script blocks that reference HTTP logging properties and attempt to set them to "false" or "dontLog". Disabling HTTP logging is significant as it can be used by adversaries to cover their tracks and delete logs, hindering forensic investigations. If confirmed malicious, this activity could allow attackers to evade detection and persist in the environment undetected. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText IN("*get-WebConfigurationProperty*","*Set-ItemProperty*") - AND ScriptBlockText IN ("*httpLogging*","*Logfile.enabled*") AND ScriptBlockText - IN ("*dontLog*", "*false*") | fillnull | stats count min(_time) as firstTime max(_time) - as lastTime by dest signature signature_id user_id vendor_product EventID Guid Opcode - Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_powershell_disable_http_logging_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - It is possible administrators or scripts may run these commands, - filtering may be required. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText IN("*get-WebConfigurationProperty*","*Set-ItemProperty*") AND ScriptBlockText IN ("*httpLogging*","*Logfile.enabled*") AND ScriptBlockText IN ("*dontLog*", "*false*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_powershell_disable_http_logging_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: It is possible administrators or scripts may run these commands, filtering may be required. references: - - https://www.microsoft.com/en-us/security/blog/2022/12/12/iis-modules-the-evolution-of-web-shells-and-how-to-detect-them/ - - https://www.crowdstrike.com/wp-content/uploads/2022/05/crowdstrike-iceapple-a-novel-internet-information-services-post-exploitation-framework-1.pdf - - https://unit42.paloaltonetworks.com/unit42-oilrig-uses-rgdoor-iis-backdoor-targets-middle-east/ - - https://www.secureworks.com/research/bronze-union + - https://www.microsoft.com/en-us/security/blog/2022/12/12/iis-modules-the-evolution-of-web-shells-and-how-to-detect-them/ + - https://www.crowdstrike.com/wp-content/uploads/2022/05/crowdstrike-iceapple-a-novel-internet-information-services-post-exploitation-framework-1.pdf + - https://unit42.paloaltonetworks.com/unit42-oilrig-uses-rgdoor-iis-backdoor-targets-middle-east/ + - https://www.secureworks.com/research/bronze-union drilldown_searches: - - name: View the detection results for - "$Computer$" - search: '%original_detection_search% | search Computer = "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$Computer$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" + search: '%original_detection_search% | search Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A PowerShell Cmdlet related to disable or modifying a IIS HTTP logging - has occurred on $dest$. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A PowerShell Cmdlet related to disable or modifying a IIS HTTP logging has occurred on $dest$. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - IIS Components - - Windows Defense Evasion Tactics - asset_type: Endpoint - mitre_attack_id: - - T1505.004 - - T1562.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - IIS Components + - Windows Defense Evasion Tactics + asset_type: Endpoint + mitre_attack_id: + - T1505.004 + - T1562.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/4104_disable_http_logging_windows-powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/4104_disable_http_logging_windows-powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_powershell_export_certificate.yml b/detections/endpoint/windows_powershell_export_certificate.yml index 35d0cc62b1..aaa36f57e3 100644 --- a/detections/endpoint/windows_powershell_export_certificate.yml +++ b/detections/endpoint/windows_powershell_export_certificate.yml @@ -1,76 +1,62 @@ name: Windows PowerShell Export Certificate id: 5e38ded4-c964-41f4-8cb6-4a1a53c6929f -version: 8 -date: '2025-06-24' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: - The following analytic detects the use of the PowerShell Cmdlet `export-certificate` - by leveraging Script Block Logging. This activity is significant as it may indicate - an adversary attempting to exfiltrate certificates from the local Certificate Store - on a Windows endpoint. Monitoring this behavior is crucial because stolen certificates - can be used to impersonate users, decrypt sensitive data, or facilitate further - attacks. If confirmed malicious, this activity could lead to unauthorized access - to encrypted communications and sensitive information, posing a severe security - risk. +description: The following analytic detects the use of the PowerShell Cmdlet `export-certificate` by leveraging Script Block Logging. This activity is significant as it may indicate an adversary attempting to exfiltrate certificates from the local Certificate Store on a Windows endpoint. Monitoring this behavior is crucial because stolen certificates can be used to impersonate users, decrypt sensitive data, or facilitate further attacks. If confirmed malicious, this activity could lead to unauthorized access to encrypted communications and sensitive information, posing a severe security risk. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText IN ("*export-certificate*") | - rename Computer as dest | fillnull | stats count min(_time) as firstTime max(_time) - as lastTime by dest signature signature_id user_id vendor_product EventID Guid Opcode - Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_powershell_export_certificate_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - It is possible administrators or scripts may run these commands, - filtering may be required. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText IN ("*export-certificate*") + | rename Computer as dest + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_powershell_export_certificate_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: It is possible administrators or scripts may run these commands, filtering may be required. references: - - https://dev.to/iamthecarisma/managing-windows-pfx-certificates-through-powershell-3pj - - https://learn.microsoft.com/en-us/powershell/module/pki/export-certificate?view=windowsserver2022-ps + - https://dev.to/iamthecarisma/managing-windows-pfx-certificates-through-powershell-3pj + - https://learn.microsoft.com/en-us/powershell/module/pki/export-certificate?view=windowsserver2022-ps drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A PowerShell Cmdlet related to exporting a Certificate was ran on $dest$, - attempting to export a certificate. - risk_objects: - - field: dest - type: system - score: 36 - threat_objects: [] + message: A PowerShell Cmdlet related to exporting a Certificate was ran on $dest$, attempting to export a certificate. + risk_objects: + - field: dest + type: system + score: 36 + threat_objects: [] tags: - analytic_story: - - Windows Certificate Services - asset_type: Endpoint - mitre_attack_id: - - T1552.004 - - T1649 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Certificate Services + asset_type: Endpoint + mitre_attack_id: + - T1552.004 + - T1649 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/4104_export_certificate.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/4104_export_certificate.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_powershell_export_pfxcertificate.yml b/detections/endpoint/windows_powershell_export_pfxcertificate.yml index 730e751da8..af10f0f283 100644 --- a/detections/endpoint/windows_powershell_export_pfxcertificate.yml +++ b/detections/endpoint/windows_powershell_export_pfxcertificate.yml @@ -1,77 +1,63 @@ name: Windows PowerShell Export PfxCertificate id: ed06725f-6da6-439f-9dcc-ab30e891297c -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: - The following analytic detects the use of the PowerShell cmdlet `export-pfxcertificate` - by leveraging Script Block Logging. This activity is significant as it may indicate - an adversary attempting to exfiltrate certificates from the Windows Certificate - Store. Monitoring this behavior is crucial for identifying potential certificate - theft, which can lead to unauthorized access and impersonation attacks. If confirmed - malicious, this activity could allow attackers to compromise secure communications, - authenticate as legitimate users, and escalate their privileges within the network. +description: The following analytic detects the use of the PowerShell cmdlet `export-pfxcertificate` by leveraging Script Block Logging. This activity is significant as it may indicate an adversary attempting to exfiltrate certificates from the Windows Certificate Store. Monitoring this behavior is crucial for identifying potential certificate theft, which can lead to unauthorized access and impersonation attacks. If confirmed malicious, this activity could allow attackers to compromise secure communications, authenticate as legitimate users, and escalate their privileges within the network. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText IN ("*export-pfxcertificate*") - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_powershell_export_pfxcertificate_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - It is possible administrators or scripts may run these commands, - filtering may be required. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText IN ("*export-pfxcertificate*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_powershell_export_pfxcertificate_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: It is possible administrators or scripts may run these commands, filtering may be required. references: - - https://dev.to/iamthecarisma/managing-windows-pfx-certificates-through-powershell-3pj - - https://learn.microsoft.com/en-us/powershell/module/pki/export-pfxcertificate?view=windowsserver2022-ps + - https://dev.to/iamthecarisma/managing-windows-pfx-certificates-through-powershell-3pj + - https://learn.microsoft.com/en-us/powershell/module/pki/export-pfxcertificate?view=windowsserver2022-ps drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A PowerShell Cmdlet related to exporting a PFX Certificate was ran on $dest$, - attempting to export a certificate. - risk_objects: - - field: dest - type: system - score: 36 - threat_objects: [] + message: A PowerShell Cmdlet related to exporting a PFX Certificate was ran on $dest$, attempting to export a certificate. + risk_objects: + - field: dest + type: system + score: 36 + threat_objects: [] tags: - analytic_story: - - Scattered Lapsus$ Hunters - - Windows Certificate Services - - Water Gamayun - asset_type: Endpoint - mitre_attack_id: - - T1552.004 - - T1649 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Scattered Lapsus$ Hunters + - Windows Certificate Services + - Water Gamayun + asset_type: Endpoint + mitre_attack_id: + - T1552.004 + - T1649 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/4104_export_pfxcertificate.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/4104_export_pfxcertificate.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_powershell_fakecaptcha_clipboard_execution.yml b/detections/endpoint/windows_powershell_fakecaptcha_clipboard_execution.yml index ca62b17265..85795b43fb 100644 --- a/detections/endpoint/windows_powershell_fakecaptcha_clipboard_execution.yml +++ b/detections/endpoint/windows_powershell_fakecaptcha_clipboard_execution.yml @@ -1,116 +1,101 @@ name: Windows PowerShell FakeCAPTCHA Clipboard Execution id: d81d4d3d-76b5-4f21-ab51-b17d5164c106 -version: 5 -date: '2025-11-20' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP description: | - This detection identifies potential FakeCAPTCHA/ClickFix clipboard hijacking campaigns by looking for PowerShell execution with hidden window parameters and distinctive strings related to fake CAPTCHA verification. These campaigns use social engineering to trick users into pasting malicious PowerShell commands from their clipboard, typically delivering information stealers or remote access trojans. + This detection identifies potential FakeCAPTCHA/ClickFix clipboard hijacking campaigns by looking for PowerShell execution with hidden window parameters and distinctive strings related to fake CAPTCHA verification. These campaigns use social engineering to trick users into pasting malicious PowerShell commands from their clipboard, typically delivering information stealers or remote access trojans. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 - - Cisco Network Visibility Module Flow Data -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Processes where `process_powershell` - AND Processes.process="*-w*h*" - AND ( - (Processes.process IN ("*robot*", "*captcha*", "*verify*", "*security check*", "*complete verification*")) - OR - ( - (Processes.process IN ("*iwr *", "*Invoke-WebRequest*", "*wget *", "*curl *", "*Net.WebClient*", "*DownloadString*", "*[Convert]::FromBase64String*")) - AND - (Processes.process IN ("*iex*", "*Invoke-Expression*")) - AND - (Processes.process IN ("*click*", "*verify*", "*check*", "*human*", "*bot*", "*token*", "*challenge*")) - ) - OR - ( - Processes.process="*clipboard*" - AND Processes.process="*iex*" - AND (Processes.process="*FromBase64String*" OR Processes.process="*decode*") - ) - ) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process - Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id - Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_powershell_fakecaptcha_clipboard_execution_filter`' -how_to_implement: To successfully implement this search, you need to be - ingesting logs with the process name, process path, and command-line - executions from your endpoints. If you are using Sysmon, you must have at - least Sysmon version 6.0.4 with EventID 1 configured. The full command line - arguments are necessary for proper detection. -known_false_positives: Legitimate PowerShell commands that use hidden windows - for automation tasks may trigger this detection. The search specifically looks - for patterns typical of FakeCAPTCHA campaigns. You may need to add additional - exclusions for legitimate administrative activities in your environment by - modifying the filter macro. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 + - Cisco Network Visibility Module Flow Data +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_powershell` + AND + Processes.process="*-w*h*" + AND + ( (Processes.process IN ("*robot*", "*captcha*", "*verify*", "*security check*", "*complete verification*")) + OR + ( (Processes.process IN ("*iwr *", "*Invoke-WebRequest*", "*wget *", "*curl *", "*Net.WebClient*", "*DownloadString*", "*[Convert]::FromBase64String*")) + AND + (Processes.process IN ("*iex*", "*Invoke-Expression*")) + AND + (Processes.process IN ("*click*", "*verify*", "*check*", "*human*", "*bot*", "*token*", "*challenge*")) ) + OR + ( Processes.process="*clipboard*" + AND + Processes.process="*iex*" + AND + (Processes.process="*FromBase64String*" + OR + Processes.process="*decode*") ) ) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_powershell_fakecaptcha_clipboard_execution_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, process path, and command-line executions from your endpoints. If you are using Sysmon, you must have at least Sysmon version 6.0.4 with EventID 1 configured. The full command line arguments are necessary for proper detection. +known_false_positives: Legitimate PowerShell commands that use hidden windows for automation tasks may trigger this detection. The search specifically looks for patterns typical of FakeCAPTCHA campaigns. You may need to add additional exclusions for legitimate administrative activities in your environment by modifying the filter macro. references: - - https://urlhaus.abuse.ch/ - - https://www.proofpoint.com/us/blog/threat-insight/security-brief-clickfix-social-engineering-technique-floods-threat-landscape - - https://reliaquest.com/blog/using-captcha-for-compromise/ - - https://attack.mitre.org/techniques/T1204/001/ - - https://github.com/MHaggis/ClickGrab + - https://urlhaus.abuse.ch/ + - https://www.proofpoint.com/us/blog/threat-insight/security-brief-clickfix-social-engineering-technique-floods-threat-landscape + - https://reliaquest.com/blog/using-captcha-for-compromise/ + - https://attack.mitre.org/techniques/T1204/001/ + - https://github.com/MHaggis/ClickGrab drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A potential FakeCAPTCHA/ClickFix campaign execution was detected on - $dest$ running a PowerShell command with hidden window and suspicious - verification strings typical of social engineering attacks. - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: - - field: process - type: process + message: A potential FakeCAPTCHA/ClickFix campaign execution was detected on $dest$ running a PowerShell command with hidden window and suspicious verification strings typical of social engineering attacks. + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: + - field: process + type: process tags: - analytic_story: - - Scattered Lapsus$ Hunters - - Fake CAPTCHA Campaigns - - Cisco Network Visibility Module Analytics - - Interlock Ransomware - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - - T1204.001 - - T1059.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - Scattered Lapsus$ Hunters + - Fake CAPTCHA Campaigns + - Cisco Network Visibility Module Analytics + - Interlock Ransomware + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + - T1204.001 + - T1059.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: - - name: True Positive Test - Sysmon - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/captcha_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata \ No newline at end of file + - name: True Positive Test - Sysmon + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/captcha_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/windows_powershell_get_ciminstance_remote_computer.yml b/detections/endpoint/windows_powershell_get_ciminstance_remote_computer.yml index 6cc2a492d6..b1b7c08e79 100644 --- a/detections/endpoint/windows_powershell_get_ciminstance_remote_computer.yml +++ b/detections/endpoint/windows_powershell_get_ciminstance_remote_computer.yml @@ -1,74 +1,59 @@ name: Windows PowerShell Get CIMInstance Remote Computer id: d8c972eb-ed84-431a-8869-ca4bd83257d1 -version: 7 -date: '2025-06-24' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk type: Anomaly status: production data_source: - - Powershell Script Block Logging 4104 -description: - The following analytic detects the use of the Get-CimInstance cmdlet - with the -ComputerName parameter, indicating an attempt to retrieve information - from a remote computer. It leverages PowerShell Script Block Logging to identify - this specific command execution. This activity is significant as it may indicate - unauthorized remote access or information gathering by an attacker. If confirmed - malicious, this could allow the attacker to collect sensitive data from remote systems, - potentially leading to further exploitation or lateral movement within the network. -search: - '`powershell` EventCode=4104 ScriptBlockText="*get-ciminstance*" AND ScriptBlockText="*computername*" - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_powershell_get_ciminstance_remote_computer_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - This is meant to be a low risk RBA anomaly analytic or to be - used for hunting. Enable this with a low risk score and let it generate risk in - the risk index. + - Powershell Script Block Logging 4104 +description: The following analytic detects the use of the Get-CimInstance cmdlet with the -ComputerName parameter, indicating an attempt to retrieve information from a remote computer. It leverages PowerShell Script Block Logging to identify this specific command execution. This activity is significant as it may indicate unauthorized remote access or information gathering by an attacker. If confirmed malicious, this could allow the attacker to collect sensitive data from remote systems, potentially leading to further exploitation or lateral movement within the network. +search: |- + `powershell` EventCode=4104 ScriptBlockText="*get-ciminstance*" AND ScriptBlockText="*computername*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_powershell_get_ciminstance_remote_computer_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: This is meant to be a low risk RBA anomaly analytic or to be used for hunting. Enable this with a low risk score and let it generate risk in the risk index. references: - - https://learn.microsoft.com/en-us/powershell/module/cimcmdlets/get-ciminstance?view=powershell-7.3 + - https://learn.microsoft.com/en-us/powershell/module/cimcmdlets/get-ciminstance?view=powershell-7.3 drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A PowerShell Cmdlet Get-CIMInstnace was ran on $dest$, attempting to connect - to a remote host. - risk_objects: - - field: dest - type: system - score: 15 - threat_objects: [] + message: A PowerShell Cmdlet Get-CIMInstnace was ran on $dest$, attempting to connect to a remote host. + risk_objects: + - field: dest + type: system + score: 15 + threat_objects: [] tags: - analytic_story: - - Active Directory Lateral Movement - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/get_ciminstance_windows-powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/get_ciminstance_windows-powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_powershell_history_file_deletion.yml b/detections/endpoint/windows_powershell_history_file_deletion.yml index 567659fce5..9103fd9f02 100644 --- a/detections/endpoint/windows_powershell_history_file_deletion.yml +++ b/detections/endpoint/windows_powershell_history_file_deletion.yml @@ -1,66 +1,59 @@ name: Windows Powershell History File Deletion id: f1369394-48e1-4327-bf6d-14377f4b8687 -version: 3 -date: '2025-06-24' +version: 4 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: The following analytic detects the usage of PowerShell to delete its command history file, which may indicate an attempt to evade detection by removing evidence of executed commands. PowerShell stores command history in ConsoleHost_history.txt under the user’s profile directory. Adversaries or malicious scripts may delete this file using Remove-Item, del, or similar commands. This detection focuses on file deletion events targeting the history file, correlating them with recent PowerShell activity. While legitimate users may occasionally clear history, frequent or automated deletions should be investigated for potential defense evasion or post-exploitation cleanup activities. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText = "*Remove-Item*" ScriptBlockText = "*.HistorySavePath" | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime - by dest signature signature_id user_id vendor_product Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_powershell_history_file_deletion_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - administrators may execute this command that may cause some - false positive. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*Remove-Item*" ScriptBlockText = "*.HistorySavePath" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product Guid + Opcode Name Path + ProcessID ScriptBlockId ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_powershell_history_file_deletion_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: administrators may execute this command that may cause some false positive. references: - - https://www.cisa.gov/news-events/cybersecurity-advisories/aa25-071a + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa25-071a drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A PowerShell related to deleting commandline history file deletion was executed on $dest$. - risk_objects: - - field: dest - type: system - score: 20 - threat_objects: [] + message: A PowerShell related to deleting commandline history file deletion was executed on $dest$. + risk_objects: + - field: dest + type: system + score: 20 + threat_objects: [] tags: - analytic_story: - - Medusa Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1059.003 - - T1070.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Medusa Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1059.003 + - T1070.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.003/ConsoleHost_History_deletion/HistorySavePath_powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.003/ConsoleHost_History_deletion/HistorySavePath_powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_powershell_iis_components_webglobalmodule_usage.yml b/detections/endpoint/windows_powershell_iis_components_webglobalmodule_usage.yml index 4eb08ec13a..c11e527f7f 100644 --- a/detections/endpoint/windows_powershell_iis_components_webglobalmodule_usage.yml +++ b/detections/endpoint/windows_powershell_iis_components_webglobalmodule_usage.yml @@ -1,80 +1,65 @@ name: Windows PowerShell IIS Components WebGlobalModule Usage id: 33fc9f6f-0ce7-4696-924e-a69ec61a3d57 -version: 9 -date: '2025-09-16' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: - The following analytic detects the usage of PowerShell Cmdlets - New-WebGlobalModule, - Enable-WebGlobalModule, and Set-WebGlobalModule, which are used to create, enable, - or modify IIS Modules. This detection leverages PowerShell Script Block Logging, - specifically monitoring EventCode 4104 for these cmdlets. This activity is significant - as adversaries may use these lesser-known cmdlets to manipulate IIS configurations, - similar to AppCmd.exe, potentially bypassing traditional defenses. If confirmed - malicious, this could allow attackers to persist in the environment, manipulate - web server behavior, or escalate privileges. +description: The following analytic detects the usage of PowerShell Cmdlets - New-WebGlobalModule, Enable-WebGlobalModule, and Set-WebGlobalModule, which are used to create, enable, or modify IIS Modules. This detection leverages PowerShell Script Block Logging, specifically monitoring EventCode 4104 for these cmdlets. This activity is significant as adversaries may use these lesser-known cmdlets to manipulate IIS configurations, similar to AppCmd.exe, potentially bypassing traditional defenses. If confirmed malicious, this could allow attackers to persist in the environment, manipulate web server behavior, or escalate privileges. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText IN("*New-WebGlobalModule*","*Enable-WebGlobalModule*","*Set-WebGlobalModule*") - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | - `windows_powershell_iis_components_webglobalmodule_usage_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - It is possible administrators or scripts may run these commands, - filtering may be required. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText IN("*New-WebGlobalModule*","*Enable-WebGlobalModule*","*Set-WebGlobalModule*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_powershell_iis_components_webglobalmodule_usage_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: It is possible administrators or scripts may run these commands, filtering may be required. references: - - https://learn.microsoft.com/en-us/powershell/module/webadministration/new-webglobalmodule?view=windowsserver2022-ps - - https://www.microsoft.com/en-us/security/blog/2022/12/12/iis-modules-the-evolution-of-web-shells-and-how-to-detect-them/ - - https://www.crowdstrike.com/wp-content/uploads/2022/05/crowdstrike-iceapple-a-novel-internet-information-services-post-exploitation-framework-1.pdf - - https://unit42.paloaltonetworks.com/unit42-oilrig-uses-rgdoor-iis-backdoor-targets-middle-east/ - - https://www.secureworks.com/research/bronze-union - - https://github.com/redcanaryco/atomic-red-team/tree/master/atomics/T1505.004 + - https://learn.microsoft.com/en-us/powershell/module/webadministration/new-webglobalmodule?view=windowsserver2022-ps + - https://www.microsoft.com/en-us/security/blog/2022/12/12/iis-modules-the-evolution-of-web-shells-and-how-to-detect-them/ + - https://www.crowdstrike.com/wp-content/uploads/2022/05/crowdstrike-iceapple-a-novel-internet-information-services-post-exploitation-framework-1.pdf + - https://unit42.paloaltonetworks.com/unit42-oilrig-uses-rgdoor-iis-backdoor-targets-middle-east/ + - https://www.secureworks.com/research/bronze-union + - https://github.com/redcanaryco/atomic-red-team/tree/master/atomics/T1505.004 drilldown_searches: - - name: View the detection results for - "$Computer$" - search: '%original_detection_search% | search Computer = "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$Computer$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" + search: '%original_detection_search% | search Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A PowerShell Cmdlet related to enabling, creating or modifying a IIS module - has occurred on $dest$. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A PowerShell Cmdlet related to enabling, creating or modifying a IIS module has occurred on $dest$. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - GhostRedirector IIS Module and Rungan Backdoor - - IIS Components - asset_type: Endpoint - mitre_attack_id: - - T1505.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - GhostRedirector IIS Module and Rungan Backdoor + - IIS Components + asset_type: Endpoint + mitre_attack_id: + - T1505.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/4104_windows-powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/4104_windows-powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_powershell_import_applocker_policy.yml b/detections/endpoint/windows_powershell_import_applocker_policy.yml index 5f67e53101..849fd6c8f3 100644 --- a/detections/endpoint/windows_powershell_import_applocker_policy.yml +++ b/detections/endpoint/windows_powershell_import_applocker_policy.yml @@ -1,78 +1,63 @@ name: Windows Powershell Import Applocker Policy id: 102af98d-0ca3-4aa4-98d6-7ab2b98b955a -version: 8 -date: '2025-06-24' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: - The following analytic detects the import of Windows PowerShell Applocker - cmdlets, specifically identifying the use of "Import-Module Applocker" and "Set-AppLockerPolicy" - with an XML policy. It leverages PowerShell Script Block Logging (EventCode 4104) - to capture and analyze script block text. This activity is significant as it may - indicate an attempt to enforce restrictive Applocker policies, potentially used - by malware like Azorult to disable antivirus products. If confirmed malicious, this - could allow an attacker to bypass security controls, leading to further system compromise - and persistence. +description: The following analytic detects the import of Windows PowerShell Applocker cmdlets, specifically identifying the use of "Import-Module Applocker" and "Set-AppLockerPolicy" with an XML policy. It leverages PowerShell Script Block Logging (EventCode 4104) to capture and analyze script block text. This activity is significant as it may indicate an attempt to enforce restrictive Applocker policies, potentially used by malware like Azorult to disable antivirus products. If confirmed malicious, this could allow an attacker to bypass security controls, leading to further system compromise and persistence. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText="*Import-Module Applocker*" ScriptBlockText="*Set-AppLockerPolicy*" - ScriptBlockText="* -XMLPolicy *" | fillnull | stats count min(_time) as firstTime - max(_time) as lastTime by dest signature signature_id user_id vendor_product EventID - Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_powershell_import_applocker_policy_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - administrators may execute this command that may cause some - false positive. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText="*Import-Module Applocker*" ScriptBlockText="*Set-AppLockerPolicy*" ScriptBlockText="* -XMLPolicy *" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_powershell_import_applocker_policy_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: administrators may execute this command that may cause some false positive. references: - - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ + - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ drilldown_searches: - - name: View the detection results for - "$dest$" and "$user_id$" - search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user_id$" + search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A PowerShell script contains Import Applocker Policy command $ScriptBlockText$ - on host $dest$ - risk_objects: - - field: dest - type: system - score: 49 - - field: user_id - type: user - score: 49 - threat_objects: [] + message: A PowerShell script contains Import Applocker Policy command $ScriptBlockText$ on host $dest$ + risk_objects: + - field: dest + type: system + score: 49 + - field: user_id + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - Azorult - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azorult + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/import_applocker_policy/windows-powershell-xml2.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/import_applocker_policy/windows-powershell-xml2.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_powershell_invoke_restmethod_ip_information_collection.yml b/detections/endpoint/windows_powershell_invoke_restmethod_ip_information_collection.yml index 4ec9f11d64..5cf485ff58 100644 --- a/detections/endpoint/windows_powershell_invoke_restmethod_ip_information_collection.yml +++ b/detections/endpoint/windows_powershell_invoke_restmethod_ip_information_collection.yml @@ -1,77 +1,60 @@ name: Windows PowerShell Invoke-RestMethod IP Information Collection id: 8db47e12-9c3e-4f5a-b0d6-e42a1895cd4f -version: 3 -date: '2025-06-24' +version: 4 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: - The following analytic detects the use of PowerShell's Invoke-RestMethod - cmdlet to collect geolocation data from ipinfo.io or IP address information from - api.ipify.org. This behavior leverages PowerShell Script Block Logging to identify - scripts that gather external IP information and potential geolocation data. This - activity is significant as it may indicate reconnaissance efforts, where threat - actors are attempting to determine the geographical location or network details - of a compromised system. While some legitimate software may use these services, - this pattern is commonly observed in malware and post-exploitation toolkits like - those used by Water Gamayun threat actors. +description: The following analytic detects the use of PowerShell's Invoke-RestMethod cmdlet to collect geolocation data from ipinfo.io or IP address information from api.ipify.org. This behavior leverages PowerShell Script Block Logging to identify scripts that gather external IP information and potential geolocation data. This activity is significant as it may indicate reconnaissance efforts, where threat actors are attempting to determine the geographical location or network details of a compromised system. While some legitimate software may use these services, this pattern is commonly observed in malware and post-exploitation toolkits like those used by Water Gamayun threat actors. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 (ScriptBlockText="*Invoke-RestMethod*" AND (ScriptBlockText="*ipinfo.io*" - OR ScriptBlockText="*api.ipify.org*")) | stats count min(_time) as firstTime max(_time) - as lastTime by dest signature signature_id user_id vendor_product Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_powershell_invoke_restmethod_ip_information_collection_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - Some legitimate applications or administrative scripts may - use these services for IP validation or geolocation. Filter as needed for approved - administrative tools. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 (ScriptBlockText="*Invoke-RestMethod*" AND (ScriptBlockText="*ipinfo.io*" OR ScriptBlockText="*api.ipify.org*")) + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product Guid + Opcode Name Path + ProcessID ScriptBlockId ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_powershell_invoke_restmethod_ip_information_collection_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: Some legitimate applications or administrative scripts may use these services for IP validation or geolocation. Filter as needed for approved administrative tools. references: - - https://securityintelligence.com/posts/new-threat-actor-water-gamayun-targets-telecom-finance/ - - https://www.ncsc.gov.uk/report/weekly-threat-report-12th-april-2024 + - https://securityintelligence.com/posts/new-threat-actor-water-gamayun-targets-telecom-finance/ + - https://www.ncsc.gov.uk/report/weekly-threat-report-12th-april-2024 drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A PowerShell script on $dest$ is collecting external IP or geolocation information using Invoke-RestMethod. - risk_objects: - - field: dest - type: system - score: 45 - threat_objects: [] + message: A PowerShell script on $dest$ is collecting external IP or geolocation information using Invoke-RestMethod. + risk_objects: + - field: dest + type: system + score: 45 + threat_objects: [] tags: - analytic_story: - - Water Gamayun - asset_type: Endpoint - mitre_attack_id: - - T1082 - - T1016 - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Water Gamayun + asset_type: Endpoint + mitre_attack_id: + - T1082 + - T1016 + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/irm_powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/irm_powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_powershell_invoke_sqlcmd_execution.yml b/detections/endpoint/windows_powershell_invoke_sqlcmd_execution.yml index 73867f8ac9..aad6d173e9 100644 --- a/detections/endpoint/windows_powershell_invoke_sqlcmd_execution.yml +++ b/detections/endpoint/windows_powershell_invoke_sqlcmd_execution.yml @@ -7,125 +7,38 @@ status: production type: Hunting description: This detection identifies potentially suspicious usage of Invoke-Sqlcmd PowerShell cmdlet, which can be used for database operations and potential data exfiltration. The detection looks for suspicious parameter combinations and query patterns that may indicate unauthorized database access, data theft, or malicious database operations. Threat actors may prefer using PowerShell Invoke-Sqlcmd over sqlcmd.exe as it provides a more flexible programmatic interface and can better evade detection. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 ScriptBlockText="*invoke-sqlcmd*" - | eval script_lower=lower(ScriptBlockText) - | eval - has_query=case( - match(script_lower, "(?i)-query\\s+"), 1, - match(script_lower, "(?i)-q\\s+"), 1, - true(), 0 - ), - has_input_file=case( - match(script_lower, "(?i)-inputfile\\s+"), 1, - match(script_lower, "(?i)-i\\s+"), 1, - true(), 0 - ), - has_url_input=case( - match(script_lower, "(?i)-inputfile\\s+https?://"), 1, - match(script_lower, "(?i)-i\\s+https?://"), 1, - match(script_lower, "(?i)-inputfile\\s+ftp://"), 1, - match(script_lower, "(?i)-i\\s+ftp://"), 1, - true(), 0 - ), - has_admin_conn=case( - match(script_lower, "(?i)-dedicatedadministratorconnection"), 1, - true(), 0 - ), - has_suspicious_auth=case( - match(script_lower, "(?i)-username\\s+sa\\b"), 1, - match(script_lower, "(?i)-u\\s+sa\\b"), 1, - match(script_lower, "(?i)-username\\s+admin\\b"), 1, - match(script_lower, "(?i)-u\\s+admin\\b"), 1, - true(), 0 - ), - has_suspicious_query=case( - match(script_lower, "(?i)(xp_cmdshell|sp_oacreate|sp_execute_external|openrowset|bulk\\s+insert)"), 1, - match(script_lower, "(?i)(master\\.\\.\\.sysdatabases|msdb\\.\\.\\.backuphistory|sysadmin|securityadmin)"), 1, - match(script_lower, "(?i)(select.*from.*sys\\.|select.*password|dump\\s+database)"), 1, - match(script_lower, "(?i)(sp_addextendedproc|sp_makewebtask|sp_addsrvrolemember)"), 1, - match(script_lower, "(?i)(sp_configure.*show\\s+advanced|reconfigure|enable_xp_cmdshell)"), 1, - match(script_lower, "(?i)(exec.*master\\.dbo\\.|exec.*msdb\\.dbo\\.)"), 1, - match(script_lower, "(?i)(sp_password|sp_control_dbmasterkey_password|sp_dropextendedproc)"), 1, - match(script_lower, "(?i)(powershell|cmd\\.exe|rundll32|regsvr32|certutil)"), 1, - true(), 0 - ), - has_data_exfil=case( - match(script_lower, "(?i)-outputas\\s+(dataset|datatables)"), 1, - match(script_lower, "(?i)-as\\s+(dataset|datatables)"), 1, - match(script_lower, "(?i)(for\\s+xml|for\\s+json)"), 1, - match(script_lower, "(?i)(select.*into.*from|select.*into.*outfile)"), 1, - true(), 0 - ), - has_cert_bypass=case( - match(script_lower, "(?i)-trustservercertificate"), 1, - true(), 0 - ) + - Powershell Script Block Logging 4104 +search: '`powershell` EventCode=4104 ScriptBlockText="*invoke-sqlcmd*" | eval script_lower=lower(ScriptBlockText) | eval has_query=case( match(script_lower, "(?i)-query\\s+"), 1, match(script_lower, "(?i)-q\\s+"), 1, true(), 0 ), has_input_file=case( match(script_lower, "(?i)-inputfile\\s+"), 1, match(script_lower, "(?i)-i\\s+"), 1, true(), 0 ), has_url_input=case( match(script_lower, "(?i)-inputfile\\s+https?://"), 1, match(script_lower, "(?i)-i\\s+https?://"), 1, match(script_lower, "(?i)-inputfile\\s+ftp://"), 1, match(script_lower, "(?i)-i\\s+ftp://"), 1, true(), 0 ), has_admin_conn=case( match(script_lower, "(?i)-dedicatedadministratorconnection"), 1, true(), 0 ), has_suspicious_auth=case( match(script_lower, "(?i)-username\\s+sa\\b"), 1, match(script_lower, "(?i)-u\\s+sa\\b"), 1, match(script_lower, "(?i)-username\\s+admin\\b"), 1, match(script_lower, "(?i)-u\\s+admin\\b"), 1, true(), 0 ), has_suspicious_query=case( match(script_lower, "(?i)(xp_cmdshell|sp_oacreate|sp_execute_external|openrowset|bulk\\s+insert)"), 1, match(script_lower, "(?i)(master\\.\\.\\.sysdatabases|msdb\\.\\.\\.backuphistory|sysadmin|securityadmin)"), 1, match(script_lower, "(?i)(select.*from.*sys\\.|select.*password|dump\\s+database)"), 1, match(script_lower, "(?i)(sp_addextendedproc|sp_makewebtask|sp_addsrvrolemember)"), 1, match(script_lower, "(?i)(sp_configure.*show\\s+advanced|reconfigure|enable_xp_cmdshell)"), 1, match(script_lower, "(?i)(exec.*master\\.dbo\\.|exec.*msdb\\.dbo\\.)"), 1, match(script_lower, "(?i)(sp_password|sp_control_dbmasterkey_password|sp_dropextendedproc)"), 1, match(script_lower, "(?i)(powershell|cmd\\.exe|rundll32|regsvr32|certutil)"), 1, true(), 0 ), has_data_exfil=case( match(script_lower, "(?i)-outputas\\s+(dataset|datatables)"), 1, match(script_lower, "(?i)-as\\s+(dataset|datatables)"), 1, match(script_lower, "(?i)(for\\s+xml|for\\s+json)"), 1, match(script_lower, "(?i)(select.*into.*from|select.*into.*outfile)"), 1, true(), 0 ), has_cert_bypass=case( match(script_lower, "(?i)-trustservercertificate"), 1, true(), 0 ) - | eval risk_score=0 - | eval risk_score=case( - has_suspicious_query=1 AND has_data_exfil=1, risk_score + 90, - has_url_input=1, risk_score + 80, - has_suspicious_query=1, risk_score + 60, - has_data_exfil=1, risk_score + 60, - has_admin_conn=1, risk_score + 50, - has_suspicious_auth=1, risk_score + 40, - has_cert_bypass=1, risk_score + 20, - true(), risk_score - ) + | eval risk_score=0 | eval risk_score=case( has_suspicious_query=1 AND has_data_exfil=1, risk_score + 90, has_url_input=1, risk_score + 80, has_suspicious_query=1, risk_score + 60, has_data_exfil=1, risk_score + 60, has_admin_conn=1, risk_score + 50, has_suspicious_auth=1, risk_score + 40, has_cert_bypass=1, risk_score + 20, true(), risk_score ) - | eval command_type=case( - match(script_lower, "xp_cmdshell"), "xp_cmdshell abuse", - match(script_lower, "https?://"), "Remote file execution", - match(script_lower, "sys\\.server_principals"), "System enumeration", - match(script_lower, "fn_my_permissions"), "Permission enumeration", - match(script_lower, "username\\s+sa\\b"), "SA account usage", - match(script_lower, "show\\s+advanced\\s+options"), "Configuration change attempt", - match(script_lower, "select.*from\\s+customers"), "Large data export", - match(script_lower, "select.*password"), "Sensitive data query", - match(script_lower, "sp_configure.*xp_cmdshell"), "Enable xp_cmdshell", - 1=1, "General database access" - ) + | eval command_type=case( match(script_lower, "xp_cmdshell"), "xp_cmdshell abuse", match(script_lower, "https?://"), "Remote file execution", match(script_lower, "sys\\.server_principals"), "System enumeration", match(script_lower, "fn_my_permissions"), "Permission enumeration", match(script_lower, "username\\s+sa\\b"), "SA account usage", match(script_lower, "show\\s+advanced\\s+options"), "Configuration change attempt", match(script_lower, "select.*from\\s+customers"), "Large data export", match(script_lower, "select.*password"), "Sensitive data query", match(script_lower, "sp_configure.*xp_cmdshell"), "Enable xp_cmdshell", 1=1, "General database access" ) - | eval risk_factors=mvappend( - if(has_suspicious_query=1 AND has_data_exfil=1, "High-risk query with data extraction: ".command_type, null()), - if(has_url_input=1, "Remote file input detected in command", null()), - if(has_suspicious_query=1, "Suspicious SQL query pattern: ".command_type, null()), - if(has_data_exfil=1, "Potential data exfiltration using ".command_type, null()), - if(has_admin_conn=1, "Administrative database connection", null()), - if(has_suspicious_auth=1, "Suspicious authentication method used", null()), - if(has_cert_bypass=1, "Certificate validation bypassed", null()) - ) - | eval risk_message="PowerShell Invoke-Sqlcmd execution with risk factors: ".mvjoin(risk_factors, ", ") + | eval risk_factors=mvappend( if(has_suspicious_query=1 AND has_data_exfil=1, "High-risk query with data extraction: ".command_type, null()), if(has_url_input=1, "Remote file input detected in command", null()), if(has_suspicious_query=1, "Suspicious SQL query pattern: ".command_type, null()), if(has_data_exfil=1, "Potential data exfiltration using ".command_type, null()), if(has_admin_conn=1, "Administrative database connection", null()), if(has_suspicious_auth=1, "Suspicious authentication method used", null()), if(has_cert_bypass=1, "Certificate validation bypassed", null()) ) | eval risk_message="PowerShell Invoke-Sqlcmd execution with risk factors: ".mvjoin(risk_factors, ", ") - | where risk_score >= 30 - | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText UserID Computer risk_message risk_score command_type - | rename Computer as dest, UserID as user - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_powershell_invoke_sqlcmd_execution_filter`' + | where risk_score >= 30 | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText UserID Computer risk_message risk_score command_type | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_invoke_sqlcmd_execution_filter`' how_to_implement: To successfully implement this detection, you need to be ingesting PowerShell logs with Script Block Logging and Module Logging enabled. The detection looks for Invoke-Sqlcmd usage in PowerShell scripts and evaluates the parameters and queries for suspicious patterns. Configure your PowerShell logging to capture script block execution and ensure the logs are mapped to the PowerShell node of the Endpoint data model. The analytic will need to be tuned based on organization specific data. Currently, set to hunting to allow for tuning. Invoke-Sqlcmd is a legitimate tool for database management and scripting tasks within enterprise environments. known_false_positives: Database administrators and developers frequently use Invoke-Sqlcmd as a legitimate tool for various database management tasks. This includes running automated database maintenance scripts, performing ETL (Extract, Transform, Load) processes, executing data migration jobs, implementing database deployment and configuration scripts, and running monitoring and reporting tasks. To effectively manage false positives in your environment, consider implementing several mitigation strategies. First, establish a whitelist of known administrator and service accounts that regularly perform these operations. Second, create exceptions for approved script paths where legitimate database operations typically occur. Additionally, it's important to baseline your environment's normal PowerShell database interaction patterns and implement monitoring for any deviations from these established patterns. Finally, consider adjusting the risk score thresholds based on your specific environment and security requirements to achieve an optimal balance between security and operational efficiency. references: -- https://learn.microsoft.com/en-us/powershell/module/sqlserver/invoke-sqlcmd -- https://attack.mitre.org/techniques/T1059.001/ -- https://attack.mitre.org/techniques/T1059.003/ + - https://learn.microsoft.com/en-us/powershell/module/sqlserver/invoke-sqlcmd + - https://attack.mitre.org/techniques/T1059.001/ + - https://attack.mitre.org/techniques/T1059.003/ tags: - analytic_story: - - SQL Server Abuse - - GhostRedirector IIS Module and Rungan Backdoor - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - - T1059.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SQL Server Abuse + - GhostRedirector IIS Module and Rungan Backdoor + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + - T1059.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/atomic_red_team/invokesqlcmd_powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/atomic_red_team/invokesqlcmd_powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_powershell_logoff_user_via_quser.yml b/detections/endpoint/windows_powershell_logoff_user_via_quser.yml index a3a98743b2..8608156f81 100644 --- a/detections/endpoint/windows_powershell_logoff_user_via_quser.yml +++ b/detections/endpoint/windows_powershell_logoff_user_via_quser.yml @@ -1,66 +1,60 @@ name: Windows Powershell Logoff User via Quser id: 6d70780d-4cfe-4820-bafd-1b43941986b5 -version: 4 -date: '2025-05-02' +version: 5 +date: '2026-02-25' author: Teoderick Contreras, Splunk data_source: -- Powershell Script Block Logging 4104 + - Powershell Script Block Logging 4104 type: Anomaly status: production -description: "The following analytic detects the process of logging off a user through\ - \ the use of the quser and logoff commands. By monitoring for these commands, the\ - \ analytic identifies actions where a user session is forcibly terminated, which\ - \ could be part of an administrative task or a potentially unauthorized access attempt.\ - \ This detection helps identify potential misuse or malicious activity where a user\u2019\ - s access is revoked without proper authorization, providing insight into potential\ - \ security incidents involving account management or session manipulation." -search: '`powershell` EventCode=4104 ScriptBlockText = "*quser*logoff*" | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_logoff_user_via_quser_filter`' -how_to_implement: The following Hunting analytic requires PowerShell operational logs - to be imported. Modify the powershell macro as needed to match the sourcetype or - add index. This analytic is specific to 4104, or PowerShell Script Block Logging. +description: "The following analytic detects the process of logging off a user through the use of the quser and logoff commands. By monitoring for these commands, the analytic identifies actions where a user session is forcibly terminated, which could be part of an administrative task or a potentially unauthorized access attempt. This detection helps identify potential misuse or malicious activity where a user’s access is revoked without proper authorization, providing insight into potential security incidents involving account management or session manipulation." +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*quser*logoff*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_powershell_logoff_user_via_quser_filter` +how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. known_false_positives: Administrators or power users may use this command. references: -- https://devblogs.microsoft.com/scripting/automating-quser-through-powershell/ + - https://devblogs.microsoft.com/scripting/automating-quser-through-powershell/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Powershell process having commandline [$ScriptBlockText$] used to logoff - user on [$dest$]. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Powershell process having commandline [$ScriptBlockText$] used to logoff user on [$dest$]. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Crypto Stealer - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - - T1531 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Crypto Stealer + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + - T1531 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1531/log_off_user/pwh_quser_logoff.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1531/log_off_user/pwh_quser_logoff.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_powershell_msix_package_installation.yml b/detections/endpoint/windows_powershell_msix_package_installation.yml index c9c2bef76d..df1d606fd2 100644 --- a/detections/endpoint/windows_powershell_msix_package_installation.yml +++ b/detections/endpoint/windows_powershell_msix_package_installation.yml @@ -1,76 +1,76 @@ name: Windows PowerShell MSIX Package Installation id: d2f77901-dbfa-42d9-8af7-dcd0f1a50a2f -version: 2 -date: '2025-09-09' +version: 3 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP description: The following analytic detects the execution of PowerShell commands to install unsigned AppX packages using Add-AppxPackage or Add-AppPackage cmdlets with the -AllowUnsigned flag. This detection leverages PowerShell Script Block Logging (EventCode=4104) to capture the full command content. This activity is significant as adversaries may use unsigned AppX packages to install malicious applications, bypass security controls, or establish persistence. If confirmed malicious, this could allow attackers to install unauthorized applications that may contain malware, backdoors, or other malicious components. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText IN("*Add-AppPackage *", "*Add-AppxPackage *") - AND ScriptBlockText IN ("* -AllowUnsigned*") | fillnull | stats count min(_time) as firstTime max(_time) - as lastTime by dest signature signature_id user_id vendor_product EventID Guid Opcode - Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_powershell_msix_package_installation_filter` - | `windows_powershell_msix_package_installation_filter`' + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText IN("*Add-AppPackage *", "*Add-AppxPackage *") AND ScriptBlockText IN ("* -AllowUnsigned*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_powershell_msix_package_installation_filter` + | `windows_powershell_msix_package_installation_filter` how_to_implement: The following analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. Ensure PowerShell Script Block Logging is enabled in your environment to capture the full command content. known_false_positives: | - Installation of unsigned packages for testing purposes by developers or system administrators. - Legitimate software development and testing activities may trigger this detection. - Internal application development teams testing MSIX packages before signing. - System administrators installing custom unsigned applications for business purposes. - Note: The -AllowUnsigned flag is only available on Windows 11 and later versions. + Installation of unsigned packages for testing purposes by developers or system administrators. + Legitimate software development and testing activities may trigger this detection. + Internal application development teams testing MSIX packages before signing. + System administrators installing custom unsigned applications for business purposes. + Note: The -AllowUnsigned flag is only available on Windows 11 and later versions. references: - - https://learn.microsoft.com/en-us/windows/msix/package/unsigned-package - - https://learn.microsoft.com/en-us/windows/msix/desktop/powershell-msix-cmdlets - - https://learn.microsoft.com/en-us/powershell/module/appx/add-appxpackage - - https://twitter.com/WindowsDocs/status/1620078135080325122 - - https://attack.mitre.org/techniques/T1059/001/ - - https://attack.mitre.org/techniques/T1547/001/ - - https://learn.microsoft.com/en-us/powershell/module/appx/add-appxpackage?view=windowsserver2025-ps + - https://learn.microsoft.com/en-us/windows/msix/package/unsigned-package + - https://learn.microsoft.com/en-us/windows/msix/desktop/powershell-msix-cmdlets + - https://learn.microsoft.com/en-us/powershell/module/appx/add-appxpackage + - https://twitter.com/WindowsDocs/status/1620078135080325122 + - https://attack.mitre.org/techniques/T1059/001/ + - https://attack.mitre.org/techniques/T1547/001/ + - https://learn.microsoft.com/en-us/powershell/module/appx/add-appxpackage?view=windowsserver2025-ps drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The user $user_id$ attempted to install an unsigned AppX package on $dest$ using PowerShell. - risk_objects: - - field: dest - type: system - score: 50 - threat_objects: - - field: ScriptBlockText - type: command + message: The user $user_id$ attempted to install an unsigned AppX package on $dest$ using PowerShell. + risk_objects: + - field: dest + type: system + score: 50 + threat_objects: + - field: ScriptBlockText + type: command tags: - analytic_story: - - Malicious PowerShell - - MSIX Package Abuse - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - - T1547.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - Malicious PowerShell + - MSIX Package Abuse + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + - T1547.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.005/msix_unsigned/windows-powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.005/msix_unsigned/windows-powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_powershell_process_implementing_manual_base64_decoder.yml b/detections/endpoint/windows_powershell_process_implementing_manual_base64_decoder.yml index 7abdd53941..e4fe41aa67 100644 --- a/detections/endpoint/windows_powershell_process_implementing_manual_base64_decoder.yml +++ b/detections/endpoint/windows_powershell_process_implementing_manual_base64_decoder.yml @@ -6,99 +6,94 @@ author: Nasreddine Bencherchali status: production type: Anomaly description: | - The following analytic identifies Windows PowerShell processes that implement a manual Base64 decoder. - Threat actors often use Base64 encoding to obfuscate malicious payloads or commands within PowerShell scripts. - By manually decoding Base64 strings, attackers can evade detection mechanisms that look for standard decoding functions like using the "-enc" flag or the "FromBase64String" function. - This detection focuses on PowerShell processes that exhibit characteristics of manual Base64 decoding, such as the presence of specific string manipulation methods and bitwise operations. - Security teams should investigate any instances of such activity, especially if found in conjunction with other suspicious behaviors or on systems that should not be using PowerShell for such tasks. + The following analytic identifies Windows PowerShell processes that implement a manual Base64 decoder. + Threat actors often use Base64 encoding to obfuscate malicious payloads or commands within PowerShell scripts. + By manually decoding Base64 strings, attackers can evade detection mechanisms that look for standard decoding functions like using the "-enc" flag or the "FromBase64String" function. + This detection focuses on PowerShell processes that exhibit characteristics of manual Base64 decoding, such as the presence of specific string manipulation methods and bitwise operations. + Security teams should investigate any instances of such activity, especially if found in conjunction with other suspicious behaviors or on systems that should not be using PowerShell for such tasks. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime - from datamodel=Endpoint.Processes where + from datamodel=Endpoint.Processes where - `process_powershell` - Processes.process = "*ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/*" - Processes.process = "*.Substring(*" - Processes.process = "*.GetString(*" - Processes.process = "*.IndexOf(*" - Processes.process IN ("*-shl*", *-shr*, "*-bxor*", "*-bor*", "*-band*") + `process_powershell` + Processes.process = "*ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/*" + Processes.process = "*.Substring(*" + Processes.process = "*.GetString(*" + Processes.process = "*.IndexOf(*" + Processes.process IN ("*-shl*", *-shr*, "*-bxor*", "*-bor*", "*-band*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id - Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id - Processes.vendor_product + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec + Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id + Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id + Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_powershell_process_implementing_manual_base64_decoder_filter` + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_powershell_process_implementing_manual_base64_decoder_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: | - Legitimate use of PowerShell for decoding Base64 strings by administrators - or power users may trigger this detection. It is essential to review the context - of the PowerShell execution to determine if it is malicious, suspicious or even authorized activity. + Legitimate use of PowerShell for decoding Base64 strings by administrators + or power users may trigger this detection. It is essential to review the context + of the PowerShell execution to determine if it is malicious, suspicious or even authorized activity. references: - - https://www.virustotal.com/gui/file/4b3ab4d9f2332da6b6cd8d9d0f4910a5eb85ac8c969108acb3ad49631812f998/behavior + - https://www.virustotal.com/gui/file/4b3ab4d9f2332da6b6cd8d9d0f4910a5eb85ac8c969108acb3ad49631812f998/behavior drilldown_searches: - - name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $process_name$ executing a manual Base64 decoding routine $process$ was identified on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 30 - threat_objects: - - field: process_name - type: process_name - - field: process - type: process_name + message: An instance of $process_name$ executing a manual Base64 decoding routine $process$ was identified on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 30 + threat_objects: + - field: process_name + type: process_name + - field: process + type: process_name tags: - analytic_story: - - Compromised Windows Host - - Deobfuscate-Decode Files or Information - asset_type: Endpoint - mitre_attack_id: - - T1027.010 - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - Deobfuscate-Decode Files or Information + asset_type: Endpoint + mitre_attack_id: + - T1027.010 + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive - attack_data: - - data: https://github.com/Splunk/attack_data/raw/master/datasets/attack_techniques/T1027.010/manual_b64_decode_pwsh/manual_b64_decode_pwsh.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive + attack_data: + - data: https://github.com/Splunk/attack_data/raw/master/datasets/attack_techniques/T1027.010/manual_b64_decode_pwsh/manual_b64_decode_pwsh.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_powershell_process_with_malicious_string.yml b/detections/endpoint/windows_powershell_process_with_malicious_string.yml index 1fce4a5aca..461015b12e 100644 --- a/detections/endpoint/windows_powershell_process_with_malicious_string.yml +++ b/detections/endpoint/windows_powershell_process_with_malicious_string.yml @@ -1,94 +1,77 @@ name: Windows PowerShell Process With Malicious String id: 5df35d50-e1a3-4a52-a337-92e69d9b1b8a -version: 5 -date: '2026-01-14' +version: 6 +date: '2026-02-25' author: Steven Dick status: production type: TTP -description: The following analytic detects the execution of multiple offensive toolkits - and commands through the process execution datamodel. This method captures commands - given directly to powershell.exe, allowing for the identification of suspicious - activities including several well-known tools used for credential theft, lateral - movement, and persistence. If confirmed malicious, this could lead to unauthorized - access, privilege escalation, and potential compromise of sensitive information - within the environment. +description: The following analytic detects the execution of multiple offensive toolkits and commands through the process execution datamodel. This method captures commands given directly to powershell.exe, allowing for the identification of suspicious activities including several well-known tools used for credential theft, lateral movement, and persistence. If confirmed malicious, this could lead to unauthorized access, privilege escalation, and potential compromise of sensitive information within the environment. data_source: -- Windows Event Log Security 4688 -- Sysmon EventID 1 -- CrowdStrike ProcessRollup2 -search: "| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)\ - \ as lastTime from datamodel=Endpoint.Processes where `process_powershell` \nby\ - \ Processes.action Processes.dest Processes.original_file_name Processes.parent_process\ - \ Processes.parent_process_exec \nProcesses.parent_process_guid Processes.parent_process_id\ - \ Processes.parent_process_name \nProcesses.parent_process_path Processes.process\ - \ Processes.process_exec Processes.process_guid Processes.process_hash \nProcesses.process_id\ - \ Processes.process_integrity_level Processes.process_name Processes.process_path\ - \ \nProcesses.user Processes.user_id Processes.vendor_product \n| `drop_dm_object_name(Processes)`\ - \ \n| `security_content_ctime(firstTime)`\n| `security_content_ctime(lastTime)`\n\ - | lookup malicious_powershell_strings command as process\n| where isnotnull(match)\n\ - | `windows_powershell_process_with_malicious_string_filter`" -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: No false positives have been identified at this time. - commands with overlap. + - Windows Event Log Security 4688 + - Sysmon EventID 1 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_powershell` + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | lookup malicious_powershell_strings command as process + | where isnotnull(match) + | `windows_powershell_process_with_malicious_string_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: No false positives have been identified at this time. commands with overlap. references: -- https://attack.mitre.org/techniques/T1059/001/ -- https://github.com/PowerShellMafia/PowerSploit -- https://github.com/PowerShellEmpire/ -- https://github.com/S3cur3Th1sSh1t/PowerSharpPack + - https://attack.mitre.org/techniques/T1059/001/ + - https://github.com/PowerShellMafia/PowerSploit + - https://github.com/PowerShellEmpire/ + - https://github.com/S3cur3Th1sSh1t/PowerSharpPack drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" AND user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$","$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate PowerShell on $dest$ - search: '| from datamodel:Endpoint.Processes | search dest=$dest|s$ process_name=$process_name$ - "*$match$*"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" AND user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$","$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate PowerShell on $dest$ + search: '| from datamodel:Endpoint.Processes | search dest=$dest|s$ process_name=$process_name$ "*$match$*"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The user $user$ ran a known malicious PowerShell string matching *$match$* - on $dest$ - risk_objects: - - field: user - type: user - score: 70 - - field: dest - type: system - score: 70 - threat_objects: - - field: process_name - type: process_name + message: The user $user$ ran a known malicious PowerShell string matching *$match$* on $dest$ + risk_objects: + - field: user + type: user + score: 70 + - field: dest + type: system + score: 70 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Malicious PowerShell - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Malicious PowerShell + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_powershell_remotesigned_file.yml b/detections/endpoint/windows_powershell_remotesigned_file.yml index 92e3930ac4..064934fa95 100644 --- a/detections/endpoint/windows_powershell_remotesigned_file.yml +++ b/detections/endpoint/windows_powershell_remotesigned_file.yml @@ -1,82 +1,66 @@ name: Windows Powershell RemoteSigned File id: f7f7456b-470d-4a95-9703-698250645ff4 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic identifies the use of the "remotesigned" execution - policy for PowerShell scripts. It leverages data from Endpoint Detection and Response - (EDR) agents, focusing on command-line executions containing "remotesigned" and - "-File". This activity is significant because the "remotesigned" policy allows locally - created scripts to run without restrictions, posing a potential security risk. If - confirmed malicious, an attacker could execute unauthorized scripts, leading to - code execution, privilege escalation, or persistence within the environment. -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_powershell` Processes.process="* - remotesigned *" Processes.process="* -File *" by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_powershell_remotesigned_file_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: It is possible administrators or scripts may run these commands, - filtering may be required. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic identifies the use of the "remotesigned" execution policy for PowerShell scripts. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions containing "remotesigned" and "-File". This activity is significant because the "remotesigned" policy allows locally created scripts to run without restrictions, posing a potential security risk. If confirmed malicious, an attacker could execute unauthorized scripts, leading to code execution, privilege escalation, or persistence within the environment. +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_powershell` Processes.process="* remotesigned *" Processes.process="* -File *" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_powershell_remotesigned_file_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: It is possible administrators or scripts may run these commands, filtering may be required. references: -- https://learn.microsoft.com/en-us/powershell/module/microsoft.powershell.security/set-executionpolicy?view=powershell-7.3 + - https://learn.microsoft.com/en-us/powershell/module/microsoft.powershell.security/set-executionpolicy?view=powershell-7.3 drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A PowerShell commandline with remotesigned policy executed on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - - field: user - type: user - score: 25 - threat_objects: [] + message: A PowerShell commandline with remotesigned policy executed on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + - field: user + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Amadey - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Amadey + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_remotesigned/remotesigned_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_remotesigned/remotesigned_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_powershell_scheduletask.yml b/detections/endpoint/windows_powershell_scheduletask.yml index 1f7a1af8c6..19adc186cc 100644 --- a/detections/endpoint/windows_powershell_scheduletask.yml +++ b/detections/endpoint/windows_powershell_scheduletask.yml @@ -1,87 +1,67 @@ name: Windows PowerShell ScheduleTask id: ddf82fcb-e9ee-40e3-8712-a50b5bf323fc -version: 9 -date: '2025-07-29' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly data_source: - - Powershell Script Block Logging 4104 -description: - The following analytic detects potential malicious activities involving - PowerShell's task scheduling cmdlets. It leverages PowerShell Script Block Logging - (EventCode 4104) to identify unusual or suspicious use of cmdlets like 'New-ScheduledTask' - and 'Set-ScheduledTask'. This activity is significant as attackers often use these - cmdlets for persistence and remote execution of malicious code. If confirmed malicious, - this could allow attackers to maintain access, deliver additional payloads, or execute - ransomware, leading to data theft or other severe impacts. Immediate investigation - and mitigation are crucial to prevent further compromise. -search: - '`powershell` EventCode=4104 ScriptBlockText IN ("*New-ScheduledTask*", "*New-ScheduledTaskAction*", - "*New-ScheduledTaskSettingsSet*", "*New-ScheduledTaskTrigger*", "*Register-ClusteredScheduledTask*", - "*Register-ScheduledTask*", "*Set-ClusteredScheduledTask*", "*Set-ScheduledTask*", - "*Start-ScheduledTask*", "*Enable-ScheduledTask*") | fillnull | stats count min(_time) - as firstTime max(_time) as lastTime by dest signature signature_id user_id vendor_product - EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_powershell_scheduletask_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - Benign administrative tasks can also trigger alerts, necessitating - a firm understanding of the typical system behavior and precise tuning of the analytic - to reduce false positives. + - Powershell Script Block Logging 4104 +description: The following analytic detects potential malicious activities involving PowerShell's task scheduling cmdlets. It leverages PowerShell Script Block Logging (EventCode 4104) to identify unusual or suspicious use of cmdlets like 'New-ScheduledTask' and 'Set-ScheduledTask'. This activity is significant as attackers often use these cmdlets for persistence and remote execution of malicious code. If confirmed malicious, this could allow attackers to maintain access, deliver additional payloads, or execute ransomware, leading to data theft or other severe impacts. Immediate investigation and mitigation are crucial to prevent further compromise. +search: |- + `powershell` EventCode=4104 ScriptBlockText IN ("*New-ScheduledTask*", "*New-ScheduledTaskAction*", "*New-ScheduledTaskSettingsSet*", "*New-ScheduledTaskTrigger*", "*Register-ClusteredScheduledTask*", "*Register-ScheduledTask*", "*Set-ClusteredScheduledTask*", "*Set-ScheduledTask*", "*Start-ScheduledTask*", "*Enable-ScheduledTask*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_powershell_scheduletask_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: Benign administrative tasks can also trigger alerts, necessitating a firm understanding of the typical system behavior and precise tuning of the analytic to reduce false positives. references: - - https://learn.microsoft.com/en-us/powershell/module/scheduledtasks/?view=windowsserver2022-ps - - https://thedfirreport.com/2023/06/12/a-truly-graceful-wipe-out/ + - https://learn.microsoft.com/en-us/powershell/module/scheduledtasks/?view=windowsserver2022-ps + - https://thedfirreport.com/2023/06/12/a-truly-graceful-wipe-out/ drilldown_searches: - - name: View the detection results for - "$Computer$" and "$user_id$" - search: - '%original_detection_search% | search Computer = "$Computer$" user_id = - "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$Computer$" and "$user_id$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" and "$user_id$" + search: '%original_detection_search% | search Computer = "$Computer$" user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" and "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - The PowerShell cmdlets related to task creation, modification and start - occurred on $dest$ by $user_id$. - risk_objects: - - field: dest - type: system - score: 25 - - field: user_id - type: user - score: 25 - threat_objects: [] + message: The PowerShell cmdlets related to task creation, modification and start occurred on $dest$ by $user_id$. + risk_objects: + - field: dest + type: system + score: 25 + - field: user_id + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Scheduled Tasks - - Scattered Spider - asset_type: Endpoint - atomic_guid: - - af9fd58f-c4ac-4bf2-a9ba-224b71ff25fd - mitre_attack_id: - - T1053.005 - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Scheduled Tasks + - Scattered Spider + asset_type: Endpoint + atomic_guid: + - af9fd58f-c4ac-4bf2-a9ba-224b71ff25fd + mitre_attack_id: + - T1053.005 + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/pwsh_scheduledtask.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/pwsh_scheduledtask.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_powershell_script_block_with_malicious_string.yml b/detections/endpoint/windows_powershell_script_block_with_malicious_string.yml index 039e284bf8..1e793fd103 100644 --- a/detections/endpoint/windows_powershell_script_block_with_malicious_string.yml +++ b/detections/endpoint/windows_powershell_script_block_with_malicious_string.yml @@ -5,71 +5,49 @@ date: '2026-01-14' author: Steven Dick status: production type: TTP -description: The following analytic detects the execution of multiple offensive toolkits - and commands by leveraging PowerShell Script Block Logging (EventCode=4104). This - method captures and logs the full command sent to PowerShell, allowing for the identification - of suspicious activities including several well-known tools used for credential - theft, lateral movement, and persistence. If confirmed malicious, this could lead - to unauthorized access, privilege escalation, and potential compromise of sensitive - information within the environment. +description: The following analytic detects the execution of multiple offensive toolkits and commands by leveraging PowerShell Script Block Logging (EventCode=4104). This method captures and logs the full command sent to PowerShell, allowing for the identification of suspicious activities including several well-known tools used for credential theft, lateral movement, and persistence. If confirmed malicious, this could lead to unauthorized access, privilege escalation, and potential compromise of sensitive information within the environment. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` ScriptBlockText=* EventCode=4104 - | stats count min(_time) as firstTime max(_time) as lastTime list(ScriptBlockText) as command values(Guid) as Guid values(Opcode) as Opcode values(Name) as Name values(Path) as Path values(ProcessID) as ProcessID values(ScriptBlockId) as ScriptBlockId values(ScriptBlockText) as ScriptBlockText by dest signature signature_id user_id vendor_product - | eval command = mvjoin(command,"\n") - | lookup malicious_powershell_strings command - | where isnotnull(match) - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_powershell_script_block_with_malicious_string_filter`' -how_to_implement: The following analytic requires PowerShell operational logs to be - imported. Modify the powershell macro as needed to match the sourcetype or add index. - This analytic is specific to 4104, or PowerShell Script Block Logging. -known_false_positives: No false positives have been identified at this time. - commands with overlap. + - Powershell Script Block Logging 4104 +search: '`powershell` ScriptBlockText=* EventCode=4104 | stats count min(_time) as firstTime max(_time) as lastTime list(ScriptBlockText) as command values(Guid) as Guid values(Opcode) as Opcode values(Name) as Name values(Path) as Path values(ProcessID) as ProcessID values(ScriptBlockId) as ScriptBlockId values(ScriptBlockText) as ScriptBlockText by dest signature signature_id user_id vendor_product | eval command = mvjoin(command,"\n") | lookup malicious_powershell_strings command | where isnotnull(match) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_script_block_with_malicious_string_filter`' +how_to_implement: The following analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. +known_false_positives: No false positives have been identified at this time. commands with overlap. references: -- https://attack.mitre.org/techniques/T1059/001/ -- https://github.com/PowerShellMafia/PowerSploit -- https://github.com/PowerShellEmpire/ -- https://github.com/S3cur3Th1sSh1t/PowerSharpPack + - https://attack.mitre.org/techniques/T1059/001/ + - https://github.com/PowerShellMafia/PowerSploit + - https://github.com/PowerShellEmpire/ + - https://github.com/S3cur3Th1sSh1t/PowerSharpPack drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The user $user_id$ ran a known malicious PowerShell string matching *$match$* - on $dest$ - risk_objects: - - field: dest - type: system - score: 70 - threat_objects: - - field: signature_id - type: signature + message: The user $user_id$ ran a known malicious PowerShell string matching *$match$* on $dest$ + risk_objects: + - field: dest + type: system + score: 70 + threat_objects: + - field: signature_id + type: signature tags: - analytic_story: - - Malicious PowerShell - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Malicious PowerShell + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.006/powershell_gpp_discovery/win-powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.006/powershell_gpp_discovery/win-powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_powershell_script_from_windowsapps_directory.yml b/detections/endpoint/windows_powershell_script_from_windowsapps_directory.yml index eae636015b..b336635197 100644 --- a/detections/endpoint/windows_powershell_script_from_windowsapps_directory.yml +++ b/detections/endpoint/windows_powershell_script_from_windowsapps_directory.yml @@ -7,73 +7,56 @@ status: production type: TTP description: The following analytic identifies the execution of PowerShell scripts from the WindowsApps directory, which is a common technique used in malicious MSIX package execution. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process command lines and parent process paths. This activity is significant as adversaries have been observed using MSIX packages with embedded PowerShell scripts (particularly StartingScriptWrapper.ps1) to execute malicious code. If confirmed malicious, this could allow attackers to execute arbitrary code, establish persistence, or deliver malware while evading traditional detection mechanisms. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name="powershell.exe" AND - (Processes.parent_process_path="*\\WindowsApps\\*" OR Processes.process="*WindowsApps*-file *" OR - Processes.process="*WindowsApps*.ps1*") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name("Processes")` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_powershell_script_from_windowsapps_directory_filter`' + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name="powershell.exe" AND (Processes.parent_process_path="*\\WindowsApps\\*" OR Processes.process="*WindowsApps*-file *" OR Processes.process="*WindowsApps*.ps1*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_script_from_windowsapps_directory_filter`' how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain process execution information, including process paths and command lines. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Legitimate applications installed via the Microsoft Store or MSIX packages may execute PowerShell scripts from the WindowsApps directory as part of their normal operation. Verify if the MSIX package is from a trusted source and signed by a trusted publisher before taking action. Look for additional suspicious activities like network connections to unknown domains or execution of known malicious payloads. references: -- https://redcanary.com/blog/threat-intelligence/msix-installers/ -- https://redcanary.com/threat-detection-report/techniques/installer-packages/ -- https://learn.microsoft.com/en-us/windows/msix/package/package-support-framework -- https://attack.mitre.org/techniques/T1059/001/ + - https://redcanary.com/blog/threat-intelligence/msix-installers/ + - https://redcanary.com/threat-detection-report/techniques/installer-packages/ + - https://learn.microsoft.com/en-us/windows/msix/package/package-support-framework + - https://attack.mitre.org/techniques/T1059/001/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Look for specific StartingScriptWrapper.ps1 execution - search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name="powershell.exe" AND Processes.process="*StartingScriptWrapper.ps1*" by Processes.dest Processes.process Processes.parent_process_name' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Look for specific StartingScriptWrapper.ps1 execution + search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name="powershell.exe" AND Processes.process="*StartingScriptWrapper.ps1*" by Processes.dest Processes.process Processes.parent_process_name' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: PowerShell script execution from WindowsApps directory detected on $dest$ by user $user$. This may indicate malicious MSIX package execution. - risk_objects: - - field: dest - type: system - score: 55 - threat_objects: - - field: process - type: command + message: PowerShell script execution from WindowsApps directory detected on $dest$ by user $user$. This may indicate malicious MSIX package execution. + risk_objects: + - field: dest + type: system + score: 55 + threat_objects: + - field: process + type: command tags: - analytic_story: - - MSIX Package Abuse - - Malicious PowerShell - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - - T1204.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - MSIX Package Abuse + - Malicious PowerShell + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + - T1204.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/msix_powershell/windows-sysmon.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/msix_powershell/windows-sysmon.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational diff --git a/detections/endpoint/windows_powershell_wmi_win32_scheduledjob.yml b/detections/endpoint/windows_powershell_wmi_win32_scheduledjob.yml index 9e97f03876..ddc5f1df1e 100644 --- a/detections/endpoint/windows_powershell_wmi_win32_scheduledjob.yml +++ b/detections/endpoint/windows_powershell_wmi_win32_scheduledjob.yml @@ -1,77 +1,60 @@ name: Windows PowerShell WMI Win32 ScheduledJob id: 47c69803-2c09-408b-b40a-063c064cbb16 -version: 8 -date: '2025-06-24' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk type: TTP status: production data_source: - - Powershell Script Block Logging 4104 -description: - The following analytic detects the use of the Win32_ScheduledJob WMI - class via PowerShell script block logging. This class, which manages scheduled tasks, - is disabled by default due to security concerns and must be explicitly enabled through - registry modifications. The detection leverages PowerShell event code 4104 and script - block text analysis. Monitoring this activity is crucial as it may indicate malicious - intent, especially if the class was enabled by an attacker. If confirmed malicious, - this could allow attackers to persist in the environment by creating scheduled tasks. -search: - '`powershell` EventCode=4104 ScriptBlockText="*win32_scheduledjob*" | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_wmi_win32_scheduledjob_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. -known_false_positives: - False positives may be present based on legacy applications - or utilities. Win32_ScheduledJob uses the Remote Procedure Call (RPC) protocol to - create scheduled tasks on remote computers. It uses the DCOM (Distributed Component - Object Model) infrastructure to establish a connection with the remote computer - and invoke the necessary methods. The RPC service needs to be running on both the - local and remote computers for the communication to take place. + - Powershell Script Block Logging 4104 +description: The following analytic detects the use of the Win32_ScheduledJob WMI class via PowerShell script block logging. This class, which manages scheduled tasks, is disabled by default due to security concerns and must be explicitly enabled through registry modifications. The detection leverages PowerShell event code 4104 and script block text analysis. Monitoring this activity is crucial as it may indicate malicious intent, especially if the class was enabled by an attacker. If confirmed malicious, this could allow attackers to persist in the environment by creating scheduled tasks. +search: |- + `powershell` EventCode=4104 ScriptBlockText="*win32_scheduledjob*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_powershell_wmi_win32_scheduledjob_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. +known_false_positives: False positives may be present based on legacy applications or utilities. Win32_ScheduledJob uses the Remote Procedure Call (RPC) protocol to create scheduled tasks on remote computers. It uses the DCOM (Distributed Component Object Model) infrastructure to establish a connection with the remote computer and invoke the necessary methods. The RPC service needs to be running on both the local and remote computers for the communication to take place. references: - - https://securityonline.info/wmiexec-regout-get-outputdata-response-from-registry/ - - https://learn.microsoft.com/en-us/windows/win32/cimwin32prov/win32-scheduledjob + - https://securityonline.info/wmiexec-regout-get-outputdata-response-from-registry/ + - https://learn.microsoft.com/en-us/windows/win32/cimwin32prov/win32-scheduledjob drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - PowerShell attempting to create a task via WMI - Win32_ScheduledJob, was - ran on $dest$. - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: [] + message: PowerShell attempting to create a task via WMI - Win32_ScheduledJob, was ran on $dest$. + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: [] tags: - analytic_story: - - Active Directory Lateral Movement - asset_type: Endpoint - mitre_attack_id: - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + asset_type: Endpoint + mitre_attack_id: + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/win32_scheduledjob_windows-powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/win32_scheduledjob_windows-powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_powersploit_gpp_discovery.yml b/detections/endpoint/windows_powersploit_gpp_discovery.yml index 8e47246c86..e109c8fad8 100644 --- a/detections/endpoint/windows_powersploit_gpp_discovery.yml +++ b/detections/endpoint/windows_powersploit_gpp_discovery.yml @@ -1,78 +1,67 @@ name: Windows PowerSploit GPP Discovery id: 0130a0df-83a1-4647-9011-841e950ff302 -version: 11 -date: '2026-01-14' +version: 12 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP data_source: - - Powershell Script Block Logging 4104 -description: - The following analytic detects the execution of the Get-GPPPassword PowerShell - cmdlet, which is used to search for unsecured credentials in Group Policy Preferences - (GPP). This detection leverages PowerShell Script Block Logging to identify specific - script block text associated with this cmdlet. Monitoring this activity is crucial - as it can indicate an attempt to retrieve and decrypt stored credentials from SYSVOL, - potentially leading to unauthorized access. If confirmed malicious, this activity - could allow an attacker to escalate privileges or move laterally within the network - by exploiting exposed credentials. -search: - '`powershell` EventCode=4104 (ScriptBlockText=Get-GPPPassword OR ScriptBlockText=Get-CachedGPPPassword) - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_powersploit_gpp_discovery_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - Powershell Script Block Logging 4104 +description: The following analytic detects the execution of the Get-GPPPassword PowerShell cmdlet, which is used to search for unsecured credentials in Group Policy Preferences (GPP). This detection leverages PowerShell Script Block Logging to identify specific script block text associated with this cmdlet. Monitoring this activity is crucial as it can indicate an attempt to retrieve and decrypt stored credentials from SYSVOL, potentially leading to unauthorized access. If confirmed malicious, this activity could allow an attacker to escalate privileges or move laterally within the network by exploiting exposed credentials. +search: |- + `powershell` EventCode=4104 (ScriptBlockText=Get-GPPPassword OR ScriptBlockText=Get-CachedGPPPassword) + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_powersploit_gpp_discovery_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. known_false_positives: No false positives have been identified at this time. references: - - https://attack.mitre.org/techniques/T1552/006/ - - https://pentestlab.blog/2017/03/20/group-policy-preferences/ - - https://adsecurity.org/?p=2288 - - https://www.hackingarticles.in/credential-dumping-group-policy-preferences-gpp/ - - https://adsecurity.org/?p=2288 - - https://support.microsoft.com/en-us/topic/ms14-025-vulnerability-in-group-policy-preferences-could-allow-elevation-of-privilege-may-13-2014-60734e15-af79-26ca-ea53-8cd617073c30 + - https://attack.mitre.org/techniques/T1552/006/ + - https://pentestlab.blog/2017/03/20/group-policy-preferences/ + - https://adsecurity.org/?p=2288 + - https://www.hackingarticles.in/credential-dumping-group-policy-preferences-gpp/ + - https://adsecurity.org/?p=2288 + - https://support.microsoft.com/en-us/topic/ms14-025-vulnerability-in-group-policy-preferences-could-allow-elevation-of-privilege-may-13-2014-60734e15-af79-26ca-ea53-8cd617073c30 drilldown_searches: - - name: View the detection results for - "$Computer$" and "$user$" - search: '%original_detection_search% | search Computer = "$Computer$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$Computer$" and "$user$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" and "$user$" + search: '%original_detection_search% | search Computer = "$Computer$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Commandlets leveraged to discover GPP credentials were executed on $dest$ - risk_objects: - - field: dest - type: system - score: 56 - - field: user_id - type: user - score: 56 - threat_objects: [] + message: Commandlets leveraged to discover GPP credentials were executed on $dest$ + risk_objects: + - field: dest + type: system + score: 56 + - field: user_id + type: user + score: 56 + threat_objects: [] tags: - analytic_story: - - Active Directory Privilege Escalation - asset_type: Endpoint - mitre_attack_id: - - T1552.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Privilege Escalation + asset_type: Endpoint + mitre_attack_id: + - T1552.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.006/powershell_gpp_discovery/win-powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.006/powershell_gpp_discovery/win-powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_powerview_ad_access_control_list_enumeration.yml b/detections/endpoint/windows_powerview_ad_access_control_list_enumeration.yml index 586a92b8fd..a584ee5584 100644 --- a/detections/endpoint/windows_powerview_ad_access_control_list_enumeration.yml +++ b/detections/endpoint/windows_powerview_ad_access_control_list_enumeration.yml @@ -1,78 +1,65 @@ name: Windows PowerView AD Access Control List Enumeration id: 39405650-c364-4e1e-a740-32a63ef042a6 -version: 8 -date: '2025-06-24' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP data_source: - - Powershell Script Block Logging 4104 -description: - The following analytic detects the execution of PowerView PowerShell - cmdlets `Get-ObjectAcl` or `Get-DomainObjectAcl`, which are used to enumerate Access - Control List (ACL) permissions for Active Directory objects. It leverages Event - ID 4104 from PowerShell Script Block Logging to identify this activity. This behavior - is significant as it may indicate an attempt to discover weak permissions in Active - Directory, potentially leading to privilege escalation. If confirmed malicious, - attackers could exploit these permissions to gain unauthorized access or escalate - their privileges within the network. -search: - '`powershell` EventCode=4104 (ScriptBlockText=*get-objectacl* OR ScriptBlockText=*Get-DomainObjectAcl*) - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_powerview_ad_access_control_list_enumeration_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba.= -known_false_positives: - Administrators may leverage PowerView for legitimate purposes, - filter as needed. + - Powershell Script Block Logging 4104 +description: The following analytic detects the execution of PowerView PowerShell cmdlets `Get-ObjectAcl` or `Get-DomainObjectAcl`, which are used to enumerate Access Control List (ACL) permissions for Active Directory objects. It leverages Event ID 4104 from PowerShell Script Block Logging to identify this activity. This behavior is significant as it may indicate an attempt to discover weak permissions in Active Directory, potentially leading to privilege escalation. If confirmed malicious, attackers could exploit these permissions to gain unauthorized access or escalate their privileges within the network. +search: |- + `powershell` EventCode=4104 (ScriptBlockText=*get-objectacl* OR ScriptBlockText=*Get-DomainObjectAcl*) + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_powerview_ad_access_control_list_enumeration_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba.= +known_false_positives: Administrators may leverage PowerView for legitimate purposes, filter as needed. references: - - https://attack.mitre.org/techniques/T1078/002/ - - https://medium.com/r3d-buck3t/enumerating-access-controls-in-active-directory-c06e2efa8b89 - - https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/abusing-active-directory-acls-aces - - https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainObjectAcl/ + - https://attack.mitre.org/techniques/T1078/002/ + - https://medium.com/r3d-buck3t/enumerating-access-controls-in-active-directory-c06e2efa8b89 + - https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/abusing-active-directory-acls-aces + - https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainObjectAcl/ drilldown_searches: - - name: View the detection results for - "$Computer$" - search: '%original_detection_search% | search Computer = "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$Computer$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" + search: '%original_detection_search% | search Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: PowerView AD acccess control list enumeration detected on $dest$ - risk_objects: - - field: dest - type: system - score: 20 - threat_objects: [] + message: PowerView AD acccess control list enumeration detected on $dest$ + risk_objects: + - field: dest + type: system + score: 20 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - - Active Directory Privilege Escalation - - Rhysida Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1078.002 - - T1069 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - Active Directory Privilege Escalation + - Rhysida Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1078.002 + - T1069 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/powerview_acl_enumeration/windows-powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/powerview_acl_enumeration/windows-powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_powerview_constrained_delegation_discovery.yml b/detections/endpoint/windows_powerview_constrained_delegation_discovery.yml index 5e67cf580e..12d68ee70c 100644 --- a/detections/endpoint/windows_powerview_constrained_delegation_discovery.yml +++ b/detections/endpoint/windows_powerview_constrained_delegation_discovery.yml @@ -1,78 +1,69 @@ name: Windows PowerView Constrained Delegation Discovery id: 86dc8176-6e6c-42d6-9684-5444c6557ab3 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the use of PowerView commandlets to discover - Windows endpoints with Kerberos Constrained Delegation. It leverages PowerShell - Script Block Logging (EventCode=4104) to identify specific commandlets like `Get-DomainComputer` - or `Get-NetComputer` with the `-TrustedToAuth` parameter. This activity is significant - as it indicates potential reconnaissance efforts by adversaries or Red Teams to - map out privileged delegation settings in Active Directory. If confirmed malicious, - this could allow attackers to identify high-value targets for further exploitation, - potentially leading to privilege escalation or lateral movement within the network. +description: The following analytic detects the use of PowerView commandlets to discover Windows endpoints with Kerberos Constrained Delegation. It leverages PowerShell Script Block Logging (EventCode=4104) to identify specific commandlets like `Get-DomainComputer` or `Get-NetComputer` with the `-TrustedToAuth` parameter. This activity is significant as it indicates potential reconnaissance efforts by adversaries or Red Teams to map out privileged delegation settings in Active Directory. If confirmed malicious, this could allow attackers to identify high-value targets for further exploitation, potentially leading to privilege escalation or lateral movement within the network. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainComputer*" OR - ScriptBlockText = "*Get-NetComputer*") AND (ScriptBlockText = "*-TrustedToAuth*") - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_powerview_constrained_delegation_discovery_filter`' -how_to_implement: The following analytic requires PowerShell operational logs to - be imported. Modify the powershell macro as needed to match the sourcetype or add - index. This analytic is specific to 4104, or PowerShell Script Block Logging. -known_false_positives: Administrators or power users may leverage PowerView for system - management or troubleshooting. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainComputer*" OR ScriptBlockText = "*Get-NetComputer*") AND (ScriptBlockText = "*-TrustedToAuth*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_powerview_constrained_delegation_discovery_filter` +how_to_implement: The following analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. +known_false_positives: Administrators or power users may leverage PowerView for system management or troubleshooting. references: -- https://attack.mitre.org/techniques/T1018/ -- https://adsecurity.org/?p=1667 -- https://docs.microsoft.com/en-us/defender-for-identity/cas-isp-unconstrained-kerberos -- https://www.guidepointsecurity.com/blog/delegating-like-a-boss-abusing-kerberos-delegation-in-active-directory/ -- https://book.hacktricks.xyz/windows-hardening/active-directory-methodology/constrained-delegation -- https://www.cyberark.com/resources/threat-research-blog/weakness-within-kerberos-delegation + - https://attack.mitre.org/techniques/T1018/ + - https://adsecurity.org/?p=1667 + - https://docs.microsoft.com/en-us/defender-for-identity/cas-isp-unconstrained-kerberos + - https://www.guidepointsecurity.com/blog/delegating-like-a-boss-abusing-kerberos-delegation-in-active-directory/ + - https://book.hacktricks.xyz/windows-hardening/active-directory-methodology/constrained-delegation + - https://www.cyberark.com/resources/threat-research-blog/weakness-within-kerberos-delegation drilldown_searches: -- name: View the detection results for - "$dest$" and "$user_id$" - search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user_id$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user_id$" + search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious PowerShell Get-DomainComputer was identified on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 35 - - field: user_id - type: user - score: 35 - threat_objects: [] + message: Suspicious PowerShell Get-DomainComputer was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 35 + - field: user_id + type: user + score: 35 + threat_objects: [] tags: - analytic_story: - - CISA AA23-347A - - Rhysida Ransomware - - Active Directory Kerberos Attacks - asset_type: Endpoint - mitre_attack_id: - - T1018 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA23-347A + - Rhysida Ransomware + - Active Directory Kerberos Attacks + asset_type: Endpoint + mitre_attack_id: + - T1018 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/windows_powerview_constrained_delegation_discovery/windows-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/windows_powerview_constrained_delegation_discovery/windows-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_powerview_kerberos_service_ticket_request.yml b/detections/endpoint/windows_powerview_kerberos_service_ticket_request.yml index 0eb13eb8ee..41187bb353 100644 --- a/detections/endpoint/windows_powerview_kerberos_service_ticket_request.yml +++ b/detections/endpoint/windows_powerview_kerberos_service_ticket_request.yml @@ -1,72 +1,64 @@ name: Windows PowerView Kerberos Service Ticket Request id: 970455a1-4ac2-47e1-a9a5-9e75443ddcb9 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: TTP -description: The following analytic detects the execution of the `Get-DomainSPNTicket` - commandlet, part of the PowerView tool, by leveraging PowerShell Script Block Logging - (EventCode=4104). This commandlet requests Kerberos service tickets for specified - service principal names (SPNs). Monitoring this activity is crucial as it can indicate - attempts to perform Kerberoasting, a technique used to extract SPN account passwords - via cracking tools like hashcat. If confirmed malicious, this activity could allow - attackers to gain unauthorized access to sensitive accounts, potentially leading - to privilege escalation and further network compromise. +description: The following analytic detects the execution of the `Get-DomainSPNTicket` commandlet, part of the PowerView tool, by leveraging PowerShell Script Block Logging (EventCode=4104). This commandlet requests Kerberos service tickets for specified service principal names (SPNs). Monitoring this activity is crucial as it can indicate attempts to perform Kerberoasting, a technique used to extract SPN account passwords via cracking tools like hashcat. If confirmed malicious, this activity could allow attackers to gain unauthorized access to sensitive accounts, potentially leading to privilege escalation and further network compromise. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 ScriptBlockText=*Get-DomainSPNTicket* | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id - user_id vendor_product EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powerview_kerberos_service_ticket_request_filter`' -how_to_implement: The following analytic requires PowerShell operational logs to be - imported. Modify the powershell macro as needed to match the sourcetype or add index. - This analytic is specific to 4104, or PowerShell Script Block Logging. -known_false_positives: False positive may include Administrators using PowerView for - troubleshooting and management. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText=*Get-DomainSPNTicket* + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_powerview_kerberos_service_ticket_request_filter` +how_to_implement: The following analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. +known_false_positives: False positive may include Administrators using PowerView for troubleshooting and management. references: -- https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainSPNTicket/ -- https://book.hacktricks.xyz/windows-hardening/active-directory-methodology/kerberoast -- https://github.com/PowerShellMafia/PowerSploit/blob/master/Recon/PowerView.ps1 -- https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/t1208-kerberoasting -- https://attack.mitre.org/techniques/T1558/003 + - https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainSPNTicket/ + - https://book.hacktricks.xyz/windows-hardening/active-directory-methodology/kerberoast + - https://github.com/PowerShellMafia/PowerSploit/blob/master/Recon/PowerView.ps1 + - https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/t1208-kerberoasting + - https://attack.mitre.org/techniques/T1558/003 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: PowerView commandlets used for requesting SPN service ticket executed on - $dest$ - risk_objects: - - field: dest - type: system - score: 27 - threat_objects: [] + message: PowerView commandlets used for requesting SPN service ticket executed on $dest$ + risk_objects: + - field: dest + type: system + score: 27 + threat_objects: [] tags: - analytic_story: - - Active Directory Kerberos Attacks - - Rhysida Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1558.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Kerberos Attacks + - Rhysida Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1558.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/powerview/windows-powershell-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/powerview/windows-powershell-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_powerview_spn_discovery.yml b/detections/endpoint/windows_powerview_spn_discovery.yml index 25a2fc747f..a176d17a4b 100644 --- a/detections/endpoint/windows_powerview_spn_discovery.yml +++ b/detections/endpoint/windows_powerview_spn_discovery.yml @@ -1,76 +1,65 @@ name: Windows PowerView SPN Discovery id: a7093c28-796c-4ebb-9997-e2c18b870837 -version: 8 -date: '2025-07-28' +version: 9 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: TTP -description: The following analytic detects the execution of the - `Get-DomainUser` or `Get-NetUser` PowerShell cmdlets with the `-SPN` - parameter, indicating the use of PowerView for SPN discovery. It leverages - PowerShell Script Block Logging (EventCode=4104) to identify these specific - commands. This activity is significant as it suggests an attempt to enumerate - domain accounts associated with Service Principal Names (SPNs), a common - precursor to Kerberoasting attacks. If confirmed malicious, this could allow - an attacker to identify and target accounts for credential theft, potentially - leading to unauthorized access and privilege escalation within the network. +description: The following analytic detects the execution of the `Get-DomainUser` or `Get-NetUser` PowerShell cmdlets with the `-SPN` parameter, indicating the use of PowerView for SPN discovery. It leverages PowerShell Script Block Logging (EventCode=4104) to identify these specific commands. This activity is significant as it suggests an attempt to enumerate domain accounts associated with Service Principal Names (SPNs), a common precursor to Kerberoasting attacks. If confirmed malicious, this could allow an attacker to identify and target accounts for credential theft, potentially leading to unauthorized access and privilege escalation within the network. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 (ScriptBlockText =*Get-NetUser* OR ScriptBlockText=*Get-DomainUser*) - ScriptBlockText= *-SPN* | fillnull | stats count min(_time) as firstTime max(_time) - as lastTime by dest signature signature_id user_id vendor_product EventID Guid Opcode - Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`| `windows_powerview_spn_discovery_filter`' -how_to_implement: The following analytic requires PowerShell operational logs to - be imported. Modify the powershell macro as needed to match the sourcetype or - add index. This analytic is specific to 4104, or PowerShell Script Block - Logging. -known_false_positives: False positive may include Administrators using PowerView - for troubleshooting and management. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 (ScriptBlockText =*Get-NetUser* OR ScriptBlockText=*Get-DomainUser*) ScriptBlockText= *-SPN* + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_powerview_spn_discovery_filter` +how_to_implement: The following analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. +known_false_positives: False positive may include Administrators using PowerView for troubleshooting and management. references: -- https://book.hacktricks.xyz/windows-hardening/active-directory-methodology/kerberoast -- https://github.com/PowerShellMafia/PowerSploit/blob/master/Recon/PowerView.ps1 -- https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/t1208-kerberoasting -- https://attack.mitre.org/techniques/T1558/003 + - https://book.hacktricks.xyz/windows-hardening/active-directory-methodology/kerberoast + - https://github.com/PowerShellMafia/PowerSploit/blob/master/Recon/PowerView.ps1 + - https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/t1208-kerberoasting + - https://attack.mitre.org/techniques/T1558/003 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: PowerView commandlets used for SPN discovery executed on $dest$ - risk_objects: - - field: dest - type: system - score: 27 - threat_objects: [] + message: PowerView commandlets used for SPN discovery executed on $dest$ + risk_objects: + - field: dest + type: system + score: 27 + threat_objects: [] tags: - analytic_story: - - CISA AA23-347A - - Rhysida Ransomware - - Active Directory Kerberos Attacks - - Interlock Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1558.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA23-347A + - Rhysida Ransomware + - Active Directory Kerberos Attacks + - Interlock Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1558.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/powerview-2/windows-powershell.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/powerview-2/windows-powershell.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_powerview_unconstrained_delegation_discovery.yml b/detections/endpoint/windows_powerview_unconstrained_delegation_discovery.yml index f302764b2c..3de2fe54f3 100644 --- a/detections/endpoint/windows_powerview_unconstrained_delegation_discovery.yml +++ b/detections/endpoint/windows_powerview_unconstrained_delegation_discovery.yml @@ -1,77 +1,68 @@ name: Windows PowerView Unconstrained Delegation Discovery id: fbf9e47f-e531-4fea-942d-5c95af7ed4d6 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the use of PowerView commandlets to discover - Windows endpoints with Kerberos Unconstrained Delegation. It leverages PowerShell - Script Block Logging (EventCode=4104) to identify specific commands like `Get-DomainComputer` - or `Get-NetComputer` with the `-Unconstrained` parameter. This activity is significant - as it indicates potential reconnaissance efforts by adversaries or Red Teams to - map out privileged delegation settings in Active Directory. If confirmed malicious, - this could allow attackers to identify high-value targets for further exploitation, - potentially leading to privilege escalation or lateral movement within the network. +description: The following analytic detects the use of PowerView commandlets to discover Windows endpoints with Kerberos Unconstrained Delegation. It leverages PowerShell Script Block Logging (EventCode=4104) to identify specific commands like `Get-DomainComputer` or `Get-NetComputer` with the `-Unconstrained` parameter. This activity is significant as it indicates potential reconnaissance efforts by adversaries or Red Teams to map out privileged delegation settings in Active Directory. If confirmed malicious, this could allow attackers to identify high-value targets for further exploitation, potentially leading to privilege escalation or lateral movement within the network. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainComputer*" OR - ScriptBlockText = "*Get-NetComputer*") AND (ScriptBlockText = "*-Unconstrained*") - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_powerview_unconstrained_delegation_discovery_filter`' -how_to_implement: The following analytic requires PowerShell operational logs to - be imported. Modify the powershell macro as needed to match the sourcetype or add - index. This analytic is specific to 4104, or PowerShell Script Block Logging. -known_false_positives: Administrators or power users may leverage PowerView for system - management or troubleshooting. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainComputer*" OR ScriptBlockText = "*Get-NetComputer*") AND (ScriptBlockText = "*-Unconstrained*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_powerview_unconstrained_delegation_discovery_filter` +how_to_implement: The following analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. +known_false_positives: Administrators or power users may leverage PowerView for system management or troubleshooting. references: -- https://attack.mitre.org/techniques/T1018/ -- https://adsecurity.org/?p=1667 -- https://docs.microsoft.com/en-us/defender-for-identity/cas-isp-unconstrained-kerberos -- https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/domain-compromise-via-unrestricted-kerberos-delegation -- https://www.cyberark.com/resources/threat-research-blog/weakness-within-kerberos-delegation + - https://attack.mitre.org/techniques/T1018/ + - https://adsecurity.org/?p=1667 + - https://docs.microsoft.com/en-us/defender-for-identity/cas-isp-unconstrained-kerberos + - https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/domain-compromise-via-unrestricted-kerberos-delegation + - https://www.cyberark.com/resources/threat-research-blog/weakness-within-kerberos-delegation drilldown_searches: -- name: View the detection results for - "$dest$" and "$user_id$" - search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user_id$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user_id$" + search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious PowerShell Get-DomainComputer was identified on endpoint $dest$ - risk_objects: - - field: dest - type: system - score: 35 - - field: user_id - type: user - score: 35 - threat_objects: [] + message: Suspicious PowerShell Get-DomainComputer was identified on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 35 + - field: user_id + type: user + score: 35 + threat_objects: [] tags: - analytic_story: - - CISA AA23-347A - - Rhysida Ransomware - - Active Directory Kerberos Attacks - asset_type: Endpoint - mitre_attack_id: - - T1018 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA23-347A + - Rhysida Ransomware + - Active Directory Kerberos Attacks + asset_type: Endpoint + mitre_attack_id: + - T1018 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/windows_powerview_constrained_delegation_discovery/windows-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/windows_powerview_constrained_delegation_discovery/windows-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_private_keys_discovery.yml b/detections/endpoint/windows_private_keys_discovery.yml index c2a84d9da7..5146e6afa5 100644 --- a/detections/endpoint/windows_private_keys_discovery.yml +++ b/detections/endpoint/windows_private_keys_discovery.yml @@ -1,86 +1,70 @@ name: Windows Private Keys Discovery id: 5c1c2877-06c0-40ee-a1a2-db71f1372b5b -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies processes that retrieve information - related to private key files, often used by post-exploitation tools like winpeas. - This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on command-line executions that search for private key certificates. This - activity is significant as it indicates potential attempts to locate insecurely - stored credentials, which adversaries can exploit for privilege escalation, persistence, - or remote service authentication. If confirmed malicious, this behavior could allow - attackers to access sensitive information, escalate privileges, or maintain persistence - within the compromised environment. +description: The following analytic identifies processes that retrieve information related to private key files, often used by post-exploitation tools like winpeas. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions that search for private key certificates. This activity is significant as it indicates potential attempts to locate insecurely stored credentials, which adversaries can exploit for privilege escalation, persistence, or remote service authentication. If confirmed malicious, this behavior could allow attackers to access sensitive information, escalate privileges, or maintain persistence within the compromised environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process = "*dir *" - OR Processes.process = "*findstr*" AND Processes.process IN ( "*.rdg*", "*.gpg*", - "*.pgp*", "*.p12*", "*.der*", "*.csr*", "*.cer*", "*.ovpn*", "*.key*", "*.ppk*", - "*.p12*", "*.pem*", "*.pfx*", "*.p7b*", "*.asc*") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_private_keys_discovery_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process = "*dir *" + OR + Processes.process = "*findstr*" + AND + Processes.process IN ( "*.rdg*", "*.gpg*", "*.pgp*", "*.p12*", "*.der*", "*.csr*", "*.cer*", "*.ovpn*", "*.key*", "*.ppk*", "*.p12*", "*.pem*", "*.pfx*", "*.p7b*", "*.asc*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_private_keys_discovery_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://attack.mitre.org/techniques/T1552/004/ -- https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS -- https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ + - https://attack.mitre.org/techniques/T1552/004/ + - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS + - https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a process with commandline $process$ that can retrieve information related - to private keys on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: a process with commandline $process$ that can retrieve information related to private keys on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Windows Post-Exploitation - - Prestige Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1552.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Post-Exploitation + - Prestige Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1552.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/winpeas_search_private_key/dir-private-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/winpeas_search_private_key/dir-private-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_privilege_escalation_suspicious_process_elevation.yml b/detections/endpoint/windows_privilege_escalation_suspicious_process_elevation.yml index 9496bc35f2..50f389c919 100644 --- a/detections/endpoint/windows_privilege_escalation_suspicious_process_elevation.yml +++ b/detections/endpoint/windows_privilege_escalation_suspicious_process_elevation.yml @@ -5,101 +5,59 @@ date: '2025-09-16' author: Steven Dick status: production type: TTP -description: The following analytic detects when a process running with low or medium - integrity from a user account spawns an elevated process with high or system integrity - in suspicious locations. This behavior is identified using process execution data - from Windows process monitoring or Sysmon EventID 1. This activity is significant - as it may indicate a threat actor successfully elevating privileges, which is a - common tactic in advanced attacks. If confirmed malicious, this could allow the - attacker to execute code with higher privileges, potentially leading to full system - compromise and persistent access. +description: The following analytic detects when a process running with low or medium integrity from a user account spawns an elevated process with high or system integrity in suspicious locations. This behavior is identified using process execution data from Windows process monitoring or Sysmon EventID 1. This activity is significant as it may indicate a threat actor successfully elevating privileges, which is a common tactic in advanced attacks. If confirmed malicious, this could allow the attacker to execute code with higher privileges, potentially leading to full system compromise and persistent access. data_source: -- Sysmon EventID 1 AND Sysmon EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime from - datamodel=Endpoint.Processes where Processes.process_integrity_level IN ("low","medium","high") - NOT Processes.user IN ("*SYSTEM","*LOCAL SERVICE","*NETWORK SERVICE","DWM-*","*$") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | eval join_guid = process_guid, integrity_level - = CASE(match(process_integrity_level,"low"),1,match(process_integrity_level,"medium"),2,match(process_integrity_level,"high"),3,match(process_integrity_level,"system"),4,true(),0) - | rename user as src_user, parent_process* as orig_parent_process*, process* as - parent_process* | join max=0 dest join_guid [| tstats `security_content_summariesonly` - count max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_integrity_level - IN ("system") NOT Processes.user IN ("*SYSTEM","*LOCAL SERVICE","*NETWORK SERVICE","DWM-*","*$")) - OR (Processes.process_integrity_level IN ("high","system") AND (Processes.parent_process_path - IN ("*\\\\*","*\\Users\\*","*\\Temp\\*","*\\ProgramData\\*") OR Processes.process_path - IN ("*\\\\*","*\\Users\\*","*\\Temp\\*","*\\ProgramData\\*"))) by Processes.dest, - Processes.user, Processes.parent_process_guid, Processes.process_name, Processes.process, - Processes.process_path, Processes.process_integrity_level, Processes.process_current_directory - | `drop_dm_object_name(Processes)` | eval elevated_integrity_level = CASE(match(process_integrity_level,"low"),1,match(process_integrity_level,"medium"),2,match(process_integrity_level,"high"),3,match(process_integrity_level,"system"),4,true(),0) - | rename parent_process_guid as join_guid ] | where elevated_integrity_level > integrity_level - OR user != elevated_user | fields dest, user, src_user, parent_process_name, parent_process, - parent_process_path, parent_process_guid, parent_process_integrity_level, parent_process_current_directory, - process_name, process, process_path, process_guid, process_integrity_level, process_current_directory, - orig_parent_process_name, orig_parent_process, orig_parent_process_guid, firstTime, - lastTime, count | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_privilege_escalation_suspicious_process_elevation_filter`' -how_to_implement: Target environment must ingest process execution data sources such - as Windows process monitoring and/or Sysmon EID 1. -known_false_positives: False positives may be generated by administrators installing - benign applications using run-as/elevation. + - Sysmon EventID 1 AND Sysmon EventID 1 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime from datamodel=Endpoint.Processes where Processes.process_integrity_level IN ("low","medium","high") NOT Processes.user IN ("*SYSTEM","*LOCAL SERVICE","*NETWORK SERVICE","DWM-*","*$") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | eval join_guid = process_guid, integrity_level = CASE(match(process_integrity_level,"low"),1,match(process_integrity_level,"medium"),2,match(process_integrity_level,"high"),3,match(process_integrity_level,"system"),4,true(),0) | rename user as src_user, parent_process* as orig_parent_process*, process* as parent_process* | join max=0 dest join_guid [| tstats `security_content_summariesonly` count max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_integrity_level IN ("system") NOT Processes.user IN ("*SYSTEM","*LOCAL SERVICE","*NETWORK SERVICE","DWM-*","*$")) OR (Processes.process_integrity_level IN ("high","system") AND (Processes.parent_process_path IN ("*\\\\*","*\\Users\\*","*\\Temp\\*","*\\ProgramData\\*") OR Processes.process_path IN ("*\\\\*","*\\Users\\*","*\\Temp\\*","*\\ProgramData\\*"))) by Processes.dest, Processes.user, Processes.parent_process_guid, Processes.process_name, Processes.process, Processes.process_path, Processes.process_integrity_level, Processes.process_current_directory | `drop_dm_object_name(Processes)` | eval elevated_integrity_level = CASE(match(process_integrity_level,"low"),1,match(process_integrity_level,"medium"),2,match(process_integrity_level,"high"),3,match(process_integrity_level,"system"),4,true(),0) | rename parent_process_guid as join_guid ] | where elevated_integrity_level > integrity_level OR user != elevated_user | fields dest, user, src_user, parent_process_name, parent_process, parent_process_path, parent_process_guid, parent_process_integrity_level, parent_process_current_directory, process_name, process, process_path, process_guid, process_integrity_level, process_current_directory, orig_parent_process_name, orig_parent_process, orig_parent_process_guid, firstTime, lastTime, count | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_privilege_escalation_suspicious_process_elevation_filter`' +how_to_implement: Target environment must ingest process execution data sources such as Windows process monitoring and/or Sysmon EID 1. +known_false_positives: False positives may be generated by administrators installing benign applications using run-as/elevation. references: -- https://attack.mitre.org/techniques/T1068/ -- https://vuls.cert.org/confluence/display/Wiki/2021/06/21/Finding+Privilege+Escalation+Vulnerabilities+in+Windows+using+Process+Monitor -- https://redcanary.com/blog/getsystem-offsec/ -- https://atomicredteam.io/privilege-escalation/T1134.001/ + - https://attack.mitre.org/techniques/T1068/ + - https://vuls.cert.org/confluence/display/Wiki/2021/06/21/Finding+Privilege+Escalation+Vulnerabilities+in+Windows+using+Process+Monitor + - https://redcanary.com/blog/getsystem-offsec/ + - https://atomicredteam.io/privilege-escalation/T1134.001/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$" src_user - = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$", "$src_user$") starthoursago=168 | stats count min(_time) as firstTime - max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$" src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$", "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The user $src_user$ launched a process [$parent_process_name$] which spawned - a suspicious elevated integrity process [$process_name$]. - risk_objects: - - field: dest - type: system - score: 40 - - field: user - type: user - score: 40 - - field: src_user - type: user - score: 40 - threat_objects: - - field: process_name - type: process_name + message: The user $src_user$ launched a process [$parent_process_name$] which spawned a suspicious elevated integrity process [$process_name$]. + risk_objects: + - field: dest + type: system + score: 40 + - field: user + type: user + score: 40 + - field: src_user + type: user + score: 40 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Windows Privilege Escalation - - BlackSuit Ransomware - - GhostRedirector IIS Module and Rungan Backdoor - asset_type: Endpoint - mitre_attack_id: - - T1068 - - T1548 - - T1134 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Privilege Escalation + - BlackSuit Ransomware + - GhostRedirector IIS Module and Rungan Backdoor + asset_type: Endpoint + mitre_attack_id: + - T1068 + - T1548 + - T1134 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/windows_escalation_behavior/windows_escalation_behavior_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - name: True Positive Test + - attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/windows_escalation_behavior/windows_escalation_behavior_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + name: True Positive Test diff --git a/detections/endpoint/windows_privilege_escalation_system_process_without_system_parent.yml b/detections/endpoint/windows_privilege_escalation_system_process_without_system_parent.yml index 595bf4dd07..a4b8893618 100644 --- a/detections/endpoint/windows_privilege_escalation_system_process_without_system_parent.yml +++ b/detections/endpoint/windows_privilege_escalation_system_process_without_system_parent.yml @@ -5,74 +5,55 @@ date: '2026-01-14' author: Steven Dick status: production type: TTP -description: The following analytic detects any system integrity level process spawned - by a non-system account. It leverages Sysmon EventID 1, focusing on process integrity - and parent user data. This behavior is significant as it often indicates successful - privilege escalation to SYSTEM from a user-controlled process or service. If confirmed - malicious, this activity could allow an attacker to gain full control over the system, - execute arbitrary code, and potentially compromise the entire environment. +description: The following analytic detects any system integrity level process spawned by a non-system account. It leverages Sysmon EventID 1, focusing on process integrity and parent user data. This behavior is significant as it often indicates successful privilege escalation to SYSTEM from a user-controlled process or service. If confirmed malicious, this activity could allow an attacker to gain full control over the system, execute arbitrary code, and potentially compromise the entire environment. data_source: -- Sysmon EventID 1 -search: '`sysmon` EventCode=1 IntegrityLevel="system" ParentUser=* NOT ParentUser - IN ("*SYSTEM","*LOCAL SERVICE","*NETWORK SERVICE","*DWM-*","*$","-") | eval src_user - = replace(ParentUser,"^[^\\\]+\\\\","") | stats count min(_time) as firstTime max(_time) - as lastTime by action dest original_file_name parent_process parent_process_exec - parent_process_guid parent_process_id parent_process_name parent_process_path process - process_exec process_guid process_hash process_id process_integrity_level process_name - process_path user user_id vendor_product src_user | `security_content_ctime(firstTime)` | - `security_content_ctime(lastTime)` | `windows_privilege_escalation_system_process_without_system_parent_filter`' -how_to_implement: Target environment must ingest sysmon data, specifically Event ID - 1 with process integrity and parent user data. + - Sysmon EventID 1 +search: '`sysmon` EventCode=1 IntegrityLevel="system" ParentUser=* NOT ParentUser IN ("*SYSTEM","*LOCAL SERVICE","*NETWORK SERVICE","*DWM-*","*$","-") | eval src_user = replace(ParentUser,"^[^\\\]+\\\\","") | stats count min(_time) as firstTime max(_time) as lastTime by action dest original_file_name parent_process parent_process_exec parent_process_guid parent_process_id parent_process_name parent_process_path process process_exec process_guid process_hash process_id process_integrity_level process_name process_path user user_id vendor_product src_user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_privilege_escalation_system_process_without_system_parent_filter`' +how_to_implement: Target environment must ingest sysmon data, specifically Event ID 1 with process integrity and parent user data. known_false_positives: No false positives have been identified at this time. references: -- https://attack.mitre.org/techniques/T1068/ -- https://vuls.cert.org/confluence/display/Wiki/2021/06/21/Finding+Privilege+Escalation+Vulnerabilities+in+Windows+using+Process+Monitor -- https://redcanary.com/blog/getsystem-offsec/ -- https://atomicredteam.io/privilege-escalation/T1134.001/ + - https://attack.mitre.org/techniques/T1068/ + - https://vuls.cert.org/confluence/display/Wiki/2021/06/21/Finding+Privilege+Escalation+Vulnerabilities+in+Windows+using+Process+Monitor + - https://redcanary.com/blog/getsystem-offsec/ + - https://atomicredteam.io/privilege-escalation/T1134.001/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$src_user$" - search: '%original_detection_search% | search dest = "$dest$" src_user = "$src_user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$src_user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$src_user$" + search: '%original_detection_search% | search dest = "$dest$" src_user = "$src_user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$src_user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$src_user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The process [$process_name$] on $dest$ was launched with system level integrity - by $src_user$. - risk_objects: - - field: dest - type: system - score: 80 - - field: src_user - type: user - score: 80 - threat_objects: - - field: process_name - type: process_name + message: The process [$process_name$] on $dest$ was launched with system level integrity by $src_user$. + risk_objects: + - field: dest + type: system + score: 80 + - field: src_user + type: user + score: 80 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Windows Privilege Escalation - - BlackSuit Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1068 - - T1548 - - T1134 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Privilege Escalation + - BlackSuit Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1068 + - T1548 + - T1134 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/windows_escalation_behavior/windows_escalation_behavior_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - name: True Positive Test + - attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/windows_escalation_behavior/windows_escalation_behavior_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + name: True Positive Test diff --git a/detections/endpoint/windows_privilege_escalation_user_process_spawn_system_process.yml b/detections/endpoint/windows_privilege_escalation_user_process_spawn_system_process.yml index 8a572aabde..55d12a59fd 100644 --- a/detections/endpoint/windows_privilege_escalation_user_process_spawn_system_process.yml +++ b/detections/endpoint/windows_privilege_escalation_user_process_spawn_system_process.yml @@ -5,92 +5,57 @@ date: '2026-01-14' author: Steven Dick status: production type: TTP -description: The following analytic detects when a process with low, medium, or high - integrity spawns a system integrity process from a user-controlled location. This - behavior is indicative of privilege escalation attempts where attackers elevate - their privileges to SYSTEM level from a user-controlled process or service. The - detection leverages Sysmon data, specifically Event ID 15, to identify such transitions. - Monitoring this activity is crucial as it can signify an attacker gaining SYSTEM-level - access, potentially leading to full control over the affected system, unauthorized - access to sensitive data, and further malicious activities. +description: The following analytic detects when a process with low, medium, or high integrity spawns a system integrity process from a user-controlled location. This behavior is indicative of privilege escalation attempts where attackers elevate their privileges to SYSTEM level from a user-controlled process or service. The detection leverages Sysmon data, specifically Event ID 15, to identify such transitions. Monitoring this activity is crucial as it can signify an attacker gaining SYSTEM-level access, potentially leading to full control over the affected system, unauthorized access to sensitive data, and further malicious activities. data_source: -- Sysmon EventID 1 AND Sysmon EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime from - datamodel=Endpoint.Processes where Processes.process_integrity_level IN ("low","medium","high") - NOT Processes.user IN ("*SYSTEM","*LOCAL SERVICE","*NETWORK SERVICE","DWM-*","*$") - AND Processes.process_path IN ("*\\\\*","*\\Users\\*","*\\Temp\\*","*\\ProgramData\\*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | eval join_guid = process_guid | join max=0 - dest join_guid [| tstats `security_content_summariesonly` count max(_time) as lastTime - from datamodel=Endpoint.Processes where Processes.process_integrity_level IN ("system") - AND Processes.parent_process_path IN ("*\\\\*","*\\Users\\*","*\\Temp\\*","*\\ProgramData\\*") - by Processes.dest, Processes.user, Processes.parent_process_guid, Processes.process_name, - Processes.process, Processes.process_path, Processes.process_integrity_level, Processes.process_current_directory - | `drop_dm_object_name(Processes)` | rename parent_process_guid as join_guid, process* - as system_process*, user as system_user ] | fields dest, user, parent_process, parent_process_name, - parent_process_guid, process, process_name, process_guid, process_integrity_level,process_path, - process_current_directory, system_process_name, system_process, system_process_path, - system_process_integrity_level, system_process_current_directory, system_user, firstTime, - lastTime, count | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_privilege_escalation_user_process_spawn_system_process_filter`' -how_to_implement: Target environment must ingest sysmon data, specifically Event ID - 15. + - Sysmon EventID 1 AND Sysmon EventID 1 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime from datamodel=Endpoint.Processes where Processes.process_integrity_level IN ("low","medium","high") NOT Processes.user IN ("*SYSTEM","*LOCAL SERVICE","*NETWORK SERVICE","DWM-*","*$") AND Processes.process_path IN ("*\\\\*","*\\Users\\*","*\\Temp\\*","*\\ProgramData\\*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | eval join_guid = process_guid | join max=0 dest join_guid [| tstats `security_content_summariesonly` count max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_integrity_level IN ("system") AND Processes.parent_process_path IN ("*\\\\*","*\\Users\\*","*\\Temp\\*","*\\ProgramData\\*") by Processes.dest, Processes.user, Processes.parent_process_guid, Processes.process_name, Processes.process, Processes.process_path, Processes.process_integrity_level, Processes.process_current_directory | `drop_dm_object_name(Processes)` | rename parent_process_guid as join_guid, process* as system_process*, user as system_user ] | fields dest, user, parent_process, parent_process_name, parent_process_guid, process, process_name, process_guid, process_integrity_level,process_path, process_current_directory, system_process_name, system_process, system_process_path, system_process_integrity_level, system_process_current_directory, system_user, firstTime, lastTime, count | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_privilege_escalation_user_process_spawn_system_process_filter`' +how_to_implement: Target environment must ingest sysmon data, specifically Event ID 15. known_false_positives: No false positives have been identified at this time. references: -- https://attack.mitre.org/techniques/T1068/ -- https://vuls.cert.org/confluence/display/Wiki/2021/06/21/Finding+Privilege+Escalation+Vulnerabilities+in+Windows+using+Process+Monitor -- https://redcanary.com/blog/getsystem-offsec/ -- https://atomicredteam.io/privilege-escalation/T1134.001/ + - https://attack.mitre.org/techniques/T1068/ + - https://vuls.cert.org/confluence/display/Wiki/2021/06/21/Finding+Privilege+Escalation+Vulnerabilities+in+Windows+using+Process+Monitor + - https://redcanary.com/blog/getsystem-offsec/ + - https://atomicredteam.io/privilege-escalation/T1134.001/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The user $user$ launched the process $process_name$ which spawned a system - level integrity process. - risk_objects: - - field: dest - type: system - score: 80 - - field: user - type: user - score: 80 - threat_objects: - - field: process_name - type: process_name + message: The user $user$ launched the process $process_name$ which spawned a system level integrity process. + risk_objects: + - field: dest + type: system + score: 80 + - field: user + type: user + score: 80 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Windows Privilege Escalation - - Compromised Windows Host - - BlackSuit Ransomware - - GhostRedirector IIS Module and Rungan Backdoor - asset_type: Endpoint - mitre_attack_id: - - T1068 - - T1548 - - T1134 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Privilege Escalation + - Compromised Windows Host + - BlackSuit Ransomware + - GhostRedirector IIS Module and Rungan Backdoor + asset_type: Endpoint + mitre_attack_id: + - T1068 + - T1548 + - T1134 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/windows_escalation_behavior/windows_escalation_behavior_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - name: True Positive Test + - attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/windows_escalation_behavior/windows_escalation_behavior_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + name: True Positive Test diff --git a/detections/endpoint/windows_privileged_group_modification.yml b/detections/endpoint/windows_privileged_group_modification.yml index b3472f87f6..a8a364342d 100644 --- a/detections/endpoint/windows_privileged_group_modification.yml +++ b/detections/endpoint/windows_privileged_group_modification.yml @@ -1,97 +1,72 @@ name: Windows Privileged Group Modification id: b8cbef2c-2cc3-4550-b0fc-9715b7852df9 -version: 7 -date: '2025-10-14' +version: 8 +date: '2026-02-25' author: Brandon Sternfield, Optiv + ClearShark data_source: -- Windows Event Log Security 4727 -- Windows Event Log Security 4731 -- Windows Event Log Security 4744 -- Windows Event Log Security 4749 -- Windows Event Log Security 4754 -- Windows Event Log Security 4759 -- Windows Event Log Security 4783 -- Windows Event Log Security 4790 + - Windows Event Log Security 4727 + - Windows Event Log Security 4731 + - Windows Event Log Security 4744 + - Windows Event Log Security 4749 + - Windows Event Log Security 4754 + - Windows Event Log Security 4759 + - Windows Event Log Security 4783 + - Windows Event Log Security 4790 type: TTP status: production -description: This analytic detects modifications to privileged groups in Active Directory, - including creation, deletion, and changes to various types of groups such as local, - global, universal, and LDAP query groups. It specifically monitors for changes to - high-privilege groups like "Administrators", "Domain Admins", "Enterprise Admins", - and "ESX Admins", among others. This detection is particularly relevant in the context - of potential exploitation of vulnerabilities like the VMware ESXi Active Directory - Integration Authentication Bypass (CVE-2024-37085), where attackers may attempt - to manipulate privileged groups to gain unauthorized access to systems. -search: '`wineventlog_security` EventCode IN (4727,4731,4744,4749,4754,4759,4783,4790) - TargetUserName IN ("Account Operators", "Administrators", "Admins DNS", "Backup - Operators", "DnsAdmins", "Domain Admins", "Enterprise Admins", "Enterprise Key Admins", - "ESX Admins", "ESXi Admins", "Group Policy Creator Owners", "Hyper-V Administrators", - "Key Admins", "Print Operators", "Remote Desktop Users", "Remote Management Users", - "Replicators", "Schema Admins", "Server Operators") | eval object_category=case( - EventCode="4731", "Local Group (Security)", EventCode="4744", "Local Group (Distribution)", - EventCode="4727", "Global Group (Security)", EventCode="4749", "Global Group (Distribution)", - EventCode="4754", "Universal Group (Security)", EventCode="4759", "Universal Group - (Distribution)", EventCode="4783", "Basic Application Group", EventCode="4790", - "LDAP Query Group") | rename Computer as dest, result AS change_type, TargetUserName - AS object, TargetSid AS object_path | stats count min(_time) as firstTime max(_time) - as lastTime by EventCode src_user object_category object object_path dest change_type - status | `windows_privileged_group_modification_filter`' -how_to_implement: To successfully implement this search, ensure that Windows Security - Event logging is enabled and being ingested into Splunk, particularly for event - codes 4727, 4730, and 4737. Configure Group Policy settings to audit these specific - events. -known_false_positives: Legitimate administrators might create, delete, or modify an - a privileged group for valid reasons. Verify that the group changes are authorized - and part of normal administrative tasks. Consider the context of the action, such - as the user performing it and any related activities. +description: This analytic detects modifications to privileged groups in Active Directory, including creation, deletion, and changes to various types of groups such as local, global, universal, and LDAP query groups. It specifically monitors for changes to high-privilege groups like "Administrators", "Domain Admins", "Enterprise Admins", and "ESX Admins", among others. This detection is particularly relevant in the context of potential exploitation of vulnerabilities like the VMware ESXi Active Directory Integration Authentication Bypass (CVE-2024-37085), where attackers may attempt to manipulate privileged groups to gain unauthorized access to systems. +search: |- + `wineventlog_security` EventCode IN (4727,4731,4744,4749,4754,4759,4783,4790) TargetUserName IN ("Account Operators", "Administrators", "Admins DNS", "Backup Operators", "DnsAdmins", "Domain Admins", "Enterprise Admins", "Enterprise Key Admins", "ESX Admins", "ESXi Admins", "Group Policy Creator Owners", "Hyper-V Administrators", "Key Admins", "Print Operators", "Remote Desktop Users", "Remote Management Users", "Replicators", "Schema Admins", "Server Operators") + | eval object_category=case( EventCode="4731", "Local Group (Security)", EventCode="4744", "Local Group (Distribution)", EventCode="4727", "Global Group (Security)", EventCode="4749", "Global Group (Distribution)", EventCode="4754", "Universal Group (Security)", EventCode="4759", "Universal Group (Distribution)", EventCode="4783", "Basic Application Group", EventCode="4790", "LDAP Query Group") + | rename Computer as dest, result AS change_type, TargetUserName AS object, TargetSid AS object_path + | stats count min(_time) as firstTime max(_time) as lastTime + BY EventCode src_user object_category + object object_path dest + change_type status + | `windows_privileged_group_modification_filter` +how_to_implement: To successfully implement this search, ensure that Windows Security Event logging is enabled and being ingested into Splunk, particularly for event codes 4727, 4730, and 4737. Configure Group Policy settings to audit these specific events. +known_false_positives: Legitimate administrators might create, delete, or modify an a privileged group for valid reasons. Verify that the group changes are authorized and part of normal administrative tasks. Consider the context of the action, such as the user performing it and any related activities. references: -- https://nvd.nist.gov/vuln/detail/CVE-2024-37085 -- https://www.rapid7.com/blog/post/2024/07/30/vmware-esxi-cve-2024-37085-targeted-in-ransomware-campaigns/%5C -- https://x.com/mthcht/status/1818196168515461431?s=12&t=kwffmj0KM1sZtg3MrqC0QQ + - https://nvd.nist.gov/vuln/detail/CVE-2024-37085 + - https://www.rapid7.com/blog/post/2024/07/30/vmware-esxi-cve-2024-37085-targeted-in-ransomware-campaigns/%5C + - https://x.com/mthcht/status/1818196168515461431?s=12&t=kwffmj0KM1sZtg3MrqC0QQ drilldown_searches: -- name: View the detection results for - "$src_user$" and "$dest$" - search: '%original_detection_search% | search src_user = "$src_user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_user$" and "$dest$" + search: '%original_detection_search% | search src_user = "$src_user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A privileged group modification was detected. Group "$object$" ($object_category$) - was $change_type$ on $dest$ by user $src_user$. - risk_objects: - - field: src_user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: [] + message: A privileged group modification was detected. Group "$object$" ($object_category$) was $change_type$ on $dest$ by user $src_user$. + risk_objects: + - field: src_user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: [] tags: - analytic_story: - - VMware ESXi AD Integration Authentication Bypass CVE-2024-37085 - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1136.001 - - T1136.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: - - CVE-2024-37085 + analytic_story: + - VMware ESXi AD Integration Authentication Bypass CVE-2024-37085 + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1136.001 + - T1136.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: + - CVE-2024-37085 tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security-esxadmins.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security-esxadmins.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_process_commandline_discovery.yml b/detections/endpoint/windows_process_commandline_discovery.yml index 078b2034ce..11a6cc01cd 100644 --- a/detections/endpoint/windows_process_commandline_discovery.yml +++ b/detections/endpoint/windows_process_commandline_discovery.yml @@ -1,60 +1,47 @@ name: Windows Process Commandline Discovery id: 67d2a52e-a7e2-4a5d-ae44-a21212048bc2 -version: 7 -date: '2025-05-19' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Hunting data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic detects the use of Windows Management Instrumentation - Command-line (WMIC) to retrieve information about running processes, specifically - targeting the command lines used to launch those processes. This detection leverages - data from Endpoint Detection and Response (EDR) agents, focusing on logs containing - process details and command-line executions. This activity is significant as it - may indicate suspicious behavior, such as a user or process gathering detailed process - information, which is uncommon for non-technical users. If confirmed malicious, - this could allow an attacker to gain insights into running processes, aiding in - further exploitation or lateral movement. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_wmic` Processes.process= - "* process *" Processes.process= "* get *" Processes.process= "*CommandLine*" by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_process_commandline_discovery_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrators or power users may use this command for troubleshooting. - Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic detects the use of Windows Management Instrumentation Command-line (WMIC) to retrieve information about running processes, specifically targeting the command lines used to launch those processes. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on logs containing process details and command-line executions. This activity is significant as it may indicate suspicious behavior, such as a user or process gathering detailed process information, which is uncommon for non-technical users. If confirmed malicious, this could allow an attacker to gain insights into running processes, aiding in further exploitation or lateral movement. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_wmic` Processes.process= "* process *" Processes.process= "* get *" Processes.process= "*CommandLine*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_process_commandline_discovery_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators or power users may use this command for troubleshooting. Filter as needed. references: -- https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a tags: - analytic_story: - - CISA AA23-347A - asset_type: Endpoint - mitre_attack_id: - - T1057 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA23-347A + asset_type: Endpoint + mitre_attack_id: + - T1057 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1057/process_commandline_discovery/wmic-cmdline-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1057/process_commandline_discovery/wmic-cmdline-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_process_executed_from_removable_media.yml b/detections/endpoint/windows_process_executed_from_removable_media.yml index e06a31f4e5..195548dc73 100644 --- a/detections/endpoint/windows_process_executed_from_removable_media.yml +++ b/detections/endpoint/windows_process_executed_from_removable_media.yml @@ -1,107 +1,95 @@ name: Windows Process Executed From Removable Media id: b483804a-4cc0-49a4-9f00-ac29ba844d08 -version: 6 -date: '2025-09-18' +version: 7 +date: '2026-02-25' author: Steven Dick status: production type: Anomaly -description: This analytic is used to identify when a removable media device is attached - to a machine and then a process is executed from the same drive letter assigned - to the removable media device. Adversaries and Insider Threats may use removable - media devices for several malicious activities, including initial access, execution, - and exfiltration. +description: This analytic is used to identify when a removable media device is attached to a machine and then a process is executed from the same drive letter assigned to the removable media device. Adversaries and Insider Threats may use removable media devices for several malicious activities, including initial access, execution, and exfiltration. data_source: -- Sysmon EventID 1 AND Sysmon EventID 13 -search: "| tstats `security_content_summariesonly` count values(Processes.process)\ - \ as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes\ - \ where Processes.process_current_directory=* AND NOT Processes.process_current_directory\ - \ IN (\"C:\\\\*\",\"*\\\\sysvol\\\\*\") \nby Processes.action Processes.dest Processes.original_file_name\ - \ Processes.parent_process Processes.parent_process_exec \nProcesses.parent_process_guid\ - \ Processes.parent_process_id Processes.parent_process_name \nProcesses.parent_process_path\ - \ Processes.process Processes.process_exec Processes.process_guid Processes.process_hash\ - \ \nProcesses.process_id Processes.process_integrity_level Processes.process_name\ - \ Processes.process_path \nProcesses.user Processes.user_id Processes.vendor_product\ - \ Processes.process_current_directory\n| `drop_dm_object_name(Processes)` \n| rex\ - \ field=process_current_directory \"^(?[^\\\\\\]+\\\\\\)\" \n| where\ - \ isnotnull(object_handle) \n| `security_content_ctime(firstTime)` \n| `security_content_ctime(lastTime)`\ - \ \n| join dest,object_handle \n [| tstats `security_content_summariesonly` count\ - \ values(Registry.action) as action values(Registry.process_guid) as process_guid\ - \ values(Registry.process_id) as process_id values(Registry.registry_hive) as registry_hive\ - \ values(Registry.registry_key_name) as registry_key_name values(Registry.registry_value_name)\ - \ as registry_value_name values(Registry.registry_value_type) as registry_value_type\ - \ values(Registry.status) as status values(Registry.user) as user values(Registry.vendor_product)\ - \ as vendor_product from datamodel=Endpoint.Registry where Registry.registry_value_data=\"\ - *:\\\\*\" AND Registry.registry_path=\"*USBSTOR*\" AND Registry.registry_path IN\ - \ (\"HKLM\\\\SOFTWARE\\\\Microsoft\\\\Windows Portable Devices\\\\Devices\\\\*\"\ - ,\"HKLM\\\\System\\\\CurrentControlSet\\\\Enum\\\\SWD\\\\WPDBUSENUM\\\\*\") by Registry.dest,Registry.registry_value_data,\ - \ Registry.registry_path \n | `drop_dm_object_name(Registry)` \n | eval object_handle\ - \ = registry_value_data, object_name = replace(mvindex(split(mvindex(split(registry_path,\ - \ \"??\"),1),\"&\"),2),\"PROD_\",\"\")\n ]\n| `windows_process_executed_from_removable_media_filter`" -how_to_implement: To successfully implement this search, you must ingest endpoint - logging that tracks changes to the HKLM\SOFTWARE\Microsoft\Windows Portable Devices\Devices\ - or HKLM\System\CurrentControlSet\Enum\SWD\WPDBUSENUM\ registry keys as well as Process - Execution commands. Ensure that the field from the event logs is being mapped to - the proper fields in the Endpoint.Registry data model. This analytic joins the Process - and Registry datamodels together based on the drive letter extract to the "object_handle" - field from both datasets. -known_false_positives: Legitimate USB activity will also be detected. Please verify - and investigate as appropriate. + - Sysmon EventID 1 AND Sysmon EventID 13 +search: |- + | tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_current_directory=* + AND + NOT Processes.process_current_directory IN ("C:\\*","*\\sysvol\\*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product Processes.process_current_directory + | `drop_dm_object_name(Processes)` + | rex field=process_current_directory "^(?[^\\\]+\\\)" + | where isnotnull(object_handle) + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | join dest,object_handle + [ + | tstats `security_content_summariesonly` count values(Registry.action) as action values(Registry.process_guid) as process_guid values(Registry.process_id) as process_id values(Registry.registry_hive) as registry_hive values(Registry.registry_key_name) as registry_key_name values(Registry.registry_value_name) as registry_value_name values(Registry.registry_value_type) as registry_value_type values(Registry.status) as status values(Registry.user) as user values(Registry.vendor_product) as vendor_product FROM datamodel=Endpoint.Registry + WHERE Registry.registry_value_data="*:\\*" + AND + Registry.registry_path="*USBSTOR*" + AND + Registry.registry_path IN ("HKLM\\SOFTWARE\\Microsoft\\Windows Portable Devices\\Devices\\*","HKLM\\System\\CurrentControlSet\\Enum\\SWD\\WPDBUSENUM\\*") + BY Registry.dest,Registry.registry_value_data, Registry.registry_path + | `drop_dm_object_name(Registry)` + | eval object_handle = registry_value_data, object_name = replace(mvindex(split(mvindex(split(registry_path, "??"),1),"&"),2),"PROD_","") + ] + | `windows_process_executed_from_removable_media_filter` +how_to_implement: To successfully implement this search, you must ingest endpoint logging that tracks changes to the HKLM\SOFTWARE\Microsoft\Windows Portable Devices\Devices\ or HKLM\System\CurrentControlSet\Enum\SWD\WPDBUSENUM\ registry keys as well as Process Execution commands. Ensure that the field from the event logs is being mapped to the proper fields in the Endpoint.Registry data model. This analytic joins the Process and Registry datamodels together based on the drive letter extract to the "object_handle" field from both datasets. +known_false_positives: Legitimate USB activity will also be detected. Please verify and investigate as appropriate. references: -- https://attack.mitre.org/techniques/T1200/ -- https://www.cisa.gov/news-events/news/using-caution-usb-drives -- https://www.bleepingcomputer.com/news/security/fbi-hackers-use-badusb-to-target-defense-firms-with-ransomware/ + - https://attack.mitre.org/techniques/T1200/ + - https://www.cisa.gov/news-events/news/using-caution-usb-drives + - https://www.bleepingcomputer.com/news/security/fbi-hackers-use-badusb-to-target-defense-firms-with-ransomware/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" and user= "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$" - , "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate USB events on $dest$ - search: '| from datamodel:Endpoint.Processes | search dest=$dest$ process_current_directory=$object_handle$*' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" and user= "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$" , "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate USB events on $dest$ + search: '| from datamodel:Endpoint.Processes | search dest=$dest$ process_current_directory=$object_handle$*' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The process [$process_name$] was launched using files on a removable storage - device named [$object_name$] by [$user$] on $dest$ - risk_objects: - - field: user - type: user - score: 35 - - field: dest - type: system - score: 35 - threat_objects: - - field: process_name - type: process_name - - field: object_name - type: registry_value_name - - field: object_handle - type: registry_value_text + message: The process [$process_name$] was launched using files on a removable storage device named [$object_name$] by [$user$] on $dest$ + risk_objects: + - field: user + type: user + score: 35 + - field: dest + type: system + score: 35 + threat_objects: + - field: process_name + type: process_name + - field: object_name + type: registry_value_name + - field: object_handle + type: registry_value_text tags: - analytic_story: - - Data Protection - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1200 - - T1025 - - T1091 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Protection + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1200 + - T1025 + - T1091 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1200/sysmon_usb_use_execution/sysmon_usb_use_execution.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1200/sysmon_usb_use_execution/sysmon_usb_use_execution.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_process_execution_from_programdata.yml b/detections/endpoint/windows_process_execution_from_programdata.yml index 02cdbbfda4..fbc5e8d319 100644 --- a/detections/endpoint/windows_process_execution_from_programdata.yml +++ b/detections/endpoint/windows_process_execution_from_programdata.yml @@ -6,69 +6,60 @@ author: Teoderick Contreras, Splunk status: production type: Hunting description: | - The following analytic identifies processes running from file paths within - the ProgramData directory, a common location abused by adversaries for executing - malicious code while evading detection. Threat actors often drop and execute payloads - from this directory to bypass security controls, as it typically has write permissions - for standard users. While this behavior can indicate malware execution or persistence - techniques, it is important to note that some legitimate software, installers, and - update mechanisms also run from ProgramData, leading to potential false positives. - Security teams should validate detections by correlating with other indicators, - such as unusual parent processes, unsigned binaries, or anomalous network activity. + The following analytic identifies processes running from file paths within + the ProgramData directory, a common location abused by adversaries for executing + malicious code while evading detection. Threat actors often drop and execute payloads + from this directory to bypass security controls, as it typically has write permissions + for standard users. While this behavior can indicate malware execution or persistence + techniques, it is important to note that some legitimate software, installers, and + update mechanisms also run from ProgramData, leading to potential false positives. + Security teams should validate detections by correlating with other indicators, + such as unusual parent processes, unsigned binaries, or anomalous network activity. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count values(Processes.process_name) - as process_name values(Processes.process) as process min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - - Processes.process_path = "*:\\ProgramData\\*" - - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_process_execution_from_programdata_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrators may allow execution of specific binaries in - non-standard paths. Filter as needed. + | tstats `security_content_summariesonly` count values(Processes.process_name) + as process_name values(Processes.process) as process min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where + + Processes.process_path = "*:\\ProgramData\\*" + + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_process_execution_from_programdata_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators may allow execution of specific binaries in non-standard paths. Filter as needed. references: -- https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ + - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ tags: - analytic_story: - - SolarWinds WHD RCE Post Exploitation - - StealC Stealer - - SnappyBee - - XWorm - - Salt Typhoon - - China-Nexus Threat Activity - - APT37 Rustonotto and FadeStealer - - GhostRedirector IIS Module and Rungan Backdoor - asset_type: Endpoint - mitre_attack_id: - - T1036.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SolarWinds WHD RCE Post Exploitation + - StealC Stealer + - SnappyBee + - XWorm + - Salt Typhoon + - China-Nexus Threat Activity + - APT37 Rustonotto and FadeStealer + - GhostRedirector IIS Module and Rungan Backdoor + asset_type: Endpoint + mitre_attack_id: + - T1036.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.005/process_in_programdata/exec_programdata.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.005/process_in_programdata/exec_programdata.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_process_execution_from_rdp_share.yml b/detections/endpoint/windows_process_execution_from_rdp_share.yml index af73e223de..52a57ff962 100644 --- a/detections/endpoint/windows_process_execution_from_rdp_share.yml +++ b/detections/endpoint/windows_process_execution_from_rdp_share.yml @@ -1,98 +1,93 @@ name: Windows Process Execution From RDP Share id: 6b1b84c4-3834-4dee-b062-9b79bdb31d15 -version: 1 -date: '2025-10-21' +version: 2 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - The following analytic identifies process executions originating from RDP shares on Windows endpoints. - Remote Desktop Protocol (RDP) shares, typically accessed via the "tsclient" path, allow users to share files between their local machine and a remote desktop session. However, threat actors may exploit RDP shares to execute malicious processes or transfer harmful files onto a compromised system. - This detection focuses on identifying any process executions that originate from RDP shares, which could indicate unauthorized access or malicious activity. - Security teams should investigate any instances of such process executions, especially if they are found on systems that should not be using RDP shares or if the executed processes are unfamiliar or suspicious. + The following analytic identifies process executions originating from RDP shares on Windows endpoints. + Remote Desktop Protocol (RDP) shares, typically accessed via the "tsclient" path, allow users to share files between their local machine and a remote desktop session. However, threat actors may exploit RDP shares to execute malicious processes or transfer harmful files onto a compromised system. + This detection focuses on identifying any process executions that originate from RDP shares, which could indicate unauthorized access or malicious activity. + Security teams should investigate any instances of such process executions, especially if they are found on systems that should not be using RDP shares or if the executed processes are unfamiliar or suspicious. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` + | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime - - from datamodel=Endpoint.Processes where + count min(_time) as firstTime + max(_time) as lastTime - Processes.process = "*\\\\tsclient\\*" + from datamodel=Endpoint.Processes where - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + Processes.process = "*\\\\tsclient\\*" - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_process_execution_from_rdp_share_filter` + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_process_execution_from_rdp_share_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: | - Legitimate use of RDP shares by users to transfer and execute files may trigger - this detection. It is essential to review the context of the process execution - to determine if it is authorized activity. + Legitimate use of RDP shares by users to transfer and execute files may trigger + this detection. It is essential to review the context of the process execution + to determine if it is authorized activity. references: -- https://jsac.jpcert.or.jp/archive/2024/pdf/JSAC2024_2_7_hara_shoji_higashi_vickie-su_nick-dai_en.pdf -- https://thedfirreport.com/2020/04/04/gogoogle-ransomware/ -- https://www.welivesecurity.com/2022/09/30/amazon-themed-campaigns-lazarus-netherlands-belgium/ + - https://jsac.jpcert.or.jp/archive/2024/pdf/JSAC2024_2_7_hara_shoji_higashi_vickie-su_nick-dai_en.pdf + - https://thedfirreport.com/2020/04/04/gogoogle-ransomware/ + - https://www.welivesecurity.com/2022/09/30/amazon-themed-campaigns-lazarus-netherlands-belgium/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Process $process_name$ executed $process$ from RDP share on host $dest$ - risk_objects: - - field: dest - type: system - score: 35 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: Process $process_name$ executed $process$ from RDP share on host $dest$ + risk_objects: + - field: dest + type: system + score: 35 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Hidden Cobra Malware - asset_type: Endpoint - mitre_attack_id: - - T1021.001 - - T1105 - - T1059 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Hidden Cobra Malware + asset_type: Endpoint + mitre_attack_id: + - T1021.001 + - T1105 + - T1059 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.001/execution_from_rdp_share/execution_from_rdp_share.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.001/execution_from_rdp_share/execution_from_rdp_share.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_process_execution_in_temp_dir.yml b/detections/endpoint/windows_process_execution_in_temp_dir.yml index 01d565781c..1e2ba73b05 100644 --- a/detections/endpoint/windows_process_execution_in_temp_dir.yml +++ b/detections/endpoint/windows_process_execution_in_temp_dir.yml @@ -5,97 +5,64 @@ date: '2025-12-10' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies processes running from %temp% - directory file paths. It leverages data from Endpoint Detection and Response - (EDR) agents, focusing on specific process paths within the Endpoint data - model. This activity is significant because adversaries often use - unconventional file paths to execute malicious code without requiring - administrative privileges. If confirmed malicious, this behavior could - indicate an attempt to bypass security controls, leading to unauthorized - software execution, potential system compromise, and further malicious - activities within the environment. +description: The following analytic identifies processes running from %temp% directory file paths. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific process paths within the Endpoint data model. This activity is significant because adversaries often use unconventional file paths to execute malicious code without requiring administrative privileges. If confirmed malicious, this behavior could indicate an attempt to bypass security controls, leading to unauthorized software execution, potential system compromise, and further malicious activities within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_path IN("*\\temp\\*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_process_execution_in_temp_dir_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: Administrators may allow execution of specific binaries - in non-standard paths. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_path IN("*\\temp\\*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_execution_in_temp_dir_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators may allow execution of specific binaries in non-standard paths. Filter as needed. references: -- https://www.trendmicro.com/vinfo/hk/threat-encyclopedia/malware/trojan.ps1.powtran.a/ -- https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ -- https://twitter.com/pr0xylife/status/1590394227758104576 -- https://malpedia.caad.fkie.fraunhofer.de/details/win.asyncrat -- https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ + - https://www.trendmicro.com/vinfo/hk/threat-encyclopedia/malware/trojan.ps1.powtran.a/ + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://twitter.com/pr0xylife/status/1590394227758104576 + - https://malpedia.caad.fkie.fraunhofer.de/details/win.asyncrat + - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious process $process_name$ running from temp directory- - $process_path$ on host- $dest$ - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: - - field: process_path - type: process_name + message: Suspicious process $process_name$ running from temp directory- $process_path$ on host- $dest$ + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: + - field: process_path + type: process_name tags: - analytic_story: - - AgentTesla - - XWorm - - NjRAT - - Remcos - - Ryuk Ransomware - - Ransomware - - Qakbot - - Trickbot - - PathWiper - - PromptLock - - Lokibot - - SesameOp - asset_type: Endpoint - mitre_attack_id: - - T1543 - - T1036.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AgentTesla + - XWorm + - NjRAT + - Remcos + - Ryuk Ransomware + - Ransomware + - Qakbot + - Trickbot + - PathWiper + - PromptLock + - Lokibot + - SesameOp + asset_type: Endpoint + mitre_attack_id: + - T1543 + - T1036.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/process_temp_path/process_temp_path.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/process_temp_path/process_temp_path.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_process_injection_in_non_service_searchindexer.yml b/detections/endpoint/windows_process_injection_in_non_service_searchindexer.yml index f96e4c9d1e..6d0d5f9cbd 100644 --- a/detections/endpoint/windows_process_injection_in_non_service_searchindexer.yml +++ b/detections/endpoint/windows_process_injection_in_non_service_searchindexer.yml @@ -1,80 +1,64 @@ name: Windows Process Injection In Non-Service SearchIndexer id: d131673f-ede1-47f2-93a1-0108d3e7fafd -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic identifies instances of the searchindexer.exe - process that are not spawned by services.exe, indicating potential process injection. - This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process names and parent processes. This activity is significant because - QakBot malware often uses a fake searchindexer.exe to evade detection and perform - malicious actions such as data exfiltration and keystroke logging. If confirmed - malicious, this activity could allow attackers to maintain persistence, steal sensitive - information, and communicate with command and control servers. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name - != services.exe Processes.process_name=searchindexer.exe by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_process_injection_in_non_service_searchindexer_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic identifies instances of the searchindexer.exe process that are not spawned by services.exe, indicating potential process injection. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and parent processes. This activity is significant because QakBot malware often uses a fake searchindexer.exe to evade detection and perform malicious actions such as data exfiltration and keystroke logging. If confirmed malicious, this activity could allow attackers to maintain persistence, steal sensitive information, and communicate with command and control servers. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name != services.exe Processes.process_name=searchindexer.exe + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_process_injection_in_non_service_searchindexer_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://twitter.com/Max_Mal_/status/1736392741758611607 -- https://twitter.com/1ZRR4H/status/1735944522075386332 + - https://twitter.com/Max_Mal_/status/1736392741758611607 + - https://twitter.com/1ZRR4H/status/1735944522075386332 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An uncommon non-service searchindexer.exe process on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: An uncommon non-service searchindexer.exe process on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Qakbot - asset_type: Endpoint - mitre_attack_id: - - T1055 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Qakbot + asset_type: Endpoint + mitre_attack_id: + - T1055 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/non-service-searchindexer/seaarch-indexer-non-service.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/non-service-searchindexer/seaarch-indexer-non-service.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_process_injection_into_commonly_abused_processes.yml b/detections/endpoint/windows_process_injection_into_commonly_abused_processes.yml index c4c46bd29a..248daee2b6 100644 --- a/detections/endpoint/windows_process_injection_into_commonly_abused_processes.yml +++ b/detections/endpoint/windows_process_injection_into_commonly_abused_processes.yml @@ -6,83 +6,52 @@ author: 0xC0FFEEEE, Github Community type: Anomaly status: production data_source: -- Sysmon EventID 10 -description: The following analytic detects process injection into executables that - are commonly abused using Sysmon EventCode 10. It identifies suspicious GrantedAccess - requests (0x40 and 0x1fffff) to processes such as notepad.exe, wordpad.exe and calc.exe, - excluding common system paths like System32, Syswow64, and Program Files. This behavior - is often associated with the SliverC2 framework by BishopFox. Monitoring this activity - is crucial as it may indicate an initial payload attempting to execute malicious - code. If confirmed malicious, this could allow attackers to execute arbitrary code, - potentially leading to privilege escalation or persistent access within the environment. -search: '`sysmon` EventCode=10 TargetImage IN ("*\\notepad.exe", "*\\wordpad.exe", - "*\\calc.exe", "*\\mspaint.exe", "*\\lsass.exe", "*\\svchost.exe", "*\\backgroundtaskhost.exe", - "*\\dllhost.exe", "*\\regsvr32.exe", "*\\searchprotocolhost.exe", "*\\werfault.exe", - "*\\wuauclt.exe", "*\\spoolsv.exe", "*\\chrome.exe", "*\\edge.exe", "*\\firefox.exe") - NOT (SourceImage IN ("*\\system32\\*","*\\syswow64\\*","*\\Program Files\\*", "*\\Program - Files (x86)\\*")) GrantedAccess IN ("0x40","0x1fffff", "0x1f3fff") | stats values(user) - as user, min(_time) as firstTime, max(_time) as lastTime, count by dest user_id - parent_process_name parent_process_guid process_name process_guid process_id signature - SourceImage TargetImage GrantedAccess CallTrace | eval CallTrace=split(CallTrace, - "|") | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | table firstTime lastTime dest user_id parent_process_name parent_process_guid - process_name process_guid process_id signature SourceImage TargetImage GrantedAccess - CallTrace| `windows_process_injection_into_commonly_abused_processes_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. -known_false_positives: False positives may be present based on SourceImage paths, - particularly those with a legitimate reason for accessing lsass.exe or regsvr32.exe. - If removing the paths is important, realize svchost and many native binaries inject - into processes consistently. Restrict or tune as needed. + - Sysmon EventID 10 +description: The following analytic detects process injection into executables that are commonly abused using Sysmon EventCode 10. It identifies suspicious GrantedAccess requests (0x40 and 0x1fffff) to processes such as notepad.exe, wordpad.exe and calc.exe, excluding common system paths like System32, Syswow64, and Program Files. This behavior is often associated with the SliverC2 framework by BishopFox. Monitoring this activity is crucial as it may indicate an initial payload attempting to execute malicious code. If confirmed malicious, this could allow attackers to execute arbitrary code, potentially leading to privilege escalation or persistent access within the environment. +search: '`sysmon` EventCode=10 TargetImage IN ("*\\notepad.exe", "*\\wordpad.exe", "*\\calc.exe", "*\\mspaint.exe", "*\\lsass.exe", "*\\svchost.exe", "*\\backgroundtaskhost.exe", "*\\dllhost.exe", "*\\regsvr32.exe", "*\\searchprotocolhost.exe", "*\\werfault.exe", "*\\wuauclt.exe", "*\\spoolsv.exe", "*\\chrome.exe", "*\\edge.exe", "*\\firefox.exe") NOT (SourceImage IN ("*\\system32\\*","*\\syswow64\\*","*\\Program Files\\*", "*\\Program Files (x86)\\*")) GrantedAccess IN ("0x40","0x1fffff", "0x1f3fff") | stats values(user) as user, min(_time) as firstTime, max(_time) as lastTime, count by dest user_id parent_process_name parent_process_guid process_name process_guid process_id signature SourceImage TargetImage GrantedAccess CallTrace | eval CallTrace=split(CallTrace, "|") | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime dest user_id parent_process_name parent_process_guid process_name process_guid process_id signature SourceImage TargetImage GrantedAccess CallTrace| `windows_process_injection_into_commonly_abused_processes_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: False positives may be present based on SourceImage paths, particularly those with a legitimate reason for accessing lsass.exe or regsvr32.exe. If removing the paths is important, realize svchost and many native binaries inject into processes consistently. Restrict or tune as needed. references: -- https://dominicbreuker.com/post/learning_sliver_c2_08_implant_basics/ -- https://www.cybereason.com/blog/sliver-c2-leveraged-by-many-threat-actors -- https://redcanary.com/threat-detection-report/techniques/process-injection/ + - https://dominicbreuker.com/post/learning_sliver_c2_08_implant_basics/ + - https://www.cybereason.com/blog/sliver-c2-leveraged-by-many-threat-actors + - https://redcanary.com/threat-detection-report/techniques/process-injection/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $SourceImage$ injecting into $TargetImage$ was identified - on endpoint $dest$. - risk_objects: - - field: dest - type: system - score: 32 - threat_objects: - - field: SourceImage - type: process - - field: TargetImage - type: process + message: An instance of $SourceImage$ injecting into $TargetImage$ was identified on endpoint $dest$. + risk_objects: + - field: dest + type: system + score: 32 + threat_objects: + - field: SourceImage + type: process + - field: TargetImage + type: process tags: - analytic_story: - - BishopFox Sliver Adversary Emulation Framework - - Earth Alux - - SAP NetWeaver Exploitation - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1055.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - BishopFox Sliver Adversary Emulation Framework + - Earth Alux + - SAP NetWeaver Exploitation + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1055.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/sliver/T1055_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/sliver/T1055_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_process_injection_into_notepad.yml b/detections/endpoint/windows_process_injection_into_notepad.yml index 0c8460f12b..0086d668c1 100644 --- a/detections/endpoint/windows_process_injection_into_notepad.yml +++ b/detections/endpoint/windows_process_injection_into_notepad.yml @@ -6,76 +6,50 @@ author: Michael Haag, Splunk type: Anomaly status: production data_source: -- Sysmon EventID 10 -description: The following analytic detects process injection into Notepad.exe using - Sysmon EventCode 10. It identifies suspicious GrantedAccess requests (0x40 and 0x1fffff) - to Notepad.exe, excluding common system paths like System32, Syswow64, and Program - Files. This behavior is often associated with the SliverC2 framework by BishopFox. - Monitoring this activity is crucial as it may indicate an initial payload attempting - to execute malicious code within Notepad.exe. If confirmed malicious, this could - allow attackers to execute arbitrary code, potentially leading to privilege escalation - or persistent access within the environment. -search: '`sysmon` EventCode=10 TargetImage IN (*\\notepad.exe) NOT (SourceImage IN - ("*\\system32\\*","*\\syswow64\\*","*\\Program Files\\*")) GrantedAccess IN ("0x40","0x1fffff") - | stats count min(_time) as firstTime max(_time) as lastTime by CallTrace EventID - GrantedAccess Guid Opcode ProcessID SecurityID SourceImage SourceProcessGUID SourceProcessId - TargetImage TargetProcessGUID TargetProcessId UserID dest granted_access parent_process_exec - parent_process_guid parent_process_id parent_process_name parent_process_path process_exec - process_guid process_id process_name process_path signature signature_id user_id - vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_process_injection_into_notepad_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. -known_false_positives: False positives may be present based on SourceImage paths. - If removing the paths is important, realize svchost and many native binaries inject - into notepad consistently. Restrict or tune as needed. + - Sysmon EventID 10 +description: The following analytic detects process injection into Notepad.exe using Sysmon EventCode 10. It identifies suspicious GrantedAccess requests (0x40 and 0x1fffff) to Notepad.exe, excluding common system paths like System32, Syswow64, and Program Files. This behavior is often associated with the SliverC2 framework by BishopFox. Monitoring this activity is crucial as it may indicate an initial payload attempting to execute malicious code within Notepad.exe. If confirmed malicious, this could allow attackers to execute arbitrary code, potentially leading to privilege escalation or persistent access within the environment. +search: '`sysmon` EventCode=10 TargetImage IN (*\\notepad.exe) NOT (SourceImage IN ("*\\system32\\*","*\\syswow64\\*","*\\Program Files\\*")) GrantedAccess IN ("0x40","0x1fffff") | stats count min(_time) as firstTime max(_time) as lastTime by CallTrace EventID GrantedAccess Guid Opcode ProcessID SecurityID SourceImage SourceProcessGUID SourceProcessId TargetImage TargetProcessGUID TargetProcessId UserID dest granted_access parent_process_exec parent_process_guid parent_process_id parent_process_name parent_process_path process_exec process_guid process_id process_name process_path signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_injection_into_notepad_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: False positives may be present based on SourceImage paths. If removing the paths is important, realize svchost and many native binaries inject into notepad consistently. Restrict or tune as needed. references: -- https://dominicbreuker.com/post/learning_sliver_c2_08_implant_basics/ -- https://www.cybereason.com/blog/sliver-c2-leveraged-by-many-threat-actors + - https://dominicbreuker.com/post/learning_sliver_c2_08_implant_basics/ + - https://www.cybereason.com/blog/sliver-c2-leveraged-by-many-threat-actors drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $SourceImage$ injecting into $TargetImage$ was identified - on endpoint $dest$. - risk_objects: - - field: dest - type: system - score: 32 - threat_objects: - - field: SourceImage - type: process - - field: TargetImage - type: process + message: An instance of $SourceImage$ injecting into $TargetImage$ was identified on endpoint $dest$. + risk_objects: + - field: dest + type: system + score: 32 + threat_objects: + - field: SourceImage + type: process + - field: TargetImage + type: process tags: - analytic_story: - - BishopFox Sliver Adversary Emulation Framework - - Earth Alux - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1055.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - BishopFox Sliver Adversary Emulation Framework + - Earth Alux + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1055.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/sliver/T1055_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/sliver/T1055_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_process_injection_of_wermgr_to_known_browser.yml b/detections/endpoint/windows_process_injection_of_wermgr_to_known_browser.yml index 275d59a911..1bcde4f0ca 100644 --- a/detections/endpoint/windows_process_injection_of_wermgr_to_known_browser.yml +++ b/detections/endpoint/windows_process_injection_of_wermgr_to_known_browser.yml @@ -5,68 +5,45 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic identifies the suspicious remote thread execution - of the wermgr.exe process into known browsers such as firefox.exe, chrome.exe, and - others. It leverages Sysmon EventCode 8 logs to detect this behavior by monitoring - SourceImage and TargetImage fields. This activity is significant because it is indicative - of Qakbot malware, which injects malicious code into legitimate processes to steal - information. If confirmed malicious, this activity could allow attackers to execute - arbitrary code, escalate privileges, and exfiltrate sensitive data from the compromised - host. +description: The following analytic identifies the suspicious remote thread execution of the wermgr.exe process into known browsers such as firefox.exe, chrome.exe, and others. It leverages Sysmon EventCode 8 logs to detect this behavior by monitoring SourceImage and TargetImage fields. This activity is significant because it is indicative of Qakbot malware, which injects malicious code into legitimate processes to steal information. If confirmed malicious, this activity could allow attackers to execute arbitrary code, escalate privileges, and exfiltrate sensitive data from the compromised host. data_source: -- Sysmon EventID 8 -search: '`sysmon` EventCode=8 SourceImage = "*\\wermgr.exe" TargetImage IN ("*\\firefox.exe", - "*\\chrome.exe", "*\\iexplore.exe","*\\microsoftedgecp.exe") | stats count min(_time) - as firstTime max(_time) as lastTime by EventID Guid NewThreadId ProcessID SecurityID - SourceImage SourceProcessGuid SourceProcessId StartAddress StartFunction StartModule - TargetImage TargetProcessGuid TargetProcessId UserID dest parent_process_exec parent_process_guid - parent_process_id parent_process_name parent_process_path process_exec process_guid - process_id process_name process_path signature signature_id user_id vendor_product - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_injection_of_wermgr_to_known_browser_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the SourceImage, TargetImage, and EventCode executions from your endpoints - related to create remote thread or injecting codes. If you are using Sysmon, you - must have at least version 6.0.4 of the Sysmon TA. + - Sysmon EventID 8 +search: '`sysmon` EventCode=8 SourceImage = "*\\wermgr.exe" TargetImage IN ("*\\firefox.exe", "*\\chrome.exe", "*\\iexplore.exe","*\\microsoftedgecp.exe") | stats count min(_time) as firstTime max(_time) as lastTime by EventID Guid NewThreadId ProcessID SecurityID SourceImage SourceProcessGuid SourceProcessId StartAddress StartFunction StartModule TargetImage TargetProcessGuid TargetProcessId UserID dest parent_process_exec parent_process_guid parent_process_id parent_process_name parent_process_path process_exec process_guid process_id process_name process_path signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_injection_of_wermgr_to_known_browser_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the SourceImage, TargetImage, and EventCode executions from your endpoints related to create remote thread or injecting codes. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: No false positives have been identified at this time. references: -- https://news.sophos.com/en-us/2022/03/10/qakbot-decoded/ -- https://www.trellix.com/en-us/about/newsroom/stories/research/demystifying-qbot-malware.html + - https://news.sophos.com/en-us/2022/03/10/qakbot-decoded/ + - https://www.trellix.com/en-us/about/newsroom/stories/research/demystifying-qbot-malware.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: wermgr.exe process $SourceImage$ create a remote thread to a browser process - $TargetImage$ in host $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: wermgr.exe process $SourceImage$ create a remote thread to a browser process $TargetImage$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Qakbot - asset_type: Endpoint - mitre_attack_id: - - T1055.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Qakbot + asset_type: Endpoint + mitre_attack_id: + - T1055.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/remote_thread/sysmon_wermgr_remote.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/remote_thread/sysmon_wermgr_remote.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_process_injection_remote_thread.yml b/detections/endpoint/windows_process_injection_remote_thread.yml index 07b54ccad6..a2eaba484a 100644 --- a/detections/endpoint/windows_process_injection_remote_thread.yml +++ b/detections/endpoint/windows_process_injection_remote_thread.yml @@ -5,81 +5,51 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk status: production type: TTP -description: - The following analytic detects suspicious remote thread execution in - processes such as Taskmgr.exe, calc.exe, and notepad.exe, which may indicate process - injection by malware like Qakbot. This detection leverages Sysmon EventCode 8 to - identify remote thread creation in specific target processes. This activity is significant - as it often signifies an attempt by malware to inject malicious code into legitimate - processes, potentially leading to unauthorized code execution. If confirmed malicious, - this could allow attackers to execute arbitrary code, escalate privileges, or maintain - persistence on the compromised host. +description: The following analytic detects suspicious remote thread execution in processes such as Taskmgr.exe, calc.exe, and notepad.exe, which may indicate process injection by malware like Qakbot. This detection leverages Sysmon EventCode 8 to identify remote thread creation in specific target processes. This activity is significant as it often signifies an attempt by malware to inject malicious code into legitimate processes, potentially leading to unauthorized code execution. If confirmed malicious, this could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence on the compromised host. data_source: - - Sysmon EventID 8 -search: - '`sysmon` EventCode=8 TargetImage IN ("*\\Taskmgr.exe", "*\\calc.exe", "*\\notepad.exe", - "*\\rdpclip.exe", "*\\explorer.exe", "*\\wermgr.exe", "*\\ping.exe", "*\\OneDriveSetup.exe", - "*\\dxdiag.exe", "*\\mobsync.exe", "*\\msra.exe", "*\\xwizard.exe","*\\cmd.exe", - "*\\powershell.exe") | stats count min(_time) as firstTime max(_time) as lastTime - by EventID Guid NewThreadId ProcessID SecurityID SourceImage SourceProcessGuid SourceProcessId - StartAddress StartFunction StartModule TargetImage TargetProcessGuid TargetProcessId - UserID dest parent_process_exec parent_process_guid parent_process_id parent_process_name - parent_process_path process_exec process_guid process_id process_name process_path - signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_process_injection_remote_thread_filter`' -how_to_implement: - To successfully implement this search, you must be ingesting data - that records process activity from your hosts like remote thread EventCode=8 of - sysmon. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon - TA. + - Sysmon EventID 8 +search: '`sysmon` EventCode=8 TargetImage IN ("*\\Taskmgr.exe", "*\\calc.exe", "*\\notepad.exe", "*\\rdpclip.exe", "*\\explorer.exe", "*\\wermgr.exe", "*\\ping.exe", "*\\OneDriveSetup.exe", "*\\dxdiag.exe", "*\\mobsync.exe", "*\\msra.exe", "*\\xwizard.exe","*\\cmd.exe", "*\\powershell.exe") | stats count min(_time) as firstTime max(_time) as lastTime by EventID Guid NewThreadId ProcessID SecurityID SourceImage SourceProcessGuid SourceProcessId StartAddress StartFunction StartModule TargetImage TargetProcessGuid TargetProcessId UserID dest parent_process_exec parent_process_guid parent_process_id parent_process_name parent_process_path process_exec process_guid process_id process_name process_path signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_injection_remote_thread_filter`' +how_to_implement: To successfully implement this search, you must be ingesting data that records process activity from your hosts like remote thread EventCode=8 of sysmon. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: No false positives have been identified at this time. references: - - https://twitter.com/pr0xylife/status/1585612370441031680?s=46&t=Dc3CJi4AnM-8rNoacLbScg - - https://thedfirreport.com/2023/06/12/a-truly-graceful-wipe-out/ + - https://twitter.com/pr0xylife/status/1585612370441031680?s=46&t=Dc3CJi4AnM-8rNoacLbScg + - https://thedfirreport.com/2023/06/12/a-truly-graceful-wipe-out/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - process $SourceImage$ create a remote thread to process $TargetImage$ on - host $dest$ - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: - - field: SourceImage - type: process + message: process $SourceImage$ create a remote thread to process $TargetImage$ on host $dest$ + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: + - field: SourceImage + type: process tags: - analytic_story: - - Qakbot - - Graceful Wipe Out Attack - - Warzone RAT - - Earth Alux - - Water Gamayun - asset_type: Endpoint - mitre_attack_id: - - T1055.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Qakbot + - Graceful Wipe Out Attack + - Warzone RAT + - Earth Alux + - Water Gamayun + asset_type: Endpoint + mitre_attack_id: + - T1055.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot_wermgr2/sysmon_wermgr2.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot_wermgr2/sysmon_wermgr2.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_process_injection_wermgr_child_process.yml b/detections/endpoint/windows_process_injection_wermgr_child_process.yml index 2029171236..37c9f8a636 100644 --- a/detections/endpoint/windows_process_injection_wermgr_child_process.yml +++ b/detections/endpoint/windows_process_injection_wermgr_child_process.yml @@ -1,80 +1,66 @@ name: Windows Process Injection Wermgr Child Process id: 360ae6b0-38b5-4328-9e2b-bc9436cddb17 -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies a suspicious instance of wermgr.exe - spawning a child process unrelated to error or fault handling. This detection leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process relationships - and command-line executions. This activity is significant as it can indicate Qakbot - malware, which injects malicious code into wermgr.exe to evade detection and execute - malicious actions. If confirmed malicious, this behavior could allow an attacker - to conduct reconnaissance, execute arbitrary code, and persist within the network, - posing a severe security risk. +description: The following analytic identifies a suspicious instance of wermgr.exe spawning a child process unrelated to error or fault handling. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process relationships and command-line executions. This activity is significant as it can indicate Qakbot malware, which injects malicious code into wermgr.exe to evade detection and execute malicious actions. If confirmed malicious, this behavior could allow an attacker to conduct reconnaissance, execute arbitrary code, and persist within the network, posing a severe security risk. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name - = "wermgr.exe" AND NOT (Processes.process_name IN ("WerFaultSecure.exe", "wermgr.exe", - "WerFault.exe")) by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_injection_wermgr_child_process_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name = "wermgr.exe" + AND + NOT (Processes.process_name IN ("WerFaultSecure.exe", "wermgr.exe", "WerFault.exe")) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_process_injection_wermgr_child_process_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://twitter.com/pr0xylife/status/1585612370441031680?s=46&t=Dc3CJi4AnM-8rNoacLbScg + - https://twitter.com/pr0xylife/status/1585612370441031680?s=46&t=Dc3CJi4AnM-8rNoacLbScg drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: wermgr parent process has a child process $process_name$ on $dest$ - risk_objects: - - field: dest - type: system - score: 56 - threat_objects: [] + message: wermgr parent process has a child process $process_name$ on $dest$ + risk_objects: + - field: dest + type: system + score: 56 + threat_objects: [] tags: - analytic_story: - - Qakbot - - Windows Error Reporting Service Elevation of Privilege Vulnerability - asset_type: Endpoint - mitre_attack_id: - - T1055 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Qakbot + - Windows Error Reporting Service Elevation of Privilege Vulnerability + asset_type: Endpoint + mitre_attack_id: + - T1055 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot_wermgr/sysmon_wermgr.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot_wermgr/sysmon_wermgr.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_process_injection_with_public_source_path.yml b/detections/endpoint/windows_process_injection_with_public_source_path.yml index b4c3e4ea6c..9fef0dfa92 100644 --- a/detections/endpoint/windows_process_injection_with_public_source_path.yml +++ b/detections/endpoint/windows_process_injection_with_public_source_path.yml @@ -5,47 +5,29 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects a process from a non-standard file path - on Windows attempting to create a remote thread in another process. This is identified - using Sysmon EventCode 8, focusing on processes not originating from typical system - directories. This behavior is significant as it often indicates process injection, - a technique used by adversaries to evade detection or escalate privileges. If confirmed - malicious, this activity could allow an attacker to execute arbitrary code within - another process, potentially leading to unauthorized actions and further compromise - of the system. +description: The following analytic detects a process from a non-standard file path on Windows attempting to create a remote thread in another process. This is identified using Sysmon EventCode 8, focusing on processes not originating from typical system directories. This behavior is significant as it often indicates process injection, a technique used by adversaries to evade detection or escalate privileges. If confirmed malicious, this activity could allow an attacker to execute arbitrary code within another process, potentially leading to unauthorized actions and further compromise of the system. data_source: -- Sysmon EventID 8 -search: '`sysmon` EventCode=8 TargetImage = "*.exe" AND NOT(SourceImage IN("C:\\Windows\\*", - "C:\\Program File*", "%systemroot%\\*")) | stats count min(_time) as firstTime max(_time) - as lastTime by EventID Guid NewThreadId ProcessID SecurityID SourceImage SourceProcessGuid - SourceProcessId StartAddress StartFunction StartModule TargetImage TargetProcessGuid - TargetProcessId UserID dest parent_process_exec parent_process_guid parent_process_id - parent_process_name parent_process_path process_exec process_guid process_id process_name - process_path signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_process_injection_with_public_source_path_filter`' -how_to_implement: To successfully implement this search, you must be ingesting data - that records process activity from your hosts to populate the endpoint data model - in the processes node. If you are using Sysmon, you must have at least version 6.0.4 - of the Sysmon TA. -known_false_positives: Some security products or third party applications may utilize - CreateRemoteThread, filter as needed. + - Sysmon EventID 8 +search: '`sysmon` EventCode=8 TargetImage = "*.exe" AND NOT(SourceImage IN("C:\\Windows\\*", "C:\\Program File*", "%systemroot%\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by EventID Guid NewThreadId ProcessID SecurityID SourceImage SourceProcessGuid SourceProcessId StartAddress StartFunction StartModule TargetImage TargetProcessGuid TargetProcessId UserID dest parent_process_exec parent_process_guid parent_process_id parent_process_name parent_process_path process_exec process_guid process_id process_name process_path signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_injection_with_public_source_path_filter`' +how_to_implement: To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: Some security products or third party applications may utilize CreateRemoteThread, filter as needed. references: -- https://unit42.paloaltonetworks.com/brute-ratel-c4-tool/ + - https://unit42.paloaltonetworks.com/brute-ratel-c4-tool/ tags: - analytic_story: - - Brute Ratel C4 - - Earth Alux - asset_type: Endpoint - mitre_attack_id: - - T1055.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Brute Ratel C4 + - Earth Alux + asset_type: Endpoint + mitre_attack_id: + - T1055.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/create_remote_thread/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/create_remote_thread/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_process_with_namedpipe_commandline.yml b/detections/endpoint/windows_process_with_namedpipe_commandline.yml index e5069026bf..7e8d4a77c4 100644 --- a/detections/endpoint/windows_process_with_namedpipe_commandline.yml +++ b/detections/endpoint/windows_process_with_namedpipe_commandline.yml @@ -5,75 +5,46 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects processes with command lines containing - named pipes. It leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process command-line executions. This behavior is significant as it - is often used by adversaries, such as those behind the Olympic Destroyer malware, - for inter-process communication post-injection, aiding in defense evasion and privilege - escalation. If confirmed malicious, this activity could allow attackers to maintain - persistence, escalate privileges, or evade defenses, potentially leading to further - compromise of the system. +description: The following analytic detects processes with command lines containing named pipes. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process command-line executions. This behavior is significant as it is often used by adversaries, such as those behind the Olympic Destroyer malware, for inter-process communication post-injection, aiding in defense evasion and privilege escalation. If confirmed malicious, this activity could allow attackers to maintain persistence, escalate privileges, or evade defenses, potentially leading to further compromise of the system. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process = "*\\\\.\\pipe\\*" - NOT Processes.process_path IN ("C:\\Program Files\\*", "C:\\Program Files (x86)\\*") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_with_namedpipe_commandline_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Normal browser application may use this technique. Please update - the filter macros to remove false positives. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*\\\\.\\pipe\\*" NOT Processes.process_path IN ("C:\\Program Files\\*", "C:\\Program Files (x86)\\*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_with_namedpipe_commandline_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Normal browser application may use this technique. Please update the filter macros to remove false positives. references: -- https://blog.talosintelligence.com/2018/02/olympic-destroyer.html + - https://blog.talosintelligence.com/2018/02/olympic-destroyer.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Process with named pipe in $process$ on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Process with named pipe in $process$ on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - asset_type: Endpoint - mitre_attack_id: - - T1055 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + asset_type: Endpoint + mitre_attack_id: + - T1055 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/olympic_destroyer/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/olympic_destroyer/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_process_with_netexec_command_line_parameters.yml b/detections/endpoint/windows_process_with_netexec_command_line_parameters.yml index 9bf25ce3e5..c4359441b0 100644 --- a/detections/endpoint/windows_process_with_netexec_command_line_parameters.yml +++ b/detections/endpoint/windows_process_with_netexec_command_line_parameters.yml @@ -1,99 +1,87 @@ name: Windows Process With NetExec Command Line Parameters id: adbff89c-c1f2-4a2e-88a4-b5e645856510 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Steven Dick, Github Community status: production type: TTP -description: The following analytic detects the use of NetExec (formally CrackmapExec) - a toolset used for post-exploitation enumeration and attack within Active Directory - environments through command line parameters. It leverages Endpoint Detection and - Response (EDR) data to identify specific command-line arguments associated with - actions like ticket manipulation, kerberoasting, and password spraying. This activity - is significant as NetExec is used by adversaries to exploit Kerberos for privilege - escalation and lateral movement. If confirmed malicious, this could lead to unauthorized - access, persistence, and potential compromise of sensitive information within the - network. +description: The following analytic detects the use of NetExec (formally CrackmapExec) a toolset used for post-exploitation enumeration and attack within Active Directory environments through command line parameters. It leverages Endpoint Detection and Response (EDR) data to identify specific command-line arguments associated with actions like ticket manipulation, kerberoasting, and password spraying. This activity is significant as NetExec is used by adversaries to exploit Kerberos for privilege escalation and lateral movement. If confirmed malicious, this could lead to unauthorized access, persistence, and potential compromise of sensitive information within the network. data_source: -- Windows Event Log Security 4688 -- Sysmon EventID 1 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) AS firstTime, - max(_time) AS lastTime FROM datamodel=Endpoint.Processes where NOT Processes.os="Linux" - Processes.process_name IN ("nxc.exe") OR Processes.original_file_name IN ("nxc.exe") - OR (Processes.process IN ("* smb *","* ssh *","* ldap *","* ftp *","* wmi *","* - winrm *","* rdp *","* vnc *","* mssql *","* nfs *") AND ((Processes.process = "* - -p *" AND Processes.process = "* -u *") OR Processes.process IN ("* -x *","* -M - *","* --*"))) by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - |`drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_process_with_netexec_command_line_parameters_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although unlikely, legitimate applications may use the same - command line parameters as NetExec. Filter as needed. + - Windows Event Log Security 4688 + - Sysmon EventID 1 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) AS firstTime, max(_time) AS lastTime FROM datamodel=Endpoint.Processes + WHERE NOT Processes.os="Linux" Processes.process_name IN ("nxc.exe") + OR + Processes.original_file_name IN ("nxc.exe") + OR + (Processes.process IN ("* smb *","* ssh *","* ldap *","* ftp *","* wmi *","* winrm *","* rdp *","* vnc *","* mssql *","* nfs *") + AND + ((Processes.process = "* -p *" + AND + Processes.process = "* -u *") + OR + Processes.process IN ("* -x *","* -M *","* --*"))) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_process_with_netexec_command_line_parameters_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although unlikely, legitimate applications may use the same command line parameters as NetExec. Filter as needed. references: -- https://www.netexec.wiki/ -- https://www.johnvictorwolfe.com/2024/07/21/the-successor-to-crackmapexec/ -- https://attack.mitre.org/software/S0488/ + - https://www.netexec.wiki/ + - https://www.johnvictorwolfe.com/2024/07/21/the-successor-to-crackmapexec/ + - https://attack.mitre.org/software/S0488/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$","$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate processes on $dest$ - search: '| from datamodel:Endpoint.Processes | search dest=$dest$ process_name = - $process_name$' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$","$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate processes on $dest$ + search: '| from datamodel:Endpoint.Processes | search dest=$dest$ process_name = $process_name$' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: NetExec command line parameters were used on $dest$ by $user$ - risk_objects: - - field: user - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: NetExec command line parameters were used on $dest$ by $user$ + risk_objects: + - field: user + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - Active Directory Kerberos Attacks - - Active Directory Privilege Escalation - asset_type: Endpoint - mitre_attack_id: - - T1550.003 - - T1558.003 - - T1558.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Kerberos Attacks + - Active Directory Privilege Escalation + asset_type: Endpoint + mitre_attack_id: + - T1550.003 + - T1558.003 + - T1558.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550/netexec_toolkit_usage/netexec_toolkit_usage.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550/netexec_toolkit_usage/netexec_toolkit_usage.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_process_writing_file_to_world_writable_path.yml b/detections/endpoint/windows_process_writing_file_to_world_writable_path.yml index 9cdfa1d000..a55139675b 100644 --- a/detections/endpoint/windows_process_writing_file_to_world_writable_path.yml +++ b/detections/endpoint/windows_process_writing_file_to_world_writable_path.yml @@ -4,66 +4,32 @@ version: 8 date: '2025-10-21' author: Michael Haag, Splunk data_source: - - Sysmon EventID 11 + - Sysmon EventID 11 type: Hunting status: production -description: The following analytic identifies a process writing a .txt file to - a world writable path. This detection leverages data from Endpoint Detection - and Response (EDR) agents, focusing on file creation events within specific - directories. This activity is significant as adversaries often use such - techniques to deliver payloads to a system, which is uncommon for legitimate - processes. If confirmed malicious, this behavior could allow attackers to - execute arbitrary code, escalate privileges, or maintain persistence within - the environment, posing a significant security risk. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name=*.txt - Filesystem.file_path IN ("*\\Windows\\Tasks\\*", "*\\Windows\\Temp\\*", "*\\Windows\\tracing\\*", - "*\\Windows\\PLA\\Reports\\*", "*\\Windows\\PLA\\Rules\\*", "*\\Windows\\PLA\\Templates\\*", - "*\\Windows\\PLA\\Reports\\en-US\\*", "*\\Windows\\PLA\\Rules\\en-US\\*", "*\\Windows\\Registration\\CRMLog\\*", - "*\\Windows\\System32\\Tasks\\*", "*\\Windows\\System32\\Com\\dmp\\*", "*\\Windows\\System32\\LogFiles\\WMI\\*", - "*\\Windows\\System32\\Microsoft\\Crypto\\RSA\\MachineKeys\\*", "*\\Windows\\System32\\spool\\PRINTERS\\*", - "*\\Windows\\System32\\spool\\SERVERS\\*", "*\\Windows\\System32\\spool\\drivers\\color\\*", - "*\\Windows\\System32\\Tasks\\Microsoft\\Windows\\RemoteApp and Desktop Connections - Update\\*", "*\\Windows\\SysWOW64\\Tasks\\*", "*\\Windows\\SysWOW64\\Com\\dmp\\*", - "*\\Windows\\SysWOW64\\Tasks\\Microsoft\\Windows\\PLA\\*", "*\\Windows\\SysWOW64\\Tasks\\Microsoft\\Windows\\RemoteApp - and Desktop Connections Update\\*", "*\\Windows\\SysWOW64\\Tasks\\Microsoft\\Windows\\PLA\\System\\*") - by Filesystem.dest, Filesystem.user, Filesystem.file_name Filesystem.file_path | - `drop_dm_object_name("Filesystem")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_process_writing_file_to_world_writable_path_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the file creation - event, process name, file path and, file name. These logs must be processed - using the appropriate Splunk Technology Add-ons that are specific to the EDR - product. The logs must also be mapped to the `Filesystem` node of the - `Endpoint` data model. Use the Splunk Common Information Model (CIM) to - normalize the field names and speed up the data modeling process. -known_false_positives: False positives may occur if legitimate software writes - to these paths. Modify the search to include additional file name extensions. - To enhance it further, adding a join on Processes.process_name may assist with - restricting the analytic to specific process names. Investigate the process - and file to determine if it is malicious. +description: The following analytic identifies a process writing a .txt file to a world writable path. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on file creation events within specific directories. This activity is significant as adversaries often use such techniques to deliver payloads to a system, which is uncommon for legitimate processes. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment, posing a significant security risk. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name=*.txt Filesystem.file_path IN ("*\\Windows\\Tasks\\*", "*\\Windows\\Temp\\*", "*\\Windows\\tracing\\*", "*\\Windows\\PLA\\Reports\\*", "*\\Windows\\PLA\\Rules\\*", "*\\Windows\\PLA\\Templates\\*", "*\\Windows\\PLA\\Reports\\en-US\\*", "*\\Windows\\PLA\\Rules\\en-US\\*", "*\\Windows\\Registration\\CRMLog\\*", "*\\Windows\\System32\\Tasks\\*", "*\\Windows\\System32\\Com\\dmp\\*", "*\\Windows\\System32\\LogFiles\\WMI\\*", "*\\Windows\\System32\\Microsoft\\Crypto\\RSA\\MachineKeys\\*", "*\\Windows\\System32\\spool\\PRINTERS\\*", "*\\Windows\\System32\\spool\\SERVERS\\*", "*\\Windows\\System32\\spool\\drivers\\color\\*", "*\\Windows\\System32\\Tasks\\Microsoft\\Windows\\RemoteApp and Desktop Connections Update\\*", "*\\Windows\\SysWOW64\\Tasks\\*", "*\\Windows\\SysWOW64\\Com\\dmp\\*", "*\\Windows\\SysWOW64\\Tasks\\Microsoft\\Windows\\PLA\\*", "*\\Windows\\SysWOW64\\Tasks\\Microsoft\\Windows\\RemoteApp and Desktop Connections Update\\*", "*\\Windows\\SysWOW64\\Tasks\\Microsoft\\Windows\\PLA\\System\\*") by Filesystem.dest, Filesystem.user, Filesystem.file_name Filesystem.file_path | `drop_dm_object_name("Filesystem")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_writing_file_to_world_writable_path_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the file creation event, process name, file path and, file name. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Filesystem` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may occur if legitimate software writes to these paths. Modify the search to include additional file name extensions. To enhance it further, adding a join on Processes.process_name may assist with restricting the analytic to specific process names. Investigate the process and file to determine if it is malicious. references: -- https://research.splunk.com/endpoint/efbcf8ee-bc75-47f1-8985-a5c638c4faf0/ + - https://research.splunk.com/endpoint/efbcf8ee-bc75-47f1-8985-a5c638c4faf0/ tags: - analytic_story: - - APT29 Diplomatic Deceptions with WINELOADER - - PHP-CGI RCE Attack on Japanese Organizations - - PathWiper - asset_type: Endpoint - mitre_attack_id: - - T1218.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - APT29 Diplomatic Deceptions with WINELOADER + - PHP-CGI RCE Attack on Japanese Organizations + - PathWiper + asset_type: Endpoint + mitre_attack_id: + - T1218.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/mshta_tasks_windows-sysmon.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/mshta_tasks_windows-sysmon.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational diff --git a/detections/endpoint/windows_processes_killed_by_industroyer2_malware.yml b/detections/endpoint/windows_processes_killed_by_industroyer2_malware.yml index 7feb10adf2..c1a6585b7f 100644 --- a/detections/endpoint/windows_processes_killed_by_industroyer2_malware.yml +++ b/detections/endpoint/windows_processes_killed_by_industroyer2_malware.yml @@ -1,69 +1,58 @@ name: Windows Processes Killed By Industroyer2 Malware id: d8bea5ca-9d4a-4249-8b56-64a619109835 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the termination of specific processes - by the Industroyer2 malware. It leverages Sysmon EventCode 5 to identify when processes - like "PServiceControl.exe" and "PService_PPD.exe" are killed. This activity is significant - as it targets processes related to energy facility networks, indicating a potential - attack on critical infrastructure. If confirmed malicious, this could lead to disruption - of essential services, loss of control over energy systems, and significant operational - impact. Immediate investigation is required to determine the cause and mitigate - any potential threats. +description: The following analytic detects the termination of specific processes by the Industroyer2 malware. It leverages Sysmon EventCode 5 to identify when processes like "PServiceControl.exe" and "PService_PPD.exe" are killed. This activity is significant as it targets processes related to energy facility networks, indicating a potential attack on critical infrastructure. If confirmed malicious, this could lead to disruption of essential services, loss of control over energy systems, and significant operational impact. Immediate investigation is required to determine the cause and mitigate any potential threats. data_source: -- Sysmon EventID 5 -search: '`sysmon` EventCode=5 process_name IN ("PServiceControl.exe", "PService_PPD.exe") - | stats min(_time) as firstTime max(_time) as lastTime count by dest process process_exec - process_guid process_id process_name process_path signature signature_id user_id - vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_processes_killed_by_industroyer2_malware_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also - required. -known_false_positives: False positives are possible if legitimate applications are - allowed to terminate this process during testing or updates. Filter as needed based - on paths that are used legitimately. + - Sysmon EventID 5 +search: |- + `sysmon` EventCode=5 process_name IN ("PServiceControl.exe", "PService_PPD.exe") + | stats min(_time) as firstTime max(_time) as lastTime count + BY dest process process_exec + process_guid process_id process_name + process_path signature signature_id + user_id vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_processes_killed_by_industroyer2_malware_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also required. +known_false_positives: False positives are possible if legitimate applications are allowed to terminate this process during testing or updates. Filter as needed based on paths that are used legitimately. references: -- https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ + - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: process was terminated $process_name$ on $dest$ - risk_objects: - - field: dest - type: system - score: 36 - threat_objects: [] + message: process was terminated $process_name$ on $dest$ + risk_objects: + - field: dest + type: system + score: 36 + threat_objects: [] tags: - analytic_story: - - Data Destruction - - Industroyer2 - asset_type: Endpoint - mitre_attack_id: - - T1489 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Destruction + - Industroyer2 + asset_type: Endpoint + mitre_attack_id: + - T1489 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/industroyer2/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/industroyer2/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_protocol_tunneling_with_plink.yml b/detections/endpoint/windows_protocol_tunneling_with_plink.yml index db766dd883..4a7bd3e9e1 100644 --- a/detections/endpoint/windows_protocol_tunneling_with_plink.yml +++ b/detections/endpoint/windows_protocol_tunneling_with_plink.yml @@ -1,98 +1,83 @@ name: Windows Protocol Tunneling with Plink id: 8aac5e1e-0fab-4437-af0b-c6e60af23eed -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: This analytic detects the use of Plink (including renamed versions like - pvhost.exe) for protocol tunneling, which may be used for egress or lateral movement - within an organization. It identifies specific command-line options (-R, -L, -D, - -l, -N, -P, -pw) commonly used for port forwarding and tunneling by analyzing process - execution logs from Endpoint Detection and Response (EDR) agents. This activity - is significant as it may indicate an attempt to bypass network security controls - or establish unauthorized connections. If confirmed malicious, this could allow - an attacker to exfiltrate data, move laterally across the network, or maintain persistent - access, posing a severe threat to the organization's security. The detection covers - both the original Plink executable and potential renamed versions, enhancing its - ability to catch evasion attempts. +description: This analytic detects the use of Plink (including renamed versions like pvhost.exe) for protocol tunneling, which may be used for egress or lateral movement within an organization. It identifies specific command-line options (-R, -L, -D, -l, -N, -P, -pw) commonly used for port forwarding and tunneling by analyzing process execution logs from Endpoint Detection and Response (EDR) agents. This activity is significant as it may indicate an attempt to bypass network security controls or establish unauthorized connections. If confirmed malicious, this could allow an attacker to exfiltrate data, move laterally across the network, or maintain persistent access, posing a severe threat to the organization's security. The detection covers both the original Plink executable and potential renamed versions, enhancing its ability to catch evasion attempts. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=plink.exe - OR Processes.process_name=pvhost.exe OR Processes.original_file_name=Plink) AND - Processes.process IN ("*-R *", "*-L *", "*-D *", "*-l *", "*-N *", "*-P *", "*-pw - *") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| - `windows_protocol_tunneling_with_plink_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present if the organization allows for - SSH tunneling outbound or internally. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name=plink.exe + OR + Processes.process_name=pvhost.exe + OR + Processes.original_file_name=Plink + ) + AND Processes.process IN ("*-R *", "*-L *", "*-D *", "*-l *", "*-N *", "*-P *", "*-pw *") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_protocol_tunneling_with_plink_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present if the organization allows for SSH tunneling outbound or internally. Filter as needed. references: -- https://thedfirreport.com/2022/06/06/will-the-real-msiexec-please-stand-up-exploit-leads-to-data-exfiltration/ -- https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html -- https://attack.mitre.org/techniques/T1572/ -- https://documentation.help/PuTTY/using-cmdline-portfwd.html#S3.8.3.5 -- https://media.defense.gov/2024/Jul/25/2003510137/-1/-1/0/Joint-CSA-North-Korea-Cyber-Espionage-Advance-Military-Nuclear-Programs.PDF -- https://blog.talosintelligence.com/lazarus-three-rats/ + - https://thedfirreport.com/2022/06/06/will-the-real-msiexec-please-stand-up-exploit-leads-to-data-exfiltration/ + - https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html + - https://attack.mitre.org/techniques/T1572/ + - https://documentation.help/PuTTY/using-cmdline-portfwd.html#S3.8.3.5 + - https://media.defense.gov/2024/Jul/25/2003510137/-1/-1/0/Joint-CSA-North-Korea-Cyber-Espionage-Advance-Military-Nuclear-Programs.PDF + - https://blog.talosintelligence.com/lazarus-three-rats/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to tunnel to a remote destination. - risk_objects: - - field: user - type: user - score: 56 - - field: dest - type: system - score: 56 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to tunnel to a remote destination. + risk_objects: + - field: user + type: user + score: 56 + - field: dest + type: system + score: 56 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - CISA AA22-257A - asset_type: Endpoint - mitre_attack_id: - - T1572 - - T1021.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA22-257A + asset_type: Endpoint + mitre_attack_id: + - T1572 + - T1021.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1572/plink/plink-windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1572/plink/plink-windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_proxy_via_netsh.yml b/detections/endpoint/windows_proxy_via_netsh.yml index c4bca1a5b5..76c2610d06 100644 --- a/detections/endpoint/windows_proxy_via_netsh.yml +++ b/detections/endpoint/windows_proxy_via_netsh.yml @@ -1,88 +1,68 @@ name: Windows Proxy Via Netsh id: c137bfe8-6036-4cff-b77b-4e327dd0a1cf -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic identifies the use of netsh.exe to configure a - connection proxy, which can be leveraged for persistence by executing a helper DLL. - It detects this activity by analyzing process creation events from Endpoint Detection - and Response (EDR) agents, focusing on command-line executions involving "portproxy" - and "v4tov4" parameters. This activity is significant because it indicates potential - unauthorized network configuration changes, which could be used to maintain persistence - or redirect network traffic. If confirmed malicious, this could allow an attacker - to maintain covert access or manipulate network communications, posing a significant - security risk. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_netsh` Processes.process - = "* portproxy *" Processes.process = "* v4tov4 *" by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - |`drop_dm_object_name("Processes")` |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` - | `windows_proxy_via_netsh_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Some VPN applications are known to launch netsh.exe. Outside - of these instances, it is unusual for an executable to launch netsh.exe and run - commands. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic identifies the use of netsh.exe to configure a connection proxy, which can be leveraged for persistence by executing a helper DLL. It detects this activity by analyzing process creation events from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving "portproxy" and "v4tov4" parameters. This activity is significant because it indicates potential unauthorized network configuration changes, which could be used to maintain persistence or redirect network traffic. If confirmed malicious, this could allow an attacker to maintain covert access or manipulate network communications, posing a significant security risk. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_netsh` Processes.process = "* portproxy *" Processes.process = "* v4tov4 *" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_proxy_via_netsh_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Some VPN applications are known to launch netsh.exe. Outside of these instances, it is unusual for an executable to launch netsh.exe and run commands. references: -- https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ + - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process $process_name$ has launched netsh with command-line $process$ - on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - - field: user - type: user - score: 49 - threat_objects: [] + message: A process $process_name$ has launched netsh with command-line $process$ on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - Volt Typhoon - asset_type: Endpoint - atomic_guid: - - b8223ea9-4be2-44a6-b50a-9657a3d4e72a - mitre_attack_id: - - T1090.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Volt Typhoon + asset_type: Endpoint + atomic_guid: + - b8223ea9-4be2-44a6-b50a-9657a3d4e72a + mitre_attack_id: + - T1090.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1090.001/netsh_portproxy/volt_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1090.001/netsh_portproxy/volt_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_proxy_via_registry.yml b/detections/endpoint/windows_proxy_via_registry.yml index d327c6d0ea..aea95cfe07 100644 --- a/detections/endpoint/windows_proxy_via_registry.yml +++ b/detections/endpoint/windows_proxy_via_registry.yml @@ -6,67 +6,45 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: -- Sysmon EventID 13 -description: The following analytic detects the modification of registry keys related - to the Windows Proxy settings via netsh.exe. It leverages data from the Endpoint.Registry - data model, focusing on changes to the registry path "*\\System\\CurrentControlSet\\Services\\PortProxy\\v4tov4\\tcp*". - This activity is significant because netsh.exe can be used to establish a persistent - proxy, potentially allowing an attacker to execute a helper DLL whenever netsh.exe - runs. If confirmed malicious, this could enable the attacker to maintain persistence, - manipulate network configurations, and potentially exfiltrate data or further compromise - the system. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime - max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path - ="*\\System\\CurrentControlSet\\Services\\PortProxy\\v4tov4\\tcp*" by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` - | `windows_proxy_via_registry_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 + - Sysmon EventID 13 +description: The following analytic detects the modification of registry keys related to the Windows Proxy settings via netsh.exe. It leverages data from the Endpoint.Registry data model, focusing on changes to the registry path "*\\System\\CurrentControlSet\\Services\\PortProxy\\v4tov4\\tcp*". This activity is significant because netsh.exe can be used to establish a persistent proxy, potentially allowing an attacker to execute a helper DLL whenever netsh.exe runs. If confirmed malicious, this could enable the attacker to maintain persistence, manipulate network configurations, and potentially exfiltrate data or further compromise the system. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path ="*\\System\\CurrentControlSet\\Services\\PortProxy\\v4tov4\\tcp*" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `windows_proxy_via_registry_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 known_false_positives: No false positives have been identified at this time. references: -- https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ + - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A registry modification for port proxy in$dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: A registry modification for port proxy in$dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Volt Typhoon - asset_type: Endpoint - atomic_guid: - - b8223ea9-4be2-44a6-b50a-9657a3d4e72a - mitre_attack_id: - - T1090.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Volt Typhoon + asset_type: Endpoint + atomic_guid: + - b8223ea9-4be2-44a6-b50a-9657a3d4e72a + mitre_attack_id: + - T1090.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1090.001/netsh_portproxy/volt_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1090.001/netsh_portproxy/volt_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_pstools_recon_usage.yml b/detections/endpoint/windows_pstools_recon_usage.yml index 21fa6687ec..e3aad94f67 100644 --- a/detections/endpoint/windows_pstools_recon_usage.yml +++ b/detections/endpoint/windows_pstools_recon_usage.yml @@ -6,123 +6,118 @@ author: Nasreddine Bencherchali status: production type: Anomaly description: | - The following analytic identifies execution of Sysinternals PsTools and Sysinternals Suit binaries that are commonly used for reconnaissance and information gathering on - Windows endpoints. - PsTools (PsExec, PsFile, PsGetSid, PsInfo, PsPing, etc.) or Sysinternals Suit tools, are frequently used by administrators for legitimate maintenance but are also leveraged by threat actors to collect system, account, network and service information during discovery and lateral movement. - This detection focuses on process execution and PE metadata telemetry (OriginalFileName). - If confirmed malicious, this activity can indicate targeted reconnaissance and foothold escalation, enabling subsequent lateral movement or credential abuse. + The following analytic identifies execution of Sysinternals PsTools and Sysinternals Suit binaries that are commonly used for reconnaissance and information gathering on + Windows endpoints. + PsTools (PsExec, PsFile, PsGetSid, PsInfo, PsPing, etc.) or Sysinternals Suit tools, are frequently used by administrators for legitimate maintenance but are also leveraged by threat actors to collect system, account, network and service information during discovery and lateral movement. + This detection focuses on process execution and PE metadata telemetry (OriginalFileName). + If confirmed malicious, this activity can indicate targeted reconnaissance and foothold escalation, enabling subsequent lateral movement or credential abuse. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime - from datamodel=Endpoint.Processes where + from datamodel=Endpoint.Processes where - Processes.process_name IN ( - "PsGetsid.exe", - "PsGetsid64.exe", - "PsInfo.exe", - "PsInfo64.exe", - "pslist.exe", - "pslist64.exe", - "PsLoggedon.exe", - "PsLoggedon64.exe", - "psloglist.exe", - "psloglist64.exe", - "PsPing.exe", - "PsPing64.exe", - "PsService.exe", - "PsService64.exe", - "Tcpvcon.exe", - "Tcpvcon64.exe", - "Tcpvcon64a.exe" - ) - OR - Processes.original_file_name IN ( - "PsGetSid.exe", - "Psinfo.exe", - "pslist.exe", - "psloggedon.exe", - "psloglist.exe", - "psping.exe", - "psservice.exe", - "Tcpvcon.exe" - ) + Processes.process_name IN ( + "PsGetsid.exe", + "PsGetsid64.exe", + "PsInfo.exe", + "PsInfo64.exe", + "pslist.exe", + "pslist64.exe", + "PsLoggedon.exe", + "PsLoggedon64.exe", + "psloglist.exe", + "psloglist64.exe", + "PsPing.exe", + "PsPing64.exe", + "PsService.exe", + "PsService64.exe", + "Tcpvcon.exe", + "Tcpvcon64.exe", + "Tcpvcon64a.exe" + ) + OR + Processes.original_file_name IN ( + "PsGetSid.exe", + "Psinfo.exe", + "pslist.exe", + "psloggedon.exe", + "psloglist.exe", + "psping.exe", + "psservice.exe", + "Tcpvcon.exe" + ) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id - Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id - Processes.vendor_product + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec + Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id + Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id + Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_pstools_recon_usage_filter` + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_pstools_recon_usage_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: | - Legitimate administrators commonly use PsTools for troubleshooting and management. - False positives are likely in environments where PsTools are an approved operational toolset, or where automated management systems invoke them. - Tune by whitelisting approved management hosts, service accounts, and scheduled maintenance windows. + Legitimate administrators commonly use PsTools for troubleshooting and management. + False positives are likely in environments where PsTools are an approved operational toolset, or where automated management systems invoke them. + Tune by whitelisting approved management hosts, service accounts, and scheduled maintenance windows. references: - - https://learn.microsoft.com/sysinternals/downloads/pstools - - https://github.com/CyberMonitor/APT_CyberCriminal_Campagin_Collections/raw/master/2015/2015.09.17.Operation_Iron_Tiger/wp-operation-iron-tiger.pdf - - https://go.crowdstrike.com/rs/281-OBQ-266/images/Report2018OverwatchReport.pdf + - https://learn.microsoft.com/sysinternals/downloads/pstools + - https://github.com/CyberMonitor/APT_CyberCriminal_Campagin_Collections/raw/master/2015/2015.09.17.Operation_Iron_Tiger/wp-operation-iron-tiger.pdf + - https://go.crowdstrike.com/rs/281-OBQ-266/images/Report2018OverwatchReport.pdf drilldown_searches: - - name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: PsTools binary $process_name$ was executed on host $dest$. - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 30 - threat_objects: - - field: process_name - type: process_name + message: PsTools binary $process_name$ was executed on host $dest$. + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 30 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1082 - - T1046 - - T1018 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1082 + - T1046 + - T1018 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive - attack_data: - - data: https://github.com/Splunk/attack_data/raw/master/datasets/attack_techniques/T1082/sysinternals_pstools/sysinternals_pstools.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive + attack_data: + - data: https://github.com/Splunk/attack_data/raw/master/datasets/attack_techniques/T1082/sysinternals_pstools/sysinternals_pstools.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_pua_named_pipe.yml b/detections/endpoint/windows_pua_named_pipe.yml index 23b840bffa..bf343237bb 100644 --- a/detections/endpoint/windows_pua_named_pipe.yml +++ b/detections/endpoint/windows_pua_named_pipe.yml @@ -1,112 +1,106 @@ name: Windows PUA Named Pipe id: 95b11d20-e2c6-46a5-b526-8629f5f0860a -version: 1 -date: '2025-12-05' +version: 2 +date: '2026-02-25' author: Raven Tait, Splunk status: production type: Anomaly description: | - The following analytic detects the creation or connection to named pipes used by potentially unwanted applications (PUAs) like VPNs or utilities like PsExec. - It leverages Sysmon EventCodes 17 and 18. - If confirmed malicious, this could allow an attacker to abuse these to potentially gain persistence, command and control, or further system compromise. + The following analytic detects the creation or connection to named pipes used by potentially unwanted applications (PUAs) like VPNs or utilities like PsExec. + It leverages Sysmon EventCodes 17 and 18. + If confirmed malicious, this could allow an attacker to abuse these to potentially gain persistence, command and control, or further system compromise. data_source: - - Sysmon EventID 17 - - Sysmon EventID 18 + - Sysmon EventID 17 + - Sysmon EventID 18 search: | - `sysmon` - (EventCode=17 OR EventCode=18) - NOT process_path IN ( - "*:\\Program Files \(x86\)\\Adobe*", - "*:\\Program Files \(x86\)\\Google*", - "*:\\Program Files \(x86\)\\Microsoft*", - "*:\\Program Files\\Adobe*", - "*:\\Program Files\\dotnet\\dotnet.exe", - "*:\\Program Files\\Google*", - "*:\\Program Files\\Microsoft*", - "*:\\Windows\\system32\\SearchIndexer.exe", - "*:\\Windows\\System32\\svchost.exe", - "*:\\Windows\\SystemApps\\Microsoft*", - "*\\Amazon\\SSM\\Instance*", - "*\\AppData\\Local\\Google*", - "*\\AppData\\Local\\Kingsoft\\*", - "*\\AppData\\Local\\Microsoft*", - "System" - ) - - | stats min(_time) as firstTime max(_time) as lastTime - count by dest dvc process_exec process_guid process_id process_path signature signature_id - vendor_product pipe_name user_id Image process_name + `sysmon` + (EventCode=17 OR EventCode=18) + NOT process_path IN ( + "*:\\Program Files \(x86\)\\Adobe*", + "*:\\Program Files \(x86\)\\Google*", + "*:\\Program Files \(x86\)\\Microsoft*", + "*:\\Program Files\\Adobe*", + "*:\\Program Files\\dotnet\\dotnet.exe", + "*:\\Program Files\\Google*", + "*:\\Program Files\\Microsoft*", + "*:\\Windows\\system32\\SearchIndexer.exe", + "*:\\Windows\\System32\\svchost.exe", + "*:\\Windows\\SystemApps\\Microsoft*", + "*\\Amazon\\SSM\\Instance*", + "*\\AppData\\Local\\Google*", + "*\\AppData\\Local\\Kingsoft\\*", + "*\\AppData\\Local\\Microsoft*", + "System" + ) - | lookup pua_named_pipes pua_pipe_name AS pipe_name OUTPUT tool, description - | where isnotnull(tool) - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_pua_named_pipe_filter` + | stats min(_time) as firstTime max(_time) as lastTime + count by dest dvc process_exec process_guid process_id process_path signature signature_id + vendor_product pipe_name user_id Image process_name + + | lookup pua_named_pipes pua_pipe_name AS pipe_name OUTPUT tool, description + | where isnotnull(tool) + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_pua_named_pipe_filter` how_to_implement: | - To successfully implement this search, you need to be ingesting - logs with the process name and pipename from your endpoints. If you are using Sysmon, - you must have at least version 6.0.4 of the Sysmon TA. + To successfully implement this search, you need to be ingesting + logs with the process name and pipename from your endpoints. If you are using Sysmon, + you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: | - PUAs can be used in a legitimate manner. Therefore, some of the named pipes identified and added may cause false positives. - Filter by process name or pipe name to reduce false positives. + PUAs can be used in a legitimate manner. Therefore, some of the named pipes identified and added may cause false positives. + Filter by process name or pipe name to reduce false positives. references: -- https://attack.mitre.org/techniques/T1218/009/ -- https://docs.microsoft.com/en-us/windows/win32/ipc/named-pipes + - https://attack.mitre.org/techniques/T1218/009/ + - https://docs.microsoft.com/en-us/windows/win32/ipc/named-pipes drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $process_name$ located in $process_path$ was identified on endpoint $dest$ accessing - known named pipe $pipe_name$ from a potentially unwanted application in your environment. - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: - - field: process_name - type: process_name + message: An instance of $process_name$ located in $process_path$ was identified on endpoint $dest$ accessing known named pipe $pipe_name$ from a potentially unwanted application in your environment. + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Active Directory Lateral Movement - - BlackByte Ransomware - - Cactus Ransomware - - CISA AA22-320A - - DarkGate Malware - - DarkSide Ransomware - - DHS Report TA18-074A - - HAFNIUM Group - - IcedID - - Medusa Ransomware - - Rhysida Ransomware - - SamSam Ransomware - - Sandworm Tools - - Seashell Blizzard - - VanHelsing Ransomware - - Volt Typhoon - asset_type: Endpoint - mitre_attack_id: - - T1559 - - T1021.002 - - T1055 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + - BlackByte Ransomware + - Cactus Ransomware + - CISA AA22-320A + - DarkGate Malware + - DarkSide Ransomware + - DHS Report TA18-074A + - HAFNIUM Group + - IcedID + - Medusa Ransomware + - Rhysida Ransomware + - SamSam Ransomware + - Sandworm Tools + - Seashell Blizzard + - VanHelsing Ransomware + - Volt Typhoon + asset_type: Endpoint + mitre_attack_id: + - T1559 + - T1021.002 + - T1055 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/named_pipes/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/named_pipes/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_query_registry_browser_list_application.yml b/detections/endpoint/windows_query_registry_browser_list_application.yml index 044b5d333b..587045c9bc 100644 --- a/detections/endpoint/windows_query_registry_browser_list_application.yml +++ b/detections/endpoint/windows_query_registry_browser_list_application.yml @@ -6,66 +6,46 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: -- Windows Event Log Security 4663 -description: The following analytic detects a suspicious process accessing the registry - entries for default internet browsers. It leverages Windows Security Event logs, - specifically event code 4663, to identify access attempts to these registry paths. - This activity is significant because adversaries can exploit this registry key to - gather information about installed browsers and their settings, potentially leading - to the theft of sensitive data such as login credentials and browsing history. If - confirmed malicious, this behavior could enable attackers to exfiltrate sensitive - information and compromise user accounts. -search: '`wineventlog_security` EventCode=4663 object_file_path IN ("*\\SOFTWARE\\Clients\\StartMenuInternet\\*", - "*\\SOFTWARE\\Clients\\StartMenuInternet\\*") AND NOT process_path IN ("*:\\Windows\\System32\\*", - "*:\\Windows\\SysWow64\\*", *:\\Windows\\WinSxS\\*, "*:\\Program Files\\*", "*:\\Program Files (x86)\\*") | stats count - min(_time) as firstTime max(_time) as lastTime by object_file_name object_file_path - process_name process_path process_id EventCode dest | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_query_registry_browser_list_application_filter`' -how_to_implement: To successfully implement this search, you must ingest Windows Security - Event logs and track event code 4663. For 4663, enable "Audit Object Access" in - Group Policy. Then check the two boxes listed for both "Success" and "Failure." -known_false_positives: uninstall application may access this registry to remove the - entry of the target application. filter is needed. + - Windows Event Log Security 4663 +description: The following analytic detects a suspicious process accessing the registry entries for default internet browsers. It leverages Windows Security Event logs, specifically event code 4663, to identify access attempts to these registry paths. This activity is significant because adversaries can exploit this registry key to gather information about installed browsers and their settings, potentially leading to the theft of sensitive data such as login credentials and browsing history. If confirmed malicious, this behavior could enable attackers to exfiltrate sensitive information and compromise user accounts. +search: '`wineventlog_security` EventCode=4663 object_file_path IN ("*\\SOFTWARE\\Clients\\StartMenuInternet\\*", "*\\SOFTWARE\\Clients\\StartMenuInternet\\*") AND NOT process_path IN ("*:\\Windows\\System32\\*", "*:\\Windows\\SysWow64\\*", *:\\Windows\\WinSxS\\*, "*:\\Program Files\\*", "*:\\Program Files (x86)\\*") | stats count min(_time) as firstTime max(_time) as lastTime by object_file_name object_file_path process_name process_path process_id EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_query_registry_browser_list_application_filter`' +how_to_implement: To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure." +known_false_positives: uninstall application may access this registry to remove the entry of the target application. filter is needed. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.redline_stealer + - https://malpedia.caad.fkie.fraunhofer.de/details/win.redline_stealer drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious process accessing installed default browser registry on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A suspicious process accessing installed default browser registry on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - China-Nexus Threat Activity - - SnappyBee - - RedLine Stealer - - Salt Typhoon - asset_type: Endpoint - mitre_attack_id: - - T1012 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - China-Nexus Threat Activity + - SnappyBee + - RedLine Stealer + - Salt Typhoon + asset_type: Endpoint + mitre_attack_id: + - T1012 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/browser_list/ar3_4663_redline_reg.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/browser_list/ar3_4663_redline_reg.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_query_registry_uninstall_program_list.yml b/detections/endpoint/windows_query_registry_uninstall_program_list.yml index 69ebada9b6..13f412caf2 100644 --- a/detections/endpoint/windows_query_registry_uninstall_program_list.yml +++ b/detections/endpoint/windows_query_registry_uninstall_program_list.yml @@ -6,63 +6,45 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: -- Windows Event Log Security 4663 -description: The following analytic detects an access request on the uninstall registry - key. It leverages Windows Security Event logs, specifically event code 4663. This - activity is significant because adversaries or malware can exploit this key to gather - information about installed applications, aiding in further attacks. If confirmed - malicious, this behavior could allow attackers to map out installed software, potentially - identifying vulnerabilities or software to exploit, leading to further system compromise. -search: '`wineventlog_security` EventCode=4663 object_file_path="*\\Microsoft\\Windows\\CurrentVersion\\Uninstall\\*" - | stats count min(_time) as firstTime max(_time) as lastTime by object_file_name - object_file_path process_name process_path process_id EventCode dest | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_query_registry_uninstall_program_list_filter`' -how_to_implement: To successfully implement this search, you must ingest Windows Security - Event logs and track event code 4663. For Event code 4663, enable the "Audit Object - Access" in Group Policy. Then check the two boxes listed for both "Success" and - "Failure." -known_false_positives: Uninstallers may access this registry to remove the entry of - the target application. Filter as needed. + - Windows Event Log Security 4663 +description: The following analytic detects an access request on the uninstall registry key. It leverages Windows Security Event logs, specifically event code 4663. This activity is significant because adversaries or malware can exploit this key to gather information about installed applications, aiding in further attacks. If confirmed malicious, this behavior could allow attackers to map out installed software, potentially identifying vulnerabilities or software to exploit, leading to further system compromise. +search: '`wineventlog_security` EventCode=4663 object_file_path="*\\Microsoft\\Windows\\CurrentVersion\\Uninstall\\*" | stats count min(_time) as firstTime max(_time) as lastTime by object_file_name object_file_path process_name process_path process_id EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_query_registry_uninstall_program_list_filter`' +how_to_implement: To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For Event code 4663, enable the "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure." +known_false_positives: Uninstallers may access this registry to remove the entry of the target application. Filter as needed. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.redline_stealer + - https://malpedia.caad.fkie.fraunhofer.de/details/win.redline_stealer drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious process $process_name$ accessing uninstall registry on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A suspicious process $process_name$ accessing uninstall registry on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - StealC Stealer - - RedLine Stealer - - Meduza Stealer - asset_type: Endpoint - mitre_attack_id: - - T1012 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - StealC Stealer + - RedLine Stealer + - Meduza Stealer + asset_type: Endpoint + mitre_attack_id: + - T1012 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/recon_registry/recon-reg-redline-security-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/recon_registry/recon-reg-redline-security-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_raccine_scheduled_task_deletion.yml b/detections/endpoint/windows_raccine_scheduled_task_deletion.yml index ad39dc67d3..0957a3cf1b 100644 --- a/detections/endpoint/windows_raccine_scheduled_task_deletion.yml +++ b/detections/endpoint/windows_raccine_scheduled_task_deletion.yml @@ -1,88 +1,74 @@ name: Windows Raccine Scheduled Task Deletion id: c9f010da-57ab-11ec-82bd-acde48001122 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies the deletion of the Raccine Rules Updater - scheduled task using the `schtasks.exe` command. This detection leverages data from - Endpoint Detection and Response (EDR) agents, focusing on process names and command-line - executions. This activity is significant because adversaries may delete this task - to disable Raccine, a tool designed to prevent ransomware attacks. If confirmed - malicious, this action could allow ransomware to execute without interference, leading - to potential data encryption and loss. +description: The following analytic identifies the deletion of the Raccine Rules Updater scheduled task using the `schtasks.exe` command. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because adversaries may delete this task to disable Raccine, a tool designed to prevent ransomware attacks. If confirmed malicious, this action could allow ransomware to execute without interference, leading to potential data encryption and loss. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe - Processes.process="*delete*" AND Processes.process="*Raccine*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_raccine_scheduled_task_deletion_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=schtasks.exe Processes.process="*delete*" + AND + Processes.process="*Raccine*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_raccine_scheduled_task_deletion_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives should be limited, however filter as needed. references: -- https://redcanary.com/blog/blackbyte-ransomware/ -- https://github.com/Neo23x0/Raccine + - https://redcanary.com/blog/blackbyte-ransomware/ + - https://github.com/Neo23x0/Raccine drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to disable Raccines scheduled task. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to disable Raccines scheduled task. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Ransomware - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon_raccine.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon_raccine.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_rapid_authentication_on_multiple_hosts.yml b/detections/endpoint/windows_rapid_authentication_on_multiple_hosts.yml index 7b57e89d13..676ab9898c 100644 --- a/detections/endpoint/windows_rapid_authentication_on_multiple_hosts.yml +++ b/detections/endpoint/windows_rapid_authentication_on_multiple_hosts.yml @@ -1,73 +1,61 @@ name: Windows Rapid Authentication On Multiple Hosts id: 62606c77-d53d-4182-9371-b02cdbbbcef7 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Mauricio Velazco, Splunk type: TTP status: production data_source: -- Windows Event Log Security 4624 -description: The following analytic detects a source computer authenticating to 30 - or more remote endpoints within a 5-minute timespan using Event ID 4624. This behavior - is identified by analyzing Windows Event Logs for LogonType 3 events and counting - unique target computers. Such activity is significant as it may indicate lateral - movement or network share enumeration by an adversary. If confirmed malicious, this - could lead to unauthorized access to multiple systems, potentially compromising - sensitive data and escalating privileges within the network. -search: '`wineventlog_security` EventCode=4624 LogonType=3 TargetUserName!="ANONYMOUS - LOGON" TargetUserName!="*$" | bucket span=5m _time | stats dc(Computer) AS unique_targets - values(Computer) as host_targets values(dest) as dest values(src) as src values(user) - as user by _time, IpAddress, TargetUserName, action, app, authentication_method, - signature, signature_id | where unique_targets > 30 | `windows_rapid_authentication_on_multiple_hosts_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Windows Event Logs from domain controllers as well as member servers and workstations. - The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs - to be enabled. -known_false_positives: Vulnerability scanners or system administration tools may also - trigger this detection. Filter as needed. + - Windows Event Log Security 4624 +description: The following analytic detects a source computer authenticating to 30 or more remote endpoints within a 5-minute timespan using Event ID 4624. This behavior is identified by analyzing Windows Event Logs for LogonType 3 events and counting unique target computers. Such activity is significant as it may indicate lateral movement or network share enumeration by an adversary. If confirmed malicious, this could lead to unauthorized access to multiple systems, potentially compromising sensitive data and escalating privileges within the network. +search: |- + `wineventlog_security` EventCode=4624 LogonType=3 TargetUserName!="ANONYMOUS LOGON" TargetUserName!="*$" + | bucket span=5m _time + | stats dc(Computer) AS unique_targets values(Computer) as host_targets values(dest) as dest values(src) as src values(user) as user + BY _time, IpAddress, TargetUserName, + action, app, authentication_method, + signature, signature_id + | where unique_targets > 30 + | `windows_rapid_authentication_on_multiple_hosts_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled. +known_false_positives: Vulnerability scanners or system administration tools may also trigger this detection. Filter as needed. references: -- https://attack.mitre.org/techniques/T1135/ -- https://thedfirreport.com/2023/01/23/sharefinder-how-threat-actors-discover-file-shares/ -- https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4624 + - https://attack.mitre.org/techniques/T1135/ + - https://thedfirreport.com/2023/01/23/sharefinder-how-threat-actors-discover-file-shares/ + - https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4624 drilldown_searches: -- name: View the detection results for - "$host_targets$" - search: '%original_detection_search% | search host_targets = "$host_targets$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$host_targets$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host_targets$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$host_targets$" + search: '%original_detection_search% | search host_targets = "$host_targets$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$host_targets$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host_targets$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The source computer with ip address $IpAddress$ authenticated to a large - number of remote endpoints within 5 minutes. - risk_objects: - - field: host_targets - type: system - score: 48 - threat_objects: - - field: IpAddress - type: ip_address + message: The source computer with ip address $IpAddress$ authenticated to a large number of remote endpoints within 5 minutes. + risk_objects: + - field: host_targets + type: system + score: 48 + threat_objects: + - field: IpAddress + type: ip_address tags: - analytic_story: - - Active Directory Privilege Escalation - - Active Directory Lateral Movement - asset_type: Endpoint - mitre_attack_id: - - T1003.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Privilege Escalation + - Active Directory Lateral Movement + asset_type: Endpoint + mitre_attack_id: + - T1003.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1135/rapid_authentication_multiple_hosts/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1135/rapid_authentication_multiple_hosts/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_rasautou_dll_execution.yml b/detections/endpoint/windows_rasautou_dll_execution.yml index 24e0bb0359..d0d78b02f4 100644 --- a/detections/endpoint/windows_rasautou_dll_execution.yml +++ b/detections/endpoint/windows_rasautou_dll_execution.yml @@ -1,91 +1,73 @@ name: Windows Rasautou DLL Execution id: 6f42b8be-8e96-11ec-ad5a-acde48001122 -version: 11 -date: '2025-10-14' +version: 12 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of an arbitrary DLL by the - Windows Remote Auto Dialer (rasautou.exe). This behavior is identified by analyzing - process creation events where rasautou.exe is executed with specific command-line - arguments. This activity is significant because it leverages a Living Off The Land - Binary (LOLBin) to execute potentially malicious code, bypassing traditional security - controls. If confirmed malicious, this technique could allow an attacker to execute - arbitrary code, potentially leading to system compromise, privilege escalation, - or persistent access within the environment. +description: The following analytic detects the execution of an arbitrary DLL by the Windows Remote Auto Dialer (rasautou.exe). This behavior is identified by analyzing process creation events where rasautou.exe is executed with specific command-line arguments. This activity is significant because it leverages a Living Off The Land Binary (LOLBin) to execute potentially malicious code, bypassing traditional security controls. If confirmed malicious, this technique could allow an attacker to execute arbitrary code, potentially leading to system compromise, privilege escalation, or persistent access within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rasautou.exe - Processes.process="* -d *"AND Processes.process="* -p *" by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_rasautou_dll_execution_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives will be limited to applications that require - Rasautou.exe to load a DLL from disk. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=rasautou.exe Processes.process="* -d *"AND Processes.process="* -p *" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_rasautou_dll_execution_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives will be limited to applications that require Rasautou.exe to load a DLL from disk. Filter as needed. references: -- https://github.com/mandiant/DueDLLigence -- https://github.com/MHaggis/notes/blob/master/utilities/Invoke-SPLDLLigence.ps1 -- https://gist.github.com/NickTyrer/c6043e4b302d5424f701f15baf136513 -- https://www.mandiant.com/resources/staying-hidden-on-the-endpoint-evading-detection-with-shellcode + - https://github.com/mandiant/DueDLLigence + - https://github.com/MHaggis/notes/blob/master/utilities/Invoke-SPLDLLigence.ps1 + - https://gist.github.com/NickTyrer/c6043e4b302d5424f701f15baf136513 + - https://www.mandiant.com/resources/staying-hidden-on-the-endpoint-evading-detection-with-shellcode drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ attempting to load a DLL in a suspicious manner. - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ attempting to load a DLL in a suspicious manner. + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Compromised Windows Host - - Windows Defense Evasion Tactics - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1055.001 - - T1218 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - Windows Defense Evasion Tactics + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1055.001 + - T1218 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055.001/rasautou/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055.001/rasautou/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_raw_access_to_disk_volume_partition.yml b/detections/endpoint/windows_raw_access_to_disk_volume_partition.yml index fcf8dae3b2..e36c4e7377 100644 --- a/detections/endpoint/windows_raw_access_to_disk_volume_partition.yml +++ b/detections/endpoint/windows_raw_access_to_disk_volume_partition.yml @@ -5,75 +5,52 @@ date: '2025-08-20' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects suspicious raw access reads to the - device disk partition of a host machine. It leverages Sysmon EventCode 9 logs - to identify processes attempting to read or write to the boot sector, - excluding legitimate system processes. This activity is significant as it is - commonly associated with destructive actions by adversaries, such as wiping, - encrypting, or overwriting the boot sector, as seen in attacks involving - malware like HermeticWiper. If confirmed malicious, this behavior could lead - to severe impacts, including system inoperability, data loss, or compromised - boot integrity. +description: The following analytic detects suspicious raw access reads to the device disk partition of a host machine. It leverages Sysmon EventCode 9 logs to identify processes attempting to read or write to the boot sector, excluding legitimate system processes. This activity is significant as it is commonly associated with destructive actions by adversaries, such as wiping, encrypting, or overwriting the boot sector, as seen in attacks involving malware like HermeticWiper. If confirmed malicious, this behavior could lead to severe impacts, including system inoperability, data loss, or compromised boot integrity. data_source: -- Sysmon EventID 9 -search: '`sysmon` EventCode=9 Device = \\Device\\HarddiskVolume* NOT (Image IN("*\\Windows\\System32\\*", - "*\\Windows\\SysWOW64\\*")) | stats count min(_time) as firstTime max(_time) as - lastTime by dest dvc process_exec process_guid process_id process_name process_path - signature signature_id user_id vendor_product Device Image | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_raw_access_to_disk_volume_partition_filter`' -how_to_implement: To successfully implement this search, you need to be - ingesting logs with the raw access read event (like sysmon eventcode 9), - process name and process guid from your endpoints. If you are using Sysmon, - you must have at least version 6.0.4 of the Sysmon TA. -known_false_positives: There are som minimal number of normal applications from - system32 folder like svchost.exe accessing the MBR. In this case we used - 'system32' and 'syswow64' path as a filter for this detection. + - Sysmon EventID 9 +search: '`sysmon` EventCode=9 Device = \\Device\\HarddiskVolume* NOT (Image IN("*\\Windows\\System32\\*", "*\\Windows\\SysWOW64\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by dest dvc process_exec process_guid process_id process_name process_path signature signature_id user_id vendor_product Device Image | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_raw_access_to_disk_volume_partition_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the raw access read event (like sysmon eventcode 9), process name and process guid from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: There are som minimal number of normal applications from system32 folder like svchost.exe accessing the MBR. In this case we used 'system32' and 'syswow64' path as a filter for this detection. references: -- https://blog.talosintelligence.com/2022/02/threat-advisory-hermeticwiper.html + - https://blog.talosintelligence.com/2022/02/threat-advisory-hermeticwiper.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Process accessing disk partition $Device$ on $dest$ - risk_objects: - - field: dest - type: system - score: 90 - threat_objects: [] + message: Process accessing disk partition $Device$ on $dest$ + risk_objects: + - field: dest + type: system + score: 90 + threat_objects: [] tags: - analytic_story: - - CISA AA22-264A - - Graceful Wipe Out Attack - - Data Destruction - - Hermetic Wiper - - Caddy Wiper - - BlackByte Ransomware - - NjRAT - - Disk Wiper - - PathWiper - asset_type: Endpoint - mitre_attack_id: - - T1561.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA22-264A + - Graceful Wipe Out Attack + - Data Destruction + - Hermetic Wiper + - Caddy Wiper + - BlackByte Ransomware + - NjRAT + - Disk Wiper + - PathWiper + asset_type: Endpoint + mitre_attack_id: + - T1561.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/hermetic_wiper/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/hermetic_wiper/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_raw_access_to_master_boot_record_drive.yml b/detections/endpoint/windows_raw_access_to_master_boot_record_drive.yml index 24bcc3edb0..8b614befc6 100644 --- a/detections/endpoint/windows_raw_access_to_master_boot_record_drive.yml +++ b/detections/endpoint/windows_raw_access_to_master_boot_record_drive.yml @@ -5,77 +5,55 @@ date: '2025-08-20' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects suspicious raw access reads to the - drive containing the Master Boot Record (MBR). It leverages Sysmon EventCode 9 - to identify processes attempting to read or write to the MBR sector, excluding - legitimate system processes. This activity is significant because adversaries - often target the MBR to wipe, encrypt, or overwrite it as part of their impact - payload. If confirmed malicious, this could lead to system instability, data - loss, or a complete system compromise, severely impacting the organization's - operations. +description: The following analytic detects suspicious raw access reads to the drive containing the Master Boot Record (MBR). It leverages Sysmon EventCode 9 to identify processes attempting to read or write to the MBR sector, excluding legitimate system processes. This activity is significant because adversaries often target the MBR to wipe, encrypt, or overwrite it as part of their impact payload. If confirmed malicious, this could lead to system instability, data loss, or a complete system compromise, severely impacting the organization's operations. data_source: -- Sysmon EventID 9 -search: '`sysmon` EventCode=9 Device = \\Device\\Harddisk0\\DR0 NOT (Image IN("*\\Windows\\System32\\*", - "*\\Windows\\SysWOW64\\*")) | stats count min(_time) as firstTime max(_time) as - lastTime by dest dvc process_exec process_guid process_id process_name process_path - signature signature_id user_id vendor_product Device Image | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_raw_access_to_master_boot_record_drive_filter`' -how_to_implement: To successfully implement this search, you need to be - ingesting logs with the raw access read event (like sysmon eventcode 9), - process name and process guid from your endpoints. If you are using Sysmon, - you must have at least version 6.0.4 of the Sysmon TA. -known_false_positives: There are som minimal number of normal applications from - system32 folder like svchost.exe accessing the MBR. In this case we used - 'system32' and 'syswow64' path as a filter for this detection. + - Sysmon EventID 9 +search: '`sysmon` EventCode=9 Device = \\Device\\Harddisk0\\DR0 NOT (Image IN("*\\Windows\\System32\\*", "*\\Windows\\SysWOW64\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by dest dvc process_exec process_guid process_id process_name process_path signature signature_id user_id vendor_product Device Image | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_raw_access_to_master_boot_record_drive_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the raw access read event (like sysmon eventcode 9), process name and process guid from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: There are som minimal number of normal applications from system32 folder like svchost.exe accessing the MBR. In this case we used 'system32' and 'syswow64' path as a filter for this detection. references: -- https://www.splunk.com/en_us/blog/security/threat-advisory-strt-ta02-destructive-software.html -- https://www.crowdstrike.com/blog/technical-analysis-of-whispergate-malware/ -- https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://www.splunk.com/en_us/blog/security/threat-advisory-strt-ta02-destructive-software.html + - https://www.crowdstrike.com/blog/technical-analysis-of-whispergate-malware/ + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: process accessing MBR $Device$ on $dest$ - risk_objects: - - field: dest - type: system - score: 90 - threat_objects: [] + message: process accessing MBR $Device$ on $dest$ + risk_objects: + - field: dest + type: system + score: 90 + threat_objects: [] tags: - analytic_story: - - CISA AA22-264A - - WhisperGate - - Graceful Wipe Out Attack - - Data Destruction - - Hermetic Wiper - - Caddy Wiper - - BlackByte Ransomware - - NjRAT - - Disk Wiper - - PathWiper - asset_type: Endpoint - mitre_attack_id: - - T1561.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA22-264A + - WhisperGate + - Graceful Wipe Out Attack + - Data Destruction + - Hermetic Wiper + - Caddy Wiper + - BlackByte Ransomware + - NjRAT + - Disk Wiper + - PathWiper + asset_type: Endpoint + mitre_attack_id: + - T1561.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1561.002/mbr_raw_access/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1561.002/mbr_raw_access/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_rdp_automaticdestinations_deletion.yml b/detections/endpoint/windows_rdp_automaticdestinations_deletion.yml index 06edb79cd9..9bb047be88 100644 --- a/detections/endpoint/windows_rdp_automaticdestinations_deletion.yml +++ b/detections/endpoint/windows_rdp_automaticdestinations_deletion.yml @@ -7,58 +7,44 @@ status: production type: Anomaly description: This detection identifies the deletion of files within the AutomaticDestinations folder, located under a user’s AppData\Roaming\Microsoft\Windows\Recent directory. These files are part of the Windows Jump List feature, which records recently accessed files and folders tied to specific applications. Each .automaticDestinations-ms file corresponds to a program (e.g., Explorer, Word, Notepad) and can be valuable for forensic analysis of user activity. Adversaries may target this folder to erase evidence of their actions, such as which documents or directories were accessed during a session. This type of deletion is rarely seen during normal user activity and may indicate deliberate anti-forensic behavior. When correlated with suspicious logon events, RDP usage, or script execution, this activity may represent an attempt to cover tracks after data access, lateral movement, or staging for exfiltration. Detecting removal of these artifacts can highlight post-compromise cleanup efforts and help analysts reconstruct attacker behavior. data_source: -- Sysmon EventID 23 -- Sysmon EventID 26 -search: '`sysmon` EventCode=23 TargetFilename IN ("*\\Recent\\AutomaticDestinations*") - | stats count min(_time) as firstTime, max(_time) as lastTime - by action dest dvc file_path file_hash file_name file_modify_time process_exec process_guid process_id process_name process_path signature signature_id user user_id vendor_product - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_rdp_automaticdestinations_deletion_filter`' -how_to_implement: To successfully implement this search, you must ingest logs that - include the process name, TargetFilename, and ProcessID executions from your endpoints. - If you are utilizing Sysmon, ensure you have at least version 2.0 of the Sysmon - TA installed. -known_false_positives: False positives will be present, filter as needed or restrict - to critical assets on the perimeter. + - Sysmon EventID 23 + - Sysmon EventID 26 +search: '`sysmon` EventCode=23 TargetFilename IN ("*\\Recent\\AutomaticDestinations*") | stats count min(_time) as firstTime, max(_time) as lastTime by action dest dvc file_path file_hash file_name file_modify_time process_exec process_guid process_id process_name process_path signature signature_id user user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_rdp_automaticdestinations_deletion_filter`' +how_to_implement: To successfully implement this search, you must ingest logs that include the process name, TargetFilename, and ProcessID executions from your endpoints. If you are utilizing Sysmon, ensure you have at least version 2.0 of the Sysmon TA installed. +known_false_positives: False positives will be present, filter as needed or restrict to critical assets on the perimeter. references: -- https://medium.com/@bonguides25/how-to-clear-rdp-connections-history-in-windows-cf0ffb67f344 -- https://thelocalh0st.github.io/posts/rdp/ + - https://medium.com/@bonguides25/how-to-clear-rdp-connections-history-in-windows-cf0ffb67f344 + - https://thelocalh0st.github.io/posts/rdp/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A file related to rdp automatic destination folder has been deleted on $dest$. - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: [] + message: A file related to rdp automatic destination folder has been deleted on $dest$. + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: [] tags: - analytic_story: - - Windows RDP Artifacts and Defense Evasion - asset_type: Endpoint - mitre_attack_id: - - T1070.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows RDP Artifacts and Defense Evasion + asset_type: Endpoint + mitre_attack_id: + - T1070.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.004/automatic_file_deleted/automatic_file_deleted.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.004/automatic_file_deleted/automatic_file_deleted.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_rdp_bitmap_cache_file_creation.yml b/detections/endpoint/windows_rdp_bitmap_cache_file_creation.yml index ff96ac0ee0..ebea4b647f 100644 --- a/detections/endpoint/windows_rdp_bitmap_cache_file_creation.yml +++ b/detections/endpoint/windows_rdp_bitmap_cache_file_creation.yml @@ -7,60 +7,43 @@ status: production type: Anomaly description: This detection identifies the creation of Remote Desktop Protocol (RDP) bitmap cache files on a Windows system, typically located in the user’s profile under the Terminal Server Client cache directory. These files (*.bmc, cache*.bin) are generated when a user initiates an RDP session using the built-in mstsc.exe client. Their presence can indicate interactive remote access activity and may be useful in detecting lateral movement or unauthorized RDP usage. Monitoring this behavior is especially important, as attackers may attempt to delete or suppress these artifacts to evade forensic analysis. data_source: -- Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime - FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\Terminal Server Client\\Cache\\*.bmc", "*\\Terminal Server Client\\Cache\\cache*.bin") - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path - Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product - | `security_content_ctime(lastTime)` - | `security_content_ctime(firstTime)` - |`drop_dm_object_name(Filesystem)` - | `windows_rdp_bitmap_cache_file_creation_filter`' -how_to_implement: To successfully implement this search, you must ingest logs that - include the process name, TargetFilename, and ProcessID executions from your endpoints. - If you are utilizing Sysmon, ensure you have at least version 2.0 of the Sysmon - TA installed. -known_false_positives: False positives will be present, filter as needed or restrict - to critical assets on the perimeter. + - Sysmon EventID 11 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\Terminal Server Client\\Cache\\*.bmc", "*\\Terminal Server Client\\Cache\\cache*.bin") by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` |`drop_dm_object_name(Filesystem)` | `windows_rdp_bitmap_cache_file_creation_filter`' +how_to_implement: To successfully implement this search, you must ingest logs that include the process name, TargetFilename, and ProcessID executions from your endpoints. If you are utilizing Sysmon, ensure you have at least version 2.0 of the Sysmon TA installed. +known_false_positives: False positives will be present, filter as needed or restrict to critical assets on the perimeter. references: -- https://medium.com/@bonguides25/how-to-clear-rdp-connections-history-in-windows-cf0ffb67f344 -- https://thelocalh0st.github.io/posts/rdp/ + - https://medium.com/@bonguides25/how-to-clear-rdp-connections-history-in-windows-cf0ffb67f344 + - https://thelocalh0st.github.io/posts/rdp/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A rdp bitmap cache has been identified on $dest$. - risk_objects: - - field: dest - type: system - score: 20 - threat_objects: [] + message: A rdp bitmap cache has been identified on $dest$. + risk_objects: + - field: dest + type: system + score: 20 + threat_objects: [] tags: - analytic_story: - - Windows RDP Artifacts and Defense Evasion - asset_type: Endpoint - mitre_attack_id: - - T1021.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows RDP Artifacts and Defense Evasion + asset_type: Endpoint + mitre_attack_id: + - T1021.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.001/bmc_creation/bmc_creation.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.001/bmc_creation/bmc_creation.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_rdp_cache_file_deletion.yml b/detections/endpoint/windows_rdp_cache_file_deletion.yml index 24499aaceb..b8b89f0f6b 100644 --- a/detections/endpoint/windows_rdp_cache_file_deletion.yml +++ b/detections/endpoint/windows_rdp_cache_file_deletion.yml @@ -7,60 +7,46 @@ status: production type: Anomaly description: This detection identifies the deletion of RDP bitmap cache files—specifically .bmc and .bin files—typically stored in the user profile under the Terminal Server Client\Cache directory. These files are created by the native Windows Remote Desktop Client (mstsc.exe) and store graphical elements from remote sessions to improve performance. Deleting these files may indicate an attempt to remove forensic evidence of RDP usage. While rare in legitimate user behavior, this action is commonly associated with defense evasion techniques used by attackers or red teamers who wish to hide traces of interactive remote access. When observed in conjunction with recent logon activity, RDP session indicators, or script execution, this behavior should be treated as potentially malicious. Monitoring for deletion of these files provides valuable visibility into anti-forensic actions that often follow lateral movement or hands-on-keyboard activity. data_source: -- Sysmon EventID 23 -- Sysmon EventID 26 -search: '`sysmon` EventCode IN ("23", "26") TargetFilename IN ("*\\Terminal Server Client\\Cache\\*.bmc", "*\\Terminal Server Client\\Cache\\cache*.bin") - | stats count min(_time) as firstTime, max(_time) as lastTime - by action dest dvc file_path file_hash file_name file_modify_time process_exec process_guid process_id process_name process_path signature signature_id user user_id vendor_product - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_rdp_cache_file_deletion_filter`' -how_to_implement: To successfully implement this search, you must ingest logs that - include the process name, TargetFilename, and ProcessID executions from your endpoints. - If you are utilizing Sysmon, ensure you have at least version 2.0 of the Sysmon - TA installed. -known_false_positives: False positives will be present, filter as needed or restrict - to critical assets on the perimeter. + - Sysmon EventID 23 + - Sysmon EventID 26 +search: '`sysmon` EventCode IN ("23", "26") TargetFilename IN ("*\\Terminal Server Client\\Cache\\*.bmc", "*\\Terminal Server Client\\Cache\\cache*.bin") | stats count min(_time) as firstTime, max(_time) as lastTime by action dest dvc file_path file_hash file_name file_modify_time process_exec process_guid process_id process_name process_path signature signature_id user user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_rdp_cache_file_deletion_filter`' +how_to_implement: To successfully implement this search, you must ingest logs that include the process name, TargetFilename, and ProcessID executions from your endpoints. If you are utilizing Sysmon, ensure you have at least version 2.0 of the Sysmon TA installed. +known_false_positives: False positives will be present, filter as needed or restrict to critical assets on the perimeter. references: -- https://medium.com/@bonguides25/how-to-clear-rdp-connections-history-in-windows-cf0ffb67f344 -- https://thelocalh0st.github.io/posts/rdp/ + - https://medium.com/@bonguides25/how-to-clear-rdp-connections-history-in-windows-cf0ffb67f344 + - https://thelocalh0st.github.io/posts/rdp/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a file related to rdp connection cached has been deleted on $dest$. - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: - - field: process_name - type: process_name + message: a file related to rdp connection cached has been deleted on $dest$. + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Windows RDP Artifacts and Defense Evasion - asset_type: Endpoint - mitre_attack_id: - - T1070.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows RDP Artifacts and Defense Evasion + asset_type: Endpoint + mitre_attack_id: + - T1070.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.004/bmc_file_deleted/bmc_file_deleted.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.004/bmc_file_deleted/bmc_file_deleted.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_rdp_client_launched_with_admin_session.yml b/detections/endpoint/windows_rdp_client_launched_with_admin_session.yml index e797ae2680..ad9cd96e04 100644 --- a/detections/endpoint/windows_rdp_client_launched_with_admin_session.yml +++ b/detections/endpoint/windows_rdp_client_launched_with_admin_session.yml @@ -1,88 +1,64 @@ name: Windows RDP Client Launched with Admin Session id: 1af84ac8-05ea-4f11-8541-b2d1e45a7744 -version: 2 -date: '2025-08-01' +version: 3 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: This detection identifies the execution of the Windows Remote - Desktop Client (mstsc.exe) with the "/v" and /admin command-line arguments. - The "/v" flag specifies the remote host to connect to, while the /admin flag - initiates a connection to the target system’s console session, often used for - administrative purposes. This combination may indicate that a user or attacker - is performing privileged remote access, potentially to manage a system without - disrupting existing user sessions. While such usage may be legitimate for IT - administrators, it is less common in typical user behavior. Threat actors may - abuse this capability during lateral movement to maintain stealthy access to - high-value systems. Monitoring for this pattern can help detect interactive - hands-on-keyboard activity, privilege abuse, or attempts to access critical - infrastructure without leaving typical login traces associated with non-admin - RDP sessions. +description: This detection identifies the execution of the Windows Remote Desktop Client (mstsc.exe) with the "/v" and /admin command-line arguments. The "/v" flag specifies the remote host to connect to, while the /admin flag initiates a connection to the target system’s console session, often used for administrative purposes. This combination may indicate that a user or attacker is performing privileged remote access, potentially to manage a system without disrupting existing user sessions. While such usage may be legitimate for IT administrators, it is less common in typical user behavior. Threat actors may abuse this capability during lateral movement to maintain stealthy access to high-value systems. Monitoring for this pattern can help detect interactive hands-on-keyboard activity, privilege abuse, or attempts to access critical infrastructure without leaving typical login traces associated with non-admin RDP sessions. data_source: -- Sysmon EventID 1 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "mstsc.exe" - Processes.process = "*/v:*" Processes.process = "*/admin*" by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_rdp_client_launched_with_admin_session_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: False positives will be present, filter as needed or - restrict to critical assets on the perimeter. + - Sysmon EventID 1 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "mstsc.exe" Processes.process = "*/v:*" Processes.process = "*/admin*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_rdp_client_launched_with_admin_session_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives will be present, filter as needed or restrict to critical assets on the perimeter. references: -- https://medium.com/@bonguides25/how-to-clear-rdp-connections-history-in-windows-cf0ffb67f344 -- https://thelocalh0st.github.io/posts/rdp/ + - https://medium.com/@bonguides25/how-to-clear-rdp-connections-history-in-windows-cf0ffb67f344 + - https://thelocalh0st.github.io/posts/rdp/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a rdp client launched with admin session on $dest$. - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: a rdp client launched with admin session on $dest$. + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - Windows RDP Artifacts and Defense Evasion - asset_type: Endpoint - mitre_attack_id: - - T1021.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows RDP Artifacts and Defense Evasion + asset_type: Endpoint + mitre_attack_id: + - T1021.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.001/mstsc_admini/mstsc_admin.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.001/mstsc_admini/mstsc_admin.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_rdp_connection_successful.yml b/detections/endpoint/windows_rdp_connection_successful.yml index 979846c9f3..58ada50bb6 100644 --- a/detections/endpoint/windows_rdp_connection_successful.yml +++ b/detections/endpoint/windows_rdp_connection_successful.yml @@ -1,54 +1,45 @@ name: Windows RDP Connection Successful id: ceaed840-56b3-4a70-b8e1-d762b1c5c08c -version: 9 -date: '2025-11-20' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting data_source: -- Windows Event Log RemoteConnectionManager 1149 -description: The following analytic detects successful Remote Desktop Protocol - (RDP) connections by monitoring EventCode 1149 from the Windows - TerminalServices RemoteConnectionManager Operational log. This detection is - significant as successful RDP connections can indicate remote access to a - system, which may be leveraged by attackers to control or exfiltrate data. If - confirmed malicious, this activity could lead to unauthorized access, data - theft, or further lateral movement within the network. Monitoring successful - RDP connections is crucial for identifying potential security breaches and - mitigating risks promptly. -search: '`remoteconnectionmanager` EventCode=1149 | stats count min(_time) as firstTime - max(_time) as lastTime by Computer, user_id | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | rename Computer as dest | `windows_rdp_connection_successful_filter`' -how_to_implement: The following analyic requires the WIndows TerminalServices - RemoteConnectionManager Operational log to be enabled and ingested into - Splunk. For the inputs, review - https://gist.github.com/MHaggis/138c6bf563bacbda4a2524f089773706. -known_false_positives: False positives will be present, filter as needed or - restrict to critical assets on the perimeter. + - Windows Event Log RemoteConnectionManager 1149 +description: The following analytic detects successful Remote Desktop Protocol (RDP) connections by monitoring EventCode 1149 from the Windows TerminalServices RemoteConnectionManager Operational log. This detection is significant as successful RDP connections can indicate remote access to a system, which may be leveraged by attackers to control or exfiltrate data. If confirmed malicious, this activity could lead to unauthorized access, data theft, or further lateral movement within the network. Monitoring successful RDP connections is crucial for identifying potential security breaches and mitigating risks promptly. +search: |- + `remoteconnectionmanager` EventCode=1149 + | stats count min(_time) as firstTime max(_time) as lastTime + BY Computer, user_id + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | rename Computer as dest + | `windows_rdp_connection_successful_filter` +how_to_implement: The following analyic requires the WIndows TerminalServices RemoteConnectionManager Operational log to be enabled and ingested into Splunk. For the inputs, review https://gist.github.com/MHaggis/138c6bf563bacbda4a2524f089773706. +known_false_positives: False positives will be present, filter as needed or restrict to critical assets on the perimeter. references: -- https://gist.github.com/MHaggis/138c6bf563bacbda4a2524f089773706 -- https://doublepulsar.com/rdp-hijacking-how-to-hijack-rds-and-remoteapp-sessions-transparently-to-move-through-an-da2a1e73a5f6 + - https://gist.github.com/MHaggis/138c6bf563bacbda4a2524f089773706 + - https://doublepulsar.com/rdp-hijacking-how-to-hijack-rds-and-remoteapp-sessions-transparently-to-move-through-an-da2a1e73a5f6 tags: - analytic_story: - - Active Directory Lateral Movement - - BlackByte Ransomware - - Windows RDP Artifacts and Defense Evasion - - Interlock Ransomware - - NetSupport RMM Tool Abuse - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1563.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + - BlackByte Ransomware + - Windows RDP Artifacts and Defense Evasion + - Interlock Ransomware + - NetSupport RMM Tool Abuse + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1563.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1563.002/windows_rdp_connection_successful/windows-xml.log - source: - WinEventLog:Microsoft-Windows-TerminalServices-RemoteConnectionManager/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1563.002/windows_rdp_connection_successful/windows-xml.log + source: WinEventLog:Microsoft-Windows-TerminalServices-RemoteConnectionManager/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_rdp_file_execution.yml b/detections/endpoint/windows_rdp_file_execution.yml index 97ef7a754e..e4b4fe437e 100644 --- a/detections/endpoint/windows_rdp_file_execution.yml +++ b/detections/endpoint/windows_rdp_file_execution.yml @@ -5,99 +5,50 @@ date: '2025-08-07' author: Michael Haag, Splunk type: TTP status: production -description: The following analytic detects when a Windows RDP client attempts - to execute an RDP file from a temporary directory, downloads directory, or - Outlook directories. This detection is significant as it can indicate an - attempt for an adversary to deliver a .rdp file, which may be leveraged by - attackers to control or exfiltrate data. If confirmed malicious, this activity - could lead to unauthorized access, data theft, or further lateral movement - within the network. +description: The following analytic detects when a Windows RDP client attempts to execute an RDP file from a temporary directory, downloads directory, or Outlook directories. This detection is significant as it can indicate an attempt for an adversary to deliver a .rdp file, which may be leveraged by attackers to control or exfiltrate data. If confirmed malicious, this activity could lead to unauthorized access, data theft, or further lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.process IN ("*\\AppData\\Local\\Temp\\*", "*\\Olk\\Attachments\\*", - "*\\AppData\\Local\\Microsoft\\Outlook\\*", "*\\Content.Outlook\\*", "*\\Downloads\\*") - AND Processes.process="*.rdp*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval - execution_type=case( match(process, "\\\\Temp\\\\.*\\.(zip|7z|rar|cab|tgz|gz|tar|iso|img|vhd|vhdx).*\\.*\\.rdp"), - "temp_archive_execution", match(process, "\\\\Downloads\\\\"), "downloads_execution", - match(process, "\\\\Temp\\\\"), "temp_execution", match(process, "\\\\Microsoft\\\\Outlook\\\\"), - "outlook_execution", match(process, "\\\\Olk\\\\Attachments\\\\"), "outlook_execution", - match(process, "\\\\Content.Outlook\\\\"), "outlook_execution", true(), "other" - ), risk_score=case( execution_type="temp_archive_execution", "Critical", execution_type - IN ("temp_execution", "outlook_execution"), "High", execution_type="downloads_execution", - "Medium", true(), "Low" ), risk_reason=case( execution_type="temp_archive_execution", - "RDP file executed directly from archive/disk image in Temp directory", execution_type="downloads_execution", - "RDP file executed from Downloads directory (Could be legitimate admin activity)", - execution_type="temp_execution", "RDP file executed from Temp directory", execution_type="outlook_execution", - "RDP file executed from Outlook directories", true(), "Standard RDP file execution" - ) | sort - risk_score | rename process_name as "RDP Process", parent_process_name - as "Parent Process", process as "Command Line", user as "User", execution_type as - "Execution Context", risk_score as "Risk Level", risk_reason as "Risk Details" | - fields - parent_process | `windows_rdp_file_execution_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: False positives may be present based on administrators - using RDP files for legitimate purposes. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*\\AppData\\Local\\Temp\\*", "*\\Olk\\Attachments\\*", "*\\AppData\\Local\\Microsoft\\Outlook\\*", "*\\Content.Outlook\\*", "*\\Downloads\\*") AND Processes.process="*.rdp*" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval execution_type=case( match(process, "\\\\Temp\\\\.*\\.(zip|7z|rar|cab|tgz|gz|tar|iso|img|vhd|vhdx).*\\.*\\.rdp"), "temp_archive_execution", match(process, "\\\\Downloads\\\\"), "downloads_execution", match(process, "\\\\Temp\\\\"), "temp_execution", match(process, "\\\\Microsoft\\\\Outlook\\\\"), "outlook_execution", match(process, "\\\\Olk\\\\Attachments\\\\"), "outlook_execution", match(process, "\\\\Content.Outlook\\\\"), "outlook_execution", true(), "other" ), risk_score=case( execution_type="temp_archive_execution", "Critical", execution_type IN ("temp_execution", "outlook_execution"), "High", execution_type="downloads_execution", "Medium", true(), "Low" ), risk_reason=case( execution_type="temp_archive_execution", "RDP file executed directly from archive/disk image in Temp directory", execution_type="downloads_execution", "RDP file executed from Downloads directory (Could be legitimate admin activity)", execution_type="temp_execution", "RDP file executed from Temp directory", execution_type="outlook_execution", "RDP file executed from Outlook directories", true(), "Standard RDP file execution" ) | sort - risk_score | rename process_name as "RDP Process", parent_process_name as "Parent Process", process as "Command Line", user as "User", execution_type as "Execution Context", risk_score as "Risk Level", risk_reason as "Risk Details" | fields - parent_process | `windows_rdp_file_execution_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present based on administrators using RDP files for legitimate purposes. Filter as needed. references: -- https://www.microsoft.com/en-us/security/blog/2024/10/29/midnight-blizzard-conducts-large-scale-spear-phishing-campaign-using-rdp-files/ + - https://www.microsoft.com/en-us/security/blog/2024/10/29/midnight-blizzard-conducts-large-scale-spear-phishing-campaign-using-rdp-files/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Windows RDP client attempted to execute an RDP file from a - temporary directory, downloads directory, or Outlook directories on the - endpoint $dest$. - risk_objects: - - field: dest - type: system - score: 42 - threat_objects: [] + message: A Windows RDP client attempted to execute an RDP file from a temporary directory, downloads directory, or Outlook directories on the endpoint $dest$. + risk_objects: + - field: dest + type: system + score: 42 + threat_objects: [] tags: - analytic_story: - - Spearphishing Attachments - - Windows RDP Artifacts and Defense Evasion - - Interlock Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1598.002 - - T1021.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - Spearphishing Attachments + - Windows RDP Artifacts and Defense Evasion + - Interlock Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1598.002 + - T1021.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1598.002/rdp/mstsc_rdpfile-windows-sysmon.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1598.002/rdp/mstsc_rdpfile-windows-sysmon.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational diff --git a/detections/endpoint/windows_rdp_login_session_was_established.yml b/detections/endpoint/windows_rdp_login_session_was_established.yml index ee5c01fcb6..c52a40a684 100644 --- a/detections/endpoint/windows_rdp_login_session_was_established.yml +++ b/detections/endpoint/windows_rdp_login_session_was_established.yml @@ -1,63 +1,62 @@ name: Windows RDP Login Session Was Established id: 00ca7f9e-88ab-4841-a6c2-83979ab1ed29 -version: 2 -date: '2025-10-14' +version: 3 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: The following analytic detects instances where a successful Remote Desktop Protocol (RDP) login session was established, as indicated by Windows Security Event ID 4624 with Logon Type 10. This event confirms that a user has not only provided valid credentials but has also initiated a full interactive RDP session. It is a key indicator of successful remote access to a Windows system. When correlated with Event ID 1149, which logs RDP authentication success, this analytic helps distinguish between mere credential acceptance and actual session establishment—critical for effective monitoring and threat detection. data_source: -- Windows Event Log Security 4624 -search: '`wineventlog_security` EventCode=4624 Logon_Type=10 - | stats count min(_time) as firstTime max(_time) as lastTime - by action app authentication_method dest dvc process process_id process_name process_path signature signature_id src src_port status subject user user_group vendor_product Logon_Type - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_rdp_login_session_was_established_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Windows Event Logs from domain controllers as well as member servers and workstations. - The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs - to be enabled. + - Windows Event Log Security 4624 +search: |- + `wineventlog_security` EventCode=4624 Logon_Type=10 + | stats count min(_time) as firstTime max(_time) as lastTime + BY action app authentication_method + dest dvc process + process_id process_name process_path + signature signature_id src + src_port status subject + user user_group vendor_product + Logon_Type + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_rdp_login_session_was_established_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled. known_false_positives: This detection can catch for third party application updates or installation. In this scenario false positive filter is needed. references: -- https://medium.com/@bonguides25/how-to-clear-rdp-connections-history-in-windows-cf0ffb67f344 -- https://thelocalh0st.github.io/posts/rdp/ + - https://medium.com/@bonguides25/how-to-clear-rdp-connections-history-in-windows-cf0ffb67f344 + - https://thelocalh0st.github.io/posts/rdp/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: RDP Login Session was established on $dest$. - risk_objects: - - field: dest - type: system - score: 20 - threat_objects: [] + message: RDP Login Session was established on $dest$. + risk_objects: + - field: dest + type: system + score: 20 + threat_objects: [] tags: - analytic_story: - - Windows RDP Artifacts and Defense Evasion - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1021.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows RDP Artifacts and Defense Evasion + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1021.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.001/rdp_session_established/4624_10_logon.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.001/rdp_session_established/4624_10_logon.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_rdp_server_registry_deletion.yml b/detections/endpoint/windows_rdp_server_registry_deletion.yml index e874463d9f..b5fff106cd 100644 --- a/detections/endpoint/windows_rdp_server_registry_deletion.yml +++ b/detections/endpoint/windows_rdp_server_registry_deletion.yml @@ -7,65 +7,44 @@ status: production type: Anomaly description: This detection identifies the deletion of registry keys under HKEY_CURRENT_USER\Software\Microsoft\Terminal Server Client\Servers\, which store records of previously connected remote systems via Remote Desktop Protocol (RDP). These keys are created automatically when a user connects to a remote host using the native Windows RDP client (mstsc.exe) and can be valuable forensic artifacts for tracking remote access activity. Malicious actors aware of this behavior may delete these keys after using RDP to hide evidence of their activity and avoid detection during incident response. This form of artifact cleanup is a known defense evasion technique, often performed during or after lateral movement. Legitimate users rarely delete these keys manually, making such actions highly suspicious—especially when correlated with RDP usage, unusual logon behavior, or other signs of compromise. Detecting the deletion of these registry entries can provide crucial insight into attempts to cover tracks following interactive remote access. data_source: -- Sysmon EventID 12 -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry - where Registry.registry_path="*\\Microsoft\\Terminal Server Client\\Servers\\*" Registry.action = deleted - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` - | `security_content_ctime(lastTime)` - | `security_content_ctime(firstTime)` - | `windows_rdp_server_registry_deletion_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: - This detection can catch for third party application updates - or installation. In this scenario false positive filter is needed. + - Sysmon EventID 12 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\Microsoft\\Terminal Server Client\\Servers\\*" Registry.action = deleted by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_rdp_server_registry_deletion_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: This detection can catch for third party application updates or installation. In this scenario false positive filter is needed. references: -- https://medium.com/@bonguides25/how-to-clear-rdp-connections-history-in-windows-cf0ffb67f344 -- https://thelocalh0st.github.io/posts/rdp/ + - https://medium.com/@bonguides25/how-to-clear-rdp-connections-history-in-windows-cf0ffb67f344 + - https://thelocalh0st.github.io/posts/rdp/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The registry was deleted on dest $dest$. - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: [] + message: The registry was deleted on dest $dest$. + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: [] tags: - analytic_story: - - Windows RDP Artifacts and Defense Evasion - asset_type: Endpoint - mitre_attack_id: - - T1070.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows RDP Artifacts and Defense Evasion + asset_type: Endpoint + mitre_attack_id: + - T1070.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.004/terminal_server_reg_deleted/terminal_server_client_reg_deleted.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.004/terminal_server_reg_deleted/terminal_server_client_reg_deleted.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_rdp_server_registry_entry_created.yml b/detections/endpoint/windows_rdp_server_registry_entry_created.yml index 096447385f..cd767ebc19 100644 --- a/detections/endpoint/windows_rdp_server_registry_entry_created.yml +++ b/detections/endpoint/windows_rdp_server_registry_entry_created.yml @@ -7,58 +7,43 @@ status: production type: Anomaly description: This detection identifies the creation of registry keys under HKEY_CURRENT_USER\Software\Microsoft\Terminal Server Client\Servers\, which occur when a user initiates a Remote Desktop Protocol (RDP) connection using the built-in Windows RDP client (mstsc.exe). These registry entries store information about previously connected remote hosts, including usernames and display settings. Their creation is a strong indicator that an outbound RDP session was initiated from the system. While the presence of these keys is normal during legitimate RDP use, their appearance can be used to track remote access activity, especially in environments where RDP is tightly controlled. In post-compromise scenarios, these artifacts may be created by threat actors using RDP for lateral movement or command-and-control. Monitoring the creation of these registry entries can help defenders detect initial use of RDP from a compromised host, particularly when correlated with unusual user behavior, logon patterns, or network activity. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry - where Registry.registry_path="*\\Microsoft\\Terminal Server Client\\Servers\\*" Registry.action != deleted - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` - | `security_content_ctime(lastTime)` - | `security_content_ctime(firstTime)` - | `windows_rdp_server_registry_entry_created_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 -known_false_positives: False positives will be present, filter as needed or restrict - to critical assets on the perimeter. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\Microsoft\\Terminal Server Client\\Servers\\*" Registry.action != deleted by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_rdp_server_registry_entry_created_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 +known_false_positives: False positives will be present, filter as needed or restrict to critical assets on the perimeter. references: -- https://medium.com/@bonguides25/how-to-clear-rdp-connections-history-in-windows-cf0ffb67f344 -- https://thelocalh0st.github.io/posts/rdp/ + - https://medium.com/@bonguides25/how-to-clear-rdp-connections-history-in-windows-cf0ffb67f344 + - https://thelocalh0st.github.io/posts/rdp/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Risk Message goes here - risk_objects: - - field: dest - type: system - score: 10 - threat_objects: [] + message: Risk Message goes here + risk_objects: + - field: dest + type: system + score: 10 + threat_objects: [] tags: - analytic_story: - - Windows RDP Artifacts and Defense Evasion - asset_type: Endpoint - mitre_attack_id: - - T1021.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows RDP Artifacts and Defense Evasion + asset_type: Endpoint + mitre_attack_id: + - T1021.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.001/terminal_server_reg_created/terminal_sever_client_Reg_created.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.001/terminal_server_reg_created/terminal_sever_client_Reg_created.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_rdpclient_connection_sequence_events.yml b/detections/endpoint/windows_rdpclient_connection_sequence_events.yml index 9781dedc38..b36cac70d1 100644 --- a/detections/endpoint/windows_rdpclient_connection_sequence_events.yml +++ b/detections/endpoint/windows_rdpclient_connection_sequence_events.yml @@ -1,77 +1,59 @@ name: Windows RDPClient Connection Sequence Events id: 67340df1-3f1d-4470-93c8-9ac7249d11b0 -version: 5 -date: '2025-08-07' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk type: Anomaly status: production -description: This analytic monitors Windows RDP client connection sequence - events (EventCode 1024) from the - Microsoft-Windows-TerminalServices-RDPClient/Operational log. These events - track when RDP ClientActiveX initiates connection attempts to remote servers. - The connection sequence is a critical phase of RDP where the client and server - exchange settings and establish common parameters for the session. Monitoring - these events can help identify unusual RDP connection patterns, potential - lateral movement attempts, unauthorized remote access activity, and RDP - connection chains that may indicate compromised systems. NOTE the analytic was - written for Multi-Line as XML was not properly parsed out. +description: This analytic monitors Windows RDP client connection sequence events (EventCode 1024) from the Microsoft-Windows-TerminalServices-RDPClient/Operational log. These events track when RDP ClientActiveX initiates connection attempts to remote servers. The connection sequence is a critical phase of RDP where the client and server exchange settings and establish common parameters for the session. Monitoring these events can help identify unusual RDP connection patterns, potential lateral movement attempts, unauthorized remote access activity, and RDP connection chains that may indicate compromised systems. NOTE the analytic was written for Multi-Line as XML was not properly parsed out. data_source: -- Windows Event Log Microsoft Windows TerminalServices RDPClient 1024 -search: '`wineventlog_rdp` EventCode=1024 | rename host as dest | stats count as "Event - Count", min(_time) as firstTime, max(_time) as lastTime, values(Message) as messages - by dest, source, LogName, EventCode, category | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_rdpclient_connection_sequence_events_filter`' -how_to_implement: To successfully implement this detection, ensure Windows RDP - Client Operational logs are being collected and forwarded to Splunk. Enable - logging for "Microsoft-Windows-TerminalServices-RDPClient/Operational", via a - new inputs.conf input. See references for more details. -known_false_positives: Legitimate RDP connections from authorized administrators - and users will generate these events. To reduce false positives, you should - baseline normal RDP connection patterns in your environment, whitelist - expected RDP connection chains between known administrative workstations and - servers, and track authorized remote support sessions. + - Windows Event Log Microsoft Windows TerminalServices RDPClient 1024 +search: |- + `wineventlog_rdp` EventCode=1024 + | rename host as dest + | stats count as "Event Count", min(_time) as firstTime, max(_time) as lastTime, values(Message) as messages + BY dest, source, LogName, + EventCode, category + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_rdpclient_connection_sequence_events_filter` +how_to_implement: To successfully implement this detection, ensure Windows RDP Client Operational logs are being collected and forwarded to Splunk. Enable logging for "Microsoft-Windows-TerminalServices-RDPClient/Operational", via a new inputs.conf input. See references for more details. +known_false_positives: Legitimate RDP connections from authorized administrators and users will generate these events. To reduce false positives, you should baseline normal RDP connection patterns in your environment, whitelist expected RDP connection chains between known administrative workstations and servers, and track authorized remote support sessions. references: -- https://gist.github.com/MHaggis/acd5dcbf1d4fb705b77f0a48e772eefc -- https://www.microsoft.com/en-us/security/blog/2024/10/29/midnight-blizzard-conducts-large-scale-spear-phishing-campaign-using-rdp-files/ + - https://gist.github.com/MHaggis/acd5dcbf1d4fb705b77f0a48e772eefc + - https://www.microsoft.com/en-us/security/blog/2024/10/29/midnight-blizzard-conducts-large-scale-spear-phishing-campaign-using-rdp-files/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Windows RDP client initiated a connection sequence event (EventCode - 1024) on host $dest$. - risk_objects: - - field: dest - type: system - score: 7 - threat_objects: [] + message: A Windows RDP client initiated a connection sequence event (EventCode 1024) on host $dest$. + risk_objects: + - field: dest + type: system + score: 7 + threat_objects: [] tags: - analytic_story: - - Spearphishing Attachments - - Windows RDP Artifacts and Defense Evasion - asset_type: Endpoint - mitre_attack_id: - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - Spearphishing Attachments + - Windows RDP Artifacts and Defense Evasion + asset_type: Endpoint + mitre_attack_id: + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1133/rdp/terminalservices-rdpclient.log - sourcetype: WinEventLog - source: WinEventLog:Microsoft-Windows-TerminalServices-RDPClient/Operational + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1133/rdp/terminalservices-rdpclient.log + sourcetype: WinEventLog + source: WinEventLog:Microsoft-Windows-TerminalServices-RDPClient/Operational diff --git a/detections/endpoint/windows_registry_bootexecute_modification.yml b/detections/endpoint/windows_registry_bootexecute_modification.yml index 2f51562867..57aa3acc1b 100644 --- a/detections/endpoint/windows_registry_bootexecute_modification.yml +++ b/detections/endpoint/windows_registry_bootexecute_modification.yml @@ -6,68 +6,45 @@ author: Michael Haag, Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects modifications to the BootExecute registry - key, which manages applications and services executed during system boot. It leverages - data from the Endpoint.Registry data model, focusing on changes to the registry - path "HKLM\\System\\CurrentControlSet\\Control\\Session Manager\\BootExecute". This - activity is significant because unauthorized changes to this key can indicate attempts - to achieve persistence, load malicious code, or tamper with the boot process. If - confirmed malicious, this could allow an attacker to maintain persistence, execute - arbitrary code at boot, or disrupt system operations. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path="*\\System\\CurrentControlSet\\Control\\Session - Manager\\BootExecute" by Registry.action Registry.dest Registry.process_guid Registry.process_id - Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data - Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user - Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_registry_bootexecute_modification_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on Windows Registry that include the name of the path and key responsible for the - changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. - In addition, confirm the latest CIM App 4.20 or higher is installed and the latest - TA for the endpoint product. + - Sysmon EventID 13 +description: The following analytic detects modifications to the BootExecute registry key, which manages applications and services executed during system boot. It leverages data from the Endpoint.Registry data model, focusing on changes to the registry path "HKLM\\System\\CurrentControlSet\\Control\\Session Manager\\BootExecute". This activity is significant because unauthorized changes to this key can indicate attempts to achieve persistence, load malicious code, or tamper with the boot process. If confirmed malicious, this could allow an attacker to maintain persistence, execute arbitrary code at boot, or disrupt system operations. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path="*\\System\\CurrentControlSet\\Control\\Session Manager\\BootExecute" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_registry_bootexecute_modification_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on Windows Registry that include the name of the path and key responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. known_false_positives: False positives may be present and will need to be filtered. references: -- https://www.microsoft.com/en-us/security/blog/2023/04/11/guidance-for-investigating-attacks-using-cve-2022-21894-the-blacklotus-campaign/ + - https://www.microsoft.com/en-us/security/blog/2023/04/11/guidance-for-investigating-attacks-using-cve-2022-21894-the-blacklotus-campaign/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The Registry BootExecute value was modified on $dest$ and should be reviewed - immediately. - risk_objects: - - field: dest - type: system - score: 100 - threat_objects: [] + message: The Registry BootExecute value was modified on $dest$ and should be reviewed immediately. + risk_objects: + - field: dest + type: system + score: 100 + threat_objects: [] tags: - analytic_story: - - Windows BootKits - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1542 - - T1547.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows BootKits + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1542 + - T1547.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.001/atomic_red_team/bootexecute-windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.001/atomic_red_team/bootexecute-windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_registry_certificate_added.yml b/detections/endpoint/windows_registry_certificate_added.yml index 0330f2d301..b7a9760e17 100644 --- a/detections/endpoint/windows_registry_certificate_added.yml +++ b/detections/endpoint/windows_registry_certificate_added.yml @@ -5,70 +5,46 @@ date: '2025-05-02' author: Michael Haag, Teodeerick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the installation of a root CA certificate - by monitoring specific registry paths for SetValue events. It leverages data from - the Endpoint datamodel, focusing on registry paths containing "certificates" and - registry values named "Blob." This activity is significant because unauthorized - root CA certificates can compromise the integrity of encrypted communications and - facilitate man-in-the-middle attacks. If confirmed malicious, this could allow an - attacker to intercept, decrypt, or manipulate sensitive data, leading to severe - security breaches. +description: The following analytic detects the installation of a root CA certificate by monitoring specific registry paths for SetValue events. It leverages data from the Endpoint datamodel, focusing on registry paths containing "certificates" and registry values named "Blob." This activity is significant because unauthorized root CA certificates can compromise the integrity of encrypted communications and facilitate man-in-the-middle attacks. If confirmed malicious, this could allow an attacker to intercept, decrypt, or manipulate sensitive data, leading to severe security breaches. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path IN ("*\\certificates\\*") - AND Registry.registry_value_name="Blob" by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_registry_certificate_added_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` node. - In addition, confirm the latest CIM App 4.20 or higher is installed and the latest - TA for the endpoint product. -known_false_positives: False positives will be limited to a legitimate business applicating - consistently adding new root certificates to the endpoint. Filter by user, process, - or thumbprint. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path IN ("*\\certificates\\*") AND Registry.registry_value_name="Blob" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_registry_certificate_added_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: False positives will be limited to a legitimate business applicating consistently adding new root certificates to the endpoint. Filter by user, process, or thumbprint. references: -- https://posts.specterops.io/code-signing-certificate-cloning-attacks-and-defenses-6f98657fc6ec -- https://github.com/redcanaryco/atomic-red-team/tree/master/atomics/T1553.004 + - https://posts.specterops.io/code-signing-certificate-cloning-attacks-and-defenses-6f98657fc6ec + - https://github.com/redcanaryco/atomic-red-team/tree/master/atomics/T1553.004 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A root certificate was added on $dest$. - risk_objects: - - field: dest - type: system - score: 42 - threat_objects: [] + message: A root certificate was added on $dest$. + risk_objects: + - field: dest + type: system + score: 42 + threat_objects: [] tags: - analytic_story: - - Windows Drivers - - Windows Registry Abuse - asset_type: Endpoint - mitre_attack_id: - - T1553.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Drivers + - Windows Registry Abuse + asset_type: Endpoint + mitre_attack_id: + - T1553.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1587.002/atomic_red_team/certblob_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1587.002/atomic_red_team/certblob_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_registry_delete_task_sd.yml b/detections/endpoint/windows_registry_delete_task_sd.yml index a1c4ab6484..5bc0a60532 100644 --- a/detections/endpoint/windows_registry_delete_task_sd.yml +++ b/detections/endpoint/windows_registry_delete_task_sd.yml @@ -6,90 +6,81 @@ author: Michael Haag, Teoderick Contreras, Splunk status: production type: Anomaly description: | - The following analytic detects a process attempting to delete a scheduled task's Security Descriptor (SD) from the registry path of that task. - It leverages the Endpoint.Registry data model to identify registry actions performed by the SYSTEM user, specifically targeting deletions of the SD value. - This activity is significant as it may indicate an attempt to remove evidence of a scheduled task for defense evasion. - If confirmed malicious, it suggests an attacker with privileged access trying to hide their tracks, potentially compromising system integrity and security. + The following analytic detects a process attempting to delete a scheduled task's Security Descriptor (SD) from the registry path of that task. + It leverages the Endpoint.Registry data model to identify registry actions performed by the SYSTEM user, specifically targeting deletions of the SD value. + This activity is significant as it may indicate an attempt to remove evidence of a scheduled task for defense evasion. + If confirmed malicious, it suggests an attacker with privileged access trying to hide their tracks, potentially compromising system integrity and security. data_source: - - Sysmon EventID 12 + - Sysmon EventID 12 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime - FROM datamodel=Endpoint.Registry where + FROM datamodel=Endpoint.Registry where - Registry.registry_path IN ("*\\Schedule\\TaskCache\\Tree\\*") - Registry.user="SYSTEM" - ( - Registry.registry_value_name="SD" - OR - Registry.registry_key_name="SD" - ) - Registry.action=deleted + Registry.registry_path IN ("*\\Schedule\\TaskCache\\Tree\\*") + Registry.user="SYSTEM" + ( + Registry.registry_value_name="SD" + OR + Registry.registry_key_name="SD" + ) + Registry.action=deleted - by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user - Registry.vendor_product + by Registry.action Registry.dest Registry.process_guid + Registry.process_id Registry.registry_hive + Registry.registry_path Registry.registry_key_name + Registry.registry_value_data Registry.registry_value_name + Registry.registry_value_type Registry.status Registry.user + Registry.vendor_product - | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_registry_delete_task_sd_filter` -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. + | `drop_dm_object_name(Registry)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_registry_delete_task_sd_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. known_false_positives: | - False positives should be limited as the activity is not common to delete ONLY the SD from the registry. + False positives should be limited as the activity is not common to delete ONLY the SD from the registry. references: - - https://www.microsoft.com/security/blog/2022/04/12/tarrask-malware-uses-scheduled-tasks-for-defense-evasion/ - - https://gist.github.com/MHaggis/5f7fd6745915166fc6da863d685e2728 - - https://gist.github.com/MHaggis/b246e2fae6213e762a6e694cabaf0c17 + - https://www.microsoft.com/security/blog/2022/04/12/tarrask-malware-uses-scheduled-tasks-for-defense-evasion/ + - https://gist.github.com/MHaggis/5f7fd6745915166fc6da863d685e2728 + - https://gist.github.com/MHaggis/b246e2fae6213e762a6e694cabaf0c17 drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A scheduled task security descriptor $registry_path$ was deleted from the registry on $dest$. - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: - - field: registry_path - type: registry_path + message: A scheduled task security descriptor $registry_path$ was deleted from the registry on $dest$. + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: + - field: registry_path + type: registry_path tags: - analytic_story: - - Windows Registry Abuse - - Windows Persistence Techniques - - Scheduled Tasks - asset_type: Endpoint - mitre_attack_id: - - T1053.005 - - T1562 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Registry Abuse + - Windows Persistence Techniques + - Scheduled Tasks + asset_type: Endpoint + mitre_attack_id: + - T1053.005 + - T1562 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/taskschedule/sd_delete_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/taskschedule/sd_delete_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_registry_dotnet_etw_disabled_via_env_variable.yml b/detections/endpoint/windows_registry_dotnet_etw_disabled_via_env_variable.yml index 54b9bca399..efa27a7445 100644 --- a/detections/endpoint/windows_registry_dotnet_etw_disabled_via_env_variable.yml +++ b/detections/endpoint/windows_registry_dotnet_etw_disabled_via_env_variable.yml @@ -5,76 +5,50 @@ date: '2025-05-02' author: Nasreddine Bencherchali, Splunk status: production type: TTP -description: The following analytic detects a registry modification that disables - the ETW for the .NET Framework. It leverages data from the Endpoint.Registry data - model, specifically monitoring changes to the COMPlus_ETWEnabled registry value - under the "Environment" registry key path for both user (HKCU\Environment) and machine - (HKLM\SYSTEM\CurrentControlSet\Control\Session Manager\Environment) scopes. This - activity is significant because disabling ETW can allow attackers to evade Endpoint - Detection and Response (EDR) tools and hide their execution from audit logs. If - confirmed malicious, this action could enable attackers to operate undetected, potentially - leading to further compromise and persistent access within the environment. +description: The following analytic detects a registry modification that disables the ETW for the .NET Framework. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the COMPlus_ETWEnabled registry value under the "Environment" registry key path for both user (HKCU\Environment) and machine (HKLM\SYSTEM\CurrentControlSet\Control\Session Manager\Environment) scopes. This activity is significant because disabling ETW can allow attackers to evade Endpoint Detection and Response (EDR) tools and hide their execution from audit logs. If confirmed malicious, this action could enable attackers to operate undetected, potentially leading to further compromise and persistent access within the environment. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path="*\\Environment*" - Registry.registry_value_name="COMPlus_ETWEnabled" (Registry.registry_value_data=0x000000000 - OR Registry.registry_value_data=0) by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_registry_dotnet_etw_disabled_via_env_variable_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 -known_false_positives: Setting the "COMPlus_ETWEnabled" value as a global environment - variable either in user or machine scope should only happens during debugging use - cases, hence the false positives rate should be very minimal. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path="*\\Environment*" Registry.registry_value_name="COMPlus_ETWEnabled" (Registry.registry_value_data=0x000000000 OR Registry.registry_value_data=0) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_registry_dotnet_etw_disabled_via_env_variable_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 +known_false_positives: Setting the "COMPlus_ETWEnabled" value as a global environment variable either in user or machine scope should only happens during debugging use cases, hence the false positives rate should be very minimal. references: -- https://gist.github.com/Cyb3rWard0g/a4a115fd3ab518a0e593525a379adee3 -- https://blog.xpnsec.com/hiding-your-dotnet-complus-etwenabled/ -- https://attack.mitre.org/techniques/T1562/006/ + - https://gist.github.com/Cyb3rWard0g/a4a115fd3ab518a0e593525a379adee3 + - https://blog.xpnsec.com/hiding-your-dotnet-complus-etwenabled/ + - https://attack.mitre.org/techniques/T1562/006/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Modified registry entry $registry_path$ in $dest$ - risk_objects: - - field: dest - type: system - score: 90 - - field: user - type: user - score: 90 - threat_objects: [] + message: Modified registry entry $registry_path$ in $dest$ + risk_objects: + - field: dest + type: system + score: 90 + - field: user + type: user + score: 90 + threat_objects: [] tags: - analytic_story: - - Windows Registry Abuse - - Windows Defense Evasion Tactics - asset_type: Endpoint - mitre_attack_id: - - T1562.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Registry Abuse + - Windows Defense Evasion Tactics + asset_type: Endpoint + mitre_attack_id: + - T1562.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.006/dotnet_etw_bypass/dotnet_etw_bypass.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.006/dotnet_etw_bypass/dotnet_etw_bypass.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_registry_entries_exported_via_reg.yml b/detections/endpoint/windows_registry_entries_exported_via_reg.yml index 9c17c1602b..a1ae7f0cc1 100644 --- a/detections/endpoint/windows_registry_entries_exported_via_reg.yml +++ b/detections/endpoint/windows_registry_entries_exported_via_reg.yml @@ -1,62 +1,53 @@ name: Windows Registry Entries Exported Via Reg id: 466379bc-0f47-476c-8202-16ef38112e0d -version: 3 -date: '2025-05-02' +version: 4 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects the execution of the reg.exe process with - either the "save" or "export" parameters. This detection leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process execution logs and command-line - arguments. This activity is significant because threat actors often use the "reg - save" or "reg export" command to dump credentials or test registry modification - capabilities on compromised hosts. If confirmed malicious, this behavior could allow - attackers to escalate privileges, persist in the environment, or access sensitive - information stored in the registry. +description: The following analytic detects the execution of the reg.exe process with either the "save" or "export" parameters. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant because threat actors often use the "reg save" or "reg export" command to dump credentials or test registry modification capabilities on compromised hosts. If confirmed malicious, this behavior could allow attackers to escalate privileges, persist in the environment, or access sensitive information stored in the registry. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_reg` AND Processes.process - IN ("* save *", "* export *") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_registry_entries_exported_via_reg_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: network administrator can use this command tool to backup registry - before updates or modifying critical registries. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_reg` + AND + Processes.process IN ("* save *", "* export *") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_registry_entries_exported_via_reg_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: network administrator can use this command tool to backup registry before updates or modifying critical registries. references: -- https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/quser -- https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS -- https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ + - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/quser + - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS + - https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ tags: - analytic_story: - - Windows Post-Exploitation - - CISA AA23-347A - - Prestige Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1012 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Post-Exploitation + - CISA AA23-347A + - Prestige Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1012 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_registry_entries_restored_via_reg.yml b/detections/endpoint/windows_registry_entries_restored_via_reg.yml index 232a82c169..36b4a23a80 100644 --- a/detections/endpoint/windows_registry_entries_restored_via_reg.yml +++ b/detections/endpoint/windows_registry_entries_restored_via_reg.yml @@ -1,61 +1,52 @@ name: Windows Registry Entries Restored Via Reg id: a17af481-e2ad-494c-9da6-afb4d243a019 -version: 3 -date: '2025-05-02' +version: 4 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects the execution of reg.exe with the "restore" - parameter, indicating an attempt to restore registry backup data on a host. This - detection leverages data from Endpoint Detection and Response (EDR) agents, focusing - on process execution logs and command-line arguments. This activity is significant - as it may indicate post-exploitation actions, such as those performed by tools like - winpeas, which use "reg save" and "reg restore" to manipulate registry settings. - If confirmed malicious, this could allow an attacker to revert registry changes, - potentially bypassing security controls and maintaining persistence. +description: The following analytic detects the execution of reg.exe with the "restore" parameter, indicating an attempt to restore registry backup data on a host. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant as it may indicate post-exploitation actions, such as those performed by tools like winpeas, which use "reg save" and "reg restore" to manipulate registry settings. If confirmed malicious, this could allow an attacker to revert registry changes, potentially bypassing security controls and maintaining persistence. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_reg` AND Processes.process - = "* restore *" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_registry_entries_restored_via_reg_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: network administrator can use this command tool to backup registry - before updates or modifying critical registries. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_reg` + AND + Processes.process = "* restore *" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_registry_entries_restored_via_reg_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: network administrator can use this command tool to backup registry before updates or modifying critical registries. references: -- https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/quser -- https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS -- https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ + - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/quser + - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS + - https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ tags: - analytic_story: - - Windows Post-Exploitation - - Prestige Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1012 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Post-Exploitation + - Prestige Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1012 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_registry_modification_for_safe_mode_persistence.yml b/detections/endpoint/windows_registry_modification_for_safe_mode_persistence.yml index 47f091e8f4..d6fd5eeb9c 100644 --- a/detections/endpoint/windows_registry_modification_for_safe_mode_persistence.yml +++ b/detections/endpoint/windows_registry_modification_for_safe_mode_persistence.yml @@ -5,74 +5,49 @@ date: '2025-05-02' author: Teoderick Contreras, Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies modifications to the SafeBoot registry - keys, specifically within the Minimal and Network paths. This detection leverages - registry activity logs from endpoint data sources like Sysmon or EDR tools. Monitoring - these keys is crucial as adversaries can use them to persist drivers or services - in Safe Mode, with Network allowing network connections. If confirmed malicious, - this activity could enable attackers to maintain persistence even in Safe Mode, - potentially bypassing certain security measures and facilitating further malicious - actions. +description: The following analytic identifies modifications to the SafeBoot registry keys, specifically within the Minimal and Network paths. This detection leverages registry activity logs from endpoint data sources like Sysmon or EDR tools. Monitoring these keys is crucial as adversaries can use them to persist drivers or services in Safe Mode, with Network allowing network connections. If confirmed malicious, this activity could enable attackers to maintain persistence even in Safe Mode, potentially bypassing certain security measures and facilitating further malicious actions. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path IN ("*SYSTEM\\CurrentControlSet\\Control\\SafeBoot\\Minimal\\*","*SYSTEM\\CurrentControlSet\\Control\\SafeBoot\\Network\\*") - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_registry_modification_for_safe_mode_persistence_filter`' -how_to_implement: To successfully implement this search, you must be ingesting data - that records registry activity from your hosts to populate the endpoint data model - in the registry node. This is typically populated via endpoint detection-and-response - product, such as Carbon Black or endpoint data sources, such as Sysmon. The data - used for this search is typically generated via logs that report reads and writes - to the registry. -known_false_positives: updated windows application needed in safe boot may used this - registry + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path IN ("*SYSTEM\\CurrentControlSet\\Control\\SafeBoot\\Minimal\\*","*SYSTEM\\CurrentControlSet\\Control\\SafeBoot\\Network\\*") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_registry_modification_for_safe_mode_persistence_filter`' +how_to_implement: To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. +known_false_positives: updated windows application needed in safe boot may used this registry references: -- https://malware.news/t/threat-analysis-unit-tau-threat-intelligence-notification-snatch-ransomware/36365 -- https://redcanary.com/blog/tracking-driver-inventory-to-expose-rootkits/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1112/T1112.md -- https://blog.didierstevens.com/2007/03/26/playing-with-safe-mode/ + - https://malware.news/t/threat-analysis-unit-tau-threat-intelligence-notification-snatch-ransomware/36365 + - https://redcanary.com/blog/tracking-driver-inventory-to-expose-rootkits/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1112/T1112.md + - https://blog.didierstevens.com/2007/03/26/playing-with-safe-mode/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Safeboot registry $registry_path$ was added or modified with a new value - $registry_value_name$ on $dest$ - risk_objects: - - field: dest - type: system - score: 42 - threat_objects: [] + message: Safeboot registry $registry_path$ was added or modified with a new value $registry_value_name$ on $dest$ + risk_objects: + - field: dest + type: system + score: 42 + threat_objects: [] tags: - analytic_story: - - Ransomware - - Windows Registry Abuse - - Windows Drivers - asset_type: Endpoint - mitre_attack_id: - - T1547.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - Windows Registry Abuse + - Windows Drivers + asset_type: Endpoint + mitre_attack_id: + - T1547.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_registry_payload_injection.yml b/detections/endpoint/windows_registry_payload_injection.yml index 3b911f948d..90b35c32b5 100644 --- a/detections/endpoint/windows_registry_payload_injection.yml +++ b/detections/endpoint/windows_registry_payload_injection.yml @@ -1,80 +1,63 @@ name: Windows Registry Payload Injection id: c6b2d80f-179a-41a1-b95e-ce5601d7427a -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Steven Dick status: production type: TTP -description: The following analytic detects suspiciously long data written to the - Windows registry, a behavior often linked to fileless malware or persistence techniques. - It leverages Endpoint Detection and Response (EDR) telemetry, focusing on registry - events with data lengths exceeding 512 characters. This activity is significant - as it can indicate an attempt to evade traditional file-based defenses, making it - crucial for SOC monitoring. If confirmed malicious, this technique could allow attackers - to maintain persistence, execute code, or manipulate system configurations without - leaving a conventional file footprint. +description: The following analytic detects suspiciously long data written to the Windows registry, a behavior often linked to fileless malware or persistence techniques. It leverages Endpoint Detection and Response (EDR) telemetry, focusing on registry events with data lengths exceeding 512 characters. This activity is significant as it can indicate an attempt to evade traditional file-based defenses, making it crucial for SOC monitoring. If confirmed malicious, this technique could allow attackers to maintain persistence, execute code, or manipulate system configurations without leaving a conventional file footprint. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count - from datamodel=Endpoint.Registry where Registry.registry_value_data=* by _time span=1h - Registry.dest Registry.registry_path Registry.registry_value_name Registry.process_guid - Registry.registry_value_data Registry.registry_key_name Registry.registry_hive Registry.status - Registry.action Registry.process_id Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` - | eval reg_data_len = len(registry_value_data) - | where reg_data_len > 512 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_registry_payload_injection_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 13 +search: |- + | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry + WHERE Registry.registry_value_data=* + BY _time span=1h Registry.dest + Registry.registry_path Registry.registry_value_name Registry.process_guid + Registry.registry_value_data Registry.registry_key_name Registry.registry_hive + Registry.status Registry.action Registry.process_id + Registry.user Registry.vendor_product + | `drop_dm_object_name(Registry)` + | eval reg_data_len = len(registry_value_data) + | where reg_data_len > 512 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_registry_payload_injection_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://www.mandiant.com/resources/blog/tracking-evolution-gootloader-operations -- https://www.trendmicro.com/vinfo/us/security/news/cybercrime-and-digital-threats/kovter-an-evolving-malware-gone-fileless -- https://attack.mitre.org/techniques/T1027/011/ + - https://www.mandiant.com/resources/blog/tracking-evolution-gootloader-operations + - https://www.trendmicro.com/vinfo/us/security/news/cybercrime-and-digital-threats/kovter-an-evolving-malware-gone-fileless + - https://attack.mitre.org/techniques/T1027/011/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process added a suspicious length of registry data on $dest$. - risk_objects: - - field: dest - type: system - score: 60 - threat_objects: [] + message: A process added a suspicious length of registry data on $dest$. + risk_objects: + - field: dest + type: system + score: 60 + threat_objects: [] tags: - analytic_story: - - Unusual Processes - asset_type: Endpoint - mitre_attack_id: - - T1027.011 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Unusual Processes + asset_type: Endpoint + mitre_attack_id: + - T1027.011 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/gootloader/partial_ttps/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/gootloader/partial_ttps/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_registry_sip_provider_modification.yml b/detections/endpoint/windows_registry_sip_provider_modification.yml index f5d17bca4d..49a4c6c0ec 100644 --- a/detections/endpoint/windows_registry_sip_provider_modification.yml +++ b/detections/endpoint/windows_registry_sip_provider_modification.yml @@ -6,76 +6,49 @@ author: Michael Haag, Splunk status: production type: TTP data_source: -- Sysmon EventID 13 -description: The following analytic detects modifications to the Windows Registry - SIP Provider. It leverages Sysmon EventID 7 to monitor registry changes in paths - and values related to Cryptography Providers and OID Encoding Types. This activity - is significant as it may indicate an attempt to subvert trust controls, a common - tactic for bypassing security measures and maintaining persistence. If confirmed - malicious, an attacker could manipulate the system's cryptographic functions, potentially - leading to unauthorized access, data theft, or other damaging outcomes. Review the - modified registry paths and concurrent processes to identify the attack source. -search: '| tstats `security_content_summariesonly` count values(Registry.registry_key_name) - as registry_key_name values(Registry.registry_path) as registry_path min(_time) - as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path - IN ("*\\SOFTWARE\\Microsoft\\Cryptography\\Providers\\*", "*\\SOFTWARE\\Microsoft\\Cryptography\\OID\\EncodingType*", - "*\\SOFTWARE\\WOW6432Node\\Microsoft\\Cryptography\\Providers\\*", "*\\SOFTWARE\\WOW6432Node\\Microsoft\\Cryptography\\OID\\EncodingType*") - Registry.registry_value_name IN ("Dll","$DLL") by Registry.action Registry.dest - Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)`| - `windows_registry_sip_provider_modification_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: Be aware of potential false positives - legitimate applications - may cause benign activities to be flagged. + - Sysmon EventID 13 +description: The following analytic detects modifications to the Windows Registry SIP Provider. It leverages Sysmon EventID 7 to monitor registry changes in paths and values related to Cryptography Providers and OID Encoding Types. This activity is significant as it may indicate an attempt to subvert trust controls, a common tactic for bypassing security measures and maintaining persistence. If confirmed malicious, an attacker could manipulate the system's cryptographic functions, potentially leading to unauthorized access, data theft, or other damaging outcomes. Review the modified registry paths and concurrent processes to identify the attack source. +search: '| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path IN ("*\\SOFTWARE\\Microsoft\\Cryptography\\Providers\\*", "*\\SOFTWARE\\Microsoft\\Cryptography\\OID\\EncodingType*", "*\\SOFTWARE\\WOW6432Node\\Microsoft\\Cryptography\\Providers\\*", "*\\SOFTWARE\\WOW6432Node\\Microsoft\\Cryptography\\OID\\EncodingType*") Registry.registry_value_name IN ("Dll","$DLL") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)`| `windows_registry_sip_provider_modification_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: Be aware of potential false positives - legitimate applications may cause benign activities to be flagged. references: -- https://attack.mitre.org/techniques/T1553/003/ -- https://github.com/SigmaHQ/sigma/blob/master/rules/windows/registry/registry_set/registry_set_sip_persistence.yml -- https://specterops.io/wp-content/uploads/sites/3/2022/06/SpecterOps_Subverting_Trust_in_Windows.pdf -- https://github.com/gtworek/PSBits/tree/master/SIP -- https://github.com/mattifestation/PoCSubjectInterfacePackage -- https://pentestlab.blog/2017/11/06/hijacking-digital-signatures/ + - https://attack.mitre.org/techniques/T1553/003/ + - https://github.com/SigmaHQ/sigma/blob/master/rules/windows/registry/registry_set/registry_set_sip_persistence.yml + - https://specterops.io/wp-content/uploads/sites/3/2022/06/SpecterOps_Subverting_Trust_in_Windows.pdf + - https://github.com/gtworek/PSBits/tree/master/SIP + - https://github.com/mattifestation/PoCSubjectInterfacePackage + - https://pentestlab.blog/2017/11/06/hijacking-digital-signatures/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows Registry SIP Provider Modification detected on $dest$. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: Windows Registry SIP Provider Modification detected on $dest$. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Subvert Trust Controls SIP and Trust Provider Hijacking - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1553.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Subvert Trust Controls SIP and Trust Provider Hijacking + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1553.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.003/sip/sip_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.003/sip/sip_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_regsvr32_renamed_binary.yml b/detections/endpoint/windows_regsvr32_renamed_binary.yml index 5a0c8f870f..79a1f0dfe5 100644 --- a/detections/endpoint/windows_regsvr32_renamed_binary.yml +++ b/detections/endpoint/windows_regsvr32_renamed_binary.yml @@ -1,79 +1,66 @@ name: Windows Regsvr32 Renamed Binary id: 7349a9e9-3cf6-4171-bb0c-75607a8dcd1a -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic identifies instances where the regsvr32.exe binary - has been renamed and executed. This detection leverages Endpoint Detection and Response - (EDR) data, specifically focusing on the original filename metadata. Renaming regsvr32.exe - is significant as it can be an evasion technique used by attackers to bypass security - controls. If confirmed malicious, this activity could allow an attacker to execute - arbitrary DLLs, potentially leading to code execution, privilege escalation, or - persistence within the environment. +description: The following analytic identifies instances where the regsvr32.exe binary has been renamed and executed. This detection leverages Endpoint Detection and Response (EDR) data, specifically focusing on the original filename metadata. Renaming regsvr32.exe is significant as it can be an evasion technique used by attackers to bypass security controls. If confirmed malicious, this activity could allow an attacker to execute arbitrary DLLs, potentially leading to code execution, privilege escalation, or persistence within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name != regsvr32.exe - AND Processes.original_file_name=regsvr32.exe by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_regsvr32_renamed_binary_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name != regsvr32.exe + AND + Processes.original_file_name=regsvr32.exe + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_regsvr32_renamed_binary_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://twitter.com/pr0xylife/status/1585612370441031680?s=46&t=Dc3CJi4AnM-8rNoacLbScg + - https://twitter.com/pr0xylife/status/1585612370441031680?s=46&t=Dc3CJi4AnM-8rNoacLbScg drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: regsvr32 was renamed as $process_name$ on $dest$ - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: regsvr32 was renamed as $process_name$ on $dest$ + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Qakbot - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1218.010 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Qakbot + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1218.010 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot_3/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot_3/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_remote_access_software_brc4_loaded_dll.yml b/detections/endpoint/windows_remote_access_software_brc4_loaded_dll.yml index 586ed8859f..c04d0e54d7 100644 --- a/detections/endpoint/windows_remote_access_software_brc4_loaded_dll.yml +++ b/detections/endpoint/windows_remote_access_software_brc4_loaded_dll.yml @@ -1,86 +1,66 @@ name: Windows Remote Access Software BRC4 Loaded Dll id: 73cf5dcb-cf36-4167-8bbe-384fe5384d05 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies the loading of four specific Windows - DLLs (credui.dll, dbghelp.dll, samcli.dll, winhttp.dll) by a non-standard process. - This detection leverages Sysmon EventCode 7 to monitor DLL load events and flags - when all four DLLs are loaded within a short time frame. This activity is significant - as it may indicate the presence of Brute Ratel C4, a sophisticated remote access - tool used for credential dumping and other malicious activities. If confirmed malicious, - this behavior could lead to unauthorized access, credential theft, and further compromise - of the affected system. +description: The following analytic identifies the loading of four specific Windows DLLs (credui.dll, dbghelp.dll, samcli.dll, winhttp.dll) by a non-standard process. This detection leverages Sysmon EventCode 7 to monitor DLL load events and flags when all four DLLs are loaded within a short time frame. This activity is significant as it may indicate the presence of Brute Ratel C4, a sophisticated remote access tool used for credential dumping and other malicious activities. If confirmed malicious, this behavior could lead to unauthorized access, credential theft, and further compromise of the affected system. data_source: -- Sysmon EventID 7 -search: '`sysmon` EventCode=7 | bin _time span=30s | eval BRC4_AnomalyLoadedDll=case(OriginalFileName=="credui.dll", - 1, OriginalFileName=="DBGHELP.DLL", 1, OriginalFileName=="SAMCLI.DLL", 1, OriginalFileName=="winhttp.dll", - 1, 1=1, 0) | eval BRC4_LoadedDllPath=case(match(ImageLoaded, "credui.dll"), 1, match(ImageLoaded, - "dbghelp.dll"), 1, match(ImageLoaded, "samcli.dll"), 1, match(ImageLoaded, "winhttp.dll"), - 1, 1=1, 0) | stats count min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) - as ImageLoaded values(OriginalFileName) as OriginalFileName dc(ImageLoaded) as ImageLoadedCount - values(loaded_file) as loaded_file values(loaded_file_path) as loaded_file_path - values(original_file_name) as original_file_name values(process_exec) as process_exec - values(process_guid) as process_guid values(process_hash) as process_hash values(process_id) - as process_id values(process_name) as process_name values(process_path) as process_path - values(service_dll_signature_exists) as service_dll_signature_exists values(service_dll_signature_verified) - as service_dll_signature_verified values(signature) as signature values(signature_id) - as signature_id values(user_id) as user_id values(vendor_product) as vendor_product - by Image BRC4_LoadedDllPath BRC4_AnomalyLoadedDll dest Signed | where ImageLoadedCount - == 4 AND (BRC4_LoadedDllPath == 1 OR BRC4_AnomalyLoadedDll == 1) | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_remote_access_software_brc4_loaded_dll_filter`' -how_to_implement: The latest Sysmon TA 3.0 https://splunkbase.splunk.com/app/5709 - will add the ImageLoaded name to the process_name field, allowing this query to - work. Use as an example and implement for other products. -known_false_positives: This module can be loaded by a third party application. Filter - is needed. + - Sysmon EventID 7 +search: |- + `sysmon` EventCode=7 + | bin _time span=30s + | eval BRC4_AnomalyLoadedDll=case(OriginalFileName=="credui.dll", 1, OriginalFileName=="DBGHELP.DLL", 1, OriginalFileName=="SAMCLI.DLL", 1, OriginalFileName=="winhttp.dll", 1, 1=1, 0) + | eval BRC4_LoadedDllPath=case(match(ImageLoaded, "credui.dll"), 1, match(ImageLoaded, "dbghelp.dll"), 1, match(ImageLoaded, "samcli.dll"), 1, match(ImageLoaded, "winhttp.dll"), 1, 1=1, 0) + | stats count min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) as ImageLoaded values(OriginalFileName) as OriginalFileName dc(ImageLoaded) as ImageLoadedCount values(loaded_file) as loaded_file values(loaded_file_path) as loaded_file_path values(original_file_name) as original_file_name values(process_exec) as process_exec values(process_guid) as process_guid values(process_hash) as process_hash values(process_id) as process_id values(process_name) as process_name values(process_path) as process_path values(service_dll_signature_exists) as service_dll_signature_exists values(service_dll_signature_verified) as service_dll_signature_verified values(signature) as signature values(signature_id) as signature_id values(user_id) as user_id values(vendor_product) as vendor_product + BY Image BRC4_LoadedDllPath BRC4_AnomalyLoadedDll + dest Signed + | where ImageLoadedCount == 4 AND (BRC4_LoadedDllPath == 1 OR BRC4_AnomalyLoadedDll == 1) + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_remote_access_software_brc4_loaded_dll_filter` +how_to_implement: The latest Sysmon TA 3.0 https://splunkbase.splunk.com/app/5709 will add the ImageLoaded name to the process_name field, allowing this query to work. Use as an example and implement for other products. +known_false_positives: This module can be loaded by a third party application. Filter is needed. references: -- https://unit42.paloaltonetworks.com/brute-ratel-c4-tool/ -- https://www.mdsec.co.uk/2022/08/part-3-how-i-met-your-beacon-brute-ratel/ -- https://strontic.github.io/xcyclopedia/library/logoncli.dll-138871DBE68D0696D3D7FA91BC2873B1.html -- https://strontic.github.io/xcyclopedia/library/credui.dll-A5BD797BBC2DD55231B9DE99837E5461.html -- https://docs.microsoft.com/en-us/windows/win32/secauthn/credential-manager -- https://strontic.github.io/xcyclopedia/library/samcli.dll-522D6D616EF142CDE965BD3A450A9E4C.html -- https://strontic.github.io/xcyclopedia/library/dbghelp.dll-15A55EAB307EF8C190FE6135C0A86F7C.html + - https://unit42.paloaltonetworks.com/brute-ratel-c4-tool/ + - https://www.mdsec.co.uk/2022/08/part-3-how-i-met-your-beacon-brute-ratel/ + - https://strontic.github.io/xcyclopedia/library/logoncli.dll-138871DBE68D0696D3D7FA91BC2873B1.html + - https://strontic.github.io/xcyclopedia/library/credui.dll-A5BD797BBC2DD55231B9DE99837E5461.html + - https://docs.microsoft.com/en-us/windows/win32/secauthn/credential-manager + - https://strontic.github.io/xcyclopedia/library/samcli.dll-522D6D616EF142CDE965BD3A450A9E4C.html + - https://strontic.github.io/xcyclopedia/library/dbghelp.dll-15A55EAB307EF8C190FE6135C0A86F7C.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a process $Image$ loaded several modules $ImageLoaded$ that might related - to credential access on $dest$. - risk_objects: - - field: dest - type: system - score: 9 - threat_objects: [] + message: a process $Image$ loaded several modules $ImageLoaded$ that might related to credential access on $dest$. + risk_objects: + - field: dest + type: system + score: 9 + threat_objects: [] tags: - analytic_story: - - Brute Ratel C4 - asset_type: Endpoint - mitre_attack_id: - - T1219 - - T1003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Brute Ratel C4 + asset_type: Endpoint + mitre_attack_id: + - T1219 + - T1003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/iso_version_dll_campaign/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/iso_version_dll_campaign/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_remote_access_software_rms_registry.yml b/detections/endpoint/windows_remote_access_software_rms_registry.yml index cec020a736..3efcc01657 100644 --- a/detections/endpoint/windows_remote_access_software_rms_registry.yml +++ b/detections/endpoint/windows_remote_access_software_rms_registry.yml @@ -5,67 +5,45 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the creation or modification of Windows - registry entries related to the Remote Manipulator System (RMS) Remote Admin tool. - It leverages data from the Endpoint.Registry datamodel, focusing on registry paths - containing "SYSTEM\\Remote Manipulator System." This activity is significant because - RMS, while legitimate, is often abused by adversaries, such as in the Azorult malware - campaigns, to gain unauthorized remote access. If confirmed malicious, this could - allow attackers to remotely control the targeted host, leading to potential data - exfiltration, system manipulation, or further network compromise. +description: The following analytic detects the creation or modification of Windows registry entries related to the Remote Manipulator System (RMS) Remote Admin tool. It leverages data from the Endpoint.Registry datamodel, focusing on registry paths containing "SYSTEM\\Remote Manipulator System." This activity is significant because RMS, while legitimate, is often abused by adversaries, such as in the Azorult malware campaigns, to gain unauthorized remote access. If confirmed malicious, this could allow attackers to remotely control the targeted host, leading to potential data exfiltration, system manipulation, or further network compromise. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SYSTEM\\Remote - Manipulator System*" by Registry.action Registry.dest Registry.process_guid Registry.process_id - Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data - Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user - Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_remote_access_software_rms_registry_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure - that this registry was included in your config files ex. sysmon config to be monitored. -known_false_positives: administrators may enable or disable this feature that may - cause some false positive. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SYSTEM\\Remote Manipulator System*" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_access_software_rms_registry_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored. +known_false_positives: administrators may enable or disable this feature that may cause some false positive. references: -- https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ -- https://malpedia.caad.fkie.fraunhofer.de/details/win.rms + - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ + - https://malpedia.caad.fkie.fraunhofer.de/details/win.rms drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: the registry related to RMS tool is created on $dest$ - risk_objects: - - field: dest - type: system - score: 90 - threat_objects: [] + message: the registry related to RMS tool is created on $dest$ + risk_objects: + - field: dest + type: system + score: 90 + threat_objects: [] tags: - analytic_story: - - Azorult - asset_type: Endpoint - mitre_attack_id: - - T1219 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azorult + asset_type: Endpoint + mitre_attack_id: + - T1219 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_remote_assistance_spawning_process.yml b/detections/endpoint/windows_remote_assistance_spawning_process.yml index 571a0289bc..98de742aa2 100644 --- a/detections/endpoint/windows_remote_assistance_spawning_process.yml +++ b/detections/endpoint/windows_remote_assistance_spawning_process.yml @@ -1,86 +1,69 @@ name: Windows Remote Assistance Spawning Process id: ced50492-8849-11ec-9f68-acde48001122 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects Microsoft Remote Assistance (msra.exe) - spawning PowerShell.exe or cmd.exe as a child process. This detection leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process creation - events where msra.exe is the parent process. This activity is significant because - msra.exe typically does not spawn command-line interfaces, indicating potential - process injection or misuse. If confirmed malicious, an attacker could use this - technique to execute arbitrary commands, escalate privileges, or maintain persistence - on the compromised system. +description: The following analytic detects Microsoft Remote Assistance (msra.exe) spawning PowerShell.exe or cmd.exe as a child process. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where msra.exe is the parent process. This activity is significant because msra.exe typically does not spawn command-line interfaces, indicating potential process injection or misuse. If confirmed malicious, an attacker could use this technique to execute arbitrary commands, escalate privileges, or maintain persistence on the compromised system. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=msra.exe - `windows_shells` by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_assistance_spawning_process_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives should be limited, filter as needed. Add additional - shells as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name=msra.exe `windows_shells` + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_remote_assistance_spawning_process_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives should be limited, filter as needed. Add additional shells as needed. references: -- https://thedfirreport.com/2022/02/07/qbot-likes-to-move-it-move-it/ -- https://app.any.run/tasks/ca1616de-89a1-4afc-a3e4-09d428df2420/ + - https://thedfirreport.com/2022/02/07/qbot-likes-to-move-it-move-it/ + - https://app.any.run/tasks/ca1616de-89a1-4afc-a3e4-09d428df2420/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$, generating behavior not common with msra.exe. - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$, generating behavior not common with msra.exe. + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Unusual Processes - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1055 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Unusual Processes + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1055 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/msra/msra-windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/msra/msra-windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_remote_create_service.yml b/detections/endpoint/windows_remote_create_service.yml index be43dffe24..66b59dbe38 100644 --- a/detections/endpoint/windows_remote_create_service.yml +++ b/detections/endpoint/windows_remote_create_service.yml @@ -6,85 +6,54 @@ author: Michael Haag, Splunk status: production type: Anomaly data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic identifies the creation of a new service on a - remote endpoint using sc.exe. It leverages data from Endpoint Detection and Response - (EDR) agents, specifically monitoring for EventCode 7045, which indicates a new - service creation. This activity is significant as it may indicate lateral movement - or remote code execution attempts by an attacker. If confirmed malicious, this could - allow the attacker to establish persistence, escalate privileges, or execute arbitrary - code on the remote system, potentially leading to further compromise of the network. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=sc.exe - Processes.process IN ("*create*") Processes.process="*\\\\*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_remote_create_service_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Note that false positives may occur, and filtering may be necessary, - especially when it comes to remote service creation by administrators or software - management utilities. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic identifies the creation of a new service on a remote endpoint using sc.exe. It leverages data from Endpoint Detection and Response (EDR) agents, specifically monitoring for EventCode 7045, which indicates a new service creation. This activity is significant as it may indicate lateral movement or remote code execution attempts by an attacker. If confirmed malicious, this could allow the attacker to establish persistence, escalate privileges, or execute arbitrary code on the remote system, potentially leading to further compromise of the network. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=sc.exe Processes.process IN ("*create*") Processes.process="*\\\\*" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_create_service_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Note that false positives may occur, and filtering may be necessary, especially when it comes to remote service creation by administrators or software management utilities. references: -- https://attack.mitre.org/techniques/T1543/003/ + - https://attack.mitre.org/techniques/T1543/003/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to create a remote service. - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to create a remote service. + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Active Directory Lateral Movement - - CISA AA23-347A - - BlackSuit Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1543.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + - CISA AA23-347A + - BlackSuit Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1543.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/atomic_red_team/remote_service_create_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/atomic_red_team/remote_service_create_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_remote_host_computer_management_access.yml b/detections/endpoint/windows_remote_host_computer_management_access.yml index 183600a92c..8b283566ab 100644 --- a/detections/endpoint/windows_remote_host_computer_management_access.yml +++ b/detections/endpoint/windows_remote_host_computer_management_access.yml @@ -1,62 +1,68 @@ name: Windows Remote Host Computer Management Access id: 455da527-0047-4610-a3ca-b4a005c2d346 -version: 2 -date: '2025-05-02' +version: 3 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: The following analytic detects the use of mmc.exe to launch Computer Management (compmgmt.msc) and connect to a remote machine. This technique allows administrators to access system management tools, including Event Viewer, Services, Shared Folders, and Local Users & Groups, without initiating a full remote desktop session. While commonly used for legitimate administrative purposes, adversaries may leverage this method for remote reconnaissance, privilege escalation, or persistence. Monitoring the execution of mmc.exe with the /computer:{hostname/ip} argument can help detect unauthorized system administration attempts or lateral movement within a network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.process_name="mmc.exe" AND Processes.process = "*compmgmt.msc *" AND Processes.process = "*/computer:*" - by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_remote_host_computer_management_access_filter`' + - Sysmon EventID 1 + - Windows Event Log Security 4688 +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name="mmc.exe" + AND + Processes.process = "*compmgmt.msc *" + AND + Processes.process = "*/computer:*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_remote_host_computer_management_access_filter` how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: administrator or power user can execute command shell or script to access Windows Remote Management. references: -- https://www.cisa.gov/news-events/cybersecurity-advisories/aa25-071a + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa25-071a drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a computer management process command $process$ executed on $dest$. - risk_objects: - - field: dest - type: system - score: 20 - threat_objects: - - field: process_name - type: process_name + message: a computer management process command $process$ executed on $dest$. + risk_objects: + - field: dest + type: system + score: 20 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Medusa Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1021.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Medusa Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1021.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/compmgtm_access/compmgmt_load.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/compmgtm_access/compmgmt_load.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_remote_management_execute_shell.yml b/detections/endpoint/windows_remote_management_execute_shell.yml index 0506e9b2d3..9dd8f8c463 100644 --- a/detections/endpoint/windows_remote_management_execute_shell.yml +++ b/detections/endpoint/windows_remote_management_execute_shell.yml @@ -1,80 +1,64 @@ name: Windows Remote Management Execute Shell id: 28b80028-851d-4b8d-88a5-375ba115418a -version: 4 -date: '2025-10-07' +version: 5 +date: '2026-02-25' author: Teoderick Contreras, Splunk data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 + - Sysmon EventID 1 + - Windows Event Log Security 4688 type: Anomaly status: production -description: The following analytic detects the execution of winrshost.exe initiating - CMD or PowerShell processes as part of a potential payload execution. winrshost.exe - is associated with Windows Remote Management (WinRM) and is typically used for remote - execution. By monitoring for this behavior, the detection identifies instances where - winrshost.exe is leveraged to run potentially malicious commands or payloads via - CMD or PowerShell. This behavior may indicate exploitation of remote management - tools for unauthorized access or lateral movement within a compromised environment, - signaling a potential security incident. -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="winrshost.exe" - AND Processes.process_name IN ("cmd.exe","*powershell*", "pwsh.exe") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_remote_management_execute_shell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: administrator or power user can execute command shell or script - remotely using WINRM. +description: The following analytic detects the execution of winrshost.exe initiating CMD or PowerShell processes as part of a potential payload execution. winrshost.exe is associated with Windows Remote Management (WinRM) and is typically used for remote execution. By monitoring for this behavior, the detection identifies instances where winrshost.exe is leveraged to run potentially malicious commands or payloads via CMD or PowerShell. This behavior may indicate exploitation of remote management tools for unauthorized access or lateral movement within a compromised environment, signaling a potential security incident. +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name="winrshost.exe" + AND + Processes.process_name IN ("cmd.exe","*powershell*", "pwsh.exe") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_remote_management_execute_shell_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: administrator or power user can execute command shell or script remotely using WINRM. references: -- https://strontic.github.io/xcyclopedia/library/winrshost.exe-6790044CEB4BA5BE6AA8161460D990FD.html + - https://strontic.github.io/xcyclopedia/library/winrshost.exe-6790044CEB4BA5BE6AA8161460D990FD.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a winrm remote proces [$parent_process_name$] execute [$process_name$] - shell on [$dest$]. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: a winrm remote proces [$parent_process_name$] execute [$process_name$] shell on [$dest$]. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Crypto Stealer - asset_type: Endpoint - mitre_attack_id: - - T1021.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Crypto Stealer + asset_type: Endpoint + mitre_attack_id: + - T1021.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/wirm_execute_shell/winrshost_pwh.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/wirm_execute_shell/winrshost_pwh.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_remote_service_rdpwinst_tool_execution.yml b/detections/endpoint/windows_remote_service_rdpwinst_tool_execution.yml index 869f63c7f0..3c432d7cc8 100644 --- a/detections/endpoint/windows_remote_service_rdpwinst_tool_execution.yml +++ b/detections/endpoint/windows_remote_service_rdpwinst_tool_execution.yml @@ -1,87 +1,71 @@ name: Windows Remote Service Rdpwinst Tool Execution id: c8127f87-c7c9-4036-89ed-8fe4b30e678c -version: 10 -date: '2025-10-14' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of the RDPWInst.exe - tool, which is an RDP wrapper library used to enable remote desktop host - support and concurrent RDP sessions. This detection leverages data from - Endpoint Detection and Response (EDR) agents, focusing on process names, - original file names, and specific command-line arguments. This activity is - significant because adversaries can abuse this tool to establish unauthorized - RDP connections, facilitating remote access and potential lateral movement - within the network. If confirmed malicious, this could lead to unauthorized - access, data exfiltration, and further compromise of the targeted host. +description: The following analytic detects the execution of the RDPWInst.exe tool, which is an RDP wrapper library used to enable remote desktop host support and concurrent RDP sessions. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, original file names, and specific command-line arguments. This activity is significant because adversaries can abuse this tool to establish unauthorized RDP connections, facilitating remote access and potential lateral movement within the network. If confirmed malicious, this could lead to unauthorized access, data exfiltration, and further compromise of the targeted host. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where (Processes.process_name="RDPWInst.exe" OR Processes.original_file_name="RDPWInst.exe") - AND Processes.process IN ("* -i*", "* -s*", "* -o*", "* -w*", "* -r*") by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_remote_service_rdpwinst_tool_execution_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: This tool was designed for home usage and not commonly - seen in production environment. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="RDPWInst.exe" + OR + Processes.original_file_name="RDPWInst.exe" + ) + AND Processes.process IN ("* -i*", "* -s*", "* -o*", "* -w*", "* -r*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_remote_service_rdpwinst_tool_execution_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: This tool was designed for home usage and not commonly seen in production environment. Filter as needed. references: -- https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ + - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Rdpwinst.exe executed on $dest$. - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: [] + message: Rdpwinst.exe executed on $dest$. + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: [] tags: - analytic_story: - - Azorult - - Compromised Windows Host - - Windows RDP Artifacts and Defense Evasion - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1021.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azorult + - Compromised Windows Host + - Windows RDP Artifacts and Defense Evasion + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1021.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_remote_services_allow_rdp_in_firewall.yml b/detections/endpoint/windows_remote_services_allow_rdp_in_firewall.yml index fd218ba14a..be555269c9 100644 --- a/detections/endpoint/windows_remote_services_allow_rdp_in_firewall.yml +++ b/detections/endpoint/windows_remote_services_allow_rdp_in_firewall.yml @@ -1,86 +1,69 @@ name: Windows Remote Services Allow Rdp In Firewall id: 9170cb54-ea15-41e1-9dfc-9f3363ce9b02 -version: 8 -date: '2025-08-01' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects modifications to the Windows - firewall to enable Remote Desktop Protocol (RDP) on a targeted machine. It - leverages data from Endpoint Detection and Response (EDR) agents, focusing on - command-line executions involving "netsh.exe" to allow TCP port 3389. This - activity is significant as it may indicate an adversary attempting to gain - remote access to a compromised host, a common tactic for lateral movement. If - confirmed malicious, this could allow attackers to remotely control the - system, leading to potential data exfiltration or further network compromise. +description: The following analytic detects modifications to the Windows firewall to enable Remote Desktop Protocol (RDP) on a targeted machine. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving "netsh.exe" to allow TCP port 3389. This activity is significant as it may indicate an adversary attempting to gain remote access to a compromised host, a common tactic for lateral movement. If confirmed malicious, this could allow attackers to remotely control the system, leading to potential data exfiltration or further network compromise. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as cmdline - values(Processes.parent_process_name) as parent_process values(Processes.process_name) - count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where (Processes.process_name = "netsh.exe" OR Processes.original_file_name= "netsh.exe") - AND Processes.process = "*firewall*" AND Processes.process = "*add*" AND Processes.process - = "*protocol=TCP*" AND Processes.process = "*localport=3389*" AND Processes.process - = "*action=allow*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_services_allow_rdp_in_firewall_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: administrators may enable or disable this feature that - may cause some false positive. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as cmdline values(Processes.parent_process_name) as parent_process values(Processes.process_name) count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name = "netsh.exe" + OR + Processes.original_file_name= "netsh.exe" + ) + AND Processes.process = "*firewall*" AND Processes.process = "*add*" AND Processes.process = "*protocol=TCP*" AND Processes.process = "*localport=3389*" AND Processes.process = "*action=allow*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_remote_services_allow_rdp_in_firewall_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: administrators may enable or disable this feature that may cause some false positive. references: -- https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ + - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: new firewall rules was added to allow rdp connection to $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: new firewall rules was added to allow rdp connection to $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Azorult - - Windows RDP Artifacts and Defense Evasion - asset_type: Endpoint - mitre_attack_id: - - T1021.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azorult + - Windows RDP Artifacts and Defense Evasion + asset_type: Endpoint + mitre_attack_id: + - T1021.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_remote_services_allow_remote_assistance.yml b/detections/endpoint/windows_remote_services_allow_remote_assistance.yml index 52d4cefb5f..f74c8cb499 100644 --- a/detections/endpoint/windows_remote_services_allow_remote_assistance.yml +++ b/detections/endpoint/windows_remote_services_allow_remote_assistance.yml @@ -5,68 +5,45 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects modifications in the Windows registry - to enable remote desktop assistance on a targeted machine. It leverages data from - the Endpoint.Registry datamodel, specifically monitoring changes to the "Control\\Terminal - Server\\fAllowToGetHelp" registry path. This activity is significant because enabling - remote assistance via registry is uncommon and often associated with adversaries - or malware like Azorult. If confirmed malicious, this could allow an attacker to - remotely access and control the compromised host, leading to potential data exfiltration - or further system compromise. +description: The following analytic detects modifications in the Windows registry to enable remote desktop assistance on a targeted machine. It leverages data from the Endpoint.Registry datamodel, specifically monitoring changes to the "Control\\Terminal Server\\fAllowToGetHelp" registry path. This activity is significant because enabling remote assistance via registry is uncommon and often associated with adversaries or malware like Azorult. If confirmed malicious, this could allow an attacker to remotely access and control the compromised host, leading to potential data exfiltration or further system compromise. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Control\\Terminal - Server\\fAllowToGetHelp*" Registry.registry_value_data="0x00000001" by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_remote_services_allow_remote_assistance_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure - that this registry was included in your config files ex. sysmon config to be monitored. -known_false_positives: administrators may enable or disable this feature that may - cause some false positive. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Control\\Terminal Server\\fAllowToGetHelp*" Registry.registry_value_data="0x00000001" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_services_allow_remote_assistance_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored. +known_false_positives: administrators may enable or disable this feature that may cause some false positive. references: -- https://docs.microsoft.com/en-us/windows-hardware/customize/desktop/unattend/microsoft-windows-remoteassistance-exe-fallowtogethelp -- https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ + - https://docs.microsoft.com/en-us/windows-hardware/customize/desktop/unattend/microsoft-windows-remoteassistance-exe-fallowtogethelp + - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: the registry for rdp protocol was modified to enable on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: the registry for rdp protocol was modified to enable on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Azorult - asset_type: Endpoint - mitre_attack_id: - - T1021.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azorult + asset_type: Endpoint + mitre_attack_id: + - T1021.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_remote_services_rdp_enable.yml b/detections/endpoint/windows_remote_services_rdp_enable.yml index 331db7aff4..79720a747d 100644 --- a/detections/endpoint/windows_remote_services_rdp_enable.yml +++ b/detections/endpoint/windows_remote_services_rdp_enable.yml @@ -5,73 +5,47 @@ date: '2025-08-01' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects modifications in the Windows - registry to enable Remote Desktop Protocol (RDP) on a targeted machine. It - leverages data from the Endpoint.Registry datamodel, specifically monitoring - changes to the "fDenyTSConnections" registry value. This activity is - significant as enabling RDP via registry is uncommon and often associated with - adversaries or malware attempting to gain remote access. If confirmed - malicious, this could allow attackers to remotely control the compromised - host, potentially leading to further exploitation and lateral movement within - the network. +description: The following analytic detects modifications in the Windows registry to enable Remote Desktop Protocol (RDP) on a targeted machine. It leverages data from the Endpoint.Registry datamodel, specifically monitoring changes to the "fDenyTSConnections" registry value. This activity is significant as enabling RDP via registry is uncommon and often associated with adversaries or malware attempting to gain remote access. If confirmed malicious, this could allow attackers to remotely control the compromised host, potentially leading to further exploitation and lateral movement within the network. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Control\\Terminal - Server\\fDenyTSConnections*" Registry.registry_value_data="0x00000000" by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_remote_services_rdp_enable_filter`' -how_to_implement: To successfully implement this search you need to be ingesting - information on process that include the name of the process responsible for - the changes from your endpoints into the `Endpoint` datamodel in the - `Registry` node. Also make sure that this registry was included in your config - files ex. sysmon config to be monitored. -known_false_positives: administrators may enable or disable this feature that - may cause some false positive. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Control\\Terminal Server\\fDenyTSConnections*" Registry.registry_value_data="0x00000000" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_services_rdp_enable_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored. +known_false_positives: administrators may enable or disable this feature that may cause some false positive. references: -- https://www.hybrid-analysis.com/sample/9d6611c2779316f1ef4b4a6edcfdfb5e770fe32b31ec2200df268c3bd236ed75?environmentId=100 + - https://www.hybrid-analysis.com/sample/9d6611c2779316f1ef4b4a6edcfdfb5e770fe32b31ec2200df268c3bd236ed75?environmentId=100 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: the registry for rdp protocol was modified to enable on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: the registry for rdp protocol was modified to enable on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Medusa Ransomware - - BlackSuit Ransomware - - Azorult - - Windows RDP Artifacts and Defense Evasion - asset_type: Endpoint - mitre_attack_id: - - T1021.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Medusa Ransomware + - BlackSuit Ransomware + - Azorult + - Windows RDP Artifacts and Defense Evasion + asset_type: Endpoint + mitre_attack_id: + - T1021.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_renamed_powershell_execution.yml b/detections/endpoint/windows_renamed_powershell_execution.yml index b5a3c307ec..c748bc7b19 100644 --- a/detections/endpoint/windows_renamed_powershell_execution.yml +++ b/detections/endpoint/windows_renamed_powershell_execution.yml @@ -1,94 +1,81 @@ name: Windows Renamed Powershell Execution id: c08014de-cc5a-42de-9775-76ecd5b37bbd -version: 4 -date: '2026-01-14' +version: 5 +date: '2026-02-25' author: Teoderick Contreras, Nasreddine Bencherchali, Splunk status: production type: TTP description: The following analytic identifies instances where the PowerShell executable has been renamed and executed under an alternate filename. This behavior is commonly associated with attempts to evade security controls or bypass logging mechanisms that monitor standard PowerShell usage. While rare in legitimate environments, renamed PowerShell binaries are frequently observed in malicious campaigns leveraging Living-off-the-Land Binaries (LOLBins) and fileless malware techniques. This detection flags executions of PowerShell where the process name does not match the default powershell.exe or pwsh.exe, especially when invoked from unusual paths or accompanied by suspicious command-line arguments. data_source: -- Sysmon EventID 1 + - Sysmon EventID 1 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime - from datamodel=Endpoint.Processes where - - ( - Processes.original_file_name = PowerShell.EXE - Processes.process_name != powershell.exe - ) - OR - ( - Processes.original_file_name = pwsh.dll - Processes.process_name != pwsh.exe - ) - OR - ( - Processes.original_file_name = powershell_ise.EXE - Processes.process_name != powershell_ise.exe - ) - - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process - Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id - Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user - Processes.user_id Processes.vendor_product - - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_renamed_powershell_execution_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + from datamodel=Endpoint.Processes where + + ( + Processes.original_file_name = PowerShell.EXE + Processes.process_name != powershell.exe + ) + OR + ( + Processes.original_file_name = pwsh.dll + Processes.process_name != pwsh.exe + ) + OR + ( + Processes.original_file_name = powershell_ise.EXE + Processes.process_name != powershell_ise.exe + ) + + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process + Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id + Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_renamed_powershell_execution_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.xworm + - https://malpedia.caad.fkie.fraunhofer.de/details/win.xworm drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: powershell was renamed as $process_name$ on $dest$ - risk_objects: - - field: dest - type: system - score: 50 - threat_objects: [] + message: powershell was renamed as $process_name$ on $dest$ + risk_objects: + - field: dest + type: system + score: 50 + threat_objects: [] tags: - analytic_story: - - XWorm - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1036.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - XWorm + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1036.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/renamed_powershell/renamed_powershell.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/renamed_powershell/renamed_powershell.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_replication_through_removable_media.yml b/detections/endpoint/windows_replication_through_removable_media.yml index 6f35ec2683..13ae6db0c4 100644 --- a/detections/endpoint/windows_replication_through_removable_media.yml +++ b/detections/endpoint/windows_replication_through_removable_media.yml @@ -5,79 +5,53 @@ date: '2025-09-18' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the creation or dropping of executable - or script files in the root directory of a removable drive. It leverages data from - the Endpoint.Filesystem datamodel, focusing on specific file types and their creation - paths. This activity is significant as it may indicate an attempt to spread malware, - such as ransomware, via removable media. If confirmed malicious, this behavior could - lead to unauthorized code execution, lateral movement, or persistence within the - network, potentially compromising sensitive data and systems. +description: The following analytic detects the creation or dropping of executable or script files in the root directory of a removable drive. It leverages data from the Endpoint.Filesystem datamodel, focusing on specific file types and their creation paths. This activity is significant as it may indicate an attempt to spread malware, such as ransomware, via removable media. If confirmed malicious, this behavior could lead to unauthorized code execution, lateral movement, or persistence within the network, potentially compromising sensitive data and systems. data_source: -- Sysmon EventID 11 -search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Filesystem - where Filesystem.file_name IN ("*.exe", "*.dll", "*.sys", "*.com", "*.vbs", "*.vbe", "*.js", "*.bat", "*.cmd", "*.pif", "*.lnk", "*.url") - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path - Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | - eval dropped_file_path = split(file_path, "\\") | eval dropped_file_path_split_count - = mvcount(dropped_file_path) | eval root_drive = mvindex(dropped_file_path,0) | - where LIKE(root_drive, "%:") AND dropped_file_path_split_count = 2 AND root_drive!= - "C:" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_replication_through_removable_media_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the Filesystem responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Filesystem` node. -known_false_positives: Administrators may allow creation of script or exe in the paths - specified. Filter as needed. + - Sysmon EventID 11 +search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.exe", "*.dll", "*.sys", "*.com", "*.vbs", "*.vbe", "*.js", "*.bat", "*.cmd", "*.pif", "*.lnk", "*.url") by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | eval dropped_file_path = split(file_path, "\\") | eval dropped_file_path_split_count = mvcount(dropped_file_path) | eval root_drive = mvindex(dropped_file_path,0) | where LIKE(root_drive, "%:") AND dropped_file_path_split_count = 2 AND root_drive!= "C:" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_replication_through_removable_media_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. +known_false_positives: Administrators may allow creation of script or exe in the paths specified. Filter as needed. references: -- https://attack.mitre.org/techniques/T1204/002/ -- https://www.fortinet.com/blog/threat-research/chaos-ransomware-variant-sides-with-russia + - https://attack.mitre.org/techniques/T1204/002/ + - https://www.fortinet.com/blog/threat-research/chaos-ransomware-variant-sides-with-russia drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: executable or script $file_path$ was dropped in root drive $root_drive$ - on $dest$ - risk_objects: - - field: user - type: user - score: 64 - threat_objects: - - field: file_name - type: file_name + message: executable or script $file_path$ was dropped in root drive $root_drive$ on $dest$ + risk_objects: + - field: user + type: user + score: 64 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - PlugX - - China-Nexus Threat Activity - - Chaos Ransomware - - Derusbi - - Salt Typhoon - - NjRAT - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1091 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - PlugX + - China-Nexus Threat Activity + - Chaos Ransomware + - Derusbi + - Salt Typhoon + - NjRAT + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1091 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/chaos_ransomware/spread_in_root_drives/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/chaos_ransomware/spread_in_root_drives/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_rmm_named_pipe.yml b/detections/endpoint/windows_rmm_named_pipe.yml index d8916542bd..385dbb98c9 100644 --- a/detections/endpoint/windows_rmm_named_pipe.yml +++ b/detections/endpoint/windows_rmm_named_pipe.yml @@ -1,108 +1,101 @@ name: Windows RMM Named Pipe id: c07c7138-edf5-4a16-8b24-3842599235bf -version: 1 -date: '2025-12-05' +version: 2 +date: '2026-02-25' author: Raven Tait, Splunk status: production type: Anomaly description: | - The following analytic detects the creation or connection to known suspicious named pipes, which is a technique often used by offensive tools. - It leverages Sysmon EventCodes 17 and 18 to identify known default pipe names used by RMM tools. - If confirmed malicious, this could allow an attacker to abuse these to potentially gain persistence, command and control, or further system compromise. + The following analytic detects the creation or connection to known suspicious named pipes, which is a technique often used by offensive tools. + It leverages Sysmon EventCodes 17 and 18 to identify known default pipe names used by RMM tools. + If confirmed malicious, this could allow an attacker to abuse these to potentially gain persistence, command and control, or further system compromise. data_source: - - Sysmon EventID 17 - - Sysmon EventID 18 + - Sysmon EventID 17 + - Sysmon EventID 18 search: | - `sysmon` - (EventCode=17 OR EventCode=18) - NOT process_path IN ( - "*:\\Program Files \(x86\)\\Adobe*", - "*:\\Program Files \(x86\)\\Google*", - "*:\\Program Files \(x86\)\\Microsoft*", - "*:\\Program Files\\Adobe*", - "*:\\Program Files\\Google*", - "*:\\Program Files\\Microsoft*", - "*:\\Windows\\system32\\SearchIndexer.exe", - "*:\\Windows\\System32\\svchost.exe", - "*:\\Windows\\SystemApps\\Microsoft*", - "*\\Amazon\\SSM\\Instance*", - "*\\AppData\\Local\\Google*", - "*\\AppData\\Local\\Kingsoft\\*", - "*\\AppData\\Local\\Microsoft*", - "System" - ) + `sysmon` + (EventCode=17 OR EventCode=18) + NOT process_path IN ( + "*:\\Program Files \(x86\)\\Adobe*", + "*:\\Program Files \(x86\)\\Google*", + "*:\\Program Files \(x86\)\\Microsoft*", + "*:\\Program Files\\Adobe*", + "*:\\Program Files\\Google*", + "*:\\Program Files\\Microsoft*", + "*:\\Windows\\system32\\SearchIndexer.exe", + "*:\\Windows\\System32\\svchost.exe", + "*:\\Windows\\SystemApps\\Microsoft*", + "*\\Amazon\\SSM\\Instance*", + "*\\AppData\\Local\\Google*", + "*\\AppData\\Local\\Kingsoft\\*", + "*\\AppData\\Local\\Microsoft*", + "System" + ) - | stats min(_time) as firstTime max(_time) as lastTime - count by dest dvc process_exec process_guid process_id process_path signature signature_id - vendor_product pipe_name user_id Image process_name - - | lookup suspicious_rmm_named_pipes suspicious_pipe_name AS pipe_name OUTPUT tool, description - | where isnotnull(tool) - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_rmm_named_pipe_filter` + | stats min(_time) as firstTime max(_time) as lastTime + count by dest dvc process_exec process_guid process_id process_path signature signature_id + vendor_product pipe_name user_id Image process_name + + | lookup suspicious_rmm_named_pipes suspicious_pipe_name AS pipe_name OUTPUT tool, description + | where isnotnull(tool) + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_rmm_named_pipe_filter` how_to_implement: | - To successfully implement this search, you need to be ingesting - logs with the process name and pipename from your endpoints. If you are using Sysmon, - you must have at least version 6.0.4 of the Sysmon TA. + To successfully implement this search, you need to be ingesting + logs with the process name and pipename from your endpoints. If you are using Sysmon, + you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: | - Some false positives may occur from RMM software used in your environment. Apply filters - based on known legitimate RMM software in your environment to reduce false positives. + Some false positives may occur from RMM software used in your environment. Apply filters + based on known legitimate RMM software in your environment to reduce false positives. references: - - https://attack.mitre.org/techniques/T1218/009/ - - https://docs.microsoft.com/en-us/windows/win32/ipc/named-pipes + - https://attack.mitre.org/techniques/T1218/009/ + - https://docs.microsoft.com/en-us/windows/win32/ipc/named-pipes drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $process_name$ located in $process_path$ was identified on endpoint $dest$ accessing - known RMM named pipe $pipe_name$. - risk_objects: - - field: dest - type: system - score: 52 - threat_objects: - - field: process_name - type: process_name + message: An instance of $process_name$ located in $process_path$ was identified on endpoint $dest$ accessing known RMM named pipe $pipe_name$. + risk_objects: + - field: dest + type: system + score: 52 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Cactus Ransomware - - CISA AA24-241A - - Command And Control - - GhostRedirector IIS Module and Rungan Backdoor - - Gozi Malware - - Insider Threat - - Interlock Ransomware - - Ransomware - - Remote Monitoring and Management Software - - Scattered Lapsus$ Hunters - - Scattered Spider - - Seashell Blizzard - asset_type: Endpoint - mitre_attack_id: - - T1559 - - T1021.002 - - T1055 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Cactus Ransomware + - CISA AA24-241A + - Command And Control + - GhostRedirector IIS Module and Rungan Backdoor + - Gozi Malware + - Insider Threat + - Interlock Ransomware + - Ransomware + - Remote Monitoring and Management Software + - Scattered Lapsus$ Hunters + - Scattered Spider + - Seashell Blizzard + asset_type: Endpoint + mitre_attack_id: + - T1559 + - T1021.002 + - T1055 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/named_pipes/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/named_pipes/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_root_domain_linked_policies_discovery.yml b/detections/endpoint/windows_root_domain_linked_policies_discovery.yml index a036a9a9da..b7105ad515 100644 --- a/detections/endpoint/windows_root_domain_linked_policies_discovery.yml +++ b/detections/endpoint/windows_root_domain_linked_policies_discovery.yml @@ -1,69 +1,62 @@ name: Windows Root Domain linked policies Discovery id: 80ffaede-1f12-49d5-a86e-b4b599b68b3c -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the use of the `[Adsisearcher]` type accelerator - in PowerShell to query Active Directory for root domain linked policies. It leverages - PowerShell Script Block Logging (EventCode=4104) to identify this activity. This - behavior is significant as it may indicate an attempt by adversaries or Red Teams - to gain situational awareness and perform Active Directory Discovery. If confirmed - malicious, this activity could allow attackers to map out domain policies, potentially - aiding in further exploitation or lateral movement within the network. +description: The following analytic detects the use of the `[Adsisearcher]` type accelerator in PowerShell to query Active Directory for root domain linked policies. It leverages PowerShell Script Block Logging (EventCode=4104) to identify this activity. This behavior is significant as it may indicate an attempt by adversaries or Red Teams to gain situational awareness and perform Active Directory Discovery. If confirmed malicious, this activity could allow attackers to map out domain policies, potentially aiding in further exploitation or lateral movement within the network. data_source: -- Powershell Script Block Logging 4104 -search: '`powershell` EventCode=4104 ScriptBlockText = "*[adsisearcher]*" ScriptBlockText - = "*.SearchRooT*" ScriptBlockText = "*.gplink*" | fillnull | stats count min(_time) - as firstTime max(_time) as lastTime by dest signature signature_id user_id vendor_product - EventID Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_root_domain_linked_policies_discovery_filter`' -how_to_implement: The following Hunting analytic requires PowerShell operational logs - to be imported. Modify the powershell macro as needed to match the sourcetype or - add index. This analytic is specific to 4104, or PowerShell Script Block Logging. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*[adsisearcher]*" ScriptBlockText = "*.SearchRooT*" ScriptBlockText = "*.gplink*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_root_domain_linked_policies_discovery_filter` +how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ -- https://medium.com/@pentesttas/discover-hidden-gpo-s-on-active-directory-using-ps-adsi-a284b6814c81 + - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/ + - https://medium.com/@pentesttas/discover-hidden-gpo-s-on-active-directory-using-ps-adsi-a284b6814c81 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows PowerShell [Adsisearcher] was used user enumeration on endpoint - $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: Windows PowerShell [Adsisearcher] was used user enumeration on endpoint $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Data Destruction - - Active Directory Discovery - - Industroyer2 - asset_type: Endpoint - mitre_attack_id: - - T1087.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Destruction + - Active Directory Discovery + - Industroyer2 + asset_type: Endpoint + mitre_attack_id: + - T1087.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/adsi_discovery/windows-powershell-xml1.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/adsi_discovery/windows-powershell-xml1.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_rundll32_apply_user_settings_changes.yml b/detections/endpoint/windows_rundll32_apply_user_settings_changes.yml index ac3a0f693b..46bcdf44ae 100644 --- a/detections/endpoint/windows_rundll32_apply_user_settings_changes.yml +++ b/detections/endpoint/windows_rundll32_apply_user_settings_changes.yml @@ -1,92 +1,79 @@ name: Windows Rundll32 Apply User Settings Changes id: b9fb8d97-dbc9-4a09-804c-ff0e3862bb2d -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - The following analytic detects the execution of rundll32 with a call to the user32 DLL, specifically the UpdatePerUserSystemParameters function. - This function is responsible for updating system parameters, such as desktop backgrounds, display settings, and visual themes. - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions. - This activity can be significant as it is an uncommon way to apply settings. It was also observed as part of Rhysida Ransomware activity. - If confirmed malicious, this could allow an attacker to disguise activities or make unauthorized system changes, potentially leading to persistent unauthorized access. + The following analytic detects the execution of rundll32 with a call to the user32 DLL, specifically the UpdatePerUserSystemParameters function. + This function is responsible for updating system parameters, such as desktop backgrounds, display settings, and visual themes. + It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions. + This activity can be significant as it is an uncommon way to apply settings. It was also observed as part of Rhysida Ransomware activity. + If confirmed malicious, this could allow an attacker to disguise activities or make unauthorized system changes, potentially leading to persistent unauthorized access. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime - - from datamodel=Endpoint.Processes where - - `process_rundll32` - Processes.process = "*user32.dll*" - Processes.process = "*UpdatePerUserSystemParameters*" - - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_rundll32_apply_user_settings_changes_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + from datamodel=Endpoint.Processes where + + `process_rundll32` + Processes.process = "*user32.dll*" + Processes.process = "*UpdatePerUserSystemParameters*" + + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_rundll32_apply_user_settings_changes_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-319a -- https://www.cisa.gov/sites/default/files/publications/aa22-264a-iranian-cyber-actors-conduct-cyber-operations-against-the-government-of-albania.pdf -- https://cdn.pathfactory.com/assets/10555/contents/400686/13f4424c-05b4-46db-bb9c-6bf9b5436ec4.pdf + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-319a + - https://www.cisa.gov/sites/default/files/publications/aa22-264a-iranian-cyber-actors-conduct-cyber-operations-against-the-government-of-albania.pdf + - https://cdn.pathfactory.com/assets/10555/contents/400686/13f4424c-05b4-46db-bb9c-6bf9b5436ec4.pdf drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Process $process_name$ with cmdline $process$ in host $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: - - field: process_name - type: process_name + message: Process $process_name$ with cmdline $process$ in host $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Rhysida Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1218.011 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Rhysida Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1218.011 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/update_per_user_system/rundll32_updateperusersystem.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/update_per_user_system/rundll32_updateperusersystem.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_rundll32_load_dll_in_temp_dir.yml b/detections/endpoint/windows_rundll32_load_dll_in_temp_dir.yml index c5af1d28a1..7d4a02ae2e 100644 --- a/detections/endpoint/windows_rundll32_load_dll_in_temp_dir.yml +++ b/detections/endpoint/windows_rundll32_load_dll_in_temp_dir.yml @@ -7,66 +7,44 @@ status: production type: Anomaly description: This detection identifies instances where rundll32.exe is used to load a DLL from a temporary directory, such as C:\Users\\AppData\Local\Temp\ or C:\Windows\Temp\. While rundll32.exe is a legitimate Windows utility used to execute functions exported from DLLs, its use to load libraries from temporary locations is highly suspicious. These directories are commonly used by malware and red team tools to stage payloads or execute code in-memory without writing it to more persistent locations. This behavior often indicates defense evasion, initial access, or privilege escalation, especially when the DLL is unsigned, recently written, or executed shortly after download. In normal user workflows, DLLs are not typically loaded from Temp paths, making this a high-fidelity indicator of potentially malicious activity. Monitoring this pattern is essential for detecting threats that attempt to blend in with native system processes while bypassing traditional application controls. data_source: - - Sysmon EventID 1 -search: - '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where `process_rundll32` AND Processes.process IN ("*temp\\*", "*\\tmp\\*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_rundll32_load_dll_in_temp_dir_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 +search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` AND Processes.process IN ("*temp\\*", "*\\tmp\\*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_rundll32_load_dll_in_temp_dir_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: - - https://blog.sekoia.io/interlock-ransomware-evolving-under-the-radar/ + - https://blog.sekoia.io/interlock-ransomware-evolving-under-the-radar/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Risk Message goes here - risk_objects: - - field: dest - type: system - score: 50 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: Risk Message goes here + risk_objects: + - field: dest + type: system + score: 50 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - Interlock Rat - asset_type: Endpoint - mitre_attack_id: - - T1218.011 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Interlock Rat + asset_type: Endpoint + mitre_attack_id: + - T1218.011 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/rundll32_dll_in_temp/rundll32_tmp.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/rundll32_dll_in_temp/rundll32_tmp.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_rundll32_webdav_request.yml b/detections/endpoint/windows_rundll32_webdav_request.yml index 7201e3f800..540554e331 100644 --- a/detections/endpoint/windows_rundll32_webdav_request.yml +++ b/detections/endpoint/windows_rundll32_webdav_request.yml @@ -6,88 +6,58 @@ author: Michael Haag, Splunk type: TTP status: production data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic identifies the execution of rundll32.exe with - command-line arguments loading davclnt.dll and the davsetcookie function to access - a remote WebDAV instance. This detection leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process names and command-line executions. - This activity is significant as it may indicate an attempt to exploit CVE-2023-23397, - a known vulnerability. If confirmed malicious, this could allow an attacker to execute - remote code or exfiltrate data, posing a severe threat to the environment. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe - Processes.process IN ("*\\windows\\system32\\davclnt.dll,*davsetcookie*","*\\windows\\syswow64\\davclnt.dll,*davsetcookie*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_rundll32_webdav_request_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives will be present based on legitimate software, - filtering may need to occur. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic identifies the execution of rundll32.exe with command-line arguments loading davclnt.dll and the davsetcookie function to access a remote WebDAV instance. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it may indicate an attempt to exploit CVE-2023-23397, a known vulnerability. If confirmed malicious, this could allow an attacker to execute remote code or exfiltrate data, posing a severe threat to the environment. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process IN ("*\\windows\\system32\\davclnt.dll,*davsetcookie*","*\\windows\\syswow64\\davclnt.dll,*davsetcookie*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_rundll32_webdav_request_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives will be present based on legitimate software, filtering may need to occur. references: -- https://strontic.github.io/xcyclopedia/library/davclnt.dll-0EA3050E7CC710526E330C413C165DA0.html -- https://twitter.com/ACEResponder/status/1636116096506818562?s=20 -- https://twitter.com/domchell/status/1635999068282408962?s=20 -- https://msrc.microsoft.com/blog/2023/03/microsoft-mitigates-outlook-elevation-of-privilege-vulnerability/ -- https://www.pwndefend.com/2023/03/15/the-long-game-persistent-hash-theft/ + - https://strontic.github.io/xcyclopedia/library/davclnt.dll-0EA3050E7CC710526E330C413C165DA0.html + - https://twitter.com/ACEResponder/status/1636116096506818562?s=20 + - https://twitter.com/domchell/status/1635999068282408962?s=20 + - https://msrc.microsoft.com/blog/2023/03/microsoft-mitigates-outlook-elevation-of-privilege-vulnerability/ + - https://www.pwndefend.com/2023/03/15/the-long-game-persistent-hash-theft/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to contact a remote WebDav server. - risk_objects: - - field: user - type: user - score: 48 - - field: dest - type: system - score: 48 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to contact a remote WebDav server. + risk_objects: + - field: user + type: user + score: 48 + - field: dest + type: system + score: 48 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - CVE-2023-23397 Outlook Elevation of Privilege - asset_type: Endpoint - cve: - - CVE-2023-23397 - mitre_attack_id: - - T1048.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CVE-2023-23397 Outlook Elevation of Privilege + asset_type: Endpoint + cve: + - CVE-2023-23397 + mitre_attack_id: + - T1048.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/cve-2023-23397/webdav_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/cve-2023-23397/webdav_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_rundll32_webdav_with_network_connection.yml b/detections/endpoint/windows_rundll32_webdav_with_network_connection.yml index 8f7e452577..fe34e57b25 100644 --- a/detections/endpoint/windows_rundll32_webdav_with_network_connection.yml +++ b/detections/endpoint/windows_rundll32_webdav_with_network_connection.yml @@ -1,120 +1,107 @@ name: Windows Rundll32 WebDav With Network Connection id: f03355e0-28b5-4e9b-815a-6adffc63b38c -version: 8 -date: '2025-06-10' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk type: TTP status: production data_source: -- Sysmon EventID 1 AND Sysmon EventID 3 -description: The following analytic detects the execution of rundll32.exe with command-line - arguments loading davclnt.dll and the davsetcookie function to access a remote WebDav - instance. It uses data from Endpoint Detection and Response (EDR) agents, correlating - process execution and network traffic data. This activity is significant as it may - indicate exploitation of CVE-2023-23397, a known vulnerability. If confirmed malicious, - this could allow an attacker to establish unauthorized remote connections, potentially - leading to data exfiltration or further network compromise. + - Sysmon EventID 1 AND Sysmon EventID 3 +description: The following analytic detects the execution of rundll32.exe with command-line arguments loading davclnt.dll and the davsetcookie function to access a remote WebDav instance. It uses data from Endpoint Detection and Response (EDR) agents, correlating process execution and network traffic data. This activity is significant as it may indicate exploitation of CVE-2023-23397, a known vulnerability. If confirmed malicious, this could allow an attacker to establish unauthorized remote connections, potentially leading to data exfiltration or further network compromise. search: | - | tstats `security_content_summariesonly` count - min(_time) as firstTime - max(_time) as lastTime - FROM datamodel=Endpoint.Processes where - Processes.parent_process_name=svchost.exe - `process_rundll32` - Processes.process IN ( - "*\\windows\\system32\\davclnt.dll,*davsetcookie*", - "*\\windows\\syswow64\\davclnt.dll,*davsetcookie*") - by host _time span=1h - Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | rename dest as src - | join host process_id - [ - | tstats `security_content_summariesonly` count - latest(All_Traffic.dest) as dest - latest(All_Traffic.dest_ip) as dest_ip - latest(All_Traffic.dest_port) as dest_port - FROM datamodel=Network_Traffic.All_Traffic where - All_Traffic.dest_port!=0 - NOT (All_Traffic.dest_ip IN (10.0.0.0/8, 172.16.0.0/12, 192.168.0.0/16)) - by host All_Traffic.action All_Traffic.app All_Traffic.bytes All_Traffic.bytes_in All_Traffic.bytes_out - All_Traffic.dest All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.dvc All_Traffic.protocol - All_Traffic.protocol_version All_Traffic.src All_Traffic.src_ip All_Traffic.src_port - All_Traffic.transport All_Traffic.user All_Traffic.vendor_product All_Traffic.direction - All_Traffic.process_id - | `drop_dm_object_name(All_Traffic)` - ] - | `windows_rundll32_webdav_with_network_connection_filter` + | tstats `security_content_summariesonly` count + min(_time) as firstTime + max(_time) as lastTime + FROM datamodel=Endpoint.Processes where + Processes.parent_process_name=svchost.exe + `process_rundll32` + Processes.process IN ( + "*\\windows\\system32\\davclnt.dll,*davsetcookie*", + "*\\windows\\syswow64\\davclnt.dll,*davsetcookie*") + by host _time span=1h + Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | rename dest as src + | join host process_id + [ + | tstats `security_content_summariesonly` count + latest(All_Traffic.dest) as dest + latest(All_Traffic.dest_ip) as dest_ip + latest(All_Traffic.dest_port) as dest_port + FROM datamodel=Network_Traffic.All_Traffic where + All_Traffic.dest_port!=0 + NOT (All_Traffic.dest_ip IN (10.0.0.0/8, 172.16.0.0/12, 192.168.0.0/16)) + by host All_Traffic.action All_Traffic.app All_Traffic.bytes All_Traffic.bytes_in All_Traffic.bytes_out + All_Traffic.dest All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.dvc All_Traffic.protocol + All_Traffic.protocol_version All_Traffic.src All_Traffic.src_ip All_Traffic.src_port + All_Traffic.transport All_Traffic.user All_Traffic.vendor_product All_Traffic.direction + All_Traffic.process_id + | `drop_dm_object_name(All_Traffic)` + ] + | `windows_rundll32_webdav_with_network_connection_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: | - False positives will be present based on legitimate software, filtering may need to occur. + False positives will be present based on legitimate software, filtering may need to occur. references: -- https://strontic.github.io/xcyclopedia/library/davclnt.dll-0EA3050E7CC710526E330C413C165DA0.html -- https://twitter.com/ACEResponder/status/1636116096506818562?s=20 -- https://twitter.com/domchell/status/1635999068282408962?s=20 -- https://msrc.microsoft.com/blog/2023/03/microsoft-mitigates-outlook-elevation-of-privilege-vulnerability/ -- https://www.pwndefend.com/2023/03/15/the-long-game-persistent-hash-theft/ + - https://strontic.github.io/xcyclopedia/library/davclnt.dll-0EA3050E7CC710526E330C413C165DA0.html + - https://twitter.com/ACEResponder/status/1636116096506818562?s=20 + - https://twitter.com/domchell/status/1635999068282408962?s=20 + - https://msrc.microsoft.com/blog/2023/03/microsoft-mitigates-outlook-elevation-of-privilege-vulnerability/ + - https://www.pwndefend.com/2023/03/15/the-long-game-persistent-hash-theft/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to contact a remote WebDav server. - risk_objects: - - field: user - type: user - score: 48 - - field: dest - type: system - score: 48 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to contact a remote WebDav server. + risk_objects: + - field: user + type: user + score: 48 + - field: dest + type: system + score: 48 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - CVE-2023-23397 Outlook Elevation of Privilege - asset_type: Endpoint - cve: - - CVE-2023-23397 - mitre_attack_id: - - T1048.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CVE-2023-23397 Outlook Elevation of Privilege + asset_type: Endpoint + cve: + - CVE-2023-23397 + mitre_attack_id: + - T1048.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/cve-2023-23397/webdav_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/cve-2023-23397/webdav_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_runmru_command_execution.yml b/detections/endpoint/windows_runmru_command_execution.yml index 9d93e42bee..df5189a4e4 100644 --- a/detections/endpoint/windows_runmru_command_execution.yml +++ b/detections/endpoint/windows_runmru_command_execution.yml @@ -5,81 +5,56 @@ date: '2025-06-10' author: Nasreddine Bencherchali, Michael Haag, Splunk status: production type: Anomaly -description: The following analytic detects modifications to the Windows RunMRU registry - key, which stores a history of commands executed through the Run dialog box (Windows+R). - It leverages Endpoint Detection and Response (EDR) telemetry to monitor registry - events targeting this key. This activity is significant as malware often uses the - Run dialog to execute malicious commands while attempting to appear legitimate. - If confirmed malicious, this could indicate an attacker using indirect command execution - techniques for defense evasion or persistence. The detection excludes MRUList value - changes to focus on actual command entries. +description: The following analytic detects modifications to the Windows RunMRU registry key, which stores a history of commands executed through the Run dialog box (Windows+R). It leverages Endpoint Detection and Response (EDR) telemetry to monitor registry events targeting this key. This activity is significant as malware often uses the Run dialog to execute malicious commands while attempting to appear legitimate. If confirmed malicious, this could indicate an attacker using indirect command execution techniques for defense evasion or persistence. The detection excludes MRUList value changes to focus on actual command entries. data_source: -- Sysmon EventID 13 + - Sysmon EventID 13 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\Microsoft\\Windows\\CurrentVersion\\Explorer\\RunMRU*" NOT Registry.registry_value_name="MRUList" NOT Registry.registry_value_data="unknown" by Registry.dest Registry.registry_value_data Registry.action Registry.process_guid Registry.process_id Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_hive Registry.registry_value_name Registry.status Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_runmru_command_execution_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Registry` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: This detection may generate a few false positives, such as - legitimate software updates or legitimate system maintenance activities that modify - the RunMRU key. However, the exclusion of MRUList value changes helps reduce the - number of false positives by focusing only on actual command entries. Add any specific - false positives to the built in filter to reduce findings as needed. +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Registry` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: This detection may generate a few false positives, such as legitimate software updates or legitimate system maintenance activities that modify the RunMRU key. However, the exclusion of MRUList value changes helps reduce the number of false positives by focusing only on actual command entries. Add any specific false positives to the built in filter to reduce findings as needed. references: -- https://medium.com/@ahmed.moh.farou2/fake-captcha-campaign-on-arabic-pirated-movie-sites-delivers-lumma-stealer-4f203f7adabf -- https://medium.com/@shaherzakaria8/downloading-trojan-lumma-infostealer-through-capatcha-1f25255a0e71 -- https://www.forensafe.com/blogs/runmrukey.html -- https://github.com/SigmaHQ/sigma/blob/master/rules-threat-hunting/windows/registry/registry_set/registry_set_runmru_command_execution.yml + - https://medium.com/@ahmed.moh.farou2/fake-captcha-campaign-on-arabic-pirated-movie-sites-delivers-lumma-stealer-4f203f7adabf + - https://medium.com/@shaherzakaria8/downloading-trojan-lumma-infostealer-through-capatcha-1f25255a0e71 + - https://www.forensafe.com/blogs/runmrukey.html + - https://github.com/SigmaHQ/sigma/blob/master/rules-threat-hunting/windows/registry/registry_set/registry_set_runmru_command_execution.yml drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $registry_value_data$ was identified on endpoint $dest$ - by user $user$ attempting to execute a command through the Run dialog box. - risk_objects: - - field: dest - type: system - score: 48 - - field: user - type: user - score: 48 - threat_objects: - - field: registry_value_data - type: registry_value_text + message: An instance of $registry_value_data$ was identified on endpoint $dest$ by user $user$ attempting to execute a command through the Run dialog box. + risk_objects: + - field: dest + type: system + score: 48 + - field: user + type: user + score: 48 + threat_objects: + - field: registry_value_data + type: registry_value_text tags: - analytic_story: - - Lumma Stealer - - Fake CAPTCHA Campaigns - asset_type: Endpoint - mitre_attack_id: - - T1202 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] - atomic_guid: - - de323a93-2f18-4bd5-ba60-d6fca6aeff76 + analytic_story: + - Lumma Stealer + - Fake CAPTCHA Campaigns + asset_type: Endpoint + mitre_attack_id: + - T1202 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] + atomic_guid: + - de323a93-2f18-4bd5-ba60-d6fca6aeff76 tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1202/atomic_red_team/windows-sysmon_runmru.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1202/atomic_red_team/windows-sysmon_runmru.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_runmru_registry_key_or_value_deleted.yml b/detections/endpoint/windows_runmru_registry_key_or_value_deleted.yml index 8966e273a7..a127b83dff 100644 --- a/detections/endpoint/windows_runmru_registry_key_or_value_deleted.yml +++ b/detections/endpoint/windows_runmru_registry_key_or_value_deleted.yml @@ -7,63 +7,44 @@ status: production type: Anomaly description: The following analytic detects the deletion or modification of Most Recently Used (MRU) command entries stored within the Windows Registry. Adversaries often clear these registry keys, such as HKCU\Software\Microsoft\Windows\CurrentVersion\Explorer\RunMRU, to remove forensic evidence of commands executed via the Windows Run dialog or other system utilities. This activity aims to obscure their actions, hinder incident response efforts, and evade detection. Detection focuses on monitoring for changes (deletion of values or modification of the MRUList value) to these specific registry paths, particularly when performed by unusual processes or outside of typical user behavior. Anomalous deletion events can indicate an attempt at defense evasion or post-exploitation cleanup by a malicious actor. data_source: -- Sysmon EventID 12 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry - where Registry.registry_path = "*\\Software\\Microsoft\\Windows\\CurrentVersion\\Explorer\\RunMRU*" Registry.action = deleted - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_runmru_registry_key_or_value_deleted_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: This event can be seen when administrator delete a history manually - or uninstall/reinstall a software that creates MRU registry entry. It is recommended - to check this alert with high priority. + - Sysmon EventID 12 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Software\\Microsoft\\Windows\\CurrentVersion\\Explorer\\RunMRU*" Registry.action = deleted by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_runmru_registry_key_or_value_deleted_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: This event can be seen when administrator delete a history manually or uninstall/reinstall a software that creates MRU registry entry. It is recommended to check this alert with high priority. references: -- https://www.linkedin.com/posts/mauricefielenbach_cybersecurity-incidentresponse-dfir-activity-7394805779448418304-g0gZ?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAuFTjIB5weY_kcyu4qp3kHbI4v49tO0zEk -- https://thedfirreport.com/2023/10/30/netsupport-intrusion-results-in-domain-compromise/ -- https://www.esentire.com/blog/evalusion-campaign-delivers-amatera-stealer-and-netsupport-rat + - https://www.linkedin.com/posts/mauricefielenbach_cybersecurity-incidentresponse-dfir-activity-7394805779448418304-g0gZ?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAuFTjIB5weY_kcyu4qp3kHbI4v49tO0zEk + - https://thedfirreport.com/2023/10/30/netsupport-intrusion-results-in-domain-compromise/ + - https://www.esentire.com/blog/evalusion-campaign-delivers-amatera-stealer-and-netsupport-rat drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A most recent used entry was deleted on $dest$ within the Windows registry. - risk_objects: - - field: dest - type: system - score: 10 - threat_objects: [] + message: A most recent used entry was deleted on $dest$ within the Windows registry. + risk_objects: + - field: dest + type: system + score: 10 + threat_objects: [] tags: - analytic_story: - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/delete_runmru_reg/runmru_deletion.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/delete_runmru_reg/runmru_deletion.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_scheduled_task_created_via_xml.yml b/detections/endpoint/windows_scheduled_task_created_via_xml.yml index a4958d0b3d..7725f64e6f 100644 --- a/detections/endpoint/windows_scheduled_task_created_via_xml.yml +++ b/detections/endpoint/windows_scheduled_task_created_via_xml.yml @@ -1,96 +1,82 @@ name: Windows Scheduled Task Created Via XML id: 7e03b682-3965-4598-8e91-a60a40a3f7e4 -version: 11 -date: '2025-12-18' +version: 12 +date: '2026-02-25' author: Teoderick Contreras, Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - The following analytic detects the creation of scheduled tasks in Windows using schtasks.exe with the "XML" parameter. - This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process details. - This activity is significant as it is a common technique for establishing persistence or achieving privilege escalation, often used by malware like Trickbot and Winter-Vivern. While creating a scheduled task via XML may be legitimate, it can also be abused by attackers. If confirmed malicious, this could allow attackers to maintain access, execute additional payloads, and potentially lead to data theft or ransomware deployment. + The following analytic detects the creation of scheduled tasks in Windows using schtasks.exe with the "XML" parameter. + This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process details. + This activity is significant as it is a common technique for establishing persistence or achieving privilege escalation, often used by malware like Trickbot and Winter-Vivern. While creating a scheduled task via XML may be legitimate, it can also be abused by attackers. If confirmed malicious, this could allow attackers to maintain access, execute additional payloads, and potentially lead to data theft or ransomware deployment. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime - from datamodel=Endpoint.Processes where - - (Processes.process_name=schtasks.exe OR Processes.original_file_name=schtasks.exe) - Processes.process IN ("* /create *", "* -create *") - Processes.process IN ("* /xml *", "* -xml *") - - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + from datamodel=Endpoint.Processes where - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_scheduled_task_created_via_xml_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Installers are known to create scheduled tasks via XML. Apply additional filters as needed. + (Processes.process_name=schtasks.exe OR Processes.original_file_name=schtasks.exe) + Processes.process IN ("* /create *", "* -create *") + Processes.process IN ("* /xml *", "* -xml *") + + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_scheduled_task_created_via_xml_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Installers are known to create scheduled tasks via XML. Apply additional filters as needed. references: -- https://twitter.com/_CERT_UA/status/1620781684257091584 -- https://cert.gov.ua/article/3761104 + - https://twitter.com/_CERT_UA/status/1620781684257091584 + - https://cert.gov.ua/article/3761104 drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A scheduled task was created via $process$, based on an XML file by user $user$ on host $dest$ - risk_objects: - - field: dest - type: system - score: 40 - - field: user - type: user - score: 40 - threat_objects: [] + message: A scheduled task was created via $process$, based on an XML file by user $user$ on host $dest$ + risk_objects: + - field: dest + type: system + score: 40 + - field: user + type: user + score: 40 + threat_objects: [] tags: - analytic_story: - - Winter Vivern - - Malicious Inno Setup Loader - - CISA AA23-347A - - Scheduled Tasks - - MoonPeak - - Lokibot - asset_type: Endpoint - mitre_attack_id: - - T1053.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Winter Vivern + - Malicious Inno Setup Loader + - CISA AA23-347A + - Scheduled Tasks + - MoonPeak + - Lokibot + asset_type: Endpoint + mitre_attack_id: + - T1053.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winter-vivern/scheduledtask/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winter-vivern/scheduledtask/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_scheduled_task_dll_module_loaded.yml b/detections/endpoint/windows_scheduled_task_dll_module_loaded.yml index 253c5f7c23..a1b865baf8 100644 --- a/detections/endpoint/windows_scheduled_task_dll_module_loaded.yml +++ b/detections/endpoint/windows_scheduled_task_dll_module_loaded.yml @@ -4,69 +4,46 @@ version: 6 date: '2025-05-02' author: Teoderick Contreras, Splunk data_source: -- Sysmon EventID 7 + - Sysmon EventID 7 type: TTP status: production -description: The following analytic detects instances where the taskschd.dll is loaded - by processes running in suspicious or writable directories. This activity is unusual, - as legitimate processes that load taskschd.dll typically reside in protected system - locations. Malware or threat actors may attempt to load this DLL from writable or - non-standard directories to manipulate the Task Scheduler and execute malicious - tasks. By identifying processes that load taskschd.dll in these unsafe locations, - this detection helps security analysts flag potentially malicious activity and investigate - further to prevent unauthorized system modifications. -search: '`sysmon` EventCode=7 Image IN ("*\\windows\\fonts\\*", "*\\windows\\temp\\*", - "*\\users\\public\\*", "*\\windows\\debug\\*", "*\\Users\\Administrator\\Music\\*", - "*\\Windows\\servicing\\*", "*\\Users\\Default\\*", "*Recycle.bin*", "*\\Windows\\Media\\*", - "\\Windows\\repair\\*", "*\\temp\\*", "*\\PerfLogs\\*") ImageLoaded = "*\\taskschd.dll" - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image - ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid - process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified - signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_scheduled_task_dll_module_loaded_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name and imageloaded executions from your endpoints. If you - are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -known_false_positives: Third party Legitimate application may load this task schedule - dll module. +description: The following analytic detects instances where the taskschd.dll is loaded by processes running in suspicious or writable directories. This activity is unusual, as legitimate processes that load taskschd.dll typically reside in protected system locations. Malware or threat actors may attempt to load this DLL from writable or non-standard directories to manipulate the Task Scheduler and execute malicious tasks. By identifying processes that load taskschd.dll in these unsafe locations, this detection helps security analysts flag potentially malicious activity and investigate further to prevent unauthorized system modifications. +search: '`sysmon` EventCode=7 Image IN ("*\\windows\\fonts\\*", "*\\windows\\temp\\*", "*\\users\\public\\*", "*\\windows\\debug\\*", "*\\Users\\Administrator\\Music\\*", "*\\Windows\\servicing\\*", "*\\Users\\Default\\*", "*Recycle.bin*", "*\\Windows\\Media\\*", "\\Windows\\repair\\*", "*\\temp\\*", "*\\PerfLogs\\*") ImageLoaded = "*\\taskschd.dll" | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_scheduled_task_dll_module_loaded_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: Third party Legitimate application may load this task schedule dll module. references: -- https://www.proofpoint.com/us/blog/threat-insight/chinese-malware-appears-earnest-across-cybercrime-threat-landscape -- https://www.fortinet.com/blog/threat-research/valleyrat-campaign-targeting-chinese-speakers + - https://www.proofpoint.com/us/blog/threat-insight/chinese-malware-appears-earnest-across-cybercrime-threat-landscape + - https://www.fortinet.com/blog/threat-research/valleyrat-campaign-targeting-chinese-speakers drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A taskschd.dll was loaded by a process - [$Image$] on [$dest$] - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A taskschd.dll was loaded by a process - [$Image$] on [$dest$] + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - ValleyRAT - asset_type: Endpoint - mitre_attack_id: - - T1053 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ValleyRAT + asset_type: Endpoint + mitre_attack_id: + - T1053 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053/taskschd_dll/taskschd_dll.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053/taskschd_dll/taskschd_dll.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_scheduled_task_service_spawned_shell.yml b/detections/endpoint/windows_scheduled_task_service_spawned_shell.yml index 505421d209..5defc2a9ba 100644 --- a/detections/endpoint/windows_scheduled_task_service_spawned_shell.yml +++ b/detections/endpoint/windows_scheduled_task_service_spawned_shell.yml @@ -5,86 +5,53 @@ date: '2026-01-14' author: Steven Dick status: production type: TTP -description: The following analytic detects when the Task Scheduler service ("svchost.exe - -k netsvcs -p -s Schedule") spawns common command line, scripting, or shell execution - binaries such as "powershell.exe" or "cmd.exe". This detection leverages data from - Endpoint Detection and Response (EDR) agents, focusing on process and parent process - relationships. This activity is significant as attackers often abuse the Task Scheduler - for execution and persistence, blending in with legitimate Windows operations. If - confirmed malicious, this could allow attackers to execute arbitrary code, maintain - persistence, or escalate privileges within the environment. +description: The following analytic detects when the Task Scheduler service ("svchost.exe -k netsvcs -p -s Schedule") spawns common command line, scripting, or shell execution binaries such as "powershell.exe" or "cmd.exe". This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process relationships. This activity is significant as attackers often abuse the Task Scheduler for execution and persistence, blending in with legitimate Windows operations. If confirmed malicious, this could allow attackers to execute arbitrary code, maintain persistence, or escalate privileges within the environment. data_source: -- Sysmon EventID 1 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process="*\\system32\\svchost.exe*" - AND Processes.parent_process="*-k*" AND Processes.parent_process= "*netsvcs*" AND - Processes.parent_process="*-p*" AND Processes.parent_process="*-s*" AND Processes.parent_process="*Schedule*" - Processes.process_name IN("powershell.exe", "wscript.exe", "cscript.exe", "cmd.exe", - "sh.exe", "ksh.exe", "zsh.exe", "bash.exe", "scrcons.exe","pwsh.exe") by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_scheduled_task_service_spawned_shell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process="*\\system32\\svchost.exe*" AND Processes.parent_process="*-k*" AND Processes.parent_process= "*netsvcs*" AND Processes.parent_process="*-p*" AND Processes.parent_process="*-s*" AND Processes.parent_process="*Schedule*" Processes.process_name IN("powershell.exe", "wscript.exe", "cscript.exe", "cmd.exe", "sh.exe", "ksh.exe", "zsh.exe", "bash.exe", "scrcons.exe","pwsh.exe") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_scheduled_task_service_spawned_shell_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://www.mandiant.com/resources/blog/tracking-evolution-gootloader-operations -- https://nasbench.medium.com/a-deep-dive-into-windows-scheduled-tasks-and-the-processes-running-them-218d1eed4cce -- https://attack.mitre.org/techniques/T1053/005/ + - https://www.mandiant.com/resources/blog/tracking-evolution-gootloader-operations + - https://nasbench.medium.com/a-deep-dive-into-windows-scheduled-tasks-and-the-processes-running-them-218d1eed4cce + - https://attack.mitre.org/techniques/T1053/005/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A windows scheduled task spawned the shell application $process_name$ on - $dest$. - risk_objects: - - field: dest - type: system - score: 20 - - field: user - type: user - score: 20 - threat_objects: - - field: process_name - type: process_name + message: A windows scheduled task spawned the shell application $process_name$ on $dest$. + risk_objects: + - field: dest + type: system + score: 20 + - field: user + type: user + score: 20 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Windows Persistence Techniques - asset_type: Endpoint - mitre_attack_id: - - T1053.005 - - T1059 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1053.005 + - T1059 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/gootloader/partial_ttps/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/gootloader/partial_ttps/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_scheduled_task_with_highest_privileges.yml b/detections/endpoint/windows_scheduled_task_with_highest_privileges.yml index 8f45aad7be..6def17c147 100644 --- a/detections/endpoint/windows_scheduled_task_with_highest_privileges.yml +++ b/detections/endpoint/windows_scheduled_task_with_highest_privileges.yml @@ -5,90 +5,55 @@ date: '2026-02-09' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the creation of a new scheduled task - with the highest execution privileges via Schtasks.exe. It leverages Endpoint - Detection and Response (EDR) logs to monitor for specific command-line - parameters ('/rl' and 'highest') in schtasks.exe executions. This activity is - significant as it is commonly used in AsyncRAT attacks for persistence and - privilege escalation. If confirmed malicious, this could allow an attacker to - maintain persistent access and execute tasks with elevated privileges, - potentially leading to unauthorized system access and data breaches. +description: The following analytic detects the creation of a new scheduled task with the highest execution privileges via Schtasks.exe. It leverages Endpoint Detection and Response (EDR) logs to monitor for specific command-line parameters ('/rl' and 'highest') in schtasks.exe executions. This activity is significant as it is commonly used in AsyncRAT attacks for persistence and privilege escalation. If confirmed malicious, this could allow an attacker to maintain persistent access and execute tasks with elevated privileges, potentially leading to unauthorized system access and data breaches. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "schtasks.exe" - Processes.process = "*/rl *" Processes.process = "* highest *" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_scheduled_task_with_highest_privileges_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: False positives may arise from legitimate applications - that create tasks to run as SYSTEM. Therefore, it's recommended to adjust - filters based on parent process or modify the query to include world writable - paths for restriction. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "schtasks.exe" Processes.process = "*/rl *" Processes.process = "* highest *" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_scheduled_task_with_highest_privileges_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may arise from legitimate applications that create tasks to run as SYSTEM. Therefore, it's recommended to adjust filters based on parent process or modify the query to include world writable paths for restriction. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.asyncrat + - https://malpedia.caad.fkie.fraunhofer.de/details/win.asyncrat drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A $process_name$ process created a scheduled task $process$ with - highest run level privilege on $dest$ - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A $process_name$ process created a scheduled task $process$ with highest run level privilege on $dest$ + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - SolarWinds WHD RCE Post Exploitation - - XWorm - - CISA AA23-347A - - Scheduled Tasks - - Quasar RAT - - AsyncRAT - - RedLine Stealer - - Compromised Windows Host - - Castle RAT - - NetSupport RMM Tool Abuse - asset_type: Endpoint - mitre_attack_id: - - T1053.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SolarWinds WHD RCE Post Exploitation + - XWorm + - CISA AA23-347A + - Scheduled Tasks + - Quasar RAT + - AsyncRAT + - RedLine Stealer + - Compromised Windows Host + - Castle RAT + - NetSupport RMM Tool Abuse + asset_type: Endpoint + mitre_attack_id: + - T1053.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/asyncrat_highest_priv_schtasks/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/asyncrat_highest_priv_schtasks/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_scheduled_task_with_suspicious_command.yml b/detections/endpoint/windows_scheduled_task_with_suspicious_command.yml index dbfbf51276..b2e8f744c8 100644 --- a/detections/endpoint/windows_scheduled_task_with_suspicious_command.yml +++ b/detections/endpoint/windows_scheduled_task_with_suspicious_command.yml @@ -5,93 +5,65 @@ date: '2026-02-09' author: Steven Dick status: production type: TTP -description: The following analytic detects the creation of scheduled tasks designed - to execute commands using native Windows shells like PowerShell, Cmd, Wscript, or - Cscript or from public folders such as Users, Temp, or ProgramData. It leverages - Windows Security EventCode 4698, 4700, and 4702 to identify when such tasks are - registered, enabled, or modified. This activity is significant as it may indicate - an attempt to establish persistence or execute malicious commands on a system. If - confirmed malicious, this could allow an attacker to maintain access, execute arbitrary - code, or escalate privileges, posing a severe threat to the environment. +description: The following analytic detects the creation of scheduled tasks designed to execute commands using native Windows shells like PowerShell, Cmd, Wscript, or Cscript or from public folders such as Users, Temp, or ProgramData. It leverages Windows Security EventCode 4698, 4700, and 4702 to identify when such tasks are registered, enabled, or modified. This activity is significant as it may indicate an attempt to establish persistence or execute malicious commands on a system. If confirmed malicious, this could allow an attacker to maintain access, execute arbitrary code, or escalate privileges, posing a severe threat to the environment. data_source: -- Windows Event Log Security 4698 -- Windows Event Log Security 4700 -- Windows Event Log Security 4702 -search: "`wineventlog_security` EventCode IN (4698,4700,4702)\n| eval TaskContent\ - \ = case(isnotnull(TaskContentNew),TaskContentNew,true(),TaskContent)\n| xmlkv TaskContent\n\ - | stats count min(_time) as firstTime max(_time) as lastTime latest(Arguments) as\ - \ Arguments latest(Author) as Author by Computer, Caller_User_Name, TaskName, Command,\ - \ Enabled, Hidden, EventCode\n| lookup windows_suspicious_tasks task_command as\ - \ Command \n| where tool == \"shell command use\" OR tool == \"suspicious paths\"\ - \n| eval command=TaskName, process=Command+if(isnotnull(Arguments),\" \".Arguments,\"\ - \"), src_user=Author, user = Caller_User_Name, dest = Computer, signature_id = EventCode\ - \ \n| `security_content_ctime(firstTime)` \n| `security_content_ctime(lastTime)`\n\ - | `windows_scheduled_task_with_suspicious_command_filter` " -how_to_implement: To successfully implement this search, you need to be ingesting - Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also - required. -known_false_positives: False positives are possible if legitimate applications are - allowed to register tasks that call a shell to be spawned. Filter as needed based - on command-line or processes that are used legitimately. Windows Defender, Google - Chrome, and MS Edge updates may trigger this detection. + - Windows Event Log Security 4698 + - Windows Event Log Security 4700 + - Windows Event Log Security 4702 +search: "`wineventlog_security` EventCode IN (4698,4700,4702)\n| eval TaskContent = case(isnotnull(TaskContentNew),TaskContentNew,true(),TaskContent)\n| xmlkv TaskContent\n| stats count min(_time) as firstTime max(_time) as lastTime latest(Arguments) as Arguments latest(Author) as Author by Computer, Caller_User_Name, TaskName, Command, Enabled, Hidden, EventCode\n| lookup windows_suspicious_tasks task_command as Command \n| where tool == \"shell command use\" OR tool == \"suspicious paths\"\n| eval command=TaskName, process=Command+if(isnotnull(Arguments),\" \".Arguments,\"\"), src_user=Author, user = Caller_User_Name, dest = Computer, signature_id = EventCode \n| `security_content_ctime(firstTime)` \n| `security_content_ctime(lastTime)`\n| `windows_scheduled_task_with_suspicious_command_filter` " +how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also required. +known_false_positives: False positives are possible if legitimate applications are allowed to register tasks that call a shell to be spawned. Filter as needed based on command-line or processes that are used legitimately. Windows Defender, Google Chrome, and MS Edge updates may trigger this detection. references: -- https://attack.mitre.org/techniques/T1053/005/ -- https://www.ic3.gov/CSA/2023/231213.pdf -- https://news.sophos.com/en-us/2024/11/06/bengal-cat-lovers-in-australia-get-psspsspssd-in-google-driven-gootloader-campaign/ -- https://github.com/mthcht/awesome-lists/blob/main/Lists/suspicious_windows_tasks_list.csv + - https://attack.mitre.org/techniques/T1053/005/ + - https://www.ic3.gov/CSA/2023/231213.pdf + - https://news.sophos.com/en-us/2024/11/06/bengal-cat-lovers-in-australia-get-psspsspssd-in-google-driven-gootloader-campaign/ + - https://github.com/mthcht/awesome-lists/blob/main/Lists/suspicious_windows_tasks_list.csv drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$","$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate schedule tasks on $dest$ - search: '`wineventlog_security` EventCode IN (4698,4700,4702) Computer="$dest$" - Caller_User_Name="$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$","$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate schedule tasks on $dest$ + search: '`wineventlog_security` EventCode IN (4698,4700,4702) Computer="$dest$" Caller_User_Name="$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious windows scheduled task named [$TaskName$] was detected on - $dest$, this may be an indicator of [$tool$] - risk_objects: - - field: dest - type: system - score: 70 - - field: user - type: user - score: 70 - threat_objects: - - field: Command - type: signature + message: A suspicious windows scheduled task named [$TaskName$] was detected on $dest$, this may be an indicator of [$tool$] + risk_objects: + - field: dest + type: system + score: 70 + - field: user + type: user + score: 70 + threat_objects: + - field: Command + type: signature tags: - analytic_story: - - SolarWinds WHD RCE Post Exploitation - - Scheduled Tasks - - Ransomware - - Quasar RAT - - Ryuk Ransomware - - Windows Persistence Techniques - - Seashell Blizzard - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1053.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SolarWinds WHD RCE Post Exploitation + - Scheduled Tasks + - Ransomware + - Quasar RAT + - Ryuk Ransomware + - Windows Persistence Techniques + - Seashell Blizzard + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1053.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/winevent_scheduled_task_created_to_spawn_shell/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/winevent_scheduled_task_created_to_spawn_shell/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_scheduled_task_with_suspicious_name.yml b/detections/endpoint/windows_scheduled_task_with_suspicious_name.yml index 6a8fc824ea..2fec3024d6 100644 --- a/detections/endpoint/windows_scheduled_task_with_suspicious_name.yml +++ b/detections/endpoint/windows_scheduled_task_with_suspicious_name.yml @@ -1,97 +1,78 @@ name: Windows Scheduled Task with Suspicious Name id: 9e9ab4e3-c9d0-4967-a197-6d755e8a7e6e -version: 5 -date: '2025-10-31' +version: 6 +date: '2026-02-25' author: Steven Dick status: production type: TTP -description: The following analytic detects the creation, modification, or - enabling of scheduled tasks with known suspicious or malicious task names. It - leverages Windows Security EventCode 4698, 4700, and 4702 to identify when - such tasks are registered, modified, or enabled. This activity is significant - as it may indicate an attempt to establish persistence or execute malicious - commands on a system. If confirmed malicious, this could allow an attacker to - maintain access, execute arbitrary code, or escalate privileges, posing a - severe threat to the environment. +description: The following analytic detects the creation, modification, or enabling of scheduled tasks with known suspicious or malicious task names. It leverages Windows Security EventCode 4698, 4700, and 4702 to identify when such tasks are registered, modified, or enabled. This activity is significant as it may indicate an attempt to establish persistence or execute malicious commands on a system. If confirmed malicious, this could allow an attacker to maintain access, execute arbitrary code, or escalate privileges, posing a severe threat to the environment. data_source: -- Windows Event Log Security 4698 -- Windows Event Log Security 4700 -- Windows Event Log Security 4702 + - Windows Event Log Security 4698 + - Windows Event Log Security 4700 + - Windows Event Log Security 4702 search: |- - `wineventlog_security` EventCode IN (4698,4700,4702) - | eval TaskContent = case(isnotnull(TaskContentNew),TaskContentNew,true(),TaskContent) - | xmlkv TaskContent - | stats count min(_time) as firstTime max(_time) as lastTime latest(Arguments) as Arguments latest(Author) as Author by Computer, TaskName, Command, Enabled, Hidden,Caller_User_Name, EventCode - | lookup windows_suspicious_tasks task_name as TaskName - | where isnotnull(tool_type) - | eval command=TaskName, process=Command+if(isnotnull(Arguments)," ".Arguments,""), src_user=Author, user = Caller_User_Name, dest = Computer - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_scheduled_task_with_suspicious_name_filter` -how_to_implement: To successfully implement this search, you need to be - ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows - TA is also required. -known_false_positives: False positives are possible if legitimate applications - are allowed to register tasks that call a shell to be spawned. Filter as - needed based on command-line or processes that are used legitimately. + `wineventlog_security` EventCode IN (4698,4700,4702) + | eval TaskContent = case(isnotnull(TaskContentNew),TaskContentNew,true(),TaskContent) + | xmlkv TaskContent + | stats count min(_time) as firstTime max(_time) as lastTime latest(Arguments) as Arguments latest(Author) as Author by Computer, TaskName, Command, Enabled, Hidden,Caller_User_Name, EventCode + | lookup windows_suspicious_tasks task_name as TaskName + | where isnotnull(tool_type) + | eval command=TaskName, process=Command+if(isnotnull(Arguments)," ".Arguments,""), src_user=Author, user = Caller_User_Name, dest = Computer + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_scheduled_task_with_suspicious_name_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also required. +known_false_positives: False positives are possible if legitimate applications are allowed to register tasks that call a shell to be spawned. Filter as needed based on command-line or processes that are used legitimately. references: -- https://attack.mitre.org/techniques/T1053/005/ -- https://www.ic3.gov/CSA/2023/231213.pdf -- https://news.sophos.com/en-us/2024/11/06/bengal-cat-lovers-in-australia-get-psspsspssd-in-google-driven-gootloader-campaign/ -- https://github.com/mthcht/awesome-lists/blob/main/Lists/suspicious_windows_tasks_list.csv + - https://attack.mitre.org/techniques/T1053/005/ + - https://www.ic3.gov/CSA/2023/231213.pdf + - https://news.sophos.com/en-us/2024/11/06/bengal-cat-lovers-in-australia-get-psspsspssd-in-google-driven-gootloader-campaign/ + - https://github.com/mthcht/awesome-lists/blob/main/Lists/suspicious_windows_tasks_list.csv drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$","$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate schedule tasks on $dest$ - search: '`wineventlog_security` EventCode IN (4698,4700,4702) | xmlkv TaskContent - | search dest="$dest$" AND TaskName = "$TaskName$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$","$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate schedule tasks on $dest$ + search: '`wineventlog_security` EventCode IN (4698,4700,4702) | xmlkv TaskContent | search dest="$dest$" AND TaskName = "$TaskName$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A windows scheduled task was created with known suspicious task name - [$TaskName$] on $dest$, this may be a [$tool$] indicator - risk_objects: - - field: dest - type: system - score: 70 - - field: user - type: user - score: 70 - threat_objects: - - field: Command - type: signature + message: A windows scheduled task was created with known suspicious task name [$TaskName$] on $dest$, this may be a [$tool$] indicator + risk_objects: + - field: dest + type: system + score: 70 + - field: user + type: user + score: 70 + threat_objects: + - field: Command + type: signature tags: - analytic_story: - - Scheduled Tasks - - Windows Persistence Techniques - - Ransomware - - Ryuk Ransomware - - 0bj3ctivity Stealer - - APT37 Rustonotto and FadeStealer - - Castle RAT - asset_type: Endpoint - mitre_attack_id: - - T1053.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Scheduled Tasks + - Windows Persistence Techniques + - Ransomware + - Ryuk Ransomware + - 0bj3ctivity Stealer + - APT37 Rustonotto and FadeStealer + - Castle RAT + asset_type: Endpoint + mitre_attack_id: + - T1053.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/winevent_scheduled_task_with_suspect_name/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/winevent_scheduled_task_with_suspect_name/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_scheduled_tasks_for_compmgmtlauncher_or_eventvwr.yml b/detections/endpoint/windows_scheduled_tasks_for_compmgmtlauncher_or_eventvwr.yml index 754f8b79bc..5cb50d576b 100644 --- a/detections/endpoint/windows_scheduled_tasks_for_compmgmtlauncher_or_eventvwr.yml +++ b/detections/endpoint/windows_scheduled_tasks_for_compmgmtlauncher_or_eventvwr.yml @@ -4,70 +4,47 @@ version: 8 date: '2026-01-14' author: Teoderick Contreras, Splunk data_source: - - Windows Event Log Security 4698 + - Windows Event Log Security 4698 type: TTP status: production -description: - The following analytic detects the creation or modification of Windows - Scheduled Tasks related to CompMgmtLauncher or Eventvwr. These legitimate system - utilities, used for launching the Computer Management Console and Event Viewer, - can be abused by attackers to execute malicious payloads under the guise of normal - system processes. By leveraging these tasks, adversaries can establish persistence - or elevate privileges without raising suspicion. This detection helps security analysts - identify unusual or unauthorized scheduled tasks involving these executables, allowing - for timely investigation and remediation of potential threats. -search: - '`wineventlog_security` EventCode=4698 TaskContent = "*<Command>C:\\Windows\\System32\\CompMgmtLauncher.exe</Command>*" - OR TaskContent = "*<Command>C:\\Windows\\System32\\zh-CN\\eventvwr.msc</Command>*" - OR TaskContent = "*<Command>C:\\Windows\\System32\\eventvwr.msc</Command>*" - | stats count min(_time) as firstTime max(_time) as lastTime by dest action EventData_Xml - TaskContent TaskName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_scheduled_tasks_for_compmgmtlauncher_or_eventvwr_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - Windows Security Event Logs with 4698 EventCode enabled. The Windows TA as well - as the URL ToolBox application are also required. +description: The following analytic detects the creation or modification of Windows Scheduled Tasks related to CompMgmtLauncher or Eventvwr. These legitimate system utilities, used for launching the Computer Management Console and Event Viewer, can be abused by attackers to execute malicious payloads under the guise of normal system processes. By leveraging these tasks, adversaries can establish persistence or elevate privileges without raising suspicion. This detection helps security analysts identify unusual or unauthorized scheduled tasks involving these executables, allowing for timely investigation and remediation of potential threats. +search: '`wineventlog_security` EventCode=4698 TaskContent = "*<Command>C:\\Windows\\System32\\CompMgmtLauncher.exe</Command>*" OR TaskContent = "*<Command>C:\\Windows\\System32\\zh-CN\\eventvwr.msc</Command>*" OR TaskContent = "*<Command>C:\\Windows\\System32\\eventvwr.msc</Command>*" | stats count min(_time) as firstTime max(_time) as lastTime by dest action EventData_Xml TaskContent TaskName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_scheduled_tasks_for_compmgmtlauncher_or_eventvwr_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA as well as the URL ToolBox application are also required. known_false_positives: No false positives have been identified at this time. references: - - https://www.proofpoint.com/us/blog/threat-insight/chinese-malware-appears-earnest-across-cybercrime-threat-landscape - - https://www.fortinet.com/blog/threat-research/valleyrat-campaign-targeting-chinese-speakers + - https://www.proofpoint.com/us/blog/threat-insight/chinese-malware-appears-earnest-across-cybercrime-threat-landscape + - https://www.fortinet.com/blog/threat-research/valleyrat-campaign-targeting-chinese-speakers drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A schedule task created for CompMgmtLauncher or Eventvwr on [$dest$]. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A schedule task created for CompMgmtLauncher or Eventvwr on [$dest$]. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - ValleyRAT - - Water Gamayun - asset_type: Endpoint - mitre_attack_id: - - T1053 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ValleyRAT + - Water Gamayun + asset_type: Endpoint + mitre_attack_id: + - T1053 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053/valleyrat_schedtask/valleyrat_schedtask.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053/valleyrat_schedtask/valleyrat_schedtask.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_schtasks_create_run_as_system.yml b/detections/endpoint/windows_schtasks_create_run_as_system.yml index be5defce69..f6b83e012c 100644 --- a/detections/endpoint/windows_schtasks_create_run_as_system.yml +++ b/detections/endpoint/windows_schtasks_create_run_as_system.yml @@ -5,94 +5,55 @@ date: '2026-02-09' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the creation of a new scheduled task - using Schtasks.exe to run as the SYSTEM user. This detection leverages data - from Endpoint Detection and Response (EDR) agents, focusing on command-line - executions and process details. This activity is significant as it often - indicates an attempt to gain elevated privileges or maintain persistence - within the environment. If confirmed malicious, an attacker could execute code - with SYSTEM-level privileges, potentially leading to data theft, ransomware - deployment, or further system compromise. Immediate investigation and - mitigation are crucial to prevent further damage. +description: The following analytic detects the creation of a new scheduled task using Schtasks.exe to run as the SYSTEM user. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process details. This activity is significant as it often indicates an attempt to gain elevated privileges or maintain persistence within the environment. If confirmed malicious, an attacker could execute code with SYSTEM-level privileges, potentially leading to data theft, ransomware deployment, or further system compromise. Immediate investigation and mitigation are crucial to prevent further damage. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - (Processes.process_name=schtasks.exe OR Processes.original_file_name=schtasks.exe) - Processes.process="*/create *" - Processes.process="*/ru *" - Processes.process="*system*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_schtasks_create_run_as_system_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: False positives will be limited to legitimate - applications creating a task to run as SYSTEM. Filter as needed based on - parent process, or modify the query to have world writeable paths to restrict - it. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=schtasks.exe OR Processes.original_file_name=schtasks.exe) Processes.process="*/create *" Processes.process="*/ru *" Processes.process="*system*" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_schtasks_create_run_as_system_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives will be limited to legitimate applications creating a task to run as SYSTEM. Filter as needed based on parent process, or modify the query to have world writeable paths to restrict it. references: -- https://pentestlab.blog/2019/11/04/persistence-scheduled-tasks/ -- https://www.ired.team/offensive-security/persistence/t1053-schtask -- https://thedfirreport.com/2022/02/07/qbot-likes-to-move-it-move-it/ + - https://pentestlab.blog/2019/11/04/persistence-scheduled-tasks/ + - https://www.ired.team/offensive-security/persistence/t1053-schtask + - https://thedfirreport.com/2022/02/07/qbot-likes-to-move-it-move-it/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An $process_name$ was created on endpoint $dest$ attempting to spawn - as SYSTEM. - risk_objects: - - field: dest - type: system - score: 48 - threat_objects: - - field: process_name - type: process_name + message: An $process_name$ was created on endpoint $dest$ attempting to spawn as SYSTEM. + risk_objects: + - field: dest + type: system + score: 48 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - SolarWinds WHD RCE Post Exploitation - - Medusa Ransomware - - Windows Persistence Techniques - - Qakbot - - Scheduled Tasks - - Castle RAT - asset_type: Endpoint - mitre_attack_id: - - T1053.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SolarWinds WHD RCE Post Exploitation + - Medusa Ransomware + - Windows Persistence Techniques + - Qakbot + - Scheduled Tasks + - Castle RAT + asset_type: Endpoint + mitre_attack_id: + - T1053.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/schtask_system/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/schtask_system/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_scmanager_security_descriptor_tampering_via_sc_exe.yml b/detections/endpoint/windows_scmanager_security_descriptor_tampering_via_sc_exe.yml index 86a3f5fe03..c7632ea8b3 100644 --- a/detections/endpoint/windows_scmanager_security_descriptor_tampering_via_sc_exe.yml +++ b/detections/endpoint/windows_scmanager_security_descriptor_tampering_via_sc_exe.yml @@ -1,86 +1,75 @@ name: Windows ScManager Security Descriptor Tampering Via Sc.EXE id: 04023928-0381-4935-82cb-03372b2ef644 -version: 5 -date: '2026-01-14' +version: 6 +date: '2026-02-25' author: Nasreddine Bencherchali, Michael Haag, Splunk status: production type: TTP -description: The following analytic detects changes in the ScManager service security - descriptor settings. It leverages data from Endpoint Detection and Response (EDR) - agents, specifically searching for any process execution involving the "sc.exe" - binary with the "sdset" flag targeting the "scmanager" service. If confirmed malicious, - this could allow an attacker to escalate their privileges. +description: The following analytic detects changes in the ScManager service security descriptor settings. It leverages data from Endpoint Detection and Response (EDR) agents, specifically searching for any process execution involving the "sc.exe" binary with the "sdset" flag targeting the "scmanager" service. If confirmed malicious, this could allow an attacker to escalate their privileges. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where (Processes.process_name=sc.exe OR Processes.original_file_name=sc.exe) Processes.process="*sdset - *" Processes.process="*scmanager*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_scmanager_security_descriptor_tampering_via_sc_exe_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process name, and process original file name. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: No false positives have been identified at this time. - descriptor settings of the scmanager service should be immediately investigated - and understood. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name=sc.exe + OR + Processes.original_file_name=sc.exe + ) + Processes.process="*sdset *" Processes.process="*scmanager*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_scmanager_security_descriptor_tampering_via_sc_exe_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process name, and process original file name. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: No false positives have been identified at this time. descriptor settings of the scmanager service should be immediately investigated and understood. references: -- https://github.com/redcanaryco/atomic-red-team/blob/8ac5c4f84692b11ea2832d18d3dc6f1ce7fb4e41/atomics/T1569.002/T1569.002.md#atomic-test-7---modifying-acl-of-service-control-manager-via-sdet -- https://0xv1n.github.io/posts/scmanager/ -- https://attack.mitre.org/techniques/T1569/002/ + - https://github.com/redcanaryco/atomic-red-team/blob/8ac5c4f84692b11ea2832d18d3dc6f1ce7fb4e41/atomics/T1569.002/T1569.002.md#atomic-test-7---modifying-acl-of-service-control-manager-via-sdet + - https://0xv1n.github.io/posts/scmanager/ + - https://attack.mitre.org/techniques/T1569/002/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - attempting to disable security services on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified attempting to disable security services on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Defense Evasion or Unauthorized Access Via SDDL Tampering - asset_type: Endpoint - mitre_attack_id: - - T1569.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Defense Evasion or Unauthorized Access Via SDDL Tampering + asset_type: Endpoint + mitre_attack_id: + - T1569.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/scmanager_sddl_tamper/scmanager_sddl_tamper_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/scmanager_sddl_tamper/scmanager_sddl_tamper_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_screen_capture_in_temp_folder.yml b/detections/endpoint/windows_screen_capture_in_temp_folder.yml index 4c9ca5432c..59f4076fa7 100644 --- a/detections/endpoint/windows_screen_capture_in_temp_folder.yml +++ b/detections/endpoint/windows_screen_capture_in_temp_folder.yml @@ -4,73 +4,50 @@ version: 8 date: '2026-01-14' author: Teoderick Contreras, Splunk data_source: -- Sysmon EventID 11 + - Sysmon EventID 11 type: TTP status: production -description: The following analytic detects the creation of screen capture files by - the Braodo stealer malware. This stealer is known to capture screenshots of the - victim's desktop as part of its data theft activities. The detection focuses on - identifying unusual screen capture activity, especially when images are saved in - directories often used by malware, such as temporary or hidden folders. Monitoring - for these files helps to quickly identify malicious screen capture attempts, allowing - security teams to respond and mitigate potential information exposure before sensitive - data is compromised. -search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("screenshot.png", - "screenshot.jpg","screenshot.bmp") Filesystem.file_path = "*\\temp\\*" by Filesystem.action - Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash - Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl - Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user - Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_screen_capture_in_temp_folder_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. +description: The following analytic detects the creation of screen capture files by the Braodo stealer malware. This stealer is known to capture screenshots of the victim's desktop as part of its data theft activities. The detection focuses on identifying unusual screen capture activity, especially when images are saved in directories often used by malware, such as temporary or hidden folders. Monitoring for these files helps to quickly identify malicious screen capture attempts, allowing security teams to respond and mitigate potential information exposure before sensitive data is compromised. +search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("screenshot.png", "screenshot.jpg","screenshot.bmp") Filesystem.file_path = "*\\temp\\*" by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_screen_capture_in_temp_folder_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. known_false_positives: No false positives have been identified at this time. references: -- https://x.com/suyog41/status/1825869470323056748 -- https://g0njxa.medium.com/from-vietnam-to-united-states-malware-fraud-and-dropshipping-98b7a7b2c36d + - https://x.com/suyog41/status/1825869470323056748 + - https://g0njxa.medium.com/from-vietnam-to-united-states-malware-fraud-and-dropshipping-98b7a7b2c36d drilldown_searches: -- name: View the detection results for "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A screen capture named as $file_name$ was created on $dest$. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A screen capture named as $file_name$ was created on $dest$. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - StealC Stealer - - Crypto Stealer - - Braodo Stealer - - APT37 Rustonotto and FadeStealer - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1113 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - StealC Stealer + - Crypto Stealer + - Braodo Stealer + - APT37 Rustonotto and FadeStealer + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1113 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1113/braodo_screenshot/braodo_screenshot.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1113/braodo_screenshot/braodo_screenshot.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_screen_capture_via_powershell.yml b/detections/endpoint/windows_screen_capture_via_powershell.yml index 7e375ad519..6342be1474 100644 --- a/detections/endpoint/windows_screen_capture_via_powershell.yml +++ b/detections/endpoint/windows_screen_capture_via_powershell.yml @@ -1,75 +1,62 @@ name: Windows Screen Capture Via Powershell id: 5e0b1936-8f99-4399-8ee2-9edc5b32e170 -version: 10 -date: '2026-01-14' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP data_source: - - Powershell Script Block Logging 4104 -description: - The following analytic detects the execution of a PowerShell script designed - to capture screen images on a host. It leverages PowerShell Script Block Logging - to identify specific script block text patterns associated with screen capture activities. - This behavior is significant as it may indicate an attempt to exfiltrate sensitive - information by capturing desktop screenshots. If confirmed malicious, this activity - could allow an attacker to gather visual data from the compromised system, potentially - leading to data breaches or further exploitation. -search: - '`powershell` EventCode=4104 ScriptBlockText = "*[Drawing.Graphics]::FromImage(*" - AND ScriptBlockText = "*New-Object Drawing.Bitmap*" AND ScriptBlockText = "*.CopyFromScreen*" - | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by dest - signature signature_id user_id vendor_product EventID Guid Opcode Name Path ProcessID - ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_screen_capture_via_powershell_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - Powershell Script Block Logging 4104 +description: The following analytic detects the execution of a PowerShell script designed to capture screen images on a host. It leverages PowerShell Script Block Logging to identify specific script block text patterns associated with screen capture activities. This behavior is significant as it may indicate an attempt to exfiltrate sensitive information by capturing desktop screenshots. If confirmed malicious, this activity could allow an attacker to gather visual data from the compromised system, potentially leading to data breaches or further exploitation. +search: |- + `powershell` EventCode=4104 ScriptBlockText = "*[Drawing.Graphics]::FromImage(*" AND ScriptBlockText = "*New-Object Drawing.Bitmap*" AND ScriptBlockText = "*.CopyFromScreen*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_screen_capture_via_powershell_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. known_false_positives: No false positives have been identified at this time. references: - - https://twitter.com/_CERT_UA/status/1620781684257091584 - - https://cert.gov.ua/article/3761104 + - https://twitter.com/_CERT_UA/status/1620781684257091584 + - https://cert.gov.ua/article/3761104 drilldown_searches: - - name: View the detection results for - "$Computer$" - search: '%original_detection_search% | search Computer = "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$Computer$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$Computer$" + search: '%original_detection_search% | search Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A PowerShell script was identified possibly performing screen captures - on $dest$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: A PowerShell script was identified possibly performing screen captures on $dest$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - APT37 Rustonotto and FadeStealer - - Winter Vivern - - Water Gamayun - asset_type: Endpoint - mitre_attack_id: - - T1113 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - APT37 Rustonotto and FadeStealer + - Winter Vivern + - Water Gamayun + asset_type: Endpoint + mitre_attack_id: + - T1113 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winter-vivern/pwh_exfiltration/windows-powershell-xml.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winter-vivern/pwh_exfiltration/windows-powershell-xml.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_security_account_manager_stopped.yml b/detections/endpoint/windows_security_account_manager_stopped.yml index 1ce998482c..e135d51d7d 100644 --- a/detections/endpoint/windows_security_account_manager_stopped.yml +++ b/detections/endpoint/windows_security_account_manager_stopped.yml @@ -1,85 +1,69 @@ name: Windows Security Account Manager Stopped id: 69c12d59-d951-431e-ab77-ec426b8d65e6 -version: 9 -date: '2025-10-14' +version: 10 +date: '2026-02-25' author: Rod Soto, Jose Hernandez, Splunk status: production type: TTP -description: The following analytic detects the stopping of the Windows Security Account - Manager (SAM) service via command-line, typically using the "net stop samss" command. - This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process names and command-line executions. This activity is significant - because stopping the SAM service can disrupt authentication mechanisms and is often - associated with ransomware attacks like Ryuk. If confirmed malicious, this action - could lead to unauthorized access, privilege escalation, and potential system-wide - compromise. +description: The following analytic detects the stopping of the Windows Security Account Manager (SAM) service via command-line, typically using the "net stop samss" command. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because stopping the SAM service can disrupt authentication mechanisms and is often associated with ransomware attacks like Ryuk. If confirmed malicious, this action could lead to unauthorized access, privilege escalation, and potential system-wide compromise. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Processes WHERE ("Processes.process_name"="net*.exe" - "Processes.process"="*stop \"samss\"*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_security_account_manager_stopped_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: SAM is a critical windows service, stopping it would cause - major issues on an endpoint this makes false positive rare. AlthoughNo false positives - have been identified. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + "Processes.process_name"="net*.exe" "Processes.process"="*stop \"samss\"*" + ) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` + | `windows_security_account_manager_stopped_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: SAM is a critical windows service, stopping it would cause major issues on an endpoint this makes false positive rare. AlthoughNo false positives have been identified. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: 'The Windows Security Account Manager (SAM) was stopped via cli by $user$ - on $dest$ by this command: $process$' - risk_objects: - - field: dest - type: system - score: 70 - - field: user - type: user - score: 70 - threat_objects: [] + message: 'The Windows Security Account Manager (SAM) was stopped via cli by $user$ on $dest$ by this command: $process$' + risk_objects: + - field: dest + type: system + score: 70 + - field: user + type: user + score: 70 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - Ryuk Ransomware - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1489 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - Ryuk Ransomware + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1489 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_security_and_backup_services_stop.yml b/detections/endpoint/windows_security_and_backup_services_stop.yml index 32b06baed0..fbb5b93fdd 100644 --- a/detections/endpoint/windows_security_and_backup_services_stop.yml +++ b/detections/endpoint/windows_security_and_backup_services_stop.yml @@ -1,81 +1,68 @@ name: Windows Security And Backup Services Stop id: 9c24aef6-cad9-4931-acce-74318aa5663b -version: 4 -date: '2025-10-14' +version: 5 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the suspicious termination of known services - commonly targeted by ransomware before file encryption. It leverages Windows System - Event Logs (EventCode 7036) to identify when critical services such as Volume Shadow - Copy, backup, and antivirus services are stopped. This activity is significant because - ransomware often disables these services to avoid errors and ensure successful file - encryption. If confirmed malicious, this behavior could lead to widespread data - encryption, rendering files inaccessible and potentially causing significant operational - disruption and data loss. +description: The following analytic detects the suspicious termination of known services commonly targeted by ransomware before file encryption. It leverages Windows System Event Logs (EventCode 7036) to identify when critical services such as Volume Shadow Copy, backup, and antivirus services are stopped. This activity is significant because ransomware often disables these services to avoid errors and ensure successful file encryption. If confirmed malicious, this behavior could lead to widespread data encryption, rendering files inaccessible and potentially causing significant operational disruption and data loss. data_source: -- Windows Event Log System 7036 -search: '`wineventlog_system` `normalized_service_binary_field` - | rename param1 as display_name - | where param2="stopped" AND (match(display_name, "(?i)(Volume Shadow Copy|VSS|backup|sophos|sql|memtas|mepocs|veeam|svc\$|DefWatch|ccEvtMgr|ccSetMgr|SavRoam|RTVscan|QBFCService|QBIDPService|Intuit\.QuickBooks\.FCS|QBCFMonitorService|YooBackup|YooIT|Veeam|PDVFSService|BackupExec|WdBoot|WdFilter|WdNisDrv|WdNisSvc|WinDefend|wscsvc|Sense|sppsvc|SecurityHealthService)") - OR match(normalized_service_name, "(?i)(Volume Shadow Copy|VSS|backup|sophos|sql|memtas|mepocs|veeam|svc\$|DefWatch|ccEvtMgr|ccSetMgr|SavRoam|RTVscan|QBFCService|QBIDPService|Intuit\.QuickBooks\.FCS|QBCFMonitorService|YooBackup|YooIT|Veeam|PDVFSService|BackupExec|WdBoot|WdFilter|WdNisDrv|WdNisSvc|WinDefend|wscsvc|Sense|sppsvc|SecurityHealthService)")) - | stats count min(_time) as firstTime max(_time) as lastTime by EventCode display_name dest normalized_service_name - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_security_and_backup_services_stop_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the 7036 EventCode ScManager in System audit Logs from your endpoints. -known_false_positives: Admin activities or installing related updates may do a sudden - stop to list of services we monitor. + - Windows Event Log System 7036 +search: |- + `wineventlog_system` + `normalized_service_binary_field` + | rename param1 as display_name + | where param2="stopped" AND (match(display_name, "(?i)(Volume Shadow Copy|VSS|backup|sophos|sql|memtas|mepocs|veeam|svc\$|DefWatch|ccEvtMgr|ccSetMgr|SavRoam|RTVscan|QBFCService|QBIDPService|Intuit\.QuickBooks\.FCS|QBCFMonitorService|YooBackup|YooIT|Veeam|PDVFSService|BackupExec|WdBoot|WdFilter|WdNisDrv|WdNisSvc|WinDefend|wscsvc|Sense|sppsvc|SecurityHealthService)") OR match(normalized_service_name, "(?i)(Volume Shadow Copy|VSS|backup|sophos|sql|memtas|mepocs|veeam|svc\$|DefWatch|ccEvtMgr|ccSetMgr|SavRoam|RTVscan|QBFCService|QBIDPService|Intuit\.QuickBooks\.FCS|QBCFMonitorService|YooBackup|YooIT|Veeam|PDVFSService|BackupExec|WdBoot|WdFilter|WdNisDrv|WdNisSvc|WinDefend|wscsvc|Sense|sppsvc|SecurityHealthService)")) + + | stats count min(_time) as firstTime max(_time) as lastTime by EventCode display_name dest normalized_service_name + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_security_and_backup_services_stop_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the 7036 EventCode ScManager in System audit Logs from your endpoints. +known_false_positives: Admin activities or installing related updates may do a sudden stop to list of services we monitor. references: -- https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/ -- https://www.mcafee.com/blogs/other-blogs/mcafee-labs/mcafee-atr-analyzes-sodinokibi-aka-revil-ransomware-as-a-service-what-the-code-tells-us/ -- https://news.sophos.com/en-us/2020/04/24/lockbit-ransomware-borrows-tricks-to-keep-up-with-revil-and-maze/ -- https://blogs.vmware.com/security/2022/10/lockbit-3-0-also-known-as-lockbit-black.html + - https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/ + - https://www.mcafee.com/blogs/other-blogs/mcafee-labs/mcafee-atr-analyzes-sodinokibi-aka-revil-ransomware-as-a-service-what-the-code-tells-us/ + - https://news.sophos.com/en-us/2020/04/24/lockbit-ransomware-borrows-tricks-to-keep-up-with-revil-and-maze/ + - https://blogs.vmware.com/security/2022/10/lockbit-3-0-also-known-as-lockbit-black.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Known services $display_name$ terminated by a potential ransomware on $dest$ - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: - - field: display_name - type: service + message: Known services $display_name$ terminated by a potential ransomware on $dest$ + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: + - field: display_name + type: service tags: - analytic_story: - - LockBit Ransomware - - Ransomware - - Compromised Windows Host - - BlackMatter Ransomware - - Termite Ransomware - - Scattered Lapsus$ Hunters - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1490 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - LockBit Ransomware + - Ransomware + - Compromised Windows Host + - BlackMatter Ransomware + - Termite Ransomware + - Scattered Lapsus$ Hunters + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1490 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/known_services_killed_by_ransomware/windows-xml.log - source: XmlWinEventLog:System - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/known_services_killed_by_ransomware/windows-xml.log + source: XmlWinEventLog:System + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_security_support_provider_reg_query.yml b/detections/endpoint/windows_security_support_provider_reg_query.yml index ff6c862409..ce49a3efcb 100644 --- a/detections/endpoint/windows_security_support_provider_reg_query.yml +++ b/detections/endpoint/windows_security_support_provider_reg_query.yml @@ -5,81 +5,50 @@ date: '2026-01-14' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies command-line activity querying the - registry for Security Support Providers (SSPs) related to Local Security Authority - (LSA) protection and configuration. This detection leverages Endpoint Detection - and Response (EDR) telemetry, focusing on processes accessing specific LSA registry - paths. Monitoring this activity is crucial as adversaries and post-exploitation - tools like winpeas may use it to gather information on LSA protections, potentially - leading to credential theft. If confirmed malicious, attackers could exploit this - to scrape password hashes or plaintext passwords from memory, significantly compromising - system security. +description: The following analytic identifies command-line activity querying the registry for Security Support Providers (SSPs) related to Local Security Authority (LSA) protection and configuration. This detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on processes accessing specific LSA registry paths. Monitoring this activity is crucial as adversaries and post-exploitation tools like winpeas may use it to gather information on LSA protections, potentially leading to credential theft. If confirmed malicious, attackers could exploit this to scrape password hashes or plaintext passwords from memory, significantly compromising system security. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_reg` AND Processes.process - = "* query *" AND Processes.process = "*\\SYSTEM\\CurrentControlSet\\Control\\LSA*" - Processes.process IN ("*RunAsPPL*" , "*LsaCfgFlags*") by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_security_support_provider_reg_query_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_reg` AND Processes.process = "* query *" AND Processes.process = "*\\SYSTEM\\CurrentControlSet\\Control\\LSA*" Processes.process IN ("*RunAsPPL*" , "*LsaCfgFlags*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_security_support_provider_reg_query_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://blog.netwrix.com/2022/01/11/understanding-lsa-protection/ -- https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS -- https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ + - https://blog.netwrix.com/2022/01/11/understanding-lsa-protection/ + - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS + - https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: process with reg query command line $process$ on $dest$ - risk_objects: - - field: dest - type: system - score: 9 - threat_objects: [] + message: process with reg query command line $process$ on $dest$ + risk_objects: + - field: dest + type: system + score: 9 + threat_objects: [] tags: - analytic_story: - - Windows Post-Exploitation - - Prestige Ransomware - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1547.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Post-Exploitation + - Prestige Ransomware + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1547.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_sensitive_group_discovery_with_net.yml b/detections/endpoint/windows_sensitive_group_discovery_with_net.yml index 9d42b75791..c243000f72 100644 --- a/detections/endpoint/windows_sensitive_group_discovery_with_net.yml +++ b/detections/endpoint/windows_sensitive_group_discovery_with_net.yml @@ -1,89 +1,72 @@ name: Windows Sensitive Group Discovery With Net id: d9eb7cda-5622-4722-bc88-7f2442f4b5af -version: 5 -date: '2025-10-24' +version: 6 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: Anomaly -description: The following analytic detects the execution of `net.exe` with command-line - arguments used to query elevated domain or sensitive groups. It leverages data from - Endpoint Detection and Response (EDR) agents, focusing on process names and command-line - executions. This activity is significant as it indicates potential reconnaissance - efforts by adversaries to identify high-privileged users within Active Directory. - If confirmed malicious, this behavior could lead to further attacks aimed at compromising - privileged accounts, escalating privileges, or gaining unauthorized access to sensitive - systems and data. +description: The following analytic detects the execution of `net.exe` with command-line arguments used to query elevated domain or sensitive groups. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential reconnaissance efforts by adversaries to identify high-privileged users within Active Directory. If confirmed malicious, this behavior could lead to further attacks aimed at compromising privileged accounts, escalating privileges, or gaining unauthorized access to sensitive systems and data. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_net` Processes.process="*group*" - Processes.process IN ("*Domain Admins*", "*Enterprise Admins*", "*Schema Admins*", - "*Account Operators*", "*Server Operators*", "*Protected Users*", "*Dns Admins*", - "*Domain Computers*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_sensitive_group_discovery_with_net_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_net` Processes.process="*group*" Processes.process IN ("*Domain Admins*", "*Enterprise Admins*", "*Schema Admins*", "*Account Operators*", "*Server Operators*", "*Protected Users*", "*Dns Admins*", "*Domain Computers*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_sensitive_group_discovery_with_net_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1069/002/ -- https://docs.microsoft.com/en-us/windows-server/identity/ad-ds/plan/security-best-practices/appendix-b--privileged-accounts-and-groups-in-active-directory -- https://adsecurity.org/?p=3658 -- https://media.defense.gov/2023/May/24/2003229517/-1/-1/0/CSA_Living_off_the_Land.PDF -- https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/ + - https://attack.mitre.org/techniques/T1069/002/ + - https://docs.microsoft.com/en-us/windows-server/identity/ad-ds/plan/security-best-practices/appendix-b--privileged-accounts-and-groups-in-active-directory + - https://adsecurity.org/?p=3658 + - https://media.defense.gov/2023/May/24/2003229517/-1/-1/0/CSA_Living_off_the_Land.PDF + - https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Elevated domain group discovery enumeration on $dest$ by $user$ - risk_objects: - - field: dest - type: system - score: 21 - threat_objects: [] + message: Elevated domain group discovery enumeration on $dest$ by $user$ + risk_objects: + - field: dest + type: system + score: 21 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - - Volt Typhoon - - Rhysida Ransomware - - BlackSuit Ransomware - - IcedID - - Microsoft WSUS CVE-2025-59287 - asset_type: Endpoint - mitre_attack_id: - - T1069.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - Volt Typhoon + - Rhysida Ransomware + - BlackSuit Ransomware + - IcedID + - Microsoft WSUS CVE-2025-59287 + asset_type: Endpoint + mitre_attack_id: + - T1069.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_sensitive_registry_hive_dump_via_commandline.yml b/detections/endpoint/windows_sensitive_registry_hive_dump_via_commandline.yml index 3e5a7dc77a..67dbdce9e2 100644 --- a/detections/endpoint/windows_sensitive_registry_hive_dump_via_commandline.yml +++ b/detections/endpoint/windows_sensitive_registry_hive_dump_via_commandline.yml @@ -1,119 +1,97 @@ name: Windows Sensitive Registry Hive Dump Via CommandLine id: 5aaff29d-0cce-405b-9ee8-5d06b49d045e -version: 7 -date: '2025-12-15' +version: 8 +date: '2026-02-25' author: Michael Haag, Patrick Bareiss, Nasreddine Bencherchali, Splunk status: production type: TTP -description: The following analytic detects the use of `reg.exe` to export Windows - Registry hives, which may contain sensitive credentials. This detection leverages - data from Endpoint Detection and Response (EDR) agents, focusing on command-line - executions involving `save` or `export` actions targeting the `sam`, `system`, or - `security` hives. This activity is significant as it indicates potential offline - credential access attacks, often executed from untrusted processes or scripts. If - confirmed malicious, attackers could gain access to credential data, enabling further - compromise and lateral movement within the network. +description: The following analytic detects the use of `reg.exe` to export Windows Registry hives, which may contain sensitive credentials. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving `save` or `export` actions targeting the `sam`, `system`, or `security` hives. This activity is significant as it indicates potential offline credential access attacks, often executed from untrusted processes or scripts. If confirmed malicious, attackers could gain access to credential data, enabling further compromise and lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - ( + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where ( - (Processes.process_name=reg.exe OR Processes.original_file_name=reg.exe) - Processes.process IN ("*save*", "*export*") + ( + (Processes.process_name=reg.exe OR Processes.original_file_name=reg.exe) + Processes.process IN ("*save*", "*export*") + ) + OR + ( + (Processes.process_name=regedit.exe OR Processes.original_file_name=REGEDIT.exe) + Processes.process IN ("*/E *", "*-E *") + ) ) - OR - ( - (Processes.process_name=regedit.exe OR Processes.original_file_name=REGEDIT.exe) - Processes.process IN ("*/E *", "*-E *") + Processes.process IN ( + "*HKEY_LOCAL_MACHINE\\SAM*", + "*HKEY_LOCAL_MACHINE\\Security*", + "*HKEY_LOCAL_MACHINE\\System*", + "*HKLM\\SAM*", + "*HKLM\\Security*", + "*HKLM\\System*", ) - ) - Processes.process IN ( - "*HKEY_LOCAL_MACHINE\\SAM*", - "*HKEY_LOCAL_MACHINE\\Security*", - "*HKEY_LOCAL_MACHINE\\System*", - "*HKLM\\SAM*", - "*HKLM\\Security*", - "*HKLM\\System*", - ) - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_sensitive_registry_hive_dump_via_commandline_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: It is possible some agent based products will generate false - positives. Filter as needed. + by Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_sensitive_registry_hive_dump_via_commandline_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: It is possible some agent based products will generate false positives. Filter as needed. references: -- https://www.mandiant.com/resources/shining-a-light-on-darkside-ransomware-operations -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.002/T1003.002.md -- https://media.defense.gov/2023/May/24/2003229517/-1/-1/0/CSA_Living_off_the_Land.PDF + - https://www.mandiant.com/resources/shining-a-light-on-darkside-ransomware-operations + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.002/T1003.002.md + - https://media.defense.gov/2023/May/24/2003229517/-1/-1/0/CSA_Living_off_the_Land.PDF drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious use of `reg.exe` exporting Windows Registry hives containing - credentials executed on $dest$ by user $user$, with a parent process of $parent_process_id$ - risk_objects: - - field: user - type: user - score: 56 - - field: dest - type: system - score: 56 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: Suspicious use of `reg.exe` exporting Windows Registry hives containing credentials executed on $dest$ by user $user$, with a parent process of $parent_process_id$ + risk_objects: + - field: user + type: user + score: 56 + - field: dest + type: system + score: 56 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - CISA AA22-257A - - CISA AA23-347A - - Compromised Windows Host - - Credential Dumping - - DarkSide Ransomware - - Data Destruction - - Industroyer2 - - Volt Typhoon - - Windows Registry Abuse - - Seashell Blizzard - asset_type: Endpoint - mitre_attack_id: - - T1003.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA22-257A + - CISA AA23-347A + - Compromised Windows Host + - Credential Dumping + - DarkSide Ransomware + - Data Destruction + - Industroyer2 + - Volt Typhoon + - Windows Registry Abuse + - Seashell Blizzard + asset_type: Endpoint + mitre_attack_id: + - T1003.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - Sysmon - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test - Sysmon + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_server_software_component_gacutil_install_to_gac.yml b/detections/endpoint/windows_server_software_component_gacutil_install_to_gac.yml index 2a5ea58abc..0e117b8ad0 100644 --- a/detections/endpoint/windows_server_software_component_gacutil_install_to_gac.yml +++ b/detections/endpoint/windows_server_software_component_gacutil_install_to_gac.yml @@ -1,90 +1,73 @@ name: Windows Server Software Component GACUtil Install to GAC id: 7c025ef0-9e65-4c57-be39-1c13dbb1613e -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the use of GACUtil.exe to add a DLL into - the Global Assembly Cache (GAC). It leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process names and command-line executions. This activity - is significant because adding a DLL to the GAC allows it to be called by any application, - potentially enabling widespread code execution. If confirmed malicious, this could - allow an attacker to execute arbitrary code across the operating system, leading - to privilege escalation or persistent access. +description: The following analytic detects the use of GACUtil.exe to add a DLL into the Global Assembly Cache (GAC). It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because adding a DLL to the GAC allows it to be called by any application, potentially enabling widespread code execution. If confirmed malicious, this could allow an attacker to execute arbitrary code across the operating system, leading to privilege escalation or persistent access. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=gacutil.exe - Processes.process IN ("*-i *","*/i *") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_server_software_component_gacutil_install_to_gac_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present if gacutil.exe is utilized day - to day by developers. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=gacutil.exe Processes.process IN ("*-i *","*/i *") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_server_software_component_gacutil_install_to_gac_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present if gacutil.exe is utilized day to day by developers. Filter as needed. references: -- https://strontic.github.io/xcyclopedia/library/gacutil.exe-F2FE4DF74BD214EDDC1A658043828089.html -- https://www.microsoft.com/en-us/security/blog/2022/12/12/iis-modules-the-evolution-of-web-shells-and-how-to-detect-them/ -- https://www.microsoft.com/en-us/security/blog/2022/07/26/malicious-iis-extensions-quietly-open-persistent-backdoors-into-servers/ -- https://learn.microsoft.com/en-us/dotnet/framework/app-domains/gac + - https://strontic.github.io/xcyclopedia/library/gacutil.exe-F2FE4DF74BD214EDDC1A658043828089.html + - https://www.microsoft.com/en-us/security/blog/2022/12/12/iis-modules-the-evolution-of-web-shells-and-how-to-detect-them/ + - https://www.microsoft.com/en-us/security/blog/2022/07/26/malicious-iis-extensions-quietly-open-persistent-backdoors-into-servers/ + - https://learn.microsoft.com/en-us/dotnet/framework/app-domains/gac drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to add a module to the global assembly - cache. - risk_objects: - - field: user - type: user - score: 49 - - field: dest - type: system - score: 49 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to add a module to the global assembly cache. + risk_objects: + - field: user + type: user + score: 49 + - field: dest + type: system + score: 49 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - IIS Components - asset_type: Endpoint - mitre_attack_id: - - T1505.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - IIS Components + asset_type: Endpoint + mitre_attack_id: + - T1505.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/gacutil_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/gacutil_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_service_create_kernel_mode_driver.yml b/detections/endpoint/windows_service_create_kernel_mode_driver.yml index c19c7a0a3d..842a47bc1d 100644 --- a/detections/endpoint/windows_service_create_kernel_mode_driver.yml +++ b/detections/endpoint/windows_service_create_kernel_mode_driver.yml @@ -1,91 +1,69 @@ name: Windows Service Create Kernel Mode Driver id: 0b4e3b06-1b2b-4885-b752-cf06d12a90cb -version: 8 -date: '2025-12-19' +version: 9 +date: '2026-02-25' author: Michael Haag, Teoderick Contreras Splunk status: production type: TTP -description: The following analytic identifies the creation of a new kernel mode driver - using the sc.exe command. This detection leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process execution logs that include command-line - details. The activity is significant because adding a kernel driver is uncommon - in regular operations and can indicate an attempt to gain low-level access to the - system. If confirmed malicious, this could allow an attacker to execute code with - high privileges, potentially compromising the entire system and evading traditional - security measures. +description: The following analytic identifies the creation of a new kernel mode driver using the sc.exe command. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. The activity is significant because adding a kernel driver is uncommon in regular operations and can indicate an attempt to gain low-level access to the system. If confirmed malicious, this could allow an attacker to execute code with high privileges, potentially compromising the entire system and evading traditional security measures. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where (Processes.process_name=sc.exe OR Processes.original_file_name=sc.exe) Processes.process IN ("*kernel*", "*filesys*") Processes.process="*type*" - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_service_create_kernel_mode_driver_filter` -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present based on common applications - adding new drivers, however, filter as needed. + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes + where (Processes.process_name=sc.exe OR Processes.original_file_name=sc.exe) Processes.process IN ("*kernel*", "*filesys*") Processes.process="*type*" + by Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_service_create_kernel_mode_driver_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present based on common applications adding new drivers, however, filter as needed. references: -- https://www.aon.com/cyber-solutions/aon_cyber_labs/yours-truly-signed-av-driver-weaponizing-an-antivirus-driver/ -- https://whiteknightlabs.com/2025/11/25/discreet-driver-loading-in-windows/ -- https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/sc-config + - https://www.aon.com/cyber-solutions/aon_cyber_labs/yours-truly-signed-av-driver-weaponizing-an-antivirus-driver/ + - https://whiteknightlabs.com/2025/11/25/discreet-driver-loading-in-windows/ + - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/sc-config drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Service control, $process_name$, loaded a new kernel mode driver on $dest$ - by $user$. - risk_objects: - - field: user - type: user - score: 48 - - field: dest - type: system - score: 48 - threat_objects: [] + message: Service control, $process_name$, loaded a new kernel mode driver on $dest$ by $user$. + risk_objects: + - field: user + type: user + score: 48 + - field: dest + type: system + score: 48 + threat_objects: [] tags: - analytic_story: - - Windows Drivers - - CISA AA22-320A - asset_type: Endpoint - mitre_attack_id: - - T1068 - - T1543.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Drivers + - CISA AA22-320A + asset_type: Endpoint + mitre_attack_id: + - T1068 + - T1543.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/drivers/sc_kernel.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/drivers/sc_kernel.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_service_create_remcomsvc.yml b/detections/endpoint/windows_service_create_remcomsvc.yml index 0c4d51c18c..7ea6a2e15e 100644 --- a/detections/endpoint/windows_service_create_remcomsvc.yml +++ b/detections/endpoint/windows_service_create_remcomsvc.yml @@ -1,67 +1,56 @@ name: Windows Service Create RemComSvc id: 0be4b5d6-c449-4084-b945-2392b519c33b -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk type: Anomaly status: production data_source: -- Windows Event Log System 7045 -description: The following analytic detects the creation of the RemComSvc service - on a Windows endpoint, typically indicating lateral movement using RemCom.exe. It - leverages Windows EventCode 7045 from the System event log, specifically looking - for the "RemCom Service" name. This activity is significant as it often signifies - unauthorized lateral movement within the network, which is a common tactic used - by attackers to spread malware or gain further access. If confirmed malicious, this - could lead to unauthorized access to sensitive systems, data exfiltration, or further - compromise of the network. -search: '`wineventlog_system` EventCode=7045 ServiceName="RemCom Service" | stats - count min(_time) as firstTime max(_time) as lastTime by dest ImagePath ServiceName - ServiceType | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_service_create_remcomsvc_filter`' -how_to_implement: To implement this analytic, the Windows EventCode 7045 will need - to be logged. The Windows TA for Splunk is also recommended. -known_false_positives: False positives may be present, filter as needed based on administrative - activity. + - Windows Event Log System 7045 +description: The following analytic detects the creation of the RemComSvc service on a Windows endpoint, typically indicating lateral movement using RemCom.exe. It leverages Windows EventCode 7045 from the System event log, specifically looking for the "RemCom Service" name. This activity is significant as it often signifies unauthorized lateral movement within the network, which is a common tactic used by attackers to spread malware or gain further access. If confirmed malicious, this could lead to unauthorized access to sensitive systems, data exfiltration, or further compromise of the network. +search: |- + `wineventlog_system` EventCode=7045 ServiceName="RemCom Service" + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest ImagePath ServiceName + ServiceType + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_service_create_remcomsvc_filter` +how_to_implement: To implement this analytic, the Windows EventCode 7045 will need to be logged. The Windows TA for Splunk is also recommended. +known_false_positives: False positives may be present, filter as needed based on administrative activity. references: -- https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/ -- https://github.com/kavika13/RemCom + - https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/ + - https://github.com/kavika13/RemCom drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A new service was created related to RemCom on $dest$. - risk_objects: - - field: dest - type: system - score: 32 - threat_objects: [] + message: A new service was created related to RemCom on $dest$. + risk_objects: + - field: dest + type: system + score: 32 + threat_objects: [] tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1543.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1543.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/atomic_red_team/remcom_windows-system.log - source: XmlWinEventLog:System - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/atomic_red_team/remcom_windows-system.log + source: XmlWinEventLog:System + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_service_create_sliverc2.yml b/detections/endpoint/windows_service_create_sliverc2.yml index 9530c84e18..b8f3b0b21a 100644 --- a/detections/endpoint/windows_service_create_sliverc2.yml +++ b/detections/endpoint/windows_service_create_sliverc2.yml @@ -1,69 +1,60 @@ name: Windows Service Create SliverC2 id: 89dad3ee-57ec-43dc-9044-131c4edd663f -version: 8 -date: '2025-10-14' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk type: TTP status: production data_source: -- Windows Event Log System 7045 -description: The following analytic detects the creation of a Windows service named - "Sliver" with the description "Sliver Implant," indicative of SliverC2 lateral movement - using the PsExec module. It leverages Windows EventCode 7045 from the System Event - log to identify this activity. This behavior is significant as it may indicate an - adversary's attempt to establish persistence or execute commands remotely. If confirmed - malicious, this activity could allow attackers to maintain control over the compromised - system, execute arbitrary code, and further infiltrate the network. -search: '`wineventlog_system` EventCode=7045 ServiceName="sliver" | stats count min(_time) - as firstTime max(_time) as lastTime by Computer EventCode ImagePath ServiceName - ServiceType | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_service_create_sliverc2_filter`' -how_to_implement: To implement this analytic, the Windows EventCode 7045 will need - to be logged from the System Event log. The Windows TA for Splunk is also recommended. -known_false_positives: False positives should be limited, but if another service out - there is named Sliver, filtering may be needed. + - Windows Event Log System 7045 +description: The following analytic detects the creation of a Windows service named "Sliver" with the description "Sliver Implant," indicative of SliverC2 lateral movement using the PsExec module. It leverages Windows EventCode 7045 from the System Event log to identify this activity. This behavior is significant as it may indicate an adversary's attempt to establish persistence or execute commands remotely. If confirmed malicious, this activity could allow attackers to maintain control over the compromised system, execute arbitrary code, and further infiltrate the network. +search: |- + `wineventlog_system` EventCode=7045 ServiceName="sliver" + | stats count min(_time) as firstTime max(_time) as lastTime + BY Computer EventCode ImagePath + ServiceName ServiceType + | rename Computer as dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_service_create_sliverc2_filter` +how_to_implement: To implement this analytic, the Windows EventCode 7045 will need to be logged from the System Event log. The Windows TA for Splunk is also recommended. +known_false_positives: False positives should be limited, but if another service out there is named Sliver, filtering may be needed. references: -- https://github.com/BishopFox/sliver/blob/71f94928bf36c1557ea5fbeffa161b71116f56b2/client/command/exec/psexec.go#LL61C5-L61C16 -- https://www.microsoft.com/en-us/security/blog/2022/08/24/looking-for-the-sliver-lining-hunting-for-emerging-command-and-control-frameworks/ -- https://regex101.com/r/DWkkXm/1 + - https://github.com/BishopFox/sliver/blob/71f94928bf36c1557ea5fbeffa161b71116f56b2/client/command/exec/psexec.go#LL61C5-L61C16 + - https://www.microsoft.com/en-us/security/blog/2022/08/24/looking-for-the-sliver-lining-hunting-for-emerging-command-and-control-frameworks/ + - https://regex101.com/r/DWkkXm/1 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A user mode service was created on $dest$ related to SliverC2. - risk_objects: - - field: dest - type: system - score: 90 - threat_objects: [] + message: A user mode service was created on $dest$ related to SliverC2. + risk_objects: + - field: dest + type: system + score: 90 + threat_objects: [] tags: - analytic_story: - - BishopFox Sliver Adversary Emulation Framework - - Compromised Windows Host - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1569.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - BishopFox Sliver Adversary Emulation Framework + - Compromised Windows Host + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1569.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/sliver/sliver_windows-system.log - source: XmlWinEventLog:System - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/sliver/sliver_windows-system.log + source: XmlWinEventLog:System + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_service_create_with_tscon.yml b/detections/endpoint/windows_service_create_with_tscon.yml index c1bc5b5585..bae7f4bda2 100644 --- a/detections/endpoint/windows_service_create_with_tscon.yml +++ b/detections/endpoint/windows_service_create_with_tscon.yml @@ -1,105 +1,74 @@ name: Windows Service Create with Tscon id: c13b3d74-6b63-4db5-a841-4206f0370077 -version: 11 -date: '2025-08-01' +version: 12 +date: '2026-02-25' author: Michael Haag, Splunk type: TTP status: production data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic detects potential RDP Hijacking attempts by - identifying the creation of a Windows service using sc.exe with a binary path - that includes tscon.exe. This detection leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process creation events and - command-line arguments. This activity is significant as it indicates an - attacker may be trying to hijack a disconnected RDP session, posing a risk of - unauthorized access. If confirmed malicious, the attacker could gain control - over an existing user session, leading to potential data theft or further - system compromise. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=sc.exe - Processes.process="*/dest:rdp-tcp*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_create_with_tscon_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: False positives may arise in the RDP Hijacking analytic - when legitimate administrators access remote sessions for maintenance or - troubleshooting purposes. These activities might resemble an attacker''s - attempt to hijack a disconnected session, leading to false alarms. To mitigate - the risk of false positives and improve the overall security posture, - organizations can implement Group Policy to automatically disconnect RDP - sessions when they are complete. By enforcing this policy, administrators - ensure that disconnected sessions are promptly terminated, reducing the window - of opportunity for an attacker to hijack a session. Additionally, - organizations can also implement access control mechanisms and monitor the - behavior of privileged accounts to further enhance security and reduce the - chances of false positives in RDP Hijacking detection. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic detects potential RDP Hijacking attempts by identifying the creation of a Windows service using sc.exe with a binary path that includes tscon.exe. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events and command-line arguments. This activity is significant as it indicates an attacker may be trying to hijack a disconnected RDP session, posing a risk of unauthorized access. If confirmed malicious, the attacker could gain control over an existing user session, leading to potential data theft or further system compromise. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=sc.exe Processes.process="*/dest:rdp-tcp*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_service_create_with_tscon_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may arise in the RDP Hijacking analytic when legitimate administrators access remote sessions for maintenance or troubleshooting purposes. These activities might resemble an attacker''s attempt to hijack a disconnected session, leading to false alarms. To mitigate the risk of false positives and improve the overall security posture, organizations can implement Group Policy to automatically disconnect RDP sessions when they are complete. By enforcing this policy, administrators ensure that disconnected sessions are promptly terminated, reducing the window of opportunity for an attacker to hijack a session. Additionally, organizations can also implement access control mechanisms and monitor the behavior of privileged accounts to further enhance security and reduce the chances of false positives in RDP Hijacking detection. references: -- https://doublepulsar.com/rdp-hijacking-how-to-hijack-rds-and-remoteapp-sessions-transparently-to-move-through-an-da2a1e73a5f6 -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1563.002/T1563.002.md + - https://doublepulsar.com/rdp-hijacking-how-to-hijack-rds-and-remoteapp-sessions-transparently-to-move-through-an-da2a1e73a5f6 + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1563.002/T1563.002.md drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was - identified on endpoint $dest$ by user $user$ attempting to hijack a RDP - session. - risk_objects: - - field: user - type: user - score: 64 - - field: dest - type: system - score: 64 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to hijack a RDP session. + risk_objects: + - field: user + type: user + score: 64 + - field: dest + type: system + score: 64 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Active Directory Lateral Movement - - Compromised Windows Host - - Windows RDP Artifacts and Defense Evasion - asset_type: Endpoint - mitre_attack_id: - - T1543.003 - - T1563.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + - Compromised Windows Host + - Windows RDP Artifacts and Defense Evasion + asset_type: Endpoint + mitre_attack_id: + - T1543.003 + - T1563.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1563.002/rdphijack/tscon_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1563.002/rdphijack/tscon_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_service_created_with_suspicious_service_name.yml b/detections/endpoint/windows_service_created_with_suspicious_service_name.yml index 041143ef1f..2756bdee70 100644 --- a/detections/endpoint/windows_service_created_with_suspicious_service_name.yml +++ b/detections/endpoint/windows_service_created_with_suspicious_service_name.yml @@ -1,76 +1,76 @@ name: Windows Service Created with Suspicious Service Name id: 35eb6d19-a497-400c-93c5-645562804b11 -version: 4 -date: '2025-12-04' +version: 5 +date: '2026-02-25' author: Steven Dick status: production type: Anomaly -description: The following analytic detects the creation of a Windows Service with a known suspicious or malicious name using Windows Event ID 7045. It leverages logs from the `wineventlog_system` to identify these services installations. This activity is significant as adversaries, including those deploying Clop ransomware, often create malicious services for lateral movement, remote code execution, persistence, and execution. If confirmed malicious, this could allow attackers to maintain persistence, execute arbitrary code, and potentially escalate privileges, posing a severe threat to the environment. -data_source: -- Windows Event Log System 7045 +description: The following analytic detects the creation of a Windows Service with a known suspicious or malicious name using Windows Event ID 7045. It leverages logs from the `wineventlog_system` to identify these services installations. This activity is significant as adversaries, including those deploying Clop ransomware, often create malicious services for lateral movement, remote code execution, persistence, and execution. If confirmed malicious, this could allow attackers to maintain persistence, execute arbitrary code, and potentially escalate privileges, posing a severe threat to the environment. +data_source: + - Windows Event Log System 7045 search: |- - `wineventlog_system` EventCode=7045 - | stats values(ImagePath) as process, count, min(_time) as firstTime, max(_time) as lastTime values(EventCode) as signature by Computer, ServiceName, StartType, ServiceType, UserID - | eval process_name = replace(mvindex(split(process,"\\"),-1), "\"", "") - | rename Computer as dest, ServiceName as object_name, ServiceType as object_type, UserID as user_id - | lookup windows_suspicious_services service_name as object_name - | where isnotnull(tool_name) - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_service_created_with_suspicious_service_name_filter` + `wineventlog_system` EventCode=7045 + | stats values(ImagePath) as process, count, min(_time) as firstTime, max(_time) as lastTime values(EventCode) as signature by Computer, ServiceName, StartType, ServiceType, UserID + | eval process_name = replace(mvindex(split(process,"\\"),-1), "\"", "") + | rename Computer as dest, ServiceName as object_name, ServiceType as object_type, UserID as user_id + | lookup windows_suspicious_services service_name as object_name + | where isnotnull(tool_name) + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_service_created_with_suspicious_service_name_filter` how_to_implement: To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints. known_false_positives: Legitimate applications may install services with uncommon services paths. references: -- https://attack.mitre.org/techniques/T1569/002/ -- https://github.com/BishopFox/sliver/blob/71f94928bf36c1557ea5fbeffa161b71116f56b2/client/command/exec/psexec.go#LL61C5-L61C16 -- https://www.microsoft.com/en-us/security/blog/2022/08/24/looking-for-the-sliver-lining-hunting-for-emerging-command-and-control-frameworks/ -- https://github.com/mthcht/awesome-lists/blob/main/Lists/suspicious_windows_services_names_list.csv + - https://attack.mitre.org/techniques/T1569/002/ + - https://github.com/BishopFox/sliver/blob/71f94928bf36c1557ea5fbeffa161b71116f56b2/client/command/exec/psexec.go#LL61C5-L61C16 + - https://www.microsoft.com/en-us/security/blog/2022/08/24/looking-for-the-sliver-lining-hunting-for-emerging-command-and-control-frameworks/ + - https://github.com/mthcht/awesome-lists/blob/main/Lists/suspicious_windows_services_names_list.csv drilldown_searches: -- name: View the detection results for - "$dest$"" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate service events on $dest$ - search: '`wineventlog_system` EventCode=7045 ServiceName = "$object_name$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$"" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate service events on $dest$ + search: '`wineventlog_system` EventCode=7045 ServiceName = "$object_name$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A known malicious service name $object_name$ was created using $process$ on $dest$, this may indicate the presence of [$tool_name$] - risk_objects: - - field: dest - type: system - score: 75 - threat_objects: - - field: process - type: process - - field: object_name - type: signature + message: A known malicious service name $object_name$ was created using $process$ on $dest$, this may indicate the presence of [$tool_name$] + risk_objects: + - field: dest + type: system + score: 75 + threat_objects: + - field: process + type: process + - field: object_name + type: signature tags: - analytic_story: - - Active Directory Lateral Movement - - Brute Ratel C4 - - CISA AA23-347A - - Clop Ransomware - - Flax Typhoon - - PlugX - - Qakbot - - Snake Malware - - Tuoni - asset_type: Endpoint - mitre_attack_id: - - T1569.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + - Brute Ratel C4 + - CISA AA23-347A + - Clop Ransomware + - Flax Typhoon + - PlugX + - Qakbot + - Snake Malware + - Tuoni + asset_type: Endpoint + mitre_attack_id: + - T1569.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/sliver/sliver_windows-system.log - source: XmlWinEventLog:System - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/sliver/sliver_windows-system.log + source: XmlWinEventLog:System + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_service_created_with_suspicious_service_path.yml b/detections/endpoint/windows_service_created_with_suspicious_service_path.yml index 827f8d2065..cdd1224f1d 100644 --- a/detections/endpoint/windows_service_created_with_suspicious_service_path.yml +++ b/detections/endpoint/windows_service_created_with_suspicious_service_path.yml @@ -5,79 +5,59 @@ date: '2025-09-18' author: Teoderick Contreras, Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the creation of a Windows Service with - a binary path located in uncommon directories, using Windows Event ID 7045. It leverages - logs from the `wineventlog_system` to identify services installed outside typical - system directories. This activity is significant as adversaries, including those - deploying Clop ransomware, often create malicious services for lateral movement, - remote code execution, persistence, and execution. If confirmed malicious, this - could allow attackers to maintain persistence, execute arbitrary code, and potentially - escalate privileges, posing a severe threat to the environment. +description: The following analytic detects the creation of a Windows Service with a binary path located in uncommon directories, using Windows Event ID 7045. It leverages logs from the `wineventlog_system` to identify services installed outside typical system directories. This activity is significant as adversaries, including those deploying Clop ransomware, often create malicious services for lateral movement, remote code execution, persistence, and execution. If confirmed malicious, this could allow attackers to maintain persistence, execute arbitrary code, and potentially escalate privileges, posing a severe threat to the environment. data_source: -- Windows Event Log System 7045 -search: '`wineventlog_system` EventCode=7045 ImagePath = "*.exe" NOT (ImagePath IN - ("*:\\Windows\\*", "*:\\Program File*", "*:\\Programdata\\*", "*%systemroot%\\*")) - | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ImagePath - ServiceName ServiceType StartType Computer UserID | rename Computer as dest| `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_service_created_with_suspicious_service_path_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the Service name, Service File Name Service Start type, and Service Type - from your endpoints. -known_false_positives: Legitimate applications may install services with uncommon - services paths. + - Windows Event Log System 7045 +search: '`wineventlog_system` EventCode=7045 ImagePath = "*.exe" NOT (ImagePath IN ("*:\\Windows\\*", "*:\\Program File*", "*:\\Programdata\\*", "*%systemroot%\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ImagePath ServiceName ServiceType StartType Computer UserID | rename Computer as dest| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_created_with_suspicious_service_path_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints. +known_false_positives: Legitimate applications may install services with uncommon services paths. references: -- https://www.mandiant.com/resources/fin11-email-campaigns-precursor-for-ransomware-data-theft -- https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html + - https://www.mandiant.com/resources/fin11-email-campaigns-precursor-for-ransomware-data-theft + - https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A service $ImagePath$ was created from a non-standard path using $ServiceName$ - on $dest$ - risk_objects: - - field: dest - type: system - score: 56 - threat_objects: - - field: ImagePath - type: service + message: A service $ImagePath$ was created from a non-standard path using $ServiceName$ on $dest$ + risk_objects: + - field: dest + type: system + score: 56 + threat_objects: + - field: ImagePath + type: service tags: - analytic_story: - - PlugX - - Qakbot - - China-Nexus Threat Activity - - CISA AA23-347A - - Flax Typhoon - - Derusbi - - Salt Typhoon - - Active Directory Lateral Movement - - Snake Malware - - Clop Ransomware - - Crypto Stealer - - Brute Ratel C4 - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1569.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - PlugX + - Qakbot + - China-Nexus Threat Activity + - CISA AA23-347A + - Flax Typhoon + - Derusbi + - Salt Typhoon + - Active Directory Lateral Movement + - Snake Malware + - Clop Ransomware + - Crypto Stealer + - Brute Ratel C4 + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1569.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/windows_service_created_with_suspicious_service_path/windows-xml.log - source: XmlWinEventLog:System - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/windows_service_created_with_suspicious_service_path/windows-xml.log + source: XmlWinEventLog:System + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_service_creation_on_remote_endpoint.yml b/detections/endpoint/windows_service_creation_on_remote_endpoint.yml index e9a29da606..0f780c2e5c 100644 --- a/detections/endpoint/windows_service_creation_on_remote_endpoint.yml +++ b/detections/endpoint/windows_service_creation_on_remote_endpoint.yml @@ -5,82 +5,52 @@ date: '2025-05-02' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic identifies the creation of a Windows Service on - a remote endpoint using `sc.exe`. It detects this activity by analyzing process - execution logs from Endpoint Detection and Response (EDR) agents, focusing on command-line - arguments that include remote paths and service creation commands. This behavior - is significant because adversaries often exploit the Service Control Manager for - lateral movement and remote code execution. If confirmed malicious, this activity - could allow attackers to execute arbitrary code on remote systems, potentially leading - to further compromise and persistence within the network. +description: The following analytic identifies the creation of a Windows Service on a remote endpoint using `sc.exe`. It detects this activity by analyzing process execution logs from Endpoint Detection and Response (EDR) agents, focusing on command-line arguments that include remote paths and service creation commands. This behavior is significant because adversaries often exploit the Service Control Manager for lateral movement and remote code execution. If confirmed malicious, this activity could allow attackers to execute arbitrary code on remote systems, potentially leading to further compromise and persistence within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=sc.exe - OR Processes.original_file_name=sc.exe) (Processes.process=*\\\\* AND Processes.process=*create* - AND Processes.process=*binpath*) by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_service_creation_on_remote_endpoint_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrators may create Windows Services on remote systems, - but this activity is usually limited to a small set of hosts or users. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=sc.exe OR Processes.original_file_name=sc.exe) (Processes.process=*\\\\* AND Processes.process=*create* AND Processes.process=*binpath*) by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_service_creation_on_remote_endpoint_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators may create Windows Services on remote systems, but this activity is usually limited to a small set of hosts or users. references: -- https://docs.microsoft.com/en-us/windows/win32/services/service-control-manager -- https://docs.microsoft.com/en-us/windows/win32/services/controlling-a-service-using-sc -- https://attack.mitre.org/techniques/T1543/003/ + - https://docs.microsoft.com/en-us/windows/win32/services/service-control-manager + - https://docs.microsoft.com/en-us/windows/win32/services/controlling-a-service-using-sc + - https://attack.mitre.org/techniques/T1543/003/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Windows Service was created on a remote endpoint from $dest$ - risk_objects: - - field: dest - type: system - score: 54 - threat_objects: [] + message: A Windows Service was created on a remote endpoint from $dest$ + risk_objects: + - field: dest + type: system + score: 54 + threat_objects: [] tags: - analytic_story: - - China-Nexus Threat Activity - - CISA AA23-347A - - SnappyBee - - Salt Typhoon - - Active Directory Lateral Movement - asset_type: Endpoint - mitre_attack_id: - - T1543.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - China-Nexus Threat Activity + - CISA AA23-347A + - SnappyBee + - Salt Typhoon + - Active Directory Lateral Movement + asset_type: Endpoint + mitre_attack_id: + - T1543.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/lateral_movement/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/lateral_movement/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_service_creation_using_registry_entry.yml b/detections/endpoint/windows_service_creation_using_registry_entry.yml index 4e73c7b5f4..a8cda9104c 100644 --- a/detections/endpoint/windows_service_creation_using_registry_entry.yml +++ b/detections/endpoint/windows_service_creation_using_registry_entry.yml @@ -5,79 +5,56 @@ date: '2026-02-09' author: Teoderick Contreras, Splunk, Steven Dick status: production type: Anomaly -description: The following analytic detects the modification of registry keys that - define Windows services using reg.exe. This detection leverages Splunk to search - for specific keywords in the registry path, value name, and value data fields. This - activity is significant because it indicates potential unauthorized changes to service - configurations, a common persistence technique used by attackers. If confirmed malicious, - this could allow an attacker to maintain access, escalate privileges, or move laterally - within the network, leading to data theft, ransomware, or other damaging outcomes. +description: The following analytic detects the modification of registry keys that define Windows services using reg.exe. This detection leverages Splunk to search for specific keywords in the registry path, value name, and value data fields. This activity is significant because it indicates potential unauthorized changes to service configurations, a common persistence technique used by attackers. If confirmed malicious, this could allow an attacker to maintain access, escalate privileges, or move laterally within the network, leading to data theft, ransomware, or other damaging outcomes. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\SYSTEM\\CurrentControlSet\\Services*" - Registry.registry_value_name = ImagePath) by Registry.action Registry.dest Registry.process_guid - Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name - Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type - Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` - | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_service_creation_using_registry_entry_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the registry value name, registry path, and registry value data from your - endpoints. If you are using Sysmon, you must have at least version 2.0 of the official - Sysmon TA. https://splunkbase.splunk.com/app/5709 -known_false_positives: Third party tools may used this technique to create services - but not so common. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\SYSTEM\\CurrentControlSet\\Services*" Registry.registry_value_name = ImagePath) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_creation_using_registry_entry_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709 +known_false_positives: Third party tools may used this technique to create services but not so common. references: -- https://github.com/redcanaryco/atomic-red-team/blob/36d49de4c8b00bf36054294b4a1fcbab3917d7c5/atomics/T1574.011/T1574.011.md + - https://github.com/redcanaryco/atomic-red-team/blob/36d49de4c8b00bf36054294b4a1fcbab3917d7c5/atomics/T1574.011/T1574.011.md drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Windows Service was created on a endpoint from $dest$ using a registry - entry - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: A Windows Service was created on a endpoint from $dest$ using a registry entry + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - SolarWinds WHD RCE Post Exploitation - - PlugX - - CISA AA23-347A - - China-Nexus Threat Activity - - Windows Persistence Techniques - - SnappyBee - - Derusbi - - Windows Registry Abuse - - Salt Typhoon - - Active Directory Lateral Movement - - Suspicious Windows Registry Activities - - Crypto Stealer - - Brute Ratel C4 - asset_type: Endpoint - mitre_attack_id: - - T1574.011 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SolarWinds WHD RCE Post Exploitation + - PlugX + - CISA AA23-347A + - China-Nexus Threat Activity + - Windows Persistence Techniques + - SnappyBee + - Derusbi + - Windows Registry Abuse + - Salt Typhoon + - Active Directory Lateral Movement + - Suspicious Windows Registry Activities + - Crypto Stealer + - Brute Ratel C4 + asset_type: Endpoint + mitre_attack_id: + - T1574.011 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.011/change_registry_path_service/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.011/change_registry_path_service/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_service_deletion_in_registry.yml b/detections/endpoint/windows_service_deletion_in_registry.yml index 29351e4989..09469e3363 100644 --- a/detections/endpoint/windows_service_deletion_in_registry.yml +++ b/detections/endpoint/windows_service_deletion_in_registry.yml @@ -5,71 +5,46 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the deletion of a service from the Windows - Registry under CurrentControlSet\Services. It leverages data from the Endpoint.Registry - datamodel, specifically monitoring registry paths and actions related to service - deletion. This activity is significant as adversaries may delete services to evade - detection and hinder incident response efforts. If confirmed malicious, this action - could disrupt legitimate services, impair system functionality, and potentially - allow attackers to maintain a lower profile within the environment, complicating - detection and remediation efforts. +description: The following analytic detects the deletion of a service from the Windows Registry under CurrentControlSet\Services. It leverages data from the Endpoint.Registry datamodel, specifically monitoring registry paths and actions related to service deletion. This activity is significant as adversaries may delete services to evade detection and hinder incident response efforts. If confirmed malicious, this action could disrupt legitimate services, impair system functionality, and potentially allow attackers to maintain a lower profile within the environment, complicating detection and remediation efforts. data_source: -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SYSTEM\\CurrentControlSet\\Services*" - AND (Registry.action = deleted OR (Registry.registry_value_name = DeleteFlag AND - Registry.registry_value_data = 0x00000001 AND Registry.action=modified)) by Registry.action - Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path - Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_service_deletion_in_registry_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure - that this registry was included in your config files ex. sysmon config to be monitored. -known_false_positives: This event can be seen when administrator delete a service - or uninstall/reinstall a software that creates service entry, but it is still recommended - to check this alert with high priority. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SYSTEM\\CurrentControlSet\\Services*" AND (Registry.action = deleted OR (Registry.registry_value_name = DeleteFlag AND Registry.registry_value_data = 0x00000001 AND Registry.action=modified)) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_deletion_in_registry_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored. +known_false_positives: This event can be seen when administrator delete a service or uninstall/reinstall a software that creates service entry, but it is still recommended to check this alert with high priority. references: -- https://unit42.paloaltonetworks.com/brute-ratel-c4-tool/ + - https://unit42.paloaltonetworks.com/brute-ratel-c4-tool/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A service was deleted on $dest$ within the Windows registry. - risk_objects: - - field: dest - type: system - score: 18 - threat_objects: [] + message: A service was deleted on $dest$ within the Windows registry. + risk_objects: + - field: dest + type: system + score: 18 + threat_objects: [] tags: - analytic_story: - - PlugX - - Crypto Stealer - - Brute Ratel C4 - asset_type: Endpoint - mitre_attack_id: - - T1489 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - PlugX + - Crypto Stealer + - Brute Ratel C4 + asset_type: Endpoint + mitre_attack_id: + - T1489 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/service_deletion/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/service_deletion/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_service_execution_remcom.yml b/detections/endpoint/windows_service_execution_remcom.yml index c7adfb89de..7a42bb680a 100644 --- a/detections/endpoint/windows_service_execution_remcom.yml +++ b/detections/endpoint/windows_service_execution_remcom.yml @@ -6,83 +6,53 @@ author: Michael Haag, Splunk type: TTP status: production data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic identifies the execution of RemCom.exe, an open-source - alternative to PsExec, used for lateral movement and remote command execution. It - leverages data from Endpoint Detection and Response (EDR) agents, focusing on process - names, original file names, and command-line arguments. This activity is significant - as it indicates potential lateral movement within the network. If confirmed malicious, - this could allow an attacker to execute commands remotely, potentially leading to - further compromise and control over additional systems within the network. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=remcom.exe - OR Processes.original_file_name=RemCom.exe) Processes.process="*\\*" Processes.process - IN ("*/user:*", "*/pwd:*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_execution_remcom_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present based on Administrative use. - Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic identifies the execution of RemCom.exe, an open-source alternative to PsExec, used for lateral movement and remote command execution. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, original file names, and command-line arguments. This activity is significant as it indicates potential lateral movement within the network. If confirmed malicious, this could allow an attacker to execute commands remotely, potentially leading to further compromise and control over additional systems within the network. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=remcom.exe OR Processes.original_file_name=RemCom.exe) Processes.process="*\\*" Processes.process IN ("*/user:*", "*/pwd:*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_execution_remcom_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present based on Administrative use. Filter as needed. references: -- https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/ -- https://github.com/kavika13/RemCom + - https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/ + - https://github.com/kavika13/RemCom drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to move laterally. - risk_objects: - - field: user - type: user - score: 40 - - field: dest - type: system - score: 40 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to move laterally. + risk_objects: + - field: user + type: user + score: 40 + - field: dest + type: system + score: 40 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1569.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1569.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/remcom/remcom_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/remcom/remcom_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_service_initiation_on_remote_endpoint.yml b/detections/endpoint/windows_service_initiation_on_remote_endpoint.yml index 9b341a0643..53c5c4d189 100644 --- a/detections/endpoint/windows_service_initiation_on_remote_endpoint.yml +++ b/detections/endpoint/windows_service_initiation_on_remote_endpoint.yml @@ -5,77 +5,48 @@ date: '2025-05-02' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the execution of `sc.exe` with command-line - arguments used to start a Windows Service on a remote endpoint. It leverages data - from Endpoint Detection and Response (EDR) agents, focusing on process names and - command-line executions. This activity is significant because adversaries may exploit - the Service Control Manager for lateral movement and remote code execution. If confirmed - malicious, this could allow attackers to execute arbitrary code on remote systems, - potentially leading to further compromise and persistence within the network. +description: The following analytic detects the execution of `sc.exe` with command-line arguments used to start a Windows Service on a remote endpoint. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because adversaries may exploit the Service Control Manager for lateral movement and remote code execution. If confirmed malicious, this could allow attackers to execute arbitrary code on remote systems, potentially leading to further compromise and persistence within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=sc.exe - OR Processes.original_file_name=sc.exe) (Processes.process=*\\\\* AND Processes.process=*start*) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `windows_service_initiation_on_remote_endpoint_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrators may start Windows Services on remote systems, - but this activity is usually limited to a small set of hosts or users. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=sc.exe OR Processes.original_file_name=sc.exe) (Processes.process=*\\\\* AND Processes.process=*start*) by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_service_initiation_on_remote_endpoint_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators may start Windows Services on remote systems, but this activity is usually limited to a small set of hosts or users. references: -- https://docs.microsoft.com/en-us/windows/win32/services/controlling-a-service-using-sc -- https://attack.mitre.org/techniques/T1543/003/ + - https://docs.microsoft.com/en-us/windows/win32/services/controlling-a-service-using-sc + - https://attack.mitre.org/techniques/T1543/003/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Windows Service was started on a remote endpoint from $dest$ - risk_objects: - - field: dest - type: system - score: 54 - threat_objects: [] + message: A Windows Service was started on a remote endpoint from $dest$ + risk_objects: + - field: dest + type: system + score: 54 + threat_objects: [] tags: - analytic_story: - - Active Directory Lateral Movement - - CISA AA23-347A - asset_type: Endpoint - mitre_attack_id: - - T1543.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + - CISA AA23-347A + asset_type: Endpoint + mitre_attack_id: + - T1543.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/lateral_movement/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/lateral_movement/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_service_stop_attempt.yml b/detections/endpoint/windows_service_stop_attempt.yml index 3ca4fd7441..926efc77f1 100644 --- a/detections/endpoint/windows_service_stop_attempt.yml +++ b/detections/endpoint/windows_service_stop_attempt.yml @@ -1,74 +1,63 @@ name: Windows Service Stop Attempt id: dd0f07ea-f08f-4d88-96e5-cb58156e82b6 -version: 4 -date: '2025-10-14' +version: 5 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic identifies attempts to stop services on a system - using `net.exe`, `sc.exe` or the "Stop-Service" cmdlet. It leverages Endpoint Detection - and Response (EDR) telemetry. This activity can be significant as adversaries often - terminate security or critical services to evade detection and further their objectives. - If confirmed malicious, this behavior could allow attackers to disable security - defenses, facilitate ransomware encryption, or disrupt essential services, leading - to potential data loss or system compromise. +description: The following analytic identifies attempts to stop services on a system using `net.exe`, `sc.exe` or the "Stop-Service" cmdlet. It leverages Endpoint Detection and Response (EDR) telemetry. This activity can be significant as adversaries often terminate security or critical services to evade detection and further their objectives. If confirmed malicious, this behavior could allow attackers to disable security defenses, facilitate ransomware encryption, or disrupt essential services, leading to potential data loss or system compromise. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where ((`process_net` OR `process_sc`) - Processes.process="* stop *") OR Processes.process="*Stop-Service *" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_service_stop_attempt_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Windows OS or software may stop and restart services due to - some critical update. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + (`process_net` + OR + `process_sc`) Processes.process="* stop *" + ) + OR Processes.process="*Stop-Service *" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_service_stop_attempt_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Windows OS or software may stop and restart services due to some critical update. references: -- https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ + - https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ tags: - analytic_story: - - Prestige Ransomware - - Graceful Wipe Out Attack - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1489 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Prestige Ransomware + - Graceful Wipe Out Attack + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1489 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/prestige_ransomware/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/prestige_ransomware/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_service_stop_by_deletion.yml b/detections/endpoint/windows_service_stop_by_deletion.yml index 5910c67f65..8dc7cbafa3 100644 --- a/detections/endpoint/windows_service_stop_by_deletion.yml +++ b/detections/endpoint/windows_service_stop_by_deletion.yml @@ -1,85 +1,72 @@ name: Windows Service Stop By Deletion id: 196ff536-58d9-4d1b-9686-b176b04e430b -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the use of `sc.exe` to delete a Windows - service. It leverages Endpoint Detection and Response (EDR) data, focusing on process - execution logs that capture command-line arguments. This activity is significant - because adversaries often delete services to disable security mechanisms or critical - system functions, aiding in evasion and persistence. If confirmed malicious, this - action could lead to the termination of essential security services, allowing attackers - to operate undetected and potentially escalate their privileges or maintain long-term - access to the compromised system. +description: The following analytic detects the use of `sc.exe` to delete a Windows service. It leverages Endpoint Detection and Response (EDR) data, focusing on process execution logs that capture command-line arguments. This activity is significant because adversaries often delete services to disable security mechanisms or critical system functions, aiding in evasion and persistence. If confirmed malicious, this action could lead to the termination of essential security services, allowing attackers to operate undetected and potentially escalate their privileges or maintain long-term access to the compromised system. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where (Processes.process_name = sc.exe OR Processes.original_file_name = sc.exe) - Processes.process="* delete *" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_stop_by_deletion_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: It is possible administrative scripts may start/stop/delete - services. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name = sc.exe + OR + Processes.original_file_name = sc.exe + ) + Processes.process="* delete *" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_service_stop_by_deletion_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: It is possible administrative scripts may start/stop/delete services. Filter as needed. references: -- https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ -- https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1543.003/T1543.003.md + - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ + - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1543.003/T1543.003.md drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ attempting to delete a service. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ attempting to delete a service. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Azorult - - Graceful Wipe Out Attack - - Crypto Stealer - asset_type: Endpoint - mitre_attack_id: - - T1489 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azorult + - Graceful Wipe Out Attack + - Crypto Stealer + asset_type: Endpoint + mitre_attack_id: + - T1489 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_service_stop_win_updates.yml b/detections/endpoint/windows_service_stop_win_updates.yml index 1d67a3c05c..0e0b067fce 100644 --- a/detections/endpoint/windows_service_stop_win_updates.yml +++ b/detections/endpoint/windows_service_stop_win_updates.yml @@ -1,70 +1,58 @@ name: Windows Service Stop Win Updates id: 0dc25c24-6fcf-456f-b08b-dd55a183e4de -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: -- Windows Event Log System 7040 -description: The following analytic detects the disabling of Windows Update services, - such as "Update Orchestrator Service for Windows Update," "WaaSMedicSvc," and "Windows - Update." It leverages Windows System Event ID 7040 logs to identify changes in service - start modes to 'disabled.' This activity is significant as it can indicate an adversary's - attempt to evade defenses by preventing critical updates, leaving the system vulnerable - to exploits. If confirmed malicious, this could allow attackers to maintain persistence - and exploit unpatched vulnerabilities, compromising the integrity and security of - the affected host. -search: '`wineventlog_system` EventCode=7040 (service_name IN ("Update Orchestrator - Service for Windows Update", "WaaSMedicSvc", "Windows Update") OR param1 IN ("UsoSvc", - "WaaSMedicSvc", "wuauserv")) AND (param3=disabled OR start_mode = disabled) | stats - count min(_time) as firstTime max(_time) as lastTime by Computer Error_Code service_name - start_mode param1 param2 param3 param4 | rename Computer as dest | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_service_stop_win_updates_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the Service name, Service File Name Service Start type, and Service Type - from your endpoints (like Windows system.log Event ID 7040) -known_false_positives: Network administrator may disable this services as part of - its audit process within the network. Filter is needed. + - Windows Event Log System 7040 +description: The following analytic detects the disabling of Windows Update services, such as "Update Orchestrator Service for Windows Update," "WaaSMedicSvc," and "Windows Update." It leverages Windows System Event ID 7040 logs to identify changes in service start modes to 'disabled.' This activity is significant as it can indicate an adversary's attempt to evade defenses by preventing critical updates, leaving the system vulnerable to exploits. If confirmed malicious, this could allow attackers to maintain persistence and exploit unpatched vulnerabilities, compromising the integrity and security of the affected host. +search: |- + `wineventlog_system` EventCode=7040 (service_name IN ("Update Orchestrator Service for Windows Update", "WaaSMedicSvc", "Windows Update") OR param1 IN ("UsoSvc", "WaaSMedicSvc", "wuauserv")) AND (param3=disabled OR start_mode = disabled) + | stats count min(_time) as firstTime max(_time) as lastTime + BY Computer Error_Code service_name + start_mode param1 param2 + param3 param4 + | rename Computer as dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_service_stop_win_updates_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints (like Windows system.log Event ID 7040) +known_false_positives: Network administrator may disable this services as part of its audit process within the network. Filter is needed. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.redline_stealer + - https://malpedia.caad.fkie.fraunhofer.de/details/win.redline_stealer drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Windows update services $service_name$ was being disabled on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Windows update services $service_name$ was being disabled on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - CISA AA23-347A - - RedLine Stealer - asset_type: Endpoint - mitre_attack_id: - - T1489 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA23-347A + - RedLine Stealer + asset_type: Endpoint + mitre_attack_id: + - T1489 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/win_update_services_stop/system.log - source: XmlWinEventLog:System - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/win_update_services_stop/system.log + source: XmlWinEventLog:System + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_set_account_password_policy_to_unlimited_via_net.yml b/detections/endpoint/windows_set_account_password_policy_to_unlimited_via_net.yml index 5954408df5..161338fa2d 100644 --- a/detections/endpoint/windows_set_account_password_policy_to_unlimited_via_net.yml +++ b/detections/endpoint/windows_set_account_password_policy_to_unlimited_via_net.yml @@ -1,86 +1,73 @@ name: Windows Set Account Password Policy To Unlimited Via Net id: 11f93009-8083-43fd-82a7-821fcbdc8342 -version: 3 -date: '2025-05-02' +version: 4 +date: '2026-02-25' author: Teoderick Contreras, Nasreddine Bencherchali, Splunk status: production type: Anomaly -description: The following analytic detects the use of net.exe to update user account - policies to set passwords as non-expiring. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on command-line executions involving "/maxpwage:unlimited" - or "/maxpwage:49710", which achieve a similar outcome theoretically. This activity - is significant as it can indicate an attempt to maintain persistence, escalate privileges, - evade defenses, or facilitate lateral movement. If confirmed malicious, this behavior - could allow an attacker to maintain long-term access to compromised accounts, potentially - leading to further exploitation and unauthorized access to sensitive information. +description: The following analytic detects the use of net.exe to update user account policies to set passwords as non-expiring. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving "/maxpwage:unlimited" or "/maxpwage:49710", which achieve a similar outcome theoretically. This activity is significant as it can indicate an attempt to maintain persistence, escalate privileges, evade defenses, or facilitate lateral movement. If confirmed malicious, this behavior could allow an attacker to maintain long-term access to compromised accounts, potentially leading to further exploitation and unauthorized access to sensitive information. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where `process_net` AND Processes.process="* accounts *" AND (Processes.process="* - /maxpwage:unlimited" OR Processes.process="/maxpwage:49710") by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_set_account_password_policy_to_unlimited_via_net_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: This behavior is not commonly seen in production environment - and not advisable, filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_net` + AND + Processes.process="* accounts *" + AND + (Processes.process="* /maxpwage:unlimited" + OR + Processes.process="/maxpwage:49710") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_set_account_password_policy_to_unlimited_via_net_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: This behavior is not commonly seen in production environment and not advisable, filter as needed. references: -- https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ -- https://docs.microsoft.com/en-us/troubleshoot/windows-server/networking/net-commands-on-operating-systems + - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ + - https://docs.microsoft.com/en-us/troubleshoot/windows-server/networking/net-commands-on-operating-systems drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ attempting to make non-expiring password on host user accounts. - risk_objects: - - field: dest - type: system - score: 100 - threat_objects: [] + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ attempting to make non-expiring password on host user accounts. + risk_objects: + - field: dest + type: system + score: 100 + threat_objects: [] tags: - analytic_story: - - Ransomware - - BlackByte Ransomware - - Crypto Stealer - - XMRig - asset_type: Endpoint - mitre_attack_id: - - T1489 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Ransomware + - BlackByte Ransomware + - Crypto Stealer + - XMRig + asset_type: Endpoint + mitre_attack_id: + - T1489 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_set_network_profile_category_to_private_via_registry.yml b/detections/endpoint/windows_set_network_profile_category_to_private_via_registry.yml index f9bded433c..c9116a89df 100644 --- a/detections/endpoint/windows_set_network_profile_category_to_private_via_registry.yml +++ b/detections/endpoint/windows_set_network_profile_category_to_private_via_registry.yml @@ -7,63 +7,42 @@ status: production type: Anomaly description: The following analytic detects attempts to modify the Windows Registry to change a network profile's category to "Private", which may indicate an adversary is preparing the environment for lateral movement or reducing firewall restrictions. Specifically, this activity involves changes to the Category value within the HKLM\SOFTWARE\Microsoft\Windows NT\CurrentVersion\NetworkList\Profiles\{GUID} registry path. A value of 1 corresponds to a private network profile, which typically enables less restrictive firewall policies. While this action can occur during legitimate network configuration, it may also be a sign of malicious behavior when combined with other indicators such as suspicious account activity, unexpected administrative privilege usage, or execution of unsigned binaries. Monitoring for this registry modification—especially outside standard IT processes or correlated with persistence mechanisms—can help identify stealthy post-exploitation activity. data_source: - - Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry - WHERE - Registry.registry_path = "*\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\NetworkList\\Profiles\\*" - Registry.registry_value_name = "Category" - Registry.registry_value_data = 0x00000001 - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_set_network_profile_category_to_private_via_registry_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure - that this registry was included in your config files ex. sysmon config to be monitored. -known_false_positives: - Administrators may enable or disable this feature that may - cause some false positive, however is not common. Filter as needed. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path = "*\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\NetworkList\\Profiles\\*" Registry.registry_value_name = "Category" Registry.registry_value_data = 0x00000001 by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_set_network_profile_category_to_private_via_registry_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored. +known_false_positives: Administrators may enable or disable this feature that may cause some false positive, however is not common. Filter as needed. references: -- https://www.microsoft.com/en-us/security/blog/2025/07/31/frozen-in-transit-secret-blizzards-aitm-campaign-against-diplomats/ + - https://www.microsoft.com/en-us/security/blog/2025/07/31/frozen-in-transit-secret-blizzards-aitm-campaign-against-diplomats/ drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A registry modification that set network profile to private on [$dest$] - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: [] + message: A registry modification that set network profile to private on [$dest$] + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: [] tags: - analytic_story: - - Secret Blizzard - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Secret Blizzard + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/reg_profiles_private2/reg_profiles_private2.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/reg_profiles_private2/reg_profiles_private2.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_sharepoint_spinstall0_webshell_file_creation.yml b/detections/endpoint/windows_sharepoint_spinstall0_webshell_file_creation.yml index dbb14b75db..82e394ac22 100644 --- a/detections/endpoint/windows_sharepoint_spinstall0_webshell_file_creation.yml +++ b/detections/endpoint/windows_sharepoint_spinstall0_webshell_file_creation.yml @@ -7,54 +7,49 @@ status: production type: TTP description: This detection identifies the creation or modification of the "spinstall0.aspx" webshell file in Microsoft SharePoint directories. This file is a known indicator of compromise associated with the exploitation of CVE-2025-53770 (ToolShell vulnerability). Attackers exploit the vulnerability to drop webshells that provide persistent access to compromised SharePoint servers, allowing them to execute arbitrary commands, access sensitive data, and move laterally within the network. data_source: -- Sysmon EventID 11 + - Sysmon EventID 11 search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name="spinstall0.aspx" AND (Filesystem.file_path="*\\microsoft shared\\Web Server Extensions\\16\\TEMPLATE\\LAYOUTS*" OR Filesystem.file_path="*\\microsoft shared\\Web Server Extensions\\15\\TEMPLATE\\LAYOUTS*") by Filesystem.dest Filesystem.user Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.action Filesystem.process_guid Filesystem.process_id Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_sharepoint_spinstall0_webshell_file_creation_filter`' how_to_implement: To successfully implement this search, you need to be ingesting logs with the file name, file path, and process information from your endpoints. If you are using Sysmon, you must have at least Sysmon version 6.0.4 with Event Code 11 enabled. You can also use other EDR products or Windows Event Logs that capture file creation events. The detection requires the Endpoint data model, populated with file creation events in the SharePoint directories. known_false_positives: Limited false positives are expected as the spinstall0.aspx file is not a legitimate SharePoint component. However, there might be rare cases where legitimate files with similar names are created during SharePoint updates or maintenance. Verify the process that created the file and the file content to confirm malicious intent. references: -- https://research.eye.security/sharepoint-under-siege/ -- https://www.cisa.gov/news-events/alerts/2025/07/20/microsoft-releases-guidance-exploitation-sharepoint-vulnerability-cve-2025-53770 -- https://msrc.microsoft.com/blog/2025/07/customer-guidance-for-sharepoint-vulnerability-cve-2025-53770/ + - https://research.eye.security/sharepoint-under-siege/ + - https://www.cisa.gov/news-events/alerts/2025/07/20/microsoft-releases-guidance-exploitation-sharepoint-vulnerability-cve-2025-53770 + - https://msrc.microsoft.com/blog/2025/07/customer-guidance-for-sharepoint-vulnerability-cve-2025-53770/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential SharePoint webshell (spinstall0.aspx) detected on $dest$ related to CVE-2025-53770. - risk_objects: - - field: dest - type: system - score: 90 - threat_objects: - - field: file_name - type: file_name + message: Potential SharePoint webshell (spinstall0.aspx) detected on $dest$ related to CVE-2025-53770. + risk_objects: + - field: dest + type: system + score: 90 + threat_objects: + - field: file_name + type: file_name tags: - analytic_story: - - Microsoft SharePoint Vulnerabilities - asset_type: Web Server - mitre_attack_id: - - T1190 - - T1505.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: - - CVE-2025-53770 + analytic_story: + - Microsoft SharePoint Vulnerabilities + asset_type: Web Server + mitre_attack_id: + - T1190 + - T1505.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: + - CVE-2025-53770 tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/sharepoint_webshell/sysmon_spinstall0.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/sharepoint_webshell/sysmon_spinstall0.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_shell_process_from_crushftp.yml b/detections/endpoint/windows_shell_process_from_crushftp.yml index 4649544064..02583acc21 100644 --- a/detections/endpoint/windows_shell_process_from_crushftp.yml +++ b/detections/endpoint/windows_shell_process_from_crushftp.yml @@ -1,59 +1,73 @@ name: Windows Shell Process from CrushFTP id: 459628e3-1b00-4e9b-9e5b-7da8961aea35 -version: 3 -date: '2026-01-14' +version: 4 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP description: The following analytic identifies instances where CrushFTP's service process (crushftpservice.exe) spawns shell processes like cmd.exe or powershell.exe. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events. This activity is significant because CrushFTP should not normally spawn interactive shell processes during regular operations. If confirmed malicious, this behavior could indicate successful exploitation of vulnerabilities like CVE-2025-31161, potentially allowing attackers to execute arbitrary commands with the privileges of the CrushFTP service. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=crushftpservice.exe AND `process_cmd` OR `process_powershell` by Processes.dest Processes.parent_process Processes.original_file_name Processes.user Processes.action Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_path Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_path Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_shell_process_from_crushftp_filter`' + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name=crushftpservice.exe + AND + `process_cmd` + OR + `process_powershell` + BY Processes.dest Processes.parent_process Processes.original_file_name + Processes.user Processes.action Processes.parent_process_exec + Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_path + Processes.process_exec Processes.process_guid Processes.process_hash + Processes.process_id Processes.process_integrity_level Processes.process_path + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_shell_process_from_crushftp_filter` how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. known_false_positives: No false positives have been identified at this time. references: - - https://nvd.nist.gov/vuln/detail/CVE-2025-31161 - - https://www.crushftp.com/crush11wiki/Wiki.jsp?page=Update - - https://www.huntress.com/blog/crushftp-cve-2025-31161-auth-bypass-and-post-exploitation + - https://nvd.nist.gov/vuln/detail/CVE-2025-31161 + - https://www.crushftp.com/crush11wiki/Wiki.jsp?page=Update + - https://www.huntress.com/blog/crushftp-cve-2025-31161-auth-bypass-and-post-exploitation drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible CrushFTP exploitation detected on $dest$ related to CVE-2025-31161. - risk_objects: - - field: dest - type: system - score: 65 - threat_objects: [] + message: Possible CrushFTP exploitation detected on $dest$ related to CVE-2025-31161. + risk_objects: + - field: dest + type: system + score: 65 + threat_objects: [] tags: - analytic_story: - - CrushFTP Vulnerabilities - asset_type: Endpoint - cve: - - CVE-2025-31161 - mitre_attack_id: - - T1059.001 - - T1059.003 - - T1190 - - T1505 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CrushFTP Vulnerabilities + asset_type: Endpoint + cve: + - CVE-2025-31161 + mitre_attack_id: + - T1059.001 + - T1059.003 + - T1190 + - T1505 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/crushftp/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - - \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/crushftp/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_short_lived_dns_record.yml b/detections/endpoint/windows_short_lived_dns_record.yml index 1f42dee507..e69f896e7c 100644 --- a/detections/endpoint/windows_short_lived_dns_record.yml +++ b/detections/endpoint/windows_short_lived_dns_record.yml @@ -1,89 +1,71 @@ name: Windows Short Lived DNS Record id: d585e253-1859-4170-977d-09376c731f74 -version: 1 -date: '2025-11-13' +version: 2 +date: '2026-02-25' author: Raven Tait, Splunk status: production type: TTP -description: The following analytic identifies the creation and quick deletion of - a DNS object within 300 seconds in an Active Directory environment, - indicative of a potential attack abusing DNS. This detection leverages Windows Security - Event Codes 5136 and 5137, analyzing the duration between these events. This activity - is significant as temporary DNS entries allows attackers to cause unexpecting network trafficking, - leading to potential compromise. +description: The following analytic identifies the creation and quick deletion of a DNS object within 300 seconds in an Active Directory environment, indicative of a potential attack abusing DNS. This detection leverages Windows Security Event Codes 5136 and 5137, analyzing the duration between these events. This activity is significant as temporary DNS entries allows attackers to cause unexpecting network trafficking, leading to potential compromise. data_source: -- Windows Event Log Security 5136 -- Windows Event Log Security 5137 -search: '`wineventlog_security` ((EventCode=5137 ObjectClass="dnsNode") OR (EventCode=5136 - ObjectClass="dnsNode" AttributeLDAPDisplayName="dNSTombstoned" AttributeValue="TRUE")) - | stats min(_time) as firstTime - max(_time) as lastTime - values(EventCode) as event_codes - values(ObjectDN) as dns_record - values(SubjectUserName) as user - values(Computer) as dest - by ObjectGUID - | where mvcount(event_codes)=2 - | eval time_diff=lastTime - firstTime - | where time_diff <= 300 - | table firstTime, lastTime, dns_record, user, dest, time_diff, ObjectGUID - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_short_lived_dns_record_filter`' -how_to_implement: To successfully implement this search, you ned to be ingesting Event - codes `5136` and `5137`. The Advanced Security Audit policy setting `Audit Directory - Services Changes` within `DS Access` needs to be enabled. For these event codes - to be generated, specific SACLs are required. -known_false_positives: Creating and deleting a DNS server object within 30 seconds or - less is unusual but not impossible in a production environment. Filter as needed. + - Windows Event Log Security 5136 + - Windows Event Log Security 5137 +search: |- + `wineventlog_security` ((EventCode=5137 ObjectClass="dnsNode") OR (EventCode=5136 ObjectClass="dnsNode" AttributeLDAPDisplayName="dNSTombstoned" AttributeValue="TRUE")) + | stats min(_time) as firstTime max(_time) as lastTime values(EventCode) as event_codes values(ObjectDN) as dns_record values(SubjectUserName) as user values(Computer) as dest + BY ObjectGUID + | where mvcount(event_codes)=2 + | eval time_diff=lastTime - firstTime + | where time_diff <= 300 + | table firstTime, lastTime, dns_record, user, dest, time_diff, ObjectGUID + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_short_lived_dns_record_filter` +how_to_implement: To successfully implement this search, you ned to be ingesting Event codes `5136` and `5137`. The Advanced Security Audit policy setting `Audit Directory Services Changes` within `DS Access` needs to be enabled. For these event codes to be generated, specific SACLs are required. +known_false_positives: Creating and deleting a DNS server object within 30 seconds or less is unusual but not impossible in a production environment. Filter as needed. references: -- https://web.archive.org/web/20250617122747/https://www.synacktiv.com/publications/ntlm-reflection-is-dead-long-live-ntlm-reflection-an-in-depth-analysis-of-cve-2025 -- https://www.synacktiv.com/publications/relaying-kerberos-over-smb-using-krbrelayx -- https://www.guidepointsecurity.com/blog/the-birth-and-death-of-loopyticket/ + - https://web.archive.org/web/20250617122747/https://www.synacktiv.com/publications/ntlm-reflection-is-dead-long-live-ntlm-reflection-an-in-depth-analysis-of-cve-2025 + - https://www.synacktiv.com/publications/relaying-kerberos-over-smb-using-krbrelayx + - https://www.guidepointsecurity.com/blog/the-birth-and-death-of-loopyticket/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search Computer = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search Computer = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A short-lived DNS object was created and deleted on $dest$ - risk_objects: - - field: dest - type: system - score: 64 - - field: user - type: user - score: 64 - threat_objects: [] + message: A short-lived DNS object was created and deleted on $dest$ + risk_objects: + - field: dest + type: system + score: 64 + - field: user + type: user + score: 64 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - Suspicious DNS Traffic - - Local Privilege Escalation With KrbRelayUp - - Kerberos Coercion with DNS - asset_type: Endpoint - mitre_attack_id: - - T1071.004 - - T1557.001 - - T1187 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: - - CVE-2025-33073 + analytic_story: + - Compromised Windows Host + - Suspicious DNS Traffic + - Local Privilege Escalation With KrbRelayUp + - Kerberos Coercion with DNS + asset_type: Endpoint + mitre_attack_id: + - T1071.004 + - T1557.001 + - T1187 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: + - CVE-2025-33073 tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1071.004/kerberos_coercion/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1071.004/kerberos_coercion/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_sip_provider_inventory.yml b/detections/endpoint/windows_sip_provider_inventory.yml index b341c856dd..bc3ff6fa14 100644 --- a/detections/endpoint/windows_sip_provider_inventory.yml +++ b/detections/endpoint/windows_sip_provider_inventory.yml @@ -6,39 +6,27 @@ author: Michael Haag, Splunk status: production type: Hunting data_source: [] -description: The following analytic identifies all SIP (Subject Interface Package) - providers on a Windows system using PowerShell scripted inputs. It detects SIP providers - by capturing DLL paths from relevant events. This activity is significant because - malicious SIP providers can be used to bypass trust controls, potentially allowing - unauthorized code execution. If confirmed malicious, this activity could enable - attackers to subvert system integrity, leading to unauthorized access or persistent - threats within the environment. Analysts should review for new and non-standard - paths to identify potential threats. -search: '`subjectinterfacepackage` Dll=*\\*.dll | stats count min(_time) as firstTime - max(_time) as lastTime values(Dll) by Path host| `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`| `windows_sip_provider_inventory_filter`' -how_to_implement: To implement this analytic, one must first perform inventory using - a scripted inputs. Review the following Gist - https://gist.github.com/MHaggis/75dd5db546c143ea67703d0e86cdbbd1 -known_false_positives: False positives are limited as this is a hunting query for - inventory. +description: The following analytic identifies all SIP (Subject Interface Package) providers on a Windows system using PowerShell scripted inputs. It detects SIP providers by capturing DLL paths from relevant events. This activity is significant because malicious SIP providers can be used to bypass trust controls, potentially allowing unauthorized code execution. If confirmed malicious, this activity could enable attackers to subvert system integrity, leading to unauthorized access or persistent threats within the environment. Analysts should review for new and non-standard paths to identify potential threats. +search: '`subjectinterfacepackage` Dll=*\\*.dll | stats count min(_time) as firstTime max(_time) as lastTime values(Dll) by Path host| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_sip_provider_inventory_filter`' +how_to_implement: To implement this analytic, one must first perform inventory using a scripted inputs. Review the following Gist - https://gist.github.com/MHaggis/75dd5db546c143ea67703d0e86cdbbd1 +known_false_positives: False positives are limited as this is a hunting query for inventory. references: -- https://gist.github.com/MHaggis/75dd5db546c143ea67703d0e86cdbbd1 + - https://gist.github.com/MHaggis/75dd5db546c143ea67703d0e86cdbbd1 tags: - analytic_story: - - Subvert Trust Controls SIP and Trust Provider Hijacking - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1553.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Subvert Trust Controls SIP and Trust Provider Hijacking + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1553.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.003/sip/sip_inventory.log - source: powershell://SubjectInterfacePackage - sourcetype: PwSh:SubjectInterfacePackage + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.003/sip/sip_inventory.log + source: powershell://SubjectInterfacePackage + sourcetype: PwSh:SubjectInterfacePackage diff --git a/detections/endpoint/windows_sip_winverifytrust_failed_trust_validation.yml b/detections/endpoint/windows_sip_winverifytrust_failed_trust_validation.yml index 0109f66a1e..c1cefbd8b6 100644 --- a/detections/endpoint/windows_sip_winverifytrust_failed_trust_validation.yml +++ b/detections/endpoint/windows_sip_winverifytrust_failed_trust_validation.yml @@ -1,72 +1,59 @@ name: Windows SIP WinVerifyTrust Failed Trust Validation id: 6ffc7f88-415b-4278-a80d-b957d6539e1a -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly data_source: -- Windows Event Log CAPI2 81 -description: The following analytic detects failed trust validation attempts using - Windows Event Log - CAPI2 (CryptoAPI 2). It specifically triggers on EventID 81, - which indicates that "The digital signature of the object did not verify." This - detection leverages the CAPI2 Operational log to identify instances where digital - signatures fail to validate. Monitoring this activity is crucial as it can indicate - attempts to execute untrusted or potentially malicious binaries. If confirmed malicious, - this activity could allow attackers to bypass security controls and execute unauthorized - code, leading to potential system compromise. -search: '`capi2_operational` EventID=81 "The digital signature of the object did not - verify." | xmlkv UserData_Xml | stats count min(_time) as firstTime max(_time) as - lastTime by Computer, UserData_Xml | rename Computer as dest | `windows_sip_winverifytrust_failed_trust_validation_filter`' -how_to_implement: To implement this analytic, one will need to enable the Microsoft-Windows-CAPI2/Operational - log within the Windows Event Log. Note this is a debug log for many purposes, and - the analytic only focuses in on EventID 81. Review the following gist for additional - enabling information. -known_false_positives: False positives may be present in some instances of legitimate - binaries with invalid signatures. Filter as needed. + - Windows Event Log CAPI2 81 +description: The following analytic detects failed trust validation attempts using Windows Event Log - CAPI2 (CryptoAPI 2). It specifically triggers on EventID 81, which indicates that "The digital signature of the object did not verify." This detection leverages the CAPI2 Operational log to identify instances where digital signatures fail to validate. Monitoring this activity is crucial as it can indicate attempts to execute untrusted or potentially malicious binaries. If confirmed malicious, this activity could allow attackers to bypass security controls and execute unauthorized code, leading to potential system compromise. +search: |- + `capi2_operational` EventID=81 "The digital signature of the object did not verify." + | xmlkv UserData_Xml + | stats count min(_time) as firstTime max(_time) as lastTime + BY Computer, UserData_Xml + | rename Computer as dest + | `windows_sip_winverifytrust_failed_trust_validation_filter` +how_to_implement: To implement this analytic, one will need to enable the Microsoft-Windows-CAPI2/Operational log within the Windows Event Log. Note this is a debug log for many purposes, and the analytic only focuses in on EventID 81. Review the following gist for additional enabling information. +known_false_positives: False positives may be present in some instances of legitimate binaries with invalid signatures. Filter as needed. references: -- https://attack.mitre.org/techniques/T1553/003/ -- https://specterops.io/wp-content/uploads/sites/3/2022/06/SpecterOps_Subverting_Trust_in_Windows.pdf -- https://github.com/gtworek/PSBits/tree/master/SIP -- https://github.com/mattifestation/PoCSubjectInterfacePackage -- https://pentestlab.blog/2017/11/06/hijacking-digital-signatures/ + - https://attack.mitre.org/techniques/T1553/003/ + - https://specterops.io/wp-content/uploads/sites/3/2022/06/SpecterOps_Subverting_Trust_in_Windows.pdf + - https://github.com/gtworek/PSBits/tree/master/SIP + - https://github.com/mattifestation/PoCSubjectInterfacePackage + - https://pentestlab.blog/2017/11/06/hijacking-digital-signatures/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Failed trust validation via the CryptoAPI 2 on $dest$ for a binary. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: Failed trust validation via the CryptoAPI 2 on $dest$ for a binary. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Subvert Trust Controls SIP and Trust Provider Hijacking - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1553.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Subvert Trust Controls SIP and Trust Provider Hijacking + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1553.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.003/sip/capi2-operational.log - source: XmlWinEventLog:Microsoft-Windows-CAPI2/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.003/sip/capi2-operational.log + source: XmlWinEventLog:Microsoft-Windows-CAPI2/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_snake_malware_file_modification_crmlog.yml b/detections/endpoint/windows_snake_malware_file_modification_crmlog.yml index 78b84216dd..24c9c8a94a 100644 --- a/detections/endpoint/windows_snake_malware_file_modification_crmlog.yml +++ b/detections/endpoint/windows_snake_malware_file_modification_crmlog.yml @@ -6,69 +6,45 @@ author: Michael Haag, Splunk status: production type: TTP data_source: -- Sysmon EventID 11 -description: The following analytic identifies the creation of a .crmlog file within - the %windows%\Registration directory, typically with a format of ..crmlog. - This detection leverages the Endpoint.Filesystem datamodel to monitor file creation - events in the specified directory. This activity is significant as it is associated - with the Snake malware, which uses this file for its operations. If confirmed malicious, - this could indicate the presence of Snake malware, leading to potential data exfiltration, - system compromise, and further malicious activities. Immediate investigation is - required to mitigate the threat. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_path="*\\windows\\registration\\*" - AND Filesystem.file_name="*.crmlog" by Filesystem.action Filesystem.dest Filesystem.file_access_time - Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name - Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid - Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_snake_malware_file_modification_crmlog_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: False positives may be present as the file pattern does match - legitimate files on disk. It is possible other native tools write the same file - name scheme. + - Sysmon EventID 11 +description: The following analytic identifies the creation of a .crmlog file within the %windows%\Registration directory, typically with a format of ..crmlog. This detection leverages the Endpoint.Filesystem datamodel to monitor file creation events in the specified directory. This activity is significant as it is associated with the Snake malware, which uses this file for its operations. If confirmed malicious, this could indicate the presence of Snake malware, leading to potential data exfiltration, system compromise, and further malicious activities. Immediate investigation is required to mitigate the threat. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_path="*\\windows\\registration\\*" AND Filesystem.file_name="*.crmlog" by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_snake_malware_file_modification_crmlog_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: False positives may be present as the file pattern does match legitimate files on disk. It is possible other native tools write the same file name scheme. references: -- https://media.defense.gov/2023/May/09/2003218554/-1/-1/0/JOINT_CSA_HUNTING_RU_INTEL_SNAKE_MALWARE_20230509.PDF + - https://media.defense.gov/2023/May/09/2003218554/-1/-1/0/JOINT_CSA_HUNTING_RU_INTEL_SNAKE_MALWARE_20230509.PDF drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A file related to Snake Malware has been identified on $dest$. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A file related to Snake Malware has been identified on $dest$. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Snake Malware - asset_type: Endpoint - atomic_guid: - - 7e47ee60-9dd1-4269-9c4f-97953b183268 - mitre_attack_id: - - T1027 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Snake Malware + asset_type: Endpoint + atomic_guid: + - 7e47ee60-9dd1-4269-9c4f-97953b183268 + mitre_attack_id: + - T1027 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/snakemalware/snake_crmlog-windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/snakemalware/snake_crmlog-windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_snake_malware_kernel_driver_comadmin.yml b/detections/endpoint/windows_snake_malware_kernel_driver_comadmin.yml index da2eb3d9ca..4844cb284e 100644 --- a/detections/endpoint/windows_snake_malware_kernel_driver_comadmin.yml +++ b/detections/endpoint/windows_snake_malware_kernel_driver_comadmin.yml @@ -6,68 +6,45 @@ author: Michael Haag, Splunk status: production type: TTP data_source: -- Sysmon EventID 11 -description: The following analytic detects the creation of the comadmin.dat file - in the %windows%\system32\Com directory, which is associated with Snake Malware. - This detection leverages the Endpoint.Filesystem data model to identify file creation - events matching the specified path and filename. This activity is significant because - the comadmin.dat file is part of Snake Malware's installation process, which includes - dropping a kernel driver and a custom DLL. If confirmed malicious, this activity - could allow an attacker to load a malicious driver, potentially leading to privilege - escalation and persistent access to the compromised system. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_path="*\\windows\\system32\\com\\*" - AND Filesystem.file_name="comadmin.dat" by Filesystem.action Filesystem.dest Filesystem.file_access_time - Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name - Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid - Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_snake_malware_kernel_driver_comadmin_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. + - Sysmon EventID 11 +description: The following analytic detects the creation of the comadmin.dat file in the %windows%\system32\Com directory, which is associated with Snake Malware. This detection leverages the Endpoint.Filesystem data model to identify file creation events matching the specified path and filename. This activity is significant because the comadmin.dat file is part of Snake Malware's installation process, which includes dropping a kernel driver and a custom DLL. If confirmed malicious, this activity could allow an attacker to load a malicious driver, potentially leading to privilege escalation and persistent access to the compromised system. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_path="*\\windows\\system32\\com\\*" AND Filesystem.file_name="comadmin.dat" by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_snake_malware_kernel_driver_comadmin_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. known_false_positives: False positives may be present, filter as needed. references: -- https://media.defense.gov/2023/May/09/2003218554/-1/-1/0/JOINT_CSA_HUNTING_RU_INTEL_SNAKE_MALWARE_20230509.PDF + - https://media.defense.gov/2023/May/09/2003218554/-1/-1/0/JOINT_CSA_HUNTING_RU_INTEL_SNAKE_MALWARE_20230509.PDF drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A kernel driver comadmin.dat related to Snake Malware was written to disk - on $dest$. - risk_objects: - - field: dest - type: system - score: 56 - threat_objects: [] + message: A kernel driver comadmin.dat related to Snake Malware was written to disk on $dest$. + risk_objects: + - field: dest + type: system + score: 56 + threat_objects: [] tags: - analytic_story: - - Snake Malware - asset_type: Endpoint - atomic_guid: - - e5cb5564-cc7b-4050-86e8-f2d9eec1941f - mitre_attack_id: - - T1547.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Snake Malware + asset_type: Endpoint + atomic_guid: + - e5cb5564-cc7b-4050-86e8-f2d9eec1941f + mitre_attack_id: + - T1547.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/snakemalware/comadmin_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/snakemalware/comadmin_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_snake_malware_registry_modification_wav_openwithprogids.yml b/detections/endpoint/windows_snake_malware_registry_modification_wav_openwithprogids.yml index 621f8e5be2..ee41aac984 100644 --- a/detections/endpoint/windows_snake_malware_registry_modification_wav_openwithprogids.yml +++ b/detections/endpoint/windows_snake_malware_registry_modification_wav_openwithprogids.yml @@ -6,76 +6,45 @@ author: Michael Haag, Splunk status: production type: TTP data_source: - - Sysmon EventID 13 -description: - The following analytic identifies modifications to the registry path - .wav\\OpenWithProgIds, associated with the Snake Malware campaign. It leverages - data from the Endpoint.Registry datamodel to detect changes in this specific registry - location. This activity is significant because Snake's WerFault.exe uses this registry - path to decrypt an encrypted blob containing critical components like the AES key, - IV, and paths for its kernel driver and loader. If confirmed malicious, this could - allow the attacker to load and execute Snake's kernel driver, leading to potential - system compromise and persistent access. -search: - '| tstats `security_content_summariesonly` count values(Registry.registry_key_name) - as registry_key_name values(Registry.registry_path) as registry_path min(_time) - as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\.wav\\OpenWithProgIds\\*" - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` - | `windows_snake_malware_registry_modification_wav_openwithprogids_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, - confirm the latest CIM App 4.20 or higher is installed and the latest TA for the - endpoint product. -known_false_positives: - False positives may be present and will require tuning based - on program Ids in large organizations. + - Sysmon EventID 13 +description: The following analytic identifies modifications to the registry path .wav\\OpenWithProgIds, associated with the Snake Malware campaign. It leverages data from the Endpoint.Registry datamodel to detect changes in this specific registry location. This activity is significant because Snake's WerFault.exe uses this registry path to decrypt an encrypted blob containing critical components like the AES key, IV, and paths for its kernel driver and loader. If confirmed malicious, this could allow the attacker to load and execute Snake's kernel driver, leading to potential system compromise and persistent access. +search: '| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\.wav\\OpenWithProgIds\\*" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `windows_snake_malware_registry_modification_wav_openwithprogids_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +known_false_positives: False positives may be present and will require tuning based on program Ids in large organizations. references: - - https://media.defense.gov/2023/May/09/2003218554/-1/-1/0/JOINT_CSA_HUNTING_RU_INTEL_SNAKE_MALWARE_20230509.PDF + - https://media.defense.gov/2023/May/09/2003218554/-1/-1/0/JOINT_CSA_HUNTING_RU_INTEL_SNAKE_MALWARE_20230509.PDF drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - A registry modification related to Snake Malware has been identified on - $dest$. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A registry modification related to Snake Malware has been identified on $dest$. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Snake Malware - asset_type: Endpoint - atomic_guid: - - 8318ad20-0488-4a64-98f4-72525a012f6b - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Snake Malware + asset_type: Endpoint + atomic_guid: + - 8318ad20-0488-4a64-98f4-72525a012f6b + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/snakemalware/snake_malware_regblob-windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/snakemalware/snake_malware_regblob-windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_snake_malware_service_create.yml b/detections/endpoint/windows_snake_malware_service_create.yml index 40d57186cd..1a9138bd1a 100644 --- a/detections/endpoint/windows_snake_malware_service_create.yml +++ b/detections/endpoint/windows_snake_malware_service_create.yml @@ -6,66 +6,47 @@ author: Michael Haag, Splunk status: production type: TTP data_source: -- Windows Event Log System 7045 -description: The following analytic detects the creation of a new service named WerFaultSvc - with a binary path in the Windows WinSxS directory. It leverages Windows System - logs, specifically EventCode 7045, to identify this activity. This behavior is significant - because it indicates the presence of Snake malware, which uses this service to maintain - persistence by blending in with legitimate Windows services. If confirmed malicious, - this activity could allow an attacker to execute Snake malware components, leading - to potential data exfiltration, system compromise, and long-term persistence within - the environment. -search: '`wineventlog_system` EventCode=7045 ImagePath="*\\windows\\winSxS\\*" ImagePath="*\Werfault.exe" - | stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode - ImagePath ServiceName ServiceType | rename Computer as dest | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_snake_malware_service_create_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - Windows System logs with the Service name, Service File Name Service Start type, - and Service Type from your endpoints. -known_false_positives: False positives should be limited as this is a strict primary - indicator used by Snake Malware. + - Windows Event Log System 7045 +description: The following analytic detects the creation of a new service named WerFaultSvc with a binary path in the Windows WinSxS directory. It leverages Windows System logs, specifically EventCode 7045, to identify this activity. This behavior is significant because it indicates the presence of Snake malware, which uses this service to maintain persistence by blending in with legitimate Windows services. If confirmed malicious, this activity could allow an attacker to execute Snake malware components, leading to potential data exfiltration, system compromise, and long-term persistence within the environment. +search: '`wineventlog_system` EventCode=7045 ImagePath="*\\windows\\winSxS\\*" ImagePath="*\Werfault.exe" | stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode ImagePath ServiceName ServiceType | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_snake_malware_service_create_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting Windows System logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints. +known_false_positives: False positives should be limited as this is a strict primary indicator used by Snake Malware. references: -- https://media.defense.gov/2023/May/09/2003218554/-1/-1/0/JOINT_CSA_HUNTING_RU_INTEL_SNAKE_MALWARE_20230509.PDF + - https://media.defense.gov/2023/May/09/2003218554/-1/-1/0/JOINT_CSA_HUNTING_RU_INTEL_SNAKE_MALWARE_20230509.PDF drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A service, WerFaultSvc, was created on $dest$ and is related to Snake Malware. - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: [] + message: A service, WerFaultSvc, was created on $dest$ and is related to Snake Malware. + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: [] tags: - analytic_story: - - Snake Malware - - Compromised Windows Host - asset_type: Endpoint - atomic_guid: - - b8db787e-dbea-493c-96cb-9272296ddc49 - mitre_attack_id: - - T1547.006 - - T1569.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Snake Malware + - Compromised Windows Host + asset_type: Endpoint + atomic_guid: + - b8db787e-dbea-493c-96cb-9272296ddc49 + mitre_attack_id: + - T1547.006 + - T1569.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/snakemalware/snake-service-windows-system.log - source: XmlWinEventLog:System - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/snakemalware/snake-service-windows-system.log + source: XmlWinEventLog:System + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_snappybee_create_test_registry.yml b/detections/endpoint/windows_snappybee_create_test_registry.yml index 769a9e1fba..907f533404 100644 --- a/detections/endpoint/windows_snappybee_create_test_registry.yml +++ b/detections/endpoint/windows_snappybee_create_test_registry.yml @@ -5,77 +5,49 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: TTP -description: - The following analytic detects modifications to the Windows registry - under `SOFTWARE\Microsoft\Test`, a location rarely used by legitimate applications - in a production environment. Monitoring this key is crucial, as adversaries may - create or alter values here for monitoring update of itself file path, updated configuration - file, or system mark compromised. The detection leverages **Sysmon Event ID 13** - (Registry Value Set) to identify unauthorized changes. Analysts should investigate - processes associated with these modifications, particularly unsigned executables - or suspicious command-line activity, as they may indicate malware or unauthorized - software behavior. +description: The following analytic detects modifications to the Windows registry under `SOFTWARE\Microsoft\Test`, a location rarely used by legitimate applications in a production environment. Monitoring this key is crucial, as adversaries may create or alter values here for monitoring update of itself file path, updated configuration file, or system mark compromised. The detection leverages **Sysmon Event ID 13** (Registry Value Set) to identify unauthorized changes. Analysts should investigate processes associated with these modifications, particularly unsigned executables or suspicious command-line activity, as they may indicate malware or unauthorized software behavior. data_source: - - Sysmon EventID 13 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path = "*\\SOFTWARE\\Microsoft\\Test\\*" - by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive - Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name - Registry.registry_value_type Registry.status Registry.user Registry.vendor_product - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_snappybee_create_test_registry_filter`' -how_to_implement: - To successfully implement this search you need to be ingesting information - on process that include the name of the process responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure - that this registry was included in your config files ex. sysmon config to be monitored. -known_false_positives: - Administrators and third party software may create this registry - entry. + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path = "*\\SOFTWARE\\Microsoft\\Test\\*" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_snappybee_create_test_registry_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored. +known_false_positives: Administrators and third party software may create this registry entry. references: - - https://www.trendmicro.com/en_nl/research/24/k/earth-estries.html + - https://www.trendmicro.com/en_nl/research/24/k/earth-estries.html drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a Test registry Entry [$registry_path$] was created on [$dest$]. - risk_objects: - - field: dest - type: system - score: 60 - - field: user - type: user - score: 60 - threat_objects: [] + message: a Test registry Entry [$registry_path$] was created on [$dest$]. + risk_objects: + - field: dest + type: system + score: 60 + - field: user + type: user + score: 60 + threat_objects: [] tags: - analytic_story: - - Salt Typhoon - - China-Nexus Threat Activity - - SnappyBee - asset_type: Endpoint - mitre_attack_id: - - T1112 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Salt Typhoon + - China-Nexus Threat Activity + - SnappyBee + asset_type: Endpoint + mitre_attack_id: + - T1112 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/test_registry/test_reg.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/test_registry/test_reg.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_soaphound_binary_execution.yml b/detections/endpoint/windows_soaphound_binary_execution.yml index 23215328fd..4e5ea90fd7 100644 --- a/detections/endpoint/windows_soaphound_binary_execution.yml +++ b/detections/endpoint/windows_soaphound_binary_execution.yml @@ -1,89 +1,75 @@ name: Windows SOAPHound Binary Execution id: 8e53f839-e127-4d6d-a54d-a2f67044a57f -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic detects the execution of the SOAPHound binary - (`soaphound.exe`) with specific command-line arguments. It leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process names, command-line arguments, - and other process-related metadata. This activity is significant because SOAPHound - is a known tool used for credential dumping and other malicious activities. If confirmed - malicious, this behavior could allow an attacker to extract sensitive information, - escalate privileges, or persist within the environment, posing a severe threat to - organizational security. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name="soaphound.exe" - OR Processes.original_file_name="soaphound.exe" AND Processes.process IN ("*--buildcache - *", "*--bhdump *", "*--certdump *", "*--dnsdump *", "*-c *", "*--cachefilename *", - "*-o *", "*--outputdirectory *") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_soaphound_binary_execution_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives should be limited as the command-line arguments - are specific to SOAPHound. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic detects the execution of the SOAPHound binary (`soaphound.exe`) with specific command-line arguments. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, command-line arguments, and other process-related metadata. This activity is significant because SOAPHound is a known tool used for credential dumping and other malicious activities. If confirmed malicious, this behavior could allow an attacker to extract sensitive information, escalate privileges, or persist within the environment, posing a severe threat to organizational security. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name="soaphound.exe" + OR + Processes.original_file_name="soaphound.exe" + AND + Processes.process IN ("*--buildcache *", "*--bhdump *", "*--certdump *", "*--dnsdump *", "*-c *", "*--cachefilename *", "*-o *", "*--outputdirectory *") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_soaphound_binary_execution_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives should be limited as the command-line arguments are specific to SOAPHound. Filter as needed. references: -- https://github.com/FalconForceTeam/SOAPHound + - https://github.com/FalconForceTeam/SOAPHound drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The process $process_name$ was executed on $dest$ related to SOAPHound. - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: - - field: process_name - type: process + message: The process $process_name$ was executed on $dest$ related to SOAPHound. + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: + - field: process_name + type: process tags: - analytic_story: - - Windows Discovery Techniques - - Compromised Windows Host - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1069.001 - - T1069.002 - - T1087.001 - - T1087.002 - - T1482 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Discovery Techniques + - Compromised Windows Host + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1069.001 + - T1069.002 + - T1087.001 + - T1087.002 + - T1482 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/soaphound/sysmon_soaphound.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/soaphound/sysmon_soaphound.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_spearphishing_attachment_onenote_spawn_mshta.yml b/detections/endpoint/windows_spearphishing_attachment_onenote_spawn_mshta.yml index 02172c4cbc..a81bd819fc 100644 --- a/detections/endpoint/windows_spearphishing_attachment_onenote_spawn_mshta.yml +++ b/detections/endpoint/windows_spearphishing_attachment_onenote_spawn_mshta.yml @@ -1,86 +1,69 @@ name: Windows Spearphishing Attachment Onenote Spawn Mshta id: 35aeb0e7-7de5-444a-ac45-24d6788796ec -version: 9 -date: '2025-09-18' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects OneNote spawning `mshta.exe`, a behavior - often associated with spearphishing attacks. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process creation events where OneNote is - the parent process. This activity is significant as it is commonly used by malware - families like TA551, AsyncRat, Redline, and DCRAT to execute malicious scripts. - If confirmed malicious, this could allow attackers to execute arbitrary code, potentially - leading to data exfiltration, system compromise, or further malware deployment. - Immediate investigation and containment are recommended. +description: The following analytic detects OneNote spawning `mshta.exe`, a behavior often associated with spearphishing attacks. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where OneNote is the parent process. This activity is significant as it is commonly used by malware families like TA551, AsyncRat, Redline, and DCRAT to execute malicious scripts. If confirmed malicious, this could allow attackers to execute arbitrary code, potentially leading to data exfiltration, system compromise, or further malware deployment. Immediate investigation and containment are recommended. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name - IN ("onenote.exe", "onenotem.exe") `process_mshta` by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_spearphishing_attachment_onenote_spawn_mshta_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name IN ("onenote.exe", "onenotem.exe") `process_mshta` + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_spearphishing_attachment_onenote_spawn_mshta_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives known. Filter as needed. references: -- https://www.bleepingcomputer.com/news/security/hackers-now-use-microsoft-onenote-attachments-to-spread-malware/ -- https://malpedia.caad.fkie.fraunhofer.de/details/win.asyncrat + - https://www.bleepingcomputer.com/news/security/hackers-now-use-microsoft-onenote-attachments-to-spread-malware/ + - https://malpedia.caad.fkie.fraunhofer.de/details/win.asyncrat drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Office process $parent_process_name$ observed executing a suspicious child - process $process_name$ with process ID $process_id$ on host $dest$ - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: - - field: process_name - type: process_name + message: Office process $parent_process_name$ observed executing a suspicious child process $process_name$ with process ID $process_id$ on host $dest$ + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Spearphishing Attachments - - Compromised Windows Host - - AsyncRAT - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1566.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Spearphishing Attachments + - Compromised Windows Host + - AsyncRAT + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1566.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/onenote_spear_phishing/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/onenote_spear_phishing/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_special_privileged_logon_on_multiple_hosts.yml b/detections/endpoint/windows_special_privileged_logon_on_multiple_hosts.yml index 8b6c5c2c70..2888afe6c1 100644 --- a/detections/endpoint/windows_special_privileged_logon_on_multiple_hosts.yml +++ b/detections/endpoint/windows_special_privileged_logon_on_multiple_hosts.yml @@ -1,76 +1,62 @@ name: Windows Special Privileged Logon On Multiple Hosts id: 4c461f5a-c2cc-4e86-b132-c262fc9edca7 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk type: TTP status: production data_source: -- Windows Event Log Security 4672 -description: The following analytic detects a user authenticating with special privileges - on 30 or more remote endpoints within a 5-minute window. It leverages Event ID 4672 - from Windows Security logs to identify this behavior. This activity is significant - as it may indicate lateral movement or remote code execution by an adversary. If - confirmed malicious, the attacker could gain extensive control over the network, - potentially leading to privilege escalation, data exfiltration, or further compromise - of the environment. Security teams should adjust detection thresholds based on their - specific environment. -search: '`wineventlog_security` EventCode=4672 AND NOT(Caller_User_Name IN ("DWM-1","DWM-2","DWM-3","LOCAL - SERVICE","NETWORK SERVICE","SYSTEM","*$")) | bucket span=5m _time | stats dc(Computer) - AS unique_targets values(Computer) as dest values(PrivilegeList) as privileges by - _time, Caller_User_Name | rename Caller_User_Name as user| where unique_targets - > 30 | `windows_special_privileged_logon_on_multiple_hosts_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - special logon events. The Advanced Security Audit policy setting `Audit Special - Logon` within `Logon/Logoff` need to be enabled. -known_false_positives: Vulnerability scanners or system administration tools may also - trigger this detection. Filter as needed. + - Windows Event Log Security 4672 +description: The following analytic detects a user authenticating with special privileges on 30 or more remote endpoints within a 5-minute window. It leverages Event ID 4672 from Windows Security logs to identify this behavior. This activity is significant as it may indicate lateral movement or remote code execution by an adversary. If confirmed malicious, the attacker could gain extensive control over the network, potentially leading to privilege escalation, data exfiltration, or further compromise of the environment. Security teams should adjust detection thresholds based on their specific environment. +search: |- + `wineventlog_security` EventCode=4672 AND NOT(Caller_User_Name IN ("DWM-1","DWM-2","DWM-3","LOCAL SERVICE","NETWORK SERVICE","SYSTEM","*$")) + | bucket span=5m _time + | stats dc(Computer) AS unique_targets values(Computer) as dest values(PrivilegeList) as privileges + BY _time, Caller_User_Name + | rename Caller_User_Name as user + | where unique_targets > 30 + | `windows_special_privileged_logon_on_multiple_hosts_filter` +how_to_implement: To successfully implement this search, you need to be ingesting special logon events. The Advanced Security Audit policy setting `Audit Special Logon` within `Logon/Logoff` need to be enabled. +known_false_positives: Vulnerability scanners or system administration tools may also trigger this detection. Filter as needed. references: -- https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4672 -- https://learn.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-r2-and-2012/dn319113(v=ws.11) -- https://thedfirreport.com/2023/01/23/sharefinder-how-threat-actors-discover-file-shares/ -- https://attack.mitre.org/tactics/TA0008/ + - https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4672 + - https://learn.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-r2-and-2012/dn319113(v=ws.11) + - https://thedfirreport.com/2023/01/23/sharefinder-how-threat-actors-discover-file-shares/ + - https://attack.mitre.org/tactics/TA0008/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: 'A user $user$ obtained special privileges on a large number of endpoints - (Count: $unique_targets$) within 5 minutes.' - risk_objects: - - field: user - type: user - score: 64 - threat_objects: [] + message: 'A user $user$ obtained special privileges on a large number of endpoints (Count: $unique_targets$) within 5 minutes.' + risk_objects: + - field: user + type: user + score: 64 + threat_objects: [] tags: - analytic_story: - - Active Directory Privilege Escalation - - Active Directory Lateral Movement - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1087 - - T1021.002 - - T1135 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Privilege Escalation + - Active Directory Lateral Movement + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1087 + - T1021.002 + - T1135 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/special_logon_on_mulitple_hosts/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/special_logon_on_mulitple_hosts/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_speechruntime_com_hijacking_dll_load.yml b/detections/endpoint/windows_speechruntime_com_hijacking_dll_load.yml index 1cb59c2818..b4ccafd0eb 100644 --- a/detections/endpoint/windows_speechruntime_com_hijacking_dll_load.yml +++ b/detections/endpoint/windows_speechruntime_com_hijacking_dll_load.yml @@ -1,70 +1,65 @@ name: Windows SpeechRuntime COM Hijacking DLL Load id: bd35738c-e93a-4e4f-be24-f6a3680b950a -version: 2 -date: '2025-10-14' +version: 3 +date: '2026-02-25' author: Raven Tait, Splunk status: production type: TTP -description: SpeechRuntime is vulnerable to an attack that allows a user to run code on another user's - session remotely and stealthily by exploiting a Windows COM class. When this class - is invoked, it launches SpeechRuntime.exe in the context of the currently logged-on user. Because this - COM class is susceptible to COM Hijacking, the attacker can alter the registry remotely to point to a - malicious DLL. By dropping that DLL on the target system (e.g., via SMB) and triggering the COM object, - the attacker causes the malicious DLL to load into SpeechRuntime.exe and executing under the user's context. This - detection identifies suspicious DLL loads by SpeechRuntime.exe from outside the expected locations. +description: SpeechRuntime is vulnerable to an attack that allows a user to run code on another user's session remotely and stealthily by exploiting a Windows COM class. When this class is invoked, it launches SpeechRuntime.exe in the context of the currently logged-on user. Because this COM class is susceptible to COM Hijacking, the attacker can alter the registry remotely to point to a malicious DLL. By dropping that DLL on the target system (e.g., via SMB) and triggering the COM object, the attacker causes the malicious DLL to load into SpeechRuntime.exe and executing under the user's context. This detection identifies suspicious DLL loads by SpeechRuntime.exe from outside the expected locations. data_source: -- Sysmon EventID 7 -search: '`sysmon` EventCode=7 Image="*SpeechRuntime.exe" | eval image_loaded_lower = lower(ImageLoaded) - | search NOT image_loaded_lower="*system32*" | fillnull | stats count min(_time) as firstTime - max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name - parent_process_name parent_process_guid - process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists - service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_speechruntime_com_hijacking_dll_load_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name and ImageLoaded (Like sysmon EventCode 7) from your endpoints. - If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. - Also be sure to include those monitored dll to your own sysmon config. + - Sysmon EventID 7 +search: |- + `sysmon` EventCode=7 Image="*SpeechRuntime.exe" + | eval image_loaded_lower = lower(ImageLoaded) + | search NOT image_loaded_lower="*system32*" + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY Image ImageLoaded dest + loaded_file loaded_file_path original_file_name + parent_process_name parent_process_guid process_exec + process_guid process_hash process_id + process_name process_path service_dll_signature_exists + service_dll_signature_verified signature signature_id + user_id vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_speechruntime_com_hijacking_dll_load_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and ImageLoaded (Like sysmon EventCode 7) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Also be sure to include those monitored dll to your own sysmon config. known_false_positives: This process should normally never be loading dlls from outside the Windows system directory. references: -- https://github.com/rtecCyberSec/SpeechRuntimeMove + - https://github.com/rtecCyberSec/SpeechRuntimeMove drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible Lateral Movement abusing Speech Runtime on $dest$ - risk_objects: - - field: dest - type: system - score: 55 - threat_objects: [] + message: Possible Lateral Movement abusing Speech Runtime on $dest$ + risk_objects: + - field: dest + type: system + score: 55 + threat_objects: [] tags: - analytic_story: - - Active Directory Lateral Movement - - Compromised Windows Host - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1021.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + - Compromised Windows Host + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1021.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.003/lateral_movement_speechruntime/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.003/lateral_movement_speechruntime/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_speechruntime_suspicious_child_process.yml b/detections/endpoint/windows_speechruntime_suspicious_child_process.yml index 5c42fc66c2..21cfca4e26 100644 --- a/detections/endpoint/windows_speechruntime_suspicious_child_process.yml +++ b/detections/endpoint/windows_speechruntime_suspicious_child_process.yml @@ -1,75 +1,69 @@ name: Windows SpeechRuntime Suspicious Child Process id: f7bb956f-b956-42a5-8c2c-ff9cdbbf7526 -version: 1 -date: '2025-08-22' +version: 2 +date: '2026-02-25' author: Raven Tait, Splunk status: production type: TTP -description: SpeechRuntime is vulnerable to an attack that allows a user to run code on another user's - session remotely and stealthily by exploiting a Windows COM class. When this class - is invoked, it launches SpeechRuntime.exe in the context of the currently logged-on user. Because this - COM class is susceptible to COM Hijacking, the attacker can alter the registry remotely to point to a - malicious DLL. By dropping that DLL on the target system (e.g., via SMB) and triggering the COM object, - the attacker causes the malicious DLL to load into SpeechRuntime.exe and executing under the user's context. - This detection identifies suspicious child processes of SpeechRuntime.exe that could indicate abuse - of this vulnerability. +description: SpeechRuntime is vulnerable to an attack that allows a user to run code on another user's session remotely and stealthily by exploiting a Windows COM class. When this class is invoked, it launches SpeechRuntime.exe in the context of the currently logged-on user. Because this COM class is susceptible to COM Hijacking, the attacker can alter the registry remotely to point to a malicious DLL. By dropping that DLL on the target system (e.g., via SMB) and triggering the COM object, the attacker causes the malicious DLL to load into SpeechRuntime.exe and executing under the user's context. This detection identifies suspicious child processes of SpeechRuntime.exe that could indicate abuse of this vulnerability. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime - from datamodel=Endpoint.Processes where (Processes.parent_process_name="*SpeechRuntime.exe*") - Processes.process IN ("*cmd.exe*","*powershell.exe*","*rundll32.exe*","*bitsadmin.exe*","*wmic.exe*","*cscript.exe*") - by Processes.dest Processes.user Processes.original_file_name Processes.parent_process Processes.process_name Processes.process - Processes.process_id Processes.parent_process_id Processes.parent_process_name action parent_process_exec - parent_process_guid parent_process_path process_exec process_guid process_hash process_integrity_level - process_path user_id vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`| `windows_speechruntime_suspicious_child_process_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.parent_process_name="*SpeechRuntime.exe*" + ) + Processes.process IN ("*cmd.exe*","*powershell.exe*","*rundll32.exe*","*bitsadmin.exe*","*wmic.exe*","*cscript.exe*") + BY Processes.dest Processes.user Processes.original_file_name + Processes.parent_process Processes.process_name Processes.process + Processes.process_id Processes.parent_process_id Processes.parent_process_name + action parent_process_exec parent_process_guid + parent_process_path process_exec process_guid + process_hash process_integrity_level process_path + user_id vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_speechruntime_suspicious_child_process_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: This process should normally never be spawning these child processes. references: -- https://github.com/rtecCyberSec/SpeechRuntimeMove + - https://github.com/rtecCyberSec/SpeechRuntimeMove drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible Lateral Movement on $dest$ by abusing SpeechRuntime. - risk_objects: - - field: dest - type: system - score: 65 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: Possible Lateral Movement on $dest$ by abusing SpeechRuntime. + risk_objects: + - field: dest + type: system + score: 65 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - Active Directory Lateral Movement - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1021.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1021.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.003/lateral_movement_speechruntime/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.003/lateral_movement_speechruntime/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_sql_server_configuration_option_hunt.yml b/detections/endpoint/windows_sql_server_configuration_option_hunt.yml index 777f1014c0..25cc3b78c6 100644 --- a/detections/endpoint/windows_sql_server_configuration_option_hunt.yml +++ b/detections/endpoint/windows_sql_server_configuration_option_hunt.yml @@ -1,51 +1,47 @@ name: Windows SQL Server Configuration Option Hunt id: 8dc9efd5-805a-460e-889e-bc79e5477af9 -version: 3 -date: '2025-08-27' +version: 4 +date: '2026-02-25' author: Michael Haag, Splunk, sidoyle from Splunk Community status: production type: Hunting description: This detection helps hunt for changes to SQL Server configuration options that could indicate malicious activity. It monitors for modifications to any SQL Server configuration settings, allowing analysts to identify potentially suspicious changes that may be part of an attack, such as enabling dangerous features or modifying security-relevant settings. data_source: -- Windows Event Log Application 15457 -search: '`wineventlog_application` EventCode=15457 - | rex field=EventData_Xml "(?[^<]+)(?[^<]+)(?[^<]+)" - | rename host as dest - | eval change_type=case( - old_value="0" AND new_value="1", "enabled", - old_value="1" AND new_value="0", "disabled", - true(), "modified" - ) - | eval risk_score=case( - change_type="enabled", 90, - change_type="disabled", 60, - true(), 70 - ) - | eval risk_message="SQL Server ".config_name." was ".change_type." on host ".dest - | stats count min(_time) as firstTime max(_time) as lastTime by dest EventCode config_name change_type risk_message risk_score - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_sql_server_configuration_option_hunt_filter`' + - Windows Event Log Application 15457 +search: |- + `wineventlog_application` EventCode=15457 + | rex field=EventData_Xml "(?[^<]+)(?[^<]+)(?[^<]+)" + | rename host as dest + | eval change_type=case( old_value="0" AND new_value="1", "enabled", old_value="1" AND new_value="0", "disabled", true(), "modified" ) + | eval risk_score=case( change_type="enabled", 90, change_type="disabled", 60, true(), 70 ) + | eval risk_message="SQL Server ".config_name." was ".change_type." on host ".dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest EventCode config_name + change_type risk_message risk_score + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_sql_server_configuration_option_hunt_filter` how_to_implement: To successfully implement this detection, you need to be ingesting Windows Application Event Logs from SQL Server instances. The detection specifically looks for EventID 15457 which indicates configuration changes to SQL Server settings. Ensure proper logging is enabled for SQL Server configuration changes and that the logs are being forwarded to your SIEM. known_false_positives: Database administrators frequently make legitimate configuration changes for maintenance, performance tuning, and security hardening. To reduce false positives, establish a baseline of normal configuration changes, document approved configuration modifications, implement change control procedures, and maintain an inventory of expected settings. references: -- https://learn.microsoft.com/en-us/sql/database-engine/configure-windows/server-configuration-options-sql-server -- https://attack.mitre.org/techniques/T1505/001/ -- https://www.netspi.com/blog/technical/network-penetration-testing/sql-server-persistence-part-1-startup-stored-procedures/ + - https://learn.microsoft.com/en-us/sql/database-engine/configure-windows/server-configuration-options-sql-server + - https://attack.mitre.org/techniques/T1505/001/ + - https://www.netspi.com/blog/technical/network-penetration-testing/sql-server-persistence-part-1-startup-stored-procedures/ tags: - analytic_story: - - SQL Server Abuse - asset_type: Windows - mitre_attack_id: - - T1505.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - SQL Server Abuse + asset_type: Windows + mitre_attack_id: + - T1505.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.001/simulation/windows-application.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Application + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.001/simulation/windows-application.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Application diff --git a/detections/endpoint/windows_sql_server_critical_procedures_enabled.yml b/detections/endpoint/windows_sql_server_critical_procedures_enabled.yml index 94f0625f65..a5ed4d8b3f 100644 --- a/detections/endpoint/windows_sql_server_critical_procedures_enabled.yml +++ b/detections/endpoint/windows_sql_server_critical_procedures_enabled.yml @@ -1,80 +1,71 @@ name: Windows SQL Server Critical Procedures Enabled id: d0434864-b043-41e3-8c08-30e53605e9cb -version: 3 -date: '2025-08-27' +version: 4 +date: '2026-02-25' author: Michael Haag, Splunk, sidoyle from Splunk Community status: production type: TTP description: This detection identifies when critical SQL Server configuration options are modified, including "Ad Hoc Distributed Queries", "external scripts enabled", "Ole Automation Procedures", "clr enabled", and "clr strict security". These features can be abused by attackers for various malicious purposes - Ad Hoc Distributed Queries enables Active Directory reconnaissance through ADSI provider, external scripts and Ole Automation allow execution of arbitrary code, and CLR features can be used to run custom assemblies. Enabling these features could indicate attempts to gain code execution or perform reconnaissance through SQL Server. data_source: -- Windows Event Log Application 15457 -search: '`wineventlog_application` EventCode=15457 - | rex field=EventData_Xml "(?[^<]+)(?[^<]+)(?[^<]+)" - | where config_name IN ("Ad Hoc Distributed Queries", "external scripts enabled", "Ole Automation Procedures", "clr enabled", "clr strict security") - | rename host as dest - | eval change_type=case( - old_value="0" AND new_value="1", "enabled", - old_value="1" AND new_value="0", "disabled", - true(), "modified" - ) - | eval risk_score=case( - change_type="enabled", 90, - change_type="disabled", 60, - true(), 70 - ) - | eval risk_message="SQL Server critical procedure ".config_name." was ".change_type." on host ".dest.", which may indicate attempts to gain code execution or perform reconnaissance" - | stats count min(_time) as firstTime max(_time) as lastTime by dest EventCode config_name change_type risk_message risk_score - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_sql_server_critical_procedures_enabled_filter`' + - Windows Event Log Application 15457 +search: |- + `wineventlog_application` EventCode=15457 + | rex field=EventData_Xml "(?[^<]+)(?[^<]+)(?[^<]+)" + | where config_name IN ("Ad Hoc Distributed Queries", "external scripts enabled", "Ole Automation Procedures", "clr enabled", "clr strict security") + | rename host as dest + | eval change_type=case( old_value="0" AND new_value="1", "enabled", old_value="1" AND new_value="0", "disabled", true(), "modified" ) + | eval risk_score=case( change_type="enabled", 90, change_type="disabled", 60, true(), 70 ) + | eval risk_message="SQL Server critical procedure ".config_name." was ".change_type." on host ".dest.", which may indicate attempts to gain code execution or perform reconnaissance" + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest EventCode config_name + change_type risk_message risk_score + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_sql_server_critical_procedures_enabled_filter` how_to_implement: To successfully implement this detection, you need to be ingesting Windows Application Event Logs from SQL Server instances where SQL Server is installed. The detection specifically looks for EventID 15457 which indicates configuration changes to SQL Server features. Ensure proper logging is enabled for SQL Server configuration changes and that the logs are being forwarded to your SIEM. known_false_positives: Database administrators may legitimately enable these features for valid business purposes such as cross-database queries, custom CLR assemblies, automation scripts, or application requirements. To reduce false positives, document when these features are required, monitor for unauthorized changes, create change control procedures for configuration modifications, and consider alerting on the enabled state rather than configuration changes if preferred. references: -- https://learn.microsoft.com/en-us/sql/database-engine/configure-windows/ad-hoc-distributed-queries-server-configuration-option -- https://learn.microsoft.com/en-us/sql/database-engine/configure-windows/external-scripts-enabled-server-configuration-option -- https://learn.microsoft.com/en-us/sql/database-engine/configure-windows/ole-automation-procedures-server-configuration-option -- https://learn.microsoft.com/en-us/sql/database-engine/configure-windows/clr-enabled-server-configuration-option -- https://www.netspi.com/blog/technical/network-penetration-testing/enumerating-domain-accounts-via-sql-server-using-adsi/ -- https://attack.mitre.org/techniques/T1505/001/ -- https://www.netspi.com/blog/technical-blog/adversary-simulation/attacking-sql-server-clr-assemblies/ + - https://learn.microsoft.com/en-us/sql/database-engine/configure-windows/ad-hoc-distributed-queries-server-configuration-option + - https://learn.microsoft.com/en-us/sql/database-engine/configure-windows/external-scripts-enabled-server-configuration-option + - https://learn.microsoft.com/en-us/sql/database-engine/configure-windows/ole-automation-procedures-server-configuration-option + - https://learn.microsoft.com/en-us/sql/database-engine/configure-windows/clr-enabled-server-configuration-option + - https://www.netspi.com/blog/technical/network-penetration-testing/enumerating-domain-accounts-via-sql-server-using-adsi/ + - https://attack.mitre.org/techniques/T1505/001/ + - https://www.netspi.com/blog/technical-blog/adversary-simulation/attacking-sql-server-clr-assemblies/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: SQL Server critical procedure "$config_name$" was $change_type$ on host $dest$, which could indicate an attempt to gain code execution or perform reconnaissance - risk_objects: - - field: dest - type: system - score: 90 - - field: config_name - type: other - score: 90 - threat_objects: [] + message: SQL Server critical procedure "$config_name$" was $change_type$ on host $dest$, which could indicate an attempt to gain code execution or perform reconnaissance + risk_objects: + - field: dest + type: system + score: 90 + - field: config_name + type: other + score: 90 + threat_objects: [] tags: - analytic_story: - - SQL Server Abuse - asset_type: Windows - mitre_attack_id: - - T1505.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - manual_test: The risk message is dynamically generated in the SPL and it needs to be manually tested for integration testing. + analytic_story: + - SQL Server Abuse + asset_type: Windows + mitre_attack_id: + - T1505.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + manual_test: The risk message is dynamically generated in the SPL and it needs to be manually tested for integration testing. tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.001/simulation/adhocdq_windows_application.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Application + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.001/simulation/adhocdq_windows_application.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Application diff --git a/detections/endpoint/windows_sql_server_extended_procedure_dll_loading_hunt.yml b/detections/endpoint/windows_sql_server_extended_procedure_dll_loading_hunt.yml index 000945efcf..296e60431a 100644 --- a/detections/endpoint/windows_sql_server_extended_procedure_dll_loading_hunt.yml +++ b/detections/endpoint/windows_sql_server_extended_procedure_dll_loading_hunt.yml @@ -1,49 +1,45 @@ name: Windows SQL Server Extended Procedure DLL Loading Hunt id: 182ba99f-2dde-4cdb-8e5c-e3b1e251cb10 -version: 2 -date: '2025-05-02' +version: 3 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting description: This analytic detects when SQL Server loads DLLs to execute extended stored procedures. This is particularly important for security monitoring as it indicates the first-time use or version changes of potentially dangerous procedures like xp_cmdshell, sp_OACreate, and others. While this is a legitimate operation, adversaries may abuse these procedures for execution, discovery, or privilege escalation. data_source: -- Windows Event Log Application 8128 -search: '`wineventlog_application` EventCode=8128 - | rex field=EventData_Xml "(?[^<]+)(?[^<]+)(?[^<]+)" - | rename host as dest - | eval dll_category=case( - dll_name=="xpstar.dll", "Extended Procedures", - dll_name=="odsole70.dll", "OLE Automation", - dll_name=="xplog70.dll", "Logging Procedures", - true(), "Other") - | stats - count as execution_count, - values(procedure_name) as procedures_used, - latest(_time) as last_seen - by dest dll_name dll_category dll_version - | sort - execution_count | `windows_sql_server_extended_procedure_dll_loading_hunt_filter`' + - Windows Event Log Application 8128 +search: |- + `wineventlog_application` EventCode=8128 + | rex field=EventData_Xml "(?[^<]+)(?[^<]+)(?[^<]+)" + | rename host as dest + | eval dll_category=case( dll_name=="xpstar.dll", "Extended Procedures", dll_name=="odsole70.dll", "OLE Automation", dll_name=="xplog70.dll", "Logging Procedures", true(), "Other") + | stats count as execution_count, values(procedure_name) as procedures_used, latest(_time) as last_seen + BY dest dll_name dll_category + dll_version + | sort - execution_count + | `windows_sql_server_extended_procedure_dll_loading_hunt_filter` how_to_implement: To successfully implement this detection, ensure Windows Event Log collection is enabled and collecting from the Application channel. SQL Server must be configured to log to the Windows Application log (enabled by default). The Splunk Windows TA is also required. known_false_positives: Legitimate administrative activity and normal database operations may trigger this detection. Common false positives include initial database startup and configuration, patch deployment and version updates, regular administrative tasks using extended stored procedures, and application servers that legitimately use OLE automation. references: -- https://learn.microsoft.com/en-us/sql/relational-databases/system-stored-procedures/general-extended-stored-procedures-transact-sql -- https://learn.microsoft.com/en-us/previous-versions/sql/sql-server-2008-r2/ms175543(v=sql.105) -- https://learn.microsoft.com/en-us/sql/relational-databases/extended-stored-procedures-programming/using-extended-stored-procedures + - https://learn.microsoft.com/en-us/sql/relational-databases/system-stored-procedures/general-extended-stored-procedures-transact-sql + - https://learn.microsoft.com/en-us/previous-versions/sql/sql-server-2008-r2/ms175543(v=sql.105) + - https://learn.microsoft.com/en-us/sql/relational-databases/extended-stored-procedures-programming/using-extended-stored-procedures tags: - analytic_story: - - SQL Server Abuse - asset_type: Windows - mitre_attack_id: - - T1505.001 - - T1059.009 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - SQL Server Abuse + asset_type: Windows + mitre_attack_id: + - T1505.001 + - T1059.009 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.001/simulation/dllprocedureload_windows-application.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Application + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.001/simulation/dllprocedureload_windows-application.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Application diff --git a/detections/endpoint/windows_sql_server_startup_procedure.yml b/detections/endpoint/windows_sql_server_startup_procedure.yml index 5f4ea551b2..eaac1163ae 100644 --- a/detections/endpoint/windows_sql_server_startup_procedure.yml +++ b/detections/endpoint/windows_sql_server_startup_procedure.yml @@ -7,64 +7,49 @@ status: production type: Anomaly description: This detection identifies when a startup procedure is registered or executed in SQL Server. Startup procedures automatically execute when SQL Server starts, making them an attractive persistence mechanism for attackers. The detection monitors for suspicious stored procedure names and patterns that may indicate malicious activity, such as attempts to execute operating system commands or gain elevated privileges. data_source: -- Windows Event Log Application 17135 -search: '`wineventlog_application` EventCode=17135 - | rex field=EventData_Xml "(?[^<]+)" - | rename host as dest - | eval risk_score=case( - match(lower(startup_procedure), "xp_|sp_|cmdshell|shell|exec"), 90, - true(), 70 - ) - | eval risk_message="SQL Server startup procedure ''".startup_procedure."'' was launched on host ".dest - | stats count min(_time) as firstTime max(_time) as lastTime by dest EventCode startup_procedure risk_message risk_score - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_sql_server_startup_procedure_filter`' + - Windows Event Log Application 17135 +search: '`wineventlog_application` EventCode=17135 | rex field=EventData_Xml "(?[^<]+)" | rename host as dest | eval risk_score=case( match(lower(startup_procedure), "xp_|sp_|cmdshell|shell|exec"), 90, true(), 70 ) | eval risk_message="SQL Server startup procedure ''".startup_procedure."'' was launched on host ".dest | stats count min(_time) as firstTime max(_time) as lastTime by dest EventCode startup_procedure risk_message risk_score | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_sql_server_startup_procedure_filter`' how_to_implement: To successfully implement this detection, you need to be ingesting Windows Application Event Logs from SQL Server instances. The detection specifically looks for EventID 17135 which indicates startup procedure execution. Ensure proper logging is enabled for SQL Server startup events and that the logs are being forwarded to your SIEM. known_false_positives: Legitimate startup procedures may be used by database administrators for maintenance, monitoring, or application functionality. Common legitimate uses include database maintenance and cleanup jobs, performance monitoring and statistics collection, application initialization procedures, and system health checks. To reduce false positives, organizations should document approved startup procedures, maintain an inventory of expected startup procedures, monitor for changes to startup procedure configurations, and create exceptions for known good procedures. references: -- https://learn.microsoft.com/en-us/sql/relational-databases/system-stored-procedures/sp-procoption-transact-sql -- https://www.netspi.com/blog/technical-blog/network-penetration-testing/sql-server-persistence-part-1-startup-stored-procedures/ -- https://attack.mitre.org/techniques/T1505/001/ + - https://learn.microsoft.com/en-us/sql/relational-databases/system-stored-procedures/sp-procoption-transact-sql + - https://www.netspi.com/blog/technical-blog/network-penetration-testing/sql-server-persistence-part-1-startup-stored-procedures/ + - https://attack.mitre.org/techniques/T1505/001/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A SQL Server startup procedure "$startup_procedure$" was executed on host $dest$, which could indicate an attempt to establish persistence - risk_objects: - - field: dest - type: system - score: 90 - - field: startup_procedure - type: other - score: 70 - threat_objects: [] + message: A SQL Server startup procedure "$startup_procedure$" was executed on host $dest$, which could indicate an attempt to establish persistence + risk_objects: + - field: dest + type: system + score: 90 + - field: startup_procedure + type: other + score: 70 + threat_objects: [] tags: - analytic_story: - - SQL Server Abuse - - Hellcat Ransomware - asset_type: Windows - mitre_attack_id: - - T1505.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - manual_test: The risk message is dynamically generated in the SPL and it needs to be manually tested for integration testing. + analytic_story: + - SQL Server Abuse + - Hellcat Ransomware + asset_type: Windows + mitre_attack_id: + - T1505.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + manual_test: The risk message is dynamically generated in the SPL and it needs to be manually tested for integration testing. tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.001/simulation/sql_startupprocedure_widows-application.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Application + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.001/simulation/sql_startupprocedure_widows-application.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Application diff --git a/detections/endpoint/windows_sql_server_xp_cmdshell_config_change.yml b/detections/endpoint/windows_sql_server_xp_cmdshell_config_change.yml index 98b88bca4c..776d54a4e7 100644 --- a/detections/endpoint/windows_sql_server_xp_cmdshell_config_change.yml +++ b/detections/endpoint/windows_sql_server_xp_cmdshell_config_change.yml @@ -1,32 +1,27 @@ name: Windows SQL Server xp_cmdshell Config Change id: 5eb76fe2-a869-4865-8c4c-8cff424b18b1 -version: 6 -date: '2025-09-16' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk, sidoyle from Splunk Community status: production type: TTP description: This detection identifies when the xp_cmdshell configuration is modified in SQL Server. The xp_cmdshell extended stored procedure allows execution of operating system commands and programs from SQL Server, making it a high-risk feature commonly abused by attackers for privilege escalation and lateral movement. data_source: -- Windows Event Log Application 15457 -search: '`wineventlog_application` EventCode=15457 - | rex field=EventData_Xml "(?[^<]+)(?[^<]+)(?[^<]+)" - | rename host as dest - | where config_name="xp_cmdshell" - | eval change_type=case( - old_value="0" AND new_value="1", "enabled", - old_value="1" AND new_value="0", "disabled", - true(), "modified" - ) - | eval risk_score=case( - change_type="enabled", 90, - change_type="disabled", 60, - true(), 70 - ) - | eval risk_message="SQL Server xp_cmdshell was ".change_type." on host ".dest - | stats count min(_time) as firstTime max(_time) as lastTime by dest EventCode config_name change_type risk_message risk_score - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_sql_server_xp_cmdshell_config_change_filter`' + - Windows Event Log Application 15457 +search: |- + `wineventlog_application` EventCode=15457 + | rex field=EventData_Xml "(?[^<]+)(?[^<]+)(?[^<]+)" + | rename host as dest + | where config_name="xp_cmdshell" + | eval change_type=case( old_value="0" AND new_value="1", "enabled", old_value="1" AND new_value="0", "disabled", true(), "modified" ) + | eval risk_score=case( change_type="enabled", 90, change_type="disabled", 60, true(), 70 ) + | eval risk_message="SQL Server xp_cmdshell was ".change_type." on host ".dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest EventCode config_name + change_type risk_message risk_score + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_sql_server_xp_cmdshell_config_change_filter` how_to_implement: To successfully implement this detection, you need to be ingesting Windows Application Event Logs from SQL Server instances where SQL Server is installed. The detection specifically looks for EventID 15457 which indicates configuration changes to extended stored procedures. known_false_positives: Database administrators may legitimately enable xp_cmdshell for maintenance tasks, such as database maintenance scripts requiring OS-level operations, legacy applications, or automated system management tasks; however, this feature should generally remain disabled in production environments due to security risks. To reduce false positives, document when xp_cmdshell is required, monitor for unauthorized changes, create change control procedures for xp_cmdshell modifications, and consider alerting on the enabled state rather than configuration changes if preferred. references: @@ -34,50 +29,45 @@ references: - https://attack.mitre.org/techniques/T1505/003/ - https://learn.microsoft.com/en-us/sql/database-engine/configure-windows/xp-cmdshell-server-configuration-option drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View all SQL Server configuration changes on this host in the last 7 days - search: '`wineventlog_application` EventCode=15457 host="$dest$" | rex field=EventData_Xml "(?[^<]+)(?[^<]+)(?[^<]+)" | stats count values(config_name) as "Changed Settings" values(new_value) as "New Values" by _time dest' - earliest_offset: -7d - latest_offset: now + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View all SQL Server configuration changes on this host in the last 7 days + search: '`wineventlog_application` EventCode=15457 host="$dest$" | rex field=EventData_Xml "(?[^<]+)(?[^<]+)(?[^<]+)" | stats count values(config_name) as "Changed Settings" values(new_value) as "New Values" by _time dest' + earliest_offset: -7d + latest_offset: now rba: - message: SQL Server xp_cmdshell configuration was $change_type$ on host $dest$, which could indicate an attempt to gain operating system command execution capabilities - risk_objects: - - field: dest - type: system - score: 90 - - field: config_name - type: other - score: 90 - threat_objects: [] + message: SQL Server xp_cmdshell configuration was $change_type$ on host $dest$, which could indicate an attempt to gain operating system command execution capabilities + risk_objects: + - field: dest + type: system + score: 90 + - field: config_name + type: other + score: 90 + threat_objects: [] tags: analytic_story: - - SQL Server Abuse - - Seashell Blizzard - - GhostRedirector IIS Module and Rungan Backdoor + - SQL Server Abuse + - Seashell Blizzard + - GhostRedirector IIS Module and Rungan Backdoor asset_type: Windows mitre_attack_id: - - T1505.001 + - T1505.001 product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud security_domain: endpoint - manual_test: The risk message is dynamically generated in the SPL and it needs to be manually tested for integration testing. + manual_test: The risk message is dynamically generated in the SPL and it needs to be manually tested for integration testing. tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.001/simulation/windows-application.log - source: XmlWinEventLog:Application - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.001/simulation/windows-application.log + source: XmlWinEventLog:Application + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_sql_spawning_certutil.yml b/detections/endpoint/windows_sql_spawning_certutil.yml index 708c0abeee..6e36b8348e 100644 --- a/detections/endpoint/windows_sql_spawning_certutil.yml +++ b/detections/endpoint/windows_sql_spawning_certutil.yml @@ -1,70 +1,58 @@ name: Windows SQL Spawning CertUtil id: dfc18a5a-946e-44ee-a373-c0f60d06e676 -version: 11 -date: '2025-05-02' +version: 12 +date: '2026-02-25' author: Michael Haag, Splunk status: experimental type: TTP data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic detects the use of certutil to download software, - specifically when spawned by SQL-related processes. This detection leverages Endpoint - Detection and Response (EDR) data, focusing on command-line executions involving - certutil with parameters like *urlcache* and *split*. This activity is significant - as it may indicate a compromise by threat actors, such as Flax Typhoon, who use - certutil to establish persistent VPN connections. If confirmed malicious, this behavior - could allow attackers to maintain access, monitor system availability, and potentially - escalate to data theft or ransomware deployment. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name - IN ("sqlservr.exe", "sqlagent.exe", "sqlps.exe", "launchpad.exe", "sqldumper.exe") - `process_certutil` (Processes.process="*urlcache*" OR Processes.process="*verifyctl*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_sql_spawning_certutil_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: The occurrence of false positives should be minimal, given - that the SQL agent does not typically download software using CertUtil. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic detects the use of certutil to download software, specifically when spawned by SQL-related processes. This detection leverages Endpoint Detection and Response (EDR) data, focusing on command-line executions involving certutil with parameters like *urlcache* and *split*. This activity is significant as it may indicate a compromise by threat actors, such as Flax Typhoon, who use certutil to establish persistent VPN connections. If confirmed malicious, this behavior could allow attackers to maintain access, monitor system availability, and potentially escalate to data theft or ransomware deployment. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name IN ("sqlservr.exe", "sqlagent.exe", "sqlps.exe", "launchpad.exe", "sqldumper.exe") `process_certutil` (Processes.process="*urlcache*" + OR + Processes.process="*verifyctl*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_sql_spawning_certutil_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: The occurrence of false positives should be minimal, given that the SQL agent does not typically download software using CertUtil. references: -- https://www.microsoft.com/en-us/security/blog/2023/08/24/flax-typhoon-using-legitimate-software-to-quietly-access-taiwanese-organizations/ + - https://www.microsoft.com/en-us/security/blog/2023/08/24/flax-typhoon-using-legitimate-software-to-quietly-access-taiwanese-organizations/ rba: - message: $process_name$ was launched on $dest$ by $user$. This behavior is uncommon - with the SQL process identified. - risk_objects: - - field: dest - type: system - score: 90 - - field: user - type: user - score: 90 - threat_objects: - - field: process_name - type: process_name + message: $process_name$ was launched on $dest$ by $user$. This behavior is uncommon with the SQL process identified. + risk_objects: + - field: dest + type: system + score: 90 + - field: user + type: user + score: 90 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - SQL Server Abuse - - Flax Typhoon - - Storm-2460 CLFS Zero Day Exploitation - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1105 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SQL Server Abuse + - Flax Typhoon + - Storm-2460 CLFS Zero Day Exploitation + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1105 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/windows_sqlcmd_execution.yml b/detections/endpoint/windows_sqlcmd_execution.yml index beeb68e590..217fc3bec4 100644 --- a/detections/endpoint/windows_sqlcmd_execution.yml +++ b/detections/endpoint/windows_sqlcmd_execution.yml @@ -7,167 +7,17 @@ status: production type: Hunting description: This detection identifies potentially suspicious usage of sqlcmd.exe, focusing on command patterns that may indicate data exfiltration, reconnaissance, or malicious database operations. The detection looks for both short-form (-X) and long-form (--flag) suspicious parameter combinations, which have been observed in APT campaigns targeting high-value organizations. For example, threat actors like CL-STA-0048 have been known to abuse sqlcmd.exe for data theft and exfiltration from compromised MSSQL servers. The detection monitors for suspicious authentication attempts, output redirection, and potentially malicious query patterns that could indicate unauthorized database access or data theft. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime - from datamodel=Endpoint.Processes - where (Processes.process_name=sqlcmd.exe OR Processes.original_file_name=sqlcmd.exe) - by Processes.dest Processes.user Processes.parent_process_name Processes.process_name - Processes.process Processes.process_id Processes.parent_process_id - | `drop_dm_object_name(Processes)` - | eval process_lower=lower(process) - | eval - is_help_check=case( - match(process, "(?i)-[?]"), 1, - match(process_lower, "(?i)--help"), 1, - match(process_lower, "(?i)--version"), 1, - true(), 0 - ), - has_parameters=if(match(process, "-[A-Za-z]"), 1, 0), - has_query=case( - match(process, "-[Qq]\\s+"), 1, - match(process_lower, "--query\\s+"), 1, - match(process_lower, "--initial-query\\s+"), 1, - true(), 0 - ), - has_output=case( - match(process, "-[oO]\\s+"), 1, - match(process_lower, "--output-file\\s+"), 1, - true(), 0 - ), - has_input=case( - match(process, "-[iI]\\s+"), 1, - match(process_lower, "--input-file\\s+"), 1, - true(), 0 - ), - has_url_input=case( - match(process, "-[iI]\\s+https?://"), 1, - match(process_lower, "--input-file\\s+https?://"), 1, - match(process, "-[iI]\\s+ftp://"), 1, - match(process_lower, "--input-file\\s+ftp://"), 1, - true(), 0 - ), - has_admin_conn=case( - match(process, "-A"), 1, - match(process_lower, "--dedicated-admin-connection"), 1, - true(), 0 - ), - has_suspicious_auth=case( - match(process, "-U\\s+sa\\b"), 1, - match(process_lower, "--user-name\\s+sa\\b"), 1, - match(process, "-U\\s+admin\\b"), 1, - match(process_lower, "--user-name\\s+admin\\b"), 1, - match(process, "-E\\b"), 1, - match(process_lower, "--use-trusted-connection"), 1, - true(), 0 - ), - has_local_server=case( - match(process, "-S\\s+127\\.0\\.0\\.1"), 1, - match(process_lower, "--server\\s+127\\.0\\.0\\.1"), 1, - match(process, "-S\\s+localhost"), 1, - match(process_lower, "--server\\s+localhost"), 1, - true(), 0 - ), - has_suspicious_output=case( - match(process_lower, "-o\\s+.*\\.(txt|csv|dat)"), 1, - match(process_lower, "--output-file\\s+.*\\.(txt|csv|dat)"), 1, - true(), 0 - ), - has_cert_bypass=case( - match(process, "-C"), 1, - match(process_lower, "--trust-server-certificate"), 1, - true(), 0 - ), - has_suspicious_query=case( - match(process_lower, "(xp_cmdshell|sp_oacreate|sp_execute_external|openrowset|bulk\\s+insert)"), 1, - match(process_lower, "(master\\.\\.\\.sysdatabases|msdb\\.\\.\\.backuphistory|sysadmin|securityadmin)"), 1, - match(process_lower, "(select.*from.*sys\\.|select.*password|dump\\s+database)"), 1, - match(process_lower, "(sp_addextendedproc|sp_makewebtask|sp_addsrvrolemember)"), 1, - match(process_lower, "(sp_configure.*show\\s+advanced|reconfigure|enable_xp_cmdshell)"), 1, - match(process_lower, "(exec.*master\\.dbo\\.|exec.*msdb\\.dbo\\.)"), 1, - match(process_lower, "(sp_password|sp_control_dbmasterkey_password|sp_dropextendedproc)"), 1, - match(process_lower, "(powershell|cmd\\.exe|rundll32|regsvr32|certutil)"), 1, - true(), 0 - ), - has_suspicious_path=case( - match(process_lower, "(\\\\temp\\\\|\\\\windows\\\\|\\\\public\\\\|\\\\users\\\\public\\\\|\\\\programdata\\\\)"), 1, - match(process_lower, "(\\\\desktop\\\\.*\\.(zip|rar|7z|tar|gz))"), 1, - match(process_lower, "(\\\\downloads\\\\.*\\.(dat|bin|tmp))"), 1, - match(process_lower, "(\\\\appdata\\\\local\\\\temp\\\\|\\\\windows\\\\tasks\\\\)"), 1, - match(process_lower, "(\\\\recycler\\\\|\\\\system32\\\\|\\\\system volume information\\\\)"), 1, - match(process_lower, "(\\.vbs|\\.ps1|\\.bat|\\.cmd|\\.exe)$"), 1, - true(), 0 - ), - has_suspicious_combo=case( - match(process, "-E") AND match(process_lower, "(?i)xp_cmdshell"), 1, - match(process, "-Q") AND match(process_lower, "(?i)exec\\s+master"), 1, - has_local_server=1 AND has_suspicious_query=1, 1, - true(), 0 - ), - has_obfuscation=case( - match(process_lower, "(char\\(|convert\\(|cast\\(|declare\\s+@)"), 1, - match(process_lower, "(exec\\s+\\(|exec\\s+@|;\\s*exec)"), 1, - match(process, "\\^|\\%|\\+\\+|\\-\\-"), 1, - len(process) > 500, 1, - true(), 0 - ), - has_data_exfil=case( - match(process_lower, "(for\\s+xml|for\\s+json)"), 1, - match(process_lower, "(bulk\\s+insert.*from)"), 1, - match(process_lower, "(bcp.*queryout|bcp.*out)"), 1, - match(process_lower, "(select.*into.*from|select.*into.*outfile)"), 1, - true(), 0 - ) + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=sqlcmd.exe OR Processes.original_file_name=sqlcmd.exe) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | eval process_lower=lower(process) | eval is_help_check=case( match(process, "(?i)-[?]"), 1, match(process_lower, "(?i)--help"), 1, match(process_lower, "(?i)--version"), 1, true(), 0 ), has_parameters=if(match(process, "-[A-Za-z]"), 1, 0), has_query=case( match(process, "-[Qq]\\s+"), 1, match(process_lower, "--query\\s+"), 1, match(process_lower, "--initial-query\\s+"), 1, true(), 0 ), has_output=case( match(process, "-[oO]\\s+"), 1, match(process_lower, "--output-file\\s+"), 1, true(), 0 ), has_input=case( match(process, "-[iI]\\s+"), 1, match(process_lower, "--input-file\\s+"), 1, true(), 0 ), has_url_input=case( match(process, "-[iI]\\s+https?://"), 1, match(process_lower, "--input-file\\s+https?://"), 1, match(process, "-[iI]\\s+ftp://"), 1, match(process_lower, "--input-file\\s+ftp://"), 1, true(), 0 ), has_admin_conn=case( match(process, "-A"), 1, match(process_lower, "--dedicated-admin-connection"), 1, true(), 0 ), has_suspicious_auth=case( match(process, "-U\\s+sa\\b"), 1, match(process_lower, "--user-name\\s+sa\\b"), 1, match(process, "-U\\s+admin\\b"), 1, match(process_lower, "--user-name\\s+admin\\b"), 1, match(process, "-E\\b"), 1, match(process_lower, "--use-trusted-connection"), 1, true(), 0 ), has_local_server=case( match(process, "-S\\s+127\\.0\\.0\\.1"), 1, match(process_lower, "--server\\s+127\\.0\\.0\\.1"), 1, match(process, "-S\\s+localhost"), 1, match(process_lower, "--server\\s+localhost"), 1, true(), 0 ), has_suspicious_output=case( match(process_lower, "-o\\s+.*\\.(txt|csv|dat)"), 1, match(process_lower, "--output-file\\s+.*\\.(txt|csv|dat)"), 1, true(), 0 ), has_cert_bypass=case( match(process, "-C"), 1, match(process_lower, "--trust-server-certificate"), 1, true(), 0 ), has_suspicious_query=case( match(process_lower, "(xp_cmdshell|sp_oacreate|sp_execute_external|openrowset|bulk\\s+insert)"), 1, match(process_lower, "(master\\.\\.\\.sysdatabases|msdb\\.\\.\\.backuphistory|sysadmin|securityadmin)"), 1, match(process_lower, "(select.*from.*sys\\.|select.*password|dump\\s+database)"), 1, match(process_lower, "(sp_addextendedproc|sp_makewebtask|sp_addsrvrolemember)"), 1, match(process_lower, "(sp_configure.*show\\s+advanced|reconfigure|enable_xp_cmdshell)"), 1, match(process_lower, "(exec.*master\\.dbo\\.|exec.*msdb\\.dbo\\.)"), 1, match(process_lower, "(sp_password|sp_control_dbmasterkey_password|sp_dropextendedproc)"), 1, match(process_lower, "(powershell|cmd\\.exe|rundll32|regsvr32|certutil)"), 1, true(), 0 ), has_suspicious_path=case( match(process_lower, "(\\\\temp\\\\|\\\\windows\\\\|\\\\public\\\\|\\\\users\\\\public\\\\|\\\\programdata\\\\)"), 1, match(process_lower, "(\\\\desktop\\\\.*\\.(zip|rar|7z|tar|gz))"), 1, match(process_lower, "(\\\\downloads\\\\.*\\.(dat|bin|tmp))"), 1, match(process_lower, "(\\\\appdata\\\\local\\\\temp\\\\|\\\\windows\\\\tasks\\\\)"), 1, match(process_lower, "(\\\\recycler\\\\|\\\\system32\\\\|\\\\system volume information\\\\)"), 1, match(process_lower, "(\\.vbs|\\.ps1|\\.bat|\\.cmd|\\.exe)$"), 1, true(), 0 ), has_suspicious_combo=case( match(process, "-E") AND match(process_lower, "(?i)xp_cmdshell"), 1, match(process, "-Q") AND match(process_lower, "(?i)exec\\s+master"), 1, has_local_server=1 AND has_suspicious_query=1, 1, true(), 0 ), has_obfuscation=case( match(process_lower, "(char\\(|convert\\(|cast\\(|declare\\s+@)"), 1, match(process_lower, "(exec\\s+\\(|exec\\s+@|;\\s*exec)"), 1, match(process, "\\^|\\%|\\+\\+|\\-\\-"), 1, len(process) > 500, 1, true(), 0 ), has_data_exfil=case( match(process_lower, "(for\\s+xml|for\\s+json)"), 1, match(process_lower, "(bulk\\s+insert.*from)"), 1, match(process_lower, "(bcp.*queryout|bcp.*out)"), 1, match(process_lower, "(select.*into.*from|select.*into.*outfile)"), 1, true(), 0 ) - | eval risk_score=0 - | eval risk_score=case( - is_help_check=1, 0, - has_parameters=0, 0, - has_suspicious_combo=1, risk_score + 90, - has_suspicious_query=1, risk_score + 60, - has_suspicious_path=1, risk_score + 40, - has_url_input=1 AND has_output=1, risk_score + 80, - has_query=1 AND has_output=1, risk_score + 30, - has_query=1 AND has_suspicious_output=1, risk_score + 40, - has_admin_conn=1, risk_score + 50, - has_suspicious_auth=1, risk_score + 40, - has_local_server=1 AND has_query=1, risk_score + 30, - has_cert_bypass=1, risk_score + 20, - has_obfuscation=1, risk_score + 70, - has_data_exfil=1, risk_score + 60, - true(), risk_score - ) + | eval risk_score=0 | eval risk_score=case( is_help_check=1, 0, has_parameters=0, 0, has_suspicious_combo=1, risk_score + 90, has_suspicious_query=1, risk_score + 60, has_suspicious_path=1, risk_score + 40, has_url_input=1 AND has_output=1, risk_score + 80, has_query=1 AND has_output=1, risk_score + 30, has_query=1 AND has_suspicious_output=1, risk_score + 40, has_admin_conn=1, risk_score + 50, has_suspicious_auth=1, risk_score + 40, has_local_server=1 AND has_query=1, risk_score + 30, has_cert_bypass=1, risk_score + 20, has_obfuscation=1, risk_score + 70, has_data_exfil=1, risk_score + 60, true(), risk_score ) - | eval risk_factors=mvappend( - if((is_help_check=0 AND has_parameters=0), null(), - if(has_suspicious_combo=1, "High-risk command combination detected", null())), - if((is_help_check=0 AND has_parameters=0), null(), - if(has_suspicious_query=1, "Suspicious SQL query pattern", null())), - if(has_suspicious_path=1, "Suspicious output path", null()), - if(has_url_input=1 AND has_output=1, "File download attempt", null()), - if(has_query=1 AND has_output=1, "Query output to file", null()), - if(has_admin_conn=1, "Admin connection", null()), - if(has_suspicious_auth=1, "Suspicious authentication", null()), - if(has_local_server=1, "Local server connection", null()), - if(has_cert_bypass=1, "Certificate validation bypass", null()), - if(has_obfuscation=1, "Command obfuscation detected", null()), - if(has_data_exfil=1, "Potential data exfiltration", null()) - ) - | eval risk_message="SQLCMD execution with risk factors: ".mvjoin(risk_factors, ", ") + | eval risk_factors=mvappend( if((is_help_check=0 AND has_parameters=0), null(), if(has_suspicious_combo=1, "High-risk command combination detected", null())), if((is_help_check=0 AND has_parameters=0), null(), if(has_suspicious_query=1, "Suspicious SQL query pattern", null())), if(has_suspicious_path=1, "Suspicious output path", null()), if(has_url_input=1 AND has_output=1, "File download attempt", null()), if(has_query=1 AND has_output=1, "Query output to file", null()), if(has_admin_conn=1, "Admin connection", null()), if(has_suspicious_auth=1, "Suspicious authentication", null()), if(has_local_server=1, "Local server connection", null()), if(has_cert_bypass=1, "Certificate validation bypass", null()), if(has_obfuscation=1, "Command obfuscation detected", null()), if(has_data_exfil=1, "Potential data exfiltration", null()) ) | eval risk_message="SQLCMD execution with risk factors: ".mvjoin(risk_factors, ", ") - | where is_help_check=0 AND (risk_score >= 30 OR (has_parameters=1 AND has_suspicious_query=1)) - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_sqlcmd_execution_filter`' -how_to_implement: The analytic will need to be tuned based on organization specific data. Currently, set to hunting to allow for tuning. SQLCmd is a legitimate tool for database management and scripting tasks within enterprise environments. The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + | where is_help_check=0 AND (risk_score >= 30 OR (has_parameters=1 AND has_suspicious_query=1)) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_sqlcmd_execution_filter`' +how_to_implement: The analytic will need to be tuned based on organization specific data. Currently, set to hunting to allow for tuning. SQLCmd is a legitimate tool for database management and scripting tasks within enterprise environments. The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: | Database administrators and developers commonly use sqlcmd.exe legitimately for database management and scripting tasks within enterprise environments. These legitimate activities often include database backups and restores, schema deployment scripts, automated database maintenance, and ETL processes. However, it's important to note that some organizations may have no sqlcmd.exe usage at all, making any detection highly suspicious. To effectively manage false positives, organizations should whitelist known administrator accounts, create exceptions for approved script paths and output locations, and add legitimate usage patterns to the filter macro as needed. Recommend running this detection first as a hunt to review usage patterns. Following, modify the risk score and false positive list as needed. @@ -178,21 +28,21 @@ references: - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1105/T1105.md#atomic-test-32---file-download-with-sqlcmdexe - https://unit42.paloaltonetworks.com/espionage-campaign-targets-south-asian-entities/ tags: - analytic_story: - - SQL Server Abuse - - GhostRedirector IIS Module and Rungan Backdoor - asset_type: Endpoint - mitre_attack_id: - - T1059.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - SQL Server Abuse + - GhostRedirector IIS Module and Rungan Backdoor + asset_type: Endpoint + mitre_attack_id: + - T1059.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/atomic_red_team/sqlcmd_windows_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/atomic_red_team/sqlcmd_windows_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_sqlservr_spawning_shell.yml b/detections/endpoint/windows_sqlservr_spawning_shell.yml index a0990cb12c..f97993a6eb 100644 --- a/detections/endpoint/windows_sqlservr_spawning_shell.yml +++ b/detections/endpoint/windows_sqlservr_spawning_shell.yml @@ -1,81 +1,73 @@ name: Windows Sqlservr Spawning Shell id: d33aac9f-030c-4830-8701-0c2dd75bb6cb -version: 3 -date: '2025-05-02' +version: 4 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: This analytic detects instances where the sqlservr.exe process spawns - a command shell (cmd.exe) or PowerShell process. This behavior is often indicative - of command execution initiated from within the SQL Server process, potentially due - to exploitation of SQL injection vulnerabilities or the use of extended stored procedures - like xp_cmdshell. +description: This analytic detects instances where the sqlservr.exe process spawns a command shell (cmd.exe) or PowerShell process. This behavior is often indicative of command execution initiated from within the SQL Server process, potentially due to exploitation of SQL injection vulnerabilities or the use of extended stored procedures like xp_cmdshell. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="sqlservr.exe" - `process_cmd` OR `process_powershell` by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_sqlservr_spawning_shell_filter`' -how_to_implement: To implement this detection, you need to be ingesting endpoint data - that captures process creation events, specifically the parent-child process relationships. - Ensure that you are collecting Sysmon Event ID 1 or Windows Event Log Security 4688 - events. The data should be mapped to the Endpoint data model in Splunk. -known_false_positives: Legitimate administrative activities or monitoring tools might - occasionally spawn command shells from sqlservr.exe. Review the process command-line - arguments and consider filtering out known legitimate processes or users. + - Sysmon EventID 1 + - Windows Event Log Security 4688 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name="sqlservr.exe" `process_cmd` + OR + `process_powershell` + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_sqlservr_spawning_shell_filter` +how_to_implement: To implement this detection, you need to be ingesting endpoint data that captures process creation events, specifically the parent-child process relationships. Ensure that you are collecting Sysmon Event ID 1 or Windows Event Log Security 4688 events. The data should be mapped to the Endpoint data model in Splunk. +known_false_positives: Legitimate administrative activities or monitoring tools might occasionally spawn command shells from sqlservr.exe. Review the process command-line arguments and consider filtering out known legitimate processes or users. references: -- https://attack.mitre.org/techniques/T1505/001/ -- https://github.com/MHaggis/notes/tree/master/utilities/SQLSSTT + - https://attack.mitre.org/techniques/T1505/001/ + - https://github.com/MHaggis/notes/tree/master/utilities/SQLSSTT drilldown_searches: -- name: View the detection results for - "$dest$" and "$process_name$" - search: '%original_detection_search% | search dest = "$dest$" process_name = "$process_name$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$process_name$" + search: '%original_detection_search% | search dest = "$dest$" process_name = "$process_name$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A command shell was spawned by sqlservr.exe on host $dest$ by user $user$. - This may indicate unauthorized command execution. - risk_objects: - - field: dest - type: system - score: 90 - - field: user - type: user - score: 90 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: A command shell was spawned by sqlservr.exe on host $dest$ by user $user$. This may indicate unauthorized command execution. + risk_objects: + - field: dest + type: system + score: 90 + - field: user + type: user + score: 90 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - SQL Server Abuse - asset_type: Endpoint - mitre_attack_id: - - T1505.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - SQL Server Abuse + asset_type: Endpoint + mitre_attack_id: + - T1505.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.001/simulation/sqlservr-windows_sysmon.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.001/simulation/sqlservr-windows_sysmon.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational diff --git a/detections/endpoint/windows_sqlwriter_sqldumper_dll_sideload.yml b/detections/endpoint/windows_sqlwriter_sqldumper_dll_sideload.yml index 5d42b5b098..1f215010b4 100644 --- a/detections/endpoint/windows_sqlwriter_sqldumper_dll_sideload.yml +++ b/detections/endpoint/windows_sqlwriter_sqldumper_dll_sideload.yml @@ -4,81 +4,53 @@ version: 7 date: '2025-05-02' author: Michael Haag, Teoderick Contreras, Splunk data_source: -- Sysmon EventID 7 + - Sysmon EventID 7 type: TTP status: production -description: The following analytic detects the abuse of SqlWriter and SQLDumper executables - to sideload the vcruntime140.dll library. It leverages Sysmon EventCode 7 logs, - focusing on instances where SQLDumper.exe or SQLWriter.exe load vcruntime140.dll, - excluding legitimate loads from the System32 directory. This activity is significant - as it indicates potential DLL sideloading, a technique used by adversaries to execute - malicious code within trusted processes. If confirmed malicious, this could allow - attackers to execute arbitrary code, maintain persistence, and evade detection by - blending with legitimate processes. -search: '`sysmon` EventCode=7 (Image="*\\SQLDumper.exe" OR Image="*\\SQLWriter.exe") - ImageLoaded="*\\vcruntime140.dll" NOT ImageLoaded="C:\\Windows\\System32\\*" | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded - dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash - process_id process_name process_path service_dll_signature_exists service_dll_signature_verified - signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`| `windows_sqlwriter_sqldumper_dll_sideload_filter`' -how_to_implement: The analytic is designed to be run against Sysmon event logs collected - from endpoints. The analytic requires the Sysmon event logs to be ingested into - Splunk. The analytic searches for EventCode 7 where the Image is either SQLDumper.exe - or SQLWriter.exe and the ImageLoaded is vcruntime140.dll. The search also filters - out the legitimate loading of vcruntime140.dll from the System32 directory to reduce - false positives. The analytic can be modified to include additional known good paths - for vcruntime140.dll to further reduce false positives. -known_false_positives: False positives are possible if legitimate processes are loading - vcruntime140.dll from non-standard directories. It is recommended to investigate - the context of the process loading vcruntime140.dll to determine if it is malicious - or not. Modify the search to include additional known good paths for vcruntime140.dll - to reduce false positives. +description: The following analytic detects the abuse of SqlWriter and SQLDumper executables to sideload the vcruntime140.dll library. It leverages Sysmon EventCode 7 logs, focusing on instances where SQLDumper.exe or SQLWriter.exe load vcruntime140.dll, excluding legitimate loads from the System32 directory. This activity is significant as it indicates potential DLL sideloading, a technique used by adversaries to execute malicious code within trusted processes. If confirmed malicious, this could allow attackers to execute arbitrary code, maintain persistence, and evade detection by blending with legitimate processes. +search: '`sysmon` EventCode=7 (Image="*\\SQLDumper.exe" OR Image="*\\SQLWriter.exe") ImageLoaded="*\\vcruntime140.dll" NOT ImageLoaded="C:\\Windows\\System32\\*" | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_sqlwriter_sqldumper_dll_sideload_filter`' +how_to_implement: The analytic is designed to be run against Sysmon event logs collected from endpoints. The analytic requires the Sysmon event logs to be ingested into Splunk. The analytic searches for EventCode 7 where the Image is either SQLDumper.exe or SQLWriter.exe and the ImageLoaded is vcruntime140.dll. The search also filters out the legitimate loading of vcruntime140.dll from the System32 directory to reduce false positives. The analytic can be modified to include additional known good paths for vcruntime140.dll to further reduce false positives. +known_false_positives: False positives are possible if legitimate processes are loading vcruntime140.dll from non-standard directories. It is recommended to investigate the context of the process loading vcruntime140.dll to determine if it is malicious or not. Modify the search to include additional known good paths for vcruntime140.dll to reduce false positives. references: -- https://www.mandiant.com/resources/blog/apt29-wineloader-german-political-parties -- https://www.zscaler.com/blogs/security-research/european-diplomats-targeted-spikedwine-wineloader + - https://www.mandiant.com/resources/blog/apt29-wineloader-german-political-parties + - https://www.zscaler.com/blogs/security-research/european-diplomats-targeted-spikedwine-wineloader drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $Image$ loading $ImageLoaded$ was detected on $dest$. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: - - field: Image - type: file_name + message: An instance of $Image$ loading $ImageLoaded$ was detected on $dest$. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: + - field: Image + type: file_name tags: - analytic_story: - - APT29 Diplomatic Deceptions with WINELOADER - group: - - APT29 - - Cozy Bear - - Midnight Blizzard - asset_type: Endpoint - mitre_attack_id: - - T1574.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - APT29 Diplomatic Deceptions with WINELOADER + group: + - APT29 + - Cozy Bear + - Midnight Blizzard + asset_type: Endpoint + mitre_attack_id: + - T1574.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/wineloader/sqlwriter_sqldumper_sideload_windows-sysmon.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/wineloader/sqlwriter_sqldumper_sideload_windows-sysmon.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational diff --git a/detections/endpoint/windows_ssh_proxy_command.yml b/detections/endpoint/windows_ssh_proxy_command.yml index f3e9b2feeb..5a81e5bb9f 100644 --- a/detections/endpoint/windows_ssh_proxy_command.yml +++ b/detections/endpoint/windows_ssh_proxy_command.yml @@ -6,101 +6,101 @@ author: Michael Haag, AJ King, Nasreddine Bencherchali, Splunk, Jesse Hunter, Sp status: production type: Anomaly description: | - This detection identifies potential abuse of SSH "ProxyCommand" or "LocalCommand" by monitoring for suspicious process execution patterns. - Specifically, it looks for instances where ssh.exe (as a parent process) containing "ProxyCommand" or "LocalCommand" in its arguments spawns potentially malicious child processes like mshta, powershell, wscript, or cscript, or processes containing "http" in their command line. - This technique can be used by attackers to execute arbitrary commands through SSH proxy configurations, potentially enabling command & control activities or remote code execution. The detection focuses on commonly abused Windows scripting engines and web requests that may indicate malicious activity when spawned through SSH proxy commands. + This detection identifies potential abuse of SSH "ProxyCommand" or "LocalCommand" by monitoring for suspicious process execution patterns. + Specifically, it looks for instances where ssh.exe (as a parent process) containing "ProxyCommand" or "LocalCommand" in its arguments spawns potentially malicious child processes like mshta, powershell, wscript, or cscript, or processes containing "http" in their command line. + This technique can be used by attackers to execute arbitrary commands through SSH proxy configurations, potentially enabling command & control activities or remote code execution. The detection focuses on commonly abused Windows scripting engines and web requests that may indicate malicious activity when spawned through SSH proxy commands. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 + - Sysmon EventID 1 + - Windows Event Log Security 4688 search: |- - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime - - from datamodel=Endpoint.Processes where + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime - Processes.parent_process_name="ssh.exe" - ( - Processes.parent_process = "*ProxyCommand=*" - OR + from datamodel=Endpoint.Processes where + + Processes.parent_process_name="ssh.exe" ( - Processes.parent_process = "* PermitLocalCommand=yes*" - Processes.parent_process = "* LocalCommand=*" + Processes.parent_process = "*ProxyCommand=*" + OR + ( + Processes.parent_process = "* PermitLocalCommand=yes*" + Processes.parent_process = "* LocalCommand=*" + ) ) - ) - Processes.process IN ( - "*cscript*", - "*http*", - "*mshta*", - "*powershell*", - "*pwsh*", - "*wmic*", - "*wscript*" - ) + Processes.process IN ( + "*cscript*", + "*http*", + "*mshta*", + "*powershell*", + "*pwsh*", + "*wmic*", + "*wscript*" + ) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id - Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id - Processes.vendor_product + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec + Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id + Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id + Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_ssh_proxy_command_filter` + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_ssh_proxy_command_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: | - Legitimate use of SSH ProxyCommand or LocalCommand with scripting engines may trigger this detection. - Filter as needed based on your environment's normal SSH usage patterns and authorized scripting activities. + Legitimate use of SSH ProxyCommand or LocalCommand with scripting engines may trigger this detection. + Filter as needed based on your environment's normal SSH usage patterns and authorized scripting activities. references: - - https://www.virustotal.com/gui/file/c33f82868dbbfc3ab03918f430b1a348499f5baf047b136ff0a4fc3e8addaa9b/detection - - https://attack.mitre.org/techniques/T1572/ - - https://lolbas-project.github.io/lolbas/Binaries/Ssh/ - - https://man.openbsd.org/ssh_config#ProxyCommand - - https://man.openbsd.org/ssh_config#LocalCommand + - https://www.virustotal.com/gui/file/c33f82868dbbfc3ab03918f430b1a348499f5baf047b136ff0a4fc3e8addaa9b/detection + - https://attack.mitre.org/techniques/T1572/ + - https://lolbas-project.github.io/lolbas/Binaries/Ssh/ + - https://man.openbsd.org/ssh_config#ProxyCommand + - https://man.openbsd.org/ssh_config#LocalCommand drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious process execution $process$ detected through SSH $parent_process$ on $dest$ by user $user$ - risk_objects: - - field: dest - type: system - score: 60 - - field: user - type: user - score: 40 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: Suspicious process execution $process$ detected through SSH $parent_process$ on $dest$ by user $user$ + risk_objects: + - field: dest + type: system + score: 60 + - field: user + type: user + score: 40 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - ZDI-CAN-25373 Windows Shortcut Exploit Abused as Zero-Day - - Living Off The Land - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1572 - - T1059.001 - - T1105 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ZDI-CAN-25373 Windows Shortcut Exploit Abused as Zero-Day + - Living Off The Land + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1572 + - T1059.001 + - T1105 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1572/ssh_proxy_command/sshproxycommand_windows-sysmon.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1572/ssh_proxy_command/sshproxycommand_windows-sysmon.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational diff --git a/detections/endpoint/windows_steal_authentication_certificates___esc1_abuse.yml b/detections/endpoint/windows_steal_authentication_certificates___esc1_abuse.yml index 16d0c27513..c3e7ae8d33 100644 --- a/detections/endpoint/windows_steal_authentication_certificates___esc1_abuse.yml +++ b/detections/endpoint/windows_steal_authentication_certificates___esc1_abuse.yml @@ -5,83 +5,50 @@ date: '2025-05-02' author: Steven Dick status: production type: TTP -description: The following analytic detects when a new certificate is requested or - granted against Active Directory Certificate Services (AD CS) using a Subject Alternative - Name (SAN). It leverages Windows Security Event Codes 4886 and 4887 to identify - these actions. This activity is significant because improperly configured certificate - templates can be exploited for privilege escalation and environment compromise. - If confirmed malicious, an attacker could gain elevated privileges or persist within - the environment, potentially leading to unauthorized access to sensitive information - and further exploitation. +description: The following analytic detects when a new certificate is requested or granted against Active Directory Certificate Services (AD CS) using a Subject Alternative Name (SAN). It leverages Windows Security Event Codes 4886 and 4887 to identify these actions. This activity is significant because improperly configured certificate templates can be exploited for privilege escalation and environment compromise. If confirmed malicious, an attacker could gain elevated privileges or persist within the environment, potentially leading to unauthorized access to sensitive information and further exploitation. data_source: -- Windows Event Log Security 4886 -- Windows Event Log Security 4887 -search: "`wineventlog_security` EventCode IN (4886,4887) Attributes=\"*SAN:*upn*\"\ - \ Attributes=\"*CertificateTemplate:*\" | stats count min(_time) as firstTime max(_time) - as lastTime values(name) as name values(status) as status values(Subject) as ssl_subject - values(SubjectKeyIdentifier) as ssl_hash by Computer, EventCode, Requester, Attributes, - RequestId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| - fillnull | rex field=Attributes \"(?i)CertificateTemplate:(?[^\\r\\n]+)\"\ - \ | rex field=Attributes \"(?i)ccm:(?[^\\r\\n]+)\" | rex max_match=10 field=Attributes - \"(?i)(upn=(?[^\\r\\n&]+))\" | rex max_match=10 field=Attributes \"\ - (?i)(dns=(?[^\\r\\n&]+))\" | rex field=Requester \"(.+\\\\\\\\)?(?[^\\\ - r\\n]+)\" | eval flavor_text = case(EventCode==\"4886\",\"A suspicious certificate - was requested using request ID: \".'RequestId',EventCode==\"4887\", \"A suspicious - certificate was issued using request ID: \".'RequestId'.\". To revoke this certifacte - use this request ID or the SSL fingerprint [\".'ssl_hash'.\"]\"), dest = upper(coalesce(req_dest_1,req_dest_2)), - src = upper(coalesce(req_src,Computer)) | fields - req_* | rename Attributes as - object_attrs, EventCode as signature_id, name as signature, RequestId as ssl_serial, - Requester as ssl_subject_common_name| `windows_steal_authentication_certificates___esc1_abuse_filter`" -how_to_implement: To implement this analytic, enhanced Audit Logging must be enabled - on AD CS and within Group Policy Management for CS server. See Page 115 of first - reference. Recommend throttle correlation by RequestId/ssl_serial at minimum. -known_false_positives: False positives may be generated in environments where administrative - users or processes are allowed to generate certificates with Subject Alternative - Names. Sources or templates used in these processes may need to be tuned out for - accurate function. + - Windows Event Log Security 4886 + - Windows Event Log Security 4887 +search: "`wineventlog_security` EventCode IN (4886,4887) Attributes=\"*SAN:*upn*\" Attributes=\"*CertificateTemplate:*\" | stats count min(_time) as firstTime max(_time) as lastTime values(name) as name values(status) as status values(Subject) as ssl_subject values(SubjectKeyIdentifier) as ssl_hash by Computer, EventCode, Requester, Attributes, RequestId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| fillnull | rex field=Attributes \"(?i)CertificateTemplate:(?[^\\r\\n]+)\" | rex field=Attributes \"(?i)ccm:(?[^\\r\\n]+)\" | rex max_match=10 field=Attributes \"(?i)(upn=(?[^\\r\\n&]+))\" | rex max_match=10 field=Attributes \"(?i)(dns=(?[^\\r\\n&]+))\" | rex field=Requester \"(.+\\\\\\\\)?(?[^\\r\\n]+)\" | eval flavor_text = case(EventCode==\"4886\",\"A suspicious certificate was requested using request ID: \".'RequestId',EventCode==\"4887\", \"A suspicious certificate was issued using request ID: \".'RequestId'.\". To revoke this certifacte use this request ID or the SSL fingerprint [\".'ssl_hash'.\"]\"), dest = upper(coalesce(req_dest_1,req_dest_2)), src = upper(coalesce(req_src,Computer)) | fields - req_* | rename Attributes as object_attrs, EventCode as signature_id, name as signature, RequestId as ssl_serial, Requester as ssl_subject_common_name| `windows_steal_authentication_certificates___esc1_abuse_filter`" +how_to_implement: To implement this analytic, enhanced Audit Logging must be enabled on AD CS and within Group Policy Management for CS server. See Page 115 of first reference. Recommend throttle correlation by RequestId/ssl_serial at minimum. +known_false_positives: False positives may be generated in environments where administrative users or processes are allowed to generate certificates with Subject Alternative Names. Sources or templates used in these processes may need to be tuned out for accurate function. references: -- https://specterops.io/wp-content/uploads/sites/3/2022/06/Certified_Pre-Owned.pdf -- https://github.com/ly4k/Certipy#esc1 -- https://pentestlaboratories.com/2021/11/08/threat-hunting-certificate-account-persistence/ + - https://specterops.io/wp-content/uploads/sites/3/2022/06/Certified_Pre-Owned.pdf + - https://github.com/ly4k/Certipy#esc1 + - https://pentestlaboratories.com/2021/11/08/threat-hunting-certificate-account-persistence/ drilldown_searches: -- name: View the detection results for - "$src$" and "$dest$" - search: '%original_detection_search% | search src = "$src$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" and "$dest$" + search: '%original_detection_search% | search src = "$src$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible AD CS ESC1 activity by $src_user$ - $flavor_text$ - risk_objects: - - field: src - type: system - score: 60 - - field: src_user - type: user - score: 60 - threat_objects: [] + message: Possible AD CS ESC1 activity by $src_user$ - $flavor_text$ + risk_objects: + - field: src + type: system + score: 60 + - field: src_user + type: user + score: 60 + threat_objects: [] tags: - analytic_story: - - Windows Certificate Services - asset_type: Endpoint - mitre_attack_id: - - T1649 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Certificate Services + asset_type: Endpoint + mitre_attack_id: + - T1649 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/certify_abuse/certify_esc1_abuse_winsecurity.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/certify_abuse/certify_esc1_abuse_winsecurity.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_steal_authentication_certificates___esc1_authentication.yml b/detections/endpoint/windows_steal_authentication_certificates___esc1_authentication.yml index 3748d2a95d..5a3bdff380 100644 --- a/detections/endpoint/windows_steal_authentication_certificates___esc1_authentication.yml +++ b/detections/endpoint/windows_steal_authentication_certificates___esc1_authentication.yml @@ -5,98 +5,62 @@ date: '2025-05-02' author: Steven Dick status: production type: TTP -description: The following analytic detects when a suspicious certificate with a Subject - Alternative Name (SAN) is issued using Active Directory Certificate Services (AD - CS) and then immediately used for authentication. This detection leverages Windows - Security Event Logs, specifically EventCode 4887, to identify the issuance and subsequent - use of the certificate. This activity is significant because improperly configured - certificate templates can be exploited for privilege escalation and environment - compromise. If confirmed malicious, an attacker could gain unauthorized access, - escalate privileges, and potentially compromise the entire environment. +description: The following analytic detects when a suspicious certificate with a Subject Alternative Name (SAN) is issued using Active Directory Certificate Services (AD CS) and then immediately used for authentication. This detection leverages Windows Security Event Logs, specifically EventCode 4887, to identify the issuance and subsequent use of the certificate. This activity is significant because improperly configured certificate templates can be exploited for privilege escalation and environment compromise. If confirmed malicious, an attacker could gain unauthorized access, escalate privileges, and potentially compromise the entire environment. data_source: -- Windows Event Log Security 4887 -- Windows Event Log Security 4768 -search: "`wineventlog_security` EventCode IN (4887) Attributes=\"*SAN:*upn*\" Attributes=\"\ - *CertificateTemplate:*\" | stats count min(_time) as firstTime max(_time) as lastTime - values(name) as name values(status) as status values(Subject) as ssl_subject values(SubjectKeyIdentifier) - as ssl_hash by Computer, EventCode, Requester, Attributes, RequestId | rex field=Attributes - \"(?i)CertificateTemplate:(?[^\\r\\n]+)\" | rex field=Attributes \"(?i)ccm:(?[^\\\ - r\\n]+)\" | rex max_match=10 field=Attributes \"(?i)(upn=(?[^\\r\\n&]+))\"\ - \ | rex max_match=10 field=Attributes \"(?i)(dns=(?[^\\r\\n&]+))\" | - rex field=Requester \"(.+\\\\\\\\)?(?[^\\r\\n]+)\" | rename Attributes - as object_attrs, EventCode as signature_id, name as signature, RequestId as ssl_serial, - Requester as ssl_subject_common_name | eval user = lower(coalesce(req_user_1,req_user_2))\ - \ | join user [ | search `wineventlog_security` EventCode=4768 CertThumbprint=* - | rename TargetUserName as user, Computer as auth_dest, IpAddress as auth_src | - fields auth_src,auth_dest,user ] | eval src = upper(coalesce(auth_src,req_src)), - dest = upper(coalesce(auth_dest,req_dest_1,req_dest_2)), risk_score = 90 | eval - flavor_text = case(signature_id==\"4887\", \"User account [\".'user'.\"] authenticated - after a suspicious certificate was issued for it by [\".'src_user'.\"] using certificate - request ID: \".'ssl_serial') | fields - req_* auth_* | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_steal_authentication_certificates___esc1_authentication_filter`" -how_to_implement: To implement this analytic, enhanced Audit Logging must be enabled - on AD CS and within Group Policy Management for CS server. See Page 115 of first - reference. Recommend throttle correlation by RequestId/ssl_serial at minimum. -known_false_positives: False positives may be generated in environments where administrative - users or processes are allowed to generate certificates with Subject Alternative - Names for authentication. Sources or templates used in these processes may need - to be tuned out for accurate function. + - Windows Event Log Security 4887 + - Windows Event Log Security 4768 +search: "`wineventlog_security` EventCode IN (4887) Attributes=\"*SAN:*upn*\" Attributes=\"*CertificateTemplate:*\" | stats count min(_time) as firstTime max(_time) as lastTime values(name) as name values(status) as status values(Subject) as ssl_subject values(SubjectKeyIdentifier) as ssl_hash by Computer, EventCode, Requester, Attributes, RequestId | rex field=Attributes \"(?i)CertificateTemplate:(?[^\\r\\n]+)\" | rex field=Attributes \"(?i)ccm:(?[^\\r\\n]+)\" | rex max_match=10 field=Attributes \"(?i)(upn=(?[^\\r\\n&]+))\" | rex max_match=10 field=Attributes \"(?i)(dns=(?[^\\r\\n&]+))\" | rex field=Requester \"(.+\\\\\\\\)?(?[^\\r\\n]+)\" | rename Attributes as object_attrs, EventCode as signature_id, name as signature, RequestId as ssl_serial, Requester as ssl_subject_common_name | eval user = lower(coalesce(req_user_1,req_user_2)) | join user [ | search `wineventlog_security` EventCode=4768 CertThumbprint=* | rename TargetUserName as user, Computer as auth_dest, IpAddress as auth_src | fields auth_src,auth_dest,user ] | eval src = upper(coalesce(auth_src,req_src)), dest = upper(coalesce(auth_dest,req_dest_1,req_dest_2)), risk_score = 90 | eval flavor_text = case(signature_id==\"4887\", \"User account [\".'user'.\"] authenticated after a suspicious certificate was issued for it by [\".'src_user'.\"] using certificate request ID: \".'ssl_serial') | fields - req_* auth_* | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_steal_authentication_certificates___esc1_authentication_filter`" +how_to_implement: To implement this analytic, enhanced Audit Logging must be enabled on AD CS and within Group Policy Management for CS server. See Page 115 of first reference. Recommend throttle correlation by RequestId/ssl_serial at minimum. +known_false_positives: False positives may be generated in environments where administrative users or processes are allowed to generate certificates with Subject Alternative Names for authentication. Sources or templates used in these processes may need to be tuned out for accurate function. references: -- https://specterops.io/wp-content/uploads/sites/3/2022/06/Certified_Pre-Owned.pdf -- https://github.com/ly4k/Certipy#esc1 -- https://pentestlaboratories.com/2021/11/08/threat-hunting-certificate-account-persistence/ + - https://specterops.io/wp-content/uploads/sites/3/2022/06/Certified_Pre-Owned.pdf + - https://github.com/ly4k/Certipy#esc1 + - https://pentestlaboratories.com/2021/11/08/threat-hunting-certificate-account-persistence/ drilldown_searches: -- name: View the detection results for - "$src$" and "$dest$" - search: '%original_detection_search% | search src = "$src$" dest = "$dest$" src_user - = "$src_user$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", - "$dest$", "$src_user$", "$user$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" and "$dest$" + search: '%original_detection_search% | search src = "$src$" dest = "$dest$" src_user = "$src_user$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$dest$", "$src_user$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible AD CS ESC1 authentication on $dest$ - risk_objects: - - field: src - type: system - score: 90 - - field: dest - type: system - score: 90 - - field: src_user - type: user - score: 90 - - field: user - type: user - score: 90 - threat_objects: - - field: ssl_hash - type: tls_hash - - field: ssl_serial - type: certificate_serial + message: Possible AD CS ESC1 authentication on $dest$ + risk_objects: + - field: src + type: system + score: 90 + - field: dest + type: system + score: 90 + - field: src_user + type: user + score: 90 + - field: user + type: user + score: 90 + threat_objects: + - field: ssl_hash + type: tls_hash + - field: ssl_serial + type: certificate_serial tags: - analytic_story: - - Windows Certificate Services - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1649 - - T1550 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Certificate Services + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1649 + - T1550 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/certify_abuse/certify_esc1_abuse_winsecurity.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/certify_abuse/certify_esc1_abuse_winsecurity.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_steal_authentication_certificates_certificate_issued.yml b/detections/endpoint/windows_steal_authentication_certificates_certificate_issued.yml index 168bae0148..15e86169fd 100644 --- a/detections/endpoint/windows_steal_authentication_certificates_certificate_issued.yml +++ b/detections/endpoint/windows_steal_authentication_certificates_certificate_issued.yml @@ -1,66 +1,55 @@ name: Windows Steal Authentication Certificates Certificate Issued id: 9b1a5385-0c31-4c39-9753-dc26b8ce64c2 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic identifies the issuance of a new certificate by - Certificate Services - AD CS, detected via Event ID 4887. This event logs the requester - user context, DNS hostname of the requesting machine, and the request time. Monitoring - this activity is crucial as it can indicate potential misuse of authentication certificates. - If confirmed malicious, an attacker could use the issued certificate to impersonate - users, escalate privileges, or maintain persistence within the environment. This - detection helps in identifying and correlating suspicious certificate-related activities - for further investigation. +description: The following analytic identifies the issuance of a new certificate by Certificate Services - AD CS, detected via Event ID 4887. This event logs the requester user context, DNS hostname of the requesting machine, and the request time. Monitoring this activity is crucial as it can indicate potential misuse of authentication certificates. If confirmed malicious, an attacker could use the issued certificate to impersonate users, escalate privileges, or maintain persistence within the environment. This detection helps in identifying and correlating suspicious certificate-related activities for further investigation. data_source: -- Windows Event Log Security 4887 -search: '`wineventlog_security` EventCode=4887 | stats count min(_time) as firstTime - max(_time) as lastTime by dest, name, Requester, action, Attributes, Subject | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`| `windows_steal_authentication_certificates_certificate_issued_filter`' -how_to_implement: To implement this analytic, enhanced Audit Logging must be enabled - on AD CS and within Group Policy Management for CS server. See Page 115 of first - reference. -known_false_positives: False positives will be generated based on normal certificates - issued. Leave enabled to generate Risk, as this is meant to be an anomaly analytic. + - Windows Event Log Security 4887 +search: |- + `wineventlog_security` EventCode=4887 + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest, name, Requester, + action, Attributes, Subject + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_steal_authentication_certificates_certificate_issued_filter` +how_to_implement: To implement this analytic, enhanced Audit Logging must be enabled on AD CS and within Group Policy Management for CS server. See Page 115 of first reference. +known_false_positives: False positives will be generated based on normal certificates issued. Leave enabled to generate Risk, as this is meant to be an anomaly analytic. references: -- https://specterops.io/wp-content/uploads/sites/3/2022/06/Certified_Pre-Owned.pdf + - https://specterops.io/wp-content/uploads/sites/3/2022/06/Certified_Pre-Owned.pdf drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A certificate was issued to $dest$. - risk_objects: - - field: dest - type: system - score: 8 - threat_objects: [] + message: A certificate was issued to $dest$. + risk_objects: + - field: dest + type: system + score: 8 + threat_objects: [] tags: - analytic_story: - - Windows Certificate Services - asset_type: Endpoint - mitre_attack_id: - - T1649 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Certificate Services + asset_type: Endpoint + mitre_attack_id: + - T1649 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/4887_windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/4887_windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_steal_authentication_certificates_certificate_request.yml b/detections/endpoint/windows_steal_authentication_certificates_certificate_request.yml index 274b6899d9..d2e699611c 100644 --- a/detections/endpoint/windows_steal_authentication_certificates_certificate_request.yml +++ b/detections/endpoint/windows_steal_authentication_certificates_certificate_request.yml @@ -1,66 +1,55 @@ name: Windows Steal Authentication Certificates Certificate Request id: 747d7800-2eaa-422d-b994-04d8bb9e06d0 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic detects when a new certificate is requested from - Certificate Services - AD CS. It leverages Event ID 4886, which indicates that a - certificate request has been received. This activity is significant because unauthorized - certificate requests can be part of credential theft or lateral movement tactics. - If confirmed malicious, an attacker could use the certificate to impersonate users, - gain unauthorized access to resources, or establish persistent access within the - environment. Monitoring and correlating this event with other suspicious activities - is crucial for identifying potential security incidents. +description: The following analytic detects when a new certificate is requested from Certificate Services - AD CS. It leverages Event ID 4886, which indicates that a certificate request has been received. This activity is significant because unauthorized certificate requests can be part of credential theft or lateral movement tactics. If confirmed malicious, an attacker could use the certificate to impersonate users, gain unauthorized access to resources, or establish persistent access within the environment. Monitoring and correlating this event with other suspicious activities is crucial for identifying potential security incidents. data_source: -- Windows Event Log Security 4886 -search: '`wineventlog_security` EventCode=4886 | stats count min(_time) as firstTime - max(_time) as lastTime by dest, name, Requester, action, Attributes | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_steal_authentication_certificates_certificate_request_filter`' -how_to_implement: To implement this analytic, enhanced Audit Logging must be enabled - on AD CS and within Group Policy Management for CS server. See Page 115 of first - reference. -known_false_positives: False positives will be generated based on normal certificate - requests. Leave enabled to generate Risk, as this is meant to be an anomaly analytic. + - Windows Event Log Security 4886 +search: |- + `wineventlog_security` EventCode=4886 + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest, name, Requester, + action, Attributes + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_steal_authentication_certificates_certificate_request_filter` +how_to_implement: To implement this analytic, enhanced Audit Logging must be enabled on AD CS and within Group Policy Management for CS server. See Page 115 of first reference. +known_false_positives: False positives will be generated based on normal certificate requests. Leave enabled to generate Risk, as this is meant to be an anomaly analytic. references: -- https://specterops.io/wp-content/uploads/sites/3/2022/06/Certified_Pre-Owned.pdf + - https://specterops.io/wp-content/uploads/sites/3/2022/06/Certified_Pre-Owned.pdf drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A certificate was requested by $dest$. - risk_objects: - - field: dest - type: system - score: 8 - threat_objects: [] + message: A certificate was requested by $dest$. + risk_objects: + - field: dest + type: system + score: 8 + threat_objects: [] tags: - analytic_story: - - Windows Certificate Services - asset_type: Endpoint - mitre_attack_id: - - T1649 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Certificate Services + asset_type: Endpoint + mitre_attack_id: + - T1649 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/4886_windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/4886_windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_steal_authentication_certificates_certutil_backup.yml b/detections/endpoint/windows_steal_authentication_certificates_certutil_backup.yml index c52e331ba1..2dd93e2759 100644 --- a/detections/endpoint/windows_steal_authentication_certificates_certutil_backup.yml +++ b/detections/endpoint/windows_steal_authentication_certificates_certutil_backup.yml @@ -1,89 +1,71 @@ name: Windows Steal Authentication Certificates CertUtil Backup id: bac85b56-0b65-4ce5-aad5-d94880df0967 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic detects CertUtil.exe performing a backup of the - Certificate Store. It leverages data from Endpoint Detection and Response (EDR) - agents, focusing on specific command-line executions involving CertUtil with backup - parameters. This activity is significant because it may indicate an attempt to steal - authentication certificates, which are critical for secure communications. If confirmed - malicious, an attacker could use the stolen certificates to impersonate users, decrypt - sensitive data, or gain unauthorized access to systems, leading to severe security - breaches. +description: The following analytic detects CertUtil.exe performing a backup of the Certificate Store. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line executions involving CertUtil with backup parameters. This activity is significant because it may indicate an attempt to steal authentication certificates, which are critical for secure communications. If confirmed malicious, an attacker could use the stolen certificates to impersonate users, decrypt sensitive data, or gain unauthorized access to systems, leading to severe security breaches. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_certutil` Processes.process - IN ("*-backupdb *", "*-backup *") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_steal_authentication_certificates_certutil_backup_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives will be generated based on normal certificate - store backups. Leave enabled to generate Risk, as this is meant to be an anomaly - analytic. If CS backups are not normal, enable as TTP. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_certutil` Processes.process IN ("*-backupdb *", "*-backup *") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_steal_authentication_certificates_certutil_backup_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives will be generated based on normal certificate store backups. Leave enabled to generate Risk, as this is meant to be an anomaly analytic. If CS backups are not normal, enable as TTP. references: -- https://specterops.io/wp-content/uploads/sites/3/2022/06/Certified_Pre-Owned.pdf + - https://specterops.io/wp-content/uploads/sites/3/2022/06/Certified_Pre-Owned.pdf drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to backup the Certificate Store. - risk_objects: - - field: user - type: user - score: 40 - - field: dest - type: system - score: 40 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to backup the Certificate Store. + risk_objects: + - field: user + type: user + score: 40 + - field: dest + type: system + score: 40 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Windows Certificate Services - - Storm-2460 CLFS Zero Day Exploitation - asset_type: Endpoint - mitre_attack_id: - - T1649 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Certificate Services + - Storm-2460 CLFS Zero Day Exploitation + asset_type: Endpoint + mitre_attack_id: + - T1649 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/backupdb_certutil_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/backupdb_certutil_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_steal_authentication_certificates_cryptoapi.yml b/detections/endpoint/windows_steal_authentication_certificates_cryptoapi.yml index ff29ea4b52..28a75257a5 100644 --- a/detections/endpoint/windows_steal_authentication_certificates_cryptoapi.yml +++ b/detections/endpoint/windows_steal_authentication_certificates_cryptoapi.yml @@ -1,68 +1,57 @@ name: Windows Steal Authentication Certificates CryptoAPI id: 905d5692-6d7c-432f-bc7e-a6b4f464d40e -version: 6 -date: '2025-10-14' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic detects the extraction of authentication certificates - using Windows Event Log - CAPI2 (CryptoAPI 2). It leverages EventID 70, which is - generated when a certificate's private key is acquired. This detection is significant - because it can identify potential misuse of certificates, such as those extracted - by tools like Mimikatz or Cobalt Strike. If confirmed malicious, this activity could - allow attackers to impersonate users, escalate privileges, or access sensitive information, - posing a severe risk to the organization's security. +description: The following analytic detects the extraction of authentication certificates using Windows Event Log - CAPI2 (CryptoAPI 2). It leverages EventID 70, which is generated when a certificate's private key is acquired. This detection is significant because it can identify potential misuse of certificates, such as those extracted by tools like Mimikatz or Cobalt Strike. If confirmed malicious, this activity could allow attackers to impersonate users, escalate privileges, or access sensitive information, posing a severe risk to the organization's security. data_source: -- Windows Event Log CAPI2 70 -search: '`capi2_operational` EventCode=70 | xmlkv UserData_Xml | stats count min(_time) - as firstTime max(_time) as lastTime by Computer, UserData_Xml | rename Computer - as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `windows_steal_authentication_certificates_cryptoapi_filter`' -how_to_implement: To implement this analytic, one will need to enable the Microsoft-Windows-CAPI2/Operational - log within the Windows Event Log. Note this is a debug log for many purposes, and - the analytic only focuses in on EventID 70. Review the following gist for additional - enabling information. -known_false_positives: False positives may be present in some instances of legitimate - applications requiring to export certificates. Filter as needed. + - Windows Event Log CAPI2 70 +search: |- + `capi2_operational` EventCode=70 + | xmlkv UserData_Xml + | stats count min(_time) as firstTime max(_time) as lastTime + BY Computer, UserData_Xml + | rename Computer as dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_steal_authentication_certificates_cryptoapi_filter` +how_to_implement: To implement this analytic, one will need to enable the Microsoft-Windows-CAPI2/Operational log within the Windows Event Log. Note this is a debug log for many purposes, and the analytic only focuses in on EventID 70. Review the following gist for additional enabling information. +known_false_positives: False positives may be present in some instances of legitimate applications requiring to export certificates. Filter as needed. references: -- https://learn.microsoft.com/en-us/previous-versions/windows/it-pro/windows-vista/cc749296(v=ws.10) + - https://learn.microsoft.com/en-us/previous-versions/windows/it-pro/windows-vista/cc749296(v=ws.10) drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Certificates were exported via the CryptoAPI 2 on $dest$. - risk_objects: - - field: dest - type: system - score: 24 - threat_objects: [] + message: Certificates were exported via the CryptoAPI 2 on $dest$. + risk_objects: + - field: dest + type: system + score: 24 + threat_objects: [] tags: - analytic_story: - - Windows Certificate Services - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1649 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Certificate Services + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1649 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/capi2-operational.log - source: XmlWinEventLog:Microsoft-Windows-CAPI2/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/capi2-operational.log + source: XmlWinEventLog:Microsoft-Windows-CAPI2/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_steal_authentication_certificates_cs_backup.yml b/detections/endpoint/windows_steal_authentication_certificates_cs_backup.yml index b36fb400c3..6152ffe483 100644 --- a/detections/endpoint/windows_steal_authentication_certificates_cs_backup.yml +++ b/detections/endpoint/windows_steal_authentication_certificates_cs_backup.yml @@ -1,67 +1,55 @@ name: Windows Steal Authentication Certificates CS Backup id: a2f4cc7f-6503-4078-b206-f83a29f408a7 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic identifies the backup of the Active Directory - Certificate Services (AD CS) store, detected via Event ID 4876. This event is logged - when a backup is performed using the CertSrv.msc UI or the CertUtil.exe -BackupDB - command. Monitoring this activity is crucial as unauthorized backups can indicate - an attempt to steal authentication certificates, which are critical for secure communications. - If confirmed malicious, this activity could allow an attacker to impersonate users, - escalate privileges, or access sensitive information, severely compromising the - security of the environment. +description: The following analytic identifies the backup of the Active Directory Certificate Services (AD CS) store, detected via Event ID 4876. This event is logged when a backup is performed using the CertSrv.msc UI or the CertUtil.exe -BackupDB command. Monitoring this activity is crucial as unauthorized backups can indicate an attempt to steal authentication certificates, which are critical for secure communications. If confirmed malicious, this activity could allow an attacker to impersonate users, escalate privileges, or access sensitive information, severely compromising the security of the environment. data_source: -- Windows Event Log Security 4876 -search: '`wineventlog_security` EventCode=4876| stats count min(_time) as firstTime - max(_time) as lastTime by dest, name, action, Caller_Domain ,Caller_User_Name | - `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_steal_authentication_certificates_cs_backup_filter`' -how_to_implement: To implement this analytic, enhanced Audit Logging must be enabled - on AD CS and within Group Policy Management for CS server. See Page 128 of first - reference. -known_false_positives: False positives will be generated based on normal certificate - store backups. Leave enabled to generate Risk, as this is meant to be an anomaly - analytic. If CS backups are not normal, enable as TTP. + - Windows Event Log Security 4876 +search: |- + `wineventlog_security` EventCode=4876 + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest, name, action, + Caller_Domain ,Caller_User_Name + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_steal_authentication_certificates_cs_backup_filter` +how_to_implement: To implement this analytic, enhanced Audit Logging must be enabled on AD CS and within Group Policy Management for CS server. See Page 128 of first reference. +known_false_positives: False positives will be generated based on normal certificate store backups. Leave enabled to generate Risk, as this is meant to be an anomaly analytic. If CS backups are not normal, enable as TTP. references: -- https://specterops.io/wp-content/uploads/sites/3/2022/06/Certified_Pre-Owned.pdf + - https://specterops.io/wp-content/uploads/sites/3/2022/06/Certified_Pre-Owned.pdf drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The Active Directory Certiciate Services was backed up on $dest$. - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: [] + message: The Active Directory Certiciate Services was backed up on $dest$. + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: [] tags: - analytic_story: - - Windows Certificate Services - asset_type: Endpoint - mitre_attack_id: - - T1649 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Certificate Services + asset_type: Endpoint + mitre_attack_id: + - T1649 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/4876_windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/4876_windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_steal_authentication_certificates_export_certificate.yml b/detections/endpoint/windows_steal_authentication_certificates_export_certificate.yml index 368c077349..76e6c36006 100644 --- a/detections/endpoint/windows_steal_authentication_certificates_export_certificate.yml +++ b/detections/endpoint/windows_steal_authentication_certificates_export_certificate.yml @@ -1,89 +1,71 @@ name: Windows Steal Authentication Certificates Export Certificate id: e39dc429-c2a5-4f1f-9c3c-6b211af6b332 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic detects the use of the PowerShell cmdlet 'export-certificate' - executed via the command line, indicating an attempt to export a certificate from - the local Windows Certificate Store. This detection leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process execution logs and command-line - arguments. Exporting certificates is significant as it may indicate credential theft - or preparation for man-in-the-middle attacks. If confirmed malicious, this activity - could allow an attacker to impersonate users, decrypt sensitive communications, - or gain unauthorized access to systems and data. +description: The following analytic detects the use of the PowerShell cmdlet 'export-certificate' executed via the command line, indicating an attempt to export a certificate from the local Windows Certificate Store. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. Exporting certificates is significant as it may indicate credential theft or preparation for man-in-the-middle attacks. If confirmed malicious, this activity could allow an attacker to impersonate users, decrypt sensitive communications, or gain unauthorized access to systems and data. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*export-certificate*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_steal_authentication_certificates_export_certificate_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Filtering may be requried based on automated utilities and - third party applications that may export certificates. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*export-certificate*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_steal_authentication_certificates_export_certificate_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Filtering may be requried based on automated utilities and third party applications that may export certificates. references: -- https://dev.to/iamthecarisma/managing-windows-pfx-certificates-through-powershell-3pj -- https://learn.microsoft.com/en-us/powershell/module/pki/export-certificate?view=windowsserver2022-ps + - https://dev.to/iamthecarisma/managing-windows-pfx-certificates-through-powershell-3pj + - https://learn.microsoft.com/en-us/powershell/module/pki/export-certificate?view=windowsserver2022-ps drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to export a certificate from the - local Windows Certificate Store. - risk_objects: - - field: user - type: user - score: 36 - - field: dest - type: system - score: 36 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to export a certificate from the local Windows Certificate Store. + risk_objects: + - field: user + type: user + score: 36 + - field: dest + type: system + score: 36 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Windows Certificate Services - asset_type: Endpoint - mitre_attack_id: - - T1649 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Certificate Services + asset_type: Endpoint + mitre_attack_id: + - T1649 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/export_certificate_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/export_certificate_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_steal_authentication_certificates_export_pfxcertificate.yml b/detections/endpoint/windows_steal_authentication_certificates_export_pfxcertificate.yml index f1282bd238..248229ee26 100644 --- a/detections/endpoint/windows_steal_authentication_certificates_export_pfxcertificate.yml +++ b/detections/endpoint/windows_steal_authentication_certificates_export_pfxcertificate.yml @@ -1,89 +1,71 @@ name: Windows Steal Authentication Certificates Export PfxCertificate id: 391329f3-c14b-4b8d-8b37-ac5012637360 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic detects the use of the PowerShell cmdlet `export-pfxcertificate` - on the command line, indicating an attempt to export a certificate from the local - Windows Certificate Store. This detection leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process execution logs and command-line arguments. - This activity is significant as it may indicate an attempt to exfiltrate authentication - certificates, which can be used to impersonate users or decrypt sensitive data. - If confirmed malicious, this could lead to unauthorized access and potential data - breaches. +description: The following analytic detects the use of the PowerShell cmdlet `export-pfxcertificate` on the command line, indicating an attempt to export a certificate from the local Windows Certificate Store. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant as it may indicate an attempt to exfiltrate authentication certificates, which can be used to impersonate users or decrypt sensitive data. If confirmed malicious, this could lead to unauthorized access and potential data breaches. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process="*export-pfxcertificate*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_steal_authentication_certificates_export_pfxcertificate_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Filtering may be requried based on automated utilities and - third party applications that may export certificates. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process="*export-pfxcertificate*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_steal_authentication_certificates_export_pfxcertificate_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Filtering may be requried based on automated utilities and third party applications that may export certificates. references: -- https://dev.to/iamthecarisma/managing-windows-pfx-certificates-through-powershell-3pj -- https://learn.microsoft.com/en-us/powershell/module/pki/export-pfxcertificate?view=windowsserver2022-ps + - https://dev.to/iamthecarisma/managing-windows-pfx-certificates-through-powershell-3pj + - https://learn.microsoft.com/en-us/powershell/module/pki/export-pfxcertificate?view=windowsserver2022-ps drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to export a certificate from the - local Windows Certificate Store. - risk_objects: - - field: user - type: user - score: 36 - - field: dest - type: system - score: 36 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to export a certificate from the local Windows Certificate Store. + risk_objects: + - field: user + type: user + score: 36 + - field: dest + type: system + score: 36 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Windows Certificate Services - asset_type: Endpoint - mitre_attack_id: - - T1649 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Certificate Services + asset_type: Endpoint + mitre_attack_id: + - T1649 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/export_pfxcertificate_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/export_pfxcertificate_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_steal_or_forge_kerberos_tickets_klist.yml b/detections/endpoint/windows_steal_or_forge_kerberos_tickets_klist.yml index cadcfe0dfc..4512251f98 100644 --- a/detections/endpoint/windows_steal_or_forge_kerberos_tickets_klist.yml +++ b/detections/endpoint/windows_steal_or_forge_kerberos_tickets_klist.yml @@ -1,60 +1,51 @@ name: Windows Steal or Forge Kerberos Tickets Klist id: 09d88404-1e29-46cb-806c-1eedbc85ad5d -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic identifies the execution of the Windows OS tool - klist.exe, often used by post-exploitation tools like winpeas. This detection leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process and - parent process details. Monitoring klist.exe is significant as it can indicate attempts - to list or gather cached Kerberos tickets, which are crucial for lateral movement - or privilege escalation. If confirmed malicious, this activity could enable attackers - to move laterally within the network or escalate privileges, posing a severe security - risk. +description: The following analytic identifies the execution of the Windows OS tool klist.exe, often used by post-exploitation tools like winpeas. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process details. Monitoring klist.exe is significant as it can indicate attempts to list or gather cached Kerberos tickets, which are crucial for lateral movement or privilege escalation. If confirmed malicious, this activity could enable attackers to move laterally within the network or escalate privileges, posing a severe security risk. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name="klist.exe" - OR Processes.original_file_name = "klist.exe" Processes.parent_process_name IN ("cmd.exe", - "powershell*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_steal_or_forge_kerberos_tickets_klist_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name="klist.exe" + OR + Processes.original_file_name = "klist.exe" Processes.parent_process_name IN ("cmd.exe", "powershell*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_steal_or_forge_kerberos_tickets_klist_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS -- https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ + - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS + - https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ tags: - analytic_story: - - Windows Post-Exploitation - - Prestige Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1558 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Post-Exploitation + - Prestige Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1558 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_subinacl_execution.yml b/detections/endpoint/windows_subinacl_execution.yml index ef3fc103c9..405a657a76 100644 --- a/detections/endpoint/windows_subinacl_execution.yml +++ b/detections/endpoint/windows_subinacl_execution.yml @@ -1,88 +1,73 @@ name: Windows SubInAcl Execution id: 12491419-1a6f-4af4-afc3-4e2052f0610e -version: 5 -date: '2026-01-14' +version: 6 +date: '2026-02-25' author: Nasreddine Bencherchali, Michael Haag, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the SubInAcl utility. - SubInAcl is a legacy Windows Resource Kit tool from the Windows 2003 era, used to - manipulate security descriptors of securable objects. It leverages data from Endpoint - Detection and Response (EDR) agents, specifically searching for any process execution - involving "SubInAcl.exe" binary. This activity can be significant because the utility - should be rarely found on modern Windows machines, which mean any execution could - potentially be considered suspicious. If confirmed malicious, this could allow an - attacker to blind defenses by tampering with EventLog ACLs or modifying the access - to a previously denied resource. +description: The following analytic detects the execution of the SubInAcl utility. SubInAcl is a legacy Windows Resource Kit tool from the Windows 2003 era, used to manipulate security descriptors of securable objects. It leverages data from Endpoint Detection and Response (EDR) agents, specifically searching for any process execution involving "SubInAcl.exe" binary. This activity can be significant because the utility should be rarely found on modern Windows machines, which mean any execution could potentially be considered suspicious. If confirmed malicious, this could allow an attacker to blind defenses by tampering with EventLog ACLs or modifying the access to a previously denied resource. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where (Processes.process_name=subinacl.exe OR Processes.original_file_name=SubInAcl.exe) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_subinacl_execution_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process name, and process original file name. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: No false positives have been identified at this time. - should be identified and understood. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name=subinacl.exe + OR + Processes.original_file_name=SubInAcl.exe + ) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_subinacl_execution_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process name, and process original file name. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: No false positives have been identified at this time. should be identified and understood. references: -- https://www.pwc.com/gx/en/issues/cybersecurity/cyber-threat-intelligence/cyber-year-in-retrospect/yir-cyber-threats-report-download.pdf -- https://attack.mitre.org/techniques/T1222/001/ + - https://www.pwc.com/gx/en/issues/cybersecurity/cyber-threat-intelligence/cyber-year-in-retrospect/yir-cyber-threats-report-download.pdf + - https://attack.mitre.org/techniques/T1222/001/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - attempting to disable security services on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 16 - - field: dest - type: system - score: 16 - threat_objects: - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified attempting to disable security services on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 16 + - field: dest + type: system + score: 16 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Defense Evasion or Unauthorized Access Via SDDL Tampering - asset_type: Endpoint - mitre_attack_id: - - T1222.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Defense Evasion or Unauthorized Access Via SDDL Tampering + asset_type: Endpoint + mitre_attack_id: + - T1222.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/subinacl/subinacl_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/subinacl/subinacl_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_suspect_process_with_authentication_traffic.yml b/detections/endpoint/windows_suspect_process_with_authentication_traffic.yml index 41c672eaf0..c0cb29c39d 100644 --- a/detections/endpoint/windows_suspect_process_with_authentication_traffic.yml +++ b/detections/endpoint/windows_suspect_process_with_authentication_traffic.yml @@ -5,79 +5,54 @@ date: '2025-05-02' author: Steven Dick status: production type: Anomaly -description: The following analytic detects executables running from public or temporary - locations that are communicating over Windows domain authentication ports/protocols - such as LDAP (389), LDAPS (636), and Kerberos (88). It leverages network traffic - data to identify processes originating from user-controlled directories. This activity - is significant because legitimate applications rarely run from these locations and - attempt domain authentication, making it a potential indicator of compromise. If - confirmed malicious, attackers could leverage this to access domain resources, potentially - leading to further exploitation and lateral movement within the network. +description: The following analytic detects executables running from public or temporary locations that are communicating over Windows domain authentication ports/protocols such as LDAP (389), LDAPS (636), and Kerberos (88). It leverages network traffic data to identify processes originating from user-controlled directories. This activity is significant because legitimate applications rarely run from these locations and attempt domain authentication, making it a potential indicator of compromise. If confirmed malicious, attackers could leverage this to access domain resources, potentially leading to further exploitation and lateral movement within the network. data_source: -- Sysmon EventID 3 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime values(All_Traffic.process_id) as process_id from datamodel=Network_Traffic.All_Traffic - where All_Traffic.dest_port IN ("88","389","636") AND All_Traffic.app IN ("*\\users\\*", - "*\\programdata\\*", "*\\temp\\*", "*\\Windows\\Tasks\\*", "*\\appdata\\*", "*\\perflogs\\*") - by All_Traffic.action All_Traffic.app All_Traffic.dest All_Traffic.dest_ip All_Traffic.dest_port - All_Traffic.direction All_Traffic.dvc All_Traffic.protocol All_Traffic.protocol_version - All_Traffic.src All_Traffic.src_ip All_Traffic.src_port All_Traffic.transport All_Traffic.user - All_Traffic.vendor_product | `drop_dm_object_name(All_Traffic)` | rex field=app - ".*\\\(?.*)$" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_suspect_process_with_authentication_traffic_filter`' -how_to_implement: To implement this analytic, Sysmon should be installed in the environment - and generating network events for userland and/or known public writable locations. -known_false_positives: Known applications running from these locations for legitimate - purposes. Targeting only kerberos (port 88) may significantly reduce noise. + - Sysmon EventID 3 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_Traffic.process_id) as process_id from datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port IN ("88","389","636") AND All_Traffic.app IN ("*\\users\\*", "*\\programdata\\*", "*\\temp\\*", "*\\Windows\\Tasks\\*", "*\\appdata\\*", "*\\perflogs\\*") by All_Traffic.action All_Traffic.app All_Traffic.dest All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.direction All_Traffic.dvc All_Traffic.protocol All_Traffic.protocol_version All_Traffic.src All_Traffic.src_ip All_Traffic.src_port All_Traffic.transport All_Traffic.user All_Traffic.vendor_product | `drop_dm_object_name(All_Traffic)` | rex field=app ".*\\\(?.*)$" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_suspect_process_with_authentication_traffic_filter`' +how_to_implement: To implement this analytic, Sysmon should be installed in the environment and generating network events for userland and/or known public writable locations. +known_false_positives: Known applications running from these locations for legitimate purposes. Targeting only kerberos (port 88) may significantly reduce noise. references: -- https://attack.mitre.org/techniques/T1069/002/ -- https://book.hacktricks.xyz/network-services-pentesting/pentesting-kerberos-88 + - https://attack.mitre.org/techniques/T1069/002/ + - https://book.hacktricks.xyz/network-services-pentesting/pentesting-kerberos-88 drilldown_searches: -- name: View the detection results for - "$src$" and "$dest$" - search: '%original_detection_search% | search src = "$src$" dest = "$dest$" user - = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", - "$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" and "$dest$" + search: '%original_detection_search% | search src = "$src$" dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The process $process_name$ on $src$ has been communicating with $dest$ - on $dest_port$. - risk_objects: - - field: src - type: system - score: 25 - - field: dest - type: system - score: 25 - - field: user - type: user - score: 25 - threat_objects: - - field: process_name - type: process_name + message: The process $process_name$ on $src$ has been communicating with $dest$ on $dest_port$. + risk_objects: + - field: src + type: system + score: 25 + - field: dest + type: system + score: 25 + - field: user + type: user + score: 25 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Active Directory Discovery - asset_type: Endpoint - mitre_attack_id: - - T1087.002 - - T1204.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + asset_type: Endpoint + mitre_attack_id: + - T1087.002 + - T1204.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/certify_abuse/certify_esc1_abuse_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/certify_abuse/certify_esc1_abuse_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_suspicious_c2_named_pipe.yml b/detections/endpoint/windows_suspicious_c2_named_pipe.yml index aef9a9764f..6f8028a76e 100644 --- a/detections/endpoint/windows_suspicious_c2_named_pipe.yml +++ b/detections/endpoint/windows_suspicious_c2_named_pipe.yml @@ -1,108 +1,99 @@ name: Windows Suspicious C2 Named Pipe id: 90599d85-dc2a-4d4c-8c59-9485c3665828 -version: 2 -date: '2026-01-20' +version: 3 +date: '2026-02-25' author: Raven Tait, Splunk status: production type: TTP description: | - The following analytic detects the creation or connection to known suspicious C2 named pipes. - It leverages Sysmon EventCodes 17 and 18 to identify known default pipe names used by C2 tools. - If confirmed malicious, this could allow an attacker to abuse these to potentially gain persistence, command and control, or further system compromise. + The following analytic detects the creation or connection to known suspicious C2 named pipes. + It leverages Sysmon EventCodes 17 and 18 to identify known default pipe names used by C2 tools. + If confirmed malicious, this could allow an attacker to abuse these to potentially gain persistence, command and control, or further system compromise. data_source: - - Sysmon EventID 17 - - Sysmon EventID 18 + - Sysmon EventID 17 + - Sysmon EventID 18 search: | - `sysmon` - (EventCode=17 OR EventCode=18) - NOT process_path IN ( - "*:\\Program Files \(x86\)\\Adobe*", - "*:\\Program Files \(x86\)\\Google*", - "*:\\Program Files \(x86\)\\Microsoft*", - "*:\\Program Files\\Adobe*", - "*:\\Program Files\\Google*", - "*:\\Program Files\\Microsoft*", - "*:\\Windows\\system32\\SearchIndexer.exe", - "*:\\Windows\\System32\\svchost.exe", - "*:\\Windows\\SystemApps\\Microsoft*", - "*\\Amazon\\SSM\\Instance*", - "*\\AppData\\Local\\Google*", - "*\\AppData\\Local\\Kingsoft\\*", - "*\\AppData\\Local\\Microsoft*", - "System" - ) - - | stats min(_time) as firstTime max(_time) as lastTime - count by dest dvc process_exec process_guid process_id process_path signature signature_id - vendor_product pipe_name user_id Image process_name + `sysmon` + (EventCode=17 OR EventCode=18) + NOT process_path IN ( + "*:\\Program Files \(x86\)\\Adobe*", + "*:\\Program Files \(x86\)\\Google*", + "*:\\Program Files \(x86\)\\Microsoft*", + "*:\\Program Files\\Adobe*", + "*:\\Program Files\\Google*", + "*:\\Program Files\\Microsoft*", + "*:\\Windows\\system32\\SearchIndexer.exe", + "*:\\Windows\\System32\\svchost.exe", + "*:\\Windows\\SystemApps\\Microsoft*", + "*\\Amazon\\SSM\\Instance*", + "*\\AppData\\Local\\Google*", + "*\\AppData\\Local\\Kingsoft\\*", + "*\\AppData\\Local\\Microsoft*", + "System" + ) - | lookup suspicious_c2_named_pipes suspicious_pipe_name AS pipe_name OUTPUT tool, description - | where isnotnull(tool) - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_suspicious_c2_named_pipe_filter` -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name and pipename from your endpoints. If you are using Sysmon, - you must have at least version 6.0.4 of the Sysmon TA. + | stats min(_time) as firstTime max(_time) as lastTime + count by dest dvc process_exec process_guid process_id process_path signature signature_id + vendor_product pipe_name user_id Image process_name + + | lookup suspicious_c2_named_pipes suspicious_pipe_name AS pipe_name OUTPUT tool, description + | where isnotnull(tool) + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_suspicious_c2_named_pipe_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and pipename from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: | - False positives should be rare, investigate matches and apply additional filters as needed. + False positives should be rare, investigate matches and apply additional filters as needed. references: -- https://attack.mitre.org/techniques/T1218/009/ -- https://docs.microsoft.com/en-us/windows/win32/ipc/named-pipes + - https://attack.mitre.org/techniques/T1218/009/ + - https://docs.microsoft.com/en-us/windows/win32/ipc/named-pipes drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $process_name$ located in $process_path$ was identified on endpoint $dest$ accessing - known suspicious C2 named pipe $pipe_name$. - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: - - field: process_name - type: process_name + message: An instance of $process_name$ located in $process_path$ was identified on endpoint $dest$ accessing known suspicious C2 named pipe $pipe_name$. + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Storm-0501 Ransomware - - APT37 Rustonotto and FadeStealer - - BlackByte Ransomware - - Brute Ratel C4 - - Cobalt Strike - - DarkSide Ransomware - - Gozi Malware - - Graceful Wipe Out Attack - - Hellcat Ransomware - - LockBit Ransomware - - Meterpreter - - Remote Monitoring and Management Software - - Trickbot - - Tuoni - asset_type: Endpoint - mitre_attack_id: - - T1559 - - T1021.002 - - T1055 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Storm-0501 Ransomware + - APT37 Rustonotto and FadeStealer + - BlackByte Ransomware + - Brute Ratel C4 + - Cobalt Strike + - DarkSide Ransomware + - Gozi Malware + - Graceful Wipe Out Attack + - Hellcat Ransomware + - LockBit Ransomware + - Meterpreter + - Remote Monitoring and Management Software + - Trickbot + - Tuoni + asset_type: Endpoint + mitre_attack_id: + - T1559 + - T1021.002 + - T1055 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_suspicious_child_process_spawned_from_webserver.yml b/detections/endpoint/windows_suspicious_child_process_spawned_from_webserver.yml index f0c1d374a4..527c07cb2c 100644 --- a/detections/endpoint/windows_suspicious_child_process_spawned_from_webserver.yml +++ b/detections/endpoint/windows_suspicious_child_process_spawned_from_webserver.yml @@ -1,109 +1,88 @@ name: Windows Suspicious Child Process Spawned From WebServer id: 2d4470ef-7158-4b47-b68b-1f7f16382156 -version: 7 -date: '2025-10-24' +version: 8 +date: '2026-02-25' author: Steven Dick status: production type: TTP -description: The following analytic identifies the execution of suspicious processes - typically associated with WebShell activity on web servers. It detects when processes - like `cmd.exe`, `powershell.exe`, or `bash.exe` are spawned by web server processes - such as `w3wp.exe` or `nginx.exe`. This behavior is significant as it may indicate - an adversary exploiting a web application vulnerability to install a WebShell, providing - persistent access and command execution capabilities. If confirmed malicious, this - activity could allow attackers to maintain control over the compromised server, - execute arbitrary commands, and potentially escalate privileges or exfiltrate sensitive - data. +description: The following analytic identifies the execution of suspicious processes typically associated with WebShell activity on web servers. It detects when processes like `cmd.exe`, `powershell.exe`, or `bash.exe` are spawned by web server processes such as `w3wp.exe` or `nginx.exe`. This behavior is significant as it may indicate an adversary exploiting a web application vulnerability to install a WebShell, providing persistent access and command execution capabilities. If confirmed malicious, this activity could allow attackers to maintain control over the compromised server, execute arbitrary commands, and potentially escalate privileges or exfiltrate sensitive data. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count max(_time) as lastTime, min(_time) - as firstTime from datamodel=Endpoint.Processes where (Processes.process_name IN - ("arp.exe","at.exe","bash.exe","bitsadmin.exe","certutil.exe","cmd.exe","cscript.exe", - "dsget.exe","dsquery.exe","find.exe","findstr.exe","fsutil.exe","hostname.exe","ipconfig.exe","ksh.exe","nbstat.exe", - "net.exe","net1.exe","netdom.exe","netsh.exe","netstat.exe","nltest.exe","nslookup.exe","ntdsutil.exe","pathping.exe", - "ping.exe","powershell.exe","pwsh.exe","qprocess.exe","query.exe","qwinsta.exe","reg.exe","rundll32.exe","sc.exe", - "scrcons.exe","schtasks.exe","sh.exe","systeminfo.exe","tasklist.exe","tracert.exe","ver.exe","vssadmin.exe", - "wevtutil.exe","whoami.exe","wmic.exe","wscript.exe","wusa.exe","zsh.exe") AND Processes.parent_process_name - IN ("w3wp.exe", "http*.exe", "nginx*.exe", "php*.exe", "php-cgi*.exe","tomcat*.exe")) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_suspicious_child_process_spawned_from_webserver_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Legitimate OS functions called by vendor applications, baseline - the environment and filter before enabling. Recommend throttle by dest/process_name + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count max(_time) as lastTime, min(_time) as firstTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name IN ("arp.exe","at.exe","bash.exe","bitsadmin.exe","certutil.exe","cmd.exe","cscript.exe", "dsget.exe","dsquery.exe","find.exe","findstr.exe","fsutil.exe","hostname.exe","ipconfig.exe","ksh.exe","nbstat.exe", "net.exe","net1.exe","netdom.exe","netsh.exe","netstat.exe","nltest.exe","nslookup.exe","ntdsutil.exe","pathping.exe", "ping.exe","powershell.exe","pwsh.exe","qprocess.exe","query.exe","qwinsta.exe","reg.exe","rundll32.exe","sc.exe", "scrcons.exe","schtasks.exe","sh.exe","systeminfo.exe","tasklist.exe","tracert.exe","ver.exe","vssadmin.exe", "wevtutil.exe","whoami.exe","wmic.exe","wscript.exe","wusa.exe","zsh.exe") + AND + Processes.parent_process_name IN ("w3wp.exe", "http*.exe", "nginx*.exe", "php*.exe", "php-cgi*.exe","tomcat*.exe") + ) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_suspicious_child_process_spawned_from_webserver_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Legitimate OS functions called by vendor applications, baseline the environment and filter before enabling. Recommend throttle by dest/process_name references: -- https://attack.mitre.org/techniques/T1505/003/ -- https://github.com/nsacyber/Mitigating-Web-Shells -- https://www.hackingarticles.in/multiple-ways-to-exploit-tomcat-manager/ + - https://attack.mitre.org/techniques/T1505/003/ + - https://github.com/nsacyber/Mitigating-Web-Shells + - https://www.hackingarticles.in/multiple-ways-to-exploit-tomcat-manager/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Webshell Exploit Behavior - $parent_process_name$ spawned $process_name$ - on $dest$. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: - - field: process_name - type: process_name + message: Webshell Exploit Behavior - $parent_process_name$ spawned $process_name$ on $dest$. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Flax Typhoon - - BlackByte Ransomware - - CISA AA22-257A - - HAFNIUM Group - - CISA AA22-264A - - ProxyShell - - SysAid On-Prem Software CVE-2023-47246 Vulnerability - - ProxyNotShell - - Medusa Ransomware - - WS FTP Server Critical Vulnerabilities - - Compromised Windows Host - - Citrix ShareFile RCE CVE-2023-24489 - - Microsoft SharePoint Vulnerabilities - - GhostRedirector IIS Module and Rungan Backdoor - - Microsoft WSUS CVE-2025-59287 - asset_type: Endpoint - mitre_attack_id: - - T1505.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Flax Typhoon + - BlackByte Ransomware + - CISA AA22-257A + - HAFNIUM Group + - CISA AA22-264A + - ProxyShell + - SysAid On-Prem Software CVE-2023-47246 Vulnerability + - ProxyNotShell + - Medusa Ransomware + - WS FTP Server Critical Vulnerabilities + - Compromised Windows Host + - Citrix ShareFile RCE CVE-2023-24489 + - Microsoft SharePoint Vulnerabilities + - GhostRedirector IIS Module and Rungan Backdoor + - Microsoft WSUS CVE-2025-59287 + asset_type: Endpoint + mitre_attack_id: + - T1505.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/generic_webshell_exploit/generic_webshell_exploit.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/generic_webshell_exploit/generic_webshell_exploit.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_suspicious_driver_loaded_path.yml b/detections/endpoint/windows_suspicious_driver_loaded_path.yml index 2faaec0f34..86c0d6beb7 100644 --- a/detections/endpoint/windows_suspicious_driver_loaded_path.yml +++ b/detections/endpoint/windows_suspicious_driver_loaded_path.yml @@ -5,74 +5,52 @@ date: '2025-12-19' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the loading of drivers from - suspicious paths, which is a technique often used by malicious software such - as coin miners (e.g., xmrig). It leverages Sysmon EventCode 6 to identify - drivers loaded from non-standard directories. This activity is significant - because legitimate drivers typically reside in specific system directories, - and deviations may indicate malicious activity. If confirmed malicious, this - could allow an attacker to execute code at the kernel level, potentially - leading to privilege escalation, persistence, or further system compromise. +description: The following analytic detects the loading of drivers from suspicious paths, which is a technique often used by malicious software such as coin miners (e.g., xmrig). It leverages Sysmon EventCode 6 to identify drivers loaded from non-standard directories. This activity is significant because legitimate drivers typically reside in specific system directories, and deviations may indicate malicious activity. If confirmed malicious, this could allow an attacker to execute code at the kernel level, potentially leading to privilege escalation, persistence, or further system compromise. data_source: -- Sysmon EventID 6 -search: '`sysmon` EventCode=6 - ImageLoaded IN("*\\windows\\fonts\\*", "*\\users\\public\\*", "*\\windows\\debug\\*", "*\\Users\\Administrator\\Music\\*", - "*Recycle.bin*", "*\\Windows\\Media\\*","\\Windows\\repair\\*", "*\\PerfLogs\\*", "*:\\Windows\\Prefetch\\*", "*:\\Windows\\Cursors\\*", "*\\temp\\*", "*\\download*", "*\\appdata\\*") - | stats min(_time) as firstTime max(_time) as lastTime count by ImageLoaded dest dvc process_hash process_path signature signature_id user_id vendor_product - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_suspicious_driver_loaded_path_filter`' -how_to_implement: To successfully implement this search, you need to be - ingesting logs with the driver loaded and Signature from your endpoints. If - you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -known_false_positives: Limited false positives will be present. Some - applications do load drivers + - Sysmon EventID 6 +search: '`sysmon` EventCode=6 ImageLoaded IN("*\\windows\\fonts\\*", "*\\users\\public\\*", "*\\windows\\debug\\*", "*\\Users\\Administrator\\Music\\*", "*Recycle.bin*", "*\\Windows\\Media\\*","\\Windows\\repair\\*", "*\\PerfLogs\\*", "*:\\Windows\\Prefetch\\*", "*:\\Windows\\Cursors\\*", "*\\temp\\*", "*\\download*", "*\\appdata\\*") | stats min(_time) as firstTime max(_time) as lastTime count by ImageLoaded dest dvc process_hash process_path signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_suspicious_driver_loaded_path_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the driver loaded and Signature from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: Limited false positives will be present. Some applications do load drivers references: -- https://www.trendmicro.com/vinfo/hk/threat-encyclopedia/malware/trojan.ps1.powtran.a/ -- https://redcanary.com/blog/tracking-driver-inventory-to-expose-rootkits/ -- https://whiteknightlabs.com/2025/11/25/discreet-driver-loading-in-windows/ + - https://www.trendmicro.com/vinfo/hk/threat-encyclopedia/malware/trojan.ps1.powtran.a/ + - https://redcanary.com/blog/tracking-driver-inventory-to-expose-rootkits/ + - https://whiteknightlabs.com/2025/11/25/discreet-driver-loading-in-windows/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious driver $ImageLoaded$ on $dest$ - risk_objects: - - field: dest - type: system - score: 60 - threat_objects: [] + message: Suspicious driver $ImageLoaded$ on $dest$ + risk_objects: + - field: dest + type: system + score: 60 + threat_objects: [] tags: - analytic_story: - - XMRig - - CISA AA22-320A - - AgentTesla - - BlackByte Ransomware - - Snake Keylogger - - Interlock Ransomware - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1543.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - XMRig + - CISA AA22-320A + - AgentTesla + - BlackByte Ransomware + - Snake Keylogger + - Interlock Ransomware + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1543.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_suspicious_named_pipe.yml b/detections/endpoint/windows_suspicious_named_pipe.yml index 60c2d46d53..449d98d8ef 100644 --- a/detections/endpoint/windows_suspicious_named_pipe.yml +++ b/detections/endpoint/windows_suspicious_named_pipe.yml @@ -1,108 +1,102 @@ name: Windows Suspicious Named Pipe id: 3a76d52f-a007-4a65-a37d-f313c2c83f31 -version: 1 -date: '2025-12-01' +version: 2 +date: '2026-02-25' author: Raven Tait, Splunk status: production type: TTP description: | - The following analytic detects the creation or connection to known suspicious named pipes. - It leverages Sysmon EventCodes 17 and 18 to identify known default pipe names used by malicious or suspicious tools. - If confirmed malicious, this could allow an attacker to abuse these to potentially gain privilege escalation, - persistence, c2 communications, or further system compromise. + The following analytic detects the creation or connection to known suspicious named pipes. + It leverages Sysmon EventCodes 17 and 18 to identify known default pipe names used by malicious or suspicious tools. + If confirmed malicious, this could allow an attacker to abuse these to potentially gain privilege escalation, + persistence, c2 communications, or further system compromise. data_source: - - Sysmon EventID 17 - - Sysmon EventID 18 + - Sysmon EventID 17 + - Sysmon EventID 18 search: | - `sysmon` - EventCode IN (17, 18) - NOT process_path IN ( - "*:\\Program Files \(x86\)\\Adobe*", - "*:\\Program Files \(x86\)\\Google*", - "*:\\Program Files \(x86\)\\Microsoft*", - "*:\\Program Files\\Adobe*", - "*:\\Program Files\\Google*", - "*:\\Program Files\\Microsoft*", - "*:\\Windows\\system32\\SearchIndexer.exe", - "*:\\Windows\\System32\\svchost.exe", - "*:\\Windows\\SystemApps\\Microsoft*", - "*\\Amazon\\SSM\\Instance*", - "*\\AppData\\Local\\Google*", - "*\\AppData\\Local\\Kingsoft\\*", - "*\\AppData\\Local\\Microsoft*", - "System", - ) - - | stats min(_time) as firstTime max(_time) as lastTime - count by dest dvc process_exec process_guid process_id process_path - pipe_name user_id process_name signature signature_id vendor_product - - | lookup suspicious_named_pipes suspicious_pipe_name AS pipe_name OUTPUT tool, type, description - | where isnotnull(tool) - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_suspicious_named_pipe_filter` + `sysmon` + EventCode IN (17, 18) + NOT process_path IN ( + "*:\\Program Files \(x86\)\\Adobe*", + "*:\\Program Files \(x86\)\\Google*", + "*:\\Program Files \(x86\)\\Microsoft*", + "*:\\Program Files\\Adobe*", + "*:\\Program Files\\Google*", + "*:\\Program Files\\Microsoft*", + "*:\\Windows\\system32\\SearchIndexer.exe", + "*:\\Windows\\System32\\svchost.exe", + "*:\\Windows\\SystemApps\\Microsoft*", + "*\\Amazon\\SSM\\Instance*", + "*\\AppData\\Local\\Google*", + "*\\AppData\\Local\\Kingsoft\\*", + "*\\AppData\\Local\\Microsoft*", + "System", + ) + + | stats min(_time) as firstTime max(_time) as lastTime + count by dest dvc process_exec process_guid process_id process_path + pipe_name user_id process_name signature signature_id vendor_product + + | lookup suspicious_named_pipes suspicious_pipe_name AS pipe_name OUTPUT tool, type, description + | where isnotnull(tool) + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_suspicious_named_pipe_filter` how_to_implement: | - To successfully implement this search, you need to be ingesting - logs with the process name and pipename from your endpoints. If you are using Sysmon, - you must have at least version 6.0.4 of the Sysmon TA. + To successfully implement this search, you need to be ingesting + logs with the process name and pipename from your endpoints. If you are using Sysmon, + you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: | - False positives should be rare, investigate matches and apply additional filters as needed. + False positives should be rare, investigate matches and apply additional filters as needed. references: - - https://attack.mitre.org/techniques/T1218/009/ - - https://docs.microsoft.com/en-us/windows/win32/ipc/named-pipes + - https://attack.mitre.org/techniques/T1218/009/ + - https://docs.microsoft.com/en-us/windows/win32/ipc/named-pipes drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $process_name$ located in $process_path$ was identified on endpoint $dest$ accessing - known suspicious named pipe $pipe_name$. - risk_objects: - - field: dest - type: system - score: 60 - threat_objects: - - field: process_name - type: process_name + message: An instance of $process_name$ located in $process_path$ was identified on endpoint $dest$ accessing known suspicious named pipe $pipe_name$. + risk_objects: + - field: dest + type: system + score: 60 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - APT37 Rustonotto and FadeStealer - - BlackByte Ransomware - - Brute Ratel C4 - - Cobalt Strike - - DarkSide Ransomware - - Gozi Malware - - Graceful Wipe Out Attack - - Hellcat Ransomware - - LockBit Ransomware - - Meterpreter - - Remote Monitoring and Management Software - - Trickbot - - Tuoni - asset_type: Endpoint - mitre_attack_id: - - T1559 - - T1021.002 - - T1055 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - APT37 Rustonotto and FadeStealer + - BlackByte Ransomware + - Brute Ratel C4 + - Cobalt Strike + - DarkSide Ransomware + - Gozi Malware + - Graceful Wipe Out Attack + - Hellcat Ransomware + - LockBit Ransomware + - Meterpreter + - Remote Monitoring and Management Software + - Trickbot + - Tuoni + asset_type: Endpoint + mitre_attack_id: + - T1559 + - T1021.002 + - T1055 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/named_pipes/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/named_pipes/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_suspicious_process_file_path.yml b/detections/endpoint/windows_suspicious_process_file_path.yml index 505025b223..a817f377a0 100644 --- a/detections/endpoint/windows_suspicious_process_file_path.yml +++ b/detections/endpoint/windows_suspicious_process_file_path.yml @@ -5,142 +5,104 @@ date: '2025-12-15' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic identifies processes running from file paths - not typically associated with legitimate software. It leverages data from - Endpoint Detection and Response (EDR) agents, focusing on specific process - paths within the Endpoint data model. This activity is significant because - adversaries often use unconventional file paths to execute malicious code - without requiring administrative privileges. If confirmed malicious, this - behavior could indicate an attempt to bypass security controls, leading to - unauthorized software execution, potential system compromise, and further - malicious activities within the environment. +description: The following analytic identifies processes running from file paths not typically associated with legitimate software. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific process paths within the Endpoint data model. This activity is significant because adversaries often use unconventional file paths to execute malicious code without requiring administrative privileges. If confirmed malicious, this behavior could indicate an attempt to bypass security controls, leading to unauthorized software execution, potential system compromise, and further malicious activities within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count values(Processes.process_name) - as process_name values(Processes.process) as process min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_path IN("*\\windows\\fonts\\*", - "*\\users\\public\\*", "*\\windows\\debug\\*", "*\\Users\\Administrator\\Music\\*", - "*Recycle.bin*", "*\\Windows\\Media\\*","\\Windows\\repair\\*", "*\\PerfLogs\\*", - "*:\\Windows\\Prefetch\\*", "*:\\Windows\\Cursors\\*", "*:\\Windows\\INF\\*") AND - NOT(Processes.process_path IN ("*\\temp\\*")) by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_suspicious_process_file_path_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: Administrators may allow execution of specific binaries - in non-standard paths. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_path IN("*\\windows\\fonts\\*", "*\\users\\public\\*", "*\\windows\\debug\\*", "*\\Users\\Administrator\\Music\\*", "*Recycle.bin*", "*\\Windows\\Media\\*","\\Windows\\repair\\*", "*\\PerfLogs\\*", "*:\\Windows\\Prefetch\\*", "*:\\Windows\\Cursors\\*", "*:\\Windows\\INF\\*") AND NOT(Processes.process_path IN ("*\\temp\\*")) by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_suspicious_process_file_path_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators may allow execution of specific binaries in non-standard paths. Filter as needed. references: -- https://www.trendmicro.com/vinfo/hk/threat-encyclopedia/malware/trojan.ps1.powtran.a/ -- https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ -- https://twitter.com/pr0xylife/status/1590394227758104576 -- https://malpedia.caad.fkie.fraunhofer.de/details/win.asyncrat -- https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ + - https://www.trendmicro.com/vinfo/hk/threat-encyclopedia/malware/trojan.ps1.powtran.a/ + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://twitter.com/pr0xylife/status/1590394227758104576 + - https://malpedia.caad.fkie.fraunhofer.de/details/win.asyncrat + - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious process $process_name$ running from a suspicious process - path- $process_path$ on host- $dest$ - risk_objects: - - field: dest - type: system - score: 60 - threat_objects: - - field: process_path - type: process_name + message: Suspicious process $process_name$ running from a suspicious process path- $process_path$ on host- $dest$ + risk_objects: + - field: dest + type: system + score: 60 + threat_objects: + - field: process_path + type: process_name tags: - analytic_story: - - StealC Stealer - - PlugX - - Water Gamayun - - Warzone RAT - - Swift Slicer - - Data Destruction - - AgentTesla - - LockBit Ransomware - - Volt Typhoon - - Brute Ratel C4 - - WhisperGate - - Industroyer2 - - DarkGate Malware - - ValleyRAT - - XMRig - - Chaos Ransomware - - Hermetic Wiper - - Remcos - - Quasar RAT - - Rhysida Ransomware - - DarkCrystal RAT - - Qakbot - - China-Nexus Threat Activity - - XWorm - - IcedID - - CISA AA23-347A - - Azorult - - Handala Wiper - - Salt Typhoon - - Earth Alux - - Double Zero Destructor - - Trickbot - - Malicious Inno Setup Loader - - BlackByte Ransomware - - SystemBC - - Phemedrone Stealer - - Graceful Wipe Out Attack - - Prestige Ransomware - - Amadey - - AsyncRAT - - RedLine Stealer - - SnappyBee - - Meduza Stealer - - MoonPeak - - Interlock Ransomware - - Interlock Rat - - NailaoLocker Ransomware - - PromptLock - - GhostRedirector IIS Module and Rungan Backdoor - - Lokibot - - Castle RAT - - SesameOp - asset_type: Endpoint - mitre_attack_id: - - T1543 - - T1036.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - StealC Stealer + - PlugX + - Water Gamayun + - Warzone RAT + - Swift Slicer + - Data Destruction + - AgentTesla + - LockBit Ransomware + - Volt Typhoon + - Brute Ratel C4 + - WhisperGate + - Industroyer2 + - DarkGate Malware + - ValleyRAT + - XMRig + - Chaos Ransomware + - Hermetic Wiper + - Remcos + - Quasar RAT + - Rhysida Ransomware + - DarkCrystal RAT + - Qakbot + - China-Nexus Threat Activity + - XWorm + - IcedID + - CISA AA23-347A + - Azorult + - Handala Wiper + - Salt Typhoon + - Earth Alux + - Double Zero Destructor + - Trickbot + - Malicious Inno Setup Loader + - BlackByte Ransomware + - SystemBC + - Phemedrone Stealer + - Graceful Wipe Out Attack + - Prestige Ransomware + - Amadey + - AsyncRAT + - RedLine Stealer + - SnappyBee + - Meduza Stealer + - MoonPeak + - Interlock Ransomware + - Interlock Rat + - NailaoLocker Ransomware + - PromptLock + - GhostRedirector IIS Module and Rungan Backdoor + - Lokibot + - Castle RAT + - SesameOp + asset_type: Endpoint + mitre_attack_id: + - T1543 + - T1036.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/suspicious_process_path/susp_path_sysmon1.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/suspicious_process_path/susp_path_sysmon1.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_suspicious_react_or_next_js_child_process.yml b/detections/endpoint/windows_suspicious_react_or_next_js_child_process.yml index a02863e83f..d19c8924e1 100644 --- a/detections/endpoint/windows_suspicious_react_or_next_js_child_process.yml +++ b/detections/endpoint/windows_suspicious_react_or_next_js_child_process.yml @@ -6,157 +6,148 @@ author: Nasreddine Bencherchali, Splunk status: production type: TTP description: | - This analytic detects Windows processes such as cmd.exe, PowerShell, and common Windows LOLBINs being spawned by React or Next.js application servers. - In the context of CVE-2025-55182 / React2Shell / CVE-2025-66478 for Next.js, successful exploitation can lead to arbitrary JavaScript execution on the server, which in turn is used to invoke Node's child_process APIs (for example child_process.execSync) to run OS-level commands. - This detection focuses on suspicious child processes where a Next/React server process spawns an uncommon process. - Such activity might be a strong indicator of exploitation of the aforementioned vulnerability. + This analytic detects Windows processes such as cmd.exe, PowerShell, and common Windows LOLBINs being spawned by React or Next.js application servers. + In the context of CVE-2025-55182 / React2Shell / CVE-2025-66478 for Next.js, successful exploitation can lead to arbitrary JavaScript execution on the server, which in turn is used to invoke Node's child_process APIs (for example child_process.execSync) to run OS-level commands. + This detection focuses on suspicious child processes where a Next/React server process spawns an uncommon process. + Such activity might be a strong indicator of exploitation of the aforementioned vulnerability. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime - from datamodel=Endpoint.Processes - where - Processes.parent_process_name = "node.exe" - Processes.parent_process IN ( - "*--experimental-https*", - "*--experimental-next-config-strip-types*", - "*\\node_modules\\next*", - "*next dev*", - "*next start*", - "*next\" start*", - "*node_modules\\.bin\\\\..\\next*", - "*react-scripts start*", - "*start-server.js*", - ) - AND - ( - Processes.process_name IN ( - "bash.exe", - "bitsadmin.exe", - "calc.exe", - "certutil.exe", - "cscript.exe", - "curl.exe", - "ftp.exe", - "ipconfig.exe", - "mshta.exe", - "netstat.exe", - "OpenConsole.exe", - "powershell.exe", - "pwsh.exe", - "regsvr32.exe", - "rundll32.exe", - "sh.exe", - "tftp.exe", - "wget.exe", - "wmic.exe", - "wscript.exe", - "wsl.exe", - "wt.exe" + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime + from datamodel=Endpoint.Processes + where + Processes.parent_process_name = "node.exe" + Processes.parent_process IN ( + "*--experimental-https*", + "*--experimental-next-config-strip-types*", + "*\\node_modules\\next*", + "*next dev*", + "*next start*", + "*next\" start*", + "*node_modules\\.bin\\\\..\\next*", + "*react-scripts start*", + "*start-server.js*", ) - OR + AND ( - Processes.process_name = "cmd.exe" - AND NOT Processes.process = "*/d /s /c *" - ) - OR - ( - Processes.process_name = "cmd.exe" - Processes.process = "*/d /s /c *" - AND NOT ( - Processes.process = "*git config --local --get remote.origin.url*" - OR - ( - Processes.process = "*netstat -ano | findstr /C:*" - Processes.process = "* | findstr LISTENING*" - ) - OR - ( - Processes.parent_process = "*--experimental-https*" - Processes.process = "*\\mkcert\\*" - Processes.process IN ("* -CAROOT*", "* -install *") + Processes.process_name IN ( + "bash.exe", + "bitsadmin.exe", + "calc.exe", + "certutil.exe", + "cscript.exe", + "curl.exe", + "ftp.exe", + "ipconfig.exe", + "mshta.exe", + "netstat.exe", + "OpenConsole.exe", + "powershell.exe", + "pwsh.exe", + "regsvr32.exe", + "rundll32.exe", + "sh.exe", + "tftp.exe", + "wget.exe", + "wmic.exe", + "wscript.exe", + "wsl.exe", + "wt.exe" + ) + OR + ( + Processes.process_name = "cmd.exe" + AND NOT Processes.process = "*/d /s /c *" + ) + OR + ( + Processes.process_name = "cmd.exe" + Processes.process = "*/d /s /c *" + AND NOT ( + Processes.process = "*git config --local --get remote.origin.url*" + OR + ( + Processes.process = "*netstat -ano | findstr /C:*" + Processes.process = "* | findstr LISTENING*" + ) + OR + ( + Processes.parent_process = "*--experimental-https*" + Processes.process = "*\\mkcert\\*" + Processes.process IN ("* -CAROOT*", "* -install *") + ) ) ) ) - ) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process - Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id - Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process + Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id + Processes.process_integrity_level Processes.process_name Processes.process_path + Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_suspicious_react_or_next_js_child_process_filter` + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_suspicious_react_or_next_js_child_process_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. - These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. - To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. - These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. - Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. + These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. + To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. + These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. + Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: | - Rare false positives might show up from child processes such as cmd or powershell. Apply additional filters as needed. + Rare false positives might show up from child processes such as cmd or powershell. Apply additional filters as needed. references: - - https://react.dev/blog/2025/12/03/critical-security-vulnerability-in-react-server-components - - https://nextjs.org/blog/CVE-2025-66478 - - https://nvd.nist.gov/vuln/detail/CVE-2025-55182 - - https://gist.github.com/maple3142/48bc9393f45e068cf8c90ab865c0f5f3 - - https://www.wiz.io/blog/critical-vulnerability-in-react-cve-2025-55182 + - https://react.dev/blog/2025/12/03/critical-security-vulnerability-in-react-server-components + - https://nextjs.org/blog/CVE-2025-66478 + - https://nvd.nist.gov/vuln/detail/CVE-2025-55182 + - https://gist.github.com/maple3142/48bc9393f45e068cf8c90ab865c0f5f3 + - https://www.wiz.io/blog/critical-vulnerability-in-react-cve-2025-55182 drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Node-based server process ($parent_process_name$) spawned the child - process $process_name$ with command-line $process$ on host $dest$ by user $user$, which may indicate - remote code execution via React Server Components (CVE-2025-55182 / - React2Shell) or abuse of a similar Node.js RCE vector. - risk_objects: - - field: user - type: user - score: 70 - - field: dest - type: system - score: 70 - threat_objects: - - field: parent_process_name - type: process - - field: process_name - type: process - - field: process - type: process + message: A Node-based server process ($parent_process_name$) spawned the child process $process_name$ with command-line $process$ on host $dest$ by user $user$, which may indicate remote code execution via React Server Components (CVE-2025-55182 / React2Shell) or abuse of a similar Node.js RCE vector. + risk_objects: + - field: user + type: user + score: 70 + - field: dest + type: system + score: 70 + threat_objects: + - field: parent_process_name + type: process + - field: process_name + type: process + - field: process + type: process tags: - analytic_story: - - React2Shell - asset_type: Endpoint - mitre_attack_id: - - T1190 - - T1059.003 - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - React2Shell + asset_type: Endpoint + mitre_attack_id: + - T1190 + - T1059.003 + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/emerging_threats/react2shell/react2shell_windows.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/emerging_threats/react2shell/react2shell_windows.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_suspicious_vmware_tools_child_process.yml b/detections/endpoint/windows_suspicious_vmware_tools_child_process.yml index 97e0450a3a..447b3229a9 100644 --- a/detections/endpoint/windows_suspicious_vmware_tools_child_process.yml +++ b/detections/endpoint/windows_suspicious_vmware_tools_child_process.yml @@ -1,86 +1,70 @@ name: Windows Suspicious VMWare Tools Child Process id: 1f77661a-0fe3-4b8d-a62c-7dff06906d26 -version: 1 -date: '2025-07-30' +version: 2 +date: '2026-02-25' author: Raven Tait, Splunk status: production type: TTP -description: The following analytic identifies child processes spawned by vmtoolsd.exe, - the VMWare Tools service in Windows, which typically runs with SYSTEM privileges. - This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process and parent process relationships. Monitoring this activity is - crucial as it can indicate exploitation attempts, such as CVE-2023-20867. If confirmed - malicious, attackers could gain SYSTEM-level access, allowing them to execute arbitrary - code, escalate privileges, and potentially compromise the entire system. +description: The following analytic identifies child processes spawned by vmtoolsd.exe, the VMWare Tools service in Windows, which typically runs with SYSTEM privileges. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process relationships. Monitoring this activity is crucial as it can indicate exploitation attempts, such as CVE-2023-20867. If confirmed malicious, attackers could gain SYSTEM-level access, allowing them to execute arbitrary code, escalate privileges, and potentially compromise the entire system. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=vmtoolsd.exe - AND Processes.process_name IN ("powershell.exe","cmd.exe", "msbuild.exe", "microsoft.workflow.compiler.exe", - "searchprotocolhost.exe", "scrcons.exe", "cscript.exe", "wscript.exe","bitsadmin.exe", "rundll32.exe", - "wmic.exe", "mshta.exe", "certutil.exe", "schtasks.exe") - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_suspicious_vmware_tools_child_process_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Although unlikely, some legitimate Administrative scripts may utilize VMWare Tools - to execute commands on virtual machines. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name=vmtoolsd.exe + AND + Processes.process_name IN ("powershell.exe","cmd.exe", "msbuild.exe", "microsoft.workflow.compiler.exe", "searchprotocolhost.exe", "scrcons.exe", "cscript.exe", "wscript.exe","bitsadmin.exe", "rundll32.exe", "wmic.exe", "mshta.exe", "certutil.exe", "schtasks.exe") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_suspicious_vmware_tools_child_process_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Although unlikely, some legitimate Administrative scripts may utilize VMWare Tools to execute commands on virtual machines. references: -- https://cloud.google.com/blog/topics/threat-intelligence/vmware-esxi-zero-day-bypass/ + - https://cloud.google.com/blog/topics/threat-intelligence/vmware-esxi-zero-day-bypass/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious process spawned by vmtoolsd.exe on $dest$ - risk_objects: - - field: dest - type: system - score: 62 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: Suspicious process spawned by vmtoolsd.exe on $dest$ + risk_objects: + - field: dest + type: system + score: 62 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - ESXi Post Compromise - - China-Nexus Threat Activity - asset_type: Endpoint - cve: - - CVE-2023-20867 - mitre_attack_id: - - T1059 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - ESXi Post Compromise + - China-Nexus Threat Activity + asset_type: Endpoint + cve: + - CVE-2023-20867 + mitre_attack_id: + - T1059 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/vmtoolsd/vmtoolsd_execution.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/vmtoolsd/vmtoolsd_execution.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_svchost_exe_parent_process_anomaly.yml b/detections/endpoint/windows_svchost_exe_parent_process_anomaly.yml index 46cb8004fd..4f9605f8b0 100644 --- a/detections/endpoint/windows_svchost_exe_parent_process_anomaly.yml +++ b/detections/endpoint/windows_svchost_exe_parent_process_anomaly.yml @@ -7,76 +7,50 @@ status: production type: Anomaly description: The following analytic detects an anomaly where an svchost.exe process is spawned by a parent process other than the standard services.exe. In a typical Windows environment, svchost.exe is a system process that hosts Windows service DLLs, and is expected to be a child of services.exe. A process deviation from this hierarchy may indicate suspicious behavior, such as malicious code attempting to masquerade as a legitimate system process or evade detection. It is essential to investigate the parent process and associated behavior for further signs of compromise or unauthorized activity. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.parent_process_name != "services.exe" AND Processes.process_name = "svchost.exe" AND Processes.process != unknown - AND Processes.parent_process_path != "C:\\ProgramData\\Microsoft\\Windows Defender\\Platform\\*\\MsMpEng.exe" - AND Processes.parent_process_path != "C:\\Program Files\\Windows Defender\\MsMpEng.exe" - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_svchost_exe_parent_process_anomaly_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name != "services.exe" AND Processes.process_name = "svchost.exe" AND Processes.process != unknown AND Processes.parent_process_path != "C:\\ProgramData\\Microsoft\\Windows Defender\\Platform\\*\\MsMpEng.exe" AND Processes.parent_process_path != "C:\\Program Files\\Windows Defender\\MsMpEng.exe" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_svchost_exe_parent_process_anomaly_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Windows Update or other Windows Installer processes may launch their own svchost.exe processes that are not directly spawned by services.exe in certain edge cases (e.g., during patches or updates). references: -- https://attack.mitre.org/techniques/T1036/009/ -- https://www.trendmicro.com/en_nl/research/24/k/earth-estries.html + - https://attack.mitre.org/techniques/T1036/009/ + - https://www.trendmicro.com/en_nl/research/24/k/earth-estries.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An svchost.exe process was spawned by an unexpected parent process [$parent_process_name$] instead of services.exe on [$dest$]. - risk_objects: - - field: dest - type: system - score: 50 - - field: user - type: user - score: 50 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: An svchost.exe process was spawned by an unexpected parent process [$parent_process_name$] instead of services.exe on [$dest$]. + risk_objects: + - field: dest + type: system + score: 50 + - field: user + type: user + score: 50 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - SnappyBee - - China-Nexus Threat Activity - asset_type: Endpoint - mitre_attack_id: - - T1036.009 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SnappyBee + - China-Nexus Threat Activity + asset_type: Endpoint + mitre_attack_id: + - T1036.009 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1035.009/suspicious_spawn_svchost/susp_svchost_proc.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1035.009/suspicious_spawn_svchost/susp_svchost_proc.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_symlink_evaluation_change_via_fsutil.yml b/detections/endpoint/windows_symlink_evaluation_change_via_fsutil.yml index d3229fb99f..95ef970342 100644 --- a/detections/endpoint/windows_symlink_evaluation_change_via_fsutil.yml +++ b/detections/endpoint/windows_symlink_evaluation_change_via_fsutil.yml @@ -1,97 +1,92 @@ name: Windows Symlink Evaluation Change via Fsutil id: 9777e7e3-2499-4a16-a519-ebe33630c1e8 -version: 1 -date: '2025-10-07' +version: 2 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - This analytic detects the execution of the Windows built-in tool Fsutil.exe with - the "behavior", "set" and "SymlinkEvaluation" parameters. - Attackers can abuse this to alter symlink evaluation behavior on Windows, potentially enabling remote traversal over SMB shares or evading defenses. - Such changes should be uncommon or even rare in enterprise environments and should be investigated. + This analytic detects the execution of the Windows built-in tool Fsutil.exe with + the "behavior", "set" and "SymlinkEvaluation" parameters. + Attackers can abuse this to alter symlink evaluation behavior on Windows, potentially enabling remote traversal over SMB shares or evading defenses. + Such changes should be uncommon or even rare in enterprise environments and should be investigated. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime max(_time) as lastTime - from datamodel=Endpoint.Processes where - - ( - Processes.process_name="fsutil.exe" - OR Processes.original_file_name="fsutil.exe" - ) + | tstats `security_content_summariesonly` + count min(_time) as firstTime max(_time) as lastTime + from datamodel=Endpoint.Processes where - Processes.process="*behavior*" - Processes.process="*set*" - Processes.process="*SymlinkEvaluation*" - - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_symlink_evaluation_change_via_fsutil_filter` + ( + Processes.process_name="fsutil.exe" + OR Processes.original_file_name="fsutil.exe" + ) + + Processes.process="*behavior*" + Processes.process="*set*" + Processes.process="*SymlinkEvaluation*" + + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product + + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_symlink_evaluation_change_via_fsutil_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: | - Edge cases may exist in environments where this command is used for legitimate purposes. - However, such usage is expected to be uncommon. It is recommended to investigate any occurrences of this command, and apply filters as necessary. + Edge cases may exist in environments where this command is used for legitimate purposes. + However, such usage is expected to be uncommon. It is recommended to investigate any occurrences of this command, and apply filters as necessary. references: -- https://learn.microsoft.com/windows-server/administration/windows-commands/fsutil-behavior -- https://www.group-ib.com/blog/blackcat/ -- https://www.intrinsec.com/alphv-ransomware-gang-analysis/ + - https://learn.microsoft.com/windows-server/administration/windows-commands/fsutil-behavior + - https://www.group-ib.com/blog/blackcat/ + - https://www.intrinsec.com/alphv-ransomware-gang-analysis/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: process $process_name$ with command line "$process$" modified SymlinkEvaluation on $dest$ - risk_objects: - - field: dest - type: system - score: 35 - threat_objects: - - field: parent_process_name - type: parent_process_name + message: process $process_name$ with command line "$process$" modified SymlinkEvaluation on $dest$ + risk_objects: + - field: dest + type: system + score: 35 + threat_objects: + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - Windows Post-Exploitation - asset_type: Endpoint - mitre_attack_id: - - T1222.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Post-Exploitation + asset_type: Endpoint + mitre_attack_id: + - T1222.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/fsutil_symlink_eval/fsutil_symlink_eval.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/fsutil_symlink_eval/fsutil_symlink_eval.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_system_binary_proxy_execution_compiled_html_file_decompile.yml b/detections/endpoint/windows_system_binary_proxy_execution_compiled_html_file_decompile.yml index 24d1519215..0fba4cf8f0 100644 --- a/detections/endpoint/windows_system_binary_proxy_execution_compiled_html_file_decompile.yml +++ b/detections/endpoint/windows_system_binary_proxy_execution_compiled_html_file_decompile.yml @@ -1,90 +1,74 @@ name: Windows System Binary Proxy Execution Compiled HTML File Decompile id: 2acf0e19-4149-451c-a3f3-39cd3c77e37d -version: 11 -date: '2025-09-18' +version: 12 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the use of the decompile parameter with - the HTML Help application (HH.exe). This behavior is identified through Endpoint - Detection and Response (EDR) telemetry, focusing on command-line executions involving - the decompile parameter. This activity is significant because it is an uncommon - command and has been associated with APT41 campaigns, where it was used to unpack - HTML help files for further malicious actions. If confirmed malicious, this technique - could allow attackers to execute arbitrary commands, potentially leading to further - compromise and persistence within the environment. +description: The following analytic detects the use of the decompile parameter with the HTML Help application (HH.exe). This behavior is identified through Endpoint Detection and Response (EDR) telemetry, focusing on command-line executions involving the decompile parameter. This activity is significant because it is an uncommon command and has been associated with APT41 campaigns, where it was used to unpack HTML help files for further malicious actions. If confirmed malicious, this technique could allow attackers to execute arbitrary commands, potentially leading to further compromise and persistence within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_hh` Processes.process=*-decompile* - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_system_binary_proxy_execution_compiled_html_file_decompile_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_hh` Processes.process=*-decompile* + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_system_binary_proxy_execution_compiled_html_file_decompile_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: False positives should be limited, filter as needed. references: -- https://www.ptsecurity.com/ww-en/analytics/pt-esc-threat-intelligence/higaisa-or-winnti-apt-41-backdoors-old-and-new/ -- https://redcanary.com/blog/introducing-atomictestharnesses/ -- https://attack.mitre.org/techniques/T1218/001/ -- https://docs.microsoft.com/en-us/windows/win32/api/htmlhelp/nf-htmlhelp-htmlhelpa + - https://www.ptsecurity.com/ww-en/analytics/pt-esc-threat-intelligence/higaisa-or-winnti-apt-41-backdoors-old-and-new/ + - https://redcanary.com/blog/introducing-atomictestharnesses/ + - https://attack.mitre.org/techniques/T1218/001/ + - https://docs.microsoft.com/en-us/windows/win32/api/htmlhelp/nf-htmlhelp-htmlhelpa drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $process_name$ has been identified using decompile against a CHM on $dest$ - under user $user$. - risk_objects: - - field: user - type: user - score: 90 - - field: dest - type: system - score: 90 - threat_objects: - - field: process_name - type: process_name + message: $process_name$ has been identified using decompile against a CHM on $dest$ under user $user$. + risk_objects: + - field: user + type: user + score: 90 + - field: dest + type: system + score: 90 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Suspicious Compiled HTML Activity - - Living Off The Land - - Compromised Windows Host - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1218.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious Compiled HTML Activity + - Living Off The Land + - Compromised Windows Host + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1218.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/hh_decom_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/hh_decom_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_system_discovery_using_ldap_nslookup.yml b/detections/endpoint/windows_system_discovery_using_ldap_nslookup.yml index 0c1879e904..4aa981c83f 100644 --- a/detections/endpoint/windows_system_discovery_using_ldap_nslookup.yml +++ b/detections/endpoint/windows_system_discovery_using_ldap_nslookup.yml @@ -1,81 +1,69 @@ name: Windows System Discovery Using ldap Nslookup id: 2418780f-7c3e-4c45-b8b4-996ea850cd49 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the execution of nslookup.exe to query - domain information using LDAP. It leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process names and command-line arguments. This activity - is significant as nslookup.exe can be abused by malware like Qakbot to gather critical - domain details, such as SRV records and server names. If confirmed malicious, this - behavior could allow attackers to map the network, identify key servers, and plan - further attacks, potentially leading to data exfiltration or lateral movement within - the network. +description: The following analytic detects the execution of nslookup.exe to query domain information using LDAP. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant as nslookup.exe can be abused by malware like Qakbot to gather critical domain details, such as SRV records and server names. If confirmed malicious, this behavior could allow attackers to map the network, identify key servers, and plan further attacks, potentially leading to data exfiltration or lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = "nslookup.exe" - OR Processes.original_file_name = "nslookup.exe") AND Processes.process = "*_ldap._tcp.dc._msdcs*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` - | `windows_system_discovery_using_ldap_nslookup_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: dministrator may execute this commandline tool for auditing - purposes. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name = "nslookup.exe" + OR + Processes.original_file_name = "nslookup.exe" + ) + AND Processes.process = "*_ldap._tcp.dc._msdcs*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_system_discovery_using_ldap_nslookup_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: dministrator may execute this commandline tool for auditing purposes. Filter as needed. references: -- https://securelist.com/qakbot-technical-analysis/103931/ -- https://learn.microsoft.com/en-us/troubleshoot/windows-server/networking/verify-srv-dns-records-have-been-created + - https://securelist.com/qakbot-technical-analysis/103931/ + - https://learn.microsoft.com/en-us/troubleshoot/windows-server/networking/verify-srv-dns-records-have-been-created drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: System nslookup domain discovery on $dest$ - risk_objects: - - field: dest - type: system - score: 1 - threat_objects: [] + message: System nslookup domain discovery on $dest$ + risk_objects: + - field: dest + type: system + score: 1 + threat_objects: [] tags: - analytic_story: - - Qakbot - asset_type: Endpoint - mitre_attack_id: - - T1033 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Qakbot + asset_type: Endpoint + mitre_attack_id: + - T1033 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/qakbot_discovery_cmdline/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/qakbot_discovery_cmdline/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_system_discovery_using_qwinsta.yml b/detections/endpoint/windows_system_discovery_using_qwinsta.yml index 2e31ba02fa..c9f99db762 100644 --- a/detections/endpoint/windows_system_discovery_using_qwinsta.yml +++ b/detections/endpoint/windows_system_discovery_using_qwinsta.yml @@ -1,60 +1,50 @@ name: Windows System Discovery Using Qwinsta id: 2e765c1b-144a-49f0-93d0-1df4287cca04 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects the execution of "qwinsta.exe" on a Windows - operating system. This detection leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process execution logs. The "qwinsta.exe" tool is significant - because it can display detailed session information on a remote desktop session - host server. This behavior is noteworthy as it is commonly abused by Qakbot malware - to gather system information and send it back to its Command and Control (C2) server. - If confirmed malicious, this activity could lead to unauthorized data exfiltration - and further compromise of the host. +description: The following analytic detects the execution of "qwinsta.exe" on a Windows operating system. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. The "qwinsta.exe" tool is significant because it can display detailed session information on a remote desktop session host server. This behavior is noteworthy as it is commonly abused by Qakbot malware to gather system information and send it back to its Command and Control (C2) server. If confirmed malicious, this activity could lead to unauthorized data exfiltration and further compromise of the host. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "qwinsta.exe" - OR Processes.original_file_name = "qwinsta.exe" by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` - | `windows_system_discovery_using_qwinsta_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator may execute this commandline tool for auditing - purposes. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "qwinsta.exe" + OR + Processes.original_file_name = "qwinsta.exe" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_system_discovery_using_qwinsta_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator may execute this commandline tool for auditing purposes. Filter as needed. references: -- https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/qwinsta -- https://securelist.com/qakbot-technical-analysis/103931/ + - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/qwinsta + - https://securelist.com/qakbot-technical-analysis/103931/ tags: - analytic_story: - - Qakbot - asset_type: Endpoint - mitre_attack_id: - - T1033 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Qakbot + asset_type: Endpoint + mitre_attack_id: + - T1033 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/qakbot_discovery_cmdline/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/qakbot_discovery_cmdline/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_system_file_on_disk.yml b/detections/endpoint/windows_system_file_on_disk.yml index 79fc66f390..2bfaa98130 100644 --- a/detections/endpoint/windows_system_file_on_disk.yml +++ b/detections/endpoint/windows_system_file_on_disk.yml @@ -1,51 +1,45 @@ name: Windows System File on Disk id: 993ce99d-9cdd-42c7-a2cf-733d5954e5a6 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic detects the creation of new .sys files on disk. - It leverages the Endpoint.Filesystem data model to identify and log instances where - .sys files are written to the filesystem. This activity is significant because .sys - files are often used as kernel mode drivers, and their unauthorized creation can - indicate malicious activity such as rootkit installation. If confirmed malicious, - this could allow an attacker to gain kernel-level access, leading to full system - compromise, persistent control, and the ability to bypass security mechanisms. +description: The following analytic detects the creation of new .sys files on disk. It leverages the Endpoint.Filesystem data model to identify and log instances where .sys files are written to the filesystem. This activity is significant because .sys files are often used as kernel mode drivers, and their unauthorized creation can indicate malicious activity such as rootkit installation. If confirmed malicious, this could allow an attacker to gain kernel-level access, leading to full system compromise, persistent control, and the ability to bypass security mechanisms. data_source: -- Sysmon EventID 11 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.sys*" - by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time - Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path - Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id - Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | - `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_system_file_on_disk_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on files from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. - In addition, confirm the latest CIM App 4.20 or higher is installed and the latest - TA for the endpoint product. In addition, filtering may occur by adding NOT (Filesystem.file_path - IN ("*\\Windows\\*", "*\\Program File*", "*\\systemroot\\*","%SystemRoot%*", "system32\*")). - This will level out the noise generated to potentally lead to generating findings. + - Sysmon EventID 11 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem + WHERE Filesystem.file_name="*.sys*" + BY Filesystem.action Filesystem.dest Filesystem.file_access_time + Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time + Filesystem.file_name Filesystem.file_path Filesystem.file_acl + Filesystem.file_size Filesystem.process_guid Filesystem.process_id + Filesystem.user Filesystem.vendor_product + | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_system_file_on_disk_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on files from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. In addition, filtering may occur by adding NOT (Filesystem.file_path IN ("*\\Windows\\*", "*\\Program File*", "*\\systemroot\\*","%SystemRoot%*", "system32\*")). This will level out the noise generated to potentally lead to generating findings. known_false_positives: False positives will be present. Filter as needed. references: -- https://redcanary.com/blog/tracking-driver-inventory-to-expose-rootkits/ + - https://redcanary.com/blog/tracking-driver-inventory-to-expose-rootkits/ tags: - analytic_story: - - CISA AA22-264A - - Windows Drivers - - Crypto Stealer - asset_type: Endpoint - mitre_attack_id: - - T1068 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA22-264A + - Windows Drivers + - Crypto Stealer + asset_type: Endpoint + mitre_attack_id: + - T1068 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/drivers/sysmon_sys_filemod.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/drivers/sysmon_sys_filemod.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_system_logoff_commandline.yml b/detections/endpoint/windows_system_logoff_commandline.yml index 098593a4d1..e79be5aa9f 100644 --- a/detections/endpoint/windows_system_logoff_commandline.yml +++ b/detections/endpoint/windows_system_logoff_commandline.yml @@ -1,85 +1,72 @@ name: Windows System LogOff Commandline id: 74a8133f-93e7-4b71-9bd3-13a66124fd57 -version: 8 -date: '2025-10-14' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the Windows command line - to log off a host machine. It leverages data from Endpoint Detection and Response - (EDR) agents, focusing on processes involving `shutdown.exe` with specific parameters. - This activity is significant as it is often associated with Advanced Persistent - Threats (APTs) and Remote Access Trojans (RATs) like dcrat, which use this technique - to disrupt operations, aid in system destruction, or inhibit recovery. If confirmed - malicious, this could lead to system downtime, data loss, or hindered incident response - efforts. +description: The following analytic detects the execution of the Windows command line to log off a host machine. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on processes involving `shutdown.exe` with specific parameters. This activity is significant as it is often associated with Advanced Persistent Threats (APTs) and Remote Access Trojans (RATs) like dcrat, which use this technique to disrupt operations, aid in system destruction, or inhibit recovery. If confirmed malicious, this could lead to system downtime, data loss, or hindered incident response efforts. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where (Processes.process_name = shutdown.exe OR Processes.original_file_name = shutdown.exe) - Processes.process="*shutdown*" Processes.process IN ("* /l*", "* -l*") Processes.process - IN ("* /t*","* -t*","* /f*","* -f*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_logoff_commandline_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator may execute this commandline to trigger shutdown, - logoff or restart the host machine. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name = shutdown.exe + OR + Processes.original_file_name = shutdown.exe + ) + Processes.process="*shutdown*" Processes.process IN ("* /l*", "* -l*") Processes.process IN ("* /t*","* -t*","* /f*","* -f*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_system_logoff_commandline_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator may execute this commandline to trigger shutdown, logoff or restart the host machine. references: -- https://attack.mitre.org/techniques/T1529/ -- https://www.mandiant.com/resources/analyzing-dark-crystal-rat-backdoor + - https://attack.mitre.org/techniques/T1529/ + - https://www.mandiant.com/resources/analyzing-dark-crystal-rat-backdoor drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Process name $process_name$ is seen to execute logoff commandline on $dest$ - risk_objects: - - field: dest - type: system - score: 56 - threat_objects: [] + message: Process name $process_name$ is seen to execute logoff commandline on $dest$ + risk_objects: + - field: dest + type: system + score: 56 + threat_objects: [] tags: - analytic_story: - - NjRAT - - DarkCrystal RAT - - XWorm - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1529 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - NjRAT + - DarkCrystal RAT + - XWorm + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1529 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/reboot_logoff_commandline/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/reboot_logoff_commandline/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_system_network_config_discovery_display_dns.yml b/detections/endpoint/windows_system_network_config_discovery_display_dns.yml index 2fb045b251..bcdd992554 100644 --- a/detections/endpoint/windows_system_network_config_discovery_display_dns.yml +++ b/detections/endpoint/windows_system_network_config_discovery_display_dns.yml @@ -1,88 +1,72 @@ name: Windows System Network Config Discovery Display DNS id: e24f0a0e-41a9-419f-9999-eacab15efc36 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: - The following analytic identifies the execution of the "ipconfig /displaydns" - command, which retrieves DNS reply information using the built-in Windows tool IPConfig. - This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on process command-line executions. Monitoring this activity is significant - as threat actors and post-exploitation tools like WINPEAS often abuse this command - to gather network information. If confirmed malicious, this activity could allow - attackers to map the network, identify DNS servers, and potentially facilitate further - network-based attacks or lateral movement. +description: The following analytic identifies the execution of the "ipconfig /displaydns" command, which retrieves DNS reply information using the built-in Windows tool IPConfig. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process command-line executions. Monitoring this activity is significant as threat actors and post-exploitation tools like WINPEAS often abuse this command to gather network information. If confirmed malicious, this activity could allow attackers to map the network, identify DNS servers, and potentially facilitate further network-based attacks or lateral movement. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 -search: - '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name="ipconfig.exe" - OR Processes.original_file_name = "ipconfig.exe" AND Processes.process = "*/displaydns*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_system_network_config_discovery_display_dns_filter`' -how_to_implement: - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name="ipconfig.exe" + OR + Processes.original_file_name = "ipconfig.exe" + AND + Processes.process = "*/displaydns*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_system_network_config_discovery_display_dns_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: - - https://superuser.com/questions/230308/explain-output-of-ipconfig-displaydns - - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS - - https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ + - https://superuser.com/questions/230308/explain-output-of-ipconfig-displaydns + - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS + - https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: process $process_name$ with commandline $process$ is executed on $dest$ - risk_objects: - - field: dest - type: system - score: 9 - threat_objects: [] + message: process $process_name$ with commandline $process$ is executed on $dest$ + risk_objects: + - field: dest + type: system + score: 9 + threat_objects: [] tags: - analytic_story: - - Medusa Ransomware - - Windows Post-Exploitation - - Prestige Ransomware - - Water Gamayun - asset_type: Endpoint - mitre_attack_id: - - T1016 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Medusa Ransomware + - Windows Post-Exploitation + - Prestige Ransomware + - Water Gamayun + asset_type: Endpoint + mitre_attack_id: + - T1016 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_system_network_connections_discovery_netsh.yml b/detections/endpoint/windows_system_network_connections_discovery_netsh.yml index 03bb969b7c..32fc561d39 100644 --- a/detections/endpoint/windows_system_network_connections_discovery_netsh.yml +++ b/detections/endpoint/windows_system_network_connections_discovery_netsh.yml @@ -1,83 +1,67 @@ name: Windows System Network Connections Discovery Netsh id: abfb7cc5-c275-4a97-9029-62cd8d4ffeca -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the execution of the Windows built-in - tool netsh.exe to display the state, configuration, and profile of the host firewall. - This detection leverages data from Endpoint Detection and Response (EDR) agents, - focusing on command-line executions and process metadata. Monitoring this activity - is crucial as netsh.exe can be used by adversaries to bypass firewall rules or discover - firewall settings. If confirmed malicious, this activity could allow attackers to - manipulate firewall configurations, potentially leading to unauthorized network - access or data exfiltration. +description: The following analytic detects the execution of the Windows built-in tool netsh.exe to display the state, configuration, and profile of the host firewall. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process metadata. Monitoring this activity is crucial as netsh.exe can be used by adversaries to bypass firewall rules or discover firewall settings. If confirmed malicious, this activity could allow attackers to manipulate firewall configurations, potentially leading to unauthorized network access or data exfiltration. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_netsh`AND Processes.process - = "* show *" Processes.process IN ("*state*", "*config*", "*wlan*", "*profile*") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_system_network_connections_discovery_netsh_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_netsh`AND Processes.process = "* show *" Processes.process IN ("*state*", "*config*", "*wlan*", "*profile*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_system_network_connections_discovery_netsh_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: network administrator can use this tool for auditing process. references: -- https://attack.mitre.org/techniques/T1049/ -- https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS -- https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ + - https://attack.mitre.org/techniques/T1049/ + - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS + - https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: netsh process with command line $process$ on $dest$ - risk_objects: - - field: dest - type: system - score: 9 - threat_objects: [] + message: netsh process with command line $process$ on $dest$ + risk_objects: + - field: dest + type: system + score: 9 + threat_objects: [] tags: - analytic_story: - - Windows Post-Exploitation - - Prestige Ransomware - - Snake Keylogger - asset_type: Endpoint - mitre_attack_id: - - T1049 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Post-Exploitation + - Prestige Ransomware + - Snake Keylogger + asset_type: Endpoint + mitre_attack_id: + - T1049 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_system_reboot_commandline.yml b/detections/endpoint/windows_system_reboot_commandline.yml index 3f56fe9b32..74e30768d8 100644 --- a/detections/endpoint/windows_system_reboot_commandline.yml +++ b/detections/endpoint/windows_system_reboot_commandline.yml @@ -1,88 +1,75 @@ name: Windows System Reboot CommandLine id: 97fc2b60-c8eb-4711-93f7-d26fade3686f -version: 9 -date: '2025-10-14' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies the execution of the Windows command - line to reboot a host machine using "shutdown.exe" with specific parameters. This - detection leverages data from Endpoint Detection and Response (EDR) agents, focusing - on process names and command-line arguments. This activity is significant as it - is often associated with advanced persistent threats (APTs) and remote access trojans - (RATs) like dcrat, which may use system reboots to disrupt operations, aid in system - destruction, or inhibit recovery. If confirmed malicious, this could lead to system - downtime, data loss, or hindered incident response efforts. +description: The following analytic identifies the execution of the Windows command line to reboot a host machine using "shutdown.exe" with specific parameters. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant as it is often associated with advanced persistent threats (APTs) and remote access trojans (RATs) like dcrat, which may use system reboots to disrupt operations, aid in system destruction, or inhibit recovery. If confirmed malicious, this could lead to system downtime, data loss, or hindered incident response efforts. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where (Processes.process_name = shutdown.exe OR Processes.original_file_name = shutdown.exe) - Processes.process="*shutdown*" Processes.process IN ("* /r*", "* -r*") Processes.process - IN ("* /t*","* -t*","* /f*","* -f*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_reboot_commandline_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator may execute this commandline to trigger shutdown - or restart the host machine. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name = shutdown.exe + OR + Processes.original_file_name = shutdown.exe + ) + Processes.process="*shutdown*" Processes.process IN ("* /r*", "* -r*") Processes.process IN ("* /t*","* -t*","* /f*","* -f*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_system_reboot_commandline_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator may execute this commandline to trigger shutdown or restart the host machine. references: -- https://attack.mitre.org/techniques/T1529/ -- https://www.mandiant.com/resources/analyzing-dark-crystal-rat-backdoor + - https://attack.mitre.org/techniques/T1529/ + - https://www.mandiant.com/resources/analyzing-dark-crystal-rat-backdoor drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Process $process_name$ that executed reboot via commandline on $dest$ - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: [] + message: Process $process_name$ that executed reboot via commandline on $dest$ + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: [] tags: - analytic_story: - - XWorm - - DarkGate Malware - - NjRAT - - Quasar RAT - - DarkCrystal RAT - - MoonPeak - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1529 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - XWorm + - DarkGate Malware + - NjRAT + - Quasar RAT + - DarkCrystal RAT + - MoonPeak + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1529 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/reboot_logoff_commandline/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/reboot_logoff_commandline/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_system_remote_discovery_with_query.yml b/detections/endpoint/windows_system_remote_discovery_with_query.yml index c480f4e5c1..dae983390e 100644 --- a/detections/endpoint/windows_system_remote_discovery_with_query.yml +++ b/detections/endpoint/windows_system_remote_discovery_with_query.yml @@ -1,92 +1,78 @@ name: Windows System Remote Discovery With Query id: 94859172-a521-474f-97ac-4cf4b09634a3 -version: 4 -date: '2025-05-02' +version: 5 +date: '2026-02-25' author: Steven Dick status: production type: Anomaly -description: The following analytic detects the execution of `query.exe` with command-line - arguments aimed at discovering data on remote devices. It leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process names and command-line - executions. This activity is significant as adversaries may use `query.exe` to gain - situational awareness and perform Active Directory discovery on compromised endpoints. - If confirmed malicious, this behavior could allow attackers to identify various - details about a system, aiding in further lateral movement and privilege escalation - within the network. +description: The following analytic detects the execution of `query.exe` with command-line arguments aimed at discovering data on remote devices. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as adversaries may use `query.exe` to gain situational awareness and perform Active Directory discovery on compromised endpoints. If confirmed malicious, this behavior could allow attackers to identify various details about a system, aiding in further lateral movement and privilege escalation within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: "| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)\ - \ as lastTime from datamodel=Endpoint.Processes \nwhere (Processes.process_name=\"\ - query.exe\" OR Processes.original_file_name=\"query.exe\") AND (Processes.process=\"\ - */server*\") AND NOT Processes.process IN (\"*/server:localhost*\", \"*/server:127.0.0.1*\"\ - ) \nby Processes.action Processes.dest Processes.original_file_name Processes.parent_process\ - \ Processes.parent_process_exec \nProcesses.parent_process_guid Processes.parent_process_id\ - \ Processes.parent_process_name \nProcesses.parent_process_path Processes.process\ - \ Processes.process_exec Processes.process_guid Processes.process_hash \nProcesses.process_id\ - \ Processes.process_integrity_level Processes.process_name Processes.process_path\ - \ \nProcesses.user Processes.user_id Processes.vendor_product \n| `drop_dm_object_name(Processes)`\ - \ \n| `security_content_ctime(firstTime)` \n| `security_content_ctime(lastTime)`\ - \ \n| `windows_system_remote_discovery_with_query_filter`" -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.process_name="query.exe" + OR + Processes.original_file_name="query.exe" + ) + AND (Processes.process="*/server*") AND NOT Processes.process IN ("*/server:localhost*", "*/server:127.0.0.1*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_system_remote_discovery_with_query_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1033/ + - https://attack.mitre.org/techniques/T1033/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$","$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate processes on $dest$ - search: '| from datamodel:Endpoint.Processes | search dest=$dest$ process_name = - $process_name|s$' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$","$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate processes on $dest$ + search: '| from datamodel:Endpoint.Processes | search dest=$dest$ process_name = $process_name|s$' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The user $user$ ran the Query command to enumerate the remote system $dest$ - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: - - field: process_name - type: process_name + message: The user $user$ ran the Query command to enumerate the remote system $dest$ + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Active Directory Discovery - - Medusa Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1033 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - Medusa Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1033 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/query_remote_usage/query_remote_usage.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/query_remote_usage/query_remote_usage.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_system_script_proxy_execution_syncappvpublishingserver.yml b/detections/endpoint/windows_system_script_proxy_execution_syncappvpublishingserver.yml index 8f4f338ee2..ab02469795 100644 --- a/detections/endpoint/windows_system_script_proxy_execution_syncappvpublishingserver.yml +++ b/detections/endpoint/windows_system_script_proxy_execution_syncappvpublishingserver.yml @@ -1,92 +1,72 @@ name: Windows System Script Proxy Execution Syncappvpublishingserver id: 8dd73f89-682d-444c-8b41-8e679966ad3c -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the execution of Syncappvpublishingserver.vbs - via wscript.exe or cscript.exe, which may indicate an attempt to download remote - files or perform privilege escalation. This detection leverages data from Endpoint - Detection and Response (EDR) agents, focusing on process names and command-line - executions. Monitoring this activity is crucial as it can signify malicious use - of a native Windows script for unauthorized actions. If confirmed malicious, this - behavior could lead to unauthorized file downloads or elevated privileges, posing - a significant security risk. +description: The following analytic detects the execution of Syncappvpublishingserver.vbs via wscript.exe or cscript.exe, which may indicate an attempt to download remote files or perform privilege escalation. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. Monitoring this activity is crucial as it can signify malicious use of a native Windows script for unauthorized actions. If confirmed malicious, this behavior could lead to unauthorized file downloads or elevated privileges, posing a significant security risk. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("wscript.exe","cscript.exe") - Processes.process="*syncappvpublishingserver.vbs*" by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_system_script_proxy_execution_syncappvpublishingserver_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives may be present if the vbscript syncappvpublishingserver - is used for legitimate purposes. Filter as needed. Adding a n; to the command-line - arguments may help reduce any noise. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name IN ("wscript.exe","cscript.exe") Processes.process="*syncappvpublishingserver.vbs*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_system_script_proxy_execution_syncappvpublishingserver_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives may be present if the vbscript syncappvpublishingserver is used for legitimate purposes. Filter as needed. Adding a n; to the command-line arguments may help reduce any noise. references: -- https://lolbas-project.github.io/lolbas/Scripts/Syncappvpublishingserver/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1216/T1216.md#atomic-test-1---syncappvpublishingserver-signed-script-powershell-command-execution + - https://lolbas-project.github.io/lolbas/Scripts/Syncappvpublishingserver/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1216/T1216.md#atomic-test-1---syncappvpublishingserver-signed-script-powershell-command-execution drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to download files or evade critical - controls. - risk_objects: - - field: user - type: user - score: 30 - - field: dest - type: system - score: 30 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to download files or evade critical controls. + risk_objects: + - field: user + type: user + score: 30 + - field: dest + type: system + score: 30 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1216 - - T1218 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1216 + - T1218 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1216/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1216/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_system_shutdown_commandline.yml b/detections/endpoint/windows_system_shutdown_commandline.yml index fc90c4f51e..6134a981f8 100644 --- a/detections/endpoint/windows_system_shutdown_commandline.yml +++ b/detections/endpoint/windows_system_shutdown_commandline.yml @@ -5,85 +5,55 @@ date: '2026-02-12' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies the execution of the Windows shutdown - command via the command line interface. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process names and command-line arguments. - This activity is significant because attackers may use the shutdown command to erase - tracks, cause disruption, or ensure changes take effect after installing backdoors. - If confirmed malicious, this activity could lead to system downtime, denial of service, - or evasion of security tools, impacting the overall security posture of the network. +description: The following analytic identifies the execution of the Windows shutdown command via the command line interface. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because attackers may use the shutdown command to erase tracks, cause disruption, or ensure changes take effect after installing backdoors. If confirmed malicious, this activity could lead to system downtime, denial of service, or evasion of security tools, impacting the overall security posture of the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where (Processes.process_name = shutdown.exe OR Processes.original_file_name = shutdown.exe) - Processes.process="*shutdown*" AND Processes.process IN("* /s*", "* -s*") AND Processes.process - IN ("* /t*","* -t*","* /f*","* -f*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_shutdown_commandline_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrator may execute this commandline to trigger shutdown - or restart the host machine. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = shutdown.exe OR Processes.original_file_name = shutdown.exe) Processes.process="*shutdown*" AND Processes.process IN("* /s*", "* -s*") AND Processes.process IN ("* /t*","* -t*","* /f*","* -f*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_shutdown_commandline_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrator may execute this commandline to trigger shutdown or restart the host machine. references: -- https://attack.mitre.org/techniques/T1529/ -- https://www.mandiant.com/resources/analyzing-dark-crystal-rat-backdoor + - https://attack.mitre.org/techniques/T1529/ + - https://www.mandiant.com/resources/analyzing-dark-crystal-rat-backdoor drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Process $process_name$ seen to execute shutdown via commandline on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: Process $process_name$ seen to execute shutdown via commandline on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - XWorm - - DarkGate Malware - - NjRAT - - Quasar RAT - - Sandworm Tools - - DarkCrystal RAT - - MoonPeak - - Scattered Lapsus$ Hunters - - ZOVWiper - asset_type: Endpoint - mitre_attack_id: - - T1529 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - XWorm + - DarkGate Malware + - NjRAT + - Quasar RAT + - Sandworm Tools + - DarkCrystal RAT + - MoonPeak + - Scattered Lapsus$ Hunters + - ZOVWiper + asset_type: Endpoint + mitre_attack_id: + - T1529 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/shutdown_commandline/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/shutdown_commandline/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_system_time_discovery_w32tm_delay.yml b/detections/endpoint/windows_system_time_discovery_w32tm_delay.yml index 98ec0c4d4a..8e24d90699 100644 --- a/detections/endpoint/windows_system_time_discovery_w32tm_delay.yml +++ b/detections/endpoint/windows_system_time_discovery_w32tm_delay.yml @@ -1,84 +1,65 @@ name: Windows System Time Discovery W32tm Delay id: b2cc69e7-11ba-42dc-a269-59c069a48870 -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies the use of the w32tm.exe utility with - the /stripchart function, which is indicative of DCRat malware delaying its payload - execution. This detection leverages data from Endpoint Detection and Response (EDR) - agents, focusing on specific command-line arguments used by w32tm.exe. This activity - is significant as it may indicate an attempt to evade detection by delaying malicious - actions such as C2 communication and beaconing. If confirmed malicious, this behavior - could allow an attacker to maintain persistence and execute further malicious activities - undetected. +description: The following analytic identifies the use of the w32tm.exe utility with the /stripchart function, which is indicative of DCRat malware delaying its payload execution. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line arguments used by w32tm.exe. This activity is significant as it may indicate an attempt to evade detection by delaying malicious actions such as C2 communication and beaconing. If confirmed malicious, this behavior could allow an attacker to maintain persistence and execute further malicious activities undetected. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.process_name = w32tm.exe Processes.process= "* /stripchart *" Processes.process= - "* /computer:localhost *" Processes.process= "* /period:*" Processes.process= "* - /dataonly *" Processes.process= "* /samples:*" by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_system_time_discovery_w32tm_delay_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = w32tm.exe Processes.process= "* /stripchart *" Processes.process= "* /computer:localhost *" Processes.process= "* /period:*" Processes.process= "* /dataonly *" Processes.process= "* /samples:*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_system_time_discovery_w32tm_delay_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://cert.gov.ua/article/405538 -- https://malpedia.caad.fkie.fraunhofer.de/details/win.dcrat -- https://www.mandiant.com/resources/analyzing-dark-crystal-rat-backdoor + - https://cert.gov.ua/article/405538 + - https://malpedia.caad.fkie.fraunhofer.de/details/win.dcrat + - https://www.mandiant.com/resources/analyzing-dark-crystal-rat-backdoor drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Process name w32tm.exe is using suspcicious command line arguments $process$ - on host $dest$. - risk_objects: - - field: dest - type: system - score: 36 - threat_objects: [] + message: Process name w32tm.exe is using suspcicious command line arguments $process$ on host $dest$. + risk_objects: + - field: dest + type: system + score: 36 + threat_objects: [] tags: - analytic_story: - - DarkCrystal RAT - asset_type: Endpoint - mitre_attack_id: - - T1124 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - DarkCrystal RAT + asset_type: Endpoint + mitre_attack_id: + - T1124 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/dcrat_delay_execution/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/dcrat_delay_execution/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_system_user_discovery_via_quser.yml b/detections/endpoint/windows_system_user_discovery_via_quser.yml index 8ea17deee7..40764f39a8 100644 --- a/detections/endpoint/windows_system_user_discovery_via_quser.yml +++ b/detections/endpoint/windows_system_user_discovery_via_quser.yml @@ -1,63 +1,53 @@ name: Windows System User Discovery Via Quser id: 0c3f3e09-e47a-410e-856f-a02a5c5fafb0 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects the execution of the Windows OS tool quser.exe, - commonly used to gather information about user sessions on a Remote Desktop Session - Host server. This detection leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process execution logs. Monitoring this activity is crucial - as quser.exe is often abused by post-exploitation tools like winpeas, used in ransomware - attacks to enumerate user sessions. If confirmed malicious, attackers could leverage - this information to further compromise the system, maintain persistence, or escalate - privileges. +description: The following analytic detects the execution of the Windows OS tool quser.exe, commonly used to gather information about user sessions on a Remote Desktop Session Host server. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. Monitoring this activity is crucial as quser.exe is often abused by post-exploitation tools like winpeas, used in ransomware attacks to enumerate user sessions. If confirmed malicious, attackers could leverage this information to further compromise the system, maintain persistence, or escalate privileges. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name="quser.exe" - OR Processes.original_file_name = "quser.exe" by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_system_user_discovery_via_quser_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: network administrator can use this command tool to audit RDP - access of user in specific network or host. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name="quser.exe" + OR + Processes.original_file_name = "quser.exe" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_system_user_discovery_via_quser_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: network administrator can use this command tool to audit RDP access of user in specific network or host. references: -- https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/quser -- https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS -- https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ + - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/quser + - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS + - https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ tags: - analytic_story: - - Prestige Ransomware - - Crypto Stealer - - Windows Post-Exploitation - asset_type: Endpoint - mitre_attack_id: - - T1033 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Prestige Ransomware + - Crypto Stealer + - Windows Post-Exploitation + asset_type: Endpoint + mitre_attack_id: + - T1033 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_system_user_privilege_discovery.yml b/detections/endpoint/windows_system_user_privilege_discovery.yml index 28d4d6cc5d..e97be3abcc 100644 --- a/detections/endpoint/windows_system_user_privilege_discovery.yml +++ b/detections/endpoint/windows_system_user_privilege_discovery.yml @@ -1,58 +1,48 @@ name: Windows System User Privilege Discovery id: 8c9a06bc-9939-4425-9bb9-be2371f7fb7e -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Hunting data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic detects the execution of `whoami.exe` with the - `/priv` parameter, which displays the privileges assigned to the current user account. - It leverages data from Endpoint Detection and Response (EDR) agents, focusing on - process names and command-line executions. This activity is significant as it may - indicate an adversary attempting to enumerate user privileges, a common step in - the reconnaissance phase of an attack. If confirmed malicious, this could lead to - privilege escalation or further exploitation within the environment. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name="whoami.exe" - Processes.process= "*/priv*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_user_privilege_discovery_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Administrators or power users may use this command for troubleshooting. - Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic detects the execution of `whoami.exe` with the `/priv` parameter, which displays the privileges assigned to the current user account. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it may indicate an adversary attempting to enumerate user privileges, a common step in the reconnaissance phase of an attack. If confirmed malicious, this could lead to privilege escalation or further exploitation within the environment. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name="whoami.exe" Processes.process= "*/priv*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_system_user_privilege_discovery_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators or power users may use this command for troubleshooting. Filter as needed. references: -- https://attack.mitre.org/techniques/T1033/ -- https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a + - https://attack.mitre.org/techniques/T1033/ + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a tags: - analytic_story: - - CISA AA23-347A - asset_type: Endpoint - mitre_attack_id: - - T1033 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA23-347A + asset_type: Endpoint + mitre_attack_id: + - T1033 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/whoami_priv/whoami-priv-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/whoami_priv/whoami-priv-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_terminating_lsass_process.yml b/detections/endpoint/windows_terminating_lsass_process.yml index 35cb856845..870de1ab26 100644 --- a/detections/endpoint/windows_terminating_lsass_process.yml +++ b/detections/endpoint/windows_terminating_lsass_process.yml @@ -1,76 +1,67 @@ name: Windows Terminating Lsass Process id: 7ab3c319-a4e7-4211-9e8c-40a049d0dba6 -version: 10 -date: '2026-01-14' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects a suspicious process attempting to terminate - the Lsass.exe process. It leverages Sysmon EventCode 10 logs to identify processes - granted PROCESS_TERMINATE access to Lsass.exe. This activity is significant because - Lsass.exe is a critical process responsible for enforcing security policies and - handling user credentials. If confirmed malicious, this behavior could indicate - an attempt to perform credential dumping, privilege escalation, or evasion of security - policies, potentially leading to unauthorized access and persistence within the - environment. +description: The following analytic detects a suspicious process attempting to terminate the Lsass.exe process. It leverages Sysmon EventCode 10 logs to identify processes granted PROCESS_TERMINATE access to Lsass.exe. This activity is significant because Lsass.exe is a critical process responsible for enforcing security policies and handling user credentials. If confirmed malicious, this behavior could indicate an attempt to perform credential dumping, privilege escalation, or evasion of security policies, potentially leading to unauthorized access and persistence within the environment. data_source: -- Sysmon EventID 10 -search: '`sysmon` EventCode=10 TargetImage=*lsass.exe GrantedAccess = 0x1 | stats - count min(_time) as firstTime max(_time) as lastTime by CallTrace EventID GrantedAccess - Guid Opcode ProcessID SecurityID SourceImage SourceProcessGUID SourceProcessId TargetImage - TargetProcessGUID TargetProcessId UserID dest granted_access parent_process_exec - parent_process_guid parent_process_id parent_process_name parent_process_path process_exec - process_guid process_id process_name process_path signature signature_id user_id - vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_terminating_lsass_process_filter`' -how_to_implement: This search requires Sysmon Logs and a Sysmon configuration, which - includes EventCode 10 for lsass.exe. This search uses an input macro named `sysmon`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. + - Sysmon EventID 10 +search: |- + `sysmon` EventCode=10 TargetImage=*lsass.exe GrantedAccess = 0x1 + | stats count min(_time) as firstTime max(_time) as lastTime + BY CallTrace EventID GrantedAccess + Guid Opcode ProcessID + SecurityID SourceImage SourceProcessGUID + SourceProcessId TargetImage TargetProcessGUID + TargetProcessId UserID dest + granted_access parent_process_exec parent_process_guid + parent_process_id parent_process_name parent_process_path + process_exec process_guid process_id + process_name process_path signature + signature_id user_id vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_terminating_lsass_process_filter` +how_to_implement: This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. known_false_positives: No false positives have been identified at this time. references: -- https://blog.talosintelligence.com/2022/03/threat-advisory-doublezero.html + - https://blog.talosintelligence.com/2022/03/threat-advisory-doublezero.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a process $SourceImage$ terminates Lsass process on $dest$ - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: - - field: TargetImage - type: process + message: a process $SourceImage$ terminates Lsass process on $dest$ + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: + - field: TargetImage + type: process tags: - analytic_story: - - Data Destruction - - Double Zero Destructor - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Destruction + - Double Zero Destructor + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/doublezero_wiper/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/doublezero_wiper/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_time_based_evasion.yml b/detections/endpoint/windows_time_based_evasion.yml index 8d772925fc..65b609d48e 100644 --- a/detections/endpoint/windows_time_based_evasion.yml +++ b/detections/endpoint/windows_time_based_evasion.yml @@ -1,78 +1,64 @@ name: Windows Time Based Evasion id: 34502357-deb1-499a-8261-ffe144abf561 -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 1 -- CrowdStrike ProcessRollup2 -description: The following analytic detects potentially malicious processes that initiate - a ping delay using an invalid IP address. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on command-line executions involving "ping 0 - -n". This behavior is significant as it is commonly used by malware like NJRAT to - introduce time delays for evasion tactics, such as delaying self-deletion. If confirmed - malicious, this activity could indicate an active infection attempting to evade - detection, potentially leading to further compromise and persistence within the - environment. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "ping.exe" - Processes.parent_process = "* ping 0 -n *" OR Processes.process = "* ping 0 -n *" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_time_based_evasion_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - CrowdStrike ProcessRollup2 +description: The following analytic detects potentially malicious processes that initiate a ping delay using an invalid IP address. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving "ping 0 -n". This behavior is significant as it is commonly used by malware like NJRAT to introduce time delays for evasion tactics, such as delaying self-deletion. If confirmed malicious, this activity could indicate an active infection attempting to evade detection, potentially leading to further compromise and persistence within the environment. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name = "ping.exe" Processes.parent_process = "* ping 0 -n *" + OR + Processes.process = "* ping 0 -n *" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name("Processes")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_time_based_evasion_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.njrat + - https://malpedia.caad.fkie.fraunhofer.de/details/win.njrat drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A $process_name$ did a suspicious ping to invalid IP address on $dest$ - risk_objects: - - field: dest - type: system - score: 36 - threat_objects: [] + message: A $process_name$ did a suspicious ping to invalid IP address on $dest$ + risk_objects: + - field: dest + type: system + score: 36 + threat_objects: [] tags: - analytic_story: - - NjRAT - asset_type: Endpoint - mitre_attack_id: - - T1497.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - NjRAT + asset_type: Endpoint + mitre_attack_id: + - T1497.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497.003/njrat_ping_delay_before_delete/ping_0.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497.003/njrat_ping_delay_before_delete/ping_0.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_time_based_evasion_via_choice_exec.yml b/detections/endpoint/windows_time_based_evasion_via_choice_exec.yml index 3c7bccd801..b5971c4121 100644 --- a/detections/endpoint/windows_time_based_evasion_via_choice_exec.yml +++ b/detections/endpoint/windows_time_based_evasion_via_choice_exec.yml @@ -1,83 +1,65 @@ name: Windows Time Based Evasion via Choice Exec id: d5f54b38-10bf-4b3a-b6fc-85949862ed50 -version: 8 -date: '2025-08-22' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic detects the use of choice.exe in batch files - as a delay tactic, a technique observed in SnakeKeylogger malware. It - leverages data from Endpoint Detection and Response (EDR) agents, focusing on - process names and command-line executions. This activity is significant as it - indicates potential time-based evasion techniques used by malware to avoid - detection. If confirmed malicious, this behavior could allow attackers to - execute code stealthily, delete malicious files, and persist on compromised - hosts, making it crucial for SOC analysts to investigate promptly. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name =choice.exe Processes.process - = "*/T*" Processes.process = "*/N*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_time_based_evasion_via_choice_exec_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: administrator may use choice.exe to allow user to choose - from and indexes of choices from a batch script. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic detects the use of choice.exe in batch files as a delay tactic, a technique observed in SnakeKeylogger malware. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential time-based evasion techniques used by malware to avoid detection. If confirmed malicious, this behavior could allow attackers to execute code stealthily, delete malicious files, and persist on compromised hosts, making it crucial for SOC analysts to investigate promptly. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name =choice.exe Processes.process = "*/T*" Processes.process = "*/N*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_time_based_evasion_via_choice_exec_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: administrator may use choice.exe to allow user to choose from and indexes of choices from a batch script. references: -- https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/choice -- https://malpedia.caad.fkie.fraunhofer.de/details/win.404keylogger + - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/choice + - https://malpedia.caad.fkie.fraunhofer.de/details/win.404keylogger drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A $process_name$ has a choice time delay commandline on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: A $process_name$ has a choice time delay commandline on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Snake Keylogger - - 0bj3ctivity Stealer - asset_type: Endpoint - mitre_attack_id: - - T1497.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Snake Keylogger + - 0bj3ctivity Stealer + asset_type: Endpoint + mitre_attack_id: + - T1497.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497.003/time_delay_using_choice_exe/snakekeylogger_choice.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497.003/time_delay_using_choice_exe/snakekeylogger_choice.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_tor_client_execution.yml b/detections/endpoint/windows_tor_client_execution.yml index ab4e66b0be..f2ba1a20be 100644 --- a/detections/endpoint/windows_tor_client_execution.yml +++ b/detections/endpoint/windows_tor_client_execution.yml @@ -6,101 +6,95 @@ author: Vignesh Subramanian, Splunk status: production type: Anomaly description: | - The following analytic detects the execution of the TOR Browser and related TOR components on Windows endpoints by monitoring process creation activity. - Adversaries and insider threats leverage TOR to anonymize command-and-control traffic, facilitate data exfiltration, and evade network monitoring and policy enforcement. - While TOR can be used for legitimate research and privacy purposes, its presence on enterprise endpoints is often unusual and should be investigated to determine intent, scope, and any associated malicious behavior. + The following analytic detects the execution of the TOR Browser and related TOR components on Windows endpoints by monitoring process creation activity. + Adversaries and insider threats leverage TOR to anonymize command-and-control traffic, facilitate data exfiltration, and evade network monitoring and policy enforcement. + While TOR can be used for legitimate research and privacy purposes, its presence on enterprise endpoints is often unusual and should be investigated to determine intent, scope, and any associated malicious behavior. data_source: - - CrowdStrike ProcessRollup2 - - Sysmon EventID 1 - - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime - from datamodel=Endpoint.Processes where - ( - Processes.process_name = "tor.exe" - OR + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime + from datamodel=Endpoint.Processes where ( - Processes.process_path = "*\\BraveSoftware\\Brave-Browser*" - Processes.process_path = "*\\tor-*" + Processes.process_name = "tor.exe" + OR + ( + Processes.process_path = "*\\BraveSoftware\\Brave-Browser*" + Processes.process_path = "*\\tor-*" + ) ) - ) - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user - Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_tor_client_execution_filter` + by Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec + Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_tor_client_execution_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: | - This detection focuses on TOR-related processes and may generate benign matches in environments where TOR is intentionally used, such as security testing, research, or lab environments. + This detection focuses on TOR-related processes and may generate benign matches in environments where TOR is intentionally used, such as security testing, research, or lab environments. references: - - https://unit42.paloaltonetworks.com/tor-traffic-enterprise-networks/ - - https://attack.mitre.org/software/S0183/ - - https://attack.mitre.org/techniques/T1090/003/ + - https://unit42.paloaltonetworks.com/tor-traffic-enterprise-networks/ + - https://attack.mitre.org/software/S0183/ + - https://attack.mitre.org/techniques/T1090/003/ drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: TOR client process $process_name$ was launched by parent process $parent_process_name$ on host $dest$ by the user $user$ with command line $process$ - risk_objects: - - field: dest - type: system - score: 40 - - field: user - type: user - score: 40 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name - - field: process - type: process + message: TOR client process $process_name$ was launched by parent process $parent_process_name$ on host $dest$ by the user $user$ with command line $process$ + risk_objects: + - field: dest + type: system + score: 40 + - field: user + type: user + score: 40 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name + - field: process + type: process tags: - analytic_story: - - Compromised Windows Host - - Windows Post-Exploitation - - Command And Control - - Data Exfiltration - - Data Protection - asset_type: Endpoint - mitre_attack_id: - - T1090.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - Windows Post-Exploitation + - Command And Control + - Data Exfiltration + - Data Protection + asset_type: Endpoint + mitre_attack_id: + - T1090.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/refs/heads/master/datasets/attack_techniques/T1090.003/windows_tor_client_execution/windows-sysmon.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/refs/heads/master/datasets/attack_techniques/T1090.003/windows_tor_client_execution/windows-sysmon.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational diff --git a/detections/endpoint/windows_uac_bypass_suspicious_child_process.yml b/detections/endpoint/windows_uac_bypass_suspicious_child_process.yml index 202cb9b808..1d37196c72 100644 --- a/detections/endpoint/windows_uac_bypass_suspicious_child_process.yml +++ b/detections/endpoint/windows_uac_bypass_suspicious_child_process.yml @@ -5,85 +5,56 @@ date: '2025-10-31' author: Steven Dick status: production type: TTP -description: The following analytic detects when an executable known for User - Account Control (UAC) bypass exploitation spawns a child process in a - user-controlled location or a command shell executable (e.g., cmd.exe, - powershell.exe). This detection leverages Sysmon EventID 1 data, focusing on - high or system integrity level processes with specific parent-child process - relationships. This activity is significant as it may indicate an attacker has - successfully used a UAC bypass exploit to escalate privileges. If confirmed - malicious, this could allow the attacker to execute arbitrary commands with - elevated privileges, potentially compromising the entire system. +description: The following analytic detects when an executable known for User Account Control (UAC) bypass exploitation spawns a child process in a user-controlled location or a command shell executable (e.g., cmd.exe, powershell.exe). This detection leverages Sysmon EventID 1 data, focusing on high or system integrity level processes with specific parent-child process relationships. This activity is significant as it may indicate an attacker has successfully used a UAC bypass exploit to escalate privileges. If confirmed malicious, this could allow the attacker to execute arbitrary commands with elevated privileges, potentially compromising the entire system. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_integrity_level - IN ("high","system") AND Processes.parent_process_name IN (`uacbypass_process_name`) - AND (Processes.process_name IN ("cmd.exe","powershell.exe","pwsh.exe","wscript","cscript.exe","bash.exe","werfault.exe") - OR Processes.process IN ("*\\\\*","*\\Users\\*","*\\ProgramData\\*","*\\Temp\\*")) - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | where parent_process_name != process_name | - `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_uac_bypass_suspicious_child_process_filter`' -how_to_implement: Target environment must ingest sysmon data, specifically Event - ID 1 with process integrity level data. -known_false_positives: Including Werfault.exe may cause some unintended false - positives related to normal application faulting, but is used in a number of - UAC bypass techniques. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_integrity_level IN ("high","system") AND Processes.parent_process_name IN (`uacbypass_process_name`) AND (Processes.process_name IN ("cmd.exe","powershell.exe","pwsh.exe","wscript","cscript.exe","bash.exe","werfault.exe") OR Processes.process IN ("*\\\\*","*\\Users\\*","*\\ProgramData\\*","*\\Temp\\*")) by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | where parent_process_name != process_name | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_uac_bypass_suspicious_child_process_filter`' +how_to_implement: Target environment must ingest sysmon data, specifically Event ID 1 with process integrity level data. +known_false_positives: Including Werfault.exe may cause some unintended false positives related to normal application faulting, but is used in a number of UAC bypass techniques. references: -- https://attack.mitre.org/techniques/T1548/002/ -- https://atomicredteam.io/defense-evasion/T1548.002/ -- https://hadess.io/user-account-control-uncontrol-mastering-the-art-of-bypassing-windows-uac/ -- https://enigma0x3.net/2016/08/15/fileless-uac-bypass-using-eventvwr-exe-and-registry-hijacking/ + - https://attack.mitre.org/techniques/T1548/002/ + - https://atomicredteam.io/defense-evasion/T1548.002/ + - https://hadess.io/user-account-control-uncontrol-mastering-the-art-of-bypassing-windows-uac/ + - https://enigma0x3.net/2016/08/15/fileless-uac-bypass-using-eventvwr-exe-and-registry-hijacking/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A UAC bypass parent process- $parent_process_name$ on host- $dest$ - launched a suspicious child process - $process_name$. - risk_objects: - - field: dest - type: system - score: 45 - - field: user - type: user - score: 45 - threat_objects: - - field: process_name - type: process_name + message: A UAC bypass parent process- $parent_process_name$ on host- $dest$ launched a suspicious child process - $process_name$. + risk_objects: + - field: dest + type: system + score: 45 + - field: user + type: user + score: 45 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Windows Defense Evasion Tactics - - Living Off The Land - - Castle RAT - asset_type: Endpoint - mitre_attack_id: - - T1548.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Living Off The Land + - Castle RAT + asset_type: Endpoint + mitre_attack_id: + - T1548.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/uac_behavior/uac_behavior_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/uac_behavior/uac_behavior_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_uac_bypass_suspicious_escalation_behavior.yml b/detections/endpoint/windows_uac_bypass_suspicious_escalation_behavior.yml index 8079e34b13..bda7da7b39 100644 --- a/detections/endpoint/windows_uac_bypass_suspicious_escalation_behavior.yml +++ b/detections/endpoint/windows_uac_bypass_suspicious_escalation_behavior.yml @@ -1,99 +1,98 @@ name: Windows UAC Bypass Suspicious Escalation Behavior id: 00d050d3-a5b4-4565-a6a5-a31f69681dc3 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Steven Dick status: production type: TTP -description: The following analytic detects when a process spawns an executable known - for User Account Control (UAC) bypass exploitation and subsequently monitors for - any child processes with a higher integrity level than the original process. This - detection leverages Sysmon EventID 1 data, focusing on process integrity levels - and known UAC bypass executables. This activity is significant as it may indicate - an attacker has successfully used a UAC bypass exploit to escalate privileges. If - confirmed malicious, the attacker could gain elevated privileges, potentially leading - to further system compromise and persistent access. +description: The following analytic detects when a process spawns an executable known for User Account Control (UAC) bypass exploitation and subsequently monitors for any child processes with a higher integrity level than the original process. This detection leverages Sysmon EventID 1 data, focusing on process integrity levels and known UAC bypass executables. This activity is significant as it may indicate an attacker has successfully used a UAC bypass exploit to escalate privileges. If confirmed malicious, the attacker could gain elevated privileges, potentially leading to further system compromise and persistent access. data_source: -- Sysmon EventID 1 AND Sysmon EventID 1 -search: '| tstats `security_content_summariesonly` count max(_time) as lastTime from - datamodel=Endpoint.Processes where Processes.process_integrity_level IN ("low","medium") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | eval original_integrity_level = CASE(match(process_integrity_level,"low"),1,match(process_integrity_level,"medium"),2,match(process_integrity_level,"high"),3,match(process_integrity_level,"system"),4,true(),0) - | rename process_guid as join_guid_1, process* as parent_process* | join max=0 dest - join_guid_1 [| tstats `security_content_summariesonly` count min(_time) as firstTime - from datamodel=Endpoint.Processes where Processes.process_integrity_level IN ("high","system") - AND Processes.process_name IN (`uacbypass_process_name`) by Processes.dest, Processes.parent_process_guid, - Processes.process_name, Processes.process_guid | `drop_dm_object_name(Processes)` - | rename parent_process_guid as join_guid_1, process_guid as join_guid_2, process_name - as uac_process_name ] | join max=0 dest join_guid_2 [| tstats `security_content_summariesonly` - count min(_time) as firstTime from datamodel=Endpoint.Processes where Processes.parent_process_name - IN (`uacbypass_process_name`) AND Processes.process_integrity_level IN ("high","system") - by Processes.dest, Processes.parent_process_guid, Processes.process_name, Processes.process, - Processes.process_guid, Processes.process_path, Processes.process_integrity_level, - Processes.process_current_directory | `drop_dm_object_name(Processes)` | rename - parent_process_guid as join_guid_2 | eval elevated_integrity_level = CASE(match(process_integrity_level,"low"),1,match(process_integrity_level,"medium"),2,match(process_integrity_level,"high"),3,match(process_integrity_level,"system"),4,true(),0)] - | where elevated_integrity_level > original_integrity_level | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_uac_bypass_suspicious_escalation_behavior_filter`' -how_to_implement: Target environment must ingest sysmon data, specifically Event ID - 1 with process integrity level data. -known_false_positives: Including Werfault.exe may cause some unintended false positives - related to normal application faulting, but is used in a number of UAC bypass techniques. + - Sysmon EventID 1 AND Sysmon EventID 1 +search: |- + | tstats `security_content_summariesonly` count max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_integrity_level IN ("low","medium") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | eval original_integrity_level = CASE(match(process_integrity_level,"low"),1,match(process_integrity_level,"medium"),2,match(process_integrity_level,"high"),3,match(process_integrity_level,"system"),4,true(),0) + | rename process_guid as join_guid_1, process* as parent_process* + | join max=0 dest join_guid_1 [ + | tstats `security_content_summariesonly` count min(_time) as firstTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_integrity_level IN ("high","system") + AND + Processes.process_name IN (`uacbypass_process_name`) + BY Processes.dest, Processes.parent_process_guid, Processes.process_name, + Processes.process_guid + | `drop_dm_object_name(Processes)` + | rename parent_process_guid as join_guid_1, process_guid as join_guid_2, process_name as uac_process_name ] + | join max=0 dest join_guid_2 [ + | tstats `security_content_summariesonly` count min(_time) as firstTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name IN (`uacbypass_process_name`) + AND + Processes.process_integrity_level IN ("high","system") + BY Processes.dest, Processes.parent_process_guid, Processes.process_name, + Processes.process, Processes.process_guid, Processes.process_path, + Processes.process_integrity_level, Processes.process_current_directory + | `drop_dm_object_name(Processes)` + | rename parent_process_guid as join_guid_2 + | eval elevated_integrity_level = CASE(match(process_integrity_level,"low"),1,match(process_integrity_level,"medium"),2,match(process_integrity_level,"high"),3,match(process_integrity_level,"system"),4,true(),0)] + | where elevated_integrity_level > original_integrity_level + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_uac_bypass_suspicious_escalation_behavior_filter` +how_to_implement: Target environment must ingest sysmon data, specifically Event ID 1 with process integrity level data. +known_false_positives: Including Werfault.exe may cause some unintended false positives related to normal application faulting, but is used in a number of UAC bypass techniques. references: -- https://attack.mitre.org/techniques/T1548/002/ -- https://atomicredteam.io/defense-evasion/T1548.002/ -- https://hadess.io/user-account-control-uncontrol-mastering-the-art-of-bypassing-windows-uac/ -- https://enigma0x3.net/2016/08/15/fileless-uac-bypass-using-eventvwr-exe-and-registry-hijacking/ + - https://attack.mitre.org/techniques/T1548/002/ + - https://atomicredteam.io/defense-evasion/T1548.002/ + - https://hadess.io/user-account-control-uncontrol-mastering-the-art-of-bypassing-windows-uac/ + - https://enigma0x3.net/2016/08/15/fileless-uac-bypass-using-eventvwr-exe-and-registry-hijacking/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A UAC bypass behavior was detected by process $parent_process_name$ on - host $dest$ by $user$. - risk_objects: - - field: dest - type: system - score: 64 - - field: user - type: user - score: 64 - threat_objects: - - field: process_name - type: process_name - - field: process_name - type: process_name - - field: parent_process_name - type: parent_process_name + message: A UAC bypass behavior was detected by process $parent_process_name$ on host $dest$ by $user$. + risk_objects: + - field: dest + type: system + score: 64 + - field: user + type: user + score: 64 + threat_objects: + - field: process_name + type: process_name + - field: process_name + type: process_name + - field: parent_process_name + type: parent_process_name tags: - analytic_story: - - Living Off The Land - - Compromised Windows Host - - Windows Defense Evasion Tactics - asset_type: Endpoint - mitre_attack_id: - - T1548.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Living Off The Land + - Compromised Windows Host + - Windows Defense Evasion Tactics + asset_type: Endpoint + mitre_attack_id: + - T1548.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/uac_behavior/uac_behavior_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/uac_behavior/uac_behavior_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_unsecured_outlook_credentials_access_in_registry.yml b/detections/endpoint/windows_unsecured_outlook_credentials_access_in_registry.yml index d7a6740bcd..0b26bf21f5 100644 --- a/detections/endpoint/windows_unsecured_outlook_credentials_access_in_registry.yml +++ b/detections/endpoint/windows_unsecured_outlook_credentials_access_in_registry.yml @@ -6,71 +6,48 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: -- Windows Event Log Security 4663 -description: The following analytic detects unauthorized access to Outlook - credentials stored in the Windows registry. It leverages Windows Security - Event logs, specifically EventCode 4663, to identify access attempts to - registry paths associated with Outlook profiles. This activity is significant - as it may indicate attempts to steal sensitive email credentials, which could - lead to unauthorized access to email accounts. If confirmed malicious, this - could allow attackers to exfiltrate sensitive information, impersonate users, - or execute further unauthorized actions within Outlook, posing a significant - security risk. -search: '`wineventlog_security` EventCode=4663 object_file_path IN ("*\\Profiles\\Outlook\\9375CFF0413111d3B88A00104B2A6676*", - "*\\Windows Messaging Subsystem\\Profiles\\9375CFF0413111d3B88A00104B2A6676*") AND - process_name != *\\outlook.exe | stats count min(_time) as firstTime max(_time) - as lastTime by object_file_name object_file_path process_name process_path process_id - EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_unsecured_outlook_credentials_access_in_registry_filter`' -how_to_implement: To successfully implement this search, you must ingest Windows - Security Event logs and track event code 4663. For 4663, enable "Audit Object - Access" in Group Policy. Then check the two boxes listed for both "Success" - and "Failure." + - Windows Event Log Security 4663 +description: The following analytic detects unauthorized access to Outlook credentials stored in the Windows registry. It leverages Windows Security Event logs, specifically EventCode 4663, to identify access attempts to registry paths associated with Outlook profiles. This activity is significant as it may indicate attempts to steal sensitive email credentials, which could lead to unauthorized access to email accounts. If confirmed malicious, this could allow attackers to exfiltrate sensitive information, impersonate users, or execute further unauthorized actions within Outlook, posing a significant security risk. +search: '`wineventlog_security` EventCode=4663 object_file_path IN ("*\\Profiles\\Outlook\\9375CFF0413111d3B88A00104B2A6676*", "*\\Windows Messaging Subsystem\\Profiles\\9375CFF0413111d3B88A00104B2A6676*") AND process_name != *\\outlook.exe | stats count min(_time) as firstTime max(_time) as lastTime by object_file_name object_file_path process_name process_path process_id EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_unsecured_outlook_credentials_access_in_registry_filter`' +how_to_implement: To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure." known_false_positives: third party software may access this outlook registry. references: -- https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/choice -- https://malpedia.caad.fkie.fraunhofer.de/details/win.404keylogger + - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/choice + - https://malpedia.caad.fkie.fraunhofer.de/details/win.404keylogger drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious process $process_name$ accessing outlook credentials - registry on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: A suspicious process $process_name$ accessing outlook credentials registry on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - StealC Stealer - - Snake Keylogger - - Meduza Stealer - - 0bj3ctivity Stealer - - Lokibot - asset_type: Endpoint - mitre_attack_id: - - T1552 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - StealC Stealer + - Snake Keylogger + - Meduza Stealer + - 0bj3ctivity Stealer + - Lokibot + asset_type: Endpoint + mitre_attack_id: + - T1552 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552/snakey_keylogger_outlook_reg_access/snakekeylogger_4663.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552/snakey_keylogger_outlook_reg_access/snakekeylogger_4663.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_unsigned_dll_side_loading.yml b/detections/endpoint/windows_unsigned_dll_side_loading.yml index 9ac4fa558d..cc60826d85 100644 --- a/detections/endpoint/windows_unsigned_dll_side_loading.yml +++ b/detections/endpoint/windows_unsigned_dll_side_loading.yml @@ -6,76 +6,50 @@ author: Teoderick Contreras, Splunk status: production type: Anomaly data_source: - - Sysmon EventID 7 -description: - The following analytic detects the creation of potentially malicious - unsigned DLLs in the c:\windows\system32 or c:\windows\syswow64 folders. It leverages - Sysmon EventCode 7 logs to identify unsigned DLLs with unavailable signatures loaded - in these critical directories. This activity is significant as it may indicate a - DLL hijacking attempt, a technique used by attackers to gain unauthorized access - and execute malicious code. If confirmed malicious, this could lead to privilege - escalation, allowing the attacker to gain elevated privileges and further compromise - the target system. -search: - '`sysmon` EventCode=7 Signed=false OriginalFileName = "-" SignatureStatus="unavailable" - ImageLoaded IN ("*:\\windows\\system32\\*", "*:\\windows\\syswow64\\*") | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded - dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash - process_id process_name process_path service_dll_signature_exists service_dll_signature_verified - signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_unsigned_dll_side_loading_filter`' -how_to_implement: - To successfully implement this search, you need to be ingesting - logs with the process name and imageloaded executions from your endpoints. If you - are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -known_false_positives: - It is possible some Administrative utilities will load dismcore.dll - outside of normal system paths, filter as needed. + - Sysmon EventID 7 +description: The following analytic detects the creation of potentially malicious unsigned DLLs in the c:\windows\system32 or c:\windows\syswow64 folders. It leverages Sysmon EventCode 7 logs to identify unsigned DLLs with unavailable signatures loaded in these critical directories. This activity is significant as it may indicate a DLL hijacking attempt, a technique used by attackers to gain unauthorized access and execute malicious code. If confirmed malicious, this could lead to privilege escalation, allowing the attacker to gain elevated privileges and further compromise the target system. +search: '`sysmon` EventCode=7 Signed=false OriginalFileName = "-" SignatureStatus="unavailable" ImageLoaded IN ("*:\\windows\\system32\\*", "*:\\windows\\syswow64\\*") | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_unsigned_dll_side_loading_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: It is possible some Administrative utilities will load dismcore.dll outside of normal system paths, filter as needed. references: - - https://asec.ahnlab.com/en/17692/ - - https://www.blackberry.com/us/en/solutions/endpoint-security/ransomware-protection/warzone#:~:text=Warzone%20RAT%20(AKA%20Ave%20Maria)%20is%20a%20remote%20access%20trojan,is%20as%20an%20information%20stealer. + - https://asec.ahnlab.com/en/17692/ + - https://www.blackberry.com/us/en/solutions/endpoint-security/ransomware-protection/warzone#:~:text=Warzone%20RAT%20(AKA%20Ave%20Maria)%20is%20a%20remote%20access%20trojan,is%20as%20an%20information%20stealer. drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An unsigned dll module was loaded on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: An unsigned dll module was loaded on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - China-Nexus Threat Activity - - Derusbi - - Warzone RAT - - Salt Typhoon - - NjRAT - - Earth Alux - - SolarWinds WHD RCE Post Exploitation - asset_type: Endpoint - mitre_attack_id: - - T1574.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - China-Nexus Threat Activity + - Derusbi + - Warzone RAT + - Salt Typhoon + - NjRAT + - Earth Alux + - SolarWinds WHD RCE Post Exploitation + asset_type: Endpoint + mitre_attack_id: + - T1574.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/warzone_rat/unsigned_dll_loaded/loaded_unsigned_dll.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/warzone_rat/unsigned_dll_loaded/loaded_unsigned_dll.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_unsigned_dll_side_loading_in_same_process_path.yml b/detections/endpoint/windows_unsigned_dll_side_loading_in_same_process_path.yml index d5abe87e5c..4fe0767411 100644 --- a/detections/endpoint/windows_unsigned_dll_side_loading_in_same_process_path.yml +++ b/detections/endpoint/windows_unsigned_dll_side_loading_in_same_process_path.yml @@ -5,81 +5,55 @@ date: '2026-02-09' author: Teoderick Contreras, Splunk type: TTP status: production -description: This detection identifies unsigned DLLs loaded through DLL - side-loading with same file path with the process loaded the DLL, a technique - observed in DarkGate malware. This detection monitors DLL loading, verifies - signatures, and flags unsigned DLLs. Suspicious file paths and known - executable associations are checked. Detecting such suspicious DLLs is crucial - in preventing privilege escalation attacks and other potential security - breaches. Regular security assessments, thorough monitoring, and implementing - security best practices are essential in safeguarding systems from such - threats. +description: This detection identifies unsigned DLLs loaded through DLL side-loading with same file path with the process loaded the DLL, a technique observed in DarkGate malware. This detection monitors DLL loading, verifies signatures, and flags unsigned DLLs. Suspicious file paths and known executable associations are checked. Detecting such suspicious DLLs is crucial in preventing privilege escalation attacks and other potential security breaches. Regular security assessments, thorough monitoring, and implementing security best practices are essential in safeguarding systems from such threats. data_source: -- Sysmon EventID 7 -search: '`sysmon` EventCode=7 Signed=false SignatureStatus != Valid NOT (Image IN - ("*:\\windows\\system32\\*", "*:\\windows\\syswow64\\*", "c:\\Program Files*")) - NOT (ImageLoaded IN ("*:\\windows\\system32\\*", "*:\\windows\\syswow64\\*", "c:\\Program - Files*")) | rex field=Image "(?.+\\\)" | rex field=ImageLoaded - "(?.+\\\)" | where ImageFolderPath = ImageLoadedFolderPath - | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded - dest loaded_file loaded_file_path process_exec process_guid process_hash process_id - process_name process_path service_dll_signature_exists service_dll_signature_verified - signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_unsigned_dll_side_loading_in_same_process_path_filter`' -how_to_implement: To successfully implement this search, you need to be - ingesting logs with the process name and imageloaded executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of - the Sysmon TA. + - Sysmon EventID 7 +search: '`sysmon` EventCode=7 Signed=false SignatureStatus != Valid NOT (Image IN ("*:\\windows\\system32\\*", "*:\\windows\\syswow64\\*", "c:\\Program Files*")) NOT (ImageLoaded IN ("*:\\windows\\system32\\*", "*:\\windows\\syswow64\\*", "c:\\Program Files*")) | rex field=Image "(?.+\\\)" | rex field=ImageLoaded "(?.+\\\)" | where ImageFolderPath = ImageLoadedFolderPath | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_unsigned_dll_side_loading_in_same_process_path_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: No false positives have been identified at this time. references: -- https://www.splunk.com/en_us/blog/security/enter-the-gates-an-analysis-of-the-darkgate-autoit-loader.html -- https://www.trendmicro.com/en_us/research/23/b/investigating-the-plugx-trojan-disguised-as-a-legitimate-windows.html + - https://www.splunk.com/en_us/blog/security/enter-the-gates-an-analysis-of-the-darkgate-autoit-loader.html + - https://www.trendmicro.com/en_us/research/23/b/investigating-the-plugx-trojan-disguised-as-a-legitimate-windows.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An unsigned dll module was loaded on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: An unsigned dll module was loaded on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - PlugX - - DarkGate Malware - - Derusbi - - China-Nexus Threat Activity - - Malicious Inno Setup Loader - - Salt Typhoon - - XWorm - - SnappyBee - - NailaoLocker Ransomware - - Lokibot - - SolarWinds WHD RCE Post Exploitation - asset_type: Endpoint - mitre_attack_id: - - T1574.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - PlugX + - DarkGate Malware + - Derusbi + - China-Nexus Threat Activity + - Malicious Inno Setup Loader + - Salt Typhoon + - XWorm + - SnappyBee + - NailaoLocker Ransomware + - Lokibot + - SolarWinds WHD RCE Post Exploitation + asset_type: Endpoint + mitre_attack_id: + - T1574.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/unsigned_dll_loaded_same_process_path/unsigned_dll_process_path.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/unsigned_dll_loaded_same_process_path/unsigned_dll_process_path.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_unsigned_ms_dll_side_loading.yml b/detections/endpoint/windows_unsigned_ms_dll_side_loading.yml index e7dd41142a..c56d4c3971 100644 --- a/detections/endpoint/windows_unsigned_ms_dll_side_loading.yml +++ b/detections/endpoint/windows_unsigned_ms_dll_side_loading.yml @@ -4,110 +4,88 @@ version: 13 date: '2026-01-13' author: Teoderick Contreras, Splunk data_source: -- Sysmon EventID 7 + - Sysmon EventID 7 type: Anomaly status: production -description: The following analytic identifies potential DLL side-loading instances - involving unsigned DLLs mimicking Microsoft signatures. It detects this activity - by analyzing Sysmon logs for Event Code 7, where both the `Image` and `ImageLoaded` - paths do not match system directories like `system32`, `syswow64`, and `programfiles`. - This behavior is significant as adversaries often exploit DLL side-loading to execute - malicious code via legitimate processes. If confirmed malicious, this activity could - allow attackers to execute arbitrary code, potentially leading to privilege escalation, - persistence, and unauthorized access to sensitive information. +description: The following analytic identifies potential DLL side-loading instances involving unsigned DLLs mimicking Microsoft signatures. It detects this activity by analyzing Sysmon logs for Event Code 7, where both the `Image` and `ImageLoaded` paths do not match system directories like `system32`, `syswow64`, and `programfiles`. This behavior is significant as adversaries often exploit DLL side-loading to execute malicious code via legitimate processes. If confirmed malicious, this activity could allow attackers to execute arbitrary code, potentially leading to privilege escalation, persistence, and unauthorized access to sensitive information. search: | - `sysmon` - EventCode=7 - Company="Microsoft Corporation" - Signed=false - SignatureStatus!= Valid - NOT (Image IN ( - "C:\\Program Files \(x86\)\\*", - "C:\\Program Files\\*", - "C:\\Windows\\System32\\*", - "C:\\Windows\\SysWow64\\*" - ) - ) - NOT (ImageLoaded IN ( - "C:\\Program Files \(x86\)\\*", - "C:\\Program Files\\*", - "C:\\Windows\\System32\\*", - "C:\\Windows\\SysWow64\\*" - ) - ) - | rex field=Image "(?.+\\\)" - | rex field=ImageLoaded "(?.+\\\)" - | where ImageFolderPath = ImageLoadedFolderPath - | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded - dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash - process_id process_name process_path service_dll_signature_exists service_dll_signature_verified - signature signature_id user_id vendor_product - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_unsigned_ms_dll_side_loading_filter` -how_to_implement: The analytic is designed to be run against Sysmon event logs collected - from endpoints. The analytic requires the Sysmon event logs to be ingested into - Splunk. The analytic searches for EventCode 7 where the Image is either SQLDumper.exe - or SQLWriter.exe and the ImageLoaded is vcruntime140.dll. The search also filters - out the legitimate loading of vcruntime140.dll from the System32 directory to reduce - false positives. The analytic can be modified to include additional known good paths - for vcruntime140.dll to further reduce false positives. -known_false_positives: False positives are possible if legitimate processes are loading - vcruntime140.dll from non-standard directories. It is recommended to investigate - the context of the process loading vcruntime140.dll to determine if it is malicious - or not. Modify the search to include additional known good paths for vcruntime140.dll - to reduce false positives. + `sysmon` + EventCode=7 + Company="Microsoft Corporation" + Signed=false + SignatureStatus!= Valid + NOT (Image IN ( + "C:\\Program Files \(x86\)\\*", + "C:\\Program Files\\*", + "C:\\Windows\\System32\\*", + "C:\\Windows\\SysWow64\\*" + ) + ) + NOT (ImageLoaded IN ( + "C:\\Program Files \(x86\)\\*", + "C:\\Program Files\\*", + "C:\\Windows\\System32\\*", + "C:\\Windows\\SysWow64\\*" + ) + ) + | rex field=Image "(?.+\\\)" + | rex field=ImageLoaded "(?.+\\\)" + | where ImageFolderPath = ImageLoadedFolderPath + | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded + dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash + process_id process_name process_path service_dll_signature_exists service_dll_signature_verified + signature signature_id user_id vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_unsigned_ms_dll_side_loading_filter` +how_to_implement: The analytic is designed to be run against Sysmon event logs collected from endpoints. The analytic requires the Sysmon event logs to be ingested into Splunk. The analytic searches for EventCode 7 where the Image is either SQLDumper.exe or SQLWriter.exe and the ImageLoaded is vcruntime140.dll. The search also filters out the legitimate loading of vcruntime140.dll from the System32 directory to reduce false positives. The analytic can be modified to include additional known good paths for vcruntime140.dll to further reduce false positives. +known_false_positives: False positives are possible if legitimate processes are loading vcruntime140.dll from non-standard directories. It is recommended to investigate the context of the process loading vcruntime140.dll to determine if it is malicious or not. Modify the search to include additional known good paths for vcruntime140.dll to reduce false positives. references: -- https://www.mandiant.com/resources/blog/apt29-wineloader-german-political-parties -- https://www.zscaler.com/blogs/security-research/european-diplomats-targeted-spikedwine-wineloader + - https://www.mandiant.com/resources/blog/apt29-wineloader-german-political-parties + - https://www.zscaler.com/blogs/security-research/european-diplomats-targeted-spikedwine-wineloader drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $Image$ loading Unsigned $ImageLoaded$ was detected on $dest$. - risk_objects: - - field: dest - type: system - score: 9 - threat_objects: - - field: Image - type: file_name + message: An instance of $Image$ loading Unsigned $ImageLoaded$ was detected on $dest$. + risk_objects: + - field: dest + type: system + score: 9 + threat_objects: + - field: Image + type: file_name tags: - analytic_story: - - China-Nexus Threat Activity - - Derusbi - - APT29 Diplomatic Deceptions with WINELOADER - - Salt Typhoon - - Earth Alux - - XWorm - group: - - APT29 - - Cozy Bear - - Midnight Blizzard - asset_type: Endpoint - mitre_attack_id: - - T1574.001 - - T1547 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - cve: [] + analytic_story: + - China-Nexus Threat Activity + - Derusbi + - APT29 Diplomatic Deceptions with WINELOADER + - Salt Typhoon + - Earth Alux + - XWorm + group: + - APT29 + - Cozy Bear + - Midnight Blizzard + asset_type: Endpoint + mitre_attack_id: + - T1574.001 + - T1547 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + cve: [] tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/unsigned_dll_load//wineloader_dll_sideload.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/unsigned_dll_load//wineloader_dll_sideload.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational diff --git a/detections/endpoint/windows_unusual_count_of_disabled_users_failed_auth_using_kerberos.yml b/detections/endpoint/windows_unusual_count_of_disabled_users_failed_auth_using_kerberos.yml index 5bf01922dc..920f3c4966 100644 --- a/detections/endpoint/windows_unusual_count_of_disabled_users_failed_auth_using_kerberos.yml +++ b/detections/endpoint/windows_unusual_count_of_disabled_users_failed_auth_using_kerberos.yml @@ -1,74 +1,62 @@ name: Windows Unusual Count Of Disabled Users Failed Auth Using Kerberos id: f65aa026-b811-42ab-b4b9-d9088137648f -date: '2025-05-02' +date: '2026-02-25' type: Anomaly -version: 7 +version: 8 status: production author: Mauricio Velazco, Splunk data_source: -- Windows Event Log Security 4768 -description: The following analytic identifies a source endpoint failing to authenticate - with multiple disabled domain users using the Kerberos protocol. It leverages EventCode - 4768, which is generated when the Key Distribution Center issues a Kerberos Ticket - Granting Ticket (TGT) and detects failure code `0x12` (credentials revoked). This - behavior is significant as it may indicate a Password Spraying attack targeting - disabled accounts, potentially leading to initial access or privilege escalation. - If confirmed malicious, attackers could gain unauthorized access or elevate privileges - within the Active Directory environment. -how_to_implement: To successfully implement this search, you need to be ingesting - Domain Controller and Kerberos events. The Advanced Security Audit policy setting - `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. -known_false_positives: A host failing to authenticate with multiple disabled domain - users is not a common behavior for legitimate systems. Possible false positive scenarios - include but are not limited to vulnerability scanners, multi-user systems missconfigured - systems. + - Windows Event Log Security 4768 +description: The following analytic identifies a source endpoint failing to authenticate with multiple disabled domain users using the Kerberos protocol. It leverages EventCode 4768, which is generated when the Key Distribution Center issues a Kerberos Ticket Granting Ticket (TGT) and detects failure code `0x12` (credentials revoked). This behavior is significant as it may indicate a Password Spraying attack targeting disabled accounts, potentially leading to initial access or privilege escalation. If confirmed malicious, attackers could gain unauthorized access or elevate privileges within the Active Directory environment. +how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. +known_false_positives: A host failing to authenticate with multiple disabled domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, multi-user systems missconfigured systems. references: -- https://attack.mitre.org/techniques/T1110/003/ + - https://attack.mitre.org/techniques/T1110/003/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -search: '`wineventlog_security` EventCode=4768 TargetUserName!=*$ Status=0x12 | bucket - span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) - as user values(dest) as dest by _time, IpAddress | eventstats avg(unique_accounts) - as comp_avg , stdev(unique_accounts) as comp_std by IpAddress | eval upperBound=(comp_avg+comp_std*3) - | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) - | search isOutlier=1 | `windows_unusual_count_of_disabled_users_failed_auth_using_kerberos_filter`' + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ +search: |- + `wineventlog_security` EventCode=4768 TargetUserName!=*$ Status=0x12 + | bucket span=5m _time + | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as user values(dest) as dest + BY _time, IpAddress + | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std + BY IpAddress + | eval upperBound=(comp_avg+comp_std*3) + | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) + | search isOutlier=1 + | `windows_unusual_count_of_disabled_users_failed_auth_using_kerberos_filter` rba: - message: Potential Kerberos based password spraying attack from $IpAddress$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: IpAddress - type: ip_address + message: Potential Kerberos based password spraying attack from $IpAddress$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: IpAddress + type: ip_address tags: - analytic_story: - - Active Directory Password Spraying - - Active Directory Kerberos Attacks - - Volt Typhoon - asset_type: Endpoint - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Password Spraying + - Active Directory Kerberos Attacks + - Volt Typhoon + asset_type: Endpoint + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_disabled_users_kerberos_xml/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog - name: True Positive Test + - attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_disabled_users_kerberos_xml/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog + name: True Positive Test diff --git a/detections/endpoint/windows_unusual_count_of_invalid_users_fail_to_auth_using_kerberos.yml b/detections/endpoint/windows_unusual_count_of_invalid_users_fail_to_auth_using_kerberos.yml index 2b02f85f59..2ed3d33171 100644 --- a/detections/endpoint/windows_unusual_count_of_invalid_users_fail_to_auth_using_kerberos.yml +++ b/detections/endpoint/windows_unusual_count_of_invalid_users_fail_to_auth_using_kerberos.yml @@ -1,74 +1,62 @@ name: Windows Unusual Count Of Invalid Users Fail To Auth Using Kerberos id: f122cb2e-d773-4f11-8399-62a3572d8dd7 type: Anomaly -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' status: production author: Mauricio Velazco, Splunk data_source: -- Windows Event Log Security 4768 -description: The following analytic identifies a source endpoint failing to authenticate - with multiple invalid domain users using the Kerberos protocol. It leverages Event - ID 4768, which is generated when the Key Distribution Center issues a Kerberos Ticket - Granting Ticket (TGT) and detects failure code 0x6, indicating the user is not found - in the Kerberos database. This behavior is significant as it may indicate a Password - Spraying attack, where an adversary attempts to gain initial access or elevate privileges. - If confirmed malicious, this activity could lead to unauthorized access and potential - privilege escalation within the Active Directory environment. -how_to_implement: To successfully implement this search, you need to be ingesting - Domain Controller and Kerberos events. The Advanced Security Audit policy setting - `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. -known_false_positives: A host failing to authenticate with multiple invalid domain - users is not a common behavior for legitimate systems. Possible false positive scenarios - include but are not limited to vulnerability scanners, multi-user systems and missconfigured - systems. + - Windows Event Log Security 4768 +description: The following analytic identifies a source endpoint failing to authenticate with multiple invalid domain users using the Kerberos protocol. It leverages Event ID 4768, which is generated when the Key Distribution Center issues a Kerberos Ticket Granting Ticket (TGT) and detects failure code 0x6, indicating the user is not found in the Kerberos database. This behavior is significant as it may indicate a Password Spraying attack, where an adversary attempts to gain initial access or elevate privileges. If confirmed malicious, this activity could lead to unauthorized access and potential privilege escalation within the Active Directory environment. +how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. +known_false_positives: A host failing to authenticate with multiple invalid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, multi-user systems and missconfigured systems. references: -- https://attack.mitre.org/techniques/T1110/003/ + - https://attack.mitre.org/techniques/T1110/003/ drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -search: '`wineventlog_security` EventCode=4768 TargetUserName!=*$ Status=0x6 | bucket - span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) - as user values(dest) as dest by _time, IpAddress | eventstats avg(unique_accounts) - as comp_avg , stdev(unique_accounts) as comp_std by IpAddress | eval upperBound=(comp_avg+comp_std*3) - | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) - | search isOutlier=1 | `windows_unusual_count_of_invalid_users_fail_to_auth_using_kerberos_filter`' + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ +search: |- + `wineventlog_security` EventCode=4768 TargetUserName!=*$ Status=0x6 + | bucket span=5m _time + | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as user values(dest) as dest + BY _time, IpAddress + | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std + BY IpAddress + | eval upperBound=(comp_avg+comp_std*3) + | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) + | search isOutlier=1 + | `windows_unusual_count_of_invalid_users_fail_to_auth_using_kerberos_filter` rba: - message: Potential Kerberos based password spraying attack from $IpAddress$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: IpAddress - type: ip_address + message: Potential Kerberos based password spraying attack from $IpAddress$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: IpAddress + type: ip_address tags: - analytic_story: - - Active Directory Password Spraying - - Active Directory Kerberos Attacks - - Volt Typhoon - asset_type: Endpoint - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Password Spraying + - Active Directory Kerberos Attacks + - Volt Typhoon + asset_type: Endpoint + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_invalid_users_kerberos_xml/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog - name: True Positive Test + - attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_invalid_users_kerberos_xml/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog + name: True Positive Test diff --git a/detections/endpoint/windows_unusual_count_of_invalid_users_failed_to_auth_using_ntlm.yml b/detections/endpoint/windows_unusual_count_of_invalid_users_failed_to_auth_using_ntlm.yml index 21748b4a50..131dd5fbd8 100644 --- a/detections/endpoint/windows_unusual_count_of_invalid_users_failed_to_auth_using_ntlm.yml +++ b/detections/endpoint/windows_unusual_count_of_invalid_users_failed_to_auth_using_ntlm.yml @@ -1,77 +1,65 @@ name: Windows Unusual Count Of Invalid Users Failed To Auth Using NTLM id: 15603165-147d-4a6e-9778-bd0ff39e668f type: Anomaly -version: 8 +version: 9 status: production -date: '2025-05-02' +date: '2026-02-25' author: Mauricio Velazco, Splunk data_source: -- Windows Event Log Security 4776 -description: The following analytic identifies a source endpoint failing to authenticate - with multiple invalid users using the NTLM protocol. It leverages EventCode 4776 - and calculates the standard deviation for each host, using the 3-sigma rule to detect - anomalies. This behavior is significant as it may indicate a Password Spraying attack, - where an adversary attempts to gain initial access or elevate privileges. If confirmed - malicious, this activity could lead to unauthorized access or privilege escalation, - posing a significant threat to the Active Directory environment. This detection - is focused on domain controllers. -how_to_implement: To successfully implement this search, you need to be ingesting - Domain Controller events. The Advanced Security Audit policy setting `Audit Credential - Validation' within `Account Logon` needs to be enabled. -known_false_positives: A host failing to authenticate with multiple invalid domain - users is not a common behavior for legitimate systems. Possible false positive scenarios - include but are not limited to vulnerability scanners and missconfigured systems. - If this detection triggers on a host other than a Domain Controller, the behavior - could represent a password spraying attack against the host's local accounts. + - Windows Event Log Security 4776 +description: The following analytic identifies a source endpoint failing to authenticate with multiple invalid users using the NTLM protocol. It leverages EventCode 4776 and calculates the standard deviation for each host, using the 3-sigma rule to detect anomalies. This behavior is significant as it may indicate a Password Spraying attack, where an adversary attempts to gain initial access or elevate privileges. If confirmed malicious, this activity could lead to unauthorized access or privilege escalation, posing a significant threat to the Active Directory environment. This detection is focused on domain controllers. +how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller events. The Advanced Security Audit policy setting `Audit Credential Validation' within `Account Logon` needs to be enabled. +known_false_positives: A host failing to authenticate with multiple invalid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. If this detection triggers on a host other than a Domain Controller, the behavior could represent a password spraying attack against the host's local accounts. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/audit-credential-validation -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4776 + - https://attack.mitre.org/techniques/T1110/003/ + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/audit-credential-validation + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4776 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -search: '`wineventlog_security` EventCode=4776 TargetUserName!=*$ Status=0xc0000064 - | bucket span=2m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) - as user values(dest) as dest by _time, Workstation | eventstats avg(unique_accounts) - as comp_avg , stdev(unique_accounts) as comp_std by Workstation | eval upperBound=(comp_avg+comp_std*3) - | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) - | search isOutlier=1 | rename Workstation as src |`windows_unusual_count_of_invalid_users_failed_to_auth_using_ntlm_filter`' + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ +search: |- + `wineventlog_security` EventCode=4776 TargetUserName!=*$ Status=0xc0000064 + | bucket span=2m _time + | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as user values(dest) as dest + BY _time, Workstation + | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std + BY Workstation + | eval upperBound=(comp_avg+comp_std*3) + | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) + | search isOutlier=1 + | rename Workstation as src + | `windows_unusual_count_of_invalid_users_failed_to_auth_using_ntlm_filter` rba: - message: Potential NTLM based password spraying attack from $src$ - risk_objects: - - field: user - type: user - score: 49 - - field: src - type: system - score: 49 - threat_objects: [] + message: Potential NTLM based password spraying attack from $src$ + risk_objects: + - field: user + type: user + score: 49 + - field: src + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Active Directory Password Spraying - - Volt Typhoon - asset_type: Endpoint - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Password Spraying + - Volt Typhoon + asset_type: Endpoint + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_invalid_users_ntlm_xml/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog - name: True Positive Test + - attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_invalid_users_ntlm_xml/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog + name: True Positive Test diff --git a/detections/endpoint/windows_unusual_count_of_users_fail_to_auth_wth_explicitcredentials.yml b/detections/endpoint/windows_unusual_count_of_users_fail_to_auth_wth_explicitcredentials.yml index 1afe9bd374..e5520c5a18 100644 --- a/detections/endpoint/windows_unusual_count_of_users_fail_to_auth_wth_explicitcredentials.yml +++ b/detections/endpoint/windows_unusual_count_of_users_fail_to_auth_wth_explicitcredentials.yml @@ -1,78 +1,65 @@ name: Windows Unusual Count Of Users Fail To Auth Wth ExplicitCredentials id: 14f414cf-3080-4b9b-aaf6-55a4ce947b93 type: Anomaly -version: 8 +version: 9 status: production -date: '2025-05-02' +date: '2026-02-25' author: Mauricio Velazco, Splunk data_source: -- Windows Event Log Security 4648 -description: The following analytic identifies a source user failing to authenticate - with multiple users using explicit credentials on a host. It leverages Windows Event - Code 4648 and calculates the standard deviation for each host, using the 3-sigma - rule to detect anomalies. This behavior is significant as it may indicate a Password - Spraying attack, where an adversary attempts to gain initial access or elevate privileges. - If confirmed malicious, this activity could lead to unauthorized access, privilege - escalation, or further compromise of the Active Directory environment. -how_to_implement: To successfully implement this search, you need to be ingesting - Windows Event Logs from domain controllers as well as member servers and workstations. - The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs - to be enabled. -known_false_positives: A source user failing attempting to authenticate multiple users - on a host is not a common behavior for regular systems. Some applications, however, - may exhibit this behavior in which case sets of users hosts can be added to an allow - list. Possible false positive scenarios include systems where several users connect - to like Mail servers, identity providers, remote desktop services, Citrix, etc. + - Windows Event Log Security 4648 +description: The following analytic identifies a source user failing to authenticate with multiple users using explicit credentials on a host. It leverages Windows Event Code 4648 and calculates the standard deviation for each host, using the 3-sigma rule to detect anomalies. This behavior is significant as it may indicate a Password Spraying attack, where an adversary attempts to gain initial access or elevate privileges. If confirmed malicious, this activity could lead to unauthorized access, privilege escalation, or further compromise of the Active Directory environment. +how_to_implement: To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled. +known_false_positives: A source user failing attempting to authenticate multiple users on a host is not a common behavior for regular systems. Some applications, however, may exhibit this behavior in which case sets of users hosts can be added to an allow list. Possible false positive scenarios include systems where several users connect to like Mail servers, identity providers, remote desktop services, Citrix, etc. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4648 -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/basic-audit-logon-events + - https://attack.mitre.org/techniques/T1110/003/ + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4648 + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/basic-audit-logon-events drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -search: '`wineventlog_security` EventCode=4648 Caller_User_Name!=*$ Target_User_Name!=*$ - | bucket span=5m _time | stats dc(Target_User_Name) AS unique_accounts values(Target_User_Name) - as user values(dest) as dest values(src_ip) as src_ip by _time, Computer, Caller_User_Name - | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std - by Computer | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts - > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `windows_unusual_count_of_users_fail_to_auth_wth_explicitcredentials_filter`' + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ +search: |- + `wineventlog_security` EventCode=4648 Caller_User_Name!=*$ Target_User_Name!=*$ + | bucket span=5m _time + | stats dc(Target_User_Name) AS unique_accounts values(Target_User_Name) as user values(dest) as dest values(src_ip) as src_ip + BY _time, Computer, Caller_User_Name + | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std + BY Computer + | eval upperBound=(comp_avg+comp_std*3) + | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) + | search isOutlier=1 + | `windows_unusual_count_of_users_fail_to_auth_wth_explicitcredentials_filter` rba: - message: Potential password spraying attack from $Computer$ - risk_objects: - - field: user - type: user - score: 49 - - field: Computer - type: system - score: 49 - threat_objects: [] + message: Potential password spraying attack from $Computer$ + risk_objects: + - field: user + type: user + score: 49 + - field: Computer + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Active Directory Password Spraying - - Insider Threat - - Volt Typhoon - asset_type: Endpoint - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Password Spraying + - Insider Threat + - Volt Typhoon + asset_type: Endpoint + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_explicit_credential_spray_xml/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog - name: True Positive Test + - attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_explicit_credential_spray_xml/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog + name: True Positive Test diff --git a/detections/endpoint/windows_unusual_count_of_users_failed_to_auth_using_kerberos.yml b/detections/endpoint/windows_unusual_count_of_users_failed_to_auth_using_kerberos.yml index 377e5497bb..2ecbba57f9 100644 --- a/detections/endpoint/windows_unusual_count_of_users_failed_to_auth_using_kerberos.yml +++ b/detections/endpoint/windows_unusual_count_of_users_failed_to_auth_using_kerberos.yml @@ -1,76 +1,64 @@ name: Windows Unusual Count Of Users Failed To Auth Using Kerberos id: bc9cb715-08ba-40c3-9758-6e2b26e455cb -date: '2025-05-02' +date: '2026-02-25' type: Anomaly -version: 7 +version: 8 status: production author: Mauricio Velazco, Splunk data_source: -- Windows Event Log Security 4771 -description: The following analytic identifies a source endpoint failing to authenticate - multiple valid users using the Kerberos protocol, potentially indicating a Password - Spraying attack. It leverages Event 4771, which is generated when the Key Distribution - Center fails to issue a Kerberos Ticket Granting Ticket (TGT) due to a wrong password - (failure code 0x18). This detection uses statistical analysis, specifically the - 3-sigma rule, to identify unusual authentication failures. If confirmed malicious, - this activity could allow an attacker to gain initial access or elevate privileges - within an Active Directory environment. -how_to_implement: To successfully implement this search, you need to be ingesting - Domain Controller and Kerberos events. The Advanced Security Audit policy setting - `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. -known_false_positives: A host failing to authenticate with multiple valid domain users - is not a common behavior for legitimate systems. Possible false positive scenarios - include but are not limited to vulnerability scanners, missconfigured systems and - multi-user systems like Citrix farms. + - Windows Event Log Security 4771 +description: The following analytic identifies a source endpoint failing to authenticate multiple valid users using the Kerberos protocol, potentially indicating a Password Spraying attack. It leverages Event 4771, which is generated when the Key Distribution Center fails to issue a Kerberos Ticket Granting Ticket (TGT) due to a wrong password (failure code 0x18). This detection uses statistical analysis, specifically the 3-sigma rule, to identify unusual authentication failures. If confirmed malicious, this activity could allow an attacker to gain initial access or elevate privileges within an Active Directory environment. +how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. +known_false_positives: A host failing to authenticate with multiple valid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, missconfigured systems and multi-user systems like Citrix farms. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-r2-and-2012/dn319109(v=ws.11) -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4771 + - https://attack.mitre.org/techniques/T1110/003/ + - https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-r2-and-2012/dn319109(v=ws.11) + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4771 drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -search: '`wineventlog_security` EventCode=4771 TargetUserName!="*$" Status=0x18 | - bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) - as user values(dest) as dest by _time, IpAddress | eventstats avg(unique_accounts) - as comp_avg , stdev(unique_accounts) as comp_std by IpAddress | eval upperBound=(comp_avg+comp_std*3) - | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) - | search isOutlier=1 | `windows_unusual_count_of_users_failed_to_auth_using_kerberos_filter`' + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ +search: |- + `wineventlog_security` EventCode=4771 TargetUserName!="*$" Status=0x18 + | bucket span=5m _time + | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as user values(dest) as dest + BY _time, IpAddress + | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std + BY IpAddress + | eval upperBound=(comp_avg+comp_std*3) + | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) + | search isOutlier=1 + | `windows_unusual_count_of_users_failed_to_auth_using_kerberos_filter` rba: - message: Potential Kerberos based password spraying attack from $IpAddress$ - risk_objects: - - field: user - type: user - score: 49 - threat_objects: - - field: IpAddress - type: ip_address + message: Potential Kerberos based password spraying attack from $IpAddress$ + risk_objects: + - field: user + type: user + score: 49 + threat_objects: + - field: IpAddress + type: ip_address tags: - analytic_story: - - Active Directory Password Spraying - - Active Directory Kerberos Attacks - - Volt Typhoon - asset_type: Endpoint - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Password Spraying + - Active Directory Kerberos Attacks + - Volt Typhoon + asset_type: Endpoint + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_valid_users_kerberos_xml/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog - name: True Positive Test + - attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_valid_users_kerberos_xml/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog + name: True Positive Test diff --git a/detections/endpoint/windows_unusual_count_of_users_failed_to_authenticate_from_process.yml b/detections/endpoint/windows_unusual_count_of_users_failed_to_authenticate_from_process.yml index 97bdfeb5a1..6e2cf3944b 100644 --- a/detections/endpoint/windows_unusual_count_of_users_failed_to_authenticate_from_process.yml +++ b/detections/endpoint/windows_unusual_count_of_users_failed_to_authenticate_from_process.yml @@ -1,79 +1,68 @@ name: Windows Unusual Count Of Users Failed To Authenticate From Process id: 25bdb6cb-2e49-4d34-a93c-d6c567c122fe type: Anomaly -version: 8 +version: 9 status: production -date: '2025-05-02' +date: '2026-02-25' author: Mauricio Velazco, Splunk data_source: -- Windows Event Log Security 4625 -description: The following analytic identifies a source process failing to authenticate - multiple users, potentially indicating a Password Spraying attack. It leverages - Windows Event 4625, which logs failed logon attempts, and uses statistical analysis - to detect anomalies. This activity is significant as it may represent an adversary - attempting to gain initial access or elevate privileges within an Active Directory - environment. If confirmed malicious, the attacker could compromise multiple accounts, - leading to unauthorized access, data exfiltration, or further lateral movement within - the network. -how_to_implement: To successfully implement this search, you need to be ingesting - Windows Event Logs from domain controllers aas well as member servers and workstations. - The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs - to be enabled. -known_false_positives: A process failing to authenticate with multiple users is not - a common behavior for legitimate user sessions. Possible false positive scenarios - include but are not limited to vulnerability scanners and missconfigured systems. + - Windows Event Log Security 4625 +description: The following analytic identifies a source process failing to authenticate multiple users, potentially indicating a Password Spraying attack. It leverages Windows Event 4625, which logs failed logon attempts, and uses statistical analysis to detect anomalies. This activity is significant as it may represent an adversary attempting to gain initial access or elevate privileges within an Active Directory environment. If confirmed malicious, the attacker could compromise multiple accounts, leading to unauthorized access, data exfiltration, or further lateral movement within the network. +how_to_implement: To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers aas well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled. +known_false_positives: A process failing to authenticate with multiple users is not a common behavior for legitimate user sessions. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4625 -- https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4625 -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/basic-audit-logon-events + - https://attack.mitre.org/techniques/T1110/003/ + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4625 + - https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4625 + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/basic-audit-logon-events drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -search: '`wineventlog_security` EventCode=4625 Logon_Type=2 ProcessName!="-" | bucket - span=2m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) - as user values(dest) as dest values(src) as src by _time, ProcessName, SubjectUserName, - Computer, action, app, authentication_method, signature, signature_id | eventstats - avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by ProcessName, - SubjectUserName, Computer | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts - > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `windows_unusual_count_of_users_failed_to_authenticate_from_process_filter`' + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ +search: |- + `wineventlog_security` EventCode=4625 Logon_Type=2 ProcessName!="-" + | bucket span=2m _time + | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as user values(dest) as dest values(src) as src + BY _time, ProcessName, SubjectUserName, + Computer, action, app, + authentication_method, signature, signature_id + | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std + BY ProcessName, SubjectUserName, Computer + | eval upperBound=(comp_avg+comp_std*3) + | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) + | search isOutlier=1 + | `windows_unusual_count_of_users_failed_to_authenticate_from_process_filter` rba: - message: Potential password spraying attack from $Computer$ - risk_objects: - - field: user - type: user - score: 49 - - field: Computer - type: system - score: 49 - threat_objects: [] + message: Potential password spraying attack from $Computer$ + risk_objects: + - field: user + type: user + score: 49 + - field: Computer + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Active Directory Password Spraying - - Insider Threat - - Volt Typhoon - asset_type: Endpoint - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Password Spraying + - Insider Threat + - Volt Typhoon + asset_type: Endpoint + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_multiple_users_from_process_xml/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog - name: True Positive Test + - attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_multiple_users_from_process_xml/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog + name: True Positive Test diff --git a/detections/endpoint/windows_unusual_count_of_users_failed_to_authenticate_using_ntlm.yml b/detections/endpoint/windows_unusual_count_of_users_failed_to_authenticate_using_ntlm.yml index 8e25ae8bb8..2c09e742c2 100644 --- a/detections/endpoint/windows_unusual_count_of_users_failed_to_authenticate_using_ntlm.yml +++ b/detections/endpoint/windows_unusual_count_of_users_failed_to_authenticate_using_ntlm.yml @@ -1,74 +1,61 @@ name: Windows Unusual Count Of Users Failed To Authenticate Using NTLM id: 6f6c8fd7-6a6b-4af9-a0e9-57cfc47a58b4 type: Anomaly -version: 8 +version: 9 status: production -date: '2025-05-02' +date: '2026-02-25' author: Mauricio Velazco, Splunk data_source: -- Windows Event Log Security 4776 -description: The following analytic identifies a source endpoint failing to authenticate - multiple valid users using the NTLM protocol, potentially indicating a Password - Spraying attack. It leverages Event 4776 from Domain Controllers, calculating the - standard deviation for each host and applying the 3-sigma rule to detect anomalies. - This activity is significant as it may represent an adversary attempting to gain - initial access or elevate privileges. If confirmed malicious, the attacker could - compromise multiple accounts, leading to unauthorized access and potential lateral - movement within the network. -how_to_implement: To successfully implement this search, you need to be ingesting - Domain Controller events. The Advanced Security Audit policy setting `Audit Credential - Validation` within `Account Logon` needs to be enabled. -known_false_positives: A host failing to authenticate with multiple valid domain users - is not a common behavior for legitimate systems. Possible false positive scenarios - include but are not limited to vulnerability scanners and missconfigured systems. - If this detection triggers on a host other than a Domain Controller, the behavior - could represent a password spraying attack against the host's local accounts. + - Windows Event Log Security 4776 +description: The following analytic identifies a source endpoint failing to authenticate multiple valid users using the NTLM protocol, potentially indicating a Password Spraying attack. It leverages Event 4776 from Domain Controllers, calculating the standard deviation for each host and applying the 3-sigma rule to detect anomalies. This activity is significant as it may represent an adversary attempting to gain initial access or elevate privileges. If confirmed malicious, the attacker could compromise multiple accounts, leading to unauthorized access and potential lateral movement within the network. +how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller events. The Advanced Security Audit policy setting `Audit Credential Validation` within `Account Logon` needs to be enabled. +known_false_positives: A host failing to authenticate with multiple valid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. If this detection triggers on a host other than a Domain Controller, the behavior could represent a password spraying attack against the host's local accounts. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/audit-credential-validation -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4776 + - https://attack.mitre.org/techniques/T1110/003/ + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/audit-credential-validation + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4776 drilldown_searches: -- name: View the detection results for - "$Workstation$" - search: '%original_detection_search% | search Workstation = "$Workstation$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$Workstation$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Workstation$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -search: '`wineventlog_security` EventCode=4776 TargetUserName!=*$ Status=0xC000006A - | bucket span=2m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) - as tried_accounts values(dest) as dest by _time, Workstation | eventstats avg(unique_accounts) - as comp_avg , stdev(unique_accounts) as comp_std by Workstation | eval upperBound=(comp_avg+comp_std*3) - | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) - | search isOutlier=1 | `windows_unusual_count_of_users_failed_to_authenticate_using_ntlm_filter`' + - name: View the detection results for - "$Workstation$" + search: '%original_detection_search% | search Workstation = "$Workstation$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Workstation$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Workstation$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ +search: |- + `wineventlog_security` EventCode=4776 TargetUserName!=*$ Status=0xC000006A + | bucket span=2m _time + | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts values(dest) as dest + BY _time, Workstation + | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std + BY Workstation + | eval upperBound=(comp_avg+comp_std*3) + | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) + | search isOutlier=1 + | `windows_unusual_count_of_users_failed_to_authenticate_using_ntlm_filter` rba: - message: Potential NTLM based password spraying attack from $Workstation$ - risk_objects: - - field: Workstation - type: system - score: 49 - threat_objects: [] + message: Potential NTLM based password spraying attack from $Workstation$ + risk_objects: + - field: Workstation + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Active Directory Password Spraying - - Volt Typhoon - asset_type: Endpoint - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Password Spraying + - Volt Typhoon + asset_type: Endpoint + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_valid_users_ntlm_xml/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog - name: True Positive Test + - attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_valid_users_ntlm_xml/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog + name: True Positive Test diff --git a/detections/endpoint/windows_unusual_count_of_users_remotely_failed_to_auth_from_host.yml b/detections/endpoint/windows_unusual_count_of_users_remotely_failed_to_auth_from_host.yml index 9c70f9bde7..3b856fb421 100644 --- a/detections/endpoint/windows_unusual_count_of_users_remotely_failed_to_auth_from_host.yml +++ b/detections/endpoint/windows_unusual_count_of_users_remotely_failed_to_auth_from_host.yml @@ -1,75 +1,64 @@ name: Windows Unusual Count Of Users Remotely Failed To Auth From Host id: cf06a0ee-ffa9-4ed3-be77-0670ed9bab52 type: Anomaly -version: 8 +version: 9 status: production -date: '2025-05-02' +date: '2026-02-25' author: Mauricio Velazco, Splunk data_source: -- Windows Event Log Security 4625 -description: The following analytic identifies a source host failing to authenticate - against a remote host with multiple users, potentially indicating a Password Spraying - attack. It leverages Windows Event 4625 (failed logon attempts) and Logon Type 3 - (remote authentication) to detect this behavior. This activity is significant as - it may represent an adversary attempting to gain initial access or elevate privileges - within an Active Directory environment. If confirmed malicious, this could lead - to unauthorized access, privilege escalation, and further compromise of the network. -how_to_implement: To successfully implement this search, you need to be ingesting - Windows Event Logs from domain controllers as as well as member servers and workstations. - The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs - to be enabled. -known_false_positives: A host failing to authenticate with multiple valid users against - a remote host is not a common behavior for legitimate systems. Possible false positive - scenarios include but are not limited to vulnerability scanners, remote administration - tools, missconfigyred systems, etc. + - Windows Event Log Security 4625 +description: The following analytic identifies a source host failing to authenticate against a remote host with multiple users, potentially indicating a Password Spraying attack. It leverages Windows Event 4625 (failed logon attempts) and Logon Type 3 (remote authentication) to detect this behavior. This activity is significant as it may represent an adversary attempting to gain initial access or elevate privileges within an Active Directory environment. If confirmed malicious, this could lead to unauthorized access, privilege escalation, and further compromise of the network. +how_to_implement: To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as as well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled. +known_false_positives: A host failing to authenticate with multiple valid users against a remote host is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, remote administration tools, missconfigyred systems, etc. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4625 -- https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4625 -- https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/basic-audit-logon-events + - https://attack.mitre.org/techniques/T1110/003/ + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4625 + - https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4625 + - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/basic-audit-logon-events drilldown_searches: -- name: View the detection results for - "$Computer$" - search: '%original_detection_search% | search Computer = "$Computer$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$Computer$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -search: '`wineventlog_security` EventCode=4625 Logon_Type=3 IpAddress!="-" | bucket - span=2m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) - as tried_accounts values(dest) as dest values(src) as src values(user) as user by - _time, IpAddress, Computer, action, app, authentication_method, signature, signature_id - | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std - by IpAddress, Computer | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts - > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `windows_unusual_count_of_users_remotely_failed_to_auth_from_host_filter`' + - name: View the detection results for - "$Computer$" + search: '%original_detection_search% | search Computer = "$Computer$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$Computer$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ +search: |- + `wineventlog_security` EventCode=4625 Logon_Type=3 IpAddress!="-" + | bucket span=2m _time + | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts values(dest) as dest values(src) as src values(user) as user + BY _time, IpAddress, Computer, + action, app, authentication_method, + signature, signature_id + | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std + BY IpAddress, Computer + | eval upperBound=(comp_avg+comp_std*3) + | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) + | search isOutlier=1 + | `windows_unusual_count_of_users_remotely_failed_to_auth_from_host_filter` rba: - message: Potential password spraying attack on $Computer$ - risk_objects: - - field: Computer - type: system - score: 49 - threat_objects: [] + message: Potential password spraying attack on $Computer$ + risk_objects: + - field: Computer + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - Active Directory Password Spraying - - Volt Typhoon - asset_type: Endpoint - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Password Spraying + - Volt Typhoon + asset_type: Endpoint + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_remote_spray_xml/windows-security.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog - name: True Positive Test + - attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_remote_spray_xml/windows-security.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog + name: True Positive Test diff --git a/detections/endpoint/windows_unusual_filezilla_xml_config_access.yml b/detections/endpoint/windows_unusual_filezilla_xml_config_access.yml index cff3f41b4d..e1370b5d71 100644 --- a/detections/endpoint/windows_unusual_filezilla_xml_config_access.yml +++ b/detections/endpoint/windows_unusual_filezilla_xml_config_access.yml @@ -5,68 +5,46 @@ date: '2025-07-16' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies processes accessing FileZilla XML config files - such as recentservers.xml and sitemanager.xml. It leverages Windows Security Event - logs, specifically monitoring EventCode 4663, which tracks object access events. - This activity is significant because it can indicate unauthorized access or manipulation - of sensitive configuration files used by FileZilla, a popular FTP client. If confirmed - malicious, this could lead to data exfiltration, credential theft, or further compromise - of the system. +description: The following analytic identifies processes accessing FileZilla XML config files such as recentservers.xml and sitemanager.xml. It leverages Windows Security Event logs, specifically monitoring EventCode 4663, which tracks object access events. This activity is significant because it can indicate unauthorized access or manipulation of sensitive configuration files used by FileZilla, a popular FTP client. If confirmed malicious, this could lead to data exfiltration, credential theft, or further compromise of the system. data_source: -- Windows Event Log Security 4663 -search: '`wineventlog_security` EventCode=4663 NOT (ProcessName IN("C:\\Program Files\\FileZilla FTP Client\\filezilla.exe", "C:\Program Files (x86)\\FileZilla FTP Client\\filezilla.exe", "C:\\Program Files\\Microsoft OneDrive\\OneDrive.exe", "C:\\Program Files (x86)\\Microsoft OneDrive\\OneDrive.exe")) - file_path IN ("*FileZilla\\recentservers.xml*", "*FileZilla\\sitemanager.xml*") - | stats count min(_time) as firstTime max(_time) as lastTime by ObjectName ObjectType - ProcessName AccessMask process_id EventCode Computer Caller_User_Name - | rename Computer as dest Caller_User_Name as user ProcessName as process_name - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_unusual_filezilla_xml_config_access_filter`' -how_to_implement: To successfully implement this search, you must ingest Windows Security - Event logs and track event code 4663. For 4663, enable "Audit Object Access" in - Group Policy. Then check the two boxes listed for both "Success" and "Failure." -known_false_positives: a third party application can access the FileZilla XML config files. - Filter is needed. + - Windows Event Log Security 4663 +search: '`wineventlog_security` EventCode=4663 NOT (ProcessName IN("C:\\Program Files\\FileZilla FTP Client\\filezilla.exe", "C:\Program Files (x86)\\FileZilla FTP Client\\filezilla.exe", "C:\\Program Files\\Microsoft OneDrive\\OneDrive.exe", "C:\\Program Files (x86)\\Microsoft OneDrive\\OneDrive.exe")) file_path IN ("*FileZilla\\recentservers.xml*", "*FileZilla\\sitemanager.xml*") | stats count min(_time) as firstTime max(_time) as lastTime by ObjectName ObjectType ProcessName AccessMask process_id EventCode Computer Caller_User_Name | rename Computer as dest Caller_User_Name as user ProcessName as process_name | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_unusual_filezilla_xml_config_access_filter`' +how_to_implement: To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure." +known_false_positives: a third party application can access the FileZilla XML config files. Filter is needed. references: -- https://www.trendmicro.com/en_us/research/18/k/trickbot-shows-off-new-trick-password-grabber-module.html + - https://www.trendmicro.com/en_us/research/18/k/trickbot-shows-off-new-trick-password-grabber-module.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a non filezilla process $process_name$ with $process_id$ accessed - FileZilla XML config files on host $dest$ - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: - - field: process_name - type: process_name + message: a non filezilla process $process_name$ with $process_id$ accessed FileZilla XML config files on host $dest$ + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Quasar RAT - asset_type: Endpoint - mitre_attack_id: - - T1552.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Quasar RAT + asset_type: Endpoint + mitre_attack_id: + - T1552.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.001/file_xml_config/filezilla_obj.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.001/file_xml_config/filezilla_obj.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_unusual_intelliform_storage_registry_access.yml b/detections/endpoint/windows_unusual_intelliform_storage_registry_access.yml index d02b2384db..7104f375e0 100644 --- a/detections/endpoint/windows_unusual_intelliform_storage_registry_access.yml +++ b/detections/endpoint/windows_unusual_intelliform_storage_registry_access.yml @@ -5,70 +5,47 @@ date: '2025-09-30' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies processes accessing Intelliform - Storage Registry keys used by Internet Explorer. It leverages Windows Security - Event logs, specifically monitoring EventCode 4663, which tracks object access - events. This activity is significant because it can indicate unauthorized - access or manipulation of sensitive registry keys used for storing form data - in Internet Explorer. If confirmed malicious, this could lead to data - exfiltration, credential theft, or further compromise of the system. +description: The following analytic identifies processes accessing Intelliform Storage Registry keys used by Internet Explorer. It leverages Windows Security Event logs, specifically monitoring EventCode 4663, which tracks object access events. This activity is significant because it can indicate unauthorized access or manipulation of sensitive registry keys used for storing form data in Internet Explorer. If confirmed malicious, this could lead to data exfiltration, credential theft, or further compromise of the system. data_source: -- Windows Event Log Security 4663 -search: '`wineventlog_security` EventCode=4663 NOT (ProcessName IN("C:\\Program Files\\Internet - Explorer\\iexplore.exe", "C:\\Windows\\System32\\dllhost.exe", "C:\\Windows\\SysWow64\\dllhost.exe")) - ObjectName IN ("*Software\\microsoft\\Internet Explorer\\Intelliforms\\storage2*") - | stats count min(_time) as firstTime max(_time) as lastTime by ObjectName ObjectType - ProcessName AccessMask process_id EventCode Computer Caller_User_Name | rename Computer - as dest Caller_User_Name as user ProcessName as process_name | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_unusual_intelliform_storage_registry_access_filter`' -how_to_implement: To successfully implement this search, you must ingest Windows - Security Event logs and track event code 4663. For 4663, enable "Audit Object - Access" in Group Policy. Then check the two boxes listed for both "Success" - and "Failure." -known_false_positives: a third party application can access the FileZilla XML - config files. Filter is needed. + - Windows Event Log Security 4663 +search: '`wineventlog_security` EventCode=4663 NOT (ProcessName IN("C:\\Program Files\\Internet Explorer\\iexplore.exe", "C:\\Windows\\System32\\dllhost.exe", "C:\\Windows\\SysWow64\\dllhost.exe")) ObjectName IN ("*Software\\microsoft\\Internet Explorer\\Intelliforms\\storage2*") | stats count min(_time) as firstTime max(_time) as lastTime by ObjectName ObjectType ProcessName AccessMask process_id EventCode Computer Caller_User_Name | rename Computer as dest Caller_User_Name as user ProcessName as process_name | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_unusual_intelliform_storage_registry_access_filter`' +how_to_implement: To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure." +known_false_positives: a third party application can access the FileZilla XML config files. Filter is needed. references: -- https://stackoverflow.com/questions/1276700/where-does-internet-explorer-stores-its-form-data-history-that-is-uses-for-auto + - https://stackoverflow.com/questions/1276700/where-does-internet-explorer-stores-its-form-data-history-that-is-uses-for-auto drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a non Internet Explorer process $process_name$ with $process_id$ - accessed Intelliform Storage Registry keys on host $dest$ - risk_objects: - - field: dest - type: system - score: 35 - threat_objects: - - field: process_name - type: process_name + message: a non Internet Explorer process $process_name$ with $process_id$ accessed Intelliform Storage Registry keys on host $dest$ + risk_objects: + - field: dest + type: system + score: 35 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Quasar RAT - - Lokibot - asset_type: Endpoint - mitre_attack_id: - - T1552.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Quasar RAT + - Lokibot + asset_type: Endpoint + mitre_attack_id: + - T1552.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.001/ie_intelliform_storage/storage2_sim.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.001/ie_intelliform_storage/storage2_sim.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_unusual_ntlm_authentication_destinations_by_source.yml b/detections/endpoint/windows_unusual_ntlm_authentication_destinations_by_source.yml index 4b433b959c..4b7af0e2d8 100644 --- a/detections/endpoint/windows_unusual_ntlm_authentication_destinations_by_source.yml +++ b/detections/endpoint/windows_unusual_ntlm_authentication_destinations_by_source.yml @@ -5,91 +5,74 @@ date: '2026-01-14' author: Steven Dick status: production type: Anomaly -description: The following analytic detects when an unusual number NTLM authentications - is attempted by the same source against multiple destinations. This activity generally - results when an attacker attempts to brute force, password spray, or otherwise authenticate - to a multiple domain joined Windows devices using an NTLM based process/attack. - This same activity may also generate a large number of EventID 4776 events as well. +description: The following analytic detects when an unusual number NTLM authentications is attempted by the same source against multiple destinations. This activity generally results when an attacker attempts to brute force, password spray, or otherwise authenticate to a multiple domain joined Windows devices using an NTLM based process/attack. This same activity may also generate a large number of EventID 4776 events as well. data_source: -- NTLM Operational 8004 -- NTLM Operational 8005 -- NTLM Operational 8006 + - NTLM Operational 8004 + - NTLM Operational 8005 + - NTLM Operational 8006 search: | - `ntlm_audit` - EventCode = 8004 - SChannelName=* WorkstationName=* - ```CIM alignment, remove leading \\ from some auth attempts ``` - | eval src = replace(WorkstationName,"\\\\","") - | eval dest = SChannelName, user = UserName + `ntlm_audit` + EventCode = 8004 + SChannelName=* WorkstationName=* + ```CIM alignment, remove leading \\ from some auth attempts ``` + | eval src = replace(WorkstationName,"\\\\","") + | eval dest = SChannelName, user = UserName - ``` Remove NTLM auths to self, improves accuracy for certain applications ``` - | where SChannelName!=src + ``` Remove NTLM auths to self, improves accuracy for certain applications ``` + | where SChannelName!=src - | stats count min(_time) as firstTime - max(_time) as lastTime - dc(eval(upper(dest))) as unique_count by src - | eventstats avg(unique_count) as unique_avg - stdev(unique_count) as unique_std + | stats count min(_time) as firstTime + max(_time) as lastTime + dc(eval(upper(dest))) as unique_count by src + | eventstats avg(unique_count) as unique_avg + stdev(unique_count) as unique_std - ``` adjust formula for sensitivity``` - | eval upperBound_unique=(1+unique_avg+unique_std*3) + ``` adjust formula for sensitivity``` + | eval upperBound_unique=(1+unique_avg+unique_std*3) - | eval isOutlier=CASE(unique_count > upperBound_unique, 1, true(), 0) - | where isOutlier==1 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_unusual_ntlm_authentication_destinations_by_source_filter` -how_to_implement: The following analytic requires that NTLM Operational logs to be - imported from the environment Domain Controllers. This requires configuration of - specific auditing settings, see Microsoft references for further guidance. This - analytic is specific to EventID 8004~8006. -known_false_positives: Vulnerability scanners, print servers, and applications that - deal with non-domain joined authentications. Recommend adjusting the upperBound_unique - eval for tailoring the correlation to your environment, running with a 24hr search - window will smooth out some statistical noise. + | eval isOutlier=CASE(unique_count > upperBound_unique, 1, true(), 0) + | where isOutlier==1 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_unusual_ntlm_authentication_destinations_by_source_filter` +how_to_implement: The following analytic requires that NTLM Operational logs to be imported from the environment Domain Controllers. This requires configuration of specific auditing settings, see Microsoft references for further guidance. This analytic is specific to EventID 8004~8006. +known_false_positives: Vulnerability scanners, print servers, and applications that deal with non-domain joined authentications. Recommend adjusting the upperBound_unique eval for tailoring the correlation to your environment, running with a 24hr search window will smooth out some statistical noise. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://techcommunity.microsoft.com/t5/ask-the-directory-services-team/ntlm-blocking-and-you-application-analysis-and-auditing/ba-p/397191 -- https://techcommunity.microsoft.com/t5/microsoft-defender-for-identity/enriched-ntlm-authentication-data-using-windows-event-8004/m-p/871827 -- https://www.varonis.com/blog/investigate-ntlm-brute-force -- https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-nrpc/4d1235e3-2c96-4e9f-a147-3cb338a0d09f + - https://attack.mitre.org/techniques/T1110/003/ + - https://techcommunity.microsoft.com/t5/ask-the-directory-services-team/ntlm-blocking-and-you-application-analysis-and-auditing/ba-p/397191 + - https://techcommunity.microsoft.com/t5/microsoft-defender-for-identity/enriched-ntlm-authentication-data-using-windows-event-8004/m-p/871827 + - https://www.varonis.com/blog/investigate-ntlm-brute-force + - https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-nrpc/4d1235e3-2c96-4e9f-a147-3cb338a0d09f drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The device [$src$] attempted $count$ NTLM authentications against $unique_count$ - destinations. - risk_objects: - - field: src - type: system - score: 25 - threat_objects: [] + message: The device [$src$] attempted $count$ NTLM authentications against $unique_count$ destinations. + risk_objects: + - field: src + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Active Directory Password Spraying - asset_type: Endpoint - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Password Spraying + asset_type: Endpoint + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/ntlm_bruteforce/ntlm_bruteforce.log - source: XmlWinEventLog:Microsoft-Windows-NTLM/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/ntlm_bruteforce/ntlm_bruteforce.log + source: XmlWinEventLog:Microsoft-Windows-NTLM/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_unusual_ntlm_authentication_destinations_by_user.yml b/detections/endpoint/windows_unusual_ntlm_authentication_destinations_by_user.yml index 9c29d9cada..5438682739 100644 --- a/detections/endpoint/windows_unusual_ntlm_authentication_destinations_by_user.yml +++ b/detections/endpoint/windows_unusual_ntlm_authentication_destinations_by_user.yml @@ -5,94 +5,77 @@ date: '2026-01-14' author: Steven Dick status: production type: Anomaly -description: The following analytic detects when an unusual number of NTLM authentications - is attempted by the same user account against multiple destinations. This activity - generally results when an attacker attempts to brute force, password spray, or otherwise - authenticate to numerous domain joined Windows devices using an NTLM based process/attack. - This same activity may also generate a large number of EventID 4776 events as well. +description: The following analytic detects when an unusual number of NTLM authentications is attempted by the same user account against multiple destinations. This activity generally results when an attacker attempts to brute force, password spray, or otherwise authenticate to numerous domain joined Windows devices using an NTLM based process/attack. This same activity may also generate a large number of EventID 4776 events as well. data_source: -- NTLM Operational 8004 -- NTLM Operational 8005 -- NTLM Operational 8006 + - NTLM Operational 8004 + - NTLM Operational 8005 + - NTLM Operational 8006 search: | - `ntlm_audit` - EventCode = 8004 - SChannelName=* WorkstationName=* + `ntlm_audit` + EventCode = 8004 + SChannelName=* WorkstationName=* - ```CIM alignment, remove leading \\ from some auth attempts ``` - | eval src = replace(WorkstationName,"\\\\","") + ```CIM alignment, remove leading \\ from some auth attempts ``` + | eval src = replace(WorkstationName,"\\\\","") - ``` CIM alignment``` - | eval dest = SChannelName, user = UserName + ``` CIM alignment``` + | eval dest = SChannelName, user = UserName - ``` Remove NTLM auths to self, improves accuracy for certain applications ``` - | where SChannelName!=src + ``` Remove NTLM auths to self, improves accuracy for certain applications ``` + | where SChannelName!=src - | stats count min(_time) as firstTime - max(_time) as lastTime - dc(eval(upper(dest))) as unique_count by user - | eventstats avg(unique_count) as unique_avg - stdev(unique_count) as unique_std + | stats count min(_time) as firstTime + max(_time) as lastTime + dc(eval(upper(dest))) as unique_count by user + | eventstats avg(unique_count) as unique_avg + stdev(unique_count) as unique_std - ``` adjust formula for sensitivity``` - | eval upperBound_unique=(1+unique_avg+unique_std*3) + ``` adjust formula for sensitivity``` + | eval upperBound_unique=(1+unique_avg+unique_std*3) - | eval isOutlier=CASE(unique_count > upperBound_unique, 1, true(), 0) - | where isOutlier==1 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_unusual_ntlm_authentication_destinations_by_user_filter` -how_to_implement: The following analytic requires that NTLM Operational logs to be - imported from the environment Domain Controllers. This requires configuration of - specific auditing settings, see Microsoft references for further guidance. This - analytic is specific to EventID 8004~8006. -known_false_positives: Vulnerability scanners, print servers, and applications that - deal with non-domain joined authentications. Recommend adjusting the upperBound_unique - eval for tailoring the correlation to your environment, running with a 24hr search - window will smooth out some statistical noise. + | eval isOutlier=CASE(unique_count > upperBound_unique, 1, true(), 0) + | where isOutlier==1 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_unusual_ntlm_authentication_destinations_by_user_filter` +how_to_implement: The following analytic requires that NTLM Operational logs to be imported from the environment Domain Controllers. This requires configuration of specific auditing settings, see Microsoft references for further guidance. This analytic is specific to EventID 8004~8006. +known_false_positives: Vulnerability scanners, print servers, and applications that deal with non-domain joined authentications. Recommend adjusting the upperBound_unique eval for tailoring the correlation to your environment, running with a 24hr search window will smooth out some statistical noise. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://techcommunity.microsoft.com/t5/ask-the-directory-services-team/ntlm-blocking-and-you-application-analysis-and-auditing/ba-p/397191 -- https://techcommunity.microsoft.com/t5/microsoft-defender-for-identity/enriched-ntlm-authentication-data-using-windows-event-8004/m-p/871827 -- https://www.varonis.com/blog/investigate-ntlm-brute-force -- https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-nrpc/4d1235e3-2c96-4e9f-a147-3cb338a0d09f + - https://attack.mitre.org/techniques/T1110/003/ + - https://techcommunity.microsoft.com/t5/ask-the-directory-services-team/ntlm-blocking-and-you-application-analysis-and-auditing/ba-p/397191 + - https://techcommunity.microsoft.com/t5/microsoft-defender-for-identity/enriched-ntlm-authentication-data-using-windows-event-8004/m-p/871827 + - https://www.varonis.com/blog/investigate-ntlm-brute-force + - https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-nrpc/4d1235e3-2c96-4e9f-a147-3cb338a0d09f drilldown_searches: -- name: View the detection results for - "$user$" - search: '%original_detection_search% | search user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" + search: '%original_detection_search% | search user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The user [$user$] attempted $count$ NTLM authentications against $unique_count$ - destinations. - risk_objects: - - field: user - type: user - score: 25 - threat_objects: [] + message: The user [$user$] attempted $count$ NTLM authentications against $unique_count$ destinations. + risk_objects: + - field: user + type: user + score: 25 + threat_objects: [] tags: - analytic_story: - - Active Directory Password Spraying - asset_type: Endpoint - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Password Spraying + asset_type: Endpoint + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/ntlm_bruteforce/ntlm_bruteforce.log - source: XmlWinEventLog:Microsoft-Windows-NTLM/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/ntlm_bruteforce/ntlm_bruteforce.log + source: XmlWinEventLog:Microsoft-Windows-NTLM/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_unusual_ntlm_authentication_users_by_destination.yml b/detections/endpoint/windows_unusual_ntlm_authentication_users_by_destination.yml index 325a4a25a7..10d6567451 100644 --- a/detections/endpoint/windows_unusual_ntlm_authentication_users_by_destination.yml +++ b/detections/endpoint/windows_unusual_ntlm_authentication_users_by_destination.yml @@ -5,95 +5,76 @@ date: '2026-01-14' author: Steven Dick status: production type: Anomaly -description: The following analytic detects when a device is the target of numerous - NTLM authentications using a null domain. This activity generally results when an - attacker attempts to brute force, password spray, or otherwise authenticate to a - domain joined Windows device from a non-domain device. This activity may also generate - a large number of EventID 4776 events in tandem, however these events will not indicate - the attacker or target device. +description: The following analytic detects when a device is the target of numerous NTLM authentications using a null domain. This activity generally results when an attacker attempts to brute force, password spray, or otherwise authenticate to a domain joined Windows device from a non-domain device. This activity may also generate a large number of EventID 4776 events in tandem, however these events will not indicate the attacker or target device. data_source: -- NTLM Operational 8004 -- NTLM Operational 8005 -- NTLM Operational 8006 + - NTLM Operational 8004 + - NTLM Operational 8005 + - NTLM Operational 8006 search: | - `ntlm_audit` - EventCode = 8004 - SChannelName=* - WorkstationName=* - ```CIM alignment, remove leading \\ from some auth attempts ``` - | eval src = replace(WorkstationName,"\\\\","") - | eval dest = SChannelName, user = UserName + `ntlm_audit` + EventCode = 8004 + SChannelName=* + WorkstationName=* + ```CIM alignment, remove leading \\ from some auth attempts ``` + | eval src = replace(WorkstationName,"\\\\","") + | eval dest = SChannelName, user = UserName - ``` Remove NTLM auths to self, improves accuracy for certain applications ``` - | where SChannelName!=src + ``` Remove NTLM auths to self, improves accuracy for certain applications ``` + | where SChannelName!=src - | stats count min(_time) as firstTime - max(_time) as lastTime - dc(eval(upper(user))) as unique_count by dest + | stats count min(_time) as firstTime + max(_time) as lastTime + dc(eval(upper(user))) as unique_count by dest - | eventstats avg(unique_count) as unique_avg - stdev(unique_count) as unique_std + | eventstats avg(unique_count) as unique_avg + stdev(unique_count) as unique_std - ```adjust formula for sensitivity``` - | eval upperBound_unique=(1+unique_avg+unique_std*3) + ```adjust formula for sensitivity``` + | eval upperBound_unique=(1+unique_avg+unique_std*3) - | eval isOutlier=CASE(unique_count > upperBound_unique, 1, true(), 0) - | where isOutlier==1 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_unusual_ntlm_authentication_users_by_destination_filter` -how_to_implement: The following analytic detects when an unusual number of NTLM authentications - is attempted against the same destination. This activity generally results when - an attacker attempts to brute force, password spray, or otherwise authenticate to - a domain joined Windows device using an NTLM based process/attack. This same activity - may also generate a large number of EventID 4776 events as well. -known_false_positives: Vulnerability scanners, print servers, and applications that - deal with non-domain joined authentications. Recommend adjusting the upperBound_unique - eval for tailoring the correlation to your environment, running with a 24hr search - window will smooth out some statistical noise. + | eval isOutlier=CASE(unique_count > upperBound_unique, 1, true(), 0) + | where isOutlier==1 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_unusual_ntlm_authentication_users_by_destination_filter` +how_to_implement: The following analytic detects when an unusual number of NTLM authentications is attempted against the same destination. This activity generally results when an attacker attempts to brute force, password spray, or otherwise authenticate to a domain joined Windows device using an NTLM based process/attack. This same activity may also generate a large number of EventID 4776 events as well. +known_false_positives: Vulnerability scanners, print servers, and applications that deal with non-domain joined authentications. Recommend adjusting the upperBound_unique eval for tailoring the correlation to your environment, running with a 24hr search window will smooth out some statistical noise. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://techcommunity.microsoft.com/t5/ask-the-directory-services-team/ntlm-blocking-and-you-application-analysis-and-auditing/ba-p/397191 -- https://techcommunity.microsoft.com/t5/microsoft-defender-for-identity/enriched-ntlm-authentication-data-using-windows-event-8004/m-p/871827 -- https://www.varonis.com/blog/investigate-ntlm-brute-force -- https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-nrpc/4d1235e3-2c96-4e9f-a147-3cb338a0d09f + - https://attack.mitre.org/techniques/T1110/003/ + - https://techcommunity.microsoft.com/t5/ask-the-directory-services-team/ntlm-blocking-and-you-application-analysis-and-auditing/ba-p/397191 + - https://techcommunity.microsoft.com/t5/microsoft-defender-for-identity/enriched-ntlm-authentication-data-using-windows-event-8004/m-p/871827 + - https://www.varonis.com/blog/investigate-ntlm-brute-force + - https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-nrpc/4d1235e3-2c96-4e9f-a147-3cb338a0d09f drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The device [$dest$] was the target of $count$ NTLM authentications using - $unique_count$ unique user accounts. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: The device [$dest$] was the target of $count$ NTLM authentications using $unique_count$ unique user accounts. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Active Directory Password Spraying - asset_type: Endpoint - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Password Spraying + asset_type: Endpoint + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/ntlm_bruteforce/ntlm_bruteforce.log - source: XmlWinEventLog:Microsoft-Windows-NTLM/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/ntlm_bruteforce/ntlm_bruteforce.log + source: XmlWinEventLog:Microsoft-Windows-NTLM/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_unusual_ntlm_authentication_users_by_source.yml b/detections/endpoint/windows_unusual_ntlm_authentication_users_by_source.yml index ff2ca67084..cbf8c3478c 100644 --- a/detections/endpoint/windows_unusual_ntlm_authentication_users_by_source.yml +++ b/detections/endpoint/windows_unusual_ntlm_authentication_users_by_source.yml @@ -5,93 +5,76 @@ date: '2026-01-14' author: Steven Dick status: production type: Anomaly -description: The following analytic detects when an unusual number of NTLM authentications - is attempted by the same source. This activity generally results when an attacker - attempts to brute force, password spray, or otherwise authenticate to a domain joined - Windows device using an NTLM based process/attack. This same activity may also generate - a large number of EventID 4776 events in as well. +description: The following analytic detects when an unusual number of NTLM authentications is attempted by the same source. This activity generally results when an attacker attempts to brute force, password spray, or otherwise authenticate to a domain joined Windows device using an NTLM based process/attack. This same activity may also generate a large number of EventID 4776 events in as well. data_source: -- NTLM Operational 8004 -- NTLM Operational 8005 -- NTLM Operational 8006 + - NTLM Operational 8004 + - NTLM Operational 8005 + - NTLM Operational 8006 search: | - `ntlm_audit` - EventCode = 8004 - SChannelName=* - WorkstationName=* + `ntlm_audit` + EventCode = 8004 + SChannelName=* + WorkstationName=* - ```CIM alignment, remove leading \\ from some auth attempts``` - | eval src = replace(WorkstationName,"\\\\","") - | eval dest = SChannelName, user = UserName + ```CIM alignment, remove leading \\ from some auth attempts``` + | eval src = replace(WorkstationName,"\\\\","") + | eval dest = SChannelName, user = UserName - ``` Remove NTLM auths to self, improves accuracy for certain applications``` - | where SChannelName!=src + ``` Remove NTLM auths to self, improves accuracy for certain applications``` + | where SChannelName!=src - | stats count min(_time) as firstTime - max(_time) as lastTime - dc(eval(upper(user))) as unique_count by src - | eventstats avg(unique_count) as unique_avg - stdev(unique_count) as unique_std + | stats count min(_time) as firstTime + max(_time) as lastTime + dc(eval(upper(user))) as unique_count by src + | eventstats avg(unique_count) as unique_avg + stdev(unique_count) as unique_std - ``` adjust formula for sensitivity``` - | eval upperBound_unique=(1+unique_avg+unique_std*3) + ``` adjust formula for sensitivity``` + | eval upperBound_unique=(1+unique_avg+unique_std*3) - | eval isOutlier=CASE(unique_count > upperBound_unique, 1, true(), 0) - | where isOutlier==1 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_unusual_ntlm_authentication_users_by_source_filter` -how_to_implement: The following analytic requires that NTLM Operational logs to be - imported from the environment Domain Controllers. This requires configuration of - specific auditing settings, see Microsoft references for further guidance. This - analytic is specific to EventID 8004~8006. -known_false_positives: Vulnerability scanners, print servers, and applications that - deal with non-domain joined authentications. Recommend adjusting the upperBound_unique - eval for tailoring the correlation to your environment, running with a 24hr search - window will smooth out some statistical noise. + | eval isOutlier=CASE(unique_count > upperBound_unique, 1, true(), 0) + | where isOutlier==1 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_unusual_ntlm_authentication_users_by_source_filter` +how_to_implement: The following analytic requires that NTLM Operational logs to be imported from the environment Domain Controllers. This requires configuration of specific auditing settings, see Microsoft references for further guidance. This analytic is specific to EventID 8004~8006. +known_false_positives: Vulnerability scanners, print servers, and applications that deal with non-domain joined authentications. Recommend adjusting the upperBound_unique eval for tailoring the correlation to your environment, running with a 24hr search window will smooth out some statistical noise. references: -- https://attack.mitre.org/techniques/T1110/003/ -- https://techcommunity.microsoft.com/t5/ask-the-directory-services-team/ntlm-blocking-and-you-application-analysis-and-auditing/ba-p/397191 -- https://techcommunity.microsoft.com/t5/microsoft-defender-for-identity/enriched-ntlm-authentication-data-using-windows-event-8004/m-p/871827 -- https://www.varonis.com/blog/investigate-ntlm-brute-force -- https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-nrpc/4d1235e3-2c96-4e9f-a147-3cb338a0d09f + - https://attack.mitre.org/techniques/T1110/003/ + - https://techcommunity.microsoft.com/t5/ask-the-directory-services-team/ntlm-blocking-and-you-application-analysis-and-auditing/ba-p/397191 + - https://techcommunity.microsoft.com/t5/microsoft-defender-for-identity/enriched-ntlm-authentication-data-using-windows-event-8004/m-p/871827 + - https://www.varonis.com/blog/investigate-ntlm-brute-force + - https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-nrpc/4d1235e3-2c96-4e9f-a147-3cb338a0d09f drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The device [$src$] attempted $count$ NTLM authentications using $unique_count$ - user accounts. - risk_objects: - - field: src - type: system - score: 25 - threat_objects: [] + message: The device [$src$] attempted $count$ NTLM authentications using $unique_count$ user accounts. + risk_objects: + - field: src + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Active Directory Password Spraying - asset_type: Endpoint - mitre_attack_id: - - T1110.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Password Spraying + asset_type: Endpoint + mitre_attack_id: + - T1110.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/ntlm_bruteforce/ntlm_bruteforce.log - source: XmlWinEventLog:Microsoft-Windows-NTLM/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/ntlm_bruteforce/ntlm_bruteforce.log + source: XmlWinEventLog:Microsoft-Windows-NTLM/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_unusual_process_load_mozilla_nss_mozglue_module.yml b/detections/endpoint/windows_unusual_process_load_mozilla_nss_mozglue_module.yml index 1795010c85..43d53ed122 100644 --- a/detections/endpoint/windows_unusual_process_load_mozilla_nss_mozglue_module.yml +++ b/detections/endpoint/windows_unusual_process_load_mozilla_nss_mozglue_module.yml @@ -5,82 +5,49 @@ date: '2025-12-16' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies processes loading Mozilla - NSS-Mozglue libraries such as mozglue.dll and nss3.dll. It leverages Sysmon - Event logs, specifically monitoring EventCode 7, which tracks image loaded - events. This activity is significant because it can indicate unauthorized - access or manipulation of these libraries, which are commonly used by Mozilla - applications like Firefox and Thunderbird. If confirmed malicious, this could - lead to data exfiltration, credential theft, or further compromise of the - system. +description: The following analytic identifies processes loading Mozilla NSS-Mozglue libraries such as mozglue.dll and nss3.dll. It leverages Sysmon Event logs, specifically monitoring EventCode 7, which tracks image loaded events. This activity is significant because it can indicate unauthorized access or manipulation of these libraries, which are commonly used by Mozilla applications like Firefox and Thunderbird. If confirmed malicious, this could lead to data exfiltration, credential theft, or further compromise of the system. data_source: -- Sysmon EventID 7 -search: '`sysmon` EventCode=7 ImageLoaded IN ("*\\mozglue.dll", "*\\nss3.dll") NOT(process_path - IN("*:\\Program Files\Mozilla Firefox\\firefox.exe", "*:\\Program Files (x86)\Mozilla - Firefox\\firefox.exe", "*:\\Program Files\\Mozilla Thunderbird\\thunderbird.exe", - "*:\\Program Files (x86)\\Mozilla Thunderbird\\thunderbird.exe", "*\\Tor Browser\\Browser\\firefox.exe","*:\\Program - Files\\Code42\\CrashPlan\\Code42Service.exe", "*:\\Program Files (x86)\\Code42\\CrashPlan\\Code42Service.exe", - "*:\\Program Files\\Pale Moon\\palemoon.exe", "*:\\Program Files (x86)\\Pale Moon\\palemoon.exe", - "*:\\Program Files\\Waterfox\\waterfox.exe","*:\\Program Files (x86)\\Waterfox\\waterfox.exe", - "*:\\Program Files\\Cyberfox\cyberfox.exe", "*:\\Program Files (x86)\\Cyberfox\\cyberfox.exe", - "*\\AppData\\Local\\slack\\slack.exe", "*:\\Program Files (x86)\\VMware\\VMware - Horizon View Client\\vmware-view.exe", "*:\\Program Files (x86)\\Dropbox\\Client\\Dropbox.exe", - "*:\\Program Files\\Google\\Google Earth Pro\\client\\googleearth.exe")) | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded - dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash - process_id process_name process_path service_dll_signature_exists service_dll_signature_verified - signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_unusual_process_load_mozilla_nss_mozglue_module_filter`' -how_to_implement: To successfully implement this search, you need to be - ingesting logs with the process name and imageloaded executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of - the Sysmon TA. -known_false_positives: Legitimate windows application that are not on the list - loading this dll. Filter as needed. + - Sysmon EventID 7 +search: '`sysmon` EventCode=7 ImageLoaded IN ("*\\mozglue.dll", "*\\nss3.dll") NOT(process_path IN("*:\\Program Files\Mozilla Firefox\\firefox.exe", "*:\\Program Files (x86)\Mozilla Firefox\\firefox.exe", "*:\\Program Files\\Mozilla Thunderbird\\thunderbird.exe", "*:\\Program Files (x86)\\Mozilla Thunderbird\\thunderbird.exe", "*\\Tor Browser\\Browser\\firefox.exe","*:\\Program Files\\Code42\\CrashPlan\\Code42Service.exe", "*:\\Program Files (x86)\\Code42\\CrashPlan\\Code42Service.exe", "*:\\Program Files\\Pale Moon\\palemoon.exe", "*:\\Program Files (x86)\\Pale Moon\\palemoon.exe", "*:\\Program Files\\Waterfox\\waterfox.exe","*:\\Program Files (x86)\\Waterfox\\waterfox.exe", "*:\\Program Files\\Cyberfox\cyberfox.exe", "*:\\Program Files (x86)\\Cyberfox\\cyberfox.exe", "*\\AppData\\Local\\slack\\slack.exe", "*:\\Program Files (x86)\\VMware\\VMware Horizon View Client\\vmware-view.exe", "*:\\Program Files (x86)\\Dropbox\\Client\\Dropbox.exe", "*:\\Program Files\\Google\\Google Earth Pro\\client\\googleearth.exe")) | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded dest loaded_file loaded_file_path original_file_name process_exec process_guid process_hash process_id process_name process_path service_dll_signature_exists service_dll_signature_verified signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_unusual_process_load_mozilla_nss_mozglue_module_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: Legitimate windows application that are not on the list loading this dll. Filter as needed. references: -- https://www.trendmicro.com/vinfo/nz/threat-encyclopedia/malware/trojanspy.win32.vidar.yxdftz + - https://www.trendmicro.com/vinfo/nz/threat-encyclopedia/malware/trojanspy.win32.vidar.yxdftz drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a non Firefox or Thunderbird process $process_name$ with $process_id$ - loaded the Mozilla NSS-Mozglue libraries on host $dest$. - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: - - field: process_name - type: process_name + message: a non Firefox or Thunderbird process $process_name$ with $process_id$ loaded the Mozilla NSS-Mozglue libraries on host $dest$. + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - StealC Stealer - - Quasar RAT - - 0bj3ctivity Stealer - - Lokibot - asset_type: Endpoint - mitre_attack_id: - - T1218.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - StealC Stealer + - Quasar RAT + - 0bj3ctivity Stealer + - Lokibot + asset_type: Endpoint + mitre_attack_id: + - T1218.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.003/moz_lib_loaded/mozilla_lib.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.003/moz_lib_loaded/mozilla_lib.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_unusual_syswow64_process_run_system32_executable.yml b/detections/endpoint/windows_unusual_syswow64_process_run_system32_executable.yml index ceae9da7e8..f04ddaf6a3 100644 --- a/detections/endpoint/windows_unusual_syswow64_process_run_system32_executable.yml +++ b/detections/endpoint/windows_unusual_syswow64_process_run_system32_executable.yml @@ -5,80 +5,49 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects an unusual process execution pattern where - a process running from C:\Windows\SysWOW64\ attempts to execute a binary from C:\Windows\System32\. - In a typical Windows environment, 32-bit processes under SysWOW64 should primarily - interact with 32-bit binaries within the same directory. However, an execution flow - where a 32-bit process spawns a 64-bit binary from System32 can indicate potential - process injection, privilege escalation, evasion techniques, or unauthorized execution - hijacking. +description: The following analytic detects an unusual process execution pattern where a process running from C:\Windows\SysWOW64\ attempts to execute a binary from C:\Windows\System32\. In a typical Windows environment, 32-bit processes under SysWOW64 should primarily interact with 32-bit binaries within the same directory. However, an execution flow where a 32-bit process spawns a 64-bit binary from System32 can indicate potential process injection, privilege escalation, evasion techniques, or unauthorized execution hijacking. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_path = "*\\Windows\\SysWOW64\\*" - AND Processes.process = "*windows\\system32\\*" by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_unusual_syswow64_process_run_system32_executable_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: some legitimate system processes, software updaters, or compatibility - tools may trigger this behavior, occurrences involving unknown, unsigned, or unusual - parent processes should be investigated for potential malware activity, persistence - mechanisms, or execution flow hijacking. + - Sysmon EventID 1 + - Windows Event Log Security 4688 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_path = "*\\Windows\\SysWOW64\\*" AND Processes.process = "*windows\\system32\\*" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_unusual_syswow64_process_run_system32_executable_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: some legitimate system processes, software updaters, or compatibility tools may trigger this behavior, occurrences involving unknown, unsigned, or unusual parent processes should be investigated for potential malware activity, persistence mechanisms, or execution flow hijacking. references: -- https://www.trendmicro.com/en_nl/research/24/k/earth-estries.html + - https://www.trendmicro.com/en_nl/research/24/k/earth-estries.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a 32 bit process execute 64 bit executable on [$dest$]. - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: - - field: process_path - type: process_name + message: a 32 bit process execute 64 bit executable on [$dest$]. + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: + - field: process_path + type: process_name tags: - analytic_story: - - DarkGate Malware - - Salt Typhoon - - China-Nexus Threat Activity - asset_type: Endpoint - mitre_attack_id: - - T1036.009 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - DarkGate Malware + - Salt Typhoon + - China-Nexus Threat Activity + asset_type: Endpoint + mitre_attack_id: + - T1036.009 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.009/32bit_process_execute_64bit/32bit_spawn_64bit.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.009/32bit_process_execute_64bit/32bit_spawn_64bit.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_usbstor_registry_key_modification.yml b/detections/endpoint/windows_usbstor_registry_key_modification.yml index 11b5e05056..2c980641f8 100644 --- a/detections/endpoint/windows_usbstor_registry_key_modification.yml +++ b/detections/endpoint/windows_usbstor_registry_key_modification.yml @@ -1,87 +1,76 @@ name: Windows USBSTOR Registry Key Modification id: a345980a-417d-4ed3-9fb4-cac30c9405a0 -version: 4 -date: '2025-09-18' +version: 5 +date: '2026-02-25' author: Steven Dick status: production type: Anomaly -description: This analytic is used to identify when a USB removable media device is - attached to a Windows host. In this scenario we are querying the Endpoint Registry - data model to look for modifications to the HKLM\System\CurrentControlSet\Enum\USBSTOR\ - key. Adversaries and Insider Threats may use removable media devices for several - malicious activities, including initial access, execution, and exfiltration. +description: This analytic is used to identify when a USB removable media device is attached to a Windows host. In this scenario we are querying the Endpoint Registry data model to look for modifications to the HKLM\System\CurrentControlSet\Enum\USBSTOR\ key. Adversaries and Insider Threats may use removable media devices for several malicious activities, including initial access, execution, and exfiltration. data_source: -- Sysmon EventID 12 -- Sysmon EventID 13 -search: "| tstats `security_content_summariesonly` min(_time) as firstTime, max(_time)\ - \ as lastTime, count from datamodel=Endpoint.Registry where Registry.registry_path\ - \ IN (\"HKLM\\\\System\\\\CurrentControlSet\\\\Enum\\\\USBSTOR\\\\*\") \nAND Registry.registry_value_name\ - \ =\"FriendlyName\" \nby Registry.action Registry.dest Registry.process_guid Registry.process_id\ - \ Registry.registry_hive Registry.registry_path \nRegistry.registry_key_name Registry.registry_value_data\ - \ Registry.registry_value_name \nRegistry.registry_value_type Registry.status Registry.user\ - \ Registry.vendor_product \n| `drop_dm_object_name(Registry)`\n| eval object_name\ - \ = registry_value_data, object_handle = split(mvindex(split(registry_path, \"\\\ - \\\"),6),\"&\"), object_handle = mvindex(mvfilter(NOT len(object_handle)=1),0)\n\ - | `security_content_ctime(firstTime)` \n| `security_content_ctime(lastTime)` \n\ - | `windows_usbstor_registry_key_modification_filter`" -how_to_implement: To successfully implement this search, you must ingest endpoint - logging that tracks changes to the HKLM\System\CurrentControlSet\Enum\USBSTOR\ registry - keys. Ensure that the field from the event logs is being mapped to the proper fields - in the Endpoint.Registry data model. -known_false_positives: Legitimate USB activity will also be detected. Please verify - and investigate as appropriate. + - Sysmon EventID 12 + - Sysmon EventID 13 +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime, max(_time) as lastTime, count FROM datamodel=Endpoint.Registry + WHERE Registry.registry_path IN ("HKLM\\System\\CurrentControlSet\\Enum\\USBSTOR\\*") + AND + Registry.registry_value_name ="FriendlyName" + BY Registry.action Registry.dest Registry.process_guid + Registry.process_id Registry.registry_hive Registry.registry_path + Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name + Registry.registry_value_type Registry.status Registry.user + Registry.vendor_product + | `drop_dm_object_name(Registry)` + | eval object_name = registry_value_data, object_handle = split(mvindex(split(registry_path, "\\"),6),"&"), object_handle = mvindex(mvfilter(NOT len(object_handle)=1),0) + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_usbstor_registry_key_modification_filter` +how_to_implement: To successfully implement this search, you must ingest endpoint logging that tracks changes to the HKLM\System\CurrentControlSet\Enum\USBSTOR\ registry keys. Ensure that the field from the event logs is being mapped to the proper fields in the Endpoint.Registry data model. +known_false_positives: Legitimate USB activity will also be detected. Please verify and investigate as appropriate. references: -- https://attack.mitre.org/techniques/T1200/ -- https://www.cisa.gov/news-events/news/using-caution-usb-drives -- https://www.bleepingcomputer.com/news/security/fbi-hackers-use-badusb-to-target-defense-firms-with-ransomware/ + - https://attack.mitre.org/techniques/T1200/ + - https://www.cisa.gov/news-events/news/using-caution-usb-drives + - https://www.bleepingcomputer.com/news/security/fbi-hackers-use-badusb-to-target-defense-firms-with-ransomware/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate USB events on $dest$ - search: '| from datamodel:Endpoint.Registry | search dest=$dest$ registry_path IN - ("HKLM\\System\\CurrentControlSet\\Enum\\USBSTOR\\*")' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate USB events on $dest$ + search: '| from datamodel:Endpoint.Registry | search dest=$dest$ registry_path IN ("HKLM\\System\\CurrentControlSet\\Enum\\USBSTOR\\*")' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A removable storage device named [$object_name$] with drive letter [$object_handle$] - was attached to $dest$ - risk_objects: - - field: dest - type: system - score: 10 - threat_objects: - - field: object_name - type: registry_value_name - - field: object_handle - type: registry_value_text + message: A removable storage device named [$object_name$] with drive letter [$object_handle$] was attached to $dest$ + risk_objects: + - field: dest + type: system + score: 10 + threat_objects: + - field: object_name + type: registry_value_name + - field: object_handle + type: registry_value_text tags: - analytic_story: - - Data Protection - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1200 - - T1025 - - T1091 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Protection + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1200 + - T1025 + - T1091 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1200/sysmon_usb_use_execution/sysmon_usb_use_execution.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1200/sysmon_usb_use_execution/sysmon_usb_use_execution.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_user_deletion_via_net.yml b/detections/endpoint/windows_user_deletion_via_net.yml index 78d63e9cb2..4db2aca5f1 100644 --- a/detections/endpoint/windows_user_deletion_via_net.yml +++ b/detections/endpoint/windows_user_deletion_via_net.yml @@ -1,88 +1,76 @@ name: Windows User Deletion Via Net id: b0b6fd2c-8953-4d1b-8f7b-56075ea6ab3e -version: 4 -date: '2025-05-02' +version: 5 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the use of net.exe or net1.exe command-line - to delete a user account on a system. It leverages data from Endpoint Detection - and Response (EDR) agents, focusing on process and command-line execution logs. - This activity is significant as it may indicate an attempt to impair user accounts - or cover tracks during lateral movement. If confirmed malicious, this could lead - to unauthorized access removal, disruption of legitimate user activities, or concealment - of adversarial actions, complicating incident response and forensic investigations. +description: The following analytic detects the use of net.exe or net1.exe command-line to delete a user account on a system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and command-line execution logs. This activity is significant as it may indicate an attempt to impair user accounts or cover tracks during lateral movement. If confirmed malicious, this could lead to unauthorized access removal, disruption of legitimate user activities, or concealment of adversarial actions, complicating incident response and forensic investigations. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_net` AND Processes.process="*user*" - AND Processes.process="*/delete*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_user_deletion_via_net_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: System administrators or scripts may delete user accounts via - this technique. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_net` + AND + Processes.process="*user*" + AND + Processes.process="*/delete*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_user_deletion_via_net_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: System administrators or scripts may delete user accounts via this technique. Filter as needed. references: -- https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ + - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to delete accounts. - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to delete accounts. + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - XMRig - - Graceful Wipe Out Attack - - DarkGate Malware - asset_type: Endpoint - mitre_attack_id: - - T1531 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - XMRig + - Graceful Wipe Out Attack + - DarkGate Malware + asset_type: Endpoint + mitre_attack_id: + - T1531 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_user_disabled_via_net.yml b/detections/endpoint/windows_user_disabled_via_net.yml index 8d1ff31c0b..e0ee3315fc 100644 --- a/detections/endpoint/windows_user_disabled_via_net.yml +++ b/detections/endpoint/windows_user_disabled_via_net.yml @@ -1,85 +1,74 @@ name: Windows User Disabled Via Net id: b0359e05-c87b-4354-83d8-aee0d890243f -version: 5 -date: '2026-01-14' +version: 6 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects the use of the `net.exe` utility to disable - a user account via the command line. It leverages data from Endpoint Detection and - Response (EDR) agents, focusing on process execution logs and command-line arguments. - This activity is significant as it may indicate an adversary's attempt to disrupt - user availability, potentially as a precursor to further malicious actions. If confirmed - malicious, this could lead to denial of service for legitimate users, aiding the - attacker in maintaining control or covering their tracks. +description: The following analytic detects the use of the `net.exe` utility to disable a user account via the command line. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant as it may indicate an adversary's attempt to disrupt user availability, potentially as a precursor to further malicious actions. If confirmed malicious, this could lead to denial of service for legitimate users, aiding the attacker in maintaining control or covering their tracks. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_net` AND Processes.process="*user*" - AND Processes.process="*/active:no*" by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_user_disabled_via_net_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_net` + AND + Processes.process="*user*" + AND + Processes.process="*/active:no*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_user_disabled_via_net_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ + - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - disabling a user account on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 42 - - field: dest - type: system - score: 42 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified disabling a user account on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 42 + - field: dest + type: system + score: 42 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - XMRig - asset_type: Endpoint - mitre_attack_id: - - T1531 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - XMRig + asset_type: Endpoint + mitre_attack_id: + - T1531 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_user_discovery_via_net.yml b/detections/endpoint/windows_user_discovery_via_net.yml index 7e40065738..9978b67aa8 100644 --- a/detections/endpoint/windows_user_discovery_via_net.yml +++ b/detections/endpoint/windows_user_discovery_via_net.yml @@ -1,61 +1,59 @@ name: Windows User Discovery Via Net id: 7742987e-88c1-476b-a626-a869e088ab72 -version: 4 -date: '2025-05-02' +version: 5 +date: '2026-02-25' author: Mauricio Velazco, Teoderick Contreras, Nasreddine Bencherchali, Splunk status: production type: Hunting -description: The following analytic detects the execution of `net.exe` or `net1.exe` - with command-line arguments `user` or `users` to query local user accounts. It leverages - data from Endpoint Detection and Response (EDR) agents, focusing on process names - and command-line executions. This activity is significant as it indicates potential - reconnaissance efforts by adversaries to enumerate local users, which is a common - step in situational awareness and Active Directory discovery. If confirmed malicious, - this behavior could lead to further attacks, including privilege escalation and - lateral movement within the network. +description: The following analytic detects the execution of `net.exe` or `net1.exe` with command-line arguments `user` or `users` to query local user accounts. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential reconnaissance efforts by adversaries to enumerate local users, which is a common step in situational awareness and Active Directory discovery. If confirmed malicious, this behavior could lead to further attacks, including privilege escalation and lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_net` (Processes.process="*user" - OR Processes.process="*users" OR Processes.process="*users *" OR Processes.process="*user - *") AND NOT (Processes.process="*/add" OR Processes.process="*/delete") by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_user_discovery_via_net_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_net` (Processes.process="*user" + OR + Processes.process="*users" + OR + Processes.process="*users *" + OR + Processes.process="*user *") + AND + NOT (Processes.process="*/add" + OR + Processes.process="*/delete") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_user_discovery_via_net_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1087/001/ + - https://attack.mitre.org/techniques/T1087/001/ tags: - analytic_story: - - Active Directory Discovery - - Sandworm Tools - - Medusa Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1087.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - Sandworm Tools + - Medusa Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1087.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.001/AD_discovery/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.001/AD_discovery/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_user_execution_malicious_url_shortcut_file.yml b/detections/endpoint/windows_user_execution_malicious_url_shortcut_file.yml index 31573d9fa1..63a5a708f4 100644 --- a/detections/endpoint/windows_user_execution_malicious_url_shortcut_file.yml +++ b/detections/endpoint/windows_user_execution_malicious_url_shortcut_file.yml @@ -5,77 +5,53 @@ date: '2025-09-18' author: Teoderick Contreras, Nasreddine Bencherchali, Splunk status: production type: Anomaly -description: The following analytic detects the creation URL shortcut files, often - used by malware like CHAOS ransomware. It leverages the Endpoint.Filesystem datamodel - to identify ".url" files created outside common directories, such as "Program Files". - This activity can be significant as ".URL" files can be used as mean to trick the - user into visiting certain websites unknowingly, or when placed in certain locations - such as "\\AppData\\Roaming\\Microsoft\\Windows\\Start Menu\\Programs\\Startup\\", - it may allow the execution of malicious code upon system reboot. If confirmed malicious, - this could allow an attacker to achieve persistence and execute harmful payloads, - potentially leading to further system compromise and data loss. +description: The following analytic detects the creation URL shortcut files, often used by malware like CHAOS ransomware. It leverages the Endpoint.Filesystem datamodel to identify ".url" files created outside common directories, such as "Program Files". This activity can be significant as ".URL" files can be used as mean to trick the user into visiting certain websites unknowingly, or when placed in certain locations such as "\\AppData\\Roaming\\Microsoft\\Windows\\Start Menu\\Programs\\Startup\\", it may allow the execution of malicious code upon system reboot. If confirmed malicious, this could allow an attacker to achieve persistence and execute harmful payloads, potentially leading to further system compromise and data loss. data_source: -- Sysmon EventID 11 -search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Filesystem where NOT Filesystem.file_path IN - ("*:\\Program Files\\*", "*:\\Program Files (x86)\\*", "*\\AppData\\Roaming\\Microsoft\\Office\\Recent\\*", - "*:\\Windows\\WinSxS\\*") Filesystem.file_name=*.url by Filesystem.action Filesystem.dest - Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time - Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size - Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product - | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_user_execution_malicious_url_shortcut_file_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on process that include the name of the Filesystem responsible for the changes from - your endpoints into the `Endpoint` datamodel in the `Filesystem` node. -known_false_positives: Administrators may allow creation of script or exe in this - path. + - Sysmon EventID 11 +search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where NOT Filesystem.file_path IN ("*:\\Program Files\\*", "*:\\Program Files (x86)\\*", "*\\AppData\\Roaming\\Microsoft\\Office\\Recent\\*", "*:\\Windows\\WinSxS\\*") Filesystem.file_name=*.url by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_user_execution_malicious_url_shortcut_file_filter`' +how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. +known_false_positives: Administrators may allow creation of script or exe in this path. references: -- https://attack.mitre.org/techniques/T1204/002/ -- https://www.fortinet.com/blog/threat-research/chaos-ransomware-variant-sides-with-russia + - https://attack.mitre.org/techniques/T1204/002/ + - https://www.fortinet.com/blog/threat-research/chaos-ransomware-variant-sides-with-russia drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A process created a .URL shortcut file in $file_path$ of $dest$ - risk_objects: - - field: user - type: user - score: 50 - - field: dest - type: system - score: 50 - threat_objects: [] + message: A process created a .URL shortcut file in $file_path$ of $dest$ + risk_objects: + - field: user + type: user + score: 50 + - field: dest + type: system + score: 50 + threat_objects: [] tags: - analytic_story: - - XWorm - - Chaos Ransomware - - NjRAT - - Quasar RAT - - Snake Keylogger - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1204.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - XWorm + - Chaos Ransomware + - NjRAT + - Quasar RAT + - Snake Keylogger + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1204.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/chaos_ransomware/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/chaos_ransomware/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_visual_basic_commandline_compiler_dnsquery.yml b/detections/endpoint/windows_visual_basic_commandline_compiler_dnsquery.yml index 420916b4f9..13976b53cf 100644 --- a/detections/endpoint/windows_visual_basic_commandline_compiler_dnsquery.yml +++ b/detections/endpoint/windows_visual_basic_commandline_compiler_dnsquery.yml @@ -1,65 +1,62 @@ name: Windows Visual Basic Commandline Compiler DNSQuery id: 8976744a-ae7a-46a4-8128-690df85c2af4 -version: 2 -date: '2026-01-14' +version: 3 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP description: The following analytic detects instances where vbc.exe, the Visual Basic Command Line Compiler, initiates DNS queries. Normally, vbc.exe operates locally to compile Visual Basic code and does not require internet access or to perform DNS lookups. Therefore, any observed DNS activity originating from vbc.exe is highly suspicious and indicative of potential malicious activity. This behavior often suggests that a malicious payload is masquerading as the legitimate vbc.exe process to establish command-and-control (C2) communication, resolve domains for data exfiltration, or download additional stages of malware. Security teams should investigate the process's parent, command-line arguments, and the resolved domains for further indicators of compromise. data_source: -- Sysmon EventID 22 -search: '`sysmon` EventCode=22 process_name="vbc.exe" - | rename dvc as dest - | stats count min(_time) as firstTime max(_time) as lastTime - by answer answer_count dest process_exec process_guid process_name query query_count reply_code_id - signature signature_id src user_id vendor_product QueryName QueryResults QueryStatus - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_visual_basic_commandline_compiler_dnsquery_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name and eventcode = 22 dnsquery executions from your endpoints. - If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. - Tune and filter known instances where renamed rundll32.exe may be used. + - Sysmon EventID 22 +search: |- + `sysmon` EventCode=22 process_name="vbc.exe" + | rename dvc as dest + | stats count min(_time) as firstTime max(_time) as lastTime + BY answer answer_count dest + process_exec process_guid process_name + query query_count reply_code_id + signature signature_id src + user_id vendor_product QueryName + QueryResults QueryStatus + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_visual_basic_commandline_compiler_dnsquery_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and eventcode = 22 dnsquery executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used. known_false_positives: No false positives have been identified at this time. references: -- https://www.cisa.gov/news-events/cybersecurity-advisories/aa20-266a + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa20-266a drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: vbc.exe process [$process_name$] made a DNS query for $query$ from host $dest$. - risk_objects: - - field: dest - type: system - score: 50 - threat_objects: - - field: process_name - type: process_name + message: vbc.exe process [$process_name$] made a DNS query for $query$ from host $dest$. + risk_objects: + - field: dest + type: system + score: 50 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Lokibot - asset_type: Endpoint - mitre_attack_id: - - T1071.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Lokibot + asset_type: Endpoint + mitre_attack_id: + - T1071.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1071.004/vbc_dnsquery/vbc_dns_query.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1071.004/vbc_dnsquery/vbc_dns_query.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_vulnerable_3cx_software.yml b/detections/endpoint/windows_vulnerable_3cx_software.yml index fc3046c6b0..92b610e344 100644 --- a/detections/endpoint/windows_vulnerable_3cx_software.yml +++ b/detections/endpoint/windows_vulnerable_3cx_software.yml @@ -1,77 +1,68 @@ name: Windows Vulnerable 3CX Software id: f2cc1584-46ee-485b-b905-977c067f36de -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk type: TTP status: production data_source: -- Sysmon EventID 1 -description: The following analytic detects instances of the 3CXDesktopApp.exe with - a FileVersion of 18.12.x, leveraging Sysmon logs. This detection focuses on identifying - vulnerable versions 18.12.407 and 18.12.416 of the 3CX desktop app. Monitoring this - activity is crucial as these specific versions have known vulnerabilities that could - be exploited by attackers. If confirmed malicious, exploitation of this vulnerability - could lead to unauthorized access, code execution, or further compromise of the - affected system, posing significant security risks. -search: '`sysmon` (process_name=3CXDesktopApp.exe OR OriginalFileName=3CXDesktopApp.exe) FileVersion=18.12.* - | stats count min(_time) as firstTime max(_time) as lastTime by action dest original_file_name - parent_process parent_process_exec parent_process_guid parent_process_id parent_process_name - parent_process_path process process_exec process_guid process_hash process_id process_integrity_level - process_name process_path user user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_vulnerable_3cx_software_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. -known_false_positives: False positives may be present based on file version, modify - the analytic to only look for version between 18.12.407 and 18.12.416 as needed. + - Sysmon EventID 1 +description: The following analytic detects instances of the 3CXDesktopApp.exe with a FileVersion of 18.12.x, leveraging Sysmon logs. This detection focuses on identifying vulnerable versions 18.12.407 and 18.12.416 of the 3CX desktop app. Monitoring this activity is crucial as these specific versions have known vulnerabilities that could be exploited by attackers. If confirmed malicious, exploitation of this vulnerability could lead to unauthorized access, code execution, or further compromise of the affected system, posing significant security risks. +search: |- + `sysmon` (process_name=3CXDesktopApp.exe OR OriginalFileName=3CXDesktopApp.exe) FileVersion=18.12.* + | stats count min(_time) as firstTime max(_time) as lastTime + BY action dest original_file_name + parent_process parent_process_exec parent_process_guid + parent_process_id parent_process_name parent_process_path + process process_exec process_guid + process_hash process_id process_integrity_level + process_name process_path user + user_id vendor_product + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_vulnerable_3cx_software_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: False positives may be present based on file version, modify the analytic to only look for version between 18.12.407 and 18.12.416 as needed. references: -- https://www.sentinelone.com/blog/smoothoperator-ongoing-campaign-trojanizes-3cx-software-in-software-supply-chain-attack/ -- https://www.cisa.gov/news-events/alerts/2023/03/30/supply-chain-attack-against-3cxdesktopapp -- https://www.reddit.com/r/crowdstrike/comments/125r3uu/20230329_situational_awareness_crowdstrike/ -- https://www.3cx.com/community/threads/crowdstrike-endpoint-security-detection-re-3cx-desktop-app.119934/page-2#post-558898 -- https://www.3cx.com/community/threads/3cx-desktopapp-security-alert.119951/ + - https://www.sentinelone.com/blog/smoothoperator-ongoing-campaign-trojanizes-3cx-software-in-software-supply-chain-attack/ + - https://www.cisa.gov/news-events/alerts/2023/03/30/supply-chain-attack-against-3cxdesktopapp + - https://www.reddit.com/r/crowdstrike/comments/125r3uu/20230329_situational_awareness_crowdstrike/ + - https://www.3cx.com/community/threads/crowdstrike-endpoint-security-detection-re-3cx-desktop-app.119934/page-2#post-558898 + - https://www.3cx.com/community/threads/3cx-desktopapp-security-alert.119951/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A known vulnerable instance of 3CX Software $process_name$ ran on $dest$, - related to a supply chain attack. - risk_objects: - - field: dest - type: system - score: 90 - threat_objects: - - field: process_name - type: process_name + message: A known vulnerable instance of 3CX Software $process_name$ ran on $dest$, related to a supply chain attack. + risk_objects: + - field: dest + type: system + score: 90 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - 3CX Supply Chain Attack - asset_type: Endpoint - cve: - - CVE-2023-29059 - mitre_attack_id: - - T1195.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - 3CX Supply Chain Attack + asset_type: Endpoint + cve: + - CVE-2023-29059 + mitre_attack_id: + - T1195.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.002/3CX/3cx_windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.002/3CX/3cx_windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_vulnerable_driver_installed.yml b/detections/endpoint/windows_vulnerable_driver_installed.yml index 22648436e9..0daf557983 100644 --- a/detections/endpoint/windows_vulnerable_driver_installed.yml +++ b/detections/endpoint/windows_vulnerable_driver_installed.yml @@ -1,73 +1,57 @@ name: Windows Vulnerable Driver Installed id: 1dda7586-57be-4a1b-8de1-a9ad802b9a7f -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Dean Luxton status: production type: TTP data_source: -- Windows Event Log System 7045 -description: The following analytic detects the loading of known vulnerable Windows - drivers, which may indicate potential persistence or privilege escalation attempts. - It leverages Windows System service install EventCode 7045 to identify driver loading - events and cross-references them with a list of vulnerable drivers. This activity - is significant as attackers often exploit vulnerable drivers to gain elevated privileges - or maintain persistence on a system. If confirmed malicious, this could allow attackers - to execute arbitrary code with high privileges, leading to further system compromise - and potential data exfiltration. This detection is a Windows Event Log adaptation - of the Sysmon driver loaded detection written by Michael Haag. -search: '`wineventlog_system` EventCode=7045 ServiceType="kernel mode driver" | table - _time dest EventCode ImagePath ServiceName ServiceType | lookup loldrivers driver_name - AS ImagePath OUTPUT is_driver driver_description | search is_driver = TRUE | `windows_vulnerable_driver_installed_filter`' -how_to_implement: Ensure the Splunk is collecting XmlWinEventLog:System events and - the EventCode 7045 is being ingested. -known_false_positives: False positives will be present. Drill down into the driver - further by version number and cross reference by signer. Review the reference material - in the lookup. In addition, modify the query to look within specific paths, which - will remove a lot of "normal" drivers. + - Windows Event Log System 7045 +description: The following analytic detects the loading of known vulnerable Windows drivers, which may indicate potential persistence or privilege escalation attempts. It leverages Windows System service install EventCode 7045 to identify driver loading events and cross-references them with a list of vulnerable drivers. This activity is significant as attackers often exploit vulnerable drivers to gain elevated privileges or maintain persistence on a system. If confirmed malicious, this could allow attackers to execute arbitrary code with high privileges, leading to further system compromise and potential data exfiltration. This detection is a Windows Event Log adaptation of the Sysmon driver loaded detection written by Michael Haag. +search: |- + `wineventlog_system` EventCode=7045 ServiceType="kernel mode driver" + | table _time dest EventCode ImagePath ServiceName ServiceType + | lookup loldrivers driver_name AS ImagePath OUTPUT is_driver driver_description + | search is_driver = TRUE + | `windows_vulnerable_driver_installed_filter` +how_to_implement: Ensure the Splunk is collecting XmlWinEventLog:System events and the EventCode 7045 is being ingested. +known_false_positives: False positives will be present. Drill down into the driver further by version number and cross reference by signer. Review the reference material in the lookup. In addition, modify the query to look within specific paths, which will remove a lot of "normal" drivers. references: -- https://loldrivers.io/ -- https://github.com/SpikySabra/Kernel-Cactus -- https://github.com/wavestone-cdt/EDRSandblast -- https://research.splunk.com/endpoint/a2b1f1ef-221f-4187-b2a4-d4b08ec745f4/ -- https://www.splunk.com/en_us/blog/security/these-are-the-drivers-you-are-looking-for-detect-and-prevent-malicious-drivers.html + - https://loldrivers.io/ + - https://github.com/SpikySabra/Kernel-Cactus + - https://github.com/wavestone-cdt/EDRSandblast + - https://research.splunk.com/endpoint/a2b1f1ef-221f-4187-b2a4-d4b08ec745f4/ + - https://www.splunk.com/en_us/blog/security/these-are-the-drivers-you-are-looking-for-detect-and-prevent-malicious-drivers.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potentially vulnerable/malicious driver [$ImagePath$] has been installed - on $dest$ - risk_objects: - - field: dest - type: system - score: 50 - threat_objects: [] + message: Potentially vulnerable/malicious driver [$ImagePath$] has been installed on $dest$ + risk_objects: + - field: dest + type: system + score: 50 + threat_objects: [] tags: - analytic_story: - - Windows Drivers - asset_type: Endpoint - mitre_attack_id: - - T1543.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Drivers + asset_type: Endpoint + mitre_attack_id: + - T1543.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1014/windows-system.log - source: XmlWinEventLog:System - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1014/windows-system.log + source: XmlWinEventLog:System + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_vulnerable_driver_loaded.yml b/detections/endpoint/windows_vulnerable_driver_loaded.yml index 2fc3d10676..3e0e06b5b6 100644 --- a/detections/endpoint/windows_vulnerable_driver_loaded.yml +++ b/detections/endpoint/windows_vulnerable_driver_loaded.yml @@ -1,61 +1,55 @@ name: Windows Vulnerable Driver Loaded id: a2b1f1ef-221f-4187-b2a4-d4b08ec745f4 -version: 7 -date: '2025-06-10' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic detects the loading of known vulnerable Windows - drivers, which may indicate potential persistence or privilege escalation attempts. - It leverages Sysmon EventCode 6 to identify driver loading events and cross-references - them with a list of vulnerable drivers. This activity is significant as attackers - often exploit vulnerable drivers to gain elevated privileges or maintain persistence - on a system. If confirmed malicious, this could allow attackers to execute arbitrary - code with high privileges, leading to further system compromise and potential data - exfiltration. +description: The following analytic detects the loading of known vulnerable Windows drivers, which may indicate potential persistence or privilege escalation attempts. It leverages Sysmon EventCode 6 to identify driver loading events and cross-references them with a list of vulnerable drivers. This activity is significant as attackers often exploit vulnerable drivers to gain elevated privileges or maintain persistence on a system. If confirmed malicious, this could allow attackers to execute arbitrary code with high privileges, leading to further system compromise and potential data exfiltration. data_source: -- Sysmon EventID 6 -search: '`sysmon` EventCode=6 | stats min(_time) as firstTime max(_time) as lastTime - count by ImageLoaded dest dvc process_hash process_path signature signature_id user_id - vendor_product | lookup loldrivers driver_name AS ImageLoaded OUTPUT is_driver driver_description - | search is_driver = TRUE | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_vulnerable_driver_loaded_filter`' -how_to_implement: Sysmon collects driver loads via EventID 6, however you may modify - the query to utilize this lookup to identify potentially persistent drivers that - are known to be vulnerable. -known_false_positives: False positives will be present. Drill down into the driver - further by version number and cross reference by signer. Review the reference material - in the lookup. In addition, modify the query to look within specific paths, which - will remove a lot of "normal" drivers. + - Sysmon EventID 6 +search: |- + `sysmon` EventCode=6 + | stats min(_time) as firstTime max(_time) as lastTime count + BY ImageLoaded dest dvc + process_hash process_path signature + signature_id user_id vendor_product + | lookup loldrivers driver_name AS ImageLoaded OUTPUT is_driver driver_description + | search is_driver = TRUE + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_vulnerable_driver_loaded_filter` +how_to_implement: Sysmon collects driver loads via EventID 6, however you may modify the query to utilize this lookup to identify potentially persistent drivers that are known to be vulnerable. +known_false_positives: False positives will be present. Drill down into the driver further by version number and cross reference by signer. Review the reference material in the lookup. In addition, modify the query to look within specific paths, which will remove a lot of "normal" drivers. references: -- https://github.com/SigmaHQ/sigma/blob/master/rules/windows/driver_load/driver_load_vuln_drivers_names.yml -- https://github.com/eclypsium/Screwed-Drivers/blob/master/DRIVERS.md -- https://docs.microsoft.com/en-us/windows/security/threat-protection/windows-defender-application-control/microsoft-recommended-driver-block-rules -- https://www.rapid7.com/blog/post/2021/12/13/driver-based-attacks-past-and-present/ -- https://github.com/jbaines-r7/dellicious -- https://github.com/MicrosoftDocs/windows-itpro-docs/blob/public/windows/security/threat-protection/windows-defender-application-control/microsoft-recommended-driver-block-rules.md -- https://github.com/namazso/physmem_drivers -- https://github.com/stong/CVE-2020-15368 -- https://github.com/CaledoniaProject/drivers-binaries -- https://github.com/Chigusa0w0/AsusDriversPrivEscala -- https://www.welivesecurity.com/2022/01/11/signed-kernel-drivers-unguarded-gateway-windows-core/ -- https://eclypsium.com/2019/11/12/mother-of-all-drivers/ -- https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-37969 + - https://github.com/SigmaHQ/sigma/blob/master/rules/windows/driver_load/driver_load_vuln_drivers_names.yml + - https://github.com/eclypsium/Screwed-Drivers/blob/master/DRIVERS.md + - https://docs.microsoft.com/en-us/windows/security/threat-protection/windows-defender-application-control/microsoft-recommended-driver-block-rules + - https://www.rapid7.com/blog/post/2021/12/13/driver-based-attacks-past-and-present/ + - https://github.com/jbaines-r7/dellicious + - https://github.com/MicrosoftDocs/windows-itpro-docs/blob/public/windows/security/threat-protection/windows-defender-application-control/microsoft-recommended-driver-block-rules.md + - https://github.com/namazso/physmem_drivers + - https://github.com/stong/CVE-2020-15368 + - https://github.com/CaledoniaProject/drivers-binaries + - https://github.com/Chigusa0w0/AsusDriversPrivEscala + - https://www.welivesecurity.com/2022/01/11/signed-kernel-drivers-unguarded-gateway-windows-core/ + - https://eclypsium.com/2019/11/12/mother-of-all-drivers/ + - https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-37969 tags: - analytic_story: - - Windows Drivers - - BlackByte Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1543.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Drivers + - BlackByte Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1543.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1014/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1014/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_wbadmin_file_recovery_from_backup.yml b/detections/endpoint/windows_wbadmin_file_recovery_from_backup.yml index 2aa9948c85..ba1129687e 100644 --- a/detections/endpoint/windows_wbadmin_file_recovery_from_backup.yml +++ b/detections/endpoint/windows_wbadmin_file_recovery_from_backup.yml @@ -1,93 +1,88 @@ name: Windows WBAdmin File Recovery From Backup id: 0175f0b7-728d-4038-bbf1-1c30d6ee3d31 -version: 2 -date: '2025-12-18' +version: 3 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - The following analytic identifies the execution of wbadmin.exe with arguments indicative of restoring files from an existing backup. - WBAdmin is a legitimate Windows Backup utility used for creating, managing, and restoring backups. However, adversaries may abuse it to restore specific files (e.g., sensitive credentials, configuration files, or malware stagers) from prior backups to regain access or re-establish persistence after cleanup or encryption events. - Monitoring this behavior is important because restoring individual files from a system backup outside of approved recovery workflows may indicate an attacker attempting to retrieve deleted or encrypted data, recover previously dropped payloads, or access prior system states as part of post-compromise activity. - If confirmed malicious, this action could enable attackers to regain operational footholds, extract sensitive data, or restore tampered components, undermining remediation and containment efforts. + The following analytic identifies the execution of wbadmin.exe with arguments indicative of restoring files from an existing backup. + WBAdmin is a legitimate Windows Backup utility used for creating, managing, and restoring backups. However, adversaries may abuse it to restore specific files (e.g., sensitive credentials, configuration files, or malware stagers) from prior backups to regain access or re-establish persistence after cleanup or encryption events. + Monitoring this behavior is important because restoring individual files from a system backup outside of approved recovery workflows may indicate an attacker attempting to retrieve deleted or encrypted data, recover previously dropped payloads, or access prior system states as part of post-compromise activity. + If confirmed malicious, this action could enable attackers to regain operational footholds, extract sensitive data, or restore tampered components, undermining remediation and containment efforts. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime - - from datamodel=Endpoint.Processes where - - (Processes.process_name=wbadmin.exe OR Processes.original_file_name=WBADMIN.EXE) - Processes.process = "*start*" - Processes.process = "*recovery*" - Processes.process = "*itemtype:file*" - - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id - Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id - Processes.vendor_product - - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_wbadmin_file_recovery_from_backup_filter` + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + + from datamodel=Endpoint.Processes where + + (Processes.process_name=wbadmin.exe OR Processes.original_file_name=WBADMIN.EXE) + Processes.process = "*start*" + Processes.process = "*recovery*" + Processes.process = "*itemtype:file*" + + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec + Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id + Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id + Processes.vendor_product + + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_wbadmin_file_recovery_from_backup_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: | - Administrators may use WBAdmin to restore files during approved recovery or testing activities. Validate the users and context of the operation and apply additional filters as needed. + Administrators may use WBAdmin to restore files during approved recovery or testing activities. Validate the users and context of the operation and apply additional filters as needed. references: -- https://lolbas-project.github.io/lolbas/Binaries/Wbadmin/ -- https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/wbadmin-start-recovery -- https://redmondmag.com/articles/2025/07/18/restoring-a-file-from-a-windows-image-backup.aspx + - https://lolbas-project.github.io/lolbas/Binaries/Wbadmin/ + - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/wbadmin-start-recovery + - https://redmondmag.com/articles/2025/07/18/restoring-a-file-from-a-windows-image-backup.aspx drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An attempt to restore a file from a backup via WBAdmin $process$ was observed on $dest$ - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: [] + message: An attempt to restore a file from a backup via WBAdmin $process$ was observed on $dest$ + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: [] tags: - analytic_story: - - Credential Dumping - asset_type: Endpoint - mitre_attack_id: - - T1490 - - T1565.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Credential Dumping + asset_type: Endpoint + mitre_attack_id: + - T1490 + - T1565.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1565.001/wbadmin_recovery/wbadmin_recovery.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1565.001/wbadmin_recovery/wbadmin_recovery.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_windbg_spawning_autoit3.yml b/detections/endpoint/windows_windbg_spawning_autoit3.yml index 8ec5880f70..66f6571180 100644 --- a/detections/endpoint/windows_windbg_spawning_autoit3.yml +++ b/detections/endpoint/windows_windbg_spawning_autoit3.yml @@ -6,87 +6,54 @@ author: Michael Haag, Splunk status: production type: TTP data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic identifies instances of the WinDBG process spawning - AutoIt3. This behavior is detected by monitoring endpoint telemetry for processes - where 'windbg.exe' is the parent process and 'autoit3.exe' or similar is the child - process. This activity is significant because AutoIt3 is frequently used by threat - actors for scripting malicious automation, potentially indicating an ongoing attack. - If confirmed malicious, this could allow attackers to automate tasks, execute arbitrary - code, and further compromise the system, leading to data exfiltration or additional - malware deployment. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=windbg.exe AND - (Processes.process_name IN ("autoit3.exe", "autoit*.exe") OR Processes.original_file_name - IN ("autoit3.exe", "autoit*.exe")) by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval - matches_extension=if(match(process, "\\.(au3|a3x|exe|aut|aup)$"), "Yes", "No") | - search matches_extension="Yes" | `windows_windbg_spawning_autoit3_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives will only be present if the WinDBG process - legitimately spawns AutoIt3. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic identifies instances of the WinDBG process spawning AutoIt3. This behavior is detected by monitoring endpoint telemetry for processes where 'windbg.exe' is the parent process and 'autoit3.exe' or similar is the child process. This activity is significant because AutoIt3 is frequently used by threat actors for scripting malicious automation, potentially indicating an ongoing attack. If confirmed malicious, this could allow attackers to automate tasks, execute arbitrary code, and further compromise the system, leading to data exfiltration or additional malware deployment. +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=windbg.exe AND (Processes.process_name IN ("autoit3.exe", "autoit*.exe") OR Processes.original_file_name IN ("autoit3.exe", "autoit*.exe")) by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval matches_extension=if(match(process, "\\.(au3|a3x|exe|aut|aup)$"), "Yes", "No") | search matches_extension="Yes" | `windows_windbg_spawning_autoit3_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives will only be present if the WinDBG process legitimately spawns AutoIt3. Filter as needed. references: -- https://github.com/PaloAltoNetworks/Unit42-timely-threat-intel/blob/main/2023-10-25-IOCs-from-DarkGate-activity.txt + - https://github.com/PaloAltoNetworks/Unit42-timely-threat-intel/blob/main/2023-10-25-IOCs-from-DarkGate-activity.txt drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 100 - - field: dest - type: system - score: 100 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 100 + - field: dest + type: system + score: 100 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Compromised Windows Host - - DarkGate Malware - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1059 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - DarkGate Malware + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1059 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/autoit/windbg_autoit.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/autoit/windbg_autoit.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_winlogon_with_public_network_connection.yml b/detections/endpoint/windows_winlogon_with_public_network_connection.yml index 64068803f6..b1631c8377 100644 --- a/detections/endpoint/windows_winlogon_with_public_network_connection.yml +++ b/detections/endpoint/windows_winlogon_with_public_network_connection.yml @@ -1,62 +1,53 @@ name: Windows WinLogon with Public Network Connection id: 65615b3a-62ea-4d65-bb9f-6f07c17df4ea -version: 9 -date: '2025-06-10' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting data_source: -- Sysmon EventID 1 AND Sysmon EventID 3 -description: The following analytic detects instances of Winlogon.exe, a critical - Windows process, connecting to public IP addresses. This behavior is identified - using Endpoint Detection and Response (EDR) telemetry, focusing on network connections - made by Winlogon.exe. Under normal circumstances, Winlogon.exe should not connect - to public IPs, and such activity may indicate a compromise, such as the BlackLotus - bootkit attack. This detection is significant as it highlights potential system - integrity breaches. If confirmed malicious, attackers could maintain persistence, - bypass security measures, and compromise the system at a fundamental level. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN (winlogon.exe) Processes.process!=unknown - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join process_id - [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic - where All_Traffic.dest_port != 0 NOT (All_Traffic.dest IN (127.0.0.1,10.0.0.0/8,172.16.0.0/12, - 192.168.0.0/16, 0:0:0:0:0:0:0:1)) by All_Traffic.process_id All_Traffic.dest All_Traffic.dest_port - | `drop_dm_object_name(All_Traffic)` | rename dest as publicIp ] | table dest parent_process_name - process_name process_path process process_id dest_port publicIp | `windows_winlogon_with_public_network_connection_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives will be present and filtering will be required. - Legitimate IPs will be present and need to be filtered. + - Sysmon EventID 1 AND Sysmon EventID 3 +description: The following analytic detects instances of Winlogon.exe, a critical Windows process, connecting to public IP addresses. This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on network connections made by Winlogon.exe. Under normal circumstances, Winlogon.exe should not connect to public IPs, and such activity may indicate a compromise, such as the BlackLotus bootkit attack. This detection is significant as it highlights potential system integrity breaches. If confirmed malicious, attackers could maintain persistence, bypass security measures, and compromise the system at a fundamental level. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name IN (winlogon.exe) Processes.process!=unknown + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | join process_id [ + | tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic + WHERE All_Traffic.dest_port != 0 NOT (All_Traffic.dest IN (127.0.0.1,10.0.0.0/8,172.16.0.0/12, 192.168.0.0/16, 0:0:0:0:0:0:0:1)) + BY All_Traffic.process_id All_Traffic.dest All_Traffic.dest_port + | `drop_dm_object_name(All_Traffic)` + | rename dest as publicIp ] + | table dest parent_process_name process_name process_path process process_id dest_port publicIp + | `windows_winlogon_with_public_network_connection_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives will be present and filtering will be required. Legitimate IPs will be present and need to be filtered. references: -- https://www.microsoft.com/en-us/security/blog/2023/04/11/guidance-for-investigating-attacks-using-cve-2022-21894-the-blacklotus-campaign/ + - https://www.microsoft.com/en-us/security/blog/2023/04/11/guidance-for-investigating-attacks-using-cve-2022-21894-the-blacklotus-campaign/ tags: - analytic_story: - - BlackLotus Campaign - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1542.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - BlackLotus Campaign + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1542.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1542.003/bootkits/network-winlogon-windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1542.003/bootkits/network-winlogon-windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_wmi_impersonate_token.yml b/detections/endpoint/windows_wmi_impersonate_token.yml index 8e2fa65ac4..6abfbf5240 100644 --- a/detections/endpoint/windows_wmi_impersonate_token.yml +++ b/detections/endpoint/windows_wmi_impersonate_token.yml @@ -5,77 +5,46 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: - The following analytic detects potential WMI token impersonation activities - in a process or command. It leverages Sysmon EventCode 10 to identify instances - where `wmiprvse.exe` has a duplicate handle or full granted access in a target process. - This behavior is significant as it is commonly used by malware like Qakbot for privilege - escalation or defense evasion. If confirmed malicious, this activity could allow - an attacker to gain elevated privileges, evade defenses, and maintain persistence - within the environment. +description: The following analytic detects potential WMI token impersonation activities in a process or command. It leverages Sysmon EventCode 10 to identify instances where `wmiprvse.exe` has a duplicate handle or full granted access in a target process. This behavior is significant as it is commonly used by malware like Qakbot for privilege escalation or defense evasion. If confirmed malicious, this activity could allow an attacker to gain elevated privileges, evade defenses, and maintain persistence within the environment. data_source: - - Sysmon EventID 10 -search: - '`sysmon` EventCode=10 SourceImage = "*\\wmiprvse.exe" GrantedAccess IN ("0x1478", - "0x1fffff") | stats count min(_time) as firstTime max(_time) as lastTime by CallTrace - EventID GrantedAccess Guid Opcode ProcessID SecurityID SourceImage SourceProcessGUID - SourceProcessId TargetImage TargetProcessGUID TargetProcessId UserID dest granted_access - parent_process_exec parent_process_guid parent_process_id parent_process_name parent_process_path - process_exec process_guid process_id process_name process_path signature signature_id - user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_wmi_impersonate_token_filter`' -how_to_implement: - This search requires Sysmon Logs and a Sysmon configuration, which - includes EventCode 10. This search uses an input macro named `sysmon`. We strongly - recommend that you specify your environment-specific configurations (index, source, - sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations - for your Splunk environment. The search also uses a post-filter macro designed to - filter out known false positives. -known_false_positives: - administrator may execute impersonate wmi object script for - auditing. Filter is needed. + - Sysmon EventID 10 +search: '`sysmon` EventCode=10 SourceImage = "*\\wmiprvse.exe" GrantedAccess IN ("0x1478", "0x1fffff") | stats count min(_time) as firstTime max(_time) as lastTime by CallTrace EventID GrantedAccess Guid Opcode ProcessID SecurityID SourceImage SourceProcessGUID SourceProcessId TargetImage TargetProcessGUID TargetProcessId UserID dest granted_access parent_process_exec parent_process_guid parent_process_id parent_process_name parent_process_path process_exec process_guid process_id process_name process_path signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_wmi_impersonate_token_filter`' +how_to_implement: This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. +known_false_positives: administrator may execute impersonate wmi object script for auditing. Filter is needed. references: - - https://github.com/trustedsec/SysmonCommunityGuide/blob/master/chapters/process-access.md - - https://www.joesandbox.com/analysis/278341/0/html + - https://github.com/trustedsec/SysmonCommunityGuide/blob/master/chapters/process-access.md + - https://www.joesandbox.com/analysis/278341/0/html drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - wmiprvse.exe process having a duplicate or full Granted Access $GrantedAccess$ - to $TargetImage$ process on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: wmiprvse.exe process having a duplicate or full Granted Access $GrantedAccess$ to $TargetImage$ process on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Qakbot - - Water Gamayun - asset_type: Endpoint - mitre_attack_id: - - T1047 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Qakbot + - Water Gamayun + asset_type: Endpoint + mitre_attack_id: + - T1047 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/wmi_impersonate/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/wmi_impersonate/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_wmi_process_and_service_list.yml b/detections/endpoint/windows_wmi_process_and_service_list.yml index 259f7d4999..4e6817c1b9 100644 --- a/detections/endpoint/windows_wmi_process_and_service_list.yml +++ b/detections/endpoint/windows_wmi_process_and_service_list.yml @@ -1,86 +1,65 @@ name: Windows WMI Process And Service List id: ef3c5ef2-3f6d-4087-aa75-49bf746dc907 -version: 7 -date: '2025-08-25' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies suspicious WMI command lines querying - for running processes or services. It leverages data from Endpoint Detection and - Response (EDR) agents, focusing on specific process and command-line events. This - activity is significant as adversaries often use WMI to gather system information - and identify services on compromised machines. If confirmed malicious, this behavior - could allow attackers to map out the system, identify critical services, and plan - further attacks, potentially leading to privilege escalation or persistence within - the environment. +description: The following analytic identifies suspicious WMI command lines querying for running processes or services. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific process and command-line events. This activity is significant as adversaries often use WMI to gather system information and identify services on compromised machines. If confirmed malicious, this behavior could allow attackers to map out the system, identify critical services, and plan further attacks, potentially leading to privilege escalation or persistence within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - `process_wmic` - Processes.process IN ("*process*", "*service*") - Processes.process = "*list*" - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_wmi_process_and_service_list_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: netowrk administrator or IT may execute this command for auditing - processes and services. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_wmic` Processes.process IN ("*process*", "*service*") Processes.process = "*list*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_wmi_process_and_service_list_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: netowrk administrator or IT may execute this command for auditing processes and services. references: -- https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS -- https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ + - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS + - https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: wmi command $process$ to list processes and services on $dest$ - risk_objects: - - field: dest - type: system - score: 4 - threat_objects: [] + message: wmi command $process$ to list processes and services on $dest$ + risk_objects: + - field: dest + type: system + score: 4 + threat_objects: [] tags: - analytic_story: - - Windows Post-Exploitation - - Prestige Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1047 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Post-Exploitation + - Prestige Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1047 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_wmi_process_call_create.yml b/detections/endpoint/windows_wmi_process_call_create.yml index 7954fe00e4..ba83dc5045 100644 --- a/detections/endpoint/windows_wmi_process_call_create.yml +++ b/detections/endpoint/windows_wmi_process_call_create.yml @@ -1,66 +1,55 @@ name: Windows WMI Process Call Create id: 0661c2de-93de-11ec-9833-acde48001122 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic detects the execution of WMI command lines used - to create or execute processes. It leverages data from Endpoint Detection and Response - (EDR) agents, focusing on command-line events that include specific keywords like - "process," "call," and "create." This activity is significant because adversaries - often use WMI to execute malicious payloads on local or remote hosts, potentially - bypassing traditional security controls. If confirmed malicious, this behavior could - allow attackers to execute arbitrary code, escalate privileges, or maintain persistence - within the environment, posing a severe threat to organizational security. +description: The following analytic detects the execution of WMI command lines used to create or execute processes. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line events that include specific keywords like "process," "call," and "create." This activity is significant because adversaries often use WMI to execute malicious payloads on local or remote hosts, potentially bypassing traditional security controls. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment, posing a severe threat to organizational security. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_wmic` Processes.process - = "* process *" Processes.process = "* call *" Processes.process = "* create *" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_wmi_process_call_create_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_wmic` Processes.process = "* process *" Processes.process = "* call *" Processes.process = "* create *" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_wmi_process_call_create_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators may execute this command for testing or auditing. references: -- https://github.com/NVISOsecurity/sigma-public/blob/master/rules/windows/process_creation/win_susp_wmi_execution.yml -- https://github.com/redcanaryco/atomic-red-team/blob/2b804d25418004a5f1ba50e9dc637946ab8733c7/atomics/T1047/T1047.md -- https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ -- https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/ + - https://github.com/NVISOsecurity/sigma-public/blob/master/rules/windows/process_creation/win_susp_wmi_execution.yml + - https://github.com/redcanaryco/atomic-red-team/blob/2b804d25418004a5f1ba50e9dc637946ab8733c7/atomics/T1047/T1047.md + - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/ + - https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/ tags: - analytic_story: - - Volt Typhoon - - Qakbot - - IcedID - - Suspicious WMI Use - - CISA AA23-347A - - Cactus Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1047 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Volt Typhoon + - Qakbot + - IcedID + - Suspicious WMI Use + - CISA AA23-347A + - Cactus Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1047 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_wmic_cpu_discovery.yml b/detections/endpoint/windows_wmic_cpu_discovery.yml index e525d5e35c..05696809b2 100644 --- a/detections/endpoint/windows_wmic_cpu_discovery.yml +++ b/detections/endpoint/windows_wmic_cpu_discovery.yml @@ -1,82 +1,70 @@ name: Windows Wmic CPU Discovery id: 6fc46cae-a8c0-4296-b07a-8e52d4322587 -version: 1 -date: '2025-08-25' +version: 2 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: The following analytic detects the use of WMIC (Windows Management Instrumentation Command-line) for CPU discovery, often executed with commands such as “wmic cpu get name” This behavior is commonly associated with reconnaissance, where adversaries seek to gather details about system hardware, assess processing power, or determine if the environment is virtualized. While WMIC is a legitimate administrative tool, its use for CPU queries outside of normal inventory or management scripts can indicate malicious intent. Monitoring command-line executions of WMIC with CPU-related arguments and correlating with other discovery activity can help identify attacker reconnaissance. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where `process_wmic` (Processes.process="* cpu*") - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_wmic_cpu_discovery_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_wmic` (Processes.process="* cpu*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_wmic_cpu_discovery_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators may execute this command for testing or auditing. references: -- https://cert.gov.ua/article/6284730 + - https://cert.gov.ua/article/6284730 drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to gather CPU information. - risk_objects: - - field: user - type: user - score: 3 - - field: dest - type: system - score: 3 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to gather CPU information. + risk_objects: + - field: user + type: user + score: 3 + - field: dest + type: system + score: 3 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - LAMEHUG - asset_type: Endpoint - mitre_attack_id: - - T1082 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - LAMEHUG + asset_type: Endpoint + mitre_attack_id: + - T1082 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/lamehug/T1082/wmic_cmd/wmic_cmd.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/lamehug/T1082/wmic_cmd/wmic_cmd.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_wmic_diskdrive_discovery.yml b/detections/endpoint/windows_wmic_diskdrive_discovery.yml index 7047e20bae..ea2b29d2ca 100644 --- a/detections/endpoint/windows_wmic_diskdrive_discovery.yml +++ b/detections/endpoint/windows_wmic_diskdrive_discovery.yml @@ -1,82 +1,70 @@ name: Windows Wmic DiskDrive Discovery id: 85e88c80-e4ee-4c65-b02e-3c54d94c7a51 -version: 1 -date: '2025-08-25' +version: 2 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: The following analytic detects the use of Windows Management Instrumentation Command-line (WMIC) for disk drive discovery activities on a Windows system. This process involves monitoring commands such as “wmic diskdrive” which are often used by administrators for inventory and diagnostics but can also be leveraged by attackers to enumerate hardware details for malicious purposes. Detecting these commands is essential for identifying potentially unauthorized asset reconnaissance or pre-attack mapping behaviors. By capturing and analyzing WMIC disk drive queries, security teams can gain visibility into suspicious activities, enabling them to respond promptly and strengthen the organization’s security posture against insider threats or lateral movement attempts. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where `process_wmic` (Processes.process="* diskdrive*") - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_wmic_diskdrive_discovery_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_wmic` (Processes.process="* diskdrive*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_wmic_diskdrive_discovery_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators may execute this command for testing or auditing. references: -- https://cert.gov.ua/article/6284730 + - https://cert.gov.ua/article/6284730 drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to gather disk drive information. - risk_objects: - - field: user - type: user - score: 3 - - field: dest - type: system - score: 3 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to gather disk drive information. + risk_objects: + - field: user + type: user + score: 3 + - field: dest + type: system + score: 3 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - LAMEHUG - asset_type: Endpoint - mitre_attack_id: - - T1082 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - LAMEHUG + asset_type: Endpoint + mitre_attack_id: + - T1082 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/lamehug/T1082/wmic_cmd/wmic_cmd.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/lamehug/T1082/wmic_cmd/wmic_cmd.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_wmic_memory_chip_discovery.yml b/detections/endpoint/windows_wmic_memory_chip_discovery.yml index 8f2438363f..dfee0d6bd1 100644 --- a/detections/endpoint/windows_wmic_memory_chip_discovery.yml +++ b/detections/endpoint/windows_wmic_memory_chip_discovery.yml @@ -1,82 +1,70 @@ name: Windows Wmic Memory Chip Discovery id: aecaddaa-5885-4e44-a724-1edd5ecbc79f -version: 1 -date: '2025-08-25' +version: 2 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: The following analytic detects the execution of Windows Management Instrumentation Command-line (WMIC) commands related to memory chip discovery on a Windows system. Specifically, it monitors instances where commands such as “wmic memorychip” are used to retrieve detailed information about installed RAM modules. While these commands can serve legitimate administrative and troubleshooting purposes, they may also be employed by adversaries to gather system hardware specifications as part of their reconnaissance activities. By identifying and alerting on WMIC memory chip queries, security teams can enhance their ability to spot unauthorized information gathering and take proactive measures to mitigate potential threats. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where `process_wmic` (Processes.process="* memorychip*") - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_wmic_memory_chip_discovery_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_wmic` (Processes.process="* memorychip*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_wmic_memory_chip_discovery_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators may execute this command for testing or auditing. references: -- https://cert.gov.ua/article/6284730 + - https://cert.gov.ua/article/6284730 drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to gather Memory Chip information. - risk_objects: - - field: user - type: user - score: 3 - - field: dest - type: system - score: 3 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to gather Memory Chip information. + risk_objects: + - field: user + type: user + score: 3 + - field: dest + type: system + score: 3 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - LAMEHUG - asset_type: Endpoint - mitre_attack_id: - - T1082 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - LAMEHUG + asset_type: Endpoint + mitre_attack_id: + - T1082 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/lamehug/T1082/wmic_cmd/wmic_cmd.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/lamehug/T1082/wmic_cmd/wmic_cmd.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_wmic_network_discovery.yml b/detections/endpoint/windows_wmic_network_discovery.yml index d63a44ca39..c1530f1497 100644 --- a/detections/endpoint/windows_wmic_network_discovery.yml +++ b/detections/endpoint/windows_wmic_network_discovery.yml @@ -1,82 +1,70 @@ name: Windows Wmic Network Discovery id: cce82b81-c716-4b6c-bac9-33e6a6925cc2 -version: 1 -date: '2025-08-25' +version: 2 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: The following analytic detects the execution of Windows Management Instrumentation Command-line (WMIC) commands used for network interface discovery on a Windows system. Specifically, it identifies commands such as “wmic nic” that retrieve detailed information about the network adapters installed on the device. While these commands are commonly used by IT administrators for legitimate network inventory and diagnostics, they can also be leveraged by malicious actors for reconnaissance, enabling them to map network configurations and identify potential targets. Monitoring WMIC network interface queries allows security teams to detect suspicious or unauthorized enumeration activities, supporting early threat identification and response.ß data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where `process_wmic` (Processes.process="* nic*") - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_wmic_network_discovery_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_wmic` (Processes.process="* nic*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_wmic_network_discovery_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators may execute this command for testing or auditing. references: -- https://cert.gov.ua/article/6284730 + - https://cert.gov.ua/article/6284730 drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to gather Network information. - risk_objects: - - field: user - type: user - score: 3 - - field: dest - type: system - score: 3 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to gather Network information. + risk_objects: + - field: user + type: user + score: 3 + - field: dest + type: system + score: 3 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - LAMEHUG - asset_type: Endpoint - mitre_attack_id: - - T1082 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - LAMEHUG + asset_type: Endpoint + mitre_attack_id: + - T1082 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/lamehug/T1082/wmic_cmd/wmic_cmd.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/lamehug/T1082/wmic_cmd/wmic_cmd.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_wmic_shadowcopy_delete.yml b/detections/endpoint/windows_wmic_shadowcopy_delete.yml index 2d88c34d5a..feee823bf0 100644 --- a/detections/endpoint/windows_wmic_shadowcopy_delete.yml +++ b/detections/endpoint/windows_wmic_shadowcopy_delete.yml @@ -7,54 +7,54 @@ status: production type: Anomaly description: This analytic detects the use of WMIC to delete volume shadow copies, which is a common technique used by ransomware actors to prevent system recovery. Ransomware like Cactus often delete shadow copies before encrypting files to ensure victims cannot recover their data without paying the ransom. This behavior is particularly concerning as it indicates potential ransomware activity or malicious actors attempting to prevent system recovery. data_source: -- Sysmon EventID 1 + - Sysmon EventID 1 search: |- - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe Processes.process = "*shadowcopy*" Processes.process = "*delete*" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_wmic_shadowcopy_delete_filter` + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe Processes.process = "*shadowcopy*" Processes.process = "*delete*" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec + Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_wmic_shadowcopy_delete_filter` how_to_implement: This detection requires Sysmon logging with Event ID 1 (Process Create) enabled. The logs must be processed using the appropriate Splunk Technology Add-ons and mapped to the Endpoint.Processes data model. Ensure that command-line arguments are being logged and that the appropriate permissions are in place to capture this data. known_false_positives: Legitimate system maintenance or backup operations may occasionally delete shadow copies. However, this activity should be rare and typically performed through approved administrative tools rather than direct WMIC commands. Tune and modify the search to fit your environment, enable as TTP. references: -- https://any.run/malware-trends/cactus -- https://attack.mitre.org/techniques/T1490/ + - https://any.run/malware-trends/cactus + - https://attack.mitre.org/techniques/T1490/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$process_name$" - search: '%original_detection_search% | search dest = "$dest$" process_name = "$process_name$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$process_name$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$process_name$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$process_name$" + search: '%original_detection_search% | search dest = "$dest$" process_name = "$process_name$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$process_name$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$process_name$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A WMIC command, $process_name$, was detected attempting to delete volume shadow copies spawned off of $parent_process_name$ on $dest$. This is a common ransomware technique used to prevent system recovery. - risk_objects: - - field: dest - type: system - score: 10 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: A WMIC command, $process_name$, was detected attempting to delete volume shadow copies spawned off of $parent_process_name$ on $dest$. This is a common ransomware technique used to prevent system recovery. + risk_objects: + - field: dest + type: system + score: 10 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Cactus Ransomware - - Volt Typhoon - - Suspicious WMI Use - asset_type: Endpoint - mitre_attack_id: - - T1490 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Cactus Ransomware + - Volt Typhoon + - Suspicious WMI Use + asset_type: Endpoint + mitre_attack_id: + - T1490 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/shadowcopy_del/wmicshadowcopydelete_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/shadowcopy_del/wmicshadowcopydelete_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_wmic_systeminfo_discovery.yml b/detections/endpoint/windows_wmic_systeminfo_discovery.yml index cd61e778f1..6fc27c8e74 100644 --- a/detections/endpoint/windows_wmic_systeminfo_discovery.yml +++ b/detections/endpoint/windows_wmic_systeminfo_discovery.yml @@ -1,82 +1,70 @@ name: Windows Wmic Systeminfo Discovery id: 97937ece-cb13-4dbc-9684-c0dc3afd400a -version: 1 -date: '2025-08-25' +version: 2 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly description: The following analytic detects the execution of Windows Management Instrumentation Command-line (WMIC) commands used for computer system discovery on a Windows system. Specifically, it monitors for commands such as “wmic computersystem” that retrieve detailed information about the computer’s model, manufacturer, name, domain, and other system attributes. While these commands are commonly used by administrators for inventory and troubleshooting, they may also be exploited by adversaries to gain insight into the target environment during the reconnaissance phase of an attack. Identifying and alerting on WMIC computer system queries helps security teams recognize unauthorized information gathering and take steps to mitigate potential threats. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where `process_wmic` (Processes.process="* computersystem*") - by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `windows_wmic_systeminfo_discovery_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_wmic` (Processes.process="* computersystem*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_wmic_systeminfo_discovery_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: Administrators may execute this command for testing or auditing. references: -- https://cert.gov.ua/article/6284730 + - https://cert.gov.ua/article/6284730 drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to gather system information. - risk_objects: - - field: user - type: user - score: 3 - - field: dest - type: system - score: 3 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to gather system information. + risk_objects: + - field: user + type: user + score: 3 + - field: dest + type: system + score: 3 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - LAMEHUG - asset_type: Endpoint - mitre_attack_id: - - T1082 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - LAMEHUG + asset_type: Endpoint + mitre_attack_id: + - T1082 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/lamehug/T1082/wmic_cmd/wmic_cmd.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/lamehug/T1082/wmic_cmd/wmic_cmd.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_wpdbusenum_registry_key_modification.yml b/detections/endpoint/windows_wpdbusenum_registry_key_modification.yml index f77db48d24..0f28f9cee8 100644 --- a/detections/endpoint/windows_wpdbusenum_registry_key_modification.yml +++ b/detections/endpoint/windows_wpdbusenum_registry_key_modification.yml @@ -1,90 +1,78 @@ name: Windows WPDBusEnum Registry Key Modification id: 52b48e8b-eb6e-48b0-b8f1-73273f6b134e -version: 4 -date: '2025-09-18' +version: 5 +date: '2026-02-25' author: Steven Dick status: production type: Anomaly -description: This analytic is used to identify when a USB removable media device is - attached to a Windows host. In this scenario we are querying the Endpoint Registry - data model to look for modifications to the Windows Portable Device keys HKLM\SOFTWARE\Microsoft\Windows - Portable Devices\Devices\ or HKLM\System\CurrentControlSet\Enum\SWD\WPDBUSENUM\ - . Adversaries and Insider Threats may use removable media devices for several malicious - activities, including initial access, execution, and exfiltration. +description: This analytic is used to identify when a USB removable media device is attached to a Windows host. In this scenario we are querying the Endpoint Registry data model to look for modifications to the Windows Portable Device keys HKLM\SOFTWARE\Microsoft\Windows Portable Devices\Devices\ or HKLM\System\CurrentControlSet\Enum\SWD\WPDBUSENUM\ . Adversaries and Insider Threats may use removable media devices for several malicious activities, including initial access, execution, and exfiltration. data_source: -- Sysmon EventID 12 -- Sysmon EventID 13 -search: "| tstats `security_content_summariesonly` min(_time) as firstTime, max(_time)\ - \ as lastTime, count from datamodel=Endpoint.Registry \nwhere Registry.registry_path\ - \ IN (\"HKLM\\\\SOFTWARE\\\\Microsoft\\\\Windows Portable Devices\\\\Devices\\\\\ - *\",\"HKLM\\\\System\\\\CurrentControlSet\\\\Enum\\\\SWD\\\\WPDBUSENUM\\\\*\") \n\ - AND Registry.registry_value_name =\"FriendlyName\" AND Registry.registry_path=\"\ - *USBSTOR*\" \nby Registry.action Registry.dest Registry.process_guid Registry.process_id\ - \ Registry.registry_hive Registry.registry_path \nRegistry.registry_key_name Registry.registry_value_data\ - \ Registry.registry_value_name \nRegistry.registry_value_type Registry.status Registry.user\ - \ Registry.vendor_product \n| `drop_dm_object_name(Registry)`\n| eval object_handle\ - \ = registry_value_data, object_name = replace(mvindex(split(mvindex(split(registry_path,\ - \ \"??\"),1),\"&\"),2),\"PROD_\",\"\")\n| `security_content_ctime(firstTime)`\ - \ \n| `security_content_ctime(lastTime)`\n| `windows_wpdbusenum_registry_key_modification_filter`" -how_to_implement: To successfully implement this search, you must ingest endpoint - logging that tracks changes to the HKLM\SOFTWARE\Microsoft\Windows Portable Devices\Devices\ - or HKLM\System\CurrentControlSet\Enum\SWD\WPDBUSENUM\ registry keys. Ensure that - the field from the event logs is being mapped to the proper fields in the Endpoint.Registry - data model. -known_false_positives: Legitimate USB activity will also be detected. Please verify - and investigate as appropriate. + - Sysmon EventID 12 + - Sysmon EventID 13 +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime, max(_time) as lastTime, count FROM datamodel=Endpoint.Registry + WHERE Registry.registry_path IN ("HKLM\\SOFTWARE\\Microsoft\\Windows Portable Devices\\Devices\\*","HKLM\\System\\CurrentControlSet\\Enum\\SWD\\WPDBUSENUM\\*") + AND + Registry.registry_value_name ="FriendlyName" + AND + Registry.registry_path="*USBSTOR*" + BY Registry.action Registry.dest Registry.process_guid + Registry.process_id Registry.registry_hive Registry.registry_path + Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name + Registry.registry_value_type Registry.status Registry.user + Registry.vendor_product + | `drop_dm_object_name(Registry)` + | eval object_handle = registry_value_data, object_name = replace(mvindex(split(mvindex(split(registry_path, "??"),1),"&"),2),"PROD_","") + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_wpdbusenum_registry_key_modification_filter` +how_to_implement: To successfully implement this search, you must ingest endpoint logging that tracks changes to the HKLM\SOFTWARE\Microsoft\Windows Portable Devices\Devices\ or HKLM\System\CurrentControlSet\Enum\SWD\WPDBUSENUM\ registry keys. Ensure that the field from the event logs is being mapped to the proper fields in the Endpoint.Registry data model. +known_false_positives: Legitimate USB activity will also be detected. Please verify and investigate as appropriate. references: -- https://attack.mitre.org/techniques/T1200/ -- https://www.cisa.gov/news-events/news/using-caution-usb-drives -- https://www.bleepingcomputer.com/news/security/fbi-hackers-use-badusb-to-target-defense-firms-with-ransomware/ + - https://attack.mitre.org/techniques/T1200/ + - https://www.cisa.gov/news-events/news/using-caution-usb-drives + - https://www.bleepingcomputer.com/news/security/fbi-hackers-use-badusb-to-target-defense-firms-with-ransomware/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate USB events on $dest$ - search: '| from datamodel:Endpoint.Registry | search dest=$dest$ registry_path IN - ("HKLM\\SOFTWARE\\Microsoft\\Windows Portable Devices\\Devices\\*","HKLM\\System\\CurrentControlSet\\Enum\\SWD\\WPDBUSENUM\\*")' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate USB events on $dest$ + search: '| from datamodel:Endpoint.Registry | search dest=$dest$ registry_path IN ("HKLM\\SOFTWARE\\Microsoft\\Windows Portable Devices\\Devices\\*","HKLM\\System\\CurrentControlSet\\Enum\\SWD\\WPDBUSENUM\\*")' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A removable storage device named [$object_name$] with drive letter [$object_handle$] - was attached to $dest$ - risk_objects: - - field: dest - type: system - score: 10 - threat_objects: - - field: object_name - type: registry_value_name - - field: object_handle - type: registry_value_text + message: A removable storage device named [$object_name$] with drive letter [$object_handle$] was attached to $dest$ + risk_objects: + - field: dest + type: system + score: 10 + threat_objects: + - field: object_name + type: registry_value_name + - field: object_handle + type: registry_value_text tags: - analytic_story: - - Data Protection - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1200 - - T1025 - - T1091 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Protection + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1200 + - T1025 + - T1091 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1200/sysmon_usb_use_execution/sysmon_usb_use_execution.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1200/sysmon_usb_use_execution/sysmon_usb_use_execution.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/windows_wsus_spawning_shell.yml b/detections/endpoint/windows_wsus_spawning_shell.yml index 1d6a82efeb..69ef1b35b5 100644 --- a/detections/endpoint/windows_wsus_spawning_shell.yml +++ b/detections/endpoint/windows_wsus_spawning_shell.yml @@ -1,94 +1,77 @@ name: Windows WSUS Spawning Shell id: 76ea28ac-6f10-43fd-b5fe-340022ad0fd3 -version: 1 -date: '2025-10-24' +version: 2 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies instances where a shell (PowerShell.exe - or Cmd.exe) is spawned from wsusservice.exe, the Windows Server Update Services - process. This detection leverages data from Endpoint Detection and Response (EDR) - agents, focusing on process creation events where the parent process is wsusservice.exe. - This activity is significant as it may indicate exploitation of CVE-2025-59287, a - critical deserialization vulnerability in WSUS that allows unauthenticated remote - code execution. If confirmed malicious, this behavior could allow attackers to - execute arbitrary commands on WSUS servers, potentially leading to system compromise, - data exfiltration, domain enumeration, or further lateral movement within the network. +description: The following analytic identifies instances where a shell (PowerShell.exe or Cmd.exe) is spawned from wsusservice.exe, the Windows Server Update Services process. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where the parent process is wsusservice.exe. This activity is significant as it may indicate exploitation of CVE-2025-59287, a critical deserialization vulnerability in WSUS that allows unauthenticated remote code execution. If confirmed malicious, this behavior could allow attackers to execute arbitrary commands on WSUS servers, potentially leading to system compromise, data exfiltration, domain enumeration, or further lateral movement within the network. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count values(Processes.process_name) - as process_name values(Processes.process) as process min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wsusservice.exe - AND `process_cmd` OR `process_powershell` by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_wsus_spawning_shell_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Legitimate WSUS maintenance scripts or administrative tools - may spawn shells in rare cases. However, wsusservice.exe spawning interactive shells - is highly abnormal behavior. Review the command line arguments and user context - to determine legitimacy. Filter known administrative scripts if needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name=wsusservice.exe + AND + `process_cmd` + OR + `process_powershell` + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_wsus_spawning_shell_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Legitimate WSUS maintenance scripts or administrative tools may spawn shells in rare cases. However, wsusservice.exe spawning interactive shells is highly abnormal behavior. Review the command line arguments and user context to determine legitimacy. Filter known administrative scripts if needed. references: -- https://www.huntress.com/blog/exploitation-of-windows-server-update-services-remote-code-execution-vulnerability -- https://msrc.microsoft.com/update-guide/vulnerability/CVE-2025-59287 -- https://hawktrace.com/blog/CVE-2025-59287-UNAUTH + - https://www.huntress.com/blog/exploitation-of-windows-server-update-services-remote-code-execution-vulnerability + - https://msrc.microsoft.com/update-guide/vulnerability/CVE-2025-59287 + - https://hawktrace.com/blog/CVE-2025-59287-UNAUTH drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: WSUS service process wsusservice.exe spawned shell process $process_name$ - on $dest$ by $user$, indicating possible CVE-2025-59287 exploitation - risk_objects: - - field: dest - type: system - score: 90 - - field: user - type: user - score: 80 - threat_objects: - - field: process_name - type: process_name + message: WSUS service process wsusservice.exe spawned shell process $process_name$ on $dest$ by $user$, indicating possible CVE-2025-59287 exploitation + risk_objects: + - field: dest + type: system + score: 90 + - field: user + type: user + score: 80 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Microsoft WSUS CVE-2025-59287 - asset_type: Endpoint - cve: - - CVE-2025-59287 - mitre_attack_id: - - T1190 - - T1505.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Microsoft WSUS CVE-2025-59287 + asset_type: Endpoint + cve: + - CVE-2025-59287 + mitre_attack_id: + - T1190 + - T1505.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/wsus-windows-sysmon.log - sourcetype: XmlWinEventLog - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/wsus-windows-sysmon.log + sourcetype: XmlWinEventLog + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational diff --git a/detections/endpoint/winevent_scheduled_task_created_to_spawn_shell.yml b/detections/endpoint/winevent_scheduled_task_created_to_spawn_shell.yml index 3c38612615..53ce48b144 100644 --- a/detections/endpoint/winevent_scheduled_task_created_to_spawn_shell.yml +++ b/detections/endpoint/winevent_scheduled_task_created_to_spawn_shell.yml @@ -1,86 +1,71 @@ name: WinEvent Scheduled Task Created to Spawn Shell id: 203ef0ea-9bd8-11eb-8201-acde48001122 -version: 16 -date: '2025-10-31' +version: 17 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the creation of scheduled tasks - designed to execute commands using native Windows shells like PowerShell, Cmd, - Wscript, or Cscript. It leverages Windows Security EventCode 4698 to identify - when such tasks are registered. This activity is significant as it may - indicate an attempt to establish persistence or execute malicious commands on - a system. If confirmed malicious, this could allow an attacker to maintain - access, execute arbitrary code, or escalate privileges, posing a severe threat - to the environment. +description: The following analytic detects the creation of scheduled tasks designed to execute commands using native Windows shells like PowerShell, Cmd, Wscript, or Cscript. It leverages Windows Security EventCode 4698 to identify when such tasks are registered. This activity is significant as it may indicate an attempt to establish persistence or execute malicious commands on a system. If confirmed malicious, this could allow an attacker to maintain access, execute arbitrary code, or escalate privileges, posing a severe threat to the environment. data_source: -- Windows Event Log Security 4698 -search: '`wineventlog_security` EventCode=4698 TaskContent IN ("*powershell.exe*", - "*wscript.exe*", "*cscript.exe*", "*cmd.exe*", "*sh.exe*", "*ksh.exe*", "*zsh.exe*", - "*bash.exe*", "*scrcons.exe*", "*pwsh.exe*") | stats count min(_time) as firstTime - max(_time) as lastTime by Computer, TaskName, TaskContent | rename Computer as dest - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winevent_scheduled_task_created_to_spawn_shell_filter`' -how_to_implement: To successfully implement this search, you need to be - ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows - TA is also required. -known_false_positives: False positives are possible if legitimate applications - are allowed to register tasks that call a shell to be spawned. Filter as - needed based on command-line or processes that are used legitimately. + - Windows Event Log Security 4698 +search: |- + `wineventlog_security` EventCode=4698 TaskContent IN ("*powershell.exe*", "*wscript.exe*", "*cscript.exe*", "*cmd.exe*", "*sh.exe*", "*ksh.exe*", "*zsh.exe*", "*bash.exe*", "*scrcons.exe*", "*pwsh.exe*") + | stats count min(_time) as firstTime max(_time) as lastTime + BY Computer, TaskName, TaskContent + | rename Computer as dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `winevent_scheduled_task_created_to_spawn_shell_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also required. +known_false_positives: False positives are possible if legitimate applications are allowed to register tasks that call a shell to be spawned. Filter as needed based on command-line or processes that are used legitimately. references: -- https://research.checkpoint.com/2021/irans-apt34-returns-with-an-updated-arsenal/ -- https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4698 -- https://redcanary.com/threat-detection-report/techniques/scheduled-task-job/ -- https://docs.microsoft.com/en-us/windows/win32/taskschd/time-trigger-example--scripting-?redirectedfrom=MSDN + - https://research.checkpoint.com/2021/irans-apt34-returns-with-an-updated-arsenal/ + - https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4698 + - https://redcanary.com/threat-detection-report/techniques/scheduled-task-job/ + - https://docs.microsoft.com/en-us/windows/win32/taskschd/time-trigger-example--scripting-?redirectedfrom=MSDN drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: 'A Windows Scheduled Task was created (task name=$TaskName$) on $dest$ - with the following contents: $TaskContent$' - risk_objects: - - field: dest - type: system - score: 70 - threat_objects: [] + message: 'A Windows Scheduled Task was created (task name=$TaskName$) on $dest$ with the following contents: $TaskContent$' + risk_objects: + - field: dest + type: system + score: 70 + threat_objects: [] tags: - analytic_story: - - CISA AA22-257A - - China-Nexus Threat Activity - - Compromised Windows Host - - Medusa Ransomware - - Ransomware - - Ryuk Ransomware - - Salt Typhoon - - Scheduled Tasks - - SystemBC - - Windows Error Reporting Service Elevation of Privilege Vulnerability - - Windows Persistence Techniques - - Winter Vivern - - 0bj3ctivity Stealer - - Castle RAT - asset_type: Endpoint - mitre_attack_id: - - T1053.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA22-257A + - China-Nexus Threat Activity + - Compromised Windows Host + - Medusa Ransomware + - Ransomware + - Ryuk Ransomware + - Salt Typhoon + - Scheduled Tasks + - SystemBC + - Windows Error Reporting Service Elevation of Privilege Vulnerability + - Windows Persistence Techniques + - Winter Vivern + - 0bj3ctivity Stealer + - Castle RAT + asset_type: Endpoint + mitre_attack_id: + - T1053.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/winevent_scheduled_task_created_to_spawn_shell/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/winevent_scheduled_task_created_to_spawn_shell/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/winevent_scheduled_task_created_within_public_path.yml b/detections/endpoint/winevent_scheduled_task_created_within_public_path.yml index f51e57a96d..69b1a652e1 100644 --- a/detections/endpoint/winevent_scheduled_task_created_within_public_path.yml +++ b/detections/endpoint/winevent_scheduled_task_created_within_public_path.yml @@ -1,107 +1,90 @@ name: WinEvent Scheduled Task Created Within Public Path id: 5d9c6eee-988c-11eb-8253-acde48001122 -version: 21 -date: '2025-12-10' +version: 22 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects the creation of scheduled tasks - within user-writable paths using Windows Security EventCode 4698. It - identifies tasks registered via schtasks.exe or TaskService that execute - commands from directories like Public, ProgramData, Temp, and AppData. This - behavior is significant as it may indicate an attempt to establish persistence - or execute unauthorized commands. If confirmed malicious, an attacker could - maintain long-term access, escalate privileges, or execute arbitrary code, - posing a severe threat to system integrity and security. +description: The following analytic detects the creation of scheduled tasks within user-writable paths using Windows Security EventCode 4698. It identifies tasks registered via schtasks.exe or TaskService that execute commands from directories like Public, ProgramData, Temp, and AppData. This behavior is significant as it may indicate an attempt to establish persistence or execute unauthorized commands. If confirmed malicious, an attacker could maintain long-term access, escalate privileges, or execute arbitrary code, posing a severe threat to system integrity and security. data_source: -- Windows Event Log Security 4698 + - Windows Event Log Security 4698 search: | - `wineventlog_security` - EventCode=4698 - TaskContent IN ( - "*\\users\\public\\*", "*\\programdata\\*", "*\\temp\\*", - "*\\Windows\\Tasks\\*", "*\\appdata\\*", "*\\perflogs\\*" - ) - | stats count min(_time) as firstTime max(_time) as lastTime - by Computer, TaskName, TaskContent, user - | rename Computer as dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `winevent_scheduled_task_created_within_public_path_filter` -how_to_implement: To successfully implement this search, you need to be - ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows - TA is also required. -known_false_positives: False positives are possible if legitimate applications - are allowed to register tasks in public paths. Filter as needed based on paths - that are used legitimately. + `wineventlog_security` + EventCode=4698 + TaskContent IN ( + "*\\users\\public\\*", "*\\programdata\\*", "*\\temp\\*", + "*\\Windows\\Tasks\\*", "*\\appdata\\*", "*\\perflogs\\*" + ) + | stats count min(_time) as firstTime max(_time) as lastTime + by Computer, TaskName, TaskContent, user + | rename Computer as dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `winevent_scheduled_task_created_within_public_path_filter` +how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also required. +known_false_positives: False positives are possible if legitimate applications are allowed to register tasks in public paths. Filter as needed based on paths that are used legitimately. references: -- https://research.checkpoint.com/2021/irans-apt34-returns-with-an-updated-arsenal/ -- https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4698 -- https://redcanary.com/threat-detection-report/techniques/scheduled-task-job/ -- https://docs.microsoft.com/en-us/windows/win32/taskschd/time-trigger-example--scripting-?redirectedfrom=MSDN -- https://app.any.run/tasks/e26f1b2e-befa-483b-91d2-e18636e2faf3/ + - https://research.checkpoint.com/2021/irans-apt34-returns-with-an-updated-arsenal/ + - https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4698 + - https://redcanary.com/threat-detection-report/techniques/scheduled-task-job/ + - https://docs.microsoft.com/en-us/windows/win32/taskschd/time-trigger-example--scripting-?redirectedfrom=MSDN + - https://app.any.run/tasks/e26f1b2e-befa-483b-91d2-e18636e2faf3/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A windows scheduled task was created (task name=$TaskName$) on $dest$ - risk_objects: - - field: dest - type: system - score: 70 - threat_objects: [] + message: A windows scheduled task was created (task name=$TaskName$) on $dest$ + risk_objects: + - field: dest + type: system + score: 70 + threat_objects: [] tags: - analytic_story: - - Data Destruction - - Winter Vivern - - Industroyer2 - - Compromised Windows Host - - Quasar RAT - - China-Nexus Threat Activity - - XWorm - - Ransomware - - IcedID - - CISA AA23-347A - - Salt Typhoon - - Ryuk Ransomware - - Active Directory Lateral Movement - - Malicious Inno Setup Loader - - CISA AA22-257A - - Medusa Ransomware - - SystemBC - - Scheduled Tasks - - Prestige Ransomware - - AsyncRAT - - Windows Persistence Techniques - - 0bj3ctivity Stealer - - APT37 Rustonotto and FadeStealer - - Castle RAT - - ValleyRAT - - PlugX - - Remcos - asset_type: Endpoint - mitre_attack_id: - - T1053.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Destruction + - Winter Vivern + - Industroyer2 + - Compromised Windows Host + - Quasar RAT + - China-Nexus Threat Activity + - XWorm + - Ransomware + - IcedID + - CISA AA23-347A + - Salt Typhoon + - Ryuk Ransomware + - Active Directory Lateral Movement + - Malicious Inno Setup Loader + - CISA AA22-257A + - Medusa Ransomware + - SystemBC + - Scheduled Tasks + - Prestige Ransomware + - AsyncRAT + - Windows Persistence Techniques + - 0bj3ctivity Stealer + - APT37 Rustonotto and FadeStealer + - Castle RAT + - ValleyRAT + - PlugX + - Remcos + asset_type: Endpoint + mitre_attack_id: + - T1053.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/winevent_scheduled_task_created_to_spawn_shell/windows-xml.log - source: XmlWinEventLog:Security - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/winevent_scheduled_task_created_to_spawn_shell/windows-xml.log + source: XmlWinEventLog:Security + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/winevent_windows_task_scheduler_event_action_started.yml b/detections/endpoint/winevent_windows_task_scheduler_event_action_started.yml index a12388e078..dbaedf7e83 100644 --- a/detections/endpoint/winevent_windows_task_scheduler_event_action_started.yml +++ b/detections/endpoint/winevent_windows_task_scheduler_event_action_started.yml @@ -5,63 +5,50 @@ date: '2026-02-09' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic detects the execution of tasks registered in Windows - Task Scheduler by monitoring EventID 200 (action run) and 201 (action completed) - from the Task Scheduler logs. This detection leverages Task Scheduler logs to identify - potentially suspicious or unauthorized task executions. Monitoring these events - is significant for a SOC as it helps uncover evasive techniques used for persistence, - unauthorized code execution, or other malicious activities. If confirmed malicious, - this activity could lead to unauthorized access, data exfiltration, or the execution - of harmful payloads, posing a significant threat to the environment. +description: The following analytic detects the execution of tasks registered in Windows Task Scheduler by monitoring EventID 200 (action run) and 201 (action completed) from the Task Scheduler logs. This detection leverages Task Scheduler logs to identify potentially suspicious or unauthorized task executions. Monitoring these events is significant for a SOC as it helps uncover evasive techniques used for persistence, unauthorized code execution, or other malicious activities. If confirmed malicious, this activity could lead to unauthorized access, data exfiltration, or the execution of harmful payloads, posing a significant threat to the environment. data_source: -- Windows Event Log TaskScheduler 200 -- Windows Event Log TaskScheduler 201 -search: '`wineventlog_task_scheduler` EventCode IN ("200","201") | stats count min(_time) - as firstTime max(_time) as lastTime by TaskName dvc EventCode | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `winevent_windows_task_scheduler_event_action_started_filter`' -how_to_implement: Task Scheduler logs are required to be collected. Enable logging - with inputs.conf by adding a stanza for [WinEventLog://Microsoft-Windows-TaskScheduler/Operational] - and renderXml=false. Note, not translating it in XML may require a proper extraction - of specific items in the Message. -known_false_positives: False positives will be present. Filter based on ActionName - paths or specify keywords of interest. + - Windows Event Log TaskScheduler 200 + - Windows Event Log TaskScheduler 201 +search: '`wineventlog_task_scheduler` EventCode IN ("200","201") | stats count min(_time) as firstTime max(_time) as lastTime by TaskName dvc EventCode | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winevent_windows_task_scheduler_event_action_started_filter`' +how_to_implement: Task Scheduler logs are required to be collected. Enable logging with inputs.conf by adding a stanza for [WinEventLog://Microsoft-Windows-TaskScheduler/Operational] and renderXml=false. Note, not translating it in XML may require a proper extraction of specific items in the Message. +known_false_positives: False positives will be present. Filter based on ActionName paths or specify keywords of interest. references: -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1053.005/T1053.005.md -- https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1053.005/T1053.005.md + - https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ tags: - analytic_story: - - SolarWinds WHD RCE Post Exploitation - - IcedID - - BlackSuit Ransomware - - Windows Persistence Techniques - - Prestige Ransomware - - Winter Vivern - - CISA AA22-257A - - Amadey - - AsyncRAT - - ValleyRAT - - SystemBC - - Malicious Inno Setup Loader - - Scheduled Tasks - - Data Destruction - - CISA AA24-241A - - DarkCrystal RAT - - Qakbot - - Sandworm Tools - - Industroyer2 - - PlugX - - Remcos - asset_type: Endpoint - mitre_attack_id: - - T1053.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - SolarWinds WHD RCE Post Exploitation + - IcedID + - BlackSuit Ransomware + - Windows Persistence Techniques + - Prestige Ransomware + - Winter Vivern + - CISA AA22-257A + - Amadey + - AsyncRAT + - ValleyRAT + - SystemBC + - Malicious Inno Setup Loader + - Scheduled Tasks + - Data Destruction + - CISA AA24-241A + - DarkCrystal RAT + - Qakbot + - Sandworm Tools + - Industroyer2 + - PlugX + - Remcos + asset_type: Endpoint + mitre_attack_id: + - T1053.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/winevent_windows_task_scheduler_event_action_started/windows-xml.log - source: XmlWinEventLog:Microsoft-Windows-TaskScheduler/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/winevent_windows_task_scheduler_event_action_started/windows-xml.log + source: XmlWinEventLog:Microsoft-Windows-TaskScheduler/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/winhlp32_spawning_a_process.yml b/detections/endpoint/winhlp32_spawning_a_process.yml index f9e0927926..f8c204adc9 100644 --- a/detections/endpoint/winhlp32_spawning_a_process.yml +++ b/detections/endpoint/winhlp32_spawning_a_process.yml @@ -5,87 +5,56 @@ date: '2025-05-02' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects winhlp32.exe spawning a child process - that loads a file from appdata, programdata, or temp directories. This detection - leverages data from Endpoint Detection and Response (EDR) agents, focusing on process - creation events. This activity is significant because winhlp32.exe has known vulnerabilities - and can be exploited to execute malicious code. If confirmed malicious, an attacker - could use this technique to execute arbitrary scripts, escalate privileges, or maintain - persistence within the environment. Analysts should review parallel processes, module - loads, and file modifications for further suspicious behavior. +description: The following analytic detects winhlp32.exe spawning a child process that loads a file from appdata, programdata, or temp directories. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events. This activity is significant because winhlp32.exe has known vulnerabilities and can be exploited to execute malicious code. If confirmed malicious, an attacker could use this technique to execute arbitrary scripts, escalate privileges, or maintain persistence within the environment. Analysts should review parallel processes, module loads, and file modifications for further suspicious behavior. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=winhlp32.exe - Processes.process IN ("*\\appdata\\*","*\\programdata\\*", "*\\temp\\*") by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `winhlp32_spawning_a_process_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: False positives should be limited as winhlp32.exe is typically - not used with the latest flavors of Windows OS. However, filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=winhlp32.exe Processes.process IN ("*\\appdata\\*","*\\programdata\\*", "*\\temp\\*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winhlp32_spawning_a_process_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: False positives should be limited as winhlp32.exe is typically not used with the latest flavors of Windows OS. However, filter as needed. references: -- https://www.exploit-db.com/exploits/16541 -- https://tria.ge/210929-ap75vsddan -- https://www.virustotal.com/gui/file/cb77b93150cb0f7fe65ce8a7e2a5781e727419451355a7736db84109fa215a89 + - https://www.exploit-db.com/exploits/16541 + - https://tria.ge/210929-ap75vsddan + - https://www.virustotal.com/gui/file/cb77b93150cb0f7fe65ce8a7e2a5781e727419451355a7736db84109fa215a89 drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$, and is not typical activity for this process. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$, and is not typical activity for this process. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Remcos - - Compromised Windows Host - asset_type: Endpoint - mitre_attack_id: - - T1055 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Remcos + - Compromised Windows Host + asset_type: Endpoint + mitre_attack_id: + - T1055 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/winrar_spawning_shell_application.yml b/detections/endpoint/winrar_spawning_shell_application.yml index 6799699221..68a51596e8 100644 --- a/detections/endpoint/winrar_spawning_shell_application.yml +++ b/detections/endpoint/winrar_spawning_shell_application.yml @@ -1,98 +1,78 @@ name: WinRAR Spawning Shell Application id: d2f36034-37fa-4bd4-8801-26807c15540f -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -description: The following analytic detects the execution of Windows shell processes - initiated by WinRAR, such as "cmd.exe", "powershell.exe", "certutil.exe", "mshta.exe", - or "bitsadmin.exe". This detection leverages data from Endpoint Detection and Response - (EDR) agents, focusing on process and parent process relationships. This activity - is significant because it may indicate exploitation of the WinRAR CVE-2023-38831 - vulnerability, where malicious scripts are executed from spoofed ZIP archives. If - confirmed malicious, this could lead to unauthorized access, financial loss, and - further malicious activities like data theft or ransomware attacks. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=winrar.exe - `windows_shells` OR Processes.process_name IN ("certutil.exe","mshta.exe","bitsadmin.exe") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `winrar_spawning_shell_application_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Be aware of potential false positives - legitimate uses of - WinRAR and the listed processes in your environment may cause benign activities - to be flagged. Upon triage, review the destination, user, parent process, and process - name involved in the flagged activity. Capture and inspect any relevant on-disk - artifacts, and look for concurrent processes to identify the attack source. This - approach helps analysts detect potential threats earlier and mitigate the risks. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +description: The following analytic detects the execution of Windows shell processes initiated by WinRAR, such as "cmd.exe", "powershell.exe", "certutil.exe", "mshta.exe", or "bitsadmin.exe". This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process relationships. This activity is significant because it may indicate exploitation of the WinRAR CVE-2023-38831 vulnerability, where malicious scripts are executed from spoofed ZIP archives. If confirmed malicious, this could lead to unauthorized access, financial loss, and further malicious activities like data theft or ransomware attacks. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name=winrar.exe `windows_shells` + OR + Processes.process_name IN ("certutil.exe","mshta.exe","bitsadmin.exe") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `winrar_spawning_shell_application_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Be aware of potential false positives - legitimate uses of WinRAR and the listed processes in your environment may cause benign activities to be flagged. Upon triage, review the destination, user, parent process, and process name involved in the flagged activity. Capture and inspect any relevant on-disk artifacts, and look for concurrent processes to identify the attack source. This approach helps analysts detect potential threats earlier and mitigate the risks. references: -- https://www.group-ib.com/blog/cve-2023-38831-winrar-zero-day/ -- https://github.com/BoredHackerBlog/winrar_CVE-2023-38831_lazy_poc -- https://github.com/b1tg/CVE-2023-38831-winrar-exploit + - https://www.group-ib.com/blog/cve-2023-38831-winrar-zero-day/ + - https://github.com/BoredHackerBlog/winrar_CVE-2023-38831_lazy_poc + - https://github.com/b1tg/CVE-2023-38831-winrar-exploit drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ attempting to decode a file. - risk_objects: - - field: user - type: user - score: 70 - - field: dest - type: system - score: 70 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to decode a file. + risk_objects: + - field: user + type: user + score: 70 + - field: dest + type: system + score: 70 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Compromised Windows Host - - WinRAR Spoofing Attack CVE-2023-38831 - cve: - - CVE-2023-38831 - asset_type: Endpoint - atomic_guid: [] - mitre_attack_id: - - T1105 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - WinRAR Spoofing Attack CVE-2023-38831 + cve: + - CVE-2023-38831 + asset_type: Endpoint + atomic_guid: [] + mitre_attack_id: + - T1105 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/winrar.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/winrar.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/winrm_spawning_a_process.yml b/detections/endpoint/winrm_spawning_a_process.yml index 633e949f37..0eeb3996b9 100644 --- a/detections/endpoint/winrm_spawning_a_process.yml +++ b/detections/endpoint/winrm_spawning_a_process.yml @@ -1,69 +1,58 @@ name: WinRM Spawning a Process id: a081836a-ba4d-11eb-8593-acde48001122 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Drew Church, Michael Haag, Splunk status: experimental type: TTP -description: The following analytic detects suspicious processes spawned by WinRM - (wsmprovhost.exe). It leverages data from Endpoint Detection and Response (EDR) - agents, focusing on specific child processes like cmd.exe, powershell.exe, and others. - This activity is significant as it may indicate exploitation attempts of vulnerabilities - like CVE-2021-31166, which could lead to system instability or compromise. If confirmed - malicious, attackers could execute arbitrary commands, escalate privileges, or maintain - persistence, posing a severe threat to the environment. +description: The following analytic detects suspicious processes spawned by WinRM (wsmprovhost.exe). It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific child processes like cmd.exe, powershell.exe, and others. This activity is significant as it may indicate exploitation attempts of vulnerabilities like CVE-2021-31166, which could lead to system instability or compromise. If confirmed malicious, attackers could execute arbitrary commands, escalate privileges, or maintain persistence, posing a severe threat to the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wsmprovhost.exe - Processes.process_name IN ("cmd.exe","sh.exe","bash.exe","powershell.exe","pwsh.exe","schtasks.exe","certutil.exe","whoami.exe","bitsadmin.exe","scp.exe") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `winrm_spawning_a_process_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: No false positives have been identified at this time. - system management software may spawn processes from `wsmprovhost.exe`. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name=wsmprovhost.exe Processes.process_name IN ("cmd.exe","sh.exe","bash.exe","powershell.exe","pwsh.exe","schtasks.exe","certutil.exe","whoami.exe","bitsadmin.exe","scp.exe") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `winrm_spawning_a_process_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: No false positives have been identified at this time. system management software may spawn processes from `wsmprovhost.exe`. references: -- https://github.com/SigmaHQ/sigma/blob/9b7fb0c0f3af2e53ed483e29e0d0f88ccf1c08ca/rules/windows/process_access/win_susp_shell_spawn_from_winrm.yml -- https://www.zerodayinitiative.com/blog/2021/5/17/cve-2021-31166-a-wormable-code-execution-bug-in-httpsys -- https://github.com/0vercl0k/CVE-2021-31166/blob/main/cve-2021-31166.py + - https://github.com/SigmaHQ/sigma/blob/9b7fb0c0f3af2e53ed483e29e0d0f88ccf1c08ca/rules/windows/process_access/win_susp_shell_spawn_from_winrm.yml + - https://www.zerodayinitiative.com/blog/2021/5/17/cve-2021-31166-a-wormable-code-execution-bug-in-httpsys + - https://github.com/0vercl0k/CVE-2021-31166/blob/main/cve-2021-31166.py rba: - message: winrm.exe spawning a process observed on $dest$ - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: winrm.exe spawning a process observed on $dest$ + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - CISA AA23-347A - - Rhysida Ransomware - - Unusual Processes - - Microsoft WSUS CVE-2025-59287 - asset_type: Endpoint - cve: - - CVE-2021-31166 - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA23-347A + - Rhysida Ransomware + - Unusual Processes + - Microsoft WSUS CVE-2025-59287 + asset_type: Endpoint + cve: + - CVE-2021-31166 + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/wmi_permanent_event_subscription.yml b/detections/endpoint/wmi_permanent_event_subscription.yml index 41b473a98d..03f0f82373 100644 --- a/detections/endpoint/wmi_permanent_event_subscription.yml +++ b/detections/endpoint/wmi_permanent_event_subscription.yml @@ -1,45 +1,41 @@ name: WMI Permanent Event Subscription id: 71bfdb13-f200-4c6c-b2c9-a2e07adf437d -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Rico Valdez, Splunk status: experimental type: TTP -description: The following analytic detects the creation of permanent event subscriptions - using Windows Management Instrumentation (WMI). It leverages Sysmon EventID 5 data - to identify instances where the event consumers are not the expected "NTEventLogEventConsumer." - This activity is significant because it suggests an attacker is attempting to achieve - persistence by running malicious scripts or binaries in response to specific system - events. If confirmed malicious, this could lead to severe impacts such as data theft, - ransomware deployment, or other damaging outcomes. Investigate the associated scripts - or binaries to identify the source of the attack. +description: The following analytic detects the creation of permanent event subscriptions using Windows Management Instrumentation (WMI). It leverages Sysmon EventID 5 data to identify instances where the event consumers are not the expected "NTEventLogEventConsumer." This activity is significant because it suggests an attacker is attempting to achieve persistence by running malicious scripts or binaries in response to specific system events. If confirmed malicious, this could lead to severe impacts such as data theft, ransomware deployment, or other damaging outcomes. Investigate the associated scripts or binaries to identify the source of the attack. data_source: [] -search: '`wmi` EventCode=5861 Binding | rex field=Message "Consumer =\s+(?[^;|^$]+)" - | search consumer!="NTEventLogEventConsumer=\"SCM Event Log Consumer\"" | stats - count min(_time) as firstTime max(_time) as lastTime by ComputerName, consumer, - Message | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | rename ComputerName as dest | `wmi_permanent_event_subscription_filter`' -how_to_implement: To successfully implement this search, you must be ingesting the - Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on - the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. -known_false_positives: Although unlikely, administrators may use event subscriptions - for legitimate purposes. +search: |- + `wmi` EventCode=5861 Binding + | rex field=Message "Consumer =\s+(?[^; + | ^$]+)" + | search consumer!="NTEventLogEventConsumer=\"SCM Event Log Consumer\"" + | stats count min(_time) as firstTime max(_time) as lastTime + BY ComputerName, consumer, Message + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | rename ComputerName as dest + | `wmi_permanent_event_subscription_filter` +how_to_implement: To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. +known_false_positives: Although unlikely, administrators may use event subscriptions for legitimate purposes. references: [] rba: - message: WMI Permanent Event Subscription detected on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: WMI Permanent Event Subscription detected on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Suspicious WMI Use - asset_type: Endpoint - mitre_attack_id: - - T1047 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious WMI Use + asset_type: Endpoint + mitre_attack_id: + - T1047 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/wmi_permanent_event_subscription___sysmon.yml b/detections/endpoint/wmi_permanent_event_subscription___sysmon.yml index c1c779e0ea..8d96e0cdac 100644 --- a/detections/endpoint/wmi_permanent_event_subscription___sysmon.yml +++ b/detections/endpoint/wmi_permanent_event_subscription___sysmon.yml @@ -1,73 +1,65 @@ name: WMI Permanent Event Subscription - Sysmon id: ad05aae6-3b2a-4f73-af97-57bd26cee3b9 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: Rico Valdez, Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies the creation of WMI permanent event - subscriptions, which can be used to establish persistence or perform privilege escalation. - It leverages Sysmon data, specifically EventCodes 19, 20, and 21, to detect the - creation of WMI EventFilters, EventConsumers, and FilterToConsumerBindings. This - activity is significant as it may indicate an attacker setting up mechanisms to - execute code with elevated SYSTEM privileges when specific events occur. If confirmed - malicious, this could allow the attacker to maintain persistence, escalate privileges, - and execute arbitrary code, posing a severe threat to the environment. +description: The following analytic identifies the creation of WMI permanent event subscriptions, which can be used to establish persistence or perform privilege escalation. It leverages Sysmon data, specifically EventCodes 19, 20, and 21, to detect the creation of WMI EventFilters, EventConsumers, and FilterToConsumerBindings. This activity is significant as it may indicate an attacker setting up mechanisms to execute code with elevated SYSTEM privileges when specific events occur. If confirmed malicious, this could allow the attacker to maintain persistence, escalate privileges, and execute arbitrary code, posing a severe threat to the environment. data_source: -- Sysmon EventID 21 -search: '`sysmon` EventCode=21 | stats count min(_time) as firstTime max(_time) as - lastTime by dest dvc object object_attrs object_category object_path signature signature_id - src status user user_id vendor_product Consumer ConsumerNoQuotes Filter FilterNoQuotes - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `wmi_permanent_event_subscription___sysmon_filter`' -how_to_implement: To successfully implement this search, you must be collecting Sysmon - data using Sysmon version 6.1 or greater and have Sysmon configured to generate - alerts for WMI activity (eventID= 19, 20, 21). In addition, you must have at least - version 6.0.4 of the Sysmon TA installed to properly parse the fields. -known_false_positives: Although unlikely, administrators may use event subscriptions - for legitimate purposes. + - Sysmon EventID 21 +search: |- + `sysmon` EventCode=21 + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest dvc object + object_attrs object_category object_path + signature signature_id src + status user user_id + vendor_product Consumer ConsumerNoQuotes + Filter FilterNoQuotes + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `wmi_permanent_event_subscription___sysmon_filter` +how_to_implement: To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate alerts for WMI activity (eventID= 19, 20, 21). In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields. +known_false_positives: Although unlikely, administrators may use event subscriptions for legitimate purposes. references: -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1546.003/T1546.003.md -- https://www.eideon.com/2018-03-02-THL03-WMIBackdoors/ -- https://github.com/trustedsec/SysmonCommunityGuide/blob/master/chapters/WMI-events.md -- https://in.security/2019/04/03/an-intro-into-abusing-and-identifying-wmi-event-subscriptions-for-persistence/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1546.003/T1546.003.md + - https://www.eideon.com/2018-03-02-THL03-WMIBackdoors/ + - https://github.com/trustedsec/SysmonCommunityGuide/blob/master/chapters/WMI-events.md + - https://in.security/2019/04/03/an-intro-into-abusing-and-identifying-wmi-event-subscriptions-for-persistence/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: WMI Permanent Event Subscription detected on $dest$ by $user$ - risk_objects: - - field: dest - type: system - score: 30 - - field: user - type: user - score: 30 - threat_objects: [] + message: WMI Permanent Event Subscription detected on $dest$ by $user$ + risk_objects: + - field: dest + type: system + score: 30 + - field: user + type: user + score: 30 + threat_objects: [] tags: - analytic_story: - - Suspicious WMI Use - asset_type: Endpoint - mitre_attack_id: - - T1546.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious WMI Use + asset_type: Endpoint + mitre_attack_id: + - T1546.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.003/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.003/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/wmi_recon_running_process_or_services.yml b/detections/endpoint/wmi_recon_running_process_or_services.yml index 747e7d9b3f..0d4eb32a2a 100644 --- a/detections/endpoint/wmi_recon_running_process_or_services.yml +++ b/detections/endpoint/wmi_recon_running_process_or_services.yml @@ -1,81 +1,67 @@ name: WMI Recon Running Process Or Services id: b5cd5526-cce7-11eb-b3bd-acde48001122 -version: 9 -date: '2025-06-24' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: - The following analytic identifies suspicious PowerShell script execution - via EventCode 4104, where WMI performs an event query to list running processes - or services. This detection leverages PowerShell Script Block Logging to capture - and analyze script block text for specific WMI queries. This activity is significant - as it is commonly used by malware and APT actors to map security applications or - services on a compromised machine. If confirmed malicious, this could allow attackers - to identify and potentially disable security defenses, facilitating further compromise - and persistence within the environment. +description: The following analytic identifies suspicious PowerShell script execution via EventCode 4104, where WMI performs an event query to list running processes or services. This detection leverages PowerShell Script Block Logging to capture and analyze script block text for specific WMI queries. This activity is significant as it is commonly used by malware and APT actors to map security applications or services on a compromised machine. If confirmed malicious, this could allow attackers to identify and potentially disable security defenses, facilitating further compromise and persistence within the environment. data_source: - - Powershell Script Block Logging 4104 -search: - '`powershell` EventCode=4104 ScriptBlockText= "*SELECT*" AND (ScriptBlockText="*Win32_Process*" - OR ScriptBlockText="*Win32_Service*") | fillnull | stats count min(_time) as firstTime - max(_time) as lastTime by dest signature signature_id user_id vendor_product EventID - Guid Opcode Name Path ProcessID ScriptBlockId ScriptBlockText | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `wmi_recon_running_process_or_services_filter`' -how_to_implement: - To successfully implement this analytic, you will need to enable - PowerShell Script Block Logging on some or all endpoints. Additional setup here - https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. + - Powershell Script Block Logging 4104 +search: |- + `powershell` EventCode=4104 ScriptBlockText= "*SELECT*" AND (ScriptBlockText="*Win32_Process*" OR ScriptBlockText="*Win32_Service*") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest signature signature_id + user_id vendor_product EventID + Guid Opcode Name + Path ProcessID ScriptBlockId + ScriptBlockText + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `wmi_recon_running_process_or_services_filter` +how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://help.splunk.com/en/security-offerings/splunk-user-behavior-analytics/get-data-in/5.4.1/add-other-data-to-splunk-uba/configure-powershell-logging-to-see-powershell-anomalies-in-splunk-uba. known_false_positives: Network administrator may used this command for checking purposes references: - - https://news.sophos.com/en-us/2020/05/12/maze-ransomware-1-year-counting/ - - https://www.eideon.com/2018-03-02-THL03-WMIBackdoors/ - - https://github.com/trustedsec/SysmonCommunityGuide/blob/master/chapters/WMI-events.md - - https://in.security/2019/04/03/an-intro-into-abusing-and-identifying-wmi-event-subscriptions-for-persistence/ + - https://news.sophos.com/en-us/2020/05/12/maze-ransomware-1-year-counting/ + - https://www.eideon.com/2018-03-02-THL03-WMIBackdoors/ + - https://github.com/trustedsec/SysmonCommunityGuide/blob/master/chapters/WMI-events.md + - https://in.security/2019/04/03/an-intro-into-abusing-and-identifying-wmi-event-subscriptions-for-persistence/ drilldown_searches: - - name: View the detection results for - "$dest$" and "$user_id$" - search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user_id$" + search: '%original_detection_search% | search dest = "$dest$" user_id = "$user_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - Suspicious powerShell script execution by $user_id$ on $dest$ via EventCode - 4104, where WMI is performing an event query looking for running processes or - running services - risk_objects: - - field: dest - type: system - score: 20 - - field: user_id - type: user - score: 20 - threat_objects: [] + message: Suspicious powerShell script execution by $user_id$ on $dest$ via EventCode 4104, where WMI is performing an event query looking for running processes or running services + risk_objects: + - field: dest + type: system + score: 20 + - field: user_id + type: user + score: 20 + threat_objects: [] tags: - analytic_story: - - Malicious PowerShell - - Hermetic Wiper - - Data Destruction - asset_type: Endpoint - mitre_attack_id: - - T1592 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Malicious PowerShell + - Hermetic Wiper + - Data Destruction + asset_type: Endpoint + mitre_attack_id: + - T1592 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/win32process.log - source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/win32process.log + source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/wmi_temporary_event_subscription.yml b/detections/endpoint/wmi_temporary_event_subscription.yml index d1980ecc9f..0bbf428fd1 100644 --- a/detections/endpoint/wmi_temporary_event_subscription.yml +++ b/detections/endpoint/wmi_temporary_event_subscription.yml @@ -1,49 +1,40 @@ name: WMI Temporary Event Subscription id: 38cbd42c-1098-41bb-99cf-9d6d2b296d83 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Rico Valdez, Splunk status: experimental type: TTP -description: The following analytic detects the creation of WMI temporary event subscriptions. - It leverages Windows Event Logs, specifically EventCode 5860, to identify these - activities. This detection is significant because attackers often use WMI to execute - commands, gather information, or maintain persistence within a compromised system. - If confirmed malicious, this activity could allow an attacker to execute arbitrary - code, escalate privileges, or persist in the environment. Analysts should review - the specific WMI queries and assess their intent, considering potential false positives - from legitimate administrative tasks. +description: The following analytic detects the creation of WMI temporary event subscriptions. It leverages Windows Event Logs, specifically EventCode 5860, to identify these activities. This detection is significant because attackers often use WMI to execute commands, gather information, or maintain persistence within a compromised system. If confirmed malicious, this activity could allow an attacker to execute arbitrary code, escalate privileges, or persist in the environment. Analysts should review the specific WMI queries and assess their intent, considering potential false positives from legitimate administrative tasks. data_source: [] -search: "`wmi` EventCode=5860 Temporary | rex field=Message \"NotificationQuery =\\\ - s+(?[^;|^$]+)\" | search query!=\"SELECT * FROM Win32_ProcessStartTrace WHERE - ProcessName = 'wsmprovhost.exe'\" AND query!=\"SELECT * FROM __InstanceOperationEvent - WHERE TargetInstance ISA 'AntiVirusProduct' OR TargetInstance ISA 'FirewallProduct' - OR TargetInstance ISA 'AntiSpywareProduct'\" | stats count min(_time) as firstTime - max(_time) as lastTime by ComputerName, query | `security_content_ctime(firstTime)`| - `security_content_ctime(lastTime)` | `wmi_temporary_event_subscription_filter`" -how_to_implement: To successfully implement this search, you must be ingesting the - Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on - the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. -known_false_positives: Some software may create WMI temporary event subscriptions - for various purposes. The included search contains an exception for two of these - that occur by default on Windows 10 systems. You may need to modify the search to - create exceptions for other legitimate events. +search: |- + `wmi` EventCode=5860 Temporary + | rex field=Message "NotificationQuery =\s+(?[^; + | ^$]+)" + | search query!="SELECT * FROM Win32_ProcessStartTrace WHERE ProcessName = 'wsmprovhost.exe'" AND query!="SELECT * FROM __InstanceOperationEvent WHERE TargetInstance ISA 'AntiVirusProduct' OR TargetInstance ISA 'FirewallProduct' OR TargetInstance ISA 'AntiSpywareProduct'" + | stats count min(_time) as firstTime max(_time) as lastTime + BY ComputerName, query + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `wmi_temporary_event_subscription_filter` +how_to_implement: To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. +known_false_positives: Some software may create WMI temporary event subscriptions for various purposes. The included search contains an exception for two of these that occur by default on Windows 10 systems. You may need to modify the search to create exceptions for other legitimate events. references: [] rba: - message: WMI Temporary event subscription detected on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: WMI Temporary event subscription detected on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Suspicious WMI Use - asset_type: Endpoint - mitre_attack_id: - - T1047 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Suspicious WMI Use + asset_type: Endpoint + mitre_attack_id: + - T1047 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/endpoint/wmic_group_discovery.yml b/detections/endpoint/wmic_group_discovery.yml index 1b2c7a128f..c244c00d03 100644 --- a/detections/endpoint/wmic_group_discovery.yml +++ b/detections/endpoint/wmic_group_discovery.yml @@ -1,92 +1,72 @@ name: Wmic Group Discovery id: 83317b08-155b-11ec-8e00-acde48001122 -version: 8 -date: '2025-08-27' +version: 9 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic identifies the use of `wmic.exe` to - enumerate local groups on an endpoint. This detection leverages data from - Endpoint Detection and Response (EDR) agents, focusing on process execution - logs, including command-line details. Monitoring this activity is significant - as it can indicate reconnaissance efforts by an attacker to understand group - memberships, which could be a precursor to privilege escalation or lateral - movement. If confirmed malicious, this activity could allow an attacker to map - out privileged groups, aiding in further exploitation and persistence within - the environment. +description: The following analytic identifies the use of `wmic.exe` to enumerate local groups on an endpoint. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs, including command-line details. Monitoring this activity is significant as it can indicate reconnaissance efforts by an attacker to understand group memberships, which could be a precursor to privilege escalation or lateral movement. If confirmed malicious, this activity could allow an attacker to map out privileged groups, aiding in further exploitation and persistence within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe - (Processes.process="*group get name*") by Processes.action Processes.dest Processes.original_file_name - Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid - Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path - Processes.process Processes.process_exec Processes.process_guid Processes.process_hash - Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path - Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `wmic_group_discovery_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: Administrators or power users may use this command for - troubleshooting. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=wmic.exe (Processes.process="*group get name*") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `wmic_group_discovery_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators or power users may use this command for troubleshooting. references: -- https://attack.mitre.org/techniques/T1069/001/ -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1069.001/T1069.001.md + - https://attack.mitre.org/techniques/T1069/001/ + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1069.001/T1069.001.md drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$. - risk_objects: - - field: user - type: user - score: 3 - - field: dest - type: system - score: 3 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$. + risk_objects: + - field: user + type: user + score: 3 + - field: dest + type: system + score: 3 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Active Directory Discovery - - LAMEHUG - asset_type: Endpoint - mitre_attack_id: - - T1069.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Discovery + - LAMEHUG + asset_type: Endpoint + mitre_attack_id: + - T1069.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.001/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.001/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/wmic_noninteractive_app_uninstallation.yml b/detections/endpoint/wmic_noninteractive_app_uninstallation.yml index 577a38605a..1303332e6b 100644 --- a/detections/endpoint/wmic_noninteractive_app_uninstallation.yml +++ b/detections/endpoint/wmic_noninteractive_app_uninstallation.yml @@ -1,61 +1,48 @@ name: Wmic NonInteractive App Uninstallation id: bff0e7a0-317f-11ec-ab4e-acde48001122 -version: 9 -date: '2025-05-02' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic identifies the use of the WMIC command-line tool - attempting to uninstall applications non-interactively. It leverages data from Endpoint - Detection and Response (EDR) agents, focusing on specific command-line patterns - associated with WMIC. This activity is significant because it is uncommon and may - indicate an attempt to evade detection by uninstalling security software, as seen - in IcedID malware campaigns. If confirmed malicious, this behavior could allow an - attacker to disable security defenses, facilitating further compromise and persistence - within the environment. +description: The following analytic identifies the use of the WMIC command-line tool attempting to uninstall applications non-interactively. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns associated with WMIC. This activity is significant because it is uncommon and may indicate an attempt to evade detection by uninstalling security software, as seen in IcedID malware campaigns. If confirmed malicious, this behavior could allow an attacker to disable security defenses, facilitating further compromise and persistence within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe - Processes.process="* product *" Processes.process="*where name*" Processes.process="*call - uninstall*" Processes.process="*/nointeractive*" by Processes.action Processes.dest - Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `wmic_noninteractive_app_uninstallation_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Third party application may use this approach to uninstall - applications. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.process_name=wmic.exe Processes.process="* product *" Processes.process="*where name*" Processes.process="*call uninstall*" Processes.process="*/nointeractive*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `wmic_noninteractive_app_uninstallation_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Third party application may use this approach to uninstall applications. references: -- https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ + - https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/ tags: - analytic_story: - - IcedID - - Azorult - asset_type: Endpoint - mitre_attack_id: - - T1562.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - IcedID + - Azorult + asset_type: Endpoint + mitre_attack_id: + - T1562.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon2.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon2.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/wmic_xsl_execution_via_url.yml b/detections/endpoint/wmic_xsl_execution_via_url.yml index 5a3883fafc..0aa503c08c 100644 --- a/detections/endpoint/wmic_xsl_execution_via_url.yml +++ b/detections/endpoint/wmic_xsl_execution_via_url.yml @@ -6,104 +6,96 @@ author: Michael Haag, Splunk status: production type: TTP description: | - The following analytic detects `wmic.exe` loading a remote XSL script - via a URL. This detection leverages Endpoint Detection and Response (EDR) data, - focusing on command-line executions that include HTTP/HTTPS URLs and the /FORMAT - switch. This activity is significant as it indicates a potential application control - bypass, allowing adversaries to execute JScript or VBScript within an XSL file. - If confirmed malicious, this technique can enable attackers to execute arbitrary - code, escalate privileges, or maintain persistence using a trusted Windows tool, - posing a severe threat to the environment. + The following analytic detects `wmic.exe` loading a remote XSL script + via a URL. This detection leverages Endpoint Detection and Response (EDR) data, + focusing on command-line executions that include HTTP/HTTPS URLs and the /FORMAT + switch. This activity is significant as it indicates a potential application control + bypass, allowing adversaries to execute JScript or VBScript within an XSL file. + If confirmed malicious, this technique can enable attackers to execute arbitrary + code, escalate privileges, or maintain persistence using a trusted Windows tool, + posing a severe threat to the environment. data_source: - - Sysmon EventID 1 - - Windows Event Log Security 4688 - - CrowdStrike ProcessRollup2 - - Cisco Network Visibility Module Flow Data + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 + - Cisco Network Visibility Module Flow Data search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where - `process_wmic` - Processes.process IN ("*http://*", "*https://*") - Processes.process="*/format:*" - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process - Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id - Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user - Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `wmic_xsl_execution_via_url_filter` + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where + `process_wmic` + Processes.process IN ("*http://*", "*https://*") + Processes.process="*/format:*" + by Processes.action Processes.dest Processes.original_file_name Processes.parent_process + Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id + Processes.parent_process_name Processes.parent_process_path Processes.process + Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id + Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `wmic_xsl_execution_via_url_filter` how_to_implement: | - The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + The detection is based on data that originates from Endpoint Detection + and Response (EDR) agents. These agents are designed to provide security-related + telemetry from the endpoints where the agent is installed. To implement this search, + you must ingest logs that contain the process GUID, process name, and parent process. + Additionally, you must ingest complete command-line executions. These logs must + be processed using the appropriate Splunk Technology Add-ons that are specific to + the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` + data model. Use the Splunk Common Information Model (CIM) to normalize the field + names and speed up the data modeling process. known_false_positives: | - False positives are limited as legitimate applications typically do not download files or xsl using WMIC. Filter as needed. + False positives are limited as legitimate applications typically do not download files or xsl using WMIC. Filter as needed. references: - - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1220/T1220.md - - https://web.archive.org/web/20190814201250/https://subt0x11.blogspot.com/2018/04/wmicexe-whitelisting-bypass-hacking.html - - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1220/T1220.md#atomic-test-4---wmic-bypass-using-remote-xsl-file - - https://securitydatasets.com/notebooks/atomic/windows/defense_evasion/SDWIN-201017061100.html + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1220/T1220.md + - https://web.archive.org/web/20190814201250/https://subt0x11.blogspot.com/2018/04/wmicexe-whitelisting-bypass-hacking.html + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1220/T1220.md#atomic-test-4---wmic-bypass-using-remote-xsl-file + - https://securitydatasets.com/notebooks/atomic/windows/defense_evasion/SDWIN-201017061100.html drilldown_searches: - - name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ utilizing wmic to download a remote XSL script. - risk_objects: - - field: user - type: user - score: 80 - - field: dest - type: system - score: 80 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ utilizing wmic to download a remote XSL script. + risk_objects: + - field: user + type: user + score: 80 + - field: dest + type: system + score: 80 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - Compromised Windows Host - - Suspicious WMI Use - - Cisco Network Visibility Module Analytics - asset_type: Endpoint - mitre_attack_id: - - T1220 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Compromised Windows Host + - Suspicious WMI Use + - Cisco Network Visibility Module Analytics + asset_type: Endpoint + mitre_attack_id: + - T1220 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1220/atomic_red_team/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog - - name: True Positive Test - Cisco NVM - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log - source: not_applicable - sourcetype: cisco:nvm:flowdata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1220/atomic_red_team/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog + - name: True Positive Test - Cisco NVM + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_network_visibility_module/cisco_nvm_flowdata/nvm_flowdata.log + source: not_applicable + sourcetype: cisco:nvm:flowdata diff --git a/detections/endpoint/wmiprvse_lolbas_execution_process_spawn.yml b/detections/endpoint/wmiprvse_lolbas_execution_process_spawn.yml index ad75609825..e5195d9d36 100644 --- a/detections/endpoint/wmiprvse_lolbas_execution_process_spawn.yml +++ b/detections/endpoint/wmiprvse_lolbas_execution_process_spawn.yml @@ -1,92 +1,68 @@ name: Wmiprvse LOLBAS Execution Process Spawn id: b7e11721-08b1-4d8b-9628-813bb2380514 -version: 1 -date: '2025-05-02' +version: 2 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects `wmiprvse.exe` spawning a LOLBAS execution - process. It leverages data from Endpoint Detection and Response (EDR) agents, focusing - on process creation events where `wmiprvse.exe` is the parent process and the child - process is a known LOLBAS binary. This activity is significant as it may indicate - lateral movement or remote code execution by an adversary abusing Windows Management - Instrumentation (WMI). If confirmed malicious, this behavior could allow attackers - to execute arbitrary code, escalate privileges, or maintain persistence within the - environment, posing a severe security risk. +description: The following analytic detects `wmiprvse.exe` spawning a LOLBAS execution process. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where `wmiprvse.exe` is the parent process and the child process is a known LOLBAS binary. This activity is significant as it may indicate lateral movement or remote code execution by an adversary abusing Windows Management Instrumentation (WMI). If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment, posing a severe security risk. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=wmiprvse.exe) - (Processes.process_name IN ("Regsvcs.exe", "Ftp.exe", "OfflineScannerShell.exe", - "Rasautou.exe", "Schtasks.exe", "Xwizard.exe", "Dllhost.exe", "Pnputil.exe", "Atbroker.exe", - "Pcwrun.exe", "Ttdinject.exe","Mshta.exe", "Bitsadmin.exe", "Certoc.exe", "Ieexec.exe", - "Microsoft.Workflow.Compiler.exe", "Runscripthelper.exe", "Forfiles.exe", "Msbuild.exe", - "Register-cimprovider.exe", "Tttracer.exe", "Ie4uinit.exe", "Bash.exe", "Hh.exe", - "SettingSyncHost.exe", "Cmstp.exe", "Mmc.exe", "Stordiag.exe", "Scriptrunner.exe", - "Odbcconf.exe", "Extexport.exe", "Msdt.exe", "WorkFolders.exe", "Diskshadow.exe", - "Mavinject.exe", "Regasm.exe", "Gpscript.exe", "Rundll32.exe", "Regsvr32.exe", "Msiexec.exe", - "Wuauclt.exe", "Presentationhost.exe", "Wmic.exe", "Runonce.exe", "Syncappvpublishingserver.exe", - "Verclsid.exe", "Infdefaultinstall.exe", "Explorer.exe", "Installutil.exe", "Netsh.exe", - "Wab.exe", "Dnscmd.exe", "At.exe", "Pcalua.exe", "Msconfig.exe")) by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `wmiprvse_lolbas_execution_process_spawn_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Legitimate applications may trigger this behavior, filter as - needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE ( + Processes.parent_process_name=wmiprvse.exe + ) + (Processes.process_name IN ("Regsvcs.exe", "Ftp.exe", "OfflineScannerShell.exe", "Rasautou.exe", "Schtasks.exe", "Xwizard.exe", "Dllhost.exe", "Pnputil.exe", "Atbroker.exe", "Pcwrun.exe", "Ttdinject.exe","Mshta.exe", "Bitsadmin.exe", "Certoc.exe", "Ieexec.exe", "Microsoft.Workflow.Compiler.exe", "Runscripthelper.exe", "Forfiles.exe", "Msbuild.exe", "Register-cimprovider.exe", "Tttracer.exe", "Ie4uinit.exe", "Bash.exe", "Hh.exe", "SettingSyncHost.exe", "Cmstp.exe", "Mmc.exe", "Stordiag.exe", "Scriptrunner.exe", "Odbcconf.exe", "Extexport.exe", "Msdt.exe", "WorkFolders.exe", "Diskshadow.exe", "Mavinject.exe", "Regasm.exe", "Gpscript.exe", "Rundll32.exe", "Regsvr32.exe", "Msiexec.exe", "Wuauclt.exe", "Presentationhost.exe", "Wmic.exe", "Runonce.exe", "Syncappvpublishingserver.exe", "Verclsid.exe", "Infdefaultinstall.exe", "Explorer.exe", "Installutil.exe", "Netsh.exe", "Wab.exe", "Dnscmd.exe", "At.exe", "Pcalua.exe", "Msconfig.exe")) + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `wmiprvse_lolbas_execution_process_spawn_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Legitimate applications may trigger this behavior, filter as needed. references: -- https://attack.mitre.org/techniques/T1047/ -- https://www.ired.team/offensive-security/lateral-movement/t1047-wmi-for-lateral-movement -- https://lolbas-project.github.io/ + - https://attack.mitre.org/techniques/T1047/ + - https://www.ired.team/offensive-security/lateral-movement/t1047-wmi-for-lateral-movement + - https://lolbas-project.github.io/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Wmiprvse.exe spawned a LOLBAS process on $dest$. - risk_objects: - - field: dest - type: system - score: 54 - threat_objects: [] + message: Wmiprvse.exe spawned a LOLBAS process on $dest$. + risk_objects: + - field: dest + type: system + score: 54 + threat_objects: [] tags: - analytic_story: - - Active Directory Lateral Movement - asset_type: Endpoint - mitre_attack_id: - - T1047 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + asset_type: Endpoint + mitre_attack_id: + - T1047 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/lateral_movement_lolbas/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/lateral_movement_lolbas/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/wscript_or_cscript_suspicious_child_process.yml b/detections/endpoint/wscript_or_cscript_suspicious_child_process.yml index 510892c8ec..513ae4c761 100644 --- a/detections/endpoint/wscript_or_cscript_suspicious_child_process.yml +++ b/detections/endpoint/wscript_or_cscript_suspicious_child_process.yml @@ -1,95 +1,77 @@ name: Wscript Or Cscript Suspicious Child Process id: 1f35e1da-267b-11ec-90a9-acde48001122 -version: 10 -date: '2025-08-22' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: This analytic identifies a suspicious spawned process by WScript or - CScript process. This technique was a common technique used by adversaries and - malware to execute different LOLBIN, other scripts like PowerShell or spawn a - suspended process to inject its code as a defense evasion. This TTP may detect - some normal script that uses several application tools that are in the list of - the child process it detects but a good pivot and indicator that a script may - execute suspicious code. +description: This analytic identifies a suspicious spawned process by WScript or CScript process. This technique was a common technique used by adversaries and malware to execute different LOLBIN, other scripts like PowerShell or spawn a suspended process to inject its code as a defense evasion. This TTP may detect some normal script that uses several application tools that are in the list of the child process it detects but a good pivot and indicator that a script may execute suspicious code. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name - IN ("cscript.exe", "wscript.exe") Processes.process_name IN ("regsvr32.exe", "rundll32.exe","winhlp32.exe","certutil.exe","msbuild.exe","cmd.exe","powershell*","pwsh.exe","wmic.exe","mshta.exe") - by Processes.action Processes.dest Processes.original_file_name Processes.parent_process - Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id - Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec - Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level - Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `wscript_or_cscript_suspicious_child_process_filter`' -how_to_implement: The detection is based on data that originates from Endpoint - Detection and Response (EDR) agents. These agents are designed to provide - security-related telemetry from the endpoints where the agent is installed. To - implement this search, you must ingest logs that contain the process GUID, - process name, and parent process. Additionally, you must ingest complete - command-line executions. These logs must be processed using the appropriate - Splunk Technology Add-ons that are specific to the EDR product. The logs must - also be mapped to the `Processes` node of the `Endpoint` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. -known_false_positives: Administrators may create vbs or js script that use - several tool as part of its execution. Filter as needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE Processes.parent_process_name IN ("cscript.exe", "wscript.exe") Processes.process_name IN ("regsvr32.exe", "rundll32.exe","winhlp32.exe","certutil.exe","msbuild.exe","cmd.exe","powershell*","pwsh.exe","wmic.exe","mshta.exe") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `wscript_or_cscript_suspicious_child_process_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Administrators may create vbs or js script that use several tool as part of its execution. Filter as needed. references: -- https://www.hybrid-analysis.com/sample/8da5b75b6380a41eee3a399c43dfe0d99eeefaa1fd21027a07b1ecaa4cd96fdd?environmentId=120 -- https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://www.hybrid-analysis.com/sample/8da5b75b6380a41eee3a399c43dfe0d99eeefaa1fd21027a07b1ecaa4cd96fdd?environmentId=120 + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ drilldown_searches: -- name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: wscript or cscript parent process spawned $process_name$ on $dest$ - risk_objects: - - field: dest - type: system - score: 49 - - field: user - type: user - score: 49 - threat_objects: [] + message: wscript or cscript parent process spawned $process_name$ on $dest$ + risk_objects: + - field: dest + type: system + score: 49 + - field: user + type: user + score: 49 + threat_objects: [] tags: - analytic_story: - - Data Destruction - - FIN7 - - NjRAT - - Remcos - - XWorm - - WhisperGate - - Unusual Processes - - ShrinkLocker - - 0bj3ctivity Stealer - asset_type: Endpoint - mitre_attack_id: - - T1055 - - T1134.004 - - T1543 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Destruction + - FIN7 + - NjRAT + - Remcos + - XWorm + - WhisperGate + - Unusual Processes + - ShrinkLocker + - 0bj3ctivity Stealer + asset_type: Endpoint + mitre_attack_id: + - T1055 + - T1134.004 + - T1543 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.005/vbs_wscript/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.005/vbs_wscript/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/wsmprovhost_lolbas_execution_process_spawn.yml b/detections/endpoint/wsmprovhost_lolbas_execution_process_spawn.yml index 494a902859..af2edf005b 100644 --- a/detections/endpoint/wsmprovhost_lolbas_execution_process_spawn.yml +++ b/detections/endpoint/wsmprovhost_lolbas_execution_process_spawn.yml @@ -1,94 +1,68 @@ name: Wsmprovhost LOLBAS Execution Process Spawn id: 2eed004c-4c0d-11ec-93e8-3e22fbd008af -version: 8 -date: '2025-10-14' +version: 9 +date: '2026-02-25' author: Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic identifies `Wsmprovhost.exe` spawning a LOLBAS - execution process. It leverages Endpoint Detection and Response (EDR) data to detect - when `Wsmprovhost.exe` spawns child processes that are known LOLBAS (Living Off - the Land Binaries and Scripts) executables. This activity is significant because - it may indicate an adversary using Windows Remote Management (WinRM) to execute - code on remote endpoints, a common technique for lateral movement. If confirmed - malicious, this could allow attackers to execute arbitrary code, escalate privileges, - or maintain persistence within the environment. +description: The following analytic identifies `Wsmprovhost.exe` spawning a LOLBAS execution process. It leverages Endpoint Detection and Response (EDR) data to detect when `Wsmprovhost.exe` spawns child processes that are known LOLBAS (Living Off the Land Binaries and Scripts) executables. This activity is significant because it may indicate an adversary using Windows Remote Management (WinRM) to execute code on remote endpoints, a common technique for lateral movement. If confirmed malicious, this could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=wsmprovhost.exe) - (Processes.process_name IN ("Regsvcs.exe", "Ftp.exe", "OfflineScannerShell.exe", - "Rasautou.exe", "Schtasks.exe", "Xwizard.exe", "Dllhost.exe", "Pnputil.exe", "Atbroker.exe", - "Pcwrun.exe", "Ttdinject.exe","Mshta.exe", "Bitsadmin.exe", "Certoc.exe", "Ieexec.exe", - "Microsoft.Workflow.Compiler.exe", "Runscripthelper.exe", "Forfiles.exe", "Msbuild.exe", - "Register-cimprovider.exe", "Tttracer.exe", "Ie4uinit.exe", "Bash.exe", "Hh.exe", - "SettingSyncHost.exe", "Cmstp.exe", "Mmc.exe", "Stordiag.exe", "Scriptrunner.exe", - "Odbcconf.exe", "Extexport.exe", "Msdt.exe", "WorkFolders.exe", "Diskshadow.exe", - "Mavinject.exe", "Regasm.exe", "Gpscript.exe", "Rundll32.exe", "Regsvr32.exe", "Msiexec.exe", - "Wuauclt.exe", "Presentationhost.exe", "Wmic.exe", "Runonce.exe", "Syncappvpublishingserver.exe", - "Verclsid.exe", "Infdefaultinstall.exe", "Explorer.exe", "Installutil.exe", "Netsh.exe", - "Wab.exe", "Dnscmd.exe", "At.exe", "Pcalua.exe", "Msconfig.exe")) by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| - `wsmprovhost_lolbas_execution_process_spawn_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: Legitimate applications may trigger this behavior, filter as - needed. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE + Processes.parent_process_name=wsmprovhost.exe + Processes.process_name IN ("Regsvcs.exe", "Ftp.exe", "OfflineScannerShell.exe", "Rasautou.exe", "Schtasks.exe", "Xwizard.exe", "Dllhost.exe", "Pnputil.exe", "Atbroker.exe", "Pcwrun.exe", "Ttdinject.exe","Mshta.exe", "Bitsadmin.exe", "Certoc.exe", "Ieexec.exe", "Microsoft.Workflow.Compiler.exe", "Runscripthelper.exe", "Forfiles.exe", "Msbuild.exe", "Register-cimprovider.exe", "Tttracer.exe", "Ie4uinit.exe", "Bash.exe", "Hh.exe", "SettingSyncHost.exe", "Cmstp.exe", "Mmc.exe", "Stordiag.exe", "Scriptrunner.exe", "Odbcconf.exe", "Extexport.exe", "Msdt.exe", "WorkFolders.exe", "Diskshadow.exe", "Mavinject.exe", "Regasm.exe", "Gpscript.exe", "Rundll32.exe", "Regsvr32.exe", "Msiexec.exe", "Wuauclt.exe", "Presentationhost.exe", "Wmic.exe", "Runonce.exe", "Syncappvpublishingserver.exe", "Verclsid.exe", "Infdefaultinstall.exe", "Explorer.exe", "Installutil.exe", "Netsh.exe", "Wab.exe", "Dnscmd.exe", "At.exe", "Pcalua.exe", "Msconfig.exe") + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `wsmprovhost_lolbas_execution_process_spawn_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: Legitimate applications may trigger this behavior, filter as needed. references: -- https://attack.mitre.org/techniques/T1021/006/ -- https://lolbas-project.github.io/ -- https://pentestlab.blog/2018/05/15/lateral-movement-winrm/ + - https://attack.mitre.org/techniques/T1021/006/ + - https://lolbas-project.github.io/ + - https://pentestlab.blog/2018/05/15/lateral-movement-winrm/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Wsmprovhost.exe spawned a LOLBAS process on $dest$. - risk_objects: - - field: dest - type: system - score: 54 - threat_objects: [] + message: Wsmprovhost.exe spawned a LOLBAS process on $dest$. + risk_objects: + - field: dest + type: system + score: 54 + threat_objects: [] tags: - analytic_story: - - Active Directory Lateral Movement - - CISA AA24-241A - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1021.006 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Active Directory Lateral Movement + - CISA AA24-241A + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1021.006 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/lateral_movement_lolbas/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/lateral_movement_lolbas/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/wsreset_uac_bypass.yml b/detections/endpoint/wsreset_uac_bypass.yml index a70d88a129..f35f4b6e28 100644 --- a/detections/endpoint/wsreset_uac_bypass.yml +++ b/detections/endpoint/wsreset_uac_bypass.yml @@ -5,77 +5,49 @@ date: '2026-01-14' author: Steven Dick, Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects a suspicious modification of the registry - aimed at bypassing User Account Control (UAC) by leveraging WSReset.exe. It identifies - the creation or modification of specific registry values under the path "*\\AppX82a6gwre4fdg3bt635tn5ctqjf8msdd2\\Shell\\open\\command*". - This detection uses data from Endpoint Detection and Response (EDR) agents, focusing - on process and registry events. This activity is significant because UAC bypass - techniques can allow attackers to execute high-privilege actions without user consent. - If confirmed malicious, this could lead to unauthorized code execution and potential - system compromise. +description: The following analytic detects a suspicious modification of the registry aimed at bypassing User Account Control (UAC) by leveraging WSReset.exe. It identifies the creation or modification of specific registry values under the path "*\\AppX82a6gwre4fdg3bt635tn5ctqjf8msdd2\\Shell\\open\\command*". This detection uses data from Endpoint Detection and Response (EDR) agents, focusing on process and registry events. This activity is significant because UAC bypass techniques can allow attackers to execute high-privilege actions without user consent. If confirmed malicious, this could lead to unauthorized code execution and potential system compromise. data_source: -- Sysmon EventID 12 -- Sysmon EventID 13 -search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry - WHERE Registry.registry_path= "*\\AppX82a6gwre4fdg3bt635tn5ctqjf8msdd2\\Shell\\open\\command*" - AND (Registry.registry_value_name = "(Default)" OR Registry.registry_value_name - = "DelegateExecute") by Registry.action Registry.dest Registry.process_guid Registry.process_id - Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data - Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user - Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `wsreset_uac_bypass_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 12 + - Sysmon EventID 13 +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE Registry.registry_path= "*\\AppX82a6gwre4fdg3bt635tn5ctqjf8msdd2\\Shell\\open\\command*" AND (Registry.registry_value_name = "(Default)" OR Registry.registry_value_name = "DelegateExecute") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wsreset_uac_bypass_filter`' +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://github.com/hfiref0x/UACME -- https://blog.morphisec.com/trickbot-uses-a-new-windows-10-uac-bypass + - https://github.com/hfiref0x/UACME + - https://blog.morphisec.com/trickbot-uses-a-new-windows-10-uac-bypass drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious modification of registry $registry_path$ with possible payload - path $registry_value_name$ on $dest$ - risk_objects: - - field: dest - type: system - score: 63 - threat_objects: [] + message: Suspicious modification of registry $registry_path$ with possible payload path $registry_value_name$ on $dest$ + risk_objects: + - field: dest + type: system + score: 63 + threat_objects: [] tags: - analytic_story: - - Windows Defense Evasion Tactics - - Living Off The Land - - Windows Registry Abuse - - MoonPeak - asset_type: Endpoint - mitre_attack_id: - - T1548.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows Defense Evasion Tactics + - Living Off The Land + - Windows Registry Abuse + - MoonPeak + asset_type: Endpoint + mitre_attack_id: + - T1548.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/uac_bypass/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/uac_bypass/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/xmrig_driver_loaded.yml b/detections/endpoint/xmrig_driver_loaded.yml index 7fb5f9e4f9..68b812038d 100644 --- a/detections/endpoint/xmrig_driver_loaded.yml +++ b/detections/endpoint/xmrig_driver_loaded.yml @@ -5,63 +5,46 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the installation of the XMRIG coinminer - driver on a system. It identifies the loading of the `WinRing0x64.sys` driver, commonly - associated with XMRIG, by analyzing Sysmon EventCode 6 logs for specific signatures - and image loads. This activity is significant because XMRIG is an open-source CPU - miner frequently exploited by adversaries to mine cryptocurrency illicitly. If confirmed - malicious, this activity could lead to unauthorized resource consumption, degraded - system performance, and potential financial loss due to unauthorized cryptocurrency - mining. +description: The following analytic detects the installation of the XMRIG coinminer driver on a system. It identifies the loading of the `WinRing0x64.sys` driver, commonly associated with XMRIG, by analyzing Sysmon EventCode 6 logs for specific signatures and image loads. This activity is significant because XMRIG is an open-source CPU miner frequently exploited by adversaries to mine cryptocurrency illicitly. If confirmed malicious, this activity could lead to unauthorized resource consumption, degraded system performance, and potential financial loss due to unauthorized cryptocurrency mining. data_source: -- Sysmon EventID 6 -search: '`sysmon` EventCode=6 Signature="Noriyuki MIYAZAKI" OR ImageLoaded= "*\\WinRing0x64.sys" - | stats min(_time) as firstTime max(_time) as lastTime count by ImageLoaded dest - dvc process_hash process_path signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `xmrig_driver_loaded_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the driver loaded and Signature from your endpoints. If you are using - Sysmon, you must have at least version 6.0.4 of the Sysmon TA. + - Sysmon EventID 6 +search: '`sysmon` EventCode=6 Signature="Noriyuki MIYAZAKI" OR ImageLoaded= "*\\WinRing0x64.sys" | stats min(_time) as firstTime max(_time) as lastTime count by ImageLoaded dest dvc process_hash process_path signature signature_id user_id vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `xmrig_driver_loaded_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the driver loaded and Signature from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: False positives should be limited. references: -- https://www.trendmicro.com/vinfo/hk/threat-encyclopedia/malware/trojan.ps1.powtran.a/ + - https://www.trendmicro.com/vinfo/hk/threat-encyclopedia/malware/trojan.ps1.powtran.a/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A driver $ImageLoaded$ related to xmrig crytominer loaded in host $dest$ - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: [] + message: A driver $ImageLoaded$ related to xmrig crytominer loaded in host $dest$ + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: [] tags: - analytic_story: - - CISA AA22-320A - - Crypto Stealer - - XMRig - asset_type: Endpoint - mitre_attack_id: - - T1543.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - CISA AA22-320A + - Crypto Stealer + - XMRig + asset_type: Endpoint + mitre_attack_id: + - T1543.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/endpoint/xsl_script_execution_with_wmic.yml b/detections/endpoint/xsl_script_execution_with_wmic.yml index 8418e29507..53d3fe2313 100644 --- a/detections/endpoint/xsl_script_execution_with_wmic.yml +++ b/detections/endpoint/xsl_script_execution_with_wmic.yml @@ -1,91 +1,74 @@ name: XSL Script Execution With WMIC id: 004e32e2-146d-11ec-a83f-acde48001122 -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the execution of an XSL script using the - WMIC process, which is often indicative of malicious activity. It leverages data - from Endpoint Detection and Response (EDR) agents, focusing on command-line executions - involving WMIC and XSL files. This behavior is significant as it has been associated - with the FIN7 group, known for using this technique to execute malicious scripts. - If confirmed malicious, this activity could allow attackers to execute arbitrary - code, potentially leading to system compromise and further malicious actions within - the environment. +description: The following analytic detects the execution of an XSL script using the WMIC process, which is often indicative of malicious activity. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving WMIC and XSL files. This behavior is significant as it has been associated with the FIN7 group, known for using this technique to execute malicious scripts. If confirmed malicious, this activity could allow attackers to execute arbitrary code, potentially leading to system compromise and further malicious actions within the environment. data_source: -- Sysmon EventID 1 -- Windows Event Log Security 4688 -- CrowdStrike ProcessRollup2 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Endpoint.Processes where `process_wmic` Processes.process - = "*os get*" Processes.process="*/format:*" Processes.process = "*.xsl*" by Processes.action - Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec - Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name - Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid - Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name - Processes.process_path Processes.user Processes.user_id Processes.vendor_product - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `xsl_script_execution_with_wmic_filter`' -how_to_implement: The detection is based on data that originates from Endpoint Detection - and Response (EDR) agents. These agents are designed to provide security-related - telemetry from the endpoints where the agent is installed. To implement this search, - you must ingest logs that contain the process GUID, process name, and parent process. - Additionally, you must ingest complete command-line executions. These logs must - be processed using the appropriate Splunk Technology Add-ons that are specific to - the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. + - Sysmon EventID 1 + - Windows Event Log Security 4688 + - CrowdStrike ProcessRollup2 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + WHERE `process_wmic` Processes.process = "*os get*" Processes.process="*/format:*" Processes.process = "*.xsl*" + BY Processes.action Processes.dest Processes.original_file_name + Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid + Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id Processes.process_integrity_level + Processes.process_name Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `xsl_script_execution_with_wmic_filter` +how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives: No false positives have been identified at this time. references: -- https://www.mandiant.com/resources/fin7-pursuing-an-enigmatic-and-evasive-global-criminal-operation -- https://attack.mitre.org/groups/G0046/ -- https://web.archive.org/web/20190814201250/https://subt0x11.blogspot.com/2018/04/wmicexe-whitelisting-bypass-hacking.html -- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1220/T1220.md#atomic-test-3---wmic-bypass-using-local-xsl-file + - https://www.mandiant.com/resources/fin7-pursuing-an-enigmatic-and-evasive-global-criminal-operation + - https://attack.mitre.org/groups/G0046/ + - https://web.archive.org/web/20190814201250/https://subt0x11.blogspot.com/2018/04/wmicexe-whitelisting-bypass-hacking.html + - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1220/T1220.md#atomic-test-3---wmic-bypass-using-local-xsl-file drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An instance of $parent_process_name$ spawning $process_name$ was identified - on endpoint $dest$ by user $user$ utilizing wmic to load a XSL script. - risk_objects: - - field: user - type: user - score: 49 - - field: dest - type: system - score: 49 - threat_objects: - - field: parent_process_name - type: parent_process_name - - field: process_name - type: process_name + message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ utilizing wmic to load a XSL script. + risk_objects: + - field: user + type: user + score: 49 + - field: dest + type: system + score: 49 + threat_objects: + - field: parent_process_name + type: parent_process_name + - field: process_name + type: process_name tags: - analytic_story: - - FIN7 - - Suspicious WMI Use - asset_type: Endpoint - mitre_attack_id: - - T1220 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - FIN7 + - Suspicious WMI Use + asset_type: Endpoint + mitre_attack_id: + - T1220 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/fin7_macro_js_1/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/fin7_macro_js_1/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/network/3cx_supply_chain_attack_network_indicators.yml b/detections/network/3cx_supply_chain_attack_network_indicators.yml index bf335a0b14..07353b89a8 100644 --- a/detections/network/3cx_supply_chain_attack_network_indicators.yml +++ b/detections/network/3cx_supply_chain_attack_network_indicators.yml @@ -5,82 +5,67 @@ date: '2026-01-19' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies DNS queries to domains associated with - the 3CX supply chain attack. It leverages the Network_Resolution datamodel to detect - these suspicious domain indicators. This activity is significant because it can - indicate a potential compromise stemming from the 3CX supply chain attack, which - is known for distributing malicious software through trusted updates. If confirmed - malicious, this activity could allow attackers to establish a foothold in the network, - exfiltrate sensitive data, or further propagate malware, leading to extensive damage - and data breaches. +description: The following analytic identifies DNS queries to domains associated with the 3CX supply chain attack. It leverages the Network_Resolution datamodel to detect these suspicious domain indicators. This activity is significant because it can indicate a potential compromise stemming from the 3CX supply chain attack, which is known for distributing malicious software through trusted updates. If confirmed malicious, this activity could allow attackers to establish a foothold in the network, exfiltrate sensitive data, or further propagate malware, leading to extensive damage and data breaches. data_source: -- Sysmon EventID 22 + - Sysmon EventID 22 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime - from datamodel=Network_Resolution where - DNS.query=* - NOT DNS.query IN ("-", "unknown") - by DNS.answer DNS.answer_count DNS.query - DNS.query_count DNS.reply_code_id DNS.src - DNS.vendor_product - | `drop_dm_object_name(DNS)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | lookup 3cx_ioc_domains domain as query OUTPUT Description isIOC - | search isIOC=true - | `3cx_supply_chain_attack_network_indicators_filter` -how_to_implement: To successfully implement this search you need to be ingesting information - into the `Network Resolution` datamodel in the `DNS` node. In addition, confirm - the latest CIM App 4.20 or higher is installed and the latest TA''s are installed. -known_false_positives: False positives will be present for accessing the 3cx[.]com - website. Remove from the lookup as needed. + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + from datamodel=Network_Resolution where + DNS.query=* + NOT DNS.query IN ("-", "unknown") + by DNS.answer DNS.answer_count DNS.query + DNS.query_count DNS.reply_code_id DNS.src + DNS.vendor_product + | `drop_dm_object_name(DNS)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | lookup 3cx_ioc_domains domain as query OUTPUT Description isIOC + | search isIOC=true + | `3cx_supply_chain_attack_network_indicators_filter` +how_to_implement: To successfully implement this search you need to be ingesting information into the `Network Resolution` datamodel in the `DNS` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA''s are installed. +known_false_positives: False positives will be present for accessing the 3cx[.]com website. Remove from the lookup as needed. references: -- https://www.sentinelone.com/blog/smoothoperator-ongoing-campaign-trojanizes-3cx-software-in-software-supply-chain-attack/ -- https://www.cisa.gov/news-events/alerts/2023/03/30/supply-chain-attack-against-3cxdesktopapp -- https://www.reddit.com/r/crowdstrike/comments/125r3uu/20230329_situational_awareness_crowdstrike/ -- https://www.3cx.com/community/threads/crowdstrike-endpoint-security-detection-re-3cx-desktop-app.119934/page-2#post-558898 -- https://www.3cx.com/community/threads/3cx-desktopapp-security-alert.119951/ + - https://www.sentinelone.com/blog/smoothoperator-ongoing-campaign-trojanizes-3cx-software-in-software-supply-chain-attack/ + - https://www.cisa.gov/news-events/alerts/2023/03/30/supply-chain-attack-against-3cxdesktopapp + - https://www.reddit.com/r/crowdstrike/comments/125r3uu/20230329_situational_awareness_crowdstrike/ + - https://www.3cx.com/community/threads/crowdstrike-endpoint-security-detection-re-3cx-desktop-app.119934/page-2#post-558898 + - https://www.3cx.com/community/threads/3cx-desktopapp-security-alert.119951/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Indicators related to 3CX supply chain attack have been identified on $src$. - risk_objects: - - field: src - type: system - score: 100 - threat_objects: - - field: query - type: domain + message: Indicators related to 3CX supply chain attack have been identified on $src$. + risk_objects: + - field: src + type: system + score: 100 + threat_objects: + - field: query + type: domain tags: - analytic_story: - - 3CX Supply Chain Attack - asset_type: Network - cve: - - CVE-2023-29059 - mitre_attack_id: - - T1195.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - 3CX Supply Chain Attack + asset_type: Network + cve: + - CVE-2023-29059 + mitre_attack_id: + - T1195.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.002/3CX/3cx_network-windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.002/3CX/3cx_network-windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/network/cisco_configuration_archive_logging_analysis.yml b/detections/network/cisco_configuration_archive_logging_analysis.yml index 5b606a727b..7db0da04ed 100644 --- a/detections/network/cisco_configuration_archive_logging_analysis.yml +++ b/detections/network/cisco_configuration_archive_logging_analysis.yml @@ -1,67 +1,65 @@ name: Cisco Configuration Archive Logging Analysis id: f52d5c0b-d45d-4304-b300-a4f6a1130dec -version: 1 -date: '2025-08-21' +version: 2 +date: '2026-02-25' author: Bhavin Patel, Michael Haag, Splunk status: production type: Hunting description: This analytic provides comprehensive monitoring of configuration changes on Cisco devices by analyzing archive logs. Configuration archive logging captures all changes made to a device's configuration, providing a detailed audit trail that can be used to identify suspicious or malicious activities. This detection is particularly valuable for identifying patterns of malicious configuration changes that might indicate an attacker's presence, such as the creation of backdoor accounts, SNMP community string modifications, and TFTP server configurations for data exfiltration. By analyzing these logs, security teams can gain a holistic view of configuration changes across sessions and users, helping to detect sophisticated attack campaigns like those conducted by threat actors such as Static Tundra. data_source: - - Cisco IOS Logs -search: - '| tstats `security_content_summariesonly` count values(All_Changes.command) as commands min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes - where ( - (All_Changes.command="*username*privilege 15*") OR - (All_Changes.command="*username*password*") OR - (All_Changes.command="*USER TABLE MODIFIED*") OR - (All_Changes.command="*tftp-server*") OR - (All_Changes.command="*snmp-server community*") - ) - by All_Changes.dvc All_Changes.user - | `drop_dm_object_name("All_Changes")` - | rename dvc as dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_configuration_archive_logging_analysis_filter`' + - Cisco IOS Logs +search: |- + | tstats `security_content_summariesonly` count values(All_Changes.command) as commands min(_time) as firstTime max(_time) as lastTime FROM datamodel=Change.All_Changes + WHERE ( + (All_Changes.command="*username*privilege 15*") + OR + (All_Changes.command="*username*password*") + OR + (All_Changes.command="*USER TABLE MODIFIED*") + OR + (All_Changes.command="*tftp-server*") + OR + (All_Changes.command="*snmp-server community*") + ) + BY All_Changes.dvc All_Changes.user + | `drop_dm_object_name("All_Changes")` + | rename dvc as dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_configuration_archive_logging_analysis_filter` how_to_implement: To implement this search, you need to be ingesting Cisco IOS logs with the sourcetype "cisco:ios" and have these logs mapped to the Change datamodel. Ensure that your Cisco IOS devices are configured to send logs to your Splunk environment, with configuration archive logging enabled. On Cisco devices, enable archive logging with the commands "archive" and "log config" in global configuration mode. Configure command logging with "archive log config logging enable" and ensure that the appropriate logging levels are set with "logging trap informational". The detection looks for patterns of suspicious configuration changes across sessions, focusing on account creation, SNMP modifications, and TFTP server configurations. known_false_positives: Legitimate configuration changes during routine maintenance or device setup may trigger this detection, especially when multiple related changes are made in a single session. Network administrators often make several configuration changes in sequence during maintenance windows. To reduce false positives, consider implementing a baseline of expected administrative activities, including approved administrative usernames and scheduled maintenance windows. The detection includes a threshold (count > 2) to filter out isolated configuration changes, but this threshold may need to be adjusted based on your environment's normal activity patterns. references: - - https://blog.talosintelligence.com/static-tundra/ - - https://tools.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-20180328-smi2 - - https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/config-mgmt/configuration/15-mt/config-mgmt-15-mt-book/cm-config-logger.html + - https://blog.talosintelligence.com/static-tundra/ + - https://tools.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-20180328-smi2 + - https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/config-mgmt/configuration/15-mt/config-mgmt-15-mt-book/cm-config-logger.html drilldown_searches: - - name: View the detection results for - "$dest$" and "$user$" - search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" and "$user$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", - "$user$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$user$" + search: '%original_detection_search% | search dest = "$dest$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ tags: - analytic_story: - - Cisco Smart Install Remote Code Execution CVE-2018-0171 - asset_type: Network - mitre_attack_id: - - T1562.001 - - T1098 - - T1505.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: - - CVE-2018-0171 + analytic_story: + - Cisco Smart Install Remote Code Execution CVE-2018-0171 + asset_type: Network + mitre_attack_id: + - T1562.001 + - T1098 + - T1505.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: + - CVE-2018-0171 tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/cisco/cisco_smart_install/cisco_ios.log - sourcetype: cisco:ios - source: cisco:ios + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/cisco/cisco_smart_install/cisco_ios.log + sourcetype: cisco:ios + source: cisco:ios diff --git a/detections/network/cisco_ios_suspicious_privileged_account_creation.yml b/detections/network/cisco_ios_suspicious_privileged_account_creation.yml index 06c2d33de7..cda2499616 100644 --- a/detections/network/cisco_ios_suspicious_privileged_account_creation.yml +++ b/detections/network/cisco_ios_suspicious_privileged_account_creation.yml @@ -1,75 +1,74 @@ name: Cisco IOS Suspicious Privileged Account Creation id: 63e3aff9-45d7-4d41-bcdb-9da561fb4533 -version: 1 -date: '2025-08-21' +version: 2 +date: '2026-02-25' author: Bhavin Patel, Michael Haag, Splunk status: production type: Anomaly description: This analytic detects the creation of privileged user accounts on Cisco IOS devices, which could indicate an attacker establishing backdoor access. The detection focuses on identifying when user accounts are created with privilege level 15 (the highest administrative privilege level in Cisco IOS) or when existing accounts have their privileges elevated. This type of activity is particularly concerning when performed by unauthorized users or during unusual hours, as it may represent a key step in establishing persistence following the exploitation of vulnerabilities like CVE-2018-0171 in Cisco Smart Install. Threat actors like Static Tundra have been observed creating privileged accounts as part of their attack chain after gaining initial access to network devices. data_source: - - Cisco IOS Logs -search: - '| tstats `security_content_summariesonly` count values(All_Changes.command) as command min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes - where ( - (All_Changes.command="*username * privilege 15*") OR - (All_Changes.command="*username * password*" AND All_Changes.command="*USER TABLE MODIFIED*") OR - (All_Changes.command="*USER_PRIVILEGE_UPDATE*priv-15*") - ) - by All_Changes.dvc All_Changes.user - | `drop_dm_object_name("All_Changes")` - | rename dvc as dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_ios_suspicious_privileged_account_creation_filter`' + - Cisco IOS Logs +search: |- + | tstats `security_content_summariesonly` count values(All_Changes.command) as command min(_time) as firstTime max(_time) as lastTime FROM datamodel=Change.All_Changes + WHERE ( + (All_Changes.command="*username * privilege 15*") + OR + (All_Changes.command="*username * password*" + AND + All_Changes.command="*USER TABLE MODIFIED*") + OR + (All_Changes.command="*USER_PRIVILEGE_UPDATE*priv-15*") + ) + BY All_Changes.dvc All_Changes.user + | `drop_dm_object_name("All_Changes")` + | rename dvc as dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_ios_suspicious_privileged_account_creation_filter` how_to_implement: To implement this search, you need to be ingesting Cisco IOS logs with the sourcetype "cisco:ios" and have these logs mapped to the Change datamodel, with AAA accounting and command logging enabled on your Cisco devices. known_false_positives: Legitimate account creation and privilege elevation activities by authorized administrators will generate alerts with this detection. To reduce false positives, consider implementing a baseline of expected administrative activities, including approved administrative usernames, typical times for account management, and authorized administrators who regularly perform these actions. You may also want to create a lookup table of approved administrative accounts and filter out alerts for these accounts. Additionally, scheduled maintenance windows should be taken into account when evaluating alerts. references: - - https://blog.talosintelligence.com/static-tundra/ - - https://tools.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-20180328-smi2 - - https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/security/a1/sec-a1-cr-book/sec-cr-a2.html#wp3796044403 + - https://blog.talosintelligence.com/static-tundra/ + - https://tools.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-20180328-smi2 + - https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/security/a1/sec-a1-cr-book/sec-cr-a2.html#wp3796044403 drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious privileged account was created or modified on Cisco IOS device $dest$ by user $user$ - risk_objects: - - field: dest - type: system - score: 50 - - field: user - type: user - score: 50 - threat_objects: - - field: command - type: command + message: A suspicious privileged account was created or modified on Cisco IOS device $dest$ by user $user$ + risk_objects: + - field: dest + type: system + score: 50 + - field: user + type: user + score: 50 + threat_objects: + - field: command + type: command tags: - analytic_story: - - Cisco Smart Install Remote Code Execution CVE-2018-0171 - asset_type: Network - mitre_attack_id: - - T1136 - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: - - CVE-2018-0171 + analytic_story: + - Cisco Smart Install Remote Code Execution CVE-2018-0171 + asset_type: Network + mitre_attack_id: + - T1136 + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: + - CVE-2018-0171 tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/cisco/cisco_smart_install/cisco_ios.log - sourcetype: cisco:ios - source: cisco:ios + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/cisco/cisco_smart_install/cisco_ios.log + sourcetype: cisco:ios + source: cisco:ios diff --git a/detections/network/cisco_network_interface_modifications.yml b/detections/network/cisco_network_interface_modifications.yml index 65db302b9e..93ec00f847 100644 --- a/detections/network/cisco_network_interface_modifications.yml +++ b/detections/network/cisco_network_interface_modifications.yml @@ -1,74 +1,73 @@ name: Cisco Network Interface Modifications id: 61ae09c2-079e-44b1-8be0-74e35c5a679e -version: 1 -date: '2025-08-21' +version: 2 +date: '2026-02-25' author: Bhavin Patel, Michael Haag, Splunk status: production type: Anomaly description: This analytic detects the creation or modification of network interfaces on Cisco devices, which could indicate an attacker establishing persistence or preparing for lateral movement. After gaining initial access to network devices, threat actors like Static Tundra often create new interfaces (particularly loopback interfaces) to establish covert communication channels or maintain persistence. This detection specifically looks for the configuration of new interfaces, interface state changes, and the assignment of IP addresses to interfaces. These activities are particularly concerning when they involve unusual interface names or descriptions containing suspicious terms. data_source: -- Cisco IOS Logs -search: '| tstats `security_content_summariesonly` count values(All_Changes.command) as command min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes - where ( - (All_Changes.command="*interface*") OR - (All_Changes.command="*LINEPROTO-5-UPDOWN*") OR - (All_Changes.command="*ip address*") - ) - by All_Changes.dvc All_Changes.user -| `drop_dm_object_name("All_Changes")` -| rename dvc as dest -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `cisco_network_interface_modifications_filter`' + - Cisco IOS Logs +search: |- + | tstats `security_content_summariesonly` count values(All_Changes.command) as command min(_time) as firstTime max(_time) as lastTime FROM datamodel=Change.All_Changes + WHERE ( + (All_Changes.command="*interface*") + OR + (All_Changes.command="*LINEPROTO-5-UPDOWN*") + OR + (All_Changes.command="*ip address*") + ) + BY All_Changes.dvc All_Changes.user + | `drop_dm_object_name("All_Changes")` + | rename dvc as dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_network_interface_modifications_filter` how_to_implement: To implement this search, you need to be ingesting Cisco IOS logs with the sourcetype "cisco:ios" and have these logs mapped to the Change datamodel. Ensure that your Cisco IOS devices are configured to send logs to your Splunk environment, with appropriate logging levels enabled to capture interface configuration changes and line protocol state changes. Configure command logging on Cisco IOS devices using the "archive log config logging enable" command and ensure that syslog is properly configured to capture LINEPROTO-5-UPDOWN messages. known_false_positives: Legitimate network interface configuration changes may trigger this detection during routine network maintenance or initial device setup. Network administrators often need to create or modify interfaces as part of normal operations. To reduce false positives, consider implementing a baseline of expected administrative activities, including approved administrative usernames, typical times for interface configuration changes, and scheduled maintenance windows. You may also want to create a lookup table of approved interface naming conventions and filter out alerts for standard interface configurations. references: -- https://blog.talosintelligence.com/static-tundra/ -- https://tools.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-20180328-smi2 -- https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/interface/command/ir-cr-book/ir-i1.html#wp1389942834 + - https://blog.talosintelligence.com/static-tundra/ + - https://tools.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-20180328-smi2 + - https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/interface/command/ir-cr-book/ir-i1.html#wp1389942834 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious network interface modifications detected on Cisco device $dest$ by user $user$, which may indicate persistence establishment - risk_objects: - - field: dest - type: system - score: 55 - - field: user - type: user - score: 45 - threat_objects: - - field: command - type: command + message: Suspicious network interface modifications detected on Cisco device $dest$ by user $user$, which may indicate persistence establishment + risk_objects: + - field: dest + type: system + score: 55 + - field: user + type: user + score: 45 + threat_objects: + - field: command + type: command tags: - analytic_story: - - Cisco Smart Install Remote Code Execution CVE-2018-0171 - asset_type: Network - mitre_attack_id: - - T1556 - - T1021 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: - - CVE-2018-0171 + analytic_story: + - Cisco Smart Install Remote Code Execution CVE-2018-0171 + asset_type: Network + mitre_attack_id: + - T1556 + - T1021 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: + - CVE-2018-0171 tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/cisco/cisco_smart_install/cisco_ios.log - sourcetype: cisco:ios - source: cisco:ios + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/cisco/cisco_smart_install/cisco_ios.log + sourcetype: cisco:ios + source: cisco:ios diff --git a/detections/network/cisco_privileged_account_creation_with_http_command_execution.yml b/detections/network/cisco_privileged_account_creation_with_http_command_execution.yml index a1ce0dab74..12401c80b0 100644 --- a/detections/network/cisco_privileged_account_creation_with_http_command_execution.yml +++ b/detections/network/cisco_privileged_account_creation_with_http_command_execution.yml @@ -6,83 +6,77 @@ author: Nasreddine Bencherchali, Splunk status: production type: Correlation description: | - This analytic correlates risk events between privileged account creation on Cisco IOS devices and HTTP requests to privileged execution paths such as `/level/15/exec/-/*`. - APT actors have been observed creating privileged accounts and then running commands on routers via HTTP GET or POST requests that target privileged execution paths. - These requests allow attackers to execute commands with the highest privilege level (15) on Cisco devices without requiring interactive SSH access. - This correlation identifies when both "Cisco IOS Suspicious Privileged Account Creation" and "Privileged Command Execution via HTTP" Snort detections fire for the same network device. - This behavior indicates an attacker leveraging the newly created account to execute commands remotely via HTTP. + This analytic correlates risk events between privileged account creation on Cisco IOS devices and HTTP requests to privileged execution paths such as `/level/15/exec/-/*`. + APT actors have been observed creating privileged accounts and then running commands on routers via HTTP GET or POST requests that target privileged execution paths. + These requests allow attackers to execute commands with the highest privilege level (15) on Cisco devices without requiring interactive SSH access. + This correlation identifies when both "Cisco IOS Suspicious Privileged Account Creation" and "Privileged Command Execution via HTTP" Snort detections fire for the same network device. + This behavior indicates an attacker leveraging the newly created account to execute commands remotely via HTTP. data_source: [] search: | - | tstats `security_content_summariesonly` - min(_time) as firstTime - max(_time) as lastTime - sum(All_Risk.calculated_risk_score) as risk_score - count(All_Risk.calculated_risk_score) as risk_event_count + | tstats `security_content_summariesonly` + min(_time) as firstTime + max(_time) as lastTime + sum(All_Risk.calculated_risk_score) as risk_score + count(All_Risk.calculated_risk_score) as risk_event_count - values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id - dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count + values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id + dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count - values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id - dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count + values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id + dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count - values(All_Risk.tag) as tag - values(source) as source - dc(source) as source_count + values(All_Risk.tag) as tag + values(source) as source + dc(source) as source_count - values(contributing_events_search) + values(contributing_events_search) - values(All_Risk.threat_object) + values(All_Risk.threat_object) - from datamodel=Risk.All_Risk where + from datamodel=Risk.All_Risk where - source IN ( - "*Cisco IOS Suspicious Privileged Account Creation*", - "*Cisco Secure Firewall - Privileged Command Execution via HTTP*" - ) - by All_Risk.normalized_risk_object - | `drop_dm_object_name(All_Risk)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | where source_count >= 2 - | `cisco_privileged_account_creation_with_http_command_execution_filter` + source IN ( + "*Cisco IOS Suspicious Privileged Account Creation*", + "*Cisco Secure Firewall - Privileged Command Execution via HTTP*" + ) + by All_Risk.normalized_risk_object + | `drop_dm_object_name(All_Risk)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | where source_count >= 2 + | `cisco_privileged_account_creation_with_http_command_execution_filter` how_to_implement: | - This correlation search requires that the following detections are enabled and generating risk events - "Cisco IOS Suspicious Privileged Account Creation" and "Cisco Secure Firewall - Privileged Command Execution via HTTP". These detections must be configured to generate risk on the same entity field (the network device IP). The search correlates risk events within a 24-hour time window. Ensure that both Cisco IOS logs (sourcetype "cisco:ios") and Cisco Secure Firewall Threat Defense Intrusion Event logs are being ingested and that the underlying detections are properly configured. + This correlation search requires that the following detections are enabled and generating risk events - "Cisco IOS Suspicious Privileged Account Creation" and "Cisco Secure Firewall - Privileged Command Execution via HTTP". These detections must be configured to generate risk on the same entity field (the network device IP). The search correlates risk events within a 24-hour time window. Ensure that both Cisco IOS logs (sourcetype "cisco:ios") and Cisco Secure Firewall Threat Defense Intrusion Event logs are being ingested and that the underlying detections are properly configured. known_false_positives: | - No false positives have been identified yet. + No false positives have been identified yet. references: - - https://www.cisa.gov/news-events/cybersecurity-advisories/aa25-239a + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa25-239a drilldown_searches: - - name: View the detection results for - "$risk_object$" - search: '%original_detection_search% | search risk_object = "$risk_object$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$risk_object$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$risk_object$" + search: '%original_detection_search% | search risk_object = "$risk_object$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$risk_object$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - - Salt Typhoon - asset_type: Network - mitre_attack_id: - - T1021.004 - - T1136 - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + - Salt Typhoon + asset_type: Network + mitre_attack_id: + - T1021.004 + - T1136 + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/emerging_threats/SaltTyphoon/salttyphoon_correlation.log - source: not_applicable - sourcetype: stash + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/emerging_threats/SaltTyphoon/salttyphoon_correlation.log + source: not_applicable + sourcetype: stash diff --git a/detections/network/cisco_privileged_account_creation_with_suspicious_ssh_activity.yml b/detections/network/cisco_privileged_account_creation_with_suspicious_ssh_activity.yml index 770cf4b311..c289529767 100644 --- a/detections/network/cisco_privileged_account_creation_with_suspicious_ssh_activity.yml +++ b/detections/network/cisco_privileged_account_creation_with_suspicious_ssh_activity.yml @@ -1,100 +1,94 @@ name: Cisco Privileged Account Creation with Suspicious SSH Activity id: 7f8e2b4c-9a3d-4e1f-8c5b-6d7e8f9a0b1c -version: 2 -date: '2026-01-22' +version: 3 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Correlation description: | - This analytic detects a correlation between privileged account creation on Cisco IOS devices and subsequent inbound SSH connections to non-standard ports or sshd_operns by correlating risk events - This correlation identifies when both "Cisco IOS Suspicious Privileged Account Creation" and SSH-related Snort detections ("SSH Connection to sshd_operns" or "SSH Connection to Non-Standard Port") fire for the same network device. - This behavior is highly indicative of persistence establishment following initial compromise. + This analytic detects a correlation between privileged account creation on Cisco IOS devices and subsequent inbound SSH connections to non-standard ports or sshd_operns by correlating risk events + This correlation identifies when both "Cisco IOS Suspicious Privileged Account Creation" and SSH-related Snort detections ("SSH Connection to sshd_operns" or "SSH Connection to Non-Standard Port") fire for the same network device. + This behavior is highly indicative of persistence establishment following initial compromise. data_source: [] search: | - | tstats `security_content_summariesonly` - min(_time) as firstTime - max(_time) as lastTime + | tstats `security_content_summariesonly` + min(_time) as firstTime + max(_time) as lastTime - sum(All_Risk.calculated_risk_score) as risk_score - count(All_Risk.calculated_risk_score) as risk_event_count + sum(All_Risk.calculated_risk_score) as risk_score + count(All_Risk.calculated_risk_score) as risk_event_count - values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id - dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count + values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id + dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count - values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id - dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count + values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id + dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count - values(All_Risk.tag) as tag - values(source) as source - dc(source) as source_count - - values(contributing_events_search) + values(All_Risk.tag) as tag + values(source) as source + dc(source) as source_count - values(All_Risk.threat_object) + values(contributing_events_search) - from datamodel=Risk.All_Risk where + values(All_Risk.threat_object) - source IN ( - "*Cisco IOS Suspicious Privileged Account Creation*", - "*Cisco Secure Firewall - SSH Connection to sshd_operns*", - "*Cisco Secure Firewall - SSH Connection to Non-Standard Port*" - ) - by All_Risk.normalized_risk_object - | `drop_dm_object_name(All_Risk)` - | eval has_account_creation=if( - match(source, "Cisco IOS Suspicious Privileged Account Creation"), + from datamodel=Risk.All_Risk where + + source IN ( + "*Cisco IOS Suspicious Privileged Account Creation*", + "*Cisco Secure Firewall - SSH Connection to sshd_operns*", + "*Cisco Secure Firewall - SSH Connection to Non-Standard Port*" + ) + by All_Risk.normalized_risk_object + | `drop_dm_object_name(All_Risk)` + | eval has_account_creation=if( + match(source, "Cisco IOS Suspicious Privileged Account Creation"), + 1, 0 + ) + | eval has_ssh_detection=if( + match(source, "SSH Connection to sshd_operns") + OR + match(source, "SSH Connection to Non-Standard Port"), 1, 0 ) - | eval has_ssh_detection=if( - match(source, "SSH Connection to sshd_operns") - OR - match(source, "SSH Connection to Non-Standard Port"), - 1, 0 - ) - | where has_account_creation=1 - AND - has_ssh_detection=1 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_privileged_account_creation_with_suspicious_ssh_activity_filter` + | where has_account_creation=1 + AND + has_ssh_detection=1 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_privileged_account_creation_with_suspicious_ssh_activity_filter` how_to_implement: | - This correlation search requires that the following detections are enabled and generating risk events - "Cisco IOS Suspicious Privileged Account Creation", "Cisco Secure Firewall - SSH Connection to sshd_operns", and "Cisco Secure Firewall - SSH Connection to Non-Standard Port". These detections must be configured to generate risk on the same entity field (the network device IP). The search correlates risk events within a 24-hour time window. Ensure that both Cisco IOS logs (sourcetype "cisco:ios") and Cisco Secure Firewall Threat Defense Intrusion Event logs are being ingested and that the underlying detections are properly configured. + This correlation search requires that the following detections are enabled and generating risk events - "Cisco IOS Suspicious Privileged Account Creation", "Cisco Secure Firewall - SSH Connection to sshd_operns", and "Cisco Secure Firewall - SSH Connection to Non-Standard Port". These detections must be configured to generate risk on the same entity field (the network device IP). The search correlates risk events within a 24-hour time window. Ensure that both Cisco IOS logs (sourcetype "cisco:ios") and Cisco Secure Firewall Threat Defense Intrusion Event logs are being ingested and that the underlying detections are properly configured. known_false_positives: | - No false positives have been identified yet. + No false positives have been identified yet. references: - - https://www.cisa.gov/news-events/cybersecurity-advisories/aa25-239a + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa25-239a drilldown_searches: - - name: View the detection results for - "$normalized_risk_object$" - search: '%original_detection_search% | search normalized_risk_object = "$normalized_risk_object$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$normalized_risk_object$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$normalized_risk_object$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$normalized_risk_object$" + search: '%original_detection_search% | search normalized_risk_object = "$normalized_risk_object$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$normalized_risk_object$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$normalized_risk_object$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - - Salt Typhoon - asset_type: Network - mitre_attack_id: - - T1021.004 - - T1136 - - T1078 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + - Salt Typhoon + asset_type: Network + mitre_attack_id: + - T1021.004 + - T1136 + - T1078 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/emerging_threats/SaltTyphoon/salttyphoon_correlation.log - source: not_applicable - sourcetype: stash + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/emerging_threats/SaltTyphoon/salttyphoon_correlation.log + source: not_applicable + sourcetype: stash diff --git a/detections/network/cisco_secure_firewall___binary_file_type_download.yml b/detections/network/cisco_secure_firewall___binary_file_type_download.yml index f6b3813525..9cd49478f1 100644 --- a/detections/network/cisco_secure_firewall___binary_file_type_download.yml +++ b/detections/network/cisco_secure_firewall___binary_file_type_download.yml @@ -1,81 +1,77 @@ name: Cisco Secure Firewall - Binary File Type Download id: 24b2c2e3-2ff7-4a23-b814-87f8a62028cd -version: 4 -date: '2026-01-21' +version: 5 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - The following analytic detects file downloads involving executable, archive, or scripting-related file types that are commonly used in malware delivery. - These file types include formats like PE executables, shell scripts, autorun files, installers, and known testing samples such as EICAR. - This detection leverages Cisco Secure Firewall Threat Defense logs and enriches the results using a filetype lookup to provide context. - If confirmed malicious, these downloads could indicate the initial infection vector, malware staging, or scripting abuse. + The following analytic detects file downloads involving executable, archive, or scripting-related file types that are commonly used in malware delivery. + These file types include formats like PE executables, shell scripts, autorun files, installers, and known testing samples such as EICAR. + This detection leverages Cisco Secure Firewall Threat Defense logs and enriches the results using a filetype lookup to provide context. + If confirmed malicious, these downloads could indicate the initial infection vector, malware staging, or scripting abuse. data_source: - - Cisco Secure Firewall Threat Defense File Event + - Cisco Secure Firewall Threat Defense File Event search: | - `cisco_secure_firewall` EventType=FileEvent FileDirection="Download" - FileType IN ("ISHIELD_MSI", "BINHEX", "BINARY_DATA", "ELF", "MACHO", "JARPACK", "TORRENT", "AUTORUN", "EICAR", "LNK", "SCR", "UNIX_SCRIPT") - | lookup cisco_secure_firewall_filetype_lookup Name as FileType OUTPUT Description - | stats count min(_time) as firstTime max(_time) as lastTime - values(uri) as uri - values(ClientApplication) as ClientApplication - values(file_hash) as file_hash - values(SHA_Disposition) as SHA_Disposition - by FileDirection FileType src dest app file_name ThreatName dest_port Description - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | table firstTime lastTime src dest dest_port FileDirection FileType Description uri file_name file_hash app ClientApplication SHA_Disposition ThreatName - | `cisco_secure_firewall___binary_file_type_download_filter` + `cisco_secure_firewall` EventType=FileEvent FileDirection="Download" + FileType IN ("ISHIELD_MSI", "BINHEX", "BINARY_DATA", "ELF", "MACHO", "JARPACK", "TORRENT", "AUTORUN", "EICAR", "LNK", "SCR", "UNIX_SCRIPT") + | lookup cisco_secure_firewall_filetype_lookup Name as FileType OUTPUT Description + | stats count min(_time) as firstTime max(_time) as lastTime + values(uri) as uri + values(ClientApplication) as ClientApplication + values(file_hash) as file_hash + values(SHA_Disposition) as SHA_Disposition + by FileDirection FileType src dest app file_name ThreatName dest_port Description + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table firstTime lastTime src dest dest_port FileDirection FileType Description uri file_name file_hash app ClientApplication SHA_Disposition ThreatName + | `cisco_secure_firewall___binary_file_type_download_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the FileEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The malware & file access policy must also enable logging. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the FileEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The malware & file access policy must also enable logging. known_false_positives: IT admins or developers may legitimately download executables or scripts as part of their normal workflow. Apply additional filters accordingly. references: - - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf + - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The host $src$ downloaded a file $file_name$ of type $FileType$ from $dest$. - risk_objects: - - field: src - type: system - score: 30 - threat_objects: - - field: file_name - type: file_name - - field: file_hash - type: file_hash + message: The host $src$ downloaded a file $file_name$ of type $FileType$ from $dest$. + risk_objects: + - field: src + type: system + score: 30 + threat_objects: + - field: file_name + type: file_name + - field: file_hash + type: file_hash tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - asset_type: Endpoint - mitre_attack_id: - - T1203 - - T1059 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: endpoint + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + asset_type: Endpoint + mitre_attack_id: + - T1203 + - T1059 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/file_event/file_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/file_event/file_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___bits_network_activity.yml b/detections/network/cisco_secure_firewall___bits_network_activity.yml index f5471b243c..717304d74a 100644 --- a/detections/network/cisco_secure_firewall___bits_network_activity.yml +++ b/detections/network/cisco_secure_firewall___bits_network_activity.yml @@ -1,72 +1,68 @@ name: Cisco Secure Firewall - Bits Network Activity id: b08e69d4-b42d-494c-bd30-abaaa3571ba4 -version: 5 -date: '2026-01-21' +version: 6 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: The following analytic detects the use of the Background Intelligent Transfer Service (BITS) client application in allowed outbound connections. It leverages logs from Cisco Secure Firewall Threat Defense devices and identifies instances where BITS is used to initiate downloads from non-standard or unexpected domains. While BITS is a legitimate Windows service used for downloading updates, it is also commonly abused by adversaries to stealthily retrieve payloads or tools. This analytic filters out known Microsoft Edge update URLs and focuses on connections that may indicate suspicious or unauthorized file transfers. If confirmed malicious, this could represent a command and control (C2) channel or a download of malware or tooling as part of an attack chain. data_source: -- Cisco Secure Firewall Threat Defense Connection Event + - Cisco Secure Firewall Threat Defense Connection Event search: | - `cisco_secure_firewall` EventType=ConnectionEvent action IN ("Trust", "Allow", "allowed") ClientApplication="BITS" AND NOT url IN ("*://msedge.b.tlu.dl*") - | stats count min(_time) as firstTime max(_time) as lastTime by src, dest, dest_port, transport, rule, url, EVE_Process, ClientApplication, ClientApplicationVersion, action - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___bits_network_activity_filter` + `cisco_secure_firewall` EventType=ConnectionEvent action IN ("Trust", "Allow", "allowed") ClientApplication="BITS" AND NOT url IN ("*://msedge.b.tlu.dl*") + | stats count min(_time) as firstTime max(_time) as lastTime by src, dest, dest_port, transport, rule, url, EVE_Process, ClientApplication, ClientApplicationVersion, action + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___bits_network_activity_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the ConnectionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The access policy must also enable logging. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the ConnectionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The access policy must also enable logging. known_false_positives: | - BITS is a legitimate Windows component used by Microsoft services such as Windows Update or Microsoft Edge for downloading updates. - Although this analytic filters known Microsoft Edge update URLs, false positives may still occur from other legitimate enterprise applications or software distribution platforms that utilize BITS. - Additional tuning may be required to account for internal application distribution systems or approved update mechanisms that also rely on BITS. + BITS is a legitimate Windows component used by Microsoft services such as Windows Update or Microsoft Edge for downloading updates. + Although this analytic filters known Microsoft Edge update URLs, false positives may still occur from other legitimate enterprise applications or software distribution platforms that utilize BITS. + Additional tuning may be required to account for internal application distribution systems or approved update mechanisms that also rely on BITS. references: -- https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf + - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $src$ downloaded a file from $url$ via BITS Service - risk_objects: - - field: src - type: system - score: 25 - - field: dest - type: system - score: 25 - threat_objects: - - field: url - type: url + message: $src$ downloaded a file from $url$ via BITS Service + risk_objects: + - field: src + type: system + score: 25 + - field: dest + type: system + score: 25 + threat_objects: + - field: url + type: url tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - asset_type: Network - mitre_attack_id: [] - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + asset_type: Network + mitre_attack_id: [] + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___blacklisted_ssl_certificate_fingerprint.yml b/detections/network/cisco_secure_firewall___blacklisted_ssl_certificate_fingerprint.yml index e140127258..b8d583eaeb 100644 --- a/detections/network/cisco_secure_firewall___blacklisted_ssl_certificate_fingerprint.yml +++ b/detections/network/cisco_secure_firewall___blacklisted_ssl_certificate_fingerprint.yml @@ -6,78 +6,74 @@ author: Nasreddine Bencherchali, Splunk status: production type: TTP description: | - The following analytic detects the use of known suspicious SSL certificates in any observed event where the SSL_CertFingerprint field is present. It leverages Cisco Secure Firewall logs and compares the SSL certificate SHA1 fingerprint against a blacklist of certificates associated with malware distribution, command and control (C2) infrastructure, or phishing campaigns. This activity is significant as adversaries often reuse or self-sign certificates across malicious infrastructure, allowing defenders to track and detect encrypted sessions even when domains or IPs change. If confirmed malicious, this may indicate beaconing, malware download, or data exfiltration over TLS/SSL. + The following analytic detects the use of known suspicious SSL certificates in any observed event where the SSL_CertFingerprint field is present. It leverages Cisco Secure Firewall logs and compares the SSL certificate SHA1 fingerprint against a blacklist of certificates associated with malware distribution, command and control (C2) infrastructure, or phishing campaigns. This activity is significant as adversaries often reuse or self-sign certificates across malicious infrastructure, allowing defenders to track and detect encrypted sessions even when domains or IPs change. If confirmed malicious, this may indicate beaconing, malware download, or data exfiltration over TLS/SSL. data_source: -- Cisco Secure Firewall Threat Defense Connection Event + - Cisco Secure Firewall Threat Defense Connection Event search: | - `cisco_secure_firewall` EventType=* SSL_CertFingerprint=* - | lookup sslbl_ssl_certificate_blacklist SHA1 as SSL_CertFingerprint OUTPUT Listingdate, Listingreason - | where isnotnull(Listingreason) - | stats min(_time) as firstTime max(_time) as lastTime - values(dest) as dest - values(dest_port) as dest_port - values(rule) as rule - values(url) as url - values(Listingreason) as Reasons - values(Listingdate) as "SSL Cert Listing Dates" - count by SSL_CertFingerprint src transport action - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___blacklisted_ssl_certificate_fingerprint_filter` + `cisco_secure_firewall` EventType=* SSL_CertFingerprint=* + | lookup sslbl_ssl_certificate_blacklist SHA1 as SSL_CertFingerprint OUTPUT Listingdate, Listingreason + | where isnotnull(Listingreason) + | stats min(_time) as firstTime max(_time) as lastTime + values(dest) as dest + values(dest_port) as dest_port + values(rule) as rule + values(url) as url + values(Listingreason) as Reasons + values(Listingdate) as "SSL Cert Listing Dates" + count by SSL_CertFingerprint src transport action + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___blacklisted_ssl_certificate_fingerprint_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the ConnectionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The access policy must also enable logging. - This search also make use of lookup based on https://sslbl.abuse.ch/blacklist/sslblacklist.csv. Make sure its available + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the ConnectionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The access policy must also enable logging. + This search also make use of lookup based on https://sslbl.abuse.ch/blacklist/sslblacklist.csv. Make sure its available known_false_positives: Certain SSL certificates may be flagged in threat intelligence feeds due to historical misuse, yet still be used by legitimate services, particularly in content delivery or shared hosting environments. Internal or self-signed certificates used in testing or development environments may inadvertently match known blacklisted fingerprints. It is recommended to validate the connection context (destination IP, domain, ClientApplication) and correlate with other indicators before taking action. references: - - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf + - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious SSL certificate fingerprint - [$SSL_CertFingerprint$] used in connections [ListingReason - $Reasons$] from $src$ - risk_objects: - - field: src - type: system - score: 20 - threat_objects: - - field: SSL_CertFingerprint - type: tls_hash - - field: url - type: url + message: Suspicious SSL certificate fingerprint - [$SSL_CertFingerprint$] used in connections [ListingReason - $Reasons$] from $src$ + risk_objects: + - field: src + type: system + score: 20 + threat_objects: + - field: SSL_CertFingerprint + type: tls_hash + - field: url + type: url tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - asset_type: Network - security_domain: network - mitre_attack_id: - - T1587.002 - - T1588.004 - - T1071.001 - - T1573.002 - product: - - Splunk Enterprise - - Splunk Cloud - - Splunk Enterprise Security + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + asset_type: Network + security_domain: network + mitre_attack_id: + - T1587.002 + - T1588.004 + - T1071.001 + - T1573.002 + product: + - Splunk Enterprise + - Splunk Cloud + - Splunk Enterprise Security tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___blocked_connection.yml b/detections/network/cisco_secure_firewall___blocked_connection.yml index 97905f1d0b..5f73ada5a3 100644 --- a/detections/network/cisco_secure_firewall___blocked_connection.yml +++ b/detections/network/cisco_secure_firewall___blocked_connection.yml @@ -1,74 +1,69 @@ name: Cisco Secure Firewall - Blocked Connection id: 17e9b764-3a2b-4d36-9751-32d13ce4718b -version: 5 -date: '2026-01-21' +version: 6 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly -description: The following analytic detects a blocked connection event by - identifying a "Block" value in the action field. It leverages logs from Cisco Secure Firewall Threat Defense devices. This activity is significant as it can identify attempts from users or applications initiating network connection to explicitly or implicitly blocked range or zones. If confirmed malicious, attackers could be attempting to perform a forbidden action on the network such as data exfiltration, lateral movement, or network disruption. +description: The following analytic detects a blocked connection event by identifying a "Block" value in the action field. It leverages logs from Cisco Secure Firewall Threat Defense devices. This activity is significant as it can identify attempts from users or applications initiating network connection to explicitly or implicitly blocked range or zones. If confirmed malicious, attackers could be attempting to perform a forbidden action on the network such as data exfiltration, lateral movement, or network disruption. data_source: -- Cisco Secure Firewall Threat Defense Connection Event + - Cisco Secure Firewall Threat Defense Connection Event search: | - `cisco_secure_firewall` EventType=ConnectionEvent action IN ("Block with reset", "Block", "blocked") - | stats count min(_time) as firstTime max(_time) as lastTime by src, dest, dest_port, transport, rule, url, EVE_Process, action - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___blocked_connection_filter` + `cisco_secure_firewall` EventType=ConnectionEvent action IN ("Block with reset", "Block", "blocked") + | stats count min(_time) as firstTime max(_time) as lastTime by src, dest, dest_port, transport, rule, url, EVE_Process, action + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___blocked_connection_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the ConnectionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The access policy must also enable logging. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the ConnectionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The access policy must also enable logging. known_false_positives: Blocked connection events are generated via an Access Control policy on the Firewall management console. Hence no false positives should be present. references: -- https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf + - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A connection request from $src$ to $dest$ has been blocked according to the configured firewall rule $rule$ - risk_objects: - - field: src - type: system - score: 25 - threat_objects: - - field: EVE_Process - type: process_name - - field: url - type: url + message: A connection request from $src$ to $dest$ has been blocked according to the configured firewall rule $rule$ + risk_objects: + - field: src + type: system + score: 25 + threat_objects: + - field: EVE_Process + type: process_name + - field: url + type: url tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - asset_type: Network - mitre_attack_id: - - T1018 - - T1046 - - T1110 - - T1203 - - T1595.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + asset_type: Network + mitre_attack_id: + - T1018 + - T1046 + - T1110 + - T1203 + - T1595.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___citrix_netscaler_memory_overread_attempt.yml b/detections/network/cisco_secure_firewall___citrix_netscaler_memory_overread_attempt.yml index b7e9b4bbda..4a0d78ee19 100644 --- a/detections/network/cisco_secure_firewall___citrix_netscaler_memory_overread_attempt.yml +++ b/detections/network/cisco_secure_firewall___citrix_netscaler_memory_overread_attempt.yml @@ -1,91 +1,91 @@ name: Cisco Secure Firewall - Citrix NetScaler Memory Overread Attempt id: 93db24a0-fd21-45d7-9daf-84afd5a8cca2 -version: 3 -date: '2026-01-21' +version: 4 +date: '2026-02-25' author: Michael Haag, Nasreddine Bencherchali, Splunk, Talos NTDR status: production type: TTP description: | - This analytic detects exploitation activity of CVE-2025-5777 using Cisco Secure Firewall Intrusion Events. - It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where Snort signature 65118 (Citrix NetScaler memory overread attempt) is triggered - If confirmed malicious, this behavior is highly indicative of a potential exploitation of CVE-2025-5777. + This analytic detects exploitation activity of CVE-2025-5777 using Cisco Secure Firewall Intrusion Events. + It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where Snort signature 65118 (Citrix NetScaler memory overread attempt) is triggered + If confirmed malicious, this behavior is highly indicative of a potential exploitation of CVE-2025-5777. data_source: - - Cisco Secure Firewall Threat Defense Intrusion Event + - Cisco Secure Firewall Threat Defense Intrusion Event search: | - `cisco_secure_firewall` - EventType=IntrusionEvent - signature_id = 65118 - | fillnull - | stats min(_time) as firstTime - max(_time) as lastTime - values(signature_id) as signature_id - values(signature) as signature - values(class_desc) as class_desc - values(MitreAttackGroups) as MitreAttackGroups - values(InlineResult) as InlineResult - values(InlineResultReason) as InlineResultReason - values(src) as src - values(dest_port) as dest_port - values(rule) as rule - values(transport) as transport - values(app) as app - by dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___citrix_netscaler_memory_overread_attempt_filter` + `cisco_secure_firewall` + EventType=IntrusionEvent + signature_id = 65118 + | fillnull + | stats min(_time) as firstTime + max(_time) as lastTime + values(signature_id) as signature_id + values(signature) as signature + values(class_desc) as class_desc + values(MitreAttackGroups) as MitreAttackGroups + values(InlineResult) as InlineResult + values(InlineResultReason) as InlineResultReason + values(src) as src + values(dest_port) as dest_port + values(rule) as rule + values(transport) as transport + values(app) as app + by dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___citrix_netscaler_memory_overread_attempt_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the FileEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The malware & file access policy must also enable logging. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the FileEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The malware & file access policy must also enable logging. known_false_positives: | - Security testing or vulnerability scanners might trigger this. Investigate any potential - matches to determine if they're legitimate. + Security testing or vulnerability scanners might trigger this. Investigate any potential + matches to determine if they're legitimate. references: - - https://support.citrix.com/support-home/kbsearch/article?articleNumber=CTX693420 - - https://support.citrix.com/support-home/kbsearch/article?articleNumber=CTX693420 - - https://www.netscaler.com/blog/news/critical-security-updates-for-netscaler-netscaler-gateway-and-netscaler-console/ - - https://github.com/mingshenhk/CitrixBleed-2-CVE-2025-5777-PoC- - - https://horizon3.ai/attack-research/attack-blogs/cve-2025-5777-citrixbleed-2-write-up-maybe/ - - https://labs.watchtowr.com/how-much-more-must-we-bleed-citrix-netscaler-memory-disclosure-citrixbleed-2-cve-2025-5777/ - - https://github.com/projectdiscovery/nuclei-templates/blob/main/http/cves/2025/CVE-2025-5777.yaml + - https://support.citrix.com/support-home/kbsearch/article?articleNumber=CTX693420 + - https://support.citrix.com/support-home/kbsearch/article?articleNumber=CTX693420 + - https://www.netscaler.com/blog/news/critical-security-updates-for-netscaler-netscaler-gateway-and-netscaler-console/ + - https://github.com/mingshenhk/CitrixBleed-2-CVE-2025-5777-PoC- + - https://horizon3.ai/attack-research/attack-blogs/cve-2025-5777-citrixbleed-2-write-up-maybe/ + - https://labs.watchtowr.com/how-much-more-must-we-bleed-citrix-netscaler-memory-disclosure-citrixbleed-2-cve-2025-5777/ + - https://github.com/projectdiscovery/nuclei-templates/blob/main/http/cves/2025/CVE-2025-5777.yaml drilldown_searches: - - name: View the detection results for - "$src$" and "$dest$" - search: '%original_detection_search% | search src="$src$" dest="$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$src$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" and "$dest$" + search: '%original_detection_search% | search src="$src$" dest="$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential exploitation of CVE-2025-5777 from $src$ - risk_objects: - - field: dest - type: system - score: 85 - threat_objects: - - field: src - type: system + message: Potential exploitation of CVE-2025-5777 from $src$ + risk_objects: + - field: dest + type: system + score: 85 + threat_objects: + - field: src + type: system tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - - Citrix NetScaler ADC and NetScaler Gateway CVE-2025-5777 - asset_type: Endpoint - mitre_attack_id: - - T1203 - - T1059 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: endpoint + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + - Citrix NetScaler ADC and NetScaler Gateway CVE-2025-5777 + asset_type: Endpoint + mitre_attack_id: + - T1203 + - T1059 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/intrusion_event/intrusion_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/intrusion_event/intrusion_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___communication_over_suspicious_ports.yml b/detections/network/cisco_secure_firewall___communication_over_suspicious_ports.yml index dbad761fd1..4bfc6951a9 100644 --- a/detections/network/cisco_secure_firewall___communication_over_suspicious_ports.yml +++ b/detections/network/cisco_secure_firewall___communication_over_suspicious_ports.yml @@ -1,81 +1,77 @@ name: Cisco Secure Firewall - Communication Over Suspicious Ports id: d85c05c8-42c0-4e4a-87e7-4e1bb3e844e3 -version: 4 -date: '2026-01-21' +version: 5 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - The following analytic detects potential reverse shell activity by identifying connections involving ports commonly associated with remote access tools, shell listeners, or tunneling utilities. It leverages Cisco Secure Firewall Threat Defense logs and monitors destination ports against a list of non-standard, high-risk port values often used in post-exploitation scenarios. Adversaries frequently configure tools like netcat, Meterpreter, or other backdoors to listen or connect over uncommon ports such as 4444, 2222, or 51820 to bypass standard monitoring and firewall rules. If confirmed malicious, this activity may represent command and control (C2) tunneling, lateral movement, or unauthorized remote access. + The following analytic detects potential reverse shell activity by identifying connections involving ports commonly associated with remote access tools, shell listeners, or tunneling utilities. It leverages Cisco Secure Firewall Threat Defense logs and monitors destination ports against a list of non-standard, high-risk port values often used in post-exploitation scenarios. Adversaries frequently configure tools like netcat, Meterpreter, or other backdoors to listen or connect over uncommon ports such as 4444, 2222, or 51820 to bypass standard monitoring and firewall rules. If confirmed malicious, this activity may represent command and control (C2) tunneling, lateral movement, or unauthorized remote access. data_source: -- Cisco Secure Firewall Threat Defense Connection Event + - Cisco Secure Firewall Threat Defense Connection Event search: | - `cisco_secure_firewall` EventType=ConnectionEvent dest_port IN ("888", "999", "2200", "2222", "4000", "4444", "6789", "8531", "50501", "51820") | fillnull value="unknown" url - | stats min(_time) as firstTime max(_time) as lastTime - values(src_port) as src_port - values(url) as url - values(rule) as rule - count by src, dest, dest_port, transport, action - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___communication_over_suspicious_ports_filter` + `cisco_secure_firewall` EventType=ConnectionEvent dest_port IN ("888", "999", "2200", "2222", "4000", "4444", "6789", "8531", "50501", "51820") | fillnull value="unknown" url + | stats min(_time) as firstTime max(_time) as lastTime + values(src_port) as src_port + values(url) as url + values(rule) as rule + count by src, dest, dest_port, transport, action + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___communication_over_suspicious_ports_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the ConnectionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The access policy must also enable logging. - The search uses a hardcoded list of suspicious ports, you might want to tune those according to your environment + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the ConnectionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The access policy must also enable logging. + The search uses a hardcoded list of suspicious ports, you might want to tune those according to your environment known_false_positives: | - Some legitimate services or custom applications may use non-standard ports for development, remote management, or internal communication. - Ephemeral ports in test environments may occasionally overlap with ports used in this detection. - Additional context such as process name, user behavior, or endpoint telemetry should be used to validate suspicious sessions before escalation. + Some legitimate services or custom applications may use non-standard ports for development, remote management, or internal communication. + Ephemeral ports in test environments may occasionally overlap with ports used in this detection. + Additional context such as process name, user behavior, or endpoint telemetry should be used to validate suspicious sessions before escalation. references: - - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf + - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious communication detected from $src$ to $dest$ over port $dest_port$. - risk_objects: - - field: src - type: system - score: 20 - threat_objects: - - field: url - type: url + message: Suspicious communication detected from $src$ to $dest$ over port $dest_port$. + risk_objects: + - field: src + type: system + score: 20 + threat_objects: + - field: url + type: url tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - asset_type: Network - security_domain: network - mitre_attack_id: - - T1021 - - T1055 - - T1059.001 - - T1105 - - T1219 - - T1571 - product: - - Splunk Enterprise - - Splunk Cloud - - Splunk Enterprise Security + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + asset_type: Network + security_domain: network + mitre_attack_id: + - T1021 + - T1055 + - T1059.001 + - T1105 + - T1219 + - T1571 + product: + - Splunk Enterprise + - Splunk Cloud + - Splunk Enterprise Security tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___connection_to_file_sharing_domain.yml b/detections/network/cisco_secure_firewall___connection_to_file_sharing_domain.yml index 6ed40a06f9..b796274aa4 100644 --- a/detections/network/cisco_secure_firewall___connection_to_file_sharing_domain.yml +++ b/detections/network/cisco_secure_firewall___connection_to_file_sharing_domain.yml @@ -1,83 +1,79 @@ name: Cisco Secure Firewall - Connection to File Sharing Domain id: f7e5e792-d907-46c1-a58e-4ff974dc462a -version: 6 -date: '2026-01-21' +version: 7 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - The following analytic detects outbound connections to commonly abused file sharing and pastebin-style hosting domains. It leverages Cisco Secure Firewall Threat Defense logs and focuses on allowed connections (action=Allow) where the url field matches a list of known data hosting or temporary storage services. While many of these platforms serve legitimate purposes, they are frequently leveraged by adversaries for malware delivery, data exfiltration, command and control (C2) beacons, or staging of encoded payloads. This analytic is valuable for identifying potential abuse of legitimate infrastructure as part of an attacker's kill chain. If confirmed malicious, this activity may indicate tool staging, credential dumping, or outbound data leaks over HTTP(S). + The following analytic detects outbound connections to commonly abused file sharing and pastebin-style hosting domains. It leverages Cisco Secure Firewall Threat Defense logs and focuses on allowed connections (action=Allow) where the url field matches a list of known data hosting or temporary storage services. While many of these platforms serve legitimate purposes, they are frequently leveraged by adversaries for malware delivery, data exfiltration, command and control (C2) beacons, or staging of encoded payloads. This analytic is valuable for identifying potential abuse of legitimate infrastructure as part of an attacker's kill chain. If confirmed malicious, this activity may indicate tool staging, credential dumping, or outbound data leaks over HTTP(S). data_source: -- Cisco Secure Firewall Threat Defense Connection Event + - Cisco Secure Firewall Threat Defense Connection Event search: | - `cisco_secure_firewall` action IN ("Trust", "Allow", "allowed") EventType=ConnectionEvent url IN ("*//objects.githubusercontent.com*", "*anonfiles.com*", "*cdn.discordapp.com*", "*ddns.net*", "*dl.dropboxusercontent.com*", "*ghostbin.co*", "*glitch.me*", "*gofile.io*", "*hastebin.com*", "*mediafire.com*", "*mega.nz*", "*onrender.com*", "*pages.dev*", "*paste.ee*", "*pastebin.com*", "*pastebin.pl*", "*pastetext.net*", "*privatlab.com*", "*privatlab.net*", "*send.exploit.in*", "*sendspace.com*", "*storage.googleapis.com*", "*storjshare.io*", "*supabase.co*", "*temp.sh*", "*transfer.sh*", "*trycloudflare.com*", "*ufile.io*", "*w3spaces.com*", "*workers.dev*") - | stats count min(_time) as firstTime max(_time) as lastTime - Values(src_port) as src_port - Values(dest) as dest - Values(dest_port) as dest_port - Values(rule) as rule - Values(url) as url - Values(EVE_Process) as EVE_Process - by src, transport, action - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___connection_to_file_sharing_domain_filter` + `cisco_secure_firewall` action IN ("Trust", "Allow", "allowed") EventType=ConnectionEvent url IN ("*//objects.githubusercontent.com*", "*anonfiles.com*", "*cdn.discordapp.com*", "*ddns.net*", "*dl.dropboxusercontent.com*", "*ghostbin.co*", "*glitch.me*", "*gofile.io*", "*hastebin.com*", "*mediafire.com*", "*mega.nz*", "*onrender.com*", "*pages.dev*", "*paste.ee*", "*pastebin.com*", "*pastebin.pl*", "*pastetext.net*", "*privatlab.com*", "*privatlab.net*", "*send.exploit.in*", "*sendspace.com*", "*storage.googleapis.com*", "*storjshare.io*", "*supabase.co*", "*temp.sh*", "*transfer.sh*", "*trycloudflare.com*", "*ufile.io*", "*w3spaces.com*", "*workers.dev*") + | stats count min(_time) as firstTime max(_time) as lastTime + Values(src_port) as src_port + Values(dest) as dest + Values(dest_port) as dest_port + Values(rule) as rule + Values(url) as url + Values(EVE_Process) as EVE_Process + by src, transport, action + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___connection_to_file_sharing_domain_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the ConnectionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The access policy must also enable logging. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the ConnectionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The access policy must also enable logging. known_false_positives: | - Legitimate users and applications may use these domains for benign purposes such as file transfers, collaborative development, or storing public content. - Developer tools, browser extensions, or open-source software may connect to githubusercontent.com or cdn.discordapp.com as part of normal operation. - It is recommended to review the associated process (`EVE_Process`), user behavior, and frequency of access before classifying the activity as suspicious. + Legitimate users and applications may use these domains for benign purposes such as file transfers, collaborative development, or storing public content. + Developer tools, browser extensions, or open-source software may connect to githubusercontent.com or cdn.discordapp.com as part of normal operation. + It is recommended to review the associated process (`EVE_Process`), user behavior, and frequency of access before classifying the activity as suspicious. references: -- https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf + - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The host $src$ initiated a connection to the file sharing or pastebin domain $url$. - risk_objects: - - field: src - type: system - score: 30 - threat_objects: - - field: url - type: url + message: The host $src$ initiated a connection to the file sharing or pastebin domain $url$. + risk_objects: + - field: src + type: system + score: 30 + threat_objects: + - field: url + type: url tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - - Scattered Lapsus$ Hunters - asset_type: Network - mitre_attack_id: - - T1071.001 - - T1090.002 - - T1105 - - T1567.002 - - T1588.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + - Scattered Lapsus$ Hunters + asset_type: Network + mitre_attack_id: + - T1071.001 + - T1090.002 + - T1105 + - T1567.002 + - T1588.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___file_download_over_uncommon_port.yml b/detections/network/cisco_secure_firewall___file_download_over_uncommon_port.yml index ef74e6d224..7e8cece1a6 100644 --- a/detections/network/cisco_secure_firewall___file_download_over_uncommon_port.yml +++ b/detections/network/cisco_secure_firewall___file_download_over_uncommon_port.yml @@ -1,78 +1,74 @@ name: Cisco Secure Firewall - File Download Over Uncommon Port id: f26445a8-a6a2-4855-bec0-0c39e52e5b8f -version: 4 -date: '2026-01-21' +version: 5 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - The following analytic detects file transfers flagged as malware that occurred over non-standard ports (other than 80 and 443). Adversaries may attempt to bypass protocol-based detection or use alternate ports to blend in with other traffic. This analytic identifies these non-conventional flows and surfaces potential evasion techniques. If confirmed malicious this indicate potential malware delivery or other nefarious activity. + The following analytic detects file transfers flagged as malware that occurred over non-standard ports (other than 80 and 443). Adversaries may attempt to bypass protocol-based detection or use alternate ports to blend in with other traffic. This analytic identifies these non-conventional flows and surfaces potential evasion techniques. If confirmed malicious this indicate potential malware delivery or other nefarious activity. data_source: - - Cisco Secure Firewall Threat Defense File Event + - Cisco Secure Firewall Threat Defense File Event search: | - `cisco_secure_firewall` EventType=FileEvent FileDirection="Download" NOT dest_port IN (80, 443) - | lookup cisco_secure_firewall_filetype_lookup Name as FileType OUTPUT Description - | stats count min(_time) as firstTime max(_time) as lastTime - values(file_name) as file_name - values(uri) as uri - values(ClientApplication) as ClientApplication - values(file_hash) as file_hash - values(SHA_Disposition) as SHA_Disposition - by FileDirection FileType app ThreatName dest_port Description src dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | table firstTime lastTime src dest dest_port FileDirection FileType Description uri ClientApplication file_name file_hash SHA_Disposition ThreatName - | `cisco_secure_firewall___file_download_over_uncommon_port_filter` + `cisco_secure_firewall` EventType=FileEvent FileDirection="Download" NOT dest_port IN (80, 443) + | lookup cisco_secure_firewall_filetype_lookup Name as FileType OUTPUT Description + | stats count min(_time) as firstTime max(_time) as lastTime + values(file_name) as file_name + values(uri) as uri + values(ClientApplication) as ClientApplication + values(file_hash) as file_hash + values(SHA_Disposition) as SHA_Disposition + by FileDirection FileType app ThreatName dest_port Description src dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table firstTime lastTime src dest dest_port FileDirection FileType Description uri ClientApplication file_name file_hash SHA_Disposition ThreatName + | `cisco_secure_firewall___file_download_over_uncommon_port_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the FileEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The malware & file access policy must also enable logging. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the FileEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The malware & file access policy must also enable logging. known_false_positives: Some legitimate applications may download files over custom ports (e.g., CDN mirrors, APIs). Apply additional filters accordingly. references: - - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf + - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The host $src$ downloaded a file $file_name$ of type $FileType$ from $dest$ over the uncommon port $dest_port$ - risk_objects: - - field: src - type: system - score: 30 - threat_objects: - - field: file_name - type: file_name - - field: file_hash - type: file_hash + message: The host $src$ downloaded a file $file_name$ of type $FileType$ from $dest$ over the uncommon port $dest_port$ + risk_objects: + - field: src + type: system + score: 30 + threat_objects: + - field: file_name + type: file_name + - field: file_hash + type: file_hash tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - asset_type: Endpoint - mitre_attack_id: - - T1105 - - T1571 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: endpoint + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + asset_type: Endpoint + mitre_attack_id: + - T1105 + - T1571 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/file_event/file_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/file_event/file_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___high_eve_threat_confidence.yml b/detections/network/cisco_secure_firewall___high_eve_threat_confidence.yml index 552bee9308..08d58e9368 100644 --- a/detections/network/cisco_secure_firewall___high_eve_threat_confidence.yml +++ b/detections/network/cisco_secure_firewall___high_eve_threat_confidence.yml @@ -6,72 +6,68 @@ author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - The following analytic detects connections with a high Encrypted Visibility Engine (EVE) threat confidence score, indicating potentially malicious behavior within encrypted traffic. It leverages Cisco Secure Firewall Threat Defense logs and evaluates the EVE_ThreatConfidencePct field, which reflects the system's confidence in classifying encrypted sessions as threats based on machine learning models and behavioral analysis. A score equal to or greater than 80 suggests the connection is highly likely to be associated with malware command and control (C2), remote access tools, or suspicious tunneling behavior. If confirmed malicious, this may indicate covert communication over TLS from compromised hosts. + The following analytic detects connections with a high Encrypted Visibility Engine (EVE) threat confidence score, indicating potentially malicious behavior within encrypted traffic. It leverages Cisco Secure Firewall Threat Defense logs and evaluates the EVE_ThreatConfidencePct field, which reflects the system's confidence in classifying encrypted sessions as threats based on machine learning models and behavioral analysis. A score equal to or greater than 80 suggests the connection is highly likely to be associated with malware command and control (C2), remote access tools, or suspicious tunneling behavior. If confirmed malicious, this may indicate covert communication over TLS from compromised hosts. data_source: -- Cisco Secure Firewall Threat Defense Connection Event + - Cisco Secure Firewall Threat Defense Connection Event search: | - `cisco_secure_firewall` EventType=ConnectionEvent EVE_ThreatConfidencePct >= 80 - | stats count min(_time) as firstTime max(_time) as lastTime - Values(rule) as rule - Values(url) as url - by EVE_Process, EVE_ThreatConfidencePct, src, dest, dest_port, transport, action - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___high_eve_threat_confidence_filter` + `cisco_secure_firewall` EventType=ConnectionEvent EVE_ThreatConfidencePct >= 80 + | stats count min(_time) as firstTime max(_time) as lastTime + Values(rule) as rule + Values(url) as url + by EVE_Process, EVE_ThreatConfidencePct, src, dest, dest_port, transport, action + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___high_eve_threat_confidence_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the ConnectionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The access policy must also enable logging. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the ConnectionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The access policy must also enable logging. known_false_positives: | - Some benign applications may exhibit behaviors that resemble encrypted threat patterns, especially if they use uncommon encryption libraries or custom protocols. - Custom-developed or internal tools may trigger high EVE confidence scores depending on how they encrypt data. - It is recommended to validate the associated process (`EVE_Process`) and destination context, and correlate with other logs (e.g., endpoint or threat intel) before taking response action. + Some benign applications may exhibit behaviors that resemble encrypted threat patterns, especially if they use uncommon encryption libraries or custom protocols. + Custom-developed or internal tools may trigger high EVE confidence scores depending on how they encrypt data. + It is recommended to validate the associated process (`EVE_Process`) and destination context, and correlate with other logs (e.g., endpoint or threat intel) before taking response action. references: - - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf + - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: High threat confidence ($EVE_ThreatConfidencePct$%) from $EVE_Process$ on $src$" - risk_objects: - - field: src - type: system - score: 20 - threat_objects: - - field: EVE_Process - type: process_name + message: High threat confidence ($EVE_ThreatConfidencePct$%) from $EVE_Process$ on $src$" + risk_objects: + - field: src + type: system + score: 20 + threat_objects: + - field: EVE_Process + type: process_name tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - asset_type: Network - security_domain: network - mitre_attack_id: - - T1041 - - T1071.001 - - T1105 - - T1573.002 - product: - - Splunk Enterprise - - Splunk Cloud - - Splunk Enterprise Security + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + asset_type: Network + security_domain: network + mitre_attack_id: + - T1041 + - T1071.001 + - T1105 + - T1573.002 + product: + - Splunk Enterprise + - Splunk Cloud + - Splunk Enterprise Security tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___high_priority_intrusion_classification.yml b/detections/network/cisco_secure_firewall___high_priority_intrusion_classification.yml index 70c0f787d5..84007ee123 100644 --- a/detections/network/cisco_secure_firewall___high_priority_intrusion_classification.yml +++ b/detections/network/cisco_secure_firewall___high_priority_intrusion_classification.yml @@ -1,97 +1,93 @@ name: Cisco Secure Firewall - High Priority Intrusion Classification id: ec99bb81-c31b-4837-8c7d-1b32aa70b337 -version: 3 -date: '2026-01-21' +version: 4 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: TTP description: | - This analytic identifies high-severity intrusion events based on the classification assigned to Snort rules within Cisco Secure Firewall logs. - It leverages Cisco Secure Firewall Threat Defense logs and focuses on events classified as: - - - A Network Trojan was Detected - - Successful Administrator Privilege Gain - - Successful User Privilege Gain - - Attempt to Login By a Default Username and Password - - Known malware command and control traffic - - Known malicious file or file based exploit - - Known client side exploit attempt - - Large Scale Information Leak" + This analytic identifies high-severity intrusion events based on the classification assigned to Snort rules within Cisco Secure Firewall logs. + It leverages Cisco Secure Firewall Threat Defense logs and focuses on events classified as: - These classifications typically represent significant threats such as remote code execution, credential theft, lateral movement, or malware communication. Detection of these classifications should be prioritized for immediate investigation. + - A Network Trojan was Detected + - Successful Administrator Privilege Gain + - Successful User Privilege Gain + - Attempt to Login By a Default Username and Password + - Known malware command and control traffic + - Known malicious file or file based exploit + - Known client side exploit attempt + - Large Scale Information Leak" + + These classifications typically represent significant threats such as remote code execution, credential theft, lateral movement, or malware communication. Detection of these classifications should be prioritized for immediate investigation. data_source: - - Cisco Secure Firewall Threat Defense Intrusion Event + - Cisco Secure Firewall Threat Defense Intrusion Event search: | - `cisco_secure_firewall` EventType=IntrusionEvent - class_desc IN ("A Network Trojan was Detected", "Successful Administrator Privilege Gain", "Successful User Privilege Gain", "Attempt to Login By a Default Username and Password", "Known malware command and control traffic", "Known malicious file or file based exploit", "Known client side exploit attempt", "Large Scale Information Leak") - | fillnull - | stats count min(_time) as firstTime max(_time) as lastTime - values(signature_id) as signature_id - values(MitreAttackGroups) as MitreAttackGroups - values(InlineResult) as InlineResult - values(InlineResultReason) as InlineResultReason - values(dest_port) as dest_port - values(rule) as rule - values(transport) as transport - values(app) as app - by src, dest, signature, class_desc - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___high_priority_intrusion_classification_filter` + `cisco_secure_firewall` EventType=IntrusionEvent + class_desc IN ("A Network Trojan was Detected", "Successful Administrator Privilege Gain", "Successful User Privilege Gain", "Attempt to Login By a Default Username and Password", "Known malware command and control traffic", "Known malicious file or file based exploit", "Known client side exploit attempt", "Large Scale Information Leak") + | fillnull + | stats count min(_time) as firstTime max(_time) as lastTime + values(signature_id) as signature_id + values(MitreAttackGroups) as MitreAttackGroups + values(InlineResult) as InlineResult + values(InlineResultReason) as InlineResultReason + values(dest_port) as dest_port + values(rule) as rule + values(transport) as transport + values(app) as app + by src, dest, signature, class_desc + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___high_priority_intrusion_classification_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the IntrusionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The intrusion access policy must also be configured. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the IntrusionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The intrusion access policy must also be configured. known_false_positives: Some intrusion events that are linked to these classifications might be noisy in certain environments. Apply a combination of filters for specific snort IDs and other indicators. references: - - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf + - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf drilldown_searches: -- name: View the detection results for - "$dest$" and "$src$" - search: '%original_detection_search% | search dest = "$dest$" and src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$src$" + search: '%original_detection_search% | search dest = "$dest$" and src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A high priority intrusion event with classification ($class_desc$) was detected from $src$ to $dest$, indicating potential suspicious activity. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: - - field: signature - type: signature - - field: src - type: ip_address + message: A high priority intrusion event with classification ($class_desc$) was detected from $src$ to $dest$, indicating potential suspicious activity. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: + - field: signature + type: signature + - field: src + type: ip_address tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - asset_type: Network - security_domain: network - mitre_attack_id: - - T1203 - - T1003 - - T1071 - - T1190 - - T1078 - product: - - Splunk Enterprise - - Splunk Cloud - - Splunk Enterprise Security + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + asset_type: Network + security_domain: network + mitre_attack_id: + - T1203 + - T1003 + - T1071 + - T1190 + - T1078 + product: + - Splunk Enterprise + - Splunk Cloud + - Splunk Enterprise Security tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/intrusion_event/intrusion_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/intrusion_event/intrusion_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___high_volume_of_intrusion_events_per_host.yml b/detections/network/cisco_secure_firewall___high_volume_of_intrusion_events_per_host.yml index 621ce5ac27..547ae3abe2 100644 --- a/detections/network/cisco_secure_firewall___high_volume_of_intrusion_events_per_host.yml +++ b/detections/network/cisco_secure_firewall___high_volume_of_intrusion_events_per_host.yml @@ -1,78 +1,74 @@ name: Cisco Secure Firewall - High Volume of Intrusion Events Per Host id: 9f2295a0-0dcb-4a5f-b013-8a6f2a3c11f6 -version: 4 -date: '2026-01-21' +version: 5 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - The following analytic detects internal systems that generate an unusually high volume of intrusion detections within a 30-minute window. It leverages Cisco Secure Firewall Threat Defense logs, specifically focusing on the IntrusionEvent event type, to identify hosts that trigger more than 15 Snort-based signatures during that time. A sudden spike in intrusion alerts originating from a single host may indicate suspicious or malicious activity such as malware execution, command-and-control communication, vulnerability scanning, or lateral movement. In some cases, this behavior may also be caused by misconfigured or outdated software repeatedly tripping detection rules. Systems exhibiting this pattern should be triaged promptly, as repeated Snort rule matches from a single source are often early indicators of compromise, persistence, or active exploitation attempts. + The following analytic detects internal systems that generate an unusually high volume of intrusion detections within a 30-minute window. It leverages Cisco Secure Firewall Threat Defense logs, specifically focusing on the IntrusionEvent event type, to identify hosts that trigger more than 15 Snort-based signatures during that time. A sudden spike in intrusion alerts originating from a single host may indicate suspicious or malicious activity such as malware execution, command-and-control communication, vulnerability scanning, or lateral movement. In some cases, this behavior may also be caused by misconfigured or outdated software repeatedly tripping detection rules. Systems exhibiting this pattern should be triaged promptly, as repeated Snort rule matches from a single source are often early indicators of compromise, persistence, or active exploitation attempts. data_source: - - Cisco Secure Firewall Threat Defense Intrusion Event + - Cisco Secure Firewall Threat Defense Intrusion Event search: | - `cisco_secure_firewall` EventType=IntrusionEvent - | bin _time span=30m - | stats count as TotalEvents values(signature_id) as signature_id - values(signature) as signature - values(dest) as dest - values(dest_port) as dest_port - min(_time) as firstTime max(_time) as lastTime - by src class_desc MitreAttackGroups InlineResult InlineResultReason rule transport app - | where TotalEvents >= 15 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___high_volume_of_intrusion_events_per_host_filter` + `cisco_secure_firewall` EventType=IntrusionEvent + | bin _time span=30m + | stats count as TotalEvents values(signature_id) as signature_id + values(signature) as signature + values(dest) as dest + values(dest_port) as dest_port + min(_time) as firstTime max(_time) as lastTime + by src class_desc MitreAttackGroups InlineResult InlineResultReason rule transport app + | where TotalEvents >= 15 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___high_volume_of_intrusion_events_per_host_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the IntrusionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The intrusion access policy must also be configured. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the IntrusionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The intrusion access policy must also be configured. known_false_positives: | - False positives can occur in environments where vulnerability scanners or malware sandboxes are actively generating simulated attacks. Additionally, noisy or overly aggressive Snort rules may produce bursts of alerts from legitimate applications. Review host context before escalating. + False positives can occur in environments where vulnerability scanners or malware sandboxes are actively generating simulated attacks. Additionally, noisy or overly aggressive Snort rules may produce bursts of alerts from legitimate applications. Review host context before escalating. references: - - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf + - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A high number [$TotalEvents$] of Snort intrusion detections for [$signature$] were triggered by [$src$] in a 30-minute time window. - risk_objects: - - field: src - type: system - score: 40 - threat_objects: - - field: signature - type: signature + message: A high number [$TotalEvents$] of Snort intrusion detections for [$signature$] were triggered by [$src$] in a 30-minute time window. + risk_objects: + - field: src + type: system + score: 40 + threat_objects: + - field: signature + type: signature tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - asset_type: Network - security_domain: network - mitre_attack_id: - - T1059 # Command and Scripting Interpreter - - T1071 # Application Layer Protocol - - T1595.002 # Active Scanning: Vulnerability Scanning - product: - - Splunk Enterprise - - Splunk Cloud - - Splunk Enterprise Security + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + asset_type: Network + security_domain: network + mitre_attack_id: + - T1059 # Command and Scripting Interpreter + - T1071 # Application Layer Protocol + - T1595.002 # Active Scanning: Vulnerability Scanning + product: + - Splunk Enterprise + - Splunk Cloud + - Splunk Enterprise Security tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/intrusion_event/intrusion_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/intrusion_event/intrusion_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___intrusion_events_by_threat_activity.yml b/detections/network/cisco_secure_firewall___intrusion_events_by_threat_activity.yml index ed06071809..fe1af08b56 100644 --- a/detections/network/cisco_secure_firewall___intrusion_events_by_threat_activity.yml +++ b/detections/network/cisco_secure_firewall___intrusion_events_by_threat_activity.yml @@ -6,98 +6,94 @@ author: Bhavin Patel, Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - This analytic detects intrusion events from known threat activity using Cisco Secure Firewall Intrusion Events. - It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where one or multiple Snort signatures - associated with a known threat or threat actor activity have been triggered within a one-hour time window. The detection uses a - lookup table (cisco_snort_ids_to_threat_mapping) to map Snort signature IDs to known threat actors and their techniques. - When multiple signatures associated with the same threat actor are triggered within the time window, and the count of - unique signatures matches or exceeds the expected number of signatures for that threat technique, an alert is generated. - This helps identify potential coordinated threat activity in your network environment by correlating related intrusion - events that occur in close temporal proximity. + This analytic detects intrusion events from known threat activity using Cisco Secure Firewall Intrusion Events. + It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where one or multiple Snort signatures + associated with a known threat or threat actor activity have been triggered within a one-hour time window. The detection uses a + lookup table (cisco_snort_ids_to_threat_mapping) to map Snort signature IDs to known threat actors and their techniques. + When multiple signatures associated with the same threat actor are triggered within the time window, and the count of + unique signatures matches or exceeds the expected number of signatures for that threat technique, an alert is generated. + This helps identify potential coordinated threat activity in your network environment by correlating related intrusion + events that occur in close temporal proximity. - Currently, this detection will alert on the following threat actors or malware families as defined in the cisco_snort_ids_to_threat_mapping lookup: + Currently, this detection will alert on the following threat actors or malware families as defined in the cisco_snort_ids_to_threat_mapping lookup: - * AgentTesla - * Amadey - * ArcaneDoor - * AsyncRAT - * CastleRAT - * Chafer - * DCRAT - * LokiBot - * Lumma Stealer - * Nobelium - * Quasar - * Remcos - * Snake - * Static Tundra - * Xworm + * AgentTesla + * Amadey + * ArcaneDoor + * AsyncRAT + * CastleRAT + * Chafer + * DCRAT + * LokiBot + * Lumma Stealer + * Nobelium + * Quasar + * Remcos + * Snake + * Static Tundra + * Xworm - To add or update threat actors, update the cisco_snort_ids_to_threat_mapping.csv lookup file with new or modified threat names and associated Snort signature IDs. + To add or update threat actors, update the cisco_snort_ids_to_threat_mapping.csv lookup file with new or modified threat names and associated Snort signature IDs. data_source: - - Cisco Secure Firewall Threat Defense Intrusion Event + - Cisco Secure Firewall Threat Defense Intrusion Event search: | - `cisco_secure_firewall` EventType=IntrusionEvent - | stats count AS total_alerts, dc(signature_id) AS sig_count, values(SnortRuleGroups) AS snort_rule_groups, values(connection_id) AS connection_id, values(rule) AS rule, values(dest_port) AS dest_port, values(transport) AS transport, values(app) AS app, values(signature) AS signature, values(src) AS src BY _time dest signature_id - | lookup cisco_snort_ids_to_threat_mapping signature_id OUTPUT threat, category, message - | where isnotnull(threat) - | bin _time span=1d - | stats count AS Total_Alerts, dc(signature_id) AS sig_count, values(signature_id) AS signature_id, values(category) AS category, values(message) AS message, values(snort_rule_groups) AS snort_rule_groups, values(connection_id) AS connection_id, values(rule) AS rule, values(dest_port) AS dest_port, values(transport) AS transport, values(app) AS app, values(signature) AS signature, values(src) AS src BY _time dest threat - | lookup threat_snort_count threat OUTPUT description, distinct_count_snort_ids - | table _time, dest, src, threat, category, message, description, signature_id, signature, snort_rule_groups, sig_count, distinct_count_snort_ids, connection_id, rule, dest_port, transport, app - | where sig_count >= distinct_count_snort_ids - | `cisco_secure_firewall___intrusion_events_by_threat_activity_filter` + `cisco_secure_firewall` EventType=IntrusionEvent + | stats count AS total_alerts, dc(signature_id) AS sig_count, values(SnortRuleGroups) AS snort_rule_groups, values(connection_id) AS connection_id, values(rule) AS rule, values(dest_port) AS dest_port, values(transport) AS transport, values(app) AS app, values(signature) AS signature, values(src) AS src BY _time dest signature_id + | lookup cisco_snort_ids_to_threat_mapping signature_id OUTPUT threat, category, message + | where isnotnull(threat) + | bin _time span=1d + | stats count AS Total_Alerts, dc(signature_id) AS sig_count, values(signature_id) AS signature_id, values(category) AS category, values(message) AS message, values(snort_rule_groups) AS snort_rule_groups, values(connection_id) AS connection_id, values(rule) AS rule, values(dest_port) AS dest_port, values(transport) AS transport, values(app) AS app, values(signature) AS signature, values(src) AS src BY _time dest threat + | lookup threat_snort_count threat OUTPUT description, distinct_count_snort_ids + | table _time, dest, src, threat, category, message, description, signature_id, signature, snort_rule_groups, sig_count, distinct_count_snort_ids, connection_id, rule, dest_port, transport, app + | where sig_count >= distinct_count_snort_ids + | `cisco_secure_firewall___intrusion_events_by_threat_activity_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the IntrusionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The intrusion access policy must also be configured. This detection is based on the cisco_snort_ids_to_threat_mapping.csv mapping file - please update the lookup file with the latest Snort IDs to Threat Actors if you would like to modify the distinct count of Snort IDs needed to trigger the detection or if you would like to add new Snort IDs to Threat Actors. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the IntrusionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The intrusion access policy must also be configured. This detection is based on the cisco_snort_ids_to_threat_mapping.csv mapping file - please update the lookup file with the latest Snort IDs to Threat Actors if you would like to modify the distinct count of Snort IDs needed to trigger the detection or if you would like to add new Snort IDs to Threat Actors. known_false_positives: False positives may occur due to legitimate security testing or research activities. references: - - https://www.cisco.com/c/en/us/products/security/firewalls/index.html - - https://blog.talosintelligence.com/static-tundra/ + - https://www.cisco.com/c/en/us/products/security/firewalls/index.html + - https://blog.talosintelligence.com/static-tundra/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$"" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$"" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential $threat$ activity detected on $dest$ originating from $src$. - risk_objects: - - field: dest - type: system - score: 50 - threat_objects: - - field: signature - type: signature + message: Potential $threat$ activity detected on $dest$ originating from $src$. + risk_objects: + - field: dest + type: system + score: 50 + threat_objects: + - field: signature + type: signature tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - - ArcaneDoor - asset_type: Network - security_domain: network - mitre_attack_id: - - T1041 - - T1573.002 - product: - - Splunk Enterprise - - Splunk Cloud - - Splunk Enterprise Security + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + - ArcaneDoor + asset_type: Network + security_domain: network + mitre_attack_id: + - T1041 + - T1573.002 + product: + - Splunk Enterprise + - Splunk Cloud + - Splunk Enterprise Security tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/lumma_stealer/lumma_stealer_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/lumma_stealer/lumma_stealer_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___lumma_stealer_activity.yml b/detections/network/cisco_secure_firewall___lumma_stealer_activity.yml index f61c300d77..b802433301 100644 --- a/detections/network/cisco_secure_firewall___lumma_stealer_activity.yml +++ b/detections/network/cisco_secure_firewall___lumma_stealer_activity.yml @@ -1,93 +1,89 @@ name: Cisco Secure Firewall - Lumma Stealer Activity id: 96bce783-c22e-4e48-8cf1-3eb2794c5083 -version: 3 -date: '2026-01-21' +version: 4 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk, Talos NTDR status: production type: TTP description: | - This analytic detects Lumma Stealer activity using Cisco Secure Firewall Intrusion Events. - It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where four of the following Snort signature IDs 64793, 64794, 64797, 64798, 64799, 64800, 64801, 62709, 64167, 64168, 64169, 64796, 62710, 62711, 62712, 62713, 62714, 62715, 62716, 62717, 64812, 64810, 64811 occurs in the span of 15 minutes from the same host. - If confirmed malicious, this behavior is highly indicative of a successful infection of Lumma Stealer. + This analytic detects Lumma Stealer activity using Cisco Secure Firewall Intrusion Events. + It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where four of the following Snort signature IDs 64793, 64794, 64797, 64798, 64799, 64800, 64801, 62709, 64167, 64168, 64169, 64796, 62710, 62711, 62712, 62713, 62714, 62715, 62716, 62717, 64812, 64810, 64811 occurs in the span of 15 minutes from the same host. + If confirmed malicious, this behavior is highly indicative of a successful infection of Lumma Stealer. data_source: - - Cisco Secure Firewall Threat Defense Intrusion Event + - Cisco Secure Firewall Threat Defense Intrusion Event search: | - `cisco_secure_firewall` EventType=IntrusionEvent signature_id IN (64793, 64794, 64797, 64798, 64799, 64800, 64801, 62709, 64167, 64168, 64169, 64796, 62710, 62711, 62712, 62713, 62714, 62715, 62716, 62717, 64812, 64810, 64811) - | bin _time span=15m - | fillnull - | stats dc(signature_id) as unique_signature_count - values(signature_id) as signature_id - values(signature) as signature - values(class_desc) as class_desc - values(MitreAttackGroups) as MitreAttackGroups - values(InlineResult) as InlineResult - values(InlineResultReason) as InlineResultReason - values(dest) as dest - values(dest_port) as dest_port - values(rule) as rule - values(transport) as transport - values(app) as app - min(_time) as firstTime - max(_time) as lastTime - by src - | where unique_signature_count >= 3 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___lumma_stealer_activity_filter` + `cisco_secure_firewall` EventType=IntrusionEvent signature_id IN (64793, 64794, 64797, 64798, 64799, 64800, 64801, 62709, 64167, 64168, 64169, 64796, 62710, 62711, 62712, 62713, 62714, 62715, 62716, 62717, 64812, 64810, 64811) + | bin _time span=15m + | fillnull + | stats dc(signature_id) as unique_signature_count + values(signature_id) as signature_id + values(signature) as signature + values(class_desc) as class_desc + values(MitreAttackGroups) as MitreAttackGroups + values(InlineResult) as InlineResult + values(InlineResultReason) as InlineResultReason + values(dest) as dest + values(dest_port) as dest_port + values(rule) as rule + values(transport) as transport + values(app) as app + min(_time) as firstTime + max(_time) as lastTime + by src + | where unique_signature_count >= 3 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___lumma_stealer_activity_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the IntrusionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The intrusion access policy must also be configured. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the IntrusionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The intrusion access policy must also be configured. known_false_positives: False positives should be very unlikely. references: - - https://malpedia.caad.fkie.fraunhofer.de/details/win.lumma + - https://malpedia.caad.fkie.fraunhofer.de/details/win.lumma drilldown_searches: -- name: View the detection results for - "$dest$" and "$src$" - search: '%original_detection_search% | search dest = "$dest$" and src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$src$" + search: '%original_detection_search% | search dest = "$dest$" and src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Lumma Stealer Activity on host $dest$ origniating from $src$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: - - field: signature - type: signature - - field: src - type: ip_address + message: Lumma Stealer Activity on host $dest$ origniating from $src$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: + - field: signature + type: signature + - field: src + type: ip_address tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - - Lumma Stealer - asset_type: Network - security_domain: network - mitre_attack_id: - - T1190 - - T1210 - - T1027 - - T1204 - product: - - Splunk Enterprise - - Splunk Cloud - - Splunk Enterprise Security + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + - Lumma Stealer + asset_type: Network + security_domain: network + mitre_attack_id: + - T1190 + - T1210 + - T1027 + - T1204 + product: + - Splunk Enterprise + - Splunk Cloud + - Splunk Enterprise Security tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/lumma_stealer/lumma_stealer_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/lumma_stealer/lumma_stealer_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___lumma_stealer_download_attempt.yml b/detections/network/cisco_secure_firewall___lumma_stealer_download_attempt.yml index 62c4b99ddb..84218a91aa 100644 --- a/detections/network/cisco_secure_firewall___lumma_stealer_download_attempt.yml +++ b/detections/network/cisco_secure_firewall___lumma_stealer_download_attempt.yml @@ -1,75 +1,71 @@ name: Cisco Secure Firewall - Lumma Stealer Download Attempt id: 66f22f52-fbae-4be7-a263-561dacb63613 -version: 3 -date: '2026-01-21' +version: 4 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk, Talos NTDR status: production type: Anomaly description: | - This analytic detects Lumma Stealer download attempts using Cisco Secure Firewall Intrusion Events. - It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where Snort signatures with IDs 64797, 64798, 64799, 64800, 64801, 64167, 64168, 64169 have been triggered. If confirmed malicious, this behavior could indicate an active infection of Lumma Stealer. + This analytic detects Lumma Stealer download attempts using Cisco Secure Firewall Intrusion Events. + It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where Snort signatures with IDs 64797, 64798, 64799, 64800, 64801, 64167, 64168, 64169 have been triggered. If confirmed malicious, this behavior could indicate an active infection of Lumma Stealer. data_source: - - Cisco Secure Firewall Threat Defense Intrusion Event + - Cisco Secure Firewall Threat Defense Intrusion Event search: | - `cisco_secure_firewall` EventType=IntrusionEvent signature_id IN (62710, 62711, 62712, 62713, 62714, 62715, 62716, 62717, 64810, 64811) - | fillnull - | stats min(_time) as firstTime max(_time) as lastTime - by src dest dest_port transport signature_id signature class_desc MitreAttackGroups rule InlineResult InlineResultReason app - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___lumma_stealer_download_attempt_filter` + `cisco_secure_firewall` EventType=IntrusionEvent signature_id IN (62710, 62711, 62712, 62713, 62714, 62715, 62716, 62717, 64810, 64811) + | fillnull + | stats min(_time) as firstTime max(_time) as lastTime + by src dest dest_port transport signature_id signature class_desc MitreAttackGroups rule InlineResult InlineResultReason app + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___lumma_stealer_download_attempt_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the IntrusionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The intrusion access policy must also be configured. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the IntrusionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The intrusion access policy must also be configured. known_false_positives: False positives should be unlikely. references: - - https://malpedia.caad.fkie.fraunhofer.de/details/win.lumma + - https://malpedia.caad.fkie.fraunhofer.de/details/win.lumma drilldown_searches: -- name: View the detection results for - "$dest$" and "$src$" - search: '%original_detection_search% | search dest = "$dest$" and src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$src$" + search: '%original_detection_search% | search dest = "$dest$" and src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Lumma Stealer Download Attempt detected on host $dest$ origniating from $src$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: - - field: signature - type: signature - - field: src - type: ip_address + message: Lumma Stealer Download Attempt detected on host $dest$ origniating from $src$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: + - field: signature + type: signature + - field: src + type: ip_address tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - - Lumma Stealer - asset_type: Network - security_domain: network - mitre_attack_id: - - T1041 - - T1573.002 - product: - - Splunk Enterprise - - Splunk Cloud - - Splunk Enterprise Security + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + - Lumma Stealer + asset_type: Network + security_domain: network + mitre_attack_id: + - T1041 + - T1573.002 + product: + - Splunk Enterprise + - Splunk Cloud + - Splunk Enterprise Security tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/lumma_stealer/lumma_stealer_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/lumma_stealer/lumma_stealer_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___lumma_stealer_outbound_connection_attempt.yml b/detections/network/cisco_secure_firewall___lumma_stealer_outbound_connection_attempt.yml index 269205d64d..1093f601ae 100644 --- a/detections/network/cisco_secure_firewall___lumma_stealer_outbound_connection_attempt.yml +++ b/detections/network/cisco_secure_firewall___lumma_stealer_outbound_connection_attempt.yml @@ -1,75 +1,71 @@ name: Cisco Secure Firewall - Lumma Stealer Outbound Connection Attempt id: 66f22f52-fbae-4be7-a263-561dacb63612 -version: 3 -date: '2026-01-21' +version: 4 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk, Talos NTDR status: production type: Anomaly description: | - This analytic detects Lumma Stealer outbound connection attempts using Cisco Secure Firewall Intrusion Events. - It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where Snort signatures with IDs 64797, 64798, 64799, 64800, 64801, 64167, 64168, 64169, 62709 have been triggered. If confirmed malicious, this behavior could indicate an active infection of Lumma Stealer. + This analytic detects Lumma Stealer outbound connection attempts using Cisco Secure Firewall Intrusion Events. + It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where Snort signatures with IDs 64797, 64798, 64799, 64800, 64801, 64167, 64168, 64169, 62709 have been triggered. If confirmed malicious, this behavior could indicate an active infection of Lumma Stealer. data_source: - - Cisco Secure Firewall Threat Defense Intrusion Event + - Cisco Secure Firewall Threat Defense Intrusion Event search: | - `cisco_secure_firewall` EventType=IntrusionEvent signature_id IN (64797, 64798, 64799, 64800, 64801, 64167, 64168, 64169, 62709) - | fillnull - | stats min(_time) as firstTime max(_time) as lastTime - by src dest dest_port transport signature_id signature class_desc MitreAttackGroups rule InlineResult InlineResultReason app - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___lumma_stealer_outbound_connection_attempt_filter` + `cisco_secure_firewall` EventType=IntrusionEvent signature_id IN (64797, 64798, 64799, 64800, 64801, 64167, 64168, 64169, 62709) + | fillnull + | stats min(_time) as firstTime max(_time) as lastTime + by src dest dest_port transport signature_id signature class_desc MitreAttackGroups rule InlineResult InlineResultReason app + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___lumma_stealer_outbound_connection_attempt_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the IntrusionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The intrusion access policy must also be configured. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the IntrusionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The intrusion access policy must also be configured. known_false_positives: False positives should be unlikely. references: - - https://malpedia.caad.fkie.fraunhofer.de/details/win.lumma + - https://malpedia.caad.fkie.fraunhofer.de/details/win.lumma drilldown_searches: -- name: View the detection results for - "$dest$" and "$src$" - search: '%original_detection_search% | search dest = "$dest$" and src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$src$" + search: '%original_detection_search% | search dest = "$dest$" and src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Lumma Stealer Outbound Connection Attempt detected on host $dest$ origniating from $src$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: - - field: signature - type: signature - - field: src - type: ip_address + message: Lumma Stealer Outbound Connection Attempt detected on host $dest$ origniating from $src$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: + - field: signature + type: signature + - field: src + type: ip_address tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - - Lumma Stealer - asset_type: Network - security_domain: network - mitre_attack_id: - - T1041 - - T1573.002 - product: - - Splunk Enterprise - - Splunk Cloud - - Splunk Enterprise Security + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + - Lumma Stealer + asset_type: Network + security_domain: network + mitre_attack_id: + - T1041 + - T1573.002 + product: + - Splunk Enterprise + - Splunk Cloud + - Splunk Enterprise Security tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/lumma_stealer/lumma_stealer_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/lumma_stealer/lumma_stealer_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___malware_file_downloaded.yml b/detections/network/cisco_secure_firewall___malware_file_downloaded.yml index f338b4e72e..7f413a676d 100644 --- a/detections/network/cisco_secure_firewall___malware_file_downloaded.yml +++ b/detections/network/cisco_secure_firewall___malware_file_downloaded.yml @@ -6,71 +6,67 @@ author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - The following analytic detects file downloads that were classified as malware by Cisco Secure Firewall Threat Defense. It relies on the `SHA_Disposition` field with a value of "Malware" and includes metadata such as file name, file_hash hash, and threat classification. This analytic is critical for surfacing file-based threats that are identified via Cisco's AMP or Threat Grid integrations. If confirmed malicious, this could indicate delivery of malware. + The following analytic detects file downloads that were classified as malware by Cisco Secure Firewall Threat Defense. It relies on the `SHA_Disposition` field with a value of "Malware" and includes metadata such as file name, file_hash hash, and threat classification. This analytic is critical for surfacing file-based threats that are identified via Cisco's AMP or Threat Grid integrations. If confirmed malicious, this could indicate delivery of malware. data_source: - - Cisco Secure Firewall Threat Defense File Event + - Cisco Secure Firewall Threat Defense File Event search: | - `cisco_secure_firewall` EventType=FileEvent SHA_Disposition="Malware" FileDirection="Download" - | lookup cisco_secure_firewall_filetype_lookup Name as FileType OUTPUT Description - | stats count min(_time) as firstTime max(_time) as lastTime - values(uri) as uri - values(ClientApplication) as ClientApplication - values(file_hash) as file_hash - by FileDirection dest src dest_port FileType app file_name ThreatName Description - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | table firstTime lastTime src dest dest_port FileDirection FileType Description uri file_name file_hash app ClientApplication ThreatName SHA_Disposition - | `cisco_secure_firewall___malware_file_downloaded_filter` + `cisco_secure_firewall` EventType=FileEvent SHA_Disposition="Malware" FileDirection="Download" + | lookup cisco_secure_firewall_filetype_lookup Name as FileType OUTPUT Description + | stats count min(_time) as firstTime max(_time) as lastTime + values(uri) as uri + values(ClientApplication) as ClientApplication + values(file_hash) as file_hash + by FileDirection dest src dest_port FileType app file_name ThreatName Description + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table firstTime lastTime src dest dest_port FileDirection FileType Description uri file_name file_hash app ClientApplication ThreatName SHA_Disposition + | `cisco_secure_firewall___malware_file_downloaded_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the FileEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The malware & file access policy must also enable logging. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the FileEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The malware & file access policy must also enable logging. known_false_positives: Malicious verdicts could be outdated or incorrect due to retroactive threat intel. references: - - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf + - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: File with Malware disposition downloaded from $dest$ over port $dest_port$ by $src$ - risk_objects: - - field: src - type: system - score: 30 - threat_objects: - - field: file_name - type: file_name - - field: file_hash - type: file_hash + message: File with Malware disposition downloaded from $dest$ over port $dest_port$ by $src$ + risk_objects: + - field: src + type: system + score: 30 + threat_objects: + - field: file_name + type: file_name + - field: file_hash + type: file_hash tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - asset_type: Endpoint - mitre_attack_id: - - T1203 - - T1105 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: endpoint + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + asset_type: Endpoint + mitre_attack_id: + - T1203 + - T1105 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/file_event/file_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/file_event/file_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___oracle_e_business_suite_correlation.yml b/detections/network/cisco_secure_firewall___oracle_e_business_suite_correlation.yml index 33c5506392..a37e3f7918 100644 --- a/detections/network/cisco_secure_firewall___oracle_e_business_suite_correlation.yml +++ b/detections/network/cisco_secure_firewall___oracle_e_business_suite_correlation.yml @@ -1,117 +1,113 @@ name: Cisco Secure Firewall - Oracle E-Business Suite Correlation id: 9e995d21-6870-43de-acd9-76f372bcf323 -version: 3 -date: '2026-01-21' +version: 4 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk, Talos NTDR status: production type: TTP description: | - This correlation rule identifies potential exploitation attempts of Oracle E-Business Suite vulnerabilities (CVE-2025-61882 and CVE-2025-61884) by correlating multiple intrusion signatures from Cisco Secure Firewall Threat Defense logs. - The detection looks for specific signatures that indicate attempts to exploit the TemplatePreview functionality and vulnerable SyncServlet endpoints as well as post compromise activity involving Cl0p. - By correlating these signatures, the analytic aims to identify coordinated exploitation attempts that may indicate an attacker is targeting Oracle E-Business Suite installations. - Security teams should investigate any instances of these correlated signatures, especially if they are found in conjunction with other suspicious network activity or on systems that should not be exposed to such threats. + This correlation rule identifies potential exploitation attempts of Oracle E-Business Suite vulnerabilities (CVE-2025-61882 and CVE-2025-61884) by correlating multiple intrusion signatures from Cisco Secure Firewall Threat Defense logs. + The detection looks for specific signatures that indicate attempts to exploit the TemplatePreview functionality and vulnerable SyncServlet endpoints as well as post compromise activity involving Cl0p. + By correlating these signatures, the analytic aims to identify coordinated exploitation attempts that may indicate an attacker is targeting Oracle E-Business Suite installations. + Security teams should investigate any instances of these correlated signatures, especially if they are found in conjunction with other suspicious network activity or on systems that should not be exposed to such threats. data_source: - - Cisco Secure Firewall Threat Defense Intrusion Event + - Cisco Secure Firewall Threat Defense Intrusion Event search: | - `cisco_secure_firewall` EventType=IntrusionEvent signature_id IN (65454, 65455, 65377, 65378, 65413, 65414, 65415, 65456) - | bin _time span=5m - | fillnull - | stats dc(signature_id) as unique_signature_count - values(signature_id) as signature_id - values(signature) as signature - values(class_desc) as class_desc - values(MitreAttackGroups) as MitreAttackGroups - values(InlineResult) as InlineResult - values(InlineResultReason) as InlineResultReason - values(dest_port) as dest_port - values(rule) as rule - values(transport) as transport - values(app) as app - min(_time) as firstTime - max(_time) as lastTime - sum(eval(signature_id==65454)) as sig_template_preview - sum(eval(signature_id==65455)) as sig_sync_servlet - sum(eval(signature_id IN (65377,65378,65413,65414,65415,65456))) as sig_exploit_activity - by src dest - | where ( - ( - sig_exploit_activity >= 1 - AND + `cisco_secure_firewall` EventType=IntrusionEvent signature_id IN (65454, 65455, 65377, 65378, 65413, 65414, 65415, 65456) + | bin _time span=5m + | fillnull + | stats dc(signature_id) as unique_signature_count + values(signature_id) as signature_id + values(signature) as signature + values(class_desc) as class_desc + values(MitreAttackGroups) as MitreAttackGroups + values(InlineResult) as InlineResult + values(InlineResultReason) as InlineResultReason + values(dest_port) as dest_port + values(rule) as rule + values(transport) as transport + values(app) as app + min(_time) as firstTime + max(_time) as lastTime + sum(eval(signature_id==65454)) as sig_template_preview + sum(eval(signature_id==65455)) as sig_sync_servlet + sum(eval(signature_id IN (65377,65378,65413,65414,65415,65456))) as sig_exploit_activity + by src dest + | where ( ( - sig_template_preview >= 1 - OR + sig_exploit_activity >= 1 + AND + ( + sig_template_preview >= 1 + OR + sig_sync_servlet >= 1 + ) + ) + OR + ( + sig_template_preview >= 1 + AND sig_sync_servlet >= 1 ) + OR + unique_signature_count >= 2 ) - OR - ( - sig_template_preview >= 1 - AND - sig_sync_servlet >= 1 - ) - OR - unique_signature_count >= 2 - ) - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___oracle_e_business_suite_correlation_filter` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___oracle_e_business_suite_correlation_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the IntrusionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The intrusion access policy must also be configured. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the IntrusionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The intrusion access policy must also be configured. known_false_positives: False positives should be very unlikely. references: - - https://www.oracle.com/security-alerts/alert-cve-2025-61882.html - - https://www.oracle.com/security-alerts/alert-cve-2025-61884.html - - https://labs.watchtowr.com/well-well-well-its-another-day-oracle-e-business-suite-pre-auth-rce-chain-cve-2025-61882well-well-well-its-another-day-oracle-e-business-suite-pre-auth-rce-chain-cve-2025-61882/ - - https://cloud.google.com/blog/topics/threat-intelligence/oracle-ebusiness-suite-zero-day-exploitation + - https://www.oracle.com/security-alerts/alert-cve-2025-61882.html + - https://www.oracle.com/security-alerts/alert-cve-2025-61884.html + - https://labs.watchtowr.com/well-well-well-its-another-day-oracle-e-business-suite-pre-auth-rce-chain-cve-2025-61882well-well-well-its-another-day-oracle-e-business-suite-pre-auth-rce-chain-cve-2025-61882/ + - https://cloud.google.com/blog/topics/threat-intelligence/oracle-ebusiness-suite-zero-day-exploitation drilldown_searches: -- name: View the detection results for - "$dest$" and "$src$" - search: '%original_detection_search% | search dest = "$dest$" and src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$src$" + search: '%original_detection_search% | search dest = "$dest$" and src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Multiple Oracle E-Business Suite exploitation signatures $signature_id$ detected from source IP $src$ to destination IP $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: - - field: signature - type: signature - - field: src - type: ip_address + message: Multiple Oracle E-Business Suite exploitation signatures $signature_id$ detected from source IP $src$ to destination IP $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: + - field: signature + type: signature + - field: src + type: ip_address tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - - Oracle E-Business Suite Exploitation - asset_type: Network - cve: - - CVE-2025-61882 - - CVE-2025-61884 - security_domain: network - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Cloud - - Splunk Enterprise Security + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + - Oracle E-Business Suite Exploitation + asset_type: Network + cve: + - CVE-2025-61882 + - CVE-2025-61884 + security_domain: network + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Cloud + - Splunk Enterprise Security tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/oracle_e_business_suite/oracle_e_business_suite.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/oracle_e_business_suite/oracle_e_business_suite.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___oracle_e_business_suite_exploitation.yml b/detections/network/cisco_secure_firewall___oracle_e_business_suite_exploitation.yml index 232a21c2a5..cddbf3124a 100644 --- a/detections/network/cisco_secure_firewall___oracle_e_business_suite_exploitation.yml +++ b/detections/network/cisco_secure_firewall___oracle_e_business_suite_exploitation.yml @@ -1,93 +1,89 @@ name: Cisco Secure Firewall - Oracle E-Business Suite Exploitation id: 1c077b8a-95a3-4692-980d-c72fc50e9930 -version: 3 -date: '2026-01-21' +version: 4 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk, Talos NTDR status: production type: TTP description: | - This analytic detects vulnerability exploitation and post-compromise activity associated with Oracle E-Business Suite web-application vulnerabilities, CVE-2025-61882 and CVE-2025-61884. - SIDs 65413-65415 detect detect Java.Backdoor.Cl0p variant payload downloads and Java.Backdoor.Cl0p outbound - command-and-control connection attempts. - SIDs 65456, 65377 and 65378 detect attempts to exploit these vulnerabilities. - Security teams should investigate any instances of these signatures, especially if they are found in conjunction with other suspicious network activity or on systems that should not be exposed to such threats. + This analytic detects vulnerability exploitation and post-compromise activity associated with Oracle E-Business Suite web-application vulnerabilities, CVE-2025-61882 and CVE-2025-61884. + SIDs 65413-65415 detect detect Java.Backdoor.Cl0p variant payload downloads and Java.Backdoor.Cl0p outbound + command-and-control connection attempts. + SIDs 65456, 65377 and 65378 detect attempts to exploit these vulnerabilities. + Security teams should investigate any instances of these signatures, especially if they are found in conjunction with other suspicious network activity or on systems that should not be exposed to such threats. data_source: - - Cisco Secure Firewall Threat Defense Intrusion Event + - Cisco Secure Firewall Threat Defense Intrusion Event search: | - `cisco_secure_firewall` - EventType=IntrusionEvent - signature_id IN (65377, 65378, 65413, 65414, 65415, 65456) - | fillnull - | stats values(signature_id) as signature_id - values(signature) as signature - values(class_desc) as class_desc - values(MitreAttackGroups) as MitreAttackGroups - values(InlineResult) as InlineResult - values(InlineResultReason) as InlineResultReason - values(dest_port) as dest_port - values(rule) as rule - values(transport) as transport - values(app) as app - min(_time) as firstTime - max(_time) as lastTime - by src dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___oracle_e_business_suite_exploitation_filter` + `cisco_secure_firewall` + EventType=IntrusionEvent + signature_id IN (65377, 65378, 65413, 65414, 65415, 65456) + | fillnull + | stats values(signature_id) as signature_id + values(signature) as signature + values(class_desc) as class_desc + values(MitreAttackGroups) as MitreAttackGroups + values(InlineResult) as InlineResult + values(InlineResultReason) as InlineResultReason + values(dest_port) as dest_port + values(rule) as rule + values(transport) as transport + values(app) as app + min(_time) as firstTime + max(_time) as lastTime + by src dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___oracle_e_business_suite_exploitation_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the IntrusionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The intrusion access policy must also be configured. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the IntrusionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The intrusion access policy must also be configured. known_false_positives: False positives should be unlikely. references: - - https://www.oracle.com/security-alerts/alert-cve-2025-61882.html - - https://www.oracle.com/security-alerts/alert-cve-2025-61884.html - - https://labs.watchtowr.com/well-well-well-its-another-day-oracle-e-business-suite-pre-auth-rce-chain-cve-2025-61882well-well-well-its-another-day-oracle-e-business-suite-pre-auth-rce-chain-cve-2025-61882/ - - https://cloud.google.com/blog/topics/threat-intelligence/oracle-ebusiness-suite-zero-day-exploitation + - https://www.oracle.com/security-alerts/alert-cve-2025-61882.html + - https://www.oracle.com/security-alerts/alert-cve-2025-61884.html + - https://labs.watchtowr.com/well-well-well-its-another-day-oracle-e-business-suite-pre-auth-rce-chain-cve-2025-61882well-well-well-its-another-day-oracle-e-business-suite-pre-auth-rce-chain-cve-2025-61882/ + - https://cloud.google.com/blog/topics/threat-intelligence/oracle-ebusiness-suite-zero-day-exploitation drilldown_searches: -- name: View the detection results for - "$dest$" and "$src$" - search: '%original_detection_search% | search dest = "$dest$" and src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$src$" + search: '%original_detection_search% | search dest = "$dest$" and src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Network activity associated with Oracle E-Business Suite exploitation detected from source IP $src$ to destination IP $dest$. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: - - field: signature - type: signature - - field: src - type: ip_address + message: Network activity associated with Oracle E-Business Suite exploitation detected from source IP $src$ to destination IP $dest$. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: + - field: signature + type: signature + - field: src + type: ip_address tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - - Oracle E-Business Suite Exploitation - asset_type: Network - security_domain: network - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Cloud - - Splunk Enterprise Security + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + - Oracle E-Business Suite Exploitation + asset_type: Network + security_domain: network + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Cloud + - Splunk Enterprise Security tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/oracle_e_business_suite/oracle_e_business_suite.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/oracle_e_business_suite/oracle_e_business_suite.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___possibly_compromised_host.yml b/detections/network/cisco_secure_firewall___possibly_compromised_host.yml index 093018d81b..e3f95f4292 100644 --- a/detections/network/cisco_secure_firewall___possibly_compromised_host.yml +++ b/detections/network/cisco_secure_firewall___possibly_compromised_host.yml @@ -1,69 +1,65 @@ name: Cisco Secure Firewall - Possibly Compromised Host id: 244a77bb-3b2a-46f1-bf2c-b4f7cd29276d -version: 4 -date: '2026-01-21' +version: 5 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: experimental type: Anomaly description: | - The following analytic highlights high-impact intrusion events assigned by Cisco Secure Firewall. - This detection leverages Cisco Secure Firewall Threat Defense logs and specifically the IntrusionEvent event type and `Impact` field assigned by Cisco Secure Firewall looking for an impact score of 1 or 2. If confirmed malicious this may indicate a potential compromised host. + The following analytic highlights high-impact intrusion events assigned by Cisco Secure Firewall. + This detection leverages Cisco Secure Firewall Threat Defense logs and specifically the IntrusionEvent event type and `Impact` field assigned by Cisco Secure Firewall looking for an impact score of 1 or 2. If confirmed malicious this may indicate a potential compromised host. data_source: - - Cisco Secure Firewall Threat Defense Intrusion Event + - Cisco Secure Firewall Threat Defense Intrusion Event search: | - `cisco_secure_firewall` EventType=IntrusionEvent Impact IN (1,2) - | stats count as TotalDetections values(signature_id) as signature_id - values(signature) as signature - values(rule) as rule - min(_time) as firstTime max(_time) as lastTime - by src dest dest_port transport Impact app impact_desc class_desc MitreAttackGroups InlineResult InlineResultReason - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___possibly_compromised_host_filter` + `cisco_secure_firewall` EventType=IntrusionEvent Impact IN (1,2) + | stats count as TotalDetections values(signature_id) as signature_id + values(signature) as signature + values(rule) as rule + min(_time) as firstTime max(_time) as lastTime + by src dest dest_port transport Impact app impact_desc class_desc MitreAttackGroups InlineResult InlineResultReason + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___possibly_compromised_host_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the IntrusionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The intrusion access policy must also be configured. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the IntrusionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The intrusion access policy must also be configured. known_false_positives: False positives are directly related to their snort rules triggering and the firewall scoring. Apply additional filters if the rules are too noisy by disabling them or simply ignoring certain IP ranges that trigger it. references: - - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf + - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A high impact IntrusionEvent was detected from $src$ to $dest$. - risk_objects: - - field: src - type: system - score: 35 - threat_objects: - - field: signature - type: signature + message: A high impact IntrusionEvent was detected from $src$ to $dest$. + risk_objects: + - field: src + type: system + score: 35 + threat_objects: + - field: signature + type: signature tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - asset_type: Network - security_domain: network - mitre_attack_id: - - T1203 - - T1059 - - T1587.001 - product: - - Splunk Enterprise - - Splunk Cloud - - Splunk Enterprise Security + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + asset_type: Network + security_domain: network + mitre_attack_id: + - T1203 + - T1059 + - T1587.001 + product: + - Splunk Enterprise + - Splunk Cloud + - Splunk Enterprise Security diff --git a/detections/network/cisco_secure_firewall___potential_data_exfiltration.yml b/detections/network/cisco_secure_firewall___potential_data_exfiltration.yml index c4e9a1c036..3c1ab21583 100644 --- a/detections/network/cisco_secure_firewall___potential_data_exfiltration.yml +++ b/detections/network/cisco_secure_firewall___potential_data_exfiltration.yml @@ -6,76 +6,72 @@ author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - The following analytic detects potentially suspicious large outbound data transfers from internal to external networks. It leverages Cisco Secure Firewall Threat Defense logs and calculates the total volume of data exchanged per connection by summing InitiatorBytes and ResponderBytes. Connections exceeding 100 MB are flagged, as these may indicate unauthorized data exfiltration, especially if initiated by unusual users, hosts, or processes. This analytic is scoped to inside-to-outside flows using a macro (cisco_secure_firewall_inside_to_outside) to abstract environment-specific zone definitions. If confirmed malicious, this behavior may reflect data staging and exfiltration over an encrypted or stealthy transport. + The following analytic detects potentially suspicious large outbound data transfers from internal to external networks. It leverages Cisco Secure Firewall Threat Defense logs and calculates the total volume of data exchanged per connection by summing InitiatorBytes and ResponderBytes. Connections exceeding 100 MB are flagged, as these may indicate unauthorized data exfiltration, especially if initiated by unusual users, hosts, or processes. This analytic is scoped to inside-to-outside flows using a macro (cisco_secure_firewall_inside_to_outside) to abstract environment-specific zone definitions. If confirmed malicious, this behavior may reflect data staging and exfiltration over an encrypted or stealthy transport. data_source: - - Cisco Secure Firewall Threat Defense Connection Event + - Cisco Secure Firewall Threat Defense Connection Event search: | - `cisco_secure_firewall` EventType=ConnectionEvent `cisco_secure_firewall_inside_to_outside` - | eval total_bytes = InitiatorBytes + ResponderBytes - | eval total_mb = round(total_bytes / 1024 / 1024, 2) - | where total_mb >= 100 - | eval Exfiltrated = total_mb + " MB" - | stats min(_time) as firstTime max(_time) as lastTime - Values(url) as url - Values(rule) as rule - Values(dest_port) as dest_port - by src, dest, Exfiltrated, transport, action - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___potential_data_exfiltration_filter` + `cisco_secure_firewall` EventType=ConnectionEvent `cisco_secure_firewall_inside_to_outside` + | eval total_bytes = InitiatorBytes + ResponderBytes + | eval total_mb = round(total_bytes / 1024 / 1024, 2) + | where total_mb >= 100 + | eval Exfiltrated = total_mb + " MB" + | stats min(_time) as firstTime max(_time) as lastTime + Values(url) as url + Values(rule) as rule + Values(dest_port) as dest_port + by src, dest, Exfiltrated, transport, action + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___potential_data_exfiltration_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the ConnectionEvent EventType. This search uses two input macros named `cisco_secure_firewall` and `cisco_secure_firewall_inside_to_outside`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definitions - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The access policy must also enable logging. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the ConnectionEvent EventType. This search uses two input macros named `cisco_secure_firewall` and `cisco_secure_firewall_inside_to_outside`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definitions + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The access policy must also enable logging. known_false_positives: | - Large outbound transfers may occur due to legitimate activities such as cloud backups, file syncing, OS or application updates, or developer build deployments. - Backup servers, CI/CD pipelines, and enterprise sync tools (e.g., OneDrive, Dropbox) may exhibit similar patterns. - Additional validation using user context, scheduled task windows, or endpoint telemetry is recommended to reduce false positives. + Large outbound transfers may occur due to legitimate activities such as cloud backups, file syncing, OS or application updates, or developer build deployments. + Backup servers, CI/CD pipelines, and enterprise sync tools (e.g., OneDrive, Dropbox) may exhibit similar patterns. + Additional validation using user context, scheduled task windows, or endpoint telemetry is recommended to reduce false positives. references: - - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf + - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential data exfiltration from $src$ to $dest$ with $Exfiltrated$ MB of data exfiltrated" - risk_objects: - - field: src - type: system - score: 40 - threat_objects: - - field: url - type: url + message: Potential data exfiltration from $src$ to $dest$ with $Exfiltrated$ MB of data exfiltrated" + risk_objects: + - field: src + type: system + score: 40 + threat_objects: + - field: url + type: url tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - asset_type: Network - security_domain: network - mitre_attack_id: - - T1041 - - T1567.002 - - T1048.003 - product: - - Splunk Enterprise - - Splunk Cloud - - Splunk Enterprise Security + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + asset_type: Network + security_domain: network + mitre_attack_id: + - T1041 + - T1567.002 + - T1048.003 + product: + - Splunk Enterprise + - Splunk Cloud + - Splunk Enterprise Security tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___privileged_command_execution_via_http.yml b/detections/network/cisco_secure_firewall___privileged_command_execution_via_http.yml index fc288fdb88..f36eeeaff7 100644 --- a/detections/network/cisco_secure_firewall___privileged_command_execution_via_http.yml +++ b/detections/network/cisco_secure_firewall___privileged_command_execution_via_http.yml @@ -1,90 +1,85 @@ name: Cisco Secure Firewall - Privileged Command Execution via HTTP id: 0c1d2e3f-4a5b-6c7d-8e9f-0a1b2c3d4e5f -version: 1 -date: '2026-01-06' +version: 2 +date: '2026-02-25' author: Nasreddine Bencherchali, Michael Haag, Splunk status: production type: Anomaly description: | - This analytic detects HTTP requests to privileged execution paths on Cisco routers, specifically targeting the `/level/15/exec/-/*` endpoint using Cisco Secure Firewall Intrusion Events. - This detection leverages Snort signature 65370 to identify requests to these sensitive endpoints, which when combined with other indicators may signal active exploitation or post-compromise activity. + This analytic detects HTTP requests to privileged execution paths on Cisco routers, specifically targeting the `/level/15/exec/-/*` endpoint using Cisco Secure Firewall Intrusion Events. + This detection leverages Snort signature 65370 to identify requests to these sensitive endpoints, which when combined with other indicators may signal active exploitation or post-compromise activity. data_source: - - Cisco Secure Firewall Threat Defense Intrusion Event + - Cisco Secure Firewall Threat Defense Intrusion Event search: | - `cisco_secure_firewall` - EventType=IntrusionEvent - signature_id=65370 - | fillnull - | stats dc(signature_id) as unique_signature_count - values(signature_id) as signature_id - values(signature) as signature - values(class_desc) as class_desc - values(MitreAttackGroups) as MitreAttackGroups - values(InlineResult) as InlineResult - values(InlineResultReason) as InlineResultReason - values(src) as src - values(dest_port) as dest_port - values(rule) as rule - values(transport) as transport - values(app) as app - min(_time) as firstTime - max(_time) as lastTime - by dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___privileged_command_execution_via_http_filter` + `cisco_secure_firewall` + EventType=IntrusionEvent + signature_id=65370 + | fillnull + | stats dc(signature_id) as unique_signature_count + values(signature_id) as signature_id + values(signature) as signature + values(class_desc) as class_desc + values(MitreAttackGroups) as MitreAttackGroups + values(InlineResult) as InlineResult + values(InlineResultReason) as InlineResultReason + values(src) as src + values(dest_port) as dest_port + values(rule) as rule + values(transport) as transport + values(app) as app + min(_time) as firstTime + max(_time) as lastTime + by dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___privileged_command_execution_via_http_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the FileEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The malware & file access policy must also enable logging. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the FileEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The malware & file access policy must also enable logging. known_false_positives: | - No false positives have been identified yet. + No false positives have been identified yet. references: - - https://www.cisa.gov/news-events/cybersecurity-advisories/aa25-239a + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa25-239a drilldown_searches: - - name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: HTTP request to privileged execution path detected from $src$ to Cisco router $dest$ - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: - - field: src - type: ip_address + message: HTTP request to privileged execution path detected from $src$ to Cisco router $dest$ + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - - Salt Typhoon - asset_type: Network - mitre_attack_id: - - T1059 - - T1505.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + - Salt Typhoon + asset_type: Network + mitre_attack_id: + - T1059 + - T1505.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/intrusion_event/intrusion_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/intrusion_event/intrusion_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___rare_snort_rule_triggered.yml b/detections/network/cisco_secure_firewall___rare_snort_rule_triggered.yml index c9d9e885f4..549dcf4016 100644 --- a/detections/network/cisco_secure_firewall___rare_snort_rule_triggered.yml +++ b/detections/network/cisco_secure_firewall___rare_snort_rule_triggered.yml @@ -1,57 +1,57 @@ name: Cisco Secure Firewall - Rare Snort Rule Triggered id: e20313d2-7d63-4fcf-b2d9-d6e12c6c7bd7 -version: 4 -date: '2026-01-21' +version: 5 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Hunting description: | - This analytic identifies Snort signatures that have triggered only once in the past 7 days across all Cisco Secure Firewall IntrusionEvent logs. While these rules typically do not trigger in day-to-day network activity, their sudden appearance may indicate early-stage compromise, previously unseen malware, or reconnaissance activity against less commonly exposed services. Investigating these outliers can provide valuable insight into new or low-noise adversary behaviors. + This analytic identifies Snort signatures that have triggered only once in the past 7 days across all Cisco Secure Firewall IntrusionEvent logs. While these rules typically do not trigger in day-to-day network activity, their sudden appearance may indicate early-stage compromise, previously unseen malware, or reconnaissance activity against less commonly exposed services. Investigating these outliers can provide valuable insight into new or low-noise adversary behaviors. data_source: - - Cisco Secure Firewall Threat Defense Intrusion Event + - Cisco Secure Firewall Threat Defense Intrusion Event search: | - `cisco_secure_firewall` EventType=IntrusionEvent earliest=-7d - | stats dc(_time) as TriggerCount min(_time) as firstTime max(_time) as lastTime - values(signature) as signature - values(src) as src - values(dest) as dest - values(dest_port) as dest_port - values(transport) as transport - values(app) as app - values(rule) as rule - by signature_id class_desc MitreAttackGroups InlineResult InlineResultReason - | where TriggerCount = 1 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___rare_snort_rule_triggered_filter` + `cisco_secure_firewall` EventType=IntrusionEvent earliest=-7d + | stats dc(_time) as TriggerCount min(_time) as firstTime max(_time) as lastTime + values(signature) as signature + values(src) as src + values(dest) as dest + values(dest_port) as dest_port + values(transport) as transport + values(app) as app + values(rule) as rule + by signature_id class_desc MitreAttackGroups InlineResult InlineResultReason + | where TriggerCount = 1 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___rare_snort_rule_triggered_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the IntrusionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The intrusion access policy must also be configured. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the IntrusionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The intrusion access policy must also be configured. known_false_positives: False positives may occur with certain rare activity. Apply additional filters where required. references: - - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf + - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - asset_type: Network - security_domain: network - mitre_attack_id: - - T1598 - - T1583.006 - product: - - Splunk Enterprise - - Splunk Cloud - - Splunk Enterprise Security - manual_test: This detection is a hunting search that has the fixed time range of 7 days baked into the search. Hence based on the time range of the data in the logs, the detection may or may not return results with TriggerCount = 1 in testing. + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + asset_type: Network + security_domain: network + mitre_attack_id: + - T1598 + - T1583.006 + product: + - Splunk Enterprise + - Splunk Cloud + - Splunk Enterprise Security + manual_test: This detection is a hunting search that has the fixed time range of 7 days baked into the search. Hence based on the time range of the data in the logs, the detection may or may not return results with TriggerCount = 1 in testing. tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/intrusion_event/intrusion_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/intrusion_event/intrusion_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___react_server_components_rce_attempt.yml b/detections/network/cisco_secure_firewall___react_server_components_rce_attempt.yml index dc791ec373..ccf9a7e454 100644 --- a/detections/network/cisco_secure_firewall___react_server_components_rce_attempt.yml +++ b/detections/network/cisco_secure_firewall___react_server_components_rce_attempt.yml @@ -1,87 +1,87 @@ name: Cisco Secure Firewall - React Server Components RCE Attempt id: d36459b1-7901-401a-a67e-44426c15b168 -version: 3 -date: '2026-01-21' +version: 4 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk, Talos NTDR status: production type: TTP description: | - This analytic detects exploitation activity of CVE-2025-55182 using Cisco Secure Firewall Intrusion Events. - It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where Snort signature 65554 (React Server Components remote code execution attempt) is triggered - If confirmed malicious, this behavior could be indicative of a potential exploitation of CVE-2025-55182. + This analytic detects exploitation activity of CVE-2025-55182 using Cisco Secure Firewall Intrusion Events. + It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where Snort signature 65554 (React Server Components remote code execution attempt) is triggered + If confirmed malicious, this behavior could be indicative of a potential exploitation of CVE-2025-55182. data_source: - - Cisco Secure Firewall Threat Defense Intrusion Event + - Cisco Secure Firewall Threat Defense Intrusion Event search: | - `cisco_secure_firewall` - EventType=IntrusionEvent - signature_id = 65554 - | fillnull - | stats min(_time) as firstTime - max(_time) as lastTime - values(signature_id) as signature_id - values(signature) as signature - values(class_desc) as class_desc - values(MitreAttackGroups) as MitreAttackGroups - values(InlineResult) as InlineResult - values(InlineResultReason) as InlineResultReason - values(src) as src - values(dest_port) as dest_port - values(rule) as rule - values(transport) as transport - values(app) as app - by dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___react_server_components_rce_attempt_filter` + `cisco_secure_firewall` + EventType=IntrusionEvent + signature_id = 65554 + | fillnull + | stats min(_time) as firstTime + max(_time) as lastTime + values(signature_id) as signature_id + values(signature) as signature + values(class_desc) as class_desc + values(MitreAttackGroups) as MitreAttackGroups + values(InlineResult) as InlineResult + values(InlineResultReason) as InlineResultReason + values(src) as src + values(dest_port) as dest_port + values(rule) as rule + values(transport) as transport + values(app) as app + by dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___react_server_components_rce_attempt_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the FileEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The malware & file access policy must also enable logging. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the FileEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The malware & file access policy must also enable logging. known_false_positives: | - Security testing or vulnerability scanners might trigger this. Investigate any potential - matches to determine if they're legitimate. + Security testing or vulnerability scanners might trigger this. Investigate any potential + matches to determine if they're legitimate. references: - - https://react.dev/blog/2025/12/03/critical-security-vulnerability-in-react-server-components - - https://nextjs.org/blog/CVE-2025-66478 - - https://nvd.nist.gov/vuln/detail/CVE-2025-55182 - - https://gist.github.com/maple3142/48bc9393f45e068cf8c90ab865c0f5f3 - - https://www.wiz.io/blog/critical-vulnerability-in-react-cve-2025-55182 + - https://react.dev/blog/2025/12/03/critical-security-vulnerability-in-react-server-components + - https://nextjs.org/blog/CVE-2025-66478 + - https://nvd.nist.gov/vuln/detail/CVE-2025-55182 + - https://gist.github.com/maple3142/48bc9393f45e068cf8c90ab865c0f5f3 + - https://www.wiz.io/blog/critical-vulnerability-in-react-cve-2025-55182 drilldown_searches: - - name: View the detection results for - "$src$" and "$dest$" - search: '%original_detection_search% | search src="$src$" dest="$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$src$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" and "$dest$" + search: '%original_detection_search% | search src="$src$" dest="$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential exploitation of CVE-2025-65554 from $src$ - risk_objects: - - field: dest - type: system - score: 85 - threat_objects: - - field: src - type: system + message: Potential exploitation of CVE-2025-65554 from $src$ + risk_objects: + - field: dest + type: system + score: 85 + threat_objects: + - field: src + type: system tags: - analytic_story: - - React2Shell - asset_type: Endpoint - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - security_domain: endpoint + analytic_story: + - React2Shell + asset_type: Endpoint + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + security_domain: endpoint tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/react2shell/react2shell.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/react2shell/react2shell.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___remote_access_software_usage_traffic.yml b/detections/network/cisco_secure_firewall___remote_access_software_usage_traffic.yml index be46028cf9..c001c134ce 100644 --- a/detections/network/cisco_secure_firewall___remote_access_software_usage_traffic.yml +++ b/detections/network/cisco_secure_firewall___remote_access_software_usage_traffic.yml @@ -1,98 +1,91 @@ name: Cisco Secure Firewall - Remote Access Software Usage Traffic id: ac54d39e-a75d-4f42-971d-006db3a0423a -version: 5 -date: '2026-01-21' +version: 6 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - The following analytic detects network traffic associated with known remote access software applications - that are covered by Cisco Secure Firewall Application Detectors, such as AnyDesk, GoToMyPC, LogMeIn, and TeamViewer. - It leverages Cisco Secure Firewall Threat Defense Connection Event. - This activity is significant because adversaries often use remote access tools to maintain unauthorized access to compromised environments. - If confirmed malicious, this activity could allow attackers to control systems remotely, exfiltrate - data, or deploy additional malware, posing a severe threat to the organization's security. + The following analytic detects network traffic associated with known remote access software applications + that are covered by Cisco Secure Firewall Application Detectors, such as AnyDesk, GoToMyPC, LogMeIn, and TeamViewer. + It leverages Cisco Secure Firewall Threat Defense Connection Event. + This activity is significant because adversaries often use remote access tools to maintain unauthorized access to compromised environments. + If confirmed malicious, this activity could allow attackers to control systems remotely, exfiltrate + data, or deploy additional malware, posing a severe threat to the organization's security. data_source: -- Cisco Secure Firewall Threat Defense Connection Event + - Cisco Secure Firewall Threat Defense Connection Event search: | - `cisco_secure_firewall` EventType=ConnectionEvent - | stats min(_time) as firstTime max(_time) as lastTime - values(dest_port) as dest_port - values(dest) as dest - values(transport) as transport - values(url) as url - values(rule) as rule - count by src ClientApplication action - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | lookup cisco_secure_firewall_appid_remote_mgmt_and_desktop_tools appName AS ClientApplication OUTPUT category, appDescription as Description - | search category IN ("remote administration", "remote desktop control") - | `remote_access_software_usage_exceptions` - | `cisco_secure_firewall___remote_access_software_usage_traffic_filter` + `cisco_secure_firewall` EventType=ConnectionEvent + | stats min(_time) as firstTime max(_time) as lastTime + values(dest_port) as dest_port + values(dest) as dest + values(transport) as transport + values(url) as url + values(rule) as rule + count by src ClientApplication action + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | lookup cisco_secure_firewall_appid_remote_mgmt_and_desktop_tools appName AS ClientApplication OUTPUT category, appDescription as Description + | search category IN ("remote administration", "remote desktop control") + | `remote_access_software_usage_exceptions` + | `cisco_secure_firewall___remote_access_software_usage_traffic_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the ConnectionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The access policy must also enable logging. - The "exceptions" macro leverages both an Assets and Identities lookup, as well as a KVStore collection called "remote_software_exceptions" - that lets you track and maintain device- based exceptions for this set of detections. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the ConnectionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The access policy must also enable logging. + The "exceptions" macro leverages both an Assets and Identities lookup, as well as a KVStore collection called "remote_software_exceptions" + that lets you track and maintain device- based exceptions for this set of detections. known_false_positives: | - It is possible that legitimate remote access software is used within the environment. Known false positives can be added to the remote_access_software_usage_exception.csv lookup to globally suppress these situations across all remote access content + It is possible that legitimate remote access software is used within the environment. Known false positives can be added to the remote_access_software_usage_exception.csv lookup to globally suppress these situations across all remote access content references: -- https://attack.mitre.org/techniques/T1219/ -- https://thedfirreport.com/2022/08/08/bumblebee-roasts-its-way-to-domain-admin/ -- https://thedfirreport.com/2022/11/28/emotet-strikes-again-lnk-file-leads-to-domain-wide-ransomware/ + - https://attack.mitre.org/techniques/T1219/ + - https://thedfirreport.com/2022/08/08/bumblebee-roasts-its-way-to-domain-admin/ + - https://thedfirreport.com/2022/11/28/emotet-strikes-again-lnk-file-leads-to-domain-wide-ransomware/ drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Traffic to known remote access software [$ClientApplication$] was - detected from $src$. - risk_objects: - - field: src - type: system - score: 25 - threat_objects: - - field: ClientApplication - type: signature + message: Traffic to known remote access software [$ClientApplication$] was detected from $src$. + risk_objects: + - field: src + type: system + score: 25 + threat_objects: + - field: ClientApplication + type: signature tags: - analytic_story: - - Insider Threat - - Command And Control - - Ransomware - - Remote Monitoring and Management Software - - Cisco Secure Firewall Threat Defense Analytics - - Scattered Spider - - Interlock Ransomware - - Scattered Lapsus$ Hunters - asset_type: Network - mitre_attack_id: - - T1219 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - manual_test: This detection uses A&I lookups from Enterprise Security. + analytic_story: + - Insider Threat + - Command And Control + - Ransomware + - Remote Monitoring and Management Software + - Cisco Secure Firewall Threat Defense Analytics + - Scattered Spider + - Interlock Ransomware + - Scattered Lapsus$ Hunters + asset_type: Network + mitre_attack_id: + - T1219 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + manual_test: This detection uses A&I lookups from Enterprise Security. tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___repeated_blocked_connections.yml b/detections/network/cisco_secure_firewall___repeated_blocked_connections.yml index 4e8a383f0e..fa1b8c984f 100644 --- a/detections/network/cisco_secure_firewall___repeated_blocked_connections.yml +++ b/detections/network/cisco_secure_firewall___repeated_blocked_connections.yml @@ -1,80 +1,76 @@ name: Cisco Secure Firewall - Repeated Blocked Connections id: 1f57f10e-1dc5-47ea-852c-2e85b2503d79 -version: 5 -date: '2026-01-21' +version: 6 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - The following analytic detects repeated blocked connection attempts from the same initiator to the same responder within a short time window. It leverages Cisco Secure Firewall Threat Defense logs and identifies connections where the action is set to Block, and the number of occurrences reaches or exceeds a threshold of ten within a one-minute span. This pattern may indicate a misconfigured application, unauthorized access attempts, or early stages of a brute-force or scanning operation. If confirmed malicious, this behavior may represent an attacker probing the network, attempting lateral movement, or testing firewall rules for weaknesses. + The following analytic detects repeated blocked connection attempts from the same initiator to the same responder within a short time window. It leverages Cisco Secure Firewall Threat Defense logs and identifies connections where the action is set to Block, and the number of occurrences reaches or exceeds a threshold of ten within a one-minute span. This pattern may indicate a misconfigured application, unauthorized access attempts, or early stages of a brute-force or scanning operation. If confirmed malicious, this behavior may represent an attacker probing the network, attempting lateral movement, or testing firewall rules for weaknesses. data_source: -- Cisco Secure Firewall Threat Defense Connection Event + - Cisco Secure Firewall Threat Defense Connection Event search: | - `cisco_secure_firewall` EventType=ConnectionEvent action IN ("Block with reset", "Block", "blocked") - | bin _time span=1m - | stats count min(_time) as firstTime max(_time) as lastTime - Values(dest_port) as dest_port - Values(url) as url - by src, dest, transport, rule, action - | where count >= 10 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___repeated_blocked_connections_filter` + `cisco_secure_firewall` EventType=ConnectionEvent action IN ("Block with reset", "Block", "blocked") + | bin _time span=1m + | stats count min(_time) as firstTime max(_time) as lastTime + Values(dest_port) as dest_port + Values(url) as url + by src, dest, transport, rule, action + | where count >= 10 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___repeated_blocked_connections_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the ConnectionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The access policy must also enable logging. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the ConnectionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The access policy must also enable logging. known_false_positives: | - Misconfigured applications or automated scripts may generate repeated blocked traffic, particularly if attempting to reach decommissioned or restricted resources. - Vulnerability scanners or penetration testing tools running in authorized environments may trigger this alert. - Tuning may be required to exclude known internal tools or scanner IPs from detection. + Misconfigured applications or automated scripts may generate repeated blocked traffic, particularly if attempting to reach decommissioned or restricted resources. + Vulnerability scanners or penetration testing tools running in authorized environments may trigger this alert. + Tuning may be required to exclude known internal tools or scanner IPs from detection. references: - - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf + - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Repeated blocked connections detected from $src$ to $dest$ according to the configured firewall rule $rule$ - risk_objects: - - field: src - type: system - score: 25 - threat_objects: - - field: url - type: url + message: Repeated blocked connections detected from $src$ to $dest$ according to the configured firewall rule $rule$ + risk_objects: + - field: src + type: system + score: 25 + threat_objects: + - field: url + type: url tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - asset_type: Network - security_domain: network - mitre_attack_id: - - T1018 - - T1046 - - T1110 - - T1203 - - T1595.002 - product: - - Splunk Enterprise - - Splunk Cloud - - Splunk Enterprise Security + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + asset_type: Network + security_domain: network + mitre_attack_id: + - T1018 + - T1046 + - T1110 + - T1203 + - T1595.002 + product: + - Splunk Enterprise + - Splunk Cloud + - Splunk Enterprise Security tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___repeated_malware_downloads.yml b/detections/network/cisco_secure_firewall___repeated_malware_downloads.yml index ed521faf3f..e3c71c8dcf 100644 --- a/detections/network/cisco_secure_firewall___repeated_malware_downloads.yml +++ b/detections/network/cisco_secure_firewall___repeated_malware_downloads.yml @@ -1,86 +1,82 @@ name: Cisco Secure Firewall - Repeated Malware Downloads id: aeff2bb5-3483-48d4-9be8-c8976194be1e -version: 6 -date: '2026-01-21' +version: 7 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - The following analytic detects repeated malware file downloads initiated by the same internal host (src) within a short time window. It leverages Cisco Secure Firewall Threat Defense logs and identifies `FileEvent` events with a `SHA_Disposition` of "Malware" and `FileDirection` set to "Download". If ten or more such events occur from the same host within five minutes, this analytic will trigger. This activity may indicate the host is compromised and repeatedly retrieving malicious content either due to command-and-control, malware staging, or automation. If confirmed malicious, this behavior may represent an infection in progress, persistence mechanism, or a malicious downloader. + The following analytic detects repeated malware file downloads initiated by the same internal host (src) within a short time window. It leverages Cisco Secure Firewall Threat Defense logs and identifies `FileEvent` events with a `SHA_Disposition` of "Malware" and `FileDirection` set to "Download". If ten or more such events occur from the same host within five minutes, this analytic will trigger. This activity may indicate the host is compromised and repeatedly retrieving malicious content either due to command-and-control, malware staging, or automation. If confirmed malicious, this behavior may represent an infection in progress, persistence mechanism, or a malicious downloader. data_source: - - Cisco Secure Firewall Threat Defense File Event + - Cisco Secure Firewall Threat Defense File Event search: | - `cisco_secure_firewall` EventType=FileEvent SHA_Disposition="Malware" FileDirection="Download" - | lookup cisco_secure_firewall_filetype_lookup Name as FileType OUTPUT Description - | bin _time span=5m - | stats count min(_time) as firstTime max(_time) as lastTime - values(uri) as uri - values(ClientApplication) as ClientApplication - values(app) as app - values(file_hash) as file_hash - values(SHA_Disposition) as SHA_Disposition - values(file_name) as file_name - values(ThreatName) as ThreatName - values(dest) as dest - values(dest_port) as dest_port - by src FileDirection FileType Description - | where count >= 10 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | table firstTime lastTime src dest dest_port FileDirection FileType Description uri file_name file_hash app ClientApplication ThreatName SHA_Disposition - | `cisco_secure_firewall___repeated_malware_downloads_filter` + `cisco_secure_firewall` EventType=FileEvent SHA_Disposition="Malware" FileDirection="Download" + | lookup cisco_secure_firewall_filetype_lookup Name as FileType OUTPUT Description + | bin _time span=5m + | stats count min(_time) as firstTime max(_time) as lastTime + values(uri) as uri + values(ClientApplication) as ClientApplication + values(app) as app + values(file_hash) as file_hash + values(SHA_Disposition) as SHA_Disposition + values(file_name) as file_name + values(ThreatName) as ThreatName + values(dest) as dest + values(dest_port) as dest_port + by src FileDirection FileType Description + | where count >= 10 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table firstTime lastTime src dest dest_port FileDirection FileType Description uri file_name file_hash app ClientApplication ThreatName SHA_Disposition + | `cisco_secure_firewall___repeated_malware_downloads_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the FileEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The malware & file access policy must also enable logging. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the FileEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The malware & file access policy must also enable logging. known_false_positives: False positives should be minimal here, tuning may be required to exclude known test machines or development hosts. references: - - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf + - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Repeated malware file downloads detected from $src$ involving $ThreatName$. - risk_objects: - - field: src - type: system - score: 30 - threat_objects: - - field: file_name - type: file_name - - field: file_hash - type: file_hash + message: Repeated malware file downloads detected from $src$ involving $ThreatName$. + risk_objects: + - field: src + type: system + score: 30 + threat_objects: + - field: file_name + type: file_name + - field: file_hash + type: file_hash tags: - analytic_story: - - Hellcat Ransomware - - Cisco Secure Firewall Threat Defense Analytics - asset_type: Network - security_domain: network - mitre_attack_id: - - T1105 - - T1027 - product: - - Splunk Enterprise - - Splunk Cloud - - Splunk Enterprise Security + analytic_story: + - Hellcat Ransomware + - Cisco Secure Firewall Threat Defense Analytics + asset_type: Network + security_domain: network + mitre_attack_id: + - T1105 + - T1027 + product: + - Splunk Enterprise + - Splunk Cloud + - Splunk Enterprise Security tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/file_event/file_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/file_event/file_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___snort_rule_triggered_across_multiple_hosts.yml b/detections/network/cisco_secure_firewall___snort_rule_triggered_across_multiple_hosts.yml index 337e79a7a6..c12a3eb57c 100644 --- a/detections/network/cisco_secure_firewall___snort_rule_triggered_across_multiple_hosts.yml +++ b/detections/network/cisco_secure_firewall___snort_rule_triggered_across_multiple_hosts.yml @@ -1,78 +1,74 @@ name: Cisco Secure Firewall - Snort Rule Triggered Across Multiple Hosts id: a4c76d0a-56b6-44be-814b-939746c4d406 -version: 4 -date: '2026-01-21' +version: 5 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - This analytic identifies Snort intrusion signatures that have been triggered by ten or more distinct internal IP addresses within a one-hour window. It leverages Cisco Secure Firewall Threat Defense logs and focuses on the IntrusionEvent event type to detect activity that may indicate broad targeting or mass exploitation attempts. This behavior is often associated with opportunistic scanning, worm propagation, or automated exploitation of known vulnerabilities across multiple systems. If confirmed malicious, this could represent the early phase of a coordinated attack aiming to gain a foothold on several hosts or move laterally across the environment. + This analytic identifies Snort intrusion signatures that have been triggered by ten or more distinct internal IP addresses within a one-hour window. It leverages Cisco Secure Firewall Threat Defense logs and focuses on the IntrusionEvent event type to detect activity that may indicate broad targeting or mass exploitation attempts. This behavior is often associated with opportunistic scanning, worm propagation, or automated exploitation of known vulnerabilities across multiple systems. If confirmed malicious, this could represent the early phase of a coordinated attack aiming to gain a foothold on several hosts or move laterally across the environment. data_source: - - Cisco Secure Firewall Threat Defense Intrusion Event + - Cisco Secure Firewall Threat Defense Intrusion Event search: | - `cisco_secure_firewall` EventType=IntrusionEvent - | bin _time span=1h - | stats dc(src) as unique_src_ips, values(src) as src - min(_time) as firstTime max(_time) as lastTime - Values(dest) as dest - Values(dest_port) as dest_port - Values(rule) as rule - Values(transport) as transport - Values(app) as app - by signature_id, signature class_desc MitreAttackGroups InlineResult InlineResultReason - | where unique_src_ips >= 10 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___snort_rule_triggered_across_multiple_hosts_filter` + `cisco_secure_firewall` EventType=IntrusionEvent + | bin _time span=1h + | stats dc(src) as unique_src_ips, values(src) as src + min(_time) as firstTime max(_time) as lastTime + Values(dest) as dest + Values(dest_port) as dest_port + Values(rule) as rule + Values(transport) as transport + Values(app) as app + by signature_id, signature class_desc MitreAttackGroups InlineResult InlineResultReason + | where unique_src_ips >= 10 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___snort_rule_triggered_across_multiple_hosts_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the IntrusionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The intrusion access policy must also be configured. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the IntrusionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The intrusion access policy must also be configured. known_false_positives: False positives should be minimal. Simultaneous vulnerability scanning across multiple internal hosts might trigger this, as well as some snort rules that are noisy. Disable those if necessary or increase the threshold. references: - - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf + - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf drilldown_searches: -- name: View the detection results for - "$src$" and "$signature_id$" - search: '%original_detection_search% | search src = "$src$" and signature_id = "$signature_id$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" and "$signature_id$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$signature_id$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" and "$signature_id$" + search: '%original_detection_search% | search src = "$src$" and signature_id = "$signature_id$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" and "$signature_id$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$signature_id$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The Snort rule $signature$ was triggered by $unique_src_ips$ unique internal hosts within a one-hour window, indicating potential widespread exploitation or coordinated targeting activity. - risk_objects: - - field: src - type: system - score: 25 - threat_objects: - - field: signature - type: signature + message: The Snort rule $signature$ was triggered by $unique_src_ips$ unique internal hosts within a one-hour window, indicating potential widespread exploitation or coordinated targeting activity. + risk_objects: + - field: src + type: system + score: 25 + threat_objects: + - field: signature + type: signature tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - asset_type: Network - security_domain: network - mitre_attack_id: - - T1105 - - T1027 - product: - - Splunk Enterprise - - Splunk Cloud - - Splunk Enterprise Security + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + asset_type: Network + security_domain: network + mitre_attack_id: + - T1105 + - T1027 + product: + - Splunk Enterprise + - Splunk Cloud + - Splunk Enterprise Security tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/intrusion_event/intrusion_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/intrusion_event/intrusion_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___ssh_connection_to_non_standard_port.yml b/detections/network/cisco_secure_firewall___ssh_connection_to_non_standard_port.yml index 5cd0b30883..ffb365ab6a 100644 --- a/detections/network/cisco_secure_firewall___ssh_connection_to_non_standard_port.yml +++ b/detections/network/cisco_secure_firewall___ssh_connection_to_non_standard_port.yml @@ -1,89 +1,84 @@ name: Cisco Secure Firewall - SSH Connection to Non-Standard Port id: 9b0c2d3e-4f5a-6b7c-8d9e-0f1a2b3c4d5e -version: 1 -date: '2026-01-06' +version: 2 +date: '2026-02-25' author: Nasreddine Bencherchali, Michael Haag, Splunk status: production type: Anomaly description: | - This analytic detects inbound SSH connections to non-standard ports on network devices using Cisco Secure Firewall Intrusion Events. APT actors have been observed enabling SSH servers on high, non-default TCP ports to maintain encrypted remote access to compromised network infrastructure. - This detection leverages Snort signature 65369 to identify SSH protocol traffic on unusual ports, which may indicate persistence mechanisms or backdoor access established by threat actors. + This analytic detects inbound SSH connections to non-standard ports on network devices using Cisco Secure Firewall Intrusion Events. APT actors have been observed enabling SSH servers on high, non-default TCP ports to maintain encrypted remote access to compromised network infrastructure. + This detection leverages Snort signature 65369 to identify SSH protocol traffic on unusual ports, which may indicate persistence mechanisms or backdoor access established by threat actors. data_source: - - Cisco Secure Firewall Threat Defense Intrusion Event + - Cisco Secure Firewall Threat Defense Intrusion Event search: | - `cisco_secure_firewall` - EventType=IntrusionEvent - signature_id=65369 - | fillnull - | stats dc(signature_id) as unique_signature_count - values(signature_id) as signature_id - values(signature) as signature - values(class_desc) as class_desc - values(MitreAttackGroups) as MitreAttackGroups - values(InlineResult) as InlineResult - values(InlineResultReason) as InlineResultReason - values(src) as src - values(dest_port) as dest_port - values(rule) as rule - values(transport) as transport - values(app) as app - min(_time) as firstTime - max(_time) as lastTime - by dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___ssh_connection_to_non_standard_port_filter` + `cisco_secure_firewall` + EventType=IntrusionEvent + signature_id=65369 + | fillnull + | stats dc(signature_id) as unique_signature_count + values(signature_id) as signature_id + values(signature) as signature + values(class_desc) as class_desc + values(MitreAttackGroups) as MitreAttackGroups + values(InlineResult) as InlineResult + values(InlineResultReason) as InlineResultReason + values(src) as src + values(dest_port) as dest_port + values(rule) as rule + values(transport) as transport + values(app) as app + min(_time) as firstTime + max(_time) as lastTime + by dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___ssh_connection_to_non_standard_port_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the FileEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The malware & file access policy must also enable logging. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the FileEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The malware & file access policy must also enable logging. known_false_positives: | - No false positives have been identified yet. + No false positives have been identified yet. references: - - https://www.cisa.gov/news-events/cybersecurity-advisories/aa25-239a + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa25-239a drilldown_searches: - - name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Inbound SSH connection to non-standard port $dest_port$ detected from $src$ to network device $dest$ - risk_objects: - - field: dest - type: system - score: 50 - threat_objects: - - field: src - type: ip_address + message: Inbound SSH connection to non-standard port $dest_port$ detected from $src$ to network device $dest$ + risk_objects: + - field: dest + type: system + score: 50 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - - Salt Typhoon - asset_type: Network - mitre_attack_id: - - T1021.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + - Salt Typhoon + asset_type: Network + mitre_attack_id: + - T1021.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/intrusion_event/intrusion_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/intrusion_event/intrusion_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___ssh_connection_to_sshd_operns.yml b/detections/network/cisco_secure_firewall___ssh_connection_to_sshd_operns.yml index a193d3ce4b..855cecd6a0 100644 --- a/detections/network/cisco_secure_firewall___ssh_connection_to_sshd_operns.yml +++ b/detections/network/cisco_secure_firewall___ssh_connection_to_sshd_operns.yml @@ -1,90 +1,85 @@ name: Cisco Secure Firewall - SSH Connection to sshd_operns id: 8a9c1d2e-3f4b-5c6d-7e8f-9a0b1c2d3e4f -version: 1 -date: '2026-01-06' +version: 2 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - This analytic detects inbound SSH connections to the sshd_operns service on network devices using Cisco Secure Firewall Intrusion Events. - APT actors have been observed enabling sshd_operns and opening it on non-standard ports to maintain encrypted remote access to compromised network infrastructure. - This detection leverages Snort signature 65368 to identify connections to this service, which when combined with other indicators may signal persistent access mechanisms established by threat actors. + This analytic detects inbound SSH connections to the sshd_operns service on network devices using Cisco Secure Firewall Intrusion Events. + APT actors have been observed enabling sshd_operns and opening it on non-standard ports to maintain encrypted remote access to compromised network infrastructure. + This detection leverages Snort signature 65368 to identify connections to this service, which when combined with other indicators may signal persistent access mechanisms established by threat actors. data_source: - - Cisco Secure Firewall Threat Defense Intrusion Event + - Cisco Secure Firewall Threat Defense Intrusion Event search: | - `cisco_secure_firewall` - EventType=IntrusionEvent - signature_id=65368 - | fillnull - | stats dc(signature_id) as unique_signature_count - values(signature_id) as signature_id - values(signature) as signature - values(class_desc) as class_desc - values(MitreAttackGroups) as MitreAttackGroups - values(InlineResult) as InlineResult - values(InlineResultReason) as InlineResultReason - values(src) as src - values(dest_port) as dest_port - values(rule) as rule - values(transport) as transport - values(app) as app - min(_time) as firstTime - max(_time) as lastTime - by dest - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___ssh_connection_to_sshd_operns_filter` + `cisco_secure_firewall` + EventType=IntrusionEvent + signature_id=65368 + | fillnull + | stats dc(signature_id) as unique_signature_count + values(signature_id) as signature_id + values(signature) as signature + values(class_desc) as class_desc + values(MitreAttackGroups) as MitreAttackGroups + values(InlineResult) as InlineResult + values(InlineResultReason) as InlineResultReason + values(src) as src + values(dest_port) as dest_port + values(rule) as rule + values(transport) as transport + values(app) as app + min(_time) as firstTime + max(_time) as lastTime + by dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___ssh_connection_to_sshd_operns_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the FileEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The malware & file access policy must also enable logging. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the FileEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The malware & file access policy must also enable logging. known_false_positives: | - No false positives have been identified yet. + No false positives have been identified yet. references: - - https://www.cisa.gov/news-events/cybersecurity-advisories/aa25-239a + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa25-239a drilldown_searches: - - name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Inbound SSH connection to sshd_operns detected from $src$ to network device $dest$ - risk_objects: - - field: dest - type: system - score: 50 - threat_objects: - - field: src - type: ip_address + message: Inbound SSH connection to sshd_operns detected from $src$ to network device $dest$ + risk_objects: + - field: dest + type: system + score: 50 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - - Salt Typhoon - asset_type: Network - mitre_attack_id: - - T1021.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + - Salt Typhoon + asset_type: Network + mitre_attack_id: + - T1021.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/intrusion_event/intrusion_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/intrusion_event/intrusion_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___static_tundra_smart_install_abuse.yml b/detections/network/cisco_secure_firewall___static_tundra_smart_install_abuse.yml index 809ea5c241..7046bf9d97 100644 --- a/detections/network/cisco_secure_firewall___static_tundra_smart_install_abuse.yml +++ b/detections/network/cisco_secure_firewall___static_tundra_smart_install_abuse.yml @@ -1,93 +1,89 @@ name: Cisco Secure Firewall - Static Tundra Smart Install Abuse id: 7e9a5a2c-2f1a-4b6a-9a4b-9e7d9c8f5a21 -version: 3 -date: '2026-01-21' +version: 4 +date: '2026-02-25' author: Bhavin Patel, Michael Haag, Splunk status: production type: TTP description: | - This analytic detects activity associated with "Static Tundra" threat actor abuse of the Cisco Smart Install (SMI) protocol - using Cisco Secure Firewall Intrusion Events. It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to - identify occurrences of Smart Install exploitation and protocol abuse, including denial-of-service and buffer overflow - attempts. The detection triggers when multiple Cisco Smart Install-related Snort signatures are observed in a short period from the - same source, which is indicative of active exploitation or reconnaissance against Cisco devices that expose SMI. + This analytic detects activity associated with "Static Tundra" threat actor abuse of the Cisco Smart Install (SMI) protocol + using Cisco Secure Firewall Intrusion Events. It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to + identify occurrences of Smart Install exploitation and protocol abuse, including denial-of-service and buffer overflow + attempts. The detection triggers when multiple Cisco Smart Install-related Snort signatures are observed in a short period from the + same source, which is indicative of active exploitation or reconnaissance against Cisco devices that expose SMI. data_source: - - Cisco Secure Firewall Threat Defense Intrusion Event + - Cisco Secure Firewall Threat Defense Intrusion Event search: | - `cisco_secure_firewall` EventType=IntrusionEvent signature_id IN (46468, 46096, 41722, 41723, 41724, 41725) - | bin _time span=15m - | fillnull - | stats dc(signature_id) as unique_signature_count - values(signature_id) as signature_id - values(signature) as signature - values(class_desc) as class_desc - values(MitreAttackGroups) as MitreAttackGroups - values(InlineResult) as InlineResult - values(InlineResultReason) as InlineResultReason - values(dest) as dest - values(dest_port) as dest_port - values(rule) as rule - values(transport) as transport - values(app) as app - min(_time) as firstTime - max(_time) as lastTime - by src - | where unique_signature_count >= 2 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___static_tundra_smart_install_abuse_filter` + `cisco_secure_firewall` EventType=IntrusionEvent signature_id IN (46468, 46096, 41722, 41723, 41724, 41725) + | bin _time span=15m + | fillnull + | stats dc(signature_id) as unique_signature_count + values(signature_id) as signature_id + values(signature) as signature + values(class_desc) as class_desc + values(MitreAttackGroups) as MitreAttackGroups + values(InlineResult) as InlineResult + values(InlineResultReason) as InlineResultReason + values(dest) as dest + values(dest_port) as dest_port + values(rule) as rule + values(transport) as transport + values(app) as app + min(_time) as firstTime + max(_time) as lastTime + by src + | where unique_signature_count >= 2 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___static_tundra_smart_install_abuse_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense logs with IntrusionEvent data. It uses the `cisco_secure_firewall` - input macro; configure it to match your environment (index, source, sourcetype). Ensure your intrusion policies include the - relevant Smart Install signatures and that events are collected via the Splunk Add-on for Cisco Security Cloud - (https://splunkbase.splunk.com/app/7404). The post-filter macro helps reduce known noise. + This search requires Cisco Secure Firewall Threat Defense logs with IntrusionEvent data. It uses the `cisco_secure_firewall` + input macro; configure it to match your environment (index, source, sourcetype). Ensure your intrusion policies include the + relevant Smart Install signatures and that events are collected via the Splunk Add-on for Cisco Security Cloud + (https://splunkbase.splunk.com/app/7404). The post-filter macro helps reduce known noise. known_false_positives: | - Network scanning or testing tools that probe Cisco Smart Install endpoints may trigger similar signatures. - Validate against maintenance windows or approved security assessments. + Network scanning or testing tools that probe Cisco Smart Install endpoints may trigger similar signatures. + Validate against maintenance windows or approved security assessments. references: -- https://blog.talosintelligence.com/static-tundra/ -- https://sec.cloudapps.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-20180328-smi2 + - https://blog.talosintelligence.com/static-tundra/ + - https://sec.cloudapps.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-20180328-smi2 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Smart Install exploitation or protocol abuse targeting $dest$ originating from $src$ - risk_objects: - - field: dest - type: system - score: 30 - threat_objects: - - field: src - type: ip_address - - field: signature - type: signature + message: Smart Install exploitation or protocol abuse targeting $dest$ originating from $src$ + risk_objects: + - field: dest + type: system + score: 30 + threat_objects: + - field: src + type: ip_address + - field: signature + type: signature tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - - Cisco Smart Install Remote Code Execution CVE-2018-0171 - asset_type: Network - security_domain: network - mitre_attack_id: - - T1190 - - T1210 - - T1499 - product: - - Splunk Enterprise - - Splunk Cloud - - Splunk Enterprise Security + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + - Cisco Smart Install Remote Code Execution CVE-2018-0171 + asset_type: Network + security_domain: network + mitre_attack_id: + - T1190 + - T1210 + - T1499 + product: + - Splunk Enterprise + - Splunk Cloud + - Splunk Enterprise Security tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/static_tundra/static_tundra.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/static_tundra/static_tundra.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___veeam_cve_2023_27532_exploitation_activity.yml b/detections/network/cisco_secure_firewall___veeam_cve_2023_27532_exploitation_activity.yml index 3f3798cd50..c3f01fbf9d 100644 --- a/detections/network/cisco_secure_firewall___veeam_cve_2023_27532_exploitation_activity.yml +++ b/detections/network/cisco_secure_firewall___veeam_cve_2023_27532_exploitation_activity.yml @@ -1,94 +1,90 @@ name: Cisco Secure Firewall - Veeam CVE-2023-27532 Exploitation Activity id: 7b7c2e92-f0b2-48d2-9c9b-b8de52b6b2ae -version: 3 -date: '2026-01-21' +version: 4 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk, Talos NTDR status: production type: TTP description: | - This analytic detects exploitation activity of CVE-2023-27532 using Cisco Secure Firewall Intrusion Events. - It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where Snort signature 61514 (Veeam Backup and Replication credential dump attempt) - is followed within a 5-minute window by 64795 (Veeam Backup and Replication xp_cmdshell invocation attempt), which detects the use of `xp_cmdshell`, a common post-exploitation technique. - If confirmed malicious, this behavior is highly indicative of a successful exploitation of CVE-2023-27532, followed by remote command execution or credential dumping. + This analytic detects exploitation activity of CVE-2023-27532 using Cisco Secure Firewall Intrusion Events. + It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where Snort signature 61514 (Veeam Backup and Replication credential dump attempt) + is followed within a 5-minute window by 64795 (Veeam Backup and Replication xp_cmdshell invocation attempt), which detects the use of `xp_cmdshell`, a common post-exploitation technique. + If confirmed malicious, this behavior is highly indicative of a successful exploitation of CVE-2023-27532, followed by remote command execution or credential dumping. data_source: - - Cisco Secure Firewall Threat Defense Intrusion Event + - Cisco Secure Firewall Threat Defense Intrusion Event search: | - `cisco_secure_firewall` EventType=IntrusionEvent signature_id IN (61514, 64795) - | bin _time span=5m - | fillnull - | stats dc(signature_id) as unique_signature_count - values(signature_id) as signature_id - values(signature) as signature - values(class_desc) as class_desc - values(MitreAttackGroups) as MitreAttackGroups - values(InlineResult) as InlineResult - values(InlineResultReason) as InlineResultReason - values(src) as src - values(dest_port) as dest_port - values(rule) as rule - values(transport) as transport - values(app) as app - min(_time) as firstTime - max(_time) as lastTime - by dest - | where unique_signature_count = 2 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___veeam_cve_2023_27532_exploitation_activity_filter` + `cisco_secure_firewall` EventType=IntrusionEvent signature_id IN (61514, 64795) + | bin _time span=5m + | fillnull + | stats dc(signature_id) as unique_signature_count + values(signature_id) as signature_id + values(signature) as signature + values(class_desc) as class_desc + values(MitreAttackGroups) as MitreAttackGroups + values(InlineResult) as InlineResult + values(InlineResultReason) as InlineResultReason + values(src) as src + values(dest_port) as dest_port + values(rule) as rule + values(transport) as transport + values(app) as app + min(_time) as firstTime + max(_time) as lastTime + by dest + | where unique_signature_count = 2 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___veeam_cve_2023_27532_exploitation_activity_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the IntrusionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The intrusion access policy must also be configured. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the IntrusionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The intrusion access policy must also be configured. known_false_positives: False positives should be very unlikely. references: - - https://nvd.nist.gov/vuln/detail/CVE-2023-27532 - - https://www.veeam.com/kb4424 + - https://nvd.nist.gov/vuln/detail/CVE-2023-27532 + - https://www.veeam.com/kb4424 drilldown_searches: -- name: View the detection results for - "$dest$" and "$src$" - search: '%original_detection_search% | search dest = "$dest$" and src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$src$" + search: '%original_detection_search% | search dest = "$dest$" and src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Exploitation attempt of Veeam CVE-2023-27532 on host $dest$ by $src$. - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: - - field: signature - type: signature - - field: src - type: ip_address + message: Exploitation attempt of Veeam CVE-2023-27532 on host $dest$ by $src$. + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: + - field: signature + type: signature + - field: src + type: ip_address tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - asset_type: Network - security_domain: network - mitre_attack_id: - - T1190 - - T1210 - - T1059.001 - - T1003.001 - product: - - Splunk Enterprise - - Splunk Cloud - - Splunk Enterprise Security + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + asset_type: Network + security_domain: network + mitre_attack_id: + - T1190 + - T1210 + - T1059.001 + - T1003.001 + product: + - Splunk Enterprise + - Splunk Cloud + - Splunk Enterprise Security tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/intrusion_event/intrusion_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/intrusion_event/intrusion_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_secure_firewall___wget_or_curl_download.yml b/detections/network/cisco_secure_firewall___wget_or_curl_download.yml index 736ed255da..889a8c46d3 100644 --- a/detections/network/cisco_secure_firewall___wget_or_curl_download.yml +++ b/detections/network/cisco_secure_firewall___wget_or_curl_download.yml @@ -1,84 +1,80 @@ name: Cisco Secure Firewall - Wget or Curl Download id: 173a1cb9-1814-4128-a9dc-f29dade89957 -version: 5 -date: '2026-01-21' +version: 6 +date: '2026-02-25' author: Nasreddine Bencherchali, Splunk status: production type: Anomaly description: | - The following analytic detects outbound connections initiated by command-line tools such as curl or wget. It leverages Cisco Secure Firewall Threat Defense logs and identifies allowed connections (action=Allow) where either the EVE_Process or ClientApplication fields indicate use of these utilities. While curl and wget are legitimate tools commonly used for software updates and scripting, adversaries often abuse them to download payloads, retrieve additional tools, or establish staging infrastructure from compromised systems. If confirmed malicious, this behavior may indicate the download phase of an attack chain or a command-and-control utility retrieval. + The following analytic detects outbound connections initiated by command-line tools such as curl or wget. It leverages Cisco Secure Firewall Threat Defense logs and identifies allowed connections (action=Allow) where either the EVE_Process or ClientApplication fields indicate use of these utilities. While curl and wget are legitimate tools commonly used for software updates and scripting, adversaries often abuse them to download payloads, retrieve additional tools, or establish staging infrastructure from compromised systems. If confirmed malicious, this behavior may indicate the download phase of an attack chain or a command-and-control utility retrieval. data_source: -- Cisco Secure Firewall Threat Defense Connection Event + - Cisco Secure Firewall Threat Defense Connection Event search: | - `cisco_secure_firewall` EventType=ConnectionEvent action IN ("Trust", "Allow", "allowed") AND - ( EVE_Process IN ("*curl*", "*wget*") OR ClientApplication IN ("cURL", "Wget") ) - | stats count min(_time) as firstTime max(_time) as lastTime - Values(rule) as rule - Values(url) as url - Values(dest_port) as dest_port - Values(ClientApplicationVersion) as ClientApplicationVersion - Values(src_port) as src_port - by src, dest, transport, EVE_Process, ClientApplication, action - | table src src_port dest dest_port transport url EVE_Process ClientApplication ClientApplicationVersion rule firstTime lastTime - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_secure_firewall___wget_or_curl_download_filter` + `cisco_secure_firewall` EventType=ConnectionEvent action IN ("Trust", "Allow", "allowed") AND + ( EVE_Process IN ("*curl*", "*wget*") OR ClientApplication IN ("cURL", "Wget") ) + | stats count min(_time) as firstTime max(_time) as lastTime + Values(rule) as rule + Values(url) as url + Values(dest_port) as dest_port + Values(ClientApplicationVersion) as ClientApplicationVersion + Values(src_port) as src_port + by src, dest, transport, EVE_Process, ClientApplication, action + | table src src_port dest dest_port transport url EVE_Process ClientApplication ClientApplicationVersion rule firstTime lastTime + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_secure_firewall___wget_or_curl_download_filter` how_to_implement: | - This search requires Cisco Secure Firewall Threat Defense Logs, which - includes the ConnectionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. - We strongly recommend that you specify your environment-specific configurations - (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition - with configurations for your Splunk environment. The search also uses a post-filter - macro designed to filter out known false positives. - The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). - The access policy must also enable logging. + This search requires Cisco Secure Firewall Threat Defense Logs, which + includes the ConnectionEvent EventType. This search uses an input macro named `cisco_secure_firewall`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Cisco Secure Firewall Threat Defense logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. + The logs are to be ingested using the Splunk Add-on for Cisco Security Cloud (https://splunkbase.splunk.com/app/7404). + The access policy must also enable logging. known_false_positives: | - Developers, administrators, or automation tools may use `curl` or `wget` for legitimate purposes such as software installation, configuration scripts, or CI/CD tasks. - Security tools or health monitoring scripts may also use these utilities to check service availability or download updates. - Review the destination `url`, frequency, and process context to validate whether the download activity is authorized. + Developers, administrators, or automation tools may use `curl` or `wget` for legitimate purposes such as software installation, configuration scripts, or CI/CD tasks. + Security tools or health monitoring scripts may also use these utilities to check service availability or download updates. + Review the destination `url`, frequency, and process context to validate whether the download activity is authorized. references: -- https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf + - https://www.cisco.com/c/en/us/td/docs/security/firepower/741/api/FQE/secure_firewall_estreamer_fqe_guide_740.pdf drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: The process $EVE_Process$ initiated an allowed connection to download content using a command-line utility ($ClientApplication$) from $url$. This behavior may indicate tool staging or payload retrieval via curl or wget. - risk_objects: - - field: src - type: system - score: 25 - threat_objects: - - field: EVE_Process - type: process_name - - field: url - type: url + message: The process $EVE_Process$ initiated an allowed connection to download content using a command-line utility ($ClientApplication$) from $url$. This behavior may indicate tool staging or payload retrieval via curl or wget. + risk_objects: + - field: src + type: system + score: 25 + threat_objects: + - field: EVE_Process + type: process_name + - field: url + type: url tags: - analytic_story: - - Cisco Secure Firewall Threat Defense Analytics - asset_type: Network - mitre_attack_id: - - T1053.003 - - T1059 - - T1071.001 - - T1105 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Cisco Secure Firewall Threat Defense Analytics + asset_type: Network + mitre_attack_id: + - T1053.003 + - T1059 + - T1071.001 + - T1105 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/cisco_smart_install_oversized_packet_detection.yml b/detections/network/cisco_smart_install_oversized_packet_detection.yml index 3c2ffa4a90..42297e78ee 100644 --- a/detections/network/cisco_smart_install_oversized_packet_detection.yml +++ b/detections/network/cisco_smart_install_oversized_packet_detection.yml @@ -6,75 +6,70 @@ author: Bhavin Patel, Michael Haag, Splunk status: production type: TTP description: | - This analytic detects oversized Cisco Smart Install (SMI) protocol messages by inspecting traffic to TCP port 4786 - within the Network_Traffic data model. Abnormally large SMI payloads have been associated with exploitation and - protocol abuse (e.g., CVE-2018-0171; activity reported by the "Static Tundra" threat actor). Monitoring message - sizes over time can help identify possible attempts at remote code execution, denial of service, or reconnaissance - against Cisco devices exposing Smart Install. + This analytic detects oversized Cisco Smart Install (SMI) protocol messages by inspecting traffic to TCP port 4786 + within the Network_Traffic data model. Abnormally large SMI payloads have been associated with exploitation and + protocol abuse (e.g., CVE-2018-0171; activity reported by the "Static Tundra" threat actor). Monitoring message + sizes over time can help identify possible attempts at remote code execution, denial of service, or reconnaissance + against Cisco devices exposing Smart Install. data_source: - - Splunk Stream TCP + - Splunk Stream TCP search: | - | tstats `security_content_summariesonly` - avg(All_Traffic.packets) as avg_packets, - max(All_Traffic.bytes) as max_bytes - from datamodel=Network_Traffic - where All_Traffic.dest_port=4786 AND All_Traffic.transport=tcp - by All_Traffic.src_ip, All_Traffic.dest_ip, _time span=1h - | `drop_dm_object_name("All_Traffic")` - | where max_bytes > 500 - | eval severity=case(max_bytes>1400, "critical", max_bytes>1000, "high", 1=1, "medium") - | `cisco_smart_install_oversized_packet_detection_filter` + | tstats `security_content_summariesonly` + avg(All_Traffic.packets) as avg_packets, + max(All_Traffic.bytes) as max_bytes + from datamodel=Network_Traffic + where All_Traffic.dest_port=4786 AND All_Traffic.transport=tcp + by All_Traffic.src_ip, All_Traffic.dest_ip, _time span=1h + | `drop_dm_object_name("All_Traffic")` + | where max_bytes > 500 + | eval severity=case(max_bytes>1400, "critical", max_bytes>1000, "high", 1=1, "medium") + | `cisco_smart_install_oversized_packet_detection_filter` how_to_implement: | - To implement this search, ingest network traffic into the Network_Traffic data model (e.g., via Splunk Stream with - sourcetype "stream:tcp"). The search analyzes TCP traffic to destination port 4786 (Cisco Smart Install) over hourly - buckets, flags sessions with unusually large maximum bytes, and assigns a basic severity based on size thresholds. - You may tune thresholds or restrict to perimeter-facing traffic. Consider blocking or disabling Smart Install where - not required. + To implement this search, ingest network traffic into the Network_Traffic data model (e.g., via Splunk Stream with + sourcetype "stream:tcp"). The search analyzes TCP traffic to destination port 4786 (Cisco Smart Install) over hourly + buckets, flags sessions with unusually large maximum bytes, and assigns a basic severity based on size thresholds. + You may tune thresholds or restrict to perimeter-facing traffic. Consider blocking or disabling Smart Install where + not required. known_false_positives: | - Legitimate Smart Install operations (e.g., image/config transfers) can produce larger payloads. Baseline typical sizes - for your environment and allowlist known management stations when appropriate. + Legitimate Smart Install operations (e.g., image/config transfers) can produce larger payloads. Baseline typical sizes + for your environment and allowlist known management stations when appropriate. references: - - https://blog.talosintelligence.com/static-tundra/ - - https://sec.cloudapps.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-20180328-smi2 + - https://blog.talosintelligence.com/static-tundra/ + - https://sec.cloudapps.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-20180328-smi2 drilldown_searches: - - name: View the detection results for - "$dest_ip$" - search: '%original_detection_search% | search dest_ip = "$dest_ip$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest_ip$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest_ip$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest_ip$" + search: '%original_detection_search% | search dest_ip = "$dest_ip$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest_ip$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest_ip$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Buffer overflow attempt detected in Cisco Smart Install message to $dest_ip$ from $src_ip$ - risk_objects: - - field: dest_ip - type: system - score: 45 - threat_objects: - - field: src_ip - type: ip_address + message: Buffer overflow attempt detected in Cisco Smart Install message to $dest_ip$ from $src_ip$ + risk_objects: + - field: dest_ip + type: system + score: 45 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Cisco Smart Install Remote Code Execution CVE-2018-0171 - asset_type: Network - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: - - CVE-2018-0171 + analytic_story: + - Cisco Smart Install Remote Code Execution CVE-2018-0171 + asset_type: Network + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: + - CVE-2018-0171 tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/cisco/cisco_smart_install/stream_tcp.log - sourcetype: stream:tcp - source: stream:tcp + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/cisco/cisco_smart_install/stream_tcp.log + sourcetype: stream:tcp + source: stream:tcp diff --git a/detections/network/cisco_smart_install_port_discovery_and_status.yml b/detections/network/cisco_smart_install_port_discovery_and_status.yml index ae6bbd967e..d4af12accf 100644 --- a/detections/network/cisco_smart_install_port_discovery_and_status.yml +++ b/detections/network/cisco_smart_install_port_discovery_and_status.yml @@ -1,75 +1,65 @@ name: Cisco Smart Install Port Discovery and Status id: ded9f9d7-edb8-48cf-8b72-1b459eee6785 -version: 2 -date: '2025-10-14' +version: 3 +date: '2026-02-25' author: Bhavin Patel, Michael Haag, Splunk status: production type: TTP description: This analytic detects network traffic to TCP port 4786, which is used by the Cisco Smart Install protocol. Smart Install is a plug-and-play configuration and image-management feature that helps customers to deploy Cisco switches. This protocol has been exploited via CVE-2018-0171, a vulnerability that allows unauthenticated remote attackers to execute arbitrary code or cause denial of service conditions. Recently, Cisco Talos reported that a Russian state-sponsored threat actor called "Static Tundra" has been actively exploiting this vulnerability to compromise unpatched and end-of-life network devices. Monitoring for traffic to this port can help identify potential exploitation attempts or unauthorized Smart Install activity. data_source: - - Splunk Stream TCP -search: '| tstats `security_content_summariesonly` - count - values(All_Traffic.src_ip) as src_ip - values(All_Traffic.src_port) as src_port - values(All_Traffic.dest_ip) as dest_ip - earliest(_time) as firstTime - latest(_time) as lastTime - from datamodel=Network_Traffic - where All_Traffic.dest_port=4786 AND All_Traffic.transport=tcp - by All_Traffic.dest_ip All_Traffic.dest_port - | `drop_dm_object_name("All_Traffic")` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `cisco_smart_install_port_discovery_and_status_filter`' + - Splunk Stream TCP +search: |- + | tstats `security_content_summariesonly` count values(All_Traffic.src_ip) as src_ip values(All_Traffic.src_port) as src_port values(All_Traffic.dest_ip) as dest_ip earliest(_time) as firstTime latest(_time) as lastTime FROM datamodel=Network_Traffic + WHERE All_Traffic.dest_port=4786 + AND + All_Traffic.transport=tcp + BY All_Traffic.dest_ip All_Traffic.dest_port + | `drop_dm_object_name("All_Traffic")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_smart_install_port_discovery_and_status_filter` how_to_implement: To implement this search, you need to be ingesting network traffic data into the Network_Traffic data model. This can be accomplished using Splunk Stream, which captures and indexes network traffic. Specifically, you need to ensure that TCP traffic is being monitored and indexed with the sourcetype "stream:tcp". The search looks for traffic destined to port 4786, which is used by the Cisco Smart Install protocol. For optimal security, organizations should consider blocking this port at their network perimeter to prevent external exploitation attempts. However, monitoring for both external and internal traffic to this port is valuable for detecting potential malicious activity. You may need to modify this search to focus on traffic patterns specific to your environment, such as monitoring only for inbound traffic from external sources or for unexpected internal communications using this protocol. known_false_positives: Legitimate use of Cisco Smart Install may generate traffic to port 4786 within environments that actively use this feature for switch deployment and management. Network administrators might use Smart Install for legitimate device configuration purposes, especially during network deployment or maintenance windows. To reduce false positives, baseline normal Smart Install usage patterns in your environment and consider implementing time-based filtering to alert only on unexpected usage outside of scheduled maintenance periods. Additionally, consider whitelisting known management stations that legitimately use Smart Install. references: - - https://blog.talosintelligence.com/static-tundra/ - - https://tools.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-20180328-smi2 - - https://github.com/AlrikRr/Cisco-Smart-Exploit - - https://www.exploit-db.com/exploits/44451 + - https://blog.talosintelligence.com/static-tundra/ + - https://tools.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-20180328-smi2 + - https://github.com/AlrikRr/Cisco-Smart-Exploit + - https://www.exploit-db.com/exploits/44451 drilldown_searches: - - name: View the detection results for - "$dest_ip$" - search: '%original_detection_search% | search dest_ip = "$dest_ip$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest_ip$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest_ip$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" - values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" - values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest_ip$" + search: '%original_detection_search% | search dest_ip = "$dest_ip$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest_ip$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest_ip$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Detected network traffic to Cisco Smart Install port (4786) on $dest_ip$. Possible access to Cisco Smart Install. - risk_objects: - - field: dest_ip - type: system - score: 50 - threat_objects: - - field: src_ip - type: ip_address + message: Detected network traffic to Cisco Smart Install port (4786) on $dest_ip$. Possible access to Cisco Smart Install. + risk_objects: + - field: dest_ip + type: system + score: 50 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Scattered Lapsus$ Hunters - - Cisco Smart Install Remote Code Execution CVE-2018-0171 - asset_type: Network - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: - - CVE-2018-0171 + analytic_story: + - Scattered Lapsus$ Hunters + - Cisco Smart Install Remote Code Execution CVE-2018-0171 + asset_type: Network + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: + - CVE-2018-0171 tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/cisco/cisco_smart_install/stream_tcp.log - sourcetype: stream:tcp - source: stream:tcp + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/cisco/cisco_smart_install/stream_tcp.log + sourcetype: stream:tcp + source: stream:tcp diff --git a/detections/network/cisco_snmp_community_string_configuration_changes.yml b/detections/network/cisco_snmp_community_string_configuration_changes.yml index 4e77aa8f51..024b217ddd 100644 --- a/detections/network/cisco_snmp_community_string_configuration_changes.yml +++ b/detections/network/cisco_snmp_community_string_configuration_changes.yml @@ -1,74 +1,73 @@ name: Cisco SNMP Community String Configuration Changes id: b0ce5521-2533-4f24-b8d5-c2ff977aae08 -version: 1 -date: '2025-08-21' +version: 2 +date: '2026-02-25' author: Bhavin Patel, Michael Haag, Splunk status: production type: Anomaly description: This analytic detects changes to SNMP community strings on Cisco devices, which could indicate an attacker establishing persistence or attempting to extract credentials. After gaining initial access to network devices, threat actors like Static Tundra often modify SNMP configurations to enable unauthorized monitoring and data collection. This detection specifically looks for the configuration of SNMP community strings with read-write (rw) or read-only (ro) permissions, as well as the configuration of SNMP hosts that may be used to exfiltrate data. These activities are particularly concerning as they may represent attempts to establish persistent access or extract sensitive information from compromised devices. data_source: -- Cisco IOS Logs -search: '| tstats `security_content_summariesonly` count values(All_Changes.command) as command min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes - where ( - (All_Changes.command="*snmp-server community*rw*") OR - (All_Changes.command="*snmp-server community*ro*") OR - (All_Changes.command="*snmp-server host*") - ) - by All_Changes.dvc All_Changes.user -| `drop_dm_object_name("All_Changes")` -| rename dvc as dest -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `cisco_snmp_community_string_configuration_changes_filter`' + - Cisco IOS Logs +search: |- + | tstats `security_content_summariesonly` count values(All_Changes.command) as command min(_time) as firstTime max(_time) as lastTime FROM datamodel=Change.All_Changes + WHERE ( + (All_Changes.command="*snmp-server community*rw*") + OR + (All_Changes.command="*snmp-server community*ro*") + OR + (All_Changes.command="*snmp-server host*") + ) + BY All_Changes.dvc All_Changes.user + | `drop_dm_object_name("All_Changes")` + | rename dvc as dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_snmp_community_string_configuration_changes_filter` how_to_implement: To implement this search, you need to be ingesting Cisco IOS logs with the sourcetype "cisco:ios" and have these logs mapped to the Change datamodel. Ensure that your Cisco IOS devices are configured to send logs to your Splunk environment, with appropriate logging levels enabled to capture configuration commands. Configure command logging on Cisco IOS devices using the "archive log config logging enable" command to ensure that SNMP configuration changes are properly logged. known_false_positives: Legitimate SNMP configuration changes may trigger this detection during routine network maintenance or initial device setup. Network administrators often need to configure SNMP for monitoring and management purposes. To reduce false positives, consider implementing a baseline of expected administrative activities, including approved administrative usernames, typical times for SNMP configuration changes, and scheduled maintenance windows. You may also want to create a lookup table of approved SNMP hosts and filter out alerts for these destinations. references: -- https://blog.talosintelligence.com/static-tundra/ -- https://tools.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-20180328-smi2 -- https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/snmp/command/snmp-cr-book/snmp-s1.html#wp1307296356 + - https://blog.talosintelligence.com/static-tundra/ + - https://tools.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-20180328-smi2 + - https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/snmp/command/snmp-cr-book/snmp-s1.html#wp1307296356 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious SNMP community string configuration changes detected on Cisco device $dest$ by user $user$, which may indicate persistence establishment - risk_objects: - - field: dest - type: system - score: 60 - - field: user - type: user - score: 40 - threat_objects: - - field: command - type: command + message: Suspicious SNMP community string configuration changes detected on Cisco device $dest$ by user $user$, which may indicate persistence establishment + risk_objects: + - field: dest + type: system + score: 60 + - field: user + type: user + score: 40 + threat_objects: + - field: command + type: command tags: - analytic_story: - - Cisco Smart Install Remote Code Execution CVE-2018-0171 - asset_type: Network - mitre_attack_id: - - T1562.001 - - T1040 - - T1552 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: - - CVE-2018-0171 + analytic_story: + - Cisco Smart Install Remote Code Execution CVE-2018-0171 + asset_type: Network + mitre_attack_id: + - T1562.001 + - T1040 + - T1552 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: + - CVE-2018-0171 tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/cisco/cisco_smart_install/cisco_ios.log - sourcetype: cisco:ios - source: cisco:ios + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/cisco/cisco_smart_install/cisco_ios.log + sourcetype: cisco:ios + source: cisco:ios diff --git a/detections/network/cisco_tftp_server_configuration_for_data_exfiltration.yml b/detections/network/cisco_tftp_server_configuration_for_data_exfiltration.yml index b9518352ed..23d73dcc7e 100644 --- a/detections/network/cisco_tftp_server_configuration_for_data_exfiltration.yml +++ b/detections/network/cisco_tftp_server_configuration_for_data_exfiltration.yml @@ -1,77 +1,69 @@ name: Cisco TFTP Server Configuration for Data Exfiltration id: 1abce487-f480-4d5f-a551-01de0bece0bd -version: 1 -date: '2025-08-21' +version: 2 +date: '2026-02-25' author: Bhavin Patel, Michael Haag, Splunk status: production type: TTP description: This analytic detects the configuration of TFTP services on Cisco IOS devices that could be used to exfiltrate sensitive configuration files. Threat actors like Static Tundra have been observed configuring TFTP servers to make device configuration files accessible for exfiltration after gaining initial access. The detection specifically looks for commands that expose critical configuration files such as startup-config, running-config, and other sensitive system information through TFTP. This activity is particularly concerning as it may represent an attempt to steal credentials, network topology information, and other sensitive data stored in device configurations. data_source: -- Cisco IOS Logs -search: '| tstats `security_content_summariesonly` count values(All_Changes.command) as command min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes - where (All_Changes.command="*tftp-server*") - AND ( - All_Changes.command="*nvram:startup-config*" OR - All_Changes.command="*bootflash:running-config*" OR - All_Changes.command="*system:running-config*" OR - All_Changes.command="*bootflash:info*" OR - All_Changes.command="*startup-config*" OR - All_Changes.command="*running-config*" - ) - by All_Changes.dvc All_Changes.user -| `drop_dm_object_name("All_Changes")` -| rename dvc as dest -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| `cisco_tftp_server_configuration_for_data_exfiltration_filter`' + - Cisco IOS Logs +search: |- + | tstats `security_content_summariesonly` count values(All_Changes.command) as command min(_time) as firstTime max(_time) as lastTime FROM datamodel=Change.All_Changes + WHERE ( + All_Changes.command="*tftp-server*" + ) + AND ( All_Changes.command="*nvram:startup-config*" OR All_Changes.command="*bootflash:running-config*" OR All_Changes.command="*system:running-config*" OR All_Changes.command="*bootflash:info*" OR All_Changes.command="*startup-config*" OR All_Changes.command="*running-config*" ) + BY All_Changes.dvc All_Changes.user + | `drop_dm_object_name("All_Changes")` + | rename dvc as dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_tftp_server_configuration_for_data_exfiltration_filter` how_to_implement: To implement this search, you need to be ingesting Cisco IOS logs with the sourcetype "cisco:ios" and have these logs mapped to the Change datamodel. Ensure that your Cisco IOS devices are configured to send logs to your Splunk environment, with appropriate logging levels enabled to capture command logging events (PARSER-5-CFGLOG_LOGGEDCMD). Configure command logging on Cisco IOS devices using the "archive log config logging enable" command. known_false_positives: Legitimate TFTP server configurations may be detected by this analytic during authorized backup operations or device maintenance. Network administrators sometimes use TFTP for legitimate configuration backups, firmware updates, or during troubleshooting. To reduce false positives, consider implementing a baseline of expected administrative activities, including approved administrative usernames and scheduled maintenance windows. references: -- https://blog.talosintelligence.com/static-tundra/ -- https://tools.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-20180328-smi2 -- https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/fundamentals/command/cf_command_ref/T_through_X.html#wp3081407060 + - https://blog.talosintelligence.com/static-tundra/ + - https://tools.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-20180328-smi2 + - https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/fundamentals/command/cf_command_ref/T_through_X.html#wp3081407060 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) - as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) - as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious TFTP server configuration detected on Cisco device $dest$ by user $user$, potentially exposing sensitive configuration files - risk_objects: - - field: dest - type: system - score: 60 - - field: user - type: user - score: 40 - threat_objects: - - field: command - type: command + message: Suspicious TFTP server configuration detected on Cisco device $dest$ by user $user$, potentially exposing sensitive configuration files + risk_objects: + - field: dest + type: system + score: 60 + - field: user + type: user + score: 40 + threat_objects: + - field: command + type: command tags: - analytic_story: - - Cisco Smart Install Remote Code Execution CVE-2018-0171 - asset_type: Network - mitre_attack_id: - - T1567 - - T1005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: - - CVE-2018-0171 + analytic_story: + - Cisco Smart Install Remote Code Execution CVE-2018-0171 + asset_type: Network + mitre_attack_id: + - T1567 + - T1005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: + - CVE-2018-0171 tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/cisco/cisco_smart_install/cisco_ios.log - sourcetype: cisco:ios - source: cisco:ios + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/cisco/cisco_smart_install/cisco_ios.log + sourcetype: cisco:ios + source: cisco:ios diff --git a/detections/network/detect_arp_poisoning.yml b/detections/network/detect_arp_poisoning.yml index d6368f710b..a51e12ff80 100644 --- a/detections/network/detect_arp_poisoning.yml +++ b/detections/network/detect_arp_poisoning.yml @@ -1,53 +1,41 @@ name: Detect ARP Poisoning id: b44bebd6-bd39-467b-9321-73971bcd1aac -version: 8 -date: '2025-10-21' +version: 9 +date: '2026-02-25' author: Mikael Bjerkeland, Splunk status: experimental type: TTP -description: The following analytic detects ARP Poisoning attacks by monitoring for - Dynamic ARP Inspection (DAI) errors on Cisco network devices. It leverages logs - from Cisco devices, specifically looking for events where the ARP inspection feature - has disabled an interface due to suspicious activity. This activity is significant - because ARP Poisoning can allow attackers to intercept, modify, or disrupt network - traffic, leading to potential data breaches or denial of service. If confirmed malicious, - this could enable attackers to perform man-in-the-middle attacks, compromising the - integrity and confidentiality of network communications. +description: The following analytic detects ARP Poisoning attacks by monitoring for Dynamic ARP Inspection (DAI) errors on Cisco network devices. It leverages logs from Cisco devices, specifically looking for events where the ARP inspection feature has disabled an interface due to suspicious activity. This activity is significant because ARP Poisoning can allow attackers to intercept, modify, or disrupt network traffic, leading to potential data breaches or denial of service. If confirmed malicious, this could enable attackers to perform man-in-the-middle attacks, compromising the integrity and confidentiality of network communications. data_source: - - Cisco IOS Logs -search: '`cisco_networks` facility="PM" mnemonic="ERR_DISABLE" disable_cause="arp-inspection" - | eval src_interface=src_int_prefix_long+src_int_suffix | stats min(_time) AS firstTime - max(_time) AS lastTime count BY host src_interface | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| - `detect_arp_poisoning_filter`' -how_to_implement: This search uses a standard SPL query on logs from Cisco Network - devices. The network devices must be configured with DHCP Snooping (see - https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) - and Dynamic ARP Inspection (see - https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-2_2_e/security/configuration_guide/b_sec_1522e_2960x_cg/b_sec_1522e_2960x_cg_chapter_01111.html) - and log with a severity level of minimum "5 - notification". The search also requires - that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) - is used to parse the logs from the Cisco network devices. -known_false_positives: This search might be prone to high false positives if DHCP - Snooping or ARP inspection has been incorrectly configured, or if a device normally - sends many ARP packets (unlikely). + - Cisco IOS Logs +search: |- + `cisco_networks` facility="PM" mnemonic="ERR_DISABLE" disable_cause="arp-inspection" + | eval src_interface=src_int_prefix_long+src_int_suffix + | stats min(_time) AS firstTime max(_time) AS lastTime count + BY host src_interface + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_arp_poisoning_filter` +how_to_implement: This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and Dynamic ARP Inspection (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-2_2_e/security/configuration_guide/b_sec_1522e_2960x_cg/b_sec_1522e_2960x_cg_chapter_01111.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. +known_false_positives: This search might be prone to high false positives if DHCP Snooping or ARP inspection has been incorrectly configured, or if a device normally sends many ARP packets (unlikely). references: [] rba: - message: Potential ARP poisoning detected on $host$ - risk_objects: - - field: host - type: system - score: 25 - threat_objects: [] + message: Potential ARP poisoning detected on $host$ + risk_objects: + - field: host + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Router and Infrastructure Security - asset_type: Infrastructure - mitre_attack_id: - - T1200 - - T1498 - - T1557.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Router and Infrastructure Security + asset_type: Infrastructure + mitre_attack_id: + - T1200 + - T1498 + - T1557.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/network/detect_dga_domains_using_pretrained_model_in_dsdl.yml b/detections/network/detect_dga_domains_using_pretrained_model_in_dsdl.yml index 6c9c2e2f60..45fa505511 100644 --- a/detections/network/detect_dga_domains_using_pretrained_model_in_dsdl.yml +++ b/detections/network/detect_dga_domains_using_pretrained_model_in_dsdl.yml @@ -1,69 +1,52 @@ name: Detect DGA domains using pretrained model in DSDL id: 92e24f32-9b9a-4060-bba2-2a0eb31f3493 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Abhinav Mishra, Kumar Sharad and Namratha Sreekanta, Splunk status: experimental type: Anomaly -description: The following analytic identifies Domain Generation Algorithm (DGA) generated - domains using a pre-trained deep learning model. It leverages the Network Resolution - data model to analyze domain names and detect unusual character sequences indicative - of DGA activity. This behavior is significant as adversaries often use DGAs to generate - numerous domain names for command-and-control servers, making it harder to block - malicious traffic. If confirmed malicious, this activity could enable attackers - to maintain persistent communication with compromised systems, evade detection, - and execute further malicious actions. +description: The following analytic identifies Domain Generation Algorithm (DGA) generated domains using a pre-trained deep learning model. It leverages the Network Resolution data model to analyze domain names and detect unusual character sequences indicative of DGA activity. This behavior is significant as adversaries often use DGAs to generate numerous domain names for command-and-control servers, making it harder to block malicious traffic. If confirmed malicious, this activity could enable attackers to maintain persistent communication with compromised systems, evade detection, and execute further malicious actions. data_source: [] -search: '| tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time) - as firstTime max(_time) as lastTime from datamodel=Network_Resolution by DNS.src, - DNS.query | `drop_dm_object_name(DNS)` | rename query AS domain | fields IPs, src, - domain, firstTime, lastTime | apply pretrained_dga_model_dsdl | rename pred_dga_proba - AS dga_score | where dga_score>0.5 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | table src, domain, IPs, firstTime, lastTime, dga_score | `detect_dga_domains_using_pretrained_model_in_dsdl_filter`' -how_to_implement: "Steps to deploy DGA detection model into Splunk App DSDL.\\ This - detection depends on the Splunk app for Data Science and Deep Learning which can - be found here - https://splunkbase.splunk.com/app/4607/ and the Network Resolution - datamodel which can be found here - https://splunkbase.splunk.com/app/1621/. The - detection uses a pre-trained deep learning model that needs to be deployed in DSDL - app. Follow the steps for deployment here - https://github.com/splunk/security_content/wiki/How-to-deploy-pre-trained-Deep-Learning-models-for-ESCU. - * Download the artifacts .tar.gz file from the link `https://seal.splunkresearch.com/pretrained_dga_model_dsdl.tar.gz`\n - * Download the pretrained_dga_model_dsdl.ipynb Jupyter notebook from `https://github.com/splunk/security_content/notebooks`\n - * Login to the Jupyter Lab for pretrained_dga_model_dsdl container. This container - should be listed on Containers page for DSDL app.\n* Below steps need to be followed - inside Jupyter lab\n* Upload the pretrained_dga_model_dsdl.tar.gz file into `app/model/data` - path using the upload option in the jupyter notebook.\n* Untar the artifact `pretrained_dga_model_dsdl.tar.gz` - using `tar -xf app/model/data/pretrained_dga_model_dsdl.tar.gz -C app/model/data`\n - * Upload `pretrained_dga_model_dsdl.pynb` into Jupyter lab notebooks folder using - the upload option in Jupyter lab\n* Save the notebook using the save option in jupyter - notebook.\n* Upload `pretrained_dga_model_dsdl.json` into `notebooks/data` folder." -known_false_positives: False positives may be present if domain name is similar to - dga generated domains. +search: |- + | tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time) as firstTime max(_time) as lastTime FROM datamodel=Network_Resolution + BY DNS.src, DNS.query + | `drop_dm_object_name(DNS)` + | rename query AS domain + | fields IPs, src, domain, firstTime, lastTime + | apply pretrained_dga_model_dsdl + | rename pred_dga_proba AS dga_score + | where dga_score>0.5 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table src, domain, IPs, firstTime, lastTime, dga_score + | `detect_dga_domains_using_pretrained_model_in_dsdl_filter` +how_to_implement: "Steps to deploy DGA detection model into Splunk App DSDL.\\ This detection depends on the Splunk app for Data Science and Deep Learning which can be found here - https://splunkbase.splunk.com/app/4607/ and the Network Resolution datamodel which can be found here - https://splunkbase.splunk.com/app/1621/. The detection uses a pre-trained deep learning model that needs to be deployed in DSDL app. Follow the steps for deployment here - https://github.com/splunk/security_content/wiki/How-to-deploy-pre-trained-Deep-Learning-models-for-ESCU. * Download the artifacts .tar.gz file from the link `https://seal.splunkresearch.com/pretrained_dga_model_dsdl.tar.gz`\n * Download the pretrained_dga_model_dsdl.ipynb Jupyter notebook from `https://github.com/splunk/security_content/notebooks`\n * Login to the Jupyter Lab for pretrained_dga_model_dsdl container. This container should be listed on Containers page for DSDL app.\n* Below steps need to be followed inside Jupyter lab\n* Upload the pretrained_dga_model_dsdl.tar.gz file into `app/model/data` path using the upload option in the jupyter notebook.\n* Untar the artifact `pretrained_dga_model_dsdl.tar.gz` using `tar -xf app/model/data/pretrained_dga_model_dsdl.tar.gz -C app/model/data`\n * Upload `pretrained_dga_model_dsdl.pynb` into Jupyter lab notebooks folder using the upload option in Jupyter lab\n* Save the notebook using the save option in jupyter notebook.\n* Upload `pretrained_dga_model_dsdl.json` into `notebooks/data` folder." +known_false_positives: False positives may be present if domain name is similar to dga generated domains. references: -- https://attack.mitre.org/techniques/T1568/002/ -- https://unit42.paloaltonetworks.com/threat-brief-understanding-domain-generation-algorithms-dga/ -- https://en.wikipedia.org/wiki/Domain_generation_algorithm + - https://attack.mitre.org/techniques/T1568/002/ + - https://unit42.paloaltonetworks.com/threat-brief-understanding-domain-generation-algorithms-dga/ + - https://en.wikipedia.org/wiki/Domain_generation_algorithm rba: - message: A potential connection to a DGA domain $domain$ was detected from host - $src$, kindly review. - risk_objects: - - field: src - type: system - score: 63 - threat_objects: - - field: domain - type: url + message: A potential connection to a DGA domain $domain$ was detected from host $src$, kindly review. + risk_objects: + - field: src + type: system + score: 63 + threat_objects: + - field: domain + type: url tags: - analytic_story: - - Data Exfiltration - - DNS Hijacking - - Suspicious DNS Traffic - - Dynamic DNS - - Command And Control - asset_type: Endpoint - mitre_attack_id: - - T1568.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Data Exfiltration + - DNS Hijacking + - Suspicious DNS Traffic + - Dynamic DNS + - Command And Control + asset_type: Endpoint + mitre_attack_id: + - T1568.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/network/detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.yml b/detections/network/detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.yml index 3757f228fe..644f6f3403 100644 --- a/detections/network/detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.yml +++ b/detections/network/detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.yml @@ -1,72 +1,56 @@ name: Detect DNS Data Exfiltration using pretrained model in DSDL id: 92f65c3a-168c-11ed-71eb-0242ac120012 -version: 6 -date: '2026-01-20' +version: 7 +date: '2026-02-25' status: experimental author: Abhinav Mishra, Kumar Sharad and Namratha Sreekanta, Splunk type: Anomaly data_source: [] -description: The following analytic identifies potential DNS data exfiltration using - a pre-trained deep learning model. It leverages DNS request data from the Network - Resolution datamodel and computes features from past events between the same source - and domain. The model generates a probability score (pred_is_exfiltration_proba) - indicating the likelihood of data exfiltration. This activity is significant as - DNS tunneling can be used by attackers to covertly exfiltrate sensitive data. If - confirmed malicious, this could lead to unauthorized data access and potential data - breaches, compromising the organization's security posture. -search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution - by DNS.src _time DNS.query | `drop_dm_object_name("DNS")` | sort - _time,src, query - | streamstats count as rank by src query | where rank < 10 | table src,query,rank,_time - | apply detect_dns_data_exfiltration_using_pretrained_model_in_dsdl | table src,_time,query,rank,pred_is_dns_data_exfiltration_proba,pred_is_dns_data_exfiltration - | where rank == 1 | rename pred_is_dns_data_exfiltration_proba as is_exfiltration_score - | rename pred_is_dns_data_exfiltration as is_exfiltration | where is_exfiltration_score - > 0.5 | `security_content_ctime(_time)` | table src, _time,query,is_exfiltration_score,is_exfiltration - | `detect_dns_data_exfiltration_using_pretrained_model_in_dsdl_filter`' -how_to_implement: "Steps to deploy detect DNS data exfiltration model into Splunk - App DSDL. This detection depends on the Splunk app for Data Science and Deep Learning - which can be found here - https://splunkbase.splunk.com/app/4607/ and the Network - Resolution datamodel which can be found here - https://splunkbase.splunk.com/app/1621/. - The detection uses a pre-trained deep learning model that needs to be deployed in - DSDL app. Follow the steps for deployment here - `https://github.com/splunk/security_content/wiki/How-to-deploy-pre-trained-Deep-Learning-models-for-ESCU`.\n - * Download the `artifacts .tar.gz` file from the link - https://seal.splunkresearch.com/detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.tar.gz - Download the `detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.ipynb` - Jupyter notebook from https://github.com/splunk/security_content/notebooks\n* Login - to the Jupyter Lab assigned for detect_dns_data_exfiltration_using_pretrained_model_in_dsdl - container. This container should be listed on Containers page for DSDL app.\n* Below - steps need to be followed inside Jupyter lab\n* Upload the detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.tar.gz - file into `app/model/data` path using the upload option in the jupyter notebook.\n - * Untar the artifact detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.tar.gz - using `tar -xf app/model/data/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz - -C app/model/data`\n* Upload detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.pynb - into Jupyter lab notebooks folder using the upload option in Jupyter lab\n* Save - the notebook using the save option in jupyter notebook.\n* Upload `detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.json` - into `notebooks/data` folder." -known_false_positives: False positives may be present if DNS data exfiltration request - look very similar to benign DNS requests. +description: The following analytic identifies potential DNS data exfiltration using a pre-trained deep learning model. It leverages DNS request data from the Network Resolution datamodel and computes features from past events between the same source and domain. The model generates a probability score (pred_is_exfiltration_proba) indicating the likelihood of data exfiltration. This activity is significant as DNS tunneling can be used by attackers to covertly exfiltrate sensitive data. If confirmed malicious, this could lead to unauthorized data access and potential data breaches, compromising the organization's security posture. +search: |- + | tstats `security_content_summariesonly` count FROM datamodel=Network_Resolution + BY DNS.src _time DNS.query + | `drop_dm_object_name("DNS")` + | sort - _time,src, query + | streamstats count as rank + BY src query + | where rank < 10 + | table src,query,rank,_time + | apply detect_dns_data_exfiltration_using_pretrained_model_in_dsdl + | table src,_time,query,rank,pred_is_dns_data_exfiltration_proba,pred_is_dns_data_exfiltration + | where rank == 1 + | rename pred_is_dns_data_exfiltration_proba as is_exfiltration_score + | rename pred_is_dns_data_exfiltration as is_exfiltration + | where is_exfiltration_score > 0.5 + | `security_content_ctime(_time)` + | table src, _time,query,is_exfiltration_score,is_exfiltration + | `detect_dns_data_exfiltration_using_pretrained_model_in_dsdl_filter` +how_to_implement: "Steps to deploy detect DNS data exfiltration model into Splunk App DSDL. This detection depends on the Splunk app for Data Science and Deep Learning which can be found here - https://splunkbase.splunk.com/app/4607/ and the Network Resolution datamodel which can be found here - https://splunkbase.splunk.com/app/1621/. The detection uses a pre-trained deep learning model that needs to be deployed in DSDL app. Follow the steps for deployment here - `https://github.com/splunk/security_content/wiki/How-to-deploy-pre-trained-Deep-Learning-models-for-ESCU`.\n * Download the `artifacts .tar.gz` file from the link - https://seal.splunkresearch.com/detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.tar.gz Download the `detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.ipynb` Jupyter notebook from https://github.com/splunk/security_content/notebooks\n* Login to the Jupyter Lab assigned for detect_dns_data_exfiltration_using_pretrained_model_in_dsdl container. This container should be listed on Containers page for DSDL app.\n* Below steps need to be followed inside Jupyter lab\n* Upload the detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.tar.gz file into `app/model/data` path using the upload option in the jupyter notebook.\n * Untar the artifact detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.tar.gz using `tar -xf app/model/data/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz -C app/model/data`\n* Upload detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.pynb into Jupyter lab notebooks folder using the upload option in Jupyter lab\n* Save the notebook using the save option in jupyter notebook.\n* Upload `detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.json` into `notebooks/data` folder." +known_false_positives: False positives may be present if DNS data exfiltration request look very similar to benign DNS requests. references: -- https://attack.mitre.org/techniques/T1048/003/ -- https://unit42.paloaltonetworks.com/dns-tunneling-how-dns-can-be-abused-by-malicious-actors/ -- https://en.wikipedia.org/wiki/Data_exfiltration + - https://attack.mitre.org/techniques/T1048/003/ + - https://unit42.paloaltonetworks.com/dns-tunneling-how-dns-can-be-abused-by-malicious-actors/ + - https://en.wikipedia.org/wiki/Data_exfiltration rba: - message: A DNS data exfiltration request was sent by this host $src$ , kindly review. - risk_objects: - - field: src - type: system - score: 45 - threat_objects: - - field: query - type: domain + message: A DNS data exfiltration request was sent by this host $src$ , kindly review. + risk_objects: + - field: src + type: system + score: 45 + threat_objects: + - field: query + type: domain tags: - analytic_story: - - DNS Hijacking - - Suspicious DNS Traffic - - Command And Control - - VoidLink Cloud-Native Linux Malware - asset_type: Endpoint - mitre_attack_id: - - T1048.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - DNS Hijacking + - Suspicious DNS Traffic + - Command And Control + - VoidLink Cloud-Native Linux Malware + asset_type: Endpoint + mitre_attack_id: + - T1048.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/network/detect_dns_query_to_decommissioned_s3_bucket.yml b/detections/network/detect_dns_query_to_decommissioned_s3_bucket.yml index 2e2822d3f1..a4f816e598 100644 --- a/detections/network/detect_dns_query_to_decommissioned_s3_bucket.yml +++ b/detections/network/detect_dns_query_to_decommissioned_s3_bucket.yml @@ -1,69 +1,65 @@ name: Detect DNS Query to Decommissioned S3 Bucket id: 2f1c5fd1-4b8a-4f5d-a0e9-7d6a8e2f5e1e -version: 3 -date: '2025-05-02' +version: 4 +date: '2026-02-25' author: Jose Hernandez, Splunk status: experimental type: Anomaly -description: This detection identifies DNS queries to domains that match previously - decommissioned S3 buckets. This activity is significant because attackers may attempt - to recreate deleted S3 buckets that were previously public to hijack them for malicious - purposes. If successful, this could allow attackers to host malicious content or - exfiltrate data through compromised bucket names that may still be referenced by - legitimate applications. +description: This detection identifies DNS queries to domains that match previously decommissioned S3 buckets. This activity is significant because attackers may attempt to recreate deleted S3 buckets that were previously public to hijack them for malicious purposes. If successful, this could allow attackers to host malicious content or exfiltrate data through compromised bucket names that may still be referenced by legitimate applications. data_source: -- Sysmon EventID 22 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Network_Resolution where DNS.message_type=QUERY by DNS.answer - DNS.answer_count DNS.query DNS.query_count DNS.reply_code_id DNS.src DNS.vendor_product - | `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | eval bucket_domain = lower(query) | lookup decommissioned_buckets bucketName as - bucket_domain OUTPUT bucketName as match | where isnotnull(match) | `detect_dns_query_to_decommissioned_s3_bucket_filter`' -how_to_implement: To successfully implement this detection, you need to be ingesting - DNS query logs and have them mapped to the Network_Resolution data model. Additionally, - ensure that the baseline search "Baseline Of Open S3 Bucket Decommissioning" is - running and populating the decommissioned_buckets KVstore lookup. -known_false_positives: Some applications or scripts may continue to reference old - S3 bucket names after they have been decommissioned. These should be investigated - and updated to prevent potential security risks. + - Sysmon EventID 22 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Network_Resolution + WHERE DNS.message_type=QUERY + BY DNS.answer DNS.answer_count DNS.query + DNS.query_count DNS.reply_code_id DNS.src + DNS.vendor_product + | `drop_dm_object_name("DNS")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | eval bucket_domain = lower(query) + | lookup decommissioned_buckets bucketName as bucket_domain OUTPUT bucketName as match + | where isnotnull(match) + | `detect_dns_query_to_decommissioned_s3_bucket_filter` +how_to_implement: To successfully implement this detection, you need to be ingesting DNS query logs and have them mapped to the Network_Resolution data model. Additionally, ensure that the baseline search "Baseline Of Open S3 Bucket Decommissioning" is running and populating the decommissioned_buckets KVstore lookup. +known_false_positives: Some applications or scripts may continue to reference old S3 bucket names after they have been decommissioned. These should be investigated and updated to prevent potential security risks. references: -- https://docs.aws.amazon.com/AmazonS3/latest/userguide/access-control-block-public-access.html -- https://labs.watchtowr.com/8-million-requests-later-we-made-the-solarwinds-supply-chain-attack-look-amateur/ + - https://docs.aws.amazon.com/AmazonS3/latest/userguide/access-control-block-public-access.html + - https://labs.watchtowr.com/8-million-requests-later-we-made-the-solarwinds-supply-chain-attack-look-amateur/ drilldown_searches: -- name: DNS Activity for Host - search: '| from datamodel:Network_Resolution | search src="$src$"' - earliest_offset: -7d@d - latest_offset: now + - name: DNS Activity for Host + search: '| from datamodel:Network_Resolution | search src="$src$"' + earliest_offset: -7d@d + latest_offset: now rba: - message: A DNS query to decommissioned S3 bucket $query$ was detected from host - $src$ - risk_objects: - - field: src - type: system - score: 30 - threat_objects: - - field: query - type: domain + message: A DNS query to decommissioned S3 bucket $query$ was detected from host $src$ + risk_objects: + - field: src + type: system + score: 30 + threat_objects: + - field: query + type: domain tags: - analytic_story: - - AWS S3 Bucket Security Monitoring - - Data Destruction - asset_type: Network - mitre_attack_id: - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - AWS S3 Bucket Security Monitoring + - Data Destruction + asset_type: Network + mitre_attack_id: + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: Baseline Dataset Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/decommissioned_buckets/cloudtrail.json - source: cloudtrail - sourcetype: aws:cloudtrail -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/decommissioned_buckets/dns.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: Baseline Dataset Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/decommissioned_buckets/cloudtrail.json + source: cloudtrail + sourcetype: aws:cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/decommissioned_buckets/dns.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/network/detect_hosts_connecting_to_dynamic_domain_providers.yml b/detections/network/detect_hosts_connecting_to_dynamic_domain_providers.yml index f43de519f6..2b11193230 100644 --- a/detections/network/detect_hosts_connecting_to_dynamic_domain_providers.yml +++ b/detections/network/detect_hosts_connecting_to_dynamic_domain_providers.yml @@ -5,89 +5,61 @@ date: '2026-01-22' author: Bhavin Patel, Splunk status: production type: TTP -description: The following analytic identifies DNS queries from internal hosts to - dynamic domain providers. It leverages DNS query logs from the `Network_Resolution` - data model and cross-references them with a lookup file containing known dynamic - DNS providers. This activity is significant because attackers often use dynamic - DNS services to host malicious payloads or command-and-control servers, making it - crucial for security teams to monitor. If confirmed malicious, this activity could - allow attackers to bypass firewall blocks, evade detection, and maintain persistent - access to the network. +description: The following analytic identifies DNS queries from internal hosts to dynamic domain providers. It leverages DNS query logs from the `Network_Resolution` data model and cross-references them with a lookup file containing known dynamic DNS providers. This activity is significant because attackers often use dynamic DNS services to host malicious payloads or command-and-control servers, making it crucial for security teams to monitor. If confirmed malicious, this activity could allow attackers to bypass firewall blocks, evade detection, and maintain persistent access to the network. data_source: -- Sysmon EventID 22 + - Sysmon EventID 22 search: | - | tstats `security_content_summariesonly` count min(_time) as firstTime - from datamodel=Network_Resolution where - DNS.query=* - NOT DNS.query IN ("-", "unknown") - by DNS.answer DNS.answer_count DNS.query DNS.query_count - DNS.reply_code_id DNS.src DNS.vendor_product - | `drop_dm_object_name("DNS")` - | `security_content_ctime(firstTime)` - | lookup update=true dynamic_dns_providers_default dynamic_dns_domains as query OUTPUTNEW isDynDNS_default - | lookup update=true dynamic_dns_providers_local dynamic_dns_domains as query OUTPUTNEW isDynDNS_local - | eval isDynDNS = coalesce(isDynDNS_local,isDynDNS_default) - |fields - isDynDNS_default, isDynDNS_local| search isDynDNS=True - | `detect_hosts_connecting_to_dynamic_domain_providers_filter` -how_to_implement: "First, you'll need to ingest data from your DNS operations. This\ - \ can be done by ingesting logs from your server or data, collected passively by\ - \ Splunk Stream or a similar solution. Specifically, data that contains the domain\ - \ that is being queried and the IP of the host originating the request must be populating\ - \ the `Network_Resolution` data model. This search also leverages a lookup file,\ - \ `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of Dynamic\ - \ DNS providers. Please consider updating the local lookup periodically by adding\ - \ new domains to the list of `dynamic_dns_providers_local.csv`.\n This search produces\ - \ fields (query, answer, isDynDNS) that are not yet supported by Mission Control Queue\ - \ and therefore cannot be viewed when a finding is raised. These fields contribute\ - \ additional context to the finding. To see the additional metadata, add the following\ - \ fields, if not already present, to Mission Control Queue. Event Attributes (Configure\ - \ > Findings and Investigations > Add New field):\n* **Label:**\ - \ DNS Query, **Field:** query\n* **Label:** DNS Answer, **Field:** answer\n* **Label:**\ - \ IsDynamicDNS, **Field:** isDynDNS\n" -known_false_positives: Some users and applications may leverage Dynamic DNS to reach - out to some domains on the Internet since dynamic DNS by itself is not malicious, - however this activity must be verified. + | tstats `security_content_summariesonly` count min(_time) as firstTime + from datamodel=Network_Resolution where + DNS.query=* + NOT DNS.query IN ("-", "unknown") + by DNS.answer DNS.answer_count DNS.query DNS.query_count + DNS.reply_code_id DNS.src DNS.vendor_product + | `drop_dm_object_name("DNS")` + | `security_content_ctime(firstTime)` + | lookup update=true dynamic_dns_providers_default dynamic_dns_domains as query OUTPUTNEW isDynDNS_default + | lookup update=true dynamic_dns_providers_local dynamic_dns_domains as query OUTPUTNEW isDynDNS_local + | eval isDynDNS = coalesce(isDynDNS_local,isDynDNS_default) + |fields - isDynDNS_default, isDynDNS_local| search isDynDNS=True + | `detect_hosts_connecting_to_dynamic_domain_providers_filter` +how_to_implement: "First, you'll need to ingest data from your DNS operations. This can be done by ingesting logs from your server or data, collected passively by Splunk Stream or a similar solution. Specifically, data that contains the domain that is being queried and the IP of the host originating the request must be populating the `Network_Resolution` data model. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of Dynamic DNS providers. Please consider updating the local lookup periodically by adding new domains to the list of `dynamic_dns_providers_local.csv`.\n This search produces fields (query, answer, isDynDNS) that are not yet supported by Mission Control Queue and therefore cannot be viewed when a finding is raised. These fields contribute additional context to the finding. To see the additional metadata, add the following fields, if not already present, to Mission Control Queue. Event Attributes (Configure > Findings and Investigations > Add New field):\n* **Label:** DNS Query, **Field:** query\n* **Label:** DNS Answer, **Field:** answer\n* **Label:** IsDynamicDNS, **Field:** isDynDNS\n" +known_false_positives: Some users and applications may leverage Dynamic DNS to reach out to some domains on the Internet since dynamic DNS by itself is not malicious, however this activity must be verified. references: [] drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A dns query $query$ from your infra connecting to suspicious domain - risk_objects: - - field: src - type: system - score: 56 - threat_objects: [] + message: A dns query $query$ from your infra connecting to suspicious domain + risk_objects: + - field: src + type: system + score: 56 + threat_objects: [] tags: - analytic_story: - - Data Protection - - Prohibited Traffic Allowed or Protocol Mismatch - - DNS Hijacking - - Suspicious DNS Traffic - - Dynamic DNS - - Command And Control - asset_type: Endpoint - mitre_attack_id: - - T1189 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Data Protection + - Prohibited Traffic Allowed or Protocol Mismatch + - DNS Hijacking + - Suspicious DNS Traffic + - Dynamic DNS + - Command And Control + asset_type: Endpoint + mitre_attack_id: + - T1189 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1189/dyn_dns_site/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1189/dyn_dns_site/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/network/detect_ipv6_network_infrastructure_threats.yml b/detections/network/detect_ipv6_network_infrastructure_threats.yml index c519514a79..8a3df4f9d9 100644 --- a/detections/network/detect_ipv6_network_infrastructure_threats.yml +++ b/detections/network/detect_ipv6_network_infrastructure_threats.yml @@ -1,62 +1,52 @@ name: Detect IPv6 Network Infrastructure Threats id: c3be767e-7959-44c5-8976-0e9c12a91ad2 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Mikael Bjerkeland, Splunk status: experimental type: TTP -description: The following analytic detects IPv6 network infrastructure threats by - identifying suspicious activities such as IP and MAC address theft or packet drops. - It leverages logs from Cisco network devices configured with First Hop Security - measures like RA Guard and DHCP Guard. This activity is significant as it can indicate - attempts to compromise network integrity and security. If confirmed malicious, attackers - could manipulate network traffic, leading to potential data interception, unauthorized - access, or network disruption. +description: The following analytic detects IPv6 network infrastructure threats by identifying suspicious activities such as IP and MAC address theft or packet drops. It leverages logs from Cisco network devices configured with First Hop Security measures like RA Guard and DHCP Guard. This activity is significant as it can indicate attempts to compromise network integrity and security. If confirmed malicious, attackers could manipulate network traffic, leading to potential data interception, unauthorized access, or network disruption. data_source: - - Cisco IOS Logs -search: '`cisco_networks` facility="SISF" mnemonic IN ("IP_THEFT","MAC_THEFT","MAC_AND_IP_THEFT","PAK_DROP") - | eval src_interface=src_int_prefix_long+src_int_suffix | eval dest_interface=dest_int_prefix_long+dest_int_suffix - | stats min(_time) AS firstTime max(_time) AS lastTime values(src_mac) AS src_mac - values(src_vlan) AS src_vlan values(mnemonic) AS mnemonic values(vendor_explanation) - AS vendor_explanation values(src_ip) AS src_ip values(dest_ip) AS dest_ip values(dest_interface) - AS dest_interface values(action) AS action count BY host src_interface | table host - src_interface dest_interface src_mac src_ip dest_ip src_vlan mnemonic vendor_explanation - action count | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` - | `detect_ipv6_network_infrastructure_threats_filter`' -how_to_implement: This search uses a standard SPL query on logs from Cisco Network - devices. The network devices must be configured with one or more First Hop Security - measures such as RA Guard, DHCP Guard and/or device tracking. See References for - more information. The search also requires that the Cisco Networks Add-on for Splunk - (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco - network devices. + - Cisco IOS Logs +search: |- + `cisco_networks` facility="SISF" mnemonic IN ("IP_THEFT","MAC_THEFT","MAC_AND_IP_THEFT","PAK_DROP") + | eval src_interface=src_int_prefix_long+src_int_suffix + | eval dest_interface=dest_int_prefix_long+dest_int_suffix + | stats min(_time) AS firstTime max(_time) AS lastTime values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(mnemonic) AS mnemonic values(vendor_explanation) AS vendor_explanation values(src_ip) AS src_ip values(dest_ip) AS dest_ip values(dest_interface) AS dest_interface values(action) AS action count + BY host src_interface + | table host src_interface dest_interface src_mac src_ip dest_ip src_vlan mnemonic vendor_explanation action count + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_ipv6_network_infrastructure_threats_filter` +how_to_implement: This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with one or more First Hop Security measures such as RA Guard, DHCP Guard and/or device tracking. See References for more information. The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. known_false_positives: No false positives have been identified at this time. references: -- https://www.ciscolive.com/c/dam/r/ciscolive/emea/docs/2020/pdf/BRKSEC-3200.pdf -- https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-ra-guard.html -- https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-snooping.html -- https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dad-proxy.html -- https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-nd-mcast-supp.html -- https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dhcpv6-guard.html -- https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-src-guard.html -- https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ipv6-dest-guard.html + - https://www.ciscolive.com/c/dam/r/ciscolive/emea/docs/2020/pdf/BRKSEC-3200.pdf + - https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-ra-guard.html + - https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-snooping.html + - https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dad-proxy.html + - https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-nd-mcast-supp.html + - https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dhcpv6-guard.html + - https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-src-guard.html + - https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ipv6-dest-guard.html rba: - message: Suspicious IPv6 Activity on $host$ - risk_objects: - - field: host - type: system - score: 25 - threat_objects: [] + message: Suspicious IPv6 Activity on $host$ + risk_objects: + - field: host + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Router and Infrastructure Security - - Scattered Lapsus$ Hunters - asset_type: Infrastructure - mitre_attack_id: - - T1200 - - T1498 - - T1557.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Router and Infrastructure Security + - Scattered Lapsus$ Hunters + asset_type: Infrastructure + mitre_attack_id: + - T1200 + - T1498 + - T1557.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/network/detect_large_icmp_traffic.yml b/detections/network/detect_large_icmp_traffic.yml index ddc10cf0db..375d256154 100644 --- a/detections/network/detect_large_icmp_traffic.yml +++ b/detections/network/detect_large_icmp_traffic.yml @@ -1,75 +1,61 @@ name: Detect Large ICMP Traffic id: 9cd6d066-94d5-4ccd-a8b9-28c03ca91be8 -version: 2 -date: '2025-05-02' +version: 3 +date: '2026-02-25' author: Rico Valdez, Dean Luxton, Bhavin Patel, Splunk status: production type: TTP description: The following analytic identifies ICMP traffic to external IP addresses with total bytes (sum of bytes in and bytes out) greater than 1,000 bytes. It leverages the Network_Traffic data model to detect large ICMP packet that aren't blocked and are directed toward external networks. We use All_Traffic.bytes in the detection to capture variations in inbound versus outbound traffic sizes, as significant discrepancies or unusually large ICMP exchanges can indicate information smuggling, covert communication, or command-and-control (C2) activities. If validated as malicious, this could signal ICMP tunneling, unauthorized data transfer, or compromised endpoints requiring immediate investigation. data_source: -- Palo Alto Network Traffic + - Palo Alto Network Traffic search: |- - | tstats `security_content_summariesonly` count earliest(_time) as firstTime latest(_time) as lastTime values(All_Traffic.action) as action - from datamodel=Network_Traffic where All_Traffic.bytes > 1000 AND All_Traffic.action != blocked AND (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) AND NOT All_Traffic.dest_ip IN ("10.0.0.0/8","172.16.0.0/12","192.168.0.0/16") - by All_Traffic.src_ip, All_Traffic.dest_ip, All_Traffic.protocol, All_Traffic.bytes, All_Traffic.app, All_Traffic.bytes_in, All_Traffic.bytes_out, All_Traffic.dest_port, All_Traffic.dvc, All_Traffic.protocol_version, - All_Traffic.src_port, All_Traffic.user, All_Traffic.vendor_product - | `drop_dm_object_name("All_Traffic")` - | iplocation dest_ip - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `detect_large_icmp_traffic_filter` -how_to_implement: The following analytic was developed with Palo Alto traffic logs. - Ensure that the logs are being ingested into Splunk and mapped to the Network_Traffic - data model. Use the Splunk Common Information Model (CIM) to normalize the field - names and speed up the data modeling process. -known_false_positives: ICMP packets are used in a variety of ways to help troubleshoot - networking issues and ensure the proper flow of traffic. As such, it is possible - that a large ICMP packet could be perfectly legitimate. If large ICMP packets are - associated with Command And Control traffic, there will typically be a large number - of these packets observed over time. If the search is providing a large number of - false positives, you can modify the macro `detect_large_icmp_traffic_filter` - to adjust the byte threshold or add specific IP addresses to an allow list. + | tstats `security_content_summariesonly` count earliest(_time) as firstTime latest(_time) as lastTime values(All_Traffic.action) as action + from datamodel=Network_Traffic where All_Traffic.bytes > 1000 AND All_Traffic.action != blocked AND (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) AND NOT All_Traffic.dest_ip IN ("10.0.0.0/8","172.16.0.0/12","192.168.0.0/16") + by All_Traffic.src_ip, All_Traffic.dest_ip, All_Traffic.protocol, All_Traffic.bytes, All_Traffic.app, All_Traffic.bytes_in, All_Traffic.bytes_out, All_Traffic.dest_port, All_Traffic.dvc, All_Traffic.protocol_version, + All_Traffic.src_port, All_Traffic.user, All_Traffic.vendor_product + | `drop_dm_object_name("All_Traffic")` + | iplocation dest_ip + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_large_icmp_traffic_filter` +how_to_implement: The following analytic was developed with Palo Alto traffic logs. Ensure that the logs are being ingested into Splunk and mapped to the Network_Traffic data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. +known_false_positives: ICMP packets are used in a variety of ways to help troubleshoot networking issues and ensure the proper flow of traffic. As such, it is possible that a large ICMP packet could be perfectly legitimate. If large ICMP packets are associated with Command And Control traffic, there will typically be a large number of these packets observed over time. If the search is providing a large number of false positives, you can modify the macro `detect_large_icmp_traffic_filter` to adjust the byte threshold or add specific IP addresses to an allow list. references: [] drilldown_searches: -- name: View the detection results for - "$src_ip$" and "$dest_ip$" - search: '%original_detection_search% | search src_ip = "$src_ip$" dest_ip = "$dest_ip$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_ip$" and "$dest_ip$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$", - "$dest_ip$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_ip$" and "$dest_ip$" + search: '%original_detection_search% | search src_ip = "$src_ip$" dest_ip = "$dest_ip$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_ip$" and "$dest_ip$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$", "$dest_ip$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Large ICMP traffic greater than a 1000 bytes detected from $src_ip$ to $dest_ip$ - risk_objects: - - field: dest_ip - type: system - score: 25 - - field: src_ip - type: system - score: 25 - threat_objects: [] + message: Large ICMP traffic greater than a 1000 bytes detected from $src_ip$ to $dest_ip$ + risk_objects: + - field: dest_ip + type: system + score: 25 + - field: src_ip + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Command And Control - - China-Nexus Threat Activity - - Backdoor Pingpong - asset_type: Endpoint - mitre_attack_id: - - T1095 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Command And Control + - China-Nexus Threat Activity + - Backdoor Pingpong + asset_type: Endpoint + mitre_attack_id: + - T1095 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1095/palologs/large_icmp.log - sourcetype: pan:traffic - source: pan:traffic + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1095/palologs/large_icmp.log + sourcetype: pan:traffic + source: pan:traffic diff --git a/detections/network/detect_outbound_ldap_traffic.yml b/detections/network/detect_outbound_ldap_traffic.yml index f59fb2aceb..66816bdf90 100644 --- a/detections/network/detect_outbound_ldap_traffic.yml +++ b/detections/network/detect_outbound_ldap_traffic.yml @@ -1,62 +1,63 @@ name: Detect Outbound LDAP Traffic id: 5e06e262-d7cd-4216-b2f8-27b437e18458 -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Bhavin Patel, Johan Bjerke, Splunk status: production type: Hunting -description: The following analytic identifies outbound LDAP traffic to external IP - addresses. It leverages the Network_Traffic data model to detect connections on - ports 389 or 636 that are not directed to private IP ranges (RFC1918). This activity - is significant because outbound LDAP traffic can indicate potential data exfiltration - or unauthorized access attempts. If confirmed malicious, attackers could exploit - this to access sensitive directory information, leading to data breaches or further - network compromise. +description: The following analytic identifies outbound LDAP traffic to external IP addresses. It leverages the Network_Traffic data model to detect connections on ports 389 or 636 that are not directed to private IP ranges (RFC1918). This activity is significant because outbound LDAP traffic can indicate potential data exfiltration or unauthorized access attempts. If confirmed malicious, attackers could exploit this to access sensitive directory information, leading to data breaches or further network compromise. data_source: -- Palo Alto Network Traffic -- Cisco Secure Firewall Threat Defense Connection Event -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime values(All_Traffic.dest_ip) as dest_ip from datamodel=Network_Traffic.All_Traffic - where All_Traffic.dest_port = 389 OR All_Traffic.dest_port = 636 AND NOT (All_Traffic.dest_ip - = 10.0.0.0/8 OR All_Traffic.dest_ip=192.168.0.0/16 OR All_Traffic.dest_ip = 172.16.0.0/12) - by All_Traffic.action All_Traffic.app All_Traffic.bytes All_Traffic.bytes_in All_Traffic.bytes_out - All_Traffic.dest All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.dvc All_Traffic.protocol - All_Traffic.protocol_version All_Traffic.src All_Traffic.src_ip All_Traffic.src_port - All_Traffic.transport All_Traffic.user All_Traffic.vendor_product All_Traffic.rule |`drop_dm_object_name("All_Traffic")` - | where src_ip != dest_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - |`detect_outbound_ldap_traffic_filter`' -how_to_implement: In order to properly run this search, Splunk needs to ingest data - from Next Generation Firewalls like, Cisco Secure Firewall Threat Defense, Palo Alto Networks Firewalls - or other network control devices that mediate the traffic allowed into an environment. - The search requires the Network_Traffic data model to be populated. -known_false_positives: No false positives have been identified at this time. - allowed outbound through your perimeter firewall. Please check those servers to - verify if the activity is legitimate. + - Palo Alto Network Traffic + - Cisco Secure Firewall Threat Defense Connection Event +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_Traffic.dest_ip) as dest_ip FROM datamodel=Network_Traffic.All_Traffic + WHERE All_Traffic.dest_port = 389 + OR + All_Traffic.dest_port = 636 + AND + NOT (All_Traffic.dest_ip = 10.0.0.0/8 + OR + All_Traffic.dest_ip=192.168.0.0/16 + OR + All_Traffic.dest_ip = 172.16.0.0/12) + BY All_Traffic.action All_Traffic.app All_Traffic.bytes + All_Traffic.bytes_in All_Traffic.bytes_out All_Traffic.dest + All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.dvc + All_Traffic.protocol All_Traffic.protocol_version All_Traffic.src + All_Traffic.src_ip All_Traffic.src_port All_Traffic.transport + All_Traffic.user All_Traffic.vendor_product All_Traffic.rule + | `drop_dm_object_name("All_Traffic")` + | where src_ip != dest_ip + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_outbound_ldap_traffic_filter` +how_to_implement: In order to properly run this search, Splunk needs to ingest data from Next Generation Firewalls like, Cisco Secure Firewall Threat Defense, Palo Alto Networks Firewalls or other network control devices that mediate the traffic allowed into an environment. The search requires the Network_Traffic data model to be populated. +known_false_positives: No false positives have been identified at this time. allowed outbound through your perimeter firewall. Please check those servers to verify if the activity is legitimate. references: -- https://www.govcert.ch/blog/zero-day-exploit-targeting-popular-java-library-log4j/ + - https://www.govcert.ch/blog/zero-day-exploit-targeting-popular-java-library-log4j/ tags: - analytic_story: - - Log4Shell CVE-2021-44228 - - Cisco Secure Firewall Threat Defense Analytics - asset_type: Endpoint - cve: - - CVE-2021-44228 - mitre_attack_id: - - T1190 - - T1059 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Log4Shell CVE-2021-44228 + - Cisco Secure Firewall Threat Defense Analytics + asset_type: Endpoint + cve: + - CVE-2021-44228 + mitre_attack_id: + - T1190 + - T1059 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: Palo Alto True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/log4shell_ldap_traffic/pantraffic.log - sourcetype: pan:traffic - source: pan:traffic -- name: Cisco Secure Firewall True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: Palo Alto True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/log4shell_ldap_traffic/pantraffic.log + sourcetype: pan:traffic + source: pan:traffic + - name: Cisco Secure Firewall True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/detect_outbound_smb_traffic.yml b/detections/network/detect_outbound_smb_traffic.yml index c89773f07c..75af69177c 100644 --- a/detections/network/detect_outbound_smb_traffic.yml +++ b/detections/network/detect_outbound_smb_traffic.yml @@ -1,100 +1,80 @@ name: Detect Outbound SMB Traffic id: 1bed7774-304a-4e8f-9d72-d80e45ff492b -version: 12 -date: '2025-06-10' +version: 13 +date: '2026-02-25' author: Bhavin Patel, Stuart Hopkins, Patrick Bareiss status: production type: TTP -description: The following analytic detects outbound SMB (Server Message Block) connections - from internal hosts to external servers. It identifies this activity by monitoring - network traffic for SMB requests directed towards the Internet, which are unusual - for standard operations. This detection is significant for a SOC as it can indicate - an attacker's attempt to retrieve credential hashes through compromised servers, - a key step in lateral movement and privilege escalation. If confirmed malicious, - this activity could lead to unauthorized access to sensitive data and potential - full system compromise. +description: The following analytic detects outbound SMB (Server Message Block) connections from internal hosts to external servers. It identifies this activity by monitoring network traffic for SMB requests directed towards the Internet, which are unusual for standard operations. This detection is significant for a SOC as it can indicate an attacker's attempt to retrieve credential hashes through compromised servers, a key step in lateral movement and privilege escalation. If confirmed malicious, this activity could lead to unauthorized access to sensitive data and potential full system compromise. data_source: -- Cisco Secure Firewall Threat Defense Connection Event + - Cisco Secure Firewall Threat Defense Connection Event search: | - | tstats `security_content_summariesonly` - earliest(_time) as start_time - latest(_time) as end_time - values(All_Traffic.action) as action - values(All_Traffic.app) as app - values(sourcetype) as sourcetype count - from datamodel=Network_Traffic where - All_Traffic.action IN ("allowed", "allow") AND - (All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app="smb") - AND All_Traffic.src_ip IN ( - "10.0.0.0/8","172.16.0.0/12","192.168.0.0/16" - ) - AND NOT All_Traffic.dest_ip IN ( - "10.0.0.0/8", "172.16.0.0/12", "192.168.0.0/16", "100.64.0.0/10", - "127.0.0.0/8", "169.254.0.0/16", "192.0.0.0/24", "192.0.0.0/29", "192.0.0.8/32", - "192.0.0.9/32", "192.0.0.10/32", "192.0.0.170/32", "192.0.0.171/32", "192.0.2.0/24", - "192.31.196.0/24", "192.52.193.0/24", "192.88.99.0/24", "224.0.0.0/4", "192.175.48.0/24", - "198.18.0.0/15", "198.51.100.0/24", "203.0.113.0/24", "240.0.0.0/4", "::1" - ) - by All_Traffic.action All_Traffic.app All_Traffic.bytes All_Traffic.bytes_in All_Traffic.bytes_out - All_Traffic.dest All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.dvc All_Traffic.protocol - All_Traffic.protocol_version All_Traffic.src All_Traffic.src_ip All_Traffic.src_port - All_Traffic.transport All_Traffic.user All_Traffic.vendor_product All_Traffic.rule - | `drop_dm_object_name("All_Traffic")` - | `security_content_ctime(start_time)` - | `security_content_ctime(end_time)` - | iplocation dest_ip - | `detect_outbound_smb_traffic_filter` -how_to_implement: This search requires you to be ingesting your network traffic - and populating the Network_Traffic data model. -known_false_positives: It is likely that the outbound Server Message Block (SMB) traffic - is legitimate, if the company's internal networks are not well-defined in the Assets - and Identity Framework. Categorize the internal CIDR blocks as `internal` in the - lookup file to avoid creating findings for traffic destined to those CIDR blocks. - Any other network connection that is going out to the Internet should be investigated - and blocked. Best practices suggest preventing external communications of all SMB - versions and related protocols at the network boundary. + | tstats `security_content_summariesonly` + earliest(_time) as start_time + latest(_time) as end_time + values(All_Traffic.action) as action + values(All_Traffic.app) as app + values(sourcetype) as sourcetype count + from datamodel=Network_Traffic where + All_Traffic.action IN ("allowed", "allow") AND + (All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app="smb") + AND All_Traffic.src_ip IN ( + "10.0.0.0/8","172.16.0.0/12","192.168.0.0/16" + ) + AND NOT All_Traffic.dest_ip IN ( + "10.0.0.0/8", "172.16.0.0/12", "192.168.0.0/16", "100.64.0.0/10", + "127.0.0.0/8", "169.254.0.0/16", "192.0.0.0/24", "192.0.0.0/29", "192.0.0.8/32", + "192.0.0.9/32", "192.0.0.10/32", "192.0.0.170/32", "192.0.0.171/32", "192.0.2.0/24", + "192.31.196.0/24", "192.52.193.0/24", "192.88.99.0/24", "224.0.0.0/4", "192.175.48.0/24", + "198.18.0.0/15", "198.51.100.0/24", "203.0.113.0/24", "240.0.0.0/4", "::1" + ) + by All_Traffic.action All_Traffic.app All_Traffic.bytes All_Traffic.bytes_in All_Traffic.bytes_out + All_Traffic.dest All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.dvc All_Traffic.protocol + All_Traffic.protocol_version All_Traffic.src All_Traffic.src_ip All_Traffic.src_port + All_Traffic.transport All_Traffic.user All_Traffic.vendor_product All_Traffic.rule + | `drop_dm_object_name("All_Traffic")` + | `security_content_ctime(start_time)` + | `security_content_ctime(end_time)` + | iplocation dest_ip + | `detect_outbound_smb_traffic_filter` +how_to_implement: This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. +known_false_positives: It is likely that the outbound Server Message Block (SMB) traffic is legitimate, if the company's internal networks are not well-defined in the Assets and Identity Framework. Categorize the internal CIDR blocks as `internal` in the lookup file to avoid creating findings for traffic destined to those CIDR blocks. Any other network connection that is going out to the Internet should be investigated and blocked. Best practices suggest preventing external communications of all SMB versions and related protocols at the network boundary. references: [] drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An outbound SMB connection from $src_ip$ in your infrastructure connecting - to dest ip $dest_ip$ - risk_objects: - - field: src_ip - type: system - score: 25 - threat_objects: - - field: dest_ip - type: ip_address + message: An outbound SMB connection from $src_ip$ in your infrastructure connecting to dest ip $dest_ip$ + risk_objects: + - field: src_ip + type: system + score: 25 + threat_objects: + - field: dest_ip + type: ip_address tags: - analytic_story: - - Hidden Cobra Malware - - DHS Report TA18-074A - - NOBELIUM Group - - Cisco Secure Firewall Threat Defense Analytics - asset_type: Endpoint - mitre_attack_id: - - T1071.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Hidden Cobra Malware + - DHS Report TA18-074A + - NOBELIUM Group + - Cisco Secure Firewall Threat Defense Analytics + asset_type: Endpoint + mitre_attack_id: + - T1071.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: Cisco Secure Firewall True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: Cisco Secure Firewall True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/detect_port_security_violation.yml b/detections/network/detect_port_security_violation.yml index a0f27a2919..4f9e50bb6b 100644 --- a/detections/network/detect_port_security_violation.yml +++ b/detections/network/detect_port_security_violation.yml @@ -1,53 +1,41 @@ name: Detect Port Security Violation id: 2de3d5b8-a4fa-45c5-8540-6d071c194d24 -version: 8 -date: '2025-10-21' +version: 9 +date: '2026-02-25' author: Mikael Bjerkeland, Splunk status: experimental type: TTP -description: The following analytic detects port security violations on Cisco switches. - It leverages logs from Cisco network devices, specifically looking for events with - mnemonics indicating port security violations. This activity is significant because - it indicates an unauthorized device attempting to connect to a secured port, potentially - bypassing network access controls. If confirmed malicious, this could allow an attacker - to gain unauthorized access to the network, leading to data exfiltration, network - disruption, or further lateral movement within the environment. +description: The following analytic detects port security violations on Cisco switches. It leverages logs from Cisco network devices, specifically looking for events with mnemonics indicating port security violations. This activity is significant because it indicates an unauthorized device attempting to connect to a secured port, potentially bypassing network access controls. If confirmed malicious, this could allow an attacker to gain unauthorized access to the network, leading to data exfiltration, network disruption, or further lateral movement within the environment. data_source: - - Cisco IOS Logs -search: '`cisco_networks` (facility="PM" mnemonic="ERR_DISABLE" disable_cause="psecure-violation") - OR (facility="PORT_SECURITY" mnemonic="PSECURE_VIOLATION" OR mnemonic="PSECURE_VIOLATION_VLAN") - | eval src_interface=src_int_prefix_long+src_int_suffix | stats min(_time) AS firstTime - max(_time) AS lastTime values(disable_cause) AS disable_cause values(src_mac) AS - src_mac values(src_vlan) AS src_vlan values(action) AS action count by host src_interface - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_port_security_violation_filter`' -how_to_implement: This search uses a standard SPL query on logs from Cisco Network - devices. The network devices must be configured with Port Security and Error Disable - for this to work (see - https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst4500/12-2/25ew/configuration/guide/conf/port_sec.html) - and log with a severity level of minimum "5 - notification". The search also requires - that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) - is used to parse the logs from the Cisco network devices. -known_false_positives: This search might be prone to high false positives if you have - malfunctioning devices connected to your ethernet ports or if end users periodically - connect physical devices to the network. + - Cisco IOS Logs +search: |- + `cisco_networks` (facility="PM" mnemonic="ERR_DISABLE" disable_cause="psecure-violation") OR (facility="PORT_SECURITY" mnemonic="PSECURE_VIOLATION" OR mnemonic="PSECURE_VIOLATION_VLAN") + | eval src_interface=src_int_prefix_long+src_int_suffix + | stats min(_time) AS firstTime max(_time) AS lastTime values(disable_cause) AS disable_cause values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(action) AS action count + BY host src_interface + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_port_security_violation_filter` +how_to_implement: This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with Port Security and Error Disable for this to work (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst4500/12-2/25ew/configuration/guide/conf/port_sec.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. +known_false_positives: This search might be prone to high false positives if you have malfunctioning devices connected to your ethernet ports or if end users periodically connect physical devices to the network. references: [] rba: - message: Port Securtiy Violation on $host$ - risk_objects: - - field: host - type: system - score: 25 - threat_objects: [] + message: Port Securtiy Violation on $host$ + risk_objects: + - field: host + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Router and Infrastructure Security - asset_type: Infrastructure - mitre_attack_id: - - T1200 - - T1498 - - T1557.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Router and Infrastructure Security + asset_type: Infrastructure + mitre_attack_id: + - T1200 + - T1498 + - T1557.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/network/detect_remote_access_software_usage_dns.yml b/detections/network/detect_remote_access_software_usage_dns.yml index dabd85d7a9..0a6168405c 100644 --- a/detections/network/detect_remote_access_software_usage_dns.yml +++ b/detections/network/detect_remote_access_software_usage_dns.yml @@ -5,106 +5,80 @@ date: '2026-01-19' author: Steven Dick status: production type: Anomaly -description: The following analytic detects DNS queries to domains associated - with known remote access software such as AnyDesk, GoToMyPC, LogMeIn, and - TeamViewer. This detection is crucial as adversaries often use these tools to - maintain access and control over compromised environments. Identifying such - behavior is vital for a Security Operations Center (SOC) because unauthorized - remote access can lead to data breaches, ransomware attacks, and other severe - impacts if these threats are not mitigated promptly. +description: The following analytic detects DNS queries to domains associated with known remote access software such as AnyDesk, GoToMyPC, LogMeIn, and TeamViewer. This detection is crucial as adversaries often use these tools to maintain access and control over compromised environments. Identifying such behavior is vital for a Security Operations Center (SOC) because unauthorized remote access can lead to data breaches, ransomware attacks, and other severe impacts if these threats are not mitigated promptly. data_source: -- Sysmon EventID 22 + - Sysmon EventID 22 search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime - from datamodel=Network_Resolution where - DNS.query=* - NOT DNS.query IN ("-", "unknown") - by DNS.answer DNS.answer_count DNS.query - DNS.query_count DNS.reply_code_id DNS.src - DNS.vendor_product - | `drop_dm_object_name("DNS")` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | lookup remote_access_software remote_domain AS query OUTPUT isutility, description as signature, - comment_reference as desc, category - | eval dest = query - | search isutility = True - | `remote_access_software_usage_exceptions` - | `detect_remote_access_software_usage_dns_filter` -how_to_implement: To implement this search, you must ingest logs that contain - the DNS query and the source of the query. These logs must be processed using - the appropriate Splunk Technology Add-ons that are specific to the DNS logs. - The logs must also be mapped to the `Network_Resolution` data model. Use the - Splunk Common Information Model (CIM) to normalize the field names and speed - up the data modeling process. The "exceptions" macro leverages both an Assets - and Identities lookup, as well as a KVStore collection called - "remote_software_exceptions" that lets you track and maintain device-based - exceptions for this set of detections. -known_false_positives: It is possible that legitimate remote access software is - used within the environment. Ensure that the lookup is reviewed and updated - with any additional remote access software that is used within the - environment. Known false positives can be added to the - remote_access_software_usage_exception.csv lookup to globally suppress these - situations across all remote access content + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + from datamodel=Network_Resolution where + DNS.query=* + NOT DNS.query IN ("-", "unknown") + by DNS.answer DNS.answer_count DNS.query + DNS.query_count DNS.reply_code_id DNS.src + DNS.vendor_product + | `drop_dm_object_name("DNS")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | lookup remote_access_software remote_domain AS query OUTPUT isutility, description as signature, + comment_reference as desc, category + | eval dest = query + | search isutility = True + | `remote_access_software_usage_exceptions` + | `detect_remote_access_software_usage_dns_filter` +how_to_implement: To implement this search, you must ingest logs that contain the DNS query and the source of the query. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the DNS logs. The logs must also be mapped to the `Network_Resolution` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. The "exceptions" macro leverages both an Assets and Identities lookup, as well as a KVStore collection called "remote_software_exceptions" that lets you track and maintain device-based exceptions for this set of detections. +known_false_positives: It is possible that legitimate remote access software is used within the environment. Ensure that the lookup is reviewed and updated with any additional remote access software that is used within the environment. Known false positives can be added to the remote_access_software_usage_exception.csv lookup to globally suppress these situations across all remote access content references: -- https://attack.mitre.org/techniques/T1219/ -- https://thedfirreport.com/2022/08/08/bumblebee-roasts-its-way-to-domain-admin/ -- https://thedfirreport.com/2022/11/28/emotet-strikes-again-lnk-file-leads-to-domain-wide-ransomware/ + - https://attack.mitre.org/techniques/T1219/ + - https://thedfirreport.com/2022/08/08/bumblebee-roasts-its-way-to-domain-admin/ + - https://thedfirreport.com/2022/11/28/emotet-strikes-again-lnk-file-leads-to-domain-wide-ransomware/ drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate traffic to $query$ - search: '| from datamodel:Network_Resolution.DNS | search src=$src$ query=$query$' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate traffic to $query$ + search: '| from datamodel:Network_Resolution.DNS | search src=$src$ query=$query$' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A domain for a known remote access software $query$ was contacted by - $src$. - risk_objects: - - field: src - type: system - score: 25 - threat_objects: - - field: query - type: domain - - field: signature - type: signature + message: A domain for a known remote access software $query$ was contacted by $src$. + risk_objects: + - field: src + type: system + score: 25 + threat_objects: + - field: query + type: domain + - field: signature + type: signature tags: - analytic_story: - - Insider Threat - - Command And Control - - Ransomware - - CISA AA24-241A - - Remote Monitoring and Management Software - - Scattered Spider - - Interlock Ransomware - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1219 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint - manual_test: This detection uses A&I lookups from Enterprise Security. + analytic_story: + - Insider Threat + - Command And Control + - Ransomware + - CISA AA24-241A + - Remote Monitoring and Management Software + - Scattered Spider + - Interlock Ransomware + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1219 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint + manual_test: This detection uses A&I lookups from Enterprise Security. tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/network/detect_remote_access_software_usage_traffic.yml b/detections/network/detect_remote_access_software_usage_traffic.yml index ea97e9fa3b..4ea3803f9b 100644 --- a/detections/network/detect_remote_access_software_usage_traffic.yml +++ b/detections/network/detect_remote_access_software_usage_traffic.yml @@ -5,109 +5,84 @@ date: '2026-01-19' author: Steven Dick status: production type: Anomaly -description: The following analytic detects network traffic associated with - known remote access software applications, such as AnyDesk, GoToMyPC, LogMeIn, - and TeamViewer. It leverages Palo Alto traffic logs mapped to the - Network_Traffic data model in Splunk. This activity is significant because - adversaries often use remote access tools to maintain unauthorized access to - compromised environments. If confirmed malicious, this activity could allow - attackers to control systems remotely, exfiltrate data, or deploy additional - malware, posing a severe threat to the organization's security. +description: The following analytic detects network traffic associated with known remote access software applications, such as AnyDesk, GoToMyPC, LogMeIn, and TeamViewer. It leverages Palo Alto traffic logs mapped to the Network_Traffic data model in Splunk. This activity is significant because adversaries often use remote access tools to maintain unauthorized access to compromised environments. If confirmed malicious, this activity could allow attackers to control systems remotely, exfiltrate data, or deploy additional malware, posing a severe threat to the organization's security. data_source: -- Palo Alto Network Traffic + - Palo Alto Network Traffic search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime - values(All_Traffic.dest_port) as dest_port - latest(All_Traffic.user) as user - from datamodel=Network_Traffic where - All_Traffic.app=* - NOT All_Traffic.app IN ("-", "unknown") - by All_Traffic.action All_Traffic.app All_Traffic.bytes All_Traffic.bytes_in - All_Traffic.bytes_out All_Traffic.dest All_Traffic.dest_ip - All_Traffic.dest_port All_Traffic.dvc All_Traffic.protocol - All_Traffic.protocol_version All_Traffic.src All_Traffic.src_ip - All_Traffic.src_port All_Traffic.transport All_Traffic.user - All_Traffic.vendor_product - | `drop_dm_object_name("All_Traffic")` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | lookup remote_access_software remote_appid AS app OUTPUT isutility, description as signature, comment_reference as desc, category - | search isutility = True - | `remote_access_software_usage_exceptions` - | `detect_remote_access_software_usage_traffic_filter` -how_to_implement: The following analytic was developed with Palo Alto traffic - logs. Ensure that the logs are being ingested into Splunk and mapped to the - Network_Traffic data model. Use the Splunk Common Information Model (CIM) to - normalize the field names and speed up the data modeling process. The - "exceptions" macro leverages both an Assets and Identities lookup, as well as - a KVStore collection called "remote_software_exceptions" that lets you track - and maintain device- based exceptions for this set of detections. -known_false_positives: It is possible that legitimate remote access software is - used within the environment. Ensure that the lookup is reviewed and updated - with any additional remote access software that is used within the - environment. Known false positives can be added to the - remote_access_software_usage_exception.csv lookup to globally suppress these - situations across all remote access content + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + values(All_Traffic.dest_port) as dest_port + latest(All_Traffic.user) as user + from datamodel=Network_Traffic where + All_Traffic.app=* + NOT All_Traffic.app IN ("-", "unknown") + by All_Traffic.action All_Traffic.app All_Traffic.bytes All_Traffic.bytes_in + All_Traffic.bytes_out All_Traffic.dest All_Traffic.dest_ip + All_Traffic.dest_port All_Traffic.dvc All_Traffic.protocol + All_Traffic.protocol_version All_Traffic.src All_Traffic.src_ip + All_Traffic.src_port All_Traffic.transport All_Traffic.user + All_Traffic.vendor_product + | `drop_dm_object_name("All_Traffic")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | lookup remote_access_software remote_appid AS app OUTPUT isutility, description as signature, comment_reference as desc, category + | search isutility = True + | `remote_access_software_usage_exceptions` + | `detect_remote_access_software_usage_traffic_filter` +how_to_implement: The following analytic was developed with Palo Alto traffic logs. Ensure that the logs are being ingested into Splunk and mapped to the Network_Traffic data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. The "exceptions" macro leverages both an Assets and Identities lookup, as well as a KVStore collection called "remote_software_exceptions" that lets you track and maintain device- based exceptions for this set of detections. +known_false_positives: It is possible that legitimate remote access software is used within the environment. Ensure that the lookup is reviewed and updated with any additional remote access software that is used within the environment. Known false positives can be added to the remote_access_software_usage_exception.csv lookup to globally suppress these situations across all remote access content references: -- https://attack.mitre.org/techniques/T1219/ -- https://thedfirreport.com/2022/08/08/bumblebee-roasts-its-way-to-domain-admin/ -- https://thedfirreport.com/2022/11/28/emotet-strikes-again-lnk-file-leads-to-domain-wide-ransomware/ -- https://applipedia.paloaltonetworks.com/ + - https://attack.mitre.org/techniques/T1219/ + - https://thedfirreport.com/2022/08/08/bumblebee-roasts-its-way-to-domain-admin/ + - https://thedfirreport.com/2022/11/28/emotet-strikes-again-lnk-file-leads-to-domain-wide-ransomware/ + - https://applipedia.paloaltonetworks.com/ drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: Investigate application traffic for $app$ - search: '| from datamodel:Network_Traffic.All_Traffic | search src=$src$ app=$app$' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate application traffic for $app$ + search: '| from datamodel:Network_Traffic.All_Traffic | search src=$src$ app=$app$' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Application traffic for a known remote access software [$signature$] - was detected from $src$. - risk_objects: - - field: src - type: system - score: 25 - - field: user - type: user - score: 25 - threat_objects: - - field: signature - type: signature + message: Application traffic for a known remote access software [$signature$] was detected from $src$. + risk_objects: + - field: src + type: system + score: 25 + - field: user + type: user + score: 25 + threat_objects: + - field: signature + type: signature tags: - analytic_story: - - Insider Threat - - Command And Control - - Ransomware - - Remote Monitoring and Management Software - - Scattered Spider - - Interlock Ransomware - - Scattered Lapsus$ Hunters - asset_type: Network - mitre_attack_id: - - T1219 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - manual_test: This detection uses A&I lookups from Enterprise Security. + analytic_story: + - Insider Threat + - Command And Control + - Ransomware + - Remote Monitoring and Management Software + - Scattered Spider + - Interlock Ransomware + - Scattered Lapsus$ Hunters + asset_type: Network + mitre_attack_id: + - T1219 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + manual_test: This detection uses A&I lookups from Enterprise Security. tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_palo_traffic.log - source: screenconnect_palo_traffic - sourcetype: pan:traffic + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_palo_traffic.log + source: screenconnect_palo_traffic + sourcetype: pan:traffic diff --git a/detections/network/detect_rogue_dhcp_server.yml b/detections/network/detect_rogue_dhcp_server.yml index 562f31e161..b82e1b757a 100644 --- a/detections/network/detect_rogue_dhcp_server.yml +++ b/detections/network/detect_rogue_dhcp_server.yml @@ -1,51 +1,41 @@ name: Detect Rogue DHCP Server id: 6e1ada88-7a0d-4ac1-92c6-03d354686079 -version: 7 -date: '2025-10-21' +version: 8 +date: '2026-02-25' author: Mikael Bjerkeland, Splunk status: experimental type: TTP -description: The following analytic identifies the presence of unauthorized DHCP servers - on the network. It leverages logs from Cisco network devices with DHCP Snooping - enabled, specifically looking for events where DHCP leases are issued from untrusted - ports. This activity is significant because rogue DHCP servers can facilitate Man-in-the-Middle - attacks, leading to potential data interception and network disruption. If confirmed - malicious, this could allow attackers to redirect network traffic, capture sensitive - information, and compromise the integrity of the network. +description: The following analytic identifies the presence of unauthorized DHCP servers on the network. It leverages logs from Cisco network devices with DHCP Snooping enabled, specifically looking for events where DHCP leases are issued from untrusted ports. This activity is significant because rogue DHCP servers can facilitate Man-in-the-Middle attacks, leading to potential data interception and network disruption. If confirmed malicious, this could allow attackers to redirect network traffic, capture sensitive information, and compromise the integrity of the network. data_source: - - Cisco IOS Logs -search: '`cisco_networks` facility="DHCP_SNOOPING" mnemonic="DHCP_SNOOPING_UNTRUSTED_PORT" - | stats min(_time) AS firstTime max(_time) AS lastTime count values(message_type) - AS message_type values(src_mac) AS src_mac BY host | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| - `detect_rogue_dhcp_server_filter`' -how_to_implement: This search uses a standard SPL query on logs from Cisco Network - devices. The network devices must be configured with DHCP Snooping enabled (see - https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) - and log with a severity level of minimum "5 - notification". The search also requires - that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) - is used to parse the logs from the Cisco network devices. -known_false_positives: This search might be prone to high false positives if DHCP - Snooping has been incorrectly configured or in the unlikely event that the DHCP - server has been moved to another network interface. + - Cisco IOS Logs +search: |- + `cisco_networks` facility="DHCP_SNOOPING" mnemonic="DHCP_SNOOPING_UNTRUSTED_PORT" + | stats min(_time) AS firstTime max(_time) AS lastTime count values(message_type) AS message_type values(src_mac) AS src_mac + BY host + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_rogue_dhcp_server_filter` +how_to_implement: This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping enabled (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. +known_false_positives: This search might be prone to high false positives if DHCP Snooping has been incorrectly configured or in the unlikely event that the DHCP server has been moved to another network interface. references: [] rba: - message: DHCP Snooping detected by $host$ - risk_objects: - - field: host - type: system - score: 25 - threat_objects: [] + message: DHCP Snooping detected by $host$ + risk_objects: + - field: host + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Router and Infrastructure Security - - Scattered Lapsus$ Hunters - asset_type: Infrastructure - mitre_attack_id: - - T1200 - - T1498 - - T1557 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Router and Infrastructure Security + - Scattered Lapsus$ Hunters + asset_type: Infrastructure + mitre_attack_id: + - T1200 + - T1498 + - T1557 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/network/detect_snicat_sni_exfiltration.yml b/detections/network/detect_snicat_sni_exfiltration.yml index 33f35fcce3..0012a150ff 100644 --- a/detections/network/detect_snicat_sni_exfiltration.yml +++ b/detections/network/detect_snicat_sni_exfiltration.yml @@ -1,47 +1,52 @@ name: Detect SNICat SNI Exfiltration id: 82d06410-134c-11eb-adc1-0242ac120002 -version: 6 -date: '2026-01-14' +version: 7 +date: '2026-02-25' author: Shannon Davis, Splunk status: experimental type: TTP -description: The following analytic identifies the use of SNICat tool commands within - the TLS SNI field, indicating potential data exfiltration attempts. It leverages - Zeek SSL data to detect specific SNICat commands such as LIST, LS, SIZE, LD, CB, - EX, ALIVE, EXIT, WHERE, and finito in the server_name field. This activity is significant - as SNICat is a known tool for covert data exfiltration using TLS. If confirmed malicious, - this could allow attackers to exfiltrate sensitive data undetected, posing a severe - threat to data confidentiality and integrity. +description: The following analytic identifies the use of SNICat tool commands within the TLS SNI field, indicating potential data exfiltration attempts. It leverages Zeek SSL data to detect specific SNICat commands such as LIST, LS, SIZE, LD, CB, EX, ALIVE, EXIT, WHERE, and finito in the server_name field. This activity is significant as SNICat is a known tool for covert data exfiltration using TLS. If confirmed malicious, this could allow attackers to exfiltrate sensitive data undetected, posing a severe threat to data confidentiality and integrity. data_source: [] -search: '`zeek_ssl` | rex field=server_name "(?(LIST|LS|SIZE|LD|CB|CD|EX|ALIVE|EXIT|WHERE|finito)-[A-Za-z0-9]{16}\.)" - | stats count by src_ip dest_ip server_name snicat | where count>0 | table src_ip - dest_ip server_name snicat | `detect_snicat_sni_exfiltration_filter`' -how_to_implement: You must be ingesting Zeek SSL data into Splunk. Zeek data should - also be getting ingested in JSON format. We are detecting when any of the predefined - SNICat commands are found within the server_name (SNI) field. These commands are - LIST, LS, SIZE, LD, CB, EX, ALIVE, EXIT, WHERE, and finito. You can go further - once this has been detected, and run other searches to decode the SNI data to prove - or disprove if any data exfiltration has taken place. +search: |- + `zeek_ssl` + | rex field=server_name "(?(LIST + | LS + | SIZE + | LD + | CB + | CD + | EX + | ALIVE + | EXIT + | WHERE + | finito)-[A-Za-z0-9]{16}\.)" + | stats count + BY src_ip dest_ip server_name + snicat + | where count>0 + | table src_ip dest_ip server_name snicat + | `detect_snicat_sni_exfiltration_filter` +how_to_implement: You must be ingesting Zeek SSL data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting when any of the predefined SNICat commands are found within the server_name (SNI) field. These commands are LIST, LS, SIZE, LD, CB, EX, ALIVE, EXIT, WHERE, and finito. You can go further once this has been detected, and run other searches to decode the SNI data to prove or disprove if any data exfiltration has taken place. known_false_positives: No false positives have been identified at this time. references: -- https://www.mnemonic.io/resources/blog/introducing-snicat/ -- https://github.com/mnemonic-no/SNIcat -- https://attack.mitre.org/techniques/T1041/ + - https://www.mnemonic.io/resources/blog/introducing-snicat/ + - https://github.com/mnemonic-no/SNIcat + - https://attack.mitre.org/techniques/T1041/ rba: - message: Possible SNICat activity from $src_ip$ - risk_objects: - - field: src_ip - type: system - score: 25 - threat_objects: [] + message: Possible SNICat activity from $src_ip$ + risk_objects: + - field: src_ip + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Data Exfiltration - asset_type: Network - mitre_attack_id: - - T1041 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Data Exfiltration + asset_type: Network + mitre_attack_id: + - T1041 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/network/detect_software_download_to_network_device.yml b/detections/network/detect_software_download_to_network_device.yml index ed6059ee67..ec636205ea 100644 --- a/detections/network/detect_software_download_to_network_device.yml +++ b/detections/network/detect_software_download_to_network_device.yml @@ -1,51 +1,43 @@ name: Detect Software Download To Network Device id: cc590c66-f65f-48f2-986a-4797244762f8 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Mikael Bjerkeland, Splunk status: experimental type: TTP -description: The following analytic identifies unauthorized software downloads to - network devices via TFTP, FTP, or SSH/SCP. It detects this activity by analyzing - network traffic events on specific ports (69, 21, 22) from devices categorized as - network, router, or switch. This activity is significant because adversaries may - exploit netbooting to load unauthorized operating systems, potentially compromising - network integrity. If confirmed malicious, this could lead to unauthorized control - over network devices, enabling further attacks, data exfiltration, or persistent - access within the network. +description: The following analytic identifies unauthorized software downloads to network devices via TFTP, FTP, or SSH/SCP. It detects this activity by analyzing network traffic events on specific ports (69, 21, 22) from devices categorized as network, router, or switch. This activity is significant because adversaries may exploit netbooting to load unauthorized operating systems, potentially compromising network integrity. If confirmed malicious, this could lead to unauthorized control over network devices, enabling further attacks, data exfiltration, or persistent access within the network. data_source: [] -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Network_Traffic where (All_Traffic.transport=udp AND - All_Traffic.dest_port=69) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=21) - OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=22) AND All_Traffic.dest_category!=common_software_repo_destination - AND All_Traffic.src_category=network OR All_Traffic.src_category=router OR All_Traffic.src_category=switch - by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_software_download_to_network_device_filter`' -how_to_implement: This search looks for Network Traffic events to TFTP, FTP or SSH/SCP - ports from network devices. Make sure to tag any network devices as network, router - or switch in order for this detection to work. If the TFTP traffic doesn't traverse - a firewall nor packet inspection, these events will not be logged. This is typically - an issue if the TFTP server is on the same subnet as the network device. There is - also a chance of the network device loading software using a DHCP assigned IP address - (netboot) which is not in the Asset inventory. -known_false_positives: This search will also report any legitimate attempts of software - downloads to network devices as well as outbound SSH sessions from network devices. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Network_Traffic + WHERE ( + All_Traffic.transport=udp + AND + All_Traffic.dest_port=69 + ) + OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=21) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=22) AND All_Traffic.dest_category!=common_software_repo_destination AND All_Traffic.src_category=network OR All_Traffic.src_category=router OR All_Traffic.src_category=switch + BY All_Traffic.src All_Traffic.dest All_Traffic.dest_port + | `drop_dm_object_name("All_Traffic")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_software_download_to_network_device_filter` +how_to_implement: This search looks for Network Traffic events to TFTP, FTP or SSH/SCP ports from network devices. Make sure to tag any network devices as network, router or switch in order for this detection to work. If the TFTP traffic doesn't traverse a firewall nor packet inspection, these events will not be logged. This is typically an issue if the TFTP server is on the same subnet as the network device. There is also a chance of the network device loading software using a DHCP assigned IP address (netboot) which is not in the Asset inventory. +known_false_positives: This search will also report any legitimate attempts of software downloads to network devices as well as outbound SSH sessions from network devices. references: [] rba: - message: Potentially unauthorized software download to $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: Potentially unauthorized software download to $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Router and Infrastructure Security - asset_type: Infrastructure - mitre_attack_id: - - T1542.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Router and Infrastructure Security + asset_type: Infrastructure + mitre_attack_id: + - T1542.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/network/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.yml b/detections/network/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.yml index 2a02d63d49..0ae70bedad 100644 --- a/detections/network/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.yml +++ b/detections/network/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.yml @@ -1,69 +1,53 @@ name: Detect suspicious DNS TXT records using pretrained model in DSDL id: 92f65c3a-968c-11ed-a1eb-0242ac120002 -version: 6 -date: '2026-01-20' +version: 7 +date: '2026-02-25' author: Abhinav Mishra, Kumar Sharad and Namratha Sreekanta, Splunk status: experimental type: Anomaly -description: The following analytic identifies suspicious DNS TXT records using a - pre-trained deep learning model. It leverages DNS response data from the Network - Resolution data model, categorizing TXT records into known types via regular expressions. - Records that do not match known patterns are flagged as suspicious. This activity - is significant as DNS TXT records can be used for data exfiltration or command-and-control - communication. If confirmed malicious, attackers could use these records to covertly - transfer data or receive instructions, posing a severe threat to network security. +description: The following analytic identifies suspicious DNS TXT records using a pre-trained deep learning model. It leverages DNS response data from the Network Resolution data model, categorizing TXT records into known types via regular expressions. Records that do not match known patterns are flagged as suspicious. This activity is significant as DNS TXT records can be used for data exfiltration or command-and-control communication. If confirmed malicious, attackers could use these records to covertly transfer data or receive instructions, posing a severe threat to network security. data_source: [] -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND - DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type | `drop_dm_object_name("DNS")` - | rename answer as text | fields firstTime, lastTime, message_type,record_type,src,dest, - text | apply detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl | - rename predicted_is_unknown as is_suspicious_score | where is_suspicious_score > - 0.5 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | - table src,dest,text,record_type, firstTime, lastTime,is_suspicious_score | `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl_filter`' -how_to_implement: "Steps to deploy detect suspicious DNS TXT records model into Splunk - App DSDL. This detection depends on the Splunk app for Data Science and Deep Learning - which can be found here - `https://splunkbase.splunk.com/app/4607/` and the Network - Resolution datamodel which can be found here - `https://splunkbase.splunk.com/app/1621/`. - The detection uses a pre-trained deep learning model that needs to be deployed in - DSDL app. Follow the steps for deployment here - `https://github.com/splunk/security_content/wiki/How-to-deploy-pre-trained-Deep-Learning-models-for-ESCU`.\n - * Download the `artifacts .tar.gz` file from the link - `https://seal.splunkresearch.com/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz`.\n - * Download the `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.ipynb` - Jupyter notebook from `https://github.com/splunk/security_content/notebooks`.\n - * Login to the Jupyter Lab assigned for `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl` - container. This container should be listed on Containers page for DSDL app.\n* Below - steps need to be followed inside Jupyter lab.\n* Upload the `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz` - file into `app/model/data` path using the upload option in the jupyter notebook.\n - * Untar the artifact `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz` - using `tar -xf app/model/data/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz - -C app/model/data`.\n* Upload detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.ipynb` - into Jupyter lab notebooks folder using the upload option in Jupyter lab.\n* Save - the notebook using the save option in Jupyter notebook.\n* Upload `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.json` - into `notebooks/data` folder." -known_false_positives: False positives may be present if DNS TXT record contents are - similar to benign DNS TXT record contents. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Network_Resolution + WHERE DNS.message_type=response + AND + DNS.record_type=TXT + BY DNS.src DNS.dest DNS.answer + DNS.record_type + | `drop_dm_object_name("DNS")` + | rename answer as text + | fields firstTime, lastTime, message_type,record_type,src,dest, text + | apply detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl + | rename predicted_is_unknown as is_suspicious_score + | where is_suspicious_score > 0.5 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table src,dest,text,record_type, firstTime, lastTime,is_suspicious_score + | `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl_filter` +how_to_implement: "Steps to deploy detect suspicious DNS TXT records model into Splunk App DSDL. This detection depends on the Splunk app for Data Science and Deep Learning which can be found here - `https://splunkbase.splunk.com/app/4607/` and the Network Resolution datamodel which can be found here - `https://splunkbase.splunk.com/app/1621/`. The detection uses a pre-trained deep learning model that needs to be deployed in DSDL app. Follow the steps for deployment here - `https://github.com/splunk/security_content/wiki/How-to-deploy-pre-trained-Deep-Learning-models-for-ESCU`.\n * Download the `artifacts .tar.gz` file from the link - `https://seal.splunkresearch.com/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz`.\n * Download the `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.ipynb` Jupyter notebook from `https://github.com/splunk/security_content/notebooks`.\n * Login to the Jupyter Lab assigned for `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl` container. This container should be listed on Containers page for DSDL app.\n* Below steps need to be followed inside Jupyter lab.\n* Upload the `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz` file into `app/model/data` path using the upload option in the jupyter notebook.\n * Untar the artifact `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz` using `tar -xf app/model/data/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz -C app/model/data`.\n* Upload detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.ipynb` into Jupyter lab notebooks folder using the upload option in Jupyter lab.\n* Save the notebook using the save option in Jupyter notebook.\n* Upload `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.json` into `notebooks/data` folder." +known_false_positives: False positives may be present if DNS TXT record contents are similar to benign DNS TXT record contents. references: -- https://attack.mitre.org/techniques/T1071/004/ -- https://unit42.paloaltonetworks.com/dns-tunneling-how-dns-can-be-abused-by-malicious-actors/ -- https://en.wikipedia.org/wiki/TXT_record + - https://attack.mitre.org/techniques/T1071/004/ + - https://unit42.paloaltonetworks.com/dns-tunneling-how-dns-can-be-abused-by-malicious-actors/ + - https://en.wikipedia.org/wiki/TXT_record rba: - message: A suspicious DNS TXT response was detected on host $src$ , kindly review. - risk_objects: - - field: src - type: system - score: 45 - threat_objects: [] + message: A suspicious DNS TXT response was detected on host $src$ , kindly review. + risk_objects: + - field: src + type: system + score: 45 + threat_objects: [] tags: - analytic_story: - - DNS Hijacking - - Suspicious DNS Traffic - - Command And Control - - VoidLink Cloud-Native Linux Malware - asset_type: Endpoint - mitre_attack_id: - - T1568.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - DNS Hijacking + - Suspicious DNS Traffic + - Command And Control + - VoidLink Cloud-Native Linux Malware + asset_type: Endpoint + mitre_attack_id: + - T1568.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/network/detect_traffic_mirroring.yml b/detections/network/detect_traffic_mirroring.yml index b42902d56d..7b09d7dd3f 100644 --- a/detections/network/detect_traffic_mirroring.yml +++ b/detections/network/detect_traffic_mirroring.yml @@ -1,52 +1,40 @@ name: Detect Traffic Mirroring id: 42b3b753-5925-49c5-9742-36fa40a73990 -version: 8 -date: '2025-10-21' +version: 9 +date: '2026-02-25' author: Mikael Bjerkeland, Splunk status: experimental type: TTP -description: The following analytic detects the initiation of traffic mirroring sessions - on Cisco network devices. It leverages logs with specific mnemonics and facilities - related to traffic mirroring, such as "ETH_SPAN_SESSION_UP" and "PKTCAP_START." - This activity is significant because adversaries may use traffic mirroring to exfiltrate - data by duplicating and forwarding network traffic to an external destination. If - confirmed malicious, this could allow attackers to capture sensitive information, - monitor network communications, and potentially compromise the integrity and confidentiality - of the network. +description: The following analytic detects the initiation of traffic mirroring sessions on Cisco network devices. It leverages logs with specific mnemonics and facilities related to traffic mirroring, such as "ETH_SPAN_SESSION_UP" and "PKTCAP_START." This activity is significant because adversaries may use traffic mirroring to exfiltrate data by duplicating and forwarding network traffic to an external destination. If confirmed malicious, this could allow attackers to capture sensitive information, monitor network communications, and potentially compromise the integrity and confidentiality of the network. data_source: - - Cisco IOS Logs -search: '`cisco_networks` (facility="MIRROR" mnemonic="ETH_SPAN_SESSION_UP") OR (facility="SPAN" - mnemonic="SESSION_UP") OR (facility="SPAN" mnemonic="PKTCAP_START") OR (mnemonic="CFGLOG_LOGGEDCMD" - command="monitor session*") | stats min(_time) AS firstTime max(_time) AS lastTime - count BY host facility mnemonic | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` - | `detect_traffic_mirroring_filter`' -how_to_implement: This search uses a standard SPL query on logs from Cisco Network - devices. The network devices must log with a severity level of minimum "5 - notification". - The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) - is used to parse the logs from the Cisco network devices and that the devices have - been configured according to the documentation of the Cisco Networks Add-on. Also - note that an attacker may disable logging from the device prior to enabling traffic - mirroring. -known_false_positives: This search will return false positives for any legitimate - traffic captures by network administrators. + - Cisco IOS Logs +search: |- + `cisco_networks` (facility="MIRROR" mnemonic="ETH_SPAN_SESSION_UP") OR (facility="SPAN" mnemonic="SESSION_UP") OR (facility="SPAN" mnemonic="PKTCAP_START") OR (mnemonic="CFGLOG_LOGGEDCMD" command="monitor session*") + | stats min(_time) AS firstTime max(_time) AS lastTime count + BY host facility mnemonic + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_traffic_mirroring_filter` +how_to_implement: This search uses a standard SPL query on logs from Cisco Network devices. The network devices must log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices and that the devices have been configured according to the documentation of the Cisco Networks Add-on. Also note that an attacker may disable logging from the device prior to enabling traffic mirroring. +known_false_positives: This search will return false positives for any legitimate traffic captures by network administrators. references: [] rba: - message: Traffic Mirroring Session observed on $host$ - risk_objects: - - field: host - type: system - score: 25 - threat_objects: [] + message: Traffic Mirroring Session observed on $host$ + risk_objects: + - field: host + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Router and Infrastructure Security - asset_type: Infrastructure - mitre_attack_id: - - T1020.001 - - T1200 - - T1498 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Router and Infrastructure Security + asset_type: Infrastructure + mitre_attack_id: + - T1020.001 + - T1200 + - T1498 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/network/detect_unauthorized_assets_by_mac_address.yml b/detections/network/detect_unauthorized_assets_by_mac_address.yml index 2a6317f6a5..31250cb86d 100644 --- a/detections/network/detect_unauthorized_assets_by_mac_address.yml +++ b/detections/network/detect_unauthorized_assets_by_mac_address.yml @@ -1,48 +1,40 @@ name: Detect Unauthorized Assets by MAC address id: dcfd6b40-42f9-469d-a433-2e53f7489ff4 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Bhavin Patel, Splunk status: experimental type: TTP -description: The following analytic identifies unauthorized devices attempting to - connect to the organization's network by inspecting DHCP request packets. It detects - this activity by comparing the MAC addresses in DHCP requests against a list of - known authorized devices stored in the assets_by_str.csv file. This activity is - significant for a SOC because unauthorized devices can pose security risks, including - potential data breaches or network disruptions. If confirmed malicious, this activity - could allow an attacker to gain unauthorized network access, potentially leading - to further exploitation or data exfiltration. +description: The following analytic identifies unauthorized devices attempting to connect to the organization's network by inspecting DHCP request packets. It detects this activity by comparing the MAC addresses in DHCP requests against a list of known authorized devices stored in the assets_by_str.csv file. This activity is significant for a SOC because unauthorized devices can pose security risks, including potential data breaches or network disruptions. If confirmed malicious, this activity could allow an attacker to gain unauthorized network access, potentially leading to further exploitation or data exfiltration. data_source: [] -search: '| tstats `security_content_summariesonly` count from datamodel=Network_Sessions - where nodename=All_Sessions.DHCP All_Sessions.tag=dhcp by All_Sessions.dest_ip All_Sessions.dest_mac - | dedup All_Sessions.dest_mac| `drop_dm_object_name("Network_Sessions")`|`drop_dm_object_name("All_Sessions")` - | search NOT [| inputlookup asset_lookup_by_str |rename mac as dest_mac | fields - + dest_mac] | `detect_unauthorized_assets_by_mac_address_filter`' -how_to_implement: This search uses the Network_Sessions data model shipped with Enterprise - Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv - file located in SA-IdentityManagement, which will contain a list of known authorized - organizational assets including their MAC addresses. Ensure that all inventoried - systems have their MAC address populated. -known_false_positives: This search might be prone to high false positives. Please - consider this when conducting analysis or investigations. Authorized devices may - be detected as unauthorized. If this is the case, verify the MAC address of the - system responsible for the false positive and add it to the Assets and Identity - framework with the proper information. +search: |- + | tstats `security_content_summariesonly` count FROM datamodel=Network_Sessions + WHERE nodename=All_Sessions.DHCP All_Sessions.tag=dhcp + BY All_Sessions.dest_ip All_Sessions.dest_mac + | dedup All_Sessions.dest_mac + | `drop_dm_object_name("Network_Sessions")` + | `drop_dm_object_name("All_Sessions")` + | search NOT [ + | inputlookup asset_lookup_by_str + | rename mac as dest_mac + | fields + dest_mac] + | `detect_unauthorized_assets_by_mac_address_filter` +how_to_implement: This search uses the Network_Sessions data model shipped with Enterprise Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv file located in SA-IdentityManagement, which will contain a list of known authorized organizational assets including their MAC addresses. Ensure that all inventoried systems have their MAC address populated. +known_false_positives: This search might be prone to high false positives. Please consider this when conducting analysis or investigations. Authorized devices may be detected as unauthorized. If this is the case, verify the MAC address of the system responsible for the false positive and add it to the Assets and Identity framework with the proper information. references: [] rba: - message: Potentially Unauthorized Device observed - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: Potentially Unauthorized Device observed + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Asset Tracking - asset_type: Infrastructure - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Asset Tracking + asset_type: Infrastructure + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/network/detect_windows_dns_sigred_via_splunk_stream.yml b/detections/network/detect_windows_dns_sigred_via_splunk_stream.yml index 1d86b37576..f152046f98 100644 --- a/detections/network/detect_windows_dns_sigred_via_splunk_stream.yml +++ b/detections/network/detect_windows_dns_sigred_via_splunk_stream.yml @@ -5,49 +5,39 @@ date: '2026-01-14' author: Shannon Davis, Splunk status: experimental type: TTP -description: The following analytic detects attempts to exploit the SIGRed vulnerability - (CVE-2020-1350) in Windows DNS servers. It leverages Splunk Stream DNS and TCP data - to identify DNS SIG and KEY records, as well as TCP payloads exceeding 65KB. This - activity is significant because SIGRed is a critical wormable vulnerability that - allows remote code execution. If confirmed malicious, an attacker could gain unauthorized - access, execute arbitrary code, and potentially disrupt services, leading to severe - data breaches and infrastructure compromise. Immediate investigation and remediation - are crucial to mitigate these risks. +description: The following analytic detects attempts to exploit the SIGRed vulnerability (CVE-2020-1350) in Windows DNS servers. It leverages Splunk Stream DNS and TCP data to identify DNS SIG and KEY records, as well as TCP payloads exceeding 65KB. This activity is significant because SIGRed is a critical wormable vulnerability that allows remote code execution. If confirmed malicious, an attacker could gain unauthorized access, execute arbitrary code, and potentially disrupt services, leading to severe data breaches and infrastructure compromise. Immediate investigation and remediation are crucial to mitigate these risks. data_source: [] search: | - `stream_dns` - | spath "query_type{}" - | search "query_type{}" IN (SIG,KEY) - | spath protocol_stack - | search protocol_stack="ip:tcp:dns" - | append [search `stream_tcp` bytes_out>65000] - | stats count by flow_id - | where count>1 - | fields - count - | `detect_windows_dns_sigred_via_splunk_stream_filter` -how_to_implement: You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We - are detecting SIG and KEY records via stream:dns and TCP payload over 65KB in size - via stream:tcp. Replace the macro definitions ('stream:dns' and 'stream:tcp') with - configurations for your Splunk environment. + `stream_dns` + | spath "query_type{}" + | search "query_type{}" IN (SIG,KEY) + | spath protocol_stack + | search protocol_stack="ip:tcp:dns" + | append [search `stream_tcp` bytes_out>65000] + | stats count by flow_id + | where count>1 + | fields - count + | `detect_windows_dns_sigred_via_splunk_stream_filter` +how_to_implement: You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We are detecting SIG and KEY records via stream:dns and TCP payload over 65KB in size via stream:tcp. Replace the macro definitions ('stream:dns' and 'stream:tcp') with configurations for your Splunk environment. known_false_positives: No false positives have been identified at this time. references: [] rba: - message: Potential SIGRed activity detected - risk_objects: - - field: flow_id - type: other - score: 25 - threat_objects: [] + message: Potential SIGRed activity detected + risk_objects: + - field: flow_id + type: other + score: 25 + threat_objects: [] tags: - analytic_story: - - Windows DNS SIGRed CVE-2020-1350 - asset_type: Endpoint - cve: - - CVE-2020-1350 - mitre_attack_id: - - T1203 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Windows DNS SIGRed CVE-2020-1350 + asset_type: Endpoint + cve: + - CVE-2020-1350 + mitre_attack_id: + - T1203 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/network/detect_windows_dns_sigred_via_zeek.yml b/detections/network/detect_windows_dns_sigred_via_zeek.yml index 11437e0782..c0ce0f2f85 100644 --- a/detections/network/detect_windows_dns_sigred_via_zeek.yml +++ b/detections/network/detect_windows_dns_sigred_via_zeek.yml @@ -1,57 +1,47 @@ name: Detect Windows DNS SIGRed via Zeek id: c5c622e4-d073-11ea-87d0-0242ac130003 -version: 8 -date: '2026-01-14' +version: 9 +date: '2026-02-25' author: Shannon Davis, Splunk status: experimental type: TTP -description: The following analytic detects the presence of SIGRed, a critical DNS - vulnerability, using Zeek DNS and Zeek Conn data. It identifies specific DNS query - types (SIG and KEY) and checks for high data transfer within a flow. This detection - is significant because SIGRed allows attackers to execute remote code on Windows - DNS servers, potentially leading to unauthorized access and control. If confirmed - malicious, this activity could result in data exfiltration, service disruption, - or further network compromise. Immediate investigation and mitigation, such as patching - or isolating the affected server, are crucial. +description: The following analytic detects the presence of SIGRed, a critical DNS vulnerability, using Zeek DNS and Zeek Conn data. It identifies specific DNS query types (SIG and KEY) and checks for high data transfer within a flow. This detection is significant because SIGRed allows attackers to execute remote code on Windows DNS servers, potentially leading to unauthorized access and control. If confirmed malicious, this activity could result in data exfiltration, service disruption, or further network compromise. Immediate investigation and mitigation, such as patching or isolating the affected server, are crucial. data_source: [] search: | - | tstats `security_content_summariesonly` count from datamodel=Network_Resolution where - DNS.query_type IN (SIG,KEY) by DNS.flow_id - | rename DNS.flow_id as flow_id - | append [ - | tstats `security_content_summariesonly` count - from datamodel=Network_Traffic where - All_Traffic.bytes_in>65000 - by All_Traffic.flow_id - | rename All_Traffic.flow_id as flow_id - ] - | stats count by flow_id - | where count>1 - | fields - count' - | `detect_windows_dns_sigred_via_zeek_filter` -how_to_implement: You must be ingesting Zeek DNS and Zeek Conn data into Splunk. Zeek - data should also be getting ingested in JSON format. We are detecting SIG and KEY - records via bro:dns:json and TCP payload over 65KB in size via bro:conn:json. The - Network Resolution and Network Traffic datamodels are in use for this search. + | tstats `security_content_summariesonly` count from datamodel=Network_Resolution where + DNS.query_type IN (SIG,KEY) by DNS.flow_id + | rename DNS.flow_id as flow_id + | append [ + | tstats `security_content_summariesonly` count + from datamodel=Network_Traffic where + All_Traffic.bytes_in>65000 + by All_Traffic.flow_id + | rename All_Traffic.flow_id as flow_id + ] + | stats count by flow_id + | where count>1 + | fields - count' + | `detect_windows_dns_sigred_via_zeek_filter` +how_to_implement: You must be ingesting Zeek DNS and Zeek Conn data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting SIG and KEY records via bro:dns:json and TCP payload over 65KB in size via bro:conn:json. The Network Resolution and Network Traffic datamodels are in use for this search. known_false_positives: No false positives have been identified at this time. references: [] rba: - message: Potential SIGRed activity detected - risk_objects: - - field: flow_id - type: other - score: 25 - threat_objects: [] + message: Potential SIGRed activity detected + risk_objects: + - field: flow_id + type: other + score: 25 + threat_objects: [] tags: - analytic_story: - - Windows DNS SIGRed CVE-2020-1350 - asset_type: Endpoint - cve: - - CVE-2020-1350 - mitre_attack_id: - - T1203 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Windows DNS SIGRed CVE-2020-1350 + asset_type: Endpoint + cve: + - CVE-2020-1350 + mitre_attack_id: + - T1203 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint diff --git a/detections/network/detect_zerologon_via_zeek.yml b/detections/network/detect_zerologon_via_zeek.yml index 3f4d160478..f1dfe18998 100644 --- a/detections/network/detect_zerologon_via_zeek.yml +++ b/detections/network/detect_zerologon_via_zeek.yml @@ -1,53 +1,45 @@ name: Detect Zerologon via Zeek id: bf7a06ec-f703-11ea-adc1-0242ac120002 -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: Shannon Davis, Splunk status: experimental type: TTP -description: 'The following analytic detects attempts to exploit the Zerologon CVE-2020-1472 - vulnerability via Zeek RPC. It leverages Zeek DCE-RPC data to identify specific - operations: NetrServerPasswordSet2, NetrServerReqChallenge, and NetrServerAuthenticate3. - This activity is significant because it indicates an attempt to gain unauthorized - access to a domain controller, potentially leading to a complete takeover of an - organization''s IT infrastructure. If confirmed malicious, the impact could be severe, - including data theft, ransomware deployment, or other devastating outcomes. Immediate - investigation of the identified IP addresses and RPC operations is crucial.' +description: 'The following analytic detects attempts to exploit the Zerologon CVE-2020-1472 vulnerability via Zeek RPC. It leverages Zeek DCE-RPC data to identify specific operations: NetrServerPasswordSet2, NetrServerReqChallenge, and NetrServerAuthenticate3. This activity is significant because it indicates an attempt to gain unauthorized access to a domain controller, potentially leading to a complete takeover of an organization''s IT infrastructure. If confirmed malicious, the impact could be severe, including data theft, ransomware deployment, or other devastating outcomes. Immediate investigation of the identified IP addresses and RPC operations is crucial.' data_source: [] -search: '`zeek_rpc` operation IN (NetrServerPasswordSet2,NetrServerReqChallenge,NetrServerAuthenticate3) - | bin span=5m _time | stats values(operation) dc(operation) as opscount count(eval(operation=="NetrServerReqChallenge")) - as challenge count(eval(operation=="NetrServerAuthenticate3")) as authcount count(eval(operation=="NetrServerPasswordSet2")) - as passcount count as totalcount by _time,src_ip,dest_ip | search opscount=3 authcount>4 - passcount>0 | search `detect_zerologon_via_zeek_filter`' -how_to_implement: You must be ingesting Zeek DCE-RPC data into Splunk. Zeek data should - also be getting ingested in JSON format. We are detecting when all three RPC operations - (NetrServerReqChallenge, NetrServerAuthenticate3, NetrServerPasswordSet2) are splunk_security_essentials_app - via bro:rpc:json. These three operations are then correlated on the Zeek UID field. +search: |- + `zeek_rpc` operation IN (NetrServerPasswordSet2,NetrServerReqChallenge,NetrServerAuthenticate3) + | bin span=5m _time + | stats values(operation) dc(operation) as opscount count(eval(operation=="NetrServerReqChallenge")) as challenge count(eval(operation=="NetrServerAuthenticate3")) as authcount count(eval(operation=="NetrServerPasswordSet2")) as passcount count as totalcount + BY _time,src_ip,dest_ip + | search opscount=3 authcount>4 passcount>0 + | search `detect_zerologon_via_zeek_filter` +how_to_implement: You must be ingesting Zeek DCE-RPC data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting when all three RPC operations (NetrServerReqChallenge, NetrServerAuthenticate3, NetrServerPasswordSet2) are splunk_security_essentials_app via bro:rpc:json. These three operations are then correlated on the Zeek UID field. known_false_positives: No false positives have been identified at this time. references: -- https://www.secura.com/blog/zero-logon -- https://github.com/SecuraBV/CVE-2020-1472 -- https://msrc.microsoft.com/en-US/security-guidance/advisory/CVE-2020-1472 -- https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-319a + - https://www.secura.com/blog/zero-logon + - https://github.com/SecuraBV/CVE-2020-1472 + - https://msrc.microsoft.com/en-US/security-guidance/advisory/CVE-2020-1472 + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-319a rba: - message: Potential Zerologon activity detected - risk_objects: - - field: dest_ip - type: system - score: 25 - threat_objects: [] + message: Potential Zerologon activity detected + risk_objects: + - field: dest_ip + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Detect Zerologon Attack - - Rhysida Ransomware - - Black Basta Ransomware - asset_type: Network - cve: - - CVE-2020-1472 - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Detect Zerologon Attack + - Rhysida Ransomware + - Black Basta Ransomware + asset_type: Network + cve: + - CVE-2020-1472 + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/network/dns_kerberos_coercion.yml b/detections/network/dns_kerberos_coercion.yml index 9d284b1bf4..900b368929 100644 --- a/detections/network/dns_kerberos_coercion.yml +++ b/detections/network/dns_kerberos_coercion.yml @@ -1,79 +1,71 @@ name: DNS Kerberos Coercion id: 8551252d-b5b6-4b6e-8a82-51460aeb29a3 -version: 1 -date: '2025-11-14' +version: 2 +date: '2026-02-25' author: Raven Tait, Splunk status: production type: TTP -description: Detects DNS-based Kerberos coercion attacks where adversaries - inject marshaled credential structures into DNS records to spoof SPNs and - redirect authentication such as in CVE-2025-33073. This detection leverages - suricata looking for specific CREDENTIAL_TARGET_INFORMATION structures in DNS - queries. +description: Detects DNS-based Kerberos coercion attacks where adversaries inject marshaled credential structures into DNS records to spoof SPNs and redirect authentication such as in CVE-2025-33073. This detection leverages suricata looking for specific CREDENTIAL_TARGET_INFORMATION structures in DNS queries. data_source: -- Suricata -- Sysmon EventID 22 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime - max(_time) as lastTime values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution - where DNS.query="*1UWhRC*" DNS.query="*AAAAA*" DNS.query="*YBAAAA*" by DNS.answer - DNS.answer_count DNS.query DNS.query_count DNS.reply_code_id DNS.src DNS.vendor_product - | `drop_dm_object_name(DNS)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | table firstTime lastTime query count src dest - | `dns_kerberos_coercion_filter`' -how_to_implement: To successfully implement this search, you will need to ensure that - DNS data is populating the Network_Resolution data model. -known_false_positives: It's unlikely that a DNS entry contains the specific structure used by - this attack. Filter as needed for your organization. + - Suricata + - Sysmon EventID 22 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(DNS.src) as src values(DNS.dest) as dest FROM datamodel=Network_Resolution + WHERE DNS.query="*1UWhRC*" DNS.query="*AAAAA*" DNS.query="*YBAAAA*" + BY DNS.answer DNS.answer_count DNS.query + DNS.query_count DNS.reply_code_id DNS.src + DNS.vendor_product + | `drop_dm_object_name(DNS)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table firstTime lastTime query count src dest + | `dns_kerberos_coercion_filter` +how_to_implement: To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. +known_false_positives: It's unlikely that a DNS entry contains the specific structure used by this attack. Filter as needed for your organization. references: -- https://web.archive.org/web/20250617122747/https://www.synacktiv.com/publications/ntlm-reflection-is-dead-long-live-ntlm-reflection-an-in-depth-analysis-of-cve-2025 -- https://www.synacktiv.com/publications/relaying-kerberos-over-smb-using-krbrelayx -- https://www.guidepointsecurity.com/blog/the-birth-and-death-of-loopyticket/ + - https://web.archive.org/web/20250617122747/https://www.synacktiv.com/publications/ntlm-reflection-is-dead-long-live-ntlm-reflection-an-in-depth-analysis-of-cve-2025 + - https://www.synacktiv.com/publications/relaying-kerberos-over-smb-using-krbrelayx + - https://www.guidepointsecurity.com/blog/the-birth-and-death-of-loopyticket/ drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search host = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search host = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A dns query $query$ with marshalled CREDENTIAL_TARGET_INFORMATION seen from $src$ - risk_objects: - - field: src - type: system - score: 56 - threat_objects: [] + message: A dns query $query$ with marshalled CREDENTIAL_TARGET_INFORMATION seen from $src$ + risk_objects: + - field: src + type: system + score: 56 + threat_objects: [] tags: - analytic_story: - - Compromised Windows Host - - Suspicious DNS Traffic - - Local Privilege Escalation With KrbRelayUp - - Kerberos Coercion with DNS - asset_type: Endpoint - mitre_attack_id: - - T1557.001 - - T1187 - - T1071.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: - - CVE-2025-33073 + analytic_story: + - Compromised Windows Host + - Suspicious DNS Traffic + - Local Privilege Escalation With KrbRelayUp + - Kerberos Coercion with DNS + asset_type: Endpoint + mitre_attack_id: + - T1557.001 + - T1187 + - T1071.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: + - CVE-2025-33073 tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1071.004/kerberos_coercion/suricata.log - source: Suricata - sourcetype: suricata - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1071.004/kerberos_coercion/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1071.004/kerberos_coercion/suricata.log + source: Suricata + sourcetype: suricata + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1071.004/kerberos_coercion/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/network/dns_query_length_outliers___mltk.yml b/detections/network/dns_query_length_outliers___mltk.yml index b9f1bf58ae..5e9aeef268 100644 --- a/detections/network/dns_query_length_outliers___mltk.yml +++ b/detections/network/dns_query_length_outliers___mltk.yml @@ -1,62 +1,46 @@ name: DNS Query Length Outliers - MLTK id: 85fbcfe8-9718-4911-adf6-7000d077a3a9 -version: 8 -date: '2026-01-22' +version: 9 +date: '2026-02-25' author: Rico Valdez, Splunk status: experimental type: Anomaly -description: The following analytic identifies DNS requests with unusually large query - lengths for the record type being requested. It leverages the Network_Resolution - data model and applies a machine learning model to detect outliers in DNS query - lengths. This activity is significant because unusually large DNS queries can indicate - data exfiltration or command-and-control communication attempts. If confirmed malicious, - this activity could allow attackers to exfiltrate sensitive data or maintain persistent - communication channels with compromised systems. +description: The following analytic identifies DNS requests with unusually large query lengths for the record type being requested. It leverages the Network_Resolution data model and applies a machine learning model to detect outliers in DNS query lengths. This activity is significant because unusually large DNS queries can indicate data exfiltration or command-and-control communication attempts. If confirmed malicious, this activity could allow attackers to exfiltrate sensitive data or maintain persistent communication channels with compromised systems. data_source: [] -search: '| tstats `security_content_summariesonly` count min(_time) as start_time - max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution - by DNS.query DNS.record_type | search DNS.record_type=* | `drop_dm_object_name(DNS)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval - query_length = len(query) | apply dns_query_pdfmodel threshold=0.01 | rename "IsOutlier(query_length)" - as isOutlier | search isOutlier > 0 | sort -query_length | table start_time end_time - query record_type count src dest query_length | `dns_query_length_outliers___mltk_filter`' -how_to_implement: "To successfully implement this search, you will need to ensure - that DNS data is populating the Network_Resolution data model. In addition, the - Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your - search heads, along with any required dependencies. Finally, the support search - \"Baseline of DNS Query Length - MLTK\" must be executed before this detection search, - because it builds a machine-learning (ML) model over the historical data used by - this search. It is important that this search is run in the same app context as - the associated support search, so that the model created by the support search is - available for use. You should periodically re-run the support search to rebuild - the model with the latest data available in your environment.\nThis search produces - fields (`query`,`query_length`,`count`) that are not yet supported by Mission Control Queue and therefore cannot be viewed when a finding event is raised. These fields - contribute additional context to the finding. To see the additional metadata, add - the following fields, if not already present, to Mission Control Queue - (Configure > Findings and Investigations > Add New Entry):\n - * **Label:** DNS Query, **Field:** query\n* **Label:** DNS Query Length, **Field:** - query_length\n* **Label:** Number of events, **Field:** count\n" -known_false_positives: If you are seeing more results than desired, you may consider - reducing the value for threshold in the search. You should also periodically re-run - the support search to re-build the ML model on the latest data. +search: |- + | tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest FROM datamodel=Network_Resolution + BY DNS.query DNS.record_type + | search DNS.record_type=* + | `drop_dm_object_name(DNS)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | eval query_length = len(query) + | apply dns_query_pdfmodel threshold=0.01 + | rename "IsOutlier(query_length)" as isOutlier + | search isOutlier > 0 + | sort -query_length + | table start_time end_time query record_type count src dest query_length + | `dns_query_length_outliers___mltk_filter` +how_to_implement: "To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search \"Baseline of DNS Query Length - MLTK\" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\nThis search produces fields (`query`,`query_length`,`count`) that are not yet supported by Mission Control Queue and therefore cannot be viewed when a finding event is raised. These fields contribute additional context to the finding. To see the additional metadata, add the following fields, if not already present, to Mission Control Queue (Configure > Findings and Investigations > Add New Entry):\n * **Label:** DNS Query, **Field:** query\n* **Label:** DNS Query Length, **Field:** query_length\n* **Label:** Number of events, **Field:** count\n" +known_false_positives: If you are seeing more results than desired, you may consider reducing the value for threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. references: [] rba: - message: DNS Query Length Outliers - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: DNS Query Length Outliers + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Hidden Cobra Malware - - Suspicious DNS Traffic - - Command And Control - asset_type: Endpoint - mitre_attack_id: - - T1071.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Hidden Cobra Malware + - Suspicious DNS Traffic + - Command And Control + asset_type: Endpoint + mitre_attack_id: + - T1071.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/network/dns_query_length_with_high_standard_deviation.yml b/detections/network/dns_query_length_with_high_standard_deviation.yml index e21203c917..0f2734beeb 100644 --- a/detections/network/dns_query_length_with_high_standard_deviation.yml +++ b/detections/network/dns_query_length_with_high_standard_deviation.yml @@ -1,83 +1,71 @@ name: DNS Query Length With High Standard Deviation id: 1a67f15a-f4ff-4170-84e9-08cf6f75d6f5 -version: 12 -date: '2026-01-22' +version: 13 +date: '2026-02-25' author: Bhavin Patel, Splunk status: production type: Anomaly -description: The following analytic identifies DNS queries with unusually large lengths - by computing the standard deviation of query lengths and filtering those exceeding - two times the standard deviation. It leverages DNS query data from the Network_Resolution - data model, focusing on the length of the domain names being resolved. This activity - is significant as unusually long DNS queries can indicate data exfiltration or command-and-control - communication attempts. If confirmed malicious, this activity could allow attackers - to stealthily transfer data or maintain persistent communication channels within - the network. +description: The following analytic identifies DNS queries with unusually large lengths by computing the standard deviation of query lengths and filtering those exceeding two times the standard deviation. It leverages DNS query data from the Network_Resolution data model, focusing on the length of the domain names being resolved. This activity is significant as unusually long DNS queries can indicate data exfiltration or command-and-control communication attempts. If confirmed malicious, this activity could allow attackers to stealthily transfer data or maintain persistent communication channels within the network. data_source: -- Sysmon EventID 22 -search: ' - | tstats `security_content_summariesonly` count min(_time) as firstTime - max(_time) as lastTime from datamodel=Network_Resolution - where NOT DNS.record_type IN ("Pointer","PTR","SOA", "SRV") DNS.query != *. by DNS.answer DNS.answer_count DNS.query - DNS.query_count DNS.reply_code_id DNS.src DNS.vendor_product DNS.dest DNS.record_type - | `drop_dm_object_name("DNS")` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | eval tlds=split(query,".") - | eval tld=mvindex(tlds,-1) - | eval tld_len=len(tld) - | search tld_len<=20 - | eval query_length = len(query) - | table firstTime lastTime src dest query query_length record_type count record_type - | eventstats stdev(query_length) AS stdev avg(query_length) AS - avg p50(query_length) AS p50 - | where query_length>(avg+stdev*2) - | eval z_score=(query_length-avg)/stdev - | stats count values(query) as query values(dest) as dest avg(query_length) as avg_query_length values(record_type) as record_type min(firstTime) as firstTime latest(lastTime) as lastTime by src - | `dns_query_length_with_high_standard_deviation_filter`' -how_to_implement: To successfully implement this search, you will need to ensure that - DNS data is populating the Network_Resolution data model. + - Sysmon EventID 22 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Network_Resolution + WHERE NOT DNS.record_type IN ("Pointer","PTR","SOA", "SRV") DNS.query != *. + BY DNS.answer DNS.answer_count DNS.query + DNS.query_count DNS.reply_code_id DNS.src + DNS.vendor_product DNS.dest DNS.record_type + | `drop_dm_object_name("DNS")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | eval tlds=split(query,".") + | eval tld=mvindex(tlds,-1) + | eval tld_len=len(tld) + | search tld_len<=20 + | eval query_length = len(query) + | table firstTime lastTime src dest query query_length record_type count record_type + | eventstats stdev(query_length) AS stdev avg(query_length) AS avg p50(query_length) AS p50 + | where query_length>(avg+stdev*2) + | eval z_score=(query_length-avg)/stdev + | stats count values(query) as query values(dest) as dest avg(query_length) as avg_query_length values(record_type) as record_type min(firstTime) as firstTime latest(lastTime) as lastTime + BY src + | `dns_query_length_with_high_standard_deviation_filter` +how_to_implement: To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. known_false_positives: It's possible there can be long domain names that are legitimate. references: [] drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potentially suspicious DNS query [$query$] with high standard deviation from src - [$src$] - risk_objects: - - field: src - type: system - score: 30 - threat_objects: - - field: query - type: url + message: Potentially suspicious DNS query [$query$] with high standard deviation from src - [$src$] + risk_objects: + - field: src + type: system + score: 30 + threat_objects: + - field: query + type: url tags: - analytic_story: - - Hidden Cobra Malware - - Suspicious DNS Traffic - - Command And Control - asset_type: Endpoint - mitre_attack_id: - - T1048.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Hidden Cobra Malware + - Suspicious DNS Traffic + - Command And Control + asset_type: Endpoint + mitre_attack_id: + - T1048.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1071.004/long_dns_query/dns-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1071.004/long_dns_query/dns-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/network/excessive_dns_failures.yml b/detections/network/excessive_dns_failures.yml index c7cf916538..7c7c53af46 100644 --- a/detections/network/excessive_dns_failures.yml +++ b/detections/network/excessive_dns_failures.yml @@ -1,50 +1,48 @@ name: Excessive DNS Failures id: 104658f4-afdc-499e-9719-17243f9826f1 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: bowesmana, Bhavin Patel, Splunk status: experimental type: Anomaly -description: The following analytic identifies excessive DNS query failures by counting - DNS responses that do not indicate success, triggering when there are more than - 50 occurrences. It leverages the Network_Resolution data model, focusing on DNS - reply codes that signify errors. This activity is significant because a high number - of DNS failures can indicate potential network misconfigurations, DNS poisoning - attempts, or malware communication issues. If confirmed malicious, this activity - could lead to disrupted network services, hindered communication, or data exfiltration - attempts by attackers. +description: The following analytic identifies excessive DNS query failures by counting DNS responses that do not indicate success, triggering when there are more than 50 occurrences. It leverages the Network_Resolution data model, focusing on DNS reply codes that signify errors. This activity is significant because a high number of DNS failures can indicate potential network misconfigurations, DNS poisoning attempts, or malware communication issues. If confirmed malicious, this activity could lead to disrupted network services, hindered communication, or data exfiltration attempts by attackers. data_source: [] -search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution - where nodename=DNS "DNS.reply_code"!="No Error" "DNS.reply_code"!="NoError" DNS.reply_code!="unknown" - NOT "DNS.query"="*.arpa" "DNS.query"="*.*" by "DNS.src" "DNS.query" "DNS.reply_code" - | `drop_dm_object_name("DNS")` | lookup cim_corporate_web_domain_lookup domain as - query OUTPUT domain | where isnull(domain) | lookup update=true alexa_lookup_by_str - domain as query OUTPUT rank | where isnull(rank) | eventstats max(count) as mc by - src reply_code | eval mode_query=if(count=mc, query, null()) | stats sum(count) - as count values(mode_query) as query values(mc) as max_query_count by src reply_code - | where count>50 | `get_asset(src)` | `excessive_dns_failures_filter`' -how_to_implement: To successfully implement this search you must ensure that DNS data - is populating the Network_Resolution data model. -known_false_positives: It is possible legitimate traffic can trigger this rule. Please - investigate as appropriate. The threshold for generating an event can also be customized - to better suit your environment. +search: |- + | tstats `security_content_summariesonly` count FROM datamodel=Network_Resolution + WHERE nodename=DNS "DNS.reply_code"!="No Error" "DNS.reply_code"!="NoError" DNS.reply_code!="unknown" NOT "DNS.query"="*.arpa" "DNS.query"="*.*" + BY "DNS.src" "DNS.query" "DNS.reply_code" + | `drop_dm_object_name("DNS")` + | lookup cim_corporate_web_domain_lookup domain as query OUTPUT domain + | where isnull(domain) + | lookup update=true alexa_lookup_by_str domain as query OUTPUT rank + | where isnull(rank) + | eventstats max(count) as mc + BY src reply_code + | eval mode_query=if(count=mc, query, null()) + | stats sum(count) as count values(mode_query) as query values(mc) as max_query_count + BY src reply_code + | where count>50 + | `get_asset(src)` + | `excessive_dns_failures_filter` +how_to_implement: To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. +known_false_positives: It is possible legitimate traffic can trigger this rule. Please investigate as appropriate. The threshold for generating an event can also be customized to better suit your environment. references: [] rba: - message: Excessive DNS failures detected on $src$ - risk_objects: - - field: src - type: system - score: 25 - threat_objects: [] + message: Excessive DNS failures detected on $src$ + risk_objects: + - field: src + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Suspicious DNS Traffic - - Command And Control - asset_type: Endpoint - mitre_attack_id: - - T1071.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Suspicious DNS Traffic + - Command And Control + asset_type: Endpoint + mitre_attack_id: + - T1071.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/network/f5_big_ip_icontrol_rest_vulnerability_cve_2022_1388.yml b/detections/network/f5_big_ip_icontrol_rest_vulnerability_cve_2022_1388.yml index fd9d35d815..4310354226 100644 --- a/detections/network/f5_big_ip_icontrol_rest_vulnerability_cve_2022_1388.yml +++ b/detections/network/f5_big_ip_icontrol_rest_vulnerability_cve_2022_1388.yml @@ -1,77 +1,64 @@ name: F5 BIG-IP iControl REST Vulnerability CVE-2022-1388 id: bb1c2c30-107a-4e56-a4b9-1f7022867bfe -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects attempts to exploit the F5 BIG-IP iControl - REST API vulnerability (CVE-2022-1388) for unauthenticated remote code execution. - It identifies suspicious URI paths and POST HTTP methods, along with specific request - headers containing potential commands in the `utilcmdargs` field and a random base64 - encoded value in the `X-F5-Auth-Token` field. This activity is significant as it - targets a critical vulnerability that can allow attackers to execute arbitrary commands - on the affected system. If confirmed malicious, this could lead to full system compromise - and unauthorized access to sensitive data. +description: The following analytic detects attempts to exploit the F5 BIG-IP iControl REST API vulnerability (CVE-2022-1388) for unauthenticated remote code execution. It identifies suspicious URI paths and POST HTTP methods, along with specific request headers containing potential commands in the `utilcmdargs` field and a random base64 encoded value in the `X-F5-Auth-Token` field. This activity is significant as it targets a critical vulnerability that can allow attackers to execute arbitrary commands on the affected system. If confirmed malicious, this could lead to full system compromise and unauthorized access to sensitive data. data_source: -- Palo Alto Network Threat -search: '| tstats count from datamodel=Web where Web.url="*/mgmt/tm/util/bash*" Web.http_method="POST" - by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest - | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `f5_big_ip_icontrol_rest_vulnerability_cve_2022_1388_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - web or proxy logs, or ensure it is being filled by a proxy like device, into the - Web Datamodel. For additional filtering, allow list private IP space or restrict - by known good. -known_false_positives: False positives may be present if the activity is blocked or - was not successful. Filter known vulnerablity scanners. Filter as needed. + - Palo Alto Network Threat +search: |- + | tstats count FROM datamodel=Web + WHERE Web.url="*/mgmt/tm/util/bash*" Web.http_method="POST" + BY Web.http_user_agent Web.http_method, Web.url,Web.url_length + Web.src, Web.dest + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `f5_big_ip_icontrol_rest_vulnerability_cve_2022_1388_filter` +how_to_implement: To successfully implement this search, you need to be ingesting web or proxy logs, or ensure it is being filled by a proxy like device, into the Web Datamodel. For additional filtering, allow list private IP space or restrict by known good. +known_false_positives: False positives may be present if the activity is blocked or was not successful. Filter known vulnerablity scanners. Filter as needed. references: -- https://github.com/dk4trin/templates-nuclei/blob/main/CVE-2022-1388.yaml -- https://www.randori.com/blog/vulnerability-analysis-cve-2022-1388/ -- https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-1388 -- https://twitter.com/da_667/status/1523770267327250438?s=20&t=-JnB_aNWuJFsmcOmxGUWLQ -- https://github.com/horizon3ai/CVE-2022-1388/blob/main/CVE-2022-1388.py + - https://github.com/dk4trin/templates-nuclei/blob/main/CVE-2022-1388.yaml + - https://www.randori.com/blog/vulnerability-analysis-cve-2022-1388/ + - https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-1388 + - https://twitter.com/da_667/status/1523770267327250438?s=20&t=-JnB_aNWuJFsmcOmxGUWLQ + - https://github.com/horizon3ai/CVE-2022-1388/blob/main/CVE-2022-1388.py drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An attempt to exploit CVE-2022-1388 against an F5 appliance $dest$ has - occurred. - risk_objects: - - field: dest - type: system - score: 70 - threat_objects: [] + message: An attempt to exploit CVE-2022-1388 against an F5 appliance $dest$ has occurred. + risk_objects: + - field: dest + type: system + score: 70 + threat_objects: [] tags: - analytic_story: - - F5 BIG-IP Vulnerability CVE-2022-1388 - - CISA AA24-241A - asset_type: Web Server - cve: - - CVE-2022-1388 - mitre_attack_id: - - T1190 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - F5 BIG-IP Vulnerability CVE-2022-1388 + - CISA AA24-241A + asset_type: Web Server + cve: + - CVE-2022-1388 + mitre_attack_id: + - T1190 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/f5/f5.log - source: pan:threat - sourcetype: pan:threat + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/f5/f5.log + source: pan:threat + sourcetype: pan:threat diff --git a/detections/network/hosts_receiving_high_volume_of_network_traffic_from_email_server.yml b/detections/network/hosts_receiving_high_volume_of_network_traffic_from_email_server.yml index 2774a09a10..3064879113 100644 --- a/detections/network/hosts_receiving_high_volume_of_network_traffic_from_email_server.yml +++ b/detections/network/hosts_receiving_high_volume_of_network_traffic_from_email_server.yml @@ -1,58 +1,37 @@ name: Hosts receiving high volume of network traffic from email server id: 7f5fb3e1-4209-4914-90db-0ec21b556368 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Bhavin Patel, Splunk status: experimental type: Anomaly -description: The following analytic identifies hosts receiving an unusually high volume - of network traffic from an email server. It leverages the Network_Traffic data model - to sum incoming bytes to clients from email servers, comparing current traffic against - historical averages and standard deviations. This activity is significant as it - may indicate data exfiltration by a malicious actor using the email server. If confirmed - malicious, this could lead to unauthorized data access and potential data breaches, - compromising sensitive information and impacting organizational security. +description: | + The following analytic identifies hosts receiving an unusually high volume of network traffic from an email server. It leverages the Network_Traffic data model to sum incoming bytes to clients from email servers, comparing current traffic against historical averages and standard deviations. This activity is significant as it may indicate data exfiltration by a malicious actor using the email server. If confirmed malicious, this could lead to unauthorized data access and potential data breaches, compromising sensitive information and impacting organizational security. data_source: [] -search: '| tstats `security_content_summariesonly` sum(All_Traffic.bytes_in) as bytes_in - from datamodel=Network_Traffic where All_Traffic.dest_category=email_server by All_Traffic.src_ip - _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_in) - as avg_bytes_in stdev(bytes_in) as stdev_bytes_in | eventstats count as num_data_samples - avg(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_avg_bytes_in - stdev(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_stdev_bytes_in - by src_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples - >= minimum_data_samples AND bytes_in > (avg_bytes_in + (deviation_threshold * stdev_bytes_in)) - AND bytes_in > (per_source_avg_bytes_in + (deviation_threshold * per_source_stdev_bytes_in)) - AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average - = round(abs(bytes_in - avg_bytes_in) / stdev_bytes_in, 2), num_standard_deviations_away_from_client_average - = round(abs(bytes_in - per_source_avg_bytes_in) / per_source_stdev_bytes_in, 2) - | table src_ip, _time, bytes_in, avg_bytes_in, per_source_avg_bytes_in, num_standard_deviations_away_from_server_average, - num_standard_deviations_away_from_client_average | `hosts_receiving_high_volume_of_network_traffic_from_email_server_filter`' -how_to_implement: This search requires you to be ingesting your network traffic and - populating the Network_Traffic data model. Your email servers must be categorized - as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold - and minimum_data_samples values based on the network traffic in your environment. - The "deviation_threshold" field is a multiplying factor to control how much variation - you're willing to tolerate. The "minimum_data_samples" field is the minimum number - of connections of data samples required for the statistic to be valid. -known_false_positives: The false-positive rate will vary based on how you set the - deviation_threshold and data_samples values. Our recommendation is to adjust these - values based on your network traffic to and from your email servers. +search: | + | tstats `security_content_summariesonly` sum(All_Traffic.bytes_in) as bytes_in from datamodel=Network_Traffic where All_Traffic.dest_category=email_server by All_Traffic.src_ip _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_in) as avg_bytes_in stdev(bytes_in) as stdev_bytes_in | eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_avg_bytes_in stdev(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_stdev_bytes_in by src_ip | eval minimum_data_samples = 4, deviation_threshold = 3 + | where num_data_samples >= minimum_data_samples AND bytes_in > (avg_bytes_in + (deviation_threshold * stdev_bytes_in)) AND bytes_in > (per_source_avg_bytes_in + (deviation_threshold * per_source_stdev_bytes_in)) AND _time >= relative_time(now(), "@d") + | eval num_standard_deviations_away_from_server_average = round(abs(bytes_in - avg_bytes_in) / stdev_bytes_in, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_in - per_source_avg_bytes_in) / per_source_stdev_bytes_in, 2) + | table src_ip, _time, bytes_in, avg_bytes_in, per_source_avg_bytes_in, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average + | `hosts_receiving_high_volume_of_network_traffic_from_email_server_filter` +how_to_implement: This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. +known_false_positives: The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. references: [] rba: - message: High volume of traffic from email servers to $src_ip$ - risk_objects: - - field: src_ip - type: system - score: 25 - threat_objects: [] + message: High volume of traffic from email servers to $src_ip$ + risk_objects: + - field: src_ip + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Collection and Staging - asset_type: Endpoint - mitre_attack_id: - - T1114.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Collection and Staging + asset_type: Endpoint + mitre_attack_id: + - T1114.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/network/http_c2_framework_user_agent.yml b/detections/network/http_c2_framework_user_agent.yml index 1f7ef40276..1b7f018282 100644 --- a/detections/network/http_c2_framework_user_agent.yml +++ b/detections/network/http_c2_framework_user_agent.yml @@ -1,80 +1,74 @@ name: HTTP C2 Framework User Agent id: 229dc225-6abe-4d28-89fd-edf874086162 -version: 1 -date: '2025-12-15' +version: 2 +date: '2026-02-25' author: Ravent Tait, Splunk status: production type: TTP -description: This Splunk query analyzes web logs to identify and categorize - user agents, detecting various types of c2 frameworks. This activity can signify malicious actors - attempting to interact with hosts on the network using known default configurations of command - and control tools. +description: This Splunk query analyzes web logs to identify and categorize user agents, detecting various types of c2 frameworks. This activity can signify malicious actors attempting to interact with hosts on the network using known default configurations of command and control tools. data_source: -- Suricata -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime - from datamodel=Web where Web.http_user_agent != null by Web.http_user_agent Web.http_method, Web.url, Web.url_length Web.src, Web.dest - | `drop_dm_object_name("Web")` - | lookup suspicious_c2_user_agents c2_user_agent AS http_user_agent OUTPUT tool, description - | where isnotnull(tool) - | stats count min(firstTime) as first_seen max(lastTime) as last_seen - by tool url http_user_agent src dest description - | `security_content_ctime(first_seen)` | `security_content_ctime(last_seen)` - | `http_c2_framework_user_agent_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - web or proxy logs, or ensure it is being filled by a proxy like device, into the - Web Datamodel. For additional filtering, allow list private IP space or restrict - by known good. + - Suricata +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.http_user_agent != null + BY Web.http_user_agent Web.http_method, Web.url, + Web.url_length Web.src, Web.dest + | `drop_dm_object_name("Web")` + | lookup suspicious_c2_user_agents c2_user_agent AS http_user_agent OUTPUT tool, description + | where isnotnull(tool) + | stats count min(firstTime) as first_seen max(lastTime) as last_seen + BY tool url http_user_agent + src dest description + | `security_content_ctime(first_seen)` + | `security_content_ctime(last_seen)` + | `http_c2_framework_user_agent_filter` +how_to_implement: To successfully implement this search, you need to be ingesting web or proxy logs, or ensure it is being filled by a proxy like device, into the Web Datamodel. For additional filtering, allow list private IP space or restrict by known good. known_false_positives: Filtering may be required in some instances depending on legacy system usage, filter as needed. references: -- https://github.com/BC-SECURITY/Malleable-C2-Profiles -- https://www.keysight.com/blogs/en/tech/nwvs/2021/07/28/koadic-c3-command-control-decoded -- https://github.com/mthcht/awesome-lists/blob/main/Lists/suspicious_http_user_agents_list.csv + - https://github.com/BC-SECURITY/Malleable-C2-Profiles + - https://www.keysight.com/blogs/en/tech/nwvs/2021/07/28/koadic-c3-command-control-decoded + - https://github.com/mthcht/awesome-lists/blob/main/Lists/suspicious_http_user_agents_list.csv drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A known C2 Framework user agent $http_user_agent$ was performing a request from $src$ to $dest$. - risk_objects: - - field: src - type: system - score: 60 - threat_objects: - - field: http_user_agent - type: http_user_agent - - field: dest - type: system + message: A known C2 Framework user agent $http_user_agent$ was performing a request from $src$ to $dest$. + risk_objects: + - field: src + type: system + score: 60 + threat_objects: + - field: http_user_agent + type: http_user_agent + - field: dest + type: system tags: - analytic_story: - - Cobalt Strike - - Brute Ratel C4 - - Tuoni - - Meterpreter - - Spearphishing Attachments - - Malicious PowerShell - - BishopFox Sliver Adversary Emulation Framework - - Suspicious User Agents - asset_type: Network - mitre_attack_id: - - T1071.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Cobalt Strike + - Brute Ratel C4 + - Tuoni + - Meterpreter + - Spearphishing Attachments + - Malicious PowerShell + - BishopFox Sliver Adversary Emulation Framework + - Suspicious User Agents + asset_type: Network + mitre_attack_id: + - T1071.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1071.001/http_user_agents/suricata_c2.log - sourcetype: suricata - source: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1071.001/http_user_agents/suricata_c2.log + sourcetype: suricata + source: suricata diff --git a/detections/network/http_malware_user_agent.yml b/detections/network/http_malware_user_agent.yml index cf8685af10..978274753c 100644 --- a/detections/network/http_malware_user_agent.yml +++ b/detections/network/http_malware_user_agent.yml @@ -1,73 +1,68 @@ name: HTTP Malware User Agent id: 8c4866e4-f488-4253-8537-7dc4f954c292 -version: 1 -date: '2025-12-16' +version: 2 +date: '2026-02-25' author: Raven Tait, Splunk status: production type: TTP -description: This Splunk query analyzes web logs to identify and categorize - user agents, detecting various types of malware. This activity can signify possible - compromised hosts on the network. +description: This Splunk query analyzes web logs to identify and categorize user agents, detecting various types of malware. This activity can signify possible compromised hosts on the network. data_source: -- Suricata -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime - from datamodel=Web where Web.http_user_agent != null by Web.http_user_agent Web.http_method, Web.url, Web.url_length Web.src, Web.dest - | `drop_dm_object_name("Web")` - | lookup malware_user_agents malware_user_agent AS http_user_agent OUTPUT malware - | where isnotnull(malware) - | stats count min(firstTime) as first_seen max(lastTime) as last_seen - by malware url http_user_agent src dest - | `security_content_ctime(first_seen)` | `security_content_ctime(last_seen)` - | `http_malware_user_agent_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - web or proxy logs, or ensure it is being filled by a proxy like device, into the - Web Datamodel. For additional filtering, allow list private IP space or restrict - by known good. + - Suricata +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.http_user_agent != null + BY Web.http_user_agent Web.http_method, Web.url, + Web.url_length Web.src, Web.dest + | `drop_dm_object_name("Web")` + | lookup malware_user_agents malware_user_agent AS http_user_agent OUTPUT malware + | where isnotnull(malware) + | stats count min(firstTime) as first_seen max(lastTime) as last_seen + BY malware url http_user_agent + src dest + | `security_content_ctime(first_seen)` + | `security_content_ctime(last_seen)` + | `http_malware_user_agent_filter` +how_to_implement: To successfully implement this search, you need to be ingesting web or proxy logs, or ensure it is being filled by a proxy like device, into the Web Datamodel. For additional filtering, allow list private IP space or restrict by known good. known_false_positives: Filtering may be required in some instances depending on legacy system usage, filter as needed. references: -- https://github.com/mthcht/awesome-lists/blob/main/Lists/suspicious_http_user_agents_list.csv + - https://github.com/mthcht/awesome-lists/blob/main/Lists/suspicious_http_user_agents_list.csv drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A known malware user agent $http_user_agent$ was performing a request from $src$. - risk_objects: - - field: src - type: system - score: 45 - threat_objects: - - field: http_user_agent - type: http_user_agent + message: A known malware user agent $http_user_agent$ was performing a request from $src$. + risk_objects: + - field: src + type: system + score: 45 + threat_objects: + - field: http_user_agent + type: http_user_agent tags: - analytic_story: - - Lokibot - - Lumma Stealer - - Meduza Stealer - - Crypto Stealer - - RedLine Stealer - - Suspicious User Agents - asset_type: Network - mitre_attack_id: - - T1071.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Lokibot + - Lumma Stealer + - Meduza Stealer + - Crypto Stealer + - RedLine Stealer + - Suspicious User Agents + asset_type: Network + mitre_attack_id: + - T1071.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1071.001/http_user_agents/suricata_malware.log - sourcetype: suricata - source: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1071.001/http_user_agents/suricata_malware.log + sourcetype: suricata + source: suricata diff --git a/detections/network/http_pua_user_agent.yml b/detections/network/http_pua_user_agent.yml index 2e16e1e4df..43f630884f 100644 --- a/detections/network/http_pua_user_agent.yml +++ b/detections/network/http_pua_user_agent.yml @@ -1,73 +1,66 @@ name: HTTP PUA User Agent id: 21af5447-734f-4549-956b-7a255cb2b032 -version: 1 -date: '2025-12-17' +version: 2 +date: '2026-02-25' author: Raven Tait, Splunk status: production type: Anomaly -description: This Splunk query analyzes web logs to identify and categorize user agents, - detecting various types of unwanted applications. This activity can signify possible - compromised hosts on the network. +description: This Splunk query analyzes web logs to identify and categorize user agents, detecting various types of unwanted applications. This activity can signify possible compromised hosts on the network. data_source: -- Suricata -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime - from datamodel=Web where Web.http_user_agent != null by Web.http_user_agent Web.http_method, Web.url, Web.url_length Web.src, Web.dest - | `drop_dm_object_name("Web")` - | lookup pua_user_agents pua_user_agent AS http_user_agent OUTPUT tool - | where isnotnull(tool) - | stats count min(firstTime) as first_seen max(lastTime) as last_seen - by tool url http_user_agent src dest - | `security_content_ctime(first_seen)` | `security_content_ctime(last_seen)` - | `http_pua_user_agent_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - web or proxy logs, or ensure it is being filled by a proxy like device, into the - Web Datamodel. For additional filtering, allow list private IP space or restrict - by known good. -known_false_positives: Noise and false positive can be seen if these programs - are allowed to be used within corporate network. In this case, a filter is - needed. + - Suricata +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.http_user_agent != null + BY Web.http_user_agent Web.http_method, Web.url, + Web.url_length Web.src, Web.dest + | `drop_dm_object_name("Web")` + | lookup pua_user_agents pua_user_agent AS http_user_agent OUTPUT tool + | where isnotnull(tool) + | stats count min(firstTime) as first_seen max(lastTime) as last_seen + BY tool url http_user_agent + src dest + | `security_content_ctime(first_seen)` + | `security_content_ctime(last_seen)` + | `http_pua_user_agent_filter` +how_to_implement: To successfully implement this search, you need to be ingesting web or proxy logs, or ensure it is being filled by a proxy like device, into the Web Datamodel. For additional filtering, allow list private IP space or restrict by known good. +known_false_positives: Noise and false positive can be seen if these programs are allowed to be used within corporate network. In this case, a filter is needed. references: -- https://github.com/mthcht/awesome-lists/blob/main/Lists/suspicious_http_user_agents_list.csv + - https://github.com/mthcht/awesome-lists/blob/main/Lists/suspicious_http_user_agents_list.csv drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A known user agent ($http_user_agent$) associated with unusual programs was performing a request from $src$. - risk_objects: - - field: src - type: system - score: 32 - threat_objects: - - field: http_user_agent - type: http_user_agent + message: A known user agent ($http_user_agent$) associated with unusual programs was performing a request from $src$. + risk_objects: + - field: src + type: system + score: 32 + threat_objects: + - field: http_user_agent + type: http_user_agent tags: - analytic_story: - - Local Privilege Escalation With KrbRelayUp - - BlackSuit Ransomware - - Cactus Ransomware - - Suspicious User Agents - asset_type: Network - mitre_attack_id: - - T1071.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Local Privilege Escalation With KrbRelayUp + - BlackSuit Ransomware + - Cactus Ransomware + - Suspicious User Agents + asset_type: Network + mitre_attack_id: + - T1071.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1071.001/http_user_agents/suricata_pua.log - sourcetype: suricata - source: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1071.001/http_user_agents/suricata_pua.log + sourcetype: suricata + source: suricata diff --git a/detections/network/http_rmm_user_agent.yml b/detections/network/http_rmm_user_agent.yml index 65a737d963..4c28b34721 100644 --- a/detections/network/http_rmm_user_agent.yml +++ b/detections/network/http_rmm_user_agent.yml @@ -1,71 +1,65 @@ name: HTTP RMM User Agent id: 61884b02-0dcf-44c5-9094-db33bac09fa6 -version: 1 -date: '2025-12-18' +version: 2 +date: '2026-02-25' author: Raven Tait, Splunk status: production type: Anomaly -description: This Splunk query analyzes web logs to identify and categorize user agents, - detecting various types of Remote Monitoring and Mangement applications. This activity - can signify possible compromised hosts on the network. +description: This Splunk query analyzes web logs to identify and categorize user agents, detecting various types of Remote Monitoring and Mangement applications. This activity can signify possible compromised hosts on the network. data_source: -- Suricata -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime - from datamodel=Web where Web.http_user_agent != null by Web.http_user_agent Web.http_method, Web.url, Web.url_length Web.src, Web.dest - | `drop_dm_object_name("Web")` - | lookup rmm_user_agents rmm_user_agent AS http_user_agent OUTPUT tool - | where isnotnull(tool) - | stats count min(firstTime) as first_seen max(lastTime) as last_seen - by tool url http_user_agent src dest - | `security_content_ctime(first_seen)` | `security_content_ctime(last_seen)`| `http_rmm_user_agent_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - web or proxy logs, or ensure it is being filled by a proxy like device, into the - Web Datamodel. For additional filtering, allow list private IP space or restrict - by known good. -known_false_positives: Noise and false positive can be seen if these programs - are allowed to be used within corporate network. In this case, a filter is - needed. + - Suricata +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.http_user_agent != null + BY Web.http_user_agent Web.http_method, Web.url, + Web.url_length Web.src, Web.dest + | `drop_dm_object_name("Web")` + | lookup rmm_user_agents rmm_user_agent AS http_user_agent OUTPUT tool + | where isnotnull(tool) + | stats count min(firstTime) as first_seen max(lastTime) as last_seen + BY tool url http_user_agent + src dest + | `security_content_ctime(first_seen)` + | `security_content_ctime(last_seen)` + | `http_rmm_user_agent_filter` +how_to_implement: To successfully implement this search, you need to be ingesting web or proxy logs, or ensure it is being filled by a proxy like device, into the Web Datamodel. For additional filtering, allow list private IP space or restrict by known good. +known_false_positives: Noise and false positive can be seen if these programs are allowed to be used within corporate network. In this case, a filter is needed. references: -- https://github.com/mthcht/awesome-lists/blob/main/Lists/suspicious_http_user_agents_list.csv + - https://github.com/mthcht/awesome-lists/blob/main/Lists/suspicious_http_user_agents_list.csv drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A known rmm user agent $http_user_agent$ was performing a request from $src$. - risk_objects: - - field: src - type: system - score: 44 - threat_objects: - - field: http_user_agent - type: http_user_agent + message: A known rmm user agent $http_user_agent$ was performing a request from $src$. + risk_objects: + - field: src + type: system + score: 44 + threat_objects: + - field: http_user_agent + type: http_user_agent tags: - analytic_story: - - Remote Monitoring and Management Software - - Suspicious User Agents - asset_type: Network - mitre_attack_id: - - T1071.001 - - T1219 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Remote Monitoring and Management Software + - Suspicious User Agents + asset_type: Network + mitre_attack_id: + - T1071.001 + - T1219 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1071.001/http_user_agents/suricata_rmm.log - sourcetype: suricata - source: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1071.001/http_user_agents/suricata_rmm.log + sourcetype: suricata + source: suricata diff --git a/detections/network/internal_horizontal_port_scan.yml b/detections/network/internal_horizontal_port_scan.yml index 11bc58a973..3f6b429ef0 100644 --- a/detections/network/internal_horizontal_port_scan.yml +++ b/detections/network/internal_horizontal_port_scan.yml @@ -1,83 +1,75 @@ name: Internal Horizontal Port Scan id: 1ff9eb9a-7d72-4993-a55e-59a839e607f1 -version: 10 -date: '2026-01-14' +version: 11 +date: '2026-02-25' author: Dean Luxton status: production type: TTP data_source: -- AWS CloudWatchLogs VPCflow -- Cisco Secure Firewall Threat Defense Connection Event -description: This analytic identifies instances where an internal host has attempted - to communicate with 250 or more destination IP addresses using the same port and - protocol. Horizontal port scans from internal hosts can indicate reconnaissance - or scanning activities, potentially signaling malicious intent or misconfiguration. - By monitoring network traffic logs, this detection helps detect and respond to such - behavior promptly, enhancing network security and preventing potential threats. -search: '| tstats `security_content_summariesonly` values(All_Traffic.action) as action - values(All_Traffic.src_category) as src_category values(All_Traffic.dest_zone) as - dest_zone values(All_Traffic.src_zone) as src_zone values(All_Traffic.src_port) - as src_port count from datamodel=Network_Traffic where All_Traffic.src_ip IN ("10.0.0.0/8","172.16.0.0/12","192.168.0.0/16") - by All_Traffic.src_ip All_Traffic.dest_port All_Traffic.dest_ip All_Traffic.transport All_Traffic.rule span=1s _time - | `drop_dm_object_name("All_Traffic")` | eval gtime=_time | bin span=1h gtime | - stats min(_time) as _time values(action) as action dc(dest_ip) as totalDestIPCount - values(src_category) as src_category values(dest_zone) as dest_zone values(src_zone) - as src_zone by src_ip dest_port gtime transport | where totalDestIPCount>=250 | - eval dest_port=transport + "/" + dest_port | stats min(_time) as _time values(action) - as action sum(totalDestIPCount) as totalDestIPCount values(src_category) as src_category - values(dest_port) as dest_ports values(dest_zone) as dest_zone values(src_zone) - as src_zone by src_ip gtime | fields - gtime | `internal_horizontal_port_scan_filter`' -how_to_implement: To properly run this search, Splunk needs to ingest data from networking - telemetry sources such as firewalls, NetFlow, or host-based networking events. Ensure - that the Network_Traffic data model is populated to enable this search effectively. + - AWS CloudWatchLogs VPCflow + - Cisco Secure Firewall Threat Defense Connection Event +description: This analytic identifies instances where an internal host has attempted to communicate with 250 or more destination IP addresses using the same port and protocol. Horizontal port scans from internal hosts can indicate reconnaissance or scanning activities, potentially signaling malicious intent or misconfiguration. By monitoring network traffic logs, this detection helps detect and respond to such behavior promptly, enhancing network security and preventing potential threats. +search: |- + | tstats `security_content_summariesonly` values(All_Traffic.action) as action values(All_Traffic.src_category) as src_category values(All_Traffic.dest_zone) as dest_zone values(All_Traffic.src_zone) as src_zone values(All_Traffic.src_port) as src_port count FROM datamodel=Network_Traffic + WHERE All_Traffic.src_ip IN ("10.0.0.0/8","172.16.0.0/12","192.168.0.0/16") + BY All_Traffic.src_ip All_Traffic.dest_port All_Traffic.dest_ip + All_Traffic.transport All_Traffic.rule span=1s + _time + | `drop_dm_object_name("All_Traffic")` + | eval gtime=_time + | bin span=1h gtime + | stats min(_time) as _time values(action) as action dc(dest_ip) as totalDestIPCount values(src_category) as src_category values(dest_zone) as dest_zone values(src_zone) as src_zone + BY src_ip dest_port gtime + transport + | where totalDestIPCount>=250 + | eval dest_port=transport + "/" + dest_port + | stats min(_time) as _time values(action) as action sum(totalDestIPCount) as totalDestIPCount values(src_category) as src_category values(dest_port) as dest_ports values(dest_zone) as dest_zone values(src_zone) as src_zone + BY src_ip gtime + | fields - gtime + | `internal_horizontal_port_scan_filter` +how_to_implement: To properly run this search, Splunk needs to ingest data from networking telemetry sources such as firewalls, NetFlow, or host-based networking events. Ensure that the Network_Traffic data model is populated to enable this search effectively. known_false_positives: No false positives have been identified at this time. references: [] drilldown_searches: -- name: View the detection results for - "$src_ip$" - search: '%original_detection_search% | search src_ip = "$src_ip$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_ip$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_ip$" + search: '%original_detection_search% | search src_ip = "$src_ip$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_ip$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $src_ip$ has scanned for ports $dest_ports$ across $totalDestIPCount$ destination - IPs - risk_objects: - - field: dest_ports - type: system - score: 64 - threat_objects: - - field: src_ip - type: ip_address + message: $src_ip$ has scanned for ports $dest_ports$ across $totalDestIPCount$ destination IPs + risk_objects: + - field: dest_ports + type: system + score: 64 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - Network Discovery - - Cisco Secure Firewall Threat Defense Analytics - - China-Nexus Threat Activity - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1046 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Network Discovery + - Cisco Secure Firewall Threat Defense Analytics + - China-Nexus Threat Activity + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1046 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: AWS CloudWatch True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1046/nmap/horizontal.log - source: aws:cloudwatchlogs:vpcflow - sourcetype: aws:cloudwatchlogs:vpcflow -- name: Cisco Secure Firewall True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: AWS CloudWatch True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1046/nmap/horizontal.log + source: aws:cloudwatchlogs:vpcflow + sourcetype: aws:cloudwatchlogs:vpcflow + - name: Cisco Secure Firewall True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/internal_horizontal_port_scan_nmap_top_20.yml b/detections/network/internal_horizontal_port_scan_nmap_top_20.yml index ff0dcc5d5c..128bdd3bcf 100644 --- a/detections/network/internal_horizontal_port_scan_nmap_top_20.yml +++ b/detections/network/internal_horizontal_port_scan_nmap_top_20.yml @@ -6,111 +6,99 @@ author: Dean Luxton status: production type: TTP data_source: -- AWS CloudWatchLogs VPCflow -- Cisco Secure Firewall Threat Defense Connection Event -description: This analytic identifies instances where an internal host has attempted - to communicate with 250 or more destination IP addresses using on of the NMAP top - 20 ports. Horizontal port scans from internal hosts can indicate reconnaissance - or scanning activities, potentially signaling malicious intent or misconfiguration. - By monitoring network traffic logs, this detection helps detect and respond to such - behavior promptly, enhancing network security and preventing potential threats. + - AWS CloudWatchLogs VPCflow + - Cisco Secure Firewall Threat Defense Connection Event +description: This analytic identifies instances where an internal host has attempted to communicate with 250 or more destination IP addresses using on of the NMAP top 20 ports. Horizontal port scans from internal hosts can indicate reconnaissance or scanning activities, potentially signaling malicious intent or misconfiguration. By monitoring network traffic logs, this detection helps detect and respond to such behavior promptly, enhancing network security and preventing potential threats. search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime - dc(All_Traffic.dest_ip) as totalDestIPCount - values(All_Traffic.action) as action - values(All_Traffic.dest_zone) as dest_zone - values(All_Traffic.rule) as rule - values(All_Traffic.src_category) as src_category - values(All_Traffic.src_port) as src_port - values(All_Traffic.src_zone) as src_zone + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + dc(All_Traffic.dest_ip) as totalDestIPCount + values(All_Traffic.action) as action + values(All_Traffic.dest_zone) as dest_zone + values(All_Traffic.rule) as rule + values(All_Traffic.src_category) as src_category + values(All_Traffic.src_port) as src_port + values(All_Traffic.src_zone) as src_zone - from datamodel=Network_Traffic where + from datamodel=Network_Traffic where - All_Traffic.src_ip IN ( - "10.0.0.0/8", "172.16.0.0/12", "192.168.0.0/16", "100.64.0.0/10", - "127.0.0.0/8", "169.254.0.0/16", "192.0.0.0/24", "192.0.0.0/29", "192.0.0.8/32", - "192.0.0.9/32", "192.0.0.10/32", "192.0.0.170/32", "192.0.0.171/32", "192.0.2.0/24", - "192.31.196.0/24", "192.52.193.0/24", "192.88.99.0/24", "224.0.0.0/4", "192.175.48.0/24", - "198.18.0.0/15", "198.51.100.0/24", "203.0.113.0/24", "240.0.0.0/4" - ) - All_Traffic.dest_port IN ( - 21, 22, 23, 25, 53, 80, 110, 111, - 135, 139, 143, 443, 445, 993, 995, - 1723, 3306, 3389, 5900, 8080 - ) + All_Traffic.src_ip IN ( + "10.0.0.0/8", "172.16.0.0/12", "192.168.0.0/16", "100.64.0.0/10", + "127.0.0.0/8", "169.254.0.0/16", "192.0.0.0/24", "192.0.0.0/29", "192.0.0.8/32", + "192.0.0.9/32", "192.0.0.10/32", "192.0.0.170/32", "192.0.0.171/32", "192.0.2.0/24", + "192.31.196.0/24", "192.52.193.0/24", "192.88.99.0/24", "224.0.0.0/4", "192.175.48.0/24", + "198.18.0.0/15", "198.51.100.0/24", "203.0.113.0/24", "240.0.0.0/4" + ) + All_Traffic.dest_port IN ( + 21, 22, 23, 25, 53, 80, 110, 111, + 135, 139, 143, 443, 445, 993, 995, + 1723, 3306, 3389, 5900, 8080 + ) - by span=1h _time - All_Traffic.src_ip All_Traffic.dest_port - All_Traffic.transport + by span=1h _time + All_Traffic.src_ip All_Traffic.dest_port + All_Traffic.transport - | `drop_dm_object_name("All_Traffic")` - | where totalDestIPCount>=250 - | eval dest_port=transport + "/" + dest_port - | stats min(firstTime) as firstTime - max(lastTime) as lastTime - dc(dest_port) as num_ports_scanned - sum(totalDestIPCount) as totalDestIPCount - values(action) as action - values(dest_port) as dest_ports - values(dest_zone) as dest_zone - values(rule) as rule - values(src_category) as src_category - values(src_zone) as src_zone - by _time src_ip - | fields - _time - | `security_content_ctime(lastTime)` - | `security_content_ctime(firstTime)` - | `internal_horizontal_port_scan_nmap_top_20_filter` -how_to_implement: To properly run this search, Splunk needs to ingest data from networking - telemetry sources such as firewalls like Cisco Secure Firewall, NetFlow, or host-based networking events. Ensure - that the Network_Traffic data model is populated to enable this search effectively. + | `drop_dm_object_name("All_Traffic")` + | where totalDestIPCount>=250 + | eval dest_port=transport + "/" + dest_port + | stats min(firstTime) as firstTime + max(lastTime) as lastTime + dc(dest_port) as num_ports_scanned + sum(totalDestIPCount) as totalDestIPCount + values(action) as action + values(dest_port) as dest_ports + values(dest_zone) as dest_zone + values(rule) as rule + values(src_category) as src_category + values(src_zone) as src_zone + by _time src_ip + | fields - _time + | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` + | `internal_horizontal_port_scan_nmap_top_20_filter` +how_to_implement: To properly run this search, Splunk needs to ingest data from networking telemetry sources such as firewalls like Cisco Secure Firewall, NetFlow, or host-based networking events. Ensure that the Network_Traffic data model is populated to enable this search effectively. known_false_positives: No false positives have been identified at this time. references: [] drilldown_searches: -- name: View the detection results for $src_ip$ - search: '%original_detection_search% | search src_ip = $src_ip$' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for $src_ip$ - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($src_ip$) - starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for $src_ip$ + search: '%original_detection_search% | search src_ip = $src_ip$' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for $src_ip$ + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($src_ip$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $src_ip$ has scanned for ports $dest_ports$ across $totalDestIPCount$ destination IPs - risk_objects: - - field: src_ip - type: system - score: 50 - threat_objects: [] + message: $src_ip$ has scanned for ports $dest_ports$ across $totalDestIPCount$ destination IPs + risk_objects: + - field: src_ip + type: system + score: 50 + threat_objects: [] tags: - analytic_story: - - Network Discovery - - Cisco Secure Firewall Threat Defense Analytics - - China-Nexus Threat Activity - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1046 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Network Discovery + - Cisco Secure Firewall Threat Defense Analytics + - China-Nexus Threat Activity + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1046 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: AWS CloudWatch True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1046/nmap/horizontal.log - source: aws:cloudwatchlogs:vpcflow - sourcetype: aws:cloudwatchlogs:vpcflow -- name: Cisco Secure Firewall True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: AWS CloudWatch True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1046/nmap/horizontal.log + source: aws:cloudwatchlogs:vpcflow + sourcetype: aws:cloudwatchlogs:vpcflow + - name: Cisco Secure Firewall True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/internal_vertical_port_scan.yml b/detections/network/internal_vertical_port_scan.yml index 2fcc9e1e62..fe195b6a3b 100644 --- a/detections/network/internal_vertical_port_scan.yml +++ b/detections/network/internal_vertical_port_scan.yml @@ -3,110 +3,98 @@ id: 40d2dc41-9bbf-421a-a34b-8611271a6770 version: 10 date: '2026-01-29' author: Dean Luxton, Splunk -status: production +status: production type: TTP data_source: -- AWS CloudWatchLogs VPCflow -- Cisco Secure Firewall Threat Defense Connection Event -description: This analytic detects instances where an internal host attempts to communicate - with over 500 ports on a single destination IP address. It includes filtering criteria - to exclude applications performing scans over ephemeral port ranges, focusing on - potential reconnaissance or scanning activities. Monitoring network traffic logs - allows for timely detection and response to such behavior, enhancing network security - by identifying and mitigating potential threats promptly. + - AWS CloudWatchLogs VPCflow + - Cisco Secure Firewall Threat Defense Connection Event +description: This analytic detects instances where an internal host attempts to communicate with over 500 ports on a single destination IP address. It includes filtering criteria to exclude applications performing scans over ephemeral port ranges, focusing on potential reconnaissance or scanning activities. Monitoring network traffic logs allows for timely detection and response to such behavior, enhancing network security by identifying and mitigating potential threats promptly. search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime - values(All_Traffic.action) as action - values(All_Traffic.src_category) as src_category - values(All_Traffic.dest_zone) as dest_zone - values(All_Traffic.src_zone) as src_zone - - from datamodel=Network_Traffic where - - All_Traffic.src_ip IN ( - "10.0.0.0/8", "172.16.0.0/12", "192.168.0.0/16", "100.64.0.0/10", - "127.0.0.0/8", "169.254.0.0/16", "192.0.0.0/24", "192.0.0.0/29", "192.0.0.8/32", - "192.0.0.9/32", "192.0.0.10/32", "192.0.0.170/32", "192.0.0.171/32", "192.0.2.0/24", - "192.31.196.0/24", "192.52.193.0/24", "192.88.99.0/24", "224.0.0.0/4", "192.175.48.0/24", - "198.18.0.0/15", "198.51.100.0/24", "203.0.113.0/24", "240.0.0.0/4" - ) - - by span=1s _time - All_Traffic.src_ip All_Traffic.dest_port - All_Traffic.dest_ip All_Traffic.transport - All_Traffic.rule + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + values(All_Traffic.action) as action + values(All_Traffic.src_category) as src_category + values(All_Traffic.dest_zone) as dest_zone + values(All_Traffic.src_zone) as src_zone - | `drop_dm_object_name("All_Traffic")` - | eval gtime=_time - | bin span=1h gtime + from datamodel=Network_Traffic where - | stats min(_time) as _time - values(action) as action - dc(eval(if(dest_port<1024 AND transport="tcp",dest_port,null))) as privilegedDestTcpPortCount - dc(eval(if(transport="tcp",dest_port,null))) as totalDestTcpPortCount - dc(eval(if(dest_port<1024 AND transport="udp",dest_port,null))) as privilegedDestUdpPortCount - dc(eval(if(transport="udp",dest_port,null))) as totalDestUdpPortCount - values(src_category) as src_category - values(dest_zone) as dest_zone - values(src_zone) as src_zone - by src_ip dest_ip transport gtime - | eval totalDestPortCount=totalDestUdpPortCount+totalDestTcpPortCount, - privilegedDestPortCount=privilegedDestTcpPortCount+privilegedDestUdpPortCount - | where (totalDestPortCount>=500 AND privilegedDestPortCount>=20) - | fields - gtime - | `internal_vertical_port_scan_filter` -how_to_implement: To properly run this search, Splunk needs to ingest data from networking - telemetry sources such as firewalls, NetFlow, or host-based networking events. Ensure - that the Network_Traffic data model is populated to enable this search effectively. + All_Traffic.src_ip IN ( + "10.0.0.0/8", "172.16.0.0/12", "192.168.0.0/16", "100.64.0.0/10", + "127.0.0.0/8", "169.254.0.0/16", "192.0.0.0/24", "192.0.0.0/29", "192.0.0.8/32", + "192.0.0.9/32", "192.0.0.10/32", "192.0.0.170/32", "192.0.0.171/32", "192.0.2.0/24", + "192.31.196.0/24", "192.52.193.0/24", "192.88.99.0/24", "224.0.0.0/4", "192.175.48.0/24", + "198.18.0.0/15", "198.51.100.0/24", "203.0.113.0/24", "240.0.0.0/4" + ) + + by span=1s _time + All_Traffic.src_ip All_Traffic.dest_port + All_Traffic.dest_ip All_Traffic.transport + All_Traffic.rule + + | `drop_dm_object_name("All_Traffic")` + | eval gtime=_time + | bin span=1h gtime + + | stats min(_time) as _time + values(action) as action + dc(eval(if(dest_port<1024 AND transport="tcp",dest_port,null))) as privilegedDestTcpPortCount + dc(eval(if(transport="tcp",dest_port,null))) as totalDestTcpPortCount + dc(eval(if(dest_port<1024 AND transport="udp",dest_port,null))) as privilegedDestUdpPortCount + dc(eval(if(transport="udp",dest_port,null))) as totalDestUdpPortCount + values(src_category) as src_category + values(dest_zone) as dest_zone + values(src_zone) as src_zone + by src_ip dest_ip transport gtime + | eval totalDestPortCount=totalDestUdpPortCount+totalDestTcpPortCount, + privilegedDestPortCount=privilegedDestTcpPortCount+privilegedDestUdpPortCount + | where (totalDestPortCount>=500 AND privilegedDestPortCount>=20) + | fields - gtime + | `internal_vertical_port_scan_filter` +how_to_implement: To properly run this search, Splunk needs to ingest data from networking telemetry sources such as firewalls, NetFlow, or host-based networking events. Ensure that the Network_Traffic data model is populated to enable this search effectively. known_false_positives: No false positives have been identified at this time. references: [] drilldown_searches: -- name: View the detection results for - "$src_ip$" - search: '%original_detection_search% | search src_ip = "$src_ip$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_ip$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_ip$" + search: '%original_detection_search% | search src_ip = "$src_ip$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_ip$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $src_ip$ has scanned $totalDestPortCount$ ports on $dest_ip$ - risk_objects: - - field: src_ip - type: system - score: 60 - threat_objects: - - field: dest_ip - type: ip_address + message: $src_ip$ has scanned $totalDestPortCount$ ports on $dest_ip$ + risk_objects: + - field: src_ip + type: system + score: 60 + threat_objects: + - field: dest_ip + type: ip_address tags: - analytic_story: - - Network Discovery - - Cisco Secure Firewall Threat Defense Analytics - - China-Nexus Threat Activity - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1046 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Network Discovery + - Cisco Secure Firewall Threat Defense Analytics + - China-Nexus Threat Activity + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1046 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: AWS CloudWatch True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1046/nmap/vertical.log - source: aws:cloudwatchlogs:vpcflow - sourcetype: aws:cloudwatchlogs:vpcflow -- name: Cisco Secure Firewall True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: AWS CloudWatch True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1046/nmap/vertical.log + source: aws:cloudwatchlogs:vpcflow + sourcetype: aws:cloudwatchlogs:vpcflow + - name: Cisco Secure Firewall True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/internal_vulnerability_scan.yml b/detections/network/internal_vulnerability_scan.yml index a495a2ea08..a03303f32f 100644 --- a/detections/network/internal_vulnerability_scan.yml +++ b/detections/network/internal_vulnerability_scan.yml @@ -1,54 +1,53 @@ name: Internal Vulnerability Scan id: 46f946ed-1c78-4e96-9906-c7a4be15e39b -version: 5 -date: '2025-10-14' +version: 6 +date: '2026-02-25' author: Dean Luxton status: experimental type: TTP data_source: [] -description: This analytic detects internal hosts triggering multiple IDS signatures, - which may include either more than 25 signatures against a single host or a single - signature across over 25 destination IP addresses. Such patterns can indicate active - vulnerability scanning activities within the network. By monitoring IDS logs, this - detection helps identify and respond to potential vulnerability scanning attempts, - enhancing the network's security posture and preventing potential exploits. -search: '| tstats `security_content_summariesonly` values(IDS_Attacks.action) as action - values(IDS_Attacks.src_category) as src_category values(IDS_Attacks.dest_category) - as dest_category count from datamodel=Intrusion_Detection.IDS_Attacks where IDS_Attacks.src - IN (10.0.0.0/8,192.168.0.0/16,172.16.0.0/12) IDS_Attacks.severity IN (critical, - high, medium) by IDS_Attacks.src IDS_Attacks.severity IDS_Attacks.signature IDS_Attacks.dest - IDS_Attacks.dest_port IDS_Attacks.transport span=1s _time | `drop_dm_object_name("IDS_Attacks")` - | eval gtime=_time | bin span=1h gtime | eventstats count as sevCount by severity - src | eventstats count as sigCount by signature src | eval severity=severity +"("+sevCount+")" - | eval signature=signature +"("+sigCount+")" | eval dest_port=transport + "/" + - dest_port | stats min(_time) as _time values(action) as action dc(dest) as destCount - dc(signature) as sigCount values(signature) values(src_category) as src_category - values(dest_category) as dest_category values(severity) as severity values(dest_port) - as dest_ports by src gtime | fields - gtime | where destCount>25 OR sigCount>25 - | `internal_vulnerability_scan_filter`' -how_to_implement: For this detection to function effectively, it is essential to ingest - IDS/IPS logs that are mapped to the Common Information Model (CIM). These logs provide - the necessary security-related telemetry and contextual information needed to accurately - identify and analyze potential threats. +description: This analytic detects internal hosts triggering multiple IDS signatures, which may include either more than 25 signatures against a single host or a single signature across over 25 destination IP addresses. Such patterns can indicate active vulnerability scanning activities within the network. By monitoring IDS logs, this detection helps identify and respond to potential vulnerability scanning attempts, enhancing the network's security posture and preventing potential exploits. +search: |- + | tstats `security_content_summariesonly` values(IDS_Attacks.action) as action values(IDS_Attacks.src_category) as src_category values(IDS_Attacks.dest_category) as dest_category count FROM datamodel=Intrusion_Detection.IDS_Attacks + WHERE IDS_Attacks.src IN (10.0.0.0/8,192.168.0.0/16,172.16.0.0/12) IDS_Attacks.severity IN (critical, high, medium) + BY IDS_Attacks.src IDS_Attacks.severity IDS_Attacks.signature + IDS_Attacks.dest IDS_Attacks.dest_port IDS_Attacks.transport + span=1s _time + | `drop_dm_object_name("IDS_Attacks")` + | eval gtime=_time + | bin span=1h gtime + | eventstats count as sevCount + BY severity src + | eventstats count as sigCount + BY signature src + | eval severity=severity +"("+sevCount+")" + | eval signature=signature +"("+sigCount+")" + | eval dest_port=transport + "/" + dest_port + | stats min(_time) as _time values(action) as action dc(dest) as destCount dc(signature) as sigCount values(signature) values(src_category) as src_category values(dest_category) as dest_category values(severity) as severity values(dest_port) as dest_ports + BY src gtime + | fields - gtime + | where destCount>25 OR sigCount>25 + | `internal_vulnerability_scan_filter` +how_to_implement: For this detection to function effectively, it is essential to ingest IDS/IPS logs that are mapped to the Common Information Model (CIM). These logs provide the necessary security-related telemetry and contextual information needed to accurately identify and analyze potential threats. known_false_positives: Internal vulnerability scanners will trigger this detection. references: [] rba: - message: Large volume of IDS signatures triggered by $src$ - risk_objects: - - field: src - type: system - score: 64 - threat_objects: [] + message: Large volume of IDS signatures triggered by $src$ + risk_objects: + - field: src + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Network Discovery - - Scattered Lapsus$ Hunters - asset_type: Endpoint - mitre_attack_id: - - T1595.002 - - T1046 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Network Discovery + - Scattered Lapsus$ Hunters + asset_type: Endpoint + mitre_attack_id: + - T1595.002 + - T1046 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/network/large_volume_of_dns_any_queries.yml b/detections/network/large_volume_of_dns_any_queries.yml index b95887131f..25e4a21f99 100644 --- a/detections/network/large_volume_of_dns_any_queries.yml +++ b/detections/network/large_volume_of_dns_any_queries.yml @@ -1,43 +1,37 @@ name: Large Volume of DNS ANY Queries id: 8fa891f7-a533-4b3c-af85-5aa2e7c1f1eb -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Bhavin Patel, Splunk status: experimental type: Anomaly -description: The following analytic identifies a large volume of DNS ANY queries, - which may indicate a DNS amplification attack. It leverages the Network_Resolution - data model to count DNS queries of type "ANY" directed to specific destinations. - This activity is significant because DNS amplification attacks can overwhelm network - resources, leading to Denial of Service (DoS) conditions. If confirmed malicious, - this activity could disrupt services, degrade network performance, and potentially - be part of a larger Distributed Denial of Service (DDoS) attack, impacting the availability - of critical infrastructure. +description: The following analytic identifies a large volume of DNS ANY queries, which may indicate a DNS amplification attack. It leverages the Network_Resolution data model to count DNS queries of type "ANY" directed to specific destinations. This activity is significant because DNS amplification attacks can overwhelm network resources, leading to Denial of Service (DoS) conditions. If confirmed malicious, this activity could disrupt services, degrade network performance, and potentially be part of a larger Distributed Denial of Service (DDoS) attack, impacting the availability of critical infrastructure. data_source: [] -search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution - where nodename=DNS "DNS.message_type"="QUERY" "DNS.record_type"="ANY" by "DNS.dest" - | `drop_dm_object_name("DNS")` | where count>200 | `large_volume_of_dns_any_queries_filter`' -how_to_implement: To successfully implement this search you must ensure that DNS data - is populating the Network_Resolution data model. -known_false_positives: Legitimate ANY requests may trigger this search, however it - is unusual to see a large volume of them under typical circumstances. You may modify - the threshold in the search to better suit your environment. +search: |- + | tstats `security_content_summariesonly` count FROM datamodel=Network_Resolution + WHERE nodename=DNS "DNS.message_type"="QUERY" "DNS.record_type"="ANY" + BY "DNS.dest" + | `drop_dm_object_name("DNS")` + | where count>200 + | `large_volume_of_dns_any_queries_filter` +how_to_implement: To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. +known_false_positives: Legitimate ANY requests may trigger this search, however it is unusual to see a large volume of them under typical circumstances. You may modify the threshold in the search to better suit your environment. references: [] rba: - message: Large Volume of DNS ANY Queries by $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: Large Volume of DNS ANY Queries by $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - DNS Amplification Attacks - asset_type: DNS Servers - mitre_attack_id: - - T1498.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - DNS Amplification Attacks + asset_type: DNS Servers + mitre_attack_id: + - T1498.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/network/ngrok_reverse_proxy_on_network.yml b/detections/network/ngrok_reverse_proxy_on_network.yml index 046adf999f..3444e93eca 100644 --- a/detections/network/ngrok_reverse_proxy_on_network.yml +++ b/detections/network/ngrok_reverse_proxy_on_network.yml @@ -1,70 +1,61 @@ name: Ngrok Reverse Proxy on Network id: 5790a766-53b8-40d3-a696-3547b978fcf0 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic detects DNS queries to common Ngrok domains, indicating - potential use of the Ngrok reverse proxy tool. It leverages the Network Resolution - datamodel to identify queries to domains such as "*.ngrok.com" and "*.ngrok.io". - While Ngrok usage is not inherently malicious, it has been increasingly adopted - by adversaries for covert communication and data exfiltration. If confirmed malicious, - this activity could allow attackers to bypass network defenses, establish persistent - connections, and exfiltrate sensitive data, posing a significant threat to the network's - security. +description: The following analytic detects DNS queries to common Ngrok domains, indicating potential use of the Ngrok reverse proxy tool. It leverages the Network Resolution datamodel to identify queries to domains such as "*.ngrok.com" and "*.ngrok.io". While Ngrok usage is not inherently malicious, it has been increasingly adopted by adversaries for covert communication and data exfiltration. If confirmed malicious, this activity could allow attackers to bypass network defenses, establish persistent connections, and exfiltrate sensitive data, posing a significant threat to the network's security. data_source: -- Sysmon EventID 22 -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Network_Resolution where DNS.query IN ("*.ngrok.com","*.ngrok.io", - "ngrok.*.tunnel.com", "korgn.*.lennut.com") by DNS.answer DNS.answer_count DNS.query - DNS.query_count DNS.reply_code_id DNS.src DNS.vendor_product | `drop_dm_object_name("DNS")` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ngrok_reverse_proxy_on_network_filter`' -how_to_implement: The Network Resolution Datamodel will need to have data mapped to - it regarding DNS queries. Modify query as needed to use another source. -known_false_positives: False positives will be present based on organizations that - allow the use of Ngrok. Filter or monitor as needed. + - Sysmon EventID 22 +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Network_Resolution + WHERE DNS.query IN ("*.ngrok.com","*.ngrok.io", "ngrok.*.tunnel.com", "korgn.*.lennut.com") + BY DNS.answer DNS.answer_count DNS.query + DNS.query_count DNS.reply_code_id DNS.src + DNS.vendor_product + | `drop_dm_object_name("DNS")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `ngrok_reverse_proxy_on_network_filter` +how_to_implement: The Network Resolution Datamodel will need to have data mapped to it regarding DNS queries. Modify query as needed to use another source. +known_false_positives: False positives will be present based on organizations that allow the use of Ngrok. Filter or monitor as needed. references: -- https://www.cisa.gov/uscert/sites/default/files/publications/aa22-320a_joint_csa_iranian_government-sponsored_apt_actors_compromise_federal%20network_deploy_crypto%20miner_credential_harvester.pdf + - https://www.cisa.gov/uscert/sites/default/files/publications/aa22-320a_joint_csa_iranian_government-sponsored_apt_actors_compromise_federal%20network_deploy_crypto%20miner_credential_harvester.pdf drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An endpoint, $src$, is beaconing out to the reverse proxy service of Ngrok. - risk_objects: - - field: src - type: system - score: 50 - threat_objects: [] + message: An endpoint, $src$, is beaconing out to the reverse proxy service of Ngrok. + risk_objects: + - field: src + type: system + score: 50 + threat_objects: [] tags: - analytic_story: - - Reverse Network Proxy - - CISA AA22-320A - - CISA AA24-241A - asset_type: Endpoint - mitre_attack_id: - - T1572 - - T1090 - - T1102 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Reverse Network Proxy + - CISA AA22-320A + - CISA AA24-241A + asset_type: Endpoint + mitre_attack_id: + - T1572 + - T1090 + - T1102 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1572/ngrok/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1572/ngrok/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/network/prohibited_network_traffic_allowed.yml b/detections/network/prohibited_network_traffic_allowed.yml index acd2987217..81ee3706a1 100644 --- a/detections/network/prohibited_network_traffic_allowed.yml +++ b/detections/network/prohibited_network_traffic_allowed.yml @@ -1,80 +1,65 @@ name: Prohibited Network Traffic Allowed id: ce5a0962-849f-4720-a678-753fe6674479 -version: 10 -date: '2026-01-16' +version: 11 +date: '2026-02-25' author: Rico Valdez, Splunk status: production type: TTP -description: The following analytic detects instances where network traffic, identified - by port and transport layer protocol as prohibited in the "lookup_interesting_ports" - table, is allowed. It uses the Network_Traffic data model to cross-reference traffic - data against predefined security policies. This activity is significant for a SOC - as it highlights potential misconfigurations or policy violations that could lead - to unauthorized access or data exfiltration. If confirmed malicious, this could - allow attackers to bypass network defenses, leading to potential data breaches and - compromising the organization's security posture. +description: The following analytic detects instances where network traffic, identified by port and transport layer protocol as prohibited in the "lookup_interesting_ports" table, is allowed. It uses the Network_Traffic data model to cross-reference traffic data against predefined security policies. This activity is significant for a SOC as it highlights potential misconfigurations or policy violations that could lead to unauthorized access or data exfiltration. If confirmed malicious, this could allow attackers to bypass network defenses, leading to potential data breaches and compromising the organization's security posture. data_source: -- Cisco Secure Firewall Threat Defense Connection Event -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime - from datamodel=Network_Traffic - where All_Traffic.action IN ("allowed", "allow") - [| inputlookup interesting_ports_lookup where is_prohibited="true"  - | table dest_port transport | dedup dest_port transport  - | rename dest_port as All_Traffic.dest_port | rename transport as All_Traffic.transport] - by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action All_Traffic.dvc All_Traffic.src_port All_Traffic.vendor_product All_Traffic.rule - | lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port transport as All_Traffic.transport OUTPUT app is_prohibited note - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `drop_dm_object_name("All_Traffic")` - | `prohibited_network_traffic_allowed_filter`' -how_to_implement: In order to properly run this search, Splunk needs to ingest data - from firewalls or other network control devices that mediate the traffic allowed - into an environment. This is necessary so that the search can identify an 'action' - taken on the traffic of interest. The search requires the Network_Traffic data model - be populated. + - Cisco Secure Firewall Threat Defense Connection Event +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Network_Traffic + WHERE All_Traffic.action IN ("allowed", "allow") [ + | inputlookup interesting_ports_lookup where is_prohibited="true" + | table dest_port transport + | dedup dest_port transport + | rename dest_port as All_Traffic.dest_port + | rename transport as All_Traffic.transport] by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action All_Traffic.dvc All_Traffic.src_port All_Traffic.vendor_product All_Traffic.rule + | lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port transport as All_Traffic.transport OUTPUT app is_prohibited note + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `drop_dm_object_name("All_Traffic")` + | `prohibited_network_traffic_allowed_filter` +how_to_implement: In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. known_false_positives: No false positives have been identified at this time. references: [] drilldown_searches: -- name: View the detection results for - "$src_ip$" - search: '%original_detection_search% | search src_ip = "$src_ip$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_ip$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_ip$" + search: '%original_detection_search% | search src_ip = "$src_ip$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_ip$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potentially Prohibited Network Traffic allowed - risk_objects: - - field: src_ip - type: system - score: 25 - threat_objects: - - field: dest_ip - type: ip_address + message: Potentially Prohibited Network Traffic allowed + risk_objects: + - field: src_ip + type: system + score: 25 + threat_objects: + - field: dest_ip + type: ip_address tags: - analytic_story: - - Prohibited Traffic Allowed or Protocol Mismatch - - Ransomware - - Command And Control - - Cisco Secure Firewall Threat Defense Analytics - asset_type: Endpoint - mitre_attack_id: - - T1048 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - manual_test: This detection uses a builtin lookup from Enterprise Security. + analytic_story: + - Prohibited Traffic Allowed or Protocol Mismatch + - Ransomware + - Command And Control + - Cisco Secure Firewall Threat Defense Analytics + asset_type: Endpoint + mitre_attack_id: + - T1048 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + manual_test: This detection uses a builtin lookup from Enterprise Security. tests: -- name: Cisco Secure Firewall True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: Cisco Secure Firewall True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/protocol_or_port_mismatch.yml b/detections/network/protocol_or_port_mismatch.yml index aa96a38c75..ca3b471029 100644 --- a/detections/network/protocol_or_port_mismatch.yml +++ b/detections/network/protocol_or_port_mismatch.yml @@ -5,96 +5,81 @@ date: '2026-01-29' author: Rico Valdez, Splunk status: production type: Anomaly -description: The following analytic identifies network traffic where the higher layer - protocol does not match the expected port, such as non-HTTP traffic on TCP port - 80. It leverages data from network traffic inspection technologies like Bro or Palo - Alto Networks firewalls. This activity is significant because it may indicate attempts - to bypass firewall restrictions or conceal malicious communications. If confirmed - malicious, this behavior could allow attackers to evade detection, maintain persistence, - or exfiltrate data through commonly allowed ports, posing a significant threat to - network security. +description: The following analytic identifies network traffic where the higher layer protocol does not match the expected port, such as non-HTTP traffic on TCP port 80. It leverages data from network traffic inspection technologies like Bro or Palo Alto Networks firewalls. This activity is significant because it may indicate attempts to bypass firewall restrictions or conceal malicious communications. If confirmed malicious, this behavior could allow attackers to evade detection, maintain persistence, or exfiltrate data through commonly allowed ports, posing a significant threat to network security. data_source: -- Cisco Secure Firewall Threat Defense Connection Event + - Cisco Secure Firewall Threat Defense Connection Event search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime - from datamodel=Network_Traffic where + from datamodel=Network_Traffic where - ( - All_Traffic.app=dns - NOT All_Traffic.dest_port IN (53) - ) - OR - ( - All_Traffic.app IN (web-browsing, http) - NOT All_Traffic.dest_port IN (80, 8000, 8080) - ) - OR - ( - All_Traffic.app=ssl - NOT All_Traffic.dest_port IN (443, 465, 993, 8443) - ) - OR - ( - All_Traffic.app=smtp - NOT All_Traffic.dest_port IN (25, 587, 2525) - ) + ( + All_Traffic.app=dns + NOT All_Traffic.dest_port IN (53) + ) + OR + ( + All_Traffic.app IN (web-browsing, http) + NOT All_Traffic.dest_port IN (80, 8000, 8080) + ) + OR + ( + All_Traffic.app=ssl + NOT All_Traffic.dest_port IN (443, 465, 993, 8443) + ) + OR + ( + All_Traffic.app=smtp + NOT All_Traffic.dest_port IN (25, 587, 2525) + ) - by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.app, - All_Traffic.dest_port All_Traffic.transport - All_Traffic.action All_Traffic.rule + by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.app, + All_Traffic.dest_port All_Traffic.transport + All_Traffic.action All_Traffic.rule - |`security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `drop_dm_object_name("All_Traffic")` - | `protocol_or_port_mismatch_filter` -how_to_implement: Running this search properly requires a technology that can inspect - network traffic and identify common protocols. Technologies such as Zeek, Cisco Secure Firewall or Palo - Alto Networks firewalls are examples that will identify protocols via inspection, - and not just assume a specific protocol based on the transport protocol and ports. + |`security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `drop_dm_object_name("All_Traffic")` + | `protocol_or_port_mismatch_filter` +how_to_implement: Running this search properly requires a technology that can inspect network traffic and identify common protocols. Technologies such as Zeek, Cisco Secure Firewall or Palo Alto Networks firewalls are examples that will identify protocols via inspection, and not just assume a specific protocol based on the transport protocol and ports. known_false_positives: Some false positive could occur with some applications that change their default communication port for an added layer of obscurity. references: [] drilldown_searches: -- name: View the detection results for - "$src_ip$" - search: '%original_detection_search% | search src_ip = "$src_ip$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_ip$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_ip$" + search: '%original_detection_search% | search src_ip = "$src_ip$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_ip$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Port or Protocol Traffic Mismatch - risk_objects: - - field: src_ip - type: system - score: 25 - threat_objects: - - field: dest_ip - type: ip_address + message: Port or Protocol Traffic Mismatch + risk_objects: + - field: src_ip + type: system + score: 25 + threat_objects: + - field: dest_ip + type: ip_address tags: - analytic_story: - - Prohibited Traffic Allowed or Protocol Mismatch - - Command And Control - - Cisco Secure Firewall Threat Defense Analytics - asset_type: Endpoint - mitre_attack_id: - - T1048.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Prohibited Traffic Allowed or Protocol Mismatch + - Command And Control + - Cisco Secure Firewall Threat Defense Analytics + asset_type: Endpoint + mitre_attack_id: + - T1048.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: Cisco Secure Firewall True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: Cisco Secure Firewall True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/protocols_passing_authentication_in_cleartext.yml b/detections/network/protocols_passing_authentication_in_cleartext.yml index 23af73f6c4..2cf108ba0a 100644 --- a/detections/network/protocols_passing_authentication_in_cleartext.yml +++ b/detections/network/protocols_passing_authentication_in_cleartext.yml @@ -1,75 +1,74 @@ name: Protocols passing authentication in cleartext id: 6923cd64-17a0-453c-b945-81ac2d8c6db9 -version: 9 -date: '2025-10-14' +version: 10 +date: '2026-02-25' author: Rico Valdez, Splunk status: production type: Anomaly -description: The following analytic identifies the use of cleartext protocols that - risk leaking sensitive information. It detects network traffic on legacy protocols - such as Telnet (port 23), POP3 (port 110), IMAP (port 143), and non-anonymous FTP - (port 21). The detection leverages the Network_Traffic data model to identify TCP - traffic on these ports. Monitoring this activity is crucial as it can expose credentials - and other sensitive data to interception. If confirmed malicious, attackers could - capture authentication details, leading to unauthorized access and potential data - breaches. +description: The following analytic identifies the use of cleartext protocols that risk leaking sensitive information. It detects network traffic on legacy protocols such as Telnet (port 23), POP3 (port 110), IMAP (port 143), and non-anonymous FTP (port 21). The detection leverages the Network_Traffic data model to identify TCP traffic on these ports. Monitoring this activity is crucial as it can expose credentials and other sensitive data to interception. If confirmed malicious, attackers could capture authentication details, leading to unauthorized access and potential data breaches. data_source: -- Cisco Secure Firewall Threat Defense Connection Event -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Network_Traffic where NOT All_Traffic.action IN ("blocked", "block") AND - All_Traffic.transport="tcp" AND (All_Traffic.dest_port="23" OR All_Traffic.dest_port="143" - OR All_Traffic.dest_port="110" OR (All_Traffic.dest_port="21" AND All_Traffic.user - != "anonymous")) by All_Traffic.user All_Traffic.src_ip All_Traffic.dest All_Traffic.dest_port All_Traffic.rule - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` - | `protocols_passing_authentication_in_cleartext_filter`' -how_to_implement: This search requires you to be ingesting your network traffic, and - populating the Network_Traffic data model. For more accurate result it's better - to limit destination to organization private and public IP range, like All_Traffic.dest - IN(192.168.0.0/16,172.16.0.0/12,10.0.0.0/8, x.x.x.x/22) + - Cisco Secure Firewall Threat Defense Connection Event +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Network_Traffic + WHERE NOT All_Traffic.action IN ("blocked", "block") + AND + All_Traffic.transport="tcp" + AND + (All_Traffic.dest_port="23" + OR + All_Traffic.dest_port="143" + OR + All_Traffic.dest_port="110" + OR + (All_Traffic.dest_port="21" + AND + All_Traffic.user != "anonymous")) + BY All_Traffic.user All_Traffic.src_ip All_Traffic.dest + All_Traffic.dest_port All_Traffic.rule + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `drop_dm_object_name("All_Traffic")` + | `protocols_passing_authentication_in_cleartext_filter` +how_to_implement: This search requires you to be ingesting your network traffic, and populating the Network_Traffic data model. For more accurate result it's better to limit destination to organization private and public IP range, like All_Traffic.dest IN(192.168.0.0/16,172.16.0.0/12,10.0.0.0/8, x.x.x.x/22) known_false_positives: Some networks may use leverage clear text protocols such as kerberos, FTP or telnet servers. Apply the necessary exclusions where needed. references: -- https://www.rackaid.com/blog/secure-your-email-and-file-transfers/ -- https://www.infosecmatter.com/capture-passwords-using-wireshark/ + - https://www.rackaid.com/blog/secure-your-email-and-file-transfers/ + - https://www.infosecmatter.com/capture-passwords-using-wireshark/ drilldown_searches: -- name: View the detection results for - "$src_ip$" - search: '%original_detection_search% | search src_ip = "$src_ip$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_ip$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_ip$" + search: '%original_detection_search% | search src_ip = "$src_ip$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_ip$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Allowed Traffic from $src_ip$ to $dest$ over port $dest_port$. Which might indicate a potential authentication attempts over a cleartext protocol. - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: - - field: dest - type: ip_address + message: Allowed Traffic from $src_ip$ to $dest$ over port $dest_port$. Which might indicate a potential authentication attempts over a cleartext protocol. + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: + - field: dest + type: ip_address tags: - analytic_story: - - Use of Cleartext Protocols - - Cisco Secure Firewall Threat Defense Analytics - - Scattered Lapsus$ Hunters - asset_type: Endpoint - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Use of Cleartext Protocols + - Cisco Secure Firewall Threat Defense Analytics + - Scattered Lapsus$ Hunters + asset_type: Endpoint + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: Cisco Secure Firewall True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: Cisco Secure Firewall True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/remote_desktop_network_traffic.yml b/detections/network/remote_desktop_network_traffic.yml index 9ac6c6975e..7693fb305b 100644 --- a/detections/network/remote_desktop_network_traffic.yml +++ b/detections/network/remote_desktop_network_traffic.yml @@ -1,85 +1,69 @@ name: Remote Desktop Network Traffic id: 272b8407-842d-4b3d-bead-a704584003d3 -version: 13 -date: '2025-08-07' +version: 14 +date: '2026-02-25' author: David Dorsey, Splunk status: production type: Anomaly -description: The following analytic detects unusual Remote Desktop Protocol - (RDP) traffic on TCP/3389 by filtering out known RDP sources and destinations, - focusing on atypical connections within the network. This detection leverages - network traffic data to identify potentially unauthorized RDP access. - Monitoring this activity is crucial for a SOC as unauthorized RDP access can - indicate an attacker's attempt to control networked systems, leading to data - theft, ransomware deployment, or further network compromise. If confirmed - malicious, this activity could result in significant data breaches or complete - system and network control loss. +description: The following analytic detects unusual Remote Desktop Protocol (RDP) traffic on TCP/3389 by filtering out known RDP sources and destinations, focusing on atypical connections within the network. This detection leverages network traffic data to identify potentially unauthorized RDP access. Monitoring this activity is crucial for a SOC as unauthorized RDP access can indicate an attacker's attempt to control networked systems, leading to data theft, ransomware deployment, or further network compromise. If confirmed malicious, this activity could result in significant data breaches or complete system and network control loss. data_source: -- Zeek Conn -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND - All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source - AND All_Traffic.action="allowed" by All_Traffic.src All_Traffic.dest All_Traffic.dest_port - All_Traffic.dest_ip All_Traffic.dvc All_Traffic.src_ip All_Traffic.src_port All_Traffic.vendor_product - | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `remote_desktop_network_traffic_filter`' -how_to_implement: To successfully implement this search you need to identify - systems that commonly originate remote desktop traffic and that commonly - receive remote desktop traffic. You can use the included support search - "Identify Systems Creating Remote Desktop Traffic" to identify systems that - originate the traffic and the search "Identify Systems Receiving Remote - Desktop Traffic" to identify systems that receive a lot of remote desktop - traffic. After identifying these systems, you will need to add the - "common_rdp_source" or "common_rdp_destination" category to that system - depending on the usage, using the Enterprise Security Assets and Identities - framework. This can be done by adding an entry in the assets.csv file located - in SA-IdentityManagement/lookups. -known_false_positives: Remote Desktop may be used legitimately by users on the - network. + - Zeek Conn +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Network_Traffic + WHERE All_Traffic.dest_port=3389 + AND + All_Traffic.dest_category!=common_rdp_destination + AND + All_Traffic.src_category!=common_rdp_source + AND + All_Traffic.action="allowed" + BY All_Traffic.src All_Traffic.dest All_Traffic.dest_port + All_Traffic.dest_ip All_Traffic.dvc All_Traffic.src_ip + All_Traffic.src_port All_Traffic.vendor_product + | `drop_dm_object_name("All_Traffic")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `remote_desktop_network_traffic_filter` +how_to_implement: To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups. +known_false_positives: Remote Desktop may be used legitimately by users on the network. references: [] drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Remote Desktop Network Traffic Anomaly Detected from $src$ to $dest$ - risk_objects: - - field: src - type: system - score: 25 - threat_objects: - - field: dest - type: ip_address + message: Remote Desktop Network Traffic Anomaly Detected from $src$ to $dest$ + risk_objects: + - field: src + type: system + score: 25 + threat_objects: + - field: dest + type: ip_address tags: - analytic_story: - - SamSam Ransomware - - Ryuk Ransomware - - Hidden Cobra Malware - - Active Directory Lateral Movement - - Windows RDP Artifacts and Defense Evasion - asset_type: Endpoint - mitre_attack_id: - - T1021.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - manual_test: This detection uses builtin lookup from Enterprise Security. + analytic_story: + - SamSam Ransomware + - Ryuk Ransomware + - Hidden Cobra Malware + - Active Directory Lateral Movement + - Windows RDP Artifacts and Defense Evasion + asset_type: Endpoint + mitre_attack_id: + - T1021.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + manual_test: This detection uses builtin lookup from Enterprise Security. tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.001/remote_desktop_connection/zeek_conn.log - sourcetype: bro:conn:json - source: conn.log + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.001/remote_desktop_connection/zeek_conn.log + sourcetype: bro:conn:json + source: conn.log diff --git a/detections/network/rundll32_dnsquery.yml b/detections/network/rundll32_dnsquery.yml index f63e7bea02..fdd0dac5d6 100644 --- a/detections/network/rundll32_dnsquery.yml +++ b/detections/network/rundll32_dnsquery.yml @@ -1,71 +1,62 @@ name: Rundll32 DNSQuery id: f1483f5e-ee29-11eb-9d23-acde48001122 -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects a suspicious `rundll32.exe` process making - HTTP connections and performing DNS queries to web domains. It leverages Sysmon - EventCode 22 logs to identify these activities. This behavior is significant as - it is commonly associated with IcedID malware, where `rundll32.exe` checks internet - connectivity and communicates with C&C servers to download configurations and other - components. If confirmed malicious, this activity could allow attackers to establish - persistence, download additional payloads, and exfiltrate sensitive data, posing - a severe threat to the network. +description: The following analytic detects a suspicious `rundll32.exe` process making HTTP connections and performing DNS queries to web domains. It leverages Sysmon EventCode 22 logs to identify these activities. This behavior is significant as it is commonly associated with IcedID malware, where `rundll32.exe` checks internet connectivity and communicates with C&C servers to download configurations and other components. If confirmed malicious, this activity could allow attackers to establish persistence, download additional payloads, and exfiltrate sensitive data, posing a severe threat to the network. data_source: -- Sysmon EventID 22 -search: '`sysmon` EventCode=22 process_name="rundll32.exe" | stats count min(_time) - as firstTime max(_time) as lastTime by answer answer_count dvc process_exec process_guid - process_name query query_count reply_code_id signature signature_id src user_id - vendor_product QueryName QueryResults QueryStatus | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `rundll32_dnsquery_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name and eventcode = 22 dnsquery executions from your endpoints. - If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. - Tune and filter known instances where renamed rundll32.exe may be used. + - Sysmon EventID 22 +search: |- + `sysmon` EventCode=22 process_name="rundll32.exe" + | stats count min(_time) as firstTime max(_time) as lastTime + BY answer answer_count dvc + process_exec process_guid process_name + query query_count reply_code_id + signature signature_id src + user_id vendor_product QueryName + QueryResults QueryStatus + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `rundll32_dnsquery_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and eventcode = 22 dnsquery executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used. known_false_positives: No false positives have been identified at this time. references: -- https://any.run/malware-trends/icedid + - https://any.run/malware-trends/icedid drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: rundll32 process $process_name$ made a DNS query for $query$ from host - $dvc$ - risk_objects: - - field: dvc - type: system - score: 56 - threat_objects: - - field: process_name - type: process_name + message: rundll32 process $process_name$ made a DNS query for $query$ from host $dvc$ + risk_objects: + - field: dvc + type: system + score: 56 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - IcedID - - Living Off The Land - asset_type: Endpoint - mitre_attack_id: - - T1218.011 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - IcedID + - Living Off The Land + asset_type: Endpoint + mitre_attack_id: + - T1218.011 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/network/smb_traffic_spike.yml b/detections/network/smb_traffic_spike.yml index e1e3244b39..70b4484e0d 100644 --- a/detections/network/smb_traffic_spike.yml +++ b/detections/network/smb_traffic_spike.yml @@ -1,50 +1,49 @@ name: SMB Traffic Spike id: 7f5fb3e1-4209-4914-90db-0ec21b936378 -version: 8 -date: '2025-05-02' +version: 9 +date: '2026-02-25' author: David Dorsey, Splunk status: experimental type: Anomaly -description: The following analytic detects spikes in Server Message Block (SMB) traffic - connections, which are used for sharing files and resources between computers. It - leverages network traffic logs to monitor connections on ports 139 and 445, and - SMB application usage. By calculating the average and standard deviation of SMB - connections over the past 70 minutes, it identifies sources exceeding two standard - deviations from the average. This activity is significant as it may indicate potential - SMB-based attacks, such as ransomware or data theft. If confirmed malicious, attackers - could exfiltrate data or spread malware within the network. +description: The following analytic detects spikes in Server Message Block (SMB) traffic connections, which are used for sharing files and resources between computers. It leverages network traffic logs to monitor connections on ports 139 and 445, and SMB application usage. By calculating the average and standard deviation of SMB connections over the past 70 minutes, it identifies sources exceeding two standard deviations from the average. This activity is significant as it may indicate potential SMB-based attacks, such as ransomware or data theft. If confirmed malicious, attackers could exfiltrate data or spread malware within the network. data_source: [] -search: '| tstats `security_content_summariesonly` count from datamodel=Network_Traffic - where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb - by _time span=1h, All_Traffic.src | `drop_dm_object_name("All_Traffic")` | eventstats - max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, - "-70m@m"), count, null))) as count avg(eval(if(_time upperBound - AND num_data_samples >=50, 1, 0) | where isOutlier=1 | table src count | `smb_traffic_spike_filter`' -how_to_implement: This search requires you to be ingesting your network traffic logs - and populating the `Network_Traffic` data model. -known_false_positives: A file server may experience high-demand loads that could cause - this analytic to trigger. +search: |- + | tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic + WHERE All_Traffic.dest_port=139 + OR + All_Traffic.dest_port=445 + OR + All_Traffic.app=smb + BY _time span=1h, All_Traffic.src + | `drop_dm_object_name("All_Traffic")` + | eventstats max(_time) as maxtime + | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-70m@m"), count, null))) as count avg(eval(if(_time upperBound AND num_data_samples >=50, 1, 0) + | where isOutlier=1 + | table src count + | `smb_traffic_spike_filter` +how_to_implement: This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model. +known_false_positives: A file server may experience high-demand loads that could cause this analytic to trigger. references: [] rba: - message: Anomalous splike of SMB traffic sent from $src$ - risk_objects: - - field: src - type: system - score: 25 - threat_objects: [] + message: Anomalous splike of SMB traffic sent from $src$ + risk_objects: + - field: src + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Emotet Malware DHS Report TA18-201A - - Hidden Cobra Malware - - Ransomware - - DHS Report TA18-074A - asset_type: Endpoint - mitre_attack_id: - - T1021.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Emotet Malware DHS Report TA18-201A + - Hidden Cobra Malware + - Ransomware + - DHS Report TA18-074A + asset_type: Endpoint + mitre_attack_id: + - T1021.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/network/smb_traffic_spike___mltk.yml b/detections/network/smb_traffic_spike___mltk.yml index dca4f8663b..822346a3d6 100644 --- a/detections/network/smb_traffic_spike___mltk.yml +++ b/detections/network/smb_traffic_spike___mltk.yml @@ -1,62 +1,50 @@ name: SMB Traffic Spike - MLTK id: d25773ba-9ad8-48d1-858e-07ad0bbeb828 -version: 9 -date: '2026-01-22' +version: 10 +date: '2026-02-25' author: Rico Valdez, Splunk status: experimental type: Anomaly -description: The following analytic identifies spikes in the number of Server Message - Block (SMB) connections using the Machine Learning Toolkit (MLTK). It leverages - the Network_Traffic data model to monitor SMB traffic on ports 139 and 445, applying - a machine learning model to detect anomalies. This activity is significant because - sudden increases in SMB traffic can indicate lateral movement or data exfiltration - attempts by attackers. If confirmed malicious, this behavior could lead to unauthorized - access, data theft, or further compromise of the network. +description: The following analytic identifies spikes in the number of Server Message Block (SMB) connections using the Machine Learning Toolkit (MLTK). It leverages the Network_Traffic data model to monitor SMB traffic on ports 139 and 445, applying a machine learning model to detect anomalies. This activity is significant because sudden increases in SMB traffic can indicate lateral movement or data exfiltration attempts by attackers. If confirmed malicious, this behavior could lead to unauthorized access, data theft, or further compromise of the network. data_source: [] -search: '| tstats `security_content_summariesonly` count values(All_Traffic.dest_ip) - as dest values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where - All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by - _time span=1h, All_Traffic.src | eval HourOfDay=strftime(_time, "%H") | eval DayOfWeek=strftime(_time, - "%A") | `drop_dm_object_name(All_Traffic)` | apply smb_pdfmodel threshold=0.001 - | rename "IsOutlier(count)" as isOutlier | search isOutlier > 0 | sort -count | - table _time src dest port count | `smb_traffic_spike___mltk_filter`' -how_to_implement: "To successfully implement this search, you will need to ensure - that DNS data is populating the Network_Traffic data model. In addition, the latest - version of Machine Learning Toolkit (MLTK) must be installed on your search heads, - along with any required dependencies. Finally, the support search \"Baseline of - SMB Traffic - MLTK\" must be executed before this detection search, because it builds - a machine-learning (ML) model over the historical data used by this search. It is - important that this search is run in the same app context as the associated support - search, so that the model created by the support search is available for use. You - should periodically re-run the support search to rebuild the model with the latest - data available in your environment.\nThis search produces a field (Number of events,count) - that are not yet supported by Mission Control Analyst Queue and therefore cannot be viewed - when a finding is raised. This field contributes additional context to the finding. - To see the additional metadata, add the following field, if not already present, - to Mission Control Analyst Queue (Configure > Findings and Investigations > Add New Entry):\n* **Label:** Number of events, **Field:** count" -known_false_positives: If you are seeing more results than desired, you may consider - reducing the value of the threshold in the search. You should also periodically - re-run the support search to re-build the ML model on the latest data. Please update - the `smb_traffic_spike_mltk_filter` macro to filter out false positive results +search: |- + | tstats `security_content_summariesonly` count values(All_Traffic.dest_ip) as dest values(All_Traffic.dest_port) as port FROM datamodel=Network_Traffic + WHERE All_Traffic.dest_port=139 + OR + All_Traffic.dest_port=445 + OR + All_Traffic.app=smb + BY _time span=1h, All_Traffic.src + | eval HourOfDay=strftime(_time, "%H") + | eval DayOfWeek=strftime(_time, "%A") + | `drop_dm_object_name(All_Traffic)` + | apply smb_pdfmodel threshold=0.001 + | rename "IsOutlier(count)" as isOutlier + | search isOutlier > 0 + | sort -count + | table _time src dest port count + | `smb_traffic_spike___mltk_filter` +how_to_implement: "To successfully implement this search, you will need to ensure that DNS data is populating the Network_Traffic data model. In addition, the latest version of Machine Learning Toolkit (MLTK) must be installed on your search heads, along with any required dependencies. Finally, the support search \"Baseline of SMB Traffic - MLTK\" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\nThis search produces a field (Number of events,count) that are not yet supported by Mission Control Analyst Queue and therefore cannot be viewed when a finding is raised. This field contributes additional context to the finding. To see the additional metadata, add the following field, if not already present, to Mission Control Analyst Queue (Configure > Findings and Investigations > Add New Entry):\n* **Label:** Number of events, **Field:** count" +known_false_positives: If you are seeing more results than desired, you may consider reducing the value of the threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. Please update the `smb_traffic_spike_mltk_filter` macro to filter out false positive results references: [] rba: - message: SMB Traffic Spike from $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: SMB Traffic Spike from $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Emotet Malware DHS Report TA18-201A - - Hidden Cobra Malware - - Ransomware - - DHS Report TA18-074A - asset_type: Endpoint - mitre_attack_id: - - T1021.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Emotet Malware DHS Report TA18-201A + - Hidden Cobra Malware + - Ransomware + - DHS Report TA18-074A + asset_type: Endpoint + mitre_attack_id: + - T1021.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/network/ssl_certificates_with_punycode.yml b/detections/network/ssl_certificates_with_punycode.yml index 3546c513b3..c3e33ddea1 100644 --- a/detections/network/ssl_certificates_with_punycode.yml +++ b/detections/network/ssl_certificates_with_punycode.yml @@ -1,44 +1,38 @@ name: SSL Certificates with Punycode id: 696694df-5706-495a-81f2-79501fa11b90 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: experimental type: Hunting -description: The following analytic detects SSL certificates with Punycode domains - in the SSL issuer email domain, identified by the prefix "xn--". It leverages the - Certificates Datamodel to flag these domains and uses CyberChef for decoding. This - activity is significant as Punycode can be used for domain spoofing and phishing - attacks. If confirmed malicious, attackers could deceive users and systems, potentially - leading to unauthorized access and data breaches. +description: The following analytic detects SSL certificates with Punycode domains in the SSL issuer email domain, identified by the prefix "xn--". It leverages the Certificates Datamodel to flag these domains and uses CyberChef for decoding. This activity is significant as Punycode can be used for domain spoofing and phishing attacks. If confirmed malicious, attackers could deceive users and systems, potentially leading to unauthorized access and data breaches. data_source: [] -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Certificates.All_Certificates by All_Certificates.SSL.ssl_issuer_email_domain - All_Certificates.SSL.ssl_issuer All_Certificates.SSL.ssl_subject_email All_Certificates.SSL.dest - All_Certificates.SSL.src All_Certificates.SSL.sourcetype All_Certificates.SSL.ssl_subject_email_domain - | `drop_dm_object_name("All_Certificates.SSL")` | eval punycode=if(like(ssl_issuer_email_domain,"%xn--%"),1,0) - | where punycode=1 | cyberchef infield="ssl_issuer_email_domain" outfield="convertedPuny" - jsonrecipe="[{"op":"From Punycode","args":[true]}]" | table ssl_issuer_email_domain - convertedPuny ssl_issuer ssl_subject_email dest src sourcetype ssl_subject_email_domain - | `ssl_certificates_with_punycode_filter`' -how_to_implement: Ensure data is properly being ingested into the Certificates datamodel. - If decoding the of interest, the CyberChef app is needed https://splunkbase.splunk.com/app/5348. - If decoding is not needed, remove the cyberchef lines. -known_false_positives: False positives may be present if the organization works with - international businesses. Filter as needed. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Certificates.All_Certificates + BY All_Certificates.SSL.ssl_issuer_email_domain All_Certificates.SSL.ssl_issuer All_Certificates.SSL.ssl_subject_email + All_Certificates.SSL.dest All_Certificates.SSL.src All_Certificates.SSL.sourcetype + All_Certificates.SSL.ssl_subject_email_domain + | `drop_dm_object_name("All_Certificates.SSL")` + | eval punycode=if(like(ssl_issuer_email_domain,"%xn--%"),1,0) + | where punycode=1 + | cyberchef infield="ssl_issuer_email_domain" outfield="convertedPuny" jsonrecipe="[{"op":"From Punycode","args":[true]}]" + | table ssl_issuer_email_domain convertedPuny ssl_issuer ssl_subject_email dest src sourcetype ssl_subject_email_domain + | `ssl_certificates_with_punycode_filter` +how_to_implement: Ensure data is properly being ingested into the Certificates datamodel. If decoding the of interest, the CyberChef app is needed https://splunkbase.splunk.com/app/5348. If decoding is not needed, remove the cyberchef lines. +known_false_positives: False positives may be present if the organization works with international businesses. Filter as needed. references: -- https://www.splunk.com/en_us/blog/security/nothing-puny-about-cve-2022-3602.html -- https://www.openssl.org/blog/blog/2022/11/01/email-address-overflows/ -- https://community.emergingthreats.net/t/out-of-band-ruleset-update-summary-2022-11-01/117 -- https://github.com/corelight/CVE-2022-3602/tree/master/scripts + - https://www.splunk.com/en_us/blog/security/nothing-puny-about-cve-2022-3602.html + - https://www.openssl.org/blog/blog/2022/11/01/email-address-overflows/ + - https://community.emergingthreats.net/t/out-of-band-ruleset-update-summary-2022-11-01/117 + - https://github.com/corelight/CVE-2022-3602/tree/master/scripts tags: - analytic_story: - - OpenSSL CVE-2022-3602 - asset_type: Network - mitre_attack_id: - - T1573 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - OpenSSL CVE-2022-3602 + asset_type: Network + mitre_attack_id: + - T1573 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/network/suspicious_process_dns_query_known_abuse_web_services.yml b/detections/network/suspicious_process_dns_query_known_abuse_web_services.yml index ecf3121ba0..1f78f230e8 100644 --- a/detections/network/suspicious_process_dns_query_known_abuse_web_services.yml +++ b/detections/network/suspicious_process_dns_query_known_abuse_web_services.yml @@ -5,78 +5,57 @@ date: '2025-12-10' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects a suspicious process making DNS queries - to known, abused text-paste web services, VoIP, instant messaging, and digital distribution - platforms. It leverages Sysmon EventID 22 logs to identify queries from processes - like cmd.exe, powershell.exe, and others. This activity is significant as it may - indicate an attempt to download malicious files, a common initial access technique. - If confirmed malicious, this could lead to unauthorized code execution, data exfiltration, - or further compromise of the target host. +description: The following analytic detects a suspicious process making DNS queries to known, abused text-paste web services, VoIP, instant messaging, and digital distribution platforms. It leverages Sysmon EventID 22 logs to identify queries from processes like cmd.exe, powershell.exe, and others. This activity is significant as it may indicate an attempt to download malicious files, a common initial access technique. If confirmed malicious, this could lead to unauthorized code execution, data exfiltration, or further compromise of the target host. data_source: -- Sysmon EventID 22 -search: '`sysmon` EventCode=22 QueryName IN ("*pastebin*", "*discord*", "*api.telegram*","*t.me*") - process_name IN ("cmd.exe", "*powershell*", "pwsh.exe", "wscript.exe","cscript.exe") - OR Image IN ("*\\users\\public\\*", "*\\programdata\\*", "*\\temp\\*", "*\\Windows\\Tasks\\*", - "*\\appdata\\*", "*\\perflogs\\*") | stats count min(_time) as firstTime max(_time) - as lastTime by answer answer_count dvc process_exec process_guid process_name query - query_count reply_code_id signature signature_id src user_id vendor_product QueryName - QueryResults QueryStatus | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `suspicious_process_dns_query_known_abuse_web_services_filter`' -how_to_implement: This detection relies on sysmon logs with the Event ID 22, DNS Query. - We suggest you run this detection at least once a day over the last 14 days. -known_false_positives: Noise and false positive can be seen if the following instant - messaging is allowed to use within corporate network. In this case, a filter is - needed. + - Sysmon EventID 22 +search: '`sysmon` EventCode=22 QueryName IN ("*pastebin*", "*discord*", "*api.telegram*","*t.me*") process_name IN ("cmd.exe", "*powershell*", "pwsh.exe", "wscript.exe","cscript.exe") OR Image IN ("*\\users\\public\\*", "*\\programdata\\*", "*\\temp\\*", "*\\Windows\\Tasks\\*", "*\\appdata\\*", "*\\perflogs\\*") | stats count min(_time) as firstTime max(_time) as lastTime by answer answer_count dvc process_exec process_guid process_name query query_count reply_code_id signature signature_id src user_id vendor_product QueryName QueryResults QueryStatus | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_process_dns_query_known_abuse_web_services_filter`' +how_to_implement: This detection relies on sysmon logs with the Event ID 22, DNS Query. We suggest you run this detection at least once a day over the last 14 days. +known_false_positives: Noise and false positive can be seen if the following instant messaging is allowed to use within corporate network. In this case, a filter is needed. references: -- https://urlhaus.abuse.ch/url/1798923/ -- https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://urlhaus.abuse.ch/url/1798923/ + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious process $process_name$ made a DNS query for $QueryName$ on $dvc$ - risk_objects: - - field: dvc - type: system - score: 64 - threat_objects: - - field: process_name - type: process_name + message: Suspicious process $process_name$ made a DNS query for $QueryName$ on $dvc$ + risk_objects: + - field: dvc + type: system + score: 64 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Snake Keylogger - - Meduza Stealer - - Malicious Inno Setup Loader - - Phemedrone Stealer - - Remcos - - Data Destruction - - PXA Stealer - - WhisperGate - - Cactus Ransomware - - Braodo Stealer - - RedLine Stealer - asset_type: Endpoint - mitre_attack_id: - - T1059.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Snake Keylogger + - Meduza Stealer + - Malicious Inno Setup Loader + - Phemedrone Stealer + - Remcos + - Data Destruction + - PXA Stealer + - WhisperGate + - Cactus Ransomware + - Braodo Stealer + - RedLine Stealer + asset_type: Endpoint + mitre_attack_id: + - T1059.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos_pastebin_download/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos_pastebin_download/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/network/suspicious_process_with_discord_dns_query.yml b/detections/network/suspicious_process_with_discord_dns_query.yml index 2131e91792..50f2b7b6de 100644 --- a/detections/network/suspicious_process_with_discord_dns_query.yml +++ b/detections/network/suspicious_process_with_discord_dns_query.yml @@ -5,69 +5,51 @@ date: '2025-05-02' author: Teoderick Contreras, Mauricio Velazco, Splunk status: production type: Anomaly -description: The following analytic identifies a process making a DNS query to Discord, - excluding legitimate Discord application paths. It leverages Sysmon logs with Event - ID 22 to detect DNS queries containing "discord" in the QueryName field. This activity - is significant because Discord can be abused by adversaries to host and download - malicious files, as seen in the WhisperGate campaign. If confirmed malicious, this - could indicate malware attempting to download additional payloads from Discord, - potentially leading to further code execution and compromise of the affected system. +description: The following analytic identifies a process making a DNS query to Discord, excluding legitimate Discord application paths. It leverages Sysmon logs with Event ID 22 to detect DNS queries containing "discord" in the QueryName field. This activity is significant because Discord can be abused by adversaries to host and download malicious files, as seen in the WhisperGate campaign. If confirmed malicious, this could indicate malware attempting to download additional payloads from Discord, potentially leading to further code execution and compromise of the affected system. data_source: -- Sysmon EventID 22 -search: '`sysmon` EventCode=22 QueryName IN ("*discord*") Image != "*\\AppData\\Local\\Discord\\*" - AND Image != "*\\Program Files*" AND Image != "discord.exe" | stats count min(_time) - as firstTime max(_time) as lastTime by answer answer_count dvc process_exec process_guid - process_name query query_count reply_code_id signature signature_id src user_id - vendor_product QueryName QueryResults QueryStatus | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `suspicious_process_with_discord_dns_query_filter`' + - Sysmon EventID 22 +search: '`sysmon` EventCode=22 QueryName IN ("*discord*") Image != "*\\AppData\\Local\\Discord\\*" AND Image != "*\\Program Files*" AND Image != "discord.exe" | stats count min(_time) as firstTime max(_time) as lastTime by answer answer_count dvc process_exec process_guid process_name query query_count reply_code_id signature signature_id src user_id vendor_product QueryName QueryResults QueryStatus | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_process_with_discord_dns_query_filter`' how_to_implement: his detection relies on sysmon logs with the Event ID 22, DNS Query. -known_false_positives: Noise and false positive can be seen if the following instant - messaging is allowed to use within corporate network. In this case, a filter is - needed. +known_false_positives: Noise and false positive can be seen if the following instant messaging is allowed to use within corporate network. In this case, a filter is needed. references: -- https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ -- https://medium.com/s2wblog/analysis-of-destructive-malware-whispergate-targeting-ukraine-9d5d158f19f3 -- https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ + - https://medium.com/s2wblog/analysis-of-destructive-malware-whispergate-targeting-ukraine-9d5d158f19f3 + - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: suspicious process $process_name$ has a dns query in $QueryName$ on $dvc$ - risk_objects: - - field: dvc - type: system - score: 64 - threat_objects: - - field: process_name - type: process_name + message: suspicious process $process_name$ has a dns query in $QueryName$ on $dvc$ + risk_objects: + - field: dvc + type: system + score: 64 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Data Destruction - - WhisperGate - - PXA Stealer - - Cactus Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1059.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Data Destruction + - WhisperGate + - PXA Stealer + - Cactus Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1059.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.005/discord_dnsquery/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.005/discord_dnsquery/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/network/tor_traffic.yml b/detections/network/tor_traffic.yml index 81f97afa86..a2d0988252 100644 --- a/detections/network/tor_traffic.yml +++ b/detections/network/tor_traffic.yml @@ -1,88 +1,74 @@ name: TOR Traffic id: ea688274-9c06-4473-b951-e4cb7a5d7a45 -version: 12 -date: '2026-01-14' +version: 13 +date: '2026-02-25' author: David Dorsey, Bhavin Patel, Splunk status: production type: TTP -description: The following analytic identifies allowed network traffic to The - Onion Router (TOR), an anonymity network often exploited for malicious - activities. It leverages data from Next Generation Firewalls, using the - Network_Traffic data model to detect traffic where the application is TOR and - the action is allowed. This activity is significant as TOR can be used to - bypass conventional monitoring, facilitating hacking, data breaches, and - illicit content dissemination. If confirmed malicious, this could lead to - unauthorized access, data exfiltration, and severe compliance violations, - compromising the integrity and security of the network. +description: The following analytic identifies allowed network traffic to The Onion Router (TOR), an anonymity network often exploited for malicious activities. It leverages data from Next Generation Firewalls, using the Network_Traffic data model to detect traffic where the application is TOR and the action is allowed. This activity is significant as TOR can be used to bypass conventional monitoring, facilitating hacking, data breaches, and illicit content dissemination. If confirmed malicious, this could lead to unauthorized access, data exfiltration, and severe compliance violations, compromising the integrity and security of the network. data_source: -- Palo Alto Network Traffic -- Cisco Secure Firewall Threat Defense Connection Event -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Network_Traffic where All_Traffic.app=tor AND All_Traffic.action - IN ("allowed", "allow") by All_Traffic.action All_Traffic.app All_Traffic.bytes - All_Traffic.bytes_in All_Traffic.bytes_out All_Traffic.dest All_Traffic.dest_ip - All_Traffic.dest_port All_Traffic.dvc All_Traffic.protocol All_Traffic.protocol_version - All_Traffic.src All_Traffic.src_ip All_Traffic.src_port All_Traffic.transport All_Traffic.user - All_Traffic.vendor_product All_Traffic.rule | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `tor_traffic_filter`' -how_to_implement: In order to properly run this search, Splunk needs to ingest - data from Next Generation Firewalls like, Cisco Secure Firewall Threat - Defense, Palo Alto Networks Firewalls or other network control devices that - mediate the traffic allowed into an environment. This is necessary so that the - search can identify an 'action' taken on the traffic of interest. The search - requires the Network_Traffic data model to be populated. + - Palo Alto Network Traffic + - Cisco Secure Firewall Threat Defense Connection Event +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Network_Traffic + WHERE All_Traffic.app=tor + AND + All_Traffic.action IN ("allowed", "allow") + BY All_Traffic.action All_Traffic.app All_Traffic.bytes + All_Traffic.bytes_in All_Traffic.bytes_out All_Traffic.dest + All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.dvc + All_Traffic.protocol All_Traffic.protocol_version All_Traffic.src + All_Traffic.src_ip All_Traffic.src_port All_Traffic.transport + All_Traffic.user All_Traffic.vendor_product All_Traffic.rule + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `drop_dm_object_name("All_Traffic")` + | `tor_traffic_filter` +how_to_implement: In order to properly run this search, Splunk needs to ingest data from Next Generation Firewalls like, Cisco Secure Firewall Threat Defense, Palo Alto Networks Firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model to be populated. known_false_positives: No false positives have been identified at this time. references: -- https://knowledgebase.paloaltonetworks.com/KCSArticleDetail?id=kA10g000000ClRtCAK -- https://unit42.paloaltonetworks.com/tor-traffic-enterprise-networks/#:~:text=For%20enterprises%20concerned%20about%20the,the%20most%20important%20security%20risks. + - https://knowledgebase.paloaltonetworks.com/KCSArticleDetail?id=kA10g000000ClRtCAK + - https://unit42.paloaltonetworks.com/tor-traffic-enterprise-networks/#:~:text=For%20enterprises%20concerned%20about%20the,the%20most%20important%20security%20risks. drilldown_searches: -- name: View the detection results for - "$src_ip$" - search: '%original_detection_search% | search src_ip = "$src_ip$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_ip$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_ip$" + search: '%original_detection_search% | search src_ip = "$src_ip$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_ip$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Suspicious network traffic allowed using TOR has been detected from - $src_ip$ to $dest_ip$ - risk_objects: - - field: src_ip - type: system - score: 80 - threat_objects: [] + message: Suspicious network traffic allowed using TOR has been detected from $src_ip$ to $dest_ip$ + risk_objects: + - field: src_ip + type: system + score: 80 + threat_objects: [] tags: - analytic_story: - - Prohibited Traffic Allowed or Protocol Mismatch - - Ransomware - - NOBELIUM Group - - Command And Control - - Cisco Secure Firewall Threat Defense Analytics - - Interlock Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1090.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Prohibited Traffic Allowed or Protocol Mismatch + - Ransomware + - NOBELIUM Group + - Command And Control + - Cisco Secure Firewall Threat Defense Analytics + - Interlock Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1090.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: Palo Alto True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1090.003/pan_tor_allowed/pan_tor_allowed.log - source: pan_tor_allowed - sourcetype: pan:traffic -- name: Cisco Secure Firewall True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log - source: not_applicable - sourcetype: cisco:sfw:estreamer + - name: Palo Alto True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1090.003/pan_tor_allowed/pan_tor_allowed.log + source: pan_tor_allowed + sourcetype: pan:traffic + - name: Cisco Secure Firewall True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/cisco_secure_firewall_threat_defense/connection_event/connection_events.log + source: not_applicable + sourcetype: cisco:sfw:estreamer diff --git a/detections/network/wermgr_process_connecting_to_ip_check_web_services.yml b/detections/network/wermgr_process_connecting_to_ip_check_web_services.yml index 3484186ed6..c256e9a48c 100644 --- a/detections/network/wermgr_process_connecting_to_ip_check_web_services.yml +++ b/detections/network/wermgr_process_connecting_to_ip_check_web_services.yml @@ -1,70 +1,60 @@ name: Wermgr Process Connecting To IP Check Web Services id: ed313326-a0f9-11eb-a89c-acde48001122 -version: 10 -date: '2026-01-14' +version: 11 +date: '2026-02-25' author: Teoderick Contreras, Mauricio Velazco, Splunk status: production type: TTP -description: The following analytic detects the wermgr.exe process attempting to connect - to known IP check web services. It leverages Sysmon EventCode 22 to identify DNS - queries made by wermgr.exe to specific IP check services. This activity is significant - because wermgr.exe is typically used for Windows error reporting, and its connection - to these services may indicate malicious code injection, often associated with malware - like Trickbot. If confirmed malicious, this behavior could allow attackers to recon - the infected machine's IP address, aiding in further exploitation and evasion tactics. +description: The following analytic detects the wermgr.exe process attempting to connect to known IP check web services. It leverages Sysmon EventCode 22 to identify DNS queries made by wermgr.exe to specific IP check services. This activity is significant because wermgr.exe is typically used for Windows error reporting, and its connection to these services may indicate malicious code injection, often associated with malware like Trickbot. If confirmed malicious, this behavior could allow attackers to recon the infected machine's IP address, aiding in further exploitation and evasion tactics. data_source: -- Sysmon EventID 22 -search: '`sysmon` EventCode =22 process_name = wermgr.exe QueryName IN ("*wtfismyip.com", - "*checkip.amazonaws.com", "*ipecho.net", "*ipinfo.io", "*api.ipify.org", "*icanhazip.com", - "*ip.anysrc.com","*api.ip.sb", "ident.me", "www.myexternalip.com", "*zen.spamhaus.org", - "*cbl.abuseat.org", "*b.barracudacentral.org","*dnsbl-1.uceprotect.net", "*spam.dnsbl.sorbs.net") - | stats min(_time) as firstTime max(_time) as lastTime count by answer answer_count - dvc process_exec process_guid process_name query query_count reply_code_id signature - signature_id src user_id vendor_product QueryName QueryResults QueryStatus | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `wermgr_process_connecting_to_ip_check_web_services_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, dns query name process path , and query ststus from - your endpoints like EventCode 22. If you are using Sysmon, you must have at least - version 12 of the Sysmon TA. + - Sysmon EventID 22 +search: |- + `sysmon` EventCode =22 process_name = wermgr.exe QueryName IN ("*wtfismyip.com", "*checkip.amazonaws.com", "*ipecho.net", "*ipinfo.io", "*api.ipify.org", "*icanhazip.com", "*ip.anysrc.com","*api.ip.sb", "ident.me", "www.myexternalip.com", "*zen.spamhaus.org", "*cbl.abuseat.org", "*b.barracudacentral.org","*dnsbl-1.uceprotect.net", "*spam.dnsbl.sorbs.net") + | stats min(_time) as firstTime max(_time) as lastTime count + BY answer answer_count dvc + process_exec process_guid process_name + query query_count reply_code_id + signature signature_id src + user_id vendor_product QueryName + QueryResults QueryStatus + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `wermgr_process_connecting_to_ip_check_web_services_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, dns query name process path , and query ststus from your endpoints like EventCode 22. If you are using Sysmon, you must have at least version 12 of the Sysmon TA. known_false_positives: No false positives have been identified at this time. references: -- https://labs.vipre.com/trickbot-and-its-modules/ -- https://whitehat.eu/incident-response-case-study-featuring-ryuk-and-trickbot-part-2/ + - https://labs.vipre.com/trickbot-and-its-modules/ + - https://whitehat.eu/incident-response-case-study-featuring-ryuk-and-trickbot-part-2/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Wermgr.exe process connecting IP location web services on $dvc$ - risk_objects: - - field: dvc - type: system - score: 56 - threat_objects: [] + message: Wermgr.exe process connecting IP location web services on $dvc$ + risk_objects: + - field: dvc + type: system + score: 56 + threat_objects: [] tags: - analytic_story: - - Trickbot - asset_type: Endpoint - mitre_attack_id: - - T1590.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Trickbot + asset_type: Endpoint + mitre_attack_id: + - T1590.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/network/windows_abused_web_services.yml b/detections/network/windows_abused_web_services.yml index 97f15ab38d..169cfe5d0b 100644 --- a/detections/network/windows_abused_web_services.yml +++ b/detections/network/windows_abused_web_services.yml @@ -1,71 +1,63 @@ name: Windows Abused Web Services id: 01f0aef4-8591-4daa-a53d-0ed49823b681 -version: 8 -date: '2026-01-24' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP data_source: -- Sysmon EventID 22 -description: The following analytic detects a suspicious process making DNS queries - to known, abused web services such as text-paste sites, VoIP, secure tunneling, - instant messaging, and digital distribution platforms. This detection leverages - Sysmon logs with Event ID 22, focusing on specific query names. This activity is - significant as it may indicate an adversary attempting to download malicious files, - a common initial access technique. If confirmed malicious, this could lead to unauthorized - code execution, data exfiltration, or further compromise of the target host. -search: '`sysmon` EventCode=22 QueryName IN ("*pastebin*","*textbin*", "*ngrok.io*", - "*discord*", "*duckdns.org*", "*pasteio.com*") | stats count min(_time) as firstTime - max(_time) as lastTime by answer answer_count dvc process_exec process_guid process_name - query query_count reply_code_id signature signature_id src user_id vendor_product - QueryName QueryResults QueryStatus | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `windows_abused_web_services_filter`' -how_to_implement: This detection relies on sysmon logs with the Event ID 22, DNS Query. - We suggest you run this detection at least once a day over the last 14 days. -known_false_positives: Noise and false positive can be seen if the following instant - messaging is allowed to use within corporate network. In this case, a filter is - needed. + - Sysmon EventID 22 +description: The following analytic detects a suspicious process making DNS queries to known, abused web services such as text-paste sites, VoIP, secure tunneling, instant messaging, and digital distribution platforms. This detection leverages Sysmon logs with Event ID 22, focusing on specific query names. This activity is significant as it may indicate an adversary attempting to download malicious files, a common initial access technique. If confirmed malicious, this could lead to unauthorized code execution, data exfiltration, or further compromise of the target host. +search: |- + `sysmon` EventCode=22 QueryName IN ("*pastebin*","*textbin*", "*ngrok.io*", "*discord*", "*duckdns.org*", "*pasteio.com*") + | stats count min(_time) as firstTime max(_time) as lastTime + BY answer answer_count dvc + process_exec process_guid process_name + query query_count reply_code_id + signature signature_id src + user_id vendor_product QueryName + QueryResults QueryStatus + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_abused_web_services_filter` +how_to_implement: This detection relies on sysmon logs with the Event ID 22, DNS Query. We suggest you run this detection at least once a day over the last 14 days. +known_false_positives: Noise and false positive can be seen if the following instant messaging is allowed to use within corporate network. In this case, a filter is needed. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.njrat + - https://malpedia.caad.fkie.fraunhofer.de/details/win.njrat drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a network connection on known abused web services from $dvc$ - risk_objects: - - field: dvc - type: system - score: 36 - threat_objects: - - field: process_name - type: process_name + message: a network connection on known abused web services from $dvc$ + risk_objects: + - field: dvc + type: system + score: 36 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - NjRAT - - CISA AA24-241A - - Malicious Inno Setup Loader - asset_type: Endpoint - mitre_attack_id: - - T1102 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - NjRAT + - CISA AA24-241A + - Malicious Inno Setup Loader + asset_type: Endpoint + mitre_attack_id: + - T1102 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1102/njrat_ngrok_connection/ngrok.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1102/njrat_ngrok_connection/ngrok.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/network/windows_ad_replication_service_traffic.yml b/detections/network/windows_ad_replication_service_traffic.yml index 1755d1bd9c..2aa00cfbfc 100644 --- a/detections/network/windows_ad_replication_service_traffic.yml +++ b/detections/network/windows_ad_replication_service_traffic.yml @@ -1,51 +1,44 @@ name: Windows AD Replication Service Traffic id: c6e24183-a5f4-4b2a-ad01-2eb456d09b67 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Steven Dick type: TTP status: experimental data_source: [] -description: The following analytic identifies unexpected Active Directory replication - traffic from non-domain controller sources. It leverages data from the Network Traffic - datamodel, specifically looking for applications related to AD replication. This - activity is significant because AD replication traffic should typically only occur - between domain controllers. Detection of such traffic from other sources may indicate - malicious activities like DCSync or DCShadow, which are used for credential dumping. - If confirmed malicious, this could allow attackers to exfiltrate sensitive credentials, - leading to unauthorized access and potential domain-wide compromise. -search: '| tstats `security_content_summariesonly` count values(All_Traffic.transport) - as transport values(All_Traffic.user) as user values(All_Traffic.src_category) as - src_category values(All_Traffic.dest_category) as dest_category min(_time) as firstTime - max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app IN ("ms-dc-replication","*drsr*","ad - drs") by All_Traffic.src All_Traffic.dest All_Traffic.app | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `windows_ad_replication_service_traffic_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - application aware firewall or proxy logs into the Network Datamodel. Categorize - all known domain controller Assets servers with an appropriate category for filtering. +description: The following analytic identifies unexpected Active Directory replication traffic from non-domain controller sources. It leverages data from the Network Traffic datamodel, specifically looking for applications related to AD replication. This activity is significant because AD replication traffic should typically only occur between domain controllers. Detection of such traffic from other sources may indicate malicious activities like DCSync or DCShadow, which are used for credential dumping. If confirmed malicious, this could allow attackers to exfiltrate sensitive credentials, leading to unauthorized access and potential domain-wide compromise. +search: |- + | tstats `security_content_summariesonly` count values(All_Traffic.transport) as transport values(All_Traffic.user) as user values(All_Traffic.src_category) as src_category values(All_Traffic.dest_category) as dest_category min(_time) as firstTime max(_time) as lastTime FROM datamodel=Network_Traffic + WHERE All_Traffic.app IN ("ms-dc-replication","*drsr*","ad drs") + BY All_Traffic.src All_Traffic.dest All_Traffic.app + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `drop_dm_object_name("All_Traffic")` + | `windows_ad_replication_service_traffic_filter` +how_to_implement: To successfully implement this search, you need to be ingesting application aware firewall or proxy logs into the Network Datamodel. Categorize all known domain controller Assets servers with an appropriate category for filtering. known_false_positives: New domain controllers or certian scripts run by administrators. references: -- https://adsecurity.org/?p=1729 -- https://attack.mitre.org/techniques/T1003/006/ -- https://attack.mitre.org/techniques/T1207/ + - https://adsecurity.org/?p=1729 + - https://attack.mitre.org/techniques/T1003/006/ + - https://attack.mitre.org/techniques/T1207/ rba: - message: Active Directory Replication Traffic from Unknown Source - $src$ - risk_objects: - - field: dest - type: system - score: 100 - threat_objects: - - field: src - type: ip_address + message: Active Directory Replication Traffic from Unknown Source - $src$ + risk_objects: + - field: dest + type: system + score: 100 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1003.006 - - T1207 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1003.006 + - T1207 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/network/windows_ad_rogue_domain_controller_network_activity.yml b/detections/network/windows_ad_rogue_domain_controller_network_activity.yml index a11d8f76cd..e67848efb2 100644 --- a/detections/network/windows_ad_rogue_domain_controller_network_activity.yml +++ b/detections/network/windows_ad_rogue_domain_controller_network_activity.yml @@ -1,45 +1,39 @@ name: Windows AD Rogue Domain Controller Network Activity id: c4aeeeef-da7f-4338-b3ba-553cbcbe2138 -version: 6 -date: '2026-01-14' +version: 7 +date: '2026-02-25' author: Dean Luxton type: TTP status: experimental data_source: [] -description: The following analytic identifies unauthorized replication RPC calls - from non-domain controller devices. It leverages Zeek wire data to detect specific - RPC operations like DrsReplicaAdd and DRSGetNCChanges, filtering out legitimate - domain controllers. This activity is significant as it may indicate an attempt to - introduce a rogue domain controller, which can compromise the integrity of the Active - Directory environment. If confirmed malicious, this could allow attackers to manipulate - directory data, escalate privileges, and persist within the network, posing a severe - security risk. -search: '`zeek_rpc` DrsReplicaAdd OR DRSGetNCChanges | where NOT (dest_category="Domain - Controller") OR NOT (src_category="Domain Controller") | fillnull value="Unknown" - src_category, dest_category | table _time endpoint operation src src_category dest - dest_category | `windows_ad_rogue_domain_controller_network_activity_filter`' -how_to_implement: Run zeek on domain controllers to capture the DCE RPC calls, ensure - the domain controller categories are defined in Assets and Identities. +description: The following analytic identifies unauthorized replication RPC calls from non-domain controller devices. It leverages Zeek wire data to detect specific RPC operations like DrsReplicaAdd and DRSGetNCChanges, filtering out legitimate domain controllers. This activity is significant as it may indicate an attempt to introduce a rogue domain controller, which can compromise the integrity of the Active Directory environment. If confirmed malicious, this could allow attackers to manipulate directory data, escalate privileges, and persist within the network, posing a severe security risk. +search: |- + `zeek_rpc` DrsReplicaAdd OR DRSGetNCChanges + | where NOT (dest_category="Domain Controller") OR NOT (src_category="Domain Controller") + | fillnull value="Unknown" src_category, dest_category + | table _time endpoint operation src src_category dest dest_category + | `windows_ad_rogue_domain_controller_network_activity_filter` +how_to_implement: Run zeek on domain controllers to capture the DCE RPC calls, ensure the domain controller categories are defined in Assets and Identities. known_false_positives: No false positives have been identified at this time. references: -- https://adsecurity.org/?p=1729 + - https://adsecurity.org/?p=1729 rba: - message: Rogue DC Activity Detected from $src_category$ device $src$ to $dest$ ($dest_category$) - risk_objects: - - field: dest - type: system - score: 100 - threat_objects: - - field: src - type: ip_address + message: Rogue DC Activity Detected from $src_category$ device $src$ to $dest$ ($dest_category$) + risk_objects: + - field: dest + type: system + score: 100 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Sneaky Active Directory Persistence Tricks - asset_type: Endpoint - mitre_attack_id: - - T1207 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Sneaky Active Directory Persistence Tricks + asset_type: Endpoint + mitre_attack_id: + - T1207 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/network/windows_dns_query_request_by_telegram_bot_api.yml b/detections/network/windows_dns_query_request_by_telegram_bot_api.yml index c5ad936ee7..deac2b6ad7 100644 --- a/detections/network/windows_dns_query_request_by_telegram_bot_api.yml +++ b/detections/network/windows_dns_query_request_by_telegram_bot_api.yml @@ -1,71 +1,61 @@ name: Windows DNS Query Request by Telegram Bot API id: 86f66f44-94d9-412d-a71d-5d8ed0fef72e -version: 5 -date: '2025-08-22' +version: 6 +date: '2026-02-25' author: Teoderick Contreras, Splunk data_source: -- Sysmon EventID 22 + - Sysmon EventID 22 type: Anomaly status: production -description: The following analytic detects the execution of a DNS query by a - process to the associated Telegram API domain, which could indicate access via - a Telegram bot commonly used by malware for command and control (C2) - communications. By monitoring DNS queries related to Telegram's - infrastructure, the detection identifies potential attempts to establish - covert communication channels between a compromised system and external - malicious actors. This behavior is often observed in cyberattacks where - Telegram bots are used to receive commands or exfiltrate data, making it a key - indicator of suspicious or malicious activity within a network. -search: '`sysmon` EventCode=22 query = "api.telegram.org" process_name != "telegram.exe" - | stats count min(_time) as firstTime max(_time) as lastTime by answer answer_count - dvc process_exec process_guid process_name query query_count reply_code_id signature - signature_id src user_id vendor_product QueryName QueryResults QueryStatus | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_dns_query_request_by_telegram_bot_api_filter`' -how_to_implement: To successfully implement this search, you need to be - ingesting logs with the process name and eventcode = 22 dnsquery executions - from your endpoints. If you are using Sysmon, you must have at least version - 6.0.4 of the Sysmon TA. +description: The following analytic detects the execution of a DNS query by a process to the associated Telegram API domain, which could indicate access via a Telegram bot commonly used by malware for command and control (C2) communications. By monitoring DNS queries related to Telegram's infrastructure, the detection identifies potential attempts to establish covert communication channels between a compromised system and external malicious actors. This behavior is often observed in cyberattacks where Telegram bots are used to receive commands or exfiltrate data, making it a key indicator of suspicious or malicious activity within a network. +search: |- + `sysmon` EventCode=22 query = "api.telegram.org" process_name != "telegram.exe" + | stats count min(_time) as firstTime max(_time) as lastTime + BY answer answer_count dvc + process_exec process_guid process_name + query query_count reply_code_id + signature signature_id src + user_id vendor_product QueryName + QueryResults QueryStatus + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_dns_query_request_by_telegram_bot_api_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and eventcode = 22 dnsquery executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. known_false_positives: a third part automation using telegram API. references: -- https://www.splunk.com/en_us/blog/security/threat-advisory-telegram-crypto-botnet-strt-ta01.html + - https://www.splunk.com/en_us/blog/security/threat-advisory-telegram-crypto-botnet-strt-ta01.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: DNS query by a telegram bot [$query$] on [$dvc$]. - risk_objects: - - field: dvc - type: system - score: 36 - threat_objects: [] + message: DNS query by a telegram bot [$query$] on [$dvc$]. + risk_objects: + - field: dvc + type: system + score: 36 + threat_objects: [] tags: - analytic_story: - - Crypto Stealer - - 0bj3ctivity Stealer - asset_type: Endpoint - mitre_attack_id: - - T1071.004 - - T1102.002 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Crypto Stealer + - 0bj3ctivity Stealer + asset_type: Endpoint + mitre_attack_id: + - T1071.004 + - T1102.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1102.002/telegram_api_dns/telegram_dns.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1102.002/telegram_api_dns/telegram_dns.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/network/windows_gather_victim_network_info_through_ip_check_web_services.yml b/detections/network/windows_gather_victim_network_info_through_ip_check_web_services.yml index e0f63f05ac..9d29cccb88 100644 --- a/detections/network/windows_gather_victim_network_info_through_ip_check_web_services.yml +++ b/detections/network/windows_gather_victim_network_info_through_ip_check_web_services.yml @@ -1,87 +1,71 @@ name: Windows Gather Victim Network Info Through Ip Check Web Services id: 70f7c952-0758-46d6-9148-d8969c4481d1 -version: 15 -date: '2025-10-31' +version: 16 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic detects processes attempting to connect to - known IP check web services. This behavior is identified using Sysmon - EventCode 22 logs, specifically monitoring DNS queries to services like - "wtfismyip.com" and "ipinfo.io". This activity is significant as it is - commonly used by malware, such as Trickbot, for reconnaissance to determine - the infected machine's IP address. If confirmed malicious, this could allow - attackers to gather network information, aiding in further attacks or lateral - movement within the network. +description: The following analytic detects processes attempting to connect to known IP check web services. This behavior is identified using Sysmon EventCode 22 logs, specifically monitoring DNS queries to services like "wtfismyip.com" and "ipinfo.io". This activity is significant as it is commonly used by malware, such as Trickbot, for reconnaissance to determine the infected machine's IP address. If confirmed malicious, this could allow attackers to gather network information, aiding in further attacks or lateral movement within the network. data_source: -- Sysmon EventID 22 -search: '`sysmon` EventCode=22 QueryName IN ("*wtfismyip.com", "*checkip.*", "*ipecho.net", - "*ipinfo.io", "*api.ipify.org", "*icanhazip.com", "*ip.anysrc.com","*api.ip.sb", - "ident.me", "www.myexternalip.com", "*zen.spamhaus.org", "*cbl.abuseat.org", "*b.barracudacentral.org", - "*dnsbl-1.uceprotect.net", "*spam.dnsbl.sorbs.net", "*iplogger.org*", "*ip-api.com*", - "*geoip.*", "*icanhazip.*", "*ipwho.is*", "*ifconfig.me*", "*myip.com*", "*ipstack.com*", - "*myexternalip.com*", "*ip-api.io*", "*trackip.net*", "*ipgeolocation.io*", "*ipfind.io*", - "*freegeoip.app*", "*ipv4bot.whatismyipaddress.com*") | stats min(_time) as firstTime - max(_time) as lastTime count by answer answer_count dvc process_exec process_guid - process_name query query_count reply_code_id signature signature_id src user_id - vendor_product QueryName QueryResults QueryStatus | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_gather_victim_network_info_through_ip_check_web_services_filter`' -how_to_implement: To successfully implement this search, you need to be - ingesting logs with the process name, dns query name process path , and query - ststus from your endpoints like EventCode 22. If you are using Sysmon, you - must have at least version 12 of the Sysmon TA. -known_false_positives: Filter internet browser application to minimize the false - positive of this detection. + - Sysmon EventID 22 +search: |- + `sysmon` EventCode=22 QueryName IN ("*wtfismyip.com", "*checkip.*", "*ipecho.net", "*ipinfo.io", "*api.ipify.org", "*icanhazip.com", "*ip.anysrc.com","*api.ip.sb", "ident.me", "www.myexternalip.com", "*zen.spamhaus.org", "*cbl.abuseat.org", "*b.barracudacentral.org", "*dnsbl-1.uceprotect.net", "*spam.dnsbl.sorbs.net", "*iplogger.org*", "*ip-api.com*", "*geoip.*", "*icanhazip.*", "*ipwho.is*", "*ifconfig.me*", "*myip.com*", "*ipstack.com*", "*myexternalip.com*", "*ip-api.io*", "*trackip.net*", "*ipgeolocation.io*", "*ipfind.io*", "*freegeoip.app*", "*ipv4bot.whatismyipaddress.com*") + | stats min(_time) as firstTime max(_time) as lastTime count + BY answer answer_count dvc + process_exec process_guid process_name + query query_count reply_code_id + signature signature_id src + user_id vendor_product QueryName + QueryResults QueryStatus + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_gather_victim_network_info_through_ip_check_web_services_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, dns query name process path , and query ststus from your endpoints like EventCode 22. If you are using Sysmon, you must have at least version 12 of the Sysmon TA. +known_false_positives: Filter internet browser application to minimize the false positive of this detection. references: -- https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ + - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a network connection on known abused web services from $dvc$ - risk_objects: - - field: dvc - type: system - score: 36 - threat_objects: - - field: process_name - type: process_name + message: a network connection on known abused web services from $dvc$ + risk_objects: + - field: dvc + type: system + score: 36 + threat_objects: + - field: process_name + type: process_name tags: - analytic_story: - - Azorult - - DarkCrystal RAT - - Phemedrone Stealer - - Snake Keylogger - - Handala Wiper - - PXA Stealer - - Meduza Stealer - - Water Gamayun - - Quasar RAT - - 0bj3ctivity Stealer - - Castle RAT - asset_type: Endpoint - mitre_attack_id: - - T1590.005 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Azorult + - DarkCrystal RAT + - Phemedrone Stealer + - Snake Keylogger + - Handala Wiper + - PXA Stealer + - Meduza Stealer + - Water Gamayun + - Quasar RAT + - 0bj3ctivity Stealer + - Castle RAT + asset_type: Endpoint + mitre_attack_id: + - T1590.005 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/network/windows_multi_hop_proxy_tor_website_query.yml b/detections/network/windows_multi_hop_proxy_tor_website_query.yml index 32944339a1..15832cda67 100644 --- a/detections/network/windows_multi_hop_proxy_tor_website_query.yml +++ b/detections/network/windows_multi_hop_proxy_tor_website_query.yml @@ -1,72 +1,60 @@ name: Windows Multi hop Proxy TOR Website Query id: 4c2d198b-da58-48d7-ba27-9368732d0054 -version: 8 -date: '2025-07-28' +version: 9 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: Anomaly -description: The following analytic identifies DNS queries to known TOR proxy - websites, such as "*.torproject.org" and "www.theonionrouter.com". It - leverages Sysmon EventCode 22 to detect these queries by monitoring DNS query - events from endpoints. This activity is significant because adversaries often - use TOR proxies to disguise the source of their malicious traffic, making it - harder to trace their actions. If confirmed malicious, this behavior could - indicate an attempt to obfuscate network traffic, potentially allowing - attackers to exfiltrate data or communicate with command and control servers - undetected. +description: The following analytic identifies DNS queries to known TOR proxy websites, such as "*.torproject.org" and "www.theonionrouter.com". It leverages Sysmon EventCode 22 to detect these queries by monitoring DNS query events from endpoints. This activity is significant because adversaries often use TOR proxies to disguise the source of their malicious traffic, making it harder to trace their actions. If confirmed malicious, this behavior could indicate an attempt to obfuscate network traffic, potentially allowing attackers to exfiltrate data or communicate with command and control servers undetected. data_source: -- Sysmon EventID 22 -search: '`sysmon` EventCode=22 QueryName IN ("*.torproject.org", "www.theonionrouter.com") - | stats count min(_time) as firstTime max(_time) as lastTime by answer answer_count - dvc process_exec process_guid process_name query query_count reply_code_id signature - signature_id src user_id vendor_product QueryName QueryResults QueryStatus | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_multi_hop_proxy_tor_website_query_filter`' -how_to_implement: To successfully implement this search, you need to be - ingesting logs with the process name and sysmon eventcode = 22 dns query - events from your endpoints. If you are using Sysmon, you must have at least - version 6.0.4 of the Sysmon TA. -known_false_positives: third party application may use this proxies if allowed - in production environment. Filter is needed. + - Sysmon EventID 22 +search: |- + `sysmon` EventCode=22 QueryName IN ("*.torproject.org", "www.theonionrouter.com") + | stats count min(_time) as firstTime max(_time) as lastTime + BY answer answer_count dvc + process_exec process_guid process_name + query query_count reply_code_id + signature signature_id src + user_id vendor_product QueryName + QueryResults QueryStatus + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_multi_hop_proxy_tor_website_query_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and sysmon eventcode = 22 dns query events from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: third party application may use this proxies if allowed in production environment. Filter is needed. references: -- https://malpedia.caad.fkie.fraunhofer.de/details/win.agent_tesla + - https://malpedia.caad.fkie.fraunhofer.de/details/win.agent_tesla drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: a process $process_name$ is having a dns query in a tor domain - $QueryName$ in $dvc$ - risk_objects: - - field: dvc - type: system - score: 25 - threat_objects: [] + message: a process $process_name$ is having a dns query in a tor domain $QueryName$ in $dvc$ + risk_objects: + - field: dvc + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - AgentTesla - - Interlock Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1071.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - AgentTesla + - Interlock Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1071.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/agent_tesla/agent_tesla_tor_dns_query/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/agent_tesla/agent_tesla_tor_dns_query/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/network/windows_remote_desktop_network_bruteforce_attempt.yml b/detections/network/windows_remote_desktop_network_bruteforce_attempt.yml index 2e84fcca24..acbec1e59a 100644 --- a/detections/network/windows_remote_desktop_network_bruteforce_attempt.yml +++ b/detections/network/windows_remote_desktop_network_bruteforce_attempt.yml @@ -1,79 +1,71 @@ name: Windows Remote Desktop Network Bruteforce Attempt id: 908bf0d5-0983-4afd-b6a4-e9eb5d361a7d -version: 5 -date: '2025-08-01' +version: 6 +date: '2026-02-25' author: Jose Hernandez, Bhavin Patel, Splunk status: production type: Anomaly -description: The following analytic identifies potential Remote Desktop Protocol - (RDP) brute force attacks by monitoring network traffic for RDP application - activity. This query detects potential RDP brute force attacks by identifying - source IPs that have made more than 10 connection attempts to the same RDP - port on a host within a one-hour window. The results are presented in a table - that includes the source and destination IPs, destination port, number of - attempts, and the times of the first and last connection attempts, helping to - prioritize IPs based on the intensity of activity. +description: The following analytic identifies potential Remote Desktop Protocol (RDP) brute force attacks by monitoring network traffic for RDP application activity. This query detects potential RDP brute force attacks by identifying source IPs that have made more than 10 connection attempts to the same RDP port on a host within a one-hour window. The results are presented in a table that includes the source and destination IPs, destination port, number of attempts, and the times of the first and last connection attempts, helping to prioritize IPs based on the intensity of activity. data_source: -- Sysmon EventID 3 -search: '| tstats `security_content_summariesonly` count, min(_time) as firstTime, - max(_time) as lastTime values(Al_Traffic.src_port) as src_port from datamodel=Network_Traffic - where (All_Traffic.app=rdp OR All_Traffic.dest_port=3389) by All_Traffic.action - All_Traffic.app All_Traffic.dest All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.direction All_Traffic.dvc - All_Traffic.protocol All_Traffic.protocol_version All_Traffic.src All_Traffic.src_ip All_Traffic.transport - All_Traffic.user All_Traffic.vendor_product | `drop_dm_object_name("All_Traffic")` | - eval duration=lastTime-firstTime | where count > 10 AND duration < 3600 | `security_content_ctime(firstTime)` | - `security_content_ctime(lastTime)` | `windows_remote_desktop_network_bruteforce_attempt_filter`' -how_to_implement: You must ensure that your network traffic data is populating - the Network_Traffic data model. Adjust the count and duration thresholds as - necessary to tune the sensitivity of your detection. -known_false_positives: RDP gateways may have unusually high amounts of traffic - from all other hosts' RDP applications in the network.Any legitimate RDP - traffic using wrong/expired credentials will be also detected as a false - positive. + - Sysmon EventID 3 +search: |- + | tstats `security_content_summariesonly` count, min(_time) as firstTime, max(_time) as lastTime values(Al_Traffic.src_port) as src_port FROM datamodel=Network_Traffic + WHERE ( + All_Traffic.app=rdp + OR + All_Traffic.dest_port=3389 + ) + BY All_Traffic.action All_Traffic.app All_Traffic.dest + All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.direction + All_Traffic.dvc All_Traffic.protocol All_Traffic.protocol_version + All_Traffic.src All_Traffic.src_ip All_Traffic.transport + All_Traffic.user All_Traffic.vendor_product + | `drop_dm_object_name("All_Traffic")` + | eval duration=lastTime-firstTime + | where count > 10 AND duration < 3600 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_remote_desktop_network_bruteforce_attempt_filter` +how_to_implement: You must ensure that your network traffic data is populating the Network_Traffic data model. Adjust the count and duration thresholds as necessary to tune the sensitivity of your detection. +known_false_positives: RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network.Any legitimate RDP traffic using wrong/expired credentials will be also detected as a false positive. references: -- https://www.zscaler.com/blogs/security-research/ransomware-delivered-using-rdp-brute-force-attack -- https://www.reliaquest.com/blog/rdp-brute-force-attacks/ + - https://www.zscaler.com/blogs/security-research/ransomware-delivered-using-rdp-brute-force-attack + - https://www.reliaquest.com/blog/rdp-brute-force-attacks/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: $dest$ may be the target of an RDP Bruteforce from $src$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: - - field: src - type: ip_address + message: $dest$ may be the target of an RDP Bruteforce from $src$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - SamSam Ransomware - - Ryuk Ransomware - - Compromised User Account - - Windows RDP Artifacts and Defense Evasion - asset_type: Endpoint - mitre_attack_id: - - T1110.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - SamSam Ransomware + - Ryuk Ransomware + - Compromised User Account + - Windows RDP Artifacts and Defense Evasion + asset_type: Endpoint + mitre_attack_id: + - T1110.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/rdp_brute_sysmon/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/rdp_brute_sysmon/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/network/windows_spearphishing_attachment_connect_to_none_ms_office_domain.yml b/detections/network/windows_spearphishing_attachment_connect_to_none_ms_office_domain.yml index a259d4cefc..bc9c1a5439 100644 --- a/detections/network/windows_spearphishing_attachment_connect_to_none_ms_office_domain.yml +++ b/detections/network/windows_spearphishing_attachment_connect_to_none_ms_office_domain.yml @@ -5,46 +5,30 @@ date: '2025-05-02' author: Teoderick Contreras, Splunk status: production type: Hunting -description: The following analytic identifies suspicious Office documents that connect - to non-Microsoft Office domains. It leverages Sysmon EventCode 22 to detect processes - like winword.exe or excel.exe making DNS queries to domains outside of *.office.com - or *.office.net. This activity is significant as it may indicate a spearphishing - attempt using malicious documents to download or connect to harmful content. If - confirmed malicious, this could lead to unauthorized data access, malware infection, - or further network compromise. +description: The following analytic identifies suspicious Office documents that connect to non-Microsoft Office domains. It leverages Sysmon EventCode 22 to detect processes like winword.exe or excel.exe making DNS queries to domains outside of *.office.com or *.office.net. This activity is significant as it may indicate a spearphishing attempt using malicious documents to download or connect to harmful content. If confirmed malicious, this could lead to unauthorized data access, malware infection, or further network compromise. data_source: -- Sysmon EventID 22 -search: '`sysmon` EventCode=22 Image IN ("*\\winword.exe","*\\excel.exe","*\\powerpnt.exe","*\\mspub.exe","*\\visio.exe","*\\wordpad.exe","*\\wordview.exe","*\\onenote.exe", - "*\\onenotem.exe","*\\onenoteviewer.exe","*\\onenoteim.exe", "*\\msaccess.exe") - AND NOT(QueryName IN ("*.office.com", "*.office.net")) | stats count min(_time) - as firstTime max(_time) as lastTime by answer answer_count dvc process_exec process_guid - process_name query query_count reply_code_id signature signature_id src user_id - vendor_product QueryName QueryResults QueryStatus | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `windows_spearphishing_attachment_connect_to_none_ms_office_domain_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. -known_false_positives: Windows Office document may contain legitimate url link other - than MS office Domain. filter is needed + - Sysmon EventID 22 +search: '`sysmon` EventCode=22 Image IN ("*\\winword.exe","*\\excel.exe","*\\powerpnt.exe","*\\mspub.exe","*\\visio.exe","*\\wordpad.exe","*\\wordview.exe","*\\onenote.exe", "*\\onenotem.exe","*\\onenoteviewer.exe","*\\onenoteim.exe", "*\\msaccess.exe") AND NOT(QueryName IN ("*.office.com", "*.office.net")) | stats count min(_time) as firstTime max(_time) as lastTime by answer answer_count dvc process_exec process_guid process_name query query_count reply_code_id signature signature_id src user_id vendor_product QueryName QueryResults QueryStatus | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_spearphishing_attachment_connect_to_none_ms_office_domain_filter`' +how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +known_false_positives: Windows Office document may contain legitimate url link other than MS office Domain. filter is needed references: -- https://www.netskope.com/blog/asyncrat-using-fully-undetected-downloader -- https://malpedia.caad.fkie.fraunhofer.de/details/win.asyncrat + - https://www.netskope.com/blog/asyncrat-using-fully-undetected-downloader + - https://malpedia.caad.fkie.fraunhofer.de/details/win.asyncrat tags: - analytic_story: - - Spearphishing Attachments - - AsyncRAT - asset_type: Endpoint - mitre_attack_id: - - T1566.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Spearphishing Attachments + - AsyncRAT + asset_type: Endpoint + mitre_attack_id: + - T1566.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/office_doc_abuses_rels/sysmon.log - source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - sourcetype: XmlWinEventLog + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/office_doc_abuses_rels/sysmon.log + source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational + sourcetype: XmlWinEventLog diff --git a/detections/network/zeek_x509_certificate_with_punycode.yml b/detections/network/zeek_x509_certificate_with_punycode.yml index 13b767d411..ce96863688 100644 --- a/detections/network/zeek_x509_certificate_with_punycode.yml +++ b/detections/network/zeek_x509_certificate_with_punycode.yml @@ -1,44 +1,36 @@ name: Zeek x509 Certificate with Punycode id: 029d6fe4-a5fe-43af-827e-c78c50e81d81 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: experimental type: Hunting -description: The following analytic detects the presence of punycode within x509 certificates - using Zeek x509 logs. It identifies punycode in the subject alternative name email - and other fields by searching for the "xn--" prefix. This activity is significant - as punycode can be used in phishing attacks or to bypass domain filters, posing - a security risk. If confirmed malicious, attackers could use these certificates - to impersonate legitimate domains, potentially leading to unauthorized access or - data breaches. +description: The following analytic detects the presence of punycode within x509 certificates using Zeek x509 logs. It identifies punycode in the subject alternative name email and other fields by searching for the "xn--" prefix. This activity is significant as punycode can be used in phishing attacks or to bypass domain filters, posing a security risk. If confirmed malicious, attackers could use these certificates to impersonate legitimate domains, potentially leading to unauthorized access or data breaches. data_source: [] -search: '`zeek_x509` | rex field=san.email{} "\@(?xn--.*)" | rex - field=san.other_fields{} "\@(?xn--.*)" | stats values(domain_detected) - by basic_constraints.ca source host | `zeek_x509_certificate_with_punycode_filter`' -how_to_implement: The following analytic requires x509 certificate data to be logged - entirely. In particular, for CVE-2022-3602, the punycode will be within the leaf - certificate. The analytic may be modified to look for all xn--, or utilize a network - IDS/monitoring tool like Zeek or Suricata to drill down into cert captured. Note - for Suricata, the certificate is base64 encoded and will need to be decoded to capture - the punycode (punycode will need to be decoded after). -known_false_positives: False positives may be present if the organization works with - international businesses. Filter as needed. +search: |- + `zeek_x509` + | rex field=san.email{} "\@(?xn--.*)" + | rex field=san.other_fields{} "\@(?xn--.*)" + | stats values(domain_detected) + BY basic_constraints.ca source host + | `zeek_x509_certificate_with_punycode_filter` +how_to_implement: The following analytic requires x509 certificate data to be logged entirely. In particular, for CVE-2022-3602, the punycode will be within the leaf certificate. The analytic may be modified to look for all xn--, or utilize a network IDS/monitoring tool like Zeek or Suricata to drill down into cert captured. Note for Suricata, the certificate is base64 encoded and will need to be decoded to capture the punycode (punycode will need to be decoded after). +known_false_positives: False positives may be present if the organization works with international businesses. Filter as needed. references: -- https://community.emergingthreats.net/t/out-of-band-ruleset-update-summary-2022-11-01/117 -- https://github.com/corelight/CVE-2022-3602/tree/master/scripts -- https://docs.zeek.org/en/master/logs/x509.html -- https://www.splunk.com/en_us/blog/security/nothing-puny-about-cve-2022-3602.html -- https://www.openssl.org/blog/blog/2022/11/01/email-address-overflows/ -- https://docs.zeek.org/en/master/scripts/base/init-bare.zeek.html#type-X509::SubjectAlternativeName + - https://community.emergingthreats.net/t/out-of-band-ruleset-update-summary-2022-11-01/117 + - https://github.com/corelight/CVE-2022-3602/tree/master/scripts + - https://docs.zeek.org/en/master/logs/x509.html + - https://www.splunk.com/en_us/blog/security/nothing-puny-about-cve-2022-3602.html + - https://www.openssl.org/blog/blog/2022/11/01/email-address-overflows/ + - https://docs.zeek.org/en/master/scripts/base/init-bare.zeek.html#type-X509::SubjectAlternativeName tags: - analytic_story: - - OpenSSL CVE-2022-3602 - asset_type: Network - mitre_attack_id: - - T1573 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - OpenSSL CVE-2022-3602 + asset_type: Network + mitre_attack_id: + - T1573 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/web/access_to_vulnerable_ivanti_connect_secure_bookmark_endpoint.yml b/detections/web/access_to_vulnerable_ivanti_connect_secure_bookmark_endpoint.yml index c36fb76ea8..265e6757da 100644 --- a/detections/web/access_to_vulnerable_ivanti_connect_secure_bookmark_endpoint.yml +++ b/detections/web/access_to_vulnerable_ivanti_connect_secure_bookmark_endpoint.yml @@ -1,77 +1,63 @@ name: Access to Vulnerable Ivanti Connect Secure Bookmark Endpoint id: 15838756-f425-43fa-9d88-a7f88063e81a -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Suricata -description: The following analytic identifies access to the - /api/v1/configuration/users/user-roles/user-role/rest-userrole1/web/web-bookmarks/bookmark - endpoint, which is associated with CVE-2023-46805 and CVE-2024-21887 vulnerabilities. - It detects this activity by monitoring for GET requests that receive a 403 Forbidden - response with an empty body. This behavior is significant as it indicates potential - exploitation attempts against Ivanti Connect Secure systems. If confirmed malicious, - attackers could exploit these vulnerabilities to gain unauthorized access or control - over the affected systems, leading to potential data breaches or system compromise. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url="*/api/v1/configuration/users/user-roles/user-role/rest-userrole1/web/web-bookmarks/bookmark*" - Web.http_method=GET Web.status=403 by Web.src, Web.dest, Web.http_user_agent, Web.status, - Web.url source | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `access_to_vulnerable_ivanti_connect_secure_bookmark_endpoint_filter`' -how_to_implement: This detection requires the Web datamodel to be populated from a - supported Technology Add-On like Suricata, Splunk for Apache, Splunk for Nginx, - or Splunk for Palo Alto. -known_false_positives: This analytic is limited to HTTP Status 403; adjust as necessary. - False positives may occur if the URI path is IP-restricted or externally blocked. - It's recommended to review the context of the alerts and adjust the analytic parameters - to better fit the specific environment. + - Suricata +description: The following analytic identifies access to the /api/v1/configuration/users/user-roles/user-role/rest-userrole1/web/web-bookmarks/bookmark endpoint, which is associated with CVE-2023-46805 and CVE-2024-21887 vulnerabilities. It detects this activity by monitoring for GET requests that receive a 403 Forbidden response with an empty body. This behavior is significant as it indicates potential exploitation attempts against Ivanti Connect Secure systems. If confirmed malicious, attackers could exploit these vulnerabilities to gain unauthorized access or control over the affected systems, leading to potential data breaches or system compromise. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url="*/api/v1/configuration/users/user-roles/user-role/rest-userrole1/web/web-bookmarks/bookmark*" Web.http_method=GET Web.status=403 + BY Web.src, Web.dest, Web.http_user_agent, + Web.status, Web.url source + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `access_to_vulnerable_ivanti_connect_secure_bookmark_endpoint_filter` +how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Suricata, Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto. +known_false_positives: This analytic is limited to HTTP Status 403; adjust as necessary. False positives may occur if the URI path is IP-restricted or externally blocked. It's recommended to review the context of the alerts and adjust the analytic parameters to better fit the specific environment. references: -- https://github.com/RootUp/PersonalStuff/blob/master/http-vuln-cve2023-46805_2024_21887.nse -- https://github.com/projectdiscovery/nuclei-templates/blob/c6b351e71b0fb0e40e222e97038f1fe09ac58194/http/misconfiguration/ivanti/CVE-2023-46085-CVE-2024-21887-mitigation-not-applied.yaml -- https://github.com/rapid7/metasploit-framework/pull/18708/files + - https://github.com/RootUp/PersonalStuff/blob/master/http-vuln-cve2023-46805_2024_21887.nse + - https://github.com/projectdiscovery/nuclei-templates/blob/c6b351e71b0fb0e40e222e97038f1fe09ac58194/http/misconfiguration/ivanti/CVE-2023-46085-CVE-2024-21887-mitigation-not-applied.yaml + - https://github.com/rapid7/metasploit-framework/pull/18708/files drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible exploitation of CVE-2023-46805 and CVE-2024-21887 against $dest$. - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: [] + message: Possible exploitation of CVE-2023-46805 and CVE-2024-21887 against $dest$. + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: [] tags: - cve: - - CVE-2023-46805 - - CVE-2024-21887 - analytic_story: - - Ivanti Connect Secure VPN Vulnerabilities - - CISA AA24-241A - asset_type: VPN Appliance - atomic_guid: [] - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + cve: + - CVE-2023-46805 + - CVE-2024-21887 + analytic_story: + - Ivanti Connect Secure VPN Vulnerabilities + - CISA AA24-241A + asset_type: VPN Appliance + atomic_guid: [] + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/ivanti_bookmark_web_access.log - source: suricata - sourcetype: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/ivanti_bookmark_web_access.log + source: suricata + sourcetype: suricata diff --git a/detections/web/adobe_coldfusion_access_control_bypass.yml b/detections/web/adobe_coldfusion_access_control_bypass.yml index 10c813edda..b74aa188c5 100644 --- a/detections/web/adobe_coldfusion_access_control_bypass.yml +++ b/detections/web/adobe_coldfusion_access_control_bypass.yml @@ -1,78 +1,62 @@ name: Adobe ColdFusion Access Control Bypass id: d6821c0b-fcdc-4c95-a77f-e10752fae41a -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Suricata -description: The following analytic detects potential exploitation attempts against - Adobe ColdFusion vulnerabilities CVE-2023-29298 and CVE-2023-26360. It monitors - requests to specific ColdFusion Administrator endpoints, especially those with an - unexpected additional forward slash, using the Web datamodel. This activity is significant - for a SOC as it indicates attempts to bypass access controls, which can lead to - unauthorized access to ColdFusion administration endpoints. If confirmed malicious, - this could result in data theft, brute force attacks, or further exploitation of - other vulnerabilities, posing a serious security risk to the environment. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url IN ("//restplay*", "//CFIDE/restplay*", "//CFIDE/administrator*", - "//CFIDE/adminapi*", "//CFIDE/main*", "//CFIDE/componentutils*", "//CFIDE/wizards*", - "//CFIDE/servermanager*","/restplay*", "/CFIDE/restplay*", "/CFIDE/administrator*", - "/CFIDE/adminapi*", "/CFIDE/main*", "/CFIDE/componentutils*", "/CFIDE/wizards*", - "/CFIDE/servermanager*") Web.status=200 by Web.http_user_agent, Web.status, Web.http_method, - Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `adobe_coldfusion_access_control_bypass_filter`' -how_to_implement: This detection requires the Web datamodel to be populated from a - supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk - for Palo Alto. -known_false_positives: This analytic is limited to HTTP Status 200; adjust as necessary. - False positives may occur if the URI path is IP-restricted or externally blocked. - It's recommended to review the context of the alerts and adjust the analytic parameters - to better fit the specific environment. + - Suricata +description: The following analytic detects potential exploitation attempts against Adobe ColdFusion vulnerabilities CVE-2023-29298 and CVE-2023-26360. It monitors requests to specific ColdFusion Administrator endpoints, especially those with an unexpected additional forward slash, using the Web datamodel. This activity is significant for a SOC as it indicates attempts to bypass access controls, which can lead to unauthorized access to ColdFusion administration endpoints. If confirmed malicious, this could result in data theft, brute force attacks, or further exploitation of other vulnerabilities, posing a serious security risk to the environment. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN ("//restplay*", "//CFIDE/restplay*", "//CFIDE/administrator*", "//CFIDE/adminapi*", "//CFIDE/main*", "//CFIDE/componentutils*", "//CFIDE/wizards*", "//CFIDE/servermanager*","/restplay*", "/CFIDE/restplay*", "/CFIDE/administrator*", "/CFIDE/adminapi*", "/CFIDE/main*", "/CFIDE/componentutils*", "/CFIDE/wizards*", "/CFIDE/servermanager*") Web.status=200 + BY Web.http_user_agent, Web.status, Web.http_method, + Web.url, Web.url_length, Web.src, + Web.dest, sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `adobe_coldfusion_access_control_bypass_filter` +how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto. +known_false_positives: This analytic is limited to HTTP Status 200; adjust as necessary. False positives may occur if the URI path is IP-restricted or externally blocked. It's recommended to review the context of the alerts and adjust the analytic parameters to better fit the specific environment. references: -- https://www.rapid7.com/blog/post/2023/07/11/cve-2023-29298-adobe-coldfusion-access-control-bypass/ + - https://www.rapid7.com/blog/post/2023/07/11/cve-2023-29298-adobe-coldfusion-access-control-bypass/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible exploitation of CVE-2023-29298 against $dest$. - risk_objects: - - field: dest - type: system - score: 45 - threat_objects: - - field: src - type: ip_address + message: Possible exploitation of CVE-2023-29298 against $dest$. + risk_objects: + - field: dest + type: system + score: 45 + threat_objects: + - field: src + type: ip_address tags: - cve: - - CVE-2023-29298 - analytic_story: - - Adobe ColdFusion Arbitrary Code Execution CVE-2023-29298 CVE-2023-26360 - asset_type: Network - atomic_guid: [] - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + cve: + - CVE-2023-29298 + analytic_story: + - Adobe ColdFusion Arbitrary Code Execution CVE-2023-29298 CVE-2023-26360 + asset_type: Network + atomic_guid: [] + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/adobe/coldfusion_cve_2023_29298.log - source: suricata - sourcetype: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/adobe/coldfusion_cve_2023_29298.log + source: suricata + sourcetype: suricata diff --git a/detections/web/adobe_coldfusion_unauthenticated_arbitrary_file_read.yml b/detections/web/adobe_coldfusion_unauthenticated_arbitrary_file_read.yml index 6d6148c921..1ff0b0f58e 100644 --- a/detections/web/adobe_coldfusion_unauthenticated_arbitrary_file_read.yml +++ b/detections/web/adobe_coldfusion_unauthenticated_arbitrary_file_read.yml @@ -1,78 +1,63 @@ name: Adobe ColdFusion Unauthenticated Arbitrary File Read id: 695aceae-21db-4e7f-93ac-a52e39d02b93 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Suricata -description: The following analytic detects potential exploitation of the Adobe ColdFusion - vulnerability, CVE-2023-26360, which allows unauthenticated arbitrary file read. - It monitors web requests to the "/cf_scripts/scripts/ajax/ckeditor/*" path using - the Web datamodel, focusing on specific ColdFusion paths to differentiate malicious - activity from normal traffic. This activity is significant due to the vulnerability's - high CVSS score of 9.8, indicating severe risk. If confirmed malicious, it could - lead to unauthorized data access, further attacks, or severe operational disruptions, - necessitating immediate investigation. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url IN ("/cf_scripts/scripts/ajax/ckeditor/*") Web.status=200 by Web.http_user_agent, - Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype - | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `adobe_coldfusion_unauthenticated_arbitrary_file_read_filter`' -how_to_implement: This detection requires the Web datamodel to be populated from a - supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk - for Palo Alto. -known_false_positives: 'In the wild, we have observed three different types of attempts - that could potentially trigger false positives if the HTTP status code is not in - the query. Please check this github gist for the specific URIs : https://gist.github.com/patel-bhavin/d10830f3f375a2397233f6a4fe38d5c9 - . These could be legitimate requests depending on the context of your organization. - Therefore, it is recommended to modify the analytic as needed to suit your specific - environment.' + - Suricata +description: The following analytic detects potential exploitation of the Adobe ColdFusion vulnerability, CVE-2023-26360, which allows unauthenticated arbitrary file read. It monitors web requests to the "/cf_scripts/scripts/ajax/ckeditor/*" path using the Web datamodel, focusing on specific ColdFusion paths to differentiate malicious activity from normal traffic. This activity is significant due to the vulnerability's high CVSS score of 9.8, indicating severe risk. If confirmed malicious, it could lead to unauthorized data access, further attacks, or severe operational disruptions, necessitating immediate investigation. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN ("/cf_scripts/scripts/ajax/ckeditor/*") Web.status=200 + BY Web.http_user_agent, Web.status Web.http_method, + Web.url, Web.url_length, Web.src, + Web.dest, sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `adobe_coldfusion_unauthenticated_arbitrary_file_read_filter` +how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto. +known_false_positives: 'In the wild, we have observed three different types of attempts that could potentially trigger false positives if the HTTP status code is not in the query. Please check this github gist for the specific URIs : https://gist.github.com/patel-bhavin/d10830f3f375a2397233f6a4fe38d5c9 . These could be legitimate requests depending on the context of your organization. Therefore, it is recommended to modify the analytic as needed to suit your specific environment.' references: -- https://www.rapid7.com/db/modules/auxiliary/gather/adobe_coldfusion_fileread_cve_2023_26360/ -- https://github.com/projectdiscovery/nuclei-templates/blob/main/http/cves/2023/CVE-2023-26360.yaml + - https://www.rapid7.com/db/modules/auxiliary/gather/adobe_coldfusion_fileread_cve_2023_26360/ + - https://github.com/projectdiscovery/nuclei-templates/blob/main/http/cves/2023/CVE-2023-26360.yaml drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible exploitation of CVE-2023-26360 against $dest$. - risk_objects: - - field: dest - type: system - score: 45 - threat_objects: - - field: src - type: ip_address + message: Possible exploitation of CVE-2023-26360 against $dest$. + risk_objects: + - field: dest + type: system + score: 45 + threat_objects: + - field: src + type: ip_address tags: - cve: - - CVE-2023-26360 - analytic_story: - - Adobe ColdFusion Arbitrary Code Execution CVE-2023-29298 CVE-2023-26360 - asset_type: Network - atomic_guid: [] - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + cve: + - CVE-2023-26360 + analytic_story: + - Adobe ColdFusion Arbitrary Code Execution CVE-2023-29298 CVE-2023-26360 + asset_type: Network + atomic_guid: [] + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/adobe/cve_2023_29360_coldfusion.log - source: suricata - sourcetype: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/adobe/cve_2023_29360_coldfusion.log + source: suricata + sourcetype: suricata diff --git a/detections/web/cisco_ios_xe_implant_access.yml b/detections/web/cisco_ios_xe_implant_access.yml index b44f36947c..66d16cf11d 100644 --- a/detections/web/cisco_ios_xe_implant_access.yml +++ b/detections/web/cisco_ios_xe_implant_access.yml @@ -1,75 +1,63 @@ name: Cisco IOS XE Implant Access id: 07c36cda-6567-43c3-bc1a-89dff61e2cd9 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Suricata -description: The following analytic identifies the potential exploitation of a vulnerability - (CVE-2023-20198) in the Web User Interface of Cisco IOS XE software. It detects - suspicious account creation and subsequent actions, including the deployment of - a non-persistent implant configuration file. The detection leverages the Web datamodel, - focusing on specific URL patterns and HTTP methods. This activity is significant - as it indicates unauthorized administrative access, which can lead to full control - of the device. If confirmed malicious, attackers could maintain privileged access, - compromising the device's integrity and security. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url IN ("/webui/logoutconfirm.html?logon_hash=*") Web.http_method=POST - Web.status=200 by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, - Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`| `cisco_ios_xe_implant_access_filter`' -how_to_implement: This detection requires the Web datamodel to be populated from a - supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk - for Palo Alto. -known_false_positives: False positives may be present, restrict to Cisco IOS XE devices - or perimeter appliances. Modify the analytic as needed based on hunting for successful - exploitation of CVE-2023-20198. + - Suricata +description: The following analytic identifies the potential exploitation of a vulnerability (CVE-2023-20198) in the Web User Interface of Cisco IOS XE software. It detects suspicious account creation and subsequent actions, including the deployment of a non-persistent implant configuration file. The detection leverages the Web datamodel, focusing on specific URL patterns and HTTP methods. This activity is significant as it indicates unauthorized administrative access, which can lead to full control of the device. If confirmed malicious, attackers could maintain privileged access, compromising the device's integrity and security. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN ("/webui/logoutconfirm.html?logon_hash=*") Web.http_method=POST Web.status=200 + BY Web.http_user_agent, Web.status Web.http_method, + Web.url, Web.url_length, Web.src, + Web.dest, sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `cisco_ios_xe_implant_access_filter` +how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto. +known_false_positives: False positives may be present, restrict to Cisco IOS XE devices or perimeter appliances. Modify the analytic as needed based on hunting for successful exploitation of CVE-2023-20198. references: -- https://blog.talosintelligence.com/active-exploitation-of-cisco-ios-xe-software/ -- https://github.com/vulncheck-oss/cisco-ios-xe-implant-scanner + - https://blog.talosintelligence.com/active-exploitation-of-cisco-ios-xe-software/ + - https://github.com/vulncheck-oss/cisco-ios-xe-implant-scanner drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible exploitation of CVE-2023-20198 against $dest$ by $src$. - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: - - field: src - type: ip_address + message: Possible exploitation of CVE-2023-20198 against $dest$ by $src$. + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: + - field: src + type: ip_address tags: - cve: - - CVE-2023-20198 - analytic_story: - - Cisco IOS XE Software Web Management User Interface vulnerability - asset_type: Network - atomic_guid: [] - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + cve: + - CVE-2023-20198 + analytic_story: + - Cisco IOS XE Software Web Management User Interface vulnerability + asset_type: Network + atomic_guid: [] + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/cisco/iosxe/ciscocve202320198.log - source: suricata - sourcetype: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/cisco/iosxe/ciscocve202320198.log + source: suricata + sourcetype: suricata diff --git a/detections/web/citrix_adc_and_gateway_citrixbleed_2_memory_disclosure.yml b/detections/web/citrix_adc_and_gateway_citrixbleed_2_memory_disclosure.yml index 39a907c7ff..e2f1df3dca 100644 --- a/detections/web/citrix_adc_and_gateway_citrixbleed_2_memory_disclosure.yml +++ b/detections/web/citrix_adc_and_gateway_citrixbleed_2_memory_disclosure.yml @@ -1,75 +1,75 @@ name: Citrix ADC and Gateway CitrixBleed 2 Memory Disclosure id: bef92f3f-7dc8-413a-8989-50581039e250 -version: 1 -date: '2025-01-07' +version: 2 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly description: | - This detection identifies potential exploitation attempts of CVE-2025-5777 (CitrixBleed 2), a memory disclosure vulnerability in Citrix NetScaler ADC and Gateway. - The vulnerability is triggered by sending POST requests with incomplete form data to the /p/u/doAuthentication.do endpoint, causing the device to leak memory contents including session tokens and authentication materials. - This search looks for POST requests to the vulnerable endpoint that may indicate scanning or exploitation attempts. + This detection identifies potential exploitation attempts of CVE-2025-5777 (CitrixBleed 2), a memory disclosure vulnerability in Citrix NetScaler ADC and Gateway. + The vulnerability is triggered by sending POST requests with incomplete form data to the /p/u/doAuthentication.do endpoint, causing the device to leak memory contents including session tokens and authentication materials. + This search looks for POST requests to the vulnerable endpoint that may indicate scanning or exploitation attempts. data_source: - - Suricata + - Suricata search: | - | tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where - Web.url IN ("*/p/u/doAuthentication.do*") - Web.http_method="POST" - Web.status=200 - by Web.http_user_agent, Web.status, Web.http_method, - Web.url, Web.url_length, Web.src, Web.dest, sourcetype - | `drop_dm_object_name("Web")` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `citrix_adc_and_gateway_citrixbleed_2_memory_disclosure_filter` + | tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where + Web.url IN ("*/p/u/doAuthentication.do*") + Web.http_method="POST" + Web.status=200 + by Web.http_user_agent, Web.status, Web.http_method, + Web.url, Web.url_length, Web.src, Web.dest, sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `citrix_adc_and_gateway_citrixbleed_2_memory_disclosure_filter` how_to_implement: | - To implement this search, ensure that web traffic logs from Citrix NetScaler ADC and Gateway devices are being ingested into Splunk and mapped to the Web data model. - The logs should include URL paths, HTTP methods, status codes, source and destination IPs, and user agents. - Look specifically for POST requests to /p/u/doAuthentication.do endpoint which is the primary attack vector for CVE-2025-5777. + To implement this search, ensure that web traffic logs from Citrix NetScaler ADC and Gateway devices are being ingested into Splunk and mapped to the Web data model. + The logs should include URL paths, HTTP methods, status codes, source and destination IPs, and user agents. + Look specifically for POST requests to /p/u/doAuthentication.do endpoint which is the primary attack vector for CVE-2025-5777. known_false_positives: | - Legitimate authentication flows will trigger this detection as they access the doAuthentication.do endpoint. However, repeated automated requests, especially from HeadlessChrome user agents or with incomplete form data, should be investigated. - Focus on unusual patterns like multiple rapid requests or non-standard user agents. + Legitimate authentication flows will trigger this detection as they access the doAuthentication.do endpoint. However, repeated automated requests, especially from HeadlessChrome user agents or with incomplete form data, should be investigated. + Focus on unusual patterns like multiple rapid requests or non-standard user agents. references: - - https://support.citrix.com/support-home/kbsearch/article?articleNumber=CTX693420 - - https://www.netscaler.com/blog/news/critical-security-updates-for-netscaler-netscaler-gateway-and-netscaler-console/ - - https://github.com/mingshenhk/CitrixBleed-2-CVE-2025-5777-PoC- - - https://horizon3.ai/attack-research/attack-blogs/cve-2025-5777-citrixbleed-2-write-up-maybe/ - - https://labs.watchtowr.com/how-much-more-must-we-bleed-citrix-netscaler-memory-disclosure-citrixbleed-2-cve-2025-5777/ - - https://github.com/projectdiscovery/nuclei-templates/blob/main/http/cves/2025/CVE-2025-5777.yaml + - https://support.citrix.com/support-home/kbsearch/article?articleNumber=CTX693420 + - https://www.netscaler.com/blog/news/critical-security-updates-for-netscaler-netscaler-gateway-and-netscaler-console/ + - https://github.com/mingshenhk/CitrixBleed-2-CVE-2025-5777-PoC- + - https://horizon3.ai/attack-research/attack-blogs/cve-2025-5777-citrixbleed-2-write-up-maybe/ + - https://labs.watchtowr.com/how-much-more-must-we-bleed-citrix-netscaler-memory-disclosure-citrixbleed-2-cve-2025-5777/ + - https://github.com/projectdiscovery/nuclei-templates/blob/main/http/cves/2025/CVE-2025-5777.yaml drilldown_searches: - - name: View the detection results for - "$src$" and "$dest$" - search: '%original_detection_search% | search src="$src$" dest="$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$src$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" and "$dest$" + search: '%original_detection_search% | search src="$src$" dest="$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential CitrixBleed 2 (CVE-2025-5777) exploitation from $src$ to $dest$ detected. POST requests to /p/u/doAuthentication.do may indicate memory disclosure vulnerability exploitation. - risk_objects: - - field: dest - type: system - score: 85 - threat_objects: - - field: src - type: system + message: Potential CitrixBleed 2 (CVE-2025-5777) exploitation from $src$ to $dest$ detected. POST requests to /p/u/doAuthentication.do may indicate memory disclosure vulnerability exploitation. + risk_objects: + - field: dest + type: system + score: 85 + threat_objects: + - field: src + type: system tags: - analytic_story: - - Citrix NetScaler ADC and NetScaler Gateway CVE-2025-5777 - asset_type: Web Application - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: - - CVE-2025-5777 + analytic_story: + - Citrix NetScaler ADC and NetScaler Gateway CVE-2025-5777 + asset_type: Web Application + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: + - CVE-2025-5777 tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/citrix/suricata_citrixbleed2.log - source: suricata - sourcetype: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/citrix/suricata_citrixbleed2.log + source: suricata + sourcetype: suricata diff --git a/detections/web/citrix_adc_and_gateway_unauthorized_data_disclosure.yml b/detections/web/citrix_adc_and_gateway_unauthorized_data_disclosure.yml index 2675079d0f..56f3337566 100644 --- a/detections/web/citrix_adc_and_gateway_unauthorized_data_disclosure.yml +++ b/detections/web/citrix_adc_and_gateway_unauthorized_data_disclosure.yml @@ -1,77 +1,62 @@ name: Citrix ADC and Gateway Unauthorized Data Disclosure id: b593cac5-dd20-4358-972a-d945fefdaf17 -version: 6 -date: '2025-10-14' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Suricata -description: The following analytic detects attempts to exploit the Citrix Bleed vulnerability - (CVE-2023-4966), which can lead to the leaking of session tokens. It identifies - HTTP requests with a 200 status code targeting the /oauth/idp/.well-known/openid-configuration - URL endpoint. By parsing web traffic and filtering based on user agent details, - HTTP method, source and destination IPs, and sourcetype, it aims to identify potentially - malicious requests. This activity is significant for a SOC because successful exploitation - can allow attackers to impersonate legitimate users, bypass authentication, and - access sensitive data. If confirmed malicious, it could lead to unauthorized data - access, network propagation, and critical information exfiltration. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url IN ("*/oauth/idp/.well-known/openid-configuration*") Web.status=200 - by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, - Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `citrix_adc_and_gateway_unauthorized_data_disclosure_filter`' -how_to_implement: This detection requires the Web datamodel to be populated from a - supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk - for Palo Alto. We recommend hunting in the environment first to understand the scope - of the issue and then deploying this detection to monitor for future exploitation - attempts. Limit or restrict to Citrix devices only if possible. -known_false_positives: False positives may be present based on organization use of - Citrix ADC and Gateway. Filter, or restrict the analytic to Citrix devices only. + - Suricata +description: The following analytic detects attempts to exploit the Citrix Bleed vulnerability (CVE-2023-4966), which can lead to the leaking of session tokens. It identifies HTTP requests with a 200 status code targeting the /oauth/idp/.well-known/openid-configuration URL endpoint. By parsing web traffic and filtering based on user agent details, HTTP method, source and destination IPs, and sourcetype, it aims to identify potentially malicious requests. This activity is significant for a SOC because successful exploitation can allow attackers to impersonate legitimate users, bypass authentication, and access sensitive data. If confirmed malicious, it could lead to unauthorized data access, network propagation, and critical information exfiltration. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN ("*/oauth/idp/.well-known/openid-configuration*") Web.status=200 + BY Web.http_user_agent, Web.status Web.http_method, + Web.url, Web.url_length, Web.src, + Web.dest, sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `citrix_adc_and_gateway_unauthorized_data_disclosure_filter` +how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto. We recommend hunting in the environment first to understand the scope of the issue and then deploying this detection to monitor for future exploitation attempts. Limit or restrict to Citrix devices only if possible. +known_false_positives: False positives may be present based on organization use of Citrix ADC and Gateway. Filter, or restrict the analytic to Citrix devices only. references: -- https://www.assetnote.io/resources/research/citrix-bleed-leaking-session-tokens-with-cve-2023-4966 -- https://github.com/assetnote/exploits/tree/main/citrix/CVE-2023-4966 + - https://www.assetnote.io/resources/research/citrix-bleed-leaking-session-tokens-with-cve-2023-4966 + - https://github.com/assetnote/exploits/tree/main/citrix/CVE-2023-4966 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible exploitation of Citrix Bleed vulnerability against $dest$ fron - $src$. - risk_objects: - - field: dest - type: system - score: 90 - threat_objects: - - field: src - type: ip_address + message: Possible exploitation of Citrix Bleed vulnerability against $dest$ fron $src$. + risk_objects: + - field: dest + type: system + score: 90 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Citrix NetScaler ADC and NetScaler Gateway CVE-2023-4966 - - Scattered Lapsus$ Hunters - asset_type: Web Server - atomic_guid: [] - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Citrix NetScaler ADC and NetScaler Gateway CVE-2023-4966 + - Scattered Lapsus$ Hunters + asset_type: Web Server + atomic_guid: [] + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/citrix/cve-2023-4966-citrix.log - source: suricata - sourcetype: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/citrix/cve-2023-4966-citrix.log + source: suricata + sourcetype: suricata diff --git a/detections/web/citrix_adc_exploitation_cve_2023_3519.yml b/detections/web/citrix_adc_exploitation_cve_2023_3519.yml index a118ae7fa5..69b28dd33b 100644 --- a/detections/web/citrix_adc_exploitation_cve_2023_3519.yml +++ b/detections/web/citrix_adc_exploitation_cve_2023_3519.yml @@ -1,53 +1,48 @@ name: Citrix ADC Exploitation CVE-2023-3519 id: 76ac2dcb-333c-4a77-8ae9-2720cfae47a8 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting data_source: -- Palo Alto Network Threat -description: The following analytic identifies potential exploitation attempts against - Citrix ADC related to CVE-2023-3519. It detects POST requests to specific web endpoints - associated with this vulnerability by leveraging the Web datamodel. This activity - is significant as CVE-2023-3519 involves a SAML processing overflow issue that can - lead to memory corruption, posing a high risk. If confirmed malicious, attackers - could exploit this to execute arbitrary code, escalate privileges, or disrupt services, - making it crucial for SOC analysts to monitor and investigate these alerts promptly. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url IN ("*/saml/login","/cgi/samlauth","*/saml/activelogin","/cgi/samlart?samlart=*","*/cgi/logout","/gwtest/formssso?event=start&target=*","/netscaler/ns_gui/vpn/*") Web.http_method=POST - by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, - Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `citrix_adc_exploitation_cve_2023_3519_filter`' -how_to_implement: This detection requires the Web datamodel to be populated from a - supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk - for Palo Alto. -known_false_positives: False positives may be present based on organization use of - SAML utilities. Filter, or restrict the analytic to Citrix devices only. + - Palo Alto Network Threat +description: The following analytic identifies potential exploitation attempts against Citrix ADC related to CVE-2023-3519. It detects POST requests to specific web endpoints associated with this vulnerability by leveraging the Web datamodel. This activity is significant as CVE-2023-3519 involves a SAML processing overflow issue that can lead to memory corruption, posing a high risk. If confirmed malicious, attackers could exploit this to execute arbitrary code, escalate privileges, or disrupt services, making it crucial for SOC analysts to monitor and investigate these alerts promptly. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN ("*/saml/login","/cgi/samlauth","*/saml/activelogin","/cgi/samlart?samlart=*","*/cgi/logout","/gwtest/formssso?event=start&target=*","/netscaler/ns_gui/vpn/*") Web.http_method=POST + BY Web.http_user_agent, Web.status Web.http_method, + Web.url, Web.url_length, Web.src, + Web.dest, sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `citrix_adc_exploitation_cve_2023_3519_filter` +how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto. +known_false_positives: False positives may be present based on organization use of SAML utilities. Filter, or restrict the analytic to Citrix devices only. references: -- https://blog.assetnote.io/2023/07/21/citrix-CVE-2023-3519-analysis/ -- https://support.citrix.com/article/CTX561482/citrix-adc-and-citrix-gateway-security-bulletin-for-cve20233519-cve20233466-cve20233467 -- https://securityintelligence.com/x-force/x-force-uncovers-global-netscaler-gateway-credential-harvesting-campaign/ -- https://support.citrix.com/article/CTX579459/netscaler-adc-and-netscaler-gateway-security-bulletin-for-cve20234966-and-cve20234967 + - https://blog.assetnote.io/2023/07/21/citrix-CVE-2023-3519-analysis/ + - https://support.citrix.com/article/CTX561482/citrix-adc-and-citrix-gateway-security-bulletin-for-cve20233519-cve20233466-cve20233467 + - https://securityintelligence.com/x-force/x-force-uncovers-global-netscaler-gateway-credential-harvesting-campaign/ + - https://support.citrix.com/article/CTX579459/netscaler-adc-and-netscaler-gateway-security-bulletin-for-cve20234966-and-cve20234967 tags: - analytic_story: - - Citrix Netscaler ADC CVE-2023-3519 - - CISA AA24-241A - cve: - - CVE-2023-3519 - asset_type: Network - atomic_guid: [] - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - Citrix Netscaler ADC CVE-2023-3519 + - CISA AA24-241A + cve: + - CVE-2023-3519 + asset_type: Network + atomic_guid: [] + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/citrix/citrix-cve20233519.log - source: pan:threat - sourcetype: pan:threat + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/citrix/citrix-cve20233519.log + source: pan:threat + sourcetype: pan:threat diff --git a/detections/web/citrix_sharefile_exploitation_cve_2023_24489.yml b/detections/web/citrix_sharefile_exploitation_cve_2023_24489.yml index 8c454bf04a..7bf826cf6e 100644 --- a/detections/web/citrix_sharefile_exploitation_cve_2023_24489.yml +++ b/detections/web/citrix_sharefile_exploitation_cve_2023_24489.yml @@ -1,55 +1,48 @@ name: Citrix ShareFile Exploitation CVE-2023-24489 id: 172c59f2-5fae-45e5-8e51-94445143e93f -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting data_source: -- Suricata -description: The following analytic detects potentially malicious file upload attempts - to Citrix ShareFile via specific suspicious URLs and the HTTP POST method. It leverages - the Web datamodel to identify URL patterns such as "/documentum/upload.aspx?parentid=", - "/documentum/upload.aspx?filename=", and "/documentum/upload.aspx?uploadId=*", combined - with the HTTP POST method. This activity is significant for a SOC as it may indicate - an attempt to upload harmful scripts or content, potentially compromising the Documentum - application. If confirmed malicious, this could lead to unauthorized access, data - breaches, and operational disruptions. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url="/documentum/upload.aspx?*" AND Web.url IN ("*parentid=*","*filename=*","*uploadId=*") - AND Web.url IN ("*unzip=*", "*raw=*") Web.http_method=POST by Web.http_user_agent, - Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype - | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| - `citrix_sharefile_exploitation_cve_2023_24489_filter`' -how_to_implement: Dependent upon the placement of the ShareFile application, ensure - the latest Technology Add-On is eneabled. This detection requires the Web datamodel - to be populated from a supported Technology Add-On like Suricata, Splunk for Apache, - Splunk for Nginx, or Splunk for Palo Alto. The ShareFile application is IIS based, - therefore ingesting IIS logs and reviewing for the same pattern would identify this - activity, successful or not. -known_false_positives: False positives may be present, filtering may be needed. Also, - restricting to known web servers running IIS or ShareFile will change this from - Hunting to TTP. + - Suricata +description: The following analytic detects potentially malicious file upload attempts to Citrix ShareFile via specific suspicious URLs and the HTTP POST method. It leverages the Web datamodel to identify URL patterns such as "/documentum/upload.aspx?parentid=", "/documentum/upload.aspx?filename=", and "/documentum/upload.aspx?uploadId=*", combined with the HTTP POST method. This activity is significant for a SOC as it may indicate an attempt to upload harmful scripts or content, potentially compromising the Documentum application. If confirmed malicious, this could lead to unauthorized access, data breaches, and operational disruptions. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url="/documentum/upload.aspx?*" + AND + Web.url IN ("*parentid=*","*filename=*","*uploadId=*") + AND + Web.url IN ("*unzip=*", "*raw=*") Web.http_method=POST + BY Web.http_user_agent, Web.status Web.http_method, + Web.url, Web.url_length, Web.src, + Web.dest, sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `citrix_sharefile_exploitation_cve_2023_24489_filter` +how_to_implement: Dependent upon the placement of the ShareFile application, ensure the latest Technology Add-On is eneabled. This detection requires the Web datamodel to be populated from a supported Technology Add-On like Suricata, Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto. The ShareFile application is IIS based, therefore ingesting IIS logs and reviewing for the same pattern would identify this activity, successful or not. +known_false_positives: False positives may be present, filtering may be needed. Also, restricting to known web servers running IIS or ShareFile will change this from Hunting to TTP. references: -- https://blog.assetnote.io/2023/07/04/citrix-sharefile-rce/ + - https://blog.assetnote.io/2023/07/04/citrix-sharefile-rce/ tags: - analytic_story: - - Citrix ShareFile RCE CVE-2023-24489 - cve: - - CVE-2023-24489 - asset_type: Network - atomic_guid: [] - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Citrix ShareFile RCE CVE-2023-24489 + cve: + - CVE-2023-24489 + asset_type: Network + atomic_guid: [] + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/citrix/citrix-cve_2023_24489.log - source: suricata - sourcetype: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/citrix/citrix-cve_2023_24489.log + source: suricata + sourcetype: suricata diff --git a/detections/web/confluence_cve_2023_22515_trigger_vulnerability.yml b/detections/web/confluence_cve_2023_22515_trigger_vulnerability.yml index adfaaf8747..d5c62fd26d 100644 --- a/detections/web/confluence_cve_2023_22515_trigger_vulnerability.yml +++ b/detections/web/confluence_cve_2023_22515_trigger_vulnerability.yml @@ -1,73 +1,62 @@ name: Confluence CVE-2023-22515 Trigger Vulnerability id: 630ea8b2-2800-4f5d-9cbc-d65c567349b0 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Suricata -description: The following analytic identifies potential exploitation attempts of - the Confluence CVE-2023-22515 vulnerability. It detects successful accesses (HTTP - status 200) to specific vulnerable endpoints by analyzing web logs within the Splunk - 'Web' Data Model. This activity is significant for a SOC as it indicates possible - privilege escalation attempts in Confluence. If confirmed malicious, attackers could - gain unauthorized access or create accounts with escalated privileges, leading to - potential data breaches or further exploitation within the environment. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url IN ("*/server-info.action?bootstrapStatusProvider.applicationConfig.setupComplete=false*","*/server-info.action?bootstrapStatusProvider.applicationConfig.setupComplete=0&*") - Web.http_method=GET Web.status=200 by Web.http_user_agent, Web.status Web.http_method, - Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `confluence_cve_2023_22515_trigger_vulnerability_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on Web traffic that include fields relavent for traffic into the `Web` datamodel. - Tested with Suricata and nginx:plus:kv. -known_false_positives: False positives may be present with legitimate applications. - Attempt to filter by dest IP or use Asset groups to restrict to Confluence servers. + - Suricata +description: The following analytic identifies potential exploitation attempts of the Confluence CVE-2023-22515 vulnerability. It detects successful accesses (HTTP status 200) to specific vulnerable endpoints by analyzing web logs within the Splunk 'Web' Data Model. This activity is significant for a SOC as it indicates possible privilege escalation attempts in Confluence. If confirmed malicious, attackers could gain unauthorized access or create accounts with escalated privileges, leading to potential data breaches or further exploitation within the environment. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN ("*/server-info.action?bootstrapStatusProvider.applicationConfig.setupComplete=false*","*/server-info.action?bootstrapStatusProvider.applicationConfig.setupComplete=0&*") Web.http_method=GET Web.status=200 + BY Web.http_user_agent, Web.status Web.http_method, + Web.url, Web.url_length, Web.src, + Web.dest, sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `confluence_cve_2023_22515_trigger_vulnerability_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on Web traffic that include fields relavent for traffic into the `Web` datamodel. Tested with Suricata and nginx:plus:kv. +known_false_positives: False positives may be present with legitimate applications. Attempt to filter by dest IP or use Asset groups to restrict to Confluence servers. references: -- https://github.com/Chocapikk/CVE-2023-22515/blob/main/exploit.py -- https://x.com/Shadowserver/status/1712378833536741430?s=20 -- https://github.com/j3seer/CVE-2023-22515-POC + - https://github.com/Chocapikk/CVE-2023-22515/blob/main/exploit.py + - https://x.com/Shadowserver/status/1712378833536741430?s=20 + - https://github.com/j3seer/CVE-2023-22515-POC drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential exploitation attempts on a known vulnerability in Atlassian Confluence - detected. The source IP is $src$ and the destination hostname is $dest$. - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: - - field: src - type: ip_address + message: Potential exploitation attempts on a known vulnerability in Atlassian Confluence detected. The source IP is $src$ and the destination hostname is $dest$. + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - CVE-2023-22515 Privilege Escalation Vulnerability Confluence Data Center and Server - asset_type: Web Server - atomic_guid: [] - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - CVE-2023-22515 Privilege Escalation Vulnerability Confluence Data Center and Server + asset_type: Web Server + atomic_guid: [] + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/confluence/confluence_vuln_trigger_cve-2023-22515.log - source: suricata - sourcetype: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/confluence/confluence_vuln_trigger_cve-2023-22515.log + source: suricata + sourcetype: suricata diff --git a/detections/web/confluence_data_center_and_server_privilege_escalation.yml b/detections/web/confluence_data_center_and_server_privilege_escalation.yml index 3b530a0970..ed7347f8d6 100644 --- a/detections/web/confluence_data_center_and_server_privilege_escalation.yml +++ b/detections/web/confluence_data_center_and_server_privilege_escalation.yml @@ -1,78 +1,66 @@ name: Confluence Data Center and Server Privilege Escalation id: 115bebac-0976-4f7d-a3ec-d1fb45a39a11 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Nginx Access -description: The following analytic identifies potential exploitation attempts on - a known vulnerability in Atlassian Confluence, specifically targeting the /setup/*.action* - URL pattern. It leverages web logs within the Splunk 'Web' Data Model, filtering - for successful accesses (HTTP status 200) to these endpoints. This activity is significant - as it suggests attackers might be exploiting a privilege escalation flaw in Confluence. - If confirmed malicious, it could result in unauthorized access or account creation - with escalated privileges, leading to potential data breaches or further exploitation - within the environment. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url IN ("*/setup/setupadministrator.action*", "*/setup/finishsetup.action*", - "*/json/setup-restore-local.action*", "*/json/setup-restore-progress.action*", "*/json/setup-restore.action*", - "*/bootstrap/selectsetupstep.action*") Web.status=200 by Web.http_user_agent, Web.status - Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `confluence_data_center_and_server_privilege_escalation_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on Web traffic that include fields relavent for traffic into the `Web` datamodel. -known_false_positives: False positives may be present with legitimate applications. - Attempt to filter by dest IP or use Asset groups to restrict to confluence servers. + - Nginx Access +description: The following analytic identifies potential exploitation attempts on a known vulnerability in Atlassian Confluence, specifically targeting the /setup/*.action* URL pattern. It leverages web logs within the Splunk 'Web' Data Model, filtering for successful accesses (HTTP status 200) to these endpoints. This activity is significant as it suggests attackers might be exploiting a privilege escalation flaw in Confluence. If confirmed malicious, it could result in unauthorized access or account creation with escalated privileges, leading to potential data breaches or further exploitation within the environment. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN ("*/setup/setupadministrator.action*", "*/setup/finishsetup.action*", "*/json/setup-restore-local.action*", "*/json/setup-restore-progress.action*", "*/json/setup-restore.action*", "*/bootstrap/selectsetupstep.action*") Web.status=200 + BY Web.http_user_agent, Web.status Web.http_method, + Web.url, Web.url_length, Web.src, + Web.dest, sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `confluence_data_center_and_server_privilege_escalation_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on Web traffic that include fields relavent for traffic into the `Web` datamodel. +known_false_positives: False positives may be present with legitimate applications. Attempt to filter by dest IP or use Asset groups to restrict to confluence servers. references: -- https://confluence.atlassian.com/security/cve-2023-22515-privilege-escalation-vulnerability-in-confluence-data-center-and-server-1295682276.html -- https://confluence.atlassian.com/security/cve-2023-22518-improper-authorization-vulnerability-in-confluence-data-center-and-server-1311473907.html -- https://www.rapid7.com/blog/post/2023/10/04/etr-cve-2023-22515-zero-day-privilege-escalation-in-confluence-server-and-data-center/ -- https://attackerkb.com/topics/Q5f0ItSzw5/cve-2023-22515/rapid7-analysis + - https://confluence.atlassian.com/security/cve-2023-22515-privilege-escalation-vulnerability-in-confluence-data-center-and-server-1295682276.html + - https://confluence.atlassian.com/security/cve-2023-22518-improper-authorization-vulnerability-in-confluence-data-center-and-server-1311473907.html + - https://www.rapid7.com/blog/post/2023/10/04/etr-cve-2023-22515-zero-day-privilege-escalation-in-confluence-server-and-data-center/ + - https://attackerkb.com/topics/Q5f0ItSzw5/cve-2023-22515/rapid7-analysis drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential exploitation attempts on a known vulnerability in Atlassian Confluence - detected. The source IP is $src$ and the destination hostname is $dest$. - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: - - field: src - type: ip_address + message: Potential exploitation attempts on a known vulnerability in Atlassian Confluence detected. The source IP is $src$ and the destination hostname is $dest$. + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - CVE-2023-22515 Privilege Escalation Vulnerability Confluence Data Center and Server - - Confluence Data Center and Confluence Server Vulnerabilities - cve: - - CVE-2023-22518 - asset_type: Web Server - atomic_guid: [] - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - CVE-2023-22515 Privilege Escalation Vulnerability Confluence Data Center and Server + - Confluence Data Center and Confluence Server Vulnerabilities + cve: + - CVE-2023-22518 + asset_type: Web Server + atomic_guid: [] + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: Nginx Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/confluence/nginx_plus_kv_confluence.log - source: nginx:plus:kv - sourcetype: nginx:plus:kv + - name: Nginx Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/confluence/nginx_plus_kv_confluence.log + source: nginx:plus:kv + sourcetype: nginx:plus:kv diff --git a/detections/web/confluence_pre_auth_rce_via_ognl_injection_cve_2023_22527.yml b/detections/web/confluence_pre_auth_rce_via_ognl_injection_cve_2023_22527.yml index 67bf2703f3..40ab8b4c74 100644 --- a/detections/web/confluence_pre_auth_rce_via_ognl_injection_cve_2023_22527.yml +++ b/detections/web/confluence_pre_auth_rce_via_ognl_injection_cve_2023_22527.yml @@ -1,74 +1,62 @@ name: Confluence Pre-Auth RCE via OGNL Injection CVE-2023-22527 id: f56936c0-ae6f-4eeb-91ff-ecc1448c6105 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Suricata -description: The following analytic identifies attempts to exploit a critical template - injection vulnerability (CVE-2023-22527) in outdated Confluence Data Center and - Server versions. It detects POST requests to the "/template/aui/text-inline.vm" - endpoint with HTTP status codes 200 or 202, indicating potential OGNL injection - attacks. This activity is significant as it allows unauthenticated attackers to - execute arbitrary code remotely. If confirmed malicious, attackers could gain full - control over the affected Confluence instance, leading to data breaches, system - compromise, and further network infiltration. Immediate patching is essential to - mitigate this threat. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url="*/template/aui/text-inline.vm*" Web.http_method=POST Web.status IN - (200, 202) by Web.src, Web.dest, Web.http_user_agent, Web.url, Web.status | `drop_dm_object_name("Web")` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `confluence_pre_auth_rce_via_ognl_injection_cve_2023_22527_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on Web traffic that include fields relavent for traffic into the `Web` datamodel. -known_false_positives: False positives may be present with legitimate applications. - Attempt to filter by dest IP or use Asset groups to restrict to confluence servers. + - Suricata +description: The following analytic identifies attempts to exploit a critical template injection vulnerability (CVE-2023-22527) in outdated Confluence Data Center and Server versions. It detects POST requests to the "/template/aui/text-inline.vm" endpoint with HTTP status codes 200 or 202, indicating potential OGNL injection attacks. This activity is significant as it allows unauthenticated attackers to execute arbitrary code remotely. If confirmed malicious, attackers could gain full control over the affected Confluence instance, leading to data breaches, system compromise, and further network infiltration. Immediate patching is essential to mitigate this threat. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url="*/template/aui/text-inline.vm*" Web.http_method=POST Web.status IN (200, 202) + BY Web.src, Web.dest, Web.http_user_agent, + Web.url, Web.status + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `confluence_pre_auth_rce_via_ognl_injection_cve_2023_22527_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on Web traffic that include fields relavent for traffic into the `Web` datamodel. +known_false_positives: False positives may be present with legitimate applications. Attempt to filter by dest IP or use Asset groups to restrict to confluence servers. references: -- https://github.com/cleverg0d/CVE-2023-22527 -- https://confluence.atlassian.com/security/cve-2023-22527-rce-remote-code-execution-vulnerability-in-confluence-data-center-and-confluence-server-1333990257.html + - https://github.com/cleverg0d/CVE-2023-22527 + - https://confluence.atlassian.com/security/cve-2023-22527-rce-remote-code-execution-vulnerability-in-confluence-data-center-and-confluence-server-1333990257.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Exploitation attempts on a known vulnerability in Atlassian Confluence - detected. The source IP is $src$ and the destination hostname is $dest$. - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: - - field: src - type: ip_address + message: Exploitation attempts on a known vulnerability in Atlassian Confluence detected. The source IP is $src$ and the destination hostname is $dest$. + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: + - field: src + type: ip_address tags: - cve: - - CVE-2023-22527 - analytic_story: - - Confluence Data Center and Confluence Server Vulnerabilities - asset_type: Web Application - atomic_guid: [] - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + cve: + - CVE-2023-22527 + analytic_story: + - Confluence Data Center and Confluence Server Vulnerabilities + asset_type: Web Application + atomic_guid: [] + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/confluence/suricata_confluence_cve-2023-22527.log - source: suricata - sourcetype: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/confluence/suricata_confluence_cve-2023-22527.log + source: suricata + sourcetype: suricata diff --git a/detections/web/confluence_unauthenticated_remote_code_execution_cve_2022_26134.yml b/detections/web/confluence_unauthenticated_remote_code_execution_cve_2022_26134.yml index 1d6714b703..70e1422372 100644 --- a/detections/web/confluence_unauthenticated_remote_code_execution_cve_2022_26134.yml +++ b/detections/web/confluence_unauthenticated_remote_code_execution_cve_2022_26134.yml @@ -1,83 +1,74 @@ name: Confluence Unauthenticated Remote Code Execution CVE-2022-26134 id: fcf4bd3f-a79f-4b7a-83bf-2692d60b859c -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects attempts to exploit CVE-2022-26134, an - unauthenticated remote code execution vulnerability in Confluence. It leverages - the Web datamodel to analyze network and CIM-compliant web logs, identifying suspicious - URL patterns and parameters indicative of exploitation attempts. This activity is - significant as it allows attackers to execute arbitrary code on the Confluence server - without authentication, potentially leading to full system compromise. If confirmed - malicious, this could result in unauthorized access, data exfiltration, and further - lateral movement within the network. Immediate investigation and remediation are - crucial to prevent extensive damage. +description: The following analytic detects attempts to exploit CVE-2022-26134, an unauthenticated remote code execution vulnerability in Confluence. It leverages the Web datamodel to analyze network and CIM-compliant web logs, identifying suspicious URL patterns and parameters indicative of exploitation attempts. This activity is significant as it allows attackers to execute arbitrary code on the Confluence server without authentication, potentially leading to full system compromise. If confirmed malicious, this could result in unauthorized access, data exfiltration, and further lateral movement within the network. Immediate investigation and remediation are crucial to prevent extensive damage. data_source: -- Palo Alto Network Threat -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url IN ("*${*", "*%2F%7B*") (Web.url="*org.apache.commons.io.IOUtils*" - Web.url="*java.lang.Runtime@getRuntime().exec*") OR (Web.url="*java.lang.Runtime%40getRuntime%28%29.exec*") - OR (Web.url="*getEngineByName*" AND Web.url="*nashorn*" AND Web.url="*ProcessBuilder*") - by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest - sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `confluence_unauthenticated_remote_code_execution_cve_2022_26134_filter`' -how_to_implement: This detection requires the Web datamodel to be populated from a - supported Technology Add-On like Splunk for Apache or Splunk for Nginx. In addition, - network based logs or event data like PAN Threat. -known_false_positives: Tune based on assets if possible, or restrict to known Confluence - servers. Remove the ${ for a more broad query. To identify more exec, remove everything - up to the last parameter (Runtime().exec) for a broad query. + - Palo Alto Network Threat +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN ("*${*", "*%2F%7B*") (Web.url="*org.apache.commons.io.IOUtils*" Web.url="*java.lang.Runtime@getRuntime().exec*") + OR + (Web.url="*java.lang.Runtime%40getRuntime%28%29.exec*") + OR + (Web.url="*getEngineByName*" + AND + Web.url="*nashorn*" + AND + Web.url="*ProcessBuilder*") + BY Web.http_user_agent Web.http_method, Web.url,Web.url_length + Web.src, Web.dest sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `confluence_unauthenticated_remote_code_execution_cve_2022_26134_filter` +how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache or Splunk for Nginx. In addition, network based logs or event data like PAN Threat. +known_false_positives: Tune based on assets if possible, or restrict to known Confluence servers. Remove the ${ for a more broad query. To identify more exec, remove everything up to the last parameter (Runtime().exec) for a broad query. references: -- https://confluence.atlassian.com/doc/confluence-security-advisory-2022-06-02-1130377146.html -- https://www.splunk.com/en_us/blog/security/atlassian-confluence-vulnerability-cve-2022-26134.html -- https://www.rapid7.com/blog/post/2022/06/02/active-exploitation-of-confluence-cve-2022-26134/ -- https://www.volexity.com/blog/2022/06/02/zero-day-exploitation-of-atlassian-confluence/ + - https://confluence.atlassian.com/doc/confluence-security-advisory-2022-06-02-1130377146.html + - https://www.splunk.com/en_us/blog/security/atlassian-confluence-vulnerability-cve-2022-26134.html + - https://www.rapid7.com/blog/post/2022/06/02/active-exploitation-of-confluence-cve-2022-26134/ + - https://www.volexity.com/blog/2022/06/02/zero-day-exploitation-of-atlassian-confluence/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A URL was requested related to CVE-2022-26134, a unauthenticated remote - code execution vulnerability, on $dest$ by $src$. - risk_objects: - - field: dest - type: system - score: 100 - threat_objects: - - field: src - type: ip_address + message: A URL was requested related to CVE-2022-26134, a unauthenticated remote code execution vulnerability, on $dest$ by $src$. + risk_objects: + - field: dest + type: system + score: 100 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Atlassian Confluence Server and Data Center CVE-2022-26134 - - Confluence Data Center and Confluence Server Vulnerabilities - asset_type: Web Server - cve: - - CVE-2022-26134 - mitre_attack_id: - - T1505 - - T1190 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Atlassian Confluence Server and Data Center CVE-2022-26134 + - Confluence Data Center and Confluence Server Vulnerabilities + asset_type: Web Server + cve: + - CVE-2022-26134 + mitre_attack_id: + - T1505 + - T1190 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/java/confluence.log - source: pan:threat - sourcetype: pan:threat + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/java/confluence.log + source: pan:threat + sourcetype: pan:threat diff --git a/detections/web/connectwise_screenconnect_authentication_bypass.yml b/detections/web/connectwise_screenconnect_authentication_bypass.yml index 8748c27d7a..a3df5d9efb 100644 --- a/detections/web/connectwise_screenconnect_authentication_bypass.yml +++ b/detections/web/connectwise_screenconnect_authentication_bypass.yml @@ -1,82 +1,64 @@ name: ConnectWise ScreenConnect Authentication Bypass id: d3f7a803-e802-448b-8eb2-e796b223bfff -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk data_source: -- Suricata + - Suricata type: TTP status: production -description: The following analytic detects attempts to exploit the ConnectWise ScreenConnect - CVE-2024-1709 vulnerability, which allows attackers to bypass authentication via - an alternate path or channel. It leverages web request logs to identify access to - the SetupWizard.aspx page, indicating potential exploitation. This activity is significant - as it can lead to unauthorized administrative access and remote code execution. - If confirmed malicious, attackers could create administrative users and gain full - control over the affected system, posing severe security risks. Immediate remediation - by updating to version 23.9.8 or above is recommended. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url IN ("*/SetupWizard.aspx/*","*/SetupWizard/") Web.status=200 Web.http_method=POST - by Web.src, Web.dest, Web.http_user_agent, Web.url, Web.status, Web.http_method, - sourcetype, source | rex field=Web.url "/SetupWizard.aspx/(?.+)" | `drop_dm_object_name("Web")` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `connectwise_screenconnect_authentication_bypass_filter`' -how_to_implement: To implement this analytic, ensure proper logging is occurring with - IIS, Apache, or a Proxy server and that these logs are being ingested into Splunk. - The analytic was written against Suricata. The proper TA will need to be enabled - and should be mapped to CIM and the Web datamodel. Ingestion of the data source - is required to utilize this detection. In addition, if it is not mapped to the datamodel, - modify the query for your application logs to look for requests the same URI and - investigate further. -known_false_positives: False positives are not expected, as the detection is based - on the presence of web requests to the SetupWizard.aspx page, which is not a common - page to be accessed by legitimate users. Note that the analytic is limited to HTTP - POST and a status of 200 to reduce false positives. Modify the query as needed to - reduce false positives or hunt for additional indicators of compromise. +description: The following analytic detects attempts to exploit the ConnectWise ScreenConnect CVE-2024-1709 vulnerability, which allows attackers to bypass authentication via an alternate path or channel. It leverages web request logs to identify access to the SetupWizard.aspx page, indicating potential exploitation. This activity is significant as it can lead to unauthorized administrative access and remote code execution. If confirmed malicious, attackers could create administrative users and gain full control over the affected system, posing severe security risks. Immediate remediation by updating to version 23.9.8 or above is recommended. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN ("*/SetupWizard.aspx/*","*/SetupWizard/") Web.status=200 Web.http_method=POST + BY Web.src, Web.dest, Web.http_user_agent, + Web.url, Web.status, Web.http_method, + sourcetype, source + | rex field=Web.url "/SetupWizard.aspx/(?.+)" + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `connectwise_screenconnect_authentication_bypass_filter` +how_to_implement: To implement this analytic, ensure proper logging is occurring with IIS, Apache, or a Proxy server and that these logs are being ingested into Splunk. The analytic was written against Suricata. The proper TA will need to be enabled and should be mapped to CIM and the Web datamodel. Ingestion of the data source is required to utilize this detection. In addition, if it is not mapped to the datamodel, modify the query for your application logs to look for requests the same URI and investigate further. +known_false_positives: False positives are not expected, as the detection is based on the presence of web requests to the SetupWizard.aspx page, which is not a common page to be accessed by legitimate users. Note that the analytic is limited to HTTP POST and a status of 200 to reduce false positives. Modify the query as needed to reduce false positives or hunt for additional indicators of compromise. references: -- https://www.huntress.com/blog/a-catastrophe-for-control-understanding-the-screenconnect-authentication-bypass -- https://www.huntress.com/blog/detection-guidance-for-connectwise-cwe-288-2 -- https://www.connectwise.com/company/trust/security-bulletins/connectwise-screenconnect-23.9.8 + - https://www.huntress.com/blog/a-catastrophe-for-control-understanding-the-screenconnect-authentication-bypass + - https://www.huntress.com/blog/detection-guidance-for-connectwise-cwe-288-2 + - https://www.connectwise.com/company/trust/security-bulletins/connectwise-screenconnect-23.9.8 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An authentication bypass attempt against ScreenConnect has been detected - on $dest$. - risk_objects: - - field: dest - type: system - score: 100 - threat_objects: [] + message: An authentication bypass attempt against ScreenConnect has been detected on $dest$. + risk_objects: + - field: dest + type: system + score: 100 + threat_objects: [] tags: - analytic_story: - - ConnectWise ScreenConnect Vulnerabilities - - Seashell Blizzard - asset_type: Web Server - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: - - CVE-2024-1708 - - CVE-2024-1709 + analytic_story: + - ConnectWise ScreenConnect Vulnerabilities + - Seashell Blizzard + asset_type: Web Server + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: + - CVE-2024-1708 + - CVE-2024-1709 tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/screenconnect/connectwise_auth_suricata.log - sourcetype: suricata - source: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/screenconnect/connectwise_auth_suricata.log + sourcetype: suricata + source: suricata diff --git a/detections/web/crushftp_authentication_bypass_exploitation.yml b/detections/web/crushftp_authentication_bypass_exploitation.yml index 8ea437c10d..a99d3061d4 100644 --- a/detections/web/crushftp_authentication_bypass_exploitation.yml +++ b/detections/web/crushftp_authentication_bypass_exploitation.yml @@ -7,52 +7,52 @@ status: production type: TTP description: The following analytic detects potential exploitation of the CrushFTP authentication bypass vulnerability (CVE-2025-31161). This detection identifies suspicious command execution patterns associated with exploitation of this vulnerability, such as executing mesch.exe with specific arguments like b64exec or fullinstall. This activity is indicative of an attacker exploiting CVE-2025-31161 to gain unauthorized access to the CrushFTP server and perform post-exploitation activities. data_source: -- CrushFTP + - CrushFTP search: '`crushftp` | rex field=_raw "\\[HTTP:[^:]+:(?[^:]+):(?[^\\]]+)\\]" | rex field=_raw "cmd:(?[^\\*\\r\\n]+)" | where isnotnull(process) AND (match(process, "mesch\.exe") OR match(process, "b64exec") OR match(process, "fullinstall") OR match(process, "run")) | stats count min(_time) as firstTime max(_time) as lastTime by src_ip, user, process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `crushftp_authentication_bypass_exploitation_filter`' how_to_implement: To implement this detection, you need to ingest CrushFTP logs into your Splunk environment. Configure CrushFTP to forward logs to Splunk via a syslog forwarder or direct file monitoring. This detection searches for CrushFTP logs containing suspicious command execution patterns commonly associated with exploitation of the CVE-2025-31161 vulnerability. known_false_positives: False positives may occur if there are legitimate administrative commands being executed on the CrushFTP server that match the suspicious patterns. Review the commands being executed to determine if the activity is legitimate administrative work or potential malicious activity. references: -- https://www.huntress.com/blog/crushftp-cve-2025-31161-auth-bypass-and-post-exploitation -- https://nvd.nist.gov/vuln/detail/CVE-2025-31161 -- https://www.crushftp.com/crush11wiki/Wiki.jsp?page=Update + - https://www.huntress.com/blog/crushftp-cve-2025-31161-auth-bypass-and-post-exploitation + - https://nvd.nist.gov/vuln/detail/CVE-2025-31161 + - https://www.crushftp.com/crush11wiki/Wiki.jsp?page=Update drilldown_searches: -- name: View the detection results for - "$src_ip$" - search: '%original_detection_search% | search src_ip = "$src_ip$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_ip$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_ip$" + search: '%original_detection_search% | search src_ip = "$src_ip$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_ip$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential CrushFTP authentication bypass exploitation from IP $src_ip$ as user $user$ - risk_objects: - - field: src_ip - type: system - score: 80 - - field: user - type: user - score: 60 - threat_objects: [] + message: Potential CrushFTP authentication bypass exploitation from IP $src_ip$ as user $user$ + risk_objects: + - field: src_ip + type: system + score: 80 + - field: user + type: user + score: 60 + threat_objects: [] tags: - analytic_story: - - CrushFTP Vulnerabilities - - Hellcat Ransomware - asset_type: Web Server - mitre_attack_id: - - T1190 - - T1059.003 - - T1059.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: - - CVE-2025-31161 + analytic_story: + - CrushFTP Vulnerabilities + - Hellcat Ransomware + asset_type: Web Server + mitre_attack_id: + - T1190 + - T1059.003 + - T1059.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: + - CVE-2025-31161 tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/crushftp/crushftp11_session.log - sourcetype: crushftp:sessionlogs - source: crushftp \ No newline at end of file + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/crushftp/crushftp11_session.log + sourcetype: crushftp:sessionlogs + source: crushftp diff --git a/detections/web/crushftp_max_simultaneous_users_from_ip.yml b/detections/web/crushftp_max_simultaneous_users_from_ip.yml index 239d84edc7..215853c547 100644 --- a/detections/web/crushftp_max_simultaneous_users_from_ip.yml +++ b/detections/web/crushftp_max_simultaneous_users_from_ip.yml @@ -7,47 +7,47 @@ status: production type: Anomaly description: The following analytic identifies instances where CrushFTP has blocked access due to exceeding the maximum number of simultaneous connections from a single IP address. This activity may indicate brute force attempts, credential stuffing, or automated attacks against the CrushFTP server. This detection is particularly relevant following the discovery of CVE-2025-31161, an authentication bypass vulnerability in CrushFTP versions 10.0.0 through 10.8.3 and 11.0.0 through 11.3.0. data_source: -- CrushFTP + - CrushFTP search: '`crushftp` "*User access not allowed. Max simultaneous users from your IP*" | rex field=_raw "SESSION\\|\\d+\\/\\d+\\/\\d+ \\d+:\\d+:\\d+\\.\\d+\\|\\[HTTP:[^:]+:(?[^:]+):(?[0-9\\.]+)\\]" | stats count min(_time) as firstTime max(_time) as lastTime values(user) as user by src_ip | where count >= 3 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `crushftp_max_simultaneous_users_from_ip_filter`' how_to_implement: To implement this detection, you need to ingest CrushFTP logs into your Splunk environment. Configure CrushFTP to forward logs to Splunk via a syslog forwarder or direct file monitoring. Ensure the sourcetype is correctly set for the CrushFTP logs. The detection requires the SESSION field and the "[HTTP:*:user:IP]" format in the logs. Adjust the threshold in the "where count >= 3" clause based on your environment's normal behavior. known_false_positives: In environments where multiple users legitimately access CrushFTP from behind the same NAT or proxy, this may generate false positives. Tune the threshold based on your organization's usage patterns. references: -- https://www.huntress.com/blog/crushftp-cve-2025-31161-auth-bypass-and-post-exploitation -- https://nvd.nist.gov/vuln/detail/CVE-2025-31161 -- https://www.crushftp.com/crush11wiki/Wiki.jsp?page=Update + - https://www.huntress.com/blog/crushftp-cve-2025-31161-auth-bypass-and-post-exploitation + - https://nvd.nist.gov/vuln/detail/CVE-2025-31161 + - https://www.crushftp.com/crush11wiki/Wiki.jsp?page=Update drilldown_searches: -- name: View the detection results for - "$src_ip$" - search: '%original_detection_search% | search src_ip = "$src_ip$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_ip$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_ip$" + search: '%original_detection_search% | search src_ip = "$src_ip$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_ip$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential brute force or automated attack against CrushFTP detected from IP $src_ip$ - risk_objects: - - field: src_ip - type: system - score: 45 - threat_objects: [] + message: Potential brute force or automated attack against CrushFTP detected from IP $src_ip$ + risk_objects: + - field: src_ip + type: system + score: 45 + threat_objects: [] tags: - analytic_story: - - CrushFTP Vulnerabilities - asset_type: Web Server - mitre_attack_id: - - T1110.001 - - T1110.004 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: - - CVE-2025-31161 + analytic_story: + - CrushFTP Vulnerabilities + asset_type: Web Server + mitre_attack_id: + - T1110.001 + - T1110.004 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: + - CVE-2025-31161 tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/crushftp/crushftp11_session.log - sourcetype: crushftp:sessionlogs - source: crushftp + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/crushftp/crushftp11_session.log + sourcetype: crushftp:sessionlogs + source: crushftp diff --git a/detections/web/detect_attackers_scanning_for_vulnerable_jboss_servers.yml b/detections/web/detect_attackers_scanning_for_vulnerable_jboss_servers.yml index b122e04f98..19f10d44f5 100644 --- a/detections/web/detect_attackers_scanning_for_vulnerable_jboss_servers.yml +++ b/detections/web/detect_attackers_scanning_for_vulnerable_jboss_servers.yml @@ -1,46 +1,46 @@ name: Detect attackers scanning for vulnerable JBoss servers id: 104658f4-afdc-499e-9719-17243f982681 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Bhavin Patel, Splunk status: experimental type: TTP -description: The following analytic identifies specific GET or HEAD requests to web - servers that indicate reconnaissance attempts to find vulnerable JBoss servers. - It leverages data from the Web data model, focusing on HTTP methods and URLs associated - with JBoss management interfaces. This activity is significant because it often - precedes exploitation attempts using tools like JexBoss, which can compromise the - server. If confirmed malicious, attackers could gain unauthorized access, execute - arbitrary code, or escalate privileges, leading to potential data breaches and system - compromise. +description: The following analytic identifies specific GET or HEAD requests to web servers that indicate reconnaissance attempts to find vulnerable JBoss servers. It leverages data from the Web data model, focusing on HTTP methods and URLs associated with JBoss management interfaces. This activity is significant because it often precedes exploitation attempts using tools like JexBoss, which can compromise the server. If confirmed malicious, attackers could gain unauthorized access, execute arbitrary code, or escalate privileges, leading to potential data breaches and system compromise. data_source: [] -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") - AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR Web.url="*jmx-console*" - OR Web.url = "*invoker*") by Web.http_method, Web.url, Web.src, Web.dest | `drop_dm_object_name("Web")` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_attackers_scanning_for_vulnerable_jboss_servers_filter`' -how_to_implement: You must be ingesting data from the web server or network traffic - that contains web specific information, and populating the Web data model. -known_false_positives: It's possible for legitimate HTTP requests to be made to URLs - containing the suspicious paths. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE ( + Web.http_method="GET" + OR + Web.http_method="HEAD" + ) + AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR Web.url="*jmx-console*" OR Web.url = "*invoker*") + BY Web.http_method, Web.url, Web.src, + Web.dest + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `detect_attackers_scanning_for_vulnerable_jboss_servers_filter` +how_to_implement: You must be ingesting data from the web server or network traffic that contains web specific information, and populating the Web data model. +known_false_positives: It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths. references: [] rba: - message: Potential Scanning for Vulnerable JBoss Servers - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: Potential Scanning for Vulnerable JBoss Servers + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - JBoss Vulnerability - - SamSam Ransomware - asset_type: Web Server - mitre_attack_id: - - T1082 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - JBoss Vulnerability + - SamSam Ransomware + asset_type: Web Server + mitre_attack_id: + - T1082 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/web/detect_f5_tmui_rce_cve_2020_5902.yml b/detections/web/detect_f5_tmui_rce_cve_2020_5902.yml index c3a2080ab9..537ffb24ea 100644 --- a/detections/web/detect_f5_tmui_rce_cve_2020_5902.yml +++ b/detections/web/detect_f5_tmui_rce_cve_2020_5902.yml @@ -5,44 +5,31 @@ date: '2026-01-14' author: Shannon Davis, Splunk status: experimental type: TTP -description: The following analytic identifies remote code execution (RCE) attempts - targeting F5 BIG-IP, BIG-IQ, and Traffix SDC devices, specifically exploiting CVE-2020-5902. - It uses regex to detect patterns in syslog data that match known exploit strings - such as "hsqldb;" and directory traversal sequences. This activity is significant - because successful exploitation can allow attackers to execute arbitrary commands - on the affected devices, leading to full system compromise. If confirmed malicious, - this could result in unauthorized access, data exfiltration, or further lateral - movement within the network. +description: The following analytic identifies remote code execution (RCE) attempts targeting F5 BIG-IP, BIG-IQ, and Traffix SDC devices, specifically exploiting CVE-2020-5902. It uses regex to detect patterns in syslog data that match known exploit strings such as "hsqldb;" and directory traversal sequences. This activity is significant because successful exploitation can allow attackers to execute arbitrary commands on the affected devices, leading to full system compromise. If confirmed malicious, this could result in unauthorized access, data exfiltration, or further lateral movement within the network. data_source: [] search: '`f5_bigip_rogue` | regex _raw="(hsqldb;|.*\\.\\.;.*)" | search `detect_f5_tmui_rce_cve_2020_5902_filter`' -how_to_implement: To consistently detect exploit attempts on F5 devices using the - vulnerabilities contained within CVE-2020-5902 it is recommended to ingest logs - via syslog. As many BIG-IP devices will have SSL enabled on their management interfaces, - detections via wire data may not pick anything up unless you are decrypting SSL - traffic in order to inspect it. I am using a regex string from a Cloudflare mitigation - technique to try and always catch the offending string (..;), along with the other - exploit of using (hsqldb;). +how_to_implement: To consistently detect exploit attempts on F5 devices using the vulnerabilities contained within CVE-2020-5902 it is recommended to ingest logs via syslog. As many BIG-IP devices will have SSL enabled on their management interfaces, detections via wire data may not pick anything up unless you are decrypting SSL traffic in order to inspect it. I am using a regex string from a Cloudflare mitigation technique to try and always catch the offending string (..;), along with the other exploit of using (hsqldb;). known_false_positives: No false positives have been identified at this time. references: -- https://www.ptsecurity.com/ww-en/about/news/f5-fixes-critical-vulnerability-discovered-by-positive-technologies-in-big-ip-application-delivery-controller/ -- https://support.f5.com/csp/article/K52145254 + - https://www.ptsecurity.com/ww-en/about/news/f5-fixes-critical-vulnerability-discovered-by-positive-technologies-in-big-ip-application-delivery-controller/ + - https://support.f5.com/csp/article/K52145254 rba: - message: Potential F5 TMUI RCE traffic - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: Potential F5 TMUI RCE traffic + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - F5 TMUI RCE CVE-2020-5902 - asset_type: Network - cve: - - CVE-2020-5902 - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - F5 TMUI RCE CVE-2020-5902 + asset_type: Network + cve: + - CVE-2020-5902 + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/web/detect_malicious_requests_to_exploit_jboss_servers.yml b/detections/web/detect_malicious_requests_to_exploit_jboss_servers.yml index 11fbae3f11..9cb68cb6f1 100644 --- a/detections/web/detect_malicious_requests_to_exploit_jboss_servers.yml +++ b/detections/web/detect_malicious_requests_to_exploit_jboss_servers.yml @@ -1,44 +1,44 @@ name: Detect malicious requests to exploit JBoss servers id: c8bff7a4-11ea-4416-a27d-c5bca472913d -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Bhavin Patel, Splunk status: experimental type: TTP -description: The following analytic identifies malicious HTTP requests targeting the - jmx-console in JBoss servers. It detects unusually long URLs, indicative of embedded - payloads, by analyzing web server logs for GET or HEAD requests with specific URL - patterns and lengths. This activity is significant as it may indicate an attempt - to exploit JBoss vulnerabilities, potentially leading to unauthorized remote code - execution. If confirmed malicious, attackers could gain control over the server, - escalate privileges, and compromise sensitive data, posing a severe threat to the - organization's security. +description: The following analytic identifies malicious HTTP requests targeting the jmx-console in JBoss servers. It detects unusually long URLs, indicative of embedded payloads, by analyzing web server logs for GET or HEAD requests with specific URL patterns and lengths. This activity is significant as it may indicate an attempt to exploit JBoss vulnerabilities, potentially leading to unauthorized remote code execution. If confirmed malicious, attackers could gain control over the server, escalate privileges, and compromise sensitive data, posing a severe threat to the organization's security. data_source: [] -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") - by Web.http_method, Web.url,Web.url_length Web.src, Web.dest | search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*" - AND Web.url_length > 200 | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | table src, dest_ip, http_method, url, firstTime, - lastTime | `detect_malicious_requests_to_exploit_jboss_servers_filter`' -how_to_implement: You must ingest data from the web server or capture network data - that contains web specific information with solutions such as Bro or Splunk Stream, - and populating the Web data model +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE ( + Web.http_method="GET" + OR + Web.http_method="HEAD" + ) + BY Web.http_method, Web.url,Web.url_length Web.src, + Web.dest + | search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*" AND Web.url_length > 200 + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table src, dest_ip, http_method, url, firstTime, lastTime + | `detect_malicious_requests_to_exploit_jboss_servers_filter` +how_to_implement: You must ingest data from the web server or capture network data that contains web specific information with solutions such as Bro or Splunk Stream, and populating the Web data model known_false_positives: No known false positives for this detection. references: [] rba: - message: Potentially malicious traffic exploiting JBoss servers - risk_objects: - - field: dest_ip - type: system - score: 25 - threat_objects: [] + message: Potentially malicious traffic exploiting JBoss servers + risk_objects: + - field: dest_ip + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - JBoss Vulnerability - - SamSam Ransomware - asset_type: Web Server - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - JBoss Vulnerability + - SamSam Ransomware + asset_type: Web Server + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/web/detect_remote_access_software_usage_url.yml b/detections/web/detect_remote_access_software_usage_url.yml index 8831b6aa88..9f5f764341 100644 --- a/detections/web/detect_remote_access_software_usage_url.yml +++ b/detections/web/detect_remote_access_software_usage_url.yml @@ -5,109 +5,83 @@ date: '2026-01-19' author: Steven Dick status: production type: Anomaly -description: The following analytic detects the execution of known remote access - software within the environment. It leverages network logs mapped to the Web - data model, identifying specific URLs and user agents associated with remote - access tools like AnyDesk, GoToMyPC, LogMeIn, and TeamViewer. This activity is - significant as adversaries often use these utilities to maintain unauthorized - remote access. If confirmed malicious, this could allow attackers to control - systems remotely, exfiltrate data, or further compromise the network, posing a - severe security risk. +description: The following analytic detects the execution of known remote access software within the environment. It leverages network logs mapped to the Web data model, identifying specific URLs and user agents associated with remote access tools like AnyDesk, GoToMyPC, LogMeIn, and TeamViewer. This activity is significant as adversaries often use these utilities to maintain unauthorized remote access. If confirmed malicious, this could allow attackers to control systems remotely, exfiltrate data, or further compromise the network, posing a severe security risk. data_source: -- Palo Alto Network Threat + - Palo Alto Network Threat search: | - | tstats count min(_time) as firstTime - max(_time) as lastTime - latest(Web.http_method) as http_method - latest(Web.http_user_agent) as http_user_agent - latest(Web.url) as url - latest(Web.user) as user - latest(Web.dest) as dest - from datamodel=Web where - Web.url_domain=* - NOT Web.url_domain IN ("-", "unknown") - by Web.action Web.src Web.category Web.url_domain Web.url_length - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `drop_dm_object_name("Web")` - | lookup remote_access_software remote_domain AS url_domain OUTPUT isutility, description as signature, comment_reference - as desc, category - | search isutility = True - | `remote_access_software_usage_exceptions` - | `detect_remote_access_software_usage_url_filter` -how_to_implement: The detection is based on data that originates from network - logs. These logs must be processed using the appropriate Splunk Technology - Add-ons that are specific to the network logs. The logs must also be mapped to - the `Web` data model. Use the Splunk Common Information Model (CIM) to - normalize the field names and speed up the data modeling process. The - "exceptions" macro leverages both an Assets and Identities lookup, as well as - a KVStore collection called "remote_software_exceptions" that lets you track - and maintain device- based exceptions for this set of detections. -known_false_positives: It is possible that legitimate remote access software is - used within the environment. Ensure that the lookup is reviewed and updated - with any additional remote access software that is used within the - environment. Known false positives can be added to the - remote_access_software_usage_exception.csv lookup to globally suppress these - situations across all remote access content + | tstats count min(_time) as firstTime + max(_time) as lastTime + latest(Web.http_method) as http_method + latest(Web.http_user_agent) as http_user_agent + latest(Web.url) as url + latest(Web.user) as user + latest(Web.dest) as dest + from datamodel=Web where + Web.url_domain=* + NOT Web.url_domain IN ("-", "unknown") + by Web.action Web.src Web.category Web.url_domain Web.url_length + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `drop_dm_object_name("Web")` + | lookup remote_access_software remote_domain AS url_domain OUTPUT isutility, description as signature, comment_reference + as desc, category + | search isutility = True + | `remote_access_software_usage_exceptions` + | `detect_remote_access_software_usage_url_filter` +how_to_implement: The detection is based on data that originates from network logs. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the network logs. The logs must also be mapped to the `Web` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. The "exceptions" macro leverages both an Assets and Identities lookup, as well as a KVStore collection called "remote_software_exceptions" that lets you track and maintain device- based exceptions for this set of detections. +known_false_positives: It is possible that legitimate remote access software is used within the environment. Ensure that the lookup is reviewed and updated with any additional remote access software that is used within the environment. Known false positives can be added to the remote_access_software_usage_exception.csv lookup to globally suppress these situations across all remote access content references: - - https://attack.mitre.org/techniques/T1219/ - - https://thedfirreport.com/2022/08/08/bumblebee-roasts-its-way-to-domain-admin/ - - https://thedfirreport.com/2022/11/28/emotet-strikes-again-lnk-file-leads-to-domain-wide-ransomware/ + - https://attack.mitre.org/techniques/T1219/ + - https://thedfirreport.com/2022/08/08/bumblebee-roasts-its-way-to-domain-admin/ + - https://thedfirreport.com/2022/11/28/emotet-strikes-again-lnk-file-leads-to-domain-wide-ransomware/ drilldown_searches: - - name: View the detection results for - "$src$" and "$user$" - search: '%original_detection_search% | search src = "$src$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$src$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: Investigate traffic to $url_domain$ - search: '| from datamodel:Web | search src=$src$ url_domain=$url_domain$' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" and "$user$" + search: '%original_detection_search% | search src = "$src$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: Investigate traffic to $url_domain$ + search: '| from datamodel:Web | search src=$src$ url_domain=$url_domain$' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A domain for a known remote access software $url_domain$ was - contacted by $src$. - risk_objects: - - field: src - type: system - score: 25 - - field: user - type: user - score: 25 - threat_objects: - - field: url_domain - type: domain - - field: signature - type: signature + message: A domain for a known remote access software $url_domain$ was contacted by $src$. + risk_objects: + - field: src + type: system + score: 25 + - field: user + type: user + score: 25 + threat_objects: + - field: url_domain + type: domain + - field: signature + type: signature tags: - analytic_story: - - Insider Threat - - Command And Control - - Ransomware - - CISA AA24-241A - - Remote Monitoring and Management Software - - Interlock Ransomware - - Scattered Lapsus$ Hunters - asset_type: Network - mitre_attack_id: - - T1219 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - manual_test: This detection uses A&I lookups from Enterprise Security. + analytic_story: + - Insider Threat + - Command And Control + - Ransomware + - CISA AA24-241A + - Remote Monitoring and Management Software + - Interlock Ransomware + - Scattered Lapsus$ Hunters + asset_type: Network + mitre_attack_id: + - T1219 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + manual_test: This detection uses A&I lookups from Enterprise Security. tests: - - name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_palo.log - source: screenconnect_palo - sourcetype: pan:threat + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_palo.log + source: screenconnect_palo + sourcetype: pan:threat diff --git a/detections/web/detect_web_access_to_decommissioned_s3_bucket.yml b/detections/web/detect_web_access_to_decommissioned_s3_bucket.yml index d4d94a87f1..9009ab8375 100644 --- a/detections/web/detect_web_access_to_decommissioned_s3_bucket.yml +++ b/detections/web/detect_web_access_to_decommissioned_s3_bucket.yml @@ -1,64 +1,67 @@ name: Detect Web Access to Decommissioned S3 Bucket id: 3a1d8f62-5b9c-4e7d-b8f3-9d6a8e2f5e1f -version: 2 -date: '2025-05-02' +version: 3 +date: '2026-02-25' author: Jose Hernandez, Splunk status: experimental type: Anomaly description: This detection identifies web requests to domains that match previously decommissioned S3 buckets through web proxy logs. This activity is significant because attackers may attempt to access or recreate deleted S3 buckets that were previously public to hijack them for malicious purposes. If successful, this could allow attackers to host malicious content or exfiltrate data through compromised bucket names that may still be referenced by legitimate applications. data_source: -- AWS Cloudfront -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Web.http_method) as http_method values(Web.http_user_agent) as http_user_agent values(Web.url) as url values(Web.user) as user from datamodel=Web where Web.url_domain!="" by Web.src Web.url_domain -| `drop_dm_object_name("Web")` -| `security_content_ctime(firstTime)` -| `security_content_ctime(lastTime)` -| eval bucket_domain = lower(url_domain) -| lookup decommissioned_buckets bucketName as bucket_domain OUTPUT bucketName as match -| where isnotnull(match) -| `detect_web_access_to_decommissioned_s3_bucket_filter`' + - AWS Cloudfront +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Web.http_method) as http_method values(Web.http_user_agent) as http_user_agent values(Web.url) as url values(Web.user) as user FROM datamodel=Web + WHERE Web.url_domain!="" + BY Web.src Web.url_domain + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | eval bucket_domain = lower(url_domain) + | lookup decommissioned_buckets bucketName as bucket_domain OUTPUT bucketName as match + | where isnotnull(match) + | `detect_web_access_to_decommissioned_s3_bucket_filter` how_to_implement: To successfully implement this detection, you need to be ingesting web proxy logs and have them mapped to the Web data model. Additionally, ensure that the baseline search "Baseline Of Open S3 Bucket Decommissioning" is running and populating the decommissioned_buckets KVStore Lookup. known_false_positives: Some applications or web pages may continue to reference old S3 bucket URLs after they have been decommissioned. These should be investigated and updated to prevent potential security risks. references: -- https://docs.aws.amazon.com/AmazonS3/latest/userguide/access-control-block-public-access.html -- https://labs.watchtowr.com/8-million-requests-later-we-made-the-solarwinds-supply-chain-attack-look-amateur/ + - https://docs.aws.amazon.com/AmazonS3/latest/userguide/access-control-block-public-access.html + - https://labs.watchtowr.com/8-million-requests-later-we-made-the-solarwinds-supply-chain-attack-look-amateur/ drilldown_searches: -- name: Web Activity for Host and User - search: '| from datamodel:Web | search src="$src$" user="$user$"' - earliest_offset: -7d@d - latest_offset: now -- name: Investigate traffic to domain - search: '| from datamodel:Web | search src="$src$" url_domain="$url_domain$"' - earliest_offset: -7d@d - latest_offset: now + - name: Web Activity for Host and User + search: '| from datamodel:Web | search src="$src$" user="$user$"' + earliest_offset: -7d@d + latest_offset: now + - name: Investigate traffic to domain + search: '| from datamodel:Web | search src="$src$" url_domain="$url_domain$"' + earliest_offset: -7d@d + latest_offset: now rba: - message: A web request to decommissioned S3 bucket domain $url_domain$ was detected from host $src$ by user $user$ - risk_objects: - - field: src - type: system - score: 30 - threat_objects: - - field: url_domain - type: domain + message: A web request to decommissioned S3 bucket domain $url_domain$ was detected from host $src$ by user $user$ + risk_objects: + - field: src + type: system + score: 30 + threat_objects: + - field: url_domain + type: domain tags: - analytic_story: - - AWS S3 Bucket Security Monitoring - - Data Destruction - asset_type: S3 Bucket - mitre_attack_id: - - T1485 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - AWS S3 Bucket Security Monitoring + - Data Destruction + asset_type: S3 Bucket + mitre_attack_id: + - T1485 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: Baseline Dataset Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/decommissioned_buckets/cloudtrail.json - source: cloudtrail - sourcetype: aws:cloudtrail -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/decommissioned_buckets/web_cloudfront_access.log - source: aws_cloudfront_accesslogs - sourcetype: aws:cloudfront:accesslogs + - name: Baseline Dataset Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/decommissioned_buckets/cloudtrail.json + source: cloudtrail + sourcetype: aws:cloudtrail + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/decommissioned_buckets/web_cloudfront_access.log + source: aws_cloudfront_accesslogs + sourcetype: aws:cloudfront:accesslogs diff --git a/detections/web/exploit_public_facing_application_via_apache_commons_text.yml b/detections/web/exploit_public_facing_application_via_apache_commons_text.yml index 4780d50e62..778800bbaf 100644 --- a/detections/web/exploit_public_facing_application_via_apache_commons_text.yml +++ b/detections/web/exploit_public_facing_application_via_apache_commons_text.yml @@ -1,89 +1,74 @@ name: Exploit Public Facing Application via Apache Commons Text id: 19a481e0-c97c-4d14-b1db-75a708eb592e -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic detects attempts to exploit the CVE-2022-42889 - vulnerability in the Apache Commons Text Library, known as Text4Shell. It leverages - the Web datamodel to identify suspicious HTTP requests containing specific lookup - keys (url, dns, script) that can lead to Remote Code Execution (RCE). This activity - is significant as it targets a critical vulnerability that can allow attackers to - execute arbitrary code on the server. If confirmed malicious, this could lead to - full system compromise, data exfiltration, or further lateral movement within the - network. +description: The following analytic detects attempts to exploit the CVE-2022-42889 vulnerability in the Apache Commons Text Library, known as Text4Shell. It leverages the Web datamodel to identify suspicious HTTP requests containing specific lookup keys (url, dns, script) that can lead to Remote Code Execution (RCE). This activity is significant as it targets a critical vulnerability that can allow attackers to execute arbitrary code on the server. If confirmed malicious, this could lead to full system compromise, data exfiltration, or further lateral movement within the network. data_source: -- Nginx Access -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Web where Web.http_method IN (POST, GET) by Web.src Web.status - Web.uri_path Web.dest Web.http_method Web.uri_query Web.http_user_agent | `drop_dm_object_name("Web")` - | eval utf=if(like(lower(uri_query),"%:utf-8:http%"),2,0) | eval lookup = if(like(lower(uri_query), - "%url%") OR like(lower(uri_query), "%dns%") OR like(lower(uri_query), "%script%"),2,0) - | eval other_lookups = if(like(lower(uri_query), "%env%") OR like(lower(uri_query), - "%file%") OR like(lower(uri_query), "%getRuntime%") OR like(lower(uri_query), "%java%") - OR like(lower(uri_query), "%localhost%") OR like(lower(uri_query), "%properties%") - OR like(lower(uri_query), "%resource%") OR like(lower(uri_query), "%sys%") OR like(lower(uri_query), - "%xml%") OR like(lower(uri_query), "%base%"),1,0) | addtotals fieldname=Score utf - lookup other_lookups | fields Score, src, dest, status, uri_query, uri_path, http_method, - http_user_agent firstTime lastTime | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | where Score >= 3 | `exploit_public_facing_application_via_apache_commons_text_filter`' -how_to_implement: To implement, one must be collecting network traffic that is normalized - in CIM and able to be queried via the Web datamodel. Or, take the chunks out needed - and tie to a specific network source type to hunt in. Tune as needed, or remove - the other_lookups statement. -known_false_positives: False positives are present when the values are set to 1 for - utf and lookup. It's possible to raise this to TTP (direct finding) if removal of - other_lookups occur and Score is raised to 2 (down from 4). + - Nginx Access +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.http_method IN (POST, GET) + BY Web.src Web.status Web.uri_path + Web.dest Web.http_method Web.uri_query + Web.http_user_agent + | `drop_dm_object_name("Web")` + | eval utf=if(like(lower(uri_query),"%:utf-8:http%"),2,0) + | eval lookup = if(like(lower(uri_query), "%url%") OR like(lower(uri_query), "%dns%") OR like(lower(uri_query), "%script%"),2,0) + | eval other_lookups = if(like(lower(uri_query), "%env%") OR like(lower(uri_query), "%file%") OR like(lower(uri_query), "%getRuntime%") OR like(lower(uri_query), "%java%") OR like(lower(uri_query), "%localhost%") OR like(lower(uri_query), "%properties%") OR like(lower(uri_query), "%resource%") OR like(lower(uri_query), "%sys%") OR like(lower(uri_query), "%xml%") OR like(lower(uri_query), "%base%"),1,0) + | addtotals fieldname=Score utf lookup other_lookups + | fields Score, src, dest, status, uri_query, uri_path, http_method, http_user_agent firstTime lastTime + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | where Score >= 3 + | `exploit_public_facing_application_via_apache_commons_text_filter` +how_to_implement: To implement, one must be collecting network traffic that is normalized in CIM and able to be queried via the Web datamodel. Or, take the chunks out needed and tie to a specific network source type to hunt in. Tune as needed, or remove the other_lookups statement. +known_false_positives: False positives are present when the values are set to 1 for utf and lookup. It's possible to raise this to TTP (direct finding) if removal of other_lookups occur and Score is raised to 2 (down from 4). references: -- https://sysdig.com/blog/cve-2022-42889-text4shell/ -- https://nvd.nist.gov/vuln/detail/CVE-2022-42889 -- https://lists.apache.org/thread/n2bd4vdsgkqh2tm14l1wyc3jyol7s1om -- https://www.rapid7.com/blog/post/2022/10/17/cve-2022-42889-keep-calm-and-stop-saying-4shell/ -- https://github.com/kljunowsky/CVE-2022-42889-text4shell -- https://medium.com/geekculture/text4shell-exploit-walkthrough-ebc02a01f035 + - https://sysdig.com/blog/cve-2022-42889-text4shell/ + - https://nvd.nist.gov/vuln/detail/CVE-2022-42889 + - https://lists.apache.org/thread/n2bd4vdsgkqh2tm14l1wyc3jyol7s1om + - https://www.rapid7.com/blog/post/2022/10/17/cve-2022-42889-keep-calm-and-stop-saying-4shell/ + - https://github.com/kljunowsky/CVE-2022-42889-text4shell + - https://medium.com/geekculture/text4shell-exploit-walkthrough-ebc02a01f035 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A URL was requested related to Text4Shell on $dest$ by $src$. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: - - field: src - type: ip_address + message: A URL was requested related to Text4Shell on $dest$ by $src$. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Text4Shell CVE-2022-42889 - asset_type: Web Server - cve: - - CVE-2022-42889 - mitre_attack_id: - - T1133 - - T1190 - - T1505.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Text4Shell CVE-2022-42889 + asset_type: Web Server + cve: + - CVE-2022-42889 + mitre_attack_id: + - T1133 + - T1190 + - T1505.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/text4shell/text4shell_nginx.log - source: nginx:plus:kv - sourcetype: nginx:plus:kv + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/text4shell/text4shell_nginx.log + source: nginx:plus:kv + sourcetype: nginx:plus:kv diff --git a/detections/web/exploit_public_facing_fortinet_fortinac_cve_2022_39952.yml b/detections/web/exploit_public_facing_fortinet_fortinac_cve_2022_39952.yml index 9114c9eb59..a19fb42566 100644 --- a/detections/web/exploit_public_facing_fortinet_fortinac_cve_2022_39952.yml +++ b/detections/web/exploit_public_facing_fortinet_fortinac_cve_2022_39952.yml @@ -1,74 +1,63 @@ name: Exploit Public-Facing Fortinet FortiNAC CVE-2022-39952 id: 2038f5c6-5aba-4221-8ae2-ca76e2ca8b97 -version: 6 -date: '2025-10-14' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects attempts to exploit the Fortinet FortiNAC - CVE-2022-39952 vulnerability. It identifies HTTP POST requests to the URI configWizard/keyUpload.jsp - with a payload.zip file. The detection leverages the Web datamodel, analyzing fields - such as URL, HTTP method, and user agent. This activity is significant as it indicates - an attempt to exploit a known vulnerability, potentially leading to remote code - execution. If confirmed malicious, attackers could gain control over the affected - system, schedule malicious tasks, and establish persistent access via a remote command - and control (C2) server. +description: The following analytic detects attempts to exploit the Fortinet FortiNAC CVE-2022-39952 vulnerability. It identifies HTTP POST requests to the URI configWizard/keyUpload.jsp with a payload.zip file. The detection leverages the Web datamodel, analyzing fields such as URL, HTTP method, and user agent. This activity is significant as it indicates an attempt to exploit a known vulnerability, potentially leading to remote code execution. If confirmed malicious, attackers could gain control over the affected system, schedule malicious tasks, and establish persistent access via a remote command and control (C2) server. data_source: -- Palo Alto Network Threat -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url IN ("*configWizard/keyUpload.jsp*") by Web.http_user_agent, Web.status - Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `exploit_public_facing_fortinet_fortinac_cve_2022_39952_filter`' -how_to_implement: This detection requires the Web datamodel to be populated from a - supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk - for Palo Alto. -known_false_positives: False positives may be present. Modify the query as needed - to POST, or add additional filtering (based on log source). + - Palo Alto Network Threat +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN ("*configWizard/keyUpload.jsp*") + BY Web.http_user_agent, Web.status Web.http_method, + Web.url, Web.url_length, Web.src, + Web.dest, sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `exploit_public_facing_fortinet_fortinac_cve_2022_39952_filter` +how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto. +known_false_positives: False positives may be present. Modify the query as needed to POST, or add additional filtering (based on log source). references: -- https://github.com/horizon3ai/CVE-2022-39952 -- https://www.horizon3.ai/fortinet-fortinac-cve-2022-39952-deep-dive-and-iocs/ -- https://viz.greynoise.io/tag/fortinac-rce-attempt?days=30 + - https://github.com/horizon3ai/CVE-2022-39952 + - https://www.horizon3.ai/fortinet-fortinac-cve-2022-39952-deep-dive-and-iocs/ + - https://viz.greynoise.io/tag/fortinac-rce-attempt?days=30 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential CVE-2022-39952 against a Fortinet NAC may be occurring against - $dest$. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: Potential CVE-2022-39952 against a Fortinet NAC may be occurring against $dest$. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Fortinet FortiNAC CVE-2022-39952 - - Hellcat Ransomware - asset_type: Network - cve: - - CVE-2022-39952 - mitre_attack_id: - - T1190 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Fortinet FortiNAC CVE-2022-39952 + - Hellcat Ransomware + asset_type: Network + cve: + - CVE-2022-39952 + mitre_attack_id: + - T1190 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/fortigate/web_fortinetnac.log - source: pan:threat - sourcetype: pan:threat + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/fortigate/web_fortinetnac.log + source: pan:threat + sourcetype: pan:threat diff --git a/detections/web/f5_tmui_authentication_bypass.yml b/detections/web/f5_tmui_authentication_bypass.yml index 733aa9504b..afe6067d03 100644 --- a/detections/web/f5_tmui_authentication_bypass.yml +++ b/detections/web/f5_tmui_authentication_bypass.yml @@ -1,73 +1,61 @@ name: F5 TMUI Authentication Bypass id: 88bf127c-613e-4579-99e4-c4d4b02f3840 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Suricata -description: The following analytic detects attempts to exploit the CVE-2023-46747 - vulnerability, an authentication bypass flaw in F5 BIG-IP's Configuration utility - (TMUI). It identifies this activity by monitoring for specific URI paths such as - "*/mgmt/tm/auth/user/*" with the PATCH method and a 200 status code. This behavior - is significant for a SOC as it indicates potential unauthorized access attempts, - leading to remote code execution. If confirmed malicious, an attacker could gain - unauthorized access, execute arbitrary code, steal data, disrupt systems, or conduct - further malicious activities within the network. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url IN ("*/mgmt/tm/auth/user/*") Web.http_method=PATCH Web.status=200 - by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, - Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`| `f5_tmui_authentication_bypass_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on Web traffic that include fields relevant for traffic into the `Web` datamodel. -known_false_positives: False positives should be limited to as this is strict to active - exploitation. Reduce noise by filtering to F5 devices with TMUI enabled or filter - data as needed. + - Suricata +description: The following analytic detects attempts to exploit the CVE-2023-46747 vulnerability, an authentication bypass flaw in F5 BIG-IP's Configuration utility (TMUI). It identifies this activity by monitoring for specific URI paths such as "*/mgmt/tm/auth/user/*" with the PATCH method and a 200 status code. This behavior is significant for a SOC as it indicates potential unauthorized access attempts, leading to remote code execution. If confirmed malicious, an attacker could gain unauthorized access, execute arbitrary code, steal data, disrupt systems, or conduct further malicious activities within the network. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN ("*/mgmt/tm/auth/user/*") Web.http_method=PATCH Web.status=200 + BY Web.http_user_agent, Web.status Web.http_method, + Web.url, Web.url_length, Web.src, + Web.dest, sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `f5_tmui_authentication_bypass_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on Web traffic that include fields relevant for traffic into the `Web` datamodel. +known_false_positives: False positives should be limited to as this is strict to active exploitation. Reduce noise by filtering to F5 devices with TMUI enabled or filter data as needed. references: -- https://www.praetorian.com/blog/refresh-compromising-f5-big-ip-with-request-smuggling-cve-2023-46747/ -- https://github.com/projectdiscovery/nuclei-templates/blob/3b0bb71bd627c6c3139e1d06c866f8402aa228ae/http/cves/2023/CVE-2023-46747.yaml + - https://www.praetorian.com/blog/refresh-compromising-f5-big-ip-with-request-smuggling-cve-2023-46747/ + - https://github.com/projectdiscovery/nuclei-templates/blob/3b0bb71bd627c6c3139e1d06c866f8402aa228ae/http/cves/2023/CVE-2023-46747.yaml drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential CVE-2023-46747 F5 TMUI Authentication Bypass may be occurring - against $dest$ from $src$. - risk_objects: - - field: dest - type: system - score: 90 - threat_objects: - - field: src - type: ip_address + message: Potential CVE-2023-46747 F5 TMUI Authentication Bypass may be occurring against $dest$ from $src$. + risk_objects: + - field: dest + type: system + score: 90 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - F5 Authentication Bypass with TMUI - asset_type: Network - atomic_guid: [] - cve: - - CVE-2023-46747 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: endpoint + analytic_story: + - F5 Authentication Bypass with TMUI + asset_type: Network + atomic_guid: [] + cve: + - CVE-2023-46747 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/f5/f5_tmui.log - source: suricata - sourcetype: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/f5/f5_tmui.log + source: suricata + sourcetype: suricata diff --git a/detections/web/fortinet_appliance_auth_bypass.yml b/detections/web/fortinet_appliance_auth_bypass.yml index b7b8546144..20fd4bfadb 100644 --- a/detections/web/fortinet_appliance_auth_bypass.yml +++ b/detections/web/fortinet_appliance_auth_bypass.yml @@ -1,80 +1,65 @@ name: Fortinet Appliance Auth bypass id: a83122f2-fa09-4868-a230-544dbc54bc1c -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects attempts to exploit CVE-2022-40684, a - Fortinet appliance authentication bypass vulnerability. It identifies REST API requests - to the /api/v2/ endpoint using various HTTP methods (GET, POST, PUT, DELETE) that - may indicate unauthorized modifications, such as adding SSH keys or creating new - users. This detection leverages the Web datamodel to monitor specific URL patterns - and HTTP methods. This activity is significant as it can lead to unauthorized access - and control over the appliance. If confirmed malicious, attackers could gain persistent - access, reroute network traffic, or capture sensitive information. +description: The following analytic detects attempts to exploit CVE-2022-40684, a Fortinet appliance authentication bypass vulnerability. It identifies REST API requests to the /api/v2/ endpoint using various HTTP methods (GET, POST, PUT, DELETE) that may indicate unauthorized modifications, such as adding SSH keys or creating new users. This detection leverages the Web datamodel to monitor specific URL patterns and HTTP methods. This activity is significant as it can lead to unauthorized access and control over the appliance. If confirmed malicious, attackers could gain persistent access, reroute network traffic, or capture sensitive information. data_source: -- Palo Alto Network Threat -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url IN ("*/api/v2/cmdb/system/admin*") Web.http_method IN ("GET", "PUT") - by Web.http_user_agent, Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, - sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `fortinet_appliance_auth_bypass_filter`' -how_to_implement: This detection requires the Web datamodel to be populated from a - supported Technology Add-On like Splunk for Apache. Splunk for Nginx, or Splunk - for Palo Alto. -known_false_positives: GET requests will be noisy and need to be filtered out or removed - from the query based on volume. Restrict analytic to known publically facing Fortigates, - or run analytic as a Hunt until properly tuned. It is also possible the user agent - may be filtered on Report Runner or Node.js only for the exploit, however, it is - unknown at this if other user agents may be used. + - Palo Alto Network Threat +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN ("*/api/v2/cmdb/system/admin*") Web.http_method IN ("GET", "PUT") + BY Web.http_user_agent, Web.http_method, Web.url, + Web.url_length, Web.src, Web.dest, + sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `fortinet_appliance_auth_bypass_filter` +how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache. Splunk for Nginx, or Splunk for Palo Alto. +known_false_positives: GET requests will be noisy and need to be filtered out or removed from the query based on volume. Restrict analytic to known publically facing Fortigates, or run analytic as a Hunt until properly tuned. It is also possible the user agent may be filtered on Report Runner or Node.js only for the exploit, however, it is unknown at this if other user agents may be used. references: -- https://www.wordfence.com/blog/2022/10/threat-advisory-cve-2022-40684-fortinet-appliance-auth-bypass/ -- https://www.horizon3.ai/fortios-fortiproxy-and-fortiswitchmanager-authentication-bypass-technical-deep-dive-cve-2022-40684/ -- https://github.com/horizon3ai/CVE-2022-40684 -- https://www.horizon3.ai/fortinet-iocs-cve-2022-40684/ -- https://attackerkb.com/topics/QWOxGIKkGx/cve-2022-40684/rapid7-analysis -- https://github.com/rapid7/metasploit-framework/pull/17143 + - https://www.wordfence.com/blog/2022/10/threat-advisory-cve-2022-40684-fortinet-appliance-auth-bypass/ + - https://www.horizon3.ai/fortios-fortiproxy-and-fortiswitchmanager-authentication-bypass-technical-deep-dive-cve-2022-40684/ + - https://github.com/horizon3ai/CVE-2022-40684 + - https://www.horizon3.ai/fortinet-iocs-cve-2022-40684/ + - https://attackerkb.com/topics/QWOxGIKkGx/cve-2022-40684/rapid7-analysis + - https://github.com/rapid7/metasploit-framework/pull/17143 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential CVE-2022-40684 against a Fortinet appliance may be occurring - against $dest$. - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: [] + message: Potential CVE-2022-40684 against a Fortinet appliance may be occurring against $dest$. + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: [] tags: - analytic_story: - - CVE-2022-40684 Fortinet Appliance Auth bypass - asset_type: Network - cve: - - CVE-2022-40684 - mitre_attack_id: - - T1190 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - CVE-2022-40684 Fortinet Appliance Auth bypass + asset_type: Network + cve: + - CVE-2022-40684 + mitre_attack_id: + - T1190 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/fortigate/fortinetcve202240684.log - source: pan:threat - sourcetype: pan:threat + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/fortigate/fortinetcve202240684.log + source: pan:threat + sourcetype: pan:threat diff --git a/detections/web/high_volume_of_bytes_out_to_url.yml b/detections/web/high_volume_of_bytes_out_to_url.yml index 66f122934e..a14d7dd301 100644 --- a/detections/web/high_volume_of_bytes_out_to_url.yml +++ b/detections/web/high_volume_of_bytes_out_to_url.yml @@ -1,76 +1,59 @@ name: High Volume of Bytes Out to Url id: c8a6b56d-16dd-4e9c-b4bd-527742ead98d -version: 6 -date: '2025-10-14' +version: 7 +date: '2026-02-25' author: Bhavin Patel, Splunk data_source: -- Nginx Access + - Nginx Access type: Anomaly status: production -description: The following analytic detects a high volume of outbound web traffic, - specifically over 1GB of data sent to a URL within a 2-minute window. It leverages - the Web data model to identify significant uploads by analyzing the sum of bytes - out. This activity is significant as it may indicate potential data exfiltration - by malware or malicious insiders. If confirmed as malicious, this behavior could - lead to unauthorized data transfer, resulting in data breaches and loss of sensitive - information. Immediate investigation is required to determine the legitimacy of - the transfer and mitigate any potential threats. -search: '| tstats `security_content_summariesonly` count sum(Web.bytes_out) as sum_bytes_out - values(Web.user) as user values(Web.app) as app values(Web.dest) as dest from datamodel=Web - by _time span=2m Web.url Web.src sourcetype | search sum_bytes_out > 1070000000 - | `drop_dm_object_name("Web")`| `high_volume_of_bytes_out_to_url_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on Web traffic that include fields relavent for traffic into the `Web` datamodel. - Please adjust the threshold for the sum of bytes out as per your environment and - user behavior. -known_false_positives: This search may trigger false positives if there is a legitimate - reason for a high volume of bytes out to a URL. We recommend to investigate these - findings. Consider updating the filter macro to exclude the applications that are - relevant to your environment. +description: The following analytic detects a high volume of outbound web traffic, specifically over 1GB of data sent to a URL within a 2-minute window. It leverages the Web data model to identify significant uploads by analyzing the sum of bytes out. This activity is significant as it may indicate potential data exfiltration by malware or malicious insiders. If confirmed as malicious, this behavior could lead to unauthorized data transfer, resulting in data breaches and loss of sensitive information. Immediate investigation is required to determine the legitimacy of the transfer and mitigate any potential threats. +search: |- + | tstats `security_content_summariesonly` count sum(Web.bytes_out) as sum_bytes_out values(Web.user) as user values(Web.app) as app values(Web.dest) as dest FROM datamodel=Web + BY _time span=2m Web.url + Web.src sourcetype + | search sum_bytes_out > 1070000000 + | `drop_dm_object_name("Web")` + | `high_volume_of_bytes_out_to_url_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on Web traffic that include fields relavent for traffic into the `Web` datamodel. Please adjust the threshold for the sum of bytes out as per your environment and user behavior. +known_false_positives: This search may trigger false positives if there is a legitimate reason for a high volume of bytes out to a URL. We recommend to investigate these findings. Consider updating the filter macro to exclude the applications that are relevant to your environment. references: -- https://attack.mitre.org/techniques/T1567/ -- https://www.trendmicro.com/en_us/research/20/l/pawn-storm-lack-of-sophistication-as-a-strategy.html -- https://www.bleepingcomputer.com/news/security/hacking-group-s-new-malware-abuses-google-and-facebook-services/ + - https://attack.mitre.org/techniques/T1567/ + - https://www.trendmicro.com/en_us/research/20/l/pawn-storm-lack-of-sophistication-as-a-strategy.html + - https://www.bleepingcomputer.com/news/security/hacking-group-s-new-malware-abuses-google-and-facebook-services/ drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A high volume of bytes out to a URL $url$ was detected from src $src$ to - dest $dest$. - risk_objects: - - field: src - type: system - score: 9 - threat_objects: - - field: dest - type: ip_address + message: A high volume of bytes out to a URL $url$ was detected from src $src$ to dest $dest$. + risk_objects: + - field: src + type: system + score: 9 + threat_objects: + - field: dest + type: ip_address tags: - analytic_story: - - Data Exfiltration - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1567 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Data Exfiltration + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1567 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1567/web_upload_nginx/web_upload_nginx.log - source: /var/log/nginx/access.log - sourcetype: nginx:plus:kv + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1567/web_upload_nginx/web_upload_nginx.log + source: /var/log/nginx/access.log + sourcetype: nginx:plus:kv diff --git a/detections/web/http_duplicated_header.yml b/detections/web/http_duplicated_header.yml index 723ba0df7b..44fe601d8d 100644 --- a/detections/web/http_duplicated_header.yml +++ b/detections/web/http_duplicated_header.yml @@ -1,79 +1,67 @@ name: HTTP Duplicated Header id: 1606cc5b-fd5f-4865-9fe3-0ed1eaec2df6 -version: 1 -date: '2025-10-15' +version: 2 +date: '2026-02-25' author: Raven Tait, Splunk status: production type: Anomaly -description: Detects when a request has more than one of the same header. This is commonly used - in request smuggling and other web based attacks. HTTP Request Smuggling exploits inconsistencies in how front-end - and back-end servers parse HTTP requests by using ambiguous or malformed headers to hide malicious - requests within legitimate ones. Attackers leverage duplicate headers, particularly Content-Length - and Transfer-Encoding, to cause different servers in the chain to disagree on where one request - ends and another begins. RFC7230 states that a sender MUST NOT generate multiple header fields with the same field - name in a message unless either the entire field value for that header field is defined as a comma-separated - list or the header field is a well-known exception. +description: Detects when a request has more than one of the same header. This is commonly used in request smuggling and other web based attacks. HTTP Request Smuggling exploits inconsistencies in how front-end and back-end servers parse HTTP requests by using ambiguous or malformed headers to hide malicious requests within legitimate ones. Attackers leverage duplicate headers, particularly Content-Length and Transfer-Encoding, to cause different servers in the chain to disagree on where one request ends and another begins. RFC7230 states that a sender MUST NOT generate multiple header fields with the same field name in a message unless either the entire field value for that header field is defined as a comma-separated list or the header field is a well-known exception. data_source: -- Suricata -search: '`suricata` http.request_headers{}.name="*" - | rename dest_ip as dest - | spath path=http.request_headers{}.name output=header_names - | mvexpand header_names - | where lower(header_names) != "set-cookie" - | stats count by _raw, header_names, src_ip, dest - | where count > 1 - | stats values(header_names) as duplicate_headers by _raw, count, src_ip, dest - | `http_duplicated_header_filter`' -how_to_implement: This detection requires the Web datamodel - to be populated from a supported Technology Add-On like Suricata, Splunk for Apache, - Splunk for Nginx, or Splunk for Palo Alto. Some of these will need to have all headers - dumped to contain the necessary fields. -known_false_positives: False positives are not expected, however, monitor, filter, - and tune as needed based on organization log sources. + - Suricata +search: |- + `suricata` http.request_headers{}.name="*" + | rename dest_ip as dest + | spath path=http.request_headers{}.name output=header_names + | mvexpand header_names + | where lower(header_names) != "set-cookie" + | stats count + BY _raw, header_names, src_ip, + dest + | where count > 1 + | stats values(header_names) as duplicate_headers + BY _raw, count, src_ip, + dest + | `http_duplicated_header_filter` +how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Suricata, Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto. Some of these will need to have all headers dumped to contain the necessary fields. +known_false_positives: False positives are not expected, however, monitor, filter, and tune as needed based on organization log sources. references: - - https://portswigger.net/web-security/request-smuggling#what-is-http-request-smuggling - - https://portswigger.net/research/http1-must-die - - https://www.vaadata.com/blog/what-is-http-request-smuggling-exploitations-and-security-best-practices/ - - https://www.securityweek.com/new-http-request-smuggling-attacks-impacted-cdns-major-orgs-millions-of-websites/ + - https://portswigger.net/web-security/request-smuggling#what-is-http-request-smuggling + - https://portswigger.net/research/http1-must-die + - https://www.vaadata.com/blog/what-is-http-request-smuggling-exploitations-and-security-best-practices/ + - https://www.securityweek.com/new-http-request-smuggling-attacks-impacted-cdns-major-orgs-millions-of-websites/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Duplicated headers within a web request was detected. - The source IP is $src_ip$ and the destination is $dest$. - risk_objects: - - field: dest - type: system - score: 51 - threat_objects: - - field: src_ip - type: ip_address + message: Duplicated headers within a web request was detected. The source IP is $src_ip$ and the destination is $dest$. + risk_objects: + - field: dest + type: system + score: 51 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - HTTP Request Smuggling - asset_type: Network - mitre_attack_id: - - T1071.001 - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - HTTP Request Smuggling + asset_type: Network + mitre_attack_id: + - T1071.001 + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/request_smuggling/suricata_request_smuggling.log - sourcetype: suricata - source: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/request_smuggling/suricata_request_smuggling.log + sourcetype: suricata + source: suricata diff --git a/detections/web/http_possible_request_smuggling.yml b/detections/web/http_possible_request_smuggling.yml index 6014dd1fc4..adb81b78f5 100644 --- a/detections/web/http_possible_request_smuggling.yml +++ b/detections/web/http_possible_request_smuggling.yml @@ -1,78 +1,63 @@ name: HTTP Possible Request Smuggling id: 97d85f98-9d15-41a0-8682-7030454875e7 -version: 1 -date: '2025-10-06' +version: 2 +date: '2026-02-25' author: Raven Tait, Splunk status: production type: TTP -description: HTTP request smuggling is a technique for interfering with the way a web site processes sequences - of HTTP requests that are received from one or more users. Request smuggling vulnerabilities are often - critical in nature, allowing an attacker to bypass security controls, gain unauthorized access to - sensitive data, and directly compromise other application users. This detection identifies a common request - smuggling technique of using both Content-Length and Transfer-Encoding headers to cause a parsing confusion - between the frontend and backend. +description: HTTP request smuggling is a technique for interfering with the way a web site processes sequences of HTTP requests that are received from one or more users. Request smuggling vulnerabilities are often critical in nature, allowing an attacker to bypass security controls, gain unauthorized access to sensitive data, and directly compromise other application users. This detection identifies a common request smuggling technique of using both Content-Length and Transfer-Encoding headers to cause a parsing confusion between the frontend and backend. data_source: -- Suricata -search: '`suricata` (http.request_headers{}.name="*Content-Length*" http.request_headers{}.name="*Transfer-Encoding*") - OR (http.request_headers{}.name="*Content-Length*" http.request_headers{}.value="*Transfer-Encoding*") - OR (http.request_headers{}.value="*Content-Length*" http.request_headers{}.name="*Transfer-Encoding*") - OR (http.request_headers{}.name="*Content-Length*" http.request_headers{}.value="0") - | rename dest_ip as dest - | rex field=_raw "request_headers.:\[(?.*)\]" - | stats count min(_time) as firstTime max(_time) as lastTime by dest, dest_port, src_ip, http.url, - http.http_method, http.http_user_agent, http.protocol, http.status, headers - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `http_possible_request_smuggling_filter`' -how_to_implement: This detection requires the Web datamodel - to be populated from a supported Technology Add-On like Suricata, Splunk for Apache, - Splunk for Nginx, or Splunk for Palo Alto. Some of these will need to have all headers - dumped to contain the necessary fields. -known_false_positives: False positives are not expected, however, monitor, filter, - and tune as needed based on organization log sources. + - Suricata +search: |- + `suricata` (http.request_headers{}.name="*Content-Length*" http.request_headers{}.name="*Transfer-Encoding*") OR (http.request_headers{}.name="*Content-Length*" http.request_headers{}.value="*Transfer-Encoding*") OR (http.request_headers{}.value="*Content-Length*" http.request_headers{}.name="*Transfer-Encoding*") OR (http.request_headers{}.name="*Content-Length*" http.request_headers{}.value="0") + | rename dest_ip as dest + | rex field=_raw "request_headers.:\[(?.*)\]" + | stats count min(_time) as firstTime max(_time) as lastTime + BY dest, dest_port, src_ip, + http.url, http.http_method, http.http_user_agent, + http.protocol, http.status, headers + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `http_possible_request_smuggling_filter` +how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Suricata, Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto. Some of these will need to have all headers dumped to contain the necessary fields. +known_false_positives: False positives are not expected, however, monitor, filter, and tune as needed based on organization log sources. references: - - https://portswigger.net/web-security/request-smuggling#what-is-http-request-smuggling - - https://portswigger.net/research/http1-must-die - - https://www.vaadata.com/blog/what-is-http-request-smuggling-exploitations-and-security-best-practices/ - - https://www.securityweek.com/new-http-request-smuggling-attacks-impacted-cdns-major-orgs-millions-of-websites/ + - https://portswigger.net/web-security/request-smuggling#what-is-http-request-smuggling + - https://portswigger.net/research/http1-must-die + - https://www.vaadata.com/blog/what-is-http-request-smuggling-exploitations-and-security-best-practices/ + - https://www.securityweek.com/new-http-request-smuggling-attacks-impacted-cdns-major-orgs-millions-of-websites/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible request smuggling against a web request was detected. - The source IP is $src_ip$ and the destination is $dest$. - risk_objects: - - field: dest - type: system - score: 60 - threat_objects: - - field: src_ip - type: ip_address + message: Possible request smuggling against a web request was detected. The source IP is $src_ip$ and the destination is $dest$. + risk_objects: + - field: dest + type: system + score: 60 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - HTTP Request Smuggling - asset_type: Network - mitre_attack_id: - - T1071.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - HTTP Request Smuggling + asset_type: Network + mitre_attack_id: + - T1071.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/request_smuggling/suricata_request_smuggling.log - sourcetype: suricata - source: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/request_smuggling/suricata_request_smuggling.log + sourcetype: suricata + source: suricata diff --git a/detections/web/http_rapid_post_with_mixed_status_codes.yml b/detections/web/http_rapid_post_with_mixed_status_codes.yml index 183a78b407..111eeeb3c8 100644 --- a/detections/web/http_rapid_post_with_mixed_status_codes.yml +++ b/detections/web/http_rapid_post_with_mixed_status_codes.yml @@ -1,73 +1,64 @@ name: HTTP Rapid POST with Mixed Status Codes id: c8c987d6-3a1a-4555-9a52-eea0741b6113 -version: 1 -date: '2025-10-14' +version: 2 +date: '2026-02-25' author: Raven Tait, Splunk status: production type: Anomaly -description: This detection identifies rapid-fire POST request attacks where an attacker - sends more than 20 POST requests within a 5-second window, potentially attempting to - exploit race conditions or overwhelm request handling. The pattern is particularly - suspicious when responses vary in size or status codes, indicating successful - exploitation attempts or probing for vulnerable endpoints. +description: This detection identifies rapid-fire POST request attacks where an attacker sends more than 20 POST requests within a 5-second window, potentially attempting to exploit race conditions or overwhelm request handling. The pattern is particularly suspicious when responses vary in size or status codes, indicating successful exploitation attempts or probing for vulnerable endpoints. data_source: -- Nginx Access -search: '`nginx_access_logs` http_method="POST"| bin _time span=5s - | rename dest_ip as dest - | stats count, values(status) as status_codes, values(bytes_out) as bytes_out, values(uri_path) as uris by _time, src_ip, dest, http_user_agent - | where count>20 - | table _time, dest, src_ip, count, status_codes, bytes_out, http_user_agent - | `http_rapid_post_with_mixed_status_codes_filter`' -how_to_implement: This analytic necessitates the collection of web data, which can - be achieved through Splunk Stream or by utilizing the Splunk Add-on for Apache Web - Server. No additional configuration is required for this analytic. -known_false_positives: False positives may be present if the activity is part of diagnostics - or testing. Filter as needed. + - Nginx Access +search: |- + `nginx_access_logs` http_method="POST" + | bin _time span=5s + | rename dest_ip as dest + | stats count, values(status) as status_codes, values(bytes_out) as bytes_out, values(uri_path) as uris + BY _time, src_ip, dest, + http_user_agent + | where count>20 + | table _time, dest, src_ip, count, status_codes, bytes_out, http_user_agent + | `http_rapid_post_with_mixed_status_codes_filter` +how_to_implement: This analytic necessitates the collection of web data, which can be achieved through Splunk Stream or by utilizing the Splunk Add-on for Apache Web Server. No additional configuration is required for this analytic. +known_false_positives: False positives may be present if the activity is part of diagnostics or testing. Filter as needed. references: - - https://portswigger.net/web-security/request-smuggling#what-is-http-request-smuggling - - https://portswigger.net/research/http1-must-die - - https://www.vaadata.com/blog/what-is-http-request-smuggling-exploitations-and-security-best-practices/ - - https://www.securityweek.com/new-http-request-smuggling-attacks-impacted-cdns-major-orgs-millions-of-websites/ + - https://portswigger.net/web-security/request-smuggling#what-is-http-request-smuggling + - https://portswigger.net/research/http1-must-die + - https://www.vaadata.com/blog/what-is-http-request-smuggling-exploitations-and-security-best-practices/ + - https://www.securityweek.com/new-http-request-smuggling-attacks-impacted-cdns-major-orgs-millions-of-websites/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A potential attempt to perform request smuggling against a web server was detected. - The source IP is $src_ip$ and the destination is $dest$. - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: - - field: src_ip - type: ip_address + message: A potential attempt to perform request smuggling against a web server was detected. The source IP is $src_ip$ and the destination is $dest$. + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - HTTP Request Smuggling - asset_type: Web Server - mitre_attack_id: - - T1071.001 - - T1190 - - T1595 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - HTTP Request Smuggling + asset_type: Web Server + mitre_attack_id: + - T1071.001 + - T1190 + - T1595 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/request_smuggling/nginx_request_smuggling.log - source: nginx:plus:kv - sourcetype: nginx:plus:kv + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/request_smuggling/nginx_request_smuggling.log + source: nginx:plus:kv + sourcetype: nginx:plus:kv diff --git a/detections/web/http_request_to_reserved_name_on_iis_server.yml b/detections/web/http_request_to_reserved_name_on_iis_server.yml index 155bf8604c..38bc2ba07a 100644 --- a/detections/web/http_request_to_reserved_name_on_iis_server.yml +++ b/detections/web/http_request_to_reserved_name_on_iis_server.yml @@ -1,79 +1,62 @@ name: HTTP Request to Reserved Name on IIS Server id: 1e45e6a8-110b-4886-b815-8d69cf35bf0a -version: 1 -date: '2025-10-17' +version: 2 +date: '2026-02-25' author: Raven Tait, Splunk status: production type: TTP -description: Detects attempts to exploit a request smuggling technique against IIS that leverages - a Windows quirk where requests for reserved Windows device names such as "/con" trigger an early - server response before the request body is received. When combined with a Content-Length desynchronization, - this behavior can lead to a parsing confusion between frontend and backend. +description: Detects attempts to exploit a request smuggling technique against IIS that leverages a Windows quirk where requests for reserved Windows device names such as "/con" trigger an early server response before the request body is received. When combined with a Content-Length desynchronization, this behavior can lead to a parsing confusion between frontend and backend. data_source: -- Suricata -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url IN ("/con", "/prn", "/aux", "/nul", "/com1","/com2","/com3","/com4", - "/com5","/com6","/com7") by Web.src, Web.dest, Web.http_user_agent, Web.url, Web.status, - Web.http_method - | `drop_dm_object_name("Web")` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `http_request_to_reserved_name_on_iis_server_filter`' -how_to_implement: To implement this analytic, ensure proper logging is occurring with - IIS, Apache, or a Proxy server and that these logs are being ingested into Splunk. - The analytic was written against Suricata. The proper TA will need to be enabled - and should be mapped to CIM and the Web datamodel. Ingestion of the data source - is required to utilize this detection. In addition, if it is not mapped to the datamodel, - modify the query for your application logs to look for requests the same URI and - investigate further. -known_false_positives: False positives are not expected on IIS servers, as the detection is based - on the presence of web requests to reserved names, which is not a common - page to be accessed by legitimate users. Modify the query as needed to - reduce false positives or hunt for additional indicators of compromise. + - Suricata +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN ("/con", "/prn", "/aux", "/nul", "/com1","/com2","/com3","/com4", "/com5","/com6","/com7") + BY Web.src, Web.dest, Web.http_user_agent, + Web.url, Web.status, Web.http_method + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `http_request_to_reserved_name_on_iis_server_filter` +how_to_implement: To implement this analytic, ensure proper logging is occurring with IIS, Apache, or a Proxy server and that these logs are being ingested into Splunk. The analytic was written against Suricata. The proper TA will need to be enabled and should be mapped to CIM and the Web datamodel. Ingestion of the data source is required to utilize this detection. In addition, if it is not mapped to the datamodel, modify the query for your application logs to look for requests the same URI and investigate further. +known_false_positives: False positives are not expected on IIS servers, as the detection is based on the presence of web requests to reserved names, which is not a common page to be accessed by legitimate users. Modify the query as needed to reduce false positives or hunt for additional indicators of compromise. references: - - https://portswigger.net/web-security/request-smuggling#what-is-http-request-smuggling - - https://portswigger.net/research/http1-must-die - - https://www.vaadata.com/blog/what-is-http-request-smuggling-exploitations-and-security-best-practices/ - - https://www.securityweek.com/new-http-request-smuggling-attacks-impacted-cdns-major-orgs-millions-of-websites/ + - https://portswigger.net/web-security/request-smuggling#what-is-http-request-smuggling + - https://portswigger.net/research/http1-must-die + - https://www.vaadata.com/blog/what-is-http-request-smuggling-exploitations-and-security-best-practices/ + - https://www.securityweek.com/new-http-request-smuggling-attacks-impacted-cdns-major-orgs-millions-of-websites/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Known scripting tool was used against a web request. - The source IP is $src$ and the destination is $dest$. - risk_objects: - - field: dest - type: system - score: 31 - threat_objects: - - field: src - type: ip_address + message: Known scripting tool was used against a web request. The source IP is $src$ and the destination is $dest$. + risk_objects: + - field: dest + type: system + score: 31 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - HTTP Request Smuggling - asset_type: Network - mitre_attack_id: - - T1071.001 - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - HTTP Request Smuggling + asset_type: Network + mitre_attack_id: + - T1071.001 + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/request_smuggling/suricata_reserved_names.log - sourcetype: suricata - source: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/request_smuggling/suricata_reserved_names.log + sourcetype: suricata + source: suricata diff --git a/detections/web/http_scripting_tool_user_agent.yml b/detections/web/http_scripting_tool_user_agent.yml index 315ab611f7..249f417802 100644 --- a/detections/web/http_scripting_tool_user_agent.yml +++ b/detections/web/http_scripting_tool_user_agent.yml @@ -1,78 +1,68 @@ name: HTTP Scripting Tool User Agent id: 04430b4e-5ca8-4e88-98b5-d6bcf54f8393 -version: 1 -date: '2025-10-09' +version: 2 +date: '2026-02-25' author: Raven Tait, Splunk status: production type: Anomaly -description: This Splunk query analyzes web access logs to identify and categorize - non-browser user agents, detecting various types of security tools, scripting languages, - automation frameworks, and suspicious patterns. This activity can signify malicious actors - attempting to interact with web endpoints in non-standard ways. +description: This Splunk query analyzes web access logs to identify and categorize non-browser user agents, detecting various types of security tools, scripting languages, automation frameworks, and suspicious patterns. This activity can signify malicious actors attempting to interact with web endpoints in non-standard ways. data_source: -- Nginx Access -search: '`nginx_access_logs` - | eval http_user_agent = lower(http_user_agent) - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `drop_dm_object_name(Web)` - | lookup scripting_tools_user_agents tool_user_agent AS http_user_agent OUTPUT tool - | where isnotnull(tool) - | rename dest_ip as dest - | stats count min(firstTime) as first_seen max(lastTime) as last_seen values(tool) as tool - by http_user_agent dest src_ip status - | `http_scripting_tool_user_agent_filter`' -how_to_implement: This analytic necessitates the collection of web data, which can - be achieved through Splunk Stream or by utilizing the Splunk Add-on for Apache Web - Server. No additional configuration is required for this analytic. -known_false_positives: False positives may be present if the activity is part of diagnostics - or testing. Filter as needed. + - Nginx Access +search: |- + `nginx_access_logs` + | eval http_user_agent = lower(http_user_agent) + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `drop_dm_object_name(Web)` + | lookup scripting_tools_user_agents tool_user_agent AS http_user_agent OUTPUT tool + | where isnotnull(tool) + | rename dest_ip as dest + | stats count min(firstTime) as first_seen max(lastTime) as last_seen values(tool) as tool + BY http_user_agent dest src_ip + status + | `http_scripting_tool_user_agent_filter` +how_to_implement: This analytic necessitates the collection of web data, which can be achieved through Splunk Stream or by utilizing the Splunk Add-on for Apache Web Server. No additional configuration is required for this analytic. +known_false_positives: False positives may be present if the activity is part of diagnostics or testing. Filter as needed. references: - - https://portswigger.net/web-security/request-smuggling#what-is-http-request-smuggling - - https://portswigger.net/research/http1-must-die - - https://www.vaadata.com/blog/what-is-http-request-smuggling-exploitations-and-security-best-practices/ - - https://www.securityweek.com/new-http-request-smuggling-attacks-impacted-cdns-major-orgs-millions-of-websites/ - - https://github.com/SigmaHQ/sigma/blob/master/rules/web/proxy_generic/proxy_ua_hacktool.yml - - https://help.aikido.dev/zen-firewall/miscellaneous/bot-protection-details + - https://portswigger.net/web-security/request-smuggling#what-is-http-request-smuggling + - https://portswigger.net/research/http1-must-die + - https://www.vaadata.com/blog/what-is-http-request-smuggling-exploitations-and-security-best-practices/ + - https://www.securityweek.com/new-http-request-smuggling-attacks-impacted-cdns-major-orgs-millions-of-websites/ + - https://github.com/SigmaHQ/sigma/blob/master/rules/web/proxy_generic/proxy_ua_hacktool.yml + - https://help.aikido.dev/zen-firewall/miscellaneous/bot-protection-details drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Known scripting tool was used against a web request. - The source IP is $src_ip$ and the destination is $dest$. - risk_objects: - - field: dest - type: system - score: 31 - threat_objects: - - field: src_ip - type: ip_address + message: Known scripting tool was used against a web request. The source IP is $src_ip$ and the destination is $dest$. + risk_objects: + - field: dest + type: system + score: 31 + threat_objects: + - field: src_ip + type: ip_address tags: - analytic_story: - - HTTP Request Smuggling - - Suspicious User Agents - asset_type: Network - mitre_attack_id: - - T1071.001 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - HTTP Request Smuggling + - Suspicious User Agents + asset_type: Network + mitre_attack_id: + - T1071.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/request_smuggling/nginx_scripting_tools.log - source: nginx:plus:kv - sourcetype: nginx:plus:kv + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/request_smuggling/nginx_scripting_tools.log + source: nginx:plus:kv + sourcetype: nginx:plus:kv diff --git a/detections/web/hunting_for_log4shell.yml b/detections/web/hunting_for_log4shell.yml index 5b435a24ec..9525ead0cc 100644 --- a/detections/web/hunting_for_log4shell.yml +++ b/detections/web/hunting_for_log4shell.yml @@ -5,63 +5,38 @@ date: '2025-05-02' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic detects potential exploitation attempts of the - Log4Shell vulnerability (CVE-2021-44228) by analyzing HTTP headers for specific - patterns. It leverages the Web Datamodel and evaluates various indicators such as - the presence of `{jndi:`, environment variables, and common URI paths. This detection - is significant as Log4Shell allows remote code execution, posing a severe threat - to systems. If confirmed malicious, attackers could gain unauthorized access, execute - arbitrary code, and potentially compromise sensitive data, leading to extensive - damage and data breaches. +description: The following analytic detects potential exploitation attempts of the Log4Shell vulnerability (CVE-2021-44228) by analyzing HTTP headers for specific patterns. It leverages the Web Datamodel and evaluates various indicators such as the presence of `{jndi:`, environment variables, and common URI paths. This detection is significant as Log4Shell allows remote code execution, posing a severe threat to systems. If confirmed malicious, attackers could gain unauthorized access, execute arbitrary code, and potentially compromise sensitive data, leading to extensive damage and data breaches. data_source: -- Nginx Access -search: '| from datamodel Web.Web | eval jndi=if(match(_raw, "(\{|%7B)[jJnNdDiI]{4}:"),4,0) - | eval jndi_fastmatch=if(match(_raw, "[jJnNdDiI]{4}"),2,0) | eval jndi_proto=if(match(_raw,"(?i)jndi:(ldap[s]?|rmi|dns|nis|iiop|corba|nds|http|https):"),5,0) - | eval all_match = if(match(_raw, "(?i)(%(25){0,}20|\s)*(%(25){0,}24|\$)(%(25){0,}20|\s)*(%(25){0,}7B|{)(%(25){0,}20|\s)*(%(25){0,}(6A|4A)|J)(%(25){0,}(6E|4E)|N)(%(25){0,}(64|44)|D)(%(25){0,}(69|49)|I)(%(25){0,}20|\s)*(%(25){0,}3A|:)[\w\%]+(%(25){1,}3A|:)(%(25){1,}2F|\/)[^\n]+"),5,0) - | eval env_var = if(match(_raw, "env:") OR match(_raw, "env:AWS_ACCESS_KEY_ID") - OR match(_raw, "env:AWS_SECRET_ACCESS_KEY"),5,0) | eval uridetect = if(match(_raw, - "(?i)Basic\/Command\/Base64|Basic\/ReverseShell|Basic\/TomcatMemshell|Basic\/JBossMemshell|Basic\/WebsphereMemshell|Basic\/SpringMemshell|Basic\/Command|Deserialization\/CommonsCollectionsK|Deserialization\/CommonsBeanutils|Deserialization\/Jre8u20\/TomcatMemshell|Deserialization\/CVE_2020_2555\/WeblogicMemshell|TomcatBypass|GroovyBypass|WebsphereBypass"),4,0) - | eval keywords = if(match(_raw,"(?i)\$\{ctx\:loginId\}|\$\{map\:type\}|\$\{filename\}|\$\{date\:MM-dd-yyyy\}|\$\{docker\:containerId\}|\$\{docker\:containerName\}|\$\{docker\:imageName\}|\$\{env\:USER\}|\$\{event\:Marker\}|\$\{mdc\:UserId\}|\$\{java\:runtime\}|\$\{java\:vm\}|\$\{java\:os\}|\$\{jndi\:logging/context-name\}|\$\{hostName\}|\$\{docker\:containerId\}|\$\{k8s\:accountName\}|\$\{k8s\:clusterName\}|\$\{k8s\:containerId\}|\$\{k8s\:containerName\}|\$\{k8s\:host\}|\$\{k8s\:labels.app\}|\$\{k8s\:labels.podTemplateHash\}|\$\{k8s\:masterUrl\}|\$\{k8s\:namespaceId\}|\$\{k8s\:namespaceName\}|\$\{k8s\:podId\}|\$\{k8s\:podIp\}|\$\{k8s\:podName\}|\$\{k8s\:imageId\}|\$\{k8s\:imageName\}|\$\{log4j\:configLocation\}|\$\{log4j\:configParentLocation\}|\$\{spring\:spring.application.name\}|\$\{main\:myString\}|\$\{main\:0\}|\$\{main\:1\}|\$\{main\:2\}|\$\{main\:3\}|\$\{main\:4\}|\$\{main\:bar\}|\$\{name\}|\$\{marker\}|\$\{marker\:name\}|\$\{spring\:profiles.active[0]|\$\{sys\:logPath\}|\$\{web\:rootDir\}|\$\{sys\:user.name\}"),4,0) - | eval obf = if(match(_raw, "(\$|%24)[^ /]*({|%7b)[^ /]*(j|%6a)[^ /]*(n|%6e)[^ /]*(d|%64)[^ - /]*(i|%69)[^ /]*(:|%3a)[^ /]*(:|%3a)[^ /]*(/|%2f)"),5,0) | eval lookups = if(match(_raw, - "(?i)({|%7b)(main|sys|k8s|spring|lower|upper|env|date|sd)"),4,0) | addtotals fieldname=Score, - jndi, jndi_proto, env_var, uridetect, all_match, jndi_fastmatch, keywords, obf, - lookups | where Score > 2 | stats values(Score) by jndi, jndi_proto, env_var, uridetect, - all_match, jndi_fastmatch, keywords, lookups, obf, dest, src, http_method, _raw - | `hunting_for_log4shell_filter`' -how_to_implement: Out of the box, the Web datamodel is required to be pre-filled. - However, tested was performed against raw httpd access logs. Change the first line - to any dataset to pass the regex's against. -known_false_positives: It is highly possible you will find false positives, however, - the base score is set to 2 for _any_ jndi found in raw logs. tune and change as - needed, include any filtering. + - Nginx Access +search: '| from datamodel Web.Web | eval jndi=if(match(_raw, "(\{|%7B)[jJnNdDiI]{4}:"),4,0) | eval jndi_fastmatch=if(match(_raw, "[jJnNdDiI]{4}"),2,0) | eval jndi_proto=if(match(_raw,"(?i)jndi:(ldap[s]?|rmi|dns|nis|iiop|corba|nds|http|https):"),5,0) | eval all_match = if(match(_raw, "(?i)(%(25){0,}20|\s)*(%(25){0,}24|\$)(%(25){0,}20|\s)*(%(25){0,}7B|{)(%(25){0,}20|\s)*(%(25){0,}(6A|4A)|J)(%(25){0,}(6E|4E)|N)(%(25){0,}(64|44)|D)(%(25){0,}(69|49)|I)(%(25){0,}20|\s)*(%(25){0,}3A|:)[\w\%]+(%(25){1,}3A|:)(%(25){1,}2F|\/)[^\n]+"),5,0) | eval env_var = if(match(_raw, "env:") OR match(_raw, "env:AWS_ACCESS_KEY_ID") OR match(_raw, "env:AWS_SECRET_ACCESS_KEY"),5,0) | eval uridetect = if(match(_raw, "(?i)Basic\/Command\/Base64|Basic\/ReverseShell|Basic\/TomcatMemshell|Basic\/JBossMemshell|Basic\/WebsphereMemshell|Basic\/SpringMemshell|Basic\/Command|Deserialization\/CommonsCollectionsK|Deserialization\/CommonsBeanutils|Deserialization\/Jre8u20\/TomcatMemshell|Deserialization\/CVE_2020_2555\/WeblogicMemshell|TomcatBypass|GroovyBypass|WebsphereBypass"),4,0) | eval keywords = if(match(_raw,"(?i)\$\{ctx\:loginId\}|\$\{map\:type\}|\$\{filename\}|\$\{date\:MM-dd-yyyy\}|\$\{docker\:containerId\}|\$\{docker\:containerName\}|\$\{docker\:imageName\}|\$\{env\:USER\}|\$\{event\:Marker\}|\$\{mdc\:UserId\}|\$\{java\:runtime\}|\$\{java\:vm\}|\$\{java\:os\}|\$\{jndi\:logging/context-name\}|\$\{hostName\}|\$\{docker\:containerId\}|\$\{k8s\:accountName\}|\$\{k8s\:clusterName\}|\$\{k8s\:containerId\}|\$\{k8s\:containerName\}|\$\{k8s\:host\}|\$\{k8s\:labels.app\}|\$\{k8s\:labels.podTemplateHash\}|\$\{k8s\:masterUrl\}|\$\{k8s\:namespaceId\}|\$\{k8s\:namespaceName\}|\$\{k8s\:podId\}|\$\{k8s\:podIp\}|\$\{k8s\:podName\}|\$\{k8s\:imageId\}|\$\{k8s\:imageName\}|\$\{log4j\:configLocation\}|\$\{log4j\:configParentLocation\}|\$\{spring\:spring.application.name\}|\$\{main\:myString\}|\$\{main\:0\}|\$\{main\:1\}|\$\{main\:2\}|\$\{main\:3\}|\$\{main\:4\}|\$\{main\:bar\}|\$\{name\}|\$\{marker\}|\$\{marker\:name\}|\$\{spring\:profiles.active[0]|\$\{sys\:logPath\}|\$\{web\:rootDir\}|\$\{sys\:user.name\}"),4,0) | eval obf = if(match(_raw, "(\$|%24)[^ /]*({|%7b)[^ /]*(j|%6a)[^ /]*(n|%6e)[^ /]*(d|%64)[^ /]*(i|%69)[^ /]*(:|%3a)[^ /]*(:|%3a)[^ /]*(/|%2f)"),5,0) | eval lookups = if(match(_raw, "(?i)({|%7b)(main|sys|k8s|spring|lower|upper|env|date|sd)"),4,0) | addtotals fieldname=Score, jndi, jndi_proto, env_var, uridetect, all_match, jndi_fastmatch, keywords, obf, lookups | where Score > 2 | stats values(Score) by jndi, jndi_proto, env_var, uridetect, all_match, jndi_fastmatch, keywords, lookups, obf, dest, src, http_method, _raw | `hunting_for_log4shell_filter`' +how_to_implement: Out of the box, the Web datamodel is required to be pre-filled. However, tested was performed against raw httpd access logs. Change the first line to any dataset to pass the regex's against. +known_false_positives: It is highly possible you will find false positives, however, the base score is set to 2 for _any_ jndi found in raw logs. tune and change as needed, include any filtering. references: -- https://gist.github.com/olafhartong/916ebc673ba066537740164f7e7e1d72 -- https://gist.github.com/Neo23x0/e4c8b03ff8cdf1fa63b7d15db6e3860b#gistcomment-3994449 -- https://regex101.com/r/OSrm0q/1/ -- https://github.com/Neo23x0/signature-base/blob/master/yara/expl_log4j_cve_2021_44228.yar -- https://news.sophos.com/en-us/2021/12/12/log4shell-hell-anatomy-of-an-exploit-outbreak/ -- https://gist.github.com/MHaggis/1899b8554f38c8692a9fb0ceba60b44c -- https://twitter.com/sasi2103/status/1469764719850442760?s=20 + - https://gist.github.com/olafhartong/916ebc673ba066537740164f7e7e1d72 + - https://gist.github.com/Neo23x0/e4c8b03ff8cdf1fa63b7d15db6e3860b#gistcomment-3994449 + - https://regex101.com/r/OSrm0q/1/ + - https://github.com/Neo23x0/signature-base/blob/master/yara/expl_log4j_cve_2021_44228.yar + - https://news.sophos.com/en-us/2021/12/12/log4shell-hell-anatomy-of-an-exploit-outbreak/ + - https://gist.github.com/MHaggis/1899b8554f38c8692a9fb0ceba60b44c + - https://twitter.com/sasi2103/status/1469764719850442760?s=20 tags: - analytic_story: - - Log4Shell CVE-2021-44228 - - CISA AA22-320A - asset_type: Web Server - cve: - - CVE-2021-44228 - mitre_attack_id: - - T1190 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Log4Shell CVE-2021-44228 + - CISA AA22-320A + asset_type: Web Server + cve: + - CVE-2021-44228 + mitre_attack_id: + - T1190 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/java/log4shell-nginx.log - source: /var/log/nginx/access.log - sourcetype: nginx:plus:kv + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/java/log4shell-nginx.log + source: /var/log/nginx/access.log + sourcetype: nginx:plus:kv diff --git a/detections/web/ivanti_connect_secure_command_injection_attempts.yml b/detections/web/ivanti_connect_secure_command_injection_attempts.yml index 978180327b..503ce3b5e0 100644 --- a/detections/web/ivanti_connect_secure_command_injection_attempts.yml +++ b/detections/web/ivanti_connect_secure_command_injection_attempts.yml @@ -1,80 +1,66 @@ name: Ivanti Connect Secure Command Injection Attempts id: 1f32a7e0-a060-4545-b7de-73fcf9ad536e -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Suricata -description: The following analytic identifies attempts to exploit the CVE-2023-46805 - and CVE-2024-21887 vulnerabilities in Ivanti Connect Secure. It detects POST requests - to specific URIs that leverage command injection to execute arbitrary commands. - The detection uses the Web datamodel to monitor for these requests and checks for - a 200 OK response, indicating a successful exploit attempt. This activity is significant - as it can lead to unauthorized command execution on the server. If confirmed malicious, - attackers could gain control over the system, leading to potential data breaches - or further network compromise. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url IN("*/api/v1/totp/user-backup-code/../../system/maintenance/archiving/cloud-server-test-connection*","*/api/v1/totp/user-backup-code/../../license/keys-status/*") - Web.http_method IN ("POST", "GET") Web.status=200 by Web.src, Web.dest, Web.http_user_agent, - Web.url, Web.http_method, Web.status | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `ivanti_connect_secure_command_injection_attempts_filter`' -how_to_implement: This detection requires the Web datamodel to be populated from a - supported Technology Add-On like Suricata, Splunk for Apache, Splunk for Nginx, - or Splunk for Palo Alto. -known_false_positives: This analytic is limited to HTTP Status 200; adjust as necessary. - False positives may occur if the URI path is IP-restricted or externally blocked. - It's recommended to review the context of the alerts and adjust the analytic parameters - to better fit the specific environment. + - Suricata +description: The following analytic identifies attempts to exploit the CVE-2023-46805 and CVE-2024-21887 vulnerabilities in Ivanti Connect Secure. It detects POST requests to specific URIs that leverage command injection to execute arbitrary commands. The detection uses the Web datamodel to monitor for these requests and checks for a 200 OK response, indicating a successful exploit attempt. This activity is significant as it can lead to unauthorized command execution on the server. If confirmed malicious, attackers could gain control over the system, leading to potential data breaches or further network compromise. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN("*/api/v1/totp/user-backup-code/../../system/maintenance/archiving/cloud-server-test-connection*","*/api/v1/totp/user-backup-code/../../license/keys-status/*") Web.http_method IN ("POST", "GET") Web.status=200 + BY Web.src, Web.dest, Web.http_user_agent, + Web.url, Web.http_method, Web.status + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `ivanti_connect_secure_command_injection_attempts_filter` +how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Suricata, Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto. +known_false_positives: This analytic is limited to HTTP Status 200; adjust as necessary. False positives may occur if the URI path is IP-restricted or externally blocked. It's recommended to review the context of the alerts and adjust the analytic parameters to better fit the specific environment. references: -- https://github.com/RootUp/PersonalStuff/blob/master/http-vuln-cve2023-46805_2024_21887.nse -- https://github.com/projectdiscovery/nuclei-templates/blob/c6b351e71b0fb0e40e222e97038f1fe09ac58194/http/misconfiguration/ivanti/CVE-2023-46085-CVE-2024-21887-mitigation-not-applied.yaml -- https://github.com/rapid7/metasploit-framework/pull/18708/files -- https://attackerkb.com/topics/AdUh6by52K/cve-2023-46805/rapid7-analysis -- https://labs.watchtowr.com/welcome-to-2024-the-sslvpn-chaos-continues-ivanti-cve-2023-46805-cve-2024-21887/ -- https://twitter.com/GreyNoiseIO/status/1747711939466453301 + - https://github.com/RootUp/PersonalStuff/blob/master/http-vuln-cve2023-46805_2024_21887.nse + - https://github.com/projectdiscovery/nuclei-templates/blob/c6b351e71b0fb0e40e222e97038f1fe09ac58194/http/misconfiguration/ivanti/CVE-2023-46085-CVE-2024-21887-mitigation-not-applied.yaml + - https://github.com/rapid7/metasploit-framework/pull/18708/files + - https://attackerkb.com/topics/AdUh6by52K/cve-2023-46805/rapid7-analysis + - https://labs.watchtowr.com/welcome-to-2024-the-sslvpn-chaos-continues-ivanti-cve-2023-46805-cve-2024-21887/ + - https://twitter.com/GreyNoiseIO/status/1747711939466453301 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible exploitation of CVE-2023-46805 and CVE-2024-21887 against $dest$. - risk_objects: - - field: dest - type: system - score: 90 - threat_objects: [] + message: Possible exploitation of CVE-2023-46805 and CVE-2024-21887 against $dest$. + risk_objects: + - field: dest + type: system + score: 90 + threat_objects: [] tags: - cve: - - CVE-2023-46805 - - CVE-2024-21887 - analytic_story: - - Ivanti Connect Secure VPN Vulnerabilities - - CISA AA24-241A - asset_type: VPN Appliance - atomic_guid: [] - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + cve: + - CVE-2023-46805 + - CVE-2024-21887 + analytic_story: + - Ivanti Connect Secure VPN Vulnerabilities + - CISA AA24-241A + asset_type: VPN Appliance + atomic_guid: [] + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/suricata_ivanti_secure_connect_exploitphase.log - source: suricata - sourcetype: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/suricata_ivanti_secure_connect_exploitphase.log + source: suricata + sourcetype: suricata diff --git a/detections/web/ivanti_connect_secure_ssrf_in_saml_component.yml b/detections/web/ivanti_connect_secure_ssrf_in_saml_component.yml index fef0d6a67e..70fddb9cc3 100644 --- a/detections/web/ivanti_connect_secure_ssrf_in_saml_component.yml +++ b/detections/web/ivanti_connect_secure_ssrf_in_saml_component.yml @@ -1,76 +1,62 @@ name: Ivanti Connect Secure SSRF in SAML Component id: 8e6ca490-7af3-4299-9a24-39fb69759925 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Suricata -description: The following analytic identifies POST requests targeting endpoints vulnerable - to the SSRF issue (CVE-2024-21893) in Ivanti's products. It leverages the Web data - model, focusing on endpoints such as /dana-ws/saml20.ws, /dana-ws/saml.ws, /dana-ws/samlecp.ws, - and /dana-na/auth/saml-logout.cgi. The detection filters for POST requests that - received an HTTP 200 OK response, indicating successful execution. This activity - is significant as it may indicate an attempt to exploit SSRF vulnerabilities, potentially - allowing attackers to access internal services or sensitive data. If confirmed malicious, - this could lead to unauthorized access and data exfiltration. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url IN ("*/dana-ws/saml20.ws*","*/dana-ws/saml.ws*","*/dana-ws/samlecp.ws*","*/dana-na/auth/saml-logout.cgi/*") - Web.http_method=POST Web.status=200 by Web.src, Web.dest, Web.http_user_agent, Web.url, - Web.status, Web.http_method | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `ivanti_connect_secure_ssrf_in_saml_component_filter`' -how_to_implement: This detection requires the Web datamodel to be populated from a - supported Technology Add-On like Suricata, Splunk for Apache, Splunk for Nginx, - or Splunk for Palo Alto. -known_false_positives: This analytic is limited to HTTP Status 200; adjust as necessary. - False positives may occur if the HTTP Status is removed, as most failed attempts - result in a 301. It's recommended to review the context of the alerts and adjust - the analytic parameters to better fit the specific environment. + - Suricata +description: The following analytic identifies POST requests targeting endpoints vulnerable to the SSRF issue (CVE-2024-21893) in Ivanti's products. It leverages the Web data model, focusing on endpoints such as /dana-ws/saml20.ws, /dana-ws/saml.ws, /dana-ws/samlecp.ws, and /dana-na/auth/saml-logout.cgi. The detection filters for POST requests that received an HTTP 200 OK response, indicating successful execution. This activity is significant as it may indicate an attempt to exploit SSRF vulnerabilities, potentially allowing attackers to access internal services or sensitive data. If confirmed malicious, this could lead to unauthorized access and data exfiltration. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN ("*/dana-ws/saml20.ws*","*/dana-ws/saml.ws*","*/dana-ws/samlecp.ws*","*/dana-na/auth/saml-logout.cgi/*") Web.http_method=POST Web.status=200 + BY Web.src, Web.dest, Web.http_user_agent, + Web.url, Web.status, Web.http_method + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `ivanti_connect_secure_ssrf_in_saml_component_filter` +how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Suricata, Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto. +known_false_positives: This analytic is limited to HTTP Status 200; adjust as necessary. False positives may occur if the HTTP Status is removed, as most failed attempts result in a 301. It's recommended to review the context of the alerts and adjust the analytic parameters to better fit the specific environment. references: -- https://attackerkb.com/topics/FGlK1TVnB2/cve-2024-21893/rapid7-analysis -- https://www.assetnote.io/resources/research/ivantis-pulse-connect-secure-auth-bypass-round-two + - https://attackerkb.com/topics/FGlK1TVnB2/cve-2024-21893/rapid7-analysis + - https://www.assetnote.io/resources/research/ivantis-pulse-connect-secure-auth-bypass-round-two drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible exploitation of CVE-2024-21893 against $dest$ from $src$. - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: - - field: src - type: ip_address + message: Possible exploitation of CVE-2024-21893 against $dest$ from $src$. + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: + - field: src + type: ip_address tags: - cve: - - CVE-2024-21893 - analytic_story: - - Ivanti Connect Secure VPN Vulnerabilities - asset_type: VPN Appliance - atomic_guid: [] - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + cve: + - CVE-2024-21893 + analytic_story: + - Ivanti Connect Secure VPN Vulnerabilities + asset_type: VPN Appliance + atomic_guid: [] + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/suricata_ivanti_saml.log - source: suricata - sourcetype: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/suricata_ivanti_saml.log + source: suricata + sourcetype: suricata diff --git a/detections/web/ivanti_connect_secure_system_information_access_via_auth_bypass.yml b/detections/web/ivanti_connect_secure_system_information_access_via_auth_bypass.yml index 5bfedec959..54b19a3fb2 100644 --- a/detections/web/ivanti_connect_secure_system_information_access_via_auth_bypass.yml +++ b/detections/web/ivanti_connect_secure_system_information_access_via_auth_bypass.yml @@ -1,77 +1,63 @@ name: Ivanti Connect Secure System Information Access via Auth Bypass id: d51c13dd-a232-4c83-a2bb-72ab36233c5d -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly data_source: -- Suricata -description: The following analytic identifies attempts to exploit the CVE-2023-46805 - and CVE-2024-21887 vulnerabilities in Ivanti Connect Secure. It detects GET requests - to the /api/v1/totp/user-backup-code/../../system/system-information URI, which - leverage an authentication bypass to access system information. The detection uses - the Web datamodel to identify requests with a 200 OK response, indicating a successful - exploit attempt. This activity is significant as it reveals potential unauthorized - access to sensitive system information. If confirmed malicious, attackers could - gain critical insights into the system, facilitating further exploitation and compromise. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url="*/api/v1/totp/user-backup-code/../../system/system-information*" - Web.http_method=GET Web.status=200 by Web.src, Web.dest, Web.http_user_agent, Web.url - | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `ivanti_connect_secure_system_information_access_via_auth_bypass_filter`' -how_to_implement: This detection requires the Web datamodel to be populated from a - supported Technology Add-On like Suricata, Splunk for Apache, Splunk for Nginx, - or Splunk for Palo Alto. -known_false_positives: This analytic is limited to HTTP Status 200; adjust as necessary. - False positives may occur if the URI path is IP-restricted or externally blocked. - It's recommended to review the context of the alerts and adjust the analytic parameters - to better fit the specific environment. + - Suricata +description: The following analytic identifies attempts to exploit the CVE-2023-46805 and CVE-2024-21887 vulnerabilities in Ivanti Connect Secure. It detects GET requests to the /api/v1/totp/user-backup-code/../../system/system-information URI, which leverage an authentication bypass to access system information. The detection uses the Web datamodel to identify requests with a 200 OK response, indicating a successful exploit attempt. This activity is significant as it reveals potential unauthorized access to sensitive system information. If confirmed malicious, attackers could gain critical insights into the system, facilitating further exploitation and compromise. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url="*/api/v1/totp/user-backup-code/../../system/system-information*" Web.http_method=GET Web.status=200 + BY Web.src, Web.dest, Web.http_user_agent, + Web.url + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `ivanti_connect_secure_system_information_access_via_auth_bypass_filter` +how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Suricata, Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto. +known_false_positives: This analytic is limited to HTTP Status 200; adjust as necessary. False positives may occur if the URI path is IP-restricted or externally blocked. It's recommended to review the context of the alerts and adjust the analytic parameters to better fit the specific environment. references: -- https://github.com/RootUp/PersonalStuff/blob/master/http-vuln-cve2023-46805_2024_21887.nse -- https://github.com/projectdiscovery/nuclei-templates/blob/c6b351e71b0fb0e40e222e97038f1fe09ac58194/http/misconfiguration/ivanti/CVE-2023-46085-CVE-2024-21887-mitigation-not-applied.yaml -- https://github.com/rapid7/metasploit-framework/pull/18708/files + - https://github.com/RootUp/PersonalStuff/blob/master/http-vuln-cve2023-46805_2024_21887.nse + - https://github.com/projectdiscovery/nuclei-templates/blob/c6b351e71b0fb0e40e222e97038f1fe09ac58194/http/misconfiguration/ivanti/CVE-2023-46085-CVE-2024-21887-mitigation-not-applied.yaml + - https://github.com/rapid7/metasploit-framework/pull/18708/files drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible exploitation of CVE-2023-46805 and CVE-2024-21887 against $dest$. - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: [] + message: Possible exploitation of CVE-2023-46805 and CVE-2024-21887 against $dest$. + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: [] tags: - cve: - - CVE-2023-46805 - - CVE-2024-21887 - analytic_story: - - Ivanti Connect Secure VPN Vulnerabilities - - CISA AA24-241A - asset_type: VPN Appliance - atomic_guid: [] - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + cve: + - CVE-2023-46805 + - CVE-2024-21887 + analytic_story: + - Ivanti Connect Secure VPN Vulnerabilities + - CISA AA24-241A + asset_type: VPN Appliance + atomic_guid: [] + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/suricata_ivanti_secure_connect_checkphase.log - source: suricata - sourcetype: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/suricata_ivanti_secure_connect_checkphase.log + source: suricata + sourcetype: suricata diff --git a/detections/web/ivanti_epm_sql_injection_remote_code_execution.yml b/detections/web/ivanti_epm_sql_injection_remote_code_execution.yml index 1e3ddb8f95..3d19d4eb87 100644 --- a/detections/web/ivanti_epm_sql_injection_remote_code_execution.yml +++ b/detections/web/ivanti_epm_sql_injection_remote_code_execution.yml @@ -1,87 +1,63 @@ name: Ivanti EPM SQL Injection Remote Code Execution id: e20564ca-c86c-4e30-acdb-a8486673426f -version: 6 -date: '2025-10-14' +version: 7 +date: '2026-02-25' author: Michael Haag type: TTP status: production data_source: -- Suricata -description: This detection identifies potential exploitation of a critical SQL injection - vulnerability in Ivanti Endpoint Manager (EPM), identified as CVE-2024-29824. The - vulnerability, which has a CVSS score of 9.8, allows for remote code execution through - the `RecordGoodApp` function in the `PatchBiz.dll` file. An attacker can exploit - this vulnerability by manipulating the `goodApp.md5` value in an HTTP POST request - to the `/WSStatusEvents/EventHandler.asmx` endpoint, leading to unauthorized command - execution on the server. Monitoring for unusual SQL commands and HTTP requests to - this endpoint can help identify exploitation attempts. Note that, the detection - is focused on the URI path, HTTP method and status code of 200, indicating potential - exploitation. To properly identify if this was successful, TLS inspection and additional - network traffic analysis is required as the xp_cmdshell comes in via the request - body. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url IN ("/WSStatusEvents/EventHandler.asmx") Web.http_method=POST Web.status=200 - by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, - Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `ivanti_epm_sql_injection_remote_code_execution_filter`' -how_to_implement: The detection is based on monitoring HTTP POST requests to the `/WSStatusEvents/EventHandler.asmx` - endpoint with a status code of 200. The detection is focused on the URI path, HTTP - method, and status code, which can indicate potential exploitation of the CVE-2024-29824 - vulnerability. To implement this detection, ensure that you have enabled the necessary - data sources and are ingesting HTTP traffic data. The detection can be implemented - using Splunk Enterprise Security and Splunk Cloud with the provided search query. - The search query should be scheduled to run at regular intervals to detect potential - exploitation attempts. Additionally, consider implementing TLS inspection or network - traffic analysis (IDS/IPS) to identify successful exploitation attempts. -known_false_positives: False positives are not expected, as this detection is based - on monitoring HTTP POST requests to a specific endpoint with a status code of 200. - However, ensure that legitimate requests to the `/WSStatusEvents/EventHandler.asmx` - endpoint are accounted for in the environment to avoid false positives. + - Suricata +description: This detection identifies potential exploitation of a critical SQL injection vulnerability in Ivanti Endpoint Manager (EPM), identified as CVE-2024-29824. The vulnerability, which has a CVSS score of 9.8, allows for remote code execution through the `RecordGoodApp` function in the `PatchBiz.dll` file. An attacker can exploit this vulnerability by manipulating the `goodApp.md5` value in an HTTP POST request to the `/WSStatusEvents/EventHandler.asmx` endpoint, leading to unauthorized command execution on the server. Monitoring for unusual SQL commands and HTTP requests to this endpoint can help identify exploitation attempts. Note that, the detection is focused on the URI path, HTTP method and status code of 200, indicating potential exploitation. To properly identify if this was successful, TLS inspection and additional network traffic analysis is required as the xp_cmdshell comes in via the request body. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN ("/WSStatusEvents/EventHandler.asmx") Web.http_method=POST Web.status=200 + BY Web.http_user_agent, Web.status Web.http_method, + Web.url, Web.url_length, Web.src, + Web.dest, sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `ivanti_epm_sql_injection_remote_code_execution_filter` +how_to_implement: The detection is based on monitoring HTTP POST requests to the `/WSStatusEvents/EventHandler.asmx` endpoint with a status code of 200. The detection is focused on the URI path, HTTP method, and status code, which can indicate potential exploitation of the CVE-2024-29824 vulnerability. To implement this detection, ensure that you have enabled the necessary data sources and are ingesting HTTP traffic data. The detection can be implemented using Splunk Enterprise Security and Splunk Cloud with the provided search query. The search query should be scheduled to run at regular intervals to detect potential exploitation attempts. Additionally, consider implementing TLS inspection or network traffic analysis (IDS/IPS) to identify successful exploitation attempts. +known_false_positives: False positives are not expected, as this detection is based on monitoring HTTP POST requests to a specific endpoint with a status code of 200. However, ensure that legitimate requests to the `/WSStatusEvents/EventHandler.asmx` endpoint are accounted for in the environment to avoid false positives. references: -- https://www.horizon3.ai/attack-research/attack-blogs/cve-2024-29824-deep-dive-ivanti-epm-sql-injection-remote-code-execution-vulnerability/ -- https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2024-29824 -- https://github.com/projectdiscovery/nuclei-templates/pull/10020/files + - https://www.horizon3.ai/attack-research/attack-blogs/cve-2024-29824-deep-dive-ivanti-epm-sql-injection-remote-code-execution-vulnerability/ + - https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2024-29824 + - https://github.com/projectdiscovery/nuclei-templates/pull/10020/files drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential exploitation of a critical SQL injection vulnerability in Ivanti - Endpoint Manager (EPM), identified as CVE-2024-29824 against $dest$. - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: [] + message: Potential exploitation of a critical SQL injection vulnerability in Ivanti Endpoint Manager (EPM), identified as CVE-2024-29824 against $dest$. + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: [] tags: - analytic_story: - - Ivanti EPM Vulnerabilities - - GhostRedirector IIS Module and Rungan Backdoor - - Hellcat Ransomware - asset_type: Web Server - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: - - CVE-2024-29824 + analytic_story: + - Ivanti EPM Vulnerabilities + - GhostRedirector IIS Module and Rungan Backdoor + - Hellcat Ransomware + asset_type: Web Server + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: + - CVE-2024-29824 tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/suricata_ivanti_epm.log - sourcetype: suricata - source: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/suricata_ivanti_epm.log + sourcetype: suricata + source: suricata diff --git a/detections/web/ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35078.yml b/detections/web/ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35078.yml index 773ffb0dee..693a0348a0 100644 --- a/detections/web/ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35078.yml +++ b/detections/web/ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35078.yml @@ -1,76 +1,62 @@ name: Ivanti EPMM Remote Unauthenticated API Access CVE-2023-35078 id: 66b9c9ba-7fb2-4e80-a3a2-496e5e078167 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Suricata -description: The following analytic detects attempts to exploit CVE-2023-35078, a - vulnerability in Ivanti Endpoint Manager Mobile (EPMM) versions up to 11.4. It identifies - HTTP requests to the endpoint "/mifs/aad/api/v2/authorized/users?*" with a status - code of 200 in web logs. This activity is significant as it indicates unauthorized - remote access to restricted functionalities or resources. If confirmed malicious, - this could lead to data theft, unauthorized modifications, or further system compromise, - necessitating immediate action to mitigate potential severe impacts. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url IN ("/mifs/aad/api/v2/authorized/users?*") Web.status=200 by Web.http_user_agent, - Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype - | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35078_filter`' -how_to_implement: To implement this analytic, a network product similar to Suricata - or Palo Alto needs to be mapped to the Web datamodel. Modify accordingly to work - with your products. -known_false_positives: The Proof of Concept exploit script indicates that status=200 - is required for successful exploitation of the vulnerability. False positives may - be present if status=200 is removed from the search. If it is removed,then the - search also alert on status=301 and status=404 which indicates unsuccessful exploitation - attempts. Analysts may find it useful to hunt for these status codes as well, but - it is likely to produce a significant number of alerts as this is a widespread vulnerability. + - Suricata +description: The following analytic detects attempts to exploit CVE-2023-35078, a vulnerability in Ivanti Endpoint Manager Mobile (EPMM) versions up to 11.4. It identifies HTTP requests to the endpoint "/mifs/aad/api/v2/authorized/users?*" with a status code of 200 in web logs. This activity is significant as it indicates unauthorized remote access to restricted functionalities or resources. If confirmed malicious, this could lead to data theft, unauthorized modifications, or further system compromise, necessitating immediate action to mitigate potential severe impacts. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN ("/mifs/aad/api/v2/authorized/users?*") Web.status=200 + BY Web.http_user_agent, Web.status Web.http_method, + Web.url, Web.url_length, Web.src, + Web.dest, sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35078_filter` +how_to_implement: To implement this analytic, a network product similar to Suricata or Palo Alto needs to be mapped to the Web datamodel. Modify accordingly to work with your products. +known_false_positives: The Proof of Concept exploit script indicates that status=200 is required for successful exploitation of the vulnerability. False positives may be present if status=200 is removed from the search. If it is removed,then the search also alert on status=301 and status=404 which indicates unsuccessful exploitation attempts. Analysts may find it useful to hunt for these status codes as well, but it is likely to produce a significant number of alerts as this is a widespread vulnerability. references: -- https://forums.ivanti.com/s/article/CVE-2023-35078-Remote-unauthenticated-API-access-vulnerability?language=en_US -- https://github.com/vchan-in/CVE-2023-35078-Exploit-POC/blob/main/cve_2023_35078_poc.py + - https://forums.ivanti.com/s/article/CVE-2023-35078-Remote-unauthenticated-API-access-vulnerability?language=en_US + - https://github.com/vchan-in/CVE-2023-35078-Exploit-POC/blob/main/cve_2023_35078_poc.py drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential CVE-2023-35078 against an Ivanti EPMM appliance on $dest$. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: Potential CVE-2023-35078 against an Ivanti EPMM appliance on $dest$. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Ivanti EPMM Remote Unauthenticated Access - asset_type: Web Server - cve: - - CVE-2023-35078 - atomic_guid: [] - mitre_attack_id: - - T1190 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Ivanti EPMM Remote Unauthenticated Access + asset_type: Web Server + cve: + - CVE-2023-35078 + atomic_guid: [] + mitre_attack_id: + - T1190 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/suricata_ivanti_CVE202335078.log - source: suricata - sourcetype: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/suricata_ivanti_CVE202335078.log + source: suricata + sourcetype: suricata diff --git a/detections/web/ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35082.yml b/detections/web/ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35082.yml index e6fb321219..420983c835 100644 --- a/detections/web/ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35082.yml +++ b/detections/web/ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35082.yml @@ -1,79 +1,63 @@ name: Ivanti EPMM Remote Unauthenticated API Access CVE-2023-35082 id: e03edeba-4942-470c-a664-27253f3ad351 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Suricata -description: The following analytic detects potential unauthorized access attempts - exploiting CVE-2023-35082 within Ivanti's software products. It identifies access - to the specific URI path /mifs/asfV3/api/v2/ with an HTTP 200 response code in web - access logs, indicating successful unauthorized access. This activity is significant - for a SOC as it highlights potential security breaches that could lead to unauthorized - data access or system modifications. If confirmed malicious, an attacker could gain - unbridled access to sensitive organizational data or modify systems maliciously, - posing severe security risks. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url IN ("/mifs/asfV3/api/v2/*") Web.status=200 by Web.http_user_agent, - Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype - | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35082_filter`' -how_to_implement: To implement this analytic, a network product similar to Suricata - or Palo Alto needs to be mapped to the Web datamodel. Modify accordingly to work - with your products. -known_false_positives: Similar to CVE-2023-35078, the path for exploitation indicates - that status=200 is required for successful exploitation of the vulnerability. False - positives may be present if status=200 is removed from the search. If it is removed,then - the search also alert on status=301 and status=404 which indicates unsuccessful - exploitation attempts. Analysts may find it useful to hunt for these status codes - as well, but it is likely to produce a significant number of alerts as this is a - widespread vulnerability. + - Suricata +description: The following analytic detects potential unauthorized access attempts exploiting CVE-2023-35082 within Ivanti's software products. It identifies access to the specific URI path /mifs/asfV3/api/v2/ with an HTTP 200 response code in web access logs, indicating successful unauthorized access. This activity is significant for a SOC as it highlights potential security breaches that could lead to unauthorized data access or system modifications. If confirmed malicious, an attacker could gain unbridled access to sensitive organizational data or modify systems maliciously, posing severe security risks. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN ("/mifs/asfV3/api/v2/*") Web.status=200 + BY Web.http_user_agent, Web.status Web.http_method, + Web.url, Web.url_length, Web.src, + Web.dest, sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35082_filter` +how_to_implement: To implement this analytic, a network product similar to Suricata or Palo Alto needs to be mapped to the Web datamodel. Modify accordingly to work with your products. +known_false_positives: Similar to CVE-2023-35078, the path for exploitation indicates that status=200 is required for successful exploitation of the vulnerability. False positives may be present if status=200 is removed from the search. If it is removed,then the search also alert on status=301 and status=404 which indicates unsuccessful exploitation attempts. Analysts may find it useful to hunt for these status codes as well, but it is likely to produce a significant number of alerts as this is a widespread vulnerability. references: -- https://forums.ivanti.com/s/article/CVE-2023-35082-Remote-Unauthenticated-API-Access-Vulnerability-in-MobileIron-Core-11-2-and-older?language=en_US -- https://github.com/vchan-in/CVE-2023-35078-Exploit-POC/blob/main/cve_2023_35078_poc.py -- https://www.rapid7.com/blog/post/2023/08/02/cve-2023-35082-mobileiron-core-unauthenticated-api-access-vulnerability/ + - https://forums.ivanti.com/s/article/CVE-2023-35082-Remote-Unauthenticated-API-Access-Vulnerability-in-MobileIron-Core-11-2-and-older?language=en_US + - https://github.com/vchan-in/CVE-2023-35078-Exploit-POC/blob/main/cve_2023_35078_poc.py + - https://www.rapid7.com/blog/post/2023/08/02/cve-2023-35082-mobileiron-core-unauthenticated-api-access-vulnerability/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential CVE-2023-35082 against an Ivanti EPMM appliance on $dest$. - risk_objects: - - field: dest - type: system - score: 64 - threat_objects: [] + message: Potential CVE-2023-35082 against an Ivanti EPMM appliance on $dest$. + risk_objects: + - field: dest + type: system + score: 64 + threat_objects: [] tags: - analytic_story: - - Ivanti EPMM Remote Unauthenticated Access - asset_type: Web Server - cve: - - CVE-2023-35082 - atomic_guid: [] - mitre_attack_id: - - T1190 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Ivanti EPMM Remote Unauthenticated Access + asset_type: Web Server + cve: + - CVE-2023-35082 + atomic_guid: [] + mitre_attack_id: + - T1190 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/suricata_ivanti_CVE202335082.log - source: suricata - sourcetype: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/suricata_ivanti_CVE202335082.log + source: suricata + sourcetype: suricata diff --git a/detections/web/ivanti_sentry_authentication_bypass.yml b/detections/web/ivanti_sentry_authentication_bypass.yml index 0cf5bddae7..3ce692c25b 100644 --- a/detections/web/ivanti_sentry_authentication_bypass.yml +++ b/detections/web/ivanti_sentry_authentication_bypass.yml @@ -1,78 +1,64 @@ name: Ivanti Sentry Authentication Bypass id: b8e0d1cf-e6a8-4d46-a5ae-aebe18ead8f8 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Suricata -description: The following analytic identifies unauthenticated access attempts to - the System Manager Portal in Ivanti Sentry, exploiting CVE-2023-38035. It detects - this activity by monitoring HTTP requests to specific endpoints ("/mics/services/configservice/*", - "/mics/services/*", "/mics/services/MICSLogService*") with a status code of 200. - This behavior is significant for a SOC as it indicates potential unauthorized access, - which could lead to OS command execution as root. If confirmed malicious, this activity - could result in significant system compromise and data breaches, especially if port - 8443 is exposed to the internet. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url IN ("/mics/services/configservice/*", "/mics/services/*","/mics/services/MICSLogService*") - Web.status=200 by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, - Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `ivanti_sentry_authentication_bypass_filter`' -how_to_implement: To implement this analytic, a network product similar to Suricata - or Palo Alto needs to be mapped to the Web datamodel. Modify accordingly to work - with your products. -known_false_positives: It is important to note that false positives may occur if the - search criteria are expanded beyond the HTTP status code 200. In other words, if - the search includes other HTTP status codes, the likelihood of encountering false - positives increases. This is due to the fact that HTTP status codes other than 200 - may not necessarily indicate a successful exploitation attempt. + - Suricata +description: The following analytic identifies unauthenticated access attempts to the System Manager Portal in Ivanti Sentry, exploiting CVE-2023-38035. It detects this activity by monitoring HTTP requests to specific endpoints ("/mics/services/configservice/*", "/mics/services/*", "/mics/services/MICSLogService*") with a status code of 200. This behavior is significant for a SOC as it indicates potential unauthorized access, which could lead to OS command execution as root. If confirmed malicious, this activity could result in significant system compromise and data breaches, especially if port 8443 is exposed to the internet. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN ("/mics/services/configservice/*", "/mics/services/*","/mics/services/MICSLogService*") Web.status=200 + BY Web.http_user_agent, Web.status Web.http_method, + Web.url, Web.url_length, Web.src, + Web.dest, sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `ivanti_sentry_authentication_bypass_filter` +how_to_implement: To implement this analytic, a network product similar to Suricata or Palo Alto needs to be mapped to the Web datamodel. Modify accordingly to work with your products. +known_false_positives: It is important to note that false positives may occur if the search criteria are expanded beyond the HTTP status code 200. In other words, if the search includes other HTTP status codes, the likelihood of encountering false positives increases. This is due to the fact that HTTP status codes other than 200 may not necessarily indicate a successful exploitation attempt. references: -- https://github.com/horizon3ai/CVE-2023-38035/blob/main/CVE-2023-38035.py -- https://www.horizon3.ai/ivanti-sentry-authentication-bypass-cve-2023-38035-deep-dive/ -- https://forums.ivanti.com/s/article/KB-API-Authentication-Bypass-on-Sentry-Administrator-Interface-CVE-2023-38035?language=en_US + - https://github.com/horizon3ai/CVE-2023-38035/blob/main/CVE-2023-38035.py + - https://www.horizon3.ai/ivanti-sentry-authentication-bypass-cve-2023-38035-deep-dive/ + - https://forums.ivanti.com/s/article/KB-API-Authentication-Bypass-on-Sentry-Administrator-Interface-CVE-2023-38035?language=en_US drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible exploitation of CVE-2023-38035 against $dest$. - risk_objects: - - field: dest - type: system - score: 45 - threat_objects: - - field: src - type: ip_address + message: Possible exploitation of CVE-2023-38035 against $dest$. + risk_objects: + - field: dest + type: system + score: 45 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Ivanti Sentry Authentication Bypass CVE-2023-38035 - cve: - - CVE-2023-38035 - asset_type: Network - atomic_guid: [] - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Ivanti Sentry Authentication Bypass CVE-2023-38035 + cve: + - CVE-2023-38035 + asset_type: Network + atomic_guid: [] + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/ivanti_sentry_CVE_2023_38035.log - source: suricata - sourcetype: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/ivanti_sentry_CVE_2023_38035.log + source: suricata + sourcetype: suricata diff --git a/detections/web/java_class_file_download_by_java_user_agent.yml b/detections/web/java_class_file_download_by_java_user_agent.yml index 5c191bbbb1..1055db577a 100644 --- a/detections/web/java_class_file_download_by_java_user_agent.yml +++ b/detections/web/java_class_file_download_by_java_user_agent.yml @@ -1,71 +1,60 @@ name: Java Class File download by Java User Agent id: 8281ce42-5c50-11ec-82d2-acde48001122 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies a Java user agent performing a GET - request for a .class file from a remote site. It leverages web or proxy logs within - the Web Datamodel to detect this activity. This behavior is significant as it may - indicate exploitation attempts, such as those related to CVE-2021-44228 (Log4Shell). - If confirmed malicious, an attacker could exploit vulnerabilities in the Java application, - potentially leading to remote code execution and further compromise of the affected - system. +description: The following analytic identifies a Java user agent performing a GET request for a .class file from a remote site. It leverages web or proxy logs within the Web Datamodel to detect this activity. This behavior is significant as it may indicate exploitation attempts, such as those related to CVE-2021-44228 (Log4Shell). If confirmed malicious, an attacker could exploit vulnerabilities in the Java application, potentially leading to remote code execution and further compromise of the affected system. data_source: -- Splunk Stream HTTP -search: '| tstats `security_content_summariesonly` count from datamodel=Web where - Web.http_user_agent="*Java*" Web.http_method="GET" Web.url="*.class*" by Web.http_user_agent - Web.http_method, Web.url,Web.url_length Web.src, Web.dest | `drop_dm_object_name("Web")` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `java_class_file_download_by_java_user_agent_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - web or proxy logs, or ensure it is being filled by a proxy like device, into the - Web Datamodel. For additional filtering, allow list private IP space or restrict - by known good. + - Splunk Stream HTTP +search: |- + | tstats `security_content_summariesonly` count FROM datamodel=Web + WHERE Web.http_user_agent="*Java*" Web.http_method="GET" Web.url="*.class*" + BY Web.http_user_agent Web.http_method, Web.url,Web.url_length + Web.src, Web.dest + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `java_class_file_download_by_java_user_agent_filter` +how_to_implement: To successfully implement this search, you need to be ingesting web or proxy logs, or ensure it is being filled by a proxy like device, into the Web Datamodel. For additional filtering, allow list private IP space or restrict by known good. known_false_positives: Filtering may be required in some instances, filter as needed. references: -- https://arstechnica.com/information-technology/2021/12/as-log4shell-wreaks-havoc-payroll-service-reports-ransomware-attack/ + - https://arstechnica.com/information-technology/2021/12/as-log4shell-wreaks-havoc-payroll-service-reports-ransomware-attack/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Java user agent $http_user_agent$ was performing a $http_method$ to retrieve - a remote class file. - risk_objects: - - field: dest - type: system - score: 40 - threat_objects: - - field: http_user_agent - type: http_user_agent + message: A Java user agent $http_user_agent$ was performing a $http_method$ to retrieve a remote class file. + risk_objects: + - field: dest + type: system + score: 40 + threat_objects: + - field: http_user_agent + type: http_user_agent tags: - analytic_story: - - Log4Shell CVE-2021-44228 - asset_type: Web Server - cve: - - CVE-2021-44228 - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Log4Shell CVE-2021-44228 + asset_type: Web Server + cve: + - CVE-2021-44228 + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/java/java.log - source: stream:http - sourcetype: stream:http + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/java/java.log + source: stream:http + sourcetype: stream:http diff --git a/detections/web/jenkins_arbitrary_file_read_cve_2024_23897.yml b/detections/web/jenkins_arbitrary_file_read_cve_2024_23897.yml index 8830d60caa..8b263ab13d 100644 --- a/detections/web/jenkins_arbitrary_file_read_cve_2024_23897.yml +++ b/detections/web/jenkins_arbitrary_file_read_cve_2024_23897.yml @@ -1,79 +1,68 @@ name: Jenkins Arbitrary File Read CVE-2024-23897 id: c641260d-2b48-4eb1-b1e8-2cc5b8b99ab1 -version: 6 -date: '2025-10-14' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Nginx Access -description: The following analytic identifies attempts to exploit Jenkins Arbitrary - File Read CVE-2024-23897. It detects HTTP POST requests to Jenkins URLs containing - "*/cli?remoting=false*" with a 200 status code. This activity is significant as - it indicates potential unauthorized access to sensitive files on the Jenkins server, - such as credentials and private keys. If confirmed malicious, this could lead to - severe data breaches, unauthorized access, and further exploitation within the environment. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url="*/cli?remoting=false*" Web.status=200 Web.http_method=POST by Web.src, - Web.dest, Web.http_user_agent, Web.url Web.status, Web.http_method | `drop_dm_object_name("Web")` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `jenkins_arbitrary_file_read_cve_2024_23897_filter`' -how_to_implement: This detection requires the Web datamodel to be populated from a - supported Technology Add-On like Suricata, Splunk for Apache, Splunk for Nginx, - or Splunk for Palo Alto. If unable to utilize the Web datamodel, modify query to - your data source. -known_false_positives: False positives should be limited as this detection is based - on a specific URL path and HTTP status code. Adjust the search as necessary to fit - the environment. + - Nginx Access +description: The following analytic identifies attempts to exploit Jenkins Arbitrary File Read CVE-2024-23897. It detects HTTP POST requests to Jenkins URLs containing "*/cli?remoting=false*" with a 200 status code. This activity is significant as it indicates potential unauthorized access to sensitive files on the Jenkins server, such as credentials and private keys. If confirmed malicious, this could lead to severe data breaches, unauthorized access, and further exploitation within the environment. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url="*/cli?remoting=false*" Web.status=200 Web.http_method=POST + BY Web.src, Web.dest, Web.http_user_agent, + Web.url Web.status, Web.http_method + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `jenkins_arbitrary_file_read_cve_2024_23897_filter` +how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Suricata, Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto. If unable to utilize the Web datamodel, modify query to your data source. +known_false_positives: False positives should be limited as this detection is based on a specific URL path and HTTP status code. Adjust the search as necessary to fit the environment. references: -- https://github.com/projectdiscovery/nuclei-templates/pull/9025 -- https://github.com/jenkinsci-cert/SECURITY-3314-3315 -- https://github.com/binganao/CVE-2024-23897 -- https://github.com/h4x0r-dz/CVE-2024-23897 -- https://www.sonarsource.com/blog/excessive-expansion-uncovering-critical-security-vulnerabilities-in-jenkins/ -- https://www.shodan.io/search?query=product%3A%22Jenkins%22 -- https://thehackernews.com/2024/01/critical-jenkins-vulnerability-exposes.html + - https://github.com/projectdiscovery/nuclei-templates/pull/9025 + - https://github.com/jenkinsci-cert/SECURITY-3314-3315 + - https://github.com/binganao/CVE-2024-23897 + - https://github.com/h4x0r-dz/CVE-2024-23897 + - https://www.sonarsource.com/blog/excessive-expansion-uncovering-critical-security-vulnerabilities-in-jenkins/ + - https://www.shodan.io/search?query=product%3A%22Jenkins%22 + - https://thehackernews.com/2024/01/critical-jenkins-vulnerability-exposes.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Jenkins Arbitrary File Read CVE-2024-23897 against $dest$ by $src$. - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: - - field: src - type: ip_address + message: Jenkins Arbitrary File Read CVE-2024-23897 against $dest$ by $src$. + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: + - field: src + type: ip_address tags: - cve: - - CVE-2024-23897 - analytic_story: - - Jenkins Server Vulnerabilities - - Hellcat Ransomware - asset_type: Web Server - atomic_guid: [] - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + cve: + - CVE-2024-23897 + analytic_story: + - Jenkins Server Vulnerabilities + - Hellcat Ransomware + asset_type: Web Server + atomic_guid: [] + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/jenkins/nginx_jenkins_cve_2023_23897.log - source: nginx:plus:kv - sourcetype: nginx:plus:kv + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/jenkins/nginx_jenkins_cve_2023_23897.log + source: nginx:plus:kv + sourcetype: nginx:plus:kv diff --git a/detections/web/jetbrains_teamcity_authentication_bypass_cve_2024_27198.yml b/detections/web/jetbrains_teamcity_authentication_bypass_cve_2024_27198.yml index b7e684e51d..542e749bc3 100644 --- a/detections/web/jetbrains_teamcity_authentication_bypass_cve_2024_27198.yml +++ b/detections/web/jetbrains_teamcity_authentication_bypass_cve_2024_27198.yml @@ -1,80 +1,70 @@ name: JetBrains TeamCity Authentication Bypass CVE-2024-27198 id: fbcc04c7-8a79-453c-b3a9-c232c423bdd4 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk data_source: -- Suricata + - Suricata type: TTP status: production -description: The following analytic identifies attempts to exploit the JetBrains TeamCity - Authentication Bypass vulnerability (CVE-2024-27198). It detects suspicious POST - requests to the `/app/rest/users` and `/app/rest/users/id:1/tokens` endpoints, which - are indicative of attempts to create new administrator users or generate admin access - tokens without authentication. This detection leverages the Web datamodel and CIM-compliant - log sources, such as Nginx or TeamCity logs. This activity is significant as it - can lead to full control over the TeamCity server, including all projects, builds, - agents, and artifacts. If confirmed malicious, attackers could gain unauthorized - administrative access, leading to severe security breaches. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where ((Web.url="*?jsp=*" AND Web.url="*;.jsp*") Web.status=200 Web.http_method=POST) - OR (Web.url IN ("*jsp=/app/rest/users;.jsp","*?jsp=/app/rest/users;.jsp","*?jsp=.*/app/rest/users/id:*/tokens;*") - Web.status=200 Web.http_method=POST ) by Web.src, Web.dest, Web.http_user_agent, - Web.url, Web.status, Web.http_method, sourcetype, source | `drop_dm_object_name("Web")` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `jetbrains_teamcity_authentication_bypass_cve_2024_27198_filter`' -how_to_implement: The detection relies on the Web datamodel and a CIM compliant log - source, that may include Nginx, TeamCity logs, or other web server logs. -known_false_positives: False positives are not expected, as this detection is based - on the presence of specific URI paths and HTTP methods that are indicative of the - CVE-2024-27198 vulnerability exploitation. Monitor, filter and tune as needed based - on organization log sources. +description: The following analytic identifies attempts to exploit the JetBrains TeamCity Authentication Bypass vulnerability (CVE-2024-27198). It detects suspicious POST requests to the `/app/rest/users` and `/app/rest/users/id:1/tokens` endpoints, which are indicative of attempts to create new administrator users or generate admin access tokens without authentication. This detection leverages the Web datamodel and CIM-compliant log sources, such as Nginx or TeamCity logs. This activity is significant as it can lead to full control over the TeamCity server, including all projects, builds, agents, and artifacts. If confirmed malicious, attackers could gain unauthorized administrative access, leading to severe security breaches. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE ( + (Web.url="*?jsp=*" + AND + Web.url="*;.jsp*") Web.status=200 Web.http_method=POST + ) + OR (Web.url IN ("*jsp=/app/rest/users;.jsp","*?jsp=/app/rest/users;.jsp","*?jsp=.*/app/rest/users/id:*/tokens;*") Web.status=200 Web.http_method=POST ) + BY Web.src, Web.dest, Web.http_user_agent, + Web.url, Web.status, Web.http_method, + sourcetype, source + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `jetbrains_teamcity_authentication_bypass_cve_2024_27198_filter` +how_to_implement: The detection relies on the Web datamodel and a CIM compliant log source, that may include Nginx, TeamCity logs, or other web server logs. +known_false_positives: False positives are not expected, as this detection is based on the presence of specific URI paths and HTTP methods that are indicative of the CVE-2024-27198 vulnerability exploitation. Monitor, filter and tune as needed based on organization log sources. references: -- https://github.com/projectdiscovery/nuclei-templates/pull/9279/files -- https://www.rapid7.com/blog/post/2024/03/04/etr-cve-2024-27198-and-cve-2024-27199-jetbrains-teamcity-multiple-authentication-bypass-vulnerabilities-fixed/ -- https://blog.jetbrains.com/teamcity/2024/03/teamcity-2023-11-4-is-out/ -- https://blog.jetbrains.com/teamcity/2024/03/additional-critical-security-issues-affecting-teamcity-on-premises-cve-2024-27198-and-cve-2024-27199-update-to-2023-11-4-now/ -- https://github.com/yoryio/CVE-2024-27198/blob/main/CVE-2024-27198.py + - https://github.com/projectdiscovery/nuclei-templates/pull/9279/files + - https://www.rapid7.com/blog/post/2024/03/04/etr-cve-2024-27198-and-cve-2024-27199-jetbrains-teamcity-multiple-authentication-bypass-vulnerabilities-fixed/ + - https://blog.jetbrains.com/teamcity/2024/03/teamcity-2023-11-4-is-out/ + - https://blog.jetbrains.com/teamcity/2024/03/additional-critical-security-issues-affecting-teamcity-on-premises-cve-2024-27198-and-cve-2024-27199-update-to-2023-11-4-now/ + - https://github.com/yoryio/CVE-2024-27198/blob/main/CVE-2024-27198.py drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible JetBrains TeamCity Authentication Bypass CVE-2024-27198 Attempt - against $dest$ from $src$. - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: - - field: src - type: ip_address + message: Possible JetBrains TeamCity Authentication Bypass CVE-2024-27198 Attempt against $dest$ from $src$. + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - JetBrains TeamCity Vulnerabilities - asset_type: Web Server - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: - - CVE-2024-27198 + analytic_story: + - JetBrains TeamCity Vulnerabilities + asset_type: Web Server + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: + - CVE-2024-27198 tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/jetbrains/teamcity_cve_2024_27198.log - sourcetype: suricata - source: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/jetbrains/teamcity_cve_2024_27198.log + sourcetype: suricata + source: suricata diff --git a/detections/web/jetbrains_teamcity_authentication_bypass_suricata_cve_2024_27198.yml b/detections/web/jetbrains_teamcity_authentication_bypass_suricata_cve_2024_27198.yml index 6d311e929f..67024d5749 100644 --- a/detections/web/jetbrains_teamcity_authentication_bypass_suricata_cve_2024_27198.yml +++ b/detections/web/jetbrains_teamcity_authentication_bypass_suricata_cve_2024_27198.yml @@ -1,79 +1,63 @@ name: JetBrains TeamCity Authentication Bypass Suricata CVE-2024-27198 id: fbcc04c7-8a79-453c-b3a9-c232c423bdd3 -version: 6 -date: '2025-10-14' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk data_source: -- Suricata + - Suricata type: TTP status: production -description: The following analytic detects attempts to exploit the CVE-2024-27198 - vulnerability in JetBrains TeamCity on-premises servers, which allows attackers - to bypass authentication mechanisms. It leverages Suricata HTTP traffic logs to - identify suspicious POST requests to the `/app/rest/users` and `/app/rest/users/id:1/tokens` - endpoints. This activity is significant because it can lead to unauthorized administrative - access, enabling attackers to gain full control over the TeamCity server, including - projects, builds, agents, and artifacts. If confirmed malicious, this could result - in severe security breaches and compromise the integrity of the development environment. -search: '`suricata` ((http.url="*?jsp=*" AND http.url="*;.jsp*") http.status=200 http_method=POST) - OR (http.url IN ("*jsp=/app/rest/users;.jsp","*?jsp=/app/rest/users;.jsp","*?jsp=.*/app/rest/users/id:*/tokens;*") - http.status=200 http_method=POST ) | stats count min(_time) as firstTime max(_time) - as lastTime by src, dest, http.http_user_agent, http.url, http.status,http_method - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `jetbrains_teamcity_authentication_bypass_suricata_cve_2024_27198_filter`' -how_to_implement: The following detection relies on the Suricata TA and ensuring it - is properly configured to monitor HTTP traffic. Modify the query for your environment - and log sources as needed. -known_false_positives: False positives are not expected, as this detection is based - on the presence of specific URI paths and HTTP methods that are indicative of the - CVE-2024-27198 vulnerability exploitation. Monitor, filter and tune as needed based - on organization log sources. +description: The following analytic detects attempts to exploit the CVE-2024-27198 vulnerability in JetBrains TeamCity on-premises servers, which allows attackers to bypass authentication mechanisms. It leverages Suricata HTTP traffic logs to identify suspicious POST requests to the `/app/rest/users` and `/app/rest/users/id:1/tokens` endpoints. This activity is significant because it can lead to unauthorized administrative access, enabling attackers to gain full control over the TeamCity server, including projects, builds, agents, and artifacts. If confirmed malicious, this could result in severe security breaches and compromise the integrity of the development environment. +search: |- + `suricata` ((http.url="*?jsp=*" AND http.url="*;.jsp*") http.status=200 http_method=POST) OR (http.url IN ("*jsp=/app/rest/users;.jsp","*?jsp=/app/rest/users;.jsp","*?jsp=.*/app/rest/users/id:*/tokens;*") http.status=200 http_method=POST ) + | stats count min(_time) as firstTime max(_time) as lastTime + BY src, dest, http.http_user_agent, + http.url, http.status,http_method + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `jetbrains_teamcity_authentication_bypass_suricata_cve_2024_27198_filter` +how_to_implement: The following detection relies on the Suricata TA and ensuring it is properly configured to monitor HTTP traffic. Modify the query for your environment and log sources as needed. +known_false_positives: False positives are not expected, as this detection is based on the presence of specific URI paths and HTTP methods that are indicative of the CVE-2024-27198 vulnerability exploitation. Monitor, filter and tune as needed based on organization log sources. references: -- https://github.com/projectdiscovery/nuclei-templates/pull/9279/files -- https://www.rapid7.com/blog/post/2024/03/04/etr-cve-2024-27198-and-cve-2024-27199-jetbrains-teamcity-multiple-authentication-bypass-vulnerabilities-fixed/ -- https://blog.jetbrains.com/teamcity/2024/03/teamcity-2023-11-4-is-out/ -- https://blog.jetbrains.com/teamcity/2024/03/additional-critical-security-issues-affecting-teamcity-on-premises-cve-2024-27198-and-cve-2024-27199-update-to-2023-11-4-now/ + - https://github.com/projectdiscovery/nuclei-templates/pull/9279/files + - https://www.rapid7.com/blog/post/2024/03/04/etr-cve-2024-27198-and-cve-2024-27199-jetbrains-teamcity-multiple-authentication-bypass-vulnerabilities-fixed/ + - https://blog.jetbrains.com/teamcity/2024/03/teamcity-2023-11-4-is-out/ + - https://blog.jetbrains.com/teamcity/2024/03/additional-critical-security-issues-affecting-teamcity-on-premises-cve-2024-27198-and-cve-2024-27199-update-to-2023-11-4-now/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible JetBrains TeamCity Authentication Bypass Attempt against $dest$ - from $src$. - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: - - field: src - type: ip_address + message: Possible JetBrains TeamCity Authentication Bypass Attempt against $dest$ from $src$. + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - JetBrains TeamCity Vulnerabilities - - Hellcat Ransomware - asset_type: Web Server - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: - - CVE-2024-27198 + analytic_story: + - JetBrains TeamCity Vulnerabilities + - Hellcat Ransomware + asset_type: Web Server + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: + - CVE-2024-27198 tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/jetbrains/teamcity_cve_2024_27198.log - sourcetype: suricata - source: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/jetbrains/teamcity_cve_2024_27198.log + sourcetype: suricata + source: suricata diff --git a/detections/web/jetbrains_teamcity_limited_auth_bypass_suricata_cve_2024_27199.yml b/detections/web/jetbrains_teamcity_limited_auth_bypass_suricata_cve_2024_27199.yml index 152c75053a..4535d3d0db 100644 --- a/detections/web/jetbrains_teamcity_limited_auth_bypass_suricata_cve_2024_27199.yml +++ b/detections/web/jetbrains_teamcity_limited_auth_bypass_suricata_cve_2024_27199.yml @@ -1,83 +1,62 @@ name: JetBrains TeamCity Limited Auth Bypass Suricata CVE-2024-27199 id: a1e68dcd-2e24-4434-bd0e-b3d4de139d58 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk data_source: -- Suricata + - Suricata type: TTP status: production -description: The following analytic identifies attempts to exploit CVE-2024-27199, - a critical vulnerability in JetBrains TeamCity web server, allowing unauthenticated - access to specific endpoints. It detects unusual access patterns to vulnerable paths - such as /res/, /update/, and /.well-known/acme-challenge/ by monitoring HTTP traffic - logs via Suricata. This activity is significant as it could indicate an attacker - bypassing authentication to access or modify system settings. If confirmed malicious, - this could lead to unauthorized changes, disclosure of sensitive information, or - uploading of malicious certificates, severely compromising the server's security. -search: '`suricata` http.url IN ("*../admin/diagnostic.jsp*", "*../app/https/settings/*", - "*../app/pipeline*", "*../app/oauth/space/createBuild.html*", "*../res/*", "*../update/*", - "*../.well-known/acme-challenge/*", "*../app/availableRunners*", "*../app/https/settings/setPort*", - "*../app/https/settings/certificateInfo*", "*../app/https/settings/defaultHttpsPort*", - "*../app/https/settings/fetchFromAcme*", "*../app/https/settings/removeCertificate*", - "*../app/https/settings/uploadCertificate*", "*../app/https/settings/termsOfService*", - "*../app/https/settings/triggerAcmeChallenge*", "*../app/https/settings/cancelAcmeChallenge*", - "*../app/https/settings/getAcmeOrder*", "*../app/https/settings/setRedirectStrategy*") - http.status=200 http_method=GET | stats count min(_time) as firstTime max(_time) - as lastTime by src, dest, http_user_agent, http.url, http.status, http_method | - `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `jetbrains_teamcity_limited_auth_bypass_suricata_cve_2024_27199_filter`' -how_to_implement: The following detection relies on the Suricata TA and ensuring it - is properly configured to monitor HTTP traffic. Modify the query for your environment - and log sources as needed. -known_false_positives: False positives are not expected, however, monitor, filter, - and tune as needed based on organization log sources. The analytic is restricted - to 200 and GET requests to specific URI paths, which should limit false positives. +description: The following analytic identifies attempts to exploit CVE-2024-27199, a critical vulnerability in JetBrains TeamCity web server, allowing unauthenticated access to specific endpoints. It detects unusual access patterns to vulnerable paths such as /res/, /update/, and /.well-known/acme-challenge/ by monitoring HTTP traffic logs via Suricata. This activity is significant as it could indicate an attacker bypassing authentication to access or modify system settings. If confirmed malicious, this could lead to unauthorized changes, disclosure of sensitive information, or uploading of malicious certificates, severely compromising the server's security. +search: |- + `suricata` http.url IN ("*../admin/diagnostic.jsp*", "*../app/https/settings/*", "*../app/pipeline*", "*../app/oauth/space/createBuild.html*", "*../res/*", "*../update/*", "*../.well-known/acme-challenge/*", "*../app/availableRunners*", "*../app/https/settings/setPort*", "*../app/https/settings/certificateInfo*", "*../app/https/settings/defaultHttpsPort*", "*../app/https/settings/fetchFromAcme*", "*../app/https/settings/removeCertificate*", "*../app/https/settings/uploadCertificate*", "*../app/https/settings/termsOfService*", "*../app/https/settings/triggerAcmeChallenge*", "*../app/https/settings/cancelAcmeChallenge*", "*../app/https/settings/getAcmeOrder*", "*../app/https/settings/setRedirectStrategy*") http.status=200 http_method=GET + | stats count min(_time) as firstTime max(_time) as lastTime + BY src, dest, http_user_agent, + http.url, http.status, http_method + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `jetbrains_teamcity_limited_auth_bypass_suricata_cve_2024_27199_filter` +how_to_implement: The following detection relies on the Suricata TA and ensuring it is properly configured to monitor HTTP traffic. Modify the query for your environment and log sources as needed. +known_false_positives: False positives are not expected, however, monitor, filter, and tune as needed based on organization log sources. The analytic is restricted to 200 and GET requests to specific URI paths, which should limit false positives. references: -- https://github.com/projectdiscovery/nuclei-templates/blob/f644ec82dfe018890c6aa308967424d26c0f1522/http/cves/2024/CVE-2024-27199.yaml -- https://www.rapid7.com/blog/post/2024/03/04/etr-cve-2024-27198-and-cve-2024-27199-jetbrains-teamcity-multiple-authentication-bypass-vulnerabilities-fixed/ -- https://blog.jetbrains.com/teamcity/2024/03/teamcity-2023-11-4-is-out/ -- https://blog.jetbrains.com/teamcity/2024/03/additional-critical-security-issues-affecting-teamcity-on-premises-cve-2024-27198-and-cve-2024-27199-update-to-2023-11-4-now/ + - https://github.com/projectdiscovery/nuclei-templates/blob/f644ec82dfe018890c6aa308967424d26c0f1522/http/cves/2024/CVE-2024-27199.yaml + - https://www.rapid7.com/blog/post/2024/03/04/etr-cve-2024-27198-and-cve-2024-27199-jetbrains-teamcity-multiple-authentication-bypass-vulnerabilities-fixed/ + - https://blog.jetbrains.com/teamcity/2024/03/teamcity-2023-11-4-is-out/ + - https://blog.jetbrains.com/teamcity/2024/03/additional-critical-security-issues-affecting-teamcity-on-premises-cve-2024-27198-and-cve-2024-27199-update-to-2023-11-4-now/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible JetBrains TeamCity Limited Authentication Bypass Attempt against - $dest$ from $src$. - risk_objects: - - field: dest - type: system - score: 63 - threat_objects: - - field: src - type: ip_address + message: Possible JetBrains TeamCity Limited Authentication Bypass Attempt against $dest$ from $src$. + risk_objects: + - field: dest + type: system + score: 63 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - JetBrains TeamCity Vulnerabilities - asset_type: Web Server - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: - - CVE-2024-27199 + analytic_story: + - JetBrains TeamCity Vulnerabilities + asset_type: Web Server + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: + - CVE-2024-27199 tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/jetbrains/teamcity_cve_2024_27199.log - sourcetype: suricata - source: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/jetbrains/teamcity_cve_2024_27199.log + sourcetype: suricata + source: suricata diff --git a/detections/web/jetbrains_teamcity_rce_attempt.yml b/detections/web/jetbrains_teamcity_rce_attempt.yml index 72d5a08678..e1a3fe2dc8 100644 --- a/detections/web/jetbrains_teamcity_rce_attempt.yml +++ b/detections/web/jetbrains_teamcity_rce_attempt.yml @@ -1,78 +1,67 @@ name: JetBrains TeamCity RCE Attempt id: 89a58e5f-1365-4793-b45c-770abbb32b6c -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Suricata -description: The following analytic detects attempts to exploit the CVE-2023-42793 - vulnerability in JetBrains TeamCity On-Premises. It identifies suspicious POST requests - to /app/rest/users/id:1/tokens/RPC2, leveraging the Web datamodel to monitor specific - URL patterns and HTTP methods. This activity is significant as it may indicate an - unauthenticated attacker attempting to gain administrative access via Remote Code - Execution (RCE). If confirmed malicious, this could allow the attacker to execute - arbitrary code, potentially compromising the entire TeamCity environment and leading - to further unauthorized access and data breaches. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url IN ("/app/rest/users/id:1/tokens/RPC2*") Web.status=200 Web.http_method=POST - by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, - Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `jetbrains_teamcity_rce_attempt_filter`' -how_to_implement: The following analytic requires the Web datamodel. Ensure data source - is mapped correctly or modify and tune for your data source. -known_false_positives: If TeamCity is not in use, this analytic will not return results. - Monitor and tune for your environment. + - Suricata +description: The following analytic detects attempts to exploit the CVE-2023-42793 vulnerability in JetBrains TeamCity On-Premises. It identifies suspicious POST requests to /app/rest/users/id:1/tokens/RPC2, leveraging the Web datamodel to monitor specific URL patterns and HTTP methods. This activity is significant as it may indicate an unauthenticated attacker attempting to gain administrative access via Remote Code Execution (RCE). If confirmed malicious, this could allow the attacker to execute arbitrary code, potentially compromising the entire TeamCity environment and leading to further unauthorized access and data breaches. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN ("/app/rest/users/id:1/tokens/RPC2*") Web.status=200 Web.http_method=POST + BY Web.http_user_agent, Web.status Web.http_method, + Web.url, Web.url_length, Web.src, + Web.dest, sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `jetbrains_teamcity_rce_attempt_filter` +how_to_implement: The following analytic requires the Web datamodel. Ensure data source is mapped correctly or modify and tune for your data source. +known_false_positives: If TeamCity is not in use, this analytic will not return results. Monitor and tune for your environment. references: -- https://blog.jetbrains.com/teamcity/2023/09/critical-security-issue-affecting-teamcity-on-premises-update-to-2023-05-4-now/ -- https://www.sonarsource.com/blog/teamcity-vulnerability/ -- https://github.com/rapid7/metasploit-framework/pull/18408 -- https://attackerkb.com/topics/1XEEEkGHzt/cve-2023-42793/rapid7-analysis + - https://blog.jetbrains.com/teamcity/2023/09/critical-security-issue-affecting-teamcity-on-premises-update-to-2023-05-4-now/ + - https://www.sonarsource.com/blog/teamcity-vulnerability/ + - https://github.com/rapid7/metasploit-framework/pull/18408 + - https://attackerkb.com/topics/1XEEEkGHzt/cve-2023-42793/rapid7-analysis drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential JetBrains TeamCity RCE Attempt detected against URL $url$ on - $dest$. - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: - - field: src - type: ip_address + message: Potential JetBrains TeamCity RCE Attempt detected against URL $url$ on $dest$. + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: + - field: src + type: ip_address tags: - cve: - - CVE-2023-42793 - analytic_story: - - JetBrains TeamCity Unauthenticated RCE - - CISA AA23-347A - - JetBrains TeamCity Vulnerabilities - asset_type: Web Server - atomic_guid: [] - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + cve: + - CVE-2023-42793 + analytic_story: + - JetBrains TeamCity Unauthenticated RCE + - CISA AA23-347A + - JetBrains TeamCity Vulnerabilities + asset_type: Web Server + atomic_guid: [] + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/jetbrains/teamcity.log - source: suricata - sourcetype: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/jetbrains/teamcity.log + source: suricata + sourcetype: suricata diff --git a/detections/web/juniper_networks_remote_code_execution_exploit_detection.yml b/detections/web/juniper_networks_remote_code_execution_exploit_detection.yml index aaf8124b27..e40f66177b 100644 --- a/detections/web/juniper_networks_remote_code_execution_exploit_detection.yml +++ b/detections/web/juniper_networks_remote_code_execution_exploit_detection.yml @@ -1,89 +1,72 @@ name: Juniper Networks Remote Code Execution Exploit Detection id: 6cc4cc3d-b10a-4fac-be1e-55d384fc690e -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Suricata -description: The following analytic detects attempts to exploit a remote code execution - vulnerability in Juniper Networks devices. It identifies requests to /webauth_operation.php?PHPRC=*, - which are indicative of uploading and executing malicious PHP files. This detection - leverages the Web data model, focusing on specific URL patterns and HTTP status - codes. This activity is significant because it signals an attempt to gain unauthorized - access and execute arbitrary code on the device. If confirmed malicious, the attacker - could gain control over the device, leading to data theft, network compromise, or - other severe consequences. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url IN ("*/webauth_operation.php?PHPRC=*") Web.status=200 by Web.http_user_agent, - Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype - | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `juniper_networks_remote_code_execution_exploit_detection_filter`' -how_to_implement: To implement this search, ensure that the Web data model is populated. - The search is activated when the Web data model is accelerated. Network products, - such as Suricata or Palo Alto, need to be mapped to the Web data model. Adjust the - mapping as necessary to suit your specific products. -known_false_positives: Be aware of potential false positives - legitimate uses of - the /webauth_operation.php endpoint may cause benign activities to be flagged.The - URL in the analytic is specific to a successful attempt to exploit the vulnerability. - Review contents of the HTTP body to determine if the request is malicious. If the - request is benign, add the URL to the whitelist or continue to monitor. + - Suricata +description: The following analytic detects attempts to exploit a remote code execution vulnerability in Juniper Networks devices. It identifies requests to /webauth_operation.php?PHPRC=*, which are indicative of uploading and executing malicious PHP files. This detection leverages the Web data model, focusing on specific URL patterns and HTTP status codes. This activity is significant because it signals an attempt to gain unauthorized access and execute arbitrary code on the device. If confirmed malicious, the attacker could gain control over the device, leading to data theft, network compromise, or other severe consequences. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN ("*/webauth_operation.php?PHPRC=*") Web.status=200 + BY Web.http_user_agent, Web.status Web.http_method, + Web.url, Web.url_length, Web.src, + Web.dest, sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `juniper_networks_remote_code_execution_exploit_detection_filter` +how_to_implement: To implement this search, ensure that the Web data model is populated. The search is activated when the Web data model is accelerated. Network products, such as Suricata or Palo Alto, need to be mapped to the Web data model. Adjust the mapping as necessary to suit your specific products. +known_false_positives: Be aware of potential false positives - legitimate uses of the /webauth_operation.php endpoint may cause benign activities to be flagged.The URL in the analytic is specific to a successful attempt to exploit the vulnerability. Review contents of the HTTP body to determine if the request is malicious. If the request is benign, add the URL to the whitelist or continue to monitor. references: -- https://supportportal.juniper.net/s/article/2023-08-Out-of-Cycle-Security-Bulletin-Junos-OS-SRX-Series-and-EX-Series-Multiple-vulnerabilities-in-J-Web-can-be-combined-to-allow-a-preAuth-Remote-Code-Execution?language=en_US -- https://github.com/projectdiscovery/nuclei-templates/blob/main/http/cves/2023/CVE-2023-36844.yaml -- https://thehackernews.com/2023/08/new-juniper-junos-os-flaws-expose.html -- https://github.com/watchtowrlabs/juniper-rce_cve-2023-36844 -- https://labs.watchtowr.com/cve-2023-36844-and-friends-rce-in-juniper-firewalls/ -- https://vulncheck.com/blog/juniper-cve-2023-36845 + - https://supportportal.juniper.net/s/article/2023-08-Out-of-Cycle-Security-Bulletin-Junos-OS-SRX-Series-and-EX-Series-Multiple-vulnerabilities-in-J-Web-can-be-combined-to-allow-a-preAuth-Remote-Code-Execution?language=en_US + - https://github.com/projectdiscovery/nuclei-templates/blob/main/http/cves/2023/CVE-2023-36844.yaml + - https://thehackernews.com/2023/08/new-juniper-junos-os-flaws-expose.html + - https://github.com/watchtowrlabs/juniper-rce_cve-2023-36844 + - https://labs.watchtowr.com/cve-2023-36844-and-friends-rce-in-juniper-firewalls/ + - https://vulncheck.com/blog/juniper-cve-2023-36845 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: This analytic has identified a potential exploitation of a remote code - execution vulnerability in Juniper Networks devices on $dest$ on the URL $url$ - used for the exploit. - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: - - field: url - type: url + message: This analytic has identified a potential exploitation of a remote code execution vulnerability in Juniper Networks devices on $dest$ on the URL $url$ used for the exploit. + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: + - field: url + type: url tags: - analytic_story: - - Juniper JunOS Remote Code Execution - cve: - - CVE-2023-36844 - - CVE-2023-36845 - - CVE-2023-36846 - - CVE-2023-36847 - asset_type: Web Server - atomic_guid: [] - mitre_attack_id: - - T1190 - - T1105 - - T1059 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Juniper JunOS Remote Code Execution + cve: + - CVE-2023-36844 + - CVE-2023-36845 + - CVE-2023-36846 + - CVE-2023-36847 + asset_type: Web Server + atomic_guid: [] + mitre_attack_id: + - T1190 + - T1105 + - T1059 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/juniper/suricata_junos_cvemegazord.log - source: suricata - sourcetype: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/juniper/suricata_junos_cvemegazord.log + source: suricata + sourcetype: suricata diff --git a/detections/web/log4shell_jndi_payload_injection_attempt.yml b/detections/web/log4shell_jndi_payload_injection_attempt.yml index c423de6925..d2dac9ac8d 100644 --- a/detections/web/log4shell_jndi_payload_injection_attempt.yml +++ b/detections/web/log4shell_jndi_payload_injection_attempt.yml @@ -1,74 +1,61 @@ name: Log4Shell JNDI Payload Injection Attempt id: c184f12e-5c90-11ec-bf1f-497c9a704a72 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Jose Hernandez status: production type: Anomaly -description: The following analytic identifies attempts to inject Log4Shell JNDI payloads - via web calls. It leverages the Web datamodel and uses regex to detect patterns - like `${jndi:ldap://` in raw web event data, including HTTP headers. This activity - is significant because it targets vulnerabilities in Java web applications using - Log4j, such as Apache Struts and Solr. If confirmed malicious, this could allow - attackers to execute arbitrary code, potentially leading to full system compromise. - Immediate investigation is required to determine if the attempt was successful and - to mitigate any potential exploitation. +description: The following analytic identifies attempts to inject Log4Shell JNDI payloads via web calls. It leverages the Web datamodel and uses regex to detect patterns like `${jndi:ldap://` in raw web event data, including HTTP headers. This activity is significant because it targets vulnerabilities in Java web applications using Log4j, such as Apache Struts and Solr. If confirmed malicious, this could allow attackers to execute arbitrary code, potentially leading to full system compromise. Immediate investigation is required to determine if the attempt was successful and to mitigate any potential exploitation. data_source: -- Nginx Access -search: '| from datamodel Web.Web | regex _raw="[jJnNdDiI]{4}(\:|\%3A|\/|\%2F)\w+(\:\/\/|\%3A\%2F\%2F)(\$\{.*?\}(\.)?)?" - | fillnull | stats count by action, category, dest, dest_port, http_content_type, - http_method, http_referrer, http_user_agent, site, src, url, url_domain, user | - `log4shell_jndi_payload_injection_attempt_filter`' -how_to_implement: This detection requires the Web datamodel to be populated from a - supported Technology Add-On like Splunk for Apache or Splunk for Nginx. -known_false_positives: If there is a vulnerablility scannner looking for log4shells - this will trigger, otherwise likely to have low false positives. + - Nginx Access +search: |- + | from datamodel Web.Web + | regex _raw="[jJnNdDiI]{4}(\:|\%3A|\/|\%2F)\w+(\:\/\/|\%3A\%2F\%2F)(\$\{.*?\}(\.)?)?" + | fillnull + | stats count by action, category, dest, dest_port, http_content_type, http_method, http_referrer, http_user_agent, site, src, url, url_domain, user + | `log4shell_jndi_payload_injection_attempt_filter` +how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache or Splunk for Nginx. +known_false_positives: If there is a vulnerablility scannner looking for log4shells this will trigger, otherwise likely to have low false positives. references: -- https://www.lunasec.io/docs/blog/log4j-zero-day/ + - https://www.lunasec.io/docs/blog/log4j-zero-day/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: CVE-2021-44228 Log4Shell triggered for host $dest$ - risk_objects: - - field: user - type: user - score: 15 - - field: dest - type: system - score: 15 - threat_objects: [] + message: CVE-2021-44228 Log4Shell triggered for host $dest$ + risk_objects: + - field: user + type: user + score: 15 + - field: dest + type: system + score: 15 + threat_objects: [] tags: - analytic_story: - - Log4Shell CVE-2021-44228 - - CISA AA22-257A - - CISA AA22-320A - asset_type: Endpoint - cve: - - CVE-2021-44228 - mitre_attack_id: - - T1190 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Log4Shell CVE-2021-44228 + - CISA AA22-257A + - CISA AA22-320A + asset_type: Endpoint + cve: + - CVE-2021-44228 + mitre_attack_id: + - T1190 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/log4j_proxy_logs/log4j_proxy_logs.log - source: nginx - sourcetype: nginx:plus:kv + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/log4j_proxy_logs/log4j_proxy_logs.log + source: nginx + sourcetype: nginx:plus:kv diff --git a/detections/web/log4shell_jndi_payload_injection_with_outbound_connection.yml b/detections/web/log4shell_jndi_payload_injection_with_outbound_connection.yml index e0bff0db78..8f422a17d1 100644 --- a/detections/web/log4shell_jndi_payload_injection_with_outbound_connection.yml +++ b/detections/web/log4shell_jndi_payload_injection_with_outbound_connection.yml @@ -1,79 +1,62 @@ name: Log4Shell JNDI Payload Injection with Outbound Connection id: 69afee44-5c91-11ec-bf1f-497c9a704a72 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Jose Hernandez status: production type: Anomaly -description: The following analytic detects Log4Shell JNDI payload injections via - outbound connections. It identifies suspicious LDAP lookup functions in web logs, - such as `${jndi:ldap://PAYLOAD_INJECTED}`, and correlates them with network traffic - to known malicious IP addresses. This detection leverages the Web and Network_Traffic - data models in Splunk. Monitoring this activity is crucial as it targets vulnerabilities - in Java web applications using log4j, potentially leading to remote code execution. - If confirmed malicious, attackers could gain unauthorized access, execute arbitrary - code, and compromise sensitive data within the affected environment. +description: The following analytic detects Log4Shell JNDI payload injections via outbound connections. It identifies suspicious LDAP lookup functions in web logs, such as `${jndi:ldap://PAYLOAD_INJECTED}`, and correlates them with network traffic to known malicious IP addresses. This detection leverages the Web and Network_Traffic data models in Splunk. Monitoring this activity is crucial as it targets vulnerabilities in Java web applications using log4j, potentially leading to remote code execution. If confirmed malicious, attackers could gain unauthorized access, execute arbitrary code, and compromise sensitive data within the affected environment. data_source: [] -search: '| from datamodel Web.Web | rex field=_raw max_match=0 "[jJnNdDiI]{4}(\:|\%3A|\/|\%2F)(?\w+)(\:\/\/|\%3A\%2F\%2F)(\$\{.*?\}(\.)?)?(?[a-zA-Z0-9\.\-\_\$]+)" - | join affected_host type=inner [| tstats `security_content_summariesonly` count - min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic.All_Traffic - by All_Traffic.dest | `drop_dm_object_name(All_Traffic)` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | rename dest AS affected_host] | fillnull - | stats count by action, category, dest, dest_port, http_content_type, http_method, - http_referrer, http_user_agent, site, src, url, url_domain, user | `log4shell_jndi_payload_injection_with_outbound_connection_filter`' -how_to_implement: This detection requires the Web datamodel to be populated from a - supported Technology Add-On like Splunk for Apache or Splunk for Nginx. -known_false_positives: If there is a vulnerablility scannner looking for log4shells - this will trigger, otherwise likely to have low false positives. +search: |- + | from datamodel Web.Web + | rex field=_raw max_match=0 "[jJnNdDiI]{4}(\:|\%3A|\/|\%2F)(?\w+)(\:\/\/|\%3A\%2F\%2F)(\$\{.*?\}(\.)?)?(?[a-zA-Z0-9\.\-\_\$]+)" | join affected_host type=inner [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic.All_Traffic by All_Traffic.dest | `drop_dm_object_name(All_Traffic)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename dest AS affected_host] + | fillnull + | stats count by action, category, dest, dest_port, http_content_type, http_method, http_referrer, http_user_agent, site, src, url, url_domain, user + | `log4shell_jndi_payload_injection_with_outbound_connection_filter` +how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache or Splunk for Nginx. +known_false_positives: If there is a vulnerablility scannner looking for log4shells this will trigger, otherwise likely to have low false positives. references: -- https://www.lunasec.io/docs/blog/log4j-zero-day/ + - https://www.lunasec.io/docs/blog/log4j-zero-day/ drilldown_searches: -- name: View the detection results for - "$user$" and "$dest$" - search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$user$" and "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", - "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: CVE-2021-44228 Log4Shell triggered for host $dest$ - risk_objects: - - field: user - type: user - score: 15 - - field: dest - type: system - score: 15 - threat_objects: [] + message: CVE-2021-44228 Log4Shell triggered for host $dest$ + risk_objects: + - field: user + type: user + score: 15 + - field: dest + type: system + score: 15 + threat_objects: [] tags: - analytic_story: - - Log4Shell CVE-2021-44228 - - CISA AA22-320A - asset_type: Endpoint - cve: - - CVE-2021-44228 - mitre_attack_id: - - T1190 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Log4Shell CVE-2021-44228 + - CISA AA22-320A + asset_type: Endpoint + cve: + - CVE-2021-44228 + mitre_attack_id: + - T1190 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/log4j_proxy_logs/log4j_proxy_logs.log - source: nginx - sourcetype: nginx:plus:kv - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/log4j_network_logs/log4j_network_logs.log - source: stream:Splunk_IP - sourcetype: stream:ip + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/log4j_proxy_logs/log4j_proxy_logs.log + source: nginx + sourcetype: nginx:plus:kv + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/log4j_network_logs/log4j_network_logs.log + source: stream:Splunk_IP + sourcetype: stream:ip diff --git a/detections/web/microsoft_sharepoint_server_elevation_of_privilege.yml b/detections/web/microsoft_sharepoint_server_elevation_of_privilege.yml index 09fb3f8f24..168caccc97 100644 --- a/detections/web/microsoft_sharepoint_server_elevation_of_privilege.yml +++ b/detections/web/microsoft_sharepoint_server_elevation_of_privilege.yml @@ -1,74 +1,63 @@ name: Microsoft SharePoint Server Elevation of Privilege id: fcf4bd3f-a79f-4b7a-83bf-2692d60b859d -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Gowthamaraj Rajendran, Splunk status: production type: TTP data_source: -- Suricata -description: The following analytic detects potential exploitation attempts against - Microsoft SharePoint Server vulnerability CVE-2023-29357. It leverages the Web datamodel - to monitor for specific API calls and HTTP methods indicative of privilege escalation - attempts. This activity is significant as it may indicate an attacker is trying - to gain unauthorized privileged access to the SharePoint environment. If confirmed - malicious, the impact could include unauthorized access to sensitive data, potential - data theft, and further compromise of the SharePoint server, leading to a broader - security breach. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url IN ("/_api/web/siteusers*","/_api/web/currentuser*") Web.status=200 - Web.http_method=GET by Web.http_user_agent, Web.status Web.http_method, Web.url, - Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `microsoft_sharepoint_server_elevation_of_privilege_filter`' -how_to_implement: This detection requires the Web datamodel to be populated from a - supported Technology Add-On like Splunk for Microsoft SharePoint. -known_false_positives: False positives may occur if there are legitimate activities - that mimic the exploitation pattern. It's recommended to review the context of the - alerts and adjust the analytic parameters to better fit the specific environment. + - Suricata +description: The following analytic detects potential exploitation attempts against Microsoft SharePoint Server vulnerability CVE-2023-29357. It leverages the Web datamodel to monitor for specific API calls and HTTP methods indicative of privilege escalation attempts. This activity is significant as it may indicate an attacker is trying to gain unauthorized privileged access to the SharePoint environment. If confirmed malicious, the impact could include unauthorized access to sensitive data, potential data theft, and further compromise of the SharePoint server, leading to a broader security breach. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN ("/_api/web/siteusers*","/_api/web/currentuser*") Web.status=200 Web.http_method=GET + BY Web.http_user_agent, Web.status Web.http_method, + Web.url, Web.url_length, Web.src, + Web.dest, sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `microsoft_sharepoint_server_elevation_of_privilege_filter` +how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Microsoft SharePoint. +known_false_positives: False positives may occur if there are legitimate activities that mimic the exploitation pattern. It's recommended to review the context of the alerts and adjust the analytic parameters to better fit the specific environment. references: -- https://socradar.io/microsoft-sharepoint-server-elevation-of-privilege-vulnerability-exploit-cve-2023-29357/ -- https://github.com/LuemmelSec/CVE-2023-29357/blob/main/CVE-2023-29357/Program.cs + - https://socradar.io/microsoft-sharepoint-server-elevation-of-privilege-vulnerability-exploit-cve-2023-29357/ + - https://github.com/LuemmelSec/CVE-2023-29357/blob/main/CVE-2023-29357/Program.cs drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Possible exploitation of CVE-2023-29357 against $dest$ from $src$. - risk_objects: - - field: dest - type: system - score: 45 - threat_objects: - - field: src - type: ip_address + message: Possible exploitation of CVE-2023-29357 against $dest$ from $src$. + risk_objects: + - field: dest + type: system + score: 45 + threat_objects: + - field: src + type: ip_address tags: - cve: - - CVE-2023-29357 - analytic_story: - - Microsoft SharePoint Server Elevation of Privilege CVE-2023-29357 - asset_type: Web Server - atomic_guid: [] - mitre_attack_id: - - T1068 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + cve: + - CVE-2023-29357 + analytic_story: + - Microsoft SharePoint Server Elevation of Privilege CVE-2023-29357 + asset_type: Web Server + atomic_guid: [] + mitre_attack_id: + - T1068 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/sharepoint/sharepointeop.log - source: suricata - sourcetype: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/sharepoint/sharepointeop.log + source: suricata + sourcetype: suricata diff --git a/detections/web/monitor_web_traffic_for_brand_abuse.yml b/detections/web/monitor_web_traffic_for_brand_abuse.yml index 399a33fc84..99d448594c 100644 --- a/detections/web/monitor_web_traffic_for_brand_abuse.yml +++ b/detections/web/monitor_web_traffic_for_brand_abuse.yml @@ -1,49 +1,39 @@ name: Monitor Web Traffic For Brand Abuse id: 134da869-e264-4a8f-8d7e-fcd0ec88f301 -version: 7 -date: '2026-01-14' +version: 8 +date: '2026-02-25' author: David Dorsey, Splunk status: experimental type: TTP -description: The following analytic identifies web requests to domains that closely - resemble your monitored brand's domain, indicating potential brand abuse. It leverages - data from web traffic sources, such as web proxies or network traffic analysis tools, - and cross-references these with known domain permutations generated by the "ESCU - - DNSTwist Domain Names" search. This activity is significant as it can indicate - phishing attempts or other malicious activities targeting your brand. If confirmed - malicious, attackers could deceive users, steal credentials, or distribute malware, - leading to significant reputational and financial damage. +description: The following analytic identifies web requests to domains that closely resemble your monitored brand's domain, indicating potential brand abuse. It leverages data from web traffic sources, such as web proxies or network traffic analysis tools, and cross-references these with known domain permutations generated by the "ESCU - DNSTwist Domain Names" search. This activity is significant as it can indicate phishing attempts or other malicious activities targeting your brand. If confirmed malicious, attackers could deceive users, steal credentials, or distribute malware, leading to significant reputational and financial damage. data_source: [] search: | - | tstats `security_content_summariesonly` - values(Web.url) as urls - min(_time) as firstTime - from datamodel=Web - by Web.src - | `drop_dm_object_name("Web")` - | `security_content_ctime(firstTime)` - | lookup update=true brandMonitoring_lookup domain as urls OUTPUT domain_abuse - | search domain_abuse=true - | `monitor_web_traffic_for_brand_abuse_filter` -how_to_implement: You need to ingest data from your web traffic. This can be accomplished - by indexing data from a web proxy, or using a network traffic analysis tool, such - as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain - Names", which creates the permutations of the domain that will be checked for. + | tstats `security_content_summariesonly` + values(Web.url) as urls + min(_time) as firstTime + from datamodel=Web + by Web.src + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | lookup update=true brandMonitoring_lookup domain as urls OUTPUT domain_abuse + | search domain_abuse=true + | `monitor_web_traffic_for_brand_abuse_filter` +how_to_implement: You need to ingest data from your web traffic. This can be accomplished by indexing data from a web proxy, or using a network traffic analysis tool, such as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. known_false_positives: No false positives have been identified at this time. references: [] rba: - message: Potential Brand Abus discovered in web logs - risk_objects: - - field: src - type: system - score: 25 - threat_objects: [] + message: Potential Brand Abus discovered in web logs + risk_objects: + - field: src + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Brand Monitoring - asset_type: Endpoint - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Brand Monitoring + asset_type: Endpoint + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/web/multiple_archive_files_http_post_traffic.yml b/detections/web/multiple_archive_files_http_post_traffic.yml index 6878d883f2..f874d735d4 100644 --- a/detections/web/multiple_archive_files_http_post_traffic.yml +++ b/detections/web/multiple_archive_files_http_post_traffic.yml @@ -1,76 +1,67 @@ name: Multiple Archive Files Http Post Traffic id: 4477f3ea-a28f-11eb-b762-acde48001122 -version: 9 -date: '2025-10-14' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects the high-frequency exfiltration of archive - files via HTTP POST requests. It leverages HTTP stream logs to identify specific - archive file headers within the request body. This activity is significant as it - often indicates data exfiltration by APTs or trojan spyware after data collection. - If confirmed malicious, this behavior could lead to the unauthorized transfer of - sensitive data to an attacker’s command and control server, potentially resulting - in severe data breaches and loss of confidential information. +description: The following analytic detects the high-frequency exfiltration of archive files via HTTP POST requests. It leverages HTTP stream logs to identify specific archive file headers within the request body. This activity is significant as it often indicates data exfiltration by APTs or trojan spyware after data collection. If confirmed malicious, this behavior could lead to the unauthorized transfer of sensitive data to an attacker’s command and control server, potentially resulting in severe data breaches and loss of confidential information. data_source: -- Splunk Stream HTTP -search: '`stream_http` http_method=POST |eval archive_hdr1=substr(form_data,1,2) | - eval archive_hdr2 = substr(form_data,1,4) |stats values(form_data) as http_request_body - min(_time) as firstTime max(_time) as lastTime count by src_ip dest_ip http_method - http_user_agent uri_path url bytes_in bytes_out archive_hdr1 archive_hdr2 |where - count >20 AND (archive_hdr1 = "7z" OR archive_hdr1 = "PK" OR archive_hdr2="Rar!") - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `multiple_archive_files_http_post_traffic_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the stream HTTP logs or network logs that catch network traffic. Make - sure that the http-request-body, payload, or request field is enabled in stream - http configuration. + - Splunk Stream HTTP +search: |- + `stream_http` http_method=POST + | eval archive_hdr1=substr(form_data,1,2) + | eval archive_hdr2 = substr(form_data,1,4) + | stats values(form_data) as http_request_body min(_time) as firstTime max(_time) as lastTime count + BY src_ip dest_ip http_method + http_user_agent uri_path url + bytes_in bytes_out archive_hdr1 + archive_hdr2 + | where count >20 AND (archive_hdr1 = "7z" OR archive_hdr1 = "PK" OR archive_hdr2="Rar!") + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `multiple_archive_files_http_post_traffic_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the stream HTTP logs or network logs that catch network traffic. Make sure that the http-request-body, payload, or request field is enabled in stream http configuration. known_false_positives: Normal archive transfer via HTTP protocol may trip this detection. references: -- https://attack.mitre.org/techniques/T1560/001/ -- https://www.mandiant.com/resources/apt39-iranian-cyber-espionage-group-focused-on-personal-information -- https://www.microsoft.com/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/ + - https://attack.mitre.org/techniques/T1560/001/ + - https://www.mandiant.com/resources/apt39-iranian-cyber-espionage-group-focused-on-personal-information + - https://www.microsoft.com/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/ drilldown_searches: -- name: View the detection results for - "$src_ip$" - search: '%original_detection_search% | search src_ip = "$src_ip$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_ip$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_ip$" + search: '%original_detection_search% | search src_ip = "$src_ip$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_ip$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A http post $http_method$ sending packet with possible archive bytes header - in uri path $uri_path$ - risk_objects: - - field: src_ip - type: system - score: 25 - threat_objects: - - field: url - type: url + message: A http post $http_method$ sending packet with possible archive bytes header in uri path $uri_path$ + risk_objects: + - field: src_ip + type: system + score: 25 + threat_objects: + - field: url + type: url tags: - analytic_story: - - Data Exfiltration - - Command And Control - - APT37 Rustonotto and FadeStealer - - Hellcat Ransomware - asset_type: Endpoint - mitre_attack_id: - - T1048.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Data Exfiltration + - Command And Control + - APT37 Rustonotto and FadeStealer + - Hellcat Ransomware + asset_type: Endpoint + mitre_attack_id: + - T1048.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/archive_http_post/stream_http_events.log - source: stream - sourcetype: stream:http + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/archive_http_post/stream_http_events.log + source: stream + sourcetype: stream:http diff --git a/detections/web/nginx_connectwise_screenconnect_authentication_bypass.yml b/detections/web/nginx_connectwise_screenconnect_authentication_bypass.yml index ebf4bdd4ea..6c06f633c6 100644 --- a/detections/web/nginx_connectwise_screenconnect_authentication_bypass.yml +++ b/detections/web/nginx_connectwise_screenconnect_authentication_bypass.yml @@ -1,83 +1,66 @@ name: Nginx ConnectWise ScreenConnect Authentication Bypass id: b3f7a803-e802-448b-8eb2-e796b223bccc -version: 7 -date: '2025-10-14' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk data_source: -- Nginx Access + - Nginx Access type: TTP status: production -description: The following analytic detects attempts to exploit the ConnectWise ScreenConnect - CVE-2024-1709 vulnerability, which allows attackers to bypass authentication via - alternate paths or channels. It leverages Nginx access logs to identify web requests - to the SetupWizard.aspx page, indicating potential exploitation. This activity is - significant as it can lead to unauthorized administrative access and remote code - execution. If confirmed malicious, attackers could create administrative users and - gain full control over the affected ScreenConnect instance, posing severe security - risks. Immediate remediation by updating to version 23.9.8 or above is recommended. -search: '`nginx_access_logs` uri_path IN ("*/SetupWizard.aspx/*","*/SetupWizard/") - status=200 http_method=POST | stats count min(_time) as firstTime max(_time) as - lastTime by src, dest, http_user_agent, url, uri_path, status, http_method, sourcetype, - source | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `nginx_connectwise_screenconnect_authentication_bypass_filter`' -how_to_implement: To implement this analytic, ensure proper logging is occurring with - Nginx, access.log and error.log, and that these logs are being ingested into Splunk. - STRT utilizes this nginx.conf https://gist.github.com/MHaggis/26f59108b04da8f1d870c9cc3a3c8eec - to properly log as much data with Nginx. -known_false_positives: False positives are not expected, as the detection is based - on the presence of web requests to the SetupWizard.aspx page, which is not a common - page to be accessed by legitimate users. Note that the analytic is limited to HTTP - POST and a status of 200 to reduce false positives. Modify the query as needed to - reduce false positives or hunt for additional indicators of compromise. +description: The following analytic detects attempts to exploit the ConnectWise ScreenConnect CVE-2024-1709 vulnerability, which allows attackers to bypass authentication via alternate paths or channels. It leverages Nginx access logs to identify web requests to the SetupWizard.aspx page, indicating potential exploitation. This activity is significant as it can lead to unauthorized administrative access and remote code execution. If confirmed malicious, attackers could create administrative users and gain full control over the affected ScreenConnect instance, posing severe security risks. Immediate remediation by updating to version 23.9.8 or above is recommended. +search: |- + `nginx_access_logs` uri_path IN ("*/SetupWizard.aspx/*","*/SetupWizard/") status=200 http_method=POST + | stats count min(_time) as firstTime max(_time) as lastTime + BY src, dest, http_user_agent, + url, uri_path, status, + http_method, sourcetype, source + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `nginx_connectwise_screenconnect_authentication_bypass_filter` +how_to_implement: To implement this analytic, ensure proper logging is occurring with Nginx, access.log and error.log, and that these logs are being ingested into Splunk. STRT utilizes this nginx.conf https://gist.github.com/MHaggis/26f59108b04da8f1d870c9cc3a3c8eec to properly log as much data with Nginx. +known_false_positives: False positives are not expected, as the detection is based on the presence of web requests to the SetupWizard.aspx page, which is not a common page to be accessed by legitimate users. Note that the analytic is limited to HTTP POST and a status of 200 to reduce false positives. Modify the query as needed to reduce false positives or hunt for additional indicators of compromise. references: -- https://docs.splunk.com/Documentation/AddOns/released/NGINX/Sourcetypes -- https://gist.github.com/MHaggis/26f59108b04da8f1d870c9cc3a3c8eec -- https://www.huntress.com/blog/a-catastrophe-for-control-understanding-the-screenconnect-authentication-bypass -- https://www.huntress.com/blog/detection-guidance-for-connectwise-cwe-288-2 -- https://www.connectwise.com/company/trust/security-bulletins/connectwise-screenconnect-23.9.8 + - https://docs.splunk.com/Documentation/AddOns/released/NGINX/Sourcetypes + - https://gist.github.com/MHaggis/26f59108b04da8f1d870c9cc3a3c8eec + - https://www.huntress.com/blog/a-catastrophe-for-control-understanding-the-screenconnect-authentication-bypass + - https://www.huntress.com/blog/detection-guidance-for-connectwise-cwe-288-2 + - https://www.connectwise.com/company/trust/security-bulletins/connectwise-screenconnect-23.9.8 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An authentication bypass attempt against ScreenConnect has been detected - on $dest$. - risk_objects: - - field: dest - type: system - score: 100 - threat_objects: [] + message: An authentication bypass attempt against ScreenConnect has been detected on $dest$. + risk_objects: + - field: dest + type: system + score: 100 + threat_objects: [] tags: - analytic_story: - - ConnectWise ScreenConnect Vulnerabilities - - Seashell Blizzard - - Scattered Lapsus$ Hunters - - Hellcat Ransomware - asset_type: Web Proxy - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: - - CVE-2024-1708 - - CVE-2024-1709 + analytic_story: + - ConnectWise ScreenConnect Vulnerabilities + - Seashell Blizzard + - Scattered Lapsus$ Hunters + - Hellcat Ransomware + asset_type: Web Proxy + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: + - CVE-2024-1708 + - CVE-2024-1709 tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/screenconnect/nginx_screenconnect.log - sourcetype: nginx:plus:kv - source: nginx:plus:kv + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/screenconnect/nginx_screenconnect.log + sourcetype: nginx:plus:kv + source: nginx:plus:kv diff --git a/detections/web/papercut_ng_remote_web_access_attempt.yml b/detections/web/papercut_ng_remote_web_access_attempt.yml index 29ddec40c2..832255dead 100644 --- a/detections/web/papercut_ng_remote_web_access_attempt.yml +++ b/detections/web/papercut_ng_remote_web_access_attempt.yml @@ -6,107 +6,100 @@ author: Michael Haag, Splunk status: production type: TTP data_source: - - Suricata + - Suricata description: | - The following analytic detects potential exploitation attempts on publicly accessible PaperCut NG servers. - It identifies connections from public IP addresses to the server, specifically monitoring URI paths commonly used in proof-of-concept scripts for exploiting PaperCut NG vulnerabilities. - This detection leverages web traffic data from the `Web` datamodel, focusing on specific URI paths and excluding internal IP ranges. - This activity is significant as it may indicate an attempt to exploit known vulnerabilities in PaperCut NG, potentially leading to unauthorized access or control of the server. - If confirmed malicious, attackers could gain administrative access, leading to data breaches or further network compromise. + The following analytic detects potential exploitation attempts on publicly accessible PaperCut NG servers. + It identifies connections from public IP addresses to the server, specifically monitoring URI paths commonly used in proof-of-concept scripts for exploiting PaperCut NG vulnerabilities. + This detection leverages web traffic data from the `Web` datamodel, focusing on specific URI paths and excluding internal IP ranges. + This activity is significant as it may indicate an attempt to exploit known vulnerabilities in PaperCut NG, potentially leading to unauthorized access or control of the server. + If confirmed malicious, attackers could gain administrative access, leading to data breaches or further network compromise. search: | - | tstats `security_content_summariesonly` - count min(_time) as firstTime - max(_time) as lastTime + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime - from datamodel=Web WHERE + from datamodel=Web WHERE - Web.url IN ( - "/app?service=direct/1/PrinterDetails/printerOptionsTab.tab" - "/app?service=direct/1/PrinterList/selectPrinter&sp=*", - "/app?service=page/PrinterList", - "/app?service=page/SetupCompleted" - ) - NOT src IN ( - "10.0.0.0/8", - "172.16.0.0/12", - "192.168.0.0/16", - "100.64.0.0/10", - "127.0.0.0/8", - "169.254.0.0/16", - "192.0.0.0/24", - "192.0.0.0/29", - "192.0.0.8/32", - "192.0.0.9/32", - "192.0.0.10/32", - "192.0.0.170/32", - "192.0.0.171/32", - "192.0.2.0/24", - "192.31.196.0/24", - "192.52.193.0/24", - "192.88.99.0/24", - "224.0.0.0/4", - "192.175.48.0/24", - "198.18.0.0/15", - "198.51.100.0/24", - "203.0.113.0/24", - "240.0.0.0/4", - "::1" - ) - by Web.http_user_agent Web.http_method - Web.url,Web.url_length Web.src - Web.dest Web.dest_port + Web.url IN ( + "/app?service=direct/1/PrinterDetails/printerOptionsTab.tab" + "/app?service=direct/1/PrinterList/selectPrinter&sp=*", + "/app?service=page/PrinterList", + "/app?service=page/SetupCompleted" + ) + NOT src IN ( + "10.0.0.0/8", + "172.16.0.0/12", + "192.168.0.0/16", + "100.64.0.0/10", + "127.0.0.0/8", + "169.254.0.0/16", + "192.0.0.0/24", + "192.0.0.0/29", + "192.0.0.8/32", + "192.0.0.9/32", + "192.0.0.10/32", + "192.0.0.170/32", + "192.0.0.171/32", + "192.0.2.0/24", + "192.31.196.0/24", + "192.52.193.0/24", + "192.88.99.0/24", + "224.0.0.0/4", + "192.175.48.0/24", + "198.18.0.0/15", + "198.51.100.0/24", + "203.0.113.0/24", + "240.0.0.0/4", + "::1" + ) + by Web.http_user_agent Web.http_method + Web.url,Web.url_length Web.src + Web.dest Web.dest_port - | `drop_dm_object_name("Web")` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `papercut_ng_remote_web_access_attempt_filter` -how_to_implement: To successfully implement this search you need to be ingesting information - on Web traffic that include fields relevant for traffic into the `Web` datamodel. + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `papercut_ng_remote_web_access_attempt_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on Web traffic that include fields relevant for traffic into the `Web` datamodel. known_false_positives: False positives may be present, filter as needed. references: - - https://www.cisa.gov/news-events/alerts/2023/05/11/cisa-and-fbi-release-joint-advisory-response-active-exploitation-papercut-vulnerability - - https://www.papercut.com/kb/Main/PO-1216-and-PO-1219 - - https://www.horizon3.ai/papercut-cve-2023-27350-deep-dive-and-indicators-of-compromise/ - - https://www.bleepingcomputer.com/news/security/hackers-actively-exploit-critical-rce-bug-in-papercut-servers/ - - https://www.huntress.com/blog/critical-vulnerabilities-in-papercut-print-management-software + - https://www.cisa.gov/news-events/alerts/2023/05/11/cisa-and-fbi-release-joint-advisory-response-active-exploitation-papercut-vulnerability + - https://www.papercut.com/kb/Main/PO-1216-and-PO-1219 + - https://www.horizon3.ai/papercut-cve-2023-27350-deep-dive-and-indicators-of-compromise/ + - https://www.bleepingcomputer.com/news/security/hackers-actively-exploit-critical-rce-bug-in-papercut-servers/ + - https://www.huntress.com/blog/critical-vulnerabilities-in-papercut-print-management-software drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: URIs specific to PaperCut NG have been access by a public IP $src$ against $dest$. - risk_objects: - - field: dest - type: system - score: 50 - threat_objects: [] + message: URIs specific to PaperCut NG have been access by a public IP $src$ against $dest$. + risk_objects: + - field: dest + type: system + score: 50 + threat_objects: [] tags: - analytic_story: - - PaperCut MF NG Vulnerability - asset_type: Web Server - atomic_guid: [] - mitre_attack_id: - - T1190 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - PaperCut MF NG Vulnerability + asset_type: Web Server + atomic_guid: [] + mitre_attack_id: + - T1190 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/papercut/papercutng-suricata.log - source: suricata - sourcetype: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/papercut/papercutng-suricata.log + source: suricata + sourcetype: suricata diff --git a/detections/web/plain_http_post_exfiltrated_data.yml b/detections/web/plain_http_post_exfiltrated_data.yml index 51277f421f..60be7d2891 100644 --- a/detections/web/plain_http_post_exfiltrated_data.yml +++ b/detections/web/plain_http_post_exfiltrated_data.yml @@ -1,70 +1,58 @@ name: Plain HTTP POST Exfiltrated Data id: e2b36208-a364-11eb-8909-acde48001122 -version: 9 -date: '2026-01-14' +version: 10 +date: '2026-02-25' author: Teoderick Contreras, Splunk status: production type: TTP -description: The following analytic detects potential data exfiltration using plain - HTTP POST requests. It leverages network traffic logs, specifically monitoring the - `stream_http` data source for POST methods containing suspicious form data such - as "wermgr.exe" or "svchost.exe". This activity is significant because it is commonly - associated with malware like Trickbot, trojans, keyloggers, or APT adversaries, - which use plain text HTTP POST requests to communicate with remote C2 servers. If - confirmed malicious, this activity could lead to unauthorized data exfiltration, - compromising sensitive information and potentially leading to further network infiltration. +description: The following analytic detects potential data exfiltration using plain HTTP POST requests. It leverages network traffic logs, specifically monitoring the `stream_http` data source for POST methods containing suspicious form data such as "wermgr.exe" or "svchost.exe". This activity is significant because it is commonly associated with malware like Trickbot, trojans, keyloggers, or APT adversaries, which use plain text HTTP POST requests to communicate with remote C2 servers. If confirmed malicious, this activity could lead to unauthorized data exfiltration, compromising sensitive information and potentially leading to further network infiltration. data_source: -- Splunk Stream HTTP -search: '`stream_http` http_method=POST form_data IN ("*wermgr.exe*","*svchost.exe*", - "*name=\"proclist\"*","*ipconfig*", "*name=\"sysinfo\"*", "*net view*") |stats values(form_data) - as http_request_body min(_time) as firstTime max(_time) as lastTime count by src_ip - dest_ip http_method http_user_agent uri_path url bytes_in bytes_out | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `plain_http_post_exfiltrated_data_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the stream HTTP logs or network logs that catch network traffic. Make - sure that the http-request-body, payload, or request field is enabled. + - Splunk Stream HTTP +search: |- + `stream_http` http_method=POST form_data IN ("*wermgr.exe*","*svchost.exe*", "*name=\"proclist\"*","*ipconfig*", "*name=\"sysinfo\"*", "*net view*") + | stats values(form_data) as http_request_body min(_time) as firstTime max(_time) as lastTime count + BY src_ip dest_ip http_method + http_user_agent uri_path url + bytes_in bytes_out + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `plain_http_post_exfiltrated_data_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the stream HTTP logs or network logs that catch network traffic. Make sure that the http-request-body, payload, or request field is enabled. known_false_positives: No false positives have been identified at this time. references: -- https://blog.talosintelligence.com/2020/03/trickbot-primer.html + - https://blog.talosintelligence.com/2020/03/trickbot-primer.html drilldown_searches: -- name: View the detection results for - "$src_ip$" - search: '%original_detection_search% | search src_ip = "$src_ip$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src_ip$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src_ip$" + search: '%original_detection_search% | search src_ip = "$src_ip$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src_ip$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A http post $http_method$ sending packet with plain text of information - in uri path $uri_path$ - risk_objects: - - field: src_ip - type: system - score: 63 - threat_objects: [] + message: A http post $http_method$ sending packet with plain text of information in uri path $uri_path$ + risk_objects: + - field: src_ip + type: system + score: 63 + threat_objects: [] tags: - analytic_story: - - Data Exfiltration - - Command And Control - - APT37 Rustonotto and FadeStealer - asset_type: Endpoint - mitre_attack_id: - - T1048.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Data Exfiltration + - Command And Control + - APT37 Rustonotto and FadeStealer + asset_type: Endpoint + mitre_attack_id: + - T1048.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/plain_exfil_data/stream_http_events.log - source: stream - sourcetype: stream:http + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/plain_exfil_data/stream_http_events.log + source: stream + sourcetype: stream:http diff --git a/detections/web/proxyshell_proxynotshell_behavior_detected.yml b/detections/web/proxyshell_proxynotshell_behavior_detected.yml index 9e55d33ddb..2bdc684553 100644 --- a/detections/web/proxyshell_proxynotshell_behavior_detected.yml +++ b/detections/web/proxyshell_proxynotshell_behavior_detected.yml @@ -1,74 +1,57 @@ name: ProxyShell ProxyNotShell Behavior Detected id: c32fab32-6aaf-492d-bfaf-acbed8e50cdf -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Correlation -description: The following analytic identifies potential exploitation of Windows Exchange - servers via ProxyShell or ProxyNotShell vulnerabilities, followed by post-exploitation - activities such as running nltest, Cobalt Strike, Mimikatz, and adding new users. - It leverages data from multiple analytic stories, requiring at least five distinct - sources to trigger, thus reducing noise. This activity is significant as it indicates - a high likelihood of an active compromise, potentially leading to unauthorized access, - privilege escalation, and persistent threats within the environment. If confirmed - malicious, attackers could gain control over the Exchange server, exfiltrate data, - and maintain long-term access. +description: The following analytic identifies potential exploitation of Windows Exchange servers via ProxyShell or ProxyNotShell vulnerabilities, followed by post-exploitation activities such as running nltest, Cobalt Strike, Mimikatz, and adding new users. It leverages data from multiple analytic stories, requiring at least five distinct sources to trigger, thus reducing noise. This activity is significant as it indicates a high likelihood of an active compromise, potentially leading to unauthorized access, privilege escalation, and persistent threats within the environment. If confirmed malicious, attackers could gain control over the Exchange server, exfiltrate data, and maintain long-term access. data_source: [] -search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) - as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) - as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as - annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) - as mitre_tactic_id_count, values(All_Risk.analyticstories) as analyticstories values(All_Risk.annotations.mitre_attack.mitre_technique_id) - as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) - as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, - dc(source) as source_count dc(All_Risk.analyticstories) as dc_analyticstories from - datamodel=Risk.All_Risk where All_Risk.analyticstories IN ("ProxyNotShell","ProxyShell") - OR (All_Risk.analyticstories IN ("ProxyNotShell","ProxyShell") AND All_Risk.analyticstories="Cobalt - Strike") All_Risk.risk_object_type="system" by _time span=1h All_Risk.risk_object - All_Risk.risk_object_type | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`| where source_count >=5 | `proxyshell_proxynotshell_behavior_detected_filter`' -how_to_implement: To implement this correlation, you will need to enable ProxyShell, - ProxyNotShell and Cobalt Strike analytic stories (the anaytics themselves) and ensure - proper data is being collected for Web and Endpoint datamodels. Run the correlation - rule seperately to validate it is not triggering too much or generating incorrectly. - Validate by running ProxyShell POC code and Cobalt Strike behavior. -known_false_positives: False positives will be limited, however tune or modify the - query as needed. +search: |- + | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.analyticstories) as analyticstories values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count dc(All_Risk.analyticstories) as dc_analyticstories FROM datamodel=Risk.All_Risk + WHERE All_Risk.analyticstories IN ("ProxyNotShell","ProxyShell") + OR + (All_Risk.analyticstories IN ("ProxyNotShell","ProxyShell") + AND + All_Risk.analyticstories="Cobalt Strike") All_Risk.risk_object_type="system" + BY _time span=1h All_Risk.risk_object + All_Risk.risk_object_type + | `drop_dm_object_name(All_Risk)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | where source_count >=5 + | `proxyshell_proxynotshell_behavior_detected_filter` +how_to_implement: To implement this correlation, you will need to enable ProxyShell, ProxyNotShell and Cobalt Strike analytic stories (the anaytics themselves) and ensure proper data is being collected for Web and Endpoint datamodels. Run the correlation rule seperately to validate it is not triggering too much or generating incorrectly. Validate by running ProxyShell POC code and Cobalt Strike behavior. +known_false_positives: False positives will be limited, however tune or modify the query as needed. references: -- https://www.gteltsc.vn/blog/warning-new-attack-campaign-utilized-a-new-0day-rce-vulnerability-on-microsoft-exchange-server-12715.html -- https://msrc-blog.microsoft.com/2022/09/29/customer-guidance-for-reported-zero-day-vulnerabilities-in-microsoft-exchange-server/ + - https://www.gteltsc.vn/blog/warning-new-attack-campaign-utilized-a-new-0day-rce-vulnerability-on-microsoft-exchange-server-12715.html + - https://msrc-blog.microsoft.com/2022/09/29/customer-guidance-for-reported-zero-day-vulnerabilities-in-microsoft-exchange-server/ drilldown_searches: -- name: View the detection results for - "$risk_object$" - search: '%original_detection_search% | search risk_object = "$risk_object$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$risk_object$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$risk_object$" + search: '%original_detection_search% | search risk_object = "$risk_object$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$risk_object$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ tags: - analytic_story: - - ProxyShell - - ProxyNotShell - - Seashell Blizzard - asset_type: Web Server - mitre_attack_id: - - T1190 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - ProxyShell + - ProxyNotShell + - Seashell Blizzard + asset_type: Web Server + mitre_attack_id: + - T1190 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/proxyshell/proxyshell-risk.log - source: proxyshell - sourcetype: stash + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/proxyshell/proxyshell-risk.log + source: proxyshell + sourcetype: stash diff --git a/detections/web/sap_netweaver_visual_composer_exploitation_attempt.yml b/detections/web/sap_netweaver_visual_composer_exploitation_attempt.yml index c47b5ca8f3..32c8a2c509 100644 --- a/detections/web/sap_netweaver_visual_composer_exploitation_attempt.yml +++ b/detections/web/sap_netweaver_visual_composer_exploitation_attempt.yml @@ -1,52 +1,55 @@ name: SAP NetWeaver Visual Composer Exploitation Attempt id: a583b9f1-9c3a-4402-9441-b981654dea6c -version: 2 -date: '2025-05-02' +version: 3 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting description: | - Detects potential exploitation attempts targeting CVE-2025-31324, a critical unauthenticated file upload vulnerability in SAP NetWeaver Visual Composer. This flaw allows remote attackers to send specially crafted POST requests to the /developmentserver/metadatauploader endpoint, enabling arbitrary file uploads—commonly webshells—resulting in full system compromise. The detection looks for HTTP HEAD or POST requests with a 200 OK status to sensitive Visual Composer endpoints, which may indicate reconnaissance or active exploitation. Successful exploitation can lead to attackers gaining privileged access, deploying malware, and impacting business-critical SAP resources. Immediate patching and investigation of suspicious activity are strongly recommended, as this vulnerability is being actively exploited in the wild. + Detects potential exploitation attempts targeting CVE-2025-31324, a critical unauthenticated file upload vulnerability in SAP NetWeaver Visual Composer. This flaw allows remote attackers to send specially crafted POST requests to the /developmentserver/metadatauploader endpoint, enabling arbitrary file uploads—commonly webshells—resulting in full system compromise. The detection looks for HTTP HEAD or POST requests with a 200 OK status to sensitive Visual Composer endpoints, which may indicate reconnaissance or active exploitation. Successful exploitation can lead to attackers gaining privileged access, deploying malware, and impacting business-critical SAP resources. Immediate patching and investigation of suspicious activity are strongly recommended, as this vulnerability is being actively exploited in the wild. data_source: -- Suricata -search: '| tstats count min(_time) as firstTime max(_time) as lastTime - from datamodel=Web.Web - where (Web.url IN ("/CTCWebService/CTCWebServiceBean", "/VisualComposer/services/DesignTimeService", "/ctc/CTCWebService/CTCWebServiceBean")) - AND Web.http_method IN ("HEAD", "POST") - AND Web.status=200 - by Web.src, Web.dest, Web.http_method, Web.url, Web.http_user_agent, Web.url_length, sourcetype - | `drop_dm_object_name("Web")` - | eval action=case(http_method="HEAD", "Recon/Probe", http_method="POST", "Possible Exploitation") - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | table firstTime, lastTime, src, dest, http_method, action, url, user_agent, url_length, sourcetype - | `sap_netweaver_visual_composer_exploitation_attempt_filter`' + - Suricata +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web.Web + WHERE ( + Web.url IN ("/CTCWebService/CTCWebServiceBean", "/VisualComposer/services/DesignTimeService", "/ctc/CTCWebService/CTCWebServiceBean") + ) + AND Web.http_method IN ("HEAD", "POST") AND Web.status=200 + BY Web.src, Web.dest, Web.http_method, + Web.url, Web.http_user_agent, Web.url_length, + sourcetype + | `drop_dm_object_name("Web")` + | eval action=case(http_method="HEAD", "Recon/Probe", http_method="POST", "Possible Exploitation") + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | table firstTime, lastTime, src, dest, http_method, action, url, user_agent, url_length, sourcetype + | `sap_netweaver_visual_composer_exploitation_attempt_filter` how_to_implement: | - Ensure that the Web data model is accelerated and populated with web server or web proxy logs capturing HTTP request and response data. - This search relies on HTTP method, status code, and URL path fields to identify suspicious access patterns against SAP NetWeaver endpoints. + Ensure that the Web data model is accelerated and populated with web server or web proxy logs capturing HTTP request and response data. + This search relies on HTTP method, status code, and URL path fields to identify suspicious access patterns against SAP NetWeaver endpoints. known_false_positives: | - Some legitimate administrative activity may access SAP NetWeaver services. However, HEAD or POST requests directly resulting in a 200 OK - to Visual Composer endpoints are uncommon and should be investigated carefully. + Some legitimate administrative activity may access SAP NetWeaver services. However, HEAD or POST requests directly resulting in a 200 OK + to Visual Composer endpoints are uncommon and should be investigated carefully. references: -- https://onapsis.com/blog/active-exploitation-of-sap-vulnerability-cve-2025-31324/ -- https://reliaquest.com/blog/threat-spotlight-reliaquest-uncovers-vulnerability-behind-sap-netweaver-compromise/ -- https://www.rapid7.com/blog/post/2025/04/28/etr-active-exploitation-of-sap-netweaver-visual-composer-cve-2025-31324/ + - https://onapsis.com/blog/active-exploitation-of-sap-vulnerability-cve-2025-31324/ + - https://reliaquest.com/blog/threat-spotlight-reliaquest-uncovers-vulnerability-behind-sap-netweaver-compromise/ + - https://www.rapid7.com/blog/post/2025/04/28/etr-active-exploitation-of-sap-netweaver-visual-composer-cve-2025-31324/ tags: - analytic_story: - - SAP NetWeaver Exploitation - asset_type: Web Server - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: - - CVE-2025-31324 + analytic_story: + - SAP NetWeaver Exploitation + asset_type: Web Server + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: + - CVE-2025-31324 tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/sap/suricata_sapnetweaver.log - sourcetype: suricata - source: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/sap/suricata_sapnetweaver.log + sourcetype: suricata + source: suricata diff --git a/detections/web/spring4shell_payload_url_request.yml b/detections/web/spring4shell_payload_url_request.yml index 08dd88cb22..e3e37560ff 100644 --- a/detections/web/spring4shell_payload_url_request.yml +++ b/detections/web/spring4shell_payload_url_request.yml @@ -1,74 +1,64 @@ name: Spring4Shell Payload URL Request id: 9d44d649-7d67-4559-95c1-8022ff49420b -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects attempts to exploit the Spring4Shell vulnerability - (CVE-2022-22963) by identifying specific URL patterns associated with web shell - payloads. It leverages web traffic data, focusing on HTTP GET requests with URLs - containing indicators like "tomcatwar.jsp," "poc.jsp," and "shell.jsp." This activity - is significant as it suggests an attacker is trying to deploy a web shell, which - can lead to remote code execution. If confirmed malicious, this could allow the - attacker to gain persistent access, execute arbitrary commands, and potentially - escalate privileges within the compromised environment. +description: The following analytic detects attempts to exploit the Spring4Shell vulnerability (CVE-2022-22963) by identifying specific URL patterns associated with web shell payloads. It leverages web traffic data, focusing on HTTP GET requests with URLs containing indicators like "tomcatwar.jsp," "poc.jsp," and "shell.jsp." This activity is significant as it suggests an attacker is trying to deploy a web shell, which can lead to remote code execution. If confirmed malicious, this could allow the attacker to gain persistent access, execute arbitrary commands, and potentially escalate privileges within the compromised environment. data_source: -- Nginx Access -search: '| tstats count from datamodel=Web where Web.http_method IN ("GET") Web.url - IN ("*tomcatwar.jsp*","*poc.jsp*","*shell.jsp*") by Web.http_user_agent Web.http_method, - Web.url,Web.url_length Web.src, Web.dest sourcetype | `drop_dm_object_name("Web")` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spring4shell_payload_url_request_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on Web traffic that include fields relavent for traffic into the `Web` datamodel. -known_false_positives: The jsp file names are static names used in current proof of - concept code. = + - Nginx Access +search: |- + | tstats count FROM datamodel=Web + WHERE Web.http_method IN ("GET") Web.url IN ("*tomcatwar.jsp*","*poc.jsp*","*shell.jsp*") + BY Web.http_user_agent Web.http_method, Web.url,Web.url_length + Web.src, Web.dest sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `spring4shell_payload_url_request_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on Web traffic that include fields relavent for traffic into the `Web` datamodel. +known_false_positives: The jsp file names are static names used in current proof of concept code. = references: -- https://www.microsoft.com/security/blog/2022/04/04/springshell-rce-vulnerability-guidance-for-protecting-against-and-detecting-cve-2022-22965/ -- https://github.com/TheGejr/SpringShell -- https://www.tenable.com/blog/spring4shell-faq-spring-framework-remote-code-execution-vulnerability + - https://www.microsoft.com/security/blog/2022/04/04/springshell-rce-vulnerability-guidance-for-protecting-against-and-detecting-cve-2022-22965/ + - https://github.com/TheGejr/SpringShell + - https://www.tenable.com/blog/spring4shell-faq-spring-framework-remote-code-execution-vulnerability drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A URL was requested related to Spring4Shell POC code on $dest$ by $src$. - risk_objects: - - field: dest - type: system - score: 36 - threat_objects: - - field: src - type: ip_address + message: A URL was requested related to Spring4Shell POC code on $dest$ by $src$. + risk_objects: + - field: dest + type: system + score: 36 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Spring4Shell CVE-2022-22965 - asset_type: Web Server - cve: - - CVE-2022-22965 - mitre_attack_id: - - T1133 - - T1190 - - T1505.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Spring4Shell CVE-2022-22965 + asset_type: Web Server + cve: + - CVE-2022-22965 + mitre_attack_id: + - T1133 + - T1190 + - T1505.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/spring4shell/spring4shell_nginx.log - source: /var/log/nginx/access.log - sourcetype: nginx:plus:kv + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/spring4shell/spring4shell_nginx.log + source: /var/log/nginx/access.log + sourcetype: nginx:plus:kv diff --git a/detections/web/sql_injection_with_long_urls.yml b/detections/web/sql_injection_with_long_urls.yml index b96974a782..f090692ea1 100644 --- a/detections/web/sql_injection_with_long_urls.yml +++ b/detections/web/sql_injection_with_long_urls.yml @@ -1,57 +1,45 @@ name: SQL Injection with Long URLs id: e0aad4cf-0790-423b-8328-7564d0d938f9 -version: 8 -date: '2025-09-16' +version: 9 +date: '2026-02-25' author: Bhavin Patel, Splunk status: experimental type: TTP -description: The following analytic detects long URLs containing multiple SQL commands, - indicating a potential SQL injection attack. This detection leverages web traffic - data, specifically targeting web server destinations with URLs longer than 1024 - characters or HTTP user agents longer than 200 characters. SQL injection is significant - as it allows attackers to manipulate a web application's database, potentially leading - to unauthorized data access or modification. If confirmed malicious, this activity - could result in data breaches, unauthorized access, and complete system compromise. - Immediate investigation and validation of alerts are crucial to mitigate these risks. +description: The following analytic detects long URLs containing multiple SQL commands, indicating a potential SQL injection attack. This detection leverages web traffic data, specifically targeting web server destinations with URLs longer than 1024 characters or HTTP user agents longer than 200 characters. SQL injection is significant as it allows attackers to manipulate a web application's database, potentially leading to unauthorized data access or modification. If confirmed malicious, this activity could result in data breaches, unauthorized access, and complete system compromise. Immediate investigation and validation of alerts are crucial to mitigate these risks. data_source: [] -search: '| tstats `security_content_summariesonly` count from datamodel=Web where - Web.dest_category=web_server AND (Web.url_length > 1024 OR Web.http_user_agent_length - > 200) by Web.src Web.dest Web.url Web.url_length Web.http_user_agent | `drop_dm_object_name("Web")` - | eval url=lower(url) | eval num_sql_cmds=mvcount(split(url, "alter%20table")) + - mvcount(split(url, "between")) + mvcount(split(url, "create%20table")) + mvcount(split(url, - "create%20database")) + mvcount(split(url, "create%20index")) + mvcount(split(url, - "create%20view")) + mvcount(split(url, "delete")) + mvcount(split(url, "drop%20database")) - + mvcount(split(url, "drop%20index")) + mvcount(split(url, "drop%20table")) + mvcount(split(url, - "exists")) + mvcount(split(url, "exec")) + mvcount(split(url, "group%20by")) + mvcount(split(url, - "having")) + mvcount(split(url, "insert%20into")) + mvcount(split(url, "inner%20join")) - + mvcount(split(url, "left%20join")) + mvcount(split(url, "right%20join")) + mvcount(split(url, - "full%20join")) + mvcount(split(url, "select")) + mvcount(split(url, "distinct")) - + mvcount(split(url, "select%20top")) + mvcount(split(url, "union")) + mvcount(split(url, - "xp_cmdshell")) - 24 | where num_sql_cmds > 3 | `sql_injection_with_long_urls_filter`' -how_to_implement: To successfully implement this search, you need to be monitoring - network communications to your web servers or ingesting your HTTP logs and populating - the Web data model. You must also identify your web servers in the Enterprise Security - assets table. -known_false_positives: It's possible that legitimate traffic will have long URLs or - long user agent strings and that common SQL commands may be found within the URL. - Please investigate as appropriate. +search: |- + | tstats `security_content_summariesonly` count FROM datamodel=Web + WHERE Web.dest_category=web_server + AND + (Web.url_length > 1024 + OR + Web.http_user_agent_length > 200) + BY Web.src Web.dest Web.url + Web.url_length Web.http_user_agent + | `drop_dm_object_name("Web")` + | eval url=lower(url) + | eval num_sql_cmds=mvcount(split(url, "alter%20table")) + mvcount(split(url, "between")) + mvcount(split(url, "create%20table")) + mvcount(split(url, "create%20database")) + mvcount(split(url, "create%20index")) + mvcount(split(url, "create%20view")) + mvcount(split(url, "delete")) + mvcount(split(url, "drop%20database")) + mvcount(split(url, "drop%20index")) + mvcount(split(url, "drop%20table")) + mvcount(split(url, "exists")) + mvcount(split(url, "exec")) + mvcount(split(url, "group%20by")) + mvcount(split(url, "having")) + mvcount(split(url, "insert%20into")) + mvcount(split(url, "inner%20join")) + mvcount(split(url, "left%20join")) + mvcount(split(url, "right%20join")) + mvcount(split(url, "full%20join")) + mvcount(split(url, "select")) + mvcount(split(url, "distinct")) + mvcount(split(url, "select%20top")) + mvcount(split(url, "union")) + mvcount(split(url, "xp_cmdshell")) - 24 + | where num_sql_cmds > 3 + | `sql_injection_with_long_urls_filter` +how_to_implement: To successfully implement this search, you need to be monitoring network communications to your web servers or ingesting your HTTP logs and populating the Web data model. You must also identify your web servers in the Enterprise Security assets table. +known_false_positives: It's possible that legitimate traffic will have long URLs or long user agent strings and that common SQL commands may be found within the URL. Please investigate as appropriate. references: [] rba: - message: SQL injection attempt with url $url$ detected on $dest$ - risk_objects: - - field: dest - type: system - score: 25 - threat_objects: [] + message: SQL injection attempt with url $url$ detected on $dest$ + risk_objects: + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - SQL Injection - - GhostRedirector IIS Module and Rungan Backdoor - asset_type: Database Server - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - SQL Injection + - GhostRedirector IIS Module and Rungan Backdoor + asset_type: Database Server + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/web/supernova_webshell.yml b/detections/web/supernova_webshell.yml index 61505d7d67..8ea37d00b1 100644 --- a/detections/web/supernova_webshell.yml +++ b/detections/web/supernova_webshell.yml @@ -1,54 +1,51 @@ name: Supernova Webshell id: 2ec08a09-9ff1-4dac-b59f-1efd57972ec1 -version: 7 -date: '2025-09-16' +version: 8 +date: '2026-02-25' author: John Stoner, Splunk status: experimental type: TTP -description: The following analytic detects the presence of the Supernova webshell, - used in the SUNBURST attack, by identifying specific patterns in web URLs. The detection - leverages Splunk to search for URLs containing "*logoimagehandler.ashx*codes*", - "*logoimagehandler.ashx*clazz*", "*logoimagehandler.ashx*method*", and "*logoimagehandler.ashx*args*". - This activity is significant as it indicates potential unauthorized access and arbitrary - code execution on a compromised system. If confirmed malicious, this could lead - to data theft, ransomware deployment, or other severe outcomes. Immediate steps - include reviewing the web URLs, inspecting on-disk artifacts, and analyzing concurrent - processes and network connections. +description: The following analytic detects the presence of the Supernova webshell, used in the SUNBURST attack, by identifying specific patterns in web URLs. The detection leverages Splunk to search for URLs containing "*logoimagehandler.ashx*codes*", "*logoimagehandler.ashx*clazz*", "*logoimagehandler.ashx*method*", and "*logoimagehandler.ashx*args*". This activity is significant as it indicates potential unauthorized access and arbitrary code execution on a compromised system. If confirmed malicious, this could lead to data theft, ransomware deployment, or other severe outcomes. Immediate steps include reviewing the web URLs, inspecting on-disk artifacts, and analyzing concurrent processes and network connections. data_source: [] -search: '| tstats `security_content_summariesonly` count from datamodel=Web.Web where - web.url=*logoimagehandler.ashx*codes* OR Web.url=*logoimagehandler.ashx*clazz* OR - Web.url=*logoimagehandler.ashx*method* OR Web.url=*logoimagehandler.ashx*args* by - Web.src Web.dest Web.url Web.vendor_product Web.user Web.http_user_agent _time span=1s - | `supernova_webshell_filter`' -how_to_implement: To successfully implement this search, you need to be monitoring - web traffic to your Solarwinds Orion. The logs should be ingested into splunk and - populating/mapped to the Web data model. -known_false_positives: There might be false positives associted with this detection - since items like args as a web argument is pretty generic. +search: |- + | tstats `security_content_summariesonly` count FROM datamodel=Web.Web + WHERE web.url=*logoimagehandler.ashx*codes* + OR + Web.url=*logoimagehandler.ashx*clazz* + OR + Web.url=*logoimagehandler.ashx*method* + OR + Web.url=*logoimagehandler.ashx*args* + BY Web.src Web.dest Web.url + Web.vendor_product Web.user Web.http_user_agent + _time span=1s + | `supernova_webshell_filter` +how_to_implement: To successfully implement this search, you need to be monitoring web traffic to your Solarwinds Orion. The logs should be ingested into splunk and populating/mapped to the Web data model. +known_false_positives: There might be false positives associted with this detection since items like args as a web argument is pretty generic. references: -- https://www.splunk.com/en_us/blog/security/detecting-supernova-malware-solarwinds-continued.html -- https://www.guidepointsecurity.com/blog/supernova-solarwinds-net-webshell-analysis/ + - https://www.splunk.com/en_us/blog/security/detecting-supernova-malware-solarwinds-continued.html + - https://www.guidepointsecurity.com/blog/supernova-solarwinds-net-webshell-analysis/ rba: - message: Potential Supernova Webshell on $dest$ - risk_objects: - - field: user - type: user - score: 25 - - field: dest - type: system - score: 25 - threat_objects: [] + message: Potential Supernova Webshell on $dest$ + risk_objects: + - field: user + type: user + score: 25 + - field: dest + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - NOBELIUM Group - - Earth Alux - - GhostRedirector IIS Module and Rungan Backdoor - asset_type: Web Server - mitre_attack_id: - - T1505.003 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - NOBELIUM Group + - Earth Alux + - GhostRedirector IIS Module and Rungan Backdoor + asset_type: Web Server + mitre_attack_id: + - T1505.003 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/web/tomcat_session_deserialization_attempt.yml b/detections/web/tomcat_session_deserialization_attempt.yml index 8632fd2baa..a3206323a1 100644 --- a/detections/web/tomcat_session_deserialization_attempt.yml +++ b/detections/web/tomcat_session_deserialization_attempt.yml @@ -1,75 +1,76 @@ name: Tomcat Session Deserialization Attempt id: e28b4fd4-8f5a-41cd-8222-2f1ccca53ef1 -version: 2 -date: '2025-05-02' +version: 3 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly description: This detection identifies potential exploitation of CVE-2025-24813 in Apache Tomcat through the second stage of the attack. This phase occurs when an attacker attempts to trigger deserialization of a previously uploaded malicious session file by sending a GET request with a specially crafted JSESSIONID cookie. These requests typically have specific characteristics, including a JSESSIONID cookie with a leading dot that matches a previously uploaded filename, and typically result in a HTTP 500 error when the exploitation succeeds. data_source: -- Nginx Access -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.http_method=GET AND Web.cookie="*JSESSIONID=.*" AND Web.status=500 - by Web.src, Web.dest, Web.http_user_agent, Web.uri_path, Web.cookie, Web.status - | `drop_dm_object_name("Web")` - | where match(cookie, "^JSESSIONID=\.") - | rex field=cookie "JSESSIONID=\.(?[^;]+)" - | eval severity="High" - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `tomcat_session_deserialization_attempt_filter`' + - Nginx Access +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.http_method=GET + AND + Web.cookie="*JSESSIONID=.*" + AND + Web.status=500 + BY Web.src, Web.dest, Web.http_user_agent, + Web.uri_path, Web.cookie, Web.status + | `drop_dm_object_name("Web")` + | where match(cookie, "^JSESSIONID=\.") + | rex field=cookie "JSESSIONID=\.(?[^;]+)" + | eval severity="High" + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `tomcat_session_deserialization_attempt_filter` how_to_implement: To successfully implement this search, you need to be ingesting logs from your web servers, proxies, or WAFs that process web traffic to Tomcat instances. The data must be mapped to the Web datamodel in the Web node. Ensure your web servers are logging requests that include HTTP methods, status codes, URI paths, and cookie information. Particularly important is capturing the JSESSIONID cookie values. The detection specifically looks for GET requests with a JSESSIONID cookie that starts with a dot (.) and results in a 500 status code, which is characteristic of successful deserialization attempts. known_false_positives: Limited false positives should occur as this pattern is highly specific to CVE-2025-24813 exploitation. However, legitimate application errors that use similar cookie patterns and result in 500 status codes might trigger false positives. Review the JSESSIONID cookie format and the associated request context to confirm exploitation attempts. references: -- https://lists.apache.org/thread/j5fkjv2k477os90nczf2v9l61fb0kkgq -- https://nvd.nist.gov/vuln/detail/CVE-2025-24813 -- https://github.com/vulhub/vulhub/tree/master/tomcat/CVE-2025-24813 -- https://www.rapid7.com/db/vulnerabilities/apache-tomcat-cve-2025-24813/ -- https://gist.github.com/MHaggis/e106367f6649fbb09ab27e7b4a01cf73 + - https://lists.apache.org/thread/j5fkjv2k477os90nczf2v9l61fb0kkgq + - https://nvd.nist.gov/vuln/detail/CVE-2025-24813 + - https://github.com/vulhub/vulhub/tree/master/tomcat/CVE-2025-24813 + - https://www.rapid7.com/db/vulnerabilities/apache-tomcat-cve-2025-24813/ + - https://gist.github.com/MHaggis/e106367f6649fbb09ab27e7b4a01cf73 drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View suspicious JSESSIONID cookies - search: '| from datamodel Web.Web | search http_method=GET AND cookie="*JSESSIONID=.*" src=$src$ | table src dest http_method uri_path http_user_agent status' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View suspicious JSESSIONID cookies + search: '| from datamodel Web.Web | search http_method=GET AND cookie="*JSESSIONID=.*" src=$src$ | table src dest http_method uri_path http_user_agent status' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Tomcat session deserialization attempt has been detected from IP $src$ targeting $dest$ with a suspicious JSESSIONID cookie. This could indicate exploitation of CVE-2025-24813. - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: - - field: src - type: ip_address + message: A Tomcat session deserialization attempt has been detected from IP $src$ targeting $dest$ with a suspicious JSESSIONID cookie. This could indicate exploitation of CVE-2025-24813. + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Apache Tomcat Session Deserialization Attacks - asset_type: Web Application - mitre_attack_id: - - T1190 - - T1505.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: - - CVE-2025-24813 + analytic_story: + - Apache Tomcat Session Deserialization Attacks + asset_type: Web Application + mitre_attack_id: + - T1190 + - T1505.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: + - CVE-2025-24813 tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/tomcat/tomcat_nginx_access.log - sourcetype: nginx:plus:kv - source: nginx + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/tomcat/tomcat_nginx_access.log + sourcetype: nginx:plus:kv + source: nginx diff --git a/detections/web/tomcat_session_file_upload_attempt.yml b/detections/web/tomcat_session_file_upload_attempt.yml index a0e8406997..c17eea7110 100644 --- a/detections/web/tomcat_session_file_upload_attempt.yml +++ b/detections/web/tomcat_session_file_upload_attempt.yml @@ -1,74 +1,77 @@ name: Tomcat Session File Upload Attempt id: a1d8f5c3-9b7e-4f2d-8c51-3bca5e672410 -version: 2 -date: '2025-05-02' +version: 3 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly description: This detection identifies potential exploitation of CVE-2025-24813 in Apache Tomcat through the initial stage of the attack. This first phase occurs when an attacker attempts to upload a malicious serialized Java object with a .session file extension via an HTTP PUT request. When successful, these uploads typically result in HTTP status codes 201 (Created) or 409 (Conflict) and create the foundation for subsequent deserialization attacks by placing malicious content in a location where Tomcat's session management can access it. data_source: -- Nginx Access -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.http_method=PUT AND Web.uri_path="*.session" AND (Web.status=201 OR Web.status=409) - by Web.src, Web.dest, Web.http_user_agent, Web.uri_path, Web.status - | `drop_dm_object_name("Web")` - | rex field=uri_path "/(?[^/]+)\.session$" - | eval severity="High" - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `tomcat_session_file_upload_attempt_filter`' + - Nginx Access +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.http_method=PUT + AND + Web.uri_path="*.session" + AND + (Web.status=201 + OR + Web.status=409) + BY Web.src, Web.dest, Web.http_user_agent, + Web.uri_path, Web.status + | `drop_dm_object_name("Web")` + | rex field=uri_path "/(?[^/]+)\.session$" + | eval severity="High" + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `tomcat_session_file_upload_attempt_filter` how_to_implement: To successfully implement this search, you need to be ingesting logs from your web servers, proxies, or WAFs that process web traffic to Tomcat instances. The data must be mapped to the Web datamodel in the Web node. Ensure your web servers are logging HTTP PUT requests, including status codes and URI paths. This detection specifically looks for PUT requests targeting files with a .session extension that result in HTTP status codes 201 or 409, which indicate successful creation of files - a pattern consistent with the first stage of CVE-2025-24813 exploitation. known_false_positives: Some legitimate applications might use PUT requests to create .session files, especially in custom implementations that leverage Tomcat's session persistence mechanism. Verify if the detected activity is part of a normal application flow or if it correlates with other suspicious behavior, such as subsequent GET requests with manipulated JSESSIONID cookies. references: -- https://lists.apache.org/thread/j5fkjv2k477os90nczf2v9l61fb0kkgq -- https://nvd.nist.gov/vuln/detail/CVE-2025-24813 -- https://github.com/vulhub/vulhub/tree/master/tomcat/CVE-2025-24813 -- https://www.rapid7.com/db/vulnerabilities/apache-tomcat-cve-2025-24813/ -- https://gist.github.com/MHaggis/e106367f6649fbb09ab27e7b4a01cf73 + - https://lists.apache.org/thread/j5fkjv2k477os90nczf2v9l61fb0kkgq + - https://nvd.nist.gov/vuln/detail/CVE-2025-24813 + - https://github.com/vulhub/vulhub/tree/master/tomcat/CVE-2025-24813 + - https://www.rapid7.com/db/vulnerabilities/apache-tomcat-cve-2025-24813/ + - https://gist.github.com/MHaggis/e106367f6649fbb09ab27e7b4a01cf73 drilldown_searches: -- name: View the detection results for - "$src$" - search: '%original_detection_search% | search src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View all PUT requests to .session files - search: '| from datamodel Web.Web | search http_method = PUT uri_path="*.session" src=$src$ | table src dest http_method uri_path http_user_agent status' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" + search: '%original_detection_search% | search src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View all PUT requests to .session files + search: '| from datamodel Web.Web | search http_method = PUT uri_path="*.session" src=$src$ | table src dest http_method uri_path http_user_agent status' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A Tomcat session file upload attempt has been detected from IP $src$ targeting $dest$ with a suspicious .session file. This could indicate the first stage of CVE-2025-24813 exploitation. - risk_objects: - - field: dest - type: system - score: 70 - threat_objects: - - field: src - type: ip_address + message: A Tomcat session file upload attempt has been detected from IP $src$ targeting $dest$ with a suspicious .session file. This could indicate the first stage of CVE-2025-24813 exploitation. + risk_objects: + - field: dest + type: system + score: 70 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Apache Tomcat Session Deserialization Attacks - asset_type: Web Application - mitre_attack_id: - - T1190 - - T1505.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: - - CVE-2025-24813 + analytic_story: + - Apache Tomcat Session Deserialization Attacks + asset_type: Web Application + mitre_attack_id: + - T1190 + - T1505.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: + - CVE-2025-24813 tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/tomcat/tomcat_nginx_access.log - sourcetype: nginx:plus:kv - source: nginx + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/tomcat/tomcat_nginx_access.log + sourcetype: nginx:plus:kv + source: nginx diff --git a/detections/web/unusually_long_content_type_length.yml b/detections/web/unusually_long_content_type_length.yml index 163284b10d..72d1f1cf72 100644 --- a/detections/web/unusually_long_content_type_length.yml +++ b/detections/web/unusually_long_content_type_length.yml @@ -5,46 +5,35 @@ date: '2025-05-02' author: Bhavin Patel, Splunk status: experimental type: Anomaly -description: The following analytic identifies unusually long strings in the Content-Type - HTTP header sent by the client to the server. It uses data from the Stream:HTTP - source, specifically evaluating the length of the `cs_content_type` field. This - activity is significant because excessively long Content-Type headers can indicate - attempts to exploit vulnerabilities or evade detection mechanisms. If confirmed - malicious, this behavior could allow attackers to execute code, manipulate data, - or bypass security controls, potentially leading to unauthorized access or data - breaches. +description: The following analytic identifies unusually long strings in the Content-Type HTTP header sent by the client to the server. It uses data from the Stream:HTTP source, specifically evaluating the length of the `cs_content_type` field. This activity is significant because excessively long Content-Type headers can indicate attempts to exploit vulnerabilities or evade detection mechanisms. If confirmed malicious, this behavior could allow attackers to execute code, manipulate data, or bypass security controls, potentially leading to unauthorized access or data breaches. data_source: [] search: >- - | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Web by Web.src Web.dest Web.url Web.http_user_agent Web.http_content_type - | `drop_dm_object_name("Web")` | eval http_content_type_length = len(http_content_type) | - where http_content_type_length > 100 - | table firstTime lastTime src dest http_content_type_length http_content_type url - http_user_agent - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `unusually_long_content_type_length_filter` -how_to_implement: This particular search leverages data extracted from Stream:HTTP. - You must configure the http stream using the Splunk Stream App on your Splunk Stream - deployment server to extract the cs_content_type field. -known_false_positives: Very few legitimate Content-Type fields will have a length - greater than 100 characters. + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Web by Web.src Web.dest Web.url Web.http_user_agent Web.http_content_type + | `drop_dm_object_name("Web")` | eval http_content_type_length = len(http_content_type) | + where http_content_type_length > 100 + | table firstTime lastTime src dest http_content_type_length http_content_type url + http_user_agent + | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `unusually_long_content_type_length_filter` +how_to_implement: This particular search leverages data extracted from Stream:HTTP. You must configure the http stream using the Splunk Stream App on your Splunk Stream deployment server to extract the cs_content_type field. +known_false_positives: Very few legitimate Content-Type fields will have a length greater than 100 characters. references: [] rba: - message: Unusually Long Content-Type Length ($http_content_type_length$ characters) - In Web Request from $src$ - risk_objects: - - field: dest - type: system - score: 25 - - field: src - type: system - score: 25 - threat_objects: [] + message: Unusually Long Content-Type Length ($http_content_type_length$ characters) In Web Request from $src$ + risk_objects: + - field: dest + type: system + score: 25 + - field: src + type: system + score: 25 + threat_objects: [] tags: - analytic_story: - - Apache Struts Vulnerability - asset_type: Web Server - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Apache Struts Vulnerability + asset_type: Web Server + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network diff --git a/detections/web/vmware_aria_operations_exploit_attempt.yml b/detections/web/vmware_aria_operations_exploit_attempt.yml index b7d0c31c24..7d1257ad51 100644 --- a/detections/web/vmware_aria_operations_exploit_attempt.yml +++ b/detections/web/vmware_aria_operations_exploit_attempt.yml @@ -1,80 +1,68 @@ name: VMWare Aria Operations Exploit Attempt id: d5d865e4-03e6-43da-98f4-28a4f42d4df7 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Palo Alto Network Threat -description: The following analytic detects potential exploitation attempts against - VMWare vRealize Network Insight, specifically targeting the CVE-2023-20887 vulnerability. - It monitors web traffic for HTTP POST requests directed at the vulnerable endpoint - "/saas./resttosaasservlet." This detection leverages web traffic data, focusing - on specific URL patterns and HTTP methods. Identifying this behavior is crucial - for a SOC as it indicates an active exploit attempt. If confirmed malicious, the - attacker could execute arbitrary code, leading to unauthorized access, data theft, - or further network compromise. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url IN ("*/saas./resttosaasservlet*") Web.http_method=POST Web.status - IN ("unknown", "200") by Web.http_user_agent, Web.status Web.http_method, Web.url, - Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `vmware_aria_operations_exploit_attempt_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - web or proxy logs, or ensure it is being filled by a proxy like device, into the - Web Datamodel. Restrict to specific dest assets to reduce false positives. -known_false_positives: False positives will be present based on gateways in use, modify - the status field as needed. + - Palo Alto Network Threat +description: The following analytic detects potential exploitation attempts against VMWare vRealize Network Insight, specifically targeting the CVE-2023-20887 vulnerability. It monitors web traffic for HTTP POST requests directed at the vulnerable endpoint "/saas./resttosaasservlet." This detection leverages web traffic data, focusing on specific URL patterns and HTTP methods. Identifying this behavior is crucial for a SOC as it indicates an active exploit attempt. If confirmed malicious, the attacker could execute arbitrary code, leading to unauthorized access, data theft, or further network compromise. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN ("*/saas./resttosaasservlet*") Web.http_method=POST Web.status IN ("unknown", "200") + BY Web.http_user_agent, Web.status Web.http_method, + Web.url, Web.url_length, Web.src, + Web.dest, sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `vmware_aria_operations_exploit_attempt_filter` +how_to_implement: To successfully implement this search, you need to be ingesting web or proxy logs, or ensure it is being filled by a proxy like device, into the Web Datamodel. Restrict to specific dest assets to reduce false positives. +known_false_positives: False positives will be present based on gateways in use, modify the status field as needed. references: -- https://nvd.nist.gov/vuln/detail/CVE-2023-20887 -- https://viz.greynoise.io/tag/vmware-aria-operations-for-networks-rce-attempt?days=30 -- https://github.com/sinsinology/CVE-2023-20887 -- https://summoning.team/blog/vmware-vrealize-network-insight-rce-cve-2023-20887/ + - https://nvd.nist.gov/vuln/detail/CVE-2023-20887 + - https://viz.greynoise.io/tag/vmware-aria-operations-for-networks-rce-attempt?days=30 + - https://github.com/sinsinology/CVE-2023-20887 + - https://summoning.team/blog/vmware-vrealize-network-insight-rce-cve-2023-20887/ drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An exploitation attempt has occurred against $dest$ from $src$ related - to CVE-2023-20887 - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: - - field: src - type: ip_address + message: An exploitation attempt has occurred against $dest$ from $src$ related to CVE-2023-20887 + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: + - field: src + type: ip_address tags: - cve: - - CVE-2023-20887 - analytic_story: - - VMware Aria Operations vRealize CVE-2023-20887 - asset_type: Web Server - atomic_guid: [] - mitre_attack_id: - - T1133 - - T1190 - - T1210 - - T1068 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + cve: + - CVE-2023-20887 + analytic_story: + - VMware Aria Operations vRealize CVE-2023-20887 + asset_type: Web Server + atomic_guid: [] + mitre_attack_id: + - T1133 + - T1190 + - T1210 + - T1068 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/vmware/vmware_aria.log - source: pan:threat - sourcetype: pan:threat + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/vmware/vmware_aria.log + source: pan:threat + sourcetype: pan:threat diff --git a/detections/web/vmware_server_side_template_injection_hunt.yml b/detections/web/vmware_server_side_template_injection_hunt.yml index 2e9ebe6761..e4c92642c6 100644 --- a/detections/web/vmware_server_side_template_injection_hunt.yml +++ b/detections/web/vmware_server_side_template_injection_hunt.yml @@ -1,56 +1,50 @@ name: VMware Server Side Template Injection Hunt id: 5796b570-ad12-44df-b1b5-b7e6ae3aabb0 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Hunting -description: The following analytic identifies potential server-side template injection - attempts related to CVE-2022-22954. It detects suspicious URL patterns containing - "deviceudid" and keywords like "java.lang.ProcessBuilder" or "freemarker.template.utility.ObjectConstructor" - using web or proxy logs within the Web Datamodel. This activity is significant as - it may indicate an attempt to exploit a known vulnerability in VMware, potentially - leading to remote code execution. If confirmed malicious, attackers could gain unauthorized - access, execute arbitrary code, and compromise the affected system, posing a severe - security risk. +description: The following analytic identifies potential server-side template injection attempts related to CVE-2022-22954. It detects suspicious URL patterns containing "deviceudid" and keywords like "java.lang.ProcessBuilder" or "freemarker.template.utility.ObjectConstructor" using web or proxy logs within the Web Datamodel. This activity is significant as it may indicate an attempt to exploit a known vulnerability in VMware, potentially leading to remote code execution. If confirmed malicious, attackers could gain unauthorized access, execute arbitrary code, and compromise the affected system, posing a severe security risk. data_source: -- Palo Alto Network Threat -search: '| tstats count from datamodel=Web where Web.http_method IN ("GET") Web.url="*deviceudid=*" - AND Web.url IN ("*java.lang.ProcessBuilder*","*freemarker.template.utility.ObjectConstructor*") - by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest - sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `vmware_server_side_template_injection_hunt_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - web or proxy logs, or ensure it is being filled by a proxy like device, into the - Web Datamodel. For additional filtering, allow list private IP space or restrict - by known good. -known_false_positives: False positives may be present if the activity is blocked or - was not successful. Filter known vulnerablity scanners. Filter as needed. + - Palo Alto Network Threat +search: |- + | tstats count FROM datamodel=Web + WHERE Web.http_method IN ("GET") Web.url="*deviceudid=*" + AND + Web.url IN ("*java.lang.ProcessBuilder*","*freemarker.template.utility.ObjectConstructor*") + BY Web.http_user_agent Web.http_method, Web.url,Web.url_length + Web.src, Web.dest sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `vmware_server_side_template_injection_hunt_filter` +how_to_implement: To successfully implement this search, you need to be ingesting web or proxy logs, or ensure it is being filled by a proxy like device, into the Web Datamodel. For additional filtering, allow list private IP space or restrict by known good. +known_false_positives: False positives may be present if the activity is blocked or was not successful. Filter known vulnerablity scanners. Filter as needed. references: -- https://www.cisa.gov/uscert/ncas/alerts/aa22-138b -- https://github.com/wvu/metasploit-framework/blob/master/modules/exploits/linux/http/vmware_workspace_one_access_cve_2022_22954.rb -- https://github.com/sherlocksecurity/VMware-CVE-2022-22954 -- https://www.vmware.com/security/advisories/VMSA-2022-0011.html -- https://attackerkb.com/topics/BDXyTqY1ld/cve-2022-22954/rapid7-analysis -- https://twitter.com/wvuuuuuuuuuuuuu/status/1519476924757778433 + - https://www.cisa.gov/uscert/ncas/alerts/aa22-138b + - https://github.com/wvu/metasploit-framework/blob/master/modules/exploits/linux/http/vmware_workspace_one_access_cve_2022_22954.rb + - https://github.com/sherlocksecurity/VMware-CVE-2022-22954 + - https://www.vmware.com/security/advisories/VMSA-2022-0011.html + - https://attackerkb.com/topics/BDXyTqY1ld/cve-2022-22954/rapid7-analysis + - https://twitter.com/wvuuuuuuuuuuuuu/status/1519476924757778433 tags: - analytic_story: - - VMware Server Side Injection and Privilege Escalation - asset_type: Web Server - cve: - - CVE-2022-22954 - mitre_attack_id: - - T1190 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - VMware Server Side Injection and Privilege Escalation + asset_type: Web Server + cve: + - CVE-2022-22954 + mitre_attack_id: + - T1190 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/vmware/vmware_scanning_pan_threat.log - source: pan:threat - sourcetype: pan:threat + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/vmware/vmware_scanning_pan_threat.log + source: pan:threat + sourcetype: pan:threat diff --git a/detections/web/vmware_workspace_one_freemarker_server_side_template_injection.yml b/detections/web/vmware_workspace_one_freemarker_server_side_template_injection.yml index da0870e8c7..89a7930ec9 100644 --- a/detections/web/vmware_workspace_one_freemarker_server_side_template_injection.yml +++ b/detections/web/vmware_workspace_one_freemarker_server_side_template_injection.yml @@ -1,76 +1,65 @@ name: VMware Workspace ONE Freemarker Server-side Template Injection id: 9e5726fe-8fde-460e-bd74-cddcf6c86113 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: Anomaly -description: The following analytic detects server-side template injection attempts - related to CVE-2022-22954 in VMware Workspace ONE. It leverages web or proxy logs - to identify HTTP GET requests to the endpoint catalog-portal/ui/oauth/verify with - the freemarker.template.utility.Execute command. This activity is significant as - it indicates potential exploitation attempts that could lead to remote code execution. - If confirmed malicious, an attacker could execute arbitrary commands on the server, - leading to full system compromise, data exfiltration, or further lateral movement - within the network. +description: The following analytic detects server-side template injection attempts related to CVE-2022-22954 in VMware Workspace ONE. It leverages web or proxy logs to identify HTTP GET requests to the endpoint catalog-portal/ui/oauth/verify with the freemarker.template.utility.Execute command. This activity is significant as it indicates potential exploitation attempts that could lead to remote code execution. If confirmed malicious, an attacker could execute arbitrary commands on the server, leading to full system compromise, data exfiltration, or further lateral movement within the network. data_source: -- Palo Alto Network Threat -search: '| tstats count from datamodel=Web where Web.http_method IN ("GET") Web.url="*/catalog-portal/ui/oauth/verify?error=&deviceudid=*" - AND Web.url="*freemarker.template.utility.Execute*" by Web.http_user_agent Web.http_method, - Web.url,Web.url_length Web.src, Web.dest sourcetype | `drop_dm_object_name("Web")` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `vmware_workspace_one_freemarker_server_side_template_injection_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - web or proxy logs, or ensure it is being filled by a proxy like device, into the - Web Datamodel. For additional filtering, allow list private IP space or restrict - by known good. -known_false_positives: False positives may be present if the activity is blocked or - was not successful. Filter known vulnerablity scanners. Filter as needed. + - Palo Alto Network Threat +search: |- + | tstats count FROM datamodel=Web + WHERE Web.http_method IN ("GET") Web.url="*/catalog-portal/ui/oauth/verify?error=&deviceudid=*" + AND + Web.url="*freemarker.template.utility.Execute*" + BY Web.http_user_agent Web.http_method, Web.url,Web.url_length + Web.src, Web.dest sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `vmware_workspace_one_freemarker_server_side_template_injection_filter` +how_to_implement: To successfully implement this search, you need to be ingesting web or proxy logs, or ensure it is being filled by a proxy like device, into the Web Datamodel. For additional filtering, allow list private IP space or restrict by known good. +known_false_positives: False positives may be present if the activity is blocked or was not successful. Filter known vulnerablity scanners. Filter as needed. references: -- https://www.cisa.gov/uscert/ncas/alerts/aa22-138b -- https://github.com/wvu/metasploit-framework/blob/master/modules/exploits/linux/http/vmware_workspace_one_access_cve_2022_22954.rb -- https://github.com/sherlocksecurity/VMware-CVE-2022-22954 -- https://www.vmware.com/security/advisories/VMSA-2022-0011.html -- https://attackerkb.com/topics/BDXyTqY1ld/cve-2022-22954/rapid7-analysis + - https://www.cisa.gov/uscert/ncas/alerts/aa22-138b + - https://github.com/wvu/metasploit-framework/blob/master/modules/exploits/linux/http/vmware_workspace_one_access_cve_2022_22954.rb + - https://github.com/sherlocksecurity/VMware-CVE-2022-22954 + - https://www.vmware.com/security/advisories/VMSA-2022-0011.html + - https://attackerkb.com/topics/BDXyTqY1ld/cve-2022-22954/rapid7-analysis drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An attempt to exploit a VMware Server Side Injection CVE-2022-22954 on - $dest$ has occurred. - risk_objects: - - field: dest - type: system - score: 49 - threat_objects: [] + message: An attempt to exploit a VMware Server Side Injection CVE-2022-22954 on $dest$ has occurred. + risk_objects: + - field: dest + type: system + score: 49 + threat_objects: [] tags: - analytic_story: - - VMware Server Side Injection and Privilege Escalation - asset_type: Web Server - cve: - - CVE-2022-22954 - mitre_attack_id: - - T1190 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - VMware Server Side Injection and Privilege Escalation + asset_type: Web Server + cve: + - CVE-2022-22954 + mitre_attack_id: + - T1190 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/vmware/vmware_scanning_pan_threat.log - source: pan:threat - sourcetype: pan:threat + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/vmware/vmware_scanning_pan_threat.log + source: pan:threat + sourcetype: pan:threat diff --git a/detections/web/web_jsp_request_via_url.yml b/detections/web/web_jsp_request_via_url.yml index 75deef22c9..0c0ee8191c 100644 --- a/detections/web/web_jsp_request_via_url.yml +++ b/detections/web/web_jsp_request_via_url.yml @@ -1,76 +1,65 @@ name: Web JSP Request via URL id: 2850c734-2d44-4431-8139-1a56f6f54c01 -version: 7 -date: '2025-05-02' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies URL requests associated with CVE-2022-22965 - (Spring4Shell) exploitation attempts, specifically targeting webshell access on - a remote webserver. It detects HTTP GET requests with URLs containing ".jsp?cmd=" - or "j&cmd=" patterns. This activity is significant as it indicates potential webshell - deployment, which can lead to unauthorized remote command execution. If confirmed - malicious, attackers could gain control over the webserver, execute arbitrary commands, - and potentially escalate privileges, leading to severe data breaches and system - compromise. +description: The following analytic identifies URL requests associated with CVE-2022-22965 (Spring4Shell) exploitation attempts, specifically targeting webshell access on a remote webserver. It detects HTTP GET requests with URLs containing ".jsp?cmd=" or "j&cmd=" patterns. This activity is significant as it indicates potential webshell deployment, which can lead to unauthorized remote command execution. If confirmed malicious, attackers could gain control over the webserver, execute arbitrary commands, and potentially escalate privileges, leading to severe data breaches and system compromise. data_source: -- Nginx Access -search: '| tstats count from datamodel=Web where Web.http_method IN ("GET") Web.url - IN ("*.jsp?cmd=*","*j&cmd=*") by Web.http_user_agent Web.http_method, Web.url,Web.url_length - Web.src, Web.dest sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `web_jsp_request_via_url_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on Web traffic that include fields relavent for traffic into the `Web` datamodel. -known_false_positives: False positives may be present with legitimate applications. - Attempt to filter by dest IP or use Asset groups to restrict to servers. + - Nginx Access +search: |- + | tstats count FROM datamodel=Web + WHERE Web.http_method IN ("GET") Web.url IN ("*.jsp?cmd=*","*j&cmd=*") + BY Web.http_user_agent Web.http_method, Web.url,Web.url_length + Web.src, Web.dest sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `web_jsp_request_via_url_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on Web traffic that include fields relavent for traffic into the `Web` datamodel. +known_false_positives: False positives may be present with legitimate applications. Attempt to filter by dest IP or use Asset groups to restrict to servers. references: -- https://www.microsoft.com/security/blog/2022/04/04/springshell-rce-vulnerability-guidance-for-protecting-against-and-detecting-cve-2022-22965/ -- https://github.com/TheGejr/SpringShell -- https://www.tenable.com/blog/spring4shell-faq-spring-framework-remote-code-execution-vulnerability + - https://www.microsoft.com/security/blog/2022/04/04/springshell-rce-vulnerability-guidance-for-protecting-against-and-detecting-cve-2022-22965/ + - https://github.com/TheGejr/SpringShell + - https://www.tenable.com/blog/spring4shell-faq-spring-framework-remote-code-execution-vulnerability drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious URL has been requested against $dest$ by $src$, related to - web shell activity. - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: - - field: src - type: ip_address + message: A suspicious URL has been requested against $dest$ by $src$, related to web shell activity. + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Spring4Shell CVE-2022-22965 - - Earth Alux - asset_type: Web Server - cve: - - CVE-2022-22965 - mitre_attack_id: - - T1133 - - T1190 - - T1505.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Spring4Shell CVE-2022-22965 + - Earth Alux + asset_type: Web Server + cve: + - CVE-2022-22965 + mitre_attack_id: + - T1133 + - T1190 + - T1505.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/spring4shell/spring4shell_nginx.log - source: /var/log/nginx/access.log - sourcetype: nginx:plus:kv + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/spring4shell/spring4shell_nginx.log + source: /var/log/nginx/access.log + sourcetype: nginx:plus:kv diff --git a/detections/web/web_remote_shellservlet_access.yml b/detections/web/web_remote_shellservlet_access.yml index 6ca1c0d3fd..da42d30442 100644 --- a/detections/web/web_remote_shellservlet_access.yml +++ b/detections/web/web_remote_shellservlet_access.yml @@ -1,73 +1,61 @@ name: Web Remote ShellServlet Access id: c2a332c3-24a2-4e24-9455-0e80332e6746 -version: 7 -date: '2025-09-16' +version: 8 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Nginx Access -description: The following analytic identifies attempts to access the Remote ShellServlet - on a web server, specifically targeting Confluence servers vulnerable to CVE-2023-22518 - and CVE-2023-22515. It leverages web data to detect URLs containing "*plugins/servlet/com.jsos.shell/*" - with a status code of 200. This activity is significant as it is commonly associated - with web shells and other malicious behaviors, potentially leading to unauthorized - command execution. If confirmed malicious, attackers could gain remote code execution - capabilities, compromising the server and potentially the entire network. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url IN ("*plugins/servlet/com.jsos.shell/*") Web.status=200 by Web.http_user_agent, - Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype - | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `web_remote_shellservlet_access_filter`' -how_to_implement: This analytic necessitates the collection of web data, which can - be achieved through Splunk Stream or by utilizing the Splunk Add-on for Apache Web - Server. No additional configuration is required for this analytic. -known_false_positives: False positives may occur depending on the web server's configuration. - If the web server is intentionally configured to utilize the Remote ShellServlet, - then the detections by this analytic would not be considered true positives. + - Nginx Access +description: The following analytic identifies attempts to access the Remote ShellServlet on a web server, specifically targeting Confluence servers vulnerable to CVE-2023-22518 and CVE-2023-22515. It leverages web data to detect URLs containing "*plugins/servlet/com.jsos.shell/*" with a status code of 200. This activity is significant as it is commonly associated with web shells and other malicious behaviors, potentially leading to unauthorized command execution. If confirmed malicious, attackers could gain remote code execution capabilities, compromising the server and potentially the entire network. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN ("*plugins/servlet/com.jsos.shell/*") Web.status=200 + BY Web.http_user_agent, Web.status Web.http_method, + Web.url, Web.url_length, Web.src, + Web.dest, sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `web_remote_shellservlet_access_filter` +how_to_implement: This analytic necessitates the collection of web data, which can be achieved through Splunk Stream or by utilizing the Splunk Add-on for Apache Web Server. No additional configuration is required for this analytic. +known_false_positives: False positives may occur depending on the web server's configuration. If the web server is intentionally configured to utilize the Remote ShellServlet, then the detections by this analytic would not be considered true positives. references: -- http://www.servletsuite.com/servlets/shell.htm + - http://www.servletsuite.com/servlets/shell.htm drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: An attempt to access the Remote ShellServlet on a web server was detected. - The source IP is $src$ and the destination hostname is $dest$. - risk_objects: - - field: dest - type: system - score: 81 - threat_objects: - - field: src - type: ip_address + message: An attempt to access the Remote ShellServlet on a web server was detected. The source IP is $src$ and the destination hostname is $dest$. + risk_objects: + - field: dest + type: system + score: 81 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - CVE-2023-22515 Privilege Escalation Vulnerability Confluence Data Center and Server - - GhostRedirector IIS Module and Rungan Backdoor - asset_type: Web Server - atomic_guid: [] - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - CVE-2023-22515 Privilege Escalation Vulnerability Confluence Data Center and Server + - GhostRedirector IIS Module and Rungan Backdoor + asset_type: Web Server + atomic_guid: [] + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/confluence/nginx_shellservlet.log - source: /var/log/nginx/access.log - sourcetype: nginx:plus:kv + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/confluence/nginx_shellservlet.log + source: /var/log/nginx/access.log + sourcetype: nginx:plus:kv diff --git a/detections/web/web_spring4shell_http_request_class_module.yml b/detections/web/web_spring4shell_http_request_class_module.yml index 45867601a1..f13151c12f 100644 --- a/detections/web/web_spring4shell_http_request_class_module.yml +++ b/detections/web/web_spring4shell_http_request_class_module.yml @@ -1,74 +1,62 @@ name: Web Spring4Shell HTTP Request Class Module id: fcdfd69d-0ca3-4476-920e-9b633cb4593e -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic detects HTTP requests containing payloads related - to the Spring4Shell vulnerability (CVE-2022-22965). It leverages Splunk Stream HTTP - data to inspect the HTTP request body and form data for specific fields such as - "class.module.classLoader.resources.context.parent.pipeline.first". This activity - is significant as it indicates an attempt to exploit a critical vulnerability in - Spring Framework, potentially leading to remote code execution. If confirmed malicious, - this could allow attackers to gain unauthorized access, execute arbitrary code, - and compromise the affected system. +description: The following analytic detects HTTP requests containing payloads related to the Spring4Shell vulnerability (CVE-2022-22965). It leverages Splunk Stream HTTP data to inspect the HTTP request body and form data for specific fields such as "class.module.classLoader.resources.context.parent.pipeline.first". This activity is significant as it indicates an attempt to exploit a critical vulnerability in Spring Framework, potentially leading to remote code execution. If confirmed malicious, this could allow attackers to gain unauthorized access, execute arbitrary code, and compromise the affected system. data_source: -- Splunk Stream HTTP -search: '`stream_http` http_method IN ("POST") | stats values(form_data) as http_request_body - min(_time) as firstTime max(_time) as lastTime count by src dest http_method http_user_agent - uri_path url bytes_in bytes_out | search http_request_body IN ("*class.module.classLoader.resources.context.parent.pipeline.first.fileDateFormat=_*", - "*class.module.classLoader.resources.context.parent.pipeline.first.pattern*","*suffix=.jsp*") - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `web_spring4shell_http_request_class_module_filter`' -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the stream HTTP logs or network logs that catch network traffic. Make - sure that the http-request-body, payload, or request field is enabled. -known_false_positives: False positives may occur and filtering may be required. Restrict - analytic to asset type. + - Splunk Stream HTTP +search: |- + `stream_http` http_method IN ("POST") + | stats values(form_data) as http_request_body min(_time) as firstTime max(_time) as lastTime count + BY src dest http_method + http_user_agent uri_path url + bytes_in bytes_out + | search http_request_body IN ("*class.module.classLoader.resources.context.parent.pipeline.first.fileDateFormat=_*", "*class.module.classLoader.resources.context.parent.pipeline.first.pattern*","*suffix=.jsp*") + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `web_spring4shell_http_request_class_module_filter` +how_to_implement: To successfully implement this search, you need to be ingesting logs with the stream HTTP logs or network logs that catch network traffic. Make sure that the http-request-body, payload, or request field is enabled. +known_false_positives: False positives may occur and filtering may be required. Restrict analytic to asset type. references: -- https://github.com/DDuarte/springshell-rce-poc/blob/master/poc.py + - https://github.com/DDuarte/springshell-rce-poc/blob/master/poc.py drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A http body request related to Spring4Shell has been sent to $dest$ by - $src$. - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: - - field: src - type: ip_address + message: A http body request related to Spring4Shell has been sent to $dest$ by $src$. + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Spring4Shell CVE-2022-22965 - asset_type: Web Server - cve: - - CVE-2022-22965 - mitre_attack_id: - - T1190 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Spring4Shell CVE-2022-22965 + asset_type: Web Server + cve: + - CVE-2022-22965 + mitre_attack_id: + - T1190 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/spring4shell/http_request_body_streams.log - source: stream:http - sourcetype: stream:http + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/spring4shell/http_request_body_streams.log + source: stream:http + sourcetype: stream:http diff --git a/detections/web/web_spring_cloud_function_functionrouter.yml b/detections/web/web_spring_cloud_function_functionrouter.yml index 0fb0680305..a79d654587 100644 --- a/detections/web/web_spring_cloud_function_functionrouter.yml +++ b/detections/web/web_spring_cloud_function_functionrouter.yml @@ -1,73 +1,63 @@ name: Web Spring Cloud Function FunctionRouter id: 89dddbad-369a-4f8a-ace2-2439218735bc -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP -description: The following analytic identifies HTTP POST requests to the Spring Cloud - Function endpoint containing "functionRouter" in the URL. It leverages the Web data - model to detect these requests based on specific fields such as http_method, url, - and http_user_agent. This activity is significant because it targets CVE-2022-22963, - a known vulnerability in Spring Cloud Function, which has multiple proof-of-concept - exploits available. If confirmed malicious, this activity could allow attackers - to execute arbitrary code, potentially leading to unauthorized access, data exfiltration, - or further compromise of the affected system. +description: The following analytic identifies HTTP POST requests to the Spring Cloud Function endpoint containing "functionRouter" in the URL. It leverages the Web data model to detect these requests based on specific fields such as http_method, url, and http_user_agent. This activity is significant because it targets CVE-2022-22963, a known vulnerability in Spring Cloud Function, which has multiple proof-of-concept exploits available. If confirmed malicious, this activity could allow attackers to execute arbitrary code, potentially leading to unauthorized access, data exfiltration, or further compromise of the affected system. data_source: -- Splunk Stream HTTP -search: '| tstats count from datamodel=Web where Web.http_method IN ("POST") Web.url="*/functionRouter*" - by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest - Web.status sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `web_spring_cloud_function_functionrouter_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on Web traffic that include fields relavent for traffic into the `Web` datamodel. -known_false_positives: False positives may be present with legitimate applications. - Attempt to filter by dest IP or use Asset groups to restrict to servers. + - Splunk Stream HTTP +search: |- + | tstats count FROM datamodel=Web + WHERE Web.http_method IN ("POST") Web.url="*/functionRouter*" + BY Web.http_user_agent Web.http_method, Web.url,Web.url_length + Web.src, Web.dest Web.status + sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `web_spring_cloud_function_functionrouter_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on Web traffic that include fields relavent for traffic into the `Web` datamodel. +known_false_positives: False positives may be present with legitimate applications. Attempt to filter by dest IP or use Asset groups to restrict to servers. references: -- https://github.com/rapid7/metasploit-framework/pull/16395 -- https://github.com/hktalent/spring-spel-0day-poc + - https://github.com/rapid7/metasploit-framework/pull/16395 + - https://github.com/hktalent/spring-spel-0day-poc drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: A suspicious URL has been requested against $dest$ by $src$, related to - a vulnerability in Spring Cloud. - risk_objects: - - field: dest - type: system - score: 42 - threat_objects: - - field: src - type: ip_address + message: A suspicious URL has been requested against $dest$ by $src$, related to a vulnerability in Spring Cloud. + risk_objects: + - field: dest + type: system + score: 42 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Spring4Shell CVE-2022-22965 - asset_type: Web Server - cve: - - CVE-2022-22963 - mitre_attack_id: - - T1190 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - Spring4Shell CVE-2022-22965 + asset_type: Web Server + cve: + - CVE-2022-22963 + mitre_attack_id: + - T1190 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/spring4shell/all_functionrouter_http_streams.log - source: stream:http - sourcetype: stream:http + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/spring4shell/all_functionrouter_http_streams.log + source: stream:http + sourcetype: stream:http diff --git a/detections/web/windows_exchange_autodiscover_ssrf_abuse.yml b/detections/web/windows_exchange_autodiscover_ssrf_abuse.yml index 1cddec1440..6e1546476b 100644 --- a/detections/web/windows_exchange_autodiscover_ssrf_abuse.yml +++ b/detections/web/windows_exchange_autodiscover_ssrf_abuse.yml @@ -1,86 +1,86 @@ name: Windows Exchange Autodiscover SSRF Abuse id: d436f9e7-0ee7-4a47-864b-6dea2c4e2752 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Nathaniel Stearns, Splunk status: production type: TTP description: This analytic identifies potential exploitation attempts of ProxyShell (CVE-2021-34473, CVE-2021-34523, CVE-2021-31207) and ProxyNotShell (CVE-2022-41040, CVE-2022-41082) vulnerabilities in Microsoft Exchange Server. The detection focuses on identifying the SSRF attack patterns used in these exploit chains. The analytic monitors for suspicious POST requests to /autodiscover/autodiscover.json endpoints that may indicate attempts to enumerate LegacyDN attributes as part of initial reconnaissance. It also detects requests containing X-Rps-CAT parameters that could indicate attempts to impersonate Exchange users and access the PowerShell backend. Additionally, it looks for MAPI requests that may be used to obtain user SIDs, along with suspicious user agents (particularly Python-based) commonly used in automated exploit attempts. If successful, these attacks can lead to remote code execution as SYSTEM, allowing attackers to deploy webshells, access mailboxes, or gain persistent access to the Exchange server and potentially the broader network environment. data_source: - - Windows IIS -search: - ' | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.status=200) AND Web.http_method=POST by Web.src Web.status Web.uri_path Web.dest Web.http_method Web.uri_query Web.http_user_agent - | `drop_dm_object_name("Web")` - | eval is_autodiscover=if(like(lower(uri_path),"%autodiscover/autodiscover.json%"),1,0) - | eval has_rps_cat=if(like(lower(uri_query),"%x-rps-cat=%"),1,0) - | eval exchange_backend=if(like(lower(uri_query),"%/powershell/?%"),1,0) - | eval mapi=if(like(uri_query,"%/mapi/%"),1,0) - | eval suspicious_agent=if(match(lower(http_user_agent), "python|urllib"),1,0) - | addtotals fieldname=Score is_autodiscover, has_rps_cat, exchange_backend, mapi, suspicious_agent - | where Score >= 3 - | fields Score, src, dest, status, uri_query, uri_path, http_method, http_user_agent - | `windows_exchange_autodiscover_ssrf_abuse_filter`' + - Windows IIS +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE ( + Web.status=200 + ) + AND Web.http_method=POST + BY Web.src Web.status Web.uri_path + Web.dest Web.http_method Web.uri_query + Web.http_user_agent + | `drop_dm_object_name("Web")` + | eval is_autodiscover=if(like(lower(uri_path),"%autodiscover/autodiscover.json%"),1,0) + | eval has_rps_cat=if(like(lower(uri_query),"%x-rps-cat=%"),1,0) + | eval exchange_backend=if(like(lower(uri_query),"%/powershell/?%"),1,0) + | eval mapi=if(like(uri_query,"%/mapi/%"),1,0) + | eval suspicious_agent=if(match(lower(http_user_agent), "python + | urllib"),1,0) + | addtotals fieldname=Score is_autodiscover, has_rps_cat, exchange_backend, mapi, suspicious_agent + | where Score >= 3 + | fields Score, src, dest, status, uri_query, uri_path, http_method, http_user_agent + | `windows_exchange_autodiscover_ssrf_abuse_filter` how_to_implement: To successfully implement this search you need to be ingesting information on Web traffic, Exchange OR IIS logs, mapped to `Web` datamodel in the `Web` node. In addition, confirm the latest CIM App 4.20 or higher is installed. known_false_positives: False positives are limited. references: - - https://www.gteltsc.vn/blog/warning-new-attack-campaign-utilized-a-new-0day-rce-vulnerability-on-microsoft-exchange-server-12715.html - - https://msrc-blog.microsoft.com/2022/09/29/customer-guidance-for-reported-zero-day-vulnerabilities-in-microsoft-exchange-server/ - - https://twitter.com/GossiTheDog/status/1575762721353916417?s=20&t=67gq9xCWuyPm1VEm8ydfyA - - https://twitter.com/cglyer/status/1575793769814728705?s=20&t=67gq9xCWuyPm1VEm8ydfyA - - https://www.gteltsc.vn/blog/warning-new-attack-campaign-utilized-a-new-0day-rce-vulnerability-on-microsoft-exchange-server-12715.html - - https://research.splunk.com/stories/proxyshell/ - - https://splunk.github.io/splunk-add-on-for-microsoft-iis/ - - https://highon.coffee/blog/ssrf-cheat-sheet/ - - https://owasp.org/Top10/A10_2021-Server-Side_Request_Forgery_%28SSRF%29/ - - https://m365internals.com/2022/10/18/hunting-and-responding-to-proxyshell-attacks/ + - https://www.gteltsc.vn/blog/warning-new-attack-campaign-utilized-a-new-0day-rce-vulnerability-on-microsoft-exchange-server-12715.html + - https://msrc-blog.microsoft.com/2022/09/29/customer-guidance-for-reported-zero-day-vulnerabilities-in-microsoft-exchange-server/ + - https://twitter.com/GossiTheDog/status/1575762721353916417?s=20&t=67gq9xCWuyPm1VEm8ydfyA + - https://twitter.com/cglyer/status/1575793769814728705?s=20&t=67gq9xCWuyPm1VEm8ydfyA + - https://www.gteltsc.vn/blog/warning-new-attack-campaign-utilized-a-new-0day-rce-vulnerability-on-microsoft-exchange-server-12715.html + - https://research.splunk.com/stories/proxyshell/ + - https://splunk.github.io/splunk-add-on-for-microsoft-iis/ + - https://highon.coffee/blog/ssrf-cheat-sheet/ + - https://owasp.org/Top10/A10_2021-Server-Side_Request_Forgery_%28SSRF%29/ + - https://m365internals.com/2022/10/18/hunting-and-responding-to-proxyshell-attacks/ drilldown_searches: - - name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ - - name: View risk events for the last 7 days for - "$dest$" - search: - '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: - Activity related to ProxyShell or ProxyNotShell has been identified on - $dest$. Review events and take action accordingly. - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: [] + message: Activity related to ProxyShell or ProxyNotShell has been identified on $dest$. Review events and take action accordingly. + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: [] tags: - analytic_story: - - ProxyShell - - BlackByte Ransomware - - ProxyNotShell - - Seashell Blizzard - asset_type: Web Server - cve: - - CVE-2021-34523 - - CVE-2021-34473 - - CVE-2021-31207 - - CVE-2022-41040 - - CVE-2022-41082 - mitre_attack_id: - - T1190 - - T1133 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + analytic_story: + - ProxyShell + - BlackByte Ransomware + - ProxyNotShell + - Seashell Blizzard + asset_type: Web Server + cve: + - CVE-2021-34523 + - CVE-2021-34473 + - CVE-2021-31207 + - CVE-2022-41040 + - CVE-2022-41082 + mitre_attack_id: + - T1190 + - T1133 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: - - name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/proxyshell/proxyshell.log - source: ms:iis:splunk - sourcetype: ms:iis:splunk + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/proxyshell/proxyshell.log + source: ms:iis:splunk + sourcetype: ms:iis:splunk diff --git a/detections/web/windows_iis_server_pswa_console_access.yml b/detections/web/windows_iis_server_pswa_console_access.yml index 48048264ce..938ccab42a 100644 --- a/detections/web/windows_iis_server_pswa_console_access.yml +++ b/detections/web/windows_iis_server_pswa_console_access.yml @@ -1,48 +1,41 @@ name: Windows IIS Server PSWA Console Access id: 914ab191-fa8a-48cb-83a6-0565e061f934 -version: 4 -date: '2025-05-02' +version: 5 +date: '2026-02-25' author: Michael Haag, Splunk data_source: -- Windows IIS + - Windows IIS type: Hunting status: production -description: This analytic detects access attempts to the PowerShell Web Access (PSWA) - console on Windows IIS servers. It monitors web traffic for requests to PSWA-related - URIs, which could indicate legitimate administrative activity or potential unauthorized - access attempts. By tracking source IP, HTTP status, URI path, and HTTP method, - it helps identify suspicious patterns or brute-force attacks targeting PSWA. This - detection is crucial for maintaining the security of remote PowerShell management - interfaces and preventing potential exploitation of this powerful administrative - tool. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Web where Web.dest IN ("/pswa/*") by Web.src Web.status - Web.uri_path Web.dest Web.http_method Web.uri_query | `drop_dm_object_name("Web")`| - `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_iis_server_pswa_console_access_filter`' -how_to_implement: To successfully implement this search you need to be ingesting information - on Web traffic, Exchange OR IIS logs, mapped to `Web` datamodel in the `Web` node. - In addition, confirm the latest CIM App 4.20 or higher is installed. -known_false_positives: False positives may occur if legitimate PSWA processes are - used for administrative tasks. Careful review of the logs is recommended to distinguish - between legitimate and malicious activity. +description: This analytic detects access attempts to the PowerShell Web Access (PSWA) console on Windows IIS servers. It monitors web traffic for requests to PSWA-related URIs, which could indicate legitimate administrative activity or potential unauthorized access attempts. By tracking source IP, HTTP status, URI path, and HTTP method, it helps identify suspicious patterns or brute-force attacks targeting PSWA. This detection is crucial for maintaining the security of remote PowerShell management interfaces and preventing potential exploitation of this powerful administrative tool. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.dest IN ("/pswa/*") + BY Web.src Web.status Web.uri_path + Web.dest Web.http_method Web.uri_query + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_iis_server_pswa_console_access_filter` +how_to_implement: To successfully implement this search you need to be ingesting information on Web traffic, Exchange OR IIS logs, mapped to `Web` datamodel in the `Web` node. In addition, confirm the latest CIM App 4.20 or higher is installed. +known_false_positives: False positives may occur if legitimate PSWA processes are used for administrative tasks. Careful review of the logs is recommended to distinguish between legitimate and malicious activity. references: -- https://www.cisa.gov/news-events/cybersecurity-advisories/aa24-241a + - https://www.cisa.gov/news-events/cybersecurity-advisories/aa24-241a tags: - analytic_story: - - CISA AA24-241A - asset_type: Web Server - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: [] + analytic_story: + - CISA AA24-241A + asset_type: Web Server + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: [] tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/pswa/iis_pswaaccess.log - sourcetype: ms:iis:splunk - source: ms:iis:splunk + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/pswa/iis_pswaaccess.log + sourcetype: ms:iis:splunk + source: ms:iis:splunk diff --git a/detections/web/windows_sharepoint_spinstall0_get_request.yml b/detections/web/windows_sharepoint_spinstall0_get_request.yml index 63f899791b..e14d1c4434 100644 --- a/detections/web/windows_sharepoint_spinstall0_get_request.yml +++ b/detections/web/windows_sharepoint_spinstall0_get_request.yml @@ -1,62 +1,66 @@ name: Windows SharePoint Spinstall0 GET Request id: ac490de2-ee39-421c-b61b-1c4005dde427 -version: 1 -date: '2025-07-21' +version: 2 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP description: The following analytic detects potential post-exploitation activity related to the Microsoft SharePoint CVE-2025-53770 vulnerability. After successful exploitation via the ToolPane.aspx endpoint, attackers typically deploy a webshell named "spinstall0.aspx" in the SharePoint layouts directory. This detection identifies GET requests to this webshell, which indicates active use of the backdoor for command execution, data exfiltration, or credential/key extraction. Attackers commonly use these webshells to extract encryption keys, authentication tokens, and other sensitive information from the compromised SharePoint server. data_source: -- Suricata -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url="*/_layouts/15/spinstall0.aspx*" Web.http_method="GET" by Web.url Web.src Web.dest Web.status Web.http_user_agent Web.url_length sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_sharepoint_spinstall0_get_request_filter`' + - Suricata +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url="*/_layouts/15/spinstall0.aspx*" Web.http_method="GET" + BY Web.url Web.src Web.dest + Web.status Web.http_user_agent Web.url_length + sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_sharepoint_spinstall0_get_request_filter` how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Microsoft SharePoint or web proxy logs that capture SharePoint traffic. The detection focuses on GET requests to the spinstall0.aspx file, which is a known webshell deployed after successful exploitation of the CVE-2025-53770 vulnerability. Configure comprehensive logging for your SharePoint web servers and ensure that all HTTP requests are being captured and forwarded to your SIEM. Ensure proper IIS logging is enabled. Tune, modify the analytic as needed based on HTTP methods. known_false_positives: Limited false positives are expected as spinstall0.aspx is not a legitimate SharePoint component. However, security teams investigating the incident might also access this file for analysis purposes. Verify the source IP addresses against known security team IPs and the timing of the requests in relation to the initial exploitation attempt. references: -- https://research.eye.security/sharepoint-under-siege/ -- https://www.cisa.gov/news-events/alerts/2025/07/20/microsoft-releases-guidance-exploitation-sharepoint-vulnerability-cve-2025-53770 -- https://msrc.microsoft.com/blog/2025/07/customer-guidance-for-sharepoint-vulnerability-cve-2025-53770/ -- https://splunkbase.splunk.com/app/3185 + - https://research.eye.security/sharepoint-under-siege/ + - https://www.cisa.gov/news-events/alerts/2025/07/20/microsoft-releases-guidance-exploitation-sharepoint-vulnerability-cve-2025-53770 + - https://msrc.microsoft.com/blog/2025/07/customer-guidance-for-sharepoint-vulnerability-cve-2025-53770/ + - https://splunkbase.splunk.com/app/3185 drilldown_searches: -- name: View the detection results for - "$dest$" and "$src$" - search: '%original_detection_search% | search dest = "$dest$" src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$src$" + search: '%original_detection_search% | search dest = "$dest$" src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential access to SharePoint webshell (spinstall0.aspx) detected from $src$ targeting $dest$ - risk_objects: - - field: dest - type: system - score: 85 - threat_objects: - - field: src - type: ip_address + message: Potential access to SharePoint webshell (spinstall0.aspx) detected from $src$ targeting $dest$ + risk_objects: + - field: dest + type: system + score: 85 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Microsoft SharePoint Vulnerabilities - asset_type: Web Server - mitre_attack_id: - - T1190 - - T1505.003 - - T1552 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: - - CVE-2025-53770 + analytic_story: + - Microsoft SharePoint Vulnerabilities + asset_type: Web Server + mitre_attack_id: + - T1190 + - T1505.003 + - T1552 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: + - CVE-2025-53770 tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/sharepoint/spinstall0.log - sourcetype: suricata - source: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/sharepoint/spinstall0.log + sourcetype: suricata + source: suricata diff --git a/detections/web/windows_sharepoint_toolpane_endpoint_exploitation_attempt.yml b/detections/web/windows_sharepoint_toolpane_endpoint_exploitation_attempt.yml index d4864d3e46..8f2bb23e66 100644 --- a/detections/web/windows_sharepoint_toolpane_endpoint_exploitation_attempt.yml +++ b/detections/web/windows_sharepoint_toolpane_endpoint_exploitation_attempt.yml @@ -1,62 +1,68 @@ name: Windows SharePoint ToolPane Endpoint Exploitation Attempt id: 508b2649-3a1e-4a4c-ba9d-3cc05e1a1b70 -version: 1 -date: '2025-07-20' +version: 2 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP description: The following analytic detects potential exploitation attempts against Microsoft SharePoint Server vulnerability CVE-2025-53770, also known as "ToolShell". This detection monitors for POST requests to the ToolPane.aspx endpoint with specific DisplayMode parameter, which is a key indicator of the exploit. This vulnerability allows unauthenticated remote code execution on affected SharePoint servers, enabling attackers to fully access SharePoint content, file systems, internal configurations, and execute code over the network. data_source: -- Suricata -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url="*/_layouts/15/ToolPane.aspx*" AND Web.url="*DisplayMode=Edit*" Web.http_method=POST by Web.http_user_agent, Web.status, Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_sharepoint_toolpane_endpoint_exploitation_attempt_filter`' + - Suricata +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url="*/_layouts/15/ToolPane.aspx*" + AND + Web.url="*DisplayMode=Edit*" Web.http_method=POST + BY Web.http_user_agent, Web.status, Web.http_method, + Web.url, Web.url_length, Web.src, + Web.dest, sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `windows_sharepoint_toolpane_endpoint_exploitation_attempt_filter` how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Microsoft SharePoint or web proxy logs that capture SharePoint traffic. Configure AMSI integration in SharePoint and deploy Microsoft Defender AV on all SharePoint servers as recommended by Microsoft to provide additional protection. known_false_positives: Limited false positives are expected as legitimate use of the ToolPane.aspx endpoint with DisplayMode=Edit parameter in POST requests is uncommon. However, some SharePoint administration activities might trigger this detection. Verify against known administrator IPs and activity patterns. references: -- https://research.eye.security/sharepoint-under-siege/ -- https://cybersecuritynews.com/sharepoint-0-day-rce-vulnerability-exploited/ -- https://msrc.microsoft.com/blog/2025/07/customer-guidance-for-sharepoint-vulnerability-cve-2025-53770/ -- https://www.cisa.gov/news-events/alerts/2025/07/20/microsoft-releases-guidance-exploitation-sharepoint-vulnerability-cve-2025-53770 -- https://splunkbase.splunk.com/app/3185 + - https://research.eye.security/sharepoint-under-siege/ + - https://cybersecuritynews.com/sharepoint-0-day-rce-vulnerability-exploited/ + - https://msrc.microsoft.com/blog/2025/07/customer-guidance-for-sharepoint-vulnerability-cve-2025-53770/ + - https://www.cisa.gov/news-events/alerts/2025/07/20/microsoft-releases-guidance-exploitation-sharepoint-vulnerability-cve-2025-53770 + - https://splunkbase.splunk.com/app/3185 drilldown_searches: -- name: View the detection results for - "$dest$" and "$src$" - search: '%original_detection_search% | search dest = "$dest$" src = "$src$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" and "$src$" + search: '%original_detection_search% | search dest = "$dest$" src = "$src$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential SharePoint ToolPane exploitation (CVE-2025-53770) detected from $src$ targeting $dest$ - risk_objects: - - field: dest - type: system - score: 80 - threat_objects: - - field: src - type: ip_address + message: Potential SharePoint ToolPane exploitation (CVE-2025-53770) detected from $src$ targeting $dest$ + risk_objects: + - field: dest + type: system + score: 80 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - Microsoft SharePoint Vulnerabilities - asset_type: Web Server - mitre_attack_id: - - T1190 - - T1505.003 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: - - CVE-2025-53770 + analytic_story: + - Microsoft SharePoint Vulnerabilities + asset_type: Web Server + mitre_attack_id: + - T1190 + - T1505.003 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: + - CVE-2025-53770 tests: -- name: True Positive Test - attack_data: - - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/sharepoint/toolpane.log - sourcetype: suricata - source: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/sharepoint/toolpane.log + sourcetype: suricata + source: suricata diff --git a/detections/web/wordpress_bricks_builder_plugin_rce.yml b/detections/web/wordpress_bricks_builder_plugin_rce.yml index 25f4774ba8..7ccdbab8a9 100644 --- a/detections/web/wordpress_bricks_builder_plugin_rce.yml +++ b/detections/web/wordpress_bricks_builder_plugin_rce.yml @@ -1,79 +1,66 @@ name: WordPress Bricks Builder plugin RCE id: 56a8771a-3fda-4959-b81d-2f266e2f679f -version: 6 -date: '2025-10-14' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk data_source: -- Nginx Access + - Nginx Access type: TTP status: production -description: The following analytic identifies potential exploitation of the WordPress - Bricks Builder plugin RCE vulnerability. It detects HTTP POST requests to the URL - path "/wp-json/bricks/v1/render_element" with a status code of 200, leveraging the - Web datamodel. This activity is significant as it indicates an attempt to exploit - CVE-2024-25600, a known vulnerability that allows remote code execution. If confirmed - malicious, an attacker could execute arbitrary commands on the target server, leading - to potential full system compromise and unauthorized access to sensitive data. -search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Web where Web.url IN ("*/wp-json/bricks/v1/render_element") - Web.status=200 Web.http_method=POST by Web.src, Web.dest, Web.http_user_agent, Web.url, - Web.uri_path, Web.status, Web.http_method, sourcetype, source | `drop_dm_object_name("Web")` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wordpress_bricks_builder_plugin_rce_filter`' -how_to_implement: The search is based on data in the Web datamodel and was modeled - from NGINX logs. Ensure that the Web datamodel is accelerated and that the data - source for the Web datamodel is properly configured. If using other web sources, - modify they query, or review the data, as needed. -known_false_positives: False positives may be possible, however we restricted it to - HTTP Status 200 and POST requests, based on the POC. Upon investigation review the - POST body for the actual payload - or command - being executed. +description: The following analytic identifies potential exploitation of the WordPress Bricks Builder plugin RCE vulnerability. It detects HTTP POST requests to the URL path "/wp-json/bricks/v1/render_element" with a status code of 200, leveraging the Web datamodel. This activity is significant as it indicates an attempt to exploit CVE-2024-25600, a known vulnerability that allows remote code execution. If confirmed malicious, an attacker could execute arbitrary commands on the target server, leading to potential full system compromise and unauthorized access to sensitive data. +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN ("*/wp-json/bricks/v1/render_element") Web.status=200 Web.http_method=POST + BY Web.src, Web.dest, Web.http_user_agent, + Web.url, Web.uri_path, Web.status, + Web.http_method, sourcetype, source + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `wordpress_bricks_builder_plugin_rce_filter` +how_to_implement: The search is based on data in the Web datamodel and was modeled from NGINX logs. Ensure that the Web datamodel is accelerated and that the data source for the Web datamodel is properly configured. If using other web sources, modify they query, or review the data, as needed. +known_false_positives: False positives may be possible, however we restricted it to HTTP Status 200 and POST requests, based on the POC. Upon investigation review the POST body for the actual payload - or command - being executed. references: -- https://attack.mitre.org/techniques/T1190 -- https://github.com/Tornad0007/CVE-2024-25600-Bricks-Builder-plugin-for-WordPress/blob/main/exploit.py -- https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2024-25600 -- https://op-c.net/blog/cve-2024-25600-wordpresss-bricks-builder-rce-flaw-under-active-exploitation/ -- https://thehackernews.com/2024/02/wordpress-bricks-theme-under-active.html + - https://attack.mitre.org/techniques/T1190 + - https://github.com/Tornad0007/CVE-2024-25600-Bricks-Builder-plugin-for-WordPress/blob/main/exploit.py + - https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2024-25600 + - https://op-c.net/blog/cve-2024-25600-wordpresss-bricks-builder-rce-flaw-under-active-exploitation/ + - https://thehackernews.com/2024/02/wordpress-bricks-theme-under-active.html drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential exploitation of the WordPress Bricks Builder plugin RCE vulnerability - on $dest$ by $src$. - risk_objects: - - field: dest - type: system - score: 100 - threat_objects: - - field: src - type: ip_address + message: Potential exploitation of the WordPress Bricks Builder plugin RCE vulnerability on $dest$ by $src$. + risk_objects: + - field: dest + type: system + score: 100 + threat_objects: + - field: src + type: ip_address tags: - analytic_story: - - WordPress Vulnerabilities - - Hellcat Ransomware - asset_type: Web Server - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network - cve: - - CVE-2024-25600 + analytic_story: + - WordPress Vulnerabilities + - Hellcat Ransomware + asset_type: Web Server + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network + cve: + - CVE-2024-25600 tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/wordpress/bricks_cve_2024_25600.log - source: nginx:plus:kv - sourcetype: nginx:plus:kv + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/wordpress/bricks_cve_2024_25600.log + source: nginx:plus:kv + sourcetype: nginx:plus:kv diff --git a/detections/web/ws_ftp_remote_code_execution.yml b/detections/web/ws_ftp_remote_code_execution.yml index d5e1e1563e..6bf3431cb2 100644 --- a/detections/web/ws_ftp_remote_code_execution.yml +++ b/detections/web/ws_ftp_remote_code_execution.yml @@ -1,76 +1,64 @@ name: WS FTP Remote Code Execution id: b84e8f39-4e7b-4d4f-9e7c-fcd29a227845 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Michael Haag, Splunk status: production type: TTP data_source: -- Suricata -description: The following analytic detects potential Remote Code Execution (RCE) - attempts exploiting CVE-2023-40044 in WS_FTP software. It identifies HTTP POST requests - to the "/AHT/AhtApiService.asmx/AuthUser" URL with a status code of 200. This detection - leverages the Web datamodel to monitor specific URL patterns and HTTP status codes. - This activity is significant as it may indicate an exploitation attempt, potentially - allowing an attacker to execute arbitrary code on the server. If confirmed malicious, - this could lead to unauthorized access, data exfiltration, or further compromise - of the affected system. -search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web - where Web.url IN ("/AHT/AhtApiService.asmx/AuthUser") Web.status=200 Web.http_method=POST - by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, - Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `ws_ftp_remote_code_execution_filter`' -how_to_implement: The following analytic requires the Web datamodel. Ensure data source - is mapped correctly or modify and tune for your data source. -known_false_positives: If WS_FTP Server is not in use, this analytic will not return - results. Monitor and tune for your environment. Note the MetaSploit module is focused - on only hitting /AHT/ and not the full /AHT/AhtApiService.asmx/AuthUser URL. + - Suricata +description: The following analytic detects potential Remote Code Execution (RCE) attempts exploiting CVE-2023-40044 in WS_FTP software. It identifies HTTP POST requests to the "/AHT/AhtApiService.asmx/AuthUser" URL with a status code of 200. This detection leverages the Web datamodel to monitor specific URL patterns and HTTP status codes. This activity is significant as it may indicate an exploitation attempt, potentially allowing an attacker to execute arbitrary code on the server. If confirmed malicious, this could lead to unauthorized access, data exfiltration, or further compromise of the affected system. +search: |- + | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web + WHERE Web.url IN ("/AHT/AhtApiService.asmx/AuthUser") Web.status=200 Web.http_method=POST + BY Web.http_user_agent, Web.status Web.http_method, + Web.url, Web.url_length, Web.src, + Web.dest, sourcetype + | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `ws_ftp_remote_code_execution_filter` +how_to_implement: The following analytic requires the Web datamodel. Ensure data source is mapped correctly or modify and tune for your data source. +known_false_positives: If WS_FTP Server is not in use, this analytic will not return results. Monitor and tune for your environment. Note the MetaSploit module is focused on only hitting /AHT/ and not the full /AHT/AhtApiService.asmx/AuthUser URL. references: -- https://github.com/projectdiscovery/nuclei-templates/pull/8296/files -- https://www.assetnote.io/resources/research/rce-in-progress-ws-ftp-ad-hoc-via-iis-http-modules-cve-2023-40044 -- https://github.com/rapid7/metasploit-framework/pull/18414 + - https://github.com/projectdiscovery/nuclei-templates/pull/8296/files + - https://www.assetnote.io/resources/research/rce-in-progress-ws-ftp-ad-hoc-via-iis-http-modules-cve-2023-40044 + - https://github.com/rapid7/metasploit-framework/pull/18414 drilldown_searches: -- name: View the detection results for - "$dest$" - search: '%original_detection_search% | search dest = "$dest$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$dest$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") - starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime - values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) - as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) - as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$dest$" + search: '%original_detection_search% | search dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential WS FTP Remote Code Execution detected against URL $url$ on $dest$ - from $src$ - risk_objects: - - field: dest - type: system - score: 72 - threat_objects: - - field: src - type: ip_address + message: Potential WS FTP Remote Code Execution detected against URL $url$ on $dest$ from $src$ + risk_objects: + - field: dest + type: system + score: 72 + threat_objects: + - field: src + type: ip_address tags: - cve: - - CVE-2023-40044 - analytic_story: - - WS FTP Server Critical Vulnerabilities - asset_type: Web Server - atomic_guid: [] - mitre_attack_id: - - T1190 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: network + cve: + - CVE-2023-40044 + analytic_story: + - WS FTP Server Critical Vulnerabilities + asset_type: Web Server + atomic_guid: [] + mitre_attack_id: + - T1190 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: network tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ws_ftp/wsftpweb.log - source: suricata - sourcetype: suricata + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ws_ftp/wsftpweb.log + source: suricata + sourcetype: suricata diff --git a/detections/web/zscaler_adware_activities_threat_blocked.yml b/detections/web/zscaler_adware_activities_threat_blocked.yml index 2f441e91db..e2e29d6d60 100644 --- a/detections/web/zscaler_adware_activities_threat_blocked.yml +++ b/detections/web/zscaler_adware_activities_threat_blocked.yml @@ -1,73 +1,60 @@ name: Zscaler Adware Activities Threat Blocked id: 3407b250-345a-4d71-80db-c91e555a3ece -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Gowthamaraj Rajendran, Splunk status: production type: Anomaly data_source: [] -description: The following analytic identifies potential adware activity blocked by - Zscaler. It leverages web proxy logs to detect blocked actions associated with adware - threats. Key data points such as device owner, user, URL category, destination URL, - and IP are analyzed. This activity is significant as adware can degrade system performance, - lead to unwanted advertisements, and potentially expose users to further malicious - content. If confirmed malicious, it could indicate an attempt to compromise user - systems, necessitating further investigation and remediation to prevent potential - data breaches or system exploitation. -search: '`zscaler_proxy` action=blocked threatname=*adware* | stats count min(_time) - as firstTime max(_time) as lastTime by action deviceowner user urlcategory url - src dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `zscaler_adware_activities_threat_blocked_filter`' -how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. - You must be ingesting Zscaler events into your Splunk environment through an ingester. - This analytic was written to be used with the "zscalernss-web" sourcetype leveraging - the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. - Security teams are encouraged to adjust the detection parameters, ensuring the detection - is tailored to their specific environment. +description: The following analytic identifies potential adware activity blocked by Zscaler. It leverages web proxy logs to detect blocked actions associated with adware threats. Key data points such as device owner, user, URL category, destination URL, and IP are analyzed. This activity is significant as adware can degrade system performance, lead to unwanted advertisements, and potentially expose users to further malicious content. If confirmed malicious, it could indicate an attempt to compromise user systems, necessitating further investigation and remediation to prevent potential data breaches or system exploitation. +search: |- + `zscaler_proxy` action=blocked threatname=*adware* + | stats count min(_time) as firstTime max(_time) as lastTime + BY action deviceowner user + urlcategory url src + dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `zscaler_adware_activities_threat_blocked_filter` +how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. You must be ingesting Zscaler events into your Splunk environment through an ingester. This analytic was written to be used with the "zscalernss-web" sourcetype leveraging the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. Security teams are encouraged to adjust the detection parameters, ensuring the detection is tailored to their specific environment. known_false_positives: False positives are limited to Zscaler configuration. references: -- https://help.zscaler.com/zia/nss-feed-output-format-web-logs + - https://help.zscaler.com/zia/nss-feed-output-format-web-logs drilldown_searches: -- name: View the detection results for - "$src$" and "$user$" - search: '%original_detection_search% | search src = "$src$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" and "$user$" + search: '%original_detection_search% | search src = "$src$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential Adware Activity blocked from dest -[$dest$] on $src$ for user-[$user$]. - risk_objects: - - field: src - type: system - score: 8 - - field: user - type: user - score: 8 - threat_objects: - - field: url - type: url + message: Potential Adware Activity blocked from dest -[$dest$] on $src$ for user-[$user$]. + risk_objects: + - field: src + type: system + score: 8 + - field: user + type: user + score: 8 + threat_objects: + - field: url + type: url tags: - analytic_story: - - Zscaler Browser Proxy Threats - asset_type: Web Server - mitre_attack_id: - - T1566 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Zscaler Browser Proxy Threats + asset_type: Web Server + mitre_attack_id: + - T1566 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json - source: zscaler - sourcetype: zscalernss-web + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json + source: zscaler + sourcetype: zscalernss-web diff --git a/detections/web/zscaler_behavior_analysis_threat_blocked.yml b/detections/web/zscaler_behavior_analysis_threat_blocked.yml index 763fb765ba..e6929b39e8 100644 --- a/detections/web/zscaler_behavior_analysis_threat_blocked.yml +++ b/detections/web/zscaler_behavior_analysis_threat_blocked.yml @@ -1,73 +1,60 @@ name: Zscaler Behavior Analysis Threat Blocked id: 289ad59f-8939-4331-b805-f2bd51d36fb8 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Rod Soto, Gowthamaraj Rajendran, Splunk status: production type: Anomaly data_source: [] -description: The following analytic identifies threats blocked by the Zscaler proxy - based on behavior analysis. It leverages web proxy logs to detect entries where - actions are blocked and threat names and classes are specified. This detection is - significant as it highlights potential malicious activities that were intercepted - by Zscaler's behavior analysis, providing early indicators of threats. If confirmed - malicious, these blocked threats could indicate attempted breaches or malware infections, - helping security teams to understand and mitigate potential risks in their environment. -search: '`zscaler_proxy` action=blocked threatname!="None" threatclass="Behavior Analysis" - | stats count min(_time) as firstTime max(_time) as lastTime by action deviceowner - user threatname url src dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `zscaler_behavior_analysis_threat_blocked_filter`' -how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. - You must be ingesting Zscaler events into your Splunk environment through an ingester. - This analytic was written to be used with the "zscalernss-web" sourcetype leveraging - the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. - Security teams are encouraged to adjust the detection parameters, ensuring the detection - is tailored to their specific environment. +description: The following analytic identifies threats blocked by the Zscaler proxy based on behavior analysis. It leverages web proxy logs to detect entries where actions are blocked and threat names and classes are specified. This detection is significant as it highlights potential malicious activities that were intercepted by Zscaler's behavior analysis, providing early indicators of threats. If confirmed malicious, these blocked threats could indicate attempted breaches or malware infections, helping security teams to understand and mitigate potential risks in their environment. +search: |- + `zscaler_proxy` action=blocked threatname!="None" threatclass="Behavior Analysis" + | stats count min(_time) as firstTime max(_time) as lastTime + BY action deviceowner user + threatname url src + dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `zscaler_behavior_analysis_threat_blocked_filter` +how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. You must be ingesting Zscaler events into your Splunk environment through an ingester. This analytic was written to be used with the "zscalernss-web" sourcetype leveraging the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. Security teams are encouraged to adjust the detection parameters, ensuring the detection is tailored to their specific environment. known_false_positives: False positives are limited to Zscalar configuration. references: -- https://help.zscaler.com/zia/nss-feed-output-format-web-logs + - https://help.zscaler.com/zia/nss-feed-output-format-web-logs drilldown_searches: -- name: View the detection results for - "$src$" and "$user$" - search: '%original_detection_search% | search src = "$src$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" and "$user$" + search: '%original_detection_search% | search src = "$src$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential Adware Behavior Analysis Threat from dest -[$dest$] on $src$ - for user-[$user$]. - risk_objects: - - field: src - type: system - score: 8 - - field: user - type: user - score: 8 - threat_objects: - - field: url - type: url + message: Potential Adware Behavior Analysis Threat from dest -[$dest$] on $src$ for user-[$user$]. + risk_objects: + - field: src + type: system + score: 8 + - field: user + type: user + score: 8 + threat_objects: + - field: url + type: url tags: - analytic_story: - - Zscaler Browser Proxy Threats - asset_type: Web Server - mitre_attack_id: - - T1566 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Zscaler Browser Proxy Threats + asset_type: Web Server + mitre_attack_id: + - T1566 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json - source: zscaler - sourcetype: zscalernss-web + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json + source: zscaler + sourcetype: zscalernss-web diff --git a/detections/web/zscaler_cryptominer_downloaded_threat_blocked.yml b/detections/web/zscaler_cryptominer_downloaded_threat_blocked.yml index 208c4b53f7..c3b31cf018 100644 --- a/detections/web/zscaler_cryptominer_downloaded_threat_blocked.yml +++ b/detections/web/zscaler_cryptominer_downloaded_threat_blocked.yml @@ -1,74 +1,60 @@ name: Zscaler CryptoMiner Downloaded Threat Blocked id: ed76ce37-bab9-4ec0-bf3e-9c6a6cf43365 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Gowthamaraj Rajendran, Rod Soto, Splunk status: production type: Anomaly data_source: [] -description: The following analytic identifies attempts to download cryptomining software - that are blocked by Zscaler. It leverages web proxy logs to detect blocked actions - associated with cryptominer threats, analyzing key data points such as device owner, - user, URL category, destination URL, and IP. This activity is significant for a - SOC as it helps in early identification and mitigation of cryptomining activities, - which can compromise network integrity and resource availability. If confirmed malicious, - this activity could lead to unauthorized use of network resources for cryptomining, - potentially degrading system performance and increasing operational costs. -search: '`zscaler_proxy` action=blocked threatname=*miner* | stats count min(_time) - as firstTime max(_time) as lastTime by action deviceowner user urlcategory url src - dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `zscaler_cryptominer_downloaded_threat_blocked_filter`' -how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. - You must be ingesting Zscaler events into your Splunk environment through an ingester. - This analytic was written to be used with the "zscalernss-web" sourcetype leveraging - the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. - Security teams are encouraged to adjust the detection parameters, ensuring the detection - is tailored to their specific environment. +description: The following analytic identifies attempts to download cryptomining software that are blocked by Zscaler. It leverages web proxy logs to detect blocked actions associated with cryptominer threats, analyzing key data points such as device owner, user, URL category, destination URL, and IP. This activity is significant for a SOC as it helps in early identification and mitigation of cryptomining activities, which can compromise network integrity and resource availability. If confirmed malicious, this activity could lead to unauthorized use of network resources for cryptomining, potentially degrading system performance and increasing operational costs. +search: |- + `zscaler_proxy` action=blocked threatname=*miner* + | stats count min(_time) as firstTime max(_time) as lastTime + BY action deviceowner user + urlcategory url src + dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `zscaler_cryptominer_downloaded_threat_blocked_filter` +how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. You must be ingesting Zscaler events into your Splunk environment through an ingester. This analytic was written to be used with the "zscalernss-web" sourcetype leveraging the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. Security teams are encouraged to adjust the detection parameters, ensuring the detection is tailored to their specific environment. known_false_positives: False positives are limited to Zscaler configuration. references: -- https://help.zscaler.com/zia/nss-feed-output-format-web-logs + - https://help.zscaler.com/zia/nss-feed-output-format-web-logs drilldown_searches: -- name: View the detection results for - "$src$" and "$user$" - search: '%original_detection_search% | search src = "$src$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" and "$user$" + search: '%original_detection_search% | search src = "$src$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential CryptoMiner Downloaded Threat from dest -[$dest$] on $src$ for - user-[$user$]. - risk_objects: - - field: src - type: system - score: 32 - - field: user - type: user - score: 32 - threat_objects: - - field: url - type: url + message: Potential CryptoMiner Downloaded Threat from dest -[$dest$] on $src$ for user-[$user$]. + risk_objects: + - field: src + type: system + score: 32 + - field: user + type: user + score: 32 + threat_objects: + - field: url + type: url tags: - analytic_story: - - Zscaler Browser Proxy Threats - asset_type: Web Server - mitre_attack_id: - - T1566 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Zscaler Browser Proxy Threats + asset_type: Web Server + mitre_attack_id: + - T1566 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json - source: zscaler - sourcetype: zscalernss-web + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json + source: zscaler + sourcetype: zscalernss-web diff --git a/detections/web/zscaler_employment_search_web_activity.yml b/detections/web/zscaler_employment_search_web_activity.yml index 7dda32b79e..7b59d5275f 100644 --- a/detections/web/zscaler_employment_search_web_activity.yml +++ b/detections/web/zscaler_employment_search_web_activity.yml @@ -1,74 +1,60 @@ name: Zscaler Employment Search Web Activity id: 5456bdef-d765-4565-8e1f-61ca027bc50e -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Gowthamaraj Rajendran, Rod Soto, Splunk status: production type: Anomaly data_source: [] -description: The following analytic identifies web activity related to employment - searches within a network. It leverages Zscaler web proxy logs, focusing on entries - categorized as 'Job/Employment Search'. Key data points such as device owner, user, - URL category, destination URL, and IP are analyzed. This detection is significant - for SOCs as it helps monitor potential insider threats by identifying users who - may be seeking new employment. If confirmed malicious, this activity could indicate - a risk of data exfiltration or other insider threats, potentially leading to sensitive - information leakage or other security breaches. -search: '`zscaler_proxy` urlsupercategory="Job/Employment Search" | stats count min(_time) - as firstTime max(_time) as lastTime by action deviceowner user urlcategory url src - dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `zscaler_employment_search_web_activity_filter`' -how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. - You must be ingesting Zscaler events into your Splunk environment through an ingester. - This analytic was written to be used with the "zscalernss-web" sourcetype leveraging - the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. - Security teams are encouraged to adjust the detection parameters, ensuring the detection - is tailored to their specific environment. +description: The following analytic identifies web activity related to employment searches within a network. It leverages Zscaler web proxy logs, focusing on entries categorized as 'Job/Employment Search'. Key data points such as device owner, user, URL category, destination URL, and IP are analyzed. This detection is significant for SOCs as it helps monitor potential insider threats by identifying users who may be seeking new employment. If confirmed malicious, this activity could indicate a risk of data exfiltration or other insider threats, potentially leading to sensitive information leakage or other security breaches. +search: |- + `zscaler_proxy` urlsupercategory="Job/Employment Search" + | stats count min(_time) as firstTime max(_time) as lastTime + BY action deviceowner user + urlcategory url src + dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `zscaler_employment_search_web_activity_filter` +how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. You must be ingesting Zscaler events into your Splunk environment through an ingester. This analytic was written to be used with the "zscalernss-web" sourcetype leveraging the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. Security teams are encouraged to adjust the detection parameters, ensuring the detection is tailored to their specific environment. known_false_positives: False positives are limited to Zscaler configuration. references: -- https://help.zscaler.com/zia/nss-feed-output-format-web-logs + - https://help.zscaler.com/zia/nss-feed-output-format-web-logs drilldown_searches: -- name: View the detection results for - "$src$" and "$user$" - search: '%original_detection_search% | search src = "$src$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" and "$user$" + search: '%original_detection_search% | search src = "$src$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential Employment Search Web Activity from dest -[$dest$] on $src$ for - user-[$user$]. - risk_objects: - - field: src - type: system - score: 4 - - field: user - type: user - score: 4 - threat_objects: - - field: url - type: url + message: Potential Employment Search Web Activity from dest -[$dest$] on $src$ for user-[$user$]. + risk_objects: + - field: src + type: system + score: 4 + - field: user + type: user + score: 4 + threat_objects: + - field: url + type: url tags: - analytic_story: - - Zscaler Browser Proxy Threats - asset_type: Web Server - mitre_attack_id: - - T1566 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Zscaler Browser Proxy Threats + asset_type: Web Server + mitre_attack_id: + - T1566 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json - source: zscaler - sourcetype: zscalernss-web + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json + source: zscaler + sourcetype: zscalernss-web diff --git a/detections/web/zscaler_exploit_threat_blocked.yml b/detections/web/zscaler_exploit_threat_blocked.yml index 9914cef212..c01f463b80 100644 --- a/detections/web/zscaler_exploit_threat_blocked.yml +++ b/detections/web/zscaler_exploit_threat_blocked.yml @@ -1,73 +1,60 @@ name: Zscaler Exploit Threat Blocked id: 94665d8c-b841-4ff4-acb4-34d613e2cbfe -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Rod Soto, Gowthamaraj Rajendran, Splunk status: production type: TTP data_source: [] -description: The following analytic identifies potential exploit attempts involving - command and script interpreters blocked by Zscaler. It leverages web proxy logs - to detect incidents where actions are blocked due to exploit references. The detection - compiles statistics by user, threat name, URL, hostname, file class, and filename. - This activity is significant as it helps identify and mitigate exploit attempts, - which are critical for maintaining security. If confirmed malicious, such activity - could lead to unauthorized code execution, privilege escalation, or persistent access - within the environment, posing a severe threat to organizational security. -search: '`zscaler_proxy` action=blocked threatname=*exploit* | stats count min(_time) - as firstTime max(_time) as lastTime by user threatname src hostname fileclass filename - url dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `zscaler_exploit_threat_blocked_filter`' -how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. - You must be ingesting Zscaler events into your Splunk environment through an ingester. - This analytic was written to be used with the "zscalernss-web" sourcetype leveraging - the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. - Security teams are encouraged to adjust the detection parameters, ensuring the detection - is tailored to their specific environment. +description: The following analytic identifies potential exploit attempts involving command and script interpreters blocked by Zscaler. It leverages web proxy logs to detect incidents where actions are blocked due to exploit references. The detection compiles statistics by user, threat name, URL, hostname, file class, and filename. This activity is significant as it helps identify and mitigate exploit attempts, which are critical for maintaining security. If confirmed malicious, such activity could lead to unauthorized code execution, privilege escalation, or persistent access within the environment, posing a severe threat to organizational security. +search: |- + `zscaler_proxy` action=blocked threatname=*exploit* + | stats count min(_time) as firstTime max(_time) as lastTime + BY user threatname src + hostname fileclass filename + url dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `zscaler_exploit_threat_blocked_filter` +how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. You must be ingesting Zscaler events into your Splunk environment through an ingester. This analytic was written to be used with the "zscalernss-web" sourcetype leveraging the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. Security teams are encouraged to adjust the detection parameters, ensuring the detection is tailored to their specific environment. known_false_positives: False positives are limited to Zscaler configuration. references: -- https://help.zscaler.com/zia/nss-feed-output-format-web-logs + - https://help.zscaler.com/zia/nss-feed-output-format-web-logs drilldown_searches: -- name: View the detection results for - "$src$" and "$user$" - search: '%original_detection_search% | search src = "$src$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" and "$user$" + search: '%original_detection_search% | search src = "$src$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential Exploit Threat from dest -[$dest$] on $src$ for user-[$user$]. - risk_objects: - - field: src - type: system - score: 40 - - field: user - type: user - score: 40 - threat_objects: - - field: url - type: url + message: Potential Exploit Threat from dest -[$dest$] on $src$ for user-[$user$]. + risk_objects: + - field: src + type: system + score: 40 + - field: user + type: user + score: 40 + threat_objects: + - field: url + type: url tags: - analytic_story: - - Zscaler Browser Proxy Threats - asset_type: Web Server - mitre_attack_id: - - T1566 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Zscaler Browser Proxy Threats + asset_type: Web Server + mitre_attack_id: + - T1566 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json - source: zscaler - sourcetype: zscalernss-web + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json + source: zscaler + sourcetype: zscalernss-web diff --git a/detections/web/zscaler_legal_liability_threat_blocked.yml b/detections/web/zscaler_legal_liability_threat_blocked.yml index 676e50dc16..909f70bbad 100644 --- a/detections/web/zscaler_legal_liability_threat_blocked.yml +++ b/detections/web/zscaler_legal_liability_threat_blocked.yml @@ -1,72 +1,61 @@ name: Zscaler Legal Liability Threat Blocked id: bbf55ebf-c416-4f62-94d9-4064f2a28014 -version: 5 -date: '2025-05-02' +version: 6 +date: '2026-02-25' author: Rod Soto, Gowthamaraj Rajendran, Splunk status: production type: Anomaly data_source: [] -description: The following analytic identifies significant legal liability threats - blocked by the Zscaler web proxy. It uses web proxy logs to track destinations, - device owners, users, URL categories, and actions associated with legal liability. - By leveraging statistics on unique fields, it ensures a precise focus on these threats. - This activity is significant for SOC as it helps enforce legal compliance and risk - management. If confirmed malicious, it could indicate attempts to access legally - sensitive or restricted content, potentially leading to legal repercussions and - compliance violations. -search: '`zscaler_proxy` urlclass="Legal Liability" | stats count min(_time) as firstTime - max(_time) as lastTime by action deviceowner user urlcategory url src dest | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | dedup urlcategory | `zscaler_legal_liability_threat_blocked_filter`' -how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. - You must be ingesting Zscaler events into your Splunk environment through an ingester. - This analytic was written to be used with the "zscalernss-web" sourcetype leveraging - the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. - Security teams are encouraged to adjust the detection parameters, ensuring the detection - is tailored to their specific environment. +description: The following analytic identifies significant legal liability threats blocked by the Zscaler web proxy. It uses web proxy logs to track destinations, device owners, users, URL categories, and actions associated with legal liability. By leveraging statistics on unique fields, it ensures a precise focus on these threats. This activity is significant for SOC as it helps enforce legal compliance and risk management. If confirmed malicious, it could indicate attempts to access legally sensitive or restricted content, potentially leading to legal repercussions and compliance violations. +search: |- + `zscaler_proxy` urlclass="Legal Liability" + | stats count min(_time) as firstTime max(_time) as lastTime + BY action deviceowner user + urlcategory url src + dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | dedup urlcategory + | `zscaler_legal_liability_threat_blocked_filter` +how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. You must be ingesting Zscaler events into your Splunk environment through an ingester. This analytic was written to be used with the "zscalernss-web" sourcetype leveraging the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. Security teams are encouraged to adjust the detection parameters, ensuring the detection is tailored to their specific environment. known_false_positives: False positives are limited to Zscaler configuration. references: -- https://help.zscaler.com/zia/nss-feed-output-format-web-logs + - https://help.zscaler.com/zia/nss-feed-output-format-web-logs drilldown_searches: -- name: View the detection results for - "$src$" and "$user$" - search: '%original_detection_search% | search src = "$src$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" and "$user$" + search: '%original_detection_search% | search src = "$src$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential Legal Liability Threat from dest -[$dest$] on $src$ for user-[$user$]. - risk_objects: - - field: src - type: system - score: 16 - - field: user - type: user - score: 16 - threat_objects: - - field: url - type: url + message: Potential Legal Liability Threat from dest -[$dest$] on $src$ for user-[$user$]. + risk_objects: + - field: src + type: system + score: 16 + - field: user + type: user + score: 16 + threat_objects: + - field: url + type: url tags: - analytic_story: - - Zscaler Browser Proxy Threats - asset_type: Web Server - mitre_attack_id: - - T1566 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Zscaler Browser Proxy Threats + asset_type: Web Server + mitre_attack_id: + - T1566 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json - source: zscaler - sourcetype: zscalernss-web + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json + source: zscaler + sourcetype: zscalernss-web diff --git a/detections/web/zscaler_malware_activity_threat_blocked.yml b/detections/web/zscaler_malware_activity_threat_blocked.yml index 9b3c6c29b2..a9962370cd 100644 --- a/detections/web/zscaler_malware_activity_threat_blocked.yml +++ b/detections/web/zscaler_malware_activity_threat_blocked.yml @@ -1,73 +1,60 @@ name: Zscaler Malware Activity Threat Blocked id: ae874ad8-e353-40a7-87d4-420cdfb27d1a -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Rod Soto, Gowthamaraj Rajendran, Splunk status: production type: Anomaly data_source: [] -description: The following analytic identifies potential malware activities within - a network that are blocked by Zscaler. It leverages web proxy logs to filter for - blocked actions associated with malware, aggregating occurrences by user, URL, and - threat category. This detection is significant for SOC as it highlights attempts - to access malicious content, indicating potential compromise or targeted attacks. - If confirmed malicious, this activity could signify an ongoing attempt to infiltrate - the network, necessitating immediate investigation to prevent further threats and - ensure network integrity. -search: '`zscaler_proxy` action=blocked threatname=*malware* threatcategory!=None - | stats count min(_time) as firstTime max(_time) as lastTime by action deviceowner - user urlcategory url src dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `zscaler_malware_activity_threat_blocked_filter`' -how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. - You must be ingesting Zscaler events into your Splunk environment through an ingester. - This analytic was written to be used with the "zscalernss-web" sourcetype leveraging - the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. - Security teams are encouraged to adjust the detection parameters, ensuring the detection - is tailored to their specific environment. +description: The following analytic identifies potential malware activities within a network that are blocked by Zscaler. It leverages web proxy logs to filter for blocked actions associated with malware, aggregating occurrences by user, URL, and threat category. This detection is significant for SOC as it highlights attempts to access malicious content, indicating potential compromise or targeted attacks. If confirmed malicious, this activity could signify an ongoing attempt to infiltrate the network, necessitating immediate investigation to prevent further threats and ensure network integrity. +search: |- + `zscaler_proxy` action=blocked threatname=*malware* threatcategory!=None + | stats count min(_time) as firstTime max(_time) as lastTime + BY action deviceowner user + urlcategory url src + dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `zscaler_malware_activity_threat_blocked_filter` +how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. You must be ingesting Zscaler events into your Splunk environment through an ingester. This analytic was written to be used with the "zscalernss-web" sourcetype leveraging the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. Security teams are encouraged to adjust the detection parameters, ensuring the detection is tailored to their specific environment. known_false_positives: False positives are limited to Zscalar configuration. references: -- https://help.zscaler.com/zia/nss-feed-output-format-web-logs + - https://help.zscaler.com/zia/nss-feed-output-format-web-logs drilldown_searches: -- name: View the detection results for - "$src$" and "$user$" - search: '%original_detection_search% | search src = "$src$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" and "$user$" + search: '%original_detection_search% | search src = "$src$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential Malware Activity from dest -[$dest$] on $src$ for user-[$user$]. - risk_objects: - - field: src - type: system - score: 40 - - field: user - type: user - score: 40 - threat_objects: - - field: url - type: url + message: Potential Malware Activity from dest -[$dest$] on $src$ for user-[$user$]. + risk_objects: + - field: src + type: system + score: 40 + - field: user + type: user + score: 40 + threat_objects: + - field: url + type: url tags: - analytic_story: - - Zscaler Browser Proxy Threats - asset_type: Web Server - mitre_attack_id: - - T1566 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Zscaler Browser Proxy Threats + asset_type: Web Server + mitre_attack_id: + - T1566 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json - source: zscaler - sourcetype: zscalernss-web + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json + source: zscaler + sourcetype: zscalernss-web diff --git a/detections/web/zscaler_phishing_activity_threat_blocked.yml b/detections/web/zscaler_phishing_activity_threat_blocked.yml index 438488097a..f10da211a6 100644 --- a/detections/web/zscaler_phishing_activity_threat_blocked.yml +++ b/detections/web/zscaler_phishing_activity_threat_blocked.yml @@ -1,74 +1,61 @@ name: Zscaler Phishing Activity Threat Blocked id: 68d3e2c1-e97f-4310-b080-dea180b48aa9 -version: 6 -date: '2025-10-14' +version: 7 +date: '2026-02-25' author: Gowthamaraj Rajendran, Rod Soto, Splunk status: production type: Anomaly data_source: [] -description: The following analytic identifies potential phishing attempts blocked - by Zscaler within a network. It leverages web proxy logs to detect actions tagged - as HTML.Phish. The detection method involves analyzing critical data points such - as user, threat name, URL, and hostname. This activity is significant for a SOC - as it serves as an early warning system for phishing threats, enabling prompt investigation - and mitigation. If confirmed malicious, this activity could indicate an attempt - to deceive users into divulging sensitive information, potentially leading to data - breaches or credential theft. -search: '`zscaler_proxy` action=blocked threatname="HTML.Phish*" | stats count min(_time) - as firstTime max(_time) as lastTime by action deviceowner user threatname url src - dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `zscaler_phishing_activity_threat_blocked_filter`' -how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. - You must be ingesting Zscaler events into your Splunk environment through an ingester. - This analytic was written to be used with the "zscalernss-web" sourcetype leveraging - the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. - Security teams are encouraged to adjust the detection parameters, ensuring the detection - is tailored to their specific environment. +description: The following analytic identifies potential phishing attempts blocked by Zscaler within a network. It leverages web proxy logs to detect actions tagged as HTML.Phish. The detection method involves analyzing critical data points such as user, threat name, URL, and hostname. This activity is significant for a SOC as it serves as an early warning system for phishing threats, enabling prompt investigation and mitigation. If confirmed malicious, this activity could indicate an attempt to deceive users into divulging sensitive information, potentially leading to data breaches or credential theft. +search: |- + `zscaler_proxy` action=blocked threatname="HTML.Phish*" + | stats count min(_time) as firstTime max(_time) as lastTime + BY action deviceowner user + threatname url src + dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `zscaler_phishing_activity_threat_blocked_filter` +how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. You must be ingesting Zscaler events into your Splunk environment through an ingester. This analytic was written to be used with the "zscalernss-web" sourcetype leveraging the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. Security teams are encouraged to adjust the detection parameters, ensuring the detection is tailored to their specific environment. known_false_positives: False positives are limited to Zscalar configuration. references: -- https://help.zscaler.com/zia/nss-feed-output-format-web-logs + - https://help.zscaler.com/zia/nss-feed-output-format-web-logs drilldown_searches: -- name: View the detection results for - "$src$" and "$user$" - search: '%original_detection_search% | search src = "$src$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" and "$user$" + search: '%original_detection_search% | search src = "$src$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential Phishing Activity from dest -[$dest$] on $src$ for user-[$user$]. - risk_objects: - - field: src - type: system - score: 16 - - field: user - type: user - score: 16 - threat_objects: - - field: url - type: url + message: Potential Phishing Activity from dest -[$dest$] on $src$ for user-[$user$]. + risk_objects: + - field: src + type: system + score: 16 + - field: user + type: user + score: 16 + threat_objects: + - field: url + type: url tags: - analytic_story: - - Zscaler Browser Proxy Threats - - Hellcat Ransomware - asset_type: Web Server - mitre_attack_id: - - T1566 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Zscaler Browser Proxy Threats + - Hellcat Ransomware + asset_type: Web Server + mitre_attack_id: + - T1566 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json - source: zscaler - sourcetype: zscalernss-web + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json + source: zscaler + sourcetype: zscalernss-web diff --git a/detections/web/zscaler_potentially_abused_file_download.yml b/detections/web/zscaler_potentially_abused_file_download.yml index d790d8232e..21d87b9208 100644 --- a/detections/web/zscaler_potentially_abused_file_download.yml +++ b/detections/web/zscaler_potentially_abused_file_download.yml @@ -1,72 +1,60 @@ name: Zscaler Potentially Abused File Download id: b0c21379-f4ba-4bac-a958-897e260f964a -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Gowthamaraj Rajendran, Rod Soto, Splunk status: production type: Anomaly data_source: [] -description: The following analytic identifies the download of potentially malicious - file types, such as .scr, .dll, .bat, and .lnk, within a network. It leverages web - proxy logs from Zscaler, focusing on blocked actions and analyzing fields like deviceowner, - user, urlcategory, url, dest, and filename. This activity is significant as these - file types are often used to spread malware, posing a threat to network security. - If confirmed malicious, this activity could lead to malware execution, data compromise, - or further network infiltration. -search: '`zscaler_proxy` url IN ("*.scr", "*.dll", "*.bat", "*.lnk") | stats count - min(_time) as firstTime max(_time) as lastTime by deviceowner user urlcategory url - src filename dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `zscaler_potentially_abused_file_download_filter`' -how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. - You must be ingesting Zscaler events into your Splunk environment through an ingester. - This analytic was written to be used with the "zscalernss-web" sourcetype leveraging - the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. - Security teams are encouraged to adjust the detection parameters, ensuring the detection - is tailored to their specific environment. +description: The following analytic identifies the download of potentially malicious file types, such as .scr, .dll, .bat, and .lnk, within a network. It leverages web proxy logs from Zscaler, focusing on blocked actions and analyzing fields like deviceowner, user, urlcategory, url, dest, and filename. This activity is significant as these file types are often used to spread malware, posing a threat to network security. If confirmed malicious, this activity could lead to malware execution, data compromise, or further network infiltration. +search: |- + `zscaler_proxy` url IN ("*.scr", "*.dll", "*.bat", "*.lnk") + | stats count min(_time) as firstTime max(_time) as lastTime + BY deviceowner user urlcategory + url src filename + dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `zscaler_potentially_abused_file_download_filter` +how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. You must be ingesting Zscaler events into your Splunk environment through an ingester. This analytic was written to be used with the "zscalernss-web" sourcetype leveraging the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. Security teams are encouraged to adjust the detection parameters, ensuring the detection is tailored to their specific environment. known_false_positives: False positives are limited to Zscaler configuration. references: -- https://help.zscaler.com/zia/nss-feed-output-format-web-logs + - https://help.zscaler.com/zia/nss-feed-output-format-web-logs drilldown_searches: -- name: View the detection results for - "$src$" and "$user$" - search: '%original_detection_search% | search src = "$src$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" and "$user$" + search: '%original_detection_search% | search src = "$src$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential Abused File Download from dest -[$dest$] on $src$ for user-[$user$]. - risk_objects: - - field: src - type: system - score: 8 - - field: user - type: user - score: 8 - threat_objects: - - field: url - type: url + message: Potential Abused File Download from dest -[$dest$] on $src$ for user-[$user$]. + risk_objects: + - field: src + type: system + score: 8 + - field: user + type: user + score: 8 + threat_objects: + - field: url + type: url tags: - analytic_story: - - Zscaler Browser Proxy Threats - asset_type: Web Server - mitre_attack_id: - - T1566 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Zscaler Browser Proxy Threats + asset_type: Web Server + mitre_attack_id: + - T1566 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json - source: zscaler - sourcetype: zscalernss-web + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json + source: zscaler + sourcetype: zscalernss-web diff --git a/detections/web/zscaler_privacy_risk_destinations_threat_blocked.yml b/detections/web/zscaler_privacy_risk_destinations_threat_blocked.yml index a94d17248c..790fdee6d1 100644 --- a/detections/web/zscaler_privacy_risk_destinations_threat_blocked.yml +++ b/detections/web/zscaler_privacy_risk_destinations_threat_blocked.yml @@ -1,73 +1,61 @@ name: Zscaler Privacy Risk Destinations Threat Blocked id: 5456bdef-d765-4565-8e1f-61ca027bc50d -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Gowthamaraj Rajendran, Rod Soto, Splunk status: production type: Anomaly data_source: [] -description: The following analytic identifies blocked destinations within a network - that are deemed privacy risks by Zscaler. It leverages web proxy logs, focusing - on entries marked as "Privacy Risk." Key data points such as device owner, user, - URL category, destination URL, and IP are analyzed. This activity is significant - for a SOC as it helps monitor and manage privacy risks, ensuring a secure network - environment. If confirmed malicious, this activity could indicate attempts to access - or exfiltrate sensitive information, posing a significant threat to data privacy - and security. -search: '`zscaler_proxy` action=blocked urlclass="Privacy Risk" | stats count min(_time) - as firstTime max(_time) as lastTime by action deviceowner user urlcategory url src - dest | dedup urlcategory | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `zscaler_privacy_risk_destinations_threat_blocked_filter`' -how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. - You must be ingesting Zscaler events into your Splunk environment through an ingester. - This analytic was written to be used with the "zscalernss-web" sourcetype leveraging - the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. - Security teams are encouraged to adjust the detection parameters, ensuring the detection - is tailored to their specific environment. +description: The following analytic identifies blocked destinations within a network that are deemed privacy risks by Zscaler. It leverages web proxy logs, focusing on entries marked as "Privacy Risk." Key data points such as device owner, user, URL category, destination URL, and IP are analyzed. This activity is significant for a SOC as it helps monitor and manage privacy risks, ensuring a secure network environment. If confirmed malicious, this activity could indicate attempts to access or exfiltrate sensitive information, posing a significant threat to data privacy and security. +search: |- + `zscaler_proxy` action=blocked urlclass="Privacy Risk" + | stats count min(_time) as firstTime max(_time) as lastTime + BY action deviceowner user + urlcategory url src + dest + | dedup urlcategory + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `zscaler_privacy_risk_destinations_threat_blocked_filter` +how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. You must be ingesting Zscaler events into your Splunk environment through an ingester. This analytic was written to be used with the "zscalernss-web" sourcetype leveraging the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. Security teams are encouraged to adjust the detection parameters, ensuring the detection is tailored to their specific environment. known_false_positives: False positives are limited to Zscaler configuration. references: -- https://help.zscaler.com/zia/nss-feed-output-format-web-logs + - https://help.zscaler.com/zia/nss-feed-output-format-web-logs drilldown_searches: -- name: View the detection results for - "$src$" and "$user$" - search: '%original_detection_search% | search src = "$src$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" and "$user$" + search: '%original_detection_search% | search src = "$src$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential Privacy Risk Destinations from dest -[$dest$] on $src$ for user-[$user$]. - risk_objects: - - field: src - type: system - score: 8 - - field: user - type: user - score: 8 - threat_objects: - - field: url - type: url + message: Potential Privacy Risk Destinations from dest -[$dest$] on $src$ for user-[$user$]. + risk_objects: + - field: src + type: system + score: 8 + - field: user + type: user + score: 8 + threat_objects: + - field: url + type: url tags: - analytic_story: - - Zscaler Browser Proxy Threats - asset_type: Web Server - mitre_attack_id: - - T1566 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Zscaler Browser Proxy Threats + asset_type: Web Server + mitre_attack_id: + - T1566 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json - source: zscaler - sourcetype: zscalernss-web + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json + source: zscaler + sourcetype: zscalernss-web diff --git a/detections/web/zscaler_scam_destinations_threat_blocked.yml b/detections/web/zscaler_scam_destinations_threat_blocked.yml index 52f8a5ba23..f37f6b77ce 100644 --- a/detections/web/zscaler_scam_destinations_threat_blocked.yml +++ b/detections/web/zscaler_scam_destinations_threat_blocked.yml @@ -1,72 +1,60 @@ name: Zscaler Scam Destinations Threat Blocked id: a0c21379-f4ba-4bac-a958-897e260f964a -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Gowthamaraj Rajendran, Rod Soto, Splunk status: production type: Anomaly data_source: [] -description: The following analytic identifies blocked scam-related activities detected - by Zscaler within a network. It leverages web proxy logs to examine actions flagged - as scam threats, focusing on data points such as device owner, user, URL category, - destination URL, and IP. This detection is significant for SOC as it helps in the - early identification and mitigation of scam activities, ensuring network safety. - If confirmed malicious, this activity could indicate attempts to deceive users, - potentially leading to data theft or financial loss. -search: '`zscaler_proxy` action=blocked threatname=*scam* | stats count min(_time) - as firstTime max(_time) as lastTime by action deviceowner user urlcategory url src - dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `zscaler_scam_destinations_threat_blocked_filter`' -how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. - You must be ingesting Zscaler events into your Splunk environment through an ingester. - This analytic was written to be used with the "zscalernss-web" sourcetype leveraging - the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. - Security teams are encouraged to adjust the detection parameters, ensuring the detection - is tailored to their specific environment. +description: The following analytic identifies blocked scam-related activities detected by Zscaler within a network. It leverages web proxy logs to examine actions flagged as scam threats, focusing on data points such as device owner, user, URL category, destination URL, and IP. This detection is significant for SOC as it helps in the early identification and mitigation of scam activities, ensuring network safety. If confirmed malicious, this activity could indicate attempts to deceive users, potentially leading to data theft or financial loss. +search: |- + `zscaler_proxy` action=blocked threatname=*scam* + | stats count min(_time) as firstTime max(_time) as lastTime + BY action deviceowner user + urlcategory url src + dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `zscaler_scam_destinations_threat_blocked_filter` +how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. You must be ingesting Zscaler events into your Splunk environment through an ingester. This analytic was written to be used with the "zscalernss-web" sourcetype leveraging the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. Security teams are encouraged to adjust the detection parameters, ensuring the detection is tailored to their specific environment. known_false_positives: False positives are limited to Zscaler configuration. references: -- https://help.zscaler.com/zia/nss-feed-output-format-web-logs + - https://help.zscaler.com/zia/nss-feed-output-format-web-logs drilldown_searches: -- name: View the detection results for - "$src$" and "$user$" - search: '%original_detection_search% | search src = "$src$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" and "$user$" + search: '%original_detection_search% | search src = "$src$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential Scam Threat from dest -[$dest$] on $src$ for user-[$user$]. - risk_objects: - - field: src - type: system - score: 8 - - field: user - type: user - score: 8 - threat_objects: - - field: url - type: url + message: Potential Scam Threat from dest -[$dest$] on $src$ for user-[$user$]. + risk_objects: + - field: src + type: system + score: 8 + - field: user + type: user + score: 8 + threat_objects: + - field: url + type: url tags: - analytic_story: - - Zscaler Browser Proxy Threats - asset_type: Web Server - mitre_attack_id: - - T1566 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Zscaler Browser Proxy Threats + asset_type: Web Server + mitre_attack_id: + - T1566 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json - source: zscaler - sourcetype: zscalernss-web + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json + source: zscaler + sourcetype: zscalernss-web diff --git a/detections/web/zscaler_virus_download_threat_blocked.yml b/detections/web/zscaler_virus_download_threat_blocked.yml index 00cd865b94..a6c1cf5704 100644 --- a/detections/web/zscaler_virus_download_threat_blocked.yml +++ b/detections/web/zscaler_virus_download_threat_blocked.yml @@ -1,73 +1,60 @@ name: Zscaler Virus Download threat blocked id: aa19e627-d448-4a31-85cd-82068dec5691 -version: 6 -date: '2025-05-02' +version: 7 +date: '2026-02-25' author: Gowthamaraj Rajendran, Rod Soto, Splunk status: production type: Anomaly data_source: [] -description: The following analytic identifies attempts to download viruses that were - blocked by Zscaler within a network. It leverages web proxy logs to detect blocked - actions indicative of virus download attempts. Key data points such as device owner, - user, URL category, destination URL, and IP are analyzed. This activity is significant - as it helps in early detection and remediation of potential virus threats, enhancing - network security. If confirmed malicious, this activity could indicate an attempt - to compromise the network, potentially leading to data breaches or further malware - infections. -search: '`zscaler_proxy` action=blocked threatname!="None" threatclass=Virus | stats - count min(_time) as firstTime max(_time) as lastTime by action deviceowner user - urlcategory url src dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `zscaler_virus_download_threat_blocked_filter`' -how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. - You must be ingesting Zscaler events into your Splunk environment through an ingester. - This analytic was written to be used with the "zscalernss-web" sourcetype leveraging - the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. - Security teams are encouraged to adjust the detection parameters, ensuring the detection - is tailored to their specific environment. +description: The following analytic identifies attempts to download viruses that were blocked by Zscaler within a network. It leverages web proxy logs to detect blocked actions indicative of virus download attempts. Key data points such as device owner, user, URL category, destination URL, and IP are analyzed. This activity is significant as it helps in early detection and remediation of potential virus threats, enhancing network security. If confirmed malicious, this activity could indicate an attempt to compromise the network, potentially leading to data breaches or further malware infections. +search: |- + `zscaler_proxy` action=blocked threatname!="None" threatclass=Virus + | stats count min(_time) as firstTime max(_time) as lastTime + BY action deviceowner user + urlcategory url src + dest + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `zscaler_virus_download_threat_blocked_filter` +how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. You must be ingesting Zscaler events into your Splunk environment through an ingester. This analytic was written to be used with the "zscalernss-web" sourcetype leveraging the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. Security teams are encouraged to adjust the detection parameters, ensuring the detection is tailored to their specific environment. known_false_positives: False positives are limited to Zscaler configuration. references: -- https://help.zscaler.com/zia/nss-feed-output-format-web-logs + - https://help.zscaler.com/zia/nss-feed-output-format-web-logs drilldown_searches: -- name: View the detection results for - "$src$" and "$user$" - search: '%original_detection_search% | search src = "$src$" user = "$user$"' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ -- name: View risk events for the last 7 days for - "$src$" and "$user$" - search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", - "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) - as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk - Message" values(analyticstories) as "Analytic Stories" values(annotations._all) - as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" - by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - earliest_offset: $info_min_time$ - latest_offset: $info_max_time$ + - name: View the detection results for - "$src$" and "$user$" + search: '%original_detection_search% | search src = "$src$" user = "$user$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$src$" and "$user$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ rba: - message: Potential Virus Download Threat from dest -[$dest$] on $src$ for user-[$user$]. - risk_objects: - - field: src - type: system - score: 40 - - field: user - type: user - score: 40 - threat_objects: - - field: url - type: url + message: Potential Virus Download Threat from dest -[$dest$] on $src$ for user-[$user$]. + risk_objects: + - field: src + type: system + score: 40 + - field: user + type: user + score: 40 + threat_objects: + - field: url + type: url tags: - analytic_story: - - Zscaler Browser Proxy Threats - asset_type: Web Server - mitre_attack_id: - - T1566 - product: - - Splunk Enterprise - - Splunk Enterprise Security - - Splunk Cloud - security_domain: threat + analytic_story: + - Zscaler Browser Proxy Threats + asset_type: Web Server + mitre_attack_id: + - T1566 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: threat tests: -- name: True Positive Test - attack_data: - - data: - https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json - source: zscaler - sourcetype: zscalernss-web + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json + source: zscaler + sourcetype: zscalernss-web diff --git a/docs/ci/yaml_formatting.md b/docs/ci/yaml_formatting.md new file mode 100644 index 0000000000..8682b28cc2 --- /dev/null +++ b/docs/ci/yaml_formatting.md @@ -0,0 +1,124 @@ +# YAML Formatting & Validation Setup + +## Quick Setup + +### 1. Install Pre-commit Hook + +```bash +# Install pre-commit +pip install pre-commit + +# Install the git hooks +pre-commit install +``` + +### 2. Install yamlfmt + +#### Option A: Using Go + +```bash +go install github.com/google/yamlfmt/cmd/yamlfmt@latest +``` + +#### Option B: Download Standalone Binary + +- Download from: +- Place in your PATH or `~/go/bin/` +- **Alternatively**, use `--yamlfmt-path` flag to specify custom location (see below) + +### 3. Install yamllint + +```bash +pip install yamllint +``` + +--- + +## Usage + +### Pre-commit (Automatic) + +Pre-commit hook runs automatically when you commit files in `detections/`: + +```bash +git add detections/endpoint/my_detection.yml +git commit -m "Add detection" +# yamlfmt runs automatically +``` + +**Skip hook if needed:** + +```bash +git commit --no-verify -m "Skip hooks" +``` + +### Manual Formatting + +```bash +# Format all detection files +yamlfmt detections/ + +# Format specific file +yamlfmt detections/endpoint/my_detection.yml + +# Check what would change (dry run) +yamlfmt -dry detections/ +``` + +### Validation (CI) + +```bash +# Validate all detections +python scripts/validate_yaml.py detections/ + +# Validate specific files +python scripts/validate_yaml.py detections/endpoint/file1.yml detections/cloud/file2.yml + +# Validate changed files only +python scripts/validate_yaml.py $(git diff --name-only --diff-filter=ACM develop...HEAD | grep '^detections/.*\.yml$') + +# Use custom yamlfmt binary path (when Go is not available) +python scripts/validate_yaml.py --yamlfmt-path /path/to/yamlfmt detections/ +``` + +--- + +## Troubleshooting + +**yamlfmt not found:** +```bash +# Make sure Go bin is in PATH +export PATH="$HOME/go/bin:$PATH" + +# Or use absolute path +~/go/bin/yamlfmt detections/ + +# Or specify path with --yamlfmt-path flag +python scripts/validate_yaml.py --yamlfmt-path /path/to/yamlfmt detections/ +``` + +**Pre-commit hook fails:** +```bash +# Run hook manually to see errors +pre-commit run yamlfmt --files detections/endpoint/file.yml + +# Update hooks +pre-commit autoupdate + +# Use custom yamlfmt binary in pre-commit +# Edit .pre-commit-config.yaml and add to yamlfmt hook args: +# args: [--yamlfmt-path, /path/to/yamlfmt] +``` + +**Validation fails in CI:** +- Check the script output for specific errors +- Run locally: `python scripts/validate_yaml.py detections/` +- Ensure files pass both yamllint and yamlfmt --lint + +--- + +## Configuration Files + +- `.yamlfmt` - yamlfmt formatting rules (4-space indent, LF line endings) +- `.yamllint` - yamllint validation rules (syntax checks, no duplicate keys) +- `.pre-commit-config.yaml` - Pre-commit hook configuration diff --git a/scripts/validate_yaml.py b/scripts/validate_yaml.py new file mode 100644 index 0000000000..f946d1fdc5 --- /dev/null +++ b/scripts/validate_yaml.py @@ -0,0 +1,348 @@ +#!/usr/bin/env python3 +""" +CI Validation Script for YAML Files +Runs yamllint and yamlfmt --lint with beautified error output +Usage: python scripts/validate_yaml.py [path_to_yaml_files...] +""" +import argparse +import subprocess +import sys +from pathlib import Path +from typing import List, Tuple, Optional + + +def find_yamlfmt(custom_path: Optional[str] = None) -> Optional[str]: + """Find yamlfmt executable in common locations or use custom path + + Args: + custom_path: Optional path to yamlfmt binary + + Returns: + Path to yamlfmt executable or None if not found + """ + # If custom path provided, verify and use it + if custom_path: + custom_path_obj = Path(custom_path) + if custom_path_obj.exists(): + return str(custom_path_obj) + else: + print_error(f"yamlfmt not found at specified path: {custom_path}") + return None + + # Check if yamlfmt is in PATH + for cmd in ['yamlfmt', 'yamlfmt.exe']: + try: + result = subprocess.run([cmd, '--version'], capture_output=True, text=True) + if result.returncode == 0: + return cmd + except FileNotFoundError: + pass + + # Check common installation paths + possible_paths = [ + Path.home() / 'go' / 'bin' / 'yamlfmt', + Path.home() / 'go' / 'bin' / 'yamlfmt.exe', + Path('/usr/local/bin/yamlfmt'), + Path('/usr/bin/yamlfmt'), + ] + + for path in possible_paths: + if path.exists(): + return str(path) + + print_error("yamlfmt not found. Install with: go install github.com/google/yamlfmt/cmd/yamlfmt@latest") + print("Make sure $GOPATH/bin is in your PATH") + print("Or use --yamlfmt-path to specify a custom yamlfmt binary location") + return None + + +class Colors: + """ANSI color codes for terminal output""" + RED = '\033[91m' + GREEN = '\033[92m' + YELLOW = '\033[93m' + BLUE = '\033[94m' + MAGENTA = '\033[95m' + CYAN = '\033[96m' + BOLD = '\033[1m' + RESET = '\033[0m' + + @classmethod + def disable(cls): + """Disable colors for non-terminal output""" + cls.RED = cls.GREEN = cls.YELLOW = cls.BLUE = '' + cls.MAGENTA = cls.CYAN = cls.BOLD = cls.RESET = '' + + +def print_header(text: str): + """Print a formatted header""" + print(f"\n{Colors.BOLD}{Colors.CYAN}{'=' * 80}{Colors.RESET}") + print(f"{Colors.BOLD}{Colors.CYAN}{text:^80}{Colors.RESET}") + print(f"{Colors.BOLD}{Colors.CYAN}{'=' * 80}{Colors.RESET}\n") + + +def print_error(text: str): + """Print an error message""" + print(f"{Colors.RED}[ERROR] {text}{Colors.RESET}") + + +def print_success(text: str): + """Print a success message""" + print(f"{Colors.GREEN}[PASS] {text}{Colors.RESET}") + + +def print_warning(text: str): + """Print a warning message""" + print(f"{Colors.YELLOW}[WARN] {text}{Colors.RESET}") + + +def find_yaml_files(paths: List[str]) -> List[Path]: + """Find all YAML files in detections/ from given paths""" + yaml_files = [] + + for path_str in paths: + path = Path(path_str) + + # Make relative to repo root if absolute + if path.is_absolute(): + try: + repo_root = Path(subprocess.run( + ['git', 'rev-parse', '--show-toplevel'], + capture_output=True, + text=True, + check=True + ).stdout.strip()) + path = path.relative_to(repo_root) + except: + pass + + if path.is_file(): + # Check if path contains 'detections' or if it's in detections/ + path_str_normalized = str(path).replace('\\', '/') + if 'detections/' in path_str_normalized and path.suffix in ['.yml', '.yaml']: + yaml_files.append(path) + elif path.is_dir(): + for yaml_file in path.rglob('*.yml'): + path_str_normalized = str(yaml_file).replace('\\', '/') + if 'detections/' in path_str_normalized: + yaml_files.append(yaml_file) + for yaml_file in path.rglob('*.yaml'): + path_str_normalized = str(yaml_file).replace('\\', '/') + if 'detections/' in path_str_normalized: + yaml_files.append(yaml_file) + + return sorted(set(yaml_files)) + + +def run_yamllint(files: List[Path], config: Path) -> Tuple[bool, dict]: + """Run yamllint on files and return success status and errors by file + + Returns: + Tuple of (success, file_errors_dict) + """ + if not files: + return True, {} + + # Process files in batches to avoid command line length limits on Windows + batch_size = 50 + all_file_errors = {} + all_passed = True + + for i in range(0, len(files), batch_size): + batch = files[i:i + batch_size] + cmd = ['yamllint', '-c', str(config)] + [str(f) for f in batch] + result = subprocess.run(cmd, capture_output=True, text=True, encoding='utf-8', errors='replace') + + if result.returncode != 0: + all_passed = False + + # Parse yamllint output + errors = result.stdout.strip().split('\n') + current_file = None + + for line in errors: + if not line.strip(): + continue + + # Check if this is a file path line + if not line.startswith(' '): + current_file = line + if current_file not in all_file_errors: + all_file_errors[current_file] = [] + else: + # This is an error line + if current_file: + all_file_errors[current_file].append(line.strip()) + + return all_passed, all_file_errors + + +def run_yamlfmt_lint(files: List[Path], config: Path, yamlfmt_path: Optional[str] = None) -> Tuple[bool, set]: + """Run yamlfmt --lint on files and return success status and set of files with issues + + Returns: + Tuple of (success, set_of_unformatted_files) + """ + if not files: + return True, set() + + # Find yamlfmt executable + yamlfmt = find_yamlfmt(yamlfmt_path) + if not yamlfmt: + return False, set() + + # Process files in batches to avoid command line length limits on Windows + batch_size = 50 + all_unformatted_files = set() + all_passed = True + + for i in range(0, len(files), batch_size): + batch = files[i:i + batch_size] + + # yamlfmt --lint returns non-zero if files need formatting + cmd = [yamlfmt, '-lint', '-conf', str(config)] + [str(f) for f in batch] + result = subprocess.run(cmd, capture_output=True, text=True, encoding='utf-8', errors='replace') + + if result.returncode != 0: + all_passed = False + + # Parse yamlfmt output to find files that need formatting + output = result.stdout.strip() + + if output: + lines = output.split('\n') + for line in lines: + line = line.strip() + # yamlfmt outputs the file path, often followed by a colon + if (line.endswith('.yml') or line.endswith('.yaml') or + line.endswith('.yml:') or line.endswith('.yaml:')): + # Remove trailing colon if present + file_path = line.rstrip(':') + if file_path: + all_unformatted_files.add(file_path) + + # If we couldn't parse files from this batch, add all batch files + if not output and result.returncode != 0: + all_unformatted_files.update(str(f) for f in batch) + + return all_passed, all_unformatted_files + + +def main(): + """Main entry point""" + parser = argparse.ArgumentParser( + description='Validate YAML files with yamllint and yamlfmt', + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=r""" +Examples: + # Validate all detection files + python scripts/validate_yaml.py detections/ + + # Validate specific files + python scripts/validate_yaml.py detections/endpoint/file.yml + + # Validate changed files (for CI) + python scripts/validate_yaml.py $(git diff --name-only --diff-filter=ACM origin/develop...HEAD | grep '^detections/.*\.yml$') + """ + ) + parser.add_argument( + 'paths', + nargs='*', + default=['detections/'], + help='Paths to YAML files or directories to validate (default: detections/)' + ) + parser.add_argument( + '--no-color', + action='store_true', + help='Disable colored output' + ) + parser.add_argument( + '--yamlfmt-path', + help='Path to yamlfmt binary (useful when Go is not available but standalone binary is)' + ) + + args = parser.parse_args() + + if args.no_color or not sys.stdout.isatty(): + Colors.disable() + + # Find repo root + try: + repo_root = subprocess.run( + ['git', 'rev-parse', '--show-toplevel'], + capture_output=True, + text=True, + check=True + ).stdout.strip() + repo_root = Path(repo_root) + except (subprocess.CalledProcessError, FileNotFoundError): + repo_root = Path.cwd() + + # Find config files + yamllint_config = repo_root / '.yamllint' + yamlfmt_config = repo_root / '.yamlfmt' + + if not yamllint_config.exists(): + print_error(f".yamllint config not found at {yamllint_config}") + return 1 + + if not yamlfmt_config.exists(): + print_error(f".yamlfmt config not found at {yamlfmt_config}") + return 1 + + # Find YAML files + yaml_files = find_yaml_files(args.paths) + + if not yaml_files: + print_warning("No YAML files found in detections/ directory") + return 0 + + print(f"{Colors.BOLD}Found {len(yaml_files)} YAML file(s) to validate{Colors.RESET}") + + print_header("Validating YAML Files") + + # Run both validations + yamllint_passed, yamllint_errors = run_yamllint(yaml_files, yamllint_config) + yamlfmt_passed, yamlfmt_files = run_yamlfmt_lint(yaml_files, yamlfmt_config, args.yamlfmt_path) + + # Combine results by file + all_issues = {} # file_path -> list of errors + + # Add yamllint errors + for file_path, errors in yamllint_errors.items(): + if file_path not in all_issues: + all_issues[file_path] = [] + all_issues[file_path].extend(errors) + + # Add yamlfmt formatting issues + for file_path in yamlfmt_files: + if file_path not in all_issues: + all_issues[file_path] = [] + all_issues[file_path].append("Formatting differences detected") + + # Display combined results + if not all_issues: + print_success(f"All {len(yaml_files)} file(s) passed validation!") + return 0 + + # Show issues grouped by file + total_errors = sum(len(errors) for errors in all_issues.values()) + print(f"\n{Colors.RED}[X] Found issues in {len(all_issues)} file(s):{Colors.RESET}\n") + + for file_path, errors in sorted(all_issues.items()): + print(f" {Colors.BOLD}{Colors.MAGENTA}> {file_path}{Colors.RESET}") + for error in errors: + print(f" {Colors.RED}- {error}{Colors.RESET}") + print() # Empty line between files + + # Show fix instructions + print(f"{Colors.CYAN}[TIP] To fix these issues, run:{Colors.RESET}") + print(f" {Colors.BOLD}yamlfmt -conf .yamlfmt detections/{Colors.RESET}") + print(f"\n{Colors.CYAN} Or for specific files:{Colors.RESET}") + print(f" {Colors.BOLD}yamlfmt -conf .yamlfmt {Colors.RESET}\n") + + return 1 + + +if __name__ == '__main__': + sys.exit(main())